Miracle-dz/UniFAR-FTUs huggingface dataset:
PROGRESA and Its Impacts on the Welfare of Rural Households in Mexico

"This document synthesizes the findings contained in a series of reports prepared by IFPRI for PROGRESA between November 1998 and November 2000. PROGRESA is one of the major programs of the Mexican government aimed at developing the human capital of poor households. Targeting its benefits directly to the population in extreme poverty in rural areas, PROGRESA aims to alleviate current and future poverty levels through cash transfers to mothers in households. One of the most important contributions of IFPRI's evaluation of PROGRESA has been the continuation of the program in spite of the historic change in the government of Mexico in the 2000 elections. The overwhelming (and unprecedented) evidence that a poverty alleviation program shows strong signs of having a significant impact on the welfare and human capital investment of poor rural families in Mexico has contributed to the decision of the Fox administration to continue with the program and to expand its coverage in the poor urban areas of the country after some improvements in the design of the program. The majority of the improvements in the design of PROGRESA (renamed Oportunidades by the Fox administration) were based on findings of the evaluation of PROGRESA that revealed areas of needed improvements in some of the structural components and the operation of the program. Yet in spite of these improvements in the program, the evaluation findings suggest that some issues remain to be resolved. " from Text
Using Hessian Locally Linear Embedding for autonomic failure prediction

The increasing complexity of modern distributed systems makes conventional fault tolerance and recovery prohibitively expensive. One of the promising approaches is online failure prediction. However, the process of feature extraction depends on the experienced administrators and their domain knowledge to filtering and compressing error events into a form that is easy for failure prediction. In this paper, we present a novel performance-centric approach to automate failure prediction with Manifold Learning techniques. More specifically, we focus on methods that use Supervised Hessian Locally Embedding algorithm to achieve autonomic failure prediction. In our experimental work we found that our method can automatically predict more than 60% of the CPU and memory failures, and around 70% of the network failure based on the runtime monitoring of the performance metrics.
Exploiting Doppler Diversity In Mobile Wireless Communications

Signal fading due to variations in channel characteristics is one of the major factors limiting the performance of modern mobile wireless communication systems. To overcome fading, various diversity techniques have been developed that eeectively transmit multiple copies of the signal over the channel. The state-of-the-art receiver used in code-division multiple access systems is the RAKE receiver that exploits multipath diversity. However, the RAKE receiver assumes slow channel variations over time and the noncoherent RAKE is often used in practice for robustness to such variations. Temporal channel variations are caused by relative motion between the transmitter and receiver and often become signiicant in mobile communications scenarios. Even though the performance of the RAKE receiver substantially degrades in such situations, we show that the Doppler induced by rapid channel variations in fact provides another means for diversity that can be further exploited to combat fading. We develop the concept of Doppler diversity and propose a new framework that exploits joint mul-tipath-Doppler diversity in an optimal fashion. The framework is applicable in several existing and proposed mobile wireless multiple access systems, and can provide substantial performance improvement over the RAKE receiver.
Some issues in dense linear algebra for multicore and special purpose architectures

We address some key issues in designing dense linear algebra (DLA) algorithms that are common for both multi/many-cores and special purpose architectures (in particular GPUs). We present them in the context of an LU factorization algorithm, where randomization techniques are used as an alternative to pivoting. This approach yields an algorithm based entirely on a collection of small Level 3 BLAS type computational tasks, which has emerged as a common goal in designing DLA algorithms for new architectures. Other common trends, also considered here, are block asynchronous task execution and “Block” layouts for the data associated with the separate tasks. We present numerical results and other specific experiments with DLA algorithms on NVIDIA GPUs using CUDA. The GPU results are also of interest themselves as we show a performance of up to 160 Glop/s on a single Quadro FX 5600 card.
A NOTE ON THE LASSO AND RELATED PROCEDURES IN MODEL SELECTION

