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arXiv 2024年

作者： Duan, Yueran Nurek, Mateusz Guan, Qing Michalski, Radoslaw Holme, Petter School of Information Engineering China University of Geosciences Beijing China Department of Artificial Intelligence Wroclaw University of Science and Technology Wroclaw Poland Department of Computer Science Aalto University Finland The Center for Computational Social Science Kobe University Japan

Temporality, a crucial characteristic in the formation of social relationships, was used to quantify the long-term time effects of networks for link prediction models, ignoring the heterogeneity of time effects on different time scales. In this work, we propose a novel approach to link prediction in temporal networks, extending existing methods with a cognitive mechanism that captures the dynamics of the interactions. Our approach computes the weight of the edges and their change over time, similar to memory traces in the human brain, by simulating the process of forgetting and strengthening connections depending on the intensity of interactions. We utilized five ground-truth datasets, which were used to predict social ties, missing events, and potential links. We found: (a) the cognitive mechanism enables more accurate capture of the heterogeneity of the temporal effect, leading to an average precision improvement of 9% compared to baselines with competitive AUC. (b) the local structure and synchronous agent behavior contribute differently to different types of datasets. (c) appropriately increasing the time intervals, which may reduce the negative impact from noise when dividing time windows to calculate the behavioral synchrony of agents, is effective for link prediction tasks. © 2024, CC BY.

关键词： Forecasting

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Intra-Datacenter Load Balancing in a Federated Cloud with Throttled Algorithm

Intra-Datacenter Load Balancing in a Federated Cloud with Th...

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2024 International Conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Akinola, Damola Gideon Adetiba, Emmanuel Abayomi, Abdultaofeek Nnaji, Uche Thakur, Surendra Moyo, Sibusiso Covenant University Covenant Applied Informatics and Communications African Center of Excellence Department of Electrical and Information Engineering Ota Nigeria Durban University of Technology HRA Institute for Systems Science Durban South Africa Mangosuthu University of Technology Department of Information and Communication Technology Durban South Africa Covenant University Dept. of Computer and Information Science Covenant Applied Informatics and Communications African Center of Excellence Ota Nigeria Durban University of Technology KZN E-Skills CoLab Durban South Africa Stellenbosch University School for Data Science and Computational Thinking Department of Mathematical Sciences Stellenbosch7602 South Africa

ISBN: (纸本)9798350358155

The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users' requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named;the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment. © 2024 IEEE.

关键词： Cloud platforms

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A Detailed Structured Monitoting System for ICU Patient Mortality rate by using AI Algorithm

A Detailed Structured Monitoting System for ICU Patient Mort...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Shivani Joshi Rajiv Kumar Vipin Rai Avinash Dwivedi Vikash Chandra Tripathi Department of Computer Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida India Department of Applied Computational Science and Engineering NVIDIA AI and Learning & Research Center G L Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science & Engineering Galgotias University Greater Noida India Department of Computer Science & Engineering Galgotias University Greater Noida

The complexity and seriousness of diseases in ICU patients, though, make it difficult for medical practitioners to identify high-risk patients who may face irreversible harm. With ANN (Artificial Neural Network), this study will provide an ICU admission prediction model to help identify high-risk patients at an earlier stage. After collecting and analyzing extensive data from various perspectives including demographics, medical history, vital signs, test results and treatment modalities, exploratory data analysis has been conducted. These are used as markers on how statistical techniques and machine learning algorithms can be used to predict mortality among critically ill patients. Consequently, the parameters are designed within the ANN model to create a predictive model that will be able to effectively classify those at higher risk. Advanced machine learning algorithms together with modern tools such as deep learning techniques will aid in the prompt analysis of complex information for accurate forecasts by this predictive model. The ANN model’s training set will include a large dataset of ICU patients so that it can become reliable and valid. This makes the predictive model developed capable of accurately identifying high risk cases expeditiously

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Identifying a Workplace from a Monitor Snapshot

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Moscow University computational Mathematics and Cybernetics 2023年第3期47卷 166-181页

作者： Piskovskii, V. O. Seminikhin, D. A. Grusho, A. A. Zabezhailo, M. I. Department of Computational Mathematics and Cybernetics Moscow State University Moscow Russia National University of Science and Technology MISiS Moscow Russia Federal Research Center of Computer Science and Management Russian Academy of Sciences Moscow Russia

The authors consider developing a system for identifying the channel of information leakage from a photograph of a document taken from a computer screen using digital watermarks integrated into the image on the monitor. Results are presented from an experimental study to prove the properties of the algorithm and its resistance to deliberate attacks.

