Miracle-dz/UniFAR-FTUs huggingface dataset:
In Mexico, Mother's Education and Remittances Matter in School Outcomes

Yet much remains to be learned about the extent and complexities of the changes migration to the United States has caused. Remittances provide a useful independent variable as they are one of the most concrete and tangible results of migration. Also, remittances are hypothesized — based on both direct and indirect evidence — to boost educational opportunity within migrantsending communities. This article, by examining how remittances affect schooling outcomes in one migrant-sending community in the southern Mexican state of Oaxaca, contributes to existing knowledge of how migration is related to development in Mexico. Remittances alone do not appear to influence a child's educational aspirations and attainment, but the effect of remittances is related to the mother's education. Specifically, receiving remittances is associated with increased schooling aspirations for youth whose mothers have below-average levels of education. Remittances also provide an apparent boost in improving the chances of high school completion for youth whose mothers have above-average levels of education. In other words, having remittances to pay for schooling expenses can help, but only in conjunction with other scholastically important aspects of a youth's background. Thus, money alone does not improve schooling outcomes in the educationally marginalized, migrant-sending regions of southern Mexico. Remittances and Educational Opportunity in Mexico
Analysis and comparison of transmit diversity scheme using MRC and EGC

MIMO systems have become fundamental part of present day communication framework. The main issues that arise in cellular structures are to provide proper administration to clients, communication without fading and interference, to increase the capacity and data rates, expand the limit of the cell with minimum bit error ratio and signal-noise ratio for which we use equal gain combiner (EGC). EGC system reduces to MRC as show closer error performance where MRC measures signal phase and amplitude. [2]
Ephemeral-Secret-Leakage Secure ID-Based Three-Party Authenticated Key Agreement Protocol for Mobile Distributed Computing Environments

A three-party Authenticated Key Agreement (AKA) protocol in the distributed computing environment is a client that requests services from an application server through an authentication server. The authentication server is responsible for authenticating the participating entities and helping them to construct a common session key. Adopting the Key Transfer Authentication Protocol (KTAP) in such an environment, the authentication server is able to monitor the communication messages to prevent and trace network crime. However, the session key in the KTAP setting is created only by the authentication server and is vulnerable to the resilience of key control. On the other hand, with the rapid growth of network technologies, mobile devices are widely used by people to access servers in the Internet. Many AKA protocols for mobile devices have been proposed, however, most protocols are vulnerable to Ephemeral Secret Leakage (ESL) attacks which compromise the private keys of clients and the session key by an adversary from eavesdropped messages. This paper proposes a novel ESL-secure ID-based three-party AKA protocol for mobile distributed computing environments based on ESL-secure ID-based Authenticated Key Exchange (ID-AKE) protocol. The proposed protocol solves the key control problem in KTAP while retaining the advantages of preventing and tracing network crime in KTAP and also resists ESL attacks. The AVISPA tool simulation results confirm the correctness of the protocol security analysis. Furthermore, we present a parallel version of the proposed ESL-secure ID-based three-party AKA protocol that is communication-efficient.
The Capital Asset Pricing Model (CAPM) Applied to Paintings

The Capital Asset Pricing Model (CAPM) has been used in a number of studies to explore the features of the art market (or individual artists). We claim that such studies have been based on unsuitable estimates of art market returns in the context of the CAPM methodology. The CAPM calls for employing total returns whereas most art-related studies rely on return estimates based on the time-dummies of hedonic pricing models. This choice is conceptually flawed since it is based on an ideal or average painting whose characteristics do not change overtime, and therefore, it does not capture total returns. We also claim that most indexes used in previous studies as proxy for the market are not able to capture the dynamics of the art market. These two observations call into question at least some of the findings of earlier researchers. Finally, we illustrate these points with several examples using actual auction prices for a group of surrealist artists. We also advance some proposals to overcome the above-mentioned shortcomings.
Radar Target Recognition Using Salient Keypoint Descriptors and Multitask Sparse Representation

