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International Journal of Information Technology (Singapore) 2024年 1-11页

作者： Mokhtari, Bilal Merizig, Abdelhak Zerdoumi, Hadjer Department of Computer Science Mohamed Khider University Biskra 0700 Algeria Laboratoire d’Applications des mathématiques à l’informatique et à l’électronique: LAMIE Batna 2 University N 3 RN3 Batna Route de Fesdis 05000 Algeria LINFI Laboratory Mohamed Khider University Biskra 07000 Algeria

Advancement of technologies has enabled the creation of smart solutions to improve healthcare. The primary objective is to help healthcare practitioners make efficient and fast decisions and diagnoses. Vital signs provide meaningful information while monitoring patient health, which allows assessing patients’ emotional well-being and adjust therapy. This study introduces an AI-powered patient monitoring system and healthcare platform. The proposed healthcare platform uses Internet of Medical Things (IoMT) and machine learning to monitor and classify emotional states using ECG signals. Thus, a label transformation and Random Forest classifier are used to identify patient emotions as positive or negative based on selected features from Heart Rate Variability (HRV) provided by ECG sensor. Moreover, a data augmentation technique is used to expand the primary used dataset, DREAMER which is afterward integrated with SWELL and WESAD datasets to increase data variety. The model presents emotion identification on the platform with an accuracy of 99.89%, outperforming most existing related works. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Data augmentation E-health ECG Emotion recognition and classification IMoT Internet of Things Machine learning

A system capable of verifiably and privately screening global DNA synthesis

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arXiv 2024年

作者： Baum, Carsten Berlips, Jens Chen, Walther Cui, Hongrui Damgard, Ivan Dong, Jiangbin Esvelt, Kevin M. Foner, Leonard Gao, Mingyu Gretton, Dana Kysel, Martin Li, Juanru Li, Xiang Paneth, Omer Rivest, Ronald L. Sage-Ling, Francesca Shamir, Adi Shen, Yue Sun, Meicen Vaikuntanathan, Vinod Van Hauwe, Lynn Vogel, Theia Weinstein-Raun, Benjamin Wang, Yun Wichs, Daniel Wooster, Stephen Yao, Andrew C. Yu, Yu Zhang, Haoling Zhang, Kaiyi Department of Computer Science Aarhus University Denmark DTU Compute Technical University of Denmark Denmark SecureDNA Foundation Switzerland Department of Computer Science and Engineering Shanghai Jiao Tong University China Institute for Interdisciplinary Information Sciences Tsinghua University China Media Lab Massachusetts Institute of Technology United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Department of Applied Mathematics Weizmann Institute of Science Israel Department of Computer Science Northeastern University United States China National GeneBank China Department of Political Science Massachusetts Institute of Technology United States Shanghai Qi Zhi Institute China

Printing custom DNA sequences is essential to scientific and biomedical research, but the technology can be used to manufacture plagues as well as cures. Just as ink printers recognize and reject attempts to counterfeit money, DNA synthesizers and assemblers should deny unauthorized requests to make viral DNA that could be used to ignite a pandemic. There are three complications. First, we don't need to quickly update printers to deal with newly discovered currencies, whereas we regularly learn of new viruses and other biological threats. Second, anti-counterfeiting specifications on a local printer can't be extracted and misused by malicious actors, unlike information on biological threats. Finally, any screening must keep the inspected DNA sequences private, as they may constitute valuable trade secrets. Here we describe SecureDNA, a free, privacy-preserving, and fully automated system capable of verifiably screening all DNA synthesis orders of 30+ base pairs against an up-to-date database of hazards, and its operational performance and specificity when applied to 67 million base pairs of DNA synthesized by providers in the United States, Europe, and China. Copyright © 2024, The Authors. All rights reserved.

