Miracle-dz/UniFAR-FTUs huggingface dataset:
High effectiveness of peginterferon alfa-2a plus ribavirin therapy in Korean patients with chronic hepatitis C in clinical practice

Background/Aims Identifying the impact of a patient's ethnicity on treatment responses in clinical practice may assist in providing individualized treatment regimens for chronic hepatitis C (CHC). The effectiveness of standard peginterferon plus ribavirin therapy and the need for triple combination therapy with protease inhibitors in Koreans remain matters of debate. These issues were investigated in the present study. Methods The clinical data of 272 treatment-naïve Korean CHC patients who were treated in a community-based clinical trial (Clinical Trial group; n=51) and in clinical practice (Cohort group; n=221), were analyzed and compared. All were treated with standard protocols of peginterferon alfa-2a plus ribavirin therapy. Results For patients with hepatitis C virus (HCV) genotype 1, the sustained virological response (SVR) rates in the Clinical Trial and Cohort groups were 81% (21/26) and 55% (58/106), respectively, by intention-to-treat (ITT) analysis (P=0.02), and 100% (13/13) and 80% (32/40), respectively, in treatment-adherent patients (P=0.18). For patients with HCV genotype 2, the SVR rates in these two groups were 96% (24/25) and 88% (101/115), respectively, by ITT analysis (P=0.31). Adherence and treatment duration were independent predictors of SVR for genotypes 1 and 2, respectively (P<0.01 for each). Korean patients with CHC achieved high SVR rates with peginterferon alfa-2a plus ribavirin in both the clinical trial and clinical practice settings. Conclusions Measures to raise adherence to standard therapy in clinical practice may improve the SVR rates in these patients as effectively as adding protease inhibitors, thus obviating the need for the latter.
Confidence and prediction intervals for neural network ensembles

We propose a technique that uses the bootstrap method to estimate confidence and prediction intervals for neural network (regression) ensembles. Our proposed technique can be applied to any ensemble technique that uses the bootstrap to generate the training sets for the ensemble, such as bagging and balancing. Confidence and prediction intervals are estimated that include a significantly improved estimate of underlying model uncertainty (i.e.) the uncertainty of our estimate of the "true" regression. Unlike existing techniques, this estimate of uncertainty will vary according to which ensemble technique is used if the effect of using a specific ensemble technique is to produce less model uncertainty than using another ensemble technique, then this will be reflected in the confidence and prediction intervals. Preliminary results illustrate how our technique can provide more accurate confidence and prediction intervals (intervals that better reflect the desired level of confidence (e.g.) 90%, 95%, etc.) for neural network ensembles than previous attempts.
Modular and Efficient Resource Management in the Exedra Media Server

Explosive growth in online services has recently renewed the interest for building modular and efficient network server systems. System design complications coupled with excessive expectations from technological progress previously discouraged the development of media servers efficiently supporting video streams with variable bit rates. In this paper, we describe the design of a distributed media server architecture, and the implementation details of a prototype. Native support is provided for variable bit rate streams, by considering their special features in the resource management policies. We identify several problems, and propose new approaches for scheduling the playback requests, organizing the memory buffers, allocating the storage space, and structuring the disk metadata. We justify several of our decisions with comparative performance measurements using both synthetic benchmarks and actual experiments with variable bit rate MPEG-2 streams over SCSI disks.
Eigenfunctions of Laplace operators

Most explicit information on the eigenfunctions of a Laplace operator on a compact manifold comes from computations where a high degree of symmetry is present. In these cases, eigenspaces may be of large dimension, the zeros of the eigenfunctions are often critical points, and the eigenfunctions usually have degenerate critical points. However, these properties are all unstable under small perturbations of the metric, and are therefore rather misleading to one's intuition. From the point of view of differential topology, the best possible properties the eigenfunctions can have would be the following: Property A. The eigenspaces are one-dimensional. Property B. 0 is not a critical value of the eigenfunctions (so the zero or nodal set is a manifold of codimension 1). Property C. The eigenfunctions are Morse functions (they have nondegenerate critical points). These properties are true for a residual set of metrics on a manifold. In the same vein we also establish similiar generic properties for bifurcations, and discuss how this approach can be used to attack the problem of invariant properties of nth eigenfunctions. The idea for this work originated in some similiar work of J. Albert on eigenfunctions [3]. His methods show that the use of transversality arguments can be avoided. More general theorems and more detailed proofs will appear elsewhere [7]. The main technical tools used are the Sard-Smale theorem and the transversality theorems which follow from it, although all the proofs can be carried out directly by exhibiting open dense sets. A map/: H -• E between two Banach manifolds is Fredholm if Dfx: TX(H)-> Tfix)(E) has finite-dimensional kernel and cokernel, and its index is the difference in these two dimensions. A point y e E is a regular value for ƒ if x e f ~ (y) implies Dfx is onto. A set of second Baire category is a residual set, and we have used generic to describe a property which is true for a residual set.
Emergency Resource Planning by Using Spatial Data Association Rule Mining and Linear Programming Method

