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arXiv 2025年

作者： He, Pan Zhang, Min Cao, Jin Li, Jingru Liu, Hao Zhai, Jinfeng Wang, Ruibo Xiao, Cong Yang, Shengyuan A. Shen, Jian State Key Laboratory of Surface Physics Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Hefei National Laboratory Hefei230088 China Institute of Applied Physics and Materials Engineering Faculty of Science and Technology University of Macau China Fudan University Shanghai200433 China Department of Physics Fudan University Shanghai China Shanghai Research Center for Quantum Sciences Shanghai China Zhangjiang Fudan International Innovation Center Fudan University Shanghai201210 China Collaborative Innovation Center of Advanced Microstructures Nanjing210093 China

The valley Hall effect (VHE) holds great promise for valleytronic applications by leveraging the valley degree of freedom. To date, research on VHE has focused on its linear response to an applied current, leaving nonlinear valley responses undetected and nonlinear valleytronic devices undeveloped. Here, we report the experimental observation of a nonlinear VHE in a graphene-hBN moiré superlattice, evidenced by the generation of second-harmonic nonlocal voltages under AC currents. Remarkably, the nonlinear VHE has magnitude surpassing the linear VHE and is highly tunable via a gate voltage, which exhibits a pair of opposite peaks on the two sides of a Dirac gap. The nonlinear signal shows quadratic scaling with driving current and quartic scaling with local resistance, setting it apart from the linear counterpart. These experimental features are consistent with the theoretical picture of nonlocal transport mediated by nonlinear VHE and linear inverse VHE. We further reveal a nonlinear inverse VHE by observing the third- and fourth-harmonic nonlocal voltages. The nonlinear VHE provides a novel mechanism for valley manipulation and enables a novel valleytronic device—the valley rectifier—that converts AC charge current into DC valley current. Copyright © 2025, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)

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Speaker Extraction with Co-Speech Gestures Cue

arXiv

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arXiv 2022年

作者： Pan, Zexu Qian, Xinyuan Li, Haizhou The Integrative Sciences and Engineering Programme The Institute of Data Science National University of Singapore 119077 Singapore The Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore The Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China The School of Data Science The Chinese University of Hong Kong Shenzhen518172 China The University of Bremen 28359 Germany

Speaker extraction seeks to extract the clean speech of a target speaker from a multi-talker mixture speech. There have been studies to use a pre-recorded speech sample or face image of the target speaker as the speaker cue. In human communication, co-speech gestures that are naturally timed with speech also contribute to speech perception. In this work, we explore the use of co-speech gestures sequence, e.g. hand and body movements, as the speaker cue for speaker extraction, which could be easily obtained from low-resolution video recordings, thus more available than face recordings. We propose two networks using the co-speech gestures cue to perform attentive listening on the target speaker, one that implicitly fuses the co-speech gestures cue in the speaker extraction process, the other performs speech separation first, followed by explicitly using the co-speech gestures cue to associate a separated speech to the target speaker. The experimental results show that the co-speech gestures cue is informative in associating with the target speaker. Copyright © 2022, The Authors. All rights reserved.

关键词： Extraction

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Multiscale Graph Neural Networks for Protein Residue Contact Map Prediction

arXiv

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arXiv 2022年

作者： Liu, Kuang Kalia, Rajiv K. Liu, Xinlian Nakano, Aiichiro Nomura, Ken-Ichi Vashishta, Priya Zamora-Resendiz, Rafael Collaboratory for Advanced Computing and Simulations Department of Computer Science Department of Physics & Astronomy Department of Chemical Engineering & Materials Science Department of Quantitative & Computational Biology University of Southern California Los AngelesCA90089 United States Department of Computer Science Hood College FrederickMD21701 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