关键词： DNA sequences

Perivascular spaces relate to the course and cognition of Huntington’s disease

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Translational Neurodegeneration 2023年第1期12卷 546-549页

作者： Xiao-Yan Li Juan-Juan Xie Jin-Hong Wang Yu-Feng Bao Yi Dong Bin Gao Ting Shen Pei-Yu Huang Hao-Chao Ying Han Xu Anna Wang Roe Hsin-Yi Lai Zhi-Ying Wu Department of Medical Genetics and Center for Rare Diseases Department of Neurology in Second Affiliated HospitalKey Laboratory of Medical Neurobiology of Zhejiang ProvinceZhejiang University School of MedicineHangzhouChina Interdisciplinary Institute of Neuroscience and Technology College of Biomedical Engineering and Instrument ScienceKey Laboratory for Biomedical Engineering of Ministry of EducationZhejiang UniversityHangzhouChina College of Computer Science and Technology Zhejiang UniversityHangzhouChina Department of Radiology Second Affiliated HospitalZhejiang University School of MedicineHangzhouChina School of Public Health Zhejiang UniversityHangzhouChina MOE Frontier Science Center for Brain Research and Brain-Machine Integration School of Brain Science and Brain MedicineZhejiang UniversityHangzhouChina CAS Center for Excellence in Brain Science and Intelligence Technology ShanghaiChina

Huntington’s disease(HD)is an autosomal dominant neurodegenerative disease that is caused by a cytosine-adenine-guanine(CAG)expansion in the first exon of the huntingtin(HTT)gene,which codes for the hun-tingtin *** typically manifests with a triad of symptoms,including motor disorders,cognitive impair-ment and psychiatric disturbances[1].HD primar-ily affects the basal ganglia(BG),especially the caudate and putamen,after which it extends to more widespread gray and white matter[2].Perivascular spaces(PVSs)are fluid-filled extensions of the subarachnoid spaces that enclose cerebral blood vessels and extend from the cer-ebral cortex through the brain *** physi-ological role of PVSs is the drainage of brain interstitial fluid into perivascular pathways for the elimination of waste products through the glymphatic drainage *** increasing number of studies have demonstrated that enlarged PVSs indicate glymphatic dysfunction and are associated with many neurological diseases,such as Alzheimer’s disease,Parkinson’s disease and small vessel disease[3].With the advantage of strong field strengths,7.0 T images show superior resolution and signal-to-noise ratios than 3.0 T,which facilitate the visualization of *** automated segmentation methods could accurately identify PVS in a short time with no inter-rater *** the current study,we used U-shaped networks(U-net),a class of deep learning methods,to explore the PVS distribution in HD and *** date,PVS distribution in HD is still *** two studies have investigated PVSs in HD,and both dem-onstrated increased visible PVS burden in manifest HD compared to controls[4,5].However,whether PVS bur-den increases in premanifest HD(pre-HD)individuals remains unknown,and the relationship of PVS with cog-nitive decline has never been studied.

关键词： Huntington filled elimination

Dear-DIA^(XMBD): Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics

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Research 2024年第1期6卷 707-720页

作者： Qingzu He Chuan-Qi Zhong Xiang Li Huan Guo Yiming Li Mingxuan Gao Rongshan Yu Xianming Liu Fangfei Zhang Donghui Guo Fangfu Ye Tiannan Guo Jianwei Shuai Jiahuai Han Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China Oujiang Laboratory(Zhejiang Lab for Regenerative Medicine Vision and Brain Health)and Wenzhou InstituteUniversity of Chinese Academy of SciencesWenzhouZhejiang 325001China School of Life Sciences Xiamen UniversityXiamen 361102China State Key Laboratory of Cellular Stress Biology Innovation Center for Cell Signaling NetworkXiamen 361102China Department of Computer Science Xiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine School of MedicineXiamen UniversityXiamen 361102China Bruker(Beijing)Scientific Technology Co.Ltd. BeijingChina Westlake Laboratory of Life Sciences and Biomedicine Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake University18 Shilongshan RoadHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced Study18 Shilongshan RoadHangzhou 310024China Westlake Omics Ltd. Yunmeng Road 1HangzhouChina Department of Electronic Engineering Xiamen UniversityXiamen 361005China

Data-independent acquisition(DIA)technology for protein identification from mass spectrometry and related algorithms is developing *** spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising *** this paper,we proposed an untargeted analysis method,Dear-DIA^(XMBD),for direct analysis of DIA ***-DIA^(XMBD) first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms,then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes,and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and *** show that Dear-DIA^(XMBD) performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms.

