ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
VoxelMorph: A Learning Framework for Deformable Medical Image Registration

We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each pair of images, which can be time-consuming for large datasets or rich deformation models. In contrast to this approach and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images. We parameterize the function via a convolutional neural network and optimize the parameters of the neural network on a set of images. Given a new pair of scans, VoxelMorph rapidly computes a deformation field by directly evaluating the function. In this paper, we explore two different training strategies. In the first (unsupervised) setting, we train the model to maximize standard image matching objective functions that are based on the image intensities. In the second setting, we leverage auxiliary segmentations available in the training data. We demonstrate that the unsupervised model's accuracy is comparable to the state-of-the-art methods while operating orders of magnitude faster. We also show that VoxelMorph trained with auxiliary data improves registration accuracy at test time and evaluate the effect of training set size on registration. Our method promises to speed up medical image analysis and processing pipelines while facilitating novel directions in learning-based registration and its applications. Our code is freely available at https://github.com/voxelmorph/voxelmorph.
Design of radiation-hardened memory cell by polar design for space applications

This paper proposed a radiation-hardened memory cell (RHMC12T) by polar design for space applications. The proposed cell has the following advantages: (1) it can tolerate all single-node upset and partial double-node upset based on combining radiation hardened by polar design technology together with reasonable layout topology; (2) comparing with the state-of-the-art radiation hardened SRAM cells, simulation results show the proposed RHMC12T cell has lower write access time, higher wordline write trip voltage, larger static noise margin, and larger critical charge. And Monte Carlo simulation results have shown that RHMC12T has good robustness; (3) electrical quality metric is widely used to evaluate the overall performance of SRAM cells. And RHMC12T has the largest EQM compared with the state-of-the-art radiation hardened SRAM cells, which suggests the proposed RHMC12T exhibits good circuit performance (including write/read access time, static noise margin et al.) as well as good radiation resistance performance with sacrificing a large area overhead.
GAN computers generate arts? A survey on visual arts, music, and literary text generation using generative adversarial network

Art is the lie that enables us to realize the truth. - Pablo Picasso. For centuries, humans have dedicated themselves to producing arts to convey their imagination. The advancement in technology and deep learning in particular, has caught the attention of many researchers trying to investigate whether art generation is possible by computers and algorithms. Using generative adversarial networks (GANs), applications such as synthesizing photorealistic human faces and generating captions automatically from images were realized. This survey takes a comprehensive look at the recent works using GANs for generating visual arts, music, and literary text. A performance comparison and description of the various GAN architecture are also presented. Finally, some of the key challenges in GAN-based art generation are highlighted along with recommendations for future work.
Cannabinoid Photochemistry: An Underexplored Opportunity

Photochemistry is a powerful synthetic tool resulting in the construction of unique substances. Remarkably, photochemistry has been relatively underexplored in the cannabinoid area and represents a valuable opportunity for further discovery.
Hybrid SFNet Model for Bone Fracture Detection and Classification Using ML/DL

An expert performs bone fracture diagnosis using an X-ray image manually, which is a time-consuming process. The development of machine learning (ML), as well as deep learning (DL), has set a new path in medical image diagnosis. In this study, we proposed a novel multi-scale feature fusion of a convolution neural network (CNN) and an improved canny edge algorithm that segregate fracture and healthy bone image. The hybrid scale fracture network (SFNet) is a novel two-scale sequential DL model. This model is highly efficient for bone fracture diagnosis and takes less computation time compared to other state-of-the-art deep CNN models. The innovation behind this research is that it works with an improved canny edge algorithm to obtain edges in the images that localize the fracture region. After that, grey images and their corresponding canny edge images are fed to the proposed hybrid SFNet for training and evaluation. Furthermore, the performance is also compared with the state-of-the-art deep CNN models on a bone image dataset. Our results showed that SFNet with canny (SFNet + canny) achieved the highest accuracy, F1-score and recall of 99.12%, 99% and 100%, respectively, for bone fracture diagnosis. It showed that using a canny edge algorithm improves the performance of CNN.
Breaking (Global) Barriers in Parallel Stochastic Optimization With Wait-Avoiding Group Averaging

Deep learning at scale is dominated by communication time. Distributing samples across nodes usually yields the best performance, but poses scaling challenges due to global information dissemination and load imbalance across uneven sample lengths. State-of-the-art decentralized optimizers mitigate the problem, but require more iterations to achieve the same accuracy as their globally-communicating counterparts. We present Wait-Avoiding Group Model Averaging (WAGMA) SGD, a wait-avoiding stochastic optimizer that reduces global communication via subgroup weight exchange. The key insight is a combination of algorithmic changes to the averaging scheme and the use of a group allreduce operation. We prove the convergence of WAGMA-SGD, and empirically show that it retains convergence rates similar to Allreduce-SGD. For evaluation, we train ResNet-50 on ImageNet; Transformer for machine translation; and deep reinforcement learning for navigation at scale. Compared with state-of-the-art decentralized SGD variants, WAGMA-SGD significantly improves training throughput (e.g., 2.1x on 1,024 GPUs for reinforcement learning), and achieves the fastest time-to-solution (e.g., the highest score using the shortest training time for Transformer).
The Generalized Log-Ratio Transformation: Learning Shape and Adjacency Priors for Simultaneous Thigh Muscle Segmentation

