Diffusion Policy has demonstrated strong visuomotor modeling capabilities, but its high computational cost renders it impractical for real-time robotic control. Despite huge redundancy across repetitive denoising steps, existing diffusion acceleration techniques fail to generalize to Diffusion Policy due to fundamental architectural and data divergences. In this paper, we propose Block-wise A daptive C aching ( BAC), a method to accelerate Diffusion Policy by caching intermediate action features. BAC achieves lossless action generation acceleration by adap-tively updating and reusing cached features at the block level, based on a key observation that feature similarities vary non-uniformly across timesteps and blocks. To operationalize this insight, we first propose the Adaptive Caching Scheduler, designed to identify optimal update timesteps by maximizing the global feature similarities between cached and skipped features. However, applying this scheduler for each block leads to significant error surges due to the inter-block propagation of caching errors, particularly within Feed-Forward Network (FFN) blocks. To mitigate this issue, we develop the Bubbling Union Algorithm, which truncates these errors by updating the upstream blocks with significant caching errors before downstream FFNs. As a training-free plugin, BAC is readily integrable with existing transformer-based Diffusion Policy and vision-language-action models. Extensive experiments on multiple robotic benchmarks demonstrate that BAC achieves up to 3 inference speedup for free.

IMPORTANCE Women are underdiagnosed and undertreated for cardiovascular disease. Automatic quantification of breast arterial calcification on screening mammography can identify women at risk for cardiovascular disease and enable earlier treatment and management of disease. OBJECTIVE To determine whether artificial-intelligence based automatic quantification of BAC from screening mammograms predicts cardiovascular disease and mortality in a large, racially diverse, multi-institutional population, both independently and beyond traditional risk factors and ASCVD scores. DESIGN, SETTING, AND PARTICIPANTS Retrospective cohort study of 116,135 women from two healthcare systems (Emory Healthcare and Mayo Clinic Enterprise) who had screening mammograms and either experienced a major adverse cardiovascular event, death, or had at least 5 years of clinical follow-up. BAC was quantified using a novel transformer-based neural network architecture for semantic segmentation. BAC severity was categorized into four groups (no BAC, mild, moderate, and severe), with outcomes assessed using Kaplan-Meier analysis and Cox proportional-hazards models. MAIN OUTCOMES AND MEASURES Major Adverse Cardiovascular Events (MACE), including acute myocardial infarction, stroke, heart failure, and all-cause mortality, adjusted for traditional risk factors and Atherosclerotic CVD (ASCVD) risk scores. RESULTS BAC severity was independently associated with MACE after adjusting for cardiovascular risk factors, with increasing hazard ratios from mild (HR 1.18-1.22),

Bb-Simplex is centered around the Simplex control architecture, which consists of a high-performance advanced controller that is not guaranteed to maintain safety of the plant, a verified-safe baseline controller, and a decision module that switches control of the plant between the two controllers to ensure safety without sacrificing performance. In Bb-Simplex, Barrier certificates are used to prove that the baseline controller ensures safety. Furthermore, Bb-Simplex features a new automated method for deriving, from the barrier certificate, the conditions for switching between the controllers. Our method is based on the Taylor expansion of the barrier certificate and yields computationally inexpensive switching conditions. We also propose extensions to Bb-Simplex to enable its use in hybrid systems, which have multiple modes each with its own dynamics, and to support its use when only approximate dynamics (not exact dynamics) are available, for both continuous-time and hybrid dynamical systems. We consider significant applications of Bb-Simplex to microgrids featuring advanced controllers in the form of neural networks trained using reinforcement learning. These microgrids are modeled in RTDS, an industry-standard high-fidelity, real-time power systems simulator. Our results demonstrate that Bb-Simplex can automatically derive switching conditions for complex continuous-time and hybrid systems, the switching conditions are not overly conservative, and Bb-Simplex ensures safety even in the presence of adversarial attacks on the neural controller when only approximate dynamics (with an error bound) are available.

Learning high-quality Q-value functions plays a key role in the success of many modern off-policy deep reinforcement learning (RL) algorithms. Previous works focus on addressing the value overestimation issue, an outcome of adopting function approximators and off-policy learning. Deviating from the common viewpoint, we observe that Q-values are indeed underestimated in the latter stage of the RL training process, primarily related to the use of inferior actions from the current policy in Bellman updates as compared to the more optimal action samples in the replay buffer. We hypothesize that this long-neglected phenomenon potentially hinders policy learning and reduces sample efficiency. Our insight to address this issue is to incorporate sufficient exploitation of past successes while maintaining exploration optimism. We propose the Blended Exploitation and Exploration (BEE) operator, a simple yet effective approach that updates Q-value using both historical best-performing actions and the current policy. The instantiations of our method in both model-free and model-based settings outperform state-of-the-art methods in various continuous control tasks and achieve strong performance in failure-prone scenarios and real-world robot tasks.

