Miracle-dz/UniFAR-FTUs huggingface dataset:
Should Nike and Reebok Pay Higher Wages? The Impact of Minimum Wages on Employment in a Low Income Country

The low level of minimum wages in developing countries has been the source of much international controversy. Raising these wages will only benefit the workers in these countries if there are not large subsequent job losses. Unlike the well-developed literature on the employment impact of the minimum wage in industrial nations, very little is known about minimum wage effects in low-income countries. Minimum wages increased sharply in Indonesia between 1990 and 1996 and by more in some provinces than in others. We exploit the large difference in the rate of increase on either side of the Jakarta-West Java border. Household level labor market data are used to establish compliance with the legislation. Matched difference-in-difference estimates of the employment impact in the clothing, textiles, footwear and leather industries are then calculated from a census of all large and medium-sized firms. We find no evidence of a negative employment impact for large firms, both foreign and domestic, but some evidence that workers in small, domestic firms may lose their jobs.
The Iterative Solver RISOLV with Application to the Exterior Helmholtz Problem

The innermost computational kernel of many large-scale scientific applications is often a large set of linear equations of the form $Ax=b$ which typically consumes a significant portion of the overall computational time required by the simulation. The traditional approach for solving this problem is to use direct methods. This approach is often preferred in industry because direct solvers are robust and effective for moderate size problems. However, direct methods can consume a huge amount of memory, and CPU time, in large-scale cases. In these cases, iterative techniques are the only viable alternative. Unfortunately, iterative methods lack the robustness of direct methods. The situation is especially difficult when the matrix is nonsymmetric. A lot of research has been devoted to trying to develop a robust iterative algorithm for nonsymmetric systems. The present paper describes a new robust and efficient algorithm aimed at solving iteratively nonsymmetric linear systems. It is based on looking for an approximation to the “optimal” polynomial $P_m(z)$ which satisfies $||P_m(z)||_{\infty}=\min_{Q\in\Pi_m}||Q(z)||_{\infty}$, $z\in D$, where $\Pi_m$ is the set of all polynomials of degree $m$ which satisfies $Q_m(0)=1$ and $D$ is a domain in the complex plane which includes all the eigenvalues of $A$. The resulting algorithm is an efficient one, especially in the case where we have a set of linear systems which share the same matrix $A$. We present several applications, including the exterior Helmholtz problem, which leads to a large indefinite, nonsymmetric, and complex system.
An ASP based method for subassembly identification

Subassembly identification is a key technique in mechanical assembly sequence planning (MASP). Declarative methods for MASP admit highly efficient and understandable problem description. This paper proposes an Answer Set Programming (ASP) based subassembly identification technique, which can be viewed as a part of the declarative solution to MASP. Given the liaison and interference relation of an assembly, all assembly knowledge and necessary constraints are produced automatically. It is shown that the space complexity of our representation method is polynomial. With the answer set solver DLV, feasible subassemblies could be generated. And thanks to the declarativeness of our approach, its representation efficiency and time efficiency can be improved separately.
Ku-band filters using open-loop and quarter-wavelength resonators based on LTCC technology

A novel Ku-band single layer bandpass filter using open-loop and quarter-wavelength resonators based on LTCC technology is proposed A pair of transmission zeros is introduced by the cross coupling, which can improve the attenuation near the passband. The size of the filter is about 6mm × 6mm × 0.288mm, about 0.75λ<sub>g</sub> × 0.75 λ<sub>g</sub>. To reduce the filter's size, a modified filter with multilayer structure is designed. It has a center frequency of 15.4 GHz with bandwidth of 800 MHz, a size of only 0.75 λ<sub>g</sub> × 0.5 λ<sub>g</sub>. After verification, these filters are suitable for the Ku-band integrated RF front end.
A Note on the Benefits of Homeownership

This brief note adds to recent work that attempts to identify externalities associated with homeownership. The results suggest that some of the homeownership effect found in Green and White [5] is driven by family characteristics associated with homeownership, especially residential stability. However, as much as homeownership increases residential stability, it appears to be correlated with higher school attainment. Attempts to control for endogeneity cannot eliminate this finding. ; Originally issued as WP 98-20 (June 1999)
Transcriptional Activation of the Human CD2AP Promoter by E2F1

