64,378 results on '"P. Rocha"'
Search Results
2. Exploring Influences on ICT Adoption in German Schools: A UTAUT-Based Structural Equation Model
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Daniel Petri Rocha, Arne Bewersdorff, and Claudia Nerdel
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The 2018 International Computer and Information Literacy Study (ICILS) provides a comprehensive dataset with contextual information on German school environments. The data comes from responses by eighth-grade teachers and principals to a questionnaire about Information and Communication Technology (ICT) for teaching. We performed an exploratory re-analysis examining what influences teachers' adoption and use of ICT by employing six factors -- Social Influence, Effort Expectancy, Performance Expectancy, Facilitating Conditions, Behavioural Intention, and Use Behaviour -- from the Unified Theory of Acceptance and Use of Technology (UTAUT). We measured relationships between these constructs with a Structural Equation Model (SEM) consisting of 15 indicators provided by the ICILS study. The goodness of fit measures indicated confirmation of the model (RMSEA = 0.05, SRMR = 0.05, CFI = 0.93, TLI = 0.91). Our findings show that teachers' attitudes toward ICT and the school administration's support affected how ICT was applied in the classroom.
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- 2024
3. Problem-Solving in a Real-Life Context: An Approach during the Learning of Inequalities
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Helena Rocha, Floriano Viseu, and Sara Matos
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This study was conducted while 9th grade students learn to solve inequalities and seeks to understand their approach to solving problems with a real-life context. Specifically, the aim is to understand: (1) What are the main characteristics of the students' approaches to the proposed problems? (2) What is the impact of the real context on the students' resolutions? A qualitative and interpretative methodology is adopted, based on case studies, with data collected through documentary collection and audio recording of discussions between a pair of students while solving problems. The main conclusions suggest a trend to approach problems without establishing immediate connections with what was being done in the classroom, with students' decisions being essentially guided by criteria of simplicity. The real context of the problems seems to have the potential to develop in students a more integrated mathematics, focused on understanding and not so much on the repetition of mechanical and meaning-independent procedures. The students' familiarization with the context in question is one of the aspects highlighted by this study.
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- 2024
4. In vitro amoebicidal effects of arabinogalactan-based ophthalmic solution
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M. Reyes-Batlle, I. Rodríguez-Talavera, I. Sifaoui, R.L. Rodríguez-Expósito, P. Rocha-Cabrera, J.E. Piñero, and J. Lorenzo-Morales
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Matrix Ocular® ,Ophthalmic solution ,Acanthamoeba ,Programmed cell death ,Arabinogalactan ,Infectious and parasitic diseases ,RC109-216 - Abstract
The main corneal infections reported worldwide are caused by bacteria and viruses but, recently, the number of Acanthamoeba keratitis (AK) cases has increased. Acanthamoeba genus is an opportunistic free living protozoa widely distributed in environmental and clinical sources, with two life-cycle stages: the trophozoite and the cyst. AK presents as primary symptoms eye redness, epithelial defects, photophobia and intense pain. An early diagnosis and an effective treatment are crucial to avoid blindness or eye removal but, so far, there is no established treatment to this corneal infection. Diverse research studies have reported the efficacy of commercialized eye drops and ophthalmic solutions against the two life cycle stages of Acanthamoeba strains, that usually present preservatives such as Propylene Glycol of Benzalkonium chloride (BAK). These compounds present toxic effects in corneal cells, favouring the inflammatory response in the so sensitive eye tissue. In the present work we have evaluated the efficacy of nine proprietary ophthalmic solutions with and without preservatives (ASDA Dry Eyes Eyedrops, Miren®, ODM5®, Ectodol®, Systane® Complete, Ocudox®, Matrix Ocular®, Alins® and Coqun®) against the two life cycle stages of three Acanthamoeba strains. Our work has demonstrated the high anti-Acanthamoeba activity of Matrix Ocular®, which induces the programmed cell death mechanisms in Acanthamoeba spp. trophozoites. The high efficacy and the absence of ocular toxic effects of Matrix Ocular®, evidences the use of the Arabinogalactan derivatives as a new source of anti-AK compounds.
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- 2021
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5. Deformations in spinor bundles: Lorentz violation and further physical implications
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da Silva, J. M. Hoff, Cavalcanti, R. T., and da Rocha, G. M. Caires
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Mathematical Physics ,High Energy Physics - Theory - Abstract
This paper delves into the deformation of spinor structures within nontrivial topologies and their physical implications. The deformation is modeled by introducing real functions that modify the standard spinor dynamics, leading to distinct physical regions characterized by varying degrees of Lorentz symmetry violation. It allows us to investigate the effects in the dynamical equation and a geometrized nonlinear sigma model. The findings suggest significant implications for the spinor fields in regions with nontrivial topologies, providing a robust mathematical approach to studying exotic spinor behavior., Comment: 14 pages, no figures
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- 2024
6. Consensus in Models for Opinion Dynamics with Generalized-Bias
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Paz, Juan, Rocha, Camilo, Tobòn, Luis, and Valencia, Frank
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Computer Science - Social and Information Networks ,Mathematics - Dynamical Systems - Abstract
Interest is growing in social learning models where users share opinions and adjust their beliefs in response to others. This paper introduces generalized-bias opinion models, an extension of the DeGroot model, that captures a broader range of cognitive biases. These models can capture, among others, dynamic (changing) influences as well as ingroup favoritism and out-group hostility, a bias where agents may react differently to opinions from members of their own group compared to those from outside. The reactions are formalized as arbitrary functions that depend, not only on opinion difference, but also on the particular opinions of the individuals interacting. Under certain reasonable conditions, all agents (despite their biases) will converge to a consensus if the influence graph is strongly connected, as in the original DeGroot model. The proposed approach combines different biases, providing deeper insights into the mechanics of opinion dynamics and influence within social networks.
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- 2024
7. Hyperedge Representations with Hypergraph Wavelets: Applications to Spatial Transcriptomics
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Sun, Xingzhi, Xu, Charles, Rocha, João F., Liu, Chen, Hollander-Bodie, Benjamin, Goldman, Laney, DiStasio, Marcello, Perlmutter, Michael, and Krishnaswamy, Smita
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Statistics - Machine Learning ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Signal Processing ,Quantitative Biology - Quantitative Methods - Abstract
In many data-driven applications, higher-order relationships among multiple objects are essential in capturing complex interactions. Hypergraphs, which generalize graphs by allowing edges to connect any number of nodes, provide a flexible and powerful framework for modeling such higher-order relationships. In this work, we introduce hypergraph diffusion wavelets and describe their favorable spectral and spatial properties. We demonstrate their utility for biomedical discovery in spatially resolved transcriptomics by applying the method to represent disease-relevant cellular niches for Alzheimer's disease.
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- 2024
8. JWST ice band profiles reveal mixed ice compositions in the HH 48 NE disk
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Bergner, Jennifer B., Sturm, J. A., Piacentino, Elettra L., McClure, M. K., Oberg, Karin I., Boogert, A. C. A., Dartois, E., Drozdovskaya, M. N., Fraser, H. J., Harsono, Daniel, Ioppolo, Sergio, Law, Charles J., Lis, Dariusz C., McGuire, Brett A., Melnick, Gary J., Noble, Jennifer A., Palumbo, M. E., Pendleton, Yvonne J., Perotti, Giulia, Qasim, Danna, Rocha, W. R. M., and van Dishoeck, E. F.
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Astrophysics - Earth and Planetary Astrophysics ,Astrophysics - Instrumentation and Methods for Astrophysics - Abstract
Planet formation is strongly influenced by the composition and distribution of volatiles within protoplanetary disks. With JWST, it is now possible to obtain direct observational constraints on disk ices, as recently demonstrated by the detection of ice absorption features towards the edge-on HH 48 NE disk as part of the Ice Age Early Release Science program. Here, we introduce a new radiative transfer modeling framework designed to retrieve the composition and mixing status of disk ices using their band profiles, and apply it to interpret the H2O, CO2, and CO ice bands observed towards the HH 48 NE disk. We show that the ices are largely present as mixtures, with strong evidence for CO trapping in both H2O and CO2 ice. The HH 48 NE disk ice composition (pure vs. polar vs. apolar fractions) is markedly different from earlier protostellar stages, implying thermal and/or chemical reprocessing during the formation or evolution of the disk. We infer low ice-phase C/O ratios around 0.1 throughout the disk, and also demonstrate that the mixing and entrapment of disk ices can dramatically affect the radial dependence of the C/O ratio. It is therefore imperative that realistic disk ice compositions are considered when comparing planetary compositions with potential formation scenarios, which will fortunately be possible for an increasing number of disks with JWST., Comment: Accepted to ApJ. 24 pages, 15 figures
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- 2024
9. Sturmian external angles of primitive components in the Mandelbrot set
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Itzá-Ortiz, Benjamín A., Rocha, Mónica Moreno, and Nopal-Coello, Víctor
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Mathematics - Dynamical Systems ,37F10 (Primary), 37B10 (Secondary) - Abstract
In this work we introduce the broken line construction, which is a geometric and combinatorial algorithm that computes periodic Sturmian angles of a given period, yielding the locations of their landing parameters in the Mandelbrot set. An easy to implement method to compute the conjugated angle of a periodic Sturmian angle is also provided. Furthermore, if $\theta$ is a periodic Sturmian angle computed by the broken line construction, then we show the existence of a one-to-one correspondence between its binary expansion and its associated kneading sequence.
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- 2024
10. Suppression of the Mott insulating phase in the particle-hole asymmetric Hubbard model
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Marques, Mateus, Melo, Bruno M. de Souza, Rocha, Alexandre R., Lewenkopf, Caio, and da Silva, Luis G. G. V. Dias
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Condensed Matter - Strongly Correlated Electrons - Abstract
We explore the phase diagram of the Mott metal-insulator transition (MIT), focusing on the effects of particle-hole asymmetry (PHA) in the single-band Hubbard model. Our dynamical mean-field theory (DMFT) study reveals that the introduction of PHA in the model significantly influences the critical temperature ($T_c$) and interaction strength ($U_c$), as well as the size of the co-existence region of metallic and insulating phases at low temperatures. Specifically, as the system is moved away from particle-hole symmetry, $T_c$ decreases and $U_c$ increases, indicating a suppression of the insulating phase and the strengthening of the metallic behavior. Additionally, the first-order transition line between metallic and insulating phases is better defined in the model with PHA, leading to a reduced co-existence region at $T
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- 2024
11. Constructions of well-rounded algebraic lattices over odd prime degree cyclic number fields
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de Araujo, Robson Ricardo, de Andrade, Antônio Aparecido, Neto, Trajano Pires da Nóbrega, and Bastos, Jéfferson Luiz Rocha
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Mathematics - Number Theory - Abstract
Algebraic lattices are those obtained from modules in the ring of integers of algebraic number fields through the canonical or twisted embeddings. In turn, well-rounded lattices are those with maximal cardinality of linearly independent vectors in its set of minimal vectors. Both classes of lattices have been applied for signal transmission in some channels, such as wiretap channels. Recently, some advances have been made in the search for well-rounded lattices that can be realized as algebraic lattices. Moreover, some works have been published studying algebraic lattices obtained from modules in cyclic number fields of odd prime degree $p$. In this work, we generalize some results of a recent work of Tran et al. and we provide new constructions of well-rounded algebraic lattices from a certain family of modules in the ring of integers of each of these fields when $p$ is ramified in its extension over the field of rational numbers., Comment: 11 pages
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- 2024
12. Fast and light-efficient wavefront shaping with a MEMS phase-only light modulator
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Rocha, José C. A., Wright, Terry, Būtaitė, Unė G, Carpenter, Joel, Gordon, George S. D., and Phillips, David B.
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Physics - Optics - Abstract
Over the last two decades, spatial light modulators (SLMs) have revolutionised our ability to shape optical fields. They grant independent dynamic control over thousands of degrees-of-freedom within a single light beam. In this work we test a new type of SLM, known as a phase-only light modulator (PLM), that blends the high efficiency of liquid crystal SLMs with the fast switching rates of binary digital micro-mirror devices (DMDs). A PLM has a 2D mega-pixel array of micro-mirrors. The vertical height of each micro-mirror can be independently adjusted with 4-bit precision. Here we provide a concise tutorial on the operation and calibration of a PLM. We demonstrate arbitrary pattern projection, aberration correction, and control of light transport through complex media. We show high-speed wavefront shaping through a multimode optical fibre -- scanning over 2000 points at 1.44 kHz. We make available our custom high-speed PLM control software library developed in C++. As PLMs are based upon micro-electromechanical system (MEMS) technology, they are polarisation agnostic, and possess fundamental switching rate limitations equivalent to that of DMDs -- with operation at up to 10 kHz anticipated in the near future. We expect PLMs will find high-speed light shaping applications across a range of fields including adaptive optics, microscopy, optogenetics and quantum optics., Comment: 15 pages, 5 figures
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- 2024
13. Discovering Dark Matter with the MUonE Experiment
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Krnjaic, Gordan, Rocha, Duncan, and Wang, Isaac R.
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High Energy Physics - Phenomenology - Abstract
The MUonE experiment aims to extract the hadronic contribution to the muon anomalous magnetic moment from a precise measurement of the muon-electron differential scattering cross section. We show that MUonE can also discover thermal relic dark matter using only its nominal experimental setup. Our search strategy is sensitive to models of dark matter in which pairs of pseudo-Dirac fermions are produced in muon-nucleus scattering in the target, and the heavier state decays semi-visibly to yield dilepton pairs displaced downstream from the interaction point. This approach can probe sub-GeV thermal-relic dark matter whose cosmological abundance is governed by the same model parameters that set the MUonE signal strength. Furthermore, our results show that the downstream ECAL plays a key role in rejecting backgrounds for this search, thereby providing strong motivation for the MUonE to keep this component in the final experimental design., Comment: 8 pages, 6 figures
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- 2024
14. Data-driven methods for computational mechanics: A fair comparison between neural networks based and model-free approaches
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Zlatić, Martin, Rocha, Felipe, Stainier, Laurent, and Čanađija, Marko
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Computer Science - Computational Engineering, Finance, and Science - Abstract
We present a comparison between two approaches to modelling hyperelastic material behaviour using data. The first approach is a novel approach based on Data-driven Computational Mechanics (DDCM) that completely bypasses the definition of a material model by using only data from simulations or real-life experiments to perform computations. The second is a neural network (NN) based approach, where a neural network is used as a constitutive model. It is trained on data to learn the underlying material behaviour and is implemented in the same way as conventional models. The DDCM approach has been extended to include strategies for recovering isotropic behaviour and local smoothing of data. These have proven to be critical in certain cases and increase accuracy in most cases. The NN approach contains certain elements to enforce principles such as material symmetry, thermodynamic consistency, and convexity. In order to provide a fair comparison between the approaches, they use the same data and solve the same numerical problems with a selection of problems highlighting the advantages and disadvantages of each approach. Both the DDCM and the NNs have shown acceptable performance. The DDCM performed better when applied to cases similar to those from which the data is gathered from, albeit at the expense of generality, whereas NN models were more advantageous when applied to wider range of applications.
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- 2024
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15. 3D{\pi}: Three-Dimensional Positron Imaging, A Novel Total-Body PET Scanner Using Xenon-Doped Liquid Argon Scintillator
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Zabihi, Azam, Li, Xinran, Ramirez, Alejandro, Rolo, Manuel D. Da Rocha, Franco, Davide, Gabriele, Federico, Galbiati, Cristiano, Lai, Michela, Marlow, Daniel R., Renshaw, Andrew, Westerdale, Shawn, and Wada, Masayuki
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Physics - Medical Physics - Abstract
Objective: This paper introduces a novel PET imaging methodology called 3-dimensional positron imaging (3D{\pi}), which integrates total-body (TB) coverage, time-of-flight (TOF) technology, ultra-low dose imaging capabilities, and ultra-fast readout electronics inspired by emerging technology from the DarkSide collaboration. Approach: The study evaluates the performance of 3D{\pi} using Monte Carlo simulations based on NEMA NU 2-2018 protocols. The methodology employs a homogenous, monolithic scintillator composed of liquid argon (LAr) doped with xenon (Xe) with silicon photomultipliers (SiPM) operating at cryogenic temperatures. Main results: Significant enhancements in system performance are observed, with the 3D{\pi} system achieving a noise equivalent count rate (NECR) of 3.2 Mcps which is approximately two times higher than uEXPLORER's peak NECR (1.5 Mcps) at 17.3 (kBq/mL). Spatial resolution measurements show an average FWHM of 2.7 mm across both axial positions. The system exhibits superior sensitivity, with values reaching 373 kcps/MBq with a line source at the center of the field of view. Additionally, 3D{\pi} achieves a TOF resolution of 151 ps at 5.3 kBq/mL, highlighting its potential to produce high-quality images with reduced noise levels. Significance: The study underscores the potential of 3D{\pi} in improving PET imaging performance, offering the potential for shorter scan times and reduced radiation exposure for patients. The Xe-doped LAr offers advantages such as fast scintillation, enhanced light yield, and cost-effectiveness. Future research will focus on optimizing system geometry and further refining reconstruction algorithms to exploit the strengths of 3D{\pi} for clinical applications., Comment: 8 pages, 8 figures, 6 tables
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- 2024
16. Vibration Sensor Dataset for Estimating Fan Coil Motor Health
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Lifsitch, Heitor, Rocha, Gabriel, Bragança, Hendrio, Filho, Cláudio, Okimoto, Leandro, Amorin, Allan, and Cardoso, Fábio
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Electrical Engineering and Systems Science - Signal Processing - Abstract
To enhance the field of continuous motor health monitoring, we present FAN-COIL-I, an extensive vibration sensor dataset derived from a Fan Coil motor. This dataset is uniquely positioned to facilitate the detection and prediction of motor health issues, enabling a more efficient maintenance scheduling process that can potentially obviate the need for regular checks. Unlike existing datasets, often created under controlled conditions or through simulations, FAN-COIL-I is compiled from real-world operational data, providing an invaluable resource for authentic motor diagnosis and predictive maintenance research. Gathered using a high-resolution 32KHz sampling rate, the dataset encompasses comprehensive vibration readings from both the forward and rear sides of the Fan Coil motor over a continuous two-week period, offering a rare glimpse into the dynamic operational patterns of these systems in a corporate setting. FAN-COIL-I stands out not only for its real-world applicability but also for its potential to serve as a reliable benchmark for researchers and practitioners seeking to validate their models against genuine engine conditions.
