109,372 results on '"Wang, Lei"'
Search Results
2. Quantum Entanglement Path Selection and Qubit Allocation via Adversarial Group Neural Bandits
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Huang, Yin, Wang, Lei, and Xu, Jie
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Quantum Physics ,Computer Science - Networking and Internet Architecture - Abstract
Quantum Data Networks (QDNs) have emerged as a promising framework in the field of information processing and transmission, harnessing the principles of quantum mechanics. QDNs utilize a quantum teleportation technique through long-distance entanglement connections, encoding data information in quantum bits (qubits). Despite being a cornerstone in various quantum applications, quantum entanglement encounters challenges in establishing connections over extended distances due to probabilistic processes influenced by factors like optical fiber losses. The creation of long-distance entanglement connections between quantum computers involves multiple entanglement links and entanglement swapping techniques through successive quantum nodes, including quantum computers and quantum repeaters, necessitating optimal path selection and qubit allocation. Current research predominantly assumes known success rates of entanglement links between neighboring quantum nodes and overlooks potential network attackers. This paper addresses the online challenge of optimal path selection and qubit allocation, aiming to learn the best strategy for achieving the highest success rate of entanglement connections between two chosen quantum computers without prior knowledge of the success rate and in the presence of a QDN attacker. The proposed approach is based on multi-armed bandits, specifically adversarial group neural bandits, which treat each path as a group and view qubit allocation as arm selection. Our contributions encompass formulating an online adversarial optimization problem, introducing the EXPNeuralUCB bandits algorithm with theoretical performance guarantees, and conducting comprehensive simulations to showcase its superiority over established advanced algorithms., Comment: Accepted by IEEE/ACM Transactions on Networking
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- 2024
3. Einstein Probe discovery of EP240408a: a peculiar X-ray transient with an intermediate timescale
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Zhang, Wenda, Yuan, Weimin, Ling, Zhixing, Chen, Yong, Rea, Nanda, Rau, Arne, Cai, Zhiming, Cheng, Huaqing, Zelati, Francesco Coti, Dai, Lixin, Hu, Jingwei, Jia, Shumei, Jin, Chichuan, Li, Dongyue, O'Brien, Paul, Shen, Rongfeng, Shu, Xinwen, Sun, Shengli, Sun, Xiaojin, Wang, Xiaofeng, Yang, Lei, Zhang, Bing, Zhang, Chen, Zhang, Shuang-Nan, Zhang, Yonghe, An, Jie, Buckley, David, Coleiro, Alexis, Cordier, Bertrand, Dou, Liming, Eyles-Ferris, Rob, Fan, Zhou, Feng, Hua, Fu, Shaoyu, Fynbo, Johan P. U., Galbany, Lluis, Jha, Saurabh W., Jiang, Shuaiqing, Kong, Albert, Kuulkers, Erik, Lei, Weihua, Li, Wenxiong, Liu, Bifang, Liu, Mingjun, Liu, Xing, Liu, Yuan, Liu, Zhu, Maitra, Chandreyee, Marino, Alessio, Monageng, Itumeleng, Nandra, Kirpal, Sanders, Jeremy, Soria, Roberto, Tao, Lian, Wang, Junfeng, Wang, Song, Wang, Tinggui, Wang, Zhongxiang, Wu, Qingwen, Wu, Xuefeng, Xu, Dong, Xu, Yanjun, Xue, Suijian, Xue, Yongquan, Zhang, Zijian, Zhu, Zipei, Zou, Hu, Bao, Congying, Chen, Fansheng, Chen, Houlei, Chen, Tianxiang, Chen, Wei, Chen, Yehai, Chen, Yifan, Cui, Chenzhou, Cui, Weiwei, Dai, Yanfeng, Fan, Dongwei, Guan, Ju, Han, Dawei, Hou, Dongjie, Hu, Haibo, Huang, Maohai, Huo, Jia, Jia, Zhenqing, Jiang, Bowen, Jin, Ge, Li, Chengkui, Li, Junfei, Li, Longhui, Li, Maoshun, Li, Wei, Li, Zhengda, Lian, Tianying, Liu, Congzhan, Liu, Heyang, Liu, Huaqiu, Lu, Fangjun, Luo, Laidan, Ma, Jia, Mao, Xuan, Pan, Haiwu, Pan, Xin, Song, Liming, Sun, Hui, Tan, Yunyin, Tang, Qingjun, Tao, Yihan, Wang, Hao, Wang, Juan, Wang, Lei, Wang, Wenxin, Wang, Yilong, Wang, Yusa, Wu, Qinyu, Xu, Haitao, Xu, Jingjing, Xu, Xinpeng, Xu, Yunfei, Xu, Zhao, Xue, Changbin, Xue, Yulong, Yan, Ailiang, Yang, Haonan, Yang, Xiongtao, Yang, Yanji, Zhang, Juan, Zhang, Mo, Zhang, Wenjie, Zhang, Zhen, Zhang, Ziliang, Zhao, Donghua, Zhao, Haisheng, Zhao, Xiaofan, Zhao, Zijian, Zhou, Hongyan, Zhou, Yilin, Zhu, Yuxuan, and Zhu, Zhencai
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Astrophysics - High Energy Astrophysical Phenomena - Abstract
We report the discovery of a peculiar X-ray transient, EP240408a, by Einstein Probe (EP) and follow-up studies made with EP, Swift, NICER, GROND, ATCA and other ground-based multi-wavelength telescopes. The new transient was first detected with Wide-field X-ray Telescope (WXT) on board EP on April 8th, 2024, manifested in an intense yet brief X-ray flare lasting for 12 seconds. The flare reached a peak flux of 3.9x10^(-9) erg/cm2/s in 0.5-4 keV, about 300 times brighter than the underlying X-ray emission detected throughout the observation. Rapid and more precise follow-up observations by EP/FXT, Swift and NICER confirmed the finding of this new transient. Its X-ray spectrum is non-thermal in 0.5-10 keV, with a power-law photon index varying within 1.8-2.5. The X-ray light curve shows a plateau lasting for about 4 days, followed by a steep decay till becoming undetectable about 10 days after the initial detection. Based on its temporal property and constraints from previous EP observations, an unusual timescale in the range of 7-23 days is found for EP240408a, which is intermediate between the commonly found fast and long-term transients. No counterparts have been found in optical and near-infrared, with the earliest observation at 17 hours after the initial X-ray detection, suggestive of intrinsically weak emission in these bands. We demonstrate that the remarkable properties of EP240408a are inconsistent with any of the transient types known so far, by comparison with, in particular, jetted tidal disruption events, gamma-ray bursts, X-ray binaries and fast blue optical transients. The nature of EP240408a thus remains an enigma. We suggest that EP240408a may represent a new type of transients with intermediate timescales of the order of about 10 days. The detection and follow-ups of more of such objects are essential for revealing their origin., Comment: 25 pages, 11 figures
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- 2024
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4. EMOCPD: Efficient Attention-based Models for Computational Protein Design Using Amino Acid Microenvironment
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Ling, Xiaoqi, Cai, Cheng, Kong, Demin, Wei, Zhisheng, Wu, Jing, Wang, Lei, and Deng, Zhaohong
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Computer Science - Machine Learning ,Computer Science - Artificial Intelligence ,Quantitative Biology - Biomolecules - Abstract
Computational protein design (CPD) refers to the use of computational methods to design proteins. Traditional methods relying on energy functions and heuristic algorithms for sequence design are inefficient and do not meet the demands of the big data era in biomolecules, with their accuracy limited by the energy functions and search algorithms. Existing deep learning methods are constrained by the learning capabilities of the networks, failing to extract effective information from sparse protein structures, which limits the accuracy of protein design. To address these shortcomings, we developed an Efficient attention-based Models for Computational Protein Design using amino acid microenvironment (EMOCPD). It aims to predict the category of each amino acid in a protein by analyzing the three-dimensional atomic environment surrounding the amino acids, and optimize the protein based on the predicted high-probability potential amino acid categories. EMOCPD employs a multi-head attention mechanism to focus on important features in the sparse protein microenvironment and utilizes an inverse residual structure to optimize the network architecture. The proposed EMOCPD achieves over 80% accuracy on the training set and 68.33% and 62.32% accuracy on two independent test sets, respectively, surpassing the best comparative methods by over 10%. In protein design, the thermal stability and protein expression of the predicted mutants from EMOCPD show significant improvements compared to the wild type, effectively validating EMOCPD's potential in designing superior proteins. Furthermore, the predictions of EMOCPD are influenced positively, negatively, or have minimal impact based on the content of the 20 amino acids, categorizing amino acids as positive, negative, or neutral. Research findings indicate that EMOCPD is more suitable for designing proteins with lower contents of negative amino acids.
