ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Accessible Visual Artworks for Blind and Visually Impaired People: Comparing a Multimodal Approach with Tactile Graphics

Despite the use of tactile graphics and audio guides, blind and visually impaired people still face challenges to experience and understand visual artworks independently at art exhibitions. Art museums and other art places are increasingly exploring the use of interactive guides to make their collections more accessible. In this work, we describe our approach to an interactive multimodal guide prototype that uses audio and tactile modalities to improve the autonomous access to information and experience of visual artworks. The prototype is composed of a touch-sensitive 2.5D artwork relief model that can be freely explored by touch. Users can access localized verbal descriptions and audio by performing touch gestures on the surface while listening to themed background music along. We present the design requirements derived from a formative study realized with the help of eight blind and visually impaired participants, art museum and gallery staff, and artists. We extended the formative study by organizing two accessible art exhibitions. There, eighteen participants evaluated and compared multimodal and tactile graphic accessible exhibits. Results from a usability survey indicate that our multimodal approach is simple, easy to use, and improves confidence and independence when exploring visual artworks.
Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks

During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance improvements, their hardware implementation using conventional technologies, such as Complementary Metal-Oxide-Semiconductor (CMOS), in power and areaconstrained settings remains a challenging task; especially when many recording channels are used. In this paper, we propose a novel low-latency parallel Convolutional Neural Network (CNN) architecture that has between 2-2,800x fewer network parameters compared to State-Of-The-Art (SOTA) CNN architectures and achieves 5-fold cross validation accuracy of 99.84% for epileptic seizure detection, and 99.01% and 97.54% for epileptic seizure prediction, when evaluated using the University of Bonn Electroencephalogram (EEG), CHB-MIT and SWEC-ETHZ seizure datasets, respectively. We subsequently implement our network onto analog crossbar arrays comprising Resistive Random-Access Memory (RRAM) devices, and provide a comprehensive benchmark by simulating, laying out, and determining hardware requirements of theCNNcomponent of our system. We parallelize the execution of convolution layer kernels on separate analog crossbars to enable 2 orders of magnitude reduction in latency compared to SOTA hybrid Memristive-CMOS Deep Learning (DL) accelerators. Furthermore, we investigate the effects of non-idealities on our system and investigate Quantization Aware Training (QAT) to mitigate the performance degradation due to lowAnalog-to-Digital Converter (ADC)/Digital-to-Analog Converter (DAC) resolution. Finally, we propose a stuck weight offsetting methodology to mitigate performance degradation due to stuck RON/ROFF memristor weights, recovering up to 32% accuracy, without requiring retraining. The CNN component of our platform is estimated to consume approximately 2.791Wof power while occupying an area of 31.255 mm(2) in a 22 nm FDSOI CMOS process.
Long-Term Effectiveness of Antiretroviral Therapy in China: An Observational Cohort Study from 2003-2014

In order to assess the effectiveness of the Chinese government's expanded access program, a cohort study on all adult HIV patients in Shenzhen was conducted from December 2003 to February 2014 to estimate the effects of antiretroviral therapy (ART) on mortality, tuberculosis and CD4 cell counts. Marginal structural regression models adjusted for baseline and time-varying covariates. Of the 6897 patients enrolled and followed up for a maximum of 178 months, 44.92% received ART. Among patients who commenced receiving ART during the study, there were 98 deaths and 59 new tuberculosis diagnoses, while there were 410 deaths and 201 new tuberculosis diagnoses among those without ART. ART was associated with both lower mortality (hazard ratio [HR] = 0.18; 95% confidence interval [CI] = 0.11-0.27) and the presence of tuberculosis (HR = 0.27; 95% CI = 0.19-0.37). Each month of ART was associated with an average increase in CD4 cell count of 6.52 cells/mu L (95% CI = 6.08-7.12 cells/mu L). In conclusions, the effectiveness of ART provided by China government health services is the same as that in higher-income countries. Accounting to higher mortality rates from the delay of starting ART, faster expansion and timely imitation of ART are urgent.
Real-Time Facial Affective Computing on Mobile Devices

Convolutional Neural Networks (CNNs) have become one of the state-of-the-art methods for various computer vision and pattern recognition tasks including facial affective computing. Although impressive results have been obtained in facial affective computing using CNNs, the computational complexity of CNNs has also increased significantly. This means high performance hardware is typically indispensable. Most existing CNNs are thus not generalizable enough for mobile devices, where the storage, memory and computational power are limited. In this paper, we focus on the design and implementation of CNNs on mobile devices for real-time facial affective computing tasks. We propose a light-weight CNN architecture which well balances the performance and computational complexity. The experimental results show that the proposed architecture achieves high performance while retaining the low computational complexity compared with state-of-the-art methods. We demonstrate the feasibility of a CNN architecture in terms of speed, memory and storage consumption for mobile devices by implementing a real-time facial affective computing application on an actual mobile device.
A phenotype-based forward genetic screen identifies Dnajb6 as a sick sinus syndrome gene

