Miracle-dz/UniFAR-FTUs huggingface dataset:
A Compact Slotted Bowtie Patch Antenna

Communication systems are growing up to achieve smaller sizes with better performances. Compact circuits have improved by advances in microelectronics, but providing integrated and compact antenna is one of the most important fields of antenna study. In new antenna design, smaller antenna with wider bandwidth is the objective to achieve. Microstrip antennas have good characteristics to compact an antenna. They are lightweight, small volume and low profile. They can be made integrated with MICs with low cost and also they allow for dualor triple frequency operations. Beside these advantages they also have some drawbacks; they have narrow bandwidth and lower gain [2-3]. Antenna size depends on the desired resonance frequency [1]. In all Microstrip antenna designs, there is a compromising challenge between the size of the antenna and its bandwidth; therefore, antenna researchers have attempt to present small area microstrip antenna with larger bandwidth [4-6]. Different techniques have been used to reduce the size of the microstrip antenna, such as using substrate with larger permittivity [2, 6], imposing a shorting plate or a shorting pin [7] and embedding proper slots in the patch [8-9]. With a size reduction at a fixed operating frequency, the bandwidth of a microstrip antenna is decreased. There are some techniques to improve the bandwidth of the microstrip antenna, for instance, using different resonator [10], impedance matching [11] and applying slots to the geometry [6]. Slots based on their location and geometry have different effects on the antenna bandwidth. This work presents a probe-feed compact slotted bowtie patch antenna operating at the resonance frequency of 2.1GHz. It has significant reduction in size and achieved to larger bandwidth, in comparison to a rectangular patch presented in [3] and a bowtie patch proposed in [1]. Simulation results prove the design.
New better than used processes

A stochastic process , such that P{Z(0) = 0} = 1, is said to be new better than used (NBU) if, for every x, the first-passage time Tx = inf {t: Z(t) > x} satisfies P{TX > s + t} for everys . In this paper it is shown that many useful processes are NBU. Examples of such processes include processes with shocks and recovery, processes with random repair-times, various Gaver–Miller processes and some strong Markov processes. Applications in reliability theory, queueing, dams, inventory and electrical activity of neurons are indicated. It is shown that various waiting times for clusters of events and for short and wide gaps in some renewal processes are NBU random variables. The NBU property of processes and random variables can be used to obtain bounds on various probabilistic quantities of interest; this is illustrated numerically.
Business Students’ Preference for Cooperative Learning

Prince Yeboah Asare University of Cape Coast pprof8@gmail.com Abstract The thrust of this survey was to investigate university business students’ preference for cooperative learning using their attitudes as a proxy. In all, 400 third year business students were involved in the study. Data was collected using a 5-point Likert scale questionnaire. The study found that business students had positive attitudes towards cooperative learning. There was no statistically significant difference between male and female business students’ attitudes towards cooperative learning. Again, there was no statistically significant difference between the attitudes of Accounting major students and Management major students. Entrenchment of cooperative learning strategy into the teaching of university courses was recommended to the academic departments within the University. The use of such cooperative learning strategies should not be gender sensitive. The same strategy can be used in both accounting and management business programmes.
Extending the Space Available for TCP Options

This document describes a method for increasing the space available for TCP options. Two new TCP options (LO and SLO) are detailed which reduce the limitations imposed by the TCP header's Data Offset field. The LO option provides this extension after connection establishment, and the SLO option aids in transmission of lengthy connection initialization and configuration options.
Nörogenez Belirteci Olan Doublecortin Proteininin Elektron Mikroskopik Düzeyde Gösterilmesi

