Miracle-dz/UniFAR-FTUs huggingface dataset:
Model of latent profile factor analysis for ordered categorical data

In the literature factor analysis is admittedly a well-known and effective multivariate method in the reduction of extensive and broad data, e.g., in the analysis of too many variables. It is also known for the process of unidimensional or multidimensional scale/s construction. Typically, in many studies (especially those pertaining to market research area) a common factor analysis solution is used (based on continuous data). However, there are rarely ever undertaken studies pertaining to latent variable models where other type of data is used based on discrete variables. One of these models might be called Latent Profile Factor Analysis - LPFA. In this article author’s main objective is to propose and discuss its (LPFA) main assumptions. In order to prove the model’s functionality in practice of market research, a brief example of LPFA model for ordered categorical data (based on one-factorial solution) in reference to hedonic consumption data is given at the end of the paper.
On the parameter estimation of ARMA(p,q) model by approximate Bayesian computation

In this paper, the parameter estimation of ARMA(p,q) model is given by approximate Bayesian computation algorithm. In order to improve the sampling efficiency of the algorithm, approximate Bayesian computation should select as many statistics as possible with parameter information in low dimension. Firstly, we use the autocorrelation coefficient of the first p+q order sample as the statistic and obtain an approximate Bayesian estimation of the AR coefficient, transforming the ARMA(p,q) model into the MA(q) model. Considering the first q order sample autocorrelation functions and sample variance as the statistics, the approximate Bayesian estimation of MA coefficient and white noise variances can be given. The method mentioned above is more accurate and powerful than the maximum likelihood estimation, which is verified by the numerical simulations and experiment study.
Impact of loop transformations on software reliability

Application-level correctness is a useful and widely accepted concept for many kinds of applications in this modern world. The results of some applications, such as multimedia, may be incorrect due to transient hardware faults or soft-errors, but they are still acceptable from a user's perspective. Thus, it is worthwhile to develop approaches to guarantee application-level correctness in software, instead of hardware, to reduce cost and save energy. Many previous research efforts presented solutions to identify parts of programs that may potentially cause unacceptable results, and placed error detectors to improve reliability. On the other hand, we observe that loop transformations have the ability to improve reliability. By applying suitable loop transformations, some critical instructions may become non-critical. In this paper we propose a metric to analyze the reliability impact of each loop transformation. Thus, we can guide a compiler to optimize programs not only for reliability improvement, but for energy saving. The experimental results show that our analysis perfectly matches the results of fault injection, and achieves a 39.72% energy saving while improving performance by 52.16% when compared with [1]. To our knowledge, this is the first work that considers a software reliability by loop transformations.
Approximation algorithms for allocation problems: Improving the factor of 1 - 1/e

Combinatorial allocation problems require allocating items to players in a way that maximizes the total utility. Two such problems received attention recently, and were addressed using the same linear programming (LP) relaxation. In the maximum submodular welfare (SMW) problem, utility functions of players are submodular, and for this case Dobzinski and Schapira [SODA 2006] showed an approximation ratio of 1 - 1/e. In the generalized assignment problem (GAP) utility functions are linear but players also have capacity constraints. GAP admits a (1 - 1/e)-approximation as well, as shown by Fleischer, Goemans, Mirrokni and Sviridenko [SODA 2006]. In both cases, the approximation ratio was in fact shown for a more general version of the problem, for which improving 1 - 1/e is NP-hard. In this paper, we show how to improve the 1 - 1/e approximation ratio, both for SMW and for GAP. A common theme in both improvements is the use of a new and optimal fair contention resolution technique. However, each of the improvements involves a different rounding procedure for the above mentioned LP. In addition, we prove APX-hardness results for SMW (such results were known for GAP). An important feature of our hardness results is that they apply even in very restricted settings, e.g. when every player has nonzero utility only for a constant number of items
Effects of 2-Deoxyglucose on ischemic brain injuries in rats

