It is an online romance that has overcome a 3,500-mile distance, and also the Covid pandemic - which meant they had to get married virtually. Welsh cheesemaker Lewis Relfe struck up a relationship with Ameila Henderson, from Virginia, USA, while playing the Friday the 13th horror video game in 2017. She made a number of visits across the Atlantic, including one for six months, and he proposed on Aberystwyth Pier, dressed as the game's main character, Jason Voorhees. While they admit to seeing the humour in being the couple that met and married virtually, they now live together in Ceredigion, with daughter Evelyn. But because of parental responsibilities, they no longer get to enjoy the thing that brought them together.

Ed Zitron on big tech, backlash, boom and bust: 'AI has taught us that people are excited to replace human beings' His blunt, brash scepticism has made the podcaster and writer something of a cult figure. But as concern over large language models builds, he's no longer the outsider he once was I f some time in an entirely possible future they come to make a movie about "how the AI bubble burst", Ed Zitron will doubtless be a main character. He's the perfect outsider figure: the eccentric loner who saw all this coming and screamed from the sidelines that the sky was falling, but nobody would listen. Just as Christian Bale portrayed Michael Burry, the investor who predicted the 2008 financial crash, in The Big Short, you can well imagine Robert Pattinson fighting Paul Mescal, say, to portray Zitron, the animated, colourfully obnoxious but doggedly detail-oriented Brit, who's become one of big tech's noisiest critics. This is not to say the AI bubble burst, necessarily, but against a tidal wave of AI boosterism, Zitron's blunt, brash scepticism has made him something of a cult figure. His tech newsletter, Where's Your Ed At, now has more than 80,000 subscribers; his weekly podcast, Better Offline, is well within the Top 20 on the tech charts; he's a regular dissenting voice in the media; and his subreddit has become a safe space for AI sceptics, including those within the tech industry itself - one user describes him as "a lighthouse in a storm of insane hypercapitalist bullshit".

The chef behind Wales' only two-Michelin-star restaurant has said he is not embarrassed after it was awarded a one-star hygiene rating. Ynyshir Restaurant and Rooms, near Machynlleth in Ceredigion, which charges nearly £500 per head, received the rating after a visit by food safety officers on 5 November. According to the Food Standards Agency (FSA), a score of one out of five means major improvement is necessary. But chef patron Gareth Ward, a contestant on MasterChef The Professionals, said the restaurant was working at the highest standard in the world and doing something different with how it approaches raw ingredients and techniques. Ynyshir offers a high-end dining experience starting at £468 per person, including a 30-course tasting menu and an in-house DJ.

Cross-domain facial expression recognition (CD-FER) remains difficult due to severe domain shift between training and deployment data. We propose Graph-Attention Network with Adversarial Domain Alignment (GAT-ADA), a hybrid framework that couples a ResNet-50 as backbone with a batch-level Graph Attention Network (GAT) to model inter-sample relations under shift. Each mini-batch is cast as a sparse ring graph so that attention aggregates cross-sample cues that are informative for adaptation. To align distributions, GAT-ADA combines adversarial learning via a Gradient Reversal Layer (GRL) with statistical alignment using CORAL and MMD. GAT-ADA is evaluated under a standard unsupervised domain adaptation protocol: training on one labeled source (RAF-DB) and adapting to multiple unlabeled targets (CK+, JAFFE, SFEW 2.0, FER2013, and ExpW). GAT-ADA attains 74.39% mean cross-domain accuracy. On RAF-DB to FER2013, it reaches 98.0% accuracy, corresponding to approximately a 36-point improvement over the best baseline we re-implemented with the same backbone and preprocessing.

Quality-Diversity (QD) algorithms constitute a branch of optimization that is concerned with discovering a diverse and high-quality set of solutions to an optimization problem. Current QD methods commonly maintain diversity by dividing the behavior space into discrete regions, ensuring that solutions are distributed across different parts of the space. The QD problem is then solved by searching for the best solution in each region. This approach to QD optimization poses challenges in large solution spaces, where storing many solutions is impractical, and in high-dimensional behavior spaces, where discretization becomes ineffective due to the curse of dimensionality. We present an alternative framing of the QD problem, called \emph{Soft QD}, that sidesteps the need for discretizations. We validate this formulation by demonstrating its desirable properties, such as monotonicity, and by relating its limiting behavior to the widely used QD Score metric. Furthermore, we leverage it to derive a novel differentiable QD algorithm, \emph{Soft QD Using Approximated Diversity (SQUAD)}, and demonstrate empirically that it is competitive with current state of the art methods on standard benchmarks while offering better scalability to higher dimensional problems.

Personalization in federated learning has emerged as a crucial strategy to cater to diverse client-specific data characteristics.

The field has seen many efforts to measure 3D and motion understanding from videos, each contributing a part of the overall goal.

We consider a structured multi-label prediction problem where the labels are organized under implication and mutual exclusion constraints.