Author: "Miné, P." - Searchworks@Jio Institute Digital Library Search Results

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3,351 results on '"Miné, P."'

1. Neutron multiplicity measurement in muon capture on oxygen nuclei in the Gd-loaded Super-Kamiokande detector

Author: Collaboration, The Super-Kamiokande, Miki, S., Abe, K., Abe, S., Asaoka, Y., Bronner, C., Harada, M., Hayato, Y., Hiraide, K., Hosokawa, K., Ieki, K., Ikeda, M., Kameda, J., Kanemura, Y., Kaneshima, R., Kashiwagi, Y., Kataoka, Y., Mine, S., Miura, M., Moriyama, S., Nakahata, M., Nakayama, S., Noguchi, Y., Okamoto, K., Pronost, G., Sato, K., Sekiya, H., Shiba, H., Shimizu, K., Shiozawa, M., Sonoda, Y., Suzuki, Y., Takeda, A., Takemoto, Y., Takenaka, A., Tanaka, H., Watanabe, S., Yano, T., Kajita, T., Okumura, K., Tashiro, T., Tomiya, T., Wang, X., Yoshida, S., Megias, G. D., Fernandez, P., Labarga, L., Ospina, N., Zaldivar, B., Pointon, B. W., Yanagisawa, C., Kearns, E., Raaf, J. L., Wan, L., Wester, T., Bian, J., Cortez, B., Griskevich, N. J., Locke, S., Smy, M. B., Sobel, H. W., Takhistov, V., Yankelevich, A., Hill, J., Jang, M. C., Lee, S. H., Moon, D. H., Park, R. G., Yang, B. S., Bodur, B., Scholberg, K., Walter, C. W., Beauchêne, A., Drapier, O., Ershova, A., Giampaolo, A., Mueller, Th. A., Santos, A. D., Paganini, P., Quach, C., Quilain, B., Rogly, R., Nakamura, T., Jang, J. S., Litchfield, R. P., Machado, L. N., Soler, F. J. P., Learned, J. G., Choi, K., Iovine, N., Cao, S., Anthony, L. H. V., Martin, D., Prouse, N. W., Scott, M., Sztuc, A. A., Uchida, Y., Berardi, V., Calabria, N. F., Catanesi, M. G., Radicioni, E., Langella, A., De Rosa, G., Collazuol, G., Feltre, M., Iacob, F., Lamoureux, M., Mattiazzi, M., Ludovici, L., Gonin, M., Périssé, L., Fujisawa, C., Horiuchi, S., Kobayashi, M., Liu, Y. M., Maekawa, Y., Nishimura, Y., Okazaki, R., Akutsu, R., Friend, M., Hasegawa, T., Ishida, T., Kobayashi, T., Jakkapu, M., Matsubara, T., Nakadaira, T., Nakamura, K., Oyama, Y., Yrey, A. Portocarrero, Sakashita, K., Sekiguchi, T., Tsukamoto, T., Bhuiyan, N., Burton, G. T., Di Lodovico, F., Gao, J., Goldsack, A., Katori, T., Migenda, J., Ramsden, R. M., Xie, Z., Zsoldos, S., Kotsar, Y., Ozaki, H., Suzuki, A. T., Takagi, Y., Takeuchi, Y., Zhong, H., Feng, J., Feng, L., Han, S., Hu, J. R., Hu, Z., Kawaue, M., Kikawa, T., Mori, M., Nakaya, T., Ngoc, T. V., Wendell, R. A., Yasutome, K., Jenkins, S. J., McCauley, N., Mehta, P., Tarrant, A., Wilking, M. J., Fukuda, Y., Itow, Y., Menjo, H., Ninomiya, K., Yoshioka, Y., Lagoda, J., Mandal, M., Mijakowski, P., Prabhu, Y. S., Zalipska, J., Jia, M., Jiang, J., Jung, C. K., Shi, W., Hino, Y., Ishino, H., Kitagawa, H., Koshio, Y., Nakanishi, F., Sakai, S., Tada, T., Tano, T., Ishizuka, T., Barr, G., Barrow, D., Cook, L., Samani, S., Wark, D., Holin, A., Nova, F., Jung, S., Yang, J. Y., Yoo, J., Fannon, J. E. P., Kneale, L., Malek, M., McElwee, J. M., Peacock, T., Stowell, P., Thiesse, M. D., Thompson, L. F., Wilson, S. T., Okazawa, H., Lakshmi, S. M., Kim, S. B., Kwon, E., Lee, M. W., Seo, J. W., Yu, I., Ichikawa, A. K., Nakamura, K. D., Tairafune, S., Nishijima, K., Eguchi, A., Goto, S., Mizuno, Y., Muro, T., Nakagiri, K., Nakajima, Y., Shima, S., Taniuchi, N., Watanabe, E., Yokoyama, M., de Perio, P., Fujita, S., Jesús-Valls, C., Martens, K., Marti, Ll., Tsui, K. M., Vagins, M. R., Xia, J., Kuze, M., Izumiyama, S., Matsumoto, R., Terada, K., Asaka, R., Ishitsuka, M., Ito, H., Kinoshita, T., Ommura, Y., Shigeta, N., Shinoki, M., Suganuma, T., Yamauchi, K., Yoshida, T., Martin, J. F., Tanaka, H. A., Towstego, T., Nakano, Y., Cormier, F., Gaur, R., Gousy-Leblanc, V., Hartz, M., Konaka, A., Li, X., Smithers, B. R., Chen, S., Wu, Y., Xu, B. D., Zhang, A. Q., Zhang, B., Girgus, M., Govindaraj, P., Posiadala-Zezula, M., Boyd, S. B., Edwards, R., Hadley, D., Nicholson, M., O'Flaherty, M., Richards, B., Ali, A., Jamieson, B., Amanai, S., Minamino, A., Pintaudi, G., Sano, S., Shibayama, R., Shimamura, R., Suzuki, S., and Wada, K.
Subjects: High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract: In recent neutrino detectors, neutrons produced in neutrino reactions play an important role. Muon capture on oxygen nuclei is one of the processes that produce neutrons in water Cherenkov detectors. We measured neutron multiplicity in the process using cosmic ray muons that stop in the gadolinium-loaded Super-Kamiokande detector. For this measurement, neutron detection efficiency is obtained with the muon capture events followed by gamma rays to be $50.2^{+2.0}_{-2.1}\%$. By fitting the observed multiplicity considering the detection efficiency, we measure neutron multiplicity in muon capture as $P(0)=24\pm3\%$, $P(1)=70^{+3}_{-2}\%$, $P(2)=6.1\pm0.5\%$, $P(3)=0.38\pm0.09\%$. This is the first measurement of the multiplicity of neutrons associated with muon capture without neutron energy threshold.
Published: 2025

2. VECT-GAN: A variationally encoded generative model for overcoming data scarcity in pharmaceutical science

Author: Abdalla, Youssef, Taub, Marrisa, Hilton, Eleanor, Akkaraju, Priya, Milanovic, Alexander, Orlu, Mine, Basit, Abdul W., Cook, Michael T, Chakraborti, Tapabrata, and Shorthouse, David
Subjects: Computer Science - Machine Learning
Abstract: Data scarcity in pharmaceutical research has led to reliance on labour-intensive trial-and-error approaches for development rather than data-driven methods. While Machine Learning offers a solution, existing datasets are often small and noisy, limiting their utility. To address this, we developed a Variationally Encoded Conditional Tabular Generative Adversarial Network (VECT-GAN), a novel generative model specifically designed for augmenting small, noisy datasets. We introduce a pipeline where data is augmented before regression model development and demonstrate that this consistently and significantly improves performance over other state-of-the-art tabular generative models. We apply this pipeline across six pharmaceutical datasets, and highlight its real-world applicability by developing novel polymers with medically desirable mucoadhesive properties, which we made and experimentally characterised. Additionally, we pre-train the model on the ChEMBL database of drug-like molecules, leveraging knowledge distillation to enhance its generalisability, making it readily available for use on pharmaceutical datasets containing small molecules, an extremely common pharmaceutical task. We demonstrate the power of synthetic data for regularising small tabular datasets, highlighting its potential to become standard practice in pharmaceutical model development, and make our method, including VECT-GAN pre-trained on ChEMBL available as a pip package., Comment: 30 pages, 6 primary figures, 3 supplementary figures
Published: 2025

3. Thermal circuit model for silicon quantum-dot array structures

Author: Utsugi, Takeru, Kusuno, Nobuhiro, Kuno, Takuma, Lee, Noriyuki, Yanagi, Itaru, Mine, Toshiyuki, Saito, Shinichi, Hisamoto, Digh, Tsuchiya, Ryuta, and Mizuno, Hiroyuki
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics, Physics - Applied Physics
Abstract: Temperature rise of qubits due to heating is a critical issue in large-scale quantum computers based on quantum-dot (QD) arrays. This leads to shorter coherence times, induced readout errors, and increased charge noise. Here, we propose a simple thermal circuit model to describe the heating effect on silicon QD array structures. Noting that the QD array is a periodic structure, we represent it as a thermal distributed-element circuit, forming a thermal transmission line. We validate this model by measuring the electron temperature in a QD array device using Coulomb blockade thermometry, finding that the model effectively reproduces experimental results. This simple and scalable model can be used to develop the thermal design of large-scale silicon-based quantum computers., Comment: 8 pages, 7 figures
Published: 2024

4. Long Time Behavior of General Markov Additive Processes

Author: Yaran, Celal Umut and Çağlar, Mine
Subjects: Mathematics - Probability, 60G18, 60G51, 60J25
Abstract: We study general Markov additive processes when the state space of the modulator is a Polish space. Under some regularity assumptions, our main result is the characterization of the long-time behavior of the ordinate in terms of the associated ladder time process and the excursion measure. An important application of Markov additive processes is the Lamperti-Kiu transform, which gives a correspondence between $\mathbb{R}^d\backslash \{0\}$-valued self-similar Markov processes and $S^{d-1}\times \mathbb{R}$-valued Markov additive processes. The asymptotic behavior of the radial distance from the origin of a self-similar Markov process can be characterized by the long-time behavior of the ordinate of the corresponding Markov additive process. We show the applicability of our assumptions on some well-known self-similar Markov processes., Comment: 29 pages
Published: 2024

5. Developing a Tutoring Dialog Dataset to Optimize LLMs for Educational Use

Author: Fateen, Menna and Mine, Tsunenori
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: Recent advances in large language models (LLMs) have shown promise for scalable educational applications, but their use in dialog-based tutoring systems remains challenging due to the need for effective pedagogical strategies and the high costs associated with expert-curated datasets. Our study explores the use of smaller, more affordable LLMs for one-on-one tutoring in the context of solving reading comprehension problems. We developed a synthetic tutoring dialog dataset, evaluated by human teachers, and fine-tuned a smaller LLM using this dataset. Furthermore, we conducted an interactive experiment comparing the performance of the fine-tuned model with a larger model in real-world tutoring scenarios. Our results show that the fine-tuned model performs on par with the larger model but at a lower cost, demonstrating a viable, cost-effective approach for implementing LLM-based tutoring systems in educational settings.
Published: 2024

6. An upper bound for the number of smooth values of a polynomial and its applications

Author: Mine, Masahiro
Subjects: Mathematics - Number Theory, Primary 11N32, Secondary 11N25
Abstract: We provide a new upper bound for the number of smooth values of a polynomial with integer coefficients. This improves Timofeev's previous result unless the polynomial is a product of linear polynomials with integer coefficients. As an application, we fix an error in the proof of a result of Cassels which was used to prove that the Hurwitz zeta-function with algebraic irrational parameter has infinitely many zeros on the domain of convergence. We also apply the main result to a problem on primitive divisors of quadratic polynomials., Comment: 30 pages
Published: 2024

7. Beyond Scores: A Modular RAG-Based System for Automatic Short Answer Scoring with Feedback

Author: Fateen, Menna, Wang, Bo, and Mine, Tsunenori
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract: Automatic short answer scoring (ASAS) helps reduce the grading burden on educators but often lacks detailed, explainable feedback. Existing methods in ASAS with feedback (ASAS-F) rely on fine-tuning language models with limited datasets, which is resource-intensive and struggles to generalize across contexts. Recent approaches using large language models (LLMs) have focused on scoring without extensive fine-tuning. However, they often rely heavily on prompt engineering and either fail to generate elaborated feedback or do not adequately evaluate it. In this paper, we propose a modular retrieval augmented generation based ASAS-F system that scores answers and generates feedback in strict zero-shot and few-shot learning scenarios. We design our system to be adaptable to various educational tasks without extensive prompt engineering using an automatic prompt generation framework. Results show an improvement in scoring accuracy by 9\% on unseen questions compared to fine-tuning, offering a scalable and cost-effective solution.
Published: 2024

8. Search for proton decay via $p\rightarrow{e^+\eta}$ and $p\rightarrow{\mu^+\eta}$ with a 0.37 Mton-year exposure of Super-Kamiokande

Author: Collaboration, Super-Kamiokande, Taniuchi, N., Abe, K., Abe, S., Asaoka, Y., Bronner, C., Harada, M., Hayato, Y., Hiraide, K., Hosokawa, K., Ieki, K., Ikeda, M., Kameda, J., Kanemura, Y., Kaneshima, R., Kashiwagi, Y., Kataoka, Y., Miki, S., Mine, S., Miura, M., Moriyama, S., Nakahata, M., Nakayama, S., Noguchi, Y., Pronost, G., Okamoto, K., Sato, K., Sekiya, H., Shiba, H., Shimizu, K., Shiozawa, M., Sonoda, Y., Suzuki, Y., Takeda, A., Takemoto, Y., Takenaka, A., Tanaka, H., Watanabe, S., Yano, T., Kajita, T., Okumura, K., Tashiro, T., Tomiya, T., Wang, X., Yoshida, S., Megias, G. D., Fernandez, P., Labarga, L., Ospina, N., Zaldivar, B., Pointon, B. W., Kearns, E., Mirabito, J., Raaf, J. L., Wan, L., Wester, T., Bian, J., Griskevich, N. J., Kropp, W. R., Locke, S., Smy, M. B., Sobel, H. W., Takhistov, V., Yankelevich, A., Hill, J., Jang, M. C., Kim, J. Y., Lee, S. H., Lim, I. T., Moon, D. H., Park, R. G., Yang, B. S., Bodur, B., Scholberg, K., Walter, C. W., Beauchêne, A., Bernard, L., Coffani, A., Drapier, O., Hedri, S. El, Giampaolo, A., Mueller, Th. A., Santos, A. D., Paganini, P., Rogly, R., Nakamura, T., Jang, J. S., Machado, L. N., Learned, J. G., Choi, K., Iovine, N., Cao, S., Anthony, L. H. V., Martin, D., Prouse, N. W., Scott, M., Sztuc, A. A., Uchida, Y., Berardi, V., Calabria, N. F., Catanesi, M. G., Radicioni, E., Langella, A., De Rosa, G., Collazuol, G., Feltre, M., Iacob, F., Lamoureux, M., Mattiazzi, M., Ludovici, L., Gonin, M., Périssé, L., Quilain, B., Fujisawa, C., Horiuchi, S., Kobayashi, M., Liu, Y. M., Maekawa, Y., Nishimura, Y., Okazaki, R., Akutsu, R., Friend, M., Hasegawa, T., Ishida, T., Kobayashi, T., Jakkapu, M., Matsubara, T., Nakadaira, T., Nakamura, K., Oyama, Y., Yrey, A. Portocarrero, Sakashita, K., Sekiguchi, T., Tsukamoto, T., Bhuiyan, N., Boschi, T., Burton, G. T., Di Lodovico, F., Gao, J., Goldsack, A., Katori, T., Migenda, J., Ramsden, R. M., Taani, M., Xie, Z., Zsoldos, S., Kotsar, Y., Ozaki, H., Suzuki, A. T., Takagi, Y., Takeuchi, Y., Yamamoto, S., Zhong, H., Feng, J., Feng, L., Han, S., Hu, J. R., Hu, Z., Kawaue, M., Kikawa, T., Mori, M., Nakaya, T., Wendell, R. A., Yasutome, K., Jenkins, S. J., McCauley, N., Mehta, P., Tarrant, A., Wilking, M. J., Fukuda, Y., Itow, Y., Menjo, H., Ninomiya, K., Yoshioka, Y., Lagoda, J., Mandal, M., Mijakowski, P., Prabhu, Y. S., Zalipska, J., Jia, M., Jiang, J., Jung, C. K., Shi, W., Yanagisawa, C., Hino, Y., Ishino, H., Ito, S., Kitagawa, H., Koshio, Y., Ma, W., Nakanishi, F., Sakai, S., Tada, T., Tano, T., Ishizuka, T., Barr, G., Barrow, D., Cook, L., Samani, S., Wark, D., Holin, A., Nova, F., Jung, S., Yang, J. Y., Yoo, J., Fannon, J. E. P., Kneale, L., Malek, M., McElwee, J. M., Stone, O., Stowell, P., Thiesse, M. D., Thompson, L. F., Wilson, S. T., Okazawa, H., Lakshmi, S. M., Kim, S. B., Kwon, E., Lee, M. W., Seo, J. W., Yu, I., Ichikawa, A. K., Nakamura, K. D., Tairafune, S., Nishijima, K., Koshiba, M., Eguchi, A., Goto, S., Iwamoto, K., Mizuno, Y., Muro, T., Nakagiri, K., Nakajima, Y., Shima, S., Watanabe, E., Yokoyama, M., de Perio, P., Fujita, S., Jesús-Valls, C., Martens, K., Marti, Ll., Tsui, K. M., Vagins, M. R., Xia, J., Izumiyama, S., Kuze, M., Matsumoto, R., Terada, K., Asaka, R., Inomoto, M., Ishitsuka, M., Ito, H., Kinoshita, T., Ommura, Y., Shigeta, N., Shinoki, M., Suganuma, T., Yamauchi, K., Yoshida, T., Nakano, Y., Martin, J. F., Tanaka, H. A., Towstego, T., Gaur, R., Gousy-Leblanc, V., Hartz, M., Konaka, A., Li, X., Chen, S., Wu, Y., Xu, B. D., Zhang, A. Q., Zhang, B., Posiadala-Zezula, M., Boyd, S. B., Edwards, R., Hadley, D., Nicholson, M., O'Flaherty, M., Richards, B., Ali, A., Jamieson, B., Amanai, S., Minamino, A., Pintaudi, G., Sano, S., Sasaki, R., Shibayama, R., Shimamura, R., Suzuki, S., and Wada, K.
Subjects: High Energy Physics - Experiment
Abstract: A search for proton decay into $e^+/\mu^+$ and a $\eta$ meson has been performed using data from a 0.373 Mton$\cdot$year exposure (6050.3 live days) of Super-Kamiokande. Compared to previous searches this work introduces an improved model of the intranuclear $\eta$ interaction cross section, resulting in a factor of two reduction in uncertainties from this source and $\sim$10\% increase in signal efficiency. No significant data excess was found above the expected number of atmospheric neutrino background events resulting in no indication of proton decay into either mode. Lower limits on the proton partial lifetime of $1.4\times\mathrm{10^{34}~years}$ for $p\rightarrow e^+\eta$ and $7.3\times\mathrm{10^{33}~years}$ for $p\rightarrow \mu^+\eta$ at the 90$\%$ C.L. were set. These limits are around 1.5 times longer than our previous study and are the most stringent to date.
Published: 2024

