Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Named Entity Recognition Method of Metro On-Board Equipment Based on Multiheaded Self-Attention Mechanism and CNN-BiLSTM-CRF

Massive and complex unstructured fault text data will be generated during the operation of subway trains. A named entity recognition model of subway on-board equipment based on Multiheaded Self-attention mechanism and CNN-BiLSTM-CRF is proposed to address the issue of low recognition accuracy and incomplete recognition features of unstructured fault data named entity recognition task of subway on-board equipment: BiLSTM-CNN parallel network extracts context feature information and local attention information, respectively; In the MHA layer, the features learned from different dimensions are fused through the Multiheaded Self-attention mechanism, and the dependencies of various ranges in the sequence are captured to yield the internal structure information of the features. The conditional random field CRF is used to learn the internal relationship between tags to ensure their sequence. This model is tested with other named entity recognition models on the marked subway on-board fault data. The experimental results demonstrate that this model is able to recognize 10 kinds of labels in the dataset. Moreover, the recognition effect of each label has a good performance in the three evaluation indexes of P, R, and F1 score. Moreover, the weighted average evaluation indexes Avg − P, Avg − R, and Avg − F1 of 10 labels in this model reach the highest 95.39%, 95.48%, and 95.37%, which has high evaluation indexes and can be applied to the named entity recognition of Metro on-board equipment.
The Mechanism of Wnt Pathway Regulated by Telocytes to Promote the Regeneration and Repair of Intrauterine Adhesions

Background Telocytes (TCs), a novel interstitial cell type in the reproductive tract, participating in pathophysiology of intrauterine adhesions (IUA). This study further investigates the hypothesis that TCs, a source of Wnt, promote the regeneration and repair of IUA. Methods RNA sequencing datasets of IUA patient (GSE160633) and mouse intestine mesenchymal cells (GSE94072) in GEO database were analyzed for differentially expressed genes (DEGs), and quantitative real-time PCR (qRT-PCR) measured indicated gene expression in TC-educated endometrial stromal cells (ESCs) and noneducated ESCs and verified the results of data mining from GEO database. Results The expression levels of Wnt genes were downregulated in IUA compared to the control and were upregulated in TCs. In particular, the changes of Wnt5a expression level were the most significant (logFC = 4.0314 and adjusted P value = 0.0023), and the relative Wnt5a expression level was remarkably higher in TC-educated ESCs than noneducated ESCs verified by qRT-PCR (P = 0.0027). Conclusions TCs may enhance the regeneration and repair of IUA through the Wnt signaling pathway.
WATERPIPE TOBACCO SMOKING AMONG SEXUAL MINORITIES IN THE UNITED STATES: EVIDENCE FROM THE NATIONAL ADULT TOBACCO SURVEY, 2012-2013 & 2013-2014

Objective: The current study examined differences in waterpipe smoking (both lifetime and current) comparing sexual minority populations - those identifying with lesbian, gay, or bisexual identity- to their heterosexual counterparts using a nationally representative dataset. Methods: The current study used pooled data from the 2012-2013 & 2013-2014 National Adult Tobacco Survey (NATS). Log-Poisson multivariable regression models were deployed to determine the risk of waterpipe smoking behavior among sexual minority individuals controlling for sociodemographic characteristics and stratified by current gender status. Results: In fully-adjusted models assessing lifetime WTS, lesbian/gay and bisexual respondents reported higher risk of WTS compared to their heterosexual counterparts. This trend held true in gender-stratified models among gay men [gay men: RR 1.25, 95%CI [1.06, 1.47] and women ([lesbians: RR 1.38, 95%CI [1.12, 1.69] and bisexual women: 1.69, 95%CI [1.45, 1.97]). In fully-adjusted models assessing current WTS, lesbian/gay and bisexual respondents reported higher risk of WTS compared to their heterosexual counterparts. This trend held true in gender-stratified models, only for among gay men [gay men: RR 1.56, 95%CI [1.18, 2.05] and bisexual women: 2.38, 95%CI [1.84, 3.09]). Conclusions: Among the US general adult population, sexual minorities are at an increased risk of current waterpipe smoking compared to their heterosexual counterparts. This pattern is also shaped by gender and variation of sexual orientation identification (e.g., lesbian/gay vs. bisexual). This warrants development of tailored interventions aimed at decreasing waterpipe smoking among sexual minority populations.
QoS-Aware Scale Up on IaaS Clouds

