In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

In this paper we present the first freely available dataset for the development and evaluation of domain adaptation methods, for the sound event detection task. The dataset contains 40 log mel-band energies extracted from $100$ different synthetic sound event tracks, with additive noise from nine different acoustic scenes (from indoor, outdoor, and vehicle environments), mixed at six different sound-to-noise ratios, SNRs, (from -12 to -27 dB with a step of -3 dB), and totaling to 5400 (9 * 100 * 6) sound files and a total length of 30 564 minutes. We provide the dataset as is, the code to re-create the dataset and remix the sound event tracks and the acoustic scenes with different SNRs, and a baseline method that tests the adaptation performance with the proposed dataset and establishes some first results.
The very first freely available domain adaptation dataset for sound event detection.

Recent DNN pruning algorithms have succeeded in reducing the number of parameters in fully connected layers often with little or no drop in classification accuracy. However most of the existing pruning schemes either have to be applied during training or require a costly retraining procedure after pruning to regain classification accuracy. In this paper we propose a cheap pruning algorithm based on difference of convex (DC) optimisation. We also provide theoretical analysis for the growth in the Generalisation Error (GE) of the new pruned network. Our method can be used with any convex regulariser and allows for a controlled degradation in classification accuracy while being orders of magnitude faster than competing approaches. Experiments on common feedforward neural networks show that for sparsity levels above 90% our method achieves 10% higher classification accuracy compared to Hard Thresholding.
A fast pruning algorithm for fully connected DNN layers with theoretical analysis of degradation in Generalisation Error.

All living organisms struggle against the forces of nature to carve out niches where
 they can maintain relative stasis. We propose that such a search for order amidst
 chaos might offer a unifying principle for the emergence of useful behaviors in
 artificial agents. We formalize this idea into an unsupervised reinforcement learning
 method called surprise minimizing RL (SMiRL). SMiRL trains an agent with the
 objective of maximizing the probability of observed states under a model trained on
 all previously seen states. The resulting agents acquire several proactive behaviors
 to seek and maintain stable states such as balancing and damage avoidance, that
 are closely tied to the affordances of the environment and its prevailing sources
 of entropy, such as winds, earthquakes, and other agents.  We demonstrate that
 our surprise minimizing agents can successfully play Tetris, Doom, and control
 a  humanoid  to  avoid  falls,  without  any  task-specific  reward  supervision.    We
further show that SMiRL can be used as an unsupervised pre-training objective
 that substantially accelerates subsequent reward-driven learning

Learning emergent behavior by minimizing Bayesian surprise with RL in natural environments with entropy.