Learning safe reinforcement learning (RL) policies from offline multi-task datasets without direct environmental interaction is crucial for efficient and reliable deployment of RL agents. Benefiting from their scalability and strong in-context learning capabilities, recent approaches attempt to utilize Decision Transformer (DT) architectures for offline safe RL, demonstrating promising adaptability across varying safety budgets. However, these methods primarily focus on single-constraint scenarios and struggle with diverse constraint configurations across multiple tasks. Additionally, their reliance on heuristically defined Return-To-Go (RTG) inputs limits flexibility and reduces learning efficiency, particularly in complex multi-task scenarios. To address these limitations, we propose CoPDT, a novel DT-based framework designed to enhance adaptability to diverse constraints (i.e., cost functions) and varying budgets. Specifically, CoPDT introduces a constraint prioritized prompt encoder, which leverages sparse binary cost signals to accurately identify constraints, and a constraint prioritized Return-To-Go (CPRTG) token mechanism, which dynamically generates RTGs based on identified constraints and corresponding safety budgets. Extensive experiments on the OSRL benchmark demonstrate that CoPDT achieves superior efficiency and significantly enhanced safety compliance across diverse multi-task scenarios, surpassing state-of-the-art DT-based methods by satisfying safety constraints in more than twice as many tasks.

A study of 140,000 people suggests that a broadening of the diagnostic criteria for autism and ADHD explains the sharp rise in diagnoses, but that doesn't mean too many people are being told they are autistic or have ADHD We may be beginning to understand what is behind the recent explosion in diagnoses of ADHD and autism . A study of 140,000 people in Denmark reveals that those recently diagnosed with ADHD or autism have fewer genetic variations associated with them than people diagnosed a decade earlier. This suggests that a broadening of the diagnostic criteria is behind the rise, but it doesn't support claims that ADHD and autism are being overdiagnosed. Diagnoses for autism and ADHD have risen up to tenfold around the world over the past two decades, particularly among girls and adults. Several possibilities have been put forward to explain this, including better awareness and understanding, a broadening of the diagnostic criteria, and even the commercial interests of pharmaceutical companies and private diagnostic clinics.

The remains of 42 hunter-gatherers show that the Black Death was already lethal 5,500 years ago. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Ust'Ida I Burial #33; this shared grave contained a boy (aged 12-15 years old) and a girl (aged 13-16 years old) who were found to not be closely related, and plague DNA was obtained from their remains. That they were very close in age but not biologically related, and buried in the same grave, hints at the relationship they might have had when alive. Breakthroughs, discoveries, and DIY tips sent six days a week.

The field of Fake Image Detection and Localization (FIDL) is highly fragmented, encompassing four domains: deepfake detection (Deepfake), image manipulation detection and localization (IMDL), artificial intelligence-generated image detection (AIGC), and document image manipulation localization (Doc). Although individual benchmarks exist in some domains, a unified benchmark for all domains in FIDL remains blank.

Achieving generalization in neural approaches across different scales and distributions remains a significant challenge for routing problems. A key obstacle is that neural networks often fail to learn robust principles for identifying universal patterns and deriving optimal solutions from diverse instances. In this paper, we first uncover Purity Law, a fundamental structural principle for optimal solutions of routing problems, defining that edge prevalence grows exponentially with the sparsity of surrounding vertices. Statistically and theoretically validated across diverse instances, Purity Law reveals a consistent bias toward local sparsity in global optima. Building on this insight, we propose Purity Policy Optimization (PUPO), a novel training paradigm that explicitly aligns characteristics of neural solutions with Purity Law during the solution construction process to enhance generalization. Extensive experiments demonstrate that PUPO can be seamlessly integrated with popular neural solvers, significantly enhancing their generalization performance without incurring additional computational overhead during inference. The code is available at https://github.com/Kejun0627/PUPO.

