Learning to Determine the Quality of News Headlines

Today, most newsreaders read the online version of news articles rather than traditional paper-based newspapers. Also, news media publishers rely heavily on the income generated from subscriptions and website visits made by newsreaders. Thus, online user engagement is a very important issue for online newspapers. Much effort has been spent on writing interesting headlines to catch the attention of online users. On the other hand, headlines should not be misleading (e.g., clickbaits); otherwise, readers would be disappointed when reading the content. In this paper, we propose four indicators to determine the quality of published news headlines based on their click count and dwell time, which are obtained by website log analysis. Then, we use soft target distribution of the calculated quality indicators to train our proposed deep learning model which can predict the quality of unpublished news headlines. The proposed model not only processes the latent features of both headline and body of the article to predict its headline quality but also considers the semantic relation between headline and body as well. To evaluate our model, we use a real dataset from a major Canadian newspaper. Results show our proposed model outperforms other state-of-the-art NLP models.Comment: 10 Pages, Accepted at the 12th International Conference on Agents and Artificial Intelligence (ICAART) 202

Differences in glucose control, insulin sensitivity, and body composition between metabolically healthy and unhealthy people with obesity

Poster presented at the 2017 Health Sciences Research Day which was organized and sponsored by the University of Missouri School of Medicine Research Council and held on November 9, 2017.Obesity is a significant risk factor for cardiometabolic complications, including type 2 diabetes and cardiovascular disease. However, approximately 25% of individuals with obesity are seemingly protected from these complications (Wildman et al. Arch Intern Med, 168, 1617-24, 2008). The purpose of this study was to provide a careful characterization of body composition and metabolic function in people who are: (i) lean and metabolically normal (MNL); (ii) obese and metabolically-normal (MN)); and (iii) obese and metabolically-abnormal (MAO). (Introduction & study aims) Although the glycemic responses of MNL individuals demonstrate a "metabolically healthy" state, more rigorous measures of insulin sensitivity show insulin resistance in this population, demonstrating people with MNO are insulin-resistant with respect to glucose metabolism but are able to maintain normal glycemic control by increased insulin secretion. Adipose tissue distribution is a marker of metabolic health in people with obesity, as greater intra-abdominal adipose tissue volume and intrahepatic triglyceride content are associated with metabolic dysfunction

Stochastic Constrained Extended System Dynamics for Solving Charge Equilibration Models

We present a new stochastic extended Lagrangian solution to charge equilibration that eliminates self-consistent field (SCF) calculations, eliminating the computational bottleneck in solving the many-body solution with standard SCF solvers. By formulating both charges and chemical potential as latent variables, and introducing a holonomic constraint that satisfies charge conservation, the SC-XLMD method accurately reproduces structural, thermodynamic, and dynamics properties using ReaxFF, and shows excellent weak- and strong-scaling performance in the LAMMPS molecular simulation package

A Spitzer-IRS Detection of Crystalline Silicates in a Protostellar Envelope

We present the Spitzer Space Telescope Infrared Spectrograph spectrum of the Orion A protostar HOPS-68. The mid-infrared spectrum reveals crystalline substructure at 11.1, 16.1, 18.8, 23.6, 27.9, and 33.6 microns superimposed on the broad 9.7 and 18 micron amorphous silicate features; the substructure is well matched by the presence of the olivine end-member forsterite. Crystalline silicates are often observed as infrared emission features around the circumstellar disks of Herbig Ae/Be stars and T Tauri stars. However, this is the first unambiguous detection of crystalline silicate absorption in a cold, infalling, protostellar envelope. We estimate the crystalline mass fraction along the line-of-sight by first assuming that the crystalline silicates are located in a cold absorbing screen and secondly by utilizing radiative transfer models. The resulting crystalline mass fractions of 0.14 and 0.17, respectively, are significantly greater than the upper limit found in the interstellar medium (< 0.02-0.05). We propose that the amorphous silicates were annealed within the hot inner disk and/or envelope regions and subsequently transported outward into the envelope by entrainment in a protostellar outflowComment: Accepted to Astrophysical Journal Letters, 2011 April 19: 6 pages, 3 figures, 2 table

