Miracle-dz/UniFAR-FTUs huggingface dataset:
Local weather, food resources and breeding stage influence Thrush Nightingale movement

Insectivorous migrants breeding at northern latitudes often time the breeding period with the seasonal peak of food resources. Whether this general pattern transfers to movement behaviour during the breeding season requires detailed study from a local perspective. We investigated fine-scale environmental correlates of movements by six actively-breeding adult Thrush Nightingales (Luscinia luscinia) at a breeding site in Denmark, using radio tracking and multiple regression models. Overall, the chick-rearing period coincided with the peak of vegetation greenness at the site. Adults flew further from nesting areas during stable weather and as vegetation greenness decreased. Adults were more active at higher environmental temperatures and when fledglings were older but still dependent on adults. These changes in local movements likely reflect adjustments to chick developmental needs and to specific local environmental conditions, including resource availability.
On the Role of Productivity in Economic Structure and Development Job Market Paper

There exist "stages of diversi cation" along the development path: countries tend to start out with employment specialized in a few industries, then they diversify, and nally they re-specialize as they develop. The existing literature relates this pattern of industry specialization to openness to trade and volatility. However, this paper shows that in an autarkic economy without uncertainty, these "stages of diversi cation" may be obtained if the economy starts out being specialized in industries that are not those that dominate the economic structure in the long run, as a matter of productivity-driven structural change. A calibrated multi-industry growth model with many industries that experience di¤erent rates of productivity growth replicates the main features of the "stages of diversi cation. " This paper also presents evidence that, in the long run, high-TFP growth manufacturing industries tend to dominate economic structure. Keywords: Stages of diversi cation, productivity di¤erences, structural change, development. JEL Codes: O11 O14 O33 O41. Department of Economics, The George Washington University, 2115 G St NW Suite 340, Washington, DC 20052. Tel: +1 (202) 994-6150, Fax: +1 (202) 994-6147, Email: yusun03@gwmail.gwu. edu. I am grateful to Jean Imbs and Romain Wacziarg for providing me with their dataset. I am deeply indebted to invaluable guidance and comments from Professor Roberto Samaniego. I am also deeply grateful to Professor Pamela Labadie and Chao Wei for comments. All errors are the authors.
econstor Make Your Publications Visible . A Service of zbw

Wages, Productivity and Aging In this article, we estimate age based wage and productivity differentials using linked employer-employee Canadian data from the Workplace and Employee Survey 1999-2003. Data on the firm side is used to estimate production functions taking into account the age profile of the firm’s workforce. Data on the workers’ side is used to estimate wage equations that also depend on age. Results show concave age-wage and age-productivity profiles. Wage-productivity comparisons show that the productivity of workers aged 55 and more with at least an undergraduate degree is lower than their wages. For other groups, we find that wages do not deviate significantly from productivity estimates. JEL Classification: J31
A Local and Global Perspective on Cervical Screening with HPV – An Educational Forum Welcome

Welcome to this review of the Roche Diagnostics NZ-sponsored educational forum, A Local and Global Perspective on Cervical Screening with HPV, which was held in Auckland on 1st May 2014. The forum featured presentations from international and local experts on the topics of human papillomavirus (HPV) infection and cervical cancer, cervical screening with HPV, Australia’s COMPASS trial comparing 3-yearly cytology screening with 6-yearly HPV screening, and New Zealand’s involvement in the COMPASS trial, the Service Evaluation Project. Also featured are concluding comments from Dr Hazel Lewis, the Clinical Leader of New Zealand’s National Cervical Screening Programme.
Health Care Management in ALS Patients

An optimized management of amyotrophic lateral sclerosis (ALS) is an added value of a follow-up by an interdisciplinary team. The aim of this study was to identify which professionals are involved in care of ALS patients at Portugal and their impact on quality of life (QoL). We divided the sample in Group 1 (G1; Southern) and Group 2 (G2; Northern) and used a semi-structured questionnaire, functional status scale (Amyotrophic Lateral Sclerosis Functional Rating Scale–Revised [ALSFRS-R]), and QoL evaluation to perform the study. There were observed differences at admission in ALSFRS-R, higher in G1, presence of specialized professionals higher in G2; non-invasive ventilation equipment had higher use and compliance in G1. Multiple regression analysis shows gender, education level, and technical aids as predictors of QoL. The study showed that, despite regional differences, there were external factors common to both groups that influenced QoL.
The Qualitative Interview in Marketing and Consumer Research : Paradigmatic Approaches and Guideline

