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52,708 results on '"Seto, A."'

1. Deep Learning Predicts Mammographic Breast Density in Clinical Breast Ultrasound Images

Author: Bunnell, Arianna, Valdez, Dustin, Wolfgruber, Thomas K., Quon, Brandon, Hung, Kailee, Hernandez, Brenda Y., Seto, Todd B., Killeen, Jeffrey, Miyoshi, Marshall, Sadowski, Peter, and Shepherd, John A.
Subjects: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract: Background: Breast density, as derived from mammographic images and defined by the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS), is one of the strongest risk factors for breast cancer. Breast ultrasound (BUS) is an alternative breast cancer screening modality, particularly useful for early detection in low-resource, rural contexts. The purpose of this study was to explore an artificial intelligence (AI) model to predict BI-RADS mammographic breast density category from clinical, handheld BUS imaging. Methods: All data are sourced from the Hawaii and Pacific Islands Mammography Registry. We compared deep learning methods from BUS imaging, as well as machine learning models from image statistics alone. The use of AI-derived BUS density as a risk factor for breast cancer was then compared to clinical BI-RADS breast density while adjusting for age. The BUS data were split by individual into 70/20/10% groups for training, validation, and testing. Results: 405,120 clinical BUS images from 14.066 women were selected for inclusion in this study, resulting in 9.846 women for training (302,574 images), 2,813 for validation (11,223 images), and 1,406 for testing (4,042 images). On the held-out testing set, the strongest AI model achieves AUROC 0.854 predicting BI-RADS mammographic breast density from BUS imaging and outperforms all shallow machine learning methods based on image statistics. In cancer risk prediction, age-adjusted AI BUS breast density predicted 5-year breast cancer risk with 0.633 AUROC, as compared to 0.637 AUROC from age-adjusted clinical breast density. Conclusions: BI-RADS mammographic breast density can be estimated from BUS imaging with high accuracy using a deep learning model. Furthermore, we demonstrate that AI-derived BUS breast density is predictive of 5-year breast cancer risk in our population.
Published: 2024

2. Aggregate-and-Adapt Natural Language Prompts for Downstream Generalization of CLIP

Author: Huang, Chen, Seto, Skyler, Abnar, Samira, Grangier, David, Jaitly, Navdeep, and Susskind, Josh
Subjects: Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract: Large pretrained vision-language models like CLIP have shown promising generalization capability, but may struggle in specialized domains (e.g., satellite imagery) or fine-grained classification (e.g., car models) where the visual concepts are unseen or under-represented during pretraining. Prompt learning offers a parameter-efficient finetuning framework that can adapt CLIP to downstream tasks even when limited annotation data are available. In this paper, we improve prompt learning by distilling the textual knowledge from natural language prompts (either human- or LLM-generated) to provide rich priors for those under-represented concepts. We first obtain a prompt ``summary'' aligned to each input image via a learned prompt aggregator. Then we jointly train a prompt generator, optimized to produce a prompt embedding that stays close to the aggregated summary while minimizing task loss at the same time. We dub such prompt embedding as Aggregate-and-Adapted Prompt Embedding (AAPE). AAPE is shown to be able to generalize to different downstream data distributions and tasks, including vision-language understanding tasks (e.g., few-shot classification, VQA) and generation tasks (image captioning) where AAPE achieves competitive performance. We also show AAPE is particularly helpful to handle non-canonical and OOD examples. Furthermore, AAPE learning eliminates LLM-based inference cost as required by baselines, and scales better with data and LLM model size., Comment: NeurIPS 2024
Published: 2024

3. Redshift-Space Distortion constraints on neutrino mass and models to alleviate the Hubble tension

Author: Toda, Yo and Seto, Osamu
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, High Energy Physics - Phenomenology
Abstract: We discuss the neutrino mass and Hubble tension solutions and examine their effects on the Redshift-Space Distortion (RSD) observations. An analysis with RSD data indicates smaller amplitude of perturbation. Including RSD data results in a slightly weaker upper limit on the neutrino mass than that derived for data without RSD, which is common in other extended models too. We have evaluated the impacts of RSD observations on some extended models, including the varying electron mass model, a time-dependent dark energy model with two parameter equations of state (EOS), and a model where the number of neutrino species is free. When we estimate the cosmological parameters for data including RSD, we found that the EOS parameter for dark energy is larger than that of the cosmological constant, and the effective number of neutrino species is smaller than the standard value, which infers a smaller present Hubble parameter $H_0$. From the viewpoint of cosmological tensions, the varying electron mass model with non-zero neutrino mass option looks promising to relax the Hubble tension and the $S_8$ tension simultaneously., Comment: 22 pages, 6 figures
Published: 2024

4. ProcBench: Benchmark for Multi-Step Reasoning and Following Procedure

Author: Fujisawa, Ippei, Nobe, Sensho, Seto, Hiroki, Onda, Rina, Uchida, Yoshiaki, Ikoma, Hiroki, Chien, Pei-Chun, and Kanai, Ryota
Subjects: Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: Reasoning is central to a wide range of intellectual activities, and while the capabilities of large language models (LLMs) continue to advance, their performance in reasoning tasks remains limited. The processes and mechanisms underlying reasoning are not yet fully understood, but key elements include path exploration, selection of relevant knowledge, and multi-step inference. Problems are solved through the synthesis of these components. In this paper, we propose a benchmark that focuses on a specific aspect of reasoning ability: the direct evaluation of multi-step inference. To this end, we design a special reasoning task where multi-step inference is specifically focused by largely eliminating path exploration and implicit knowledge utilization. Our dataset comprises pairs of explicit instructions and corresponding questions, where the procedures necessary for solving the questions are entirely detailed within the instructions. This setup allows models to solve problems solely by following the provided directives. By constructing problems that require varying numbers of steps to solve and evaluating responses at each step, we enable a thorough assessment of state-of-the-art LLMs' ability to follow instructions. To ensure the robustness of our evaluation, we include multiple distinct tasks. Furthermore, by comparing accuracy across tasks, utilizing step-aware metrics, and applying separately defined measures of complexity, we conduct experiments that offer insights into the capabilities and limitations of LLMs in reasoning tasks. Our findings have significant implications for the development of LLMs and highlight areas for future research in advancing their reasoning abilities. Our dataset is available at \url{https://huggingface.co/datasets/ifujisawa/procbench} and code at \url{https://github.com/ifujisawa/proc-bench}.
Published: 2024

5. Task-Adaptive Pretrained Language Models via Clustered-Importance Sampling

Author: Grangier, David, Fan, Simin, Seto, Skyler, and Ablin, Pierre
Subjects: Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract: Specialist language models (LMs) focus on a specific task or domain on which they often outperform generalist LMs of the same size. However, the specialist data needed to pretrain these models is only available in limited amount for most tasks. In this work, we build specialist models from large generalist training sets instead. We adjust the training distribution of the generalist data with guidance from the limited domain-specific data. We explore several approaches, with clustered importance sampling standing out. This method clusters the generalist dataset and samples from these clusters based on their frequencies in the smaller specialist dataset. It is scalable, suitable for pretraining and continued pretraining, it works well in multi-task settings. Our findings demonstrate improvements across different domains in terms of language modeling perplexity and accuracy on multiple-choice question tasks. We also present ablation studies that examine the impact of dataset sizes, clustering configurations, and model sizes.
Published: 2024

