1,595,870 results on '"Bo An"'
Search Results
2. SNAP25-induced MYC upregulation promotes high-grade neuroendocrine lung carcinoma progression
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Zhiqiang Chen, Shujing Wang, Jingrui Wang, Ying Wang, Xiangjun Qi, Bo An, Lingling Sun, and Lizhu Lin
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high-grade neuroendocrine carcinoma ,synaptosome associated protein 25 ,c-Myc ,MEK ,ERK ,Immunologic diseases. Allergy ,RC581-607 - Abstract
BackgroundThis study investigated the expression and role of Synaptosome associated protein 25 (SNAP25) in high-grade neuroendocrine carcinoma (HGNEC).MethodsWe used differentially expressed analysis and weighted gene co-expression network analysis (WGCNA) to identify key genes and modules in HGNEC. KEGG and GO analyses helped understand these genes’ roles, and ROC curves assessed their diagnostic value. We also studied SNAP25’s relation to immune infiltration and confirmed findings with in vitro and vivo experiments and datasets.ResultsWGCNA identified 595 key genes related to pathways like MAPK signaling, GABAergic synapse, and cancer-related transcriptional misregulation. Top genes included SNAP25, MYC, NRXN1, GAD2, and SYT1. SNAP25 was notably associated with M2 macrophage infiltration. Dataset GSE40275 confirmed SNAP25’s high expression and poor prognosis in HGNEC. qRT-PCR and WB analyses showed increased SNAP25 and c-MYC levels in HGNEC, promoting MEK/ERK pathway activity. Reducing SNAP25 decreased H1299 cell proliferation, migration, invasion, and levels of c-MYC, MEK, and ERK. Finally, in vivo experiments further confirmed that SNAP25 knockout can inhibit tumor growth.ConclusionSNAP25 regulates c-MYC activation by stimulating the MEK/ERK pathway, ultimately influencing the development of HGNEC.
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- 2024
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3. The characteristics and corrections of ventral support interferences in the transonic-speed wind tunnel for the blended-wing-body aircraft
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Aoxiang Qiu, Weimin Sang, Shuya Du, Bo An, Dong Li, and Binqian Zhang
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Blended-wing-body ,Transonic-speed wind tunnel test ,Ventral sting interference ,Numerical simulation ,Aerodynamic characteristic ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Motor vehicles. Aeronautics. Astronautics ,TL1-4050 - Abstract
Abstract For the problem of ventral support interference in a transonic-speed wind tunnel with the blended-wing-body aircraft NPU-BWB-300 installed, the numerical simulation method based on Reynolds-averaged Navier–Stokes (RANS) equations is used to study the influence law of aerodynamic characteristic interference with the variation of Mach numbers and angles of attack. Moreover, the characteristics of ventral support interference for blended-wing-body aircraft and conventional aircraft are compared. The relevant mechanism of the generation and change of ventral support interference is revealed by employing analysis of the body surface pressure, the shock wave of the strut, and the separation area between the strut and the aircraft. The aerodynamic characteristic interference obtained from the numerical simulation is linearized based on the principle of the least square method. Afterward, a numerical simulation correction method of ventral support interference in the transonic-speed wind tunnel for the blended-wing-body aircraft is developed. Finally, the test results after the corrections of ventral support interferences in the transonic-speed wind tunnel for NPU-BWB-300 are obtained, which is significant for the evaluation of current aerodynamic performances and subsequent optimization designs.
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- 2024
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4. Robust control of wind turbines to reduce wind power fluctuation
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Minan Tang, Wenjuan Wang, Xiaofei Zhen, Bo An, Yaqi Zhang, and Yaguang Yan
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model predictive control ,robust control ,turbulent wind ,uncertain system ,wind power generation system ,Technology ,Science - Abstract
Abstract The wind power generation system of a 5 MW horizontal axis wind turbine has a high wind energy conversion efficiency. The proportion of installed capacity in practical production applications is increasing year on year, so that the stability of its operation becomes a central factor in determining the productivity of the wind farm in question. This paper takes a 5 MW wind turbine as the research object and proposes a parameter‐adaptive robust model predictive control method to achieve self‐optimization of controller parameters through a Bayesian optimization approach. A robust model predictive control strategy, aiming to reduce the power fluctuation while maximizing the power output, is developed in this paper to enhance the dynamic economic performance under uncertain wind speed variation. A Bayesian algorithm is used in this paper to optimize the parameters of the controller. Moreover, wind speeds are simulated using TurbSim for different turbulence intensities of 5%, 10%, and 15% turbulence. Finally, the robust model predictive control toolbox in MATLAB is designed and simulated. The results show that the operational instability of the wind energy system is overcome. Meanwhile, the robustness of the wind energy system operation is improved compared to the traditional model predictive control approach.
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- 2024
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5. Clinical Analysis of Venetoclax Combined with Azacitidinein Hig-risk Myelodysplastic Syndrome
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Tianjiao Huang, Songtao Liu, Qinglan Zeng, Hong Zhou, Xuemei Wang, Chunye You, Bo An, Bowen Jiang, and Heng Guo
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hig-risk myelodysplastic syndrome ,venetoclax ,azacitidine ,Geriatrics ,RC952-954.6 - Abstract
Objective To investigate the efficacy and safety of Venetoclax combined with Azacitidine in the treatment of high-risk myelodysplastic syndrome. Methods A total of 56 patients with high-risk myelodysplastic syndrome were enrolled from June 2019 to June 2022 in the Second Affiliated Hospital of Qiqihar Medical University.The patients were divided into a control group(n=30) and a study group(n=26) by simple random sampling.The control group received Azacitidine chemotherapy.The study group received Venetoclax combined with Azacitidine chemotherapy.The efficacy, adverse reactions, lactate dehydrogenase, β2 microglobulin, and folic acid were compared between the two groups. Results The overall response rate in the study group was higher than that in the control group(P0.05).After treatment, the serum levels of lactate dehydrogenase, β2 microglobulin and folic acid in the study group were all lower than those in the control group(P
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- 2024
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6. Electric Vehicle Charging Load Demand Forecasting in Different Functional Areas of Cities with Weighted Measurement Fusion UKF Algorithm
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Minan Tang, Xi Guo, Jiandong Qiu, Jinping Li, and Bo An
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electric vehicle charging ,load demand forecasting ,unscented Kalman filter ,weighted measurement fusion ,urban functional zoning divide ,Technology - Abstract
The forecasting of charging demand for electric vehicles (EVs) plays a vital role in maintaining grid stability and optimizing energy distribution. Therefore, an innovative method for the prediction of EV charging load demand is proposed in this study to address the downside of the existing techniques in capturing the spatial–temporal heterogeneity of electric vehicle (EV) charging loads and predicting the charging demand of electric vehicles. Additionally, an innovative method of electric vehicle charging load demand forecasting is proposed, which is based on the weighted measurement fusion unscented Kalman filter (UKF) algorithm, to improve the accuracy and efficiency of forecasting. First, the data collected from OpenStreetMap and Amap are used to analyze the distribution of urban point-of-interest (POI), to accurately classify the functional areas of the city, and to determine the distribution of the urban road network, laying a foundation for modeling. Second, the travel chain theory was applied to thoroughly analyze the travel characteristics of EV users. The Improved Floyd (IFloyd) algorithm is used to determine the optimal route. Also, a Monte Carlo simulation is performed to estimate the charging load for electric vehicle users in a specific region. Then, a weighted measurement fusion UKF (WMF–UKF) state estimator is introduced to enhance the accuracy of prediction, which effectively integrates multi-source data and enables a more detailed prediction of the spatial–temporal distribution of load demand. Finally, the proposed method is validated comparatively against traffic survey data and the existing methods by conducting a simulation experiment in an urban area. The results show that the method proposed in this paper is applicable to predict the peak hours more accurately compared to the reference method, with the accuracy of first peak prediction improved by 53.53% and that of second peak prediction improved by 23.23%. The results not only demonstrate the high performance of the WMF–UKF prediction model in forecasting peak periods but also underscore its potential in supporting grid operations and management, which provides a new solution to improving the accuracy of EV load demand forecasting.
