ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Blockchain-Based Decentralized Model Aggregation for Cross-Silo Federated Learning in Industry 4.0

Traditional federated learning (FL) adopts a client-server architecture where FL clients (e.g., IoT edge devices) train a common global model with the help of a centralized orchestrator (cloud server). However, current approaches are moving away from centralized orchestration toward a decentralized one in order to fully adapt FL for a cross-silo configuration with multiple organizations acting as clients. State-of-the-art decentralized FL mechanisms make at least one of the following assumptions: 1) clients are trusted organizations and cannot inject low-quality model updates for aggregation and 2) client local models can be shared with other clients or a third party for verification of low-quality updates. This article proposes a Blockchain-based decentralized framework for scenarios where participatory organizations are believed to be fully capable of injecting low-quality model updates as they are not willing to expose their local models to any other entity for verification purpose. The proposed decentralized FL framework adopts a novel hierarchical network of aggregators with the ability to punish/reward organizations in proportion to their local model quality updates. The framework is flexible and unlike state-of-the-art solutions, prevents a single entity from possessing the aggregated model in any FL round of training. The proposed framework is tested with respect to off-chain and on-chain performance in two Industry 4.0 use cases: 1) predictive maintenance and 2) product visual inspection. A comparative evaluation against the state-of-the-art reveals the proposed framework's utility in terms of minimizing model convergence time and latency while maximizing accuracy and throughput.
Estimated specific antibody-based true sero-prevalences of canine filariosis in dogs in Central Europe and the UK

Dirofilariosis is a vector-borne disease mainly caused by Dirofilaria immitis and Dirofilaria repens. In contrast to the known endemicity of dirofilariosis in southern and south-eastern Europe, information on the distribution of D. repens in Central-Europe is fragmentary. We tested 8877 serum samples from dogs from Austria, Denmark, Germany, Italy, Lithuania, Poland, Switzerland and the UK using an ELISA detecting filarial-specific antibodies, hypothesising higher occurrence of D. repens. Based on two overlapping frequency distributions, presumed negative samples had a mean optical density (OD) value of 0.097, representing 97.45% of all samples. Presumed positive samples, representing 2.55% of all sera, had a mean OD value of 0.287. Test prevalence based on the calculated cut-off was 3.51% for all sera (4.36% for Austria, 1.94% for Denmark, 1.39% for Germany, 3.37% for Italy, 6.90% for Lithuania, 6.99% for Poland, 0.77% for Switzerland and 0.0% for the UK, respectively). The bimodal distribution, representing overlapping distributions of OD values from positive and negative dogs, enabled the assignment of a probability of true infection status to each dog. Mean probabilities of true infection status across groups, based on the postal codes of origin, allowed us to estimate and map true prevalences. For all countries, except the UK, the true prevalence was lower than the test prevalence. The large number of serum samples and the use of a non-gold standard analytical method allowed us to create a more realistic picture of the distribution of D. repens in Central Europe and the UK.
ELECTRIcity: An Efficient Transformer for Non-Intrusive Load Monitoring

Non-Intrusive Load Monitoring (NILM) describes the process of inferring the consumption pattern of appliances by only having access to the aggregated household signal. Sequence-to-sequence deep learning models have been firmly established as state-of-the-art approaches for NILM, in an attempt to identify the pattern of the appliance power consumption signal into the aggregated power signal. Exceeding the limitations of recurrent models that have been widely used in sequential modeling, this paper proposes a transformer-based architecture for NILM. Our approach, called ELECTRIcity, utilizes transformer layers to accurately estimate the power signal of domestic appliances by relying entirely on attention mechanisms to extract global dependencies between the aggregate and the domestic appliance signals. Another additive value of the proposed model is that ELECTRIcity works with minimal dataset pre-processing and without requiring data balancing. Furthermore, ELECTRIcity introduces an efficient training routine compared to other traditional transformer-based architectures. According to this routine, ELECTRIcity splits model training into unsupervised pre-training and downstream task fine-tuning, which yields performance increases in both predictive accuracy and training time decrease. Experimental results indicate ELECTRIcity's superiority compared to several state-of-the-art methods.
Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field Experiments

