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Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms

作者： Danny Z. Chen Ovidiu Daescu Yang Dai Naoki Katoh Xiadong Wu Jinhui Xu Department of Computer Science and Engineering University of Notre Dame Notre Dame IN Department of Computer Science and Engineering University of Notre Dame Notre Dame IN and Center for Applied Mathematics University of Notre Dame Notre Dame Indiana Department of Mathematical and Computing Sciences Tokyo Institute of Technology 2-12-1 O-okayama Meguro-ku Tokyo 152 Japan and Grant-in-Aid of Ministry of Science Culture and Education of Japan Department of Architecture and Architectural Systems Graduate School of Engineering Kyoto University Yoshida-Honmachi Sakyo-ku Kyoto 606-8501 Japan Department of Computer Science and Engineering University of Notre Dame Notre Dame IN and Graduate School University of Notre Dame Notre Dame Indiana

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Jamming Modulation: An Active Anti-Jamming Scheme

arXiv

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arXiv 2022年

作者： Ma, Jianhui Li, Qiang Liu, Zilong Du, Linsong Chen, Hongyang Ansari, Nirwan Chengdu611731 China The School of Computer Science and Electronics Engineering University of Essex ColchesterCO4 3SQ United Kingdom The School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China The Research Center for Graph Computing Zhejiang Lab Hangzhou311121 China The Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Providing quality communications under adversarial electronic attacks, e.g., broadband jamming attacks, is a challenging task. Unlike state-of-the-art approaches which treat jamming signals as destructive interference, this paper presents a novel active anti-jamming (AAJ) scheme for a jammed channel to enhance the communication quality between a transmitter node (TN) and receiver node (RN), where the TN actively exploits the jamming signal as a carrier to send messages. Specifically, the TN is equipped with a programmable-gain amplifier, which is capable of re-modulating the jamming signals for jamming modulation. Considering four typical jamming types, we derive both the bit error rates (BER) and the corresponding optimal detection thresholds of the AAJ scheme. The asymptotic performances of the AAJ scheme are discussed under the high jamming-to-noise ratio (JNR) and sampling rate cases. Our analysis shows that there exists a BER floor for sufficiently large JNR. Simulation results indicate that the proposed AAJ scheme allows the TN to communicate with the RN reliably even under extremely strong and/or broadband jamming. Additionally, we investigate the channel capacity of the proposed AAJ scheme, and show that the channel capacity of the AAJ scheme outperforms that of the direct transmission when the JNR is relatively high. Copyright © 2022, The Authors. All rights reserved.

关键词： Jamming

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Beyond Model Scale Limits: End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration

arXiv

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arXiv 2024年

作者： Wu, Zhiyuan Sun, Sheng Wang, Yuwei Liu, Min Xu, Ke Pan, Quyang Gao, Bo Wen, Tian The State Key Lab of Processers Institute of Computing Technology Chinese Academy of Sciences Beijing China The University of Chinese Academy of Sciences Beijing China The Zhongguancun Laboratory Beijing China The Department of Computer Science and Technology Tsinghua University Beijing China The School of Computer Science and Technology The Engineering Research Center of Network Management Technology for High-Speed Railway of Ministry of Education Beijing Jiaotong University Beijing China