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- 2024
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7. Azimuthally extreme-ultraviolet focal splitter by modified spiral photon sieves
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Yujie Shen, Yuni Zheng, Huaiyu Cui, Dongdi Zhao, Bo An, Saiyao Miao, Junyong Zhang, and Yongpeng Zhao
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Applied optics. Photonics ,TA1501-1820 - Abstract
Extreme Ultraviolet (EUV) radiation is a short-wavelength light source that has important applications in many fields, such as optical communication, particle manipulation, and ultrahigh resolution imaging. However, the highly absorptive nature of EUV light makes it challenging to design suitable focusing optics, such as focal splitters, to properly manipulate the energetic light. Here, we propose modified spiral photon sieves to transform EUV laser light into azimuthally splitting focusing. A genetic algorithm was used to design and optimize the azimuthally focal splitters. A capillary discharge EUV laser at 46.9 nm was used to verify the effectiveness of our proposed method, and PMMA targets were used to record the focused laser spot. The profile of the recorded patterns measured by atomic force microscopy shows that the focal spots in the experiment are diffraction-limited and agreed with the theoretical analysis. The proposed technique provides a new way for manipulating EUV light and further extends the applications ranging from EUV to soft x rays.
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- 2024
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8. Yaw Stability Control of Unmanned Emergency Supplies Transportation Vehicle Considering Two-Layer Model Predictive Control
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Minan Tang, Yaqi Zhang, Wenjuan Wang, Bo An, and Yaguang Yan
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emergency supplies transportation vehicle ,yaw stability ,two-layer model predictive control ,improved Sage–Husa adaptive extended Kalman filter ,dynamics model ,Materials of engineering and construction. Mechanics of materials ,TA401-492 ,Production of electric energy or power. Powerplants. Central stations ,TK1001-1841 - Abstract
The transportation of emergency supplies is characterized by real-time, urgent, and non-contact, which constitute the basic guarantee for emergency rescue and disposal. To improve the yaw stability of the four-wheel-drive unmanned emergency supplies transportation vehicle (ESTV) during operation, a two-layer model predictive controller (MPC) method based on a Kalman filter is proposed in this paper. Firstly, the dynamics model of the ESTV is established. Secondly, the improved Sage–Husa adaptive extended Kalman filter (SHAEKF) is used to decrease the impact of noise on the ESTV system. Thirdly, a two-layer MPC is designed for the yaw stability control of the ESTV. The upper-layer controller solves the yaw moment and the front wheel steering angle of the ESTV. The lower-layer controller optimizes the torque distribution of the four tires of the ESTV to ensure the self-stabilization of the ESTV operation. Finally, analysis and verification are carried out. The simulation results have verified that the improved SHAEKF can decrease the state estimation error by more than 78% and achieve the noise reduction of the ESTV state. Under extreme conditions of high velocity and low adhesion, the average relative error is within 6.77%. The proposed control method can effectively prevent the instability of the ESTV and maintain good yaw stability.
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- 2024
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9. Robust model predictive control of wind turbines based on Bayesian parameter self-optimization
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Minan Tang, Wenjuan Wang, Yaguang Yan, Yaqi Zhang, and Bo An
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Bayesian optimization ,parameter self-optimization ,robust model predictive control ,5 MW wind turbine ,high altitude areas in northwestern China ,General Works - Abstract
This paper studies the effect of different turbulent wind speeds on the operation of wind turbines. The proportion of wind power in the field of new energy generation has increased massively and has gained wide application and attention. However, the smooth operation and the stability of the output power of the wind power generation system are susceptible to wind speed fluctuations. To tackle this problem, this paper takes a 5 MW horizontal axis wind turbine as the research object that proposes a parameter adaptive robust control method to achieve self-optimization of controller parameters by means of Bayesian optimization. The 5 MW wind turbine model is utilized to verify the feasibility of the algorithm by combining the wind speed types commonly found in a high-altitude region in northwestern. The simulation results validate the effectiveness of the proposed scheme. The outcomes demonstrate that Bayesian optimization can significantly decrease the effects of wind speed instability. The output power increases by 1.9% on average at low wind speed and stabilizes on 5 MW at high wind speed. Therefore, the stable controller for wind power output is the robust model predictive controller with parameter improvement.
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- 2023
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10. Construction and application of Chinese breast cancer knowledge graph based on multi-source heterogeneous data
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Bo An
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knowledge graph ,medical knowledge graph ,information etraction ,deep learning ,pre-trained language model ,Biotechnology ,TP248.13-248.65 ,Mathematics ,QA1-939 - Abstract
The knowledge graph is a critical resource for medical intelligence. The general medical knowledge graph tries to include all diseases and contains much medical knowledge. However, it is challenging to review all the triples manually. Therefore the quality of the knowledge graph can not support intelligence medical applications. Breast cancer is one of the highest incidences of cancer at present. It is urgent to improve the efficiency of breast cancer diagnosis and treatment through artificial intelligence technology and improve the postoperative health status of breast cancer patients. This paper proposes a framework to construct a breast cancer knowledge graph from heterogeneous data resources in response to this demand. Specifically, this paper extracts knowledge triple from clinical guidelines, medical encyclopedias and electronic medical records. Furthermore, the triples from different data resources are fused to build a breast cancer knowledge graph (BCKG). Experimental results demonstrate that BCKG can support knowledge-based question answering, breast cancer postoperative follow-up and healthcare, and improve the quality and efficiency of breast cancer diagnosis, treatment and management.
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- 2023
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11. In-depth mapping of protein localizations in whole tissue by micro-scaffold assisted spatial proteomics (MASP)
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Min Ma, Shihan Huo, Ming Zhang, Shuo Qian, Xiaoyu Zhu, Jie Pu, Sailee Rasam, Chao Xue, Shichen Shen, Bo An, Jianmin Wang, and Jun Qu
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Science - Abstract
Accurate protein mapping on whole-tissue levels provides critical insights into diseases/therapies. Here, the authors described a novel spatial proteomics method, based on tissue compartmentalization using a 3D-printed micro-scaffold, generated thousands of protein maps across a whole-tissue slice.
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- 2022
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12. Quest for Equitable Education in Phases: Insights from an NGO in China
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Shirley Pan and Bo Wang
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Among the East Asian nations, a recurring predicament faced by educational institutions is that of providing inclusive but high-quality education. Active involvement of non-governmental organizations (NGOs) in education is valuable in China. Adream was such an NGO on education in China, established in 2008 with a singular and noble objective: promotion of equitable access to quality education within the disadvantaged regions of China. The trajectory of Adream's endeavor to secure equitable access to quality education in rural China stands as a compelling exemplar of the transformative potential that NGOs wield within the realm of education.
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- 2024
13. A century-long eddy-resolving simulation of global oceanic large- and mesoscale state
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Mengrong Ding, Hailong Liu, Pengfei Lin, Yao Meng, Weipeng Zheng, Bo An, Yihua Luan, Yongqiang Yu, Zipeng Yu, Yiwen Li, Jinfeng Ma, Jian Chen, and Kangjun Chen
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Science - Abstract
Measurement(s) ocean Technology Type(s) Model Factor Type(s) Sea Surface Height Above Geoid • Sea Water Surface Downward X Stress • Sea Water Surface Downward Y Stress • Downwelling Shortwave Radiation in Sea Water • Surface Upwelling Longwave Radiation • Ocean Mixed Layer Thickness Defined by Sigma T • Water Flux into Sea Water • Sea-Ice Area Percentage • Surface Upward Sensible Heat Flux • Sea Water Potential Temperature • Sea Water Salinity • Sea Water X velocity • Sea Water Y velocity • Sea Water Vertical Velocity Sample Characteristic - Environment ocean Sample Characteristic - Location global ocean
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- 2022
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14. Molecular programming modulates hepatic lipid metabolism and adult metabolic risk in the offspring of obese mothers in a sex-specific manner
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Christina Savva, Luisa A. Helguero, Marcela González-Granillo, Tânia Melo, Daniela Couto, Bo Angelin, Maria Rosário Domingues, Xidan Li, Claudia Kutter, and Marion Korach-André
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Biology (General) ,QH301-705.5 - Abstract
Sex and maternal obesity drive differently transcriptional and posttranscriptional regulation of major metabolic processes in the livers of female and male offspring, contributing to the sexual dimorphism in obesity-associated metabolic risk.