Interviews are the most pervasive means of collecting data in qualitative research projects. The purpose of this paper is to discuss how the practice of conducting interviews has evolved within consumer and marketing research and to offer insights for those who would like to gather qualitative data via interviews. To do so, we first identify and differentiate two broad perspectives on qualitative marketing and consumer research: the phenomenological approach and the post-structuralist approach. We then develop a step by step guideline on how to conceive, conduct, and analyze interviews under both approaches. As much as the paper provides a useful toolkit to researchers with initial and intermediate degrees of intimacy with qualitative methods, it also contributes to the mapping of important ongoing paradigmatic discussions in the field of qualitative marketing and consumer research.
Flexible Deep Learning in Edge Computing for IoT

Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Traditional edge computing models have rigid characteristics. Flexible edge computing architecture solves rigidity in IoT edge computing. Proposed model combines deep learning into edge computing and flexible edge computing architecture using multiple agents. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. FEC architecture is a flexible and advanced IoT system model characterized by environment adaptation ability and user orientation ability. In the performance evaluation, we test the performance of executing deep learning tasks in FEC architecture for edge computing environment. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT.
The effect of transportation and safety attitudes, personality, and lifestyle on vehicle type choice: a case study of Tehran

In recent years, researchers have paid specific attention to safety, air pollution, and energy consumption, which are all affected by the vehicle type. The primary objective of this study is to achieve a thorough understanding of the relationships between transportation attitudes, safety attitudes, personality, as well as lifestyle and vehicle type for the first time in Iran, as a developing country. For this purpose, an interview questionnaire was designed, and 740 participants were interviewed in Tehran. Vehicle types are categorized into seven groups: mini, compact, medium, heavy, SUV, pickup, and motorcycle. In the first stage, factor analysis was used to identify the latent variables regarding transportation attitudes, safety attitudes, personality, and lifestyle. In the second step, by using these latent variables, individuals are classified, and a two-level clustering is formed. The correlation analysis performed in the third step shows a significant relationship between each category of people and the type of car. Likewise, the analyses carried out in this study reveal the significant relations between the vehicle type (classified based on fuel economy level, production country, vehicle emission, and vintage) and the attitudes of its owner. This information is beneficial to both vehicle manufacturers and transportation planners.
Directional wavelet transforms for prediction residuals in video coding

Various directional transforms have been developed recently to improve image compression. In video compression, however, prediction residuals of image intensities, such as the motion compensation residual or the resolution enhancement residual, are transformed. The applicability of the directional transforms on prediction residuals have not been carefully investigated. In this paper, we brie y discuss differing characteristics of prediction residuals and images, and propose directional transforms speci cally designed for prediction residuals. We compare these transforms with the directional transforms proposed for images using prediction residuals. The results of the comparison indicate that our proposed directional transforms can provide better compression of prediction residuals than the directional transforms proposed for images.
Apparent pressures on multi-propped retaining walls in soils under drained conditions with shallow water table

In the design of flexible retaining walls supported by several levels of struts, apparent design diagrams are frequently used, particularly to predict the loads on the struts. In the case of sandy soils, the traditional design diagrams do not consider water table. The present work deals with apparent design diagrams for cases considering water table. A brief analysis of the problem based on published information is presented. Stress-strain finite element analyses are applied to a case study, comparing situations without water table with others where the water table is assumed. For the cases where water table is assumed two types of conditions are considered: those where seepage is allowed and those where it is not. A parametric analysis is performed to study the effects on soil pressures and on apparent pressures of geometrical and mechanical parameters of the soil and structure. From the analysis of the results a proposal for the apparent diagrams for the design of retaining walls performed in soils under drained conditions with water table above the bottom of the excavation is presented.
Weak Stochastic Increasingness , Rank Exchangeability , and Partial Identification of The Distribution of Treatment Effects

