Miracle-dz/UniFAR-FTUs huggingface dataset:
Millimeter-Wave Frequency Radio over Fiber Systems: A Survey

In recent years considerable attention has been devoted to the merging of radio frequency and optical fiber technologies aiming to the distribution of millimeter-wave (mm-wave) signals. This effort has given birth to the field of Radio over Fiber (RoF) technologies and systems. This sort of systems have a great potential to support secure, cost-effective, and high-capacity vehicular/mobile/wireless access for the future provisioning of broadband, interactive, and multimedia wireless services. In this paper we present a comprehensive review of mm-wave frequency RoF systems. In our integral approach, we identified the most important figures of merit of an RoF system, which is divided into three main subsystems: Central Station (CS), Optical Distribution Network (ODN) and Base Station (BS). In each subsystem, the most promising technologies are classified: downlink transmission techniques at the CS, ODN architectures, and optical configurations of the BS. The impact of technology choice on the overall system performance is discussed, and the figures of merit are studied and used to assess the subsystem's performance. Finally, we suggest technological opportunities and future developments that should be attracting the attention of researchers and developers.
Efficient Sampling through Variable Splitting-inspired Bayesian Hierarchical Models

Markov chain Monte Carlo (MCMC) methods are an important class of computation techniques to solve Bayesian inference problems. Much recent research has been dedicated to scale these algorithms in high-dimensional settings by relying on powerful optimization tools such as gradient information or proximity operators. In a similar vein, this paper proposes a new Bayesian hierarchical model to solve large scale inference problems by taking inspiration from variable splitting methods. Similarly to the latter, the derived Gibbs sampler permits to divide the initial sampling task into simpler ones. As a result, the proposed Bayesian framework can lead to a faster sampling scheme than state-of-the-art methods by embedding them. The strength of the proposed methodology is illustrated on two often-studied image processing problems.
How Contractor Behavior Affects Engineering Project Value-Added Performance

Contractors are one of the most important project participants, and their behavior has gained increasing attention for improving performance, but few studies have been devoted to focusing on the forms of contractor behavior and project value-added performance. This study developed and tested a theoretical model to investigate how different types of contractor behavior (perfunctory, consummate, and opportunistic) affect engineering project value added (EPVA) and analyze the moderating role of contract complexity and task complexity. The multiple regression analysis approach is used to analyze data collected from a survey of 290 Chinese project professionals. The results show that three types of contractor behavior have a significant impact on EPVA, that is, contractor perfunctory and consummate behaviors have a significant positive impact, whereas opportunistic behavior has a significant negative impact. In addition, contractor consummate behavior had the strongest impact, followed by perfunctory and opportunistic behaviors. The moderation results showed that the positive impact between perfunctory behavior and EPVA is strengthened and weakened by contract and task complexity, respectively, whereas the negative impact of opportunistic behavior is weakened and strengthened, respectively. The contract and task complexity have no significant moderating impact on the relationship between contractor consummate behavior and EPVA. The findings of this study contribute to the project governance and project performance literature and provide new knowledge for academe and practice for a deeper understanding of how to realize EPVA from the perspective of contractor behavior.
Opportunities and Challenges with J2SE 5 for Introductory Programming Teaching

The recent release of the Java version 5.0 "Tiger" introduces some significant language changes. For educators, some of these changes provide opportunities to improve teaching, while others pose additional problems that require awareness to avoid them. The authors have recently completed the inclusion of support for all new language features into a wellknown educational IDE for Java – BlueJ – and in the course of doing so evaluated each of them for usefulness in education, and developed pedagogic strategies to handle the inherent opportunities and challenges. This has formed the basis of the design of the features in BlueJ which support the language changes. In this paper, we describe the results of our evaluation, provide recommendations on treatment of the new features in introductory courses and discuss how BlueJ may be used to illustrate important aspects.
Effects of desktop 3D world design on user navigation and search performance

Desktop virtual reality (VR) offers a powerful environment for visualizing structure in large information sets. In well-designed virtual worlds, users can employ skills from wayfinding in the real world. The paper reports on the development and testing of a series of prototype VR worlds, designed to support navigation during information visualization and retrieval. Results indicated that users subjectively preferred naturalistic environments over abstract ones, but that users objectively searched better in environments that had rigorous hierarchical structure, and support for both overview and detail. In comparison with a hypertext interface, the final virtual world design elicited more enjoyment, without worsening search performance significantly, when appropriate training time was allowed. This prototype shows the potential value of navigable VR that is engaging and useful for everyday information exploration.
An Effective Method of 1-D Bar Code Image Identification

