Gradient boosting has become a cornerstone of machine learning, enabling base learners such as decision trees to achieve exceptional predictive performance. While existing algorithms primarily handle scalar or Euclidean outputs, increasingly prevalent complex-structured data, such as distributions, networks, and manifold-valued outputs, present challenges for traditional methods. Such non-Euclidean data lack algebraic structures such as addition, subtraction, or scalar multiplication required by standard gradient boosting frameworks. To address these challenges, we introduce Fréchet geodesic boosting (FGBoost), a novel approach tailored for outputs residing in geodesic metric spaces. FGBoost leverages geodesics as proxies for residuals and constructs ensembles in a way that respects the intrinsic geometry of the output space. Through theoretical analysis, extensive simulations, and real-world applications, we demonstrate the strong performance and adaptability of FGBoost, showcasing its potential for modeling complex data.

Despite growing research on bias in large language models (LLMs), most work has focused on gender and race, with little attention to religious identity. This paper explores how religion is internally represented in LLMs and how it intersects with concepts of violence and geography. Using mechanistic interpretability and Sparse Autoencoders (SAEs) via the Neuronpedia API, we analyze latent feature activations across five models. We measure overlap between religion- and violence-related prompts and probe semantic patterns in activation contexts. While all five religions show comparable internal cohesion, Islam is more frequently linked to features associated with violent language. In contrast, geographic associations largely reflect real-world religious demographics, revealing how models embed both factual distributions and cultural stereotypes. These findings highlight the value of structural analysis in auditing not just outputs but also internal representations that shape model behavior.

Coral aquaculture for reef restoration requires accurate and continuous spawn counting for resource distribution and larval health monitoring, but current methods are labor-intensive and represent a critical bottleneck in the coral production pipeline. We propose the Coral Spawn and Larvae Imaging Camera System (CSLICS), which uses low cost modular cameras and object detectors trained using human-in-the-loop labeling approaches for automated spawn counting in larval rearing tanks. This paper details the system engineering, dataset collection, and computer vision techniques to detect, classify and count coral spawn. Experimental results from mass spawning events demonstrate an F1 score of 82.4\% for surface spawn detection at different embryogenesis stages, 65.3\% F1 score for sub-surface spawn detection, and a saving of 5,720 hours of labor per spawning event compared to manual sampling methods at the same frequency. Comparison of manual counts with CSLICS monitoring during a mass coral spawning event on the Great Barrier Reef demonstrates CSLICS' accurate measurement of fertilization success and sub-surface spawn counts. These findings enhance the coral aquaculture process and enable upscaling of coral reef restoration efforts to address climate change threats facing ecosystems like the Great Barrier Reef.

Accurate modeling of personalized cardiovascular dynamics is crucial for non-invasive monitoring and therapy planning. State-of-the-art physics-informed neural network (PINN) approaches employ deep, multi-branch architectures with adversarial or contrastive objectives to enforce partial differential equation constraints. While effective, these enhancements introduce significant training and implementation complexity, limiting scalability and practical deployment. We investigate physics-informed neural operator learning models as efficient supervisory signals for training simplified architectures through knowledge distillation. Our approach pre-trains a physics-informed DeepONet (PI-DeepONet) on high-fidelity cuffless blood pressure recordings to learn operator mappings from raw wearable waveforms to beat-to-beat pressure signals under embedded physics constraints. This pre-trained operator serves as a frozen supervisor in a lightweight knowledge-distillation pipeline, guiding streamlined base models that eliminate complex adversarial and contrastive learning components while maintaining performance. We characterize the role of physics-informed regularization in operator learning and demonstrate its effectiveness for supervisory guidance. Through extensive experiments, our operator-supervised approach achieves performance parity with complex baselines (correlation: 0.766 vs. 0.770, RMSE: 4.452 vs. 4.501), while dramatically reducing architectural complexity from eight critical hyperparameters to a single regularization coefficient and decreasing training overhead by 4%. Our results demonstrate that operator-based supervision effectively replaces intricate multi-component training strategies, offering a more scalable and interpretable approach to physiological modeling with reduced implementation burden.

Visual teach-and-repeat navigation enables robots to autonomously traverse previously demonstrated paths by comparing current sensory input with recorded trajectories. However, conventional frame-based cameras fundamentally limit system responsiveness: their fixed frame rates (typically 30-60 Hz) create inherent latency between environmental changes and control responses. Here we present the first event-camera-based visual teach-and-repeat system. To achieve this, we develop a frequency-domain cross-correlation framework that transforms the event stream matching problem into computationally efficient Fourier space multiplications, capable of exceeding 300Hz processing rates, an order of magnitude faster than frame-based approaches. By exploiting the binary nature of event frames and applying image compression techniques, we further enhance the computational speed of the cross-correlation process without sacrificing localization accuracy. Extensive experiments using a Prophesee EVK4 HD event camera mounted on an AgileX Scout Mini robot demonstrate successful autonomous navigation across 4000+ meters of indoor and outdoor trajectories. Our system achieves ATEs below 24 cm while maintaining consistent high-frequency control updates. Our evaluations show that our approach achieves substantially higher update rates compared to conventional frame-based systems, underscoring the practical viability of event-based perception for real-time robotic navigation.

