Journal of Computational Mathematics and Data Science最新文献_第5页

Variably Scaled Persistence Kernels (VSPKs) for persistent homology applications 用于持久同构应用程序的可变缩放持久化内核(vspk)

Journal of Computational Mathematics and Data Science Pub Date : 2022-08-01 DOI: 10.1016/j.jcmds.2022.100050

Stefano De Marchi , Federico Lot , Francesco Marchetti , Davide Poggiali

引用次数: 2

Reducing the Gibbs effect in multimodal medical imaging by the Fake Nodes approach 假淋巴结法减少多模态医学成像中的吉布斯效应

Journal of Computational Mathematics and Data Science Pub Date : 2022-08-01 DOI: 10.1016/j.jcmds.2022.100040

Davide Poggiali , Diego Cecchin , Stefano De Marchi

引用次数: 1

Deep learning, stochastic gradient descent and diffusion maps 深度学习，随机梯度下降和扩散图

Journal of Computational Mathematics and Data Science Pub Date : 2022-08-01 DOI: 10.1016/j.jcmds.2022.100054

Carmina Fjellström, Kaj Nyström

{"title":"Deep learning, stochastic gradient descent and diffusion maps","authors":"Carmina Fjellström, Kaj Nyström","doi":"10.1016/j.jcmds.2022.100054","DOIUrl":"https://doi.org/10.1016/j.jcmds.2022.100054","url":null,"abstract":"<div><p>Stochastic gradient descent (SGD) is widely used in deep learning due to its computational efficiency, but a complete understanding of why SGD performs so well remains a major challenge. It has been observed empirically that most eigenvalues of the Hessian of the loss functions on the loss landscape of over-parametrized deep neural networks are close to zero, while only a small number of eigenvalues are large. Zero eigenvalues indicate zero diffusion along the corresponding directions. This indicates that the process of minima selection mainly happens in the relatively low-dimensional subspace corresponding to the top eigenvalues of the Hessian. Although the parameter space is very high-dimensional, these findings seems to indicate that the SGD dynamics may mainly live on a low-dimensional manifold. In this paper, we pursue a truly data driven approach to the problem of getting a potentially deeper understanding of the high-dimensional parameter surface, and in particular, of the landscape traced out by SGD by analyzing the data generated through SGD, or any other optimizer for that matter, in order to possibly discover (local) low-dimensional representations of the optimization landscape. As our vehicle for the exploration, we use diffusion maps introduced by R. Coifman and coauthors.</p></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"4 ","pages":"Article 100054"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772415822000177/pdfft?md5=38c0dff05f24faf5b0990bd6aa9fd984&pid=1-s2.0-S2772415822000177-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137407104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal learning of Markov k-tree topology 马尔可夫k树拓扑的最优学习

Journal of Computational Mathematics and Data Science Pub Date : 2022-08-01 DOI: 10.1016/j.jcmds.2022.100046

Di Chang , Liang Ding , Russell Malmberg , David Robinson , Matthew Wicker , Hongfei Yan , Aaron Martinez , Liming Cai

{"title":"Optimal learning of Markov k-tree topology","authors":"Di Chang , Liang Ding , Russell Malmberg , David Robinson , Matthew Wicker , Hongfei Yan , Aaron Martinez , Liming Cai","doi":"10.1016/j.jcmds.2022.100046","DOIUrl":"10.1016/j.jcmds.2022.100046","url":null,"abstract":"<div><p>The seminal work of Chow and Liu (1968) shows that approximation of a finite probabilistic system by Markov trees can achieve the minimum information loss with the topology of a maximum spanning tree. Our current paper generalizes the result to Markov networks of tree-width <span><math><mrow><mo>≤</mo><mi>k</mi></mrow></math></span>, for every fixed <span><math><mrow><mi>k</mi><mo>≥</mo><mn>2</mn></mrow></math></span>. In particular, we prove that approximation of a finite probabilistic system with such Markov networks has the minimum information loss when the network topology is achieved with a maximum spanning <span><math><mi>k</mi></math></span>-tree. While constructing a maximum spanning <span><math><mi>k</mi></math></span>-tree is intractable for even <span><math><mrow><mi>k</mi><mo>=</mo><mn>2</mn></mrow></math></span>, we show that polynomial algorithms can be ensured by a sufficient condition accommodated by many meaningful applications. In particular, we show an efficient algorithm for learning the optimal topology of higher order correlations among random variables that belong to an underlying linear structure. As an application, we demonstrate effectiveness of this efficient algorithm applied to biomolecular 3D structure prediction.</p></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"4 ","pages":"Article 100046"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277241582200013X/pdfft?md5=aad4f1525f5d10d5657975c4606226da&pid=1-s2.0-S277241582200013X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77532056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Understanding the assumptions of an SEIR compartmental model using agentization and a complexity hierarchy 理解使用代理和复杂性层次结构的SEIR分区模型的假设

