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$_{20}$-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