Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bayesian Approach to Shape Reconstruction of a Compact Object from a Few Number of Projections

Image reconstruction in X ray tomography consists in determining an object from its projections. In many applications such as non destructive testing, we look for an image who has a constant value inside a region (default) and another constant value outside that region (homogeneous region surrounding the default). The image reconstruction problem becomes then the determination of the shape of that region. In this work we model the object (the default region) as a polygonal disc and propose a new method for the estimation of the coordinates of its vertices directly from a very limited number of its projections. Keywords: Computed Imaging, Tomography, Shape reconstruction, Non destructive testing, Regularization, Bayesian estimation, Deformable contours.
Kernel Fisher Discriminants and Kernel Nearest Neighbor Classifiers: A Comparative Study for Large-Scale Learning Problems

One of solutions for kernel Fisher discriminants (KFDs) to solve the large-scale learning problems is that all the training samples in a class are covered by multiple hyperspheres one by one, and each sphere should include as many samples from the class as possible. In that way, the KFDs can be carried out in some relatively lower dimensional kernel space. This paper clarifies the fact that a nonlinearly separable dataset in the input space does not certainly become linearly separable in the kernel space. We thus propose a kernel nearest neighbor classification method, i.e., a sample is labeled according to the minimum distance between it and the surfaces of the existing kernels. The experimental results for the letter and the handwritten digit recognitions show that the presented method is quite effective for solving the large-scale learning problems.
Airborne Lidar: Advances in Discrete Return Technology for 3D Vegetation Mapping

Conventional discrete return airborne lidar systems, used in the commercial sector for efficient generation of high quality spatial data, have been considered for the past decade to be an ideal choice for various mapping applications. Unlike two-dimensional aerial imagery, the elevation component of airborne lidar data provides the ability to represent vertical structure details with very high precision, which is an advantage for many lidar applications focusing on the analysis of elevated features such as 3D vegetation mapping. However, the use of conventional airborne discrete return lidar systems for some of these applications has often been limited, mostly due to relatively coarse vertical resolution and insufficient number of multiple measurements in vertical domain. For this reason, full waveform airborne sensors providing more detailed representation of target vertical structure have often been considered as a preferable choice in some areas of 3D vegetation mapping application, such as forestry research. This paper presents an overview of the specific features of airborne lidar technology concerning 3D mapping applications, particularly vegetation mapping. Certain key performance characteristics of lidar sensors important for the quality of vegetation mapping are discussed and illustrated by the advanced capabilities of the ALTM-Orion, a new discrete return sensor manufactured by Optech Incorporated. It is demonstrated that advanced discrete return sensors with enhanced 3D mapping capabilities can produce data of enhanced quality, which can represent complex structures of vegetation targets at the level of details equivalent in some aspects to the content of full waveform data. It is also shown that recent advances in conventional airborne lidar technology bear the potential to create a new application niche, where high quality dense point clouds, enhanced by fully recorded intensity for multiple returns, may provide sufficient information for modeling and analysis, which have traditionally been applied mostly to full waveform data.
Win Some Lose Some? Evidence from a Randomized Microcredit Program Placement Experiment by Compartamos Banco

Theory and evidence have raised concerns that microcredit does more harm than good, particularly when offered at high interest rates. We use a clustered randomized trial, and household surveys of eligible borrowers and their businesses, to estimate impacts from an expansion of group lending at 110% APR by the largest microlender in Mexico. Average effects on a rich set of outcomes measured 18-34 months post-expansion suggest some good and little harm. Other estimators identify heterogeneous treatment effects and effects on outcome distributions, but again yield little support for the hypothesis that microcredit causes harm.
Trends in Distance Education Research: A Content Analysis of Journals 2009-2013.

This study intends to explore the current trends in the field of distance education research during the period of 2009-2013. The trends were identified by an extensive review of seven peer reviewed scholarly journals: The American Journal of Distance Education (AJDE), Distance Education (DE), The European Journal of Open, Distance and e-Learning (EURODL), The Journal of Distance Education (JDE), The Journal of Online Learning and Technology (JOLT), Open Learning: The Journal of Open, Distance and e-Learning (OL) and The International Review of Research in Open and Distributed Learning (IRRODL). A total of 861 research articles was reviewed. Mainly content analysis was employed to be able to analyze the current research. Also, a social network analysis (SNA) was used to interpret the interrelationship between keywords indicated in these articles. Themes were developed and the content of the articles in the selected journals were coded according to categories derived from earlier studies. The results were interpreted using descriptive analysis (frequencies) and social network analysis. The reporting of the results were organized into the following categories: research areas, theoretical and conceptual frameworks, variables, methods, models, strategies, data collection and analysis methods, and the participants. The study also identified the most commonly used keywords, and the most frequently cited authors and studies in distance education. The findings obtained in this study may be useful in the exploration of potential research areas and identification of neglected areas in the field of distance education.
The Effects of Commodity Price Stabilization Programs

