The Relational Vector-space Model

This paper addresses the classification of linked entities. We introduce a relational vector (VS) model (in analogy to the VS model used in information retrieval) that abstracts the linked structure, representing entities by vectors of weights. Given labeled data as background knowledge training data, classification procedures can be defined for this model, including a straightforward, "direct" model using weighted adjacency vectors. Using a large set of tasks from the domain of company affiliation identification, we demonstrate that such classification procedures can be effective. We then examine the method in more detail, showing that as expected the classification performance correlates with the- relational auto correlation of the data set. We then turn the tables and use the relational VS scores as a way to analyze/visualize the relational autocorrelation present in a complex linked structure. The main contribution of the paper 1s to introduce the relational VS model as a potentially useful addition to the toolkit for relational data mining. It could provide useful constructed features for domains with low to moderate relational autocorrelation; it may be effective by itself for domains with high levels of relational autocorrelation, and it provides a useful abstraction for analyzing the properties of linked data.Information Systems Working Papers Serie

Discovering Knowledge from Relational Data Extracted from Business News

Thousands of business news stories (including press releases, earnings reports, general business news, etc.) are released each day. Recently, information technology advances have partially automated the processing of documents, reducing the amount of text that must be read. Current techniques (e.g., text classification and information extraction) for full-text analysis for the most part are limited to discovering information that can be found in single documents. Often, however, important information does not reside in a single document, but in the relationships between information distributed over multiple documents. This paper reports on an investigation into whether knowledge can be discovered automatically from relational data extracted from large corpora of business news stories. We use a combination of information extraction, network analysis, and statistical techniques. We show that relationally interlinked patterns distributed over multiple documents can indeed be extracted, and (specifically) that knowledge about companiesÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂàinterrelationships can be discovered. We evaluate the extracted relationships in several ways: we give a broad visualization of related companies, showing intuitive industry clusters; we use network analysis to ask who are the central players, and finally, we show that the extracted interrelationships can be used for important tasks, such as for classifying companies by industry membership.Information Systems Working Papers Serie

Quantum nonlinear lattices and coherent state vectors

Quantized nonlinear lattice models are considered for two different classes, boson and fermionic ones. The quantum discrete nonlinear Schroedinger model (DNLS) is our main objective, but its so called modified discrete nonlinear (MDNLS) version is also included, together with the fermionic polaron (FP) model. Based on the respective dynamical symmetries of the models, a method is put forward which by use of the associated boson and spin coherent state vectors (CSV) and a factorization ansatz for the solution of the Schroedinger equation, leads to quasiclassical Hamiltonian equations of motion for the CSV parameters. Analysing the geometrical content of the factorization ansatz made for the state vectors invokes the study of the Riemannian and symplectic geometry of the CSV manifolds as generalized phase spaces. Next, we investigate analytically and numerically the behavior of mean values and uncertainties of some physically interesting observables as well as the modifications in the quantum regime of processes such as the discrete self trapping (DST), in terms of the Q-function and the distribution of excitation quanta of the lattice sites. Quantum DST in the symmetric ordering of lattice operators is found to be relatively enhanced with respect to the classical DST. Finally, the meaning of the factorization ansatz for the lattice wave function is explained in terms of disregarded quantum correlations, and as a quantitative figure of merit for that ansatz a correlation index is introduced.Comment: 17 pages, Latex, 9 figures in ps forma

Discovering Knowledge from Relational Data Extracted from Business News

Thousands of business news stories (including press releases, earnings reports, general business news, etc.) are released each day. Recently, information technology advances have partially automated the processing of documents, reducing the amount of text that must be read. Current techniques (e.g., text classification and information extraction) for full-text analysis for the most part are limited to discovering information that can be found in single documents. Often, however, important information does not reside in a single document, but in the relationships between information distributed over multiple documents. This paper reports on an investigation into whether knowledge can be discovered automatically from relational data extracted from large corpora of business news stories. We use a combination of information extraction, network analysis, and statistical techniques. We show that relationally interlinked patterns distributed over multiple documents can indeed be extracted, and (specifically) that knowledge about companiesÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂàinterrelationships can be discovered. We evaluate the extracted relationships in several ways: we give a broad visualization of related companies, showing intuitive industry clusters; we use network analysis to ask who are the central players, and finally, we show that the extracted interrelationships can be used for important tasks, such as for classifying companies by industry membership.Information Systems Working Papers Serie

