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

Communication Over MIMO Broadcast Channels Using Lattice-Basis Reduction

A simple scheme for communication over MIMO broadcast channels is introduced which adopts the lattice reduction technique to improve the naive channel inversion method. Lattice basis reduction helps us to reduce the average transmitted energy by modifying the region which includes the constellation points. Simulation results show that the proposed scheme performs well, and as compared to the more complex methods (such as the perturbation method) has a negligible loss. Moreover, the proposed method is extended to the case of different rates for different users. The asymptotic behavior of the symbol error rate of the proposed method and the perturbation technique, and also the outage probability for the case of fixed-rate users is analyzed. It is shown that the proposed method, based on LLL lattice reduction, achieves the optimum asymptotic slope of symbol-error-rate (called the precoding diversity). Also, the outage probability for the case of fixed sum-rate is analyzed.Comment: Submitted to IEEE Trans. on Info. Theory (Jan. 15, 2006), Revised (Jun. 12, 2007

Power Electronics Platforms for Grid-Tied Smart Buildings

Renewable energy sources (such as sun, wind, water, or fuel cells) are attracting great interest for either grid-tied or off-grid arrangements in smart green buildings. It must be either used when generated, stored for future use on-site, delivered to the power grid, or shared among combination of these. Grid-tied buildings are connected to the utility grid service lines. Off-grid buildings have no connection to utility service lines. Both types employ inverters to convert power from direct current (DC) to alternating current (AC), and most off-grid systems have batteries to store energy for use when needed. Accordingly, power electronics systems are playing an important role as the enabling technology for smart grid. In addition, smart meter represents the interface part between the green building and the utility grid. In order to realize the interaction between both systems, a bidirectional power conditioning module is needed. This chapter introduces the different power electronics platforms suitable for grid-tied smart green buildings (such as residential homes, commercial, and industrial) as well as its integrative functionality with advanced metering infrastructure (AMI). In order to show the superiority of these platforms in conjunction with smart meters, a hardware case study with one of the most popular power electronics topologies is presented

Confusion Matrix in Three-class Classification Problems: A Step-by-Step Tutorial

The confusion matrix is a specific table used in machine learning to describe and assess the performance of a classification model (e.g., an artificial neural network) for a set of test data whose actual distinguishing features are known. The confusion matrix for an n-class classification problem is square, with n rows and n columns. The rows represent the class actual samples (instances), which are the classifier inputs, and the columns represent the class predicted samples, which are the classifier outputs. Binary class classifiers have been presented in a previous paper, where in this paper, we are concerned with three-class classification performance measures. We also clarify the concept with numerical examples to make it close to the reader mind

Confusion Matrix in Binary Classification Problems: A Step-by-Step Tutorial

In the field of machine learning, the confusion matrix is a specific table adopted to describe and assess the performance of a classification model (e.g. an artificial neural network) for a set of test data whose actual distinguishing features are known. The learning algorithm is thus of the supervised learning category. For an n-class classification problem, the confusion matrix is square with n rows and n columns. The rows represent the class actual samples (instances) which are the inputs to the classifier, while the columns represent the class predicted samples, the classifier outputs. (The converse is also valid, i.e. the two dimensions \u27actual\u27 and \u27predicted\u27 can be assigned to columns and rows, respectively). Binary as well as multiple-class classifiers can be dealt with. It is worth noting that the term \u27matrix\u27 here has nothing to do with the theorems of matrix algebra; it is regarded just as an information-conveying table. The descriptive word ‘confusion’ stems from the fact that the matrix clarifies to what extent the model confuses the classes — mislabels one as another. The essential concept was introduced in 1904 by the British statistician Karl Pearson (1857 — 1936)