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- 2022
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15. Demonstration of multi-pass amplification of 46.9 nm laser pumped by capillary discharge
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Dongdi Zhao, Yongpeng Zhao, Bo An, Jiaqi Li, and Huaiyu Cui
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Nuclear and particle physics. Atomic energy. Radioactivity ,QC770-798 - Abstract
Using a plane–plane resonator composed of silicon carbide mirrors, we achieve for the first time multi-pass amplification of a 46.9 nm laser pumped by capillary discharge. In terms of the temporal characteristics, for an initial argon pressure of 17 Pa, triple-pass amplification of the laser is obtained at a delay time between the pre-pulse and the main pulse currents of 40 µs, and quadruple-pass amplification is obtained at a delay time of 50 µs. The experimental results show that the gain duration of the plasma column is more than 6 ns. In terms of spatial characteristics, the spot of the output laser has a reduced full width at half maximum divergence compared with that from a laser without a resonator.
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- 2023
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16. A Dual-Layer MPC of Coordinated Control of Battery Load Demand and Grid-Side Supply Matching at Electric Vehicle Swapping Stations
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Minan Tang, Chenchen Zhang, Yaqi Zhang, Yaguang Yan, Wenjuan Wang, and Bo An
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electric vehicles ,battery swapping station ,model predictive control ,multi-stage optimization ,peak shaving ,Technology - Abstract
The uncontrolled charging of electric vehicles may cause damage to the electrical system as the number of electric vehicles continues to rise. This paper aims to construct a new model of the power system and investigates the rational regulation and efficient control of electric vehicle battery charging at electric vehicle exchange battery stations in response to the real-time grid-side supply situation. Firstly, a multi-objective optimization strategy is established to meet the day-ahead forecasted swap demand and grid-side supply with the maximization of day-ahead electric vehicle battery swapping station (BSS) revenue in the core. Secondly, considering the variable tariff strategy, a two-layer Model Predictive Control (MPC) coordinated control system under real-time conditions is constructed with the objective function of maximizing the revenue of BSS and smoothing the load fluctuation of the power system. Then, the day-ahead optimization results are adopted as the reference value for in-day rolling optimization, and the reference value for in-day optimization is dynamically adjusted according to the real-time number of electric car changes and power system demand. Finally, verified by experimental simulation, the results show that the day-ahead-intraday optimization model can increase the economic benefits of BSS and reduce the pressure on the grid to a certain extent, and it can ensure the fast, accurate, and reasonable allocation of batteries in BSS, and realize the flexible, efficient, and reasonable distribution of batteries in BSS.
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- 2024
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17. MPPT Strategy of Waterborne Bifacial Photovoltaic Power Generation System Based on Economic Model Predictive Control
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Minan Tang, Jinping Li, Jiandong Qiu, Xi Guo, Bo An, Yaqi Zhang, and Wenjuan Wang
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economic model predictive control ,waterborne bifacial photovoltaics ,maximum power point tracking ,Technology - Abstract
At present, the new energy industry represented by photovoltaics has become the main force to realize the optimization of China’s energy structure and the goal of “double carbon”; with the absence of land resources, the waterborne bifacial photovoltaic has ushered in a new opportunity. Therefore, in order to address the problem that the maximum power point tracking (MPPT) of photovoltaics (PV) could not take into account, the dynamic economic performance in the control process, an economic model predictive control (EMPC), is proposed in this work to realize the MPPT of the waterborne bifacial PV power generation system. Firstly, the model of the bifacial PV module is constructed by combining the ray-tracing irradiance model and considering the effect of water surface albedo on the irradiance absorbed by the module. Secondly, the EMPC controller is designed based on the state-space model of the system to maximize the power generation as the economic performance index, and to solve the optimal input variables time by time to achieve a rolling optimization with the operational requirements of the system itself as the constraints. Thirdly, the MATLAB/Simulink (R2022a) simulation experimental results verify that the EMPC strategy could be utilized to achieve MPPT of the waterborne bifacial PV power generation system, according to the changes of environment. Finally, it is also demonstrated that the bifacial PV power generation system that employed the EMPC strategy outperformed the traditional MPPT algorithm, with respect to both output power tracking velocity and accuracy, and the power generation could be improved by about 6% to 14.5%, which significantly enhances the system’s dynamic process economics.
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- 2023
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18. Decision Making in Team-Adversary Games with Combinatorial Action Space
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Shuxin Li, Youzhi Zhang, Xinrun Wang, Wanqi Xue, and Bo An
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decision making ,team-adversary games ,nash equilibrium ,counterfactual regret minimization (cfr) ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
The team-adversary game simulates many real-world scenarios in which a team of agents competes cooperatively against an adversary. However, decision-making in this type of game is a big challenge since the joint action space of the team is combinatorial and exponentially related to the number of team members. It also hampers the existing equilibrium finding algorithms from solving team-adversary games efficiently. To solve this issue caused by the combinatorial action space, we propose a novel framework based on Counterfactual Regret Minimization (CFR) framework: CFR-MIX. Firstly, we propose a new strategy representation to replace the traditional joint action strategy by using the individual action strategies of all the team members, which can significantly reduce the strategy space. To maintain the cooperation between team members, a strategy consistency relationship is proposed. Then, we transform the consistency relationship of the strategy to the regret consistency for computing the equilibrium strategy with the new strategy representation under the CFR framework. To guarantee the regret consistency relationship, a product-form decomposition method over cumulative regret values is proposed. To implement this decomposition method, our CFR-MIX framework employs a mixing layer under the CFR framework to get the final decision strategy for the team, i.e., the Nash equilibrium strategy. Finally, we conduct experiments on games in different domains. Extensive results show that CFR-MIX significantly outperforms state-of-the-art algorithms. We hope it can help the team make decisions in large-scale team-adversary games.
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- 2023
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19. Estimating the strength of Lorentzian distribution in non-commutative geometry by solar system tests
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Wang, Rui-Bo, Ma, Shi-Jie, Deng, Jian-Bo, and Hu, Xian-Ru
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General Relativity and Quantum Cosmology - Abstract
In this paper, we study four classical tests of Schwarzschild space-time with Lorentzian distribution in non-commutative geometry. We performed detailed calculations of the first-order corrections induced by the non-commutative parameter on planetary orbital precession, light deflection, radar wave delay, and gravitational redshift. The study showed that the impact of the non-commutative parameter on timelike geodesics is significantly greater than its effect on null geodesics. Using precise experimental observations, the allowable range for the non-commutative parameter is ultimately constrained within $\Theta\leq0.067579~\mathrm{m}^{2}$, which is given by Mercury's orbital precession. This result aligns with the view that $\sqrt{\Theta}$ is on the order of the Planck length. Moreover, the constrained parameter range exceeds the Planck scale by a significant margin., Comment: 33pages, 3 figures, 1 table
