Ghislaine Maxwell's ultimate humiliation: Epstein's sex trafficker girlfriend poses in outrageous outfits and exposes herself in dozens of photos released from the billionaire paedophile's files Silent Trump flees growing storm over Epstein'cover-up' as he jets off for holidays without ANY comment How you can ease the agony of carpal tunnel syndrome. The'change of pace' sex move that sends ANY woman wild. Here's the precise moment to deploy it and what to do with your eyes. Corey Feldman walks back claim that Corey Haim'molested' him after late star's mother slammed his comments Emily in Paris cast left'aghast' and'walking on eggshells' as off-camera drama becomes overwhelming... and whispers swirl about a CURSE Truth about THIS photo of Karoline Leavitt's face... and why if she was non-binary and disabled, Vanity Fair would never have done this: KENNEDY After 27 years as a TV anchor I was suddenly pulled off screens. My boss's explanation was a brutal lesson in loyalty I was dead for 105 minutes and learned exactly how you get into heaven... then Jesus spoke six words into my mind and sent me back Jake Paul's jaw is broken in Anthony Joshua battering: YouTuber-turned-boxer rushes to hospital I was falsely accused of being the Brown University shooter... America's great divide laid bare as Wall Street splurges record bonuses on outrageously lavish homes while the rest of the country struggles Andrew's fury at anyone who doesn't bow and scrape.

Scientists Thought Parkinson's Was in Our Genes. New ideas about chronic illness could revolutionize treatment, if we take the research seriously. Amy Lindberg spent 26 years in the Navy and she still walked like it--with intention, like her chin had someplace to be. But around 2017, her right foot stopped following orders. Lindberg and her husband Brad were five years into their retirement. After moving 10 times for Uncle Sam, they'd bought their dream house near the North Carolina coast. They had a backyard that spilled out onto wetlands. From the kitchen, you could see cranes hunting. They kept bees and played pickleball and watched their children grow. But now Lindberg's right foot was out of rhythm. She worked hard to ignore it, but she couldn't disregard the tremors.

Ghislaine Maxwell's ultimate humiliation: Epstein's sex trafficker girlfriend poses in outrageous outfits and exposes herself in dozens of photos released from the billionaire paedophile's files Silent Trump flees growing storm over Epstein'cover-up' as he jets off for holidays without ANY comment How you can ease the agony of carpal tunnel syndrome. The'change of pace' sex move that sends ANY woman wild. Here's the precise moment to deploy it and what to do with your eyes. Corey Feldman walks back claim that Corey Haim'molested' him after late star's mother slammed his comments Emily in Paris cast left'aghast' and'walking on eggshells' as off-camera drama becomes overwhelming... and whispers swirl about a CURSE Truth about THIS photo of Karoline Leavitt's face... and why if she was non-binary and disabled, Vanity Fair would never have done this: KENNEDY After 27 years as a TV anchor I was suddenly pulled off screens. My boss's explanation was a brutal lesson in loyalty I was dead for 105 minutes and learned exactly how you get into heaven... then Jesus spoke six words into my mind and sent me back Jake Paul's jaw is broken in Anthony Joshua battering: YouTuber-turned-boxer rushes to hospital I was falsely accused of being the Brown University shooter... America's great divide laid bare as Wall Street splurges record bonuses on outrageously lavish homes while the rest of the country struggles Andrew's fury at anyone who doesn't bow and scrape.

Until now, Tokyo police had identified suspicious posts on social media platforms by manually searching for them using keywords. Tokyo's Metropolitan Police Department has introduced an artificial intelligence tool that automatically gathers and analyzes social media posts that appear to offer dark part-time jobs, it announced Tuesday. For example, if a post on X is deemed to be an offer for shady jobs, police post a reply warning users the message in question may contain inappropriate content soliciting people who are prepared to do illicit part-time jobs. The number of such warning posts made between August and November this year came to around 18,500, more than double the roughly 8,800 replies made in the entire year of 2024. Until now, Tokyo police had identified suspicious posts by manually searching for such messages using keywords.

Sam Altman's appearance on is part of a larger charm offensive currently being waged by the tech establishment. OpenAI CEO Sam Altman was at the center of Silicon Valley's most visible publicity push in recent memory Monday night when he appeared on . In a predictably softball interview with host Jimmy Fallon, Altman explained how ChatGPT has helped him alleviate the anxiety that comes with being a new parent. It was a distinctly clever, if somewhat surprising, choice from Altman who has mostly kept his personal life out of the media spotlight. But Altman is a salesman, and a good salesman understands the optics of good television.

