We develop a framework for task-specific active next-best-view selection in 3D reconstruction from point clouds, by casting the problem in the language of Bayesian decision theory. Our framework works by (a) placing a prior distribution over the space of implicit surfaces, (b) using recently-developed stochastic surface reconstruction methods to calculate the resulting posterior distribution, then (c) using the posterior distribution to carefully reason about which view to scan next. This enables us to perform camera selection in a manner that is directly optimized for the intended use of the reconstructed data - meaning, we reduce uncertainty only in those regions that make a difference in the task at hand, as opposed to prior approaches that reduce it uniformly across space. We evaluate our method across three distinct downstream tasks: semantic classification, segmentation, and PDE-guided physics simulation. Experimental results demonstrate that our framework achieves superior task performance with fewer views compared to commonly used baselines and prior general uncertainty-reduction techniques.

Inflatable tube men depicting Elon Musk are displayed during the'Tyrant Diner' protest, calling for a boycott of Tesla, outside the Tesla Diner in LA. Inflatable tube men depicting Elon Musk are displayed during the'Tyrant Diner' protest, calling for a boycott of Tesla, outside the Tesla Diner in LA. What happened after Tesla opened a diner in Los Angeles? L ess than six months since it opened, Elon Musk's Tesla Diner has the feel of a ghost town. Gone is the Optimus robot serving popcorn, gone are the carnivore-diet-inspired "Epic Bacon" strips, gone are the hours-long, hundred-person lines wrapped around the block.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Woman gives birth in a driverless Waymo taxi in San Francisco. Waymo taxis navigate a street in San Francisco in 2023. This is read by an automated voice. Please report any issues or inconsistencies here .

Things to Do in L.A. Tap to enable a layout that focuses on the article. The Audrey Irmas Pavilion, left, at the Wilshire Boulevard Temple, center in background, in 2021. This is read by an automated voice. Please report any issues or inconsistencies here . Two were arrested during a pro-Palestinian protest at Wilshire Boulevard Temple that ended in confrontation.

What it's like to be in the middle of a conspiracy theory (according to a conspiracy theory expert) Mike Rothschild has spent years studying the rise of QAnon and antivaccine conspiracism. After his house in Altadena, California, burned down, he found himself mired in similarly sticky webs of misinformation. On a gloomy Saturday morning this past May, a few months after entire blocks of Altadena, California, were destroyed by wildfires, several dozen survivors met at a local church to vent their built-up frustration, anger, blame, and anguish. As I sat there listening to one horror story after another, I almost felt sorry for the very polite consultants who were being paid to sit there, and who couldn't do a thing about what they were hearing. Hosted by a third-party arbiter at the behest of Los Angeles County, the gathering was a listening session in which survivors could "share their experiences with emergency alerts and evacuations" for a report on how the response to the Eaton Fire months earlier had succeeded and failed. It didn't take long to see just how much failure there had been. After a small fire started in the bone-dry brush of Pasadena's Eaton Canyon early in the evening of Tuesday, January 7, 2025, the raging Santa Ana winds blew its embers into nearby Altadena, the historically Black and middle-class town just to the north. By Wednesday morning, much of it was burning.

The rapid progress of Large Language Models (LLMs) and their multimodal extensions (MLLMs) has enabled agentic systems capable of perceiving and acting across diverse environments. A challenging yet impactful frontier is the development of GUI agents, which must navigate complex desktop and web interfaces while maintaining robustness and generalization. Existing paradigms typically model tasks as long-chain executions, concatenating historical trajectories into the context. While approaches such as Mirage and GTA1 refine planning or introduce multi-branch action selection, they remain constrained by two persistent issues: Dependence on historical trajectories, which amplifies error propagation. And Local exploration bias, where "decision-first, observation-later" mechanisms overlook critical interface cues. We introduce the Memory-Driven GUI Agent (MGA), which reframes GUI interaction around the principle of observe first, then decide. MGA models each step as an independent, context-rich environment state represented by a triad: current screenshot, task-agnostic spatial information, and a dynamically updated structured memory. Experiments on OSworld benchmarks, real desktop applications (Chrome, VSCode, VLC), and cross-task transfer demonstrate that MGA achieves substantial gains in robustness, generalization, and efficiency compared to state-of-the-art baselines. The code is publicly available at: {https://anonymous.4open.science/r/MGA-3571}.

