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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Leo Zhou Joao Basso Song Mei Walter Burke Institute for Theoretical Physics and the IQIM California Institute of Technology Pasadena CA and Electrical and Computer Engineering Department University of California Los Angeles CA Department of Mathematics University of California Berkeley CA and Quantum Artificial Intelligence Lab (QuAIL) NASA Ames Research Center Moffett Field CA and Research Institute for Advanced Computer Science (RIACS) USRA Mountain View CA Department of Statistics and Department of EECS University of California Berkeley CA

ISBN: (纸本)9798331314385

The quantum approximate optimization algorithm (QAOA) is a general-purpose algorithm for combinatorial optimization that has been a promising avenue for near-term quantum advantage. In this paper, we analyze the performance of the QAOA on the spiked tensor model, a statistical estimation problem that exhibits a large computational-statistical gap classically. We prove that the weak recovery threshold of 1-step QAOA matches that of 1-step tensor power iteration. Additional heuristic calculations suggest that the weak recovery threshold of p-step QAOA matches that of p-step tensor power iteration when p is a fixed constant. This further implies that multi-step QAOA with tensor unfolding could achieve, but not surpass, the asymptotic classical computation threshold Θ(n(q-2)/4) for spiked q-tensors. Meanwhile, we characterize the asymptotic overlap distribution for p-step QAOA, discovering an intriguing sine-Gaussian law verified through simulations. For some p and q, the QAOA has an effective recovery threshold that is a constant factor better than tensor power iteration. Of independent interest, our proof techniques employ the Fourier transform to handle difficult combinatorial sums, a novel approach differing from prior QAOA analyses on spin-glass models without planted structure.

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AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis of the Femoral Head Detection 37

AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis o...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Chen, Haoyang Liu, Shuai Lu, Feng Li, Wei Sheng, Bin Li, Mi Jin, Hai Zomaya, Albert Y. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

ISBN: (纸本)9781577358800

Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Large dataset

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Fast multiplication of random dense matrices with sparse matrices

Fast multiplication of random dense matrices with sparse mat...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Tianyu Liang Riley Murray Aydın Buluç James Demmel Electrical Engineering and Computer Science Department UC Berkeley Computational Research Division Lawrence Berkeley National Lab International Computer Science Institute Sandia National Laboratories

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

This work focuses on accelerating the multiplication of a dense random matrix with a (fixed) sparse matrix, which is frequently used in sketching algorithms. We develop a novel scheme that takes advantage of blocking and recomputation (on-the-fly random number generation) to accelerate this operation. The techniques we propose decrease memory movement, thereby increasing the algorithm’s parallel scalability in shared memory architectures. On the Intel Frontera architecture, our algorithm can achieve 2x speedups over libraries such as Eigen and Intel MKL on some examples. In addition, with 32 threads, we can obtain a parallel efficiency of up to 45%.We also present a theoretical analysis for the memory movement lower bound of our algorithm, showing that under mild assumptions, it’s possible to beat the data movement lower bound of general matrix-matrix multiply (GEMM) by a factor of M, where M is the cache size. Finally, we incorporate our sketching method into a randomized algorithm for overdetermined least squares with sparse data matrices. Our results are competitive with SuiteSparse for highly overdetermined problems; in some cases, we obtain a speedup of 10x over SuiteSparse.

关键词： Distributed processing Scalability Instruction sets Memory management Memory architecture Libraries Sparse matrices

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A Hybrid Wireless Image Transmission Scheme with Diffusion 24

A Hybrid Wireless Image Transmission Scheme with Diffusion

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Niu, Xueyan Wang, Xu Gunduz, Deniz Bai, Bo Chen, Weichao Zhou, Guohua City University of Hong Kong Department of Computer Science Hong Kong Huawei Technologies Co. Ltd. Theory Lab Central Research Institute 2012 Labs China Imperial College London Department of Electrical and Electronic Engineering London United Kingdom Huawei Technologies Co. Ltd Ran Research Department Wn China

ISBN: (纸本)9781665496261

We propose a hybrid joint source-channel coding (JSCC) scheme, in which the conventional digital communication scheme is complemented with a generative refinement component to improve the perceptual quality of the reconstruction. The input image is decomposed into two components: the first is a coarse compressed version, and is transmitted following the conventional separation based approach. An additional component is obtained through the diffusion process by adding independent Gaussian noise to the input image, and is transmitted using DeepJSCC. The decoder combines the two signals to produce a high quality reconstruction of the source. Experimental results show that the hybrid design provides bandwidth savings and enables graceful performance improvement as the channel quality improves. © 2023 IEEE.

关键词： Diffusion

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Exploitation or Exploration Next? User Behavior Decoupling and Emerging Intent Modeling for Next-Item Recommendation

Exploitation or Exploration Next? User Behavior Decoupling a...

