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1. Cebp1 and Cebpβ transcriptional axis controls eosinophilopoiesis in zebrafish

Author

Gaofei Li, Yicong Sun, Immanuel Kwok, Liting Yang, Wanying Wen, Peixian Huang, Mei Wu, Jing Li, Zhibin Huang, Zhaoyuan Liu, Shuai He, Wan Peng, Jin-Xin Bei, Florent Ginhoux, Lai Guan Ng, and Yiyue Zhang

Subjects
Science
Abstract

Abstract Eosinophils are a group of granulocytes well known for their capacity to protect the host from parasites and regulate immune function. Diverse biological roles for eosinophils have been increasingly identified, but the developmental pattern and regulation of the eosinophil lineage remain largely unknown. Herein, we utilize the zebrafish model to analyze eosinophilic cell differentiation, distribution, and regulation. By identifying eslec as an eosinophil lineage-specific marker, we establish a Tg(eslec:eGFP) reporter line, which specifically labeled cells of the eosinophil lineage from early life through adulthood. Spatial-temporal analysis of eslec + cells demonstrates their organ distribution from larval stage to adulthood. By single-cell RNA-Seq analysis, we decipher the eosinophil lineage cells from lineage-committed progenitors to mature eosinophils. Through further genetic analysis, we demonstrate the role of Cebp1 in balancing neutrophil and eosinophil lineages, and a Cebp1-Cebpβ transcriptional axis that regulates the commitment and differentiation of the eosinophil lineage. Cross-species functional comparisons reveals that zebrafish Cebp1 is the functional orthologue of human C/EBPεP27 in suppressing eosinophilopoiesis. Our study characterizes eosinophil development in multiple dimensions including spatial-temporal patterns, expression profiles, and genetic regulators, providing for a better understanding of eosinophilopoiesis.

Published
2024
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2. Quantitative thresholds for variant enrichment in 13,845 cases: improving pathogenicity classification in genetic hearing loss

Author

Sihan Liu, Mingjun Zhong, Yu Huang, Qian Zhang, Ting Chen, Xiaofei Xu, Wan Peng, Xiaolu Wang, Xiaoshu Feng, Lu Kang, Yu Lu, Jing Cheng, Fengxiao Bu, and Huijun Yuan

Subjects
PS4, ACMG/AMP guidelines, Variant classification, Cohort dataset, Hearing loss, Medicine, Genetics, QH426-470
Abstract

Abstract Background The American College of Medical Genetics and Genomics (ACMG)/Association for Molecular Pathology (AMP) guidelines recommend using variant enrichment among cases as "strong" evidence for pathogenicity per the PS4 criterion. However, quantitative support for PS4 thresholds from real-world Mendelian case–control cohorts is lacking. Methods To address this gap, we evaluated and established PS4 thresholds using data from the Chinese Deafness Genetics Consortium. A total of 9,050 variants from 13,845 patients with hearing loss (HL) and 6,570 ancestry-matched controls were analyzed. Positive likelihood ratio and local positive likelihood ratio values were calculated to determine the thresholds corresponding to each strength of evidence across three variant subsets. Results In subset 1, consisting of variants present in both cases and controls with an allele frequency (AF) in cases ≥ 0.0005, an odds ratio (OR) ≥ 6 achieved strong evidence, while OR ≥ 3 represented moderate evidence. For subset 2, which encompassed variants present in both cases and controls with a case AF 2.27 or allele count ≥ 3) and the PS4_Moderate threshold (allele count ≥ 6), respectively. Reanalysis applying the adjusted PS4 criteria changed the classification of 15 variants and enabled diagnosis of an additional four patients. Conclusions Our study quantified evidence strength thresholds for variant enrichment in genetic HL cases, highlighting the importance of defining disease/gene-specific thresholds to improve the precision and accuracy of clinical genetic testing.

Published
2023
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3. Evaluation of the IDEA Public Schools Education Innovation and Research Grant [U411C190117]: Impact of IDEA Public Schools' Advanced Placement Computer Science Principles Curriculum Study

Author

American Institutes for Research (AIR), Elizabeth Kurban, Dana Shaat, Mariesa Hawkins, and Yinmei Wan

Abstract

Purpose: The purpose of the study was to evaluate the implementation and impact of a computer science-integrated math curriculum, the Beauty and Joy of Computing (BJC), combined with curriculum and professional development support for Computer Science Principles (CSP) teachers, funded by an Innovation and Research (EIR) grant. Methods: The study used a randomized controlled trial design where schools from the IDEA public schools were randomly assigned to use the BJC curriculum (treatment) or, the business as usual, Code.org (comparison). The study compared AP CSP test scores and student attitudes towards CSP between students in the treatment and comparison group. Results: The study found that the average AP CSP scores were low in both the treatment and comparison groups. About one-quarter of test takers (25%) earned a passing score of 3 or higher (compared to national average of 64%). The results of the analyses show that there were no statistically significant differences between the BJC and comparison groups. The study also showed that students in the treatment group responded more favorably on all student engagement in CSP constructs than comparison students, the size of the differences between the treatment and comparison condition were small and none were statistically significant. Implications: Since the evaluation examined student outcomes in the same year that the new curriculum was implemented for this first time, finding significant impacts on student outcomes may require more time. Nevertheless, the results of this evaluation inform IDEA staff, practitioners, and other researchers on the intervention's effectiveness, and they provide insights on how to improve curriculum use to further improve student outcomes.

Published
2024

4. Evaluation of the IDEA Public Schools Education Innovation and Research Grant [U411C190117]: Math Curriculum Redesign

Author

American Institutes for Research (AIR), Elizabeth Kurban, Yinmei Wan, Mariesa Hawkins, and Dana Shaat

Abstract

Purpose: The purpose of the study was to evaluate the implementation and impact of the IDEA Public Schools' Mathematics Curriculum Redesign (MCR) effort, funded by an Innovation and Research (EIR) grant. Methods: The study used a quasi-experimental matching design to examine the effect of MCR on student achievement outcomes. The study compared outcomes between the two cohorts of students at IDEA Public Schools that piloted the MCR and matched cohorts of students in IDEA schools that had delayed implementation of MCR. Results:?The study found statistically significant differences in IDEA Geometry test scores (ES = 0.39) between treatment and comparison group students in Grade 9 and on AP Calculus exam scores between treatment and comparison group students in Grade 12 (ES = 0.57). The study also found that the MCR program met the evaluation threshold for fidelity of implementation for the two out of three key program components (development of redesigned math curriculum and teacher use of math curriculum) in some years. The program did not meet the evaluation threshold for fidelity of implementation for the third key program components (teacher training and professional development to use redesigned math curriculum) in any of the years. Implications:?Because curriculum development and implementation are ongoing and iterative, realizing all of the benefits is not immediate and may take more time to achieve desired results. Continuous improvement of the curriculum and consistent supports to teachers will contribute to continued benefits for teachers and students while moving toward reaching anticipated student outcomes.

Published
2024

5. A time-frequency feature prediction network for time-varying radio frequency interference

Author

WAN Pengcheng, FENG Weike, TONG Ningning, and WEI Wei

Subjects
频谱预测, 射频干扰, 深度神经网络, 循环神经网络, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Abstract

The time-varying radio frequency interference has strong nonlinear dynamic characteristics, which is difficult to be predicted by linear method effectively, making the anti-interference decision without sufficient information support. To solve this problem, a recurrent neural network for spectrum prediction based on time-frequency correlation features is proposed. A sliding window is used to characterize the two-dimensional correlation of time-frequency series, and the spectrum prediction problem is transformed into a problem similar to spatiotemporal sequence prediction. A gradient bridge structure across time frames is added to reduce the attenuation of the gradient in the long time and multi-level network propagation. The training efficiency and network performance are improved by the loss function with better matching. Simulation and experimental results verify the validity of the network prediction results.

Published
2023
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6. Zwitterionic betaines over HEPES as the new generation biocompatible pH buffers for cell culture

Author

Peiming Liu, Jin Sun, Wan Peng, Yahui Gu, Xiaoxue Ji, Zhi Su, Pingsheng Liu, and Jian Shen

Subjects
Good's buffers, Zwitterionic betaines, ROS conversion, Cellular functions, Biocompatibility, Materials of engineering and construction. Mechanics of materials, TA401-492, Biology (General), QH301-705.5
Abstract

Good's buffers have been widely applied in cell/organ culture over the past half a century as biocompatible pH stabilizers. However, the emergence of severe adverse effects, such as cellular uptake, lysosomal autophagic activation, and visible light-induced cytotoxicity, raises serious questions over its biocompatibility while underlying mechanism was unclear. Here we report that riboflavin (RF, component of cell culture medium) generates 1O2, ·OH, and O2•- under visible light exposure during regular cell manipulation. These short half-life reactive oxygen species (ROS) react with tertiary amine groups of HEPES, producing 106.6 μM of H2O2. Orders of magnitude elevated half-life of ROS in the medium caused severe cytotoxicity and systematic disorder of normal cell functions. We have further designed and validated zwitterionic betaines as the new generation biocompatible organic pH buffers, which is able to completely avoid the adverse effects that found on HEPES and derivate Good's buffers. These findings may also open a new avenue for zwitterionic betaine based materials for biomedical applications.

Published
2023
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8. Mechanism of short-wall block backfill water-preserved mining based on water-conducting fractures development-heavy metal ions migration

Author

ZHANG Yun, LIU Yongzi, LAI Xingping, SONG Tianqi, ZHANG Leiming, SUN Haoqiang, WAN Peifeng, and ZHAN Rui

Subjects
water resources protection, water-conducting fractures, heavy metal ions in gangue, water-preserved mining, short-wall block backfill mining, Mining engineering. Metallurgy, TN1-997
Abstract

Short-wall block backfill mining (SBBM) technology can effectively solve the problems of coal resources waste, water resources loss and gangue waste accumulation. However, after the long-term effect of mine water, the internal heavy metal ions in the gangue backfill materials may be leached, which have a certain potential impact on the groundwater environment in the mining area. Therefore, according to the technical characteristics of SBBM technology, comprehensive prevention of water resources loss and water resources pollution during SBBM was studied systematically. First, prevention and control of water resources loss in the mining area. Based on the combination relationship of “coal pillar, backfill body-barrier layer-aquifuge”, The development of water-conducting fractures in overlying strata under SBBM were analysed. The control mechanism of water resources protection under the evolution of overlying strata structure induced during SBBM was revealed. Secondly, prevention and control of water resources pollution in mining areas. The migration model of heavy metal ions in the gob was established. Then, the pollution mechanism of gangue backfill materials to water resources was revealed, the migration law of heavy metal ions in gangue backfill materials was analyzed, the comprehensive prevention and control technology of water resources destruction in mining area was summarized, the comprehensive prevention and control technology of water resources loss-pollution in mining area was summarized, and the design method of filling ratio based on water resources loss-pollution prevention was put forward. The results show that the gangue materials were filled into the gob as the backfill body, the gangue materials would be the permanent bearing body, which bore the load of the overlying strata accompanied with the protective coal pillar among the blocks, which could greatly prevents the collapse of the low-level stratum, restrict the water-conducting fractures to penetrate the barrier layer and protect the integrity of the high-level stratum. In addition, the heavy metal ions in gangue backfill materials were subjected to the coupled action of seepage, concentration, and stress and then driven by water head pressure and gravitational potential energy to migrate along the side of the coal pillar and below the floor stratum, during which mine water served as the carrier. The migration distance of heavy metal ions increased with increasing of the permeability of floor strata, leaching concentration of heavy metal ions/pollution source intensity, depth of floor breaks and water level heights, and decreases with the increase of gangue particle size and surrounding rock stress. Based on this, the comprehensive prevention and control technology of water resources loss-pollution in mining area was summarized, it included mining parameter control technology, backfill parameter control technology, pollution source control technology, transmission route control technology and in-situ water control technology. and the design method of filling ratio based on water resource loss-pollution prevention and control was put forward, which can realize the comprehensive prevention and control of water resource loss-pollution in mining area. This study will provide scientific theoretical basis and guidance for the comprehensive prevention and control of the water resources destruction problems caused by coal mining.