In this paper, the authors analyze the effects of commodity programs in which the government attempts to support and stabilize price through open market purchases and sales. Specifically, they as sess the effects of such programs on the U.S. soybean market using a rational-expectations model that allows for private storage, expected -price-responsive production, and arbitrary support and release price s. A salient feature of the model is that private storage and product ion behavior adjusts to changes in government policy. Stochastic simu lations of the market model demonstrate that price stabilization prog rams can reduce long-run market price and destabilize producer revenu e. Copyright 1988 by American Economic Association.
P-Type ATPase TAT-2 Negatively Regulates Monomethyl Branched-Chain Fatty Acid Mediated Function in Post-Embryonic Growth and Development in C. elegans

Monomethyl branched-chain fatty acids (mmBCFAs) are essential for Caenorhabditis elegans growth and development. To identify factors acting downstream of mmBCFAs for their function in growth regulation, we conducted a genetic screen for suppressors of the L1 arrest that occurs in animals depleted of the 17-carbon mmBCFA C17ISO. Three of the suppressor mutations defined an unexpected player, the P-type ATPase TAT-2, which belongs to the flippase family of proteins that are implicated in mediating phospholipid bilayer asymmetry. We provide evidence that TAT-2, but not other TAT genes, has a specific role in antagonizing the regulatory activity of mmBCFAs in intestinal cells. Interestingly, we found that mutations in tat-2 also suppress the lethality caused by inhibition of the first step in sphingolipid biosynthesis. We further showed that the fatty acid side-chains of glycosylceramides contain 20%–30% mmBCFAs and that this fraction is greatly diminished in the absence of mmBCFA biosynthesis. These results suggest a model in which a C17ISO-containing sphingolipid may mediate the regulatory functions of mmBCFAs and is negatively regulated by TAT-2 in intestinal cells. This work indicates a novel connection between a P-type ATPase and the critical regulatory function of a specific fatty acid.
Mobile Cloud Service Selection using Back Propagation Neural Network

Cloud computing is a paradigm in high performance computing, focuses on provisioning ubiquitous computing with the help of Software and/or Hardware Virtualization. In Mobile Cloud Computing (MCC), mobile/portable devices access cloud resources through wireless communication(GPRS/3G/WiFi etc). MCC enhances the mobility of the cloud user which solves cloud computing issues such as Unreliability, Quality-of-Service (QoS), etc. Recently QoS has emerged as a one of the challenging issue in MCC which impact to the large number of mobile users and businesses. The QoS in MCC degrades mainly due to its limited bandwidth, network congestion, user mobility, etc. In this paper, we have proposed a mobile cloud computing framework that facilitates the mobile client to access cloud services with a high degree of QoS based on the network condition of the connection. We proposed a new QoS based mobile cloud computing framework . Back Propagation Neural Network (BPNN) is being used for predicting and selecting appropriate cloud service for the mobile client. We have implemented the proposed framework taking QoS parameters: Packet Delivery Ratio (PDR), Transmission Rate, and Delay in a mobile cloud computing environment. At the end, we have compared our model with the random selection approach and it shows that the proposed model gives better performance.
Optimal , Nonmasking Fault-Tolerant Recon guration of Trees and Rings

We design two programs that maintain the processes of an arbitrary distributed system in a rooted spanning tree and in a unidirectional ring, respectively, in the presence of fail-stop failures and repairs of both processes and communication channels. Our programs are notable as they (i) are fully distributed, (ii) have optimal time complexity, and (iii) demonstrate two di erent approaches to designing nonmasking fault-tolerant programs.
Mobile Database System: Role of Mobility on the Query Processing

The rapidly expanding technology of mobile communication will give mobile users capability of accessing information from anywhere and any time. The wireless technology has made it possible to achieve continuous connectivity in mobile environment. When the query is specified as continuous, the requesting mobile user can obtain continuously changing result. In order to provide accurate and timely outcome to requesting mobile user, the locations of moving object has to be closely monitored. The objective of paper is to discuss the problem related to the role of personal and terminal mobility and query processing in the mobile environment.
Multi_Objective Solid Transportation Problem with Interval Cost in Source and Demand Parameters

