Bill Clinton's birthday letter to Jeffrey Epstein praising'childlike curiosity' is revealed along with creepy drawing of billionaire pedophile Revealed: Squalid campsite where fugitive New Zealand father's kids were found hiding after four years on the run'She was so f***ed up': Carolyn Bessette's friends tell MAUREEN CALLAHAN of her secret Daddy issue, JFK Jr's murder brag that drove her mad... and why everything we know about her is a lie Infamous'Uranus in retrograde' will turn the lives of three zodiac signs upside-down Greta Thunberg's Gaza flotilla was NOT hit by a drone and their claims'have no basis in truth', Tunisian authorities say - after the group said they were attacked Turn back the clock with the K-beauty retinol cream Amazon shoppers say leaves their skin'silky smooth' - and it's now $10 Idyllic Midwest town is torn apart as mystery tech giant plots $1.6B takeover They were locked in a dungeon inside a house of horrors. But incredible footage shows five kids' daring acts while their parents were out... and it left neighbors speechless This is the extraordinary story of the Kansas City Chiefs' secret weapon Charming town with just 5,000 people named Georgia's prettiest place to live Sharia patrols' spotted in Texas demanding stores stop selling alcohol and pork Glamorous TikToker charged with using medic's identity to carry out cosmetic procedures without a license New photo from Epstein'birthday book' shows joke about Trump'buying girl' after his'lewd birthday message' was revealed Billionaire turns his back on Trump as he blasts President's'risky' financial move that could cost Americans their savings Woman's butt dial voicemail exposes plot to help man dump a dead body '90s TV star cuts a youthful figure at 81 on rare sighting... can you guess who? Whether it's an affectionate cuddle or an awkward squeeze, everyone has their own style of hug. But the way you embrace could reveal parts of your personality, according to a new study. Experts used advanced AI video analysis technology to investigate hugs carried out between friends and romantic partners.

'No one is irreplaceable', says BBC chief after scandals BBC director general Tim Davie has said he is not letting anything lie when it comes to rooting out abuses of power within the corporation. If you're not living the values, it is clear you leave the BBC or there are consequences, he told MPs on Tuesday, adding that no one was irreplaceable. Davie is facing questions from the Culture, Media and Sport Committee on a number of scandals. One of the topics discussed was the MasterChef crisis, after both of its presenters - Gregg Wallace and John Torode - were sacked following a report which upheld allegations against them. During the hearing, Davie discussed some of the changes that have been made to how abuses of power are dealt with following a recent review into the BBC's workplace culture.

More than 20 people have been killed in a Russian air strike on a village in eastern Ukraine, President Volodymr Zelensky has said, citing initial reports. The victims were ordinary people collecting their pensions in the Donetsk settlement of Yarova, he said. Yarova, to the north of Sloviansk, is one of the big cities in the region and not far from the front line as Russian forces advance slowly in the east. If confirmed, the death toll would be among the heaviest attacks on Ukrainian civilians in recent weeks, 42 months into Russia's full-scale invasion. At least 23 people were killed in overnight air strikes on Ukraine's capital Kyiv at the end of August.

Thailand's top court has ruled that former prime minister Thaksin Shinawatra must serve a year in jail, in yet another blow to the influential political dynasty. The decision relates to a previous case where he was sentenced to years in prison for corruption, but ended up spending less than a day in a jail cell as he was moved to a hospital. On Tuesday, the Supreme Court ruled that this transfer was unlawful - and that the 76-year-old would have to serve his sentence in jail. Thaksin and his family have dominated Thai politics since he was first elected PM in 2001. His daughter Paetongtarn previously served as leader but was removed from office last month over a leaked phone call.

Meeting minimum data rate constraints is a significant challenge in wireless communication systems, particularly as network complexity grows. Traditional deep learning approaches often address these constraints by incorporating penalty terms into the loss function and tuning hyperparameters empirically. However, this heuristic treatment offers no theoretical convergence guarantees and frequently fails to satisfy QoS requirements in practical scenarios. Building upon the structure of the WMMSE algorithm, we first extend it to a multi-channel setting with QoS constraints, resulting in the enhanced WMMSE (eWMMSE) algorithm, which is provably convergent to a locally optimal solution when the problem is feasible. To further reduce computational complexity and improve scalability, we develop a GNN-based algorithm, JCPGNN-M, capable of supporting simultaneous multi-channel allocation per user. To overcome the limitations of traditional deep learning methods, we propose a principled framework that integrates GNN with a Lagrangian-based primal-dual optimization method. By training the GNN within the Lagrangian framework, we ensure satisfaction of QoS constraints and convergence to a stationary point. Extensive simulations demonstrate that JCPGNN-M matches the performance of eWMMSE while offering significant gains in inference speed, generalization to larger networks, and robustness under imperfect channel state information. This work presents a scalable and theoretically grounded solution for constrained resource allocation in future wireless networks.

Traditional Chinese Medicine (TCM) formulas play a significant role in treating epidemics and complex diseases. Existing models for TCM utilize traditional algorithms or deep learning techniques to analyze formula relationships, yet lack comprehensive results, such as complete formula compositions and detailed explanations. Although recent efforts have used TCM instruction datasets to fine-tune Large Language Models (LLMs) for explainable formula generation, existing datasets lack sufficient details, such as the roles of the formula's sovereign, minister, assistant, courier; efficacy; contraindications; tongue and pulse diagnosis-limiting the depth of model outputs. To address these challenges, we propose ZhiFangDanTai, a framework combining Graph-based Retrieval-Augmented Generation (GraphRAG) with LLM fine-tuning. ZhiFangDanTai uses GraphRAG to retrieve and synthesize structured TCM knowledge into concise summaries, while also constructing an enhanced instruction dataset to improve LLMs' ability to integrate retrieved information. Furthermore, we provide novel theoretical proofs demonstrating that integrating GraphRAG with fine-tuning techniques can reduce generalization error and hallucination rates in the TCM formula task. Experimental results on both collected and clinical datasets demonstrate that ZhiFangDanTai achieves significant improvements over state-of-the-art models. Our model is open-sourced at https://huggingface.co/tczzx6/ZhiFangDanTai1.0.