Previously we showed the generation of a protein trap library made with the gene-break transposon (GBT) in zebrafish (Danio rerio) that could be used to facilitate novel functional genome annotation towards understanding molecular underpinnings of human diseases (Ichino et al, 2020). Here, we report a significant application of this library for discovering essential genes for heart rhythm disorders such as sick sinus syndrome (SSS). SSS is a group of heart rhythm disorders caused by malfunction of the sinus node, the heart's primary pacemaker. Partially owing to its aging-associated phenotypic manifestation and low expressivity, molecular mechanisms of SSS remain difficult to decipher. From 609 GBT lines screened, we generated a collection of 35 zebrafish insertional cardiac (ZIC) mutants in which each mutant traps a gene with cardiac expression. We further employed electrocardiographic measurements to screen these 35 ZIC lines and identified three GBT mutants with SSS-like phenotypes. More detailed functional studies on one of the arrhythmogenic mutants, GBT411, in both zebrafish and mouse models unveiled Dnajb6 as a novel SSS causative gene with a unique expression pattern within the subpopulation of sinus node pacemaker cells that partially overlaps with the expression of hyperpolarization activated cyclic nucleotide gated channel 4 (HCN4), supporting heterogeneity of the cardiac pacemaker cells.
ROS System Facial Emotion Detection Using Machine Learning for a Low-Cost Robot Based on Raspberry Pi

Facial emotion recognition (FER) is a field of research with multiple solutions in the state-of-the-art, focused on fields such as security, marketing or robotics. In the literature, several articles can be found in which algorithms are presented from different perspectives for detecting emotions. More specifically, in those emotion detection systems in the literature whose computational cores are low-cost, the results presented are usually in simulation or with quite limited real tests. This article presents a facial emotion detection system-detecting emotions such as anger, happiness, sadness or surprise-that was implemented under the Robot Operating System (ROS), Noetic version, and is based on the latest machine learning (ML) techniques proposed in the state-of-the-art. To make these techniques more efficient, and that they can be executed in real time on a low-cost board, extensive experiments were conducted in a real-world environment using a low-cost general purpose board, the Raspberry Pi 4 Model B. The final achieved FER system proposed in this article is capable of plausibly running in real time, operating at more than 13 fps, without using any external accelerator hardware, as other works (widely introduced in this article) do need in order to achieve the same purpose.
A Novel Machine Learning Approach for Android Malware Detection Based on the Co-Existence of Features

This paper proposes a machine learning model based on the co-existence of static features for Android malware detection. The proposed model assumes that Android malware requests an abnormal set of co-existed permissions and APIs in comparing to those requested by benign applications. To prove this assumption, the paper created a new dataset of co-existed permissions and API calls at different levels of combinations, which are the second level, the third level, the fourth level and the fifth level. The extracted datasets of co-existed features at different levels were applied on permissions only, APIs only, permissions and APIs, and APIs and APIs frequencies. To extract the most relevant co-existed features, the frequent pattern growth (FP-growth) algorithm, which is an association rule mining technique, was used. The new datasets were extracted using Android APK samples from the Drebin, Malgenome and MalDroid2020 datasets. To evaluate the proposed model, several conventional machine learning algorithms were used. The results show that the model can successfully classify Android malware with a high accuracy using machine learning algorithms and the co-existence of features. Moreover, the results show that the achieved classification accuracy depends on the classifier and the type of co-existed features. The maximum accuracy, which is 98%, was achieved using the Random Forest algorithm and the co-existence of permissions features at the second combination level. Furthermore, the results show that the proposed approach outperforms the state-of-the-art model. Using Malgenome dataset, the proposed approach achieved an accuracy of about 98%, while the state-of-the-art achieved an accuracy of about 87%. In addition, the experiments show that using the Drebin dataset, the proposed approach achieved an accuracy of about 95%, while the state-of-the-art achieved an accuracy of about 93%.
Universal blind image quality assessment using contourlet transform and singular-value decomposition

Most current state-of-the-art blind image quality assessment (IQA) algorithms usually require process training or learning. Here, we have developed a completely blind IQA model that uses features derived from an image's contourlet transform and singular-value decomposition. The model is used to build algorithms that can predict image quality without any training or any prior knowledge of the images or their distortions. The new method consists of three steps: first, the contourlet transform is used on the image to obtain detailed high-frequency structural information from the image; second, the singular values of the just-obtained "structural image" are computed; and finally, two new universal blind IQA indices are constructed utilizing the area and slope of the truncated singular-value curves of the "structural image." Experimental results on three open databases show that the proposed algorithms deliver quality predictions that have high correlations against human subjective judgments and are highly competitive with the state-of-the-art. (C) 2014 SPIE and IS&T
Construction of a novel plasmid for an industrial yeast Candida glycerinogenes by dual-autonomously replicating sequence strategy

