Multi-robot systems have emerged as a key technology for addressing the efficiency and cost challenges in labor-intensive industries. In the representative scenario of smart farming, planning efficient harvesting schedules for a fleet of electric robots presents a highly challenging frontier problem. The complexity arises not only from the need to find Pareto-optimal solutions for the conflicting objectives of makespan and transportation cost, but also from the necessity to simultaneously manage payload constraints and finite battery capacity. When robot loads are dynamically updated during planned multi-trip operations, a mandatory recharge triggered by energy constraints introduces an unscheduled load reset. This interaction creates a complex cascading effect that disrupts the entire schedule and renders traditional optimization methods ineffective. To address this challenge, this paper proposes the segment anchoring-based balancing algorithm (SABA). The core of SABA lies in the organic combination of two synergistic mechanisms: the sequential anchoring and balancing mechanism, which leverages charging decisions as `anchors' to systematically reconstruct disrupted routes, while the proportional splitting-based rebalancing mechanism is responsible for the fine-grained balancing and tuning of the final solutions' makespans. Extensive comparative experiments, conducted on a real-world case study and a suite of benchmark instances, demonstrate that SABA comprehensively outperforms 6 state-of-the-art algorithms in terms of both solution convergence and diversity. This research provides a novel theoretical perspective and an effective solution for the multi-robot task allocation problem under energy constraints.

Figure 1: Convergence curves of the two proposed methods on a toy problem.

INFRASTRUCTURE SENSOR-ENABLED VEHICLE DA T A GENERA TION USING MUL TI-SENSOR FUSION FOR PROACTIVE SAFETY APPLICA TIONS A T WORK ZONE Suhala Rabab Saba Department of Civil, Construction & Environmental Engineering, The University of Alabama Smart Communities and Innovation Building (SCIB), 28 Kirkbride Lane, Tuscaloosa, AL 35487-0288 Email: ssaba@crimson.ua.edu Saba, Khan, Ahmad, Cao, Rahman, Zhao, Huynh, and Ozguven 3 ABSTRACT Infrastructure-based sensing and real-time trajectory generation hold significant promise for improving safety in high-risk roadway segments like work zones, yet practical deployments are hindered by perspective distortion, complex geometry, occlusions, and costs. This study tackles these barriers by (i) integrating roadside camera and LiDAR sensors into a cosimulation environment to develop a scalable, cost-effective vehicle detection and localization framework, and (ii) employing a Kalman Filter-based late fusion strategy to enhance trajectory consistency and accuracy. In simulation, the fusion algorithm reduced longitudinal error by up to 70% compared to individual sensors while preserving lateral accuracy within 1-3 meters. Field validation in an active work zone, using LiDAR, a radar-camera rig, and RTK-GPS as ground truth, demonstrated that the fused trajectories closely match real vehicle paths, even when single-sensor data are intermittent or degraded. These results confirm that KF based sensor fusion can reliably compensate for individual sensor limitations, providing precise and robust vehicle tracking capabilities. Our approach thus offers a practical pathway to deploy infrastructure-enabled multi-sensor systems for proactive safety measures in complex traffic environments. Keywords: work zone, fusion, lidar, camera, localization, safety Saba, Khan, Ahmad, Cao, Rahman, Zhao, Huynh, and Ozguven 4 INTRODUCTION Work zone crashes do not necessarily impact only the vehicles and people directly involved; instead, they have cascading effects that cause operational delays for passing vehicles and project completion delays for work zone contractors. The Federal Motor Carrier Safety Administration (FMCSA) report indicates that commercial motor vehicles (CMVs) are involved in one-third of work zone fatal crashes, although they represent only 5% of all vehicular traffic (1). In addition, speed is a contributing factor in 26% of all fatal work zone crashes (2). According to Jiao (2022) (3), 13% of CMV drivers are fatigued when they are involved in crashes.

Figure 1: Convergence curves of the two proposed methods on a toy problem.