Published
2023
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9. Study on the retention effect of condensed water at different soil levels in exposed areas of Hoh Xil lake upon water recession

Author

Bei Li, Jing Tao Liu, Wan Peng Shi, Yu Xi Zhang, Fie Xie, and Bing Zhou

Subjects
artificial water retention layer, condensation, micro-osmometer, receding waters in the lake, sandy area, water absorption capacity, Water supply for domestic and industrial purposes, TD201-500, River, lake, and water-supply engineering (General), TC401-506
Abstract

In order to investigate the absorption and retention characteristics of condensed water at different soil layers in the bare desertification area at the bottom of a receding lake in Hoh Xil, Qinghai Province, the characteristics of condensate variation, condensate formation time, condensate volume, and its absorption capacity were investigated in July 2021 using a micro-osmometer. The research area was the artificial water retention layer and bare ground of the exposed sandy bottom formed under the influence of the warming–wetting trend that occurs when water recedes in the salt lake area of the Qinghai-Tibet Plateau, as well as the two conditions of underpass and underseal. According to the results, the time of condensation generation during the observation period in the salt lake area of Hoh Xil begins at about 0:00 and ends at about 10:00. The artificial water retention layer had little influence on the condensation generation time, and the trend of the condensation rate is the same. The unidirectional condensation phenomenon of water vapor in the near-surface air at a depth range of 0–5 cm when an artificial water retention layer is applied is clearly superior to that of other layers; accordingly, its frequency of condensation phenomenon is also the highest. The amount of condensed soil water vapor from the lower part of the ground when an artificial water retention layer is applied at depth ranges of 0–5 cm, 5–10 cm, and 0–10 cm were 3.60, 6.39, and 10.27 times that of the near-surface air, respectively. The total amount of bidirectional condensed water under the condition of the artificial water retention layer at 0–10 cm was the largest. Conversely, the total amount of condensed water from the near-surface air at a depth range of 0–5 cm under the unidirectional condensation condition was the highest, being twice as great as the total amount of condensed water in other layers, which demonstrates the promoting effect that the artificial water retention layer has on the adsorption of water vapor in the near-surface air. Due to the existence of permafrost in the Qinghai-Tibet Plateau, the heat zero-flux plane in the area of the Salt Lake has a migration range of divergence of approximately 30 cm from the surface to the ground. An analogous humidity coefficient characterizes the retention effect of condensed water in certain areas, which serve to demonstrate how the artificial water retention layer distributed at a depth of 0–5 cm, regardless of bidirectional or unidirectional condensation, has the best capacity for adsorption. The results of this study provide a theoretical basis for condensate absorption capacity and vegetation restoration in the bare ecologically degraded areas of the lake bottom. HIGHLIGHTS Explores the exposed sandy area of the Hoh Xil Lake to observe condensation generation patterns and absorption effects.; Investigates the use of artificial retention layers.; Provides a theoretical basis for condensate absorption capacity and vegetation restoration in bare ecologically degraded areas of a lake bottom.; Provides basic data for the rational management, utilization, and ecological restoration of fragile ecosystems.;

Published
2022
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10. Exploring the Relationship between Test-Optional Admissions and Selectivity and Enrollment Outcomes during the Pandemic. EdWorkingPaper No. 24-982

Author

Annenberg Institute for School Reform at Brown University, Kelly Rosinger, Dominique J. Baker, Joseph Sturm, Wan Yu, Julie J. Park, OiYan Poon, Brian Heseung Kim, and Stephanie Breen

Abstract

Most selective colleges implemented test-optional admissions during the pandemic, making college entrance exam scores optional for applicants. We draw on descriptive, two-way fixed effects, and event study methods to examine variation in test-optional implementation during the pandemic and how implementation relates to selectivity and enrollment. For "test-optional" colleges during the pandemic, we found substantial variation in policy type (e.g., test optional, test free) and whether the policy extended to all applicants and scholarship consideration. Findings suggest test-optional implementation related to increases in Black student enrollment, mostly at moderately selective colleges and when policies extended to all applicants and scholarships. At highly selective colleges, findings suggest test-optional implementation related to an increase in applications but not consistent gains in enrollment.

Published
2024

11. An Adhesive/Anti‐Adhesive Janus Tissue Patch for Efficient Closure of Bleeding Tissue with Inhibited Postoperative Adhesion

Author

Wan Peng, Cheng Liu, Youjin Lai, Yanting Wang, Pingsheng Liu, and Jian Shen

Subjects
anti‐postoperative adhesion, hemostasis, injured bleeding tissues, Janus tissue patch, wet tissue adhesive, °C, Science
Abstract

Abstract Most of the current bioadhesives cannot perform well on bleeding tissues while postoperative adhesion is a general but serious clinical issue. Here, a three‐layer biodegradable Janus tissue patch (J‐TP) that is able to simultaneously enable efficient closure of bleeding wounds with significantly promoted clotting ability and suppressed postoperative adhesion of tissues is reported. A dry adhesive hydrogel bottom layer of the J‐TP can form rapid (within 15 s) and strong (tensile strength up to 98 kPa) adhesion to bleeding/wet tissues with high bursting pressure (about 312.5 mmHg on a sealed porcine skin) through hydrogen binding and covalent conjugation between the carboxyl & N‐hydroxy succinimide (NHS) groups of hydrogel and the primary amine groups of tissues, while the phosphonic motifs can significantly reduce blood loss (by 81% on a rat bleeding liver model) of bleeding wounds. A thin polylactic acid (PLA) middle layer can improve the tensile strength (by 132%) of the J‐TP in wet conditions while the grafted zwitterionic polymers can effectively prevent postoperative tissue adhesion and inflammatory reaction. This J‐TP may be a promising tissue patch to assist the clinical treatment of injured bleeding tissues with inhibited postoperative adhesion.

Published
2023
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12. Towards Efficient Neuro-Symbolic AI: From Workload Characterization to Hardware Architecture

Author

Wan, Zishen, Liu, Che-Kai, Yang, Hanchen, Raj, Ritik, Li, Chaojian, You, Haoran, Fu, Yonggan, Wan, Cheng, Li, Sixu, Kim, Youbin, Samajdar, Ananda, Lin, Yingyan Celine, Ibrahim, Mohamed, Rabaey, Jan M., Krishna, Tushar, and Raychowdhury, Arijit

Subjects
Computer Science - Hardware Architecture, Computer Science - Artificial Intelligence
Abstract

The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, are facing challenges surrounding unsustainable computational trajectories, limited robustness, and a lack of explainability. To develop next-generation cognitive AI systems, neuro-symbolic AI emerges as a promising paradigm, fusing neural and symbolic approaches to enhance interpretability, robustness, and trustworthiness, while facilitating learning from much less data. Recent neuro-symbolic systems have demonstrated great potential in collaborative human-AI scenarios with reasoning and cognitive capabilities. In this paper, we aim to understand the workload characteristics and potential architectures for neuro-symbolic AI. We first systematically categorize neuro-symbolic AI algorithms, and then experimentally evaluate and analyze them in terms of runtime, memory, computational operators, sparsity, and system characteristics on CPUs, GPUs, and edge SoCs. Our studies reveal that neuro-symbolic models suffer from inefficiencies on off-the-shelf hardware, due to the memory-bound nature of vector-symbolic and logical operations, complex flow control, data dependencies, sparsity variations, and limited scalability. Based on profiling insights, we suggest cross-layer optimization solutions and present a hardware acceleration case study for vector-symbolic architecture to improve the performance, efficiency, and scalability of neuro-symbolic computing. Finally, we discuss the challenges and potential future directions of neuro-symbolic AI from both system and architectural perspectives., Comment: 14 pages, 11 figures, 7 tables; IEEE Transactions on Circuits and Systems for Artificial Intelligence (TCASAI), 2024

Published
2024

13. Exploring the Key Features of Repeating Fast Radio Bursts with Machine Learning

Author

Sun, Wan-Peng, Zhang, Ji-Guo, Li, Yichao, Hou, Wan-Ting, Zhang, Fu-Wen, Zhang, Jing-Fei, and Zhang, Xin

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics, General Relativity and Quantum Cosmology, High Energy Physics - Phenomenology
Abstract

Fast radio bursts (FRBs) are enigmatic high-energy events with unknown origins, which are observationally divided into two categories, i.e., repeaters and non-repeaters. However, there are potentially a number of non-repeaters that may be misclassified, as repeating bursts are missed due to the limited sensitivity and observation periods, thus misleading the investigation of their physical properties. In this work, we propose a repeater identification method based on the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm and apply the classification to the first Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) catalog. We find that the spectral morphology parameters, specifically spectral running ($r$), represent the key features for identifying repeaters from the non-repeaters. Also, the results suggest that repeaters are more biased towards narrowband emission, whereas non-repeaters are inclined toward broadband emission. We provide a list of 163 repeater candidates, with $5$ of which are confirmed with an updated repeater catalog from CHIME/FRB. Our findings help to the understanding of the various properties underlying repeaters and non-repeaters, as well as guidelines for future FRB detection and categorization., Comment: 15 pages, 7 figures

Published
2024

14. Cloning and Functional Analysis of Calcineurin Subunits A and B in Development and Fecundity of Nilaparvata lugens (Stål)

Author

Wang Weixia, Zhu Tingheng, Wan Pinjun, Wei Qi, He Jiachun, Lai Fengxiang, and Fu Qiang

Subjects
Nilaparvata lugens, Calcineurin, Calcineurin catalytic subunit, Calcineurin regulatory subunit, 20-hydroxyecdysone, Phenoloxidase, Plant culture, SB1-1110
Abstract

Serine/threonine phosphatase calcineurin (CN) is a unique but confounding calcium/calmodulin- mediated enzyme, which is composed of a catalytic subunit A (CNA) and a regulatory subunit B (CNB). We cloned six transcripts for CNA named from NlCNA-X1 to NlCNA-X6, one CNB named NlCNB1 and one CNB homologous gene NlCNBH1 from Nilaparvata lugens. All of them are constitutively transcripted in various tissues and developmental stages. The primary structure of the six isoforms showed obvious differences in the length and composition of the amino acid sequence between the two binding domains of Ca2+/calmodulin (CaM) and CNB. Ca2+-binding EF-hand motifs were found in NlCNB1 and NlCNBH1. The specific gene silencing of NlCNA, NlCNB1 and NlCNBH1 respectively by RNAi resulted in drastical reduction in survival rate, female weight, eclosion rate and fecundity of N. lugens. These results showed that NlCNA, NlCNB1 and NlCNBH1 were required for N. lugens growth and reproduction. The negative effects of NlCNB1 silence on nymph mortality (97%), molting malformation (90%) and female sterile (50%) were more serious than those of NlCNA or NlCNBH1. qRT-PCR and enzyme-linked immunosorbent assay (ELISA) analyses indicated that the nymphs with silenced NlCNA, NlCNB1 or NlCNBH1 showed impaired hormone and energy metabolism. In nymphs, the contents of 20-hydroxyecdysone (20E) after NlCNB1 RNAi and phenoloxidase after NlCNA RNAi were particularly decreased. These results suggested that NlCNA is involved in immunity of N. lugens by regulation of phenoloxidase, while NlCNB1 may control the growth and development of N. lugens by 20E signaling pathway in addition to interact with CNA. Injection of 70 ng/μL dsNlCNB1 resulted in 77.0% down regulation of NlCNB1, and the nymph mortality was up to 57.9% at 10 d after injection. Therefore, NlCNB1 could be a potential candidate target used for strategy design in control of N. lugens. Our results revealed the importance of CN in the regulation of the growth and development of N. lugens, which provided a basis for further study of the molecular mechanism of CN.

Published
2022
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15. Teachers' Perspectives on Applying Online Learning Tools through Learning Management System: The Need for an Online Malay Language Teaching Module

Author

M. Badrul Hisyam Sulong, M. Khalid M. Nasir, and Wan Muna Ruzanna Wan Mohamad

Abstract

This study examines Malay language teachers' perceptions of using Google tools in their online teaching in primary schools. It uses a basic interpretative research methodology to investigate the perspectives of six primary school Malay language teachers from Terengganu, Malaysia. Interviews with semi-structured questions were used to collect data and analyzed thematically. All the themes and sub-themes were organized using NVivo 12 software. The data show that the predominant topic in teachers' thoughts on the importance of Google tools was the reasons for their absence of use. There were two essential sub-themes: a lack of skills and proficiency and a need for more awareness of how to use Google tools. Furthermore, this study highlighted personal support of module development as a secondary theme which included two sub-themes: agreement with the need for modules and the importance of providing them. This study also underlined the importance of module preparation which was reinforced by two sub-themes: facilitating teachers' implementation of online teaching and serving as a significant reference for teachers. This study shows that effective online education requires competency, skills and knowledge of Google Tools. It suggests creating a Google Tools module to guide and serve as a resource for Malay language teachers.

Published
2024

16. Fostering Creative Thinking Skills through Digital Storytelling

Author

Wan Mohd Faizal Wan Mohd Nasir, Lilia Halim, and Nurazidawati Mohamad Arsad

Abstract

Mastering creative thinking skills (CTS) is crucial for enhancing students' science conceptual understanding. However, conventional teaching prioritizes linear thinking over the three sub-CTS essential to science learning: associative, visual, and divergent thinking. Weaknesses in these three sub-CTS cause students to have misconceptions, difficulty in creating mental images, and an inability to generate ideas on abstract scientific concepts. The aim of this study is to evaluate the effectiveness of the Digital Storytelling Science Creative (DSSC) module in enhancing these three sub-CTS, based on the topic of acids and bases. The ASSURE instructional design model guided the DSSC module design and development process. This quasi-experimental design study involved 63 eighth-grade students. Analysis of two-way MANCOVA using the data collected through the administered Creative Thinking Skills Test revealed that the DSSC module group performed significantly better than the control group in divergent thinking. However, the DSSC module did not significantly affect students' associative and visual thinking or gender.

Published
2024

17. International Faculty Members' Intention to Leave South Korea: Do Acculturation and Cultural Advantage Matter?

Author

Jung Cheol Shin, Douglas R. Gress, Byung Shik Rhee, Kiyong Byun, Jang Wan Ko, and Heejin Lim

Abstract

The percentage of international faculty members at Korean universities peaked in 2013 and has been decreasing since, suggesting that Korean universities have a problem with international faculty member retention. This study investigates whether international faculty members' intention to leave is associated with their degree of acculturation. Results are based on analyses of data from 325 international faculty members with doctorates working at Korean universities nationwide. Regression analyses reveal that acculturation, work-related conditions (salary, workload, workplace climate) and mobility-related motivational factors (job market in home country, interest in Korea) explain international faculty members' intention to leave. A 'cultural advantage' (ethnically Korean or living with a Korean spouse) is not a significant predictor of intention to leave. Suggestions to support international faculty members, and to help bring about synergistic organizational change are ventured in the hopes of bolstering retention at Korean universities going forward.

Published
2024

18. The Development of E-book Based on Realistic Mathematics Education (PMR) Approach to Improve Mathematical Communication Skills in Class VIII Junior High School Students

Author

Wan Putri Tania and Nurhasanah Siregar

Subjects
Education, Education (General), L7-991, Mathematics, QA1-939
Abstract

This study aims to obtain an e-book based on the Realistic Mathematics Education (PMR) approach that is valid, practical, and effective so that it can improve students' mathematical communication skills on the Two-Variable Linear Equation System (SPLDV) material. This type of research is Research and Development (R&D) using the ADDIE model which consists of five stages, namely analyze, design, development, implementation, and evaluation. The research instruments used were questionnaire and instrument validation sheets, lesson plans validation sheets, e-book validation sheets, mathematical communication skills tests, and student and teacher response questionnaires to e-books. Based on the research, the results of the RPP validation were obtained with a percentage of 88% (very valid), e-book validation with a percentage of 82% for material, and 90% for media with a very valid category. The results of student responses with the percentage of the practicality of 86% (very practical) and the teacher's response questionnaire with the percentage of the practicality of 86% (very practical). The results obtained by meeting the effective criteria with a classical student learning completeness score of 96%, more than 65% of students achieving 75% of the learning objectives for each indicator, and the learning time is the same as ordinary learning in field trials and student responses are good to the e-book provided. developed. Through the Gain test, it is seen that students' mathematical communication skills using e-books based on the PMR approach have increased by 0.7, meaning that they are in the high category. Keywords: e-book, realistic mathematics education, mathematical communication.

