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Purpose: Accurately evaluating fluid flow behaviors and determining permeability for deforming porous media is time-consuming and remains challenging. This paper aims to propose a novel machine-learning method for the rapid estimation of permeability of porous media at different deformation stages constrained by hydro-mechanical coupling analysis. Design/methodology/approach: A convolutional neural network (CNN) is proposed in this paper, which is guided by the results of finite element coupling analysis of equilibrium equation for mechanical deformation and Boltzmann equation for fluid dynamics during the hydro-mechanical coupling process [denoted as Finite element lattice Boltzmann model (FELBM) in this paper]. The FELBM ensures the Lattice Boltzmann analysis of coupled fluid flow with an unstructured mesh, which varies with the corresponding nodal displacement resulting from mechanical deformation. It provides reliable label data for permeability estimation at different stages using CNN. Findings: The proposed CNN can rapidly and accurately estimate the permeability of deformable porous media, significantly reducing processing time. The application studies demonstrate high accuracy in predicting the permeability of deformable porous media for both the test and validation sets. The corresponding correlation coefficients (R2) is 0.93 for the validation set, and the R2 for the test set A and test set B are 0.93 and 0.94, respectively. Originality/value: This study proposes an innovative approach with the CNN to rapidly estimate permeability in porous media under dynamic deformations, guided by FELBM coupling analysis. The fast and accurate performance of CNN underscores its promising potential for future applications. [ABSTRACT FROM AUTHOR]

The article presents several news briefs, statistics, and quotations, including information on the amount of moldy paper money that was found in a state park in Pennsylvania. Maryland Prosecutor Marilyn J. Mosby is quoted regarding justice and six police officers who have been charged in the death of Baltimore, Maryland resident Freddie Gray. Nepal's Information Minister Minendra Rijal claims that his nation needs the international community's help to recover from an earthquake in April 2015.

Over the past few years, tasks such as automatic information extraction and object surface detection from 3D medical images have become increasingly popular, providing clinicians with numerous new opportunities for exploration and diagnostic assistance. Nonetheless, the segmentation process can present various challenges. This paper introduces an automatic 3D deep parametric active surface model (3D B-snake) approximated by bivariate Unified and Extended spline (UE-spline) functions with a parameter controlling the shape of the snake. We introduce an energy term that enables to separate the different textures present in an image using a Convolutional Neural Network (CNN) learning model, namely U-Net. After balancing our snake surface to align it with the complex parts of the object to be segmented, an object snake alignment strategy is proposed using an interpolation scheme that exploits some geometric properties of the object. Experiments performed on the BraTS21 dataset show that our model is very powerful to deal with various difficulties encountered in medical image segmentation. [ABSTRACT FROM AUTHOR]

Purpose: Considering the problem that the high recognition rate of deep learning requires the support of mass data, this study aims to propose an insulating fault identification method based on small data set convolutional neural network (CNN). Design/methodology/approach: Because of the chaotic characteristics of partial discharge (PD) signals, the equivalent transformation of the PD signal of unit power frequency period is carried out by phase space reconstruction to derive the chaotic features. At the same time, geometric, fractal, entropy and time domain features are extracted to increase the volume of feature data. Finally, the combined features are constructed and imported into CNN to complete PD recognition. Findings: The results of the case study show that the proposed method can realize the PD recognition of small data set and make up for the shortcomings of the methods based on CNN. Also, the 1-CNN built in this paper has better recognition performance for four typical insulation faults of cable accessories. The recognition performance is improved by 4.37% and 1.25%, respectively, compared with similar methods based on support vector machine and BPNN. Originality/value: In this paper, a method of insulation fault recognition based on CNN with small data set is proposed, which can solve the difficulty to realize insulation fault recognition of cable accessories and deep data mining because of insufficient measure data. [ABSTRACT FROM AUTHOR]

