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Deep neural networks are extensively used in text mining and Natural Language Processing is to enable computers to understand, analyze, and generate natural language data, such as text or speech, but semantic resources, such as taxonomies and ontologies, are not fully included in deep learning. In this paper, we use Deep Convolutional Neural Network (Deep CNN) to classify research papers using the Computer Science Ontology, an ontology of research areas in the field of computer science. It takes as input the abstract and keywords of a particular research paper and returns the relevant research topic. To evaluate our ontology, we used a gold standard dataset that includes research articles. To further improve text classification results, we propose to design a Deep CNN model. We then used ontology matching to reduce the classes and get better results. Experimental results show that the proposed approach outperforms the one with the highest precision, recall, and F1-score. [ABSTRACT FROM AUTHOR]

In recent years, cellular neural networks (CNNs) have become a popular apparatus for simulations in neuroscience, biology, medicine, computer sciences and engineering. In order to create more adequate models, researchers have considered memory effects, reaction–diffusion structures, impulsive perturbations, uncertain terms and fractional-order dynamics. The design, cellular aspects, functioning and behavioral aspects of such CNN models depend on efficient stability and control strategies. In many practical cases, the classical stability approaches are useless. Recently, in a series of papers, we have proposed several extended stability and control concepts that are more appropriate from the applied point of view. This paper is an overview of our main results and focuses on extended stability and control notions including practical stability, stability with respect to sets and manifolds and Lipschitz stability. We outline the recent progress in the stability and control methods and provide diverse mechanisms that can be used by the researchers in the field. The proposed stability techniques are presented through several types of impulsive and fractional-order CNN models. Examples are elaborated to demonstrate the feasibility of different technologies. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate for the first time how the Integrated Finite Element Neural Network (I-FENN) framework, previously proposed by the authors [1,2] , can efficiently simulate the entire loading history of non-local gradient damage propagation. To achieve this goal, we first adopt a Temporal Convolutional Network (TCN) as the neural network of choice to capture the history-dependent evolution of the non-local strain in a coarsely meshed domain. The quality of the network predictions governs the computational performance of I-FENN, and therefore we perform an extended investigation aimed at enhancing them. We explore a data-driven vs. physics-informed TCN setup to arrive at an optimum network training, evaluating the network based on a coherent set of relevant performance metrics. We address the crucial issue of training a physics-informed network with input data that span vastly different length scales by proposing a systematic way of input normalization and output un-normalization. We then integrate the trained TCN within the nonlinear iterative FEM solver and apply I-FENN to simulate the damage propagation analysis. I-FENN is always applied in mesh idealizations different from the one used for the TCN training, showcasing the framework's ability to be used at progressively refined mesh resolutions. We illustrate several cases that I-FENN completes the simulation using either a modified or a full Newton–Raphson scheme, and we showcase its computational savings compared to both the classical monolithic and staggered FEM solvers. We underline that we satisfy very strict convergence criteria for every increment across the entire simulation, providing clear evidence of the robustness and accuracy of I-FENN. All the code and data used in this work will be made publicly available upon publication of the article. • Non-local damage propagation is modeled for the first time with a TCN-based I-FENN. • I-FENN is faster than monolithic and staggered FEM and savings scale with model size. • I-FENN's robustness is shown by satisfying very strict convergence criteria. • Input/output normalization methods are applied to enhance the network training. • I-FENN is always applied in different training and testing mesh idealizations. [ABSTRACT FROM AUTHOR]

"Deep Learning and Scientific Computing with R torch" by Sigrid Keydana is a book that introduces an R interface for PyTorch, a popular deep learning framework. The book is well-written and easy to follow, with plenty of illustrations and examples. It is divided into three parts: the basics of torch, the implementation of deep neural networks, and the utility of torch beyond deep learning. The book covers topics such as tensors, automatic differentiation, neural network architecture, optimization algorithms, and various applications of torch. Overall, it is a valuable resource for R users interested in deep learning and scientific computing. [Extracted from the article]

Pests and diseases have posed a severe threat to the production of grapes, causing irreversible damage on the growth cycle of grapes. The treatments vary significantly in the types of grape diseases. Therefore, it is highly demanding to detect the diseases of grapes timely, while accurately identify the types of diseases to avoid the spread of diseases. In the early days, the diseases of grapes were mainly identified by experienced grape and pest experts recognizing the grape leaves manually, which is time-consuming and laborious. It is necessary to develop a grape disease recognition using computer science technology. Convolutional neural network (CNN) has been used to automatically recognize agricultural pests in the field of artificial intelligence (AI) in recent years. However, the general CNN cannot be well trained to achieve high accuracy, due mainly to few large datasets of diseased grape leaves. In this study, a new recognition network of grape leaf disease was proposed using CNN. Transfer learning and data enhancement were also applied for the new network to enlarge the small dataset of grape leaves. The new network was named Grape-VGG-16 (GV for short) using VGG-16 network (one of the most classical CNN). GV generally contained 5 convolutions, 2 full connection 1 flatten, 1 dropout, and 1 SoftMax layer. The knowledge was first learned from the ImageNet image dataset using transfer learning, and then be transferred to the GV in the disease recognition of grape leaf. The output number was modified to 4 for the last fully connected layer using fine tuning, representing the 4 types of grapes leaves (healthy, brown spot, wheel spot, and black rot). A TensorFlow framework was selected to verify the network in the experiments using Python programming language. 300 images were collected for each leaf category (the total number of images was 1200), 250 of which were randomly selected as the training set, and 50 of which as the testing set. Then the dataset of images was augmented under the operations, such as random rotation, random horizontal and vertical translation, as well as horizontal flipping on the image. Finally, the augmented dataset was used to train the GV network, which was uniformly scaled to 224×224 pixels. 18 groups of experiments were performed on a computing server with Nvidia GPU using a combination of three learning (new learning, transfer learning with training the full connection layer, as well as transfer learning with training both the last convolution and full connection layer), two dataset with/without augmentation, and three learning rates (0.01, 0.001, and 0.0001). The experimental results demonstrated that: 1) Transfer learning improved the model accuracy, while shortened the convergence time of the model; 2) Data augmentation greatly increased the diversity of data, while effectively alleviated the over-fitting. Consequently, the GV network achieved an overall recognition accuracy of 96.48% under both transfer learning and data augmentation. The recognizing accuracies of healthy leaves, brown spot, wheel spot and black rot were 98.04%, 98.04%, 95.83%, and 94.00%, respectively. The GV network was also embedded in the mobile application, and then deployed on the mobile terminal, for the detection and recognition of grape leaf diseases in the field. The finding can offer a potential promising reference for the intelligent diagnosis of grape leaf diseases in fruit production. [ABSTRACT FROM AUTHOR]