Published
2022
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19. Upregulation of TMEM45A Promoted the Progression of Clear Cell Renal Cell Carcinoma in vitro

Author

Jiang H, Chen H, Wan P, Liang M, and Chen N

Subjects
biomarker, clear cell renal cell carcinoma, prognosis, tmem45a, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract

Huiming Jiang,1,* Haibin Chen,2,* Pei Wan,1 Meng Liang,3 Nanhui Chen1 1Department of Urology, Meizhou People’s Hospital, Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, People’s Republic of China; 2Department of Histology and Embryology, Shantou University Medical College, Shantou, People’s Republic of China; 3Gannan Medical University, Ganzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Huiming Jiang; Nanhui Chen Tel +86-13560990839Email jianghm0228@163.com; chennanhuiht@163.comBackground: Clear cell renal cell carcinoma (ccRCC) is the most common and aggressive type of primary kidney cancer worldwide. Transmembrane protein 45A (TMEM45A) has been reported to be closely associated with the progression of several cancers. However, the role of TMEM45A in ccRCC remains unclear. Our study intended to explore the potential role of TMEM45A in ccRCC.Methods: Data on the expression of TMEM45A were obtained from multiple databases, including UCSC, GEPIA2, Oncomine and TIMER. Real-world samples of ccRCC and paired normal renal tissues were used to confirm the information obtained from the databases. In addition, the prognostic value of TMEM45A was evaluated. Loss-of-function assays were performed using TMEM45A-targeting lentivirus to evaluate the biological role of TMEM45A in renal cancer cells. Gene set enrichment analysis (GSEA) was performed to investigate the potential molecular mechanisms.Results: TMEM45A was significantly overexpressed in patients with ccRCC and correlated with poor overall survival and disease-free survival. In addition, the expression of TMEM45A was closely associated with various clinicopathological parameters such as histological grade and TNM stage. Knockdown of TMEM45A inhibited the proliferation and migration and promoted the apoptosis of ccRCC cells in vitro. The results of the GSEA suggested that TMEM45A was potentially involved in the promotion of epithelial–mesenchymal transition (EMT) and inflammatory response in ccRCC.Conclusion: TMEM45A was overexpressed and associated with poor survival and acted as a tumour promoter in ccRCC; therefore, might be a potential prognostic marker and therapeutic target.Keywords: biomarker, clear cell renal cell carcinoma, prognosis, TMEM45A

Published
2021

20. Effect of novel turtle shell decoction on cycle, apoptosis, CTGF/Itgα 5 pathway and expression of Col1a1 in rat hepatic stellate cells

Author

CAI Xiuhua, DONG Anying, ZHANG Ronghua, BAI Ganping, WAN Ping, JIANG Yi, and ZHU Qi

Subjects
novel turtle shell decoction, hepatic stellate cells, connective tissue growth factor, integrin alpha 5, collagen type i, cell cycle, apoptosis, Medicine (General), R5-920
Abstract

Objective To investigate the effects of novel turtle shell decoction (NTSD) on the cycle, apoptosis, connective tissue growth factor/integrin alpha 5 (CTGF/Itgα5) pathway and the expression of Collagen type I (Col1a1) in rat hepatic stellate cell line T6 (HSCT6). Methods HSCT6 cells was treated with NTSD containing crude drug 0.71 g/mL as experimental group, treated with turtle shell pills (TSP) at the same dose as positive control, and cultured routinely as normal control. Cell cycle and apoptosis of HSCT6 cells were tested by flow cytometry (FCM). Real time-qPCR, Western blotting and immunofluorescence assay were employed to detect the expression changes of CTGF, Itgα5 and Col1a1 genes and their coding proteins in each group of HSCT6, respectively. Results FCM showed that NTSD treatment blocked the cells at S stage, and induced cell apoptosis. Real time-qPCR, Western blotting and immunofluorescence assay revealed that NTSD can significantly down-regulate the expression of CTGF, Itgα5 and Col1a1 in mRNA and protein levels in HSC-T6 cells (P < 0.01). Conclusion NTSD treatment results in cell arrest at S phage through down-regulating CTGF/Itgα5, induction of cell apoptosis, decrease of Col1a1 expression, and then inhibition of cell growth in HSCT6 cells.

Published
2021
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21. Law of interest rate changes in financial markets based on the differential equation model of liquidity

Author

Wan Pengfei and Hamad Arbad Ahmed Mohamed

Subjects
rate of return, liquidity, differential equation, the principle of equilibrium, financial market, interest rate changes, 91g30, Mathematics, QA1-939
Abstract

The paper establishes a related differential equation model about changes in financial interest rates. It uses information related to liquidity to feedback the law and stability of differential equations in interest rate changes. The article applies stochastic processes and partial differential equations to complex financial networks to confirm node yields in financial market networks. It confirms the existence of interest rate stickiness in Chinese financial markets. The advantage of this interest rate model is that when the external economic environment changes, the state of interest rates will also change accordingly.

Published
2021
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22. Sonoclot Signature Analysis: A New Point-of-Care Testing Method for Defining Heat Stroke-Induced Coagulopathy

Author

Min J, Wan P, Liu G, Yu M, and Su L

Subjects
heat stroke, sonoclot signature analysis, coagulopathy, prognosis, Medicine (General), R5-920
Abstract

Jinyi Min,1,&ast; Peng Wan,2,&ast; Guiwei Liu,1 Min Yu,2 Lei Su3 1Department of Critical Care Medicine, The People’s Hospital, Dangyang City, Hubei, 444100, People’s Republic of China; 2Department of Critical Care Medicine, The People’s Hospital of China Three Gorges University, Yichang City, Hubei, 443000, People’s Republic of China; 3Department of Critical Care Medicine, Guangzhou General Hospital of Guangzhou Military Command, The Military Key Laboratory of Trauma Care in Hot Zone and Tissue Repair in PLA, Guangzhou, 510000, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Peng WanDepartment of Critical Care Medicine, The People’s Hospital of China Three Gorges University, 4th Street Hudi, Xiling District, Yichang City, Hubei, 443000, People’s Republic of ChinaTel + 86717-6287551Fax + 86717-6221636Email wanpeng0a0@163.comPurpose: Data regarding the incidence of a coagulable state following heat stroke as assessed by Sonoclot signature analysis are limited. Our purpose was to appraise coagulopathy using a dynamic test capable of analyzing the entire coagulation cascade and to characterize coagulation in patients with heat stroke prior to transfusion.Materials and Methods: The data of 106 patients were collected prospectively from the Critical Care Center of the General Hospital of Guangzhou Military Command. Coagulable state was defined as normal. Both hyper- and hypo-coagulable states were defined as coagulation defects. Hypercoagulability was defined as an activated clotting time (ACT) ≦195s and a clot rate (CR) > 23, and hypocoagulability was defined as an ACT ≧119s and a CR < 7. The Sonoclot signature t examination was performed at the time of admission. Conventional tests, such as the prothrombin time (PT) and activated partial thromboplastin time (aPTT), were compared with Sonoclot monitoring to identify coagulation defects.Results: The average age of the 106 patients was 23.2± 2.5 years. There were 102 males (96.3%) and 4 females (3.7%). Thirty-four patients (32.1%) were hypercoagulable and 44 patients (41.5%) were hypocoagulable at the time of admission; 28 patients (26.4%) had no evidence of a coagulopathy. Patients with hypocoagulability, unlike patients with hypercoagulability, had a higher sequential Organ Failure Assessment score, indicating a more severe multiple organ dysfunction score. Mortality was 5.9% in patients with hypercoagulability compared with 3.5% in patients with normal coagulation, and 18.1% in patients with a hypocoagulable state (P < 0.05). ACT was a predictor of mortality, while the CR and platelet function did not show statistical significance.Conclusion: This study determined the clinical outcomes and prognostic value of coagulability in patients with heat stroke, as defined by Sonoclot signature analysis at the time of admission.Keywords: heat stroke, coagulopathy, prognosis

Published
2021

23. Overview of recent experimental results on the EAST Tokamak

Author

Gong, X, Team and Collaborators, on behalf of EAST, Team:, The EAST, Song, Yuntao, Wan, Baonian, Li, Jiangang, Wan, Yuanxi, Wu, Xinchao, Liu, Fukun, Chen, Junling, Hu, Jiansheng, Xu, Guosheng, Lu, Kun, Gong, Xianzu, Xiao, Bingjia, Wu, Yu, Gao, Xiang, Yao, Damao, Xiang, Nong, Hu, Liqun, Hu, Chundong, Wu, Jiefeng, Shen, Biao, Gao, Ge, Huang, Yiyun, Xu, Liuwei, Zhang, Qiyong, Bae, Cheonho, Cao, Bin, Cao, Lei, Chang, Jiafeng, Chen, Dalong, Chen, Ran, Chen, Xiaojiao, Chen, Yebin, Chen, Yue, Cheng, Yunxin, Cheng, Yong, Ding, Bojiang, Ding, Fang, Ding, Rui, Du, Shijun, Duan, Yanmin, Fu, Jia, Gao, Daming, Gao, Wei, Gu, Yongqi, Guo, Bin, Guo, Fei, Guo, Yong, Han, Xiaofeng, He, Shiying, Hu, Ailan, Hu, Chang, Hu, Guanghai, Hu, Huaichuan, Hu, Qingsheng, Hu, Yanlan, Hu, Zhenhua, Huang, Juan, Huang, Liansheng, Huang, Ming, Huang, Ronglin, Ji, Xiang, Jia, Hua, Jiang, Caichao, Jie, Yinxian, Ju, Songqing, Kong, Defeng, Li, Erzhong, Li, Guoqiang, Li, Jiahong, Li, Junjun, Li, Miaohui, Li, Pan, Li, Kedong, Li, Shi, Li, Yadong, Liang, Lizhen, Liao, Yanchuan, Lin, Shiyao, Lin, Xin, Ling, Bili, Liu, Haiqing, Liu, Huajun, Liu, Jianwen, Liu, Liang, Liu, Shaocheng, Liu, Sheng, Liu, Wenbin, Liu, Xiaoju, Liu, Xiaoyan, Liu, Yong, Liu, Zhihong, Liu, Zhimin, Lu, Jianhua, Luo, Zhengping, Ma, Dengkui, Mao, Huafeng, and Ma, Wendong

Subjects
Nuclear and Plasma Physics, Physical Sciences, Atomic, Molecular, Nuclear, Particle and Plasma Physics, Fluids & Plasmas, Nuclear and plasma physics
Abstract

Since the last IAEA-FEC in 2021, significant progress on the development of long pulse steady state scenario and its related key physics and technologies have been achieved, including the reproducible 403 s long-pulse steady-state H-mode plasma with pure radio frequency (RF) power heating. A thousand-second time scale (∼1056 s) fully non-inductive plasma with high injected energy up to 1.73 GJ has also been achieved. The EAST operational regime of high βP has been significantly extended (H98y2 > 1.3, βP ∼ 4.0, βN ∼ 2.4 and ne/nGW ∼ 1.0) using RF and neutral beam injection (NBI). The full edge localized mode suppression using the n = 4 resonant magnetic perturbations has been achieved in ITER-like standard type-I ELMy H-mode plasmas with q95 ≈ 3.1 on EAST, extrapolating favorably to the ITER baseline scenario. The sustained large ELM control and stable partial detachment have been achieved with Ne seeding. The underlying physics of plasma-beta effect for error field penetration, where toroidal effect dominates, is disclosed by comparing the results in cylindrical theory and MARS-Q simulation in EAST. Breakdown and plasma initiation at low toroidal electric fields (