9. SeqRisk: Transformer-augmented latent variable model for improved survival prediction with longitudinal data

Author: Öğretir, Mine, Koskinen, Miika, Sinisalo, Juha, Renkonen, Risto, and Lähdesmäki, Harri
Subjects: Computer Science - Machine Learning
Abstract: In healthcare, risk assessment of different patient outcomes has for long time been based on survival analysis, i.e.\ modeling time-to-event associations. However, conventional approaches rely on data from a single time-point, making them suboptimal for fully leveraging longitudinal patient history and capturing temporal regularities. Focusing on clinical real-world data and acknowledging its challenges, we utilize latent variable models to effectively handle irregular, noisy, and sparsely observed longitudinal data. We propose SeqRisk, a method that combines variational autoencoder (VAE) or longitudinal VAE (LVAE) with a transformer encoder and Cox proportional hazards module for risk prediction. SeqRisk captures long-range interactions, improves patient trajectory representations, enhances predictive accuracy and generalizability, as well as provides partial explainability for sample population characteristics in attempts to identify high-risk patients. We demonstrate that SeqRisk performs competitively compared to existing approaches on both simulated and real-world datasets.
Published: 2024

10. Gestalt driven augmented collimator widget for precise 5 dof dental drill tool positioning in 3d space

Author: Dastan, Mine, Uva, Antonio E., and Fiorentino, Michele
Subjects: Computer Science - Human-Computer Interaction
Abstract: Drill tool positioning in dental implantology is a challenging task requiring 5DOF precision as the rotation around the tool axis is not influential. This work improves the quasi-static visual elements of the state-of-the-art with a novel Augmented Collimation Widget (ACW), an interactive tool of position and angle error visualization based on the gestalt reification, the human ability to group geometric elements. The user can seek in a quick, pre-attentive way the collimation of five (three positional and two rotational) error component widgets (ECWs), taking advantage of three key aspects: component separation and reification, error visual amplification, and dynamic hiding of the collimated components. We compared the ACW with the golden standard in a within-subjects (N=30) user test using 32 implant targets, measuring the time, error, and usability. ACW performed significantly better in positional (+19%) and angular (+47%) precision accuracy and with less mental demand (-6%) and frustration (-13%), but with an expected increase in task time (+59%) and physical demand (+64%). The interview indicated the ACW as the main preference and aesthetically more pleasant than GSW, candidating it as the new golden standard for implantology, but also for other applications where 5DOF positioning is key.
Published: 2024
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11. Co-Designing Dynamic Mixed Reality Drill Positioning Widgets: A Collaborative Approach with Dentists in a Realistic Setup

Author: Dastan, Mine, Fiorentino, Michele, Walter, Elias D., Diegritz, Christian, Uva, Antonio E., Eck, Ulrich, and Navab, Nassir
Subjects: Computer Science - Human-Computer Interaction
Abstract: Mixed Reality (MR) is proven in the literature to support precise spatial dental drill positioning by superimposing 3D widgets. Despite this, the related knowledge about widget's visual design and interactive user feedback is still limited. Therefore, this study is contributed to by co-designed MR drill tool positioning widgets with two expert dentists and three MR experts. The results of co-design are two static widgets (SWs): a simple entry point, a target axis, and two dynamic widgets (DWs), variants of dynamic error visualization with and without a target axis (DWTA and DWEP). We evaluated the co-designed widgets in a virtual reality simulation supported by a realistic setup with a tracked phantom patient, a virtual magnifying loupe, and a dentist's foot pedal. The user study involved 35 dentists with various backgrounds and years of experience. The findings demonstrated significant results; DWs outperform SWs in positional and rotational precision, especially with younger generations and subjects with gaming experiences. The user preference remains for DWs (19) instead of SWs (16). However, findings indicated that the precision positively correlates with the time trade-off. The post-experience questionnaire (NASA-TLX) showed that DWs increase mental and physical demand, effort, and frustration more than SWs. Comparisons between DWEP and DWTA show that the DW's complexity level influences time, physical and mental demands. The DWs are extensible to diverse medical and industrial scenarios that demand precision.
Published: 2024
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12. Precise Tool to Target Positioning Widgets (TOTTA) in Spatial Environments: A Systematic Review

Author: Dastan, Mine, Fiorentino, Michele, and Uva, Antonio E.
Subjects: Computer Science - Human-Computer Interaction
Abstract: TOTTA outlines the spatial position and rotation guidance of a real/virtual tool (TO) towards a real/virtual target (TA), which is a key task in Mixed Reality applications. The task error can have critical consequences regarding safety, performance, and quality, such as in surgical implantology or industrial maintenance scenarios. The TOTTA problem lacks a dedicated study and is scattered across different domains with isolated designs. This work contributes to a systematic review of the TOTTA visual widgets, studying 70 unique designs from 24 papers. TOTTA is commonly guided by visual overlap an intuitive, pre-attentive 'collimation' feedback of simple-shaped widgets: Box, 3D Axes, 3D Model, 2D Crosshair, Globe, Tetrahedron, Line, and Plane. Our research discovers that TO and TA are often represented with the same shape. They are distinguished by topological elements (e.g., edges, vertices, faces), colors, transparency levels, and added shapes, widget quantity, and size. Meanwhile, some designs provide continuous 'during manipulation feedback' relative to the distance between TO and TA by text, dynamic color, sonification, and amplified graphical visualization. Some approaches trigger discrete 'TA reached feedback,' such as color alteration, added sound, TA shape change, and added text. We found a lack of golden standards, including in testing procedures, as current ones are limited to partial sets with different and incomparable setups (different target configurations, avatar, background, etc.). We also found a bias in participants: right-handed, young male, non-color impaired.
Published: 2024
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13. Enhanced Optimization Strategies to Design an Underactuated Hand Exoskeleton

Author: Akbas, Baris, Yuksel, Huseyin Taner, Soylemez, Aleyna, Sarac, Mine, and Stroppa, Fabio
Subjects: Computer Science - Robotics, Computer Science - Human-Computer Interaction, Computer Science - Neural and Evolutionary Computing
Abstract: Exoskeletons can boost human strength and provide assistance to individuals with physical disabilities. However, ensuring safety and optimal performance in their design poses substantial challenges. This study presents the design process for an underactuated hand exoskeleton (U-HEx), first including a single objective (maximizing force transmission), then expanding into multi objective (also minimizing torque variance and actuator displacement). The optimization relies on a Genetic Algorithm, the Big Bang-Big Crunch Algorithm, and their versions for multi-objective optimization. Analyses revealed that using Big Bang-Big Crunch provides high and more consistent results in terms of optimality with lower convergence time. In addition, adding more objectives offers a variety of trade-off solutions to the designers, who might later set priorities for the objectives without repeating the process - at the cost of complicating the optimization algorithm and computational burden. These findings underline the importance of performing proper optimization while designing exoskeletons, as well as providing a significant improvement to this specific robotic design., Comment: 12 pages, 7 figures, 8 talbes, submitted to IEEE Transactions on Robotics
Published: 2024

14. Easing Maintenance of Academic Static Analyzers

Author: Monat, Raphaël, Ouadjaout, Abdelraouf, and Miné, Antoine
Subjects: Computer Science - Programming Languages, Computer Science - Software Engineering
Abstract: Academic research in static analysis produces software implementations. These implementations are time-consuming to develop and some need to be maintained in order to enable building further research upon the implementation. While necessary, these processes can be quickly challenging. This article documents the tools and techniques we have come up with to simplify the maintenance of Mopsa since 2017. Mopsa is a static analysis platform that aims at being sound. First, we describe an automated way to measure precision that does not require any baseline of true bugs obtained by manually inspecting the results. Further, it improves transparency of the analysis, and helps discovering regressions during continuous integration. Second, we have taken inspiration from standard tools observing the concrete execution of a program to design custom tools observing the abstract execution of the analyzed program itself, such as abstract debuggers and profilers. Finally, we report on some cases of automated testcase reduction.
Published: 2024

15. One Stone, Four Birds: A Comprehensive Solution for QA System Using Supervised Contrastive Learning

Author: Wang, Bo and Mine, Tsunenori
Subjects: Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: This paper presents a novel and comprehensive solution to enhance both the robustness and efficiency of question answering (QA) systems through supervised contrastive learning (SCL). Training a high-performance QA system has become straightforward with pre-trained language models, requiring only a small amount of data and simple fine-tuning. However, despite recent advances, existing QA systems still exhibit significant deficiencies in functionality and training efficiency. We address the functionality issue by defining four key tasks: user input intent classification, out-of-domain input detection, new intent discovery, and continual learning. We then leverage a unified SCL-based representation learning method to efficiently build an intra-class compact and inter-class scattered feature space, facilitating both known intent classification and unknown intent detection and discovery. Consequently, with minimal additional tuning on downstream tasks, our approach significantly improves model efficiency and achieves new state-of-the-art performance across all tasks., Comment: 16 pages, updated to the accepted version
Published: 2024
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16. Balancing Embedding Spectrum for Recommendation

Author: Peng, Shaowen, Sugiyama, Kazunari, Liu, Xin, and Mine, Tsunenori
Subjects: Computer Science - Information Retrieval
Abstract: Modern recommender systems heavily rely on high-quality representations learned from high-dimensional sparse data. While significant efforts have been invested in designing powerful algorithms for extracting user preferences, the factors contributing to good representations have remained relatively unexplored. In this work, we shed light on an issue in the existing pair-wise learning paradigm (i.e., the embedding collapse problem), that the representations tend to span a subspace of the whole embedding space, leading to a suboptimal solution and reducing the model capacity. Specifically, optimization on observed interactions is equivalent to a low pass filter causing users/items to have the same representations and resulting in a complete collapse. While negative sampling acts as a high pass filter to alleviate the collapse by balancing the embedding spectrum, its effectiveness is only limited to certain losses, which still leads to an incomplete collapse. To tackle this issue, we propose a novel method called DirectSpec, acting as a reliable all pass filter to balance the spectrum distribution of the embeddings during training, ensuring that users/items effectively span the entire embedding space. Additionally, we provide a thorough analysis of DirectSpec from a decorrelation perspective and propose an enhanced variant, DirectSpec+, which employs self-paced gradients to optimize irrelevant samples more effectively. Moreover, we establish a close connection between DirectSpec+ and uniformity, demonstrating that contrastive learning (CL) can alleviate the collapse issue by indirectly balancing the spectrum. Finally, we implement DirectSpec and DirectSpec+ on two popular recommender models: MF and LightGCN. Our experimental results demonstrate its effectiveness and efficiency over competitive baselines., Comment: ACM Trans on Recommender Systems
Published: 2024

17. How Powerful is Graph Filtering for Recommendation

Author: Peng, Shaowen, Liu, Xin, Sugiyama, Kazunari, and Mine, Tsunenori
Subjects: Computer Science - Information Retrieval
Abstract: It has been shown that the effectiveness of graph convolutional network (GCN) for recommendation is attributed to the spectral graph filtering. Most GCN-based methods consist of a graph filter or followed by a low-rank mapping optimized based on supervised training. However, we show two limitations suppressing the power of graph filtering: (1) Lack of generality. Due to the varied noise distribution, graph filters fail to denoise sparse data where noise is scattered across all frequencies, while supervised training results in worse performance on dense data where noise is concentrated in middle frequencies that can be removed by graph filters without training. (2) Lack of expressive power. We theoretically show that linear GCN (LGCN) that is effective on collaborative filtering (CF) cannot generate arbitrary embeddings, implying the possibility that optimal data representation might be unreachable. To tackle the first limitation, we show close relation between noise distribution and the sharpness of spectrum where a sharper spectral distribution is more desirable causing data noise to be separable from important features without training. Based on this observation, we propose a generalized graph normalization G^2N to adjust the sharpness of spectral distribution in order to redistribute data noise to assure that it can be removed by graph filtering without training. As for the second limitation, we propose an individualized graph filter (IGF) adapting to the different confidence levels of the user preference that interactions can reflect, which is proved to be able to generate arbitrary embeddings. By simplifying LGCN, we further propose a simplified graph filtering (SGFCF) which only requires the top-K singular values for recommendation. Finally, experimental results on four datasets with different density settings demonstrate the effectiveness and efficiency of our proposed methods., Comment: Accepted to KDD'24
Published: 2024

18. Integrated density of states for the Poisson point interactions on $\mathbf{R}^3$

Author: Kaminaga, Masahiro, Mine, Takuya, and Nakano, Fumihiko
Subjects: Mathematical Physics
Abstract: We determine the principal term of the asymptotics of the integrated density of states (IDS) $N(\lambda)$ for the Schr\"odinger operator with point interactions on $\mathbf{R}^3$ as $\lambda \to -\infty$, provided that the set of positions of the point obstacles is the Poisson configuration, and the interaction parameters are bounded i.i.d.\ random variables. In particular, we prove $N(\lambda) =O(|\lambda|^{-3/2})$ as $\lambda\to -\infty$. In the case that all interaction parameters are equal to a constant, we give a more detailed asymptotics of $N(\lambda)$, and verify the result by a numerical method using R., Comment: 30 pages, 4 figures
Published: 2024

19. First joint oscillation analysis of Super-Kamiokande atmospheric and T2K accelerator neutrino data

Author: Super-Kamiokande, collaborations, T2K, Abe, S., Abe, K., Akhlaq, N., Akutsu, R., Alarakia-Charles, H., Ali, A., Hakim, Y. I. Alj, Monsalve, S. Alonso, Amanai, S., Andreopoulos, C., Anthony, L. H. V., Antonova, M., Aoki, S., Apte, K. A., Arai, T., Arihara, T., Arimoto, S., Asada, Y., Asaka, R., Ashida, Y., Atkin, E. T., Babu, N., Barbi, M., Barker, G. J., Barr, G., Barrow, D., Bates, P., Batkiewicz-Kwasniak, M., Beauchêne, A., Berardi, V., Berns, L., Bhadra, S., Bhuiyan, N., Bian, J., Blanchet, A., Blondel, A., Bodur, B., Bolognesi, S., Bordoni, S., Boyd, S. B., Bravar, A., Bronner, C., Bubak, A., Avanzini, M. Buizza, Burton, G. T., Caballero, J. A., Calabria, N. F., Cao, S., Carabadjac, D., Carter, A. J., Cartwright, S. L., Casado, M. P., Catanesi, M. G., Cervera, A., Chakrani, J., Chalumeau, A., Chen, S., Cherdack, D., Choi, K., Chong, P. S., Chvirova, A., Cicerchia, M., Coleman, J., Collazuol, G., Cook, L., Cormier, F., Cudd, A., Dalmazzone, C., Daret, T., Dasgupta, P., Davis, C., Davydov, Yu. I., De Roeck, A., De Rosa, G., Dealtry, T., Delogu, C. C., Densham, C., Dergacheva, A., Dharmapal, R., Di Lodovico, F., Lopez, G. Diaz, Dolan, S., Douqa, D., Doyle, T. A., Drapier, O., Duffy, K. E., Dumarchez, J., Dunne, P., Dygnarowicz, K., D'ago, D., Edwards, R., Eguchi, A., Elias, J., Emery-Schrenk, S., Erofeev, G., Ershova, A., Eurin, G., Fannon, J. E. P., Fedorova, D., Fedotov, S., Feltre, M., Feng, J., Feng, L., Ferlewicz, D., Fernandez, P., Finch, A. J., Aguirre, G. A. Fiorentini, Fiorillo, G., Fitton, M. D., Patiño, J. M. Franco, Friend, M., Fujii, Y., Fujisawa, C., Fujita, S., Fukuda, Y., Furui, Y., Gao, J., Gaur, R., Giampaolo, A., Giannessi, L., Giganti, C., Glagolev, V., Goldsack, A., Gonin, M., Rosa, J. González, Goodman, E. A. G., Gorin, A., Gorshanov, K., Gousy-Leblanc, V., Grassi, M., Griskevich, N. J., Guigue, M., Hadley, D., Haigh, J. T., Han, S., Harada, M., Harris, D. A., Hartz, M., Hasegawa, T., Hassani, S., Hastings, N. C., Hayato, Y., Heitkamp, I., Henaff, D., Hill, J., Hino, Y., Hiraide, K., Hogan, M., Holeczek, J., Holin, A., Holvey, T., Van, N. T. Hong, Honjo, T., Horiuchi, S., Hosokawa, K., Hu, Z., Hu, J., Iacob, F., Ichikawa, A. K., Ieki, K., Ikeda, M., Iovine, N., Ishida, T., Ishino, H., Ishitsuka, M., Ishizuka, T., Ito, H., Itow, Y., Izmaylov, A., Izumiyama, S., Jakkapu, M., Jamieson, B., Jang, M. C., Jang, J. S., Jenkins, S. J., Jesús-Valls, C., Ji, J. Y., Jia, M., Jiang, J., Jonsson, P., Joshi, S., Jung, C. K., Jung, S., Kabirnezhad, M., Kaboth, A. C., Kajita, T., Kakuno, H., Kameda, J., Kanemura, Y., Kaneshima, R., Karpova, S., Kasetti, S. P., Kashiwagi, Y., Kasturi, V. S., Kataoka, Y., Katori, T., Kawamura, Y., Kawaue, M., Kearns, E., Khabibullin, M., Khotjantsev, A., Kikawa, T., Kim, S. B., King, S., Kiseeva, V., Kisiel, J., Kneale, L., Kobayashi, H., Kobayashi, T., Kobayashi, M., Koch, L., Kodama, S., Kolupanova, M., Konaka, A., Kormos, L. L., Koshio, Y., Koto, T., Kowalik, K., Kudenko, Y., Kudo, Y., Kuribayashi, S., Kurjata, R., Kurochka, V., Kutter, T., Kuze, M., Kwon, E., La Commara, M., Labarga, L., Lachat, M., Lachner, K., Lagoda, J., Lakshmi, S. M., LamersJames, M., Langella, A., Laporte, J. -F., Last, D., Latham, N., Laveder, M., Lavitola, L., Lawe, M., Learned, J. G., Lee, Y., Lee, S. H., Silverio, D. Leon, Levorato, S., Lewis, S., Li, X., Li, W., Lin, C., Litchfield, R. P., Liu, S. L., Liu, Y. M., Long, K. R., Longhin, A., Moreno, A. Lopez, Lu, X., Ludovici, L., Lux, T., Machado, L. N., Maekawa, Y., Magaletti, L., Mahn, K., Mahtani, K. K., Malek, M., Mandal, M., Manly, S., Marino, A. D., Martens, K., Marti, Ll., Martin, D. G. R., Martin, J. F., Martin, D., Martini, M., Maruyama, T., Matsubara, T., Matsumoto, R., Mattiazzi, M., Matveev, V., Mauger, C., Mavrokoridis, K., Mazzucato, E., McCauley, N., McElwee, J. M., McFarland, K. S., McGrew, C., McKean, J., Mefodiev, A., Megias, G. D., Mehta, P., Mellet, L., Menjo, H., Metelko, C., Mezzetto, M., Migenda, J., Mijakowski, P., Miki, S., Miller, E., Minamino, A., Mine, S., Mineev, O., Mirabito, J., Miura, M., Bueno, L. Molina, Moon, D. H., Mori, M., Moriyama, S., Morrison, P., Muñoz, A., Mueller, Th. A., Munford, D., Munteanu, L., Nagai, Y., Nagai, K., Nakadaira, T., Nakagiri, K., Nakahata, M., Nakajima, Y., Nakamura, A., Nakamura, K., Nakamura, K. D., Nakamura, T., Nakanishi, F., Nakano, Y., Nakaya, T., Nakayama, S., Nakayoshi, K., Naseby, C. E. R., Ngoc, T. V., Nguyen, V. Q., Nguyen, D. T., Nicholson, M., Niewczas, K., Ninomiya, K., Nishijima, K., Nishimori, S., Nishimura, Y., Noguchi, Y., Nosek, T., Nova, F., Novella, P., Nugent, J. C., Odagawa, T., Okazaki, R., Okazawa, H., Okinaga, W., Okumura, K., Okusawa, T., Ommura, Y., Onda, N., Ospina, N., Osu, L., Oyama, Y., O'Flaherty, M., O'Keeffe, H. M., O'Sullivan, L., Périssé, L., Paganini, P., Palladino, V., Paolone, V., Pari, M., Park, R. G., Parlone, J., Pasternak, J., Payne, D., Penn, G. C., de Perio, P., Pershey, D., Pfaff, M., Pickering, L., Pintaudi, G., Pistillo, C., Pointon, B. W., Popov, B., Yrey, A. Portocarrero, Porwit, K., Posiadala-Zezula, M., Prabhu, Y. S., Prasad, H., Pronost, G., Prouse, N. W., Pupilli, F., Quilain, B., Quyen, P. T., Raaf, J. L., Radermacher, T., Radicioni, E., Radics, B., Ramirez, M. A., Ramsden, R. M., Ratoff, P. N., Reh, M., Riccio, C., Richards, B., Rogly, R., Rondio, E., Roth, S., Roy, N., Rubbia, A., Russo, L., Rychter, A., Saenz, W., Sakai, S., Sakashita, K., Samani, S., Santos, A. D., Sato, Y., Sato, K., Schefke, T., Schloesser, C. M., Scholberg, K., Scott, M., Seiya, Y., Sekiguchi, T., Sekiya, H., Seo, J. W., Sgalaberna, D., Shaikhiev, A., Shi, W., Shiba, H., Shibayama, R., Shigeta, N., Shima, S., Shimamura, R., Shimizu, K., Shinoki, M., Shiozawa, M., Shiraishi, Y., Shvartsman, A., Skrobova, N., Skwarczynski, K., Smy, M. B., Smyczek, D., Sobczyk, J. T., Sobel, H. W., Soler, F. J. P., Sonoda, Y., Speers, A. J., Spina, R., Stroke, Y., Suslov, I. A., Suvorov, S., Suzuki, S., Suzuki, A., Suzuki, S. Y., Suzuki, Y., Sánchez, F., Tada, T., Tada, M., Tairafune, S., Takagi, Y., Takeda, A., Takemoto, Y., Takeuchi, Y., Takhistov, V., Takifuji, K., Tanaka, H., Tanaka, H. K., Tanigawa, H., Taniuchi, N., Tano, T., Tarrant, A., Tashiro, T., Teklu, A., Terada, K., Tereshchenko, V. V., Thamm, N., Thiesse, M. D., Thompson, L. F., Toki, W., Tomiya, T., Touramanis, C., Tsui, K. M., Tsukamoto, T., Tzanov, M., Uchida, Y., Vagins, M. R., Vargas, D., Varghese, M., Vasseur, G., Villa, E., Vinning, W. G. S., Virginet, U., Vladisavljevic, T., Wachala, T., Wakabayashi, D., Wallace, H. T., Walsh, J. G., Walter, C. W., Wan, L., Wang, X., Wang, Y., Wark, D., Wascko, M. O., Watanabe, E., Weber, A., Wendell, R. A., Wester, T., Wilking, M. J., Wilkinson, C., Wilson, S. T., Wilson, J. R., Wood, K., Wret, C., Wu, Y., Xia, J., Xie, Z., Xu, B. D., Xu, Y. -H., Yamamoto, K., Yamamoto, T., Yamauchi, K., Yanagisawa, C., Yang, G., Yang, B. S., Yang, J. Y., Yankelevich, A., Yano, T., Yasutome, K., Yershov, N., Yevarouskaya, U., Yokoyama, M., Yoo, J., Yoshida, T., Yoshida, S., Yoshimoto, Y., Yoshimura, N., Yoshioka, Y., Yu, M., Yu, I., Zaki, R., Zaldivar, B., Zalewska, A., Zalipska, J., Zaremba, K., Zarnecki, G., Zhang, J., Zhang, A. Q., Zhang, B., Zhao, X. Y., Zhong, H., Zhu, T., Ziembicki, M., Zimmerman, E. D., Zito, M., and Zsoldos, S.
Subjects: High Energy Physics - Experiment
Abstract: The Super-Kamiokande and T2K collaborations present a joint measurement of neutrino oscillation parameters from their atmospheric and beam neutrino data. It uses a common interaction model for events overlapping in neutrino energy and correlated detector systematic uncertainties between the two datasets, which are found to be compatible. Using 3244.4 days of atmospheric data and a beam exposure of $19.7(16.3) \times 10^{20}$ protons on target in (anti)neutrino mode, the analysis finds a 1.9$\sigma$ exclusion of CP-conservation (defined as $J_{CP}=0$) and a preference for the normal mass ordering., Comment: 12 pages, 4 figures
Published: 2024