Spatial attributes are important factors that affect the whole process of emergency events. However, studies on this subject have not sufficiently been carried out. This paper presents a new idea that incorporates spatial predicates describing the spatial relationships between emergency locations and surrounding objects into emergency event analysis. Furthemore, a multi-level spatial data association algorithm is developed to realize knowledge discovery for emergency event analysis. Traditional linear programming model failed to give reasonable weight for different emergency events ocured in different locations. While this paper uses spatial data assocation rules which detect how spatial attributes affect emergency events as the weighting mechanism for different spots, Based on such method, we finally propose a linear programming method that realize emergency resource planning in a new perspective.
3D Acoustic Frequency-domain Full-waveform Inversion

We present one of the first attempt at implementing a massively parallel frequency-domain full-waveform inversion algorithm for imaging 3D acoustic media. The inverse method is based on a classic steepest-descent algorithm. The algorithm was designed so that one or several frequencies are inverted at a time. Wave propagation modeling, a key component of the inversion algorithm, is performed with a finite-difference frequency-domain method which requires 4 grid points per wavelength. Frequency-domain methods for wave propagation modeling requires to solve a huge sparse system of linear equations whose solutions are the computed wavefields and right-hand side (RHS) terms are the sources. We solve this system with a massively parallel direct solver in order to compute efficiently multiple-source solutions. The massively parallel direct solver leaves the multiple solutions distributed over the processors. We take advantage of this distributed storage to compute in parallel the gradient of the cost function in the inversion. The algorithm was validated with preliminary synthetic examples of limited dimensions.
NoDB in Action: Adaptive Query Processing on Raw Data

As data collections become larger and larger, users are faced with increasing bottlenecks in their data analysis. More data means more time to prepare the data, to load the data into the database and to execute the desired queries. Many applications already avoid using traditional database systems, e.g., scientific data analysis and social networks, due to their complexity and the increased data-to-query time, i.e. the time between getting the data and retrieving its first useful results. For many applications data collections keep growing fast, even on a daily basis, and this data deluge will only increase in the future, where it is expected to have much more data than what we can move or store, let alone analyze. In this demonstration, we will showcase a new philosophy for designing database systems called NoDB. NoDB aims at minimizing the data-to-query time, most prominently by removing the need to load data before launching queries. We will present our prototype implementation, PostgresRaw, built on top of PostgreSQL, which allows for efficient query execution over raw data files with zero initialization overhead. We will visually demonstrate how PostgresRaw incrementally and adaptively touches, parses, caches and indexes raw data files autonomously and exclusively as a side-effect of user queries.
Evolving GPS position correction function using genetic programming

Many mobile devices embeds Global positioning system (GPS) to enable tour navigation or path programming. Those low-cost consumer-grade GPS receivers usually do not have high accuracy so that a position correction algorithm is therefore necessary. This paper proposed a correction technique using genetic programming. This technique requires only two GPS receivers and a known position. Using position information gathered by the receivers we are capable of predicting the correct position. The proposed technique can be implemented without modifying hardware devices or settings. The algorithm generates a correction function constructed by features of NMEA (national Marine Electronics Association) sentences, which is a standard format and is common in most GPS receivers. Experiments are conducted to demonstrate performance of the proposed technique. Positioning error could be reduced significantly.
SVM-Based Multimodal Classification of Activities of Daily Living in Health Smart Homes: Sensors, Algorithms, and First Experimental Results

By 2050, about one third of the French population will be over 65. Our laboratory's current research focuses on the monitoring of elderly people at home, to detect a loss of autonomy as early as possible. Our aim is to quantify criteria such as the international activities of daily living (ADL) or the French Autonomie Gerontologie Groupes Iso-Ressources (AGGIR) scales, by automatically classifying the different ADL performed by the subject during the day. A Health Smart Home is used for this. Our Health Smart Home includes, in a real flat, infrared presence sensors (location), door contacts (to control the use of some facilities), temperature and hygrometry sensor in the bathroom, and microphones (sound classification and speech recognition). A wearable kinematic sensor also informs postural transitions (using pattern recognition) and walk periods (frequency analysis). This data collected from the various sensors are then used to classify each temporal frame into one of the ADL that was previously acquired (seven activities: hygiene, toilet use, eating, resting, sleeping, communication, and dressing/undressing). This is done using support vector machines. We performed a 1-h experimentation with 13 young and healthy subjects to determine the models of the different activities, and then we tested the classification algorithm (cross validation) with real data.
Planning and control for a fully-automatic parallel parking assist system in narrow parking spaces

