Miracle-dz/UniFAR-FTUs huggingface dataset:
Contourlet based lossy image coder with edge preserving

In this paper we present a lossy image coder based in multiscale and directional contourlet transform which allows edge preserving in decompressed images. The preserving task is made by capture the edge points of an image using a robust and efficient edge detector known as Smallest Univalue Segment Assimilating Nucleus (SUSAN). The resulting edges coordinates are used as a priori knowledge of the positions of points which are mapped to the correspondent position in the transformed domain by exploit the contourlet parent-child relationship. The contourlet points are coded and finally the image is decompressed to measure the quality. The use of contourlet transform allows us to take advantage of the directional and multiscale representation of edges which is showed in the good edge preserving results obtained.
Hydrogen-Rich Saline Attenuated Neuropathic Pain by Reducing Oxidative Stress

Background: Reactive oxygen species (ROS) are often associated with persistent pains such as neuropathic and inflammatory pain. Hydrogen gas can reduce ROS and alleviate cerebral, myocardial, and hepatic ischemia/reperfusion injuries. In the present study, we aim to investigate whether hydrogen-rich saline can reduce neuropathic pain in a rat model of chronic constriction injury (CCI). Methods: Thirty SD rats were randomly divided into three groups: sham group was administered sodium chloride by intrathecal injection (n=10); control groups underwent CCI surgery and were administered sodium chloride by intrathecal injection (n=10); vehicle group underwent CCI surgery and was administered hydrogen-rich saline by intrathecal injection (n=10). Drugs were administered in the dose of 100ul/kg once a day at 0.5 hours before and 1-7 day after CCI surgery. The mechanical thresholds were tested at one day before and 3-14 day after CCI surgery. Results: We found that hydrogen-rich saline significantly elevated the mechanical thresholds of neuropathic pain compared to vehicle (physiologic saline) control in CCI rats (p<0.05); it also decreased the levels of myeloperoxidase, maleic dialdehyde, and protein carbonyl in spinal cord by 7 days post-chronic constriction injury(p<0.05). In addition, hydrogen-rich saline also suppressed the expression of p38-mitogen-activated protein kinase (p38MAPK) and brain-derived neurotrophic factor (BDNF) in the spinal cord by 7 days post-chronic constriction injury (p<0.01, p<0.01, respectively), but had no effect on P2X4R (p>0.05), an ATP receptor. Conclusion: Intrathecal injection of hydrogen-rich saline can decrease oxidative stress and the expression of p38MAPK and BDNF that may contribute to the elevated threshold of neuropathic pain in rat CCI model.
Usefulness of the Framingham Cardiovascular Risk Profile Beyond Its Statistical Performance The Tehran Lipid and Glucose Study

The utility of a risk function in clinical practice is an important concept that has received insufficient attention. The authors evaluated the clinical usefulness of the Framingham risk function (FRF) for cardiovascular disease in a Middle Eastern population (2,640 men and 3,584 women aged 30–74 years) free of cardiovascular disease at baseline in 1999. They calculated the net benefit fraction for treatment of subjects with an estimated 10-year risk of ≥10% and also ≥20%, where the net benefit fraction is a weighted sum of true-positive and false-positive rates divided by incidence, as estimated by Kaplan-Meier analysis. The authors drew a decision curve by plotting the net benefit fraction against a wide range of risk thresholds for treatment. The cumulative incidence of cardiovascular disease was 7.6% and 12.3% in women and men, respectively. The FRF had a C index of 0.832 in women and 0.785 in men with a reasonable calibration. On the basis of the net benefit fraction, about 50% of the incidence in men and women could be appropriately treated by using the 10% threshold; however, the FRF was not useful at the 20% threshold, especially in women. In both genders, usefulness of the FRF was as good as the function derived directly from Tehrani data with the same variables; however, it could be useful in low thresholds for treatment.
Type reconstruction in the presence of polymorphic recursion