Objectives: In vivo and in vitro studies have shown that 2-deoxyglucose (2-DG) administration enhances tolerance and exerts neuroprotection against ischemic injury or oxidative stress. In this study, we investigated the effects of 2-DG on ischemic brain injuries in rats and determined whether the effects are related to sublethal endoplasmic reticulum (ER) stress. Methods: 2-DG was administered systemically 7 d before the rats were subjected to focal cerebral ischemia (2 h) followed by reperfusion. Neurological score and infarct volume were evaluated, and protein expression of ER molecular chaperone glucose-regulated protein 78 (GRP78) and X-box protein-1 (XBP-1) was determined at different time points after reperfusion. Results: 2-DG pretreatment significantly decreased neurological scores after reperfusion for 3 h, 6 h, 12 h, and 24 h, reduced infarct volume at 24 h after reperfusion compared to the corresponding control groups. ER molecular chaperone GRP78 and XBP-1 increased in 2-DG pretreatment group as compared to the control. Conclusion: Pretreatment with 2-DG improves the neurological function after cerebral ischemia-reperfusion injury. Increased expression of ER chaperone GRP78 and activation of XBP-1 may contribute to the protective effect of 2-DG against brain injury.
Impulsivity is Associated with Increased Metabolism in the Fronto-Insular Network in Parkinson’s Disease

Various neuroimaging studies demonstrated that the fronto-insular network is implicated in impulsive behavior. We compared glucose metabolism (as a proxy measure of neural activity) among 24 patients with Parkinson’s disease (PD) who presented with low or high levels of impulsivity based on the Barratt Impulsiveness Scale 11 (BIS) scores. Subjects underwent 18-fluorodeoxyglucose positron emission tomography (FDG-PET) and the voxel-wise group difference of FDG-metabolism was analyzed in Statistical Parametric Mapping (SPM8). Subsequently, we performed a partial correlation analysis between the FDG-metabolism and BIS scores, controlling for covariates (i.e., age, sex, severity of disease and levodopa equivalent daily doses). Voxel-wise group comparison revealed higher FDG-metabolism in the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), and right insula in patients with higher impulsivity scores. Moreover, there was a positive correlation between the FDG-metabolism and BIS scores. Our findings provide evidence that high impulsivity is associated with increased FDG-metabolism within the fronto-insular network in PD.
Predictive control in matrix converters — Part I: Principles, topologies and applications

This paper presents an overview of the predictive control principles applied to matrix converters and also the different topologies where this control technique is applied. It will be shown that the predictive strategy is a promising alternative to control matrix converters due to its simplicity and flexibility to include additional aspects in the control being suitable for different industrial applications.
Variation of daily warm season mortality as a function of micro-urban heat islands

Background: Little attention has been paid to how heat-related health effects vary with the micro-urban variation of outdoor temperatures. This study explored whether people located in micro-urban heat islands are at higher risk of mortality during hot summer days. Methods: Data used included (1) daily mortality for Montreal (Canada) for June–August 1990–2003, (2) daily mean ambient outdoor temperatures at the local international airport and (3) two thermal surface images (Landsat satellites, infrared wavelengths). A city-wide temperature versus daily mortality function was established on the basis of a case-crossover design; this function was stratified according to the surface temperature at decedents’ place of death. Results: The risk of death on warm summer days in areas with higher surface temperatures was greater than in areas with lower surface temperatures. Conclusions: This study suggests that measures aimed at reducing the temperature in micro-urban heat islands (eg, urban greening activities) may reduce the health impact of hot temperatures. Further studies are needed to document the variation of heat-related risks within cities and to evaluate the health benefits of measures aimed at reducing the temperature in micro-urban heat islands.
Short-term electric load forecasting using computational intelligence methods

Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce several methods for short-term electric load forecasting. All the presented methods stem from computational intelligence techniques: Random Forest, Nonlinear Autoregressive Neural Networks, Evolutionary Support Vector Machines and Fuzzy Inductive Reasoning. The performance of the suggested methods is experimentally justified with several experiments carried out, using a set of three time series from electricity consumption in the real-world domain, on different forecasting horizons.
Combining Methods for the TREC 2003 Robust Track

For ranked retrieval, we have come to rely on a statistical language model to compute query/document similarity values. Hiemstra and de Vries describe such a linguistically motivated probabilistic model and explain how it relates to both the Boolean and vector space models [4]. The model has also been cast as a rudimentary Hidden Markov Model [7]. Although the model does not explicitly incorporate inverse document frequency, it does favor documents that contain more of the rare query terms. The similarity measure can be computed as
The Progressive Simplification of the Infertility Evaluation

Objective. With the advent of evidence-based medicine, much of the complex testing that infertile couples used to undergo is no longer recommended as part of the routine evaluation. Examples of tests no longer implemented include the postcoital test, Rubin test of tubal patency, endometrial biopsy, sperm antibody testing, and the zona-free hamster test. The role of routine diagnostic laparoscopy and hysteroscopy is also reviewed. Conclusions. In 2010, the simplicity of the current evaluation of couples presenting with infertility is essential information for patients to know at the initial visit. Ideally, it can help alleviate anxiety and provide patients with a road-map during a visit that is often associated with stress and high emotions. Target Audience: Obstetricians & Gynecologists, Family Physicians. Learning Objectives: After completion of this educational activity, the obstetrician/gynecologist should be better able to assess the current recommendations for evaluating infertile couples; examine the various ways to assess ovarian reserve, the uterine cavity, and fallopian patency; and appraise the controversy surrounding diagnostic surgery.
Geographic Entity Relationship Extraction Model Based on Piecewise Convolution of Residual Network

Nowadays, geographic entity relationship extraction systems generally rely on artificial feature extraction. These features either require complex and complete data sets, or cannot describe deep features such as semantics. And data sets that can be used for geographic relationship extraction are scarce. To tackle these problems, this paper uses distant supervision to map existing knowledge bases into rich unstructured data which contributes to a large amount of training data. In training, this paper uses the deep residual network to extract more abstract and deeper features. Then the piecewise max pooling and selective attention mechanisms are used to further improve the accuracy of the model. Finally, the experimental results show that the deeper network and the piecewise max pooling significantly improve the extraction results.
Optimal self regulating stand-alone wave energy conversion scheme

The paper presents a novel AC and DC stabilization scheme system for a Wave Energy Converter (WEC) using Permanent Magnet Linear Generator PMLG connected to a six pulse diode rectifier. The proposed dynamic controller of the WEC-Battery renewable energy system is based on Particle Swarm Optimization PSO technique. The optimization search algorithm is implemented to control a novel low cost dynamic AC side dynamic filter/capacitor compensation green plug filter and PMDC speed controller scheme. The Dynamic Modulated Filter Capacitor Compensation scheme (MPFC) serves as an AC side voltage stabilization regulator for wave energy utilization enhancement. The objective is to extract available power from the wave energy system (WES) and transfer this power efficiency to the local isolated stabilized DC side electric load while dynamically controlling the speed of the PMDC motors.
Magnetic Field "Flyby" Measurement Using a Smartphone's Magnetometer and Accelerometer Simultaneously.

The spatial dependence of magnetic fields in simple configurations is a common topic in introductory electromagnetism lessons, both in high school and in university courses. In typical experiments, magnetic fields and distances are obtained taking point-by-point values using a Hall sensor and a ruler, respectively. Here, we show how to take advantage of the smartphone capabilities to get simultaneous measures with the built-in accelerometer and magnetometer and to obtain the spatial dependence of magnetic fields. We consider a simple setup consisting of a smartphone mounted on a track whose direction coincides with the axis of a coil. While the smartphone is moving on the track, both the magnetic field and the distance from the center of the coil (integrated numerically from the acceleration values) are simultaneously obtained. This methodology can easily be extended to more complicated setups.
Please, do not Decentralize the Internet with (Permissionless) Blockchains!

