Miracle-dz/UniFAR-FTUs huggingface dataset:
Anomaly distribution of quasar magnitudes: a test of lensing by a hypothetic supergiant molecular cloud in the Galactic halo

An anomaly in the distribution of quasar magnitudes based on the SDSS survey, has been recently reported by Longo (2012). The angular size of this anomaly is of the order of $\rm \pm 15^o$ on the sky. A low surface brightness smooth structure in $\gamma$-rays, coincides with the sky location and extent of the quasar anomaly, and is close to the Northern component of a pair of $\gamma$-ray bubbles discovered in the \sl Fermi Gamma-ray Space Telescope \rm survey. Molecular clouds are thought to be illuminated by cosmic rays. I test the hypothesis that the magnitude anomaly in the quasar distribution, is due to a lensing effect by an hypothetic Supergiant Molecular Cloud (SGMC) in the Galactic halo.A series of grid lens models are built by assuming firstly that a SGMC is a lattice with clumps of $\rm 10^{-3} M_\odot$, 10 AU in size, and assuming various filling factors of the cloud, and secondly a fractal structure. Local amplifications are calculated for these lenses by using the public software LensTool, and the single plane approximation. A complex network of caustics due to the clumpy structure is present. Our best single plane lens model capable of explaining Longo's effect, \sl at least in sparse regions, \rm requires a mass $\rm (1.5-4.1) \times 10^{10} ~M_\odot$ within $\rm 8.7 \times 8.7 \times (5-8.6) kpc^3$ at a lens plane distance of 20 kpc. It is constructed from a molecular cloud building block of $5 \times 10^5 M_\odot$ within a scale of 30 pc expanded by fractal scaling with dimension $D = 1.8-2$ up to 5-8.6 kpc for the SGMC. If such a Supergiant Molecular Cloud were demonstrated, it might be part of a lens explanation for the luminous anomaly discovered in quasars and in red galaxies. The mass budget may be varied by changing the cloud depth and the fractal dimension.
The Design of a Simple, Spiking Sparse Coding Algorithm for Memristive Hardware

Calculating a sparse code for signals with high dimensionality, such as highresolution images, takes substantial time to compute on a traditional computer architecture. Memristors present the opportunity to combine storage and computing elements into a single, compact device, drastically reducing the area required to perform these calculations. This work focused on the analysis of two existing sparse coding architectures, one of which utilizes memristors, as well as the design of a new, third architecture that employs a memristive crossbar. These architectures implement either a non-spiking or spiking variety of sparse coding based on the Locally Competitive Algorithm (LCA) introduced by Rozell et al. in 2008. Each architecture receives an arbitrary number of input lines and drives an arbitrary number of output lines. Training of the dictionary used for the sparse code was implemented through external control signals that approximate Oja’s rule. The resulting designs were capable of representing input in real-time: no resets would be needed between frames of a video, for instance, though some settle time would be needed. The spiking architecture proposed is novel, emphasizing simplicity to achieve lower power than existing designs. The architectures presented were tested for their ability to encode and reconstruct 8 × 8 patches of natural images. The proposed network reconstructed patches with a normalized, root-mean-square error of 0.13, while a more complicated CMOS-only approach yielded 0.095, and a non-spiking approach yielded 0.074. Several outputs competing for representation of the input was shown to improve reconstruction quality and preserve more subtle components in the final encoding; the proposed algorithm lacks this feature. Steps to address this were proposed for future work by scaling input spikes according to the current expected
A Bayesian Inference Method for Model Extrapolation Together with Qualitative Knowledge

In order to resolve the problem of model validation with limited test data in the untested domain,this paper presented an extrapolation method together with qualitative knowledge and quantitative Bayesian inference.Qualitative information such as the subject matter experts' opinions is transformed to prior probability in the proposed quantification method and applied to Bayesian inference.The Bayesian network with Monte Carlo method which is limited in sampling range is explored for extrapolating quantitatively the inference from the validated domain at the component level to the applied domain at the system level.And Bayesian interval hypothesis testing is performed on the evaluated quantity to assess the model validity.A simplified version of a static frame challenge problem developed by Sandia National Laboratories demonstrates that the method provides a valid approach to facilitate rational decisions in confidence extrapolation.
User Behavior Mining in Microblogging

