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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wangdong Guo Qing Zhu Qirong Mao School of Computer Science and Communication Engineering Jiangsu University Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Group Emotion Recognition (GER) seeks to identify emotional states within multi-person groups. The complexity of the environment and the reliance on a single group emotion label often result in noisy individuals with inconsistent emotional expressions, hampering accurate emotion classification. Mainstream GER networks attempt to downweight noisy individuals, but when their numbers are high and over-relying on a single group label, the negative impact of noise on individuals remains significant, further complicating classification and diminishing accuracy. To address these limitations, we propose the Multi-Scale Contextual and Noise Suppression Model (MCon-NSM), a novel framework that enhances GER by capturing fine-grained interaction contexts and collaboratively suppressing noise at both the individual and group label levels. Extensive experiments on the GAF series datasets demonstrate that our method achieves results comparable to state-of-the-art techniques, validating its effectiveness in mitigating noise in GER.

关键词： Emotion recognition Analytical models Accuracy Noise Noise reduction Speech recognition Complexity theory Noise measurement Speech processing Context modeling

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Optimization for Task Offloading and Downloading in UAV-Assisted MEC Systems with Aerial to Aerial Collaboration

Optimization for Task Offloading and Downloading in UAV-Assi...

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International Conference on computer Supported Cooperative Work in Design

作者： Jiawei Jiang Xiang Tian Yubing Han Chunyu Hu Bin Feng Jiguo Yu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China School of Information Science and Technology Taishan University Taian China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Big Data Institute Qilu University of Technology Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Owing to the easy deployment and mobile flexibility, Unmanned Aerial Vehicle (UAV) assisted Mobile Edge Computing (MEC) has been deemed as one potential technology for handling the computation-intensive tasks at terminal devices (TDs). In this work, a MEC architecture assisted by UAVs is designed which achieves efficient offloading, computing, and downloading for tasks from multiple TDs via aerial to aerial collaboration of two UAVs. In this architecture, the task offloading process contains two parts, i.e., the offloading from TDs to a mobile UAV which flies around TDs, and the offloading from the mobile UAV to a hovering UAV which hovers in the air. The computing tasks from TDs will be divided into three parts allocated to the TDs themselves, and both two UAVs. Upon completion of computation, the computation results are downloaded to the TDs. The optimization objective is to seek for an optimal task division strategy to attain the weighted total energy consumption minimization for all devices. Since the formulated optimization problem is not convex, we develop a two-step iteration algorithm which jointly optimizes computing frequency, task allocation volume, as well as UAV's trajectory based on the method of block coordinate descent. Simulation results confirm the effectiveness and performance advantages of the designed algorithm.

关键词： Energy consumption Simulation Collaboration computer architecture Autonomous aerial vehicles Minimization Resource management Jamming Trajectory optimization Radio spectrum management

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Deformable registration framework for glioma images with absent correspondence based on auxiliary-image-aided intensity-consistency constraint

Deformable registration framework for glioma images with abs...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tang, Kun Wang, Lihui Yang, Menglong Xu, Jingwen Cheng, Xinyu Zhang, Jian Zhu, Yuemin Wei, Hongjiang Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Guiyang China Univ Lyon Insa Lyon Cnrs Inserm Creatis Umr 5220 U1206 Lyon France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200240 China

ISBN: (纸本)9798350386226

Considering the tumor aggressive nature and the significant changes in anatomical structure, aligning the preoperative and follow up scans of glioma patients remains a challenge due to the presence of regions with absent correspondence. To address this challenge, this work proposed a novel bidirectional unsupervised deformable image registration framework for image pairs with missing correspondence based on an auxiliary-image-aided intensity-consistency constraint (ICC) strategy. Specifically, for any fixed and moving image pairs, we introduced an auxiliary image and warped it directly to fixed/moving image or warped it twice through a transition of moving/fixed image. By comparing the difference between these warped images, the weighting maps to identify and exclude regions with absent correspondence between fixed and moving image pairs can be generated. To verify the effectiveness of the proposed framework, we combined it with several deep learning-based registration models and tested it on BraTS-Reg challenge dataset, the results demonstrated that the proposed ICC strategy can improve the registration performance for all the models, with the improvement of average target registration error (TRE) and success rate (SR) being up to 44.9% and 66.7%, respectively. Comparing against the best existing forward-backward consistency strategy for dealing with missing correspondence registration, our auxiliary-image-aided ICC strategy can also decrease average TRE by 2.9%, demonstrating the superiority of the proposed framework. The present work is not limited to the glioma images, it can be used to address the registration problems for any image pairs with absent correspondence or inconsistent intensity. © 2024 IEEE.