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- 2024
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17. Image Provenance Analysis via Graph Encoding with Vision Transformer
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Zhang, Keyang, Kong, Chenqi, Wang, Shiqi, Rocha, Anderson, and Li, Haoliang
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Electrical Engineering and Systems Science - Image and Video Processing - Abstract
Recent advances in AI-powered image editing tools have significantly lowered the barrier to image modification, raising pressing security concerns those related to spreading misinformation and disinformation on social platforms. Image provenance analysis is crucial in this context, as it identifies relevant images within a database and constructs a relationship graph by mining hidden manipulation and transformation cues, thereby providing concrete evidence chains. This paper introduces a novel end-to-end deep learning framework designed to explore the structural information of provenance graphs. Our proposed method distinguishes from previous approaches in two main ways. First, unlike earlier methods that rely on prior knowledge and have limited generalizability, our framework relies upon a patch attention mechanism to capture image provenance clues for local manipulations and global transformations, thereby enhancing graph construction performance. Second, while previous methods primarily focus on identifying tampering traces only between image pairs, they often overlook the hidden information embedded in the topology of the provenance graph. Our approach aligns the model training objectives with the final graph construction task, incorporating the overall structural information of the graph into the training process. We integrate graph structure information with the attention mechanism, enabling precise determination of the direction of transformation. Experimental results show the superiority of the proposed method over previous approaches, underscoring its effectiveness in addressing the challenges of image provenance analysis., Comment: 13 pages, 10 figures
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- 2024
18. Benchmarking the design of the cryogenics system for the underground argon in DarkSide-20k
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Collaboration, DarkSide-20k, Acerbi, F., Adhikari, P., Agnes, P., Ahmad, I., Albergo, S., Albuquerque, I. F. M., Alexander, T., Alton, A. K., Amaudruz, P., Angiolilli, M., Aprile, E., Ardito, R., Corona, M. Atzori, Auty, D. J., Ave, M., Avetisov, I. C., Azzolini, O., Back, H. O., Balmforth, Z., Olmedo, A. Barrado, Barrillon, P., Batignani, G., Bhowmick, P., Blua, S., Bocci, V., Bonivento, W., Bottino, B., Boulay, M. G., Buchowicz, A., Bussino, S., Busto, J., Cadeddu, M., Cadoni, M., Calabrese, R., Camillo, V., Caminata, A., Canci, N., Capra, A., Caravati, M., Cárdenas-Montes, M., Cargioli, N., Carlini, M., Castellani, A., Castello, P., Cavalcante, P., Cebrian, S., Ruiz, J. Cela, Chashin, S., Chepurnov, A., Cifarelli, L., Cintas, D., Citterio, M., Cleveland, B., Coadou, Y., Cocco, V., Colaiuda, D., Vilda, E. Conde, Consiglio, L., Costa, B. S., Czubak, M., D'Aniello, M., D'Auria, S., Rolo, M. D. Da Rocha, Darbo, G., Davini, S., De Cecco, S., De Guido, G., Dellacasa, G., Derbin, A. V., Devoto, A., Di Capua, F., Di Ludovico, A., Di Noto, L., Di Stefano, P., Dias, L. K., Mairena, D. Díaz, Ding, X., Dionisi, C., Dolganov, G., Dordei, F., Dronik, V., Elersich, A., Ellingwood, E., Erjavec, T., Diaz, M. Fernandez, Ficorella, A., Fiorillo, G., Franchini, P., Franco, D., Gatti, H. Frandini, Frolov, E., Gabriele, F., Gahan, D., Galbiati, C., Galiński, G., Gallina, G., Gallus, G., Garbini, M., Abia, P. Garcia, Gawdzik, A., Gendotti, A., Ghisi, A., Giovanetti, G. K., Casanueva, V. Goicoechea, Gola, A., Grandi, L., Grauso, G., di Cortona, G. Grilli, Grobov, A., Gromov, M., Guerzoni, M., Gulino, M., Guo, C., Hackett, B. R., Hallin, A., Hamer, A., Haranczyk, M., Harrop, B., Hessel, T., Hill, S., Horikawa, S., Hu, J., Hubaut, F., Hucker, J., Hugues, T., Hungerford, E. V., Ianni, A., Ippolito, V., Jamil, A., Jillings, C., Jois, S., Kachru, P., Keloth, R., Kemmerich, N., Kemp, A., Kendziora, C. L., Kimura, M., Kish, A., Kondo, K., Korga, G., Kotsiopoulou, L., Koulosousas, S., Kubankin, A., Kunzé, P., Kuss, M., Kuźniak, M., Kuzwa, M., La Commara, M., Lai, M., Guirriec, E. Le, Leason, E., Leoni, A., Lidey, L., Lissia, M., Luzzi, L., Lychagina, O., Macfadyen, O., Machulin, I. N., Manecki, S., Manthos, I., Mapelli, L., Marasciulli, A., Mari, S. M., Mariani, C., Maricic, J., Martinez, M., Martoff, C. J., Matteucci, G., Mavrokoridis, K., McDonald, A. B., Mclaughlin, J., Merzi, S., Messina, A., Milincic, R., Minutoli, S., Mitra, A., Moharana, A., Moioli, S., Monroe, J., Moretti, E., Morrocchi, M., Mroz, T., Muratova, V. N., Murphy, M., Murra, M., Muscas, C., Musico, P., Nania, R., Nessi, M., Nieradka, G., Nikolopoulos, K., Nikoloudaki, E., Nowak, J., Olchanski, K., Oleinik, A., Oleynikov, V., Organtini, P., de Solórzano, A. Ortiz, Pallavicini, M., Pandola, L., Pantic, E., Paoloni, E., Papi, D., Pastuszak, G., Paternoster, G., Peck, A., Pegoraro, P. A., Pelczar, K., Pellegrini, L. A., Perez, R., Perotti, F., Pesudo, V., Piacentini, S. I., Pino, N., Plante, G., Pocar, A., Poehlmann, M., Pordes, S., Pralavorio, P., Price, D., Puglia, S., Bazetto, M. Queiroga, Ragusa, F., Ramachers, Y., Ramirez, A., Ravinthiran, S., Razeti, M., Renshaw, A. L., Rescigno, M., Retiere, F., Rignanese, L. P., Rivetti, A., Roberts, A., Roberts, C., Rogers, G., Romero, L., Rossi, M., Rubbia, A., Rudik, D., Sabia, M., Salomone, P., Samoylov, O., Sandford, E., Sanfilippo, S., Santone, D., Santorelli, R., Santos, E. M., Savarese, C., Scapparone, E., Schillaci, G., Schuckman II, F. G., Scioli, G., Semenov, D. A., Shalamova, V., Sheshukov, A., Simeone, M., Skensved, P., Skorokhvatov, M. D., Smirnov, O., Smirnova, T., Smith, B., Sotnikov, A., Spadoni, F., Spangenberg, M., Stefanizzi, R., Steri, A., Stornelli, V., Stracka, S., Sulis, S., Sung, A., Sunny, C., Suvorov, Y., Szelc, A. M., Taborda, O., Tartaglia, R., Taylor, A., Taylor, J., Tedesco, S., Testera, G., Thieme, K., Thompson, A., Thorpe, T. N., Tonazzo, A., Torres-Lara, S., Tricomi, A., Unzhakov, E. V., Vallivilayil, T. J., Van Uffelen, M., Velazquez-Fernandez, L., Viant, T., Viel, S., Vishneva, A., Vogelaar, R. B., Vossebeld, J., Vyas, B., Wada, M., Walczak, M. B., Wang, H., Wang, Y., Westerdale, S., Williams, L., Wojaczyński, R., Wojcik, M., Wojcik, M. M., Wright, T., Xiao, X., Xie, Y., Yang, C., Yin, J., Zabihi, A., Zakhary, P., Zani, A., Zhang, Y., Zhu, T., Zichichi, A., Zuzel, G., and Zykova, M. P.
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Physics - Instrumentation and Detectors ,High Energy Physics - Experiment - Abstract
DarkSide-20k (DS-20k) is a dark matter detection experiment under construction at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy. It utilises ~100 t of low radioactivity argon from an underground source (UAr) in its inner detector, with half serving as target in a dual-phase time projection chamber (TPC). The UAr cryogenics system must maintain stable thermodynamic conditions throughout the experiment's lifetime of >10 years. Continuous removal of impurities and radon from the UAr is essential for maximising signal yield and mitigating background. We are developing an efficient and powerful cryogenics system with a gas purification loop with a target circulation rate of 1000 slpm. Central to its design is a condenser operated with liquid nitrogen which is paired with a gas heat exchanger cascade, delivering a combined cooling power of >8 kW. Here we present the design choices in view of the DS-20k requirements, in particular the condenser's working principle and the cooling control, and we show test results obtained with a dedicated benchmarking platform at CERN and LNGS. We find that the thermal efficiency of the recirculation loop, defined in terms of nitrogen consumption per argon flow rate, is 95 % and the pressure in the test cryostat can be maintained within $\pm$(0.1-0.2) mbar. We further detail a 5-day cool-down procedure of the test cryostat, maintaining a cooling rate typically within -2 K/h, as required for the DS-20k inner detector. Additionally, we assess the circuit's flow resistance, and the heat transfer capabilities of two heat exchanger geometries for argon phase change, used to provide gas for recirculation. We conclude by discussing how our findings influence the finalisation of the system design, including necessary modifications to meet requirements and ongoing testing activities., Comment: 45 pages, 24 figures
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- 2024
19. Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture
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Kong, Chenqi, Luo, Anwei, Bao, Peijun, Li, Haoliang, Wan, Renjie, Zheng, Zengwei, Rocha, Anderson, and Kot, Alex C.
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Open-set face forgery detection poses significant security threats and presents substantial challenges for existing detection models. These detectors primarily have two limitations: they cannot generalize across unknown forgery domains and inefficiently adapt to new data. To address these issues, we introduce an approach that is both general and parameter-efficient for face forgery detection. It builds on the assumption that different forgery source domains exhibit distinct style statistics. Previous methods typically require fully fine-tuning pre-trained networks, consuming substantial time and computational resources. In turn, we design a forgery-style mixture formulation that augments the diversity of forgery source domains, enhancing the model's generalizability across unseen domains. Drawing on recent advancements in vision transformers (ViT) for face forgery detection, we develop a parameter-efficient ViT-based detection model that includes lightweight forgery feature extraction modules and enables the model to extract global and local forgery clues simultaneously. We only optimize the inserted lightweight modules during training, maintaining the original ViT structure with its pre-trained ImageNet weights. This training strategy effectively preserves the informative pre-trained knowledge while flexibly adapting the model to the task of Deepfake detection. Extensive experimental results demonstrate that the designed model achieves state-of-the-art generalizability with significantly reduced trainable parameters, representing an important step toward open-set Deepfake detection in the wild.
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- 2024
20. DUNE Phase II: Scientific Opportunities, Detector Concepts, Technological Solutions
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DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Akbar, F., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Alves, T., Amar, H., Amedo, P., Anderson, J., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antic, D., Antoniassi, M., Antonova, M., Antoshkin, A., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asner, D. M., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Azam, M. B., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baldonedo, J., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barbu, D., Barenboim, G., Barham~Alzás, P., Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernal, J., Bernardini, P., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Bodek, A., Bogenschuetz, J., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Merlo, R. Borges, Borkum, A., Bostan, N., Bouet, R., Boza, J., Bracinik, J., Brahma, B., Brailsford, D., Bramati, F., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brio, V., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., Bundock, A., Burgardt, D., Butchart, S., V., G. Caceres, Cagnoli, I., Cai, T., Calabrese, R., Calcutt, J., Calivers, L., Calvo, E., Caminata, A., Camino, A. F., Campanelli, W., Campani, A., Benitez, A. Campos, Canci, N., Capó, J., Caracas, I., Caratelli, D., Carber, D., Carceller, J. M., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Casarejos, E., Casazza, D., Forero, J. F. Castaño, Castaño, F. A., Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavanna, F., Centro, S., Cerati, G., Cerna, C., Cervelli, A., Villanueva, A. Cervera, Chakraborty, K., Chakraborty, S., Chalifour, M., Chappell, A., Charitonidis, N., Chatterjee, A., Chen, H., Chen, M., Chen, W. C., Chen, Y., Chen-Wishart, Z., Cherdack, D., Chi, C., Chiapponi, F., Chirco, R., Chitirasreemadam, N., Cho, K., Choate, S., Chokheli, D., Chong, P. S., Chowdhury, B., Christian, D., Chukanov, A., Chung, M., Church, E., Cicala, M. F., Cicerchia, M., Cicero, V., Ciolini, R., Clarke, P., Cline, G., Coan, T. E., Cocco, A. G., Coelho, J. A. B., Cohen, A., Collazo, J., Collot, J., Conley, E., Conrad, J. M., Convery, M., Copello, S., Cortez, A. F. V., Cova, P., Cox, C., Cremaldi, L., Cremonesi, L., Crespo-Anadón, J. I., Crisler, M., Cristaldo, E., Crnkovic, J., Crone, G., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Curciarello, F., Cussans, D., Dai, J., Dalager, O., Dallavalle, R., Dallaway, W., D'Amico, R., da Motta, H., Dar, Z. A., Darby, R., Peres, L. Da Silva, David, Q., Davies, G. S., Davini, S., Dawson, J., De Aguiar, R., De Almeida, P., Debbins, P., De Bonis, I., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., Astiz, I. L. De Icaza, De Jong, P., Sanchez, P. Del Amo, De la Torre, A., De Lauretis, G., Delbart, A., Delepine, D., Delgado, M., Dell'Acqua, A., Monache, G. Delle, Delmonte, N., De Lurgio, P., Demario, R., De Matteis, G., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., Detje, J. P., Devine, J., Dharmapalan, R., Dias, M., Diaz, A., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Falco, S., Di Giulio, L., Ding, P., Di