The rise of End-Edge-Cloud Collaboration (EECC) offers a promising paradigm for Artificial Intelligence (AI) model training across end devices, edge servers, and cloud data centers, providing enhanced reliability and reduced latency. Hierarchical Federated Learning (HFL) can benefit from this paradigm by enabling multi-tier model aggregation across distributed computing nodes. However, the potential of HFL is significantly constrained by the inherent heterogeneity and dynamic characteristics of EECC environments. Specifically, the uniform model structure bounded by the least powerful end device across all computing nodes imposes a performance bottleneck. Meanwhile, coupled heterogeneity in data distributions and resource capabilities across tiers disrupts hierarchical knowledge transfer, leading to biased updates and degraded performance. Furthermore, the mobility and fluctuating connectivity of computing nodes in EECC environments introduce complexities in dynamic node migration, further compromising the robustness of the training process. To address multiple challenges within a unified framework, we propose End-Edge-Cloud Federated Learning with Self-Rectified Knowledge Agglomeration (FedEEC), which is a novel EECC-empowered FL framework that allows the trained models from end, edge, to cloud to grow larger in size and stronger in generalization ability. FedEEC introduces two key innovations: (1) Bridge Sample Based Online Distillation Protocol (BSBODP), which enables knowledge transfer between neighboring nodes through generated bridge samples, and (2) Self-Knowledge Rectification (SKR), which refines the transferred knowledge to prevent suboptimal cloud model optimization. The proposed framework effectively handles both cross-tier resource heterogeneity and effective knowledge transfer between neighboring nodes, while satisfying the migration-resilient requirements of EECC. Extensive experiments on three datasets demonstrate that FedEEC achieves significantly hi

关键词： Federated learning

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Dynamic Asset Allocation with Expected Shortfall via Quantum Annealing

arXiv

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arXiv 2021年

作者： Xu, Hanjing Dasgupta, Samudra Pothen, Alex Banerjee, Arnab Department of Computer Science Purdue University West LafayetteIN47906 United States Department of Physics Purdue University West LafayetteIN47906 United States Oak Ridge National Laboratory Quantum Computing Institute Oak RidgeTN37831 United States Bredesen Center University of Tennessee KnoxvilleTN37996 United States

Recent advances in quantum hardware offer new approaches to solve various optimization problems that can be computationally expensive when classical algorithms are employed. We propose a hybrid quantum-classical algorithm to solve a dynamic asset allocation problem where a target return and a target risk metric (expected shortfall) are specified. We propose an iterative algorithm that treats the target return as a constraint in a Markowitz portfolio optimization model, and dynamically adjusts the target return to satisfy the targeted expected shortfall. The Markowitz optimization is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem. The use of the expected shortfall risk metric enables the modeling of extreme market events. We compare the results from D-Wave’s 2000Q and Advantage quantum annealers using real-world financial data. Both quantum annealers are able to generate portfolios with more than 80% of the return of the classical optimal solutions, while satisfying the expected shortfall. We observe that experiments on assets with higher correlations tend to perform better, which may help to design practical quantum applications in the near term. © 2021, CC BY.

关键词： Iterative methods

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Yield enhancement for 3D-stacked ICs: Recent advances and challenges

Yield enhancement for 3D-stacked ICs: Recent advances and ch...

引用

Asia and South Pacific Design Automation Conference

作者： Qiang Xu Li Jiang Huiyun Li Bill Eklow CUhk REliable Computing Laboratory (CURE) Department of Computer Science & Engineering Chinese University of Hong Kong Hong Kong China Automotive Electronics Research Center Shenzhen Institutes of Advanced Technologies Chinese Academy of Sciences China Cisco Systems Inc. San Jose CA USA

Three-dimensional (3D) integrated circuits (ICs) that stack multiple dies vertically using through-silicon vias (TSVs) have gained wide interests of the semiconductor industry. The shift towards volume production of 3D-stacked ICs, however, requires their manufacturing yield to be commercially viable. Various techniques have been presented in the literature to address this important problem, including pre-bond testing techniques to tackle the “known good die” problem, TSV redundancy designs to provide defect-tolerance, and wafter/die matching solutions to improve the overall stack yield. In this paper, we survey recent advances in this filed and point out challenges to be resolved in the future.

