The influence of an electrostatic field on cyclotron resonance behaviour of a plasma

The theoretically predicted influence of an electrostatic field on the energy gain of electrons at e.c.r. is confirmed experimentally by measuring the loss tangent of the plasma as a function of an applied D.C. voltage. The applicability of this effect as a heating scheme is discussed in general term

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

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

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

Genetics of testosterone and the aggression-hostility-anger (AHA) syndrome: a study of middle-aged male twins.

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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)

Quasi-periodic X-ray Flares from the Protostar YLW15

With ASCA, we have detected three X-ray flares from the Class I protostar YLW15. The flares occurred every ~20 hours and showed an exponential decay with time constant 30-60 ks. The X-ray spectra are explained by a thin thermal plasma emission. The plasma temperature shows a fast-rise and slow-decay for each flare with kT_{peak}~4-6 keV. The emission measure of the plasma shows this time profile only for the first flare, and remains almost constant during the second and third flares at the level of the tail of the first flare. The peak flare luminosities L_{X,peak} were ~5-20 * 10^{31} erg s^{-1}, which are among the brightest X-ray luminosities observed to date for Class I protostars. The total energy released in each flare was 3-6*10^{36} ergs. The first flare is well reproduced by the quasi-static cooling model, which is based on solar flares, and it suggests that the plasma cools mainly radiatively, confined by a semi-circular magnetic loop of length ~14 Ro with diameter-to-length ratio \~0.07. The two subsequent flares were consistent with the reheating of the same magnetic structure as of the first flare. The large-scale magnetic structure and the periodicity of the flares imply that the reheating events of the same magnetic loop originate in an interaction between the star and the disk due to the differential rotation.Comment: Accepted by ApJ, 9 pages incl. 4 ps figure