This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by Refinitiv Lipper .

In few-shot domain adaptation (FDA), classifiers for the target domain are trained with \emph{accessible} labeled data in the source domain (SD) and few labeled data in the target domain (TD). However, data usually contain private information in the current era, e.g., data distributed on personal phones. Thus, the private data will be leaked if we directly access data in SD to train a target-domain classifier (required by FDA methods). In this paper, to prevent privacy leakage in SD, we consider a very challenging problem setting, where the classifier for the TD has to be trained using few labeled target data and a well-trained SD classifier, named few-shot hypothesis adaptation (FHA). In FHA, we cannot access data in SD, as a result, the private information in SD will be protected well. To this end, we propose a target-oriented hypothesis adaptation network (TOHAN) to solve the FHA problem, where we generate highly-compatible unlabeled data (i.e., an intermediate domain) to help train a target-domain classifier. TOHAN maintains two deep networks simultaneously, in which one focuses on learning an intermediate domain and the other takes care of the intermediate-to-target distributional adaptation and the target-risk minimization. Experimental results show that TOHAN outperforms competitive baselines significantly.

We propose a novel approach that integrates machine learning into compartmental disease modeling (e.g., SEIR) to predict the progression of COVID-19. Our model is explainable by design as it explicitly shows how different compartments evolve and it uses interpretable encoders to incorporate covariates and improve performance. Explainability is valuable to ensure that the model's forecasts are credible to epidemiologists and to instill confidence in end-users such as policy makers and healthcare institutions. Our model can be applied at different geographic resolutions, and we demonstrate it for states and counties in the United States. We show that our model provides more accurate forecasts compared to the alternatives, and that it provides qualitatively meaningful explanatory insights.

Airborne Laser Scanning (ALS) technology has transformed modern archaeology by unveiling hidden landscapes beneath dense vegetation. However, the lack of expert-annotated, open-access resources has hindered the analysis of ALS data using advanced deep learning techniques. We address this limitation with Archaeoscape (available at https://archaeoscape.ai/data/2024), a novel large-scale archaeological ALS dataset spanning 888 km in Cambodia with 31,141 annotated archaeological features from the Angkorian period. Archaeoscape is over four times larger than comparable datasets, and the first ALS archaeology resource with open-access data, annotations, and models.We benchmark several recent segmentation models to demonstrate the benefits of modern vision techniques for this problem and highlight the unique challenges of discovering subtle human-made structures under dense jungle canopies. By making Archaeoscape available in open access, we hope to bridge the gap between traditional archaeology and modern computer vision methods.

Humans solving algorithmic (or) reasoning problems typically exhibit solution times that grow as a function of problem difficulty. Adaptive recurrent neural networks have been shown to exhibit this property for various language-processing tasks. However, little work has been performed to assess whether such adaptive computation can also enable vision models to extrapolate solutions beyond their training distribution's difficulty level, with prior work focusing on very simple tasks. In this study, we investigate a critical functional role of such adaptive processing using recurrent neural networks: to dynamically scale computational resources conditional on input requirements that allow for zero-shot generalization to novel difficulty levels not seen during training using two challenging visual reasoning tasks: PathFinder and Mazes. We combine convolutional recurrent neural networks (ConvRNNs) with a learnable halting mechanism based on Graves (2016). We explore various implementations of such adaptive ConvRNNs (AdRNNs) ranging from tying weights across layers to more sophisticated biologically inspired recurrent networks that possess lateral connections and gating. We show that 1) AdRNNs learn to dynamically halt processing early (or late) to solve easier (or harder) problems, 2) these RNNs zero-shot generalize to more difficult problem settings not shown during training by dynamically increasing the number of recurrent iterations at test time. Our study provides modeling evidence supporting the hypothesis that recurrent processing enables the functional advantage of adaptively allocating compute resources conditional on input requirements and hence allowing generalization to harder difficulty levels of a visual reasoning problem without training.

Federated learning methods typically learn a model by iteratively sampling updates from a population of clients. In this work, we explore how the number of clients sampled at each round (the cohort size) impacts the quality of the learned model and the training dynamics of federated learning algorithms. Our work poses three fundamental questions. First, what challenges arise when trying to scale federated learning to larger cohorts? Second, what parallels exist between cohort sizes in federated learning, and batch sizes in centralized learning? Last, how can we design federated learning methods that effectively utilize larger cohort sizes? We give partial answers to these questions based on extensive empirical evaluation. Our work highlights a number of challenges stemming from the use of larger cohorts. While some of these (such as generalization issues and diminishing returns) are analogs of large-batch training challenges, others (including catastrophic training failures and fairness concerns) are unique to federated learning.

The male leopard whiptail ray also boasts a four-foot-three-inch wingspan. Leopard whiptail rays have spotted skin and a long, thin tail they use for balance, steering, and defense. Breakthroughs, discoveries, and DIY tips sent every weekday. Getting that annual check-up can feel daunting for anyone. At the weight of an adult human with a four-foot-three-inch wingspan, just moving the giant fish from its habitat to an exam pool is an exercise in teamwork.

The rapid normalization of artificial intelligence is forcing a reckoning with how much of the future is being shaped by hype rather than utility. The writers Charles Duhigg, Cal Newport, and Anna Wiener join Tyler Foggatt for a conversation about artificial intelligence and the promises, myths, and anxieties surrounding it. The discussion was recorded before a live audience at The New Yorker Festival this fall. They explore the gap between Silicon Valley's sweeping claims and what generative A.I. can actually do today; how people are using the technology for work, creativity, and emotional support; and why the tech's most immediate political consequences may be the hardest to grapple with. " The Biggest Threat to the 2026 Economy Is Still Donald Trump," by John Cassidy What Can We Do Instead?," by Jay Caspian Kang When an Ivy League school turned against a student .

PCWorld reports that Microsoft 365 subscribers with Copilot+ PCs can now access automatic alt text generation for images in Word and PowerPoint. This accessibility feature requires Microsoft 365 version 2512 and a Copilot+ PC with at least 40 TOPS NPU to function properly. Users can approve or edit AI-generated captions for both new and existing images, enhancing document accessibility and productivity. Microsoft has announced that Microsoft 365 subscribers with certain Copilot Plus computers will now have access to a new feature: automatic captions, or alt texts, in Word and PowerPoint.

It's time for Wright to retire - Van Gerwen Michael van Gerwen says Peter Wright should retire after his emphatic defeat in the second round of the PDC World Championship. Two-time world champion Wright won just two legs and averaged below 80 as he was surprisingly beaten by debutant Arno Merk on Tuesday. Van Gerwen, who has won the world title three times, said he was not surprised by [Wright's] performance given the Scotsman's inconsistent recent record and it's time for him to retire anyway. The 36-year-old Dutchman beat William O'Connor to reach the third round at Alexandra Palace. Another two-time world champion, Gary Anderson, said players cannot play well all the time and that Wright is having a blip.