Unplanned engine failures in helicopters can lead to severe operational disruptions, safety hazards, and costly repairs. To mitigate these risks, this study compares two predictive maintenance strategies for helicopter engines: a supervised classification pipeline and an unsupervised anomaly detection approach based on autoencoders (AEs). The supervised method relies on labelled examples of both normal and faulty behaviour, while the unsupervised approach learns a model of normal operation using only healthy engine data, flagging deviations as potential faults. Both […]

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arXiv:2602.11169v1 Announce Type: new Abstract: Transformer hidden states encode information as high-dimensional vectors, yet whether direction (orientation in representational space) and magnitude (vector norm) serve distinct functional roles remains unclear. Studying Pythia-family models, we discover a striking cross-over dissociation: angular perturbations cause up to 42.9 more damage to language modeling loss, while magnitude perturbations cause disproportionately more damage to syntactic processing (20.4% vs.1.6% accuracy drop on subject-verb agreement).This finding is enabled by L2-matched perturbation analysis, a methodology ensuring […]

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arXiv:2601.00163v1 Announce Type: new Abstract: Robotic fleets such as unmanned aerial and ground vehicles have been widely used for routine inspections of static environments, where the areas of interest are known and planned in advance. However, in many applications, such areas of interest are unknown and should be identified online during exploration. Thus, this paper considers the problem of simultaneous exploration, inspection of unknown environments and then real-time communication to a mobile ground control station to report the […]

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arXiv:2602.09256v1 Announce Type: new Abstract: As more young women in China live alone, they navigate entangled privacy, security, and safety (PSS) risks across smart homes, online platforms, and public infrastructures. Drawing on six participatory threat modeling (PTM) workshops (n = 33), we present a human-centered threat model that illustrates how digitally facilitated physical violence, digital harassment and scams, and pervasive surveillance by individuals, companies, and the state are interconnected and mutually reinforcing. We also document four mitigation strategies […]

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arXiv:2604.06274v1 Announce Type: new Abstract: In recent years, the pace of development of information technology in various areas has increased drastically, forcing cybersecurity specialists to constantly review existing processes in order to prevent unauthorized access to confidential information. Using Ukraine as a primary case study, this paper explores the integration of international best practices, specifically ISO/IEC 27001 and the NIST Cybersecurity Framework, into national regulatory systems. A focus is placed on the transition from traditional compliance models to […]

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arXiv:2604.01413v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly rely on multi-turn reasoning and interaction, such as adaptive retrieval-augmented generation (RAG) and ReAct-style agents, to answer difficult questions. These methods improve accuracy by iteratively retrieving information, reasoning, or acting, but introduce a key challenge: textbf{When should the model stop?} Existing approaches rely on heuristic stopping rules or fixed turn budgets and provide no formal guarantees that the final prediction still contains the correct answer. This limitation is […]

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arXiv:2603.08832v1 Announce Type: new Abstract: Traditional Differential Privacy (DP) mechanisms are typically tailored to specific analysis tasks, which limits the reusability of protected data. DP tabular data synthesis overcomes this by generating synthetic datasets that can be shared for arbitrary downstream tasks. However, existing synthesis methods predominantly assume centralized or local settings and overlook the more practical horizontal federated scenario. Naively synthesizing data locally or perturbing individual records either produces biased mixtures or introduces excessive noise, especially under […]

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In the last 6 months, I’ve helped 3 AI startups migrate from Vercel or Cloudflare to AWS Lambda. The pattern is the same: they start on a platform with great DX. Then the wall shows up: background jobs, retries, queues, cron, and eventually a “this endpoint needs 2-8 GB RAM for 4-10 minutes” workload — and they land on AWS. To be fair: Vercel and Cloudflare captured developer attention for good reasons. Vercel ships Next.js fast — previews, […]

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arXiv:2602.01607v2 Announce Type: replace-cross Abstract: Differentially private synthetic data enables the sharing and analysis of sensitive datasets while providing rigorous privacy guarantees for individual contributors. A central challenge is to achieve strong utility guarantees for meaningful downstream analysis. Many existing methods ensure uniform accuracy over broad query classes, such as all Lipschitz functions, but this level of generality often leads to suboptimal rates for statistics of practical interest. Since many common data analysis queries exhibit smoothness beyond what […]

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