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16th International Conference on Correlation Optics, COR 2023

作者： Kotsur, M. Vikhor, L. Institute of Applied-Physics and Computer Sciences Yuriy Fedkovych Chernivtsi National University Chernivtsi58012 Ukraine Inst. of Thermoelectricity of the Natl. Acad. of Sci. and Min. of Education and Science of Ukraine Chernivtsi58029 Ukraine

ISBN: (纸本)9781510671829

Thermoelectric coolers (TECs) are widely used to ensure a stable operating temperature of IR detectors, which are the main component of most optical electronic devices. Cooling improves such parameters of IR detectors as sensitivity, detectability, speed, range of spectral sensitivity, reliability, etc. TECs provide cooling on a temperature level of up to 190 K. Modern research is carried out in the directions of expanding the temperature range of the TEC and of increasing its coefficient of performance. The paper considers the use of optimal control theory in the simulation of TEC, matched with the parameters of the photodetector. The methods make it possible to determine the most suitable thermoelectric materials, optimal structure and optimal operation mode of TEC. Examples of TEC simulation for IR detectors by means of optimal control methods are given. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Thermoelectric equipment

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Splicing ViT Features for Semantic Appearance Transfer

arXiv

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arXiv 2022年

作者： Tumanyan, Narek Bar-Tal, Omer Bagon, Shai Dekel, Tali Dept. of Computer Science and Applied Math The Weizmann Inst. of Science

We present a method for semantically transferring the visual appearance of one natural image to another. Specifically, our goal is to generate an image in which objects in a source structure image are "painted" with the visual appearance of their semantically related objects in a target appearance image. Our method works by training a generator given only a single structure/appearance image pair as input. To integrate semantic information into our framework-a pivotal component in tackling this task-our key idea is to leverage a pre-trained and fixed Vision Transformer (ViT) model which serves as an external semantic prior. Specifically, we derive novel representations of structure and appearance extracted from deep ViT features, untwisting them from the learned self-attention modules. We then establish an objective function that splices the desired structure and appearance representations, interweaving them together in the space of ViT features. Our framework, which we term "Splice", does not involve adversarial training, nor does it require any additional input information such as semantic segmentation or correspondences, and can generate high resolution results, e.g., work in HD. We demonstrate high quality results on a variety of in-the-wild image pairs, under significant variations in the number of objects, their pose and appearance. © 2022, CC BY.

关键词： Semantics

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A coupled multitemporal UAV-based LiDAR and multispectral data approach to model dry biomass of maize 43

A coupled multitemporal UAV-based LiDAR and multispectral da...

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43. Jahrestagung der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft - Resiliente Agri-Food-Systeme: Herausforderungen und Losungsansatze, GIL 2023 - 43rd Annual Conference of the Society for Informatics in Agriculture, Forestry, and Food Industry - Resilient Agri-Food Systems: Challenges and Solutions, GIL 2023

作者： Rettig, Robert Storch, Marcel Wittstruck, Lucas Ansah, Christabel Edena Bald, Richard Janis Richard, David Trautz, Dieter Jarmer, Thomas University of Applied Science Osnabrück Fac. of Agric. Sc. and Landscape Architecture Am Krümpel 31 Osnabrück49090 Germany Osnabrück University Inst. of Computer Sc. Wachsbleiche 27 Osnabrück49090 Germany

ISBN: (纸本)9783885797241

The presented approach attempts to highlight the capabilities of a data fusion approach that combines UAV LiDAR (RIEGL – miniVUX-1UAV) and multispectral data (Micasense – Altum) to assess the dry above ground biomass (AGB) for maize. The combined acquisition of both LiDAR and multispectral data not only supports estimates of AGB when fusing them, but also helps to evaluate phenological stage-specific modelling differences on the individual sensor data. A multiple linear regression was applied on the multisensorial UAV data from two appointments in 2021. The resulting R2 of 0.87 and RMSE of 14.35 g/plant for AGB was then transferred to AGB in dt/ha. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Biomass

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Heterogeneous Network Propagation with Optimal Similarity Measure for Drug-Target Associations 25

Heterogeneous Network Propagation with Optimal Similarity Me...

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25th International computer science and Engineering Conference, ICSEC 2021

作者： Tangmanussukum, Piyanut Kawichai, Thitipong Suratanee, Apichat Plaimas, Kitiporn Chulalongkorn University Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand Chulachomklao Royal Military Academy Academic Division Department of Mathematics and Computer Science Nakhon Nayok Thailand Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Sci. and Technol. Res. Inst. King Mongkut's Univ. of Technol. N. Bangkok Department of Mathematics Bangkok Thailand Center Faculty of Science Department of Mathematics and Computer Science Bangkok Thailand

ISBN: (纸本)9781665411974

Identification of drug target proteins is an important process in drug discovery and development. To perform predictions of large-scale drug-Target interactions, many computational methods have been proposed using the information of target-Target and drug-drug similarities into a network. In this work, we propose an enhanced heterogeneous network model with various biological aspects of target-Target and drug-drug similarities to predict drug-Target associations. Network propagation was performed through the heterogeneous network to predict new potential targets of a certain drug. Cross-validation and the Area Under the Receiver Operating Characteristic curve (AUROC) were then used to qualify the best model in the drug-Target association prediction. The result shows that the model using the similarity measures based on drug-disease associations and target protein sequences significantly outperforms the other models and the original model. Overall, our best model yields an accuracy of 93.5%. In conclusion, the heterogeneous model with optimal similarity measures is a well-suited approach to predict a new target for a certain drug. © 2021 IEEE.