This paper presents the design and the VLSI implementation of an asynchronous cellular logic array for fast binary image processing. The proposed processor array employs trigger-wave propagation and collision detection mechanisms for binary image skeletonization, and Voronoi tessellation. Low power, low area, and high processing speed are achieved using full custom dynamic logic design. The prototype array consisting of 64 \times 96 cells is fabricated in a standard 90 nm CMOS technology. The experimental results confirm the fast operation of the array, capable of extracting up to 2.78\times 10^6 skeletons per second, consuming less than 1 nJ/skeleton. The asynchronous operation enables circular wave contours, which improves the quality of the extracted skeletons. The proposed asynchronous processing module consists of 24 MOS transistors and occupies 5.5\ \mum\times 7.4\ \mum area. Such array can be used as a co-processing unit aiding global binary image processing in standard pixel-parallel SIMD architectures in vision chips. [ABSTRACT FROM AUTHOR]

This paper presents a field-programmable gate array (FPGA) design of a segmentation algorithm based on convolutional neural network (CNN) that can process light detection and ranging (LiDAR) data in real-time. For autonomous vehicles, drivable region segmentation is an essential step that sets up the static constraints for planning tasks. Traditional drivable region segmentation algorithms are mostly developed on camera data, so their performance is susceptible to the light conditions and the qualities of road markings. LiDAR sensors can obtain the 3D geometry information of the vehicle surroundings with high precision. However, it is a computational challenge to process a large amount of LiDAR data in real-time. In this paper, a CNN model is proposed and trained to perform semantic segmentation using data from the LiDAR sensor. An efficient hardware architecture is proposed and implemented on an FPGA that can process each LiDAR scan in 17.59 ms, which is much faster than the previous works. Evaluated using Ford and KITTI road detection benchmarks, the proposed solution achieves both high accuracy in performance and real-time processing in speed. [ABSTRACT FROM AUTHOR]

Hardware failures are undesired but a common problem in circuits. Such failures are inherently due to the aging of circuitry or variation in circumstances. In critical systems, customers demand the system never to fail. Several self-healing and fault tolerance techniques have been proposed in the literature for recovering a circuitry from a fault. Such techniques come to the rescue when a fault has already occurred but they are typically uninformed about the possibility of an impending failure (i.e., fault prediction), which can be used as a pre-stage to fault tolerance and self-healing. This paper presents an approach to early fault prediction of circuits. The proposed method uses Fast Fourier Transform (FFT) to get the fault frequency signature, Principal Component Analysis (PCA) to get the most important data with reduced dimension, and Convolutional Neural Network (CNN) to learn and classify the fault. The proposed method is validated for working in different circuits by testing it using two circuits: comparator and amplifier. The comparator and amplifier are implemented using 45 nm technology on HSPICE to extract the failures dataset in terms of voltage, current, temperature, noise, and delay. This extracted data is used for training the proposed approach using Tensorflow. To the best of our knowledge, this is the first work of fault prediction at the transistor level for hardware system. The proposed approach considers aging, short-circuit, and open-circuit faults, and it provides a fault prediction accuracy of 98.93% and 98.91% for comparator and amplifier circuits, respectively. The proposed method is tested for two different circuits for its validation, and it consumes 1.08 W for Altera Arria 10 GX FPGA device. [ABSTRACT FROM AUTHOR]

• This paper proposes a human–machine integration ECG intelligent annotation system. • The ECG intelligent annotation system can free the annotation experts from the heavy manual annotation work. • This paper proposes an accurate beat generation model suitable for all beat types. • The generated beat data can be used as a supplement to labelled data. • This paper proposes an intelligent beat pre-annotation model based on intelligent simulation generated data. • The intelligent beat pre-annotation model achieved the best performance. An electrocardiogram (ECG) consists of complex segments, such as P-QRS-T waves. Manual ECG annotation is challenging and time-consuming, even for specialist physicians. The shortage of labelled ECG data is one of the essential factors that affect ECG intelligent analysis's long-term development. This study proposes an intelligent ECG-assisted annotation system, that not only supplements labelled data, but also significantly reduces the workload compared with manual annotation. Since beat annotation is the most basic and important part, a GAN-based generation model that can generate 14 types of simulation beats and a CNN-based beat pre-annotation model are proposed. The experimental results show that the simulation beat has high similarity to real beat and the accuracy of the pre-annotation model on the test set of 14 classes of beats is 99.28%. The proposed ECG intelligent annotation system's self-learning mechanism could improve pre-annotation performance and annotation efficiency by generating more labelled data. The proposed annotation system can also be extended to other data annotation applications. [ABSTRACT FROM AUTHOR]

