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arXiv 2021年

作者： Tang, Chuan Yang, Xi Wu, Bojian Han, Zhizhong Chang, Yi School of Artificial Intelligence Jilin University China Zhejiang University China The Department of Computer Science Wayne State University United States Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MoE China

Shape-Text matching is an important task of high-level shape understanding. Current methods mainly represent a 3D shape as multiple 2D rendered views, which obviously can not be understood well due to the structural ambiguity caused by self-occlusion in the limited number of views. To resolve this issue, we directly represent 3D shapes as point clouds, and propose to learn joint embedding of point clouds and texts by bidirectional matching between parts from shapes and words from texts. Specifically, we first segment the point clouds into parts, and then leverage optimal transport method to match parts and words in an optimized feature space, where each part is represented by aggregating features of all points within it and each word is abstracted by its contextual information. We optimize the feature space in order to enlarge the similarities between the paired training samples, while simultaneously maximizing the margin between the unpaired ones. Experiments demonstrate that our method achieves a significant improvement in accuracy over the SOTAs on multi-modal retrieval tasks under the Text2Shape dataset. Codes are available at here. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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Combination of Enrichment Using Gene Ontology and Transcriptomic Analysis Revealed Contribution of Interferon Signaling to Severity of COVID-19

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Interdisciplinary Perspectives on Infectious Diseases 2022年第0期2022卷 3515001页

作者： Ramadhani, Hilmi Farhan Annisa, Annisa Tedjo, Aryo Noor, Dimas R. Kusuma, Wisnu Ananta Department of Computer Science Faculty of Mathematics and Natural Sciences IPB University Bogor 16680 Indonesia Department of Medical Chemistry Faculty of Medicine Universitas Indonesia Jakarta Indonesia Human Cancer Research Center Indonesian Medical Education and Research Institute Faculty of Medicine Universitas Indonesia Jakarta Indonesia Tropical Biopharmaca Research Center IPB University Bogor 16128 Indonesia

Introduction. The severity of coronavirus disease 2019 (COVID-19) was known to be affected by hyperinflammation. Identification of important proteins associated with hyperinflammation is critical. These proteins can be a potential target either as biomarkers or targets in drug discovery. Therefore, we combined enrichment analysis of these proteins to identify biological knowledge related to hyperinflammation. Moreover, we conducted transcriptomic data analysis to reveal genes contributing to disease severity. Methods. We performed large-scale gene function analyses using gene ontology to identify significantly enriched biological processes, molecular functions, and cellular components associated with our proteins. One of the appropriate methods to functionally group large-scale protein-protein interaction (PPI) data into small-scale clusters is fuzzy K-partite clustering. We collected the transcriptomics data from GEO Database (GSE 164805 and GPL26963 platform). Moreover, we created a data set and analyzed gene expression using Orange Data-mining version 3.30. PPI analysis was performed using the STRING database with a confidence score >0.9. Results. This study indicated that four proteins were associated with 25 molecular functions, three were associated with 22 cellular components, and one was associated with ten biological processes. All GOs of molecular function, cellular components, and 9 of 14 biological processes were associated with important cytokines related to the COVID-19 cytokine storm present in the resulting cluster. The expression analysis showed the interferon-related genes IFNAR1, IFI6, IFIT1, and IFIT3 were significant genes, whereas PPIs showed their interactions were closely related. Conclusion. A combination of enrichment using GOs and transcriptomic analysis showed that hyperinflammation and severity of COVID-19 may be caused by interferon signaling. © 2022 Hilmi Farhan Ramadhani et al.

