Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating GPU Speedups for Programs Without Writing a Single Line of GPU Code

Heterogeneous processing using GPUs is here to stay and today spans mobile devices, laptops, and supercomputers. Although modern software development frameworks like OpenCL and CUDA serve as a high productivity environment, software development for GPUs is time consuming. First, much work needs to be done to restructure software and data organization to match the GPU’s many-threaded programming model. Second, code optimization is quite time consuming and performance analysis tools require significant expertise to use effectively. Third, until the final optimized code has been derived, it is almost impossible today to know what performance advantage will be provided by porting a code to a GPU. This paper focuses on this last question and seeks to develop an automated “performance prediction” tool that can provide accurate estimate of GPU speedup when provided a piece of CPU code prior to developing the GPU code. Our paper is built on two insights: i) Ultimately speedup on a GPU for a piece of code is dependent on fundamental microarchitecture-independent program properties like available parallelism, branching behavior etc. ii) By examining a vast array of previously implemented GPU codes alongwith their CPU counterpart, we can use machine learning to learn this correlation between program properties and GPU speedup. In this paper, we use linear regression, specifically, a technique inspired by regularized regression, to build a model for speedup prediction for GPUs. When applied to a never-seen test data selected randomly from Rodinia, Parboil, Lonestar and Parsec benchmark suites, as test data (speedup range of 5.9× to 276×), our tool makes accurate predictions with an average weighted error of 32%. Our technique is also robust the errors remain similar across other “unseen” GPU platforms we test on. Essentially, we deliver an automated tool that programmers can use to estimate potential GPU speedup before writing any GPU code.
A score test for variance components in a semiparametric mixed-effects model under non-normality

In this paper, we propose a score test for variance components in a semiparametric mixed-effects model when the random-effects and measurement errors are not normally distributed. The asymptotic null distribution of the test statistic is shown to be a simple chi-squared distribution with the degrees of freedom being the number of linearlyindependent variance components. The simulation results show that the proposed score test is robust against the nonnormality of the random-effects and the measurement errors and performs well in terms of both size and power. The score test is illustrated via an application to a real longitudinal data set collected in a clinical trial study. AMS 2000 subject classifications: Primary 62G10, 62G20; secondary 62G08.
A simulated annealing approach to define the genetic structure of populations

We present a new approach for defining groups of populations that are geographically homogeneous and maximally differentiated from each other. As a by‐product, it also leads to the identification of genetic barriers between these groups. The method is based on a simulated annealing procedure that aims to maximize the proportion of total genetic variance due to differences between groups of populations (spatial analysis of molecular variance; samova). Monte Carlo simulations were used to study the performance of our approach and, for comparison, the behaviour of the Monmonier algorithm, a procedure commonly used to identify zones of sharp genetic changes in a geographical area. Simulations showed that the samova algorithm indeed finds maximally differentiated groups, which do not always correspond to the simulated group structure in the presence of isolation by distance, especially when data from a single locus are available. In this case, the Monmonier algorithm seems slightly better at finding predefined genetic barriers, but can often lead to the definition of groups of populations not differentiated genetically. The samova algorithm was then applied to a set of European roe deer populations examined for their mitochondrial DNA (mtDNA) HVRI diversity. The inferred genetic structure seemed to confirm the hypothesis that some Italian populations were recently reintroduced from a Balkanic stock, as well as the differentiation of groups of populations possibly due to the postglacial recolonization of Europe or the action of a specific barrier to gene flow.
Environmental Health: a Global Access Science Source Serum Organochlorines and Urinary Porphyrin Pattern in a Population Highly Exposed to Hexachlorobenzene

Background: Porphyria cutanea tarda (PCT) is caused by hexachlorobenzene (HCB) in several species of laboratory mammals, but the human evidence is contradictory. In a study among adults of a population highly exposed to HCB (Flix, Catalonia, Spain), the prevalence of PCT was not increased. We aimed at analysing the association of individual urinary porphyrins with the serum concentrations of HCB and other organochlorine compounds in this highly exposed population.
(Re)Searching for the development of a conceptual model of education for citizenship in the context of young people’s globalised mobility in higher education

ABSTRACT This paper proposes a conceptual model of education for global citizenship by drawing on the literature on international student mobility for higher education. This model outlines the kinds of competencies and forms of consciousness that education for citizenship should focus upon so as to address the economic and moral dilemmas of living in a globalising world. In an integrative review process, this paper identifies the challenges and opportunities that globalised mobility brings to the lives of young people, the mobilities that higher educational institutions are embracing, the mobility motivations of young students in higher education, and the consequences of this globalised mobility for their present and future life.
Motivations , Values and Emotions : Three Sides of the same Coin

