Machine learning for the design of nano-materials

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We present the deep self-correlation (DSC) descriptor for establishing dense correspondences between images taken under different imaging modalities, such as different spectral ranges or lighting conditions. We encode local self-similar structure in a pyra ...

To reduce energy consumption, it is possible to operate embedded systems at sub-nominal conditions (e.g., reduced voltage, limited eDRAM refresh rate) that can introduce bit errors in their memories. These errors can affect the stored values of CNN weights ...

Drainage canals associated with logging and agriculture dry out organic soils in tropical peatlands, thereby threatening the viability of long-term carbon stores due to increased emissions from decomposition, fire, and fluvial transport. In Southeast Asian ...

The evaluation of cardiac contractility by the assessment of the ventricular systolic elastance function is clinically challenging and cannot be easily obtained at the bedside. In this work, we present a framework characterizing left ventricular systolic f ...

Optical diffraction tomography (ODT) provides us 3D refractive index (RI) distributions of transparent samples. Since RI values differ across different materials, they serve as endogenous contrasts. It, therefore, enables us to image without pre-processing ...

The central vein sign (CVS) is an efficient imaging biomarker for multiple sclerosis (MS) diagnosis, but its application in clinical routine is limited by inter-rater variability and the expenditure of time associated with manual assessment. We describe a ...

This paper formulates a multitask optimization problem where agents in the network have individual objectives to meet, or individual parameter vectors to estimate, subject to a smoothness condition over the graph. The smoothness condition softens the trans ...

Machine learning for the design of nano-materials

Chattez avec Graph Search

Dense Cross-Modal Correspondence Estimation With the Deep Self-Correlation Descriptor

E2CNN: Ensembles of Convolutional Neural Networks to Improve Robustness Against Memory Errors in Edge-Computing Devices

Drainage Canals in Southeast Asian Peatlands Increase Carbon Emissions

Implementation and Calibration of a Deep Neural Network to Predict Parameters of Left Ventricular Systolic Function Based on Pulmonary and Systemic Arterial Pressure Signals

Learning approaches to high-fidelity optical diffraction tomography

CVSnet: A machine learning approach for automated central vein sign assessment in multiple sclerosis

Learning Over Multitask Graphs-Part I: Stability Analysis

Machine Learning for Color Generation Using Plasmonic Materials