6. Online and Offline Algorithms for Counting Distinct Closed Factors via Sliding Suffix Trees

Author: Mieno, Takuya, Takahashi, Shun, Seto, Kazuhisa, and Horiyama, Takashi
Subjects: Computer Science - Data Structures and Algorithms
Abstract: A string is said to be closed if its length is one, or if it has a non-empty factor that occurs both as a prefix and as a suffix of the string, but does not occur elsewhere. The notion of closed words was introduced by [Fici, WORDS 2011]. Recently, the maximum number of distinct closed factors occurring in a string was investigated by [Parshina and Puzynina, Theor. Comput. Sci. 2024], and an asymptotic tight bound was proved. In this paper, we propose two algorithms to count the distinct closed factors in a string T of length n over an alphabet of size \sigma. The first algorithm runs in O(n log \sigma) time using O(n) space for string T given in an online manner. The second algorithm runs in O(n) time using O(n) space for string T given in an offline manner. Both algorithms utilize suffix trees for sliding windows.
Published: 2024

7. Discovery potential of a long-lived partner of inelastic dark matter at MATHUSLA in $U(1)_{X_3}$ extension of the standard model

Author: Okada, Nobuchika and Seto, Osamu
Subjects: High Energy Physics - Phenomenology, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We investigate the discovery potential at the MATHUSLA experiment of a long-lived particle (LLP), which is the heavier state of inelastic scalar dark matter (DM) in third generation-philic $U(1)$ ($U(1)_{X_3}$) extension of the standard model. Since the heavier state and DM state form the complex scalar charged under the $U(1)_{X_3}$, it is natural that the heavier state $P$ is almost degenerate with the DM state and hence long-lived. We find that third generation-philic right-handed $U(1)$, $U(1)_{R_3}$, model is the most interesting, because third generation-philic models are less constrained by the current experimental results and right-handed $U(1)$ interactions leave visible final decay products without produing neutrinos. For a benchmark of the model parameters consistent with the current phenomenological constraints, we find that the travel distance of the LLP can be $\mathcal{O}(100)$ m and the LLP production cross section at the 14 TeV LHC can be $\mathcal{O}(10)$ fb. Thus, we conclude that the LLP can be discovered at the MATHUSLA with a sufficiently large number of LLP decay events inside the MATHUSLA detector., Comment: 16 pages, 3 figures
Published: 2024

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

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

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

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

10. Learning Spatially-Aware Language and Audio Embedding

Author: Devnani, Bhavika, Seto, Skyler, Aldeneh, Zakaria, Toso, Alessandro, Menyaylenko, Elena, Theobald, Barry-John, Sheaffer, Jonathan, and Sarabia, Miguel
Subjects: Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: Humans can picture a sound scene given an imprecise natural language description. For example, it is easy to imagine an acoustic environment given a phrase like "the lion roar came from right behind me!". For a machine to have the same degree of comprehension, the machine must know what a lion is (semantic attribute), what the concept of "behind" is (spatial attribute) and how these pieces of linguistic information align with the semantic and spatial attributes of the sound (what a roar sounds like when its coming from behind). State-of-the-art audio foundation models which learn to map between audio scenes and natural textual descriptions, are trained on non-spatial audio and text pairs, and hence lack spatial awareness. In contrast, sound event localization and detection models are limited to recognizing sounds from a fixed number of classes, and they localize the source to absolute position (e.g., 0.2m) rather than a position described using natural language (e.g., "next to me"). To address these gaps, we present ELSA a spatially aware-audio and text embedding model trained using multimodal contrastive learning. ELSA supports non-spatial audio, spatial audio, and open vocabulary text captions describing both the spatial and semantic components of sound. To train ELSA: (a) we spatially augment the audio and captions of three open-source audio datasets totaling 4,738 hours of audio, and (b) we design an encoder to capture the semantics of non-spatial audio, and the semantics and spatial attributes of spatial audio using contrastive learning. ELSA is competitive with state-of-the-art for both semantic retrieval and 3D source localization. In particular, ELSA achieves +2.8% mean audio-to-text and text-to-audio R@1 above the baseline, and outperforms by -11.6{\deg} mean-absolute-error in 3D source localization over the baseline., Comment: 25 pages, 7 figures
Published: 2024

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

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

12. On the Limited Generalization Capability of the Implicit Reward Model Induced by Direct Preference Optimization

Author: Lin, Yong, Seto, Skyler, ter Hoeve, Maartje, Metcalf, Katherine, Theobald, Barry-John, Wang, Xuan, Zhang, Yizhe, Huang, Chen, and Zhang, Tong
Subjects: Computer Science - Machine Learning, Computer Science - Computation and Language
Abstract: Reinforcement Learning from Human Feedback (RLHF) is an effective approach for aligning language models to human preferences. Central to RLHF is learning a reward function for scoring human preferences. Two main approaches for learning a reward model are 1) training an EXplicit Reward Model (EXRM) as in RLHF, and 2) using an implicit reward learned from preference data through methods such as Direct Preference Optimization (DPO). Prior work has shown that the implicit reward model of DPO (denoted as DPORM) can approximate an EXRM in the limit. DPORM's effectiveness directly implies the optimality of the learned policy, and also has practical implication for LLM alignment methods including iterative DPO. However, it is unclear how well DPORM empirically matches the performance of EXRM. This work studies the accuracy at distinguishing preferred and rejected answers for both DPORM and EXRM. Our findings indicate that even though DPORM fits the training dataset comparably, it generalizes less effectively than EXRM, especially when the validation datasets contain distribution shifts. Across five out-of-distribution settings, DPORM has a mean drop in accuracy of 3% and a maximum drop of 7%. These findings highlight that DPORM has limited generalization ability and substantiates the integration of an explicit reward model in iterative DPO approaches., Comment: 12 pages, 8 tables, 3 figures; Paper Accepted at EMNLP Findings 2024
Published: 2024

13. A Young-type integration on self-similar sets in intervals

Author: Maruyama, Takashi and Seto, Tatsuki
Subjects: Mathematics - Functional Analysis, Mathematics - Metric Geometry, Primary 26A42, Secondary 28A80
Abstract: We introduce a generalization of the Young integration on self-similar sets defined in a closed interval and give a sufficient condition of its integrability. We also prove integration by substitution, integration by parts and term-by-term integration and give examples of the properties., Comment: 24 pages
Published: 2024

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

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

15. Numerical Simulations of Spatiotemporal Instabilities in Discontinuous Shear Thickening Fluids

Author: Angerman, Peter, Sandnes, Bjornar, Seto, Ryohei, and Ellero, Marco
Subjects: Physics - Fluid Dynamics
Abstract: Discontinuous Shear Thickening (DST) fluids exhibit unique instability properties in a wide range of flow conditions. We present numerical simulations of a scalar model for DST fluids in a planar simple shear using the Smoothed Particle Hydrodynamics (SPH) approach. The model reproduces the spatially homogeneous instability mechanism based on the competition between the inertial and microstructural timescales, with good congruence to the theoretical predictions. Spatial inhomogeneities arising from a stress-splitting instability are rationalised within the context of local components of the microstructure evolution. Using this effect, the addition of non-locality in the model is found to produce an alternative mechanism of temporal instabilities, driven by the inhomogeneous pattern formation. The reported arrangement of the microstructure is generally in agreement with the experimental data on gradient pattern formation in DST. Simulations in a parameter space representative of realistic DST materials resulted in aperiodic oscillations in measured shear rate and stress, driven by formation of gap-spanning frictional structures.
Published: 2024

