Author: "Grau, A" / Publication Type: Reports - Searchworks@Jio Institute Digital Library Search Results

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656 results on '"Grau, A"'

1. A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making

Author: Kim, Yubin, Park, Chanwoo, Jeong, Hyewon, Grau-Vilchez, Cristina, Chan, Yik Siu, Xu, Xuhai, McDuff, Daniel, Lee, Hyeonhoon, Ghassemi, Marzyeh, Breazeal, Cynthia, and Park, Hae Won
Subjects: Computer Science - Computation and Language
Abstract: Medical Decision-Making (MDM) is a multi-faceted process that requires clinicians to assess complex multi-modal patient data patient, often collaboratively. Large Language Models (LLMs) promise to streamline this process by synthesizing vast medical knowledge and multi-modal health data. However, single-agent are often ill-suited for nuanced medical contexts requiring adaptable, collaborative problem-solving. Our MDAgents addresses this need by dynamically assigning collaboration structures to LLMs based on task complexity, mimicking real-world clinical collaboration and decision-making. This framework improves diagnostic accuracy and supports adaptive responses in complex, real-world medical scenarios, making it a valuable tool for clinicians in various healthcare settings, and at the same time, being more efficient in terms of computing cost than static multi-agent decision making methods.
Published: 2024

2. Insights from the first flaring activity of a high-synchrotron-peaked blazar with X-ray polarization and VHE gamma rays

Author: Abe, K., Abe, S., Abhir, J., Abhishek, A., Acciari, V. A., Aguasca-Cabot, A., Agudo, I., Aniello, T., Ansoldi, S., Antonelli, L. A., Engels, A. Arbet, Arcaro, C., Asano, K., Babić, A., de Almeida, U. Barres, Barrio, J. A., Barrios-Jiménez, L., Batković, I., Baxter, J., González, J. Becerra, Bednarek, W., Bernardini, E., Bernete, J., Berti, A., Besenrieder, J., Bigongiari, C., Biland, A., Blanch, O., Bonnoli, G., Bošnjak, Ž., Bronzini, E., Burelli, I., Campoy-Ordaz, A., Carosi, A., Carosi, R., Carretero-Castrillo, M., Castro-Tirado, A. J., Cerasole, D., Ceribella, G., Chai, Y., Chilingarian, A., Cifuentes, A., Colombo, E., Contreras, J. L., Cortina, J., Covino, S., D'Ammando, F., D'Amico, G., Da Vela, P., Dazzi, F., De Angelis, A., De Lotto, B., de Menezes, R., Delfino, M., Delgado, J., Mendez, C. Delgado, Di Pierro, F., Di Tria, R., Di Venere, L., Dinesh, A., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Eisenberger, L., Elsaesser, D., Escudero, J., Fariña, L., Foffano, L., Font, L., Fröse, S., Fukazawa, Y., López, R. J. García, Garczarczyk, M., Gasparyan, S., Gaug, M., Paiva, J. G. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Godinović, N., Gradetzke, T., Grau, R., Green, D., Green, J. G., Günther, P., Hadasch, D., Hahn, A., Hassan, T., Heckmann, L., Llorente, J. Herrera, Hrupec, D., Imazawa, R., Israyelyan, D., Itokawa, T., Martínez, I. Jiménez, Quiles, J. Jiménez, Jormanainen, J., Kankkunen, S., Kayanoki, T., Kerszberg, D., Khachatryan, M., Kluge, G. W., Kobayashi, Y., Konrad, J., Kouch, P. M., Kubo, H., Kushida, J., Láinez, M., Lamastra, A., Lindfors, E., Lombardi, S., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Lorini, A., Lyard, E., Majumdar, P., Makariev, M., Maneva, G., Manganaro, M., Mangano, S., Mannheim, K., Mariotti, M., Martínez, M., Maruševec, P., Mas-Aguilar, A., Mazin, D., Menchiari, S., Mender, S., Miceli, D., Miranda, J. M., Mirzoyan, R., González, M. Molero, Molina, E., Mondal, H. A., Moralejo, A., Nakamori, T., Nanci, C., Neustroev, V., Nickel, L., Rosillo, M. Nievas, Nigro, C., Nikolić, L., Nilsson, K., Nishijima, K., Ekoume, T. Njoh, Noda, K., Nozaki, S., Okumura, A., Paiano, S., Paneque, D., Paoletti, R., Paredes, J. M., Peresano, M., Persic, M., Pihet, M., Pirola, G., Podobnik, F., Moroni, P. G. Prada, Prandini, E., Principe, G., Rhode, W., Ribó, M., Rico, J., Righi, C., Sahakyan, N., Saito, T., Saturni, F. G., Schmuckermaier, F., Schubert, J. L., Sciaccaluga, A., Silvestri, G., Sitarek, J., Sliusar, V., Sobczynska, D., Stamerra, A., Strišković, J., Strom, D., Strzys, M., Suda, Y., Tajima, H., Takahashi, M., Takeishi, R., Temnikov, P., Terauchi, K., Terzić, T., Teshima, M., Truzzi, S., Tutone, A., Ubach, S., van Scherpenberg, J., Ventura, S., Verna, G., Viale, I., Vigliano, A., Vigorito, C. F., Vitale, V., Vovk, I., Walter, R., Wersig, F., Will, M., Yamamoto, T., Yeung, P. K. H., Liodakis, I., Middei, R., Kiehlmann, S., Gesu, L. D., Kim, D. E., Ehlert, S. R., Saade, M. L., Kaaret, P., Maksym, W. P., Chen, C. T., Pérez, I. De La Calle, Perri, M., Verrecchia, F., Domann, O., Dürr, S., Feige, M., Heidemann, M., Koppitz, O., Manhalter, G., Reinhart, D., Steineke, R., Lorey, C., McCall, C., Jermak, H. E., Steele, I. A., Ramazani, V. Fallah, Otero-Santos, J., Morcuende, D., Aceituno, F. J., Casanova, V., Sota, A., Jorstad, S. G., Marscher, A. P., Pauley, C., Sasada, M., Kawabata, K. S., Uemura, M., Mizuno, T., Nakaoka, T., Akitaya, H., Myserlis, I., Gurwell, M., Keating, G. K., Rao, R., Angelakis, E., and Kraus, A.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We study a flaring activity of the HSP Mrk421 that was characterized from radio to very-high-energy (VHE; E $>0.1$TeV) gamma rays with MAGIC, Fermi-LAT, Swift, XMM-Newton and several optical and radio telescopes. These observations included, for the first time for a gamma-ray flare of a blazar, simultaneous X-ray polarization measurements with IXPE. We find substantial variability in both X-rays and VHE gamma rays throughout the campaign, with the highest VHE flux above 0.2 TeV occurring during the IXPE observing window, and exceeding twice the flux of the Crab Nebula. However, the VHE and X-ray spectra are on average softer, and the correlation between these two bands weaker that those reported in previous flares of Mrk421. IXPE reveals an X-ray polarization degree significantly higher than that at radio and optical frequencies. The X-ray polarization angle varies by $\sim$100$^\circ$ on timescales of days, and the polarization degree changes by more than a factor 4. The highest X-ray polarization degree reaches 26%, around which a X-ray counter-clockwise hysteresis loop is measured with XMM-Newton. It suggests that the X-ray emission comes from particles close to the high-energy cutoff, hence possibly probing an extreme case of the Turbulent Extreme Multi-Zone model. We model the broadband emission with a simplified stratified jet model throughout the flare. The polarization measurements imply an electron distribution in the X-ray emitting region with a very high minimum Lorentz factor, which is expected in electron-ion plasma, as well as a variation of the emitting region size up to a factor of three during the flaring activity. We find no correlation between the fluxes and the evolution of the model parameters, which indicates a stochastic nature of the underlying physical mechanism. Such behaviour would be expected in a highly turbulent electron-ion plasma crossing a shock front., Comment: Submitted to Astronomy and Astrophysics. Corresponding authors: Axel Arbet-Engels, Lea Heckmann, David Paneque
Published: 2024

3. Multi-wavelength study of OT 081: broadband modelling of a transitional blazar

Author: MAGIC Collaboration, Abe, H., Abe, S., Acciari, V. A., Agudo, I., Aniello, T., Ansoldi, S., Antonelli, L. A., Engels, A. Arbet, Arcaro, C., Artero, M., Asano, K., Baack, D., Babić, A., Baquero, A., de Almeida, U. Barres, Batković, I., Baxter, J., Bernardini, E., Bernardos, M., Bernete, J., Berti, A., Bigongiari, C., Biland, A., Blanch, O., Bonnoli, G., Bošnjak, Ž., Burelli, I., Busetto, G., Campoy-Ordaz, A., Carosi, A., Carosi, R., Carretero-Castrillo, M., Castro-Tirado, A. J., Chai, Y., Cifuentes, A., Cikota, S., Colombo, E., Contreras, J. L., Cortina, J., Covino, S., D'Amico, G., D'Elia, V., Da Vela, P., Dazzi, F., De Angelis, A., De Lotto, B., Del Popolo, A., Delfino, M., Delgado, J., Mendez, C. Delgado, Depaoli, D., Di Pierro, F., Di Venere, L., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Elsaesser, D., Emery, G., Escudero, J., Fariña, L., Fattorini, A., Foffano, L., Font, L., Fukami, S., Fukazawa, Y., López, R. J. García, Gasparyan, S., Gaug, M., Paiva, J. G. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Grau, R., Green, J. G., Hadasch, D., Hahn, A., Heckmann, L., Herrera, J., Hrupec, D., Hütten, M., Imazawa, R., Inada, T., Iotov, R., Ishio, K., Martínez, I. Jiménez, Jormanainen, J., Kerszberg, D., Kluge, G. W., Kobayashi, Y., Kubo, H., Kushida, J., Lezáun, M. Láinez, Lamastra, A., Leone, F., Lindfors, E., Linhoff, L., Lombardi, S., Longo, F., López-Moya, M., López-Oramas, A., Loporchio, S., Lorini, A., Fraga, B. Machado de Oliveira, Majumdar, P., Makariev, M., Maneva, G., Mang, N., Manganaro, M., Mangano, S., Mannheim, K., Mariotti, M., Martínez, M., Mas-Aguilar, A., Mazin, D., Menchiari, S., Mender, S., Mićanović, S., Miceli, D., Miranda, J. M., Mirzoyan, R., Molina, E., Mondal, H. A., Morcuende, D., Nanci, C., Neustroev, V., Nigro, C., Nishijima, K., Ekoume, T. Njoh, Noda, K., Nozaki, S., Ohtani, Y., Otero-Santos, J., Paiano, S., Palatiello, M., Paneque, D., Paoletti, R., Paredes, J. M., Pavletić, L., Persic, M., Pihet, M., Pirola, G., Podobnik, F., Moroni, P. G. Prada, Prandini, E., Principe, G., Priyadarshi, C., Rhode, W., Ribó, M., Rico, J., Righi, C., Sahakyan, N., Saito, T., Satalecka, K., Saturni, F. G., Schleicher, B., Schmidt, K., Schmuckermaier, F., Schubert, J. L., Schweizer, T., Sitarek, J., Spolon, A., Stamerra, A., Strišković, J., Strom, D., Suda, Y., Surić, T., Suutarinen, S., Tajima, H., Takahashi, M., Takeishi, R., Tavecchio, F., Temnikov, P., Terzić, T., Teshima, M., Tosti, L., Truzzi, S., Ubach, S., van Scherpenberg, J., Ventura, S., Verguilov, V., Viale, I., Vigorito, C. F., Vitale, V., Walter, R., Yamamoto, T., Collaborators, Benkhali, F. Ait, Becherini, Y., Bi, B., Böttcher, M., Bolmont, J., Brown, A., Bulik, T., Casanova, S., Chand, T., Chandra, S., Chibueze, J., Chibueze, O., Egberts, K., Einecke, S., Ernenwein, J. -P., Fontaine, G., Gabici, S., Goswami, P., Holler, M., Jamrozy, M., Joshi, V., Jung-Richardt, I., Kasai, E., Katarzyński, K., Khatoon, R., Khélifi, B., Kluzniak, W., Kosack, K., Stum, S. Le, Lemière, A., Marx, R., Moderski, R., Moghadam, M. O., de Naurois, M., Niemiec, J., O'Brien, P., Ostrowski, M., Peron, G., Pita, S., Pühlhofer, G., Quirrenbach, A., Rudak, B., Sahakian, V., Sanchez, D. A., Santangelo, A., Sasaki, M., Schutte, H. M., Seglar-Arroyo, M., Shapopi, J. N. S., Steenkamp, R., Steppa, C., Suzuki, H., Tanaka, T., Tluczykont, M., Venter, C., Wagner, S. J., Wierzcholska, A., Zdziarski, A. A., Żywucka, N., Collaboration, Fermi-LAT, González, J. Becerra, Ciprini, S., Venters, T. M., collaborators, MWL, D'Ammando, F., Esteban-Gutiérrez, A., Ramazani, V. Fallah, Filippenko, A. V., Hovatta, T., Jermak, H., Jorstad, S., Kiehlmann, S., Lähteenmäki, A., Larionov, V. M., Larionova, E., Marscher, A. P., Morozova, D., Max-Moerbeck, W., Readhead, A. C. S., Reeves, R., Steele, I. A., Tornikoski, M., Verrecchia, F., Xiao, H., and Zheng, W.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: OT 081 is a well-known, luminous blazar that is remarkably variable in many energy bands. We present the first broadband study of the source which includes very-high-energy (VHE, $E>$100\,GeV) $\gamma$-ray data taken by the MAGIC and H.E.S.S. imaging Cherenkov telescopes. The discovery of VHE $\gamma$-ray emission happened during a high state of $\gamma$-ray activity in July 2016, observed by many instruments from radio to VHE $\gamma$-rays. We identify four states of activity of the source, one of which includes VHE $\gamma$-ray emission. Variability in the VHE domain is found on daily timescales. The intrinsic VHE spectrum can be described by a power-law with index $3.27\pm0.44_{\rm stat}\pm0.15_{\rm sys}$ (MAGIC) and $3.39\pm0.58_{\rm stat}\pm0.64_{\rm sys}$ (H.E.S.S.) in the energy range of 55--300\,GeV and 120--500\,GeV, respectively. The broadband emission cannot be sucessfully reproduced by a simple one-zone synchrotron self-Compton model. Instead, an additional external Compton component is required. We test a lepto-hadronic model that reproduces the dataset well and a proton-synchrotron dominated model that requires an extreme proton luminosity. Emission models that are able to successfully represent the data place the emitting region well outside of the Broad Line Region (BLR) to a location at which the radiative environment is dominated by the infrared thermal radiation field of the dusty torus. In the scenario described by this flaring activity, the source appears to be an FSRQ, in contrast with past categorizations. This suggests that the source can be considered to be a transitional blazar, intermediate between BL~Lac and FSRQ objects., Comment: Accepted on MNRAS Corresponding authors: M. Manganaro, J. Becerra Gonz\'alez, M. Seglar-Arroyo, D. A. Sanchez
Published: 2024

4. A new method of reconstructing images of gamma-ray telescopes applied to the LST-1 of CTAO

Author: Project, CTA-LST, Abe, K., Abe, S., Abhishek, A., Acero, F., Aguasca-Cabot, A., Agudo, I., Alispach, C., Crespo, N. Alvarez, Ambrosino, D., Antonelli, L. A., Aramo, C., Arbet-Engels, A., Arcaro, C., Asano, K., Aubert, P., Baktash, A., Balbo, M., Bamba, A., Larriva, A. Baquero, de Almeida, U. Barres, Barrio, J. A., Jiménez, L. Barrios, Batkovic, I., Baxter, J., González, J. Becerra, Bernardini, E., Medrano, J. Bernete, Berti, A., Bezshyiko, I., Bhattacharjee, P., Bigongiari, C., Bissaldi, E., Blanch, O., Bonnoli, G., Bordas, P., Borkowski, G., Brunelli, G., Bulgarelli, A., Burelli, I., Burmistrov, L., Buscemi, M., Cardillo, M., Caroff, S., Carosi, A., Carrasco, M. S., Cassol, F., Castrejón, N., Cauz, D., Cerasole, D., Ceribella, G., Chai, Y., Cheng, K., Chiavassa, A., Chikawa, M., Chon, G., Chytka, L., Cicciari, G. M., Cifuentes, A., Contreras, J. L., Cortina, J., Costantini, H., Da Vela, P., Dalchenko, M., Dazzi, F., De Angelis, A., de Lavergne, M. de Bony, De Lotto, B., de Menezes, R., Del Burgo, R., Del Peral, L., Delgado, C., Mengual, J. Delgado, della Volpe, D., Dellaiera, M., Di Piano, A., Di Pierro, F., Di Tria, R., Di Venere, L., Díaz, C., Dominik, R. M., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Eisenberger, L., Elsässer, D., Emery, G., Escudero, J., Ramazani, V. Fallah, Ferrarotto, F., Fiasson, A., Foffano, L., Coromina, L. Freixas, Fröse, S., Fukazawa, Y., López, R. Garcia, Gasbarra, C., Gasparrini, D., Geyer, D., Paiva, J. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Godinovic, N., Grau, R., Green, D., Green, J., Gunji, S., Günther, P., Hackfeld, J., Hadasch, D., Hahn, A., Hassan, T., Hayashi, K., Heckmann, L., Heller, M., Llorente, J. Herrera, Hirotani, K., Hoffmann, D., Horns, D., Houles, J., Hrabovsky, M., Hrupec, D., Hui, D., Iarlori, M., Imazawa, R., Inada, T., Inome, Y., Inoue, S., Ioka, K., Iori, M., Iuliano, A., Martinez, I. Jimenez, Quiles, J. Jimenez, Jurysek, J., Kagaya, M., Kalashev, O., Karas, V., Katagiri, H., Kataoka, J., Kerszberg, D., Kobayashi, Y., Kohri, K., Kong, A., Kubo, H., Kushida, J., Lainez, M., Lamanna, G., Lamastra, A., Lemoigne, L., Linhoff, M., Longo, F., López-Coto, R., López-Oramas, A., Loporchio, S., Lorini, A., Bahilo, J. Lozano, Luciani, H., Luque-Escamilla, P. L., Majumdar, P., Makariev, M., Mallamaci, M., Mandat, D., Manganaro, M., Manicò, G., Mannheim, K., Marchesi, S., Mariotti, M., Marquez, P., Marsella, G., Martí, J., Martinez, O., Martínez, G., Martínez, M., Mas-Aguilar, A., Maurin, G., Mazin, D., Méndez-Gallego, J., Guillen, E. Mestre, Micanovic, S., Miceli, D., Miener, T., Miranda, J. M., Mirzoyan, R., Mizuno, T., Gonzalez, M. Molero, Molina, E., Montaruli, T., Moralejo, A., Morcuende, D., Morselli, A., Moya, V., Muraishi, H., Nagataki, S., Nakamori, T., Neronov, A., Nickel, L., Rosillo, M. Nievas, Nikolic, L., Nishijima, K., Noda, K., Nosek, D., Novotny, V., Nozaki, S., Ohishi, M., Ohtani, Y., Oka, T., Okumura, A., Orito, R., Otero-Santos, J., Ottanelli, P., Owen, E., Palatiello, M., Paneque, D., Pantaleo, F. R., Paoletti, R., Paredes, J. M., Pech, M., Pecimotika, M., Peresano, M., Pfeifle, F., Pietropaolo, E., Pihet, M., Pirola, G., Plard, C., Podobnik, F., Pons, E., Prandini, E., Priyadarshi, C., Prouza, M., Rainò, S., Rando, R., Rhode, W., Ribó, M., Righi, C., Rizi, V., Fernandez, G. Rodriguez, Frías, M. D. Rodríguez, Ruina, A., Ruiz-Velasco, E., Saito, T., Sakurai, S., Sanchez, D. A., Sano, H., Šarić, T., Sato, Y., Saturni, F. G., Savchenko, V., Schiavone, F., Schleicher, B., Schmuckermaier, F., Schubert, J. L., Schussler, F., Schweizer, T., Arroyo, M. Seglar, Siegert, T., Sitarek, J., Sliusar, V., Strišković, J., Strzys, M., Suda, Y., Tajima, H., Takahashi, H., Takahashi, M., Takata, J., Takeishi, R., Tam, P. H. T., Tanaka, S. J., Tateishi, D., Tavernier, T., Temnikov, P., Terada, Y., Terauchi, K., Terzic, T., Teshima, M., Tluczykont, M., Tokanai, F., Torres, D. F., Travnicek, P., Tutone, A., Vacula, M., Vallania, P., van Scherpenberg, J., Acosta, M. Vázquez, Ventura, S., Verna, G., Viale, I., Vigliano, A., Vigorito, C. F., Visentin, E., Vitale, V., Voitsekhovskyi, V., Voutsinas, G., Vovk, I., Vuillaume, T., Walter, R., Wan, L., Will, M., Wójtowicz, J., Yamamoto, T., Yamazaki, R., Yeung, P. K. H., Yoshida, T., Yoshikoshi, T., Zhang, W., and Zywucka, N.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Imaging atmospheric Cherenkov telescopes (IACTs) are used to observe very high-energy photons from the ground. Gamma rays are indirectly detected through the Cherenkov light emitted by the air showers they induce. The new generation of experiments, in particular the Cherenkov Telescope Array Observatory (CTAO), sets ambitious goals for discoveries of new gamma-ray sources and precise measurements of the already discovered ones. To achieve these goals, both hardware and data analysis must employ cutting-edge techniques. This also applies to the LST-1, the first IACT built for the CTAO, which is currently taking data on the Canary island of La Palma. This paper introduces a new event reconstruction technique for IACT data, aiming to improve the image reconstruction quality and the discrimination between the signal and the background from misidentified hadrons and electrons. The technique models the development of the extensive air shower signal, recorded as a waveform per pixel, seen by CTAO telescopes' cameras. Model parameters are subsequently passed to random forest regressors and classifiers to extract information on the primary particle. The new reconstruction was applied to simulated data and to data from observations of the Crab Nebula performed by the LST-1. The event reconstruction method presented here shows promising performance improvements. The angular and energy resolution, and the sensitivity, are improved by 10 to 20% over most of the energy range. At low energy, improvements reach up to 22%, 47%, and 50%, respectively. A future extension of the method to stereoscopic analysis for telescope arrays will be the next important step., Comment: Accepted in A&A
Published: 2024