We present a novel probabilistic shape representation that implicitly includes prior anatomical volume and adjacency information, termed the generalized log-ratio (GLR) representation. We demonstrate the usefulness of this representation in the task of thigh muscle segmentation. Analysis of the shapes and sizes of thigh muscles can lead to a better understanding of the effects of chronic obstructive pulmonary disease (COPD), which often results in skeletal muscle weakness in lower limbs. However, segmenting these muscles from one another is difficult due to a lack of distinctive features and inter-muscular boundaries that are difficult to detect. We overcome these difficulties by building a shape model in the space of GLR representations. We remove pose variability from the model by employing a presegmentation-based alignment scheme. We also design a rotationally invariant random forest boundary detector that learns common appearances of the interface between muscles from training data. We combine the shape model and the boundary detector into a fully automatic globally optimal segmentation technique. Our segmentation technique produces a probabilistic segmentation that can be used to generate uncertainty information, which can be used to aid subsequent analysis. Our experiments on challenging 3D magnetic resonance imaging data sets show that the use of the GLR representation improves the segmentation accuracy, and yields an average Dice similarity coefficient of 0.808 +/- 0.074, comparable to other state-of-the-art thigh segmentation techniques.
Human actions recognition: an approach based on stable motion boundary fields

Automatic video action recognition have been a long-standing problem in computer vision. To obtain a scalable solution for actions recognition, it is important to have efficient visual representation of motions. In this paper, we propose a new visual representation for actions based in the body motion boundaries. The first step, a set of optical flow frames highlighting the principal motions in the poses is substracted. Then, the motion boundaries are computed from the previous optical flow frames. Maximum Stable Extremal Regions are then applied to motion boundaries maps in order to obtain Motion Stable Shape (MSS) features. Local descriptors were computed based on each detected MSS to capture motion patterns. To predict the classes of the different human actions, we have represented different descriptors with a bag-of-words (BOW) model and for classification, we use a non-linear support vector machine. We have performed a set of experiments on different datasets: Weizmann, KTH, UCF sport, UCF50 and Hollywood to prove the efficiency of our developed model. The achieved results improve the state-of-the-art on the KTH and Weizmann datasets and are comparable to state-of-the-art for UCF sport and UCF50 datasets.
A meta-evolutionary selection of constituents in ensemble differential evolution algorithm

A Meta-evolutionary Selection of Constituents in Ensemble DE (MeSCEDE) framework is proposed in this paper to automate the design of high-level multi-population ensemble Differential Evolution (DE) algorithms. The automated design of high-level multi-population ensemble DE algorithms involves both automated selection of constituent DE algorithms for the ensemble and automated configuration of ensemble related parameters. Grammatical Evolution has been used as the meta-evolutionary algorithm in MeSCEDE to search the space of design choices so as to evolve effective ensemble design(s) for given problem(s). The simulation experiments carried out in this paper involve applying MeSCEDE to evolve ensemble DE designs for solving 30-dimensional CEC'17 benchmark functions. The effectiveness of the evolved designs are validated on 30 and 50-dimensional CEC'14 functions as well as on 22 real-world problem instances from CEC'11 benchmark suite. The MeSCEDE evolved designs have exhibited a competitive performance against state-of-the-art ensemble DE algorithms. In addition, the potential of MeSCEDE has been demonstrated against irace, a state-of-the-art algorithm configurator. All simulation experiments reiterate the potential of MeSCEDE towards evolving effective and robust ensemble DE designs.
Enhanced Passive GNSS-Based Radar Imaging Based on Coherent Integrated Multi-Satellite Signals

Weak reflected signal is one of the main problems in a recent developing remote sensing tool-passive GNSS-based radar (GNSS radar). To address this issue, an enhanced GNSS radar imaging scheme on the basis of coherently integrating multiple satellites is proposed. In the proposed scheme, to avoid direct signal interference at surveillance antenna, the satellites that used as transmission of opportunity are in backscattering geometry model. To coherently accumulate echo signal magnitudes of the scene center in the targeted sensing region illuminated by the selected satellites, after performing the paralleled range compressions, a coordinates alignment operator is performed to the respective range domains, in which, pseudorandom noise (PRN) code phases are aligned. Thereafter, the coordinates aligned range compressed signals of the selected satellites are coherently integrated along azimuth domain so that imaging gain is improved and azimuth processing can be accomplished in only one state operation. The theoretical analysis and field proof-of-concept experimental results indicate that compared to both conventional bistatic imaging scheme and the state-of-the-art multi-image fusion scheme, the proposed scheme can provide a higher imaging gain; compared to the state-of-the-art multi-image fusion scheme, the proposed scheme has a less computational complexity and faster algorithm speed.
RandShift: An Energy-Efficient Fault-Tolerant Method in Secure Nonvolatile Main Memory

