Search

Your search keyword '"Davit, A"' showing total 7,451 results
Start Over Author "Davit, A"
7,451 results on '"Davit, A"'

1. The Impact of Socio-Economic Challenges and Technological Progress on Economic Inequality: An Estimation with the Perelman Model and Ricci Flow Methods

Author

Gondauri, Davit

Subjects
Economics - Econometrics, Mathematics - Differential Geometry
Abstract

The article examines the impact of 16 key parameters of the Georgian economy on economic inequality, using the Perelman model and Ricci flow mathematical methods. The study aims to conduct a deep analysis of the impact of socio-economic challenges and technological progress on the dynamics of the Gini coefficient. The article examines the following parameters: income distribution, productivity (GDP per hour), unemployment rate, investment rate, inflation rate, migration (net negative), education level, social mobility, trade infrastructure, capital flows, innovative activities, access to healthcare, fiscal policy (budget deficit), international trade (turnover relative to GDP), social protection programs, and technological access. The results of the study confirm that technological innovations and social protection programs have a positive impact on reducing inequality. Productivity growth, improving the quality of education, and strengthening R&D investments increase the possibility of inclusive development. Sensitivity analysis shows that social mobility and infrastructure are important factors that affect economic stability. The accuracy of the model is confirmed by high R^2 values (80-90%) and the statistical reliability of the Z-statistic (<0.05). The study uses Ricci flow methods, which allow for a geometric analysis of the transformation of economic parameters in time and space. Recommendations include the strategic introduction of technological progress, the expansion of social protection programs, improving the quality of education, and encouraging international trade, which will contribute to economic sustainability and reduce inequality. The article highlights multifaceted approaches that combine technological innovation and responses to socio-economic challenges to ensure sustainable and inclusive economic development.

Published
2025
Full Text
View/download PDF

2. Impact of R&D and AI Investments on Economic Growth and Credit Rating

Author

Gondauri, Davit and Mikautadze, Ekaterine

Subjects
Economics - Econometrics
Abstract

The research and development (R&D) phase is essential for fostering innovation and aligns with long-term strategies in both public and private sectors. This study addresses two primary research questions: (1) assessing the relationship between R&D investments and GDP through regression analysis, and (2) estimating the economic value added (EVA) that Georgia must generate to progress from a BB to a BBB credit rating. Using World Bank data from 2014-2022, this analysis found that increasing R&D, with an emphasis on AI, by 30-35% has a measurable impact on GDP. Regression results reveal a coefficient of 7.02%, indicating a 10% increase in R&D leads to a 0.70% GDP rise, with an 81.1% determination coefficient and a strong 90.1% correlation. Georgia's EVA model was calculated to determine the additional value needed for a BBB rating, comparing indicators from Greece, Hungary, India, and Kazakhstan as benchmarks. Key economic indicators considered were nominal GDP, GDP per capita, real GDP growth, and fiscal indicators (government balance/GDP, debt/GDP). The EVA model projects that to achieve a BBB rating within nine years, Georgia requires $61.7 billion in investments. Utilizing EVA and comprehensive economic indicators will support informed decision-making and enhance the analysis of Georgia's economic trajectory.

Published
2024
Full Text
View/download PDF

3. Anisotropic van der Waals Crystal with High Refractive Index and Transparency for UV-Visible Range Applications

Author

Slavich, Aleksandr, Ermolaev, Georgy, Zavidovskiy, Ilya, Grudinin, Dmitriy, Kravtsov, Konstantin, Tatmyshevskiy, Mikhail, Mironov, Mikhail, Toksumakov, Adilet, Tselikov, Gleb, Fradkin, Ilia, Voronin, Kirill, Povolotskiy, Maksim, Matveeva, Olga, Syuy, Alexander, Yakubovsky, Dmitry, Tsymbarenko, Dmitry, Kruglov, Ivan, Ghazaryan, Davit, Novikov, Sergey, Vyshnevyy, Andrey, Arsenin, Aleksey, Volkov, Valentyn, and Novoselov, Kostya

Subjects
Physics - Optics, Condensed Matter - Materials Science
Abstract

Thanks to their record high refractive index and giant optical anisotropy, van der Waals (vdW) materials have accelerated the development of nanophotonics. However, traditional high refractive index materials, such as titanium dioxide (TiO2), still dominate in the most important visible range. This is due to the current lack of transparent vdW materials across the entire visible spectrum. In this context, we propose that germanium disulfide (GeS2) could offer a significant breakthrough. With its high refractive index, negligible losses, and biaxial optical anisotropy across the whole visible range, GeS2 has the potential to complement TiO2 and close the application gap of vdW materials in the visible spectrum. The addition of GeS2 could have a profound impact on the design of van der Waals nanophotonic circuits for any operation wavelength from ultraviolet to infrared, emphasizing the significance of the potential impact of GeS2 on the field of nanophotonics., Comment: 15 pages, 4 figures

Published
2024

4. Performance of Parity QAOA for the Signed Max-Cut Problem

Author

Weidinger, Anita, Mbeng, Glen Bigan, Fellner, Michael, Khachatryan, Davit, and Lechner, Wolfgang

Subjects
Quantum Physics
Abstract

The practical implementation of quantum optimization algorithms on noisy intermediate-scale quantum devices requires accounting for their limited connectivity. As such, the Parity architecture was introduced to overcome this limitation by encoding binary optimization problems onto planar quantum chips. We investigate the performance of the Quantum Approximate Optimization Algorithm on the Parity architecture (Parity QAOA) for solving instances of the signed Max-Cut problem on complete and regular graphs. By comparing the algorithms at fixed circuit depth, we demonstrate that Parity QAOA outperforms conventional QAOA implementations based on SWAP networks. Our analysis utilizes Clifford circuits to estimate lower performance bounds for Parity QAOA for problem sizes that would be otherwise inaccessible on classical computers. For single layer circuits we additionally benchmark the recursive variant of the two algorithms, showing that their performance is equal.

Published
2024

5. Speaker Tagging Correction With Non-Autoregressive Language Models

Author

Kirakosyan, Grigor and Karamyan, Davit

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing, 68T01 General topics in artificial intelligence
Abstract

Speech applications dealing with conversations require not only recognizing the spoken words but also determining who spoke when. The task of assigning words to speakers is typically addressed by merging the outputs of two separate systems, namely, an automatic speech recognition (ASR) system and a speaker diarization (SD) system. In practical settings, speaker diarization systems can experience significant degradation in performance due to a variety of factors, including uniform segmentation with a high temporal resolution, inaccurate word timestamps, incorrect clustering and estimation of speaker numbers, as well as background noise. Therefore, it is important to automatically detect errors and make corrections if possible. We used a second-pass speaker tagging correction system based on a non-autoregressive language model to correct mistakes in words placed at the borders of sentences spoken by different speakers. We first show that the employed error correction approach leads to reductions in word diarization error rate (WDER) on two datasets: TAL and test set of Fisher. Additionally, we evaluated our system in the Post-ASR Speaker Tagging Correction challenge and observed significant improvements in cpWER compared to baseline methods., Comment: 6 pages, 7 tables

Published
2024

6. Lattice-tunable substituted iron garnets for low-temperature magnonics

Author

Legrand, William, Kemna, Yana, Schären, Stefan, Wang, Hanchen, Petrosyan, Davit, Siegl, Luise, Schlitz, Richard, Lammel, Michaela, and Gambardella, Pietro

Subjects
Condensed Matter - Materials Science, Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

The magnetic resonance of iron garnets is at the heart of rich physics and provides a crucial technology used inside microwave components. A barrier still stands in the way of integrated cryogenic devices with iron garnets, because their epitaxy requires paramagnetic substrates that cause large microwave losses at low temperatures. Finding alternative combinations of substrates and magnetic garnets is thus essential to enabling cryogenic integrated magnonics. With this motivation, we develop a new approach to the synthesis of epitaxial garnet films, based on the co-sputtering of binary oxides, enabling a precise and continuous tuning of the iron garnet composition. By substituting a controlled proportion of the iron with additional yttrium, we obtain Y$_{3}$(Y$_{x}$Fe$_{5-x}$)O$_{12}$ films of high crystalline quality that can be lattice-matched to yttrium scandium gallium garnet (YSGG), a diamagnetic substrate suitable for low-temperature applications. Ferromagnetic resonance performed at low temperatures confirms the elimination of substrate paramagnetism losses on YSGG. We also show that the Y$_{3}$(Y$_{x}$Fe$_{5-x}$)O$_{12}$ system can be matched to other substrates, such as gadolinium yttrium scandium gallium garnet and gadolinium scandium gallium garnet. Additionally, we demonstrate that Bi-substituted films of (Bi$_{0.8}$Y$_{2.2}$)Fe$_{5}$O$_{12}$ also offer ideal lattice matching to YSGG for low-temperature magnetic resonance. The present approach of controlled substitutions in iron garnets to obtain ideal lattice matching with diamagnetic substrates opens a very promising pathway to magnonic devices operating in low-temperature environments., Comment: Main: 18 pages, 6 figures; Supplement: 9 pages, 6 figures

Published
2024

7. StackRAG Agent: Improving Developer Answers with Retrieval-Augmented Generation

Author

Abrahamyan, Davit and Fard, Fatemeh H.

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

Developers spend much time finding information that is relevant to their questions. Stack Overflow has been the leading resource, and with the advent of Large Language Models (LLMs), generative models such as ChatGPT are used frequently. However, there is a catch in using each one separately. Searching for answers is time-consuming and tedious, as shown by the many tools developed by researchers to address this issue. On the other, using LLMs is not reliable, as they might produce irrelevant or unreliable answers (i.e., hallucination). In this work, we present StackRAG, a retrieval-augmented Multiagent generation tool based on LLMs that combines the two worlds: aggregating the knowledge from SO to enhance the reliability of the generated answers. Initial evaluations show that the generated answers are correct, accurate, relevant, and useful.

Published
2024

8. OPTune: Efficient Online Preference Tuning

Author

Chen, Lichang, Chen, Jiuhai, Liu, Chenxi, Kirchenbauer, John, Soselia, Davit, Zhu, Chen, Goldstein, Tom, Zhou, Tianyi, and Huang, Heng

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language
Abstract

Reinforcement learning with human feedback~(RLHF) is critical for aligning Large Language Models (LLMs) with human preference. Compared to the widely studied offline version of RLHF, \emph{e.g.} direct preference optimization (DPO), recent works have shown that the online variants achieve even better alignment. However, online alignment requires on-the-fly generation of new training data, which is costly, hard to parallelize, and suffers from varying quality and utility. In this paper, we propose a more efficient data exploration strategy for online preference tuning (OPTune), which does not rely on human-curated or pre-collected teacher responses but dynamically samples informative responses for on-policy preference alignment. During data generation, OPTune only selects prompts whose (re)generated responses can potentially provide more informative and higher-quality training signals than the existing responses. In the training objective, OPTune reweights each generated response (pair) by its utility in improving the alignment so that learning can be focused on the most helpful samples. Throughout our evaluations, OPTune'd LLMs maintain the instruction-following benefits provided by standard preference tuning whilst enjoying 1.27-1.56x faster training speed due to the efficient data exploration strategy., Comment: 16 pages, 7 figures

Published
2024

9. A Novel Two-stage Deming Regression Framework with Applications to Association Analysis between Clinical Risks

Author

Duan, Yajie, Cabrera, Javier, and Sargsyan, Davit

Subjects
Statistics - Applications
Abstract

In healthcare, clinical risks are crucial for treatment decisions, yet the analysis of their associations is often overlooked. This gap is particularly significant when balancing risks that are weighed against each other, as in the case of atrial fibrillation (AF) patients facing stroke and bleeding risks with anticoagulant medication. While traditional regression models are ill-suited for this task due to standard errors in risk estimation, a novel two-stage Deming regression framework is proposed to address this issue, offering a more accurate tool for analyzing associations between variables observed with errors of known or estimated variances. The first stage is to obtain the variable values with variances of errors either by estimation or observation, followed by the second stage that fits a Deming regression model potentially subject to a transformation. The second stage accounts for the uncertainties associated with both independent and response variables, including known or estimated variances and additional unknown variances from the model. The complexity arising from different scenarios of uncertainty is handled by existing and advanced variations of Deming regression models. An important practical application is to support personalized treatment recommendations based on clinical risk associations that were identified by the proposed framework. The model's effectiveness is demonstrated by applying it to a real-world dataset of AF-diagnosed patients to explore the relationship between stroke and bleeding risks, providing crucial guidance for making informed decisions regarding anticoagulant medication. Furthermore, the model's versatility in addressing data containing multiple sources of uncertainty such as privacy-protected data suggests promising avenues for future research in regression analysis.