20. Direct observation of anisotropic Cooper pairing in kagome superconductor CsV3Sb5

Author: Mine, Akifumi, Zhong, Yigui, Liu, Jinjin, Suzuki, Takeshi, Najafzadeh, Sahand, Uchiyama, Takumi, Yin, Jia-Xin, Wu, Xianxin, Shi, Xun, Wang, Zhiwei, Yao, Yugui, and Okazaki, Kozo
Subjects: Condensed Matter - Superconductivity, Condensed Matter - Materials Science
Abstract: In the recently discovered kagome superconductor AV3Sb5 (A = K, Rb, and Cs), the superconductivity is intertwined with an unconventional charge density wave order. Its pairing symmetry remains elusive owing to the lack of direct measurement of the superconducting gap in the momentum space. In this letter, utilizing laser-based ultra-high-resolution and low-temperature angle-resolved photoemission spectroscopy, we observe anisotropic Cooper pairing in kagome superconductor CsV3Sb5. We detect a highly anisotropic superconducting gap structure with an anisotropy over 80% and the gap maximum along the V-V bond direction on a Fermi surface originated from the 3d-orbital electrons of the V kagome lattice. It is in stark contrast to the isotropic superconducting gap structure on the other Fermi surface that is occupied by Sb 5p-orbital electrons. Our observation of the anisotropic Cooper pairing in pristine CsV3Sb5 is fundamental for understanding intertwined orders in the ground state of kagome superconductors.
Published: 2024

21. Combined Pre-Supernova Alert System with Kamland and Super-Kamiokande

Author: KamLAND, Collaborations, Super-Kamiokande, Abe, Seisho, Eizuka, Minori, Futagi, Sawako, Gando, Azusa, Gando, Yoshihito, Goto, Shun, Hachiya, Takahiko, Hata, Kazumi, Ichimura, Koichi, Ieki, Sei, Ikeda, Haruo, Inoue, Kunio, Ishidoshiro, Koji, Kamei, Yuto, Kawada, Nanami, Kishimoto, Yasuhiro, Koga, Masayuki, Kurasawa, Maho, Mitsui, Tadao, Miyake, Haruhiko, Morita, Daisuke, Nakahata, Takeshi, Nakajima, Rika, Nakamura, Kengo, Nakamura, Rikuo, Nakamura, Ryo, Nakane, Jun, Ozaki, Hideyoshi, Saito, Keita, Sakai, Taichi, Shimizu, Itaru, Shirai, Junpei, Shiraishi, Kensuke, Shoji, Ryunosuke, Suzuki, Atsuto, Takeuchi, Atsuto, Tamae, Kyoko, Watanabe, Hiroko, Watanabe, Kazuho, Yoshida, Sei, Umehara, Saori, Fushimi, Ken-Ichi, Kotera, Kenta, Urano, Yusuke, Berger, Bruce E., Fujikawa, Brian K., Larned, John G., Maricic, Jelena, Fu, Zhenghao, Smolsky, Joseph, Winslow, Lindley A., Efremenko, Yuri, Karwowski, Hugon J., Markoff, Diane M., Tornow, Werner, Dell'Oro, Stefano, O'Donnell, Thomas, Detwiler, Jason A., Enomoto, Sanshiro, Decowski, Michal P., Weerman, Kelly M., Grant, Christopher, Song, Hasung, Li, Aobo, Axani, Spencer N., Garcia, Miles, Abe, Ko, Bronner, Christophe, Hayato, Yoshinari, Hiraide, Katsuki, Hosokawa, Keishi, Ieki, Kei, Ikeda, Motoyasu, Kameda, June, Kanemura, Yuki, Kaneshima, Ryota, Kashiwagi, Yuri, Kataoka, Yousuke, Miki, Shintaro, Mine, Shunichi, Miura, Makoto, Moriyama, Shigetaka, Nakahata, Masayuki, Nakano, Yuuki, Nakayama, Shoei, Noguchi, Yohei, Sato, Kazufumi, Sekiya, Hiroyuki, Shiba, Hayato, Shimizu, Kotaro, Shiozawa, Masato, Sonoda, Yutaro, Suzuki, Yoichiro, Takeda, Atsushi, Takemoto, Yasuhiro, Tanaka, Hidekazu K., Yano, Takatomi, Han, Seungho, Kajita, Takaaki, Okumura, Kimihiro, Tashiro, Takuya, Tomiya, Takuya, Wang, Xubin, Yoshida, Shunsuke, Fernandez, Pablo, Labarga, Luis, Ospina, Nataly, Zaldivar, Bryan, Pointon, Barry W., Kearns, Edward, Raaf, Jennifer L., Wan, Linyan, Wester, Thomas, Bian, Jianming, Griskevich, Jeff, Smy, Michael B., Sobel, Henry W., Takhistov, Volodymyr, Yankelevich, Alejandro, Hill, James, Jang, MinCheol, Lee, Seonghak, Moon, DongHo, Park, RyeongGyoon, Bodur, Baran, Scholberg, Kate, Walter, Chris W., Beauchêne, Antoine, Drapier, Olivier, Giampaolo, Alberto, Mueller, Thomas A., Santos, Andrew D., Paganini, Pascal, Quilain, Benjamin, Rogly, Rudolph, Nakamura, Taku, Jang, Jee-Seung, Machado, Lucas N., Learned, John G., Choi, Koun, Iovine, Nadege, Cao, Son V., Anthony, Lauren H. V., Martin, Daniel G. R., Prouse, Nick W., Scott, Mark, Uchida, Yoshi, Berardi, Vincenzo, Calabria, Nicola F., Catanesi, M. G., Radicioni, Emilio, Langella, Aurora, de Rosa, Gianfranca, Collazuol, Gianmaria, Feltre, Matteo, Iacob, Fabio, Mattiazzi, Marco, Ludovici, Lucio, Gonin, Michel, Périssé, Lorenzo, Pronost, Guillaume, Fujisawa, Chiori, Horiuchi, Shogo, Kobayashi, Misaki, Liu, Yu-Ming, Maekawa, Yuto, Nishimura, Yasuhiro, Okazaki, Reo, Akutsu, Ryosuke, Friend, Megan, Hasegawa, Takuya, Ishida, Taku, Kobayashi, Takashi, Jakkapu, Mahesh, Matsubara, Tsunayuki, Nakadaira, Takeshi, Nakamura, Kenzo, Oyama, Yuichi, Sakashita, Ken, Sekiguchi, Tetsuro, Tsukamoto, Toshifumi, Yrey, Antoniosk Portocarrero, Bhuiyan, Nahid, Burton, George T., Di Lodovico, Francesca, Gao, Joanna, Goldsack, Alexander, Katori, Teppei, Migenda, Jost, Ramsden, Rory M., Xie, Zhenxiong, Zsoldos, Stephane, Suzuki, Atsumu T., Takagi, Yusuke, Takeuchi, Yasuo, Zhong, Haiwen, Feng, Jiahui, Feng, Li-Cheng, Hu, Jianrun, Hu, Zhuojun, Kawaue, Masaki, Kikawa, Tatsuya, Mori, Masamitsu, Nakaya, Tsuyoshi, Wendell, Roger A., Yasutome, Kenji, Jenkins, Sam J., McCauley, Neil K., Mehta, Pruthvi, Tarrant, Adam, Wilking, Mike J., Fukuda, Yoshiyuki, Itow, Yoshitaka, Menjo, Hiroaki, Ninomiya, Kotaro, Yoshioka, Yushi, Lagoda, Justyna, Mandal, Maitrayee, Mijakowski, Piotr, Prabhu, Yashwanth S., Zalipska, Joanna, Jia, Mo, Jiang, Junjie, Shi, Wei, Yanagisawa, Chiaki, Harada, Masayuki, Hino, Yota, Ishino, Hirokazu, Koshio, Yusuke, Nakanishi, Fumi, Sakai, Seiya, Tada, Tomoaki, Tano, Tomohiro, Ishizuka, Takeharu, Barr, Giles, Barrow, Daniel, Cook, Laurence, Samani, Soniya, Wark, David, Holin, Anna, Nova, Federico, Jung, Seunghyun, Yang, Byeongsu, Yang, JeongYeol, Yoo, Jonghee, Fannon, Jack E. P., Kneale, Liz, Malek, Matthew, McElwee, Jordan M., Thiesse, Matthew D., Thompson, Lee F., Wilson, Stephen T., Okazawa, Hiroko, Mohan, Lakshmi S., Kim, SooBong, Kwon, Eunhyang, Seo, Ji-Woong, Yu, Intae, Ichikawa, Atsuko K., Nakamura, Kiseki D., Tairafune, Seidai, Nishijima, Kyoshi, Eguchi, Aoi, Nakagiri, Kota, Nakajima, Yasuhiro, Shima, Shizuka, Taniuchi, Natsumi, Watanabe, Eiichiro, Yokoyama, Masashi, de Perio, Patrick, Fujita, Saki, Jesus-Valls, Cesar, Martens, Kai, Tsui, Ka M., Vagins, Mark R., Xia, Junjie, Izumiyama, Shota, Kuze, Masahiro, Matsumoto, Ryo, Terada, Kotaro, Asaka, Ryusei, Ishitsuka, Masaki, Ito, Hiroshi, Ommura, Yuga, Shigeta, Natsuki, Shinoki, Masataka, Yamauchi, Koki, Yoshida, Tsukasa, Gaur, Rhea, Gousy-Leblan, Vincent, Hartz, Mark, Konaka, Akira, Li, Xiaoyue, Chen, Shaomin, Xu, Benda, Zhang, Aiqiang, Zhang, Bin, Posiadala-Zezula, Magdalena, Boyd, Steven B., Edwards, Rory, Hadley, David, Nicholson, Matthew, O'Flaherty, Marcus, Richards, Benjamin, Ali, Ajmi, Jamieson, Blair, Amanai, Shogo, Marti-Magro, Lluis, Minamino, Akihiro, Shibayama, Ryo, and Suzuki, Serina
Subjects: High Energy Physics - Experiment, Astrophysics - High Energy Astrophysical Phenomena, Physics - Instrumentation and Detectors
Abstract: Preceding a core-collapse supernova, various processes produce an increasing amount of neutrinos of all flavors characterized by mounting energies from the interior of massive stars. Among them, the electron antineutrinos are potentially detectable by terrestrial neutrino experiments such as KamLAND and Super-Kamiokande via inverse beta decay interactions. Once these pre-supernova neutrinos are observed, an early warning of the upcoming core-collapse supernova can be provided. In light of this, KamLAND and Super-Kamiokande, both located in the Kamioka mine in Japan, have been monitoring pre-supernova neutrinos since 2015 and 2021, respectively. Recently, we performed a joint study between KamLAND and Super-Kamiokande on pre-supernova neutrino detection. A pre-supernova alert system combining the KamLAND detector and the Super-Kamiokande detector was developed and put into operation, which can provide a supernova alert to the astrophysics community. Fully leveraging the complementary properties of these two detectors, the combined alert is expected to resolve a pre-supernova neutrino signal from a 15 M$_{\odot}$ star within 510 pc of the Earth, at a significance level corresponding to a false alarm rate of no more than 1 per century. For a Betelgeuse-like model with optimistic parameters, it can provide early warnings up to 12 hours in advance., Comment: Resubmitted to ApJ. 22 pages, 16 figures, for more information about the combined pre-supernova alert system, see https://www.lowbg.org/presnalarm/
Published: 2024
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22. Development of a data overflow protection system for Super-Kamiokande to maximize data from nearby supernovae

