ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Carried Object Detection in Videos Using Color Information

Automatic baggage detection has become a subject of significant practical interest in recent years. In this paper, we propose an approach to baggage detection in CCTV video footage that uses color information to address some of the vital shortcomings of state-of-the-art algorithms. The proposed approach consists of typical steps used in baggage detection, namely, the estimation of moving direction of humans carrying baggage, construction of human-like temporal templates, and their alignment with the best matched view-specific exemplars. In addition, we utilize the color information to define the region that most likely belongs to a human torso in order to reduce the false positive detections. A key novel contribution is the person's viewing direction estimation using machine learning and shoulder shape related features. Further enhancement of baggage detection and segmentation is achieved by exploiting the CIELAB color space properties. The proposed system has been extensively tested for its effectiveness, at each stage of improvement, on PETS 2006 dataset and additional CCTV video footage captured to cover specific test scenarios. The experimental results suggest that the proposed algorithm is capable of superseding the functional performance of state-of-the-art baggage detection algorithms.
The effect of modulation of gut microbiome profile on radiation-induced carcinogenesis and survival

Non-lethal doses of ionizing radiation (IR) delivered to humans because of terrorist events, nuclear accidents or radiotherapy can result in carcinogenesis. Means of protecting against carcinogenesis are lacking. We questioned the role of the gut microbiome in IR-induced carcinogenesis. The gut microbiome was modulated by administering broad spectrum antibiotics (Ab) in the drinking water. Mice were given Ab 3 weeks before and 3 weeks after 3 Gy total body irradiation (TBI) or for 6 weeks one month after TBI. Three weeks of Ab treatment resulted in a 98% reduction in total 16S rRNA counts for 4 out of 6 of the phylum groups detected. However, 3 more weeks of Ab treatment (6 weeks total) saw an expansion in the phylum groups Proteobacteria and Actinobacteria. The Ab treatment altered the bacteria diversity in the gut, and shortened the lifespan when Ab were administered before and after TBI. Mortality studies indicated that the adverse Ab lifespan effects were due to a decrease in the time in which solid tumors started to appear and not to any changes in hematopoietic or benign tumors. In contrast, when Ab were administered one month after TBI, lifespan was unchanged compared to the control TBI group. Use of broad-spectrum antibiotics to simulate the germ-free condition did not afford an advantage on carcinogenesis or lifespan.
Pandemic's silver lining

The intense international focus on the COVID-19 pandemic has provided a unique opportunity to use a wide array of novel tools to carry out scientific studies on the SARS-CoV-2 virus. The value of these comparative studies extends far beyond their consequences for SARS-CoV-2, providing broad implications for health-related science. Here we specifically discuss the impacts of these comparisons on advances in vaccines, the analysis of host humoral immunity, and antibody discovery. As an extension, we also discuss potential synergies between these areas.Abbreviations: CoVIC: The Coronavirus Immunotherapeutic Consortium; EUA: Emergency Use Authorization.
Arts, place, and sacrifice zones: restoration of damaged relational values in a Chilean sacrifice zone

This paper aims to unpack the relational dimension of place and placemaking by analysing how creative actions underpin relational values towards socio-spatial restoration in the sacrifice zone affecting the communities of Quintero and Puchuncavi (QPSZ) in Chile. Sacrifice zones are places permanently subject to environmental damage and lack of environmental regulation. For affected populations in environmentally degraded areas, creative actions such as murals, music, and street performances have become a way to re-establish connections both among humans, and between humans and the environment. To date, little has been theorized on this connection. With this in mind, we use network analysis to analyze which and how relational values are mobilized by artistic actions, and to examine ensuing socio-spatial transformations. Drawing insights from 35 interviews with activists, artists, and residents in QPSZ, we observed relational effects of arts, especially in creation processes, and in representations of local elements and life histories. The materiality of artistic practices raised as a force of placemaking, and so did artistic spaces as promoters of networking and social cohesion, essential for socio-spatial restoration. By bringing together insights from aesthetic politics, human geography, and relational values, this paper contributes to the emerging literature on art committed to tackling socio-environmental crises, and to political-ecological theories on the transformation of degraded areas.
A high speed modulo (2(n)-2(p)+1) multiplier design

In this express, an optimized architecture for modulo (2(n) - 2(p) + 1) multipliers is proposed. Compared with the state-of-art, synthesized results demonstrate that the proposed multipliers can achieve an average delay savings of about 11.8%. With the increase of n, the average delay savings also increases remarkably.
Cyber Attacks and Faults Discrimination in Intelligent Electronic Device-Based Energy Management Systems

