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International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2022

作者： Habib, Nihal Idrissi Azami, Abdellah El Ghoubali, Douae El Ouafi, Zainab Lemsayah, Mustapha Bougdira, Abdesselam Al Idrissi, Najib Rhalem, Wajih Ezziyyani, Mostafa Nejjari, Chakib Ghazal, Hassan Laboratory of Genomics Bioinformatics and Digital Health Mohammed VI University of Health Sciences Casablanca82403 Morocco Mohammed VI Center for Research and Innovation Rabat Morocco High Technology School in Morocco Rabat10090 Morocco Laboratory of Engineering Sciences Mohamed Ben Abdellah University Fez30000 Morocco Department of Surgery Faculty of Medicine Mohammed VI University of Health Sciences Casablanca82403 Morocco Ecole Nationale Supérieure d’Arts Et Métiers ENSAM Rabat10100 Morocco Moroccan Society of Digital Health Rabat Morocco Department of Computer Sciences Faculty of Sciences and Techniques of Tangier Tangier90000 Morocco Department of Epidemiology and Biostatistics International School of Public Health Mohammed VI University of Health Sciences Casablanca82403 Morocco Department of Epidemiology and Public Health Faculty of Medicine University Sidi Mohammed Ben Abdellah Fez Morocco National Center for Scientific and Technical Research Rabat10102 Morocco

ISBN: (纸本)9783031352478

Technological and computational advancements used to generate and process large sets of biological data (omics data) are driving a critical change in the study of biomedical sciences and medicine. While genomics, transcriptomics, and proteomics, coupled with bioinformatics and biostatistics, are gaining momentum, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. The need for this type of data in a clinical setup is becoming more evident. Hence, integrating omics data in electronic health records (EHRs) is a basis for advancing toward personalized diagnosis and treatments. However, due to the heavy and complex nature of omics data, it is still an ongoing challenge to attempt to integrate them into EHRs in an easily interpretable manner. To this end, we present HER-Omics (HERO), a tool that combines both the patients’ EHR and their Omics data. HERO is lightweight, simple to use, and can be integrated with an existing EHR database. The workflow involves taking raw FASTQ files as input, performing the analysis, and giving a clear and readable output that will aid in the decision-making process. We illustrate the use of this tool by using demi transcriptomics data. This shows a proof of concept that we hope will be developed in a more advanced manner in the near future. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data integration

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Online distribution system state estimation via stochastic gradient algorithm

arXiv

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arXiv 2021年

作者： Huang, Jianqiao Zhou, Xinyang Cui, Bai Department of Electrical and Computer Engineering Illinois Institute of Technology Chicago United States Power System Engineering Department National Renewable Energy Laboratory GoldenCO80401 United States

Distribution network operation is becoming more challenging because of the growing integration of intermittent and volatile distributed energy resources (DERs). This motivates the development of new distribution system state estimation (DSSE) paradigms that can operate at fast timescale based on real-time data stream of asynchronous measurements enabled by modern information and communications technology. To solve the real-time DSSE with asynchronous measurements effectively and accurately, this paper formulates a weighted least squares DSSE problem and proposes an online stochastic gradient algorithm to solve it. The performance of the proposed scheme is analytically guaranteed and is numerically corroborated with realistic data on IEEE-123 bus feeder. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy resources

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BCW: Buffer-controlled writes to HDDs for SSD-HDD Hybrid Storage Server 18

BCW: Buffer-controlled writes to HDDs for SSD-HDD Hybrid Sto...

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18th USENIX Conference on File and Storage Technologies, FAST 2020

作者： Wang, Shucheng Lu, Ziyi Cao, Qiang Jiang, Hong Yao, Jie Dong, Yuanyuan Yang, Puyuan Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System School of Computer Science and Technology Huazhong University of Science and Technology Department of Computer Science and Engineering University of Texas at Arlington Alibaba Group