• Clinical target volume definition in radiotherapy is challenging. • The contribution of computational methods is discussed. • Goals are automation, consistency, and ultimately improvements. • Image segmentation algorithms can automate the process in parts. • Mathematical models may quantitatively describe tumor progression patterns. Treatment planning in radiotherapy distinguishes three target volume concepts: the gross tumor volume (GTV), the clinical target volume (CTV), and the planning target volume (PTV). Over time, GTV definition and PTV margins have improved through the development of novel imaging techniques and better image guidance, respectively. CTV definition is sometimes considered the weakest element in the planning process. CTV definition is particularly complex since the extension of microscopic disease cannot be seen using currently available in-vivo imaging techniques. Instead, CTV definition has to incorporate knowledge of the patterns of tumor progression. While CTV delineation has largely been considered the domain of radiation oncologists, this paper, arising from a 2019 ESTRO Physics research workshop, discusses the contributions that medical physics and computer science can make by developing computational methods to support CTV definition. First, we overview the role of image segmentation algorithms, which may in part automate CTV delineation through segmentation of lymph node stations or normal tissues representing anatomical boundaries of microscopic tumor progression. The recent success of deep convolutional neural networks has also enabled learning entire CTV delineations from examples. Second, we discuss the use of mathematical models of tumor progression for CTV definition, using as example the application of glioma growth models to facilitate GTV-to-CTV expansion for glioblastoma that is consistent with neuroanatomy. We further consider statistical machine learning models to quantify lymphatic metastatic progression of tumors, which may eventually improve elective CTV definition. Lastly, we discuss approaches to incorporate uncertainty in CTV definition into treatment plan optimization as well as general limitations of the CTV concept in the case of infiltrating tumors without natural boundaries. [ABSTRACT FROM AUTHOR]

Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional FERET and Mugshot datasets. We evaluated the average FR performance using a weighted score-level fusion of FM and DCNN based recognition methods. The experimental evaluations on FERET, CFM and Mugshot datasets show higher performances than state-of-the-art FM approaches with 99.23%, 94.64% and 97.86% accuracies in Rank-1 evaluations, respectively. Our FR performance of FM + DCNN (86.46% in TAR, 91.23% in Rank-1, 96.57% in Rank-5, 98.65% in Rank-10) is shown to be higher than state-of-the-art (83.80% in TAR@1%FAR, 90.30% in Rank-1, 96.5% in Rank-5, and 97.7% in Rank-10). Experimental results after fusion of FM + DCNN on IJB-A dataset show 2.66% FR performance improvement from the DCNN only recognition in terms of TAR@1%FAR. • Face verification with soft biometric based matching approach • Unconstrained dataset with larger variations collected from 500 subjects • Evaluations on FERET, CFM, Mugshot, and IJB-A datasets • Performance improvement with Facial mark and VGG-face based fusion matching Sidra Riaz received her BS degree in Telecom Engineering from NUCES-FAST in 2011. She received her MS and PhD degrees in Computer Engineering from Chosun University in 2013 and Sogang University in 2020, respectively. She is currently working as an AI developer in BluePrint Company, Korea. Ms. Sidra Riaz is the recipient of National ICT R&D Merit Scholarship, Global IT Scholarship, and Seoul Honorary Citizenship Awards. Her research interests include deep learning, computer vision, and pattern recognition. Unsang Park received the BS and MS degrees from the Department of Materials Engineering, Hanyang University, South Korea, in 1998 and 2000, respectively. He received the MS and Ph.D. degrees from the Department of Computer Science and Engineering, Michigan State University, in 2004 and 2009, respectively. From 2012, he is an assistant professor in the Department of Computer Science and Engineering at Sogang University. His research interests include pattern recognition, image processing, computer vision, and machine learning. Prem Natarajan is Michael Keston Executive Director of ISI, a vice dean of the USC Viterbi School of Engineering and a professor of computer science. At ISI, he leads managerial and technical directions Institute-wide, including research, development and the MOSIS electronic chip brokerage. He also heads teams in his areas of expertise: novel approaches to face, character, handwriting and speech recognition, along with other deep learning and natural language processing directions. [ABSTRACT FROM AUTHOR]