Attentive Neural Architecture Incorporating Song Features For Music Recommendation

Recommender Systems are an integral part of music sharing platforms. Often the aim of these systems is to increase the time, the user spends on the platform and hence having a high commercial value. The systems which aim at increasing the average time a user spends on the platform often need to recommend songs which the user might want to listen to next at each point in time. This is different from recommendation systems which try to predict the item which might be of interest to the user at some point in the user lifetime but not necessarily in the very near future. Prediction of the next song the user might like requires some kind of modeling of the user interests at the given point of time. Attentive neural networks have been exploiting the sequence in which the items were selected by the user to model the implicit short-term interests of the user for the task of next item prediction, however we feel that the features of the songs occurring in the sequence could also convey some important information about the short-term user interest which only the items cannot. In this direction, we propose a novel attentive neural architecture which in addition to the sequence of items selected by the user, uses the features of these items to better learn the user short-term preferences and recommend the next song to the user.Comment: Accepted as a paper at the 12th ACM Conference on Recommender Systems (RecSys 18

Collaborative Deep Learning for Recommender Systems

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art

X-Ray Spectroscopy of II Pegasi: Coronal Temperature Structure, Abundances, and Variability

We have obtained high resolution X-ray spectra of the coronally active binary, II Pegasi (HD 224085), covering the wavelength range of 1.5-25 Angstroms. For the first half of our 44 ksec observation, the source was in a quiescent state with constant X-ray flux, after which it flared, reaching twice the quiescent flux in 12 ksec, then decreasing. We analyze the emission-line spectrum and continuum during quiescent and flaring states. The differential emission measure derived from lines fluxes shows a hot corona with a continuous distribution in temperature. During the non-flare state, the distribution peaks near log T = 7.2, and when flaring, near 7.6. High-temperature lines are enhanced slightly during the flare, but most of the change occurs in the continuum. Coronal abundance anomalies are apparent, with iron very deficient relative to oxygen and significantly weaker than expected from photospheric measurements, while neon is enhanced relative to oxygen. We find no evidence of appreciable resonant scattering optical depth in line ratios of iron and oxygen. The flare light curve is consistent with Solar two-ribbon flare models, but with a very long reconnection time-constant of about 65 ks. We infer loop lengths of about 0.05 stellar radii, to about 0.25 in the flare, if the flare emission originated from a single, low-density loop.Comment: 25 pages, 5 figures, 3 tables, accepted by ApJ (scheduled for the v559 n2 p1 Oct 1, 2001 issue

X-Ray Flaring on the dMe Star, Ross 154

We present results from two Chandra imaging observations of Ross 154, a nearby flaring M dwarf star. During a 61-ks ACIS-S exposure, a very large flare occurred (the equivalent of a solar X3400 event, with L_X = 1.8x10^30 ergs/s) in which the count rate increased by a factor of over 100. The early phase of the flare shows evidence for the Neupert effect, followed by a further rise and then a two-component exponential decay. A large flare was also observed at the end of a later 48-ks HRC-I observation. Emission from the non-flaring phases of both observations was analyzed for evidence of low level flaring. From these temporal studies we find that microflaring probably accounts for most of the `quiescent' emission, and that, unlike for the Sun and the handful of other stars that have been studied, the distribution of flare intensities does not appear to follow a power-law with a single index. Analysis of the ACIS spectra, which was complicated by exclusion of the heavily piled-up source core, suggests that the quiescent Ne/O abundance ratio is enhanced by a factor of ~2.5 compared to the commonly adopted solar abundance ratio, and that the Ne/O ratio and overall coronal metallicity during the flare appear to be enhanced relative to quiescent abundances. Based on the temperatures and emission measures derived from the spectral fits, we estimate the length scales and plasma densities in the flaring volume and also track the evolution of the flare in color-intensity space. Lastly, we searched for a stellar-wind charge-exchange X-ray halo around the star but without success; because of the relationship between mass-loss rate and the halo surface brightness, not even an upper limit on the stellar mass-loss rate can be determined.Comment: 20 pages, 12 figures (4 color), accepted by ApJ, expected publication April 1, 200