This paper proposes a new approach to achieve angle correction for images with arbitrary rotated bar code. A new projection curve based bar code reading method and a new global and local based Euclidean distance decoding algorithm are proposed. The proposed method is effective for identifying bar code images which are low-resolution, non-uniform illuminated, photographed remotely, noisy or slightly blurry, and it is especially suitable for recognizing low-resolution, severely noisy, and small-scale bar code image. Also, this method has the virtue of real-time, robustness and high recognition rate, etc. Experimental results using some international UPC-A bar code library show that the proposed algorithm delivers substantial improvements when compared with some latest methods.
Topological Properties and Temporal Dynamics of Place Networks in Urban Environments

Understanding the spatial networks formed by the trajectories of mobile users can be beneficial to applications ranging from epidemiology to local search. Despite the potential for impact in a number of fields, several aspects of human mobility networks remain largely unexplored due to the lack of large-scale data at a fine spatiotemporal resolution. Using a longitudinal dataset from the location-based service Foursquare, we perform an empirical analysis of the topological properties of place networks and note their resemblance to online social networks in terms of heavy-tailed degree distributions, triadic closure mechanisms and the small world property. Unlike social networks however, place networks present a mixture of connectivity trends in terms of assortativity that are surprisingly similar to those of the web graph. We take advantage of additional semantic information to interpret how nodes that take on functional roles such as 'travel hub', or 'food spot' behave in these networks. Finally, motivated by the large volume of new links appearing in place networks over time, we formulate the classic link prediction problem in this new domain. We propose a novel variant of gravity models that brings together three essential elements of inter-place connectivity in urban environments: network-level interactions, human mobility dynamics, and geographic distance. We evaluate this model and find it outperforms a number of baseline predictors and supervised learning algorithms on a task of predicting new links in a sample of one hundred popular cities.
Subsurface oxide plays a critical role in CO2 activation by Cu(111) surfaces to form chemisorbed CO2, the first step in reduction of CO2

Significance Combining ambient pressure X-ray photoelectron spectroscopy experiments and quantum mechanical density functional theory calculations, this work reveals the essential first step for activating CO2 on a Cu surface, in particular, highlighting the importance of copper suboxide and the critical role of water. These findings provide the quintessential information needed to guide the future design of improved catalysts. A national priority is to convert CO2 into high-value chemical products such as liquid fuels. Because current electrocatalysts are not adequate, we aim to discover new catalysts by obtaining a detailed understanding of the initial steps of CO2 electroreduction on copper surfaces, the best current catalysts. Using ambient pressure X-ray photoelectron spectroscopy interpreted with quantum mechanical prediction of the structures and free energies, we show that the presence of a thin suboxide structure below the copper surface is essential to bind the CO2 in the physisorbed configuration at 298 K, and we show that this suboxide is essential for converting to the chemisorbed CO2 in the presence of water as the first step toward CO2 reduction products such as formate and CO. This optimum suboxide leads to both neutral and charged Cu surface sites, providing fresh insights into how to design improved carbon dioxide reduction catalysts.
On the design of linear arrays of fixed length for diversity reception in Rayleigh fading and cochannel interference

The problem of optimizing the placement of the antennas for an optimum combining receiver equipped with a linear antenna array of fixed length is addressed. A Rayleigh fading model is considered where the complex channel gains of both the desired user and the interfering users are assumed to be correlated due to the mutual proximity of the antennas. The average output signal-to-interference-plus-noise ratio is adopted as a measure of the system's performance and is maximized over the number and the arrangement of the antennas in the array. The optimization problem is formulated as a quadratic convex optimization problem in an infinite dimensional space where finite dimensional approximations of the problem are numerically solved using a convex optimization method. For the special case of an exponential correlation function the optimal arrangement is also derived analytically. It is shown for an exponential correlation model that the optimal number of antennas is infinite meaning that adding more antennas is always beneficial. Perhaps unexpectedly, the best arrangement of antennas in this case is not uniform, in general. On the contrary, it is shown through numerical results that for a two dimensional omnidirectional scattering correlation model the optimal number of antennas is a finite number and the optimum placement is uniform.
Multicenter retrospective study of cement-retained implant-supported anterior partial prostheses: success and restoration evaluation.

PURPOSE The aim of this multicenter study was to evaluate implant success and restorative complications of cement-retained implant-supported anterior partial prostheses in Jordan. MATERIALS AND METHODS A retrospective study of all implants with a minimum of 1-year follow-up were used to support fixed, cement-retained restorations from April 2000 until March 2007. The cement-retained implants were loaded with either single- or multiple-tooth replacements. The Fisher exact test was performed to test the presence of any statistically significant difference in success concerning gender or arch of placement. RESULTS Eighty-seven implants were placed in the anterior region of the mandible or maxilla in 49 patients at multiple clinical practices in Jordan. The age of the patients ranged from 17 to 85 years. Eighteen implants were placed in the mandible and 69 in the maxilla. Three maxillary implants in 2 male patients had 3-mm horizontal bone loss. Those 3 implants are still functioning and were considered surviving implants but not successful implants. Therefore, the implant cumulative survival rate for both arches and genders was 100%. The implant cumulative success rate was 95.78%. Three crowns (maxillary) were dislodged. No significant differences were revealed regarding gender or arch of placement (P > .05). CONCLUSIONS Cement-retained implants exhibited high survival and success rates among a Jordanian population.
Phylogenetic relationships of Peronospora and related genera based on nuclear ribosomal ITS sequences.

