Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliable diagnostics for coronary artery disease

In the past few decades, machine learning tools have been successfully used in several medical diagnostic problems. While they often significantly outperform expert physicians (in terms of diagnostic accuracy, sensitivity and specificity), they are mostly not being used in practice. One reason for this is that it is difficult to obtain an unbiased estimation of the diagnosis's reliability. We discuss how the reliability of diagnoses is assessed in medical decision-making and propose a general framework for reliability estimation in machine learning, based on transductive inference. We compare our approach with the usual machine-learning probabilistic approach, as well as with classical step-wise diagnostic process, where the reliability of a diagnosis is presented as its post-test probability. The proposed transductive approach is evaluated in a practical problem of the clinical diagnosis of coronary artery disease. Significant improvements over existing techniques are achieved.
Edge Metrics for Visual Graph Analytics: A Comparative Study

Visual graph analytics definitely relies on the use of node and edge metrics to identify salient properties in graphs. Most often, these metrics are turned into useful visual cues, or are used to interactively filter out parts of a graph while querying it, for instance. Along the years, analysts coming from different application domains have designed metrics to serve specific needs. Graph analytics, sometimes also called network science, recently developed as a cross-discipline field developing models shared by numerous application domains such as bio-informatics, social network analysis, web graphs, etc.[4] [10]. As a consequence, we end up finding various metrics in the literature aiming at similar goals; different names and analytics description often hide similarity between two metrics that originated from different fields. We survey a list of edge metrics for graphs and compare their relative value and behaviour, in an effort to organize them into a taxonomy and underline the genuine ingredients in each of them disregarding their origin.
Single nucleotide polymorphisms in ABCC2 associate with tenofovir-induced kidney tubular dysfunction in Japanese patients with HIV-1 infection: a pharmacogenetic study.

BACKGROUND Tenofovir is a widely used antiretroviral drug although it can cause kidney tubular dysfunction (KTD). The aim of this study was to determine the association between polymorphisms in genes encoding drug transporters and KTD in Japanese patients treated with tenofovir. METHODS The association between tenofovir-induced KTD and 14 single nucleotide polymorphisms (SNPs) in the ABCC2, ABCC4, ABCC10, SCL22A6, and ABCB1 genes was investigated in 190 Japanese patients. KTD was diagnosed by the presence of at least 3 abnormalities in the following parameters: fractional tubular resorption of phosphate, fractional excretion of uric acid, urinary β2-microglobulin, urinary α1-microglobulin, and urinary N-acetyl-β-D-glucosaminidase. Genotyping was performed by allelic discrimination using TaqMan 5'-nuclease assays with standard protocols. Associations between genotypes and KTD were tested by univariate and multivariate logistic regression analyses. RESULTS KTD was diagnosed in 19 of the 190 (10%) patients. Univariate and multivariate analyses showed a significant association between KTD and genotype CC at position -24 CC (adjusted odds ratio [OR], 20.08; 95% confidence interval [CI], 1.711-235.7; P= .017) and genotype AA at position 1249 (adjusted OR, 16.21; 95% CI, 1.630-161.1; P= .017) of ABCC2. Multivariate analysis showed higher adjusted OR for patients with both homozygotes (adjusted OR, 38.44; 95% CI, 2.051-720.4; P= .015). ABCC2 haplotype -24T and 1249G was a protective haplotype for KTD (OR, 0.098; 95% CI, . 002-. 603; P= . 003 CONCLUSIONS This is the first study of our knowledge to identify the association between SNPs in ABCC2 and tenofovir-induced KTD in an Asian population. Close monitoring of renal function is warranted in tenofovir-treated patients with these SNPs.
Estimations of Tariff Equivalents for the Services Sectors

Methodological issues arising from the estimation of tariff equivalents of barriers to services trade are very relevant for policy. These equivalents are used extensively to compute welfare gains and resource reallocations associated with partial liberalization of the sector; any measurement errors will strongly affect the estimated gains. Using the Global Trade Analysis Project (GTAP) database, we rely on socalled quantity based methods to derive tariff equivalents from a gravity equation estimated at sectoral level for 9 services sectors and 65 countries. We also estimate trade equations for services using cross section data, and improve on the methodology of Park (2002). We investigate whether relying on cross section rather than panel data leads to differences in the estimated equivalents. Finally, we compare the estimations based on reconstructed and actual data. We conclude that although use of partially reconstructed data (such as GTAP) affects the results, the equivalents obtained are good representations of the magnitude of protection for services in the various countries analyzed, although with larger deviations for developing economies.
Detection of Broken Rotor Bars in Induction Motor Using Derivative Free Kalman Filters