关键词： Image registration

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Electron charge qubit with 0.1 millisecond coherence time

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Nature Physics 2024年第1期20卷 116-122页

作者： Zhou, Xianjing Li, Xinhao Chen, Qianfan Koolstra, Gerwin Yang, Ge Dizdar, Brennan Huang, Yizhong Wang, Christopher S. Han, Xu Zhang, Xufeng Schuster, David I. Jin, Dafei Center for Nanoscale Materials Argonne National Laboratory LemontIL United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL United States Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions CambridgeMA United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States James Franck Institute and Department of Physics University of Chicago ChicagoIL United States Department of Electrical and Computer Engineering Northeastern University BostonMA United States Department of Applied Physics Stanford University StanfordCA United States Department of Physics and Astronomy University of Notre Dame Notre DameIN United States

Electron charge qubits are compelling candidates for solid-state quantum computing because of their inherent simplicity in qubit design, fabrication, control and readout. However, electron charge qubits built on conventional semiconductors and superconductors suffer from severe charge noise that limits their coherence time to the order of one microsecond. Here we report electron charge qubits that exceed this limit, based on isolated single electrons trapped on an ultraclean solid neon surface in a vacuum. Quantum information is encoded in the motional states of an electron that is strongly coupled with microwave photons in an on-chip superconducting resonator. The measured relaxation and coherence times are both on the order of 0.1 ms, surpassing all existing charge qubits and rivalling state-of-the-art superconducting transmon qubits. The simultaneous strong coupling of two qubits with a common resonator is also demonstrated, as the first step towards two-qubit entangling gates for universal quantum computing. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

关键词： Superconducting resonators

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Functional photonic structures for external interaction with flexible/ wearable devices

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Nano Research 2021年第9期14卷 2904-2918页

作者： Young Jin Yoo Se-Yeon Heo Yeong Jae Kim Joo Hwan Ko Zafrin Ferdous Mira Young Min Song School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology(GIST)123 Cheomdangwagi-roBukguGwangju 61005Republic of Korea Korea Institute of Ceramic Engineering&Technology Ceramics Test-Bed Center3321 Gyeongchung-daeroSindun-myeonIcheon-si Gyeonggi-do 17303Republic of Korea Anti-Viral Research Center Gwangju Institute of Science and Technology(GIST)123 Cheomdangwagi-roBukguGwangju 61005Republic of Korea AI Graduate School Gwangju Institute of Science and Technology(GIST)123 Cheomdangwagi-roBukguGwangju 61005Republic of Korea

In addition to vital functions,more subsidiary functions are being expected from wearable *** wearable technology thus far has achieved the ability to maintain homeostasis by continuously monitoring physiological *** quality of life improves if,through further developments of wearable devices to detect,announce,and even control unperceptive or noxious signals from the *** materials based on photonic engineering can fulfil the abovementioned *** to the flexibility and zero-power operation of such materials,they can be applied to conventional wearables without affecting existing *** achievements to freely tailoring a broad range of electromagnetic waves have encouraged the development of wearable systems for independent recognition/manipulation of light,pollution,chemicals,viruses and ***,the role that photonic engineering on a flexible platform plays in detecting or reacting to environmental changes is reviewed in terms of material selection,structural design,and regulation mechanisms from the ultraviolet to infrared spectral ***,issues emerging with the evolution of the wearable technology,such as Joule heating,battery durability,and user privacy,and the potential solution strategies are *** article provides a systematic review of current progress in wearable devices based on photonic structures as well as an overview of possible ubiquitous advances and their applications,providing diachronic perspectives and future outlook on the rapidly growing research field of wearable technology.

