We live in what are described as post-capitalist times, where the economic system that promotes the virtues of creating individual wealth has been variously described as broken, defunct, and even failed. It has, according to many, morphed into a system where the oligarchs control the resources required to make immense wealth and leave the rest to fend for themselves and fight over scraps. Some go to the extent of romanticizing the concept of a welfare state, where basic […]

Ver mais

arXiv:2603.08719v1 Announce Type: new Abstract: Large language models (LLMs) have recently emerged as a promising approach for automating Verilog code generation; however, existing methods primarily emphasize syntactic correctness and often rely on commercial models or external verification tools, which introduces concerns regarding cost, data privacy, and limited guarantees of functional correctness. This work proposes a unified multi-agent framework for reasoning-oriented training data generation with integrated testbench-driven verification, enabling locally fine-tuned LLMs, SiliconMind-V1, to iteratively generate, test, and debug […]

Ver mais

arXiv:2602.18535v1 Announce Type: new Abstract: Voice-based digital biomarkers can enable scalable, non-invasive screening and monitoring of Parkinson’s disease (PD) and Amyotrophic Lateral Sclerosis (ALS). However, models trained on one cohort or device often fail on new acquisition settings due to cross-device and cross-cohort domain shift. This challenge is amplified in real-world scenarios with partial-label mismatch, where datasets may contain different disease labels and only partially overlap in class space. In addition, voice-based models may exploit demographic cues, raising […]

Ver mais

Reinforcement Learning (RL) remains a central optimisation framework in machine learning. Although RL agents can converge to optimal solutions, the definition of “optimality” depends on the environment’s statistical properties. The Bellman equation, central to most RL algorithms, is formulated in terms of expected values of future rewards. However, when ergodicity is broken, long-term outcomes depend on the specific trajectory rather than on the ensemble average. In such settings, the ensemble average diverges from the time-average growth experienced by […]

Ver mais

Lagrangian Neural Networks (LNNs) can learn arbitrary Lagrangians from trajectory data, but their unusual optimization objective leads to significant training instabilities that limit their application to complex systems. We propose several improvements that address these fundamental challenges, namely, a Hessian regularization scheme that penalizes unphysical signatures in the Lagrangian’s second derivatives with respect to velocities, preventing the network from learning unstable dynamics, activation functions that are better suited to the problem of learning Lagrangians, and a physics-aware coordinate […]

Ver mais

Ver mais

As has been said by many commentators, rare earths are not particularly rare.  Via source, here is an estimate of their abundance in the Earth’s surface: Note by the way the Y-axis is logarithmic so small changes in vertical position can mean a factor of 10 or more difference in concentration.  But the rare earths are not unreasonably far off fairly common industrial metals like lead, nickel, copper, and molybdenum and well more common than gold, silver, and […]

Ver mais

In this quiz, you’ll test your understanding of Python Decorators 101. Work through this quiz to review first-class functions, inner functions, and decorators, and learn how to create, reuse, and apply them to extend behavior cleanly in Python. [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Ver mais