Machine learning (ML) is revolutionizing protein structural analysis, including an important subproblem of predicting protein residue contact maps, i.e., which amino-acid residues are in close spatial proximity given the amino-acid sequence of a protein. Despite recent progresses in ML-based protein contact prediction, predicting contacts with a wide range of distances (commonly classified into short-, medium- and long-range contacts) remains a challenge. Here, we propose a multiscale graph neural network (GNN) based approach taking a cue from multiscale physics simulations, in which a standard pipeline involving a recurrent neural network (RNN) is augmented with three GNNs to refine predictive capability for short-, medium- and long-range residue contacts, respectively. Test results on the ProteinNet dataset show improved accuracy for contacts of all ranges using the proposed multiscale RNN+GNN approach over the conventional approach, including the most challenging case of long-range contact prediction. © 2022, CC BY.

关键词： Forecasting

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EXPLAINABLE GRAPH PYRAMID AUTOFORMER FOR LONG-TERM TRAFFIC FORECASTING

arXiv

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arXiv 2022年

作者： Zhong, Weiheng Mallick, Tanwi Meidani, Hadi Macfarlane, Jane Balaprakash, Prasanna University of Illinois at Urbana-Champaign ChampaignIL United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Department of Civil and Environmental Engineering University of Illinois at Urbana-Champaign ChampaignIL United States Sustainable Energy Systems Group Lawrence Berkeley National Laboratory BerkeleyCA United States Mathematics and Computer Science Division Argonne Leadership Computing Facility Argonne National Laboratory LemontIL United States

Accurate traffic forecasting is vital to an intelligent transportation system. Although many deep learning models have achieved state-of-art performance for short-term traffic forecasting of up to 1 hour, long-term traffic forecasting that spans multiple hours remains a major challenge. Moreover, most of the existing deep learning traffic forecasting models are black box, presenting additional challenges related to explainability and interpretability. We develop Graph Pyramid Autoformer (X-GPA), an explainable attention-based spatial-temporal graph neural network that uses a novel pyramid autocorrelation attention mechanism. It enables learning from long temporal sequences on graphs and improves long-term traffic forecasting accuracy. Our model can achieve up to 35% better long-term traffic forecast accuracy than that of several state-of-the-art methods. The attention-based scores from the X-GPA model provide spatial and temporal explanations based on the traffic dynamics, which change for normal vs. peak-hour traffic and weekday vs. weekend traffic. © 2022, CC BY.

关键词： Graph neural networks

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Design and Verification of an Aromatherapy Feedback System for Mental Fatigue Based on Physiological Signals

Design and Verification of an Aromatherapy Feedback System f...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tao Sun Fuze Tian Hua Jiang Qinglin Zhao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China School of Medical Technology Beijing Institute of Technology Beijing China CAS Center for Excellence in Brain Science and Institutes for Biological Sciences Shanghai Institutes for Biological Sciences Chines Academy of Sciences Shanghai China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Chinese Academy of Sciences Lanzhou China

Mental fatigue is a prevalent issue in contemporary society and can negatively affect physical performance and concentration, increasing the likelihood of adverse consequences due to inattention during productive activities. Therefore, it becomes increasingly important to address and eliminate fatigue within a specific period of time. Aromatherapy, as a form of Complementary Alternative Medicine (CAM), is a non-invasive, cost-effective, and efficient method to combat fatigue. Previous studies have assessed the effects of specific aromatherapy oils using scales, but there is a lack of objective and reliable physiological indicators to prove the effectiveness of aromatherapy. Hence, this paper seeks to establish a model illustrating the effects of aromatic essential oil gases on the human body. A multimodal physiological fatigue signal acquisition system that integrates aromatherapy feedback was designed. In addition, an experimental paradigm was developed to explore the potential of aromatherapy in mitigating mental fatigue. Electroencephalogram (EEG) and Electrocardiogram (ECG) signals were collected, allowing for the analysis of time-frequency domain features in EEG and ECG signals, as well as Heart Rate Variability (HRV) features in ECG signals. Our findings indicate that specific aromatic gases demonstrate effectiveness in reducing mental fatigue. Furthermore, we employed the Support Vector Machine (SVM) algorithm to classify the state of human mental fatigue. Based on the classification results, the release of aromatic gas was controlled to provide targeted aromatic feedback. This innovative approach offers a promising avenue for objectively assessing and addressing mental fatigue through aromatherapy interventions.