Due to the lack of available episomal plasmid, the improvement of many industrial strains, especially exogenous gene expression, is severely restricted. The failure of autonomous replication or low copy number of episomal plasmids is the main reason for the failure of many episomal plasmids construction. In this paper, Candida glycerinogenes, an industrial strain lacking episomal plasmids, was employed as the topic. A series of GFP-based plasmids containing autonomously replicating sequence (ARS) from different strain sources were constructed and analyzed for performance, and it was found that only the panARS from Kluyveromyces lactis compared with other nine low capacity ARSs proved to have the best performance and could be used to construct episomal plasmid. Further, the dual-ARS strategy was used to optimize the episomal plasmid, and the results indicated that only the dual-ARS plasmid +PPARS2 with double different ARSs, not the dual-ARS plasmid +panARS with double same ARSs, showed an improvement in all properties, with an increase in transformation efficiency of about 36% and a synchronous trend of fluorescence intensity and copy number, both by about 40%. In addition, constructed episomal plasmids were used to express the exogenous gene CrGES, and the fact that geraniol was found proved the versatility of the plasmids. The successful construction of episomal plasmids will also substantially facilitate genetic engineering research and industrial use of C. glycerinogenes in the future, as well as providing a feasible approach to create episomal plasmids for industrial strains.
Flagellum tapering and midpiece volume in songbird spermatozoa

In contrast to numerous studies on spermatozoa length, relatively little work focuses on the width of spermatozoa, and particularly the width of the midpiece and flagellum. In flagellated spermatozoa, the flagellum provides forward thrust while energy may be provided via mitochondria in the midpiece and/or through glycolysis along the flagellum itself. Longer flagella may be able to provide greater thrust but may also require stronger structural features and more or larger mitochondria to supply sufficient energy. Here, we use scanning electron microscopy to investigate the ultrastructure of spermatozoa from 55 passerine species in 26 taxonomic families in the Passerides infraorder. Our data confirm the qualitative observation that the flagellum tapers along its length, and we show that longer flagella are wider at the neck. This pattern is similar to mammals, and likely reflects the need for longer cells to be stronger against shearing forces. We further estimate the volume of the mitochondrial helix and show that it correlates well with midpiece length, supporting the use of midpiece length as a proxy for mitochondrial volume, at least in between-species studies where midpiece length is highly variable. These results provide important context for understanding the evolutionary correlations among different sperm cell components and dimensions.
Contactless Biometric Identification Using 3D Finger Knuckle Patterns

Study on finger knuckle patterns has attracted increasing attention for the automated biometric identification. However, finger knuckle pattern is essentially a 3D biometric identifier and the usage or availability of only 2D finger knuckle databases in the literature is the key limitation to avail full potential from this biometric identifier. This paper therefore introduces (first) contactless 3D finger knuckle database in public domain, which is acquired from 130 different subjects in two-session imaging using photometric stereo approach. This paper investigates on the 3D information from the finger knuckle patterns and introduces a new feature descriptor to extract discriminative 3D features for more accurate 3D finger knuckle matching. An individuality model for the proposed feature descriptor is also presented. Comparative experimental results using the state-of-the-art feature extraction methods on this challenging 3D finger knuckle database validate the effectiveness of our approach. Although our feature descriptor is designed for 3D finger knuckle patterns, it is also attractive for other hand-based biometric identifiers with similar patterns such as the palmprint and fingerprint. This observation is validated from the outperforming results, using the state-of-the-art pixel-wise 3D palmprint and 3D fingerprint feature descriptors, on other publicly available datasets.
Kolmogorov's Theory of Computer Science

In the present work, we follow in chronological order the ideas, contributions and discoveries of the greatest Russian mathematician Andrei Kolmogorov in Computer Science. We interpret such Kolmogorov's concepts as algorithm, complexity, komputer mathematics, machine, in the context of the state-of-the art information theories and technologies. We conclude that in broad sense these theories and technologies follow the ways sketched and predicted by Kolmogorov about half century ago.
Curved scene text detection via transverse and longitudinal sequence connection