We study the problem of type-checking functional programs in three extensions of ML. One distinguishing feature of these extensions is that they allow recursive definitions to be polymorphically typed. Although the motivation for these extensions comes from pragmatic considerations of programming language design, we show that the typability problem for each one of these extensions is polynomial-time equivalent to the Semi-Unification Problem and, therefore, undecidable
Issues with Methods for Scoring Competitors in RoboCup Rescue

As part of the RoboCup yearly competition, participants in the RoboCup Rescue Simulation League compete to solve a simulated urban search and rescue problem. For the last couple of years there has been debate about what metrics are best for selecting the competition winners. We statistically evaluate a variety of the metrics used and propose new ones,with the objective of determining which has the most power in determining the winner and the final rankings of the competitors. We also suggest a variety of convenient changes to the competition that would help increase the accuracy of the rankings.
Stochastic-Based Deep Convolutional Networks with Reconfigurable Logic Fabric

Large-scale convolutional neural network is a fundamental algorithmic building block in many computer vision and artificial intelligence applications that follow the deep learning principle. However, a typically-sized CNN is well known to be computationally intensive. This work presents a novel stochastic-based and scalable hardware architecture and circuit design that computes a large-scale CNN with FPGA. The key idea is to implement all key components of a deep learning CNN, including multi-dimensional convolution, activation, and pooling layers, completely in the probabilistic computing domain in order to achieve high computing robustness, high performance, and low hardware usage. Our approach has three advantages. First, it can achieve significantly lower algorithmic complexity for any given accuracy requirement. For a <inline-formula><tex-math notation="LaTeX">$N$</tex-math><alternatives> <inline-graphic xlink:href="alawad-ieq1-2601326.gif"/></alternatives></inline-formula> dimensional image feature map, we have theoretically proven that a random sample size of <inline-formula><tex-math notation="LaTeX"> $k^* \log (N)$</tex-math><alternatives><inline-graphic xlink:href="alawad-ieq2-2601326.gif"/></alternatives> </inline-formula> is sufficient to achieve no more than 0.05 error at 95 percent confidence level, where <inline-formula><tex-math notation="LaTeX">$k^*$</tex-math><alternatives> <inline-graphic xlink:href="alawad-ieq3-2601326.gif"/></alternatives></inline-formula> is a constant of 510. This computing complexity, when compared with that of conventional multiplier-based architecture, represents on average 8.97<inline-formula><tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:href="alawad-ieq4-2601326.gif"/></alternatives></inline-formula> and 6.98<inline-formula> <tex-math notation="LaTeX">$\times$</tex-math><alternatives> <inline-graphic xlink:href="alawad-ieq5-2601326.gif"/></alternatives></inline-formula> performance improvement for SCNN and Deep SCNN, respectively. Second, this proposed stochastic-based architecture is highly fault-tolerant because the information to be processed is encoded with a large ensemble of random samples. As such, the local perturbations of its computing accuracy will be dissipated globally, thus becoming inconsequential to the final overall results. More interestingly, our measured results have shown that 0.1 percent degradation in computing accuracy of CNN can actually mitigate the well-known overfitting problem. Overall, being highly scalable and energy efficient, our stochastic-based convolutional neural network architecture is well-suited for a modular vision engine with the goal of performing real-time detection, recognition, and segmentation of mega-pixel images, especially those perception-based computing tasks that are inherently fault-tolerant, while still requiring high energy efficiency.
The Research on Algorithm of Greatest Available Logical Time in HLA

The time management service has important influence on performance of distributed simulation in high level architecture (HLA). For the conservative time advance mechanism, the algorithm of greatest available logical time (GALT) is key to decide whether time management service in the interface specification of HLA can be realized successfully. The in transit messages which had been sent by federates but have not been received by run time infrastructure (RTI) needs taking into consideration in the computation of GALT. In this paper, an algorithm for GALT is presented based on basic principle of Mattern's algorithm which can catch in transit messages and stature measurement algorithm. In the end, the experiment is carried out to evaluate algorithm for GALT computation.
Intelligent multivariate sales forecasting using wrapper approach and neural networks