5. Transesophageal Echocardiography Generation using Anatomical Models

Author: Oladokun, Emmanuel, Abdulkareem, Musa, Šprem, Jurica, and Grau, Vicente
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Through automation, deep learning (DL) can enhance the analysis of transesophageal echocardiography (TEE) images. However, DL methods require large amounts of high-quality data to produce accurate results, which is difficult to satisfy. Data augmentation is commonly used to tackle this issue. In this work, we develop a pipeline to generate synthetic TEE images and corresponding semantic labels. The proposed data generation pipeline expands on an existing pipeline that generates synthetic transthoracic echocardiography images by transforming slices from anatomical models into synthetic images. We also demonstrate that such images can improve DL network performance through a left-ventricle semantic segmentation task. For the pipeline's unpaired image-to-image (I2I) translation section, we explore two generative methods: CycleGAN and contrastive unpaired translation. Next, we evaluate the synthetic images quantitatively using the Fr\'echet Inception Distance (FID) Score and qualitatively through a human perception quiz involving expert cardiologists and the average researcher. In this study, we achieve a dice score improvement of up to 10% when we augment datasets with our synthetic images. Furthermore, we compare established methods of assessing unpaired I2I translation and observe a disagreement when evaluating the synthetic images. Finally, we see which metric better predicts the generated data's efficacy when used for data augmentation., Comment: MICCAI2023; DALI Workshop
Published: 2024
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6. Toward hybrid quantum simulations with qubits and qumodes on trapped-ion platforms

Author: Araz, Jack Y., Grau, Matt, Montgomery, Jake, and Ringer, Felix
Subjects: Quantum Physics, High Energy Physics - Lattice, Nuclear Theory, Physics - Atomic Physics
Abstract: We explore the feasibility of hybrid quantum computing using both discrete (qubit) and continuous (qumode) variables on trapped-ion platforms. Trapped-ion systems have demonstrated record one- and two-qubit gate fidelities and long qubit coherence times while qumodes, which can be represented by the collective vibrational modes of the ion chain, have remained relatively unexplored for their use in computing. Using numerical simulations, we show that high-fidelity hybrid gates and measurement operations can be achieved for existing trapped-ion quantum platforms. As an exemplary application, we consider quantum simulations of the Jaynes-Cummings-Hubbard model, which is given by a one-dimensional chain of interacting spin and boson degrees of freedom. Using classical simulations, we study its real-time evolution and we develop a suitable variational quantum algorithm for the ground state preparation. Our results motivate further studies of hybrid quantum computing in this context, which may lead to direct applications in condensed matter and fundamental particle and nuclear physics., Comment: 22 pages, 12 figures
Published: 2024

7. Hidden symmetries from extra dimensions

Author: Ciafardini, Marco, Marques, Diego, Nunez, Carmen, and Grau, Agustina Pereyra
Subjects: High Energy Physics - Theory, General Relativity and Quantum Cosmology
Abstract: In Kaluza-Klein compactifications, some symmetries of the higher dimensional theory are preserved in lower dimensions, others are broken, and occasionally, there are symmetry enhancements. The symmetries that are enhanced by toroidal compactifications were recently shown to define a symmetry principle with constrained parameters that fixes the action before dimensional reduction. Here we show the opposite: symmetries of the higher dimensional theory that are broken in the reduction process, can actually be realized after dimensional reduction as a global symmetry principle with constrained parameters that fixes couplings in the lower dimensional theory. We implement this principle in pure gravity, half-maximal supergravity and the circle reduction of 11 dimensional supergravity to Type IIA superstring theory. As a further application, we show that it can be used to constrain the quartic Ramond-Ramond couplings in Type IIA superstring theory from the four-point $\zeta(3)\, \alpha'{}^3\, t_8 t_8 R^{(-) 4}$ interactions., Comment: 36p
Published: 2024

8. Optimal Control of Fractional Punishment in Optional Public Goods Game

Author: Grau, J., Botta, R., and Schaerer, C. E.
Subjects: Electrical Engineering and Systems Science - Systems and Control, Mathematics - Optimization and Control
Abstract: Punishment is probably the most frequently used mechanism to increase cooperation in Public Goods Games (PGG); however, it is expensive. To address this problem, this paper introduces an optimal control problem that uses fractional punishment to promote cooperation. We present a series of computational experiments illustrating the effects of single and combined terms of the optimization cost function. In the findings, the optimal controller outperforms the use of constant fractional punishment and gives an insight into the period and size of the penalization to be implemented with respect to the defection in the game.
Published: 2024

9. Standardised formats and open-source analysis tools for the MAGIC telescopes data

Author: Abe, S., Abhir, J., Abhishek, A., Acciari, V. A., Aguasca-Cabot, A., Agudo, I., Aniello, T., Ansoldi, S., Antonelli, L. A., Engels, A. Arbet, Arcaro, C., Artero, M., Asano, K., Babić, A., de Almeida, U. Barres, Barrio, J. A., Batković, I., Bautista, A., Baxter, J., González, J. Becerra, Bednarek, W., Bernardini, E., Bernete, J., Berti, A., Besenrieder, J., Bigongiari, C., Biland, A., Blanch, O., Bonnoli, G., Bošnjak, Ž., Bronzini, E., Burelli, I., Busetto, G., Campoy-Ordaz, A., Carosi, A., Carosi, R., Carretero-Castrillo, M., Castro-Tirado, A. J., Cerasole, D., Ceribella, G., Chai, Y., Cifuentes, A., Colombo, E., Contreras, J. L., Cortina, J., Covino, S., D'Amico, G., D'Elia, V., Da Vela, P., Dazzi, F., De Angelis, A., De Lotto, B., de Menezes, R., Delfino, M., Delgado, J., Di Pierro, F., Di Tria, R., Di Venere, L., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Elsaesser, D., Escudero, J., Fariña, L., Fattorini, A., Foffano, L., Font, L., Fröse, S., Fukami, S., Fukazawa, Y., López, R. J. García, Garczarczyk, M., Gasparyan, S., Gaug, M., Paiva, J. G. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Gradetzke, T., Grau, R., Green, D., Green, J. G., Günther, P., Hadasch, D., Hahn, A., Hassan, T., Heckmann, L., Llorente, J. Herrera, Hrupec, D., Hütten, M., Imazawa, R., Ishio, K., Martínez, I. Jiménez, Jormanainen, J., Kayanoki, T., Kerszberg, D., Kluge, G. W., Kobayashi, Y., Kouch, P. M., Kubo, H., Kushida, J., Láinez, M., Lamastra, A., Leone, F., Lindfors, E., Lombardi, S., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Lorini, A., Lyard, E., Fraga, B. Machado de Oliveira, Majumdar, P., Makariev, M., Maneva, G., Manganaro, M., Mangano, S., Mannheim, K., Mariotti, M., Martínez, M., Martínez-Chicharro, M., Mas-Aguilar, A., Mazin, D., Menchiari, S., Mender, S., Miceli, D., Miener, T., Miranda, J. M., Mirzoyan, R., González, M. Molero, Molina, E., Mondal, H. A., Moralejo, A., Morcuende, D., Nakamori, T., Nanci, C., Neustroev, V., Nickel, L., Rosillo, M. Nievas, Nigro, C., Nikolić, L., Nishijima, K., Ekoume, T. Njoh, Noda, K., Nozaki, S., Ohtani, Y., Okumura, A., Otero-Santos, J., Paiano, S., Paneque, D., Paoletti, R., Paredes, J. M., Peresano, M., Persic, M., Pihet, M., Pirola, G., Podobnik, F., Moroni, P. G. Prada, Prandini, E., Principe, G., Priyadarshi, C., Rhode, W., Ribó, M., Rico, J., Righi, C., Sahakyan, N., Saito, T., Saturni, F. G., Schmidt, K., Schmuckermaier, F., Schubert, J. L., Schweizer, T., Sciaccaluga, A., Silvestri, G., Sitarek, J., Sliusar, V., Sobczynska, D., Spolon, A., Stamerra, A., Strišković, J., Strom, D., Strzys, M., Suda, Y., Suutarinen, S., Tajima, H., Takahashi, M., Takeishi, R., Temnikov, P., Terauchi, K., Terzić, T., Teshima, M., Truzzi, S., Tutone, A., Ubach, S., van Scherpenberg, J., Acosta, M. Vazquez, Ventura, S., Viale, I., Vigorito, C. F., Vitale, V., Vovk, I., Walter, R., Will, M., Wunderlich, C., Yamamoto, T., Jouvin, L., Linhoff, L., and Linhoff, M.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract: Instruments for gamma-ray astronomy at Very High Energies ($E>100\,{\rm GeV}$) have traditionally derived their scientific results through proprietary data and software. Data standardisation has become a prominent issue in this field both as a requirement for the dissemination of data from the next generation of gamma-ray observatories and as an effective solution to realise public data legacies of current-generation instruments. Specifications for a standardised gamma-ray data format have been proposed as a community effort and have already been successfully adopted by several instruments. We present the first production of standardised data from the Major Atmospheric Gamma-ray Imaging Cherenkov (MAGIC) telescopes. We converted $166\,{\rm h}$ of observations from different sources and validated their analysis with the open-source software Gammapy. We consider six data sets representing different scientific and technical analysis cases and compare the results obtained analysing the standardised data with open-source software against those produced with the MAGIC proprietary data and software. Aiming at a systematic production of MAGIC data in this standardised format, we also present the implementation of a database-driven pipeline automatically performing the MAGIC data reduction from the calibrated down to the standardised data level. In all the cases selected for the validation, we obtain results compatible with the MAGIC proprietary software, both for the manual and for the automatic data productions. Part of the validation data set is also made publicly available, thus representing the first large public release of MAGIC data. This effort and this first data release represent a technical milestone toward the realisation of a public MAGIC data legacy., Comment: Accepted for publication in the Journal of High Energy Astrophysics
Published: 2024

10. Spatiotemporal variability and prediction of e-bike battery levels in bike-sharing systems

Author: Bassolas, Aleix, Grau-Escolano, Jordi, and Vicens, Julian
Subjects: Physics - Physics and Society, Mathematical Physics, Statistics - Applications
Abstract: Bike Sharing Systems (BSSs) play a crucial role in promoting sustainable urban mobility by facilitating short-range trips and connecting with other transport modes. Traditionally, most BSS fleets have consisted of mechanical bikes (m-bikes), but electric bikes (e-bikes) are being progressively introduced due to their ability to cover longer distances and appeal to a wider range of users. However, the charging requirements of e-bikes often hinder their deployment and optimal functioning. This study examines the spatiotemporal variations in battery levels of Barcelona's BSS, revealing that bikes stationed near the city centre tend to have shorter rest periods and lower average battery levels. Additionally, to improve the management of e-bike fleets, a Markov-chain approach is developed to predict both bike availability and battery levels. This research offers a unique perspective on the dynamics of e-bike battery levels and provides a practical tool to overcome the main operational challenges in their implementation., Comment: 25 pages, 15 figures, 1 table
Published: 2024

11. Measurement of elliptic flow of J$/\psi$ in $\sqrt{s_{_{NN}}}=200$ GeV Au$+$Au collisions at forward rapidity

Author: PHENIX Collaboration, Abdulameer, N. J., Acharya, U., Adare, A., Aidala, C., Ajitanand, N. N., Akiba, Y., Alfred, M., Antsupov, S., Aoki, K., Apadula, N., Asano, H., Ayuso, C., Azmoun, B., Babintsev, V., Bai, M., Bandara, N. S., Bannier, B., Bannikov, E., Barish, K. N., Bathe, S., Bazilevsky, A., Beaumier, M., Beckman, S., Belmont, R., Berdnikov, A., Berdnikov, Y., Bichon, L., Blankenship, B., Blau, D. S., Boer, M., Bok, J. S., Borisov, V., Boyle, K., Brooks, M. L., Bryslawskyj, J., Bumazhnov, V., Butler, C., Campbell, S., Roman, V. Canoa, Chen, C. -H., Chen, D., Chiu, M., Chi, C. Y., Choi, I. J., Choi, J. B., Chujo, T., Citron, Z., Connors, M., Corliss, R., Csanád, M., Csörgő, T., Liu, L. D., Danley, T. W., Datta, A., Daugherity, M. S., David, G., DeBlasio, K., Dehmelt, K., Denisov, A., Deshpande, A., Desmond, E. J., Dion, A., Diss, P. B., Doomra, V., Do, J. H., Drees, A., Drees, K. A., Dumancic, M., Durham, J. M., Durum, A., Elder, T., Enokizono, A., Esha, R., Fadem, B., Fan, W., Feege, N., Fields, D. E., Finger, Jr., M., Finger, M., Firak, D., Fitzgerald, D., Fokin, S. L., Frantz, J. E., Franz, A., Frawley, A. D., Fukuda, Y., Gallus, P., Gal, C., Garg, P., Ge, H., Giordano, F., Glenn, A., Goto, Y., Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Guo, T., Hachiya, T., Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamilton, H. F., Hanks, J., Han, S. Y., Hasegawa, S., Haseler, T. O. S., Hashimoto, K., Hemmick, T. K., He, X., Hill, J. C., Hill, K., Hodges, A., Hollis, R. S., Homma, K., Hong, B., Hoshino, T., Hotvedt, N., Huang, J., Imai, K., Imrek, J., Inaba, M., Iordanova, A., Isenhower, D., Ito, Y., Ivanishchev, D., Jacak, B., Jezghani, M., Jiang, X., Ji, Z., Johnson, B. M., Jorjadze, V., Jouan, D., Jumper, D. S., Kanda, S., Kang, J. H., Kapukchyan, D., Karthas, S., Kawall, D., Kazantsev, A. V., Key, J. A., Khachatryan, V., Khanzadeev, A., Kimelman, B., Kim, C., Kim, D. J., Kim, E. -J., Kim, G. W., Kim, M., Kim, M. H., Kincses, D., Kistenev, E., Kitamura, R., Klatsky, J., Kleinjan, D., Kline, P., Koblesky, T., Komkov, B., Kotov, D., Kovacs, L., Kudo, S., Kurita, K., Kurosawa, M., Kwon, Y., Lajoie, J. G., Lallow, E. O., Lebedev, A., Lee, S., Lee, S. H., Leitch, M. J., Leung, Y. H., Lewis, N. A., Lim, S. H., Liu, M. X., Li, X., Loggins, V. -R., Lökös, S., Loomis, D. A., Lynch, D., Majoros, T., Makdisi, Y. I., Makek, M., Malaev, M., Manion, A., Manko, V. I., Mannel, E., Masuda, H., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Meles, A., Mendoza, M., Mignerey, A. C., Mihalik, D. E., Milov, A., Mishra, D. K., Mitchell, J. T., Mitrankova, M., Mitrankov, Iu., Mitsuka, G., Miyasaka, S., Mizuno, S., Mohanty, A. K., Montuenga, P., Moon, T., Morrison, D. P., Morrow, S. I., Moukhanova, T. V., Mulilo, B., Murakami, T., Murata, J., Mwai, A., Nagai, K., Nagashima, K., Nagashima, T., Nagle, J. L., Nagy, M. I., Nakagawa, I., Nakagomi, H., Nakano, K., Nattrass, C., Netrakanti, P. K., Niida, T., Nishimura, S., Nouicer, R., Novitzky, N., Novotny, R., Novák, T., Nukazuka, G., Nyanin, A. S., O'Brien, E., Ogilvie, C. A., Koop, J. D. Orjuela, Orosz, M., Osborn, J. D., Oskarsson, A., Ozawa, K., Pak, R., Pantuev, V., Papavassiliou, V., Park, J. S., Park, S., Patel, M., Pate, S. F., Peng, J. -C., Peng, W., Perepelitsa, D. V., Perera, G. D. N., Peressounko, D. Yu., PerezLara, C. E., Perry, J., Petti, R., Phipps, M., Pinkenburg, C., Pinson, R., Pisani, R. P., Potekhin, M., Pun, A., Purschke, M. L., Rak, J., Ramson, B. J., Ravinovich, I., Read, K. F., Reynolds, D., Riabov, V., Riabov, Y., Richford, D., Rinn, T., Rolnick, S. D., Rosati, M., Rowan, Z., Rubin, J. G., Runchey, J., Sahlmueller, B., Saito, N., Sakaguchi, T., Sako, H., Samsonov, V., Sarsour, M., Sato, K., Sato, S., Schaefer, B., Schmoll, B. K., Sedgwick, K., Seidl, R., Seleznev, A., Sen, A., Seto, R., Sett, P., Sexton, A., Sharma, D., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Singh, B. K., Singh, C. P., Singh, V., Slunečka, M., Smith, K. L., Snowball, M., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Stankus, P. W., Stepanov, M., Stoll, S. P., Sugitate, T., Sukhanov, A., Sumita, T., Sun, J., Sun, Z., Syed, S., Sziklai, J., Takeda, A., Taketani, A., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarnai, G., Tieulent, R., Timilsina, A., Todoroki, T., Tomášek, M., Towell, C. L., Towell, R., Towell, R. S., Tserruya, I., Ueda, Y., Ujvari, B., van Hecke, H. W., Vazquez-Carson, S., Velkovska, J., Virius, M., Vrba, V., Wang, X. R., Wang, Z., Watanabe, Y., Watanabe, Y. S., Wei, F., White, A. S., Wong, C. P., Woody, C. L., Wysocki, M., Xia, B., Xue, L., Xu, C., Xu, Q., Yalcin, S., Yamaguchi, Y. L., Yanovich, A., Yin, P., Yoon, I., Yoo, J. H., Yushmanov, I. E., Yu, H., Zajc, W. A., Zelenski, A., Zhou, S., and Zou, L.
Subjects: Nuclear Experiment
Abstract: We report the first measurement of the azimuthal anisotropy of J$/\psi$ at forward rapidity ($1.2<|\eta|<2.2$) in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV at the Relativistic Heavy Ion Collider. The data were collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The second Fourier coefficient ($v_2$) of the azimuthal distribution of $J/\psi$ is determined as a function of the transverse momentum ($p_T$) using the event-plane method. The measurements were performed for several selections of collision centrality: 0\%--50\%, 10\%--60\%, and 10\%-40\%. We find that in all cases the values of $v_2(p_T)$, which quantify the elliptic flow of J$/\psi$, are consistent with zero. The results are consistent with measurements at midrapidity, indicating no significant elliptic flow of the J$/\psi$ within the quark-gluon-plasma medium at collision energies of $\sqrt{s_{_{NN}}}=200$ GeV., Comment: 369 authors from 72 institutions, 12 pages, 7 figures, 5 tables. v1 is version submitted to Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html
Published: 2024

12. Measurements at forward rapidity of elliptic flow of charged hadrons and open-heavy-flavor muons in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV

Author: PHENIX Collaboration, Abdulameer, N. J., Acharya, U., Adare, A., Aidala, C., Ajitanand, N. N., Akiba, Y., Alfred, M., Antsupov, S., Aoki, K., Apadula, N., Asano, H., Ayuso, C., Azmoun, B., Babintsev, V., Bai, M., Bandara, N. S., Bannier, B., Bannikov, E., Barish, K. N., Bathe, S., Bazilevsky, A., Beaumier, M., Beckman, S., Belmont, R., Berdnikov, A., Berdnikov, Y., Bichon, L., Blankenship, B., Blau, D. S., Boer, M., Bok, J. S., Borisov, V., Boyle, K., Brooks, M. L., Bryslawskyj, J., Bumazhnov, V., Butler, C., Campbell, S., Roman, V. Canoa, Chen, C. -H., Chen, D., Chiu, M., Chi, C. Y., Choi, I. J., Choi, J. B., Chujo, T., Citron, Z., Connors, M., Corliss, R., Csanád, M., Csörgő, T., Liu, L. D., Danley, T. W., Datta, A., Daugherity, M. S., David, G., DeBlasio, K., Dehmelt, K., Denisov, A., Deshpande, A., Desmond, E. J., Dion, A., Diss, P. B., Doomra, V., Do, J. H., Drees, A., Drees, K. A., Dumancic, M., Durham, J. M., Durum, A., Elder, T., Enokizono, A., Esha, R., Fadem, B., Fan, W., Feege, N., Fields, D. E., Finger, Jr., M., Finger, M., Firak, D., Fitzgerald, D., Fokin, S. L., Frantz, J. E., Franz, A., Frawley, A. D., Fukuda, Y., Gallus, P., Gal, C., Garg, P., Ge, H., Giordano, F., Glenn, A., Goto, Y., Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Guo, T., Hachiya, T., Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamilton, H. F., Hanks, J., Han, S. Y., Hasegawa, S., Haseler, T. O. S., Hashimoto, K., Hemmick, T. K., He, X., Hill, J. C., Hill, K., Hodges, A., Hollis, R. S., Homma, K., Hong, B., Hoshino, T., Hotvedt, N., Huang, J., Imai, K., Imrek, J., Inaba, M., Iordanova, A., Isenhower, D., Ito, Y., Ivanishchev, D., Jacak, B., Jezghani, M., Jiang, X., Ji, Z., Johnson, B. M., Jorjadze, V., Jouan, D., Jumper, D. S., Kanda, S., Kang, J. H., Kapukchyan, D., Karthas, S., Kawall, D., Kazantsev, A. V., Key, J. A., Khachatryan, V., Khanzadeev, A., Kimelman, B., Kim, C., Kim, D. J., Kim, E. -J., Kim, G. W., Kim, M., Kim, M. H., Kincses, D., Kistenev, E., Kitamura, R., Klatsky, J., Kleinjan, D., Kline, P., Koblesky, T., Komkov, B., Kotov, D., Kovacs, L., Kudo, S., Kurita, K., Kurosawa, M., Kwon, Y., Lajoie, J. G., Lallow, E. O., Lebedev, A., Lee, S., Lee, S. H., Leitch, M. J., Leung, Y. H., Lewis, N. A., Lim, S. H., Liu, M. X., Li, X., Loggins, V. -R., Lökös, S., Loomis, D. A., Lynch, D., Majoros, T., Makdisi, Y. I., Makek, M., Malaev, M., Manion, A., Manko, V. I., Mannel, E., Masuda, H., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Meles, A., Mendoza, M., Mignerey, A. C., Mihalik, D. E., Milov, A., Mishra, D. K., Mitchell, J. T., Mitrankova, M., Mitrankov, Iu., Mitsuka, G., Miyasaka, S., Mizuno, S., Mohanty, A. K., Montuenga, P., Moon, T., Morrison, D. P., Morrow, S. I., Moukhanova, T. V., Mulilo, B., Murakami, T., Murata, J., Mwai, A., Nagai, K., Nagashima, K., Nagashima, T., Nagle, J. L., Nagy, M. I., Nakagawa, I., Nakagomi, H., Nakano, K., Nattrass, C., Netrakanti, P. K., Niida, T., Nishimura, S., Nouicer, R., Novitzky, N., Novotny, R., Novák, T., Nukazuka, G., Nyanin, A. S., O'Brien, E., Ogilvie, C. A., Koop, J. D. Orjuela, Orosz, M., Osborn, J. D., Oskarsson, A., Ozawa, K., Pak, R., Pantuev, V., Papavassiliou, V., Park, J. S., Park, S., Patel, M., Pate, S. F., Peng, J. -C., Peng, W., Perepelitsa, D. V., Perera, G. D. N., Peressounko, D. Yu., PerezLara, C. E., Perry, J., Petti, R., Phipps, M., Pinkenburg, C., Pinson, R., Pisani, R. P., Potekhin, M., Pun, A., Purschke, M. L., Rak, J., Ramson, B. J., Ravinovich, I., Read, K. F., Reynolds, D., Riabov, V., Riabov, Y., Richford, D., Rinn, T., Rolnick, S. D., Rosati, M., Rowan, Z., Rubin, J. G., Runchey, J., Sahlmueller, B., Saito, N., Sakaguchi, T., Sako, H., Samsonov, V., Sarsour, M., Sato, K., Sato, S., Schaefer, B., Schmoll, B. K., Sedgwick, K., Seidl, R., Seleznev, A., Sen, A., Seto, R., Sett, P., Sexton, A., Sharma, D., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Singh, B. K., Singh, C. P., Singh, V., Slunečka, M., Smith, K. L., Snowball, M., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Stankus, P. W., Stepanov, M., Stoll, S. P., Sugitate, T., Sukhanov, A., Sumita, T., Sun, J., Sun, Z., Syed, S., Sziklai, J., Takeda, A., Taketani, A., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarnai, G., Tieulent, R., Timilsina, A., Todoroki, T., Tomášek, M., Towell, C. L., Towell, R., Towell, R. S., Tserruya, I., Ueda, Y., Ujvari, B., van Hecke, H. W., Vazquez-Carson, S., Velkovska, J., Virius, M., Vrba, V., Wang, X. R., Wang, Z., Watanabe, Y., Watanabe, Y. S., Wei, F., White, A. S., Wong, C. P., Woody, C. L., Wysocki, M., Xia, B., Xue, L., Xu, C., Xu, Q., Yalcin, S., Yamaguchi, Y. L., Yanovich, A., Yin, P., Yoon, I., Yoo, J. H., Yushmanov, I. E., Yu, H., Zajc, W. A., Zelenski, A., Zhou, S., and Zou, L.
Subjects: Nuclear Experiment
Abstract: We present the first forward-rapidity measurements of elliptic anisotropy of open-heavy-flavor muons at the BNL Relativistic Heavy Ion Collider. The measurements are based on data samples of Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV collected by the PHENIX experiment in 2014 and 2016 with integrated luminosity of 14.5~nb$^{-1}$. The measurements are performed in the pseudorapidity range $1.2<|\eta|<2$ and cover transverse momenta $1
Published: 2024

13. NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections by Neural Implicit Representation

Author: Wang, Yiying, Banerjee, Abhirup, and Grau, Vicente
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Cardiovascular diseases (CVDs) are the most common health threats worldwide. 2D x-ray invasive coronary angiography (ICA) remains as the most widely adopted imaging modality for CVDs diagnosis. However, in current clinical practice, it is often difficult for the cardiologists to interpret the 3D geometry of coronary vessels based on 2D planes. Moreover, due to the radiation limit, in general only two angiographic projections are acquired, providing limited information of the vessel geometry and necessitating 3D coronary tree reconstruction based only on two ICA projections. In this paper, we propose a self-supervised deep learning method called NeCA, which is based on implicit neural representation using the multiresolution hash encoder and differentiable cone-beam forward projector layer in order to achieve 3D coronary artery tree reconstruction from two projections. We validate our method using six different metrics on coronary computed tomography angiography data in terms of right coronary artery and left anterior descending respectively. The evaluation results demonstrate that our NeCA method, without 3D ground truth for supervision and large datasets for training, achieves promising performance in both vessel topology preservation and branch-connectivity maintaining compared to the supervised deep learning model., Comment: 16 pages, 10 figures, 6 tables
Published: 2024

14. Multiplicity dependent $J/\psi$ and $\psi(2S)$ production at forward and backward rapidity in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV

Author: PHENIX Collaboration, Abdulameer, N. J., Acharya, U., Aidala, C., Akiba, Y., Alfred, M., Andrieux, V., Antsupov, S., Apadula, N., Asano, H., Azmoun, B., Babintsev, V., Bandara, N. S., Bannikov, E., Barish, K. N., Bathe, S., Bazilevsky, A., Beaumier, M., Belmont, R., Berdnikov, A., Berdnikov, Y., Bichon, L., Blankenship, B., Blau, D. S., Bok, J. S., Borisov, V., Brooks, M. L., Bryslawskyj, J., Bumazhnov, V., Campbell, S., Cervantes, R., Chen, D., Chiu, M., Chi, C. Y., Choi, I. J., Choi, J. B., Citron, Z., Connors, M., Corliss, R., Cronin, N., Csanád, M., Csörgő, T., Danley, T. W., Daugherity, M. S., David, G., DeBlasio, K., Dehmelt, K., Denisov, A., Deshpande, A., Desmond, E. J., Dion, A., Dixit, D., Doomra, V., Do, J. H., Drees, A., Drees, K. A., Durham, J. M., Durum, A., En'yo, H., Enokizono, A., Esha, R., Fadem, B., Fan, W., Feege, N., Fields, D. E., Finger, Jr., M., Finger, M., Firak, D., Fitzgerald, D., Fokin, S. L., Frantz, J. E., Franz, A., Frawley, A. D., Fukuda, Y., Gallus, P., Gal, C., Garg, P., Ge, H., Giordano, F., Goto, Y., Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Guo, T., Guragain, H., Hachiya, T., Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamilton, H. F., Hanks, J., Han, S. Y., Hasegawa, S., Haseler, T. O. S., Hemmick, T. K., He, X., Hill, J. C., Hill, K., Hodges, A., Hollis, R. S., Homma, K., Hong, B., Hoshino, T., Hotvedt, N., Huang, J., Imai, K., Inaba, M., Iordanova, A., Isenhower, D., Ivanishchev, D., Jacak, B., Jezghani, M., Jiang, X., Ji, Z., Johnson, B. M., Jouan, D., Jumper, D. S., Kang, J. H., Kapukchyan, D., Karthas, S., Kawall, D., Kazantsev, A. V., Khachatryan, V., Khanzadeev, A., Kim, C., Kim, E. -J., Kim, M., Kincses, D., Kistenev, E., Klatsky, J., Kline, P., Koblesky, T., Kotov, D., Kovacs, L., Kudo, S., Kurita, K., Kwon, Y., Lajoie, J. G., Lebedev, A., Lee, S., Leitch, M. J., Leung, Y. H., Lim, S. H., Liu, M. X., Li, X., Loggins, V. -R., Lökös, S., Loomis, D. A., Lovasz, K., Lynch, D., Majoros, T., Makdisi, Y. I., Makek, M., Manko, V. I., Mannel, E., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Mendoza, M., Mignerey, A. C., Milov, A., Mishra, D. K., Mitchell, J. T., Mitrankova, M., Mitrankov, Iu., Mitsuka, G., Miyasaka, S., Mizuno, S., Montuenga, P., Moon, T., Morrison, D. P., Mulilo, B., Murakami, T., Murata, J., Nagai, K., Nagashima, K., Nagashima, T., Nagle, J. L., Nagy, M. I., Nakagawa, I., Nakano, K., Nattrass, C., Niida, T., Nouicer, R., Novitzky, N., Novák, T., Nukazuka, G., Nyanin, A. S., O'Brien, E., Ogilvie, C. A., Koop, J. D. Orjuela, Orosz, M., Osborn, J. D., Oskarsson, A., Ottino, G. J., Ozawa, K., Pantuev, V., Papavassiliou, V., Park, J. S., Park, S., Patel, M., Pate, S. F., Perepelitsa, D. V., Perera, G. D. N., Peressounko, D. Yu., PerezLara, C. E., Perry, J., Petti, R., Phipps, M., Pinkenburg, C., Pisani, R. P., Potekhin, M., Purschke, M. L., Read, K. F., Reynolds, D., Riabov, V., Riabov, Y., Richford, D., Rinn, T., Rolnick, S. D., Rosati, M., Rowan, Z., Safonov, A. S., Sakaguchi, T., Sako, H., Samsonov, V., Sarsour, M., Sato, S., Schaefer, B., Schmoll, B. K., Sedgwick, K., Seidl, R., Seleznev, A., Sen, A., Seto, R., Sexton, A., Sharma, D., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shioya, T., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Singh, B. K., Singh, C. P., Singh, V., Slunečka, M., Smith, K. L., Snowball, M., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Stankus, P. W., Stoll, S. P., Sugitate, T., Sukhanov, A., Sumita, T., Sun, J., Sun, Z., Sziklai, J., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Tarnai, G., Tieulent, R., Timilsina, A., Todoroki, T., Tomášek, M., Towell, C. L., Towell, R. S., Tserruya, I., Ueda, Y., Ujvari, B., van Hecke, H. W., Velkovska, J., Virius, M., Vrba, V., Vukman, N., Wang, X. R., Watanabe, Y. S., Woody, C. L., Xue, L., Xu, C., Xu, Q., Yalcin, S., Yamaguchi, Y. L., Yamamoto, H., Yanovich, A., Yoon, I., Yoo, J. H., Yushmanov, I. E., Yu, H., Zajc, W. A., Zelenski, A., and Zou, L.
Subjects: High Energy Physics - Experiment
Abstract: The $J/\psi$ and $\psi(2S)$ charmonium states, composed of $c\bar{c}$ quark pairs and known since the 1970s, are widely believed to serve as ideal probes to test quantum chromodynamics in high-energy hadronic interactions. However, there is not yet a complete understanding of the charmonium-production mechanism. Recent measurements of $J/\psi$ production as a function of event charged-particle multiplicity at the collision energies of both the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) show enhanced $J/\psi$ production yields with increasing multiplicity. One potential explanation for this type of dependence is multiparton interactions (MPI). We carry out the first measurements of self-normalized $J/\psi$ yields and the $\psi(2S)$ to $J/\psi$ ratio at both forward and backward rapidities as a function of self-normalized charged-particle multiplicity in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV. In addition, detailed {\sc pythia} studies tuned to RHIC energies were performed to investigate the MPI impacts. We find that the PHENIX data at RHIC are consistent with recent LHC measurements and can only be described by {\sc pythia} calculations that include MPI effects. The forward and backward $\psi(2S)$ to $J/\psi$ ratio, which serves as a unique and powerful approach to study final-state effects on charmonium production, is found to be less dependent on the charged-particle multiplicity., Comment: 301 authors from 69 institutions, 8 pages, 3 figures. v1 is version submitted to Physical Review D Letters. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html
Published: 2024

15. Personalized Topology-Informed 12-Lead ECG Electrode Localization from Incomplete Cardiac MRIs for Efficient Cardiac Digital Twins

Author: Li, Lei, Smith, Hannah, Lyu, Yilin, Camps, Julia, Rodriguez, Blanca, Banerjee, Abhirup, and Grau, Vicente
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
Abstract: Cardiac digital twins (CDTs) offer personalized \textit{in-silico} cardiac representations for the inference of multi-scale properties tied to cardiac mechanisms. The creation of CDTs requires precise information about the electrode position on the torso, especially for the personalized electrocardiogram (ECG) calibration. However, current studies commonly rely on additional acquisition of torso imaging and manual/semi-automatic methods for ECG electrode localization. In this study, we propose a novel and efficient topology-informed model to fully automatically extract personalized ECG electrode locations from 2D clinically standard cardiac MRIs. Specifically, we obtain the sparse torso contours from the cardiac MRIs and then localize the electrodes from the contours. Cardiac MRIs aim at imaging of the heart instead of the torso, leading to incomplete torso geometry within the imaging. To tackle the missing topology, we incorporate the electrodes as a subset of the keypoints, which can be explicitly aligned with the 3D torso topology. The experimental results demonstrate that the proposed model outperforms the time-consuming conventional method in terms of accuracy (Euclidean distance: $1.24 \pm 0.293$ cm vs. $1.48 \pm 0.362$ cm) and efficiency ($2$~s vs. $30$-$35$~min). We further demonstrate the effectiveness of using the detected electrodes for \textit{in-silico} ECG simulation, highlighting their potential for creating accurate and efficient CDT models. The code will be released publicly after the manuscript is accepted for publication., Comment: 12 pages
Published: 2024

16. Measurement of inclusive jet cross section and substructure in $p$$+$$p$ collisions at $\sqrt{s_{_{NN}}}=200$ GeV

Author: PHENIX Collaboration, Abdulameer, N. J., Acharya, U., Aidala, C., Ajitanand, N. N., Akiba, Y., Akimoto, R., Alexander, J., Alfred, M., Andrieux, V., Antsupov, S., Aoki, K., Apadula, N., Asano, H., Atomssa, E. T., Awes, T. C., Azmoun, B., Babintsev, V., Bai, M., Bai, X., Bandara, N. S., Bannier, B., Bannikov, E., Barish, K. N., Bathe, S., Baublis, V., Baumann, C., Baumgart, S., Bazilevsky, A., Beaumier, M., Belmont, R., Berdnikov, A., Berdnikov, Y., Bichon, L., Black, D., Blankenship, B., Blau, D. S., Bok, J. S., Borisov, V., Boyle, K., Brooks, M. L., Bryslawskyj, J., Buesching, H., Bumazhnov, V., Butsyk, S., Campbell, S., Cervantes, R., Chen, C. -H., Chen, D., Chiu, M., Chi, C. Y., Choi, I. J., Choi, J. B., Choi, S., Christiansen, P., Chujo, T., Cianciolo, V., Citron, Z., Cole, B. A., Connors, M., Corliss, R., Cronin, N., Crossette, N., Csanád, M., Csörgő, T., D'Orazio, L., Danley, T. W., Datta, A., Daugherity, M. S., David, G., DeBlasio, K., Dehmelt, K., Denisov, A., Deshpande, A., Desmond, E. J., Ding, L., Dion, A., Dixit, D., Doomra, V., Do, J. H., Drapier, O., Drees, A., Drees, K. A., Durham, J. M., Durum, A., En'yo, H., Engelmore, T., Enokizono, A., Esha, R., Eyser, K. O., Fadem, B., Fan, W., Feege, N., Fields, D. E., Finger, Jr., M., Finger, M., Firak, D., Fitzgerald, D., Fleuret, F., Fokin, S. L., Frantz, J. E., Franz, A., Frawley, A. D., Fukao, Y., Fukuda, Y., Fusayasu, T., Gainey, K., Gallus, P., Gal, C., Garg, P., Garishvili, A., Garishvili, I., Ge, H., Giordano, F., Glenn, A., Gong, X., Gonin, M., Goto, Y., de Cassagnac, R. Granier, Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Guo, T., Guragain, H., Gu, Y., Hachiya, T., Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamilton, H. F., Hanks, J., Han, S. Y., Hasegawa, S., Haseler, T. O. S., Hashimoto, K., Hayano, R., Hemmick, T. K., Hester, T., He, X., Hill, J. C., Hill, K., Hodges, A., Hollis, R. S., Homma, K., Hong, B., Hoshino, T., Hotvedt, N., Huang, J., Ichihara, T., Ikeda, Y., Imai, K., Imazu, Y., Inaba, M., Iordanova, A., Isenhower, D., Isinhue, A., Ivanishchev, D., Jeon, S. J., Jezghani, M., Jiang, X., Ji, Z., Johnson, B. M., Joo, K. S., Jouan, D., Jumper, D. S., Kamin, J., Kanda, S., Kang, B. H., Kang, J. H., Kang, J. S., Kapukchyan, D., Kapustinsky, J., Karthas, S., Kawall, D., Kazantsev, A. V., Key, J. A., Khachatryan, V., Khandai, P. K., Khanzadeev, A., Kijima, K. M., Kim, C., Kim, D. J., Kim, E. -J., Kim, M., Kim, Y. -J., Kim, Y. K., Kincses, D., Kistenev, E., Klatsky, J., Kleinjan, D., Kline, P., Koblesky, T., Kofarago, M., Komkov, B., Koster, J., Kotchetkov, D., Kotov, D., Kovacs, L., Krizek, F., Kudo, S., Kurita, K., Kurosawa, M., Kwon, Y., Lai, Y. S., Lajoie, J. G., Lebedev, A., Lee, D. M., Lee, G. H., Lee, J., Lee, K. B., Lee, K. S., Lee, S., Lee, S. H., Leitch, M. J., Leitgab, M., Leung, Y. H., Lewis, B., Lim, S. H., Liu, M. X., Li, X., Loggins, V. -R., Lokos, S., Loomis, D. A., Lovasz, K., Lynch, D., Maguire, C. F., Majoros, T., Makdisi, Y. I., Makek, M., Manion, A., Manko, V. I., Mannel, E., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Meles, A., Mendoza, M., Meredith, B., Miake, Y., Mibe, T., Mignerey, A. C., Milov, A., Mishra, D. K., Mitchell, J. T., Mitrankova, M., Mitrankov, Iu., Mitsuka, G., Miyasaka, S., Mizuno, S., Mohanty, A. K., Mohapatra, S., Montuenga, P., Moon, T., Morrison, D. P., Moskowitz, M., Moukhanova, T. V., Mulilo, B., Murakami, T., Murata, J., Mwai, A., Nagae, T., Nagai, K., Nagamiya, S., Nagashima, K., Nagashima, T., Nagle, J. L., Nagy, M. I., Nakagawa, I., Nakamiya, Y., Nakamura, K. R., Nakamura, T., Nakano, K., Nattrass, C., Netrakanti, P. K., Nihashi, M., Niida, T., Nouicer, R., Novitzky, N., Novák, T., Nukazuka, G., Nyanin, A. S., O'Brien, E., Ogilvie, C. A., Oide, H., Okada, K., Koop, J. D. Orjuela, Orosz, M., Osborn, J. D., Oskarsson, A., Ottino, G. J., Ozawa, K., Pak, R., Pantuev, V., Papavassiliou, V., Park, I. H., Park, J. S., Park, S., Park, S. K., Patel, L., Patel, M., Pate, S. F., Peng, J. -C., Perepelitsa, D. V., Perera, G. D. N., Peressounko, D. Yu., PerezLara, C. E., Perry, J., Petti, R., Phipps, M., Pinkenburg, C., Pisani, R. P., Potekhin, M., Purschke, M. L., Qu, H., Rak, J., Ravinovich, I., Read, K. F., Reynolds, D., Riabov, V., Riabov, Y., Richardson, E., Richford, D., Rinn, T., Riveli, N., Roach, D., Rolnick, S. D., Rosati, M., Rowan, Z., Ryu, M. S., Safonov, A. S., Sahlmueller, B., Saito, N., Sakaguchi, T., Sako, H., Samsonov, V., Sarsour, M., Sato, S., Sawada, S., Schaefer, B., Schmoll, B. K., Sedgwick, K., Seele, J., Seidl, R., Sekiguchi, Y., Seleznev, A., Sen, A., Seto, R., Sett, P., Sexton, A., Sharma, D., Shaver, A., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shioya, T., Shoji, K., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Singh, B. K., Singh, C. P., Singh, V., Skolnik, M., Slunečka, M., Smith, K. L., Snowball, M., Solano, S., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Stankus, P. W., Steinberg, P., Stenlund, E., Stepanov, M., Ster, A., Stoll, S. P., Stone, M. R., Sugitate, T., Sukhanov, A., Sumita, T., Sun, J., Sun, Z., Sziklai, J., Takahara, A., Taketani, A., Tanaka, Y., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarnai, G., Tennant, E., Tieulent, R., Timilsina, A., Todoroki, T., Tomášek, M., Torii, H., Towell, C. L., Towell, R. S., Tserruya, I., Ueda, Y., Ujvari, B., van Hecke, H. W., Vargyas, M., Vazquez-Zambrano, E., Veicht, A., Velkovska, J., Virius, M., Vrba, V., Vukman, N., Vznuzdaev, E., Vértesi, R., Wang, X. R., Watanabe, D., Watanabe, K., Watanabe, Y., Watanabe, Y. S., Wei, F., Whitaker, S., Wolin, S., Woody, C. L., Wysocki, M., Xia, B., Xue, L., Xu, C., Xu, Q., Yalcin, S., Yamaguchi, Y. L., Yamamoto, H., Yanovich, A., Yokkaichi, S., Yoon, I., Yoo, J. H., Younus, I., You, Z., Yushmanov, I. E., Yu, H., Zajc, W. A., Zelenski, A., Zhou, S., and Zou, L.
Subjects: High Energy Physics - Experiment, Nuclear Experiment
Abstract: The jet cross-section and jet-substructure observables in $p$$+$$p$ collisions at $\sqrt{s}=200$ GeV were measured by the PHENIX Collaboration at the Relativistic Heavy Ion Collider (RHIC). Jets are reconstructed from charged-particle tracks and electromagnetic-calorimeter clusters using the anti-$k_{t}$ algorithm with a jet radius $R=0.3$ for jets with transverse momentum within $8.0
Published: 2024

17. Relational Graph Convolutional Networks Do Not Learn Sound Rules

Author: Morris, Matthew, Cucala, David J. Tena, Grau, Bernardo Cuenca, and Horrocks, Ian
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Logic in Computer Science, 03B70, I.2.6, G.2.2, I.2.4, I.2.3
Abstract: Graph neural networks (GNNs) are frequently used to predict missing facts in knowledge graphs (KGs). Motivated by the lack of explainability for the outputs of these models, recent work has aimed to explain their predictions using Datalog, a widely used logic-based formalism. However, such work has been restricted to certain subclasses of GNNs. In this paper, we consider one of the most popular GNN architectures for KGs, R-GCN, and we provide two methods to extract rules that explain its predictions and are sound, in the sense that each fact derived by the rules is also predicted by the GNN, for any input dataset. Furthermore, we provide a method that can verify that certain classes of Datalog rules are not sound for the R-GCN. In our experiments, we train R-GCNs on KG completion benchmarks, and we are able to verify that no Datalog rule is sound for these models, even though the models often obtain high to near-perfect accuracy. This raises some concerns about the ability of R-GCN models to generalise and about the explainability of their predictions. We further provide two variations to the training paradigm of R-GCN that encourage it to learn sound rules and find a trade-off between model accuracy and the number of learned sound rules., Comment: Full version (with appendices) of paper accepted to KR 2024 (21st International Conference on Principles of Knowledge Representation and Reasoning)
Published: 2024

18. Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation

Author: Wu, Junde, Zhu, Jiayuan, Qi, Yunli, Chen, Jingkun, Xu, Min, Menolascina, Filippo, and Grau, Vicente
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: We introduce a novel graph-based Retrieval-Augmented Generation (RAG) framework specifically designed for the medical domain, called \textbf{MedGraphRAG}, aimed at enhancing Large Language Model (LLM) capabilities for generating evidence-based medical responses, thereby improving safety and reliability when handling private medical data. Graph-based RAG (GraphRAG) leverages LLMs to organize RAG data into graphs, showing strong potential for gaining holistic insights from long-form documents. However, its standard implementation is overly complex for general use and lacks the ability to generate evidence-based responses, limiting its effectiveness in the medical field. To extend the capabilities of GraphRAG to the medical domain, we propose unique Triple Graph Construction and U-Retrieval techniques over it. In our graph construction, we create a triple-linked structure that connects user documents to credible medical sources and controlled vocabularies. In the retrieval process, we propose U-Retrieval which combines Top-down Precise Retrieval with Bottom-up Response Refinement to balance global context awareness with precise indexing. These effort enable both source information retrieval and comprehensive response generation. Our approach is validated on 9 medical Q\&A benchmarks, 2 health fact-checking benchmarks, and one collected dataset testing long-form generation. The results show that MedGraphRAG consistently outperforms state-of-the-art models across all benchmarks, while also ensuring that responses include credible source documentation and definitions. Our code is released at: https://github.com/MedicineToken/Medical-Graph-RAG.
Published: 2024

