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9. Signature of Seyfert-like component in a blazar 3C 273 and its reflection-based explanation

Author

Gaur, Haritma, Pal, Main, Anjum, Muhammad S., Wani, Kiran, Kushwaha, Pankaj, Pandey, Ashwani, and Chen, Liang

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We present the results of blazar 3C 273 obtained from simultaneous observations obtained using XMM-Newton and NuSTAR satellites during the period 2015-2019 in five epochs. When the spectra are modeled with a power-law, significant residuals arise below 2 keV and in the energy range of 30-78 keV in NuSTAR data. Residuals in the lower energy band represent soft X-ray excess while at higher energies it likely represents Compton reflection hump which might be a weak component arising from dense and cold material. The presence of a faint iron line is present in XMM-Newton observations. We interpret such features as attributed to the coronal emission plus those arising from reflection from an accretion disk. We model the SEDs with the single zone inverse Compton jet model based on Synchrotron Self Compton and External Compton phenomena. It is found that a one-zone synchrotron plus IC model explains quite well the SEDs but the jet component alone fails to fit the multiband X-ray emission for the low state of this object in 2018 and 2019 which arises due to spectral flattening at low energy X-rays, indicating that an additional Seyfert-like thermal component must be present at X-rays. This is further supported by a big blue bump present in the optical/ultraviolet band in all SEDs. Finally, we analyzed all the epochs using relxill model to incorporate relativistic reflection to model those residuals of soft excess and Compton hump in the X-ray bands., Comment: 12 pages and 4 figures. Accepted for publication in ApJ

Published
2025

10. Evidence of the P_ccbars(4459)0 in Upsilon(1S, 2S) inclusive decays at Belle

Author

Adachi, I., Aggarwal, L., Ahmed, H., Ahn, J. K., Aihara, H., Akopov, N., Alhakami, M., Aloisio, A., Althubiti, N., Asner, D. M., Atmacan, H., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Baghel, N. K., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Bartl, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhardwaj, V., Bhuyan, B., Bianchi, F., Biswas, D., Bodrov, D., Bolz, A., Boschetti, A., Bozek, A., Bracko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cui, J. X., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Capua, F., Dingfelder, J., Dolezal, Z., Jimenez, I. Domnguez, Dong, T. V., Dong, X., Dossett, D., Dugic, K., Dujany, G., Ecker, P., Eppelt, J., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Forti, F., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Gaudino, G., Gaur, V., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Gironell, P. Gironella, Glazov, A., Gobbo, B., Godang, R., Goldenzweig, P., Graziani, E., Greenwald, D., Gruberova, Z., Guan, Y., Gudkova, K., Haide, I., Han, Y., Harris, C., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernandez, Higuchi, T., Hoek, M., Hohmann, M., Hoppe, R., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobi, D., Jacobs, W. W., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kaleta, M., Kandra, J., Kang, K. H., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Ketter, C., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, J. -Y., Kim, K. -H., Kim, Y. -K., Kindo, H., Kinoshita, K., Kodys, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Krizan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, D., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lalwani, K., Lam, T., Lange, J. S., Lau, T. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Lemettais, C., Leo, P., Lewis, P. M., Li, C., Li, L. K., Li, Q. M., Li, W. Z., Li, Y., Li, Y. B., Liao, Y. P., Libby, J., Lin, J., Liu, M. H., Liu, Q. Y., Liu, Y., Liu, Z. Q., Liventsev, D., Longo, S., Lyu, C., Ma, Y., Madaan, C., Maggiora, M., Maharana, S. P., Maiti, R., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matvienko, D., Maurya, S. K., Maushart, M., McKenna, J. A., Mehta, R., Meier, F., Meleshko, D., Merola, M., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Miyake, H., Mizuk, R., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, H., Nakazawa, Y., Naruki, M., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Nishida, S., Ogawa, S., Ono, H., Onuki, Y., Otani, F., Pakhlova, G., Pardi, S., Parham, K., Park, H., Park, J., Park, K., Park, S. -H., Paschen, B., Patra, S., Pedlar, T. K., Peruzzi, I., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Ravindran, K., Rehman, J. U., Reif, M., Reiter, S., Remnev, M., Reuter, L., Herrmann, D. Ricalde, Ripp-Baudot, I., Rizzo, G., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sanders, D. A., Sandilya, S., Santelj, L., Savinov, V., Scavino, B., Schmitz, J., Schneider, S., Schnell, G., Schwanda, C., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Sharma, C., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Song, W., Staric, M., Stavroulakis, P., Stefkova, S., Stroili, R., Strube, J., Sue, Y., Sumihama, M., Sumisawa, K., Sutcliffe, W., Suwonjandee, N., Svidras, H., Takahashi, M., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Torassa, E., Trabelsi, K., Tsaklidis, I., Uchida, M., Ueda, I., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Vossen, A., Wakai, M., Wallner, S., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Yuan, J., Zani, L., Zeng, F., Zhang, B., Zhou, J. S., Zhou, Q. D., Zhu, L., Zhukova, V. I., Zlebck, R., and Zou, S.

Subjects
High Energy Physics - Experiment
Abstract

Using data samples of 102 million Upsilon(1S) events and 158 million Upsilon(2S) events collected by the Belle detector at the KEKB asymmetric-energy $e^+e^-$ collider, we search for [udsccbar] pentaquark states decaying to Jpsi Lambda. Using the first observations of Upsilon(1S, 2S) inclusive decays to Jpsi Lambda, we find evidence of the P_ccbars(4459)0 state with a significance of 3.3 standard deviations, including statistical and systematic uncertainties. We measure the mass and width of the Pccbars(4459)0 to be (4471.7 +- 4.8 +- 0.6) MeV/c2 and (21.9 +- 13.1 +- 2.7) MeV, respectively. The branching fractions for P_ccbars(4459)0 production are measured to be B[Upsilon(1S) -> P_ccbars(4459)0/ Pbar_ccbars(4459)0 + anything] = (3.5 +- 2.0 +- 0.2)*10-6 and B[Upsilin(2S) -> P_ccbars(4459)0/ Pbar_ccbars(4459)0 +anything] = (2.9 +- 1.7 +- 0.4)*10-6. The inclusive branching fractions of Upsilon(1S, 2S) -> Jpsi Lambda/Lambdabar are measured to be B[Upsilin(1S) -> Jpsi Lambda/Lambdabar + anything] = (36.9 +- 5.3 +- 2.4)*10-6 and B[Upsilon(2S) -> Jpsi Lambda/Lambdabar + anything] = (22.3 +- 5.7 +- 3.1)*10-6. We measure the visible cross section $\sigma(e^+e^- \to J/psi \Lambda/\bar\Lambda$ + anything) = (90 +- 14 +- 6) fb for the continuum production at $\sqrt{s} = 10.52$ GeV. In all cases, the first uncertainties are statistical and the second are systematic., Comment: 8 pages, 3 figures

Published
2025

11. End-to-End triplet loss based fine-tuning for network embedding in effective PII detection

Author

Kohli, Rishika, Gupta, Shaifu, and Gaur, Manoj Singh

Subjects
Computer Science - Machine Learning
Abstract

There are many approaches in mobile data ecosystem that inspect network traffic generated by applications running on user's device to detect personal data exfiltration from the user's device. State-of-the-art methods rely on features extracted from HTTP requests and in this context, machine learning involves training classifiers on these features and making predictions using labelled packet traces. However, most of these methods include external feature selection before model training. Deep learning, on the other hand, typically does not require such techniques, as it can autonomously learn and identify patterns in the data without external feature extraction or selection algorithms. In this article, we propose a novel deep learning based end-to-end learning framework for prediction of exposure of personally identifiable information (PII) in mobile packets. The framework employs a pre-trained large language model (LLM) and an autoencoder to generate embedding of network packets and then uses a triplet-loss based fine-tuning method to train the model, increasing detection effectiveness using two real-world datasets. We compare our proposed detection framework with other state-of-the-art works in detecting PII leaks from user's device., Comment: 13 pages, 10 figures, 5 tables

Published
2025

12. Measurement of $B^+\to\tau^+\nu_\tau$ branching fraction with a hadronic tagging method at Belle II

Author

Belle II Collaboration, Adachi, I., Adamczyk, K., Ahmed, H., Ahn, Y., Aihara, H., Akopov, N., Alhakami, M., Aloisio, A., Althubiti, N., Angelsmark, M., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, V., Aversano, M., Ayad, R., Babu, V., Baghel, N. K., Bahinipati, S., Bambade, P., Banerjee, Sw., Bartl, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Bobrov, A., Bodrov, D., Bondar, A., Borah, J., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Chang, M. -C., Cheema, P., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cui, J. X., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Di Canto, A., Dingfelder, J., Doležal, Z., Dong, T. V., Dorigo, M., Dugic, K., Dujany, G., Ecker, P., Epifanov, D., Eppelt, J., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Fulsom, B. G., Gabrielli, A., Gärtner, L., Gale, A., Garcia-Hernandez, M., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Gironell, P. Gironella, Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Graziani, E., Greenwald, D., Gruberová, Z., Guan, Y., Gudkova, K., Haide, I., Hayashii, H., Hazra, S., Hearty, C., de la Cruz, I. Heredia, Higuchi, T., Hoek, M., Hohmann, M., Hoppe, R., Horak, P., Hsu, C. -L., Huang, A., Iijima, T., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobi, D., Jacobs, W. W., Jaffe, D. E., Jang, E. -J., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kandra, J., Kang, K. H., Karyan, G., Kawasaki, T., Ketter, C., Kiesling, C., Kim, D. Y., Kim, J. -Y., Kim, K. -H., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Križan, P., Krokovny, P., Kuhr, T., Kumar, D., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lam, T., Lange, J. S., Lau, T. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Leo, P., Li, L. K., Li, W. Z., Li, Y., Libby, J., Lin, S., Liu, M. H., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Madaan, C., Maggiora, M., Maiti, R., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuoka, K., Maurya, S. K., Maushart, M., McKenna, J. A., Meier, F., Meleshko, D., Merola, M., Miller, C., Mirra, M., Miyabayashi, K., Mizuk, R., Mondal, S., Moneta, S., Moser, H. -G., Nakamura, I., Nakao, M., Nakazawa, H., Natkaniec, Z., Natochii, A., Nayak, M., Neu, M., Niiyama, M., Nishida, S., Ogawa, S., Okubo, R., Ono, H., Pakhlova, G., Pardi, S., Park, J., Park, K., Park, S. -H., Paschen, B., Patra, S., Paul, S., Pedlar, T. K., Peruzzi, I., Peschke, R., Piilonen, L. E., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Privalov, S., Prudiiev, I., Purwar, H., Raiz, S., Ravindran, K., Rehman, J. U., Reif, M., Reiter, S., Remnev, M., Herrmann, D. Ricalde, Ripp-Baudot, I., Rizzo, G., Robertson, S. H., Roney, J. M., Rostomyan, A., Rout, N., Sanders, D. A., Sandilya, S., Santelj, L., Savinov, V., Scavino, B., Schmitt, C., Schmitz, J., Schneider, S., Schwanda, C., Seino, Y., Senyo, K., Sevior, M. E., Sfienti, C., Shan, W., Shi, X. D., Shillington, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stefkova, S., Stroili, R., Sue, Y., Sumihama, M., Takizawa, M., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Torassa, E., Trabelsi, K., Tsaklidis, I., Ueda, I., Uglov, T., Unger, K., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vobbilisetti, V., Volpe, R., Wallner, S., Wang, M. -Z., Warburton, A., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Yuan, J., Zani, L., Zeng, F., Zhang, B., Zhilich, V., Zhou, Q. D., Zhu, L., and Žlebčík, R.

Subjects
High Energy Physics - Experiment
Abstract

We present a measurement of the branching fraction of $B^+\to\tau^+\nu_\tau$ decays using $(387\pm6)\times 10^6$ $\Upsilon(4S)$ collected between 2019 and 2022 with the Belle II detector at the SuperKEKB $e^+e^-$ collider. We reconstruct the accompanying $B^-$ meson using the hadronic tagging method, while $B^+\to\tau^+\nu_\tau$ candidates are identified in the recoil. We find evidence for $B^+\to\tau^+\nu_\tau$ decays at 3.0 standard deviations, including systematic uncertainties. The measured branching fraction is $\mathcal{B}(B^+\to\tau^+\nu_\tau) = [1.24 \pm 0.41 (\text{stat.}) \pm 0.19 (\text{syst.})] \times 10^{-4}$.

Published
2025

13. Design and Commissioning of Readout Electronics for a $K_L^0$ and $\mu$ Detector at the Belle II Experiment

Author

Ketter, C., Andrew, M., Aushev, T., Baghel, N. K., Banerjee, Sw., Becker, E., Beretta, M., Bernieri, E., Biswas, D., Bodrov, D., Branchini, P., Budano, A., Chen, C., Chen, Y. T., Chilikin, K., Choudhury, S., Cochran, J., De Pietro, G., de Sangro, R., Finocchiaro, G., Gaur, V., Graziani, E., Guan, Y., Jacobs, W. W., Kang, S., Kimmel, T. D., Kindo, H., Kirby, B., Kunkler, B., Lam, T., Liventsev, D., Martellini, C., Martini, A., Meier, F., Mitra, S., Mizuk, R., Mostafanezhad, I., Nakao, M., Nishimura, K., Oberhof, B., Oskin, P., Pakhlov, P., Pakhlova, G., Parham, K., Passeri, A., Pathak, A., Patra, S., Peruzzi, I., Peschke, R., Piccolo, M., Piilonen, L. E., Popov, V., Prell, S., Purwar, H., Russo, A., Sahoo, D., Schneider, S., Shebalin, V., Solovieva, E., Stottler, Z. S., Sumisawa, K., Tagnani, D., Uglov, T., Varner, G. S., Veronesi, M., Visser, G., Vossen, A., Wang, T., Wang, X. L., Wood, L., Xu, X. P., Yoshihara, K., Zhai, Y., and Zhukova, V. I.