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A smart filtering-based adaptive optimized link state routing protocol in flying ad hoc networks for traffic monitoring

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Journal of King Saud University - computer and Information sciences 2024年第4期36卷

作者： Hosseinzadeh, Mehdi Ali, Saqib Rahmani, Amir Masoud Lansky, Jan Nulicek, Vladimir Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Darwesh, Aso Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Center of Research and Strategic Studies Lebanese French University Kurdistan Region Iraq Department of Information Technology University of Human Development Sulaymaniyah Kurdistan region Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

Nowadays, the use of drones as a fundamental element of smart cities has attracted the attention of many researchers to monitor and control the traffic of vehicles. Because of the high flexibility of multi-drone systems, like flying ad hoc networks (FANETs), they provide various services and improve modern life in smart cities. However, due to the unique features of FANET, especially the high speed of drones and rapid changes in network topology, communication reliability is a serious challenge in this network. Hence, traditional routing protocols, such as optimized link state routing (OLSR) scheme, cannot work well in these networks. In this paper, a smart filtering-based adaptive optimized link state routing (SFA-OLSR) scheme is proposed in FANETs. To increase adaptability to the FANET environment, SFA-OLSR provides a new solution to adjust the hello broadcast period so that each flying node specifies its broadcast period based on a new scale called cosine similarity between real and predicted positions. Furthermore, in SFA-OLSR, each flying node develops a filtering algorithm based on two parameters, namely link lifetime and remaining energy. The purpose of this algorithm is to reduce the size of the single-hop neighboring set of each flying node and minimize the search space when finding multi-point relays (MPRs). This increases the convergence speed of the algorithm. Then, SFA-OLSR exploits the sparrow search algorithm (SSA) to single out the best MPRs. This algorithm introduces a multi-objective function by focusing on three components, including energy, link lifespan, and neighbor degree. Lastly, the simulation process of SFA-OLSR is performed by the NS3 simulator. This process evaluates the performance of the proposed method and three schemes, namely Gangopadhyay et al., P-OLSR, and OLSR-ETX. These evaluations show that SFA-OLSR has a good performance in terms of three scales, namely packet delivery ratio, delay, and throughput, but its overhead is more than

关键词： Artificial intelligence (AI) Flying ad hoc networks (FANETs) Meta-heuristic algorithms Reliability Routing

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Biological arrow of time: Emergence of tangled information hierarchies and self-modelling dynamics

arXiv

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arXiv 2024年

作者： Prokopenko, Mikhail Davies, Paul C.W. Harré, Michael Heisler, Marcus Kuncic, Zdenka Lewis, Geraint F. Livson, Ori Lizier, Joseph T. Rosas, Fernando E. The Centre for Complex Systems University of Sydney SydneyNSW2006 Australia School of Computer Science Faculty of Engineering University of Sydney SydneyNSW2006 Australia The Beyond Center for Fundamental Concepts in Science Arizona State University TempeAZ85287–0506 United States School of Life and Environmental Sciences Faculty of Science University of Sydney SydneyNSW2006 Australia School of Physics Faculty of Science University of Sydney SydneyNSW2006 Australia The Charles Perkins Centre University of Sydney SydneyNSW2006 Australia Sussex AI Centre for Consciousness Science Department of Informatics University of Sussex BrightonBN19RH United Kingdom Centre for Psychedelic Research Department of Brain Science Imperial College London LondonSW72AZ United Kingdom Centre for Complexity Science Imperial College London LondonSW72AZ United Kingdom Center for Eudaimonia and Human Flourishing University of Oxford OxfordOX39BX United Kingdom

We study open-ended evolution by focusing on computational and information-processing dynamics underlying major evolutionary transitions. In doing so, we consider biological organisms as hierarchical dynamical systems that generate regularities in their phase-spaces through interactions with their environment. These emergent information patterns can then be encoded within the organism’s components, leading to self-modelling "tangled hierarchies". Our main conjecture is that when macro-scale patterns are encoded within micro-scale components, it creates fundamental tensions (computational inconsistencies) between what is encodable at a particular evolutionary stage and what is potentially realisable in the environment. A resolution of these tensions triggers an evolutionary transition which expands the problem-space, at the cost of generating new tensions in the expanded space, in a continual process. We argue that biological complexification can be interpreted computation-theoretically, within the Gödel–Turing–Post recursion-theoretic framework, as open-ended generation of computational novelty. In general, this process can be viewed as a meta-simulation performed by higher-order systems that successively simulate the computation carried out by lower-order systems. This computation-theoretic argument provides a basis for hypothesising the biological arrow of *** Codes 03Dxx, 68Qxx, 92Dxx, 37N25 Copyright © 2024, The Authors. All rights reserved.

