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CancerSRT:a spatially resolved transcriptomics database for human cancers

Journal of Genetics and Genomics 2024年第12期51卷 1505-1508页

作者： Yuying Huo Jiakang Wang Chengcheng Liu Jinxia Wang Chen Wang Wenbo Guo Zhiyuan Yuan Tiantian Guo Jin Gu Xiangyu Li School of Software Engineering Beijing Jiaotong UniversityBeijing 100044China Department of Biological Sciences Center for Systems BiologyThe University of Texas at DallasRichardsonTX 75080USA MOE Key Laboratory of Bioinformatics BNRIST Bioinformatics DivisionDepartment of AutomationTsinghua UniversityBeijing 100084China Institute of Science and Technology for Brain-Inspired Intelligence Fudan UniversityShanghai 200433China

Human cancer is one of the leading causes of death *** heterogeneity and complex microenvironment are major challenges for anti-cancer treatment.A better understanding of the tumor heterogeneity might contribute to more precise diagnosis and *** advances in single-cell RNA-sequencing(scRNA-seq)have provided valuable insights into cell fate determination and development in cancer,but the main limitation is that cellular spatial information is ***,spatially resolved transcriptomics technologies have enabled gene expression profiling with spatial coordinates in tissues,which opens up new avenues for deciphering the cancer spatial structure and accelerating oncological research.

关键词： diagnosis spatially resolved

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Modeling and Real-Time Simulation of Microgrid Components Using Systemc-Ams

Modeling and Real-Time Simulation of Microgrid Components Us...

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2023 Winter Simulation Conference, WSC 2023

作者： Bhadani, Rahul Karsai, Gabor Tu, Hao Lukic, Srdjan Vanderbilt University Institute for Software Integrated Systems 1025 16th Ave S Suite 102 NashvilleTN37212 United States North Carolina State University Electrical and Computer Engineering 890 Oval Drive 3114 Engineering Building II RaleighNC27606 United States

ISBN: (纸本)9798350369663

Microgrids are localized power systems that can function independently or alongside the main grid. They consist of interconnected generators, energy storage, and loads that can be managed locally. Using SystemC-AMS, we demonstrate how microgrid components, including solar panels and converters, can be accurately modeled and simulated, along with their interactions. Real-time simulations are crucial for understanding microgrid behavior and optimizing components. This approach facilitates seamless integration with hardware prototypes and automation systems, supporting various development stages. Our study presents a best-case scenario for real-time simulation, assuming each loop takes less time than the simulation time step, with fallback to the previous value if data isn't received in time. This article introduces the first known real-time simulation strategy using SystemC-AMS, enabling the real-time simulation of microgrid components and integration with external devices. The implementation adopts a model-based design approach, creating increasingly complex systems with grid components and controllers. © 2023 IEEE.

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Human-Aligned Calibration for AI-Assisted Decision Making

arXiv

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arXiv 2023年

作者： Corvelo Benz, Nina L. Rodriguez, Manuel Gomez Max Planck Institute for Software Systems Germany Department of Biosystems Science and Engineering ETH Zurich Switzerland

Whenever a binary classifier is used to provide decision support, it typically provides both a label prediction and a confidence value. Then, the decision maker is supposed to use the confidence value to calibrate how much to trust the prediction. In this context, it has been often argued that the confidence value should correspond to a well calibrated estimate of the probability that the predicted label matches the ground truth label. However, multiple lines of empirical evidence suggest that decision makers have difficulties at developing a good sense on when to trust a prediction using these confidence values. In this paper, our goal is first to understand why and then investigate how to construct more useful confidence values. We first argue that, for a broad class of utility functions, there exist data distributions for which a rational decision maker is, in general, unlikely to discover the optimal decision policy using the above confidence values—an optimal decision maker would need to sometimes place more (less) trust on predictions with lower (higher) confidence values. However, we then show that, if the confidence values satisfy a natural alignment property with respect to the decision maker’s confidence on her own predictions, there always exists an optimal decision policy under which the level of trust the decision maker would need to place on predictions is monotone on the confidence values, facilitating its discoverability. Further, we show that multicalibration with respect to the decision maker’s confidence on her own predictions is a sufficient condition for alignment. Experiments on four different AI-assisted decision making tasks where a classifier provides decision support to real human experts validate our theoretical results and suggest that alignment may lead to better decisions. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Toward Coordination Control of Multiple Fish-Like Robots:Real-Time Vision-Based Pose Estimation and Tracking via Deep Neural Networks