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

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

17. Stable dark matter from Pauli blocking in the degenerate fermion background with Quantum Field Theory

Author: Cho, Wonsub, Choi, Ki-Young, Joh, Junghoon, and Seto, Osamu
Subjects: High Energy Physics - Phenomenology, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We study a mechanism to make dark matter stable based on the Pauli blocking in the fermion background. In the background where fermions occupy the states, the decay of dark matter to those final states is not allowed, as a result, DM becomes stable. We derive the evolution equations of the distribution function in the quantum field theory and compare it with the Boltzmann equation. We apply this mechanism to a realistic model of neutrino and dark matter., Comment: 19 pages, 9 figures
Published: 2024

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

Author: PHENIX Collaboration, Abdulameer, N. J., Acharya, U., Adare, A., Afanasiev, S., Aidala, C., Ajitanand, N. N., Akiba, Y., Al-Bataineh, H., Alexander, J., Alfred, M., Aoki, K., Apadula, N., Aphecetche, L., Asai, J., Asano, H., Atomssa, E. T., Averbeck, R., Awes, T. C., Azmoun, B., Babintsev, V., Bai, M., Baksay, G., Baksay, L., Baldisseri, A., Bandara, N. S., Bannier, B., Barish, K. N., Barnes, P. D., Bassalleck, B., Basye, A. T., Bathe, S., Batsouli, S., Baublis, V., Baumann, C., Bazilevsky, A., Beaumier, M., Beckman, S., Belikov, S., Belmont, R., Bennett, R., Berdnikov, A., Berdnikov, Y., Bichon, L., Bickley, A. A., Blankenship, B., Blau, D. S., Boissevain, J. G., Bok, J. S., Borel, H., Borisov, V., Boyle, K., Brooks, M. L., Bryslawskyj, J., Buesching, H., Bumazhnov, V., Bunce, G., Butsyk, S., Camacho, C. M., Campbell, S., Chang, B. S., Chang, W. C., Charvet, J. L., Chen, C. -H., Chen, D., Chernichenko, S., Chiu, M., Chi, C. Y., Choi, I. J., Choi, J. B., Choudhury, R. K., Chujo, T., Chung, P., Churyn, A., Cianciolo, V., Citron, Z., Cole, B. A., Connors, M., Constantin, P., Corliss, R., Csanád, M., Csörgő, T., d'Enterria, D., Dahms, T., Dairaku, S., Danley, T. W., Das, K., Datta, A., Daugherity, M. S., David, G., DeBlasio, K., Dehmelt, K., Denisov, A., Deshpande, A., Desmond, E. J., Dietzsch, O., Dion, A., Diss, P. B., Donadelli, M., Doomra, V., Do, J. H., Drapier, O., Drees, A., Drees, K. A., Dubey, A. K., Durham, J. M., Durum, A., Dutta, D., Dzhordzhadze, V., Efremenko, Y. V., Ellinghaus, F., En'yo, H., Engelmore, T., Enokizono, A., Esha, R., Eyser, K. O., Fadem, B., Feege, N., Fields, D. E., Finger, Jr., M., Finger, M., Firak, D., Fitzgerald, D., Fleuret, F., Fokin, S. L., Fraenkel, Z., Frantz, J. E., Franz, A., Frawley, A. D., Fujiwara, K., Fukao, Y., Fusayasu, T., Gallus, P., Gal, C., Garg, P., Garishvili, I., Ge, H., Giordano, F., Glenn, A., Gong, H., Gonin, M., Gosset, J., Goto, Y., de Cassagnac, R. Granier, Grau, N., Greene, S. V., Perdekamp, M. Grosse, Gunji, T., Guo, T., Gustafsson, H. -Å., Hachiya, T., Henni, A. Hadj, Haggerty, J. S., Hahn, K. I., Hamagaki, H., Hamilton, H. F., Hanks, J., Han, R., Han, S. Y., Hartouni, E. P., Haruna, K., Hasegawa, S., Haseler, T. O. S., Hashimoto, K., Haslum, E., Hayano, R., Heffner, M., Hemmick, T. K., Hester, T., He, X., Hill, J. C., Hodges, A., Hohlmann, M., Hollis, R. S., Holzmann, W., Homma, K., Hong, B., Horaguchi, T., Hornback, D., Hoshino, T., Hotvedt, N., Huang, J., Ichihara, T., Ichimiya, R., Iinuma, H., Ikeda, Y., Imai, K., Imrek, J., Inaba, M., Iordanova, A., Isenhower, D., Ishihara, M., Isobe, T., Issah, M., Isupov, A., Ivanishchev, D., Jacak, B. V., Jezghani, M., Jiang, X., Jin, J., Ji, Z., Johnson, B. M., Joo, K. S., Jouan, D., Jumper, D. S., Kajihara, F., Kametani, S., Kamihara, N., Kamin, J., Kanda, S., Kang, J. H., Kapustinsky, J., Kawall, D., Kazantsev, A. V., Kempel, T., Key, J. A., Khachatryan, V., Khanzadeev, A., Kijima, K. M., Kikuchi, J., Kimelman, B., Kim, B. I., Kim, C., Kim, D. H., Kim, D. J., Kim, E., Kim, E. -J., Kim, G. W., Kim, M., Kim, S. H., Kinney, E., Kiriluk, K., Kiss, Á., Kistenev, E., Kitamura, R., Klatsky, J., Klay, J., Klein-Boesing, C., Kleinjan, D., Kline, P., Koblesky, T., Kochenda, L., Komkov, B., Konno, M., Koster, J., Kotov, D., Kovacs, L., Kozlov, A., Kravitz, A., Král, A., Kunde, G. J., Kurgyis, B., Kurita, K., Kurosawa, M., Kweon, M. J., Kwon, Y., Kyle, G. S., Lai, Y. S., Lajoie, J. G., Layton, D., Lebedev, A., Lee, D. M., Lee, K. B., Lee, S., Lee, S. H., Lee, T., Leitch, M. J., Leite, M. A. L., Lenzi, B., Liebing, P., Lim, S. H., Litvinenko, A., Liu, H., Liu, M. X., Liška, T., Li, X., Lokos, S., Loomis, D. A., Love, B., Lynch, D., Maguire, C. F., Makdisi, Y. I., Makek, M., Malakhov, A., Malik, M. D., Manion, A., Manko, V. I., Mannel, E., Mao, Y., Masui, H., Matathias, F., Mašek, L., McCumber, M., McGaughey, P. L., McGlinchey, D., McKinney, C., Means, N., Meles, A., Mendoza, M., Meredith, B., Miake, Y., Mignerey, A. C., Mikeš, P., Miki, K., Milov, A., Mishra, D. K., Mishra, M., Mitchell, J. T., Mitrankova, M., Mitrankov, Iu., Miyasaka, S., Mizuno, S., Mohanty, A. K., Montuenga, P., Moon, T., Morino, Y., Morreale, A., Morrison, D. P., Moukhanova, T. V., Mukhopadhyay, D., Mulilo, B., Murakami, T., Murata, J., Mwai, A., Nagamiya, S., Nagashima, K., Nagle, J. L., Naglis, M., Nagy, M. I., Nakagawa, I., Nakagomi, H., Nakamiya, Y., Nakamura, T., Nakano, K., Nattrass, C., Netrakanti, P. K., Newby, J., Nguyen, M., Niida, T., Nishimura, S., Nouicer, R., Novitzky, N., Novák, T., Nukazuka, G., Nyanin, A. S., O'Brien, E., Oda, S. X., Ogilvie, C. A., Okada, K., Oka, M., Onuki, Y., Koop, J. D. Orjuela, Orosz, M., Osborn, J. D., Oskarsson, A., Ouchida, M., Ozawa, K., Pak, R., Palounek, A. P. T., Pantuev, V., Papavassiliou, V., Park, J., Park, J. S., Park, S., Park, W. J., Patel, M., Pate, S. F., Pei, H., Peng, J. -C., Pereira, H., Perepelitsa, D. V., Perera, G. D. N., Peresedov, V., Peressounko, D. Yu., Perry, J., Petti, R., Pinkenburg, C., Pinson, R., Pisani, R. P., Potekhin, M., Purschke, M. L., Purwar, A. K., Qu, H., Rakotozafindrabe, A., Rak, J., Ramson, B. J., Ravinovich, I., Read, K. F., Rembeczki, S., Reygers, K., Reynolds, D., Riabov, V., Riabov, Y., Richford, D., Rinn, T., Roach, D., Roche, G., Rolnick, S. D., Rosati, M., Rosendahl, S. S. E., Rosnet, P., Rowan, Z., Rubin, J. G., Rukoyatkin, P., Ružička, P., Rykov, V. L., Sahlmueller, B., Saito, N., Sakaguchi, T., Sakai, S., Sakashita, K., Sako, H., Samsonov, V., Sarsour, M., Sato, S., Sato, T., Sawada, S., Schaefer, B., Schmoll, B. K., Sedgwick, K., Seele, J., Seidl, R., Semenov, A. Yu., Semenov, V., Sen, A., Seto, R., Sett, P., Sexton, A., Sharma, D., Shein, I., Shibata, T. -A., Shigaki, K., Shimomura, M., Shoji, K., Shukla, P., Sickles, A., Silva, C. L., Silvermyr, D., Silvestre, C., Sim, K. S., Singh, B. K., Singh, C. P., Singh, V., Slunečka, M., Smith, K. L., Snowball, M., Soldatov, A., Soltz, R. A., Sondheim, W. E., Sorensen, S. P., Sourikova, I. V., Staley, F., Stankus, P. W., Stenlund, E., Stepanov, M., Ster, A., Stoll, S. P., Sugitate, T., Suire, C., Sukhanov, A., Sumita, T., Sun, J., Sun, Z., Sziklai, J., Takagui, E. M., Taketani, A., Tanabe, R., Tanaka, Y., Tanida, K., Tannenbaum, M. J., Tarafdar, S., Taranenko, A., Tarján, P., Themann, H., Thomas, T. L., Tieulent, R., Timilsina, A., Todoroki, T., Togawa, M., Toia, A., Tomita, Y., Tomášek, L., Tomášek, M., Torii, H., Towell, C. L., Towell, R., Towell, R. S., Tram, V-N., Tserruya, I., Tsuchimoto, Y., Ujvari, B., Vale, C., Valle, H., van Hecke, H. W., Veicht, A., Velkovska, J., Vinogradov, A. A., Virius, M., Vrba, V., Vznuzdaev, E., Vértesi, R., Wang, X. R., Watanabe, Y., Watanabe, Y. S., Wei, F., Wessels, J., White, A. S., White, S. N., Winter, D., Wong, C. P., Woody, C. L., Wysocki, M., Xia, B., Xie, W., Xue, L., Yalcin, S., Yamaguchi, Y. L., Yamaura, K., Yang, R., Yanovich, A., Ying, J., Yokkaichi, S., Yoon, I., Yoo, J. H., Young, G. R., Younus, I., Yushmanov, I. E., Yu, H., Zajc, W. A., Zaudtke, O., Zelenski, A., Zhang, C., Zhou, S., Zolin, L., and Zou, L.
Subjects: Nuclear Experiment
Abstract: High-momentum two-particle correlations are a useful tool for studying jet-quenching effects in the quark-gluon plasma. Angular correlations between neutral-pion triggers and charged hadrons with transverse momenta in the range 4--12~GeV/$c$ and 0.5--7~GeV/$c$, respectively, have been measured by the PHENIX experiment in 2014 for Au$+$Au collisions at $\sqrt{s_{_{NN}}}=200$~GeV. Suppression is observed in the yield of high-momentum jet fragments opposite the trigger particle, which indicates jet suppression stemming from in-medium partonic energy loss, while enhancement is observed for low-momentum particles. The ratio and differences between the yield in Au$+$Au collisions and $p$$+$$p$ collisions, $I_{AA}$ and $\Delta_{AA}$, as a function of the trigger-hadron azimuthal separation, $\Delta\phi$, are measured for the first time at the Relativistic Heavy Ion Collider. These results better quantify how the yield of low-$p_T$ associated hadrons is enhanced at wide angle, which is crucial for studying energy loss as well as medium-response effects., Comment: 535 authors from 84 institutions, 12 pages, 8 figures. v2 is version accepted for publication in Physical Review C. HEPdata tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.html
Published: 2024
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19. Student Loans: Education Should Proactively Manage Fraud Risks in Any Future Debt Relief Efforts. Report to Congressional Committees. GAO-24-107142