This position paper speaks to the interrelationships between the three concepts of motivations, values, and emotion. Motivations prime actions, values serve to choose between motivations, emotions provide a common currency for values, and emotions implement motivations. While conceptually distinct, the three are so pragmatically intertwined as to differ primarily from our taking different points of view. To make these points more transparent, we briefly describe the three in the context a cognitive architecture, the LIDA model, for software agents and robots that models human cognition, including a developmental period. We also compare the LIDA model with other models of cognition, some involving learning and emotions. Finally, we conclude that artificial emotions will prove most valuable as implementers of motivations in situations requiring learning and development.
Fear of Floating in Reverse : Exchange Rate Policy in the 2000 s 1

In recent years the term "fear of floating" has been used to describe exchange rate regimes that, while officially flexible, in practice intervene heavily to avoid sudden or large depreciations. However, the data reveals that in most cases (and increasingly so in the 2000s) intervention has been aimed at limiting appreciations rather than depreciations, often motivated by the neo-mercantilist view of a high real exchange rate as protection for domestic industries. As a first step to address the broader question of whether this view delivers on its promise, we examine whether this “fear of revaluation” has a positive impact on growth performance in developing economies. We show that undervalued exchange rates appear to induce higher growth, but that the effect, rather than through import substitution or export booms as argued by the mercantilist view, works largely through the deepening of domestic savings and capital accumulation. 1 We thank César Calderón, Luis Cubeddu, Javier Kinkman, Jeff Frankel, Ricardo Hausmann, Robert Lawrence, Ricardo Lopez Murphy, Guillermo Perry, Dani Rodrik, Luis Servén, and seminar participants at the Harvard Kennedy School and the Bank of Turkey for helpful comments. We thank Luis Casanova, Pablo Gluzmann, Victoria Coccoz and Ramiro Blázquez for excellent research assistance. The usual disclaimers apply.
Economic integration, intensity of competition and R&D incentives

A two-country differentiated duopoly model is set out in which economic integration increases firms' incentives to invest in R&D, purely through the effect of increased intensity of competition between firms. The model is extended to incorporate knowledge spillovers, which, if related to the degree of integration, give rise to an inverted u-shaped relationship between R&D incentives and integration. The model is also extended to the n-firm general equilibrium case in which integration stimulates economic growth through intensity of competition. As such, the model suggests a positive growth effect of economic integration that does not rely on the usual scale effects.
Ownership, R&D and Productivity Change: Assessing the Catch-up in China's High-tech Industries

This study contributes to the debate on whether China's domestic enterprises (DEs) have experienced a significant catch-up compared with foreign-funded enterprises (FFEs) in high-tech industries. Our paper tries to estimate a new set of capital stock and RqD capital stock by ownership for China's high-tech industries. Then, using this newly constructed data set, it assesses the comparative productivity performance of state-owned enterprises (SOEs) (as the most important proxy for DEs) in high-tech industries from 1996 to 2006. The results show that SOEs as a whole have experienced an inverted U-shape trajectory of catch-up for 1996-2006, while those SOEs which originate from competitive industries tend to show a better performance. With respect to the technology catch-up, SOEs in particular are still lagging behind because of their failure to develop indigenous technology capabilities.JEL Classification: L1, L5, O2, O3, O5, P3
An fNIRS Study of Brain State during Letter and Category Fluency Tasks

Functional Near-infrared Spectroscopy (fNIRS) is a technique that measures metabolic changes in hemoglobin oxygen in vivo, using near-infrared light. fNIRS detects changes in concentration of oxy-hemoglobin, deoxy-hemoglobin, total hemoglobin, and measures alteration in cerebral blood flow caused by neural activity [1,2]. Brain function measurement using fNIRS is noninvasive, has excellent temporal resolution, and is performed under routine conditions. In recent times, fNIRS as a tool in advanced medicine is attracting attention. fNIRS examination using Verbal Fluency Task (VFT) is widely being used in diagnosis of depression symptoms. VFT is a task that requires to generate as many words within a category or starting with a given letter as possible within a time limit. In the Letter Fluency Task (LFT), words are recalled from particular letter. In the Category Fluency Task (CFT), words are recalled from semantic categories. The characteristics of brain activity in VFT are different in depression, bipolar disorder and schizophrenia, therefore, diverse activating tasks are used for different disorders. It has been pointed out that VFT tests not only the semantic memory related to vocabulary but also many cognitive functions such as effective vocabulary search power, information processing speed, and executive function [3]. There are several studies on brain activity differences in LFT and CFT of healthy subjects and patients with a mental illness [4–8]. However, only a few studies compare LFT and CFT as such. In this study, brain activities during LFT and CFT were compared in healthy subjects. We hypothesized a difference in brain activity between LFT and CFT and therefore, the purpose of the current study was to investigate the brain status of the two versions of the VFT over a wide area of the frontal and bilateral temporal brain regions using 40 channels of fNIRS.
Achievable Rate and Energy Efficiency of Hybrid and Digital Beamforming Receivers With Low Resolution ADC