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A SSIM Guided cGAN Architecture For Clinically Driven Generative Image Synthesis of Multiplexed Spatial Proteomics Channels

arXiv

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arXiv 2022年

作者： Saurav, Jillur Rahman Nasr, Mohammad Sadegh Shang, Helen H. Koomey, Paul Robben, Michael Huber, Manfred Weidanz, Jon Ryan, Bríd Ruppin, Eytan Jiang, Peng Luber, Jacob M. Department of Computer Science University of Texas Arlington United States Multi-Interprofessional Center for Health Informatics University of Texas Arlington United States Department of Bioengineering University of Texas Arlington United States Department of Kinesiology University of Texas Arlington United States Division of Internal Medicine Ronald Reagan University of California Los Angeles Medical Center United States Laboratory of Human Carcinogenesis Center for Cancer Research National Cancer Institute National Institutes of Health United States Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health United States

Histopathological work in clinical labs often relies on immunostaining of proteins, which can be time-consuming and costly. Multiplexed spatial proteomics imaging can increase interpretive power, but current methods cannot cost-effectively sample the entire proteomic retinue important to diagnostic medicine or drug development. To address this challenge, we developed a conditional generative adversarial network (cGAN) that performs image-to-image (i2i) synthesis to generate accurate biomarker channels in multiplexed spatial proteomics *** approached this problem as missing biomarker expression generation, where we assumed that a given n-channel multiplexed image has p channels (biomarkers) present and q channels (biomarkers) absent, with p+q=n, and we aimed to generate the missing q channels. To improve accuracy, we selected p and q channels based on their structural similarity, as measured by a structural similarity index measure (SSIM). We demonstrated the effectiveness of our approach using spatial proteomic data from the human BioMolecular Atlas Program (HuBMAP)2, which we used to generate spatial representations of missing proteins through a U-Net based image synthesis pipeline. Channels were hierarchically clustered by SSIM to obtain the minimal set needed to recapitulate the underlying biology represented by the spatial landscape of proteins. We also assessed the scalability of our algorithm using regression slope analysis, which showed that it can generate increasing numbers of missing biomarkers in multiplexed spatial proteomics images. Furthermore, we validated our approach by generating a new spatial proteomics data set from human lung adenocarcinoma tissue sections and showed that our model could accurately synthesize the missing channels from this new data set. Overall, our approach provides a cost-effective and time-efficient alternative to traditional immunostaining methods for generating missing biomarker channels, while also increasing the am

关键词： Proteins

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Face-sketch learning with human sketch-drawing order enforcement

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science China(Information sciences) 2020年第11期63卷 298-311页

作者： Liang CHANG Lihua JIN Lifen WENG Wentao CHAO Xuguang WANG Xiaoming DENG Qiulei DONG School of Artificial Intelligence Beijing Normal University Department of Design Art Xiamen University of Technology Department of Automation North China Electric Power University Beijing Key Laboratory of Human Computer Interactions Institute of Software Chinese Academy of Sciences National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences

Dear editor,Although face-sketch synthesis generates a sketch from a given face photo automatically [1], it is an open research problem in computer vision [2–4]. Recently, several deep neural network (DNN)methods for... 详细信息

关键词： face sketch synthesis deep neural network order enforcement image synthesis generative adversarial network

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Analytics in Higher Education: Scoping the Landscape of Research in the Area

