Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Regular Expressions for Interpretable Medical Text Classification Using a Pool-based Simulated Annealing and Word-vector Models

In this paper, we propose a rule-based engine composed of high quality and interpretable regular expressions for medical text classification. The regular expressions are auto generated by a constructive heuristic method and optimized using a Pool-based Simulated Annealing (PSA) approach. Although existing Deep Neural Network (DNN) methods present high quality performance in most Natural Language Processing (NLP) applications, the solutions are regarded as uninterpretable black boxes to humans. Therefore, rule-based methods are often introduced when interpretable solutions are needed, especially in the medical field. However, the construction of regular expressions can be extremely labor-intensive for large data sets. This research aims to reduce the manual efforts while maintaining high-quality solutions
A Quantile-based Approach for Hyperparameter Transfer Learning

Bayesian optimization (BO) is a popular methodology to tune the hyperparameters of expensive black-box functions. Traditionally, BO focuses on a single task at a time and is not designed to leverage information from related functions, such as tuning performance objectives of the same algorithm across multiple datasets. In this work, we introduce a novel approach to achieve transfer learning across different datasets as well as different objectives. The main idea is to regress the mapping from hyperparameter to objective quantiles with a semi-parametric Gaussian Copula distribution, which provides robustness against different scales or outliers that can occur in different tasks. We introduce two methods to leverage this mapping: a Thompson sampling strategy as well as a Gaussian Copula process using such quantile estimate as a prior. We show that these strategies can combine the estimation of multiple objectives such as latency and accuracy, steering the hyperparameters optimization toward faster predictions for the same level of accuracy. Extensive experiments demonstrate significant improvements over state-of-the-art methods for both hyperparameter optimization and neural architecture search.
Eats Plants and Leaves: Tortoises and Tomatoes in Galapagos

Galapagos endemic tomatoes (of the species lycopersicon cheesmanii, L. esculentum var. minor, and L. pimpinellifolium) evolved two unique adaptive traits to ensure wide dispersal on the harsh lava-based terrain of the islands. These adaptations are a high seed dormancy and a non-abscising pedicel, which led to a symbiotic relationship with the Galapagos tortoise. The relationship shows no evidence of being an example of coevolution.
Critical Success Factors for Effective Implementation of TQM & TPM

According to the literature, quality management consists of a set of components: critical factors, tools, techniques and practices. To facilitate the implementation of Total quality management (TQM) and Total Productive Maintenance (TPM), practitioners and researchers have suggested large number of critical success factors. However, despite a broad consensus about what needs to be done, companies still struggle to implement these initiatives. Over the years, both academicians and professionals are emphasizing on the point of quality improvement of goods and services. Two of the eminent quality management philosophies are TQM and TPM. Quality management, by means of TQM, is considered to promote organizational performance characterized by competitiveness and long-term profitability. The benefits of quality management cannot be achieved without the sustained performance of equipment affecting product quality, hence TPM has become important. TQM and TPM approaches are generated in developed countries. It is found that TQM and TPM have similar background, some common factors, which have positive impact on the organizational performance. This paper includes a comparative study of literature focusing on factors of TQM and TPM implementation which have positive impact on the organizational performance. The study found some factors are common categories of activities when implementing TQM and TPM as combined strategy. The purpose of this paper is to identify the Critical Success Factors of TQM and TPM, in order to make them known to managers and thus facilitate successful quality management implementation.
Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming

Linear constrained convex programming has many practical applications, including support vector machine and machine learning portfolio problems. We propose the randomized primal-dual coordinate (RPDC) method, a randomized coordinate extension of the first-order primal-dual method by Cohen and Zhu, 1984 and Zhao and Zhu, 2019, to solve linear constrained convex programming. We randomly choose a block of variables based on a uniform distribution, linearize, and apply a Bregman-like function (core function) to the selected block to obtain simple parallel primal-dual decomposition. We then establish almost surely convergence and expected O(1/t) convergence rate, and expected linear convergence under global strong metric subregularity. Finally, we discuss implementation details for the randomized primal-dual coordinate approach and present numerical experiments on support vector machine and machine learning portfolio problems to verify the linear convergence.
Advanced space concepts bio-inspired by spider attaching mechanisms and plant roots

Performed studies show that a biomimetic approach has potential to face challenges and difficulties of modern space engineering design. Key areas of bio-inspired space applications have been identified and classified. In this paper, the concepts of spider-inspired attaching mechanisms and soil probing capabilities of plant roots are proposed for the exploration of inhomogeneous, unstructured and unknown space environments.
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics

Posterior distribution approximation is a central task in Bayesian inference. Stochastic gradient Langevin dynamics (SGLD) and its extensions have been practically used and theoretically studied. While SGLD updates a single particle at a time, ensemble methods that update multiple particles simultaneously have been recently gathering attention. Compared with the naive parallel-chain SGLD that updates multiple particles independently, ensemble methods update particles with their interactions. Thus, these methods are expected to be more particle-efﬁcient than the naive parallel-chain SGLD because particles can be aware of other particles’ behavior through their interactions. Although ensemble methods numerically demonstrated their superior performance, no theoretical guarantee exists to assure such particle-efﬁciency and it is unclear whether those ensemble methods are really superior to the naive parallel-chain SGLD in the non-asymptotic settings. To cope with this problem, we pro-pose a novel ensemble method that uses a non-reversible Markov chain for the interaction, and we present a non-asymptotic theoretical analysis for our method. Our analysis shows that, for the ﬁrst time, the interaction causes a faster convergence rate than the naive parallel-chain SGLD in the non-asymptotic setting if the discretization error is appropriately controlled. Numerical experiments show that we can control the discretization error by tuning the interaction appropriately.
Involvement of p53, p21, and Caspase-3 in Apoptosis of Coronary Artery Smooth Muscle Cells in a Kawasaki Vasculitis Mouse Model

Background Overexpression of p53, p21, and caspase-3 promotes apoptosis of vascular smooth muscle cells. However, the mechanisms that lead to apoptosis of coronary artery smooth muscle cells (CASMCs) is unclear in Kawasaki disease (KD). This study investigated involvement of p53, p21, and caspase-3 in the apoptosis of CASMCs from a Kawasaki vasculitis mouse model. Material/Methods The Kawasaki vasculitis mouse model with coronary artery lesions was generated via administration of Lactobacillus casei cell wall extract. In 2 groups of mice (healthy control and KD vasculitis mice), the levels of p53, p21, and caspase-3 protein in the root of the coronary artery were evaluated via immunohistochemistry. Receiver operating characteristic curves were plotted for determination of area under the curve, 95% confidence interval, sensitivity, specificity, and cutoff values for the ability of p53, p21, and caspase-3 expression to predict CASMC apoptosis and coronary artery lesion formation in KD vasculitis mice. Results Compared with healthy mice, KD vasculitis mice had a significantly higher apoptosis index and upregulated p53, p21, and caspase-3 expression. Also, the immunoreactive score for caspase-3 was positively correlated with the immunoreactivity scores for p53 and p21. The optimal cutoff values for p53, p21, and caspase-3 expression for predicting the presence of coronary artery lesions were 4.15, 4.18, and 4.22, respectively. Conclusions Upregulated levels of p53, p21, and caspase-3 promoted apoptosis of CASMCs in KD vasculitis mice. Thus, the levels of p53, p21, and caspase-3 may serve as valuable predictors of coronary artery lesion formation in KD.
HMCNAS: Neural Architecture Search using Hidden Markov Chains and Bayesian Optimization

Neural Architecture Search has achieved state-of-the-art performance in a variety of tasks, out-performing human-designed networks. However, many assumptions, that require human definition, related with the problems being solved or the models generated are still needed: final model architectures, number of layers to be sampled, forced operations, small search spaces, which ultimately contributes to having models with higher performances at the cost of inducing bias into the system. In this paper, we propose HMCNAS, which is composed of two novel components: i) a method that leverages information about human-designed models to autonomously generate a complex search space, and ii) an Evolutionary Algorithm with Bayesian Optimization that is capable of generating competitive CNNs from scratch, without relying on human-defined parameters or small search spaces. The experimental results show that the proposed approach results in competitive architectures obtained in a very short time. HMCNAS provides a step towards generalizing NAS, by providing a way to create competitive models, without requiring any human knowledge about the specific task.
Electromagnetic inertia of the waveguide modes

Studying electromagnetic inertia in SI units of the waveguide modes needs rearrangement of standard Maxwell’s equations to a novel format where the electric and magnetic fields have common physical dimensions. The time-domain waveguide modes are found out explicitly via solving Maxwell’s equations in their novel format in SI units within the framework of the evolutionary approach to electrodynamics. Electromagnetic mass and momentum of the waveguide modes are obtained as the mechanical equivalents of the energetic fields characteristics.
ElasticMatrix: AMATLAB toolbox for anisotropic elastic wave propagation in layeredmedia