The old mantra of decentralizing the Internet is coming again with fanfare, this time around the blockchain technology hype. We have already seen a technology supposed to change the nature of the Internet: peer-to-peer. The reality is that peer-to-peer naming systems failed, peer-to-peer social networks failed, and yes, peer-to-peer storage failed as well. In this paper, we will review the research on distributed systems in the last few years to identify the limits of open peer-to-peer networks. We will address issues like system complexity, security and frailty, instability and performance. We will show how many of the aforementioned problems also apply to the recent breed of permissionless blockchain networks. The applicability of such systems to mature industrial applications is undermined by the same properties that make them so interesting for a libertarian audience: namely, their openness, their pseudo-anonymity and their unregulated cryptocurrencies. As such, we argue that permissionless blockchain networks are unsuitable to be the substrate for a decentralized Internet. Yet, there is still hope for more decentralization, albeit in a form somewhat limited with respect to the libertarian view of decentralized Internet: in cooperation rather than in competition with the superpowerful datacenters that dominate the world today. This is derived from the recent surge in interest in byzantine fault tolerance and permissioned blockchains, which opens the door to a world where use of trusted third parties is not the only way to arbitrate an ensemble of entities. The ability of establish trust through permissioned blockchains enables to move the control from the datacenters to the edge, truly realizing the promises of edge-centric computing.
A Genetic Algorithm for Optimizing the Label Ordering in Multi-label Classifier Chains

First proposed in 2009, the classifier chains model (CC) has become one of the most influential algorithms for multi-label classification. It is distinguished by its simple and effective approach to exploit label dependencies. The CC method involves the training of q single-label binary classifiers, where each one is solely responsible for classifying a specific label in ll, . , lq. These q classifiers are linked in a chain, such that each binary classifier is able to consider the labels predicted by the previous ones as additional information at classification time. The label ordering has a strong effect on predictive accuracy, however it is decided at random and/or combining random orders via an ensemble. A disadvantage of the ensemble approach consists of the fact that it is not suitable when the goal is to generate interpretable classifiers. To tackle this problem, in this work we propose a genetic algorithm for optimizing the label ordering in classifier chains. Experiments on diverse benchmark datasets, followed by the Wilcoxon test for assessing statistical significance, indicate that the proposed strategy produces more accurate classifiers.
Accelerating statistical static timing analysis using graphics processing units

We explore the implementation of Monte Carlo based statistical static timing analysis (SSTA) on a Graphics Processing Unit (GPU). SSTA via Monte Carlo simulations is a computationally expensive, but important step required to achieve design timing closure. It provides an accurate estimate of delay variations and their impact on design yield. The large number of threads that can be computed in parallel on a GPU suggests a natural fit for the problem of Monte Carlo based SSTA to the GPU platform. Our implementation performs multiple delay simulations at a single gate in parallel. A parallel implementation of the Mersenne Twister pseudo-random number generator on the GPU, followed by Box-Muller transformations (also implemented on the GPU) is used for generating gate delay numbers from a normal distribution. The µ and σ of the pin-to-output delay distributions for all inputs and for every gate, are obtained using a memory lookup, which benefits from the large memory bandwidth of the GPU. Threads which execute in parallel have no data/control dependencies on each other. All threads compute identical instructions, but on different data, as required by the Single Instruction Multiple Data (SIMD) programming semantics of the GPU. Our approach is implemented on a NVIDIA GeForce GTX 8800 GPU card. Our results indicate that our approach can obtain an average speedup of about 260× as compared to a serial CPU implementation. With the recently announced quad 8800 GPU cards, we estimate that our approach would attain a speedup of over 785×. The correctness of the Monte Carlo based SSTA implemented on a GPU has been verified by comparing its results with a CPU based implementation.
Classification for Volume Rendering of Industrial CT Based on Moment of Histogram

