William K. Ovalle PhD, Patrick C. Nahirney PhD Netter's Essential Histology: With Correlated Histopathology (Netter Basic Science) 3rd Edition

With strong correlations between gross anatomy and the microanatomy of structures, Netter’s Essential Histology, 3rd Edition, is the perfect text for today’s evolving medical education. Concise and easy to use, it integrates gross anatomy and embryology with classic histology slides and state-of-the-art scanning electron microscopy, offering a clear, visual understanding of this complex subject. Additional histopathology images, more clinical boxes, and new histopathology content ensure that this textbook-atlas clearly presents the most indispensable histologic concepts and their clinical relevance.Helps you recognize both normal and diseased structures at the microscopic level with the aid of succinct explanatory text as well as numerous clinical boxes. Features more histopathology content and additional clinical boxes to increase your knowledge of pathophysiology and clinical relevance. Includes high-quality light and electron micrographs, including enhanced and colorized electron micrographs that show ultra-structures in 3D, side by side with classic Netter illustrations that link your knowledge of anatomy and cell biology to what is seen in the micrographs. Provides online access to author-narrated video overviews of each chapter, plus Zoomify images and Virtual Slides that include histopathology and can be viewed at different magnifications.

Применение онтологий для решения задач геофизики

The paper covers an intelligent support system that allows to describe and construct solutions to various scientific problems. In this study, in particular, we consider geophysical problems. This system is being developed at the Institute of Computational Mathematics and Mathematical Geophysics of the Russian Academy of Sciences (ICMMG SB RAS) and Institute of Informatics System of the Russian Academy of Sciences (IIS SB RAS). The system contains a knowledge base, the core of which is a set of several interconnected ontologies such as the ontology of supercomputer architectures, the ontology of algorithms and methods. Ontology can be viewed as a set of concepts and how those concepts are linked. As the result, the authors present an ontological description of two geophysical problems via the means of the intelligent support system: 1) the seismic wavefield simulation and 2) the reconstruction of a seismic image through pre-stack time or depth migration. For a better visual understanding of the system described and the results obtained, the paper also contains several schematic diagrams and images. В статье рассматривается система интеллектуальной поддержки, позволяющая описывать и выстраивать решения различных научных задач. В данной работе рассматриваются геофизические задачи. Система разрабатывается в Институте вычислительной математики и математической геофизики Российской академии наук (ИВМГ СО РАН) и Институте систем информатики Российской академии наук (ИИС СО РАН). Система содержит базу знаний, ядром которой является набор из нескольких взаимосвязанных онтологий, таких как онтология суперкомпьютерных архитектур, онтология алгоритмов и методов. Онтологию можно рассматривать как набор концепций и связей между ними. В результате авторы представляют онтологическое описание двух геофизических задач с помощью средств системы интеллектуальной поддержки: 1) моделирование сейсмического волнового поля и 2) реконструкция сейсмического изображения посредством временной или глубинной миграции до суммирования. Для лучшего визуального понимания описанной системы и полученных результатов в работе также есть несколько схематических диаграмм и изображений.

Is It a Cube? Common Visual Perception of Cuboid Drawings

A cube is one of the most fundamental shapes we can draw and can observe from a drawing. The two visualization methods most commonly applied in mathematics textbooks and education are the axonometric and the perspective representations. However, what we see in the drawing is really a cube or only a general cuboid (i.e., a polyhedron with different edge lengths). In this experimental study, 153 first-year ( 19–20-year-old) students, two-thirds of them being female, were asked to interactively adjust a cuboid figure until they believe what they see is really a cube. We were interested in how coherently people, who are actually students of arts studies and engineering with advanced spatial perception skills in most cases, evaluate these drawings. What we have experienced is that for most people there is a common visual understanding of seeing a cube (and not a general cuboid). Moreover, this common sense is surprisingly close to the conventions applied in axonometric drawings, and to the theoretical, geometric solution in the case of three-point perspective drawings, which is the most realistic visualization method.

ISDNet: AI-enabled Instance Segmentation of Aerial Scenes for Smart Cities

Aerial scenes captured by UAVs have immense potential in IoT applications related to urban surveillance, road and building segmentation, land cover classification, and so on, which are necessary for the evolution of smart cities. The advancements in deep learning have greatly enhanced visual understanding, but the domain of aerial vision remains largely unexplored. Aerial images pose many unique challenges for performing proper scene parsing such as high-resolution data, small-scaled objects, a large number of objects in the camera view, dense clustering of objects, background clutter, and so on, which greatly hinder the performance of the existing deep learning methods. In this work, we propose ISDNet (Instance Segmentation and Detection Network), a novel network to perform instance segmentation and object detection on visual data captured by UAVs. This work enables aerial image analytics for various needs in a smart city. In particular, we use dilated convolutions to generate improved spatial context, leading to better discrimination between foreground and background features. The proposed network efficiently reuses the segment-mask features by propagating them from early stages using residual connections. Furthermore, ISDNet makes use of effective anchors to accommodate varying object scales and sizes. The proposed method obtains state-of-the-art results in the aerial context.

