Vehicle trajectory prediction serves as a critical enabler for autonomous navigation and intelligent transportation systems. While existing approaches predominantly focus on pattern extraction and vehicle-environment interaction modeling, they exhibit a fundamental limitation in addressing trajectory heterogeneity originating from human driving styles. This oversight constrains prediction reliability in complex real-world scenarios. To bridge this gap, we propose the Driving-StyleAdaptive (DSA) framework, which establishes the first systematic integration of heterogeneous driving behaviors into trajectory prediction models. Specifically, our framework employs a set of basis functions tailored to each driving style to approximate the trajectory patterns. By dynamically combining and adaptively adjusting the degree of these basis functions, DSA not only enhances prediction accuracy but also provides explanations insights into the prediction process. Extensive experiments on public real-world datasets demonstrate that the DSA framework outperforms state-of-the-art methods.

Jonathan Lee is a Romani activist from Wales, working at the European Roma Rights Centre. For the Romani families living in Nea Zoi, an informal neighbourhood near Aspropyrgos, Greece, the pre-dawn hum of surveillance drones has become a regular soundtrack to their lives. By daybreak, K-9 units and tactical police have blocked narrow dirt roads, police in riot gear have formed a perimeter around the neighbourhood, and armed officers are breaking through doors to makeshift homes, all under the banner of "public order". Since late 2025, this routine has repeated with terrifying regularity: at least 76 raids in six months, involving 473 officers, targeting 152 Romani communities across Greece. Documented by the European Roma Rights Centre as the most extensive anti-Roma police operation in decades, these actions are presented by Greek politicians as a tactical response to organised crime.

Modern neural recording techniques such as two-photon imaging or Neuropixel probes allow to acquire vast time-series datasets with responses of hundreds or thousands of neurons. Contrastive learning is a powerful self-supervised framework for learning representations of complex datasets. Existing applications for neural time series rely on generic data augmentations and do not exploit the multi-trial data structure inherent in many neural datasets. Here we present TRACE, a new contrastive learning framework that averages across different subsets of trials to generate positive pairs. TRACE allows to directly learn a two-dimensional embedding, combining ideas from contrastive learning and neighbor embeddings. We show that TRACE outperforms other methods, resolving fine response differences in simulated data. Further, using in vivo recordings, we show that the representations learned by TRACE capture both biologically relevant continuous variation, cell-type-related cluster structure, and can assist data quality control.

Dexterous grasp generation is a fundamental challenge in robotics, requiring both grasp stability and adaptability across diverse objects and tasks. Analytical methods ensure stable grasps but are inefficient and lack task adaptability, while generative approaches improve efficiency and task integration but generalize poorly to unseen objects and tasks due to data limitations. In this paper, we propose a transfer-based framework for dexterous grasp generation, leveraging a conditional diffusion model to transfer high-quality grasps from shape templates to novel objects within the same category. Specifically, we reformulate the grasp transfer problem as the generation of an object contact map, incorporating object shape similarity and task specifications into the diffusion process. To handle complex shape variations, we introduce a dual mapping mechanism, capturing intricate geometric relationship between shape templates and novel objects.