Simulating the propagation of elastic waves in multi-layered media has many applications. A common approach is to use matrix methods where the elastic wave-field within each material layer is represented by a sum of partial-waves along with boundary conditions imposed at each interface. While these methods are well-known, coding the required matrix formation, inversion, and analysis for general multi-layered systems is non-trivial and time-consuming. Here, a new open-source toolbox called ElasticMatrix is described which solves the problem of acoustic and elastic wave propagation in multi-layered media for isotropic and transverse-isotropic materials where the wave propagation occurs in a material plane of symmetry. The toolbox is implemented in MATLAB using an object oriented programming framework and is designed to be easy to use and extend. Methods are provided for calculating and plotting dispersion curves, displacement and stress fields, reflection and transmission coefficients, and slowness profiles. © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Simulation of a Cascaded Multilevel Inverter Topology with Reduced Number of Switches and Sources Arranged in Matrix Structure

This paper analysis an improved topology of a cascaded multilevel inverter (CMLI) that utilizes less switch count topological structure than that of conventional topology. So with the reduced number of switches, the topological structures is designed by the mould of a matrix for a CMLI. As the numbers of switches are depleted in the conduction path, so both the switching as well as conduction losses are reduced, lower input current distortion and electromagnetic interference are also reduced. Therefore it assists for the higher efficiency of the converter. The propound inverter focus extends the outstretch to produces different number of output voltage levels from the congruent topology, there where it uses the same number of the voltage source and very less number of switches when compared to the conventional inverters. Thus the desired operation of the power modules and firing pulses are generated by the annex pulse width modulation (PWM) techniques strategy. And its changes in the harmonic spectrum will be analyzed. The converter will be modeled with the assist of MATLAB/SIMULINK. Key words—Matrix structure, CMLI, Level Shifted Carrier PWM, Reduced switch count topology.
A Novel Multiple-Cue Observational Clinical Scale for Functional Evaluation of Gait After Stroke – The Stroke Mobility Score (SMS)

Background For future development of machine learning tools for gait impairment assessment after stroke, simple observational whole-body clinical scales are required. Current observational scales regard either only leg movement or discrete overall parameters, neglecting dysfunctions in the trunk and arms. The purpose of this study was to introduce a new multiple-cue observational scale, called the stroke mobility score (SMS). Material/Methods In a group of 131 patients, we developed a 1-page manual involving 6 subscores by Delphi method using the video-based SMS: trunk posture, leg movement of the most affected side, arm movement of the most affected side, walking speed, gait fluency and stability/risk of falling. Six medical raters then validated the SMS on a sample of 60 additional stroke patients. Conventional scales (NIHSS, Timed-Up-And-Go-Test, 10-Meter-Walk-Test, Berg Balance Scale, FIM-Item L, Barthel Index) were also applied. Results (1) High consistency and excellent inter-rater reliability of the SMS were verified (Cronbach’s alpha >0.9). (2) The SMS subscores are non-redundant and reveal much more nuanced whole-body dysfunction details than conventional scores, although evident correlations as e.g. between 10-Meter-Walk-Test and subscore “gait speed” are verified. (3) The analysis of cross-correlations between SMS subscores unveils new functional interrelationships for stroke profiling. Conclusions The SMS proves to be an easy-to-use, tele-applicable, robust, consistent, reliable, and nuanced functional scale of gait impairments after stroke. Due to its sensitivity to whole-body motion criteria, it is ideally suited for machine learning algorithms and for development of new therapy strategies based on instrumented gait analysis.
Enforcing Compositionality in Sentence Embedding Models

Compositionality is considered to be among the deﬁning properties of language, and yet, capturing compositionality remains a struggle for existing sentence embedding models. We present preliminary results on a data augmentation method that helps sentence embedding models to learn the recursive compositional property of language. We add an element to the training objective, which we call compo-sitionality loss: we artiﬁcially increase the level of recursive depth in a sentence while maintaining its meaning, and then penalize the encoder model if the embeddings of the augmented and original sentences differ. The proposed method is ﬂexible and can be applied with minimal adjustments to most existing sentence embedding methods. As a proof of concept, we demonstrate the effects of our approach on an example of Sequential Denoising Au-toencoder model, where it allows to improve the model’s performance on a number of transfer tasks. Although much work is still to be done, we believe that the proposed approach shows promise and will be of interest to the community.
EFFECT OF FOLIAR SPRAYING STAGE BY MANGANESE AND BORON ON GROWTH PARAMETERS OF CORN (ZEA MAYS L.)