Author: Mori, M., Abe, K., Hayato, Y., Hiraide, K., Hosokawa, K., Ieki, K., Ikeda, M., Kameda, J., Kanemura, Y., Kaneshima, R., Kashiwagi, Y., Kataoka, Y., Miki, S., Mine, S., Miura, M., Moriyama, S., Nakano, Y., Nakahata, M., Nakayama, S., Noguchi, Y., Okamoto, K., Sato, K., Sekiya, H., Shiba, H., Shimizu, K., Shiozawa, M., Sonoda, Y., Suzuki, Y., Takeda, A., Takemoto, Y., Takenaka, A., Tanaka, H., Watanabe, S., Yano, T., Han, S., Kajita, T., Okumura, K., Tashiro, T., Tomiya, T., Wang, X., Yoshida, S., Megias, G. D., Fernandez, P., Labarga, L., Ospina, N., Zaldivar, B., Pointon, B. W., Kearns, E., Raaf, J. L., Wan, L., Wester, T., Bian, J., Griskevich, N. J., Locke, S., Smy, M. B., Sobel, H. W., Takhistov, V., Yankelevich, A., Hill, J., Jang, M. C., Lee, S. H., Moon, D. H., Park, R. G., Bodur, B., Scholberg, K., Walter, C. W., Beauchene, A., Drapier, O., Giampaolo, A., Mueller, Th. A., Santos, A. D., Paganini, P., Quilain, B., Rogly, R., Ishizuka, T., Nakamura, T., Jang, J. S., Learned, J. G., Choi, K., Iovine, N., Cao, S., Anthony, L. H. V., Martin, D., Scott, M., Sztuc, A. A., Uchida, Y., Berardi, V., Catanesi, M. G., Radicioni, E., Calabria, N. F., Langella, A., Machado, L. N., De Rosa, G., Collazuol, G., Iacob, F., Lamoureux, M., Mattiazzi, M., Ludovici, L., Gonin, M., Perisse, L., Pronost, G., Fujisawa, C., Maekawa, Y., Nishimura, Y., Okazaki, R., Akutsu, R., Friend, M., Hasegawa, T., Ishida, T., Kobayashi, T., Jakkapu, M., Matsubara, T., Nakadaira, T., Nakamura, K., Oyama, Y., Sakashita, K., Sekiguchi, T., Tsukamoto, T., Bhuiyan, N., Burton, G. T., Edwards, R., Di Lodovico, F., Gao, J., Goldsack, A., Katori, T., Migenda, J., Ramsden, R. M., Xie, Z., Zsoldos, S., Kotsar, Y., Ozaki, H., Suzuki, A. T., Takagi, Y., Takeuchi, Y., Zhong, H., Bronner, C., Feng, J., Hu, J. R., Hu, Z., Kawaune, M., Kikawa, T., LiCheng, F., Nakaya, T., Wendell, R. A., Yasutome, K., Jenkins, S. J., McCauley, N., Mehta, P., Tarant, A., Fukuda, Y., Itow, Y., Menjo, H., Ninomiya, K., Yoshioka, Y., Lagoda, J., Lakshmi, S. M., Mandal, M., Mijakowski, P., Prabhu, Y. S., Zalipska, J., Jia, M., Jiang, J., Jung, C. K., Shi, W., Wilking, M. J., Yanagisawa, C., Harada, M., Hino, Y., Ishino, H., Kitagawa, H., Koshio, Y., Nakanishi, F., Sakai, S., Tada, T., Tano, T., Barr, G., Barrow, D., Cook, L., Samani, S., Wark, D., Holin, A., Nova, F., Jung, S., Yang, B. S., Yang, J. Y., Yoo, J., Fannon, J. E. P., Kneale, L., Malek, M., McElwee, J. M., Thiesse, M. D., Thompson, L. F., Wilson, S., Okazawa, H., Kim, S. B., Kwon, E., Seo, J. W., Yu, I., Ichikawa, A. K., Nakamura, K. D., Tairafune, S., Nishijima, K., Eguchi, A., Nakagiri, K., Nakajima, Y., Shima, S., Taniuchi, N., Watanabe, E., Yokoyama, M., de Perio, P., Fujita, S., Martens, K., Tsui, K. M., Vagins, M. R., Valls, C. J., Xia, J., Kuze, M., Izumiyama, S., Ishitsuka, M., Ito, H., Kinoshita, T., Matsumoto, R., Ommura, Y., Shigeta, N., Shinoki, M., Suganuma, T., Yamauchi, K., Yoshida, T., Martin, J. F., Tanaka, H. A., Towstego, T., Gaur, R., Gousy-Leblanc, V., Hartz, M., Konaka, A., Li, X., Prouse, N. W., Chen, S., Xu, B. D., Zhang, B., Posiadala-Zezula, M., Boyd, S. B., Hadley, D., Nicholson, M., Flaherty, M. O', Richards, B., Ali, A., Jamieson, B., Amanai, S., Marti, Ll., Minamino, A., Pintaudi, G., Sano, S., Suzuki, S., and Wada, K.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Solar and Stellar Astrophysics, High Energy Physics - Experiment
Abstract: Neutrinos from very nearby supernovae, such as Betelgeuse, are expected to generate more than ten million events over 10\,s in Super-Kamokande (SK). At such large event rates, the buffers of the SK analog-to-digital conversion board (QBEE) will overflow, causing random loss of data that is critical for understanding the dynamics of the supernova explosion mechanism. In order to solve this problem, two new DAQ modules were developed to aid in the observation of very nearby supernovae. The first of these, the SN module, is designed to save only the number of hit PMTs during a supernova burst and the second, the Veto module, prescales the high rate neutrino events to prevent the QBEE from overflowing based on information from the SN module. In the event of a very nearby supernova, these modules allow SK to reconstruct the time evolution of the neutrino event rate from beginning to end using both QBEE and SN module data. This paper presents the development and testing of these modules together with an analysis of supernova-like data generated with a flashing laser diode. We demonstrate that the Veto module successfully prevents DAQ overflows for Betelgeuse-like supernovae as well as the long-term stability of the new modules. During normal running the Veto module is found to issue DAQ vetos a few times per month resulting in a total dead time less than 1\,ms, and does not influence ordinary operations. Additionally, using simulation data we find that supernovae closer than 800~pc will trigger Veto module resulting in a prescaling of the observed neutrino data., Comment: 28 pages, 18 figures. Submitted to PTEP
Published: 2024
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23. The Impact of Evolutionary Computation on Robotic Design: A Case Study with an Underactuated Hand Exoskeleton

Author: Akbas, Baris, Yuksel, Huseyin Taner, Soylemez, Aleyna, Zyada, Mazhar Eid, Sarac, Mine, and Stroppa, Fabio
Subjects: Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction
Abstract: Robotic exoskeletons can enhance human strength and aid people with physical disabilities. However, designing them to ensure safety and optimal performance presents significant challenges. Developing exoskeletons should incorporate specific optimization algorithms to find the best design. This study investigates the potential of Evolutionary Computation (EC) methods in robotic design optimization, with an underactuated hand exoskeleton (U-HEx) used as a case study. We propose improving the performance and usability of the U-HEx design, which was initially optimized using a naive brute-force approach, by integrating EC techniques such as Genetic Algorithm and Big Bang-Big Crunch Algorithm. Comparative analysis revealed that EC methods consistently yield more precise and optimal solutions than brute force in a significantly shorter time. This allowed us to improve the optimization by increasing the number of variables in the design, which was impossible with naive methods. The results show significant improvements in terms of the torque magnitude the device transfers to the user, enhancing its efficiency. These findings underline the importance of performing proper optimization while designing exoskeletons, as well as providing a significant improvement to this specific robotic design., Comment: 6 pages (+ref), 4 figures, IEEE International Conference on Robotics and Automation (ICRA) 2024
Published: 2024

24. Borrowing Treasures from Neighbors: In-Context Learning for Multimodal Learning with Missing Modalities and Data Scarcity

Author: Zhi, Zhuo, Liu, Ziquan, Elbadawi, Moe, Daneshmend, Adam, Orlu, Mine, Basit, Abdul, Demosthenous, Andreas, and Rodrigues, Miguel
Subjects: Computer Science - Machine Learning
Abstract: Multimodal machine learning with missing modalities is an increasingly relevant challenge arising in various applications such as healthcare. This paper extends the current research into missing modalities to the low-data regime, i.e., a downstream task has both missing modalities and limited sample size issues. This problem setting is particularly challenging and also practical as it is often expensive to get full-modality data and sufficient annotated training samples. We propose to use retrieval-augmented in-context learning to address these two crucial issues by unleashing the potential of a transformer's in-context learning ability. Diverging from existing methods, which primarily belong to the parametric paradigm and often require sufficient training samples, our work exploits the value of the available full-modality data, offering a novel perspective on resolving the challenge. The proposed data-dependent framework exhibits a higher degree of sample efficiency and is empirically demonstrated to enhance the classification model's performance on both full- and missing-modality data in the low-data regime across various multimodal learning tasks. When only 1% of the training data are available, our proposed method demonstrates an average improvement of 6.1% over a recent strong baseline across various datasets and missing states. Notably, our method also reduces the performance gap between full-modality and missing-modality data compared with the baseline.
Published: 2024

25. Measurements of the charge ratio and polarization of cosmic-ray muons with the Super-Kamiokande detector

Author: Kitagawa, H., Tada, T., Abe, K., Bronner, C., Hayato, Y., Hiraide, K., Hosokawa, K., Ieki, K., Ikeda, M., Kameda, J., Kanemura, Y., Kaneshima, R., Kashiwagi, Y., Kataoka, Y., Miki, S., Mine, S., Miura, M., Moriyama, S., Nakano, Y., Nakahata, M., Nakayama, S., Noguchi, Y., Okamoto, K., Sato, K., Sekiya, H., Shiba, H., Shimizu, K., Shiozawa, M., Sonoda, Y., Suzuki, Y., Takeda, A., Takemoto, Y., Takenaka, A., Tanaka, H., Watanabe, S., Yano, T., Han, S., Kajita, T., Okumura, K., Tashiro, T., Tomiya, T., Wang, X., Yoshida, S., Megias, G. D., Fernandez, P., Labarga, L., Ospina, N., Zaldivar, B., Pointon, B. W., Kearns, E., Raaf, J. L., Wan, L., Wester, T., Bian, J., Griskevich, N. J., Locke, S., Smy, M. B., Sobel, H. W., Takhistov, V., Yankelevich, A., Hill, J., Jang, M. C., Lee, S. H., Moon, D. H., Park, R. G., Bodur, B., Scholberg, K., Walter, C. W., Beauchêne, A., Drapier, O., Giampaolo, A., Mueller, Th. A., Santos, A. D., Paganini, P., Quilain, B., Rogly, R., Nakamura, T., Jang, J. S., Machado, L. N., Learned, J. G., Choi, K., Iovine, N., Cao, S., Anthony, L. H. V., Martin, D., Prouse, N. W., Scott, M., Sztuc, A. A., Uchida, Y., Berardi, V., Calabria, N. F., Catanesi, M. G., Radicioni, E., Langella, A., De Rosa, G., Collazuol, G., Iacob, F., Lamoureux, M., Mattiazzi, M., Ludovici, L., Gonin, M., Périssé, L., Pronost, G., Fujisawa, C., Maekawa, Y., Nishimura, Y., Okazaki, R., Akutsu, R., Friend, M., Hasegawa, T., Ishida, T., Kobayashi, T., Jakkapu, M., Matsubara, T., Nakadaira, T., Nakamura, K., Oyama, Y., Sakashita, K., Sekiguchi, T., Tsukamoto, T., Bhuiyan, N., Burton, G. T., Di Lodovico, F., Gao, J., Goldsack, A., Katori, T., Migenda, J., Ramsden, R. M., Xie, Z., Zsoldos, S., Kotsar, Y., Ozaki, H., Suzuki, A. T., Takagi, Y., Takeuchi, Y., Zhong, H., Feng, J., Feng, L., Hu, J. R., Hu, Z., Kawaue, M., Kikawa, T., Mori, M., Nakaya, T., Wendell, R. A., Yasutome, K., Jenkins, S. J., McCauley, N., Mehta, P., Tarant, A., Wilking, M. J., Fukuda, Y., Itow, Y., Menjo, H., Ninomiya, K., Yoshioka, Y., Lagoda, J., Mandal, M., Mijakowski, P., Prabhu, Y. S., Zalipska, J., Jia, M., Jiang, J., Jung, C. K., Shi, W., Yanagisawa, C., Harada, M., Hino, Y., Ishino, H., Koshio, Y., Nakanishi, F., Sakai, S., Tano, T., Ishizuka, T., Barr, G., Barrow, D., Cook, L., Samani, S., Wark, D., Holin, A., Nova, F., Jung, S., Yang, B. S., Yang, J. Y., Yoo, J., Fannon, J. E. P., Kneale, L., Malek, M., McElwee, J. M., Thiesse, M. D., Thompson, L. F., Wilson, S. T., Okazawa, H., Lakshmi, S. M., Kim, S. B., Kwon, E., Seo, J. W., Yu, I., Ichikawa, A. K., Nakamura, K. D., Tairafune, S., Nishijima, K., Eguchi, A., Nakagiri, K., Nakajima, Y., Shima, S., Taniuchi, N., Watanabe, E., Yokoyama, M., de Perio, P., Fujita, S., Jesús-Valls, C., Martens, K., Tsui, K. M., Vagins, M. R., Xia, J., Izumiyama, S., Kuze, M., Matsumoto, R., Terada, K., Ishitsuka, M., Ito, H., Kinoshita, T., Ommura, Y., Shigeta, N., Shinoki, M., Suganuma, T., Yamauchi, K., Yoshida, T., Martin, J. F., Tanaka, H. A., Towstego, T., Gaur, R., Gousy-Leblanc, V., Hartz, M., Konaka, A., Li, X., Chen, S., Xu, B. D., Zhang, B., Posiadala-Zezula, M., Boyd, S. B., Edwards, R., Hadley, D., Nicholson, M., O'Flaherty, M., Richards, B., Ali, A., Jamieson, B., Amanai, S., Marti, Ll., Minamino, A., Pintaudi, G., Sano, S., Suzuki, S., and Wada, K.
Subjects: High Energy Physics - Experiment, Astrophysics - High Energy Astrophysical Phenomena
Abstract: We present the results of the charge ratio ($R$) and polarization ($P^{\mu}_{0}$) measurements using the decay electron events collected from 2008 September to 2022 June by the Super-Kamiokande detector. Because of its underground location and long operation, we performed high precision measurements by accumulating cosmic-ray muons. We measured the muon charge ratio to be $R=1.32 \pm 0.02$ $(\mathrm{stat.}{+}\mathrm{syst.})$ at $E_{\mu}\cos \theta_{\mathrm{Zenith}}=0.7^{+0.3}_{-0.2}$ $\mathrm{TeV}$, where $E_{\mu}$ is the muon energy and $\theta_{\mathrm{Zenith}}$ is the zenith angle of incoming cosmic-ray muons. This result is consistent with the Honda flux model while this suggests a tension with the $\pi K$ model of $1.9\sigma$. We also measured the muon polarization at the production location to be $P^{\mu}_{0}=0.52 \pm 0.02$ $(\mathrm{stat.}{+}\mathrm{syst.})$ at the muon momentum of $0.9^{+0.6}_{-0.1}$ $\mathrm{TeV}/c$ at the surface of the mountain; this also suggests a tension with the Honda flux model of $1.5\sigma$. This is the most precise measurement ever to experimentally determine the cosmic-ray muon polarization near $1~\mathrm{TeV}/c$. These measurement results are useful to improve the atmospheric neutrino simulations., Comment: 29 pages, 45 figures
Published: 2024
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26. The transition operator of a random walk perturbated by sparse potentials

Author: Mine, Takuya and Yoshida, Nobuo
Subjects: Mathematics - Spectral Theory
Abstract: We consider an operator $P_V=(1+V)P$ on $\ell^2(Z^d)$, where $P$ is the transition operator of a symmetric irreducible random walk, and $V$ is a ``sparse'' potential. We first characterize the essential spectra of this operator. Secondly, we prove that all the eigenfunctions which correspond to discrete spectra decay exponentially fast. Thirdly, we give a sufficient condition for this operator to have an absolute spectral gap at the right edge of the spectra. Finally, as an application of the absolute spectral gap and the exponential decay of the eigenfunctions, we prove a limit theorem for the random walk under the Gibbs measure associated to the potential $V$.
Published: 2024

27. Second gadolinium loading to Super-Kamiokande

Author: Abe, K., Bronner, C., Hayato, Y., Hiraide, K., Hosokawa, K., Ieki, K., Ikeda, M., Kameda, J., Kanemura, Y., Kaneshima, R., Kashiwagi, Y., Kataoka, Y., Miki, S., Mine, S., Miura, M., Moriyama, S., Nakano, Y., Nakahata, M., Nakayama, S., Noguchi, Y., Sato, K., Sekiya, H., Shiba, H., Shimizu, K., Shiozawa, M., Sonoda, Y., Suzuki, Y., Takeda, A., Takemoto, Y., Tanaka, H., Yano, T., Han, S., Kajita, T., Okumura, K., Tashiro, T., Tomiya, T., Wang, X., Yoshida, S., Fernandez, P., Labarga, L., Ospina, N., Zaldivar, B., Pointon, B. W., Kearns, E., Raaf, J. L., Wan, L., Wester, T., Bian, J., Griskevich, N. J., Smy, M. B., Sobel, H. W., Takhistov, V., Yankelevich, A., Hill, J., Jang, M. C., Lee, S. H., Moon, D. H., Park, R. G., Bodur, B., Scholberg, K., Walter, C. W., Beauchene, A., Drapier, O., Giampaolo, A., Mueller, Th. A., Santos, A. D., Paganini, P., Quilain, B., Rogly, R., Nakamura, T., Jang, J. S., Machado, L. N., Learned, J. G., Choi, K., Iovine, N., Cao, S., Anthony, L. H. V., Martin, D., Prouse, N. W., Scott, M., Uchida, Y., Berardi, V., Calabria, N. F., Catanesi, M. G., Radicioni, E., Langella, A., De Rosa, G., Collazuol, G., Iacob, F., Mattiazzi, M., Ludovici, L., Gonin, M., Perisse, L., Pronost, G., Fujisawa, C., Maekawa, Y., Nishimura, Y., Okazaki, R., Akutsu, R., Friend, M., Hasegawa, T., Ishida, T., Kobayashi, T., Jakkapu, M., Matsubara, T., Nakadaira, T., Nakamura, K., Oyama, Y., Sakashita, K., Sekiguchi, T., Tsukamoto, T., Bhuiyan, N., Burton, G. T., Di Lodovico, F., Gao, J., Goldsack, A., Katori, T., Migenda, J., Ramsden, R. M., Xie, Z., Zsoldos, S., Suzuki, A. T., Takagi, Y., Takeuchi, Y., Zhong, H., Feng, J., Feng, L., Hu, J. R., Hu, Z., Kawaue, M., Kikawa, T., Mori, M., Nakaya, T., Wendell, R. A., Yasutome, K., Jenkins, S. J., McCauley, N., Mehta, P., Tarant, A., Wilking, M. J., Fukuda, Y., Itow, Y., Menjo, H., Ninomiya, K., Yoshioka, Y., Lagoda, J., Mandal, M., Mijakowski, P., Prabhu, Y. S., Zalipska, J., Jia, M., Jiang, J., Shi, W., Yanagisawa, C., Harada, M., Hino, Y., Ishino, H., Koshio, Y., Nakanishi, F., Sakai, S., Tada, T., Tano, T., Ishizuka, T., Barr, G., Barrow, D., Cook, L., Samani, S., Wark, D., Holin, A., Nova, F., Jung, S., Yang, B. S., Yang, J. Y., Yoo, J., Fannon, J. E. P., Kneale, L., Malek, M., McElwee, J. M., Thiesse, M. D., Thompson, L. F., Wilson, S. T., Okazawa, H., Lakshmi, S. M., Kim, S. B., Kwon, E., Seo, J. W., Yu, I., Ichikawa, A. K., Tairafune, S., Nishijima, K., Eguchi, A., Nakagiri, K., Nakajima, Y., Shima, S., Taniuchi, N., Watanabe, E., Yokoyama, M., de Perio, P., Fujita, S., Jesus-Valls, C., Martens, K., Tsui, K. M., Vagins, M. R., Xia, J., Izumiyama, S., Kuze, M., Matsumoto, R., Terada, K., Ishitsuka, M., Ito, H., Ommura, Y., Shigeta, N., Shinoki, M., Yamauchi, K., Yoshida, T., Gaur, R., Gousy-Leblanc, V., Hartz, M., Konaka, A., Li, X., Chen, S., Xu, B. D., Zhang, B., Posiadala-Zezula, M., Boyd, S. B., Edwards, R., Hadley, D., Nicholson, M., O'Flaherty, M., Richards, B., Ali, A., Jamieson, B., Amanai, S., Marti, Ll., Minamino, A., Suzuki, S., Scovell, P. R., Meehan, E., Bandac, I., Pena-Garay, C., Perez, J., Gileva, O., Lee, E. K., Leonard, D. S., Sakakieda, Y., Sakaguchi, A., Sueki, K., Takaku, Y., and Yamasaki, S.
Subjects: Physics - Instrumentation and Detectors, Astrophysics - High Energy Astrophysical Phenomena
Abstract: The first loading of gadolinium (Gd) into Super-Kamiokande in 2020 was successful, and the neutron capture efficiency on Gd reached 50\%. To further increase the Gd neutron capture efficiency to 75\%, 26.1 tons of $\rm Gd_2(\rm SO_4)_3\cdot \rm 8H_2O$ was additionally loaded into Super-Kamiokande (SK) from May 31 to July 4, 2022. As the amount of loaded $\rm Gd_2(\rm SO_4)_3\cdot \rm 8H_2O$ was doubled compared to the first loading, the capacity of the powder dissolving system was doubled. We also developed new batches of gadolinium sulfate with even further reduced radioactive impurities. In addition, a more efficient screening method was devised and implemented to evaluate these new batches of $\rm Gd_2(\rm SO_4)_3\cdot \rm 8H_2O$. Following the second loading, the Gd concentration in SK was measured to be $333.5\pm2.5$ ppm via an Atomic Absorption Spectrometer (AAS). From the mean neutron capture time constant of neutrons from an Am/Be calibration source, the Gd concentration was independently measured to be 332.7 $\pm$ 6.8(sys.) $\pm$ 1.1(stat.) ppm, consistent with the AAS result. Furthermore, during the loading the Gd concentration was monitored continually using the capture time constant of each spallation neutron produced by cosmic-ray muons,and the final neutron capture efficiency was shown to become 1.5 times higher than that of the first loaded phase, as expected., Comment: 34 pages, 13 figures, submitted to Nuclear Inst. and Methods in Physics Research, A
Published: 2024
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28. Performance of SK-Gd's Upgraded Real-time Supernova Monitoring System