Subjects
High Energy Physics - Experiment
Abstract

The K-long and muon detector (KLM) constitutes the outer-most volume of the Belle II spectrometer at the interaction region of the SuperKEKB collider in Tsukuba, Japan. The KLM detector was partially upgraded since the Belle experiment by replacing many of its resistive-plate chambers with scintillators containing wavelength-shifting fibers and instrumenting it with silicon photomultipliers. We describe the readout electronics, firmware, and software created to control and acquire data from the scintillators and resistive-plate chambers., Comment: 14 pages, 20 figures. To be submitted to Nuclear Instruments and Methods in Physics Research Section A

Published
2025

14. On-demand storage and retrieval of single photons from a semiconductor quantum dot in a room-temperature atomic vapor memory

Author

Maaß, Benjamin, Barua, Avijit, Ewald, Norman Vincenz, Robertson, Elizabeth, Gaur, Kartik, Park, Suk In, Rodt, Sven, Song, Jin-Dong, Reitzenstein, Stephan, and Wolters, Janik

Subjects
Quantum Physics, Condensed Matter - Mesoscale and Nanoscale Physics, Physics - Optics
Abstract

Interfacing light from solid-state single-photon sources with scalable and robust room-temperature quantum memories has been a long-standing challenge in photonic quantum information technologies due to inherent noise processes and time-scale mismatches between the operating conditions of solid-state and atomic systems. Here, we demonstrate on-demand storage and retrieval of single photons from a semiconductor quantum dot device in a room-temperature atomic vapor memory. A deterministically fabricated InGaAs quantum dot light source emits single photons at the wavelength of the cesium D1 line at 895\,nm which exhibit an inhomogeneously broadened linewidth of 5.1(7)\,GHz and are subsequently stored in a low-noise ladder-type cesium vapor memory. We show control over the interaction between the single photons and the atomic vapor, allowing for variable retrieval times of up to 19.8(3)\,ns at an internal efficiency of $\eta_\mathrm{int}=0.6(1)\%$. Our results significantly expand the application space of both room-temperature vapor memories and semiconductor quantum dots in future quantum network architectures.

Published
2025

15. Compositional dependence of magnetic damping in sputter-deposited CoxFe1-x thin films

Author

Gaur, Samanvaya S., Diaz, Rosa, and Marinero, Ernesto E.

Subjects
Condensed Matter - Materials Science, 03C90
Abstract

Co25Fe75 ferromagnetic films exhibit ultralow magnetic damping. The magnetic damping dependence of Cobalt Iron thin films over a Co composition (23 to 36%) is here reported. The thin film structures were sputter deposited at ambient temperature and FMR measurements in both in plane and out of plane geometries were utilized to measure magnetic damping parameters, which include intrinsic damping and contributions from spin pumping. The damping parameters, decrease as the Co content is increased, except for Co31Fe69. The smallest values of damping correspond to alloys exhibiting interface perpendicular magnetic anisotropy. A value of 0.00091 was measured for Co36Fe64, whereas for Co31Fe69 was measured as 0.002, this composition exhibits the largest in-plane anisotropy. HAADF-STEM cross-section analysis of the Co36Fe64 thin film stack revealed Cu interdiffusion into the magnetic layer. The degree of interdiffusion was found to be up to 7x higher at grain boundaries as compared the bulk of the polycrystalline grains. The incorporation of Cu into the ferromagnetic layer adversely impacts magnetic damping. Reducing impurities in the magnetic layer by improving the growth chamber base pressure resulted in a reduction of magnetic damping of 18%. The diffraction analysis revealed that the primary growth direction of Co36Fe64 is [101] and that of Cu buffer layer is [111], these planes are perpendicular to their respective [101] planes and for this composition the lattice mismatch was determined to be 0.9325%. The lattice mismatch decreases with increasing Co content and hence the lattice strain. The diffusion of Cu into the ferromagnet creates magnon scattering centers and local changes in magnetic properties. Both factors negatively influence magnetic damping., Comment: 22 pages, 11 figures

Published
2025

16. Extraction Of Cumulative Blobs From Dynamic Gestures

Author

Naulakha, Rishabh, Gaur, Shubham, Lodha, Dhairya, Tulsyan, Mehek, and Kotecha, Utsav

Subjects
Computer Science - Computer Vision and Pattern Recognition, 68T45, 68U10, I.2.10, I.5.1, H.5.2
Abstract

Gesture recognition is a perceptual user interface, which is based on CV technology that allows the computer to interpret human motions as commands, allowing users to communicate with a computer without the use of hands, thus making the mouse and keyboard superfluous. Gesture recognition's main weakness is a light condition because gesture control is based on computer vision, which heavily relies on cameras. These cameras are used to interpret gestures in 2D and 3D, so the extracted information can vary depending on the source of light. The limitation of the system cannot work in a dark environment. A simple night vision camera can be used as our camera for motion capture as they also blast out infrared light which is not visible to humans but can be clearly seen with a camera that has no infrared filter this majorly overcomes the limitation of systems which cannot work in a dark environment. So, the video stream from the camera is fed into a Raspberry Pi which has a Python program running OpenCV module which is used for detecting, isolating and tracking the path of dynamic gesture, then we use an algorithm of machine learning to recognize the pattern drawn and accordingly control the GPIOs of the raspberry pi to perform some activities.

Published
2025

17. Measurement of the branching fraction, polarization, and time-dependent $CP$ asymmetry in $B^0 \to \rho^+\rho^-$ decays and constraint on the CKM angle $\phi_2$

Author

Belle II Collaboration, Adachi, I., Aggarwal, L., Ahmed, H., Akopov, N., Alhakami, M., Aloisio, A., Althubiti, N., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, V., Aversano, M., Ayad, R., Babu, V., Baghel, N. K., Bambade, P., Banerjee, Sw., Barrett, M., Bartl, M., Baudot, J., Baur, A., Beaubien, A., Becker, J., Bennett, J. V., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Bondar, A., Borah, J., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cui, J. X., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dong, X., Dorigo, M., Dossett, D., Dugic, K., Dujany, G., Ecker, P., Eppelt, J., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Gironell, P. Gironella, Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Graziani, E., Greenwald, D., Gruberová, Z., Guan, Y., Gudkova, K., Haide, I., Hara, T., Harris, C., Hayasaka, K., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Hoppe, R., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jacobi, D., Jacobs, W. W., Jang, E. -J., Jin, Y., Johnson, A., Junkerkalefeld, H., Kaleta, M., Kaliyar, A. B., Kandra, J., Keil, F., Ketter, C., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, J. -Y., Kim, K. -H., Kim, Y. -K., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lalwani, K., Lam, T., Lanceri, L., Lange, J. S., Lau, T. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Lemettais, C., Leo, P., Li, L. K., Li, Q. M., Li, W. Z., Li, Y., Li, Y. B., Liao, Y. P., Libby, J., Lin, J., Lin, S., Liu, M. H., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Madaan, C., Maggiora, M., Maharana, S. P., Maiti, R., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuoka, K., Matvienko, D., Maurya, S. K., Maushart, M., McKenna, J. A., Meier, F., Meleshko, D., Merola, M., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Miyake, H., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, Y., Naruki, M., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Nishida, S., Ogawa, S., Okubo, R., Ono, H., Onuki, Y., Pakhlova, G., Pardi, S., Parham, K., Park, H., Park, J., Park, K., Park, S. -H., Passeri, A., Patra, S., Pedlar, T. K., Peruzzi, I., Peschke, R., Pestotnik, R., Piilonen, L. E., Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Raiz, S., Ravindran, K., Rehman, J. U., Reif, M., Reiter, S., Remnev, M., Reuter, L., Herrmann, D. Ricalde, Ripp-Baudot, I., Rizzo, G., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sakai, Y., Sanders, D. A., Sandilya, S., Santelj, L., Savinov, V., Scavino, B., Schwanda, C., Schwartz, A. J., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Shi, X. D., Shillington, T., Shiu, J. -G., Shtol, D., Shwartz, B., Sibidanov, A., Simon, F., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Song, W., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Strube, J., Sumihama, M., Sumisawa, K., Suwonjandee, N., Svidras, H., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Torassa, E., Trabelsi, K., Tsaklidis, I., Ueda, I., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wakai, M., Wallner, S., Wang, M. -Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, J., Yusa, Y., Zani, L., Zhilich, V., Zhou, J. S., Zhou, Q. D., Zhu, L., and Žlebčík, R.

Subjects
High Energy Physics - Experiment
Abstract

We present a measurement of the branching fraction and fraction of longitudinal polarization of $B^0 \to \rho^+ \rho^-$ decays, which have two $\pi^0$'s in the final state. We also measure time-dependent $CP$ violation parameters for decays into longitudinally polarized $\rho^+ \rho^-$ pairs. This analysis is based on a data sample containing $(387\pm6) \times 10^6$ \BBbar pairs collected with the Belle~II detector at the SuperKEKB asymmetric-energy $e^+e^-$ collider in 2019-2022. We obtain ${B}(B^0\to\rho^+\rho^-) = (2.88 ^{+0.23}_{-0.22} {}^{+0.29}_{-0.27}) \times 10^{-5}, f_{L} = 0.921 ^{+0.024}_{-0.025} {}^{+0.017}_{-0.015}$, $S = -0.26\pm0.19\pm0.08$, and $C = -0.02\pm0.12^{+0.06}_{-0.05}$, where the first uncertainties are statistical and the second are systematic. We use these results to perform an isospin analysis to constrain the CKM angle $\phi_2$ and obtain two solutions; the result consistent with other Standard Model constraints is $\phi_2 = (92.6^{+4.5}_{-4.8})^\circ$.

Published
2024

18. Search for lepton flavor-violating decay modes $B^0\to K_S^0\tau^\pm\ell^\mp~(\ell=\mu, e)$ with hadronic $B$-tagging at Belle and Belle II

Author

Belle, Collaborations, Belle II, Adachi, I., Adamczyk, K., Aggarwal, L., Ahmed, H., Aihara, H., Akopov, N., Alhakami, M., Aloisio, A., Althubiti, N., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Baghel, N. K., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Bartl, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhardwaj, V., Bhuyan, B., Bianchi, F., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cui, J. X., De La Cruz-Burelo, E., De La Motte, S. A., de Marino, G., De Nardo, G., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dong, X., Dorigo, M., Dossett, D., Dubey, S., Dugic, K., Dujany, G., Ecker, P., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Gironell, P. Gironella, Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Granderath, S., Graziani, E., Greenwald, D., Gruberová, Z., Guan, Y., Gudkova, K., Haide, I., Han, Y., Hara, T., Harris, C., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Hoppe, R., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Inguglia, G., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jacobi, D., Jacobs, W. W., Jaffe, D. E., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kaleta, M., Kaliyar, A. B., Kandra, J., Kang, K. H., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Ketter, C., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, J. -Y., Kim, K. -H., Kim, Y. -K., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, D., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lalwani, K., Lam, T., Lanceri, L., Lange, J. S., Lau, T. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Lemettais, C., Leo, P., Lewis, P. M., Li, C., Li, L. K., Li, Q. M., Li, W. Z., Li, Y., Li, Y. B., Liao, Y. P., Libby, J., Lin, J., Lin, S., Liu, M. H., Liu, Q. Y., Liu, Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Luo, T., Lyu, C., Ma, Y., Madaan, C., Maggiora, M., Maharana, S. P., Maiti, R., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matvienko, D., Maurya, S. K., Maushart, M., McKenna, J. A., Mehta, R., Meier, F., Meleshko, D., Merola, M., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Miyake, H., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mussa, R., Nakamura, I., Nakamura, K. R., Nakao, M., Nakazawa, Y., Naruki, M., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Nishida, S., Ogawa, S., Ono, H., Onuki, Y., Otani, F., Pakhlova, G., Paoloni, E., Pardi, S., Parham, K., Park, H., Park, J., Park, K., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Pedlar, T. K., Peruzzi, I., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Prudiiev, I., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Ravindran, K., Rehman, J. U., Reif, M., Reiter, S., Remnev, M., Reuter, L., Herrmann, D. Ricalde, Ripp-Baudot, I., Rizzo, G., Robertson, S. H., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sanders, D. A., Sandilya, S., Santelj, L., Savinov, V., Scavino, B., Schneider, S., Schnell, G., Schwanda, C., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Sharma, C., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Song, W., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Strube, J., Sue, Y., Sumihama, M., Sumisawa, K., Sutcliffe, W., Suwonjandee, N., Svidras, H., Takahashi, M., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Torassa, E., Trabelsi, K., Tsaklidis, I., Ueda, I., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wakai, M., Wallner, S., Wang, M. -Z., Wang, Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Wiechczynski, J., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, C. Z., Yuan, J., Zani, L., Zeng, F., Zhang, B., Zhou, J. S., Zhou, Q. D., Zhu, L., Zhukova, V. I., and Žlebčík, R.

Subjects
High Energy Physics - Experiment
Abstract

We present the first search for the lepton flavor-violating decay modes $B^0 \rightarrow K_S^0 \tau^\pm \ell^\mp~(\ell=\mu, e)$ using the 711 fb$^{-1}$ and 365 fb$^{-1}$ data samples recorded by the Belle and Belle II detectors, respectively. We use a hadronic $B$-tagging technique, and search for the signal decay in the system recoiling against the fully reconstructed $B$ meson. We find no evidence for $B^0 \rightarrow K_S^0 \tau^\pm \ell^\mp$ decays and set 90\% confidence level upper limits on the branching fractions in the range of $[0.8,\,3.6]\times10^{-5}$.

