Definitely not working smarter," writes one forum user. On a forum with over 5,000 members claiming to be current and former Immigration and Customs Enforcement (ICE) and Customs and Border Protection (CBP) officers, users vent their frustrations and concerns about the agency as it has become the center of public ire. Definitely not working smarter," wrote one user. The forum contains posts dating back over a decade and describes itself as an "unofficial forum for current Deportation Officers, prospective applicants and retired Deportation Officers to have a platform for discussion." In posts viewed by WIRED, users complain of long working hours, limited overtime pay, incompetent leadership, and poorly trained new recruits.

Robots operating in everyday environments must often decide when and whether to engage with people, yet such decisions often hinge on subtle nonverbal cues that unfold over time and are difficult to model explicitly. Drawing on a five-day Wizard-of-Oz deployment of a mobile service robot in a university cafe, we analyze how people signal interaction readiness through nonverbal behaviors and how expert wizards use these cues to guide engagement. Motivated by these observations, we propose a two-stage pipeline in which lightweight perceptual detectors (gaze shifts and proxemics) are used to selectively trigger heavier video-based vision-language model (VLM) queries at socially meaningful moments. We evaluate this pipeline on replayed field interactions and compare two prompting strategies. Our findings suggest that selectively using VLMs as proxies for social reasoning enables socially responsive robot behavior, allowing robots to act appropriately by attending to the cues people naturally provide in real-world interactions.

Generative AI systems may pose serious risks to individuals vulnerable to eating disorders. Existing safeguards tend to overlook subtle but clinically significant cues, leaving many risks unaddressed. To better understand the nature of these risks, we conducted semi-structured interviews with 15 clinicians, researchers, and advocates with expertise in eating disorders. Using abductive qualitative analysis, we developed an expert-guided taxonomy of generative AI risks across seven categories: (1) providing generalized health advice; (2) encouraging disordered behaviors; (3) supporting symptom concealment; (4) creating thinspiration; (5) reinforcing negative self-beliefs; (6) promoting excessive focus on the body; and (7) perpetuating narrow views about eating disorders. Our results demonstrate how certain user interactions with generative AI systems intersect with clinical features of eating disorders in ways that may intensify risk. We discuss implications of our work, including approaches for risk assessment, safeguard design, and participatory evaluation practices with domain experts.

Advances in AI and Robotics have accelerated significant initiatives in agriculture, particularly in the areas of robot navigation and 3D digital twin creation. A significant bottleneck impeding this progress is the critical lack of "in-the-wild" datasets that capture the full complexities of real farmland, including non-rigid motion from wind, drastic illumination variance, and morphological changes resulting from growth. This data gap fundamentally limits research on robust AI models for autonomous field navigation and scene-level dynamic 3D reconstruction. In this paper, we present AgriChrono, a modular robotic data collection platform and multi-modal dataset designed to capture these dynamic farmland conditions. Our platform integrates multiple sensors, enabling remote, time-synchronized acquisition of RGB, Depth, LiDAR, IMU, and Pose data for efficient and repeatable long-term data collection in real-world agricultural environments. We successfully collected 18TB of data over one month, documenting the entire growth cycle of Canola under diverse illumination conditions. We benchmark state-of-the-art 3D reconstruction methods on AgriChrono, revealing the profound challenge of reconstructing high-fidelity, dynamic non-rigid scenes in such farmland settings. This benchmark validates AgriChrono as a critical asset for advancing model generalization, and its public release is expected to significantly accelerate research and development in precision agriculture. The code and dataset are publicly available at: https://github.com/StructuresComp/agri-chrono

However, for some applications, this assumption is inappropriate.

The complexity and difficulties of Kurdish speaker detection among its several dialects are investigated in this work. Because of its great phonetic and lexical differences, Kurdish with several dialects including Kurmanji, Sorani, and Hawrami offers special challenges for speaker recognition systems. The main difficulties in building a strong speaker identification system capable of precisely identifying speakers across several dialects are investigated in this work. To raise the accuracy and dependability of these systems, it also suggests solutions like sophisticated machine learning approaches, data augmentation tactics, and the building of thorough dialect-specific corpus. The results show that customized strategies for every dialect together with cross-dialect training greatly enhance recognition performance.