Author: Kashiwagi, Y., Abe, K., Bronner, C., Hayato, Y., Hiraide, K., Hosokawa, K., Ieki, K., Ikeda, M., Kameda, J., Kanemura, Y., Kaneshima, R., Kataoka, Y., Miki, S., Mine, S., Miura, M., Moriyama, S., Nakano, Y., Nakahata, M., Nakayama, S., Noguchi, Y., Sato, K., Sekiya, H., Shiba, H., Shimizu, K., Shiozawa, M., Sonoda, Y., Suzuki, Y., Takeda, A., Takemoto, Y., Tanaka, H., Yano, T., Han, S., Kajita, T., Okumura, K., Tashiro, T., Tomiya, T., Wang, X., Yoshida, S., Fernandez, P., Labarga, L., Ospina, N., Zaldivar, B., Pointn, B. W., Kearns, E., Raaf, J. L., Wan, L., Wester, T., Bian, J., Griskevich, N. J., Locke, S., Smy, M. B., Sobel, H. W., Takhistov, V., Yankelevich, A., Hill, J., Jang, M. C., Lee, S. H., Moon, D. H., Park, R. G., Bodur, B., Scholberg, K., Walter, C. W., Beauchêne, A., Drapier, O., Giampaolo, A., Mueller, Th. A., Santos, A. D., Paganini, P., Quilain, B., Rogly, R., Nakamura, T., Jang, J. S., Machado, L. N., Learned, J. G., Choi, K., Iovine, N., Cao, S., Anthony, L. H. V., Martin, D., Prouse, N. W., Scott, M., Sztuc, A. A., Uchida, Y., Berardi, V., Catanesi, M. G., Radicioni, E., Calabria, N. F., Langella, A., De Rosa, G., Collazuol, G., Iacob, F., Mattiazzi, M., Ludovici, L., Gonin, M., Périssé, L., Pronost, G., Fujisawa, C., Maekawa, Y., Nishimura, Y., Okazaki, R., Akutsu, R., Friend, M., Hasegawa, T., Ishida, T., Kobayashi, T., Jakkapu, M., Matsubara, T., Nakadaira, T., Nakamura, K., Oyama, Y., Sakashita, K., Sekiguchi, T., Tsukamoto, T., Bhuiyan, N., Burton, G. T., Di Lodovico, F., Gao, J., Goldsack, A., Katori, T., Migenda, J., Ramsden, R. M., Xie, Z., Zsoldos, S., Suzuki, A. T., Takagi, Y., Takeuchi, Y., Zhong, H., Feng, J., Feng, L., Hu, J. R., Hu, Z., Kawaue, M., Kikawa, T., Mori, M., Nakaya, T., Wendell, R. A., Yasutome, K., Jenkins, S. J., McCauley, N., Mehta, P., Tarrant, A., Fukuda, Y., Itow, Y., Menjo, H., Ninomiya, K., Yoshioka, Y., Lagoda, J., Lakshmi, S. M., Mandal, M., Mijakowski, P., Prabhu, Y. S., Zalipska, J., Jia, M., Jiang, J., Jung, C. K., Shi, W., Wilking, M. J., Yanagisawa, C., Harada, M., Hino, Y., Ishino, H., Koshio, Y., Nakanishi, F., Sakai, S., Tada, T., Tano, T., Ishizuka, T., Barr, G., Barrow, D., Cook, L., Samani, S., Wark, D., Holin, A., Nova, F., Jung, S., Yang, B. S., Yang, J. Y., Yoo, J., Fannon, J. E. P., Kneale, L., Malek, M., McElwee, J. M., Thiesse, M. D., Thompson, L. F., Wilson, S. T., Okazawa, H., Kim, S. B., Kwon, E., Seo, J. W., Yu, I., Ichikawa, A. K., Nakamura, K. D., Tairafune, S., Nishijima, K., Eguchi, A., Nakagiri, K., Nakajima, Y., Shima, S., Taniuchi, N., Watanabe, E., Yokoyama, M., de Perio, P., Fujita, S., Jesús-Valls, C., Martens, K., Tsui, K. M., Vagins, M. R., Xia, J., Kuze, M., Izumiyama, S., Matsumoto, R., Ishitsuka, M., Ito, H., Ommura, Y., Shigeta, N., Shinoki, M., Yamauchi, K., Yoshida, T., Gaur, R., Gousy-Leblanc, V., Hartz, M., Konaka, A., Li, X., Chen, S., Xu, B. D., Zhang, B., Posiadala-Zezula, M., Boyd, S. B., Edwards, R., Hadley, D., Nicholson, M., O'Flaherty, M., Richards, B., Ali, A., Jamieson, B., Amanai, S., Marti, Ll., Minamino, A., and Suzuki, S.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Among multi-messenger observations of the next galactic core-collapse supernova, Super-Kamiokande (SK) plays a critical role in detecting the emitted supernova neutrinos, determining the direction to the supernova (SN), and notifying the astronomical community of these observations in advance of the optical signal. On 2022, SK has increased the gadolinium dissolved in its water target (SK-Gd) and has achieved a Gd concentration of 0.033%, resulting in enhanced neutron detection capability, which in turn enables more accurate determination of the supernova direction. Accordingly, SK-Gd's real-time supernova monitoring system (Abe te al. 2016b) has been upgraded. SK_SN Notice, a warning system that works together with this monitoring system, was released on December 13, 2021, and is available through GCN Notices (Barthelmy et al. 2000). When the monitoring system detects an SN-like burst of events, SK_SN Notice will automatically distribute an alarm with the reconstructed direction to the supernova candidate within a few minutes. In this paper, we present a systematic study of SK-Gd's response to a simulated galactic SN. Assuming a supernova situated at 10 kpc, neutrino fluxes from six supernova models are used to characterize SK-Gd's pointing accuracy using the same tools as the online monitoring system. The pointing accuracy is found to vary from 3-7$^\circ$ depending on the models. However, if the supernova is closer than 10 kpc, SK_SN Notice can issue an alarm with three-degree accuracy, which will benefit follow-up observations by optical telescopes with large fields of view., Comment: 38 pages, 29 figures, 6 tables
Published: 2024

29. A Systematic Literature Review of Undergraduate Data Science Education Research

Author: Dogucu, Mine, Demirci, Sinem, Bendekgey, Harry, Ricci, Federica Zoe, and Medina, Catalina M.
Subjects: Statistics - Other Statistics
Abstract: The presence of data science has been profound in the scientific community in almost every discipline. An important part of the data science education expansion has been at the undergraduate level. We conducted a systematic literature review to (1) portray current evidence and knowledge gaps in self-proclaimed undergraduate data science education research and (2) inform policymakers and the data science education community about what educators may encounter when searching for literature using the general keyword 'data science education.' While open-access publications that target a broader audience of data science educators and include multiple examples of data science programs and courses are a strength, significant knowledge gaps remain. The undergraduate data science literature that we identified often lacks empirical data, research questions and reproducibility. Certain disciplines are less visible. We recommend that we should (1) cherish data science as an interdisciplinary field; (2) adopt a consistent set of keywords/terminology to ensure data science education literature is easily identifiable; (3) prioritize investments in empirical studies., Comment: 4 figures and 2 tables
Published: 2024

30. Achieving Low Latency at Low Outage: Multilevel Coding for mmWave Channels

Author: Dogan, Mine Gokce, Shah, Jaimin, Cardone, Martina, Fragouli, Christina, Mao, Wei, Nikopour, Hosein, and Vannithamby, Rath
Subjects: Computer Science - Information Theory
Abstract: Millimeter-wave (mmWave) spectrum is expected to support data-intensive applications that require ultra-reliable low-latency communications (URLLC). However, mmWave links are highly sensitive to blockage, which may lead to disruptions in the communication. Traditional techniques that build resilience against such blockages (among which are interleaving and feedback mechanisms) incur delays that are too large to effectively support URLLC. This calls for novel techniques that ensure resilient URLLC. In this paper, we propose to deploy multilevel codes over space and over time. These codes offer several benefits, such as they allow to control what information is received and they provide different reliability guarantees for different information streams based on their priority. We also show that deploying these codes leads to attractive trade-offs between rate, delay, and outage probability. A practically-relevant aspect of the proposed technique is that it offers resilience while incurring a low operational complexity.
Published: 2024

31. HgbNet: predicting hemoglobin level/anemia degree from EHR data

Author: Zhi, Zhuo, Elbadawi, Moe, Daneshmend, Adam, Orlu, Mine, Basit, Abdul, Demosthenous, Andreas, and Rodrigues, Miguel
Subjects: Computer Science - Machine Learning
Abstract: Anemia is a prevalent medical condition that typically requires invasive blood tests for diagnosis and monitoring. Electronic health records (EHRs) have emerged as valuable data sources for numerous medical studies. EHR-based hemoglobin level/anemia degree prediction is non-invasive and rapid but still faces some challenges due to the fact that EHR data is typically an irregular multivariate time series containing a significant number of missing values and irregular time intervals. To address these issues, we introduce HgbNet, a machine learning-based prediction model that emulates clinicians' decision-making processes for hemoglobin level/anemia degree prediction. The model incorporates a NanDense layer with a missing indicator to handle missing values and employs attention mechanisms to account for both local irregularity and global irregularity. We evaluate the proposed method using two real-world datasets across two use cases. In our first use case, we predict hemoglobin level/anemia degree at moment T+1 by utilizing records from moments prior to T+1. In our second use case, we integrate all historical records with additional selected test results at moment T+1 to predict hemoglobin level/anemia degree at the same moment, T+1. HgbNet outperforms the best baseline results across all datasets and use cases. These findings demonstrate the feasibility of estimating hemoglobin levels and anemia degree from EHR data, positioning HgbNet as an effective non-invasive anemia diagnosis solution that could potentially enhance the quality of life for millions of affected individuals worldwide. To our knowledge, HgbNet is the first machine learning model leveraging EHR data for hemoglobin level/anemia degree prediction.
Published: 2024

32. An Application of HEP Track Seeding to Astrophysical Data

Author: Gökçen, Mine, Garcia-Sciveres, Maurice, and Ju, Xiangyang
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We apply methods of particle track reconstruction in High Energy Physics (HEP) to the search for distinct stellar populations in the Milky Way, using the Gaia EDR3 data set. This was motivated by analogies between the 3D space points in HEP detectors and the positions of stars (which are also points in a coordinate space) and the way collections of space points correspond to particle trajectories in the HEP, while collections of stars from distinct populations (such as stellar streams) can resemble tracks. Track reconstruction consists of multiple steps, the first one being seeding. In this note, we describe our implementation and results of the seeding step to the search for distinct stellar populations, and we indicate how the next steps will proceed. Our seeding method uses machine learning tools from the FAISS library, such as the k-nearest neighbors (kNN) search., Comment: 9 pages, 10 figures, 1 table. Conference proceedings preprint for Connecting the Dots (CTD) 2023. Updated figures, fixed typos
Published: 2024

33. Privacy-Preserving Sequential Recommendation with Collaborative Confusion

Author: Wang, Wei, Lin, Yujie, Ren, Pengjie, Chen, Zhumin, Mine, Tsunenori, Zhao, Jianli, Zhao, Qiang, Zhang, Moyan, Ben, Xianye, and Li, Yujun
Subjects: Computer Science - Information Retrieval
Abstract: Sequential recommendation has attracted a lot of attention from both academia and industry, however the privacy risks associated to gathering and transferring users' personal interaction data are often underestimated or ignored. Existing privacy-preserving studies are mainly applied to traditional collaborative filtering or matrix factorization rather than sequential recommendation. Moreover, these studies are mostly based on differential privacy or federated learning, which often leads to significant performance degradation, or has high requirements for communication. In this work, we address privacy-preserving from a different perspective. Unlike existing research, we capture collaborative signals of neighbor interaction sequences and directly inject indistinguishable items into the target sequence before the recommendation process begins, thereby increasing the perplexity of the target sequence. Even if the target interaction sequence is obtained by attackers, it is difficult to discern which ones are the actual user interaction records. To achieve this goal, we propose a CoLlaborative-cOnfusion seqUential recommenDer, namely CLOUD, which incorporates a collaborative confusion mechanism to edit the raw interaction sequences before conducting recommendation. Specifically, CLOUD first calculates the similarity between the target interaction sequence and other neighbor sequences to find similar sequences. Then, CLOUD considers the shared representation of the target sequence and similar sequences to determine the operation to be performed: keep, delete, or insert. We design a copy mechanism to make items from similar sequences have a higher probability to be inserted into the target sequence. Finally, the modified sequence is used to train the recommender and predict the next item.
Published: 2024

34. Embedded Shape Matching in Photogrammetry Data for Modeling Making Knowledge

Author: Tas, Demircan and Özkar, Mine
Subjects: Computer Science - Computer Vision and Pattern Recognition
Abstract: In three-dimensional models obtained by photogrammetry of existing structures, all of the shapes that the eye can select cannot always find their equivalents in the geometric components of the model. However, the matching of meaningful parts and assemblages with the records acquired with rapid and detailed documentation methods will provide an advantage for the creation of information models of existing structures. While aiming to produce answers to this problem and in order to overcome the difficulties of pattern recognition in three-dimensional models, we used two-dimensional samples obtained by projection. Processing techniques such as ambient occlusion, curvature and normal maps are commonly used in modern computer graphics applications that enable the representation of three-dimensional surface properties in two-dimensional data sets. The method we propose is based on the recognition of patterns through these mappings instead of the usual light-based visualization. The first stage of the application is photogrammetric capture of a few examples of Zeugma mosaics and three-dimensional digital modeling of a set of Seljuk era brick walls based on knowledge obtained through architectural history literature. The second stage covers the creation of digital models byprocessing the surface representation obtained from this data using Alice Vision, OpenCV-Python, and Autodesk Maya to include information on aspects of the making of the walls. What is envisioned for the next stages is that the mapping data contributes and supports the knowledge for rule-based design and making processesof cultural heritage., Comment: 9 pages, in Turkish language. 6 figures. In: MSTAS 2019 - (XIII. Computational Design in Architecture National Symposium) pp. 313-326., Kocaeli, Turkey (2019)
Published: 2023

