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1. Investigate on the mechanical properties and microscopic three-dimensional morphology of rock failure surfaces under different stress states

Author

Genshui Wu, Weijian Yu, Bao Pan, Yuqing Liu, Chuangfeng Fang, and Xun Liu

Subjects
Rock mechanics, Failure surface, 3D reconstruction, Fractal characteristics, Roughness, Geophysics. Cosmic physics, QC801-809
Abstract

Abstract The macro and micro morphology of rock failure surfaces play crucial roles in determining the rock mechanical and seepage properties. The morphology of unloaded deep rock failure surfaces exhibits significant variability and complexity. Surface roughness is closely linked to both shear strength and crack seepage behavior. Understanding these morphology parameters is vital for comprehending the mechanical behavior and seepage characteristics of rock masses. In this study, three-dimensional optical scanning technology was employed to analyze the micromorphological properties of limestone and sandstone failure surfaces under varying stress conditions. Line and surface roughness characteristics of different rock failure surfaces were then determined. Our findings reveal a critical confining pressure value (12 MPa) that influences the damage features of Ordovician limestone failure surfaces. With increasing confining pressure, pore depth and crack formation connecting the pores also increase. Beyond the critical confining pressure, the mesoscopic roughness of the failure surface decreases, and the range of interval-distributed pore roughness diminishes. Additionally, we conducted a detailed investigation into the water conductivity properties of rocks under different stress states using Barton's joint roughness coefficient (JRC) index and rock fractal theory. The roughness features of rock failure surfaces were classified into three categories based on mesoscopic pore and crack undulation forms: straight, wavy, and jagged. We also observed significant confining pressure effects on limestone and sandstone, which exceeding the critical confining pressure led to increased water conductivity in both rocks, albeit through different mechanisms. While sandstone exhibits fissures running across it, limestone shows shear abrasion holes. Beyond the critical confining pressure, the rock failure surface becomes smoother, leading to decreased water flow blocking capacity. The fractal dimension of Ordovician limestone increases significantly under critical confining pressure, leading to a more complex mesoscopic crack extension route.

Published
2024
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2. Visual analysis method for unmanned pumping stations on dynamic platforms based on data fusion technology

Author

Zhen Liu, Sen Chen, Zhaobo Zhang, Jiahao Qin, and Bao Peng

Subjects
Visual analysis, Transfer learning, ResNet18, LSTM, Telecommunication, TK5101-6720, Electronics, TK7800-8360
Abstract

Abstract As the scale of water conservancy projects continues to expand, the amount and complexity of analytical data have also correspondingly increased. At present, it is difficult to realize project management decision support based on a single data source, and most manual analysis methods not only have high labor costs, but also are prone to the risk of misjudgment, resulting in huge property losses. Based on this problem, this paper proposes visual analysis method for unmanned pumping stations on dynamic platforms based on data fusion technology. First, the method uses the transfer learning method to enable ResNet18 obtain generalization ability. Secondly, the method uses ResNet18 to extract image features, and outputs fixed length sequence data as the input of long short-term memory (LSTM). Finally, the method uses LSTM outputs the classification results. The experimental results demonstrate that the algorithm model can achieve an impressive accuracy of 99.032%, outperforming the combination of traditional feature extraction and machine learning methods. This model effectively recognizes and classifies images of pumping stations, significantly reducing the risk of accidents in these facilities.

Published
2024
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3. Revealing the heterogeneity of plasma protein and cognitive decline trajectory among Mild Cognitive Impairment patients by clustering of brain atrophy features

Author

My Nguyen, Bao Pham, Toi Vo, and Huong Ha

Subjects
Alzheimer's disease, Mild cognitive impairment, Clustering analysis, Brain atrophy, Plasma protein, Cognitive decline, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Alzheimer's disease (AD) is suggested to be a heterogeneous disorder, but limited studies explore the heterogeneity of the Mild Cognitive Impairment (MCI) stage. This study aimed to tackle such problems using the CIMLR (Cancer Integration via Multikernel Learning) algorithm to cluster brain structural features extracted from T1-weighted Magnetic Resonance Images of MCI patients from Alzheimer's Disease Neuroimaging Initiative. The demographic and cognitive results, characteristics of brain structural features, plasma biomarkers, and longitudinal cognitive trajectory were analyzed for each cluster. The CIMLR clustering analysis revealed four distinct clusters. Cluster 1 is the oldest group but has had mild atrophy and moderate progression with elevated Tumor Necrosis Factor Receptor 2 level; and low Brain-Derived Neurotrophic Factor and CD40 Ligand levels. Cluster 2 showed the highest risk for aggressive MCI progression, with abnormal Leptin, Adiponectin, and Creatine kinase-MB values. Cluster 3 exhibited a low level of Monokine Induced by Gamma Interferon and mild atrophy that shared similar patterns with Cluster 1. Cluster 4 represented the healthiest group during longitudinal tracking, with the mildest Parahippocampal atrophy, which was found to be positively correlated with cognitive impairment and amino acid levels. The longitudinal analyses showed the potential of Hepatocyte Growth Factor as a marker for slow cognitive impairment; Cortisol and Neurofilament Light Polypeptide as prognosis markers for aggressive MCI progression. These findings may lay out new suggestions for further research contributing to the accurate diagnosis and precision medicine for dementia and AD.

Published
2024
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4. Investigation on water inrush fracture mechanics model based on fracture mechanics and microseismic monitoring

Author

Genshui Wu, Weijian Yu, Hanxiao Guo, Bao Pan, and Xun Liu

Subjects
Water inrush, Rock mechanics, Fault, Coal mine, Fracture mechanics, Engineering (General). Civil engineering (General), TA1-2040
Abstract

North China coalfield is one of the most severe coal fields threatened by Ordovician limestone water (buried depth exceeding 1000 m). In this paper, the hydraulic mechanism of Ordovician limestone water in the complex geological structure are studied. An interesting phenomenon was found in the study, the water inrush from Ordovician limestone has a “latent period” and presents lag water inrush law. The hydrochemical characteristics of the early stage in Xingdong coal mine is non-Ordovician limestone water chemical characteristics, the later stage gradually shows the Ordovician limestone water with the continuous outflow. Meanwhile, the hyperbolic contour distribution characteristics are also observed in the spatial distribution law of microseismic in the floor working face. The minimum damage area is in the layer at the hyperbolic apex, which is the critical breakthrough point of water inrush. Furthermore, the water inrush fracture mechanics model (WIFM) of concealed fault with high confined water is established based on the fracture mechanics theory. The results show that the critical values of the length and angle of the water inrush of the conceal fault are about 50 m and 40°. It should be pointed out that the longer and larger angle conceal faults indicate the longer of the fracture expansion at the tip of the fault, and the probability of water inrush increases. Special attention should be paid to such concealed faults and methods such as grouting should be adopted to avoid the water inrush disaster caused by the activation of faults. This model provides a theoretical basis for the mechanical mechanism of the coal seam floor water inrush process.

Published
2024
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5. Serotype distribution and antimicrobial resistance of pediatric Streptococcus pneumoniae isolated from inpatients and outpatients at Beijing Children's Hospital

Author

Shuang Lyu, Wei Shi, Fang Dong, Bao Ping Xu, Gang Liu, Quan Wang, Kai Hu Yao, and Yong Hong Yang

Subjects
Streptococcus pneumoniae, Serotype distribution, Antibiotic resistance, Multilocus sequence typing, Inpatients and outpatients, Infectious and parasitic diseases, RC109-216, Microbiology, QR1-502
Abstract

Background: Understanding the epidemiology of Streptococcus pneumoniae (S. pneumoniae) isolates is important for pneumonia treatment and prevention. This research aimed to explore the epidemiological characteristics of S. pneumoniae isolated from pediatric inpatients and outpatients during the same period. Methods: S. pneumoniae were isolated from unsterile samples of inpatients and outpatients younger than five years old between March 2013 and February 2014. The serotypes were determined using diagnostic pneumococcal antisera. The resistance of each strain to 13 antibiotics was tested using either the E-test or the disc diffusion method. The Sequence Types (STs) were analyzed via Multilocus Sequence Typing (MLST). Results: The dominant serotypes obtained from inpatients were 19F (32.9 %), 19A (20.7 %), 23F (10.7 %), 6A (10.0 %), and 14 (8.6 %), while those from outpatients were 19F (13.6 %), 23F (12.9 %), 6A (10.0 %), 6B (10.0 %), and 19A (7.9 %). The coverage rates of 13-valent Pneumococcal Conjugate Vaccine (PCV) formulations were high in both groups. The nonsusceptibility to penicillin, cefuroxime, imipenem, erythromycin, and trimethoprim-sulfamethoxazole among the inpatient isolates was 7.1 %, 92.8 %, 65.7 %, 100 %, and 85.0 %, respectively, while that among the outpatient isolates was 0.7 %, 50.0 %, 38.6 %, 96.4 %, and 65.7 %, respectively. There were 45 and 81 STs detected from the pneumococci isolated from inpatients and outpatients, respectively. CC271 was common among both inpatients and outpatients (43.6 % and 14.3 %). Conclusions: Pneumococcal vaccine-related serotypes are prevalent among both inpatients and outpatients, especially among inpatients, who exhibit more severe antibiotic resistance. Therefore, universal immunization with PCV13 would decrease the hospitalization rate due to S. pneumoniae and the antibiotic resistance rate of S. pneumoniae.