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- 2024
5. Hierarchical Multiple Kernel K-Means Algorithm Based on Sparse Connectivity
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Wang, Lei, Du, Liang, and Zhou, Peng
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Computer Science - Machine Learning - Abstract
Multiple kernel learning (MKL) aims to find an optimal, consistent kernel function. In the hierarchical multiple kernel clustering (HMKC) algorithm, sample features are extracted layer by layer from a high-dimensional space to maximize the retention of effective information. However, information interaction between layers is often ignored. In this model, only corresponding nodes in adjacent layers exchange information; other nodes remain isolated, and if full connectivity is adopted, the diversity of the final consistency matrix is reduced. Therefore, this paper proposes a hierarchical multiple kernel K-Means (SCHMKKM) algorithm based on sparse connectivity, which controls the assignment matrix to achieve sparse connections through a sparsity rate, thereby locally fusing the features obtained by distilling information between layers. Finally, we conduct cluster analysis on multiple datasets and compare it with the fully connected hierarchical multiple kernel K-Means (FCHMKKM) algorithm in experiments. It is shown that more discriminative information fusion is beneficial for learning a better consistent partition matrix, and the fusion strategy based on sparse connection outperforms the full connection strategy., Comment: in Chinese language
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- 2024
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6. Magnetoresistance oscillations in vertical junctions of 2D antiferromagnetic semiconductor CrPS$_4$
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Shi, Pengyuan, Wang, Xiaoyu, Zhang, Lihao, Song, Wenqin, Yang, Kunlin, Wang, Shuxi, Zhang, Ruisheng, Zhang, Liangliang, Taniguchi, Takashi, Watanabe, Kenji, Yang, Sen, Zhang, Lei, Wang, Lei, Shi, Wu, Pan, Jie, and Wang, Zhe
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Condensed Matter - Mesoscale and Nanoscale Physics - Abstract
Magnetoresistance (MR) oscillations serve as a hallmark of intrinsic quantum behavior, traditionally observed only in conducting systems. Here we report the discovery of MR oscillations in an insulating system, the vertical junctions of CrPS$_4$ which is a two dimensional (2D) A-type antiferromagnetic semiconductor. Systematic investigations of MR peaks under varying conditions, including electrode materials, magnetic field direction, temperature, voltage bias and layer number, elucidate a correlation between MR oscillations and spin-canted states in CrPS$_4$. Experimental data and analysis point out the important role of the in-gap electronic states in generating MR oscillations, and we proposed that spin selected interlayer hopping of localized states may be responsible for it. Our findings not only illuminate the unusual electronic transport in CrPS$_4$ but also underscore the potential of van der Waals magnets for exploring interesting phenomena.
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- 2024
7. Exploring multi-step electroweak phase transitions in the 2HDM+$\boldsymbol{a}$
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Si, Zong-guo, Wang, Hong-xin, Wang, Lei, and Zhang, Yang
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High Energy Physics - Phenomenology - Abstract
Multiple electroweak phase transitions occurring sequentially in the early universe can give rise to intriguing phenomenology, compared to the typical single-step electroweak phase transition. In this work, we investigate this scenario within the framework of the two-Higgs-doublet model with a pseudoscalar, utilizing the complete one-loop finite-temperature effective potential. After considering relevant experimental and theoretical constraints, we identify four distinct types of phase transitions. In the first case, only the configuration of the CP-even Higgs acquires a non-zero value via a first-order or a cross-over electroweak phase transition, leading to electroweak symmetry breaking. In the remaining three cases, the pseudoscalar fields can obtain vacuum expectation values at different phases of the multi-step phase transition process, leading to spontaneous breaking of the CP symmetry. As the temperature decreases, the phase shifts to the vacuum observed today via first-order electroweak phase transition, at this point, the vacuum expectation value of the pseudoscalar field returns to zero, restoring the CP symmetry. Finally, we compare the transition strength and the stochastic gravitational wave background generated in the four situations along with the projected detection limits., Comment: 24 pages, 7 figures
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- 2024
8. Symmetry Nonnegative Matrix Factorization Algorithm Based on Self-paced Learning
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Wang, Lei, Du, Liang, Zhou, Peng, and Wu, Peng
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Computer Science - Machine Learning - Abstract
A symmetric nonnegative matrix factorization algorithm based on self-paced learning was proposed to improve the clustering performance of the model. It could make the model better distinguish normal samples from abnormal samples in an error-driven way. A weight variable that could measure the degree of difficulty to all samples was assigned in this method, and the variable was constrained by adopting both hard-weighting and soft-weighting strategies to ensure the rationality of the model. Cluster analysis was carried out on multiple data sets such as images and texts, and the experimental results showed the effectiveness of the proposed algorithm., Comment: in Chinese language
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- 2024
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9. ChartifyText: Automated Chart Generation from Data-Involved Texts via LLM
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Zhang, Songheng, Wang, Lei, Li, Toby Jia-Jun, Shen, Qiaomu, Cao, Yixin, and Wang, Yong
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Computer Science - Human-Computer Interaction ,Computer Science - Information Retrieval - Abstract
Text documents with numerical values involved are widely used in various applications such as scientific research, economy, public health and journalism. However, it is difficult for readers to quickly interpret such data-involved texts and gain deep insights. To fill this research gap, this work aims to automatically generate charts to accurately convey the underlying data and ideas to readers, which is essentially a challenging task. The challenges originate from text ambiguities, intrinsic sparsity and uncertainty of data in text documents, and subjective sentiment differences. Specifically, we propose ChartifyText, a novel fully-automated approach that leverages Large Language Models (LLMs) to convert complex data-involved texts to expressive charts. It consists of two major modules: tabular data inference and expressive chart generation. The tabular data inference module employs systematic prompt engineering to guide the LLM (e.g., GPT-4) to infer table data, where data ranges, uncertainties, missing data values and corresponding subjective sentiments are explicitly considered. The expressive chart generation module augments standard charts with intuitive visual encodings and concise texts to accurately convey the underlying data and insights. We extensively evaluate the effectiveness of ChartifyText on real-world data-involved text documents through case studies, in-depth interviews with three visualization experts, and a carefully-designed user study with 15 participants. The results demonstrate the usefulness and effectiveness of ChartifyText in helping readers efficiently and effectively make sense of data-involved texts.
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- 2024
10. Geometrically predictable micro fabricated continuum robot
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Su, Xiaoyu, Wang, Lei, and Chen, Zhuoran
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Computer Science - Robotics - Abstract
Compared to the micro continuum robots that use traditional manufacturing technology, the micro fabricated continuum robots are different in terms of the application of smart materials, additive manufacturing process, and physical field control. However, the existing geometrical prediction models of the micro continuum robots still follow the model frameworks designed for their larger counterparts, which is inconsistent with the real geometrical transformation principle of micro fabricated continuum robots. In this paper, we present a universal geometrical prediction method for the geometry transformation of the micro fabricated continuum robots based on their material properties and the displacement of the stress points. By discretizing of the micro fabricated continuum structure and applying force constraints between adjacent points to simulate material properties, formulations and simulations are demonstrated to prove the feasibility and effectiveness of the proposed method. Three micro fabricated continuum robots driven through different external field forces are investigated to show two superiorities: the geometrical deformation of a micro fabricated continuum robot under external disturbances can be predicted, and a targeted geometry can be shaped by predicting the sequence and directions of external forces. This pioneer research has contributed to promote understanding and operation of micro fabricated continuum robots and their deformation both from theoretical aspect and real experimental operations.
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- 2024
11. An FPGA-based Versatile and Digitalized Method for Pulse Laser Repetition Frequency Locking
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Zheng, Qibin, Tao, Zhengyi, Wang, Lei, Bu, Zhaohui, Jin, Zuanming, and Wang, Zhao
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Physics - Optics ,Physics - Instrumentation and Detectors - Abstract
This paper introduces a novel method for locking the repetition frequency of pulse lasers, adaptable to different frequencies,offering significant improvements in system integration and measurement accuracy. The method consists of two primary components: an error amplification module (EAM) and a digital frequency locking module (DFLM) based on FPGA. The EAM integrates a configurable frequency generator (CFG), a configurable frequency multiplier (CFM) and a mixer,to process the laser pulse alongside a high-stability reference source, such as an atomic clock. By employing frequency multiplication and mixing, the EAM amplifies the laser's frequency error and performs frequency down-conversion,enhancing measurement sensitivity and reducing the hardware requirements of the back-end.The CFG, implemented on a phase-locked loop (PLL) chip, allows for parameter adjustments to accommodate various laser frequencies.The DFLM processes the output from the EAM using a high-speed, ADC-based dual-mixer time-difference (DMTD) method to precisely measure frequency errors.A digital proportional-integral-derivative (PID) controller then provides feedback to achieve accurate frequency locking. To evaluate the proposed method, an FPGA-based electronic system was developed and tested. In laboratory experiment with a custom-built femtosecond fiber laser, the system demonstrated robust locking of the repetition rate, achieving an Allan deviation improvement from $1.51 \times 10^{-7}$ to $1.12 \times 10^{-12}$ at a gate time of 10 s.Further testing with a voltage-controlled oscillator (VCO) confirmed a long-term stability of $9.58 \times 10^{-14} @ 10 s$.