Hosted by The Alan Turing Institute, AI UK is a yearly event that brings together representatives from government, academia and industry to showcase data science and AI research and innovation in the UK. This year, the two-day conference featured talks, panel discussions, and hands-on workshops, and participants could attend in-person or remotely. One of the sessions focussed on an on-going case study in a London borough whereby the local council is using data and AI to help inform their decision making, and to improve what they do. Tariq set the scene by describing the borough of Camden, an area that not only houses institutions such as University College London and the Francis Crick Institute, and companies such as Google, but also some of the poorest communities in Europe. The council wants to tackle inequality and sees the use of data as one potential avenue.

Computer-aided diagnosis (CAD), a vibrant medical imaging research field, is expanding quickly. Because errors in medical diagnostic systems might lead to seriously misleading medical treatments, major efforts have been made in recent years to improve computer-aided diagnostics applications. The use of machine learning in computer-aided diagnosis is crucial. A simple equation may result in a false indication of items like organs. Therefore, learning from examples is a vital component of pattern recognition. Pattern recognition and machine learning in the biomedical area promise to increase the precision of disease detection and diagnosis. They also support the decision-making process's objectivity. Machine learning provides a practical method for creating elegant and autonomous algorithms to analyze high-dimensional and multimodal bio-medical data. This review article examines machine-learning algorithms for detecting diseases, including hepatitis, diabetes, liver disease, dengue fever, and heart disease. It draws attention to the collection of machine learning techniques and algorithms employed in studying conditions and the ensuing decision-making process.

The Winograd Schema (WS) challenge has been proposed as an alternative to the Turing Test as a test for machine intelligence. In this paper we'situate' the WS challenge in the data-information-knowledge continuum, suggesting in the process what a good WS is. Subsequently, we will argue that the WS is but a special case of a more general phenomenon in language understanding, namely the phenomenon of the'missing text'. In particular, we will argue that what we usually call thinking in the process of language understanding almost always involves discovering some missing text - text is rarely explicitly stated but is implicitly assumed as shared background knowledge. As such, we suggest extending the WS challenge to include other linguistic phenomena that also involve discovering the'missing text', such tests metonymy, quantifier scope, lexical disambiguation, and copredication, to name a few.

Bilevel optimization, the problem of minimizing a value function which involves the arg-minimum of another function, appears in many areas of machine learning. In a large scale setting where the number of samples is huge, it is crucial to develop stochastic methods, which only use a few samples at a time to progress. However, computing the gradient of the value function involves solving a linear system, which makes it difficult to derive unbiased stochastic estimates. To overcome this problem we introduce a novel framework, in which the solution of the inner problem, the solution of the linear system, and the main variable evolve at the same time. These directions are written as a sum, making it straightforward to derive unbiased estimates. The simplicity of our approach allows us to develop global variance reduction algorithms, where the dynamics of all variables is subject to variance reduction. We demonstrate that SABA, an adaptation of the celebrated SAGA algorithm in our framework, has $O(\frac1T)$ convergence rate, and that it achieves linear convergence under Polyak-Lojasciewicz assumption. This is the first stochastic algorithm for bilevel optimization that verifies either of these properties. Numerical experiments validate the usefulness of our method.

It's no longer shocking that human resources departments use artificial intelligence. In fact, according to Littler's 2018 Annual Employer Survey, 49 percent said they use AI and advanced data analytics for recruiting and hiring. So, where can HR leaders expect to see significant gains in how AI will support HR-driven uses cases? There are some caveats to consider with AI-infused human resources initiatives. For starters, companies should keep a close eye on how these AI tools perform as they risk inadvertently introducing bias, according to Armen Berjikly, head of AI at Ultimate Software.

DUBAI, UNITED ARAB EMIRATES – Yemen's Houthi forces shot down a U.S. surveillance drone in the capital, Sanaa, on Sunday, the Houthi-controlled state news agency SABA reported. The Houthi movement and its ally, former President Ali Abdullah Saleh, control much of northern Yemen, including Sanaa, and are battling a Saudi-led coalition that is trying to restore the internationally recognized government of President Abd-Rabbu Mansour Hadi. The United States backs the Saudi-led coalition by providing it with intelligence and weapons. "A military source said (Houthi) air defenses shot down a U.S. MQ-9 surveillance drone in Jader area in the Sanaa province," SABA reported. A photographer said the drone came down at around 11 am local time in a crowded area on the outskirts of the capital, but there were no reports of any casualties.