This paper deals with design and implementation of Joint Unscented Kalman filter (JUKF) and Dual Unscented Kalman filter (DUKF) for the detection and monitoring of rotor bar faults in induction motor under simulation studies. A broken rotor bar essentially leads to an increase in rotor resistance of the induction motor. The methodology used is basically model based fault detection in which the problem is treated as one of detection and estimation of parameter variation. An extensive monte carlo simulation study has been carried out to assess the relative performance of the two filters under various operating conditions. The results of the simulation studies show that DUKF is more sensitive to rotor resistance variation over wide range of tuning parameters and gives better performance than JUKF in detecting and estimating the rotor resistance . However DUKF also shows high sensitivity towards load disturbances.
BLyS and APRIL Form Biologically Active Heterotrimers That Are Expressed in Patients with Systemic Immune-Based Rheumatic Diseases1

BLyS and APRIL are two members of the TNF superfamily that are secreted by activated myeloid cells and have costimulatory activity on B cells. BLyS and APRIL share two receptors, TACI and BCMA, whereas a third receptor, BAFF-R, specifically binds BLyS. Both BLyS and APRIL have been described as homotrimeric molecules, a feature common to members of the TNF superfamily. In this study, we show that APRIL and BLyS can form active heterotrimeric molecules when coexpressed and that circulating heterotrimers are present in serum samples from patients with systemic immune-based rheumatic diseases. These findings raise the possibility that active BLyS/APRIL heterotrimers may play a role in rheumatic and other autoimmune diseases and that other members of the TNF ligand superfamily may also form active soluble heterotrimers.
Chemical Properties Associated with Bacterial Wetwood in Red Oaks

Bacterial wetwood is a major cause of value loss in the red oak lumber industry in the United States. Red oak trees in Mississippi, South Carolina, and Florida were sampled and evaluated for certain chemical properties possibly associated with the wetwood condition. Specific variables investigated were pH and concentrations of methane, cations (Na + , Ca +- , K + , and Mg ++ ), nonstructural carbohydrates, and organic acids (acetate, propionate, and butyrate). The degree of bacterial wetwood infection and development was greater in red oaks from Mississippi than from South Carolina as evidenced by increased concentrations of methane, total Na + , total K + , total Ca ++ , and by decreased concentrations of total sugar and reducing sugar. Of all the variables tested, methane concentration was the best indicator of wetwood in living red oak trees at all three locations. pH was not an indicator of wetwood in living trees or in green red oak lumber. of the remaining variables tested, greater concentrations of acetic acid, total K + , and lesser concentrations of nonstructural carbohydrates characterized wetwood-affected trees, but their potential as wetwood indicators depends on wetwood severity, not its mere presence.
A Trust Model Based on Statistical Propagation and Fuzzy Aggregation for Semantic Web

The last layer in semantic Web structure is dedicated to the aspects of trust which deals with uncertainty. In this paper a trust model is proposed which evaluates trust between trustor and trustee. This model is associated with two algorithms, an algorithm for propagation and another for aggregation. The propagation algorithm utilizes statistical techniques and the aggregation algorithm is based on fuzzy analysis. The proposed technique is named max-weight method and experimental results are presented to compare and contrast the method with other techniques. Results of experiments clearly illustrate the effectiveness and the superiority of the proposed approach.
Capacity-equivocation region of a special case of wiretap channel with noiseless feedback

The general wiretap channel with noiseless feedback is first investigated by Ahlswede and Cai, where lower and upper bounds on the secrecy capacity are provided in their work. The upper bound is met with equality only in some special cases. In this paper, we study a special case of the general wiretap channel with noiseless feedback (called non-degraded wiretap channel with noiseless feedback). Inner and outer bounds on the capacity-equivocation region of this special model are provided. The outer bound is achievable if the main channel is more capable than the wiretap channel. The inner bound is constructed especially for the case that the wiretap channel is more capable than the main channel. The results of this paper are further explained via binary and Gaussian examples. Compared with the capacity results for the non-degraded wiretap channel, we find that the security is enhanced by using the noiseless feedback.
See-Through Science : Why Public Engagement Needs to Move Upstream

Spurred on by controversies over BSE, GM crops and nanotechnology, scientists gradually started to involve the public in their work. They looked first to education as the answer, then to processes of dialogue and participation. But these efforts have not yet proved sufficient. Scientists need to find ways of listening to and valuing more diverse forms of public knowledge. Only by opening up innovation processes at an early stage can we ensure that science contributes to the common good. Debates about risk are important. But the public also want answers to the more fundamental questions at stake in any new technology: Who owns it? Who benefits from it? To what purposes will it be directed? In this influential 2004 Demos report, James Wilsdon and Rebecca Willis argued that public engagement had to move upstream. The pamphlet offers practical guidance for scientists, policymakers, research councils businesses and NGOs – anyone who is trying to make engagement work.
The influence of speciation on the toxicity of silver to fathead minnow (Pimephales promelas)