Every emotional moment from the Gilgo Beach killer's sentencing: Rex Heuermann's shocking first words... and the chilling exchange that silenced the room Don Trump Jr. says Ted Cruz is'lying through his teeth' as GOP infighting over Iran deal continues to spiral LIZ JONES: The cracks in Harry and Meghan's perfect facade have started to show. It's so obvious he's tiring of her tone-deaf approach... and I predict there's serious trouble in store All my friends are suddenly getting divorced. Mid-life wives share taboo sex confessions about why they really leave... including common position that made one hate her husband: JANA HOCKING Trump says'fools who think I haven't been tough enough on Iran' are'jealous or stupid' after signing widely-criticised deal that includes giving Tehran $300billion Taylor Swift's bottomless thirst for attention, her greed and sheer tackiness are now truly unbearable... this latest stunt has shown her true colors: MAUREEN CALLAHAN The Ring star Daveigh Chase's friends searched for her on LA's Skid Row in months before her shock death at 35 Horrific new videos blow Texas woman's mystery death wide open: Her agonizing'final gasp'... unthinkably vile corpse claims... and sick past of man who saw her last'I can still see you': Princess of Wales shares adorable moment with shy little girl at Royal Ascot Brooklyn Beckham is savaged by fans for yet another'classless' swipe at his estranged family as new DoorDash ad is branded a'giant PR mess' Sensational REAL reason Jelly Roll is divorcing Bunnie XO: Insiders reveal'preacher's wife' bombshell that's the talk of Nashville... truth about legendary rocker cuckolding rumor... and G-string mishap Watch horrifying drone video that follows woman's plunge to death after bungee team threw her from bridge without rope Kylie Jenner and Timothee Chalamet put on VERY affectionate display during NYC bike ride... as it's revealed how the relationship has'changed' the actor Luxury fashion tycoon beloved by the stars hangs her head in shame as she's indicted for allegedly exploiting her workers and stealing $50k from their wages NBA star's fiancee breaks her silence after friend, 26, mysteriously dropped dead at her luxury bachelorette party in St Barts Tragedy as 8-year-old dies during World Cup watch party with cops blaming'accidental drowning' Instead, I lost a stone and dropped a dress size in one MONTH with a meal plan that's not even a diet. It's packed with carbs and so simple - anyone can do it in time for summer Revealed: The popular UK pet foods that contain the most microplastics - so, is your dog or cat at risk? The popular UK pet foods that contain the most microplastics have been revealed in a new study.

We argue that the machine learning value chain is structurally unsustainable due to an economic data processing inequality: each state in the data cycle from inputs to model weights to synthetic outputs refines technical signal but strips economic equity from data generators. We show, by analyzing seventy-three public data deals, that the majority of value accrues to aggregators, with documented creator royalties rounding to zero and widespread opacity of deal terms. This is not just an economic welfare concern: as data and its derivatives become economic assets, the feedback loop that sustains current learning algorithms is at risk. We identify three structural faults--missing provenance, asymmetric bargaining power, and nondynamic pricing--as the operational machinery of this inequality. In our analysis, we trace these problems along the machine learning value chain and propose an Equitable Data-Value Exchange (EDVEX) Framework to enable a minimal market that benefits all participants. Finally, we outline research directions where our community can make concrete contributions to data deals and contextualize our position with related and orthogonal viewpoints.

Convolutional neural networks (CNNs) trained on object recognition achieve high task performance but continue to exhibit vulnerability under a range of visual perturbations and out-of-domain images, when compared with biological vision. Prior work has demonstrated that coupling a standard CNN with a front-end (VOneBlock) that mimics the primate primary visual cortex (V1) can improve overall model robustness. Expanding on this, we introduce Early Vision Networks (EVNets), a new class of hybrid CNNs that combine the VOneBlock with a novel SubcorticalBlock, whose architecture draws from computational models in neuroscience and is parameterized to maximize alignment with subcortical responses reported across multiple experimental studies. Without being optimized to do so, the assembly of the SubcorticalBlock with the VOneBlock improved V1 alignment across most standard V1 benchmarks, and better modeled extra-classical receptive field phenomena. In addition, EVNets exhibit stronger emergent shape bias and outperform the base CNN architecture by 9.3% on an aggregate benchmark of robustness evaluations, including adversarial perturbations, common corruptions, and domain shifts. Finally, we show that EVNets can be further improved when paired with a state-of-the-art data augmentation technique, surpassing the performance of the isolated data augmentation approach by 6.2% on our robustness benchmark. This result reveals complementary benefits between changes in architecture to better mimic biology and training-based machine learning approaches. 1