The early and accurate detection of COVID-19 is vital nowadays to avoid the vast and rapid spread of this virus and ease lockdown restrictions. As a result, researchers developed methods to diagnose COVID-19. However, these methods have several limitations. Therefore, presenting new methods is essential to improve the diagnosis of COVID-19. Recently, investigation of the electrocardiogram (ECG) signals becoming an easy way to detect COVID-19 since the ECG process is non-invasive and easy to use. Therefore, we proposed in this paper a novel end-to-end deep learning model (ECG-COVID) based on ECG for COVID-19 detection. We employed several deep Convolutional Neural Networks (CNNs) on a dataset of 1109 ECG images, which is built for screening the perception of COVID-19 and cardiac patients. After that, we selected the most efficient model as our model for evaluation. The proposed model is end-to-end where the input ECG images are fed directly to the model for the final decision without using any additional stages. The proposed method achieved an average accuracy of 98.81%, Precision of 98.8%, Sensitivity of 98.8% and, F1-score of 98.81% for COVID-19 detection. As cases of corona continue to rise and hospitalizations continue again, hospitals may find our study helpful when dealing with these patients who did not get significantly worse. [ABSTRACT FROM AUTHOR]

The optimization algorithm plays a decisive role in the performance of deep learning models. Nevertheless, the task, the data properties, and the model's architecture directly influence this latter's effectiveness. Hence, analyzing the effect of different optimizers on specific tasks, such as change detection, is very advantageous due to the ambiguity surrounding the method selection. This paper is twofold. First, we investigate the performance of five first-order optimization methods, namely Momentum GD, NAG, AdaGrad, RMSProp, and Adam, in the context of change detection using a U-Net and DenseU-Net architectures for remote sensing images. To the best of our knowledge, no comparative study involving optimization methods has been conducted for change detection. The results reveal that the Adam optimizer is the preferred choice to train a U-Net like architecture for this specific task. Challenges like dataset size and irregular change patterns warrant further exploration and optimization. The second part of this study focuses on a detailed analysis of the Adam optimizer. We specifically examine its sensitivity to both model and data sparsity. To do this, we train several Convolutional Neural Network (CNN) model architectures, each with a different degree of sparsity, on several datasets with various levels of sparsity. The experimental results provide valuable insights, showing that the Adam optimizer is more influenced by model sparsity than data sparsity, and suggesting its suitability for effectively training densely connected models. [ABSTRACT FROM AUTHOR]

Handwritten signature is one of the most important behavioral biometrics and plays an important role in the field of identity verification. It is regarded as a legal means to verify personal identity by administrative and financial institutions. Traditional manual signature verification requires large labor costs and the probability of verification error is relatively high. Nowadays, tablets are often used for signature capturing, which motivates us to explore the feasibility of using tablets for signature verification. In this paper, we propose an online handwriting s ignature v erification system based on s ound and v ibration (SVSV) generated during the signing process. We develop an application to collect signature-related vibration and sound data. We first extract the time domain features of the sound signal and use Fast Fourier Transform to extract the frequency domain features of the sound data. For vibration data, we use Discrete Cosine Transform for dimensionality reduction and feature extraction. Then we fuse the sound and vibration features. Finally, we design an efficient one-class classifier based on the Convolutional Neural Network to perform signature verification. Through extensive experiments with 12 volunteers, the results show that SVSV is a robust and efficient system with an AUC of 0.984 and an EER of 0.05. [ABSTRACT FROM AUTHOR]

