arXiv:2503.16222v2 Announce Type: replace-cross Abstract: This paper studies plug-and-play (PnP) Langevin sampling strategies for Bayesian inference in low-photon Poisson imaging problems, a challenging class of problems with significant applications in astronomy, medicine, and biology. PnP Langevin sampling offers a powerful framework for Bayesian image restoration, enabling accurate point estimation as well as advanced inference tasks, including uncertainty quantification and visualization analyses, and empirical Bayesian inference for automatic model parameter tuning. Herein, we leverage and adapt recent developments in […]

Ver mais

Python operators enable you to perform computations by combining objects and operators into expressions. Understanding Python operators is essential for manipulating data effectively. This video course covers arithmetic, comparison, Boolean, identity, membership, bitwise, concatenation, and repetition operators, along with augmented assignment operators. You’ll also learn how to build expressions using these operators and explore operator precedence to understand the order of operations in complex expressions. By the end of this video course, you’ll understand that: Arithmetic operators perform […]

Ver mais

arXiv:2601.17087v1 Announce Type: new Abstract: Agentic benchmarks increasingly rely on LLM-simulated users to scalably evaluate agent performance, yet the robustness, validity, and fairness of this approach remain unexamined. Through a user study with participants across the United States, India, Kenya, and Nigeria, we investigate whether LLM-simulated users serve as reliable proxies for real human users in evaluating agents on {tau}-Bench retail tasks. We find that user simulation lacks robustness, with agent success rates varying up to 9 percentage […]

Ver mais

A designer friend recently told me she spent three days trying to figure out why her company’s checkout flow made users confirm their email address twice. It seemed obviously redundant—just bad UX someone forgot to clean up. She asked the PM. She asked the engineer who built it. She even dug through old PRs looking for context. The best answer she got was: “There was something with the vendor API, but I don’t remember the details. Maybe check […]

Ver mais

arXiv:2601.08859v1 Announce Type: new Abstract: Researchers face a persistent barrier when applying computational algorithms with parameter configuration typically demanding programming skills, interfaces differing across environments, and settings rarely persisting between sessions. This fragmentation forces repetitive input, slows iterative exploration, and undermines reproducibility because parameter choices are difficult to record, share, and reuse. We present EZInput, a cross-runtime environment Python library enabling algorithm developers to automatically generate graphical user interfaces that make their computational tools accessible to end-users without […]

Ver mais

arXiv:2510.22848v2 Announce Type: replace-cross Abstract: Self-induced stochastic resonance (SISR) is the emergence of coherent oscillations in slow-fast excitable systems driven solely by noise, without external periodic forcing or proximity to a bifurcation. This work presents a physics-informed machine learning framework for modeling and predicting SISR in the stochastic FitzHugh-Nagumo neuron. We embed the governing stochastic differential equations and SISR-asymptotic timescale-matching constraints directly into a Physics-Informed Neural Network (PINN) based on a Noise-Augmented State Predictor architecture. The composite loss […]

Ver mais

The previous post introduced the idea of finding your location by sighting a star. There is some point on Earth that is directly underneath the star at any point in time, and that location is called the star’s GP (geographic position). That is one vertex of the navigational triangle. The other two vertices are your position and the North Pole. Unless you’re at Santa’s workshop and observing a star nearly directly overhead, the navigational triangle is a big […]

Ver mais

arXiv:2601.19040v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) are a powerful paradigm for time series analysis and are often enhanced by synthetic data augmentation to improve the training data quality. Existing augmentation methods, however, typically rely on heuristics and static paradigms. Motivated by dynamic data optimization, which shows that the contribution of samples varies across training stages, we propose OATS (Online Data Augmentation for Time Series Foundation Models), a principled strategy that generates synthetic data tailored […]

Ver mais