Journal of Computational Mathematics and Data Science Pub Date : 2022-08-01 DOI: 10.1016/j.jcmds.2022.100056

Elizabeth Hunter, John D. Kelleher

{"title":"Understanding the assumptions of an SEIR compartmental model using agentization and a complexity hierarchy","authors":"Elizabeth Hunter, John D. Kelleher","doi":"10.1016/j.jcmds.2022.100056","DOIUrl":"10.1016/j.jcmds.2022.100056","url":null,"abstract":"<div><p>Equation-based and agent-based models are popular methods in understanding disease dynamics. Although there are many types of equation-based models, the most common is the SIR compartmental model that assumes homogeneous mixing and populations. One way to understand the effects of these assumptions is by agentization. Equation-based models can be agentized by creating a simple agent-based model that replicates the results of the equation-based model, then by adding complexity to these agentized models it is possible to break the assumptions of homogeneous mixing and populations and test how breaking these assumptions results in different outputs. We report a set of experiments comparing the outputs of an SEIR model and a set of agent-based models of varying levels of complexity, using as a case study a measles outbreak in a town in Ireland. We define and use a six level complexity hierarchy for agent-based models to create a set of progressively more complex variants of an agentized SEIR model for the spread of infectious disease. We then compare the results of the agent-based model at each level of complexity with results of the SEIR model to determine when the agentization breaks. Our analysis shows this occurs on the fourth step of complexity, when scheduled movements are added into the model. When agents networks and behaviours are complex the peak of the outbreak is shifted to the right and is lower than in the SEIR model suggesting that heterogeneous populations and mixing patterns lead to slower outbreaks compared homogeneous populations and mixing patterns.</p></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"4 ","pages":"Article 100056"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772415822000189/pdfft?md5=8f941ae4468af0fee67c94e73183f140&pid=1-s2.0-S2772415822000189-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77558484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Computational treatment of MHD Maxwell nanofluid flow across a stretching sheet considering higher-order chemical reaction and thermal radiation 考虑高阶化学反应和热辐射的MHD麦克斯韦纳米流体流过拉伸片的计算处理

Journal of Computational Mathematics and Data Science Pub Date : 2022-08-01 DOI: 10.1016/j.jcmds.2022.100048

Rajib Biswas , Md. Shahadat Hossain , Rafiqul Islam , Sarder Firoz Ahmmed , S.R. Mishra , Mohammad Afikuzzaman

{"title":"Computational treatment of MHD Maxwell nanofluid flow across a stretching sheet considering higher-order chemical reaction and thermal radiation","authors":"Rajib Biswas , Md. Shahadat Hossain , Rafiqul Islam , Sarder Firoz Ahmmed , S.R. Mishra , Mohammad Afikuzzaman","doi":"10.1016/j.jcmds.2022.100048","DOIUrl":"10.1016/j.jcmds.2022.100048","url":null,"abstract":"<div><p>The present analysis reports a computational study of Magnetohydrodynamic (MHD) flow behaviour of 2D Maxwell nanofluid across a stretched sheet in appearance of Brownian motion. The substantial term thermal radiation and chemical reactions have been employed extensively in the current research. Nanofluids are usually chosen by researchers because of their rheological properties, which are important in determining their appropriateness for convective heat transfer. The present research reveals that the fluid velocity augments for the enhanced values of all the parameters. Heat source, as well as the radiation parameters, ensure that there is enough heat in the fluid, which implies escalation of the thermal boundary layer thickness by accruing radiation parameter. Moreover, streamlines and isotherms have been investigated for the different parametric values. The suggested model is valuable because it has a wide range of applications in domains including medical sciences (treatment of cancer therapeutics), microelectronics, biomedicine, biology, and industrial production processes.</p></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"4 ","pages":"Article 100048"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772415822000141/pdfft?md5=901981bc7e4956837055a6b712d8d47e&pid=1-s2.0-S2772415822000141-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88860933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 27