This dissertation addresses the modeling of factors concerning microblogging users’ content and behavior. We focus on two sets of factors. The first set includes behavioral factors of users and content items driving content propagation in microblogging. The second set consists of latent topics and communities of users as the users are engaged in content generation and behavior adoptions. These two sets of factors are extremely important in many applications, e.g., network monitoring and recommender systems. In the first part of this dissertation, we identify user virality, user susceptibility, and content virality as three behavioral factors that affect users’ behaviors in content propagation. User virality refers to the ability of a user in getting her content propagated by many other users, while user susceptibility refers to the tendency of a user to propagate other users’ content. Content virality refers to the tendency of a content item to attract propagation by users. Instead of modeling these factors independently as done in previous research, we propose to jointly model all these factors considering their inter-relationships. We develop static, temporal, and incremental models for measuring the factors based on propagation data. We also develop a static model for modeling the factors specific to topics. In the second part of this dissertation, we develop topic models for learning users’ topical interest and communities from both their content and behavior. We first propose a model to derive community affiliations of users using topics and sentiments expressed in their content as well as their behavior. We then extend the model to learn both users’ personal interest and that of their communities, distinguishing the two types of interests. Our model also learns the bias of users toward their communities when generating content and adopting behavior.
Evolutionary Dynamics of Complex Communications Networks

Until recently, most network design techniques employed a bottom-up approach with lower protocol layer mechanisms affecting the development of higher ones. This approach, however, has not yielded fascinating results in the case of wireless distributed networks. Addressing the emerging aspects of modern network analysis and design, Evolutionary Dynamics of Complex Communications Networks introduces and develops a top-bottom approach where elements of the higher layer can be exploited in modifying the lowest physical topologyclosing the network design loop in an evolutionary fashion similar to that observed in natural processes.This book provides a complete overview of contemporary design approaches from the viewpoint of network science and complex/social network analysis. A significant part of the text focuses on the classification and analysis of various network modification mechanisms for wireless decentralized networks that exploit social features from relevant online social networks. Each chapter begins with learning objectives and introductory material and slowly builds to more detailed analysis and advanced concepts. Each chapter also identifies open issues, while by the end of the book, potential research directions are summarized for the more interested researcher or graduate student.The approach outlined in the book will help network designers and administrators increase the value of their infrastructure without requiring any significant additional investment. Topics covered include: basic network graph models and properties, cognitive methods and evolutionary computing, complex and social network analysis metrics and features, and analysis and development of the distinctive structure and features of complex networks. Considering all aspects of modern network analysis and design, the text covers the necessary material and background to make it a suitable source of reference for graduate students, postdoctoral researchers, and scientists
Fluorescent Microspheres as Tags for Anti-Counterfeiting of Textiles

We demonstrate the use of non-toxic, fluorescent microspheres for creation of a new class of anticounterfeiting systems for textiles. Cotton fabric was immersed into solutions with different concentration of polystyrene microspheres. We achieved successful attachment of these particles after soaking for one hour. Stability of the particles on the fabrics was tested using pure water and water with addition of detergent. Results demonstrate that particles attach firmly to the surface. We demonstrate the ability to create different patterns of fluorescent signals from the microspheres, thus making it impossible to reproduce.
Multiple regression laser display color system establishment

By analyzing the corresponding relations between a lot of actual measurement data of CRT input color corrdinate(R,G,B) and output color corrdinate(L,a,b). Through the device-independent color space"Lab",we will set up a color convert model from laser TV to CRT.Based on the convert model,realize the CRT predicts output effect is the laser TV output effect,which makes laser TV accurately reproduce the CRT color,finally realize the effect:"what you see is you see. "
Computational modelling of multidomain proteins with covarying residue pairs

The vast majority of known protein sequences have no solved three-dimensional structure at all, and the remaining ones usually have not been completely characterised, due to the limitations of experimental structural biology techniques. Structural genomics projects have helped increase the coverage of the protein structure universe, but most available structures still consist of either individual domains or sets of relatively small ones. This has prompted the development of computational methods for protein structure prediction, as well as for multidomain architecture modelling. One appealing idea to achieve this goal consists of detecting residue-residue contacts from multiple sequence alignments, under the assumption that they covary in order to maintain the local microenvironment and the overall stability of protein structures. After early limited success, this type of analysis has lately witnessed substantial progress, thanks to theoretical advances in disentangling genuine from spurious instances of correlation. Unsurprisingly, structural bioinformatics has promptly and successfully applied these improved tools to model globular and transmembrane proteins, along with guiding the assembly of protein complexes. However, the efficacy of these methods in the context of multidomain protein modelling has not yet been investigated. In this thesis state-of-the-art methods for predicting contacts from sequence data have been evaluated and used to build models of two-domain protein structures. Firstly, the ability of alternative methods to identify interdomain contacts was examined in a reference set of experimentally solved structures. Secondly, predicted contacts were employed to score docking models and select near-native solutions accordingly. Finally, predicted contacts were used to guide the assembly of individual domains in a multidomain modelling protocol.
A Secure Cloud Computing Model and Architecture Design and Research