CD2-associated protein (CD2AP) is an adaptor molecule involved in T cell receptor signaling and podocyte homeostasis. CD2AP-deficient mice develop nephritic syndrome and renal failure caused by glomerulosclerosis. Transcription factor E2F1 is a key regulator of cell proliferation and apoptosis. Here we report that E2F1 up-regulates the human CD2AP promoter and further increases the mRNA and protein levels of the human CD2AP in human embryonic kidney (HEK) 293 cells. By semi-quantitative RT-PCR and Western blot analysis we demonstrate that ectopic expression of E2F1 elevates the mRNA and protein levels of CD2AP. Consistently, transient transfection assays prove that overexpression of E2F1 transactivates the CD2AP promoter while knocking-down of endogenous E2F1 by a shRNA strategy results in reduction of the CD2AP promoter activity. Toward understanding the underlying mechanism of this regulation, we performed chromatin immunoprecipitation and mutations of the putative Sp1 binding sites, demonstrating that E2F1 can bind to Sp1 binding site and overexpression of E2F1 is capable of increasing the binding of E2F1 and decreasing the binding of Sp1 to Sp1 binding sites.
Distribution Preserving Quantization With Dithering and Transformation

A new quantization scheme that preserves the probability distribution of the source signal is presented. The distribution preserving quantization (DPQ) achieves the optimal trade-off between mean square error and bit rate asymptotically. It provides a continuum ranging from rate-distortion optimal signal quantization to parametric coding. The method can be used as a core component for scalable coding. Its efficacy is illustrated by applying the scheme to audio coding.
Optimization of Spiking Neural Networks with dynamic synapses for spike sequence generation using PSO

We present a method that is based on Particle Swarm Optimization (PSO) for training a Spiking Neural Network (SNN) with dynamic synapses to generate precise time spike sequences. The similarity between the desired spike sequence and the actual output sequence is measured by a simple leaky integrate and fire spiking neuron. This measurement is used as a fitness function for PSO algorithm to tune the dynamic synapses until a desired spike output sequence is obtained when certain input spike sequence is presented. Simulations are made to illustrate the performance of the proposed method.
Values Underlying U.S. Low-Income Rural Mothers' Voices about Welfare and Welfare Reform: An Inductive Analysis

This study explicitly identifies the main values that rural welfare recipients reveal when they talk about their experiences with welfare and welfare reform. An inductive analysis of values is conducted using interview data from 49 current and former recipients of Temporary Assistance for Needy Families (TANF) residing in the states of Massachusetts, Maryland, Minnesota, and New York. Seven main values that emerge from the data are self-esteem, autonomy, uniqueness, advancement, security, independence, and fairness. A conceptual diagram of these values is developed to illustrate how these values are related
Disease Spread through Animal Movements: A Static and Temporal Network Analysis of Pig Trade in Germany

Background Animal trade plays an important role for the spread of infectious diseases in livestock populations. The central question of this work is how infectious diseases can potentially spread via trade in such a livestock population. We address this question by analyzing the underlying network of animal movements. In particular, we consider pig trade in Germany, where trade actors (agricultural premises) form a complex network. Methodology The considered pig trade dataset spans several years and is analyzed with respect to its potential to spread infectious diseases. Focusing on measurements of network-topological properties, we avoid the usage of external parameters, since these properties are independent of specific pathogens. They are on the contrary of great importance for understanding any general spreading process on this particular network. We analyze the system using different network models, which include varying amounts of information: (i) static network, (ii) network as a time series of uncorrelated snapshots, (iii) temporal network, where causality is explicitly taken into account. Findings We find that a static network view captures many relevant aspects of the trade system, and premises can be classified into two clearly defined risk classes. Moreover, our results allow for an efficient allocation strategy for intervention measures using centrality measures. Data on trade volume do barely alter the results and is therefore of secondary importance. Although a static network description yields useful results, the temporal resolution of data plays an outstanding role for an in-depth understanding of spreading processes. This applies in particular for an accurate calculation of the maximum outbreak size.
Double dip map-reduce for processing cross validation jobs

Cross validation is fundamental to machine learning as it provides a reliable way in which to evaluate algorithms and the overall quality of the corpora in use. In typical cross validation, the corpus is initially divided into learning and training segments, then crossed-over in successive rounds, so that each data segment is validated against the remaining ones. This process is prohibitively time and effort consuming, and often brushed off for computationally cheaper ones, such as heuristics. In this paper we introduce a cloud-based architecture for running cross validation jobs. Our solution makes heavy use of computational resources in the cloud by proposing a strategy in which there are two distinct, subsequent, map-reduce cycles: the first to perform the algorithmic target computation, and the second to provide cross validation data to retrofit the machine learning process. We demonstrate the feasibility of the proposed approach, with the implementation of a web segmentation algorithm.
Variance Based Extraction and Hidden Markov Model Based Verification of Signatures Present on Bank Cheques

In this paper a method is proposed for the extraction and verification of signatures present on images of bank documents, which include signature localization using variance, signature block extraction using entropy, normalization using desired mean and variance, and verification using a hidden Markov model (HMM) with fixed number of states. Horizontal projection, vertical projection, right envelope, left envelope, top envelope, bottom envelope, horizontal size variations and vertical size variations of the binary format of the genuine signatures are treated as the feature set of an individual. Six genuine signatures of each individual were used for training the HMM. Skilled forgeries of amateur type were used for testing the false acceptance rate of the system. It is observed that the extraction rate is 95% along with a verification rate of 85%.
Mutation analysis of HIF prolyl hydroxylases (PHD/EGLN) in individuals with features of phaeochromocytoma and renal cell carcinoma susceptibility