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IEEE/CAA Journal of Automatica Sinica 2021年第12期8卷 1964-1976页

作者： Tianhao Zhang Jiuhong Xiao Liang Li Chen Wang Guangming Xie State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design LaboratoryCollege of EngineeringPeking UniversityBeijing 100871China National Engineering Research Center of Software Engineering Peking UniversityBeijing 100871 Institute of Ocean Research Peking UniversityBeijing100871 Peng Cheng Laboratory Shenzhen 518055China

Controlling multiple multi-joint fish-like robots has long captivated the attention of engineers and biologists,for which a fundamental but challenging topic is to robustly track the postures of the individuals in real *** requires detecting multiple robots,estimating multi-joint postures,and tracking identities,as well as processing fast in real *** the best of our knowledge,this challenge has not been tackled in the previous *** this paper,to precisely track the planar postures of multiple swimming multi-joint fish-like robots in real time,we propose a novel deep neural network-based method,named *** TAB part fuses the top-down and bottom-up approaches for vision-based pose estimation,while the IOL part with long short-term memory considers the motion constraints among joints for precise pose *** satisfying performance of our TAB-IOL is verified by testing on a group of freely swimming fish-like robots in various scenarios with strong disturbances and by a deed comparison of accuracy,speed,and robustness with most state-of-the-art ***,based on the precise pose estimation and tracking realized by our TAB-IOL,several formation control experiments are conducted for the group of fish-like *** results clearly demonstrate that our TAB-IOL lays a solid foundation for the coordination control of multiple fish-like robots in a real working *** believe our proposed method will facilitate the growth and development of related fields.

关键词： Deep neural networks formation control multiple fish-like robots pose estimation pose tracking

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Recommender System for Configuration Management Process of Entrepreneurial software Designing Firms

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Computers, Materials & Continua 2021年第5期67卷 2373-2391页

作者： Muhammad Wajeeh Uz Zaman Yaser Hafeez Shariq Hussain Haris Anwaar Shunkun Yang Sadia Ali Aaqif Afzaal Abbasi Oh-Young Song University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindi46000Pakistan Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan Department of Electrical Electronics and Telecommunication EngineeringUniversity of Engineering and TechnologyLahore54000Pakistan School of Reliability and Systems Engineering Beihang UniversityBeijing100191China Department of Software Sejong UniversitySeoul05006South Korea

The rapid growth in software demand incentivizes software development organizations to develop exclusive software for their customers *** problem is addressed by the software development industry by software product line(SPL)practices that employ feature ***,optimal feature selection based on user requirements is a challenging ***,there is a requirement to resolve the challenges of software development,to increase satisfaction and maintain high product quality,for massive customer needs within limited *** this work,we propose a recommender system for the development team and clients to increase productivity and quality by utilizing historical information and prior experiences of similar developers and *** proposed system recommends features with their estimated cost concerning new software requirements,from all over the globe according to similar developers’and clients’needs and *** system guides and facilitates the development team by suggesting a list of features,code snippets,libraries,cheat sheets of programming languages,and coding references from a cloud-based knowledge management ***,a list of features is suggested to the client according to their needs and *** experimental results revealed that the proposed recommender system is feasible and effective,providing better recommendations to developers and *** provides proper and reasonably well-estimated costs to perform development tasks effectively as well as increase the client’s satisfaction *** results indicate that there is an increase in productivity,performance,and quality of products and a reduction in effort,complexity,and system ***,our proposed system facilitates developers and clients during development by providing better recommendations in terms of solutions and anticipated ***,the increase in productivity and satisfaction level maximizes the benefits and usability of SPL in the

关键词： Feature selection recommender system software reuse configuration management

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GAMS-based Efficient Approach for Coordination of Directional Overcurrent Relays in Distribution System 19

GAMS-based Efficient Approach for Coordination of Directiona...

