This paper addresses the movement and placement of mobile agents to establish a communication network in initially unknown environments. We cast the problem in a computational-geometric framework by relating the coverage problem and line-of-sight constraints to the Cooperative Guard Art Gallery Problem, and introduce its partially observable variant, the Partially Observable Cooperative Guard Art Gallery Problem (POCGAGP). We then present two algorithms that solve POCGAGP: CADENCE, a centralized planner that incrementally selects 270 degree corners at which to deploy agents, and DADENCE, a decentralized scheme that coordinates agents using local information and lightweight messaging. Both approaches operate under partial observability and target simultaneous coverage and connectivity. We evaluate the methods in simulation across 1,500 test cases of varied size and structure, demonstrating consistent success in forming connected networks while covering and exploring unknown space. These results highlight the value of geometric abstractions for communication-driven exploration and show that decentralized policies are competitive with centralized performance while retaining scalability.

Operational forecasting of the ionosphere remains a critical space weather challenge due to sparse observations, complex coupling across geospatial layers, and a growing need for timely, accurate predictions that support Global Navigation Satellite System (GNSS), communications, aviation safety, as well as satellite operations. As part of the 2025 NASA Heliolab, we present a curated, open-access dataset that integrates diverse ionospheric and heliospheric measurements into a coherent, machine learning-ready structure, designed specifically to support next-generation forecasting models and address gaps in current operational frameworks. Our workflow integrates a large selection of data sources comprising Solar Dynamic Observatory data, solar irradiance indices (F10.7), solar wind parameters (velocity and interplanetary magnetic field), geomagnetic activity indices (Kp, AE, SYM-H), and NASA JPL's Global Ionospheric Maps of Total Electron Content (GIM-TEC). We also implement geospatially sparse data such as the TEC derived from the World-Wide GNSS Receiver Network and crowdsourced Android smartphone measurements. This novel heterogeneous dataset is temporally and spatially aligned into a single, modular data structure that supports both physical and data-driven modeling. Leveraging this dataset, we train and benchmark several spatiotemporal machine learning architectures for forecasting vertical TEC under both quiet and geomagnetically active conditions. This work presents an extensive dataset and modeling pipeline that enables exploration of not only ionospheric dynamics but also broader Sun-Earth interactions, supporting both scientific inquiry and operational forecasting efforts.

Punctuation plays a vital role in structuring meaning, yet current models often struggle to restore it accurately in transcripts of spontaneous speech, especially in the presence of disfluencies such as false starts and backtracking. These limitations hinder the performance of downstream tasks like translation, text to speech, summarization, etc. where sentence boundaries are critical for preserving quality. In this work, we introduce Cadence, a generalist punctuation restoration model adapted from a pretrained large language model. Cadence is designed to handle both clean written text and highly spontaneous spoken transcripts. It surpasses the previous state of the art in performance while expanding support from 14 to all 22 Indian languages and English. We conduct a comprehensive analysis of model behavior across punctuation types and language families, identifying persistent challenges under domain shift and with rare punctuation marks. Our findings demonstrate the efficacy of utilizing pretrained language models for multilingual punctuation restoration and highlight Cadence practical value for low resource NLP pipelines at scale.

This position paper argues that token prediction is fundamentally misaligned with real creativity. While next-token models have enabled impressive advances in language generation, their architecture favours surface-level coherence over spontaneity, originality, and improvisational risk. In contrast, creative acts, particularly in live performance domains, require dynamic responsiveness and stylistic divergence, enabling humans to transcend pre-learned patterns in the moment. We use battle rap as a case study to expose the limitations of predictive systems, demonstrating that they cannot truly engage in adversarial or emotionally resonant exchanges. As a result, such models fail to support the interactive flow states where human creators "lose themselves in the moment." Rather than pursuing greater predictive accuracy, we argue that AI research should embrace dialogue as a form of co-negotiated creative agency. This shift calls for approaches that prioritize real-time interaction, rhythmic alignment, and adaptive generative control. By reframing creativity as an interactive process rather than a predictive output, we offer a vision for AI systems that are more expressive, responsive, and aligned with human creative practice.