Published
2024

19. Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context

Author

Das, Nilanjana, Raff, Edward, and Gaur, Manas

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Previous research on LLM vulnerabilities often relied on nonsensical adversarial prompts, which were easily detectable by automated methods. We address this gap by focusing on human-readable adversarial prompts, a more realistic and potent threat. Our key contributions are situation-driven attacks leveraging movie scripts to create contextually relevant, human-readable prompts that successfully deceive LLMs, adversarial suffix conversion to transform nonsensical adversarial suffixes into meaningful text, and AdvPrompter with p-nucleus sampling, a method to generate diverse, human-readable adversarial suffixes, improving attack efficacy in models like GPT-3.5 and Gemma 7B. Our findings demonstrate that LLMs can be tricked by sophisticated adversaries into producing harmful responses with human-readable adversarial prompts and that there exists a scope for improvement when it comes to robust LLMs.

Published
2024

20. Can LLMs Obfuscate Code? A Systematic Analysis of Large Language Models into Assembly Code Obfuscation

Author

Mohseni, Seyedreza, Mohammadi, Seyedali, Tilwani, Deepa, Saxena, Yash, Ndawula, Gerald Ketu, Vema, Sriram, Raff, Edward, and Gaur, Manas

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Malware authors often employ code obfuscations to make their malware harder to detect. Existing tools for generating obfuscated code often require access to the original source code (e.g., C++ or Java), and adding new obfuscations is a non-trivial, labor-intensive process. In this study, we ask the following question: Can Large Language Models (LLMs) potentially generate a new obfuscated assembly code? If so, this poses a risk to anti-virus engines and potentially increases the flexibility of attackers to create new obfuscation patterns. We answer this in the affirmative by developing the MetamorphASM benchmark comprising MetamorphASM Dataset (MAD) along with three code obfuscation techniques: dead code, register substitution, and control flow change. The MetamorphASM systematically evaluates the ability of LLMs to generate and analyze obfuscated code using MAD, which contains 328,200 obfuscated assembly code samples. We release this dataset and analyze the success rate of various LLMs (e.g., GPT-3.5/4, GPT-4o-mini, Starcoder, CodeGemma, CodeLlama, CodeT5, and LLaMA 3.1) in generating obfuscated assembly code. The evaluation was performed using established information-theoretic metrics and manual human review to ensure correctness and provide the foundation for researchers to study and develop remediations to this risk., Comment: To appear in AAAI 2025, Main Track

Published
2024

21. Transcribing and Translating, Fast and Slow: Joint Speech Translation and Recognition

Author

Moritz, Niko, Xie, Ruiming, Gaur, Yashesh, Li, Ke, Merello, Simone, Ahmed, Zeeshan, Seide, Frank, and Fuegen, Christian

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Computation and Language
Abstract

We propose the joint speech translation and recognition (JSTAR) model that leverages the fast-slow cascaded encoder architecture for simultaneous end-to-end automatic speech recognition (ASR) and speech translation (ST). The model is transducer-based and uses a multi-objective training strategy that optimizes both ASR and ST objectives simultaneously. This allows JSTAR to produce high-quality streaming ASR and ST results. We apply JSTAR in a bilingual conversational speech setting with smart-glasses, where the model is also trained to distinguish speech from different directions corresponding to the wearer and a conversational partner. Different model pre-training strategies are studied to further improve results, including training of a transducer-based streaming machine translation (MT) model for the first time and applying it for parameter initialization of JSTAR. We demonstrate superior performances of JSTAR compared to a strong cascaded ST model in both BLEU scores and latency., Comment: Submitted to ICASSP 2025

Published
2024

22. Measurement of the branching fraction and $\it CP$-violating asymmetry of the decay $B^{0} \rightarrow \pi^{0} \pi^{0}$ using $387$ million bottom-antibottom meson pairs in Belle II data

Author

Belle II Collaboration, Adachi, I., Aggarwal, L., Ahmed, H., Aihara, H., Alhakami, M., Aloisio, A., Althubiti, N., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Baghel, N. K., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Bartl, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhardwaj, V., Bhuyan, B., Bianchi, F., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Bondar, A., Borah, J., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cui, J. X., De La Cruz-Burelo, E., De La Motte, S. A., de Marino, G., De Nardo, G., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dong, X., Dorigo, M., Dossett, D., Dubey, S., Dugic, K., Dujany, G., Ecker, P., Epifanov, D., Eppelt, J., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Giakoustidis, G., Giordano, R., Giri, A., Gironell, P. Gironella, Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Granderath, S., Graziani, E., Greenwald, D., Gruberová, Z., Guan, Y., Gudkova, K., Haide, I., Halder, S., Han, Y., Hara, T., Harris, C., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Hoppe, R., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobi, D., Jacobs, W. W., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kalita, D., Kaliyar, A. B., Kandra, J., Kang, K. H., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Ketter, C., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, J. -Y., Kim, K. -H., Kim, Y. -K., Kim, Y. J., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, D., Kumar, M., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lalwani, K., Lam, T., Lanceri, L., Lange, J. S., Lau, T. S., Laurenza, M., Lautenbach, K., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Lemettais, C., Leo, P., Levit, D., Lewis, P. M., Li, C., Li, L. K., Li, Q. M., Li, W. Z., Li, Y. B., Liao, Y. P., Libby, J., Lin, J., Lin, S., Liu, M. H., Liu, Q. Y., Liu, Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Madaan, C., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuda, T., Matvienko, D., Maurya, S. K., Maushart, M., McKenna, J. A., Mehta, R., Meier, F., Meleshko, D., Merola, M., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Miyake, H., Mizuk, R., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, Y., Naruki, M., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Niebuhr, C., Nishida, S., Ogawa, S., Ono, H., Onuki, Y., Otani, F., Pakhlov, P., Pakhlova, G., Paoloni, E., Pardi, S., Parham, K., Park, H., Park, J., Park, K., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Pedlar, T. K., Peruzzi, I., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Prudiiev, I., Purwar, H., Rados, P., Raeuber, G., Raiz, S., RajG, V., Rauls, N., Ravindran, K., Rehman, J. U., Reif, M., Reiter, S., Remnev, M., Reuter, L., Herrmann, D. Ricalde, Ripp-Baudot, I., Rizzo, G., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sanders, D. A., Sandilya, S., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schmitt, C., Schneider, S., Schnepf, M., Schwanda, C., Schwartz, A. J., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Sharma, C., Shen, C. P., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Song, W., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stroili, R., Strube, J., Sue, Y., Sumihama, M., Sumisawa, K., Sutcliffe, W., Suwonjandee, N., Svidras, H., Takahashi, M., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Tonelli, D., Torassa, E., Trabelsi, K., Tsaklidis, I., Uchida, M., Ueda, I., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Vossen, A., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yang, S. B., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, J., Zani, L., Zeng, F., Zhang, B., Zhilich, V., Zhou, J. S., Zhou, Q. D., Zhukova, V. I., and Žlebčík, R.

Subjects
High Energy Physics - Experiment
Abstract

We measure the branching fraction and $\it CP$-violating flavor-dependent rate asymmetry of $B^{0} \to \pi^{0} \pi^{0}$ decays reconstructed using the Belle II detector in an electron-positron collision sample containing $387 \times 10^{6}$ $B\overline{B}$ pairs. Using an optimized event selection, we find $126\pm 20$ signal decays in a fit to background-discriminating and flavor-sensitive distributions. The resulting branching fraction is $(1.25 \pm 0.23)\times 10^{-6}$ and the $\it CP$-violating asymmetry is $0.03 \pm 0.30$.

Published
2024

23. Efficient high performance computing with the ALICE Event Processing Nodes GPU-based farm

Author

Ronchetti, Federico, Akishina, Valentina, Andreassen, Edvard, Bluhme, Nora, Dange, Gautam, de Cuveland, Jan, Erba, Giada, Gaur, Hari, Hutter, Dirk, Kozlov, Grigory, Krčál, Luboš, La Pointe, Sarah, Lehrbach, Johannes, Lindenstruth, Volker, Neskovic, Gvozden, Redelbach, Andreas, Rohr, David, Weiglhofer, Felix, and Wilhelmi, Alexander

Subjects
High Energy Physics - Experiment
Abstract

Due to the increase of data volumes expected for the LHC Run 3 and Run 4, the ALICE Collaboration designed and deployed a new, energy efficient, computing model to run Online and Offline O$^2$ data processing within a single software framework. The ALICE O$^2$ Event Processing Nodes (EPN) project performs online data reconstruction using GPUs (Graphic Processing Units) instead of CPUs and applies an efficient, entropy-based, online data compression to cope with PbPb collision data at a 50 kHz hadronic interaction rate. Also, the O$^2$ EPN farm infrastructure features an energy efficient, environmentally friendly, adiabatic cooling system which allows for operational and capital cost savings.

Published
2024

24. Observation of the decay $B^0 \to J/\psi \omega$ at Belle II

Author

Belle II Collaboration, Adachi, I., Aggarwal, L., Ahmed, H., Aihara, H., Akopov, N., Alhakami, M., Aloisio, A., Althubiti, N., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, V., Aversano, M., Ayad, R., Babu, V., Baghel, N. K., Bahinipati, S., Bambade, P., Banerjee, Sw., Barrett, M., Baudot, J., Baur, A., Beaubien, A., Becker, J., Bennett, J. V., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Bondar, A., Borah, J., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Brenny, N., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Cheaib, R., Cheema, P., Chen, C., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cui, J. X., De La Cruz-Burelo, E., De La Motte, S. A., de Marino, G., De Nardo, G., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dong, X., Dorigo, M., Dossett, D., Dugic, K., Dujany, G., Ecker, P., Epifanov, D., Eppelt, J., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Gironell, P. Gironella, Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Graziani, E., Greenwald, D., Gruberová, Z., Guan, Y., Gudkova, K., Haide, I., Harris, C., Hayasaka, K., Hayashii, H., Hazra, S., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Hoppe, R., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jacobi, D., Jacobs, W. W., Jang, E. -J., Jin, Y., Johnson, A., Junkerkalefeld, H., Kalita, D., Kaliyar, A. B., Kandra, J., Karyan, G., Kawasaki, T., Keil, F., Ketter, C., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, J. -Y., Kim, K. -H., Kim, Y. -K., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lalwani, K., Lam, T., Lanceri, L., Lange, J. S., Lau, T. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Lemettais, C., Leo, P., Li, L. K., Li, Q. M., Li, W. Z., Li, Y., Li, Y. B., Liao, Y. P., Libby, J., Lin, J., Lin, S., Liu, M. H., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Luo, T., Lyu, C., Ma, Y., Madaan, C., Maggiora, M., Maharana, S. P., Maiti, R., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuoka, K., Matvienko, D., Maurya, S. K., Maushart, M., McKenna, J. A., Meier, F., Meleshko, D., Merola, M., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Miyake, H., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, H., Nakazawa, Y., Naruki, M., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Nishida, S., Ogawa, S., Ono, H., Onuki, Y., Pakhlova, G., Pardi, S., Park, H., Park, J., Park, K., Park, S. -H., Passeri, A., Patra, S., Pedlar, T. K., Peruzzi, I., Peschke, R., Pestotnik, R., Piilonen, L. E., Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Prudiiev, I., Purwar, H., Raiz, S., Ravindran, K., Rehman, J. U., Reif, M., Reiter, S., Remnev, M., Reuter, L., Herrmann, D. Ricalde, Ripp-Baudot, I., Rizzo, G., Roehrken, M., Roney, J. M., Rostomyan, A., Sanders, D. A., Sandilya, S., Santelj, L., Savinov, V., Scavino, B., Schwanda, C., Schwartz, A. J., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Shwartz, B., Sibidanov, A., Simon, F., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Song, W., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Strube, J., Sumihama, M., Sumisawa, K., Suwonjandee, N., Svidras, H., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Torassa, E., Trabelsi, K., Tsaklidis, I., Ueda, I., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wakai, M., Wallner, S., Wang, M. -Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yelton, J., Yoshihara, K., Yuan, C. Z., Yuan, J., Yusa, Y., Zani, L., Zhilich, V., Zhou, J. S., Zhou, Q. D., Zhu, L., and Žlebčík, R.

Subjects
High Energy Physics - Experiment
Abstract

We measure the branching fraction of the decay $B^0 \to J/\psi \omega$ using data collected with the Belle II detector at the SuperKEKB collider. The data contain $(387 \pm 6) \times 10^6$ $B\overline{B}$ meson pairs produced in energy-asymmetric $e^+e^-$ collisions at the $\Upsilon (4S)$ resonance. The measured branching fraction $\mathcal{B}(B^0 \to J/\psi \omega) = \left( 2.16 \pm 0.30 \pm 0.14 \right) \times 10^{-5}$, where the first uncertainty is statistical and the second is systematic, is more precise than previous results and constitutes the first observation of the decay with a significance of $6.5$ standard deviations.