Silver sulfide, silver thiosulfate complex and silver chloride complexes were tested and compared with free silver ion for their toxic effects in 96 h flow through acute toxicity tests using fathead minnows (Pimephales promelas). Thirty day fathead minnow embryo larval tests were conducted with silver sulfide and silver thiosulfate complex. Extensive chemical analyses were performed to quantify actual exposures. Compared with free silver ion, silver chloride complexes were about 300 times less toxic acutely silver sulfide was at least 15,000 times less toxic acutely and silver thiosulfate complex was more than 17,500 times less toxic acutely. The estimated maximum acceptable toxicant concentrations (MATCs) determined from the embryo larval tests with silver sulfide and silver thiosulfate complex were greater than 11 mg/L (as total silver) and greater than 16 but less than 35 mg/L (as total silver), respectively. The MATC previously reported for free silver ion from tests with rainbow trout (Salmo gairdneri) was greater than 0 00009 but less than 0 00017 mg/L. These differences in acute toxicities and the differences of five orders of magnitude in the MATC values are attributed to the influence of chemical speciation on the effects of silver in the aquatic environment. Thus, the speciation of silver is an essential factor in its potential to affect fish and, presumably, other aquatic life, and therefore should be considered in environmental risk assessment.
Model based essential interactions cluster mining in multivariate time

Functional magnetic resonance imaging or functional MRI (fMRI) is a functional neuroimaging procedure using MRI technology that measures brain activity by detecting associated changes in blood flow. The goal of fMRI data analysis is to detect correlations between brain activation and a task the subject performs during the scan. It also aims to discover correlations with the specific cognitive states, such as memory and recognition, induced in the subject. In this system, we propose a novel framework for clustering the essential fMRI signals based on their interactions and also correlation which is generated in a multivariate time series. To formalize this framework we cluster only Important Interactions based on the patient's medical records with the help of Essential Clustering Algorithm. The Essential clusters (EC) are then clustered again based on their dependencies on various brain regions. These EC's are grouped under specific models. The changes detected are mined based on the type of cluster grouped under a certain model. Our method shows that certainly increases the efficiency of the system along with increases in the effectiveness with minimal resource utilization.
Using ontology search in the design of class diagram from business process model

Business process model describes process flow of a business and can be seen as the requirement for developing a software application. This paper discusses a BPM2CD guideline which complements the Model Driven Architecture concept by suggesting how to create a platform-independent software model in the form of a UML class diagram from a business process model. An important step is the identification of UML classes from the business process model. A technique for object-oriented analysis called domain analysis is borrowed and key concepts in the business process model will be discovered and proposed as candidate classes for the class diagram. The paper enhances this step by using ontology search to help identify important classes for the business domain. As ontology is a source of knowledge for a particular domain which itself can link to ontologies of related domains, the search can give a refined set of candidate classes for the resulting class diagram. Keywords—Business Process Model, Model Driven Architecture, Ontology, UML Class Diagram.
The pattern memory of gene-protein networks

In this paper we study the potential of gene-protein interaction networks to store input-output patterns. The central question in this study concerns the memory capacity of a network of a given number of genes and proteins, which interact according to a linear state space model with external inputs. Here it is assumed that to a certain combination of inputs there exists an optimal state of the system, i.e., values of the gene expressions and protein levels, that has been attained externally, e.g., through evolutionary learning. Given such a set of learned optimal input-output patterns, the design question here is to find a sparse and hierarchical network structure for the gene-protein interactions and the gene-input couplings. This problem is formulated as an optimization problem in a linear programming setting. Numerical analysis shows that there are clear scale-invariant continuous second-order phase transitions for the network sparsity as the number of patterns increases. These phase transitions divide the system in three regions with different memory characteristics. It is possible to formulate simple scaling rules for the behavior of the network sparsity. Finally, numerical experiments show that these patterns are stable within a certain finite range around the patterns.
ECG Signal Denoising using Discrete Wavelet Transform

In this paper, wavelet de-noising method has been examined to eliminate noise from the ECG signal. Different thresholding algorithms are analyzed both theoretically and empirically. Ideal ECG signal and noise corrupted ECG signal are evaluated using MATLAB. Removal of noise because of muscle activity is difficult to handle because of the substantial spectral overlap between the ECG and muscle noise. Averaging techniques have been successfully applied to ECG signal for reduction of baseline wander noise. DWT has good ability to decompose the signal and wavelet thresholding is good in removing noise from decomposed signal. We applied wavelet transform on the input vector, thresholded it, inverse transformed it to finally achieve a signal with very low EMG noise. The analyses of thresholding techniques have been compared based on signal to noise ratio. It is observed that “rigrsure” method gives optimum performance.
CNFET based 8-T SRAM cell to improve stability with reduced power consumption

