Spectral Clustering for Optical Confirmation and Redshift Estimation of X-ray Selected Galaxy Cluster Candidates in the SDSS Stripe 82

We develop a galaxy cluster finding algorithm based on spectral clustering technique to identify optical counterparts and estimate optical redshifts for X-ray selected cluster candidates. As an application, we run our algorithm on a sample of X-ray cluster candidates selected from the third XMM-Newton serendipitous source catalog (3XMM-DR5) that are located in the Stripe 82 of the Sloan Digital Sky Survey (SDSS). Our method works on galaxies described in the color-magnitude feature space. We begin by examining 45 galaxy clusters with published spectroscopic redshifts in the range of 0.1 to 0.8 with a median of 0.36. As a result, we are able to identify their optical counterparts and estimate their photometric redshifts, which have a typical accuracy of 0.025 and agree with the published ones. Then, we investigate another 40 X-ray cluster candidates (from the same cluster survey) with no redshift information in the literature and found that 12 candidates are considered as galaxy clusters in the redshift range from 0.29 to 0.76 with a median of 0.57. These systems are newly discovered clusters in X-rays and optical data. Among them 7 clusters have spectroscopic redshifts for at least one member galaxy.Comment: 15 pages, 7 figures, 3 tables, 1 appendix, Accepted by Journal of "Astronomy and Computing

A multistage hierarchical algorithm for hand shape recognition

This paper represents a multistage hierarchical algorithm for hand shape recognition using principal component analysis (PCA) as a dimensionality reduction and a feature extraction method. The paper discusses the effect of image blurring to build data manifolds using PCA and the different ways to construct these manifolds. In_order to classify the hand shape of an incoming sign object and to be invariant to linear transformations like translation and rotation, a multistage hierarchical classifier structure is used. Computer generated images for different Irish Sign Language shapes are used in testing. Experimental results are given to show the accuracy and performance of the proposed algorithm

Nonlinearity reduction of manifolds using Gaussian blur for handshape recognition based on multi-dimensional grids

This paper presents a hand-shape recognition algorithm based on using multi-dimensional grids (MDGs) to divide the feature space of a set of hand images. Principal Component Analysis (PCA) is used as a feature extraction and dimensionality reduction method to generate eigenspaces from example images. Images are blurred by convolving with a Gaussian kernel as a low pass filter. Image blurring is used to reduce the non-linearity in the manifolds within the eigenspaces where MDG structure can be used to divide the spaces linearly. The algorithm is invariant to linear transformations like rotation and translation. Computer generated images for different hand-shapes in Irish Sign Language are used in testing. Experimental results show accuracy and performance of the proposed algorithm in terms of blurring level and MDG size

DenMune: Density peak based clustering using mutual nearest neighbors

Many clustering algorithms fail when clusters are of arbitrary shapes, of varying densities, or the data classes are unbalanced and close to each other, even in two dimensions. A novel clustering algorithm, DenMune is presented to meet this challenge. It is based on identifying dense regions using mutual nearest neighborhoods of size K, where K is the only parameter required from the user, besides obeying the mutual nearest neighbor consistency principle. The algorithm is stable for a wide range of values of K. Moreover, it is able to automatically detect and remove noise from the clustering process as well as detecting the target clusters. It produces robust results on various low and high-dimensional datasets relative to several known state-of-the-art clustering algorithms.Comment: pyMune is a Python package that implements this clustering algorithm proposed in this paper, DenMune. It is opensource and reproducible, doi:10.1016/j.simpa.2023.10056

Trans-Sense: Real Time Transportation Schedule Estimation Using Smart Phones

Developing countries suffer from traffic congestion, poorly planned road/rail networks, and lack of access to public transportation facilities. This context results in an increase in fuel consumption, pollution level, monetary losses, massive delays, and less productivity. On the other hand, it has a negative impact on the commuters feelings and moods. Availability of real-time transit information - by providing public transportation vehicles locations using GPS devices - helps in estimating a passenger's waiting time and addressing the above issues. However, such solution is expensive for developing countries. This paper aims at designing and implementing a crowd-sourced mobile phones-based solution to estimate the expected waiting time of a passenger in public transit systems, the prediction of the remaining time to get on/off a vehicle, and to construct a real time public transit schedule. Trans-Sense has been evaluated using real data collected for over 800 hours, on a daily basis, by different Android phones, and using different light rail transit lines at different time spans. The results show that Trans-Sense can achieve an average recall and precision of 95.35% and 90.1%, respectively, in discriminating lightrail stations. Moreover, the empirical distributions governing the different time delays affecting a passenger's total trip time enable predicting the right time of arrival of a passenger to her destination with an accuracy of 91.81%.In addition, the system estimates the stations dimensions with an accuracy of 95.71%.Comment: 8 pages, 11 figures

Injudicious Provision of Subtherapeutic Doses of Antibiotics in Community Pharmacies

Background: Egyptian pharmacists routinely provide antibiotics without a prescription. A few pills of common cold products are offered under the name “cold group”. A cold group may contain one or more pills of antibiotics. This study aimed to estimate the proportion of pharmacies that provide subtherapeutic doses of antibiotics in community pharmacies as part of a CG or upon direct request from a simulated client. Methods: A probability sample of community pharmacies in Alexandria, Egypt was selected. A simulated client approached pharmacy staff using a standardized scenario. He initially requested a cold group and followed by requesting two antibiotic pills.Results: The simulated client visited 104 pharmacies and was sold an antibiotic at 68 pharmacies in total. A cold group with one or more antibiotic pills was provided in 31 pharmacies. Upon request for two antibiotic pills, 2-8 antibiotic pills were provided in 30 pharmacies whereas an antibiotic carton was provided in three pharmacies. In four pharmacies, the simulated client was sold a cold group containing an antibiotic as well as another antibiotic upon request. Beta-lactam antibiotics comprised 76% of antibiotics provided. In five encounters, the simulated client was told that the cold group contained an antibiotic when, in fact, it did not. Conclusions: Subtherapeutic doses of antibiotics are provided at dangerous rates in Alexandria’s community pharmacies. Interventions are urgently needed to tackle different factors contributing to this dangerous practice. Conflict of Interest We declare no conflicts of interest or financial interests that the authors or members of their immediate families have in any product or service discussed in the manuscript, including grants (pending or received), employment, gifts, stock holdings or options, honoraria, consultancies, expert testimony, patents and royalties   Type: Original Researc

Analyse topologique et statistique de tracés manuscrits

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A Multistage Hierarchical Algorithm for Hand Shape Recognition

Abstract—This paper represents a multistage hierarchical algorithm for hand shape recognition using principal component analysis (PCA) as a dimensionality reduction and a feature extraction method. The paper discusses the effect of image blurring to build data manifolds using PCA and the different ways to construct these manifolds. In_order to classify the hand shape of an incoming sign object and to be invariant to linear transformations like translation and rotation, a multistage hierarchical classifier structure is used. Computer generated images for different Irish Sign Language shapes are used in testing. Experimental results are given to show the accuracy and performance of the proposed algorithm. I