Nanoparticles (NPs)-based electrochemical devices are generating a growing interest and optical microscopy has recently proven to be powerful tool apprehend their behavior. Through several striking examples, this review demonstrates how label-free imaging coupled an actuation can used probe operando the physical properties of single NPs, with high resolution sensitivity without additional emitt...

Leveraging weak or noisy supervision for building effective machine learning models has long been an important research problem. Its importance further increased recently due to the growing need large-scale datasets train deep models. Weak could originate from multiple sources including non-expert annotators automatic labeling based on heuristics user interaction signals. There is extensive amo...

Multi-label text classification has been widely concerned by scholars due to its contribution practical applications. One of the key challenges in multi-label is how extract and leverage correlation among labels. However, it quite challenging directly model correlations labels a complex unknown label space. In this paper, we propose Label Prompt Text Classification (LP-MTC), which inspired idea...

Data augmentation with Mixup has been proven an effective method to regularize the current deep neural networks. generates virtual samples and corresponding labels simultaneously by linear interpolation. However, one-stage generation paradigm use of interpolation have two defects: (1) The label generated sample is simply combined from original pairs without reasonable judgment, resulting in amb...

The Ca2(+)-ATPase in native sarcoplasmic reticulum membranes was selectively spin-labeled for saturation transfer electron spin resonance (ESR) studies by prelabeling with N-ethylmaleimide and by using low label/protein ratios. Results with the nitroxide derivative of the standard sulphydryl-modifying reagent, maleimide, were compared with a series of six novel nitroxide beta-substituted vinyl ...