This paper introduces WhisperSeg, utilizing the Whisper Transformer pre-trained for Automatic Speech Recognition (ASR) for human and animal Voice Activity Detection (VAD). Contrary to traditional methods that detect human voice or animal vocalizations from a short audio frame and rely on careful threshold selection, WhisperSeg processes entire spectrograms of long audio and generates plain text representations of onset, offset, and type of voice activity. Processing a longer audio context with a larger network greatly improves detection accuracy from few labeled examples. We further demonstrate a positive transfer of detection performance to new animal species, making our approach viable in the data-scarce multi-species setting.1

The abstracts of scientific papers typically contain both premises (e.g., background and observations) and conclusions.Although conclusion sentences are highlighted in structured abstracts, in non-structured abstracts the concluding information is not explicitly marked, which makes the automatic segmentation of conclusions from scientific abstracts a challenging task.In this work, we explore Normalized Mutual Information (NMI) as a means for abstract segmentation.We consider each abstract as a recurrent cycle of sentences and place two segmentation boundaries by greedily optimizing the NMI score between the two segments, assuming that conclusions are strongly semantically linked with preceding premises.On nonstructured abstracts, our proposed unsupervised approach GreedyCAS achieves the best performance across all evaluation metrics; on structured abstracts, GreedyCAS outperforms all baseline methods measured by P k .The strong correlation of NMI to our evaluation metrics reveals the effectiveness of NMI for abstract segmentation.1

We introduce MemSum (Multi-step Episodic Markov decision process extractive SUMmarizer), a reinforcement-learning-based extractive summarizer enriched at each step with information on the current extraction history.When MemSum iteratively selects sentences into the summary, it considers a broad information set that would intuitively also be used by humans in this task: 1) the text content of the sentence, 2) the global text context of the rest of the document, and 3) the extraction history consisting of the set of sentences that have already been extracted.With a lightweight architecture, MemSum obtains state-of-the-art test-set performance (ROUGE) in summarizing long documents taken from PubMed, arXiv, and GovReport.Ablation studies demonstrate the importance of local, global, and history information.A human evaluation confirms the high quality and low redundancy of the generated summaries, stemming from MemSum's awareness of extraction history.

In this paper, we introduce the `Reverse Variational Autoencoder" (Reverse-VAE) which is a generative network. On the one hand, visual attributes can be manipulated and combined while generating images. On the other hand, anomalies, meaning deviations from the data space used for training, can be detected. During training the generator network maps samples from stochastic latent vectors to the data space. Meanwhile the encoder network takes these generated images to reconstruct the latent vector. The generator and discriminator are trained adversarially. The discriminator is trained to distinguish between real and generated data. Overall, our model tries to match the joint latent/data-space distribution of the generator and the latent/data-space joint distribution of the encoder by minimizing their Kullback-Leibler divergence. Desired visual attributes of CelebA images are successfully manipulated. The performance of anomaly detection is competitive with state-of-the-art on MNIST.