Amac : Yetiskin beyninde yeni olusan noronlarin gosterilmesi norogenez surecinin aydinlatilmasi acisindan onem tasimaktadir. Calismamizda hipokampusun dentat girus (DG) bolgesinde, yeni olusan noron belirteci olan doublecortin (DCX) proteini icin uygun gomme oncesi mikrodalga isaretleme metodu olusturmayi amacladik. Yontemler : 10 haftalik C57BJ/6J erkek farelerden perfuzyon fiksasyonu ile beyin dokulari elde edildi. Vibratom ile beyin dokularindan alinan kesitler, gomme oncesi mikrodalga yontemi ile anti-DCX antikoru ile isaretlendi. 3,3'-diaminobenzidin (DAB) ile boyanan dokular, mikrodalga yardimi ile elektron mikroskopik analizler icin hazirlandi. Epon bloklar uzerindeki dokulardan ince kesitler alinarak elektron mikroskobunda incelendi ve morfolojik analiz icin fotograflandi. Bulgular : Mikrodalga yontemi kullanilarak yapilan gomme oncesi DCX isaretlemesi hem isik hem de elektron mikroskopik (EM) duzeyde spesifik olarak gosterildi. DCX pozitif hucrelerin subgranuler zon (SGZ) ve granuler tabakada yerlesim gosterdikleri izlendi. Elektron mikroskopik incelemelerde DCX immunreaktivitesinin akson, dendrit ve hucre govdeleri uzerinde pozitif oldugu gozlendi. Sonuc : Calismamizda, mikrodalga kullanilarak yapilan gomme oncesi immun-elektron yontemi ile DG bolgesinde DCX varliginin belirlenebildigini gosterdik. Calismamiz DCX immunreaktif hucreleri, gomme oncesi DAB isaretlemesi ile elektron mikroskopik duzeyde gostererek, norogenez surecinin arastirilacagi ileri calismalar icin temel olusturmaktadir.
Detection of Multidimensional Outliers using Biplot Analysis

It is necessary to examine the valuable data being distorted by the presence of outliers before the same is subjected to necessary analysis. Outliers should be identified using reliable detection methods and tested prior to performing data analysis. Detection of outliers in multidimensional data is important in many applications as it will have far reaching consequences in its analysis. There are methods available in the literature for detecting multiple outliers but there exist no unified method for detecting the same. An attempt has been made to detect the multidimensional outliers through Biplot analysis using elliptical method with a well defined axis (a, b) based on Inter Quartile Range (IQR). The performance of the designed methods is examined by a comparison with the existing methods.
The Best of Both Worlds: Challenges in Linking Provenance and Explainability in Distributed Machine Learning

Machine learning experts prefer to think of their input as a single, homogeneous, and consistent data set. However, when analyzing large volumes of data, the entire data set may not be manageable on a single server, but must be stored on a distributed file system instead. Moreover, with the pressing demand to deliver explainable models, the experts may no longer focus on the machine learning algorithms in isolation, but must take into account the distributed nature of the data stored, as well as the impact of any data pre-processing steps upstream in their data analysis pipeline. In this paper, we make the point that even basic transformations during data preparation can impact the model learned, and that this is exacerbated in a distributed setting. We then sketch our vision of end-to-end explainability of the model learned, taking the pre-processing into account. In particular, we point out the potentials of linking the contributions of research on data provenance with the efforts on explainability in machine learning. In doing so, we highlight pitfalls we may experience in a distributed system on the way to generating more holistic explanations for our machine learning models.
Defroster nozzle shape optimization using the continuous adjoint method