Noto, L., Diociaiuti, E., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Doering, D., Dolan, S., Dolek, F., Dolinski, M. J., Domenici, D., Domine, L., Donati, S., Donon, Y., Doran, S., Douglas, D., Doyle, T. A., Dragone, A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dwyer, D. A., Dyshkant, A. S., Dytman, S., Eads, M., Earle, A., Edayath, S., Edmunds, D., Eisch, J., Englezos, P., Ereditato, A., Erjavec, T., Escobar, C. O., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Fajt, L., Falcone, A., Fani', M., Farnese, C., Farrell, S., Farzan, Y., Fedoseev, D., Felix, J., Feng, Y., Fernandez-Martinez, E., Fernández-Posada, D., Ferry, G., Fialova, E., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fine, R., Fiorillo, G., Fiorini, M., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Francis, K., Franco, D., Franklin, J., Freeman, J., Fried, J., Friedland, A., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gaba, R., Gabrielli, A., M~Gago, A., Galizzi, F., Gallagher, H., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Ganacim, F., Gandhi, R., Ganguly, S., Gao, F., Gao, S., Garcia-Gamez, D., García-Peris, M. Á., Gardim, F., Gardiner, S., Gastler, D., Gauch, A., Gauvreau, J., Gauzzi, P., Gazzana, S., Ge, G., Geffroy, N., Gelli, B., Gent, S., Gerlach, L., Ghorbani-Moghaddam, Z., Giammaria, T., Gibin, D., Gil-Botella, I., Gilligan, S., Gioiosa, A., Giovannella, S., Girerd, C., Giri, A. K., Giugliano, C., Giusti, V., Gnani, D., Gogota, O., Gollapinni, S., Gollwitzer, K., Gomes, R. A., Bermeo, L. V. Gomez, Fajardo, L. S. Gomez, Gonnella, F., Gonzalez-Diaz, D., Gonzalez-Lopez, M., Goodman, M. C., Goswami, S., Gotti, C., Goudeau, J., Goudzovski, E., Grace, C., Gramellini, E., Gran, R., Granados, E., Granger, P., Grant, C., Gratieri, D. R., Grauso, G., Green, P., Greenberg, S., Greer, J., Griffith, W. C., Groetschla, F. T., Grzelak, K., Gu, L., Gu, W., Guarino, V., Guarise, M., Guenette, R., Guerzoni, M., Guffanti, D., Guglielmi, A., Guo, B., Guo, F. Y., Gupta, A., Gupta, V., Gurung, G., Gutierrez, D., Guzowski, P., Guzzo, M. M., Gwon, S., Habig, A., Hadavand, H., Haegel, L., Haenni, R., Hagaman, L., Hahn, A., Haiston, J., Hakenmüller, J., Hamernik, T., Hamilton, P., Hancock, J., Happacher, F., Harris, D. A., Hart, A., Hartnell, J., Hartnett, T., Harton, J., Hasegawa, T., Hasnip, C. M., Hatcher, R., Hayrapetyan, K., Hays, J., Hazen, E., He, M., Heavey, A., Heeger, K. M., Heise, J., Hellmuth, P., Henry, S., Hernández-García, J., Herner, K., Hewes, V., Higuera, A., Hilgenberg, C., Hillier, S. J., Himmel, A., Hinkle, E., Hirsch, L. R., Ho, J., Hoff, J., Holin, A., Holvey, T., Hoppe, E., Horiuchi, S., Horton-Smith, G. A., Houdy, T., Howard, B., Howell, R., Hristova, I., Hronek, M. S., Huang, J., Huang, R. G., Hulcher, Z., Ibrahim, M., Iles, G., Ilic, N., Iliescu, A. M., Illingworth, R., Ingratta, G., Ioannisian, A., Irwin, B., Isenhower, L., Oliveira, M. Ismerio, Itay, R., Jackson, C. M., Jain, V., James, E., Jang, W., Jargowsky, B., Jena, D., Jentz, I., Ji, X., Jiang, C., Jiang, J., Jiang, L., Jipa, A., Jo, J. H., Joaquim, F. R., Johnson, W., Jollet, C., Jones, B., Jones, R., Jovancevic, N., Judah, M., Jung, C. K., Junk, T., Jwa, Y., Kabirnezhad, M., Kaboth, A. C., Kadenko, I., Kakorin, I., Kalitkina, A., Kalra, D., Kandemir, M., Kaplan, D. M., Karagiorgi, G., Karaman, G., Karcher, A., Karyotakis, Y., Kasai, S., Kasetti, S. P., Kashur, L., Katsioulas, I., Kauther, A., Kazaryan, N., Ke, L., Kearns, E., Keener, P. T., Kelly, K. J., Kemp, E., Kemularia, O., Kermaidic, Y., Ketchum, W., Kettell, S. H., Khabibullin, M., Khan, N., Khvedelidze, A., Kim, D., Kim, J., Kim, M. J., King, B., Kirby, B., Kirby, M., Kish, A., Klein, J., Kleykamp, J., Klustova, A., Kobilarcik, T., Koch, L., Koehler, K., Koerner, L. W., Koh, D. H., Kolupaeva, L., Korablev, D., Kordosky, M., Kosc, T., Kose, U., Kostelecký, V. A., Kothekar, K., Kotler, I., Kovalcuk, M., Kozhukalov, V., Krah, W., Kralik, R., Kramer, M., Kreczko, L., Krennrich, F., Kreslo, I., Kroupova, T., Kubota, S., Kubu, M., Kudenko, Y., Kudryavtsev, V. A., Kufatty, G., Kuhlmann, S., Kulagin, S., Kumar, J., Kumar, P., Kumaran, S., Kunzmann, J., Kuravi, R., Kurita, N., Kuruppu, C., Kus, V., Kutter, T., Kuźniak, M., Kvasnicka, J., Labree, T., Lackey, T., Lalău, I., Lambert, A., Land, B. J., Lane, C. E., Lane, N., Lang, K., Langford, T., Langstaff, M., Lanni, F., Lantwin, O., Larkin, J., Lasorak, P., Last, D., Laudrain, A., Laundrie, A., Laurenti, G., Lavaut, E., Laycock, P., Lazanu, I., LaZur, R., Lazzaroni, M., Le, T., Leardini, S., Learned, J., LeCompte, T., Legin, V., Miotto, G. Lehmann, Lehnert, R., de Oliveira, M. A. Leigui, Leitner, M., Silverio, D. Leon, Lepin, L. M., -Y~Li, J., Li, S. W., Li, Y., Liao, H., Lin, C. S., Lindebaum, D., Linden, S., Lineros, R. A., Lister, A., Littlejohn, B. R., Liu, H., Liu, J., Liu, Y., Lockwitz, S., Lokajicek, M., Lomidze, I., Long, K., Lopes, T. V., Lopez, J., de Rego, I. López, López-March, N., Lord, T., LoSecco, J. M., Louis, W. C., Sanchez, A. Lozano, Lu, X. -G., Luk, K. B., Lunday, B., Luo, X., Luppi, E., MacFarlane, D., Machado, A. A., Machado, P., Macias, C. T., Macier, J. R., MacMahon, M., Maddalena, A., Madera, A., Madigan, P., Magill, S., Magueur, C., Mahn, K., Maio, A., Major, A., Majumdar, K., Mameli, S., Man, M., Mandujano, R. C., Maneira, J., Manly, S., Mann, A., Manolopoulos, K., Plata, M. Manrique, Corchado, S. Manthey, Manyam, V. N., Marchan, M., Marchionni, A., Marciano, W., Marfatia, D., Mariani, C., Maricic, J., Marinho, F., Marino, A. D., Markiewicz, T., Marques, F. Das Chagas, Marquet, C., Marshak, M., Marshall, C. M., Marshall, J., Martina, L., Martín-Albo, J., Martinez, N., Caicedo, D. A. Martinez, López, F. Martínez, Miravé, P. Martínez, Martynenko, S., Mascagna, V., Massari, C., Mastbaum, A., Matichard, F., Matsuno, S., Matteucci, G., Matthews, J., Mauger, C., Mauri, N., Mavrokoridis, K., Mawby, I., Mazza, R., McAskill, T., McConkey, N., McFarland, K. S., McGrew, C., McNab, A., Meazza, L., Meddage, V. C. N., Mefodiev, A., Mehta, B., Mehta, P., Melas, P., Mena, O., Mendez, H., Mendez, P., Méndez, D. P., Menegolli, A., Meng, G., Mercuri, A. C. E. A., Meregaglia, A., Messier, M. D., Metallo, S., Metcalf, W., Mewes, M., Meyer, H., Miao, T., Micallef, J., Miccoli, A., Michna, G., Milincic, R., Miller, F., Miller, G., Miller, W., Mineev, O., Minotti, A., Miralles, L., Miranda, O. G., Mironov, C., Miryala, S., Miscetti, S., Mishra, C. S., Mishra, P., Mishra, S. R., Mislivec, A., Mitchell, M., Mladenov, D., Mocioiu, I., Mogan, A., Moggi, N., Mohanta, R., Mohayai, T. A., Mokhov, N., Molina, J., Bueno, L. Molina, Montagna, E., Montanari, A., Montanari, C., Montanari, D., Montanino, D., Zetina, L. M. Montaño, Mooney, M., Moor, A. F., Moore, Z., Moreno, D., Moreno-Palacios, O., Morescalchi, L., Moretti, D., Moretti, R., Morris, C., Mossey, C., Moura, C. A., Mouster, G., Mu, W., Mualem, L., Mueller, J., Muether, M., Muheim, F., Muir, A., Mulhearn, M., Munford, D., Munteanu, L. J., Muramatsu, H., Muraz, J., Murphy, M., Murphy, T., Muse, J., Mytilinaki, A., Nachtman, J., Nagai, Y., Nagu, S., Nandakumar, R., Naples, D., Narita, S., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nehm, A., Nelson, J. K., Neogi, O., Nesbit, J., Nessi, M., Newbold, D., Newcomer, M., Nichol, R., Nicolas-Arnaldos, F., Nikolica, A., Nikolov, J., Niner, E., Nishimura, K., Norman, A., Norrick, A., Novella, P., Nowak, A., Nowak, J. A., Oberling, M., Ochoa-Ricoux, J. P., Oh, S., Oh, S. B., Olivier, A., Olshevskiy, A., Olson, T., Onel, Y., Onishchuk, Y., Oranday, A., Gann, G. D. Orebi, Osbiston, M., Vélez, J. A. Osorio, O'Sullivan, L., Ormachea, L. Otiniano, Ott, J., Pagani, L., Palacio, G., Palamara, O., Palestini, S., Paley, J. M., Pallavicini, M., Palomares, C., Pan, S., Panda, P., Vazquez, W. Panduro, Pantic, E., Paolone, V., Papaleo, R., Papanestis, A., Papoulias, D., Paramesvaran, S., Paris, A., Parke, S., Parozzi, E., Parsa, S., Parsa, Z., Parveen, S., Parvu, M., Pasciuto, D., Pascoli, S., Pasqualini, L., Pasternak, J., Patrick, C., Patrizii, L., Patterson, R. B., Patzak, T., Paudel, A., Paulucci, L., Pavlovic, Z., Pawloski, G., Payne, D., Pec, V., Pedreschi, E., Peeters, S. J. M., Pellico, W., Perez, A. Pena, Pennacchio, E., Penzo, A., Peres, O. L. G., Gonzalez, Y. F. Perez, Pérez-Molina, L., Pernas, C., Perry, J., Pershey, D., Pessina, G., Petrillo, G., Petta, C., Petti, R., Pfaff, M., Pia, V., Pickering, L., Pietropaolo, F., Pimentel, V. L., Pinaroli, G., Pincha, S., Pinchault, J., Pitts, K., Plows, K., Pollack, C., Pollman, T., Pompa, F., Pons, X., Poonthottathil, N., Popov, V., Poppi, F., Porter, J., Paix{ã}o, L. G. Porto, Potekhin, M., Potenza, R., Pozimski, J., Pozzato, M., Prakash, T., Pratt, C., Prest, M., Psihas, F., Pugnere, D., Qian, X., Queen, J., Raaf, J. L., Radeka, V., Rademacker, J., Radics, B., Raffaelli, F., Rafique, A., Raguzin, E., Rai, M., Rajagopalan, S., Rajaoalisoa, M., Rakhno, I., Rakotondravohitra, L., Ralte, L., Delgado, M. A. Ramirez, Ramson, B., Rappoldi, A., Raselli, G., Ratoff, P., Ray, R., Razafinime, H., Rea, E. M., Real, J. S., Rebel, B., Rechenmacher, R., Reichenbacher, J., Reitzner, S. D., Sfar, H. Rejeb, Renner, E., Renshaw, A., Rescia, S., Resnati, F., Diego~Restrepo, Reynolds, C., Ribas, M., Riboldi, S., Riccio, C., Riccobene, G., Ricol, J. S., Rigan, M., Rincón, E. V., Ritchie-Yates, A., Ritter, S., Rivera, D., Rivera, R., Robert, A., Rocha, J. L. Rocabado, Rochester, L., Roda, M., Rodrigues, P., Alonso, M. J. Rodriguez, Rondon, J. Rodriguez, Rosauro-Alcaraz, S., Rosier, P., Ross, D., Rossella, M., Rossi, M., Ross-Lonergan, M., Roy, N., Roy, P., Rubbia, C., Ruggeri, A., Ruiz, G., Russell, B., Ruterbories, D., Rybnikov, A., Sacerdoti, S., Saha, S., Sahoo, S. K., Sahu, N., Sala, P., Samios, N., Samoylov, O., Sanchez, M. C., Bravo, A. Sánchez, Sánchez-Castillo, A., Sanchez-Lucas, P., Sandberg, V., Sanders, D. A., Sanfilippo, S., Sankey, D., Santoro, D., Saoulidou, N., Sapienza, P., Sarasty, C., Sarcevic, I., Sarra, I., Savage, G., Savinov, V., Scanavini, G., Scaramelli, A., Scarff, A., Schefke, T., Schellman, H., Schifano, S., Schlabach, P., Schmitz, D., Schneider, A. W., Scholberg, K., Schukraft, A., Schuld, B., Segade, A., Segreto, E., Selyunin, A., Senadheera, D., Senise, C. R., Sensenig, J., Seo, S. H., Shaevitz, M. H., Shanahan, P., Sharma, P., Kumar, R., Poudel, S. Sharma, Shaw, K., Shaw, T., Shchablo, K., Shen, J., Shepherd-Themistocleous, C., Sheshukov, A., Shi, J., Shi, W., Shin, S., Shivakoti, S., Shoemaker, I., Shooltz, D., Shrock, R., Siddi, B., Siden, M., Silber, J., Simard, L., Sinclair, J., Sinev, G., Singh, J., Singh, L., Singh, P., Singh, V., Chauhan, S. Singh, Sipos, R., Sironneau, C., Sirri, G., Siyeon, K., Skarpaas, K., Smedley, J., Smith, E., Smith, J., Smith, P., Smolik, J., Smy, M., Snape, M., Snider, E. L., Snopok, P., Snowden-Ifft, D., Nunes, M. Soares, Sobel, H., Soderberg, M., Sokolov, S., Salinas, C. J. Solano, Söldner-Rembold, S., Solomey, N., Solovov, V., Sondheim, W. E., Sorel, M., Sotnikov, A., Soto-Oton, J., Sousa, A., Soustruznik, K., Spinella, F., Spitz, J., Spooner, N. J. C., Spurgeon, K., Stalder, D., Stancari, M., Stanco, L., Steenis, J., Stein, R., Steiner, H. M., Lisbôa, A. F. Steklain, Stepanova, A., Stewart, J., Stillwell, B., Stock, J., Stocker, F., Stokes, T., Strait, M., Strauss, T., Strigari, L., Stuart, A., Suarez, J. G., Subash, J., Surdo, A., Suter, L., Sutera, C. M., Sutton, K., Suvorov, Y., Svoboda, R., Swain, S. K., Szczerbinska, B., Szelc, A. M., Sztuc, A., Taffara, A., Talukdar, N., Tamara, J., Tanaka, H. A., Tang, S., Taniuchi, N., Casanova, A. M. Tapia, Oregui, B. Tapia, Tapper, A., Tariq, S., Tarpara, E., Tatar, E., Tayloe, R., Tedeschi, D., Teklu, A. M., Vidal, J. Tena, Tennessen, P., Tenti, M., Terao, K., Terranova, F., Testera, G., Thakore, T., Thea, A., Thomas, S., Thompson, A., Thorn, C., Timm, S. C., Tiras, E., Tishchenko, V., Todorović, N., Tomassetti, L., Tonazzo, A., Torbunov, D., Torti, M., Tortola, M., Tortorici, F., Tosi, N., Totani, D., Toups, M., Touramanis, C., Tran, D., Travaglini, R., Trevor, J., Triller, E., Trilov, S., Truchon, J., Truncali, D., Trzaska, W. H., Tsai, Y., Tsai, Y. -T., Tsamalaidze, Z., Tsang, K. V., Tsverava, N., Tu, S. Z., Tufanli, S., Tunnell, C., Turnberg, S., Turner, J., Tuzi, M., Tyler, J., Tyley, E., Tzanov, M., Uchida, M. A., González, J. Ureña, Urheim, J., Usher, T., Utaegbulam, H., Uzunyan, S., Vagins, M. R., Vahle, P., Valder, S., Valdiviesso, G. A., Valencia, E., Valentim, R., Vallari, Z., Vallazza, E., Valle, J. W. F., Van Berg, R., Van de Water, R. G., Forero, D. V., Vannozzi, A., Van Nuland-Troost, M., Varanini, F., Oliva, D. Vargas, Vasina, S., Vaughan, N., Vaziri, K., Vázquez-Ramos, A., Vega, J., Ventura, S., Verdugo, A., Vergani, S., Verzocchi, M., Vetter, K., Vicenzi, M., de Souza, H. Vieira, Vignoli, C., Vilela, C., Villa, E., Viola, S., Viren, B., Hernandez, A. P. Vizcaya, Vuong, Q., Waldron, A. V., Wallbank, M., Walsh, J., Walton, T., Wang, H., Wang, J., Wang, L., Wang, M. H. L. S., Wang, X., Wang, Y., Warburton, K., Warner, D., Warsame, L., Wascko, M. O., Waters, D., Watson, A., Wawrowska, K., Weber, A., Weber, C. M., Weber, M., Wei, H., Weinstein, A., Westerdale, S., Wetstein, M., Whalen, K., White, A., Whitehead, L. H., Whittington, D., Wilhlemi, J., Wilking, M. J., Wilkinson, A., Wilkinson, C., Wilson, F., Wilson, R. J., Winter, P., Wisniewski, W., Wolcott, J., Wolfs, J., Wongjirad, T., Wood, A., Wood, K., Worcester, E., Worcester, M., Wospakrik, M., Wresilo, K., Wret, C., Wu, S., Wu, W., Wurm, M., Wyenberg, J., Xiao, Y., Xiotidis, I., Yaeggy, B., Yahlali, N., Yandel, E., Yang, J., Yang, K., Yang, T., Yankelevich, A., Yershov, N., Yonehara, K., Young, T., Yu, B., Yu, H., Yu, J., Yu, Y., Yuan, W., Zaki, R., Zalesak, J., Zambelli, L., Zamorano, B., Zani, A., Zapata, O., Zazueta, L., Zeller, G. P., Zennamo, J., Zeug, K., Zhang, C., Zhang, S., Zhao, M., Zhivun, E., Zimmerman, E. D., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.
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Physics - Instrumentation and Detectors ,High Energy Physics - Experiment - Abstract
The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the European Strategy for Particle Physics. While the construction of the DUNE Phase I is well underway, this White Paper focuses on DUNE Phase II planning. DUNE Phase-II consists of a third and fourth far detector (FD) module, an upgraded near detector complex, and an enhanced 2.1 MW beam. The fourth FD module is conceived as a "Module of Opportunity", aimed at expanding the physics opportunities, in addition to supporting the core DUNE science program, with more advanced technologies. This document highlights the increased science opportunities offered by the DUNE Phase II near and far detectors, including long-baseline neutrino oscillation physics, neutrino astrophysics, and physics beyond the standard model. It describes the DUNE Phase II near and far detector technologies and detector design concepts that are currently under consideration. A summary of key R&D goals and prototyping phases needed to realize the Phase II detector technical designs is also provided. DUNE's Phase II detectors, along with the increased beam power, will complete the full scope of DUNE, enabling a multi-decadal program of groundbreaking science with neutrinos.
