ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Reducing your local footprint with anyrun computing

Computational offloading is the standard approach to running computationally intensive tasks on resource-limited smart devices, while reducing the local footprint, i.e., the local resource consumption. The natural candidate for computational offloading is the cloud, but recent results point out the hidden costs of cloud reliance in terms of latency and energy. Strategies that rely on local computing power have been proposed that enable fine-grained energy-aware code offloading from a mobile device to a nearby piece of infrastructure. Even state-of-the-art cloud-free solutions are centralized and suffer from a lack of flexibility, because computational offloading is tied to the presence of a specific piece of computing infrastructure. We propose AnyRun Computing (ARC), a system to dynamically select the most adequate piece of local computing infrastructure. With ARC, code can run anywhere and be offloaded not only to nearby dedicated devices, as in existing approaches, but also to peer devices. We present a detailed system description and a thorough evaluation of ARC under a wide variety of conditions. We show that ARC matches the performance of the state-of-the-art solution (MAUI), in reducing the local footprint with stationary network topology conditions and outperforms it by up to one order of magnitude under more realistic topological conditions. (C) 2016 Elsevier B.V. All rights reserved.
Protective coatings on silicon particles and their effect on energy density and specific energy in lithium ion battery cells: A model study

Protective coating on silicon particles is a strategy reported in literature to improve capacity retention of Sicontaining lithium ion batteries. Up to date, the impact of the coating on the cell energy density and specific energy is not considered and guidelines for coating design are missing. In this paper a model is proposed to fill this gap. The model depicts how energy density and specific energy of lithium ion cells based on a Si-graphite composite electrode change in function of coating type, thickness and silicon weight fraction in the negative electrode. Volume changes during lithiation-delithiation and corresponding electrolyte displacement are also considered. Energy density depends on the ratio of coating thickness to silicon particle dimension and weight fraction of silicon in the electrode. Specific energy depends - marginally - also on the coating type. As a case study silicon spherical particles of 200 nm diameter are considered. For a 10 nm coating, the maximum energy density gain vs. state of art graphite negative electrodes is 13%, obtained with 40% weight fraction of silicon in the negative electrode. Above 60 nm thickness no improvement can be obtained vs. state of art graphite negative electrodes.
Feature Saliencies in Asymmetric Hidden Markov Models

Many real-life problems are stated as nonlabeled high-dimensional data. Current strategies to select features are mainly focused on labeled data, which reduces the options to select relevant features for unsupervised problems, such as clustering. Recently, feature saliency models have been introduced and developed as clustering models to select and detect relevant variables/features as the model is learned. Usually, these models assume that all variables are independent, which narrows their applicability. This article introduces asymmetric hidden Markov models with feature saliencies, i.e., models capable of simultaneously determining during their learning phase relevant variables/features and probabilistic relationships between variables. The proposed models are compared with other state-of-the-art approaches using synthetic data and real data related to grammatical face videos and wear in ball bearings. We show that the proposed models have better or equal fitness than other state-of-the-art models and provide further data insights.
Predictors of undocumented PTSD in persons using public mental health services

Individuals diagnosed with serious mental illness (SMI) have greater trauma exposure and are at increased risk for posttraumatic stress disorder (PTSD). However, PTSD is rarely documented in their clinical records. This study investigated the predictors of PTSD documentation among 776 clients with SMI receiving public mental health services, who had probable PTSD as indicated by a PTSD Checklist score of at least 45. Only 5.3% of clients had PTSD listed as a primary diagnosis, and 8.4% had PTSD as a secondary diagnosis, with a total 13.7% documentation rate. PTSD documentation rate was highest for clients with major depression (18.8%) compared to those with schizophrenia (4.1%) or bipolar disorder (6.3%). Factors that predicted a lower likelihood of having a chart diagnosis of PTSD included being diagnosed with schizophrenia/schizoaffective disorder or bipolar disorder. Factors that predicted a higher likelihood of having a chart diagnosis of PTSD included being of non-white race, being female, and experiencing eight or more types of traumatic events. Findings highlight the need for PTSD screening and trauma informed care for clients with SMI receiving public mental health services.
Discovery of Anti-tubercular Analogues of Bedaquiline with Modified A-, B- and C-Ring Subunits