关键词： Deep Auto Independent

Retraction Note: High performance reconciliation for practical quantum key distribution systems

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Optical and Quantum Electronics 2024年第12期56卷 1-2页

作者： Mao, ao-Kun Li, Qiong Hao, Peng-Lei Abd-El-Atty, Bassem Iliyasu, Abdullah M. Department of Computer Science and Technology Harbin Institute of Technology Harbin China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei China Department of Computer Science Faculty of Computers and Information Luxor University Luxor Egypt Electrical Engineering Department College of Engineering Prince Sattam Bin Abdulaziz University Al- Kharj Saudi Arabia School of Computing Tokyo Institute of Technology Yokohama Japan School of Computer Science and Technology Changchun University of Science and Technology Changchun China

Linear optimal transport subspaces for point set classification

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arXiv 2024年

作者： Shifat-E-Rabbi, Mohammad Pathan, Naqib Sad Li, Shiying Zhuang, Yan Rubaiyat, Abu Hasnat Mohammad Rohde, Gustavo K. The Department of Electrical and Computer Engineering North South University Dhaka Bangladesh The Imaging and Data Science Laboratory The Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA United States The Department of Mathematics University of North Carolina Chapel HillNC United States The Department of Radiology and Imaging Sciences National Institutes Health Clinical Center MD United States The Imaging and Data Science Laboratory The Department of Biomedical Engineering The Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA United States

Learning from point sets is an essential component in many computer vision and machine learning applications. Native, unordered, and permutation invariant set structure space is challenging to model, particularly for point set classification under spatial deformations. Here we propose a framework for classifying point sets experiencing certain types of spatial deformations, with a particular emphasis on datasets featuring affine deformations. Our approach employs the Linear Optimal Transport (LOT) transform to obtain a linear embedding of set-structured data. Utilizing the mathematical properties of the LOT transform, we demonstrate its capacity to accommodate variations in point sets by constructing a convex data space, effectively simplifying point set classification problems. Our method, which employs a nearest-subspace algorithm in the LOT space, demonstrates label efficiency, non-iterative behavior, and requires no hyper-parameter tuning. It achieves competitive accuracies compared to state-of-the-art methods across various point set classification tasks. Furthermore, our approach exhibits robustness in out-of-distribution scenarios where training and test distributions vary in terms of deformation magnitudes. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

Multichannel One-Time Length Encoding: Exploiting Channel Redundancy

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Multichannel One-Time Length Encoding: Exploiting Channel Re...

2023 IEEE Virtual Conference on Communications, VCC 2023

作者： Yin, Hoover H. F. Wang, Xishi Cao, Qi Liu, Shiqiu The Chinese University of Hong Kong Department of Information Engineering Hong Kong The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong N-hop Technologies Ltd Hong Kong Guangzhou Institute Xidian University China Peng Cheng Laboratory China

ISBN: (纸本)9798350318807

In some applications, communication would be terminated shortly due to reasons like security, secrecy, or energy saving. The time interval between two messages can be very long so that the idling interval is longer than sufficient to distinguish the messages. On the other hand, when there are multiple channels available, the channel-use allocation on the channels carries some information. In this paper, we exploit the use of the information carried by the channel-use allocation, specifically, the number of bits sent via each channel, so that we can transmit some extra bits implicitly at no extra cost. We propose a concrete scheme to make use of this advantage. © 2023 IEEE.

关键词： Energy conservation

More for Less: Integrating Capability-Predominant and Capacity-Predominant Computing

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arXiv 2025年

作者： Zheng, Zhong Papka, Michael E. Lan, Zhiling Department of Computer Science University of Illinois Chicago United States Argonne National Laboratory University of Illinois Chicago United States University of Illinois Argonne National Laboratory Chicago United States

