Miracle-dz/UniFAR-FTUs huggingface dataset:
Relational Query Co-Processing on Graphics Processors 1

Graphics processors (GPUs) have recently emerged as a powerful co-processor for general-purpose computation. Compared with commodity CPUs, GPUs have an order of magnitude higher computation power as well as memory bandwidth. Moreover, new-generation GPUs allow writes to random memory locations, provide e‐cient inter-processor communication through on-chip local memory, and support a general-purpose parallel programming model. Nevertheless, many of the GPU features are specialized for graphics processing, including the massively multi-threaded architecture, the Single-Instruction-Multiple-Data processing style, and the execution model of a single application at a time. Additionally, GPUs rely on a bus of limited bandwidth to transfer data from and to the CPU, do not allow dynamic memory allocation from GPU kernels, and have little hardware support for write con∞icts. Therefore, it requires a careful design and implementation to utilize the GPU for co-processing database queries. In this paper, we present our design, implementation, and evaluation of an in-memory relational query co-processing system, GDB, on the GPU. Taking advantage of the GPU hardware features, we design a set of highly optimized data-parallel primitives such as split and sort, and use these primitives to implement common relational query processing algorithms. Our algorithms utilize the high parallelism as well as the high memory bandwidth of the GPU, and use parallel computation and memory optimizations to efiectively reduce memory stalls. Furthermore, we propose co-processing techniques that take into account both the computation resources and the GPU-CPU data transfer cost so that each operator in a query can utilize suitable processors - the CPU, the GPU, or both, for an optimized overall performance. We have evaluated our GDB system on a machine with an Intel quad-core CPU and an NVIDIA GeForce 8800 GTX GPU. Our workloads include microbenchmark queries on memory-resident data as well as TPC-H queries that involve complex data types and multiple query operators on data sets larger than the GPU memory. Our results show that our GPU-based algorithms are 2-27x faster than their optimized CPU-based counterparts on in-memory data. Moreover, the performance of our co-processing scheme is similar to or better than both the GPU-only and the CPU-only schemes.
Efficient Error Detection in Double Error Correction Orthogonal Latin Squares Codes

Reliability is a major concern in advanced electronic circuits. Errors caused for example by radiation become more common as technology scales. To ensure that those errors do not affect the circuit functionality a number of mitigation techniques can be used. Among them, Error Correction Codes (ECC) are commonly used to protect memories and registers in electronic circuits. When ECCs are used, it is of interest that in addition to correcting a given number of errors, the code can also detect errors exceeding that number. This ensures that uncorrectable errors are detected and therefore silent data corruption does not occur. Among the ECCs used to protect circuits, one option is Orthogonal Latin Squares (OLS) codes for which decoding can be efficiently implemented. In this paper, an enhancement of the decoding for Double Error Correction (DEC) OLS codes is proposed. The proposed scheme tries to reduce the probability of silent data corruption by implementing mechanisms to detect errors that affect more than two bits.
Productivity and saving channels of economic growth as latent variables: an application of confirmatory factor analysis

When it comes to measure the sources of growth and draw economic policy conclusions, economists rely on growth accounting. According to this approach, per capita growth is explained by two sources: capital accumulation and total factor productivity. Our contention is that growth accounting suffers from serious pitfalls once we take into account that: (i) Total factor productivity and investment are not independent of each other, and (ii) Total factor productivity is badly measured. The result is that the sources of growth are directly unob-servable, undermining any conclusion based on available measures. To partially overcome these problems, we introduce a technique especially designed to deal with unobservable, or latent variables, called confirmatory factor analysis. We examine both the relationship between fourteen variables correlated to the growth rate and two latent variables: the “savings channel” and the “productivity channel”, and the correlation between the latter.
Parallel operation of uninterruptible power supply systems in microgrids