Published
2024

25. Observations of the singly Cabibbo-suppressed decays $\Xi_c^{+} \to pK_{S}^{0}$, $\Xi_c^+ \to \Lambda \pi^+$, and $\Xi_c^+ \to \Sigma^{0} \pi^+$ at Belle and Belle II

Author

Belle, Collaborations, Belle II, Adachi, I., Aggarwal, L., Akopov, N., Alhakami, M., Aloisio, A., Althubiti, N., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, T., Aushev, V., Aversano, M., Ayad, R., Babu, V., Baghel, N. K., Bahinipati, S., Bambade, P., Banerjee, Sw., Barrett, M., Baudot, J., Baur, A., Beaubien, A., Becker, J., Bennett, J. V., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Bianchi, F., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Bondar, A., Borah, J., Boschetti, A., Bozek, A., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Cheaib, R., Cheema, P., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Corona, L., Cui, J. X., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dorigo, M., Dossett, D., Dugic, K., Dujany, G., Ecker, P., Epifanov, D., Feichtinger, P., Ferber, T., Fillinger, T., Finocchiaro, G., Forti, F., Fulsom, B. G., Gabrielli, A., Garcia-Hernandez, M., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Gironell, P. Gironella, Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Graziani, E., Greenwald, D., Gruberová, Z., Gudkova, K., Haide, I., Harris, C., Hayashii, H., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Hoppe, R., Horak, P., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jacobi, D., Jacobs, W. W., Jaffe, D. E., Jang, E. -J., Jin, Y., Johnson, A., Junkerkalefeld, H., Kaleta, M., Kaliyar, A. B., Kandra, J., Karyan, G., Kawasaki, T., Keil, F., Ketter, C., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, J. -Y., Kim, K. -H., Kim, Y. -K., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lalwani, K., Lam, T., Lanceri, L., Lange, J. S., Lau, T. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Leo, P., Li, L. K., Li, Q. M., Li, W. Z., Li, Y., Li, Y. B., Liao, Y. P., Libby, J., Lin, J., Liu, M. H., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Madaan, C., Maggiora, M., Maiti, R., Mancinelli, G., Manfredi, R., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martinov, T., Massaccesi, L., Masuda, M., Matvienko, D., Maushart, M., McKenna, J. A., Meier, F., Meleshko, D., Merola, M., Miller, C., Mirra, M., Mitra, S., Miyake, H., Moneta, S., Moser, H. -G., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, Y., Naruki, M., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Nishida, S., Ogawa, S., Ono, H., Onuki, Y., Pakhlova, G., Pardi, S., Park, H., Park, J., Park, K., Park, S. -H., Patra, S., Pedlar, T. K., Peruzzi, I., Peschke, R., Pestotnik, R., Piilonen, L. E., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Raiz, S., Ravindran, K., Rehman, J. U., Reif, M., Reiter, S., Remnev, M., Reuter, L., Herrmann, D. Ricalde, Ripp-Baudot, I., Rizzo, G., Roehrken, M., Roney, J. M., Rostomyan, A., Sanders, D. A., Sandilya, S., Santelj, L., Savinov, V., Scavino, B., Schnell, G., Schwanda, C., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Shen, C. P., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Song, W., Starič, M., Stavroulakis, P., Stroili, R., Sumihama, M., Suwonjandee, N., Svidras, H., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Torassa, E., Trabelsi, K., Tsaklidis, I., Ueda, I., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wallner, S., Wang, M. -Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yoshihara, K., Yuan, C. Z., Yuan, J., Yusa, Y., Zani, L., Zhilich, V., Zhou, J. S., Zhou, Q. D., Zhu, L., and Žlebčík, R.

Subjects
High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract

Using data samples of 983.0~$\rm fb^{-1}$ and 427.9~$\rm fb^{-1}$ accumulated with the Belle and Belle~II detectors operating at the KEKB and SuperKEKB asymmetric-energy $e^+e^-$ colliders, singly Cabibbo-suppressed decays $\Xi_c^{+} \to pK_{S}^{0}$, $\Xi_c^+ \to \Lambda \pi^+$, and $\Xi_c^+ \to \Sigma^{0} \pi^+$ are observed for the first time. The ratios of branching fractions of $\Xi_{c}^{+}\to p K_{S}^{0}$, $\Xi_{c}^{+}\to \Lambda \pi^{+}$, and $\Xi_{c}^{+}\to \Sigma^{0} \pi^{+}$ relative to that of $\Xi_c^+ \to \Xi^- \pi^{+} \pi^{+}$ are measured to be \begin{equation} \frac{{\cal B}(\Xi_c^+ \to pK_S^0)}{{\cal B}(\Xi_c^{+} \to \Xi^{-} \pi^+ \pi^+)} = (2.47 \pm 0.16 \pm 0.07)\% \notag, \end{equation} \begin{equation} \frac{{\cal B}(\Xi_c^+ \to \Lambda \pi^+)}{{\cal B}(\Xi_c^{+} \to \Xi^{-} \pi^+ \pi^+)} = (1.56 \pm 0.14 \pm 0.09)\% \notag, \end{equation} \begin{equation} \frac{{\cal B}(\Xi_c^+ \to \Sigma^0 \pi^+)}{{\cal B}(\Xi_c^{+} \to \Xi^{-} \pi^+ \pi^+)} = (4.13 \pm 0.26 \pm 0.22)\% \notag. \end{equation} Multiplying these values by the branching fraction of the normalization channel, ${\cal B}(\Xi_c^{+} \to \Xi^{-} \pi^+\pi^+) = (2.9 \pm 1.3)\%$, the absolute branching fractions are determined to be \begin{equation} {\cal B}(\Xi_c^{+} \to p K_{S}^{0}) = (7.16 \pm 0.46 \pm 0.20 \pm 3.21) \times 10^{-4} \notag, \end{equation} \begin{equation} {\cal B}(\Xi_c^{+} \to \Lambda \pi^+) = (4.52 \pm 0.41 \pm 0.26 \pm 2.03) \times 10^{-4} \notag, \end{equation} \begin{equation} {\cal B}(\Xi_c^{+} \to \Sigma^0 \pi^+) = (1.20 \pm 0.08 \pm 0.07 \pm 0.54) \times 10^{-3} \notag. \end{equation} The first and second uncertainties above are statistical and systematic, respectively, while the third ones arise from the uncertainty in ${\cal B}(\Xi_c^{+} \to \Xi^{-} \pi^{+} \pi^{+})$., Comment: 21 pages, 5 pages

Published
2024

26. Quantum Algorithms for Optimal Power Flow

Author

Hafshejani, Sajad Fathi, Uddin, Md Mohsin, Neufeld, David, Gaur, Daya, and Benkoczi, Robert

Subjects
Quantum Physics, Electrical Engineering and Systems Science - Systems and Control, Mathematics - Optimization and Control
Abstract

This paper explores the use of quantum computing, specifically the use of HHL and VQLS algorithms, to solve optimal power flow problem in electrical grids. We investigate the effectiveness of these quantum algorithms in comparison to classical methods. The simulation results presented here which substantially improve the results in [1] indicate that quantum approaches yield similar solutions and optimal costs compared to classical methods, suggesting the potential use case of quantum computing for power system optimization.

Published
2024

27. WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks

Author

Shinde, Rajat, Phillips, Christopher E., Ankur, Kumar, Gupta, Aman, Pfreundschuh, Simon, Roy, Sujit, Kirkland, Sheyenne, Gaur, Vishal, Lin, Amy, Sheshadri, Aditi, Nair, Udaysankar, Maskey, Manil, and Ramachandran, Rahul

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

High-quality machine learning (ML)-ready datasets play a foundational role in developing new artificial intelligence (AI) models or fine-tuning existing models for scientific applications such as weather and climate analysis. Unfortunately, despite the growing development of new deep learning models for weather and climate, there is a scarcity of curated, pre-processed machine learning (ML)-ready datasets. Curating such high-quality datasets for developing new models is challenging particularly because the modality of the input data varies significantly for different downstream tasks addressing different atmospheric scales (spatial and temporal). Here we introduce WxC-Bench (Weather and Climate Bench), a multi-modal dataset designed to support the development of generalizable AI models for downstream use-cases in weather and climate research. WxC-Bench is designed as a dataset of datasets for developing ML-models for a complex weather and climate system, addressing selected downstream tasks as machine learning phenomenon. WxC-Bench encompasses several atmospheric processes from meso-$\beta$ (20 - 200 km) scale to synoptic scales (2500 km), such as aviation turbulence, hurricane intensity and track monitoring, weather analog search, gravity wave parameterization, and natural language report generation. We provide a comprehensive description of the dataset and also present a technical validation for baseline analysis. The dataset and code to prepare the ML-ready data have been made publicly available on Hugging Face -- https://huggingface.co/datasets/nasa-impact/WxC-Bench

Published
2024

28. Spectrally accurate reverse-mode differentiable bounce-averaging operator and its applications

Author

Unalmis, Kaya E., Gaur, Rahul, Conlin, Rory, Panici, Dario, and Kolemen, Egemen

Subjects
Physics - Plasma Physics
Abstract

We present a spectrally accurate bounce-averaging operator implemented as a part of the automatically differentiable DESC stellarator optimization suite. Using this operator, we calculate the proxy for neoclassical transport coefficient $\epsilon_{\mathrm{eff}}^{3/2}$ in the $1/\nu$ regime and benchmark it against the NEO code. Ultimately, by employing this differentiable approximation, for the first time, we directly optimize a finite-$\beta$ stellarator to enhance neoclassical transport using reverse-mode differentiation. This ensures that the computational cost of determining the gradients does not depend on the number of input parameters.

Published
2024

29. Initial measurement of reactor antineutrino oscillation at SNO+

Author

Allega, A, Anderson, MR, Andringa, S, Askins, M, Auty, DJ, Bacon, A, Baker, J, Barão, F, Barros, N, Bayes, R, Beier, EW, Bezerra, TS, Bialek, A, Biller, SD, Blucher, E, Caden, E, Callaghan, EJ, Chen, M, Cheng, S, Cleveland, B, Cookman, D, Corning, J, Cox, MA, Dehghani, R, Deloye, J, Depatie, MM, Di Lodovico, F, Dima, C, Dittmer, J, Dixon, KH, Esmaeilian, MS, Falk, E, Fatemighomi, N, Ford, R, Gaur, A, González-Reina, OI, Gooding, D, Grant, C, Grove, J, Hall, S, Hallin, AL, Hallman, D, Heintzelman, WJ, Helmer, RL, Hewitt, C, Howard, V, Hreljac, B, Hu, J, Huang, P, Hunt-Stokes, R, Hussain, SMA, Inácio, AS, Jillings, CJ, Kaluzienski, S, Kaptanoglu, T, Khan, H, Kladnik, J, Klein, JR, Kormos, LL, Krar, B, Kraus, C, Krauss, CB, Kroupová, T, Lake, C, Lebanowski, L, Lefebvre, C, Lozza, V, Luo, M, Maio, A, Manecki, S, Maneira, J, Martin, RD, McCauley, N, McDonald, AB, Mills, C, Milton, G, Colina, A Molina, Morris, D, Morton-Blake, I, Mubasher, M, Naugle, S, Nolan, LJ, O’Keeffe, HM, Gann, GD Orebi, Page, J, Paleshi, K, Parker, W, Paton, J, Peeters, SJM, Pickard, L, Quenallata, B, Ravi, P, Reichold, A, Riccetto, S, Rose, J, Rosero, R, Semenec, I, Simms, J, Skensved, P, and Smiley, M

Subjects
Nuclear and Plasma Physics, Particle and High Energy Physics, Physical Sciences, Atomic, Molecular, Nuclear, Particle and Plasma Physics, Quantum Physics, Nuclear & Particles Physics, Astronomical sciences, Atomic, molecular and optical physics, Particle and high energy physics
Abstract

Abstract: The SNO$$+$$ + collaboration reports its first spectral analysis of long-baseline reactor antineutrino oscillation using 114 tonne-years of data. Fitting the neutrino oscillation probability to the observed energy spectrum yields constraints on the neutrino mass-squared difference $$\Delta m^2_{21}$$ Δ m 21 2 . In the ranges allowed by previous measurements, the best-fit $$\Delta m^2_{21}$$ Δ m 21 2 is ($$8.85^{+1.10}_{-1.33}$$ 8 . 85 - 1.33 + 1.10 ) $$\times $$ × $$10^{-5}$$ 10 - 5 $$\hbox {eV}^2$$ eV 2 . This measurement is continuing in the next phases of SNO+ and is expected to surpass the present global precision on $$\Delta m^2_{21}$$ Δ m 21 2 with about three years of data.

Published
2025

30. Measurement of the B8 solar neutrino flux using the full SNO+ water phase dataset

Author

Allega, A, Anderson, MR, Andringa, S, Askins, M, Asner, DM, Auty, DJ, Bacon, A, Barão, F, Barros, N, Bayes, R, Beier, EW, Bialek, A, Biller, SD, Blucher, E, Caden, E, Callaghan, EJ, Chen, M, Cheng, S, Cleveland, B, Cookman, D, Corning, J, Cox, MA, Dehghani, R, Deloye, J, Depatie, MM, Di Lodovico, F, Dima, C, Dittmer, J, Dixon, KH, Esmaeilian, MS, Falk, E, Fatemighomi, N, Ford, R, Gaur, A, González-Reina, OI, Gooding, D, Grant, C, Grove, J, Hall, S, Hallin, AL, Hallman, D, Heintzelman, WJ, Helmer, RL, Hewitt, C, Hreljac, B, Hu, J, Huang, P, Hunt-Stokes, R, Hussain, SMA, Inácio, AS, Jillings, CJ, Kaluzienski, S, Kaptanoglu, T, Kladnik, J, Klein, JR, Kormos, LL, Krar, B, Kraus, C, Krauss, CB, Kroupová, T, Lake, C, Lebanowski, L, Lefebvre, C, Lozza, V, Luo, M, Maio, A, Manecki, S, Maneira, J, Martin, RD, McCauley, N, McDonald, AB, Milton, G, Morris, D, Mubasher, M, Naugle, S, Nolan, LJ, O’Keeffe, HM, Gann, GD Orebi, Page, J, Paleshi, K, Parker, W, Paton, J, Peeters, SJM, Pickard, L, Quenallata, B, Ravi, P, Reichold, A, Riccetto, S, Rose, J, Rosero, R, Semenec, I, Simms, J, Skensved, P, Smiley, M, Svoboda, R, Tam, B, Tseng, J, Vázquez-Jáuregui, E, Virtue, CJ, and Ward, M

Subjects
Particle and High Energy Physics, Mathematical Physics, Mathematical Sciences, Physical Sciences, Astronomical Sciences
Abstract

The SNO+ detector operated initially as a water Cherenkov detector. The implementation of a sealed cover gas system midway through water data taking resulted in a significant reduction in the activity of Rn222 daughters in the detector and allowed the lowest background to the solar electron scattering signal above 5 MeV achieved to date. This paper reports an updated SNO+ water phase B8 solar neutrino analysis with a total livetime of 282.4 days and an analysis threshold of 3.5 MeV. The B8 solar neutrino flux is found to be (2.32-0.17+0.18(stat)-0.05+0.07(syst))×106 cm-2 s-1 assuming no neutrino oscillations, or (5.36-0.39+0.41(stat)-0.16+0.17(syst))×106 cm-2 s-1 assuming standard neutrino oscillation parameters, in good agreement with both previous measurements and standard solar model calculations. The electron recoil spectrum is presented above 3.5 MeV.

