Pinching antennas systems (PASSs) have recently been proposed as a novel flexible-antenna technology. These systems are implemented by attaching low-cost pinching elements to dielectric waveguides. As the direct link is bypassed through waveguides, PASSs can effectively compensate large-scale effects of the wireless channel. This work explores the potential gains of employing PASSs for over-the-air federated learning (OTA-FL). For a PASS-assisted server, we develop a low-complexity algorithmic approach, which jointly tunes the PASS parameters and schedules the mobile devices […]

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A small Houston, Texas business just did something no mining company, no venture-backed startup, and no publicly traded hardware manufacturer has done: they brought a fully open-source touchscreen Bitcoin miner to market that outperforms every competitor in its class by a factor of two. The Bitaxe Turbo Touch. Dual BM1370 ASIC chips. 2.15 TH/s at stock. Over 3.06 TH/s overclocked. Eight dynamic displays. Every line of firmware, from the ASIC driver to the touchscreen renderer, is open source […]

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arXiv:2601.08844v1 Announce Type: new Abstract: The advent of Large Language Models (LLMs) has raised concerns about their enormous carbon footprint, starting with energy-intensive training and continuing through repeated inference. This study investigates the potential of using fine-tuned Small Language Models (SLMs) as a sustainable alternative for predefined tasks. Here, we present a comparative analysis of the performance-emissions trade-off between LLMs and fine-tuned SLMs across selected tasks under Natural Language Processing, Reasoning and Programming. Our results show that in […]

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By: Stefanie Jegelka and Ameet Talwalkar (ICML21 Communication Chairs) With ICML 2021 underway, we wanted to briefly highlight the upcoming invited talks. A general theme of the invited talks this year is “machine learning for science.” The Program Chairs (Marina Meila and Tong Zhang) have invited world-renowned scientists from various disciplines to discuss their problems and the corresponding machine learning challenges. By exposing the machine learning community to these fascinating problems, we hope that we can help to […]

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Science Robotics, Volume 11, Issue 114, May 2026.

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arXiv:2509.18349v3 Announce Type: replace Abstract: Meta-learning aims to leverage information across related tasks to improve prediction on unlabeled data for new tasks when only a small number of labeled observations are available (“few-shot” learning). Increased task diversity is often believed to enhance meta-learning by providing richer information across tasks. However, recent work by Kumar et al. (2022) shows that increasing task diversity, quantified through the overall geometric spread of task representations, can in fact degrade meta-learning prediction performance […]

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Adaptive traffic signal control (ATSC) is crucial in alleviating congestion, maximizing throughput and promoting sustainable mobility in ever-expanding cities. Multi-Agent Reinforcement Learning (MARL) has recently shown significant potential in addressing complex traffic dynamics, but the intricacies of partial observability and coordination in decentralized environments still remain key challenges in formulating scalable and efficient control strategies. To address these challenges, we present CoordLight, a MARL-based framework designed to improve intra-neighborhood traffic by enhancing decision-making at individual junctions (agents), as […]

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Agentic Engineering Patterns > I use the term agentic engineering to describe the practice of developing software with the assistance of coding agents. What are coding agents? They’re agents that can both write and execute code. Popular examples include Claude Code, OpenAI Codex, and Gemini CLI. What’s an agent? Clearly defining that term is a challenge that has frustrated AI researchers since at least the 1990s but the definition I’ve come to accept, at least in the field […]

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