A Hybrid Model-Free Data-Interpretation Approach For Damage Detection During Continuous Civil-Infrastructure Monitoring

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Demographic fairness or equity is a crucial aspect of machine learning models, particularly in critical domains such as healthcare, where errors can have severe consequences. A fair model avoids making distinctions between groups with different sensitive o ...

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

This paper presents a novel hybrid framework for generating and updating a synthetic population. We call it hybrid because it combines model-based and data-driven approaches. Existing generators produce a snapshot of synthetic data that becomes outdated ov ...

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

Motivated by global analysis of aircraft-based measurements of air pollutants and climate variables, and specifically the COVID-19 pandemic’s possible impact on ozone concentrations, a functional autoregressive model is proposed to capture global spatio-te ...

We describe a novel method to compute the components of dynamo tensors from direct magnetohydrodynamic (MHD) simulations. Our method relies upon an extension and generalization of the standard H & ouml;gbom CLEAN algorithm widely used in radio astronomy to ...

This paper proposes a data-driven control design method for nonlinear systems that builds upon the Koopman operator framework. In particular, the Koopman operator is used to lift the nonlinear dynamics to a higher-dimensional space where the so-called obse ...

Data redundancy has been one of the most important problems in data-intensive applications such as data mining and machine learning. Removing data redundancy brings many benefits in efficient data updating, effective data storage, and error-free query proc ...

We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of

\R^d

. Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...

Microscopic traffic flow models can be distinguished in lane-based or lane-free depending on the degree of lane-discipline. This distinction holds true only if motorcycles are neglected in lane-based traffic. In cities, as opposed to highways, this is an o ...

Many robotics problems are formulated as optimization problems. However, most optimization solvers in robotics are locally optimal and the performance depends a lot on the initial guess. For challenging problems, the solver will often get stuck at poor loc ...

Unlike water and sanitation infrastructures or socio-economic indicators, landscape features are seldomly considered as predictors of diarrhoea. In contexts of rapid urbanisation and changes in the physical environment, urban planners and public health man ...

Distribution-on-distribution regression considers the problem of formulating and es-timating a regression relationship where both covariate and response are probability distributions. The optimal transport distributional regression model postulates that th ...

Large amounts of data are generated in chemistry labs-nearly all instruments record data in a digital form, yet a considerable proportion is also captured non-digitally and reported in ways non-accessible to both humans and their computational agents. Chem ...

The work proposes a multi-modal regional mean speed regression analysis for the city network of Athens, Greece. The dataset from pNUEMA experiment is used in the present context. Accumulations and mean speeds of different modes are estimated and compared t ...

Using phase-modulation-induced potential gradient whose period is synchronized to a microwave opto-electronic oscillator, dissipative Kerr solitons generated in a crystalline optical microresonator are trapped by the soliton tweezing effect, exhibiting a s ...

BackgroundTinnitus is a heterogeneous condition which may be associated with moderate to severe disability, but the reasons why only a subset of individuals is burdened by the condition are not fully clear. Ecological momentary assessment (EMA) allows a be ...

In this second part of the development of a mechanistic kinetic model of the solar inactivation of E. coli enhanced with hydrogen peroxide, we evaluate the mechanisms based on photonic inactivation and integrate them into the kinetic model of the dark proc ...

The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atom ...

A Hybrid Model-Free Data-Interpretation Approach For Damage Detection During Continuous Civil-Infrastructure Monitoring

Chattez avec Graph Search

Optimizing fairness and utility in healthcare machine learning models

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Hybrid Simulator for Capturing Dynamics of Synthetic Populations

Reliable data-driven decision-making through optimal transport

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Functional autoregressive processes on a spherical domain for global aircraft-based atmospheric measurements

Iterative removal of sources to model the turbulent electromotive force

Data-Driven Control Synthesis using Koopman Operator: A Robust Approach

Validating functional redundancy with mixed generative adversarial networks

Distributional Regression and Autoregression via Optimal Transport

A hybrid microscopic model for multimodal traffic with empirical observations from aerial footage

Memory of Motion for Initializing Optimization in Robotics

Using Open-Access Data to Explore Relations between Urban Landscapes and Diarrhoeal Diseases in Côte d’Ivoire

Minimax rate for optimal transport regression between distributions

Making the collective knowledge of chemistry open and machine actionable

Linear regression analysis of regional mean speed of Athens city network using drone data: A multi-modal approach

Microresonator Dissipative Kerr Solitons Synchronized to an Optoelectronic Oscillator

Daily Contributors of Tinnitus Loudness and Distress: An Ecological Momentary Assessment Study

Mechanistic modelling of solar disinfection (SODIS) kinetics of Escherichia coli, enhanced with H2O2 - Part 2: Shine on you, crazy peroxide

Physics-Inspired Structural Representations for Molecules and Materials