This research investigated a retail sales forecasting problem based on early sales. An effective multivariate intelligent decision-making (MID) model is developed to handle this problem by integrating a data preparation and preprocessing module, a harmony search-wrapper-based variable selection (HWVS) module and a multivariate intelligent forecaster (MIF) module. The HWVS module selects out the optimal input variable subset from given candidate inputs as the inputs of MIF. The MIF is proposed to model the relationship between the selected input variables and the sales volumes of retail products, and then employed to forecast the sales volumes of retail products. Experiments were conducted to evaluate the effectiveness of the proposed model. Results show that it is statistically significant that the proposed MID model can provide superior forecasts to ELM-based model and generalized linear model.
Decision Making in Political Systems: Veto Players in Presidentialism, Parliamentarism, Multicameralism and Multipartyism

The article compares different political systems with respect to one property: their capacity to produce policy change. I define the basic concept of the article, the ‘veto player’: veto players are individual or collective actors whose agreement (by majority rule for collective actors) is required for a change of the status quo. Two categories of veto players are identified in the article: institutional and partisan. Institutional veto players (president, chambers) exist in presidential systems while partisan veto players (parties) exist at least in parliamentary systems. Westminster systems, dominant party systems and single-party minority governments have only one veto player, while coalitions in parliamentary systems, presidential or federal systems have multiple veto players. The potential for policy change decreases with the number of veto players, the lack of congruence (dissimilarity of policy positions among veto players) and the cohesion (similarity of policy positions among the constituent units of each veto player) of these players. The veto player framework produces results different from existing theories in comparative politics, but congruent with existing empirical studies. In addition, it permits comparisons across different political and party systems. Finally, the veto player framework enables predictions about government instability (in parliamentary systems) or regime instability (in presidential systems); these predictions are supported by available evidence.
Maximization of entropy in a two layer asymmetry-coupled network

More real-world complex systems contain mass of data and are composed of not only a single layer network. A fundamental problem on these systems is how to get maximum information entropy but economizing energy. Here we explore this problem on a two layer asymmetry-coupled network. On the assumption that a node could couple with more than one counterparts on the other layer network, we found for a given average degree, the entropy of the system varies differently according to the influence coefficient of asymmetry depending on the number of node's counterparts. We also show that the energy expanded increase if nodes have fewer counterparts or in a not obvious community structure of each layer network, which give a clue to maximizing entropy while consuming least energy on this two layer asymmetry-coupled network.
Empoderamiento mediático mediante e-learning. Diseño y validación de una escala

Las actuales facilidades para la educacion por medios online permiten adquirir las competencias profesionales y personales demandadas para el siglo XXI. En este articulo se estudia el empoderamiento mediatico de los alumnos como ciudadanos que usan multiples medios de comunicacion. Se establece la primera escala para la medida de dicho empoderamiento, analizando el ejercicio de la ciudadania activa y el uso de diversos medios de comunicacion, como habilidades adquiridas mediante e-learning. El estudio se realizo con antiguos estudiantes de la Universitat Oberta de Catalunya (n=544), construyendose una escala consistente y fiable para las dos subescalas que la conforman: ciudadania activa (5 items, α=0,93) y uso diversificado de los medios (3 items, α=0,95).
A Review on Video/Image Authentication and Temper Detection Techniques

With the innovations and development in sophisticated video editing technology and a wide spread of video information and services in our society, it is becoming increasingly significant to assure the trustworthiness of video information. Therefore in surveillance, medical and various other fields, video contents must be protected against attempt to manipulate them. Such malicious alterations could affect the decisions based on these videos. A lot of techniques are proposed by various researchers in the literature that assure the authenticity of video information in their own way. In this paper we present a brief survey on video authentication techniques with their classification. These authentication techniques are generally classified into following categories: digital signature based techniques, watermark based techniques, and other authentication techniques.
The gene evolution model and computing its associated probabilities