In this article, we present a simple, yet energy- and area-efficient method for tolerating the stuck-at faults caused by an endurance issue in secure-resistive main memories. In the proposed method, by employing the random characteristics of the encrypted data encoded by the Advanced Encryption Standard (AES) as well as a rotational shift operation, a large number of memory locations with stuck-at faults could be employed for correctly storing the data. Due to the simple hardware implementation of the proposed method, its energy consumption is considerably smaller than that of other recently proposed methods. The technique may be employed along with other error correction methods, including the error correction code (ECC) and the error correction pointer (ECP). To assess the efficacy of the proposed method, it is implemented in a phase-change memory (PCM)based main memory system and compared with three error tolerating methods. The results reveal that for a stuck-at fault occurrence rate of 10(-2) and with the uncorrected bit error rate of 2 x 10(-3), the proposed method achieves 82% energy reduction compared to the state-of-the-art method. More generally, using a simulation analysis technique, we show that the fault coverage of the proposed method is similar to that of the state-of-the-art method.
A state of the art system for managing time data in manual assembly

Valid time data, a prerequisite for the efficient use of manufacturing resources, directly influence planning and control quality. However, access to time data that capture real shop-floor operations in general and manual operations in particular is often assumed by both academics and practitioners. This has led to a mismatch between reality and the data found in systems for production planning and control, causing operational inefficiencies and negatively affecting decision-making in manufacturing companies. This article addresses the importance of updated and valid time data in planning and controlling production and considers how they relate to manufacturing system performance and improvement. The focus is on how to determine, utilise, and sustain valid time data for manual assembly operations through integrating enterprise information systems. The article builds on a case study performed at a large manufacturing enterprise that operates a state of the art system for managing time data in manual assembly. Findings from the case study reveal how standalone system applications can be integrated with the organisational functions of an enterprise to achieve updated and valid operation times.
ETHICS OF EXHIBITION SPACE LIGHTING IN ART MUSEUMS

Exhibition lighting is one of the most important subjects in terms of forming of an image of the entire museum as well as of perception of particular exhibits. This article reviews aspects of art museum lighting, presents examples of interaction of light and fine art pieces, and gives a perspective on possible methods of picture lighting accounting for nuances of the subject and the state of a painting. We will try to describe the main aspects and principles of work with paintings.
Bayesian compressive sensing for cluster structured sparse signals

In traditional framework of compressive sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. Other than sparse prior, structures on the sparse pattern of the signal have also been used as an additional prior, called model-based compressive sensing, such as clustered structure and tree structure on wavelet coefficients. In this paper, the cluster structured sparse signals are investigated. Under the framework of Bayesian compressive sensing, a hierarchical Bayesian model is employed to model both the sparse prior and cluster prior, then Markov Chain Monte Carlo (MCMC) sampling is implemented for the inference. unlike the state-of-the-art algorithms which are also taking into account the cluster prior, the proposed algorithm solves the inverse problem automatically prior information on the number of clusters and the size of each cluster is unknown. The experimental results show that the proposed algorithm outperforms many state-of-the-art algorithms. (C) 2011 Elsevier B.V. All rights reserved.
Cyber Insurance Against Cyberattacks on Electric Vehicle Charging Stations

Cyberattacks in the energy sector are commonplace. Load-altering cyberattacks launched via the manipulations of high-wattage appliances and assets are particularly alarming, as they are not continuously monitored by electric power utilities. Public Electric Vehicle Charging Stations (EVCSs) are among such high-wattage assets. Even EVCSs monitored by the electric power utilities and protected by state-of-the-art defense mechanisms are vulnerable to cyberattacks. Such cyberattacks cause financial losses to the EVCSs. In this paper, we propose cyber insurance for EVCSs to hedge the economic loss due to such cyberattacks and develop a data-driven cyber insurance design model for public EVCSs. Under mild modeling assumptions, we derive an optimal cyber insurance premium. Then, we ensure the robustness of this optimal premium and investigate the risk of insuring the EVCSs using a suitable risk assessment metric (Conditional Value-at-Risk). A case study with data from EVCSs in Manhattan, New York illustrates our results. Our results demonstrate that risk assessment is crucial for designing insurance premiums. Furthermore, the premium increases in proportion to the loss coverage offered for the EVCSs. This work informs the stakeholders involved in the roll-out and operation of public EVCSs about the benefits of cyber insurance and suggests that insurance premiums can be reduced by deploying state-of-the-art defense mechanisms.
Dietary zero-dimensional fullerene supplementation improves the meat quality, lipid metabolism, muscle fiber characteristics, and antioxidative status in finishing pigs