In this paper, a control scheme applied to distributed UPSs forming a microgrid is proposed. The control architecture consists of two levels of hierarchy: 1) the P/Q droop control method of every UPS unit and 2) the management of the microgrid through the control of the setpoints of the units, and the connection or disconnection to the utility mains.
Divergent role of nitric oxide in insulin-stimulated aortic vasorelaxation between low- and high-intrinsic aerobic capacity rats

Low‐intrinsic aerobic capacity is associated with increased risk for cardiovascular and metabolic diseases and is a strong predictor of early mortality. The effects of intrinsic aerobic capacity on the vascular response to insulin are largely unknown. We tested the hypothesis that rats selectively bred for a low capacity to run (LCR) exhibit vascular dysfunction and impaired vascular reactivity to insulin compared to high capacity running (HCR) rats. Mature female LCR (n = 21) and HCR (n = 17) rats were maintained under sedentary conditions, and in vitro thoracic aortic vascular function was assessed. LCR exhibited greater body mass (13%), body fat (35%), and subcutaneous, perigonadal, and retroperitoneal adipose tissue mass, than HCR. During an intraperitoneal glucose tolerance test, glucose area under the curve (AUC) was not different but insulin AUC was 2‐fold greater in LCR than HCR. Acetylcholine and insulin‐stimulated aortic vasorelaxation was significantly greater in LCR (65.2 ± 3.8%, and 32.7 ± 4.1%) than HCR (55.0 ± 3.3%, and 16.7 ± 2.8%). Inhibition of nitric oxide synthase (NOS) with L‐NAME entirely abolished insulin‐mediated vasorelaxation in the aorta of LCR, with no effect in HCR. LCR rats exhibited greater expression of Insulin Receptor protein, lower Endothelin Receptor‐A protein, a down‐regulation of transcripts for markers of immune cell infiltration (CD11C, CD4, and F4/80) and up‐regulation of pro‐atherogenic inflammatory genes (VCAM‐1 and MCP‐1) in the aorta wall. Contrary to our hypothesis, low‐aerobic capacity was associated with enhanced aortic endothelial function and NO‐mediated reactivity to insulin, despite increased adiposity and evidence of whole body insulin resistance.
A mobile robot tracking using Kalman filter-based Gaussian Process in wireless sensor networks

RSSI-based localization has a variety of possible applications, and the environment to obtain the required information is well-constructed in these days due to the prevalence of WiFi usage. However, it is difficult to apply this method directly to the real-world positioning, because there are several factors of uncertainty in the signal strength measurements. In this paper, it is proposed to incorporate dead-reckoning using encoder measurement only, and Kalman filter-based Gaussian Process to compensate the uncertainty. As encoder itself is not able to calibrate the accumulating error, and the measured RSSI data has a time-varying error, the defects of respective methods can be complemented by each other using Kalman filter. The performance of the proposed method is evaluated by two different simulations. The location of a mobile robot moving through the exact desired path is estimated first. Then, the result of controlling a mobile robot based on the estimated position is shown.
Experiments with noise ltering in a medical domain

The paper presents a series of noise detection experiments in a medical problem of coronary artery disease diagnosis. The following algorithms for noise detection and elimination are tested: a saturation lter, a classiication l-ter, a combined classiication-saturation l-ter, and a consensus saturation lter. The distinguishing feature of the novel consensus saturation lter is its high reliability which is due to the multiple detection of potentially noisy examples. Reliable detection of noisy examples is important for the analysis of patient records in medical databases, as well as for the induction of rules from ltered data, representing genuine characteristics of the diagnostic domain. Medical evaluation in the problem of coronary artery disease diagnosis shows that the detected noisy examples are indeed noisy or non-typical class representatives .
Kelvin probe microscopy and electronic transport measurements in reduced graphene oxide chemical sensors