Attitude control of fixed-wing unmanned aerial vehicles (UAVs) is a difficult control problem in part due to uncertain nonlinear dynamics, actuator constraints, and coupled longitudinal and lateral motions. Current state-of-the-art autopilots are based on linear control and are thus limited in their effectiveness and performance. drl is a machine learning method to automatically discover optimal control laws through interaction with the controlled system that can handle complex nonlinear dynamics. We show in this article that deep reinforcement learning (DRL) can successfully learn to perform attitude control of a fixed-wing UAV operating directly on the original nonlinear dynamics, requiring as little as 3 min of flight data. We initially train our model in a simulation environment and then deploy the learned controller on the UAV in flight tests, demonstrating comparable performance to the state-of-the-art ArduPlane proportional-integral-derivative (PID) attitude controller with no further online learning required. Learning with significant actuation delay and diversified simulated dynamics were found to be crucial for successful transfer to control of the real UAV. In addition to a qualitative comparison with the ArduPlane autopilot, we present a quantitative assessment based on linear analysis to better understand the learning controller's behavior.
Sparse representation with multi-manifold analysis for texture classification from few training images

Texture classification is one of the most important tasks in computer vision field and it has been extensively investigated in the last several decades. Previous texture classification methods mainly used the template matching based methods such as Support Vector Machine and k-Nearest-Neighbour for classification. Given enough training images the state-of-the-art texture classification methods could achieve very high classification accuracies on some benchmark databases. However, when the number of training images is limited, which usually happens in real-world applications because of the high cost of obtaining labelled data, the classification accuracies of those state-of-the-art methods would deteriorate due to the overfitting effect. In this paper we aim to develop a novel framework that could correctly classify textural images with only a small number of training images. By taking into account the repetition and sparsity property of textures we propose a sparse representation based multi-manifold analysis framework for texture classification from few training images. A set of new training samples are generated from each training image by a scale and spatial pyramid, and then the training samples belonging to each class are modelled by a manifold based on sparse representation. We learn a dictionary of sparse representation and a projection matrix for each class and classify the test images based on the projected reconstruction errors. The framework provides a more compact model than the template matching based texture classification methods, and mitigates the overfitting effect. Experimental results show that the proposed method could achieve reasonably high generalization capability even with as few as 3 training images, and significantly outperforms the state-of-the-art texture classification approaches on three benchmark datasets. (C) 2014 Elsevier B.V. All rights reserved.
Dynamic graph convolutional network for multi-video summarization

Multi-video summarization is an effective tool for users to browse multiple videos. In this paper, multi-video summarization is formulated as a graph analysis problem and a dynamic graph convolutional network is proposed to measure the importance and relevance of each video shot in its own video as well as in the whole video collection. Two strategies are proposed to solve the inherent class imbalance problem of video summarization task. Moreover, we propose a diversity regularization to encourage the model to generate a diverse summary. Extensive experiments are conducted, and the comparisons are carried out with the state-of-the-art video summarization methods, the traditional and novel graph models. Our method achieves state-of-the-art performances on two standard video summarization datasets. The results demonstrate the effectiveness of our proposed model in generating a representative summary for multiple videos with good diversity. (C) 2020 Elsevier Ltd. All rights reserved.
Low temperature gradient thermoelectric generator: Modelling and experimental verification