关键词： Heterogeneous networks

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EFFICIENT CPU-OPTIMIZED PARAMETER ESTIMATION FOR MODELING FISH SCHOOLING BEHAVIOR IN LARGE PARTICLE SYSTEMS

arXiv

引用

arXiv 2023年

作者： Alrabeei, Salah Subbey, Sam Department of Computer Science Western Norway Univ. of Applied Sciences Bergen Norway Inst. of Marine Research PO Box 1870 Bergen5817 Norway

The schooling behavior of fish can be studied through simulations involving a large number of interacting particles. In such systems, each individual particle is guided by behavior rules, which include aggregation towards a centroid, collision avoidance, and direction alignment. The movement vector of each particle may be expressed as a linear combination of behaviors, with unknown parameters that define a trade-off among several behavioral constraints. A fitness function for collective schooling behavior encompasses all individual particle parameters. For a large number of interacting particles in a complex environment, heuristic methods, such as evolutionary algorithms, are used to optimize the fitness function, ensuring that the resulting decision rule preserves collective behavior. However, these algorithms exhibit slow convergence, making them inefficient in terms of CPU time cost. This paper proposes a CPU-efficient iterative (Cluster, Partition, Refine – CPR) algorithm for estimating decision rule parameters for a large number of interacting particles. In the first step, we employ the K-Means (unsupervised learning) algorithm to cluster candidate solutions. Then, we partition the search space using Voronoi tessellation over the defined clusters. We assess the quality of each cluster based on the fitness function, with the centroid of their Voronoi cells representing the clusters. Subsequently, we refine the search space by introducing new cells into a number of identified well-fitting Voronoi cells. This process is repeated until convergence. A comparison of the performance of the CPR algorithm with a standard Genetic Algorithm reveals that the former converges faster than the latter. We also demonstrate that the application of the CPR algorithm results in a schooling behavior consistent with empirical *** Codes 92D40, 92C20, 65K05, 68T05 © 2023, CC BY.

关键词： K-means clustering

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Integration of NGS Genomic Metadata Analysis with Open-Source Genomic Databases for Single Cell Tissue Samples

Integration of NGS Genomic Metadata Analysis with Open-Sourc...

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International Conference on Computational Cybernetics (ICCC)

作者： Marianna Dimitrova Kucarov Béla Molnár Miklos Kozlovszky 3DHistech Ltd. Doctoral School of Applied Informatics and Applied Mathematics BioTech Óbuda University Budapest Hungary 3DHistech Ltd. Budapest Hungary John von Neumann Faculty of Informatics Óbuda University Hun Medical Device Research Group LPDS Inst. for Computer Science and Control (SZTAKI) Hungarian Research Network (HUN-REN)

ISBN: (数字)9798331502461

ISBN: (纸本)9798331502478

In this paper, we present the development and use of an automated platform that integrates the evaluation of genomic metadata from single-cells in colorectal tissues with the open source KEGG genomic database via API using the Biopython package. The system collects and fuses the information into its own PostgreSQL database, from which the results of the analysis are transferred to a graphical user interface using the NpgSql extension. Thus, the implemented application allows the identification of molecular pathways and gene-specific drug targets associated with the mutated genes per cell, providing the combined visualization of tumor diagnostics and gene therapy opportunities. Using the application, 9 predefined gene panels, containing 2800 genes, were analyzed based on which the molecular pathway and drug target coverage of the genes, as well as the correlation between the pathways and drug target values were evaluated.

关键词： Drugs Correlation Databases Genomics Metadata Gene therapy Time factors Bioinformatics Tumors Testing

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TRACKING CAPELIN SPAWNING MIGRATION - INTEGRATING ENVIRONMENTAL DATA AND INDIVIDUAL-BASED MODELING

arXiv

引用

arXiv 2023年

作者： Alrabeei, Salah Rahman, Talal Subbey, Sam Department of Computer Science Western Norway Univ. of Applied Sciences Bergen Norway Inst. of Marine Research PO Box 1870 Bergen5817 Norway Department of Computer Science Western Norway Univ. of Applied Sciences PO Box 7030 Bergen5020 Norway

This paper presents a modeling framework for tracking the spawning migration of the capelin, which is a fish species in the Barents Sea. The framework combines an individual-based model (IBM) with artificial neural networks (ANNs). The ANNs determine the direction of the fish’s movement based on local environmental information, while a genetic algorithm and fitness function assess the suitability of the proposed directions. The framework’s efficacy is demonstrated by comparing the spatial distributions of modeled and empirical potential spawners. The proposed model successfully replicates the southeastward movement of capelin during their spawning migration, accurately capturing the distribution of spawning fish over historical spawning sites along the eastern coast of northern Norway. Furthermore, the paper compares three migration models: passive swimmers, taxis movement based on temperature gradients, and restricted-area search, along with our proposed approach. The results reveal that our approach outperforms the other models in mimicking the migration pattern. Most spawning stocks managed to reach the spawning sites, unlike the other models where water currents played a significant role in pushing the fish away from the coast. The temperature gradient detection model and restricted-area search model are found to be inadequate for accurately simulating capelin spawning migration in the Barents Sea due to complex oceanographic *** Codes 92D25, 92C05, 68T05, 68U10 © 2023, CC BY.