Phylogeny is both a fundamental tool in biology and a rich source of fascinating modeling and algorithmic problems. Today's wealth of sequenced genomes makes it increasingly important to understand evolutionary events such as duplications, losses, transpositions, inversions, lateral transfers, and domain shuffling. We focus on the gene duplication event, that constitutes a major force in the creation of genes with new function [Ohno 1970; Lynch and Force 2000] and, thereby also, of biodiversity. We introduce the probabilistic gene evolution model, which describes how a gene tree evolves within a given species tree with respect to speciation, gene duplication, and gene loss. The actual relation between gene tree and species tree is captured by a reconciliation, a concept which we generalize for more expressiveness. The model is a canonical generalization of the classical linear birth-death process, obtained by replacing the interval where the process takes place by a tree. For the gene evolution model, we derive efficient algorithms for some associated probability distributions: the probability of a reconciled tree, the probability of a gene tree, the maximum probability reconciliation, the posterior probability of a reconciliation, and sampling reconciliations with respect to the posterior probability. These algorithms provides the basis for several applications, including species tree construction, reconciliation analysis, orthology analysis, biogeography, and host-parasite co-evolution.
Sparsity-Constrained fMRI Decoding of Visual Saliency in Naturalistic Video Streams

Naturalistic stimuli such as video watching have been increasingly used in functional magnetic resonance imaging (fMRI)-based brain encoding and decoding studies since they can provide real and dynamic information that the human brain has to process in everyday life. In this paper, we propose a sparsity-constrained decoding model to explore whether bottom-up visual saliency in continuous video streams can be effectively decoded by brain activity recorded by fMRI, and to examine whether sparsity constraints can improve visual saliency decoding. Specifically, we use a biologically-plausible computational model to quantify the visual saliency in video streams, and adopt a sparse representation algorithm to learn the atomic fMRI signal dictionaries that are representative of the patterns of whole-brain fMRI signals. Sparse representation also links the learned atomic dictionary with the quantified video saliency. Experimental results show that the temporal visual saliency in video stream can be well decoded and the sparse constraints can improve the performance of fMRI decoding models.
Risk Premia in the Term Structure of Swaps in Pesetas

Some characteristics of the term structure in interest rate swap (IRS) markets are influenced by the own idiosyncrasy of this financial instrument, which could explain the rejection of the Expectations Hypothesis, we present evidence supporting the existence of significant, time-varying risk premia. We then focus on characterizing some propreties of realized, ex-pst term-premia, and provide explanatory variables for them. We pay particular attention to the extent to which the levels of markets risk, default risk and liquidity risk explain the time evolution of risk premia at different maturities.
Web User Click Intention Prediction by Using Pupil Dilation Analysis

We propose a novel approach for predicting Web user click intention, using pupil dilation data generated by an eye-tracking device as input. Our goal is to determine if this variable is useful to differentiate choice and no-choice states, and if so, to generate a classification model for predicting choice understood as a click. For this, we performed an experiment with 25 healthy subjects in which gaze position and pupil size was recorded while users choose between several elements on a simulated Web site. Our results show that there is a statistical difference between pupil sizes of chosen elements compared with no chosen ones. Furthermore, we generated a click-intention prediction model, based on Artificial Neural Networks, which obtained an 82% accuracy. These results suggest that this variable could be used from a Web Intelligence point of view as a proxy of Web user behaviour, in order to generate an online recommender to improve Web site structure and content.
Coronary computed tomography angiography in planning of percutaneous coronary interventions in bifurcation lesions – study design and rationale