Published
2024

10. Prediction of Energy Resolution in the JUNO Experiment

Author

JUNO Collaboration, Abusleme, Angel, Adam, Thomas, Adamowicz, Kai, Ahmad, Shakeel, Ahmed, Rizwan, Aiello, Sebastiano, An, Fengpeng, An, Qi, Andronico, Giuseppe, Anfimov, Nikolay, Antonelli, Vito, Antoshkina, Tatiana, de André, João Pedro Athayde Marcondes, Auguste, Didier, Bai, Weidong, Balashov, Nikita, Baldini, Wander, Barresi, Andrea, Basilico, Davide, Baussan, Eric, Bellato, Marco, Beretta, Marco, Bergnoli, Antonio, Bick, Daniel, Bieger, Lukas, Biktemerova, Svetlana, Birkenfeld, Thilo, Blake, Iwan, Blum, David, Blyth, Simon, Bolshakova, Anastasia, Bongrand, Mathieu, Bordereau, Clément, Breton, Dominique, Brigatti, Augusto, Brugnera, Riccardo, Bruno, Riccardo, Budano, Antonio, Busto, Jose, Cabrera, Anatael, Caccianiga, Barbara, Cai, Hao, Cai, Xiao, Cai, Yanke, Cai, Zhiyan, Callier, Stéphane, Calvez, Steven, Cammi, Antonio, Campeny, Agustin, Cao, Chuanya, Cao, Guofu, Cao, Jun, Caruso, Rossella, Cerna, Cédric, Cerrone, Vanessa, Chang, Jinfan, Chang, Yun, Chatrabhuti, Auttakit, Chen, Chao, Chen, Guoming, Chen, Pingping, Chen, Shaomin, Chen, Xin, Chen, Yiming, Chen, Yixue, Chen, Yu, Chen, Zelin, Chen, Zhangming, Chen, Zhiyuan, Chen, Zikang, Cheng, Jie, Cheng, Yaping, Cheng, Yu Chin, Chepurnov, Alexander, Chetverikov, Alexey, Chiesa, Davide, Chimenti, Pietro, Chin, Yen-Ting, Chou, Po-Lin, Chu, Ziliang, Chukanov, Artem, Claverie, Gérard, Clementi, Catia, Clerbaux, Barbara, Molla, Marta Colomer, Di Lorenzo, Selma Conforti, Coppi, Alberto, Corti, Daniele, Csakli, Simon, Cui, Chenyang, Corso, Flavio Dal, Dalager, Olivia, Datta, Jaydeep, De La Taille, Christophe, Deng, Zhi, Deng, Ziyan, Ding, Xiaoyu, Ding, Xuefeng, Ding, Yayun, Dirgantara, Bayu, Dittrich, Carsten, Dmitrievsky, Sergey, Dohnal, Tadeas, Dolzhikov, Dmitry, Donchenko, Georgy, Dong, Jianmeng, Doroshkevich, Evgeny, Dou, Wei, Dracos, Marcos, Druillole, Frédéric, Du, Ran, Du, Shuxian, Dugas, Katherine, Dusini, Stefano, Duyang, Hongyue, Eck, Jessica, Enqvist, Timo, Fabbri, Andrea, Fahrendholz, Ulrike, Fan, Lei, Fang, Jian, Fang, Wenxing, Fedoseev, Dmitry, Feng, Li-Cheng, Feng, Qichun, Ferraro, Federico, Fournier, Amélie, Fritsch, Fritsch, Gan, Haonan, Gao, Feng, Garfagnini, Alberto, Gavrikov, Arsenii, Giammarchi, Marco, Giudice, Nunzio, Gonchar, Maxim, Gong, Guanghua, Gong, Hui, Gornushkin, Yuri, Grassi, Marco, Gromov, Maxim, Gromov, Vasily, Gu, Minghao, Gu, Xiaofei, Gu, Yu, Guan, Mengyun, Guan, Yuduo, Guardone, Nunzio, Guizzetti, Rosa Maria, Guo, Cong, Guo, Wanlei, Hagner, Caren, Han, Hechong, Han, Ran, Han, Yang, He, Miao, He, Wei, He, Xinhai, Heinz, Tobias, Hellmuth, Patrick, Heng, Yuekun, Herrera, Rafael, Hor, YuenKeung, Hou, Shaojing, Hsiung, Yee, Hu, Bei-Zhen, Hu, Hang, Hu, Jun, Hu, Peng, Hu, Shouyang, Hu, Tao, Hu, Yuxiang, Hu, Zhuojun, Huang, Guihong, Huang, Hanxiong, Huang, Jinhao, Huang, Junting, Huang, Kaixuan, Huang, Shengheng, Huang, Wenhao, Huang, Xin, Huang, Xingtao, Huang, Yongbo, Hui, Jiaqi, Huo, Lei, Huo, Wenju, Huss, Cédric, Hussain, Safeer, Imbert, Leonard, Ioannisian, Ara, Isocrate, Roberto, Jafar, Arshak, Jelmini, Beatrice, Jeria, Ignacio, Ji, Xiaolu, Jia, Huihui, Jia, Junji, Jian, Siyu, Jiang, Cailian, Jiang, Di, Jiang, Wei, Jiang, Xiaoshan, Jiang, Xiaozhao, Jiang, Yixuan, Jing, Xiaoping, Jollet, Cécile, Kang, Li, Karaparabil, Rebin, Kazarian, Narine, Khan, Ali, Khatun, Amina, Khosonthongkee, Khanchai, Korablev, Denis, Kouzakov, Konstantin, Krasnoperov, Alexey, Kuleshov, Sergey, Kumaran, Sindhujha, Kutovskiy, Nikolay, Labit, Loïc, Lachenmaier, Tobias, Landini, Cecilia, Leblanc, Sébastien, Lefevre, Frederic, Lei, Ruiting, Leitner, Rupert, Leung, Jason, Li, Demin, Li, Fei, Li, Fule, Li, Gaosong, Li, Hongjian, Li, Jiajun, Li, Min, Li, Nan, Li, Qingjiang, Li, Ruhui, Li, Rui, Li, Shanfeng, Li, Shuo, Li, Tao, Li, Teng, Li, Weidong, Li, Weiguo, Li, Xiaomei, Li, Xiaonan, Li, Xinglong, Li, Yi, Li, Yichen, Li, Yufeng, Li, Zhaohan, Li, Zhibing, Li, Ziyuan, Li, Zonghai, Liang, Hao, Liao, Jiajun, Liao, Yilin, Liao, Yuzhong, Limphirat, Ayut, Lin, Guey-Lin, Lin, Shengxin, Lin, Tao, Ling, Jiajie, Ling, Xin, Lippi, Ivano, Liu, Caimei, Liu, Fang, Liu, Fengcheng, Liu, Haidong, Liu, Haotian, Liu, Hongbang, Liu, Hongjuan, Liu, Hongtao, Liu, Hongyang, Liu, Jianglai, Liu, Jiaxi, Liu, Jinchang, Liu, Min, Liu, Qian, Liu, Qin, Liu, Runxuan, Liu, Shenghui, Liu, Shubin, Liu, Shulin, Liu, Xiaowei, Liu, Xiwen, Liu, Xuewei, Liu, Yankai, Liu, Zhen, Loi, Lorenzo, Lokhov, Alexey, Lombardi, Paolo, Lombardo, Claudio, Loo, Kai, Lu, Chuan, Lu, Haoqi, Lu, Jingbin, Lu, Junguang, Lu, Meishu, Lu, Peizhi, Lu, Shuxiang, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Ludhova, Livia, Lukanov, Arslan, Luo, Fengjiao, Luo, Guang, Luo, Jianyi, Luo, Shu, Luo, Wuming, Luo, Xiaojie, Lyashuk, Vladimir, Ma, Bangzheng, Ma, Bing, Ma, Qiumei, Ma, Si, Ma, Xiaoyan, Ma, Xubo, Maalmi, Jihane, Mai, Jingyu, Malabarba, Marco, Malyshkin, Yury, Mandujano, Roberto Carlos, Mantovani, Fabio, Mao, Xin, Mao, Yajun, Mari, Stefano M., Marini, Filippo, Martini, Agnese, Mayer, Matthias, Mayilyan, Davit, Mednieks, Ints, Meng, Yue, Meraviglia, Anita, Meregaglia, Anselmo, Meroni, Emanuela, Miramonti, Lino, Mohan, Nikhil, Montuschi, Michele, Müller, Axel, Nastasi, Massimiliano, Naumov, Dmitry V., Naumova, Elena, Navas-Nicolas, Diana, Nemchenok, Igor, Thi, Minh Thuan Nguyen, Nikolaev, Alexey, Ning, Feipeng, Ning, Zhe, Nunokawa, Hiroshi, Oberauer, Lothar, Ochoa-Ricoux, Juan Pedro, Olshevskiy, Alexander, Orestano, Domizia, Ortica, Fausto, Othegraven, Rainer, Paoloni, Alessandro, Parker, George, Parmeggiano, Sergio, Patsias, Achilleas, Pei, Yatian, Pelicci, Luca, Peng, Anguo, Peng, Haiping, Peng, Yu, Peng, Zhaoyuan, Percalli, Elisa, Perrin, Willy, Perrot, Frédéric, Petitjean, Pierre-Alexandre, Petrucci, Fabrizio, Pilarczyk, Oliver, Rico, Luis Felipe Piñeres, Popov, Artyom, Poussot, Pascal, Previtali, Ezio, Qi, Fazhi, Qi, Ming, Qi, Xiaohui, Qian, Sen, Qian, Xiaohui, Qian, Zhen, Qiao, Hao, Qin, Zhonghua, Qiu, Shoukang, Qu, Manhao, Qu, Zhenning, Ranucci, Gioacchino, Rasheed, Reem, Re, Alessandra, Rebii, Abdel, Redchuk, Mariia, Reina, Gioele, Ren, Bin, Ren, Jie, Ren, Yuhan, Ricci, Barbara, Rientong, Komkrit, Rifai, Mariam, Roche, Mathieu, Rodphai, Narongkiat, Romani, Aldo, Roskovec, Bedřich, Ruan, Xichao, Rybnikov, Arseniy, Sadovsky, Andrey, Saggese, Paolo, Sandanayake, Deshan, Sangka, Anut, Sava, Giuseppe, Sawangwit, Utane, Schever, Michaela, Schwab, Cédric, Schweizer, Konstantin, Selyunin, Alexandr, Serafini, Andrea, Settimo, Mariangela, Shao, Junyu, Sharov, Vladislav, Shi, Hexi, Shi, Jingyan, Shi, Yanan, Shutov, Vitaly, Sidorenkov, Andrey, Šimkovic, Fedor, Singhal, Apeksha, Sirignano, Chiara, Siripak, Jaruchit, Sisti, Monica, Smirnov, Mikhail, Smirnov, Oleg, Sokolov, Sergey, Songwadhana, Julanan, Soonthornthum, Boonrucksar, Sotnikov, Albert, Sreethawong, Warintorn, Stahl, Achim, Stanco, Luca, Stankevich, Konstantin, Steiger, Hans, Steinmann, Jochen, Sterr, Tobias, Stock, Matthias Raphael, Strati, Virginia, Strizh, Michail, Studenikin, Alexander, Su, Aoqi, Su, Jun, Sun, Shifeng, Sun, Xilei, Sun, Yongjie, Sun, Yongzhao, Sun, Zhengyang, Suwonjandee, Narumon, Takenaka, Akira, Tan, Xiaohan, Tang, Jian, Tang, Jingzhe, Tang, Qiang, Tang, Quan, Tang, Xiao, Hariharan, Vidhya Thara, Tietzsch, Alexander, Tkachev, Igor, Tmej, Tomas, Torri, Marco Danilo Claudio, Triossi, Andrea, Triozzi, Riccardo, Trzaska, Wladyslaw, Tung, Yu-Chen, Tuve, Cristina, Ushakov, Nikita, Vedin, Vadim, Venettacci, Carlo, Verde, Giuseppe, Vialkov, Maxim, Viaud, Benoit, Vollbrecht, Cornelius Moritz, von Sturm, Katharina, Vorobel, Vit, Voronin, Dmitriy, Votano, Lucia, Walker, Pablo, Wang, Caishen, Wang, Chung-Hsiang, Wang, En, Wang, Guoli, Wang, Jian, Wang, Jun, Wang, Li, Wang, Lu, Wang, Meng, Wang, Mingyuan, Wang, Ruiguang, Wang, Sibo, Wang, Siguang, Wang, Wei, Wang, Wenshuai, Wang, Xi, Wang, Xiangyue, Wang, Yangfu, Wang, Yaoguang, Wang, Yi, Wang, Yifang, Wang, Yuanqing, Wang, Yuyi, Wang, Zhe, Wang, Zheng, Wang, Zhimin, Watcharangkool, Apimook, Wei, Wei, Wei, Wenlu, Wei, Yadong, Wei, Yuehuan, Wen, Kaile, Wen, Liangjian, Weng, Jun, Wiebusch, Christopher, Wirth, Rosmarie, Wu, Chengxin, Wu, Diru, Wu, Qun, Wu, Yinhui, Wu, Yiyang, Wu, Zhi, Wurm, Michael, Wurtz, Jacques, Wysotzki, Christian, Xi, Yufei, Xia, Dongmei, Xian, Shishen, Xiao, Fei, Xiao, Xiang, Xie, Xiaochuan, Xie, Yijun, Xie, Yuguang, Xin, Zhao, Xing, Zhizhong, Xu, Benda, Xu, Cheng, Xu, Donglian, Xu, Fanrong, Xu, Hangkun, Xu, Jiayang, Xu, Jilei, Xu, Jing, Xu, Jinghuan, Xu, Meihang, Xu, Xunjie, Xu, Yin, Xu, Yu, Yan, Baojun, Yan, Qiyu, Yan, Taylor, Yan, Xiongbo, Yan, Yupeng, Yang, Changgen, Yang, Chengfeng, Yang, Fengfan, Yang, Jie, Yang, Lei, Yang, Pengfei, Yang, Xiaoyu, Yang, Yifan, Yang, Yixiang, Yang, Zekun, Yao, Haifeng, Ye, Jiaxuan, Ye, Mei, Ye, Ziping, Yermia, Frédéric, You, Zhengyun, Yu, Boxiang, Yu, Chiye, Yu, Chunxu, Yu, Guojun, Yu, Hongzhao, Yu, Miao, Yu, Xianghui, Yu, Zeyuan, Yu, Zezhong, Yuan, Cenxi, Yuan, Chengzhuo, Yuan, Ying, Yuan, Zhenxiong, Yue, Baobiao, Zafar, Noman, Zamogilnyi, Kirill, Zavadskyi, Vitalii, Zeng, Fanrui, Zeng, Shan, Zeng, Tingxuan, Zeng, Yuda, Zhan, Liang, Zhang, Aiqiang, Zhang, Bin, Zhang, Binting, Zhang, Feiyang, Zhang, Hangchang, Zhang, Haosen, Zhang, Honghao, Zhang, Jialiang, Zhang, Jiawen, Zhang, Jie, Zhang, Jingbo, Zhang, Jinnan, Zhang, Junwei, Zhang, Lei, Zhang, Peng, Zhang, Ping, Zhang, Qingmin, Zhang, Shiqi, Zhang, Shu, Zhang, Shuihan, Zhang, Siyuan, Zhang, Tao, Zhang, Xiaomei, Zhang, Xin, Zhang, Xuantong, Zhang, Yinhong, Zhang, Yiyu, Zhang, Yongpeng, Zhang, Yu, Zhang, Yuanyuan, Zhang, Yumei, Zhang, Zhenyu, Zhang, Zhijian, Zhao, Jie, Zhao, Rong, Zhao, Runze, Zhao, Shujun, Zhao, Tianhao, Zheng, Hua, Zheng, Yangheng, Zhou, Jing, Zhou, Li, Zhou, Nan, Zhou, Shun, Zhou, Tong, Zhou, Xiang, Zhu, Jingsen, Zhu, Kangfu, Zhu, Kejun, Zhu, Zhihang, Zhuang, Bo, Zhuang, Honglin, Zong, Liang, Zou, Jiaheng, and Züfle, Jan

Subjects
High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract

This paper presents an energy resolution study of the JUNO experiment, incorporating the latest knowledge acquired during the detector construction phase. The determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3\% at 1~MeV. To achieve this ambitious goal, significant efforts have been undertaken in the design and production of the key components of the JUNO detector. Various factors affecting the detection of inverse beta decay signals have an impact on the energy resolution, extending beyond the statistical fluctuations of the detected number of photons, such as the properties of the liquid scintillator, performance of photomultiplier tubes, and the energy reconstruction algorithm. To account for these effects, a full JUNO simulation and reconstruction approach is employed. This enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy resolution. The results of study reveal an energy resolution of 2.95\% at 1~MeV. Furthermore, this study assesses the contribution of major effects to the overall energy resolution budget. This analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data collection. Moreover, it provides a guideline for comprehending the energy resolution characteristics of liquid scintillator-based detectors.