Failure detection is employed in the industry to improve system performance and reduce costs due to unexpected malfunction events. So, a good dataset of the system is desirable for designing an automated failure detection system. However, industrial process datasets are unbalanced and contain little information about failure behavior due to the uniqueness of these events and the high cost for running the system just to get information about the undesired behaviors. For this reason, performing correct training and validation of automated failure detection methods is challenging. This paper proposes a methodology called FaultFace for failure detection on Ball-Bearing joints for rotational shafts using deep learning techniques to create balanced datasets. The FaultFace methodology uses 2D representations of vibration signals denominated faceportraits obtained by time–frequency transformation techniques. From the obtained faceportraits, a Deep Convolutional Generative Adversarial Network is employed to produce new faceportraits of the nominal and failure behaviors to get a balanced dataset. A Convolutional Neural Network is trained for fault detection employing the balanced dataset. The FaultFace methodology is compared with other deep learning techniques to evaluate its performance in for fault detection with unbalanced datasets. Obtained results show that FaultFace methodology has a good performance for failure detection for unbalanced datasets. [ABSTRACT FROM AUTHOR]

The Wall Street Journal reports the FBI was told by someone "familiar with the stored papers" that Trump may not have turned over all the classified documents that he brought back to Mar-A-Lago. [ABSTRACT FROM AUTHOR]

We propose the design of Physically Unclonable Functions (PUFs) exploiting the nonlinear behavior of Cellular Neural Networks (CNNs). Our work derives from some theoretical results achieved within the theory of CNNs, adapted to a simpler case. The theoretical analysis discussed in this work has a general validity, whereas the presented basic hardware solution (i.e., the PUF electronic implementation) has to be understood as a reference demonstrating circuit to be further optimized and developed for a profitable use of the proposed approach. Theoretical results have been validated experimentally. [ABSTRACT FROM PUBLISHER]

The natural language processing (NLP) denotes a technique used to process data such as text and speech. Some of the fundamental research in NLP includes the named entity recognition, which recognizes the named entities (i.e., persons and companies) from texts, the semantic parsing, which converts a natural language utterance to a logical form, and the co-reference resolution, which extracts the nouns (including pronouns and noun phrases) pointing to the same reference body. In this paper, we focus on the mention extraction and classification, proposing a neural-encoded mention-hypergraph model named the BILU-NEMH to extract the mention entities from a content. The proposed BILU-NEMH model combines a mention hypergraph model with the encoding schema and neural network. The proposed model can effectively capture the overlapping mention entities of an unbounded length. The proposed model was verified by the experiments, and the obtained experimental results showed that the proposed model achieved better performance and greater effectiveness than the existing related models on most standard datasets. [ABSTRACT FROM AUTHOR]

Abstract Fibrillations and flutters are serious diseases influence the normal functioning of the heart. Among the most frequently occurring heart disorders belong atrial fibrillation (A fib), atrial flutter (A fl), and ventricular fibrillation (V fib). Nowadays, heart failures are mostly detected by electrocardiogram (ECG) device by examining the signal transferred from electrodes placed on the human body to the output display. The signal is examined by professional health personnel, who are looking for an obvious pattern representing the normal or abnormal rhythm of the heart. Nevertheless, information from ECG can be distorted by noise on data transmission. Moreover,problematic pattern does not have to be so much different from normal and it can be difficult to recognize them just by human eye even by an expert in the field. An automated computer-aided diagnosis (CAD) is an approach to make decision support for elimination of these lacks. For early diagnosis, CAD tool should work in like real-time system without big time consuming and dependency on data and measuring differences of each device. This paper proposes a novel approach of a CAD system to the detection of fibrillations and flutters by our 8-layer deep convolutional neural network. Proposed model requires only basic data normalization without pre-processing and feature extraction from raw ECG samples. We have achieved the accuracy, specificity, and sensitivity of 98.45%, 99.27%, and 99.87% respectively. Designed system can be directly implemented like decision support system in clinical environment. [ABSTRACT FROM AUTHOR]