Author: US Government Accountability Office (GAO), Emrey-Arras, Melissa, and Bagdoyan, Seto J.
Abstract: In August 2022, the Department of Education announced that, to address the heightened risk of delinquency and default caused by the COVID-19 pandemic, it would provide up to $20,000 of student loan debt relief to borrowers who met certain income thresholds. Borrowers eligible for this relief were to receive up to the full $20,000 in relief if they received a Pell Grant while in college, and up to $10,000 if they did not. In total, the Congressional Budget Office estimated that Education would have relieved around $430 billion of student loan debt for more than 31 million federal student loan borrowers. To be eligible, borrowers with qualifying federal loans must have had an annual adjusted gross income in 2020 or 2021 below certain thresholds. In November 2022, as a result of court orders, Education ceased work on the student loan debt relief program. In June 2023, the U.S. Supreme Court held that the debt relief program was not authorized under the Higher Education Relief Opportunities for Students Act of 2003 (HEROES Act). As a result, Education was not able to implement the program. The Biden administration subsequently announced plans to use the department's authority under the Higher Education Act of 1965, as amended, (HEA) to pursue an alternative path to debt relief for borrowers. The goal of this report is to provide feedback on Education's originally proposed debt relief program to inform any future efforts. Specifically, it assesses the extent to which Education's policies and procedures would have mitigated certain types of fraud risk in the originally proposed student loan debt relief program and reviewed key aspects of Education's fraud risk management in the program
Published: 2023

20. Phylogeny, morphology, virulence, ecology, and host range of Ordospora pajunii (Ordosporidae), a microsporidian symbiont of Daphnia spp.