—For systems hosted in IaaS clouds that target proﬁt, like blogs and e-commerce systems, the ﬁnal revenue should be the most important metric. Balancing the QoS experienced by clients as a manner to avoid their drop-out against the cost related to leasing instances forms the basis of a good instance management for those scenarios. In this paper, we demonstrate the feasibility of using a Fuzzy Logic Inference System as a tool for maintaining the QoS. Furthermore, it was created a method for reducing the overall cost related to instances leasing using an incremental algorithm, acquiring one computational unit at a time. Finally, we used hooks to handle unpredictable burst of requests, called here as noises. Our experiments evidenced that those methods can keep the response time of all requests below a deadline, avoiding customer dissatisfaction. At the same time, the total cost of servers lease is reduced, even when the cost-beneﬁt among different conﬁgurations is not linear.
Why Do Bowerbirds Build Bowers

central element of his theory of sexual selection called female choice. The high? ly sculptured structure of a bower and a male's use of brightly colored decora? tions suggested to Darwin that female bowerbirds might shop for the most at? tractive bower, thereby directing the evolution of these display traits. Nevertheless, several other mecha? nisms could have driven the evolution of bowers. The so-called good-genes model, for instance, suggests that male-display traits, including bowers, might indicate a male's vigor and, ulti? mately, his quality as a sire. That is, more vigorous males might have better bowers. A bower could even directly benefit a female, perhaps protecting her from threats, including predators that might attack her during mating or males that might try to force her to copulate. Bower building could even arise from an arbitrary or pre-existing female preference, such as an attraction to nest-like structures. I have used Darwin's method of comparisons of related species to re? construct the evolution of bower build?
COVID-19 Diagnosis in Patients With Acute Abdominal Pain Without Respiratory Symptoms: A UK Emergency General Surgical Unit Experience

Background Recent evidence has emerged reporting atypical clinical symptoms of the novel coronavirus (COVID-19). There is a sparsity of existing studies examining COVID-19-related abdominal pain and the role of investigative imaging for the virus in these patients. Study aims were to determine COVID-19 incidence in those with acute abdominal pain in the absence of respiratory symptoms and to assess the diagnostic performance of CT thoracic imaging in such patients. Methods Retrospective analysis of all consecutive patients admitted to our emergency general surgical unit between 1st March 2020 and 31st May 2020 was performed. In adherence with national guidelines, all patients underwent nasal and oro-pharyngeal COVID-19 RT-PCR swabs as well as thoracic and abdominal computed tomography (CT) on admission. Results From 112 patients admitted with acute abdominal pain in the absence of respiratory symptoms, 16 (14.3%) tested positive for COVID-19 on RT-PCR swab testing. Overall, 50% (8/16) of these patients had no intra-abdominal pathology on CT. The sensitivity and specificity of CT thoracic imaging for diagnosing COVID-19 was 43.8% and 91.7%, respectively. Patients with positive COVID-19 swabs had higher C-reactive protein levels, lower potassium levels and a higher proportion of those with a low lymphocyte count. Discussion One in seven patients with abdominal pain without any respiratory symptoms tested positive for COVID-19. Half of these patients represented COVID-19 manifesting primarily as acute abdominal pain. Combined swab testing and CT imaging should be performed in all abdominal pain presentations due to the varying diagnostic performance of thoracic CT in diagnosing COVID-19.
The relationship between empathy and personality traits in Saudi medical students

BACKGROUND: Empathy is regarded as a fundamental personal attribute for in-training and in-practice doctors. Several factors may play a significant role to facilitate or prevent the ability of doctors' empathy toward their patients. The objective of the present study is to investigate the relationship of empathy with personality traits (extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience) among undergraduate medical students. MATERIALS AND METHODS: This study was based on a correlational, cross-sectional design and was conducted from January 2020 to April 2020. The sample consisted of 301 undergraduate medical students studying in different medical colleges situated in Riyadh, Saudi Arabia. Personal information form, the big-five personality inventory, and the interpersonal reactivity index scales were administered. Obtained data were analyzed by applying several methods such as mean, standard deviation, Mann–Whitney U test, Pearson's correlation coefficient, and multivariable linear regression. RESULTS: Regression analysis revealed that personality dimensions accounted for 10–19% of the variance in scores of empathy sub-dimensions of perspective-taking, empathetic concern, fantasy, and personal distress. Agreeableness was found to be associated with empathetic concern and perspective-taking. Openness to experience was found to be significantly related to perspective-taking and fantasy, whereas extraversion was negatively associated with perspective-taking. Neuroticism had an association with personal distress, empathetic concern, and fantasy. Additionally, no gender difference was observed and students with people-oriented specialty preferences showed significantly higher scores on empathy sub-domains than technology-oriented students. CONCLUSION: This study confirmed that personality dimensions play a notable role to predict empathetic behaviors among Saudi undergraduate medical students. This study would help to design individualized strategies by incorporating personality dimensions in the training program to enhance empathetic behavior among medical students.
Structural and functional basis for lipid synergy on the activity of the antibacterial peptide ABC transporter McjD

