Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical models for estimating herd prevalence and test accuracy in the absence of a gold standard

A common assumption made in studies involving two or more binary diagnostic tests in the absence of a gold standard is one of conditional independence among tests given disease status. Although reasonable in some cases, often this assumption is untenable or untested and may lead to biased results. We proposed a class of hierarchical models for the purpose of estimating the herd-level prevalence distribution and the accuracies of two tests in the absence of a gold standard when several exchangeable populations with differing disease prevalence are available for sampling, relaxing the assumption of conditional independence between tests. The models are used to estimate the prevalence of bovine brucellosis in Mexican cow herds and to estimate the error rates of two tests for the detection of swine pneumonia.
Undergraduate Students' Emotional Response andCharacteristics of Receptiveness as a Lecture Voice toHoarse and Normal Reading Voice

The purpose of this study is to identify undergraduate students' emotional response and characteristics of receptiveness as a lecture voice to hoarse and normal reading voice. A hoarse voice sample was collected from a 30 year-old female with functional voice disorder and normal voice sample was collected from a female with same age without voice problems. After listening the voice samples, undergraduate students filled out the adjective checklist and responded to the questionnaire about their feelings as a lecture voice. The results showed that the students had negative emotions to hoarse voice and preferred normal voice to a hoarse voice as a lecture voice. Also, these results pointed out that professors’ voice quality in class is important and prevention and care of voice problem(s) are necessary.
IKKβ-Targeted Anti-Inflammatory Activities of a Butanol Fraction of Artificially Cultivated Cordyceps pruinosa Fruit Bodies

The inhibitory activities of the Cordyceps pruinosa butanol fraction (Cp-BF) were investigated by determining inflammatory responses of lipopolysaccharide (LPS)-treated RAW264.7 macrophage cells and by evaluating HCl/ethanol (EtOH)-triggered gastric ulcers in mice. The molecular mechanisms of the inhibitory effects of Cp-BF were investigated by identifying target enzymes using biochemical and molecular biological approaches. Cp-BF strongly inhibited the production of NO and TNF-α, release of reactive oxygen species (ROS), phagocytic uptake of FITC-dextran, and mRNA expression levels of interleukin (IL)-6, inducible NO synthase (iNOS), and tumour necrosis factor-alpha (TNF)-α in activated RAW264.7 cells. Cp-BF also strongly downregulated the NF-κB pathway by suppressing IKKβ according to luciferase reporter assays and immunoblot analysis. Furthermore, Cp-BF blocked both increased levels of NF-κB-mediated luciferase activities and phosphorylation of p65/p50 observed by IKKβ overexpression. Finally, orally administered Cp-BF was found to attenuate gastric ulcer and block the phosphorylation of IκBα induced by HCl/EtOH. Therefore, these results suggest that the anti-inflammatory activity of Cp-BF may be mediated by suppression of IKKα and its downstream NF-κB activation. Since our group has established the mass cultivation conditions by developing culture conditions for Cordyceps pruinosa, the information presented in this study may be useful for developing new anti-inflammatory agents.
A Unifying Framework for Local Throughput in Wireless Networks

With the increased competition for the electromagnetic spectrum, it is important to characterize the impact of interference in the performance of a wireless network, which is traditionally measured by its throughput. This paper presents a unifying framework for characterizing the local throughput in wireless networks. We first analyze the throughput of a probe link from a connectivity perspective, in which a packet is successfully received if it does not collide with other packets from nodes within its reach (called the audible interferers). We then characterize the throughput from a signal-to-interference-plus-noise ratio (SINR) perspective, in which a packet is successfully received if the SINR exceeds some threshold, considering the interference from all emitting nodes in the network. Our main contribution is to generalize and unify various results scattered throughout the literature. In particular, the proposed framework encompasses arbitrary wireless propagation effects (e.g, Nakagami-m fading, Rician fading, or log-normal shadowing), as well as arbitrary traffic patterns (e.g., slotted-synchronous, slotted-asynchronous, or exponential-interarrivals traffic), allowing us to draw more general conclusions about network performance than previously available in the literature.
Towards an integrated view of Wnt signaling in development

Wnt signaling is crucial for embryonic development in all animal species studied to date. The interaction between Wnt proteins and cell surface receptors can result in a variety of intracellular responses. A key remaining question is how these specific responses take shape in the context of a complex, multicellular organism. Recent studies suggest that we have to revise some of our most basic ideas about Wnt signal transduction. Rather than thinking about Wnt signaling in terms of distinct, linear, cellular signaling pathways, we propose a novel view that considers the integration of multiple, often simultaneous, inputs at the level of both Wnt-receptor binding and the downstream, intracellular response.
Advanced virtual texture topics

