Inverse optimisation and linear programming have emerged as crucial instruments in addressing complex decision-making problems where underlying models must be inferred from observed behaviour. At its ...

Pioneering biostatistician and infectious disease expert Dr. Elizabeth Halloran recently transitioned to emerita after a ...

Linear and logistic regression models are essential tools for quantifying the relationship between outcomes and exposures. Understanding the mathematics behind these models and being able to apply ...

DeepSeek’s Engram separates static memory from computation, increasing efficiency in large AI models The method reduces high-speed memory needs by enabling DeepSeek models to use lookups Engram ...

The design of short LAM-PCR amplicons that do not exceed several hundred base pairs is critical for the efficiency of each analysis. Amplicons should be long enough to be resolved by electropheresis ...

Abstract: This article proposes a data-driven linear parameter variation model predictive control (DDLPVMPC) method for unknown nonlinear (NL) systems. The approach eliminates reliance on prior ...

Abstract: The fractional order Cole models are the most common form of electrical equivalent circuits (EEC) to model complex impedance (Z) of various dielectric materials like electrodes, sensors, ...

This repository provides code, dataset usage instructions, and trained model from our paper "TSalV360: A Method and Dataset for Text-driven Saliency Detection in 360-Degrees Videos", written by ...

AutoJudge is a machine learning–based system that predicts the difficulty of programming problems using only their textual descriptions. It outputs both a difficulty class (Easy / Medium / Hard) and a ...

IOD distinguishes itself as scientific home for researchers working at the boundaries of traditional academic spheres, and generating growing programs in the integration of research with informatics ...