The Lasso, the Forward Stagewise regression and the Lars are closely related procedures recently proposed for linear regression problems. Each of them can produce sparse models and can be used both for estimation and variable selec- tion. In practical implementations these algorithms are typically tuned to achieve optimal prediction accuracy. We show that, when the prediction accuracy is used as the criterion to choose the tuning parameter, in general these procedures are not consistent in terms of variable selection. That is, the sets of variables selected are not consistently the true set of important variables. In particular, we show that for any sample size n, when there are superuous variables in the linear regression model and the design matrix is orthogonal, the probability that these procedures correctly identify the true set of important variables is less than a constant (smaller than one) not depending on n. This result is also shown to hold for two-dimensional problems with general correlated design matrices. The results indicate that in prob- lems where the main goal is variable selection, prediction-accuracy-based criteria alone are not sucien t for this purpose. Adjustments will be discussed to make the Lasso and related procedures useful/consistent for variable selection.
Synthesis and Characterization of Copolymers of 2- methyl aniline with aniline and 2-aminobenzoic acid Capacity

The chemical copolymerization of 2-methyl aniline with aniline and 2-aminobenzoic acid has been performed by chemical and inverse emulsion polymerization. 2-methyl aniline–aniline copolymer and 2- methyl aniline–co–2-aminobenzoic acid were synthesized at different molar ratios and at different temperatures. The copolymers of 2-methyl aniline with aniline show a greater resemblance to the poly(2- methyl aniline) due to the higher amounts of 2- methyl aniline in the copolymer, this lead to an increase in the solubility, but a lowering in the conductivity. The synthesis of 2-methyl aniline–2-aminobenzoic acid copolymer show high yield and conductivity values at room temperature compared to those synthesized at 0 and 60OC due to higher rate of polymerization of poly (2- methyl aniline) at room temperature. The characterization of the copolymers was investigated using a number of techniques including FTIR, UV–Vis, conductivity and elemental analysis.
The Technology Dependence of Lightweight Hash Implementation Cost

The growing demand of security features in pervasive computing requires cryptographic implementations to meet tight cost constraints. Lightweight Cryptography is a generic term that captures new efforts in this area, covering lightweight cryptography proposals as well as lightweight implementation techniques. This paper demonstrates the influence of technology selection when comparing different lightweight hash designs and when using lightweight cryptography techniques to implement a hash design. First, we demonstrate the impact of technology selection to the cost analysis of existing lightweight hash designs through two case studies: the new lightweight proposal Quark [1] and a lightweight implementation of CubeHash [2]. Second, by observing the interaction of hash algorithm design, architecture design, and technology mapping, we propose a methodology for lightweight hash implementation and apply it to Cubehash optimizations. Finally, we introduce a cost model for analyzing the hardware cost of lightweight hash implementations.
Host cell interactions of outer membrane vesicle-associated virulence factors of enterohemorrhagic Escherichia coli O157: Intracellular delivery, trafficking and mechanisms of cell injury