Author: Dziuba, Marcin K, McIntire, Kristina M, Seto, Kensuke, Davenport, Elizabeth S, Rogalski, Mary A, Gowler, Camden D, Baird, Emma, Vaandrager, Megan, Huerta, Cristian, Jaye, Riley, Corcoran, Fiona E, Withrow, Alicia, Ahrendt, Steven, Salamov, Asaf, Nolan, Matt, Tejomurthula, Sravanthi, Barry, Kerrie, Grigoriev, Igor V, James, Timothy Y, and Duffy, Meghan A
Subjects: Microbiology, Medical Microbiology, Biomedical and Clinical Sciences, Biological Sciences, Infectious Diseases, Genetics, 2.1 Biological and endogenous factors, 2.2 Factors relating to the physical environment, Infection, Animals, Daphnia, Symbiosis, Host Specificity, Phylogeny, Virulence, Microsporidia, Microsporidia, Unclassified, microsporidia, mutualism-parasitism continuum, symbiosis, pathogen, zooplankton, Biochemistry and cell biology, Medical microbiology
Abstract: The impacts of microsporidia on host individuals are frequently subtle and can be context dependent. A key example of the latter comes from a recently discovered microsporidian symbiont of Daphnia, the net impact of which was found to shift from negative to positive based on environmental context. Given this, we hypothesized low baseline virulence of the microsporidian; here, we investigated the impact of infection on hosts in controlled conditions and the absence of other stressors. We also investigated its phylogenetic position, ecology, and host range. The genetic data indicate that the symbiont is Ordospora pajunii, a newly described microsporidian parasite of Daphnia. We show that O. pajunii infection damages the gut, causing infected epithelial cells to lose microvilli and then rupture. The prevalence of this microsporidian could be high (up to 100% in the lab and 77% of adults in the field). Its overall virulence was low in most cases, but some genotypes suffered reduced survival and/or reproduction. Susceptibility and virulence were strongly host-genotype dependent. We found that North American O. pajunii were able to infect multiple Daphnia species, including the European species Daphnia longispina, as well as Ceriodaphnia spp. Given the low, often undetectable virulence of this microsporidian and potentially far-reaching consequences of infections for the host when interacting with other pathogens or food, this Daphnia-O. pajunii symbiosis emerges as a valuable system for studying the mechanisms of context-dependent shifts between mutualism and parasitism, as well as for understanding how symbionts might alter host interactions with resources.ImportanceThe net outcome of symbiosis depends on the costs and benefits to each partner. Those can be context dependent, driving the potential for an interaction to change between parasitism and mutualism. Understanding the baseline fitness impact in an interaction can help us understand those shifts; for an organism that is generally parasitic, it should be easier for it to become a mutualist if its baseline virulence is relatively low. Recently, a microsporidian was found to become beneficial to its Daphnia hosts in certain ecological contexts, but little was known about the symbiont (including its species identity). Here, we identify it as the microsporidium Ordospora pajunii. Despite the parasitic nature of microsporidia, we found O. pajunii to be, at most, mildly virulent; this helps explain why it can shift toward mutualism in certain ecological contexts and helps establish O. pajunii is a valuable model for investigating shifts along the mutualism-parasitism continuum.
Published: 2024

21. Perturbative Frequency Expansion for Nearly Monochromatic Binary Black Holes Detectable with LISA

Author: Seto, Naoki
Subjects: General Relativity and Quantum Cosmology, Astrophysics - High Energy Astrophysical Phenomena
Abstract: The proposed space gravitational wave (GW) detector LISA has potential to detect stellar-mass black hole binaries (BBHs). The majority of the detected BBHs are expected to emit nearly monochromatic GWs, whose frequency evolution will be efficiently described by Taylor expansions. We study the measurability of the associated time derivative coefficients of the frequencies, by extending a recent work based on a simplified Fisher matrix analysis. Additionally, we provide qualitative discussions on how to extract astrophysical information, such as orbital eccentricity and tertiary perturbation, from the observed derivative coefficients., Comment: 11 pages, 6 figures, corrected Eq. (15) and related errors
Published: 2024

22. DESI constraints on the varying electron mass model and axion-like early dark energy

Author: Seto, Osamu and Toda, Yo
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, High Energy Physics - Phenomenology
Abstract: Baryon acoustic oscillation (BAO) is one of the important standard rulers in cosmology. The results of the latest BAO measurements by Dark Energy Spectroscopic Instrument (DESI) survey have been reported. Cosmology with the varying electron mass model and the early dark energy models are regarded as interesting models to resolve the Hubble tension. We present constraints on the varying electron mass model and early dark energy models in new DESI data as well as cosmic microwave background by Planck and the conventional BAO data from 6dF, MGS, and DR12 and supernovae light curve data into analysis. Since new DESI BAO data indicate a slightly longer sound horizon $r_dh$ than the other BAO observations, for the varying electron mass model, the larger $H_0 =69.44\pm 0.84 $ km$/$s$/$Mpc is indicated., Comment: 18 pages, 6 figures
Published: 2024

23. Controlling $^{229}$Th isomeric state population in a VUV transparent crystal

Author: Hiraki, Takahiro, Okai, Koichi, Bartokos, Michael, Beeks, Kjeld, Fujimoto, Hiroyuki, Fukunaga, Yuta, Haba, Hiromitsu, Kasamatsu, Yoshitaka, Kitao, Shinji, Leitner, Adrian, Masuda, Takahiko, Ming, Guan, Nagasawa, Nobumoto, Ogake, Ryoichiro, Pimon, Martin, Pressler, Martin, Sasao, Noboru, Schaden, Fabian, Schumm, Thorsten, Seto, Makoto, Shigekawa, Yudai, Shimizu, Koutaro, Sikorsky, Tomas, Tamasaku, Kenji, Takatori, Sayuri, Watanabe, Tsukasa, Yamaguchi, Atsushi, Yoda, Yoshitaka, Yoshimi, Akihiro, and Yoshimura, Koji
Subjects: Nuclear Experiment, Physics - Atomic Physics
Abstract: The radioisotope Th-229 is renowned for its extraordinarily low-energy, long-lived nuclear first-excited state. This isomeric state can be excited by VUV lasers and the transition from the ground state has been proposed as a reference transition for ultra-precise nuclear clocks. Such nuclear clocks will find multiple applications, ranging from fundamental physics studies to practical implementations. Recent investigations extracted valuable constraints on the nuclear transition energy and lifetime, populating the isomer in stochastic nuclear decay of U-233 or Ac-229. However, to assess the feasibility and performance of the (solid-state) nuclear clock concept, time-controlled excitation and depopulation of the $^{229}$Th isomer together with time-resolved monitoring of the radiative decay are imperative. Here we report the population of the $^{229}$Th isomeric state through resonant X-ray pumping and detection of the radiative decay in a VUV transparent $^{229}$Th-doped CaF$_2$ crystal. The decay half-life is measured to $447\pm 25$ s, with a transition wavelength of $148.18 \pm 0.42$ nm and a radiative decay fraction consistent with unity. Furthermore, we report a new ``X-ray quenching'' effect which allows to de-populate the isomer on demand and effectively reduce the half-life by at least a factor 50. Such controlled quenching can be used to significantly speed up the interrogation cycle in future nuclear clock schemes. Our results show that full control over the $^{229}$Th nuclear isomer population can be achieved in a crystal environment. In particular, non-radiative decay processes that might lead to a broadening of the isomer transition linewidth are negligible, paving the way for the development of a compact and robust solid-state nuclear clock. Further studies are needed to reveal the underlying physical mechanism of the X-ray quenching effect., Comment: 14 pages with 8 figures and 2 tables
Published: 2024
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24. Exploring the Orbital Alignments of Galactic Close White Dwarf Binaries with LISA

Author: Seto, Naoki
Subjects: Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract: Using the proposed space gravitational wave detector LISA, we will be able to measure the geometrical configurations of $\sim 10^4$ close white dwarf binaries in our Galaxy. The obtained data will be an entirely new resource to examine the randomness of their orbital orientations. Partly motivated by a recent reported on the systematic alignments of bulge planetary nebulae, we discuss the outlook of the orientational analysis with LISA. We find that a quadrupole pattern as small as $\sim 0.05$ can be detected for bulge white dwarf binaries, owing to their large available number. From such a pattern analysis, we might geometrically explore fossil records in our Galaxy billions of years ago., Comment: 6 pages, 2 figures, updated
Published: 2024
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25. Correlations of event activity with hard and soft processes in $p$ + Au collisions at $\sqrt{s_\mathrm{NN}}$ = 200 GeV at STAR