Published
2024
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6. A laboratory-testing-based study on mechanical properties and dilatancy characteristics of deeply buried mudstone under different stress loading rates

Author

Ke LI, Weijian YU, Ze LIAO, Hanxiao GUO, Bao PAN, Viengvilay KHAMPHOUVANH, and Jie YANG

Subjects
stress loading rates, deeply buried mudstone, mechanical strength, dilatancy characteristics, Geology, QE1-996.5, Mining engineering. Metallurgy, TN1-997
Abstract

The damage and failure process of rock engineering is inseparable from rock dilatancy. Dilatancy is the phenomenon of rock volume increase after the differential stress exceeds the yield limit. X-ray diffraction, scanning electron microscope, and uniaxial compression tests under different stress loading rates (0.1, 0.5, 2.0, 5.0 kN/s) were carried out on mudstone specimens from drill holes in the mine site with a depth of nearly 1300 m. The results demonstrate that the mudstone is mainly composed of plagioclase. The distribution of various minerals in the rock is not uniform. The main component plagioclase is distributed in strips, filled with other minerals, the filling material is mainly chlorite. Under different stress loading rates, the axial strain curves are typical plastic-elastic-plastic curves, and each specimen has an obvious micro-crack compaction process. The lower the stress loading rate, the more pronounced the compaction process, that is, the smoother the curve. In the elastic stage, the slope of the curve increases as the stress loading rate increases, indicating that the rock’s elastic modulus rises. The one-way analysis of variance method was used to test the significance of the differences in each analysis item’s data. The results indicate that the stress loading rate (Vload) is related to the uniaxial compressive strength (σc), elastic modulus(E), maximum axial strain(εa,max), maximum radial strain(εd,max), the initial dilatancy strength(f*), and the ratio of the initial dilatancy stress to the uniaxial compressive strength(f*/σc) of mudstone. It does not correlate with the maximum volumetric strain value (εV,max), and the ratio of the initial dilatancy volumetric strain to the maximum volumetric strain (εV,d/εV,max). As the Vload increases, σc and E increase, when Vload increases 50 times from 0.1 kN/s to 5.0 kN/s, the measured σc increases from 26.8 MPa to 36.5 MPa, and E increases from 3.5 GPa to 5.9 GPa. The εa,max and εd,max decrease with increasing Vload while axial and radial strain rates increase. Hence, the likelihood of failure of mudstone rock under insufficient deformation increases with increasing Vload. The f* and f*/σc decrease as Vload increases, meaning that it is easier to enter the dilatancy stage with higher Vload. It is considered that the strength parameters, deformation and dilatancy parameters of mudstone are significantly dependent on Vload.

Published
2023
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7. Isolation, Identification, and Characterization of Tetrodotoxin Producing Bacillus amyloliquefaciens B1 Originated from Carcinoscorpius rotundicauda

Author

Canh Le, Tra Doan, Phong Le, Bao Phan, Tu Ho, Phuc Hua, Yen Tran, and Tu Nguyen

Subjects
carcinoscorpius rotundicauda, bacillus amyloliquefaciens b1, tetrodotoxin, exopolysaccharides, characterization, Microbiology, QR1-502
Abstract

This study isolated and identified Bacillus amyloliquefaciens B1 from Carcinoscorpius rotundicauda by carrying out the 16S rRNA sequence analysis, reconstructing the phylogenetic tree based on the Environment for Tree Exploration (ETE3) v3.1.1 belonging to the GenomeNet. By an indirect competitive enzyme-labeled immunoassay, B1 could produce tetrodotoxin (TTX) in MRS was more highly than LB media. After purification, TTX producing ability in B1 could be detected in ELISA assay, high performance liquid chromatography (HPLC). The gel permeation chromatography and gas chromatography were applied to determine the molecular weight of EPS and the concentration of glucose in EPS. The results indicated the highest molecular weight of exopolysaccharides (EPS) estimated 1.33 × 106 Da consisted of glucose (150.09 µg/g). TTX yield was proportional to EPS production in the bacterium. The antimicrobial activities of EPS were determined by agar well diffusion method. Diameter of inhibition zone (mm) of Bacillus amyloliquefaciens EPS on the test microorganisms. The EPS could inhibit against Staphylococcus aureus ATCC 25923, Pseudomonas aeruginosa ATCC 27853, Escherichia coli ATCC 25922, Vibrio parahaemolyticus ATCC 17802 and Micrococcus luteus ATCC 10240. In silico prediction, TTX might interact with Bacillus amyloliquefaciens via the extracellular domain of noncanonic ABC-type transporter from gram positive bacteria. TTX might also interact with peptidase S54, mistic, metal binding protein of Bacillus subtilis and tryptophan-rich sensory protein of Bacillus cereus. This study provides the understanding of TTX producing Bacillus amyloliquefaciens B1 isolated from Carcinoscorpius rotundicauda.

Published
2023
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8. Chinese personalised text‐to‐speech synthesis for robot human–machine interaction

Author

Bao Pang, Jun Teng, Qingyang Xu, Yong Song, Xianfeng Yuan, and Yibin Li

Subjects
convolutional neural network, human robot interaction, deep learning, deep neural network, Cybernetics, Q300-390, Electronic computers. Computer science, QA75.5-76.95
Abstract

Abstract Speech interaction is an important means of robot interaction. With the rapid development of deep learning, end‐to‐end speech synthesis methods based on this technique have gradually become mainstream. Chinese deep learning‐based speech synthesis techniques suffer from problems such as unstable synthesised speech, poor naturalness and poor personalised speech synthesis, which do not satisfy some practical application scenarios. Hence, an F‐MelGAN model is adopted to improve the performance of Chinese speech synthesis. A post‐processing network is used to refine the Mel‐spectrum predicted by the decoder and alleviate the Mel‐spectrum distortion phenomenon. A phoneme‐level and sentence‐level combined module is proposed to model the personalised style of speakers. A combination of an acoustic conditioning network, speaker encoder network GCNet and feedback‐constrained training is proposed to solve the problem of poor personalised speech synthesis and achieve personalised speech customisation in Chinese. Experimental results show that the whole model can generate high‐quality speech with high speaker similarity for both speakers that appear in the training process and speakers that never appear in the training process.

Published
2023
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9. Artificial Bee Colony Algorithm Based on Dimensional Memory Mechanism and Adaptive Elite Population for Training Artificial Neural Networks

Author

Yiyang Zhang, Bao Pang, Yong Song, Qingyang Xu, and Xianfeng Yuan

Subjects
Artificial bee colony algorithm, adaptive elite population, dimensional memory mechanism, search equation, artificial neural network, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Based on dimensional memory mechanism and adaptive elite population, this paper proposes a satisfactory and efficient artificial bee colony algorithm (DMABC_elite) to solve optimization problems and train artificial neural networks (ANN). DMABC_elite proposes the concept of adaptive elite population that changes dynamically with the search process, and modifies the search equations for employed and onlooker bee phases on this basis. In addition, a dimensional memory mechanism has been introduced that allows multi-dimensional updates, which improves exploitation and speeds up convergence. Next, a new selection strategy and a Lévy flight-based solution-generating method are introduced in the scout bee phase to enhance the global search ability. Finally, the performance of DMABC_elite on two different problem groups is analyzed experimentally. On the one hand, DMABC_elite is evaluated using 22 classical benchmark functions with different dimensions and CEC 2013 test functions. Compared with basic ABC and nine state-of-the-art ABC variants, DMABC_elite achieved better results, ranking first in all 10-, 30- and 100-dimensional tests across 22 classical benchmark functions and 30-dimensional tests across CEC 2013 test functions. On the other hand, DMABC_elite is compared with traditional backpropagation-based algorithms and other ABC variants when training seven different ANNs. The results show that DMABC_elite is efficient and competitive in training ANNs.

Published
2023
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10. Analysis on incidence of stomach cancer in 2016 and trend of incidence during 2002-2016 in Shanghai

Author

BAO Pingping, WU Chunxiao, GU Kai, PANG Yi, WANG Chunfang, SHI Liang, XIANG Yongmei, GONG Yangming, DOU Jianming, WU Mengyin, FU Chen, SHI Yan

Subjects
stomach cancer, incidence, epidemiology, time trend, shanghai, Medicine
Abstract

Objective: To investigate the incidence of stomach cancer in 2016 and the trend of incidence during 2002-2016 in Shanghai. Methods: The data on new-diagnosed stomach cancer during 2002-2016 were obtained from population-based cancer registry in Shanghai Municipal Center for Disease Control and Prevention. The incidence of stomach cancer stratified by year of diagnosis, gender, and age-group were analyzed. The number, proportion, crude rate, age-specific rate, age-standardized and annual percentage change were calculated, and the trends of incidence were evaluated. Segi′s World Standard Population in 1960 was used to calculate age-standardized rates of incidence and mortality, and the Joinpoint was used to analyze the trend of incidence of stomach cancer. Results: The new stomach cancer cases in Shanghai were 5 979 in 2016. The crude rate of incidence was 41.29/105 and the age-standardized rate was 15.85/105. The stomach cancer was one the most common cancers and ranked fourth in Shanghai. The age-standardized rate in male (21.19/105) was higher than that in female (10.84/105). The age-standardized rate of stomach cancer decreased during 2002-2016 in both male and female, and the APC was -2.93% (-3.23%, -2.63%) in male and -2.70% (-2.98%, -2.42%) in female, respectively. The morbidity rates of stomach cancer increased with age, which was

Published
2022
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11. New Insights into Supradense Matter from Dissecting Scaled Stellar Structure Equations

Author

Cai, Bao-Jun and Li, Bao-An

Subjects
Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Astrophysics of Galaxies, High Energy Physics - Phenomenology, Nuclear Experiment, Nuclear Theory
Abstract

