Ensuring equitable treatment (fairness) across protected attributes (such as gender or ethnicity) is a critical issue in machine learning. Most existing literature focuses on binary classification, but achieving fairness in regression tasks-such as insurance pricing or hiring score assessments-is equally important. Moreover, anti-discrimination laws also apply to continuous attributes, such as age, for which many existing methods are not applicable. In practice, multiple protected attributes can exist simultaneously; however, methods targeting fairness across several attributes often overlook so-called "fairness gerrymandering", thereby ignoring disparities among intersectional subgroups (e.g., African-American women or Hispanic men). In this paper, we propose a distance covariance regularisation framework that mitigates the association between model predictions and protected attributes, in line with the fairness definition of demographic parity, and that captures both linear and nonlinear dependencies. To enhance applicability in the presence of multiple protected attributes, we extend our framework by incorporating two multivariate dependence measures based on distance covariance: the previously proposed joint distance covariance (JdCov) and our novel concatenated distance covariance (CCdCov), which effectively address fairness gerrymandering in both regression and classification tasks involving protected attributes of various types. We discuss and illustrate how to calibrate regularisation strength, including a method based on Jensen-Shannon divergence, which quantifies dissimilarities in prediction distributions across groups. We apply our framework to the COMPAS recidivism dataset and a large motor insurance claims dataset.

Human Activity Recognition (HAR) has seen significant advancements with the adoption of deep learning techniques, yet challenges remain in terms of data requirements, reliability and robustness. This paper explores a novel application of Ensemble Distribution Distillation (EDD) within a self-supervised learning framework for HAR aimed at overcoming these challenges. By leveraging unlabeled data and a partially supervised training strategy, our approach yields an increase in predictive accuracy, robust estimates of uncertainty, and substantial increases in robustness against adversarial perturbation; thereby significantly improving reliability in real-world scenarios without increasing computational complexity at inference. We demonstrate this with an evaluation on several publicly available datasets. The contributions of this work include the development of a self-supervised EDD framework, an innovative data augmentation technique designed for HAR, and empirical validation of the proposed method's effectiveness in increasing robustness and reliability.

Bio-inspired design is often used in autonomous UAV navigation due to the capacity of biological systems for flight and obstacle avoidance despite limited sensory and computational capabilities. In particular, honeybees mainly use the sensory input of optic flow, the apparent motion of objects in their visual field, to navigate cluttered environments. In our work, we train a Reinforcement Learning agent to navigate a tunnel with obstacles using only optic flow as sensory input. We inspect the attention patterns of trained agents to determine the regions of optic flow on which they primarily base their motor decisions. We find that agents trained in this way pay most attention to regions of discontinuity in optic flow, as well as regions with large optic flow magnitude. The trained agents appear to navigate a cluttered tunnel by avoiding the obstacles that produce large optic flow, while maintaining a centered position in their environment, which resembles the behavior seen in flying insects. This pattern persists across independently trained agents, which suggests that this could be a good strategy for developing a simple explicit control law for physical UAVs.

Honey bee colonies are essential for global food security and ecosystem stability, yet they face escalating threats from pests, diseases, and environmental stressors. Traditional hive inspections are labor-intensive and disruptive, while cloud-based monitoring solutions remain impractical for remote or resource-limited apiaries. Recent advances in Internet of Things (IoT) and Tiny Machine Learning (TinyML) enable low-power, real-time monitoring directly on edge devices, offering scalable and non-invasive alternatives. This survey synthesizes current innovations at the intersection of TinyML and apiculture, organized around four key functional areas: monitoring hive conditions, recognizing bee behaviors, detecting pests and diseases, and forecasting swarming events. We further examine supporting resources, including publicly available datasets, lightweight model architectures optimized for embedded deployment, and benchmarking strategies tailored to field constraints. Critical limitations such as data scarcity, generalization challenges, and deployment barriers in off-grid environments are highlighted, alongside emerging opportunities in ultra-efficient inference pipelines, adaptive edge learning, and dataset standardization. By consolidating research and engineering practices, this work provides a foundation for scalable, AI-driven, and ecologically informed monitoring systems to support sustainable pollinator management.