The widespread use of big data across sectors has raised major privacy concerns, especially when sensitive information is shared or analyzed. Regulations such as GDPR and HIPAA impose strict controls on data handling, making it difficult to balance the need for insights with privacy requirements. Synthetic data offers a promising solution by creating artificial datasets that reflect real patterns without exposing sensitive information. However, traditional synthetic data methods often fail to capture complex, implicit rules that link different elements of the data and are essential in domains like healthcare. They may reproduce explicit patterns but overlook domain-specific constraints that are not directly stated yet crucial for realism and utility. For example, prescription guidelines that restrict certain medications for specific conditions or prevent harmful drug interactions may not appear explicitly in the original data. Synthetic data generated without these implicit rules can lead to medically inappropriate or unrealistic profiles. To address this gap, we propose ContextGAN, a Context-Aware Differentially Private Generative Adversarial Network that integrates domain-specific rules through a constraint matrix encoding both explicit and implicit knowledge. The constraint-aware discriminator evaluates synthetic data against these rules to ensure adherence to domain constraints, while differential privacy protects sensitive details from the original data. We validate ContextGAN across healthcare, security, and finance, showing that it produces high-quality synthetic data that respects domain rules and preserves privacy. Our results demonstrate that ContextGAN improves realism and utility by enforcing domain constraints, making it suitable for applications that require compliance with both explicit patterns and implicit rules under strict privacy guarantees.

The rapid proliferation of generative components, such as LoRAs, has created a vast but unstructured ecosystem. Existing discovery methods depend on unreliable user descriptions or biased popularity metrics, hindering usability. We present CARLoS, a large-scale framework for characterizing LoRAs without requiring additional metadata. Analyzing over 650 LoRAs, we employ them in image generation over a variety of prompts and seeds, as a credible way to assess their behavior. Using CLIP embeddings and their difference to a base-model generation, we concisely define a three-part representation: Directions, defining semantic shift; Strength, quantifying the significance of the effect; and Consistency, quantifying how stable the effect is. Using these representations, we develop an efficient retrieval framework that semantically matches textual queries to relevant LoRAs while filtering overly strong or unstable ones, outperforming textual baselines in automated and human evaluations. While retrieval is our primary focus, the same representation also supports analyses linking Strength and Consistency to legal notions of substantiality and volition, key considerations in copyright, positioning CARLoS as a practical system with broader relevance for LoRA analysis.

The emerging "agentic web" envisions large populations of autonomous agents coordinating, transacting, and delegating across open networks. Yet many agent communication and commerce protocols treat agents as low-cost identities, despite the empirical reality that LLM agents remain unreliable, hallucinated, manipulable, and vulnerable to prompt-injection and tool-abuse. A natural response is "agents-at-stake": binding economically meaningful, slashable collateral to persistent identities and adjudicating misbehavior with verifiable evidence. However, heterogeneous tasks make universal verification brittle and centralization-prone, while traditional reputation struggles under rapid model drift and opaque internal states. We propose a protocol-native alternative: insured agents. Specialized insurer agents post stake on behalf of operational agents in exchange for premiums, and receive privileged, privacy-preserving audit access via TEEs to assess claims. A hierarchical insurer market calibrates stake through pricing, decentralizes verification via competitive underwriting, and yields incentive-compatible dispute resolution.

Artificial Intelligence (AI) systems are now an integral part of multiple industries. In clinical research, AI supports automated adverse event detection in clinical trials, patient eligibility screening for protocol enrollment, and data quality validation. Beyond healthcare, AI is transforming finance through real-time fraud detection, automated loan risk assessment, and algorithmic decision-making. Similarly, in manufacturing, AI enables predictive maintenance to reduce equipment downtime, enhances quality control through computer-vision inspection, and optimizes production workflows using real-time operational data. While these technologies enhance operational efficiency, they introduce new challenges regarding safety, accountability, and regulatory compliance. To address these concerns, we introduce the SMART+ Framework - a structured model built on the pillars of Safety, Monitoring, Accountability, Reliability, and Transparency, and further enhanced with Privacy & Security, Data Governance, Fairness & Bias, and Guardrails. SMART+ offers a practical, comprehensive approach to evaluating and governing AI systems across industries. This framework aligns with evolving mechanisms and regulatory guidance to integrate operational safeguards, oversight procedures, and strengthened privacy and governance controls. SMART+ demonstrates risk mitigation, trust-building, and compliance readiness. By enabling responsible AI adoption and ensuring auditability, SMART+ provides a robust foundation for effective AI governance in clinical research.

Multi-modal large reasoning models (MLRMs) pose significant privacy risks by inferring precise geographic locations from personal images through hierarchical chain-of-thought reasoning. Existing privacy protection techniques, primarily designed for perception-based models, prove ineffective against MLRMs' sophisticated multi-step reasoning processes that analyze environmental cues. We introduce \textbf{ReasonBreak}, a novel adversarial framework specifically designed to disrupt hierarchical reasoning in MLRMs through concept-aware perturbations. Our approach is founded on the key insight that effective disruption of geographic reasoning requires perturbations aligned with conceptual hierarchies rather than uniform noise. ReasonBreak strategically targets critical conceptual dependencies within reasoning chains, generating perturbations that invalidate specific inference steps and cascade through subsequent reasoning stages. To facilitate this approach, we contribute \textbf{GeoPrivacy-6K}, a comprehensive dataset comprising 6,341 ultra-high-resolution images ($\geq$2K) with hierarchical concept annotations. Extensive evaluation across seven state-of-the-art MLRMs (including GPT-o3, GPT-5, Gemini 2.5 Pro) demonstrates ReasonBreak's superior effectiveness, achieving a 14.4\% improvement in tract-level protection (33.8\% vs 19.4\%) and nearly doubling block-level protection (33.5\% vs 16.8\%). This work establishes a new paradigm for privacy protection against reasoning-based threats.