Search results for "Information Systems"
showing 10 items of 1926 documents
Finite-size effects of Kirkwood–Buff integrals from molecular simulations
2017
The modelling of thermodynamic properties of liquids from local density fluctuations is relevant to many chemical and biological processes. The Kirkwood–Buff (KB) theory connects the microscopic structure of isotropic liquids with macroscopic properties such as partial derivatives of activity coefficients, partial molar volumes and compressibilities. Originally, KB integrals were formulated for open and infinite systems which are difficult to access with standard Molecular Dynamics (MD) simulations. Recently, KB integrals for finite and open systems were formulated (J Phys Chem Lett. 2013;4:235). From the scaling of KB integrals for finite subvolumes, embedded in larger reservoirs, with the…
Spatial joins
2019
The spatial join is a popular operation in spatial database systems and its evaluation is a well-studied problem. This paper reviews research and recent trends on spatial join evaluation. The complexity of different data types, the consideration of different join predicates, the use of modern commodity hardware, and support for parallel processing open the road to a number of interesting directions for future research, some of which we outline in the paper.
LinkedSaeima: A Linked Open Dataset of Latvia’s Parliamentary Debates
2019
This paper describes the LinkedSaeima dataset that contains structured data about Latvia’s parliamentary debates from 1993 until 2017. This information is published at http://dati.saeima.korpuss.lv as Linked Open Data. It is a part of the Corpus of Saeima (the Parliament of Latvia) released as open data for multidisciplinary research. The data model of LinkedSaeima follows the data structure of the LinkedEP dataset with a few modifications. The dataset is augmented with links to the Wikidata knowledge base that provide additional information about the speakers and named entities mentioned in the corpus.
Notice of Violation of IEEE Publication Principles: New Delay-Dependent Exponential $H_{\infty}$ Synchronization for Uncertain Neural Networks With M…
2010
This paper establishes an exponential H infin synchronization method for a class of uncertain master and slave neural networks (MSNNs) with mixed time delays, where the mixed delays comprise different neutral, discrete, and distributed time delays. The polytopic and the norm-bounded uncertainties are separately taken into consideration. An appropriate discretized Lyapunov-Krasovskii functional and some free-weighting matrices are utilized to establish some delay-dependent sufficient conditions for designing delayed state-feedback control as a synchronization law in terms of linear matrix inequalities under less restrictive conditions. The controller guarantees the exponential H infin synchr…
Asynchronously switched control of discrete impulsive switched systems with time delays
2013
This paper is concerned with the stabilization problem for a class of uncertain discrete impulsive switched delay systems under asynchronous switching. The so-called asynchronous switching means that the switches between the candidate controllers and system modes are asynchronous. By using the average dwell time (ADT) approach, sufficient conditions for the existence of an asynchronously switched controller is derived such that the resulting closed-loop system is exponentially stable. The desired controller gains and the admissible switching signals are obtained in terms of a set of matrix inequalities. A numerical example is given to illustrate the effectiveness of the proposed method.
Multi-label Classification Using Stacked Hierarchical Dirichlet Processes with Reduced Sampling Complexity
2018
Nonparametric topic models based on hierarchical Dirichlet processes (HDPs) allow for the number of topics to be automatically discovered from the data. The computational complexity of standard Gibbs sampling techniques for model training is linear in the number of topics. Recently, it was reduced to be linear in the number of topics per word using a technique called alias sampling combined with Metropolis Hastings (MH) sampling. We propose a different proposal distribution for the MH step based on the observation that distributions on the upper hierarchy level change slower than the document-specific distributions at the lower level. This reduces the sampling complexity, making it linear i…
Comparison of MeSH terms and KeyWords Plus terms for more accurate classification in medical research fields. A case study in cannabis research
2021
Abstract KeyWords Plus and Medical Subject Headings (MeSH) are widely used in bibliometric studies for topic mapping. The objective of this study is to compare the two description systems in documents about cannabis research to find the concordance between systems and establish whether there is neutrality in topic mapping. A total of 25,593 articles from 1970 to 2019 were drawn from Web of Science's Core Collection and Medline and analyzed. The tidytext library, Zipf's law, topic modeling tools, the contingency coefficient, Cramer's V, and Cohen's kappa were used. The results included 10,107 MeSH terms and 28,870 KeyWords Plus terms. The Zipf distribution of the terms was different for each…
Online Sparse Collapsed Hybrid Variational-Gibbs Algorithm for Hierarchical Dirichlet Process Topic Models
2017
Topic models for text analysis are most commonly trained using either Gibbs sampling or variational Bayes. Recently, hybrid variational-Gibbs algorithms have been found to combine the best of both worlds. Variational algorithms are fast to converge and more efficient for inference on new documents. Gibbs sampling enables sparse updates since each token is only associated with one topic instead of a distribution over all topics. Additionally, Gibbs sampling is unbiased. Although Gibbs sampling takes longer to converge, it is guaranteed to arrive at the true posterior after infinitely many iterations. By combining the two methods it is possible to reduce the bias of variational methods while …
A Survey of Multi-Label Topic Models
2019
Every day, an enormous amount of text data is produced. Sources of text data include news, social media, emails, text messages, medical reports, scientific publications and fiction. To keep track of this data, there are categories, key words, tags or labels that are assigned to each text. Automatically predicting such labels is the task of multi-label text classification. Often however, we are interested in more than just the pure classification: rather, we would like to understand which parts of a text belong to the label, which words are important for the label or which labels occur together. Because of this, topic models may be used for multi-label classification as an interpretable mode…
The role of ICT, eWOM and guest characteristics in loyalty
2019
Purpose This study aims to examine how technologies contribute to consumer loyalty in the tourist industry. To achieve this objective, information and communication technology (ICT) development and electronic word-of-mouth (eWOM) are analysed to explore their direct and indirect effects on satisfaction and loyalty dimensions. The moderating role of customer characteristics (personal and experience-related variables) is also considered to study the complex relationship between satisfaction and loyalty. Design/methodology/approach A quantitative study based on a questionnaire structured was developed. The survey was conducted with 386 guests from Spanish hotels. SEM methodology is applied to…