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- 2024
20. What If the Input is Expanded in OOD Detection?
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Zhang, Boxuan, Zhu, Jianing, Wang, Zengmao, Liu, Tongliang, Du, Bo, and Han, Bo
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Computer Science - Machine Learning - Abstract
Out-of-distribution (OOD) detection aims to identify OOD inputs from unknown classes, which is important for the reliable deployment of machine learning models in the open world. Various scoring functions are proposed to distinguish it from in-distribution (ID) data. However, existing methods generally focus on excavating the discriminative information from a single input, which implicitly limits its representation dimension. In this work, we introduce a novel perspective, i.e., employing different common corruptions on the input space, to expand that. We reveal an interesting phenomenon termed confidence mutation, where the confidence of OOD data can decrease significantly under the corruptions, while the ID data shows a higher confidence expectation considering the resistance of semantic features. Based on that, we formalize a new scoring method, namely, Confidence aVerage (CoVer), which can capture the dynamic differences by simply averaging the scores obtained from different corrupted inputs and the original ones, making the OOD and ID distributions more separable in detection tasks. Extensive experiments and analyses have been conducted to understand and verify the effectiveness of CoVer. The code is publicly available at: https://github.com/tmlr-group/CoVer., Comment: accepted by NeurIPS 2024
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- 2024
21. Deep Learning-based Software Engineering: Progress, Challenges, and Opportunities
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Chen, Xiangping, Hu, Xing, Huang, Yuan, Jiang, He, Ji, Weixing, Jiang, Yanjie, Jiang, Yanyan, Liu, Bo, Liu, Hui, Li, Xiaochen, Lian, Xiaoli, Meng, Guozhu, Peng, Xin, Sun, Hailong, Shi, Lin, Wang, Bo, Wang, Chong, Wang, Jiayi, Wang, Tiantian, Xuan, Jifeng, Xia, Xin, Yang, Yibiao, Yang, Yixin, Zhang, Li, Zhou, Yuming, and Zhang, Lu
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Computer Science - Software Engineering - Abstract
Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many papers have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However, although several surveys have provided overall pictures of the application of deep learning techniques in software engineering, they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this paper, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out the through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically., Comment: Accepted in SCIENCE CHINA Information Sciences
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- 2024
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22. Proposal of quantum repeater architecture based on Rydberg atom quantum processors
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Zhang, Yan-Lei, Jie, Qing-Xuan, Li, Ming, Wu, Shu-Hao, Wang, Zhu-Bo, Zou, Xu-Bo, Zhang, Peng-Fei, Li, Gang, Zhang, Tiancai, Guo, Guang-Can, and Zou, Chang-Ling
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Quantum Physics - Abstract
Realizing large-scale quantum networks requires the generation of high-fidelity quantum entanglement states between remote quantum nodes, a key resource for quantum communication, distributed computation and sensing applications. However, entanglement distribution between quantum network nodes is hindered by optical transmission loss and local operation errors. Here, we propose a novel quantum repeater architecture that synergistically integrates Rydberg atom quantum processors with optical cavities to overcome these challenges. Our scheme leverages cavity-mediated interactions for efficient remote entanglement generation, followed by Rydberg interaction-based entanglement purification and swapping. Numerical simulations, incorporating realistic experimental parameters, demonstrate the generation of Bell states with 99\% fidelity at rates of 1.1\,kHz between two nodes in local-area network (distance $0.1\,\mathrm{km}$), and can be extend to metropolitan-area ($25\,\mathrm{km}$) or intercity ($\mathrm{250\,\mathrm{km}}$, with the assitance of frequency converters) network with a rate of 0.1\,kHz. This scalable approach opens up near-term opportunities for exploring quantum network applications and investigating the advantages of distributed quantum information processing., Comment: 3 figures
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- 2024
23. A Study of Decay Rate of Bound Negative Muons
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Deng, Jian-Bo, Deng, Miao-Yi, Ma, Shi-Jie, Wang, Rui-Bo, Fan, Qi-Qi, He, Peng-Zhang, He, Yi-Peng, Li, Shuo-Wen, and Hu, Xian-Ru
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High Energy Physics - Phenomenology - Abstract
A number of experiments show that the decay lifetimes of muons bound to atomic nuclei are longer than the decay lifetimes of free muons. In this paper, a scheme of extending quantum mechanics (EQM) is proposed to resolve this problem. The Schr$\ddot{\text{o}}$dinger's equation is obtained to prove the validation of this attempt. The decay ratio of bound muons is also calculated in EQM, and the result is in good agreement with the experimental data., Comment: 5 pages, 1 figure, 2 tables
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- 2024
24. HypomimiaCoach: An AU-based Digital Therapy System for Hypomimia Detection & Rehabilitation with Parkinson's Disease
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Xu, Yingjing, Cai, Xueyan, Zhou, Zihong, Xue, Mengru, Wang, Bo, Wang, Haotian, Li, Zhengke, Weng, Chentian, Luo, Wei, Yao, Cheng, Lin, Bo, and Yin, Jianwei
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Computer Science - Human-Computer Interaction ,Computer Science - Artificial Intelligence - Abstract
Hypomimia is a non-motor symptom of Parkinson's disease that manifests as delayed facial movements and expressions, along with challenges in articulation and emotion. Currently, subjective evaluation by neurologists is the primary method for hypomimia detection, and conventional rehabilitation approaches heavily rely on verbal prompts from rehabilitation physicians. There remains a deficiency in accessible, user-friendly and scientifically rigorous assistive tools for hypomimia treatments. To investigate this, we developed HypomimaCoach, an Action Unit (AU)-based digital therapy system for hypomimia detection and rehabilitation in Parkinson's disease. The HypomimaCoach system was designed to facilitate engagement through the incorporation of both relaxed and controlled rehabilitation exercises, while also stimulating initiative through the integration of digital therapies that incorporated traditional face training methods. We extract action unit(AU) features and their relationship for hypomimia detection. In order to facilitate rehabilitation, a series of training programmes have been devised based on the Action Units (AUs) and patients are provided with real-time feedback through an additional AU recognition model, which guides them through their training routines. A pilot study was conducted with seven participants in China, all of whom exhibited symptoms of Parkinson's disease hypomimia. The results of the pilot study demonstrated a positive impact on participants' self-efficacy, with favourable feedback received. Furthermore, physician evaluations validated the system's applicability in a therapeutic setting for patients with Parkinson's disease, as well as its potential value in clinical applications.
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- 2024
25. Thermodynamics of Schwarzschild-AdS black hole in non-commutative geometry
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Wang, Rui-Bo, Ma, Shi-Jie, You, Lei, Deng, Jian-Bo, and Hu, Xian-Ru
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General Relativity and Quantum Cosmology - Abstract
In this paper, we study the thermodynamics of Schwarzschild-anti-de Sitter black holes within the framework of non-commutative geometry. By solving the Einstein's equations, we derive the corrected Schwarzschild-AdS black hole with Lorentzian distribution and analyze the thermodynamics. Our results confirm that if the energy-momentum tensor outside the event horizon is related to the mass of the black hole, the conventional first law of thermodynamics will be violated. The study of criticality reveals that the black hole undergoes a small black hole-large black hole phase transition similar to that of the Van der Waals system, with a critical point and a critical ratio slightly smaller than that of the Van der Waals fluid. As the non-commutative parameter increases, the phase transition process shortens, leading to a critical point, and ultimately to the disappearance of the phase transition. The violation of the conventional first law results in a discontinuity of the Gibbs free energy during the phase transition, indicating the occurrence of zeroth-order phase transition. Moreover, we investigate the Joule-Thomson expansion, obtaining the minimum inversion temperature and the minimum inversion mass., Comment: 37pages, 11figures
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- 2024
26. Pathfinding pulsar observations with the CVN incorporating the FAST
- Author
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Yan, Zhen, Shen, Zhiqiang, Jiang, Peng, Zhang, Bo, Zhang, Haiyan, Cui, Lang, Luo, Jintao, Chen, Rurong, Jiang, Wu, Zhang, Hua, Wu, De, Zhao, Rongbing, Yuan, Jianping, Hu, Yue, Wu, Yajun, Xia, Bo, Li, Guanghui, Rao, Yongnan, Chen, Chenyu, Wang, Xiaowei, Ding, Hao, Liu, Yongpeng, Zhang, Fuchen, and Jiang, Yongbin
- Subjects
Astrophysics - High Energy Astrophysical Phenomena ,Astrophysics - Instrumentation and Methods for Astrophysics - Abstract
The importance of Very Long Baseline Interferometry (VLBI) for pulsar research is becoming increasingly prominent and receiving more and more attention. In this paper, we present pathfinding pulsar observation results with the Chinese VLBI Network (CVN) incorporating the Five-hundred-meter Aperture Spherical radio Telescope (FAST). On MJD 60045 (April 11th, 2023), PSRs B0919+06 and B1133+16 were observed with the phase-referencing mode in the L-band using four radio telescopes (FAST, TianMa, Haoping and Nanshan) and correlated with the pulsar binning mode of the distributed FX-style software correlator in Shanghai. After further data processing with the NRAO Astronomical Image Processing System (AIPS), we detected these two pulsars and fitted their current positions with accuracy at the milliarcsecond level. By comparison, our results show significantly better agreement with predicted values based on historical VLBI observations than that with previous timing observations, as pulsar astrometry with the VLBI provides a more direct and model-independent method for accurately obtaining related parameters., Comment: Accepted by the Chinese Physics Letters
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- 2024
27. Integrated photonic nonreciprocal devices based on susceptibility-programmable medium
- Author
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Zhang, Yan-Lei, Li, Ming, Xu, Xin-Biao, Wang, Zhu-Bo, Dong, Chun-Hua, Guo, Guang-Can, Zou, Chang-Ling, and Zou, Xu-Bo
- Subjects
Physics - Optics - Abstract
The switching and control of optical fields based on nonlinear optical effects are often limited to relatively weak nonlinear susceptibility and strong optical pump fields. Here, an optical medium with programmable susceptibility tensor based on polarizable atoms is proposed. Under a structured optical pump, the ground state population of atoms could be efficiently controlled by tuning the chirality and intensity of optical fields, and thus the optical response of the medium is programmable in both space and time. We demonstrate the potential of this approach by engineering the spatial distribution of the complex susceptibility tensor of the medium in photonic structures to realize nonreciprocal optical effects. Specifically, we investigate the advantages of chiral interaction between atoms and photons in an atom-cladded waveguide, theoretically showing that reconfigurable, strong, and fastly switchable isolation of optical signals in a selected optical mode is possible. The susceptibility-programmable medium provides a promising way to efficiently control the optical field, opening up a wide range of applications for integrated photonic devices and structured optics., Comment: 7 pages, 4 figures
- Published
- 2024
28. Two-Stage Robust Optimal Operation of Distribution Networks using Confidence Level Based Distributionally Information Gap Decision
- Author
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Xiong, Zhisheng, Zeng, Bo, Palensky, Peter, and Vergara, Pedro P.