19. State preparation of lattice field theories using quantum optimal control

Author: Araz, Jack Y., Bhowmick, Siddhanth, Grau, Matt, McEntire, Thomas J., and Ringer, Felix
Subjects: Quantum Physics, High Energy Physics - Lattice, High Energy Physics - Phenomenology
Abstract: We explore the application of quantum optimal control (QOC) techniques to state preparation of lattice field theories on quantum computers. As a first example, we focus on the Schwinger model, quantum electrodynamics in 1+1 dimensions. We demonstrate that QOC can significantly speed up the ground state preparation compared to gate-based methods, even for models with long-range interactions. Using classical simulations, we explore the dependence on the inter-qubit coupling strength and the device connectivity, and we study the optimization in the presence of noise. While our simulations indicate potential speedups, the results strongly depend on the device specifications. In addition, we perform exploratory studies on the preparation of thermal states. Our results motivate further studies of QOC techniques in the context of quantum simulations for fundamental physics., Comment: 13 pages + references, 11 figures, 3 tables
Published: 2024

20. Deep Learning-based 3D Coronary Tree Reconstruction from Two 2D Non-simultaneous X-ray Angiography Projections

Author: Wang, Yiying, Banerjee, Abhirup, Choudhury, Robin P., and Grau, Vicente
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract: Cardiovascular diseases (CVDs) are the most common cause of death worldwide. Invasive x-ray coronary angiography (ICA) is one of the most important imaging modalities for the diagnosis of CVDs. ICA typically acquires only two 2D projections, which makes the 3D geometry of coronary vessels difficult to interpret, thus requiring 3D coronary tree reconstruction from two projections. State-of-the-art approaches require significant manual interactions and cannot correct the non-rigid cardiac and respiratory motions between non-simultaneous projections. In this study, we propose a novel deep learning pipeline. We leverage the Wasserstein conditional generative adversarial network with gradient penalty, latent convolutional transformer layers, and a dynamic snake convolutional critic to implicitly compensate for the non-rigid motion and provide 3D coronary tree reconstruction. Through simulating projections from coronary computed tomography angiography (CCTA), we achieve the generalisation of 3D coronary tree reconstruction on real non-simultaneous ICA projections. We incorporate an application-specific evaluation metric to validate our proposed model on both a CCTA dataset and a real ICA dataset, together with Chamfer L1 distance. The results demonstrate the good performance of our model in vessel topology preservation, recovery of missing features, and generalisation ability to real ICA data. To the best of our knowledge, this is the first study that leverages deep learning to achieve 3D coronary tree reconstruction from two real non-simultaneous x-ray angiography projections., Comment: 16 pages, 13 figures, 3 tables
Published: 2024

21. Centrality dependence of L\'evy-stable two-pion Bose-Einstein correlations in $\sqrt{s_{_{NN}}}=200$ GeV Au$+$Au collisions

Author: PHENIX Collaboration, Abdulameer, N. J., Acharya, U., Adare, A., Aidala, C., Ajitanand, N. N., Akiba, Y., Akimoto, R., Al-Ta'ani, H., Alexander, J., Angerami, A., Aoki, K., Apadula, N., Aramaki, Y., Asano, H., Aschenauer, E. C., Atomssa, E. T., Awes, T. C., Azmoun, B., Babintsev, V., Bai, M., Bannier, B., Barish, K. N., Bassalleck, B., Bathe, S., Baublis, V., Baumgart, S., Bazilevsky, A., Belmont, R., Berdnikov, A., Berdnikov, Y., Bichon, L., Blankenship, B., Blau, D. S., Bok, J. S., Borisov, V., Boyle, K., Brooks, M. L., Buesching, H., Bumazhnov, V., Butsyk, S., Campbell, S., Castera, P., Chen, C. -H., Chen, D., Chiu, M., Chi, C. Y., Choi, I. J., Choi, J. B., Choi, S., Choudhury, R. K., Christiansen, P., Chujo, T., Chvala, O., Cianciolo, V., Citron, Z., Cole, B. A., Connors, M., Corliss, R., Csanád, M., Csörgő, T., D'Orazio, L., Dairaku, S., Datta, A., Daugherity, M. S., David, G., Denisov, A., Deshpande, A., Desmond, E. J., Dharmawardane, K. V., Dietzsch, O., Ding, L., Dion, A., Donadelli, M., Doomra, V., Drapier, O., Drees, A., Drees, K. A., Durham, J. M., Durum, A., Edwards, S., Efremenko, Y. V., Engelmore, T., Enokizono, A., Esha, R., Eyser, K. O., Fadem, B., Fields, D. E., Finger, Jr., M., Finger, M., Firak, D., Fitzgerald, D., Fleuret, F., Fokin, S. L., Frantz, J. E., Franz, A., Frawley, A. D., Fukao, Y., Fusayasu, T., Gainey, K., Gal, C., Garishvili, A., Garishvili, I., Glenn, A., Gong, X., Gonin, M., Goto, Y., de Cassagnac, R. Granier, Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Guo, L., Guo, T., Gustafsson, H. -Å., Hachiya, T., Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hanks, J., Hashimoto, K., Haslum, E., Hayano, R., Hemmick, T. K., Hester, T., He, X., Hill, J. C., Hodges, A., Hollis, R. S., Homma, K., Hong, B., Horaguchi, T., Hori, Y., Ichihara, T., Iinuma, H., Ikeda, Y., Imrek, J., Inaba, M., Iordanova, A., Isenhower, D., Issah, M., Ivanishchev, D., Jacak, B. V., Javani, M., Jiang, X., Ji, Z., Johnson, B. M., Joo, K. S., Jouan, D., Jumper, D. S., Kamin, J., Kaneti, S., Kang, B. H., Kang, J. H., Kang, J. S., Kapustinsky, J., Karatsu, K., Kasai, M., Kasza, G., Kawall, D., Kazantsev, A. V., Kempel, T., Khanzadeev, A., Kijima, K. M., Kim, B. I., Kim, C., Kim, D. J., Kim, E. -J., Kim, H. J., Kim, K. -B., Kim, Y. -J., Kim, Y. K., Kinney, E., Kiss, Á., Kistenev, E., Klatsky, J., Kleinjan, D., Kline, P., Komatsu, Y., Komkov, B., Koster, J., Kotchetkov, D., Kotov, D., Kovacs, L., Krizek, F., Král, A., Kunde, G. J., Kurgyis, B., Kurita, K., Kurosawa, M., Kwon, Y., Kyle, G. S., Lai, Y. S., Lajoie, J. G., Lebedev, A., Lee, B., Lee, D. M., Lee, J., Lee, K. B., Lee, K. S., Lee, S. H., Lee, S. R., Leitch, M. J., Leite, M. A. L., Leitgab, M., Lewis, B., Lim, S. H., Levy, L. A. Linden, Liu, M. X., Lökös, S., Loomis, D. A., Love, B., Maguire, C. F., Makdisi, Y. I., Makek, M., Manion, A., Manko, V. I., Mannel, E., Masumoto, S., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Mendoza, M., Meredith, B., Miake, Y., Mibe, T., Mignerey, A. C., Milov, A., Mishra, D. K., Mitchell, J. T., Mitrankova, M., Mitrankov, Iu., Miyachi, Y., Miyasaka, S., Mohanty, A. K., Mohapatra, S., Moon, H. J., Morrison, D. P., Motschwiller, S., Moukhanova, T. V., Mulilo, B., Murakami, T., Murata, J., Mwai, A., Nagae, T., Nagamiya, S., Nagle, J. L., Nagy, M. I., Nakagawa, I., Nakamiya, Y., Nakamura, K. R., Nakamura, T., Nakano, K., Nattrass, C., Nederlof, A., Nihashi, M., Nouicer, R., Novák, T., Novitzky, N., Nukazuka, G., Nyanin, A. S., O'Brien, E., Ogilvie, C. A., Okada, K., Orosz, M., Oskarsson, A., Ouchida, M., Ozawa, K., Pak, R., Pantuev, V., Papavassiliou, V., Park, B. H., Park, I. H., Park, J. S., Park, S., Park, S. K., Patel, L., Pate, S. F., Pei, H., Peng, J. -C., Pereira, H., Peressounko, D. Yu., Petti, R., Pinkenburg, C., Pisani, R. P., Potekhin, M., Proissl, M., Purschke, M. L., Qu, H., Rak, J., Ravinovich, I., Read, K. F., Reynolds, D., Riabov, V., Riabov, Y., Richardson, E., Richford, D., Roach, D., Roche, G., Rolnick, S. D., Rosati, M., Sahlmueller, B., Saito, N., Sakaguchi, T., Samsonov, V., Sano, M., Sarsour, M., Sawada, S., Sedgwick, K., Seidl, R., Sen, A., Seto, R., Sharma, D., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shoji, K., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Sim, K. S., Singh, B. K., Singh, C. P., Singh, V., Slunečka, M., Smith, K. L., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Stankus, P. W., Stenlund, E., Stepanov, M., Ster, A., Stoll, S. P., Sugitate, T., Sukhanov, A., Sun, J., Sun, Z., Sziklai, J., Takagui, E. M., Takahara, A., Taketani, A., Tanaka, Y., Taneja, S., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tennant, E., Themann, H., Todoroki, T., Tomášek, L., Tomášek, M., Torii, H., Towell, R. S., Tserruya, I., Tsuchimoto, Y., Tsuji, T., Ujvari, B., Vale, C., van Hecke, H. W., Vargyas, M., Vazquez-Zambrano, E., Veicht, A., Velkovska, J., Virius, M., Vossen, A., Vrba, V., Vznuzdaev, E., Vértesi, R., Wang, X. R., Watanabe, D., Watanabe, K., Watanabe, Y., Watanabe, Y. S., Wei, F., Wei, R., White, S. N., Winter, D., Wolin, S., Woody, C. L., Wysocki, M., Xia, B., Yamaguchi, Y. L., Yang, R., Yanovich, A., Ying, J., Yokkaichi, S., Younus, I., You, Z., Yushmanov, I. E., Zajc, W. A., and Zelenski, A.
Subjects: Nuclear Experiment
Abstract: The PHENIX experiment measured the centrality dependence of two-pion Bose-Einstein correlation functions in $\sqrt{s_{_{NN}}}=200$~GeV Au$+$Au collisions at the Relativistic Heavy Ion Collider at Brookhaven National Laboratory. The data are well represented by L\'evy-stable source distributions. The extracted source parameters are the correlation-strength parameter $\lambda$, the L\'evy index of stability $\alpha$, and the L\'evy-scale parameter $R$ as a function of transverse mass $m_T$ and centrality. The $\lambda(m_T)$ parameter is constant at larger values of $m_T$, but decreases as $m_T$ decreases. The L\'evy scale parameter $R(m_T)$ decreases with $m_T$ and exhibits proportionality to the length scale of the nuclear overlap region. The L\'evy exponent $\alpha(m_T)$ is independent of $m_T$ within uncertainties in each investigated centrality bin, but shows a clear centrality dependence. At all centralities, the L\'evy exponent $\alpha$ is significantly different from that of Gaussian ($\alpha=2$) or Cauchy ($\alpha=1$) source distributions. Comparisons to the predictions of Monte-Carlo simulations of resonance-decay chains show that in all but the most peripheral centrality class (50%-60%), the obtained results are inconsistent with the measurements, unless a significant reduction of the in-medium mass of the $\eta'$ meson is included. In each centrality class, the best value of the in-medium $\eta'$ mass is compared to the mass of the $\eta$ meson, as well as to several theoretical predictions that consider restoration of $U_A(1)$ symmetry in hot hadronic matter., Comment: 401 authors from 75 institutions, 20 pages, 15 figures, 2 tables. v1 is version submitted to Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html
Published: 2024

22. LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

Author: Huang, Jingwei, Wang, Chuansheng, Huang, Jiayan, Fan, Haoyi, Grau, Antoni, and Zhang, Fuquan
Subjects: Electrical Engineering and Systems Science - Signal Processing, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract: Driver drowsiness electroencephalography (EEG) signal monitoring can timely alert drivers of their drowsiness status, thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end lightweight dual graph convolutional network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. Furthermore, to deploy our method on the fourth-generation Raspberry Pi, we utilize Adaptive Pruning Optimization (APO) on both channels and neurons, reducing inference latency by almost half. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization compared to existing state-of-the-art algorithms. All our source code can be found at https://github.com/BryantDom/Driver-Drowsiness-Monitoring.
Published: 2024

23. A detailed study of the very-high-energy Crab pulsar emission with the LST-1

Author: Project, CTA-LST, Abe, K., Abe, S., Abhishek, A., Acero, F., Aguasca-Cabot, A., Agudo, I., Crespo, N. Alvarez, Antonelli, L. A., Aramo, C., Arbet-Engels, A., Arcaro, C., Artero, M., Asano, K., Aubert, P., Baktash, A., Bamba, A., Larriva, A. Baquero, Baroncelli, L., de Almeida, U. Barres, Barrio, J. A., Batkovic, I., Baxter, J., González, J. Becerra, Bernardini, E., Medrano, J. Bernete, Berti, A., Bhattacharjee, P., Bigongiari, C., Bissaldi, E., Blanch, O., Bonnoli, G., Bordas, P., Brunelli, G., Bulgarelli, A., Burelli, I., Burmistrov, L., Buscemi, M., Cardillo, M., Caroff, S., Carosi, A., Carrasco, M. S., Cassol, F., Castrejón, N., Cauz, D., Cerasole, D., Ceribella, G., Chai, Y., Cheng, K., Chiavassa, A., Chikawa, M., Chon, G., Chytka, L., Cicciari, G. M., Cifuentes, A., Contreras, J. L., Cortina, J., Costantini, H., Da Vela, P., Dalchenko, M., Dazzi, F., De Angelis, A., de Lavergne, M. de Bony, De Lotto, B., de Menezes, R., Del Peral, L., Delgado, C., Mengual, J. Delgado, della Volpe, D., Dellaiera, M., Di Piano, A., Di Pierro, F., Di Tria, R., Di Venere, L., Díaz, C., Dominik, R. M., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Eisenberger, L., Elsässer, D., Emery, G., Escudero, J., Ramazani, V. Fallah, Ferrarotto, F., Fiasson, A., Foffano, L., Coromina, L. Freixas, Fröse, S., Fukazawa, Y., López, R. Garcia, Gasbarra, C., Gasparrini, D., Gavira, L., Geyer, D., Paiva, J. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Godinovic, N., Grau, R., Green, D., Green, J., Gunji, S., Günther, P., Hackfeld, J., Hadasch, D., Hahn, A., Hassan, T., Hayashi, K., Heckmann, L., Heller, M., Llorente, J. Herrera, Hirotani, K., Hoffmann, D., Horns, D., Houles, J., Hrabovsky, M., Hrupec, D., Hui, D., Iarlori, M., Imazawa, R., Inada, T., Inome, Y., Ioka, K., Iori, M., Martinez, I. Jimenez, Quiles, J. Jiménez, Jurysek, J., Kagaya, M., Karas, V., Katagiri, H., Kataoka, J., Kerszberg, D., Kobayashi, Y., Kohri, K., Kong, A., Kubo, H., Kushida, J., Lainez, M., Lamanna, G., Lamastra, A., Lemoigne, L., Linhoff, M., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Lorini, A., Bahilo, J. Lozano, Luque-Escamilla, P. L., Majumdar, P., Makariev, M., Mallamaci, M., Mandat, D., Manganaro, M., Manicò, G., Mannheim, K., Marchesi, S., Mariotti, M., Marquez, P., Marsella, G., Martí, J., Martinez, O., Martínez, G., Martínez, M., Mas-Aguilar, A., Maurin, G., Mazin, D., Guillen, E. Mestre, Micanovic, S., Miceli, D., Miener, T., Miranda, J. M., Mirzoyan, R., Mizuno, T., Gonzalez, M. Molero, Molina, E., Montaruli, T., Moralejo, A., Morcuende, D., Morselli, A., Moya, V., Muraishi, H., Nagataki, S., Nakamori, T., Neronov, A., Nickel, L., Rosillo, M. Nievas, Nikolic, L., Nishijima, K., Noda, K., Nosek, D., Novotny, V., Nozaki, S., Ohishi, M., Ohtani, Y., Oka, T., Okumura, A., Orito, R., Otero-Santos, J., Ottanelli, P., Owen, E., Palatiello, M., Paneque, D., Pantaleo, F. R., Paoletti, R., Paredes, J. M., Pech, M., Pecimotika, M., Peresano, M., Pfeiffle, F., Pietropaolo, E., Pihet, M., Pirola, G., Plard, C., Podobnik, F., Pons, E., Prandini, E., Priyadarshi, C., Prouza, M., Rando, R., Rhode, W., Ribó, M., Righi, C., Rizi, V., Fernandez, G. Rodriguez, Frías, M. D. Rodríguez, Saito, T., Sakurai, S., Sanchez, D. A., Sano, H., Šarić, T., Sato, Y., Saturni, F. G., Savchenko, V., Schiavone, F., Schleicher, B., Schmuckermaier, F., Schubert, J. L., Schussler, F., Schweizer, T., Arroyo, M. Seglar, Siegert, T., Silvia, R., Sitarek, J., Sliusar, V., Strišković, J., Strzys, M., Suda, Y., Tajima, H., Takahashi, H., Takahashi, M., Takata, J., Takeishi, R., Tam, P. H. T., Tanaka, S. J., Tateishi, D., Tavernier, T., Temnikov, P., Terada, Y., Terauchi, K., Terzic, T., Teshima, M., Tluczykont, M., Tokanai, F., Torres, D. F., Travnicek, P., Truzzi, S., Tutone, A., Vacula, M., Vallania, P., van Scherpenberg, J., Acosta, M. Vázquez, Verna, G., Viale, I., Vigliano, A., Vigorito, C. F., Visentin, E., Vitale, V., Voitsekhovskyi, V., Voutsinas, G., Vovk, I., Vuillaume, T., Walter, R., Wan, L., Will, M., Yamamoto, T., Yamazaki, R., Yeung, P. K. H., Yoshida, T., Yoshikoshi, T., Zhang, W., and Zywucka, N.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Context: There are currently three pulsars firmly detected by imaging atmospheric Cherenkov telescopes (IACTs), two of them reaching TeV energies, challenging models of very-high-energy (VHE) emission in pulsars. More precise observations are needed to better characterize pulsar emission at these energies. The LST-1 is the prototype of the Large-Sized Telescope, that will be part of the Cherenkov Telescope Array Observatory (CTAO). Its improved performance over previous IACTs makes it well suited for studying pulsars. Aims: To study the Crab pulsar emission with the LST-1, improving and complementing the results from other telescopes. These observations can also be used to characterize the potential of the LST-1 to study other pulsars and detect new ones. Methods: We analyzed a total of $\sim$103 hours of gamma-ray observations of the Crab pulsar conducted with the LST-1 in the period from September 2020 to January 2023. The observations were carried out at zenith angles less than 50 degrees. A new analysis of the Fermi-LAT data was also performed, including $\sim$14 years of observations. Results: The Crab pulsar phaseogram, long-term light-curve, and phase-resolved spectra are reconstructed with the LST-1 from 20 GeV to 450 GeV for P1 and up to 700 GeV for P2. The pulsed emission is detected with a significance of 15.2$\sigma$. The two characteristic emission peaks of the Crab pulsar are clearly detected (>10$\sigma$), as well as the so-called bridge emission (5.7$\sigma$). We find that both peaks are well described by power laws, with spectral indices of $\sim$3.44 and $\sim$3.03 respectively. The joint analysis of Fermi-LAT and LST-1 data shows a good agreement between both instruments in the overlapping energy range. The detailed results obtained in the first observations of the Crab pulsar with LST-1 show the potential that CTAO will have to study this type of sources., Comment: Accepted by A&A
Published: 2024

24. General collections demography model with multiple risks

Author: Grau-Bové, Josep and Andrews, Miriam
Subjects: Computer Science - Computers and Society
Abstract: This note presents an Agent-Based Model (ABM) with Monte Carlo sampling, designed to simulate the behaviour of a population of objects over time. The model incorporates damage functions with the risk parameters of the ABC framework to simulate adverse events. As a result, it combines continuous and probabilistic degradation. This hybrid approach allows us to study the emergent behavior of the system and explore the range of possible lifetimes of a collection. The main outcome of the model is the decay in condition of a collection as a consequence of all the combined degradation processes. The model is based on six hypotheses that are described for further testing. This paper presents a first attempt at an universal implementation of Collections Demography principles, with the hope that it will generate discussion and the identification of research gaps., Comment: 20 pages
Published: 2024

25. Probing the Extragalactic Background Light with the MAGIC telescopes

Author: Grau, R. and Moralejo, A.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The Extragalactic Background Light (EBL) is the accumulated light produced throughout the universe history, spanning the UV, optical, and IR spectral ranges and mostly originating from stars, directly or re-processed by dust. However, measuring the EBL total intensity (beyond the contribution of resolved discrete sources) is challenging due to its faintness compared to foreground diffuse light like zodiacal light. A possible technique exploits the Very High Energy (VHE) photons coming from sources at cosmological distances. VHE photons can interact with the EBL and produce electron-positron pairs, a process that leaves an imprint in the observed gamma-ray spectrum. Determining the EBL with this method requires assumptions on the intrinsic spectrum of the source, which can affect the robustness of EBL constraints. In this contribution, through the use of Monte Carlo simulations, and archival data of the MAGIC telescopes, we have studied the impact that the assumptions so far adopted in the literature have in the estimates of the EBL density, and how the use of more generic ones would modify the results. These studies can impact our understanding of the evolution of the Universe, gamma-ray propagation, and large-scale structure formation., Comment: TAUP proceedings, 5 pages, 3 figures. XVIII International Conference on Topics in Astroparticle and Underground Physics. 2024
Published: 2024
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26. Unitarity Method for Holographic Defects

Author: Chen, Junding, Gimenez-Grau, Aleix, Paul, Hynek, and Zhou, Xinan
Subjects: High Energy Physics - Theory
Abstract: We initiate the study of loop-level holographic correlators in the presence of defects. We present a unitarity method which constructs loop corrections from lower order data. As an example, we apply this method to 6d $\mathcal{N}=(2,0)$ theories with $\frac{1}{2}$-BPS surface defects and report the first holographic two-point function at one loop., Comment: 9 pages, 2 figures
Published: 2024

27. Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey

Author: Li, Lei, Camps, Julia, Rodriguez, Blanca, and Grau, Vicente
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: Cardiac digital twins (CDTs) are personalized virtual representations used to understand complex cardiac mechanisms. A critical component of CDT development is solving the ECG inverse problem, which enables the reconstruction of cardiac sources and the estimation of patient-specific electrophysiology (EP) parameters from surface ECG data. Despite challenges from complex cardiac anatomy, noisy ECG data, and the ill-posed nature of the inverse problem, recent advances in computational methods have greatly improved the accuracy and efficiency of ECG inverse inference, strengthening the fidelity of CDTs. This paper aims to provide a comprehensive review of the methods of solving ECG inverse problem, the validation strategies, the clinical applications, and future perspectives. For the methodologies, we broadly classify state-of-the-art approaches into two categories: deterministic and probabilistic methods, including both conventional and deep learning-based techniques. Integrating physics laws with deep learning models holds promise, but challenges such as capturing dynamic electrophysiology accurately, accessing accurate domain knowledge, and quantifying prediction uncertainty persist. Integrating models into clinical workflows while ensuring interpretability and usability for healthcare professionals is essential. Overcoming these challenges will drive further research in CDTs.
Published: 2024

28. Jet modification via $\pi^0$-hadron correlations in Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV

Author: PHENIX Collaboration, Abdulameer, N. J., Acharya, U., Adare, A., Afanasiev, S., Aidala, C., Ajitanand, N. N., Akiba, Y., Al-Bataineh, H., Alexander, J., Alfred, M., Aoki, K., Apadula, N., Aphecetche, L., Asai, J., Asano, H., Atomssa, E. T., Averbeck, R., Awes, T. C., Azmoun, B., Babintsev, V., Bai, M., Baksay, G., Baksay, L., Baldisseri, A., Bandara, N. S., Bannier, B., Barish, K. N., Barnes, P. D., Bassalleck, B., Basye, A. T., Bathe, S., Batsouli, S., Baublis, V., Baumann, C., Bazilevsky, A., Beaumier, M., Beckman, S., Belikov, S., Belmont, R., Bennett, R., Berdnikov, A., Berdnikov, Y., Bichon, L., Bickley, A. A., Blankenship, B., Blau, D. S., Boissevain, J. G., Bok, J. S., Borel, H., Borisov, V., Boyle, K., Brooks, M. L., Bryslawskyj, J., Buesching, H., Bumazhnov, V., Bunce, G., Butsyk, S., Camacho, C. M., Campbell, S., Chang, B. S., Chang, W. C., Charvet, J. L., Chen, C. -H., Chen, D., Chernichenko, S., Chiu, M., Chi, C. Y., Choi, I. J., Choi, J. B., Choudhury, R. K., Chujo, T., Chung, P., Churyn, A., Cianciolo, V., Citron, Z., Cole, B. A., Connors, M., Constantin, P., Corliss, R., Csanád, M., Csörgő, T., d'Enterria, D., Dahms, T., Dairaku, S., Danley, T. W., Das, K., Datta, A., Daugherity, M. S., David, G., DeBlasio, K., Dehmelt, K., Denisov, A., Deshpande, A., Desmond, E. J., Dietzsch, O., Dion, A., Diss, P. B., Donadelli, M., Doomra, V., Do, J. H., Drapier, O., Drees, A., Drees, K. A., Dubey, A. K., Durham, J. M., Durum, A., Dutta, D., Dzhordzhadze, V., Efremenko, Y. V., Ellinghaus, F., En'yo, H., Engelmore, T., Enokizono, A., Esha, R., Eyser, K. O., Fadem, B., Feege, N., Fields, D. E., Finger, Jr., M., Finger, M., Firak, D., Fitzgerald, D., Fleuret, F., Fokin, S. L., Fraenkel, Z., Frantz, J. E., Franz, A., Frawley, A. D., Fujiwara, K., Fukao, Y., Fusayasu, T., Gallus, P., Gal, C., Garg, P., Garishvili, I., Ge, H., Giordano, F., Glenn, A., Gong, H., Gonin, M., Gosset, J., Goto, Y., de Cassagnac, R. Granier, Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Guo, T., Gustafsson, H. -Å., Hachiya, T., Henni, A. Hadj, Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamilton, H. F., Hanks, J., Han, R., Han, S. Y., Hartouni, E. P., Haruna, K., Hasegawa, S., Haseler, T. O. S., Hashimoto, K., Haslum, E., Hayano, R., Heffner, M., Hemmick, T. K., Hester, T., He, X., Hill, J. C., Hodges, A., Hohlmann, M., Hollis, R. S., Holzmann, W., Homma, K., Hong, B., Horaguchi, T., Hornback, D., Hoshino, T., Hotvedt, N., Huang, J., Ichihara, T., Ichimiya, R., Iinuma, H., Ikeda, Y., Imai, K., Imrek, J., Inaba, M., Iordanova, A., Isenhower, D., Ishihara, M., Isobe, T., Issah, M., Isupov, A., Ivanishchev, D., Jacak, B. V., Jezghani, M., Jiang, X., Jin, J., Ji, Z., Johnson, B. M., Joo, K. S., Jouan, D., Jumper, D. S., Kajihara, F., Kametani, S., Kamihara, N., Kamin, J., Kanda, S., Kang, J. H., Kapustinsky, J., Kawall, D., Kazantsev, A. V., Kempel, T., Key, J. A., Khachatryan, V., Khanzadeev, A., Kijima, K. M., Kikuchi, J., Kimelman, B., Kim, B. I., Kim, C., Kim, D. H., Kim, D. J., Kim, E., Kim, E. -J., Kim, G. W., Kim, M., Kim, S. H., Kinney, E., Kiriluk, K., Kiss, Á., Kistenev, E., Kitamura, R., Klatsky, J., Klay, J., Klein-Boesing, C., Kleinjan, D., Kline, P., Koblesky, T., Kochenda, L., Komkov, B., Konno, M., Koster, J., Kotov, D., Kovacs, L., Kozlov, A., Kravitz, A., Král, A., Kunde, G. J., Kurgyis, B., Kurita, K., Kurosawa, M., Kweon, M. J., Kwon, Y., Kyle, G. S., Lai, Y. S., Lajoie, J. G., Layton, D., Lebedev, A., Lee, D. M., Lee, K. B., Lee, S., Lee, S. H., Lee, T., Leitch, M. J., Leite, M. A. L., Lenzi, B., Liebing, P., Lim, S. H., Litvinenko, A., Liu, H., Liu, M. X., Liška, T., Li, X., Lokos, S., Loomis, D. A., Love, B., Lynch, D., Maguire, C. F., Makdisi, Y. I., Makek, M., Malakhov, A., Malik, M. D., Manion, A., Manko, V. I., Mannel, E., Mao, Y., Masui, H., Matathias, F., Mašek, L., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Means, N., Meles, A., Mendoza, M., Meredith, B., Miake, Y., Mignerey, A. C., Mikeš, P., Miki, K., Milov, A., Mishra, D. K., Mishra, M., Mitchell, J. T., Mitrankova, M., Mitrankov, Iu., Miyasaka, S., Mizuno, S., Mohanty, A. K., Montuenga, P., Moon, T., Morino, Y., Morreale, A., Morrison, D. P., Moukhanova, T. V., Mukhopadhyay, D., Mulilo, B., Murakami, T., Murata, J., Mwai, A., Nagamiya, S., Nagashima, K., Nagle, J. L., Naglis, M., Nagy, M. I., Nakagawa, I., Nakagomi, H., Nakamiya, Y., Nakamura, T., Nakano, K., Nattrass, C., Netrakanti, P. K., Newby, J., Nguyen, M., Niida, T., Nishimura, S., Nouicer, R., Novitzky, N., Novák, T., Nukazuka, G., Nyanin, A. S., O'Brien, E., Oda, S. X., Ogilvie, C. A., Okada, K., Oka, M., Onuki, Y., Koop, J. D. Orjuela, Orosz, M., Osborn, J. D., Oskarsson, A., Ouchida, M., Ozawa, K., Pak, R., Palounek, A. P. T., Pantuev, V., Papavassiliou, V., Park, J., Park, J. S., Park, S., Park, W. J., Patel, M., Pate, S. F., Pei, H., Peng, J. -C., Pereira, H., Perepelitsa, D. V., Perera, G. D. N., Peresedov, V., Peressounko, D. Yu., Perry, J., Petti, R., Pinkenburg, C., Pinson, R., Pisani, R. P., Potekhin, M., Purschke, M. L., Purwar, A. K., Qu, H., Rakotozafindrabe, A., Rak, J., Ramson, B. J., Ravinovich, I., Read, K. F., Rembeczki, S., Reygers, K., Reynolds, D., Riabov, V., Riabov, Y., Richford, D., Rinn, T., Roach, D., Roche, G., Rolnick, S. D., Rosati, M., Rosendahl, S. S. E., Rosnet, P., Rowan, Z., Rubin, J. G., Rukoyatkin, P., Ružička, P., Rykov, V. L., Sahlmueller, B., Saito, N., Sakaguchi, T., Sakai, S., Sakashita, K., Sako, H., Samsonov, V., Sarsour, M., Sato, S., Sato, T., Sawada, S., Schaefer, B., Schmoll, B. K., Sedgwick, K., Seele, J., Seidl, R., Semenov, A. Yu., Semenov, V., Sen, A., Seto, R., Sett, P., Sexton, A., Sharma, D., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shoji, K., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Silvestre, C., Sim, K. S., Singh, B. K., Singh, C. P., Singh, V., Slunečka, M., Smith, K. L., Snowball, M., Soldatov, A., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Staley, F., Stankus, P. W., Stenlund, E., Stepanov, M., Ster, A., Stoll, S. P., Sugitate, T., Suire, C., Sukhanov, A., Sumita, T., Sun, J., Sun, Z., Sziklai, J., Takagui, E. M., Taketani, A., Tanabe, R., Tanaka, Y., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarján, P., Themann, H., Thomas, T. L., Tieulent, R., Timilsina, A., Todoroki, T., Togawa, M., Toia, A., Tomita, Y., Tomášek, L., Tomášek, M., Torii, H., Towell, C. L., Towell, R., Towell, R. S., Tram, V-N., Tserruya, I., Tsuchimoto, Y., Ujvari, B., Vale, C., Valle, H., van Hecke, H. W., Veicht, A., Velkovska, J., Vinogradov, A. A., Virius, M., Vrba, V., Vznuzdaev, E., Vértesi, R., Wang, X. R., Watanabe, Y., Watanabe, Y. S., Wei, F., Wessels, J., White, A. S., White, S. N., Winter, D., Wong, C. P., Woody, C. L., Wysocki, M., Xia, B., Xie, W., Xue, L., Yalcin, S., Yamaguchi, Y. L., Yamaura, K., Yang, R., Yanovich, A., Ying, J., Yokkaichi, S., Yoon, I., Yoo, J. H., Young, G. R., Younus, I., Yushmanov, I. E., Yu, H., Zajc, W. A., Zaudtke, O., Zelenski, A., Zhang, C., Zhou, S., Zolin, L., and Zou, L.
Subjects: Nuclear Experiment
Abstract: High-momentum two-particle correlations are a useful tool for studying jet-quenching effects in the quark-gluon plasma. Angular correlations between neutral-pion triggers and charged hadrons with transverse momenta in the range 4--12~GeV/$c$ and 0.5--7~GeV/$c$, respectively, have been measured by the PHENIX experiment in 2014 for Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$~GeV. Suppression is observed in the yield of high-momentum jet fragments opposite the trigger particle, which indicates jet suppression stemming from in-medium partonic energy loss, while enhancement is observed for low-momentum particles. The ratio and differences between the yield in Au$+$Au collisions and $p$$+$$p$ collisions, $I_{AA}$ and $\Delta_{AA}$, as a function of the trigger-hadron azimuthal separation, $\Delta\phi$, are measured for the first time at the Relativistic Heavy Ion Collider. These results better quantify how the yield of low-$p_T$ associated hadrons is enhanced at wide angle, which is crucial for studying energy loss as well as medium-response effects., Comment: 535 authors from 84 institutions, 12 pages, 8 figures. v2 is version accepted for publication in Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html
Published: 2024
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29. Constraints on Lorentz invariance violation from the extraordinary Mrk 421 flare of 2014 using a novel analysis method

Author: MAGIC Collaboration, Abe, S., Abhir, J., Abhishek, A., Acciari, V. A., Aguasca-Cabot, A., Agudo, I., Aniello, T., Ansoldi, S., Antonelli, L. A., Engels, A. Arbet, Arcaro, C., Artero, M., Asano, K., Babić, A., Baquero, A., de Almeida, U. Barres, Barrio, J. A., Batković, I., Bautista, A., Baxter, J., González, J. Becerra, Bednarek, W., Bernardini, E., Bernete, J., Berti, A., Besenrieder, J., Bigongiari, C., Biland, A., Blanch, O., Bonnoli, G., Bošnjak, Ž., Bronzini, E., Burelli, I., Busetto, G., Campoy-Ordaz, A., Carosi, A., Carosi, R., Carretero-Castrillo, M., Castro-Tirado, A. J., Cerasole, D., Ceribella, G., Chai, Y., Cifuentes, A., Colombo, E., Contreras, J. L., Cortina, J., Covino, S., D'Amico, G., D'Elia, V., Da Vela, P., Dazzi, F., De Angelis, A., De Lotto, B., de Menezes, R., Del Popolo, A., Delfino, M., Delgado, J., Mendez, C. Delgado, Di Pierro, F., Di Tria, R., Di Venere, L., Donini, A., Dorner, D., Doro, M., Elsaesser, D., Emery, G., Escudero, J., Fariña, L., Fattorini, A., Foffano, L., Font, L., Fröse, S., Fukami, S., Fukazawa, Y., López, R. J. García, Garczarczyk, M., Gasparyan, S., Gaug, M., Paiva, J. G. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Godinović, N., Gradetzke, T., Grau, R., Green, D., Green, J. G., Günther, P., Hadasch, D., Hahn, A., Hassan, T., Heckmann, L., Llorente, J. Herrera, Hrupec, D., Hütten, M., Imazawa, R., Ishio, K., Martínez, I. Jiménez, Jormanainen, J., Kankkunen, S., Kayanoki, T., Kerszberg, D., Kluge, G. W., Kobayashi, Y., Kouch, P. M., Kubo, H., Kushida, J., Láinez, M., Lamastra, A., Leone, F., Lindfors, E., Linhoff, L., Lombardi, S., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Lorini, A., Lyard, E., Fraga, B. Machado de Oliveira, Majumdar, P., Makariev, M., Maneva, G., Manganaro, M., Mangano, S., Mannheim, K., Mariotti, M., Martínez, M., Martínez-Chicharro, M., Mas-Aguilar, A., Mazin, D., Menchiari, S., Mender, S., Miceli, D., Miener, T., Miranda, J. M., Mirzoyan, R., González, M. Molero, Molina, E., Mondal, H. A., Moralejo, A., Morcuende, D., Nakamori, T., Nanci, C., Neustroev, V., Nickel, L., Rosillo, M. Nievas, Nigro, C., Nikolić, L., Nilsson, K., Nishijima, K., Ekoume, T. Njoh, Noda, K., Nogues, L., Nozaki, S., Ohtani, Y., Okumura, A., Otero-Santos, J., Paiano, S., Palatiello, M., Paneque, D., Paoletti, R., Paredes, J. M., Peresano, M., Persic, M., Pihet, M., Pirola, G., Podobnik, F., Moroni, P. G. Prada, Prandini, E., Principe, G., Priyadarshi, C., Rhode, W., Ribó, M., Rico, J., Righi, C., Sahakyan, N., Saito, T., Saturni, F. G., Schleicher, B., Schmidt, K., Schmuckermaier, F., Schubert, J. L., Schweizer, T., Sciaccaluga, A., Silvestri, G., Sitarek, J., Sliusar, V., Sobczynska, D., Stamerra, A., Strišković, J., Strom, D., Suda, Y., Tajima, H., Takahashi, M., Takeishi, R., Tavecchio, F., Temnikov, P., Terauchi, K., Terzić, T., Teshima, M., Truzzi, S., Tutone, A., Ubach, S., van Scherpenberg, J., Acosta, M. Vazquez, Ventura, S., Viale, I., Vigorito, C. F., Vitale, V., Vovk, I., Walter, R., Will, M., Wunderlich, C., and Yamamoto, T.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Phenomenology
Abstract: The Lorentz Invariance Violation (LIV), a proposed consequence of certain quantum gravity (QG) scenarios, could instigate an energy-dependent group velocity for ultra-relativistic particles. This energy dependence, although suppressed by the massive QG energy scale $E_\mathrm{QG}$, expected to be on the level of the Planck energy $1.22 \times 10^{19}$ GeV, is potentially detectable in astrophysical observations. In this scenario, the cosmological distances traversed by photons act as an amplifier for this effect. By leveraging the observation of a remarkable flare from the blazar Mrk\,421, recorded at energies above 100 GeV by the MAGIC telescopes on the night of April 25 to 26, 2014, we look for time delays scaling linearly and quadratically with the photon energies. Using for the first time in LIV studies a binned-likelihood approach we set constraints on the QG energy scale. For the linear scenario, we set $95\%$ lower limits $E_\mathrm{QG}>2.7\times10^{17}$ GeV for the subluminal case and $E_\mathrm{QG}> 3.6 \times10^{17}$ GeV for the superluminal case. For the quadratic scenario, the $95\%$ lower limits for the subluminal and superluminal cases are $E_\mathrm{QG}>2.6 \times10^{10}$ GeV and $E_\mathrm{QG}>2.5\times10^{10}$ GeV, respectively.
Published: 2024

30. Automatic Counting and Classification of Mosquito Eggs in Field Traps

Author: Naranjo-Alcazar, Javier, Grau-Haro, Jordi, Zuccarello, Pedro, Almenar, David, and Lopez-Ballester, Jesus
Subjects: Computer Science - Artificial Intelligence
Abstract: Insect pest control poses a global challenge, affecting public health, food safety, and the environment. Diseases transmitted by mosquitoes are expanding beyond tropical regions due to climate change. Agricultural pests further exacerbate economic losses by damaging crops. The Sterile Insect Technique (SIT) emerges as an eco-friendly alternative to chemical pesticides, involving the sterilization and release of male insects to curb population growth. This work focuses on the automation of the analysis of field ovitraps used to follow-up a SIT program for the Aedes albopictus mosquito in the Valencian Community, Spain, funded by the Conselleria de Agricultura, Agua, Ganaderia y Pesca. Previous research has leveraged deep learning algorithms to automate egg counting in ovitraps, yet faced challenges such as manual handling and limited analysis capacity. Innovations in our study include classifying eggs as hatched or unhatched and reconstructing ovitraps from partial images, mitigating issues of duplicity and cut eggs. Also, our device can analyze multiple ovitraps simultaneously without the need of manual replacement. This approach significantly enhances the accuracy of egg counting and classification, providing a valuable tool for large-scale field studies. This document describes part of the work of the project Application of Industry 4.0 techniques to the production of tiger mosquitoes for the Sterile Insect Technique (MoTIA2,IMDEEA/2022/70), financed by the Valencian Institute for Business Competitiveness (IVACE) and the FEDER funds. The participation of J.Naranjo-Alcazar, J.Grau-Haro and P.Zuccarello has been possible thanks to funding from IVACE and FEDER funds. The participation of D.Almenar has been financed by the Conselleria de Agricultura, Agua, Ganaderia y Pesca of the Generalitat Valenciana and the Subdireccion de Innovacion y Desarrollo de Servicios (TRAGSA group).
Published: 2024

31. A Data-Centric Framework for Machine Listening Projects: Addressing Large-Scale Data Acquisition and Labeling through Active Learning

Author: Naranjo-Alcazar, Javier, Grau-Haro, Jordi, Ribes-Serrano, Ruben, and Zuccarello, Pedro
Subjects: Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: Machine Listening focuses on developing technologies to extract relevant information from audio signals. A critical aspect of these projects is the acquisition and labeling of contextualized data, which is inherently complex and requires specific resources and strategies. Despite the availability of some audio datasets, many are unsuitable for commercial applications. The paper emphasizes the importance of Active Learning (AL) using expert labelers over crowdsourcing, which often lacks detailed insights into dataset structures. AL is an iterative process combining human labelers and AI models to optimize the labeling budget by intelligently selecting samples for human review. This approach addresses the challenge of handling large, constantly growing datasets that exceed available computational resources and memory. The paper presents a comprehensive data-centric framework for Machine Listening projects, detailing the configuration of recording nodes, database structure, and labeling budget optimization in resource-constrained scenarios. Applied to an industrial port in Valencia, Spain, the framework successfully labeled 6540 ten-second audio samples over five months with a small team, demonstrating its effectiveness and adaptability to various resource availability situations. Acknowledgments: The participation of Javier Naranjo-Alcazar, Jordi Grau-Haro and Pedro Zuccarello in this research was funded by the Valencian Institute for Business Competitiveness (IVACE) and the FEDER funds by means of project Soroll-IA2 (IMDEEA/2023/91). The research carried out for this publication has been partially funded by the project STARRING-NEURO (PID2022-137048OA-C44) funded by the Ministry of Science, Innovation and Universities of Spain and the European Union., Comment: Paper accepted at 8th Future of Information and Communication Conference 2025, 28-29 April, Berlin
Published: 2024

32. Early Results from the HUMDRUM Survey: A Small, Earth-mass Planet Orbits TOI-1450A

Author: Brady, M., Bean, J., Seifahrt, A., Kasper, D., Luque, R., Stefánsson, G., Stürmer, J., Charbonneau, D., Collins, K., Doty, J., Essack, Z., Fukui, A., Horta, F. Grau, Hedges, C., Hellier, C., Jenkins, J., Narita, N., Quinn, S., Shporer, A., Schwarz, R., Seager, S., Stassun, K., Striegel, S., Watkins, C., Winn, J., and Zambelli, R.
Subjects: Astrophysics - Earth and Planetary Astrophysics
Abstract: M dwarf stars provide us with an ideal opportunity to study nearby small planets. The HUMDRUM (HUnting for M Dwarf Rocky planets Using MAROON-X) survey uses the MAROON-X spectrograph, which is ideally suited to studying these stars, to measure precise masses of a volume-limited ($<\,30$ pc) sample of transiting M dwarf planets. TOI-1450 is a nearby (22.5 pc) binary system containing a M3 dwarf with a roughly 3000 K companion. Its primary star, TOI-1450A, was identified by $TESS$ to have a 2.04d transit signal, and is included in the HUMDRUM sample. In this paper, we present MAROON-X radial velocities which confirm the planetary nature of this signal and measure its mass at a nearly 10% precision. The 2.04d planet, TOI-1450Ab, has $R_b\,=\,1.13\,\pm\,0.04\,R_\oplus$ and $M_b\,=\,1.26\,\pm\,0.13\,M_\oplus$. It is the second-lowest-mass transiting planet with a high-precision RV mass measurement. With this mass and radius, the planet's mean density is compatible with an Earth-like composition. Given its short orbital period and slightly sub-Earth density, it may be amenable to $JWST$ follow-up to test whether the planet has retained an atmosphere despite extreme heating from the nearby star. We also discover a non-transiting planet in the system with a period of 5.07 days and a $M\mathrm{sin}i_c\,=\,1.53\,\pm\,0.18\,M_\oplus$. We also find a 2.01d signal present in the systems's $TESS$ photometry that likely corresponds to the rotation period of TOI-1450A's binary companion, TOI-1450B. TOI-1450A, meanwhile, appears to have a rotation period of approximately 40 days, which is in-line with our expectations for a mid-M dwarf., Comment: 31 pages, 17 figures, accepted in AJ
Published: 2024