Internet of Things (IoT) and wearable sensing paradigm assume the sensing devices are available 24/7 and can be accessed from anywhere. This vision implies strict requirements to the power supply and energy harvesting which are expected to guarantee 'perpetual' operation of IoT devices. This paper reports on modelling and experimental verification of low temperature gradient thermoelectric generator. Obtained under the conditions of low gradient temperature approximation, the model accounts for the key physical phenomena and enables the accurate output power calculations using a closed-form expression. We perform a comparative study on the state-of-the-art models against the obtained solution and show the simplicity and performance of the proposed approach. For demonstrating practical feasibility of the model, we develop an experimental testbed consisting of the power generator, temperature control and data acquisition units. Experimental results demonstrate the average error 5.5% which improves the state-of-the-art results.
A High-Accuracy and Energy-Efficient CORDIC Based Izhikevich Neuron With Error Suppression and Compensation

Bio-inspired neuron models are the key building blocks of brain-like neural networks for brain-science exploration and neuromorphic engineering applications. The efficient hardware design of bio-inspired neuron models is one of the challenges to implement brain-like neural networks, as the balancing of model accuracy, energy consumption and hardware cost is very challenging. This paper proposes a high-accuracy and energy-efficient Fast-Convergence COordinate Rotation DIgital Computer (FC-CORDIC) based Izhikevich neuron design. For ensuring the model accuracy, an error propagation model of the Izhikevich neuron is presented for systematic error analysis and effective error reduction. Parameter-Tuning Error Compensation (PTEC) method and Bitwidth-Extension Error Suppression (BEES) method are proposed to reduce the error of Izhikevich neuron design effectively. In addition, by utilizing the FC-CORDIC instead of conventional CORDIC for square calculation in the Izhikevich model, the redundant CORDIC iterations are removed and therefore, both the accumulated errors and required computation are effectively reduced, which significantly improve the accuracy and energy efficiency. An optimized fixed-point design of FC-CORDIC is also proposed to save hardware overhead while ensuring the accuracy. FPGA implementation results exhibit that the proposed Izhikevich neuron design can achieve high accuracy and energy efficiency with an acceptable hardware overhead, among the state-of-the-art designs.
High-Density Lipoprotein Cholesterol as a Potential Medium between Depletion of Lachnospiraceae Genera and Hypertension under a High-Calorie Diet

Gut microbial dysbiosis has been associated with hypertension. An extremely high incidence of essential hypertension was found in the Han and the Yugur people who resided in Sunan County in China's nomadic steppes, with little population movement. To investigate gut microbial contributions to this high incidence of hypertension, we recruited a total of 1, 242 Yugur and Han people, who had resided in Sunan County for more than 15 years and accounted for 3% of the local population. The epidemiological survey of 1,089 individuals indicated their nearly 1.8-times-higher prevalence of hypertension (38.2 to 43.3%) than the average in China (23.2%), under a special high-calorie diet based on wheat, cattle, mutton, and animal offal. Investigations of the fecal microbiota of another cohort of 153 individuals revealed that certain Lachnospiraceae genera were positively correlated with high-density lipoprotein cholesterol (HDL-C) but negatively correlated with systolic blood pressure (SBP) and diastolic blood pressure (DBP). HDL-C was negatively correlated with SBP and DBP. We further observed that the serum butyrate content was lower in both Han and Yugur people with hypertension than in those without hypertension. This study gives novel insight into the role of gut microbial dysbiosis in hypertension modulation under a high-calorie diet, where the notable depletion of Lachnospiraceae genera might lead to less production of butyrate, contributing to the lower level of HDL-C and elevating blood pressure in hypertension. IMPORTANCE Dietary nutrients can be converted by the gut microbiota into metabolites such as short-chain fatty acids, which may serve as disease-preventing agents in hypertension. Due to the limited population mobility and unique high-calorie dietary habits, the cohort of this study can serve as a representative cohort for elucidating the associations between the gut microbiota and hypertension under a high-calorie diet. Moreover, low levels of HDL-C have previously been associated with an increased risk of various cardiovascular diseases (CVDs). Our findings provide new insight showing that low levels of HDL-C may be a potential medium between the depletion of Lachnospiraceae genera and hypertension under a high-calorie diet, which might also be a potential candidate for other CVDs.
Novel Bacterial Topoisomerase Inhibitor Gepotidacin Demonstrates Absence of Fluoroquinolone-Like Arthropathy in Juvenile Rats