An Intelligent Garbage Sorting System Based on Edge Computing and Visual Understanding of Social Internet of Vehicles

In order to enable Social Internet of Vehicles devices to achieve the purpose of intelligent and autonomous garbage classification in a public environment, while avoiding network congestion caused by a large amount of data accessing the cloud at the same time, it is therefore considered to combine mobile edge computing with Social Internet of Vehicles to give full play to mobile edge computing features of high bandwidth and low latency. At the same time, based on cutting-edge technologies such as deep learning, knowledge graph, and 5G transmission, the paper builds an intelligent garbage sorting system based on edge computing and visual understanding of Social Internet of Vehicles. First of all, for the massive multisource heterogeneous Social Internet of Vehicles big data in the public environment, different item modal data adopts different processing methods, aiming to obtain a visual understanding model. Secondly, using the 5G network, the model is deployed on the edge device and the cloud for cloud-side collaborative management, aiming to avoid the waste of edge node resources, while ensuring the data privacy of the edge node. Finally, the Social Internet of Vehicles devices is used to make intelligent decision-making on the big data of the items. First, the items are judged as garbage, and then the category is judged, and finally the task of grabbing and sorting is realized. The experimental results show that the system proposed in this paper can efficiently process the big data of Social Internet of Vehicles and make valuable intelligent decisions. At the same time, it also has a certain role in promoting the promotion of Social Internet of Vehicles devices.

CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation

Scene graphs are semantic abstraction of images that encourage visual understanding and reasoning. However, the performance of Scene Graph Generation (SGG) is unsatisfactory when faced with biased data in real-world scenarios. Conventional debiasing research mainly studies from the view of balancing data distribution or learning unbiased models and representations, ignoring the correlations among the biased classes. In this work, we analyze this problem from a novel cognition perspective: automatically building a hierarchical cognitive structure from the biased predictions and navigating that hierarchy to locate the relationships, making the tail relationships receive more attention in a coarse-to-fine mode. To this end, we propose a novel debiasing Cognition Tree (CogTree) loss for unbiased SGG. We first build a cognitive structure CogTree to organize the relationships based on the prediction of a biased SGG model. The CogTree distinguishes remarkably different relationships at first and then focuses on a small portion of easily confused ones. Then, we propose a debiasing loss specially for this cognitive structure, which supports coarse-to-fine distinction for the correct relationships. The loss is model-agnostic and consistently boosting the performance of several state-of-the-art models. The code is available at: https://github.com/CYVincent/Scene-Graph-Transformer-CogTree.

CNN-LRP: Understanding Convolutional Neural Networks Performance for Target Recognition in SAR Images

Target recognition is one of the most challenging tasks in synthetic aperture radar (SAR) image processing since it is highly affected by a series of pre-processing techniques which usually require sophisticated manipulation for different data and consume huge calculation resources. To alleviate this limitation, numerous deep-learning based target recognition methods are proposed, particularly combined with convolutional neural network (CNN) due to its strong capability of data abstraction and end-to-end structure. In this case, although complex pre-processing can be avoided, the inner mechanism of CNN is still unclear. Such a “black box” only tells a result but not what CNN learned from the input data, thus it is difficult for researchers to further analyze the causes of errors. Layer-wise relevance propagation (LRP) is a prevalent pixel-level rearrangement algorithm to visualize neural networks’ inner mechanism. LRP is usually applied in sparse auto-encoder with only fully-connected layers rather than CNN, but such network structure usually obtains much lower recognition accuracy than CNN. In this paper, we propose a novel LRP algorithm particularly designed for understanding CNN’s performance on SAR image target recognition. We provide a concise form of the correlation between output of a layer and weights of the next layer in CNNs. The proposed method can provide positive and negative contributions in input SAR images for CNN’s classification, viewed as a clear visual understanding of CNN’s recognition mechanism. Numerous experimental results demonstrate the proposed method outperforms common LRP.

Re-reading Ernbodied Texts - An Interpretation of Rune-stones

Rune-stones have been a major field of research in philology, archaeology, art history and history during the 20th century. Most of these studies have been based on the thorough editions of rune-stones published in Scandinavia during the century. The aim of this article is to question some of the fundamental principles of these editions, and to initiate a new type of interpretation based on the complex interplay between images and texts on the rune-stones. Elements of a more visual understanding of the monuments are presented, as well as some examples of a new contextual reading, which sometimes alter the philological interpretations in the rune-stone publications.