关键词： Fish

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Graph Attention Networks and Deep Q-Learning for Service Mesh Optimization: A Digital Twinning Approach

Graph Attention Networks and Deep Q-Learning for Service Mes...

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IEEE International Conference on Communications (ICC)

作者： Michel Gokan Khan Javid Taheri Andreas Kassler Arsineh Boodaghian Asl Dep. of Mathematics and Computer Science Karlstad University Karlstad Sweden School of Electronics Electrical Eng. and Computer Science Queen's University Belfast Belfast United Kingdom Faculty of Computer Science Deggendorf Institute of Technology Deggendorf Germany School of Eng. Sciences in Chemistry Biotech. and Health KTH Royal Inst. of Technology Stockholm Sweden

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In the realm of cloud native environments, Ku-bernetes has emerged as the de facto orchestration system for containers, and the service mesh architecture, with its interconnected microservices, has become increasingly prominent. Efficient scheduling and resource allocation for these microservices play a pivotal role in achieving high performance and maintaining system reliability. In this paper, we introduce a novel approach for container scheduling within Kubernetes clusters, leveraging Graph Attention Networks (GATs) for representation learning. Our proposed method captures the intricate dependencies among containers and services by constructing a representation graph. The deep Q-learning algorithm is then employed to optimize scheduling decisions, focusing on container-to-node placements, CPU request-response allocation, and adherence to node affinity and anti-affinity rules. Our experiments demonstrate that our GATs-based method outperforms traditional scheduling strategies, leading to enhanced resource utilization, reduced service latency, and improved overall system throughput. The insights gleaned from this study pave the way for a new frontier in cloud native performance optimization and offer tangible benefits to industries adopting microservice-based architectures.

关键词： Industries Cloud computing Job shop scheduling Q-learning Microservice architectures computer architecture Containers

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Automated Bell Inequality Violation using Uncalibrated Fiber Squeezing Polarization Controllers

Automated Bell Inequality Violation using Uncalibrated Fiber...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Dowling, Evan Morris, Mark Baumgartner, Gerald Roy, Rajarshi Murphy, Thomas E. Inst. for Research in Electronics & Applied Physics University of Maryland College ParkMD20740 United States Dept. of Physics University of Maryland College ParkMD20740 United States Laboratory for Telecommunication Sciences College ParkMD20740 United States Inst. for Physical Science & Technology University of Maryland College ParkMD20740 United States Dept. of Electrical & Computer Engineering University of Maryland College ParkMD20740 United States

We experimentally report on a real-time self-guided method to search for maximal CHSH violations between two observers sharing polarization entangled photon pairs using uncalibrated piezoelectric fiber squeezers as po...

ISBN: (纸本)9781957171258

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Optimizing Future Predictions in Children's Health: Implementing OGPA-enhanced Deep Learning for Precise Child Height Forecasting in the Social Media Age

Optimizing Future Predictions in Children's Health: Implemen...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Yantao Shao Tianxiang He Yan Mao Kai Fang Wei Wang Keji Mao School of Computer Science and Technology Zhejiang University of Technology Hangzhou China School of Mathematics and Computer Science Zhejiang A&F University Hangzhou China Guangdong-HK-Macao Jt. Lab for Emotion Intell. & Pervasive Comp AI Res. Inst. Shenzhen MSU-BIT Univ Shenzhen China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In recent years, the issue of children's height has garnered widespread attention on social media. Social media platforms serve as pivotal communication channels for parents, doctors, educators, and researchers, with children's height, a crucial health indicator, becoming one of the hot topics. Inaccurate prediction methods might mislead the public, resulting in parents harboring erroneous expectations regarding their children's future height. Such misplaced expectations may culminate in unwarranted worries or pressure. In pursuit of devising an accurate height prediction model, this paper thoroughly leverages a substantial data sample obtained from the physical health examinations of primary and secondary school students in Zhejiang Province, as well as continuous observation samples provided by the Zhejiang Provincial Bone Age Research Center, to delve deeply into the issue of height prediction in children and adolescents. In this research, we have developed a lightweight neural net-work model suitable for particle swarm optimization to predict children’s stage-wise height. When the difference between the actual and predicted values is within ±2cm, the prediction accuracy for boys reached 86.67%, and for girls, it was 85.32%, with an RMSE of 1.3503.

关键词： Deep learning Pediatrics Accuracy Social networking (online) Federated learning Medical services Predictive models

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