Long-term trajectory anomaly detection is a challenging problem due to the diversity and complex spatiotemporal dependencies in trajectory data. Existing trajectory anomaly detection methods fail to simultaneously consider both the high-level intentions of agents as well as the low-level details of the agent's navigation when analysing an agent's trajectories. This limits their ability to capture the full diversity of normal trajectories. In this paper, we propose an unsupervised trajectory anomaly detection method named Intention-aware Hierarchical Diffusion model (IHiD), which detects anomalies through both high-level intent evaluation and low-level sub-trajectory analysis. Our approach leverages Inverse Q Learning as the high-level model to assess whether a selected subgoal aligns with an agent's intention based on predicted Q-values. Meanwhile, a diffusion model serves as the low-level model to generate sub-trajectories conditioned on subgoal information, with anomaly detection based on reconstruction error. By integrating both models, IHiD effectively utilises subgoal transition knowledge and is designed to capture the diverse distribution of normal trajectories. Our experiments show that the proposed method IHiD achieves up to 30.2% improvement in anomaly detection performance in terms of F1 score over state-of-the-art baselines.

Traditional Human-Swarm Interaction (HSI) methods often lack intuitive real-time adaptive interfaces, making decision making slower and increasing cognitive load while limiting command flexibility. To solve this, we present SwarmChat, a context-aware, multimodal interaction system powered by Large Language Models (LLMs). SwarmChat enables users to issue natural language commands to robotic swarms using multiple modalities, such as text, voice, or teleoperation. The system integrates four LLM-based modules: Context Generator, Intent Recognition, Task Planner, and Modality Selector. These modules collaboratively generate context from keywords, detect user intent, adapt commands based on real-time robot state, and suggest optimal communication modalities. Its three-layer architecture offers a dynamic interface with both fixed and customizable command options, supporting flexible control while optimizing cognitive effort. The preliminary evaluation also shows that the SwarmChat's LLM modules provide accurate context interpretation, relevant intent recognition, and effective command delivery, achieving high user satisfaction.

Large Language Models (LLMs) have enabled the emergence of autonomous agents capable of complex reasoning, planning, and interaction. However, coordinating such agents at scale remains a fundamental challenge, particularly in decentralized environments where communication lacks transparency and agent behavior cannot be shaped through centralized incentives. We propose a blockchain-based framework that enables transparent agent registration, verifiable task allocation, and dynamic reputation tracking through smart contracts. The core of our design lies in two mechanisms: a matching score-based task allocation protocol that evaluates agents by reputation, capability match, and workload; and a behavior-shaping incentive mechanism that adjusts agent behavior via feedback on performance and reward. Our implementation integrates GPT-4 agents with Solidity contracts and demonstrates, through 50-round simulations, strong task success rates, stable utility distribution, and emergent agent specialization. The results underscore the potential for trustworthy, incentive-compatible multi-agent coordination in open environments.

Trump drops bombshell Tylenol autism announcement as he vows to rip up'disgraceful' vaccine schedule in major medical shake-up Jimmy Kimmel Live! will return TOMORROW after host was canned over Charlie Kirk comments Tylenol maker responds to Trump's plans to link everyday drug to autism The six hidden messages in the texts between Charlie Kirk's'assassin' and his trans lover DECODED Top plastic surgeon reveals secrets behind Catherine Zeta-Jones' youthful appearance: 'This isn't what happens when we age' Common kitchen spice may reverse Alzheimer's disease, study suggests Barack and Michelle Obama arrive apart on Spielberg's yacht after ex-president's startling marriage confession Miley Cyrus shares the medicine that has kept her'grounded in a sober lifestyle' for 5 years Jimmy Kimmel steps in to'protect' Ivanka Trump from handsy comedian in resurfaced video Seven charities including Teenage Cancer Trust cut ties with Sarah Ferguson after leaked email showed her apologising to'supreme friend' Jeffrey Epstein Heather Locklear fans can't believe how amazing the Melrose Place vet looks at 63... 40 years after fame hit'Brazilian Josef Fritzl' makes chilling claim to police after'holding stepdaughter captive for 22 years' American'sexpert' deported from Indonesia after furious Muslim officials accused her of hosting KAMA SUTRA demonstrations Beloved actress who played loud, loving matriarch in 2002 romcom makes rare outing in LA...can you guess who? Face of man who tried to'murder Jeffrey Epstein' days before pedophile financier's mysterious suicide Candace Owens says she'll make Brigitte Macron submit to MEDICAL EXAM after'French first lady is a man' claim sparked lawsuit Blindfolded and awaiting death: Horrifying footage shows Hamas executing'Israeli collaborators' in Gaza streets as baying crowd screams'Allahu Akbar' All the'Biblical signs' pointing to the Rapture coming TOMORROW as believers spread fears the end is nigh New'loaded water' trend slashes cravings so you can lose weight... as expert reveals how to maximize benefits Clear and startling images of what appears to be a UFO were captured over Los Angeles, sparking fresh debate about what's flying over America's biggest cities. A pair of Los Angeles residents were on their balcony when they spotted a black, V-shaped craft covered in lights moving slowly over the city on August 28. The sighting went on for roughly 25 minutes, with the UFO flying south until the witnesses eventually lost sight of it around 11:38pm local time (2:38am ET). The pair was able to capture both pictures and clear videos with a cellphone camera, zooming in to see nine white lights along the UFO's hull.

After deflecting the US Department of Justice's attack on its illegal monopoly in online search, Google is facing another attempt to dismantle its internet empire in a trial focused on abusive tactics in digital advertising. The trial that opened Monday in an Alexandria, Virginia, federal court revolves around the harmful conduct that resulted in US district Judge Leonie Brinkema declaring parts of Google's digital advertising technology to be an illegal monopoly in April. The judge found that Google has been engaging in behavior that stifles competition to the detriment of online publishers that depend on the system for revenue. Google and the justice department will spend the next two weeks in court presenting evidence in a "remedy" trial that will culminate in Brinkema issuing a ruling on how to restore fair market conditions. If the justice department gets its way, Brinkema will order Google to sell parts of its ad technology - a proposal that the company's lawyers warned would "invite disruption and damage" to consumers and the internet's ecosystem.