Fluoroquinolone use in children is limited due to its potential toxicity and negative effects on skeletal development, but the actual effects/risks of fluoroquinolones on bone growth and the mechanisms behind fluoroquinolone-driven arthropathy remain unknown. Gepotidacin is a novel, bactericidal, first-in-class triazaacenaphthylene antibiotic with a unique mechanism of action that is not anticipated to have the same risks to bone growth as those of fluoroquinolones. Gepotidacin is in phase III clinical development for uncomplicated urinary tract infections (ClinicalTrials.gov identifiers NCT04020341 and NCT04187144) and urogenital gonorrhea (ClinicalTrials.gov identifier NCT04010539) in adults and adolescents ≥12 years of age. To inform arthropathy and other potential toxicity risks of gepotidacin in pediatric studies, this nonclinical study assessed oral gepotidacin toxicity in juvenile rats from postnatal day (PND) 4 to PND 32/35 (approximately equivalent to human ages from newborn to 11 years), using both in-life assessments (tolerability, toxicity, and toxicokinetics) and terminal assessments (necropsy with macroscopic and microscopic skeletal femoral head and/or stifle joint examinations). Gepotidacin doses of ≤300 mg/kg of body weight/day were well tolerated from PND 4 to PND 21, and higher doses of ≤1,250 mg/kg/day were well tolerated from PND 22 when the dose levels were escalated to maintain systemic exposure levels up to PND 35, with no observed treatment-related clinical signs, effects on mean body weight gain, or macroscopic findings on articular surfaces. A dose of 1,000 mg/kg/day was not tolerated during the dosing period from PND 4 to 21, with effects on body weight gain, fecal consistency, and body condition. Microscopic effects on articular surfaces were evaluated after 32 days of gepotidacin treatment at the highest tolerated dose. After 32 days of treatment with the highest tolerated gepotidacin dose of 300/1,250 mg/kg/day (systemic concentrations [area under the curve {AUC} values] of 93.7 μg · h/mL [males] and 121 μg · h/mL [females]), no skeletal effects on articular surfaces of the femoral head or stifle joint were observed. The absence of treatment-related clinical signs and arthropathy in juvenile rats provides evidence to support the potential future use of gepotidacin in children.
Sinonasal Teratocarcinosarcoma Involving Orbital and Intracranial Extension: A Rare Case Report

Sinonasal teratocarcinosarcoma (SNTCS) is one of the rarest and most highly invasive malignant neoplasms often found in the nasal cavity and paranasal sinuses. SNTCS is often misdiagnosed because of its morphological heterogeneity. Due to its rarity, clinical characteristics and optimal therapy have not been well-established. Here, we present a case of SNTCS with orbital and intracranial extensions. A 48-year-old male patient presented with left-side nasal obstruction for 3 years. He appeared with visual and neurological symptoms 2 months ago. On radiographic examination, a mass was observed in the left paranasal sinuses with orbital and intracranial extension involvement. The mass was surgically resected. In the future, knowledge of this entity may assist in the accurate diagnosis and proper management of SNTCS.
Purinergic Signaling and Its Role in Mobilization of Bone Marrow Stem Cells

Mobilization or egress of stem cells from bone marrow (BM) into peripheral blood (PB) is an evolutionary preserved and important mechanism in an organism for self-defense and regeneration. BM-derived stem cells circulate always at steady-state conditions in PB, and their number increases during stress situations related to (a) infections, (b) tissue organ injury, (c) stress, and (d) strenuous exercise. Stem cells also show a circadian pattern of their PB circulating level with peak in early morning hours and nadir late at night. The number of circulating in PB stem cells could be pharmacologically increased after administration of some drugs such as cytokine granulocyte colony-stimulating factor (G-CSF) or small molecular antagonist of CXCR4 receptor AMD3100 (Plerixafor) that promote their egress from BM into PB and lymphatic vessels. Circulating can be isolated from PB for transplantation purposes by leukapheresis. This important homeostatic mechanism is governed by several intrinsic complementary pathways. In this chapter, we will discuss the role of purinergic signaling and extracellular nucleotides in regulating this process and review experimental strategies to study their involvement in mobilization of various types of stem cells that reside in murine BM.
Interleaved Sketch: Toward Consistent Network Telemetry for Commodity Programmable Switches