Theoretical efforts to prove advantages of Transformers in comparison with classical architectures such as feedforward and recurrent neural networks have mostly focused on representational power. In this work, we take an alternative perspective and prove that even with infinite compute, feedforward and recurrent networks may suffer from larger sample complexity compared to Transformers, as the latter can adapt to a form of dynamic sparsity. Specifically, we consider a sequence-tosequence data generating model on sequences of length N, where the output at each position only depends on q N relevant tokens, and the positions of these tokens are described in the input prompt. We prove that a single-layer Transformer can learn this model if and only if its number of attention heads is at least q, in which case it achieves a sample complexity almost independent of N, while recurrent networks require NΩ(1) samples on the same problem. If we simplify this model, recurrent networks may achieve a complexity almost independent of N, while feedforward networks still require N samples. Our proposed sparse retrieval model illustrates a natural hierarchy in sample complexity across these architectures.

Nvidia ACE, a new AI technology for creating realistic gaming NPCs, is now available in PUBG's special Ally Duo Mode until June 30th. PCWorld reports that ACE uses small language models to enable dynamic NPC interactions and real-time speech synthesis without pre-recorded dialogue. The technology requires an Nvidia graphics card with 8GB+ memory and could revolutionize gaming by making NPCs more lifelike and interactive. One of the frustrating things about the proliferation of "AI", in the large language model sense, is how it muddles previously serviceable terms. It's neither artificial intelligence, in the sci-fi robot sense, nor artificial intelligence in the video game sense -- i.e., deliberate programmed behavior not controlled by the player.

Graph heterophily, where connected nodes have different labels, has attracted significant interest recently. Most existing works adopt a simplified approach using low-pass filters for homophilic graphs and high-pass filters for heterophilic graphs. However, we discover that the relationship between graph heterophily and spectral filters is more complex - the optimal filter response varies across frequency components and does not follow a strict monotonic correlation with heterophily degree. This finding challenges conventional fixed filter designs and suggests the need for adaptive filtering to preserve expressiveness in graph embeddings. Formally, natural questions arise: Given a heterophilic graph G, how and to what extent will the varying heterophily degree of G affect the performance of GNNs? How can we design adaptive filters to fit those varying heterophilic connections? Our theoretical analysis reveals that the average frequency response of GNNs and graph heterophily degree do not follow a strict monotonic correlation, necessitating adaptive graph filters to guarantee good generalization performance. Hence, we propose HEROFILTER, a simple yet powerful GNN, which extracts information across the heterophily spectrum and combines salient representations through adaptive mixing. HEROFILTER's superior performance achieves up to 9.2% accuracy improvement over leading baselines across homophilic and heterophilic graphs.

This work introduces a novel approach, Pairwise Epistemic Estimators (PairEpEsts), for epistemic uncertainty estimation in ensemble models for regression tasks using pairwise-distance estimators (PaiDEs). By utilizing the pairwise distances between model components, PaiDEs establish bounds on entropy. We leverage this capability to enhance the performance of Bayesian Active Learning by Disagreement (BALD). Notably, unlike sample-based Monte Carlo estimators, PairEpEsts can estimate epistemic uncertainty up to 100 times faster and demonstrate superior performance in higher dimensions. To validate our approach, we conducted a varied series of regression experiments on commonly used benchmarks: 1D sinusoidal data, Pendulum, Hopper, Ant, and Humanoid, demonstrating PairEpEsts' advantage over baselines in high-dimensional regression active learning.

Fluorescence microscopy, while being a key driver for progress in the life sciences, is also subject to technical limitations. To overcome them, computational multiplexing techniques have recently been proposed, which allow multiple cellular structures to be captured in a single image and later be unmixed. Existing image decomposition methods are trained on a set of superimposed input images and the respective unmixed target images. It is critical to note that the relative strength (mixing ratio) of the superimposed images for a given input is a priori unknown. However, existing methods are trained on a fixed intensity ratio of superimposed inputs, making them not cognizant of the range of relative intensities that can occur in fluorescence microscopy.