Curved text detection is a difficult problem that has not been addressed sufficiently. To highlight the difficulties in reading curved text in a real environment, we constructed a curved text dataset called CTW1500, which includes over 10,000 text annotations in 1500 images, and used it to formulate a polygon-based curved text detector that can detect curved text without using an empirical combination. With the seamless integration of recurrent transverse and longitudinal offset connection, our method explores context information instead of predicting points independently, resulting in smoother and more accurate detection. Our approach is designed as a universal method, meaning it can be trained using rectangular or quadrilateral bounding boxes, requiring no extra effort. Experimental results on the CTW1500 dataset and Total-text demonstrated that our method with only a light backbone can outperform stateof-the-art methods by a large margin. Our method also achieved state-of-the-art performance on the MSRA-TD500 dataset, demonstrating its promising generalization ability. Code, datasets, and label-tool are available at https://github.com/Yuliang-Liu/Curve-Text-Detector. (C) 2019 The Authors. Published by Elsevier Ltd.
The Presence and Profile of Neurological Conditions and Associated Psychiatric Comorbidities in U.S. Resettled Refugees: A Retrospective Single Center Study

Refugees are a vulnerable, growing population who confront a myriad of traumas leading to their forced migration. Although psychiatric illnesses of resettled refugees are well-documented, there is a paucity of studies characterizing their neurological disease profiles. This study aimed to characterize the frequency and range of neurological disorders in a sample of resettled refugees receiving care at a community health center in Massachusetts, U.S.A. We performed a retrospective medical record review of adult (≥ 18 years) resettled refugees between May 2001 and December 2012 at a community health center in Northeast Massachusetts. Sociodemographic and clinical characteristics pertaining to neuropsychiatric health were collected from medical records using a standardized data extraction tool. Group comparisons between those with and without neurological illness and associated sociodemographic and psychiatric characteristics were evaluated using χ2 and independent samples two-tailed t-tests. In our sample (n = 779), 53.3% (n = 415) were male and 48.8% (n = 380) were from the African continent. The mean age was 33.2 ± 12.4 years (range 18-85). 36.8% were diagnosed with at least one neurological disorder and 18.1% with more than one neurological illness. The most common diagnoses were headaches (28.3%), sleep disorders (11.2%), cognitive impairment/ dementia (5.5%), and head trauma (5.5%). Exploratory analyses revealed that participants with neurological illness were more likely to be older (p < .001), female (p = .002), and diagnosed with co-morbid psychiatric diagnoses (p < .001) than those without neurological illness. Neurological disorders frequently co-occur with psychiatric comorbidities among U.S. resettled adult refugees. Standard refugee health assessments should incorporate screening and treatment for neurological illnesses.
Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts

Weakly supervised object detection (WSOD) using only image-level annotations has attracted a growing attention over the past few years. Whereas such task is typically addressed with a domain-specific solution focused on natural images, we show that a simple multiple instance approach applied on pre-trained deep features yields excellent performances on non-photographic datasets, possibly including new classes. The approach does not include any fine-tuning or cross-domain learning and is therefore efficient and possibly applicable to arbitrary datasets and classes. We investigate several flavors of the proposed approach, some including multi layers perceptron and polyhedral classifiers. Despite its simplicity, our method shows competitive results on a range of publicly available datasets, including paintings (People-Art, IconArt), watercolors, cliparts and comics and allows to quickly learn unseen visual categories.
Group-Wise Registration of Point Sets for Statistical Shape Models

This paper presents a novel, fast, group-wise registration technique based on establishing soft correspondences between groups of point sets. The registration approach is used to create a statistical shape model, capable of learning the shape variations within a training set. The shape model consists of a mean shape and its transformations to all training shapes. We decouple the procedure into two steps: updating the mean shape and registering it to the training shapes. The algorithm alternates between these two steps until convergence. Following the generation of the statistical shape model, we use the soft correspondence approach to register the model to a new observation. We perform extensive experiments on two data sets: lumbar spine and hippocampi. We compare our algorithm to available state-of-the-art group-wise registration algorithms including feature-based and volume-based approaches. We demonstrate improved generalization, specificity and compactness compared to these algorithms.
Overcoming Electrostatic Interaction via Strong Complexation for Highly Selective Reduction of CN- into N2

Limited by the electrostatic interaction, the oxidation reaction of cations at the anode and the reduction reaction of anions at the cathode in the electrocatalytic system nearly cannot be achieved. This study proposes a novel strategy to overcome electrostatic interaction via strong complexation, realizing the electrocatalytic reduction of cyanide (CN- ) at the cathode and then converting the generated reduction products into nitrogen (N2 ) at the anode. Theoretical calculations and experimental results confirm that the polarization of the transition metal oxide cathodes under the electric field causes the strong chemisorption between CN- and cathode, inducing the preferential enrichment of CN- to the cathode. CN- is hydrogenated by atomic hydrogen at the cathode to methylamine/ammonia, which are further oxidized into N2 by free chlorine derived from the anode. This paper provides a new idea for realizing the unconventional and unrealizable reactions in the electrocatalytic system.