This paper presents the use of the continuous adjoint method, developed by the Parallel CFD & Optimization Unit of National Technical University of Athens (NTUA) in the OpenFOAM environment, for the shape optimization of a passenger car defroster nozzle, including experimental validation performed at Toyota Motor Europe (TME). The defroster nozzle plays a major role in the demisting-defogging of the windshield, by blowing high velocity hot air jets supplied by the HVAC (Heating, Ventilation and Air Conditioning) unit of the vehicle. For a well–designed defroster nozzle, the time required for dispelling condensation or frost on the windshield must be reasonable; the nozzle must also have the capability to perform uniform defrosting from the bottom of the windshield to its top, without patches of condensation. In view of the above, an appropriate objective function, to be minimized, is the integral of the difference of the air velocity from a target (desirable) one over a thin control volume defined close to the windshield, inside the car cabin. To set up the optimization problem, the shape of a reference defroster nozzle is allowed to vary according to the morphing capabilities of a volumetric NURBS tool developed by NTUA; the latter is also used for deforming the computational mesh at each optimization cycle, by adapting it to the changed defroster shape. The CFD analysis is based on RANS, using the kturbulence model. The optimization loop uses the gradient of the objective function with respect to the coordinates of the control points of a volumetric B–splines lattice, which is computed using the continuous adjoint method. Experimental tests performed to measure the actual velocity pattern on the windshield include velocity measurements with a hot-wire anemometer. A convincing comparison between CFD analysis and measurements is presented. The improved demisting performance of the geometry resulted from the adjoint optimization was also experimentally validated, using rapid prototyping to manufacture the designed defroster nozzle. L.A. Germanou, E.M. Papoutsis-Kiachagias, A. Delacroix and K.C. Giannakoglou
Central limit theorems for martingales and for processes with stationary increments using a Skorokhod representation approach

The Skorokhod representation for martingales is used to obtain a functional central limit theorem (or invariance principle) for martingales. It is clear from the method of proof that this result may in fact be extended to the case of triangular arrays in which each row is a martingale sequence and the second main result is a functional central limit theorem for such arrays. These results are then used to obtain two functional central limit theorems for processes with stationary ergodic increments following on from the work of Gordin. The first of these theorems extends a result of Billingsley for Φ-mixing sequences.
Polymer physics of nuclear organization and function

We review here recent progress to link the nuclear organization to its function, based on elementary physical processes such as diffusion, polymer dynamics of DNA, chromatin and the search mechanism for a small target by double-stranded DNA (dsDNA) break. These physical models and their analysis make it possible to compute critical rates involved in cell reorganization timing, which depends on many parameters. In the framework of polymer models, various empirical observations are interpreted as anomalous diffusion of chromatin at various time scales. The reviewed theoretical approaches offer a framework for extracting features, biophysical parameters, predictions, and so on, based on a large variety of experimental data, such as chromosomal capture data, single particle trajectories, and more. Combining theoretical approaches with live cell microscopy data should unveil some of the still unexplained behavior of the nucleus in carrying out some of its key function involved in survival, DNA repair or gene activation.
Object detection based on deformable part model

In complex scene, considering traditional object detection methods based on feature points have exposed many problems, such as undetected points, low detected ratio and cannot well process object occlusion and scaling situation, this paper proposes a detection method which based on a deformable part model. The method uses histogram of oriented gradient (HOG) feature as the object description, and the deformable part model includes a global template and several high-resolution templates. And the method uses the support vector machine (SVM) training the object model. In the learning process, after the HOG feature extracted, the method modifies the HOG feature, and then uses the principal component analysis (PCA) method reducing feature dimensions to avoid over-learning, and improve the detection rate in the detection process. The experiment results shows that the method proposed can better process object occlusion or scaled situation, and there’s also an improvement in detection ratio.
Confidence Intervals and Prediction Intervals for Feed-Forward Neural Networks

The chapter opens with an introduction to regression and its implementation within the maximum-likelihood framework. This is followed by a general introduction to classical confidence intervals and prediction intervals. We set the scene by first considering confidence and prediction intervals based on univariate samples, and then we progress to regarding these intervals in the context of linear regression and logistic regression. Since a feed-forward neural network is a type of regression model, the concepts of confidence and prediction intervals are applicable to these networks, and we look at several techniques for doing this via maximum-likelihood estimation. An alternative to the maximum-likelihood framework is Bayesian statistics, and we examine the notions of Bayesian confidence and predictions intervals as applied to feed-forward networks. This includes a critique on Bayesian confidence intervals and classification.
Machine Learning Methods for Brain Image Analysis

