Terrifying stomach cancer explosion sweeps the US: After fitness influencer's shock death, experts reveal subtle early signs that are too often ignored... and lifestyle tweaks that can PREVENT it Actress, 43, announces she is expecting with sweet video after detailing'complicated' journey to motherhood and hope of having third child Trump foe Rosie O'Donnell to replace Jimmy Kimmel as he steps back from his show Deadly secrets of gorgeous California enclave where college girls were killed by a'sneaker'... now experts say they could have been SAVED The other women left devastated by Jelly Roll's divorce: Why his daughter is now'disgusted'... as Bunnie's baby bombshell rocks Nashville The shaming of America's original mommy influencer after tragedy that divided the nation: Bode Miller's wife Morgan breaks cover to reveal agonizing regret that still haunts her since daughter's drowning Trump boasts there's'no limits' to his power and posts bizarre memo by fake historian comparing him to Hitler More young Americans are living with their parents than ever before... and there is a shocking reason behind the boomerang trend I was mortified when my husband always said no to sex. Then I realised the mistake I was making. This is the change that's completely transformed marital love-making in middle age: ALICE SNAPE Revealed: Hero, 24, who saved man's LIFE in dramatic rescue during New York Knicks victory parade after defying cops' orders: 'I'm just another New Yorker' REVEALED: Gavin Newsom steered millions of dollars of donations to nonprofits connected to his wife... as Trump's DOJ probes couple The shingles vaccine could lower dementia risk'by up to a quarter' - but scientists are still puzzled why Farce of Obama's $850m'monstrosity': As clucking liberal elite cheer Barack's grand opening, outraged Chicago locals tell HARRIET ALEXANDER awkward truth about library Why turnips MUST be in your grocery cart if you're trying to lose weight Taco Bell's finally fixes a glaring menu gap - and brings back a fan favorite after years Mom thought popular'natural' health supplement was safer than Xanax. She took it... then never woke up. Don't make the same mistake Mother and child in critical condition after being swallowed into ocean by ANOTHER monstrous California wave... just days after college students were killed by breaker'I was taken from school and trained to fly UFOs with my mind,' claims child genius A former gifted child has come forward with claims that he was removed from public school and secretly trained to develop psychic abilities for military and UFO-related applications.

Is the war entering a new phase? Russia has renewed its strikes on Ukraine, killing three people including an eight-year-old girl, Ukrainian officials said. The Russian strikes on Friday come a day after Ukraine launched its biggest-ever drone attack on Moscow, killing a different eight-year-old girl and sparking an inferno at a major oil refinery, according to Russian officials. Between late Thursday and early Friday, Russia launched 90 drones at Ukraine, according to the Ukrainian air force. "An eight-year-old girl was killed. These are the consequences of this morning's enemy attack on Pavlohrad," Oleksandr Ganzha, the governor of Ukraine's central Dnipropetrovsk region, said.

Generalized policy and execution efficiency constitute the two critical challenges in robotic manipulation. While recent foundation policies benefit from the commonsense reasoning capabilities of internet-scale pretrained vision-language models (VLMs), they often suffer from low execution frequency. To mitigate this dilemma, dual-system approaches have been proposed to leverage a VLM-based System 2 module for handling high-level decision-making, and a separate System 1 action module for ensuring real-time control. However, existing designs maintain both systems as separate models, limiting System 1 from fully leveraging the rich pretrained knowledge from the VLM-based System 2. In this work, we propose Fast-in-Slow (FiS), a unified dual-system vision-language-action (VLA) model that embeds the System 1 execution module within the VLM-based System 2 by partially sharing parameters. This innovative paradigm not only enables high-frequency execution in System 1, but also facilitates coordination between multimodal reasoning and execution components within a single foundation model of System 2. Given their fundamentally distinct roles within FiS-VLA, we design the two systems to incorporate heterogeneous modality inputs alongside asynchronous operating frequencies, enabling both fast and precise manipulation. To enable coordination between the two systems, a dual-aware co-training strategy is proposed that equips System 1 with action generation capabilities while preserving System 2's contextual understanding to provide stable latent conditions for System 1. For evaluation, FiS-VLA outperforms previous state-of-the-art methods by 8% in simulation and 11% in realworld tasks in terms of average success rate, while achieving a 117.7 Hz control frequency with action chunk set to eight.