Author: STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Broodo, C., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Deppner, I. M., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Khanal, A., Khyzhniak, Y. V., Kikoła, D. P., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kołaś, J., Kosarzewski, L. K., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Manikandhan, R., Margetis, S., Markert, C., Matonoha, O., McNamara, G., Mezhanska, O., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, B., Mondal, M. M., Mooney, I., Mrazkova, J., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nie, M., Nigmatkulov, G., Niida, T., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pal, S., Pandav, A., Panday, A., Pandey, A. K., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T. L., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Reed, R., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Su, Y., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Svoboda, M., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Trentalange, S., Tribedy, P., Tripathy, S. K., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.
Subjects: Nuclear Experiment
Abstract: With the STAR experiment at the BNL Relativisic Heavy Ion Collider, we characterize $\sqrt{s_\mathrm{NN}}$ = 200 GeV p+Au collisions by event activity (EA) measured within the pseudorapidity range $eta$ $in$ [-5, -3.4] in the Au-going direction and report correlations between this EA and hard- and soft- scale particle production at midrapidity ($\eta$ $\in$ [-1, 1]). At the soft scale, charged particle production in low-EA p+Au collisions is comparable to that in p+p collisions and increases monotonically with increasing EA. At the hard scale, we report measurements of high transverse momentum (pT) jets in events of different EAs. In contrast with the soft particle production, high-pT particle production and EA are found to be inversely related. To investigate whether this is a signal of jet quenching in high-EA events, we also report ratios of pT imbalance and azimuthal separation of dijets in high- and low-EA events. Within our measurement precision, no significant differences are observed, disfavoring the presence of jet quenching in the highest 30% EA p+Au collisions at $\sqrt{s_\mathrm{NN}}$ = 200 GeV., Comment: 12 page, 9 figures
Published: 2024
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26. A proposal for enhancing technosignature search toward the Galactic center

Author: Seto, Naoki
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies, Physics - Popular Physics
Abstract: We discuss the possibility of enhancing intelligent life searches toward the Galactic center. From the clockwork orbital motions of stars around the Sgr A${}^*$ black hole, we can determine the distance to the Galactic center at an exceptional accuracy, despite its remoteness $\sim 8.3$kpc. In addition, we can define precise reference epochs by selecting a prominent object such as the bright B-type star S2. These properties have a particular affinity for the coordinated signaling scheme which was hypothesized by Seto (2019) for systematically connecting intentional senders to searchers without a prior communication. If S2 is actually being used as a common reference clock, we can compress the search directions around the Galactic center by more than two orders of magnitude, with the scanning interval of $\sim16$yr., Comment: 7 pages, 6 figures, accepted for publication in ApJ
Published: 2024

27. Freeze-in sterile neutrino dark matter in feeble gauged $B-L$ model

Author: Seto, Osamu, Shimomura, Takashi, and Uchida, Yoshiki
Subjects: High Energy Physics - Phenomenology
Abstract: We consider the gauged $U(1)_{B-L}$ model and examine the situation where the sterile neutrino is a dark matter candidate produced by the freeze-in mechanism. In our model, the dark matter $N$ is mainly produced by the decay of a $U(1)_{B-L}$ breaking scalar field $\phi$. We point out that the on-shell production of $\phi$ through annihilation of the $U(1)_{B-L}$ gauge field $Z'$ plays an important role. We find that the single production of $Z'$ from the photon and the gluon bath can become the main production modes for $Z'$ in some parameter regions. To prevent $N$ from being overproduced, we show that the $U(1)_{B-L}$ gauge coupling constant $g_{B-L}$ must be as small as $10^{-14}$--$10^{-10}$. We also consider the case where the decay of $\phi$ into $N$ is kinematically forbidden. In this case, $N$ is generated by the scattering of $Z'$ and the $g_{B-L}$ takes values of $10^{-7}$--$10^{-6}$, which can be explored in collider experiments like FASER., Comment: 18 pages, 4 figures
Published: 2024

28. Bending-Rotation coupling in the viscoelasticity of semiflexible polymers -- Rigorous perturbation analysis from the rod limit

Author: Xiong, Zhongqiang, Seto, Ryohei, and Doi, Masao
Subjects: Condensed Matter - Soft Condensed Matter
Abstract: Brownian motion and viscoelasticity of semiflexible polymers is a subject that has been studied for many years. Still, rigorous analysis has been hindered due to the difficulty in handling the constraint that polymer chains cannot be stretched along the contour. Here, we show a straightforward method to solve the problem. We consider a stiff polymer that has a persistent length $L_p$ much larger than the contour length $L$. We express the polymer configuration using three types of variables: the position vector of the center of mass $R_c$, the unit vector $n$ along the main axis, and the normal coordinates $u_p$ for bending. Solving the Smoluchowski equation for the distribution function of these variables, we calculate the equilibrium time correlation function $ \langle P(t)\cdot P(0) \rangle$ of the end-to-end vector $P$ and the complex modulus $G^*(\omega)$ of dilute solution. They include the bending effect to the first order in $\theta \equiv L/L_p$ and reduce to the exact results for the rigid rod in the limit of $\theta \to 0$. The rotational diffusion coefficient increases slightly by the semiflexibility because the equilibrium length of the semiflexible polymer is smaller than that of the rigid rod with the same contour length. The storage modulus shows the same asymptotic dependence $G'(\omega) \sim \omega^{3/4}$ predicted by Shankar, Pasquali, and Morse [J. Rheol. 2002, 46, 1111--1154]. The high-frequency viscosity is predicted to be dependent on the thickness of the semiflexible polymers.
Published: 2024
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29. Implementation of Conceptual Design and Taguchi Optimization Method for the Construction of Multi-Resin 3D Printer

Author: Romario, Yulius Shan, Bhat, Chinmai, Riyanto, Seto Agung, Sanjoto, Samuel Bobby, Toyserkani, Ehsan, Ramezani, Maziar, and Jiang, Cho-Pei
Published: 2024
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30. Is the Screening Scale for Pedophilic Interest-2 a Measure of Pedophilic Interests or a Measure of Behavioral Propensity to Sexually Offend Against Children?