MACHINE LEARNING METHODS FOR BRAIN IMAGE ANALYSIS Ahmed Fakhry Old Dominion University, 2016 Director: Dr. Shuiwang Ji Understanding how the brain functions and quantifying compound interactions between complex synaptic networks inside the brain remain some of the most challenging problems in neuroscience. Lack or abundance of data, shortage of manpower along with heterogeneity of data following from various species all served as an added complexity to the already perplexing problem. The ability to process vast amount of brain data need to be performed automatically, yet with an accuracy close to manual human-level performance. These automated methods essentially need to generalize well to be able to accommodate data from different species. Also, novel approaches and techniques are becoming a necessity to reveal the correlations between different data modalities in the brain at the global level. In this dissertation, I mainly focus on two problems: automatic segmentation of brain electron microscopy (EM) images and stacks, and integrative analysis of the gene expression and synaptic connectivity in the brain. I propose to use deep learning algorithms for the 2D segmentation of EM images. I designed an automated pipeline with novel insights that was able to achieve state-of-the-art performance on the segmentation of the Drosophila brain. I also propose a novel technique for 3D segmentation of EM image stacks that can be trained end-to-end with no prior knowledge of the data. This technique was evaluated in an ongoing online challenge for 3D segmentation of neurites where it achieved accuracy close to a second human observer. Later, I employed ensemble learning methods to perform the first systematic integrative analysis of the genome and connectome in the mouse brain at both the regionaland voxel-level. I show that the connectivity signals can be predicted from the gene expression signatures with an extremely high accuracy. Furthermore, I show that only a certain fraction of genes are responsible for this predictive aspect. Rich functional and cellular analysis of these genes are detailed to validate these findings.
Hierarchical high‐order conforming C1 bases for quadrangular and triangular finite elements

We present three new sets of C1 hierarchical high‐order tensor‐product bases for conforming finite elements. The first basis is a high‐order extension of the Bogner–Fox–Schmit basis. The edge and face functions are constructed using a combination of cubic Hermite and Jacobi polynomials with C1 global continuity on the common edges of elements. The second basis uses the tensor product of fifth‐order Hermite polynomials and high‐order functions and achieves global C1 continuity for meshes of quadrilaterals and C2 continuity on the element vertices. The third basis for triangles is also constructed using the tensor product of one‐dimensional functions defined in barycentric coordinates. It also has global C1 continuity on edges and C2 continuity on vertices. A patch test is applied to the three considered elements. Projection and plate problems with smooth fabricated solutions are solved, and the performance of the h‐ and p‐refinements are evaluated by comparing the approximation errors in the L2‐ and energy norms. A plate with singularity is then studied, and h‐ and p‐refinements are analysed. Finally, a transient problem with implicit time integration is considered. The results show exponential convergence rates with increasing polynomial order for the triangular and quadrilateral meshes of non‐distorted and distorted elements. Copyright © 2016 John Wiley & Sons, Ltd.
Self-Consistent Treatment of Neutrino Physics in Cosmology

Author(s): Grohs, Evan | Abstract: We are developing a code to self--consistently treat beyond--standard--model physics in the early universe. This work details the calculations done with regards to the epochs of weak decoupling, weak freeze--out, big bang nucleosynthesis, recombination, and includes exploratory work on dilution and electron--positron annihilation. We assess the predictions made by the standard model, and investigate how new physics in the neutrino sector may alter or preserve those predictions. Throughout this work, we use natural units : hbar=c=k_b=1
Empirical Bayes Credibility Models for Economic Catastrophic Losses by Regions

Catastrophic events affect various regions of the world with increasing frequency and intensity. The number of catastrophic events and the amount of economic losses is varying in different world regions. Part of these losses is covered by insurance. Catastrophe events in last years are associated with increases in premiums for some lines of business. The article focus on estimating the amount of net premiums that would be needed to cover the total or insured catastrophic losses in different world regions using Buhlmann and Buhlmann-Straub empirical credibility models based on data from Sigma Swiss Re 2010-2016. The empirical credibility models have been developed to estimate insurance premiums for short term insurance contracts using two ingredients: past data from the risk itself and collateral data from other sources considered to be relevant. In this article we deal with application of these models based on the real data about number of catastrophic events and about the total economic and insured catastrophe losses in seven regions of the world in time period 2009-2015. Estimated credible premiums by world regions provide information how much money in the monitored regions will be need to cover total and insured catastrophic losses in next year.
GPU Implementation of Gaussian Processes