Reduced graphene oxide (RGO) is an electronically hybrid material that displays remarkable chemical sensing properties. Here, we present a quantitative analysis of the chemical gating effects in RGO-based chemical sensors. The gas sensing devices are patterned in a field-effect transistor geometry, by dielectrophoretic assembly of RGO platelets between gold electrodes deposited on SiO2/Si substrates. We show that these sensors display highly selective and reversible responses to the measured analytes, as well as fast response and recovery times (tens of seconds). We use combined electronic transport/Kelvin probe microscopy measurements to quantify the amount of charge transferred to RGO due to chemical doping when the device is exposed to electron-acceptor (acetone) and electron-donor (ammonia) analytes. We demonstrate that this method allows us to obtain high-resolution maps of the surface potential and local charge distribution both before and after chemical doping, to identify local gate-susceptible areas on the RGO surface, and to directly extract the contact resistance between the RGO and the metallic electrodes. The method presented is general, suggesting that these results have important implications for building graphene and other nanomaterial-based chemical sensors.
Boosting Clustering by Active Constraint Selection

In this paper we address the problem of active query selection for clustering with constraints. The objective is to determine automatically a set of user queries to define a set of must-link or cannot-link constraints. Some works on active constraint learning have already been proposed but they are mainly applied to K-Means like clustering algorithms which are known to be limited to spherical clusters, while we are interested in clusters of arbitrary sizes and shapes. The novelty of our approach relies on the use of a k-nearest neighbor graph to determine candidate constraints coupled with a new constraint utility function. Comparative experiments conducted on real datasets from machine learning repository show that our approach significantly improves the results of constraints based clustering algorithms.
Rolling bearing fault diagnosis: A data-based method using EEMD, information entropy and one-versus-one SVM

This paper presents a rolling bearing fault diagnosis approach based on the combination of Ensemble Empirical Mode Decomposition (EEMD), Information Entropy (IE) and Support Vector Machine (SVM). The horizontal and vertical vibration signals of the bearings are utilized as the input of the method. First, the signals, after preprocess, are decomposed into certain number of intrinsic mode functions (IMF) using EEMD. Second, the IEs of the IMFs are calculated as the features for further fault diagnosis. Third, the selected features are adopted to train the SVM model using 10-fold cross validation. Fourth, the trained SVM model is used to conduct bearing fault diagnosis. To verify the effectiveness of the proposed approach, three types of faults including inner-ring fault, outer-ring fault and rolling element fault are injected and data from three individual experiments are used. The results demonstrate that the approach has desirable diagnostic performance both for cylindrical roller bearing and deep groove ball bearing.
Joint Source-Channel Coding: a Practical Approach and an implementation Example

The basic building blocks of most state-of the art source coders are: a linear transformation; scalar quantization of the transform coefficients; probability modeling of the sequence of quantization indices; an entropy coding stage. The weakness of conventional separated source-channel coding approaches lies in the catastrophic behavior of the entropy coding stage. Hence, we replace this stage with linear coding, that maps directly the sequence of redundant quantizer output symbols into a channel codeword. We show that this approach does not entail any loss of optimality in the asymptotic regime of large block length. Furthermore, for practical block lengths and moderate complexity, our approach yields very significant improvements. We develop a full implementation example based on the JPEG2000 image coder and punctured turbo codes. The gain over a conventional separated scheme is demonstrated by extensive numerical experiments on test images.
Optimal Government Spending and Taxation in Endgenous Growth Models

This paper analyzes optimal spending, tax and financial policies in models of endogenous growth where public spending is productive. We extend previous work in four directions. First, we analyze optimal policies when the government is allowed to borrow and lend, rather than being restricted to run a balanced budget in every period. Second, we develop a model with a separate human capital accumulation sector. Therefore, the properties of optimal policies depend on whether government spending affects the productivity of the final goods sector or the human capital accumulation sector. Third, we consider the policy implications of alternative assumptions about which factor of production benefits from the external effects of productive public goods. Fourth, we study the implications of restrictions on the menu of tax instruments available to the policy maker. We contrast optimal tax rates on human and physical capital under different assumptions on technology and distribution. We analyze the welfare properties of public debt and assets.
The Effect of Monitoring Committees on the Relationship between Board Structure and Firm Performance