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Infrastructuring digital literacy in K-12 education: A national case study

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International Journal of Child-computer Interaction 2024年 42卷

作者： Andersen, Lars Bo Basballe, Ditte Amund Buus, Lillian Dindler, Christian Hansen, Thomas Illum Hjorth, Mikkel Iversen, Ole Sejer Johannessen, Christian Mosbæk Kanstrup, Katrine Holm Lorentzen, Rasmus Fink Misfeldt, Morten Musaeus, Line Have Nielsen, Camilla Balslev Petersen, Marianne Graves Schrøder, Vibeke Slot, Marie Falkesgaard University College Copenhagen Denmark Program for Learning and Digital Technologies VIA University College Denmark Reseach Center for Didactics and Pedagogy VIA University College Denmark Center for Computational Thinking and Design Aarhus University Denmark Applied Research in Education and Social Science UCL University College Denmark Center for Digital Education University of Copenhagen Denmark Department of Computer Science & Center for Computational Thinking and Design Aarhus University Denmark Aarhus University Department of Computer Science & Center for Computational Thinking and Design Denmark

While much CCI research has dealt with the educational challenge of providing children with knowledge and skills for a digital society, little work has dealt with the strategic challenge of developing and implementing a digital literacy subject in K-12 education. In this paper, we explore how to develop, implement, and sustain a national program on technology comprehension by analyzing the newly established Danish knowledge center for digital technology comprehension. We draw on the concept of infrastructuring to shed light on how to create and sustain the social, material, political and organizational structures that form the basis for introducing the new national initiative. Based on our case, we distill seven propositions that describe more generally how to work strategically with this challenge. © 2024 The Authors

关键词： Digital literacy Infrastructuring Strategy Technology comprehension

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Hydrodynamic bound states of rotating microcylinders in a confining geometry

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Physical Review Fluids 2024年第1期9卷 014102-014102页

作者： Hanliang Guo Yi Man Hai Zhu Department of Mathematics and Computer Science Ohio Wesleyan University Delaware Ohio 43015 USA Department of Mechanics and Engineering Science at College of Engineering and State Key Laboratory for Turbulence and Complex Systems Peking University Beijing 100871 China Center for Computational Mathematics Flatiron Institute New York New York 10010 USA

Many microswimmers propel themselves by rotating microcylindrical organelles such as flagella or cilia. These cylindrical organelles almost never live in free space, yet their motions in a confining geometry can be counterintuitive. For example, one of the intriguing yet classical results in this regard is that a rotating cylinder next to a plane wall does not generate any net force in Newtonian fluids and therefore does not translate. In this work we employ analytical and numerical tools to investigate the motions of microcylinders under prescribed torques in a confining geometry. We show that a cylinder pair can form four nontrivial hydrodynamic bound states depending on the relative position within the confinement. Our analysis shows that the distinct states are the results of competing effects of the hydrodynamic interactions within the cylinder pair and between the active cylinders and the confinement.

关键词： Laminar flows Low Reynolds number swimmers

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Learning non-Gaussian graphical models via Hessian scores and triangular transport

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 4181-4226页

作者： Ricardo Baptista Rebecca Morrison Olivier Zahm Youssef Marzouk Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA Department of Computer Science University of Colorado Boulder Boulder CO Université Grenoble Alpes Inria CNRS Grenoble INP LJK Grenoble France Center for Computational Science and Engineering Massachusetts Institute of Technology Cambridge MA

Undirected probabilistic graphical models represent the conditional dependencies, or Markov properties, of a collection of random variables. Knowing the sparsity of such a graphical model is valuable for modeling multivariate distributions and for efficiently performing inference. While the problem of learning graph structure from data has been studied extensively for certain parametric families of distributions, most existing methods fail to consistently recover the graph structure for non-Gaussian data. Here we propose an algorithm for learning the Markov structure of continuous and non-Gaussian distributions. To characterize conditional independence, we introduce a score based on integrated Hessian information from the joint log-density, and we prove that this score upper bounds the conditional mutual information for a general class of distributions. To compute the score, our algorithm sing estimates the density using a deterministic coupling, induced by a triangular transport map, and iteratively exploits sparse structure in the map to reveal sparsity in the graph. For certain non-Gaussian datasets, we show that our algorithm recovers the graph structure even with a biased approximation to the density. Among other examples, we apply sing to learn the dependencies between the states of a chaotic dynamical system with local interactions.

