technocracy

arXiv:2601.22264v1 Announce Type: new Abstract: In principle, Continuous Integration (CI) pipeline failures provide valuable feedback to developers on code-related errors. In practice, however, pipeline jobs often fail intermittently due to non-deterministic tests, network outages, infrastructure failures, resource exhaustion, and other reliability issues. These intermittent (flaky) job failures lead to substantial inefficiencies: wasted computational resources from repeated reruns and significant diagnosis time that distracts developers from core activities and often requires intervention from specialized teams. Prior work has proposed […]

Ver mais

arXiv:2601.12286v1 Announce Type: new Abstract: The increasing prevalence of Large Language Models (LLMs) demands effective safeguards for their operation, particularly concerning their tendency to generate out-of-context responses. A key challenge is accurately detecting when LLMs stray from expected conversational norms, manifesting as topic shifts, factual inaccuracies, or outright hallucinations. Traditional anomaly detection struggles to directly apply within contextual semantics. This paper outlines our experiment in exploring the use of Representation Engineering (RepE) and One-Class Support Vector Machine (OCSVM) […]

Ver mais

An HBR Executive Masterclass with Boston Consulting Group partners Julia Dhar and Kristy Ellmer on the five phases of organizational change.

Ver mais

In late 2025, iOS 26 shipped a keyboard that couldn’t keep up with fast typists. Type quickly and characters would go missing. The tap registered, the key animated, but the letter never landed in the field. Worse, the half-finished words poisoned autocorrect, which kept “fixing” text the user never typed. Apple patched it in iOS 26.4 in March 2026. n React Native has its own version of this bug. It’s older than iOS 26’s, it’s still in your […]

Ver mais

arXiv:2601.03397v1 Announce Type: new Abstract: We present PIVONet (Physically-Informed Variational ODE Neural Network), a unified framework that integrates Neural Ordinary Differential Equations (Neuro-ODEs) with Continuous Normalizing Flows (CNFs) for stochastic fluid simulation and visualization. First, we demonstrate that a physically informed model, parameterized by CNF parameters {theta}, can be trained offline to yield an efficient surrogate simulator for a specific fluid system, eliminating the need to simulate the full dynamics explicitly. Second, by introducing a variational model with […]

Ver mais

In an era of $2,000 “pro” laptops with soldered RAM and zero-repairability scores, I decided to prove a point. As a 30-year veteran of the industry, I know that hardware doesn’t die—it just gets neglected. I recently acquired a “parts-only” Lenovo ThinkPad T450 for a handful of dollars. It was a mess: an unplugged internal battery, a faulty RAM stick, and a chassis covered in the grime of a previous life. To most, it was e-waste. To me, […]

Ver mais

Accurately predicting the state-of-health (SOH) and remaining useful life (RUL) of lithium-ion batteries is crucial for ensuring the safe and efficient operation of electric vehicles while minimizing associated risks. However, current deep learning methods are limited in their ability to selectively extract features and model time dependencies for these two parameters. Moreover, most existing methods rely on traditional recurrent neural networks, which have inherent shortcomings in long-term time-series modeling. To address these issues, this paper proposes a multi-task […]

Ver mais

arXiv:2510.00479v2 Announce Type: replace-cross Abstract: We numerically investigate the feasibility and limits of jointly estimating flow fields and unknown particle properties (e.g., position, size, and density) from Lagrangian particle tracking (LPT) data. LPT offers time-resolved, volumetric measurements of particle trajectories, which are markers of the carrier fluid motion. However, experimental tracks are spatially sparse and potentially noisy, and the problem of reconstructing flow fields may be further complicated by inertial particle transport, such that particle slip velocities must […]

Ver mais

arXiv:2603.21375v1 Announce Type: cross Abstract: We study Constrained Online Convex Optimization with Memory (COCO-M), where both the loss and the constraints depend on a finite window of past decisions made by the learner. This setting extends the previously studied unconstrained online optimization with memory framework and captures practical problems such as the control of constrained dynamical systems and scheduling with reconfiguration budgets. For this problem, we propose the first algorithms that achieve sublinear regret and sublinear cumulative constraint […]

Ver mais

arXiv:2505.15008v3 Announce Type: replace-cross Abstract: Selective classification enhances the reliability of predictive models by allowing them to abstain from making uncertain predictions. In this work, we revisit the design of optimal selection functions through the lens of the Neyman–Pearson lemma, a classical result in statistics that characterizes the optimal rejection rule as a likelihood ratio test. We show that this perspective not only unifies the behavior of several post-hoc selection baselines, but also motivates new approaches to selective […]

Ver mais