Miracle-dz/UniFAR-FTUs huggingface dataset:
Regularization of Wavelet Approximations

In this paper, we introduce nonlinear regularized wavelet estimators for estimating nonparametric regression functions when sampling points are not uniformly spaced. The approach can apply readily to many other statistical contexts. Various new penalty functions are proposed. The hard-thresholding and soft-thresholding estimators of Donoho and Johnstone are specific members of nonlinear regularized wavelet estimators. They correspond to the lower and upper envelopes of a class of the penalized least squares estimators. Necessary conditions for penalty functions are given for regularized estimators to possess thresholding properties. Oracle inequalities and universal thresholding parameters are obtained for a large class of penalty functions. The sampling properties of nonlinear regularized wavelet estimators are established and are shown to be adaptively minimax. To efficiently solve penalized least squares problems, nonlinear regularized Sobolev interpolators (NRSI) are proposed as initial estimators, which are shown to have good sampling properties. The NRSI is further ameliorated by regularized one-step estimators, which are the one-step estimators of the penalized least squares problems using the NRSI as initial estimators. The graduated nonconvexity algorithm is also introduced to handle penalized least squares problems. The newly introduced approaches are illustrated by a few numerical examples.
The performance of turbo coding over power-controlled fading channel in Ka-band LEO satellite systems

The integration of power control and turbo coding is adopted to achieve reliable communications over Ka-band code-division multiple-access (CDMA)-based low Earth orbit (LEO) satellite systems. The effect of imperfect power control on the bit error ratio (BER) performance is analyzed, and the upper bounds on BER are also derived for the case of slow and fast imperfect power control. The analytical and simulation results show that power-control error (PCE) degrades the BER performance of turbo-decoded systems significantly in the waterfall region. In the region of the error floor, the degradation effect slows down and the BER obtained under imperfect power control eventually converges with the BER under perfect power control. Moreover, in the waterfall region, the correlation characteristics of the PCE fluctuation do not degrade the BER performance as anticipated.
Efficient Training Data Enrichment and Unknown Token Handling for POS Tagging of Nonstandardized Texts

In this work we consider the problem of social media text Part-of-Speech tagging as fundamental task for Natural Language Processing. We present improvements to a social media Markov model tagger, by adapting parameter estimation methods for unknown tokens. In addition, we propose to enrich the social media text corpus by a linear combination with a newspaper training corpus. Applying our tagger to a social media text corpus results in accuracies of around 94 . 8% , which comes close to accuracies for standardized texts. 1
Assessment of 2013 AHA/ACC ASCVD risk scores with behavioral characteristics of an urban cohort in India

Abstract Cardiovascular diseases (CVDs) are the leading cause of death and disability in India. Early and sustained exposure to behavioral risk factors leads to development of CVDs. The aim of this study was to determine the baseline risk of a “hard CVD event” in subjects attending comprehensive health clinic and assess behavioral characteristics in “at risk” population. Using WHO STEPwise approach to Surveillance modified questionnaire, prevalence of noncommunicable diseases (NCDs) and risk factors was estimated in this cross-sectional study of 4507 subjects. Baseline cardiovascular risk was determined using Framingham risk score (FRS) and American College of Cardiology (ACC)/American Heart Association (AHA) atherosclerotic cardiovascular disease (ASCVD) algorithms. Modifiable behavior associated with high CVD risk was assessed. Among 40 to 59-year olds, ASCVD risk tool derived both a 10-year and lifetime risk score, which were used to stratify the cohort into 3 risk groups, namely, a high 10-year and high lifetime, a low 10-year and high lifetime, and a low 10-year and low lifetime risks. Dyslipidemia (30.6%), hypertension (25.5%), diabetes mellitus (20%), and obstructive airway disorders (17.6%) were most prevalent NCDs in our cohort. The ASCVD score stratified 26.1% subjects into high 10-yr and 59.5% into high lifetime risk while FRS classified 17.2% into high 10-year risk. Compared with FRS, the ASCVD risk estimator identified a larger proportion of subjects “at risk” of developing CVD. A high prevalence of alcohol use (38.4%), decreased intake of fruits and vegetables (96.2%) and low physical activity (58%) were observed in “at risk” population. Logistic regression analysis showed that in 40 to 59-year group, regular and occasional drinkers were 8.5- and 3.1-fold more likely to be in high 10-year and high lifetime ASCVD risk category than in low 10-year and low lifetime risk group. Similarly, regular drinkers and occasional drinkers were 2.1 and 1.3 times more likely to be in low 10-year and high lifetime risk than in low 10-year and low lifetime risk category. Subjects with inadequate intake of fruits and vegetables were 1.59 times more likely to be in low 10-year and high lifetime risk than the lower 10-year and lifetime risk group. Obese participants were 2.3-fold more likely to be in low 10-year and high lifetime risk. Identification of “at risk” subjects from seemingly healthy population will allow sustainable primary prevention strategies to reduce CVD.
SmurVEbox: a smart multi-user real-time virtual environment for generating character animations

