Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Dislocation Characterization by Electron

8 In this work, the relative capabilities and limitations of electron channeling contrast imaging 9 (ECCI) and cross-correlation electron backscattered diffraction (CC-EBSD) have been assessed 10 by studying the dislocation distributions resulting from nanoindentation in body centered cubic 11 Ta. Qualitative comparison reveals very similar dislocation distributions between the CC-EBSD 12 mapped GNDs and the ECC imaged dislocations. Approximate dislocation densities determined 13 from ECC images compare well to those determined by CC-EBSD. Nevertheless, close examina14 tion reveals subtle differences in the details of the distributions mapped by these two approaches. 15 The details of the dislocation Burgers vectors and line directions determined by ECCI have been 16 compared to those determined using CC-EBSD and reveal good agreement. 17
Probing Particle Diffusion around Two Nearby Pulsars with TeV Gamma-Ray Observations from HAWC

Nearby cosmic-ray accelerators, especially pulsar wind nebulae, are possible origins of the local multi-GeV positron excess. Pulsar Geminga and B0656+14, less than 300 pc from the Earth, have been postulated as the main sources of the positron excess. With one and half years of data, the HAWC gamma-ray observatory discovered very extended TeV gamma-ray structures produced from high-energy electrons and positrons around these two nearby middle-aged pulsars. Morphology studies suggest that the diffusion in the vicinity of these two pulsars is 100 times slower than the average in our Galaxy. This result provides important constraints on the origin of positron excess, but raises questions like why diffusion is so slow at high energies near these pulsars. With more than twice of data from the previous results, we now explore the possibility of energy-dependent diffusion at these sources.
CPU Performance Evaluation for 2D Voronoi Tessellation

Voronoi tessellation can be used within a couple of different fields. Some of these fields include healthcare, construction and urban planning. Since Voronoi tessellations are used in multiple fields, it is motivated to know the strengths and weaknesses of the algorithms used to generate them, in terms of their efficiency. The objectives of this thesis are to compare two CPU algorithm implementations for Voronoi tessellation in regards to execution time and see which of the two is the most efficient. The algorithms compared are The Bowyer-Watson algorithm and Fortunes algorithm. The Fortunes algorithm used in the research is based upon a pre-existing Fortunes implementation while the Bowyer-Watson implementation was specifically made for this research. Their differences in efficiency were determined by measuring their execution times and comparing them. This was done in an iterative manner, where for each iteration, the amount of data to be computed was continuously increased. The results show that Fortunes algorithm is more efficient on the CPU without using any acceleration techniques for any of the algorithms. It took 70 milliseconds for the Bowyer-Watson method to calculate 3000 input points while Fortunes method took 12 milliseconds under the same conditions. As a conclusion, Fortunes algorithm was more efficient due to the Bowyer-Watson algorithm doing unnecessary calculations. These calculations include checking all the triangles for every new point added. A suggestion for improving the speed of this algorithm would be to use a nearest neighbour search technique when searching through triangles.
Computing the Matrix Exponential with an Optimized Taylor Polynomial Approximation

A new way to compute the Taylor polynomial of a matrix exponential is presented which reduces the number of matrix multiplications in comparison with the de-facto standard Paterson-Stockmeyer method for polynomial evaluation. Combined with the scaling and squaring procedure, this reduction is sufficient to make the Taylor method superior in performance to Pade approximants over a range of values of the matrix norms. An efficient adjustment to make the method robust against overscaling is also introduced. Numerical experiments show the superior performance of our method to have a similar accuracy in comparison with state-of-the-art implementations, and thus, it is especially recommended to be used in conjunction with Lie-group and exponential integrators where preservation of geometric properties is at issue.
Clustering on Spatial Data Sets Using Extended Linked Clustering Algorithms

Various clustering algorithms (CA) have been reported in literature, to group data into clusters in diverse domains. Literature further reported that, these CAs work satisfactorily either on pure numerical data or on pure categorical data and perform poorly on mixed numerical and categorical data. Clustering is the process of creating distribution patterns and obtaining intrinsic correlations in large datasets by arranging the data into similarity classes. The present work pertains to reviewing the available research papers on clustering spatial data. In a web perspective, a detailed inspection of grouped patterns and their belonging to well known characters will be very useful for evolution of clusters. The review work is split into spatial data mining, clustering on spatial data sets and extended linked clustering. This review work will enable the researchers to make an in depth study of the till date research work on above areas and will pave way for developing extended linked clustering algorithms with a view to find number of clusters on mixed datasets to produce results for
Evaluation of 3D-Sensorsystems for service robotics in orcharding and viticulture

