Robert Griffin III involved in'scary' car crash with wife and kids as shocking photos emerge Shroud of Turin mystery deepens as surgeon spots hidden detail that points to Jesus' resurrection I was so happy after trying a trendy new cosmetic procedure. But 10 years later I suffered a devastating side effect... the doctor had lied I'm no longer sleeping with my husband - and never will again, says MOLLY RYDDELL. I love him, but counted down the moments until he climaxed. Then I couldn't bear it any more and the truth spilled out... so many women feel the same The'middle-class kinks' saving marriages: Wives reveal the eight buzzy sex trends that revived their lagging libidos - including the fantasy husbands are secretly obsessed with I'm a woman with autism... here are the signs you might be masking, even from yourself Lori Loughlin's husband Mossimo Giannulli seen with mystery brunette in tiny skirt day after shock split Body count from Houston's bayous rises as serial killer whispers grip city and residents are told: 'Be vigilant' Realtor with expensive ex-wife arrested over shocking $11.6m claims about how he was funding Palm Beach lifestyle Trump dollar coin design released by Treasury... and it's inspired by the most iconic political photo of the century I've loved Taylor Swift for years. Mystery deepens over Hulk Hogan's death as his widow faces fresh anguish Warning as pasta salad is recalled due to risk of'fatal infections' Plan to pump 45,000 gallons of RADIOACTIVE water into New York's Hudson River A controversial plan to release 45,000 gallons of radioactive water into the Hudson River has been approved in court.

ByteDance's AI Videos Are Scary Realistic. An image created with Bytedance's AI tool Seedream, via the platform Kapwing, of Minions playing basketball. This week, OpenAI released its latest AI video generation model, Sora 2, advertising it as a big leap forward for the space. As Sora hits the public, it will have to compete for market share in a crowded market, including with a major competitor that is rapidly gaining steam: the Chinese company ByteDance, which owns TikTok . In the past few months, ByteDance released Seedance, an AI video generator that many users are already calling the best in the world, and a new version of Seedream, an elite image model.

Shroud of Turin mystery deepens as surgeon spots hidden detail that points to Jesus' resurrection I was so happy after trying a trendy new cosmetic procedure. But 10 years later I suffered a devastating side effect... the doctor had lied I'm no longer sleeping with my husband - and never will again, says MOLLY RYDDELL. I love him, but counted down the moments until he climaxed. Then I couldn't bear it any more and the truth spilled out... so many women feel the same The'middle-class kinks' saving marriages: Wives reveal the eight buzzy sex trends that revived their lagging libidos - including the fantasy husbands are secretly obsessed with Lori Loughlin's husband Mossimo Giannulli seen with mystery brunette in tiny skirt day after shock split I'm a woman with autism... here are the signs you might be masking, even from yourself Cake-faced 90s sitcom star looks unrecognizable as she ditches the heavy eyeshadow for an LA errand run can you guess who? Trump dollar coin design released by Treasury... and it's inspired by the most iconic political photo of the century I've loved Taylor Swift for years. Mystery deepens over Hulk Hogan's death as his widow faces fresh anguish Body count from Houston's bayous rises as serial killer whispers grip city and residents are told: 'Be vigilant' Prison chief reveals exactly where Diddy could end up... and the one horrifying jail he MUST avoid Diddy sentenced to 50 MONTHS in prison for prostitution offenses as he's branded a vile and unrepentant woman beater Feline stressed: Experts urge cat owners NOT to take their pets out for walks on trendy harnesses - amid fears they leave kitties feeling'scared' A cat charity has urged owners not to use trendy harnesses on their cats - amid fears they leave felines feeling scared.

The EPA Is Ending Greenhouse Gas Data Collection. Who Will Step Up to Fill the Gap? With the agency no longer collecting emissions data from polluting companies, attention is turning to whether climate NGOs have the tools--and legal right--to fulfill this EPA function. The Environmental Protection Agency announced earlier this month that it would stop making polluting companies report their greenhouse gas emissions to it, eliminating a crucial tool the US uses to track emissions and form climate policy. Climate NGOs say their work could help plug some of the data gap, but they and other experts fear the EPA's work can't be fully matched. "I don't think this system can be fully replaced," says Joseph Goffman, the former assistant administrator at the EPA's Office of Air and Radiation.

We develop new classifiers under group fairness in the attribute-aware setting for binary classification with multiple group fairness constraints (e.g., demographic parity (DP), equalized odds (EO), and predictive parity (PP)). We propose a novel approach, applicable to linear fractional constraints, based on directly intervening on the operating characteristics of a pre-trained base classifier, by (i) identifying optimal operating characteristics using the base classifier's group-wise ROC convex hulls and (ii) post-processing the base classifier to match those targets. As practical post-processors, we consider randomizing a mixture of group-wise thresholding rules subject to minimizing the expected number of interventions. We further extend our approach to handle multiple protected attributes and multiple linear fractional constraints. On standard datasets (COMPAS and ACSIncome), our methods simultaneously satisfy approximate DP, EO, and PP with few interventions and a near-oracle drop in accuracy; comparing favorably to previous methods.

