arXiv:2601.00151v1 Announce Type: new Abstract: We study reinforcement learning methods with linear function approximation under non-Markov state and cost processes. We first consider the policy evaluation method and show that the algorithm converges under suitable ergodicity conditions on the underlying non-Markov processes. Furthermore, we show that the limit corresponds to the fixed point of a joint operator composed of an orthogonal projection and the Bellman operator of an auxiliary emph{Markov} decision process. For Q-learning with linear function approximation, […]

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arXiv:2601.02566v1 Announce Type: new Abstract: Image manipulation localization is a critical research task, given that forged images may have a significant societal impact of various aspects. Such image manipulations can be produced using traditional image editing tools (known as “shallowfakes”) or advanced artificial intelligence techniques (“deepfakes”). While numerous studies have focused on image manipulation localization on either shallowfake images or deepfake videos, few approaches address both cases. In this paper, we explore the feasibility of using a deep […]

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We submitted our rebuttal to Brain and received a prompt reply from the Editor-In-Chief, Dr. Kullman himself, offering us an opportunity to revise – with the main criticism that our letter contained unfounded insinuations and allegations. We tried to interpret his message as best we could and respond accordingly. To most readers it was pretty clear what he wrote and the message he intended to convey. Nevertheless, in our revision, we stayed much closer to the words of […]

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arXiv:2601.03386v1 Announce Type: new Abstract: Unmanned aerial vehicle (UAV) with slung load system is a classic air transportation system. In practical applications, the suspension point of the slung load does not always align with the center of mass (CoM) of the UAV due to mission requirements or mechanical interference. This offset creates coupling in the system’s nonlinear dynamics which leads to a complicated motion control problem. In existing research, modeling of the system are performed about the UAV’s […]

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In this blog post, we will be exploring, describing, and dissecting the first phase of the collaboration between Runtime Verification and Serokell. The collaboration involved work on making optimizations to K, a rewrite-based executable semantic framework in which programming languages, type systems, and formal analysis tools can be defined with the help of configurations and rewrite rules. The blog post will contain: the details and the results of the discovery phase; our recommendations (introduced in the Findings section); […]

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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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There are many obvious things that we humans do to a much larger degree than other animals. We construct great civilizations, we create advanced technology, we use complex language, we make art and tell stories. How do our unique capabilities guide us in figuring out how our brains are different from those of other animals, if they are? To me, the most revealing feature of human intelligence is that it is primarily societal, rather than individual. Most of […]

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Traditional kernel fuzzers rely on coarse-grained coverage metrics that cannot reflect complex microarchitectural behaviors. We present a hardware-assisted fuzzing framework that leverages branch buffer telemetry from modern CPUs (LBR, BTB sampling) to refine fuzzing feedback. A model-based inference algorithm aggregates branch-data patterns to estimate microarchitectural novelty and guides seed prioritization. Experiments on Intel Ice Lake and AMD Zen 3 systems demonstrate 27% improvement in unique path coverage, with 11 newly identified concurrency bugs across filesystem and scheduler subsystems. […]

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Graph Prompt Feature (GPF) learning has been widely used in adapting pre-trained GNN model on the downstream task. GPFs first introduce some prompt atoms and then learns the optimal prompt vector for each graph node using the linear combination of prompt atoms. However, existing GPFs generally conduct prompting over node’s all feature dimensions which is obviously redundant and also be sensitive to node feature noise. To overcome this issue, for the first time, this paper proposes learning sparse […]

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arXiv:2601.00212v1 Announce Type: new Abstract: Image-level domain alignment is the de facto approach for unsupervised domain adaptation, where unpaired image translation is used to minimize the domain gap. Prior studies mainly focus on the domain shift between the source and target domains, whereas the intra-domain variability remains under-explored. To address the latter, an effective strategy is to diversify the styles of the synthetic target domain data during image translation. However, previous methods typically require intra-domain variations to be […]

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