Reinforcement Learning agent and a configurator that can modify some environmental parameters to improve the agent's performance.

ICINCO 2025 (22nd International Conference on Informatics in Control, Automation and Robotics) received 158 paper submissions from 42 countries. To evaluate each submission, a double-blind paper review was performed by the Program Committee. After a stringent selection process, 43 papers were published and presented as full papers, i.e. completed work (12 pages/25' oral presentation), 86 papers were accepted as short papers (51 as oral presentation). The organizing committee included the ICINCO Conference Chair: Dimitar Filev, Ford Research, United States, and the ICINCO 2025 Program Chairs: Giuseppina Carla Gini, Politecnico di Milano, Italy, and Radu-Emil Precup, Politehnica University of Timisoara, Romania. At the closing session, the conference acknowledged a few papers that were considered excellent in their class, presenting a "Best Paper Award", "Best Student Paper Award", "Best Poster Award", and "Best Industrial Paper Award" for the conference.

Reinforcement Learning agent and a configurator that can modify some environmental parameters to improve the agent's performance.

Officially named'Essential,' the torch was designed by Carlo Ratti and weighs only 2.5 pounds. Breakthroughs, discoveries, and DIY tips sent six days a week. Every Olympic Games has a torch. Every torch has a designer. For the 2026 Milano Cortina Olympic Games and Paralympic Games, that designer is MIT engineer and architect Carlo Ratti .

NBC announced that sportscaster and former professional tennis player Mary Carillo will step in for Savannah Guthrie to host the Winter Olympics in Italy.

The rapid progress of diffusion models highlights the growing need for detecting generated images. Previous research demonstrates that incorporating diffusion-based measurements, such as reconstruction error, can enhance the gener-alizability of detectors. However, ignoring the differing impacts of aleatoric and epistemic uncertainty on reconstruction error can undermine detection performance. Aleatoric uncertainty, arising from inherent data noise, creates ambiguity that impedes accurate detection of generated images. As it reflects random variations within the data (e.g., noise in natural textures), it does not help distinguish generated images. In contrast, epistemic uncertainty, which represents the model's lack of knowledge about unfamiliar patterns, supports detection. In this paper, we propose a novel framework, Diffusion Epistemic Uncertainty with Asymmetric Learning (DEUA), for detecting diffusion-generated images. W e introduce Diffusion Epistemic Uncertainty (DEU) estimation via the Laplace approximation to assess the proximity of data to the manifold of diffusion-generated samples. Additionally, an asymmetric loss function is introduced to train a balanced classifier with larger margins, further enhancing generalizability.

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Time awareness is a fundamental ability of omni large language models, especially for understanding long videos and answering complex questions. Previous approaches mainly target vision-language scenarios and focus on the explicit temporal grounding questions, such as identifying when a visual event occurs or determining what event happens at aspecific time. However, they often make insufficient use of the audio modality, and overlook implicit temporal grounding across modalities--for example, identifying what is visually present when a character speaks, or determining what is said when a visual event occurs--despite such cross-modal temporal relations being prevalent in real-world scenarios. In this paper, we propose ChronusOmni, an omni large language model designed to enhance temporal awareness for both explicit and implicit audiovisual temporal grounding. First, we interleave text-based timestamp tokens with visual and audio representations at each time unit, enabling unified temporal modeling across modalities. Second, to enforce correct temporal ordering and strengthen fine-grained temporal reasoning, we incorporate reinforcement learning with specially designed reward functions. Moreover, we construct ChronusAV, a temporally-accurate, modality-complete, and cross-modal-aligned dataset to support the training and evaluation on audiovisual temporal grounding task. Experimental results demonstrate that ChronusOmni achieves state-of-the-art performance on ChronusAV with more than 30% improvement and top results on most metrics upon other temporal grounding benchmarks. This highlights the strong temporal awareness of our model across modalities, while preserving general video and audio understanding capabilities.