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- 2024
12. Heat transfer enhancement of N-Ga-Al semiconductors heterogeneous interfaces
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Luo, Wenzhu, Yin, Ershuai, Wang, Lei, Lian, Wenlei, Wang, Neng, and Li, Qiang
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Condensed Matter - Materials Science ,Physics - Applied Physics ,J.2.7 - Abstract
Heat transfer enhancement of N-Ga-Al semiconductor heterostructure interfaces is critical for the heat dissipation in GaN-based electronic devices, while the effect of the AlxGa(1-x)N transition layer component concentration and thickness on the heat transfer mechanism at the GaN-AlN interface is unclear. In this paper, using molecular dynamics simulations based on machine learning potentials, the interfacial thermal conductance (ITC) between GaN-AlxGa(1-x)N, AlN-AlxGa(1-x)N and GaN-AlxGa(1-x)N-AlN heterostructure interfaces are calculated for different transition layer thicknesses with different concentrations of Al fractions, and the reasons for the change of ITC and its heat transfer mechanism were explained by the phonon density of states and the spectral heat current. GaN-AlN heterostructure ITC at 300 K is calculated to be 557 MW/(m2K), and the ITCs of GaN-Al0.5Ga0.5N and AlN-Al0.5Ga0.5N are improved by 128% and 229% compared to GaN-AlN, whereas the ITCs of GaN-Al0.7Ga0.3N-AlN containing a 0.5 nm transition layer improved by 27.6%. This is because elemental doping enhances phonon scattering near the interface thereby promoting phonon energy redistribution, but the bulk thermal resistance of the AlxGa(1-x)N layer also increases rapidly with increasing doping ratio, and ITC is affected by a combination of these two factors. This work aims to understand the mechanism of transition layer component concentration and thickness on the heat transfer at the GaN-AlN contact interface, which provides a useful guide for better thermal design of the GaN-AlN heterostructure interface., Comment: 18 pages, 10 figures
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- 2024
13. Freezing dynamics of wetting droplet under a uniform electric field
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Huang, Jiangxu, Li, Hanqing, Che, Jiaqi, Chai, Zhenhua, Wang, Lei, and Shi, Baochang
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Physics - Fluid Dynamics - Abstract
Electrofreezing is a powerful technique that employs the electric field to control and enhance the freezing process. In this work, a phase-field-based lattice Boltzmann (LB) method is developed to study the electrofreezing process of sessile droplet on a cooled substrate. The accuracy of the present LB method is first validated through performing some simulations of the three-phase Stefan problem, the droplet freezing on a cold wall, and the droplet deformation under a uniform electric field. Then it is used to investigate the effect of an electric field on the freezing of a wetting droplet on a cold substrate, and the numerical results show that the electric field has a significant influence on the freezing time of the droplet mainly through changing the morphology of the droplet. In particular, under the effect of the electric field, the freezing time is increased for the droplet with a prolate pattern, while the freezing time of the droplet with an oblate pattern is decreased. These numerical results bring some new insights on the electrofreezing and provide a valuable guidance for the precise regulation of droplet freezing., Comment: 19 pages, 14 figures
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- 2024
14. Hot electron lifetime exceeds 300 nanoseconds in quantum dots with high quantum efficiency
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Tang, Beibei, Li, Bo, Sun, Yingying, Li, Jianshun, Guo, Yanheng, Song, Jiaojiao, Yan, Xiaohan, Zhang, Huimin, Wang, Xiaosuo, Chen, Fei, Wang, Lei, Du, Jiangfeng, Shen, Huaibin, and Fan, Fengjia
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Condensed Matter - Mesoscale and Nanoscale Physics - Abstract
Hot electrons are theoretically predicted to be long-lived in strongly confined quantum dots, which could play vital roles in quantum dot-based optoelectronics; however, existing photoexcitation transient spectroscopy investigations reveal that their lifetime is less than 1 ps in well-passivated quantum dots because of the ultrafast electron-hole Auger-assisted cooling. Therefore, they are generally considered absent in quantum dot optoelectronic devices. Here, by using our newly developed electrically excited transient absorption spectroscopy, we surprisingly observed abundant hot electrons in both II-VI and III-VI compound quantum dot light-emitting diodes at elevated bias (>4 V), of which the lifetimes reach 59 to 371 ns, lengthened by more than 5 orders of magnitude compared with the photoexcited hot electrons. These results experimentally prove the presence of a strong phonon bottleneck effect, refreshing our understanding of the role of hot electrons in quantum dot optoelectronics.
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- 2024
15. MathHay: An Automated Benchmark for Long-Context Mathematical Reasoning in LLMs
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Wang, Lei, Dong, Shan, Xu, Yuhui, Dong, Hanze, Wang, Yalu, Saha, Amrita, Lim, Ee-Peng, Xiong, Caiming, and Sahoo, Doyen
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Computer Science - Computation and Language - Abstract
Recent large language models (LLMs) have demonstrated versatile capabilities in long-context scenarios. Although some recent benchmarks have been developed to evaluate the long-context capabilities of LLMs, there is a lack of benchmarks evaluating the mathematical reasoning abilities of LLMs over long contexts, which is crucial for LLMs' application in real-world scenarios. In this paper, we introduce MathHay, an automated benchmark designed to assess the long-context mathematical reasoning capabilities of LLMs. Unlike previous benchmarks like Needle in a Haystack, which focus primarily on information retrieval within long texts, MathHay demands models with both information-seeking and complex mathematical reasoning abilities. We conduct extensive experiments on MathHay to assess the long-context mathematical reasoning abilities of eight top-performing LLMs. Even the best-performing model, Gemini-1.5-Pro-002, still struggles with mathematical reasoning over long contexts, achieving only 51.26% accuracy at 128K tokens. This highlights the significant room for improvement on the MathHay benchmark., Comment: Work-in-Progress
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- 2024
16. GenSim: A General Social Simulation Platform with Large Language Model based Agents
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Tang, Jiakai, Gao, Heyang, Pan, Xuchen, Wang, Lei, Tan, Haoran, Gao, Dawei, Chen, Yushuo, Chen, Xu, Lin, Yankai, Li, Yaliang, Ding, Bolin, Zhou, Jingren, Wang, Jun, and Wen, Ji-Rong
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Computer Science - Multiagent Systems ,Computer Science - Artificial Intelligence - Abstract
With the rapid advancement of large language models (LLMs), recent years have witnessed many promising studies on leveraging LLM-based agents to simulate human social behavior. While prior work has demonstrated significant potential across various domains, much of it has focused on specific scenarios involving a limited number of agents and has lacked the ability to adapt when errors occur during simulation. To overcome these limitations, we propose a novel LLM-agent-based simulation platform called \textit{GenSim}, which: (1) \textbf{Abstracts a set of general functions} to simplify the simulation of customized social scenarios; (2) \textbf{Supports one hundred thousand agents} to better simulate large-scale populations in real-world contexts; (3) \textbf{Incorporates error-correction mechanisms} to ensure more reliable and long-term simulations. To evaluate our platform, we assess both the efficiency of large-scale agent simulations and the effectiveness of the error-correction mechanisms. To our knowledge, GenSim represents an initial step toward a general, large-scale, and correctable social simulation platform based on LLM agents, promising to further advance the field of social science.
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- 2024
17. Uncertainties of the dust grain size in protoplanetary disks retrieved from millimeter continuum observations
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Li, Dafa, Liu, Yao, Wang, Hongchi, Fang, Min, and Wang, Lei
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Astrophysics - Earth and Planetary Astrophysics - Abstract
Investigating the dust grain size and its dependence on substructures in protoplanetary disks is a crucial step in understanding the initial process of planet formation. Spectral indices derived from millimeter observations are used as a common probe for grain size. Converting observed spectral indices into grain sizes is a complex task that involves solving the radiative transfer equation, taking into account the disk structure and dust properties. In this work, we ran reference radiative transfer models with known disk properties, and generated four synthetic images at wavelengths of 0.8, 1.3, 3, and 7.8 mm, representing high-resolution continuum observations. Rings and gaps were considered in the setup. We fit the synthetic images using the analytic solution of the radiative transfer equation to investigate the circumstances under which the input grain sizes can be recovered. The results show that fitting images at only two wavelengths is not sufficient to retrieve the grain size. Fitting three images improves the retrieval of grain size, but the dust surface density is still not well recovered. When taking all of the four images into account, degeneracies between different parameters are highly reduced, and consequently the best-fit grain sizes are consistent with the reference setup at almost all radii. We find that the inclination angle has a significant impact on the fitting results. For disks with low inclinations, the analytic approach works quite well. However, when the disk is tilted above about 60 degree, neither the grain size nor the dust surface density can be constrained, as the inclination effect will smooth out all substructures in the radial intensity profile of the disk., Comment: 10 pages, 9 figures, Published in the journal of A&A (ref. 2024, A&A, 688, A204)
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- 2024
18. LEGO: Learnable Expansion of Graph Operators for Multi-Modal Feature Fusion
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Ding, Dexuan, Wang, Lei, Zhu, Liyun, Gedeon, Tom, and Koniusz, Piotr
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
In computer vision tasks, features often come from diverse representations, domains, and modalities, such as text, images, and videos. Effectively fusing these features is essential for robust performance, especially with the availability of powerful pre-trained models like vision-language models. However, common fusion methods, such as concatenation, element-wise operations, and non-linear techniques, often fail to capture structural relationships, deep feature interactions, and suffer from inefficiency or misalignment of features across domains. In this paper, we shift from high-dimensional feature space to a lower-dimensional, interpretable graph space by constructing similarity graphs that encode feature relationships at different levels, e.g., clip, frame, patch, token, etc. To capture deeper interactions, we use graph power expansions and introduce a learnable graph fusion operator to combine these graph powers for more effective fusion. Our approach is relationship-centric, operates in a homogeneous space, and is mathematically principled, resembling element-wise similarity score aggregation via multilinear polynomials. We demonstrate the effectiveness of our graph-based fusion method on video anomaly detection, showing strong performance across multi-representational, multi-modal, and multi-domain feature fusion tasks., Comment: Research paper
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- 2024
19. EEG Emotion Copilot: Pruning LLMs for Emotional EEG Interpretation with Assisted Medical Record Generation
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Chen, Hongyu, Zeng, Weiming, Chen, Chengcheng, Cai, Luhui, Wang, Fei, Wang, Lei, Zhang, Wei, Li, Yueyang, Yan, Hongjie, Siok, Wai Ting, and Wang, Nizhuan
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Computer Science - Computer Vision and Pattern Recognition - Abstract
In the fields of affective computing (AC) and brain-machine interface (BMI), the analysis of physiological and behavioral signals to discern individual emotional states has emerged as a critical research frontier. While deep learning-based approaches have made notable strides in EEG emotion recognition, particularly in feature extraction and pattern recognition, significant challenges persist in achieving end-to-end emotion computation, including real-time processing, individual adaptation, and seamless user interaction. This paper presents the EEG Emotion Copilot, a system leveraging a lightweight large language model (LLM) operating in a local setting. The system is designed to first recognize emotional states directly from EEG signals, subsequently generate personalized diagnostic and treatment suggestions, and finally support the automation of electronic medical records. The proposed solution emphasizes both the accuracy of emotion recognition and an enhanced user experience, facilitated by an intuitive interface for participant interaction. We further discuss the construction of the data framework, model pruning, training, and deployment strategies aimed at improving real-time performance and computational efficiency. Privacy concerns are also addressed, with a focus on ethical data collection, processing, and the protection of users' personal information. Through these efforts, we aim to advance the application of AC in the medical domain, offering innovative approaches to mental health diagnostics and treatment., Comment: 8 pages, 9 figures
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- 2024
20. A novel brain registration model combining structural and functional MRI information
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Li, Baolong, Shi, Yuhu, Wang, Lei, Zeng, Weiming, and Zhu, Changming
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Electrical Engineering and Systems Science - Image and Video Processing - Abstract
Although developed functional magnetic resonance imaging (fMRI) registration algorithms based on deep learning have achieved a certain degree of alignment of functional area, they underutilized fine structural information. In this paper, we propose a semi-supervised convolutional neural network (CNN) registration model that integrates both structural and functional MRI information. The model first learns to generate deformation fields by inputting structural MRI (T1w-MRI) into the CNN to capture fine structural information. Then, we construct a local functional connectivity pattern to describe the local fMRI information, and use the Bhattacharyya coefficient to measure the similarity between two fMRI images, which is used as a loss function to facilitate the alignment of functional areas. In the inter-subject registration experiment, our model achieved an average number of voxels exceeding the threshold of 4.24 is 2248 in the group-level t-test maps for the four functional brain networks (default mode network, visual network, central executive network, and sensorimotor network). Additionally, the atlas-based registration experiment results show that the average number of voxels exceeding this threshold is 3620. The results are the largest among all methods. Our model achieves an excellent registration performance in fMRI and improves the consistency of functional regions. The proposed model has the potential to optimize fMRI image processing and analysis, facilitating the development of fMRI applications.