Published
2024

24. Constraints on the photon polarisation in $b \to s \gamma$ transitions using $B_s^0 \rightarrow \phi e^+e^-$ decays

Author

LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. A., Belin, S., Bellee, V., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bondar, N., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bouchiba, S. A., Bovill, M., Bowcock, T. J. V., Boyer, A., Bozzi, C., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonaura, A., Buonincontri, L., Burke, A. T., Burr, C., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A. C., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cervenkov, D., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., Chobanova, V., Cholak, S., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collins, P., Colombo, T., Colonna, M., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Davidson, A., Davies, J. E., Davis, A., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Dekkers, S., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. D., Dziurda, A., Dzyuba, A., Easo, S., Eckstein, E., Egede, U., Egorychev, A., Egorychev, V., Eisenhardt, S., Ejopu, E., Eklund, L., Elashri, M., Ellbracht, J., Ely, S., Ene, A., Eschle, J., Esen, S., Evans, T., Fabiano, F., Falcao, L. N., Fan, Y., Fang, B., Fantini, L., Faria, M., Farmer, K., Fazzini, D., Felkowski, L., Feng, M., Feo, M., Casani, A. Fernandez, Gomez, M. Fernandez, Fernez, A. D., Ferrari, F., Rodrigues, F. Ferreira, Ferrillo, M., Ferro-Luzzi, M., Filippov, S., Fini, R. A., Fiorini, M., Firlej, M., Fischer, K. L., Fitzgerald, D. S., Fitzpatrick, C., Fiutowski, T., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Lima, V. Franco, Sevilla, M. Franco, Frank, M., Franzoso, E., Frau, G., Frei, C., Friday, D. A., Fu, J., Führing, Q., Fujii, Y., Fulghesu, T., Gabriel, E., Galati, G., Galati, M. D., Torreira, A. Gallas, Galli, D., Gambetta, S., Gandelman, M., Gandini, P., Ganie, B., Gao, H., Gao, R., Gao, T. Q., Gao, Y., Martin, L. M. Garcia, Moreno, P. Garcia, Pardiñas, J. García, Garg, K. G., Garrido, L., Gaspar, C., Geertsema, R. E., Gerken, L. L., Gersabeck, E., Gersabeck, M., Gershon, T., Ghizzo, S., Ghorbanimoghaddam, Z., Giambastiani, L., Giasemis, F. I., Gibson, V., Giemza, H. K., Gilman, A. L., Giovannetti, M., Gioventù, A., Girardey, L., Gironell, P. Gironella, Giugliano, C., Giza, M. A., Gkougkousis, E. L., Glaser, F. C., Gligorov, V. V., Göbel, C., Golobardes, E., Golubkov, D., Golutvin, A., Fernandez, S. Gomez, Gomulka, W., Abrantes, F. Goncalves, Goncerz, M., Gong, G., Gooding, J. A., Gorelov, I. V., Gotti, C., Grabowski, J. P., Cardoso, L. A. Granado, Graugés, E., Graverini, E., Grazette, L., Graziani, G., Grecu, A. T., Greeven, L. M., Grieser, N. A., Grillo, L., Gromov, S., Gu, C., Guarise, M., Guerry, L., Guittiere, M., Guliaeva, V., Günther, P. A., Guseinov, A. -K., Gushchin, E., Guz, Y., Gys, T., Habermann, K., Hadavizadeh, T., Hadjivasiliou, C., Haefeli, G., Haen, C., Haimberger, J., Hajheidari, M., Hallett, G., Halvorsen, M. M., Hamilton, P. M., Hammerich, J., Han, Q., Han, X., Hansmann-Menzemer, S., Hao, L., Harnew, N., Hartmann, M., Hashmi, S., He, J., Hemmer, F., Henderson, C., Henderson, R. D. L., Hennequin, A. M., Hennessy, K., Henry, L., Herd, J., Gascon, P. Herrero, Heuel, J., Hicheur, A., Mendizabal, G. Hijano, Hill, D., Horswill, J., Hou, R., Hou, Y., Howarth, N., Hu, J., Hu, W., Hu, X., Huang, W., Hulsbergen, W., Hunter, R. J., Hushchyn, M., Hutchcroft, D., Idzik, M., Ilin, D., Ilten, P., Inglessi, A., Iniukhin, A., Ishteev, A., Ivshin, K., Jacobsson, R., Jage, H., Elles, S. J. Jaimes, Jakobsen, S., Jans, E., Jashal, B. K., Jawahery, A., Jevtic, V., Jiang, E., Jiang, X., Jiang, Y., Jiang, Y. J., John, M., Rajan, A. John Rubesh, Johnson, D., Jones, C. R., Jones, T. P., Joshi, S., Jost, B., Castella, J. Juan, Jurik, N., Juszczak, I., Kaminaris, D., Kandybei, S., Kane, M., Kang, Y., Kar, C., Karacson, M., Karpenkov, D., Kauniskangas, A., Kautz, J. W., Kazanecki, M. K., Keizer, F., Kenzie, M., Ketel, T., Khanji, B., Kharisova, A., Kholodenko, S., Khreich, G., Kirn, T., Kirsebom, V. S., Kitouni, O., Klaver, S., Kleijne, N., Klimaszewski, K., Kmiec, M. R., Koliiev, S., Kolk, L., Konoplyannikov, A., Kopciewicz, P., Koppenburg, P., Korolev, M., Kostiuk, I., Kot, O., Kotriakhova, S., Kozachuk, A., Kravchenko, P., Kravchuk, L., Kreps, M., Krokovny, P., Krupa, W., Krzemien, W., Kshyvanskyi, O., Kubis, S., Kucharczyk, M., Kudryavtsev, V., Kulikova, E., Kupsc, A., Kutsenko, B. K., Lacarrere, D., Gonzalez, P. Laguarta, Lai, A., Lampis, A., Lancierini, D., Gomez, C. Landesa, Lane, J. J., Lane, R., Lanfranchi, G., Langenbruch, C., Langer, J., Lantwin, O., Latham, T., Lazzari, F., Lazzeroni, C., Gac, R. Le, Lee, H., Lefèvre, R., Leflat, A., Legotin, S., Lehuraux, M., Cid, E. Lemos, Leroy, O., Lesiak, T., Lesser, E. D., Leverington, B., Li, A., Li, C., Li, H., Li, K., Li, L., Li, M., Li, P., Li, P. -R., Li, Q., Li, S., Li, T., Li, Y., Lian, Z., Liang, X., Libralon, S., Lin, C., Lin, T., Lindner, R., Linton, H., Lisovskyi, V., Litvinov, R., Liu, F. L., Liu, G., Liu, K., Liu, S., Liu, W., Liu, Y., Liu, Y. L., Salvia, A. Lobo, Loi, A., Castro, J. Lomba, Long, T., Lopes, J. H., Huertas, A. Lopez, Soliño, S. López, Lu, Q., Lucarelli, C., Lucchesi, D., Martinez, M. Lucio, Lukashenko, V., Luo, Y., Lupato, A., Luppi, E., Lynch, K., Lyu, X. -R., Ma, G. M., Maccolini, S., Machefert, F., Maciuc, F., Mack, B., Mackay, I., Mackey, L. M., Mohan, L. R. Madhan, Madurai, M. J., Maevskiy, A., Magdalinski, D., Maisuzenko, D., Majewski, M. W., Malczewski, J. J., Malde, S., Malentacca, L., Malinin, A., Maltsev, T., Manca, G., Mancinelli, G., Mancuso, C., Escalero, R. Manera, Manganella, F. M., Manuzzi, D., Marangotto, D., Marchand, J. F., Marchevski, R., Marconi, U., Mariani, E., Mariani, S., Benito, C. Marin, Marks, J., Marshall, A. M., Martel, L., Martelli, G., Martellotti, G., Martinazzoli, L., Martinelli, M., Gomez, D. Martinez, Santos, D. Martinez, Vidal, F. Martinez, Massafferri, A., Matev, R., Mathad, A., Matiunin, V., Matteuzzi, C., Mattioli, K. R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., de Cos, J. Mazorra, Mazurek, M., McCann, M., Mcconnell, L., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Cardenas, E. F. Molina, Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Moron, J., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Niu, Q., Nogarolli, P., Nogga, P., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Osthues, D., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Paciolla, F., Padee, A., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. Pepe, Perazzini, S., Pereima, D., Da Costa, H. Pereira, Castro, A. Pereiro, Perret, P., Perrevoort, A., Perro, A., Petridis, K., Petrolini, A., Pfaller, J. P., Pham, H., Pica, L., Piccini, M., Piccolo, L., Pietrzyk, B., Pietrzyk, G., Pinci, D., Pisani, F., Pizzichemi, M., Placinta, V., Casasus, M. Plo, Poeschl, T., Polci, F., Lener, M. Poli, Poluektov, A., Polukhina, N., Polyakov, I., Polycarpo, E., Ponce, S., Popov, D., Poslavskii, S., Prasanth, K., Prouve, C., Provenzano, D., Pugatch, V., Punzi, G., Qasim, S., Qian, Q. Q., Qian, W., Qin, N., Qu, S., Quagliani, R., Trejo, R. I. Rabadan, Rademacker, J. H., Rama, M., García, M. Ramírez, De Oliveira, V. Ramos, Pernas, M. Ramos, Rangel, M. S., Ratnikov, F., Raven, G., De Miguel, M. Rebollo, Redi, F., Reich, J., Reiss, F., Ren, Z., Resmi, P. K., Ribatti, R., Ricart, G. R., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Alvarez, A. Rodriguez, Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Roensch, J., Rogachev, A., Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Vidal, A. Romero, Romolini, G., Ronchetti, F., Rong, T., Rotondo, M., Roy, S. R., Rudolph, M. S., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzhikov, A., Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Sborzacchi, F., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Sellam, S., Semennikov, A., Senger, T., Soares, M. Senghi, Sergi, A., Serra, N., Sestini, L., Seuthe, A., Shang, Y., Shangase, D. M., Shapkin, M., Sharma, R. S., Shchemerov, I., Shchutska, L., Shears, T., Shekhtman, L., Shen, Z., Sheng, S., Shevchenko, V., Shi, B., Shi, Q., Shimizu, Y., Shmanin, E., Shorkin, R., Shupperd, J. D., Coutinho, R. Silva, Simi, G., Simone, S., Skidmore, N., Skwarnicki, T., Slater, M. W., Smallwood, J. C., Smith, E., Smith, K., Smith, M., Snoch, A., Lavra, L. Soares, Sokoloff, M. D., Soler, F. J. P., Solomin, A., Solovev, A., Solovyev, I., Sommerfeld, N. S., Song, R., Song, Y., Song, Y. S., De Almeida, F. L. Souza, De Paula, B. Souza, Norella, E. Spadaro, Spedicato, E., Speer, J. G., Spiridenkov, E., Spradlin, P., Sriskaran, V., Stagni, F., Stahl, M., Stahl, S., Stanislaus, S., Stein, E. N., Steinkamp, O., Stenyakin, O., Stevens, H., Strekalina, D., Su, Y., Suljik, F., Sun, J., Sun, L., Sundfeld, D., Sutcliffe, W., Swallow, P. N., Swientek, K., Swystun, F., Szabelski, A., Szumlak, T., Tan, Y., Tat, M. D., Terentev, A., Terzuoli, F., Teubert, F., Thomas, E., Thompson, D. J. D., Tilquin, H., Tisserand, V., T'Jampens, S., Tobin, M., Tomassetti, L., Tonani, G., Tong, X., Machado, D. Torres, Toscano, L., Tou, D. Y., Trippl, C., Tuci, G., Tuning, N., Uecker, L. H., Ukleja, A., Unverzagt, D. J., Ursov, E., Usachov, A., Ustyuzhanin, A., Uwer, U., Vagnoni, V., Cadenas, V. Valcarce, Valenti, G., Canudas, N. Valls, Van Hecke, H., van Herwijnen, E., Van Hulse, C. B., Van Laak, R., van Veghel, M., Vasquez, G., Gomez, R. Vazquez, Regueiro, P. Vazquez, Sierra, C. Vázquez, Vecchi, S., Velthuis, J. J., Veltri, M., Venkateswaran, A., Verdoglia, M., Vesterinen, M., Benet, D. Vico, Villalba, P. Vidrier, Diaz, M. Vieites, Vilasis-Cardona, X., Figueras, E. Vilella, Villa, A., Vincent, P., Volle, F. C., Bruch, D. vom, Voropaev, N., Vos, K., Vouters, G., Vrahas, C., Wagner, J., Walsh, J., Walton, E. J., Wan, G., Wang, C., Wang, G., Wang, H., Wang, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., Wang, Y. W., Wang, Z., Ward, J. A., Waterlaat, M., Watson, N. K., Websdale, D., Wei, Y., Wendel, J., Westhenry, B. D. C., White, C., Whitehead, M., Whiter, E., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. J., Williams, M. R. J., Williams, R., Williams, Z., Wilson, F. F., Winn, M., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, X., Wu, Y., Wu, Z., Wyllie, K., Xian, S., Xiang, Z., Xie, Y., Xu, A., Xu, J., Xu, L., Xu, M., Xu, Z., Yang, D., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yin, X., Yu, C. Y., Yu, J., Yuan, X., Yuan, Y, Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zenesini, F., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhao, Y., Zharkova, A., Zhelezov, A., Zheng, S. Z., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.

Subjects
High Energy Physics - Experiment
Abstract

An angular analysis of the $B_s^0 \rightarrow \phi e^+e^-$ decay is performed using the proton-proton collision dataset collected between 2011 and 2018 by the LHCb experiment, corresponding to an integrated luminosity of $9\,{\rm fb}^{-1}$ at centre-of-mass energies of 7, 8 and $13\,{\rm TeV}$. The analysis is performed in the very low dielectron invariant mass-squared region between $0.0009$ and $0.2615\,{\rm GeV}^2\!/c^4$. The longitudinal polarisation fraction of the $\phi$ meson is measured to be less than $11.5\%$ at $90\%$ confidence level. The $A_{\mathrm{T}}^{\mathcal{R}e C\!P}$ observable, which is related to the lepton forward-backward asymmetry, is measured to be $0.116 \pm 0.155 \pm 0.006$, where the first uncertainty is statistical and the second systematic. The transverse asymmetries, $A_{\mathrm{T}}^{(2)}$ and $A_{\mathrm{T}}^{\mathcal{I}m C\!P}$ , which are sensitive to the virtual photon polarisation, are found to be $-0.045 \pm 0.235 \pm 0.014$ and $0.002 \pm 0.247 \pm 0.016$, respectively. The results are consistent with Standard Model predictions., Comment: 21 pages, 4 figures. All figures and tables, along with any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/3433/(LHCb public pages)

Published
2024

25. Propensity Score Matching: Should We Use It in Designing Observational Studies?

Author

Wan, Fei

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Propensity Score Matching (PSM) stands as a widely embraced method in comparative effectiveness research. PSM crafts matched datasets, mimicking some attributes of randomized designs, from observational data. In a valid PSM design where all baseline confounders are measured and matched, the confounders would be balanced, allowing the treatment status to be considered as if it were randomly assigned. Nevertheless, recent research has unveiled a different facet of PSM, termed "the PSM paradox." As PSM approaches exact matching by progressively pruning matched sets in order of decreasing propensity score distance, it can paradoxically lead to greater covariate imbalance, heightened model dependence, and increased bias, contrary to its intended purpose. Methods: We used analytic formula, simulation, and literature to demonstrate that this paradox stems from the misuse of metrics for assessing chance imbalance and bias. Results: Firstly, matched pairs typically exhibit different covariate values despite having identical propensity scores. However, this disparity represents a "chance" difference and will average to zero over a large number of matched pairs. Common distance metrics cannot capture this ``chance" nature in covariate imbalance, instead reflecting increasing variability in chance imbalance as units are pruned and the sample size diminishes. Secondly, the largest estimate among numerous fitted models, because of uncertainty among researchers over the correct model, was used to determine statistical bias. This cherry-picking procedure ignores the most significant benefit of matching design-reducing model dependence based on its robustness against model misspecification bias. Conclusions: We conclude that the PSM paradox is not a legitimate concern and should not stop researchers from using PSM designs.