Efficient sensor systems are a prerequisite for robust automation in agriculture. Particularly in the field of autonomous guidance, the demand for reliable sensor systems is significant. A large variety of 3D systems such as laser scanners and ToF cameras are becoming increasingly popular in this field. In the project elWObot (funded by the federal ministry of food and agriculture), autonomous guidance and leaf wall detection are essential tasks. Therefore, various 3D sensor systems are evaluated to test their reliability and robustness in the field. Currently in use are the Sick LMS 511 2D laser scanner with a rotation mechanism, the Nippon Signal FX-8 3D laser scanner, and the Mesa Imaging SwissRanger 4500 3D ToF camera.
A Genetic Need for Menopause: Reflections and New Perspectives

Tradition has taught women to view menopause as the “beginning of the end”, the beginning of a decline in health, where they will have to handle the effects of illness, disability and dependency. Women have fought the effects of aging and menopause through botox injections, plastic surgery, and use in vitro fertilization and hormone replacement therapy to maintain the ability to reproduce and continue feeling young. Despite these efforts to maintain what we see as “valuable life”, genetic studies have revealed the potential for another purpose to the menopausal period in a woman’s life, the ability to grandparent and the evolutionary advantages of this. In this light, menopause should perhaps not be viewed as the “beginning of the end”, but rather the “end of the beginning”. In a proposed grandparent hypothesis, grandmothers increase the growth of infants by assisting in providing for food for nursing mothers and infants which ultimately accelerates infants’ growth, allowing for them to be weaned sooner, and thus allowing the female to reproduce more. Recent genetic literature suggests that menopause may offer the selection for alleles, specifically the CD33 allele, which may protect against cognitive decline in post-reproductive humans, allowing humans to grandparent. Without these alleles, cognitive decline in the post-reproductive population may decrease the ability of grandparents to provide food and contribute knowledge to their offspring, and deters resources to the care of the aged and demented, thus hindering their grandchildren’s reproductive success. This suggests that there is a genetic need for menopause, and serves as an opportunity to reflect on the evolution of thought from reversing menopause to viewing it as another important stage of a woman’s life, which genetics may have selected for in order to sustain the continuation of our species.
Targeting Buildings for Energy Retrofit Using Recurrent Neural Networks with Multivariate Time Series

The existing building stock accounts for over 30% of global carbon emissions and energy demand [25]. Effective building retrofits are therefore vital in reducing global emissions [17]. Current methods for building energy assessment typically rely on walk-throughs, surveys or the collection of in-situ measurements [19], none of which are scalable or cost effective. Supervised machine learning methods have the potential to overcome these issues, but their application to retrofit analysis has been limited. This paper serves as a novel showcase for how multivariate time series analysis with Gated Recurrent Units can be applied to targeted retrofit analysis via two case studies: (1) classification of building heating system type and (2) prediction of building envelope thermal properties.
A Novel Multi-Objective Electromagnetic Analysis Based on Genetic Algorithm

Correlation electromagnetic analysis (CEMA) is a method prevalent in side-channel analysis of cryptographic devices. Its success mostly depends on the quality of electromagnetic signals acquired from the devices. In the past, only one byte of the key was analyzed and other bytes were regarded as noise. Apparently, other bytes’ useful information was wasted, which may increase the difficulty of recovering the key. Multi-objective optimization is a good way to solve the problem of a single byte of the key. In this work, we applied multi-objective optimization to correlation electromagnetic analysis taking all bytes of the key into consideration. Combining the advantages of multi-objective optimization and genetic algorithm, we put forward a novel multi-objective electromagnetic analysis based on a genetic algorithm to take full advantage of information when recovering the key. Experiments with an Advanced Encryption Standard (AES) cryptographic algorithm on a Sakura-G board demonstrate the efficiency of our method in practice. The experimental results show that our method reduces the number of traces required in correlation electromagnetic analysis. It achieved approximately 42.72% improvement for the corresponding case compared with CEMA.
Reemployment Bonuses, Unemployment Duration, and Job Match Quality

This study examines the impacts of reemployment bonus, the incentive payments to unemployment insurance recipients who find a job within a specified period of time, using Korean data. A sharp discontinuity in treatment assignment at age 55 identifies the effect of increased reemployment bonus on unemployment duration and on subsequent job duration. The results indicate that increases in reemployment bonus boost the job-finding hazards of the claimants early in unemployment spells by 20.0% to 37.8%. Additionally, employment stability is not significantly affected by increased bonus, which implies no negative influence of the bonus on subsequent job match quality.
Surrogate Cost Techniques in Classification and Regression Analysis

We study the problems of classification and regression analysis when the set of classes or parameters is a σ-compact metric space, by means of surrogate cost minimization. We give a natural sufficient condition for the optimal classifier or estimator to be of the form T f when the function f minimizes a cost function which is a surrogate for the actual loss defined on pairs of classes. Sequences of functions whose expectations converge to the infimum of the expectations of all such functions can then be found by minimizing the sample averages of training sets. This result extends and sharpens previous results.
Terminology systematization for Cybersecurity domain in Italian Language