Abstract-- Traditional soil sampling and analysis methods are labor-intensive, time-consuming, and limited in spatial resolution, making them unsuitable for large-scale precision agriculture. T o address these limitations, we present a robotic solution for real-time sampling, analysis and mapping of key soil properties. Our system consists of two main sub-systems: a Sample Acquisition System (SAS) for precise, automated in-field soil sampling; and a Sample Analysis Lab (Lab) for real-time soil property analysis. The system's performance was validated through extensive field trials at a large-scale Australian farm. Experimental results show that the SAS can consistently acquire soil samples with a mass of 50g at a depth of 200mm, while the Lab can process each sample within 10 minutes to accurately measure pH and macronutrients. These results demonstrate the potential of the system to provide farmers with timely, data-driven insights for more efficient and sustainable soil management and fertilizer application. I. INTRODUCTION Achieving sustainable agricultural resource management requires accurate, high-resolution, and up-to-date data on soil properties such as pH and macronutrients [1], [2]. However, conventional soil sampling and testing methods fail to address this need at scale.

The Point Machine (PM) is a critical piece of railway equipment that switches train routes by diverting tracks through a switchblade. As with any critical safety equipment, a failure will halt operations leading to service disruptions; therefore, pre-emptive maintenance may avoid unnecessary interruptions by detecting anomalies before they become failures. Previous work relies on several inputs and crafting custom features by segmenting the signal. This not only adds additional requirements for data collection and processing, but it is also specific to the PM technology, the installed locations and operational conditions limiting scalability. Based on the available maintenance records, the main failure causes for PM are obstacles, friction, power source issues and misalignment. Those failures affect the energy consumption pattern of PMs, altering the usual (or healthy) shape of the power signal during the PM movement. In contrast to the current state-of-the-art, our method requires only one input. We apply a deep learning model to the power signal pattern to classify if the PM is nominal or associated with any failure type, achieving >99.99\% precision, <0.01\% false positives and negligible false negatives. Our methodology is generic and technology-agnostic, proven to be scalable on several electromechanical PM types deployed in both real-world and test bench environments. Finally, by using conformal prediction the maintainer gets a clear indication of the certainty of the system outputs, adding a confidence layer to operations and making the method compliant with the ISO-17359 standard.

In the wake of January's horrific fires, detractors of Los Angeles -- an urban reality often seen as a toxic mixture of unsustainable resource planning and structurally poor governance systems -- are having a field day. Los Angeles knows how to weather a crisis -- or two or three. Angelenos are tapping into that resilience, striving to build a city for everyone. Their criticism is not new: For most of the 20th century -- and certainly for the last five decades or so -- Los Angeles has been seen by many urbanists as less city and more cautionary tale -- a smoggy expanse of subdivisions and spaghetti junctions, where ambition came with a two-hour commute. Planners shuddered, while architects looked away, even as they accepted handsome commissions to build some of L.A.'s -- if not the world's -- most iconic buildings.

The rapid urbanization of cities and increasing vehicular congestion have posed significant challenges to traffic management and safety. This study explores the transformative potential of artificial intelligence (AI) and machine vision technologies in revolutionizing traffic systems. By leveraging advanced surveillance cameras and deep learning algorithms, this research proposes a system for real-time detection of vehicles, traffic anomalies, and driver behaviors. The system integrates geospatial and weather data to adapt dynamically to environmental conditions, ensuring robust performance in diverse scenarios. Using YOLOv8 and YOLOv11 models, the study achieves high accuracy in vehicle detection and anomaly recognition, optimizing traffic flow and enhancing road safety. These findings contribute to the development of intelligent traffic management solutions and align with the vision of creating smart cities with sustainable and efficient urban infrastructure.