What are the right computational primitives for exploration?

Algorithmic stability is a major theme in learning theory, where seminal results have firmly established its close relationship with generalization. Recent research has further highlighted the intricate interplay between stability and additional properties of interest beyond statistical generalization.

The new "Send to Alexa" feature lets you send Kindle Scribe notebooks to your Echo device with just a couple of taps. Alexa+ has been rolling out to users across the board (well, users with Prime, that is) as its Early Access becomes more widely available. Now, there's a new feature to explore if you're also a Kindle Scribe user: Send to Alexa. This lets you send your Kindle Scribe notes to the AI-powered assistant so you can ask questions about them without having to refer back to your Kindle. It won't automatically do this with all your notes.

Because of the generality of Definition 1.1, it may be that

Supporting Go/No-Go decisions prior to launch requires assessing real-time telemetry data against redline limits established during the design qualification phase. Family data from ground testing or previous flights is commonly used to detect initiating failure modes and their timing; however, this approach relies heavily on engineering judgment and is more error-prone for new launch vehicles. To address these limitations, we utilize Long-Term Short-Term Memory (LSTM) networks for supervised classification of time-series anomalies. Although, initial training labels derived from simulated anomaly data may be suboptimal due to variations in anomaly strength, anomaly settling times, and other factors. In this work, we propose a novel statistical detector based on the Mahalanobis distance and forward-backward detection fractions to adjust the supervised training labels. We demonstrate our method on digital twin simulations of a ground-stage propulsion system with 20.8 minutes of operation per trial and O(10^8) training timesteps. The statistical data relabeling improved precision and recall of the LSTM classifier by 7% and 22% respectively.

We study the discrete-to-continuum consistency of the training of shallow graph convolutional neural networks (GCNNs) on proximity graphs of sampled point clouds under a manifold assumption. Graph convolution is defined spectrally via the graph Laplacian, whose low-frequency spectrum approximates that of the Laplace-Beltrami operator of the underlying smooth manifold, and shallow GCNNs of possibly infinite width are linear functionals on the space of measures on the parameter space. From this functional-analytic perspective, graph signals are seen as spatial discretizations of functions on the manifold, which leads to a natural notion of training data consistent across graph resolutions. To enable convergence results, the continuum parameter space is chosen as a weakly compact product of unit balls, with Sobolev regularity imposed on the output weight and bias, but not on the convolutional parameter. The corresponding discrete parameter spaces inherit the corresponding spectral decay, and are additionally restricted by a frequency cutoff adapted to the informative spectral window of the graph Laplacians. Under these assumptions, we prove $Γ$-convergence of regularized empirical risk minimization functionals and corresponding convergence of their global minimizers, in the sense of weak convergence of the parameter measures and uniform convergence of the functions over compact sets. This provides a formalization of mesh and sample independence for the training of such networks.

Science Archaeology 3D map of Easter Island takes you places visitors aren't allowed One of the world's most isolated islands is open to virtual tourists. Breakthroughs, discoveries, and DIY tips sent every weekday. Nestled in the South Pacific Ocean, some 6,000 people live on the most isolated, inhabited island in the world: Rapa Nui. Known to many as Easter Island, a name Dutch explorer Jacob Roggeveen coined after landing on the island on Easter Sunday 1722, Rapa Nui is roughly double the size of Disney World, or 63.2 square miles. And every year, some 100,000 people visit the remote island to see the famed 13-foot-tall moai statues or Easter Island heads .

Character.AI and Google settle with families in teen suicide and self-harm lawsuits In one case, a 14-year-old bonded with a Daenerys Targaryen chatbot before taking his own life. Character.AI lets you create and share custom chatbots. Character.AI and Google have reportedly agreed to settle multiple lawsuits regarding teen suicide and self-harm. According to, the victims' families and the companies are working to finalize the settlement terms. The families of several teens sued the companies in Florida, Colorado, Texas and New York. The Orlando, FL, lawsuit was filed by the mother of 14-year-old Sewell Setzer III, who used a Character.AI chatbot tailored after Daenerys Targaryen.