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Accident anticipation aims to predict impending collisions from dashcam videos and trigger early alerts. Existing methods rely on binary supervision with manually annotated "anomaly onset" frames, which are subjective and inconsistent, leading to inaccurate risk estimation. In contrast, we propose RiskProp, a novel collision-anchored self-supervised risk propagation paradigm for early accident anticipation, which removes the need for anomaly onset annotations and leverages only the reliably annotated collision frame. RiskProp models temporal risk evolution through two observation-driven losses: first, since future frames contain more definitive evidence of an impending accident, we introduce a future-frame regularization loss that uses the model's next-frame prediction as a soft target to supervise the current frame, enabling backward propagation of risk signals; second, inspired by the empirical trend of rising risk before accidents, we design an adaptive monotonic constraint to encourage a non-decreasing progression over time. Experiments on CAP and Nexar demonstrate that RiskProp achieves state-of-the-art performance and produces smoother, more discriminative risk curves, improv

Scientific publishing systematically filters out negative results. We argue that this long-standing asymmetry has become an urgent problem in the era of large language models, which inherit the positive bias of the literature they are trained on, face an impending shortage of high-quality training data, and are increasingly deployed as both research tools and peer reviewers. We analyze three ways in which LLMs have changed the value of failure data and show that the systematic absence of such data degrades their utility as research tools, training data consumers, and peer reviewers alike. We outline experimental protocols to validate these claims and discuss the structural conditions under which a failure-inclusive publishing culture could emerge.

We construct early-warning signals for impending critical transitions in non-Markovian systems. We analyze stochastic forcings such as fractional Brownian motion, fractional Ornstein-Uhlenbeck processes and red noise in fast-slow systems exhibiting such transitions. We show that the effectiveness of indicators such as autocovariance, autocorrelation, and spectral density depends on several properties of the underlying system. In particular, we compare the influence of the Hurst index and the bifurcation type. We prove that the rotatory dynamics associated with a Hopf bifurcation substantially alters the scaling laws of these observables. Finally, we provide practical guidelines for implementing these signals and validate them on both theoretical and applied models.

The early prediction of tipping points, distinguished by sudden and catastrophic shifts from stable states, poses a challenging task that would enable us to assess the impending threat across natural and engineered systems. This threat becomes particularly acute in low-fluctuation environments, where tipping occurs through saddle-node bifurcation without prior warning in noise dynamics. In this study, we investigate the tipping point dynamics of avalanche catastrophes in low-fluctuation domain, employing model system like the zero temperature random field Ising model and thermally deposited cobalt films. As the system approaches the tipping point, avalanche activity reveals pronounced critical behaviour, including critical slowing down, variance enhancement, and a growing spatial correlation length--hallmarks that may serve as early warning signals of impending collapse. Crucially, we demonstrate that increasing disorder in the system reduces its vulnerability to catastrophic failure. In highly disorder regimes, these early warning signals emerge well before the transition, thereby providing a large margin for anticipation and mitigation. This key finding suggests a protective role

Tangential interactions between particles play a central role in suspension rheology. We show theoretically that these interactions, often attributed to contact friction, are a direct consequence of fluid flows between rough particles in relative motion. We find that small surface asperities generically lead to localized hydrodynamic sliding forces and torques that can exceed their smooth counterparts by orders of magnitude. A fully analytic thin-film theory shows that these forces grow inversely with the surface separation, significantly more singular than the logarithmic scaling for smooth particles. The impending singularity tightly constrains the particles' rotation with their translation, recovering a crucial ingredient in dense suspension rheology. Despite their purely hydrodynamic origin, these features resemble several aspects of dry rolling and sliding friction.

We propose a design space for data melodification, where standard visualization idioms and fundamental data characteristics map to rhetorical devices of music for a more affective experience of data. Traditional data sonification transforms data into sound by mapping it to different parameters such as pitch, volume, and duration. Often and regrettably, this mapping leaves behind melody, harmony, rhythm and other musical devices that compose the centuries-long persuasive and expressive power of music. What results is the occasional, unintentional sense of tinnitus and horror film-like impending doom caused by a disconnect between the semantics of data and sound. Through this work we ask, can the aestheticization of sonification through (classical) music theory make data simultaneously accessible, meaningful, and pleasing to one's ears?

The impending discovery and monitoring of hundreds of new gravitationally lensed quasars and supernovae from upcoming ground and space based large area surveys such as LSST, \textit{Euclid}, and \textit{Roman} necessitates the development of improved numerical methods for studying gravitational microlensing. We present in this work the fastest microlensing map generation code currently publicly available. We utilize graphics processing units to take advantage of the inherent parallelizable nature of creating magnification maps, in addition to using 1) the fast multipole method to reduce the runtime dependence on the number of microlenses and 2) inverse polygon mapping to reduce the number of rays required. The code is available at https://github.com/weisluke/microlensing/.

Patients with traumatic brain injury (TBI) often experience pathological increases in intracranial pressure (ICP), leading to intracranial hypertension (tIH), a common and serious complication. Early warning of an impending rise in ICP could potentially improve patient outcomes by enabling preemptive clinical intervention. However, the limited availability of patient data poses a challenge in developing reliable prediction models. In this study, we aim to determine whether foundation models, which leverage transfer learning, may offer a promising solution.

In this paper, I examine questions surrounding AI neutrality through the prism of existing literature and scholarship about mediation and media pluralism. Such traditions, I argue, provide a valuable theoretical framework for how we should approach the (likely) impending era of AI mediation. In particular, I suggest examining further the notion of algorithmic pluralism. Contrasting this notion to the dominant idea of algorithmic transparency, I seek to describe what algorithmic pluralism may be, and present both its opportunities and challenges. Implemented thoughtfully and responsibly, I argue, Algorithmic or AI pluralism has the potential to sustain the diversity, multiplicity, and inclusiveness that are so vital to democracy.