Indoor scene classification is a critical task in computer vision, with wide-ranging applications that go from robotics to sensitive content analysis, such as child sexual abuse imagery (CSAI) classification. The problem is particularly challenging due to the intricate relationships between objects and complex spatial layouts. In this work, we propose the Attention over Scene Graphs for Sensitive Content Analysis (ASGRA), a novel framework that operates on structured graph representations instead of raw pixels. By first converting images into Scene Graphs and then employing a Graph Attention Network for inference, ASGRA directly models the interactions between a scene's components. This approach offers two key benefits: (i) inherent explainability via object and relationship identification, and (ii) privacy preservation, enabling model training without direct access to sensitive images. On Places8, we achieve 81.27% balanced accuracy, surpassing image-based methods. Real-world CSAI evaluation with law enforcement yields 74.27% balanced accuracy. Our results establish structured scene representations as a robust paradigm for indoor scene classification and CSAI classification. Code is publicly available at https://github.com/tutuzeraa/ASGRA.

NLP models require test data that are sufficiently challenging. The difficulty of an example is linked to the topic it originates from (''seed topic''). The relationship between the topic and the difficulty of its instances is stochastic in nature: an example about a difficult topic can happen to be easy, and vice versa. At the scale of the Internet, there are tens of thousands of potential topics, and finding the most difficult one by drawing and evaluating a large number of examples across all topics is computationally infeasible. We formalize this task and treat it as a multi-armed bandit problem. In this framework, each topic is an ''arm,'' and pulling an arm (at a cost) involves drawing a single example, evaluating it, and measuring its difficulty. The goal is to efficiently identify the most difficult topics within a fixed computational budget. We illustrate the bandit problem setup of finding difficult examples for the task of machine translation. We find that various bandit strategies vastly outperform baseline methods like brute-force searching the most challenging topics.

Criminal investigations often involve the analysis of messages exchanged through instant messaging apps such as WhatsApp, which can be an extremely effort-consuming task. Our approach integrates knowledge graphs and NLP models to support this analysis by semantically enriching data collected from suspects' mobile phones, and help prosecutors and investigators search into the data and get valuable insights. Our semantic enrichment process involves extracting message data and modeling it using a knowledge graph, generating transcriptions of voice messages, and annotating the data using an end-to-end entity extraction approach. We adopt two different solutions to help users get insights into the data, one based on querying and visualizing the graph, and one based on semantic search. The proposed approach ensures that users can verify the information by accessing the original data. While we report about early results and prototypes developed in the context of an ongoing project, our proposal has undergone practical applications with real investigation data. As a consequence, we had the chance to interact closely with prosecutors, collecting positive feedback but also identifying interesting opportunities as well as promising research directions to share with the research community.

Many enterprises are increasingly adopting Artificial Intelligence (AI) to make internal processes more competitive and efficient. In response to public concern and new regulations for the ethical and responsible use of AI, implementing AI governance frameworks could help to integrate AI within organisations and mitigate associated risks. However, the rapid technological advances and lack of shared ethical AI infrastructures creates barriers to their practical adoption in businesses. This paper presents a real-world AI application at TVS Supply Chain Solutions, reporting on the experience developing an AI assistant underpinned by large language models and the ethical, regulatory, and sociotechnical challenges in deployment for enterprise use.

Unified Multimodal understanding and generation Models (UMMs) have demonstrated remarkable capabilities in both understanding and generation tasks. However, we identify a vulnerability arising from the generation-understanding coupling in UMMs. The attackers can use the generative function to craft an information-rich adversarial image and then leverage the understanding function to absorb it in a single pass, which we call Cross-Modal Generative Injection (CMGI). Current attack methods on malicious instructions are often limited to a single modality while also relying on prompt rewriting with semantic drift, leaving the unique vulnerabilities of UMMs unexplored. We propose STaR-Attack, the first multi-turn jailbreak attack framework that exploits unique safety weaknesses of UMMs without semantic drift. Specifically, our method defines a malicious event that is strongly correlated with the target query within a spatio-temporal context. Using the three-act narrative theory, STaR-Attack generates the pre-event and the post-event scenes while concealing the malicious event as the hidden climax. When executing the attack strategy, the opening two rounds exploit the UMM's generative ability to produce images for these scenes. Subsequently, an image-based question guessing and answering game is introduced by exploiting the understanding capability. STaR-Attack embeds the original malicious question among benign candidates, forcing the model to select and answer the most relevant one given the narrative context. Extensive experiments show that STaR-Attack consistently surpasses prior approaches, achieving up to 93.06% ASR on Gemini-2.0-Flash and surpasses the strongest prior baseline, FlipAttack. Our work uncovers a critical yet underdeveloped vulnerability and highlights the need for safety alignments in UMMs.