Published
2024

26. DarkSHINE Baseline Design Report: Physics Prospects and Detector Technologies

Author

Chen, Jing, Chen, Ji-Yuan, Chen, Jun-Feng, Chen, Xiang, Fu, Chang-Bo, Guo, Jun, Guo, Yi-Han, Khaw, Kim Siang, Li, Jia-Lin, Li, Liang, Li, Shu, Lin, Yu-ming, Liu, Dan-Ning, Liu, Kang, Liu, Kun, Liu, Qi-Bin, Liu, Zhi, Lu, Ze-Jia, Lv, Meng, Song, Si-Yuan, Sun, Tong, Tang, Jian-Nan, Wan, Wei-Shi, Wang, Dong, Wang, Xiao-Long, Wang, Yu-Feng, Wang, Zhen, Wang, Zi-Rui, Wu, Wei-Hao, Yang, Hai-Jun, Yang, Lin, Yang, Yong, Yu, Dian, Yuan, Rui, Zhang, Jun-Hua, Zhang, Yu-Lei, Zhang, Yun-Long, Zhao, Zhi-Yu, Zhou, Bai-Hong, Zhu, Chun-Xiang, Zhu, Xu-Liang, and Zhu, Yi-Fan

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

DarkSHINE is a newly proposed fixed-target experiment initiative to search for the invisible decay of Dark Photon via missing energy/momentum signatures, based on the high repetition rate electron beam to be deployed/delivered by the Shanghai High repetition rate XFEL and Extreme light facility (SHINE). This report elaborates the baseline design of DarkSHINE experiment by introducing the physics goals, experimental setups, details of each sub-detector system technical designs, signal and backgground modelings, expected search sensitivities and future prospects, which mark an important step towards the further prototyping and technical demonstrations.

Published
2024

27. Measurement of $\phi(1020)$ meson production in fixed-target $\textit{p}$Ne collisions at $\sqrt{s_{NN}}$ = 68.5 GeV

Author

LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Balboni, A., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. A., Belin, S., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bonacci, R. B., Bondar, N., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bouchiba, S. A., Bovill, M., Bowcock, T. J. V., Boyer, A., Bozzi, C., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonaura, A., Buonincontri, L., Burke, A. T., Burr, C., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A. C., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cervenkov, D., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., Chobanova, V., Cholak, S., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. 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J., Hushchyn, M., Hutchcroft, D., Idzik, M., Ilin, D., Ilten, P., Inglessi, A., Iniukhin, A., Ishteev, A., Ivshin, K., Jacobsson, R., Jage, H., Elles, S. J. Jaimes, Jakobsen, S., Jans, E., Jashal, B. K., Jawahery, A., Jevtic, V., Jiang, E., Jiang, X., Jiang, Y., Jiang, Y. J., John, M., Rajan, A. John Rubesh, Johnson, D., Jones, C. R., Jones, T. P., Joshi, S., Jost, B., Castella, J. Juan, Jurik, N., Juszczak, I., Kaminaris, D., Kandybei, S., Kane, M., Kang, Y., Kar, C., Karacson, M., Karpenkov, D., Kauniskangas, A., Kautz, J. W., Kazanecki, M. K., Keizer, F., Kenzie, M., Ketel, T., Khanji, B., Kharisova, A., Kholodenko, S., Khreich, G., Kirn, T., Kirsebom, V. S., Kitouni, O., Klaver, S., Kleijne, N., Klimaszewski, K., Kmiec, M. 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Lobo, Loi, A., Long, T., Lopes, J. H., Huertas, A. Lopez, Soliño, S. López, Lu, Q., Lucarelli, C., Lucchesi, D., Martinez, M. Lucio, Lukashenko, V., Luo, Y., Lupato, A., Luppi, E., Lynch, K., Lyu, X. -R., Ma, G. M., Maccolini, S., Machefert, F., Maciuc, F., Mack, B., Mackay, I., Mackey, L. M., Mohan, L. R. Madhan, Madurai, M. J., Maevskiy, A., Magdalinski, D., Maisuzenko, D., Majewski, M. W., Malczewski, J. J., Malde, S., Malentacca, L., Malinin, A., Maltsev, T., Manca, G., Mancinelli, G., Mancuso, C., Escalero, R. Manera, Manganella, F. M., Manuzzi, D., Marangotto, D., Marchand, J. F., Marchevski, R., Marconi, U., Mariani, E., Mariani, S., Benito, C. Marin, Marks, J., Marshall, A. M., Martel, L., Martelli, G., Martellotti, G., Martinazzoli, L., Martinelli, M., Gomez, D. Martinez, Santos, D. Martinez, Vidal, F. Martinez, Granollers, A. Martorell i, Massafferri, A., Matev, R., Mathad, A., Matiunin, V., Matteuzzi, C., Mattioli, K. R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., de Cos, J. Mazorra, Mazurek, M., McCann, M., Mcconnell, L., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Cardenas, E. F. Molina, Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Moron, J., Morren, W., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Niu, Q., Nogarolli, P., Nogga, P., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Osthues, D., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Paciolla, F., Padee, A., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Pan, X., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Parmar, D., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. Pepe, Perazzini, S., Pereima, D., Da Costa, H. Pereira, Castro, A. Pereiro, Perret, P., Perrevoort, A., Perro, A., Peters, M. J., Petridis, K., Petrolini, A., Pfaller, J. P., Pham, H., Pica, L., Piccini, M., Piccolo, L., Pietrzyk, B., Pietrzyk, G., Pinci, D., Pisani, F., Pizzichemi, M., Placinta, V., Casasus, M. Plo, Poeschl, T., Polci, F., Lener, M. Poli, Poluektov, A., Polukhina, N., Polyakov, I., Polycarpo, E., Ponce, S., Popov, D., Poslavskii, S., Prasanth, K., Prouve, C., Provenzano, D., Pugatch, V., Punzi, G., Qasim, S., Qian, Q. Q., Qian, W., Qin, N., Qu, S., Quagliani, R., Trejo, R. I. Rabadan, Rademacker, J. H., Rama, M., García, M. Ramírez, De Oliveira, V. Ramos, Pernas, M. Ramos, Rangel, M. S., Ratnikov, F., Raven, G., De Miguel, M. Rebollo, Redi, F., Reich, J., Reiss, F., Ren, Z., Resmi, P. K., Ribatti, R., Ricart, G. R., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Alvarez, A. Rodriguez, Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Roensch, J., Rogachev, A., Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Vidal, A. Romero, Romolini, G., Ronchetti, F., Rong, T., Rotondo, M., Roy, S. R., Rudolph, M. S., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzhikov, A., Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Sborzacchi, F., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Segal, I., Sellam, S., Semennikov, A., Senger, T., Soares, M. Senghi, Sergi, A., Serra, N., Sestini, L., Seuthe, A., Shang, Y., Shangase, D. M., Shapkin, M., Sharma, R. S., Shchemerov, I., Shchutska, L., Shears, T., Shekhtman, L., Shen, Z., Sheng, S., Shevchenko, V., Shi, B., Shi, Q., Shimizu, Y., Shmanin, E., Shorkin, R., Shupperd, J. D., Coutinho, R. Silva, Simi, G., Simone, S., Skidmore, N., Skwarnicki, T., Slater, M. W., Smallwood, J. C., Smith, E., Smith, K., Smith, M., Snoch, A., Lavra, L. Soares, Sokoloff, M. D., Soler, F. J. P., Solomin, A., Solovev, A., Solovyev, I., Sommerfeld, N. S., Song, R., Song, Y., Song, Y. S., De Almeida, F. L. Souza, De Paula, B. Souza, Norella, E. Spadaro, Spedicato, E., Speer, J. G., Spiridenkov, E., Spradlin, P., Sriskaran, V., Stagni, F., Stahl, M., Stahl, S., Stanislaus, S., Stein, E. N., Steinkamp, O., Stenyakin, O., Stevens, H., Strekalina, D., Su, Y., Suljik, F., Sun, J., Sun, L., Sundfeld, D., Sutcliffe, W., Swallow, P. N., Swientek, K., Swystun, F., Szabelski, A., Szumlak, T., Tan, Y., Tang, Y., Tat, M. D., Terentev, A., Terzuoli, F., Teubert, F., Thomas, E., Thompson, D. J. D., Tilquin, H., Tisserand, V., T'Jampens, S., Tobin, M., Tomassetti, L., Tonani, G., Tong, X., Machado, D. Torres, Toscano, L., Tou, D. Y., Trippl, C., Tuci, G., Tuning, N., Uecker, L. H., Ukleja, A., Unverzagt, D. J., Urbach, B., Ursov, E., Usachov, A., Ustyuzhanin, A., Uwer, U., Vagnoni, V., Cadenas, V. Valcarce, Valenti, G., Canudas, N. Valls, Van Hecke, H., van Herwijnen, E., Van Hulse, C. B., Van Laak, R., van Veghel, M., Vasquez, G., Gomez, R. Vazquez, Regueiro, P. Vazquez, Sierra, C. Vázquez, Vecchi, S., Velthuis, J. J., Veltri, M., Venkateswaran, A., Verdoglia, M., Vesterinen, M., Benet, D. Vico, Villalba, P. Vidrier, Diaz, M. Vieites, Vilasis-Cardona, X., Figueras, E. Vilella, Villa, A., Vincent, P., Volle, F. C., Bruch, D. vom, Voropaev, N., Vos, K., Vrahas, C., Wagner, J., Walsh, J., Walton, E. J., Wan, G., Wang, C., Wang, G., Wang, H., Wang, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., Wang, Y. W., Wang, Z., Ward, J. A., Waterlaat, M., Watson, N. K., Websdale, D., Wei, Y., Wendel, J., Westhenry, B. D. C., White, C., Whitehead, M., Whiter, E., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. J., Williams, M. R. J., Williams, R., Williams, Z., Wilson, F. F., Winn, M., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, X., Wu, Y., Wu, Z., Wyllie, K., Xian, S., Xiang, Z., Xie, Y., Xu, A., Xu, J., Xu, L., Xu, M., Xu, Z., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yin, X., Yu, C. Y., Yu, J., Yuan, X., Yuan, Y, Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zenesini, F., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhao, Y., Zharkova, A., Zhelezov, A., Zheng, S. Z., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.

Subjects
High Energy Physics - Experiment, Nuclear Experiment
Abstract

The first measurement of $\phi(1020)$ meson production in fixed-target $p$Ne collisions at $\sqrt{s_{NN}}=68.5$ GeV is presented. The $\phi(1020)$ mesons are reconstructed in their $K^{+}K^{-}$ decay in a data sample consisting of proton collisions on neon nuclei at rest, corresponding to an integrated luminosity of $21.7 \pm 1.4$ nb$^{-1}$, collected by the LHCb detector at CERN. The $\phi(1020)$ production cross-section in the centre-of-mass rapidity range of $-1.8

Published
2024

28. UNSCT-HRNet: Modeling Anatomical Uncertainty for Landmark Detection in Total Hip Arthroplasty

Author

Wan, Jiaxin, Liu, Lin, Wang, Haoran, Li, Liangwei, Li, Wei, Kou, Shuheng, Li, Runtian, Tang, Jiayi, Liu, Juanxiu, Zhang, Jing, Du, Xiaohui, and Hao, Ruqian

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

Total hip arthroplasty (THA) relies on accurate landmark detection from radiographic images, but unstructured data caused by irregular patient postures or occluded anatomical markers pose significant challenges for existing methods. To address this, we propose UNSCT-HRNet (Unstructured CT - High-Resolution Net), a deep learning-based framework that integrates a Spatial Relationship Fusion (SRF) module and an Uncertainty Estimation (UE) module. The SRF module, utilizing coordinate convolution and polarized attention, enhances the model's ability to capture complex spatial relationships. Meanwhile, the UE module which based on entropy ensures predictions are anatomically relevant. For unstructured data, the proposed method can predict landmarks without relying on the fixed number of points, which shows higher accuracy and better robustness comparing with the existing methods. Our UNSCT-HRNet demonstrates over a 60% improvement across multiple metrics in unstructured data. The experimental results also reveal that our approach maintains good performance on the structured dataset. Overall, the proposed UNSCT-HRNet has the potential to be used as a new reliable, automated solution for THA surgical planning and postoperative monitoring.

Published
2024

29. P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs

Author

Zhang, Yidan, Deng, Boyi, Wan, Yu, Yang, Baosong, Wei, Haoran, Huang, Fei, Yu, Bowen, Lin, Junyang, and Zhou, Jingren

Subjects
Computer Science - Computation and Language
Abstract

Recent advancements in large language models (LLMs) showcase varied multilingual capabilities across tasks like translation, code generation, and reasoning. Previous assessments often limited their scope to fundamental natural language processing (NLP) or isolated capability-specific tasks. To alleviate this drawback, we aim to present a comprehensive multilingual multitask benchmark. First, we present a pipeline for selecting available and reasonable benchmarks from massive ones, addressing the oversight in previous work regarding the utility of these benchmarks, i.e., their ability to differentiate between models being evaluated. Leveraging this pipeline, we introduce P-MMEval, a large-scale benchmark covering effective fundamental and capability-specialized datasets. Furthermore, P-MMEval delivers consistent language coverage across various datasets and provides parallel samples. Finally, we conduct extensive experiments on representative multilingual model series to compare performances across models, analyze dataset effectiveness, examine prompt impacts on model performances, and explore the relationship between multilingual performances and factors such as tasks, model sizes, and languages. These insights offer valuable guidance for future research. The dataset is available at https://huggingface.co/datasets/Qwen/P-MMEval.

Published
2024

30. Study of the light scalar $a_{0}(980)$ through the decay $D^{0} \to a_{0}(980)^-e^{+} \nu_{e}$ with $a_{0}(980)^- \to \eta \pi^-$

Author

BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.