ISBN: (纸本)9781939133120

Hybrid Storage servers combining high-speed SSDs and high-capacity HDDs are designed for high cost-effectiveness and provide μs-level responsiveness for applications. Observations from the production hybrid cloud storage system Pangu suggest that HDDs are often severely underutilized while SSDs are overused, especially for writes that dominate the hybrid storage. This lopsided utilization between HDDs and SSDs leads to not only fast wear-out in the latter but also very high tail latency due to frequent garbage collections induced by intensive writes to the latter. On the other hand, our extensive experimental study reveals that a series of sequential and continuous writes to HDDs exhibit a periodic, staircase shaped pattern of write latency, i.e., low (e.g., 35s), middle (e.g., 55s), and high latency (e.g., 12ms), resulting from buffered writes in HDD's controller. This suggests that HDDs can potentially provide μs-level write IO delay (for appropriately scheduled writes), which is close to SSDs' write performance. These observations inspire us to effectively exploit this performance potential of HDDs to absorb as many writes as possible to avoid SSD overuse without performance degradation. To achieve this goal, we first characterize performance behaviors of hybrid storage in general and its HDDs in particular. Based on the findings on sequential and continuous writes, we propose a prediction model to accurately determine next write latency state (i.e., fast, middle and slow). With this model, a Buffer-Controlled Write approach, BCW, is proposed to proactively and effectively control buffered writes so that low- and mid-latency periods in HDDs are scheduled with application write data and high-latency periods are filled with padded data. Based on BCW, we design a mixed IO scheduler (MIOS) to adaptively steer incoming data to SSDs and HDDs according to write patterns, runtime queue lengths, and disk status. We perform extensive evaluations under production workloads

关键词： Cost effectiveness

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A Continual Learning Method for Reducing Class Interference Based on Replay

A Continual Learning Method for Reducing Class Interference ...

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Chinese Control Conference (CCC)

作者： Zhibo Xu Tian Wang Jian Wang Ce Li Yao Fu Hichem Snoussi School of Automation Science and Electrical Engineering Beihang University Beijing China Institute of Artificial Intelligence SKLSDE Beihang University Beijing China Zhongguancun Laboratory Beijing China School of Computer Science Wuhan University Wuhan China Department of Computer Science and Technology China University of Mining and Technology Beijing China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun China Laboratory of Computer Science and Digital Society University of Technology of Troyes Troyes France

Although deep neural networks perform well on many individual tasks, they suffer from catastrophic forgetting when learning new tasks continually. Recently, various continual learning methods have been proposed, and some approaches based on replaying memory data achieve promising performance. Maximally Interfered Retrieval is a strong replay-based baseline, however, exists class interference due to class imbalance and lacks the ability to generalize to the real class-incremental scenario. In this paper, aiming at these problems, we design Class cumulative Classifier to replace the shared output layer of the original network, which makes it closer to practical applications, and Class balanced Buffer to address the class imbalance of stored samples. In addition, we propose Retrospect strategy to further improve the accuracy. Experimental results on benchmark datasets show that our method outperforms several strong baselines and is more suitable for complex datasets with more classes.

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Cloud-magnetic resonance imaging system: In the era of 6G and artificial intelligence

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Magnetic Resonance Letters 2025年 57-68页

作者： Yirong Zhou Yanhuang Wu Yuhan Su Jing Li Jianyu Cai Yongfu You Jianjun Zhou Di Guo Xiaobo Qu Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University Department of Electronic Science Key Laboratory of Digital Fujian on IoT Communication Xiamen University Shanghai Electric Group Co. Ltd China Telecom Group China Mobile Group Department of Radiology Zhongshan Hospital (Xiamen) Fudan University Xiamen Municipal Clinical Research Center for Medical Imaging Fujian Province Key Clinical Specialty Construction Project (Medical Imaging Department) Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence School of Computer and Information Engineering Xiamen University of Technology

Magnetic resonance imaging(MRI) plays an important role in medical diagnosis,generating petabytes of image data annually in large *** voluminous data stream requires a significant amount of network bandwidth and extensive storage ***,local data processing demands substantial manpower and hardware *** isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and *** this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain *** system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI) algorithm maintenance,hardware upgrading,and collaborative *** workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data(ISMRMRD) ***,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic ***,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other *** Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.