A virtual texture is a mip-mapped texture used as cache to allow a much higher resolution texture to be emulated for real-time rendering, while only partly residing in texture memory. This functionality is already accessible with the efficient pixel shader capabilities available on the recent generations of commodity GPUs. In this chapter we will be discussing technical implications on engine design due to virtual textures use, content creation issues, results, performance and image quality. We will also cover several practical examples to highlight the challenges and to offer solutions. These include texture filtering, block compression, float precision, disk streaming, UV borders, mip-map generation, LOD selection and more.
A Transductive SVM with quasi-linear kernel based on cluster assumption for semi-supervised classification

This paper presents a Transductive Support Vector Machine (TSVM) with quasi-linear kernel based on a clustering assumption for semi-supervised classification. Since the potential separating boundary is located in low density area between classes, a modified density clustering method by considering label information is firstly introduced to extract the information of potential separating boundary in low density region between different classes. Then the information is used to compose a quasi-linear kernel for the TSVM. The optimization of TSVM is further speeded up by developing a pairwise label switching method on minimal sets. Experiment results on benchmark datasets show that the proposed method is effective and improves classification performances.
Dense stereo-based critical area detection for active pedestrian protection system

An active pedestrian protection system is required to operate in real-time and produce an extremely low false alarm. To achieve these requirements, Broggi et al. detected critical areas using a laser scanner and found pedestrians only in these areas via a camera. However, using heterogeneous sensors requires a complicated system architecture and laser scanners have drawbacks in terms of installability and durability. Therefore, proposed is a critical area detection method using a stereo camera. This configuration can overcome the aforementioned drawbacks and detect both critical areas and pedestrians using a single sensor module. The proposed method detects critical areas by extracting local extrema of the depth change profile along a free space boundary. Experiments show its feasibility quantitatively.
Survivor or Laborer

Regardless of primary population served, human service organizations are likely to come into contact with individuals who have been currently or formerly involved in the sex trade. In the United States, social workers have had a fraught history with this population, either treating them like delinquents or like victims in need of rescue. Sex worker activists in the United States continue to decry the negative treatment provided by individuals in the helping professions, even as harm reduction, the practice of reducing the harm of risky behaviors, has entered the service provision lexicon as an antidote to abstinence-only services. This article uses qualitative interviews with managers of human service organizations in the city of Chicago to determine how they think about their work with sex workers and how they perceive the proposed solutions to “fixing” the sex trade: abolitionism and decriminalization. Findings show that despite the dominant discourse of abolitionism in the United States, most of managers in this project believe full decriminalization of sex work will best assist their sex worker clients. Future research needs to understand how this finding holds in different settings and how this affects current efforts to advocate for decriminalization.
ECE-978 December 1997 Implementations of Context Based Value Predictors

Execution paradigms that eliminate data dependences based on value prediction have been shown to have significant performance potential. High accuracy value prediction is essential for the success of such paradigms. Recently it was shown that context based prediction can predict values with high accuracy. A context based predictor learns the values that follow a particular context (sequence of values) and predicts one of the values when the context repeats. However, in the earlier study unbounded tables were used. In this paper we discuss implementations of context based value predictors using two level table organizations. The new elements introduced in this paper are fixed size tables and sharing of prediction information among instructions. A number of issues unique to context based value prediction are considered. A variety of implementations of the proposed value predictor are evaluated by comparing their prediction performance to the “ideal” performance with unbounded tables. We found that such predictors obtain accuracies that approach that of “ideal” predictors. The proposed predictor was also compared against stride and last value predictors. Stride and last value do better than context based prediction for small tables but context based prediction is superior with increasing table size. The sensitivity of the context based prediction to the size of the input data was explored and found to be relatively insignificant.
Automatic heart sound recording classification using a nested set of ensemble algorithms

Automated phonocardiogram (PCG) analysis may provide better clinical information to physicians for analyzing and diagnosing different heart abnormalities. However, despite recent advances in PCG analysis methods, it is still a challenging task to extract accurate and useful information from contaminated heart sound recordings. The main objective of this paper is to introduce a new approach for classification of normal and abnormal heart sound recordings using a nested ensemble of algorithms that includes Random Forest, LogitBoost and a Cost-Sensitive Classifier. The approach consisted of three stages: preprocessing, classification and evaluation. In the preprocessing stage, PCG signals were first downsampled to 1 kHz using a polyphase antialiasing filter. Next, each heart sound was segmented using Springer's improved version of Schmidt's method to identify four states; S1, S2, systole and diastole. Thereafter, 131 features in time, frequency, wavelet and statistical domains were extracted from the entire signal and from the timings of the states. In the classification stage, the meta-classifier was cross validated on the entire training dataset provided by Physionet Challenge 2016. In the evaluation stage, the sensitivity and specificity of the trained algorithm was tested with unseen signals selected randomly by the Challenge testing environment. Experimental results showed that the proposed approach achieved an overall score of 84.48%, ranking 5th. The use of a nested set of ensemble classifier with a combined set of features extracted from different domains helped reduce overfitting and improved classification performance.
Two-Phase Flow Simulation by AMMoC, an Adaptive Meshfree Method of Characteristics