Intelligent electronic devices (IEDs) along with advanced information and communication technology (ICT)-based networks are emerging in the legacy power grid to obtain real-time system states and provide the energy management system (EMS) with wide-area monitoring and advanced control capabilities. Cyber attackers can inject malicious data into the EMS to mislead the state estimation process and disrupt operations or initiate blackouts. A machine learning algorithm (MLA)-based approach is presented in this paper to detect false data injection attacks (FDIAs) in an IED-based EMS. In addition, stealthy construction of FDIAs and their impact on the detection rate of MLAs are analyzed. Furthermore, the impacts of natural disturbances such as faults on the system are considered, and the research work is extended to distinguish between cyber attacks and faults by using state-of-the-art MLAs. In this paper, state-of-the-art MLAs such as Random Forest, OneR, Naive Bayes, SVM, and AdaBoost are used as detection classifiers, and performance parameters such as detection rate, false positive rate, precision, recall, and f-measure are analyzed for different case scenarios on the IEEE benchmark 14-bus system. The experimental results are validated using real-time load flow data from the New York Independent System Operator (NYISO).
Optimal Distributed Nonlinear Battery Control

Energy storage plays a more important role than ever before, due to the transition to smart grid along with higher penetration of renewable resources. In this paper, we describe our optimal nonlinear battery control algorithm that can handle multiple batteries connected to the grid in a distributed and cost-optimal fashion, while maintaining low complexity of O(N-2). In contrast to the state-of-the-art models, we use a high accuracy nonlinear battery model with 2% error. We present three distributed solutions: 1) a circular negotiation ring, providing convergence rates independent of the number of batteries; 2) a mean circular negotiation ring, converging very quickly for a low number of batteries; 3) a bisection method has a convergence rate independent of battery capacities. We compare our algorithm to the state-of-the-art algorithms and show that we can decrease the utility cost of an actual building by up to 50% compared with the batteryless case by 30% over the load-following heuristic and by 60% over a state-of-the-art optimal control algorithm designed using a linear battery model. For a constant load profile, optimal linear control incurs costs higher by 150% for model predictive control and 250% for single-trajectory solutions than for our algorithm.
Relationship Induced Multi-Template Learning for Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment

As shown in the literature, methods based on multiple templates usually achieve better performance, compared with those using only a single template for processing medical images. However, most existing multi-template based methods simply average or concatenate multiple sets of features extracted from different templates, which potentially ignores important structural information contained in the multi-template data. Accordingly, in this paper, we propose a novel relationship induced multi-template learning method for automatic diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI), by explicitly modeling structural information in the multi-template data. Specifically, we first nonlinearly register each brain's magnetic resonance (MR) image separately onto multiple pre-selected templates, and then extract multiple sets of features for this MR image. Next, we develop a novel feature selection algorithm by introducing two regularization terms to model the relationships among templates and among individual subjects. Using these selected features corresponding to multiple templates, we then construct multiple support vector machine (SVM) classifiers. Finally, an ensemble classification is used to combine outputs of all SVM classifiers, for achieving the final result. We evaluate our proposed method on 459 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 97 AD patients, 128 normal controls (NC), 117 progressive MCI (pMCI) patients, and 117 stable MCI (sMCI) patients. The experimental results demonstrate promising classification performance, compared with several state-of-the-art methods for multi-template based AD/MCI classification.
Dickkopf-1 as a promising therapeutic target for autoimmune diseases

Dickkopf-1 (DKK-1) is mostly known as a mature inhibitor of classic Wnt signaling pathways, which plays a critically role in regulating bone formation and bone metastasis. In recent years, the roles of DKK-1 played in bone resorption, bone formation, immune homeostasis and inflammation have been investigated. The role of DKK-1 in the pathogenesis and treatment of autoimmune diseases (ADs), including rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), etc, has attracted widespread attention. Various studies have found that DKK-1 may be used as a biomarker for the occurrence and development of ADs, and as a potential target for the treatment of ADs. In this review, we have briefly summed up the intricate immunological functions and regulatory mechanisms of DKK-1 in ADs, aiming to further learning more about the role of DKK-1 involved in the pathogenesis of ADs and provide an outlook for the potential future researches.
Deep reinforcement learning for stock portfolio optimization by connecting with modern portfolio theory

With artificial intelligence and data quality development, portfolio optimization has improved rapidly. Traditionally, researchers in the financial market have utilized the modern portfolio theory for portfolio optimization; however, with the recent development of artificial intelligence, attempts to optimize portfolios with reinforcement learning are increasing. Many studies have developed reinforcement learning and deep learning algorithms and conducted portfolio optimization research. However, in reality, thus far, the securities industry thus has used the modern portfolio theory, which is sufficiently valuable. Nevertheless, to the best of our knowledge, there has yet to be an attempt to combine modern portfolio theory and reinforcement learning. To bridge this gap in the literature, we propose a novel deep reinforcement learning approach that combines the modern portfolio theory and a deep learning approach. As far as we know, we are the first to combine recent deep learning technology and traditional financial theory. Specifically, we solved the multimodal problem through the Tucker decomposition of a model with the input of technical analysis and stock return covariates. The results show that the proposed method outperforms state-of-the-art algorithms regarding the Sharpe ratio, annualized return, and maximum drawdown. In addition, the proposed method dynamically changes the weight according to the market trend, unlike other state-of-the-art algorithms.
Multi-Task Siamese Network for Retinal Artery/Vein Separation via Deep Convolution Along Vessel

