Multi-task learning shows strikingly inconsistent results — sometimes joint training helps substantially, sometimes it actively harms performance — yet the field lacks a principled framework for predicting these outcomes. We identify a fundamental but unstated assumption underlying gradient-based task analysis: tasks must share training instances for gradient conflicts to reveal genuine relationships. When tasks are measured on the same inputs, gradient alignment reflects shared mechanistic structure; when measured on disjoint inputs, any apparent signal conflates task relationships with […]

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arXiv:2603.08724v1 Announce Type: new Abstract: This manuscript summarizes the work and showcases the impact of the doctoral thesis by introducing novel, cost-efficient methods for assessing and enhancing the reliability of DNN hardware accelerators. A comprehensive Systematic Literature Review (SLR) was conducted, categorizing existing reliability assessment techniques, identifying research gaps, and leading to the development of new analytical reliability assessment tools. Additionally, this work explores the interplay between reliability, quantization, and approximation, proposing methodologies that optimize the trade-offs between […]

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Key Takeaways 1. The biological ceiling: Classical hierarchy exploits evolutionarily ancient stress mechanisms — which physiologically degrades employees’ cognitive capacity through what’s known as “amygdala hijack.” 2. Psychological safety isn’t about being “nice”. It’s the baseline condition your prefrontal cortex needs to run complex analysis and actually learn anything. 3. Unbossing isn’t ditching structure — it’s a deliberate tradeoff: you’re trading low coordination overhead (hierarchy) for higher adaptability and genuine engagement (self-management). Nothing is free. 4. The 2026 […]

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Understanding how humans and machines learn from sparse data is central to cognitive science and machine learning. Using a species-fair design, we compare children and convolutional neural networks (CNNs) in a few-shot semi-supervised category learning task. Both learners are exposed to novel object categories under identical conditions. Learners receive mixtures of labeled and unlabeled exemplars while we vary supervision (1/3/6 labels), target feature (size, shape, pattern), and perceptual alignment (high/low). We find that children generalize rapidly from minimal […]

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arXiv:2603.20936v1 Announce Type: cross Abstract: The Riesz representer is a central object in semiparametric statistics and debiased/doubly-robust estimation. Two literatures in econometrics have highlighted the role for directly estimating Riesz representers: the automatic debiased machine learning literature (as in Chernozhukov et al., 2022b), and an independent literature on sieve methods for conditional moment models (as in Chen et al., 2014). These two literatures solve distinct optimization problems that in the population both have the Riesz representer as their […]

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Semantic communication promises task-aligned transmission but must reconcile semantic fidelity with stringent latency guarantees in immersive and safety-critical services. This paper introduces a time-constrained human-in-the-loop reinforcement learning (TC-HITL-RL) framework that embeds human feedback, semantic utility, and latency control within a semantic-aware Open radio access network (RAN) architecture. We formulate semantic adaptation driven by human feedback as a constrained Markov decision process (CMDP) whose state captures semantic quality, human preferences, queue slack, and channel dynamics, and solve it via […]

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arXiv:2602.17685v1 Announce Type: new Abstract: This paper addresses the challenge of multi target active debris removal (ADR) in Low Earth Orbit (LEO) by introducing a unified coelliptic maneuver framework that combines Hohmann transfers, safety ellipse proximity operations, and explicit refueling logic. We benchmark three distinct planning algorithms Greedy heuristic, Monte Carlo Tree Search (MCTS), and deep reinforcement learning (RL) using Masked Proximal Policy Optimization (PPO) within a realistic orbital simulation environment featuring randomized debris fields, keep out zones, […]

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We introduce a novel machine learning method called the Penalized Profile Support Vector Machine based on the Gabriel edited set for the computation of the probability of failure for a complex system as determined by a threshold condition on a computer model of system behavior. The method is designed to minimize the number of evaluations of the computer model while preserving the geometry of the decision boundary that determines the probability. It employs an adaptive sampling strategy designed […]

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Introduction Even today, browsers still can’t provide the same smooth calling experience as native mobile apps. Because of that, many popular services simply block calls from mobile browsers and force users to install a native app instead. But sometimes a native app isn’t an option: the user didn’t install it, has no storage left, or just prefers using the web. So the question is: how close can we get to a native call experience using only the browser? […]

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:::info Astounding Stories of Super-Science April 2004, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. THE COUNTRY OF THE BLIND – XXIX. — THE MAGIC SHOP. Astounding Stories of Super-Science April 2004: THE COUNTRY OF THE BLIND – XXIX. — THE MAGIC SHOP. By H. G. Wells ::: I had seen the Magic Shop from afar several times; I had passed it once or twice, a […]

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