Machine learning (ML) is transforming modeling and control in the physical, engineering, and biological sciences. However, rapid development has outpaced the creation of standardized, objective benchmarks--leading to weak baselines, reporting bias, and inconsistent evaluations across methods. This undermines reproducibility, misguides resource allocation, and obscures scientific progress. To address this, we develop a Common Task Framework (CTF) for scientific machine learning. The CTF features a curated set of datasets and task-specific metrics spanning forecasting, state reconstruction, and generalization under realistic constraints, including noise and limited data. Inspired by the success of CTFs in fields like natural language processing and computer vision, our framework provides a structured, rigorous foundation for head-to-head evaluation of diverse algorithms.

The simple linear threshold units used in many artificial neural networks have a limited computational capacity. Famously, a single unit cannot handle nonlinearly separable problems like XOR. In contrast, real neurons exhibit complex morphologies as well as active dendritic integration, suggesting that their computational capacities outperform those of simple linear units. Considering specific families of Boolean functions, we empirically examine the computational limits of single units that incorporate more complex dendritic structures. For random Boolean functions, we show that there is a phase transition in learnability as a function of the input dimension, with most random functions below a certain critical dimension being learnable and those above not.

However, their adoption is limited by two main challenges: the accuracy gap compared to large-scale networks during training, and discrepancies between simulated and fabricated systems that further degrade accuracy. While previous work has proposed end-to-end optimizations for specific datasets (e.g., MNIST) and optical systems, these approaches typically lack generalization across tasks and hardware designs. To address these limitations, we propose a task-agnostic and hardware-agnostic pipeline that supports image classification and segmentation across diverse optical systems. To assist optical system design before training, we design the metasurface layout based on fabrication constraints. For training, we introduce Neural Tangent Knowledge Distillation (NTKD), which aligns optical models with electronic teacher networks, thereby narrowing the accuracy gap. After fabrication, NTKD also guides fine-tuning of the digital backend to compensate for implementation errors. Experiments on multiple datasets (e.g., MNIST, CIFAR, Carvana Image Masking Dataset) and hardware configurations show that our pipeline consistently improves ONN performance and enables practical deployment in both pre-fabrication simulations and physical implementations.

They were testifying at a Seattle city council meeting. Five members of Amazon Employees for Climate Justice (AECJ) previously testified at Seattle city council meetings about AI data centers . Now, three of them are apparently under investigation by the company. The AECJ has filed a civil rights complaint against the company on behalf of the three engineers, according to CNBC and GeekWire, accusing Amazon of violating a Seattle law that prohibits companies from discriminating against employees based on their political ideology, race, religion and age. The engineers spoke at Seattle city council hearings over whether to put a pause on AI data center buildouts.

The software engineers filed a complaint with Seattle's civil rights office accusing Amazon of illegally retaliating against them for expressing their personal political beliefs. Earlier this month, five current Amazon employees publicly urged Seattle City Council to regulate data centers . It was an unprecedented act of advocacy by tech workers, and now three of the staffers say they are under internal investigation for what they understand to be allegedly representing themselves as spokespeople for the company without prior approval. "It's a totally ridiculous claim," says one of the affected employees, Patrick Schloesser. The three software engineers, who work in different divisions of Amazon and all live in Seattle, believe they are being unfairly targeted for expressing their political beliefs.

Seattle has passed a year-long moratorium on the construction of new datacenters. The city council voted unanimously in favor of the temporary ban on Tuesday. A major tech hub whose metro area is home to Amazon and Microsoft, Seattle is the largest US city to have passed such a moratorium as the backlash against AI infrastructure grows across the country. Lawmakers have framed the pause as an opportunity to draft regulations specifically targeting the electricity-hungry datacenters being built nationwide to serve the AI sector, and to protect local residents from environmental risks and rising electricity bills. According to Seattle's mayor, Katie Wilson, the moratorium will also let city officials determine whether datacenters are a "good use of urban land", and potentially impose new stipulations on their approval, such as requiring developers to invest in local transit and housing initiatives in exchange for construction permits.

Reduced-order modeling of high-dimensional dynamical systems is often hindered by the non-Markovian closure term that represents the effect of unresolved variables on the resolved dynamics. Inspired by the Mori--Zwanzig formalism, in which the closure takes the form of a memory functional of the resolved trajectory, we recast closure modeling as a sequence modeling problem and propose the Mamba-Assisted Closure (MAC) framework: a Mamba-based sequence model, trained to predict the closure from the resolved trajectory, is coupled with the reduced-order governing equations through a numerical integrator to advance the resolved variables in time. A key feature of the framework is its exploitation of the dual representation of state-space models -- the model is trained in a sequence-to-sequence fashion via the convolutional form, and deployed for step-by-step autoregressive rollout via the recurrent form, yielding both efficient long-trajectory training and constant per-step inference cost. On the viscous Burgers' equation and the chaotic two-scale Lorenz '96 system, the MAC model substantially outperforms the Markovian reduced-order model, the GRU-based sequence model, and the Wilks method in predictive accuracy and long-time rollout stability.

Murl, Cutti, Pip, Checkers, and Gopher will monitor trails, haul supplies, and help with search and rescue efforts. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Mules have been helping maintain various national parks for over 100 years. Breakthroughs, discoveries, and DIY tips sent six days a week. Five new mules at Olympic National Park in Washington State are ready for the busy tourist season.

ROV specialist Phil Parisi is documenting nearly 100 underwater targets in Seattle's Lake Union, calling the urban lake a "shipwreck city" hiding a century of maritime history.

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 LSEG .