arXiv:2601.08999v1 Announce Type: new Abstract: Accurate prediction of solar energetic particle events is vital for safeguarding satellites, astronauts, and space-based infrastructure. Modern space weather monitoring generates massive volumes of high-frequency, multivariate time series (MVTS) data from sources such as the Geostationary perational Environmental Satellites (GOES). Machine learning (ML) models trained on this data show strong predictive power, but most existing methods overlook domain-specific feasibility constraints. Counterfactual explanations have emerged as a key tool for improving model interpretability, yet […]

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The life of a prescription at Amazon Pharmacy From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money. Conversational AI Alexandre Alves Anita Vila September 30, 01:32 PM October 02, 11:42 AM Pharmacies play a vital role in ensuring patients health, but the process of dispensing medications is far more complex than it may appear. At Amazon Pharmacy, we are using artificial […]

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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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In my previous analysis of Power Stranding, I explored the multi-billion-dollar art of ensuring that massive power infrastructure doesn’t sit idle. But for a data center financial analyst, solving the power equation isn’t just about building better substations. It’s about what sits inside the racks. Looking at a modern data center’s balance sheet in early 2026, the “Hardware” line item is undergoing a silent coup. For decades, the industry operated as a one-stop shop: cloud providers bought their […]

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arXiv:2601.14296v1 Announce Type: new Abstract: The study of system complexity primarily has two objectives: to explore underlying patterns and to develop theoretical explanations. Pattern exploration seeks to clarify the mechanisms behind the emergence of system complexity, while theoretical explanations aim to identify the fundamental causes of this complexity. Laws are generally defined as mappings between variables, whereas theories offer causal explanations of system behavior. Agent Based Modeling(ABM) is an important approach for studying complex systems, but it tends […]

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arXiv:2602.04667v1 Announce Type: new Abstract: Root-cause analysis in controlled time dependent systems poses a major challenge in applications. Especially energy systems are difficult to handle as they exhibit instantaneous as well as delayed effects and if equipped with storage, do have a memory. In this paper we adapt the causal root-cause analysis method of Budhathoki et al. [2022] to general time-dependent systems, as it can be regarded as a strictly causal definition of the term “root-cause”. Particularly, we […]

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Rodney v0.4.0 My Rodney CLI tool for browser automation attracted quite the flurry of PRs since I announced it last week. Here are the release notes for the just-released v0.4.0: Errors now use exit code 2, which means exit code 1 is just for for check failures. #15 New rodney assert command for running JavaScript tests, exit code 1 if they fail. #19 New directory-scoped sessions with –local/–global flags. #14 New reload –hard and clear-cache commands. #17 New […]

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arXiv:2603.08825v1 Announce Type: new Abstract: Discrete graph generation has emerged as a powerful paradigm for modeling graph data, often relying on highly expressive neural backbones such as transformers or higher-order architectures. We revisit this design choice by introducing GenGNN, a modular message-passing framework for graph generation. Diffusion models with GenGNN achieve more than 90% validity on Tree and Planar datasets, within margins of graph transformers, at 2-5x faster inference speed. For molecule generation, DiGress with a GenGNN backbone […]

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