Published
2024

31. Quantum Active Learning for Structural Determination of Doped Nanoparticles -- a Case Study of 4Al@Si$_{11}$

Author

Lourenço, Maicon Pierre, Naseri, Mosayeb, Herrera, Lizandra Barrios, Zadeh-Haghighi, Hadi, Gaur, Daya, Simon, Christoph, and Salahub, Dennis R.

Subjects
Quantum Physics, Condensed Matter - Materials Science
Abstract

Active learning (AL) has been widely applied in chemistry and materials science. In this work we propose a quantum active learning (QAL) method for automatic structural determination of doped nanoparticles, where quantum machine learning (QML) models for regression are used iteratively to indicate new structures to be calculated by DFT or DFTB and this new data acquisition is used to retrain the QML models. The QAL method is implemented in the Quantum Machine Learning Software/Agent for Material Design and Discovery (QMLMaterial), whose aim is using an artificial agent (defined by QML regression algorithms) that chooses the next doped configuration to be calculated that has a higher probability of finding the optimum structure. The QAL uses a quantum Gaussian process with a fidelity quantum kernel as well as the projected quantum kernel and different quantum circuits. For comparison, classical AL was used with a classical Gaussian process with different classical kernels. The presented QAL method was applied in the structural determination of doped Si$_{11}$ with 4 Al (4Al@Si$_{11}$) and the results indicate the QAL method is able to find the optimum 4Al@Si$_{11}$ structure. The aim of this work is to present the QAL method -- formulated in a noise-free quantum computing framework -- for automatic structural determination of doped nanoparticles and materials defects., Comment: 22 pages,6 figures

Published
2024

32. Forest Covers and Bounded Forest Covers

Author

Gaur, Daya Ram, Gorain, Barun, Patra, Shaswati, and Singh, Rishi Ranjan

Subjects
Computer Science - Data Structures and Algorithms, Computer Science - Computational Complexity, Mathematics - Combinatorics
Abstract

We study approximation algorithms for the forest cover and bounded forest cover problems. A probabilistic $2+\epsilon$ approximation algorithm for the forest cover problem is given using the method of dual fitting. A deterministic algorithm with a 2-approximation ratio that rounds the optimal solution to a linear program is given next. The 2-approximation for the forest cover is then used to give a 6-approximation for the bounded forest cover problem. The use of the probabilistic method to develop the $2+\epsilon$ approximation algorithm may be of independent interest.

Published
2024

33. Towards Robust Evaluation of Unlearning in LLMs via Data Transformations

Author

Joshi, Abhinav, Saha, Shaswati, Shukla, Divyaksh, Vema, Sriram, Jhamtani, Harsh, Gaur, Manas, and Modi, Ashutosh

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

Large Language Models (LLMs) have shown to be a great success in a wide range of applications ranging from regular NLP-based use cases to AI agents. LLMs have been trained on a vast corpus of texts from various sources; despite the best efforts during the data pre-processing stage while training the LLMs, they may pick some undesirable information such as personally identifiable information (PII). Consequently, in recent times research in the area of Machine Unlearning (MUL) has become active, the main idea is to force LLMs to forget (unlearn) certain information (e.g., PII) without suffering from performance loss on regular tasks. In this work, we examine the robustness of the existing MUL techniques for their ability to enable leakage-proof forgetting in LLMs. In particular, we examine the effect of data transformation on forgetting, i.e., is an unlearned LLM able to recall forgotten information if there is a change in the format of the input? Our findings on the TOFU dataset highlight the necessity of using diverse data formats to quantify unlearning in LLMs more reliably., Comment: Accepted at EMNLP 2024 Findings; 21 pages (5 page main content + references + appendix)

Published
2024

34. Measurement of the inclusive branching fractions for $B_s^0$ decays into $D$ mesons via hadronic tagging

Author

Belle, Collaborations, Belle II, Adachi, I., Aggarwal, L., Ahmed, H., Aihara, H., Akopov, N., Aloisio, A., Said, S. Al, Althubiti, N., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, T., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Baghel, N. K., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Bartl, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Belous, K., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhardwaj, V., Bhuyan, B., Bianchi, F., Bierwirth, L., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Bondar, A., Borah, J., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cui, J. X., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Nuccio, M., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dorner, D., Dort, K., Dossett, D., Dreyer, S., Dubey, S., Dugic, K., Dujany, G., Ecker, P., Eliachevitch, M., Epifanov, D., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Gironell, P. Gironella, Glazov, A., Gobbo, B., Godang, R., Goldenzweig, P., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Guan, Y., Gudkova, K., Haide, I., Halder, S., Han, Y., Hara, T., Harris, C., Hayasaka, K., Hayashii, H., Hazra, S., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Hoppe, R., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobs, W. W., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kaleta, M., Kalita, D., Kaliyar, A. B., Kandra, J., Kang, K. H., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Ketter, C., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, J. -Y., Kim, K. -H., Kim, Y. -K., Kim, Y. J., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, D., Kumar, J., Kumar, M., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lalwani, K., Lam, T., Lanceri, L., Lange, J. S., Lau, T. S., Laurenza, M., Lautenbach, K., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Lemettais, C., Leo, P., Levit, D., Lewis, P. M., Li, L. K., Li, Q. M., Li, S. X., Li, W. Z., Li, Y., Li, Y. B., Liao, Y. P., Libby, J., Lin, J., Liptak, Z., Liu, M. H., Liu, Q. Y., Liu, Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Ma, Y., Madaan, C., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matvienko, D., Maurya, S. K., Maushart, M., McKenna, J. A., Meier, F., Merola, M., Metzner, F., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Mizuk, R., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mrvar, M., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, Y., Naruki, M., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Niebuhr, C., Niiyama, M., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Onuki, Y., Otani, F., Pakhlov, P., Pakhlova, G., Paoloni, E., Pardi, S., Parham, K., Park, H., Park, J., Park, K., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Angioni, G. Pinna, Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Prudiiev, I., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Ravindran, K., Rehman, J. U., Reif, M., Reiter, S., Remnev, M., Reuter, L., Herrmann, D. Ricalde, Ripp-Baudot, I., Rizzo, G., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sanders, D. A., Sandilya, S., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schmitt, C., Schneider, S., Schnell, G., Schnepf, M., Schwanda, C., Schwartz, A. J., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Sharma, C., Shen, C. P., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Song, W., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Strube, J., Sue, Y., Sumihama, M., Sumisawa, K., Sutcliffe, W., Suwonjandee, N., Svidras, H., Takahashi, M., Takizawa, M., Tamponi, U., Tanaka, S., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Torassa, E., Trabelsi, K., Tsaklidis, I., Ueda, I., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Vossen, A., Wach, B., Wakai, M., Wallner, S., Wang, B., Wang, E., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Wiechczynski, J., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yang, S. B., Yasaveev, M., Yelton, J., Yin, J. H., Yook, Y. M., Yoshihara, K., Yuan, C. Z., Yuan, J., Yusa, Y., Zani, L., Zeng, F., Zhang, B., Zhilich, V., Zhou, J. S., Zhou, Q. D., Zhukova, V. I., and Žlebčík, R.

Subjects
High Energy Physics - Experiment
Abstract

We report measurements of the absolute branching fractions $\mathcal{B}(B_s^0 \to D_s^{\pm} X)$, $\mathcal{B}(B_s^0 \to D^0/\bar{D}^0 X)$, and $\mathcal{B}(B_s^0 \to D^{\pm} X)$, where the latter is measured for the first time. The results are based on a 121.4\,fb$^{-1}$ data sample collected at the $\Upsilon(10860)$ resonance by the Belle detector at the KEKB asymmetric-energy $e^+ e^-$ collider. We reconstruct one $B_s^0$ meson in $e^+e^- \to \Upsilon(10860) \to B_s^{*} \bar{B}_s^{*}$ events and measure yields of $D_s^+$, $D^0$, and $D^+$ mesons in the rest of the event. We obtain $\mathcal{B}(B_s^0 \to D_s^{\pm} X) = (68.6 \pm 7.2 \pm 4.0)\%$, $\mathcal{B}(B_s^0 \to D^0/\bar{D}^0 X) = (21.5 \pm 6.1 \pm 1.8)\%$, and $\mathcal{B}(B_s^0 \to D^{\pm} X) = (12.6 \pm 4.6 \pm 1.3)\%$, where the first uncertainty is statistical and the second is systematic. Averaging with previous Belle measurements gives $\mathcal{B}(B_s^0 \to D_s^{\pm} X) = (63.4 \pm 4.5 \pm 2.2)\%$ and $\mathcal{B}(B_s^0 \to D^0/\bar{D}^0 X) = (23.9 \pm 4.1 \pm 1.8)\%$. For the $B_s^0$ production fraction at the $\Upsilon(10860)$, we find $f_s = (21.4^{+1.5}_{-1.7})\%$., Comment: 23 pages, 9 figures, submitted to JHEP

Published
2024

35. Production cross sections of light and charmed mesons in $e^+e^-$ annihilation near 10.58 GeV

Author

Belle Collaboration, Seidl, R., Adachi, I., Aihara, H., Aushev, T., Ayad, R., Banerjee, Sw., Belous, K., Bennett, J., Bessner, M., Bhuyan, B., Biswas, D., Bodrov, D., Bračko, M., Branchini, P., Browder, T. E., Budano, A., Campajola, M., Chilikin, K., Cho, K., Choi, S. -K., Choi, Y., Choudhury, S., Das, S., De Nardo, G., De Pietro, G., Di Capua, F., Dingfelder, J., Doležal, Z., Dong, T. V., Dossett, D., Ecker, P., Ferber, T., Fulsom, B. G., Gaur, V., Giri, A., Goldenzweig, P., Graziani, E., Guan, Y., Gudkova, K., Hadjivasiliou, C., Hara, T., Hayashii, H., Herrmann, D., Hou, W. -S., Hsu, C. -L., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jacobs, W. W., Jia, S., Jin, Y., Joo, K. K., Kaliyar, A. B., Kiesling, C., Kim, C. H., Kim, D. Y., Kim, K. -H., Kodyš, P., Korobov, A., Korpar, S., Križan, P., Krokovny, P., Kumar, D., Kumara, K., Kwon, Y. -J., Lam, T., Li, L. K., Li, Y. B., Gioi, L. Li, Libby, J., Liventsev, D., Ma, Y., Masuda, M., Matsuda, T., Matvienko, D., Merola, M., Miyabayashi, K., Mussa, R., Nakao, M., Natochii, A., Niiyama, M., Nishida, S., Ogawa, S., Ono, H., Pakhlova, G., Pardi, S., Park, J., Park, S. -H., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Pestotnik, R., Piilonen, L. E., Podobnik, T., Prencipe, E., Prim, M. T., Russo, G., Sandilya, S., Santelj, L., Savinov, V., Schnell, G., Schwanda, C., Seino, Y., Senyo, K., Sevior, M. E., Shan, W., Shiu, J. -G., Shwartz, B., Singh, J. B., Solovieva, E., Starič, M., Sumihama, M., Takizawa, M., Tanida, K., Tenchini, F., Uglov, T., Unno, Y., Uno, S., Usov, Y., Van Hulse, C., Vinokurova, A., Vossen, A., Wang, M. -Z., Yabsley, B. D., Yan, W., Yook, Y., Yuan, C. Z., Yuan, L., Zhang, Z. P., and Zhilich, V.