Petroleum reservoir modelling requires effective multiscale methods for the numerical simulation of two-phase flow in porous media. This paper proposes the application of a novel meshfree particle method to the Buckley-Leverett model. The utilized meshfree advection scheme, called AMMoC, is essentially a method of characteristics, which combines an adaptive semi-Lagrangian method with local meshfree interpolation by polyharmonic splines. The method AMMoC is applied to the five-spot problem, a well-established model problem in petroleum reservoir simulation. The numerical results and subsequent numerical comparisons with two leading commercial reservoir simulators, ECLIPSE and FrontSim, show the good performance of our meshfree advection scheme AMMoC.
Implementing flexible rules of interaction for object manipulation in cluttered virtual environments

Object manipulation in cluttered virtual environments (VEs) brings additional challenges to the design of interaction algorithms, when compared with open virtual spaces. As the complexity of the algorithms increases so does the flexibility with which users can interact, but this is at the expense of much greater difficulties in implementation for developers. Three rules that increase the realism and flexibility of interaction are outlined: collision response, order of control, and physical compatibility. The implementation of each is described, highlighting the substantial increase in algorithm complexity that arises. Data are reported from an experiment in which participants manipulated a bulky virtual object through parts of a virtual building (the piano movers' problem). These data illustrate the benefits to users that accrue from implementing flexible rules of interaction.
WEIGHT FREEZING IN CONSTRUCTIVE NEURAL NETWORKS

An important research problem in constructive network algorithms is how to train the new network after the addition of a hidden unit. There are two ways to train the resultant network. One calls for freezing the input weights of the original network and training only those of the new unit, whereas the other approach allows complete retraining of the whole network. Some previous empirical analyses performed on the cascade-correlation architecture indicate that the effectiveness of freezing is different for different problem domains and hence is not conclusive. This paper describes a series of experiments with the single-hidden-layer network on a number of artificial pattern classification problems. We compare the performance of the network with and without input weight freezing, and against standard back-propagation. We also identify drawbacks with freezing and some directions for future work are discussed.
A New Approach to Tracking 3D Objects in 2D Image Sequences

We present a new technique for tracking 3D objects from 2D image sequences through the integration of qualitative and quantitative techniques. The deformable models are initialized based on a previously developed part-based qualitative shape segmentation system. Using a physics-based quantitative approach, objects are subsequently tracked without feature correspondence based on generalized forces computed from the stereo images. The automatic prediction of possible edge occlusion and disocclusion is performed using an extended Kalman filter. To cope with possible occlusion caused by a previously undetected object, we monitor the magnitude and direction of the computed image forces exerted on the models. Abrupt changes to these forces trigger scene re-segmentation and model re-initialization through the qualitative shape segmentation system. Tracking is subsequently continued using only local image forces. We demonstrate our technique in experiments involving image sequences from complex motions of 3D objects.
Using Bayesian networks for cyber security analysis

Capturing the uncertain aspects in cyber security is important for security analysis in enterprise networks. However, there has been insufficient effort in studying what modeling approaches correctly capture such uncertainty, and how to construct the models to make them useful in practice. In this paper, we present our work on justifying uncertainty modeling for cyber security, and initial evidence indicating that it is a useful approach. Our work is centered around near real-time security analysis such as intrusion response. We need to know what is really happening, the scope and severity level, possible consequences, and potential countermeasures. We report our current efforts on identifying the important types of uncertainty and on using Bayesian networks to capture them for enhanced security analysis. We build an example Bayesian network based on a current security graph model, justify our modeling approach through attack semantics and experimental study, and show that the resulting Bayesian network is not sensitive to parameter perturbation.
QU Car: a very high luminosity nova-like binary with a carbon-enriched companion

