511 research outputs found
SchNet - a deep learning architecture for molecules and materials
Deep learning has led to a paradigm shift in artificial intelligence,
including web, text and image search, speech recognition, as well as
bioinformatics, with growing impact in chemical physics. Machine learning in
general and deep learning in particular is ideally suited for representing
quantum-mechanical interactions, enabling to model nonlinear potential-energy
surfaces or enhancing the exploration of chemical compound space. Here we
present the deep learning architecture SchNet that is specifically designed to
model atomistic systems by making use of continuous-filter convolutional
layers. We demonstrate the capabilities of SchNet by accurately predicting a
range of properties across chemical space for \emph{molecules and materials}
where our model learns chemically plausible embeddings of atom types across the
periodic table. Finally, we employ SchNet to predict potential-energy surfaces
and energy-conserving force fields for molecular dynamics simulations of small
molecules and perform an exemplary study of the quantum-mechanical properties
of C-fullerene that would have been infeasible with regular ab initio
molecular dynamics
Genetics of testosterone and the aggression-hostility-anger (AHA) syndrome: a study of middle-aged male twins.
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TransNets: Learning to Transform for Recommendation
Recently, deep learning methods have been shown to improve the performance of
recommender systems over traditional methods, especially when review text is
available. For example, a recent model, DeepCoNN, uses neural nets to learn one
latent representation for the text of all reviews written by a target user, and
a second latent representation for the text of all reviews for a target item,
and then combines these latent representations to obtain state-of-the-art
performance on recommendation tasks. We show that (unsurprisingly) much of the
predictive value of review text comes from reviews of the target user for the
target item. We then introduce a way in which this information can be used in
recommendation, even when the target user's review for the target item is not
available. Our model, called TransNets, extends the DeepCoNN model by
introducing an additional latent layer representing the target user-target item
pair. We then regularize this layer, at training time, to be similar to another
latent representation of the target user's review of the target item. We show
that TransNets and extensions of it improve substantially over the previous
state-of-the-art.Comment: Accepted for publication in the 11th ACM Conference on Recommender
Systems (RecSys 2017
Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms
Neural network based models for collaborative filtering have started to gain
attention recently. One branch of research is based on using deep generative
models to model user preferences where variational autoencoders were shown to
produce state-of-the-art results. However, there are some potentially
problematic characteristics of the current variational autoencoder for CF. The
first is the too simplistic prior that VAEs incorporate for learning the latent
representations of user preference. The other is the model's inability to learn
deeper representations with more than one hidden layer for each network. Our
goal is to incorporate appropriate techniques to mitigate the aforementioned
problems of variational autoencoder CF and further improve the recommendation
performance. Our work is the first to apply flexible priors to collaborative
filtering and show that simple priors (in original VAEs) may be too restrictive
to fully model user preferences and setting a more flexible prior gives
significant gains. We experiment with the VampPrior, originally proposed for
image generation, to examine the effect of flexible priors in CF. We also show
that VampPriors coupled with gating mechanisms outperform SOTA results
including the Variational Autoencoder for Collaborative Filtering by meaningful
margins on 2 popular benchmark datasets (MovieLens & Netflix)
A Search for the Near-Infrared Counterpart to GCRT J1745-3009
We present an optical/near-infrared search for a counterpart to the
perplexing radio transient GCRT J1745-3009, a source located ~1 degree from the
Galactic Center. Motivated by some similarities to radio bursts from nearby
ultracool dwarfs, and by a distance upper limit of 70 pc for the emission to
not violate the 1e12 K brightness temperature limit for incoherent radiation,
we searched for a nearby star at the position of GCRT J1745-3009. We found only
a single marginal candidate, limiting the presence of any late-type star to >1
kpc (spectral types earlier than M9), >200 pc (spectral types L and T0-T4), and
>100 pc (spectral types T4-T7), thus severely restricting the possible local
counterparts to GCRT J1745-3009. We also exclude any white dwarf within 1 kpc
or a supergiant star out to the distance of the Galactic Center as possible
counterparts. This implies that GCRT J1745-3009 likely requires a coherent
emission process, although whether or not it reflects a new class of sources is
unclear.Comment: 10 pages, 5 figures. Accepted for publication in the Astrophysical
Journa
Structure and Stress: Trajectories of Depressive Symptoms across Adolescence and Young Adulthood
Previous research into the social distribution of early life depression has yielded inconsistent results regarding subgroup differences in depression levels and in the etiology of these differences. Using latent curve models and data from the National Longitudinal Study of Adolescent Health, this study investigates gender and racial/ethnic disparities in early life depressive symptoms and the explanatory roles of stress and socioeconomic status (SES). Results show that females and minorities experience higher levels of depressive symptoms across early life compared to males and Whites. Further, childhood SES and stressful life events (SLEs) explain much of the disparity for Blacks and Hispanics. Finally, Blacks, Hispanics, and females show greater sensitivity to the effects of low childhood SES and, in the case of females, SLEs. Overall, this study provides new insight into gender and racial/ethnic differences in the course of early life depression and in the role of the stress process during this important developmental stage
Flare energetics: analysis of a large flare on YZ Canis Minoris observed simultaneously in the ultraviolet, optical and radio.
The results of coordinated observations of the dMe star YZ CMi at optical, UV and radio wavelengths during 3-7 February 1983 are presented. YZ CMi showed repeated optical flaring with the largest flare having a magnitude of 3.8 in the U-band. This flare coincided with an IUE exposure which permits a comparison of the emission measure curves of YZ CMi in its flaring and quiescent state. During the flare a downward shift of the transition zone is observed while the radiative losses in the range 10^4^-10^7^K strongly increase. The optical flare is accompanied with a radio flare at 6cm, while at 20cm no emission is detected. The flare is interpreted in terms of optically thick synchrotron emission. We present a combined interpretation of the optical/radio flare and show that the flare can be interpreted within the context of solar two-ribbon/white-light flares. Special attention is paid to the bombardment of dMe atmospheres by particle beams. We show that the characteristic temperature of the heated atmosphere is almost independent of the beam flux and lies within the range of solar white-light flare temperatures. We also show that it is unlikely that stellar flares emit black-body spectra. The fraction of accelerated particles, as follows from our combined optical/radio interpretation is in good agreement with the fraction determined by two-ribbon flare reconnection models
A systematic method for estimating individual responses to treatment with antipsychotics in CATIE
In addition to comparing drug treatment groups, the wealth of genetic and clinical data collected in the Clinical Antipsychotic Trials of Intervention Effectiveness study offers tremendous opportunities to study individual differences in response to treatment with antipsychotics. A major challenge, however, is how to estimate the individual responses to treatments. For this purpose, we propose a systematic method that condenses all information collected during the trials in an optimal, empirical fashion
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