Bitcoin miners are producing every coin at a loss. The average cost to mine one BTC sits at $88,000 while the market price holds near $70,853, meaning every block costs $19,000 more than it earns. Mining difficulty just dropped 7.8% and hashrate has fallen to 920 EH/s, well below the 1.1 ZH/s peak from 2025. When the people who create Bitcoin cannot afford to produce it, the market is sitting at a price floor that preceded every major […]

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This paper studies graph-based higher-order structures related to metagraphs and edgelabeled hierarchical networks. After reviewing MetaGraphs and Iterated MetaGraphs, we introduce the notion of an Edge-MetaGraph, in which each edge is labeled by a two-ported internal graph, allowing edge-substitution expansion through port gluing. We then define Iterated EdgeMetaGraphs recursively, so that edges may carry nested Edge-MetaGraph structures. Concrete examples from biomedical systems, software pipelines, and logistics are presented to illustrate the expressive power of the proposed framework.

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arXiv:2602.10299v1 Announce Type: new Abstract: Recent work on network attacks have demonstrated that ML-based network intrusion detection systems (NIDS) can be evaded with adversarial perturbations. However, these attacks rely on complex optimizations that have large computational overheads, making them impractical in many real-world settings. In this paper, we introduce a lightweight adversarial agent that implements strategies (policies) trained via reinforcement learning (RL) that learn to evade ML-based NIDS without requiring online optimization. This attack proceeds by (1) offline […]

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arXiv:2603.15645v1 Announce Type: new Abstract: Time series forecasters are widely used across various domains. Among them, MLP (multi-layer perceptron)-based forecasters have been proven to be more robust to noise compared to Transformer-based forecasters. However, MLP struggles to capture complex features, resulting in limitations on capturing long-range dependencies. To address this challenge, we propose XLinear, an MLP-based forecaster for long-range forecasting. Firstly, we decompose the time series into trend and seasonal components. For the trend component which contains long-range […]

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Classical proportional–integral–derivative (PID) control is widely employed in industrial applications; however, achieving higher performance often motivates the adoption of model predictive control (MPC). Although gradient-based methods are the standard for real-time optimization, sampling-based approaches have recently gained attention. In particular, model predictive path integral (MPPI) control enables gradient-free optimization and accommodates non-differentiable models and objective functions. However, directly sampling control input sequences may yield discontinuous inputs and increase the optimization dimensionality in proportion to the prediction horizon. This […]

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arXiv:2602.18470v1 Announce Type: new Abstract: The integration of artificial intelligence (AI) into science education is transforming the design and function of learning materials, offering new affordances for personalization, authenticity, and accessibility. This chapter examines how AI technologies are transforming science learning materials across six interrelated domains: 1) integrating AI into scientific practice, 2) enabling adaptive and personalized instruction, 3) facilitating interactive simulations, 4) generating multimodal content, 5) enhancing accessibility for diverse learners, and 6) promoting co-creation through AI-supported […]

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arXiv:2603.25224v1 Announce Type: new Abstract: Demographic parity (DP) is a widely used group fairness criterion requiring predictive distributions to be invariant across sensitive groups. While natural in classification, full distributional DP is often overly restrictive in regression and can lead to substantial accuracy loss. We propose a relaxation of DP tailored to regression, enforcing parity only at a finite set of quantile levels and/or score thresholds. Concretely, we introduce a novel (${ell}$, Z)-fair predictor, which imposes groupwise CDF […]

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arXiv:2601.17001v1 Announce Type: new Abstract: Twenty-five years ago, Joel Reidenberg argued that technology itself, not just law and regulation, imposes rules on communities in the Information Society. System design choices like network architecture and configurations create regulatory norms he termed “Lex Informatica”-referencing the merchant-driven medieval “Lex Mercatoria” that emerged independent of sovereign control. Today we face different challenges requiring us to revisit Reidenberg’s insights and examine the consequences of that earlier era. While Lex Informatica provided a framework […]

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arXiv:2601.12169v1 Announce Type: new Abstract: Deep learning and model predictive control (MPC) can play complementary roles in legged robotics. However, integrating learned models with online planning remains challenging. When dynamics are learned with neural networks, three key difficulties arise: (1) stiff transitions from contact events may be inherited from the data; (2) additional non-physical local nonsmoothness can occur; and (3) training datasets can induce non-Gaussian model errors due to rapid state changes. We address (1) and (2) by […]

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arXiv:2602.13212v1 Announce Type: new Abstract: We study language-in-the-loop control for multi-drone systems that execute evolving, high-level missions while retaining formal robustness guarantees at the physical layer. We propose a three-layer architecture in which (i) a human operator issues natural-language instructions, (ii) an LLM-based supervisor periodically interprets, verifies, and corrects the commanded task in the context of the latest state and target estimates, and (iii) a distributed inner-loop controller tracks the resulting reference using only local relative information. We […]

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