Published
2024
Full Text
View/download PDF

11. JUNO Sensitivity to Invisible Decay Modes of Neutrons

Author

JUNO Collaboration, Abusleme, Angel, Adam, Thomas, Adamowicz, Kai, Ahmad, Shakeel, Ahmed, Rizwan, Aiello, Sebastiano, An, Fengpeng, An, Qi, Andronico, Giuseppe, Anfimov, Nikolay, Antonelli, Vito, Antoshkina, Tatiana, de André, João Pedro Athayde Marcondes, Auguste, Didier, Bai, Weidong, Balashov, Nikita, Baldini, Wander, Barresi, Andrea, Basilico, Davide, Baussan, Eric, Bellato, Marco, Beretta, Marco, Bergnoli, Antonio, Bick, Daniel, Bieger, Lukas, Biktemerova, Svetlana, Birkenfeld, Thilo, Blake, Iwan, Blyth, Simon, Bolshakova, Anastasia, Bongrand, Mathieu, Breton, Dominique, Brigatti, Augusto, Brugnera, Riccardo, Bruno, Riccardo, Budano, Antonio, Busto, Jose, Cabrera, Anatael, Caccianiga, Barbara, Cai, Hao, Cai, Xiao, Cai, Yanke, Cai, Zhiyan, Callier, Stéphane, Calvez, Steven, Cammi, Antonio, Campeny, Agustin, Cao, Chuanya, Cao, Guofu, Cao, Jun, Caruso, Rossella, Cerna, Cédric, Cerrone, Vanessa, Chang, Jinfan, Chang, Yun, Chatrabhuti, Auttakit, Chen, Chao, Chen, Guoming, Chen, Pingping, Chen, Shaomin, Chen, Xin, Chen, Yiming, Chen, Yixue, Chen, Yu, Chen, Zelin, Chen, Zhangming, Chen, Zhiyuan, Chen, Zikang, Cheng, Jie, Cheng, Yaping, Cheng, Yu Chin, Chepurnov, Alexander, Chetverikov, Alexey, Chiesa, Davide, Chimenti, Pietro, Chin, Yen-Ting, Chou, Po-Lin, Chu, Ziliang, Chukanov, Artem, Claverie, Gérard, Clementi, Catia, Clerbaux, Barbara, Molla, Marta Colomer, Di Lorenzo, Selma Conforti, Coppi, Alberto, Corti, Daniele, Csakli, Simon, Cui, Chenyang, Corso, Flavio Dal, Dalager, Olivia, Datta, Jaydeep, De La Taille, Christophe, Deng, Zhi, Deng, Ziyan, Ding, Xiaoyu, Ding, Xuefeng, Ding, Yayun, Dirgantara, Bayu, Dittrich, Carsten, Dmitrievsky, Sergey, Dohnal, Tadeas, Dolzhikov, Dmitry, Donchenko, Georgy, Dong, Jianmeng, Doroshkevich, Evgeny, Dou, Wei, Dracos, Marcos, Druillole, Frédéric, Du, Ran, Du, Shuxian, Duan, Yujie, Dugas, Katherine, Dusini, Stefano, Duyang, Hongyue, Eck, Jessica, Enqvist, Timo, Fabbri, Andrea, Fahrendholz, Ulrike, Fan, Lei, Fang, Jian, Fang, Wenxing, Fedoseev, Dmitry, Feng, Li-Cheng, Feng, Qichun, Ferraro, Federico, Fournier, Amélie, Fritsch, Fritsch, Gan, Haonan, Gao, Feng, Garfagnini, Alberto, Gavrikov, Arsenii, Giammarchi, Marco, Giudice, Nunzio, Gonchar, Maxim, Gong, Guanghua, Gong, Hui, Gornushkin, Yuri, Grassi, Marco, Gromov, Maxim, Gromov, Vasily, Gu, Minghao, Gu, Xiaofei, Gu, Yu, Guan, Mengyun, Guan, Yuduo, Guardone, Nunzio, Guizzetti, Rosa Maria, Guo, Cong, Guo, Wanlei, Hagner, Caren, Han, Hechong, Han, Ran, Han, Yang, He, Jinhong, He, Miao, He, Wei, He, Xinhai, Heinz, Tobias, Hellmuth, Patrick, Heng, Yuekun, Herrera, Rafael, Hor, YuenKeung, Hou, Shaojing, Hsiung, Yee, Hu, Bei-Zhen, Hu, Hang, Hu, Jun, Hu, Peng, Hu, Shouyang, Hu, Tao, Hu, Yuxiang, Hu, Zhuojun, Huang, Guihong, Huang, Hanxiong, Huang, Jinhao, Huang, Junting, Huang, Kaixuan, Huang, Shengheng, Huang, Wenhao, Huang, Xin, Huang, Xingtao, Huang, Yongbo, Hui, Jiaqi, Huo, Lei, Huo, Wenju, Huss, Cédric, Hussain, Safeer, Imbert, Leonard, Ioannisian, Ara, Isocrate, Roberto, Jafar, Arshak, Jelmini, Beatrice, Jeria, Ignacio, Ji, Xiaolu, Jia, Huihui, Jia, Junji, Jian, Siyu, Jiang, Cailian, Jiang, Di, Jiang, Guangzheng, Jiang, Wei, Jiang, Xiaoshan, Jiang, Xiaozhao, Jiang, Yixuan, Jing, Xiaoping, Jollet, Cécile, Kang, Li, Karaparabil, Rebin, Kazarian, Narine, Khan, Ali, Khatun, Amina, Khosonthongkee, Khanchai, Korablev, Denis, Kouzakov, Konstantin, Krasnoperov, Alexey, Kuleshov, Sergey, Kumaran, Sindhujha, Kutovskiy, Nikolay, Labit, Loïc, Lachenmaier, Tobias, Lai, Haojing, Landini, Cecilia, Leblanc, Sébastien, Lefevre, Frederic, Lei, Ruiting, Leitner, Rupert, Leung, Jason, Li, Demin, Li, Fei, Li, Fule, Li, Gaosong, Li, Hongjian, Li, Huang, Li, Jiajun, Li, Min, Li, Nan, Li, Qingjiang, Li, Ruhui, Li, Rui, Li, Shanfeng, Li, Shuo, Li, Tao, Li, Teng, Li, Weidong, Li, Weiguo, Li, Xiaomei, Li, Xiaonan, Li, Xinglong, Li, Yi, Li, Yichen, Li, Yufeng, Li, Zhaohan, Li, Zhibing, Li, Ziyuan, Li, Zonghai, Liang, An-An, Liang, Hao, Liao, Jiajun, Liao, Yilin, Liao, Yuzhong, Limphirat, Ayut, Lin, Guey-Lin, Lin, Shengxin, Lin, Tao, Ling, Jiajie, Ling, Xin, Lippi, Ivano, Liu, Caimei, Liu, Fang, Liu, Fengcheng, Liu, Haidong, Liu, Haotian, Liu, Hongbang, Liu, Hongjuan, Liu, Hongtao, Liu, Hongyang, Liu, Jianglai, Liu, Jiaxi, Liu, Jinchang, Liu, Min, Liu, Qian, Liu, Qin, Liu, Runxuan, Liu, Shenghui, Liu, Shubin, Liu, Shulin, Liu, Xiaowei, Liu, Xiwen, Liu, Xuewei, Liu, Yankai, Liu, Zhen, Loi, Lorenzo, Lokhov, Alexey, Lombardi, Paolo, Lombardo, Claudio, Loo, Kai, Lu, Chuan, Lu, Haoqi, Lu, Jingbin, Lu, Junguang, Lu, Meishu, Lu, Peizhi, Lu, Shuxiang, Lu, Xianguo, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Ludhova, Livia, Lukanov, Arslan, Luo, Fengjiao, Luo, Guang, Luo, Jianyi, Luo, Shu, Luo, Wuming, Luo, Xiaojie, Lyashuk, Vladimir, Ma, Bangzheng, Ma, Bing, Ma, Qiumei, Ma, Si, Ma, Xiaoyan, Ma, Xubo, Maalmi, Jihane, Mai, Jingyu, Malabarba, Marco, Malyshkin, Yury, Mandujano, Roberto Carlos, Mantovani, Fabio, Mao, Xin, Mao, Yajun, Mari, Stefano M., Marini, Filippo, Martini, Agnese, Mayer, Matthias, Mayilyan, Davit, Mednieks, Ints, Meng, Yue, Meraviglia, Anita, Meregaglia, Anselmo, Meroni, Emanuela, Miramonti, Lino, Mohan, Nikhil, Montuschi, Michele, Reveco, Cristobal Morales, Nastasi, Massimiliano, Naumov, Dmitry V., Naumova, Elena, Navas-Nicolas, Diana, Nemchenok, Igor, Thi, Minh Thuan Nguyen, Nikolaev, Alexey, Ning, Feipeng, Ning, Zhe, Nunokawa, Hiroshi, Oberauer, Lothar, Ochoa-Ricoux, Juan Pedro, Olshevskiy, Alexander, Orestano, Domizia, Ortica, Fausto, Othegraven, Rainer, Paoloni, Alessandro, Parker, George, Parmeggiano, Sergio, Patsias, Achilleas, Pei, Yatian, Pelicci, Luca, Peng, Anguo, Peng, Haiping, Peng, Yu, Peng, Zhaoyuan, Percalli, Elisa, Perrin, Willy, Perrot, Frédéric, Petitjean, Pierre-Alexandre, Petrucci, Fabrizio, Pilarczyk, Oliver, Rico, Luis Felipe Piñeres, Popov, Artyom, Poussot, Pascal, Previtali, Ezio, Qi, Fazhi, Qi, Ming, Qi, Xiaohui, Qian, Sen, Qian, Xiaohui, Qian, Zhen, Qiao, Hao, Qin, Zhonghua, Qiu, Shoukang, Qu, Manhao, Qu, Zhenning, Ranucci, Gioacchino, Re, Alessandra, Rebii, Abdel, Redchuk, Mariia, Reina, Gioele, Ren, Bin, Ren, Jie, Ren, Yuhan, Ricci, Barbara, Rientong, Komkrit, Rifai, Mariam, Roche, Mathieu, Rodphai, Narongkiat, Romani, Aldo, Roskovec, Bedřich, Ruan, Xichao, Rybnikov, Arseniy, Sadovsky, Andrey, Saggese, Paolo, Sandanayake, Deshan, Sangka, Anut, Sava, Giuseppe, Sawangwit, Utane, Schever, Michaela, Schwab, Cédric, Schweizer, Konstantin, Selyunin, Alexandr, Serafini, Andrea, Settimo, Mariangela, Shao, Junyu, Sharov, Vladislav, Shi, Hexi, Shi, Jingyan, Shi, Yanan, Shutov, Vitaly, Sidorenkov, Andrey, Šimkovic, Fedor, Singhal, Apeksha, Sirignano, Chiara, Siripak, Jaruchit, Sisti, Monica, Smirnov, Mikhail, Smirnov, Oleg, Sokolov, Sergey, Songwadhana, Julanan, Soonthornthum, Boonrucksar, Sotnikov, Albert, Sreethawong, Warintorn, Stahl, Achim, Stanco, Luca, Stankevich, Konstantin, Steiger, Hans, Steinmann, Jochen, Sterr, Tobias, Stock, Matthias Raphael, Strati, Virginia, Strizh, Michail, Studenikin, Alexander, Su, Aoqi, Su, Jun, Sun, Guangbao, Sun, Shifeng, Sun, Xilei, Sun, Yongjie, Sun, Yongzhao, Sun, Zhengyang, Suwonjandee, Narumon, Takenaka, Akira, Tan, Xiaohan, Tang, Jian, Tang, Jingzhe, Tang, Qiang, Tang, Quan, Tang, Xiao, Hariharan, Vidhya Thara, Tkachev, Igor, Tmej, Tomas, Torri, Marco Danilo Claudio, Triossi, Andrea, Trzaska, Wladyslaw, Tung, Yu-Chen, Tuve, Cristina, Ushakov, Nikita, Vedin, Vadim, Venettacci, Carlo, Verde, Giuseppe, Vialkov, Maxim, Viaud, Benoit, Vollbrecht, Cornelius Moritz, von Sturm, Katharina, Vorobel, Vit, Voronin, Dmitriy, Votano, Lucia, Walker, Pablo, Wang, Caishen, Wang, Chung-Hsiang, Wang, En, Wang, Guoli, Wang, Hanwen, Wang, Jian, Wang, Jun, Wang, Li, Wang, Lu, Wang, Meng, Wang, Mingyuan, Wang, Qianchuan, Wang, Ruiguang, Wang, Sibo, Wang, Siguang, Wang, Wei, Wang, Wenshuai, Wang, Xi, Wang, Xiangyue, Wang, Yangfu, Wang, Yaoguang, Wang, Yi, Wang, Yifang, Wang, Yuanqing, Wang, Yuyi, Wang, Zhe, Wang, Zheng, Wang, Zhimin, Watcharangkool, Apimook, Wei, Wei, Wei, Wenlu, Wei, Yadong, Wei, Yuehuan, Wen, Liangjian, Weng, Jun, Wiebusch, Christopher, Wirth, Rosmarie, Wu, Chengxin, Wu, Diru, Wu, Qun, Wu, Yinhui, Wu, Yiyang, Wu, Zhi, Wurm, Michael, Wurtz, Jacques, Wysotzki, Christian, Xi, Yufei, Xia, Dongmei, Xian, Shishen, Xiang, Ziqian, Xiao, Fei, Xiao, Xiang, Xie, Xiaochuan, Xie, Yijun, Xie, Yuguang, Xin, Zhao, Xing, Zhizhong, Xu, Benda, Xu, Cheng, Xu, Donglian, Xu, Fanrong, Xu, Hangkun, Xu, Jiayang, Xu, Jilei, Xu, Jing, Xu, Jinghuan, Xu, Meihang, Xu, Xunjie, Xu, Yin, Xu, Yu, Yan, Baojun, Yan, Qiyu, Yan, Taylor, Yan, Xiongbo, Yan, Yupeng, Yang, Changgen, Yang, Chengfeng, Yang, Fengfan, Yang, Jie, Yang, Lei, Yang, Pengfei, Yang, Xiaoyu, Yang, Yifan, Yang, Yixiang, Yang, Zekun, Yao, Haifeng, Ye, Jiaxuan, Ye, Mei, Ye, Ziping, Yermia, Frédéric, You, Zhengyun, Yu, Boxiang, Yu, Chiye, Yu, Chunxu, Yu, Guojun, Yu, Hongzhao, Yu, Miao, Yu, Xianghui, Yu, Zeyuan, Yu, Zezhong, Yuan, Cenxi, Yuan, Chengzhuo, Yuan, Ying, Yuan, Zhenxiong, Yue, Baobiao, Zafar, Noman, Zamogilnyi, Kirill, Zavadskyi, Vitalii, Zeng, Fanrui, Zeng, Shan, Zeng, Tingxuan, Zeng, Yuda, Zhan, Liang, Zhang, Aiqiang, Zhang, Bin, Zhang, Binting, Zhang, Feiyang, Zhang, Hangchang, Zhang, Haosen, Zhang, Honghao, Zhang, Jialiang, Zhang, Jiawen, Zhang, Jie, Zhang, Jingbo, Zhang, Jinnan, Zhang, Junwei, Zhang, Lei, Zhang, Peng, Zhang, Ping, Zhang, Qingmin, Zhang, Shiqi, Zhang, Shu, Zhang, Shuihan, Zhang, Siyuan, Zhang, Tao, Zhang, Xiaomei, Zhang, Xin, Zhang, Xuantong, Zhang, Yibing, Zhang, Yinhong, Zhang, Yiyu, Zhang, Yongpeng, Zhang, Yu, Zhang, Yuanyuan, Zhang, Yumei, Zhang, Zhenyu, Zhang, Zhijian, Zhao, Jie, Zhao, Rong, Zhao, Runze, Zhao, Shujun, Zhao, Tianhao, Zheng, Hua, Zheng, Yangheng, Zhou, Jing, Zhou, Li, Zhou, Nan, Zhou, Shun, Zhou, Tong, Zhou, Xiang, Zhou, Xing, Zhu, Jingsen, Zhu, Kangfu, Zhu, Kejun, Zhu, Zhihang, Zhuang, Bo, Zhuang, Honglin, Zong, Liang, and Zou, Jiaheng

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

We explore the bound neutrons decay into invisible particles (e.g., $n\rightarrow 3 \nu$ or $nn \rightarrow 2 \nu$) in the JUNO liquid scintillator detector. The invisible decay includes two decay modes: $ n \rightarrow { inv} $ and $ nn \rightarrow { inv} $. The invisible decays of $s$-shell neutrons in $^{12}{\rm C}$ will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino $\bar{\nu}_e$, natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are $\tau/B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, {\rm yr}$ and $\tau/B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, {\rm yr}$., Comment: 28 pages, 7 figures, 4 tables

Published
2024

12. Is prolonged operative time associated with postoperative complications in liver surgery? An international multicentre cohort study of 5424 patients

Author

Kuemmerli, Christoph, Sijberden, Jasper P., Cipriani, Federica, Osei-Bordom, Daniel, Aghayan, Davit, Lanari, Jacopo, de Meyere, Celine, Cacciaguerra, Andrea Benedetti, Rotellar, Fernando, Fuks, David, Liu, Rong, Besselink, Marc G., Zimmitti, Giuseppe, Ruzzenente, Andrea, di Benedetto, Fabrizio, Succandy, Iswanto, Efanov, Mikhail, Memeo, Riccardo, Jovine, Elio, Vrochides, Dionisios, Dagher, Ibrahim, Croner, Roland, Lopez-Ben, Santi, Geller, David, Ahmad, Jawad, Gallagher, Tom, White, Steven, Alseidi, Adnan, Goh, Brian K. P., Sparrelid, Ernesto, Ratti, Francesca, Marudanayagam, Ravi, Fretland, Åsmund Avdem, Vivarelli, Marco, D’Hondt, Mathieu, Cillo, Umberto, Edwin, Bjørn, Sutcliffe, Robert P., Aldrighetti, Luca A., and Hilal, Mohammed Abu

Published
2024
Full Text
View/download PDF

13. Homonym Sense Disambiguation in the Georgian Language

Author

Melikidze, Davit and Gamkrelidze, Alexander

Subjects
Computer Science - Computation and Language, Computer Science - Machine Learning
Abstract

This research proposes a novel approach to the Word Sense Disambiguation (WSD) task in the Georgian language, based on supervised fine-tuning of a pre-trained Large Language Model (LLM) on a dataset formed by filtering the Georgian Common Crawls corpus. The dataset is used to train a classifier for words with multiple senses. Additionally, we present experimental results of using LSTM for WSD. Accurately disambiguating homonyms is crucial in natural language processing. Georgian, an agglutinative language belonging to the Kartvelian language family, presents unique challenges in this context. The aim of this paper is to highlight the specific problems concerning homonym disambiguation in the Georgian language and to present our approach to solving them. The techniques discussed in the article achieve 95% accuracy for predicting lexical meanings of homonyms using a hand-classified dataset of over 7500 sentences.

Published
2024

14. Optimizing Surgical Plans for Parenchyma-Sparing Liver Resections through Contour-Guided Resection and Surface Approximation

Author

d'Albenzio, Gabriella, Meng, Ruoyan, Aghayan, Davit, Pelanis, Egidijus, Hisey, Rebecca, Drejian, Sarkis, Fretland, Åsmund Avdem, Elle, Ole Jakob, Edwin, Bjørn, and Palomar, Rafael

Subjects
Physics - Medical Physics, Computer Science - Graphics
Abstract

Objective: This study introduces a novel method for defining virtual resections in liver cancer surgery, aimed at enhancing the adaptability of parenchyma-sparing resection (PSR) plans. By comparing these with traditional anatomical resection (AR) plans, we explore the potential for optimization in surgical planning. Methods: Leveraging contours and spline surface approximations directly from the liver's surface, our method aligns closely with actual surgical procedures, offering a more realistic representation of curved resection paths. This technique, tested against 14 cases from the OSLO-COMET study, incorporates surface deformation for versatile plan modeling, comparing volumetric outcomes of PSR and AR. Results: The study highlights significant benefits of PSR over AR, including reduced resected volume ($32.71 \pm 13.80$ ml for PSR vs. $249.53 \pm 135.23$ ml for AR, $p <0.0001$) and higher remnant liver volume ($1922.77 \pm 442.86$ ml for PSR vs. $1716.87 \pm 403.00$ ml for AR, $p <0.0001$). PSR also showed a considerably higher remnant percentage ($98.16 \pm 0.81%$) compared to AR ($87.40 \pm 6.49%$, $p <0.0001$). Conclusion: The proposed approach is able to define virtual resections accommodating a wide variety of resections (i.e., PSR and AR). Careful surgical planning using virtual resections can optimize the resection strategy. Significance: This study presents a novel computer-aided planning system for liver surgery, demonstrating its efficacy and flexibility for definition of virtual resections. Virtual surgery planning can be used for optimization of resection strategies leading to increased preservation of healthy tissue.

Published
2024

15. Generation and optimization of entanglement between atoms chirally coupled to spin cavities

Author

You, Jia-Bin, Kong, Jian Feng, Aghamalyan, Davit, Mok, Wai-Keong, Lim, Kian Hwee, Ye, Jun, Png, Ching Eng, and García-Vidal, Francisco J.

Subjects
Quantum Physics, Physics - Atomic Physics
Abstract

We explore the generation and optimization of entanglement between atoms chirally coupled to finite 1D spin chains, functioning as {\it spin cavities}. By diagonalizing the spin cavity Hamiltonian, we identify a parity effect that influences entanglement, with small even-sized cavities chirally coupled to atoms expediting entanglement generation by approximately $50\%$ faster than non-chiral coupling. Applying a classical driving field to the atoms reveals oscillations in concurrence, with resonant dips at specific driving strengths due to the resonances between the driven atom and the spin cavity. Extending our study to systems with energetic disorder, we find that high concurrence can be achieved regardless of disorder strength when the inverse participation ratio of the resulting eigenstates is favorable. Finally, we demonstrate that controlled disorder within the cavity significantly enhances and expedites entanglement generation, achieving higher concurrences up to four times faster than those attained in ordered systems.

Published
2024

16. ODIN: Disentangled Reward Mitigates Hacking in RLHF

Author

Chen, Lichang, Zhu, Chen, Soselia, Davit, Chen, Jiuhai, Zhou, Tianyi, Goldstein, Tom, Huang, Heng, Shoeybi, Mohammad, and Catanzaro, Bryan

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

In this work, we study the issue of reward hacking on the response length, a challenge emerging in Reinforcement Learning from Human Feedback (RLHF) on LLMs. A well-formatted, verbose but less helpful response from the LLMs can often deceive LLMs or even human evaluators to achieve high scores. The same issue also holds for some reward models in RL. To address the challenges in both training and evaluation, we establish a more reliable evaluation protocol for comparing different training configurations, which inspects the trade-off between LLM evaluation score and response length obtained by varying training hyperparameters. Based on this evaluation, we conduct large-scale studies, where the results shed insights into the efficacy of hyperparameters and tricks used in RL on mitigating length bias. We further propose to improve the reward model by jointly training two linear heads on shared feature representations to predict the rewards, one trained to correlate with length, and the other trained to decorrelate with length and therefore focus more on the actual content. We then discard the length head in RL to prevent reward hacking on length. Experiments demonstrate that our approach almost eliminates the reward correlation with length, and improves the obtained policy by a significant margin.

Published
2024

17. Correlation Between Pre-Operative Diffusion Tensor Imaging Indices and Post-Operative Outcome in Degenerative Cervical Myelopathy: A Systematic Review and Meta-Analysis.