- Subjects
Electrical Engineering and Systems Science - Systems and Control - Abstract
This paper presents a confidence level-based distributionally information gap decision theory (CL-DIGDT) framework for the two-stage robust optimal operation of distribution networks, aiming at deriving an optimal operational scheme capable of addressing uncertainties related to renewable energy and load demands. Building on conventional IGDT, the proposed framework utilizes the confidence level to capture the asymmetric characteristics of uncertainties and maximize the risk-averse capability of the solution in a probabilistic manner. To account for the probabilistic consideration, the imprecise Dirichlet model is employed to construct the ambiguity sets of uncertainties, reducing reliance on precise probability distributions. Consequently, a two-stage robust optimal operation model for distribution networks using CL-DIGDT is developed. An iterative method is proposed to solve the model and determine the upper and lower bounds of the objective function. Case study demonstrates that the proposed approach yields a more robust and statistically optimized solution with required accuracy compared to existing method, contributing to a reduction in first-stage cost by 0.84%, second-stage average cost by 6.7%, and significantly increasing the reliability of the solution by 8%.
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- 2024
29. Self-learning path integral hybrid Monte Carlo with mixed ab initio and machine learning potentials for modeling nuclear quantum effects in water
- Author
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Thomsen, Bo, Nagai, Yuki, Kobayashi, Keita, Hamada, Ikutaro, and Shiga, Motoyuki
- Subjects
Physics - Chemical Physics ,Condensed Matter - Materials Science - Abstract
The introduction of machine learned potentials (MLPs) has greatly expanded the space available for studying Nuclear Quantum Effects computationally with ab initio path integral (PI) accuracy, with the MLPs' promise of an accuracy comparable to that of ab initio at a fraction of the cost. One of the challenges in development of MLPs is the need for a large and diverse training set calculated by ab initio methods. This data set should ideally cover the entire phase space, while not searching this space using ab initio methods, as this would be counterproductive and generally intractable with respect to computational time.In this paper, we present the self-learning PI hybrid Monte Carlo Method using a mixed ab initio and ML potential (SL-PIHMC-MIX), where the mixed potential allows for the study of larger systems and the extension of the original SL-HMC method [Nagai et al., Phys. Rev. B 102, 041124 (2020)] to PI methods and larger systems. While the MLPs generated by this method can be directly applied to run long-time ML-PIMD simulations, we demonstrate that using PIHMC-MIX with the trained MLPs allows for an exact reproduction of the structure obtained from ab initio PIMD. Specifically, we find that the PIHMC-MIX simulations require only 5,000 evaluations of the 32-bead structure, compared to the 100,000 evaluations needed for the ab initio PIMD result., Comment: 44 pages, 9 figures and 25 pages supplemental materials, accepted in J. Chem. Phys
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- 2024
30. Model Inversion Attacks: A Survey of Approaches and Countermeasures
- Author
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Zhou, Zhanke, Zhu, Jianing, Yu, Fengfei, Li, Xuan, Peng, Xiong, Liu, Tongliang, and Han, Bo
- Subjects
Computer Science - Machine Learning - Abstract
The success of deep neural networks has driven numerous research studies and applications from Euclidean to non-Euclidean data. However, there are increasing concerns about privacy leakage, as these networks rely on processing private data. Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training by abusing access to a well-trained model. The effectiveness of MIAs has been demonstrated in various domains, including images, texts, and graphs. These attacks highlight the vulnerability of neural networks and raise awareness about the risk of privacy leakage within the research community. Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs across different domains. This survey aims to summarize up-to-date MIA methods in both attacks and defenses, highlighting their contributions and limitations, underlying modeling principles, optimization challenges, and future directions. We hope this survey bridges the gap in the literature and facilitates future research in this critical area. Besides, we are maintaining a repository to keep track of relevant research at https://github.com/AndrewZhou924/Awesome-model-inversion-attack., Comment: 40 pages, 17 figures
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- 2024
31. Temporal evolution of axially standing kink motions in solar coronal slabs: An eigenfunction expansion approach
- Author
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Gao, Yuhong, Li, Bo, Shi, Mijie, Chen, Shaoxia, and Yu, Hui
- Subjects
Astrophysics - Solar and Stellar Astrophysics - Abstract
We aim to provide more insights into the applicability to solar coronal seismology of the much-studied discrete leaky modes (DLMs) in classic analyses. Under linear ideal pressureless MHD, we examine two-dimensional (2D) axial fundamental kink motions that arise when localized velocity exciters impact some symmetric slab equilibria. Continuous structuring is allowed for. A 1D initial value problem (IVP) is formulated in conjunction with an eigenvalue problem (EVP) for laterally open systems, with no strict boundary conditions (BCs) at infinity. The IVP is solved by eigenfunction expansion, allowing a clear distinction between the contributions from proper eigenmodes and improper continuum eigenmodes. Example solutions are offered for parameters typical of active region loops. Our solutions show that the system evolves towards long periodicities due to proper eigenmodes (of order the axial Alfven time), whereas the interference of the improper continuum may lead to short periodicities initially (of order the lateral Alfven time). Specializing to the slab axis, we demonstrate that the proper contribution strengthens with the density contrast, but may occasionally be stronger for less steep density profiles. Short periodicities are not guaranteed in the improper contribution, the details of the initial exciter being key. When identifiable, these periodicities tend to agree with the oscillation frequencies expected for DLMs, despite the differences in the BCs between our EVP and classic analyses. The eigenfunction expansion approach enables all qualitative features to be interpreted as the interplay between the initial exciter and some response function, the latter solely determined by the equilibria. Classic theories for DLMs can find seismological applications, with time-dependent studies offering additional ways for constraining initial exciters., Comment: accepted for publication in A&A
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- 2024
32. Random-Flux-Induced Topological Phase Transitions and Chern Insulators
- Author
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Li, Chang-An, Fu, Bo, Li, Jian, and Trauzettel, Björn
- Subjects
Condensed Matter - Mesoscale and Nanoscale Physics - Abstract
We study the relevance of random flux on stability and emergence of topological phases of matter. A particularly interesting model in the presence of random flux is the anisotropic Wilson-Dirac model in two spatial dimensions. We show that this model exhibits an intriguing topological phase transition from a weak topological insulator to a Chern insulator driven by random flux. We numerically establish a global phase diagram of this model in presence of random flux. We uncover the underlying mechanism of topological phase transitions with an analytical effective medium theory, illustrating momentum-dependent renormalizations of model parameters by random flux. This analysis allows us to identify quasi-critical phase points at transitions between weak and strong topological phases, where eigen states are extended in one spatial direction but localized in the other one. Our results describe a qualitatively new effect of disorder on topological phases of matter., Comment: 6+10 pages, 4+7 figures
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- 2024
33. A Risk Sensitive Contract-unified Reinforcement Learning Approach for Option Hedging
- Author
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Peng, Xianhua, Zhou, Xiang, Xiao, Bo, and Wu, Yi
- Subjects
Quantitative Finance - Risk Management ,Quantitative Finance - Pricing of Securities - Abstract
We propose a new risk sensitive reinforcement learning approach for the dynamic hedging of options. The approach focuses on the minimization of the tail risk of the final P&L of the seller of an option. Different from most existing reinforcement learning approaches that require a parametric model of the underlying asset, our approach can learn the optimal hedging strategy directly from the historical market data without specifying a parametric model; in addition, the learned optimal hedging strategy is contract-unified, i.e., it applies to different options contracts with different initial underlying prices, strike prices, and maturities. Our approach extends existing reinforcement learning methods by learning the tail risk measures of the final hedging P&L and the optimal hedging strategy at the same time. We carry out comprehensive empirical study to show that, in the out-of-sample tests, the proposed reinforcement learning hedging strategy can obtain statistically significantly lower tail risk and higher mean of the final P&L than delta hedging methods., Comment: 70 pages, 3 figures