The strong-field gravity in General Relativity (GR) realized in neutron stars (NSs) renders the Equation of State (EOS) $P(\varepsilon)$ of supradense neutron star (NS) matter to be essentially nonlinear and refines the upper bound for $\phi\equiv P/\varepsilon$ to be much smaller than the Special Relativity (SR) requirement with linear EOSs, where $P$ and $\varepsilon$ are respectively the pressure and energy density of the system considered. Specifically, a tight bound $\phi\lesssim0.374$ is obtained by anatomizing perturbatively the intrinsic structures of the scaled Tolman--Oppenheimer--Volkoff (TOV) equations without using any input nuclear EOS. New insights gained from this novel analysis provide EOS-model independent constraints on properties (e.g., density profiles of the sound speed squared $s^2=\d P/\d\varepsilon$ and trace anomaly $\Delta=1/3-\phi$) of cold supradense matter in NS cores. Using the gravity-matter duality in theories describing NSs, we investigate the impact of gravity on supradense matter EOS in NSs. In particular, we show that the NS mass $M_{\rm{NS}}$, radius $R$ and its compactness $\xi\equiv M_{\rm{NS}}/R$ scale with certain combinations of its central pressure and energy density (encapsulating its central EOS). Thus, observational data on these properties of NSs can straightforwardly constrain NS central EOSs without relying on any specific nuclear EOS-model., Comment: 27 pages with 10 figures. Review Article for the Research Topic "Strong and Weak Interactions in Compact Stars" hosted by Mark Alford, David Blaschke, Ignazio Bombaci, James Lattimer, and Armen Sedrakian in Frontiers in Astronomy and Space Sciences

Published
2024

12. Evaluation of OpenAI o1: Opportunities and Challenges of AGI

Author

Zhong, Tianyang, Liu, Zhengliang, Pan, Yi, Zhang, Yutong, Zhou, Yifan, Liang, Shizhe, Wu, Zihao, Lyu, Yanjun, Shu, Peng, Yu, Xiaowei, Cao, Chao, Jiang, Hanqi, Chen, Hanxu, Li, Yiwei, Chen, Junhao, Hu, Huawen, Liu, Yihen, Zhao, Huaqin, Xu, Shaochen, Dai, Haixing, Zhao, Lin, Zhang, Ruidong, Zhao, Wei, Yang, Zhenyuan, Chen, Jingyuan, Wang, Peilong, Ruan, Wei, Wang, Hui, Zhao, Huan, Zhang, Jing, Ren, Yiming, Qin, Shihuan, Chen, Tong, Li, Jiaxi, Zidan, Arif Hassan, Jahin, Afrar, Chen, Minheng, Xia, Sichen, Holmes, Jason, Zhuang, Yan, Wang, Jiaqi, Xu, Bochen, Xia, Weiran, Yu, Jichao, Tang, Kaibo, Yang, Yaxuan, Sun, Bolun, Yang, Tao, Lu, Guoyu, Wang, Xianqiao, Chai, Lilong, Li, He, Lu, Jin, Sun, Lichao, Zhang, Xin, Ge, Bao, Hu, Xintao, Zhang, Lian, Zhou, Hua, Zhang, Lu, Zhang, Shu, Liu, Ninghao, Jiang, Bei, Kong, Linglong, Xiang, Zhen, Ren, Yudan, Liu, Jun, Jiang, Xi, Bao, Yu, Zhang, Wei, Li, Xiang, Li, Gang, Liu, Wei, Shen, Dinggang, Sikora, Andrea, Zhai, Xiaoming, Zhu, Dajiang, and Liu, Tianming

Subjects
Computer Science - Computation and Language
Abstract

This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguistics, and social sciences. Through rigorous testing, o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance in areas ranging from coding challenges to scientific reasoning and from language processing to creative problem-solving. Key findings include: -83.3% success rate in solving complex competitive programming problems, surpassing many human experts. -Superior ability in generating coherent and accurate radiology reports, outperforming other evaluated models. -100% accuracy in high school-level mathematical reasoning tasks, providing detailed step-by-step solutions. -Advanced natural language inference capabilities across general and specialized domains like medicine. -Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis. -Remarkable proficiency in anthropology and geology, demonstrating deep understanding and reasoning in these specialized fields. -Strong capabilities in quantitative investing. O1 has comprehensive financial knowledge and statistical modeling skills. -Effective performance in social media analysis, including sentiment analysis and emotion recognition. The model excelled particularly in tasks requiring intricate reasoning and knowledge integration across various fields. While some limitations were observed, including occasional errors on simpler problems and challenges with certain highly specialized concepts, the overall results indicate significant progress towards artificial general intelligence.

Published
2024

13. Quantum key distribution based on mid-infrared and telecom band two-color entanglement source

Author

Li, Wu-Zhen, Zhou, Chun, Wang, Yang, Chen, Li, Chen, Ren-Hui, Han, Zhao-Qi-Zhi, Gao, Ming-Yuan, Wang, Xiao-Hua, Zheng, Di-Yuan, Xie, Meng-Yu, Li, Yin-Hai, Zhou, Zhi-Yuan, Bao, Wan-Su, and Shi, Bao-Sen

Subjects
Quantum Physics
Abstract

Due to the high noise caused by solar background radiation, the existing satellite-based free-space quantum key distribution (QKD) experiments are mainly carried out at night, hindering the establishment of a practical all-day real-time global-scale quantum network. Given that the 3-5 {\mu}m mid-infrared (MIR) band has extremely low solar background radiation and strong scattering resistance, it is one of the ideal bands for free-space quantum communication. Here, firstly, we report on the preparation of a high-quality MIR (3370 nm) and telecom band (1555 nm) two-color polarization-entangled photon source, then we use this source to realize a principle QKD based on free-space and fiber hybrid channels in a laboratory. The theoretical analysis clearly shows that a long-distance QKD over 500 km of free-space and 96 km of fiber hybrid channels can be reached simultaneously. This work represents a significant step toward developing all-day global-scale quantum communication networks., Comment: 24 pages, 9 figures

Published
2024

14. Singular limit and convergence rate via projection method in a model for plant-growth dynamics with autotoxicity

Author

Morgan, Jeff, Soresina, Cinzia, Tang, Bao Quoc, and Tran, Bao-Ngoc

Subjects
Mathematics - Analysis of PDEs
Abstract

We investigate a fast-reaction--diffusion system modelling the effect of autotoxicity on plant-growth dynamics, in which the fast-reaction terms are based on the dichotomy between healthy and exposed roots depending on the toxicity. The model was proposed in [Giannino, Iuorio, Soresina, forthcoming] to account for stable stationary spacial patterns considering only biomass and toxicity, and its fast-reaction (cross-diffusion) limit was formally derived and numerically investigated. In this paper, the cross-diffusion limiting system is rigorously obtained as the fast-reaction limit of the reaction-diffusion system with fast-reaction terms by performing a bootstrap argument involving energies. Then, a thorough well-posedness analysis of the cross-diffusion system is presented, including a $L^\infty_{x,t}$-bound, uniqueness, stability, and regularity of weak solutions. This analysis, in turn, becomes crucial to establish the convergence rate for the fast reaction limit, thanks to the key idea of using an inverse Neumann Laplacian operator. Finally, numerical experiments illustrate the analytical findings on the convergence rate.

Published
2024

15. Risk Factors Associated with Severe/Critical COVID-19 in People Living with HIV-1

Author

Antoine Bachelard, Aurelie Sautereau, Marc Digumber, Valentina Isernia, Bao Phung, Anne-Claire Lehur, Sylvie Le Gac, Roland Landman, Yazdan Yazdanpanah, and Jade Ghosn

Subjects
HIV, COVID-19, Risk factors, Infectious and parasitic diseases, RC109-216
Abstract

Introduction: Our objective was to determine the risk factors of a “severe/critical” form of COVID-19 in a cohort of people living with HIV-1 (PLWH1) followed in the Bichat University Hospital center in PARIS, FRANCE. Methods: This study was an observational retrospective monocentric cohort of PLWH1 diagnosed with COVID-19 between February 1 st and November 31 st, 2020. Risk factors associated with “severe/critical” forms were determined using stepwise forward selection. Results: One-hundred-and-twenty-nine PLWH1 with COVID-19 were included. COVID-19 diagnosis was confirmed in 98 cases (75.9%) and deemed probable according to the association of clinical criteria and contact case in 31 cases (24.1%). Clinical presentation of COVID-19 was “asymptomatic/mild/moderate” in 95 (73.6%), “severe” in 26 (21.7%) and “critical” in eight (6%). Patients with “severe/critical” COVID-19 tended to be older (median 54 year old), have a higher BMI (median 28.8 kg/m²) and were likely to have diabetes (9 versus 5) or chronic kidney disease (5 versus 2). Transgender women had higher risk too (OR: 4.9 (IC95: 1.35-24.0)). No association was observed between severity of COVID-19 and viral suppression or CD4 rates. Conclusion: Risk factors for severe COVID-19 were similar in PLWH1 than in the general population and PLWH1 transgender women were at higher risk.

Published
2022
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16. Evaluation of cabin energy consumption based on combination weighting and grey fuzzy comprehensive model

Author

Xinmin Ren, Qinghua Jiang, Jun Jiang, Zilin He, Bin Ouyang, and Bao Peng

Subjects
Special painting, Cabin energy consumption, Combined weigh, Fuzzy comprehension evaluation method, Telecommunication, TK5101-6720, Electronics, TK7800-8360
Abstract

Abstract Painting is the largest energy consumption unit in ship repair enterprises, with annual electrical energy costs amounting to millions of dollars. The energy-saving model of painting business based on artificial intelligence, Internet of Vehicles (IoV) and big data technologies has become a current research hot spot. This paper takes a cargo ship of Youlian Shipyard as an example to improve the quality of special coating construction, reduce cost and increase efficiency. In the process of special coating construction, a comprehensive analysis of equipment usage in different compartments is carried out, and a compartment energy consumption analysis model based on combined weighting and Gray Fuzzy Comprehensive Evaluation is proposed. The model uses factors such as energy consumption, hours, and number of devices as evaluation indicators. Based on the fuzzy comprehensive evaluation, the gray correlation coefficients and comprehensive weights were weighted to obtain the final comprehensive evaluation results of each planning scheme, and then compare them to determine the optimal scheme. The results show that the grey fuzzy comprehensive evaluation model with combined weighting has better results than other models, and the evaluation results are scientific and reasonable. It has certain application value in multi-objective scheme optimization.

