ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Isotropic Reconstruction of MR Images Using 3D Patch-Based Self-Similarity Learning

Isotropic three-dimensional (3D) acquisition is a challenging task in magnetic resonance imaging (MRI). Particularly in cardiac MRI, due to hardware and time limitations, current 3D acquisitions are limited by low-resolution, especially in the through-plane direction, leading to poor image quality in that dimension. To overcome this problem, super-resolution (SR) techniques have been proposed to reconstruct a single isotropic 3D volume from multiple anisotropic acquisitions. Previously, local regularization techniques such as total variation have been applied to limit noise amplification while preserving sharp edges and small features in the images. In this paper, inspired by the recent progress in patch-based reconstruction, we propose a novel isotropic 3D reconstruction scheme that integrates non-local and self-similarity information from 3D patch neighborhoods. By grouping 3D patches with similar structures, we enforce the natural sparsity of MR images, which can be expressed by a low-rank structure, leading to robust image reconstruction with high signal-to-noise ratio efficiency. An Augmented Lagrangian formulation of the problem is proposed to efficiently decompose the optimization into a low-rank volume denoising and a SR reconstruction. Experimental results in simulations, brain imaging and clinical cardiac MRI, demonstrate that the proposed joint SR and self-similarity learning framework outperforms current state-of-the-art methods. The proposed reconstruction of isotropic 3D volumes may be particularly useful for cardiac applications, such as myocardial infarction scar assessment by late gadolinium enhancement MRI.
Multiple instance learning for malware classification

This work addresses classification of unknown binaries executed in sandbox by modeling their interaction with system resources (files, mutexes, registry keys and communication with servers over the network) and error messages provided by the operating system, using vocabulary-based method from the multiple instance learning paradigm. It introduces similarities suitable for individual resource types that combined with an approximative clustering method efficiently group the system resources and define features directly from data. This approach effectively removes randomization often employed by malware authors and projects samples into low-dimensional feature space suitable for common classifiers. An extensive comparison to the state of the art on a large corpus of binaries demonstrates that the proposed solution achieves superior results using only a fraction of training samples. Moreover, it makes use of a source of information different than most of the prior art, which increases the diversity of tools detecting the malware, hence making detection evasion more difficult. (C) 2017 Elsevier Ltd. All rights reserved.
A study of artificial bee colony variants for radar waveform design

Waveform design and diversity is the technology that allows one or more sensors (e.g., radar or radar sensor networks) on board a platform to automatically change operating parameters, e.g., frequency, gain pattern, and pulse repetition frequency, to meet the varying environments. Optimization problems in the design of radar waveforms such as polyphase code design often bring troubles to designers. This paper proposes to hybridize the migration operator of biogeography-based optimization (BBO) approach with artificial bee colony (ABC) algorithm. Migration operator is able to promote information exchange of solutions in bee colony. This is useful for exploiting good information of searched solutions. Moreover, three state-of-the-art ABC variants are taken for study. A spread spectrum polyphase code design problem is chosen for experiment. The proposed ABCBBO algorithm as well as three state-of-the-art ABC algorithms are applied to solve polyphase code design. Results show that ABCBBO presents the overall best performance amongst the test algorithms. It is also the most reliable one.
Techniques and applications for soccer video analysis: A survey

Nowadays, soccer is the most popular sport in our society, followed by millions of people. Consequently, many video analysis applications have been developed in the last years to provide information that can be useful for viewers, referees, coaches and players. Some of these applications are focused on specific tasks, such as detecting players, segmenting the field of play, or registering the broadcast images. On the other hand, there are applications aimed at performing tasks of a higher level, such as event detection or game analysis. Here, the most meaningful techniques and applications that have been proposed throughout the last two decades to analyze soccer video sequences are surveyed. The aim of the paper is not to compare the existing techniques, but to represent a comprehensive and organized showcase for the state-of-the-art in the field: as such, it provides a thorough review of the existing types of soccer analysis applications and the techniques used in each one of them, along with the apparent recent technical trends identified from the most recent works, and discuses the challenges in soccer analysis that still remain unsolved.
Levodopa alters resting-state functional connectivity more selectively in Parkinson's disease with freezing of gait