This paper presents two major parts of a fully-automatic parallel parking assist system. After mapping the available parking place by perception module, the parking assist system plans an optimal path according to driver's selection, and then tracks the reference path with limited error. It is presented in details the solution of segmental path planning with back-and-forth shuttling maneuvers in the narrow parking lot. In addition, it is discussed the flatness-based steering control algorithm and the longitudinal motion control strategy. Effectiveness of the proposed method is verified by hardware-in-the-loop test results with a driving simulator.
Windows for DFT-Based Harmonic Analysis

Relation between roots location and spectrum: The first important thing in the use of windows for harmonic analysis, is the relation between the zeros of the windows, and their spectra. The plot in Figure 3.1 shows the spectrum of the rectangular window and the roots of this window. This plot outlines that the width of the main-lobe is obtained from the angle of the first root, in this case, Θ=2π/32.
Improving the Error Estimation in Cosmological Parameter by the Third Order Expansion of the Natural Logarithm of the Likelihood

The Fisher matrix formalism is a method of error forecasting and the method has been widely used in cosmological data analysis because of its convenience and effectiveness. It is based on the second-order expansion of the logarithm of the likelihood function in the parameter space, and the estimated error contours are ellipsoids in the parameter space. With real-world data, the error contours are usually have shapes more complicated than ellipsoids and hence the Fisher matrix formalism underor overestimates errors. This paper investigates a generalization of the Fisher matrix formalism based on the third order expansion in the logarithm of the likelihood function.
An Outlier Detection Algorithm Based on Spectral Clustering

Outlier detection is widely used for many areas such as credit card fraud detection, discovery of criminal activities in electronic commerce, weather prediction and marketing. In this paper, we demonstrate the effectiveness of spectral clustering in dataset with outliers. Through spectral method we can use the information of feature space with eigenvectors rather than that of the whole dataset to obtain stable clusters. Then we introduce the cluster-based local outlier factor to identify and find the outliers in dataset. The experimental results show that our outlier detection algorithm outperforms the K-means based algorithm with high precision and low false alarm rate as well as desirable coverage ratio.
Diversity and commonality in music performance: an analysis of timing microstructure in Schumann's "Träumerei".

This study attempts to characterize the temporal commonalities and differences among distinguished pianists' interpretations of a well-known piece, Robert Schumann's "Träumerei. " Intertone onset intervals (IOIs) were measured in 28 recorded performances. These data were subjected to a variety of statistical analyses, including principal components analysis of longer stretches of music and curve fitting to series of IOIs within brief melodic gestures. Global timing patterns reflected the hierarchical grouping structure of the composition, with pronounced ritardandi at the ends of major sections and frequent expressive lengthening of accented tones within melodic gestures. Analysis of local timing patterns, particularly of within-gesture ritardandi, revealed that they often followed a parabolic timing function. The major variation in these patterns can be modeled by families of parabolas with a single degree of freedom. The grouping structure, which prescribes the location of major tempo changes, and the parabolic timing function, which represents a natural manner of executing such changes, seem to be the two major constraints under which pianists are operating. Within these constraints, there is room for much individual variation, and there are always exceptions to the rules. The striking individuality of two legendary pianists, Alfred Cortot and Vladimir Horowitz, is objectively demonstrated here, as is the relative eccentricity of several other artists.
Statistical Wavelet Subband Characterization Based on Generalized Gamma Density and Its Application in Texture Retrieval

The modeling of image data by a general parametric family of statistical distributions plays an important role in many applications. In this paper, we propose to adopt the three-parameter generalized gamma density (G¿D) for modeling wavelet detail subband histograms and for texture image retrieval. The advantage of G¿D over the existing generalized Gaussian density (GGD) is that it provides more flexibility to control the shape of model which is critical for practical histogram-based applications. To measure the discrepancy between G¿Ds, we use the symmetrized Kullback-Leibler distance (SKLD) and derive a closed form for the SKLD between G¿Ds. Such a distance can be computed directly and effectively via the model parameters, making our proposed scheme particularly suitable for image retrieval systems with large image database. Experimental results on the well-known databases reveal the superior performance of our proposed method compared with the current existing approaches.
On the trade-off between accuracy and delay in cooperative UWB navigation

In ultra-wide bandwidth (UWB) cooperative navigation, nodes estimate their position by means of shared information. Such sharing has a direct impact on the position accuracy and medium access control (MAC) delay, which needs to be considered when designing UWB navigation systems. We investigate the interplay between UWB position accuracy and MAC delay for cooperative scenarios. We quantify this relation through fundamental lower bounds on position accuracy and MAC delay for arbitrary finite networks. Results show that the traditional ways to increase accuracy (e.g., increasing the number of anchors or the transmission power) as well as inter-node cooperation may lead to large MAC delays. We evaluate one method to mitigate these delays.
A low-complexity error concealment algorithm for video transmission based on non-local means denoising

