VIVALDI: steel billet tracking with advanced computer vision

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Modeling ferroelectric materials from first principles is one of the successes of density-functional theory and the driver of much development effort, requiring an accurate description of the electronic processes and the thermodynamic equilibrium that driv ...

We study the impact of pre and postprocessing for reducing discrimination in data-driven decision makers. We first analyze the fundamental trade-off between fairness and accuracy in a preprocessing approach, and propose a design for a preprocessing module ...

A prerequisite for NMR studies of organic materials is assigning each experimental chemical shift to a set of geometrically equivalent nuclei. Obtaining the assignment experimentally can be challenging and typically requires time-consuming multidimensional ...

Many important problems in contemporary machine learning involve solving highly non- convex problems in sampling, optimization, or games. The absence of convexity poses significant challenges to convergence analysis of most training algorithms, and in some ...

In sensed buildings, information related to occupant movement helps optimize important functionalities such as security enhancement, energy management, and caregiving. Typical sensing approaches for occupant tracking rely on mobile devices and cameras. The ...

The molecular dipole polarizability describes the tendency of a molecule to change its dipole moment in response to an applied electric field. This quantity governs key intra- and intermolecular interactions, such as induction and dispersion; plays a vital ...

VIVALDI: steel billet tracking with advanced computer vision

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Thermodynamics and dielectric response of BaTiO3 by data-driven modeling

Impact of Data Processing on Fairness in Supervised Learning

Bayesian probabilistic assignment of chemical shifts in organic solids

Convergence without Convexity: Sampling, Optimization, and Games

Occupancy detection and tracking in buildings using floor-vibration signals

Accurate molecular polarizabilities with coupled cluster theory and machine learning