关键词： photonic structure wearable device health care device colorimetric sensor radiative cooling infrared camouflage

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Challenging the Machine: Contestability in Government ai Systems

arXiv

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arXiv 2024年

作者： Landau, Susan Dempsey, James X. Kamar, Ece Bellovin, Steven M. Pool, Robert Columbia University United States The Cybersecurity and Privacy Center The university's Data Science Institute United States Columbia Law School United States UC Berkeley Law School United States The Department of Computer Science and Engineering The University of Washington United States The Fletcher School The School of Engineering Tufts University United States

In an October 2023 executive order (EO), President Biden issued a detailedbut largely aspirational road map for the safe and responsible development anduse of artificial intelligence (ai). The challenge for the January 24-25, 2024workshop was to transform those aspirations regarding one specific but crucialissue - the ability of individuals to challenge government decisions madeabout themselves - into actionable guidance enabling agencies to develop,procure, and use genuinely contestable advanced automated decision-makingsystems. While the Administration has taken important steps since the October2023 EO, the insights garnered from our workshop remain highly relevant, as therequirements for contestability of advanced decision-making systems are not yetfully defined or implemented. The workshop brought together technologists, members of government agenciesand civil society organizations, litigators, and researchers in an intensivetwo-day meeting that examined the challenges that users, developers, andagencies faced in enabling contestability in light of advanced automateddecision-making systems. To ensure a free and open flow of discussion, themeeting was held under a modified version of the Chatham House *** were free to use any information or details that they learned, butthey may not attribute any remarks made at the meeting by the identity or theaffiliation of the speaker. Thus, the workshop summary that follows anonymizesspeakers and their affiliation. Where an identification of an agency, company,or organization is made, it is done from a public, identified resource and doesnot necessarily reflect statements made by participants at the workshop. This document is a report of that workshop, along with recommendations andexplanatory material. © 2024, CC BY-NC-SA.

关键词： Decision making

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IKGN: Intention-aware Knowledge Graph Network for POI Recommendation

IKGN: Intention-aware Knowledge Graph Network for POI Recomm...

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IEEE International Conference on data Mining (ICDM)

作者： Xiaoyu Zhu Chenyang Bu Bingbing Dong Shengwei Ji Yi He Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology China School of Artificial Intelligence and Big Data Hefei University China Department of Computer Science Old Dominion University USA Research Center for Knowledge Engineering Zhejiang Lab China

Point-of-Interest (POI) recommendation, pivotal for guiding users to their next interested locale, grapples with the persistent challenge of data sparsity. Whereas knowledge graphs (KGs) have emerged as a favored tool to mitigate the issue, existing KG-based methods tend to overlook two crucial elements: the intention steering users’ location choices and the high-order topological structure within the KG. In this paper, we craft an Intention-aware Knowledge Graph (IKG) that harmonizes users’ visit histories, movement trajectories, and location categories to model user intentions. Building upon IKG, our novel Intention-aware Knowledge Graph Network (IKGN) delves deeper into the POI recommendation by weighing and propagating node embeddings through an attention mechanism, capturing the unique locational intent of each user. A sequential model like GRU is then employed to ensure a comprehensive representation of users’ short- and long-term location preferences. An empirical study on two real-world datasets validates the effectiveness of our proposed IKGN, with it markedly outshining seven benchmark rival models in both Recall and NDCG metrics. The code of IKGN is available at https://***/Jungle123456/IKGN.

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Environment-aware dynamic graph learning for out-of-distribution generalization 23

Environment-aware dynamic graph learning for out-of-distribu...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Haonan Yuan Qingyun Sun Xingcheng Fu Ziwei Zhang Cheng Ji Hao Peng Jianxin Li Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University and School of Computer Science and Engineering Beihang University Key Lab of Education Blockchain and Intelligent Technology Guangxi Normal University Department of Computer Science and Technology Tsinghua University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by latent environments, investigating their impacts on the out-of-distribution (OOD) generalization is critical. However, it remains unexplored with the following two major challenges: (1) How to properly model and infer the complex environments on dynamic graphs with distribution shifts? (2) How to discover invariant patterns given inferred spatio-temporal environments? To solve these challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex coupled environments and exploiting spatiotemporal invariant patterns. Specifically, we first design the environment-aware EADGNN to model environments by multi-channel environments disentangling. Then, we propose an environment instantiation mechanism for environment diversification with inferred distributions. Finally, we discriminate spatio-temporal invariant patterns for out-of-distribution prediction by the invariant pattern recognition mechanism and perform fine-grained causal interventions node-wisely with a mixture of instantiated environment samples. Experiments on real-world and synthetic dynamic graph datasets demonstrate the superiority of our method against state-of-the-art baselines under distribution shifts. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.