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- 2024
21. Fast Extrinsic Calibration for Multiple Inertial Measurement Units in Visual-Inertial System
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Yu, Youwei, Liu, Yanqing, Fu, Fengjie, He, Sihan, Zhu, Dongchen, Wang, Lei, Zhang, Xiaolin, and Li, Jiamao
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Computer Science - Robotics - Abstract
In this paper, we propose a fast extrinsic calibration method for fusing multiple inertial measurement units (MIMU) to improve visual-inertial odometry (VIO) localization accuracy. Currently, data fusion algorithms for MIMU highly depend on the number of inertial sensors. Based on the assumption that extrinsic parameters between inertial sensors are perfectly calibrated, the fusion algorithm provides better localization accuracy with more IMUs, while neglecting the effect of extrinsic calibration error. Our method builds two non-linear least-squares problems to estimate the MIMU relative position and orientation separately, independent of external sensors and inertial noises online estimation. Then we give the general form of the virtual IMU (VIMU) method and propose its propagation on manifold. We perform our method on datasets, our self-made sensor board, and board with different IMUs, validating the superiority of our method over competing methods concerning speed, accuracy, and robustness. In the simulation experiment, we show that only fusing two IMUs with our calibration method to predict motion can rival nine IMUs. Real-world experiments demonstrate better localization accuracy of the VIO integrated with our calibration method and VIMU propagation on manifold.
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- 2024
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22. TrackNetV4: Enhancing Fast Sports Object Tracking with Motion Attention Maps
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Raj, Arjun, Wang, Lei, and Gedeon, Tom
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence ,Computer Science - Machine Learning - Abstract
Accurately detecting and tracking high-speed, small objects, such as balls in sports videos, is challenging due to factors like motion blur and occlusion. Although recent deep learning frameworks like TrackNetV1, V2, and V3 have advanced tennis ball and shuttlecock tracking, they often struggle in scenarios with partial occlusion or low visibility. This is primarily because these models rely heavily on visual features without explicitly incorporating motion information, which is crucial for precise tracking and trajectory prediction. In this paper, we introduce an enhancement to the TrackNet family by fusing high-level visual features with learnable motion attention maps through a motion-aware fusion mechanism, effectively emphasizing the moving ball's location and improving tracking performance. Our approach leverages frame differencing maps, modulated by a motion prompt layer, to highlight key motion regions over time. Experimental results on the tennis ball and shuttlecock datasets show that our method enhances the tracking performance of both TrackNetV2 and V3. We refer to our lightweight, plug-and-play solution, built on top of the existing TrackNet, as TrackNetV4., Comment: Research report
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- 2024
23. Hypersphere Secure Sketch Revisited: Probabilistic Linear Regression Attack on IronMask in Multiple Usage
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Zhu, Pengxu and Wang, Lei
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Computer Science - Cryptography and Security ,Computer Science - Computer Vision and Pattern Recognition - Abstract
Protection of biometric templates is a critical and urgent area of focus. IronMask demonstrates outstanding recognition performance while protecting facial templates against existing known attacks. In high-level, IronMask can be conceptualized as a fuzzy commitment scheme building on the hypersphere directly. We devise an attack on IronMask targeting on the security notion of renewability. Our attack, termed as Probabilistic Linear Regression Attack, utilizes the linearity of underlying used error correcting code. This attack is the first algorithm to successfully recover the original template when getting multiple protected templates in acceptable time and requirement of storage. We implement experiments on IronMask applied to protect ArcFace that well verify the validity of our attacks. Furthermore, we carry out experiments in noisy environments and confirm that our attacks are still applicable. Finally, we put forward two strategies to mitigate this type of attacks.
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- 2024
24. Analytic two-Loop four-point form factor of the stress-tensor supermultiplet in ${\cal N}=4$ SYM
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Guo, Yuanhong, Wang, Lei, Yang, Gang, and Yin, YiXiong
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High Energy Physics - Theory ,High Energy Physics - Phenomenology - Abstract
We compute the two-loop four-point MHV form factor of the stress-tensor supermultiplet in planar ${\cal N}=4$ super Yang-Mills (SYM). This form factor is analogous to the Higgs plus four-gluon amplitudes in the heavy-top limit of QCD when translated to the ${\cal N}=4$ SYM context. We obtain the full $D$-dimensional integrands up to two loops via unitarity-cut methods. Subsequently, we utilize IBP reduction to express the result in terms of a set of uniformly transcendental basis integrals, incorporating the two-loop non-planar five-point one-mass integrals recently given by Abreu et al. [PRL 132 (2024) 14]. We obtain the two-loop finite remainder in the functional form in terms of the pentagon functions. The symbol of our remainder confirms the bootstrap results reported by Dixon et al. [PRL 130 (2023) 11]. We perform various non-trivial checks of our results, including the triple-collinear limit, which recovers the two-loop six-gluon remainder. We also show that the form factor has a directional dual conformal symmetry at the integrand level. Our results are expected to shed further light on the study of antipodal dualities and the computation of Higgs plus four-parton amplitudes in QCD., Comment: 48 pages, 14 figures; v2: references added, ancillary file "2_UT_Masters_Definition.m" updated to align with FF conventions
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- 2024
25. SpMis: An Investigation of Synthetic Spoken Misinformation Detection
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Liu, Peizhuo, Wang, Li, He, Renqiang, He, Haorui, Wang, Lei, Zheng, Huadi, Shi, Jie, Xiao, Tong, and Wu, Zhizheng
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Computer Science - Computation and Language - Abstract
In recent years, speech generation technology has advanced rapidly, fueled by generative models and large-scale training techniques. While these developments have enabled the production of high-quality synthetic speech, they have also raised concerns about the misuse of this technology, particularly for generating synthetic misinformation. Current research primarily focuses on distinguishing machine-generated speech from human-produced speech, but the more urgent challenge is detecting misinformation within spoken content. This task requires a thorough analysis of factors such as speaker identity, topic, and synthesis. To address this need, we conduct an initial investigation into synthetic spoken misinformation detection by introducing an open-source dataset, SpMis. SpMis includes speech synthesized from over 1,000 speakers across five common topics, utilizing state-of-the-art text-to-speech systems. Although our results show promising detection capabilities, they also reveal substantial challenges for practical implementation, underscoring the importance of ongoing research in this critical area., Comment: Accepted in SLT 2024
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- 2024
26. Crosscap states and duality of Ising field theory in two dimensions
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Zhang, Yueshui, Wu, Ying-Hai, Wang, Lei, and Tu, Hong-Hao
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Condensed Matter - Strongly Correlated Electrons ,Condensed Matter - Statistical Mechanics ,High Energy Physics - Theory ,Quantum Physics - Abstract
We propose two distinct crosscap states for the two-dimensional (2D) Ising field theory. These two crosscap states, identifying Ising spins or dual spins (domain walls) at antipodal points, are shown to be related via the Kramers-Wannier duality transformation. We derive their Majorana free field representations and extend bosonization techniques to calculate correlation functions of the 2D Ising conformal field theory (CFT) with different crosscap boundaries. We further develop a conformal perturbation theory to calculate the Klein bottle entropy as a universal scaling function [Phys. Rev. Lett. 130, 151602 (2023)] in the 2D Ising field theory. The formalism developed in this work is applicable to many other 2D CFTs perturbed by relevant operators., Comment: 6+30 pages, 1+2 figures
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- 2024
27. KARGEN: Knowledge-enhanced Automated Radiology Report Generation Using Large Language Models
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Li, Yingshu, Wang, Zhanyu, Liu, Yunyi, Wang, Lei, Liu, Lingqiao, and Zhou, Luping
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Artificial Intelligence - Abstract
Harnessing the robust capabilities of Large Language Models (LLMs) for narrative generation, logical reasoning, and common-sense knowledge integration, this study delves into utilizing LLMs to enhance automated radiology report generation (R2Gen). Despite the wealth of knowledge within LLMs, efficiently triggering relevant knowledge within these large models for specific tasks like R2Gen poses a critical research challenge. This paper presents KARGEN, a Knowledge-enhanced Automated radiology Report GENeration framework based on LLMs. Utilizing a frozen LLM to generate reports, the framework integrates a knowledge graph to unlock chest disease-related knowledge within the LLM to enhance the clinical utility of generated reports. This is achieved by leveraging the knowledge graph to distill disease-related features in a designed way. Since a radiology report encompasses both normal and disease-related findings, the extracted graph-enhanced disease-related features are integrated with regional image features, attending to both aspects. We explore two fusion methods to automatically prioritize and select the most relevant features. The fused features are employed by LLM to generate reports that are more sensitive to diseases and of improved quality. Our approach demonstrates promising results on the MIMIC-CXR and IU-Xray datasets.
