High-Resolution Predictions of the Fuel and Cladding Temperatures for the 3D PWR Core with Artificial Neural Networks trained on CTF

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Emotion recognition is usually achieved by collecting features (physiological signals, events, facial expressions, etc.) to predict an emotional ground truth. This ground truth is arguably unreliable due to its subjective nature. In this paper, we introduc ...

Image classification has significantly improved using deep learning. This is mainly due to convolutional neural networks (CNNs) that are capable of learning rich feature extractors from large datasets. However, most deep learning classification methods are ...

Continuous and reliable cardiac function monitoring could improve medication adherence in patients at risk of heart failure. This work presents an innovative implantable Fiber Bragg Grating-based soft sensor designed to sense mechanical cardiac activity. T ...

With the current exponential growth of video-based social networks, video retrieval using natural language is receiving ever-increasing attention. Most existing approaches tackle this task by extracting individual frame-level spatial features to represent ...

We consider the problem of finding a target object t using pairwise comparisons, by asking an oracle questions of the form “Which object from the pair (i, j) is more similar to t?”. Objects live in a space of latent features, from which the oracle generate ...

Accurate and fast segmentation of nuclei in histopathological images plays a crucial role in cancer research for detection and grading, as well as personal treatment. Despite the important efforts, current algorithms are still suboptimal in terms of speed, ...

The necessary and sufficient conditions for existence of a generalized representer theorem are presented for learning Hilbert space - valued functions. Representer theorems involving explicit basis functions and Reproducing Kernels are a common occurrence ...

Functional magnetic resonance imaging is a non-invasive tomographic imaging modality that has provided insights into system-level brain function. New analysis methods are emerging to study the dynamic behavior of brain activity. The innovation-driven co-ac ...

The predictive simulation of molecular liquids requires potential energy surface (PES) models that are not only accurate but also computationally efficient enough to handle the large systems and long time scales required for reliable prediction of macrosco ...

This paper studies the problem of inferring whether an agent is directly influenced by another agent over a network. Agent i influences agent j if they are connected (according to the network topology), and if agent j uses the data from agent i to update i ...

High-Resolution Predictions of the Fuel and Cladding Temperatures for the 3D PWR Core with Artificial Neural Networks trained on CTF

Chattez avec Graph Search

Towards Recognizing Emotion in the Latent Space

Fidelity Estimation Improves Noisy-Image Classification with Pretrained Networks

Implantable Fiber Bragg Grating Sensor for Continuous Heart Activity Monitoring: Ex-Vivo and In-Vivo Validation

Semantics-Aware Spatial-Temporal Binaries for Cross-Modal Video Retrieval

Scalable and Efficient Comparison-based Search without Features

Automatic Segmentation of Nuclei in Histopathology Images using Encoding-Decoding Convolutional Neural Networks

A Generalized Representer Theorem for Hilbert Space - Valued Functions

Robust Recovery of Temporal Overlap Between Network Activity Using Transient-Informed Spatio-Temporal Regression

Equation of State of Fluid Methane from First Principles with Machine Learning Potentials

Consistent Tomography Under Partial Observations Over Adaptive Networks