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AppleQSM:Geometry-Based 3D Characterization of Apple Tree Architecture in Orchards

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Plant Phenomics 2024年第3期6卷 737-754页

作者： Tian Qiu Tao Wang Tao Han Kaspar Kuehn Lailiang Cheng Cheng Meng Xiangtao Xu Kenong Xu Jiang Yu School of Electrical and Computer Engineering Cornell UniversityIthacaNYUSA Institute of Statistics and Big Data Renming University of ChinaBeijingChina Department of Ecology and Evolutionary Biology Cornell UniversityIthacaNYUSA School of Integrative Plant Science Cornell UniversityIthacaNYUSA Center for Applied Statistics Renmin University of ChinaBeijingChina School of Integrative Plant Science Cornell University.IthacaNYUSA

The architecture of apple trees plays a pivotal role in shaping their growth and fruit-bearing potential,forming the foundation for precision apple ***,2D imaging technologies were employed to delineate the architectural traits of apple trees,but their accuracy was hampered by occlusion and perspective *** study aimed to surmount these constraints by devising a 3D geometry-based processing pipeline for apple tree structure segmentation and architectural trait characterization,utilizing point clouds collected by a terrestrial laser scanner(TLS).The pipeline consisted of four modules:(a)data preprocessing module,(b)tree instance segmentation module,(c)tree structure segmentation module,and(d)architectural trait extraction *** developed pipeline was used to analyze 84 trees of two representative apple cultivars,characterizing architectural traits such as tree height,trunk diameter,branch count,branch diameter,and branch *** results indicated that the established pipeline attained an R^(2)of 0.92 and 0.83,and a mean absolute error(MAE)of 6.1cm and 4.71mm for tree height and trunk diameter at the tree level,***,at the branch level,it achieved an R^(2)of 0.77 and 0.69,and a MAE of 6.86 mm and 7.48°for branch diameter and angle,*** accurate measurement of these architectural traits can enable precision management in high-density apple orchards and bolster phenotyping endeavors in breeding ***,bottlenecks of 3D tree characterization in general were comprehensively analyzed to reveal future development.

关键词： architecture tree tion appleqsm character geometry-based orchards

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Detection and Classification of Transmission Line Transient Faults Based on Graph Convolutional Neural Network

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CSEE Journal of Power and Energy Systems 2021年第3期7卷 456-471页

作者： Houjie Tong Robert C.Qiu Dongxia Zhang Haosen Yang Qi Ding Xin Shi Department of Electrical Engineering Center for Big Data and Artificial IntelligenceShanghai Jiao Tong UniversityShanghai 200240China School of Electronic Information and Communication Huazhong University of Science and TechnologyWuhan 430000China Electric Power Research Institute Haidian DistrictBeijing 100192China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China

We present a novel transient fault detection and classification approach in power transmission lines based on graph convolutional neural *** with the existing techniques,the proposed approach considers explicit spatial information in sampling sequences as prior knowledge and it has stronger feature extraction *** this basis,a framework for transient fault detection and classification is *** structure is generated to provide topology information to the *** approach takes the adjacency matrix of topology graph and the bus voltage signals during a sampling period after transient faults as inputs,and outputs the predicted classification results ***,the proposed approach is tested in various situations and its generalization ability is verified by experimental *** results show that the proposed approach can detect and classify transient faults more effectively than the existing techniques,and it is practical for online transmission line protection for its rapidness,high robustness and generalization ability.

关键词： Graph convolutional network(GCN) power transmission line fault detection and classification spatio-temporal data topology information

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