To date, the clinical use of the anti-tubercular therapy bedaquiline has been somewhat limited due to safety concerns. Recent investigations determined that modification of the B- and C-ring units of bedaquiline delivered new diarylquinolines (for example TBAJ-587) with potent anti-tubercular activity yet an improved safety profile due to reduced affinity for the hERG channel. Building on our recent discovery that substitution of the quinoline motif (the A-ring subunit) for C5-aryl pyridine groups within bedaquiline analogues led to retention of anti-tubercular activity, we investigated the concurrent modification of A-, B- and C-ring units within bedaquiline variants. This led to the discovery that 4-trifluoromethoxyphenyl and 4-chlorophenyl pyridyl analogues of TBAJ-587 retained relatively potent anti-tubercular activity and for the 4-chlorophenyl derivative in particular, a significant reduction in hERG inhibition relative to bedaquiline was achieved, demonstrating that modifications of the A-, B- and C-ring units within the bedaquiline structure is a viable strategy for the design of effective, yet safer (and less lipophilic) anti-tubercular compounds.
A state-of-the-art differential evolution algorithm for parameter estimation of solar photovoltaic models

Photovoltaic (PV) generation systems are vital to the utilization of the sustainable and pollution-free solar energy. However, the parameter estimation of PV systems remains very challenging due to its inherent nonlinear, multi-variable, and multi-modal characteristics. In this paper, we propose a state-of-the-art optimization method, namely, directional permutation differential evolution algorithm (DPDE), to tackle the parameter estimation of several kinds of solar PV models. By fully utilizing the information arisen from the search population and the direction of differential vectors, DPDE can possess a strong global exploration ability of jumping out of the local optima. To verify the performance of DPDE, six groups of experiments based on single, double, triple diode models and PV module models are conducted. Extensive comparative results between DPDE and other fifteen representative algorithms show that DPDE outperforms its peers in terms of the solution accuracy. Additionally, statistical results based on Wilcoxon rank-sum and Friedman tests indicate that DPDE is the most robust and best performing algorithm for the parameter estimation of PV systems.
Fast and Accurate Estimation of RFID Tags

Radio frequency identification (RFID) systems have been widely deployed for various applications such as object tracking, 3-D positioning, supply chain management, inventory control, and access control. This paper concerns the fundamental problem of estimating RFID tag population size, which is needed in many applications such as tag identification, warehouse monitoring, and privacy-sensitive RFID systems. In this paper, we propose a new scheme for estimating tag population size called Average Run-based Tag estimation (ART). The technique is based on the average run length of ones in the bit string received using the standardized framed slotted Aloha protocol. ART is significantly faster than prior schemes. For example, given a required confidence interval of 0.1% and a required reliability of 99.9%, ART is consistently 7 times faster than the fastest existing schemes (UPE and EZB) for any tag population size. Furthermore, ART's estimation time is provably independent of the tag population sizes. ART works with multiple readers with overlapping regions and can estimate sizes of arbitrarily large tag populations. ART is easy to deploy because it neither requires modification to tags nor to the communication protocol between tags and readers. ART only needs to be implemented on readers as a software module.
Application of Isotopically Labeled Engineered Nanomaterials for Detection and Quantification in Soils via Single-Particle Inductively Coupled Plasma Time-of-Flight Mass Spectrometry

Finding and quantifying engineered nanomaterials (ENMs) in soil are challenging because of the abundance of natural nanomaterials (NNMs) with the same elemental composition, for example, TiO2. Isotopically enriched ENMs may be distinguished from NNMs with the same elemental composition using single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-TOF-MS) to measure multiple isotopes simultaneously within each ENM and NNM in soil, but the minimum isotope enrichment needed for detection of ENMs in soil is not known. Here, we determined the isotope enrichment needed for 47Ti-enriched TiO2 ENMs to be detectable in soil and assessed the effects of weathering on those requirements for less soluble TiO2 and more soluble CuO ENMs. The isotope-enriched ENMs were dosed into two different soils and were extracted and measured by spICP-TOF-MS after 1, 7, and 30 days. Isotope-enriched ENMs were recovered and detected for all three time points. The 47Ti-enriched TiO2 ENMs were detectable in Lufa 2.2 soil at a nominal dosed concentration of 10 mg-TiO2 kg-1 which is an environmentally relevant concentration in biosolid-amended soils. For distinguishing an ∼70 nm diameter TiO2 ENM from TiO2 NNMs in Lufa 2.2 soil, an ∼10 wt % 47Ti isotope-enrichment was required, and this enrichment requirement increases as the particle size decreases. This study is the first to evaluate the tracking ability of isotope-enriched ENMs at an individual particle level in soil and provides guidance on the isotope enrichment requirements for quantification of ENMs made from Earth-abundant elements in soils.
CAM-CAN: Class activation map-based categorical adversarial network

