2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)最新文献_第5页

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2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/synasc51798.2020.00003

引用次数: 0

Preface SYNASC 2020

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/synasc51798.2020.00005

引用次数: 0

Image Semantic Segmentation Based on High-Resolution Networks for Monitoring Agricultural Vegetation 基于高分辨率网络的农业植被监测图像语义分割

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00050

V. Ganchenko, V. Starovoitov, Xiangtao Zheng

引用次数: 1

Sustainable MLOps: Trends and Challenges 可持续MLOps:趋势与挑战

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00015

D. Tamburri

引用次数: 48

An Approach to Support Automated Deployment of Applications on Heterogeneous Cloud-HPC Infrastructures 支持在异构云-高性能计算基础设施上自动部署应用程序的方法

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00031

E. D. Nitto, Jesús Gorroñogoitia, I. Kumara, G. Meditskos, Dragan Radolovic, K. Sivalingam, Román Sosa González

引用次数: 7

Machine Learning based models for examining differences between modern and ancient DNA in dental calculus 基于机器学习的模型用于检查牙石中现代和古代DNA的差异

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00036

Maria-Iuliana Bocicor, Iulia-Monica Szuhai, Emilia-Loredana Pop, Ioan-Gabriel Mircea

{"title":"Machine Learning based models for examining differences between modern and ancient DNA in dental calculus","authors":"Maria-Iuliana Bocicor, Iulia-Monica Szuhai, Emilia-Loredana Pop, Ioan-Gabriel Mircea","doi":"10.1109/SYNASC51798.2020.00036","DOIUrl":"https://doi.org/10.1109/SYNASC51798.2020.00036","url":null,"abstract":"DNA, or deoxyribonucleic acid, carries the entirety of genetic information of any living organism. The study of the bacterial DNA extracted from human bones excavated from archaeological and anthropological sites aims to analyse the evolution of microorganisms inhabiting the human body and to contribute to new insight related to the health, diet and even migration of our ancestors. This paper aims to offer a solution for the discrimination between ancient and modern bacterial DNA in dental calculus. We propose three internal representations for the considered DNA sequences in order to analyse which captures the most information and is more informative for classification models. Two of these are text-based, while the third one takes advantage of several physical and chemical properties of nucleotides in the DNA. We use a data set containing both ancient and modern dental calculus bacterial DNA and apply two supervised models, namely artificial neural networks and support vector machines to distinguish between the two types of sequences. The two main conclusions indicated by the obtained results are: the representation based on physical and chemical properties seems to best capture relevant information for the task at hand; for the considered data set and DNA encoding proposals, support vector machines outperform artificial neural networks, although results obtained by both models are promising.","PeriodicalId":278104,"journal":{"name":"2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"31 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123408580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Hybrid Hyper-parameter Optimization for Collaborative Filtering 协同过滤的混合超参数优化

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-08-31 DOI: 10.1109/SYNASC51798.2020.00042

Peter Szabó, B. Genge

引用次数: 2

A bibliometric overview of the International Symposium on Symbolic and Numeric Algorithms for Scientific Computing between 2005 and 2018 2005年至2018年科学计算符号和数值算法国际研讨会的文献计量学综述

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-08-30 DOI: 10.1109/SYNASC51798.2020.00052

Teodor-Florin Fortiş, A. Fortis

引用次数: 1

Hierarchical Scheduling in on-demand GPU-as-a-Service Systems 按需gpu即服务系统中的分层调度

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-08-28 DOI: 10.1109/SYNASC51798.2020.00030

Federica Filippini, M. Lattuada, A. Jahani, M. Ciavotta, D. Ardagna, E. Amaldi

{"title":"Hierarchical Scheduling in on-demand GPU-as-a-Service Systems","authors":"Federica Filippini, M. Lattuada, A. Jahani, M. Ciavotta, D. Ardagna, E. Amaldi","doi":"10.1109/SYNASC51798.2020.00030","DOIUrl":"https://doi.org/10.1109/SYNASC51798.2020.00030","url":null,"abstract":"Deep learning (DL) methods have recently gained popularity. Training this class of models is, however, computing-intensive, and frequently GPUs are used to boost performance. Although the costs of GPU-based systems are gradually reducing due to the high demand, they are still prohibitive: in public clouds, GPU-powered virtual machines (VMs) time unit price is 5-8x higher than CPU-only VMs. While the cloud remains the most cost-effective and flexible deployment, operation costs can be reduced, in large settings, by rightsizing and sharing resources among multiple processes. This work addresses the online joint capacity planning and job scheduling with due dates problem and proposes alternative matheuristic solution methods. Our objective is to optimize operation costs by: i) rightsizing the VM capacities at each node, ii) partitioning the set of GPUs among multiple concurrent jobs on the same VM, and iii) determining a due-date-aware job schedule. The effectiveness of the proposed hierarchical approach, coupled with an appropriate Mixed Integer Linear Programming formulation, is validated against first-principle methods by relying on simulation. The experiments prove that the efficiency of GPU-based systems evaluated in terms of costs can be improved by 50-70%. Finally, scalability analyses show that the proposed approach enables to solve problem instances with up to 100 nodes in less than one minute on average, making it suitable for practical scenarios.","PeriodicalId":278104,"journal":{"name":"2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124940357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Tackling Morpion Solitaire with AlphaZero-like Ranked Reward Reinforcement Learning 用类似alphazero的排名奖励强化学习解决Morpion纸牌

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-06-14 DOI: 10.1109/SYNASC51798.2020.00033

Hui Wang, M. Preuss, M. Emmerich, A. Plaat

{"title":"Tackling Morpion Solitaire with AlphaZero-like Ranked Reward Reinforcement Learning","authors":"Hui Wang, M. Preuss, M. Emmerich, A. Plaat","doi":"10.1109/SYNASC51798.2020.00033","DOIUrl":"https://doi.org/10.1109/SYNASC51798.2020.00033","url":null,"abstract":"Morpion Solitaire is a popular single player game, performed with paper and pencil. Due to its large state space (on the order of the game of Go) traditional search algorithms, such as MCTS, have not been able to find good solutions. A later algorithm, Nested Rollout Policy Adaptation, was able to find a new record of 82 steps, albeit with large computational resources. After achieving this record, to the best of our knowledge, there has been no further progress reported, for about a decade. In this paper we take the recent impressive performance of deep self-learning reinforcement learning approaches from AlphaGo/AlphaZero as inspiration to design a searcher for Morpion Solitaire. A challenge of Morpion Solitaire is that the state space is sparse, there are few win/loss signals. Instead, we use an approach known as ranked reward to create a reinforcement learning self-play framework for Morpion Solitaire. This enables us to find medium-quality solutions with reasonable computational effort. Our record is a 67 steps solution, which is very close to the human best (68) without any other adaptation to the problem than using ranked reward. We list many further avenues for potential improvement.","PeriodicalId":278104,"journal":{"name":"2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132884063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8