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- 2024
21. A new formulation for the collection and delivery problem of biomedical specimen
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Rocha, Luis Aurelio, Otto, Alena, and Goerigk, Marc
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Mathematics - Optimization and Control - Abstract
We study the collection and delivery problem of biomedical specimens (CDSP) with multiple trips, time windows, a homogeneous fleet, and the objective of minimizing total completion time of delivery requests. This is a prominent problem in healthcare logistics, where specimens (blood, plasma, urin etc.) collected from patients in doctor's offices and hospitals are transported to a central laboratory for advanced analysis. To the best of our knowledge, available exact solution approaches for CDSP have been able to solve only small instances with up to 9 delivery requests. In this paper, we propose a two-index mixed-integer programming formulation that, when used with an off-the-shelf solver, results in a fast exact solution approach. Computational experiments on a benchmark data set confirm that the proposed formulation outperforms both the state-of-the-art model and the state-of-the-art metaheuristic from the literature, solving 80 out of 168 benchmark instances to optimality, including a significant number of instances with 100 delivery requests., Comment: This work has been submitted for possible publication
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- 2024
22. A molecular decomposition for $H^p(\mathbb{Z}^n)$
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Rocha, Pablo
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Mathematics - Classical Analysis and ODEs - Abstract
In this work, for the range $\frac{n-1}{n} < p \leq 1$, we give a molecular reconstruction theorem for $H^p(\mathbb{Z}^n)$. As an application of this result and the atomic decomposition developed by S. Boza and M. Carro in [Proc. R. Soc. Edinb., 132 A (1) (2002), 25-43], we prove that the discrete Riesz potential $I_{\alpha}$ defined on $\mathbb{Z}^n$ is a bounded operator $H^p(\mathbb{Z}^n) \to H^q(\mathbb{Z}^n)$ for $\frac{n-1}{n} < p < \frac{n}{\alpha}$ and $\frac{1}{q} = \frac{1}{p} - \frac{\alpha}{n}$, where $0 < \alpha < n$., Comment: 13 pages
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- 2024
23. A Strategy to Combine 1stGen Transformers and Open LLMs for Automatic Text Classification
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de Andrade, Claudio M. V., Cunha, Washington, Reis, Davi, Pagano, Adriana Silvina, Rocha, Leonardo, and Gonçalves, Marcos André
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Computer Science - Computation and Language - Abstract
Transformer models have achieved state-of-the-art results, with Large Language Models (LLMs), an evolution of first-generation transformers (1stTR), being considered the cutting edge in several NLP tasks. However, the literature has yet to conclusively demonstrate that LLMs consistently outperform 1stTRs across all NLP tasks. This study compares three 1stTRs (BERT, RoBERTa, and BART) with two open LLMs (Llama 2 and Bloom) across 11 sentiment analysis datasets. The results indicate that open LLMs may moderately outperform or match 1stTRs in 8 out of 11 datasets but only when fine-tuned. Given this substantial cost for only moderate gains, the practical applicability of these models in cost-sensitive scenarios is questionable. In this context, a confidence-based strategy that seamlessly integrates 1stTRs with open LLMs based on prediction certainty is proposed. High-confidence documents are classified by the more cost-effective 1stTRs, while uncertain cases are handled by LLMs in zero-shot or few-shot modes, at a much lower cost than fine-tuned versions. Experiments in sentiment analysis demonstrate that our solution not only outperforms 1stTRs, zero-shot, and few-shot LLMs but also competes closely with fine-tuned LLMs at a fraction of the cost., Comment: 13 pages, 3 figures, 8 tables
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- 2024
24. PATopics: An automatic framework to extract useful information from pharmaceutical patents documents
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Cecilio, Pablo, Perreira, Antônio, Viegas, Juliana Santos Rosa, Cunha, Washington, Viegas, Felipe, Tuler, Elisa, Vicentini, Fabiana Testa Moura de Carvalho, and Rocha, Leonardo
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Computer Science - Digital Libraries ,Computer Science - Information Retrieval ,Computer Science - Machine Learning - Abstract
Pharmaceutical patents play an important role by protecting the innovation from copies but also drive researchers to innovate, create new products, and promote disruptive innovations focusing on collective health. The study of patent management usually refers to an exhaustive manual search. This happens, because patent documents are complex with a lot of details regarding the claims and methodology/results explanation of the invention. To mitigate the manual search, we proposed PATopics, a framework specially designed to extract relevant information for Pharmaceutical patents. PATopics is composed of four building blocks that extract textual information from the patents, build relevant topics that are capable of summarizing the patents, correlate these topics with useful patent characteristics and then, summarize the information in a friendly web interface to final users. The general contributions of PATopics are its ability to centralize patents and to manage patents into groups based on their similarities. We extensively analyzed the framework using 4,832 pharmaceutical patents concerning 809 molecules patented by 478 companies. In our analysis, we evaluate the use of the framework considering the demands of three user profiles -- researchers, chemists, and companies. We also designed four real-world use cases to evaluate the framework's applicability. Our analysis showed how practical and helpful PATopics are in the pharmaceutical scenario., Comment: 17 pages, 5 figures, 5 tables
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- 2024
25. First Measurement of the Total Inelastic Cross-Section of Positively-Charged Kaons on Argon at Energies Between 5.0 and 7.5 GeV
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DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Akbar, F., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Alves, T., Amar, H., Amedo, P., Anderson, J., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antic, D., Antoniassi, M., Antonova, M., Antoshkin, A., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asner, D., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Azam, M. B., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baldonedo, J., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barbu, D., Barenboim, G., Barham~Alzás, P., Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernal, J., Bernardini, P., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Bodek, A., Bogenschuetz, J., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Merlo, R. Borges, Borkum, A., Bostan, N., Bouet, R., Boza, J., Bracinik, J., Brahma, B., Brailsford, D., Bramati, F., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brio, V., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., Bundock, A., Burgardt, D., Butchart, S., V., G. Caceres, Cagnoli, I., Cai, T., Calabrese, R., Calcutt, J., Calivers, L., Calvo, E., Caminata, A., Camino, A. F., Campanelli, W., Campani, A., Benitez, A. Campos, Canci, N., Capó, J., Caracas, I., Caratelli, D., Carber, D., Carceller, J. M., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Casarejos, E., Casazza, D., Forero, J. F. Castaño, Castaño, F. A., Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavanna, F., Centro, S., Cerati, G., Cerna, C., Cervelli, A., Villanueva, A. Cervera, Chakraborty, K., Chakraborty, S., Chalifour, M., Chappell, A., Charitonidis, N., Chatterjee, A., Chen, H., Chen, M., Chen, W. C., Chen, Y., Chen-Wishart, Z., Cherdack, D., Chi, C., Chiapponi, F., Chirco, R., Chitirasreemadam, N., Cho, K., Choate, S., Chokheli, D., Chong, P. S., Chowdhury, B., Christian, D., Chukanov, A., Chung, M., Church, E., Cicala, M. F., Cicerchia, M., Cicero, V., Ciolini, R., Clarke, P., Cline, G., Coan, T. E., Cocco, A. G., Coelho, J. A. B., Cohen, A., Collazo, J., Collot, J., Conley, E., Conrad, J. M., Convery, M., Copello, S., Cova, P., Cox, C., Cremaldi, L., Cremonesi, L., Crespo-Anadón, J. I., Crisler, M., Cristaldo, E., Crnkovic, J., Crone, G., Cross, R., Cudd, A., Cuesta, C., Cui, Y., Curciarello, F., Cussans, D., Dai, J., Dalager, O., Dallavalle, R., Dallaway, W., D'Amico, R., da Motta, H., Dar, Z. A., Darby, R., Peres, L. Da Silva, David, Q., Davies, G. S., Davini, S., Dawson, J., De Aguiar, R., De Almeida, P., Debbins, P., De Bonis, I., Decowski, M. P., de Gouvêa, A., De Holanda, P. C., Astiz, I. L. De Icaza, De Jong, P., Sanchez, P. Del Amo, De la Torre, A., De Lauretis, G., Delbart, A., Delepine, D., Delgado, M., Dell'Acqua, A., Monache, G. Delle, Delmonte, N., De Lurgio, P., Demario, R., De Matteis, G., Neto, J. R. T. de Mello, DeMuth, D. M., Dennis, S., Densham, C., Denton, P., Deptuch, G. W., De Roeck, A., De Romeri, V., Detje, J. P., Devine, J., Dharmapalan, R., Dias, M., Diaz, A., Díaz, J. S., Díaz, F., Di Capua, F., Di Domenico, A., Di Domizio, S., Di Falco, S., Di Giulio, L., Ding, P., Di Noto, L., Diociaiuti, E., Distefano, C., Diurba, R., Diwan, M., Djurcic, Z., Doering, D., Dolan, S., Dolek, F., Dolinski, M. J., Domenici, D., Domine, L., Donati, S., Donon, Y., Doran, S., Douglas, D., Doyle, T. A., Dragone, A., Drielsma, F., Duarte, L., Duchesneau, D., Duffy, K., Dugas, K., Dunne, P., Dutta, B., Duyang, H., Dwyer, D. A., Dyshkant, A. S., Dytman, S., Eads, M., Earle, A., Edayath, S., Edmunds, D., Eisch, J., Englezos, P., Ereditato, A., Erjavec, T., Escobar, C. O., Evans, J. J., Ewart, E., Ezeribe, A. C., Fahey, K., Fajt, L., Falcone, A., Fani', M., Farnese, C., Farrell, S., Farzan, Y., Fedoseev, D., Felix, J., Feng, Y., Fernandez-Martinez, E., Ferry, G., Fialova, E., Fields, L., Filip, P., Filkins, A., Filthaut, F., Fine, R., Fiorillo, G., Fiorini, M., Fogarty, S., Foreman, W., Fowler, J., Franc, J., Francis, K., Franco, D., Franklin, J., Freeman, J., Fried, J., Friedland, A., Fuess, S., Furic, I. K., Furman, K., Furmanski, A. P., Gaba, R., Gabrielli, A., M~Gago, A., Galizzi, F., Gallagher, H., Gallice, N., Galymov, V., Gamberini, E., Gamble, T., Ganacim, F., Gandhi, R., Ganguly, S., Gao, F., Gao, S., Garcia-Gamez, D., García-Peris, M. Á., Gardim, F., Gardiner, S., Gastler, D., Gauch, A., Gauvreau, J., Gauzzi, P., Gazzana, S., Ge, G., Geffroy, N., Gelli, B., Gent, S., Gerlach, L., Ghorbani-Moghaddam, Z., Giammaria, T., Gibin, D., Gil-Botella, I., Gilligan, S., Gioiosa, A., Giovannella, S., Girerd, C., Giri, A. K., Giugliano, C., Giusti, V., Gnani, D., Gogota, O., Gollapinni, S., Gollwitzer, K., Gomes, R. A., Bermeo, L. V. Gomez, Fajardo, L. S. Gomez, Gonnella, F., Gonzalez-Diaz, D., Gonzalez-Lopez, M., Goodman, M. C., Goswami, S., Gotti, C., Goudeau, J., Goudzovski, E., Grace, C., Gramellini, E., Gran, R., Granados, E., Granger, P., Grant, C., Gratieri, D. R., Grauso, G., Green, P., Greenberg, S., Greer, J., Griffith, W. C., Groetschla, F. T., Grzelak, K., Gu, L., Gu, W., Guarino, V., Guarise, M., Guenette, R., Guerzoni, M., Guffanti, D., Guglielmi, A., Guo, B., Guo, F. Y., Gupta, A., Gupta, V., Gurung, G., Gutierrez, D., Guzowski, P., Guzzo, M. M., Gwon, S., Habig, A., Hadavand, H., Haegel, L., Haenni, R., Hagaman, L., Hahn, A., Haiston, J., Hakenmüller, J., Hamernik, T., Hamilton, P., Hancock, J., Happacher, F., Harris, D. A., Hartnell, J., Hartnett, T., Harton, J., Hasegawa, T., Hasnip, C. M., Hatcher, R., Hayrapetyan, K., Hays, J., Hazen, E., He, M., Heavey, A., Heeger, K. M., Heise, J., Hellmuth, P., Henry, S., Herner, K., Hewes, V., Higuera, A., Hilgenberg, C., Hillier, S. J., Himmel, A., Hinkle, E., Hirsch, L. R., Ho, J., Hoff, J., Holin, A., Holvey, T., Hoppe, E., Horiuchi, S., Horton-Smith, G. A., Houdy, T., Howard, B., Howell, R., Hristova, I., Hronek, M. S., Huang, J., Huang, R. G., Hulcher, Z., Ibrahim, M., Iles, G., Ilic, N., Iliescu, A. M., Illingworth, R., Ingratta, G., Ioannisian, A., Irwin, B., Isenhower, L., Oliveira, M. Ismerio, Itay, R., Jackson, C. M., Jain, V., James, E., Jang, W., Jargowsky, B., Jena, D., Jentz, I., Ji, X., Jiang, C., Jiang, J., Jiang, L., Jipa, A., Jo, J. H., Joaquim, F. R., Johnson, W., Jollet, C., Jones, B., Jones, R., Jovancevic, N., Judah, M., Jung, C. K., Junk, T., Jwa, Y., Kabirnezhad, M., Kaboth, A. C., Kadenko, I., Kakorin, I., Kalitkina, A., Kalra, D., Kandemir, M., Kaplan, D. M., Karagiorgi, G., Karaman, G., Karcher, A., Karyotakis, Y., Kasai, S., Kasetti, S. P., Kashur, L., Katsioulas, I., Kauther, A., Kazaryan, N., Ke, L., Kearns, E., Keener, P. T., Kelly, K. J., Kemp, E., Kemularia, O., Kermaidic, Y., Ketchum, W., Kettell, S. H., Khabibullin, M., Khan, N., Khvedelidze, A., Kim, D., Kim, J., Kim, M. J., King, B., Kirby, B., Kirby, M., Kish, A., Klein, J., Kleykamp, J., Klustova, A., Kobilarcik, T., Koch, L., Koehler, K., Koerner, L. W., Koh, D. H., Kolupaeva, L., Korablev, D., Kordosky, M., Kosc, T., Kose, U., Kostelecký, V. A., Kothekar, K., Kotler, I., Kovalcuk, M., Kozhukalov, V., Krah, W., Kralik, R., Kramer, M., Kreczko, L., Krennrich, F., Kreslo, I., Kroupova, T., Kubota, S., Kubu, M., Kudenko, Y., Kudryavtsev, V. A., Kufatty, G., Kuhlmann, S., Kulagin, S., Kumar, J., Kumar, P., Kumaran, S., Kunzmann, J., Kuravi, R., Kurita, N., Kuruppu, C., Kus, V., Kutter, T., Kvasnicka, J., Labree, T., Lackey, T., Lal{ă}u, I., Lambert, A., Land, B. J., Lane, C. E., Lane, N., Lang, K., Langford, T., Langstaff, M., Lanni, F., Lantwin, O., Larkin, J., Lasorak, P., Last, D., Laudrain, A., Laundrie, A., Laurenti, G., Lavaut, E., Laycock, P., Lazanu, I., LaZur, R., Lazzaroni, M., Le, T., Leardini, S., Learned, J., LeCompte, T., Legin, V., Miotto, G. Lehmann, Lehnert, R., de Oliveira, M. A. Leigui, Leitner, M., Silverio, D. Leon, Lepin, L. M., -Y~Li, J., Li, S. W., Li, Y., Liao, H., Lin, C. S., Lindebaum, D., Linden, S., Lineros, R. A., Lister, A., Littlejohn, B. R., Liu, H., Liu, J., Liu, Y., Lockwitz, S., Lokajicek, M., Lomidze, I., Long, K., Lopes, T. V., Lopez, J., de Rego, I. López, López-March, N., Lord, T., LoSecco, J. M., Louis, W. C., Sanchez, A. Lozano, Lu, X. -G., Luk, K. B., Lunday, B., Luo, X., Luppi, E., MacFarlane, D., Machado, A. A., Machado, P., Macias, C. T., Macier, J. R., MacMahon, M., Maddalena, A., Madera, A., Madigan, P., Magill, S., Magueur, C., Mahn, K., Maio, A., Major, A., Majumdar, K., Mameli, S., Man, M., Mandujano, R. C., Maneira, J., Manly, S., Mann, A., Manolopoulos, K., Plata, M. Manrique, Corchado, S. Manthey, Manyam, V. N., Marchan, M., Marchionni, A., Marciano, W., Marfatia, D., Mariani, C., Maricic, J., Marinho, F., Marino, A. D., Markiewicz, T., Marques, F. Das