Capability jobs (e.g., large, long-running tasks) and capacity jobs (e.g., small, short-running tasks) are two common types of workloads in high-performance computing (HPC). Different HPC systems are typically deployed to handle distinct computing workloads. For example, Theta at the Argonne Leadership Computing Facility (ALCF) primarily serves capability jobs, while Cori at the National Energy Research Scientific Computing Center (NERSC) predominantly handles capacity workloads. However, this segregation often leads to inefficient resource utilization and higher costs due to the need for operating separate computing platforms. This work examines what-if scenarios for integrating siloed platforms. Specifically, we collect and characterize two real workloads from production systems at DOE laboratories, representing capability-predominant and capacity-predominant computing, respectively. We investigate two approaches to unification. Workload fusion explores how efficiently resources are utilized when a unified system accommodates diverse workloads, whereas workload injection identifies opportunities to enhance resource utilization on capability computing systems by leveraging capacity jobs. Finally, through extensive trace-based, event-driven simulations, we explore the potential benefits of co-scheduling both types of jobs on a unified system to enhance resource utilization and reduce costs, offering new insights for future research in unified computing. Copyright © 2025, The Authors. All rights reserved.

关键词： Computing power

Prompt Learning on Temporal Interaction Graphs

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arXiv 2024年

作者： Chen, Xi Zhang, Siwei Xiong, Yun Wu, Xixi Zhang, Jiawei Sun, Xiangguo Zhang, Yao Zhao, Feng Kang, Yulin Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China IFM Lab Department of Computer Science University of California DavisCA United States Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Ant Group Shanghai Hangzhou China

Temporal Interaction Graphs (TIGs) are widely utilized to represent real-world systems, such as e-commerce and social networks. To facilitate representation learning on TIGs, researchers have proposed a series of TIG models. However, these models are still facing two tough gaps between the pre-training and downstream predictions in their "pre-train, predict" training paradigm. First, there is a temporal gap that exhibits limited accommodation ability to their timely predictions. This shortcoming severely undermines their applicability in distant future predictions on the dynamically evolving TIG data. Second, there is a semantic gap due to the lack of versatility in these pre-trained models to effectively cater to diverse downstream tasks. This hinders their practical applications, as they struggle to align with their learning and prediction capabilities across various application scenarios. Recently, the "pre-train, prompt" paradigm has emerged as a lightweight mechanism for model generalization. Therefore, applying this paradigm within TIGs is a potential solution to solve the aforementioned challenges. However, the adaptation of this paradigm to TIGs is not straightforward. The prevalent application of prompting in static graph contexts falls short in temporal settings due to a lack of consideration for time-sensitive dynamics and a deficiency in expressive power. To address this issue, we introduce Temporal Interaction Graph Prompting (TIGPrompt), a versatile framework that seamlessly integrates with existing TIG models, bridging both the temporal and semantic gaps mentioned above. In detail, we propose a temporal prompt generator to offer temporally-aware prompts for different tasks. These prompts stand out for their minimalistic design, relying solely on the fine-tuning of the prompt generator with very little supervision data, which is extremely efficient. To cater to varying computational resource demands, we propose an extended "pre-train, prompt-based fine

关键词： Forecasting

Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption

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arXiv 2025年

作者： Chen, Weilin Cai, Ruichu Qiao, Jie Yan, Yuguang Hernández-Lobato, José Miguel School of Computer Science Guangdong University of Technology Guangzhou China Peng Cheng Laboratory Shenzhen China Department of Engineering University of Cambridge Cambridge United Kingdom

Estimating causal effects under networked interference is a crucial yet challenging problem. Existing methods based on observational data mainly rely on the networked unconfoundedness assumption, which guarantees the identification of networked effects. However, the networked unconfoundedness assumption is usually violated due to the latent confounders in observational data, hindering the identification of networked effects. Interestingly, in such networked settings, interactions between units provide valuable information for recovering latent confounders. In this paper, we identify three types of latent confounders in networked inference that hinder identification: those affecting only the individual, those affecting only neighbors, and those influencing both. Specifically, we devise a networked effect estimator based on identifiable representation learning techniques. Theoretically, we establish the identifiability of all latent confounders, and leveraging the identified latent confounders, we provide the networked effect identification result. Extensive experiments validate our theoretical results and demonstrate the effectiveness of the proposed method. Copyright © 2025, The Authors. All rights reserved.

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