Author

Mohammadi, Mohammad, Roohollahi, Faramarz, Mahmoudi, Mohamad, Mohammadi, Aynaz, Mohamadi, Mobin, Kankam, Samuel, Ghamari Khameneh, Afshar, Baghdasaryan, Davit, Farahbakhsh, Farzin, Martin, Allan, Harrop, James, and Rahimi-Movaghar, Vafa

Subjects
clinical outcome, degenerative cervical myelopathy, diffusion tensor imaging, prognosis
Abstract

STUDY DESIGN: Systematic review. OBJECTIVES: The correlation between pre-operative diffusion tensor imaging (DTI) metrics and post-operative clinical outcomes in patients with degenerative cervical myelopathy (DCM) has been widely investigated with different studies reporting varied findings. We conducted a systematic review to determine the association between DTI metric and clinical outcomes after surgery. METHODS: We identified relevant articles that investigated the relationship between pre-operative DTI indices and post-operative outcome in DCM patients by searching PubMed/MEDLINE, Web of Science, Scopus, and EMBASE from inception until October 2023. In addition, quantitative synthesis and meta-analyses were performed. RESULTS: FA was significantly correlated with postoperative JOA or mJOA across all age and follow up subgroups, changes observed in JOA or mJOA from preoperative to postoperative stages (Δ JOA or Δ mJOA) in subgroups aged 65 and above and in those with a follow-up period of 6 months or more, as well as recovery rate in all studies pooled together and also in the under-65 age bracket. Additionally, a significant correlation was demonstrated between recovery rate and ADC across all age groups. No other significant correlations were discovered between DTI parameters (MD, AD, and ADC) and post-operative outcomes. CONCLUSION: DTI is a quantitative noninvasive evaluation tool that correlates with severity of DCM. However, the current evidence is still elusive regarding whether DTI metric is a validated tool for predicting the degree of post-operative recovery, which could potentially be useful in patient selection for surgery.

Published
2024

18. Diffusion Tensor Imaging in Diagnosing and Evaluating Degenerative Cervical Myelopathy: A Systematic Review and Meta-Analysis.

Author

Mohammadi, Mohammad, Roohollahi, Faramarz, Farahbakhsh, Farzin, Mohammadi, Aynaz, Mortazavi Mamaghani, Elham, Kankam, Samuel, Moarrefdezfouli, Azin, Ghamari Khameneh, Afshar, Mahmoudi, Mohamad, Baghdasaryan, Davit, Martin, Allan, Harrop, James, and Rahimi-Movaghar, Vafa

Subjects
degenerative cervical myelopathy, diagnosis, diffusion tensor imaging, meta-analysis
Abstract

STUDY DESIGN: Systematic review. OBJECTIVE: Degenerative cervical myelopathy (DCM) is a common spinal cord disorder necessitating surgery. We aim to explore how effectively diffusion tensor imaging (DTI) can distinguish DCM from healthy individuals and assess the relationship between DTI metrics and symptom severity. METHODS: We included studies with adult DCM patients who had not undergone decompressive surgery and implemented correlation analyses between DTI parameters and severity, or compared healthy controls and DCM patients. RESULTS: 57 studies were included in our meta-analysis. At the maximal compression (MC) level, fractional anisotropy (FA) exhibited lower values in DCM patients, while apparent diffusion coefficient (ADC), mean diffusivity (MD), and radial diffusivity (RD) were notably higher in the DCM group. Moreover, our investigation into the diagnostic utility of DTI parameters disclosed high sensitivity, specificity, and area under the curve values for FA (.84, .80, .83 respectively) and ADC (.74, .84, .88 respectively). Additionally, we explored the correlation between DTI parameters and myelopathy severity, revealing a significant correlation of FA (.53, 95% CI:0.40 to .65) at MC level with JOA/mJOA scores. CONCLUSION: Current guidelines for DCM suggest decompressive surgery for both mild and severe cases. However, they lack clear recommendations on which mild DCM patients might benefit from conservative treatment vs immediate surgery. ADCs role here could be pivotal, potentially differentiating between healthy individuals and DCM. While it may not correlate with symptom severity, it might predict surgical outcomes, making it a valuable imaging biomarker for clearer management decisions in mild DCM.

Published
2024

19. Factors Associated with and Impact of Open Conversion in Laparoscopic and Robotic Minor Liver Resections: An International Multicenter Study of 10,541 Patients

Author

Saleh, Mansour, Pascual, Franco, Ghallab, Mohammed, Wu, Andrew G. R., Chin, Ken-Min, Ratti, Francesca, Giglio, Mariano Cesare, Garatti, Marco, Nghia, Phan Phuoc, Kato, Yutaro, Lim, Chetana, Herman, Paulo, Coelho, Fabricio Ferreira, Schmelzle, Moritz, Pratschke, Johann, Aghayan, Davit L., Liu, Qiu, Marino, Marco V., Belli, Andrea, Chiow, Adrian K. H., Sucandy, Iswanto, Ivanecz, Arpad, Di Benedetto, Fabrizio, Choi, Sung Hoon, Lee, Jae Hoon, Park, James O., Prieto, Mikel, Guzman, Yoelimar, Fondevila, Constantino, Efanov, Mikhail, Rotellar, Fernando, Choi, Gi-Hong, Robles-Campos, Ricardo, Kadam, Prashant, Sutcliffe, Robert P., Troisi, Roberto I., Tang, Chung Ngai, Chong, Charing C., D’Hondt, Mathieu, Dalla Valle, Bernardo, Ruzzenente, Andrea, Kingham, T. Peter, Scatton, Olivier, Liu, Rong, Mejia, Alejandro, Mishima, Kohei, Wakabayashi, Go, Lopez-Ben, Santiago, Wang, Xiaoying, Ferrero, Alessandro, Ettorre, Giuseppe Maria, Vivarelli, Marco, Mazzaferro, Vincenzo, Giuliante, Felice, Yong, Chee Chien, Yin, Mengqiu, Monden, Kazuteru, Geller, David, Chen, Kuo-Hsin, Sugioka, Atsushi, Edwin, Bjørn, Cheung, Tan-To, Long, Tran Cong Duy, Abu Hilal, Mohammad, Aldrighetti, Luca, Soubrane, Olivier, Fuks, David, Han, Ho-Seong, Cherqui, Daniel, and Goh, Brian K. P.

Published
2024
Full Text
View/download PDF

20. Quantum Spin Hall Effect in Magnetic Graphene

Author

Ghiasi, Talieh S., Petrosyan, Davit, Ingla-Aynés, Josep, Bras, Tristan, Watanabe, Kenji, Taniguchi, Takashi, Mañas-Valero, Samuel, Coronado, Eugenio, Zollner, Klaus, Fabian, Jaroslav, Kim, Philip, and van der Zant, Herre S. J.

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

A promising approach to attain long-distance coherent spin propagation is accessing topological spin-polarized edge states in graphene. Achieving this without external magnetic fields necessitates engineering graphene band structure, obtainable through proximity effects in van der Waals heterostructures. In particular, proximity-induced staggered potentials and spin-orbit coupling are expected to form a topological bulk gap in graphene with gapless helical edge states that are robust against disorder. In this work, we detect the spin-polarized helical edge transport in graphene at zero external magnetic field, allowed by the proximity of an interlayer antiferromagnet, CrPS$_4$. We show the coexistence of the quantum spin Hall (QSH) states and magnetism in graphene, where the induced spin-orbit and exchange couplings also give rise to a large anomalous Hall (AH) effect. The detection of the QSH states at zero external magnetic field, together with the AH signal that persists up to room temperature, opens the route for practical applications of magnetic graphene in quantum spintronic circuitries.

Published
2023

21. Infrared photodetection in graphene-based heterostructures: bolometric and thermoelectric effects at the tunneling barrier

Author

Mylnikov, Dmitry A., Kashchenko, Mikhail A., Kapralov, Kirill N., Ghazaryan, Davit A., Vdovin, Evgenii E., Morozov, Sergey V., Novoselov, Kostya S., Bandurin, Denis A., Chernov, Alexander I., and Svintsov, Dmitry A.

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics
Abstract

Graphene/hBN/graphene tunnel devices offer promise as sensitive mid-infrared photodetectors but the microscopic origin underlying the photoresponse in them remains elusive. In this work, we investigated the photocurrent generation in graphene/hBN/graphene tunnel structures with localized defect states under mid-IR illumination. We demonstrate that the photocurrent in these devices is proportional to the second derivative of the tunnel current with respect to the bias voltage, peaking during tunneling through the hBN impurity level. We revealed that the origin of the photocurrent generation lies in the change of the tunneling probability upon radiation-induced electron heating in graphene layers, in agreement with the theoretical model that we developed. Finally, we show that at a finite bias voltage, the photocurrent is proportional to the either of the graphene layers heating under the illumination, while at zero bias, it is proportional to the heating difference. Thus, the photocurrent in such devices can be used for accurate measurements of the electronic temperature providing a convenient alternative to Johnson noise thermometry.

Published
2023

22. On some weighted maximal operators of partial sums of Walsh-Fourier series in the space $H_1$

Author

Baramidze, Davit

Subjects
Mathematics - Functional Analysis, 42C10
Abstract

In the first part of this paper we describe the status of the art of this subject. In the second part we present and motivate some new results. Indeed, we introduce some new weighted maximal operators of the partial sums of the Walsh-Fourier series. We prove that for some "optimal" weights these new operators indeed are bounded from the martingale Hardy space $H_{1}(G)$ to the space $\text{weak}-L_{1}(G),$ but is not bounded from $H_{1}(G)$ to the space $L_{1}(G).$

Published
2023

23. JUNO sensitivity to invisible decay modes of neutrons

Author

Abusleme, Angel, Adam, Thomas, Adamowicz, Kai, Ahmad, Shakeel, Ahmed, Rizwan, Aiello, Sebastiano, An, Fengpeng, An, Qi, Andronico, Giuseppe, Anfimov, Nikolay, Antonelli, Vito, Antoshkina, Tatiana, de André, João Pedro Athayde Marcondes, Auguste, Didier, Bai, Weidong, Balashov, Nikita, Baldini, Wander, Barresi, Andrea, Basilico, Davide, Baussan, Eric, Bellato, Marco, Beretta, Marco, Bergnoli, Antonio, Bick, Daniel, Bieger, Lukas, Biktemerova, Svetlana, Birkenfeld, Thilo, Blake, Iwan, Blyth, Simon, Bolshakova, Anastasia, Bongrand, Mathieu, Breton, Dominique, Brigatti, Augusto, Brugnera, Riccardo, Bruno, Riccardo, Budano, Antonio, Busto, Jose, Cabrera, Anatael, Caccianiga, Barbara, Cai, Hao, Cai, Xiao, Cai, Yanke, Cai, Zhiyan, Callier, Stéphane, Calvez, Steven, Cammi, Antonio, Campeny, Agustin, Cao, Chuanya, Cao, Guofu, Cao, Jun, Caruso, Rossella, Cerna, Cédric, Cerrone, Vanessa, Chang, Jinfan, Chang, Yun, Chatrabhuti, Auttakit, Chen, Chao, Chen, Guoming, Chen, Pingping, Chen, Shaomin, Chen, Xin, Chen, Yiming, Chen, Yixue, Chen, Yu, Chen, Zelin, Chen, Zhangming, Chen, Zhiyuan, Chen, Zikang, Cheng, Jie, Cheng, Yaping, Cheng, Yu Chin, Chepurnov, Alexander, Chetverikov, Alexey, Chiesa, Davide, Chimenti, Pietro, Chin, Yen-Ting, Chou, Po-Lin, Chu, Ziliang, Chukanov, Artem, Claverie, Gérard, Clementi, Catia, Clerbaux, Barbara, Molla, Marta Colomer, Lorenzo, Selma Conforti Di, Coppi, Alberto, Corti, Daniele, Csakli, Simon, Cui, Chenyang, Corso, Flavio Dal, Dalager, Olivia, Datta, Jaydeep, De La Taille, Christophe, Deng, Zhi, Deng, Ziyan, Ding, Xiaoyu, Ding, Xuefeng, Ding, Yayun, Dirgantara, Bayu, Dittrich, Carsten, Dmitrievsky, Sergey, Dohnal, Tadeas, Dolzhikov, Dmitry, Donchenko, Georgy, Dong, Jianmeng, Doroshkevich, Evgeny, Dou, Wei, Dracos, Marcos, Druillole, Frédéric, Du, Ran, Du, Shuxian, Duan, Yujie, Dugas, Katherine, Dusini, Stefano, Duyang, Hongyue, Eck, Jessica, Enqvist, Timo, Fabbri, Andrea, Fahrendholz, Ulrike, Fan, Lei, Fang, Jian, Fang, Wenxing, Fedoseev, Dmitry, Feng, Li-Cheng, Feng, Qichun, Ferraro, Federico, Fournier, Amélie, Fritsch, Fritsch, Gan, Haonan, Gao, Feng, Gao, Feng, Garfagnini, Alberto, Gavrikov, Arsenii, Giammarchi, Marco, Giudice, Nunzio, Gonchar, Maxim, Gong, Guanghua, Gong, Hui, Gornushkin, Yuri, Grassi, Marco, Gromov, Maxim, Gromov, Vasily, Gu, Minghao, Gu, Xiaofei, Gu, Yu, Guan, Mengyun, Guan, Yuduo, Guardone, Nunzio, Guizzetti, Rosa Maria, Guo, Cong, Guo, Wanlei, Hagner, Caren, Han, Hechong, Han, Ran, Han, Yang, He, Jinhong, He, Miao, He, Wei, He, Xinhai, Heinz, Tobias, Hellmuth, Patrick, Heng, Yuekun, Herrera, Rafael, Hor, YuenKeung, Hou, Shaojing, Hsiung, Yee, Hu, Bei-Zhen, Hu, Hang, Hu, Jun, Hu, Peng, Hu, Shouyang, Hu, Tao, Hu, Yuxiang, Hu, Zhuojun, Huang, Guihong, Huang, Hanxiong, Huang, Jinhao, Huang, Junting, Huang, Kaixuan, Huang, Shengheng, Huang, Wenhao, Huang, Xin, Huang, Xingtao, Huang, Yongbo, Hui, Jiaqi, Huo, Lei, Huo, Wenju, Huss, Cédric, Hussain, Safeer, Imbert, Leonard, Ioannisian, Ara, Isocrate, Roberto, Jafar, Arshak, Jelmini, Beatrice, Jeria, Ignacio, Ji, Xiaolu, Jia, Huihui, Jia, Junji, Jian, Siyu, Jiang, Cailian, Jiang, Di, Jiang, Guangzheng, Jiang, Wei, Jiang, Xiaoshan, Jiang, Xiaozhao, Jiang, Yixuan, Jing, Xiaoping, Jollet, Cécile, Kang, Li, Karaparabil, Rebin, Kazarian, Narine, Khan, Ali, Khatun, Amina, Khosonthongkee, Khanchai, Korablev, Denis, Kouzakov, Konstantin, Krasnoperov, Alexey, Kuleshov, Sergey, Kumaran, Sindhujha, Kutovskiy, Nikolay, Labit, Loïc, Lachenmaier, Tobias, Lai, Haojing, Landini, Cecilia, Leblanc, Sébastien, Lefevre, Frederic, Lei, Ruiting, Leitner, Rupert, Leung, Jason, Li, Demin, Li, Fei, Li, Fule, Li, Gaosong, Li, Hongjian, Li, Huang, Li, Jiajun, Li, Min, Li, Nan, Li, Qingjiang, Li, Ruhui, Li, Rui, Li, Shanfeng, Li, Shuo, Li, Tao, Li, Teng, Li, Weidong, Li, Weiguo, Li, Xiaomei, Li, Xiaonan, Li, Xinglong, Li, Yi, Li, Yichen, Li, Yufeng, Li, Zhaohan, Li, Zhibing, Li, Ziyuan, Li, Zonghai, Liang, An-An, Liang, Hao, Liang, Hao, Liao, Jiajun, Liao, Yilin, Liao, Yuzhong, Limphirat, Ayut, Lin, Guey-Lin, Lin, Shengxin, Lin, Tao, Ling, Jiajie, Ling, Xin, Lippi, Ivano, Liu, Caimei, Liu, Fang, Liu, Fengcheng, Liu, Haidong, Liu, Haotian, Liu, Hongbang, Liu, Hongjuan, Liu, Hongtao, Liu, Hongyang, Liu, Jianglai, Liu, Jiaxi, Liu, Jinchang, Liu, Min, Liu, Qian, Liu, Qin, Liu, Runxuan, Liu, Shenghui, Liu, Shubin, Liu, Shulin, Liu, Xiaowei, Liu, Xiwen, Liu, Xuewei, Liu, Yankai, Liu, Zhen, Loi, Lorenzo, Lokhov, Alexey, Lombardi, Paolo, Lombardo, Claudio, Loo, Kai, Lu, Chuan, Lu, Haoqi, Lu, Jingbin, Lu, Junguang, Lu, Meishu, Lu, Peizhi, Lu, Shuxiang, Lu, Xianguo, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Ludhova, Livia, Lukanov, Arslan, Luo, Fengjiao, Luo, Guang, Luo, Jianyi, Luo, Shu, Luo, Wuming, Luo, Xiaojie, Lyashuk, Vladimir, Ma, Bangzheng, Ma, Bing, Ma, Qiumei, Ma, Si, Ma, Xiaoyan, Ma, Xubo, Maalmi, Jihane, Mai, Jingyu, Malabarba, Marco, Malyshkin, Yury, Mandujano, Roberto Carlos, Mantovani, Fabio, Mao, Xin, Mao, Yajun, Mari, Stefano M., Marini, Filippo, Martini, Agnese, Mayer, Matthias, Mayilyan, Davit, Mednieks, Ints, Meng, Yue, Meraviglia, Anita, Meregaglia, Anselmo, Meroni, Emanuela, Miramonti, Lino, Mohan, Nikhil, Montuschi, Michele, Reveco, Cristobal Morales, Nastasi, Massimiliano, Naumov, Dmitry V., Naumova, Elena, Navas-Nicolas, Diana, Nemchenok, Igor, Thi, Minh Thuan Nguyen, Nikolaev, Alexey, Ning, Feipeng, Ning, Zhe, Nunokawa, Hiroshi, Oberauer, Lothar, Ochoa-Ricoux, Juan Pedro, Olshevskiy, Alexander, Orestano, Domizia, Ortica, Fausto, Othegraven, Rainer, Paoloni, Alessandro, Parker, George, Parmeggiano, Sergio, Patsias, Achilleas, Pei, Yatian, Pelicci, Luca, Peng, Anguo, Peng, Haiping, Peng, Yu, Peng, Zhaoyuan, Percalli, Elisa, Perrin, Willy, Perrot, Frédéric, Petitjean, Pierre-Alexandre, Petrucci, Fabrizio, Pilarczyk, Oliver, Rico, Luis Felipe Piñeres, Popov, Artyom, Poussot, Pascal, Previtali, Ezio, Qi, Fazhi, Qi, Ming, Qi, Xiaohui, Qian, Sen, Qian, Xiaohui, Qian, Zhen, Qiao, Hao, Qin, Zhonghua, Qiu, Shoukang, Qu, Manhao, Qu, Zhenning, Ranucci, Gioacchino, Re, Alessandra, Rebii, Abdel, Redchuk, Mariia, Reina, Gioele, Ren, Bin, Ren, Jie, Ren, Yuhan, Ricci, Barbara, Rientong, Komkrit, Rifai, Mariam, Roche, Mathieu, Rodphai, Narongkiat, Romani, Aldo, Roskovec, Bedřich, Ruan, Xichao, Rybnikov, Arseniy, Sadovsky, Andrey, Saggese, Paolo, Sandanayake, Deshan, Sangka, Anut, Sava, Giuseppe, Sawangwit, Utane, Schever, Michaela, Schwab, Cédric, Schweizer, Konstantin, Selyunin, Alexandr, Serafini, Andrea, Settimo, Mariangela, Shao, Junyu, Sharov, Vladislav, Shi, Hexi, Shi, Jingyan, Shi, Yanan, Shutov, Vitaly, Sidorenkov, Andrey, Šimkovic, Fedor, Singhal, Apeksha, Sirignano, Chiara, Siripak, Jaruchit, Sisti, Monica, Smirnov, Mikhail, Smirnov, Oleg, Sokolov, Sergey, Songwadhana, Julanan, Soonthornthum, Boonrucksar, Sotnikov, Albert, Sreethawong, Warintorn, Stahl, Achim, Stanco, Luca, Stankevich, Konstantin, Steiger, Hans, Steinmann, Jochen, Sterr, Tobias, Stock, Matthias Raphael, Strati, Virginia, Strizh, Michail, Studenikin, Alexander, Su, Aoqi, Su, Jun, Su, Jun, Sun, Guangbao, Sun, Shifeng, Sun, Xilei, Sun, Yongjie, Sun, Yongzhao, Sun, Zhengyang, Suwonjandee, Narumon, Takenaka, Akira, Tan, Xiaohan, Tang, Jian, Tang, Jingzhe, Tang, Qiang, Tang, Quan, Tang, Xiao, Hariharan, Vidhya Thara, Tkachev, Igor, Tmej, Tomas, Torri, Marco Danilo Claudio, Triossi, Andrea, Trzaska, Wladyslaw, Tung, Yu-Chen, Tuve, Cristina, Ushakov, Nikita, Vedin, Vadim, Venettacci, Carlo, Verde, Giuseppe, Vialkov, Maxim, Viaud, Benoit, Vollbrecht, Cornelius Moritz, Sturm, Katharina von, Vorobel, Vit, Voronin, Dmitriy, Votano, Lucia, Walker, Pablo, Wang, Caishen, Wang, Chung-Hsiang, Wang, En, Wang, Guoli, Wang, Hanwen, Wang, Jian, Wang, Jun, Wang, Li, Wang, Lu, Wang, Meng, Wang, Meng, Wang, Mingyuan, Wang, Qianchuan, Wang, Ruiguang, Wang, Sibo, Wang, Siguang, Wang, Wei, Wang, Wenshuai, Wang, Xi, Wang, Xiangyue, Wang, Yangfu, Wang, Yaoguang, Wang, Yi, Wang, Yi, Wang, Yifang, Wang, Yuanqing, Wang, Yuyi, Wang, Zhe, Wang, Zheng, Wang, Zhimin, Watcharangkool, Apimook, Wei, Wei, Wei, Wei, Wei, Wenlu, Wei, Yadong, Wei, Yuehuan, Wen, Liangjian, Weng, Jun, Wiebusch, Christopher, Wirth, Rosmarie, Wu, Chengxin, Wu, Diru, Wu, Qun, Wu, Yinhui, Wu, Yiyang, Wu, Zhi, Wurm, Michael, Wurtz, Jacques, Wysotzki, Christian, Xi, Yufei, Xia, Dongmei, Xian, Shishen, Xiang, Ziqian, Xiao, Fei, Xiao, Xiang, Xie, Xiaochuan, Xie, Yijun, Xie, Yuguang, Xin, Zhao, Xing, Zhizhong, Xu, Benda, Xu, Cheng, Xu, Donglian, Xu, Fanrong, Xu, Hangkun, Xu, Jiayang, Xu, Jilei, Xu, Jing, Xu, Jinghuan, Xu, Meihang, Xu, Xunjie, Xu, Yin, Xu, Yu, Yan, Baojun, Yan, Qiyu, Yan, Taylor, Yan, Xiongbo, Yan, Yupeng, Yang, Changgen, Yang, Chengfeng, Yang, Fengfan, Yang, Jie, Yang, Lei, Yang, Pengfei, Yang, Xiaoyu, Yang, Yifan, Yang, Yixiang, Yang, Zekun, Yao, Haifeng, Ye, Jiaxuan, Ye, Mei, Ye, Ziping, Yermia, Frédéric, You, Zhengyun, Yu, Boxiang, Yu, Chiye, Yu, Chunxu, Yu, Guojun, Yu, Hongzhao, Yu, Miao, Yu, Xianghui, Yu, Zeyuan, Yu, Zezhong, Yuan, Cenxi, Yuan, Chengzhuo, Yuan, Ying, Yuan, Zhenxiong, Yue, Baobiao, Zafar, Noman, Zamogilnyi, Kirill, Zavadskyi, Vitalii, Zeng, Fanrui, Zeng, Shan, Zeng, Tingxuan, Zeng, Yuda, Zhan, Liang, Zhang, Aiqiang, Zhang, Bin, Zhang, Binting, Zhang, Feiyang, Zhang, Hangchang, Zhang, Haosen, Zhang, Honghao, Zhang, Jialiang, Zhang, Jiawen, Zhang, Jie, Zhang, Jingbo, Zhang, Jinnan, Zhang, Junwei, Zhang, Lei, Zhang, Peng, Zhang, Ping, Zhang, Qingmin, Zhang, Shiqi, Zhang, Shu, Zhang, Shuihan, Zhang, Siyuan, Zhang, Tao, Zhang, Xiaomei, Zhang, Xin, Zhang, Xuantong, Zhang, Yibing, Zhang, Yinhong, Zhang, Yiyu, Zhang, Yongpeng, Zhang, Yu, Zhang, Yuanyuan, Zhang, Yumei, Zhang, Zhenyu, Zhang, Zhijian, Zhao, Jie, Zhao, Rong, Zhao, Runze, Zhao, Shujun, Zhao, Tianhao, Zheng, Hua, Zheng, Yangheng, Zhou, Jing, Zhou, Li, Zhou, Nan, Zhou, Shun, Zhou, Tong, Zhou, Xiang, Zhou, Xing, Zhu, Jingsen, Zhu, Kangfu, Zhu, Kejun, Zhu, Zhihang, Zhuang, Bo, Zhuang, Honglin, Zong, Liang, and Zou, Jiaheng