• A shock detection method based on Convolutional Neural Networks is proposed. • A novel loss function is designed for shock waves. • Compared with existing methods, ours is more efficient or yields better results. • The good generalization of our method is demonstrated by extensive experiments. As one of the main technologies of flow visualization, shock detection plays a key role in feature identification and has been intensively studied. However, existing methods take too much execution time to meet the requirement of post-processing on large-scale Computational Fluid Dynamics (CFD) flow field data. To address this problem, in this paper, we propose a detection method for shock waves based on Convolutional Neural Networks (CNN) and design a novel loss function to optimize the detection results. In specific, the proposed method samples small patches from flow field data, and trains a detection network which includes multiple convolutional layers. This network is responsible for generating shock values and finding the location of shock waves. Compared with the existing shock detection methods which are not based on deep learning, our method has great advantages in detection time. Compared with the ones based on deep learning, our method gives a better detection result of shock waves. Extensive experimental results demonstrate the good generalization of the proposed method on many datasets. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes the hyperchaotic system of 6th-order cellular neural network (CNN), realizes its synchronization based on state observer. In addition, a multi-ary number communication system based on this hyperchaotic system is given in this paper. This communication system has the features of large capacity of signals transmission and high security. [Copyright &y& Elsevier]

Cuomo was suspended Tuesday after papers revealed he closely aided his brother Andrew Cuomo during his sexual harrasment scandal. [ABSTRACT FROM AUTHOR]

The secondary user (SU) only detects one feature of the primary user (PU) signal in conventional spectrum sensing. However, single feature detection does not fully explore the PU information. Meanwhile, non-Gaussian interference will also affect the spectrum sensing performance. In this paper, we propose a novel spectrum sensing method using multimodal fusion and convolutional neural networks (CNN). First, the received signal at multiple antennas is preprocessed, where the generalized covariance matrix and the generalized Wigner-Ville distribution are used to characterize two different modes of the received signals as the input of CNN. Then, a CNN model using multimodal fusion is constructed. In this model, the attention mechanism is first used to extract features of different modalities, and the multimodal fusion technique fuses the extracted features to obtain the feature vector. Finally, the detection statistics and detection thresholds for spectrum sensing are constructed and compared using the output feature vectors of CNN. Simulation results show that the performance of multi-mode fusion spectrum sensing is better than the single-mode counterpart. This method can effectively realize spectrum sensing with both Gaussian noise and non-Gaussian interference, and it has a high detection probability at low SNR. [ABSTRACT FROM AUTHOR]

Glaucoma progressively affects the optic nerve and may cause partial or complete vision loss. Raised intravascular pressure is the only factor which can be modified to prevent blindness from this condition. Accurate early detection and continuous screening may prevent the vision loss. Computer aided diagnosis (CAD) is a non-invasive technique which can detect the glaucoma in its early stage using digital fundus images. Developing such a system require diverse huge database in order to reach optimum performance. This paper proposes a novel CAD tool for the accurate detection of glaucoma using deep learning technique. An eighteen layer convolutional neural networks (CNN) is effectively trained in order to extract robust features from the digital fundus images. Finally these features are classified into normal and glaucoma classes during testing. We have achieved the highest accuracy of 98.13% using 1426 (589: normal and 837: glaucoma) fundus images. Our experimental results demonstrates the robustness of the system, which can be used as a supplementary tool for the clinicians to validate their decisions. [ABSTRACT FROM AUTHOR]

This paper presents an effective deep attention network for joint hand gesture localization and recognition using static RGB-D images. Our method trains a CNN framework based on a soft attention mechanism in an end-to-end manner, which is capable of automatically localizing hands and classifying gestures using a single network rather than relying on the conventional means of stage-wise hand segmentation/detection and classification. More precisely, our attention network first computes the weight for each proposal generated from the entire image, in order to judge the probability of the hand appearing in a given region. It then implements a global-sum operation for all proposals, which is influenced by their corresponding weights, in order to obtain a representation of the entire image. We demonstrate the feasibility and effectiveness of our method through extensive experiments on the NTU Hand Digits (NTU-HD) benchmark and the challenging HUST American Sign Language (HUST-ASL) dataset. Moreover, the proposed attention network is simple to train, without requiring bounding-box or segmentation mask annotations, which makes it easy to apply in hand gesture recognition systems. Based on the proposed attention network and taken RGB-D images as input, we obtain the state-of-the-art hand gesture recognition performance on the challenging HUST-ASL dataset. [ABSTRACT FROM AUTHOR]

