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

The CAD Conundrum: Lex-Least vs Order CAD难题:Lex-Least vs Order

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00017

S. McCallum, Akshar Nair, James H. Davenport, G. Sankaran

引用次数: 1

An Experiment on Mizar Adjectives with Extra Visible Arguments 带额外可视参数的米扎尔形容词实验

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00026

Adam Naumowicz

引用次数: 0

Approximate GCD in Lagrange bases 拉格朗日基的近似GCD

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00018

Leili Rafiee Sevyeri, Robert M Corless

{"title":"Approximate GCD in Lagrange bases","authors":"Leili Rafiee Sevyeri, Robert M Corless","doi":"10.1109/SYNASC51798.2020.00018","DOIUrl":"https://doi.org/10.1109/SYNASC51798.2020.00018","url":null,"abstract":"For a pair of polynomials with real or complex coefficients, given in any particular basis, the problem of finding their GCD is known to be ill-posed. An answer is still desired for many applications, however. Hence, looking for a GCD of so-called approximate polynomials where this term explicitly denotes small uncertainties in the coefficients has received significant attention in the field of hybrid symbolic-numeric computation. In this paper we give an algorithm, based on one of Victor Ya. Pan, to find an approximate GCD for a pair of approximate polynomials given in a Lagrange basis. More precisely, we suppose that these polynomials are given by their approximate values at distinct known points. We first find each of their roots by using a Lagrange basis companion matrix for each polynomial, cluster the roots of each polynomial to identify multiple roots, and then “marry” the two polynomials to find their GCD. At no point do we change to the monomial basis, thus preserving the good conditioning properties of the original Lagrange basis. We discuss advantages and drawbacks of this method. The computational cost is dominated by the rootfinding step; unless special-purpose eigenvalue algorithms are used, the cost is cubic in the degrees of the polynomials. In principle, this cost could be reduced but we do not do so here.","PeriodicalId":278104,"journal":{"name":"2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"11 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":"121506574","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

On One Approach to Goal-Driven Proof Search in Classical First-Order Logic 经典一阶逻辑中目标驱动证明搜索的一种方法

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00023

Alexander V. Lyaletski, A. Lyaletsky

引用次数: 0

DS Lab Notebook: A new tool for data science applications DS Lab Notebook:数据科学应用的新工具

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00056

Alexandru Ionascu, Sebastian-Aurelian Ștefănigă

引用次数: 0

Edge map response of dilated and reconstructed classical filters 扩展和重构经典滤波器的边缘映射响应

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00039

Ciprian Orhei, V. Bogdan, C. Bonchis

引用次数: 6

Automatic Real-Time Road Crack Identification System 道路裂缝自动实时识别系统

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00043

Lucia-Georgiana Coca, Ciprian-Gabriel Cusmuliuc, Vladut-Haralambie Morosanu, Teodora Grosu, Adrian Iftene

引用次数: 2

A Metric-Based Approach to Modelling a Virtual Machine for Smart Contract Execution 基于度量的智能合约执行虚拟机建模方法

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00055

Spataru Alexe Luca, Ciprian-Petrisor Pungila

{"title":"A Metric-Based Approach to Modelling a Virtual Machine for Smart Contract Execution","authors":"Spataru Alexe Luca, Ciprian-Petrisor Pungila","doi":"10.1109/SYNASC51798.2020.00055","DOIUrl":"https://doi.org/10.1109/SYNASC51798.2020.00055","url":null,"abstract":"Applicability of smart contracts showed significant potential to multiple industries by offering data integrity, transparency, non-repudiation, and trust automation. An essential part of programmability within a blockchain is a process virtual machine that performs the execution and outputs new valid states. In this paper, there will be proposed theoretical models that address the problems that arise in the design of a virtual machine in blockchain, by strongly emphasizing the importance of metrics for an efficient halting mechanism. A model aimed towards performing computations and tracking state changes will be presented in order to discuss the key aspects of programmability in blockchains. Also, the virtual machine will be defined as a deterministic state automaton and within the definition, additional metrics for a halting condition will be proposed. We also propose a loosely-coupled architecture such that the virtual machine's interactions with the blockchain are handled in an isolated manner from the blockchain's functionality and also introduce an additional layer on top of the virtual machine designated to add privacy and improve scalability.","PeriodicalId":278104,"journal":{"name":"2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"6 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":"124019540","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: 0

A Study of Multiparty Interactions in Continuation Semantics 延续语义中的多方交互研究

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00029

Gabriel Ciobanu, E. Todoran

引用次数: 0

Quality of Pre-trained Deep-Learning Models for Palmprint Recognition 掌纹识别中预训练深度学习模型的质量

2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Pub Date : 2020-09-01 DOI: 10.1109/SYNASC51798.2020.00041

Valentin Rosca, A. Ignat

{"title":"Quality of Pre-trained Deep-Learning Models for Palmprint Recognition","authors":"Valentin Rosca, A. Ignat","doi":"10.1109/SYNASC51798.2020.00041","DOIUrl":"https://doi.org/10.1109/SYNASC51798.2020.00041","url":null,"abstract":"This paper presents a comprehensive study of deep learning methods and datasets used for solving the palmprint recognition problem. The quality of image embeddings provided by deep neural networks, pre-trained on the ImageNet dataset, are evaluated on palmprint recognition in the visible spectrum task. In our tests, we used twelve publicly available datasets obtained with different types of acquisition procedures: constrained, partially constrained and unconstrained. Sixteen convolutional neural networks (two from the VGG family, six from ResNet, three from Inception, two from MobileNet and three from DenseNet) were evaluated. We analyzed the results from the point of view of specialization potential, dataset difficulty and general parameter tuning. For evaluation, EER (Equal Error Rate) was employed. We ranked the datasets and appraised the feature vectors computed by the pre-trained networks using this metric. The best results, on average, were provided by the deep neural networks from the MobileNet family. The distances used for comparing the feature vectors were Euclidean, Cityblock, cosine and correlation. The best results were obtained with the cosine family distances.","PeriodicalId":278104,"journal":{"name":"2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"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":"128799719","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