Text-3DScene Retrieval (T3SR) aims to retrieve relevant scenes using linguistic queries. Although traditional T3SR methods have made significant progress in capturing fine-grained associations, they implicitly assume that query descriptions are information-complete. In practical deployments, however, limited by the capabilities of users and models, it is difficult or even impossible to directly obtain a perfect textual query suiting the entire scene and model, thereby leading to performance degradation. To address this issue, we propose a novel Interactive Text-3D Scene Retrieval Method (IDeal), which promotes the enhancement of the alignment between texts and 3D scenes through continuous interaction. To achieve this, we present an Interactive Retrieval Refinement framework (IRR), which employs a questioner to pose contextually relevant questions to an answerer in successive rounds that either promote detailed probing or encourage exploratory divergence within scenes. Upon the iterative responses received from the answerer, IRR adopts a retriever to perform both feature-level and semantic-level information fusion, facilitating scene-level interaction and understanding for more precise re-rankings. To bridge the domain gap between queries and interactive texts, we propose an Interaction Adaptation Tuning strategy (IAT).

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In recent years, artificial intelligence has significantly advanced medical image segmentation. Nonetheless, challenges remain, including efficient 3D medical image processing across diverse modalities and handling data variability. In this work, we introduce Hierarchical Soft Mixture-of-Experts (HoME), a two-level token-routing layer for efficient long-context modeling, specifically designed for 3D medical image segmentation. Built on the Mamba Selective State-Space Model (SSM) backbone, HoME enhances sequential modeling through adaptive expert routing.

The expansion of instruction-tuning data has enabled foundation language models to exhibit improved instruction adherence and superior performance across diverse downstream tasks. Semantically-rich 3D human motion is being progressively integrated with these foundation models to enhance multimodal understanding and cross-modal generation capabilities. However, the modality gap between human motion and text raises unresolved concerns about catastrophic forgetting during this integration. In addition, developing autoregressive-compatible pose representations that preserve generalizability across heterogeneous downstream tasks remains a critical technical barrier. To address these issues, we propose the Human MotionVision-Language Model (HMVLM), a unified framework based on the Mixture of Expert Low-Rank Adaption(MoE LoRA) strategy. The framework leverages the gating network to dynamically allocate LoRA expert weights based on the input prompt, enabling synchronized fine-tuning of multiple tasks. To mitigate catastrophic forgetting during instruction-tuning, we introduce a novel zero expert that preserves the pre-trained parameters for general linguistic tasks. For pose representation, we implement body-part-specific tokenization by partitioning the human body into different joint groups, enhancing the spatial resolution of the representation. Experiments show that our method effectively alleviates knowledge forgetting during instruction-tuning and achieves remarkable performance across diverse human motion downstream tasks.

Recently, deep multi-agent reinforcement learning (MARL) has demonstrated promising performance for solving challenging tasks, such as long-term dependencies and non-Markovian environments. Its success is partly attributed to conditioning policies on large fixed context length. However, such large fixed context lengths may lead to limited exploration efficiency and redundant information. In this paper, we propose a novel MARL framework to obtain adaptive and effective contextual information. Specifically, we design a central agent that dynamically optimizes context length via temporal gradient analysis, enhancing exploration to facilitate convergence to global optima in MARL. Furthermore, to enhance the adaptive optimization capability of the context length, we present an efficient input representation for the central agent, which effectively filters redundant information. By leveraging a Fourier-based low-frequency truncation method, we extract global temporal trends across decentralized agents, providing an effective and efficient representation of the MARL environment. Extensive experiments demonstrate that the proposed method achieves state-of-the-art (SOTA) performance on long-term dependency tasks, including PettingZoo, MiniGrid, Google Research Football (GRF), and StarCraft Multi-Agent Challenge v2 (SMACv2).

Recent advances in data-centric deep generative models have led to significant progress in solving inverse imaging problems. However, these models (e.g., diffusion models) typically require large amounts of fully sampled (clean) training data, which is often impractical in medical and scientific settings. Training-data-free approaches like Deep Image Prior (DIP) do not require clean images but suffer from noise overfitting and can be computationally expensive as the network parameters need to be optimized for each measurement vector independently. Moreover, DIPbased methods often overlook the potential of learning a prior using a small number of sub-sampled measurements (or degraded images) available during training. In this paper, we propose UGoDIT--an Unsupervised Group DIP via Transferable weights--designed for the low-data regime where only a very small number, M, of sub-sampled measurement vectors are available during training.

This paper addresses the problem of multi-risk measure agnostic multi-armed bandits in heavy-tailed reward settings. We propose a framework that leverages novel deviation inequalities for the 1-Wasserstein distance to construct confidence intervals for Lipschitz risk measures. The distributional LCB (DistLCB) algorithm is introduced, which achieves asymptotic optimality by deriving the first lower bounds for risk measure aware bandits with explicit sub-optimality gap dependencies. The DistLCB is further extended to multi-risk objectives, which enables Pareto-optimal solutions that consider multiple aspects of reward distributions. Additionally, we provide a regret analysis that includes both gap-dependent and gap-independent bounds for multi-risk settings. Experiments validate the effectiveness of the proposed methods in synthetic and real-world applications.