In the volume rendering, the classification step is used to determine voxel visibility and plays an important role. In this paper, a new classification algorithm, which is based on moment of histogram of volume data, is proposed for volume rendering. Firstly, the histogram of volume data is partitioned into different subsections by the number of object classes, which is developed through calculating the entropy of the histogram of volume data. Secondly, in each subsection a threshold is computed according to the moment of the histogram of volume data. Finally, the opacity of each voxel is assigned by a transfer function, which is split by these thresholds into subsection. Three schemes based on the moment of histogram of volume data are applied to determine the threshold and three corresponded results is compared. Examples from industrial CT dataset are presented. In the volume rendering results, industrial components are shown explicitly and can be successfully disassembled in the simulated by the computer.
An 8-GHz to 10-GHz Distributed DLL for Multiphase Clock Generation

A distributed DLL (DDLL) with low jitter and high phase accuracy is proposed for the multiphase clock generator. The high-speed multiphase clock generator produces a five-phase clock at a frequency range of 8 to 10 GHz. Additionally, the discrete-time model for the distributed DLL and the analysis about stability and noise are proposed in this work. The measured rms jitter is 293.3 fs and the maximum phase mismatch is 1.4 ps. The proposed architecture can suppress the jitter by 58%. The distributed DLL occupies 0.03 mm2 active area in a 90-nm CMOS technology and consumes 15 mA from a 1.0-V supply.
Decentralized control design for fuzzy large-scale systems: High-order uncertainty bound

A class of lager-scale uncertain interconnected systems is introduced. The uncertainty is bounded and the bound is in high-order form that may be due to the unknown parameters and input disturbance. The available information of the uncertainty is prescribed by fuzzy characteristics. A new decentralized control design is adopted for the system. The optimal design problem is solved by obtaining the minimization of a performance index with the decentralized control method which reflects the system's average fuzzy characteristics. The solution to this optimization system performance index is proved to be always exists and is unique. Furthermore, the closed-form expression of the solution is obtained. Finally, the special case that without the consideration of transient effect in limiting performance is shown for engineering significance.
Genotyping microsatellite polymorphisms by agarose gel electrophoresis with ethidium bromide staining: application to quantitative trait loci analysis of seizure susceptibility in mice

&NA; Agarose gel electrophoresis with ethidium bromide staining (AGE/EBS) is an efficient and reliable method for analyzing microsatellite polymorphisms. We report the use of AGE/EBS for analyzing DNA microsatellite polymorphisms in a preliminary quantitative trait loci (QTL) study of seizure susceptibility in which a candidate gene strategy was used to direct initial mapping efforts. F2 intercross progeny, derived from seizure‐sensitive DBA/2J (D2) and seizure‐resistant C57BL/6J (B6) inbred strains of mice, were tested for their sensitivity to the seizure‐inducing effect of pentylenetetrazol (PTZ), a gamma‐aminobutyric acid (GABA) receptor antagonist. A semi‐automated method is described, in which DNA microsatellites were amplified by polymerase chain reaction (PCR) to yield products of 100‐200 base pair (bp) in length. Alleles were separated on 3‐6% MetaPhor agarose gels, stained with ethidium bromide, and visualized by ultraviolet (UV) illumination. Univariate analysis of genotype and phenotype data provides evidence for a seizure‐related QTL on chromosome 5, near genes coding for the GABAA receptor subunits &agr;5 and &ggr;3. Interestingly, this suggestive QTL derives from the more resistant B6 strain, but it nonetheless provides impetus for the characterization of possible strain differences in these two candidate genes. Overall, these results demonstrate that AGE/EBS can be useful for rapid screening of genomic regions of special interest in QTL mapping studies. Psychiatr Genet 8:227‐233 © 1998 Lippincott Williams & Wilkins.