A field experiment was conducted in Al-Nile district (7 km northeast of Babylon province) for the spring and autumn seasons of 2017 in sedimentary soil with Silt Clay texture. The experiment was designed according to split-split block design in The Randomized Complete Block Design (RCBD). The study included three levels of manganese with concentrations of (0, 25, 50 mg.L-1), three levels of boron with concentrations of (0, 2, 4 mg.L-1) and three stages for spraying, the period between them is 15 days, where the first spraying after 20 days of cultivation. Nitrogen, Phosphate and Potassium Fertilizers were added according to their recommendations. The results indicated that the spraying treatment with Manganese at concentration (50 mg.L-1), the spraying treatment with boron at concentration of (4 mg.L-1) and spraying stage (45 days) were significantly excelled by giving them the lowest averages for number of days from cultivation to emergence of 75% of male inflorescences, number of days from cultivation to emergence of 75% of female inflorescences, and the highest averages for the traits of plant height, leaf area, total chlorophyll and stem diameter.
The Performance Of Different Machine Learning Models On Diabetes Prediction

: Many machine learning algorithms are employed to develop predictive models that are applied in diverse areas such as disease prediction and diagnosis, recommendation systems, stock price prediction and object recognitions. In this paper, the performance of machine learning models, namely Gaussian Naïve Bayes (GNB), Linear Support Vector Machine (LSVM) and the Random Forest (RF) is evaluated by employing predicative performance accuracy, Area under Curve (AUC) score and precision as performance metrics in the evaluation of the learning models. The experimental result analysis showed that, the linear support vector machine (LSVM) performed well on diabetes prediction with an accuracy of 78.39%. The Gaussian Naïve Bayes performed better than Random Forest with an accuracy of 74.15%. The Random Forest (RF) performed the least with an average accuracy of 72.72% on prediction of the diabetes.
Structural Features of Human Memapsin 2 （β-Secretase） and Their Biological and Pathological Implications

Memapsin 2 (β-secretase) is the membrane-anchored aspartic protease that initiates the cleavage of β-amyloid precursor protein (APP) leading to the production of amyloid-β (Aβ), a major factor in the pathogenesis of Alzheimer's disease (AD). Since memapsin 2 is a major target for the development of inhibitor drugs for AD, it has been intensively studied during the past five years. Here we discuss the structural features of the catalytic/specificity apparatus, transmembrane domain, cytosolic domain and the implications of these features in the physiological and pathological roles of this protease.
A Large-Scale Empirical Study on Vulnerability Distribution within Projects and the Lessons Learned

The number of vulnerabilities increases rapidly in recent years, due to advances in vulnerability discovery solutions. It enables a thorough analysis on the vulnerability distribution and provides support for correlation analysis and prediction of vulnerabilities. Previous research either focuses on analyzing bugs rather than vulnerabilities, or only studies general vulnerability distribution among projects rather than the distribution within each project. In this paper, we collected a large vulnerability dataset, consisting of all known vulnerabilities associated with five representative open source projects, by utilizing automated crawlers and spending months of manual efforts. We then analyzed the vulnerability distribution within each project over four dimensions, including files, functions, vulnerability types and responsible developers. Based on the results analysis, we presented 12 practical insights on the distribution of vulnerabilities. Finally, we applied such insights on several vulnerability discovery solutions (including static analysis and dynamic fuzzing), and helped them find 10 zero-day vulnerabilities in target projects, showing that our insights are useful.
Tax Aggressiveness and Tax Morale

This paper investigates the impact of social norms on the tax aggressiveness of firms. Social norms can incentivize owners and managers to be less tax aggressive. As these incentives can differ across different kinds of owners, the owner type is taken into account in the empirical analysis. The results show that family-owned firms react stronger to social tax norms than other firms. This can be interpreted as evidence that personal motives of owners play an important role in corporate tax decisions. These motives could include (1) the fear of reputational damage on a personal level and (2) personally held beliefs of the owners and the ability to apply these to the actions of firms. Using information on the direct ownership shares and the similarity between the owner’s and the firm’s name does not reveal which of the channels is predominant.
Identifying Children with Autism in Regular Schools in Resource Limited Settings: A Systematic and Comprehensive Method

Autism is the most diagnosed among the autism spectrum disorders. Thus, many educators now are faced with the demands to teach children with autism in their classes. It is imperative that teachers be able to recognize the symptom presentations of children with autism and be aware of a comprehensive and systematic method of identifying them in their classrooms. The aim of this study was to bring out a reliable and systematic method of identifying children with autism in a regular classroom setting. To attain this, different strategies such as review of school records, discussions with teachers and the administration of an anecdotal case history check list to parents of these children were employed to identify those with autism in nursery schools in Buea. The data was collected using questionnaires and was analyzed using simple percentages and arithmetic mean. Results of the study revealed that15 children out of 288 in the age range 3-5 years had autism. There were more male than female children.