Genome-wide pharmacogenomic analysis of response to treatment with antipsychotics

Schizophrenia is an often devastating neuropsychiatric illness. Understanding the genetic variation affecting response to antipsychotics is important to develop novel diagnostic tests to match individual schizophrenic patients to the most effective and safe medication. Here we use a genomewide approach to detect genetic variation underlying individual differences in response to treatment with the antipsychotics olanzapine, quetiapine, risperidone, ziprasidone and perphenazine. Our sample consisted of 738 subjects with DSM-IV schizophrenia who took part in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE). Subjects were genotyped using the Affymetrix 500K genotyping platform plus a custom 164K chip to improve genomewide coverage. Treatment outcome was measured using the Positive and Negative Syndrome Scale (PANSS). Our criterion for genomewide significance was a pre-specified threshold that ensures, on average, only 10% of the significant findings are false discoveries. The top statistical result reached significance at our pre-specified threshold and involved a SNP in an intergenic region on chromosome 4p15. In addition, SNPs in ANKS1B and CNTNAP5 that mediated the effects of olanzapine and risperidone on Negative symptoms were very close to our threshold for declaring significance. The most significant SNP in CNTNAP5 is nonsynonymous, giving rise to an amino acid substitution. In addition to highlighting our top results, we provide all p-values for download as a resource for investigators with the requisite samples to carry out replication. This study demonstrates the potential of GWAS to discover novel genes that mediate effects of antipsychotics, which eventually could help to tailor drug treatment to schizophrenic patients

Stellar Coronal and Wind Models: Impact on Exoplanets

Surface magnetism is believed to be the main driver of coronal heating and stellar wind acceleration. Coronae are believed to be formed by plasma confined in closed magnetic coronal loops of the stars, with winds mainly originating in open magnetic field line regions. In this Chapter, we review some basic properties of stellar coronae and winds and present some existing models. In the last part of this Chapter, we discuss the effects of coronal winds on exoplanets.Comment: Chapter published in the "Handbook of Exoplanets", Editors in Chief: Juan Antonio Belmonte and Hans Deeg, Section Editor: Nuccio Lanza. Springer Reference Work

Recent Advances in Understanding Particle Acceleration Processes in Solar Flares

We review basic theoretical concepts in particle acceleration, with particular emphasis on processes likely to occur in regions of magnetic reconnection. Several new developments are discussed, including detailed studies of reconnection in three-dimensional magnetic field configurations (e.g., current sheets, collapsing traps, separatrix regions) and stochastic acceleration in a turbulent environment. Fluid, test-particle, and particle-in-cell approaches are used and results compared. While these studies show considerable promise in accounting for the various observational manifestations of solar flares, they are limited by a number of factors, mostly relating to available computational power. Not the least of these issues is the need to explicitly incorporate the electrodynamic feedback of the accelerated particles themselves on the environment in which they are accelerated. A brief prognosis for future advancement is offered.Comment: This is a chapter in a monograph on the physics of solar flares, inspired by RHESSI observations. The individual articles are to appear in Space Science Reviews (2011

Power calculations using exact data simulation: A useful tool for genetic study designs.

Statistical power calculations constitute an essential first step in the planning of scientific studies. If sufficient summary statistics are available, power calculations are in principle straightforward and computationally light. In designs, which comprise distinct groups (e.g., MZ & DZ twins), sufficient statistics can be calculated within each group, and analyzed in a multi-group model. However, when the number of possible groups is prohibitively large (say, in the hundreds), power calculations on the basis of the summary statistics become impractical. In that case, researchers may resort to Monte Carlo based power studies, which involve the simulation of hundreds or thousands of replicate samples for each specified set of population parameters. Here we present exact data simulation as a third method of power calculation. Exact data simulation involves a transformation of raw data so that the data fit the hypothesized model exactly. As in power calculation with summary statistics, exact data simulation is computationally light, while the number of groups in the analysis has little bearing on the practicality of the method. The method is applied to three genetic designs for illustrative purposes