Published
2022
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17. Flavonoid Biosynthesis Pathway May Indirectly Affect Outcrossing Rate of Cytoplasmic Male–Sterile Lines of Soybean

Author

Chunjing Lin, Yuetong Duan, Rong Li, Pengnian Wang, Yanyan Sun, Xiaoyang Ding, Jingyong Zhang, Hao Yan, Wei Zhang, Bao Peng, Limei Zhao, and Chunbao Zhang

Subjects
soybean, male–sterile lines, RNA–seq, metabolome, outcrossing rates, DEGs, Botany, QK1-989
Abstract

(1) Background: Cytoplasmic male sterility (CMS) is important for exploiting heterosis. Soybean (Glycine max L.) has a low outcrossing rate that is detrimental for breeding sterile lines and producing hybrid seeds. Therefore, the molecular mechanism controlling the outcrossing rate should be elucidated to increase the outcrossing rate of soybean CMS lines; (2) Methods: The male–sterile soybean lines JLCMS313A (with a high outcrossing rate; HL) and JLCMS226A (with a low outcrossing rate; LL) were used for a combined analysis of the transcriptome (RNA–seq) and the targeted phenol metabolome; (3) Results: The comparison between HL and LL detected 5946 differentially expressed genes (DEGs) and 81 phenolic metabolites. The analysis of the DEGs and differentially abundant phenolic metabolites identified only one common KEGG pathway related to flavonoid biosynthesis. The qRT–PCR expression for eight DEGs was almost consistent with the transcriptome data. The comparison of the cloned coding sequence (CDS) regions of the SUS, FLS, UGT, and F3H genes between HL and LL revealed seven single nucleotide polymorphisms (SNPs) only in the F3H CDS. Moreover, five significant differentially abundant phenolic metabolites between HL and LL were associated with flavonoid metabolic pathways. Finally, on the basis of the SNPs in the F3H CDS, one derived cleaved amplified polymorphic sequence (dCAPS) marker was developed to distinguish between HL and LL soybean lines; (4) Conclusions: The flavonoid biosynthesis pathway may indirectly affect the outcrossing rate of CMS sterile lines in soybean.

Published
2023
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18. Dynamic Response Threshold Model for Self-Organized Task Allocation in a Swarm of Foraging Robots

Author

Bao Pang, Ziqi Zhang, Yong Song, Xianfeng Yuan, and Qingyang Xu

Subjects
swarm robotics, adaptive foraging, self-organized, task allocation, dynamic response threshold model, traffic flow density, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

In swarm-robotics foraging, the purpose of task allocation is to adjust the number of active foraging robots dynamically based on the task demands and changing environment. It is a difficult challenge to generate self-organized foraging behavior in which each robot can adapt to environmental changes. To complete the foraging task efficiently, this paper presents a novel self-organized task allocation strategy known as the dynamic response threshold model (DRTM). To adjust the behavior of the active foraging robots, the proposed DRTM newly introduces the traffic flow density, which can be used to evaluate the robot density. Firstly, the traffic flow density and the amount of obstacle avoidance are used to adjust the threshold which determines the tendency of a robot to respond to a stimulus in the environment. Then, each individual robot uses the threshold and external stimulus to calculate the foraging probability that determines whether or not to go foraging. This paper completes the simulation and physical experiments, respectively, and the performance of the proposed method is evaluated using three commonly used performance indexes: the average deviation of food, the energy efficiency, and the number of obstacle avoidance events. The experimental results show that the DRTM is superior to and more efficient than the adaptive response threshold model (ARTM) in all three indexes.

Published
2023
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19. Research on the Deformation Law and Stress Distribution Characteristics of Overburden under the Influence of Multiple Faults

Author

Yaobin Qi, Weijian Yu, Desun Yu, Hanxiao Guo, and Bao Pan

Subjects
Engineering (General). Civil engineering (General), TA1-2040
Abstract

Taking the geological conditions of a coal mine in Liupanshui City, Guizhou Province, as the background. The fracture law of the roof in the coal face and the height of the “Two zones” were obtained through theoretical analysis and empirical formula calculation, and the results were verified using similar simulation experiments. Through numerical simulation were researched the stress distribution characteristics of the overlying strata on the working face under the influence of multiple faults. The results showed that in the early stage of mining, F1 undergoes activation under the influence of the main roof weighting, leading to an increase in the degree of plastic damage to the coal seam and strata behind the working face, causing a redistribution of the behind abutment pressure. Finally, under the influence of the F1 barrier effect, the vast majority of the behind abutment pressure is transferred to the lower strata of F1, leading to a continuous increase in stress concentration there. But as the working face continues to advance, the blocking effect of the roof plastic zone above the goaf on the transmission of the behind abutment pressure of the working face begins to appear and continues to increase, leading to a decrease in the peak stress at the lower strata of F1. The stress change process on the F2 side is similar to that on the F1 side, the front abutment pressure is initially distributed in the strata in front of the working face, but as the activation degree of F2 continues to intensify, it gradually shifts to the lower strata of F2, and ultimately the majority of the front abutment pressure is borne by the lower strata of F2.

Published
2023
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20. Drug-Eluting Beads Bronchial Arterial Chemoembolization in Treating Relapsed/Refractory Small Cell Lung Cancer Patients: Results from a Pilot Study

Author

Lin H, Wang Q, Tian F, Zhang R, Mu M, Zhao W, and Bao P

Subjects
small cell lung cancer, drug-eluting beads bronchial arterial chemoembolization, treatment response, survival, adverse events, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Hu Lin,1,* Qin Wang,1,2,* Fangfang Tian,1 Rui Zhang,1 Mi Mu,1 Weiguo Zhao,1 Pengtao Bao1,2 1Department of Pulmonary and Critical Care Medicine, The Eighth Medical Center of Chinese PLA General Hospital, Pulmonary and Critical Care Medicine College, Chinese PLA General Hospital, Beijing, 100093, People’s Republic of China; 2Graduate School, Department of Clinical Medicine, Hebei North University, Zhangjiakou, 075000, People’s Republic of China*These authors contributed equally to this workCorrespondence: Pengtao Bao; Weiguo ZhaoDepartment of Pulmonary and Critical Care Medicine, The Eighth Medical Center of Chinese PLA General Hospital, Pulmonary and Critical Care Medicine College, Chinese PLA General Hospital, 17th, Heishanhu Road, Haidian District, Beijing, 100093, People’s Republic of ChinaTel +86-105-5473141Fax +86-105-5473181Email pangye846574808@163.com; gulie998516@163.comBackground: We aimed to explore the efficacy and tolerance of drug-eluting beads bronchial arterial chemoembolization (DEB-BACE) treatment in relapsed/refractory small cell lung cancer (SCLC) patients.Methods: Eleven relapsed/refractory SCLC patients were enrolled and treated with DEB-BACE. Then, treatment response and tumor marker levels were assessed at the first, second and sixth month post treatment. Quality of life was assessed by the EORTC QLQ-C30 scale. Progression-free survival (PFS) and overall survival (OS) were also evaluated.Results: At the first, second and sixth month post treatment, the objective response rates were 63.6%, 54.5%, and 36.4%, respectively; and the disease control rates were 90.9%, 90.9% and 54.5%, respectively. In addition, the neuron-specific enolase (NSE) and progastrin-releasing peptide levels were reduced at the second and sixth month. Quality of life assessed by EORTC QLQ-C30 scale, which included subscales of general health status, functional domains, symptom domains, and single domains except for financial difficulty, was markedly improved at second month post treatment. Median values of PFS and OS were 5.1 (95% CI: 4.1– 5.9) months and 9.0 (95% CI: 6.0– 12.0) months, respectively. The ECOG score and preoperative NSE level were independent predictive factors for PFS, and age as well as lesion location were independent predictive factors for OS. Adverse events were all mild and manageable with chest pain and chest stuffiness the most common ones.Conclusion: DEB-BACE could be a therapeutic option for relapsed/refractory SCLC patients regarding its favorable treatment response, quality of life, survival benefit and safety profile.Keywords: small cell lung cancer, drug-eluting beads bronchial arterial chemoembolization, treatment response, survival, adverse events

Published
2021

21. Assessment and Clinical Utility of a Non-Next-Generation Sequencing-Based Non-Invasive Prenatal Testing Technology

Author

Uzay Gormus, Alka Chaubey, Suresh Shenoy, Yong Wee Wong, Lee Yin Chan, Bao Ping Choo, Liza Oraha, Anna Gousseva, Fredrik Persson, Lawrence Prensky, Ephrem Chin, and Madhuri Hegde

Subjects
cell-free DNA, noninvasive prenatal screening, noninvasive prenatal testing, NIPT, NIPS, validation study, Biology (General), QH301-705.5
Abstract

Background: Rolling-circle replication (RCR) is a novel technology that has not been applied to cell-free DNA (cfDNA) testing until recently. Given the cost and simplicity advantages of this technology compared to other platforms currently used in cfDNA analysis, an assessment of RCR in clinical laboratories was performed. Here, we present the first validation study from clinical laboratories utilizing RCR technology. Methods: 831 samples from spontaneously pregnant women carrying a singleton fetus, and 25 synthetic samples, were analyzed for the fetal risk of trisomy 21 (T21), trisomy 18 (T18) and trisomy 13 (T13), by three laboratories on three continents. All the screen-positive pregnancies were provided post-test genetic counseling and confirmatory diagnostic invasive testing (e.g., amniocentesis). The screen-negative pregnancies were routinely evaluated at birth for fetal aneuploidies, using newborn examinations, and any suspected aneuploidies would have been offered diagnostic testing or confirmed with karyotyping. Results: The study found rolling-circle replication to be a highly viable technology for the clinical assessment of fetal aneuploidies, with 100% sensitivity for T21 (95% CI: 82.35–100.00%); 100.00% sensitivity for T18 (71.51–100.00%); and 100.00% sensitivity for T13 analyses (66.37–100.00%). The specificities were >99% for each trisomy (99.7% (99.01–99.97%) for T21; 99.5% (98.62–99.85%) for T18; 99.7% (99.03–99.97%) for T13), along with a first-pass no-call rate of 0.93%. Conclusions: The study showed that using a rolling-circle replication-based cfDNA system for the evaluation of the common aneuploidies would provide greater accuracy and clinical utility compared to conventional biochemical screening, and it would provide comparable results to other reported cfDNA methodologies.