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19th International Conference on Emerging Technologies, ICET 2024

作者： Waqas, Abu Bakar Raza, Muhammad Bilal Ashraf, Muhammad Mansoor Waseem, Muhammad Pallonetto, Fabiano Kulsoom, Farzana University of Engineering and Technology Electrical Engineering Department Taxila Pakistan Maynooth University Co. International Renewable and Energy Systems Integration Research Group (IRESI) Department of Electronics Engineering Kildare Ireland School of Business Innovation Value Institute (IVI) Maynooth University Co. Lero - Science Foundation Ireland Research Centre for Software Kildare Ireland University of Engineering and Technology Telecommunication Engineering Department Taxila Pakistan

ISBN: (纸本)9798350379587

In designing a power system, efficacious synchronization of directional over-current relays (DOCRs) plays a pivotal role in guaranteeing the system's stability and ensuring continuous power flow, even in the event of disturbance. However, attaining this optimized strategy pertains to the persistence of solving extremely constrained and nonlinear optimized complications. The main intention is to formulate an optimized coordination strategy to optimize the overall operational time (OT) of DOCRs. This involves featuring optimized values of decision variables, specifically the relay plug settings (PS) and time dial settings (TDS). The designed formulation for DOCRs is employed on various IEEE bus networks like the 8, 9, and 15 bus systems. Comparative evaluation of obtained results is accomplished with numerous algorithms that showcase the competency of the GAMS-driven optimization strategy. The results depict that there is an improvement of 1.8-23 % in the total operational time of relays depending upon the test system under consideration. © 2024 IEEE.

关键词： Coordination time Interval Directional Over-current Relays General Algebraic Modelling System Plug Setting Time multiplier setting

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Online Min Cost Circulation for Multi-Object Tracking on Fragments

Online Min Cost Circulation for Multi-Object Tracking on Fra...

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International Conference on Intelligent Transportation

作者： Yanbing Wang Junyi Ji William Barbour Daniel B. Work Department of Civil and Environmental Engineering Institute for Software Integrated Systems Vanderbilt University Nashville TN USA

Multi-object tracking (MOT) or global data association problem is commonly approached as a minimum-cost-flow or minimum-cost-circulation problem on a graph. While there have been numerous studies aimed at enhancing algorithm efficiency, most of them focus on the batch problem, where all the data must be available simultaneously to construct a static graph. However, with the growing number of applications that generate streaming data, an efficient online algorithm is required to handle the streaming nature of the input. In this paper, we present an online extension of the well-known negative cycle canceling algorithm for solving the multi-object tracking problem with streaming fragmented data. We provide a proof of correctness for the proposed algorithm and demonstrate its efficiency through numerical experiments.

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Exploiting Inter-sample and Inter-feature Relations in Dataset Distillation

