The National Police Agency held its first expert panel meeting on Tuesday on countering illegal drone flights amid rising concerns over their potential use in terrorism and other threats. The National Police Agency on Tuesday held the first meeting of an expert panel on measures against illegal drone flights, in light of the growing threat of drones being used for terrorism and other purposes. The panel plans to compile a report by the end of the year on expanding the list of no-fly zones and penalties, with a view to revising the drone control law. The law was established in 2016 in the wake of an incident in which a drone fell on the roof of the Prime Minister's Office in Tokyo. It currently bans drone flights within about 300 meters of important facilities such as the National Diet Building, the Imperial Palace and nuclear power plants.

Ensuring reliability in modern software systems requires rigorous pre-production testing across highly heterogeneous and evolving environments. Because exhaustive evaluation is infeasible, practitioners must decide how to allocate limited testing resources across configurations where failure probabilities may drift over time. Existing combinatorial optimization approaches are static, ad hoc, and poorly suited to such non-stationary settings. We introduce a novel reinforcement learning (RL) framework that recasts configuration allocation as a sequential decision-making problem. Our method is the first to integrate Q-learning with a hybrid reward design that fuses simulated outcomes and real-time feedback, enabling both sample efficiency and robustness. In addition, we develop an adaptive online-offline training scheme that allows the agent to quickly track abrupt probability shifts while maintaining long-run stability. Extensive simulation studies demonstrate that our approach consistently outperforms static and optimization-based baselines, approaching oracle performance. This work establishes RL as a powerful new paradigm for adaptive configuration allocation, advancing beyond traditional methods and offering broad applicability to dynamic testing and resource scheduling domains.

Renewable energy forecasting is attaining greater importance due to its constant increase in contribution to the electrical power grids. Solar energy is one of the most significant contributors to renewable energy and is dependent on solar irradiation. For the effective management of electrical power grids, forecasting models that predict solar irradiation, with high accuracy, are needed. In the current study, Machine Learning techniques such as Linear Regression, Extreme Gradient Boosting and Genetic Algorithm Optimization are used to forecast solar irradiation. The data used for training and validation is recorded from across three different geographical stations in the United States that are part of the SURFRAD network. A Global Horizontal Index (GHI) is predicted for the models built and compared. Genetic Algorithm Optimization is applied to XGB to further improve the accuracy of solar irradiation prediction.

ABSTRACT Machine Speech Chain, simulating the human perception-production loop, proves effective in jointly improving ASR and TTS. We propose TokenChain, a fully discrete speech chain coupling semantic-token ASR with a two-stage TTS: an autoregressive text-to-semantic model co-trained with ASR and a masked-generative semantic-to-acoustic model for synthesis only. End-to-end feedback across the text interface is enabled with straight-through argmax/Gumbel-Softmax and balanced with supervised ASR via dynamic weight averaging. Ablations examine optimal temperature schedules for in-and cross-domain transfer. Evaluation reveals TokenChain surpasses baseline accuracy 2-6 epochs earlier and yields 5-13% lower equal-epoch error with stable T2S on LibriSpeech, and reduces relative ASR WER by 56% and T2S WER by 31% on TED-LIUM with minimal forgetting, showing that chain learning remains effective with token interfaces and models.