Published
2021
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22. BlinkTrack: Feature Tracking over 100 FPS via Events and Images

Author

Shen, Yichen, Li, Yijin, Chen, Shuo, Li, Guanglin, Huang, Zhaoyang, Bao, Hujun, Cui, Zhaopeng, and Zhang, Guofeng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Feature tracking is crucial for, structure from motion (SFM), simultaneous localization and mapping (SLAM), object tracking and various computer vision tasks. Event cameras, known for their high temporal resolution and ability to capture asynchronous changes, have gained significant attention for their potential in feature tracking, especially in challenging conditions. However, event cameras lack the fine-grained texture information that conventional cameras provide, leading to error accumulation in tracking. To address this, we propose a novel framework, BlinkTrack, which integrates event data with RGB images for high-frequency feature tracking. Our method extends the traditional Kalman filter into a learning-based framework, utilizing differentiable Kalman filters in both event and image branches. This approach improves single-modality tracking, resolves ambiguities, and supports asynchronous data fusion. We also introduce new synthetic and augmented datasets to better evaluate our model. Experimental results indicate that BlinkTrack significantly outperforms existing event-based methods, exceeding 100 FPS with preprocessed event data and 80 FPS with multi-modality data.

Published
2024

23. Properties of the QCD Matter: A Review of Selected Results from the ALICE Experiment

Author

Shou, Qi-Ye, Ma, Yu-Gang, Zhang, Song, Zhu, Jian-Hui, Mao, Ya-Xian, Pei, Hua, Yin, Zhong-Bao, Zhang, Xiao-Ming, Zhou, Dai-Cui, Peng, Xin-Ye, Bai, Xiao-Zhi, Tang, Ze-Bo, Zhang, Yi-Fei, and Li, Xiao-Mei

Subjects
Nuclear Experiment, High Energy Physics - Experiment
Abstract

The Large Hadron Collider (LHC), the world's largest and most powerful particle accelerator, has been a pivotal tool in advancing our understanding of fundamental physics. By colliding heavy ions (such as lead ions), the LHC recreates conditions similar to those just after the Big Bang. This allows scientists to study the Quark-Gluon Plasma (QGP), a state of matter where quarks and gluons are not confined within protons and neutrons. These studies provide insights into the strong force and the early universe's behavior. In this paper, we provide a comprehensive overview of recent significant findings from A Large Ion Collider Experiment (ALICE) at LHC. The topics encompass measurements regarding to properties of the QGP, particle production, flow and correlations, dileptons, quarkonia and electromagnetic probes, heavy flavor, and jets. Additionally, we introduce future plans for detector upgrades of the ALICE experiment., Comment: 29 pages, 32 figures. This review is dedicated to Professor Wenqing Shen in honor of his leadership and significant impact on the Chinese heavy-ion physics community. All authors contributed equally to this work

Published
2024

24. Taming Diffusion Prior for Image Super-Resolution with Domain Shift SDEs

Author

Cui, Qinpeng, Liu, Yixuan, Zhang, Xinyi, Bao, Qiqi, Wang, Zhongdao, Liao, Qingmin, Wang, Li, Lu, Tian, and Barsoum, Emad

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Diffusion-based image super-resolution (SR) models have attracted substantial interest due to their powerful image restoration capabilities. However, prevailing diffusion models often struggle to strike an optimal balance between efficiency and performance. Typically, they either neglect to exploit the potential of existing extensive pretrained models, limiting their generative capacity, or they necessitate a dozens of forward passes starting from random noises, compromising inference efficiency. In this paper, we present DoSSR, a Domain Shift diffusion-based SR model that capitalizes on the generative powers of pretrained diffusion models while significantly enhancing efficiency by initiating the diffusion process with low-resolution (LR) images. At the core of our approach is a domain shift equation that integrates seamlessly with existing diffusion models. This integration not only improves the use of diffusion prior but also boosts inference efficiency. Moreover, we advance our method by transitioning the discrete shift process to a continuous formulation, termed as DoS-SDEs. This advancement leads to the fast and customized solvers that further enhance sampling efficiency. Empirical results demonstrate that our proposed method achieves state-of-the-art performance on synthetic and real-world datasets, while notably requiring only 5 sampling steps. Compared to previous diffusion prior based methods, our approach achieves a remarkable speedup of 5-7 times, demonstrating its superior efficiency. Code: https://github.com/QinpengCui/DoSSR., Comment: This paper is accepted by NeurIPS 2024

Published
2024

25. Towards a complete classification of holographic entropy inequalities

Author

Bao, Ning, Furuya, Keiichiro, and Naskar, Joydeep

Subjects
High Energy Physics - Theory, Computer Science - Discrete Mathematics, Quantum Physics
Abstract

We propose a deterministic method to find all holographic entropy inequalities and prove the completeness of our method. We use a triality between holographic entropy inequalities, contraction maps and partial cubes. More specifically, the validity of a holographic entropy inequality is implied by the existence of a contraction map, which we prove to be equivalent to finding an isometric embedding of a contracted graph. Thus, by virtue of the completeness of the contraction map proof method, the problem of finding all holographic entropy inequalities is equivalent to the problem of finding all contraction maps, which we translate to a problem of finding all image graph partial cubes. We give an algorithmic solution to this problem and characterize the complexity of our method. We also demonstrate interesting by-products, most notably, a procedure to generate candidate quantum entropy inequalities., Comment: 23 pages, 4 figures

Published
2024

26. Scalable quality control on processing of large diffusion-weighted and structural magnetic resonance imaging datasets

Author

Kim, Michael E., Gao, Chenyu, Ramadass, Karthik, Kanakaraj, Praitayini, Newlin, Nancy R., Rudravaram, Gaurav, Schilling, Kurt G., Dewey, Blake E., Bennett, David A., OBryant, Sid, Barber, Robert C., Archer, Derek, Hohman, Timothy J., Bao, Shunxing, Li, Zhiyuan, Landman, Bennett A., Khairi, Nazirah Mohd, Initiative, The Alzheimers Disease Neuroimaging, and Team, The HABSHD Study

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

Proper quality control (QC) is time consuming when working with large-scale medical imaging datasets, yet necessary, as poor-quality data can lead to erroneous conclusions or poorly trained machine learning models. Most efforts to reduce data QC time rely on outlier detection, which cannot capture every instance of algorithm failure. Thus, there is a need to visually inspect every output of data processing pipelines in a scalable manner. We design a QC pipeline that allows for low time cost and effort across a team setting for a large database of diffusion weighted and structural magnetic resonance images. Our proposed method satisfies the following design criteria: 1.) a consistent way to perform and manage quality control across a team of researchers, 2.) quick visualization of preprocessed data that minimizes the effort and time spent on the QC process without compromising the condition or caliber of the QC, and 3.) a way to aggregate QC results across pipelines and datasets that can be easily shared. In addition to meeting these design criteria, we also provide information on what a successful output should be and common occurrences of algorithm failures for various processing pipelines. Our method reduces the time spent on QC by a factor of over 20 when compared to naively opening outputs in an image viewer and demonstrate how it can facilitate aggregation and sharing of QC results within a team. While researchers must spend time on robust visual QC of data, there are mechanisms by which the process can be streamlined and efficient., Comment: 22 pages, 12 figures, 1 table, 6 supplemental figures

Published
2024

27. Atomic Higgsings of 6D SCFTs

Author

Bao, Jiakang and Zhang, Hao Y.