Subjects
High Energy Physics - Experiment
Abstract

Using 7.93 ${\rm fb^{-1}}$ of $e^+e^-$ collision data collected at a center-of-mass energy of 3.773 ${\rm GeV}$ with the BESIII detector, we present an analysis of the decay $D^{0} \to \eta \pi^- e^+ \nu_{e}$. The branching fraction of the decay $D^{0} \to a_{0}(980)^{-} e^+ \nu_{e}$ with $a_{0}(980)^{-} \to \eta \pi^{-}$ is measured to be $(0.86\pm0.17_{\text{stat}}\pm0.05_{\text{syst}})\times 10^{-4}$. The decay dynamics of this process is studied with a single-pole parameterization of the hadronic form factor and the Flatt\'e formula describing the $a_0(980)$ line shape in the differential decay rate. The product of the form factor $f^{ a_0}_{+}(0)$ and the Cabibbo-Kobayashi-Maskawa matrix element $|V_{cd}|$ is determined for the first time with the result $f^{ a_0}_+(0)|V_{cd}|=0.126\pm0.013_{\rm stat}\pm0.003_{\rm syst}$.

Published
2024

31. Dynamic Evolutionary Game Analysis of How Fintech in Banking Mitigates Risks in Agricultural Supply Chain Finance

Author

Wan, Qiang and Cui, Jun

Subjects
Economics - Econometrics
Abstract

This paper explores the impact of banking fintech on reducing financial risks in the agricultural supply chain, focusing on the secondary allocation of commercial credit. The study constructs a three-player evolutionary game model involving banks, core enterprises, and SMEs to analyze how fintech innovations, such as big data credit assessment, blockchain, and AI-driven risk evaluation, influence financial risks and access to credit. The findings reveal that banking fintech reduces financing costs and mitigates financial risks by improving transaction reliability, enhancing risk identification, and minimizing information asymmetry. By optimizing cooperation between banks, core enterprises, and SMEs, fintech solutions enhance the stability of the agricultural supply chain, contributing to rural revitalization goals and sustainable agricultural development. The study provides new theoretical insights and practical recommendations for improving agricultural finance systems and reducing financial risks. Keywords: banking fintech, agricultural supply chain, financial risk, commercial credit, SMEs, evolutionary game model, big data, blockchain, AI-driven risk evaluation.

Published
2024

32. When Randomness Beats Redundancy: Insights into the Diffusion of Complex Contagions

Author

Wan, Allison, Riedl, Christoph, and Lazer, David

Subjects
Computer Science - Social and Information Networks, Physics - Physics and Society
Abstract

How does social network structure amplify or stifle behavior diffusion? Existing theory suggests that when social reinforcement makes the adoption of behavior more likely, it should spread more -- both farther and faster -- on clustered networks with redundant ties. Conversely, if adoption does not benefit from social reinforcement, then it should spread more on random networks without such redundancies. We develop a novel model of behavior diffusion with tunable probabilistic adoption and social reinforcement parameters to systematically evaluate the conditions under which clustered networks better spread a behavior compared to random networks. Using both simulations and analytical techniques we find precise boundaries in the parameter space where either network type outperforms the other or performs equally. We find that in most cases, random networks spread a behavior equally as far or farther compared to clustered networks despite strong social reinforcement. While there are regions in which clustered networks better diffuse contagions with social reinforcement, this only holds when the diffusion process approaches that of a deterministic threshold model and does not hold for all socially reinforced behaviors more generally. At best, clustered networks only outperform random networks by at least a five percent margin in 18\% of the parameter space, and when social reinforcement is large relative to the baseline probability of adoption.

Published
2024

33. Rendering-Oriented 3D Point Cloud Attribute Compression using Sparse Tensor-based Transformer

Author

Huo, Xiao, Ho, Junhui, Wan, Shuai, and Yang, Fuzheng

Subjects
Computer Science - Multimedia, Computer Science - Computer Vision and Pattern Recognition
Abstract

The evolution of 3D visualization techniques has fundamentally transformed how we interact with digital content. At the forefront of this change is point cloud technology, offering an immersive experience that surpasses traditional 2D representations. However, the massive data size of point clouds presents significant challenges in data compression. Current methods for lossy point cloud attribute compression (PCAC) generally focus on reconstructing the original point clouds with minimal error. However, for point cloud visualization scenarios, the reconstructed point clouds with distortion still need to undergo a complex rendering process, which affects the final user-perceived quality. In this paper, we propose an end-to-end deep learning framework that seamlessly integrates PCAC with differentiable rendering, denoted as rendering-oriented PCAC (RO-PCAC), directly targeting the quality of rendered multiview images for viewing. In a differentiable manner, the impact of the rendering process on the reconstructed point clouds is taken into account. Moreover, we characterize point clouds as sparse tensors and propose a sparse tensor-based transformer, called SP-Trans. By aligning with the local density of the point cloud and utilizing an enhanced local attention mechanism, SP-Trans captures the intricate relationships within the point cloud, further improving feature analysis and synthesis within the framework. Extensive experiments demonstrate that the proposed RO-PCAC achieves state-of-the-art compression performance, compared to existing reconstruction-oriented methods, including traditional, learning-based, and hybrid methods.

Published
2024

34. Electron dynamics and SiO2 etching profile evolution in capacitive Ar/CHF3 discharges driven by sawtooth-tailored voltage waveforms

Author

Dong, Wan, Song, Liu-Qin, Zhang, Yi-Fan, Wang, Li, Song, Yuan-Hong, and Schulze, Julian

Subjects
Physics - Plasma Physics
Abstract

The electron dynamics and SiO2 etching profile evolution in capacitively coupled Ar/CHF3 plasmas driven by sawtooth-waveforms are investigated based on a one-dimensional fluid/Monte-Carlo (MC) model coupled with an etching profile evolution model. The effects of the sawtooth-waveforms synthesized from different numbers of consecutive harmonics, N, of a fundamental frequency of 13.56 MHz on the electron dynamics, ion and neutral transport, as well as the etching profile evolution are revealed in different mixtures of Ar/CHF3. By increasing N, a reduction in electronegativity, a decrease of the DC self-bias voltage, and a transition of the discharge mode from the Drift-Ambipolar (DA) to an {\alpha}-DA hybrid mode is observed accompanied by an enhanced plasma asymmetry. As the CHF3 gas admixture increases, the electronegativity initially increases and then decreases, following a similar trend as the absolute value of the DC self-bias voltage. This is mainly caused by the change in ionization, attachment and de-attachment reaction rates. The obtained results show that placing the substrate on the grounded electrode and using a higher number of harmonic frequencies (N) can achieve a faster etching rate, since higher ion fluxes can be obtained in these scenarios. Additionally, the Ar/CHF3 gas mixing ratio impacts the neutral surface coverage, which in turn affects the etching rate. Therefore, selecting an appropriate gas mixture is also essential for optimizing etching results., Comment: slope asymmetry effect, capacitive radio frequency Ar/CHF3 plasmas, etching profile, synergy of neutral radicals and ions

Published
2024

35. Cauchy problems for Einstein equations in three-dimensional spacetimes

Author

Chruściel, Piotr T., Cong, Wan, Quéau, Théophile, and Wutte, Raphaela

Subjects
General Relativity and Quantum Cosmology, High Energy Physics - Theory, Mathematics - Differential Geometry
Abstract

We analyze existence and properties of solutions of two-dimensional general relativistic initial data sets with a negative cosmological constant, both on spacelike and characteristic surfaces. A new family of such vacuum, spacelike data parameterised by poles at the conformal boundary at infinity is constructed. We review the notions of global Hamiltonian charges, emphasising the difficulties arising in this dimension, both in a spacelike and characteristic setting. One or two, depending upon the topology, lower bounds for energy in terms of angular momentum, linear momentum, and center of mass are established., Comment: 66 pages, 1 figure

Published
2024

36. The Super Weight in Large Language Models

Author

Yu, Mengxia, Wang, De, Shan, Qi, Reed, Colorado, and Wan, Alvin

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

Recent works have shown a surprising result: a small fraction of Large Language Model (LLM) parameter outliers are disproportionately important to the quality of the model. LLMs contain billions of parameters, so these small fractions, such as 0.01%, translate to hundreds of thousands of parameters. In this work, we present an even more surprising finding: Pruning as few as a single parameter can destroy an LLM's ability to generate text -- increasing perplexity by 3 orders of magnitude and reducing zero-shot accuracy to guessing. We propose a data-free method for identifying such parameters, termed super weights, using a single forward pass through the model. We additionally find that these super weights induce correspondingly rare and large activation outliers, termed super activations. When preserved with high precision, super activations can improve simple round-to-nearest quantization to become competitive with state-of-the-art methods. For weight quantization, we similarly find that by preserving the super weight and clipping other weight outliers, round-to-nearest quantization can scale to much larger block sizes than previously considered. To facilitate further research into super weights, we provide an index of super weight coordinates for common, openly available LLMs.

Published
2024

37. Randomized Forward Mode Gradient for Spiking Neural Networks in Scientific Machine Learning

Author

Wan, Ruyin, Zhang, Qian, and Karniadakis, George Em

Subjects
Computer Science - Neural and Evolutionary Computing, Mathematics - Numerical Analysis, Mathematics - Optimization and Control
Abstract

Spiking neural networks (SNNs) represent a promising approach in machine learning, combining the hierarchical learning capabilities of deep neural networks with the energy efficiency of spike-based computations. Traditional end-to-end training of SNNs is often based on back-propagation, where weight updates are derived from gradients computed through the chain rule. However, this method encounters challenges due to its limited biological plausibility and inefficiencies on neuromorphic hardware. In this study, we introduce an alternative training approach for SNNs. Instead of using back-propagation, we leverage weight perturbation methods within a forward-mode gradient framework. Specifically, we perturb the weight matrix with a small noise term and estimate gradients by observing the changes in the network output. Experimental results on regression tasks, including solving various PDEs, show that our approach achieves competitive accuracy, suggesting its suitability for neuromorphic systems and potential hardware compatibility.

Published
2024

38. Sensing Capacity for Integrated Sensing and Communication Systems in Low-Altitude Economy

Author

Wan, Jiahua, Ren, Hong, Pan, Cunhua, Zhang, Zhenkun, Gao, Songtao, Yu, Yiming, and Wang, Chengzhong

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

The burgeoning significance of the low-altitude economy (LAE) has garnered considerable interest, largely fuelled by the widespread deployment of unmanned aerial vehicles (UAVs). To tackle the challenges associated with the detection of unauthorized UAVs and the efficient scheduling of authorized UAVs, this letter introduces a novel performance metric, termed sensing capacity, for integrated sensing and communication (ISAC) systems. This metric, which quantifies the capability of a base station (BS) to detect multiple UAVs simultaneously, leverages signal-to-noise ratio (SNR) and probability of detection (PD) as key intermediate variables. Through mathematical derivations, we can derive a closed-form solution for the maximum number of UAVs that can be detected by the BS while adhering to a specific SNR constraint. Furthermore, an approximate solution based on PD constraints is proposed to facilitate the efficient determination of the threshold for the maximum number of detectable UAVs. The accuracy of this analytical approach is verified through extensive simulation results.

Published
2024

39. Imitation from Diverse Behaviors: Wasserstein Quality Diversity Imitation Learning with Single-Step Archive Exploration

Author

Yu, Xingrui, Wan, Zhenglin, Bossens, David Mark, Lyu, Yueming, Guo, Qing, and Tsang, Ivor W.

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

Learning diverse and high-performance behaviors from a limited set of demonstrations is a grand challenge. Traditional imitation learning methods usually fail in this task because most of them are designed to learn one specific behavior even with multiple demonstrations. Therefore, novel techniques for quality diversity imitation learning are needed to solve the above challenge. This work introduces Wasserstein Quality Diversity Imitation Learning (WQDIL), which 1) improves the stability of imitation learning in the quality diversity setting with latent adversarial training based on a Wasserstein Auto-Encoder (WAE), and 2) mitigates a behavior-overfitting issue using a measure-conditioned reward function with a single-step archive exploration bonus. Empirically, our method significantly outperforms state-of-the-art IL methods, achieving near-expert or beyond-expert QD performance on the challenging continuous control tasks derived from MuJoCo environments.

Published
2024

40. ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?

Author

Chen, Canyu, Yu, Jian, Chen, Shan, Liu, Che, Wan, Zhongwei, Bitterman, Danielle, Wang, Fei, and Shu, Kai

Subjects
Computer Science - Computation and Language
Abstract

Large Language Models (LLMs) hold great promise to revolutionize current clinical systems for their superior capacities on medical text processing tasks and medical licensing exams. Meanwhile, traditional ML models such as SVM and XGBoost have still been mainly adopted in clinical prediction tasks. An emerging question is Can LLMs beat traditional ML models in clinical prediction? Thus, we build a new benchmark ClinicalBench to comprehensively study the clinical predictive modeling capacities of both general-purpose and medical LLMs, and compare them with traditional ML models. ClinicalBench embraces three common clinical prediction tasks, two databases, 14 general-purpose LLMs, 8 medical LLMs, and 11 traditional ML models. Through extensive empirical investigation, we discover that both general-purpose and medical LLMs, even with different model scales, diverse prompting or fine-tuning strategies, still cannot beat traditional ML models in clinical prediction yet, shedding light on their potential deficiency in clinical reasoning and decision-making. We call for caution when practitioners adopt LLMs in clinical applications. ClinicalBench can be utilized to bridge the gap between LLMs' development for healthcare and real-world clinical practice., Comment: The first two authors contributed equally. 10 pages for main paper, 66 pages including appendix. Project website: https://clinicalbench.github.io

Published
2024

41. Proximate Tomonaga-Luttinger liquid in a spin-1/2 ferromagnetic XXZ chain compound

Author

Li, Boqiang, Chen, Xun, Zhao, Yuqian, Ma, Zhaohua, Wan, Zongtang, and Li, Yuesheng

Subjects
Condensed Matter - Strongly Correlated Electrons
Abstract

The spin-1/2 ferromagnetic XXZ chain is a prototypical many-body quantum model, exactly solvable via the integrable Bethe ansatz method, hosting a Tomonaga-Luttinger spin liquid. However, its clear experimental realizations remain absent. Here, we present a thorough investigation of the magnetism of the structurally disorder-free compound LuCu(OH)$_3$SO$_4$. By conducting magnetization and electron-spin-resonance measurements on the single-crystal sample, we establish that the title compound approximates the spin-1/2 ferromagnetic XXZ chain model with a nearest-neighbor exchange strength of $J_1$ $\sim$ 65 K and an easy-plane anisotropy of $\sim$ 0.994. The specific heat demonstrates a distinctive power-law behavior at low magnetic fields (with energy scales $\leq$ 0.02$J_1$) and low temperatures ($T$ $\leq$ 0.03$J_1$). This behavior is consistent with the expectations of the ideal spin-1/2 ferromagnetic XXZ chain model, thereby supporting the formation of a gapless Tomonaga-Luttinger spin liquid in LuCu(OH)$_3$SO$_4$.