Freezing of gait (FOG) is a debilitating motor symptom of Parkinson's disease (PD). Although PD dopaminergic medication (L-DOPA) seems to generally reduce FOG severity, its effect on neural mechanisms of FOG remains to be determined. The purpose of this study was to quantify the effect of L-DOPA on brain resting-state functional connectivity in individuals with FOG. Functional magnetic resonance imaging was acquired at rest in 30 individuals living with PD (15 freezers) in the ON- and OFF- medication state. A seed-to-voxel analysis was performed with seeds in the bilateral basal ganglia nuclei, the thalamus and the mesencephalic locomotor region. In freezers, medication-state contrasts revealed numerous changes in resting-state functional connectivity, not modulated by L-DOPA in non-freezers. In freezers, L-DOPA increased the functional connectivity between the seeds and regions including the posterior parietal, the posterior cingulate, the motor and the medial prefrontal cortices. Comparisons with non-freezers revealed that L-DOPA generally normalizes brain functional connectivity to non-freezers levels but can also increase functional connectivity, possibly compensating for dysfunctional networks in freezers. Our findings suggest that L-DOPA could contribute to a better sensorimotor, attentional, response inhibition and limbic processing to prevent FOG when triggers are encountered but could also contribute to FOG by interfering with the processing capacity of the striatum. This study shows that levodopa taken to control PD symptoms induces changes in functional connectivity at rest, in freezers only. Increases (green) in functional connectivity of GPe, GPi, putamen and thalamus with cognitive, sensorimotor and limbic cortical regions of the Interference model (blue) was observed. Our results suggest that levodopa can normalize connections similar to non-freezers or increases connectivity to compensate for dysfunctional networks.
DIRECT Mode Early Decision Optimization Based on Rate Distortion Cost Property and Inter-view Correlation

In this paper, an Efficient DIRECT Mode Early Decision (EDMED) algorithm is proposed for low complexity multiview video coding. Two phases are included in the proposed EDMED: 1) early decision of DIRECT mode is made before doing time-consuming motion estimation/disparity estimation, where adaptive rate-distortion (RD) cost threshold, inter-view DIRECT mode correlation and coded block pattern are jointly utilized; and 2) false rejected DIRECT mode macroblocks of the first phase are then successfully terminated based on weighted RD cost comparison between 16x16 and DIRECT modes for further complexity reduction. Experimental results show that the proposed EDMED algorithm achieves 11.76% more complexity reduction than that achieved by the state-of-the-art SDMET for the temporal views. Also, it achieves a reduction of 50.98% to 81.13% (69.15% on average) in encoding time for inter-view, which is 29.31% and 15.03% more than the encoding time reduction achieved by the state-of-the-art schemes. Meanwhile, the average Peak Signal-to-Noise Ratio (PSNR) degrades 0.05 dB and average bit rate increases by -0.37%, which is negligible.
Factors Associated with patient satisfaction towards a prison detention Clinic Care among male drug-using inmates

This study assessed patient satisfaction and its associated factors among male drug-using inmates utilizing a prison detention clinic in Taiwan. A cross-sectional design and structured questionnaire were employed to recruit 580 drug-using inmates into the study. The Patient Satisfaction Questionnaire Short Form (PSQ-18), developed by the RAND Corporation, was used as the basis for the short scale of patient satisfaction, and the research data were analyzed using the SPSS for Windows 20.0 statistical software package. The results showed that the research subjects had low patient satisfaction in all the factors assessed compared with the scale's general norms. Among the original seven satisfaction subscales in this study, the highest score was for the financial aspects, and the lowest was for the amount of time spent with doctors. This study also investigated satisfaction with medical lab exams and the pharmacy at the prison's clinic, and the satisfaction scores were higher than the original seven subscales. In multiple logistic regression analyses, the final model indicated that the inmates undergoing observed rehabilitation (OR = 13.837, 95% CI = 2.736-69.983) were more likely satisfied with prison detention clinic c than those serving prison sentences. Those inmates with custodial deposits (high vs. low; OR = 1.813, 95% CI = 1.038-3.168), and meet their physical health needs (met vs. unmet; OR = 4.872, 95% CI = 2.054-11.560) had significant correlated with detention clinic care satisfactory level. Although there is only one study setting cannot give a generalizability for people who are incarcerated in Taiwan, this study highlights that the prison authorities should scrutinize factors associated with detention clinic care satisfaction, such as the type of inmate, economic status in the prison, self-reported health status, and their physical health needs, to increase the level of patient satisfaction.
MultiCycleNet: Multiple Cycles Self-Boosted Neural Network for Short-term Electric Household Load Forecasting

Household load forecasting plays an important role in future grid planning and operation. However, compared with aggregated load forecasting, household load forecasting faces the challenge of the uncertainty of prolific load profiles. This paper presents a novel multiple cycles self-boosted neural network (MultiCycleNet) framework for household load forecasting, which aims to solve the uncertainty problem of household load profiles through the correlation analysis of electricity consumption patterns in multiple cycles. The basic idea of the proposed framework is that the predictor can learn customers' power consumption patterns better by learning the features and contextual information of relevant load profiles in multiple historical cycles. We use two real-life datasets: 1. the household load consumption dataset from Low Carbon London project led by United Kingdom (UK) Power Networks and 2. the UK Domestic Appliance-Level Electricity (UK-DALE) dataset to evaluate the effectiveness of the proposed framework. Compared with the state-of-the-art methods, experimental results show that the proposed framework is effective and outperforms the state-of-the-art methods by 19.83%, 10.46%, 11.14% and 9.02% in terms of mean squared error, root mean squared error, mean absolute error and mean absolute percent error, respectively.
Breakage Assessment of Lath-Like Crystals in a Novel Laboratory-Scale Agitated Filter Bed Dryer