Germline mutations in the von Hippel–Lindau disease (VHL) and succinate dehydrogenase subunit B (SDHB) genes can cause inherited phaeochromocytoma and/or renal cell carcinoma (RCC). Dysregulation of the hypoxia-inducible factor (HIF) transcription factors has been linked to VHL and SDHB-related RCC; both HIF dysregulation and disordered function of a prolyl hydroxylase domain isoform 3 (PHD3/EGLN3)-related pathway of neuronal apoptosis have been linked to the development of phaeochromocytoma. The 2-oxoglutarate-dependent prolyl hydroxylase enzymes PHD1 (EGLN2), PHD2 (EGLN1) and PHD3 (EGLN3) have a key role in regulating the stability of HIF-α subunits (and hence expression of the HIF-α transcription factors). A germline PHD2 mutation has been reported in association with congenital erythrocytosis and recurrent extra-adrenal phaeochromocytoma. We undertook mutation analysis of PHD1, PHD2 and PHD3 in two cohorts of patients with features of inherited phaeochromocytoma (n=82) and inherited RCC (n=64) and no evidence of germline mutations in known susceptibility genes. No confirmed pathogenic mutations were detected suggesting that mutations in these genes are not a frequent cause of inherited phaeochromocytoma or RCC.
Efficient Computer Aided System Based on Mathematical Morphology and Higher Order Partial Differential Equations for Breast Cancer Detection

Quantification algorithms are applied to segment structure to extract the essential diagnosis information such as shape size texture angle and motion; because the type of measurement and tissue vary considerable numerous techniques that address specific application have been developed. The Present paper is concentrated on Breast cancer is the one of the largest human threat for women. It can be detected by the analysis the digital mammograms. However the survival rate enhanced only by early detection. In order to improve the accuracy, execution speed of the CAD system need to be modified the existing algorithm with new operators, Proposed method chosen mathematical morphology operators & partial differential equations, which is prominently have been given a efficient results in recent developments in image & feature analysis. The new algorithms were designed and, it is implemented in MATLAB7.8. Simulation of the algorithm has shown an improved results .
On the design of script languages for neural simulation

In neural network simulators, models are specified according to a language, either specific or based on a general programming language (e.g. Python). There are also ongoing efforts to develop standardized languages, for example NeuroML. When designing these languages, efforts are often focused on expressivity, that is, on maximizing the number of model types than can be described and simulated. I argue that a complementary goal should be to minimize the cognitive effort required on the part of the user to use the language. I try to formalize this notion with the concept of “language entropy”, and I propose a few practical guidelines to minimize the entropy of languages for neural simulation.
Top-down design for Low power Multi-bit Sigma-Delta Modulator

The present paper reports a top-down design for the design of a Low Power Sigma-Delta Modulator, going from determining the architecture and specifications to the transistor-level design. A Multi-bit CIFF (Chain of Integrators with Feed Forward) implementation was chosen for low power consumption. The Sigma-Delta Modulator is designed in the 0.18 μm IBM CMOS technology and has a 98dB Dynamic Range, 2 Vpp Full Scale, and 20-20 kHz input bandwidth (Audio Bandwidth). The final circuit reached a simulated power consumption of 2.77 mW (Cadence), for 1.8 V power supply.
A Brome Mosaic Virus Intergenic RNA3 Replication Signal Functions with Viral Replication Protein 1a To Dramatically Stabilize RNA In Vivo

ABSTRACT Brome mosaic virus (BMV), a positive-strand RNA virus in the alphavirus-like superfamily, encodes two RNA replication proteins. The 1a protein has putative helicase and RNA-capping domains, whereas 2a contains a polymerase-like domain. Saccharomyces cerevisiae expressing 1a and 2a is capable of replicating a BMV RNA3 template produced by in vivo transcription of a DNA copy of RNA3. Although insufficient for RNA3 replication, the expression of 1a protein alone results in a dramatic and specific stabilization of the RNA3 template in yeast. As one step toward understanding 1a-induced stabilization of RNA3, the interactions involved, and its possible relation to RNA replication, we have identified thecis-acting sequences required for this effect. We find that 1a-induced stabilization is mediated by a 150- to 190-base segment of the RNA3 intergenic region corresponding to a previously identified enhancer of RNA3 replication. Moreover, this segment is sufficient to confer 1a-induced stability on a heterologous β-globin RNA. Within this intergenic segment, partial deletions that inhibited 1a-induced stabilization in yeast expressing 1a alone resulted in parallel decreases in the levels of negative- and positive-strand RNA3 replication products in yeast expressing 1a and 2a. In particular, a small deletion encompassing a motif corresponding to the box B element of RNA polymerase III promoters dramatically reduced the ability of RNAs to respond to 1a or 1a and 2a. These and other findings suggest that 1a-induced stabilization likely reflects an early template selection step in BMV RNA replication.
Improving Retrieval Performance with Wikipedia's Category Knowledge

