technocracy

NASA chose the right crew to launch a new era of human space exploration

NASA chose the right crew to launch a new era of human space exploration

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HOUSTON—Their mission is complete. The four people who flew beyond the Moon on NASA’s Artemis II mission are back home in Houston with their families. But the lessons from Artemis II are just beginning to be told. There are tangible, objective takeaways from the nine-day mission. How did NASA’s Space Launch System rocket perform? Nearly perfectly. Was the Orion spacecraft up to the job of flying to the Moon and back? Absolutely. Will engineers need to make any […]

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EARL: Energy-Aware Optimization of Liquid State Machines for Pervasive AI

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Pervasive AI increasingly depends on on-device learning systems that deliver low-latency and energy-efficient computation under strict resource constraints. Liquid State Machines (LSMs) offer a promising approach for low-power temporal processing in pervasive and neuromorphic systems, but their deployment remains challenging due to high hyperparameter sensitivity and the computational cost of traditional optimization methods that ignore energy constraints. This work presents EARL, an energy-aware reinforcement learning framework that integrates Bayesian optimization with an adaptive reinforcement learning based selection policy […]

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Safe Multitask Molecular Graph Networks for Vapor Pressure and Odor Threshold Prediction

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arXiv:2601.16426v1 Announce Type: new Abstract: We investigate two important tasks in odor-related property modeling: Vapor Pressure (VP) and Odor Threshold (OP). To evaluate the model’s out-of-distribution (OOD) capability, we adopt the Bemis-Murcko scaffold split. In terms of features, we introduce the rich A20/E17 molecular graph features (20-dimensional atom features + 17-dimensional bond features) and systematically compare GINE and PNA backbones. The results show: for VP, PNA with a simple regression head achieves Val MSE $approx$ 0.21 (normalized space); […]

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It’s a big one

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This week’s edition of my email newsletter (aka content from this blog delivered to your inbox) features 4 pelicans riding bicycles, 1 possum on an e-scooter, up to 5 raccoons with ham radios hiding in crowds, 5 blog posts, 8 links, 3 quotes and a new chapter of my Agentic Engineering Patterns guide. Tags: newsletter

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Embodied Intelligent Spectrum Management: A New Paradigm for Dynamic Spectrum Access

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arXiv:2602.13245v1 Announce Type: new Abstract: Wireless communication is evolving into an agent era, where numerous intelligent agents equipped with perception, reasoning, and interaction capabilities will operate in highly dynamic wireless environments. To complete diverse complex tasks, agent communication will play a critical role, which enables autonomous information exchange with external tools, services, and other agents. This trendy movement will dramatically increase spectrum demand and result in unprecedented challenges for spectrum management. However, current spectrum management paradigms, including static […]

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An Algebraic Representation Theorem for Linear GENEOs in Geometric Machine Learning

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Geometric and Topological Deep Learning are rapidly growing research areas that enhance machine learning through the use of geometric and topological structures. Within this framework, Group Equivariant Non-Expansive Operators (GENEOs) have emerged as a powerful class of operators for encoding symmetries and designing efficient, interpretable neural architectures. Originally introduced in Topological Data Analysis, GENEOs have since found applications in Deep Learning as tools for constructing equivariant models with reduced parameter complexity. GENEOs provide a unifying framework bridging Geometric […]

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How DoorDash Optimized Item Availability at Scale Using Elasticsearch

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DoorDash’s homepage item carousels needed to filter millions of items by availability in under 300ms. We couldn’t call the menu service at request time (too much fan-out, too slow), so we indexed availability directly in Elasticsearch. We went through three schema iterations: nested documents (600ms), Gojek-style encoded time slots as terms (350ms but 6x storage), and finally range fields backed by BKD trees (250ms, baseline storage). The range approach won on both latency and storage.

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Evaluating Scene-based In-Situ Item Labeling for Immersive Conversational Recommendation

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arXiv:2604.09698v1 Announce Type: new Abstract: The growing ubiquity of Extended Reality (XR) is driving Conversational Recommendation Systems (CRS) toward visually immersive experiences. We formalize this paradigm as Immersive CRS (ICRS), where recommended items are highlighted directly in the user’s scene-based visual environment and augmented with in-situ labels. While item recommendation has been widely studied, the problem of how to select and evaluate which information to present as immersive labels remains an open problem. To this end, we introduce […]

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A Scoping Review of Deep Learning for Urban Visual Pollution and Proposal of a Real-Time Monitoring Framework with a Visual Pollution Index

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Urban Visual Pollution (UVP) has emerged as a critical concern, yet research on automatic detection and application remains fragmented. This scoping review maps the existing deep learning-based approaches for detecting, classifying, and designing a comprehensive application framework for visual pollution management. Following the PRISMA-ScR guidelines, seven academic databases (Scopus, Web of Science, IEEE Xplore, ACM DL, ScienceDirect, SpringerNatureLink, and Wiley) were systematically searched and reviewed, and 26 articles were found. Most research focuses on specific pollutant categories and […]

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Where Do Flow Semantics Reside? A Protocol-Native Tabular Pretraining Paradigm for Encrypted Traffic Classification

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arXiv:2603.10051v1 Announce Type: new Abstract: Self-supervised masked modeling shows promise for encrypted traffic classification by masking and reconstructing raw bytes. Yet recent work reveals these methods fail to reduce reliance on labeled data despite costly pretraining: under frozen encoder evaluation, accuracy drops from greater than 0.9 to less than 0.47. We argue the root cause is inductive bias mismatch: flattening traffic into byte sequences destroys protocol-defined semantics. We identify three specific issues: 1) field unpredictability, random fields like […]

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