Carbon fiber reinforced polymer (CFRP) could be damaged by low-velocity impacts. Precise localization of impact can increase the efficiency of nondestructive testing by narrowing the scanning in a small area. The conventional localization method is based on the time-of-arrival (ToA) of impact induced guided wave, whose localization accuracy could be influenced by the reflected waves from plate edges. This paper proposes an impact localization method based on time–frequency features of guided wave and convolutional neural network. To test the algorithm, 585 impact experiments are performed at 117 different locations of a CFRP plate. Signals corresponding to 12 randomly selected locations are treated as test set, and the remaining signals are used for training of the network. The results show that the average localization error of the proposed method is 10.2 mm. • The influence of reflected ultrasonic guided wave on the localization of impact is analyzed. • The time–frequency features of guided wave signal is analyzed with wavelet transform. • A convolutional neural network is trained to locate the impacts on a CFRP plate. [ABSTRACT FROM AUTHOR]

• The influence of haze on CIELAB color space is studied. • The MSRA-Net consists of two subnetworks dealing with luminance and chrominance components, respectively. • In MSRA-Net, the multi-scale residual attention block and feature aggregation building block are proposed. • The effectiveness of MSRA-Net is validated by the ablation analysis. • The experiments show that the proposed method has good performance on dehazing and color fidelity. Single image dehazing is a fundamental but difficult task in image processing. Although various deep learning-based methods have achieved good dehazing performance, it is still a challenge to recover a haze-free image without color distortion from a hazy image. The color performance of nowadays dehazing methods needs further improvement. In this paper, the effect of haze on the luminance and chrominance of CIELAB color space is firstly studied. Based on CIELAB color space, a novel multi-scale residual attention network (MSRA-Net) is proposed for single image dehazing. The proposed network consists of two subnetworks: one is designed to remove haze from the luminance channel; another focuses on enhancing the chrominance components and makes the color more realistic. To improve the performance of MSRA-Net, the multi-scale residual attention block (MSRA-block) and feature aggregation building block (FAB-block) are presented. Both the subjective and objective performance show that the proposed method outperforms the existing dehazing algorithms, especially in terms of color performance. [ABSTRACT FROM AUTHOR]

GPUs became the reference platform for both training and inference phases of Convolutional Neural Networks (CNN) due to their tailored architecture to the CNN operators. However, GPUs are power-hungry architectures. A path to enable the deployment of CNNs in energy-constrained devices is adopting hardware accelerators for the inference phase. The design space exploration of CNNs using standard approaches, such as RTL, is limited due to their complexity. Thus, designers need frameworks enabling design space exploration that delivers accurate hardware estimation metrics to deploy CNNs. This work proposes a framework to explore CNNs design space, providing power, performance, and area (PPA) estimations. The heart of the framework is a system simulator. The system simulator front-end is TensorFlow, and the back-end is performance estimations obtained from the physical synthesis of hardware accelerators, not only from components like multipliers and adders. The first set of results evaluate the CNN accuracy using integer quantization, the accelerators PPA after physical synthesis, and the benefits of using a system simulator. These results allow a rich design space exploration, enabling selecting the best set of CNN parameters to meet the design constraints. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel differentiable activation function for convolutional neural networks. This function is proposed using an orthogonal Legendre polynomial based linear-in-parameter model. First, we present mathematical modeling for max pooling operation in convolutional neural networks and derive the relationship between max pooling and average pooling. The representation of max pooling using signal processing elements is presented. Further, we study approximations of various nonlinearities using the proposed linear-in-parameter model. The proposed activation function can be used as an alternative in place of existing activation functions in convolutional neural networks. Finally, the effectiveness of the proposed activation function is shown with empirical evaluations on benchmark image classification datasets using convolutional neural networks. [ABSTRACT FROM AUTHOR]