Chagas, Marquet, C., Marshak, M., Marshall, C. M., Marshall, J., Martina, L., Martín-Albo, J., Martinez, N., Caicedo, D. A. Martinez, López, F. Martínez, Miravé, P. Martínez, Martynenko, S., Mascagna, V., Massari, C., Mastbaum, A., Matichard, F., Matsuno, S., Matteucci, G., Matthews, J., Mauger, C., Mauri, N., Mavrokoridis, K., Mawby, I., Mazza, R., McAskill, T., McConkey, N., McFarland, K. S., McGrew, C., McNab, A., Meazza, L., Meddage, V. C. N., Mefodiev, A., Mehta, B., Mehta, P., Melas, P., Mena, O., Mendez, H., Mendez, P., Méndez, D. P., Menegolli, A., Meng, G., Mercuri, A. C. E. A., Meregaglia, A., Messier, M. D., Metallo, S., Metcalf, W., Mewes, M., Meyer, H., Miao, T., Micallef, J., Miccoli, A., Michna, G., Milincic, R., Miller, F., Miller, G., Miller, W., Mineev, O., Minotti, A., Miralles, L., Miranda, O. G., Mironov, C., Miryala, S., Miscetti, S., Mishra, C. S., Mishra, P., Mishra, S. R., Mislivec, A., Mitchell, M., Mladenov, D., Mocioiu, I., Mogan, A., Moggi, N., Mohanta, R., Mohayai, T. A., Mokhov, N., Molina, J., Bueno, L. Molina, Montagna, E., Montanari, A., Montanari, C., Montanari, D., Montanino, D., Zetina, L. M. Montaño, Mooney, M., Moor, A. F., Moore, Z., Moreno, D., Moreno-Palacios, O., Morescalchi, L., Moretti, D., Moretti, R., Morris, C., Mossey, C., Moura, C. A., Mouster, G., Mu, W., Mualem, L., Mueller, J., Muether, M., Muheim, F., Muir, A., Mulhearn, M., Munford, D., Munteanu, L. J., Muramatsu, H., Muraz, J., Murphy, M., Murphy, T., Muse, J., Mytilinaki, A., Nachtman, J., Nagai, Y., Nagu, S., Nandakumar, R., Naples, D., Narita, S., Navrer-Agasson, A., Nayak, N., Nebot-Guinot, M., Nehm, A., Nelson, J. K., Neogi, O., Nesbit, J., Nessi, M., Newbold, D., Newcomer, M., Nichol, R., Nicolas-Arnaldos, F., Nikolica, A., Nikolov, J., Niner, E., Nishimura, K., Norman, A., Norrick, A., Novella, P., Nowak, A., Nowak, J. A., Oberling, M., Ochoa-Ricoux, J. P., Oh, S., Oh, S. B., Olivier, A., Olshevskiy, A., Olson, T., Onel, Y., Onishchuk, Y., Oranday, A., Osbiston, M., Vélez, J. A. Osorio, O'Sullivan, L., Ormachea, L. Otiniano, Ott, J., Pagani, L., Palacio, G., Palamara, O., Palestini, S., Paley, J. M., Pallavicini, M., Palomares, C., Pan, S., Panda, P., Vazquez, W. Panduro, Pantic, E., Paolone, V., Papaleo, R., Papanestis, A., Papoulias, D., Paramesvaran, S., Paris, A., Parke, S., Parozzi, E., Parsa, S., Parsa, Z., Parveen, S., Parvu, M., Pasciuto, D., Pascoli, S., Pasqualini, L., Pasternak, J., Patrick, C., Patrizii, L., Patterson, R. B., Patzak, T., Paudel, A., Paulucci, L., Pavlovic, Z., Pawloski, G., Payne, D., Pec, V., Pedreschi, E., Peeters, S. J. M., Pellico, W., Perez, A. Pena, Pennacchio, E., Penzo, A., Peres, O. L. G., Gonzalez, Y. F. Perez, Pérez-Molina, L., Pernas, C., Perry, J., Pershey, D., Pessina, G., Petrillo, G., Petta, C., Petti, R., Pfaff, M., Pia, V., Pickering, L., Pietropaolo, F., Pimentel, V. L., Pinaroli, G., Pincha, S., Pinchault, J., Pitts, K., Plows, K., Pollack, C., Pollman, T., Pompa, F., Pons, X., Poonthottathil, N., Popov, V., Poppi, F., Porter, J., Paix{ã}o, L. G. Porto, Potekhin, M., Potenza, R., Pozimski, J., Pozzato, M., Prakash, T., Pratt, C., Prest, M., Psihas, F., Pugnere, D., Qian, X., Queen, J., Raaf, J. L., Radeka, V., Rademacker, J., Radics, B., Raffaelli, F., Rafique, A., Raguzin, E., Rai, M., Rajagopalan, S., Rajaoalisoa, M., Rakhno, I., Rakotondravohitra, L., Ralte, L., Delgado, M. A. Ramirez, Ramson, B., Rappoldi, A., Raselli, G., Ratoff, P., Ray, R., Razafinime, H., Rea, E. M., Real, J. S., Rebel, B., Rechenmacher, R., Reichenbacher, J., Reitzner, S. D., Sfar, H. Rejeb, Renner, E., Renshaw, A., Rescia, S., Resnati, F., Diego~Restrepo, Reynolds, C., Ribas, M., Riboldi, S., Riccio, C., Riccobene, G., Ricol, J. S., Rigan, M., Rincón, E. V., Ritchie-Yates, A., Ritter, S., Rivera, D., Rivera, R., Robert, A., Rocha, J. L. Rocabado, Rochester, L., Roda, M., Rodrigues, P., Alonso, M. J. Rodriguez, Rondon, J. Rodriguez, Rosauro-Alcaraz, S., Rosier, P., Ross, D., Rossella, M., Rossi, M., Ross-Lonergan, M., Roy, N., Roy, P., Rubbia, C., Ruggeri, A., Ferreira, G. Ruiz, Russell, B., Ruterbories, D., Rybnikov, A., Sacerdoti, S., Saha, S., Sahoo, S. K., Sahu, N., Sala, P., Samios, N., Samoylov, O., Sanchez, M. C., Bravo, A. Sánchez, Sánchez-Castillo, A., Sanchez-Lucas, P., Sandberg, V., Sanders, D. A., Sanfilippo, S., Sankey, D., Santoro, D., Saoulidou, N., Sapienza, P., Sarasty, C., Sarcevic, I., Sarra, I., Savage, G., Savinov, V., Scanavini, G., Scaramelli, A., Scarff, A., Schefke, T., Schellman, H., Schifano, S., Schlabach, P., Schmitz, D., Schneider, A. W., Scholberg, K., Schukraft, A., Schuld, B., Segade, A., Segreto, E., Selyunin, A., Senadheera, D., Senise, C. R., Sensenig, J., Shaevitz, M. H., Shanahan, P., Sharma, P., Kumar, R., Poudel, S. Sharma, Shaw, K., Shaw, T., Shchablo, K., Shen, J., Shepherd-Themistocleous, C., Sheshukov, A., Shi, J., Shi, W., Shin, S., Shivakoti, S., Shoemaker, I., Shooltz, D., Shrock, R., Siddi, B., Siden, M., Silber, J., Simard, L., Sinclair, J., Sinev, G., Singh, Jaydip, Singh, J., Singh, L., Singh, P., Singh, V., Chauhan, S. Singh, Sipos, R., Sironneau, C., Sirri, G., Siyeon, K., Skarpaas, K., Smedley, J., Smith, E., Smith, J., Smith, P., Smolik, J., Smy, M., Snape, M., Snider, E. L., Snopok, P., Snowden-Ifft, D., Nunes, M. Soares, Sobel, H., Soderberg, M., Sokolov, S., Salinas, C. J. Solano, Söldner-Rembold, S., Solomey, N., Solovov, V., Sondheim, W. E., Sorel, M., Sotnikov, A., Soto-Oton, J., Sousa, A., Soustruznik, K., Spinella, F., Spitz, J., Spooner, N. J. C., Spurgeon, K., Stalder, D., Stancari, M., Stanco, L., Steenis, J., Stein, R., Steiner, H. M., Lisbôa, A. F. Steklain, Stepanova, A., Stewart, J., Stillwell, B., Stock, J., Stocker, F., Stokes, T., Strait, M., Strauss, T., Strigari, L., Stuart, A., Suarez, J. G., Subash, J., Surdo, A., Suter, L., Sutera, C. M., Sutton, K., Suvorov, Y., Svoboda, R., Swain, S. K., Szczerbinska, B., Szelc, A. M., Sztuc, A., Taffara, A., Talukdar, N., Tamara, J., Tanaka, H. A., Tang, S., Taniuchi, N., Casanova, A. M. Tapia, Oregui, B. Tapia, Tapper, A., Tariq, S., Tarpara, E., Tatar, E., Tayloe, R., Tedeschi, D., Teklu, A. M., Vidal, J. Tena, Tennessen, P., Tenti, M., Terao, K., Terranova, F., Testera, G., Thakore, T., Thea, A., Thomas, S., Thompson, A., Thorn, C., Timm, S. C., Tiras, E., Tishchenko, V., Todorović, N., Tomassetti, L., Tonazzo, A., Torbunov, D., Torti, M., Tortola, M., Tortorici, F., Tosi, N., Totani, D., Toups, M., Touramanis, C., Tran, D., Travaglini, R., Trevor, J., Triller, E., Trilov, S., Truchon, J., Truncali, D., Trzaska, W. H., Tsai, Y., Tsai, Y. -T., Tsamalaidze, Z., Tsang, K. V., Tsverava, N., Tu, S. Z., Tufanli, S., Tunnell, C., Turnberg, S., Turner, J., Tuzi, M., Tyler, J., Tyley, E., Tzanov, M., Uchida, M. A., González, J. Ureña, Urheim, J., Usher, T., Utaegbulam, H., Uzunyan, S., Vagins, M. R., Vahle, P., Valder, S., Valdiviesso, G. A., Valencia, E., Valentim, R., Vallari, Z., Vallazza, E., Valle, J. W. F., Van Berg, R., Van de Water, R. G., Forero, D. V., Vannozzi, A., Van Nuland-Troost, M., Varanini, F., Oliva, D. Vargas, Vasina, S., Vaughan, N., Vaziri, K., Vázquez-Ramos, A., Vega, J., Ventura, S., Verdugo, A., Vergani, S., Verzocchi, M., Vetter, K., Vicenzi, M., de Souza, H. Vieira, Vignoli, C., Vilela, C., Villa, E., Viola, S., Viren, B., Hernandez, A. P. Vizcaya, Vuong, Q., Waldron, A. V., Wallbank, M., Walsh, J., Walton, T., Wang, H., Wang, J., Wang, L., Wang, M. H. L. S., Wang, X., Wang, Y., Warburton, K., Warner, D., Warsame, L., Wascko, M. O., Waters, D., Watson, A., Wawrowska, K., Weber, A., Weber, C. M., Weber, M., Wei, H., Weinstein, A., Westerdale, S., Wetstein, M., Whalen, K., White, A., Whitehead, L. H., Whittington, D., Wilhlemi, J., Wilking, M. J., Wilkinson, A., Wilkinson, C., Wilson, F., Wilson, R. J., Winter, P., Wisniewski, W., Wolcott, J., Wolfs, J., Wongjirad, T., Wood, A., Wood, K., Worcester, E., Worcester, M., Wospakrik, M., Wresilo, K., Wret, C., Wu, S., Wu, W., Wurm, M., Wyenberg, J., Xiao, Y., Xiotidis, I., Yaeggy, B., Yahlali, N., Yandel, E., Yang, J., Yang, K., Yang, T., Yankelevich, A., Yershov, N., Yonehara, K., Young, T., Yu, B., Yu, H., Yu, J., Yu, Y., Yuan, W., Zaki, R., Zalesak, J., Zambelli, L., Zamorano, B., Zani, A., Zapata, O., Zazueta, L., Zeller, G. P., Zennamo, J., Zeug, K., Zhang, C., Zhang, S., Zhao, M., Zhivun, E., Zimmerman, E. D., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.
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High Energy Physics - Experiment ,Physics - Instrumentation and Detectors - Abstract
ProtoDUNE Single-Phase (ProtoDUNE-SP) is a 770-ton liquid argon time projection chamber that operated in a hadron test beam at the CERN Neutrino Platform in 2018. We present a measurement of the total inelastic cross section of charged kaons on argon as a function of kaon energy using 6 and 7 GeV/$c$ beam momentum settings. The flux-weighted average of the extracted inelastic cross section at each beam momentum setting was measured to be 380$\pm$26 mbarns for the 6 GeV/$c$ setting and 379$\pm$35 mbarns for the 7 GeV/$c$ setting.
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- 2024
26. TinyBird-ML: An ultra-low Power Smart Sensor Node for Bird Vocalization Analysis and Syllable Classification
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Schulthess, Lukas, Marty, Steven, Dirodi, Matilde, Rocha, Mariana D., Rüttimann, Linus, Hahnloser, Richard H. R., and Magno, Michele
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Electrical Engineering and Systems Science - Signal Processing - Abstract
Animal vocalisations serve a wide range of vital functions. Although it is possible to record animal vocalisations with external microphones, more insights are gained from miniature sensors mounted directly on animals' backs. We present TinyBird-ML; a wearable sensor node weighing only 1.4 g for acquiring, processing, and wirelessly transmitting acoustic signals to a host system using Bluetooth Low Energy. TinyBird-ML embeds low-latency tiny machine learning algorithms for song syllable classification. To optimize battery lifetime of TinyBird-ML during fault-tolerant continuous recordings, we present an efficient firmware and hardware design. We make use of standard lossy compression schemes to reduce the amount of data sent over the Bluetooth antenna, which increases battery lifetime by 70% without negative impact on offline sound analysis. Furthermore, by not transmitting signals during silent periods, we further increase battery lifetime. One advantage of our sensor is that it allows for closed-loop experiments in the microsecond range by processing sounds directly on the device instead of streaming them to a computer. We demonstrate this capability by detecting and classifying song syllables with minimal latency and a syllable error rate of 7%, using a light-weight neural network that runs directly on the sensor node itself. Thanks to our power-saving hardware and software design, during continuous operation at a sampling rate of 16 kHz, the sensor node achieves a lifetime of 25 hours on a single size 13 zinc-air battery.
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- 2024
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27. Ge-based Clinopyroxene series: first principles and experimental local probe study
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Moreira, Ricardo P., da Silva, E. Lora, Oliveira, Gonçalo N. P., Rocha-Rodrigues, Pedro, Stroppa, Alessandro, Colin, Claire V., Darie, Céline, Correia, João G., Assali, Lucy V. C., Petrilli, Helena M., Lopes, Armandina M. L., and Araújo, João P.
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Condensed Matter - Materials Science ,Condensed Matter - Strongly Correlated Electrons - Abstract
The structural and electronic properties of the CaMnGe$_2$O$_6$ and SrMnGe$_2$O$_6$ clinopyroxene systems have been investigated by means of perturbed angular correlation (PAC) measurements, performed at ISOLDE, combined with $ab-initio$ electronic structure calculations within the density functional theory (DFT) framework. The partial density of states (PDOS) of the CaMnGe$_2$O$_6$ and SrMnGe$_2$O$_6$ stable compounds has been determined, and it has been observed that the requirement of including an on-site Hubbard-$U$ potential was necessary in order to describe the highly correlated Mn $3d$-states. By considering $U_{eff}$=4 eV, we obtained a band gap width of 1.82 eV and 1.70 eV, for the CaMnGe$_2$O$_6$ and SrMnGe$_2$O$_6$, respectively. Combining electric field gradient (EFG) first principles calculations, using a supercell scheme, with experimental PAC results, we were able to infer that the Cd probe can replace either the $A$ (Ca, Sr) or the Mn sites in the crystalline structures. We also showed that Cd substitution is expected to lead to a reduction in the width of the band gap in these systems, evidencing opportunities for potential band-gap engineering.
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- 2024
28. A Sea of Black Holes: Characterizing the LISA Signature for Stellar-Origin Black Hole Binaries
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Ruiz-Rocha, Krystal, Holley-Bockelmann, Kelly, Jani, Karan, Mapelli, Michela, Dunham, Samuel, and Gabella, William
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Astrophysics - High Energy Astrophysical Phenomena - Abstract
Observations by the LIGO, Virgo and KAGRA (LVK) detectors have provided new insights in the demographics of stellar-origin black hole binaries (sBHB). A few years before gravitational-wave signals from sBHB mergers are recorded in the LVK detectors, their early coalescence will leave a unique signature in the ESA/NASA mission Laser Interferometer Space Antenna (LISA). Multiband observations of sBHB sources between LISA and LVK detectors opens an unprecedented opportunity to investigate the astrophysical environment and multi-messenger early-alerts. In this study, we report the sBHB sources that will be present in the LISA data derived directly from the hydrodynamic cosmological simulation Illustris. By surveying snapshots across cosmological volume, metallicity and look-back time, we find that about tens to thousand sBHB candidates will be present in the LISA data for various combinations of mission lifetime. For estimates consistent with the LVK rates, we find that only 20 sBHBs across Illustris snapshots will be detected with significant confidence for a 10-year LISA mission, while a 4-year LISA mission would detect only 2 sBHBs. Our work paves the way for creating LISA mock data and bench marking LISA detection pipelines directly using cosmological simulations.
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- 2024
29. Contrasting Deep Learning Models for Direct Respiratory Insufficiency Detection Versus Blood Oxygen Saturation Estimation
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Gauy, Marcelo Matheus, Koza, Natalia Hitomi, Morita, Ricardo Mikio, Stanzione, Gabriel Rocha, Junior, Arnaldo Candido, Berti, Larissa Cristina, Levin, Anna Sara Shafferman, Sabino, Ester Cerdeira, Svartman, Flaviane Romani Fernandes, and Finger, Marcelo
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Computer Science - Sound ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Audio and Speech Processing - Abstract
We contrast high effectiveness of state of the art deep learning architectures designed for general audio classification tasks, refined for respiratory insufficiency (RI) detection and blood oxygen saturation (SpO$_2$) estimation and classification through automated audio analysis. Recently, multiple deep learning architectures have been proposed to detect RI in COVID patients through audio analysis, achieving accuracy above 95% and F1-score above 0.93. RI is a condition associated with low SpO$_2$ levels, commonly defined as the threshold SpO$_2$ <92%. While SpO$_2$ serves as a crucial determinant of RI, a medical doctor's diagnosis typically relies on multiple factors. These include respiratory frequency, heart rate, SpO$_2$ levels, among others. Here we study pretrained audio neural networks (CNN6, CNN10 and CNN14) and the Masked Autoencoder (Audio-MAE) for RI detection, where these models achieve near perfect accuracy, surpassing previous results. Yet, for the regression task of estimating SpO$_2$ levels, the models achieve root mean square error values exceeding the accepted clinical range of 3.5% for finger oximeters. Additionally, Pearson correlation coefficients fail to surpass 0.3. As deep learning models perform better in classification than regression, we transform SpO$_2$-regression into a SpO$_2$-threshold binary classification problem, with a threshold of 92%. However, this task still yields an F1-score below 0.65. Thus, audio analysis offers valuable insights into a patient's RI status, but does not provide accurate information about actual SpO$_2$ levels, indicating a separation of domains in which voice and speech biomarkers may and may not be useful in medical diagnostics under current technologies., Comment: 23 pages, 4 figures, in review at Journal of Biomedical Signal Processing and Control
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- 2024
30. JOYS+: link between ice and gas of complex organic molecules. Comparing JWST and ALMA data of two low-mass protostars
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Chen, Y., Rocha, W. R. M., van Dishoeck, E. F., van Gelder, M. L., Nazari, P., Slavicinska, K., Francis, L., Tabone, B., Ressler, M. E., Klaassen, P. D., Beuther, H., Boogert, A. C. A., Gieser, C., Kavanagh, P. J., Perotti, G., Gouellec, V. J. M. Le, Majumdar, L., Güdel, M., and Henning, Th.
