3,964 research outputs found
Charged black holes in string-inspired gravity: I. Causal structures and responses of the Brans-Dicke field
We investigate gravitational collapses of charged black holes in
string-inspired gravity models, including dilaton gravity and braneworld model,
as well as f(R) gravity and the ghost limit. If we turn on gauge coupling, the
causal structures and the responses of the Brans-Dicke field depend on the
coupling between the charged matter and the Brans-Dicke field. For Type IIA
inspired models, a Cauchy horizon exists, while there is no Cauchy horizon for
Type I or Heterotic inspired models. For Type IIA inspired models, the no-hair
theorem is satisfied asymptotically, while it is biased to the weak coupling
limit for Type I or Heterotic inspired models. Apart from string theory, we
find that in the ghost limit, a gravitational collapse can induce inflation by
itself and create one-way traversable wormholes without the need of other
special initial conditions.Comment: 45 pages, 22 figure
Neural Speed Reading with Structural-Jump-LSTM
Recurrent neural networks (RNNs) can model natural language by sequentially
'reading' input tokens and outputting a distributed representation of each
token. Due to the sequential nature of RNNs, inference time is linearly
dependent on the input length, and all inputs are read regardless of their
importance. Efforts to speed up this inference, known as 'neural speed
reading', either ignore or skim over part of the input. We present
Structural-Jump-LSTM: the first neural speed reading model to both skip and
jump text during inference. The model consists of a standard LSTM and two
agents: one capable of skipping single words when reading, and one capable of
exploiting punctuation structure (sub-sentence separators (,:), sentence end
symbols (.!?), or end of text markers) to jump ahead after reading a word. A
comprehensive experimental evaluation of our model against all five
state-of-the-art neural reading models shows that Structural-Jump-LSTM achieves
the best overall floating point operations (FLOP) reduction (hence is faster),
while keeping the same accuracy or even improving it compared to a vanilla LSTM
that reads the whole text.Comment: 10 page
Green manure crops for low fertility soils
Organic crop production is growing, but crop yields are still below potential. The purpose of our project “Nutrients for higher organic yields (NutHY)” is to increase yields and resource efficiency in organic crop production by optimizing nutrient supply. Growing green manure is an important tool to improve fertilization by biological nitrogen (N) fixation but also by mobilization and release of other nutrients such as phosphorus (P). However, development and performance of green manure are affected by low soil nutrient availability that is often reported as a problem in organic arable farms, especially with regard to P.
Poster at DOK-Monte Veritá Conference, 6-11 October, 2019, Congressi Stefano Fanscini, Monte Veritá, Switzerlan
Modelling Sequential Music Track Skips using a Multi-RNN Approach
Modelling sequential music skips provides streaming companies the ability to
better understand the needs of the user base, resulting in a better user
experience by reducing the need to manually skip certain music tracks. This
paper describes the solution of the University of Copenhagen DIKU-IR team in
the 'Spotify Sequential Skip Prediction Challenge', where the task was to
predict the skip behaviour of the second half in a music listening session
conditioned on the first half. We model this task using a Multi-RNN approach
consisting of two distinct stacked recurrent neural networks, where one network
focuses on encoding the first half of the session and the other network focuses
on utilizing the encoding to make sequential skip predictions. The encoder
network is initialized by a learned session-wide music encoding, and both of
them utilize a learned track embedding. Our final model consists of a majority
voted ensemble of individually trained models, and ranked 2nd out of 45
participating teams in the competition with a mean average accuracy of 0.641
and an accuracy on the first skip prediction of 0.807. Our code is released at
https://github.com/Varyn/WSDM-challenge-2019-spotify.Comment: 4 page
Passage of radiation through wormholes
We investigate numerically the process of the passage of a radiation pulse
through a wormhole and the subsequent evolution of the wormhole that is caused
by the gravitational action of this pulse. The initial static wormhole is
modeled by the spherically symmetrical Armendariz-Picon solution with zero
mass. The radiation pulses are modeled by spherically symmetrical shells of
self-gravitating massless scalar fields. We demonstrate that the compact signal
propagates through the wormhole and investigate the dynamics of the fields in
this process for both cases: collapse of the wormhole into the black hole and
for the expanding wormhole.Comment: 18 Pages, 13 figures, minor typos corrected, updated reference
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