Network telemetry is vital to various network applications, including network anomaly detection, capacity planning, and congestion alleviation. State-of-the-art network telemetry systems are claimed to be scalable, flexible, all-purpose, and accurate. They adopt interval approaches that track network traffic in each interval and collect statistics for analysis at a specific epoch. However, interval methods are impaired by collecting inconsistency and clearing inconsistency, which pollute statistics. Moreover, The state-of-the-art centralized controllers have long latency, which aggravates the discrepancy. Accordingly, we propose the interleaved sketch, a consistent and decentralized network telemetry system across all switches. Each switch has two asymmetric sketches that work in an interleaved fashion, and is self-supervised to improve consistency. The distributed control plane extracts the flow characteristics and provides network-wide telemetry with low latency. We build a P4 prototype of our proposed interleaved sketch and test it on a Barefoot Tofino switch. Experimental results demonstrate that our interleaved sketch achieves ideal accuracy at line speed, with 6% resource overhead.
Multiaxial Haar-Like Feature and Compact Cascaded Classifier for Versatile Recognition

A versatile recognition algorithm has been proposed to process image, sound, and 3-D acceleration signals with a common framework at low calculation cost. Firstly, a novel 1-D Haar-like feature is used to roughly extract frequency information from temporal signals. Biaxial and mean-embedded Haar-like features are proposed to extract the standard deviation and the interaxial correlation from 3-D acceleration signals. Secondly, two techniques are proposed to build a compact cascaded classifier. Redundant feature selection (RFS) incorporates the features which are already selected in previous stage classifiers to reduce the calculation cost. A dynamic look-up table (DLUT) is proposed to construct a look-up table-based weak classifier with the smallest possible number of bins. A train loss function is by globally optimized using dynamic programming. The proposed algorithm is tested experimentally on speech/nonspeech classification and human activity recognition. The proposed algorithm yields a speech/nonspeech classification performance comparable to the state-of-art method called MFCC while reducing the calculation cost by 100 times. The algorithm also achieves human activity recognition accuracy of 96.1% with calculation cost reduction of 84% compared with the state-of-art method based on C4.5 decision-tree classifier using the basic statistical features. The proposed algorithm has been employed to build the versatile recognition processor.
The effect of justified video game violence on aggressive behavior and moderated immersion: An experimental approach

The effect of violent video games on aggressive behavior is an important topic in the field of game research. Recently, growing evidence suggests that justified game violence decreases feelings of guilt caused by in-game immoral behavior. However, little is known about the impact on aggressive behavior, and whether other factors moderate this effect. In a two-factor experiment, we tested the impact of justification of video game violence on aggressive behavior, and whether this effect would be enhanced by game immersion. Pilot experiment 1 (N = 60) and pilot experiment 2 (N = 40) demonstrated that the justification of violence and game immersion was successfully controlled by avatar and graphics quality. In the Main experiment, 123 participants played one of four conditions of a video game (2 [justification: justified vs. unjustified violence] × 2 [immersion: high vs. low immersion]) and it was found that participants who played in the justified violence condition reported greater aggressive behavior than those in the unjustified violence condition. In addition, participants who played in high immersion reported greater aggressive behavior than those in low immersion. However, game immersion did not moderate the effects of justified violence. This unexpected effect is likely due to participants' distancing themselves from and identifying less with their violent avatars.