Outer membrane vesicles (OMVs) are important tools in bacterial virulence but their role in the pathogenesis of infections caused by enterohemorrhagic Escherichia coli (EHEC) O157, the leading cause of life-threatening hemolytic uremic syndrome, is poorly understood. Using proteomics, electron and confocal laser scanning microscopy, immunoblotting, and bioassays, we investigated OMVs secreted by EHEC O157 clinical isolates for virulence factors cargoes, interactions with pathogenetically relevant human cells, and mechanisms of cell injury. We demonstrate that O157 OMVs carry a cocktail of key virulence factors of EHEC O157 including Shiga toxin 2a (Stx2a), cytolethal distending toxin V (CdtV), EHEC hemolysin, and flagellin. The toxins are internalized by cells via dynamin-dependent endocytosis of OMVs and differentially separate from vesicles during intracellular trafficking. Stx2a and CdtV-B, the DNase-like CdtV subunit, separate from OMVs in early endosomes. Stx2a is trafficked, in association with its receptor globotriaosylceramide within detergent-resistant membranes, to the Golgi complex and the endoplasmic reticulum from where the catalytic Stx2a A1 fragment is translocated to the cytosol. CdtV-B is, after its retrograde transport to the endoplasmic reticulum, translocated to the nucleus to reach DNA. CdtV-A and CdtV-C subunits remain OMV-associated and are sorted with OMVs to lysosomes. EHEC hemolysin separates from OMVs in lysosomes and targets mitochondria. The OMV-delivered CdtV-B causes cellular DNA damage, which activates DNA damage responses leading to G2 cell cycle arrest. The arrested cells ultimately die of apoptosis induced by Stx2a and CdtV via caspase-9 activation. By demonstrating that naturally secreted EHEC O157 OMVs carry and deliver into cells a cocktail of biologically active virulence factors, thereby causing cell death, and by performing first comprehensive analysis of intracellular trafficking of OMVs and OMV-delivered virulence factors, we provide new insights into the pathogenesis of EHEC O157 infections. Our data have implications for considering O157 OMVs as vaccine candidates.
Impact phase kinematics of instep kicking in soccer

Abstract The purpose of this study was to capture the lower limb kinematics before during and after ball impact of soccer kicking by examining the influence of both sampling rate and smoothing procedures. Nine male soccer players performed maximal instep kicks and the three-dimensional leg movements were captured at 1000 Hz. Angular and linear velocities and accelerations were determined using four different processing approaches: processed using a modified version of a time-frequency filtering algorithm (WGN), smoothed by a second-order low-pass Butterworth filter at 200 Hz cut-off (BWF), re-sampled at 250 Hz without smoothing (RSR) and re-sampled at 250 Hz but filtered by the same Butterworth filter at 10 Hz cut-off (RSF). The WGN approach appeared to establish representative kinematics, whereas the other procedures failed to remove noisy oscillation from the baseline of signal (BWF), lost the peaks of rapid changes (RSR) or produced totally distorted movement patterns (RSF). The results indicate that the procedures used by some previous studies may have been insufficient to adequately capture the lower limb motion near ball impact. We propose a new time-frequency filtering technique as a better way to smooth data whose frequency content varies dramatically.
Some results on SLAM and the closing the loop problem

This paper addresses the closing loop problem as the challenge of using all the information from the observation gathered when closing the loop in order to optimally adjust the whole map (assuming a correct data association). The proposed approach is an approximation, which allows the calculation of the gain without keeping track of all the correlations (i.e. with a complexity independent of the number of the map elements). Furthermore, the paper presents an explicit mathematical demonstration showing that the correlations computed by the EKF-based SLAM are overestimated. More precisely, it is shown that these correlations decrease exponentially with respect to the heading error of the robot. The approach is empirically demonstrated by means of meaningful simulations. The results are then discussed and conclusions are pointed out in the last section.
Preferences vs Constraints Revisited : Multilevel Modelling of Women ' s Working Time Preferences in England and Scotland

This paper investigates determinants of the probability of preferring to work less hours among parttime and full-time working women aged 16 to 59 in England and Scotland in 2005-2006, against the background of the ongoing discussion about the role of preferences and orientations to work in women! s participation in the labour market. Theoretically rooted in a Bourdieusian tradition, it investigates the patterns of association between resources available to women as well as their constraints at individual, household, as well as local authority levels and their expressed preferences for shorter hours.
Architecture of Y-Family DNA Polymerases Relevant to Translesion DNA Synthesis as Revealed in Structural and Molecular Modeling Studies