In this paper, we propose a novel approach to recognize radar targets on inverse synthetic aperture radar (ISAR) and synthetic aperture radar (SAR) images. This approach is based on the multiple salient keypoint descriptors (MSKD) and multitask sparse representation based classification (MSRC). Thus, to characterize the targets in the radar images, we combine the scale-invariant feature transform (SIFT) and the saliency map. The purpose of this combination is to reduce the number of SIFT keypoints by keeping only those located in the target area (salient region); this speeds up the recognition process. After that, we compute the feature vectors of the resulting salient SIFT keypoints (MSKD). This methodology is applied for both training and test images. The MSKD of the training images leads to constructing the dictionary of a sparse convex optimization problem. To achieve the recognition, we adopt the MSRC taking into consideration each vector in the MSKD as a task. This classifier solves the sparse representation problem for each task over the dictionary and determines the class of the radar image according to all sparse reconstruction errors (residuals). The effectiveness of the proposed approach method has been demonstrated by a set of extensive empirical results on ISAR and SAR images databases. The results show the ability of the proposed method to predict adequately the aircraft and the ground targets.
MYC gene amplification is a rare event in atypical fibroxanthoma and pleomorphic dermal sarcoma

Atypical fibroxanthoma (AFX) and pleomorphic dermal sarcoma (PDS) are rare malignancies typically occurring in elderly patients and predominantly located in skin regions exposed to UV-light. Thus, a role of UV-radiation-induced damage for AFX and PDS tumorigenesis has been postulated. MYC gene amplification has been demonstrated as a distinctive feature of radiation-induced angiosarcoma. In order to investigate whether chronic exposure to UV-light might also lead to MYC copy number changes, 51 AFX and 24 PDS samples were retrospectively analyzed for MYC amplification by fluorescence in situ hybridization using a MYC and a CEP8 gene probe. Of the 44 analyzable AFX samples, one case showed MYC amplification (defined as a MYC/CEP8 ratio ≥2.0), whereas 13 cases demonstrated low level copy number gains (defined as MYC/CEP8 ratio ≥ 1.2−< 2.0). MYC amplification was seen in an AFX sample of extraordinary tumor thickness of 17.5 mm (vs. median 3.25 mm for all samples). Of the 24 PDS cases, five specimen demonstrated MYC low level copy number gains. Immunohistochemically, neither the AFX nor the PDS cases showed MYC protein expression. In summary, these findings rule out that MYC amplification is a major genetic driver in the process of AFX or PDS tumorigenesis. However, MYC amplification may occur as a late event during AFX development and hence might only be detectable in advanced, thick lesions.
Leukotriene receptors as potential therapeutic targets

&NA; Leukotrienes, a class of arachidonic acid‐derived bioactive molecules, are known as mediators of allergic and inflammatory reactions and considered to be important drug targets. Although an inhibitor of leukotriene biosynthesis and antagonists of the cysteinyl leukotriene receptor are clinically used for bronchial asthma and allergic rhinitis, these medications were developed before the molecular identification of leukotriene receptors. Numerous studies using cloned leukotriene receptors and genetically engineered mice have unveiled new pathophysiological roles for leukotrienes. This Review covers the recent findings on leukotriene receptors to revisit them as new drug targets.
Strong associations between plant genotypes and bacterial communities in a natural salt marsh

Abstract Although microbial communities have been shown to vary among plant genotypes in a number of experiments in terrestrial ecosystems, relatively little is known about this relationship under natural conditions and outside of select model systems. We reasoned that a salt marsh ecosystem, which is characterized by twice‐daily flooding by tides, would serve as a particularly conservative test of the strength of plant–microbial associations, given the high degree of abiotic regulation of microbial community assembly resulting from alternating periods of inundation and exposure. Within a salt marsh in the northeastern United States, we characterized genotypes of the foundational plant Spartina alterniflora using microsatellite markers, and bacterial metagenomes within marsh soil based on pyrosequencing. We found significant differences in bacterial community composition and diversity between bulk and rhizosphere soil, and that the structure of rhizosphere communities varied depending on the growth form of, and genetic variation within, the foundational plant S. alterniflora. Our results indicate that there are strong plant–microbial associations within a natural salt marsh, thereby contributing to a growing body of evidence for a relationship between plant genotypes and microbial communities from terrestrial ecosystems and suggest that principles of community genetics apply to this wetland type.
Effect of wound type on Smad 2 and 4 translocation.

