This $400 set comes with minifigures of the Next Gen crew, including Picard, Riker, and Wesley Crusher's portable tractor beam. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. It is impossible to overstate just how big an impact had on an entire generation of kids growing up in the 1990s. I watched every episode with my mom.

Neural operators serve as universal approximators for general continuous operators. In this paper, we derive the approximation rate of solution operators for the nonlinear parabolic partial differential equations (PDEs), contributing to the quantitative approximation theorem for solution operators of nonlinear PDEs. Our results show that neural operators can efficiently approximate these solution operators without the exponential growth in model complexity, thus strengthening the theoretical foundation of neural operators. A key insight in our proof is to transfer PDEs into the corresponding integral equations via Duahamel's principle, and to leverage the similarity between neural operators and Picard's iteration, a classical algorithm for solving PDEs. This approach is potentially generalizable beyond parabolic PDEs to a range of other equations, including the Navier-Stokes equation, nonlinear Schr\"odinger equations and nonlinear wave equations, which can be solved by Picard's iteration.

The signature of a path is a sequence of integrals, applied iteratively to the components of the path, which allows it to describe the path precisely and to summarise its characteristics, especially the geometrical ones. This concept emerged in the 1950s and was originally studied by K. T. Chen, who developed the theory and gave the first significant results that justified the interest in the signature. After that, the signature formed part of the Terry Lyons' theory of rough paths, which is key in the field of stochastic calculus and in the study of differential equations controlled by rough paths. Thanks to the development of this theory, the signature was generalised to apply to certain paths of finite variation and regained some relevance. More recently, applications of the signature have also been found in the field of machine learning, where its properties for describing paths are useful for summarising the data sequences used in this discipline and for revealing their properties, which facilitates the training of models that have to make decisions based on the data. Before we begin, we start by defining some concepts.

There are many recent advanced developments for the Text-to-SQL task, where the Picard model is one of the the top performing models as measured by the Spider dataset competition. However, bringing Text-to-SQL systems to realistic use-cases through domain adaptation remains a tough challenge. We analyze how well the base T5 Language Model and Picard perform on query structures different from the Spider dataset, we fine-tuned the base model on the Spider data and on independent databases (DB). To avoid accessing the DB content online during inference, we also present an alternative way to disambiguate the values in an input question using a rule-based approach that relies on an intermediate representation of the semantic concepts of an input question. In our results we show in what cases T5 and Picard can deliver good performance, we share the lessons learned, and discuss current domain adaptation challenges.

Automated tumor detection in Digital Breast Tomosynthesis (DBT) is a difficult task due to natural tumor rarity, breast tissue variability, and high resolution. Given the scarcity of abnormal images and the abundance of normal images for this problem, an anomaly detection/localization approach could be well-suited. However, most anomaly localization research in machine learning focuses on non-medical datasets, and we find that these methods fall short when adapted to medical imaging datasets. The problem is alleviated when we solve the task from the image completion perspective, in which the presence of anomalies can be indicated by a discrepancy between the original appearance and its auto-completion conditioned on the surroundings. However, there are often many valid normal completions given the same surroundings, especially in the DBT dataset, making this evaluation criterion less precise. To address such an issue, we consider pluralistic image completion by exploring the distribution of possible completions instead of generating fixed predictions. This is achieved through our novel application of spatial dropout on the completion network during inference time only, which requires no additional training cost and is effective at generating diverse completions. We further propose minimum completion distance (MCD), a new metric for detecting anomalies, thanks to these stochastic completions. We provide theoretical as well as empirical support for the superiority over existing methods of using the proposed method for anomaly localization. On the DBT dataset, our model outperforms other state-of-the-art methods by at least 10\% AUROC for pixel-level detection.

William Shatner, 'Star Trek' alum and author of'Boldly Go,' spoke to Fox News Digital about his decadeslong friendship with Leonard Nimoy, as well as his iconic on-screen kiss with Nichelle Nichols. "Star Trek" fans can bask in nostalgia, as the cast of the iconic science fiction series has reunited. After more than two decades, "Star Trek: Nemesis" actors, including Gates McFadden, LeVar Burton, Jonathan Frakes and Patrick Stewart, revealed the decision to reprise their famous roles and what it was like working together on the spacecraft again on "Star Trek: Picard." Stewart, who's known for his role as Jean Luc Picard in the "Star Trek" franchise, gave fans a preview of what they can expect in the current series. "Star Trek: Nemesis" actors, including, from left, Jonathan Frakes, Patrick Stewart, Gates McFadden, LeVar Burton and Michael Dorn, reprise their famous roles on "Star Trek: Picard."

Recently, there has been increasing interest in synthesizing data to improve downstream text-to-SQL tasks. In this paper, we first examined the existing synthesized datasets and discovered that state-of-the-art text-to-SQL algorithms did not further improve on popular benchmarks when trained with augmented synthetic data. We observed two shortcomings: illogical synthetic SQL queries from independent column sampling and arbitrary table joins. To address these issues, we propose a novel synthesis framework that incorporates key relationships from schema, imposes strong typing, and conducts schema-distance-weighted column sampling. We also adopt an intermediate representation (IR) for the SQL-to-text task to further improve the quality of the generated natural language questions. When existing powerful semantic parsers are pre-finetuned on our high-quality synthesized data, our experiments show that these models have significant accuracy boosts on popular benchmarks, including new state-of-the-art performance on Spider.

One pioneering group at the Massachusetts Institute of Technology (MIT) is applying emotion AI to improve mental health and overall quality of life. Recently the Affective Computing Research Group at the MIT Media Lab published a new study that provides empirical evidence that empathetic artificial intelligence (AI) machine learning can counterbalance the adverse effects of anger on human creative problem solving. The MIT study involved over a thousand participants to play a word guessing game, Wordle, to see how anger and empathy impacts performance. Those assigned to the anger elicitation condition performed poorly compared to the control group. The research shows that an empathic AI agent can reduce the negative impact of anger on creative problem solving.