Subjects
High Energy Physics - Experiment
Abstract

We report measurements of production cross sections for $\rho^+$, $\rho^0$, $\omega$, $K^{*+}$, $K^{*0}$, $\phi$, $\eta$, $K_S^0$, $f_0(980)$, $D^+$, $D^0$, $D_s^+$, $D^{*+}$, $D^{*0}$, and $D^{*+}_s$ in $e^+e^-$ collisions at a center-of-mass energy near 10.58 GeV. The data were recorded by the Belle experiment, consisting of 571 fb$^{-1}$ at 10.58 GeV and 74 fb$^{-1}$ at 10.52 GeV. Production cross sections are extracted as a function of the fractional hadron momentum $x_p$ . The measurements are compared to {\sc pythia} Monte Carlo generator predictions with various fragmentation settings, including those that have increased fragmentation into vector mesons over pseudo-scalar mesons. The cross sections measured for light hadrons are consistent with no additional increase of vector over pseudo-scalar mesons. The charmed-meson cross sections are compared to earlier measurements -- when available -- including older Belle results, which they supersede. They are in agreement before application of an improved initial-state radiation correction procedure that causes slight changes in their \xp shapes., Comment: 21 pages, 18 figures, submitted to Phys. Rev. D

Published
2024

36. Measurement of $B \to K{}^{*}(892)\gamma$ decays at Belle II

Author

Belle II Collaboration, Adachi, I., Aggarwal, L., Ahmed, H., Aihara, H., Akopov, N., Aloisio, A., Althubiti, N., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, T., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Baghel, N. K., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Bartl, M., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhardwaj, V., Bhuyan, B., Bianchi, F., Bierwirth, L., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Bondar, A., Borah, J., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Chen, C., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cui, J. X., Dattola, F., De La Cruz-Burelo, E., De La Motte, S. A., de Marino, G., De Nardo, G., De Pietro, G., de Sangro, R., Destefanis, M., Dey, S., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Jiménez, I. Domínguez, Dong, T. V., Dorigo, M., Dort, K., Dossett, D., Dubey, S., Dugic, K., Dujany, G., Ecker, P., Eliachevitch, M., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Frey, A., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Gironell, P. Gironella, Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Graziani, E., Greenwald, D., Gruberová, Z., Gu, T., Guan, Y., Gudkova, K., Haide, I., Halder, S., Han, Y., Hara, T., Harris, C., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Hoppe, R., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobs, W. W., Jang, E. -J., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kaleta, M., Kalita, D., Kaliyar, A. B., Kandra, J., Kang, K. H., Kang, S., Karyan, G., Kawasaki, T., Keil, F., Ketter, C., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, J. -Y., Kim, K. -H., Kim, Y. -K., Kim, Y. J., Kindo, H., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, D., Kumar, M., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lalwani, K., Lam, T., Lanceri, L., Lange, J. S., Lau, T. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Lemettais, C., Leo, P., Levit, D., Lewis, P. M., Li, C., Li, L. K., Li, Q. M., Li, S. X., Li, W. Z., Li, Y., Li, Y. B., Liao, Y. P., Libby, J., Lin, J., Liptak, Z., Liu, M. H., Liu, Q. Y., Liu, Y., Liu, Z. Q., Liventsev, D., Longo, S., Lyu, C., Ma, Y., Madaan, C., Maggiora, M., Maharana, S. P., Maiti, R., Maity, S., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matsuda, T., Matsuoka, K., Matvienko, D., Maurya, S. K., Maushart, M., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Metzner, F., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Mizuk, R., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mrvar, M., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, Y., Naruki, M., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Niebuhr, C., Niiyama, M., Nishida, S., Ogawa, S., Onishchuk, Y., Ono, H., Onuki, Y., Otani, F., Pakhlov, P., Pakhlova, G., Paoloni, E., Pardi, S., Parham, K., Park, H., Park, J., Park, K., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Peruzzi, I., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Angioni, G. Pinna, Podesta-Lerma, P. L. M., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Prudiiev, I., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Ravindran, K., Rehman, J. U., Reif, M., Reiter, S., Remnev, M., Reuter, L., Herrmann, D. Ricalde, Ripp-Baudot, I., Rizzo, G., Robertson, S. H., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sanders, D. A., Sandilya, S., Santelj, L., Sato, Y., Savinov, V., Scavino, B., Schmitt, C., Schneider, S., Schnepf, M., Schwanda, C., Schwartz, A. J., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Sharma, C., Shen, C. P., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Shwartz, B., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Song, W., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Strube, J., Sue, Y., Sumihama, M., Sumisawa, K., Sutcliffe, W., Suwonjandee, N., Svidras, H., Takahashi, M., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Torassa, E., Trabelsi, K., Tsaklidis, I., Ueda, I., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Vossen, A., Wach, B., Wakai, M., Wallner, S., Wang, E., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yang, S. B., Yelton, J., Yin, J. H., Yook, Y. M., Yoshihara, K., Yuan, C. Z., Yuan, J., Zani, L., Zeng, F., Zhang, B., Zhilich, V., Zhou, J. S., Zhou, Q. D., Zhukova, V. I., and Žlebčík, R.

Subjects
High Energy Physics - Experiment
Abstract

We present measurements of $B \to K{}^{*}(892)\gamma$ decays using $365\,{\rm fb}^{-1}$ of data collected from 2019 to 2022 by the Belle~II experiment at the SuperKEKB asymmetric-energy $e^+e^-$ collider. The data sample contains $(387 \pm 6) \times 10^6$ $B\overline{B}$ events. We measure branching fractions ($\mathcal{B}$) and $C\!P$ asymmetries ($\mathcal{A}_{C\!P}$) for both $B^{0}\to K{}^{*0}\gamma$ and $B^{+}\to K{}^{*+}\gamma$ decays. The difference in $C\!P$ asymmetries ($\Delta \mathcal{A}_{C\!P}$) and the isospin asymmetry ($\Delta_{0+}$) between these neutral and charged channels are also measured. We obtain the following branching fractions and $C\!P$ asymmetries: $\mathcal{B} (B^{0} \to K{}^{*0}\gamma) = (4.14 \pm 0.10 \pm 0.11 ) \times 10^{-5}$, $\mathcal{B} (B^{+} \to K{}^{*+}\gamma) = (4.02 \pm 0.13 \pm 0.13 )\times 10^{-5}$, $\mathcal{A}_{C\!P} (B^{0} \to K{}^{*0}\gamma) = (-3.3 \pm 2.3 \pm 0.4 )\%$, and $\mathcal{A}_{C\!P} (B^{+} \to K{}^{*+}\gamma) = (-0.7 \pm 2.9 \pm 0.6 )\%$. The measured difference in $C\!P$ asymmetries is $\Delta \mathcal{A}_{C\!P} = (+2.6 \pm 3.8 \pm 0.7 )\%$, and the measured isospin asymmetry is $\Delta_{0+} = (+5.0 \pm 2.0 \pm 1.5 )\%$. The first uncertainties listed are statistical and the second are systematic. These results are consistent with world-average values and theory predictions.

Published
2024

37. $2$-R\'enyi CCNR Negativity of Compact Boson for multiple disjoint intervals

Author

Gaur, Himanshu

Subjects
High Energy Physics - Theory, Condensed Matter - Statistical Mechanics, Quantum Physics
Abstract

We investigate mixed-state bipartite entanglement between multiple disjoint intervals using the computable cross-norm criterion (CCNR). We consider entanglement between a single interval and the union of remaining disjoint intervals, and compute $2$-R\'enyi CCNR negativity for $2$d massless compact boson. The expression for $2$-R\'enyi CCNR negativity is given in terms of cross-ratios and Riemann period matrices of Riemann surfaces involved in the calculation. In general, the Riemann surfaces involved in the calculation of $n$-R\'enyi CCNR negativity do not possess a $Z_n$ symmetry. We also evaluate the Reflected R\'enyi entropy related to the $2$-R\'enyi CCNR negativity. This Reflected R\'enyi entropy is a universal quantity. We extend these calculations to the $2$d massless Dirac fermions as well. Finally, the analytical results are checked against the numerical evaluations in the tight-binding model and are found to be in good agreement., Comment: 29 pages, 11 figures

Published
2024

38. A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19

Author

Khandelwal, Vedant, Gaur, Manas, Kursuncu, Ugur, Shalin, Valerie, and Sheth, Amit

Subjects
Computer Science - Artificial Intelligence, I.2.4, I.2.6, I.2.7, I.2.0
Abstract

Monitoring public sentiment via social media is potentially helpful during health crises such as the COVID-19 pandemic. However, traditional frequency-based, data-driven neural network-based approaches can miss newly relevant content due to the evolving nature of language in a dynamically evolving environment. Human-curated symbolic knowledge sources, such as lexicons for standard language and slang terms, can potentially elevate social media signals in evolving language. We introduce a neurosymbolic method that integrates neural networks with symbolic knowledge sources, enhancing the detection and interpretation of mental health-related tweets relevant to COVID-19. Our method was evaluated using a corpus of large datasets (approximately 12 billion tweets, 2.5 million subreddit data, and 700k news articles) and multiple knowledge graphs. This method dynamically adapts to evolving language, outperforming purely data-driven models with an F1 score exceeding 92\%. This approach also showed faster adaptation to new data and lower computational demands than fine-tuning pre-trained large language models (LLMs). This study demonstrates the benefit of neurosymbolic methods in interpreting text in a dynamic environment for tasks such as health surveillance., Comment: 13 Pages, 5 Figures, 5 Tables, 2024 IEEE International Conference on Big Data, Regular Paper

Published
2024

39. Measurement of the time-integrated CP asymmetry in $D^{0}\rightarrow K^{0}_{S}K^{0}_{S}$ decays using Belle and Belle II data

Author

Belle, Collaborations, Belle II, Adachi, I., Aggarwal, L., Ahmed, H., Aihara, H., Akopov, N., Aloisio, A., Althubiti, N., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, V., Aversano, M., Ayad, R., Babu, V., Baghel, N. K., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Bartl, M., Baudot, J., Beaubien, A., Becker, J., Bennett, J. V., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Biswas, D., Bobrov, A., Bodrov, D., Bolz, A., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Chen, C., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cui, J. X., Das, S., De La Cruz-Burelo, E., De La Motte, S. A., De Pietro, G., de Sangro, R., Destefanis, M., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Dong, T. V., Dorigo, M., Dossett, D., Dujany, G., Ecker, P., Eppelt, J., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Fulsom, B. G., Gabrielli, A., Ganiev, E., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Gironell, P. Gironella, Glazov, A., Gobbo, B., Godang, R., Goldenzweig, P., Gradl, W., Graziani, E., Greenwald, D., Gruberová, Z., Guan, Y., Gudkova, K., Haide, I., Hara, T., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Hoppe, R., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jacobs, W. W., Jaffe, D. E., Jang, E. -J., Ji, Q. P., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kaliyar, A. B., Kandra, J., Karyan, G., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, J. -Y., Kim, K. -H., Kim, Y. -K., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Križan, P., Krokovny, P., Kuhr, T., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lacaprara, S., Lai, Y. -T., Lalwani, K., Lam, T., Lange, J. S., Lau, T. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Lemettais, C., Leo, P., Li, C., Li, L. K., Li, Q. M., Li, W. Z., Li, Y. B., Liao, Y. P., Libby, J., Liu, M. H., Liu, Q. Y., Liu, Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Madaan, C., Maggiora, M., Maharana, S. P., Maiti, R., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matvienko, D., Maurya, S. K., Maushart, M., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, Y., Naruki, M., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Nishida, S., Ogawa, S., Ono, H., Oxford, E. R., Pakhlova, G., Pardi, S., Parham, K., Park, H., Park, J., Park, K., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Pedlar, T. K., Peruzzi, I., Peschke, R., Piccolo, M., Piilonen, L. E., Podesta-Lerma, P. L. M., Podobnik, T., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Raiz, S., Rauls, N., Rehman, J. U., Reif, M., Reiter, S., Reuter, L., Herrmann, D. Ricalde, Ripp-Baudot, I., Rizzo, G., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sanders, D. A., Sandilya, S., Santelj, L., Savinov, V., Scavino, B., Schnepf, M., Schwanda, C., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Shi, X. D., Shiu, J. -G., Shtol, D., Shwartz, B., Sibidanov, A., Simon, F., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Strube, J., Sumihama, M., Sumisawa, K., Svidras, H., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Tittel, O., Tiwary, R., Torassa, E., Trabelsi, K., Ueda, I., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wakai, M., Wallner, S., Wang, M. -Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Won, E., Yabsley, B. D., Yamada, S., Yan, W., Yelton, J., Yin, J. H., Yoshihara, K., Yuan, J., Zani, L., Zhang, B., Zhilich, V., Zhou, J. S., Zhou, Q. D., Zhu, L., Zhukova, V. I., and Žlebčík, R.

Subjects
High Energy Physics - Experiment
Abstract

We measure the time-integrated CP asymmetry in $D^{0} \rightarrow K^{0}_{S}K^{0}_{S}$ decays reconstructed in $e^{+}e^{-} \rightarrow c\overline{c}$ events collected by the Belle and Belle II experiments. The corresponding data samples have integrated luminosities of 980 fb$^{-1}$ and 428 fb$^{-1}$, respectively. The $D^{0}$ decays are required to originate from the $D^{*+} \rightarrow D^{0}\pi^{+}$ decay, which determines the charm flavor at production time. A control sample of $D^{0} \rightarrow K^{+}K^{-}$ decays is used to correct for production and detection asymmetries. The result, $(-1.4\pm1.3{\rm(stat)}\pm0.1{\rm (syst)})\%$, is consistent with previous determinations and with CP symmetry., Comment: 10 pages, 3 figures. arXiv admin note: text overlap with arXiv:2410.22961

Published
2024
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40. Schema Augmentation for Zero-Shot Domain Adaptation in Dialogue State Tracking

Author

Richardson, Christopher, Sharma, Roshan, Gaur, Neeraj, Haghani, Parisa, Sundar, Anirudh, and Ramabhadran, Bhuvana

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Zero-shot domain adaptation for dialogue state tracking (DST) remains a challenging problem in task-oriented dialogue (TOD) systems, where models must generalize to target domains unseen at training time. Current large language model approaches for zero-shot domain adaptation rely on prompting to introduce knowledge pertaining to the target domains. However, their efficacy strongly depends on prompt engineering, as well as the zero-shot ability of the underlying language model. In this work, we devise a novel data augmentation approach, Schema Augmentation, that improves the zero-shot domain adaptation of language models through fine-tuning. Schema Augmentation is a simple but effective technique that enhances generalization by introducing variations of slot names within the schema provided in the prompt. Experiments on MultiWOZ and SpokenWOZ showed that the proposed approach resulted in a substantial improvement over the baseline, in some experiments achieving over a twofold accuracy gain over unseen domains while maintaining equal or superior performance over all domains., Comment: short paper (4 pages) submitted to ARR