Agitated filter bed dryer is often the equipment of choice in the pharmaceutical industry for the isolation of potent active pharmaceutical ingredients (API) from the mother liquor and subsequent drying through intermittent agitation. The use of an impeller to promote homogeneous drying could lead to undesirable size reduction of the crystal product due to shear deformation induced by the impeller blades during agitation, potentially causing off-specification product and further downstream processing issues. An evaluation of the breakage propensity of crystals during the initial development stage is therefore critical. A new versatile scale-down agitated filter bed dryer (AFBD) has been developed for this purpose. Carbamazepine dihydrate crystals that are prone to breakage have been used as model particles. The extent of particle breakage as a function of impeller rotational speed, size of clearance between the impeller and containing walls and base, and solvent content has been evaluated. A transition of breakage behaviour is observed, where carbamazepine dihydrate crystals undergo fragmentation first along the crystallographic plane [00l]. As the crystals become smaller and more equant, the breakage pattern switches to chipping. Unbound solvent content has a strong influence on the breakage, as particles break more readily at high solvent contents. The laboratory-scale instrument developed here provides a tool for comparative assessment of the propensity of particle attrition under agitated filter bed drying conditions.
A deep learning based unified framework to detect, segment and recognize irises using spatially corresponding features

This paper proposes a deep learning based unified and generalizable framework for accurate iris detection, segmentation and recognition. The proposed framework firstly exploits state-of-the-art and iris specific Mask R-CNN, which performs highly reliable iris detection and primary segmentation i.e., identifying iris/non-iris pixels, followed by adopting an optimized fully convolutional network (FCN), which generates spatially corresponding iris feature descriptors. A specially designed Extended Triplet Loss (ETL) function is presented to incorporate the bit-shifting and non-iris masking, which are found necessary for learning meaningful and discriminative spatial iris features. Thorough experiments on four publicly available databases suggest that the proposed framework consistently outperforms several classic and state-of-the-art iris recognition approaches. More importantly, our model exhibits superior generalization capability as, unlike popular methods in the literature, it does not essentially require database-specific parameter tuning, which is another key advantage. (C) 2019 Published by Elsevier Ltd.
Fixed Point Iteration Based Algorithm for Asynchronous TOA-Based Source Localization

This paper investigates the problem of source localization using signal time-of-arrival (TOA) measurements in the presence of unknown start transmission time. Most state-of-art methods are based on convex relaxation technologies, which possess global solution for the relaxed optimization problem. However, computational complexity of the convex optimization-based algorithm is usually large, and need CVX toolbox to solve it. Although the two stage weighted least squares (2SWLS) algorithm has very low computational complexity, its estimate performance is susceptible to sensor geometry and threshold phenomenon. A new algorithm that is directly derived from maximum likelihood estimator (MLE) is developed. The newly proposed algorithm is named as fixed point iteration (FPI); it only involves simple calculations, such as addition, multiplication, division, and square-root. Unlike state-of-the-art methods, there is no matrix inversion operation and can avoid the unstable performance incurred by singular matrix. The FPI algorithm can be easily extended to the scenario with sensor position errors. Finally, simulation results demonstrate that the proposed algorithm reaches a good balance between computational complexity and localization accuracy.
Localized bioelectrical conduction block from radiofrequency gastric ablation persists after healing: safety and feasibility in a recovery model

