How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## The Long Now of the Web: Inside the Internet Archive’s Fight Against Forgetting By @zbruceli [ 18 Min read ] A deep dive into the Internet Archive’s custom tech stack. Read More. Indie Hacking Vibe Coding Setup: What Changed in 6 Months By @ivankuznetsov […]

Ver mais

arXiv:2601.19942v1 Announce Type: new Abstract: We study the emergence of multi-step reasoning in deep Transformer language models through a geometric and statistical-physics lens. Treating the hidden-state trajectory as a flow on an implicit Riemannian manifold, we analyze the layerwise covariance spectrum of activations, where $C^{(ell)}=mathbb{E}[h^{(ell)}h^{(ell)top}]$, and track deviations from a random-matrix bulk. Across model scales (1.5B–30B), we observe a sharp reduction in effective dimensionality consistent with a phase transition: an order parameter based on sparsity/localization, $Omega(h)=1-|h|_1/(sqrt{d}|h|_2)$, exhibits a […]

Ver mais

arXiv:2510.15817v2 Announce Type: replace Abstract: Simulation-based inference (SBI) has become a widely used framework in applied sciences for estimating the parameters of stochastic models that best explain experimental observations. A central question in this setting is how to effectively combine multiple observations in order to improve parameter inference and obtain sharper posterior distributions. Recent advances in score-based diffusion methods address this problem by constructing a compositional score, obtained by aggregating individual posterior scores within the diffusion process. While […]

Ver mais

arXiv:2601.07866v1 Announce Type: new Abstract: While machine learning shows promise for maternal health risk prediction, clinical adoption in resource-constrained settings faces a critical barrier: lack of explainability and trust. This study presents a hybrid explainable AI (XAI) framework combining ante-hoc fuzzy logic with post-hoc SHAP explanations, validated through systematic clinician feedback. We developed a fuzzy-XGBoost model on 1,014 maternal health records, achieving 88.67% accuracy (ROC-AUC: 0.9703). A validation study with 14 healthcare professionals in Bangladesh revealed strong preference […]

Ver mais

arXiv:2406.04071v3 Announce Type: replace Abstract: Given an undirected measurement graph $mathcal{H} = ([n], mathcal{E})$, the classical angular synchronization problem consists of recovering unknown angles $theta_1^*,dots,theta_n^*$ from a collection of noisy pairwise measurements of the form $(theta_i^* – theta_j^*) mod 2pi$, for all ${i,j} in mathcal{E}$. This problem arises in a variety of applications, including computer vision, time synchronization of distributed networks, and ranking from pairwise comparisons. In this paper, we consider a dynamic version of this problem where […]

Ver mais

The Engineer’s Dilemma Engineers, architects, and developers share a common flaw: we hate ambiguity. We build systems based on logic, predictable inputs, and measurable outputs. Yet, the most important metric of our existence—Happiness—is usually treated as a vague, ethereal concept that “just happens.” I don’t like things that “just happen.” I like things I can track, optimize, and debug. If life is a system, then Happiness is the output. If the output is inconsistent, the code is buggy. […]

Ver mais

arXiv:2601.04361v1 Announce Type: new Abstract: We tackle a common domain adaptation setting in causal systems. In this setting, the target variable is observed in the source domain but is entirely missing in the target domain. We aim to impute the target variable in the target domain from the remaining observed variables under various shifts. We frame this as learning a compact, mechanism-stable representation. This representation preserves information relevant for predicting the target while discarding spurious variation. For linear […]

Ver mais

arXiv:2601.05219v2 Announce Type: replace Abstract: One-shot prediction enables rapid adaptation of pretrained foundation models to new tasks using only one labeled example, but lacks principled uncertainty quantification. While conformal prediction provides finite-sample coverage guarantees, standard split conformal methods are inefficient in the one-shot setting due to data splitting and reliance on a single predictor. We propose Conformal Aggregation of One-Shot Predictors (CAOS), a conformal framework that adaptively aggregates multiple one-shot predictors and uses a leave-one-out calibration scheme to […]

Ver mais

arXiv:2601.17058v1 Announce Type: new Abstract: Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them, which is essential for a wide range of data-centric applications. Driven by (i) rising demands for application-ready data (e.g., for analytics, visualization, decision-making), (ii) increasingly powerful LLM techniques, and (iii) the emergence of infrastructures that facilitate flexible agent construction (e.g., using Databricks Unity Catalog), LLM-enhanced methods are rapidly becoming a transformative and potentially dominant paradigm for data […]

Ver mais