Latent Space Model for Multi-Modal Social Data

With the emergence of social networking services, researchers enjoy the increasing availability of large-scale heterogenous datasets capturing online user interactions and behaviors. Traditional analysis of techno-social systems data has focused mainly on describing either the dynamics of social interactions, or the attributes and behaviors of the users. However, overwhelming empirical evidence suggests that the two dimensions affect one another, and therefore they should be jointly modeled and analyzed in a multi-modal framework. The benefits of such an approach include the ability to build better predictive models, leveraging social network information as well as user behavioral signals. To this purpose, here we propose the Constrained Latent Space Model (CLSM), a generalized framework that combines Mixed Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA) incorporating a constraint that forces the latent space to concurrently describe the multiple data modalities. We derive an efficient inference algorithm based on Variational Expectation Maximization that has a computational cost linear in the size of the network, thus making it feasible to analyze massive social datasets. We validate the proposed framework on two problems: prediction of social interactions from user attributes and behaviors, and behavior prediction exploiting network information. We perform experiments with a variety of multi-modal social systems, spanning location-based social networks (Gowalla), social media services (Instagram, Orkut), e-commerce and review sites (Amazon, Ciao), and finally citation networks (Cora). The results indicate significant improvement in prediction accuracy over state of the art methods, and demonstrate the flexibility of the proposed approach for addressing a variety of different learning problems commonly occurring with multi-modal social data.Comment: 12 pages, 7 figures, 2 table

A charge density study of an intramolecular charge-transfer quinoid compound with strong NLO properties

An experimental charge density investigation of 7,7-di[(S)-(+)-2-(methoxymethyl)pyrrolidino]-8,8-dicyanoquinodimethane establishes the presence of a large charge separation as well as a high in-crystal dipole moment compared to the free molecule in frozen geometry, consistent with the high SHG activity of the compound

The complexity of dominating set reconfiguration

Suppose that we are given two dominating sets $D_s$ and $D_t$ of a graph $G$ whose cardinalities are at most a given threshold $k$. Then, we are asked whether there exists a sequence of dominating sets of $G$ between $D_s$ and $D_t$ such that each dominating set in the sequence is of cardinality at most $k$ and can be obtained from the previous one by either adding or deleting exactly one vertex. This problem is known to be PSPACE-complete in general. In this paper, we study the complexity of this decision problem from the viewpoint of graph classes. We first prove that the problem remains PSPACE-complete even for planar graphs, bounded bandwidth graphs, split graphs, and bipartite graphs. We then give a general scheme to construct linear-time algorithms and show that the problem can be solved in linear time for cographs, trees, and interval graphs. Furthermore, for these tractable cases, we can obtain a desired sequence such that the number of additions and deletions is bounded by $O(n)$, where $n$ is the number of vertices in the input graph

Spin Gaps in Coupled t-J Ladders

Spin gaps in coupled $t$-$J$ ladders are investigated by exact diagonalization of small clusters up to 4$\times$8 sites. At half-filling, the numerical results for the triplet excitation spectrum are in very good agreement with a second order perturbation expansion in term of small inter-ladder and intra-ladder exchange couplings between rungs ($J/J^\prime$$<$$0.25$). The band of local triplet excitations moving coherently along the ladder (with momenta close to $\pi$) is split by the inter-ladder coupling. For intermediate couplings finite size scaling is used to estimate the spin gap. In the isotropic infinite 4-chain system (two coupled ladders) we find a spin gap of $0.245 J$, roughly half of the single ladder spin gap. When the system is hole doped, bonding and anti-bonding bound pairs of holes can propagate coherently along the chains and the spin gap remains finite.Comment: 11 pages, 5 figures, uuencoded form of postscript files of figures and text, LPQTH-94/

The vanishing atrial mass.

HEFCEThis is the final version of the article. It first appeared from Oxford University Press via https://doi.org10.1093/ehjci/jew12

Resistivity as a function of temperature for models with hot spots on the Fermi surface.