This article develops bounds on the distribution of treatment effects under testable assumptions on the joint distribution of potential outcomes, namely weak stochastic increasingness, and rank exchangeability, and shows how to test the empirical restrictions implied by those assumptions. The resulting bounds sharpen the classical bounds based on Frechet-Hoeffiding limits. An empirical application on the impacts of charter schools shows the bounds are informative.
Deep Learning Model Generalization in Side-Channel Analysis Analysing class probabilities , metrics and ensembles

The adoption of deep neural networks for profiled side-channel attacks provides different capabilities for leakage detection of secure products. Research papers provide a variety of arguments with respect to model interpretability and the selection of adequate hyper-parameters for each target under evaluation. When training a neural network for side-channel leakage classification, it is expected that the trained model is able to implement an approximation function that can detect leaking side-channel samples and, at the same time, be insensible to noisy (or non-leaking) samples. This is basically a generalization situation where the model can identify main representations learned from the training set in a separate test set. Very few understanding has been achieved in order to demonstrate if a trained model is actually generalizing for the current side-channel problem. In this paper, we provide guidelines for a correct interpretation of model’s generalization in side-channel analysis. We detail how class probabilities provided by the output layer are very informative for the understanding of generalization and how they can be used as an important validation metric. Moreover, we demonstrate that ensemble learning based on averaged class probabilities improves the generalization of neural networks in side-channel attacks.
STANCY: Stance Classification Based on Consistency Cues

Controversial claims are abundant in online media and discussion forums. A better understanding of such claims requires analyzing them from different perspectives. Stance classification is a necessary step for inferring these perspectives in terms of supporting or opposing the claim. In this work, we present a neural network model for stance classification leveraging BERT representations and augmenting them with a novel consistency constraint. Experiments on the Perspectrum dataset, consisting of claims and users’ perspectives from various debate websites, demonstrate the effectiveness of our approach over state-of-the-art baselines.
Intonational equivalence : an experimental evaluation of pitch scales

Intonationally equivalent utterances by, for instance, a man and a woman result in different pitch ‘spans’ when plotted on a Hertz scale. Alternative, psycho-acoustic, scales are available, such as semitones, mels, Bark and ERB-rate. Previous experiments have found hearers’ linguistic pitch-related perception to be well modelled by one or another of these scales or even by Hertz, but there is no consensus. In this experiment subjects were asked to replicate equivalent ‘template’ intonation patterns produced by a male and a female speaker. The utterances, by design, were in three pitch spans. The ‘goodness of fit’ of the subjects’ imitations was evaluated when pitch was represented in each of the above scales. Results for both female and male subjects showed that semitones and ERB-rate best reflect subjects’ intuitions about equivalence, with semitones marginally the better. The reasons for this result, particularly its relation to previous work using prominence judgments, are discussed.
Factors Influencing the Decision to Adopt Machine Learning Technology: a Case of Automatic number plate Recognition Systems

This research investigates the factors influencing the decision to adopt Automatic Number Plate Recognition (ANPR) technology in New Zealand organizations. We interviewed participants from six New Zealand organizations. The findings suggest that the key reason for adopting ANPR was the ability of the technology to solve an operational problem and achieve a specific organizational goal. The perceived costs of the technology and level of use associated with using ANPR were also contributing factors. Organizations that had identified a cost effective business case for adopting ANPR had either adopted or were in the process of adopting ANPR technology. The adoption by international organizations and the effect on social systems further explain the reluctance of organizations to adopt ANPR as a new technology. Other factors that were not identified prior to this research include legal concerns over privacy and using ANPR results as enforceable evidence, the limited availability of slack resources and concerns that ANPR could inadvertently change the way the organization operates.
AUTOMATIC ANNOTATION OF 3 D POINT CLOUDS FROM 2 D IMAGES A Degree

1 Resumen . 2 Resum . 3 Acknowledgements . 4 Revision history and approval record. 5 Table of contents . 6 List of Figures . 8 List of Tables: . 9
NMR Rheo-NMR : a New Application for NMR Microscopy and NMR Spectroscopy