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- 2024
28. A Double Tracking Method for Optimization with Decentralized Generalized Orthogonality Constraints
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Wang, Lei, Xiao, Nachuan, and Liu, Xin
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Mathematics - Optimization and Control ,Computer Science - Distributed, Parallel, and Cluster Computing ,Statistics - Machine Learning - Abstract
In this paper, we consider the decentralized optimization problems with generalized orthogonality constraints, where both the objective function and the constraint exhibit a distributed structure. Such optimization problems, albeit ubiquitous in practical applications, remain unsolvable by existing algorithms in the presence of distributed constraints. To address this issue, we convert the original problem into an unconstrained penalty model by resorting to the recently proposed constraint-dissolving operator. However, this transformation compromises the essential property of separability in the resulting penalty function, rendering it impossible to employ existing algorithms to solve. We overcome this difficulty by introducing a novel algorithm that tracks the gradient of the objective function and the Jacobian of the constraint mapping simultaneously. The global convergence guarantee is rigorously established with an iteration complexity. To substantiate the effectiveness and efficiency of our proposed algorithm, we present numerical results on both synthetic and real-world datasets.
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- 2024
29. Rapid Automatic Multiple Moving Objects Detection Method Based on Feature Extraction from Images with Non-sidereal Tracking
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Wang, Lei, Zhang, Xiaoming, Bai, Chunhai, Xie, Haiwen, Li, Juan, Ge, Jiayi, Wang, Jianfeng, Zeng, Xianqun, Sun, Jiantao, and Jiang, Xiaojun
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Astrophysics - Instrumentation and Methods for Astrophysics - Abstract
Optically observing and monitoring moving objects, both natural and artificial, is important to human space security. Non-sidereal tracking can improve the system's limiting magnitude for moving objects, which benefits the surveillance. However, images with non-sidereal tracking include complex background, as well as objects with different brightness and moving mode, posing a significant challenge for accurate multi-object detection in such images, especially in wide field of view (WFOV) telescope images. To achieve a higher detection precision in a higher speed, we proposed a novel object detection method, which combines the source feature extraction and the neural network. First, our method extracts object features from optical images such as centroid, shape, and flux. Then it conducts a naive labeling based on those features to distinguish moving objects from stars. After balancing the labeled data, we employ it to train a neural network aimed at creating a classification model for point-like and streak-like objects. Ultimately, based on the neural network model's classification outcomes, moving objects whose motion modes consistent with the tracked objects are detected via track association, while objects with different motion modes are detected using morphological statistics. The validation, based on the space objects images captured in target tracking mode with the 1-meter telescope at Nanshan, Xinjiang Astronomical Observatory, demonstrates that our method achieves 94.72% detection accuracy with merely 5.02% false alarm rate, and a processing time of 0.66s per frame. Consequently, our method can rapidly and accurately detect objects with different motion modes from wide-field images with non-sidereal tracking.
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- 2024
30. Searching for MeV-scale Axion-like Particles and Dark Photons with PandaX-4T
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PandaX Collaboration, Li, Tao, Bo, Zihao, Chen, Wei, Chen, Xun, Chen, Yunhua, Cheng, Zhaokan, Cui, Xiangyi, Fan, Yingjie, Fang, Deqing, Gao, Zhixing, Geng, Lisheng, Giboni, Karl, Guo, Xunan, Guo, Xuyuan, Guo, Zichao, Han, Chencheng, He, Ke HanChangda, He, Jinrong, Huang, Di, Huang, Houqi, Huang, Junting, Hou, Ruquan, Hou, Yu, Ji, Xiangdong, Ji, Xiangpan, Ju, Yonglin, Li, Chenxiang, Li, Jiafu, Li, Mingchuan, Li, Shuaijie, Li, Zhiyuan, Lin, Qing, Liu, Jianglai, Lu, Congcong, Lu, Xiaoying, Luo, Lingyin, Luo, Yunyang, Ma, Wenbo, Ma, Yugang, Mao, Yajun, Meng, Yue, Ning, Xuyang, Pang, Binyu, Qi, Ningchun, Qian, Zhicheng, Ren, Xiangxiang, Shan, Dong, Shang, Xiaofeng, Shao, Xiyuan, Shen, Guofang, Shen, Manbin, Sun, Wenliang, Tao, Yi, Wang, Anqing, Wang, Guanbo, Wang, Hao, Wang, Jiamin, Wang, Lei, Wang, Meng, Wang, Qiuhong, Wang, Shaobo, Wang, Siguang, Wang, Wei, Wang, Xiuli, Wang, Xu, Wang, Zhou, Wei, Yuehuan, Wu, Weihao, Wu, Yuan, Xiao, Mengjiao, Xiao, Xiang, Xiong, Kaizhi, Xu, Yifan, Yao, Shunyu, Yan, Binbin, Yan, Xiyu, Yang, Yong, Ye, Peihua, Yu, Chunxu, Yuan, Ying, Yuan, Zhe, Yun, Youhui, Zeng, Xinning, Zhang, Minzhen, Zhang, Peng, Zhang, Shibo, Zhang, Shu, Zhang, Tao, Zhang, Wei, Zhang, Yang, Zhang, Yingxin, Zhang, Yuanyuan, Zhao, Li, Zhou, Jifang, Zhou, Jiaxu, Zhou, Jiayi, Zhou, Ning, Zhou, Xiaopeng, Zhou, Yubo, and Zhou, Zhizhen
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High Energy Physics - Experiment - Abstract
Axion-like particles (ALPs) and dark photons (DPs) are viable dark matter particle candidates. We have searched for possible ALP/DP signals in the PandaX-4T liquid xenon detector using 94.8 days of data. A binned likelihood fit is constructed to search for possible mono-energetic peaks induced by the absorption processes between ALPs/DPs and atomic electrons of xenon. A detailed temporal model of decays associated with xenon isotopes is introduced to constrain the number of background events. No signal excess over background expectations is observed, and we have established the most stringent exclusion limits for most ALP/DP masses ranging from 150 keV/$c^2$ to 1 MeV/$c^2$.
