Approximation Algorithms for the Bottleneck Asymmetric Traveling Salesman Problem

We present the first nontrivial approximation algorithm for the bottleneck asymmetric traveling salesman problem . Given an asymmetric metric cost between n vertices, the problem is to find a Hamiltonian cycle that minimizes its bottleneck (or maximum-length edge) cost. We achieve an O (log n / log log n ) approximation performance guarantee by giving a novel algorithmic technique to shortcut Eulerian circuits while bounding the lengths of the shortcuts needed. This allows us to build on a related result of Asadpour, Goemans, Mądry, Oveis Gharan, and Saberi to obtain this guarantee. Furthermore, we show how our technique yields stronger approximation bounds in some cases, such as the bounded orientable genus case studied by Oveis Gharan and Saberi. We also explore the possibility of further improvement upon our main result through a comparison to the symmetric counterpart of the problem.

Mood Disorder Detection in Adolescents by Classification Trees, Random Forests and XGBoost in Presence of Missing Data

We apply tree-based classification algorithms, namely the classification trees, with the use of the rpart algorithm, random forests and XGBoost methods to detect mood disorder in a group of 2508 lower secondary school students. The dataset presents many challenges, the most important of which is many missing data as well as the being heavily unbalanced (there are few severe mood disorder cases). We find that all algorithms are specific, but only the rpart algorithm is sensitive; i.e., it is able to detect cases of real cases mood disorder. The conclusion of this paper is that this is caused by the fact that the rpart algorithm uses the surrogate variables to handle missing data. The most important social-studies-related result is that the adolescents’ relationships with their parents are the single most important factor in developing mood disorders—far more important than other factors, such as the socio-economic status or school success.

STRATEGIC MANAGEMENT OF PROPERTIES

This paper is dedicated to the study of strategic management techniques for improving the return on in-vestment of a real estate company with techniques of increasing the financial leverage and reduction of sys-temic risk and specific risk of this kinds of companies. Some strategies are proposed with different capital allocation hypothesis and related result are provided.

Laboratory Instruments’ Produced Scientific Data Standardization through the Use of Metadata

The progression of scientific data from various laboratory instruments is increasing these days. As different laboratory instruments hold different structures and formats of data, it became a concern in the management and analysis of data because of the heterogeneity of data structure and format. This paper offered a metadata structure to standardize the laboratory instruments' -produced scientific data to attain a standard structure and format. This paper contains explanation regarding the methodology and the use of proposed metadata structure, before summarizing the implementation and its related result analysis. The proposed metadata structure extraction shows promising results based on conducted evaluation and validation.

A Comparison between the Naïve Bayes and the C5.0 Decision Tree Algorithms for Predicting the Advice of the Student Enrollment Applications

In this preprint, we introduce a dataset containing students enrolment applications combined with the related result of their filing procedure. The dataset contains 73 variable. Student candidates, at the time of applying for study, fill a web form for filing the procedure. A committee at Tilburg University review each single application and decide if the student is admissible or not. This dataset is suitable for algorithmic studies and has been used in a comparison between the Naïve Bayes and the C5.0 Decision Tree Algorithms. They have been used for predicting the decision of the committee in admitting candidates at various bachelor programs. Our analysis shows that, in this particular case, a combination of the approaches outperform a both of them in term of precision and recall.

Several Fixed-Point Theorems for F -Contractions in Complete Branciari b -Metric Spaces and Applications

In this paper, we prove the existence and uniqueness of fixed points for F -contractions in complete Branciari b -metric spaces. Furthermore, an example for supporting the related result is shown. We also present the concept of the weak well-posedness of the fixed-point problem of the mapping T and discuss the weak well-posedness of the fixed-point problem of an F -contraction in complete Branciari b -metric spaces. Besides, we investigate the problem of common fixed points for F -contractions in above spaces. As an application, we apply our main results to solving the existence and uniqueness of solutions for a class of the integral equation and the dynamic programming problem, respectively.

Support Vector Machine (SVM) for Colorization the Grayscale Image

Recently, there have been several automatic approaches to color grayscale images, which depend on the internal features of the grayscale images. There are several scales which are considered as a prominent key to extract the corresponding chromatic value of the gray level. In this aspect, colorizing methods that rely on automatic algorithms are still under investigation, especially after the development of neural networks used to recognize the features of images. This paper develops a new model to obtain a color image from an original grayscale image through the use of the Support Vector Machine to recognize the features of grayscale images which are extracted from two stages: the first stage is Haar Discrete Wavelets Transform used to configure the vector that combines with six of Statistical Measurements: (Mean, Variance, Skewness, Kurtosis, Energy and Standard Deviation) extracts from the grayscales image in the second stage. After the Support Vector Machine recognition has been done, the colorization process uses the result of Support Vector Machine to recover the color to greyscale images by using YCbCr color system then it converts the color to default color system (RGB) to be more clear. The proposed model will be able to move away from relying on the user to identify the source image which matches in color levels and it exceeds the network determinants of image types with similar color levels. In addition, Support Vector Machine is considered to be more reliable than neural networks in classification algorithms. The model performance is evaluated by using the Root Mean Squared Error (RMSE) in measuring the success of the assumed modal of matching the coloring (resulting) images and the original color images. So, a reality-related result has been obtained at a good rate for all the tested images. This model has proved to be successful in the process of recognizing the chromatic values of greyscale images then retrieving it. It takes less time complexity in trained data, and it isn’t complex in working.

Resting-State Fluctuations Underlie Free and Creative Verbal Behaviors in the Human Brain

Resting-state fluctuations are ubiquitous and widely studied phenomena of the human brain, yet we are largely in the dark regarding their function in human cognition. Here we examined the hypothesis that resting-state fluctuations underlie the generation of free and creative human behaviors. In our experiment, participants were asked to perform three voluntary verbal tasks: a verbal fluency task, a verbal creativity task, and a divergent thinking task, during functional magnetic resonance imaging scanning. Blood oxygenation level dependent (BOLD)-activity during these tasks was contrasted with a control- deterministic verbal task, in which the behavior was fully determined by external stimuli. Our results reveal that all voluntary verbal-generation responses displayed a gradual anticipatory buildup that preceded the deterministic control-related responses. Critically, the time–frequency dynamics of these anticipatory buildups were significantly correlated with resting-state fluctuations’ dynamics. These correlations were not a general BOLD-related or verbal-response related result, as they were not found during the externally determined verbal control condition. Furthermore, they were located in brain regions known to be involved in language production, specifically the left inferior frontal gyrus. These results suggest a common function of resting-state fluctuations as the neural mechanism underlying the generation of free and creative behaviors in the human cortex.

Locally constant constructive functions and connectedness of intervals

We prove that every locally constant constructive function on an interval is in fact a constant function. This answers a question formulated by Andrej Bauer [ 1]. As a related result, we show that an interval consisting of constructive real numbers is in fact connected but can be decomposed into the disjoint union of two sequentially closed nonempty sets.

The zeros of f^{n}f^(k)-a and normal families of meromorphic functions

In this paper, we first prove that if f be a non-constant meromorphic function, all of whose zeros have multiplicity at least $k$, then f^{n}f^{(k)}-a has at least m+1 distinct zeros, where $k(\geq2),m(\geq1),n(\geq m+1)$ are three integers, and $a\in \mathbb{C}\cup\setminus\{0\}$.Also, in relation to this result, a normality criteria is given, which extends the related result.