Synchronous nanoscale topographic and chemical mapping by differential-confocal controlled Raman microscopy

Confocal Raman microscopy is currently used for label-free optical sensing and imaging within the biological, engineering, and physical sciences as well as in industry. However, currently these methods have limitations, including their low spatial resolution and poor focus stability, that restrict the breadth of new applications. This paper now introduces differential-confocal controlled Raman microscopy as a technique that fuses differential confocal microscopy and Raman spectroscopy, enabling the point-to-point collection of three-dimensional nanoscale topographic information with the simultaneous reconstruction of corresponding chemical information. The microscope collects the scattered Raman light together with the Rayleigh light, both as Rayleigh scattered and reflected light (these are normally filtered out in conventional confocal Raman systems). Inherent in the design of the instrument is a significant improvement in the axial focusing resolution of topographical features in the image (to ∼1 nm ), which, when coupled with super-resolution image restoration, gives a lateral resolution of 220 nm. By using differential confocal imaging for controlling the Raman imaging, the system presents a significant enhancement of the focusing and measurement accuracy, precision, and stability (with an antidrift capability), mitigating against both thermal and vibrational artefacts. We also demonstrate an improved scan speed, arising as a consequence of the nonaxial scanning mode

Graph Neural Networks for Natural Language Processing: A Survey

Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP). Although text inputs are typically represented as a sequence of tokens, there isa rich variety of NLP problems that can be best expressed with a graph structure. As a result, thereis a surge of interests in developing new deep learning techniques on graphs for a large numberof NLP tasks. In this survey, we present a comprehensive overview onGraph Neural Networks(GNNs) for Natural Language Processing. We propose a new taxonomy of GNNs for NLP, whichsystematically organizes existing research of GNNs for NLP along three axes: graph construction,graph representation learning, and graph based encoder-decoder models. We further introducea large number of NLP applications that are exploiting the power of GNNs and summarize thecorresponding benchmark datasets, evaluation metrics, and open-source codes. Finally, we discussvarious outstanding challenges for making the full use of GNNs for NLP as well as future researchdirections. To the best of our knowledge, this is the first comprehensive overview of Graph NeuralNetworks for Natural Language Processing.Comment: 127 page