Published
2024
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42. Cosmology From CMB Lensing and Delensed EE Power Spectra Using 2019-2020 SPT-3G Polarization Data

Author

Ge, F., Millea, M., Camphuis, E., Daley, C., Huang, N., Omori, Y., Quan, W., Anderes, E., Anderson, A. J., Ansarinejad, B., Archipley, M., Balkenhol, L., Benabed, K., Bender, A. N., Benson, B. A., Bianchini, F., Bleem, L. E., Bouchet, F. R., Bryant, L., Carlstrom, J. E., Chang, C. L., Chaubal, P., Chen, G., Chichura, P. M., Chokshi, A., Chou, T. -L., Coerver, A., Crawford, T. M., de Haan, T., Dibert, K. R., Dobbs, M. A., Doohan, M., Doussot, A., Dutcher, D., Everett, W., Feng, C., Ferguson, K. R., Fichman, K., Foster, A., Galli, S., Gambrel, A. E., Gardner, R. W., Goeckner-Wald, N., Gualtieri, R., Guidi, F., Guns, S., Halverson, N. W., Hivon, E., Holder, G. P., Holzapfel, W. L., Hood, J. C., Howe, D., Hryciuk, A., Kéruzoré, F., Khalife, A. R., Knox, L., Korman, M., Kornoelje, K., Kuo, C. -L., Lee, A. T., Levy, K., Lowitz, A. E., Lu, C., Maniyar, A., Martsen, E. S., Menanteau, F., Montgomery, J., Nakato, Y., Natoli, T., Noble, G. I., Pan, Z., Paschos, P., Phadke, K. A., Pollak, A. W., Prabhu, K., Rahimi, M., Rahlin, A., Reichardt, C. L., Riebel, D., Rouble, M., Ruhl, J. E., Schiappucci, E., Sobrin, J. A., Stark, A. A., Stephen, J., Tandoi, C., Thorne, B., Trendafilova, C., Umilta, C., Vieira, J. D., Vitrier, A., Wan, Y., Whitehorn, N., Wu, W. L. K., Young, M. R., and Zebrowski, J. A.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

From CMB polarization data alone we reconstruct the CMB lensing power spectrum, comparable in overall constraining power to previous temperature-based reconstructions, and an unlensed E-mode power spectrum. The observations, taken in 2019 and 2020 with the South Pole Telescope (SPT) and the SPT-3G camera, cover 1500 deg$^2$ at 95, 150, and 220 GHz with arcminute resolution and roughly 4.9$\mu$K-arcmin coadded noise in polarization. The power spectrum estimates, together with systematic parameter estimates and a joint covariance matrix, follow from a Bayesian analysis using the Marginal Unbiased Score Expansion (MUSE) method. The E-mode spectrum at $\ell>2000$ and lensing spectrum at $L>350$ are the most precise to date. Assuming the $\Lambda$CDM model, and using only these SPT data and priors on $\tau$ and absolute calibration from Planck, we find $H_0=66.81\pm0.81$ km/s/Mpc, comparable in precision to the Planck determination and in 5.4$\sigma$ tension with the most precise $H_0$ inference derived via the distance ladder. We also find $S_8=0.850\pm0.017$, providing further independent evidence of a slight tension with low-redshift structure probes. The $\Lambda$CDM model provides a good simultaneous fit to the combined Planck, ACT, and SPT data, and thus passes a powerful test. Combining these CMB datasets with BAO observations, we find that the effective number of neutrino species, spatial curvature, and primordial helium fraction are consistent with standard model values, and that the 95% confidence upper limit on the neutrino mass sum is 0.075 eV. The SPT data are consistent with the somewhat weak preference for excess lensing power seen in Planck and ACT data relative to predictions of the $\Lambda$CDM model. We also detect at greater than 3$\sigma$ the influence of non-linear evolution in the CMB lensing power spectrum and discuss it in the context of the $S_8$ tension.(abridged), Comment: 28 pages, 21 figures + appendices

Published
2024

43. Autoregressive Models in Vision: A Survey

Author

Xiong, Jing, Liu, Gongye, Huang, Lun, Wu, Chengyue, Wu, Taiqiang, Mu, Yao, Yao, Yuan, Shen, Hui, Wan, Zhongwei, Huang, Jinfa, Tao, Chaofan, Yan, Shen, Yao, Huaxiu, Kong, Lingpeng, Yang, Hongxia, Zhang, Mi, Sapiro, Guillermo, Luo, Jiebo, Luo, Ping, and Wong, Ngai

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computation and Language
Abstract

Autoregressive modeling has been a huge success in the field of natural language processing (NLP). Recently, autoregressive models have emerged as a significant area of focus in computer vision, where they excel in producing high-quality visual content. Autoregressive models in NLP typically operate on subword tokens. However, the representation strategy in computer vision can vary in different levels, \textit{i.e.}, pixel-level, token-level, or scale-level, reflecting the diverse and hierarchical nature of visual data compared to the sequential structure of language. This survey comprehensively examines the literature on autoregressive models applied to vision. To improve readability for researchers from diverse research backgrounds, we start with preliminary sequence representation and modeling in vision. Next, we divide the fundamental frameworks of visual autoregressive models into three general sub-categories, including pixel-based, token-based, and scale-based models based on the strategy of representation. We then explore the interconnections between autoregressive models and other generative models. Furthermore, we present a multi-faceted categorization of autoregressive models in computer vision, including image generation, video generation, 3D generation, and multi-modal generation. We also elaborate on their applications in diverse domains, including emerging domains such as embodied AI and 3D medical AI, with about 250 related references. Finally, we highlight the current challenges to autoregressive models in vision with suggestions about potential research directions. We have also set up a Github repository to organize the papers included in this survey at: \url{https://github.com/ChaofanTao/Autoregressive-Models-in-Vision-Survey}.

Published
2024

44. Humans Continue to Outperform Large Language Models in Complex Clinical Decision-Making: A Study with Medical Calculators

Author

Wan, Nicholas, Jin, Qiao, Chan, Joey, Xiong, Guangzhi, Applebaum, Serina, Gilson, Aidan, McMurry, Reid, Taylor, R. Andrew, Zhang, Aidong, Chen, Qingyu, and Lu, Zhiyong

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

Although large language models (LLMs) have been assessed for general medical knowledge using medical licensing exams, their ability to effectively support clinical decision-making tasks, such as selecting and using medical calculators, remains uncertain. Here, we evaluate the capability of both medical trainees and LLMs to recommend medical calculators in response to various multiple-choice clinical scenarios such as risk stratification, prognosis, and disease diagnosis. We assessed eight LLMs, including open-source, proprietary, and domain-specific models, with 1,009 question-answer pairs across 35 clinical calculators and measured human performance on a subset of 100 questions. While the highest-performing LLM, GPT-4o, provided an answer accuracy of 74.3% (CI: 71.5-76.9%), human annotators, on average, outperformed LLMs with an accuracy of 79.5% (CI: 73.5-85.0%). With error analysis showing that the highest-performing LLMs continue to make mistakes in comprehension (56.6%) and calculator knowledge (8.1%), our findings emphasize that humans continue to surpass LLMs on complex clinical tasks such as calculator recommendation.

Published
2024

45. Measurement of the $\psi(2S)$ to $J/\psi$ cross-section ratio as a function of centrality in PbPb collisions at $\sqrt{s_{\text{NN}}}$ = 5.02 TeV

Author

LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Balboni, A., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartolini, M., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. A., Belin, S., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bonacci, R. B., Bondar, N., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bouchiba, S. A., Bovill, M., Bowcock, T. J. V., Boyer, A., Bozzi, C., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonaura, A., Buonincontri, L., Burke, A. T., Burr, C., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A. C., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cervenkov, D., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., Chobanova, V., Cholak, S., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collaviti, S., Collins, P., Colombo, T., Colonna, M., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Darze, G., Davidson, A., Davies, J. E., Davis, A., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Dekkers, S., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. D., Dziurda, A., Dzyuba, A., Easo, S., Eckstein, E., Egede, U., Egorychev, A., Egorychev, V., Eisenhardt, S., Ejopu, E., Eklund, L., Elashri, M., Ellbracht, J., Ely, S., Ene, A., Eschle, J., Esen, S., Evans, T., Fabiano, F., Falcao, L. N., Fan, Y., Fang, B., Fantini, L., Faria, M., Farmer, K., Fazzini, D., Felkowski, L., Feng, M., Feo, M., Casani, A. Fernandez, Gomez, M. Fernandez, Fernez, A. D., Ferrari, F., Rodrigues, F. Ferreira, Ferrillo, M., Ferro-Luzzi, M., Filippov, S., Fini, R. A., Fiorini, M., Firlej, M., Fischer, K. L., Fitzgerald, D. S., Fitzpatrick, C., Fiutowski, T., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Lima, V. Franco, Sevilla, M. Franco, Frank, M., Franzoso, E., Frau, G., Frei, C., Friday, D. A., Fu, J., Führing, Q., Fujii, Y., Fulghesu, T., Gabriel, E., Galati, G., Galati, M. D., Torreira, A. Gallas, Galli, D., Gambetta, S., Gandelman, M., Gandini, P., Ganie, B., Gao, H., Gao, R., Gao, T. Q., Gao, Y., Martin, L. M. Garcia, Moreno, P. Garcia, Pardiñas, J. García, Gardner, P., Garg, K. G., Garrido, L., Gaspar, C., Geertsema, R. E., Gerken, L. L., Gersabeck, E., Gersabeck, M., Gershon, T., Ghizzo, S., Ghorbanimoghaddam, Z., Giambastiani, L., Giasemis, F. I., Gibson, V., Giemza, H. K., Gilman, A. L., Giovannetti, M., Gioventù, A., Girardey, L., Gironell, P. Gironella, Giugliano, C., Giza, M. A., Gkougkousis, E. L., Glaser, F. C., Gligorov, V. V., Göbel, C., Golobardes, E., Golubkov, D., Golutvin, A., Fernandez, S. Gomez, Gomulka, W., Abrantes, F. Goncalves, Goncerz, M., Gong, G., Gooding, J. A., Gorelov, I. V., Gotti, C., Grabowski, J. P., Cardoso, L. A. Granado, Graugés, E., Graverini, E., Grazette, L., Graziani, G., Grecu, A. T., Greeven, L. M., Grieser, N. A., Grillo, L., Gromov, S., Gu, C., Guarise, M., Guerry, L., Guittiere, M., Guliaeva, V., Günther, P. A., Guseinov, A. -K., Gushchin, E., Guz, Y., Gys, T., Habermann, K., Hadavizadeh, T., Hadjivasiliou, C., Haefeli, G., Haen, C., Hajheidari, M., Hallett, G., Halvorsen, M. M., Hamilton, P. M., Hammerich, J., Han, Q., Han, X., Hansmann-Menzemer, S., Hao, L., Harnew, N., Harris, T. H., Hartmann, M., Hashmi, S., He, J., Hemmer, F., Henderson, C., Henderson, R. D. L., Hennequin, A. M., Hennessy, K., Henry, L., Herd, J., Gascon, P. Herrero, Heuel, J., Hicheur, A., Mendizabal, G. Hijano, Horswill, J., Hou, R., Hou, Y., Howarth, N., Hu, J., Hu, W., Hu, X., Huang, W., Hulsbergen, W., Hunter, R. J., Hushchyn, M., Hutchcroft, D., Idzik, M., Ilin, D., Ilten, P., Inglessi, A., Iniukhin, A., Ishteev, A., Ivshin, K., Jacobsson, R., Jage, H., Elles, S. J. Jaimes, Jakobsen, S., Jans, E., Jashal, B. K., Jawahery, A., Jevtic, V., Jiang, E., Jiang, X., Jiang, Y., Jiang, Y. J., John, M., Rajan, A. John Rubesh, Johnson, D., Jones, C. R., Jones, T. P., Joshi, S., Jost, B., Castella, J. Juan, Jurik, N., Juszczak, I., Kaminaris, D., Kandybei, S., Kane, M., Kang, Y., Kar, C., Karacson, M., Karpenkov, D., Kauniskangas, A., Kautz, J. W., Kazanecki, M. K., Keizer, F., Kenzie, M., Ketel, T., Khanji, B., Kharisova, A., Kholodenko, S., Khreich, G., Kirn, T., Kirsebom, V. S., Kitouni, O., Klaver, S., Kleijne, N., Klimaszewski, K., Kmiec, M. R., Koliiev, S., Kolk, L., Konoplyannikov, A., Kopciewicz, P., Koppenburg, P., Korolev, M., Kostiuk, I., Kot, O., Kotriakhova, S., Kozachuk, A., Kravchenko, P., Kravchuk, L., Kreps, M., Krokovny, P., Krupa, W., Krzemien, W., Kshyvanskyi, O., Kubis, S., Kucharczyk, M., Kudryavtsev, V., Kulikova, E., Kupsc, A., Kutsenko, B. K., Lacarrere, D., Gonzalez, P. Laguarta, Lai, A., Lampis, A., Lancierini, D., Gomez, C. Landesa, Lane, J. J., Lane, R., Lanfranchi, G., Langenbruch, C., Langer, J., Lantwin, O., Latham, T., Lazzari, F., Lazzeroni, C., Gac, R. Le, Lee, H., Lefèvre, R., Leflat, A., Legotin, S., Lehuraux, M., Cid, E. Lemos, Leroy, O., Lesiak, T., Lesser, E. D., Leverington, B., Li, A., Li, C., Li, H., Li, K., Li, L., Li, M., Li, P., Li, P. -R., Li, Q., Li, S., Li, T., Li, Y., Lian, Z., Liang, X., Libralon, S., Lin, C., Lin, T., Lindner, R., Linton, H., Lisovskyi, V., Litvinov, R., Liu, F. L., Liu, G., Liu, K., Liu, S., Liu, W., Liu, Y., Liu, Y. L., Salvia, A. Lobo, Loi, A., Long, T., Lopes, J. H., Huertas, A. Lopez, Soliño, S. López, Lu, Q., Lucarelli, C., Lucchesi, D., Martinez, M. Lucio, Lukashenko, V., Luo, Y., Lupato, A., Luppi, E., Lynch, K., Lyu, X. -R., Ma, G. M., Maccolini, S., Machefert, F., Maciuc, F., Mack, B., Mackay, I., Mackey, L. M., Mohan, L. R. Madhan, Madurai, M. J., Maevskiy, A., Magdalinski, D., Maisuzenko, D., Majewski, M. W., Malczewski, J. J., Malde, S., Malentacca, L., Malinin, A., Maltsev, T., Manca, G., Mancinelli, G., Mancuso, C., Escalero, R. Manera, Manganella, F. M., Manuzzi, D., Marangotto, D., Marchand, J. F., Marchevski, R., Marconi, U., Mariani, E., Mariani, S., Benito, C. Marin, Marks, J., Marshall, A. M., Martel, L., Martelli, G., Martellotti, G., Martinazzoli, L., Martinelli, M., Gomez, D. Martinez, Santos, D. Martinez, Vidal, F. Martinez, Granollers, A. Martorell i, Massafferri, A., Matev, R., Mathad, A., Matiunin, V., Matteuzzi, C., Mattioli, K. R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., de Cos, J. Mazorra, Mazurek, M., McCann, M., Mcconnell, L., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Cardenas, E. F. Molina, Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Moron, J., Morren, W., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Niu, Q., Nogarolli, P., Nogga, P., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Osthues, D., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Paciolla, F., Padee, A., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Pan, X., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Parmar, D., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. Pepe, Perazzini, S., Pereima, D., Da Costa, H. Pereira, Castro, A. Pereiro, Perret, P., Perrevoort, A., Perro, A., Peters, M. J., Petridis, K., Petrolini, A., Pfaller, J. P., Pham, H., Pica, L., Piccini, M., Piccolo, L., Pietrzyk, B., Pietrzyk, G., Pinci, D., Pisani, F., Pizzichemi, M., Placinta, V., Casasus, M. Plo, Poeschl, T., Polci, F., Lener, M. Poli, Poluektov, A., Polukhina, N., Polyakov, I., Polycarpo, E., Ponce, S., Popov, D., Poslavskii, S., Prasanth, K., Prouve, C., Provenzano, D., Pugatch, V., Punzi, G., Qasim, S., Qian, Q. Q., Qian, W., Qin, N., Qu, S., Quagliani, R., Trejo, R. I. Rabadan, Rademacker, J. H., Rama, M., García, M. Ramírez, De Oliveira, V. Ramos, Pernas, M. Ramos, Rangel, M. S., Ratnikov, F., Raven, G., De Miguel, M. Rebollo, Redi, F., Reich, J., Reiss, F., Ren, Z., Resmi, P. K., Ribatti, R., Ricart, G. R., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Alvarez, A. Rodriguez, Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Roensch, J., Rogachev, A., Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Vidal, A. Romero, Romolini, G., Ronchetti, F., Rong, T., Rotondo, M., Roy, S. R., Rudolph, M. S., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzhikov, A., Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Sborzacchi, F., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Segal, I., Sellam, S., Semennikov, A., Senger, T., Soares, M. Senghi, Sergi, A., Serra, N., Sestini, L., Seuthe, A., Shang, Y., Shangase, D. M., Shapkin, M., Sharma, R. S., Shchemerov, I., Shchutska, L., Shears, T., Shekhtman, L., Shen, Z., Sheng, S., Shevchenko, V., Shi, B., Shi, Q., Shimizu, Y., Shmanin, E., Shorkin, R., Shupperd, J. D., Coutinho, R. Silva, Simi, G., Simone, S., Skidmore, N., Skwarnicki, T., Slater, M. W., Smallwood, J. C., Smith, E., Smith, K., Smith, M., Snoch, A., Lavra, L. Soares, Sokoloff, M. D., Soler, F. J. P., Solomin, A., Solovev, A., Solovyev, I., Sommerfeld, N. S., Song, R., Song, Y., Song, Y. S., De Almeida, F. L. Souza, De Paula, B. Souza, Norella, E. Spadaro, Spedicato, E., Speer, J. 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J., Veltri, M., Venkateswaran, A., Verdoglia, M., Vesterinen, M., Benet, D. Vico, Villalba, P. Vidrier, Diaz, M. Vieites, Vilasis-Cardona, X., Figueras, E. Vilella, Villa, A., Vincent, P., Volle, F. C., Bruch, D. vom, Voropaev, N., Vos, K., Vrahas, C., Wagner, J., Walsh, J., Walton, E. J., Wan, G., Wang, C., Wang, G., Wang, H., Wang, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., Wang, Y. W., Wang, Z., Ward, J. A., Waterlaat, M., Watson, N. K., Websdale, D., Wei, Y., Wendel, J., Westhenry, B. D. C., White, C., Whitehead, M., Whiter, E., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. J., Williams, M. R. J., Williams, R., Williams, Z., Wilson, F. F., Winn, M., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, X., Wu, Y., Wu, Z., Wyllie, K., Xian, S., Xiang, Z., Xie, Y., Xu, A., Xu, J., Xu, L., Xu, M., Xu, Z., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yin, X., Yu, C. Y., Yu, J., Yuan, X., Yuan, Y, Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zenesini, F., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhang, Z., Zhao, Y., Zharkova, A., Zhelezov, A., Zheng, S. Z., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.