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- 2024
34. Golden Noise for Diffusion Models: A Learning Framework
- Author
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Zhou, Zikai, Shao, Shitong, Bai, Lichen, Xu, Zhiqiang, Han, Bo, and Xie, Zeke
- Subjects
Computer Science - Machine Learning ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Text-to-image diffusion model is a popular paradigm that synthesizes personalized images by providing a text prompt and a random Gaussian noise. While people observe that some noises are ``golden noises'' that can achieve better text-image alignment and higher human preference than others, we still lack a machine learning framework to obtain those golden noises. To learn golden noises for diffusion sampling, we mainly make three contributions in this paper. First, we identify a new concept termed the \textit{noise prompt}, which aims at turning a random Gaussian noise into a golden noise by adding a small desirable perturbation derived from the text prompt. Following the concept, we first formulate the \textit{noise prompt learning} framework that systematically learns ``prompted'' golden noise associated with a text prompt for diffusion models. Second, we design a noise prompt data collection pipeline and collect a large-scale \textit{noise prompt dataset}~(NPD) that contains 100k pairs of random noises and golden noises with the associated text prompts. With the prepared NPD as the training dataset, we trained a small \textit{noise prompt network}~(NPNet) that can directly learn to transform a random noise into a golden noise. The learned golden noise perturbation can be considered as a kind of prompt for noise, as it is rich in semantic information and tailored to the given text prompt. Third, our extensive experiments demonstrate the impressive effectiveness and generalization of NPNet on improving the quality of synthesized images across various diffusion models, including SDXL, DreamShaper-xl-v2-turbo, and Hunyuan-DiT. Moreover, NPNet is a small and efficient controller that acts as a plug-and-play module with very limited additional inference and computational costs, as it just provides a golden noise instead of a random noise without accessing the original pipeline.
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- 2024
35. MM-Eval: A Hierarchical Benchmark for Modern Mongolian Evaluation in LLMs
- Author
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Zhang, Mengyuan, Wang, Ruihui, Xia, Bo, Sun, Yuan, and Zhao, Xiaobing
- Subjects
Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Large language models (LLMs) excel in high-resource languages but face notable challenges in low-resource languages like Mongolian. This paper addresses these challenges by categorizing capabilities into language abilities (syntax and semantics) and cognitive abilities (knowledge and reasoning). To systematically evaluate these areas, we developed MM-Eval, a specialized dataset based on Modern Mongolian Language Textbook I and enriched with WebQSP and MGSM datasets. Preliminary experiments on models including Qwen2-7B-Instruct, GLM4-9b-chat, Llama3.1-8B-Instruct, GPT-4, and DeepseekV2.5 revealed that: 1) all models performed better on syntactic tasks than semantic tasks, highlighting a gap in deeper language understanding; and 2) knowledge tasks showed a moderate decline, suggesting that models can transfer general knowledge from high-resource to low-resource contexts. The release of MM-Eval, comprising 569 syntax, 677 semantics, 344 knowledge, and 250 reasoning tasks, offers valuable insights for advancing NLP and LLMs in low-resource languages like Mongolian. The dataset is available at https://github.com/joenahm/MM-Eval.
- Published
- 2024
36. AI-driven inverse design of materials: Past, present and future
- Author
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Han, Xiao-Qi, Wang, Xin-De, Xu, Meng-Yuan, Feng, Zhen, Yao, Bo-Wen, Guo, Peng-Jie, Gao, Ze-Feng, and Lu, Zhong-Yi
- Subjects
Condensed Matter - Materials Science ,Condensed Matter - Superconductivity ,Computer Science - Artificial Intelligence - Abstract
The discovery of advanced materials is the cornerstone of human technological development and progress. The structures of materials and their corresponding properties are essentially the result of a complex interplay of multiple degrees of freedom such as lattice, charge, spin, symmetry, and topology. This poses significant challenges for the inverse design methods of materials. Humans have long explored new materials through a large number of experiments and proposed corresponding theoretical systems to predict new material properties and structures. With the improvement of computational power, researchers have gradually developed various electronic structure calculation methods, particularly such as the one based density functional theory, as well as high-throughput computational methods. Recently, the rapid development of artificial intelligence technology in the field of computer science has enabled the effective characterization of the implicit association between material properties and structures, thus opening up an efficient paradigm for the inverse design of functional materials. A significant progress has been made in inverse design of materials based on generative and discriminative models, attracting widespread attention from researchers. Considering this rapid technological progress, in this survey, we look back on the latest advancements in AI-driven inverse design of materials by introducing the background, key findings, and mainstream technological development routes. In addition, we summarize the remaining issues for future directions. This survey provides the latest overview of AI-driven inverse design of materials, which can serve as a useful resource for researchers., Comment: 43 pages, 5 figures, 2 tables
- Published
- 2024
37. Stability and Generalization for Distributed SGDA
- Author
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Zhu, Miaoxi, Sun, Yan, Shen, Li, Du, Bo, and Tao, Dacheng
- Subjects
Computer Science - Machine Learning ,Mathematics - Optimization and Control - Abstract
Minimax optimization is gaining increasing attention in modern machine learning applications. Driven by large-scale models and massive volumes of data collected from edge devices, as well as the concern to preserve client privacy, communication-efficient distributed minimax optimization algorithms become popular, such as Local Stochastic Gradient Descent Ascent (Local-SGDA), and Local Decentralized SGDA (Local-DSGDA). While most existing research on distributed minimax algorithms focuses on convergence rates, computation complexity, and communication efficiency, the generalization performance remains underdeveloped, whereas generalization ability is a pivotal indicator for evaluating the holistic performance of a model when fed with unknown data. In this paper, we propose the stability-based generalization analytical framework for Distributed-SGDA, which unifies two popular distributed minimax algorithms including Local-SGDA and Local-DSGDA, and conduct a comprehensive analysis of stability error, generalization gap, and population risk across different metrics under various settings, e.g., (S)C-(S)C, PL-SC, and NC-NC cases. Our theoretical results reveal the trade-off between the generalization gap and optimization error and suggest hyperparameters choice to obtain the optimal population risk. Numerical experiments for Local-SGDA and Local-DSGDA validate the theoretical results.
- Published
- 2024
38. DarkSHINE Baseline Design Report: Physics Prospects and Detector Technologies
- Author
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Chen, Jing, Chen, Ji-Yuan, Chen, Jun-Feng, Chen, Xiang, Fu, Chang-Bo, Guo, Jun, Guo, Yi-Han, Khaw, Kim Siang, Li, Jia-Lin, Li, Liang, Li, Shu, Lin, Yu-ming, Liu, Dan-Ning, Liu, Kang, Liu, Kun, Liu, Qi-Bin, Liu, Zhi, Lu, Ze-Jia, Lv, Meng, Song, Si-Yuan, Sun, Tong, Tang, Jian-Nan, Wan, Wei-Shi, Wang, Dong, Wang, Xiao-Long, Wang, Yu-Feng, Wang, Zhen, Wang, Zi-Rui, Wu, Wei-Hao, Yang, Hai-Jun, Yang, Lin, Yang, Yong, Yu, Dian, Yuan, Rui, Zhang, Jun-Hua, Zhang, Yu-Lei, Zhang, Yun-Long, Zhao, Zhi-Yu, Zhou, Bai-Hong, Zhu, Chun-Xiang, Zhu, Xu-Liang, and Zhu, Yi-Fan
- Subjects
Physics - Instrumentation and Detectors ,High Energy Physics - Experiment - Abstract
DarkSHINE is a newly proposed fixed-target experiment initiative to search for the invisible decay of Dark Photon via missing energy/momentum signatures, based on the high repetition rate electron beam to be deployed/delivered by the Shanghai High repetition rate XFEL and Extreme light facility (SHINE). This report elaborates the baseline design of DarkSHINE experiment by introducing the physics goals, experimental setups, details of each sub-detector system technical designs, signal and backgground modelings, expected search sensitivities and future prospects, which mark an important step towards the further prototyping and technical demonstrations.