33. Cycling into the workshop: predictive maintenance for Barcelona's bike-sharing system

Author: Grau-Escolano, Jordi, Bassolas, Aleix, and Vicens, Julian
Subjects: Computer Science - Computers and Society, Computer Science - Machine Learning, Physics - Physics and Society
Abstract: Bike-sharing systems have emerged as a significant element of urban mobility, providing an environmentally friendly transportation alternative. With the increasing integration of electric bikes alongside mechanical bikes, it is crucial to illuminate distinct usage patterns and their impact on maintenance. Accordingly, this research aims to develop a comprehensive understanding of mobility dynamics, distinguishing between different mobility modes, and introducing a novel predictive maintenance system tailored for bikes. By utilising a combination of trip information and maintenance data from Barcelona's bike-sharing system, Bicing, this study conducts an extensive analysis of mobility patterns and their relationship to failures of bike components. To accurately predict maintenance needs for essential bike parts, this research delves into various mobility metrics and applies statistical and machine learning survival models, including deep learning models. Due to their complexity, and with the objective of bolstering confidence in the system's predictions, interpretability techniques explain the main predictors of maintenance needs. The analysis reveals marked differences in the usage patterns of mechanical bikes and electric bikes, with a growing user preference for the latter despite their extra costs. These differences in mobility were found to have a considerable impact on the maintenance needs within the bike-sharing system. Moreover, the predictive maintenance models proved effective in forecasting these maintenance needs, capable of operating across an entire bike fleet. Despite challenges such as approximated bike usage metrics and data imbalances, the study successfully showcases the feasibility of an accurate predictive maintenance system capable of improving operational costs, bike availability, and security., Comment: 25 pages, 9 figures, 7 tables
Published: 2024
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34. Translational hulls of semigroups of endomorphisms of an algebra

Author: Gould, Victoria, Grau, Ambroise, Johnson, Marianne, and Kambites, Mark
Subjects: Mathematics - Rings and Algebras, 20M12, 20M25, 20M30
Abstract: We consider the translational hull $\Omega(I)$ of an arbitrary subsemigroup $I$ of an endomorphism monoid $\mathrm{End}(A)$ where $A$ is a universal algebra. We give conditions for every bi-translation of $I$ to be realised by transformations, or by endomorphisms, of $A$. We demonstrate that certain of these conditions are also sufficient to provide natural isomorphisms between the translational hull of $I$ and the idealiser of $I$ within $\mathrm{End}(A)$, which in the case where $I$ is an ideal is simply $\mathrm{End}(A)$. We describe the connection between these conditions and work of Petrich and Gluskin in the context of densely embedded ideals. Where the conditions fail, we develop a methodology to extract information concerning $\Omega(I)$ from the translational hull $\Omega(I/{\approx})$ of a quotient $I/{\approx}$ of $I$. We illustrate these concepts in detail in the cases where $A$ is: a free algebra; an independence algebra; a finite symmetric group., Comment: 31 pages, 1 figure
Published: 2024

35. AnnoCTR: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat Reports

Author: Lange, Lukas, Müller, Marc, Torbati, Ghazaleh Haratinezhad, Milchevski, Dragan, Grau, Patrick, Pujari, Subhash, and Friedrich, Annemarie
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract: Monitoring the threat landscape to be aware of actual or potential attacks is of utmost importance to cybersecurity professionals. Information about cyber threats is typically distributed using natural language reports. Natural language processing can help with managing this large amount of unstructured information, yet to date, the topic has received little attention. With this paper, we present AnnoCTR, a new CC-BY-SA-licensed dataset of cyber threat reports. The reports have been annotated by a domain expert with named entities, temporal expressions, and cybersecurity-specific concepts including implicitly mentioned techniques and tactics. Entities and concepts are linked to Wikipedia and the MITRE ATT&CK knowledge base, the most widely-used taxonomy for classifying types of attacks. Prior datasets linking to MITRE ATT&CK either provide a single label per document or annotate sentences out-of-context; our dataset annotates entire documents in a much finer-grained way. In an experimental study, we model the annotations of our dataset using state-of-the-art neural models. In our few-shot scenario, we find that for identifying the MITRE ATT&CK concepts that are mentioned explicitly or implicitly in a text, concept descriptions from MITRE ATT&CK are an effective source for training data augmentation., Comment: Accepted at LREC-COLING 2024. Corpus available at https://github.com/boschresearch/anno-ctr-lrec-coling-2024
Published: 2024

36. Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction

Author: Chen, Chen, Li, Lei, Beetz, Marcel, Banerjee, Abhirup, Gupta, Ramneek, and Grau, Vicente
Subjects: Quantitative Biology - Quantitative Methods, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract: Heart failure (HF) poses a significant public health challenge, with a rising global mortality rate. Early detection and prevention of HF could significantly reduce its impact. We introduce a novel methodology for predicting HF risk using 12-lead electrocardiograms (ECGs). We present a novel, lightweight dual-attention ECG network designed to capture complex ECG features essential for early HF risk prediction, despite the notable imbalance between low and high-risk groups. This network incorporates a cross-lead attention module and twelve lead-specific temporal attention modules, focusing on cross-lead interactions and each lead's local dynamics. To further alleviate model overfitting, we leverage a large language model (LLM) with a public ECG-Report dataset for pretraining on an ECG-report alignment task. The network is then fine-tuned for HF risk prediction using two specific cohorts from the UK Biobank study, focusing on patients with hypertension (UKB-HYP) and those who have had a myocardial infarction (UKB-MI).The results reveal that LLM-informed pre-training substantially enhances HF risk prediction in these cohorts. The dual-attention design not only improves interpretability but also predictive accuracy, outperforming existing competitive methods with C-index scores of 0.6349 for UKB-HYP and 0.5805 for UKB-MI. This demonstrates our method's potential in advancing HF risk assessment with clinical complex ECG data., Comment: Under journal revision
Published: 2024

37. Dark Matter Line Searches with the Cherenkov Telescope Array

Author: Abe, S., Abhir, J., Abhishek, A., Acero, F., Acharyya, A., Adam, R., Aguasca-Cabot, A., Agudo, I., Aguirre-Santaella, A., Alfaro, J., Alfaro, R., Alvarez-Crespo, N., Batista, R. Alves, Amans, J. -P., Amato, E., Ambrosi, G., Angel, L., Aramo, C., Arcaro, C., Arnesen, T. T. H., Arrabito, L., Asano, K., Ascasibar, Y., Aschersleben, J., Ashkar, H., Backes, M., Baktash, A., Balazs, C., Balbo, M., Larriva, A. Baquero, Martins, V. Barbosa, de Almeida, U. Barres, Barrio, J. A., Batković, I., Batzofin, R., Baxter, J., González, J. Becerra, Beck, G., Benbow, W., Berge, D., Bernardini, E., Bernete, J., Bernlöhr, K., Berti, A., Bertucci, B., Bhattacharjee, P., Bhattacharyya, S., Bigongiari, C., Biland, A., Bissaldi, E., Biteau, J., Blanch, O., Blazek, J., Bocchino, F., Boisson, C., Bolmont, J., Bonnoli, G., Bonollo, A., Bordas, P., Bosnjak, Z., Bottacini, E., Böttcher, M., Bringmann, T., Bronzini, E., Brose, R., Brown, A. M., Brunelli, G., Bulgarelli, A., Bulik, T., Burelli, I., Burmistrov, L., Burton, M., Buscemi, M., Bylund, T., Cailleux, J., Campoy-Ordaz, A., Cantlay, B. K., Capasso, G., Caproni, A., Capuzzo-Dolcetta, R., Caraveo, P., Caroff, S., Carosi, A., Carosi, R., Carquin, E., Carrasco, M. -S., Cassol, F., Castaldini, L., Castrejon, N., Castro-Tirado, A. J., Cerasole, D., Cerruti, M., Chadwick, P. M., Chaty, S., Chen, A. W., Chernyakova, M., Chiavassa, A., Chudoba, J., Chytka, L., Cicciari, G. M., Cifuentes, A., Araujo, C. H. Coimbra, Colapietro, M., Conforti, V., Conte, F., Contreras, J. L., Costa, A., Costantini, H., Cotter, G., Cristofari, P., Cuevas, O., Curtis-Ginsberg, Z., D'Amico, G., D'Ammando, F., Dai, S., Dalchenko, M., Dazzi, F., De Angelis, A., de Lavergne, M. de Bony, De Caprio, V., Pino, E. M. de Gouveia Dal, De Lotto, B., De Lucia, M., de Menezes, R., de Naurois, M., de Souza, V., del Peral, L., del Valle, M. V., Giler, A. G. Delgado, Mengual, J. Delgado, Delgado, C., Dell'aiera, M., della Volpe, D., Depaoli, D., Di Girolamo, T., Di Piano, A., Di Pierro, F., Di Tria, R., Di Venere, L., Díaz, C., Diebold, S., Dinesh, A., Djuvsland, J., Dominik, R. M., Prester, D. Dominis, Donini, A., Dorner, D., Dörner, J., Doro, M., Dournaux, J. -L., Duangchan, C., Dubos, C., Ducci, L., Dwarkadas, V. V., Ebr, J., Eckner, C., Egberts, K., Einecke, S., Elsässer, D., Emery, G., Errando, M., Escanuela, C., Escarate, P., Godoy, M. Escobar, Escudero, J., Esposito, P., Ettori, S., Falceta-Goncalves, D., Fedorova, E., Fegan, S., Feng, Q., Ferrand, G., Ferrarotto, F., Fiandrini, E., Fiasson, A., Filipovic, M., Fioretti, V., Fiori, M., Foffano, L., Guiteras, L. Font, Fontaine, G., Fröse, S., Fukazawa, Y., Fukui, Y., Furniss, A., Galanti, G., Galaz, G., Galelli, C., Gallozzi, S., Gammaldi, V., Garczarczyk, M., Gasbarra, C., Gasparrini, D., Ghalumyan, A., Gianotti, F., Giarrusso, M., Paiva, J. G. Giesbrecht Formiga, Giglietto, N., Giordano, F., Giuffrida, R., Glicenstein, J. -F., Glombitza, J., Goldoni, P., González, J. M., González, M. M., Coelho, J. Goulart, Gradetzke, T., Granot, J., Grasso, D., Grau, R., Gréaux, L., Green, D., Green, J. G., Grolleron, G., Guedes, L. M. V., Gueta, O., Hackfeld, J., Hadasch, D., Hamal, P., Hanlon, W., Hara, S., Harvey, V. M., Hassan, T., Hayashi, K., Heß, B., Heckmann, L., Heller, M., Cadena, S. Hernández, Hervet, O., Hinton, J., Hiroshima, N., Hnatyk, B., Hnatyk, R., Hofmann, W., Holder, J., Horan, D., Horvath, P., Hovatta, T., Hrabovsky, M., Hrupec, D., Iarlori, M., Inada, T., Incardona, F., Inoue, S., Inoue, Y., Iocco, F., Iori, M., Ishio, K., Jamrozy, M., Janecek, P., Jankowsky, F., Jean, P., Quiles, J. Jimenez, Jin, W., Juramy-Gilles, C., Jurysek, J., Kagaya, M., Kalekin, O., Karas, V., Katagiri, H., Kataoka, J., Kaufmann, S., Kazanas, D., Kerszberg, D., Kieda, D. B., Kleiner, T., Kluge, G., Kobayashi, Y., Kohri, K., Komin, N., Kornecki, P., Kosack, K., Kowal, G., Kubo, H., Kushida, J., La Barbera, A., La Palombara, N., Láinez, M., Lamastra, A., Lapington, J., Laporte, P., Lazarević, S., Lazendic-Galloway, J., Lemoine-Goumard, M., Lenain, J. -P., Leone, F., Leonora, E., Leto, G., Lindfors, E., Linhoff, M., Liodakis, I., Lipniacka, A., Lombardi, S., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Bahilo, J. Lozano, Luque-Escamilla, P. L., Macias, O., Majumdar, P., Mallamaci, M., Malyshev, D., Mandat, D., Manicò, G., Mariotti, M., Márquez, I., Marquez, P., Marsella, G., Martí, J., Martínez, G. A., Martínez, M., Martinez, O., Marty, C., Mas-Aguilar, A., Mastropietro, M., Mazin, D., Menchiari, S., Mestre, E., Meunier, J. -L., Meyer, D. M. -A., Meyer, M., Miceli, D., Miceli, M., Michailidis, M., Michałowski, J., Miener, T., Miranda, J. M., Mitchell, A., Mizote, M., Mizuno, T., Moderski, R., Molero, M., Molfese, C., Molina, E., Montaruli, T., Moralejo, A., Morcuende, D., Morselli, A., Moulin, E., Zamanillo, V. Moya, Munari, K., Murach, T., Muraczewski, A., Muraishi, H., Nakamori, T., Nayak, A., Nemmen, R., Neto, J. P., Nickel, L., Niemiec, J., Nieto, D., Rosillo, M. Nievas, Nikołajuk, M., Nikolić, L., Nishijima, K., Noda, K., Nosek, D., Novotny, V., Nozaki, S., Ohishi, M., Ohtani, Y., Okumura, A., Olive, J. -F., Ong, R. A., Orienti, M., Orito, R., Orlandini, M., Orlando, E., Orlando, S., Ostrowski, M., Otero-Santos, J., Oya, I., Pagano, I., Pagliaro, A., Palatiello, M., Panebianco, G., Paneque, D., Pantaleo, F. R., Paredes, J. M., Parmiggiani, N., Patricelli, B., Pe'er, A., Pech, M., Pecimotika, M., Pensec, U., Peresano, M., Pérez-Romero, J., Persic, M., Peters, K. P., Petruk, O., Piano, G., Pierre, E., Pietropaolo, E., Pihet, M., Pinchbeck, L., Pirola, G., Pittori, C., Plard, C., Podobnik, F., Pohl, M., Pollet, V., Ponti, G., Prandini, E., Principe, G., Priyadarshi, C., Produit, N., Prouza, M., Pueschel, E., Pühlhofer, G., Pumo, M. L., Queiroz, F., Quirrenbach, A., Rainò, S., Rando, R., Razzaque, S., Regeard, M., Reimer, A., Reimer, O., Reisenegger, A., Rhode, W., Ribeiro, D., Ribó, M., Ricci, C., Richtler, T., Rico, J., Rieger, F., Riitano, L., Rizi, V., Roache, E., Fernandez, G. Rodriguez, Frías, M. D. Rodríguez, Rodríguez-Vázquez, J. J., Romano, P., Romeo, G., Rosado, J., de Leon, A. Rosales, Rowell, G., Rudak, B., Ruiter, A. J., Rulten, C. B., Sadeh, I., Saha, L., Saito, T., Salzmann, H., Sánchez-Conde, M., Sandaker, H., Sangiorgi, P., Sano, H., Santander, M., Santos-Lima, R., Sapienza, V., Šarić, T., Sarkar, A., Sarkar, S., Saturni, F. G., Savarese, S., Scherer, A., Schiavone, F., Schipani, P., Schleicher, B., Schovanek, P., Schubert, J. L., Schwanke, U., Arroyo, M. Seglar, Seitenzahl, I. R., Sergijenko, O., Servillat, M., Siegert, T., Siejkowski, H., Siqueira, C., Sliusar, V., Slowikowska, A., Sol, H., Spencer, S. T., Spiga, D., Stamerra, A., Stanič, S., Starecki, T., Starling, R., Stawarz, Ł., Steppa, C., Hatlen, E. Sæther, Stolarczyk, T., Strišković, J., Suda, Y., Świerk, P., Tajima, H., Tak, D., Takahashi, M., Takeishi, R., Tavernier, T., Tejedor, L. A., Terauchi, K., Teshima, M., Testa, V., Tian, W. W., Tibaldo, L., Tibolla, O., Peixoto, C. J. Todero, Torradeflot, F., Torres, D. F., Tosti, G., Tothill, N., Toussenel, F., Tramacere, A., Travnicek, P., Tripodo, G., Trois, A., Truzzi, S., Tutone, A., Vaclavek, L., Vacula, M., Vallania, P., Vallés, R., van Eldik, C., van Scherpenberg, J., Vandenbroucke, J., Vassiliev, V., Acosta, M. Vázquez, Vecchi, M., Ventura, S., Vercellone, S., Verna, G., Viana, A., Viaux, N., Vigliano, A., Vignatti, J., Vigorito, C. F., Villanueva, J., Visentin, E., Vitale, V., Vodeb, V., Voisin, V., Voitsekhovskyi, V., Vorobiov, S., Voutsinas, G., Vovk, I., Vuillaume, T., Wagner, S. J., Walter, R., White, M., White, R., Wierzcholska, A., Will, M., Williams, D. A., Wohlleben, F., Wolter, A., Yamamoto, T., Yang, L., Yoshida, T., Yoshikoshi, T., Zaharijas, G., Zampieri, L., Sanchez, R. Zanmar, Zavrtanik, D., Zavrtanik, M., Zdziarski, A. A., Zech, A., Zhang, W., Zhdanov, V. I., Ziętara, K., Živec, M., and Zuriaga-Puig, J.
Subjects: High Energy Physics - Phenomenology, Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Experiment
Abstract: Monochromatic gamma-ray signals constitute a potential smoking gun signature for annihilating or decaying dark matter particles that could relatively easily be distinguished from astrophysical or instrumental backgrounds. We provide an updated assessment of the sensitivity of the Cherenkov Telescope Array (CTA) to such signals, based on observations of the Galactic centre region as well as of selected dwarf spheroidal galaxies. We find that current limits and detection prospects for dark matter masses above 300 GeV will be significantly improved, by up to an order of magnitude in the multi-TeV range. This demonstrates that CTA will set a new standard for gamma-ray astronomy also in this respect, as the world's largest and most sensitive high-energy gamma-ray observatory, in particular due to its exquisite energy resolution at TeV energies and the adopted observational strategy focussing on regions with large dark matter densities. Throughout our analysis, we use up-to-date instrument response functions, and we thoroughly model the effect of instrumental systematic uncertainties in our statistical treatment. We further present results for other potential signatures with sharp spectral features, e.g.~box-shaped spectra, that would likewise very clearly point to a particle dark matter origin., Comment: 44 pages JCAP style (excluding author list and references), 19 figures; minor changes to match published version
Published: 2024
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38. The variability patterns of the TeV blazar PG 1553+113 from a decade of MAGIC and multi-band observations

Author: MAGIC Collaboration, Abe, H., Abe, S., Abhir, J., Acciari, V. A., Agudo, I., Aniello, T., Ansoldi, S., Antonelli, L. A., Engels, A. Arbet, Arcaro, C., Artero, M., Asano, K., Baack, D., Babić, A., Baquero, A., de Almeida, U. Barres, Batković, I., Baxter, J., González, J. Becerra, Bernardini, E., Bernete, J., Berti, A., Besenrieder, J., Bigongiari, C., Biland, A., Blanch, O., Bonnoli, G., Bošnjak, Ž., Burelli, I., Busetto, G., Campoy-Ordaz, A., Carosi, A., Carosi, R., Carretero-Castrillo, M., Castro-Tirado, A. J., Chai, Y., Cifuentes, A., Cikota, S., Colombo, E., Contreras, J. L., Cortina, J., Covino, S., D'Amico, G., D'Elia, V., Da Vela, P., Dazzi, F., De Angelis, A., De Lotto, B., Del Popolo, A., Delfino, M., Delgado, J., Mendez, C. Delgado, Depaoli, D., Di Pierro, F., Di Venere, L., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Elsaesser, D., Emery, G., Escudero, J., Fariña, L., Fattorini, A., Foffano, L., Font, L., Fukami, S., Fukazawa, Y., López, R. J. García, Gasparyan, S., Gaug, M., Paiva, J. G. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Grau, R., Green, J. G., Hadasch, D., Hahn, A., Heckmann, L., Herrera, J., Hovatta, T., Hrupec, D., Hütten, M., Imazawa, R., Inada, T., Iotov, R., Ishio, K., Martínez, I. Jiménez, Jormanainen, J., Kerszberg, D., Kluge, G. W., Kobayashi, Y., Kouch, P. M., Kubo, H., Kushida, J., Lezáun, M. Láinez, Lamastra, A., Leone, F., Lindfors, E., Liodakis, I., Lombardi, S., Longo, F., López-Moya, M., López-Oramas, A., Loporchio, S., Lorini, A., Fraga, B. Machado de Oliveira, Majumdar, P., Makariev, M., Maneva, G., Mang, N., Manganaro, M., Mannheim, K., Mariotti, M., Martínez, M., Martínez-Chicharro, M., Mas-Aguilar, A., Mazin, D., Menchiari, S., Mender, S., Miceli, D., Miener, T., Miranda, J. M., Mirzoyan, R., González, M. Molero, Molina, E., Mondal, H. A., Moralejo, A., Morcuende, D., Nakamori, T., Nanci, C., Neustroev, V., Nigro, C., Nikolić, L., Nilsson, K., Nishijima, K., Ekoume, T. Njoh, Noda, K., Nozaki, S., Ohtani, Y., Okumura, A., Otero-Santos, J., Paiano, S., Palatiello, M., Paneque, D., Paoletti, R., Paredes, J. M., Pavlović, D., Persic, M., Pihet, M., Pirola, G., Podobnik, F., Moroni, P. G. Prada, Prandini, E., Principe, G., Priyadarshi, C., Rhode, W., Ribó, M., Rico, J., Righi, C., Sahakyan, N., Saito, T., Satalecka, K., Saturni, F. G., Schleicher, B., Schmidt, K., Schmuckermaier, F., Schubert, J. L., Schweizer, T., Sciaccaluga, A., Sitarek, J., Spolon, A., Stamerra, A., Strišković, J., Strom, D., Suda, Y., Suutarinen, S., Tajima, H., Takeishi, R., Tavecchio, F., Temnikov, P., Terauchi, K., Terzić, T., Teshima, M., Tosti, L., Truzzi, S., Tutone, A., Ubach, S., van Scherpenberg, J., Ventura, S., Verguilov, V., Viale, I., Vigorito, C. F., Vitale, V., Walter, R., Wunderlich, C., Yamamoto, T., collaborators, MWL, Jermak, H., Steele, I. A., Smith, P. S., Blinov, D., Raiteri, C. M., Villata, M., Mirzaqulov, D. O., Kurtanidze, S. O., Carosati, D., Savchenko, S. S., Acosta-Pulido, J. A., Borman, G. A., Bozhilov, V., Carnerero, M. I., Chigladze, R. A., Damljanovic, G., Ehgamberdiev, S. A., Feige, M., Grishina, T. S., Gupta, A. C., Hagen-Thorn, V. A., Ibryamov, S., Ivanidze, R. Z., Jorstad, S. G., Kania, J., Kimeridze, G. N., Kopatskaya, E. N., Kopp, M., Kunkel, L., Kurtanidze, O. M., Larionov, V. M., Larionova, E. G., Larionova, L. V., Lorey, C., Marchini, A., Marscher, A. P., Minev, M., Morozova, D. A., Nikolashvili, M. G., Ovcharov, E., Reinhart, D., Sadun, A. C., Scherbantin, A., Schneider, L., Semkov, E., Sigua, L. A., Steineke, R., Troitskaya, Yu. V., Troitskiy, I. S., Valcheva, A., Vasilyev, A. A., Vince, O., Zaharieva, E., Zottmann, N., Kiehlmann, S., Readhead, A., Max-Moerbeck, W., Reeves, R. A., Sandrinelli, A., Ramazani, V. Fallah, Giroletti, M., Righini, S., Marchili, N., Patricelli, B., Ghirlanda, G., and Lico, R.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: PG 1553+113 is one of the few blazars with a convincing quasi-periodic emission in the gamma-ray band. The source is also a very high-energy (VHE; >100 GeV) gamma-ray emitter. To better understand its properties and identify the underlying physical processes driving its variability, the MAGIC Collaboration initiated a multiyear, multiwavelength monitoring campaign in 2015 involving the OVRO 40-m and Medicina radio telescopes, REM, KVA, and the MAGIC telescopes, Swift and Fermi satellites, and the WEBT network. The analysis presented in this paper uses data until 2017 and focuses on the characterization of the variability. The gamma-ray data show a (hint of a) periodic signal compatible with literature, but the X-ray and VHE gamma-ray data do not show statistical evidence for a periodic signal. In other bands, the data are compatible with the gamma-ray period, but with a relatively high p-value. The complex connection between the low and high-energy emission and the non-monochromatic modulation and changes in flux suggests that a simple one-zone model is unable to explain all the variability. Instead, a model including a periodic component along with multiple emission zones is required., Comment: Accepted for publication in Monthly Notices of the Royal Astronomical Society. 19 pages, 9 figures. Corresponding authors: Elisa Prandini, Antonio Stamerra, Talvikki Hovatta
Published: 2024
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39. Segment anything model for head and neck tumor segmentation with CT, PET and MRI multi-modality images

