We observe that our method outperforms the baseline methods in a statistically significant way. We consider four state-of-the-art video classification models, representing diverse methodologies of learning from videos, i.e., C3D [1], SlowFast [2], TPN [3] and I3D [4], as our black-box victim models to perform adversarial attack. The C3D model applies 3D convolution to learn spatio-temporal features from videos. SlowFast uses a two-pathway architecture where the slow pathway operates at a low frame rate to capture spatial semantics and the fast pathway operates at a high frame rate to capture motion at fine temporal resolution. I3D proposes the Inflated 3DConvNet(I3D) with Inflated 2D filters and pooling kernels of traditional 2DCNNs.

We propose a novel reduction-to-binary (R2B) approach that enforces demographic parity for multiclass classification with non-binary sensitive attributes via a reduction to a sequence of binary debiasing tasks. We prove that R2B satisfies optimality and bias guarantees and demonstrate empirically that it can lead to an improvement over two baselines: (1) treating multiclass problems as multi-label by debiasing labels independently and (2) transforming the features instead of the labels. Surprisingly, we also demonstrate that independent label debiasing yields competitive results in most (but not all) settings.

We can't let them hide behind their models From Gaza to Iran, the pattern is the same: precision weapons, chosen blindness, and dead children. There is an Israeli military strategy called the "fog procedure". First used during the second intifada, it's an unofficial rule that requires soldiers guarding military posts in conditions of low visibility to shoot bursts of gunfire into the darkness, on the theory that an invisible threat might be lurking. It's violence licensed by blindness. Shoot into the darkness and call it deterrence. With the dawn of AI warfare, that same logic of chosen blindness has been refined, systematized, and handed off to a machine.

Using targeted attack strategy allows us to include the randomness of the target label sampling. We choose Jester dataset as it generally takes few queries to attack Jester dataset, thussavingtesting time. GEO-TRAP can employ different kinds of geometric transformations in theTRANS-WARP function. Thisisdemonstrated bythefactthatGEO-TRAP'sgradients generally have larger cosine similarity with the ground truth gradients. Wedenote the probability score associated with this label aspy(x).

This paper proposes a competitive and computationally efficient approach to probabilistic rainfall nowcasting. A video projector (V-JEPA Vision Transformer) associated to a lightweight probabilistic head is attached to a pre-trained satellite vision encoder (DINOv3-SAT493M) to map encoder tokens into a discrete empirical CDF (eCDF) over 4-hour accumulated rainfall. The projector-head is optimized end-to-end over the Ranked Probability Score (RPS). As an alternative, 3D-UNET baselines trained with an aggregate Rank Probability Score and a per-pixel Gamma-Hurdle objective are used. On the Weather4Cast 2025 benchmark, the proposed method achieved a promising performance, with a CRPS of 3.5102, which represents $\approx$ 26% in effectiveness gain against the best 3D-UNET.

While Vision-Language Models (VLMs) are set to transform robotic navigation, existing methods often underutilize their reasoning capabilities. To unlock the full potential of VLMs in robotics, we shift their role from passive observers to active strategists in the navigation process. Our framework outsources high-level planning to a VLM, which leverages its contextual understanding to guide a frontier-based exploration agent. This intelligent guidance is achieved through a trio of techniques: structured chain-of-thought prompting that elicits logical, step-by-step reasoning; dynamic inclusion of the agent's recent action history to prevent getting stuck in loops; and a novel capability that enables the VLM to interpret top-down obstacle maps alongside first-person views, thereby enhancing spatial awareness. When tested on challenging benchmarks like HM3D, Gibson, and MP3D, this method produces exceptionally direct and logical trajectories, marking a substantial improvement in navigation efficiency over existing approaches and charting a path toward more capable embodied agents.

This paper demonstrates the feasibility of trajectory learning for ensemble forecasts by employing the continuous ranked probability score (CRPS) as a loss function. Using the two-scale Lorenz '96 system as a case study, we develop and train both additive and multiplicative stochastic parametrizations to generate ensemble predictions. Results indicate that CRPS-based trajectory learning produces parametrizations that are both accurate and sharp. The resulting parametrizations are straightforward to calibrate and outperform derivative-fitting-based parametrizations in short-term forecasts. This approach is particularly promising for data assimilation applications due to its accuracy over short lead times.

Learning on temporal graphs has become a central topic in graph representation learning, with numerous benchmarks indicating the strong performance of state-of-the-art models. However, recent work has raised concerns about the reliability of benchmark results, noting issues with commonly used evaluation protocols and the surprising competitiveness of simple heuristics. This contrast raises the question of which properties of the underlying graphs temporal graph learning models actually use to form their predictions. We address this by systematically evaluating seven models on their ability to capture eight fundamental attributes related to the link structure of temporal graphs. These include structural characteristics such as density, temporal patterns such as recency, and edge formation mechanisms such as homophily. Using both synthetic and real-world datasets, we analyze how well models learn these attributes. Our findings reveal a mixed picture: models capture some attributes well but fail to reproduce others. With this, we expose important limitations. Overall, we believe that our results provide practical insights for the application of temporal graph learning models, and motivate more interpretability-driven evaluations in temporal graph learning research.