Abstract : In this paper, a solution procedure has been given for the Multi-Objective Interval Solid Transportation Problem under stochastic environment where the cost coefficients of the objective functions have been taken as random variables whereas the source availability, destination demand and conveyance capacities have been taken as stochastic intervals by the decision makers. The problem has been transformed into a classical multi-objective transportation problem where the multiple objective functions are minimized by using fuzzy programming approach. Numerical examples are provided to illustrate the approach.
The Effects of Price Uncertainty on Housing Demand: Empirical Evidence from U.S. Markets

This paper examines how price risk affects housing demand. It identifies two relevant channels: a financial risk effect that reduces demand, and a hedging effect that increases demand since current homes may hedge future housing costs. The latter dominates when hedging incentives are strong, namely when the likelihood of moving up the housing ladder is high and the tendency to move across markets is low. For households with weak hedging incentives, the paper finds negative effects of price risk on the timing and size of home purchases, but positive effects for households with strong hedging incentives.
Sign language recognition through fusion of 5DT data glove and camera based information

Hand gestures serve as primary tools for man-machine interaction. Hand Gesture Recognition System provides a natural, innovative and modern way on non verbal communication. A wide area of applications in Human Computer Interaction and Sign Language Recognition are available over last decade. This paper is a part of the project that aims at designing a real time system that would recognize sign language accurately. The gestures are recognized through camera based and 5DT data glove based systems respectively and these are combined to increase the recognition rate. The proposed fusion algorithm provides a high recognition rate as compared to discrete approaches.
Image restoration using prior information physics model

Images captured in haze weather suffer from serious degradation of color and contrast due to incident light scattering and absorption. However, most of the existing methods which are based on depth information could not get satisfactory haze removal effect. In this paper, a simple but effective method is presented - image restoration using improved dark channel prior. The dark channel prior is based on a large number of statistical information. Most local patches in haze-free outdoor image contain some pixels which have very low intensities in at least one color channel. Using this prior with the haze image model, we can recover a high quality haze-free image. Image or video taken in haze day is restored using guided filtering finally. The simulation experiments based on Matlab demonstrate that the method is easy to use and able to improve quality of the images in haze efficiently. Meantime, the method can meet certain practical requirements.
N-acetyltransferase 2: Slow, intermediate or fast? A booming question of the molecular epidemiology in cancer research

Throughout history, humanity has referred to reactions occurring with food, plants and, recently, medicines or drugs. The increase in pulmonary tuberculosis cases and the availability of treatment showed that genetic human differences can interfere in the capacity to metabolize drugs. There are remarkable genetic polymorphisms of N-acetyltransferase 2 (NAT2) activity that have been associated with different levels of susceptibility to developing many kinds of cancers. This review considers the field as an open window for the application of molecular epidemiology tools that led to the development of pharmacogenomics. We cover historical data and the most recent knowledge about NAT2 genetic polymorphisms and its distribution in different populations, which is an important concept being incorporated in epidemiological studies of cancer risk. We present up to date information about these studies, including meta-analysis based on the NAT2 distribution in different types of cancer. A critical broad at advances in NAT2 research, high-lighting recent studies related to NAT2 alleles in cancer susceptibility. Although there are multifactorial aspects involved in cancer risk, the variability in NAT2 allelic frequency can be related to carcinogenesis through alterations in the metabolic rate after exposure to carcinogens.
A note on conditional arbitrage-free maximum entropy densities for simulative option pricing

In this note we present a simple method to include the no-arbitrage condition into the derivation of conditional densities using the principle of maximum entropy. For the case of identically and independently distributed returns, we easily derive that the whole process estimated that way is arbitrage free. Such a process may be directly used for simulative derivation of option prices.
A Base Station Workload-Aware Dynamic Pricing Scheme for Mobile Internet Access

The ever-increasing traffic created by mobile Internet users has challenged ISPs to implement fair pricing scheme. Time-based pricing schemes could be used to address this problem giving users the opportunity to choose when they desire to spend their own resources to access the Internet. However, distinct base stations that make up a cellular data network often do not experience the same workload over time. Thus, a time-based pricing approach could create an undesirable situation to users who happen to be in a region covered by a base station that is currently overloaded. This paper presents a workload-aware dynamic time-based pricing scheme which uses historical workload of each base station to differentiate prices in order to foster the geographically distribution of the Internet accesses over the ISP's network. We evaluate the proposed pricing scheme via simulation using some human mobility models, and compare it with two other state-of-the-art pricing schemes. Our experimental results show that the proposed approach is able to better redistribute the network workload among a larger number of base stations in different human mobility models, when compared to the alternatives. This allows ISP's to accommodate a larger number of users since our approach help ISP to avoid the overload of base station resources.
Oil Spill Detection in Glint-Contaminated Near-Infrared MODIS Imagery