关键词： Through-silicon vias Three dimensional displays Testing Bonding Redundancy Semiconductor device modeling Maintenance engineering

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Ano Raw: A Deep Learning Based Approach to Transliterating the Filipino Sign Language

Ano Raw: A Deep Learning Based Approach to Transliterating t...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Mark Allen Cabutaje Kenneth Ang Brondial Alyssa Franchesca Obillo Mideth Abisado Shekinah Lor Huyo-a Gabriel Avelino Sampedro Department of Computer Science Technological University of the Philippines Manila Philippines College of Computing and Information Technologies National University Manila Philippines Research and Development Center Philippine Coding Camp Manila Philippines Faculty of Information and Communication Studies University of the Philippines Open University Los Baños Philippines

Deaf people communicate best through sign language. It is one of the most vital languages worldwide. Sign languages, like spoken languages, are sophisticated, naturally formed structures that are arranged around a set of conversational activities. In the Philippines, practicing Filipino Sign Language (FSL) already had improved communication for deaf people. However, the community’s main difficulty is that most Filipinos do not comprehend or use FSL. Within the deaf community, these gestures, whether ASL or FSL, are still limited. People who are normal and hearing would never attempt to learn sign language. This results in a significant communication gap between the deaf and the hearing. Human translators are needed in order for deaf people to be understood. While they can be helpful, they are not always available or attainable. Filipino Sign Language Alphabet Recognition using Convolutional Neural Networks (CNN) is proposed to address this growing challenge. The proposed solution can recognize and forecast a letter using an image training model. The model peaked at the 15th epoch, with an accuracy rate of 92% and validation accuracy of 93%. The study aims to bridge the gaps between the deaf community with the hearing in the Philippines.

关键词： Training Deep learning Image recognition Gesture recognition Auditory system Assistive technologies Predictive models

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PlantMWpIDB: a database for the molecular weight and isoelectric points of the plant proteomes

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Scientific Reports 2022年第1期12卷 1-7页

作者： Mohanta, Tapan Kumar Kamran, Muhammad Shahzad Omar, Muhammad Anwar, Waheed Choi, Gyu Sang Natural and Medical Sciences Research Center University of Nizwa Nizwa 616 Oman Department of Computer Science and IT The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Data Science Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Information and Communication Engineering Yeungnam University 214-1 Gyeongsan-si 712-749 South Korea

The molecular weight and isoelectric point of the proteins are very important parameters that control their subcellular localization and subsequent function. Although the genome sequence data of the plant kingdom improved enormously, the proteomic details have been poorly elaborated. Therefore, we have calculated the molecular weight and isoelectric point of the plant proteins and reported them in this database. A database, PlantMWpIDB, containing protein data from 342 plant proteomes was created to provide information on plant proteomes for hypothesis formulation in basic research and for biotechnological applications. The Molecular weight and isoelectric point (pI) are important molecular parameters of proteins that are useful when conducting protein studies involving 2D gel electrophoresis, liquid chromatography-mass spectrometry, and X-ray protein crystallography. PlantMWpIDB provides an easy-to-use and efficient interface for search options and generates a summary of basic protein parameters. The database represents a virtual 2D proteome map of plants, and the molecular weight and pI of a protein can be obtained by searching on the name of a protein, a keyword, or by a list of accession numbers. The PlantMWpIDB database also allows one to query protein sequences. The database can be found in the following link https://***/. The individual 2D virtual proteome map of the plant kingdom will enable us to understand the proteome diversity between different species. Further, the molecular weight and isoelectric point of individual proteins can enable us to understand their functional significance in different species. © 2022, The Author(s).

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An improved N-bit to N-bit reversible Haar-like transform

An improved N-bit to N-bit reversible Haar-like transform

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Pacific Conference on computer Graphics and Applications

作者： J.G. Senecal P. Lindstrom M.A. Duchaineau K.I. Joy Inst. for Sci. Comput. Res. Lawrence Livermore Nat. Lab. CA USA Center for Applied Scientific Computing Lawrence Livemore National Laboratory USA Institute for Data Analysis and Visualization Computer Science Department University of California Davis USA

We introduce the piecewise-linear Haar (PLHaar) transform, a reversible n-bit to n-bit transform that is based on the Haar wavelet transform. PLHaar is continuous, while all current n-bit to n-bit methods are not, and is therefore uniquely usable with both lossy and lossless methods (e.g. image compression). PLHaar has both integer and continuous (i.e. non-discrete) forms. By keeping the coefficients to n bits PLHaar is particularly suited for use in hardware environments where channel width is limited, such as digital video channels and graphics hardware.

