We introduce Dr. RAW, a unified and tuning-efficient framework for high-level computer vision tasks directly operating on camera RAW data. Unlike previous approaches that optimize image signal processing (ISP) pipelines and fully finetune networks for each task, Dr. RAW achieves state-of-the-art performance with minimal parameter updates and frozen backbone weights. At the input stage, we apply lightweight pre-processing steps, including sensor and illumination mapping, along with re-mosaicing, to mitigate data inconsistencies stemming from sensor variations and lighting conditions. At the network level, we introduce task-specific adaptation through two modules: Sensor Prior Prompts (SPP) and task-specific Low-Rank Adaptation (LoRA). SPP injects sensor-aware conditioning into the network via learnable prompts derived from RAW pixel distribution priors, while LoRA enables efficient task-specific tuning by updating only low-rank matrices in key backbone layers. Despite minimal tuning, Dr. RAW delivers superior results across four RAW-based tasks (object detection, semantic segmentation, instance segmentation, and pose estimation) on nine datasets encompassing various light conditions.

Federated continual learning (FCL) aims to maintain the model's performance on old tasks (i.e., stability) while enhancing its ability to acquire knowledge from current tasks (i.e., plasticity). With the development of pre-trained models (PTMs), fine-tuning PTMs on clients has become a promising approach to leveraging their extensive knowledge in FCL. In this paper, we propose MultiFCL, a novel FCL framework that fine-tunes PTMs to adapt to FCL while preserving their strong generalization capabilities. Specifically, to ensure the stability, MultiFCL introduces lightweight adapters for task adaption, which are subsequently frozen to prevent catastrophic forgetting. Moreover, by utilizing the semantic features of old tasks, MultiFCL performs multi-modal initialization of new task class prototypes. To enhance the plasticity, MultiFCL employs a multi-expert training mechanism that integrates multi-scale feature learning with multi-teacher dynamic self-distillation.

Today, people can easily record memorable moments, ranging from concerts, sports events, lectures, family gatherings, and birthday parties with multiple consumer cameras. However, synchronizing these cross-camera streams remains challenging. Existing methods assume controlled settings, specific targets, manual correction, or costly hardware. We present VisualSync, an optimization framework based on multi-view dynamics that aligns unposed, unsynchronized videos at millisecond accuracy. Our key insight is that any moving 3D point, when co-visible in two cameras, obeys epipolar constraints once properly synchronized.

Tubular-like system shape analysis is quite difficult in geometry and topology, while it is widely used in plants and organs analysis in practice. However, traditional discrete representations such as voxels and point clouds often require substantial storage and may lead to the loss of fine-grained geometric and topological details. To address these challenges, we propose SE(3)-BBSCformerGCN, a novel framework for learning shape-aware representations from continuous tubular topological manifolds with equivariance to rotations and translations. Our approach leverages Ball B-Spline Curve (BBSC) to define tubular manifolds and its functional space. We provide a formal mathematical definition and analysis of the resulting manifolds and the BBSC functional space, and incorporate an equivariant mapping that preserves geometric and topological stability. Compared to the point cloud and voxel based representations, our manifold-based formulation significantly reduces data complexity while preserving geometric attributes together with topological features.

As generative AI systems become competent and democratized in science, business, and government, deeper insight into their failure modes now poses an acute need. The occasional volatility in their behavior, such as the propensity of transformer models to hallucinate, impedes trust and adoption of emerging AI solutions in high-stakes areas. In the present work, we establish how and when hallucinations arise in pre-trained transformer models through concept representations captured by sparse autoencoders, under scenarios with experimentally controlled uncertainty in the input space. Our systematic experiments reveal that the number of semantic concepts used by the transformer model grows as the input information becomes increasingly unstructured. In the face of growing uncertainty in the input space, the transformer model becomes prone to activate coherent yet input-insensitive semantic features, leading to hallucinated output. At its extreme, for pure-noise inputs, we identify a wide variety of robustly triggered and meaningful concepts in the intermediate activations of pre-trained transformer models, whose functional integrity we confirm through targeted steering. We also show that hallucinations in the output of a transformer model can be reliably predicted from the concept patterns embedded in transformer layer activations. This collection of insights on transformer internal processing mechanics has immediate consequences for aligning AI models with human values, AI safety, opening the attack surface for potential adversarial attacks, and providing a basis for automatic quantification of a model's hallucination risk.

Reinforcement learning has achieved remarkable success in complex decisionmaking environments, yet its lack of transparency limits its deployment in practice, especially in safety-critical settings. Shapley values from cooperative game theory provide a principled framework for explaining reinforcement learning; however, the computational cost of Shapley explanations is an obstacle for their use. We introduce FastSVERL, a scalable method for explaining reinforcement learning by approximating Shapley values. FastSVERL is designed to handle the unique challenges of reinforcement learning, including temporal dependencies across multi-step trajectories, learning from off-policy data, and adapting to evolving agent behaviours in real time. FastSVERL introduces a practical, scalable approach for principled and rigourous interpretability in reinforcement learning.