Analytics in Higher Education: Scoping the Landscape of Rese...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Natashaa Kaul Amruta Deshpande Amit Mittal Vishal Jain Symbiosis Center for Management and Human Resource Development Pune India Symbiosis International University Pune India Indira School of Business Studies PGDM Pune India Chitkara Business School Chitkara University Punjab India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida U.P. India

ISBN: (纸本)9781665487351

Understanding the research landscape of any domain is of primary importance at all stages of a concept's evolution. Research in the domain of analytics has increased exponentially in the past decade, analytics is a tool being applied to increase effectiveness and efficiency within all domains. Similarly, there are numerous research studies that have applied analytics to the domain of higher education. Assessing research activity is important for planning future research and identifying the way forward to make meaningful contributions. The objective of this paper is to examine the current research in this domain and to identify emerging areas of research to make meaningful contributions. Bibliometric analysis was applied using Scopus. Documents on analytics and higher education keywords in business, accounting and management and published in journals were considered for the analysis. Vos Viewer was used for bibliometric analysis in the area, to identify emerging research themes and to understand the scope of applying analytics in higher education. The search query found 1635 documents when using the keywords of “analytics” and “higher education”. This was further narrowed down to research in Business Management and Accounting and using Journal articles and conference articles, finally yielding 103 articles. After a primary read to remove articles misplaced in the group the final number of articles include for the bibliometric analysis came down to 98. There has been an increase in publications since 2018 with interest increasing post pandemic in the area. The research categories are Student outcomes and perspective, academician outcomes and perspective, design of learning systems, etc. Based on this, it is recommended that research should focus on theoretical aspects of applying analytics to HEIs, ethical issues in data collection, etc..

关键词： Learning systems Pandemics Education Bibliometrics Data collection Market research Mathematical models

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人类泛基因组参考图谱发布开启基因组学领域的新篇章

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science Bulletin 2023年第14期68卷 1484-1487页

作者：王博党宁馨杨晓飞徐书华叶凯 School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Genome Institute the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China State Key Laboratory of Genetic Engineering Human Phenome InstituteZhangjiang Fudan International Innovation CenterCenter for Evolutionary BiologySchool of Life SciencesFudan UniversityShanghai 200032China Ministry of Education Key Laboratory of Contemporary Anthropology Collaborative Innovation Center for Genetics and DevelopmentFudan UniversityShanghai 200032China Department of Liver Surgery and Transplantation Liver Cancer InstituteZhongshan HospitalFudan UniversityShanghai 200032China School of Life Science and Technology Shanghai Tech UniversityShanghai 201210China

The significance of DNA sequencing in advancing medicalresearch,improving healthcare practices,and enhancing ourunderstanding of human evolution cannot be *** yearmarks the 20th anniversary of the completion of the human Genome Project(HGP)in 2003,with the draft human genome reference published in 2001[1,2].However,even with the latesthuman reference genome,GRCh38,there remain gaps totalingapprox.150 Mb,involving complex sequence in the pericentromeric and subtelomeric regions,as well as recent segmentalduplications and both ampliconic gene and ribosomal DNA(rDNA)arrays[3].A groundbreaking achievement was recently accomplished by the Telomere-to-Telomere(T2T)consortium whosuccessfully closed the remaining gaps and completed the firstend-to-end assembly of a human haploid genome,known as T2TCHM13[3].