Subjects
Nuclear Experiment, High Energy Physics - Experiment
Abstract

The dissociation of quarkonium states with different binding energies produced in heavy-ion collisions is a powerful probe for investigating the formation and properties of the quark-gluon plasma. The ratio of production cross-sections of $\psi(2S)$ and $J/\psi$ mesons times the ratio of their branching fractions into the dimuon final state is measured as a function of centrality using data collected by the LHCb detector in PbPb collisions at $\sqrt{s_{\text{NN}}}$ = 5.02 TeV. The measured ratio shows no dependence on the collision centrality, and is compared to the latest theory predictions and to the recent measurements in literature., Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2024-041.html (LHCb public pages)

Published
2024

46. Ising domain wall networks from intertwined charge density waves in single-layer TiSe2

Author

Wan, Wen, Gastiasoro, Maria N., Muñoz-Segovia, Daniel, Dreher, Paul, Ugeda, Miguel M., and de Juan, Fernando

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

When the period of an incommensurate charge density wave (ICDW) approaches an integer multiple of a lattice vector, the energy gain obtained from locking the period to the lattice can lead to a fascinating transition into a commensurate state. This transition actually occurs through an intermediate near-commensurate (NC) phase, with locally commensurate regions separated by an ordered array of phase slips of a complex CDW order parameter. TiSe2 is a paradigmatic CDW system where incommensuration is believed to be induced by carrier doping, yet its putative NC state has never been imaged or its nature established. Here we report the observation of a striking NC state in ultraclean, slightly doped monolayers of TiSe2, displaying an intricate network of coherent, unidirectional CDW domain walls over hundreds of nanometers. Detailed analysis reveals these are not phase slips of a complex CDW, but rather sign-changing Ising-type domain walls of two coupled real CDWs of previously known symmetry, consistent with the period doubling nature of the parent commensurate state. In addition, we observe an unexpected nematic modulation at the original lattice Bragg peaks which couples to the CDW order parameters. A Ginzburg-Landau analysis naturally explains the couplings and relative modulations of all order parameters, unveiling TiSe2 as a rare example of an NC-CDW of two intertwined real modulations and emergent nematicity.

Published
2024

47. Multiple-partition cross-modulation programmable metasurface empowering wireless communications

Author

Zhang, Jun Wei, Qi, Zhen Jie, Wu, Li Jie, Cao, Wan Wan, Gao, Xinxin, Fu, Zhi Hui, Chen, Jing Yu, Lv, Jie Ming, Wang, Zheng Xing, Wang, Si Ran, Wu, Jun Wei, Zhang, Zhen, Zhang, Jia Nan, Li, Hui Dong, Dai, Jun Yan, Cheng, Qiang, and Cui, Tie Jun

Subjects
Physics - Applied Physics
Abstract

With the versatile manipulation capability, programmable metasurfaces are rapidly advancing in their intelligence, integration, and commercialization levels. However, as the programmable metasurfaces scale up, their control configuration becomes increasingly complicated, posing significant challenges and limitations. Here, we propose a multiple-partition cross-modulation (MPCM) programmable metasurface to enhance the wireless communication coverage with low hardware complexity. We firstly propose an innovative encoding scheme to multiply the control voltage vectors of row-column crossing, achieving high beamforming precision in free space while maintaining low control hardware complexity and reducing memory requirements for coding sequences. We then design and fabricate an MPCM programmable metasurface to confirm the effectiveness of the proposed encoding scheme. The simulated and experimental results show good agreements with the theoretically calculated outcomes in beam scanning across the E and H planes and in free-space beam pointing. The MPCM programmable metasurface offers strong flexibility and low complexity by allowing various numbers and combinations of partition items in modulation methods, catering to diverse precision demands in various scenarios. We demonstrate the performance of MPCM programmable metasurface in a realistic indoor setting, where the transmissions of videos to specific receiver positions are successfully achieved, surpassing the capabilities of traditional programmable metasurfaces. We believe that the proposed programmable metasurface has great potentials in significantly empowering the wireless communications while addressing the challenges associated with the programmable metasurface's design and implementation.

Published
2024

48. On-chip Moir\'e Optical Skyrmion Clusters with Nanoscale Dynamics

Author

Zhang, Lan, Hou, Liang, Wan, Lipeng, Deng, Weimin, Zou, Qiushun, Wang, Tongbiao, Zhao, Daomu, and Yu, Tianbao

Subjects
Physics - Optics, Physics - Applied Physics
Abstract

Skyrmions are topological defects belonging to nontrivial homotopy classes in particle theory, and are recognized as a suitable unit in the high-density, low-dissipation microelectronic devices in condensed matter physics. Their remarkably stable topology has been observed in electromagnetic waves recently. For the evanescent fields near a surface, this has been realized so far only for elementary optical skyrmions, with a fixed skyrmion number. Here we introduce the concept of moir\'e optical skyrmion clusters-multiskyrmions are nested to form a large optical skyrmion cluster-crystallized or quasi-crystallized as a consequence of the twisted nanostructures. The rapid inverting of optical skyrmion number is achieved in the imperfectly aligned composite nanostructures. This moir\'e optical skyrmion interaction mechanism is described by a lattice model. Further, the nucleation and collapse of optical skyrmion are studied, where their nanoscale dynamics are revealed with a tiny change of the twist angle. The sudden reversal of the on-chip skyrmion can serve as a precise beacon of the relative alignment deviation between twisted composite nanostructures.

Published
2024

49. UKTF: Unified Knowledge Tracing Framework for Subjective and Objective Assessments

Author

Wang, Zhifeng, Wan, Jiaqin, Yang, Yang, Zeng, Chunyan, and Shen, Jialiang

Subjects
Computer Science - Computers and Society
Abstract

With the continuous deepening and development of the concept of smart education, learners' comprehensive development and individual needs have received increasing attention. However, traditional educational evaluation systems tend to assess learners' cognitive abilities solely through general test scores, failing to comprehensively consider their actual knowledge states. Knowledge tracing technology can establish knowledge state models based on learners' historical answer data, thereby enabling personalized assessment of learners. Nevertheless, current classical knowledge tracing models are primarily suited for objective test questions, while subjective test questions still confront challenges such as complex data representation, imperfect modeling, and the intricate and dynamic nature of knowledge states. Drawing on the application of knowledge tracing technology in education, this study aims to fully utilize examination data and proposes a unified knowledge tracing model that integrates both objective and subjective test questions. Recognizing the differences in question structure, assessment methods, and data characteristics between objective and subjective test questions, the model employs the same backbone network for training both types of questions. Simultaneously, it achieves knowledge tracing for subjective test questions by universally modifying the training approach of the baseline model, adding branch networks, and optimizing the method of question encoding. This study conducted multiple experiments on real datasets, and the results consistently demonstrate that the model effectively addresses knowledge tracing issues in both objective and subjective test question scenarios., Comment: 9 pages

Published
2024

50. Balancing Pipeline Parallelism with Vocabulary Parallelism

Author

Yeung, Man Tsung, Qi, Penghui, Lin, Min, and Wan, Xinyi

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Pipeline parallelism is widely used to scale the training of transformer-based large language models, various works have been done to improve its throughput and memory footprint. In this paper, we address a frequently overlooked issue: the vocabulary layers can cause imbalanced computation and memory usage across pipeline stages, worsening pipeline bubbles and the memory bottleneck. To tackle this, we partition the vocabulary layers evenly across pipeline devices and group the computation into pipeline passes. To reduce the activation memory overhead, we propose several algorithms to reduce communication barriers within vocabulary layers. Additionally, we utilize a generalizable method to integrate Vocabulary Parallelism with existing pipeline schedules. By combining these techniques, our methods effectively balance the computation and parameter memory, with only a small constant activation memory overhead. Notably, when combined with activation memory-balanced schedules like V-Half, our approach achieves perfect balance in both memory and computation. Extensive evaluations demonstrate that our method achieves computation and memory balance regardless of the vocabulary size, resulting in a 5% to 51% improvement in throughput compared to naive approaches, meanwhile significantly reducing peak memory usage especially for large vocabulary scenarios. Our implementation is open-sourced at https://github.com/sail-sg/VocabularyParallelism .

Published
2024

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