Published
2024

41. Crosstalk Attack Resilient RNS Quantum Addition

Author

Gaur, Bhaskar and Thapliyal, Himanshu

Subjects
Quantum Physics, Computer Science - Cryptography and Security
Abstract

As quantum computers scale, the rise of multi-user and cloud-based quantum platforms can lead to new security challenges. Attacks within shared execution environments become increasingly feasible due to the crosstalk noise that, in combination with quantum computer's hardware specifications, can be exploited in form of crosstalk attack. Our work pursues crosstalk attack implementation in ion-trap quantum computers. We propose three novel quantum crosstalk attacks designed for ion trap qubits: (i) Alternate CNOT attack (ii) Superposition Alternate CNOT (SAC) attack (iii) Alternate Phase Change (APC) attack. We demonstrate the effectiveness of proposed attacks by conducting noise-based simulations on a commercial 20-qubit ion-trap quantum computer. The proposed attacks achieve an impressive reduction of up to 42.2% in output probability for Quantum Full Adders (QFA) having 6 to 9-qubit output. Finally, we investigate the possibility of mitigating crosstalk attacks by using Residue Number System (RNS) based Parallel Quantum Addition (PQA). We determine that PQA achieves higher attack resilience against crosstalk attacks in the form of 24.3% to 133.5% improvement in output probability against existing Non Parallel Quantum Addition (NPQA). Through our systematic methodology, we demonstrate how quantum properties such as superposition and phase transition can lead to crosstalk attacks and also how parallel quantum computing can help secure against these attacks., Comment: 5 pages, 4 figures, 3 tables

Published
2024

42. Model-independent measurement of $D^0$-$\overline{D}{}^0$ mixing parameters in $D^0\rightarrow K^0_{S}\pi^+\pi^-$ decays at Belle and Belle II

Author

Belle, Collaborations, Belle II, Adachi, I., Aggarwal, L., Ahmed, H., Aihara, H., Akopov, N., Aloisio, A., Althubiti, N., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, V., Aversano, M., Ayad, R., Baghel, N. K., Bambade, P., Banerjee, Sw., Bansal, S., Barrett, M., Bartl, M., Baudot, J., Beaubien, A., Becker, J., Bennett, J. V., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhuyan, B., Biswas, D., Bodrov, D., Bolz, A., Bondar, A., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Casarosa, G., Cecchi, C., Chang, P., Cheaib, R., Cheema, P., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cui, J. X., Das, S., De La Cruz-Burelo, E., De La Motte, S. A., De Pietro, G., de Sangro, R., Destefanis, M., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Dong, T. V., Dorigo, M., Dossett, D., Dujany, G., Ecker, P., Epifanov, D., Eppelt, J., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Gironell, P. Gironella, Glazov, A., Gobbo, B., Godang, R., Goldenzweig, P., Gong, G., Gradl, W., Graziani, E., Greenwald, D., Gruberová, Z., Gudkova, K., Haide, I., Hara, T., Hayasaka, K., Hayashii, H., Hazra, S., Hearty, C., Hedges, M. T., Heidelbach, A., de la Cruz, I. Heredia, Higuchi, T., Hoek, M., Hohmann, M., Hoppe, R., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Itoh, R., Iwasaki, M., Jacobs, W. W., Jang, E. -J., Ji, Q. P., Jin, Y., Johnson, A., Junkerkalefeld, H., Kaliyar, A. B., Kandra, J., Karyan, G., Keil, F., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, J. -Y., Kim, K. -H., Kim, Y. -K., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Kovalenko, E., Kowalewski, R., Križan, P., Krokovny, P., Kuhr, T., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lalwani, K., Lam, T., Lange, J. S., Lau, T. S., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Lemettais, C., Leo, P., Li, C., Li, L. K., Li, Q. M., Li, W. Z., Li, Y., Li, Y. B., Libby, J., Liu, M. H., Liu, Q. Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Madaan, C., Maggiora, M., Maiti, R., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Matvienko, D., Maurya, S. K., Maushart, M., McKenna, J. A., Meier, F., Merola, M., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, H., Nakazawa, Y., Naruki, M., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Nishida, S., Ogawa, S., Ono, H., Oxford, E. R., Pakhlova, G., Pardi, S., Parham, K., Park, H., Park, J., Park, K., Park, S. -H., Paschen, B., Passeri, A., Patra, S., Pedlar, T. K., Peschke, R., Piilonen, L. E., Podesta-Lerma, P. L. M., Podobnik, T., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Purwar, H., Raiz, S., Rehman, J. U., Reif, M., Reiter, S., Reuter, L., Herrmann, D. Ricalde, Ripp-Baudot, I., Rizzo, G., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sanders, D. A., Sandilya, S., Santelj, L., Savinov, V., Scavino, B., Schwanda, C., Schwartz, A. J., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Shen, C. P., Shi, X. D., Shillington, T., Shiu, J. -G., Shtol, D., Sibidanov, A., Simon, F., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Sumihama, M., Sumisawa, K., Svidras, H., Takizawa, M., Tanida, K., Tenchini, F., Tittel, O., Tiwary, R., Torassa, E., Trabelsi, K., Uchida, M., Ueda, I., Uglov, T., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Vahsen, S. E., van Tonder, R., Varvell, K. E., Veronesi, M., Vinokurova, A., Vismaya, V. S., Vitale, L., Volpe, R., Wakai, M., Wallner, S., Wang, M. -Z., Warburton, A., Watanabe, M., Watanuki, S., Wessel, C., Yabsley, B. D., Yamada, S., Yan, W., Yin, J. H., Yoshihara, K., Yuan, J., Zhang, Z., Zhilich, V., Zhou, J. S., Zhou, Q. D., Zhu, L., Zhukova, V. I., and Žlebčík, R.

Subjects
High Energy Physics - Experiment
Abstract

We perform a model-independent measurement of the $D^0$-$\overline{D}{}^0$ mixing parameters using samples of $e^+e^-$-collision data collected by the Belle and Belle II experiments that have integrated luminosities of $951\ \text{fb}^{-1}$ and $408\ \text{fb}^{-1}$, respectively. Approximately $2.05\times10^6$ neutral $D$ mesons are reconstructed in the $D^0\rightarrow K^0_{S}\pi^+\pi^-$ channel, with the neutral $D$ flavor tagged by the charge of the pion in the $D^{*+}\rightarrow D^0\pi^+$ decay. Assuming charge-parity symmetry, the mixing parameters are measured to be $ x = (4.0\pm1.7\pm0.4)\times 10^{-3} $ and $ y = (2.9\pm1.4\pm0.3)\times 10^{-3}$, where the first uncertainties are statistical and the second systematic. The results are consistent with previous determinations.

Published
2024

43. Geometric Correction and Mosaic Generation of Geo High Resolution Camera Images

Author

Garg, Ankur, Thapa, Nitesh, Sangar, Ghansham, Gaur, Neha, Sarkar, Meenakshi, Moorthi, S. Manthira, and Dhar, Debajyoti

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

The Geo High Resolution Camera (GHRC) aboard ISRO GSAT-29 satellite is a state-of-the-art 6-band Visible and Near Infrared (VNIR) imager in geostationary orbit at 55degE longitude. It provides a ground sampling distance of 55 meters at nadir, covering 110x110 km at a time, and can image the entire Earth disk using a scan mirror mechanism. To cover India, GHRC uses a two-dimensional raster scanning technique, resulting in over 1,000 scenes that must be stitched into a seamless mosaic. This paper presents the geolocation model and examines potential sources of targeting error, with an assessment of location accuracy. Challenges in inter-band registration and inter-frame mosaicing are addressed through algorithms for geometric correction, band-to-band registration, and seamless mosaic generation. In-flight geometric calibration, including adjustments to the instrument interior alignment angles using ground reference images, has improved pointing and location accuracy. A backtracking algorithm has been developed to correct frame-to-frame mosaicing errors for large-scale mosaics, leveraging geometric models, image processing, and space resection techniques. These advancements now enable the operational generation of full India mosaics with 100-meter resolution and high geometric fidelity, enhancing the GHRC capabilities for Earth observation and monitoring applications., Comment: Preprint

Published
2024

44. Search for $h_b(2P)\to\gamma\chi_{bJ}(1P)$ at $\sqrt{s} = 10.860$ GeV

Author

Belle Collaboration, Boschetti, A., Mussa, R., Tamponi, U., Adachi, I., Aihara, H., Asner, D. M., Aushev, T., Ayad, R., Banerjee, Sw., Belous, K., Bennett, J., Bessner, M., Biswas, D., Bobrov, A., Bodrov, D., Bozek, A., Bračko, M., Branchini, P., Browder, T. E., Budano, A., Chang, M. -C., Cheon, B. G., Chilikin, K., Cho, K., Choi, S. -K., Choi, Y., Choudhury, S., De Nardo, G., De Pietro, G., Dhamija, R., Di Capua, F., Doležal, Z., Dong, T. V., Ecker, P., Epifanov, D., Ferlewicz, D., Fulsom, B. G., Garg, R., Gaur, V., Garmash, A., Giri, A., Goldenzweig, P., Graziani, E., Gu, T., Guan, Y., Gudkova, K., Hadjivasiliou, C., Hara, T., Hayasaka, K., Hayashii, H., Hazra, S., Hou, W. -S., Hsu, C. -L., Inami, K., Ipsita, N., Itoh, R., Iwasaki, M., Jacobs, W. W., Jin, Y., Kawasaki, T., Kiesling, C., Kim, C. H., Kim, D. Y., Kim, K. -H., Kim, Y. -K., Kinoshita, K., Kodyš, P., Korpar, S., Kovalenko, E., Križan, P., Krokovny, P., Kumar, R., Kumara, K., Kwon, Y. -J., Lam, T., Levit, D., Li, L. K., Li, Y. B., Gioi, L. Li, Liventsev, D., Ma, Y., Masuda, M., Matsuda, T., Matvienko, D., Meier, F., Merola, M., Miyabayashi, K., Mizuk, R., Mohanty, G. B., Nakao, M., Natkaniec, Z., Natochii, A., Nayak, L., Nayak, M., Nishida, S., Ogawa, S., Ono, H., Pakhlova, G., Park, J., Park, S. -H., Passeri, A., Patra, S., Paul, S., Pedlar, T. K., Pestotnik, R., Piilonen, L. E., Podobnik, T., Prencipe, E., Prim, M. T., Rout, N., Russo, G., Sandilya, S., Santelj, L., Savinov, V., Schnell, G., Schwanda, C., Seino, Y., Senyo, K., Shan, W., Shen, C. P., Shiu, J. -G., Sokolov, A., Solovieva, E., Starič, M., Sumihama, M., Takizawa, M., Tanida, K., Tenchini, F., Tiwary, R., Uchida, M., Unno, Y., Uno, S., Vinokurova, A., Wang, E., Wang, M. -Z., Wang, X. L., Won, E., Yabsley, B. D., Yelton, J., Yin, J. H., Yook, Y., and Yuan, L.

Subjects
High Energy Physics - Experiment
Abstract

In the bottomonium sector, the hindered magnetic dipole (M1) transitions between P-wave states $h_b(2P) \rightarrow \chi_{bJ}(1P) \gamma$, $J=0, \, 1, \, 2$, are expected to be severely suppressed according to the Relativized Quark Model, due to the spin flip of the $b$ quark. Nevertheless, a recent model following the coupled-channel approach predicts the corresponding branching fractions to be enhanced by orders of magnitude. In this Letter, we report the first search for such transitions. We find no significant signals and set upper limits at 90% CL on the corresponding branching fractions: $\mathcal{B}[h_b(2P)\to\gamma\chi_{b0}(1P)] < 2.7 \times 10^{-1}$, $\mathcal{B}[h_b(2P)\to\gamma\chi_{b1}(1P)] < 5.4 \times 10^{-3}$ and $\mathcal{B}[h_b(2P)\to\gamma\chi_{b2}(1P)] < 1.3 \times 10^{-2}$. These values help to constrain the parameters of the coupled-channel models. The results are obtained using a $121.4 \, fb^{-1}$ data sample taken around $\sqrt{s}= 10.860 \, GeV$ with the Belle detector at the KEKB asymmetric-energy $e^+e^-$ collider.

Published
2024

45. On The Global Convergence Of Online RLHF With Neural Parametrization

Author

Gaur, Mudit, Bedi, Amrit Singh, Pasupathy, Raghu, and Aggarwal, Vaneet

Subjects
Computer Science - Machine Learning
Abstract

The importance of Reinforcement Learning from Human Feedback (RLHF) in aligning large language models (LLMs) with human values cannot be overstated. RLHF is a three-stage process that includes supervised fine-tuning (SFT), reward learning, and policy learning. Although there are several offline and online approaches to aligning LLMs, they often suffer from distribution shift issues. These issues arise from the inability to accurately capture the distributional interdependence between the reward learning and policy learning stages. Consequently, this has led to various approximated approaches, but the theoretical insights and motivations remain largely limited to tabular settings, which do not hold in practice. This gap between theoretical insights and practical implementations is critical. It is challenging to address this gap as it requires analyzing the performance of AI alignment algorithms in neural network-parameterized settings. Although bi-level formulations have shown promise in addressing distribution shift issues, they suffer from the hyper-gradient problem, and current approaches lack efficient algorithms to solve this. In this work, we tackle these challenges employing the bi-level formulation laid out in Kwon et al. (2024) along with the assumption \emph{Weak Gradient Domination} to demonstrate convergence in an RLHF setup, obtaining a sample complexity of $\epsilon^{-\frac{7}{2}}$ . Our key contributions are twofold: (i) We propose a bi-level formulation for AI alignment in parameterized settings and introduce a first-order approach to solve this problem. (ii) We analyze the theoretical convergence rates of the proposed algorithm and derive state-of-the-art bounds. To the best of our knowledge, this is the first work to establish convergence rate bounds and global optimality for the RLHF framework in neural network-parameterized settings.

Published
2024

46. A hybrid quantum solver for the Lorenz system

Author

Hafshejani, Sajad Fathi, Gaur, Daya, Dasgupta, Arundhati, Benkoczi, Robert, Gosala, Narasimha, and Iorio, Alfredo

Subjects
Quantum Physics, Mathematical Physics, Mathematics - Numerical Analysis
Abstract

We develop a hybrid classical-quantum method for solving the Lorenz system. We use the forward Euler method to discretize the system in time, transforming it into a system of equations. This set of equations is solved using the Variational Quantum Linear Solver (VQLS) algorithm. We present numerical results comparing the hybrid method with the classical approach for solving the Lorenz system. The simulation results demonstrate that the VQLS method can effectively compute solutions comparable to classical methods. The method is easily extended to solving similar nonlinear differential equations.