We calculate the resistivity $\rho$ as a function of temperature $T$ for two models currently discussed in connection with high temperature superconductivity: nearly antiferromagnetic Fermi liquids and models with van Hove singularities on the Fermi surface. The resistivity is calculated semiclassicaly by making use of a Boltzmann equation which is formulated as a variational problem. For the model of nearly antiferromagnetic Fermi liquids we construct a better variational solution compared to the standard one and we find a new energy scale for the crossover to the $\rho\propto T^2$ behavior at low temperatures. This energy scale is finite even when the spin-fluctuations are assumed to be critical. The effect of additional impurity scattering is discussed. For the model with van Hove singularities a standard ansatz for the Boltzmann equation is sufficient to show that although the quasiparticle lifetime is anomalously short, the resistivity $\rho\propto T^2\ln(1/T)$.Comment: Revtex 3.0, 8 pages; figures available upon request. Submitted to Phys. Rev. B

Token Jumping in minor-closed classes

Given two $k$-independent sets $I$ and $J$ of a graph $G$, one can ask if it is possible to transform the one into the other in such a way that, at any step, we replace one vertex of the current independent set by another while keeping the property of being independent. Deciding this problem, known as the Token Jumping (TJ) reconfiguration problem, is PSPACE-complete even on planar graphs. Ito et al. proved in 2014 that the problem is FPT parameterized by $k$ if the input graph is $K_{3,\ell}$-free. We prove that the result of Ito et al. can be extended to any $K_{\ell,\ell}$-free graphs. In other words, if $G$ is a $K_{\ell,\ell}$-free graph, then it is possible to decide in FPT-time if $I$ can be transformed into $J$. As a by product, the TJ-reconfiguration problem is FPT in many well-known classes of graphs such as any minor-free class

An unusual finding in a 57-year-old woman with new onset hypertension and a diastolic murmur.

CLINICAL INTRODUCTION: A 57-year-old woman presented to our clinic with breathlessness brought on while walking uphill. She had been recently diagnosed with systemic hypertension. There was no known family history of cardiac disease, or prior smoking habit. On examination, pulse was 73 bpm and blood pressure 155/73 mm Hg, which was asymmetrical in her arms. Auscultation revealed a readily audible early diastolic murmur in the aortic area and bilateral subclavian bruits. ECG showed sinus rhythm with no abnormality. Transthoracic echocardiography demonstrated mild-to-moderate aortic regurgitation, and normal left ventricular size and function. The ascending aorta was mildly dilated (41 mm), with para-aortic thickening noted. Owing to the abnormal appearance of the aortic wall, cardiac MRI, and subsequently 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) scan was performed (figure 1). QUESTION: Which complication of the underlying disease is evident in figure 1, panel C? Aortic aneurysmAortic dissectionAortic thrombusCoronary artery aneurysmCoronary sinus fistula

Tuberculosis in North Arcot District of Tamil Nadu – a sample survey

A sample survey was carried out in the North Arcot district of Tamil Nadu with the objective of finding out the prevalence of bacteriologically positive and radiologically active pulmonary tuberculosis among persons aged 15 years and above using two screening methods viz.. elicitation of suggestive symptoms and chest X-ray examination. Another objective was to estimate the prevalence of tuberculosis infection in children aged below 10 years. A population of 1,05,339 persons was registered in a random sample of 35 villages from the rural areas and 102 town streets from the urban sector. All children aged 0-9 years were tuberculin tested with ITU RT23. Persons aged 15 years and above were screened for suggestive symptoms. and one-third of the sample was screened by X-ray of chest as well. Sputum specimens from the symptomatics and/or X-ray abnormals were subjected to bacteriological examination. The prevalence of infection among ‘below 10 years old’ children without BCG scar was 6.7%. The prevalence of disease by sputum smear and/or culture among symptomatics was 4.3 per thousand in population aged 15 years and above. The prevalence rate of bacteriological positives based on symptoms and X-ray screening, in the one-third sample was 7.9 per thousand. The prevalence of X-ray positive cases was 17.0 per 1000. Information available from similar other studies in the country has been reviewed

Ask the GRU: Multi-Task Learning for Deep Text Recommendations

In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on latent factor models can be extended naturally to leverage text by employing an explicit mapping from text to factors. This enables recommendations for new, unseen content, and may generalize better, since the factors for all items are produced by a compactly-parametrized model. Previous work has used topic models or averages of word embeddings for this mapping. In this paper we present a method leveraging deep recurrent neural networks to encode the text sequence into a latent vector, specifically gated recurrent units (GRUs) trained end-to-end on the collaborative filtering task. For the task of scientific paper recommendation, this yields models with significantly higher accuracy. In cold-start scenarios, we beat the previous state-of-the-art, all of which ignore word order. Performance is further improved by multi-task learning, where the text encoder network is trained for a combination of content recommendation and item metadata prediction. This regularizes the collaborative filtering model, ameliorating the problem of sparsity of the observed rating matrix.Comment: 8 page