Author: Ren, Jintao, Rasmussen, Mathis, Nijkamp, Jasper, Eriksen, Jesper Grau, and Korreman, Stine
Subjects: Physics - Medical Physics, Computer Science - Computer Vision and Pattern Recognition
Abstract: Deep learning presents novel opportunities for the auto-segmentation of gross tumor volume (GTV) in head and neck cancer (HNC), yet fully automatic methods usually necessitate significant manual refinement. This study investigates the Segment Anything Model (SAM), recognized for requiring minimal human prompting and its zero-shot generalization ability across natural images. We specifically examine MedSAM, a version of SAM fine-tuned with large-scale public medical images. Despite its progress, the integration of multi-modality images (CT, PET, MRI) for effective GTV delineation remains a challenge. Focusing on SAM's application in HNC GTV segmentation, we assess its performance in both zero-shot and fine-tuned scenarios using single (CT-only) and fused multi-modality images. Our study demonstrates that fine-tuning SAM significantly enhances its segmentation accuracy, building upon the already effective zero-shot results achieved with bounding box prompts. These findings open a promising avenue for semi-automatic HNC GTV segmentation.
Published: 2024

40. Performance and first measurements of the MAGIC Stellar Intensity Interferometer

Author: MAGIC Collaboration, Abe, S., Abhir, J., Acciari, V. A., Aguasca-Cabot, A., Agudo, I., Aniello, T., Ansoldi, S., Antonelli, L. A., Engels, A. Arbet, Arcaro, C., Artero, M., Asano, K., Babić, A., Baquero, A., de Almeida, U. Barres, Barrio, J. A., Batković, I., Bautista, A., Baxter, J., González, J. Becerra, Bernardini, E., Bernardos, M., Bernete, J., Berti, A., Besenrieder, J., Bigongiari, C., Biland, A., Blanch, O., Bošnjak, Ž., Burelli, I., Busetto, G., Campoy-Ordaz, A., Carosi, A., Carosi, R., Carretero-Castrillo, M., Ceribella, G., Chai, Y., Cifuentes, A., Colombo, E., Contreras, J. L., Cortina, J., Covino, S., D'Amico, G., D'Elia, V., Da Vela, P., Dazzi, F., De Angelis, A., De Lotto, B., de Menezes, R., Del Popolo, A., Delfino, M., Delgado, J., Mendez, C. Delgado, Di Pierro, F., Di Venere, L., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Elsaesser, D., Emery, G., Escudero, J., na, L. Fari, Fattorini, A., Foffano, L., Font, L., Fröse, S., Fukami, S., Fukazawa, Y., López, R. J. García, Garczarczyk, M., Gasparyan, S., Gaug, M., Paiva, J. G. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Gradetzke, T., Grau, R., Green, D., Green, J. G., Günther, P., Hadasch, D., Hahn, A., Hassan, T., Heckmann, L., Herrera, J., Hrupec, D., Hütten, M., Imazawa, R., Ishio, K., Martínez, I. Jiménez, Jormanainen, J., Kayanoki, T., Kerszberg, D., Kluge, G. W., Kobayashi, Y., Kouch, P. M., Kubo, H., Kushida, J., Láinez, M., Lamastra, A., Leone, F., Lindfors, E., Linhoff, L., Lombardi, S., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Lorini, A., Lyard, E., Fraga, B. Machado de Oliveira, Majumdar, P., Makariev, M., Maneva, G., Mang, N., Manganaro, M., Mangano, S., Mannheim, K., Mariotti, M., Martínez, M., Martínez-Chicharro, M., Mas-Aguilar, A., Mazin, D., Menchiari, S., Mender, S., Miceli, D., Miener, T., Miranda, J. M., Mirzoyan, R., González, M. Molero, Molina, E., Mondal, H. A., Moralejo, A., Morcuende, D., Nakamori, T., Nanci, C., Neustroev, V., Nickel, L., Rosillo, M. Nievas, Nigro, C., Nikolić, L., Nilsson, K., Nishijima, K., Ekoume, T. Njoh, Noda, K., Nozaki, S., Ohtani, Y., Okumura, A., Otero-Santos, J., Paiano, S., Palatiello, M., Paneque, D., Paoletti, R., Paredes, J. M., Peresano, M., Persic, M., Pihet, M., Pirola, G., Podobnik, F., Moroni, P. G. Prada, Prandini, E., Principe, G., Priyadarshi, C., Rhode, W., Ribó, M., Rico, J., Righi, C., Sahakyan, N., Saito, T., Satalecka, K., Saturni, F. G., Schleicher, B., Schmidt, K., Schmuckermaier, F., Schubert, J. L., Schweizer, T., Sciaccaluga, A., Silvestri, G., Sitarek, J., Sliusar, V., Sobczynska, D., Spolon, A., Stamerra, A., Strišković, J., Strom, D., Strzys, M., Suda, Y., Surić, T., Suutarinen, S., Tajima, H., Takahashi, M., Takeishi, R., Temnikov, P., Terauchi, K., Terzić, T., Truzzi, M. Teshima S., Tutone, A., Ubach, S., van Scherpenberg, J., Acosta, M. Vazquez, Ventura, S., Viale, I., Vigorito, C. F., Vitale, V., Walter, R., Will, M., Wunderlich, C., Yamamoto, T., Díaz, G. Chon C., Fiori, M., Lobo, M., Naletto, G., Polo, M., Rodríguez-Vázquez, J. J., Saha, P., and Zampieri, L.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics
Abstract: In recent years, a new generation of optical intensity interferometers has emerged, leveraging the existing infrastructure of Imaging Atmospheric Cherenkov Telescopes (IACTs). The MAGIC telescopes host the MAGIC-SII system (Stellar Intensity Interferometer), implemented to investigate the feasibility and potential of this technique on IACTs. After the first successful measurements in 2019, the system was upgraded and now features a real-time, dead-time-free, 4-channel, GPU-based correlator. These hardware modifications allow seamless transitions between MAGIC's standard very-high-energy gamma-ray observations and optical interferometry measurements within seconds. We establish the feasibility and potential of employing IACTs as competitive optical Intensity Interferometers with minimal hardware adjustments. The measurement of a total of 22 stellar diameters are reported, 9 corresponding to reference stars with previous comparable measurements, and 13 with no prior measurements. A prospective implementation involving telescopes from the forthcoming Cherenkov Telescope Array Observatory's northern hemisphere array, such as the first prototype of its Large-Sized Telescopes, LST-1, is technically viable. This integration would significantly enhance the sensitivity of the current system and broaden the UV-plane coverage. This advancement would enable the system to achieve competitive sensitivity with the current generation of long-baseline optical interferometers over blue wavelengths., Comment: 18 pages, 13 figures, submitted to MNRAS
Published: 2024
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41. Amortized Planning with Large-Scale Transformers: A Case Study on Chess

Author: Ruoss, Anian, Delétang, Grégoire, Medapati, Sourabh, Grau-Moya, Jordi, Wenliang, Li Kevin, Catt, Elliot, Reid, John, Lewis, Cannada A., Veness, Joel, and Genewein, Tim
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract: This paper uses chess, a landmark planning problem in AI, to assess transformers' performance on a planning task where memorization is futile $\unicode{x2013}$ even at a large scale. To this end, we release ChessBench, a large-scale benchmark dataset of 10 million chess games with legal move and value annotations (15 billion data points) provided by Stockfish 16, the state-of-the-art chess engine. We train transformers with up to 270 million parameters on ChessBench via supervised learning and perform extensive ablations to assess the impact of dataset size, model size, architecture type, and different prediction targets (state-values, action-values, and behavioral cloning). Our largest models learn to predict action-values for novel boards quite accurately, implying highly non-trivial generalization. Despite performing no explicit search, our resulting chess policy solves challenging chess puzzles and achieves a surprisingly strong Lichess blitz Elo of 2895 against humans (grandmaster level). We also compare to Leela Chess Zero and AlphaZero (trained without supervision via self-play) with and without search. We show that, although a remarkably good approximation of Stockfish's search-based algorithm can be distilled into large-scale transformers via supervised learning, perfect distillation is still beyond reach, thus making ChessBench well-suited for future research.
Published: 2024

42. The eROSITA Final Equatorial Depth Survey (eFEDS): the hard X-ray selected sample

Author: Nandra, K., Waddell, S. G. H., Liu, T., Buchner, J., Dwelly, T., Salvato, M., Shen, Y., Wu, Q., Arcodia, R., Boller, Th., Brunner, H., Brusa, M., Collmar, W., Comparat, J., Georgakakis, A., Grau, M., Hämmerich, S., Ibarra-Medel, H., Igo, Z., Krumpe, M., Lamer, G., Merloni, A., Musiimenta, B., Wolf, J., Assef, R. J., Bauer, F. E., Brandt, W. N., and Rix, H. -W.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Astrophysics of Galaxies
Abstract: During its calibration and performance verification phase, the eROSITA instrument aboard the SRG satellite performed a uniform wide--area X-ray survey of approximately 140 deg$^{2}$ in a region of the sky known as the eROSITA Final Equatorial Depth Survey (eFEDS). The primary aim of eFEDS is to demonstrate the scientific performance to be expected at the end of the 8-pass eROSITA all sky survey. This will provide the first focussed image of the whole sky in the hard X-ray ($>2$~keV) bandpass. The expected source population in this energy range is thus of great interest, particularly for AGN studies. We use the 2.3--5 keV selection presented by Brunner et al. (2022) to construct a sample of 246 point-like hard X-ray sources for further study and characterization. These are classified as either extragalactic ($\sim 90$~\%) or Galactic ($\sim 10$~\%), with the former consisting overwhelmingly of AGN and the latter active stars. We concentrate our further analysis on the extragalactic/AGN sample, describing their X-ray and multiwavelength properties and comparing them to the eFEDS main AGN sample selected in the softer 0.2-2.3 keV band. The eROSITA hard band selects a subsample of sources that is a factor $>10$ brighter than the eFEDS main sample. The AGN within the hard population reach up to $z=3.2$ but on the whole are relatively nearby, with median $z$=0.34 compared to $z$=0.94 for the main sample. The hard survey probes typical luminosities in the range $\log L_{\rm X} = 43-46$. X-ray spectral analysis shows significant intrinsic absorption (with $\log N_{\rm H}>21$) in $\sim 20$~\% of the sources, with a hard X-ray power law continuum with mean $<\Gamma>=1.83\pm0.04$, typical of AGN, but slightly harder than the soft-selected eROSITA sample. (abridged), Comment: Submitted to A&A as part of eROSITA Data Release 1
Published: 2024

43. Learning Universal Predictors

Author: Grau-Moya, Jordi, Genewein, Tim, Hutter, Marcus, Orseau, Laurent, Delétang, Grégoire, Catt, Elliot, Ruoss, Anian, Wenliang, Li Kevin, Mattern, Christopher, Aitchison, Matthew, and Veness, Joel
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: Meta-learning has emerged as a powerful approach to train neural networks to learn new tasks quickly from limited data. Broad exposure to different tasks leads to versatile representations enabling general problem solving. But, what are the limits of meta-learning? In this work, we explore the potential of amortizing the most powerful universal predictor, namely Solomonoff Induction (SI), into neural networks via leveraging meta-learning to its limits. We use Universal Turing Machines (UTMs) to generate training data used to expose networks to a broad range of patterns. We provide theoretical analysis of the UTM data generation processes and meta-training protocols. We conduct comprehensive experiments with neural architectures (e.g. LSTMs, Transformers) and algorithmic data generators of varying complexity and universality. Our results suggest that UTM data is a valuable resource for meta-learning, and that it can be used to train neural networks capable of learning universal prediction strategies., Comment: 32 pages, 11 figures
Published: 2024

44. Constraints on axion-like particles with the Perseus Galaxy Cluster with MAGIC

Author: MAGIC Collaboration, Abe, H., Abe, S., Abhir, J., Acciari, V. A., Agudo, I., Aniello, T., Ansoldi, S., Antonelli, L. A., Engels, A. Arbet, Arcaro, C., Artero, M., Asano, K., Baack, D., Babić, A., Baquero, A., de Almeida, U. Barres, Barrio, J. A., Batković, I., Baxter, J., González, J. Becerra, Bednarek, W., Bernardini, E., Bernete, J., Berti, A., Besenrieder, J., Bigongiari, C., Biland, A., Blanch, O., Bonnoli, G., Bošnjak, Ž., Burelli, I., Busetto, G., Campoy-Ordaz, A., Carosi, A., Carosi, R., Carretero-Castrillo, M., Castro-Tirado, A. J., Ceribella, G., Chai, Y., Cifuentes, A., Cikota, S., Colombo, E., Contreras, J. L., Cortina, J., Covino, S., D'Amico, G., D'Elia, V., Da Vela, P., Dazzi, F., De Angelis, A., De Lotto, B., Del Popolo, A., Delgado, J., Mendez, C. Delgado, Depaoli, D., Di Pierro, F., Di Venere, L., Donini, A., Dorner, D., Doro, M., Elsaesser, D., Emery, G., Escudero, J., Fariña, L., Fattorini, A., Foffano, L., Font, L., Fukami, S., Fukazawa, Y., López, R. J. García, Garczarczyk, M., Gasparyan, S., Gaug, M., Paiva, J. G. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Godinović, N., Grau, R., Green, D., Green, J. G., Hadasch, D., Hahn, A., Hassan, T., Heckmann, L., Herrera, J., Hrupec, D., Hütten, M., Imazawa, R., Inada, T., Iotov, R., Ishio, K., Martínez, I. Jiménez, Jormanainen, J., Kerszberg, D., Kluge, G. W., Kobayashi, Y., Kouch, P. M., Kubo, H., Kushida, J., Láinez, M., Lamastra, A., Leone, F., Lindfors, E., Linhoff, L., Lombardi, S., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Lorini, A., Fraga, B. Machado de Oliveira, Majumdar, P., Makariev, M., Maneva, G., Mang, N., Manganaro, M., Mangano, S., Mannheim, K., Mariotti, M., Martínez, M., Martínez-Chicharro, M., Mas-Aguilar, A., Mazin, D., Menchiari, S., Mender, S., Miceli, D., Miener, T., Miranda, J. M., Mirzoyan, R., González, M. Molero, Molina, E., Mondal, H. A., Moralejo, A., Morcuende, D., Nakamori, T., Nanci, C., Nava, L., Neustroev, V., Nickel, L., Rosillo, M. Nievas, Nigro, C., Nikolić, L., Nilsson, K., Nishijima, K., Ekoume, T. Njoh, Noda, K., Nozaki, S., Ohtani, Y., Okumura, A., Otero-Santos, J., Paiano, S., Palatiello, M., Paneque, D., Paoletti, R., Paredes, J. M., Pavlović, D., Persic, M., Pihet, M., Pirola, G., Podobnik, F., Moroni, P. G. Prada, Prandini, E., Principe, G., Priyadarshi, C., Rhode, W., Ribó, M., Rico, J., Righi, C., Sahakyan, N., Saito, T., Satalecka, K., Saturni, F. G., Schleicher, B., Schmidt, K., Schmuckermaier, F., Schubert, J. L., Schweizer, T., Sciaccaluga, A., Sitarek, J., Sliusar, V., Sobczynska, D., Spolon, A., Stamerra, A., Strišković, J., Strom, D., Strzys, M., Suda, Y., Suutarinen, S., Tajima, H., Takahashi, M., Takeishi, R., Tavecchio, F., Temnikov, P., Terauchi, K., Terzić, T., Teshima, M., Tosti, L., Truzzi, S., Tutone, A., Ubach, S., van Scherpenberg, J., Acosta, M. Vazquez, Ventura, S., Verguilov, V., Viale, I., Vigorito, C. F., Vitale, V., Vovk, I., Walter, R., Will, M., and Yamamoto, T.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Phenomenology
Abstract: Axion-like particles (ALPs) are pseudo-Nambu-Goldstone bosons that emerge in various theories beyond the standard model. These particles can interact with high-energy photons in external magnetic fields, influencing the observed gamma-ray spectrum. This study analyzes 41.3 hrs of observational data from the Perseus Galaxy Cluster collected with the MAGIC telescopes. We focused on the spectra the radio galaxy in the center of the cluster: NGC 1275. By modeling the magnetic field surrounding this target, we searched for spectral indications of ALP presence. Despite finding no statistical evidence of ALP signatures, we were able to exclude ALP models in the sub-micro electronvolt range. Our analysis improved upon previous work by calculating the full likelihood and statistical coverage for all considered models across the parameter space. Consequently, we achieved the most stringent limits to date for ALP masses around 50 neV, with cross sections down to $g_{a\gamma} = 3 \times 10^{-12}$ GeV$^{-1}$., Comment: 25 pages, 10 figures, accepted for publication in Physics of the Dark Universe
Published: 2024
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45. Migration and Evolution of giant ExoPlanets (MEEP) I: Nine Newly Confirmed Hot Jupiters from the TESS Mission

Author: Schulte, Jack, Rodriguez, Joseph E., Bieryla, Allyson, Quinn, Samuel N., Collins, Karen A., Yee, Samuel W., Nine, Andrew C., Soares-Furtado, Melinda, Latham, David W., Eastman, Jason D., Barkaoui, Khalid, Ciardi, David R., Dragomir, Diana, Everett, Mark E., Giacalone, Steven, Mireles, Ismael, Murgas, Felipe, Narita, Norio, Shporer, Avi, Strakhov, Ivan A., Striegel, Stephanie, Vaňko, Martin, Vowell, Noah, Wang, Gavin, Ziegler, Carl, Bellaver, Michael, Benni, Paul, Bergeron, Serge, Boffin, Henri M. J., Briceño, César, Clark, Catherine A., Collins, Kevin I., de Leon, Jerome P., Dressing, Courtney D., Evans, Phil, Esparza-Borges, Emma, Fedewa, Jeremy, Fukui, Akihiko, Gan, Tianjun, Gerasimov, Ivan S., Hartman, Joel D., Gill, Holden, Gillon, Michaël, Horne, Keith, Horta, Ferran Grau, Howell, Steve B., Isogai, Keisuke, Jehin, Emmanuël, Jenkins, Jon M., Karjalainen, Raine, Kielkopf, John F., Lester, Kathryn V., Littlefield, Colin, Lund, Michael B., Mann, Andrew W., McCormack, Mason, Michaels, Edward J., Painter, Shane, Palle, Enric, Parviainen, Hannu, Peterson, David-Michael, Pozuelos, Francisco J., Raup, Zachary, Reed, Phillip, Relles, Howard M., Ricker, George R., Savel, Arjun B., Schwarz, Richard P., Seager, Sara, Sefako, Ramotholo, Srdoc, Gregor, Stockdale, Chris, Sullivan, Hannah, Timmermans, Mathilde, and Winn, Joshua N.
Subjects: Astrophysics - Earth and Planetary Astrophysics
Abstract: Hot Jupiters were many of the first exoplanets discovered in the 1990s, but in the decades since their discovery, the mysteries surrounding their origins remain. Here, we present nine new hot Jupiters (TOI-1855 b, TOI-2107 b, TOI-2368 b, TOI-3321 b, TOI-3894 b, TOI-3919 b, TOI-4153 b, TOI-5232 b, and TOI-5301 b) discovered by NASA's TESS mission and confirmed using ground-based imaging and spectroscopy. These discoveries are the first in a series of papers named the Migration and Evolution of giant ExoPlanets (MEEP) survey and are part of an ongoing effort to build a complete sample of hot Jupiters orbiting FGK stars, with a limiting Gaia $G$-band magnitude of 12.5. This effort aims to use homogeneous detection and analysis techniques to generate a set of precisely measured stellar and planetary properties that is ripe for statistical analysis. The nine planets presented in this work occupy a range of masses (0.55 Jupiter masses (M$_{\rm{J}}$) $<$ M$_{\rm{P}}$ $<$ 3.88 M$_{\rm{J}}$) and sizes (0.967 Jupiter radii (R$_{\rm{J}}$) $<$ R$_{\rm{P}}$ $<$ 1.438 R$_{\rm{J}}$) and orbit stars that range in temperature from 5360 K $<$ Teff $<$ 6860 K with Gaia $G$-band magnitudes ranging from 11.1 to 12.7. Two of the planets in our sample have detectable orbital eccentricity: TOI-3919 b ($e = 0.259^{+0.033}_{-0.036}$) and TOI-5301 b ($e = 0.33^{+0.11}_{-0.10}$). These eccentric planets join a growing sample of eccentric hot Jupiters that are consistent with high-eccentricity tidal migration, one of the three most prominent theories explaining hot Jupiter formation and evolution., Comment: 35 pages, 7 tables, and 14 figures. Submitted to AAS Journals on 2023 Dec 28
Published: 2024

