If a major disaster like Fukushima or Chernobyl ever happens again, the world would know almost straight away, thanks to an array of government and DIY radiation-monitoring programs running globally.

Startups flush with cash are building AI-assisted laboratories to find materials far faster and more cheaply, but are still waiting for their ChatGPT moment. The microwave-size instrument at Lila Sciences in Cambridge, Massachusetts, doesn't look all that different from others that I've seen in state-of-the-art materials labs. Inside its vacuum chamber, the machine zaps a palette of different elements to create vaporized particles, which then fly through the chamber and land to create a thin film, using a technique called sputtering. What sets this instrument apart is that artificial intelligence is running the experiment; an AI agent, trained on vast amounts of scientific literature and data, has determined the recipe and is varying the combination of elements. Later, a person will walk the samples, each containing multiple potential catalysts, over to a different part of the lab for testing. Another AI agent will scan and interpret the data, using it to suggest another round of experiments to try to optimize the materials' performance. For now, a human scientist keeps a close eye on the experiments and will approve the next steps on the basis of the AI's suggestions and the test results. But the startup is convinced this AI-controlled machine is a peek into the future of materials discovery--one in which autonomous labs could make it far cheaper and faster to come up with novel and useful compounds. Flush with hundreds of millions of dollars in new funding, Lila Sciences is one of AI's latest unicorns.

Many problems in science and engineering involve time-dependent, high dimensional datasets arising from complex physical processes, which are costly to simulate. In this work, we propose WeldNet: Windowed Encoders for Learning Dynamics, a data-driven nonlinear model reduction framework to build a low-dimensional surrogate model for complex evolution systems. Given time-dependent training data, we split the time domain into multiple overlapping windows, within which nonlinear dimension reduction is performed by auto-encoders to capture latent codes. Once a low-dimensional representation of the data is learned, a propagator network is trained to capture the evolution of the latent codes in each window, and a transcoder is trained to connect the latent codes between adjacent windows. The proposed windowed decomposition significantly simplifies propagator training by breaking long-horizon dynamics into multiple short, manageable segments, while the transcoders ensure consistency across windows. In addition to the algorithmic framework, we develop a mathematical theory establishing the representation power of WeldNet under the manifold hypothesis, justifying the success of nonlinear model reduction via deep autoencoder-based architectures. Our numerical experiments on various differential equations indicate that WeldNet can capture nonlinear latent structures and their underlying dynamics, outperforming both traditional projection-based approaches and recently developed nonlinear model reduction methods.

What is in the 28-point US plan for Ukraine? 'Ukraine is running out of men, money and time' Can the US get all sides to end the war? Why is Europe opposing Trump's peace plan? Two people were killed in a Ukrainian drone strike on the Russian city of Saratov, regional Governor Roman Busargin said in a statement on Telegram. An unspecified number of people were also injured in the attack.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Ex-Trump DOJ lawyers say'fraudulent' UC antisemitism probes led them to quit Data centers for AI could nearly triple San José's energy use. This is read by an automated voice. Please report any issues or inconsistencies here . The county seat of Santa Clara is touting its partnership with Pacific Gas & Electric, claiming the city is "the West Coast's premier destination for data center development."

Fears about AI data centers' water use have exploded. Experts say the reality is far more complicated than people think. Last month, journalist Karen Hao posted a Twitter thread in which she acknowledged that there was a substantial error in her blockbuster book Empire of AI. Hao had written that a proposed Google data center in a town near Santiago, Chile, could require "more than one thousand times the amount of water consumed by the entire population"--a figure which, thanks to a unit misunderstanding, appears to have been off by a magnitude of 1,000. In the thread, Hao thanked Andy Masley, the head of an effective altruism organization in Washington, DC, for bringing the correction to her attention. Masley has spent the past several months questioning some of the numbers and rhetoric common in popular media about water use and AI on his Substack.