We present a methodology to detect oil spills using MODIS near-infrared sun glittered radiance imagery. The methodology was developed by using a set of seven MODIS images (training dataset) and validated using four other images (validation dataset). The method is based on the ratio image R = L'GN/LGN, where L'GN is the MODIS-retrieved normalized sun glint radiance image and LGN the same quantity, but obtained from the Cox and Munk isotropic (independent of wind direction) sun glint model. We show that in the R image, while clean water pixel values tend to one, oil spills stand out as anomalies. Moreover, we provide a criterion to distinguish between positive and negative oil-water contrast. A pixel in an R image is classified as a potential oil spill or water via a variable threshold Rs as a function of L'GN, where the threshold values are obtained from the slicks of our training dataset. Two different fitting curves are provided for Rs, according to the contrast sign. The selection of the correct fitting curve is based on the contrast type, resulting from the criterion above. Results indicate that the thresholding is able to isolate the spills and that the spills of the validation dataset are successfully detected. Spurious look-alike features, such as clouds, and other non-spill features, e.g., large areas at the glint region border, are also detected as oil spills and must be eliminated. We believe that our methodology represents a novel and promising, though preliminary, approach towards automatic oil spill detection in optical satellite images.
The continuum of psychotic symptoms in the general population: a cross-national study.

OBJECTIVE To identify the cross-national prevalence of psychotic symptoms in the general population and to analyze their impact on health status. METHOD The sample was composed of 256,445 subjects (55.9% women), from nationally representative samples of 52 countries worldwide participating in the World Health Organization's World Health Survey. Standardized and weighted prevalence of psychotic symptoms were calculated in addition to the impact on health status as assessed by functioning in multiple domains. RESULTS Overall prevalences for specific symptoms ranged from 4.80% (SE = 0.14) for delusions of control to 8.37% (SE = 0.20) for delusions of reference and persecution. Prevalence figures varied greatly across countries. All symptoms of psychosis produced a significant decline in health status after controlling for potential confounders. There was a clear change in health impact between subjects not reporting any symptom and those reporting at least one symptom (effect size of 0.55). CONCLUSIONS The prevalence of the presence of at least one psychotic symptom has a wide range worldwide varying as much as from 0.8% to 31.4%. Psychotic symptoms signal a problem of potential public health concern, independent of the presence of a full diagnosis of psychosis, as they are common and are related to a significant decrement in health status. The presence of at least one psychotic symptom is related to a significant poorer health status, with a regular linear decrement in health depending on the number of symptoms.
Hand recognition using texture histograms a proposed technique for image acquisition and recognition of the human palm

This paper presents a technique for biometric identification based on image acquisition of the palm of the human hand. A computer system called Palm Print Authentication System (PPAS) was implemented using the technique exposed, it identifies human hand palm by processing image data through texture identification and geometrical data by employing the Local Binary Pattern (LBP) method. The methodology proposed has four steps: image acquisition; image pre-processing (normalization), and segmentation for biometric extraction and hand recognition. The technique has been tested utilizing 50 different images and the tests have proven promising results, showing that the approach is not only robust but also quite efficient.
Nonparametric Approaches to Auctions

This chapter discusses structural econometric approaches to auctions. Remarkably, much of what can be learned from auction data can be learned without restrictions beyond those derived from the relevant economic model. This enables us to take a nonparametric perspective in discussing how the structure of auction models can be combined with observables to uncover (or test hypotheses about) primitives of interest in auction markets. We focus on first-price sealed-bid and ascending auctions, including extensions to Dutch auctions, Internet auctions, multi-unit auctions, and multi-object auctions. We consider a wide range of underlying structures of bidder demand and information, as well as a variety of types of data one may encounter in applications. We discuss identification and testable restrictions of these models and present a variety of estimation approaches.
Macroeconomic Volatility and Terms of Trade Shocks

This paper explores the effect of terms of trade volatility on macroeconomic volatility using a panel of 71 countries from 1971–2005. It finds that terms of trade volatility has a statistically significant and positive impact on the volatility of output growth and inflation, although the magnitudes of these effects depend on the policy framework and the structure of markets. Specifically, adopting a more flexible exchange rate tends to ameliorate the effect of terms of trade shocks on macroeconomic volatility. The paper also finds some evidence that a monetary policy regime that focuses on low inflation helps to moderate the volatility of output and inflation in the face of a volatile terms of trade. The same is true of financial market development in the case of output volatility. Using data on the expenditure components of GDP, the channels through which terms of trade shocks affect output are examined. The results suggest that terms of trade volatility has its largest effect on the volatility of consumption, exports and imports. There is evidence to suggest that greater financial market development helps to mitigate the effect of terms of trade volatility on consumption volatility, while monetary policy that focuses on low inflation is associated with lower volatility of imports.