Every emotional moment from the Gilgo Beach killer's sentencing: Rex Heuermann's shocking first words... and the chilling exchange that silenced the room Don Trump Jr. says Ted Cruz is'lying through his teeth' as GOP infighting over Iran deal continues to spiral LIZ JONES: The cracks in Harry and Meghan's perfect facade have started to show. It's so obvious he's tiring of her tone-deaf approach... and I predict there's serious trouble in store All my friends are suddenly getting divorced. Mid-life wives share taboo sex confessions about why they really leave... including common position that made one hate her husband: JANA HOCKING Trump says'fools who think I haven't been tough enough on Iran' are'jealous or stupid' after signing widely-criticised deal that includes giving Tehran $300billion Taylor Swift's bottomless thirst for attention, her greed and sheer tackiness are now truly unbearable... this latest stunt has shown her true colors: MAUREEN CALLAHAN The Ring star Daveigh Chase's friends searched for her on LA's Skid Row in months before her shock death at 35 Horrific new videos blow Texas woman's mystery death wide open: Her agonizing'final gasp'... unthinkably vile corpse claims... and sick past of man who saw her last'I can still see you': Princess of Wales shares adorable moment with shy little girl at Royal Ascot Brooklyn Beckham is savaged by fans for yet another'classless' swipe at his estranged family as new DoorDash ad is branded a'giant PR mess' Sensational REAL reason Jelly Roll is divorcing Bunnie XO: Insiders reveal'preacher's wife' bombshell that's the talk of Nashville... truth about legendary rocker cuckolding rumor... and G-string mishap Watch horrifying drone video that follows woman's plunge to death after bungee team threw her from bridge without rope Kylie Jenner and Timothee Chalamet put on VERY affectionate display during NYC bike ride... as it's revealed how the relationship has'changed' the actor Luxury fashion tycoon beloved by the stars hangs her head in shame as she's indicted for allegedly exploiting her workers and stealing $50k from their wages NBA star's fiancee breaks her silence after friend, 26, mysteriously dropped dead at her luxury bachelorette party in St Barts Tragedy as 8-year-old dies during World Cup watch party with cops blaming'accidental drowning' Instead, I lost a stone and dropped a dress size in one MONTH with a meal plan that's not even a diet. It's packed with carbs and so simple - anyone can do it in time for summer Gen Z are scared of ringing the DOORBELL: One in three youngsters now text or call when they arrive at someone's door because they think it's less awkward It's something most people do without thinking twice about it. But a new survey has revealed how ringing the doorbell is leaving many Gen Z petrified.

In this paper, we examine the convergence landscape of multi-agent learning under uncertainty. Specifically, we analyze two stochastic models of regularized learning in continuous games--one in continuous and one in discrete time--with the aim of characterizing the long-run behavior of the induced sequence of play. In stark contrast to deterministic, full-information models of learning (or models with a vanishing learning rate), we show that the resulting dynamics do not converge in general. In lieu of this, we ask instead which actions are played more often in the long run, and by how much. We show that, in strongly monotone games, the dynamics of regularized learning may wander away from equilibrium infinitely often, but they always return to its vicinity in finite time (which we estimate), and their long-run distribution is sharply concentrated around a neighborhood thereof. We quantify the degree of this concentration, and we show that these favorable properties may all break down if the underlying game is not strongly monotone--underscoring in this way the limits of regularized learning in the presence of persistent randomness and uncertainty.

We study how training data contributes to the emergence of toxic behaviors in large language models. Most prior work on reducing model toxicity adopts reactive approaches, such as fine-tuning pre-trained (and potentially toxic) models to align them with human values. In contrast, we propose a proactive approach-- IF-GUIDE--that leverages influence functions to identify and suppress harmful tokens in the training data. To this end, we first show that standard influence functions are ineffective at discovering harmful training records. We then present a novel adaptation that measures token-level attributions from training data to model toxicity, along with techniques for selecting toxic training documents and a learning objective that can be integrated into both pre-training and fine-tuning. Moreover, IF-GUIDE does not rely on human-preference data, which is typically required by existing alignment methods. In our evaluation, we demonstrate that IF-GUIDE substantially reduces both explicit and implicit toxicity--by up to 10 compared to uncensored models, and up to 3 compared to baseline alignment methods such as DPO and RAD--across both pre-training and fine-tuning scenarios. IF-GUIDE is computationally efficient: a billion-parameter model is not necessary for computing influence scores; a million-parameter model--with 7.5 fewer parameters--can effectively serve as a proxy for identifying harmful data.