Gear-obsessed editors choose every product we review. We may earn commission if you buy from a link. Why Trust Us? Here’s what you’ll learn when you read this story: Time crystals oscillate in ...

1 Inner Mongolia Wuhai Ultra High Voltage Power Supply Bureau, Wuhai, China 2 Inner Mongolia Electric Power Research Institute, Hohhot, China The increasing penetration of distributed generation and ...

Abstract: This paper presents an area- and power-efficient Current-to-Digital Converter (CDC) system for bioelectric current sensor applications. To address the requirements of high resolution, ...

Zeroing neural network (ZNN) is viewed as an effective solution to time-varying nonlinear equation (TVNE). In this paper, a further study is shown by proposing a novel combined discrete-time ZNN ...

Researchers demonstrate a novel method for transforming continuous time crystals into discrete ones using subharmonic injection locking, offering new insights into symmetry breaking and control in ...

A robust Python package for automated trend labelling in time series data with a strong financial flavour, implementing SOTA trend labelling algorithms (bibliography) with returns estimation and ...

Physicists in the School of Arts & Sciences at Washington University in St. Louis (WashU) have invented a new type of time crystal, described as the first-ever “time quasicrystal,” that defies common ...

This paper proposes an analytic representation of sequence-space Jacobians in heterogeneous agent models with aggregate shocks in continuous time. Our approach is based on a pen-and-paper perturbation ...

Sampling from probability distributions with known density functions (up to normalization) is a fundamental challenge across various scientific domains. From Bayesian uncertainty quantification to ...

This repository contains code for the paper Neural Continuous-Time Supermartingale Certificates by Grigory Neustroev, Mirco Giacobbe, Anna Lukina, published in AAAI 2025. To cite this work you can use ...

Masked diffusion has emerged as a promising alternative to autoregressive models for the generative modeling of discrete data. Despite its potential, existing research has been constrained by overly ...