Animating virtual characters is a complex task, which requires professional animators and performers, expensive motion capture systems, or considerable amounts of time to generate convincing results. In this paper we introduce the SmurVEbox, which is a cost-effective animating system that encompasses many important aspects of animating virtual characters by providing a novel shared user experience. SmurVEbox is a collaborative environment for generating character animations in real time, which has the potential to enhance the computer animation process. Our setup allows animators and performers to cooperate on the same virtual animation sequence in real time. Performers are able to communicate with the animator in the real space while simultaneously perceiving the effects of their actions on the virtual character in the virtual space. The animator can refine actions of a performer in real time so that both collaborate together on the same animation of a virtual character. We describe the setup and present a simple application.
Performance evaluation of the IEEE 802.11 MAC for mobile wireless sensor networks

With application development of wireless sensor networks, there are some scenarios exited mobile nodes. This motivates the research towards the mobile wireless sensor networks (MWSNs). IEEE 802.11 standard medium access control (MAC) is designed mainly for wireless local and metropolitan area network and used in Mobile Ad-hoc Networks. Its capability whether to suit MWSN has not yet been discovered. This study is to analyze and evaluate the suitability of IEEE 802.11 MAC in MWSN. The simulation is simulated on newly released Network Simulator NS-3. By means of some examples, the results show the energy consumption, delay and packet delivery ratio performance.
Short-Term Optical Variability in Broad Absorption Line QSOs

We present the first results from a pilot program to monitor the short-term optical variability in broad absorption line system QSOs. Intranight optical variations of ~5% were detected on timescales of ~1 hr in QSOs 0846+156 and 0856+172. Further, the mean magnitude level decreased in the two objects by ~0.05 and ~0.15 mag, respectively, during the period of our observations. The observed light curves are quite similar to those previously seen in the flat-spectrum radio-loud sources, especially the BL Lacertae objects, and can provide important constraints for the origin of microvariability and also a possible evolutionary link between the radio-loud and the radio-quiet QSOs.
Confidence, Crashes and Animal Spirits

This paper presents a model of the macroeconomy that reformulates what I take to be two important ideas from Keynes General Theory. The first is that there may be a continuum of steady state unemployment rates. The second is that beliefs select an equilibrium. I argue that search and matching costs in the labor market lead to the existence of a continuum of equilibria and I resolve the resulting indeterminacy by assuming that the beliefs of stock market participants are self-fulfilling. The paper reconciles Keynesian economics with general equilibrium theory without invoking the assumption of frictions that prevent wages and prices from reaching their equilibrium levels.
Emerging Wireless Technologies in the Internet of Things: a Comparative Study

The Internet of Things (IoT) incorporates multiple long-range, short-range, and personal area wireless networks and technologies into the designs of IoT applications. This enables numerous business opportunities in fields as diverse as e-health, smart cities, smart homes, among many others. This research analyses some of the major evolving and enabling wireless technologies in the IoT. Particularly, it focuses on ZigBee, 6LoWPAN, Bluetooth Low Energy, LoRa, and the different versions of Wi-Fi including the recent IEEE 802.11ah protocol. The studies evaluate the capabilities and behaviours of these technologies regarding various metrics including the data range and rate, network size, RF Channels and Bandwidth, and power consumption. It is concluded that there is a need to develop a multifaceted technology approach to enable interoperable and secure communications in the IoT.
Evolution of a superbubble in a turbulent, multi-phased and magnetized ISM

The evolution of a superbubble is simulated using a local three-dimensional, non-ideal MHD model, which includes galactic differential rotation, an external gravitational potential, heating via supernova explosions and radiative cooling of the in- terstellar medium (ISM). In our model a superbubble is formed due to the clustering of supernova activity, mimicking an OB association. Supernovae are modelled as instantaneous explo- sions that release 10 51 erg of thermal energy and3M of gas in a small volume. We implement a superbubble with the luminosity 310 37 ergs 1 into an initial ISM, which is taken from our ear- lier calculations modelling the warm and hot phases of the ISM. The simulated ISM has a multi-phase structure with hot, dilute and warm, denser gas coexisting in pressure equilibrium; there is also some cold, dense gas in the form of clouds and filaments arising from supernova compression. The multicomponent gas is in a state of developed turbulence, with r.m.s. velocity 10 and 40kms 1 for the warm and hot gas, respectively. At the devel- oped state of the simulation there is a magnetic field of 1.3 G strength having both uniform and random components. The evolution of a superbubble is rather different from that indicated by models with quasi-uniform ambient medium. The superbubble loses its spherical symmetry at very early stages of expansion. Its break-through from the disc is strongly facilitated by the nonuniformity of its environment. A superbubble which would be confined in the disc according to criteria obtained for a quasi-uniform ISM can break out to the halo.
Survey on Clustering Techniques in Data Mining

