'She Has a Presence': The Superfans Who Turned Up for Opening Weekend WIRED attended two documentary screening parties--one on each coast--for the First Lady's film. For decades now, people have been wondering: Who is Melania Trump? The First Lady opens her 2024 memoir with a story about leaving her family in Slovenia to immigrate to America as a 26-year-old model. Ten years later, she became an American citizen. "It was not an easy process," she writes. "And my personal experience dealing with the trials of the immigration process opened my eyes to the difficulties faced by all who wish to become US citizens." OK, but what does that mean, exactly? Her husband, in both his terms as president, put harshly enforcing immigration policy at the center of his domestic agenda. This is all to say that I was authentically excited to see, the documentary that Amazon paid $40 million to acquire and $35 million to market. The director, Brett Ratner, previously accused of sexual misconduct by six different women, is currently in the news thanks to his appearance in a photo included in the most recent dump of Epstein files. What is Melania like behind closed doors?

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.

Our obsession with deadly game shows--from "The Running Man" and "Squid Game" to MrBeast's real-life reënactments--reflects a shift in the national mood to something increasingly zero-sum. It seems we can't get enough of game shows in which the losers die. "The Hunger Games" became a multibillion-dollar media franchise over the past decade, with audiences returning to the theatre, time and time again, to watch adolescents try to kill one another in an enormous arena--a contest devised by the leaders of a society rife with inequality. Netflix's " Squid Game " followed four hundred and fifty-six desperate individuals into an underworld where they play lethal versions of children's games in the hope of winning a life-changing amount of money. Four weeks after its release, the show had become Netflix's most-watched series ever; to date, the first season has been viewed more than two hundred and sixty-five million times.

As a baseline for DCLM, we conduct extensive experiments and find that model-based filtering is key to assembling a high-quality training set.

Things to Do in L.A. A shadowy L.A. crime ring is hijacking the IDs of foreign scholars, fraud expert says This is read by an automated voice. Please report any issues or inconsistencies here . An identity theft ring believed to be based in the Burbank area is stealing Social Security Numbers of former foreign scholars. Private fraud investigators suspect the operation is connected to Armenian organized crime groups known for sophisticated financial crimes. Using apartments in the San Fernando Valley and Glendale area, a shadowy group of identity thieves has been quietly exploiting a new kind of victim -- foreign scholars who left the U.S. years ago but whose Social Security numbers still linger in American databases, according to a cybercrime expert.

The ubiquity of dynamic data in domains such as weather, healthcare, and energy underscores a growing need for effective interpretation and retrieval of time-series data. These data are inherently tied to domain-specific contexts, such as clinical notes or weather narratives, making cross-modal retrieval essential not only for downstream tasks but also for developing robust time-series foundation models by retrieval-augmented generation (RAG). Despite the increasing demand, time-series retrieval remains largely underexplored. Existing methods often lack semantic grounding, struggle to align heterogeneous modalities, and have limited capacity for handling multi-channel signals. To address this gap, we propose TRACE, a generic multimodal retriever that grounds time-series embeddings in aligned textual context. TRACE enables fine-grained channel-level alignment and employs hard negative mining to facilitate semantically meaningful retrieval. It supports flexible cross-modal retrieval modes, including Text-to-Timeseries and Timeseries-to-Text, effectively linking linguistic descriptions with complex temporal patterns. By retrieving semantically relevant pairs, TRACE enriches downstream models with informative context, leading to improved predictive accuracy and interpretability. Beyond a static retrieval engine, TRACE also serves as a powerful standalone encoder, with lightweight task-specific tuning that refines context-aware representations while maintaining strong cross-modal alignment. These representations achieve state-of-the-art performance on downstream forecasting and classification tasks. Extensive experiments across multiple domains highlight its dual utility, as both an effective encoder for downstream applications and a general-purpose retriever to enhance time-series models.

Despite suggestions it has been losing its edge, California is way ahead of others when it comes to the hottest technology right now: artificial intelligence. The regions around San Francisco, San José and Los Angeles are among the best prepped for AI in the country, according to a report released Wednesday by the Brookings Institution. The Washington think tank dubbed the San Francisco and San José metropolitan areas "superstars" when it comes to AI readiness. Three out of the top 10 city regions most ready for AI are in California, according to the report. No other state has more than one region in the top 10.

Because we are highly motivated to be understood, we created public external representations -- mime, language, art -- to externalise our inner states. We argue that such external representations are a pre-condition for access consciousness, the global availability of information for reasoning. Yet the bandwidth of access consciousness is tiny compared with the richness of `raw experience', so no external representation can reproduce that richness in full. Ordinarily an explanation of experience need only let an audience `grasp' the relevant pattern, not relive the phenomenon. But our drive to be understood, and our low level sensorimotor capacities for `grasping' so rich, that the demand for an explanation of the feel of experience cannot be ``satisfactory''. That inflated epistemic demand (the preeminence of our expectation that we could be perfectly understood by another or ourselves) rather than an irreducible metaphysical gulf -- keeps the hard problem of consciousness alive. But on the plus side, it seems we will simply never give up creating new ways to communicate and think about our experiences. In this view, to be consciously aware is to strive to have one's agency understood by oneself and others.

Recently, large language models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, yet they remain prone to hallucinations when reasoning with insufficient internal knowledge. While integrating LLMs with knowledge graphs (KGs) provides access to structured, verifiable information, existing approaches often generate incomplete or factually inconsistent reasoning paths. To this end, we propose Self-Reflective Planning (SRP), a framework that synergizes LLMs with KGs through iterative, reference-guided reasoning. Specifically, given a question and topic entities, SRP first searches for references to guide planning and reflection. In the planning process, it checks initial relations and generates a reasoning path. After retrieving knowledge from KGs through a reasoning path, it implements iterative reflection by judging the retrieval result and editing the reasoning path until the answer is correctly retrieved. Extensive experiments on three public datasets demonstrate that SRP surpasses various strong baselines and further underscore its reliable reasoning ability.

Interferometric Synthetic Aperture Radar (InSAR) technology uses satellite radar to detect surface deformation patterns and monitor earthquake impacts on buildings. While vital for emergency response planning, extracting multi-class building damage classifications from InSAR data faces challenges: overlapping damage signatures with environmental noise, computational complexity in multi-class scenarios, and the need for rapid regional-scale processing. Our novel multi-class variational causal Bayesian inference framework with quadratic variational bounds provides rigorous approximations while ensuring efficiency. By integrating InSAR observations with USGS ground failure models and building fragility functions, our approach separates building damage signals while maintaining computational efficiency through strategic pruning. Evaluation across five major earthquakes (Haiti 2021, Puerto Rico 2020, Zagreb 2020, Italy 2016, Ridgecrest 2019) shows improved damage classification accuracy (AUC: 0.94-0.96), achieving up to 35.7% improvement over existing methods. Our approach maintains high accuracy (AUC > 0.93) across all damage categories while reducing computational overhead by over 40% without requiring extensive ground truth data.