In order to investigate phylogenetic relationships of selected members of Peronosporaceae to the genera Pythium, Halophkytophthora, Phytophthora, and Peronophythora, Bayesian analysis of partial sequences of the ITS1-5.8S-ITS2 region was performed. In addition, sequences of the complete ITS1-5.8S-ITS2 region were analysed for 101 collections belonging to the genera Peronospora, Hyaloperonospora, Perofascia, Pseudoperonospora, Phytophthora and Peronophythora, using Bayesian inference of phylogeny and maximum parsimony. The results confirm a close relationship of these genera. The strongly supported Peronosporaceae clade is located within the paraphyletic genus Phytophthora (including Peronophythora). Monophyly of the genera Pseudoperonospora and Hyaloperonospora are strongly supported, but monophyly of Peronospora s. str. can neither be confirmed nor rejected. Within Peronospora s. str. , basic relationships often remain unclear; however, some groups form highly supported monophyletic clades. Peronospora species parasitising the same host families or orders are only partly resolved as monophyletic, indicating frequent host-jumping also between distantly related host families. All species inhabiting flowers of different host families form a strongly supported monophyletic group.
The Effect of Beverage Type on Alcoholism Mortality in Russia

Background: Alcohol is increasingly implicated in the mortality crisis observed in Russia since the collapse of the communist system. Objective: The aim of this study was to examine the relation between the consumption of different beverage types and alcoholism mortality rates in Russia. Method: Age-standardized male and female alcoholism mortality data for the period 1970-2005 and data on beverage-specific alcohol sales were obtained from Russian State Statistical Committee. Time-series analytical modeling techniques (ARIMA) were used to examine the relation between the sales of different alcoholic beverages (vodka, wine, beer) and alcoholism mortality rates.
Isometric Multi-manifold Learning for Feature Extraction

Manifold learning is an important topic in pattern recognition and computer vision. However, most manifold learning algorithms implicitly assume the data are aligned on a single manifold, which is too strict in actual applications. Isometric feature mapping (Isomap), as a promising manifold learning method, fails to work on data which distribute on clusters in a single manifold or manifolds. In this paper, we propose a new multi-manifold learning algorithm (M-Isomap). The algorithm first discovers the data manifolds and then reduces the dimensionality of the manifolds separately. Meanwhile, a skeleton representing the global structure of whole data set is built and kept in low-dimensional space. Secondly, by referring to the low-dimensional representation of the skeleton, the embeddings of the manifolds are relocated to a global coordinate system. Compared with previous methods, these algorithms can keep both of the intra and inter manifolds geodesics faithfully. The features and effectiveness of the proposed multi-manifold learning algorithms are demonstrated and compared through experiments.
Tissue Plasminogen Activator Contributes to Alterations of Neuronal Migration and Activity-Dependent Responses in Fragile X Mice

Fragile X syndrome (FXS) is the most common inherited neurodevelopmental disorder with intellectual disability. Here, we show that the expression of tissue plasminogen activator (tPA) is increased in glial cells differentiated from neural progenitors of Fmr1 knock-out mice, a mouse model for FXS, and that tPA is involved in the altered migration and differentiation of these progenitors lacking FMR1 protein (FMRP). When tPA function is blocked with an antibody, enhanced migration of doublecortin-immunoreactive neurons in 1 d differentiated FMRP-deficient neurospheres is normalized. In time-lapse imaging, blocking the tPA function promotes early glial differentiation and reduces the velocity of nuclear movement of FMRP-deficient radial glia. In addition, we show that enhanced intracellular Ca2+ responses to depolarization with potassium are prevented by the treatment with the tPA-neutralizing antibody in FMRP-deficient cells during early neural progenitor differentiation. Alterations of the tPA expression in the embryonic, postnatal, and adult brain of Fmr1 knock-out mice suggest an important role for tPA in the abnormal neuronal differentiation and plasticity in FXS. Altogether, the results indicate that tPA may prove to be an interesting potential target for pharmacological intervention in FXS.
Mining Tera-Scale Graphs: Theory, Engineering and Discoveries