关键词： undirected graphical models structure learning non-Gaussian distributions conditional mutual information transport map sparsity

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Electron charge qubit with 0.1 millisecond coherence time

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Nature Physics 2024年第1期20卷 116-122页

作者： Zhou, Xianjing Li, Xinhao Chen, Qianfan Koolstra, Gerwin Yang, Ge Dizdar, Brennan Huang, Yizhong Wang, Christopher S. Han, Xu Zhang, Xufeng Schuster, David I. Jin, Dafei Center for Nanoscale Materials Argonne National Laboratory LemontIL United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL United States Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions CambridgeMA United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States James Franck Institute and Department of Physics University of Chicago ChicagoIL United States Department of Electrical and Computer Engineering Northeastern University BostonMA United States Department of Applied Physics Stanford University StanfordCA United States Department of Physics and Astronomy University of Notre Dame Notre DameIN United States

Electron charge qubits are compelling candidates for solid-state quantum computing because of their inherent simplicity in qubit design, fabrication, control and readout. However, electron charge qubits built on conventional semiconductors and superconductors suffer from severe charge noise that limits their coherence time to the order of one microsecond. Here we report electron charge qubits that exceed this limit, based on isolated single electrons trapped on an ultraclean solid neon surface in a vacuum. Quantum information is encoded in the motional states of an electron that is strongly coupled with microwave photons in an on-chip superconducting resonator. The measured relaxation and coherence times are both on the order of 0.1 ms, surpassing all existing charge qubits and rivalling state-of-the-art superconducting transmon qubits. The simultaneous strong coupling of two qubits with a common resonator is also demonstrated, as the first step towards two-qubit entangling gates for universal quantum computing. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

关键词： Superconducting resonators

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Neural Fields for Interactive Visualization of Statistical Dependencies in 3D Simulation Ensembles

arXiv

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arXiv 2023年

作者： Farokhmanesh, F. Höhlein, K. Neuhauser, C. Necker, T. Weissmann, M. Miyoshi, T. Westermann, R. Technical University of Munich Department of Computer Science School of Computation Information and Technology Germany University of Vienna Department of Meteorology and Geophysics Austria RIKEN Center for Computational Science Kobe Japan

We present neural dependence fields (NDFs) – the first neural network that learns to compactly represent and efficiently reconstruct the statistical dependencies between the values of physical variables at different spatial locations in large 3D simulation ensembles. Going beyond linear dependencies, we consider mutual information as an exemplary measure of non-linear dependence. We demonstrate learning and reconstruction with a large weather forecast ensemble comprising 1000 members, each storing multiple physical variables at a 250 × 352 × 20 simulation grid. By circumventing compute-intensive statistical estimators at runtime, we demonstrate significantly reduced memory and computation requirements for reconstructing the major dependence structures. This enables embedding the estimator into a GPU-accelerated direct volume renderer and interactively visualizing all mutual dependencies for a selected domain point. Copyright © 2023, The Authors. All rights reserved.

关键词： Weather forecasting

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Deep learning-based activity recognition and fine motor identification using 2D skeletons of cynomolgus monkeys

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Zoological Research 2023年第5期44卷 967-980页

作者： Chuxi Li Zifan Xiao Yerong Li Zhinan Chen Xun Ji Yiqun Liu Shufei Feng Zhen Zhang Kaiming Zhang Jianfeng Feng Trevor W.Robbins Shisheng Xiong Yongchang Chen Xiao Xiao School of Information Science and Technology Micro Nano System Center Fudan UniversityShanghai 200433China Department of Anesthesiology Huashan HospitalKey Laboratory of Computational Neuroscience and Brain-Inspired IntelligenceMinistry of EducationBehavioral and Cognitive Neuroscience CenterInstitute of Science and Technology for Brain-Inspired IntelligenceMOE Frontiers Center for Brain ScienceFudan UniversityShanghai 200433China Kuang Yaming Honors School Nanjing UniversityNanjingJiangsu 210023China Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China State Key Laboratory of Primate Biomedical Research Institute of Primate Translational MedicineKunming University of Science and TechnologyKunmingYunnan 650500China New Vision World LLC. Aliso ViejoCalifornia 92656USA Behavioural and Clinical Neuroscience Institute University of CambridgeCambridgeCB21TNUK

Video-based action recognition is becoming a vital tool in clinical research and neuroscientific study for disorder detection and ***,action recognition currently used in non-human primate(NHP)research relies heavily on intense manual labor and lacks standardized *** this work,we established two standard benchmark datasets of NHPs in the laboratory:Monkeyin Lab(Mi L),which includes 13 categories of actions and postures,and MiL2D,which includes sequences of two-dimensional(2D)skeleton ***,based on recent methodological advances in deep learning and skeleton visualization,we introduced the Monkey Monitor Kit(Mon Kit)toolbox for automatic action recognition,posture estimation,and identification of fine motor activity in *** the datasets and Mon Kit,we evaluated the daily behaviors of wild-type cynomolgus monkeys within their home cages and experimental environments and compared these observations with the behaviors exhibited by cynomolgus monkeys possessing mutations in the MECP2 gene as a disease model of Rett syndrome(RTT).Mon Kit was used to assess motor function,stereotyped behaviors,and depressive phenotypes,with the outcomes compared with human manual *** Kit established consistent criteria for identifying behavior in NHPs with high accuracy and efficiency,thus providing a novel and comprehensive tool for assessing phenotypic behavior in monkeys.

关键词： Action recognition Fine motor identification Two-stream deep model 2D skeleton Non-human primates Rett syndrome

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