Gastric ablation has demonstrated potential to induce conduction blocks and correct abnormal electrical activity (i.e., ectopic slow-wave propagation) in acute, intraoperative in vivo studies. This study aimed to evaluate the safety and feasibility of gastric ablation to modulate slow-wave conduction after 2 wk of healing. Chronic in vivo experiments were performed in weaner pigs (n = 6). Animals were randomly divided into two groups: sham-ablation (n = 3, control group; no power delivery, room temperature, 5 s/point) and radiofrequency (RF) ablation (n = 3; temperature-control mode, 65°C, 5 s/point). In the initial surgery, high-resolution serosal electrical mapping (16 × 16 electrodes; 6 × 6 cm) was performed to define the baseline slow-wave activation profile. Ablation (sham/RF) was then performed in the mid-corpus, in a line around the circumferential axis of the stomach, followed by acute postablation mapping. All animals recovered from the procedure, with no sign of perforation or other complications. Two weeks later, intraoperative high-resolution mapping was repeated. High-resolution mapping showed that ablation successfully induced sustained conduction blocks in all cases in the RF-ablation group at both the acute and 2 wk time points, whereas all sham-controls had no conduction block. Histological and immunohistochemical evaluation showed that after 2 wk of healing, the lesions were in the inflammation and early proliferation phase, and interstitial cells of Cajal (ICC) were depleted and/or deformed within the ablation lesions. This safety and feasibility study demonstrates that gastric ablation can safely and effectively induce a sustained localized conduction block in the stomach without disrupting the surrounding slow-wave conduction capability.NEW & NOTEWORTHY Ablation has recently emerged as a tool for modulating gastric electrical activation and may hold interventional potential for disorders of gastric function. However, previous studies have been limited to the acute intraoperative setting. This study now presents the safety of gastric ablation after postsurgical recovery and healing. Localized electrical conduction blocks created by ablation remained after 2 wk of healing, and no perforation or other complications were observed over the postsurgical period.
Exploiting stereoscopic disparity for augmenting human activity recognition performance

This work investigates several ways to exploit scene depth information, implicitly available through the modality of stereoscopic disparity in 3D videos, with the purpose of augmenting performance in the problem of recognizing complex human activities in natural settings. The standard state-of-the-art activity recognition algorithmic pipeline consists in the consecutive stages of video description, video representation and video classification. Multimodal, depth-aware modifications to standard methods are being proposed and studied, both for video description and for video representation, that indirectly incorporate scene geometry information derived from stereo disparity. At the description level, this is made possible by suitably manipulating video interest points based on disparity data. At the representation level, the followed approach represents each video by multiple vectors corresponding to different disparity zones, resulting in multiple activity descriptions defined by disparity characteristics. In both cases, a scene segmentation is thus implicitly implemented, based on the distance of each imaged object from the camera during video acquisition. The investigated approaches are flexible and able to cooperate with any monocular low-level feature descriptor. They are evaluated using a publicly available activity recognition dataset of unconstrained stereoscopic 3D videos, consisting in extracts from Hollywood movies, and compared both against competing depth-aware approaches and a state-of-the-art monocular algorithm. Quantitative evaluation reveals that some of the examined approaches achieve state-of-the-art performance.
Remote Sensing Image Scene Classification Based on an Enhanced Attention Module

Classifying different satellite remote sensing scenes is a very important subtask in the field of remote sensing image interpretation. With the recent development of convolutional neural networks (CNNs), remote sensing scene classification methods have continued to improve. However, the use of recognition methods based on CNNs is challenging because the background of remote sensing image scenes is complex and many small objects often appear in these scenes. In this letter, to improve the feature extraction and generalization abilities of deep neural networks so that they can learn more discriminative features, an enhanced attention module (EAM) was designed. Our proposed method achieved very competitive performance-94.29% accuracy on NWPU-RESISC45 and state-of-the-art performance on different remote sensing scene recognition data sets. The experimental results show that the proposed method can learn more discriminative features than state-of-the-art methods, and it can effectively improve the accuracy of scene classification for remote sensing images. Our code is available at https://github.com/williamzhao95/Pay-More-Attention.
The magic of split augmented Lagrangians applied to K-frame-based l (0)-l (2) minimization image restoration

We propose a simple, yet efficient image deconvolution approach, which is formulated as a complementary K-frame-based l (0)-l (2) minimization problem, aiming at benefiting from the advantages of each frame. The problem is solved by borrowing the idea of alternating split augmented Lagrangians. The experimental results demonstrate that our approach has achieved competitive performance among state-of-the-art methods.
Unsupervised Two-Path Neural Network for Cell Event Detection and Classification Using Spatiotemporal Patterns

Automatic event detection in cell videos is essential for monitoring cell populations in biomedicine. Deep learning methods have advantages over traditional approaches for cell event detection due to their ability to capture more discriminative features of cellular processes. Supervised deep learning methods, however, are inherently limited due to the scarcity of annotated data. Unsupervised deep learning methods have shown promise in general (non-cell) videos because they can learn the visual appearance and motion of regularly occurring events. Cell videos, however, can have rapid, irregular changes in cell appearance and motion, such as during cell division and death, which are often the events of most interest. We propose a novel unsupervised two-path input neural network architecture to capture these irregular events with three key elements: 1) a visual encoding path to capture regular spatiotemporal patterns of observed objects with convolutional long short-term memory units; 2) an event detection path to extract information related to irregular events with max-pooling layers; and 3) integration of the hidden states of the two paths to provide a comprehensive representation of the video that is used to simultaneously locate and classify cell events. We evaluated our network in detecting cell division in densely packed stem cells in phase-contrast microscopy videos. Our unsupervised method achieved higher or comparable accuracy to standard and state-of-the-art supervised methods.