This paper aims at presenting the first steps to improve the quality of the first draft of an Italian thesaurus for Cybersecurity terminology that has been realized for a specific project activity in collaboration with CybersecurityLab at Informatics and Telematics Institute (IIT) of the National Council of Research (CNR) in Italy. In particular, the paper will focus, first, on the terminological knowledge base built to retrieve the most representative candidate terms of Cybersecurity domain in Italian language, giving examples of the main gold standard repositories that have been used to build this semantic tool. Attention will be then given to the methodology and software employed to configure a system of NLP rules to get the desired semantic results and to proceed with the enhancement of the candidate terms selection which are meant to be inserted in the controlled vocabulary.
A comparison between statistical and Data Mining methods for credit scoring in case of limited available data

Credit scoring is a method used to estimate the prob ability that a loan applicant or existing borrower will def ault or become delinquent. There are two types of methods use d for scoring: Traditional statistics models like Probit and Logistic regression and Data Mining models such as Classifica tion nd Regression Trees (CART), Multivariate Adaptive Regress ion Splines (MARS) and Bagging. In this paper, we have ex amined the performance of different models in credit scoring on real data of a bank and compared two approaches abo ve. We found out that Bootstrap Aggregating (Bagging) model in Data Mining approach and the Logit regression in tr adi ional statistical methods performs better than other meth ods in credit scoring application. We also present an empiric al study on the machine learning feature selection methods, whi ch provide an automatic technique for reducing the featur e space. The study illustrates how these methods help to imp rove the performance of scoring models.
Implementation of Recurrent Neural Network with Sequence to Sequence Model to Translate Language Based on TensorFlow

Recently, many recurrent neural network based LM, a type of deep neural network for dealing with sequential data, have been proposed and achieved remarkable results. Most deep learning frame works, support the GPU to form fast models; in particular the execution of these models on several GPUs. In this work, an automatic learning algorithm will developed and proposed to address a deep neural network with a convolutional layer and a connected layer. The proposed algorithm is an extension of Ensemble's approach and uses a multilayer perceptron for data points, and a multilayer perceptron to combine the experts and predict the end result. In order to solve this problem, in this paper, we propose a CNN based language model that deals with textual data regarding a multi-dimensional data with respect to the input of the network. To train this dimensional input to Long-Short Term Memory (LSTM), we use a convolutional neural network (CNN) for dimensionality reduction of input data to avoid the vanishing gradient problem by decreasing the time step between input words. The dataset to train and test this model was taken from the META data set database with low velocity than MNIST dataset which is the object of our future work. Our implementations can be used for fast exhaustive search in Recurrent Neural Network, which can find a textual data regarding a multi-dimensional data by using Python 3.6. to get better performance.
Obese Children Sustain Significantly More Both Bones Forearm Fractures When Compared to Non-Obese Children

Objectives: Obesity in children is associated with an increased risk for fracture. The causes remain unclear and may be related to increased bone weakness during periods of intense growth or increased kinetics (when falling). The hypothesis was that obese children sustain more severe fractures compared to non-obese children, and needed more treatment under general anesthesia (GA). Methods: 6 months prospective study on obese and non-obese children presenting to the emergency room with an upper extremity long bones fractures (ULBF). The children’s activity prior to trauma was assessed with a questionnaire. The mechanism of injury, the characteristics of the fracture defined using a validated pediatric classification and the treatment modalities were compared. Results: The calculated prevalence of obesity in children with ULBF was 28%. 46 obese children aged 2 to 13 years and 119 non-obese children aged 2 to 16 years were included in the study. The risk for sustaining both bones forearm fractures was twice higher in obese children when compared to non-obese children (p=0.012). Obese children required a higher number of manipulations under GA (p=0.092). Conclusions: The prevalence of obesity in children with ULBF was higher than in the general pediatric population. There was no statistical difference between both groups in the reported level of activity, in the kinetics and in the treatment modalities. Obese children had a significantly higher risk for a combined radius-ulna fracture. Further research is needed to evaluate the relationship between obesity, bone growth and trauma.
Alcohol Dehydrogenases from Marine Hyperthermophilic Microorganisms and Their Importance to the Pharmaceutical Industry

Alcohol dehydrogenases (ADHs) are enzymes capable of catalyzing the intercoversion of alcohols to their corresponding aldehydes or ketones by using different redox-mediating cofactors. They are valuable tools for the biocatalytic synthesis of various chiral compounds, which are potential building blocks or intermediates in the organic synthesis of therapeutics in the pharmaceutical industry. In particular, ADHs from marine hyperthermophilic microorganisms have attracted special attention as catalysts in several industrial processes over the last two decades, mainly due to some unique properties, including their relatively broad specificity towards various carbonyl substrates, their high thermal and chemical stability, as well as their suitability for cofactor regeneration. Therefore, this review article describes ADHs from marine hyperthermophiles, including their activity screening, cloning, heterologous production, and characterisation. Their potential for application in the pharmaceutical industry is discussed.
Whom to Learn From? Graph- vs. Text-based Word Embeddings