Large Vision-Language Models (LVLMs) or multimodal large language models represent a significant advancement in artificial intelligence, enabling systems to understand and generate content across both visual and textual modalities. While large-scale pretraining has driven substantial progress, fine-tuning these models for aligning with human values or engaging in specific tasks or behaviors remains a critical challenge. Deep Reinforcement Learning (DRL) and Direct Preference Optimization (DPO) offer promising frameworks for this aligning process. While DRL enables models to optimize actions using reward signals instead of relying solely on supervised preference data, DPO directly aligns the policy with preferences, eliminating the need for an explicit reward model. This overview explores paradigms for fine-tuning LVLMs, highlighting how DRL and DPO techniques can be used to align models with human preferences and values, improve task performance, and enable adaptive multimodal interaction. We categorize key approaches, examine sources of preference data, reward signals, and discuss open challenges such as scalability, sample efficiency, continual learning, generalization, and safety. The goal is to provide a clear understanding of how DRL and DPO contribute to the evolution of robust and human-aligned LVLMs.

School of ICT Griffith University Gold Coast, Australia junjun.pan@griffithuni.edu.au Abstract--Graph anomaly detection (GAD) has attracted increasing attention in recent years for identifying malicious samples in a wide range of graph-based applications, such as social media and e-commerce. However, most GAD methods assume identical training and testing distributions and are tailored to specific tasks, resulting in limited adaptability to real-world scenarios such as shifting data distributions and scarce training samples in new applications. T o address the limitations, recent work has focused on improving the generalization capability of GAD models through transfer learning that leverages knowledge from related domains to enhance detection performance, or developing "one-for-all" GAD foundation models that generalize across multiple applications. Since a systematic understanding of generalization in GAD is still lacking, in this paper, we provide a comprehensive review of generalization in GAD. We first trace the evolution of generalization in GAD and formalize the problem settings, which further leads to our systematic taxonomy. Rooted in this fine-grained taxonomy, an up-to-date and comprehensive review is conducted for the existing generalized GAD methods. Finally, we identify current open challenges and suggest future directions to inspire future research in this emerging field. With the advances in information technology, graph-structured data has become a ubiquitous data structure in online services, including social media [11], e-commerce [44], and autonomous agents [9], [23], [26], [34].

Trump's Epstein crisis explodes as lewd birthday letter showing president's signature is revealed Judge's'promise' let career criminal walk free to butcher Ukrainian refugee after his MOM said he should be locked up'She was so f***ed up': Carolyn Bessette's friends tell MAUREEN CALLAHAN of her secret Daddy issue, JFK Jr's murder brag that drove her mad... and why everything we know about her is a lie The chaos behind when Meghan Markle was told not to be at Queen Elizabeth II's deathbed They were locked in a dungeon inside a house of horrors. But incredible footage shows five kids' daring acts while their parents were out... and it left neighbors speechless Turn back the clock with the K-beauty retinol cream Amazon shoppers say leaves their skin'silky smooth' - and it's now $10 Scientists crack the ultimate answer to the meaning of life... and it's hidden among 38M obituaries CBS News hires a CONSERVATIVE to police interviews after Trump and Noem'deceptive' editing fury Scientist claims life on Earth was not random... but engineered Supreme Court LIFTS restrictions on Trump's immigration raids despite claims agents targeted people by race I was 52 with a collapsed'turkey neck'. Here's how I turned back the clock 10 years Plastic surgeons weigh in on Jessica Simpson's dramatic new look at VMAs as fans declare her'unrecognizable' Billionaire turns his back on Trump as he blasts President's'risky' financial move that could cost Americans their savings Trump loses appeal and must pay $83 million to E. Jean Carroll AMANDA PLATELL: Harry is'desperate' to come back to Britain and reclaim his royal role - but this fresh snub from William makes it clear why it will never happen... and why he'll never forgive his brother Scientists crack the ultimate answer to the meaning of life... and it's hidden among 38million obituaries Scientists on a mission to uncover what constitutes a life well lived found the answer after analyzing 38 million obituaries from the US spanning 30 years. Using automated text analysis tools, the team found that the most commonly celebrated values were tradition and benevolence. Nearly 80 percent of obituaries highlighted respect for customs or religion, while 76 percent emphasized caring, reliability and trustworthiness.

Breakthroughs, discoveries, and DIY tips sent every weekday. The smelly, brown seaweed can put a damper on a day at the beach at best and hinder baby turtles on their way to the ocean at worst. Only about one in 1,000 sea turtle hatchlings survive to adulthood, and might be added to their already long list of challenges . The new findings detailed in a study published in the explores the role that this brown seaweed plays on vulnerable sea turtle populations. "For sea turtle hatchlings, reaching the ocean is already a race against time - and survival. Now, increasingly large mats of sargassum are adding new challenges to this critical journey," study co-author and Florida Atlantic University biologist Sarah Milton, said in a statement .