Organizers say the "No Kings" protests drew more than 7 million people across 2,700 cities. The crowds included high-profile politicians, A-list celebrities, and more than a few creative inflatables. On Saturday, crowds gathered in cities across the United States to protest President Donald Trump and his administration. Organizers of the No Kings rallies claim that more than 7 million people attended in all, across 2,700 cities in the Unites States and beyond. The gatherings provided a clear picture not only of how widespread the resistance to the Trump administration has become, but also the diversity of the coalition driving it.

The rapid growth of digital pathology and advances in self-supervised deep learning have enabled the development of foundational models for various pathology tasks across diverse diseases. While multimodal approaches integrating diverse data sources have emerged, a critical gap remains in the comprehensive integration of whole-slide images (WSIs) with spatial tran-scriptomics (ST), which is crucial for capturing critical molecular heterogeneity beyond standard hematoxylin & eosin (H&E) staining. We introduce SPADE, a foundation model that integrates histopathology with ST data to guide image representation learning within a unified framework, in effect creating an ST-informed latent space. These authors contributed equally to this work. Pre-trained on the comprehensive HEST-1k dataset, SPADE is evaluated on 20 downstream tasks, demonstrating significantly superior few-shot performance compared to baseline models, highlighting the benefits of integrating morphological and molecular information into one latent space. Introduction High-resolution whole slide images (WSIs) have propelled the development of powerful deep learning foundation models in computational pathology, demonstrating robust performance across diverse tissue types and tasks [1, 2, 3, 4]. These models are typically trained using self-supervision, enabling learning from large unlabeled datasets and producing embeddings robust to institutional variations, including differences in staining procedures and other image-quality factors [5, 6, 7, 8]. By visually capturing cellular arrangement, WSIs enable the study of spatial organization and disorganization of cells in tissues, characterizations that are especially relevant in cancer research [9, 10]. In clinical settings, WSIs are commonly stained with hematoxylin & eosin (H&E), a two-color stain that highlights nuclei and cytoplasm but offers a limited view of molecular-level heterogeneity [11]. As tumor tissues are known to exhibit high variability within and across patients, deciphering the heterogeneity at the molecular level is critical for improving deep learning applications that can more precisely inform diagnosis, treatment, and prognosis [12, 13]. While H&E provides crucial morphological insights, its inability to capture molecular heterogeneity limits its utility in fully characterizing tissue complexity. Spatial transcriptomics addresses this gap by providing spatially resolved gene expression data, allowing for additional molecular context for a given tissue specimen. Although both ST and H&E data have independently proven useful in various applications, their combined potential for creating a more comprehensive representation learning framework remains unexplored. To this end, we introduce SPADE, a vision-ST foundation model that uses a mixture of experts, each trained via contrastive learning, to unify ST data and H&E images to produce slide representations that encompass both modalities.

While Large Language Models (LLMs) have shown remarkable advancements in reasoning and tool use, they often fail to generate optimal, grounded solutions under complex constraints. Real-world travel planning exemplifies these challenges, evaluating agents' abilities to handle constraints that are explicit, implicit, and even evolving based on interactions with dynamic environments and user needs. In this paper, we present ATLAS, a general multi-agent framework designed to effectively handle such complex nature of constraints awareness in real-world travel planning tasks. ATLAS introduces a principled approach to address the fundamental challenges of constraint-aware planning through dedicated mechanisms for dynamic constraint management, iterative plan critique, and adaptive interleaved search. ATLAS demonstrates state-of-the-art performance on the TravelPlanner benchmark, improving the final pass rate from 23.3% to 44.4% over its best alternative. More importantly, our work is the first to demonstrate quantitative effectiveness on real-world travel planning tasks with live information search and multi-turn feedback. In this realistic setting, ATLAS showcases its superior overall planning performance, achieving an 84% final pass rate which significantly outperforms baselines including ReAct (59%) and a monolithic agent (27%).

Police officers in San Bruno observed a Waymo car making an illegal U-turn. The incident highlights a gray area in law enforcement’s ability to cite robot cars when they violate traffic laws.