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Foundation Models for Atomistic Simulation of Chemistry and Materials

arXiv

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arXiv 2025年

作者： Yuan, Eric C.-Y. Liu, Yunsheng Chen, Junmin Zhong, Peichen Raja, Sanjeev Kreiman, Tobias Vargas, Santiago Xu, Wenbin Head-Gordon, Martin Yang, Chao Blau, Samuel M. Cheng, Bingqing Krishnapriyan, Aditi Head-Gordon, Teresa Kenneth S. Pitzer Theory Center Department of Chemistry United States Department of Chemical and Biomolecular Engineering United States Electrical Engineering and Computer Science United States Bakar Institute of Digital Materials for the Planet University of California BerkeleyCA94720 United States Chemical Sciences Division United States Applied Mathematics and Computational Research Division United States Energy Technologies Area United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States

Given the power of large language and large vision models, it is of profound and fundamental interest to ask if a foundational model based on data and parameter scaling laws and pre-training strategies is possible for learned simulations of chemistry and materials. The scaling of large and diverse datasets and highly expressive architectures for chemical and materials sciences should result in a foundation model that is more efficient and broadly transferable, robust to out-of-distribution challenges, and easily fine-tuned to a variety of downstream observables, when compared to specific training from scratch on targeted applications in atomistic simulation. In this Perspective we aim to cover the rapidly advancing field of machine learned interatomic potentials (MLIP), and to illustrate a path to create chemistry and materials MLIP foundation models at larger scale. © 2025, CC BY-SA.

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Integral Inequalities for the Analysis of Distributed Parameter systems: A Complete Characterization via the Least-Squares Principle

arXiv

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arXiv 2023年

作者： Feng, Qian Seuret, Alexandre Nguang, Sing Kiong Xiao, Feng School of Control and Computer Engineering North China Electric Power University Beijing China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China Dpto de Ingeniería de Sistemas y Automática Universidad de Sevilla Camino de los Descubrimientos Sevilla Spain Department of Electrical Computer and Software Engineering The University of Auckland New Zealand

A wide variety of integral inequalities (IIs) have been developed and studied for the stability analysis of distributed parameter systems using the Lyapunov functional approach. However, no unified mathematical framework has been proposed that could characterize the similarity and connection between these IIs, as most of them was introduced in a dispersed manner for the analysis of specific types of systems. Additionally, the extent to which the generality of these IIs can be expanded and the optimality of their lower bounds (LBs) remains open questions. In this study, we introduce two general classes of IIs that can generalize nearly all IIs in the literature. The integral kernels of the LBs of our IIs can contain an unlimited number of weighted L2 functions that are linearly independent in a Lebesgue sense. Moreover, we not only estalish the equivalence relations between the LBs of our IIs, but also demonstrate that these LBs are guaranteed by the least squares principle, implying asymptotic convergence to the upper bound when the kernels functions constitutes a Schauder basis of the underlying Hilbert space. Owing to their general structures, our IIs are applicable in a variety of contexts, such as the stability analysis of coupled PDE-ODE systems or cybernetic systems with delay structures. Copyright © 2023, The Authors. All rights reserved.

关键词： Least squares approximations

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A Joint Transmit and Receive Design for Dimmable High Speed MC MU VLC systems

A Joint Transmit and Receive Design for Dimmable High Speed ...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Yujie Yang Yang Yang Hailun Xia Caili Guo Mahdi Chehimi Walid Saad Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of. Posts and Telecommunications Beijing China Bradley Department of Electrical and Computer Engineering Wireless@VT Group Virginia Tech Blacksburg VA USA

ISBN: (纸本)9781665435413

In multi-cell multi-user multiple-input multiple-output (MC MU-MIMO) visible light communication (VLC) systems, each user is equipped with multiple closely-placed photodiodes (PDs) with similar channel gains, leading to severe inter-cell interference and intra-cell interference. To address this problem, this paper proposes a hybrid dimming (HD) scheme with MIMO VLC transceiver design, which jointly optimizes transmit and receive antenna selection (TRAS), cell clustering and precoding (TRASP-HD) for MC MU-MIMO VLC systems. In this scheme, a sum-rate maximization problem under the dimming level and illumination uniformity is formulated and solved by being divided into two sub-problems. In particular, The first sub-problem is on TRAS and cell formation based on the criterion of sum-rate maximization under the illumination uniformity constraint. With the same goal, the second sub-problem is on optimizing the precoding matrices of each cell. Finally, these two sub-problems are iteratively solved to obtain a convergent solution. Simulation results verify that in a typical indoor scenario, the mean bandwidth efficiency of TRASP-HD scheme is 2.36 bit/s/Hz higher than the conventional MC MU-MIMO system.