关键词：基因组学 breaking accomplished

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Dynamical process of a bit-width reduced Ising model with simulated annealing

arXiv

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arXiv 2023年

作者： Kikuchi, Shuta Togawa, Nozomu Tanaka, Shu Department of Applied Physics and Physico-Informatics Keio University Kanagawa223-8522 Japan Department of Computer Science and Communications Engineering Waseda University Tokyo169-8555 Japan Human Biology-Microbiome-Quantum Research Center WPI-Bio2Q Keio University Tokyo108-8345 Japan Green Computing System Research Organization Waseda University Tokyo162-0042 Japan International Research Frontiers Initiative Tokyo Institute of Technology Tokyo108-0023 Japan

Ising machines have attracted attention as efficient solvers for combinatorial optimization problems, which are formulated as ground-state (lowest-energy) search problems of the Ising model. Due to the limited bit-width of coefficients on Ising machines, the Ising model must be transformed into a bit-width reduced (BWR) Ising model. According to previous research, the bit-width reduction method, which adds auxiliary spins, ensures that the ground state of the BWR Ising model is theoretically the same as the Ising model before bit-width reduction (original Ising model). However, while the dynamical process is closely related to solution accuracy, how the BWR Ising model progresses towards the ground state remains to be elucidated. Therefore, we compared the dynamical processes of these models using simulated annealing (SA). Our findings reveal significant differences in the dynamical process across models. Analysis from the viewpoint of statistical mechanics found that the BWR Ising model has two characteristic properties: an effective temperature and a slow relaxation. These properties alter the temperature schedule and spin flip probability in the BWR Ising model, leading to differences in the dynamical process. Therefore, to obtain the same dynamical process as the original Ising model, we proposed SA parameters for the BWR Ising model. We demonstrated the proposed SA parameters using a square lattice Ising model, in which all coefficients were set uniformly to the same positive values or randomly. Our experimental evaluations demonstrated that the dynamical process of the BWR and original Ising model became closer. © 2023, CC BY.

关键词： Simulated annealing

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Surformer: An Interpretable Pattern-Perceptive Survival Transformer for Cancer Survival Prediction from Histopathology Whole Slide Images

SSRN

引用

SSRN 2023年

作者： Wang, Zhikang Gao, Qian Yi, Xiaoping Zhang, Xinyu Zhang, Yiwen Zhang, Daokun Liò, Pietro Bain, Christopher Bassed, Richard Li, Shanshan Guo, Yuming Imoto, Seiya Yao, Jianhua Daly, Roger J. Song, Jiangning Xiangya Hospital Central South University Hunan Changsha China Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University Melbourne Australia Faculty of Information Technology Monash University Melbourne Australia Climate Air Quality Research Unit School of Public Health and Preventive Medicine Monash University Melbourne Australia Department of Computer Science and Technology The University of Cambridge Cambridge United Kingdom Victorian Institute of Forensic Medicine Melbourne Australia Human Genome Center Institute of Medical Science The University of Tokyo Japan Tencent AI Lab Tencent Shenzhen China

High-resolution histopathology whole slide images (WSIs) contain abundant valuable information for cancer prognostication. However, most computational pathology methods for survival prediction have weak interpretability and cannot explain the decision-making processes reasonably. To address this issue, we propose a highly interpretable neural network termed pattern-perceptive survival transformer (Surformer, a portmanteau of survival and transformer) for cancer survival prediction from WSIs. Notably, it can quantify specific histological patterns and explicitly interpret the model prediction with statistical analysis, all of which are achieved through bag-level labels without any patch/cell-level auxiliary information. Specifically, the proposed ratio-reserved cross- attention module (RRCA) detects valuable features and quantifies them in the form of ratio factors (rfs), in which process both global and multiple local features are generated. Afterward, multi-head self&cross-attention modules proceed with the computation for feature enhancement against noise. Eventually, the designed disentangling loss function guides multiple local features to focus on distinct patterns, thereby assisting RRCA in achieving more explicit quantification. Extensive benchmarking experiments on five TCGA datasets illustrate that Surformer outperforms existing state-of-the-art methods. In addition, we highlight its excellent interpretability and utility by showing attentive visualizations and analysis. Surformer is expected to be exploited as a useful tool for performing histopathology image data-driven analysis and gaining new insights for interpreting the associations between such images and patient survival states. © 2023, The Authors. All rights reserved.

关键词： Decision making

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