Published
2025
Full Text
View/download PDF

25. The design and technology development of the JUNO central detector

Author

Abusleme, Angel, Adam, Thomas, Ahmad, Shakeel, Ahmed, Rizwan, Aiello, Sebastiano, Akram, Muhammad, Aleem, Abid, Alexandros, Tsagkarakis, An, Fengpeng, An, Qi, Andronico, Giuseppe, Anfimov, Nikolay, Antonelli, Vito, Antoshkina, Tatiana, Asavapibhop, Burin, de André, João Pedro Athayde Marcondes, Auguste, Didier, Bai, Weidong, Balashov, Nikita, Baldini, Wander, Barresi, Andrea, Basilico, Davide, Baussan, Eric, Bellato, Marco, Beretta, Marco, Bergnoli, Antonio, Bick, Daniel, Birkenfeld, Thilo, Blum, David, Blyth, Simon, Bolshakova, Anastasia, Bongrand, Mathieu, Bordereau, Clément, Breton, Dominique, Brigatti, Augusto, Brugnera, Riccardo, Bruno, Riccardo, Budano, Antonio, Busto, Jose, Cabrera, Anatael, Caccianiga, Barbara, Cai, Hao, Cai, Xiao, Cai, Yanke, Cai, Zhiyan, Callier, Stéphane, Cammi, Antonio, Campeny, Agustin, Cao, Chuanya, Cao, Guofu, Cao, Jun, Caruso, Rossella, Cerna, Cédric, Cerrone, Vanessa, Chan, Chi, Chang, Jinfan, Chang, Yun, Chen, Guoming, Chen, Pingping, Chen, Shaomin, Chen, Yixue, Chen, Yu, Chen, Zhiyuan, Chen, Zikang, Cheng, Jie, Cheng, Yaping, Cheng, Yu Chin, Chepurnov, Alexander, Chetverikov, Alexey, Chiesa, Davide, Chimenti, Pietro, Chu, Ziliang, Chukanov, Artem, Claverie, Gérard, Clementi, Catia, Clerbaux, Barbara, Molla, Marta Colomer, Lorenzo, Selma Conforti Di, Coppi, Alberto, Corti, Daniele, Corso, Flavio Dal, Dalager, Olivia, De La Taille, Christophe, Deng, Zhi, Deng, Ziyan, Depnering, Wilfried, Diaz, Marco, Ding, Xuefeng, Ding, Yayun, Dirgantara, Bayu, Dmitrievsky, Sergey, Dohnal, Tadeas, Dolzhikov, Dmitry, Donchenko, Georgy, Dong, Jianmeng, Doroshkevich, Evgeny, Dou, Wei, Dracos, Marcos, Druillole, Frédéric, Du, Ran, Du, Shuxian, Dusini, Stefano, Duyang, Hongyue, Enqvist, Timo, Fabbri, Andrea, Fahrendholz, Ulrike, Fan, Lei, Fang, Jian, Fang, Wenxing, Fargetta, Marco, Fedoseev, Dmitry, Fei, Zhengyong, Feng, Li-Cheng, Feng, Qichun, Ferraro, Federico, Fournier, Amélie, Gan, Haonan, Gao, Feng, Garfagnini, Alberto, Gavrikov, Arsenii, Giammarchi, Marco, Giudice, Nunzio, Gonchar, Maxim, Gong, Guanghua, Gong, Hui, Gornushkin, Yuri, Göttel, Alexandre, Grassi, Marco, Gromov, Maxim, Gromov, Vasily, Gu, Minghao, Gu, Xiaofei, Gu, Yu, Guan, Mengyun, Guan, Yuduo, Guardone, Nunzio, Guo, Cong, Guo, Wanlei, Guo, Xinheng, Guo, Yuhang, Hagner, Caren, Han, Ran, Han, Yang, Hao, Jiajun, He, Miao, He, Wei, Heinz, Tobias, Hellmuth, Patrick, Heng, Yuekun, Herrera, Rafael, Hor, YuenKeung, Hou, Shaojing, Hsiung, Yee, Hu, Bei-Zhen, Hu, Hang, Hu, Jianrun, Hu, Jun, Hu, Shouyang, Hu, Tao, Hu, Yuxiang, Hu, Zhuojun, Huang, Guihong, Huang, Hanxiong, Huang, Kaixi, Huang, Kaixuan, Huang, Wenhao, Huang, Xin, Huang, Xingtao, Huang, Yongbo, Hui, Jiaqi, Huo, Lei, Huo, Wenju, Huss, Cédric, Hussain, Safeer, Ioannisian, Ara, Isocrate, Roberto, Jelmini, Beatrice, Jeria, Ignacio, Ji, Xiaolu, Jia, Huihui, Jia, Junji, Jian, Siyu, Jiang, Di, Jiang, Wei, Jiang, Xiaoshan, Jing, Xiaoping, Jollet, Cécile, Kampmann, Philipp, Kang, Li, Karaparambil, Rebin, Kazarian, Narine, Khan, Ali, Khatun, Amina, Khosonthongkee, Khanchai, Korablev, Denis, Kouzakov, Konstantin, Krasnoperov, Alexey, Kutovskiy, Nikolay, Kuusiniemi, Pasi, Lachenmaier, Tobias, Landini, Cecilia, Leblanc, Sébastien, Lebrin, Victor, Lefevre, Frederic, Lei, Ruiting, Leitner, Rupert, Leung, Jason, Li, Daozheng, Li, Demin, Li, Fei, Li, Fule, Li, Gaosong, Li, Huiling, Li, Mengzhao, Li, Min, Li, Nan, Li, Qingjiang, Li, Ruhui, Li, Rui, Li, Shanfeng, Li, Tao, Li, Teng, Li, Weidong, Li, Weiguo, Li, Xiaomei, Li, Xiaonan, Li, Xinglong, Li, Xiwen, Li, Yi, Li, Yichen, Li, Yufeng, Li, Zepeng, Li, Zhaohan, Li, Zhibing, Li, Ziyuan, Li, Zonghai, Liang, Hao, Liang, Hao, Liao, Jiajun, Limphirat, Ayut, Lin, Guey-Lin, Lin, Shengxin, Lin, Tao, Ling, Jiajie, Lippi, Ivano, Liu, Caimei, Liu, Fang, Liu, Haidong, Liu, Haotian, Liu, Hongbang, Liu, Hongjuan, Liu, Hongtao, Liu, Hui, Liu, Jianglai, Liu, Jinchang, Liu, Min, Liu, Qian, Liu, Qin, Liu, Runxuan, Liu, Shubin, Liu, Shulin, Liu, Xiaowei, Liu, Xiwen, Liu, Yan, Liu, Yunzhe, Lokhov, Alexey, Lombardi, Paolo, Lombardo, Claudio, Loo, Kai, Lu, Chuan, Lu, Haoqi, Lu, Jingbin, Lu, Junguang, Lu, Peizhi, Lu, Shuxiang, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Ludhova, Livia, Lukanov, Arslan, Luo, Daibin, Luo, Fengjiao, Luo, Guang, Luo, Jianyi, Luo, Shu, Luo, Wuming, Luo, Xiaojie, Luo, Xiaolan, Lyashuk, Vladimir, Ma, Bangzheng, Ma, Bing, Ma, Qiumei, Ma, Si, Ma, Xiaoyan, Ma, Xubo, Maalmi, Jihane, Magoni, Marco, Mai, Jingyu, Malyshkin, Yury, Mandujano, Roberto Carlos, Mantovani, Fabio, Mao, Xin, Mao, Yajun, Mari, Stefano M., Marini, Filippo, Martini, Agnese, Mayer, Matthias, Mayilyan, Davit, Mednieks, Ints, Meng, Yue, Meraviglia, Anita, Meregaglia, Anselmo, Meroni, Emanuela, Meyhöfer, David, Mezzetto, Mauro, Miramonti, Lino, Montini, Paolo, Montuschi, Michele, Müller, Axel, Nastasi, Massimiliano, Naumov, Dmitry V., Naumova, Elena, Navas-Nicolas, Diana, Nemchenok, Igor, Thi, Minh Thuan Nguyen, Nikolaev, Alexey, Ning, Feipeng, Ning, Zhe, Nunokawa, Hiroshi, Oberauer, Lothar, Ochoa-Ricoux, Juan Pedro, Olshevskiy, Alexander, Orestano, Domizia, Ortica, Fausto, Othegraven, Rainer, Paoloni, Alessandro, Parmeggiano, Sergio, Pei, Yatian, Pelicci, Luca, Peng, Anguo, Peng, Haiping, Peng, Yu, Peng, Zhaoyuan, Perrot, Frédéric, Petitjean, Pierre-Alexandre, Petrucci, Fabrizio, Pilarczyk, Oliver, Rico, Luis Felipe Piñeres, Popov, Artyom, Poussot, Pascal, Previtali, Ezio, Qi, Fazhi, Qi, Ming, Qian, Sen, Qian, Xiaohui, Qian, Zhen, Qiao, Hao, Qin, Zhonghua, Qiu, Shoukang, Ranucci, Gioacchino, Rasheed, Reem, Re, Alessandra, Rebii, Abdel, Redchuk, Mariia, Ren, Bin, Ren, Jie, Ricci, Barbara, Rifai, Mariam, Roche, Mathieu, Rodphai, Narongkiat, Rodphai, Narongkiat, Romani, Aldo, Roskovec, Bedřich, Ruan, Xichao, Rybnikov, Arseniy, Sadovsky, Andrey, Saggese, Paolo, Sanfilippo, Simone, Sangka, Anut, Sawangwit, Utane, Sawatzki, Julia, Schever, Michaela, Schwab, Cédric, Schweizer, Konstantin, Selyunin, Alexandr, Serafini, Andrea, Settanta, Giulio, Settimo, Mariangela, Shao, Zhuang, Sharov, Vladislav, Shaydurova, Arina, Shi, Jingyan, Shi, Yanan, Shutov, Vitaly, Sidorenkov, Andrey, Šimkovic, Fedor, Sirignano, Chiara, Siripak, Jaruchit, Sisti, Monica, Slupecki, Maciej, Smirnov, Mikhail, Smirnov, Oleg, Sogo-Bezerra, Thiago, Sokolov, Sergey, Song, Wuying, Songwadhana, Julanan, Soonthornthum, Boonrucksar, Sotnikov, Albert, Šrámek, Ondřej, Sreethawong, Warintorn, Stahl, Achim, Stanco, Luca, Stankevich, Konstantin, Štefánik, Dušan, Steiger, Hans, Steinmann, Jochen, Sterr, Tobias, Stock, Matthias Raphael, Strati, Virginia, Studenikin, Alexander, Su, Jun, Sun, Shifeng, Sun, Xilei, Sun, Yongjie, Sun, Yongzhao, Sun, Zhengyang, Suwonjandee, Narumon, Szelezniak, Michal, Takenaka, Akira, Tang, Jian, Tang, Qiang, Tang, Quan, Tang, Xiao, Hariharan, Vidhya Thara, Theisen, Eric, Tietzsch, Alexander, Tkachev, Igor, Tmej, Tomas, Torri, Marco Danilo Claudio, Tortorici, Francesco, Treskov, Konstantin, Triossi, Andrea, Triozzi, Riccardo, Troni, Giancarlo, Trzaska, Wladyslaw, Tung, Yu-Chen, Tuve, Cristina, Ushakov, Nikita, Vedin, Vadim, Verde, Giuseppe, Vialkov, Maxim, Viaud, Benoit, Vollbrecht, Cornelius Moritz, Sturm, Katharina von, Vorobel, Vit, Voronin, Dmitriy, Votano, Lucia, Walker, Pablo, Wang, Caishen, Wang, Chung-Hsiang, Wang, Derun, Wang, En, Wang, Guoli, Wang, Jian, Wang, Jun, Wang, Lu, Wang, Meng, Wang, Meng, Wang, Ruiguang, Wang, Siguang, Wang, Wei, Wang, Wenshuai, Wang, Xi, Wang, Xiangyue, Wang, Yangfu, Wang, Yaoguang, Wang, Yi, Wang, Yi, Wang, Yifang, Wang, Yuanqing, Wang, Yuman, Wang, Zhe, Wang, Zheng, Wang, Zhimin, Watcharangkool, Apimook, Wei, Wei, Wei, Wei, Wei, Wenlu, Wei, Yadong, Wen, Kaile, Wen, Liangjian, Weng, Jun, Wiebusch, Christopher, Wirth, Rosmarie, Wonsak, Bjoern, Wu, Diru, Wu, Qun, Wu, Shuai, Wu, Zhi, Wurm, Michael, Wurtz, Jacques, Wysotzki, Christian, Xi, Yufei, Xia, Dongmei, Xiao, Xiang, Xie, Xiaochuan, Xie, Yuguang, Xie, Zhangquan, Xin, Zhao, Xing, Zhizhong, Xu, Benda, Xu, Cheng, Xu, Donglian, Xu, Fanrong, Xu, Hangkun, Xu, Jilei, Xu, Jing, Xu, Meihang, Xu, Yin, Xu, Yu, Yan, Baojun, Yan, Qiyu, Yan, Taylor, Yan, Wenqi, Yan, Xiongbo, Yan, Yupeng, Yang, Changgen, Yang, Chengfeng, Yang, Jie, Yang, Lei, Yang, Xiaoyu, Yang, Yifan, Yang, Yifan, Yao, Haifeng, Ye, Jiaxuan, Ye, Mei, Ye, Ziping, Yermia, Frédéric, You, Zhengyun, Yu, Boxiang, Yu, Chiye, Yu, Chunxu, Yu, Guojun, Yu, Hongzhao, Yu, Miao, Yu, Xianghui, Yu, Zeyuan, Yu, Zezhong, Yuan, Cenxi, Yuan, Chengzhuo, Yuan, Ying, Yuan, Zhenxiong, Yue, Baobiao, Zafar, Noman, Zavadskyi, Vitalii, Zeng, Shan, Zeng, Tingxuan, Zeng, Yuda, Zhan, Liang, Zhang, Aiqiang, Zhang, Bin, Zhang, Binting, Zhang, Feiyang, Zhang, Honghao, Zhang, Jialiang, Zhang, Jiawen, Zhang, Jie, Zhang, Jin, Zhang, Jingbo, Zhang, Jinnan, Zhang, Mohan, Zhang, Peng, Zhang, Qingmin, Zhang, Shiqi, Zhang, Shu, Zhang, Tao, Zhang, Xiaomei, Zhang, Xin, Zhang, Xuantong, Zhang, Yinhong, Zhang, Yiyu, Zhang, Yongpeng, Zhang, Yu, Zhang, Yuanyuan, Zhang, Yumei, Zhang, Zhenyu, Zhang, Zhijian, Zhao, Jie, Zhao, Rong, Zhao, Runze, Zhao, Shujun, Zheng, Dongqin, Zheng, Hua, Zheng, Yangheng, Zhong, Weirong, Zhou, Jing, Zhou, Li, Zhou, Nan, Zhou, Shun, Zhou, Tong, Zhou, Xiang, Zhu, Jingsen, Zhu, Kangfu, Zhu, Kejun, Zhu, Zhihang, Zhuang, Bo, Zhuang, Honglin, Zong, Liang, Zou, Jiaheng, and Zwickel, Sebastian