This paper examines the generalization capacity of two state-of-the-art classification and similarity learning models in reliably identifying users based on their motions in various Extended Reality (XR) applications. We developed a novel dataset containing a wide range of motion data from 49 users in five different XR applications: four XR games with distinct tasks and action patterns, and an additional social XR application with no predefined task sets. The dataset is used to evaluate the performance and, in particular, the generalization capacity of the two models across applications. Our results indicate that while the models can accurately identify individuals within the same application, their ability to identify users across different XR applications remains limited. Overall, our results provide insight into current models generalization capabilities and suitability as biometric methods for user verification and identification. The results also serve as a much-needed risk assessment of hazardous and unwanted user identification in XR and Metaverse applications. Our cross-application XR motion dataset and code are made available to the public to encourage similar research on the generalization of motion-based user identification in typical Metaverse application use cases.

Video shows conservative activist Charlie Kirk speaking to a crowd of hundreds on the campus of Utah Valley University on Wednesday. Then a single shot rang out, and students fled in every direction. The 31-year-old influencer and Trump ally was rushed to hospital but pronounced dead later. 'We love you, you will always be with us', says father of Minneapolis shooting victim Fletcher Merkel, 8, was one of two children killed in Wednesday's shooting at Annunciation Catholic School in Minneapolis. The Garnet wildfire in Fresno County has scorched nearly 14,000 acres (5,665 hectares) and remains uncontained.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Masslie Arias, of DoorDash, prepares to load a delivery package on a hovering drone on July 31 in Frisco, Texas. This is read by an automated voice. Please report any issues or inconsistencies here . Food delivery app DoorDash is setting its sights on a new destination to test out flying drone deliveries: San Francisco.

Into the two big foreign policy arenas sucking up much of the Trump administration's time and effort come two major challenges in less than 24 hours. Israel's air raid on the offices of Hamas in Doha and a Russian drone incursion deep into Polish airspace represent two massive headaches for the White House. After all, these are conflicts - Ukraine and Gaza - US President Donald Trump said he would deal with swiftly and decisively. In each case, a leader he sees as a natural, if problematic ally - Russian President Vladimir Putin and Israeli Prime Minister Benjamin Netanyahu - has thrown a massive spanner in the wheels of White House peace-making. The Doha raid came just two days after the Trump administration delivered its latest proposals to end the war in Gaza.

Russia's drone incursion into Poland is a test for Nato Wednesday morning's incursion of Russian drones into Polish airspace led to jets being scrambled, an emergency government meeting being called - and concerns that Europe and Nato's resolve against Moscow may not be up to the test. Poland's Prime Minister Donald Tusk said Polish airspace was violated 19 times and at least three drones were shot down by Warsaw's jets, aided by Dutch F-35s and an Italian early warning aircraft. Russia has pushed back against accusations that the incursion was deliberate - though it also stopped short of denying its drones had trespassed sovereign Polish airspace. No objects on Polish territory were planned to be targeted, Moscow said. But European officials have forcefully batted off suggestions the act may have been unintentional. There is no evidence whatsoever that this amount of drones flew over this route over... Polish territory by accident, Germany's Defence Minister Boris Pistorius said, while his Italian counterpart Guido Crosetto called the overnight events in Poland a deliberate attack with the double aim of provoking and testing.

Orbital missions will unlock the next phase of Starship's development, providing better data on the performance of the spacecraft's heat shield and allowing for tests of in-orbit refueling, which will be essential for missions to Mars. Save this storyIt has been two weeks since SpaceX's last Starship test flight, and engineers have diagnosed issues with its heat shield, identified improvements, and developed a preliminary plan for the next time the ship heads into space. Bill Gerstenmaier, a SpaceX executive in charge of build and flight reliability, presented the findings Monday at the American Astronautical Society's Glenn Space Technology Symposium in Cleveland. The rocket lifted off on August 26 from SpaceX's launch pad in Starbase, Texas, just north of the US-Mexico border. It was the 10th full-scale test flight of SpaceX's Super Heavy booster and Starship upper stage, combining to form the world's largest rocket. There were a couple of overarching objectives on the August 26 test flight.