Vectorial representations of meaning can be supported by empirical data from diverse sources and obtained with diverse embedding approaches. This paper aims at screening this experimental space and reports on an assessment of word embeddings supported (i) by data in raw texts vs. in lexical graphs, (ii) by lexical information encoded in association- vs. inference-based graphs, and obtained (iii) by edge reconstruction- vs. matrix factorisation vs. random walk-based graph embedding methods. The results observed with these experiments indicate that the best solutions with graph-based word embeddings are very competitive, consistently outperforming mainstream text-based ones.
Biocide Treatments for Composite Panels

This study was designed to investigate the effect of biocide addition on the properties of randomly oriented wood strandboard. Included in the study were new-generation preservative systems including copper naphthenate, betaine, and copper betaine added to a southern pine furnish containing no more than 4 percent lowand medium-density hardwoods. Study variables included treatment level, mode of biocide addition (flake pretreatment, spray incorporation), and biocide form (powdered, liquid). Comparison was with untreated panels and panels treated with the industry standard, zinc borate. Bending and dimensional properties were evaluated in this preliminary study. Results are discussed in terms of biocide and treatment mode. Few deleterious effects were found for boards treated with the copper compounds or betaine when compared to those for zinc borate treatments. Session III: Product Innovation Barnes and Kirkpatrick • 225 39th International Wood Composites Symposium
MORE ON MODELS AND MUDDLES IN THE SOCIAL-SCIENTIFIC INTERPRETATION OF THE NEW TESTAMENT: THE SOCIOLOGICAL

It is argued that the explicit use of social science models, even so-called cross-cuI tural models, does not necessarily help to eliminate anachronistic and ethnocentric Interpretations of New Te~tament texts. A historical approach (aim of interpretation) that does not assume commensurability between different cultures, which emphasises the native's point of view, has more potential in this respect. Models used not as iron matrixes but as heuristic tools promoting the dialogical interaction between model and data, carry some safeguards against the sociological fallacy . against which certain scholars have wamed.
A General Approach Based on Newton’s Method and Cyclic Coordinate Descent Method for Solving the Inverse Kinematics

The inverse kinematics of robot manipulators is a crucial problem with respect to automatically controlling robots. In this work, a Newton-improved cyclic coordinate descent (NICCD) method is proposed, which is suitable for robots with revolute or prismatic joints with degrees of freedom of any arbitrary number. Firstly, the inverse kinematics problem is transformed into the objective function optimization problem, which is based on the least-squares form of the angle error and the position error expressed by the product-of-exponentials formula. Thereafter, the optimization problem is solved by combining Newton’s method with the improved cyclic coordinate descent (ICCD) method. The difference between the proposed ICCD method and the traditional cyclic coordinate descent method is that consecutive prismatic joints and consecutive parallel revolute joints are treated as a whole in the former for the purposes of optimization. The ICCD algorithm has a convenient iterative formula for these two cases. In order to illustrate the performance of the NICCD method, its simulation results are compared with the well-known Newton–Raphson method using six different robot manipulators. The results suggest that, overall, the NICCD method is effective, accurate, robust, and generalizable. Moreover, it has advantages for the inverse kinematics calculations of continuous trajectories.
Molecular Dynamics Study of Water Molecules in Interlayer of 14 A Tobermorite

The molecular structure and dynamics of interlayer water of 14 Å tobermorite are investigated based on molecular dynamics (MD) simulations. Calculated structural parameters of the interlayer water configuration are in good agreement with current knowledge of the refined structure. The MD simulations provide detailed information on the position and mobility of the hydrogen and oxygen of interlayer water, as well as its self-diffusion coefficient, through the interlayer of 14 Å tobermorite. Comparison of the MD simulation results at 100 and 300 K demonstrates that water molecules in the interlayer maintain their structure but change their mobility. The dominant configuration and self-diffusion coefficient of interlayer water are obtained in this study.
LEXICAL STUDY OF A SPOKEN DIALOGUE CORPUS IN TUNISIAN DIALECT

The aim of this paper is to present a lexical study of a spoken dialogue corpus in Tunisian dialect since such resources does not currently existing. The lexical analysis permits to take into account the specificity of Tunisian dialect by identifying lexical varieties and significant elements used in the spoken dialogue. This can lead us to provide a useful characterization for dialogue systems and help us to develop models and methods specifically designed for Tunisian dialect.