For 5G, it will be important to leverage the available millimeter wave spectrum. To achieve an approximately omnidirectional coverage with a similar effective antenna aperture compared with the state-of-the-art cellular systems, an antenna array is required at both the mobile and base stations. Due to the large bandwidth, the analog front-end of the receiver with a large number of antennas becomes especially power hungry. Two main solutions exist to reduce the power consumption: Hybrid BeamForming (HBF) and Digital BeamForming (DBF) with low resolution Analog to Digital Converters (ADCs). An HBF system can also be combined with low resolution ADCs. This paper compares the spectral and energy efficiency based on the RF-frontend configuration. A channel with multipath propagation is used. In contrast to previous publication, we take the spatial correlation of the quantization noise into account. We show that the low resolution ADC DBF is robust to small Automatic Gain Control (AGC) imperfections. We showed that in the low SNR regime, the performance of DBF even with 1–2 bit resolution outperforms HBF. If we consider the relationship of spectral and energy efficiency, DBF with 3–5 bit resolution achieves the best ratio of spectral efficiency per power consumption of the RF receiver frontend over a wide SNR range. The power consumption model is based on components reported in the literature.
Empirical Geometry-Based Random-Cluster Model for High-Speed-Train Channels in UMTS Networks

In this paper, a recently conducted measurement campaign for high-speed-train (HST) channels is introduced, where the downlink signals of an in-service Universal Mobile Terrestrial System (UMTS) deployed along an HST railway between Beijing and Shanghai were acquired. The channel impulse responses (CIRs) are extracted from the data received in the common pilot channels (CPICHs). Within 1318 km, 144 base stations (BSs) were detected. Multipath components (MPCs) estimated from the CIRs are clustered and associated across the time slots. The results show that, limited by the sounding bandwidth of 3.84 MHz, most of the channels contain a single line-of-sight (LoS) cluster, and the rest consists of several LoS clusters due to distributed antennas, leaking cable, or neighboring BSs sharing the same CPICH. A new geometry-based random-cluster model is established for the clusters' behavior in delay and Doppler domains. Different from conventional models, the time-evolving behaviors of clusters are characterized by random geometrical parameters, i.e., the relative position of BS to railway, and the train speed. The distributions of these parameters, and the per-cluster path loss, shadowing, delay, and Doppler spreads, are extracted from the measurement data.
Adaptive neural network tracking control of manipulators using quaternion feedback

An adaptive neural network controller is proposed to deal with the task-space tracking problem of manipulators with kinematic and dynamic uncertainties. The orientation of manipulator is represented by the unit quaternion, which avoids singularities associated with three-parameter representation. By employing the adaptive Jacobian scheme, neural networks, and backstepping technique, the torque controller is obtained which is demonstrated to be stable by the Lyapunov approach. The adaptive updating laws for controller parameters are derived by the projection method, and the tracking error can be reduced as small as desired. The favorable features of the proposed controller lie in that: (1) the uncertainty in manipulator kinematics is taken into account; (2) the unit quaternion is used to represent the end-effector orientation; (3) the "linearity-in-parameters" assumption for the uncertain terms in dynamics of manipulators is no longer necessary; (4) effects of external disturbances are also considered in the controller design. Finally, the satisfactory performance of the proposed approach is illustrated by simulation results on a PUMA 560 robot.
Symmetrical and Asymmetrical H-Bridge Multilevel Inverter for DTC Induction Motor Drive Automotive Applications

AbstractSeveral works have been reported in the literature on multilevel inverters topologies, control techniques and applications. However, there are few works that actually discuss or evaluate the performance of induction motor drives associated to three-phase multilevel inverter. This paper presents then a comparison study for a cascaded H-bridge multilevel DTC induction motor drive. In this case, symmetrical and asymmetrical arrangements of five and seven level H-bridge inverters are compared in order to find an optimum arrangement with lower switching losses and optimized output voltage quality. The carried out experiments shows that an asymmetrical configuration provides nearly sinusoidal voltages with very low distortion, using less switching devices. Due to the small dv/dts, torque ripple is greatly reduced. In addition, it generates a high switching frequency output voltage with less switching losses, since only the small power cells of the inverter operate at high switching rate. Therefore a high performance and also efficient torque and flux controller is obtained, enabling a DTC solution for multilevel inverter powered induction motor drives. Index Terms Induction motor, Direct Torque Control (DTC), multilevel inverters.
Instruction Set Extension Exploration in Multiple-Issue Architecture