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- 2024
31. MoManifold: Learning to Measure 3D Human Motion via Decoupled Joint Acceleration Manifolds
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Dang, Ziqiang, Fan, Tianxing, Zhao, Boming, Shen, Xujie, Wang, Lei, Zhang, Guofeng, and Cui, Zhaopeng
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Incorporating temporal information effectively is important for accurate 3D human motion estimation and generation which have wide applications from human-computer interaction to AR/VR. In this paper, we present MoManifold, a novel human motion prior, which models plausible human motion in continuous high-dimensional motion space. Different from existing mathematical or VAE-based methods, our representation is designed based on the neural distance field, which makes human dynamics explicitly quantified to a score and thus can measure human motion plausibility. Specifically, we propose novel decoupled joint acceleration manifolds to model human dynamics from existing limited motion data. Moreover, we introduce a novel optimization method using the manifold distance as guidance, which facilitates a variety of motion-related tasks. Extensive experiments demonstrate that MoManifold outperforms existing SOTAs as a prior in several downstream tasks such as denoising real-world human mocap data, recovering human motion from partial 3D observations, mitigating jitters for SMPL-based pose estimators, and refining the results of motion in-betweening., Comment: Accepted by BMVC 2024. Supplementary material is included at the end of the main paper (12 pages, 11 figures, 5 tables)
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- 2024
32. Could Bibliometrics Reveal Top Science and Technology Achievements and Researchers? The Case for Evaluatology-based Science and Technology Evaluation
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Kang, Guoxin, Gao, Wanling, Wang, Lei, Luo, Chunjie, Ye, Hainan, He, Qian, Dai, Shaopeng, and Zhan, Jianfeng
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Computer Science - Computational Engineering, Finance, and Science ,Computer Science - Computers and Society - Abstract
By utilizing statistical methods to analyze bibliographic data, bibliometrics faces inherent limitations in identifying the most significant science and technology achievements and researchers. To overcome this challenge, we present an evaluatology-based science and technology evaluation methodology. At the heart of this approach lies the concept of an extended evaluation condition, encompassing eight crucial components derived from a field. We define four relationships that illustrate the connections among various achievements based on their mapped extended EC components, as well as their temporal and citation links. Within a relationship under an extended evaluation condition, evaluators can effectively compare these achievements by carefully addressing the influence of confounding variables. We establish a real-world evaluation system encompassing an entire collection of achievements, each of which is mapped to several components of an extended EC. Within a specific field like chip technology or open source, we construct a perfect evaluation model that can accurately trace the evolution and development of all achievements in terms of four relationships based on the real-world evaluation system. Building upon the foundation of the perfect evaluation model, we put forth four-round rules to eliminate non-significant achievements by utilizing four relationships. This process allows us to establish a pragmatic evaluation model that effectively captures the essential achievements, serving as a curated collection of the top N achievements within a specific field during a specific timeframe. We present a case study on the top 100 Chip achievements which highlights its practical application and efficacy in identifying significant achievements and researchers that otherwise can not be identified by using bibliometrics., Comment: 18 pages, 8 figures, and 2 tables
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- 2024
33. UNetMamba: An Efficient UNet-Like Mamba for Semantic Segmentation of High-Resolution Remote Sensing Images
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Zhu, Enze, Chen, Zhan, Wang, Dingkai, Shi, Hanru, Liu, Xiaoxuan, and Wang, Lei
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Semantic segmentation of high-resolution remote sensing images is vital in downstream applications such as land-cover mapping, urban planning and disaster assessment.Existing Transformer-based methods suffer from the constraint between accuracy and efficiency, while the recently proposed Mamba is renowned for being efficient. Therefore, to overcome the dilemma, we propose UNetMamba, a UNet-like semantic segmentation model based on Mamba. It incorporates a mamba segmentation decoder (MSD) that can efficiently decode the complex information within high-resolution images, and a local supervision module (LSM), which is train-only but can significantly enhance the perception of local contents. Extensive experiments demonstrate that UNetMamba outperforms the state-of-the-art methods with mIoU increased by 0.87% on LoveDA and 0.39% on ISPRS Vaihingen, while achieving high efficiency through the lightweight design, less memory footprint and reduced computational cost. The source code is available at https://github.com/EnzeZhu2001/UNetMamba., Comment: 5 pages, 3 figures
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- 2024
34. Spatio-Temporal Communication Compression for Distributed Prime-Dual Optimization
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Ren, Zihao, Wang, Lei, Yuan, Deming, Su, Hongye, and Shi, Guodong
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Electrical Engineering and Systems Science - Systems and Control - Abstract
In this paper, for the problem of distributed computing, we propose a general spatio-temporal compressor and discuss its compression methods. This compressor comprehensively considers both temporal and spatial information, encompassing many existing specific compressors. We use the average consensus algorithm as a starting point and further studies distributed optimization algorithms, the Prime-Dual algorithm as an example, in both continuous and discrete time forms. We find that under stronger additional assumptions, the spatio-temporal compressor can be directly applied to distributed computing algorithms, while its default form can also be successfully applied through observer-based differential compression methods, ensuring the linear convergence of the algorithm when the objective function is strongly convex. On this basis, we also discuss the acceleration of the algorithm, filter-based compression methods in the literature, and the addition of randomness to the spatio-temporal compressor. Finally, numerical simulations illustrate the generality of the spatio-temporal compressor, compare different compression methods, and verify the algorithm's performance in the convex objective function scenario., Comment: 21 pages. arXiv admin note: text overlap with arXiv:2408.02332
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- 2024
35. Exploring New Physics with PandaX-4T Low Energy Electronic Recoil Data
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PandaX Collaboration, Zeng, Xinning, Bo, Zihao, Chen, Wei, Chen, Xun, Chen, Yunhua, Cheng, Zhaokan, Cui, Xiangyi, Fan, Yingjie, Fang, Deqing, Gao, Zhixing, Geng, Lisheng, Giboni, Karl, Guo, Xunan, Guo, Xuyuan, Guo, Zichao, Han, Chencheng, He, Ke HanChangda, He, Jinrong, Huang, Di, Huang, Houqi, Huang, Junting, Hou, Ruquan, Hou, Yu, Ji, Xiangdong, Ji, Xiangpan, Ju, Yonglin, Li, Chenxiang, Li, Jiafu, Li, Mingchuan, Li, Shuaijie, Li, Tao, Li, Zhiyuan, Lin, Qing, Liu, Jianglai, Lu, Congcong, Lu, Xiaoying, Luo, Lingyin, Luo, Yunyang, Ma, Wenbo, Ma, Yugang, Mao, Yajun, Meng, Yue, Ning, Xuyang, Pang, Binyu, Qi, Ningchun, Qian, Zhicheng, Ren, Xiangxiang, Shan, Dong, Shang, Xiaofeng, Shao, Xiyuan, Shen, Guofang, Shen, Manbin, Sun, Wenliang, Tao, Yi, Wang, Anqing, Wang, Guanbo, Wang, Hao, Wang, Jiamin, Wang, Lei, Wang, Meng, Wang, Qiuhong, Wang, Shaobo, Wang, Siguang, Wang, Wei, Wang, Xiuli, Wang, Xu, Wang, Zhou, Wei, Yuehuan, Wu, Weihao, Wu, Yuan, Xiao, Mengjiao, Xiao, Xiang, Xiong, Kaizhi, Xu, Yifan, Yao, Shunyu, Yan, Binbin, Yan, Xiyu, Yang, Yong, Ye, Peihua, Yu, Chunxu, Yuan, Ying, Yuan, Zhe, Yun, Youhui, Zhang, Minzhen, Zhang, Peng, Zhang, Shibo, Zhang, Shu, Zhang, Tao, Zhang, Wei, Zhang, Yang, Zhang, Yingxin, Zhang, Yuanyuan, Zhao, Li, Zhou, Jifang, Zhou, Jiaxu, Zhou, Jiayi, Zhou, Ning, Zhou, Xiaopeng, Zhou, Yubo, and Zhou, Zhizhen
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High Energy Physics - Experiment - Abstract
New particles beyond the Standard Model of particle physics, such as axions, can be effectively searched through their interactions with electrons. We use the large liquid xenon detector PandaX-4T to search for novel electronic recoil signals induced by solar axions, neutrinos with anomalous magnetic moment, axion-like particles, dark photons, and light fermionic dark matter. A detailed background model is established with the latest datasets with 1.54 $\rm tonne \cdot year$ exposure. No significant excess above the background has been observed, and we have obtained competitive constraints for axion couplings, neutrino magnetic moment, and fermionic dark matter interactions.
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- 2024
36. MatterGPT: A Generative Transformer for Multi-Property Inverse Design of Solid-State Materials
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Chen, Yan, Wang, Xueru, Deng, Xiaobin, Liu, Yilun, Chen, Xi, Zhang, Yunwei, Wang, Lei, and Xiao, Hang
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Condensed Matter - Materials Science ,Physics - Computational Physics - Abstract
Inverse design of solid-state materials with desired properties represents a formidable challenge in materials science. Although recent generative models have demonstrated potential, their adoption has been hindered by limitations such as inefficiency, architectural constraints and restricted open-source availability. The representation of crystal structures using the SLICES (Simplified Line-Input Crystal-Encoding System) notation as a string of characters enables the use of state-of-the-art natural language processing models, such as Transformers, for crystal design. Drawing inspiration from the success of GPT models in generating coherent text, we trained a generative Transformer on the next-token prediction task to generate solid-state materials with targeted properties. We demonstrate MatterGPT's capability to generate de novo crystal structures with targeted single properties, including both lattice-insensitive (formation energy) and lattice-sensitive (band gap) properties. Furthermore, we extend MatterGPT to simultaneously target multiple properties, addressing the complex challenge of multi-objective inverse design of crystals. Our approach showcases high validity, uniqueness, and novelty in generated structures, as well as the ability to generate materials with properties beyond the training data distribution. This work represents a significant step forward in computational materials discovery, offering a powerful and open tool for designing materials with tailored properties for various applications in energy, electronics, and beyond., Comment: 20 pages, 6 figures
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- 2024
37. Probing a light long-lived pseudo-scalar from Higgs decay via displaced taus at the LHC
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Shan, Lianyou, Wang, Lei, Yang, Jin Min, and Zhu, Rui
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High Energy Physics - Phenomenology - Abstract
A light (GeV mass) long-lived ($c\tau$ around dozens of millimeters) CP-odd scalar can be readily predicted in new physics models. In this work we investigate the Higgs decay into such a light scalar plus a $Z$-boson and take the aligned two-Higgs-doublet model (2HDM) as an example. This light long-lived scalar, with the dominant decay to tau leptons, will fly over a distance from the production point and present a displaced vertex in an Inner Detector of a generally purposed experiment like ATLAS or CMS. In our study we focus on the LHC experiment and perform Monte Carlo simulations for the signal and backgrounds. We demonstrate some benchmark points for the aligned 2HDM and find the signal to be detectable when the luminosity is accumulated to 300 fb$^{-1}$. So our study suggests an experimental search for this process in the ongoing LHC., Comment: 18 pages, 6 figures
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- 2024
38. Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning
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Bian, Jieming, Wang, Lei, and Xu, Jie
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Computer Science - Machine Learning ,Computer Science - Distributed, Parallel, and Cluster Computing - Abstract
Federated Learning (FL) is a distributed machine learning approach that enables devices to collaboratively train models without sharing their local data, ensuring user privacy and scalability. However, applying FL to real-world data presents challenges, particularly as most existing FL research focuses on unimodal data. Multimodal Federated Learning (MFL) has emerged to address these challenges, leveraging modality-specific encoder models to process diverse datasets. Current MFL methods often uniformly allocate computational frequencies across all modalities, which is inefficient for IoT devices with limited resources. In this paper, we propose FlexMod, a novel approach to enhance computational efficiency in MFL by adaptively allocating training resources for each modality encoder based on their importance and training requirements. We employ prototype learning to assess the quality of modality encoders, use Shapley values to quantify the importance of each modality, and adopt the Deep Deterministic Policy Gradient (DDPG) method from deep reinforcement learning to optimize the allocation of training resources. Our method prioritizes critical modalities, optimizing model performance and resource utilization. Experimental results on three real-world datasets demonstrate that our proposed method significantly improves the performance of MFL models., Comment: Submitted to IEEE TMC, under review
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- 2024
39. LUT Tensor Core: Lookup Table Enables Efficient Low-Bit LLM Inference Acceleration
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Mo, Zhiwen, Wang, Lei, Wei, Jianyu, Zeng, Zhichen, Cao, Shijie, Ma, Lingxiao, Jing, Naifeng, Cao, Ting, Xue, Jilong, Yang, Fan, and Yang, Mao
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Computer Science - Hardware Architecture ,Computer Science - Machine Learning - Abstract
As large language model (LLM) inference demands ever-greater resources, there is a rapid growing trend of using low-bit weights to shrink memory usage and boost inference efficiency. However, these low-bit LLMs introduce the need for mixed-precision matrix multiplication (mpGEMM), which is a crucial yet under-explored operation that involves multiplying lower-precision weights with higher-precision activations. Unfortunately, current hardware does not natively support mpGEMM, resulting in indirect and inefficient dequantization-based implementations. To address the mpGEMM requirements in low-bit LLMs, we explored the lookup table (LUT)-based approach for mpGEMM. However, a conventional LUT implementation falls short of its potential. To fully harness the power of LUT-based mpGEMM, we introduce LUT Tensor Core, a software-hardware co-design optimized for low-bit LLM inference. Specifically, we introduce software-based operator fusion and table symmetrization techniques to optimize table precompute and table storage, respectively. Then, LUT Tensor Core proposes the hardware design featuring an elongated tiling shape design to enhance table reuse and a bit-serial design to support various precision combinations in mpGEMM. Moreover, we design an end-to-end compilation stack with new instructions for LUT-based mpGEMM, enabling efficient LLM compilation and optimizations. The evaluation on low-bit LLMs (e.g., BitNet, LLAMA) shows that LUT Tensor Core achieves more than a magnitude of improvements on both compute density and energy efficiency.