Numerous studies have investigated image classification. In particular, recent methods based on deep learning have exhibited high accuracies. However, various existing state-of-the-art methods based on deep learning show different accuracies depending on the database and environment. Accordingly, different deep learning models need to be used in image classification studies according to the database, environment, and research field. This study investigated a technique to increase the accuracy of the existing deep learning-based models. The proposed method was applied to various existing state-of-the-art methods. In the proposed method, a convolution neural network (CNN) is trained using the classification activation map (CAM) to focus on specific areas in the input image. The CAM image is used as the ground-truth image. Furthermore, the concept of the CAM-based cate-gorical adversarial network (CAM-CAN), in which the CNN is trained based on a generative adversarial network, is proposed in this paper. An action recognition experiment was performed using the self-collected Dongguk thermal image database (DTh-DB) and open database, and the results revealed that the accuracies of the existing state-of-the-art methods significantly increased after applying the proposed method. For instance, the accuracies obtained using the DTh-DB, TPR, PPV, ACC, and F1 with the conventional DenseNet201 model were 80.14%, 75.28%, 96.0%, and 75.91%, respectively. After applying the proposed method, the accuracies increased to 86.53%, 89.90%, 97.64%, and 85.84%, respectively.
The Frequency of Refractory Status Epilepticus and Its Outcome in a Tertiary Care Hospital in Pakistan: A Retrospective Study

Background Refractory status epilepticus (RSE) is a common neurologic emergency with refractory cases leading to increased rates of morbidity and mortality in patients. The lack of previous studies on the incidence, causes, and management of refractory status epilepticus in the pediatric population from our region prompted us to investigate further in this study. Methods We included retrospective data of all patients admitted to the pediatric intensive care unit (PICU) with a provisional diagnosis of RSE at a tertiary care hospital in Karachi from February 2019 to February 2021. No personal identification data was used, and confidentiality of the data was maintained throughout the analysis. The Statistical Package for the Social Sciences (SPSS) software version 22.0 (IBM SPSS Statistics, Armonk, NY, USA) was used to pool data and perform a descriptive analysis. Results Among the 687 patients who presented to the PICU with seizures, 50 (7.27%) patients were eventually diagnosed with RSE during the two-year period. The majority of the patients were male and less than one year of age. Infectious causes predominated our data cohort, and a four-drug regimen consisting of phenytoin, levetiracetam, valproic acid, and midazolam was able to terminate RSE in the majority of the patients in our setting (70%). The mortality rate was noted to be 22% among patients with RSE. Conclusion Morbidity and mortality among pediatric RSE patients are high in our settings. Urgent emergency services and timely cause-directed intervention could improve outcomes.
Learning the micro deformations by max-pooling for offline signature verification

For signature verification systems, micro deformations can be defined as the small differences in the same strokes of signatures or special writing habits of different signers. These micro deformations can reveal the core distinction between the genuine signatures and skilled forgeries. In this paper, we prove that Convolutional Neural Networks (CNNs) have the potential to extract those micro deformations by max-pooling. More specifically, the micro deformations can be determined by watching the location coordinates of the maximum values in pooling windows of max-pooling. Extensive analysis and experiments demonstrate that it is possible to achieve state-of-the-art performance by using this location information as a new feature for capturing micro deformations, along with convolutional features. The proposed method outperforms the state-of-the-art systems on four publicly available datasets of different languages, i.e., English (GPDSsynthetic, CEDAR), Persian (UTSig), and Hindi (BHSig260). (c) 2021 Elsevier Ltd. All rights reserved.
Performance benchmarking of state-of-the-art software switches for NFV

With the ultimate goal of replacing proprietary hardware appliances with Virtual Network Functions (VNFs) implemented in software, Network Function Virtualization (NFV) has gained popularity in the past few years. Software switches are widely employed to route traffic between VNFs and physical Network Interface Cards (NICs). It is thus of paramount importance to compare the performance of different switch designs and architectures. In this paper, we propose a methodology to compare fairly and comprehensively the performance of software switches. We first explore the design spaces of 7 state-of-the-art software switches and then compare their performance under four representative test scenarios. Each scenario corresponds to a specific case of routing NFV traffic between NICs and/or VNFs. In our experiments, we evaluate the throughput and latency between VNFs in two of the most popular virtualization environments, namely virtual machines (VMs) and containers. Our experimental results show that no single software switch prevails in all scenarios. It is, therefore, crucial to choose the most suitable solution for the given use case. At the same time, the presented results and analysis provide a more in-depth insight into the design tradeoffs and identify potential performance bottlenecks that could inspire new designs.
A Novel Framework for Trash Classification Using Deep Transfer Learning