With the increasing demand for high-quality pork, more nutritional substances have been studied for the regulation of meat quality. Zero-dimensional fullerenes (C60) can modulate the biological behavior of a variety of cell lines and animals. In this study, we report the biological effects of C60 on finishing pigs at different concentrations. A total of 24 barrows (Duroc × Large White × Landrace), with an average body weight of 21.01 ± 0.98 kg, were divided into 3 groups and each treated daily with C60 (100 or 200 mg per kg feed) or a control diet until the end of the experiment. Our results showed that dietary C60 supplementation improved flesh color, marbling scores, and flavor amino acid contents of longissimus dorsi (LD) of growing-finishing pigs (P < 0.05). C60 improved meat quality by regulating lipid metabolism and muscle fiber morphology by mediating the expression of genes, L-lactic dehydrogenase (LDH), myosin heavy chain (MyHC) IIa, MyHCIIb, peroxisome proliferator-activated receptor γ (PPARγ), and fatty acid transport protein 1 (FATP1) (P < 0.05). Moreover, C60 substantially promoted the mRNA expression of antioxidant enzyme genes (P < 0.05), which also contributed to improving meat quality. These findings have important implications for the application of C60 in the livestock industry, especially for improving the meat quality of fattening pigs.
Complicating cure: How Australian criminal law shapes imagined post-hepatitis C futures

In recent years, highly tolerable and effective drugs have emerged promising a radical new 'post-hepatitis C' world. Optimism about medical cure potentially overlooks discrimination and stigma associated with hepatitis C and injecting drug use. Legal frameworks are especially relevant to hepatitis futures, since the law has the potential to reinforce or alleviate stigma and discrimination. This article explores how hepatitis C figures in Australian criminal law and with what potential effects. Drawing on Bruno Latour's work on legal veridiction, Alison Kafer's work on futurity and disability and case law data collected for a major study on hepatitis C and post-cure lives, we explore how the criminal law handles hepatitis C in the age of cure. We find that law complicates cure, constituting hepatitis C as disabling despite the advent of effective cures. The law steadfastly maintains its own approach to disease, disability and illness, untouched by medical and scientific developments, in ways that might complicate straightforwardly linear imaginaries of cure, transformation and progress of the kind that dominate biomedicine. We explore the implications of these tensions between law and medicine.
Differential 3D Facial Recognition: Adding 3D to Your State-of-the-Art 2D Method

Active illumination is a prominent complement to enhance 2D face recognition and make it more robust, e.g., to spoofing attacks and low-light conditions. In the present work we show that it is possible to adopt active illumination to enhance state-of-the-art 2D face recognition approaches with 3D features, while bypassing the complicated task of 3D reconstruction. The key idea is to project over the test face a high spatial frequency pattern, which allows us to simultaneously recover real 3D information plus a standard 2D facial image. Therefore, state-of-the-art 2D face recognition solution can be transparently applied, while from the high frequency component of the input image, complementary 3D facial features are extracted. Experimental results on ND-2006 dataset show that the proposed ideas can significantly boost face recognition performance and dramatically improve the robustness to spoofing attacks.
Methods for Successful Aging: An Aesthetics-Oriented Perspective Derived from Richard Shusterman's Somaesthetics

This study explored Richard Shusterman's somaesthetics to understand the rationale for his view on enhancing the body experience of older adults and increasing their participation in art; it also examined methods or successful aging to enhance the theoretical foundation for educational gerontology. Accordingly, the research objectives were to (1) analyze the definition of successful aging; (2) clarify the role of body experience and participation in art in promoting successful aging among older adults; (3) explore and discuss Shusterman's somaesthetics; and (4) explore methods for successful aging derived from Shusterman's somaesthetics. This study mainly explored educational philosophy by collecting, reading, analyzing, logically reviewing, and interpreting the literature on this topic. During this exploration, methods for successful aging were reviewed. The findings are as follows: (1) shifting focus of successful aging to the bodies of older adults; (2) cultivating the body consciousness of older adults enables them to understand themselves and pursue virtue, happiness, and justice; (3) popular art can be integrated to promote the aesthetic ability of older adults and encourage their physical participation in the aesthetic process; (4) older adult education should cultivate the somaesthetic sensitivity of older adults; (5) older adult education should incorporate the physical training of older adults to help them enhance their self-cultivation and care for their body, cultivate virtue, and live a better life; and (6) older adult education should integrate the body and mind of older adults.