Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics in Face Embeddings

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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 ...

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 ...

Discrete Choice Models (DCMs) have a distinct advantage over Machine Learning (ML) classification algorithms, in that they employ a highly interpretable linear structure. However, a key drawback of DCMs compared to ML is the need to specify the utility fun ...

One of the main goal of Artificial Intelligence is to develop models capable of providing valuable predictions in real-world environments. In particular, Machine Learning (ML) seeks to design such models by learning from examples coming from this same envi ...

Though deep learning (DL) algorithms are very powerful for image processing tasks, they generally require a lot of data to reach their full potential. Furthermore, there is no straightforward way to impose various properties, given by the prior knowledge a ...

In this paper we propose an unsupervised feature extraction method to capture temporal information on monocular videos, where we detect and encode subject of interest in each frame and leverage contrastive self-supervised (CSS) learning to extract rich lat ...

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 ...

We present novel, low-cost and non-invasive potential diagnostic biomarkers of schizophrenia. They are based on the 'mirrorgame', a coordination task in which two partners are asked to mimic each other's hand movements. In particular, we use the patient's ...

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 ...

Early and accurate detection of epileptic seizures is an extremely important therapeutic goal due to the severity of complications it can prevent. To this end, a low-power machine learning-based seizure detection implemented on an FPGA is proposed in this ...

A Mach-Zehnder Interferometer (MZI) based optical fibre sensing technique, developed and patented by EPFL, is an efficient and economical way to detect hotspots in High Temperature Superconductor (HTS) applications. Due to the MZI sensitivity being a compo ...

Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to larg ...

This paper addresses the problem of sentiment classification of short messages on microblogging platforms. We apply machine learning and pattern recognition techniques to design and implement a classification system for microblog messages assigning them in ...

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 ...

While Machine Learning algorithms are key to automating organelle segmentation in large EM stacks, they require annotated data, which is hard to come by in sufficient quantities. Furthermore, images acquired from one part of the brain are not always repres ...

Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds. Despite promising progress, existing representations learned with neural networks sti ...

To improve the dexterity of multi-functional myoelectric prosthetic hand, more accurate hand gesture recognition based on surface electromyographic (sEMG) signal is needed. This paper evaluates two types of time-domain EMG features, one independent feature ...

With the growing number of process variation sources in deeply nano-scaled technologies, parameterized device and circuit modeling is becoming very important for chip design and verification. However, the high dimensionality of parameter space, for process ...

Little is known about the biological functions which are exerted by hormone receptors in physiological conditions. Here, we made use of the Mouse INtraDuctal (MIND) model, an innovative patient-derived xenograft (PDX) model, to characterize global gene exp ...

Interatomic potentials are essential for studying fundamental mechanisms of deformation and failure in metals and alloys because the relevant defects (dislocations, cracks, etc.) are far above the scales accessible to first-principles studies. Existing pot ...

Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics in Face Embeddings

Chattez avec Graph Search

Classification of fall directions via wearable motion sensors

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

Weak teachers: assisted specification of discrete choice models using ensemble learning algorithms

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Graph-based image representation learning

Temporal Representation Learning on Monocular Videos for 3D Human Pose Estimation

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

Unravelling socio-motor biomarkers in schizophrenia

Personalized Productive Engagement Recognition in Robot-Mediated Collaborative Learning

Hardware-Friendly Random Forest Classification of iEEG Signals for Implantable Seizure Detection

Optical fibre based quench detection in HTS applications using machine learning classifiers

Scalable Robust Graph Embedding with Spark

Sentiment Classification of Tweets using Hierarchical Classification

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

Scalable Unsupervised Domain Adaptation for Electron Microscopy

Social NCE: Contrastive Learning of Socially-aware Motion Representations

EMG Pattern Recognition Using Decomposition Techniques for Constructing Multiclass Classifiers

Efficient Statistical Parameter Selection for Nonlinear Modeling of Process/Performance Variation

Variably Scaled Kernels Improve Classification of Hormonally-Treated Patient-Derived Xenografts

Machine learning for metallurgy II. A neural-network potential for magnesium