Interactive-time Exploration, Querying, and Analysis of Large High-dimensional Datasets

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This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theor ...

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

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 ...

Dynamic downscaling of atmospheric forcing data to the hectometer resolution has shown increases in accuracy for landsurface models, but at great computational cost. Here we present a validation of a novel intermediate complexity atmospheric model, HICAR, ...

The 70 kDa heat shock protein (Hsp70) chaperones control protein homeostasis in all ATP-containing cellular compartments. J-domain proteins (JDPs) coevolved with Hsp70s to trigger ATP hydrolysis and catalytically upload various substrate polypeptides in ne ...

Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

Lobular carcinoma represent the most common special histological subtype of breast cancer, with the majority classed as hormone receptor positive. Rates of invasive lobular carcinoma in postmenopausal women have been seen to increase globally, while other ...

A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

Cutting plane methods are a fundamental approach for solving integer linear programs (ILPs). In each iteration of such methods, additional linear constraints (cuts) are introduced to the constraint set with the aim of excluding the previous fractional opti ...

Bayesian Optimization (BO) is typically used to optimize an unknown function f that is noisy and costly to evaluate, by exploiting an acquisition function that must be maximized at each optimization step. Even if provably asymptotically optimal BO algorith ...

Interactive-time Exploration, Querying, and Analysis of Large High-dimensional Datasets

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A ride time-oriented scheduling algorithm for dial-a-ride problems

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

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

Intermediate complexity atmospheric modeling in complex terrain: is it right?

Autorepression of yeast Hsp70 cochaperones by intramolecular interactions involving their J-domains

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Is There a Special Role for Ovarian Hormones in the Pathogenesis of Lobular Carcinoma?

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Extensions of Peer Prediction Incentive Mechanisms

Learning to Remove Cuts in Integer Linear Programming

Relaxing the Additivity Constraints in Decentralized No-Regret High-Dimensional Bayesian Optimization