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IEEE International Conference on Data Mining (ICDM)

作者： Nengjun Zhu Lingdan Sun Xiangfeng Luo Jian Cao Qi Zhang Xinjiang Lu School of Computer Engineering and Science Shanghai University Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science Tongji University Shanghai China Business Intelligence Lab Baidu Research Beijing China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Recent trends in next-item recommendation systems have focused on modeling user intents. Traditional methods often extract users' inherent intents from the most representative items in a session, overlooking “unexpected items” that deviate from the majority in various contextual aspects. These unexpected items, frequently present, can be crucial indicators of a user's inclination towards exploring new options, signaling emerging intents that warrant significant attention. In response, we introduce DbMei, a novel approach that decouples user behaviors and emphasizes the modeling of emerging intents. DbMei distinguishes between two user behavior types: “focused shopping”, which aligns with users' inherent intents, and”wandering shopping”, which aligns with emerging intents. Focused shopping is analyzed using topic modeling and hypergraph learning while wandering shopping is explored through session neighbor retrieval. An exploitation-exploration mechanism is employed to determine the behavioral probability distribution for upcoming items. This integrated modeling of focused and wandering shopping behaviors drives our recommendation process. Extensive empirical studies on two real-world datasets, Amazon-KDD and Beauty, showcase DbMei's superiority over leading methods regarding Recall and MRR metrics. Our code is publicly available at https://***/sunlingdan-123/DbMei.

关键词： Measurement Analytical models Codes Market research Data models Probability distribution Data mining Recommender systems Context modeling

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Burst-Sensitive Traffic Forecast via Multi-Property Personalized Fusion in Federated Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5598-5614页

作者： Xue, Jingjing Sun, Sheng Liu, Min Wang, Yuwei Meng, Xuying Wang, Jingyuan Zhang, JunBo Xu, Ke Chinese Academy of Sciences Institute of Computing Technology Beijing100045 China University of Chinese Academy of Sciences Beijing101408 China Zhongguancun Lab Beijing100190 China Beihang University School of Computer Science and Engineering Beijing100191 China Beihang University MIIT Key Laboratory of Data Intelligence and Management Beijing100191 China JD Technology JD iCity JD Intelligent Cities Research Beijing101111 China Tsinghua University Department of Computer Science and Technology Beijing100190 China Zhongguancun Laboratory Beijing100081 China

For distributed network traffic prediction with data localization and privacy protection, Federated Learning (FL) enables collaborative training without raw data exchange across Base Stations (BSs). Nevertheless, traffic across BSs exhibit inherently heterogeneous trend burst and smooth fluctuation properties, but existing FL methods model single-scale series from only one view, which cannot simultaneously capture diverse trend and fluctuation properties, especially distinct burst distributions. In this paper, we propose Personalized Federated Forecasting with Multi-property Self-fusion (P2FMS), which can represent multi-scale traffic properties from different views. With precise multi-property representations, a fusion-level prediction decision is learned for each client in a personalized manner to promptly sense traffic bursts and improve forecasting performance in non-IID settings. Specifically, P2FMS decomposes the traffic series into distinct time scales, based on which, we effectively extract closeness, period, and trend properties from different views. The closeness and period are embedded through global-view representations with spatial correlations, while non-stationary trends are individually fitted from the client-side view. Furthermore, a personalized combiner is designed to accurately quantify the proportion of general fluctuation raws (i.e., closeness and period) and specific trend property in predictions, which enables multi-property self-fusion for each client to accommodate heterogeneous traffic patterns and enhance prediction accuracy. Besides, an alternant training mechanism is introduced to optimize property representation and fusion modules with the convergence guarantee. Extensive experiments on real-world datasets show that P2FMS outperforms status quo methods in both prediction performance and convergence time. © 2002-2012 IEEE.

关键词： Weather forecasting

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An Augmented and Diminished Reality Exergame for Investigating the Embodiment of a Prosthetic Limb

An Augmented and Diminished Reality Exergame for Investigati...

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2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： MacAluso, Roberta Calandra, Davide Pratticò, Filippo Gabriele Battegazzorre, Edoardo Baiocchi, Gianmarco Boccardo, Nicolò Laffranchi, Matteo Bottino, Andrea Barresi, Giacinto Lamberti, Fabrizio Politecnico di Torino Department of Control and Computer Engineering Italy Rehab Technologies Lab Open University Affiliated Research Centre Istituto Italiano di Tecnologia Genoa Italy Istituto Italiano di Tecnologia Rehab Technologies Lab Genoa Italy Uwe Bristol Robotics Laboratory Bristol United Kingdom

ISBN: (纸本)9798331514846

Bionic limb prosthetics continue to face significant challenges in terms of artificial limb embodiment, often leading to prosthesis abandonment in patients. Following recent results in literature on digital trainings, this study investigates the impact of the visuo-attentive feedback of an Augmented/Diminished Reality (AR/DR) exergame on prosthetic embodiment. Two interaction paradigms were compared, i.e. a reference unrestricted interaction of a virtual prosthetic limb against a one in which the prosthesis and its interaction capability fades over time and requires the participant's visual-attention to reestablish it. Preliminary findings on non-amputees demonstrated hardship with the visuo-attentive approach which was associated with detrimental effects on the prosthesis embodiment, possibly because of the re-allocation of cognitive resources that are necessary to establish the process of body ownership. © 2025 IEEE.