Percutaneous treatment of coronary bifurcations is a complex issue due to numerous possible techniques and high risk of complications. Because of increasing interest in non-invasive imaging in interventional cardiology and growing quality of obtained images, we designed a prospective, randomized, single-blinded trial to evaluate the role of coronary computed tomography angiography (CCTA) in the planning of percutaneous coronary interventions (PCI) of bifurcation lesions. Eighty eligible patients scheduled for PCI of bifurcations in stable coronary artery disease will undergo additional CCTA examination and will be randomized 1: 1 to either planning of PCI using angiography and CCTA or to PCI planning with use of angiography alone. Primary endpoints will include PCI strategy (one or two stents), technique, size of implanted stents and direct angiographic effect of the procedure. Immediate PCI effect measured with intravascular ultrasound (IVUS) and the effect on fractional flow reserve (FFR) in the side branch (in a subgroup of patients), as well as plaque morphology assessed in CCTA, patient radiation exposure and amount of contrast will be compared in secondary analysis. The study is intended to clarify the influence of CCTA analysis on the technique and direct effect of PCI of bifurcations and to provide evidence on the relevance of performing a CCTA scan prior to PCI of bifurcation lesions.
Predictive Feed Forward Stereo Processing

This paper describes the preliminary stages in the development of a predictive feed-forward (PFF) stereo based tracking module. The object of the module is to exploit the spatio-temporal coherence that exists in a sequence of stereo images in the context of providing a visual control mechanism for a mobile vehicle with uncertainty in position. PFF provides a method by which the representation of a 3D scene can be maintained and evolved over time. Furthermore, quickening strategies can utilise the spatio-temporal coherence by exploiting previously obtained depth values and approximate trajectory information in order to accelerate the process that actually achieves the stereo correspondences. Much research in computer vision has been developed in snapshot mode, concentrating attention on a single image or a small number of frames obtained either in synchrony or from a short movie sequence, the computational overheads involved in the analysis of a long sequence of images having proved prohibitive. It has been assumed, albeit implicitly, that algorithms developed in this way would, given appropriate parallel computer architectures, eventually be able to perform in real time on continuous image sequences. That is, the whole process involved in the recovery of scene descriptions would begin afresh upon each image frame and that some [not fully defined] extra module would be responsible for maintaining an evolving model of the environment based upon these descriptions. In the context of the control of a mobile robotic vehicle it is important to distinguish between the twin goals of obtaining an accurate model of the environment and determining the current position within it. Hence dynamic vision can be decomposed into [at least] two important modules: 1) The maintenance of an accurate and, as far as possible, topologically complete scene model. This will include: the combination of multiple views [1,2] to give more complete and robust data; the identification of and inclusion of novel (not previously seen) features; the determination of free space within which the robot can move. 2) The use of visual tracking to provide the control signal required to navigate an autonomous vehicle through an unstructured/partially structured environment using as beacons a subset of the scene features (perhaps identified outside the tracking module itself). The visual through-put and temporal response required by each task is very different. For example, when using visual feed back as a control mechanism it will be necessary to provide a much higher sample rate than that at which the model of the environment needs to be updated. Furthermore, the actual elapsed time
Tumor Necrosis Factor-α as a Diagnostic Marker for Neonatal Sepsis: A Meta-Analysis

Neonatal sepsis (NS) is an important cause of mortality in newborns and life-threatening disorder in infants. The meta-analysis was performed to investigate the diagnosis value of tumor necrosis factor-α (TNF-α) test in NS. Our collectible studies were searched from PUBMED, EMBASE, and the Cochrane Library between March 1994 and August 2013. Accordingly, 347 studies were collected totally, in which 15 articles and 23 trials were selected to study the NS in our meta-analysis. The TNF-α test showed moderate accuracy of the diagnosis of NS both in early-onset neonatal sepsis (sensitivity = 0.66, specificity = 0.76, Q∗ = 0.74) and in late-onset neonatal sepsis (sensitivity = 0.68, specificity = 0.89, Q∗ = 0.87). We also found the northern hemisphere group in the test has higher sensitivity (0.84) and specificity (0.83). A diagnostic OR analysis found that the study population may be the major reason for the heterogeneity. Accordingly, we suggest that TNF-α is also a valuable marker in the diagnosis of NS.