DNA adducts, which block replicative DNA polymerases (DNAPs), are often bypassed by lesion-bypass DNAPs, which are mostly in the Y-Family. Y-Family DNAPs can do non-mutagenic or mutagenic dNTP insertion, and understanding this difference is important, because mutations transform normal into tumorigenic cells. Y-Family DNAP architecture that dictates mechanism, as revealed in structural and modeling studies, is considered. Steps from adduct blockage of replicative DNAPs, to bypass by a lesion-bypass DNAP, to resumption of synthesis by a replicative DNAP are described. Catalytic steps and protein conformational changes are considered. One adduct is analyzed in greater detail: the major benzo[a]pyrene adduct (B[a]P-N2-dG), which is bypassed non-mutagenically (dCTP insertion) by Y-family DNAPs in the IV/κ-class and mutagenically (dATP insertion) by V/η-class Y-Family DNAPs. Important architectural differences between IV/κ-class versus V/η-class DNAPs are discussed, including insights gained by analyzing ~400 sequences each for bacterial DNAPs IV and V, along with sequences from eukaryotic DNAPs kappa, eta and iota. The little finger domains of Y-Family DNAPs do not show sequence conservation; however, their structures are remarkably similar due to the presence of a core of hydrophobic amino acids, whose exact identity is less important than the hydrophobic amino acid spacing.
Genetic Risk Score Predicts Late-Life Cognitive Impairment

Introduction. A family history of Alzheimer's disease is a significant risk factor for its onset, but the genetic risk associated with possessing multiple risk alleles is still poorly understood. Methods. In a sample of 95 older adults (Mean age = 75.1, 64.2% female), we constructed a genetic risk score based on the accumulation of risk alleles in BDNF, COMT, and APOE. A neuropsychological evaluation and consensus determined cognitive status (44 nonimpaired, 51 impaired). Logistic regression was performed to determine whether the genetic risk score predicted cognitive impairment above and beyond that associated with each gene. Results. An increased genetic risk score was associated with a nearly 4-fold increased risk of cognitive impairment (OR = 3.824, P = .013) when including the individual gene polymorphisms as covariates in the model. Discussion. A risk score combining multiple genetic influences may be more useful in predicting late-life cognitive impairment than individual polymorphisms.
The Contribution of the Minimum Wage to U.S. Wage Inequality Over Three Decades: A Reassessment

We reassess the effect of minimum wages on US earnings inequality using additional decades of data and an IV strategy that addresses potential biases in prior work. We find that the minimum wage reduces inequality in the lower tail of the wage distribution, though by substantially less than previous estimates, suggesting that rising lower tail inequality after 1980 primarily reflects underlying wage structure changes rather than an unmasking of latent inequality. These wage effects extend to percentiles where the minimum is nominally nonbinding, implying spillovers. We are unable to reject that these spillovers are due to reporting artifacts, however. (JEL J22, J31, J38, K31)
Experimental Manipulation of Grassland Plant Diversity Induces Complex Shifts in Aboveground Arthropod Diversity

Changes in producer diversity cause multiple changes in consumer communities through various mechanisms. However, past analyses investigating the relationship between plant diversity and arthropod consumers focused only on few aspects of arthropod diversity, e.g. species richness and abundance. Yet, shifts in understudied facets of arthropod diversity like relative abundances or species dominance may have strong effects on arthropod-mediated ecosystem functions. Here we analyze the relationship between plant species richness and arthropod diversity using four complementary diversity indices, namely: abundance, species richness, evenness (equitability of the abundance distribution) and dominance (relative abundance of the dominant species). Along an experimental gradient of plant species richness (1, 2, 4, 8, 16 and 60 plant species), we sampled herbivorous and carnivorous arthropods using pitfall traps and suction sampling during a whole vegetation period. We tested whether plant species richness affects consumer diversity directly (i), or indirectly through increased productivity (ii). Further, we tested the impact of plant community composition on arthropod diversity by testing for the effects of plant functional groups (iii). Abundance and species richness of both herbivores and carnivores increased with increasing plant species richness, but the underlying mechanisms differed between the two trophic groups. While higher species richness in herbivores was caused by an increase in resource diversity, carnivore richness was driven by plant productivity. Evenness of herbivore communities did not change along the gradient in plant species richness, whereas evenness of carnivores declined. The abundance of dominant herbivore species showed no response to changes in plant species richness, but the dominant carnivores were more abundant in species-rich plant communities. The functional composition of plant communities had small impacts on herbivore communities, whereas carnivore communities were affected by forbs of small stature, grasses and legumes. Contrasting patterns in the abundance of dominant species imply different levels of resource specialization for dominant herbivores (narrow food spectrum) and carnivores (broad food spectrum). That in turn could heavily affect ecosystem functions mediated by herbivorous and carnivorous arthropods, such as herbivory or biological pest control.
A Bus Encoding Method for Crosstalk and Power Reduction in RC Coupled VLSI Interconnects