35. Solar neutrino measurements using the full data period of Super-Kamiokande-IV

Author: Collaboration, Super-Kamiokande, Abe, K., Bronner, C., Hayato, Y., Hiraide, K., Hosokawa, K., Ieki, K., Ikeda, M., Imaizumi, S., Iyogi, K., Kameda, J., Kanemura, Y., Kaneshima, R., Kashiwagi, Y., Kataoka, Y., Kato, Y., Kishimoto, Y., Miki, S., Mine, S., Miura, M., Mochizuki, T., Moriyama, S., Nagao, Y., Nakahata, M., Nakano, Y., Nakayama, S., Noguchi, Y., Okada, T., Okamoto, K., Orii, A., Sato, K., Sekiya, H., Shiba, H., Shimizu, K., Shiozawa, M., Sonoda, Y., Suzuki, Y., Takeda, A., Takemoto, Y., Takenaka, A., Tanaka, H., Watanabe, S., Yano, T., Han, S., Kajita, T., Okumura, K., Tashiro, T., Tomiya, T., Wang, R., Wang, X., Yoshida, S., Bravo-Berguno, D., Fernandez, P., Labarga, L., Ospina, N., Zaldivar, B., Pointon, B. W., Blaszczyk, F. d. M., Kachulis, C., Kearns, E., Raaf, J. L., Stone, J. L., Wan, L., Wester, T., Bian, J., Griskevich, N. J., Kropp, W. R., Locke, S., Smy, M. B., Sobel, H. W., Takhistov, V., Weatherly, P., Yankelevich, A., Ganezer, K. S., Hill, J., Jang, M. C., Kim, J. Y., Lee, S., Lim, I. T., Moon, D. H., Park, R. G., Bodur, B., Scholberg, K., Walter, C. W., Beauchene, A., Bernard, L., Coffani, A., Drapier, O., Hedri, S. El, Giampaolo, A., Imber, J., Mueller, Th. A., Paganini, P., Rogly, R., Quilain, B., Santos, A., Nakamura, T., Jang, J. S., Machado, L. N., Learned, J. G., Matsuno, S., Iovine, N., Choi, K., Cao, S., Anthony, L. H. V., Litchfield, R. P., Prouse, N., Marin, D., Scott, M., Sztuc, A. A., Uchida, Y., Berardi, V., Catanesi, M. G., Intonti, R. A., Radicioni, E., Calabria, N. F., De Rosa, G., Langella, A., Collazuol, G., Iacob, F., Lamoureux, M., Mattiazzi, M., Ludovici, L., Gonin, M., Perisse, L., Pronost, G., Fujisawa, C., Maekawa, Y., Nishimura, Y., Okazaki, R., Friend, M., Hasegawa, T., Ishida, T., Jakkapu, M., Kobayashi, T., Matsubara, T., Nakadaira, T., Nakamura, K., Oyama, Y., Sakashita, K., Sekiguchi, T., Tsukamoto, T., Boschi, T., Bhuiyan, N., Burton, G. T., Gao, J., Goldsack, A., Katori, T., Di Lodovico, F., Migenda, J., Sedgwick, S. Molina, Ramsden, R. M., Taani, M., Xie, Z., Zsoldos, S., Abe, KE., Hasegawa, M., Isobe, Y., Kotsar, Y., Miyabe, H., Ozaki, H., Shiozawa, T., Sugimoto, T., Suzuki, A. T., Takagi, Y., Takeuchi, Y., Yamamoto, S., Zhong, H., Ashida, Y., Feng, J., Feng, L., Hayashino, T., Hirota, S., Hu, J. R., Hu, Z., Jiang, M., Kawaue, M., Kikawa, T., Mori, M., Nakamura, KE., Nakaya, T., Wendell, R. A., Yasutome, K., Jenkins, S. J., McCauley, N., Mehta, P., Pritchard, A., Tarrant, A., Wilking, M. J., Fukuda, Y., Itow, Y., Menjo, H., Murase, M., Ninomiya, K., Niwa, T., Tsukada, M., Yoshioka, Y., Frankiewicz, K., Lagoda, J., Mandal, M., Mijakowski, P., Prabhu, Y. S., Zalipska, J., Jiang, J., Jia, M., Jung, C. K., Palomino, J. L., Santucci, G., Shi, W., Vilela, C., Yanagisawa, C., Fukuda, D., Hagiwara, K., Harada, M., Hino, Y., Horai, T., Ishino, H., Ito, S., Kitagawa, H., Koshio, Y., Ma, W., Nakanishi, F., Piplani, N., Sakai, S., Sakuda, M., Tada, T., Tano, T., Xu, C., Yamaguchi, R., Ishizuka, T., Kuno, Y., Barr, G., Barrow, D., Cook, L., Samani, S., Simpson, C., Wark, D., Holin, A. M., Nova, F., Jung, S., Yang, B., Yang, J. Y., Yoo, J., Fannon, J. E. P., Kneale, L., Malek, M., McElwee, J. M., Stone, O., Thiesse, M. D., Thompson, L. F., Wilson, S. T., Okazawa, H., Lakshmi, S. M., Choi, Y., Kim, S. B., Kwon, E., Seo, J. W., Yu, I., Ichikawa, A. K., Tairahune, S., Nishijima, K., Eguchi, A., Iwamoto, K., Nakagiri, K., Nakajima, Y., Ogawa, N., Shima, S., Watanabe, E., Yokoyama, M., Calland, R. G., Fujita, S., Jesus-Valls, C., Junjie, X., Ming, T. K., de Perio, P., Martens, K., Murdoch, M., Vagins, M. R., Izumiyama, S., Kuze, M., Matsumoto, R., Okajima, Y., Tanaka, M., Yoshida, T., Inomoto, M., Ishitsuka, M., Ito, H., Kinoshita, T., Ohta, K., Ommura, Y., Shinoki, M., Shigeta, N., Suganuma, T., Yamaguchi, K., Martin, J. F., Nantais, C. M., Tanaka, H. A., Towstego, T., Gaur, R., Gousy-Leblanc, V., Hartz, M., Konaka, A., Li, X., Chen, S., Xu, B. D., Zhang, B., Berkman, S., Posiadala-Zezula, M., Boyd, S. B., Edwards, R., Hadley, D., Nicholson, M., O'Flaherty, M., Richards, B., Ali, A., Jamieson, B., Walker, J., Amanai, S., Marti, Ll., Minamino, A., Pintaudi, G., Sano, S., Sasaki, R., Suzuki, S., and Wada, K.
Subjects: High Energy Physics - Experiment
Abstract: An analysis of solar neutrino data from the fourth phase of Super-Kamiokande~(SK-IV) from October 2008 to May 2018 is performed and the results are presented. The observation time of the data set of SK-IV corresponds to $2970$~days and the total live time for all four phases is $5805$~days. For more precise solar neutrino measurements, several improvements are applied in this analysis: lowering the data acquisition threshold in May 2015, further reduction of the spallation background using neutron clustering events, precise energy reconstruction considering the time variation of the PMT gain. The observed number of solar neutrino events in $3.49$--$19.49$ MeV electron kinetic energy region during SK-IV is $65,443^{+390}_{-388}\,(\mathrm{stat.})\pm 925\,(\mathrm{syst.})$ events. Corresponding $\mathrm{^{8}B}$ solar neutrino flux is $(2.314 \pm 0.014\, \rm{(stat.)} \pm 0.040 \, \rm{(syst.)}) \times 10^{6}~\mathrm{cm^{-2}\,s^{-1}}$, assuming a pure electron-neutrino flavor component without neutrino oscillations. The flux combined with all SK phases up to SK-IV is $(2.336 \pm 0.011\, \rm{(stat.)} \pm 0.043 \, \rm{(syst.)}) \times 10^{6}~\mathrm{cm^{-2}\,s^{-1}}$. Based on the neutrino oscillation analysis from all solar experiments, including the SK $5805$~days data set, the best-fit neutrino oscillation parameters are $\rm{sin^{2} \theta_{12,\,solar}} = 0.306 \pm 0.013 $ and $\Delta m^{2}_{21,\,\mathrm{solar}} = (6.10^{+ 0.95}_{-0.81}) \times 10^{-5}~\rm{eV}^{2}$, with a deviation of about 1.5$\sigma$ from the $\Delta m^{2}_{21}$ parameter obtained by KamLAND. The best-fit neutrino oscillation parameters obtained from all solar experiments and KamLAND are $\sin^{2} \theta_{12,\,\mathrm{global}} = 0.307 \pm 0.012 $ and $\Delta m^{2}_{21,\,\mathrm{global}} = (7.50^{+ 0.19}_{-0.18}) \times 10^{-5}~\rm{eV}^{2}$., Comment: 47 pages, 61 figures
Published: 2023
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36. Atmospheric neutrino oscillation analysis with neutron tagging and an expanded fiducial volume in Super-Kamiokande I-V

Author: Collaboration, Super-Kamiokande, Wester, T., Abe, K., Bronner, C., Hayato, Y., Hiraide, K., Hosokawa, K., Ieki, K., Ikeda, M., Kameda, J., Kanemura, Y., Kaneshima, R., Kashiwagi, Y., Kataoka, Y., Miki, S., Mine, S., Miura, M., Moriyama, S., Nakano, Y., Nakahata, M., Nakayama, S., Noguchi, Y., Sato, K., Sekiya, H., Shiba, H., Shimizu, K., Shiozawa, M., Sonoda, Y., Suzuki, Y., Takeda, A., Takemoto, Y., Tanaka, H., Yano, T., Han, S., Kajita, T., Okumura, K., Tashiro, T., Tomiya, T., Wang, X., Yoshida, S., Fernandez, P., Labarga, L., Ospina, N., Zaldivar, B., Pointon, B. W., Kearns, E., Raaf, J. L., Wan, L., Bian, J., Griskevich, N. J., Locke, S., Smy, M. B., Sobel, H. W., Takhistov, V., Yankelevich, A., Hill, J., Lee, S. H., Moon, D. H., Park, R. G., Bodur, B., Scholberg, K., Walter, C. W., Beauchene, A., Drapier, O., Giampaolo, A., Mueller, Th. A., Santos, A. D., Paganini, P., Quilain, B., Nakamura, T., Jang, J. S., Machado, L. N., Learned, J. G., Choi, K., Iovine, N., Cao, S., Anthony, L. H. V., Martin, D., Prouse, N. W., Scott, M., Sztuc, A. A., Uchida, Y., Berardi, V., Catanesi, M. G., Radicioni, E., Calabria, N. F., Langella, A., De Rosa, G., Collazuol, G., Iacob, F., Mattiazzi, M., Ludovici, L., Gonin, M., Pronost, G., Fujisawa, C., Maekawa, Y., Nishimura, Y., Okazaki, R., Akutsu, R., Friend, M., Hasegawa, T., Ishida, T., Kobayashi, T., Jakkapu, M., Matsubara, T., Nakadaira, T., Nakamura, K., Oyama, Y., Sakashita, K., Sekiguchi, T., Tsukamoto, T., Bhuiyan, N., Burton, G. T., Di Lodovico, F., Gao, J., Goldsack, A., Katori, T., Migenda, J., Ramsden, R., Xie, Z., Zsoldos, S., Suzuki, A. T., Takagi, Y., Takeuchi, Y., Zhong, H., Feng, J., Feng, L., Hu, J. R., Hu, Z., Kawaue, M., Kikawa, T., Mori, M., Nakaya, T., Wendell, R. A., Yasutome, K., Jenkins, S. J., McCauley, N., Mehta, P., Tarrant, A., Fukuda, Y., Itow, Y., Menjo, H., Ninomiya, K., Lagoda, J., Lakshmi, S. M., Mandal, M., Mijakowski, P., Prabhu, Y. S., Zalipska, J., Jia, M., Jiang, J., Jung, C. K., Shi, W., Wilking, M. J., Yanagisawa, C., Harada, M., Hino, Y., Ishino, H., Koshio, Y., Nakanishi, F., Sakai, S., Tada, T., Tano, T., Ishizuka, T., Barr, G., Barrow, D., Cook, L., Holin, A., Nova, F., Samani, S., Wark, D., Jung, S., Yang, B. S., Yang, J. Y., Yoo, J., Fannon, J. E. P., Kneale, L., Malek, M., McElwee, J. M., Thiesse, M. D., Thompson, L. F., Wilson, S. T., Okazawa, H., Kim, S. B., Kwon, E., Seo, J. W., Yu, I., Ichikawa, A. K., Nakamura, K. D., Tairafune, S., Nishijima, K., Koshiba, M., Eguchi, A., Nakagiri, K., Nakajima, Y., Shima, S., Taniuchi, N., Watanabe, E., Yokoyama, M., de Perio, P., Fujita, S., Martens, K., Tsui, K. M., Vagins, M. R., Xia, J., Izumiyama, S., Kuze, M., Matsumoto, R., Ishitsuka, M., Ito, H., Ommura, Y., Shigeta, N., Shinoki, M., Yamauchi, K., Yoshida, T., Gaur, R., Gousy-Leblanc, V., Hartz, M., Konaka, A., Li, X., Chen, S., Xu, B. D., Zhang, B., Posiadala-Zezula, M., Boyd, S. B., Edwards, R., Hadley, D., Nicholson, M., O'Flaherty, M., Richards, B., Ali, A., Jamieson, B., Amanai, S., Marti, Ll., Minamino, A., and Suzuki, S.
Subjects: High Energy Physics - Experiment
Abstract: We present a measurement of neutrino oscillation parameters with the Super-Kamiokande detector using atmospheric neutrinos from the complete pure-water SK I-V (April 1996-July 2020) data set, including events from an expanded fiducial volume. The data set corresponds to 6511.3 live days and an exposure of 484.2 kiloton-years. Measurements of the neutrino oscillation parameters $\Delta m^2_{32}$, $\sin^2\theta_{23}$, $\sin^2 \theta_{13}$, $\delta_{CP}$, and the preference for the neutrino mass ordering are presented with atmospheric neutrino data alone, and with constraints on $\sin^2 \theta_{13}$ from reactor neutrino experiments. Our analysis including constraints on $\sin^2 \theta_{13}$ favors the normal mass ordering at the 92.3% level., Comment: 24 pages, 18 figures
Published: 2023
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37. Measurement of the neutrino-oxygen neutral-current quasielastic cross section using atmospheric neutrinos in the SK-Gd experiment

Author: Sakai, S., Abe, K., Bronner, C., Hayato, Y., Hiraide, K., Hosokawa, K., Ieki, K., Ikeda, M., Kameda, J., Kanemura, Y., Kaneshima, R., Kashiwagi, Y., Kataoka, Y., Miki, S., Mine, S., Miura, M., Moriyama, S., Nakano, Y., Nakahata, M., Nakayama, S., Noguchi, Y., Sato, K., Sekiya, H., Shiba, H., Shimizu, K., Shiozawa, M., Sonoda, Y., Suzuki, Y., Takeda, A., Takemoto, Y., Tanaka, H., Yano, T., Han, S., Kajita, T., Okumura, K., Tashiro, T., Tomiya, T., Wang, X., Yoshida, S., Fernandez, P., Labarga, L., Ospina, N., Zaldivar, B., Pointon, B. W., Kearns, E., Raaf, J. L., Wan, L., Wester, T., Bian, J., Griskevich, N. J., Locke, S., Smy, M. B., Sobel, H. W., Takhistov, V., Yankelevich, A., Hill, J., Jang, M. C., Lee, S. H., Moon, D. H., Park, R. G., Bodur, B., Scholberg, K., Walter, C. W., Beauchene, A., Drapier, O., Giampaolo, A., Mueller, Th. A., Santos, A. D., Paganini, P., Quilain, B., Nakamura, T., Jang, J. S., Machado, L. N., Learned, J. G., Choi, K., Iovine, N., Cao, S., Anthony, L. H. V., Martin, D., Prouse, N. W., Scott, M., Sztuc, A. A., Uchida, Y., Berardi, V., Calabria, N. F., Catanesi, M. G., Radicioni, E., Langella, A., De Rosa, G., Collazuol, G., Iacob, F., Mattiazzi, M., Ludovici, L., Gonin, M., Pronost, G., Fujisawa, C., Maekawa, Y., Nishimura, Y., Okazaki, R., Akutsu, R., Friend, M., Hasegawa, T., Ishida, T., Kobayashi, T., Jakkapu, M., Matsubara, T., Nakadaira, T., Nakamura, K., Oyama, Y., Sakashita, K., Sekiguchi, T., Tsukamoto, T., Bhuiyan, N., Burton, G. T., Di Lodovico, F., Gao, J., Goldsack, A., Katori, T., Migenda, J., Ramsden, R. M., Xie, Z., Zsoldos, S., Suzuki, A. T., Takagi, Y., Zhong, H., Takeuchi, Y., Feng, J., Feng, L., Hu, J. R., Hu, Z., Kawaue, M., Kikawa, T., Mori, M., Nakaya, T., Wendell, R. A., Yasutome, K., Jenkins, S. J., McCauley, N., Mehta, P., Tarant, A., Fukuda, Y., Itow, Y., Menjo, H., Ninomiya, K., Yoshioka, Y., Lagoda, J., Lakshmi, S. M., Mandal, M., Mijakowski, P., Prabhu, Y. S., Zalipska, J., Jia, M., Jiang, J., Jung, C. K., Shi, W., Wilking, M. J., Yanagisawa, C., Harada, M., Hino, Y., Ishino, H., Koshio, Y., Nakanishi, F., Tada, T., Tano, T., Ishizuka, T., Barr, G., Barrow, D., Cook, L., Samani, S., Wark, D., Holin, A., Nova, F., Jung, S., Yang, B. S., Yang, J. Y., Yoo, J., Fannon, J. E. P., Kneale, L., Malek, M., McElwee, J. M., Thiesse, M. D., Thompson, L. F., Wilson, S. T., Okazawa, H., Kim, S. B., Kwon, E., Seo, J. W., Yu, I., Ichikawa, A. K., Nakamura, K. D., Tairafune, S., Nishijima, K., Eguchi, A., Nakagiri, K., Nakajima, Y., Shima, S., Taniuchi, N., Watanabe, E., Yokoyama, M., de Perio, P., Fujita, S., Martens, K., Tsui, K. M., Vagins, M. R., Xia, J., Izumiyama, S., Kuze, M., Matsumoto, R., Ishitsuka, M., Ito, H., Ommura, Y., Shigeta, N., Shinoki, M., Yamauchi, K., Yoshida, T., Gaur, R., Gousy-Leblanc, V., Hartz, M., Konaka, A., Li, X., Chen, S., Xu, B. D., Zhang, B., Posiadala-Zezula, M., Boyd, S. B., Edwards, R., Hadley, D., Nicholson, M., O'Flaherty, M., Richards, B., Ali, A., Jamieson, B., Amanai, S., Marti, Ll., Minamino, A., and Suzuki, S.
Subjects: High Energy Physics - Experiment
Abstract: We report the first measurement of the atmospheric neutrino-oxygen neutral-current quasielastic (NCQE) cross section in the gadolinium-loaded Super-Kamiokande (SK) water Cherenkov detector. In June 2020, SK began a new experimental phase, named SK-Gd, by loading 0.011% by mass of gadolinium into the ultrapure water of the SK detector. The introduction of gadolinium to ultrapure water has the effect of improving the neutron-tagging efficiency. Using a 552.2 day data set from August 2020 to June 2022, we measure the NCQE cross section to be 0.74 $\pm$ 0.22(stat.) $^{+0.85}_{-0.15}$ (syst.) $\times$ 10$^{-38}$ cm$^{2}$/oxygen in the energy range from 160 MeV to 10 GeV, which is consistent with the atmospheric neutrino-flux-averaged theoretical NCQE cross section and the measurement in the SK pure-water phase within the uncertainties. Furthermore, we compare the models of the nucleon-nucleus interactions in water and find that the Binary Cascade model and the Liege Intranuclear Cascade model provide a somewhat better fit to the observed data than the Bertini Cascade model. Since the atmospheric neutrino-oxygen NCQE reactions are one of the main backgrounds in the search for diffuse supernova neutrino background (DSNB), these new results will contribute to future studies - and the potential discovery - of the DSNB in SK., Comment: 8 pages, 3 figures
Published: 2023