While the transport of matter by wheeled vehicles or legged robots can be guaranteed in engineered landscapes like roads or rails, locomotion prediction in complex environments like collapsed buildings or crop fields remains challenging. Inspired by principles of information transmission which allow signals to be reliably transmitted over noisy channels, we develop a ``matter transport" framework demonstrating that non-inertial locomotion can be provably generated over ``noisy" rugose landscapes (heterogeneities on the scale of locomotor dimensions). Experiments confirm that sufficient spatial redundancy in the form of serially-connected legged robots leads to reliable transport on such terrain without requiring sensing and control. Further analogies from communication theory coupled to advances in gaits (coding) and sensor-based feedback control (error detection/correction) can lead to agile locomotion in complex terradynamic regimes.

La Trobe University researchers have developed an artificial intelligence (AI) algorithm that could work alongside expensive and potentially biased breath testing devices in pubs and clubs. The technology can instantly determine whether a person has exceeded the legal alcohol limit purely on using a 12-seconds recording of their voice. In a paper published in the journal Alcohol, the study led by Ph.D. student Abraham Albert Bonela and supervised by Professors Emmanuel Kuntsche and Associate Professor Zhen He, from the Center for Alcohol Policy Research and the Department of Computer Science and Information Technology at La Trobe University, respectively, describes the development of the Audio-based Deep Learning Algorithm to Identify Alcohol Inebriation (ADLAIA) that can determine an individual's intoxication status based on a 12-second recording of their speech. "Intoxicated individuals are usually identified by measuring their blood alcohol concentration (BAC) using breathalyzers that are expensive and labor-intensive," Albert Bonela said. "A test that could simply rely on someone speaking into a microphone would be a game changer."

In this work, we propose Behavior-Guided Actor-Critic (BAC), an off-policy actor-critic deep RL algorithm. BAC mathematically formulates the behavior of the policy through autoencoders by providing an accurate estimation of how frequently each state-action pair was visited while taking into consideration state dynamics that play a crucial role in determining the trajectories produced by the policy. The agent is encouraged to change its behavior consistently towards less-visited state-action pairs while attaining good performance by maximizing the expected discounted sum of rewards, resulting in an efficient exploration of the environment and good exploitation of all high reward regions. One prominent aspect of our approach is that it is applicable to both stochastic and deterministic actors in contrast to maximum entropy deep reinforcement learning algorithms. Results show considerably better performances of BAC when compared to several cutting-edge learning algorithms.

Digital Transformation continues to gain velocity. But even as technology takes over many tasks, it's important that a Human touch is retained. This thought-provoking article by Bryan Kramer s another in our "Great Articles You may have missed" series. A few years ago, experts were trumpeting that the future is mobile, and they weren't wrong. Some of the world's most successful new apps and business models are mobile-based -- just look at Uber, Instagram, and Snapchat.

A few years ago, experts were trumpeting that the future is mobile, and they weren't wrong. Some of the world's most successful new apps and business models are mobile-based -- just look at Uber, Instagram and Snapchat. Now, our machines and devices are beginning to understand us on an even deeper level. Machine learning and artificial intelligence are being used to improve user experience and transform the way that we interact with technology. The aim is to simplify processes using complex assistive technologies and seamlessly integrate them into our lives.

INTRODUCTION This paper is a report on some preliminary work undertaken as part of a longer term study of the problems which arise in designing and implementing digital computer programs which'learn'. A program has been written which learns to play the board game'Go-Moku' using a particular learning mechanism to be described later. The program is to be regarded as an experimental tool by means of which the particular learning mechanism can be investigated in some depth. Go-Moku is a simple but not a trivial game with an intellectual content comparable with a game of draughts (checkers). Opinions have sometimes been expressed that there is nothing to be learnt (no pun intended!) by programming simple games. Present knowledge of programming learning is such that it is useful to experiment with programs operating in a simple task environment. It is not so much what game the program learns as how it learns it. It is emphasised that the object of the present work is not to write a program which plays a difficult game better than anyone or anything has played it before, but to isolate and investigate particular aspects of a learning process which might be valid over a range of ill-structured problems. For the record, however, the current learning programs learn to play a good (basically defensive) game. The modifications currently being made to the program should give it a learning capacity to become unbeatable.