The purpose of this study is to investigate the impact of board structure on the performance of French firms in the presence of several monitoring committees. We studied 80 publicly listed French firms spanning from 2001 to 2013. We concluded that large board size has a negative effect on market performance. While large board size in combination with the existence of at least three committees enhances accounting performance and does not have any impact on market performance, the existence of a board dominated by independent directors with the presence of at least three committees seems to have only a negative impact on accounting performance. Our findings indicate that monitoring committees are beneficial for shareholders only for corporations with a large board size.
Differential Contribution of PB1-F2 to the Virulence of Highly Pathogenic H5N1 Influenza A Virus in Mammalian and Avian Species

Highly pathogenic avian influenza A viruses (HPAIV) of the H5N1 subtype occasionally transmit from birds to humans and can cause severe systemic infections in both hosts. PB1-F2 is an alternative translation product of the viral PB1 segment that was initially characterized as a pro-apoptotic mitochondrial viral pathogenicity factor. A full-length PB1-F2 has been present in all human influenza pandemic virus isolates of the 20th century, but appears to be lost evolutionarily over time as the new virus establishes itself and circulates in the human host. In contrast, the open reading frame (ORF) for PB1-F2 is exceptionally well-conserved in avian influenza virus isolates. Here we perform a comparative study to show for the first time that PB1-F2 is a pathogenicity determinant for HPAIV (A/Viet Nam/1203/2004, VN1203 (H5N1)) in both mammals and birds. In a mammalian host, the rare N66S polymorphism in PB1-F2 that was previously described to be associated with high lethality of the 1918 influenza A virus showed increased replication and virulence of a recombinant VN1203 H5N1 virus, while deletion of the entire PB1-F2 ORF had negligible effects. Interestingly, the N66S substituted virus efficiently invades the CNS and replicates in the brain of Mx+/+ mice. In ducks deletion of PB1-F2 clearly resulted in delayed onset of clinical symptoms and systemic spreading of virus, while variations at position 66 played only a minor role in pathogenesis. These data implicate PB1-F2 as an important pathogenicity factor in ducks independent of sequence variations at position 66. Our data could explain why PB1-F2 is conserved in avian influenza virus isolates and only impacts pathogenicity in mammals when containing certain amino acid motifs such as the rare N66S polymorphism.
Different Cardiovascular Responses to a Resistance Training Session in Hypertensive Women Receiving Propanolol Compared with Normotensive Controls

The aim of the present study was to compare the responses of blood pressure, heart rate, and rate-pressure product of hypertensive women using beta-blockers with healthy controls during resistance exercise (by the end of the concentric phase of the contractions) and in the postexercise period (5 and 30 minutes after). Ten untrained normotensive women (N) and 10 mildly hypertensive females receiving 40 mg/day of propanolol (H) were selected. Three sets of 10 repetitions at 80% of 10 repetitions maximum with 30 s rest interval were performed on the leg press exercise. The H group exhibited lower systolic blood pressure after the second set compared with N. Heart rate and rate-pressure product were lower in H in all analyzed periods compared with N. Propanolol attenuates the cardiovascular response to a leg press resistance exercise in mildly hypertensive women.
PLDA using Gaussian Restricted Boltzmann Machines with application to Speaker Verification

A novel approach to supervised dimensionality reduction is introduced, based on Gaussian Restricted Boltzmann Machines. The proposed model should be considered as the analogue of the probabilistic LDA, using undirected graphical models. The training algorithm of the model is presented while its close relation to the cosine distance is underlined. For the problem of speaker verification, we applied it to i-vectors and attained a significant improvement compared to the Fisher’s Discriminant LDA projection using less than half of the number of eigenvectors required by LDA.
Learning interaction force model for endodontic shaping with support vector regression