Subjects
High Energy Physics - Theory
Abstract

In this paper, we study the full Higgs branch Hasse diagram for any given 6d $\mathcal{N}=(1,0)$ SCFT constructed via F-theory. This can be done by a procedure of determining all the minimal Higgsings on the generalized quiver of the 6d SCFT. We call this procedure the atomic Higgsing, which can be implemented iteratively. We present our general algorithms with many concrete examples of Hasse diagrams. We also compare our algorithm with the Higgsings determined by the 3d $\mathcal{N} = 4$ magnetic quivers. For the cases where the magnetic quivers are unitary, we can reproduce the full Hasse diagrams. We also construct the orthosymplectic magnetic quivers from the Type IIA brane systems for some new examples. Our approach, based on F-theory, applies to the known and new orthosymplectic cases, as well as theories that do not have known descriptions in terms of magnetic quivers. We expect our geometry-based approach to help extend the horizon of the RG flows of the 6d SCFTs., Comment: 75 pages and appendices

Published
2024

28. Search for $B_{(s)}^{*0}\to\mu^+\mu^-$ in $B_c^+\to\pi^+\mu^+\mu^-$ 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., Butkevich, A., 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., Godinez, J. Castro, Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cervenkov, D., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Barajas, C. A. Chavez, 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. C., 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., Deacon, S., 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., Epple, E., 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., Fischer, K. L., Fitzgerald, D. S., Fitzpatrick, C., 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., Fuehring, 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., Garau, M., 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. G., 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., Gomes, A., Fernandez, S. Gomez, 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., Hollitt, S. E., 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., 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. K., Kubat, J., 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., Leverington, B., Li, A., Li, C., Li, H., Li, K., Li, L., 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., 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., Ma, R., Maccolini, S., Machefert, F., Maciuc, F., Mack, B., Mackay, I., Mackey, L. M., Mohan, L. R. Madhan, Madurai, M. J., Maevskiy, A., Magdalinski, D., Mahajan, V., 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, Manuzzi, D., Marangotto, D., Marchand, J. 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Rodriguez, Roensch, J., Rogachev, A., Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Lamas, M. Romero, 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., Gras, C. Sanchez, 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. 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W., Wang, Y., 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. R. J., Williams, R., Williams, Z., Wilson, F. F., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., 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., 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

A search for the very rare $B^{*0}\to\mu^+\mu^-$ and $B_{s}^{*0}\to\mu^+\mu^-$ decays is conducted by analysing the $B_c^+\to \pi^+\mu^+\mu^-$ process. The analysis uses proton-proton collision data collected with the LHCb detector between 2011 and 2018, corresponding to an integrated luminosity of 9$\text{\,fb}^{-1}$. The signal signatures correspond to simultaneous peaks in the $\mu^+\mu^-$ and $\pi^+\mu^+\mu^-$ invariant masses. No evidence for an excess of events over background is observed for either signal decay mode. Upper limits at the $90\%$ confidence level are set on the branching fractions relative to that for $B_c^+\to J\mskip -3mu/\mskip -2mu\psi\pi^+$ decays, \begin{align*} {\cal R}_{B^{*0}(\mu^+\mu^-)\pi^+/J\mskip -3mu/\mskip -2mu\psi\pi^+} &< 3.8\times 10^{-5}\ \text{ and } {\cal R}_{B_{s}^{*0}(\mu^+\mu^-)\pi^+/J\mskip -3mu/\mskip -2mu\psi\pi^+} &< 5.0\times 10^{-5}\,. \end{align*}, Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lbfence.cern.ch/alcm/public/analysis/full-details/1796/ (LHCb public pages)

Published
2024

29. SynChart: Synthesizing Charts from Language Models

Author

Liu, Mengchen, Li, Qixiu, Chen, Dongdong, Chen, Dong, Bao, Jianmin, and Li, Yunsheng

Subjects
Computer Science - Artificial Intelligence
Abstract

With the release of GPT-4V(O), its use in generating pseudo labels for multi-modality tasks has gained significant popularity. However, it is still a secret how to build such advanced models from its base large language models (LLMs). This work explores the potential of using LLMs alone for data generation and develop competitive multi-modality models focusing on chart understanding. We construct a large-scale chart dataset, SynChart, which contains approximately 4 million diverse chart images with over 75 million dense annotations, including data tables, code, descriptions, and question-answer sets. We trained a 4.2B chart-expert model using this dataset and achieve near-GPT-4O performance on the ChartQA task, surpassing GPT-4V.

Published
2024

30. Parameter Estimation for the Reduced Fracture Model via a Direct Filter Method

Author

Huynh, Phuoc Toan, Bao, Feng, and Hoang, Thi-Thao-Phuong

Subjects
Mathematics - Numerical Analysis, Mathematics - Probability
Abstract

In this work, we present a numerical method that provides accurate real-time detection for the widths of the fractures in a fractured porous medium based on observational data on porous medium fluid mass and velocity. To achieve this task, an inverse problem is formulated by first constructing a forward formulation based on the reduced fracture model of the diffusion equation. A parameter estimation problem is then performed online by utilizing a direct filter method. Numerical experiments are carried out to demonstrate the accuracy of our method in approximating the target parameters.

Published
2024

31. Interactive Example-based Explanations to Improve Health Professionals' Onboarding with AI for Human-AI Collaborative Decision Making

Author

Lee, Min Hun, Ng, Renee Bao Xuan, Choo, Silvana Xinyi, and Thilarajah, Shamala

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

A growing research explores the usage of AI explanations on user's decision phases for human-AI collaborative decision-making. However, previous studies found the issues of overreliance on `wrong' AI outputs. In this paper, we propose interactive example-based explanations to improve health professionals' onboarding with AI for their better reliance on AI during AI-assisted decision-making. We implemented an AI-based decision support system that utilizes a neural network to assess the quality of post-stroke survivors' exercises and interactive example-based explanations that systematically surface the nearest neighborhoods of a test/task sample from the training set of the AI model to assist users' onboarding with the AI model. To investigate the effect of interactive example-based explanations, we conducted a study with domain experts, health professionals to evaluate their performance and reliance on AI. Our interactive example-based explanations during onboarding assisted health professionals in having a better reliance on AI and making a higher ratio of making `right' decisions and a lower ratio of `wrong' decisions than providing only feature-based explanations during the decision-support phase. Our study discusses new challenges of assisting user's onboarding with AI for human-AI collaborative decision-making.

Published
2024

32. A conveyor-belt magneto-optical trap of CaF

Author

Yu, Scarlett S., You, Jiaqi, Bao, Yicheng, Anderegg, Loic, Hallas, Christian, Li, Grace K., Lim, Dongkyu, Chae, Eunmi, Ketterle, Wolfgang, Ni, Kang-Kuen, and Doyle, John M.

Subjects
Physics - Atomic Physics
Abstract

We report the experimental realization of a conveyor-belt magneto-optical trap for calcium monofluoride (CaF) molecules. The obtained highly-compressed cloud has a mean radius of 64(5) $\mu$m and a peak number density of $3.6(5) \times 10^{10}$ cm$^{-3}$, a 600-fold increase over the conventional red-detuned MOTs of CaF, and the densest molecular MOT observed to date. Subsequent loading of these molecules into an optical dipole trap yields up to $2.6 \times 10^4$ trapped molecules at a temperature of 14(2) $\mu$K with a peak phase-space density of $\sim 2.4 \times 10^{-6}$. This opens new possibilities for a range of applications utilizing high-density, optically trapped ultracold molecules., Comment: 7 pages; 4 figures; typos corrected

Published
2024

33. Search for $D^0\to K^-\eta e^+\nu_e$, $D^+\to K_S^0 \eta e^+\nu_e$ and $D^+\to \eta\eta e^+\nu_e$ decays

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., Chang, J. F., Che, G. R., 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, 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., Lane, J. J., 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, 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, 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, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., 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, 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, 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. 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. 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Subjects
High Energy Physics - Experiment
Abstract

By analyzing $e^+e^-$ annihilation data corresponding to an integrated luminosity of 7.93 fb$^{-1}$, collected at the center-of-mass energy of 3.773 GeV with the BESIII detector, we search for the semileptonic decays $D^0\to K^-\eta e^+\nu_e$, $D^+\to K_S^0 \eta e^+\nu_e$ and $D^+\to \eta\eta e^+\nu_e$ for the first time. We present evidence for $D^0\to K^-\eta e^+\nu_e$ with a significance of $3.3\sigma$. The branching fraction of $D^0\to K^-\eta e^+\nu_e$ is measured to be $(0.84_{-0.34}^{+0.29}\pm0.22)\times 10^{-4}$. Here, the first uncertainties are statistical and the second ones are systematic. No significant signals are observed for the decays $D^+\to K_S^0 \eta e^+\nu_e$ and $D^+\to \eta\eta e^+\nu_e$ and we set the upper limits on their branching fractions., Comment: 10 pages,4 figures

Published
2024

34. MTP: A Dataset for Multi-Modal Turning Points in Casual Conversations

Author

Ho, Gia-Bao Dinh, Tan, Chang Wei, Darban, Zahra Zamanzadeh, Salehi, Mahsa, Haffari, Gholamreza, and Buntine, Wray

Subjects
Computer Science - Computation and Language
Abstract

Detecting critical moments, such as emotional outbursts or changes in decisions during conversations, is crucial for understanding shifts in human behavior and their consequences. Our work introduces a novel problem setting focusing on these moments as turning points (TPs), accompanied by a meticulously curated, high-consensus, human-annotated multi-modal dataset. We provide precise timestamps, descriptions, and visual-textual evidence high-lighting changes in emotions, behaviors, perspectives, and decisions at these turning points. We also propose a framework, TPMaven, utilizing state-of-the-art vision-language models to construct a narrative from the videos and large language models to classify and detect turning points in our multi-modal dataset. Evaluation results show that TPMaven achieves an F1-score of 0.88 in classification and 0.61 in detection, with additional explanations aligning with human expectations., Comment: Accepted by ACL 2024 main conference

Published
2024

35. Nonabelian Fourier Kernels on $\mathrm{SL}_2$ and $\mathrm{GL}_2$

Author

Luo, Zhilin and Chau, Ngo Bao

Subjects
Mathematics - Number Theory, Mathematics - Representation Theory
Abstract

For $G=\mathrm{SL}_2$ or $\mathrm{GL}_2$, we present explicit formulas for the nonabelian Fourier kernels on $G$, as conjectured by A. Braverman and D. Kazhdan. Additionally, we furnish explicit formulas for the orbital Hankel transform on $G$, a topic investigated by the second author, and provide an explicit formula for the stable orbital integral of the basic function. These results are applicable to local fields with residual characteristics other than two., Comment: 106pp. Any comments are very welcome