关键词： Wavelet transforms Continuous wavelet transforms Hardware Dynamic range Image coding Scientific computing Image reconstruction Laboratories Data analysis Data visualization

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Deep Reinforcement Learning Guided Graph Neural Networks for Brain Network Analysis

arXiv

引用

arXiv 2022年

作者： Zhao, Xusheng Wu, Jia Peng, Hao Beheshti, Amin Monaghan, Jessica J.M. McAlpine, David Hernandez-Perez, Heivet Dras, Mark Dai, Qiong Li, Yangyang Yu, Philip S. He, Lifang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computing Macquarie University Sydney Australia Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China National Acoustic Laboratories Sydney Australia Department of Linguistics The Australian Hearing Hub Macquarie University Sydney Australia CAEIT Beijing China Department of Computer Science University of Illinois ChicagoIL United States Computer Science & Engineering Lehigh University PA United States

Modern neuroimaging techniques enable us to construct human brains as brain networks or connectomes. Capturing brain networks' structural information and hierarchical patterns is essential for understanding brain functions and disease states. Recently, the promising network representation learning capability of graph neural networks (GNNs) has prompted related methods for brain network analysis to be proposed. Specifically, these methods apply feature aggregation and global pooling to convert brain network instances into vector representations encoding brain structure induction for downstream brain network analysis tasks. However, existing GNN-based methods often neglect that brain networks of different subjects may require various aggregation iterations and use GNN with a fixed number of layers to learn all brain networks. Therefore, how to fully release the potential of GNNs to promote brain network analysis is still non-trivial. In our work, a novel brain network representation framework, BN-GNN, is proposed to solve this difficulty, which searches for the optimal GNN architecture for each brain network. Concretely, BN-GNN employs deep reinforcement learning (DRL) to automatically predict the optimal number of feature propagations (reflected in the number of GNN layers) required for a given brain network. Furthermore, BN-GNN improves the upper bound of traditional GNNs' performance in eight brain network disease analysis tasks. © 2022, CC BY.

关键词： Reinforcement learning

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A Landscape-Aware Differential Evolution for Multimodal Optimization Problems

arXiv

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arXiv 2024年

作者： Lin, Guo-Yun Chen, Zong-Gan Liu, Chuanbin Jiang, Yuncheng Kwong, Sam Zhang, Jun Zhan, Zhi-Hui School of Computer Science South China Normal University Guangzhou510631 China The Center for Scientific Research and Development in Higher Education Institutes Ministry of Education China The Department of Computing and Decision Science Lingnan University Hong Kong The College of Artificial Intelligence Nankai University Tianjin300350 China The Hanyang University ERICA 15588 Korea Republic of

How to simultaneously locate multiple global peaks and achieve certain accuracy on the found peaks are two key challenges in solving multimodal optimization problems (MMOPs). In this paper, a landscape-aware differential evolution (LADE) algorithm is proposed for MMOPs, which utilizes landscape knowledge to maintain sufficient diversity and provide efficient search guidance. In detail, the landscape knowledge is efficiently utilized in the following three aspects. First, a landscape-aware peak exploration helps each individual evolve adaptively to locate a peak and simulates the regions of the found peaks according to search history to avoid an individual locating a found peak. Second, a landscape-aware peak distinction distinguishes whether an individual locates a new global peak, a new local peak, or a found peak. Accuracy refinement can thus only be conducted on the global peaks to enhance the search efficiency. Third, a landscape-aware reinitialization specifies the initial position of an individual adaptively according to the distribution of the found peaks, which helps explore more peaks. The experiments are conducted on 20 widely-used benchmark MMOPs. Experimental results show that LADE obtains generally better or competitive performance compared with seven well-performed algorithms proposed recently and four winner algorithms in the IEEE CEC competitions for multimodal optimization. Copyright © 2024, The Authors. All rights reserved.

关键词： Optimization algorithms

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