About a month ago, Elon Musk boldly stated that “work will be optional” while discussing the future impact of AI on the workforce. While I assumed this was to evoke emotion in the general public, I felt stirred to explore this further. As adults, many of us are focused on just fulfilling basic needs of food, shelter, and safety. Psychologist Abraham Maslow’s hierarchy of needs states that we reach our full potential when we go beyond basic needs […]

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Let me set the scene. You’ve shipped three .NET applications in the last two years. Each one has a DbContext, a generic repository, probably a Unit of Work wrapper. Maybe you even went full CQRS on the last one because you’d watched enough conference talks to feel guilty about not doing it. And every single time, you wrote the same scaffolding. Different entity names. Different connection string. Same bones. At some point you stop and think — why […]

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Introduction: Educational systems have historically adapted to incorporate technological innovations, but the success of these processes may vary greatly. Some tools become embedded in established processes, while others lead to radical transformation. In this paper, we examine the development of educational technology and recommend and critique certain technologies that facilitate existing industrial education approaches and others that require the restructuring of how educational aims and methods are defined and implemented. Purpose: This study aims to evaluate this optimization-restructuring […]

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arXiv:2604.00947v1 Announce Type: cross Abstract: Since the random language model was proposed by E. DeGiuli [Phys. Rev. Lett. 122, 128301], language models have been investigated intensively from the viewpoint of statistical mechanics. Recently, the existence of a Berezinskii–Kosterlitz–Thouless transition was numerically demonstrated in models with long-range interactions between symbols. In statistical mechanics, it has long been known that long-range interactions can induce phase transitions. Therefore, it has remained unclear whether phase transitions observed in language models originate from […]

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arXiv:2602.07049v1 Announce Type: new Abstract: Online handwriting recognition using inertial measurement units opens up handwriting on paper as input for digital devices. Doing it on edge hardware improves privacy and lowers latency, but entails memory constraints. To address this, we propose Error-enhanced Contrastive Handwriting Recognition (ECHWR), a training framework designed to improve feature representation and recognition accuracy without increasing inference costs. ECHWR utilizes a temporary auxiliary branch that aligns sensor signals with semantic text embeddings during the training […]

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arXiv:2603.02268v1 Announce Type: new Abstract: EEG foundation models are typically pretrained on narrow-source clinical archives and evaluated on benchmarks from the same ecosystem, leaving unclear whether representations encode neural physiology or recording-distribution artifacts. We introduce PRISM (Population Representative Invariant Signal Model), a masked autoencoder ablated along two axes — pretraining population and downstream adaptation — with architecture and preprocessing fixed. We compare a narrow-source EU/US corpus (TUH + PhysioNet) against a geographically diverse pool augmented with multi-center South […]

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arXiv:2603.20279v1 Announce Type: new Abstract: Reinforcement learning techniques are being explored as solutions to the threat of cyber attacks on enterprise networks. Recent research in the field of AI in cyber security has investigated the ability of homogeneous multi-agent reinforcement learning agents, capable of inter-agent communication, to respond to cyberattacks. This paper advances the study of learned communication in multi-agent systems by examining heterogeneous agent capabilities within a simulated network environment. To this end, we leverage CommFormer, a […]

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arXiv:2603.28798v1 Announce Type: new Abstract: Physically Unclonable Functions (PUFs) provide promising hardware security for IoT authentication, leveraging inherent randomness suitable for resource constrained environments. However, ML/DL modeling attacks threaten PUF security by learning challenge-response patterns. This work introduces a custom resistor-capacitor (RC) based dynamically reconfigurable PUF using 32-bit challenge-response pairs (CRPs) designed to resist such attacks. We systematically evaluated robustness by generating a CRP dataset and splitting it into training, validation, and test sets. Multiple ML techniques including […]

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