The lipid bilayer is a dynamic environment that consists of a mixture of lipids with different properties that regulate the function of membrane proteins; these lipids are either annular, masking the protein hydrophobic surface, or specific lipids, essential for protein function. In this study, using tandem mass spectrometry, we have identified specific lipids associated with the Escherichia coli ABC transporter McjD, which translocates the antibacterial peptide MccJ25. Using non-denaturing mass spectrometry, we show that McjD in complex with MccJ25 survives the gas-phase. Partial delipidation of McjD resulted in reduced ATPase activity and thermostability as shown by Circular Dichroism, both of which could be restored upon addition of defined E. coli lipids. We have resolved a phosphatidylglycerol lipid associated with McjD at 3.4 Å resolution, while molecular dynamic simulations carried out in different lipid environments assessed the binding of specific lipids to McjD. Combined, our data show a synergistic effect of zwitterionic and negatively charged lipids on the activity of McjD; the zwitterionic lipids provide structural stability to McjD whereas the negatively charged lipids are essential for its function.
Performance improvement of slotted elliptical patch antenna using FSS superstrate

In this article, the slotted microstrip‐ fed elliptical patch antenna is designed and optimized to operate at the resonance frequency of 5.38 GHz. Then, the designed patch antenna is further analyzed in a resonant cavity formed by a frequency selective surface (FSS) superstrate and antenna ground plane. The highly reflective behavior of FSS from the offset of the resonance is utilized for improving the performance of the antenna. The optimized slotted geometry produces a gain improvement of 2.5 dBi, impedance bandwidth improvement of 0.21 GHz, simultaneously at the operating resonance frequency of 5.38 GHz. After placing an FSS superstrate at a height of 28.5 mm above the antenna substrate, further improvement of 5.1 dBi gain and 0.13 GHz bandwidth is attained, For validation purpose, the prototypes of the slotted patch antenna with and without FSS superstrate are fabricated and characterized. A fairly good agreement is achieved in the measured and simulated results.
Creating and Evaluating Data-Driven Ontologies

—Automatically creating data-driven ontologies is a challenge but it can save time and resources. In this paper, eight data-driven algorithms are compared to create ontologies, four ontologies based on documents and four based on keywords, on three different document sets. We evaluate the performance using three different evaluation metrics based on nodes, weights and relations. Results show that 1) keyword-based methods are in general better than document-based methods; 2) a co-occurrence-based algorithm is the best document-based method; 3) the evaluation metrics give useful insight, but need to be enhanced in future work. It is advised to a) use the created ontologies as a head start in an ontology creation session, but not use the ontologies as created; b) use word2vec to generate an ontology in a generic domain, whereas the co-occurrences algorithm should be used in speciﬁc domain.
Molecular Dynamics Simulation of Interaction of Lysine Dendrigraft of 2 nd Generation with Stack of Amyloid Peptides.

— In present paper, molecular dynamics simulation is used to study destruction of stack of short amyloid peptide molecules by oppositely charged dendrigraft of 2 nd generation. Dendrimers and dendrigraftss are often used in biomedicine for delivery of drugs and other biological molecules. They also could be used as antibacterial, antiviral and antiamyloid agents. Since lysine dendrimers and dendrigrafts are less toxic than many other conventional synthetic dendrimers they were chosen for present study and two systems consisting of 2 nd generation dendrigraft and stack of 8 or 16 short amyloid peptide molecules were simulated by the method of molecular dynamics in water. It was demonstrated that lysine dendrigraft destroys both studied amyloid stacks and forms stable complexes with their peptide molecules. The final structures of the complexes in equilibrium state were studied also. It was shown that peptides in complexes stay mainly on the surface of dendrigrafts and do not penetrate into them. The results obtained in present paper could be useful for elaboration in future the anti-amyloid agents for treatment of Alzheimer's disease, since it is believed that one of the sources of this disease is the formation of toxic amyloid oligomers and fibrils.
Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation

Using machine learning to solve combinatorial optimization (CO) problems is challenging, especially when the data is unlabeled. This work proposes an unsupervised learning framework for CO problems. Our framework follows a standard relaxation-plus-rounding approach and adopts neural networks to parameterize the relaxed solutions so that simple back-propagation can train the model end-to-end. Our key contribution is the observation that if the relaxed objective satisfies entry-wise concavity, a low optimization loss guarantees the quality of the final integral solutions. This observation significantly broadens the applicability of the previous framework inspired by Erdos' probabilistic method. In particular, this observation can guide the design of objective models in applications where the objectives are not given explicitly while requiring being modeled in prior. We evaluate our framework by solving a synthetic graph optimization problem, and two real-world applications including resource allocation in circuit design and approximate computing. Our framework largely outperforms the baselines based on na\"{i}ve relaxation, reinforcement learning, and Gumbel-softmax tricks.
A Unified Recovery of Structured Signals Using Atomic Norm