Published
2024

36. Learning to Localize Actions in Instructional Videos with LLM-Based Multi-Pathway Text-Video Alignment

Author

Chen, Yuxiao, Li, Kai, Bao, Wentao, Patel, Deep, Kong, Yu, Min, Martin Renqiang, and Metaxas, Dimitris N.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Learning to localize temporal boundaries of procedure steps in instructional videos is challenging due to the limited availability of annotated large-scale training videos. Recent works focus on learning the cross-modal alignment between video segments and ASR-transcripted narration texts through contrastive learning. However, these methods fail to account for the alignment noise, i.e., irrelevant narrations to the instructional task in videos and unreliable timestamps in narrations. To address these challenges, this work proposes a novel training framework. Motivated by the strong capabilities of Large Language Models (LLMs) in procedure understanding and text summarization, we first apply an LLM to filter out task-irrelevant information and summarize task-related procedure steps (LLM-steps) from narrations. To further generate reliable pseudo-matching between the LLM-steps and the video for training, we propose the Multi-Pathway Text-Video Alignment (MPTVA) strategy. The key idea is to measure alignment between LLM-steps and videos via multiple pathways, including: (1) step-narration-video alignment using narration timestamps, (2) direct step-to-video alignment based on their long-term semantic similarity, and (3) direct step-to-video alignment focusing on short-term fine-grained semantic similarity learned from general video domains. The results from different pathways are fused to generate reliable pseudo step-video matching. We conducted extensive experiments across various tasks and problem settings to evaluate our proposed method. Our approach surpasses state-of-the-art methods in three downstream tasks: procedure step grounding, step localization, and narration grounding by 5.9\%, 3.1\%, and 2.8\%., Comment: Accepted to ECCV 2024

Published
2024

37. SplatLoc: 3D Gaussian Splatting-based Visual Localization for Augmented Reality

Author

Zhai, Hongjia, Zhang, Xiyu, Zhao, Boming, Li, Hai, He, Yijia, Cui, Zhaopeng, Bao, Hujun, and Zhang, Guofeng

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Visual localization plays an important role in the applications of Augmented Reality (AR), which enable AR devices to obtain their 6-DoF pose in the pre-build map in order to render virtual content in real scenes. However, most existing approaches can not perform novel view rendering and require large storage capacities for maps. To overcome these limitations, we propose an efficient visual localization method capable of high-quality rendering with fewer parameters. Specifically, our approach leverages 3D Gaussian primitives as the scene representation. To ensure precise 2D-3D correspondences for pose estimation, we develop an unbiased 3D scene-specific descriptor decoder for Gaussian primitives, distilled from a constructed feature volume. Additionally, we introduce a salient 3D landmark selection algorithm that selects a suitable primitive subset based on the saliency score for localization. We further regularize key Gaussian primitives to prevent anisotropic effects, which also improves localization performance. Extensive experiments on two widely used datasets demonstrate that our method achieves superior or comparable rendering and localization performance to state-of-the-art implicit-based visual localization approaches. Project page: \href{https://zju3dv.github.io/splatloc}{https://zju3dv.github.io/splatloc}.

Published
2024

38. PureDiffusion: Using Backdoor to Counter Backdoor in Generative Diffusion Models

Author

Truong, Vu Tuan and Le, Long Bao

Subjects
Computer Science - Artificial Intelligence
Abstract

Diffusion models (DMs) are advanced deep learning models that achieved state-of-the-art capability on a wide range of generative tasks. However, recent studies have shown their vulnerability regarding backdoor attacks, in which backdoored DMs consistently generate a designated result (e.g., a harmful image) called backdoor target when the models' input contains a backdoor trigger. Although various backdoor techniques have been investigated to attack DMs, defense methods against these threats are still limited and underexplored, especially in inverting the backdoor trigger. In this paper, we introduce PureDiffusion, a novel backdoor defense framework that can efficiently detect backdoor attacks by inverting backdoor triggers embedded in DMs. Our extensive experiments on various trigger-target pairs show that PureDiffusion outperforms existing defense methods with a large gap in terms of fidelity (i.e., how much the inverted trigger resembles the original trigger) and backdoor success rate (i.e., the rate that the inverted trigger leads to the corresponding backdoor target). Notably, in certain cases, backdoor triggers inverted by PureDiffusion even achieve higher attack success rate than the original triggers.

Published
2024

39. Transfer Learning with Clinical Concept Embeddings from Large Language Models

Author

Gao, Yuhe, Bao, Runxue, Ji, Yuelyu, Sun, Yiming, Song, Chenxi, Ferraro, Jeffrey P., and Ye, Ye

Subjects
Computer Science - Computation and Language
Abstract

Knowledge sharing is crucial in healthcare, especially when leveraging data from multiple clinical sites to address data scarcity, reduce costs, and enable timely interventions. Transfer learning can facilitate cross-site knowledge transfer, but a major challenge is heterogeneity in clinical concepts across different sites. Large Language Models (LLMs) show significant potential of capturing the semantic meaning of clinical concepts and reducing heterogeneity. This study analyzed electronic health records from two large healthcare systems to assess the impact of semantic embeddings from LLMs on local, shared, and transfer learning models. Results indicate that domain-specific LLMs, such as Med-BERT, consistently outperform in local and direct transfer scenarios, while generic models like OpenAI embeddings require fine-tuning for optimal performance. However, excessive tuning of models with biomedical embeddings may reduce effectiveness, emphasizing the need for balance. This study highlights the importance of domain-specific embeddings and careful model tuning for effective knowledge transfer in healthcare.

Published
2024

40. Unlocking Memorization in Large Language Models with Dynamic Soft Prompting

Author

Wang, Zhepeng, Bao, Runxue, Wu, Yawen, Taylor, Jackson, Xiao, Cao, Zheng, Feng, Jiang, Weiwen, Gao, Shangqian, and Zhang, Yanfu

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

Pretrained large language models (LLMs) have revolutionized natural language processing (NLP) tasks such as summarization, question answering, and translation. However, LLMs pose significant security risks due to their tendency to memorize training data, leading to potential privacy breaches and copyright infringement. Accurate measurement of this memorization is essential to evaluate and mitigate these potential risks. However, previous attempts to characterize memorization are constrained by either using prefixes only or by prepending a constant soft prompt to the prefixes, which cannot react to changes in input. To address this challenge, we propose a novel method for estimating LLM memorization using dynamic, prefix-dependent soft prompts. Our approach involves training a transformer-based generator to produce soft prompts that adapt to changes in input, thereby enabling more accurate extraction of memorized data. Our method not only addresses the limitations of previous methods but also demonstrates superior performance in diverse experimental settings compared to state-of-the-art techniques. In particular, our method can achieve the maximum relative improvement of 112.75% and 32.26% over the vanilla baseline in terms of discoverable memorization rate for the text generation task and code generation task respectively.

Published
2024

41. Multi-Modality Conditioned Variational U-Net for Field-of-View Extension in Brain Diffusion MRI

Author

Li, Zhiyuan, Yao, Tianyuan, Kanakaraj, Praitayini, Gao, Chenyu, Bao, Shunxing, Zuo, Lianrui, Kim, Michael E., Newlin, Nancy R., Rudravaram, Gaurav, Khairi, Nazirah M., Huo, Yuankai, Schilling, Kurt G., Kukull, Walter A., Toga, Arthur W., Archer, Derek B., Hohman, Timothy J., and Landman, Bennett A.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

An incomplete field-of-view (FOV) in diffusion magnetic resonance imaging (dMRI) can severely hinder the volumetric and bundle analyses of whole-brain white matter connectivity. Although existing works have investigated imputing the missing regions using deep generative models, it remains unclear how to specifically utilize additional information from paired multi-modality data and whether this can enhance the imputation quality and be useful for downstream tractography. To fill this gap, we propose a novel framework for imputing dMRI scans in the incomplete part of the FOV by integrating the learned diffusion features in the acquired part of the FOV to the complete brain anatomical structure. We hypothesize that by this design the proposed framework can enhance the imputation performance of the dMRI scans and therefore be useful for repairing whole-brain tractography in corrupted dMRI scans with incomplete FOV. We tested our framework on two cohorts from different sites with a total of 96 subjects and compared it with a baseline imputation method that treats the information from T1w and dMRI scans equally. The proposed framework achieved significant improvements in imputation performance, as demonstrated by angular correlation coefficient (p < 1E-5), and in downstream tractography accuracy, as demonstrated by Dice score (p < 0.01). Results suggest that the proposed framework improved imputation performance in dMRI scans by specifically utilizing additional information from paired multi-modality data, compared with the baseline method. The imputation achieved by the proposed framework enhances whole brain tractography, and therefore reduces the uncertainty when analyzing bundles associated with neurodegenerative., Comment: 20 pages; 8 figures

Published
2024

42. A Neural Network Framework for High-Dimensional Dynamic Unbalanced Optimal Transport

Author

Wan, Wei, Pan, Jiangong, Zhang, Yuejin, Bao, Chenglong, and Shi, Zuoqiang

Subjects
Mathematics - Optimization and Control
Abstract

In this paper, we introduce a neural network-based method to address the high-dimensional dynamic unbalanced optimal transport (UOT) problem. Dynamic UOT focuses on the optimal transportation between two densities with unequal total mass, however, it introduces additional complexities compared to the traditional dynamic optimal transport (OT) problem. To efficiently solve the dynamic UOT problem in high-dimensional space, we first relax the original problem by using the generalized Kullback-Leibler (GKL) divergence to constrain the terminal density. Next, we adopt the Lagrangian discretization to address the unbalanced continuity equation and apply the Monte Carlo method to approximate the high-dimensional spatial integrals. Moreover, a carefully designed neural network is introduced for modeling the velocity field and source function. Numerous experiments demonstrate that the proposed framework performs excellently in high-dimensional cases. Additionally, this method can be easily extended to more general applications, such as crowd motion problem.

