In this paper, we theoretically and empirically characterize obstructions to training encoders with geometric latent spaces. We show that local optima can arise due to singularities (e.g.

However, this is not true in practice.

Monterey Park residents gathered at city hall on 21 January to speak out against the construction of a datacenter. Monterey Park residents gathered at city hall on 21 January to speak out against the construction of a datacenter. Sat 7 Feb 2026 11.00 ESTLast modified on Sat 7 Feb 2026 16.55 EST When a southern California city council proposed building a giant datacenter the size of four football fields last December, five residents vowed to stop it. Through a frenetic word-of-mouth campaign, the small group raised awareness about the proposed facility in Monterey Park, a small city east of Los Angeles known affectionately as the country's first suburban Chinatown. No Data Center Monterey Park organizers - working in tandem with the grassroots racial justice group San Gabriel Valley (SGV) Progressive Action - held a teach-in and rally that drew hundreds of participants, knocked on doors, and distributed flyers on busy streets.

These rare sea creatures live where the sun don't shine. Breakthroughs, discoveries, and DIY tips sent every weekday. Like a scene out of a Jules Verne novel, scientists from Schmidt Ocean Institute recently encountered a giant phantom jelly (). The enormous deep-sea jellyfish was spotted about 830 feet below the surface of the Pacific Ocean by a Remotely Operated Vehicle (ROV) exploring the Colorado-Rawson submarine canyon wall off the coast of Argentina. ROV pilots filmed this giant phantom jelly, or Stygiomedusa gigantea, at 253 meters during an ROV descent to explore the Colorado-Rawson submarine canyon wall.

Breakthroughs, discoveries, and DIY tips sent every weekday. There are plenty of annual recap lists circulating around this time of year, but few of them involve the amount of work put in by California's Monterey Bay Aquarium Research Institute (MBARI). Over the past year, researchers guided remotely operated vehicles more than 3,000 feet down to survey the vast biodiversity within some of the oceans' deepest and darkest regions. The data and footage collected during these trips will help experts fill in the gaps towards understanding the planet's hardest-to-reach ecosystems. To celebrate the past 12 months of discoveries, MBARI released a video highlighting some of 2025's most stunning, strange, and mysterious creature sightings.

Proper quantification of predictive uncertainty is essential for the use of machine learning in safety-critical applications. V arious uncertainty measures have been proposed for this purpose, typically claiming superiority over other measures. In this paper, we argue that there is no single best measure. Instead, uncertainty quantification should be tailored to the specific application. To this end, we use a flexible family of uncertainty measures that distinguishes between total, aleatoric, and epistemic uncertainty of second-order distributions. These measures can be instantiated with specific loss functions, so-called proper scoring rules, to control their characteristics, and we show that different characteristics are useful for different tasks. In particular, we show that, for the task of selective prediction, the scoring rule should ideally match the task loss. On the other hand, for out-of-distribution detection, our results confirm that mutual information, a widely used measure of epistemic uncertainty, performs best. Furthermore, in an active learning setting, epistemic uncertainty based on zero-one loss is shown to consistently outperform other uncertainty measures.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Rare, deep-sea encounter: California scientists observe'extraordinary' seven-arm octopus On November 6, 2025, MBARI Senior Scientist Steven Haddock and researchers in MBARI's Biodiversity and Biooptics Team observed a seven-arm octopus (Haliphron atlanticus) during an expedition in Monterey Bay with MBARI's remotely operated vehicle at a depth of approximately 700 meters. This is read by an automated voice. Please report any issues or inconsistencies here . California scientists captured rare footage of a seven-arm octopus eating a jellyfish.

The availability of Large Language Models (LLMs) presents a unique opportunity to reinvigorate research on Knowledge Engineering (KE) automation. This trend is already evident in recent efforts developing LLM-based methods and tools for the automatic generation of Competency Questions (CQs), natural language questions used by ontology engineers to define the functional requirements of an ontology. However, the evaluation of these tools lacks standardization. This undermines the methodological rigor and hinders the replication and comparison of results. To address this gap, we introduce Bench4KE, an extensible API-based benchmarking system for KE automation. The presented release focuses on evaluating tools that generate CQs automatically. Bench4KE provides a curated gold standard consisting of CQ datasets from 17 real-world ontology engineering projects and uses a suite of similarity metrics to assess the quality of the CQs generated. We present a comparative analysis of 6 recent CQ generation systems, which are based on LLMs, establishing a baseline for future research. Bench4KE is also designed to accommodate additional KE automation tasks, such as SPARQL query generation, ontology testing and drafting. Code and datasets are publicly available under the Apache 2.0 license.

Capsule Networks (CapsNets) show exceptional graph representation capacity via dynamic routing and vectorized hierarchical representations, but they model the complex geometries of real\-world graphs poorly by fixed\-curvature space due to the inherent geodesical disconnectedness issues, leading to suboptimal performance. Recent works find that non\-Euclidean pseudo\-Riemannian manifolds provide specific inductive biases for embedding graph data, but how to leverage them to improve CapsNets is still underexplored. Here, we extend the Euclidean capsule routing into geodesically disconnected pseudo\-Riemannian manifolds and derive a Pseudo\-Riemannian Capsule Network (PR\-CapsNet), which models data in pseudo\-Riemannian manifolds of adaptive curvature, for graph representation learning. Specifically, PR\-CapsNet enhances the CapsNet with Adaptive Pseudo\-Riemannian Tangent Space Routing by utilizing pseudo\-Riemannian geometry. Unlike single\-curvature or subspace\-partitioning methods, PR\-CapsNet concurrently models hierarchical and cluster or cyclic graph structures via its versatile pseudo\-Riemannian metric. It first deploys Pseudo\-Riemannian Tangent Space Routing to decompose capsule states into spherical\-temporal and Euclidean\-spatial subspaces with diffeomorphic transformations. Then, an Adaptive Curvature Routing is developed to adaptively fuse features from different curvature spaces for complex graphs via a learnable curvature tensor with geometric attention from local manifold properties. Finally, a geometric properties\-preserved Pseudo\-Riemannian Capsule Classifier is developed to project capsule embeddings to tangent spaces and use curvature\-weighted softmax for classification. Extensive experiments on node and graph classification benchmarks show PR\-CapsNet outperforms SOTA models, validating PR\-CapsNet's strong representation power for complex graph structures.