On April 8, 2026, Pulitzer Prize-winning journalist John Carreyrou published a major investigation in the New York Times arguing that British cryptographer Adam Back is Satoshi Nakamoto, the pseudonymous creator of Bitcoin. The piece ran across multiple formats, a 7,000-word feature, a condensed summary, and an accompanying video, and landed with the weight the Times typically commands. It also landed with significant skepticism from within the crypto community, where Adam Back has long been considered an important figure […]

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arXiv:2603.15759v1 Announce Type: new Abstract: Simulation-to-real transfer remains a central challenge in robotics, as mismatches between simulated and real-world dynamics often lead to failures. While reinforcement learning offers a principled mechanism for adaptation, existing sim-to-real finetuning methods struggle with exploration and long-horizon credit assignment in the low-data regimes typical of real-world robotics. We introduce Simulation Distillation (SimDist), a sim-to-real framework that distills structural priors from a simulator into a latent world model and enables rapid real-world adaptation via […]

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A Digital Twin (DT) may protect information that is considered private to its associated physical system. For a mobile device, this may include its mobility profile, recent location(s), and experienced channel conditions. Online schedulers, however, typically use this type of information to perform tasks such as shared bandwidth and channel time slot assignments. In this paper, we consider three transmission scheduling problems with energy constraints, where such information is needed, and yet must remain private: minimizing total transmission […]

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The SARS-CoV-2 RNA pseudoknot is a promising target for antiviral intervention, as it regulates the efficiency of $-$1 programmed ribosomal frameshifting ($-$1 PRF), a mechanism that is essential for viral protein synthesis. The pseudoknot represents a viral RNA sequence composed of helical stems that adopts two long-lived topologies, threaded and unthreaded. Ligand-induced distortion of this fold is thought to underlie the susceptibility of $-$1 PRF to small-molecule inhibitors. Resolving these distortions from unbiased molecular dynamics (MD) requires collective […]

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arXiv:2602.22229v1 Announce Type: new Abstract: Fully Homomorphic Encryption (FHE) enables computation directly on encrypted data but incurs massive computational and memory overheads, often exceeding plaintext execution by several orders of magnitude. While custom ASIC accelerators can mitigate these costs, their long time-to-market and the rapid evolution of FHE algorithms threaten their long-term relevance. GPUs, by contrast, offer scalability, programmability, and widespread availability, making them an attractive platform for FHE. However, modern GPUs are increasingly specialized for machine learning […]

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arXiv:2501.04903v4 Announce Type: replace Abstract: There is a widespread and longstanding belief that machine learning models are biased towards the majority class when learning from imbalanced binary response data, leading them to neglect or ignore the minority class. Motivated by a recent simulation study that found that decision trees can be biased towards the minority class, our paper aims to reconcile the conflict between that study and other published works. First, we critically evaluate past literature on this […]

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arXiv:2604.07539v1 Announce Type: new Abstract: We present a constructive proof that a single C program, the emph{Vulnerability Factory}, admits a countably infinite set of distinct, independently CVE-assignable software vulnerabilities. We formalise the argument using elementary set theory, verify it against MITRE’s CVE Numbering Authority counting rules, sketch a model-checking analysis that corroborates unbounded vulnerability generation, and provide a Turing-machine characterisation that situates the result within classical computability theory. We then contextualise this result within the long-running debate on […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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