Published
2024
Full Text
View/download PDF

26. Development and external evaluation of a self-learning auto-segmentation model for Colorectal Cancer Liver Metastases Assessment (COALA)

Author

Bereska, Jacqueline I., Zeeuw, Michiel, Wagenaar, Luuk, Jenssen, Håvard Bjørke, Wesdorp, Nina J., van der Meulen, Delanie, Bereska, Leonard F., Gavves, Efstratios, Janssen, Boris V., Besselink, Marc G., Marquering, Henk A., van Waesberghe, Jan-Hein T. M., Aghayan, Davit L., Pelanis, Egidijus, van den Bergh, Janneke, Nota, Irene I. M., Moos, Shira, Kemmerich, Gunter, Syversveen, Trygve, Kolrud, Finn Kristian, Huiskens, Joost, Swijnenburg, Rutger-Jan, Punt, Cornelis J. A., Stoker, Jaap, Edwin, Bjørn, Fretland, Åsmund A., Kazemier, Geert, and Verpalen, Inez M.

Published
2024
Full Text
View/download PDF

32. Wandering principal optical axes in van der Waals triclinic materials

Author

Ermolaev, Georgy A., Voronin, Kirill V., Toksumakov, Adilet N., Grudinin, Dmitriy V., Fradkin, Ilia M., Mazitov, Arslan, Slavich, Aleksandr S., Tatmyshevskiy, Mikhail K., Yakubovsky, Dmitry I., Solovey, Valentin R., Kirtaev, Roman V., Novikov, Sergey M., Zhukova, Elena S., Kruglov, Ivan, Vyshnevyy, Andrey A., Baranov, Denis G., Ghazaryan, Davit A., Arsenin, Aleksey V., Martin-Moreno, Luis, Volkov, Valentyn S., and Novoselov, Kostya S.

Published
2024
Full Text
View/download PDF

36. JUNO sensitivity to invisible decay modes of neutrons

Author

JUNO Collaboration, Angel Abusleme, Thomas Adam, Kai Adamowicz, Shakeel Ahmad, Rizwan Ahmed, Sebastiano Aiello, Fengpeng An, Qi An, Giuseppe Andronico, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, João Pedro Athayde Marcondes de André, Didier Auguste, Weidong Bai, Nikita Balashov, Wander Baldini, Andrea Barresi, Davide Basilico, Eric Baussan, Marco Bellato, Marco Beretta, Antonio Bergnoli, Daniel Bick, Lukas Bieger, Svetlana Biktemerova, Thilo Birkenfeld, Iwan Blake, Simon Blyth, Anastasia Bolshakova, Mathieu Bongrand, Dominique Breton, Augusto Brigatti, Riccardo Brugnera, Riccardo Bruno, Antonio Budano, Jose Busto, Anatael Cabrera, Barbara Caccianiga, Hao Cai, Xiao Cai, Yanke Cai, Zhiyan Cai, Stéphane Callier, Steven Calvez, Antonio Cammi, Agustin Campeny, Chuanya Cao, Guofu Cao, Jun Cao, Rossella Caruso, Cédric Cerna, Vanessa Cerrone, Jinfan Chang, Yun Chang, Auttakit Chatrabhuti, Chao Chen, Guoming Chen, Pingping Chen, Shaomin Chen, Xin Chen, Yiming Chen, Yixue Chen, Yu Chen, Zelin Chen, Zhangming Chen, Zhiyuan Chen, Zikang Chen, Jie Cheng, Yaping Cheng, Yu Chin Cheng, Alexander Chepurnov, Alexey Chetverikov, Davide Chiesa, Pietro Chimenti, Yen-Ting Chin, Po-Lin Chou, Ziliang Chu, Artem Chukanov, Gérard Claverie, Catia Clementi, Barbara Clerbaux, Marta Colomer Molla, Selma Conforti Di Lorenzo, Alberto Coppi, Daniele Corti, Simon Csakli, Chenyang Cui, Flavio Dal Corso, Olivia Dalager, Jaydeep Datta, Christophe De La Taille, Zhi Deng, Ziyan Deng, Xiaoyu Ding, Xuefeng Ding, Yayun Ding, Bayu Dirgantara, Carsten Dittrich, Sergey Dmitrievsky, Tadeas Dohnal, Dmitry Dolzhikov, Georgy Donchenko, Jianmeng Dong, Evgeny Doroshkevich, Wei Dou, Marcos Dracos, Frédéric Druillole, Ran Du, Shuxian Du, Yujie Duan, Katherine Dugas, Stefano Dusini, Hongyue Duyang, Jessica Eck, Timo Enqvist, Andrea Fabbri, Ulrike Fahrendholz, Lei Fan, Jian Fang, Wenxing Fang, Dmitry Fedoseev, Li-Cheng Feng, Qichun Feng, Federico Ferraro, Amélie Fournier, Fritsch Fritsch, Haonan Gan, Feng Gao, Alberto Garfagnini, Arsenii Gavrikov, Marco Giammarchi, Nunzio Giudice, Maxim Gonchar, Guanghua Gong, Hui Gong, Yuri Gornushkin, Marco Grassi, Maxim Gromov, Vasily Gromov, Minghao Gu, Xiaofei Gu, Yu Gu, Mengyun Guan, Yuduo Guan, Nunzio Guardone, Rosa Maria Guizzetti, Cong Guo, Wanlei Guo, Caren Hagner, Hechong Han, Ran Han, Yang Han, Jinhong He, Miao He, Wei He, Xinhai He, Tobias Heinz, Patrick Hellmuth, Yuekun Heng, Rafael Herrera, YuenKeung Hor, Shaojing Hou, Yee Hsiung, Bei-Zhen Hu, Hang Hu, Jun Hu, Peng Hu, Shouyang Hu, Tao Hu, Yuxiang Hu, Zhuojun Hu, Guihong Huang, Hanxiong Huang, Jinhao Huang, Junting Huang, Kaixuan Huang, Shengheng Huang, Wenhao Huang, Xin Huang, Xingtao Huang, Yongbo Huang, Jiaqi Hui, Lei Huo, Wenju Huo, Cédric Huss, Safeer Hussain, Leonard Imbert, Ara Ioannisian, Roberto Isocrate, Arshak Jafar, Beatrice Jelmini, Ignacio Jeria, Xiaolu Ji, Huihui Jia, Junji Jia, Siyu Jian, Cailian Jiang, Di Jiang, Guangzheng Jiang, Wei Jiang, Xiaoshan Jiang, Xiaozhao Jiang, Yixuan Jiang, Xiaoping Jing, Cécile Jollet, Li Kang, Rebin Karaparabil, Narine Kazarian, Ali Khan, Amina Khatun, Khanchai Khosonthongkee, Denis Korablev, Konstantin Kouzakov, Alexey Krasnoperov, Sergey Kuleshov, Sindhujha Kumaran, Nikolay Kutovskiy, Loïc Labit, Tobias Lachenmaier, Haojing Lai, Cecilia Landini, Sébastien Leblanc, Frederic Lefevre, Ruiting Lei, Rupert Leitner, Jason Leung, Demin Li, Fei Li, Fule Li, Gaosong Li, Hongjian Li, Huang Li, Jiajun Li, Min Li, Nan Li, Qingjiang Li, Ruhui Li, Rui Li, Shanfeng Li, Shuo Li, Tao Li, Teng Li, Weidong Li, Weiguo Li, Xiaomei Li, Xiaonan Li, Xinglong Li, Yi Li, Yichen Li, Yufeng Li, Zhaohan Li, Zhibing Li, Ziyuan Li, Zonghai Li, An-An Liang, Hao Liang, Jiajun Liao, Yilin Liao, Yuzhong Liao, Ayut Limphirat, Guey-Lin Lin, Shengxin Lin, Tao Lin, Jiajie Ling, Xin Ling, Ivano Lippi, Caimei Liu, Fang Liu, Fengcheng Liu, Haidong Liu, Haotian Liu, Hongbang Liu, Hongjuan Liu, Hongtao Liu, Hongyang Liu, Jianglai Liu, Jiaxi Liu, Jinchang Liu, Min Liu, Qian Liu, Qin Liu, Runxuan Liu, Shenghui Liu, Shubin Liu, Shulin Liu, Xiaowei Liu, Xiwen Liu, Xuewei Liu, Yankai Liu, Zhen Liu, Lorenzo Loi, Alexey Lokhov, Paolo Lombardi, Claudio Lombardo, Kai Loo, Chuan Lu, Haoqi Lu, Jingbin Lu, Junguang Lu, Meishu Lu, Peizhi Lu, Shuxiang Lu, Xianguo Lu, Bayarto Lubsandorzhiev, Sultim Lubsandorzhiev, Livia Ludhova, Arslan Lukanov, Fengjiao Luo, Guang Luo, Jianyi Luo, Shu Luo, Wuming Luo, Xiaojie Luo, Vladimir Lyashuk, Bangzheng Ma, Bing Ma, Qiumei Ma, Si Ma, Xiaoyan Ma, Xubo Ma, Jihane Maalmi, Jingyu Mai, Marco Malabarba, Yury Malyshkin, Roberto Carlos Mandujano, Fabio Mantovani, Xin Mao, Yajun Mao, Stefano M. Mari, Filippo Marini, Agnese Martini, Matthias Mayer, Davit Mayilyan, Ints Mednieks, Yue Meng, Anita Meraviglia, Anselmo Meregaglia, Emanuela Meroni, Lino Miramonti, Nikhil Mohan, Michele Montuschi, Cristobal Morales Reveco, Massimiliano Nastasi, Dmitry V. Naumov, Elena Naumova, Diana Navas-Nicolas, Igor Nemchenok, Minh Thuan Nguyen Thi, Alexey Nikolaev, Feipeng Ning, Zhe Ning, Hiroshi Nunokawa, Lothar Oberauer, Juan Pedro Ochoa-Ricoux, Alexander Olshevskiy, Domizia Orestano, Fausto Ortica, Rainer Othegraven, Alessandro Paoloni, George Parker, Sergio Parmeggiano, Achilleas Patsias, Yatian Pei, Luca Pelicci, Anguo Peng, Haiping Peng, Yu Peng, Zhaoyuan Peng, Elisa Percalli, Willy Perrin, Frédéric Perrot, Pierre-Alexandre Petitjean, Fabrizio Petrucci, Oliver Pilarczyk, Luis Felipe Piñeres Rico, Artyom Popov, Pascal Poussot, Ezio Previtali, Fazhi Qi, Ming Qi, Xiaohui Qi, Sen Qian, Xiaohui Qian, Zhen Qian, Hao Qiao, Zhonghua Qin, Shoukang Qiu, Manhao Qu, Zhenning Qu, Gioacchino Ranucci, Alessandra Re, Abdel Rebii, Mariia Redchuk, Gioele Reina, Bin Ren, Jie Ren, Yuhan Ren, Barbara Ricci, Komkrit Rientong, Mariam Rifai, Mathieu Roche, Narongkiat Rodphai, Aldo Romani, Bedřich Roskovec, Xichao Ruan, Arseniy Rybnikov, Andrey Sadovsky, Paolo Saggese, Deshan Sandanayake, Anut Sangka, Giuseppe Sava, Utane Sawangwit, Michaela Schever, Cédric Schwab, Konstantin Schweizer, Alexandr Selyunin, Andrea Serafini, Mariangela Settimo, Junyu Shao, Vladislav Sharov, Hexi Shi, Jingyan Shi, Yanan Shi, Vitaly Shutov, Andrey Sidorenkov, Fedor Šimkovic, Apeksha Singhal, Chiara Sirignano, Jaruchit Siripak, Monica Sisti, Mikhail Smirnov, Oleg Smirnov, Sergey Sokolov, Julanan Songwadhana, Boonrucksar Soonthornthum, Albert Sotnikov, Warintorn Sreethawong, Achim Stahl, Luca Stanco, Konstantin Stankevich, Hans Steiger, Jochen Steinmann, Tobias Sterr, Matthias Raphael Stock, Virginia Strati, Michail Strizh, Alexander Studenikin, Aoqi Su, Jun Su, Guangbao Sun, Shifeng Sun, Xilei Sun, Yongjie Sun, Yongzhao Sun, Zhengyang Sun, Narumon Suwonjandee, Akira Takenaka, Xiaohan Tan, Jian Tang, Jingzhe Tang, Qiang Tang, Quan Tang, Xiao Tang, Vidhya Thara Hariharan, Igor Tkachev, Tomas Tmej, Marco Danilo Claudio Torri, Andrea Triossi, Wladyslaw Trzaska, Yu-Chen Tung, Cristina Tuve, Nikita Ushakov, Vadim Vedin, Carlo Venettacci, Giuseppe Verde, Maxim Vialkov, Benoit Viaud, Cornelius Moritz Vollbrecht, Katharina von Sturm, Vit Vorobel, Dmitriy Voronin, Lucia Votano, Pablo Walker, Caishen Wang, Chung-Hsiang Wang, En Wang, Guoli Wang, Hanwen Wang, Jian Wang, Jun Wang, Li Wang, Lu Wang, Meng Wang, Mingyuan Wang, Qianchuan Wang, Ruiguang Wang, Sibo Wang, Siguang Wang, Wei Wang, Wenshuai Wang, Xi Wang, Xiangyue Wang, Yangfu Wang, Yaoguang Wang, Yi Wang, Yifang Wang, Yuanqing Wang, Yuyi Wang, Zhe Wang, Zheng Wang, Zhimin Wang, Apimook Watcharangkool, Wei Wei, Wenlu Wei, Yadong Wei, Yuehuan Wei, Liangjian Wen, Jun Weng, Christopher Wiebusch, Rosmarie Wirth, Chengxin Wu, Diru Wu, Qun Wu, Yinhui Wu, Yiyang Wu, Zhi Wu, Michael Wurm, Jacques Wurtz, Christian Wysotzki, Yufei Xi, Dongmei Xia, Shishen Xian, Ziqian Xiang, Fei Xiao, Xiang Xiao, Xiaochuan Xie, Yijun Xie, Yuguang Xie, Zhao Xin, Zhizhong Xing, Benda Xu, Cheng Xu, Donglian Xu, Fanrong Xu, Hangkun Xu, Jiayang Xu, Jilei Xu, Jing Xu, Jinghuan Xu, Meihang Xu, Xunjie Xu, Yin Xu, Yu Xu, Baojun Yan, Qiyu Yan, Taylor Yan, Xiongbo Yan, Yupeng Yan, Changgen Yang, Chengfeng Yang, Fengfan Yang, Jie Yang, Lei Yang, Pengfei Yang, Xiaoyu Yang, Yifan Yang, Yixiang Yang, Zekun Yang, Haifeng Yao, Jiaxuan Ye, Mei Ye, Ziping Ye, Frédéric Yermia, Zhengyun You, Boxiang Yu, Chiye Yu, Chunxu Yu, Guojun Yu, Hongzhao Yu, Miao Yu, Xianghui Yu, Zeyuan Yu, Zezhong Yu, Cenxi Yuan, Chengzhuo Yuan, Ying Yuan, Zhenxiong Yuan, Baobiao Yue, Noman Zafar, Kirill Zamogilnyi, Vitalii Zavadskyi, Fanrui Zeng, Shan Zeng, Tingxuan Zeng, Yuda Zeng, Liang Zhan, Aiqiang Zhang, Bin Zhang, Binting Zhang, Feiyang Zhang, Hangchang Zhang, Haosen Zhang, Honghao Zhang, Jialiang Zhang, Jiawen Zhang, Jie Zhang, Jingbo Zhang, Jinnan Zhang, Junwei Zhang, Lei Zhang, Peng Zhang, Ping Zhang, Qingmin Zhang, Shiqi Zhang, Shu Zhang, Shuihan Zhang, Siyuan Zhang, Tao Zhang, Xiaomei Zhang, Xin Zhang, Xuantong Zhang, Yibing Zhang, Yinhong Zhang, Yiyu Zhang, Yongpeng Zhang, Yu Zhang, Yuanyuan Zhang, Yumei Zhang, Zhenyu Zhang, Zhijian Zhang, Jie Zhao, Rong Zhao, Runze Zhao, Shujun Zhao, Tianhao Zhao, Hua Zheng, Yangheng Zheng, Jing Zhou, Li Zhou, Nan Zhou, Shun Zhou, Tong Zhou, Xiang Zhou, Xing Zhou, Jingsen Zhu, Kangfu Zhu, Kejun Zhu, Zhihang Zhu, Bo Zhuang, Honglin Zhuang, Liang Zong, and Jiaheng Zou