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- 2024
40. A 95 GeV Higgs boson and spontaneous CP-violation at the finite temperature
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Gao, Jing, Ma, Jinghong, Wang, Lei, and Xu, Haotian
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High Energy Physics - Phenomenology - Abstract
The ATLAS and CMS collaborations reported a diphoton excess in the invariant mass distribution around the 95.4 GeV with a local significance of $3.1\sigma$. Moreover, there is another $2.3\sigma$ local excess in the $b\bar{b}$ final state at LEP in the same mass region. A plausible solution is that the Higgs sector is extended to include an additional Higgs boson with a mass of $95.4$ GeV. We study a complex singlet scalar extension of the two-Higgs-doublet model in which the 95.4 GeV Higgs is from the mixing of three CP-even Higgs fields. In addition, the extended Higgs potential can achieve spontaneous CP-violation at the finite temperature and restore CP symmetry at the present temperature of the Universe. We find that the model can simultaneously explain the baryon asymmetry of the Universe, the diphoton and $b\bar{b}$ excesses around the 95.4 GeV while satisfying various relevant constraints including the experiments of collider and electric dipole moment., Comment: 28 pages, 8 figures, 1 tables, add references. arXiv admin note: text overlap with arXiv:2311.02828
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- 2024
41. MMRole: A Comprehensive Framework for Developing and Evaluating Multimodal Role-Playing Agents
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Dai, Yanqi, Hu, Huanran, Wang, Lei, Jin, Shengjie, Chen, Xu, and Lu, Zhiwu
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Computer Science - Artificial Intelligence - Abstract
Recently, Role-Playing Agents (RPAs) have garnered increasing attention for their potential to deliver emotional value and facilitate sociological research. However, existing studies are primarily confined to the textual modality, unable to simulate humans' multimodal perceptual capabilities. To bridge this gap, we introduce the concept of Multimodal Role-Playing Agents (MRPAs), and propose a comprehensive framework, MMRole, for their development and evaluation, which comprises a personalized multimodal dataset and a robust evaluation method. Specifically, we construct a large-scale, high-quality dataset, MMRole-Data, consisting of 85 characters, 11K images, and 14K single or multi-turn dialogues. Additionally, we present a robust evaluation method, MMRole-Eval, encompassing eight metrics across three dimensions, where a reward model is trained to score MRPAs with the constructed ground-truth data for comparison. Moreover, we develop the first specialized MRPA, MMRole-Agent. Extensive evaluation results demonstrate the improved performance of MMRole-Agent and highlight the primary challenges in developing MRPAs, emphasizing the need for enhanced multimodal understanding and role-playing consistency. The data, code, and models will be available at https://github.com/YanqiDai/MMRole.
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- 2024
42. Spatio-Temporal Communication Compression in Distributed Prime-Dual Flows
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Ren, Zihao, Wang, Lei, Yuan, Deming, Su, Hongye, and Shi, Guodong
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Electrical Engineering and Systems Science - Systems and Control - Abstract
In this paper, we study distributed prime-dual flows for multi-agent optimization with spatio-temporal compressions. The central aim of multi-agent optimization is for a network of agents to collaboratively solve a system-level optimization problem with local objective functions and node-to-node communication by distributed algorithms. The scalability of such algorithms crucially depends on the complexity of the communication messages, and a number of communication compressors for distributed optimization have recently been proposed in the literature. First of all, we introduce a general spatio-temporal compressor characterized by the stability of the resulting dynamical system along the vector field of the compressor. We show that several important distributed optimization compressors such as the greedy sparsifier, the uniform quantizer, and the scalarizer all fall into the category of this spatio-temporal compressor. Next, we propose two distributed prime-dual flows with the spatio-temporal compressors being applied to local node states and local error states, respectively, and prove (exponential) convergence of the node trajectories to the global optimizer for (strongly) convex cost functions. Finally, a few numerical examples are present to illustrate our theoretical results.
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- 2024
43. Dark Matter Search Results from 1.54 Tonne$\cdot$Year Exposure of PandaX-4T
- Author
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PandaX Collaboration, Bo, Zihao, Chen, Wei, Chen, Xun, Chen, Yunhua, Cheng, Zhaokan, Cui, Xiangyi, Fan, Yingjie, Fang, Deqing, Gao, Zhixing, Geng, Lisheng, Giboni, Karl, Guo, Xunan, Guo, Xuyuan, Guo, Zichao, Han, Chencheng, Han, Ke, He, Changda, He, Jinrong, Huang, Di, Huang, Houqi, Huang, Junting, Hou, Ruquan, Hou, Yu, Ji, Xiangdong, Ji, Xiangpan, Ju, Yonglin, Li, Chenxiang, Li, Jiafu, Li, Mingchuan, Li, Shuaijie, Li, Tao, Li, Zhiyuan, Lin, Qing, Liu, Jianglai, Lu, Congcong, Lu, Xiaoying, Luo, Lingyin, Luo, Yunyang, Ma, Wenbo, Ma, Yugang, Mao, Yajun, Meng, Yue, Ning, Xuyang, Pang, Binyu, Qi, Ningchun, Qian, Zhicheng, Ren, Xiangxiang, Shan, Dong, Shang, Xiaofeng, Shao, Xiyuan, Shen, Guofang, Shen, Manbin, Sun, Wenliang, Tao, Yi, Wang, Anqing, Wang, Guanbo, Wang, Hao, Wang, Jiamin, Wang, Lei, Wang, Meng, Wang, Qiuhong, Wang, Shaobo, Wang, Siguang, Wang, Wei, Wang, Xiuli, Wang, Xu, Wang, Zhou, Wei, Yuehuan, Wu, Weihao, Wu, Yuan, Xiao, Mengjiao, Xiao, Xiang, Xiong, Kaizhi, Xu, Yifan, Yao, Shunyu, Yan, Binbin, Yan, Xiyu, Yang, Yong, Ye, Peihua, Yu, Chunxu, Yuan, Ying, Yuan, Zhe, Yun, Youhui, Zeng, Xinning, Zhang, Minzhen, Zhang, Peng, Zhang, Shibo, Zhang, Shu, Zhang, Tao, Zhang, Wei, Zhang, Yang, Zhang, Yingxin, Zhang, Yuanyuan, Zhao, Li, Zhou, Jifang, Zhou, Jiaxu, Zhou, Jiayi, Zhou, Ning, Zhou, Xiaopeng, Zhou, Yubo, and Zhou, Zhizhen
- Subjects
High Energy Physics - Experiment - Abstract
In this letter, we report the dark matter search results from the commissioning run and the first science run of the PandaX-4T experiment. A blind analysis is carried out on the entire data set. The data processing is improved compared to previous work, unifying the low-level signal reconstruction in a wide energy range up to 120 keV. With a total exposure of 1.54 tonne$\cdot$year, no significant excess of nuclear recoil events is found. The lowest 90% confidence level exclusion on the spin-independent cross section is $1.6 \times 10^{-47} \mathrm{cm}^2$ at a dark matter mass of 40 GeV$/c^2$. Our results represent the most stringent constraint for a dark matter mass above 100 GeV$/c^2$.
