The intracrystalline microstructure of Monte Fico lizardite, by optics, <i>μ</i>-Raman spectroscopy and TEM

The Monte Fico lizardite crystals have an internal skeletal spongy microstructure, formed by two micrometric domains having different optical reliefs. This intracrystalline microstructure parallels the previously reported intercrystalline arrangement, consisting of lizardite prisms within a chrysotile plus polygonal serpentine matrix. In the high-wavenumber region, the larger and more abundant domains (that represent approximately 87 % of the total field view) produce μ-Raman spectra characterized by two major peaks at 3686 and 3705 cm−1. The smaller, less abundant domains present a wide band confined between these wavenumbers. These features are interpreted as lizardite and chrysotile, respectively. Raman results are confirmed by TEM, which emphasizes the presence of well-recognizable polygonal serpentine too. Tight crystallographic control exists between lizardite and this first serpentine generation. A second serpentine generation occurs perpendicularly to the first one. The lizardite crystals grew up with a skeletal habit, whereas chrysotile fibres and polygonal serpentine filled the voids, growing epitactically on the lizardite crystals, with fast crystal growth in a fluid-rich environment.

Skin strata delineation in reflectance confocal microscopy images using recurrent convolutional networks with attention

Reflectance confocal microscopy (RCM) is an effective non-invasive tool for cancer diagnosis. However, acquiring and reading RCM images requires extensive training and experience, and novice clinicians exhibit high discordance in diagnostic accuracy. Quantitative tools to standardize image acquisition could reduce both required training and diagnostic variability. To perform diagnostic analysis, clinicians collect a set of RCM mosaics (RCM images concatenated in a raster fashion to extend the field view) at 4–5 specific layers in skin, all localized in the junction between the epidermal and dermal layers (dermal-epidermal junction, DEJ), necessitating locating that junction before mosaic acquisition. In this study, we automate DEJ localization using deep recurrent convolutional neural networks to delineate skin strata in stacks of RCM images collected at consecutive depths. Success will guide to automated and quantitative mosaic acquisition thus reducing inter operator variability and bring standardization in imaging. Testing our model against an expert labeled dataset of 504 RCM stacks, we achieved $$88.07\%$$ 88.07 % classification accuracy and nine-fold reduction in the number of anatomically impossible errors compared to the previous state-of-the-art.

Real-Time Object Detection for LiDAR Based on LS-R-YOLOv4 Neural Network

Recently, self-driving cars became a big challenge in the automobile industry. After the DARPA challenge, which introduced the design of a self-driving system that can be classified as SAR Level 3 or higher levels, driven to focus on self-driving cars more. Later on, using these introduced design models, a lot of companies started to design self-driving cars. Various sensors, such as radar, high-resolution cameras, and LiDAR are important in self-driving cars to sense the surroundings. LiDAR acts as an eye of a self-driving vehicle, by offering 64 scanning channels, 26.9° vertical field view, and a high-precision 360° horizontal field view in real-time. The LiDAR sensor can provide 360° environmental depth information with a detection range of up to 120 meters. In addition, the left and right cameras can further assist in obtaining front image information. In this way, the surrounding environment model of the self-driving car can be accurately obtained, which is convenient for the self-driving algorithm to perform route planning. It is very important for self-driving to avoid the collision. LiDAR provides both horizontal and vertical field views and helps in avoiding collision. In an online website, the dataset provides different kinds of data like point cloud data and color images which helps this data to use for object recognition. In this paper, we used two types of publicly available datasets, namely, KITTI and PASCAL VOC. Firstly, the KITTI dataset provides in-depth data knowledge for the LiDAR segmentation (LS) of objects obtained through LiDAR point clouds. The performance of object segmentation through LiDAR cloud points is used to find the region of interest (ROI) on images. And later on, we trained the network with the PASCAL VOC dataset used for object detection by the YOLOv4 neural network. To evaluate, we used the region of interest image as input to YOLOv4. By using all these technologies, we can segment and detect objects. Our algorithm ultimately constructs a LiDAR point cloud at the same time; it also detects the image in real-time.

Fast and Accurate Light Field View Synthesis by Optimizing Input View Selection

There is a trade-off between spatial resolution and angular resolution limits in light field applications; various targeted algorithms have been proposed to enhance angular resolution while ensuring high spatial resolution simultaneously, which is also called view synthesis. Among them, depth estimation-based methods can use only four corner views to reconstruct a novel view at an arbitrary location. However, depth estimation is a time-consuming process, and the quality of the reconstructed novel view is not only related to the number of the input views, but also the location of the input views. In this paper, we explore the relationship between different input view selections with the angular super-resolution reconstruction results. Different numbers and positions of input views are selected to compare the speed of super-resolution reconstruction and the quality of novel views. Experimental results show that the speed of the algorithm decreases with the increase of the input views for each novel view, and the quality of the novel view decreases with the increase of the distance from the input views. After comparison using two input views in the same line to reconstruct the novel views between them, fast and accurate light field view synthesis is achieved.

The nationalist-indigenous and colonial modernity: an assessment of two sociologists in India

This paper analyzes the work of two Indian sociologists who defined the contours of sociology in India in the immediate post-independence decades, M. N. Srinivas and A. R. Desai. It argues that their scholarship can be linked to sociology’s legacy as anthropology in India and its embeddedness in the episteme of colonial modernity. It contends that Srinivas’s methodology, the field view, attempted to make a break with earlier methods, such as book view. However, his three concepts, that of dominant caste, Sanskritization and Westernization were perceived as civilizational attributes and which had organized social change in India. A. R. Desai, a Marxist historical sociologist, made an incisive critique of capitalist exploitation and elaborated the material conditions that led to peasant and working-class revolts. However, his sociology could not unravel the caste-class linkages that organized the Indian ‘social’ which was embedded in Indian nationalism. This paper suggests that a definitive understanding of modernity emerges in Indian sociology in the late 70s when the feminist, dalit and tribal movements interrogated the material basis of contemporary India’s developmentalism and its capitalist and exploitative character.