Normally, these models work by inverting the process of natural diffusion, where they start with a distribution of random noise and progressively transform it into a structured data distribution resembling the training data. This transformation occurs in multiple steps, which incrementally denoise the noisy sample until it reaches the desired complexity and detail. In contrast to the normal diffusion models mentioned above (Song et al. 2022, 2024), which generate synthetic images by denoising random noise distributions without incorporating any specific guidance, our GenMDI model generates a synthetic image considering the previous image and the next image surrounding the generated image. This image generation process with guidance or condition is known as the conditional diffusion process, which is often used in the generation of video frames (Voleti et al. 2022). By conditioning the reverse diffusion process on the previous and subsequent images, GenMDI ensures that the generated image maintains continuity and reflects the dynamics of the surrounding images. This approach not only preserves the natural flow and consistency of MDI time-series magnetograms but also enhances our model's ability to accurately generate synthetic images. To our knowledge, this is the first time a conditional diffusion model has been used to improve the temporal resolution of MDI magnetograms. The remainder of this paper is organized as follows. Section 2 describes the data used in this study.

"The Agency" star Katherine Waterston admitted she finds AI generally "terrifying" for Hollywood and beyond. Lisa Kudrow fears an uncertain future as artificial intelligence becomes more and more prevalent in Hollywood. During a recent appearance on the "Armchair Expert with Dax Shepard" podcast, she discussed the recent film, "Here," directed by Robert Zemeckis and starring Tom Hanks and Robin Wright. The movie used AI to allow the stars to play the same characters all the way from their teen years to old age. "They shot it, and they could actually shoot the scene and then look at the playback of them as younger, and it's ready for them to see," Kudrow said.

Arm has confirmed that it will be offering its next-gen Arm compute platform, called Arm CSS for Client, at Android smartphones. Executives also mentioned that they could be used for PCs as well. The announcement follows an earlier report that indicated that Arm might expand its traditional business model. Arm has traditionally sold CPU designs, not silicon, to partners like Qualcomm. Those companies have the freedom to adjust Arm's designs -- depending upon their license agreement -- and then ask foundries like TSMC to actually manufacture the chip.

Fox News' Martha MacCallum has the latest on her new Fox Nation documentary on'The Story.' My father-in-law passed away last month, days away from his 99th birthday. He lived with us for 13 years. He was a great man, a World War II veteran who loved his wife and raised three children. As his vascular dementia worsened – unlike Alzheimer's, his long-term memory remained intact almost until the end – my wife would set him up with a familiar film. "The Godfather" played most frequently, followed by "Patton."

People with Parkinson's Disease experience gait impairments that significantly impact their quality of life. Visual, auditory, and tactile cues can alleviate gait impairments, but they can become less effective due to the progressive nature of the disease and changes in people's motor capability. In this study, we develop a human-in-the-loop (HIL) framework that monitors two key gait parameters, stride length and cadence, and continuously learns a person-specific model of how the parameters change in response to the feedback. The model is then used in an optimization algorithm to improve the gait parameters. This feasibility study examines whether auditory cues can be used to influence stride length in people without gait impairments. The results demonstrate the benefits of the HIL framework in maintaining people's stride length in the presence of a secondary task.

Target tracking plays a crucial role in real-world scenarios, particularly in drug-trafficking interdiction, where the knowledge of an adversarial target's location is often limited. Improving autonomous tracking systems will enable unmanned aerial, surface, and underwater vehicles to better assist in interdicting smugglers that use manned surface, semi-submersible, and aerial vessels. As unmanned drones proliferate, accurate autonomous target estimation is even more crucial for security and safety. This paper presents Constrained Agent-based Diffusion for Enhanced Multi-Agent Tracking (CADENCE), an approach aimed at generating comprehensive predictions of adversary locations by leveraging past sparse state information. To assess the effectiveness of this approach, we evaluate predictions on single-target and multi-target pursuit environments, employing Monte-Carlo sampling of the diffusion model to estimate the probability associated with each generated trajectory. We propose a novel cross-attention based diffusion model that utilizes constraint-based sampling to generate multimodal track hypotheses. Our single-target model surpasses the performance of all baseline methods on Average Displacement Error (ADE) for predictions across all time horizons.