38. Search for Periodic Time Variations of the Solar $^8$B Neutrino Flux between 1996 and 2018 in Super-Kamiokande

Author: Abe, K., Bronner, C., Hayato, Y., Hiraide, K., Hosokawa, K., Ieki, K., Ikeda, M., Kameda, J., Kanemura, Y., Kaneshima, R., Kashiwagi, Y., Kataoka, Y., Miki, S., Mine, S., Miura, M., Moriyama, S., Nakano, Y., Nakahata, M., Nakayama, S., Noguchi, Y., Sato, K., Sekiya, H., Shiba, H., Shimizu, K., Shiozawa, M., Sonoda, Y., Suzuki, Y., Takeda, A., Takemoto, Y., Tanaka, H., Yano, T., Han, S., Kajita, T., Okumura, K., Tashiro, T., Tomiya, T., Wang, X., Yoshida, S., Fernandez, P., Labarga, L., Ospina, N., Zaldivar, B., Pointon, B. W., Kearns, E., Raaf, J. L., Wan, L., Wester, T., Bian, J., Griskevich, N. J., Locke, S., Smy, M. B., Sobel, H. W., Takhistov, V., Yankelevich, A., Hill, J., Lee, S. H., Moon, D. H., Park, R. G., Jang, M. C., Bodur, B., Scholberg, K., Walter, C. W., Beauchene, A., Drapier, O., Giampaolo, A., Mueller, Th. A., Santos, A. D., Paganini, P., Quilain, B., Nakamura, T., Jang, J. S., Machado, L. N., Learned, J. G., Choi, K., Iovine, N., Cao, S., Anthony, L. H. V., Martin, D., Prouse, N. W., Scott, M., Sztuc, A. A., Uchida, Y., Berardi, V., Catanesi, M. G., Radicioni, E., Calabria, N. F., Langella, A., De Rosa, G., Collazuol, G., Iacob, F., Mattiazzi, M., Ludovici, L., Gonin, M., Pronost, G., Fujisawa, C., Maekawa, Y., Nishimura, Y., Okazaki, R., Akutsu, R., Friend, M., Hasegawa, T., Ishida, T., Kobayashi, T., Jakkapu, M., Matsubara, T., Nakadaira, T., Nakamura, K., Oyama, Y., Sakashita, K., Sekiguchi, T., Tsukamoto, T., Bhuiyan, N., Burton, G. T., Di Lodovico, F., Gao, J., Goldsack, A., Katori, T., Migenda, J., Xie, Z., Ramsden, R. M., Zsoldos, S., Suzuki, A. T., Takagi, Y., Zhong, H., Takeuchi, Y., Feng, J., Feng, L., Hu, J. R., Hu, Z., Kikawa, T., Mori, M., Kawaue, M., Nakaya, T., Wendell, R. A., Yasutome, K., Jenkins, S. J., McCauley, N., Mehta, P., Tarant, A., Fukuda, Y., Itow, Y., Menjo, H., Ninomiya, K., Yoshioka, Y., Lagoda, J., Lakshmi, S. M., Mandal, M., Mijakowski, P., Prabhu, Y. S., Zalipska, J., Jia, M., Jiang, J., Jung, C. K., Wilking, M. J., Yanagisawa, C., Shi, W., Harada, M., Hino, Y., Ishino, H., Koshio, Y., Nakanishi, F., Sakai, S., Tada, T., Tano, T., Ishizuka, T., Barr, G., Barrow, D., Cook, L., Samani, S., Wark, D., Holin, A., Nova, F., Yang, B. S., Yang, J. Y., Yoo, J., Jung, S., Fannon, J. E. P., Kneale, L., Malek, M., McElwee, J. M., Thiesse, M. D., Thompson, L. F., Wilson, S. T., Okazawa, H., Kim, S. B., Kwon, E., Seo, J. W., Yu, I., Ichikawa, A. K., Nakamura, K. D., Tairafune, S., Nishijima, K., Eguchi, A., Nakagiri, K., Nakajima, Y., Shima, S., Taniuchi, N., Watanabe, E., Yokoyama, M., de Perio, P., Fujita, S., Martens, K., Tsui, K. M., Vagins, M. R., Xia, J., Izumiyama, S., Kuze, M., Matsumoto, R., Ishitsuka, M., Ito, H., Ommura, Y., Shigeta, N., Shinoki, M., Yamauchi, K., Yoshida, T., Gaur, R., Gousy-Leblanc, V., Hartz, M., Konaka, A., Li, X., Chen, S., Xu, B. D., Zhang, B., Posiadala-Zezula, M., Boyd, S. B., Edwards, R., Hadley, D., Nicholson, M., Flaherty, M. O, Richards, B., Ali, A., Jamieson, B., Amanai, S., Marti, Ll., Minamino, A., and Suzuki, S.
Subjects: High Energy Physics - Experiment
Abstract: We report a search for time variations of the solar $^8$B neutrino flux using 5804 live days of Super-Kamiokande data collected between May 31, 1996, and May 30, 2018. Super-Kamiokande measured the precise time of each solar neutrino interaction over 22 calendar years to search for solar neutrino flux modulations with unprecedented precision. Periodic modulations are searched for in a dataset comprising five-day interval solar neutrino flux measurements with a maximum likelihood method. We also applied the Lomb-Scargle method to this dataset to compare it with previous reports. The only significant modulation found is due to the elliptic orbit of the Earth around the Sun. The observed modulation is consistent with astronomical data: we measured an eccentricity of (1.53$\pm$0.35)\%, and a perihelion shift of ($-$1.5$\pm$13.5) days., Comment: 8 pages, 5 figures, 2 tables, and data file: "sksolartimevariation5804d.txt" (the data file updated with additional 3 columns -- R^2 correction, upper-error, lower-error)
Published: 2023

39. Measurements of the $\nu_{\mu}$ and $\bar{\nu}_{\mu}$-induced Coherent Charged Pion Production Cross Sections on $^{12}C$ by the T2K experiment

Author: Abe, K., Akhlaq, N., Akutsu, R., Ali, A., Monsalve, S. Alonso, Alt, C., Andreopoulos, C., Antonova, M., Aoki, S., Arihara, T., Asada, Y., Ashida, Y., Atkin, E. T., Barbi, M., Barker, G. J., Barr, G., Barrow, D., Batkiewicz-Kwasniak, M., Berardi, V., Berns, L., Bhadra, S., Blanchet, A., Blondel, A., Bolognesi, S., Bonus, T., Bordoni, S., Boyd, S. B., Bravar, A., Bronner, C., Bron, S., Bubak, A., Avanzini, M. Buizza, Caballero, J. A., Calabria, N. F., Cao, S., Carabadjac, D., Carter, A. J., Cartwright, S. L., Casado, M. P., Catanesi, M. G., Cervera, A., Chakrani, J., Cherdack, D., Chong, P. S., Christodoulou, G., Chvirova, A., Cicerchia, M., Coleman, J., Collazuol, G., Cook, L., Cudd, A., Dalmazzone, C., Daret, T., Davydov, Yu. I., De Roeck, A., De Rosa, G., Dealtry, T., Delogu, C. C., Densham, C., Dergacheva, A., Di Lodovico, F., Dolan, S., Douqa, D., Doyle, T. A., Drapier, O., Dumarchez, J., Dunne, P., Dygnarowicz, K., Eguchi, A., Emery-Schrenk, S., Erofeev, G., Ershova, A., Eurin, G., Fedorova, D., Fedotov, S., Feltre, M., Finch, A. J., Aguirre, G. A. Fiorentini, Fiorillo, G., Fitton, M. D., Patiño, J. M. Franco, Friend, M., Fujii, Y., Fukuda, Y., Furui, Y., Giannessi, L., Giganti, C., Glagolev, V., Gonin, M., Rosa, J. González, Goodman, E. A. G., Gorin, A., Grassi, M., Guigue, M., Hadley, D. R., Haigh, J. T., Hamacher-Baumann, P., Harris, D. A., Hartz, M., Hasegawa, T., Hassani, S., Hastings, N. C., Hayato, Y., Henaff, D., Hogan, M., Holeczek, J., Holin, A., Holvey, T., Van, N. T. Hong, Honjo, T., Ichikawa, A. K., Ikeda, M., Ishida, T., Ishitsuka, M., Israel, H. T., Izmaylov, A., Jakkapu, M., Jamieson, B., Jenkins, S. J., Jesús-Valls, C., Jiang, J. J., Ji, J. Y., Jonsson, P., Joshi, S., Jung, C. K., Jurj, P. B., Kabirnezhad, M., Kaboth, A. C., Kajita, T., Kakuno, H., Kameda, J., Kasetti, S. P., Kataoka, Y., Katori, T., Kawaue, M., Kearns, E., Khabibullin, M., Khotjantsev, A., Kikawa, T., King, S., Kiseeva, V., Kisiel, J., Kobayashi, H., Kobayashi, T., Koch, L., Kodama, S., Konaka, A., Kormos, L. L., Koshio, Y., Koto, T., Kowalik, K., Kudenko, Y., Kudo, Y., Kuribayashi, S., Kurjata, R., Kutter, T., Kuze, M., La Commara, M., Labarga, L., Lachner, K., Lagoda, J., Lakshmi, S. M., James, M. Lamers, Lamoureux, M., Langella, A., Laporte, J. -F., Last, D., Latham, N., Laveder, M., Lavitola, L., Lawe, M., Lee, Y., Lin, C., Lin, S. -K., Litchfield, R. P., Liu, S. L., Li, W., Longhin, A., Long, K. R., Moreno, A. Lopez, Ludovici, L., Lu, X., Lux, T., Machado, L. N., Magaletti, L., Mahn, K., Malek, M., Mandal, M., Manly, S., Marino, A. D., Marti-Magro, L., Martin, D. G. R., Martini, M., Martin, J. F., Maruyama, T., Matsubara, T., Matveev, V., Mauger, C., Mavrokoridis, K., Mazzucato, E., McCauley, N., McElwee, J., McFarland, K. S., McGrew, C., McKean, J., Mefodiev, A., Megias, G. D., Mehta, P., Mellet, L., Metelko, C., Mezzetto, M., Miller, E., Minamino, A., Mineev, O., Mine, S., Miura, M., Bueno, L. Molina, Moriyama, S., Morrison, P., Mueller, Th. A., Munford, D., Munteanu, L., Nagai, K., Nagai, Y., Nakadaira, T., Nakagiri, K., Nakahata, M., Nakajima, Y., Nakamura, A., Nakamura, H., Nakamura, K., Nakamura, K. D., Nakano, Y., Nakayama, S., Nakaya, T., Nakayoshi, K., Naseby, C. E. R., Ngoc, T. V., Nguyen, V. Q., Niewczas, K., Nishimori, S., Nishimura, Y., Nishizaki, K., Nosek, T., Nova, F., Novella, P., Nugent, J. C., O'Keeffe, H. M., O'Sullivan, L., Odagawa, T., Okinaga, W., Okumura, K., Okusawa, T., Ospina, N., Oyama, Y., Palladino, V., Paolone, V., Pari, M., Parlone, J., Pasternak, J., Pavin, M., Payne, D., Penn, G. C., Pershey, D., Pickering, L., Pidcott, C., Pintaudi, G., Pistillo, C., Popov, B., Porwit, K., Posiadala-Zezula, M., Prabhu, Y. S., Pupilli, F., Quilain, B., Radermacher, T., Radicioni, E., Radics, B., Ramírez, M. A., Ratoff, P. N., Reh, M., Riccio, C., Rondio, E., Roth, S., Roy, N., Rubbia, A., Ruggeri, A. C., Ruggles, C. A., Rychter, A., Sakashita, K., Sánchez, F., Schloesser, C. M., Scholberg, K., Scott, M., Seiya, Y., Sekiguchi, T., Sekiya, H., Sgalaberna, D., Shaikhiev, A., Shaker, F., Shiozawa, M., Shorrock, W., Shvartsman, A., Skrobova, N., Skwarczynski, K., Smyczek, D., Smy, M., Sobczyk, J. T., Sobel, H., Soler, F. J. P., Sonoda, Y., Speers, A. J., Spina, R., Suslov, I. A., Suvorov, S., Suzuki, A., Suzuki, S. Y., Suzuki, Y., Tada, M., Tairafune, S., Takayasu, S., Takeda, A., Takeuchi, Y., Takifuji, K., Tanaka, H. K., Tani, M., Teklu, A., Tereshchenko, V. V., Thamm, N., Thompson, L. F., Toki, W., Touramanis, C., Towstego, T., Tsui, K. M., Tsukamoto, T., Tzanov, M., Uchida, Y., Vagins, M., Vargas, D., Varghese, M., Vasseur, G., Vilela, C., Villa, E., Vinning, W. G. S., Virginet, U., Vladisavljevic, T., Wachala, T., Walsh, J. G., Wang, Y., Wan, L., Wark, D., Wascko, M. O., Weber, A., Wendell, R., Wilking, M. J., Wilkinson, C., Wilson, J. R., Wood, K., Wret, C., Xia, J., Xu, Y. -h., Yamamoto, K., Yamamoto, T., Yanagisawa, C., Yang, G., Yano, T., Yasutome, K., Yershov, N., Yevarouskaya, U., Yokoyama, M., Yoshimoto, Y., Yoshimura, N., Yu, M., Zaki, R., Zalewska, A., Zalipska, J., Zaremba, K., Zarnecki, G., Zhao, X., Zhu, T., Ziembicki, M., Zimmerman, E. D., Zito, M., and Zsoldos, S.
Subjects: High Energy Physics - Experiment
Abstract: We report an updated measurement of the $\nu_{\mu}$-induced, and the first measurement of the $\bar{\nu}_{\mu}$-induced coherent charged pion production cross section on $^{12}C$ nuclei in the T2K experiment. This is measured in a restricted region of the final-state phase space for which $p_{\mu,\pi} > 0.2$ GeV, $\cos(\theta_{\mu}) > 0.8$ and $\cos(\theta_{\pi}) > 0.6$, and at a mean (anti)neutrino energy of 0.85 GeV using the T2K near detector. The measured $\nu_{\mu}$ CC coherent pion production flux-averaged cross section on $^{12}C$ is $(2.98 \pm 0.37 (stat.) \pm 0.31 (syst.) \substack{ +0.49 \\ -0.00 } \mathrm{ (Q^2\,model)}) \times 10^{-40}~\mathrm{cm}^{2}$. The new measurement of the $\bar{\nu}_{\mu}$-induced cross section on $^{12}{C}$ is $(3.05 \pm 0.71 (stat.) \pm 0.39 (syst.) \substack{ +0.74 \\ -0.00 } \mathrm{(Q^2\,model)}) \times 10^{-40}~\mathrm{cm}^{2}$. The results are compatible with both the NEUT 5.4.0 Berger-Sehgal (2009) and GENIE 2.8.0 Rein-Sehgal (2007) model predictions.
Published: 2023

40. Supporting Passive Users in mmWave Networks

Author: Dogan, Mine Gokce, Cardone, Martina, and Fragouli, Christina
Subjects: Computer Science - Information Theory
Abstract: The interference from active to passive users is a well-recognized challenge in millimeter-wave (mmWave) communications. We propose a method that enables to limit the interference on passive users (whose presence may not be detected since they do not transmit) with a small penalty to the throughput of active users. Our approach abstracts away (in a simple, yet informative way) the physical layer component and it leverages the directivity of mmWave links and the available network path diversity. We provide linear programming formulations, lower bounds on active users rates, numerical evaluations, and we establish a connection with the problem of (information theoretically) secure communication over mmWave networks.
Published: 2023

41. Hand Dominance and Congruence for Wrist-worn Haptics using Custom Voice-Coil Actuation

Author: Adeyemi, Ayoade, Sen, Umit, Ercan, Samet Mert, and Sarac, Mine
Subjects: Computer Science - Human-Computer Interaction, Computer Science - Robotics
Abstract: During virtual interactions, rendering haptic feedback on a remote location (like the wrist) instead of the fingertips freeing users' hands from mechanical devices. This allows for real interactions while still providing information regarding the mechanical properties of virtual objects. In this paper, we present CoWrHap -- a novel wrist-worn haptic device with custom-made voice coil actuation to render force feedback. Then, we investigate the impact of asking participants to use their dominant or non-dominant hand for virtual interactions and the best mapping between the active hand and the wrist receiving the haptic feedback, which can be defined as hand-wrist congruence through a user experiment based on a stiffness discrimination task. Our results show that participants performed the tasks (i) better with non-congruent mapping but reported better experiences with congruent mapping, and (ii) with no statistical difference in terms of hand dominance but reported better user experience and enjoyment using their dominant hands. This study indicates that participants can perceive mechanical properties via haptic feedback provided through CoWrHap., Comment: 6 pages, 7 figures
Published: 2023
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42. Automated grading workflows for providing personalized feedback to open-ended data science assignments

Author: Ricci, Federica Zoe, Medina, Catalina Mari, and Dogucu, Mine
Subjects: Statistics - Other Statistics, Computer Science - Computers and Society, Physics - Physics Education
Abstract: Open-ended assignments - such as lab reports and semester-long projects - provide data science and statistics students with opportunities for developing communication, critical thinking, and creativity skills. However, providing grades and formative feedback to open-ended assignments can be very time consuming and difficult to do consistently across students. In this paper, we discuss the steps of a typical grading workflow and highlight which steps can be automated in an approach that we call automated grading workflow. We illustrate how gradetools, a new R package, implements this approach within RStudio to facilitate efficient and consistent grading while providing individualized feedback. By outlining the motivations behind the development of this package and the considerations underlying its design, we hope this article will provide data science and statistics educators with ideas for improving their grading workflows, possibly developing new grading tools or considering use gradetools as their grading workflow assistant., Comment: 24 pages, 3 figures
Published: 2023
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43. Single electron routing in a silicon quantum-dot array

Author: Utsugi, Takeru, Kuno, Takuma, Lee, Noriyuki, Tsuchiya, Ryuta, Mine, Toshiyuki, Hisamoto, Digh, Saito, Shinichi, and Mizuno, Hiroyuki
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: The ability to transport single electrons on a quantum dot array dramatically increases the freedom in designing quantum computation schemes that can be implemented on solid-state devices. So far, however, routing schemes to precisely control the transport paths of single electrons have yet to be established. Here, we propose a silicon single-electron router that transports pumped electrons along the desired route on the branches of a T-shaped quantum dot array by inputting a synchronous phase-controlled signal to multiple gates. Notably, we show that it is possible to achieve a routing accuracy above 99\% by boosting the accuracy of the electron-transport timing with an assist gate in front of the branching paths. We also evaluated the minimum error rate of routing by the model of electron transport based on the Wigner representation in an energy-time space. The results suggest new possibilities for fast and accurate transport of single electrons on the two-dimensional quantum dot arrays., Comment: 12 pages, 16 figures
Published: 2023
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44. Mixbiotic society measures: Comparison of organizational structures based on communication simulation

Author: Kato, Takeshi, Miyakoshi, Jyunichi, Matsumura, Tadayuki, Kudo, Yasuyuki, Mine, Ryuji, Mizuno, Hiroyuki, and Deguchi, Yasuo
Subjects: Computer Science - Social and Information Networks, Computer Science - Computers and Society, 91D30, 91C05, 62P25, J.4, G.2.2
Abstract: The philosophical world has proposed the concept of "mixbiotic society," in which individuals with freedom and diverse values mix and mingle to recognize their respective "fundamental incapability" each other and sublimate into solidarity, toward solving the issues of social isolation and fragmentation. Based on this concept, the mixbiotic society measures have been proposed to evaluate dynamic communication patterns with reference to classification in cellular automata and particle reaction-diffusion that simulate living phenomena. In this paper, we applied these measures to five typologies of organizational structure (Red: impulsive, Amber: adaptive, Orange: achievement, Green: pluralistic, and Teal: evolutionary) and evaluated their features. Specifically, we formed star, tree, tree+jumpers, tree+more jumpers, and small-world type networks corresponding to each of five typologies, conducted communication simulations on these networks, and calculated values for mixbiotic society measures. The results showed that Teal organization has the highest value of the mixism measure among mixbiotic society measures, i.e., it balances similarity (mixing) and dissimilarity (mingling) in communication, and is living and mixbiotic between order and chaos. Measures other than mixism showed that in Teal organization, information is not concentrated in a central leader and that communication takes place among various members. This evaluation of organizational structures shows that the mixbiotic society measures is also useful for assessing organizational change. In the future, these measures will be used not only in business organizations, but also in digital democratic organizations and platform cooperatives in conjunction with information technology., Comment: 25 pages, 6 figures
Published: 2023