Subjects
Astrophysics, QB460-466, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract We explore the decay of bound neutrons in the JUNO liquid scintillator detector into invisible particles (e.g., $$n\rightarrow 3 \nu $$ n → 3 ν or $$nn \rightarrow 2 \nu $$ n n → 2 ν ), which do not produce an observable signal. The invisible decay includes two decay modes: $$ n \rightarrow { inv} $$ n → inv and $$ nn \rightarrow { inv} $$ n n → inv . The invisible decays of s-shell neutrons in $$^{12}\textrm{C}$$ 12 C will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino $${\bar{\nu }}_e$$ ν ¯ e , natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are $$\tau /B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, \textrm{years}$$ τ / B ( n → inv ) > 5.0 × 10 31 years and $$\tau /B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, \textrm{years}$$ τ / B ( n n → inv ) > 1.4 × 10 32 years .

Published
2025
Full Text
View/download PDF

37. Active plasma sterilizer for planetary protection and contamination control for space missions

Author

Subrata Roy, Justin Kosky, Tamara Revazishvili, Sarthak Roy, Sierra Brown, Vladimir Vichnyakov, Nona Rurua, Emma Noelle Mastro, and Davit Kobaidze

Subjects
Plasma, Sterilization, Ozone, Portable, Smart, Plumbing-free design, Medicine, Science
Abstract

Abstract This paper introduces a novel, compact plasma sterilization system, the Active Plasma Sterilizer (APS), for planetary protection space missions. The development of the APS system is done through iterative testing and design modifications aimed at addressing decontamination modalities for time and temperature, cleaning adhesive surfaces, and cleaning protocols beyond alcohol and bleach. Decontamination testing of Deinococcus radiodurans, Geobacillus stearothermophilus (spore forming bacteria), and Aspergillus fumigatus (fungi) was verified for the APS on relevant materials of 4 to 5 log reduction up to complete killing in 45 min or less. The material compatibility testing of the APS performed with Stainless Steel 316, Teflon PTFE, and FR-4 PCB using a single exposure showed no visible material degradation through SEM analysis. This study demonstrates the efficacy of the APS technology for use with planetary protection due to its low-temperature operation, low weight and size, zero-plumbing requirements, safety features, decontamination capabilities, and material compatibility.

Published
2024
Full Text
View/download PDF

38. The Impact of Socio-Economic Challenges and Technological Progress on Economic Inequality: An Estimation with the Perelman Model and Ricci Flow Methods

Author

Davit Gondauri

Subjects
technological advances, economical inequality, gini coefficient, ricci flow, perelman models, technological innovations, research and development, innovations, sensitive analyze, automatization, economic stability, socio-economic challenges, Sociology (General), HM401-1281, Economic history and conditions, HC10-1085
Abstract

The article examines the impact of 16 key parameters of the Georgian economy on economic inequality, using the Perelman model and Ricci flow mathematical methods. The aim of the study is to conduct a deep analysis of the impact of socio-economic challenges and technological progress on the dynamics of the Gini coefficient. The article examines the following parameters: income distribution, productivity (GDP per hour), unemployment rate, investment rate, inflation rate, migration (net negative), education level, social mobility, trade infrastructure, capital flows, innovative activities, access to healthcare, fiscal policy (budget deficit), international trade (turnover relative to GDP), social protection programs, and technological access. The results of the study confirm that technological innovations and social protection programs have a positive impact on reducing inequality. Productivity growth, improving the quality of education and strengthening R&D investments increase the possibility of inclusive development. Sensitivity analysis shows that social mobility and infrastructure are important factors that affect economic stability. The accuracy of the model is confirmed by high R² values (80-90%) and the statistical reliability of the Z-statistic (

Published
2024
Full Text
View/download PDF

39. Droplet-engineered organoids recapitulate parental tissue transcriptome with inter-organoid homogeneity and inter-tumor cell heterogeneity

Author

Haoran Zhao, Yifan Cheng, Jiawei Li, Jiaqi Zhou, Haowei Yang, Feng Yu, Feihong Yu, Davit Khutsishvili, Zitian Wang, Shengwei Jiang, Kaixin Tan, Yi Kuang, Xinhui Xing, and Shaohua Ma

Subjects
Liver organoid, Tumor, Droplet-engineered organoid, RNA-seq, Droplet-based microfluidics, Science (General), Q1-390
Abstract

Organoids are expected to function as effective human organ models for precision cancer studies and drug development. Currently, primary tissue-derived organoids, termed non-engineered organoids (NEOs), are produced by manual pipetting or liquid handling that compromises organoid-organoid homogeneity and organoid-tissue consistency. Droplet-based microfluidics enables automated organoid production with high organoid-organoid homogeneity, organoid-tissue consistency, and a significantly improved production spectrum. It takes advantage of droplet-encapsulation of defined populations of cells and droplet-rendered microstructures that guide cell self-organization. Herein, we studied the droplet-engineered organoids (DEOs), derived from mouse liver tissues and human liver tumors, by using transcriptional analysis and cellular deconvolution on bulk RNA-seq data. The characteristics of DEOs are compared with the parental liver tissues (or tumors) and NEOs. The DEOs are proven higher reproducibility and consistency with the parental tissues, have a high production spectrum and shortened modeling time, and possess inter-organoid homogeneity and inter-tumor cell heterogeneity.

Published
2024
Full Text
View/download PDF

40. Development and external evaluation of a self-learning auto-segmentation model for Colorectal Cancer Liver Metastases Assessment (COALA)

Author

Jacqueline I. Bereska, Michiel Zeeuw, Luuk Wagenaar, Håvard Bjørke Jenssen, Nina J. Wesdorp, Delanie van der Meulen, Leonard F. Bereska, Efstratios Gavves, Boris V. Janssen, Marc G. Besselink, Henk A. Marquering, Jan-Hein T. M. van Waesberghe, Davit L. Aghayan, Egidijus Pelanis, Janneke van den Bergh, Irene I. M. Nota, Shira Moos, Gunter Kemmerich, Trygve Syversveen, Finn Kristian Kolrud, Joost Huiskens, Rutger-Jan Swijnenburg, Cornelis J. A. Punt, Jaap Stoker, Bjørn Edwin, Åsmund A. Fretland, Geert Kazemier, Inez M. Verpalen, for the Pancreatobiliary and Hepatic Artificial Intelligence Research (PHAIR) consortium, and the Dutch Colorectal Cancer Group Liver Expert Panel

Subjects
Colorectal neoplasms, Liver, Biomarkers, Tumor, Artificial intelligence, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract

Abstract Objectives Total tumor volume (TTV) is associated with overall and recurrence-free survival in patients with colorectal cancer liver metastases (CRLM). However, the labor-intensive nature of such manual assessments has hampered the clinical adoption of TTV as an imaging biomarker. This study aimed to develop and externally evaluate a CRLM auto-segmentation model on CT scans, to facilitate the clinical adoption of TTV. Methods We developed an auto-segmentation model to segment CRLM using 783 contrast-enhanced portal venous phase CTs (CT-PVP) of 373 patients. We used a self-learning setup whereby we first trained a teacher model on 99 manually segmented CT-PVPs from three radiologists. The teacher model was then used to segment CRLM in the remaining 663 CT-PVPs for training the student model. We used the DICE score and the intraclass correlation coefficient (ICC) to compare the student model’s segmentations and the TTV obtained from these segmentations to those obtained from the merged segmentations. We evaluated the student model in an external test set of 50 CT-PVPs from 35 patients from the Oslo University Hospital and an internal test set of 21 CT-PVPs from 10 patients from the Amsterdam University Medical Centers. Results The model reached a mean DICE score of 0.85 (IQR: 0.05) and 0.83 (IQR: 0.10) on the internal and external test sets, respectively. The ICC between the segmented volumes from the student model and from the merged segmentations was 0.97 on both test sets. Conclusion The developed colorectal cancer liver metastases auto-segmentation model achieved a high DICE score and near-perfect agreement for assessing TTV. Critical relevance statement AI model segments colorectal liver metastases on CT with high performance on two test sets. Accurate segmentation of colorectal liver metastases could facilitate the clinical adoption of total tumor volume as an imaging biomarker for prognosis and treatment response monitoring. Key Points Developed colorectal liver metastases segmentation model to facilitate total tumor volume assessment. Model achieved high performance on internal and external test sets. Model can improve prognostic stratification and treatment planning for colorectal liver metastases. Graphical Abstract

Published
2024
Full Text
View/download PDF

41. COOL Front: The FORECAST for your COMMUTE this FALL calls for luxuriously textured COATS, JACKETS, and DAYWEAR-with a LITTLE bit of LEATHER and LACE

Author

Giorgadze, Davit

Subjects
Autumn, Clothing and dress -- Forecasts and trends, Fashion -- Forecasts and trends, Market trend/market analysis, Fashion and beauty
Abstract

Photographs by DAVIT GIORGADZE Styling by DOGI Caption: This page: Jacket, trousers, and boots, FENDI. Opposite page: Coat and bag, CELINE BY HEDI SLIMANE. Love earrings and Juste un Clou [...]

Published
2024

42. Chiral photonic super-crystals based on helical van der Waals homostructures

Author

Voronin, Kirill V., Toksumakov, Adilet N., Ermolaev, Georgy A., Slavich, Aleksandr S., Tatmyshevskiy, Mikhail K., Novikov, Sergey V., Vyshnevy, Andrey A., Arsenin, Aleksey V., Novoselov, Kostya S., Ghazaryan, Davit A., Volkov, Valentyn S., and Baranov, Denis G.

Subjects
Physics - Optics
Abstract

Chirality is probably the most mysterious among all symmetry transformations. Very readily broken in biological systems, it is practically absent in naturally occurring inorganic materials and is very challenging to create artificially. Chiral optical wavefronts are often used for the identification, control and discrimination of left- and right-handed biological and other molecules. Thus, it is crucially important to create materials capable of chiral interaction with light, which would allow one to assign arbitrary chiral properties to a light field. In this paper, we utilized van der Waals technology to assemble helical homostructures with chiral properties (e. g. circular dichroism). Because of the large range of van der Waals materials available such helical homostructures can be assigned with very flexible optical properties. We demonstrate our approach by creating helical homostructures based on multilayer As$_2$S$_3$, which offers the most pronounced chiral properties even in thin structures due to its strong biaxial optically anisotropy. Our work showcases that the chirality of an electromagnetic system may emerge at an intermediate level between the molecular and the mesoscopic one due to the tailored arrangement of non-chiral layers of van der Waals crystals and without additional patterning.

Published
2023
Full Text
View/download PDF

43. Exponentially faster preparation of quantum dimers via driven-dissipative stabilization

Author

Lim, Kian Hwee, Mok, Wai-Keong, You, Jia-Bin, Kong, Jian Feng, and Aghamalyan, Davit

Subjects
Quantum Physics
Abstract

We propose a novel rapid, high-fidelity, and noise-resistant scheme to generate many-body entanglement between multiple qubits stabilized by dissipation into a 1D bath. Using a carefully designed time-dependent drive, our scheme achieves a provably exponential speedup over state-of-the-art dissipative stabilization schemes in 1D baths, which require a timescale that diverges as the target fidelity approaches unity and scales exponentially with the number of qubits. To prepare quantum dimer pairs, our scheme only requires local 2-qubit control Hamiltonians, with a protocol time that is independent of system size. This provides a scalable and robust protocol for generating a large number of entangled dimer pairs on-demand, serving as a fundamental resource for many quantum metrology and quantum information processing tasks., Comment: 17 pages, 10 figures