For text search systems, the ambiguity of short queries often leads to poor performance. To solve this problem, relevance feedback via query-expansion is considered as one effective technique. However, many methods of relevance feedback barely use the knowledge of search results and the improvement of effectiveness is limited because the knowledge used is limited. In this paper we try to include Wikipedia's category knowledge to improve the poor retrieval performance. A method of category feedback is proposed, which is based on the information of Wikipedia categories. Categories instead of terms and documents are provided to users for feedback. Finally, an experimental search system is developed which demonstrates the effectiveness of our method.
PKI Trust Models

A PKI can be described as a set of technologies, procedures and policies for propagating trust from where it initially exists to where it is needed for authentication in online environments. How the trust propagation takes place under a specific PKI depends on the PKI’s syntactic trust structure which is commonly known as a trust model. However, trust is primarily a semantic concept that can not be expressed in syntactic terms alone. In order to define meaningful trust models for PKIs it is also necessary to consider the semantic assumptions and human cognition of trust relationships, as explicitly or implicitly expressed by certification policies, legal contractual agreements between participants in a PKI, and by how identity information is displayed and represented. Of the many different PKI trust models proposed in the literature, some have been implemented and are currently used in practical settings, from small personal networks to large scale private and public networks such as the Internet. This article takes a closer look at the most prominent and widely used PKI trust models, and discusses related semantic issues.
Association mapping for kernel phytosterol content in almond

Almond kernels are a rich source of phytosterols, which are important compounds for human nutrition. The genetic control of phytosterol content has not yet been documented in almond. Association mapping (AM), also known as linkage disequilibrium (LD), was applied to an almond germplasm collection in order to provide new insight into the genetic control of total and individual sterol contents in kernels. Population structure analysis grouped the accessions into two principal groups, the Mediterranean and the non-Mediterranean. There was a strong subpopulation structure with LD decaying with increasing genetic distance, resulting in lower levels of LD between more distant markers. A significant impact of population structure on LD in the almond cultivar groups was observed. The mean r2-value for all intra-chromosomal loci pairs was 0.040, whereas, the r2 for the inter-chromosomal loci pairs was 0.036. For analysis of association between the markers and phenotypic traits five models were tested. The mixed linear model (MLM) approach using co-ancestry values from population structure and kinship estimates (K model) as covariates identified a maximum of 13 significant associations. Most of the associations found appeared to map within the interval where many candidate genes involved in the sterol biosynthesis pathway are predicted in the peach genome. These findings provide a valuable foundation for quality gene identification and molecular marker assisted breeding in almond.
Leveraging Biometric Data to Boost Software Developer Productivity

Producing great software requires great productive developers. Yet, what does it really mean for an individual developer to be productive, and what can we do to best help developers to be productive? To answer these questions, research has traditionally focused on measuring a developer's output and therefore suffered from two drawbacks: the measures can only be calculated after a developer finished her work and these measures do not account for individual differences between developers. The recent advances in biometric sensor technology offer new opportunities to measure a developer's cognitive and emotional states in real-time and thus allow us to know more about what an individual developer is currently experiencing and what might foster or impede the developer's productivity. Results from recent research studies demonstrate the potential that biometric data has to accurately predict aspects of a developer's work, such as perceived task and code difficulty, progress and interruptibility of a developer. This opens up new opportunities for better supporting developers in their work and, for instance, prevent bugs from entering the code, reduce costly interruptions, and foster a better and more productive work day. Our vision is that biometric sensing will be integrated into a developer's work and that biometrics can be used to boost the productivity of each individual developer.
2.45 GHz broadband monopole RFID reader antenna buried in the ground of parking lot near the curb

In this paper, a compact broadband monopole RFID reader antenna with operating frequency of 2.45 GHz is designed for vehicle detection application. This reader antenna is buried in the ground of parking lot along the curb. The antenna comprises a planar loop monopole element which is fed by a microstrip line. The proposed antenna is simulated using HFSS (High Frequency Structure Simulator) as a 3D electromagnetic field simulator. The antenna is deployed on an FR4-Epoxy dielectric substrate with the dimension of 34 mm × 16 mm and the thickness of 1.6 mm. The permittivity and loss tangent of the substrate are 4.4 and 0.02, respectively. The measured results show that the characteristics of realized antenna have good agreements with the simulated ones. The bandwidth for the linear polarized antenna is more than 280 MHz (2.27-2.55 GHz) and the return loss is less than -10 dB.