: The main objective of the data mining process is to extract information from a large data set and transform it into an understandable structure for further use. Clustering is a main task of exploratory data analysis and data mining applications. Clusteringis the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups (clusters). There are different types of cluster model: Connectivity models, Distribution models, Centroid models, Density models, Subspace model, Group models and Graph-based models. Clustering can be done by the different algorithms such as hierarchical, partitioning, grid, density and graph based algorithms. Hierarchical clustering, which is connectivity based clustering. Partitioning clustering is the centroid based clustering. Distribution-based clustering model most closely related to statistics is based on distribution models. Density-based clustering, clusters are defined as areas of higher density than the remainder of the data set. Grid-based clustering,partition the space into a finite number of cells that form a grid structure on which all of the operations for clustering are performed. Graph clustering is a graph based method, clustering of vertices on basis of edge structure.In this survey paper, a review of clustering and its different techniques in data mining is done.
Time‐lapse total internal reflection fluorescence video of acetylcholine receptor cluster formation on myotubes

To study when and where acetylcholine receptor (AChR) clusters appear on developing rat myotubes in primary culture, we have made time‐lapse movies of total internal reflection fluorescence (TIRF) overlaid with schlieren transmitted light images. The receptors, including the ones newly incorporated into the membrane, were labeled with rhodamine α‐bungarotoxin (R‐BT) continuously present in the medium. Since TIRF illuminates only cell‐substrate contact regions where almost all of the AChR clusters are located, background fluorescence from fluorophores either in the bulk solution or inside the cells can be suppressed. Also, because TIRF minimizes the exposure of the cell interior to light, the healthy survival of the cell culture during imaging procedures is much enhanced relative to standard epi‐ (or trans‐) illumination. During the experiment, cells were kept alive on the microscope stage at 37°C in an atmosphere of 10% CO2· Two digital images were recorded by a CCD camera every 20 min: the schlieren image of the cells and the TIRF image of the clusters. After background subtraction, the cluster image was displayed in pseudocolors, overlaid onto the cell images, and recorded as 3 frames on a videotape. The final movies are thus able to summarize a week‐long experiment in less than a minute. These movies and images show that clusters form often shortly after the myoblast fusion but sometimes much later, and the formation takes place very rapidly (a few hours). The clusters have an average lifetime of around a day, much shorter than the lifetime of a typical myotube. The brightest and largest clusters tend to be the longest‐lived. The cluster formation seems to be associated with the contacts of myotubes at the glass substrate, but not with cell‐cell contacts or myoblast fusion into myotubes. New AChR continuously appear in preexisting clusters: after photobleaching, the fluorescence of some clusters recovers within an hour. © 1994 Wiley‐Liss, Inc.
Offline QR Code Authorization Based on Visual Cryptography

It is popular to use mobile phone daily in modern life. Among the numerous applications provided by mobile phone, barcode utility is one of the important branches. Many companies supply barcode tools for mobile phones. For example, Google's mobile Android operating system supports QR codes by natively including the barcode scanner in some models, and the browser supports URI redirection function which allows QR codes to send metadata to the applications on the device. After investigated the supplied applications on mobile devices, most of the products exhibit information accessible to every user. Differ from the traditional applications of QR Code. This paper proposes an offline authentication mechanism for QR codes. Based on the technology of visual cryptography, it is possible to check the identity accessing to the QR codes and to control the permission to the protected data.
Individual Differences in Psychotherapy Change Among Ethnic Minority Patients

iv ACKNOWLEDGEMENTS I would like to thank my advisor, Michael J. Constantino, for his many years of thoughtful and patient guidance and support. I would also like to extend my gratitude to the members of my committee, Ouellett, for their helpful comments and suggestions on all stages of this project. I wish to express my appreciation to David Kraus for making the TOP data available to me and answering my questions during this process. I am grateful to Stephon Proctor and Kristin Huang for their consultation regarding the cultural sensitivity of the discussion. Finally, I would also like to thank Samuel Nordberg for his help in organizing the data and in completing the statistical analyses, as well as his support and patience over the last four years. A special thank you to all those whose support and friendship helped me to stay focused on this project and who have provided me with the encouragement to continue, especially when the end seemed very far away.
A social-spider optimization approach for support vector machines parameters tuning