Author: Lalumière, Martin L., Stephens, Skye, and Seto, Michael C.
Published: 2024
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31. Outcome research on esophagectomy analyzed using nationwide databases in Japan: evidences generated from real-world data

Author: Kakeji, Yoshihiro, Yamamoto, Hiroyuki, Watanabe, Masayuki, Kono, Koji, Ueno, Hideki, Doki, Yuichiro, Kitagawa, Yuko, Takeuchi, Hiroya, Shirabe, Ken, and Seto, Yasuyuki
Published: 2024
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32. Dual-etiology MAFLD: the interactions between viral hepatitis B, viral hepatitis C, alcohol, and MAFLD

Author: Liu, Chun-Jen, Seto, Wai Kay, and Yu, Ming-Lung
Published: 2024
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33. First case report of intestinal lymphangiectasia with refractory bleeding from the duodenum, successfully treated by intra-abdominal lymphaticovenous anastomosis with venous ligation

Author: Miyakawa, Yu, Ihara, Sozaburo, Ishii, Saaya, Rui, Yang, Yajima, Shoh, Hayakawa, Yoku, Tsuji, Yosuke, Okazaki, Mutsumi, Seto, Yasuyuki, and Fujishiro, Mitsuhiro
Published: 2024
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34. Response to chemotherapy could predict the prognosis of esophageal squamous cell carcinoma treated with neoadjuvant docetaxel, cisplatin, and fluorouracil (DCF) followed by surgery: long-term results in a single institute

Author: Sato, Yasuyoshi, Mori, Kazuhiko, Atsumi, Shinichiro, Sakamoto, Kei, Oya, Shuichiro, Okamoto, Asami, Urabe, Masayuki, Miwa, Yoshiyuki, Yajima, Shoh, Yagi, Koichi, Nomura, Sachiyo, Yamashita, Hiroharu, and Seto, Yasuyuki
Published: 2024
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35. Association between social jetlag and weight and fat reduction in dieting

Author: Minabe, Kai, Shimura, Akiyoshi, Sugiura, Ko, Hino, Hiroko, Akatsuka, Yusaku, Seto, Takeshi, Yanai, Miho, Masuya, Jiro, Tamada, Yu, and Inoue, Takeshi
Published: 2024
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36. Analysis of operative duration of image-guided brachytherapy for cervical cancer

Author: Takagawa, Yoshiaki, Machida, Masanori, Seto, Ichiro, Komori, Shinya, Sato, Hiroki, Endo, Hiromitsu, Midorikawa, Hiroko, Kato, Takahiro, Murakami, Masao, Honda, Michitaka, and Kono, Koji
Published: 2024
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37. Global urban structural growth shows a profound shift from spreading out to building up

Author: Frolking, Steve, Mahtta, Richa, Milliman, Tom, Esch, Thomas, and Seto, Karen C.
Published: 2024
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38. Validation of Quality Assessment Measures for Inpatient Gastroenterology Consults on Oncologic Patients in Non-teaching Services at a Cancer Center: A Cross-Sectional Study

Author: Kidambi, Trilokesh D., Qadri, Rateeba, Varughese, Jay, Seto, Tyler, Idos, Gregory, Lin, James, Hirsch, Peter, Trieu, Harry, Ma, Huiyan, Hein, Marjorie, Ahn, Alice, Hy-Hincy, Claire, Lew, Michael W., Kessler, Jonathan, Perumpail, Ryan B., Terdiman, Jonathan P., Lee, Jeffrey K., Day, Lukejohn, Manesh, Reza Sedighi, Taplitz, Randy, and Banciu-Odell, Cornelia
Published: 2024
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39. Janus kinase inhibitor overcomes resistance to immune checkpoint inhibitor treatment in peritoneal dissemination of gastric cancer in C57BL/6 J mice

Author: Du, Wan-Ying, Masuda, Hiroki, Nagaoka, Koji, Yasuda, Tomohiko, Kuge, Komei, Seto, Yasuyuki, Kakimi, Kazuhiro, and Nomura, Sachiyo
Published: 2024
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40. Estimating Quantum Dot Size with Pulsed Field Gradient NMR

Author: Hal Van Ryswyk, Hanna Z. Porter, Connor K. M. Seto, Ixchel Lopez, and Emma Dy
Abstract: Pulsed field gradient nuclear magnetic resonance (NMR) is used to estimate the size of quantum dots (QDs) produced in a simple, fast, small-scale synthesis. This upper-division laboratory experiment teaches the basics of QD synthesis and pulsed field gradient NMR, a powerful technique for measuring diffusion coefficients in solution. The diffusion coefficients are used to calculate hydrodynamic radii, which are compared to core radii obtained from optical absorbance sizing curves, providing insight into the structure of QDs in colloidal suspension and the nature of the information provided by pulsed field gradient NMR.
Published: 2023
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41. Shortest cover after edit

Author: Mitani, Kazuki, Mieno, Takuya, Seto, Kazuhisa, and Horiyama, Takashi
Subjects: Computer Science - Data Structures and Algorithms
Abstract: This paper investigates the (quasi-)periodicity of a string when the string is edited. A string $C$ is called a cover (as known as a quasi-period) of a string $T$ if each character of $T$ lies within some occurrence of $C$. By definition, a cover of $T$ must be a border of $T$; that is, it occurs both as a prefix and as a suffix of $T$. In this paper, we focus on the changes in the longest border and the shortest cover of a string when the string is edited only once. We propose a data structure of size $O(n)$ that computes the longest border and the shortest cover of the string in $O(\ell \log n)$ time after an edit operation (either insertion, deletion, or substitution of some string) is applied to the input string $T$ of length $n$, where $\ell$ is the length of the string being inserted or substituted. The data structure can be constructed in $O(n)$ time given string $T$.
Published: 2024

42. Can you Remove the Downstream Model for Speaker Recognition with Self-Supervised Speech Features?

Author: Aldeneh, Zakaria, Higuchi, Takuya, Jung, Jee-weon, Seto, Skyler, Likhomanenko, Tatiana, Shum, Stephen, Abdelaziz, Ahmed Hussen, Watanabe, Shinji, and Theobald, Barry-John
Subjects: Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract: Self-supervised features are typically used in place of filter-bank features in speaker verification models. However, these models were originally designed to ingest filter-bank features as inputs, and thus, training them on top of self-supervised features assumes that both feature types require the same amount of learning for the task. In this work, we observe that pre-trained self-supervised speech features inherently include information required for downstream speaker verification task, and therefore, we can simplify the downstream model without sacrificing performance. To this end, we revisit the design of the downstream model for speaker verification using self-supervised features. We show that we can simplify the model to use 97.51% fewer parameters while achieving a 29.93% average improvement in performance on SUPERB. Consequently, we show that the simplified downstream model is more data efficient compared to baseline--it achieves better performance with only 60% of the training data.
Published: 2024

43. Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling

Author: Maini, Pratyush, Seto, Skyler, Bai, He, Grangier, David, Zhang, Yizhe, and Jaitly, Navdeep
Subjects: Computer Science - Computation and Language
Abstract: Large language models are trained on massive scrapes of the web, which are often unstructured, noisy, and poorly phrased. Current scaling laws show that learning from such data requires an abundance of both compute and data, which grows with the size of the model being trained. This is infeasible both because of the large compute costs and duration associated with pre-training, and the impending scarcity of high-quality data on the web. In this work, we propose Web Rephrase Augmented Pre-training ($\textbf{WRAP}$) that uses an off-the-shelf instruction-tuned model prompted to paraphrase documents on the web in specific styles such as "like Wikipedia" or in "question-answer format" to jointly pre-train LLMs on real and synthetic rephrases. First, we show that using WRAP on the C4 dataset, which is naturally noisy, speeds up pre-training by $\sim3x$. At the same pre-training compute budget, it improves perplexity by more than 10% on average across different subsets of the Pile, and improves zero-shot question answer accuracy across 13 tasks by more than 2%. Second, we investigate the impact of the re-phrasing style on the performance of the model, offering insights into how the composition of the training data can impact the performance of LLMs in OOD settings. Our gains are attributed to the fact that re-phrased synthetic data has higher utility than just real data because it (i) incorporates style diversity that closely reflects downstream evaluation style, and (ii) has higher 'quality' than web-scraped data.
Published: 2024