Discovering Knowledge from Relational Data Extracted from Business News

Thousands of business news stories (including press releases, earnings reports, general business news, etc.) are released each day. Recently, information technology advances have partially automated the processing of documents, reducing the amount of text that must be read. Current techniques (e.g., text classification and information extraction) for full-text analysis for the most part are limited to discovering information that can be found in single documents. Often, however, important information does not reside in a single document, but in the relationships between information distributed over multiple documents. This paper reports on an investigation into whether knowledge can be discovered automatically from relational data extracted from large corpora of business news stories. We use a combination of information extraction, network analysis, and statistical techniques. We show that relationally interlinked patterns distributed over multiple documents can indeed be extracted, and (specifically) that knowledge about companiesÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂàinterrelationships can be discovered. We evaluate the extracted relationships in several ways: we give a broad visualization of related companies, showing intuitive industry clusters; we use network analysis to ask who are the central players, and finally, we show that the extracted interrelationships can be used for important tasks, such as for classifying companies by industry membership.Information Systems Working Papers Serie

Discovering Knowledge from Relational Data Extracted from Business News

Thousands of business news stories (including press releases, earnings reports, general business news, etc.) are released each day. Recently, information technology advances have partially automated the processing of documents, reducing the amount of text that must be read. Current techniques (e.g., text classification and information extraction) for full-text analysis for the most part are limited to discovering information that can be found in single documents. Often, however, important information does not reside in a single document, but in the relationships between information distributed over multiple documents. This paper reports on an investigation into whether knowledge can be discovered automatically from relational data extracted from large corpora of business news stories. We use a combination of information extraction, network analysis, and statistical techniques. We show that relationally interlinked patterns distributed over multiple documents can indeed be extracted, and (specifically) that knowledge about companiesÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂàinterrelationships can be discovered. We evaluate the extracted relationships in several ways: we give a broad visualization of related companies, showing intuitive industry clusters; we use network analysis to ask who are the central players, and finally, we show that the extracted interrelationships can be used for important tasks, such as for classifying companies by industry membership.Information Systems Working Papers Serie

Non-Newtonian characteristics of peristaltic flow of blood in micro-vessels

Of concern in the paper is a generalized theoretical study of the non-Newtonian characteristics of peristaltic flow of blood through micro-vessels, e.g. arterioles. The vessel is considered to be of variable cross-section and blood to be a Herschel-Bulkley type of fluid. The progressive wave front of the peristaltic flow is supposed sinusoidal/straight section dominated (SSD) (expansion/contraction type); Reynolds number is considered to be small with reference to blood flow in the micro-circulatory system. The equations that govern the non-Newtonian peristaltic flow of blood are considered to be non-linear. The objective of the study has been to examine the effect of amplitude ratio, mean pressure gradient, yield stress and the power law index on the velocity distribution, wall shear stress, streamline pattern and trapping. It is observed that the numerical estimates for the aforesaid quantities in the case of peristaltic transport of the blood in a channel are much different from those for flow in an axisymmetric vessel of circular cross-section. The study further shows that peristaltic pumping, flow velocity and wall shear stress are significantly altered due to the non-uniformity of the cross-sectional radius of blood vessels of the micro-circulatory system. Moreover, the magnitude of the amplitude ratio and the value of the fluid index are important parameters that affect the flow behaviour. Novel features of SSD wave propagation that affect the flow behaviour of blood have also been discussed.Comment: Accepted for publication in Communications in Nonlinear Science and Numerical Simulation, Elsevier. arXiv admin note: text overlap with arXiv:1006.017

International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci.

The risk of posttraumatic stress disorder (PTSD) following trauma is heritable, but robust common variants have yet to be identified. In a multi-ethnic cohort including over 30,000 PTSD cases and 170,000 controls we conduct a genome-wide association study of PTSD. We demonstrate SNP-based heritability estimates of 5-20%, varying by sex. Three genome-wide significant loci are identified, 2 in European and 1 in African-ancestry analyses. Analyses stratified by sex implicate 3 additional loci in men. Along with other novel genes and non-coding RNAs, a Parkinson's disease gene involved in dopamine regulation, PARK2, is associated with PTSD. Finally, we demonstrate that polygenic risk for PTSD is significantly predictive of re-experiencing symptoms in the Million Veteran Program dataset, although specific loci did not replicate. These results demonstrate the role of genetic variation in the biology of risk for PTSD and highlight the necessity of conducting sex-stratified analyses and expanding GWAS beyond European ancestry populations