PURPOSE In a prior study, it was reported that both TGF-beta receptors type-I and -II are upregulated after wounding, suggesting that TGF-beta signaling may play a role in corneal epithelial repair. The Smad proteins, which translocate into the nucleus after activation of the TGF-beta receptors, are key factors in the major TGF-beta signaling pathway. The present study was undertaken to examine whether Smads 2 and 4 translocate into the nucleus during wound repair and whether the wound type affects the extent of translocation. METHODS Either a 3-mm superficial keratectomy or epithelial debridement was performed on adult Sprague-Dawley rats. The eyes were allowed to heal from 4 hours to 2 weeks. Indirect immunofluorescence was performed with anti-Smads 2 and 4, anti-laminin, a marker of basement membrane, and anti-alphavbeta6 integrin, which has been implicated in TGF-beta activation. In addition, the effect of the p38MAPK inhibitor SB202190 on healing rates of debridement and keratectomy wounds was determined in organ culture. RESULTS In unwounded tissue, Smad 2 was cytoplasmic. By 4 hours after keratectomy, nuclear localization was visible in a few epithelial basal cells at the leading edge of the wound. The number of basal cells expressing nuclear Smad 2 in the wound area increased with time, peaking at 48 hours (95%). However, in the debridement model, Smad 2 localization remained primarily cytoplasmic. Smad 4 showed similar localization. In both wound models, p38MAPK inhibitor slowed epithelial migration, and alphavbeta6 integrin appeared to be upregulated with localization primarily observed in the basal cells migrating over the wound area. CONCLUSIONS The presence of the basement membrane appears to have an effect on the extent and duration of translocation of the Smad 2 and 4 proteins during corneal epithelial wound repair. The Smad pathway does not appear to be essential for migration; rather, it may play a role in resynthesis of the basement membrane.
Learning and Inferring a Driver's Braking Action in Car-Following Scenarios

Accurately predicting and inferring a driver's decision to brake is critical for designing warning systems and avoiding collisions. In this paper, we focus on predicting a driver's intent to brake in car-following scenarios from a perception–decision–action perspective according to his/her driving history. A learning-based inference method, using onboard data from CAN-Bus, radar, and cameras as explanatory variables, is introduced to infer drivers’ braking decisions by combining a Gaussian mixture model (GMM) with a hidden Markov model (HMM). The GMM is used to model stochastic relationships among variables, while the HMM is applied to infer drivers’ braking actions based on the GMM. Real-case driving data from 49 drivers (more than three years’ driving data per driver on average) have been collected from the University of Michigan Safety Pilot Model Deployment database. We compare the GMM-HMM method to a support vector machine (SVM) method and a SVM-Bayesian filtering method. The experimental results are evaluated by employing three performance metrics: accuracy, sensitivity, and specificity. The comparison results show that the GMM–HMM obtains the best performance, with an accuracy of 90%, sensitivity of 84%, and specificity of 97%. Thus, we believe that this method has great potential for real-world active safety systems.
Speed Control of BLDC Motor Based on Recurrent Wavelet Neural Network

In recent years, artificial intelligence techniques such as wavelet neural network have been applied to control the speed of the BLDC motor drive. The BLDC motor is a multivariable and nonlinear system due to variations in stator resistance and moment of inertia. Therefore, it is not easy to obtain a good performance by applying conventional PID controller. The Recurrent Wavelet Neural Network (RWNN) is proposed, in this paper, with PID controller in parallel to produce a modified controller called RWNN-PID controller, which combines the capability of the artificial neural networks for learning from the BLDC motor drive and the capability of wavelet decomposition for identification and control of dynamic system and also having the ability of self-learning and self-adapting. The proposed controller is applied for controlling the speed of BLDC motor which provides a better performance than using conventional controllers with a wide range of speed. The parameters of the proposed controller are optimized using Particle Swarm Optimization (PSO) algorithm. The BLDC motor drive with RWNN-PID controller through simulation results proves a better in the performance and stability compared with using conventional PID and classical WNN-PID controllers.
Does Central Clearing Affect Price Stability? Evidence from Nordic Equity Markets

We provide evidence of the effects of introducing a central clearing counterparty (CCP) on price stability by adopting as an experimental construct the 2009 clearing reform in three Nordic equity markets. We find that the daily price volatility of the affected equities experience an economically significant decline of 8.8% relative to pre-reform levels. The decrease in volatility is more pronounced for stocks with a higher margin cost impact, consistent with the predictions of dynamic asset pricing models. We also find that the reform induces a sharp decline of 9.8% in trade volume but no deterioration in market quality as captured by trading costs and information measures. Overall, our results suggest that the adoption of central clearing enhances price stability. Our results also suggest an important coordination role for policy in implementing clearing reforms, since investors failed to voluntarily clear trades in the CCP when given the option.
MICHELE VALENTI CONVOLUTIONAL NEURAL NETWORKS FOR ACOUSTIC SCENE CLASSIFICATION

TAMPERE UNIVERSITY OF TECHNOLOGY Master‘s Degree Programme in Signal Processing VALENTI, MICHELE: Convolutional neural networks for acoustic scene classification Master of Science Thesis, 49 pages September 2016 Major: Signal Processing Examiners: Prof. Tuomas Virtanen, Prof. Stefano Squartini, Diment Aleksandr
Using Classifiers for Software Defect Detection