Due to taking into account the security issues, making the original with the large-scale, dynamic, open and distributed computing environment of cloud computing, which was to build small-scale, static private cloud at present. In order to solve the security issues of the cloud platform, the cloud security services platform and related services were built with the user dimension, the data dimension and business-dimension in this project so that the cloud platform security, cloud services safe and secure cloud services were gained and met with the application of cloud computing services rendered during the required security credible demand. In the end, a lightweight cloud computing multi-demensional archetecture was designed and built based on Eucalyptus.
Case-Based Decision Support for Intelligent Patient Knowledge Management

The majority of healthcare workers in hospitals continue to record, access and update important patient information using paper charts. Disparate patient data (clinical information, laboratory results and medical imagery) is entered by different caregivers and stored at different locations around the hospital. This is a cumbersome, time consuming process that can result in critical medical errors such as documents being mislaid or prescriptions being misinterpreted due to illegible handwriting. Hospitals everywhere are moving to integrate health data sources using Electronic Health Record (EHR) systems as well as taking advantage of the flexibility and speed of wireless computing to improve the quality and reduce the cost of healthcare. We are developing a mobile application that allows doctors to efficiently access accurate real-time patient information at the point-of-care. The system can assist caregivers in automatically searching through very large repositories of previous patient cases as increasingly large hospital databases are making manual searches of such information unfeasible. The system performs computational prognosis by providing decision support for pre-screening of medical diagnosis. A presenting patient's symptoms can be input to a portable device and the application can quickly retrieve the most similar profiles with known diagnoses from large databases which can be used to compare treatments, diagnosis, test results and other information.
Novel dynamic partial reconfiguration implementations of the support vector machine classifier on FPGA

The support vector machine (SVM) is one of the highly powerful classifiers that have been shown to be capable of dealing with high-dimensional data. However, its complexity increases requirements of computational power. Recent technologies including the postgenome data of high-dimensional nature add further complexity to the construction of SVM classifiers. In order to overcome this problem, hardware implementations of the SVM classifier have been proposed to benefit from parallelism to accelerate the SVM. On the other hand, those implementations offer limited flexibility in terms of changing parameters and require the reconfiguration of the whole device. The latter interrupts the operation of other tasks placed on the hardware device. In this work, two flexible hardware implementations of the SVM classifier are proposed, namely A1 and A2 classifiers with successful applications in a microarray dataset. In addition, two dynamically and partially reconfigurable (DPR) architectures of the SVM classifier are presented. The A1 and A2 architectures have achieved up to 61× and 49× speed-up, respectively, over the equivalent general purpose processor. Furthermore, the DPR implementations achieved at least ∼8× reduction in reconfiguration time compared to non-DPR implementation. This is a significant achievement that can be easily adapted in other application domains of a similar nature.
Bit error probability for an adaptive diversity receiver in a Rayleigh fading channel

The bit error probability is determined for an adaptive diversity receiver using decision directed feedforward channel estimation. The channel is assumed to be a nonselective Rayleigh fading channel with AWGN. The analysis is valid for all feedforward estimation structures which use an FIR-type estimation filter. The analysis can be applied also to Rake receivers in a frequency selective Rayleigh fading channel. Numerical results are presented for an adaptive diversity receiver which uses either an FIR-type minimum mean-square error (MMSE) filter or a moving average (MA) filter as the estimation filter. The results show that the MA filter performs poorly compared to the MMSE filter at large signal-to-noise ratios and large Doppler spreads. However, at small signal-to-noise ratios and Doppler spreads the filters do not show any remarkable difference in the performance. <<ETX>>
Automated Multi Camera Surveillance Algorithms And Practice

Thank you very much for downloading automated multi camera surveillance algorithms and practice. As you may know, people have search hundreds times for their favorite books like this automated multi camera surveillance algorithms and practice, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some infectious virus inside their laptop.
Multimodal tracking for robust pose estimation