Published
2024

43. Federated Learning with Label-Masking Distillation

Author

Lu, Jianghu, Li, Shikun, Bao, Kexin, Wang, Pengju, Qian, Zhenxing, and Ge, Shiming

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Computer Vision and Pattern Recognition
Abstract

Federated learning provides a privacy-preserving manner to collaboratively train models on data distributed over multiple local clients via the coordination of a global server. In this paper, we focus on label distribution skew in federated learning, where due to the different user behavior of the client, label distributions between different clients are significantly different. When faced with such cases, most existing methods will lead to a suboptimal optimization due to the inadequate utilization of label distribution information in clients. Inspired by this, we propose a label-masking distillation approach termed FedLMD to facilitate federated learning via perceiving the various label distributions of each client. We classify the labels into majority and minority labels based on the number of examples per class during training. The client model learns the knowledge of majority labels from local data. The process of distillation masks out the predictions of majority labels from the global model, so that it can focus more on preserving the minority label knowledge of the client. A series of experiments show that the proposed approach can achieve state-of-the-art performance in various cases. Moreover, considering the limited resources of the clients, we propose a variant FedLMD-Tf that does not require an additional teacher, which outperforms previous lightweight approaches without increasing computational costs. Our code is available at https://github.com/wnma3mz/FedLMD., Comment: Accepted by ACM MM 2023

Published
2024

44. Analysis of $\it{\Lambda}^\mathrm{0}_b \rightarrow pK^-\mu^+\mu^-$ 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. B., 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., Godinez, J. Castro, 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. C., 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., Epple, E., 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., Fuehring, 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., Garau, M., 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. G., 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., Gomes, A., Fernandez, S. Gomez, 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. H., 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., Hollitt, S. E., 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. K., 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., 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., 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., Ma, R., 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, 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., 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., 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., 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., Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Roensch, J., Rogachev, A., Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Lamas, M. Romero, 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., 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., 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., Sun, Y., 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. V. 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, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., 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. R. J., Williams, R., Williams, Z., Wilson, F. F., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., 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., 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

The differential branching fraction and angular coefficients of \ensuremath{\it{\Lambda}^\mathrm{0}_b \rightarrow pK^-\mu^+\mu^-}\xspace decays are measured in bins of the dimuon mass squared and dihadron mass. The analysis is performed using a data set corresponding to 9$\aunit{fb}^{-1}$ of integrated luminosity collected with the $\mbox{LHCb}$ detector between 2011 and 2018. The data are consistent with receiving contributions from a mixture of $\it{\Lambda}$ resonances with different spin-parity quantum numbers. The angular coefficients show a pattern of vector--axial vector interference that is a characteristic of the type of flavour-changing neutral-current transition relevant for these decays., Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/3264.html (LHCb public pages)

Published
2024

45. Bayer-type Vis-NIR Routing via Inverse Design for Submicron-pixel Image Sensing Chip

Author

Yang, Xianguang, Xiong, Shijie, Tan, Fangchang, Lin, Zhitao, Bao, Yanjun, Wen, Long, Chen, Qin, and Li, Baojun

Subjects
Physics - Optics, Physics - Applied Physics
Abstract

With the advent of high-precision nanoscale lithography technology, high-resolution image sensing has experienced rapid development in recent years. Currently, mainstream commercial image sensors predominantly utilize Bayer array color filters to implement RGB colorful imaging strategies. However, as pixel sizes transition into the submicron dimensions, traditional dye filters used in image sensors have long been hampered by limited optical efficiency, suboptimal signal-to-noise ratios, and significant difficulties in miniaturization. In this work, a novel 4-channel RGB-IR color router for image sensing, distinct from the traditional absorption-transmission mechanisms, was proposed through inverse design methodologies. Utilizing genetic algorithms and DCGAN models, approximately 20,000 random color routing structures were generated and trained. From these, an optimized spectral splitting structure with a minimal periodic size of 1.6 um * 1.6 um was identified. This structure achieves peak optical efficiencies 1.7 times greater than those of dye filters, while also offering superior color imaging quality and signal intensity. This innovative design approach, leveraging deep learning integration, demonstrates an on-chip strategy for color realization in 4-channel image sensors, and holds significant promise for enhancing the development of next-generation high-performance image sensing chip systems., Comment: 19 pages,5 figures

Published
2024

46. Axial Attention Transformer Networks: A New Frontier in Breast Cancer Detection

Author

He, Weijie, Bao, Runyuan, Cang, Yiru, Wei, Jianjun, Zhang, Yang, and Hu, Jiacheng

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

This paper delves into the challenges and advancements in the field of medical image segmentation, particularly focusing on breast cancer diagnosis. The authors propose a novel Transformer-based segmentation model that addresses the limitations of traditional convolutional neural networks (CNNs), such as U-Net, in accurately localizing and segmenting small lesions within breast cancer images. The model introduces an axial attention mechanism to enhance the computational efficiency and address the issue of global contextual information that is often overlooked by CNNs. Additionally, the paper discusses improvements tailored to the small dataset challenge, including the incorporation of relative position information and a gated axial attention mechanism to refine the model's focus on relevant features. The proposed model aims to significantly improve the segmentation accuracy of breast cancer images, offering a more efficient and effective tool for computer-aided diagnosis.

Published
2024

47. Relative torsionfreeness and Frobenius extensions

Author

Bao, Yanhong, Lü, Jiafeng, and Zhao, Zhibing

Subjects
Mathematics - Rings and Algebras, 16D10, 16E05, 16E30
Abstract

Let $S/R$ be a Frobenius extension with $_RS_R$ centrally projective over $R$. We show that if $_R\omega$ is a Wakamatsu tilting module then so is $_SS\otimes_R\omega$, and the natural ring homomorphism from the endomorphism ring of $_R\omega$ to the endomorphism ring of $_SS\otimes_R\omega$ is a Frobenius extension in addition that pd$(\omega_T)$ is finite, where $T$ is the endomorphism ring of $_R\omega$. We also obtain that the relative $n$-torsionfreeness of modules is preserved under Frobenius extensions. Furthermore, we give an application, which shows that the generalized G-dimension with respect to a Wakamatsu module is invariant under Frobenius extensions., Comment: arXiv admin note: text overlap with arXiv:2301.01903

Published
2024

48. Photo-nuclear reaction rates of $^{157,159}$Ho and $^{163,165}$Tm and their impact in the $\gamma$--process

Author

Cheng, Hao, Sun, Bao-Hua, Zhu, Li-Hua, Kusakabe, Motohiko, Luo, Yudong, Kajino, Toshitaka, Wang, Chang-Jian, Yao, Xing-Qun, He, Chuang-Ye, Liu, Fu-Long, and Guo, Bing

Subjects
Astrophysics - Solar and Stellar Astrophysics, Nuclear Experiment, Nuclear Theory
Abstract

Reliable photo-nuclear reaction rates at the stellar conditions are essential to understand the origin of the heavy stable neutron-deficient isotopes between $^{74}$Se and $^{196}$Hg-p-nuclei, however, many reaction rates of relevance still have to rely on the Hauser-Feshbach model due to rare experimental progress. One such case is in the mass range of 160 for Dy, Er, Ho and Tm isotopes. In this work we attempt to constrain the Hauser-Feshbach model in the TALYS package by reproducing the available experimental data of $^{160}$Dy($p,\gamma$)$^{161}$Ho and $^{162}$Er($p,\gamma$)$^{163}$Tm in the $A\sim 160$ mass region, and examine the effects of level density, gamma strength function and the optical model potential. The constrained model then allows us to calculate the reaction rates of $^{157, 159}$Ho($\gamma$, $p$) and $^{163,165}$Tm($\gamma$, $p$) for the $\gamma$-process nucleosynthesis in carbon-deflagration SNe Ia model. Our recommended rates differ from the JINA REACLIB by more than 1 order of magnitude in the temperature range of 2-3 GK. This results in the changes of final abundance of $p$-nuclei in the $A\sim 160$ mass range by -5.5-3\% from those with JINA, which means that the ($\gamma$, $p$) reactions uncertainty is not predominant for the synthesis of these nuclei., Comment: 12 pages,7 figures

Published
2024
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49. Computable, obstructed Morse homology for clean intersections

Author

Bao, Erkao and Zhu, Ke

Subjects
Mathematics - Symplectic Geometry, Mathematics - Geometric Topology, 53D40, 53D58
Abstract

In this paper, we develop a method to compute the Morse homology of a manifold when descending manifolds and ascending manifolds intersect cleanly, but not necessarily transversely. While obstruction bundle gluing defined by Hutchings and Taubes is a computable tool to handle non-transverse intersections, it has only been developed for specific cases. In contrast, most virtual techniques apply to general cases but lack computational efficiency. To address this, we construct minimal semi-global Kuranishi structures for the moduli spaces of Morse trajectories, which generalize obstruction bundle gluing while maintaining its computability feature. Through this construction, we obtain iterated gluing equals simultaneous gluing., Comment: 40 pages

Published
2024

50. Leveraging AI-Generated Emotional Self-Voice to Nudge People towards their Ideal Selves

Author

Fang, Cathy Mengying, Chua, Phoebe, Chan, Samantha, Leong, Joanne, Bao, Andria, and Maes, Pattie

Subjects
Computer Science - Human-Computer Interaction
Abstract

Emotions, shaped by past experiences, significantly influence decision-making and goal pursuit. Traditional cognitive-behavioral techniques for personal development rely on mental imagery to envision ideal selves, but may be less effective for individuals who struggle with visualization. This paper introduces Emotional Self-Voice (ESV), a novel system combining emotionally expressive language models and voice cloning technologies to render customized responses in the user's own voice. We investigate the potential of ESV to nudge individuals towards their ideal selves in a study with 60 participants. Across all three conditions (ESV, text-only, and mental imagination), we observed an increase in resilience, confidence, motivation, and goal commitment, but the ESV condition was perceived as uniquely engaging and personalized. We discuss the implications of designing generated self-voice systems as a personalized behavioral intervention for different scenarios.

Published
2024

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