46. Influence of thermal effects on the optomechanical coupling rate in acousto-optic cavities

Author: Ortiz, Raúl, Mercadé, Laura, Grau, Alberto, Navarro-Urrios, Daniel, and Martínez, Alejandro
Subjects: Physics - Optics
Abstract: Optomechanical (OM) cavities simultaneously localize photons and phonons, thus enhancing their mutual interaction through radiation-pressure force. This acousto-optic interaction can be quantified by means of the optical frequency shift per mechanical displacement G. The aforesaid frequency shift can also be related to the vacuum OM coupling rate, g0, where only photoelastic (PE) and moving boundaries (MB) effects are commonly taken into account. However, the thermo-optic (TO) and thermal expansion (ThE) effects may also play a role since the material forming the OM cavity could be heated by the presence of photons, which should naturally affect the mechanical properties of the cavity. In this work, we introduce a new theoretical approach to determine how thermal effects change the canonical OM coupling rate. To test the model, a complete set of optical-thermal-mechanical simulations has been performed in two OM crystal cavities fabricated from two different materials: silicon and diamond. Our results lead us to conclude that there is a non-negligible thermal correction that is always present as a negative shift to the OM coupling rate that should be considered in order to predict more accurately the strength of the OM interaction.
Published: 2024

47. Practical Reasoning in DatalogMTL

Author: Wang, Dingmin, Wałęga, Przemysław A., Hu, Pan, and Grau, Bernardo Cuenca
Subjects: Computer Science - Logic in Computer Science
Abstract: DatalogMTL is an extension of Datalog with metric temporal operators that has found an increasing number of applications in recent years. Reasoning in DatalogMTL is, however, of high computational complexity, which makes reasoning in modern data-intensive applications challenging. In this paper we present a practical reasoning algorithm for the full DatalogMTL language, which we have implemented in a system called MeTeoR. Our approach effectively combines an optimised (but generally non-terminating) materialisation (a.k.a. forward chaining) procedure, which provides scalable behaviour, with an automata-based component that guarantees termination and completeness. To ensure favourable scalability of the materialisation component, we propose a novel semina\"ive materialisation procedure for DatalogMTL enjoying the non-repetition property, which ensures that each specific rule application will be considered at most once throughout the entire execution of the algorithm. Moreover, our materialisation procedure is enhanced with additional optimisations which further reduce the number of redundant computations performed during materialisation by disregarding rules as soon as it is certain that they cannot derive new facts in subsequent materialisation steps. Our extensive evaluation supports the practicality of our approach., Comment: Under consideration in Theory and Practice of Logic Programming (TPLP). arXiv admin note: text overlap with arXiv:2208.07100
Published: 2024

48. Anatomical basis of human sex differences in ECG identified by automated torso-cardiac three-dimensional reconstruction

Author: Smith, Hannah J., Rodriguez, Blanca, Sang, Yuling, Beetz, Marcel, Choudhury, Robin, Grau, Vicente, and Banerjee, Abhirup
Subjects: Physics - Medical Physics, Computer Science - Artificial Intelligence, Computer Science - Computational Geometry, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Quantitative Methods
Abstract: Background and Aims: The electrocardiogram (ECG) is routinely used for diagnosis and risk stratification following myocardial infarction (MI), though its interpretation is confounded by anatomical variability and sex differences. Women have a higher incidence of missed MI diagnosis and poorer outcomes following infarction. Sex differences in ECG biomarkers and torso-ventricular anatomy have not been well characterised, largely due to the absence of high-throughput torso reconstruction methods. Methods: This work presents quantification of sex differences in ECG versus anatomical biomarkers in healthy and post-MI subjects, enabled by a novel, end-to-end automated pipeline for torso-ventricular anatomical reconstruction from clinically standard cardiac magnetic resonance imaging. Personalised 3D torso-ventricular reconstructions were generated for 425 post-MI subjects and 1051 healthy controls from the UK Biobank. Regression models were created relating the extracted torso-ventricular and ECG parameters. Results: Half the sex difference in QRS durations is explained by smaller ventricles in women both in healthy ($3.4 \pm 1.3$ms of $6.0 \pm 1.5$ms) and post-MI ($4.5 \pm 1.4$ms of $8.3 \pm 2.5$ms) subjects. Lower baseline STj amplitude in women is also associated with smaller ventricles, and more superior and posterior cardiac position. Post-MI T wave amplitude and R axis deviations are more strongly associated with a more posterior and horizontal cardiac position in women rather than electrophysiology as in men. Conclusion: A novel computational pipeline enables the three-dimensional reconstruction of 1476 torso-cardiac geometries of healthy and post-myocardial infarction subjects, quantification of sex and BMI-related differences and association with ECG biomarkers. Any ECG-based tool should be reviewed considering anatomical sex differences to avoid sex-biased outcomes., Comment: Paper under revision
Published: 2023

49. First characterization of the emission behavior of Mrk421 from radio to VHE gamma rays with simultaneous X-ray polarization measurements

Author: Abe, S., Abhir, J., Acciari, V. A., Agudo, I., Aniello, T., Ansoldi, S., Antonelli, L. A., Engels, A. Arbet, Arcaro, C., Artero, M., Asano, K., Babić, A., Baquero, A., de Almeida, U. Barres, Barrio, J. A., Batković, I., Baxter, J., González, J. Becerra, Bednarek, W., Bernardini, E., Bernete, J., Berti, A., Besenrieder, J., Bigongiari, C., Biland, A., Blanch, O., Bonnoli, G., Bošnjak, Ž., Burelli, I., Busetto, G., Campoy-Ordaz, A., Carosi, A., Carosi, R., Carretero-Castrillo, M., Castro-Tirado, A. J., Ceribella, G., Chai, Y., Cifuentes, A., Cikota, S., Colombo, E., Contreras, J. L., Cortina, J., Covino, S., D'Ammando, F., D'Amico, G., D'Elia, V., Da Vela, P., Dazzi, F., De Angelis, A., De Lotto, B., de Menezes, R., Del Popolo, A., Delgado, J., Mendez, C. Delgado, Di Pierro, F., Di Venere, L., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Elsaesser, D., Emery, G., Escudero, J., Fariña, L., Fattorini, A., Foffano, L., Font, L., Fröse, S., Fukami, S., Fukazawa, Y., López, R. J. García, Garczarczyk, M., Gasparyan, S., Gaug, M., Paiva, J. G. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Godinović, N., Gradetzke, T., Grau, R., Green, D., Green, J. G., Günther, P., Hadasch, D., Hahn, A., Hassan, T., Heckmann, L., Herrera, J., Hrupec, D., Hütten, M., Imazawa, R., Inada, T., Ishio, K., Martínez, I. Jiménez, Jormanainen, J., Kerszberg, D., Kluge, G. W., Kobayashi, Y., Kouch, P. M., Kubo, H., Kushida, J., Lezáun, M. Láinez, Lamastra, A., Leone, F., Lindfors, E., Linhoff, L., Lombardi, S., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Lorini, A., Fraga, B. Machado de Oliveira, Majumdar, P., Makariev, M., Maneva, G., Mang, N., Manganaro, M., Mangano, S., Mannheim, K., Mariotti, M., Martínez, M., Martínez-Chicharro, M., Mas-Aguilar, A., Mazin, D., Menchiari, S., Mender, S., Miceli, D., Miener, T., Miranda, J. M., Mirzoyan, R., González, M. Molero, Molina, E., Mondal, H. A., Moralejo, A., Morcuende, D., Nakamori, T., Nanci, C., Nava, L., Neustroev, V., Nickel, L., Rosillo, M. Nievas, Nigro, C., Nikolić, L., Nilsson, K., Nishijima, K., Ekoume, T. Njoh, Noda, K., Nozaki, S., Ohtani, Y., Okumura, A., Otero-Santos, J., Paiano, S., Palatiello, M., Paneque, D., Paoletti, R., Paredes, J. M., Pavlović, D., Peresano, M., Persic, M., Pihet, M., Pirola, G., Podobnik, F., Moroni, P. G. Prada, Prandini, E., Principe, G., Priyadarshi, C., Rhode, W., Ribó, M., Rico, J., Righi, C., Sahakyan, N., Saito, T., Satalecka, K., Saturni, F. G., Schleicher, B., Schmidt, K., Schmuckermaier, F., Schubert, J. L., Schweizer, T., Sciaccaluga, A., Sitarek, J., Sliusar, V., Sobczynska, D., Stamerra, A., Strišković, J., Strom, D., Strzys, M., Suda, Y., Suutarinen, S., Tajima, H., Takahashi, M., Takeishi, R., Tavecchio, F., Temnikov, P., Terauchi, K., Terzić, T., Teshima, M., Tosti, L., Truzzi, S., Tutone, A., Ubach, S., van Scherpenberg, J., Acosta, M. Vazquez, Ventura, S., Viale, I., Vigorito, C. F., Vitale, V., Vovk, I., Walter, R., Will, M., Wunderlich, C., Yamamoto, T., Liodakis, I., Jorstad, S. G., Gesu, L. D., Donnarumma, I., Kim, D. E., Marscher, A. P., Middei, R., Perri, M., Puccetti, S., Verrecchia, F., Leto, C., Pérez, I. De La Calle, Jiménez-Bailón, E., Blinov, D., Bourbah, I. G., Kiehlmann, S., Kontopodis, E., Mandarakas, N., Skalidis, R., Vervelaki, A., Aceituno, F. J., Agís-González, B., Sota, A., Sasada, M., Kawabata, K. S., Uemura, M., Mizuno, T., Akitaya, H., Casadio, C., Myserlis, I., Sievers, A., Lähteenmäki, A., Syrjärinne, I., Tornikoski, M., Salomé, Q., Gurwell, M., Keating, G. K., and Rao, R.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We perform the first broadband study of Mrk421 from radio to TeV gamma rays with simultaneous measurements of the X-ray polarization from IXPE. The data were collected within an extensive multiwavelength campaign organized between May and June 2022 using MAGIC, Fermi-LAT, NuSTAR, XMM-Newton, Swift, and several optical and radio telescopes to complement IXPE. During the IXPE exposures, the measured 0.2-1 TeV flux is close to the quiescent state and ranges from 25% to 50% of the Crab Nebula without intra-night variability. Throughout the campaign, the VHE and X-ray emission are positively correlated at a $4\sigma$ significance level. The IXPE measurements unveil a X-ray polarization degree that is a factor of 2-5 higher than in the optical/radio bands; that implies an energy-stratified jet in which the VHE photons are emitted co-spatially with the X-rays, in the vicinity of a shock front. The June 2022 observations exhibit a rotation of the X-ray polarization angle. Despite no simultaneous VHE coverage being available during a large fraction of the swing, the Swift-XRT monitoring unveils an X-ray flux increase with a clear spectral hardening. It suggests that flares in high synchrotron peaked blazars can be accompanied by a polarization angle rotation, as observed in some flat spectrum radio quasars. Finally, during the polarization angle rotation, NuSTAR data reveal two contiguous spectral hysteresis loops in opposite directions (clockwise and counter-clockwise), implying important changes in the particle acceleration efficiency on $\sim$hour timescales., Comment: Accepted for publication in Astronomy & Astrophysics. 29 pages, 22 figures. Corresponding authors: Axel Arbet Engels, Felix Schmuckermaier, David Paneque
Published: 2023

50. Identified charged-hadron production in $p$$+$Al, $^3$He$+$Au, and Cu$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV and in U$+$U collisions at $\sqrt{s_{_{NN}}}=193$ GeV

Author: PHENIX Collaboration, Abdulameer, N. J., Acharya, U., Adare, A., Aidala, C., Ajitanand, N. N., Akiba, Y., Akimoto, R., Alexander, J., Alfred, M., Andrieux, V., Aoki, K., Apadula, N., Asano, H., Atomssa, E. T., Awes, T. C., Azmoun, B., Babintsev, V., Bai, M., Bai, X., Bandara, N. S., Bannier, B., Barish, K. N., Bathe, S., Baublis, V., Baumann, C., Baumgart, S., Bazilevsky, A., Beaumier, M., Beckman, S., Belmont, R., Berdnikov, A., Berdnikov, Y., Bichon, L., Black, D., Blankenship, B., Blau, D. S., Bok, J. S., Borisov, V., Boyle, K., Brooks, M. L., Bryslawskyj, J., Buesching, H., Bumazhnov, V., Butsyk, S., Campbell, S., Roman, V. Canoa, Cervantes, R., Chen, C. -H., Chiu, M., Chi, C. Y., Choi, I. J., Choi, J. B., Choi, S., Christiansen, P., Chujo, T., Cianciolo, V., Citron, Z., Cole, B. A., Connors, M., Corliss, R., Morales, Y. Corrales, Cronin, N., Crossette, N., Csanád, M., Csörgő, T., D'Orazio, L., Danley, T. W., Datta, A., Daugherity, M. S., David, G., Dean, C. T., DeBlasio, K., Dehmelt, K., Denisov, A., Deshpande, A., Desmond, E. J., Ding, L., Dion, A., Diss, P. B., Dixit, D., Doomra, V., Do, J. H., Drapier, O., Drees, A., Drees, K. A., Durham, J. M., Durum, A., En'yo, H., Engelmore, T., Enokizono, A., Esha, R., Eyser, K. O., Fadem, B., Fan, W., Feege, N., Fields, D. E., Finger, Jr., M., Finger, M., Firak, D., Fitzgerald, D., Fleuret, F., Fokin, S. L., Frantz, J. E., Franz, A., Frawley, A. D., Fukao, Y., Fukuda, Y., Fusayasu, T., Gainey, K., Gallus, P., Gal, C., Garg, P., Garishvili, A., Garishvili, I., Ge, H., Giles, M., Giordano, F., Glenn, A., Gong, X., Gonin, M., Goto, Y., de Cassagnac, R. Granier, Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gu, Y., Gunji, T., Guo, T., Guragain, H., Hachiya, T., Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamilton, H. F., Hanks, J., Han, S. Y., Harvey, M., Hasegawa, S., Haseler, T. O. S., Hashimoto, K., Hayano, R., Hemmick, T. K., Hester, T., He, X., Hill, J. C., Hill, K., Hodges, A., Hollis, R. S., Homma, K., Hong, B., Hoshino, T., Hotvedt, N., Huang, J., Ichihara, T., Ikeda, Y., Imai, K., Imazu, Y., Inaba, M., Iordanova, A., Isenhower, D., Isinhue, A., Ivanishchev, D., Jacak, B. V., Jeon, S. J., Jezghani, M., Jiang, X., Ji, Z., Johnson, B. M., Joo, K. S., Jouan, D., Jumper, D. S., Kamin, J., Kanda, S., Kang, B. H., Kang, J. H., Kang, J. S., Kapukchyan, D., Kapustinsky, J., Karthas, S., Kawall, D., Kazantsev, A. V., Key, J. A., Khachatryan, V., Khandai, P. K., Khanzadeev, A., Khatiwada, A., Kijima, K. M., Kimelman, B., Kim, C., Kim, D. J., Kim, E. -J., Kim, G. W., Kim, M., Kim, T., Kim, Y. -J., Kim, Y. K., Kincses, D., Kingan, A., Kistenev, E., Kitamura, R., Klatsky, J., Kleinjan, D., Kline, P., Koblesky, T., Kofarago, M., Komkov, B., Koster, J., Kotchetkov, D., Kotov, D., Kovacs, L., Krizek, F., Kudo, S., Kurgyis, B., Kurita, K., Kurosawa, M., Kwon, Y., Lai, Y. S., Lajoie, J. G., Larionova, D., Lebedev, A., Lee, D. M., Lee, G. H., Lee, J., Lee, K. B., Lee, K. S., Lee, S., Lee, S. H., Leitch, M. J., Leitgab, M., Leung, Y. H., Lewis, B., Lewis, N. A., Lim, S. H., Liu, M. X., Li, X., Loggins, V. -R., Loomis, D. A., Lovasz, K., Lynch, D., Lökös, S., Maguire, C. F., Majoros, T., Makdisi, Y. I., Makek, M., Manion, A., Manko, V. I., Mannel, E., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Meles, A., Mendoza, M., Meredith, B., Miake, Y., Mibe, T., Mignerey, A. C., Milov, A., Mishra, D. K., Mitchell, J. T., Mitrankova, M., Mitrankov, Iu., Mitsuka, G., Miyasaka, S., Mizuno, S., Mohanty, A. K., Mohapatra, S., Mondal, M. M., Montuenga, P., Moon, T., Morrison, D. P., Moskowitz, M., Moukhanova, T. V., Muhammad, A., Mulilo, B., Murakami, T., Murata, J., Mwai, A., Nagae, T., Nagai, K., Nagamiya, S., Nagashima, K., Nagashima, T., Nagle, J. L., Nagy, M. I., Nakagawa, I., Nakagomi, H., Nakamiya, Y., Nakamura, K. R., Nakamura, T., Nakano, K., Nattrass, C., Nelson, S., Netrakanti, P. K., Nihashi, M., Niida, T., Nishimura, S., Nouicer, R., Novitzky, N., Novák, T., Nukazuka, G., Nyanin, A. S., O'Brien, E., Ogilvie, C. A., Oh, J., Oide, H., Okada, K., Koop, J. D. Orjuela, Orosz, M., Osborn, J. D., Oskarsson, A., Ottino, G. J., Ozawa, K., Pak, R., Pantuev, V., Papavassiliou, V., Park, I. H., Park, J. S., Park, S., Park, S. K., Patel, L., Patel, M., Pate, S. F., Peng, J. -C., Peng, W., Perepelitsa, D. V., Perera, G. D. N., Peressounko, D. Yu., PerezLara, C. E., Perry, J., Petti, R., Phipps, M., Pinkenburg, C., Pinson, R., Pisani, R. P., Potekhin, M., Pun, A., Purschke, M. L., Qu, H., Radzevich, P. V., Rak, J., Ramasubramanian, N., Ramson, B. J., Ravinovich, I., Read, K. F., Reynolds, D., Riabov, V., Riabov, Y., Richardson, E., Richford, D., Rinn, T., Riveli, N., Roach, D., Rolnick, S. D., Rosati, M., Rowan, Z., Rubin, J. G., Runchey, J., Ryu, M. S., Safonov, A. S., Sahlmueller, B., Saito, N., Sakaguchi, T., Sako, H., Samsonov, V., Sarsour, M., Sato, S., Sawada, S., Schaefer, B., Schmoll, B. K., Sedgwick, K., Seele, J., Seidl, R., Sekiguchi, Y., Sen, A., Seto, R., Sett, P., Sexton, A., Sharma, D., Shaver, A., Shein, I., Shibata, M., Shibata, T. -A., Shigaki, K., Shimomura, M., Shioya, T., Shi, Z., Shoji, K., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Singh, B. K., Singh, V., Skolnik, M., Slunečka, M., Smith, K. L., Snowball, M., Solano, S., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Stankus, P. W., Steinberg, P., Stenlund, E., Stepanov, M., Ster, A., Stoll, S. P., Stone, M. R., Sugitate, T., Sukhanov, A., Sumita, T., Sun, J., Sun, Z., Sziklai, J., Takahama, R., Takahara, A., Taketani, A., Tanaka, Y., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarnai, G., Tennant, E., Tieulent, R., Timilsina, A., Todoroki, T., Tomášek, M., Torii, H., Towell, C. L., Towell, R., Towell, R. S., Tserruya, I., Ueda, Y., Ujvari, B., van Hecke, H. W., Vargyas, M., Vazquez-Zambrano, E., Veicht, A., Velkovska, J., Virius, M., Vrba, V., Vukman, N., Vznuzdaev, E., Vértesi, R., Wang, X. R., Wang, Z., Watanabe, D., Watanabe, K., Watanabe, Y., Watanabe, Y. S., Wei, F., Whitaker, S., White, A. S., Wolin, S., Wong, C. P., Woody, C. L., Wysocki, M., Xia, B., Xue, L., Xu, C., Xu, Q., Yalcin, S., Yamaguchi, Y. L., Yamamoto, H., Yanovich, A., Yokkaichi, S., Yoon, I., Yoo, J. H., Younus, I., You, Z., Yushmanov, I. E., Yu, H., Zajc, W. A., Zelenski, A., Zhou, S., and Zou, L.
Subjects: Nuclear Experiment
Abstract: The PHENIX experiment has performed a systematic study of identified charged-hadron ($\pi^\pm$, $K^\pm$, $p$, $\bar{p}$) production at midrapidity in $p$$+$Al, $^3$He$+$Au, Cu$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV and U$+$U collisions at $\sqrt{s_{_{NN}}}=193$ GeV. Identified charged-hadron invariant transverse-momentum ($p_T$) and transverse-mass ($m_T$) spectra are presented and interpreted in terms of radially expanding thermalized systems. The particle ratios of $K/\pi$ and $p/\pi$ have been measured in different centrality ranges of large (Cu$+$Au, U$+$U) and small ($p$$+$Al, $^3$He$+$Au) collision systems. The values of $K/\pi$ ratios measured in all considered collision systems were found to be consistent with those measured in $p$$+$$p$ collisions. However the values of $p/\pi$ ratios measured in large collision systems reach the values of $\approx0.6$, which is $\approx2$ times larger than in $p$$+$$p$ collisions. These results can be qualitatively understood in terms of the baryon enhancement expected from hadronization by recombination. Identified charged-hadron nuclear-modification factors ($R_{AB}$) are also presented. Enhancement of proton $R_{AB}$ values over meson $R_{AB}$ values was observed in central $^3$He$+$Au, Cu$+$Au, and U$+$U collisions. The proton $R_{AB}$ values measured in $p$$+$Al collision system were found to be consistent with $R_{AB}$ values of $\phi$, $\pi^\pm$, $K^\pm$, and $\pi^0$ mesons, which may indicate that the size of the system produced in $p$$+$Al collisions is too small for recombination to cause a noticeable increase in proton production., Comment: 480 authors from 78 institutions, 18 pages, 6 tables, 16 figures. v2 is version accepted for publication in Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html
Published: 2023
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