Deviations in local and global process parameters causes increase in threshold voltage (Vt) variation in ultra-short channel devices, such as CMOS. Thus, it is impossible to operate the CMOS based 6-Transistor (6T) SRAM cell bellow 600 mV. Hence, a CNFET based 8T SRAM cell is suggested in this article to alleviate the effect of process fluctuations. Different design metrics of an SRAM cell are accessed for the proposed cell and to show its effectiveness, the design metrics are compared with its conventional counterpart. The proposed cell dissipates less dynamic power (1.2× read power and 1.02× write power) and also consumes 16.17× less power when it is not accessed @ 200 mV compared to conventional CMOS based 8T cell. It depicts its robustness against process fluctuations by showing narrower spread in read power (97.91%), write power (96.35%), and hold power consumption (87.34%) for the same supply voltage. The proposed CNFET based 8T cell shows 59.41% higher read stability @ 400 mV than the CMOS based 8T SRAM cell.
Design implications for networked controllers using web standards in cloud robotics

Computation, networking and controls are in the midst of a true transformation. Previously it was assumed that delays in network communication would inhibit control systems from leveraging networked resources such as remote compute power. Now, industrial and residential applications of the Internet of Things force us to revisit principled deployment and support of distributed sensing and actuation. Web standards provide key resources in the development and support of flexible distributed systems, however the conventional wisdom is that their use adds to the network delay and uncertainty, and makes a difficult control problem intractable. We quantify the effect of an important set of web standards (both formal and informal) in the control of physical systems. The results reported in this paper indicate that although the use of web standards provides an effective path to flexible implementation of distributed control, there are software design choices that impact the observed performance in significant ways.
Omnidirectional Camera Motion Estimation

We present an automatic technique for computing relative ca mer motion and simultaneous omnidirectional image matching. Our technique works for small as well as larg e motions, tolerates multiple moving objects and very large occlusions in the scene. We combine three prin ci les and obtain a practical algorithm which improves the state of the art. First, we show that the correct motion is found much sooner if the tentative matches are sampled after ordering them by the similarity of their descriptors. Secondly, we show that the correct camera motion can be better found by soft voting for t he direction of the motion than by selecting the motion that is supported by the largest set of matches. Fi nally, we show that it is useful to filter out the epipolar geometries which are not generated by points recon structed in front of cameras. We demonstrate the performance of the technique in an experiment with 189 image pairs acquired in a city and in a park. All camera motions were recovered with the error of the motion di rection smaller than 8 ◦, which is 4 % of the 183◦ field of view, w.r. t. the ground truth.
Downscaling Pest Risk Analyses: Identifying Current and Future Potentially Suitable Habitats for Parthenium hysterophorus with Particular Reference to Europe and North Africa

Pest Risk Assessments (PRAs) routinely employ climatic niche models to identify endangered areas. Typically, these models consider only climatic factors, ignoring the ‘Swiss Cheese’ nature of species ranges due to the interplay of climatic and habitat factors. As part of a PRA conducted for the European and Mediterranean Plant Protection Organization, we developed a climatic niche model for Parthenium hysterophorus, explicitly including the effects of irrigation where it was known to be practiced. We then downscaled the climatic risk model using two different methods to identify the suitable habitat types: expert opinion (following the EPPO PRA guidelines) and inferred from the global spatial distribution. The PRA revealed a substantial risk to the EPPO region and Central and Western Africa, highlighting the desirability of avoiding an invasion by P. hysterophorus. We also consider the effects of climate change on the modelled risks. The climate change scenario indicated the risk of substantial further spread of P. hysterophorus in temperate northern hemisphere regions (North America, Europe and the northern Middle East), and also high elevation equatorial regions (Western Brazil, Central Africa, and South East Asia) if minimum temperatures increase substantially. Downscaling the climate model using habitat factors resulted in substantial (approximately 22–53%) reductions in the areas estimated to be endangered. Applying expert assessments as to suitable habitat classes resulted in the greatest reduction in the estimated endangered area, whereas inferring suitable habitats factors from distribution data identified more land use classes and a larger endangered area. Despite some scaling issues with using a globally conformal Land Use Systems dataset, the inferential downscaling method shows promise as a routine addition to the PRA toolkit, as either a direct model component, or simply as a means of better informing an expert assessment of the suitable habitat types.
A Survey : Web Log Mining using Genetic Algorithm

Web mining has become a vast area of Research in last few years. Web Mining Which deals with the extraction of interesting knowledge from logging information produced by web server. In this paper we present a survey generate clusters using a multi objective genetic algorithm. Genetic algorithm is also a very hot area of research. In this paper we will compare the error value between FCM(Fuzzy c-means) and FCM-MOGA(Fuzzy CMeans multi objective Genetic algorithm). Genetic algorithm follows some steps and produce optimize solution. In using GA standard deviation and iteration value also affected. In this paper we have survey various paper based on Web log mining and FCM and GA.