Exploiting Inter-sample and Inter-feature Relations in Datas...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wenxiao Deng Wenbin Li Tianyu Ding Lei Wang Hongguang Zhang Kuihua Huang Jing Huo Yang Gao State Key Laboratory for Novel Software Technology Nanjina University China Microsoft Corporation USA University of Wollongong Australia Systems Engineering Institute AMS China School of Systems Engineering University of Defense Technology China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Dataset distillation has emerged as a promising approach in deep learning, enabling efficient training with small synthetic datasets derived from larger real ones. Particularly, distribution matching-based distillation methods attract attention thanks to its effectiveness and low computational cost. However, these methods face two primary limitations: the dispersed feature distribution within the same class in synthetic datasets, reducing class discrim-ination, and an exclusive focus on mean feature consistency, lacking precision and comprehensiveness. To address these challenges, we introduce two novel constraints: a class centralization constraint and a covariance matching constraint. The class centralization constraint aims to enhance class discrimination by more closely clustering samples within classes. The covariance matching constraint seeks to achieve more accurate feature distribution matching between real and synthetic datasets through local feature covariance matrices, particularly beneficial when sample sizes are much smaller than the number of features. Experiments demonstrate notable improvements with these constraints, yielding performance boosts of up to 6.6% on CIFAR10, 2.9% on SVHN, 2.5% on CIFAR100, and 2.5% on TinyImageNet, compared to the state-of-the-art relevant methods. In addition, our method maintains robust performance in cross-architecture settings, with a maximum performance drop of 1.7% on four architectures. Code is avail-able at https://***/VincenDen/IID.

关键词： Training Deep learning Computer vision Face recognition Focusing Computer architecture Computational efficiency

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A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI

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Computers, Materials & Continua 2022年第7期72卷 73-92页

作者： Farman Ali Sadia Khan Arbab Waseem Abbas Babar Shah Tariq Hussain Dongho Song Shaker EI-Sappagh Jaiteg Singh Department of Software Sejong UniversitySeoul05006Korea Institute of Computer Sciences&Information Technology The University of AgriculturePeshawar25130Pakistan College of Technological Innovation Zayed UniversityDubai19282UAE Department of Software Korea Aerospace UniversitySeoul10540Korea Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Information Systems Department Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13518Egypt Chitkara University Institute of Engineering&Technology Chitkara UniversityRajpura140401India

Medical Image Analysis(MIA)is one of the active research areas in computer vision,where brain tumor detection is the most investigated domain among researchers due to its deadly *** tumor detection in magnetic resonance imaging(MRI)assists radiologists for better analysis about the exact size and location of the ***,the existing systems may not efficiently classify the human brain tumors with significantly higher *** addition,smart and easily implementable approaches are unavailable in 2D and 3D medical images,which is the main problem in detecting the *** this paper,we investigate various deep learning models for the detection and localization of the tumor in MRI.A novel twotier framework is proposed where the first tire classifies normal and tumor MRI followed by tumor regions localization in the second ***,in this paper,we introduce a well-annotated dataset comprised of tumor and normal *** experimental results demonstrate the effectiveness of the proposed framework by achieving 97%accuracy using GoogLeNet on the proposed dataset for classification and 83%for localization tasks after finetuning the pre-trained you only look once(YOLO)v3 model.

关键词： Tumor localization MRI Image classification GoogLeNet YOLOv3

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System to Improve the Quality of Water Resources in Sri Lanka Using Machine Learning and Image Processing 4

System to Improve the Quality of Water Resources in Sri Lank...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Liyanage, Samindi M.H. Gajanayake, G.M.B.S. Wijewickrama, Oshini Fernando, S.D.S.A. Wijendra, Dinuka Gamage, Aruna Ishara Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Department Computer Systems & Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

Water covers approximately 71% of the earth's surface, but only 1.2% of it can be used for drinking. However, due to the amount of waste water released into water resources, the presence of harmful microorganisms, and natural occurrences such as eutrophication, even that water cannot be used directly for drinking purposes without purification. One method of purifying water is chlorination. However, if the chlorine level exceeds the standard, it can cause both long-term and short-term illnesses. As a result, a system is imposed to solve four problems: predicting the pH value of chlorinated drinking water, determining the quantification value of active sludge in a wastewater plant, detecting microorganisms in drinking water, and predicting the percentage of eutrophication in a water resource. © 2022 IEEE.

关键词： Potable water

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