arXiv:2602.06157v1 Announce Type: new Abstract: DNA storage has matured from concept to practical stage, yet its integration with neural compression pipelines remains inefficient. Early DNA encoders applied redundancy-heavy constraint layers atop raw binary data – workable but primitive. Recent neural codecs compress data into learned latent representations with rich statistical structure, yet still convert these latents to DNA via naive binary-to-quaternary transcoding, discarding the entropy model’s optimization. This mismatch undermines compression efficiency and complicates the encoding stack. A […]

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arXiv:2602.13297v1 Announce Type: new Abstract: High-resolution range profiles (HRRPs) enable fast onboard processing for radar automatic target recognition, but their strong sensitivity to acquisition conditions limits robustness across operational scenarios. Conditional HRRP generation can mitigate this issue, yet prior studies are constrained by small, highly specific datasets. We study HRRP synthesis on a largescale maritime database representative of coastal surveillance variability. Our analysis indicates that the fundamental scenario drivers are geometric: ship dimensions and the desired aspect angle. […]

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arXiv:2602.03979v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) on reasoning benchmarks via reinforcement learning requires a specific reward function, often binary, for each benchmark. This comes with two potential limitations: the need to design the reward, and the potentially sparse nature of binary rewards. Here, we systematically investigate rewards derived from the probability or log-probability of emitting the reference answer (or any other prompt continuation present in the data), which have the advantage of not relying […]

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This study addresses the challenge of accurately identifying multi-task contention types in high-dimensional system environments and proposes a unified contention classification framework that integrates representation transformation, structural modeling, and a task decoupling mechanism. The method first constructs system state representations from high-dimensional metric sequences, applies nonlinear transformations to extract cross-dimensional dynamic features, and integrates multiple source information such as resource utilization, scheduling behavior, and task load variations within a shared representation space. It then introduces a graph-based modeling […]

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Both Google and Apple are cramming new AI features into their phones and other devices, and neither company has offered clear ways to control which apps those AI systems can access. Recent issues around WhatsApp on both Android and iPhone demonstrate how these interactions can go sideways, risking revealing chat conversations beyond what you intend. Users deserve better controls and clearer documentation around what these AI features can access. After diving into how Google Gemini and Apple Intelligence […]

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Author(s): Mandar Karhade, MD. PhD. Originally published on Towards AI. When an AI model does in minutes what human researchers couldn’t do in decades, the market doesn’t just notice: it panics. Here is the nuclear option nobody in Silicon Valley wanted to talk about. For years, the cybersecurity industry has been a high stakes gambling ring built on a house of cards. You pay millions for “endpoint protection” and “zero trust” wrappers that essentially act as expensive digital […]

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