关键词：

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Performance comparison of CFD-DEM solver MFiX-Exa, on GPUs and CPUs

arXiv

引用

arXiv 2021年

作者： Lao, Shandong Holt, Aaron Vaidhynathan, Deepthi Sitaraman, Hariswaran Hrenya, Christine M. Hauser, Thomas Research Computing University of Colorado Boulder United States Computational Science Center National Renewable Energy Laboratory Chemical and Biological Engineering University of Colorado Boulder United States

We present computational performance comparisons of gas-solid simulations performed on current CPU and GPU architectures using MFiX Exa, a CFD-DEM solver that leverages hybrid CPU+GPU parallelism. A representative fluidized bed simulation with varying particle numbers from 2 to 67 million is used to compare serial and parallel performance. A single GPU was observed to be about 10 times faster compared to a single CPU core. The use of 3 GPUs on a single compute node was observed to be 4x faster than using all 64 CPU cores. We also observed that using an error controlled adaptive time stepping scheme for particle advance provided a consistent 4x speedup on both CPUs and GPUs. Weak scaling results indicate superior parallel efficiencies when using GPUs compared to CPUs for the problem sizes studied in this work. © 2021, CC BY.

关键词： Program processors

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Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition

arXiv

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arXiv 2022年

作者： Chen, Huayu He, Huanhuan Zhu, Jing Sun, Shuting Li, Jianxiu Shao, Xuexiao Li, Junxiang Li, Xiaowei Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China Brain Health Engineering Laboratory School of Medical Technology Beijing Institute of Technology Beijing China School of Information Science and Engineering Inner Mongolia University Inner Mongolia China Shandong Academy of Intelligent Computing Technology Jinan China Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences Lanzhou China Open Source Software and Real-Time System Lanzhou University Ministry of Education Lanzhou China

Cross-dataset emotion recognition as an extremely challenging task in the field of EEG-based affective computing is influenced by many factors, which makes the universal models yield unsatisfactory results. Facing the situation that lacks EEG information decoding research, we first analyzed the impact of different EEG information(individual, session, emotion and trial) for emotion recognition by sample space visualization, sample aggregation phenomena quantification, and energy pattern analysis on five public datasets. Based on these phenomena and patterns, we provided the processing methods and interpretable work of various EEG differences. Through the analysis of emotional feature distribution patterns, the Individual Emotional Feature Distribution Difference(IEFDD) was found, which was also considered as the main factor of the stability for emotion recognition. After analyzing the limitations of traditional modeling approach suffering from IEFDD, the Weight-based Channel-model Matrix Framework(WCMF) was proposed. To reasonably characterize emotional feature distribution patterns, four weight extraction methods were designed, and the optimal was the correction T-test(CT) weight extraction method. Finally, the performance of WCMF was validated on cross-dataset tasks in two kinds of experiments that simulated different practical scenarios, and the results showed that WCMF had more stable and better emotion recognition ability. Copyright © 2022, The Authors. All rights reserved.

关键词： Emotion Recognition

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Simulating Parton Fragmentation on Quantum Computers

arXiv

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arXiv 2024年

作者： Li, Tianyin Xing, Hongxi Zhang, Dan-Bo Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter Institute of Quantum Matter South China Normal University Guangzhou510006 China Guangdong-Hong Kong Joint Laboratory of Quantum Matter Guangdong Provincial Key Laboratory of Nuclear Science Southern Nuclear Science Computing Center South China Normal University Guangzhou510006 China Institute of Modern Physics Chinese Academy of Sciences Huizhou516000 China Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter School of Physics South China Normal University Guangzhou510006 China Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials Guangdong-Hong Kong Joint Laboratory of Quantum Matter Frontier Research Institute for Physics South China Normal University Guangzhou510006 China