关键词： Artificial limbs

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Accelerating Multi-Agent DDPG on CPU-FPGA Heterogeneous Platform

Accelerating Multi-Agent DDPG on CPU-FPGA Heterogeneous Plat...

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Samuel Wiggins Yuan Meng Rajgopal Kannan Viktor Prasanna Ming Hsieh Department of Electrical and Computer Engineering University of Southern California DEVCOM Army Research Lab

Multi-Agent Reinforcement Learning (MARL) is a key technology in artificial intelligence applications such as robotics, surveillance, energy systems, etc. Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is a state-of-the-art MARL algorithm that has been widely adopted and considered a popular baseline for novel MARL algorithms. However, existing implementations of MADDPG on CPU and CPU-GPU platforms do not exploit fine-grained parallelism between cooperative agents and handle inter-agent communication sequentially, leading to sub-optimal throughput performance in MADDPG training. In this work, we develop the first high-throughput MADDPG accelerator on a CPU-FPGA heterogeneous platform. Specifically, we develop dedicated hardware modules that enable parallel training of each agent's internal Deep Neural Networks (DNNs) and support low-latency inter-agent communication using an on-chip agent interconnection network. Our experimental results show that the speed performance of agent neural network training improves by a factor of $3.6\times-24.3\times$ and $1.5\times-29.5\times$ compared with state-of-the-art CPU and CPU-GPU implementations. Our design achieves up to a $1.99\times$ and $1.93\times$ improvement in overall system throughput compared with CPU and CPU-GPU implementations, respectively.

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A New Identity-Based Encryption Scheme with Accountable Authority Based on SM9 11

A New Identity-Based Encryption Scheme with Accountable Auth...

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11th International Conference on Electronics, Communications and Networks, CECNet 2021

作者： Wang, Ke Zhao, Yuan Luo, Song Guan, Zhi MoE Beijing China Department of Computer Science and Technology EECS Peking University Beijing China School of Computer Science and Engineering Chongqing University of Technology Chongqing China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (纸本)9781643682402

Accountable authority identity-based encryption (A-IBE) is an extension of identity-based encryption (IBE) in which private key's source can be traced, i.e., whether the key comes from a private key generator or a user. SM9 is an official cryptography standard of China which defines a practical IBE scheme. In this paper, we construct a practical A-IBE scheme from the SM9-IBE scheme. Our A-IBE scheme has public traceability and is proven secure if the based SM9-IBE scheme is secure. Compared with other A-IBE schemes, our A-IBE scheme has better efficiency in encryption and decryption. © 2022 The authors and IOS Press.

关键词： Cryptography

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Sales Prediction in the Retail Industry Using Machine Learning: A Case Study of BigMart

Sales Prediction in the Retail Industry Using Machine Learni...

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Electrical, Electronics, Communication and computers (ELEXCOM), International Conference on

作者： Hrishabh Upadhyay Sushant Shekhar Anurag Vidyarthi Rishi Prakash R. Gowri Department of Computer Science Engineering Propagation Research Lab Graphic Era (Deemed to be University) Dehradun India Department of Electronics & Communication Engineering Propagation Research Lab Graphic Era (Deemed to be University) Dehradun India Department of Computer Science Engineering Graphic Era Hill University Dehradun India

This research paper presents a machine learning-based approach for sales prediction in the retail industry. The study was conducted using data collected from the BigMart retail chain. The research aimed to develop an accurate predictive model to forecast different products' sales in various BigMart stores. The study involved the implementation of several machine learning algorithms, including linear regression, decision tree, random forest, gradient boosting, support vector machine, convolutional neural network, and long short-term memory. The models were trained and evaluated using a dataset comprising of over 8,000 entries of sales data from different BigMart stores. The results showed that the convolutional neural network and long short-term memory models outperformed the other algorithms in terms of accuracy, with an R-squared value of 0.68 and 0.69, respectively. The study also identified the most significant features affecting sales, including item weight, store location, and promotional activities. The findings of this research have significant implications for the retail industry, as accurate sales prediction can help businesses optimize their inventory management and marketing strategies. The study also highlights the potential of machine learning algorithms for sales forecasting in the retail sector. In conclusion, this research demonstrates the effectiveness of machine learning in predicting sales and highlights the importance of leveraging advanced analytical techniques for improving business performance in the retail industry.

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