Comments and other data left on a PDF detailing Homeland Security's proposal to build "mega" detention and processing centers reveal the personnel involved in its creation. A PDF that Department of Homeland Security officials provided to New Hampshire governor Kelly Ayotte's office about a new effort to build "mega" detention and processing centers across the United States contains embedded comments and metadata identifying the people who worked on it. The seemingly accidental exposure of the identities of DHS personnel who crafted Immigration and Customs Enforcement's mega detention center plan lands amid widespread public pushback against the expansion of ICE detention centers and the department's brutal immigration enforcement tactics. Metadata in the document, which concerns ICE's "Detention Reengineering Initiative" (DRI), lists as its author Jonathan Florentino, the director of ICE's Newark, New Jersey, Field Office of Enforcement and Removal Operations. In a note embedded on top of an FAQ question, "What is the average length of stay for the aliens?"

In large-scale applications, datasets often contain billions of high-dimensional points. Grouping similar data points into clusters is crucial for understanding and organizing datasets.

We introduce the best order score search (BOSS) and grow-shrink trees (GSTs) for learning directed acyclic graphs (DAGs) in this paradigm.

's matching capacity will be reduced

Justice Department expands FACE Act enforcement to synagogue protests, with Assistant Attorney General Harmeet Dhillon citing cases against accused protesters.

Like The New Yorker, I was born in 1925. Somewhat to my surprise, I decided to keep a journal of my hundredth year. The author, who was born on December 17, 1925, notes that the magazine's first issue came out ten months before he did. Old age is no joke, but it can feel like one. You look everywhere for your glasses, until your wife points out that you're wearing them. I turn a hundred this year. People act as though this is an achievement, and I suppose it is, sort of. Nobody in my family has lived this long, and I've been lucky. I'm still in pretty good health, no wasting diseases or Alzheimer's, and friends and strangers comment on how young I look, which cues me to cite the three ages of man: Youth, Maturity, and You Look Great. On the other hand, I've lost so many useful abilities that my wife, Dodie, and I have taken to calling me Feebleman. Look, up in the sky! No, it's Dodie doesn't want me to know how old she is, but she's nearly three decades younger than I am, and I become ...

Abstract--The integration of advanced technologies, such as Artificial Intelligence (AI), into manufacturing processes is attracting significant attention, paving the way for the development of intelligent systems that enhance efficiency and automation. This paper uses the term "Gentelligent system" to refer to systems that incorporate inherent component information (akin to genes in bioinformatics--where manufacturing operations are likened to chromosomes in this study) and automated mechanisms. By implementing reliable fault detection methods, manufacturers can achieve several benefits, including improved product quality, increased yield, and reduced production costs. T o support these objectives, we propose a hybrid framework with a dominance-based multi-objective evolutionary algorithm. This mechanism enables simultaneous optimization of feature selection and classification performance by exploring Pareto-optimal solutions in a single run. This solution helps monitor various manufacturing operations, addressing a range of conflicting objectives that need to be minimized together . Manufacturers can leverage such predictive methods and better adapt to emerging trends. T o strengthen the validation of our model, we incorporate two real-world datasets from different industrial domains. The results on both datasets demonstrate the generalizability and effectiveness of our approach. ORE recently, manufacturing has embraced the Industrial Internet of Things (IIoT), where digital sensors, network technologies, and gentelligent components are integrated into manufacturing processes. A gentelligent component, as defined in the Collaborative Research Centre 653 project [1], refers to components that intrinsically store information. The focus of that work is on encoding and preserving data within physical parts throughout the product lifecycle. Inspired by this concept, we extend the notion into what we define as a "gentelligent system."

Large language models (LLMs) have achieved remarkable success and demonstrated superior performance across various tasks, including natural language processing (NLP), weather forecasting, biological protein folding, text generation, and solving mathematical problems. However, many real-world data exhibit highly non-Euclidean latent hierarchical anatomy, such as protein networks, transportation networks, financial networks, brain networks, and linguistic structures or syntactic trees in natural languages. Effectively learning intrinsic semantic entailment and hierarchical relationships from these raw, unstructured input data using LLMs remains an underexplored area. Due to its effectiveness in modeling tree-like hierarchical structures, hyperbolic geometry -- a non-Euclidean space -- has rapidly gained popularity as an expressive latent representation space for complex data modeling across domains such as graphs, images, languages, and multi-modal data. Here, we provide a comprehensive and contextual exposition of recent advancements in LLMs that leverage hyperbolic geometry as a representation space to enhance semantic representation learning and multi-scale reasoning. Specifically, the paper presents a taxonomy of the principal techniques of Hyperbolic LLMs (HypLLMs) in terms of four main categories: (1) hyperbolic LLMs through exp/log maps; (2) hyperbolic fine-tuned models; (3) fully hyperbolic LLMs, and (4) hyperbolic state-space models. We also explore crucial potential applications and outline future research directions. A repository of key papers, models, datasets, and code implementations is available at https://github.com/sarangp2402/Hyperbolic-LLM-Models.