关键词： Spectral efficiency Precoding Simulation Lighting Receiving antennas Transceivers MIMO

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Engineered Modular Neuronal Networks-on-Chip Represent Structure-Function Relationship

SSRN

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SSRN 2024年

作者： Habibey, Rouhollah Striebel, Johannes Latiftikhereshki, Roshanak Schmieder, Felix Nasiri, Rohollah Latifi, Shahrzad Universitäts-Augenklinik Bonn University of Bonn Dep. of Ophthalmology Ernst-Abbe-Straße 2 BonnD-53127 Germany CRTD - Center for Regenerative Therapies TU Dresden Dresden01307 Germany Dept. Neuroscience Italian Institute of Technology Genova Italy Department of Computer Engineering Faculty of Engineering Kermanshah Branch Azad University Kermanshah Iran Laboratory of Measurement and Sensor System Technique Faculty of Electrical and Computer Engineering TU Dresden Helmholtzstraße 18 Dresden01069 Germany Division of Nanobiotechnology Department of Protein Science Science for Life Laboratory KTH Royal Institute of Technology Solna Sweden AIMES Center for the Advancement of Integrated Medical and Engineering Sciences Department of Neuroscience Karolinska Institute Solna Sweden Department of Neurology David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Neuroscience Rockefeller Neuroscience Institute West Virginia University MorgantownWV26506 United States

Brain function is substantially linked to the highly organized structure of neuronal networks. The structures of in vitro assembled neuronal circuits often vary between samples and change over time, making it challenging to reliably record network functional output and link it to network structure. Therefore, engineering neuronal structures with predefined geometry and reproducible functional features is essential to robustly model in vivo neuronal circuits. Here, we engineered thin microchannel devices to assemble 2D and 3D modular networks. The microchannel devices were coupled with a multi-electrode array (MEA) electrophysiology system to enable long-term electrophysiology recordings from microengineered circuits. Each network consisted of 64 modules connected to their adjacent modules by micron-sized channels. The microstructures physically confined neurons to the recording electrodes, which significantly improved the efficiency of electrophysiology readout. In addition, the microstructures maintained the modular network structure for weeks. Modular circuits within microfluidic devices showed consistent spatial patterns of activity over weeks, which was lacking in the randomly formed circuits. The number of physical connections per module was shown to influence the measured activity and functional connectivity parameters, which represents the impact of network structure on its functional output. We show that microengineered 3D modular networks with profound activity and higher number of functional connections recapitulate key functional features of developing cortex. Structurally and functionally stable 2D and 3D networks mimic the modular architecture of brain circuits and provide a robust and reproducible in vitro microphysiological system for basic and translational neuroscience research. © 2024, The Authors. All rights reserved.

关键词： Microfluidics

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A Novel Air Traffic Complexity Assessment Method Based on Deep Metric Learning

SSRN

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SSRN 2023年

作者： Zhang, Linghui Chen, Haiyan Wu, Lingxiao Liu, Li Xue, Dabin College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory of Air Traffic Management System Nanjing China Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University Hong Kong Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic University Hong Kong

Air traffic complexity is related to the workload of air traffic control officers and pilots, subsequently leading to potential effects on flight safety and efficiency. However, how to assess air traffic complexity accurately is still a question in the concept of Air Traffic Management (ATM). In this study, a model for air traffic complexity assessment is proposed based on deep metric learning. Specifically, the air traffic data collected by surveillance radars are adopted to generate the air traffic image set, from which air traffic features are extracted based on the Convolutional Neural Networks (CNN) model. After that, the Deep Metric learning method based on Asymmetric distance, Aggregation loss, and Edge hard loss, called AAEDM, is applied to address the problem of class imbalance in air traffic images. Finally, the traffic complexity assessment model is proposed based on AAEDM. The proposed model is validated through comprehensive experimentation using two established standard datasets. The results of these experiments indicate the outstanding proficiency of AAEDM, particularly in scenarios involving unbalanced data. The proposed model can extract deeper features of air traffic than traditional machine learning methods, outperforming other models for air traffic complexity assessment. This study can help assess air traffic complexity and improve the robustness of the ATM system. © 2023, The Authors. All rights reserved.

关键词： Air traffic control

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