45. Mixbiotic society measures: Assessment of community well-going as living system

Author: Kato, Takeshi, Miyakoshi, Jyunichi, Matsumura, Tadayuki, Mine, Ryuji, Mizuno, Hiroyuki, and Deguchi, Yasuo
Subjects: Computer Science - Social and Information Networks, Computer Science - Computers and Society, 91D30, 91C05, 62P25, J.4, G.2.2
Abstract: Social isolation is caused by the impoverishment of community (atomism) and fragmentation is caused by the enlargement of in-group (mobism), both of which can be viewed as social problems related to communication. To solve these problems, the philosophical world has proposed the concept of "mixbiotic society," in which individuals with freedom and diverse values mix and mingle to recognize their respective "fundamental incapability" each other and sublimate into solidarity. Based on this concept, this study proposes new mixbiotic society measures to evaluate dynamic communication patterns with reference to classification in cellular automata and particle reaction diffusion that simulate living phenomena. Specifically, the hypothesis of measures corresponding to the four classes was formulated, and the hypothesis was validated by simulating the generation and disappearance of communication. As a result, considering communication patterns as multidimensional vectors, it found that the mean of Euclidean distance for "mobism," the variance of the relative change in distance for "atomism," the composite measure that multiplies the mean and variance of cosine similarity for "mixism," which corresponds to the well-going of mixbiotic society, and the almost zero measures for "nihilism," are suitable. Then, evaluating seven real-society datasets using these measures, we showed that the mixism measure is useful for assessing the livingness of communication, and that it is possible to typify communities based on plural measures. The measures established in this study are superior to conventional analysis in that they can evaluate dynamic patterns, they are simple to calculate, and their meanings are easy to interpret. As a future development, the mixbiotic society measures will be used in the fields of digital democracy and platform cooperativism toward a desirable society., Comment: 52 pages, 10 figures
Published: 2023
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46. Diverse Perspectives Can Mitigate Political Bias in Crowdsourced Content Moderation

Author: Thebault-Spieker, Jacob, Venkatagiri, Sukrit, Mine, Naomi, and Luther, Kurt
Subjects: Computer Science - Human-Computer Interaction, Computer Science - Computers and Society
Abstract: In recent years, social media companies have grappled with defining and enforcing content moderation policies surrounding political content on their platforms, due in part to concerns about political bias, disinformation, and polarization. These policies have taken many forms, including disallowing political advertising, limiting the reach of political topics, fact-checking political claims, and enabling users to hide political content altogether. However, implementing these policies requires human judgement to label political content, and it is unclear how well human labelers perform at this task, or whether biases affect this process. Therefore, in this study we experimentally evaluate the feasibility and practicality of using crowd workers to identify political content, and we uncover biases that make it difficult to identify this content. Our results problematize crowds composed of seemingly interchangeable workers, and provide preliminary evidence that aggregating judgements from heterogeneous workers may help mitigate political biases. In light of these findings, we identify strategies to achieving fairer labeling outcomes, while also better supporting crowd workers at this task and potentially mitigating biases., Comment: Accepted to the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT 2023). To cite please use the reference available at this URL: https://doi.org/10.1145/3593013.3594080
Published: 2023
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47. Updated T2K measurements of muon neutrino and antineutrino disappearance using 3.6 $\times$ 10$^{21}$ protons on target

Author: Abe, K., Akhlaq, N., Akutsu, R., Alarakia-Charles, H., Ali, A., Hakim, Y. I. Alj, Monsalve, S. Alonso, Alt, C., Andreopoulos, C., Antonova, M., Aoki, S., Arihara, T., Asada, Y., Ashida, Y., Atkin, E. T., Barbi, M., Barker, G. J., Barr, G., Barrow, D., Batkiewicz-Kwasniak, M., Bench, F., Berardi, V., Berns, L., Bhadra, S., Blanchet, A., Blondel, A., Bolognesi, S., Bonus, T., Bordoni, S., Boyd, S. B., Bravar, A., Bronner, C., Bron, S., Bubak, A., Avanzini, M. Buizza, Caballero, J. A., Calabria, N. F., Cao, S., Carabadjac, D., Carter, A. J., Cartwright, S. L., Casado, M. P., Catanesi, M. G., Cervera, A., Chakrani, J., Cherdack, D., Chong, P. S., Christodoulou, G., Chvirova, A., Cicerchia, M., Coleman, J., Collazuol, G., Cook, L., Cudd, A., Dalmazzone, C., Daret, T., Dasgupta, P., Davydov, Yu. I., De Roeck, A., De Rosa, G., Dealtry, T., Delogu, C. C., Densham, C., Dergacheva, A., Di Lodovico, F., Dolan, S., Douqa, D., Doyle, T. A., Drapier, O., Dumarchez, J., Dunne, P., Dygnarowicz, K., Eguchi, A., Emery-Schrenk, S., Erofeev, G., Ershova, A., Eurin, G., Fedorova, D., Fedotov, S., Feltre, M., Finch, A. J., Aguirre, G. A. Fiorentini, Fiorillo, G., Fitton, M. D., Patiño, J. M. Franco, Friend, M., Fujii, Y., Fukuda, Y., Furui, Y., Fusshoeller, K., Giannessi, L., Giganti, C., Glagolev, V., Gonin, M., Rosa, J. González, Goodman, E. A. G., Gorin, A., Grassi, M., Guigue, M., Hadley, D. R., Haigh, J. T., Hamacher-Baumann, P., Harris, D. A., Hartz, M., Hasegawa, T., Hassani, S., Hastings, N. C., Hayato, Y., Henaff, D., Hiramoto, A., Hogan, M., Holeczek, J., Holin, A., Holvey, T., Van, N. T. Hong, Honjo, T., Iacob, F., Ichikawa, A. K., Ikeda, M., Ishida, T., Ishitsuka, M., Israel, H. T., Iwamoto, K., Izmaylov, A., Izumi, N., Jakkapu, M., Jamieson, B., Jenkins, S. J., Jesús-Valls, C., Jiang, J. J., Ji, J. Y., Jonsson, P., Joshi, S., Jung, C. K., Jurj, P. B., Kabirnezhad, M., Kaboth, A. C., Kajita, T., Kakuno, H., Kameda, J., Kasetti, S. P., Kataoka, Y., Katayama, Y., Katori, T., Kawaue, M., Kearns, E., Khabibullin, M., Khotjantsev, A., Kikawa, T., Kikutani, H., King, S., Kiseeva, V., Kisiel, J., Kobata, T., Kobayashi, H., Kobayashi, T., Koch, L., Kodama, S., Konaka, A., Kormos, L. L., Koshio, Y., Kostin, A., Koto, T., Kowalik, K., Kudenko, Y., Kudo, Y., Kuribayashi, S., Kurjata, R., Kutter, T., Kuze, M., La Commara, M., Labarga, L., Lachner, K., Lagoda, J., Lakshmi, S. M., James, M. Lamers, Lamoureux, M., Langella, A., Laporte, J. -F., Last, D., Latham, N., Laveder, M., Lavitola, L., Lawe, M., Lee, Y., Lin, C., Lin, S. -K., Litchfield, R. P., Liu, S. L., Li, W., Longhin, A., Long, K. R., Moreno, A. Lopez, Ludovici, L., Lu, X., Lux, T., Machado, L. N., Magaletti, L., Mahn, K., Malek, M., Mandal, M., Manly, S., Marino, A. D., Marti-Magro, L., Martin, D. G. R., Martini, M., Martin, J. F., Maruyama, T., Matsubara, T., Matveev, V., Mauger, C., Mavrokoridis, K., Mazzucato, E., McCauley, N., McElwee, J., McFarland, K. S., McGrew, C., McKean, J., Mefodiev, A., Megias, G. D., Mehta, P., Mellet, L., Metelko, C., Mezzetto, M., Miller, E., Minamino, A., Mineev, O., Mine, S., Miura, M., Bueno, L. Molina, Moriyama, S., Morrison, P., Mueller, Th. A., Munford, D., Munteanu, L., Nagai, K., Nagai, Y., Nakadaira, T., Nakagiri, K., Nakahata, M., Nakajima, Y., Nakamura, A., Nakamura, H., Nakamura, K., Nakamura, K. D., Nakano, Y., Nakayama, S., Nakaya, T., Nakayoshi, K., Naseby, C. E. R., Ngoc, T. V., Nguyen, V. Q., Niewczas, K., Nishimori, S., Nishimura, Y., Nishizaki, K., Nosek, T., Nova, F., Novella, P., Nugent, J. C., O'Keeffe, H. M., O'Sullivan, L., Odagawa, T., Ogawa, T., Okada, R., Okinaga, W., Okumura, K., Okusawa, T., Ospina, N., Osu, L., Owen, R. A., Oyama, Y., Palladino, V., Paolone, V., Pari, M., Parlone, J., Parsa, S., Pasternak, J., Pavin, M., Payne, D., Penn, G. C., Pershey, D., Pickering, L., Pidcott, C., Pintaudi, G., Pistillo, C., Popov, B., Yrey, A. J. Portocarrero, Porwit, K., Posiadala-Zezula, M., Prabhu, Y. S., Pupilli, F., Quilain, B., Radermacher, T., Radicioni, E., Radics, B., Ramírez, M. A., Ratoff, P. N., Reh, M., Riccio, C., Rondio, E., Roth, S., Roy, N., Rubbia, A., Ruggeri, A. C., Ruggles, C. A., Rychter, A., Sakashita, K., Sánchez, F., Santucci, G., Schefke, T., Schloesser, C. M., Scholberg, K., Scott, M., Seiya, Y., Sekiguchi, T., Sekiya, H., Sgalaberna, D., Shaikhiev, A., Shaker, F., Shiozawa, M., Shorrock, W., Shvartsman, A., Skrobova, N., Skwarczynski, K., Smyczek, D., Smy, M., Sobczyk, J. T., Sobel, H., Soler, F. J. P., Sonoda, Y., Speers, A. J., Spina, R., Suslov, I. A., Suvorov, S., Suzuki, A., Suzuki, S. Y., Suzuki, Y., Sztuc, A. A., Tada, M., Tairafune, S., Takayasu, S., Takeda, A., Takeuchi, Y., Takifuji, K., Tanaka, H. K., Tanigawa, H., Tanihara, Y., Tani, M., Teklu, A., Tereshchenko, V. V., Teshima, N., Thamm, N., Thompson, L. F., Toki, W., Touramanis, C., Towstego, T., Tsui, K. M., Tsukamoto, T., Tzanov, M., Uchida, Y., Vagins, M., Vargas, D., Varghese, M., Vasseur, G., Vilela, C., Villa, E., Vinning, W. G. S., Virginet, U., Vladisavljevic, T., Wachala, T., Wakabayashi, D., Walsh, J. G., Wang, Y., Wan, L., Wark, D., Wascko, M. O., Weber, A., Wendell, R., Wilking, M. J., Wilkinson, C., Wilson, J. R., Wood, K., Wret, C., Xia, J., Xu, Y. -h., Yamamoto, K., Yamamoto, T., Yanagisawa, C., Yang, G., Yano, T., Yasutome, K., Yershov, N., Yevarouskaya, U., Yokoyama, M., Yoshimoto, Y., Yoshimura, N., Yu, M., Zaki, R., Zalewska, A., Zalipska, J., Zaremba, K., Zarnecki, G., Zhao, X., Zhu, T., Ziembicki, M., Zimmerman, E. D., Zito, M., and Zsoldos, S.
Subjects: High Energy Physics - Experiment
Abstract: Muon neutrino and antineutrino disappearance probabilities are identical in the standard three-flavor neutrino oscillation framework, but CPT violation and non-standard interactions can violate this symmetry. In this work we report the measurements of $\sin^{2} \theta_{23}$ and $\Delta m_{32}^2$ independently for neutrinos and antineutrinos. The aforementioned symmetry violation would manifest as an inconsistency in the neutrino and antineutrino oscillation parameters. The analysis discussed here uses a total of 1.97$\times$10$^{21}$ and 1.63$\times$10$^{21}$ protons on target taken with a neutrino and antineutrino beam respectively, and benefits from improved flux and cross-section models, new near detector samples and more than double the data reducing the overall uncertainty of the result. No significant deviation is observed, consistent with the standard neutrino oscillation picture.
Published: 2023

48. Search for astrophysical electron antineutrinos in Super-Kamiokande with 0.01wt% gadolinium-loaded water

Author: Harada, M., Abe, K., Bronner, C., Hayato, Y., Hiraide, K., Hosokawa, K., Ieki, K., Ikeda, M., Kameda, J., Kanemura, Y., Kaneshima, R., Kashiwagi, Y., Kataoka, Y., Miki, S., Mine, S., Miura, M., Moriyama, S., Nakano, Y., Nakahata, M., Nakayama, S., Noguchi, Y., Okamoto, K., Sato, K., Sekiya, H., Shiba, H., Shimizu, K., Shiozawa, M., Sonoda, Y., Suzuki, Y., Takeda, A., Takemoto, Y., Takenaka, A., Tanaka, H., Watanabe, S., Yano, T., Han, S., Kajita, T., Okumura, K., Tashiro, T., Tomiya, T., Wang, X., Yoshida, S., Megias, G. D., Fernandez, P., Labarga, L., Ospina, N., Zaldivar, B., Pointon, B. W., Kearns, E., Raaf, J. L., Wan, L., Wester, T., Bian, J., Griskevich, N. J., Locke, S., Smy, M. B., Sobel, H. W., Takhistov, V., Yankelevich, A., Hill, J., Lee, S. H., Moon, D. H., Park, R. G., Bodur, B., Scholberg, K., Walter, C. W., Beauchene, A., Drapier, O., Giampaolo, A., Mueller, Th. A., Santos, A. D., Paganini, P., Quilain, B., Ishizuka, T., Nakamura, T., Jang, J. S., Learned, J. G., Choi, K., Iovine, N., Cao, S., Anthony, L. H. V., Martin, D., Scott, M., Sztuc, A. A., Uchida, Y., Berardi, V., Catanesi, M. G., Radicioni, E., Calabria, N. F., Langella, A., Machado, L. N., De Rosa, G., Collazuol, G., Iacob, F., Lamoureux, M., Mattiazzi, M., Ludovici, L., Gonin, M., Pronost, G., Fujisawa, C., Maekawa, Y., Nishimura, Y., Okazaki, R., Akutsu, R., Friend, M., Hasegawa, T., Ishida, T., Kobayashi, T., Jakkapu, M., Matsubara, T., Nakadaira, T., Nakamura, K., Oyama, Y., Sakashita, K., Sekiguchi, T., Tsukamoto, T., Bhuiyan, N., Burton, G. T., Di Lodovico, F., Gao, J., Goldsack, A., Katori, T., Migenda, J., Xie, Z., Zsoldos, S., Kotsar, Y., Ozaki, H., Suzuki, A. T., Takagi, Y., Takeuchi, Y., Feng, J., Feng, L., Hu, J. R., Hu, Z., Kikawa, T., Mori, M., Nakaya, T., Wendell, R. A., Yasutome, K., Jenkins, S. J., McCauley, N., Mehta, P., Tarrant, A., Fukuda, Y., Itow, Y., Menjo, H., Ninomiya, K., Lagoda, J., Lakshmi, S. M., Mandal, M., Mijakowski, P., Prabhu, Y. S., Zalipska, J., Jia, M., Jiang, J., Jung, C. K., Wilking, M. J., Yanagisawa, C., Hino, Y., Ishino, H., Kitagawa, H., Koshio, Y., Nakanishi, F., Sakai, S., Tada, T., Tano, T., Barr, G., Barrow, D., Cook, L., Samani, S., Wark, D., Holin, A., Nova, F., Yang, B. S., Yang, J. Y., Yoo, J., Fannon, J. E. P., Kneale, L., Malek, M., McElwee, J. M., Thiesse, M. D., Thompson, L. F., Wilson, S. T., Okazawa, H., Kim, S. B., Kwon, E., Seo, J. W., Yu, I., Ichikawa, A. K., Nakamura, K. D., Tairafune, S., Nishijima, K., Nakagiri, K., Nakajima, Y., Shima, S., Taniuchi, N., Watanabe, E., Yokoyama, M., de Perio, P., Martens, K., Tsui, K. M., Vagins, M. R., Xia, J., Kuze, M., Izumiyama, S., Matsumoto, R., Ishitsuka, M., Ito, H., Kinoshita, T., Ommura, Y., Shigeta, N., Shinoki, M., Suganuma, T., Yamauchi, K., Martin, J. F., Tanaka, H. A., Towstego, T., Gaur, R., Gousy-Leblanc, V., Hartz, M., Konaka, A., Li, X., Prouse, N. W., Chen, S., Xu, B. D., Zhang, B., Posiadala-Zezula, M., Boyd, S. B., Edwards, R., Hadley, D., Nicholson, M., Flaherty, M. O, Richards, B., Ali, A., Jamieson, B., Marti, Ll., Minamino, A., Pintaudi, G., Sano, S., Suzuki, S., and Wada, K.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Experiment, Nuclear Experiment, Physics - Instrumentation and Detectors
Abstract: We report the first search result for the flux of astrophysical electron antineutrinos for energies O(10) MeV in the gadolinium-loaded Super-Kamiokande (SK) detector. In June 2020, gadolinium was introduced to the ultra-pure water of the SK detector in order to detect neutrons more efficiently. In this new experimental phase, SK-Gd, we can search for electron antineutrinos via inverse beta decay with efficient background rejection and higher signal efficiency thanks to the high efficiency of the neutron tagging technique. In this paper, we report the result for the initial stage of SK-Gd with a $22.5\times552$ $\rm kton\cdot day$ exposure at 0.01% Gd mass concentration. No significant excess over the expected background in the observed events is found for the neutrino energies below 31.3 MeV. Thus, the flux upper limits are placed at the 90% confidence level. The limits and sensitivities are already comparable with the previous SK result with pure-water ($22.5 \times 2970 \rm kton\cdot day$) owing to the enhanced neutron tagging.
Published: 2023
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49. New developments toward the Gonek Conjecture on the Hurwitz zeta-function

Author: Mine, Masahiro
Subjects: Mathematics - Number Theory, Primary 11M35, Secondary 41A30
Abstract: In this paper, we prove a version of the universality theorem for the Hurwitz zeta-function in the case where the parameter is algebraic and irrational. Then we apply the result to show that many of such Hurwitz zeta-functions have infinitely many zeros in the right half of the critical strip., Comment: 41 pages
Published: 2023

50. Factors determining surface oxygen vacancy formation energy in ternary spinel structure oxides with zinc

Author: Hinuma, Yoyo, Mine, Shinya, Toyao, Takashi, Kamachi, Takashi, and Shimizu, Ken-ichi
Subjects: Condensed Matter - Materials Science
Abstract: Spinel oxides are an important class of materials for heterogeneous catalysis including photocatalysis and electrocatalysis. The surface O vacancy formation energy (EOvac) is a critical quantity on catalyst performance because the surface of metal oxide catalysts often acts as reaction sites, for example, in the Mars-van Krevelen mechanism. However, experimental evaluation of EOvac is very challenging. We obtained the EOvac for (100), (110), and (111) surfaces of normal zinc-based spinel oxides ZnAl2O4, ZnGa2O4, ZnIn2O4, ZnV2O4, ZnCr2O4, ZnMn2O4, ZnFe2O4, and ZnCo2O4. The most stable surface is (100) for all compounds. The smallest EOvac for a surface is the largest in the (100) surface except for ZnCo2O4. For (100) and (110) surfaces, there is a good correlation, over all spinels, between the smallest EOvac for the surface and bulk formation energy, while the ionization potential correlates well in (111) surfaces. Machine learning over EOvac of all surface sites in all orientations and all compounds to find the important factors, or descriptors, that decide the EOvac revealed that bulk and surface-dependent descriptors are the most important, namely the bulk formation energy, a Boolean descriptor on whether the surface is (111), and the ionization potential, followed by geometrical descriptors that are different in each O site., Comment: Supplementary material available at https://doi.org/10.1039/D1CP03657B
Published: 2023
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