Accurate estimation of interaction forces for endodontic shaping is fundamental to the interactive simulation of this operation. By applying new statistical learning techniques to this problem, this paper proposes a novel estimation method to acquire an optimized interaction force model to characterize input-output force mapping for endodontic shaping. We first present a novel robotic measurement system to acquire interaction forces in endodontic shaping and establish the needed training set. Then we propose a support vector regression model to learn the input-output force mapping for endodontic shaping. The regression model uses RBF kernel for training, and the optimized parameters of which are obtained by experiments. The learned model can convincingly estimate the interaction force resulting from endodontic shaping. And the effectiveness of the model has been evaluated by error measurement results
Early experience with optimizing I/O performance using high-performance SSDs for in-memory cluster computing

This paper describes our experience with storage optimization that utilizes cost-effective PCIe solid-state drives (SSDs) to improve the overall performance of a Spark framework. A key problem we address is the limited memory system performance. In particular, we adopt high-performance SSDs to alleviate the saturated DRAM bandwidth and its limited capacity. We utilize SSDs to store shuffle data and persisted RDDs. As a result, the overall performance improves due to the larger capacity of SSDs and the increased bandwidth provided by SSDs while alleviating memory contentions. Our experiments show that we can improve the performance of data-intensive applications by 23.1% on average, compared to the performance of the memory-only approach. To our knowledge, this is the first work to demonstrate performance optimizations using PCIe SSDs on Spark.
Feature-based nonrigid image registration using a Hausdorff distance matching measure

A feature-based, nonrigid image registration method using a Hausdorff distance-based matching measure is presented. The proposed method is robust to outliers and missing features, as no correspondence is established between the features. Utilizing a B-spline-based, nonrigid deformation model, the proposed method is able to handle nonrigid deformations between the images to be registered. A gradient descent-based optimization procedure is developed to maximize the matching measure under the nonrigid transformation model. With adaptively adjustable step sizes, the optimization procedure works in a coarse-to-fine manner so that first large nonrigid deformations are compensated and then the transformation parameters are gradually refined. In addition, two acceleration techniques are devised to greatly speed up the registration method, making it more practical for real applications. The performance of the proposed method is validated in various experiments from synthetic image registration to hand-drawn Chinese character registration and brain outline registration. The limitation of the method is also analyzed and exemplified. To partly alleviate the limitation of the method, we incorporate landmark information into the method and achieve promising results. (C) 2007 Society of Photo-Optical Instrumentation Engineers.
An Approach for Identifying and Implementing Aspectual Features inSoftware Product Lines

Software Product Lines (SPL) exploits reuse by identifying, modeling, and systemically reusing software features to develop different but related software systems. Successful reuse of a product line depends greatly on the modularity of the features that characterize the product line. Traditionally, features in SPL are grouped along the dimension of commonality and variability. However, this singledimension grouping overlooks the crosscutting nature of some features in the system, which negatively impacts the reusability and modularity of the product line architecture. In this paper we address this particular problem by investigating the concept of Aspectual Feature (AF) as another grouping dimension that can be used in SPLs. To this end, the paper proposes the Aspectual Product Line Engineering (APPLE) approach for identifying, modeling, and implementing AFs to enhance the reuse of SPLs. A tool support for implementing the APPLE approach is also presented and demonstrated through a case study.
Mixed Virtual Reference Feedback Tuning — Iterative Feedback Tuning approach to the position control of a laboratory servo system

A mixed Virtual Reference Feedback Tuning (VRFT) — Iterative Feedback Tuning (IFT) approach to the position control of a laboratory servo system equipment is proposed in this paper. The meeting point between the performance objectives of VRFT and IFT is discussed. The mixed VRFT-IFT approach is expressed on the basis of a relatively simple unbiased estimate of the Hessian which is derived from the favorable parameterization of VRFT.