QU Car is listed in cataclysmic variable star catalogues as a nova-like variable. This little-studied, yet bright interacting binary is re-appraised here in the light of new high-quality ultraviolet (UV) interstellar line data obtained with STIS on board the Hubble Space Telescope. The detection of a component of interstellar absorption at a mean LSR velocity of 14 km s 1 indicates that the distance to QU Car may be �2 kpc or more – a considerable increase on the previous lower-limiting distance of 500 pc. If so, the bolometric luminosity of QU Car could exceed 10 37 ergs s 1 . This would place this binary in the luminosity domain occupied by known compact-binary supersoft X-ray sources. Even at a 500 pc, QU Car appears to be the most luminous nova-like variable known. New intermediate dispersion optical spectroscopy of QU Car spanning 3800– 7000 u is presented. These data yield the discovery that Civ ��5801,12 is present as an unusually prominent emission line in an otherwise low-contrast line spectrum. Using measurements of this and other lines in a recombination line analysis, it is shown that the C/He abundance as proxied by the n(C 4+ )/n(He 2+ ) ratio may be as high as 0.06 (an order of magnitude higher than the solar ratio). Furthermore, the C/O abundance ratio is estimated to be greater than 1. These findings suggest that the companion in QU Car is a carbon star. If so, it would be the first example of a carbon star in such a binary. An early-type R star best matches the required abundance pattern and could escape detection at optical wavelengths provided the distance to QU Car is �2 kpc or more.
Kernel Local Fuzzy Clustering Margin Fisher Discriminant Method Faced on Fault Diagnosis

In order to better identify the fault of rotor system,one new method based on local fuzzy clustering margin fisher discriminant (LFCMFD) was proposed. For each point on manifold, the farthest point in local neighborhood and the nearest point outside local neighborhood usually constituted the local margin. LFCMFD introduced fuzzy cluster analysis algorithm, eliminated the influence of pseudo-margin points, obtained real local margin, compute with-class scatter and between-class scatte, established local magin fisher discriminant function, found optimal fault diagnosis vector,and then identified the fault class of new testing data by this vector. In order to improve the nonlinear analysis ability of LFCMFD, considering kernel mapping idea, training data with supervision information were mapped to kernel space, constructed kernel fisher discriminant function, LFCMFD algorithm based on kernel method (KLFCMFD)was proposed. The experiment showed, KLFCMFD algorithm had best effect in comparison to other manifold learning algorithm to the rotor fault diagnosis,and fully identify fault class when selecting the appropriate parameters.
Detecting grey matter changes in preclinical phase of Alzheimer's disease by voxel-based morphometric and textural features: A preliminary study

In the present study, a voxel-based morphometry (VBM) and three dimensional texture analysis were used to detect brain structural changes of AD subjects in preclinical phase. Three groups of T1-weighed MRI data were selected from Open Access Series of Imaging Studies (OASIS) database, including 13 converted subjects (CS) who were cognitively healthy at baseline, 13 non-demented subjects (NS) remained cognitively healthy, and 64 demented subjects (DS) for control study. Results showed grey matter (GM) atrophy in bilateral occipital lobe, superior temporal gyrus, and left inferior temporal lobe in CS (p< 0.001); textural features of the largest cluster significantly differed from that of NS, with the classification accuracy of 88.5% through cross validation, which may highlight the key role of these regions in prediction of the disease. Morphometric measurement plus texture analysis presents feasible in detecting preclinical AD.
Art training and personality traits as predictors of aesthetic experience of different art styles among Polish students

Personality, demographics and art experience proved to play an important role in reactions to visual art. Nevertheless, research attempts that take into account all those factors when determining predictors of aesthetic responses to different artistic styles are quite rare. The study presented here investigates predictors of aesthetic experience across figurative, abstract and contemporary paintings in individuals with varying expertise. Students enrolled in Sport, Humanities and the Arts programmes (N = 181) declared their art exposure and filled out personality measures (Big Five, alexithymia, need for closure). Next participants evaluated three paintings using a tool constructed by the authors to track various dimensions of aesthetic reactions (i.e. negative/positive affective responses, self-references, explicit knowledge and perceived mastery of the artwork). Reactions to figurative painting depended mostly on formal knowledge about arts, not personality traits. Aesthetic perception of abstract art rely not only on art exposure, but also on some individual characteristics (openness to experience, tolerance of ambiguity and ability to identify one’s own emotions and track their source). Reception of contemporary art was predicted mostly by art exposure variables and in the case of negative emotionality by ability to identify one’s own emotions and track their source. Both formal art education and art experience were stronger predictors of aesthetic responses than personality traits, for all art styles and dimensions of aesthetic experience. Personality predictors were significant mostly for abstract art. Personal interest in the arts seems to be as good predictor of aesthetic reactions as formal expertise.