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Astrophysics - Astrophysics of Galaxies ,Astrophysics - Earth and Planetary Astrophysics ,Astrophysics - Solar and Stellar Astrophysics - Abstract
A rich inventory of complex organic molecules (COMs) has been observed in high abundances in the gas phase toward Class 0 protostars. These molecules are suggested to be formed in ices and sublimate in the warm inner envelope close to the protostar. However, only the most abundant COM, methanol (CH3OH), has been firmly detected in ices before the era of James Webb Space Telescope (JWST). Now it is possible to detect the interstellar ices of other COMs and constrain their ice column densities quantitatively. We aim to determine the column densities of several oxygen-bearing COMs (O-COMs) in both gas and ice for two low-mass protostellar sources, NGC 1333 IRAS 2A and B1-c, as case studies in our JWST Observations of Young protoStars (JOYS+) program. By comparing the column density ratios w.r.t. CH3OH between both phases measured in the same sources, we can probe into the evolution of COMs from ice to gas in the early stages of star formation. We are able to fit the fingerprints range of COM ices between 6.8 and 8.8 um in the JWST/MIRI-MRS spectra of B1-c using similar components as recently used for IRAS 2A. We claim detection of CH4, OCN-, HCOO-, HCOOH, CH3CHO, C2H5OH, CH3OCH3, CH3OCHO, and CH3COCH3 in B1-c, and upper limits are estimated for SO2, CH3COOH, and CH3CN. The comparison of O-COM ratios w.r.t CH3OH between ice and gas shows two different cases. 1) the column density ratios of CH3OCHO and CH3OCH3 match well between the two phases, which may be attributed to a direct inheritance from ice to gas or strong chemical links with CH3OH. 2) the ice ratios of CH3CHO and C2H5OH w.r.t. CH3OH are higher than the gas ratios by 1-2 orders of magnitudes. This difference can be explained by the gas-phase reprocessing following sublimation, or different spatial distributions of COMs in the envelope., Comment: 42 pages (22 main text, 20 appendix); 27 figures (12 in main text, 15 in appendix); 5 tables (2 in main text, 3 in appendix) Accepted for publication in A&A
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- 2024
31. A Novel Two-Step Fine-Tuning Pipeline for Cold-Start Active Learning in Text Classification Tasks
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Belém, Fabiano, Cunha, Washington, França, Celso, Andrade, Claudio, Rocha, Leonardo, and Gonçalves, Marcos André
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Computer Science - Machine Learning ,Computer Science - Databases ,Computer Science - Information Retrieval - Abstract
This is the first work to investigate the effectiveness of BERT-based contextual embeddings in active learning (AL) tasks on cold-start scenarios, where traditional fine-tuning is infeasible due to the absence of labeled data. Our primary contribution is the proposal of a more robust fine-tuning pipeline - DoTCAL - that diminishes the reliance on labeled data in AL using two steps: (1) fully leveraging unlabeled data through domain adaptation of the embeddings via masked language modeling and (2) further adjusting model weights using labeled data selected by AL. Our evaluation contrasts BERT-based embeddings with other prevalent text representation paradigms, including Bag of Words (BoW), Latent Semantic Indexing (LSI), and FastText, at two critical stages of the AL process: instance selection and classification. Experiments conducted on eight ATC benchmarks with varying AL budgets (number of labeled instances) and number of instances (about 5,000 to 300,000) demonstrate DoTCAL's superior effectiveness, achieving up to a 33% improvement in Macro-F1 while reducing labeling efforts by half compared to the traditional one-step method. We also found that in several tasks, BoW and LSI (due to information aggregation) produce results superior (up to 59% ) to BERT, especially in low-budget scenarios and hard-to-classify tasks, which is quite surprising., Comment: 11 pages, 4 figures, 2 Tables, and 1 algorithm
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- 2024
32. Classical discrete operators on variable $\ell^{p(\cdot)}(\mathbb{Z})$ spaces
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Rocha, Pablo
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Mathematics - Classical Analysis and ODEs ,Mathematics - Functional Analysis - Abstract
We show, by applying discrete weighted norm inequalities and the Rubio de Francia algorithm, that the discrete Hilbert transform and discrete Riesz potential are bounded on variable $\ell^{p(\cdot)}(\mathbb{Z})$ spaces whenever the discrete Hardy-Littlewood maximal is bounded on $\ell^{p'(\cdot)}(\mathbb{Z})$. We also obtain vector-valued inequalities for the discrete fractional maximal operator., Comment: 8 pages
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- 2024
33. A note about discrete Riesz potential on $\mathbb{Z}^n$
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Rocha, Pablo
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Mathematics - Classical Analysis and ODEs - Abstract
In this note we prove that the discrete Riesz potential $I_{\alpha}$ defined on $\mathbb{Z}^n$ is a bounded operator $H^p (\mathbb{Z}^n) \to \ell^q (\mathbb{Z}^n)$ for $0 < p \leq 1$ and $\frac{1}{q} = \frac{1}{p} - \frac{\alpha}{n}$, where $0 < \alpha < n$., Comment: 10 pages
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- 2024
34. The Non-Relativistic Effective Field Theory Of Dark Matter-Electron Interactions
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Krnjaic, Gordan, Rocha, Duncan, and Trickle, Tanner
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High Energy Physics - Phenomenology ,Astrophysics - Cosmology and Nongalactic Astrophysics - Abstract
Electronic excitations in atomic, molecular, and crystal targets are at the forefront of the ongoing search for light, sub-GeV dark matter (DM). In many light DM-electron interactions the energy and momentum deposited is much smaller than the electron mass, motivating a non-relativistic (NR) description of the electron. Thus, for any target, light DM-electron phenomenology relies on understanding the interactions between the DM and electron in the NR limit. In this work we derive the NR effective field theory (EFT) of general DM-electron interactions from a top-down perspective, starting from general high-energy DM-electron interaction Lagrangians. This provides an explicit connection between high-energy theories and their low-energy phenomenology in electron excitation based experiments. Furthermore, we derive Feynman rules for the DM-electron NR EFT, allowing observables to be computed diagrammatically, which can systematically explain the presence of in-medium screening effects in general DM models. We use these Feynman rules to compute absorption, scattering, and dark Thomson scattering rates for a wide variety of high-energy DM models., Comment: 60 pages, 2 figures
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- 2024
35. Binarity at LOw Metallicity (BLOeM): I. a spectroscopic VLT monitoring survey of massive stars in the SMC
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Shenar, T., Bodensteiner, J., Sana, H., Crowther, P. A., Lennon, D. J., Abdul-Masih, M., Almeida, L. A., Backs, F., Berlanas, S. R., Bernini-Peron, M., Bestenlehner, J. M., Bowman, D. M., Bronner, V. A., Britavskiy, N., de Koter, A., de Mink, S. E., Deshmukh, K., Evans, C. J., Fabry, M., Gieles, M., Gilkis, A., González-Torà, G., Gräfener, G., Götberg, Y., Hawcroft, C., Hénault-Brunet, V., Herrero, A., Holgado, G., Janssens, S., Johnston, C., Josiek, J., Justham, S., Kalari, V. M., Katabi, Z. Z., Keszthelyi, Z., Klencki, J., Kubát, J., Kubátová, B., Langer, N., Lefever, R. R., Ludwig, B., Mackey, J., Mahy, L., Apellániz, J. Maíz, Mandel, I., Maravelias, G., Marchant, P., Menon, A., Najarro, F., Oskinova, L. M., Ovadia, R., Patrick, L. R., Pauli, D., Pawlak, M., Ramachandran, V., Renzo, M., Rocha, D. F., Sander, A. A. C., Sayada, T., Schneider, F. R. N., Schootemeijer, A., Schösser, E. C., Schürmann, C., Sen, K., Shahaf, S., Simón-Díaz, S., Stoop, M., van Loon, J. Th., Toonen, S., Tramper, F., Valli, R., van Son, L. A. C., Vigna-Gómez, A., Villaseñor, J. I., Vink, J. S., Wang, C., and Willcox, R.
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Astrophysics - Solar and Stellar Astrophysics ,Astrophysics - Astrophysics of Galaxies - Abstract
Surveys in the Milky Way and Large Magellanic Cloud revealed that the majority of massive stars will interact with companions during their lives. However, knowledge of the binary properties of massive stars at low metallicity, which approaches the conditions of the Early Universe, remains sparse. We present the Binarity at LOw Metallicity (BLOeM) campaign - an ESO large programme designed to obtain 25 epochs of spectroscopy for 929 massive stars in the SMC - the lowest metallicity conditions in which multiplicity is probed to date (Z = 0.2 Zsun). BLOeM will provide (i) the binary fraction, (ii) the orbital configurations of systems with periods P < 3 yr, (iii) dormant OB+BH binaries, and (iv) a legacy database of physical parameters of massive stars at low metallicity. The stars are observed with the LR02 setup of the giraffe instrument of the Very Large Telescope (3960-4570A, resolving power R=6200; typical signal-to-noise ratio S/N=70-100). This paper utilises the first 9 epochs obtained over a three-month time. We describe the survey and data reduction, perform a spectral classification of the stacked spectra, and construct a Hertzsprung-Russell diagram of the sample via spectral-type and photometric calibrations. The sample covers spectral types from O4 to F5, spanning the effective temperature and luminosity ranges 6.5
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- 2024
36. The impact of higher derivative corrections to General Relativity on black hole mergers
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Dias, João M., Frassino, Antonia M., Lopes, David C., Paccoia, Valentin D., and Rocha, Jorge V.
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General Relativity and Quantum Cosmology - Abstract
The merging of two black holes is a notoriously difficult process to describe exactly. Nevertheless, the hindrances posed by gravity's nonlinearity can be circumvented by focusing on the strict extreme mass ratio limit, in which one of the black holes is infinitely larger than the other. Such an approach has been developed by Emparan and Mart\'inez and applied within General Relativity to investigate the time evolution of event horizons melding, using nothing but elementary concepts in gravitational physics and simple integrations of geodesics. We apply this strategy to study black hole mergers in higher derivative gravity, in order to assess how the defining characteristics of the fusion process change as the gravitational theory is modified. We adopt the case of Einsteinian cubic gravity for concreteness, and determine how the mergers' duration and the relative area increment change as the theory's single coupling parameter is varied., Comment: v2: 13 pages, 11 figures, minor modifications and an added subsection about entropy and area growth (including two new figures)
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- 2024
37. Unraveling Rodeo Algorithm Through the Zeeman Model
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Gomes, Raphael Fortes Infante, Rocha, Julio Cesar Siqueira, Nogueira, Wallon Anderson Tadaiesky, and Dias, Rodrigo Alves
- Subjects
Quantum Physics - Abstract
We unravel the Rodeo Algorithm to determine the eigenstates and eigenvalues spectrum for a general Hamiltonian considering arbitrary initial states. By presenting a novel methodology, we detail the original method and show how to define all properties without having prior knowledge regarding the eigenstates. To this end, we exploit Pennylane and Qiskit platforms resources to analyze scenarios where the Hamiltonians are described by the Zeeman model for one and two spins. We also introduce strategies and techniques to improve the algorithm's performance by adjusting its intrinsic parameters and reducing the fluctuations inherent to data distribution. First, we explore the dynamics of a single qubit on Xanadu simulators to set the parameters that optimize the method performance and select the best strategies to execute the algorithm. On the sequence, we extend the methodology for bipartite systems to discuss how the algorithm works when degeneracy and entanglement are taken into account. Finally, we compare the predictions with the results obtained on a real superconducting device provided by the IBM Q Experience program, establishing the conditions to increase the protocol efficiency for multi-qubit systems.
- Published
- 2024
38. Supernova Pointing Capabilities of DUNE
- Author
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DUNE Collaboration, Abud, A. Abed, Abi, B., Acciarri, R., Acero, M. A., Adames, M. R., Adamov, G., Adamowski, M., Adams, D., Adinolfi, M., Adriano, C., Aduszkiewicz, A., Aguilar, J., Aimard, B., Akbar, F., Allison, K., Monsalve, S. Alonso, Alrashed, M., Alton, A., Alvarez, R., Alves, T., Amar, H., Amedo, P., Anderson, J., Andrade, D. A., Andreopoulos, C., Andreotti, M., Andrews, M. P., Andrianala, F., Andringa, S., Anfimov, N., Ankowski, A., Antoniassi, M., Antonova, M., Antoshkin, A., Aranda-Fernandez, A., Arellano, L., Diaz, E. Arrieta, Arroyave, M. A., Asaadi, J., Ashkenazi, A., Asner, D., Asquith, L., Atkin, E., Auguste, D., Aurisano, A., Aushev, V., Autiero, D., Azfar, F., Back, A., Back, H., Back, J. J., Bagaturia, I., Bagby, L., Balashov, N., Balasubramanian, S., Baldi, P., Baldini, W., Baldonedo, J., Baller, B., Bambah, B., Banerjee, R., Barao, F., Barenboim, G., Alzás, P. Barham, Barker, G. J., Barkhouse, W., Barr, G., Monarca, J. Barranco, Barros, A., Barros, N., Barrow, D., Barrow, J. L., Basharina-Freshville, A., Bashyal, A., Basque, V., Batchelor, C., Bathe-Peters, L., Battat, J. B. R., Battisti, F., Bay, F., Bazetto, M. C. Q., Alba, J. L. L. Bazo, Beacom, J. F., Bechetoille, E., Behera, B., Belchior, E., Bell, G., Bellantoni, L., Bellettini, G., Bellini, V., Beltramello, O., Benekos, N., Montiel, C. Benitez, Benjamin, D., Neves, F. Bento, Berger, J., Berkman, S., Bernal, J., Bernardini, P., Bersani, A., Bertolucci, S., Betancourt, M., Rodríguez, A. Betancur, Bevan, A., Bezawada, Y., Bezerra, A. T., Bezerra, T. J., Bhat, A., Bhatnagar, V., Bhatt, J., Bhattacharjee, M., Bhattacharya, M., Bhuller, S., Bhuyan, B., Biagi, S., Bian, J., Biery, K., Bilki, B., Bishai, M., Bitadze, A., Blake, A., Blaszczyk, F. D., Blazey, G. C., Blucher, E., Bogenschuetz, J., Boissevain, J., Bolognesi, S., Bolton, T., Bomben, L., Bonesini, M., Bonilla-Diaz, C., Bonini, F., Booth, A., Boran, F., Bordoni, S., Merlo, R. Borges, Borkum, A., Bostan, N., Bracinik, J., Braga, D., Brahma, B., Brailsford, D., Bramati, F., Branca, A., Brandt, A., Bremer, J., Brew, C., Brice, S. J., Brio, V., Brizzolari, C., Bromberg, C., Brooke, J., Bross, A., Brunetti, G., Brunetti, M., Buchanan, N., Budd, H., Buergi, J., Burgardt, D., Butchart, S., V., G. Caceres, Cagnoli, I., Cai, T., Calabrese, R., Calcutt, J., Calin, M., Calivers, L., Calvo, E., Caminata, A., Camino, A. F., Campanelli, W., Campani, A., Benitez, A. Campos, Canci, N., Capó, J., Caracas, I., Caratelli, D., Carber, D., Carceller, J. M., Carini, G., Carlus, B., Carneiro, M. F., Carniti, P., Terrazas, I. Caro, Carranza, H., Carrara, N., Carroll, L., Carroll, T., Carter, A., Casarejos, E., Casazza, D., Forero, J. F. Castaño, Castaño, F. A., Castillo, A., Castromonte, C., Catano-Mur, E., Cattadori, C., Cavalier, F., Cavanna, F., Centro, S., Cerati, G., Cerna, C., Cervelli, A., Villanueva, A. 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M., Gwon, S., Habig, A., Hadavand, H., Haegel, L., Haenni, R., Hagaman, L., Hahn, A., Haiston, J., Hakenmüller, J., Hamernik, T., Hamilton, P., Hancock, J., Happacher, F., Harris, D. A., Hartnell, J., Hartnett, T., Harton, J., Hasegawa, T., Hasnip, C., Hatcher, R., Hayrapetyan, K., Hays, J., Hazen, E., He, M., Heavey, A., Heeger, K. M., Heise, J., Henry, S., Morquecho, M. A. Hernandez, Herner, K., Hewes, V., Higuera, A., Hilgenberg, C., Hillier, S. J., Himmel, A., Hinkle, E., Hirsch, L. R., Ho, J., Hoff, J., Holin, A., Holvey, T., Hoppe, E., Horiuchi, S., Horton-Smith, G. A., Hostert, M., Houdy, T., Howard, B., Howell, R., Hristova, I., Hronek, M. S., Huang, J., Huang, R. G., Hulcher, Z., Ibrahim, M., Iles, G., Ilic, N., Iliescu, A. M., Illingworth, R., Ingratta, G., Ioannisian, A., Irwin, B., Isenhower, L., Oliveira, M. Ismerio, Itay, R., Jackson, C. M., Jain, V., James, E., Jang, W., Jargowsky, B., Jena, D., Jentz, I., Ji, X., Jiang, C., Jiang, J., Jiang, L., Jipa, A., Joaquim, F. R., Johnson, W., Jollet, C., Jones, B., Jones, R., Fernández, D. José, Jovancevic, N., Judah, M., Jung, C. K., Junk, T., Jwa, Y., Kabirnezhad, M., Kaboth, A. C., Kadenko, I., Kakorin, I., Kalitkina, A., Kalra, D., Kandemir, M., Kaplan, D. M., Karagiorgi, G., Karaman, G., Karcher, A., Karyotakis, Y., Kasai, S., Kasetti, S. P., Kashur, L., Katsioulas, I., Kauther, A., Kazaryan, N., Ke, L., Kearns, E., Keener, P. T., Kelly, K. J., Kemp, E., Kemularia, O., Kermaidic, Y., Ketchum, W., Kettell, S. H., Khabibullin, M., Khan, N., Khvedelidze, A., Kim, D., Kim, J., King, B., Kirby, B., Kirby, M., Kish, A., Klein, J., Kleykamp, J., Klustova, A., Kobilarcik, T., Koch, L., Koehler, K., Koerner, L. W., Koh, D. H., Kolupaeva, L., Korablev, D., Kordosky, M., Kosc, T., Kose, U., Kostelecký, V. A., Kothekar, K., Kotler, I., Kovalcuk, M., Kozhukalov, V., Krah, W., Kralik, R., Kramer, M., Kreczko, L., Krennrich, F., Kreslo, I., Kroupova, T., Kubota, S., Kubu, M., Kudenko, Y., Kudryavtsev, V. 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B., Lunday, B., Luo, X., Luppi, E., Maalmi, J., MacFarlane, D., Machado, A. A., Machado, P., Macias, C. T., Macier, J. R., MacMahon, M., Maddalena, A., Madera, A., Madigan, P., Magill, S., Magueur, C., Mahn, K., Maio, A., Major, A., Majumdar, K., Man, M., Mandujano, R. C., Maneira, J., Manly, S., Mann, A., Manolopoulos, K., Plata, M. Manrique, Corchado, S. Manthey, Manyam, V. N., Marchan, M., Marchionni, A., Marciano, W., Marfatia, D., Mariani, C., Maricic, J., Marinho, F., Marino, A. D., Markiewicz, T., Marques, F. Das Chagas, Marquet, C., Marsden, D., Marshak, M., Marshall, C. M., Marshall, J., Martina, L., Martín-Albo, J., Martinez, N., Caicedo, D. A. Martinez, López, F. Martínez, Miravé, P. Martínez, Martynenko, S., Mascagna, V., Massari, C., Mastbaum, A., Matichard, F., Matsuno, S., Matteucci, G., Matthews, J., Mauger, C., Mauri, N., Mavrokoridis, K., Mawby, I., Mazza, R., Mazzacane, A., McAskill, T., McConkey, N., McFarland, K. S., McGrew, C., McNab, A., Meazza, L., Meddage, V. 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Steklain, Stepanova, A., Stewart, J., Stillwell, B., Stock, J., Stocker, F., Stokes, T., Strait, M., Strauss, T., Strigari, L., Stuart, A., Suarez, J. G., Subash, J., Surdo, A., Suter, L., Sutera, C. M., Sutton, K., Suvorov, Y., Svoboda, R., Swain, S. K., Szczerbinska, B., Szelc, A. M., Sztuc, A., Taffara, A., Talukdar, N., Tamara, J., Tanaka, H. A., Tang, S., Taniuchi, N., Casanova, A. M. Tapia, Oregui, B. Tapia, Tapper, A., Tariq, S., Tarpara, E., Tatar, E., Tayloe, R., Tedeschi, D., Teklu, A. M., Vidal, J. Tena, Tennessen, P., Tenti, M., Terao, K., Terranova, F., Testera, G., Thakore, T., Thea, A., Thiebault, A., Thomas, S., Thompson, A., Thorn, C., Timm, S. C., Tiras, E., Tishchenko, V., Todorović, N., Tomassetti, L., Tonazzo, A., Torbunov, D., Torti, M., Tortola, M., Tortorici, F., Tosi, N., Totani, D., Toups, M., Touramanis, C., Tran, D., Travaglini, R., Trevor, J., Triller, E., Trilov, S., Truchon, J., Truncali, D., Trzaska, W. H., Tsai, Y., Tsai, Y. -T., Tsamalaidze, Z., Tsang, K. V., Tsverava, N., Tu, S. Z., Tufanli, S., Tunnell, C., Turner, J., Tuzi, M., Tyler, J., Tyley, E., Tzanov, M., Uchida, M. A., González, J. Ureña, Urheim, J., Usher, T., Utaegbulam, H., Uzunyan, S., Vagins, M. R., Vahle, P., Valder, S., Valdiviesso, G. A., Valencia, E., Valentim, R., Vallari, Z., Vallazza, E., Valle, J. W. F., Van Berg, R., Van de Water, R. G., Forero, D. V., Vannozzi, A., Van Nuland-Troost, M., Varanini, F., Oliva, D. Vargas, Vasina, S., Vaughan, N., Vaziri, K., Vázquez-Ramos, A., Vega, J., Ventura, S., Verdugo, A., Vergani, S., Verzocchi, M., Vetter, K., Vicenzi, M., de Souza, H. Vieira, Vignoli, C., Vilela, C., Villa, E., Viola, S., Viren, B., Vizcaya-Hernandez, A., Vrba, T., Vuong, Q., Waldron, A. V., Wallbank, M., Walsh, J., Walton, T., Wang, H., Wang, J., Wang, L., Wang, M. H. L. S., Wang, X., Wang, Y., Warburton, K., Warner, D., Warsame, L., Wascko, M. O., Waters, D., Watson, A., Wawrowska, K., Weber, A., Weber, C. M., Weber, M., Wei, H., Weinstein, A., Wenzel, H., Westerdale, S., Wetstein, M., Whalen, K., Whilhelmi, J., White, A., Whitehead, L. H., Whittington, D., Wilking, M. J., Wilkinson, A., Wilkinson, C., Wilson, F., Wilson, R. J., Winter, P., Wisniewski, W., Wolcott, J., Wolfs, J., Wongjirad, T., Wood, A., Wood, K., Worcester, E., Worcester, M., Wospakrik, M., Wresilo, K., Wret, C., Wu, S., Wu, W., Wurm, M., Wyenberg, J., Xiao, Y., Xiotidis, I., Yaeggy, B., Yahlali, N., Yandel, E., Yang, K., Yang, T., Yankelevich, A., Yershov, N., Yonehara, K., Young, T., Yu, B., Yu, H., Yu, J., Yu, Y., Yuan, W., Zaki, R., Zalesak, J., Zambelli, L., Zamorano, B., Zani, A., Zapata, O., Zazueta, L., Zeller, G. P., Zennamo, J., Zeug, K., Zhang, C., Zhang, S., Zhao, M., Zhivun, E., Zimmerman, E. D., Zucchelli, S., Zuklin, J., Zutshi, V., and Zwaska, R.
