756 research outputs found
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-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
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