Physiological mechanisms of sustained fumagillin-induced weight loss

Current obesity interventions suffer from lack of durable effects and undesirable complications. Fumagillin, an inhibitor of methionine aminopeptidase-2, causes weight loss by reducing food intake, but with effects on weight that are superior to pair-feeding. Here, we show that feeding of rats on a high-fat diet supplemented with fumagillin (HF/FG) suppresses the aggressive feeding observed in pair-fed controls (HF/PF) and alters expression of circadian genes relative to the HF/PF group. Multiple indices of reduced energy expenditure are observed in HF/FG but not HF/PF rats. HF/FG rats also exhibit changes in gut hormones linked to food intake, increased energy harvest by gut microbiota, and caloric spilling in the urine. Studies in gnotobiotic mice reveal that effects of fumagillin on energy expenditure but not feeding behavior may be mediated by the gut microbiota. In sum, fumagillin engages weight loss-inducing behavioral and physiologic circuits distinct from those activated by simple caloric restriction

Small-Noise Analysis and Symmetrization of Implicit Monte Carlo Samplers

Implicit samplers are algorithms for producing independent, weighted samples from multivariate probability distributions. These are often applied in Bayesian data assimilation algorithms. We use Laplace asymptotic expansions to analyze two implicit samplers in the small noise regime. Our analysis suggests a symmetrization of the algorithms that leads to improved implicit sampling schemes at a relatively small additional cost. Computational experiments confirm the theory and show that symmetrization is effective for small noise sampling problems.© 2016 Wiley Periodicals, Inc

Semantic distillation: a method for clustering objects by their contextual specificity

Techniques for data-mining, latent semantic analysis, contextual search of databases, etc. have long ago been developed by computer scientists working on information retrieval (IR). Experimental scientists, from all disciplines, having to analyse large collections of raw experimental data (astronomical, physical, biological, etc.) have developed powerful methods for their statistical analysis and for clustering, categorising, and classifying objects. Finally, physicists have developed a theory of quantum measurement, unifying the logical, algebraic, and probabilistic aspects of queries into a single formalism. The purpose of this paper is twofold: first to show that when formulated at an abstract level, problems from IR, from statistical data analysis, and from physical measurement theories are very similar and hence can profitably be cross-fertilised, and, secondly, to propose a novel method of fuzzy hierarchical clustering, termed \textit{semantic distillation} -- strongly inspired from the theory of quantum measurement --, we developed to analyse raw data coming from various types of experiments on DNA arrays. We illustrate the method by analysing DNA arrays experiments and clustering the genes of the array according to their specificity.Comment: Accepted for publication in Studies in Computational Intelligence, Springer-Verla

ENV-607: SURFACTANT-MODIFIED BIOMASS ADSORBENTS FOR ENHANCED REMOVAL OF POLLUTANTS FROM AQUEOUS SOLUTION

From the view of economical efficiency and technology sustainability, considerable attention has been recently given to the use of low-cost biomass residues as adsorbents in pollution control. To achieve a desirable adsorptive efficiency, some efforts have also been made to modify biomass adsorbents through appropriate treatments. There is a particular interest in surfactant-assisted biomass surface modification. Although some findings from previous studies are encouraging, knowledge about the adsorption of pollutants onto surfactant-modified biomass is still limited. A number of issues about the characteristics of involved interface transport are poorly understood. The present study therefore aims to examine the adsorption of anionic azo dyes onto surfactant-modified biomass in the solution. Different surfactants are used for modification. The equilibrium and kinetic studies for the adsorption of anionic azo dyes on modified biomass are conducted and the effects of aqueous chemistry characteristics are also evaluated. The results present the potential of modified biomass as suitable adsorbent for the removal of anionic azo dyes from wastewater. It can help understand the migration patterns of organic pollutants at biomass-water interface