Nuclear Magnetic Resonance is a remarkably powerful technique for the investigation of molecular structure, organisation and dynamics, and this fact lies behind the wide use of NMR in so many different fields of research in physics, chemistry, biochemistry, medicine, food technology and materials science. Chemical engineering is one of the areas that has more recently begun to benefit from the insights which NMR can offer, and two research topics where NMR is making a major impact are the study of flow in porous media and rheology, the study of the mechanical properties of complex fluids. The name derives from the Greek word rheo which means to flow.
Novel view Deep Blending Ranked Pixel Contributions Predicted Blending Weights Our novel view Soft

Fig. 1. Image-based rendering blends contributions from different input images to synthesize a novel view. (a) This blending operation is complex for a variety of reasons. For example, incorrect visibility (e.g., for the non-reconstructed green surface in input view (1)) may result in wrong projections of an image into the novel view. Blending must also account for, e.g., differences in projected resolution ((2) and (3)). Previous methods have used hand-crafted heuristics to overcome these issues. (b) Our method generates a set of ranked contributions (mosaics) from the input images and uses predicted blending weights from a CNN to perform deep blending and synthesize (c) novel views. Our solution significantly reduces visible artifacts compared to (d) previous methods.
Final Report : Quantum Simulation of Spin Models

Simulation by quantum computers [1] is a promising method to investigate quantum systems which are intractable to classical computers. However, it is not explicit how quantum computers outperform classical ones in practice. In this project, we will answer this question as follows: in the first and the second section, we mainly discuss the motivation of quantum simulation and different models of quantum simulation; in the third and fourth section, we survey the recent research on the simulation of Hamiltonian dynamics; on the other hand, we illustrate how to simulate the equilibrium properties of quantum systems by implementing quantum metropolis algorithm. For simplicity, We will focus on the spin models and will not touch the detail of the physical realization.
Cellular / Molecular The Glycine Transporter GlyT 2 Controls the Dynamics of Synaptic Vesicle Refilling in Inhibitory Spinal Cord Neurons

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. The glycine transporter GlyT2 controls the dynamics of synaptic vesicle refilling in inhibitory spinal cord neurons. France Rousseau, Karin R Aubrey, Stéphane Supplisson
The collapse of Real Class building O colapso do edifício Real Class

Resumo This paper describes the scene of the Real Class building collapse, a residential building with reinforced concrete structural system and located in the urban area of the city of Belem / PA, occurred in 2011. The unconformities found in the building are displayed using data extracted from reports and verification of structural and architectural designs. The data was compared with the Brazilian code for reinforced concrete structures, NBR 6118 (2007), valid at the time of the accident. The security of the building was evaluated through a computer model with linear analysis with the software used by the designer. The conditions of the structural system designed and as built was evaluated with parameters of global stability and load capacities of columns and foundations. The results showed that the structure of the building was subject to large displacements and the sections of columns were unable to resist the stresses produced by regional wind actions.
Disambiguation of Portuguese Clitic Pronouns

Part-of-speech (POS) tagging is usually considered a solved problem in NLP, with state-of-the-art precision usually above 97% precision for most languages. However, these figures are often based on small and/or coarse POS tag sets. With highly granular tag sets, the number of ambiguous forms may increase significantly, as thiner distinctions may apply to the same word form. Portuguese is a highly inflected language, and several state-of-the-art POS taggers are already available. Nevertheless, some POS categories, which are key to later NLP tasks, remain a resilient POS-tagging problem, with suboptimal results. This is due to their level of ambiguity, particularly when a fully-fledged POS tag set is used, and also for their high frequency in texts. In Portuguese, clitic pronouns are one such categories. Correct disambiguation of personal pronouns is particularly relevant in this language, not only because they constitute a very frequent category in texts, but especially because of their prominent role in the discourse structure by their anaphoric value, and their essential contribution to the semantic interpretation of the verbs they depend on and/or of the sentences they appear in. This paper presents a solution that combines statistical POS tagging techniques with rich lexical information in order to improve the POS disambiguation of clitic personal pronouns in Portuguese. The solution is fully integrated in a NLP processing chain and is directly exploited in subsequent NLP tasks. Results on the POS tagging problem significantly outperform baseline. Consequently, improvements in several NLP tasks were also assessed.