3 Design 6 3.1 Libraries Used . 6 3.1. 1 Basic Linear Algebra Subprograms (BLAS) . 6 3.1. 2 Linear Algebra PACKage (LAPACK) . 6 3.1. 3 CUDA BLAS (cuBLAS) . 6 3.1. 4 CUDA Linear Algebra (CULA) . 6 3.1. 5 Limited Memory Broyden-Fletcher-Goldfarb-Shanno (libLBFGS) . 6 3.2 Negative Log Marginal Likelihood and Derivative Marginal Log Likelihood Execution Path . 7 3.3 Kernel Matrix Execution Path . 8 3.4 Layer Design Model . 9 3.5 Switching between CPU and GPU Computation . 9 3.6 Compute Capability . 9 3.7 Column-Major Order Storage . 10 3.8 Symmetric Matrices . 10
Extend GO Methodology to Support Common-Cause Failures Modeling Explicitly by Means of Bayesian Networks

As a success-oriented system reliability and safety-analysis technique, the GO methodology has been applied in a variety of real-world safety-critical industrial fields. Common-cause failure (CCF) is the simultaneous failure of multicomponents within a system due to the same root cause. An enhancement approach for the original GO methodology is proposed in this paper to support CCF modeling and calculation both in graphical modeling aspect and algorithm aspect. First, a new concise and formalized GO operator (named CCO) is introduced to represent complicated CCF event, which makes the CCF modeling process intuitive and concise for analysts. In algorithm aspect, the mapping rule is given and demonstrated to transform new CCO operator with impacted multiple operators to the corresponding Bayesian network (BN) fragment. Second, general mapping programmable process is presented on transforming any CCF enhanced GO model to the corresponding BN. Furthermore, using BN's inference capability, the enhanced GO model with CCF can be calculated efficiently. Nevertheless, the diagnosis process can be performed to investigate the weak points of the modeled system. Finally, a case study is performed to demonstrate the modeling process by means of CCF enhanced GO model. The calculation result shows that CCF has significant influence on the system reliability. Using diagnosis analysis, the CCF event can be confirmed as the major cause leading to system failure.
Relationship Between Body Composition, Vertical Jump, 30 M Sprint, Static Strength and Anaerobic Power for Athletes

Aim: The purpose of the study is to determine the relationship between body composition, vertical jump, sprint, static strength, and anaerobic power of athletes. Methods: 19-27 years old (training 2 hours/day, 4 days/week, training at least 4 years) 28 male (23.11 ± 1.71 years; 1 fitness, 1 mountain climber, 1 swimmer, 15 football, 2 basketball, and 8 tennis players), 19 female (21.95 ± 2.37 years; 3 Zumba, 1 cross country runner, 5 basketball, 2 football, 6 tennis, and 2 volleyball players) athletes participated voluntarily. Body composition compartments, height, skinfold thicknesses, Vertical Jump and 30 m sprint by using timer gates were measured. Wingate Anaerobic Test (WAnT) was used to determine peak anaerobic power (WAnT PP ) and mean anaerobic power (WAnT MP ). Results: There was no significant relationship between fat compartment of body composition and vertical jump or 30 m sprint performances for male athletes. In addition to that, there were no relationship between the fat compartment and any of the performance parameters of vertical jump, sprinting, WAnT results for female athletes. Conclusion: S port specific strength requirements to be discussed for performance determinants have yet been incomplete and versatile research subject. To predict the sport performance, follow-up and performance focused battery should be studied by all affecting parameters such as physiological, neurological, detailed body composition compartments.