Nowadays, society is growing and crowded, the construction of automatic smart waste sorter machine utilizing the intelligent sensors is important and necessary. To build this system, trash classification from trash images is an important issue in computer vision to be addressed for integrating into sensors. Therefore, this study proposes a robust model using deep neural networks to classify trash automatically which can be applied in smart waste sorter machines. Firstly, we collect the VN-trash dataset that consists of 5904 images belonging to three different classes including Organic, Inorganic and Medical wastes from Vietnam. Next, this study develops a deep neural network model for trash classification named DNN-TC which is an improvement of ResNext model to improve the predictive performance. Finally, the experiments are conducted to compare the performances of DNN-TC and the state-of-the-art methods for trash classification on VN-trash dataset as well as Trashnet dataset to show the effectiveness of the proposed model. The experimental results indicate that DNN-TC yields 94 and 98 in terms of accuracy for Trashnet and VN-trash datasets respectively and thus it outperforms the state-of-the-art methods for trash classification on both experimental datasets.
LOGISMOS-B: Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces for the Brain

Automated reconstruction of the cortical surface is one of the most challenging problems in the analysis of human brain magnetic resonance imaging (MRI). A desirable segmentation must be both spatially and topologically accurate, as well as robust and computationally efficient. We propose a novel algorithm, LOGISMOS-B, based on probabilistic tissue classification, generalized gradient vector flows and the LOGISMOS graph segmentation framework. Quantitative results on MRI datasets from both healthy subjects and multiple sclerosis patients using a total of 16 800 manually placed landmarks illustrate the excellent performance of our algorithm with respect to spatial accuracy. Remarkably, the average signed error was only 0.084 mm for the white matter and 0.008 mm for the gray matter, even in the presence of multiple sclerosis lesions. Statistical comparison shows that LOGISMOS-B produces a significantly more accurate cortical reconstruction than FreeSurfer, the current state-of-the-art approach (p << 0.001). Furthermore, LOGISMOS-B enjoys a run time that is less than a third of that of FreeSurfer, which is both substantial, considering the latter takes 10 h/subject on average, and a statistically significant speedup.
Can we teach computers to understand art? Domain adaptation for enhancing deep networks capacity to de-abstract art

Humans comprehend a natural scene at a single glance; painters and other visual artists, through their abstract representations, stressed this capacity to the limit. The performance of computer vision solutions matched that of humans in many problems of visual recognition. In this paper we address the problem of recognizing the genre (subject) in digitized paintings using Convolutional Neural Networks (CNN) as part of the more general dealing with abstract and/or artistic representation of scenes. Initially we establish the state of the art performance by training a CNN from scratch. In the next level of evaluation, we identify aspects that hinder the CNNs' recognition, such as artistic abstraction. Further, we test various domain adaptation methods that could enhance the subject recognition capabilities of the CNNs. The evaluation is performed on a database of 80,000 annotated digitized paintings, which is tentatively extended with artistic photographs, either original or stylized, in order to emulate artistic representations. Surprisingly, the most efficient domain adaptation is not the neural style transfer. Finally, the paper provides an experiment-based assessment of the abstraction level that CNNs are able to achieve. (C) 2018 Elsevier B.V. All rights reserved.
Nonsense-mediated mRNA decay and metal ion homeostasis and detoxification in Saccharomyces cerevisiae

The highly conserved Nonsense-mediated mRNA decay (NMD) pathway is a translation dependent mRNA degradation pathway. Although NMD is best known for its role in degrading mRNAs with premature termination codons (PTCs) generated during transcription, splicing, or damage to the mRNAs, NMD is now also recognized as a pathway with additional important functions. Notably, NMD precisely regulates protein coding natural mRNAs, hence controlling gene expression within several physiologically significant pathways. Such pathways affected by NMD include nutritional bio-metal homeostasis and metal ion detoxification, as well as crosstalk between these pathways. Here, we focus on the relationships between NMD and various metal homeostasis and detoxification pathways. We review the described role that the NMD pathway plays in magnesium, zinc, iron, and copper homeostasis, as well as cadmium detoxification.
Maximum A Posteriori Linear Regression for language recognition

This paper proposes the use of Maximum A Posteriori Linear Regression (MAPLR) transforms as feature for language recognition. Rather than estimating the transforms using maximum likelihood linear regression (MLLR), MAPLR inserts the priori information of the transforms in the estimation process using maximum a posteriori (MAP) as the estimation criterion to drive the transforms. By multi MAPLR adaptation each language spoken utterance is convert to one discriminative transform supervector consist of one target language transform vector and other non-target transform vectors. SVM classifiers are employed to model the discriminative MAPLR transform supervector. This system can achieve performance comparable to that obtained with state-of-the-art approaches and better than MLLR. Experiment results on 2007 NIST Language Recognition Evaluation (LRE) databases show that relative decline in EER of 4% and on mincost of 9% are obtained after the language recognition system using MAPLR instead of MLLR in 30-s tasks, and further improvement is gained combining with state-of-the-art systems. It leads to gains of 6% on EER and 11% on minDCF comparing with the performance of the only combination of the MMI system and the GMM-SVM system. (C) 2011 Elsevier Ltd. All rights reserved.