Arithmetic circuit optimization remains a fundamental challenge in modern integrated circuit design. Recent advances have cast this problem within the Learning to Optimize (L2O) paradigm, where intelligent agents autonomously explore high-performance design spaces with encouraging results. However, existing approaches predominantly target coarse-grained architectural configurations, while the crucial interconnect optimization stage is often relegated to oversimplified proxy models or a heuristic approach. This disconnect undermines design quality, leading to suboptimal solutions in the circuit topology search space. To bridge this gap, we present ARITH-DAS, a Differentiable Architecture Search framework for Arithmetic circuits. To the best of our knowledge, ARITH-DAS is the first to formulate interconnect optimization within arithmetic circuits as a differentiable edge prediction problem over a multi-relational directed acyclic graph, enabling fine-grained, proxy-free optimization at the interconnection level. We evaluate ARITH-DAS on a suite of representative arithmetic circuits, including multipliers and multiply-accumulate units. Experiments show substantial improvements over state-of-the-art L2O and conventional methods, achieving up to 27.05% gain in hypervolume of area-delay Pareto frontiers, a standard metric for evaluating multi-objective optimization performance.

Inside America's new fattest town: Burgers are the size of your head, gyms lie empty and custom mobility scooters carry 800lb loads... as we investigate why Ozempic just DOESN'T work Ex-partner of dad who was berated for taking his daughters into women's bathroom claims he'exploited' girls and accuses him of failing to pay child support... before he hits back The'marry me' sex move that'll make even the most commitment-phobic of men beg to see you again... and it worked for THREE of my friends Stingy fast food giant named America's favorite restaurant AGAIN... and experts think they know why Netherlands vs Sweden - World Cup Group F LIVE: Liverpool's Cody Gakpo adds to Brian Brobbey's quickfire double as Ronald Koeman's side aim for first win Meghan went into'high-performance mode' when Serena Williams's mother'ignored her' at the US Open, body language expert claims - as visit to the UK raises the intriguing possibility of the Duchess attending Wimbledon Dua Lipa stuns in a bespoke Chanel bridal gown and parties into the early hours as she shares the first pictures from her £1.5million Little-known penis condition that SHORTENS manhood: Shockingly, 1 in 10 men have it... but most miss the signs until it's too late to reverse with easy cure: DR PETAR BAJIC Jeremy Clarkson, 66, reveals he is in remission after being diagnosed with'aggressive' prostate cancer as he says he's the'world's luckiest man' Capitol Hill glam girl shares the beauty secrets of Trump's leading ladies... from go-to makeup products to tips on achieving the perfect'Mar-A-Lago face' Harrowing chain of events behind The Ring star's death at just 35 laid bare by doctors in agonizing detail... and how it could have been prevented The four mistakes that led to bungee tragedy on Skeleton Bridge: FRED KELLY saw the scene for himself, now he retraces the prelude to disaster. So was it really an accident? Taylor Swift's bombshell wedding invite'olive branch' to Blake Lively: Insiders reveal every detail of reconciliation literally no one saw coming... and the actress has a dress picked out! Furious Trump hits back at Italian Prime Minister Meloni and gives her unusual'nickname' as their photo feud ramps up World Cup commentator denies making racist comment about Ciara live on air during USA's win over Australia TV star mom, 46, who appeared on'quitting everything to change your life' show died in fire at luxury Caribbean beach resort that sent 1,700 tourists running for their lives Swedish actress, 81, was in TWO James Bond movies and also worked with Charlton Heston, who is she?

Sparse-reward reinforcement learning (RL) can model a wide range of highly complex tasks. Solving sparse-reward tasks is RL's core premise--requiring efficient exploration coupled with long-horizon credit assignment--and overcoming these challenges is key for building self-improving agents with superhuman ability. Prior work commonly explores with the objective of solving many sparse-reward tasks, making exploration of individual high-dimensional, long-horizon tasks intractable. We argue that solving such challenging tasks requires solving simpler tasks that are relevant to the target task, i.e., whose achieval will teach the agent skills required for solving the target task. We demonstrate that this sense of direction, necessary for effective exploration, can be extracted from existing RL algorithms, without leveraging any prior information. To this end, we propose a method for directed sparse-reward goal-conditioned very long-horizon RL (DISCOVER), which selects exploratory goals in the direction of the target task. We connect DISCOVER to principled exploration in bandits, formally bounding the time until the target task becomes achievable in terms of the agent's initial distance to the target, but independent of the volume of the space of all tasks. We then perform a thorough evaluation in high-dimensional environments. We find that the directed goal selection of DISCOVER solves exploration problems that are beyond the reach of prior state-of-the-art exploration methods in RL.

School children in Preston and Manchester had mixed feelings about a proposed social media ban for under-16s following an announcement from Prime Minister Sir Keir Starmer. On Monday, Starmer said under-16s will be banned from social media platforms such as Snapchat, TikTok, YouTube, Instagram, Facebook and X by spring 2027. Speaking to the BBC, some pupils described the ban as unnecessary as they asked for more responsibility for parents. One student said she hoped the ban will have a positive impact on young people's lives and their mental health. How much screen time is too much for under fives?