Published
2024

47. Observation of time-dependent $CP$ violation and measurement of the branching fraction of $B^0 \to J/\psi \pi^0$ decays

Author

Belle II Collaboration, Adachi, I., Aggarwal, L., Ahmed, H., Aihara, H., Akopov, N., Aloisio, A., Althubiti, N., Ky, N. Anh, Asner, D. M., Atmacan, H., Aushev, V., Aversano, M., Ayad, R., Babu, V., Bae, H., Baghel, N. K., Bahinipati, S., Bambade, P., Banerjee, Sw., Bansal, S., Baudot, J., Baur, A., Beaubien, A., Becherer, F., Becker, J., Bennett, J. V., Bernlochner, F. U., Bertacchi, V., Bertemes, M., Bertholet, E., Bessner, M., Bettarini, S., Bhardwaj, V., Bianchi, F., Bilka, T., Biswas, D., Bobrov, A., Bodrov, D., Bondar, A., Borah, J., Boschetti, A., Bozek, A., Bračko, M., Branchini, P., Briere, R. A., Browder, T. E., Budano, A., Bussino, S., Campagna, Q., Campajola, M., Cao, L., Casarosa, G., Cecchi, C., Cerasoli, J., Chang, M. -C., Chang, P., Cheaib, R., Cheema, P., Chen, C., Cheon, B. G., Chilikin, K., Chirapatpimol, K., Cho, H. -E., Cho, K., Cho, S. -J., Choi, S. -K., Choudhury, S., Cochran, J., Corona, L., Cui, J. X., De La Cruz-Burelo, E., De La Motte, S. A., De Nardo, G., De Pietro, G., de Sangro, R., Destefanis, M., Dhamija, R., Di Canto, A., Di Capua, F., Dingfelder, J., Doležal, Z., Dong, T. V., Dorigo, M., Dubey, S., Dugic, K., Dujany, G., Ecker, P., Epifanov, D., Feichtinger, P., Ferber, T., Fillinger, T., Finck, C., Finocchiaro, G., Fodor, A., Forti, F., Fulsom, B. G., Gabrielli, A., Ganiev, E., Garcia-Hernandez, M., Garg, R., Gaudino, G., Gaur, V., Gaz, A., Gellrich, A., Ghevondyan, G., Ghosh, D., Ghumaryan, H., Giakoustidis, G., Giordano, R., Giri, A., Gironella, P., Glazov, A., Gobbo, B., Godang, R., Gogota, O., Goldenzweig, P., Gradl, W., Granderath, S., Graziani, E., Gruberová, Z., Guan, Y., Gudkova, K., Haide, I., Han, Y., Hara, T., Hayashii, H., Hazra, S., Hearty, C., Heidelbach, A., de la Cruz, I. Heredia, Villanueva, M. Hernández, Higuchi, T., Hoek, M., Hohmann, M., Hoppe, R., Horak, P., Hsu, C. -L., Humair, T., Iijima, T., Inami, K., Ipsita, N., Ishikawa, A., Itoh, R., Iwasaki, M., Jackson, P., Jacobs, W. W., Jang, E. -J., Jia, S., Jin, Y., Johnson, A., Joo, K. K., Junkerkalefeld, H., Kalita, D., Kandra, J., Kang, K. H., Kang, S., Kawasaki, T., Keil, F., Ketter, C., Kiesling, C., Kim, C. -H., Kim, D. Y., Kim, J. -Y., Kim, K. -H., Kim, Y. -K., Kim, Y. J., Kinoshita, K., Kodyš, P., Koga, T., Kohani, S., Kojima, K., Korobov, A., Korpar, S., Kovalenko, E., Kowalewski, R., Križan, P., Krokovny, P., Kuhr, T., Kulii, Y., Kumar, D., Kumar, R., Kumara, K., Kunigo, T., Kuzmin, A., Kwon, Y. -J., Lai, Y. -T., Lalwani, K., Lam, T., Lau, T. S., Laurenza, M., Leboucher, R., Diberder, F. R. Le, Lee, M. J., Lemettais, C., Leo, P., Li, L. K., Li, Q. M., Li, W. Z., Li, Y., Li, Y. B., Liao, Y. P., Libby, J., Lin, J., Liu, M. H., Liu, Q. Y., Liu, Y., Liu, Z. Q., Liventsev, D., Longo, S., Lueck, T., Lyu, C., Maggiora, M., Maharana, S. P., Maiti, R., Mancinelli, G., Manfredi, R., Manoni, E., Mantovano, M., Marcantonio, D., Marcello, S., Marinas, C., Martellini, C., Martens, A., Martini, A., Martinov, T., Massaccesi, L., Masuda, M., Maurya, S. K., McKenna, J. A., Mehta, R., Meier, F., Merola, M., Miller, C., Mirra, M., Mitra, S., Miyabayashi, K., Mohanty, G. B., Mondal, S., Moneta, S., Moser, H. -G., Mussa, R., Nakamura, I., Nakao, M., Nakazawa, Y., Naruki, M., Natkaniec, Z., Natochii, A., Nayak, M., Nazaryan, G., Neu, M., Nishida, S., Ogawa, S., Ono, H., Onuki, Y., Otani, F., Pakhlov, P., Pakhlova, G., Paoloni, E., Pardi, S., Park, H., Park, J., Park, K., Park, S. -H., Paschen, B., Passeri, A., Pedlar, T. K., Peruzzi, I., Peschke, R., Pestotnik, R., Piccolo, M., Piilonen, L. E., Podobnik, T., Pokharel, S., Praz, C., Prell, S., Prencipe, E., Prim, M. T., Prudiiev, I., Purwar, H., Rados, P., Raeuber, G., Raiz, S., Rauls, N., Reif, M., Reiter, S., Remnev, M., Reuter, L., Ripp-Baudot, I., Rizzo, G., Roehrken, M., Roney, J. M., Rostomyan, A., Rout, N., Sanders, D. A., Sandilya, S., Santelj, L., Savinov, V., Scavino, B., Schmitt, C., Schneider, S., Schnepf, M., Schoenning, K., Schwanda, C., Schwartz, A. J., Seino, Y., Selce, A., Senyo, K., Serrano, J., Sevior, M. E., Sfienti, C., Shan, W., Sharma, C., Shen, C. P., Shi, X. D., Shillington, T., Shimasaki, T., Shiu, J. -G., Shtol, D., Shwartz, B., Sibidanov, A., Simon, F., Singh, J. B., Skorupa, J., Sobie, R. J., Sobotzik, M., Soffer, A., Sokolov, A., Solovieva, E., Spataro, S., Spruck, B., Song, W., Starič, M., Stavroulakis, P., Stefkova, S., Stroili, R., Strube, J., Sue, Y., Sumihama, M., Sumisawa, K., Sutcliffe, W., Suwonjandee, N., Svidras, H., Takahashi, M., Takizawa, M., Tamponi, U., Tanida, K., Tenchini, F., Thaller, A., Tittel, O., Tiwary, R., Torassa, E., Trabelsi, K., Tsaklidis, I., Uchida, M., Ueda, I., Unger, K., Unno, Y., Uno, K., Uno, S., Urquijo, P., Ushiroda, Y., Vahsen, S. E., van Tonder, R., Veronesi, M., Vismaya, V. S., Vitale, L., Vobbilisetti, V., Volpe, R., Wakai, M., Wallner, S., Wang, M. -Z., Wang, X. L., Wang, Z., Warburton, A., Watanuki, S., Wessel, C., Won, E., Xu, X. P., Yabsley, B. D., Yamada, S., Yan, W., Yelton, J., Yin, J. H., Yoshihara, K., Yusa, Y., Zani, L., Zeng, F., Zhang, B., Zhilich, V., Zhou, J. S., Zhou, Q. D., Zhukova, V. I., and Žlebčík, R.

Subjects
High Energy Physics - Experiment
Abstract

We present a measurement of the branching fraction and time-dependent charge-parity ($CP$) decay-rate asymmetries in $B^0 \to J/\psi \pi^0$ decays. The data sample was collected with the Belle~II detector at the SuperKEKB asymmetric $e^+e^-$ collider in 2019-2022 and contains $(387\pm 6)\times 10^6$ $B\overline{B}$ meson pairs from $\Upsilon(4S)$ decays. We reconstruct $392\pm 24$ signal decays and fit the $CP$ parameters from the distribution of the proper-decay-time difference of the two $B$ mesons. We measure the branching fraction to be $B(B^0 \to J/\psi \pi^0)=(2.00 \pm 0.12 \pm 0.09)\times 10^{-5}$ and the direct and mixing-induced $CP$ asymmetries to be $C_{CP}=0.13 \pm 0.12 \pm 0.03$ and $S_{CP}=-0.88 \pm 0.17 \pm 0.03$, respectively, where the first uncertainties are statistical and the second are systematic. We observe mixing-induced $CP$ violation with a significance of $5.0$ standard deviations for the first time in this mode.

Published
2024
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48. MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging

Author

Codella, Noel C. F., Jin, Ying, Jain, Shrey, Gu, Yu, Lee, Ho Hin, Abacha, Asma Ben, Santamaria-Pang, Alberto, Guyman, Will, Sangani, Naiteek, Zhang, Sheng, Poon, Hoifung, Hyland, Stephanie, Bannur, Shruthi, Alvarez-Valle, Javier, Li, Xue, Garrett, John, McMillan, Alan, Rajguru, Gaurav, Maddi, Madhu, Vijayrania, Nilesh, Bhimai, Rehaan, Mecklenburg, Nick, Jain, Rupal, Holstein, Daniel, Gaur, Naveen, Aski, Vijay, Hwang, Jenq-Neng, Lin, Thomas, Tarapov, Ivan, Lungren, Matthew, and Wei, Mu

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

In this work, we present MedImageInsight, an open-source medical imaging embedding model. MedImageInsight is trained on medical images with associated text and labels across a diverse collection of domains, including X-Ray, CT, MRI, dermoscopy, OCT, fundus photography, ultrasound, histopathology, and mammography. Rigorous evaluations demonstrate MedImageInsight's ability to achieve state-of-the-art (SOTA) or human expert level performance across classification, image-image search, and fine-tuning tasks. Specifically, on public datasets, MedImageInsight achieves SOTA in CT 3D medical image retrieval, as well as SOTA in disease classification and search for chest X-ray, dermatology, and OCT imaging. Furthermore, MedImageInsight achieves human expert performance in bone age estimation (on both public and partner data), as well as AUC above 0.9 in most other domains. When paired with a text decoder, MedImageInsight achieves near SOTA level single image report findings generation with less than 10\% the parameters of other models. Compared to fine-tuning GPT-4o with only MIMIC-CXR data for the same task, MedImageInsight outperforms in clinical metrics, but underperforms on lexical metrics where GPT-4o sets a new SOTA. Importantly for regulatory purposes, MedImageInsight can generate ROC curves, adjust sensitivity and specificity based on clinical need, and provide evidence-based decision support through image-image search (which can also enable retrieval augmented generation). In an independent clinical evaluation of image-image search in chest X-ray, MedImageInsight outperformed every other publicly available foundation model evaluated by large margins (over 6 points AUC), and significantly outperformed other models in terms of AI fairness (across age and gender). We hope releasing MedImageInsight will help enhance collective progress in medical imaging AI research and development.

Published
2024

49. Omnigenous stellarator equilibria with enhanced stability

Author

Gaur, Rahul, Conlin, Rory, Dickinson, David, Parisi, Jason F., Dudt, Daniel, Panici, Dario, Kim, Patrick, Unalmis, Kaya, Dorland, William D., and Kolemen, Egemen

Subjects
Physics - Plasma Physics
Abstract

To build an economically viable stellarator, it is essential to find a configuration that satisfies a set of favorable properties to achieve efficient steady-state nuclear fusion. One such property is omnigenity, which ensures confinement of trapped particles. After creating an omnigenous equilibrium, one must also ensure reduced transport resulting from kinetic and magnetohydrodynamic (MHD) instabilities. This study introduces and leverages the GPU-accelerated DESC optimization suite, which is used to design stable high-$\beta$ omnigenous equilibria, achieving Mercier, ideal ballooning, and enhanced kinetic ballooning stability. We explain the link between ideal and kinetic ballooning modes and discover stellarators with second stability, a regime of large pressure gradient where an equilibria becomes ideal ballooning stable., Comment: Supplementary material appended after the appendix. 33 pages + 10 pages supplementary material, 13 figures, 4 tables

Published
2024

50. Textless Streaming Speech-to-Speech Translation using Semantic Speech Tokens

Author

Zhao, Jinzheng, Moritz, Niko, Lakomkin, Egor, Xie, Ruiming, Xiu, Zhiping, Zmolikova, Katerina, Ahmed, Zeeshan, Gaur, Yashesh, Le, Duc, and Fuegen, Christian

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Cascaded speech-to-speech translation systems often suffer from the error accumulation problem and high latency, which is a result of cascaded modules whose inference delays accumulate. In this paper, we propose a transducer-based speech translation model that outputs discrete speech tokens in a low-latency streaming fashion. This approach eliminates the need for generating text output first, followed by machine translation (MT) and text-to-speech (TTS) systems. The produced speech tokens can be directly used to generate a speech signal with low latency by utilizing an acoustic language model (LM) to obtain acoustic tokens and an audio codec model to retrieve the waveform. Experimental results show that the proposed method outperforms other existing approaches and achieves state-of-the-art results for streaming translation in terms of BLEU, average latency, and BLASER 2.0 scores for multiple language pairs using the CVSS-C dataset as a benchmark., Comment: Submitted to ICASSP 2025

Published
2024

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