In many applications, we seek to recover signals from linear measurements far fewer than the ambient dimension, given the signals have exploitable structures such as sparse vectors or low rank matrices. In this paper, we work in a general setting where signals are approximately sparse in a so-called atomic set. We provide general recovery results stating that a convex programming can stably and robustly recover signals if the null space of the sensing map satisfies certain properties. Moreover, we argue that such null space property can be satisfied with high probability if each measurement is sub-Gaussian even when the number of measurements are very few. Some new results for recovering signals sparse in a frame, and recovering low rank matrices are also derived as a result.
Semi-supervised Vector-Quantization in Visual SLAM using HGCN

In this paper, two semi-supervised appearance based loop closure detection technique, HGCN-FABMAP and HGCN-BoW are introduced. Furthermore an extension to the current state of the art localization SLAM algorithm, ORB-SLAM, is presented. The proposed HGCN-FABMAP method is implemented in an off-line manner incorporating Bayesian probabilistic schema for loop detection decision making. Specifically, we let a Hyperbolic Graph Convolutional Neural Network (HGCN) to operate over the SURF features graph space, and perform vector quantization part of the SLAM procedure. This part previously was performed in an unsupervised manner using algorithms like HKmeans, kmeans++,. etc. The main Advantage of using HGCN, is that it scales linearly in number of graph edges. Experimental results shows that HGCN-FABMAP algorithm needs far more cluster centroids than HGCN-ORB, otherwise it fails to detect loop closures. Therefore we consider HGCN-ORB to be more efficient in terms of memory consumption, also we conclude the superiority of HGCN-BoW and HGCN-FABMAP with respect to other algorithms.
Adaptive Attitude Estimation Using a Hybrid Model-Learning Approach

Attitude determination using the smartphone’s inertial sensors poses a major challenge due to the sensor’s low-performance grade and the varying nature of the walking pedestrian. In this article, data-driven techniques are employed to address that challenge. To that end, a hybrid deep learning (DL) and model-based solution for attitude estimation is proposed. Here, classical model-based equations are applied to form an adaptive complementary filter (CF) structure. Instead of using constant or model-based adaptive weights, the accelerometer weights in each axis are determined by a unique neural network (NN). The performance of the proposed hybrid approach is evaluated relative to popular model-based approaches using experimental data.
T-RecX: Tiny-Resource Efficient Convolutional neural networks with early-eXit

Deploying Machine learning (ML) on milliwatt-scale edge devices (tinyML) is gaining popularity due to recent breakthroughs in ML and Internet of Things (IoT). Most tinyML research focuses on model compression techniques that trade accuracy (and model capacity) for compact models to fit into the KB-sized tiny-edge devices. In this paper, we show how such models can be enhanced by the addition of an early exit intermediate classifier. If the intermediate classifier exhibits sufficient confidence in its prediction, the network exits early thereby, resulting in considerable savings in time. Although early exit classifiers have been proposed in previous work, these previous proposals focus on large networks, making their techniques suboptimal/impractical for tinyML applications. Our technique is optimized specifically for tiny-CNN sized models. In addition, we present a method to alleviate the effect of network overthinking by leveraging the representations learned by the early exit. We evaluate T-RecX on three CNNs from the MLPerf tiny benchmark suite for image classification, keyword spotting and visual wake word detection tasks. Our results show that T-RecX 1) improves the accuracy of baseline network, 2) achieves 31.58% average reduction in FLOPS in exchange for one percent accuracy across all evaluated models. Furthermore, we show that our methods consistently outperform popular prior works on the tiny-CNNs we evaluate.
Multi-Agent Deep Reinforcement Learning-Driven Mitigation of Adverse Effects of Cyber-Attacks on Electric Vehicle Charging Station

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has myriads of exploitable vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. These standalone or networked EVCS open up large attack surfaces for the local or state-funded adversaries. The state-of-the-art approaches are not agile and intelligent enough to defend against and mitigate advanced persistent threats (APT). We propose the data-driven model-free distributed intelligence based on multiagent Deep Reinforcement Learning (MADRL)-- Twin Delayed Deep Deterministic Policy Gradient (TD3) -- that efficiently learns the control policy to mitigate the cyberattacks on the controllers of EVCS. Also, we have proposed two additional mitigation methods: the manual/Bruteforce mitigation and the controller clone-based mitigation. The attack model considers the APT designed to malfunction the duty cycles of the EVCS controllers with Type-I low-frequency attack and Type-II constant attack. The proposed model restores the EVCS operation under threat incidence in any/all controllers by correcting the control signals generated by the legacy controllers. Also, the TD3 algorithm provides higher granularity by learning nonlinear control policies as compared to the other two mitigation methods. Index Terms: Cyberattack, Deep Reinforcement Learning(DRL), Electric Vehicle Charging Station, Mitigation.