Kate Daniels, from Willow Farm in Worcestershire, told BBC Radio 4's Today programme that while the scientists might have been impressed, "I don't think this will come as a surprise to anyone that works with pigs".

A sequential recommender system aims to recommend attractive items to users based on behaviour patterns. The predominant sequential recommendation models are based on natural language processing models, such as the gated recurrent unit, that embed items in some defined space and grasp the user's long-term and short-term preferences based on the item embeddings. However, these approaches lack fundamental insight into how such models are related to the user's inherent decision-making process. To provide this insight, we propose a novel recurrent cell, namely FaNC, from Freudian and Newtonian perspectives. FaNC divides the user's state into conscious and unconscious states, and the user's decision process is modelled by Freud's two principles: the pleasure principle and reality principle. To model the pleasure principle, i.e., free-floating user's instinct, we place the user's unconscious state and item embeddings in the same latent space and subject them to Newton's law of gravitation. Moreover, to recommend items to users, we model the reality principle, i.e., balancing the conscious and unconscious states, via a gating function. Based on extensive experiments on various benchmark datasets, this paper provides insight into the characteristics of the proposed model. FaNC initiates a new direction of sequential recommendations at the convergence of psychoanalysis and recommender systems.

We propose a novel pitch estimation technique called DeepF0, which leverages the available annotated data to directly learns from the raw audio in a data-driven manner. F0 estimation is important in various speech processing and music information retrieval applications. Existing deep learning models for pitch estimations have relatively limited learning capabilities due to their shallow receptive field. The proposed model addresses this issue by extending the receptive field of a network by introducing the dilated convolutional blocks into the network. The dilation factor increases the network receptive field exponentially without increasing the parameters of the model exponentially. To make the training process more efficient and faster, DeepF0 is augmented with residual blocks with residual connections. Our empirical evaluation demonstrates that the proposed model outperforms the baselines in terms of raw pitch accuracy and raw chroma accuracy even using 77.4% fewer network parameters. We also show that our model can capture reasonably well pitch estimation even under the various levels of accompaniment noise.

Platforms that support online commentary, from social networks to news sites, are increasingly leveraging machine learning to assist their moderation efforts. But this process does not typically provide feedback to the author that would help them contribute according to the community guidelines. This is prohibitively time-consuming for human moderators to do, and computational approaches are still nascent. This work focuses on models that can help suggest rephrasings of toxic comments in a more civil manner. Inspired by recent progress in unpaired sequence-to-sequence tasks, a self-supervised learning model is introduced, called CAE-T5. CAE-T5 employs a pre-trained text-to-text transformer, which is fine tuned with a denoising and cyclic auto-encoder loss. Experimenting with the largest toxicity detection dataset to date (Civil Comments) our model generates sentences that are more fluent and better at preserving the initial content compared to earlier text style transfer systems which we compare with using several scoring systems and human evaluation.

Google Arts & Culture has teamed up with the Centre Pompidou, a cultural complex in Paris, to pay tribute to Vassily Kandinsky with a virtual exhibition of the artist's works and other documents. You can view some of Kandinsky's pieces in an augmented reality gallery. At the heart of the exhibit is a machine learning experiment that tries to replicate synesthesia, a condition the abstract art pioneer had. In a nutshell, synesthesia turns information that stimulates one of your senses into a multi-sensory experience. For some people (including Kandinsky, Billie Eilish and Pharrell Williams), the condition deepens the association between colors and sounds or moods.

Neural style transfer, allowing to apply the artistic style of one image to another, has become one of the most widely showcased computer vision applications shortly after its introduction. In contrast, related tasks in the music audio domain remained, until recently, largely untackled. While several style conversion methods tailored to musical signals have been proposed, most lack the 'one-shot' capability of classical image style transfer algorithms. On the other hand, the results of existing one-shot audio style transfer methods on musical inputs are not as compelling. In this work, we are specifically interested in the problem of one-shot timbre transfer. We present a novel method for this task, based on an extension of the vector-quantized variational autoencoder (VQ-VAE), along with a simple self-supervised learning strategy designed to obtain disentangled representations of timbre and pitch. We evaluate the method using a set of objective metrics and show that it is able to outperform selected baselines.

Online-dating company Match Group Inc. has reached an agreement to acquire South Korean social-media company Hyperconnect for $1.73 billion, broadening its services beyond connecting people in their love lives. The cash-and-stock deal, announced Tuesday, marks Match Group's largest acquisition to date. Hyperconnect, based in Seoul, has developed two video apps that focus on helping people interact one-on-one and with new communities. Hyperconnect's first app, Azar, offers live video and audio chat and can instantly translate voice and text for users that speak different languages. Hyperconnect's other app, Hakuna Live, is a social live-streaming app that provides group video and audio broadcasts.

Last spring, I started boiling two eggs for breakfast every morning--one for me, and one for the crows. A mated pair patrolled the rooftops around my Berlin apartment building; I'd begun luring them to my balcony with peanuts and other snacks. They loved not only eggs but also mealworms, cat food, cashews, chicken hearts, stale bread, cheese, and chunks of lamb fat; they barely touched liver, walnuts, vegetables, and dried fruit. In Germany, we were under a COVID-19 lockdown. But the birds were free.

This graduate textbook on machine learning tells a story of how patterns in data support predictions and consequential actions. Starting with the foundations of decision making, we cover representation, optimization, and generalization as the constituents of supervised learning. A chapter on datasets as benchmarks examines their histories and scientific bases. Self-contained introductions to causality, the practice of causal inference, sequential decision making, and reinforcement learning equip the reader with concepts and tools to reason about actions and their consequences. Throughout, the text discusses historical context and societal impact. We invite readers from all backgrounds; some experience with probability, calculus, and linear algebra suffices.

Models for question answering, dialogue agents, and summarization often interpret the meaning of a sentence in a rich context and use that meaning in a new context. Taking excerpts of text can be problematic, as key pieces may not be explicit in a local window. We isolate and define the problem of sentence decontextualization: taking a sentence together with its context and rewriting it to be interpretable out of context, while preserving its meaning. We describe an annotation procedure, collect data on the Wikipedia corpus, and use the data to train models to automatically decontextualize sentences. We present preliminary studies that show the value of sentence decontextualization in a user facing task, and as preprocessing for systems that perform document understanding. We argue that decontextualization is an important subtask in many downstream applications, and that the definitions and resources provided can benefit tasks that operate on sentences that occur in a richer context.