Vascular tree disentanglement and vessel type classification are two crucial steps of the graph-based method for retinal artery-vein (A/V) separation. Existing approaches treat them as two independent tasks and mostly rely on ad hoc rules (e.g. change of vessel directions) and hand-crafted features (e.g. color, thickness) to handle them respectively. However, we argue that the two tasks are highly correlated and should be handled jointly since knowing the A/V type can unravel those highly entangled vascular trees, which in turn helps to infer the types of connected vessels that are hard to classify based on only appearance. Therefore, designing features and models isolatedly for the two tasks often leads to a suboptimal solution of A/V separation. In view of this, this paper proposes a multi-task siamese network which aims to learn the two tasks jointly and thus yields more robust deep features for accurate A/V separation. Specifically, we first introduce Convolution Along Vessel (CAV) to extract the visual features by convolving a fundus image along vessel segments, and the geometric features by tracking the directions of blood flow in vessels. The siamese network is then trained to learn multiple tasks: i) classifying A/V types of vessel segments using visual features only, and ii) estimating the similarity of every two connected segments by comparing their visual and geometric features in order to disentangle the vasculature into individual vessel trees. Finally, the results of two tasks mutually correct each other to accomplish final A/V separation. Experimental results demonstrate that our method can achieve accuracy values of 94.7%, 96.9%, and 94.5% on three major databases (DRIVE, INSPIRE, WIDE) respectively, which outperforms recent state-of-the-arts.
Force Control Strategy and Bench Press Experimental Research of a Cable Driven Astronaut Rehabilitative Training Robot

This paper presents a cable driven astronaut rehabilitative training (ART) robot, which can provide astronauts with multiple physical exercises including bench press, running, and deep squat to alleviate and resist the adverse effects induced by space adaptation syndrome. First, the modular reconfiguration of the ART driven by a cable is proposed to simulate the load characteristics of the gravity environment. Second, in order to improve the accuracy of the ART, the force control strategy is presented. The controller consists of two parts: high-level controller, which to calculate the desired cable tension and low-level controller, which to make each cable achieve the desired tension. Finally, to validate the effect of the ART, three (adult male) weight lifters were recruited to perform several bench press exercises with the barbell and ART. The analysis of the surface electromyography (sEMG) of triceps brachii and pectoralis major during real bench press and bench press simulated by the ART was conducted. The performance of the ART and the data of sEMG demonstrate the controller is effective and the ART can effectively assist astronauts to carry out bench press exercises in space.
A Novel, Efficient Implementation of a Local Binary Convolutional Neural Network

In order to reduce the computational complexity of convolutional neural networks (CNNs), the local binary convolutional neural network (LBCNN) has been proposed. In the LBCNN, a convolutional layer is divided into two sublayers. Sublayer 1 is a sparse ternary-weighted convolutional layer, and Sublayer 2 is a 1x1 convolutional layer. With the use of two sublayers, the LBCNN has lower computational complexity and uses less memory than the CNN. In this brief, we propose a platform that includes a weight preprocessor and layer accelerator for the LBCNN. The proposed weight preprocessor takes advantage of the sparsity in the LBCNN and encodes the weight offline. The layer accelerator effectively uses the encoded data to reduce computational complexity and memory accesses for an inference. When compared to the state-of-the-art design, the experimental results show that the number of clock cycles are reduced by 76.32%, and memory usage is reduced by 39.41%. The synthesized results show that the clock period is reduced by 4.76%; the cell area is reduced by 46.48%, and the power consumption is reduced by 40.87%. The inference accuracy is the same as that of the state-of-the-art design.
Looking Inside Category: Subcategory-Aware Object Recognition

In this paper, we present a subcategory-aware recognition framework to boost category level object classification performance. Different from the existing monolithic model approaches, we aim to automatically leverage the embedded subcategory structure to assist the further category level recognition. Motivated by the observation of considerable intra-class diversities and inter-class ambiguities in many current object classification data sets, we explicitly split data into subcategories by ambiguity-guided subcategory mining. The resulting subcategories are seamlessly integrated into the state-of-the-art detection-assisted classification framework. In particular, we build the instance affinity graph by combining both intra-class similarity and inter-class ambiguity. Visual subcategories, which correspond to the dense subgraphs, are detected by the graph shift algorithm. We then train an individual model for each subcategory rather than an attempt to represent an object category with a monolithic model. Related samples, which are informative for subcategory classification, are utilized to regularize each subcategory model. Finally, the responses from subcategory models are aggregated by subcategory-aware kernel regression. The extensive experiments over the PASCAL visual object challenge (VOC) 2007 and PASCAL VOC 2010 databases show the state-of-the-art performance from our framework.
Axially Extended-Volume C-Arm CT Using a Reverse Helical Trajectory in the Interventional Room

