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

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

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

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