The choice of hyper-parameters in Support Vector Machines (SVM)-based learning is a crucial task, since different values may degrade its performance, as well as can increase the computational burden. In this paper, we introduce a recently developed nature-inspired optimization algorithm to find out suitable values for SVM kernel mapping named Social-Spider Optimization (SSO). We compare the results obtained by SSO against with a Grid-Search, Particle Swarm Optimization and Harmonic Search. Statistical evaluation has showed SSO can outperform the compared techniques for some sort of kernels and datasets.
Defining Components of the ßcatenin Destruction Complex and Exploring Its Regulation and Mechanisms of Action during Development

Background A subset of signaling pathways play exceptionally important roles in embryonic and post-embryonic development, and mis-regulation of these pathways occurs in most human cancers. One such pathway is the Wnt pathway. The primary mechanism keeping Wnt signaling off in the absence of ligand is regulated proteasomal destruction of the canonical Wnt effector ßcatenin (or its fly homolog Armadillo). A substantial body of evidence indicates that SCFβTrCP mediates βcat destruction, however, an essential role for Roc1 has not been demonstrated in this process, as would be predicted. In addition, other E3 ligases have also been proposed to destroy βcat, suggesting that βcat destruction may be regulated differently in different tissues. Methodology/Principal Findings Here we used cultured Drosophila cells, human colon cancer cells, and Drosophila embryos and larvae to explore the machinery that targets Armadillo for destruction. Using RNAi in Drosophila S2 cells to examine which SCF components are essential for Armadillo destruction, we find that Roc1/Roc1a is essential for regulating Armadillo stability, and that in these cells the only F-box protein playing a detectable role is Slimb. Second, we find that while embryonic and larval Drosophila tissues use the same destruction complex proteins, the response of these tissues to destruction complex inactivation differs, with Armadillo levels more elevated in embryos. We provide evidence consistent with the possibility that this is due to differences in armadillo mRNA levels. Third, we find that there is no correlation between the ability of different APC2 mutant proteins to negatively regulate Armadillo levels, and their recently described function in positively-regulating Wnt signaling. Finally, we demonstrate that APC proteins lacking the N-terminal Armadillo-repeat domain cannot restore Armadillo destruction but retain residual function in negatively-regulating Wnt signaling. Conclusions/Significance We use these data to refine our model for how Wnt signaling is regulated during normal development.
The Direction of Causality between Financial Development and Economic Growth: Evidence from Turkey

In this paper we investigate the causality relationship between financial development and economic growth using available data from 1987:1 to 2006:04 about Turkey. We take total bank credit to private sector and total market capitalization as proxies for financial development and GDP as proxy for economic growth. In this context, Vector Error Correction Model and Impulse Response Functions (IRF) are used to explain possible casual relationships between variables. Empirical findings suggest that there is a bidirectional causality relationship between variables. While the development of the stock market and banking sector has caused economic growth, economic growth has also been brought about by stock market and banking sector developments in Turkey over the same period. Moreover, the contribution of the banking sector to economic growth has been larger than that of the stock market.
The isolated fossil group RX J1119.7+2126

Fossil groups are galaxy structures that probably underwent a nearly complete fusion of all intermediate magnitude galaxies into a single large central dominant galaxy. However, the formation and evolution processes of these structures are still not well understood. In order to test this scenario and its implications we studied the fossil group RXJ1119.7+2126, based on available spectroscopy of the galaxies in the low density large scale region around the fossil group and deep B and R band imaging of its close vicinity and three comparison fields. We used spectroscopic data to investigate the degree of isolation of RXJ1119.7+2126 in terms of bright neighbour galaxies. The imaging data were used to derive the color magnitude relation and select faint galaxies statistically belonging to this structure. The structure appears as a very isolated group exhibiting a red sequence in the color magnitude relation with characteristics close to the red sequences already observed for other fossil groups. All these results can be interpreted consistently in the framework of the building up process generally proposed for fossil groups.
Understanding Terrorism from an Economic perspective

Terrorism has emerged as a major threat to the contemporary society. Nation States are reliant on their counter-terrorism laws for checking terrorism and deterring terrorists. To understand the effectiveness of these counter terrorism laws, it is important to first understand the behaviour of terrorists, so as to comprehend what actions can dissuade terrorist’s behaviour and decision to propagate violence. This paper will first look at behaviour of terrorist in decision making from an economic perspective, then will try to explore if there are any economic determinant of terrorism and finally, since the cost of terrorism is huge in terms of life, property etc, will discuss the status of counter-terrorism legislations in India. In the study it has been shown that terrorists are rational in decision making. It has also been found that economic determinants are not significant in determining terrorism; however, to a certain extent education does have a positive relation with participation in terrorism. Terrorism has huge cost, and hence it is essential to have counter-terrorist legislations. These legislations provides power to the state to deny operating space to terrorists and their supporters, deter them from carrying out terrorist acts, ensure the basic rights of the people, and uphold the Fundamental Rights enshrined in the Constitution.