- Published
- 2024
39. Memory Proxy Maps for Visual Navigation
- Author
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Johnson, Faith, Cao, Bryan Bo, Ashok, Ashwin, Jain, Shubham, and Dana, Kristin
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Visual navigation takes inspiration from humans, who navigate in previously unseen environments using vision without detailed environment maps. Inspired by this, we introduce a novel no-RL, no-graph, no-odometry approach to visual navigation using feudal learning to build a three tiered agent. Key to our approach is a memory proxy map (MPM), an intermediate representation of the environment learned in a self-supervised manner by the high-level manager agent that serves as a simplified memory, approximating what the agent has seen. We demonstrate that recording observations in this learned latent space is an effective and efficient memory proxy that can remove the need for graphs and odometry in visual navigation tasks. For the mid-level manager agent, we develop a waypoint network (WayNet) that outputs intermediate subgoals, or waypoints, imitating human waypoint selection during local navigation. For the low-level worker agent, we learn a classifier over a discrete action space that avoids local obstacles and moves the agent towards the WayNet waypoint. The resulting feudal navigation network offers a novel approach with no RL, no graph, no odometry, and no metric map; all while achieving SOTA results on the image goal navigation task., Comment: arXiv admin note: substantial text overlap with arXiv:2402.12498
- Published
- 2024
40. Spin excitations arising from anisotropic Dirac spinons in YCu$_3$(OD)$_6$Br$_2$[Br$_{0.33}$(OD)$_{0.67}$]
- Author
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Han, Lankun, Zeng, Zhenyuan, Liu, Bo, Kofu, Maiko, Nakajima, Kenji, Steffens, Paul, Hiess, Arno, Su, Yixi, and Li, Shiliang
- Subjects
Condensed Matter - Strongly Correlated Electrons - Abstract
A Dirac quantum spin liquid hosts Dirac spinons, which are low-energy fractionalized neutral quasiparticles with spin 1/2 that obey the Dirac equation. Recent studies have revealed cone spin continuum in YCu$_3$(OD)$_6$Br$_2$[Br$_{x}$(OD)$_{1-x}$], consistent with the convolution of two Dirac spinons. In this work, we further studied spin excitations using the inelastic neutron scattering technique. The width of low-energy spin excitations shows a linear temperature dependence, which can be explained by spinon-spinon interactions with a Dirac dispersion. Polarized neutron scattering measurements reveal that in-plane magnetic fluctuations are about 1.5 times stronger than the out-of-plane ones, suggesting the presence of out-of-plane Dzyaloshinskii-Moriya interaction. Moreover, the high-energy spin excitations around 14 meV agree with the one-pair spinon-antispinon excitations in Raman studies. The real part of the dynamical susceptibility derived from the Kramers-Kronig relationship also accords with the Knight shift measured by nuclear magnetic resonance. These results provide further insights for the possible Dirac quantum spin liquid in this system., Comment: 7 pages, 4 figures
- Published
- 2024
41. Face De-identification: State-of-the-art Methods and Comparative Studies
- Author
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Cao, Jingyi, Chen, Xiangyi, Liu, Bo, Ding, Ming, Xie, Rong, Song, Li, Li, Zhu, and Zhang, Wenjun
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Cryptography and Security - Abstract
The widespread use of image acquisition technologies, along with advances in facial recognition, has raised serious privacy concerns. Face de-identification usually refers to the process of concealing or replacing personal identifiers, which is regarded as an effective means to protect the privacy of facial images. A significant number of methods for face de-identification have been proposed in recent years. In this survey, we provide a comprehensive review of state-of-the-art face de-identification methods, categorized into three levels: pixel-level, representation-level, and semantic-level techniques. We systematically evaluate these methods based on two key criteria, the effectiveness of privacy protection and preservation of image utility, highlighting their advantages and limitations. Our analysis includes qualitative and quantitative comparisons of the main algorithms, demonstrating that deep learning-based approaches, particularly those using Generative Adversarial Networks (GANs) and diffusion models, have achieved significant advancements in balancing privacy and utility. Experimental results reveal that while recent methods demonstrate strong privacy protection, trade-offs remain in visual fidelity and computational complexity. This survey not only summarizes the current landscape but also identifies key challenges and future research directions in face de-identification.
- Published
- 2024
42. InterFormer: Towards Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction
- Author
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Zeng, Zhichen, Liu, Xiaolong, Hang, Mengyue, Liu, Xiaoyi, Zhou, Qinghai, Yang, Chaofei, Liu, Yiqun, Ruan, Yichen, Chen, Laming, Chen, Yuxin, Hao, Yujia, Xu, Jiaqi, Nie, Jade, Liu, Xi, Zhang, Buyun, Wen, Wei, Yuan, Siyang, Wang, Kai, Chen, Wen-Yen, Han, Yiping, Li, Huayu, Yang, Chunzhi, Long, Bo, Yu, Philip S., Tong, Hanghang, and Yang, Jiyan
- Subjects
Computer Science - Information Retrieval ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
Click-through rate (CTR) prediction, which predicts the probability of a user clicking an ad, is a fundamental task in recommender systems. The emergence of heterogeneous information, such as user profile and behavior sequences, depicts user interests from different aspects. A mutually beneficial integration of heterogeneous information is the cornerstone towards the success of CTR prediction. However, most of the existing methods suffer from two fundamental limitations, including (1) insufficient inter-mode interaction due to the unidirectional information flow between modes, and (2) aggressive information aggregation caused by early summarization, resulting in excessive information loss. To address the above limitations, we propose a novel module named InterFormer to learn heterogeneous information interaction in an interleaving style. To achieve better interaction learning, InterFormer enables bidirectional information flow for mutually beneficial learning across different modes. To avoid aggressive information aggregation, we retain complete information in each data mode and use a separate bridging arch for effective information selection and summarization. Our proposed InterFormer achieves state-of-the-art performance on three public datasets and a large-scale industrial dataset., Comment: 10 pages, 6 figures
- Published
- 2024
43. Vortex lattice states of bilayer electron-hole fluids in quantizing magnetic fields
- Author
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Zou, Bo and MacDonald, Allan H.
- Subjects
Condensed Matter - Mesoscale and Nanoscale Physics ,Condensed Matter - Quantum Gases ,Condensed Matter - Strongly Correlated Electrons - Abstract
We show that the ground state of a weakly charged two-dimensional electron-hole fluid in a strong magnetic field is a broken translational symmetry state with interpenetrating lattices of localized vortices and antivortices in the electron-hole-pair field. The vortices and antivortices carry fractional charges of equal sign but unequal magnitude and have a honeycomb lattice structure that contrasts with the triangular lattices of superconducting electron-electron-pair vortex lattices. We predict that increasing charge density and weakening magnetic fields drive vortex delocalization transitions signaled experimentally by abrupt increases in counterflow transport resistance.