Parton fragmentation functions (FFs) are indispensable for understanding processes of hadron production ubiquitously existing in high-energy collisions, but their first principle determination has never been realized due to the insurmountable difficulties in encoding their operator definition using traditional lattice methodology. We propose a framework that makes a first step for evaluating FFs utilizing quantum computing methodology. The key element is to construct a semi-inclusive hadron operator for filtering out hadrons of desired types in a collection of particles encoded in the quantum state. We illustrate the framework by elaborating on the Nambu-Jona-Lasinio model with numeral simulations. Remarkably, We show that the semi-inclusive hadron operator can be constructed efficiently with a variational quantum algorithm. Moreover, we develop error mitigation techniques tailed for accurately calculating the FFs in the presence of quantum noises. Our work opens a new avenue for investigating QCD hadronization on near-term quantum computers. © 2024, CC BY.

关键词： Hadrons

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Improved Diversity-Promoting Collaborative Metric Learning for Recommendation

arXiv

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arXiv 2024年

作者： Bao, Shilong Xu, Qianqian Yang, Zhiyong He, Yuan Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Security Department of Alibaba Group Hangzhou311121 China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and collaborative filtering. Following the convention of RS, existing practices exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to introduce a set of multiple representations for each user in the system where users' preference toward an item is aggregated by taking the minimum item-user distance among their embedding set. Specifically, we instantiate two effective assignment strategies to explore a proper quantity of vectors for each user. Meanwhile, a Diversity Control Regularization Scheme (DCRS) is developed to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could induce a smaller generalization error than traditional CML. Furthermore, we notice that CML-based approaches usually require negative sampling to reduce the heavy computational burden caused by the pairwise objective therein. In this paper, we reveal the fundamental limitation of the widely adopted hard-aware sampling from the One-Way Partial AUC (OPAUC) perspective and then develop an effective sampling alternative for the CML-based paradigm. Finally, comprehensive experiments over a range of benchmark datasets speak to the efficacy of DPCML. Code are available at https://***/statusrank/LibCML. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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Automatic Repeat Request Design in AoI-Aware Broadcast With Heterogeneous Direct and Relay-Assisted Users

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IEEE Internet of Things Journal 2025年

作者： Wang, Xueer Pan, Haoyuan Chan, Tse-Tin Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering Shenzhen China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology The College of Computer Science and Software Engineering Shenzhen China

This paper investigates the design of automatic repeat request (ARQ) protocols in age of information (AoI)-aware broadcast systems with heterogeneous users, including both direct and relay-assisted users. In this setup, a direct user receives status updates directly via a single-hop link, while a relay-assisted user can receive status updates through either a direct single-hop link or a two-hop relay-assisted link. While ARQ is commonly used to ensure reliable transmission in error-prone wireless networks, previous studies suggest that ARQ does not improve the average AoI in single-hop networks. However, its impact on AoI in systems with relays, particularly those involving heterogeneous users, remains unclear. We address this gap by analyzing the average AoI under different ARQ strategies, introducing a transmission limit k ≥ 0 at the relay. Here, k = 0, k = 1, and k > 1 correspond to non-relay, non-ARQ-at-relay, and truncated-ARQ-at-relay strategies, respectively. Utilizing a unified Markov chain framework that models the transmission processes for each user type, we derive the theoretical average AoI. Our results show that the direct user benefits most from non-relay and non-ARQ strategies, similar to single-hop systems. In contrast, the relay-assisted user achieves optimal performance with truncated-ARQ-at-relay, leveraging both direct and relay-assisted links. For the overall system, applying a non-ARQ-at-relay approach strikes a balance in AoI between the direct and relay-assisted users, leading to a more stable and lower system-wide average AoI. © 2014 IEEE.

关键词： Mobile telecommunication systems

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