C-arm computed tomography (CT) is an innovative technique that enables a C-arm system to generate 3-D images from a set of 2-D X-ray projections. This technique can reduce treatment-related complications and may improve interventional efficacy and safety. However, state-of-the-art C-arm systems rely on a circular short scan for data acquisition, which limits coverage in the axial direction. This limitation was reported as a problem in hepatic vascular interventions. To solve this problem, as well as to further extend the value of C-arm CT, axially extended-volume C-arm CT is needed. For example, such an extension would enable imaging the full aorta, the peripheral arteries or the spine in the interventional room, which is currently not feasible. In this paper, we demonstrate that performing long object imaging using a reverse helix is feasible in the interventional room. This demonstration involved developing a novel calibration method, assessing geometric repeatability, implementing a reconstruction method that applies to real reverse helical data, and quantitatively evaluating image quality. Our results show that: 1) the reverse helical trajectory can be implemented and reliably repeated on a multiaxis C-arm system; and 2) a long volume can be reconstructed with satisfactory image quality using reverse helical data.
Reassigned time-frequency representations of discrete time signals and application to the Constant-Q Transform

In this paper we provide a formal justification of the use of time frequency reassignment techniques on time frequency transforms of discrete time signals. State of the art techniques indeed rely on formulae established in the continuous case which are applied, in a somehow inaccurate manner, to discrete time signals. Here, we formally derive a general framework for discrete time reassignment. To illustrate its applicability and generality this framework is applied to a specific transform: the Constant-Q Transform.
Limited view CT reconstruction and segmentation via constrained metric labeling

This paper proposes a new discrete optimization framework for tomographic reconstruction and segmentation of CT volumes when only a few projection views are available. The problem has important clinical applications in coronary angiographic imaging. We first show that the limited view reconstruction and segmentation problem can be formulated as a 'constrained' version of the metric labeling problem. This lays the groundwork for a linear programming framework that brings metric labeling classification and classical algebraic tomographic reconstruction (ART) together in a unified model. If the imaged volume is known to be comprised of a finite set of attenuation coefficients (a realistic assumption), given a regular limited view reconstruction, we view it as a task of voxels reassignment subject to maximally maintaining consistency with the input reconstruction and the objective of ART simultaneously. The approach can reliably reconstruct (or segment) Volumes With several multiple contrast objects. We present evaluations using experiments on cone beam computed tomography. (C) 2008 Elsevier Inc. All rights reserved.
Viscous pressure forming (VPF): state-of-the-art and future trends

Viscous pressure forming (VPF) is a recently developed flexible sheet forming process. Different from conventional sheet metal forming technology, VPF uses a kind of semi-solid, flowable and viscous material as the pressure-carrying medium. In VPF, the viscous medium is applied on one side or both sides of sheet blank to improve its formability. VPF offers a new approach to the forming of high strength, difficult-to-form materials and complex-shaped parts. In this paper, the state-of-the-art of VPF in formability analysis of sheet metals, pressure control, numerical simulation, applications and forming devices is presented. VPF has shown potential application to superalloy and aluminum alloy. Several complex shaped parts formed by VPF meet the requirements of shape and dimensional accuracy and the thickness distributions are more uniform. Finally, critical issues for the future development of VPF are presented. (C) 2004 Elsevier B.V. All rights reserved.
State-of-the-Art: AI-Assisted Surrogate Modeling and Optimization for Microwave Filters

Microwave filters are indispensable passive devices for modern wireless communication systems. Nowadays, electromagnetic (EM) simulation-based design process is a norm for filter designs. Many EM-based design methodologies for microwave filter design have emerged in recent years to achieve efficiency, automation, and customizability. The majority of EM-based design methods exploit low-cost models (i.e., surrogates) in various forms, and artificial intelligence techniques assist the surrogate modeling and optimization processes. Focusing on surrogate-assisted microwave filter designs, this article first analyzes the characteristic of filter design based on different design objective functions. Then, the state-of-the-art filter design methodologies are reviewed, including surrogate modeling (machine learning) methods and advanced optimization algorithms. Three essential techniques in filter designs are included: 1) smart data sampling techniques; 2) advanced surrogate modeling techniques; and 3) advanced optimization methods and frameworks. To achieve success and stability, they have to be tailored or combined together to achieve the specific characteristics of the microwave filters. Finally, new emerging design applications and future trends in the filter design are discussed.