Classifier models have successfully been applied to the task of detecting and predicting defects in software projects. These models work by learning patterns between attributes of the software and a corresponding binary label that indicates whether or not a defect exists. In this study, we evaluate the performance of five different classifiers in the context of software defect detection – naı̈ve Bayes, neural networks, support vector machines, logistic regression, and k-nearest neighbor. We measure performance using both accuracy and F1-score, and use ANOVA to test for significance. Each classifier was run on five datasets from NASA’s metrics data program repository. Results show that all models can detect software defects using static software features with a relatively high degree of certainty, although naive Bayes and support vector machines were outperformed by the remaining algorithms for some datasets.
Organization and nucleotide sequences of two lactococcal bacteriocin operons

Two distinct regions of the Lactococcus lactis subsp. cremoris 9B4 plasmid p9B4-6, each of which specified bacteriocin production as well as immunity, have been sequenced and analyzed by deletion and frameshift mutation analyses. On a 1.8-kb ScaI-ClaI fragment specifying low antagonistic activity, three open reading frames (ORFs) were present, which were organized in an operon. The first two ORFs, containing 69 and 77 codons, respectively, were involved in bacteriocin activity, whereas the third ORF, containing 154 codons, was essential for immunity. Primer extension analysis indicated the presence of a promoter upstream of the ORFs. Two ORFs were present on a 1.3-kb ScaI-HindII fragment specifying high antagonistic activity. The first ORF, containing 75 codons, specified bacteriocin activity. The second ORF, containing 98 codons, specified immunity. The nucleotide sequences of both fragments upstream of the first ORFs as well as the first 20 bp of the first ORF of both bacteriocin operons appeared to be identical.
Inter-organizational crisis management infrastructures for electrical power breakdowns

Major electricity breakdowns like the Northeast Blackout (USA) in 2003 or the blackout in most parts of Western Europe in 2005, have shown the fundamental role of electricity in our everyday life. The experiences of these accidents show that power suppliers, firefighters, police, county administration and citizens face multifarious challenges in inter-organizational communication, information and coordination processes during coping and recovery work. In this work-in-progress paper we describe early research dealing with interorganizational issues in emergency management (EM). We are mainly focusing on supporting social practices in inter-organizational EM, for example collaborative interpretation of emergency situations, ad-hoc coordination or supporting citizen communication and helping routines. Identified from our experiences from related projects, discussions and literature studies, we suggest potential questions and future topics in user-driven software engineering processes for EM and domain specific problems, such as supporting citizen participation, coping with information uncertainties and quality variations or enhancing inter-organizational learning.
Resource diversity and provenance underpin spatial patterns in functional diversity across native and exotic species

Abstract Functional diversity metrics are increasingly used to augment or replace taxonomic diversity metrics to deliver more mechanistic insights into community structure and function. Metrics used to describe landscape structure and characteristics share many of the same limitations as taxonomy‐based metrics, particularly their reliance on anthropogenically defined typologies with little consideration of structure, management, or function. However, the development of alternative metrics to describe landscape characteristics has been limited. Here, we extend the functional diversity framework to characterize landscapes based on the diversity of resources available across habitats present. We then examine the influence of resource diversity and provenance on the functional diversities of native and exotic avian communities in New Zealand. Invasive species are increasingly prevalent and considered a global threat to ecosystem function, but the characteristics of and interactions between sympatric native and exotic communities remain unresolved. Understanding their comparative responses to environmental change and the mechanisms underpinning them is of growing importance in predicting community dynamics and changing ecosystem function. We use (i) matrices of resource use (species) and resource availability (habitats) and (ii) occurrence data for 62 native and 25 exotic species and 19 native and 13 exotic habitats in 2015 10 × 10 km quadrats to examine the relationship between native and exotic avian and landscape functional diversity. The numbers of species in, and functional diversities of, native and exotic communities were positively related. Each community displayed evidence of environmental filtering, but it was significantly stronger for exotic species. Less environmental filtering occurred in landscapes providing a more diverse combination of resources, with resource provenance also an influential factor. Landscape functional diversity explained a greater proportion of variance in native and exotic community characteristics than the number of habitat types present. Resource diversity and provenance should be explicitly accounted for when characterizing landscape structure and change as they offer additional mechanistic understanding of the links between environmental filtering and community structure. Manipulating resource diversity through the design and implementation of management actions could prove a powerful tool for the delivery of conservation objectives, be they to protect native species, control exotic species, or maintain ecosystem service provision.