The article discusses the success of U.S. Republican Party candidate Donald John Trump's presidential campaign as of August 2015, and it mentions the views of the members of America's political elite with regards to Trump's popularity and the manner in which he is conducting himself during various campaign events. Trump's views about illegal immigration and border security in America, the Islamic State terrorist group, and U.S. military policy are examined. Public opinion polls are also assessed.

The article presents the story behind the cyberattack on Sony Pictures Entertainment on November 24, 2014. It describes the events leading to the cyberattack, the release of stolen confidential files onto public file-sharing sites and Sony employees' fears of identity theft. It discusses allegations on the role of North Korea which had issued threats against the film "The Interview" due for release that Christmas, financial losses and litigations facing Sony.

The article discusses mass media firm Discovery Communications Inc.'s chief executive officer David Zaslav and his success in America's cable television industry, and it mentions Zaslav's work as the supervisor of the "Cake Boss" food-related television program and the Discovery Channel's "Shark Week" television programming event. Zaslav's 2014 pay package is mentioned, along with his work in overseeing 13 U.S. television networks. Reality television programs such as "Gold Rush" are examined.

Human pose estimation is an important problem which has found applications in different fields. One problem in this context is to estimate the human joints or keypoints under occlusion. Most of the methods in the literature use CNN structures to estimate keypoint positions. In this paper, a Bayesian Approach for occluded Keypoint Estimation, BAKE, is presented to complete the missing human pose elements. A state-of-the-art CNN-based pose estimation algorithm Openpose [1] is used for detecting the visible joints. Then a Bayesian pose estimation is applied for the missing 2D pose elements. A Gaussian model composed of the length and angle parameters of a 13-keypoint skeleton model to encode the 2D human body pose is presented. Model parameters are obtained from the global and local statistics. Global statistics are extracted from COCO database [2] whereas the local statistics are obtained from the non-occluded initial video frames under consideration. The local and global statistics are combined and updated by using the action-specific body relations. A new predictability score is proposed to determine the confidence of completing the missing human joints. This confidence score is used to develop a hybrid technique which combines the predictions of the Openpose and the proposed method, BAKE. The proposed approach can be seen as an alternative for a 3D CNN structure which is trying to model the time-dependent sequence of events. Several experiments are performed to compare the outputs of the Openpose, BAKE, and the hybrid approach. It is shown that BAKE outperforms the Openpose estimates in general and the hybrid method generates a slight improvement over the BAKE algorithm. [ABSTRACT FROM AUTHOR]

The Coronavirus disease (Covid-19) has been declared a pandemic by World Health Organisation (WHO) and till date caused 585,727 numbers of deaths all over the world. The only way to minimize the number of death is to quarantine the patients tested Corona positive. The quick spread of this disease can be reduced by automatic screening to cover the lack of radiologists. Though the researchers already have done extremely well to design pioneering deep learning models for the screening of Covid-19, most of them results in low accuracy rate. In addition, over-fitting problem increases difficulties for those models to learn on existing Covid-19 datasets. In this paper, an automated Covid-19 screening model is designed to identify the patients suffering from this disease by using their chest X-ray images. The model classifies the images in three categories – Covid-19 positive, other pneumonia infection and no infection. Three learning schemes such as CNN, VGG-16 and ResNet-50 are separately used to learn the model. A standard Covid-19 radiography dataset from the repository of Kaggle is used to get the chest X-ray images. The performance of the model with all the three learning schemes has been evaluated and it shows VGG-16 performed better as compared to CNN and ResNet-50. The model with VGG-16 gives the accuracy of 97.67%, precision of 96.65%, recall of 96.54% and F1 score of 96.59%. The performance evaluation also shows that our model outperforms two existing models to screen the Covid-19. [ABSTRACT FROM AUTHOR]