Thermodynamic analysis of a tangent hyperbolic hydromagnetic heat generating fluid in quadratic Boussinesq approximation 二次Boussinesq近似下正切双曲磁流体热流体的热力学分析

Journal of Computational Mathematics and Data Science Pub Date : 2022-08-01 DOI: 10.1016/j.jcmds.2022.100058

A.R. Hassan , S.O. Salawu , A.B. Disu , O.R. Aderele

引用次数: 8

Detection of anomalies in the proximity of a railway line: A case study 铁路附近异常的检测:一个案例研究

Journal of Computational Mathematics and Data Science Pub Date : 2022-08-01 DOI: 10.1016/j.jcmds.2022.100052

Pierluigi Amodio , Marcello De Giosa , Felice Iavernaro , Roberto La Scala , Arcangelo Labianca , Monica Lazzo , Francesca Mazzia , Lorenzo Pisani

引用次数: 0

A general framework for hypercomplex-valued extreme learning machines 超复值极限学习机的一般框架

Journal of Computational Mathematics and Data Science Pub Date : 2022-06-01 DOI: 10.1016/j.jcmds.2022.100032

Guilherme Vieira, Marcos Eduardo Valle

引用次数: 10

EUPHORIA: A neural multi-view approach to combine content and behavioral features in review spam detection EUPHORIA:一种结合评论垃圾邮件检测内容和行为特征的神经多视图方法

Journal of Computational Mathematics and Data Science Pub Date : 2022-06-01 DOI: 10.1016/j.jcmds.2022.100036

Giuseppina Andresini , Andrea Iovine , Roberto Gasbarro , Marco Lomolino , Marco de Gemmis , Annalisa Appice

{"title":"EUPHORIA: A neural multi-view approach to combine content and behavioral features in review spam detection","authors":"Giuseppina Andresini , Andrea Iovine , Roberto Gasbarro , Marco Lomolino , Marco de Gemmis , Annalisa Appice","doi":"10.1016/j.jcmds.2022.100036","DOIUrl":"10.1016/j.jcmds.2022.100036","url":null,"abstract":"<div><p>Nowadays, online reviews are the main source to customer opinions. They are especially important in the realm of e-commerce, where reviews regarding products and services influence the purchase decisions of customers, as well as the reputation of the commerce websites. Unfortunately, not all the online reviews are truthful and trustworthy. Therefore, it is crucial to develop machine learning techniques to detect review spam. This study describes <span>EUPHORIA</span> — a novel classification approach to distinguish spam from truthful reviews. This approach couples multi-view learning to deep learning, in order to gain accuracy by accounting for the variety of information possibly associated with both the reviews’ content and the reviewers’ behavior. Experiments carried out on two real review datasets from Yelp.com – Hotel and Restaurant – show that the use of multi-view learning can improve the performance of a deep learning classifier trained for review spam detection. In particular, the proposed approach achieves AUC-ROC equal to 0.813 and 0.708 in Hotel and Restaurant, respectively.</p></div>","PeriodicalId":100768,"journal":{"name":"Journal of Computational Mathematics and Data Science","volume":"3 ","pages":"Article 100036"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772415822000086/pdfft?md5=2d7de96c79d3f46c848780e22dd8e576&pid=1-s2.0-S2772415822000086-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81237745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4