The performance factors such as propagation delay, power dissipation and crosstalk in RC modelled interconnects are major design issues for the System on-chip (SoC) designs in current Deep Submicron (DSM) era. The crosstalk effect is a consequence of coupling and switching activities that is encountered when there is a transition as compared to previous state of wire and or when there are transitions in adjacent wires. Therefore, minimization or elimination of switching and coupling activities is crucial in enhancing the performance of SoC designs. There are several methods for the reduction of power dissipation, crosstalk and delay. The encoding method is most effective and popular method for enhancing the behaviour of on-chip buses. This paper proposes encoding scheme to achieve overall reduction in transitions. The reduction in transition improves the performance in terms of reduced power dissipation, coupling activity and delay in on-chip buses. This encoding method is implemented using VHDL. The result evidently demonstrates reduction in transitions which consequently improves the overall performance of on-chip buses.
Following the paths of XML Data: An algebraic framework for XML query evaluation

This paper introduces an algebraic framework for expressing and evaluating queries over XML data. It presents the underlying assumptions of the framework, describes the input and output of the algebraic operators, and defines these operators and their semantics. It evaluates the framework with regard to other proposed XML query algebras. Examples show that this framework is flexible enough to capture queries expressed in Quilt, one of the dominant XML query languages. We have used this algebra in the context of an Internet query engine, in which it is used to formulate logical plans for XML-QL queries. We define equivalence rules that provide opportunities for optimization, and give example cases that point out the usefulness of these rules.
Label-Selective )-Calculus Syntax and Confluencet

We introduce an extension of )t-calculus, called label-selective Acalculus, in which arguments of functions are selected by labels. The set of labels includes numeric positions as well as symbolic keywords. While the latter enjoy free commutation, the former must comply with relative precedence in order to preserve currying. This extension of ),-calculus is conservative in the sense that when the set of labels is the singleton { 1}, it coincides with )t-calculus. The main result of this paper is that the label-selective )t-calculus is confluent. In other words, argument selection and reduction commute.
Pemilihan Model Regresi Terbaik Menggunakan Metode Akaike’s Information Criterion dan Schwarz Information Criterion

Analisis regresi seringkali digunakan untuk mengkaji hubungan antara beberapa variabel dan meramal suatu variabel. Agar diperoleh hasil analisis yang optimal, maka diperlukan model regresi terbaik. Beberapa metode dapat digunakan untuk memilih model regresi terbaik, diantaranya adalah dengan metode Akaike’s Information Criterion (AIC) dan Schwarz Information Criterion (SIC). Penelitian ini bertujuan mengkaji pemilihan model regresi terbaik menggunakan metode AIC dan SIC pada kasus faktor-faktor yang mempengaruhi nilai ujian nasional (UNAS) siswa Sekolah Menengah Kejuruan Negeri (SMKN) 1 Samarinda. Berdasarkan metode AIC model regresi terbaik yang dapat digunakan untuk mengetahui hubungan antara rata-rata nilai UNAS siswa SMKN 1 Samarinda dengan rata-rata nilai tryout (X1), nilai kompetensi (X2) dan rata-rata nilai ujian sekolah (X3) adalah Y = -0,0094 + 0,4541 X1 + 0,2178 X2 + 0,3291 X3. Adapun model regresi terbaik menurut metode SIC adalah Y = 0,4749 + -2,6174 + + 0,5322 X1 + + 0,2636 X3