Abstract-- Hair care is an essential daily activity, yet it remains inaccessible to individuals with limited mobility and challenging for autonomous robot systems due to the fine-grained physical structure and complex dynamics of hair . We introduce a novel dynamics learning paradigm that is suited for volumetric quantities such as hair, relying on an action-conditioned latent state editing mechanism, coupled with a compact 3D latent space of diverse hairstyles to improve generalizability. This latent space is pre-trained at scale using a novel hair physics simulator, enabling generalization across previously unseen hairstyles. Experiments in simulation demonstrate that DYMO-Hair's dynamics model outperforms baselines on capturing local deformation for diverse, unseen hairstyles. DYMO-Hair further outperforms baselines in closed-loop hair styling tasks on unseen hairstyles, with an average of 22% lower final geometric error and 42% higher success rate than the state-of-the-art system. Real-world experiments exhibit zero-shot transferability of our system to wigs, achieving consistent success on challenging unseen hairstyles where the state-of-the-art system fails. T ogether, these results introduce a foundation for model-based robot hair care, advancing toward more generalizable, flexible, and accessible robot hair styling in unconstrained physical environments. Hair is central to personal identity and self-esteem [1], [2], yet routine care is difficult for individuals with limited mobility due to reduced coordination, strength, and flexibility [3]. To improve accessibility and autonomy, robot hair care systems have been explored [4]-[7], but existing approaches rely on either handcrafted trajectories or rule-based controllers, restricting generalization across diverse hairstyles and goals. To address these limitations, we propose DYMO-Hair, a model-based robot hair care system. Our system is capable of generalizable and flexible visual goal-conditioned hair manipulation, across diverse hairstyles and objectives in unconstrained physical environments. Chengyang Zhao, Uksang Y oo, Jonathan Francis (by courtesy), Jeffrey Ichnowski, and Jean Oh are with Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA. Arkadeep Narayan Chaudhury is with Epic Games, Inc., Pittsburgh, Pennsylvania, USA. Giljoo Nam is with Meta Codec Avatars Lab, Pittsburgh, Pennsylvania, USA. Jonathan Francis is with Bosch Center for Artificial Intelligence, Pittsburgh, Pennsylvania, USA. Figure 1. We introduce DYMO-Hair, a unified, model-based robot hair care system.

Planetary exploration missions require robust locomotion systems capable of operating in extreme environments over extended periods. This paper presents the DISTANT (Distant Transmission and Steering Systems) design, a novel approach for relocating rover traction and steering actuators from wheel-mounted positions to a thermally protected warm box within the rover body. The design addresses critical challenges in long-distance traversal missions by protecting sensitive components from thermal cycling, dust contamination, and mechanical wear. A double wishbone suspension configuration with cardan joints and capstan drive steering has been selected as the optimal architecture following comprehensive trade-off analysis. The system enables independent wheel traction, steering control, and suspension management whilst maintaining all motorisation within the protected environment. The design meets a 50 km traverse requirement without performance degradation, with integrated dust protection mechanisms and thermal management solutions. Testing and validation activities are planned for Q1 2026 following breadboard manufacturing at 1:3 scale.

Low-light image enhancement (LLIE) is vital for safety-critical applications such as surveillance, autonomous navigation, and medical imaging, where visibility degradation can impair downstream task performance. Recently, diffusion models have emerged as a promising generative paradigm for LLIE due to their capacity to model complex image distributions via iterative denoising. This survey provides an up-to-date critical analysis of diffusion models for LLIE, distinctively featuring an in-depth comparative performance evaluation against Generative Adversarial Network and Transformer-based state-of-the-art methods, a thorough examination of practical deployment challenges, and a forward-looking perspective on the role of emerging paradigms like foundation models. We propose a multi-perspective taxonomy encompassing six categories: Intrinsic Decomposition, Spectral & Latent, Accelerated, Guided, Multimodal, and Autonomous; that map enhancement methods across physical priors, conditioning schemes, and computational efficiency. Our taxonomy is grounded in a hybrid view of both the model mechanism and the conditioning signals. We evaluate qualitative failure modes, benchmark inconsistencies, and trade-offs between interpretability, generalization, and inference efficiency. We also discuss real-world deployment constraints (e.g., memory, energy use) and ethical considerations. This survey aims to guide the next generation of diffusion-based LLIE research by highlighting trends and surfacing open research questions, including novel conditioning, real-time adaptation, and the potential of foundation models.

A monoped's jump height and energy consumption depend on both, its mechanical design and control strategy. Existing co-design frameworks typically optimize for either maximum height or minimum energy, neglecting their trade-off. They also often omit gearbox parameter optimization and use oversimplified actuator mass models, producing designs difficult to replicate in practice. In this work, we introduce a novel three-stage co-design optimization framework that jointly maximizes jump height while minimizing mechanical energy consumption of a monoped. The proposed method explicitly incorporates realistic actuator mass models and optimizes mechanical design (including gearbox) and control parameters within a unified framework. The resulting design outputs are then used to automatically generate a parameterized CAD model suitable for direct fabrication, significantly reducing manual design iterations. Our experimental evaluations demonstrate a 50 percent reduction in mechanical energy consumption compared to the baseline design, while achieving a jump height of 0.8m. Video presentation is available at http://y2u.be/XW8IFRCcPgM