“I was curious to establish a baseline for when LLMs are effectively able to solve open math problems compared to where they ...

The development of glmSMA represents a valuable advancement in spatial transcriptomics analysis, offering a mathematically robust regression-based approach that achieves higher-resolution mapping of ...

Abstract: The sparsity-regularized linear inverse problem has been widely used in many fields, such as remote sensing imaging, image processing and analysis, seismic deconvolution, compressed sensing, ...

Samuel Alito says Supreme Court responding to Trump acting 'aggressively' Search for missing swimmer suspended; witness saw shark with body in its mouth Johnny Carson book exposes 'Tonight Show' ban ...

How to solve linear programming and quadratic programming with inequality constraint only? For LP, I tried to use OSQP and pass the objective as (None, -c), the equality constraint as (None, None), ...

A brisk April day in Manhattan, an overdue introduction on the F and more reader tales of New York City in this week’s Metropolitan Diary. Dear Diary: Hard to believe it was the middle of April. The ...

Linear inequalities are an essential component of algebra. They involve variables, constants, and inequality signs, depicting a range of solutions rather than one specific solution like an equation ...

This paper presents a novel template-based method to solve math word problems. This method learns the mappings between math concept phrases in math word problems and their math expressions from ...

Abstract: This article proposes three distributed algorithms for solving linear algebraic equations to seek a least-squares (LS) solution via multiagent networks. We consider that each agent has only ...