- Subjects
High Energy Physics - Experiment ,Astrophysics - High Energy Astrophysical Phenomena ,Astrophysics - Instrumentation and Methods for Astrophysics ,Astrophysics - Solar and Stellar Astrophysics ,Nuclear Experiment ,Physics - Instrumentation and Detectors - Abstract
The determination of the direction of a stellar core collapse via its neutrino emission is crucial for the identification of the progenitor for a multimessenger follow-up. A highly effective method of reconstructing supernova directions within the Deep Underground Neutrino Experiment (DUNE) is introduced. The supernova neutrino pointing resolution is studied by simulating and reconstructing electron-neutrino charged-current absorption on $^{40}$Ar and elastic scattering of neutrinos on electrons. Procedures to reconstruct individual interactions, including a newly developed technique called ``brems flipping'', as well as the burst direction from an ensemble of interactions are described. Performance of the burst direction reconstruction is evaluated for supernovae happening at a distance of 10 kpc for a specific supernova burst flux model. The pointing resolution is found to be 3.4 degrees at 68% coverage for a perfect interaction-channel classification and a fiducial mass of 40 kton, and 6.6 degrees for a 10 kton fiducial mass respectively. Assuming a 4% rate of charged-current interactions being misidentified as elastic scattering, DUNE's burst pointing resolution is found to be 4.3 degrees (8.7 degrees) at 68% coverage., Comment: 25 pages, 16 figures
- Published
- 2024
39. Swift-BAT GUANO follow-up of gravitational-wave triggers in the third LIGO-Virgo-KAGRA observing run
- Author
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Raman, Gayathri, Ronchini, Samuele, Delaunay, James, Tohuvavohu, Aaron, Kennea, Jamie A., Parsotan, Tyler, Ambrosi, Elena, Bernardini, Maria Grazia, Campana, Sergio, Cusumano, Giancarlo, D'Ai, Antonino, D'Avanzo, Paolo, D'Elia, Valerio, De Pasquale, Massimiliano, Dichiara, Simone, Evans, Phil, Hartmann, Dieter, Kuin, Paul, Melandri, Andrea, O'Brien, Paul, Osborne, Julian P., Page, Kim, Palmer, David M., Sbarufatti, Boris, Tagliaferri, Gianpiero, Troja, Eleonora, Abac, A. G., Abbott, R., Abe, H., Abouelfettouh, I., Acernese, F., Ackley, K., Adamcewicz, C., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Al-Shammari, S., Altin, P. A., Alvarez-Lopez, S., Amato, A., Amez-Droz, L., Amorosi, A., Amra, C., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Anglin, J., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Avallone, G., Babak, S., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bagui, E., Bai, Y., Baier, J. G., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. 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R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Craig, K., Creed, R., Creighton, J. D. E., Creighton, T. D., Cremonese, P., Criswell, A. W., Crockett-Gray, J. C. G., Croquette, M., Crouch, R., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Davari, N., Dave, I., Davenport, A., Davier, M., Davies, T. F., Davis, D., Davis, L., Davis, M. C., Daw, E. J., Dax, M., De Bolle, J., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., DePergola, N., De Pietri, R., De Rosa, R., De Rossi, C., De Simone, R., Dhani, A., Dhurandhar, S., Diab, R., Díaz, M. C., Di Cesare, M., Dideron, G., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, F., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Donahue, L., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Drori, Y., Ducoin, J. -G., Dunn, L., Dupletsa, U., D'Urso, D., Duval, H., Duverne, P. -A., Dwyer, S. E., Eassa, C., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Einsle, H., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Emma, M., Engelby, E., Engl, A. J., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fairhurst, S., Fan, P. C., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Fenyvesi, E., Ferguson, D. L., Ferrante, I., Ferreira, T. A., Fidecaro, F., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Foley, E. M., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franceschetti, K., Franchini, N., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fuentes-Garcia, M., Fujii, S., Fukunaga, I., Fulda, P., Fyffe, M., Gabella, W. 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Granda, Gras, S., Grassia, P., Gray, C., Gray, R., Greco, G., Green, A. C., Green, S. M., Green, S. R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimaud, C., Grote, H., Gruson, A. S., Guerra, D., Guetta, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H., Guo, W., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, Ish, Gupta, N. C., Gupta, P., Gupta, S. K., Gupta, T., Gupte, N., Gurav, R., Gurs, J., Gutierrez, N., Guzman, F., Haba, D., Haberland, M., Haegel, L., Hain, G., Haino, S., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanselman, A. G., Hansen, H., Hanson, J., Harada, R., Harder, T., Haris, K., Harmark, T., Harms, J., Harry, G. M., Harry, I. W., Haskell, B., Haster, C. -J., Hathaway, J. S., Haughian, K., Hayakawa, H., Hayama, K., Healy, J., Heffernan, A., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J. -S., Hennig, M., Henshaw, C., Hernandez, A., Hertog, T., Heurs, M., Hewitt, A. L., Higginbotham, S., Hild, S., Hill, P., Hill, S., Himemoto, Y., Hines, A. S., Hirata, N., Hirose, C., Ho, J., Hoang, S., Hochheim, S., Hofman, D., Holland, N. A., Holley-Bockelmann, K., Hollows, I. J., Holmes, Z. J., Holz, D. E., Hong, C., Hornung, J., Hoshino, S., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hoyland, D., Hrishikesh, C. A., Hsieh, H. -F., Hsiung, C., Hsu, H. C., Hsu, S. -C., Hsu, W. -F., Hu, P., Hu, Q., Huang, H. Y., Huang, Y. -J., Huang, Y., Huang, Y. T., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Hur, R., Husa, S., Huxford, R., Huynh-Dinh, T., Iakovlev, A., Iandolo, G. A., Iess, A., Inayoshi, K., Inoue, Y., Iorio, G., Irwin, J., Isi, M., Ismail, M. A., Itoh, Y., Iwaya, M., Iyer, B. 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A., Khursheed, M., Kiendrebeogo, W., Kijbunchoo, N., Kim, C., Kim, J. C., Kim, K., Kim, M. H., Kim, S., Kim, W. S., Kim, Y. -M., Kimball, C., Kimura, N., Kinley-Hanlon, M., Kinnear, M., Kissel, J. S., Kiyota, T., Klimenko, S., Klinger, T., Knee, A. M., Knust, N., Koch, P., Koehlenbeck, S. M., Koekoek, G., Kohri, K., Kokeyama, K., Koley, S., Kolitsidou, P., Kolstein, M., Komori, K., Kong, A. K. H., Kontos, A., Korobko, M., Kossak, R. V., Kou, X., Koushik, A., Kouvatsos, N., Kovalam, M., Koyama, N., Kozak, D. B., Kranzhoff, S. L., Kringel, V., Krishnendu, N. V., Królak, A., Kuehn, G., Kuijer, P., Kulkarni, S., Ramamohan, A. Kulur, Kumar, A., Kumar, Praveen, Kumar, Prayush, Kumar, Rahul, Kumar, Rakesh, Kume, J., Kuns, K., Kuroyanagi, S., Kuwahara, S., Kwak, K., Kwan, K., Lacaille, G., Lagabbe, P., Laghi, D., Lai, S., Laity, A. H., Lakkis, M. H., Lalande, E., Lalleman, M., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., La Rana, A., La Rosa, I., Lartaux-Vollard, A., Lasky, P. 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C., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhong, S., Zhou, R., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
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Astrophysics - High Energy Astrophysical Phenomena ,General Relativity and Quantum Cosmology - Abstract
We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables
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- 2024
40. A JWST/MIRI analysis of the ice distribution and PAH emission in the protoplanetary disk HH 48 NE
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Sturm, J. A., McClure, M. K., Harsono, D., Bergner, J. B., Dartois, E., Boogert, A. C. A., Cordiner, M. A., Drozdovskaya, M. N., Ioppolo, S., Law, C. J., Lis, D. C., McGuire, B. A., Melnick, G. J., Noble, J. A., Öberg, K. I., Palumbo, M. E., Pendleton, Y. J., Perotti, G., Rocha, W. R. M., Urso, R. G., and van Dishoeck, E. F.
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Astrophysics - Earth and Planetary Astrophysics - Abstract
Ice-coated dust grains provide the main reservoir of volatiles that play an important role in planet formation processes and may become incorporated into planetary atmospheres. However, due to observational challenges, the ice abundance distribution in protoplanetary disks is not well constrained. We present JWST/MIRI observations of the edge-on disk HH 48 NE carried out as part of the IRS program Ice Age. We detect CO$_2$, NH$_3$, H$_2$O and tentatively CH$_4$ and NH$_4^+$. Radiative transfer models suggest that ice absorption features are produced predominantly in the 50-100 au region of the disk. The CO$_2$ feature at 15 micron probes a region closer to the midplane (z/r = 0.1-0.15) than the corresponding feature at 4.3 micron (z/r = 0.2-0.6), but all observations trace regions significantly above the midplane reservoirs where we expect the bulk of the ice mass to be located. Ices must reach a high scale height (z/r ~ 0.6; corresponding to modeled dust extinction Av ~ 0.1), in order to be consistent with the observed vertical distribution of the peak ice optical depths. The weakness of the CO$_2$ feature at 15 micron relative to the 4.3 micron feature and the red emission wing of the 4.3 micron CO$_2$ feature are both consistent with ices being located at high elevation in the disk. The retrieved NH$_3$ abundance and the upper limit on the CH$_3$OH abundance relative to H$_2$O are significantly lower than those in the interstellar medium (ISM), but consistent with cometary observations. Full wavelength coverage is required to properly study the abundance distribution of ices in disks. To explain the presence of ices at high disk altitudes, we propose two possible scenarios: a disk wind that entrains sufficient amounts of dust, thus blocking part of the stellar UV radiation, or vertical mixing that cycles enough ices into the upper disk layers to balance ice photodesorption., Comment: 16 pages, 11 figures, accepted in A&A
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- 2024
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41. DarkSide-20k sensitivity to light dark matter particles
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Collaboration, DarkSide-20k, Acerbi, F., Adhikari, P., Agnes, P., Ahmad, I., Albergo, S., Albuquerque, I. F. M., Alexander, T., Alton, A. K., Amaudruz, P., Angiolilli, M., Aprile, E., Ardito, R., Corona, M. Atzori, Auty, D. J., Ave, M., Avetisov, I. C., Azzolini, O., Back, H. O., Balmforth, Z., Olmedo, A. Barrado, Barrillon, P., Batignani, G., Bhowmick, P., Blua, S., Bocci, V., Bonivento, W., Bottino, B., Boulay, M. G., Buchowicz, A., Bussino, S., Busto, J., Cadeddu, M., Cadoni, M., Calabrese, R., Camillo, V., Caminata, A., Canci, N., Capra, A., Caravati, M., Cárdenas-Montes, M., Cargioli, N., Carlini, M., Castellani, A., Castello, P., Cavalcante, P., Cebrian, S., Ruiz, J. M. Cela, Chashin, S., Chepurnov, A., Cifarelli, L., Cintas, D., Citterio, M., Cleveland, B., Coadou, Y., Cocco, V., Colaiuda, D., Vilda, E. Conde, Consiglio, L., Costa, B. S., Czubak, M., D'Aniello, M., D'Auria, S., Rolo, M. D. Da Rocha, Darbo, G., Davini, S., De Cecco, S., De Guido, G., Dellacasa, G., Derbin, A. V., Devoto, A., Di Capua, F., Di Ludovico, A., Di Noto, L., Di Stefano, P., Dias, L. K., Mairena, D. Díaz, Ding, X., Dionisi, C., Dolganov, G., Dordei, F., Dronik, V., Elersich, A., Ellingwood, E., Erjavec, T., Diaz, M. Fernandez, Ficorella, A., Fiorillo, G., Franchini, P., Franco, D., Gatti, H. Frandini, Frolov, E., Gabriele, F., Gahan, D., Galbiati, C., Galiński, G., Gallina, G., Gallus, G., Garbini, M., Abia, P. Garcia, Gawdzik, A., Gendotti, A., Ghisi, A., Giovanetti, G. K., Casanueva, V. Goicoechea, Gola, A., Grandi, L., Grauso, G., di Cortona, G. Grilli, Grobov, A., Gromov, M., Guerzoni, M., Gulino, M., Guo, C., Hackett, B. R., Hallin, A., Hamer, A., Haranczyk, M., Harrop, B., Hessel, T., Hill, S., Horikawa, S., Hu, J., Hubaut, F., Hucker, J., Hugues, T., Hungerford, E. V., Ianni, A., Ippolito, V., Jamil, A., Jillings, C., Jois, S., Kachru, P., Keloth, R., Kemmerich, N., Kemp, A., Kendziora, C. L., Kimura, M., Kondo, K., Korga, G., Kotsiopoulou, L., Koulosousas, S., Kubankin, A., Kunzé, P., Kuss, M., Kuźniak, M., Kuzwa, M., La Commara, M., Lai, M., Guirriec, E. Le, Leason, E., Leoni, A., Lidey, L., Lissia, M., Luzzi, L., Lychagina, O., Macfadyen, O., Machulin, I. N., Manecki, S., Manthos, I., Mapelli, L., Marasciulli, A., Mari, S. M., Mariani, C., Maricic, J., Martinez, M., Martoff, C. J., Matteucci, G., Mavrokoridis, K., McDonald, A. B., Mclaughlin, J., Merzi, S., Messina, A., Milincic, R., Minutoli, S., Mitra, A., Moioli, S., Monroe, J., Moretti, E., Morrocchi, M., Mroz, T., Muratova, V. N., Murphy, M., Murra, M., Muscas, C., Musico, P., Nania, R., Nessi, M., Nieradka, G., Nikolopoulos, K., Nikoloudaki, E., Nowak, J., Olchanski, K., Oleinik, A., Oleynikov, V., Organtini, P., de Solórzano, A. Ortiz, Pallavicini, M., Pandola, L., Pantic, E., Paoloni, E., Papi, D., Pastuszak, G., Paternoster, G., Peck, A., Pegoraro, P. A., Pelczar, K., Pellegrini, L. A., Perez, R., Perotti, F., Pesudo, V., Piacentini, S. I., Pino, N., Plante, G., Pocar, A., Poehlmann, M., Pordes, S., Pralavorio, P., Price, D., Puglia, S., Bazetto, M. Queiroga, Ragusa, F., Ramachers, Y., Ramirez, A., Ravinthiran, S., Razeti, M., Renshaw, A. L., Rescigno, M., Retiere, F., Rignanese, L. P., Rivetti, A., Roberts, A., Roberts, C., Rogers, G., Romero, L., Rossi, M., Rubbia, A., Rudik, D., Sabia, M., Salomone, P., Samoylov, O., Sandford, E., Sanfilippo, S., Santone, D., Santorelli, R., Santos, E. M., Savarese, C., Scapparone, E., Schillaci, G., Schuckman II, F. G., Scioli, G., Semenov, D. A., Shalamova, V., Sheshukov, A., Simeone, M., Skensved, P., Skorokhvatov, M. D., Smirnov, O., Smirnova, T., Smith, B., Sotnikov, A., Spadoni, F., Spangenberg, M., Stefanizzi, R., Steri, A., Stornelli, V., Stracka, S., Sulis, S., Sung, A., Sunny, C., Suvorov, Y., Szelc, A. M., Taborda, O., Tartaglia, R., Taylor, A., Taylor, J., Tedesco, S., Testera, G., Thieme, K., Thompson, A., Tonazzo, A., Torres-Lara, S., Tricomi, A., Unzhakov, E. V., Vallivilayil, T. J., Van Uffelen, M., Velazquez-Fernandez, L., Viant, T., Viel, S., Vishneva, A., Vogelaar, R. B., Vossebeld, J., Vyas, B., Walczak, M. B., Wang, Y., Wang, H., Westerdale, S., Williams, L., Wojaczyński, R., Wojcik, M., Wojcik, M. M., Wright, T., Xie, Y., Yang, C., Yin, J., Zabihi, A., Zakhary, P., Zani, A., Zhang, Y., Zhu, T., Zichichi, A., Zuzel, G., and Zykova, M. P.
