Purely URL-based Topic Classification

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Epilepsy is one of the most prevalent brain disorders that disrupts the lives of millions worldwide. For patients with drug-resistant seizures, there exist implantable devices capable of monitoring neural activity, promptly triggering neurostimulation to r ...

Despite the importance and pervasiveness of Wikipedia as one of the largest platforms for open knowledge, surprisingly little is known about how people navigate its content when seeking information. To bridge this gap, we present the first systematic large ...

This study presents a self-supervised Bayesian Neural Network (BNN) framework using air-borne Acoustic Emission (AE) to identify different Laser Powder Bed Fusion (LPBF) process regimes such as Lack of Fusion, conduction mode, and keyhole without ground-tr ...

Laser Powder Bed Fusion (LPBF) is an Additive Manufacturing (AM) process consolidating parts layer by layer, from a metallic powder bed. It allows no limitation in terms of geometry and is therefore of particular interest to various industries. Metallic LP ...

This paper considers the problem of resilient distributed optimization and stochastic learning in a server-based architecture. The system comprises a server and multiple agents, where each agent has its own local cost function. The agents collaborate with ...

Users rely on ad and tracker blocking tools to protect their privacy. Unfortunately, existing ad and tracker blocking tools are susceptible to mutable advertising and tracking content. In this paper, we first demonstrate that a state-of-the-art ad and trac ...

Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies.Objecti ...

In this paper, we propose and compare personalized models for Productive Engagement (PE) recognition. PE is defined as the level of engagement that maximizes learning. Previously, in the context of robot-mediated collaborative learning, a framework of prod ...

Effective fall-detection and classification systems are vital in mitigating severe medical and economical consequences of falls to people in the fall risk groups. One class of such systems is based on wearable sensors. While there is a vast amount of acade ...

Machine learning has become the state of the art for the solution of the diverse inverse problems arising from computer vision and medical imaging, e.g. denoising, super-resolution, de-blurring, reconstruction from scanner data, quantitative magnetic reson ...

A simple predictive biomarker for fatty liver disease is required for individuals with insulin resistance. Here, we developed a supervised machine learning-based classifier for fatty liver disease using fecal 16S rDNA sequencing data. Based on the Kangbuk ...

Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology unde ...

We study unbinned multivariate analysis techniques, based on Statistical Learning, for indirect new physics searches at the LHC in the Effective Field Theory framework. We focus in particular on high-energy ZW production with fully leptonic decays, modeled ...

Augmented/virtual realities (ARs/VRs) promise to revolutionize STEM education. However, most easy-to-use tools are limited to static visualizations, which limits the approachable content, whereas more interactive and dynamic alternatives require costly har ...

One paramount challenge in multi-ion-sensing arises from ion interference that degrades the accuracy of sensor calibration. Machine learning models are here proposed to optimize such multivariate calibration. However, the acquisition of big experimental da ...

The majority of currently available webpages are dynamic in nature and are changing frequently. New content gets added to webpages, and existing content gets updated or deleted. Hence, people find it useful to be alert for changes in webpages that contain ...

Background. In medicine and other applications, the copying and sharing of data is impractical for a range of well-considered reasons. With federated learning (FL) techniques, machine learning models can be trained on data spread across several locations w ...

A major challenge in the common approach of hot water generation in residential houses lies in the highly stochastic nature of domestic hot water (DHW) demand. Learning hot water use behavior enables water heating systems to continuously adapt to the stoch ...

We present a new method for electrochemical sensing, which compensates the fouling effect of propofol through machine learning (ML) model. Direct and continuous monitoring of propofol is crucial in the development of automatic systems for control of drug i ...

We propose a simulation-based decision strategy for the proactive maintenance of complex structures with a particular application to structural health monitoring (SHM). The strategy is based on a data-driven approach which exploits an offline-online decomp ...

Purely URL-based Topic Classification

Chat with Graph Search

Enhancing Epileptic Seizure Detection with EEG Feature Embeddings

A Large-Scale Characterization of How Readers Browse Wikipedia

Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Impact of Redundancy on Resilience in Distributed Optimization and Learning

WEBGRAPH: Capturing Advertising and Tracking Information Flows for Robust Blocking

Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU

Personalized Productive Engagement Recognition in Robot-Mediated Collaborative Learning

Classification of fall directions via wearable motion sensors

Combining Model Driven and Data Driven Approaches for Inverse Problems in Parameter Estimation and Image Reconstruction: From Modelling to Validation

Machine learning-derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging

Parametrized classifiers for optimal EFT sensitivity

MoleculARweb: A Web Site for Chemistry and Structural Biology Education through Interactive Augmented Reality out of the Box in Commodity Devices

Multi-ion-sensing emulator and multivariate calibration optimization by machine learning models

Change Detection and Notification of Web Pages: A Survey

Weight Erosion: An Update Aggregation Scheme for Personalized Collaborative Machine Learning

Adaptive hot water production based on Supervised Learning

Continuous monitoring of propofol in human serum with fouling compensation by support vector classifier

Simulation-based Anomaly Detection and Damage Localization: An application to Structural Health Monitoring