ACKNOWLEDGEMENTS I would like to thank my advisor Dr Henrik Christensen, for believing in me and giving me an opportunity to work on a challenging problem. He helped me figure out the path and get me through my lows. I would also like to thank my labmates Ruffin, Siddharth, Varun and Samarth with whom I spent some cherished moments of my time. I am indebted to Siddharth and Rahul for being the ideal mentors, for being accommodating and helping me as I stumbled through the dark. I have shared my time here with awesome robotics students Yannick, Zhaoyang, Natesh and Carlos from whom I learnt about the diversity of research. Finally, I would like to thank Cheshta and my parents, for always being there for me.
Accelerating a MATLAB Application with Nvidia GPUs: a Case Study for GPU Library Construction

This abstract covers a case study in using the Nvidia CUDA environment to accelerate lung tumor tracking through template matching, and will discuss how this lays the groundwork for creating high performance GPU accelerated applications through a modular library-based programming interface. The tracking algorithm uses motion-enhanced templates and Pearson's correlation. Issues that arise when using CUDA from within MATLAB are investigated, as well as how the particular application was mapped from MATLAB code to a GPU. The case study is being used to develop a tool that allows more rapid exploration of the tradespaces for GPU applications.
The Mutual Presence of RP-7 and the Future of Virtual Collaborative Writing

A recent front page article in the Sunday New York Times (Lewin, 2009) captured a prevailing perspective on digital communication technologies. Lewin’s article depicted the future as one that is exclusively digital, a departure from the materially-bound, printbased texts common to workplace and educational settings for centuries. A simple contrast represents digital texts literally as the “future,” while print texts are “history. ” There are at least two important oversights that this binary perspective perpetuates. ABSTRACT
Visuo-haptic set-up for usability analysis of industrial products

Virtual Reality (VR) technologies are able to support and facilitate the Participatory Design of industrial products by allowing designers to involve the final users of a product at the early design stages, without the need of a physical mock-up and with the advantage of being able to assess several design options. In some previous works, we have described a methodology and a system (called VP2PaD: Virtual Prototyping oriented to Participatory Design) for the usability evaluation of an industrial product based on the use of virtual prototypes. One of the main limitations of VP2PaD is the absence of tactile feedback, which complicates the user's interaction with the interface of the virtual product.This paper presents the continuation of these works, describing the first steps in the development of a new system, based on a haptic device and an augmented reality display, that aims to improve the effectiveness of VP2PaD, especially in terms of user-friendliness of the interaction devices. Thanks to the haptic device, the user may feel the presence and the contact with interface widgets like buttons and knobs. Moreover, with augmented reality the virtual product may be displayed in a real context and the user may see his/her own hand touching the digital objects. This approach presents several challenges related to the implementation of the visuo-haptic augmented reality setup. The rendering pipeline has to be modified in order to delete the haptic device from the scene and, at the same time, to display correctly the real hand avoiding any wrong occlusion by the virtual objects. In order to solve these problems, we consider ImageBased Rendering (IBR) techniques.The elimination of the haptic device from the augmented reality scene, together with the collocation of the user’s hand and the virtual object, notably enhance the friendliness of the interface. As a result, the user can focus on testing the virtual product, without distractive elements from the setup.
The Nucleolus as a Strategy for Resources Optimization in LANs on Power Line Communications

Background/Objectives: At present HPAV lacks an efficient mechanism for the optimization of resources. The objective of this paper is to propose the use of the Nucleolus as a strategy to provide a solution to the problem. Methods/Statistical Analysis: The paper raises two separate scenarios, formed by twelve nodes each, in conditions of channel and traffic clearly established. It proceeded to implement a solution to the problem through two methods: Nucleolus and Linear Programming, in order to make a comparison of treatments, through an analysis of means, under the use the test called paired-t and with this evaluate the behavior of the Nucleolus as a strategy for the optimization of resources. Findings: The use of Nucleolus is a novel strategy for optimizing resources within the PLC technology, considering the importance that game theory has been gaining as a tool to analyze complex optimization situations in various fields of knowledge and also that no found similar work in the various bibliographic sources consulted. Based on the results obtained it showed that the Nucleolus made a better allocation of BW in comparison with the optimization method of Linear Programming, to minimize the difference between BW requested and the BW allocated for each node, with a 95% confidence. However, although the method is efficient, the computational complexity of the algorithm increases dramatically as increase the number of nodes. Application/Improvements: It is recommended to investigate on alternative methods to calculate the Nucleolus that offer a computational complexity and temporary reduced, in order to facilitate its implementation in low-cost embedded systems.