In video communication, the concealment of transmission errors is important to mitigate error propagation and allow for a pleasant visual quality. The previous spatio-temporal error concealment algorithm, known as Denoised Temporal Extrapolation Refinement (DTER) used a spatial denoising algorithm to reduce the imperfectness of the temporal extrapolation. However, the computational complexity of DTER is extremely high. In this paper, a fast algorithm based on Non-Local Means (NLM) is proposed for video transmission. The proposed fast strategy mainly includes two steps. First, the search window can be narrowed down by using the characteristic that the similarity of any two image blocks will decrease with increasing Euclidean distance. Afterwards, a successive elimination method is used for cutting out some redundant points. By making use of these two steps, our proposed algorithm achieves 10~30 times faster than DTER without sacrifice in picture quality. Moreover, simulation results clearly show the proposed fast algorithm outperform other existing error concealment algorithms.
Fuzzy image matching for posture recognition in ballet dance

This work aims at designing a fuzzy matching algorithm that would automatically recognize an unknown ballet posture from seventeen fundamental ballet dance primitives. A novel and simple 7-stage system is proposed to achieve the desired objective. Minimized skeletons of the dance postures are generated after performing skin color segmentation on them. Straight line approximation on the minimized skeletons with the help of chain code and sampling generate their equivalent stick figure diagrams. Significant straight lines from the stick figure diagrams are considered, their fuzzy membership with respect to the 4 quadrants are evaluated. Finally with the help of the evaluated data, a fuzzy T-norm operator determines the proximity of a generated dance posture with the seventeen fundamental dance primitives.
Adaptive wear-leveling algorithm for PRAM main memory with a DRAM buffer

Phase Change RAM (PRAM) is a candidate to replace DRAM main memory due to its low idle power consumption and high scalability. However, its latency and endurance have generated problems in fulfilling its main memory role. The latency can be treated with a DRAM buffer, but the endurance problem remains, with three critical points that need to be improved despite the use of, existing wear-leveling algorithms. First, existing DRAM buffering schemes do not consider write count distribution. Second, swapping and shifting operations are performed statically. Finally, swapping and shifting operations are loosely coupled with a DRAM buffer. As a remedy to these drawbacks, we propose an adaptive wear-leveling algorithm that consists of three novel schemes for PRAM main memory with a DRAM buffer. The PRAM-aware DRAM buffering scheme reduces the write count and prevents skewed writing by considering the write count and clean data based on the least recently used (LRU) scheme. The adaptive multiple swapping and shifting scheme makes the write count even with the dynamic operation timing, the number of swapping pages being based on the workload pattern. Our DRAM buffer-aware swapping and shifting scheme reduces overhead by curbing additional swapping and shifting operations, thus reducing unnecessary write operations. To evaluate the wear-leveling effect, we have implemented a PIN-based wear-leveling simulator. The evaluation confirms that the PRAM lifetime increases from 0.68 years with the previous wear-leveling algorithm to 5.32 years with the adaptive wear-leveling algorithm.
Mechanisms of Motivational Interviewing in a Parent-focused Pediatric Obesity Intervention

MECHANISMS OF MOTIVATIONAL INTERVIEWING IN A PARENT-FOCUSED PEDIATRIC OBESITY INTERVENTION Nadia Yasmin Islam, M.S. A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University Virginia Commonwealth University, 2017 Co-Director: Melanie K. Bean, Ph.D. Associate Professor , Department of Pediatrics, Children’s Hospital of Richmond Co-Director: Bryce D. McLeod, Ph.D. Associate Professor, Department of Psychology Pediatric obesity is a major public health epidemic with serious physical and psychological consequences. Difficulty engaging families in treatment is a significant obstacle in addressing pediatric obesity, especially among underserved populations. Motivational interviewing (MI) is a collaborative, person-centered communication style that has been shown to reduce attrition, increase attendance, and improve patient treatment adherence; however, little is known about the process of MI and how it improves treatment engagement. This study examined clinician and parent language in a pre-treatment MI session that increased initial engagement in a parentfocused pediatric obesity intervention (N= 81). Results showed that increased parent change talk, and preparatory language in particular, was positively related to the likelihood of initial attendance at baseline. Additionally, certain types of MI consistent clinician strategies were positively associated with parent change talk. Complex positive reflections were correlated with preparatory language and overall change talk, suggesting this might be a particularly important MI skill. Findings have implications for better understanding the process of MI and mechanisms through which MI can improve treatment engagement.