Characterizing of Robo downstream signalling to promote direct neurogenesis

The size and degree of folding of the mammalian cortex are pivotal factors that affect species’ cognitive abilities and sensorimotor skills. The cerebral cortex is the main region in the mammalian brain that governs complex cognitive behaviors. The development of the cortex depends on the amplification of neural stem cells (NSCs), neural progenitors (NPs) and the generation and differentiation of postmitotic neurons. There are two main types of NPs in the mouse neocortex (NCx): apical radial glia (aRGCs) and intermediate progenitor cells (IPCs). Robo receptors play an important role in regulating the amplification of cortical progenitors. The absence of Robo receptor signalling plus the alteration of the Notch signalling pathway in the mouse NCx leads to an overproduction of poorly functional IPCs. Ancient amniotic cortices exhibit a predominance of direct neurogenesis during development, where aRGCs produce neurons directly. Intriguingly, Robo receptors as well as Notch signalling play a major role in attenuating the mode of neurogenesis. This hypothesis was validated in several brain structures with phyletic antiquity, confirming that Robo receptors are essential in the shift towards indirect neurogenesis during the evolution and expansion of the cerebral cortex. However, little is known about the precise signalling cascade or interactors employed by Robo to initiate direct neurogenesis. In this thesis, we demonstrated the transcriptomic differences between the developing mouse NCx and OB (where direct neurogenesis is predominant in the OB vs NCx) using single cell RNA sequencing (scRNA). We showed aRGCs populations that are differently enriched between these regions. We traced lineage trajectories of indirect and direct neurogenesis, as well as validating the expression of several differentially expressed genes between the two regions. We used Robo intracellular domain (ICD)—this region is considered a constitutively active form of Robo receptor—and demonstrated the protein interactors that bind it. Following that, we demonstrated Robo ICD localization to the nucleus. We discovered that Robo conserved cytoplasmic domains play an important role in Robo ICD nucleocytoplasmic localization and direct neurogenesis induction in the mouse NCx. Next, we showed that Robo ICD localizes to chromatin, and causes transcriptional changes that occur upon the experimental gain of function of Robo ICD in the NCx and in vitro. Additionally, we showed that loss of function of Nup107, a nuclear pore complex (NPC) protein and one of Robo ICD protein interactors, induces direct neurogenesis in mouse NCx and chick lateral pallium. Taken together, our findings suggest the transcriptional role Robo ICD exerts by binding DNA and, consequently, its conserved role in moderating direct neurogenesis. El tamaño y el grado de plegamiento de la corteza cerebral son factores fundamentales que afectan a las capacidades cognitivas y habilidades sensoriomotoras de los mamíferos. La corteza cerebral es la principal región del cerebro que gobierna conductas cognitivas complejas. El desarrollo de la corteza depende de la amplificación de células madre neurales (CMN), progenitores neurales (PN) y de la generación y diferenciación de neuronas postmitóticas. Hay dos tipos principales de PN en la neocorteza o neocórtex (NCx) del ratón: las células de glía radial apical (CGRa) y las células progenitoras intermedias (CPI). Los receptores Robo juegan un papel importante en la regulación de la amplificación de los progenitores corticales. La ausencia de señalización del receptor Robo sumada a la alteración de la vía de señalización de Notch en el NCx de ratón conduce a una sobreproducción de CPI poco funcionales. La corteza de especies amniotas anteriores en la evolución a los mamíferos (como los reptiles y las aves) exhiben un predominio de neurogénesis directa durante el desarrollo, por el cual las CGRa producen neuronas directamente. Curiosamente, los receptores Robo, así como la señalización de Notch, desempeñan un papel importante en la atenuación de esta modalidad de neurogénesis a lo largo de la evolución. Esta hipótesis ha sido validada en varias estructuras cerebrales con antigüedad filética, confirmando que los receptores Robo son esenciales en el cambio hacia la neurogénesis indirecta durante la evolución y la consecuente expansión de la corteza cerebral. Sin embargo, se sabe poco sobre la cascada de señalización de Robo, así como de los mensajeros secundarios empleados por este receptor para iniciar el proceso de neurogénesis directa. En esta tesis, demostramos las diferencias transcriptómicas que existen entre el NCx y el bulbo olfatorio (BO) de ratón en desarrollo (sabiendo que la neurogénesis directa es predominante en BO frente al NCx). Para ello usamos la técnica de secuenciación de ARN de células individuales (single-cell RNA sequencing (scRNAseq) en inglés). Mostramos que hay poblaciones de RGCa que están diferentemente enriquecidas entre estas regiones. Trazamos trayectorias de linaje de neurogénesis indirecta y directa y validamos la expresión de varios genes expresados diferencialmente entre las dos regiones. Utilizamos el dominio intracelular (DIC) de Robo (esta región se considera una forma constitutivamente activa del receptor) y demostramos los mensajeros secundarios que se unen. Después, demostramos la localización del DIC de Robo en el núcleo. Descubrimos que sus dominios citoplasmáticos, muy conservados a lo largo de la evolución, tienen un papel importante en la localización núcleo-citoplasmática del DIC y la inducción directa de neurogénesis en el NCx de ratón. A continuación, mostramos que una vez en el núcleo, el DIC se une a la cromatina y provoca cambios transcripcionales que tienen como resultado una la ganancia de función de Robo tanto en el NCx como in vitro. Además, demostramos que la pérdida de función de Nup107, una proteína que forma parte del complejo del poro nuclear (CPN) además de ser una proteína de interacción del DIC de Robo, induce neurogénesis directa en el NCx de ratón y en el palio lateral de pollo. En conjunto, nuestros resultados sugieren el papel de modulación transcripcional que ejerce el DIC de Robo al unirse al ADN y, en consecuencia, su rol conservado a lo largo de la evolución en la disminución de la neurogénesis directa

Foreign Direct Investment and Economic Growth Literature Review from 1994 to 2012

AbstractForeign direct investment (FDI) has been viewed as a power affecting economic growth (EG) directly and indirectly during the past few decades. This paper reviewed an amount of researches examining the relationships between FDI and EG, especially the effects of FDI on EG, from 1994 up to 2012. The results show that the main finding of the FDI-EG relation is significantly positive, but in some cases it is negative or even null. And within the relation, there exist several influencing factors such as the adequate levels of human capital, the well-developed financial markets, the complementarity between domestic and foreign investment and the open trade regimes, etc

Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey

Modern communication systems and networks, e.g., Internet of Things (IoT) and cellular networks, generate a massive and heterogeneous amount of traffic data. In such networks, the traditional network management techniques for monitoring and data analytics face some challenges and issues, e.g., accuracy, and effective processing of big data in a real-time fashion. Moreover, the pattern of network traffic, especially in cellular networks, shows very complex behavior because of various factors, such as device mobility and network heterogeneity. Deep learning has been efficiently employed to facilitate analytics and knowledge discovery in big data systems to recognize hidden and complex patterns. Motivated by these successes, researchers in the field of networking apply deep learning models for Network Traffic Monitoring and Analysis (NTMA) applications, e.g., traffic classification and prediction. This paper provides a comprehensive review on applications of deep learning in NTMA. We first provide fundamental background relevant to our review. Then, we give an insight into the confluence of deep learning and NTMA, and review deep learning techniques proposed for NTMA applications. Finally, we discuss key challenges, open issues, and future research directions for using deep learning in NTMA applications.publishedVersio