Published
2023

44. Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Author

Abusleme, Angel, Adam, Thomas, Ahmad, Shakeel, Ahmed, Rizwan, Aiello, Sebastiano, Akram, Muhammad, Aleem, Abid, An, Fengpeng, An, Qi, Andronico, Giuseppe, Anfimov, Nikolay, Antonelli, Vito, Antoshkina, Tatiana, Asavapibhop, Burin, de André, João Pedro Athayde Marcondes, Auguste, Didier, Bai, Weidong, Balashov, Nikita, Baldini, Wander, Barresi, Andrea, Basilico, Davide, Baussan, Eric, Bellato, Marco, Beretta, Marco, Bergnoli, Antonio, Bick, Daniel, Bieger, Lukas, Biktemerova, Svetlana, Birkenfeld, Thilo, Morton-Blake, Iwan, Blum, David, Blyth, Simon, Bolshakova, Anastasia, Bongrand, Mathieu, Bordereau, Clément, Breton, Dominique, Brigatti, Augusto, Brugnera, Riccardo, Bruno, Riccardo, Budano, Antonio, Busto, Jose, Cabrera, Anatael, Caccianiga, Barbara, Cai, Hao, Cai, Xiao, Cai, Yanke, Cai, Zhiyan, Callier, Stéphane, Cammi, Antonio, Campeny, Agustin, Cao, Chuanya, Cao, Guofu, Cao, Jun, Caruso, Rossella, Cerna, Cédric, Cerrone, Vanessa, Chan, Chi, Chang, Jinfan, Chang, Yun, Chatrabhuti, Auttakit, Chen, Chao, Chen, Guoming, Chen, Pingping, Chen, Shaomin, Chen, Yixue, Chen, Yu, Chen, Zhangming, Chen, Zhiyuan, Chen, Zikang, Cheng, Jie, Cheng, Yaping, Cheng, Yu Chin, Chepurnov, Alexander, Chetverikov, Alexey, Chiesa, Davide, Chimenti, Pietro, Chin, Yen-Ting, Chu, Ziliang, Chukanov, Artem, Claverie, Gérard, Clementi, Catia, Clerbaux, Barbara, Molla, Marta Colomer, Di Lorenzo, Selma Conforti, Coppi, Alberto, Corti, Daniele, Csakli, Simon, Corso, Flavio Dal, Dalager, Olivia, Datta, Jaydeep, De La Taille, Christophe, Deng, Zhi, Deng, Ziyan, Ding, Xiaoyu, Ding, Xuefeng, Ding, Yayun, Dirgantara, Bayu, Dittrich, Carsten, Dmitrievsky, Sergey, Dohnal, Tadeas, Dolzhikov, Dmitry, Donchenko, Georgy, Dong, Jianmeng, Doroshkevich, Evgeny, Dou, Wei, Dracos, Marcos, Druillole, Frédéric, Du, Ran, Du, Shuxian, Dugas, Katherine, Dusini, Stefano, Duyang, Hongyue, Eck, Jessica, Enqvist, Timo, Fabbri, Andrea, Fahrendholz, Ulrike, Fan, Lei, Fang, Jian, Fang, Wenxing, Fargetta, Marco, Fedoseev, Dmitry, Fei, Zhengyong, Feng, Li-Cheng, Feng, Qichun, Ferraro, Federico, Fournier, Amélie, Gan, Haonan, Gao, Feng, Garfagnini, Alberto, Gavrikov, Arsenii, Giammarchi, Marco, Giudice, Nunzio, Gonchar, Maxim, Gong, Guanghua, Gong, Hui, Gornushkin, Yuri, Göttel, Alexandre, Grassi, Marco, Gromov, Maxim, Gromov, Vasily, Gu, Minghao, Gu, Xiaofei, Gu, Yu, Guan, Mengyun, Guan, Yuduo, Guardone, Nunzio, Guo, Cong, Guo, Wanlei, Guo, Xinheng, Hagner, Caren, Han, Ran, Han, Yang, He, Miao, He, Wei, Heinz, Tobias, Hellmuth, Patrick, Heng, Yuekun, Herrera, Rafael, Hor, YuenKeung, Hou, Shaojing, Hsiung, Yee, Hu, Bei-Zhen, Hu, Hang, Hu, Jianrun, Hu, Jun, Hu, Shouyang, Hu, Tao, Hu, Yuxiang, Hu, Zhuojun, Huang, Guihong, Huang, Hanxiong, Huang, Jinhao, Huang, Junting, Huang, Kaixuan, Huang, Wenhao, Huang, Xin, Huang, Xingtao, Huang, Yongbo, Hui, Jiaqi, Huo, Lei, Huo, Wenju, Huss, Cédric, Hussain, Safeer, Imbert, Leonard, Ioannisian, Ara, Isocrate, Roberto, Jafar, Arshak, Jelmini, Beatrice, Jeria, Ignacio, Ji, Xiaolu, Jia, Huihui, Jia, Junji, Jian, Siyu, Jiang, Cailian, Jiang, Di, Jiang, Wei, Jiang, Xiaoshan, Jing, Xiaoping, Jollet, Cécile, Kampmann, Philipp, Kang, Li, Karaparambil, Rebin, Kazarian, Narine, Khan, Ali, Khatun, Amina, Khosonthongkee, Khanchai, Korablev, Denis, Kouzakov, Konstantin, Krasnoperov, Alexey, Kuleshov, Sergey, Kutovskiy, Nikolay, Labit, Loïc, Lachenmaier, Tobias, Landini, Cecilia, Leblanc, Sébastien, Lebrin, Victor, Lefevre, Frederic, Lei, Ruiting, Leitner, Rupert, Leung, Jason, Li, Demin, Li, Fei, Li, Fule, Li, Gaosong, Li, Huiling, Li, Jiajun, Li, Mengzhao, Li, Min, Li, Nan, Li, Qingjiang, Li, Ruhui, Li, Rui, Li, Shanfeng, Li, Tao, Li, Teng, Li, Weidong, Li, Weiguo, Li, Xiaomei, Li, Xiaonan, Li, Xinglong, Li, Yi, Li, Yichen, Li, Yufeng, Li, Zhaohan, Li, Zhibing, Li, Ziyuan, Li, Zonghai, Liang, Hao, Liao, Jiajun, Limphirat, Ayut, Lin, Guey-Lin, Lin, Shengxin, Lin, Tao, Ling, Jiajie, Ling, Xin, Lippi, Ivano, Liu, Caimei, Liu, Fang, Liu, Fengcheng, Liu, Haidong, Liu, Haotian, Liu, Hongbang, Liu, Hongjuan, Liu, Hongtao, Liu, Hui, Liu, Jianglai, Liu, Jiaxi, Liu, Jinchang, Liu, Min, Liu, Qian, Liu, Qin, Liu, Runxuan, Liu, Shenghui, Liu, Shubin, Liu, Shulin, Liu, Xiaowei, Liu, Xiwen, Liu, Xuewei, Liu, Yankai, Liu, Zhen, Lokhov, Alexey, Lombardi, Paolo, Lombardo, Claudio, Loo, Kai, Lu, Chuan, Lu, Haoqi, Lu, Jingbin, Lu, Junguang, Lu, Peizhi, Lu, Shuxiang, Lu, Xianguo, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Ludhova, Livia, Lukanov, Arslan, Luo, Daibin, Luo, Fengjiao, Luo, Guang, Luo, Jianyi, Luo, Shu, Luo, Wuming, Luo, Xiaojie, Lyashuk, Vladimir, Ma, Bangzheng, Ma, Bing, Ma, Qiumei, Ma, Si, Ma, Xiaoyan, Ma, Xubo, Maalmi, Jihane, Magoni, Marco, Mai, Jingyu, Malyshkin, Yury, Mandujano, Roberto Carlos, Mantovani, Fabio, Mao, Xin, Mao, Yajun, Mari, Stefano M., Marini, Filippo, Martini, Agnese, Mayer, Matthias, Mayilyan, Davit, Mednieks, Ints, Meng, Yue, Meraviglia, Anita, Meregaglia, Anselmo, Meroni, Emanuela, Meyhöfer, David, Miramonti, Lino, Mohan, Nikhil, Montuschi, Michele, Müller, Axel, Nastasi, Massimiliano, Naumov, Dmitry V., Naumova, Elena, Navas-Nicolas, Diana, Nemchenok, Igor, Thi, Minh Thuan Nguyen, Nikolaev, Alexey, Ning, Feipeng, Ning, Zhe, Nunokawa, Hiroshi, Oberauer, Lothar, Ochoa-Ricoux, Juan Pedro, Olshevskiy, Alexander, Orestano, Domizia, Ortica, Fausto, Othegraven, Rainer, Paoloni, Alessandro, Parmeggiano, Sergio, Pei, Yatian, Pelicci, Luca, Peng, Anguo, Peng, Haiping, Peng, Yu, Peng, Zhaoyuan, Perrot, Frédéric, Petitjean, Pierre-Alexandre, Petrucci, Fabrizio, Pilarczyk, Oliver, Rico, Luis Felipe Piñeres, Popov, Artyom, Poussot, Pascal, Previtali, Ezio, Qi, Fazhi, Qi, Ming, Qi, Xiaohui, Qian, Sen, Qian, Xiaohui, Qian, Zhen, Qiao, Hao, Qin, Zhonghua, Qiu, Shoukang, Qu, Manhao, Qu, Zhenning, Ranucci, Gioacchino, Rasheed, Reem, Re, Alessandra, Rebii, Abdel, Redchuk, Mariia, Ren, Bin, Ren, Jie, Ricci, Barbara, Rientong, Komkrit, Rifai, Mariam, Roche, Mathieu, Rodphai, Narongkiat, Romani, Aldo, Roskovec, Bedřich, Ruan, Xichao, Rybnikov, Arseniy, Sadovsky, Andrey, Saggese, Paolo, Sandanayake, Deshan, Sangka, Anut, Sava, Giuseppe, Sawangwit, Utane, Schever, Michaela, Schwab, Cédric, Schweizer, Konstantin, Selyunin, Alexandr, Serafini, Andrea, Settimo, Mariangela, Sharov, Vladislav, Shaydurova, Arina, Shi, Jingyan, Shi, Yanan, Shutov, Vitaly, Sidorenkov, Andrey, Šimkovic, Fedor, Singhal, Apeksha, Sirignano, Chiara, Siripak, Jaruchit, Sisti, Monica, Smirnov, Mikhail, Smirnov, Oleg, Sogo-Bezerra, Thiago, Sokolov, Sergey, Songwadhana, Julanan, Soonthornthum, Boonrucksar, Sotnikov, Albert, Šrámek, Ondřej, Sreethawong, Warintorn, Stahl, Achim, Stanco, Luca, Stankevich, Konstantin, Steiger, Hans, Steinmann, Jochen, Sterr, Tobias, Stock, Matthias Raphael, Strati, Virginia, Studenikin, Alexander, Su, Aoqi, Su, Jun, Sun, Shifeng, Sun, Xilei, Sun, Yongjie, Sun, Yongzhao, Sun, Zhengyang, Suwonjandee, Narumon, Szelezniak, Michal, Takenaka, Akira, Tang, Jian, Tang, Qiang, Tang, Quan, Tang, Xiao, Hariharan, Vidhya Thara, Theisen, Eric, Tietzsch, Alexander, Tkachev, Igor, Tmej, Tomas, Torri, Marco Danilo Claudio, Tortorici, Francesco, Treskov, Konstantin, Triossi, Andrea, Triozzi, Riccardo, Trzaska, Wladyslaw, Tung, Yu-Chen, Tuve, Cristina, Ushakov, Nikita, Vedin, Vadim, Venettacci, Carlo, Verde, Giuseppe, Vialkov, Maxim, Viaud, Benoit, Vollbrecht, Cornelius Moritz, von Sturm, Katharina, Vorobel, Vit, Voronin, Dmitriy, Votano, Lucia, Walker, Pablo, Wang, Caishen, Wang, Chung-Hsiang, Wang, En, Wang, Guoli, Wang, Jian, Wang, Jun, Wang, Li, Wang, Lu, Wang, Meng, Wang, Ruiguang, Wang, Siguang, Wang, Wei, Wang, Wenshuai, Wang, Xi, Wang, Xiangyue, Wang, Yangfu, Wang, Yaoguang, Wang, Yi, Wang, Yifang, Wang, Yuanqing, Wang, Yuyi, Wang, Zhe, Wang, Zheng, Wang, Zhimin, Watcharangkool, Apimook, Wei, Wei, Wei, Wenlu, Wei, Yadong, Wei, Yuehuan, Wen, Kaile, Wen, Liangjian, Weng, Jun, Wiebusch, Christopher, Wirth, Rosmarie, Wonsak, Bjoern, Wu, Diru, Wu, Qun, Wu, Yiyang, Wu, Zhi, Wurm, Michael, Wurtz, Jacques, Wysotzki, Christian, Xi, Yufei, Xia, Dongmei, Xiao, Fei, Xiao, Xiang, Xie, Xiaochuan, Xie, Yuguang, Xie, Zhangquan, Xin, Zhao, Xing, Zhizhong, Xu, Benda, Xu, Cheng, Xu, Donglian, Xu, Fanrong, Xu, Hangkun, Xu, Jilei, Xu, Jing, Xu, Meihang, Xu, Xunjie, Xu, Yin, Xu, Yu, Yan, Baojun, Yan, Qiyu, Yan, Taylor, Yan, Xiongbo, Yan, Yupeng, Yang, Changgen, Yang, Chengfeng, Yang, Jie, Yang, Lei, Yang, Xiaoyu, Yang, Yifan, Yao, Haifeng, Ye, Jiaxuan, Ye, Mei, Ye, Ziping, Yermia, Frédéric, You, Zhengyun, Yu, Boxiang, Yu, Chiye, Yu, Chunxu, Yu, Guojun, Yu, Hongzhao, Yu, Miao, Yu, Xianghui, Yu, Zeyuan, Yu, Zezhong, Yuan, Cenxi, Yuan, Chengzhuo, Yuan, Ying, Yuan, Zhenxiong, Yue, Baobiao, Zafar, Noman, Zavadskyi, Vitalii, Zeng, Fanrui, Zeng, Shan, Zeng, Tingxuan, Zeng, Yuda, Zhan, Liang, Zhang, Aiqiang, Zhang, Bin, Zhang, Binting, Zhang, Feiyang, Zhang, Haosen, Zhang, Honghao, Zhang, Jialiang, Zhang, Jiawen, Zhang, Jie, Zhang, Jingbo, Zhang, Jinnan, ZHANG, Lei, Zhang, Mohan, Zhang, Peng, Zhang, Ping, Zhang, Qingmin, Zhang, Shiqi, Zhang, Shu, Zhang, Shuihan, Zhang, Siyuan, Zhang, Tao, Zhang, Xiaomei, Zhang, Xin, Zhang, Xuantong, Zhang, Yinhong, Zhang, Yiyu, Zhang, Yongpeng, Zhang, Yu, Zhang, Yuanyuan, Zhang, Yumei, Zhang, Zhenyu, Zhang, Zhijian, Zhao, Jie, Zhao, Rong, Zhao, Runze, Zhao, Shujun, Zheng, Dongqin, Zheng, Hua, Zheng, Yangheng, Zhong, Weirong, Zhou, Jing, Zhou, Li, Zhou, Nan, Zhou, Shun, Zhou, Tong, Zhou, Xiang, Zhu, Jingsen, Zhu, Kangfu, Zhu, Kejun, Zhu, Zhihang, Zhuang, Bo, Zhuang, Honglin, Zong, Liang, Zou, Jiaheng, and Züfle, Jan

Subjects
High Energy Physics - Experiment, Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Phenomenology
Abstract

The core-collapse supernova (CCSN) is considered one of the most energetic astrophysical events in the universe. The early and prompt detection of neutrinos before (pre-SN) and during the supernova (SN) burst presents a unique opportunity for multi-messenger observations of CCSN events. In this study, we describe the monitoring concept and present the sensitivity of the system to pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton liquid scintillator detector currently under construction in South China. The real-time monitoring system is designed to ensure both prompt alert speed and comprehensive coverage of progenitor stars. It incorporates prompt monitors on the electronic board as well as online monitors at the data acquisition stage. Assuming a false alert rate of 1 per year, this monitoring system exhibits sensitivity to pre-SN neutrinos up to a distance of approximately 1.6 (0.9) kiloparsecs and SN neutrinos up to about 370 (360) kiloparsecs for a progenitor mass of 30 solar masses, considering both normal and inverted mass ordering scenarios. The pointing ability of the CCSN is evaluated by analyzing the accumulated event anisotropy of inverse beta decay interactions from pre-SN or SN neutrinos. This, along with the early alert, can play a crucial role in facilitating follow-up multi-messenger observations of the next galactic or nearby extragalactic CCSN., Comment: 24 pages, 9 figures, accepted for the publication at JCAP

Published
2023

45. Exploring van der Waals materials with high anisotropy: geometrical and optical approaches

Author

Slavich, Aleksandr S., Ermolaev, Georgy A., Tatmyshevskiy, Mikhail K., Toksumakov, Adilet N., Matveeva, Olga G., Grudinin, Dmitriy V., Mazitov, Arslan, Kravtsov, Konstantin V., Syuy, Alexander V., Tsymbarenko, Dmitry M., Mironov, Mikhail S., Novikov, Sergey M., Kruglov, Ivan, Ghazaryan, Davit A., Vyshnevyy, Andrey A., Arsenin, Aleksey V., Volkov, Valentyn S., and Novoselov, Kostya S.

Subjects
Physics - Optics, Condensed Matter - Materials Science
Abstract

The emergence of van der Waals (vdW) materials resulted in the discovery of their giant optical, mechanical, and electronic anisotropic properties, immediately enabling countless novel phenomena and applications. Such success inspired an intensive search for the highest possible anisotropic properties among vdW materials. Furthermore, the identification of the most promising among the huge family of vdW materials is a challenging quest requiring innovative approaches. Here, we suggest an easy-to-use method for such a survey based on the crystallographic geometrical perspective of vdW materials followed by their optical characterization. Using our approach, we found As2S3 as a highly anisotropic vdW material. It demonstrates rare giant in-plane optical anisotropy, high refractive index and transparency in the visible range, overcoming the century-long record set by rutile. Given these benefits, As2S3 opens a pathway towards next-generation nanophotonics as demonstrated by an ultrathin true zero-order quarter-waveplate that combines classical and the Fabry-Perot optical phase accumulations. Hence, our approach provides an effective and easy-to-use method to find vdW materials with the utmost anisotropic properties., Comment: 11 pages, 5 figures

Published
2023

46. Quantum Machine Learning for Credit Scoring

Author

Schetakis, Nikolaos, Aghamalyan, Davit, Boguslavsky, Michael, Rees, Agnieszka, Raktomalala, Marc, and Griffin, Paul

Subjects
Quantum Physics
Abstract

In this paper we explore the use of quantum machine learning (QML) applied to credit scoring for small and medium-sized enterprises (SME). A quantum/classical hybrid approach has been used with several models, activation functions, epochs and other parameters. Results are shown from the best model, using two quantum classifiers and a classical neural network, applied to data for companies in Singapore. We observe significantly more efficient training for the quantum models over the classical models with the quantum model trained for 350 epochs compared to 3500 epochs for comparable prediction performance. Surprisingly, a degradation in the accuracy was observed as the number of qubits was increased beyond 12 qubits and also with the addition of extra classifier blocks in the quantum model. Practical issues for executing on simulators and real quantum computers are also explored. Overall, we see great promise in this first in-depth exploration of the use of hybrid QML in credit scoring., Comment: 12 pages, 5 figures, article

Published
2023

48. Microglia promote anti-tumour immunity and suppress breast cancer brain metastasis

Author

Evans, Katrina T, Blake, Kerrigan, Longworth, Aaron, Coburn, Morgan A, Insua-Rodríguez, Jacob, McMullen, Timothy P, Nguyen, Quy H, Ma, Dennis, Lev, Tatyana, Hernandez, Grace A, Oganyan, Armani K, Orujyan, Davit, Edwards, Robert A, Pridans, Clare, Green, Kim N, Villalta, S Armando, Blurton-Jones, Mathew, and Lawson, Devon A

Subjects
Biochemistry and Cell Biology, Biological Sciences, Cancer, Neurosciences, Breast Cancer, Women's Health, Immunotherapy, 2.1 Biological and endogenous factors, 4.1 Discovery and preclinical testing of markers and technologies, Mice, Animals, Humans, Female, Microglia, Breast Neoplasms, Brain Neoplasms, Brain, Treatment Outcome, Medical and Health Sciences, Developmental Biology, Biochemistry and cell biology
Abstract

Breast cancer brain metastasis (BCBM) is a lethal disease with no effective treatments. Prior work has shown that brain cancers and metastases are densely infiltrated with anti-inflammatory, protumourigenic tumour-associated macrophages, but the role of brain-resident microglia remains controversial because they are challenging to discriminate from other tumour-associated macrophages. Using single-cell RNA sequencing, genetic and humanized mouse models, we specifically identify microglia and find that they play a distinct pro-inflammatory and tumour-suppressive role in BCBM. Animals lacking microglia show increased metastasis, decreased survival and reduced natural killer and T cell responses, showing that microglia are critical to promote anti-tumour immunity to suppress BCBM. We find that the pro-inflammatory response is conserved in human microglia, and markers of their response are associated with better prognosis in patients with BCBM. These findings establish an important role for microglia in anti-tumour immunity and highlight them as a potential immunotherapy target for brain metastasis.

Published
2023

Catalog

Books, media, physical & digital resources
See catalog results