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High Energy Physics - Experiment ,Astrophysics - Cosmology and Nongalactic Astrophysics - Abstract
The dual-phase liquid argon time projection chamber is presently one of the leading technologies to search for dark matter particles with masses below 10 GeV/c$^2$. This was demonstrated by the DarkSide-50 experiment with approximately 50 kg of low-radioactivity liquid argon as target material. The next generation experiment DarkSide-20k, currently under construction, will use 1,000 times more argon and is expected to start operation in 2027. Based on the DarkSide-50 experience, here we assess the DarkSide-20k sensitivity to models predicting light dark matter particles, including Weakly Interacting Massive Particles (WIMPs) and sub-GeV/c$^2$ particles interacting with electrons in argon atoms. With one year of data, a sensitivity improvement to dark matter interaction cross-sections by at least one order of magnitude with respect to DarkSide-50 is expected for all these models. A sensitivity to WIMP--nucleon interaction cross-sections below $1\times10^{-42}$ cm$^2$ is achievable for WIMP masses above 800 MeV/c$^2$. With 10 years exposure, the neutrino fog can be reached for WIMP masses around 5 GeV/c$^2$., Comment: submitted to Nature Communications
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- 2024
42. The S-PLUS Ultra-Short Survey: first data release
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Perottoni, Hélio D., Placco, Vinicius M., Almeida-Fernandes, Felipe, Herpich, Fábio R., Rossi, Silvia, Beers, Timothy C., Smiljanic, Rodolfo, Amarante, João A. S., Limberg, Guilherme, Werle, Ariel, Rocha-Pinto, Helio J., Silva, Leandro Beraldo e, Daflon, Simone, Alvarez-Candal, Alvaro, Schwarz, Gustavo B Oliveira, Schoenell, William, Ribeiro, Tiago, and Kanaan, Antonio
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Astrophysics - Solar and Stellar Astrophysics ,Astrophysics - Astrophysics of Galaxies ,Astrophysics - Instrumentation and Methods for Astrophysics - Abstract
This paper presents the first public data release of the S-PLUS Ultra-Short Survey (USS), a photometric survey with short exposure times, covering approximately 9300 deg$^{2}$ of the Southern sky. The USS utilizes the Javalambre 12-band magnitude system, including narrow and medium-band and broad-band filters targeting prominent stellar spectral features. The primary objective of the USS is to identify bright, extremely metal-poor (EMP; [Fe/H] $\leq -3$) and ultra metal-poor (UMP; [Fe/H] $\leq -4$) stars for further analysis using medium- and high-resolution spectroscopy.}{This paper provides an overview of the survey observations, calibration method, data quality, and data products. Additionally, it presents the selection of EMP and UMP candidates.}{The data from the USS were reduced and calibrated using the same methods as presented in the S-PLUS DR2. An additional step was introduced, accounting for the offset between the observed magnitudes off the USS and the predicted magnitudes from the very low-resolution Gaia XP spectra.}{This first release contains data for 163 observed fields totaling $\sim$324 deg$^{2}$ along the Celestial Equator. The magnitudes obtained from the USS are well-calibrated, showing a difference of $\sim 15$ mmag compared to the predicted magnitudes by the GaiaXPy toolkit. By combining colors and magnitudes, 140 candidates for EMP or UMP have been identified for follow-up studies.}{The S-PLUS USS DR1 is an important milestone in the search for bright metal-poor stars, with magnitudes in the range 10 $ < r \leq 14$. The USS is an ongoing survey; in the near future, it will provide many more bright metal-poor candidate stars for spectroscopic follow-up., Comment: Accepted for publication in A&A. 17 pages 6 figures. Long table at the end of the paper
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- 2024
43. Quantum dust core of black holes with central charge
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Casadio, R., da Rocha, R., Giusti, A., and Meert, P.
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General Relativity and Quantum Cosmology ,High Energy Physics - Theory - Abstract
We consider a dust ball with an electrically charged central core and study its quantum spectrum by quantising the geodesic equation for individual dust particles in the corresponding Reissner-Nordstr\"om spacetime. As in the neutral case investigated previously, we find a ground state of the dust ball with the size of a fraction of the outer horizon. Moreover, we determine a self-consistent configuration of layers in the ground state corresponding to an effective mass function that increases linearly with the areal radius and has no inner horizon., Comment: 13 pages, 6 figures
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- 2024
44. Towards Asimov's Psychohistory: Harnessing Topological Data Analysis, Artificial Intelligence and Social Media data to Forecast Societal Trends
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Rocha, Isabela
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Computer Science - Computers and Society ,Computer Science - Artificial Intelligence ,Computer Science - Social and Information Networks - Abstract
In the age of big data and advanced computational methods, the prediction of large-scale social behaviors, reminiscent of Isaac Asimov's fictional science of Psychohistory, is becoming increasingly feasible. This paper consists of a theoretical exploration of the integration of computational power and mathematical frameworks, particularly through Topological Data Analysis (TDA) (Carlsson, Vejdemo-Johansson, 2022) and Artificial Intelligence (AI), to forecast societal trends through social media data analysis. By examining social media as a reflective surface of collective human behavior through the systematic behaviorist approach (Glenn, et al., 2016), I argue that these tools provide unprecedented clarity into the dynamics of large communities. This study dialogues with Asimov's work, drawing parallels between his visionary concepts and contemporary methodologies, illustrating how modern computational techniques can uncover patterns and predict shifts in social behavior, contributing to the emerging field of digital sociology -- or even, Psychohistory itself., Comment: 21 pages, 2 figures
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- 2024
45. myAURA: Personalized health library for epilepsy management via knowledge graph sparsification and visualization
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Correia, Rion Brattig, Rozum, Jordan C., Cross, Leonard, Felag, Jack, Gallant, Michael, Guo, Ziqi, Herr II, Bruce W., Min, Aehong, Rocha, Deborah Stungis, Wang, Xuan, Börner, Katy, Miller, Wendy, and Rocha, Luis M.
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Computer Science - Information Retrieval ,Computer Science - Digital Libraries - Abstract
Objective: We report the development of the patient-centered myAURA application and suite of methods designed to aid epilepsy patients, caregivers, and researchers in making decisions about care and self-management. Materials and Methods: myAURA rests on the federation of an unprecedented collection of heterogeneous data resources relevant to epilepsy, such as biomedical databases, social media, and electronic health records. A generalizable, open-source methodology was developed to compute a multi-layer knowledge graph linking all this heterogeneous data via the terms of a human-centered biomedical dictionary. Results: The power of the approach is first exemplified in the study of the drug-drug interaction phenomenon. Furthermore, we employ a novel network sparsification methodology using the metric backbone of weighted graphs, which reveals the most important edges for inference, recommendation, and visualization, such as pharmacology factors patients discuss on social media. The network sparsification approach also allows us to extract focused digital cohorts from social media whose discourse is more relevant to epilepsy or other biomedical problems. Finally, we present our patient-centered design and pilot-testing of myAURA, including its user interface, based on focus groups and other stakeholder input. Discussion: The ability to search and explore myAURA's heterogeneous data sources via a sparsified multi-layer knowledge graph, as well as the combination of those layers in a single map, are useful features for integrating relevant information for epilepsy. Conclusion: Our stakeholder-driven, scalable approach to integrate traditional and non-traditional data sources, enables biomedical discovery and data-powered patient self-management in epilepsy, and is generalizable to other chronic conditions.
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- 2024
46. Cortical Tracking of Visual Rhythmic Speech by 5- and 8-Month-Old Infants: Individual Differences in Phase Angle Relate to Language Outcomes up to 2 Years
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Áine Ní Choisdealbha, Adam Attaheri, Sinead Rocha, Natasha Mead, Helen Olawole-Scott, Maria Alfaro e Oliveira, Carmel Brough, Perrine Brusini, Samuel Gibbon, Panagiotis Boutris, Christina Grey, Isabel Williams, Sheila Flanagan, and Usha Goswami
- Abstract
It is known that the rhythms of speech are visible on the face, accurately mirroring changes in the vocal tract. These low-frequency visual temporal movements are tightly correlated with speech output, and both visual speech (e.g., mouth motion) and the acoustic speech amplitude envelope entrain neural oscillations. Low-frequency visual temporal information ('visual prosody') is known from behavioural studies to be perceived by infants, but oscillatory studies are currently lacking. Here we measure cortical tracking of low-frequency visual temporal information by 5- and 8-month-old infants using a rhythmic speech paradigm (repetition of the syllable 'ta' at 2 Hz). Eye-tracking data were collected simultaneously with EEG, enabling computation of cortical tracking and phase angle during visual-only speech presentation. Significantly higher power at the stimulus frequency indicated that cortical tracking occurred across both ages. Further, individual differences in preferred phase to visual speech related to subsequent measures of language acquisition. The difference in phase between visual-only speech and the same speech presented as auditory-visual at 6- and 9-months was also examined. These neural data suggest that individual differences in early language acquisition may be related to the phase of entrainment to visual rhythmic input in infancy.
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- 2024
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47. Passive Data Collection on Reddit: A Practical Approach
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Tiago Rocha-Silva, Conceição Nogueira, and Liliana Rodrigues
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Since its onset, scholars have characterized social media as a valuable source for data collection since it presents several benefits (e.g. exploring research questions with hard-to-reach populations). Nonetheless, methods of online data collection are riddled with ethical and methodological challenges that researchers must consider if they want to adopt good practices when collecting and analyzing online data. Drawing from our primary research project, where we collected passive online data on Reddit, we explore and detail the steps that researchers must consider before collecting online data: (1) planning online data collection; (2) ethical considerations; and (3) data collection. We also discuss two atypical questions that researchers should also consider: (1) how to handle deleted user-generated content; and (2) how to quote user-generated content. Moving on from the dichotomous discussion between what is public and private data, we present recommendations for good practices when collecting and analyzing qualitative online data.
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- 2024
- Full Text
- View/download PDF
48. Genomic data provide insights into the classification of extant termites.
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Hellemans, Simon, Rocha, Mauricio, Wang, Menglin, Romero Arias, Johanna, Aanen, Duur, Bagnères, Anne-Geneviève, Buček, Aleš, Carrijo, Tiago, Chouvenc, Thomas, Cuezzo, Carolina, Constantini, Joice, Constantino, Reginaldo, Dedeine, Franck, Deligne, Jean, Eggleton, Paul, Evans, Theodore, Hanus, Robert, Harrison, Mark, Harry, Myriam, Josens, Guy, Jouault, Corentin, Kalleshwaraswamy, Chicknayakanahalli, Kaymak, Esra, Korb, Judith, Lee, Chow-Yang, Legendre, Frédéric, Li, Hou-Feng, Lo, Nathan, Lu, Tomer, Matsuura, Kenji, Maekawa, Kiyoto, McMahon, Dino, Mizumoto, Nobuaki, Oliveira, Danilo, Poulsen, Michael, Sillam-Dussès, David, Su, Nan-Yao, Tokuda, Gaku, Vargo, Edward, Ware, Jessica, Šobotník, Jan, Scheffrahn, Rudolf, Cancello, Eliana, Roisin, Yves, Engel, Michael, and Bourguignon, Thomas
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Isoptera ,Animals ,Phylogeny ,Genomics ,Genome ,Insect - Abstract
The higher classification of termites requires substantial revision as the Neoisoptera, the most diverse termite lineage, comprise many paraphyletic and polyphyletic higher taxa. Here, we produce an updated termite classification using genomic-scale analyses. We reconstruct phylogenies under diverse substitution models with ultraconserved elements analyzed as concatenated matrices or within the multi-species coalescence framework. Our classification is further supported by analyses controlling for rogue loci and taxa, and topological tests. We show that the Neoisoptera are composed of seven family-level monophyletic lineages, including the Heterotermitidae Froggatt, Psammotermitidae Holmgren, and Termitogetonidae Holmgren, raised from subfamilial rank. The species-rich Termitidae are composed of 18 subfamily-level monophyletic lineages, including the new subfamilies Crepititermitinae, Cylindrotermitinae, Forficulitermitinae, Neocapritermitinae, Protohamitermitinae, and Promirotermitinae; and the revived Amitermitinae Kemner, Microcerotermitinae Holmgren, and Mirocapritermitinae Kemner. Building an updated taxonomic classification on the foundation of unambiguously supported monophyletic lineages makes it highly resilient to potential destabilization caused by the future availability of novel phylogenetic markers and methods. The taxonomic stability is further guaranteed by the modularity of the new termite classification, designed to accommodate as-yet undescribed species with uncertain affinities to the herein delimited monophyletic lineages in the form of new families or subfamilies.
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- 2024
49. Eteplirsen Treatment for Duchenne Muscular Dystrophy: A Qualitative Patient Experience Study.
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Iff, Joel, Carmichael, Chloe, McKee, Stephanie, Sehinovych, Ihor, McNeill, Carolyn, Tesi-Rocha, Carolina, Henricson, Erik, Muntoni, Francesco, and Kitchen, Helen
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Duchenne muscular dystrophy ,Eteplirsen ,Health-related quality of life ,Qualitative ,Humans ,Muscular Dystrophy ,Duchenne ,Child ,Male ,Adolescent ,Quality of Life ,Activities of Daily Living ,Caregivers ,Qualitative Research ,Morpholinos - Abstract
INTRODUCTION: Duchenne muscular dystrophy (DMD) is characterized by rapid functional decline. Current available treatment options aim to delay disease progression or stabilize physical function. To aid in healthcare providers understanding of the symptoms of disease that impact patients experience, this study explored childrens physical functioning, activities of daily living (ADLs), and health-related quality of life (HRQoL) after receiving eteplirsen, a weekly infusion indicated for individuals with DMD with exon 51 skip-amenable mutations. METHODS: Fifteen caregivers of male individuals with DMD participated in a 60-min, semi-structured interview. Open-ended questioning explored changes in the childrens condition or maintenance in abilities since eteplirsen initiation. RESULTS: Children with DMD (age 7-15 years [mean 10.9]; steroid treatment at interview, n = 8; time since eteplirsen initiation 3-24 months [mean 14.9]) were described by caregivers as ambulatory (n = 9) and non-ambulatory (n = 6). Caregivers of ambulatory children reported improvements or maintenance of walking ability (n = 7/9), running (n = 6/9), and using stairs (n = 4/9). Continued decline in using stairs was reported by two caregivers. In upper-limb functioning, improvements or maintenances in fine-motor movements were reported by nearly half of all caregivers (n = 7/15), with one caregiver noting a continued decline. Subsequent improvements or maintenances in ADLs were described. Improvements or maintenances in fatigue (n = 9/15), muscle weakness (n = 7/15), and pain (n = 6/15) were reported, although some caregivers described a continued decline (n = 3/15 fatigue, n = 1/15 muscle weakness, n = 2/15 pain). Importantly, most caregivers who reported maintenances in ability perceived this as a positive outcome (n = 6/9). CONCLUSION: This exploratory study indicated that most caregivers perceived improvements or maintenances in aspects of their childs physical functioning, ADLs, and HRQoL since eteplirsen initiation, which they perceived to be a positive outcome.
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- 2024
50. Climate justice, forests, and Indigenous Peoples: toward an alternative to REDD + for the Amazon
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Osborne, Tracey, Cifuentes, Sylvia, Dev, Laura, Howard, Seánna, Marchi, Elisa, Withey, Lauren, and Santos Rocha da Silva, Marcelo
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Meteorology & Atmospheric Sciences - Published
- 2024
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