Transfer Learning (TL) is currently the most effective approach for modeling building thermal dynamics when only limited data are available. TL uses a pretrained model that is fine-tuned to a specific target building. However, it remains unclear how to proceed after initial fine-tuning, as more operational measurement data are collected over time. This challenge becomes even more complex when the dynamics of the building change, for example, after a retrofit or a change in occupancy. In Machine Learning literature, Continual Learning (CL) methods are used to update models of changing systems. TL approaches can also address this challenge by reusing the pretrained model at each update step and fine-tuning it with new measurement data. A comprehensive study on how to incorporate new measurement data over time to improve prediction accuracy and address the challenges of concept drifts (changes in dynamics) for building thermal dynamics is still missing. Therefore, this study compares several CL and TL strategies, as well as a model trained from scratch, for thermal dynamics modeling during building operation. The methods are evaluated using 5--7 years of simulated data representative of single-family houses in Central Europe, including scenarios with concept drifts from retrofits and changes in occupancy. We propose a CL strategy (Seasonal Memory Learning) that provides greater accuracy improvements than existing CL and TL methods, while maintaining low computational effort. SML outperformed the benchmark of initial fine-tuning by 28.1\% without concept drifts and 34.9\% with concept drifts.

Causal discovery methods have traditionally been developed under two distinct regimes: independent and identically distributed (i.i.d.) and timeseries data, each governed by separate modelling assumptions. In this paper, we argue that the i.i.d. setting can and should be reframed in terms of exchangeability, a strictly more general symmetry principle. We present the implications of this reframing, alongside two core arguments: (1) a conceptual argument, based on extending the dependency of experimental causal inference on exchangeability to causal discovery; and (2) an empirical argument, showing that many existing i.i.d. causal-discovery methods are predicated on exchangeability assumptions, and that the sole extensive widely-used real-world "i.i.d." benchmark (the Tübingen dataset) consists mainly of exchangeable (and not i.i.d.) examples. Building on this insight, we introduce a novel synthetic dataset that enforces only the exchangeability assumption, without imposing the stronger i.i.d. assumption. We show that our exchangeable synthetic dataset mirrors the statistical structure of the real-world "i.i.d." dataset more closely than all other i.i.d. synthetic datasets. Furthermore, we demonstrate the predictive capability of this dataset by proposing a neural-network-based causal-discovery algorithm trained exclusively on our synthetic dataset, and which performs similarly to other state-of-the-art i.i.d. methods on the real-world benchmark.

Inverse medium scattering is an ill-posed, nonlinear wave-based imaging problem arising in medical imaging, remote sensing, and non-destructive testing. Machine learning (ML) methods offer increased inference speed and flexibility in capturing prior knowledge of imaging targets relative to classical optimization-based approaches; however, they perform poorly in regimes where the scattering behavior is highly nonlinear. A key limitation is that ML methods struggle to incorporate the physics governing the scattering process, which are typically inferred implicitly from the training data or loosely enforced via architectural design. In this paper, we present a method that endows a machine learning framework with explicit knowledge of problem physics, in the form of a differentiable solver representing the forward model. The proposed method progressively refines reconstructions of the scattering potential using measurements at increasing wave frequencies, following a classical strategy to stabilize recovery. Empirically, we find that our method provides high-quality reconstructions at a fraction of the computational or sampling costs of competing approaches.

Studying how recommendation systems reshape social networks is difficult on live platforms: confounds abound, and controlled experiments risk user harm. We present an agent-based simulator where content production, tie formation, and a graph attention network (GAT) recommender co-evolve in a closed loop. We calibrate parameters using Mastodon data and validate out-of-sample against Bluesky (4--6\% error on structural metrics; 10--15\% on held-out temporal splits). Across 18 configurations at 100 agents, we find that \emph{activation timing} affects outcomes: introducing recommendations at $t=10$ vs.\ $t=40$ decreases transitivity by 10\% while engagement differs by $<$8\%. Delaying activation increases content diversity by 9\% while reducing modularity by 4\%. Scaling experiments ($n$ up to 5,000) show the effect persists but attenuates. Jacobian analysis confirms local stability under bounded reactance parameters. We release configuration schemas and reproduction scripts.