How do we find patterns and anomalies, on graphs with billions of nodes and edges, which do not fit in memory? How to use parallelism for such Tera- or Peta-scale graphs? In this thesis, we propose PEGASUS, a large scale graph mining system implemented on the top of the HADOOP platform, the open source version of MAPREDUCE . PEGASUS includes algorithms which help us spot patterns and anomalous behaviors in large graphs. PEGASUS enables the structure analysis on large graphs. We unify many different structure analysis algorithms, including the analysis on connected components, PageRank, and radius/diameter, into a general primitive called GIM-V. GIM-V is highly optimized, achieving good scale-up on the number of edges and available machines. We discover surprising patterns using GIM-V, including the 7-degrees of separation in one of the largest publicly available Web graphs, with 7 billion edges. PEGASUS also enables the inference and the spectral analysis on large graphs. We design an efficient distributed belief propagation algorithm which infer the states of unlabeled nodes given a set of labeled nodes. We also develop an eigensolver for computing top k eigenvalues and eigenvectors of the adjacency matrices of very large graphs. We use the eigensolver to discover anomalous adult advertisers in the who-follows-whom Twitter graph with 3 billion edges. In addition, we develop an efficient tensor decomposition algorithm and use it to analyze a large knowledge base tensor. Finally, PEGASUS allows the management of large graphs. We propose efficient graph storage and indexing methods to answer graph mining queries quickly. We also develop an edge layout algorithm for better compressing graphs.
Detecting Masqueraders: A Comparison of One-Class Bag-of-Words User Behavior Modeling Techniques

A masquerade attack is a consequence of identity theft. In such attacks, the impostor impersonates a legitimate insider while performing illegitimate activities. These attacks are very hard to detect and can cause considerable damage to an organization. Prior work has focused on user command modeling to identify abnormal behavior indicative of impersonation. In this paper, we investigate the performance of two one-class user behavior profiling techniques: one-class Support Vector Machines (ocSVMs) and a Hellinger distance-based user behavior profiling technique. Both techniques model bags of words or commands and do not model sequences of commands . We use both techniques for masquerade detection and compare the experimental results. The objective is to evaluate which modeling technique is most suitable for use in an operational monitoring system, hence our focus is on accuracy and operational performance characteristics. We show that one-class SVMs are most practical for deployment in sensors developed for masquerade detection in the general case. We also show that for specific users whose profile fits the average user profile, one-class SVMs may not be the best modeling approach. Such users pose a more serious threat since they may be easier to mimic.
Spiral μ-preconcentrator for gas sensor detection in the ppb range

In the present work, a silicon-based gas chromatographic microsystem is proposed and developed; it consists of a spiral-shaped μ preconcentrator followed by a μ-hotplate sensor matrix. The shape of the preconcentrator was chosen according to technological and gas flow limitations as it is required that the whole gas flow passes trough the absorbent in order to obtain a good concentration factor. Therefore, a spiral-shape configuration was adopted with 10cm length, and width/depth of 300μm. In this case, the carrier flow is high enough to avoid stagnation whereas provide a fast evacuation of the gas after applying the desorption thermal pulse. The proposed spiral μ-preconcentrator leads to a robust and highly integrated device able to be incorporated in a GC/MS-gas sensor system.
Security Analysis of Consumer-Grade Anti-Theft Solutions Provided by Android Mobile Anti-Virus

We study the “anti-theft” mechanisms available to consumers to thwart unauthorised access to personal data on stolen Android smartphones. With millions of devices stolen in the USA in 2013 alone, such attacks are a serious and growing problem. The main mitigation against unauthorised data access on stolen devices is provided by “anti-theft” apps; that is, with “remote wipe” and “remote lock” functions. We study the top 10 Mobile Anti-Virus (MAV) apps that implement these functions. They have been downloaded hundreds of millions of times. We investigate the general security practices of MAVs, as well as the implementation of their “remote wipe” and “remote lock” functions. Our analysis uncovers flaws that undermine MAV security claims and highlight the fragility of third-party security apps. We find that MAV remote locks may be unreliable due to poor implementation practices, Android API limitations and vendor customisations. Mobile OS architectures leave third-party security apps little leeway to improve built-in Factory Resets, therefore MAV remote wipe functions are not an alternative to a flawed built-in Factory Reset. We conclude the only viable solutions are those driven by vendors themselves.
PRECIOUS! Out-of-reach selection using iterative refinement in VR

Selecting objects outside user's arm-reach in Virtual Reality still poses significant challenges. Techniques proposed to overcome such limitations often follow arm-extension metaphors or favor the use of selection volumes combined with ray-casting. Nonetheless, these approaches work for room sized and sparse environments, and they do not scale to larger scenarios with many objects. We introduce PRECIOUS, a novel mid-air technique for selecting out-of-reach objects. It employs an iterative progressive refinement, using cone-casting to select multiple objects and moving users closer to them in each step, allowing accurate selections. A user evaluation showed that PRECIOUS compares favorably against existing approaches, being the most versatile.