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- 2024
44. RoCo:Robust Collaborative Perception By Iterative Object Matching and Pose Adjustment
- Author
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Huang, Zhe, Wang, Shuo, Wang, Yongcai, Li, Wanting, Li, Deying, and Wang, Lei
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Computer Science - Artificial Intelligence - Abstract
Collaborative autonomous driving with multiple vehicles usually requires the data fusion from multiple modalities. To ensure effective fusion, the data from each individual modality shall maintain a reasonably high quality. However, in collaborative perception, the quality of object detection based on a modality is highly sensitive to the relative pose errors among the agents. It leads to feature misalignment and significantly reduces collaborative performance. To address this issue, we propose RoCo, a novel unsupervised framework to conduct iterative object matching and agent pose adjustment. To the best of our knowledge, our work is the first to model the pose correction problem in collaborative perception as an object matching task, which reliably associates common objects detected by different agents. On top of this, we propose a graph optimization process to adjust the agent poses by minimizing the alignment errors of the associated objects, and the object matching is re-done based on the adjusted agent poses. This process is carried out iteratively until convergence. Experimental study on both simulated and real-world datasets demonstrates that the proposed framework RoCo consistently outperforms existing relevant methods in terms of the collaborative object detection performance, and exhibits highly desired robustness when the pose information of agents is with high-level noise. Ablation studies are also provided to show the impact of its key parameters and components. The code is released at https://github.com/HuangZhe885/RoCo., Comment: ACM MM2024
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- 2024
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45. Long-distance distribution of telecom time-energy entanglement generated on a silicon chip
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Zhao, Yuan-yuan, Yue, Fuyong, Gao, Feng, Wang, Qibing, Li, Chao, Liu, Zichen, Wang, Lei, and He, Zhixue
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Quantum Physics ,Physics - Optics - Abstract
Entanglement distribution is a critical technique that enables numerous quantum applications. Most fiber-based long-distance experiments reported to date have utilized photon pair sources generated in bulk optical crystals, with the entanglement encoded in the polarization degree of freedom. Here, we create time-energy entanglement for photon pairs generated from an on-chip silicon ring resonator via SFWM process and report the distribution of the entanglement over standard optical fiber with distance >81 km. Our work paves the way for future large-scale quantum networks with connect of distant quantum nodes., Comment: 8 pages, 4 figures
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- 2024
46. ThinK: Thinner Key Cache by Query-Driven Pruning
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Xu, Yuhui, Jie, Zhanming, Dong, Hanze, Wang, Lei, Lu, Xudong, Zhou, Aojun, Saha, Amrita, Xiong, Caiming, and Sahoo, Doyen
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence - Abstract
Large Language Models (LLMs) have revolutionized the field of natural language processing, achieving unprecedented performance across a variety of applications. However, their increased computational and memory demands present significant challenges, especially when handling long sequences. This paper focuses on the long-context scenario, addressing the inefficiencies in KV cache memory consumption during inference. Unlike existing approaches that optimize the memory based on the sequence length, we identify substantial redundancy in the channel dimension of the KV cache, as indicated by an uneven magnitude distribution and a low-rank structure in the attention weights. In response, we propose ThinK, a novel query-dependent KV cache pruning method designed to minimize attention weight loss while selectively pruning the least significant channels. Our approach not only maintains or enhances model accuracy but also achieves a reduction in KV cache memory costs by over 20% compared with vanilla KV cache eviction and quantization methods. For instance, ThinK integrated with KIVI can achieve a 2.8x reduction in peak memory usage while maintaining nearly the same quality, enabling up to a 5x increase in batch size when using a single GPU. Extensive evaluations on the LLaMA and Mistral models across various long-sequence datasets verified the efficiency of ThinK, establishing a new baseline algorithm for efficient LLM deployment without compromising performance., Comment: 20 pages, 6 figures
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- 2024
47. Distributed Adaptive Time-Varying Optimization with Global Asymptotic Convergence
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Jiang, Liangze, Wu, Zheng-Guang, and Wang, Lei
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Electrical Engineering and Systems Science - Systems and Control ,Mathematics - Optimization and Control - Abstract
In this note, we study distributed time-varying optimization for a multi-agent system. We first focus on a class of time-varying quadratic cost functions, and develop a new distributed algorithm that integrates an average estimator and an adaptive optimizer, with both bridged by a Dead Zone Algorithm. Based on a composite Lyapunov function and finite escape-time analysis, we prove the closed-loop global asymptotic convergence to the optimal solution under mild assumptions. Particularly, the introduction of the estimator relaxes the requirement for the Hessians of cost functions, and the integrated design eliminates the waiting time required in the relevant literature for estimating global parameter during algorithm implementation. We then extend this result to a more general class of time-varying cost functions. Two examples are used to verify the proposed designs., Comment: 11 pages, 7 figures
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- 2024
48. Text-Region Matching for Multi-Label Image Recognition with Missing Labels
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Ma, Leilei, Xie, Hongxing, Wang, Lei, Fu, Yanping, Sun, Dengdi, and Zhao, Haifeng
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Computer Science - Computer Vision and Pattern Recognition - Abstract
Recently, large-scale visual language pre-trained (VLP) models have demonstrated impressive performance across various downstream tasks. Motivated by these advancements, pioneering efforts have emerged in multi-label image recognition with missing labels, leveraging VLP prompt-tuning technology. However, they usually cannot match text and vision features well, due to complicated semantics gaps and missing labels in a multi-label image. To tackle this challenge, we propose $\textbf{T}$ext-$\textbf{R}$egion $\textbf{M}$atching for optimizing $\textbf{M}$ulti-$\textbf{L}$abel prompt tuning, namely TRM-ML, a novel method for enhancing meaningful cross-modal matching. Compared to existing methods, we advocate exploring the information of category-aware regions rather than the entire image or pixels, which contributes to bridging the semantic gap between textual and visual representations in a one-to-one matching manner. Concurrently, we further introduce multimodal contrastive learning to narrow the semantic gap between textual and visual modalities and establish intra-class and inter-class relationships. Additionally, to deal with missing labels, we propose a multimodal category prototype that leverages intra- and inter-category semantic relationships to estimate unknown labels, facilitating pseudo-label generation. Extensive experiments on the MS-COCO, PASCAL VOC, Visual Genome, NUS-WIDE, and CUB-200-211 benchmark datasets demonstrate that our proposed framework outperforms the state-of-the-art methods by a significant margin. Our code is available here: https://github.com/yu-gi-oh-leilei/TRM-ML., Comment: Accepted to ACM International Conference on Multimedia (ACM MM) 2024
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- 2024
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49. Neural Modulation Alteration to Positive and Negative Emotions in Depressed Patients: Insights from fMRI Using Positive/Negative Emotion Atlas
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Feng, Yu, Zeng, Weiming, Xie, Yifan, Chen, Hongyu, Wang, Lei, Wang, Yingying, Yan, Hongjie, Zhang, Kaile, Tao, Ran, Siok, Wai Ting, and Wang, Nizhuan
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Background: Although it has been noticed that depressed patients show differences in processing emotions, the precise neural modulation mechanisms of positive and negative emotions remain elusive. FMRI is a cutting-edge medical imaging technology renowned for its high spatial resolution and dynamic temporal information, making it particularly suitable for the neural dynamics of depression research. Methods: To address this gap, our study firstly leveraged fMRI to delineate activated regions associated with positive and negative emotions in healthy individuals, resulting in the creation of positive emotion atlas (PEA) and negative emotion atlas (NEA). Subsequently, we examined neuroimaging changes in depression patients using these atlases and evaluated their diagnostic performance based on machine learning. Results: Our findings demonstrate that the classification accuracy of depressed patients based on PEA and NEA exceeded 0.70, a notable improvement compared to the whole-brain atlases. Furthermore, ALFF analysis unveiled significant differences between depressed patients and healthy controls in eight functional clusters during the NEA, focusing on the left cuneus, cingulate gyrus, and superior parietal lobule. In contrast, the PEA revealed more pronounced differences across fifteen clusters, involving the right fusiform gyrus, parahippocampal gyrus, and inferior parietal lobule. Limitations: Due to the limited sample size and subtypes of depressed patients, the efficacy may need further validation in future. Conclusions: These findings emphasize the complex interplay between emotion modulation and depression, showcasing significant alterations in both PEA and NEA among depression patients. This research enhances our understanding of emotion modulation in depression, with implications for diagnosis and treatment evaluation.
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- 2024
50. Compensate Quantization Errors+: Quantized Models Are Inquisitive Learners
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Gao, Yifei, Ou, Jie, Wang, Lei, Shang, Fanhua, Wu, Jaji, and Cheng, Jun
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Computer Science - Computation and Language ,Computer Science - Artificial Intelligence ,I.2.7 - Abstract
Large Language Models (LLMs) showcase remarkable performance and robust deductive capabilities, yet their expansive size complicates deployment and raises environmental concerns due to substantial resource consumption. The recent development of a quantization technique known as Learnable Singular-value Increment (LSI) has addressed some of these quantization challenges. Leveraging insights from LSI and our extensive research, we have developed innovative methods that enhance the performance of quantized LLMs, particularly in low-bit settings. Our methods consistently deliver state-of-the-art results across various quantization scenarios and offer deep theoretical insights into the quantization process, elucidating the potential of quantized models for widespread application., Comment: Effecient Quantization Methods for LLMs
- Published
- 2024
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