A defamation lawsuit against the cable channel from Rep. Devin Nunes (R-Calif.) was dismissed on Friday, the latest suit against a media outlet to fall flat in recent months. [ABSTRACT FROM AUTHOR]

The world is suffering from an existential global health crisis known as the COVID-19 pandemic. Countries like India, Bangladesh, and other developing countries are still having a slow pace in the detection of COVID-19 cases. Therefore, there is an urgent need for fast detection with clear visualization of infection is required using which a suspected patient of COVID-19 could be saved. In the recent technological advancements, the fusion of deep learning classifiers and medical images provides more promising results corresponding to traditional RT-PCR testing while making detection and predictions about COVID-19 cases with increased accuracy. In this paper, we have proposed a deep transfer learning algorithm that accelerates the detection of COVID-19 cases by using X-ray and CT-Scan images of the chest. It is because, in COVID-19, initial screening of chest X-ray (CXR) may provide significant information in the detection of suspected COVID-19 cases. We have considered three datasets known as 1) COVID-chest X-ray, 2) SARS-COV-2 CT-scan, and 3) Chest X-Ray Images (Pneumonia). In the obtained results, the proposed deep learning model can detect the COVID-19 positive cases in ≤ 2 seconds which is faster than RT-PCR tests currently being used for detection of COVID-19 cases. We have also established a relationship between COVID-19 patients along with the Pneumonia patients which explores the pattern between Pneumonia and COVID-19 radiology images. In all the experiments, we have used the Grad-CAM based color visualization approach in order to clearly interpretate the detection of radiology images and taking further course of action. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic has challenged the world science. The international community tries to find, apply, or design novel methods for diagnosis and treatment of COVID-19 patients as soon as possible. Currently, a reliable method for the diagnosis of infected patients is a reverse transcription-polymerase chain reaction. The method is expensive and time-consuming. Therefore, designing novel methods is important. In this paper, we used three deep learning-based methods for the detection and diagnosis of COVID-19 patients with the use of X-Ray images of lungs. For the diagnosis of the disease, we presented two algorithms include deep neural network (DNN) on the fractal feature of images and convolutional neural network (CNN) methods with the use of the lung images, directly. Results classification shows that the presented CNN architecture with higher accuracy (93.2%) and sensitivity (96.1%) is outperforming than the DNN method with an accuracy of 83.4% and sensitivity of 86%. In the segmentation process, we presented a CNN architecture to find infected tissue in lung images. Results show that the presented method can almost detect infected regions with high accuracy of 83.84%. This finding also can be used to monitor and control patients from infected region growth. [ABSTRACT FROM AUTHOR]

The former president accused the network of defaming him by using the phrase "the Big Lie" while allegedly likening him to Adolf Hitler. [ABSTRACT FROM AUTHOR]

If the Ukrainians have liberated Tomyna Balka, they're within striking distance of the Russian garrison—part of the 49th Combined Arms Army—in Kherson. [ABSTRACT FROM AUTHOR]

The article focuses on the most popular viral videos as of 2015, which includes "Numa Numa Dance," "Leave Britney Alone!" and singer Rebecca Black's music video for "Friday." An interview with Trey Parker, co-creator of the television program "South Park," is presented. Information is provided on comedian Judson Laipply's "Evolution of Dance" video.

Discusses the global electronic connections that make citizens more aware of world problems and more able to contribute to their solution. More supply but less demand in information; Feeling versus thinking on television or print; Significance of the end user-friendly manichaeism; Interactivity and virtuality; Bottom-up or top-down diplomacy.

Here we attempt to answer all of major questions stemming from the indictment [ABSTRACT FROM PUBLISHER]

The article reports on the value of American cable and satellite television channel Cable News Network (CNN) which is up for sale based on the views of media analysts. It states that analysts believe CNN is worth more than the estimates of the securities industry as international, online, and mobile operations are profitable despite the declines of its ratings in the U.S. It notes that mass media firm CBS Corp. is expected to bid to acquire CNN.