44. UMBRELLA: A One-stop Shop Bridging the Gap from Lab to Real-World IoT Experimentation

Author: Mavromatis, Ioannis, Jin, Yichao, Stanoev, Aleksandar, Portelli, Anthony, Weeks, Ingram, Holden, Ben, Glasspole, Eliot, Farnham, Tim, Khan, Aftab, Raza, Usman, Aijaz, Adnan, Bierton, Thomas, Seto, Ichiro, Patel, Nita, and Sooriyabandara, Mahesh
Subjects: Computer Science - Networking and Internet Architecture, Computer Science - Software Engineering, Electrical Engineering and Systems Science - Systems and Control
Abstract: UMBRELLA is an open, large-scale IoT ecosystem deployed across South Gloucestershire, UK. It is intended to accelerate innovation across multiple technology domains. UMBRELLA is built to bridge the gap between existing specialised testbeds and address holistically real-world technological challenges in a System-of-Systems (SoS) fashion. UMBRELLA provides open access to real-world devices and infrastructure, enabling researchers and the industry to evaluate solutions for Smart Cities, Robotics, Wireless Communications, Edge Intelligence, and more. Key features include over 200 multi-sensor nodes installed on public infrastructure, a robotics arena with 20 mobile robots, a 5G network-in-a-box solution, and a unified backend platform for management, control and secure user access. The heterogeneity of hardware components, including diverse sensors, communication interfaces, and GPU-enabled edge devices, coupled with tools like digital twins, allows for comprehensive experimentation and benchmarking of innovative solutions not viable in lab environments. This paper provides a comprehensive overview of UMBRELLA's multi-domain architecture and capabilities, making it an ideal playground for Internet of Things (IoT) and Industrial IoT (IIoT) innovation. It discusses the challenges in designing, developing and operating UMBRELLA as an open, sustainable testbed and shares lessons learned to guide similar future initiatives. With its unique openness, heterogeneity, realism and tools, UMBRELLA aims to continue accelerating cutting-edge technology research, development and translation into real-world progress., Comment: Submitted for publication to IEEE Access
Published: 2024

45. Constraints on sterile neutrinos and the cosmological tensions

Author: Pan, Supriya, Seto, Osamu, Takahashi, Tomo, and Toda, Yo
Subjects: Astrophysics - Cosmology and Nongalactic Astrophysics, High Energy Physics - Phenomenology
Abstract: We investigate cosmological bounds on sterile neutrino masses in the light of the Hubble and $S_8$ tensions. We argue that non-zero masses for sterile neutrinos are inferred at 2$\sigma$ level in some extended models such as varying dark energy equation of state, when a direct measurement of the Hubble constant $H_0$ and weak lensing measurement of dark energy survey (DES) are taken into account. Furthermore, the Hubble and $S_8$ tensions are also reduced in such a framework. We also consider the case where a non-flat Universe is allowed and show that a slightly open Universe may be favored in models with sterile neutrinos in the context of the cosmological tensions., Comment: 23 pages, 8 figures
Published: 2023
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46. Theoretical Aspects of Generating Instances with Unique Solutions: Pre-assignment Models for Unique Vertex Cover

Author: Horiyama, Takashi, Kobayashi, Yasuaki, Ono, Hirotaka, Seto, Kazuhisa, and Suzuki, Ryu
Subjects: Computer Science - Data Structures and Algorithms, Computer Science - Computational Complexity
Abstract: The uniqueness of an optimal solution to a combinatorial optimization problem attracts many fields of researchers' attention because it has a wide range of applications, it is related to important classes in computational complexity, and an instance with only one solution is often critical for algorithm designs in theory. However, as the authors know, there is no major benchmark set consisting of only instances with unique solutions, and no algorithm generating instances with unique solutions is known; a systematic approach to getting a problem instance guaranteed having a unique solution would be helpful. A possible approach is as follows: Given a problem instance, we specify a small part of a solution in advance so that only one optimal solution meets the specification. This paper formulates such a ``pre-assignment'' approach for the vertex cover problem as a typical combinatorial optimization problem and discusses its computational complexity. First, we show that the problem is $\Sigma^P_2$-complete in general, while the problem becomes NP-complete when an input graph is bipartite. We then present an $O(2.1996^n)$-time algorithm for general graphs and an $O(1.9181^n)$-time algorithm for bipartite graphs, where $n$ is the number of vertices. The latter is based on an FPT algorithm with $O^*(3.6791^{\tau})$ time for vertex cover number $\tau$. Furthermore, we show that the problem for trees can be solved in $O(1.4143^n)$ time., Comment: 21 pages, 3 figures. An extended abstract will appear in AAAI2024
Published: 2023

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

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

Author: STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Broodo, C., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Q., Chen, Z., Cheng, J., Cheng, Y., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Deppner, I. M., Deshpande, A., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., Havener, L. B., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Khanal, A., Khyzhniak, Y. V., Kikoła, D. P., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kołaś, J., Kosarzewski, L. K., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Manikandhan, R., Markert, C., Matonoha, O., McNamara, G., Mezhanska, O., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, B., Mondal, M. M., Mooney, I., Mrazkova, J., Nagy, M. I., Naim, C. J., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nie, M., Nigmatkulov, G., Niida, T., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pal, S., Pandav, A., Panday, A., Pandey, A. K., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Pinto, I. D. Ponce, Posik, M., Protzman, T. L., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Reed, R., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Su, Y., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Svoboda, M., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Trentalange, S., Tribedy, P., Tripathy, S. K., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.
Subjects: Nuclear Experiment
Abstract: Flow coefficients ($v_2$ and $v_3$) are measured in high-multiplicity $p$+Au, $d$+Au, and $^{3}$He$+$Au collisions at a center-of-mass energy of $\sqrt{s_{_{\mathrm{NN}}}}$ = 200 GeV using the STAR detector. The measurements utilize two-particle correlations with a pseudorapidity requirement of $|\eta| <$ 0.9 and a pair gap of $|\Delta\eta|>1.0$. The primary focus is on analysis methods, particularly the subtraction of non-flow contributions. Four established non-flow subtraction methods are applied to determine $v_n$, validated using the HIJING event generator. $v_n$ values are compared across the three collision systems at similar multiplicities; this comparison cancels the final state effects and isolates the impact of initial geometry. While $v_2$ values show differences among these collision systems, $v_3$ values are largely similar, consistent with expectations of subnucleon fluctuations in the initial geometry. The ordering of $v_n$ differs quantitatively from previous measurements using two-particle correlations with a larger rapidity gap, which, according to model calculations, can be partially attributed to the effects of longitudinal flow decorrelations. The prospects for future measurements to improve our understanding of flow decorrelation and subnucleonic fluctuations are also discussed., Comment: 29 pages, 25 figures
Published: 2023

49. Seasonal variation of chlorophyll-a in South Java over the past quarter-century

Author: Wijaya, Yusuf Jati, Wisha, Ulung Jantama, Maslukah, Lilik, Windarto, Seto, Wirasatriya, Anindya, and Zainuri, Muhammad
Published: 2024
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50. Left Frontoparietal Control Network Connectivity Moderates the Effect of Amyloid on Cognitive Decline in Preclinical Alzheimer’s Disease: The A4 Study

Author: Boyle, R., Klinger, H. M., Shirzadi, Z., Coughlan, G. T., Seto, M., Properzi, M. J., Townsend, D. L., Yuan, Z., Scanlon, C., Jutten, R. J., Papp, K. V., Amariglio, R. E., Rentz, D. M., Chhatwal, J. P., Donohue, M. C., Sperling, R. A., Schultz, A. P., and Buckley, Rachel F.
Published: 2024
Full Text: View/download PDF

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