- Published
- 2024
44. UIFormer: A Unified Transformer-based Framework for Incremental Few-Shot Object Detection and Instance Segmentation
- Author
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Zhang, Chengyuan, Zhang, Yilin, Zhu, Lei, Liu, Deyin, Wu, Lin, Li, Bo, Zhang, Shichao, Bennamoun, Mohammed, and Boussaid, Farid
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
This paper introduces a novel framework for unified incremental few-shot object detection (iFSOD) and instance segmentation (iFSIS) using the Transformer architecture. Our goal is to create an optimal solution for situations where only a few examples of novel object classes are available, with no access to training data for base or old classes, while maintaining high performance across both base and novel classes. To achieve this, We extend Mask-DINO into a two-stage incremental learning framework. Stage 1 focuses on optimizing the model using the base dataset, while Stage 2 involves fine-tuning the model on novel classes. Besides, we incorporate a classifier selection strategy that assigns appropriate classifiers to the encoder and decoder according to their distinct functions. Empirical evidence indicates that this approach effectively mitigates the over-fitting on novel classes learning. Furthermore, we implement knowledge distillation to prevent catastrophic forgetting of base classes. Comprehensive evaluations on the COCO and LVIS datasets for both iFSIS and iFSOD tasks demonstrate that our method significantly outperforms state-of-the-art approaches., Comment: 11 pages, 3 figures
- Published
- 2024
45. A fiber array architecture for atom quantum computing
- Author
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Li, Xiao, Hou, Jia-Yi, Wang, Jia-Chao, Wang, Guang-Wei, He, Xiao-Dong, Zhou, Feng, Wang, Yi-Bo, Liu, Min, Wang, Jin, Xu, Peng, and Zhan, Ming-Sheng
- Subjects
Quantum Physics - Abstract
Arrays of single atoms trapped in optical tweezers are increasingly recognized as a promising platform for scalable quantum computing. In both the fault-tolerant and NISQ eras, the ability to individually control qubits is essential for the efficient execution of quantum circuits. Time-division multiplexed control schemes based on atom shuttling or beam scanning have been employed to build programmable neutral atom quantum processors, but achieving high-rate, highly parallel gate operations remains a challenge. Here, we propose a fiber array architecture for atom quantum computing capable of fully independent control of individual atoms. The trapping and addressing lasers for each individual atom are emitted from the same optical waveguide, enabling robust control through common-mode suppression of beam pointing noise. Using a fiber array, we experimentally demonstrate the trapping and independent control of ten single atoms in two-dimensional optical tweezers, achieving individually addressed single-qubit gate with an average fidelity of 0.9966(3). Moreover, we perform simultaneous arbitrary single-qubit gate on four randomly selected qubits, resulting in an average fidelity of 0.9961(4). Our work paves the way for time-efficient execution of quantum algorithms on neutral atom quantum computers., Comment: 12 pages
- Published
- 2024
46. Use QUDA for lattice QCD calculation with Python
- Author
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Jiang, Xiangyu, Shi, Chunjiang, Chen, Ying, Gong, Ming, and Yang, Yi-Bo
- Subjects
High Energy Physics - Lattice - Abstract
We developed PyQUDA, a Python wrapper for QUDA written in Cython, designed to facilitate lattice QCD calculations using the Python programming language. PyQUDA leverages the optimized linear algebra capabilities of NumPy/CuPy/PyTorch, along with the highly optimized lattice QCD operations provided by QUDA to accelerate research. This integration simplifies the process of writing calculation codes, enabling researchers to build more complex Python packages like EasyDistillation for specific physics objectives. PyQUDA supports a range of lattice QCD operations, including hybrid Monte Carlo (HMC) with N-flavor clover/HISQ fermions and inversion for the Wilson/clover/HISQ fermion action with the multigrid solver. It also includes utility functions for reading lattice QCD data stored in Chroma, MILC, and $\chi$QCD formats. Type hints are supported by stub files and multi-GPU support is provided through mpi4py., Comment: 11 pages, 3 listings
- Published
- 2024
47. Observation of optical chaotic solitons and modulated subharmonic route to chaos in mode-locked laser
- Author
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Kang, Huiyu, Zhou, Anran, Zhang, Ying, Wu, Xiuqi, Yuan, Bo, Peng, Junsong, Finot, Christophe, Boscolo, Sonia, and Zeng, Heping
- Subjects
Physics - Optics - Abstract
We reveal a new scenario for the transition of solitons to chaos in a mode-locked fiber laser: the modulated subharmonic route. Its universality is confirmed in two different laser configurations, namely, a figure-of-eight and a ring laser. Numerical simulations of the laser models agree well with the experiments. The modulated subharmonic route to chaos could stimulate parallel research in many nonlinear physical systems.
- Published
- 2024
48. 10 GHz Robust polarization modulation towards high-speed satellite-based quantum communication
- Author
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Wang, Zexu, Xu, Huaxing, Li, Ju, Huang, Jinquan, Han, Hui, Wang, Changlei, Zhang, Ping, Yin, Feifei, Xu, Kun, Liu, Bo, and Dai, Yitang
- Subjects
Quantum Physics - Abstract
In practical satellite-based quantum key distribution (QKD) systems, the preparation and transmission of polarization-encoding photons suffer from complex environmental effects and high channel-loss. Consequently, the hinge to enhancing the secure key rate (SKR) lies in achieving robust, low-error and high-speed polarization modulation. Although the schemes that realize self-compensation exhibit remarkable robustness. Their modulation speed is constrained to approximately 2 GHz to avoid the interaction between the electrical signal and the reverse optical pulses. Here we utilize the non-reciprocity of the lithium niobate modulators and eliminate the modulation on the reverse optical pulses. As this characteristic is widely available in the radio-frequency band, the modulation speed is no longer limited by the self-compensating optics and can be further increased. The measured average intrinsic QBER of the different polarization states at 10 GHz system repetition frequency is as low as 0.53% over 10 min without any compensation. And the experiment simulation shows that the proposed scheme extends the transmission distance to more than 350 km. Our work can be be efficient performed to the high-speed and high-loss satellite-based quantum communication scenario., Comment: 16 pages, 10 figures
- Published
- 2024
49. Gazing at Rewards: Eye Movements as a Lens into Human and AI Decision-Making in Hybrid Visual Foraging
- Author
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Wang, Bo, Tan, Dingwei, Kuo, Yen-Ling, Sun, Zhaowei, Wolfe, Jeremy M., Cham, Tat-Jen, and Zhang, Mengmi
- Subjects
Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Imagine searching a collection of coins for quarters ($0.25$), dimes ($0.10$), nickels ($0.05$), and pennies ($0.01$)-a hybrid foraging task where observers look for multiple instances of multiple target types. In such tasks, how do target values and their prevalence influence foraging and eye movement behaviors (e.g., should you prioritize rare quarters or common nickels)? To explore this, we conducted human psychophysics experiments, revealing that humans are proficient reward foragers. Their eye fixations are drawn to regions with higher average rewards, fixation durations are longer on more valuable targets, and their cumulative rewards exceed chance, approaching the upper bound of optimal foragers. To probe these decision-making processes of humans, we developed a transformer-based Visual Forager (VF) model trained via reinforcement learning. Our VF model takes a series of targets, their corresponding values, and the search image as inputs, processes the images using foveated vision, and produces a sequence of eye movements along with decisions on whether to collect each fixated item. Our model outperforms all baselines, achieves cumulative rewards comparable to those of humans, and approximates human foraging behavior in eye movements and foraging biases within time-limited environments. Furthermore, stress tests on out-of-distribution tasks with novel targets, unseen values, and varying set sizes demonstrate the VF model's effective generalization. Our work offers valuable insights into the relationship between eye movements and decision-making, with our model serving as a powerful tool for further exploration of this connection. All data, code, and models will be made publicly available.
- Published
- 2024
50. Dual-Head Knowledge Distillation: Enhancing Logits Utilization with an Auxiliary Head
- Author
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Yang, Penghui, Zong, Chen-Chen, Huang, Sheng-Jun, Feng, Lei, and An, Bo
- Subjects
Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning - Abstract
Traditional knowledge distillation focuses on aligning the student's predicted probabilities with both ground-truth labels and the teacher's predicted probabilities. However, the transition to predicted probabilities from logits would obscure certain indispensable information. To address this issue, it is intuitive to additionally introduce a logit-level loss function as a supplement to the widely used probability-level loss function, for exploiting the latent information of logits. Unfortunately, we empirically find that the amalgamation of the newly introduced logit-level loss and the previous probability-level loss will lead to performance degeneration, even trailing behind the performance of employing either loss in isolation. We attribute this phenomenon to the collapse of the classification head, which is verified by our theoretical analysis based on the neural collapse theory. Specifically, the gradients of the two loss functions exhibit contradictions in the linear classifier yet display no such conflict within the backbone. Drawing from the theoretical analysis, we propose a novel method called dual-head knowledge distillation, which partitions the linear classifier into two classification heads responsible for different losses, thereby preserving the beneficial effects of both losses on the backbone while eliminating adverse influences on the classification head. Extensive experiments validate that our method can effectively exploit the information inside the logits and achieve superior performance against state-of-the-art counterparts., Comment: Preprint
- Published
- 2024
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