To satisfy high-performance computing demand in modern embedded devices, current embedded processor architectures provide designer with possibility either to define customized instruction set extension (ISE) or to increase instruction issue width. Previous studies have shown that deploying ISE in multiple-issue architecture can significantly improve performance. However, identifying ISE for multiple-issue architecture by using current ISE exploration algorithms will result in unnecessary waste of silicon area and limitation of performance improvement. This is because most algorithms overlook two important considerations: (1) only packing the operations lying on the critical path into ISE can improve performance; (2) the critical path usually changes after packing operations into an ISE. With these considerations, this paper presents an algorithm for ISE exploration based on list scheduling and Ant Colony Optimization (ACO), in which combines ISE exploration and the critical path identification (i.e. instruction scheduling). Results indicate that our approach outperforms the previous work in both performance improvement and area efficiency.
Automatic classification of land cover with high resolution data of the Rio de Janeiro City Brazil

The city of Rio de Janeiro, with more than 5 million inhabitants, has two big mountains in its centre with natural Atlantic rain forest. The growth of the city around these two mountains puts pressure on this forest. This endangers the remaining Atlantic forest (one of the most endangered forest types of the world) and also causes landslides and mud flows. The city administration carried out a land-cover forest classification with visual interpretation using merged LANDSAT and SPOT data. This work produced a compatible thematic map in the scale 1:50,000. It took about 36 months of intensive work and high costs to produce these maps. The scale of these maps permit to have a global vision of the land change cover but unfortunately do not correspond with the geographic information system of the city, which works with a scale of 1:10,000. The city searched for options to make this work automatically and quickly to get information for planning and to propose solutions. In order to solve this problem high resolution satellite data and automatic classification of land-cover classes are needed. Consequently, images as IKONOS (multispectral with four bands and 4 meters) need to be used to produce a classification, with a scale corresponding to the GIS of the city. The automatic classification of land-cover classes provides a relatively rapid classification. Pixel based classification with high resolution data show some problems because the level of information in the data produce a lot of incorrect classified pixels. The solution to perform this classification uses the new approach that makes one "pre-classification" (classification), which transforms the pixel information in objects as well as the feature in the vector representation.
Splicing Variants of SERPINA1 Gene in Ovine Milk: Characterization of cDNA and Identification of Polymorphisms

The serine protease inhibitor, clade A, member 1 (SERPINA1) is the gene for a protein called alpha-1-antitrypsin (AAT), which is a member of the serine protease inhibitor (serpin) superfamily of proteins. By conformational change, serpins control several chemical reactions inhibiting the activity of proteases. AAT is the most abundant endogenous serpin in blood circulation and it is present in relatively high concentration in human milk as well as in bovine and porcine colostrum. Here we report for the first time the molecular characterization and sequence variability of the ovine SERPINA1 cDNA and gene. cDNAs from mammary gland and from milk were PCR amplified, and three different transcripts (1437, 1166 and 521bp) of the SERPINA1 gene were identified. We amplified and sequenced different regions of the gene (5’ UTR, from exon 2 to exon 5 and 3’ UTR), and we found that the exon-intron structure of the gene is similar to that of human and bovine. We detected a total of 97 SNPs in cDNAs and gene sequences from 10 sheep of three different breeds. In adult sheep tissues a SERPINA1 gene expression analysis indicated a differential expression of the three different transcripts. The finding reported in this paper will aid further studies on possible involvement of the SERPINA1 gene in different physiological states and its possible association with production traits.
A Recursive Hyperspheric Classification Algorithm

This paper presents a novel method for learning from a labeled dataset to accurately classify unknown data. The recursive algorithm, termed Recursive Hyperspheric Classification, or RHC, can accurately learn the classes of a labeled, n-dimensional dataset via a training method that recursively spawns a set of hyperspheres, endeavoring to separate and divide the feature space into partitions. This produces a comprehensive mapping of the space. These hyperspheres provide guidance for the search because they are recursively traversed. Some benchmarking has been performed on various data sets and has shown to yield superior results to more traditional artificial methods.
An attribute based classification of hardware trojans

This paper considers the classification of hardware trojans in semiconductor chips. The phases of the chip production life-cycle are reviewed and opportunities for trojan insertion are discussed. Trojans are classified using a comprehensive attribute taxonomy based on eight categories. A matrix describing the relationships between these attributes is defined which can be used to identify hardware trojans.
Substance Abuse Treatment, Prevention, and Policy High School Drinking Mediates the Relationship between Parental Monitoring and College Drinking: a Longitudinal Analysis

Background: College drinking is a significant public health problem. Although parental monitoring and supervision reduces the risk for alcohol consumption among younger adolescents, few studies have investigated the impact of earlier parental monitoring on later college drinking. This study examined whether parental monitoring indirectly exerts a protective effect on college drinking by reducing high school alcohol consumption.