bluuebunny/crossref_metadata_2025 huggingface dataset:
Implicações das tecnologias e da organização escolar no aprender matemática: o olhar do professor
Authors: Cardoso de Oliveira Macedo Aline , Laurino Débora Pereira
Published: August 2019

Para grande parte dos alunos do Ensino Médio, utilizar as várias tecnologias existentes não é desafio. O desafio consiste na escolha das tecnologias que eles empregam para o aprender. Para tentar compreender essas escolhas, trazemos o presente artigo que analisa os discursos, organizados a partir de entrevistas semiestruturadas, feitas com professores de Matemática de um Instituto Federal. As perguntas versaram sobre a percepção do comprometimento de seus alunos com a sua própria aprendizagem e as tecnologias escolhidas no aprender. Para organizar as discursividades, escolhemos a técnica Discurso do Sujeito Coletivo, proposta por Fernando Lefèvre e Ana Maria Lefèvre. Ao analisarmos esses discursos, com o aporte teórico da Biologia do Conhecer desenvolvida por Humberto Maturana e Francisco Varela, compreendemos que o professor está procurando maneiras diferentes para trabalhar, visando atender às necessidades dos seus alunos, pois trabalhar pedagogicamente com a tecnologia requer apropriação da mesma. Percebemos que o comprometimento de alunos e professores é ponto de partida para que se ampliem as possibilidades de cooperação e de significados do próprio aprender Matemática. É em um ambiente cooperativo que, professores e alunos, poderão conviver e encontrar as tecnologias que mediarão esse aprender.
PENGUATAN PENDIDIKAN KARAKTER PESERTA DIDIK MELALUI PROGRAM PENDIDIKAN BERNUANSA SURAU DAN BUDAYA MINANGKABAU
Authors: Afridinata Heri , Samad Duski , Kosim Muhammad
Published: August 2019

This study aims to describe and analyze the application of the character education program based surau and the Minangkabau culture strengthening program in intracurricular, extracurricular activities; and through school culture. This study used a qualitative approach and interviews, observation and documentation use to collect the data. Some teachers are respondents of the research. The results showed that the implementation of the Minangkabau culture and character education program in intracuricular activities were carried out by the subject teacher by incorporating the values of the Minangkabau nuanced character and culture into each learning. Character values were embedded in learning tools such as prayer and reading the Al- Qur’an before and after learning activities. Whereas the implementation of the character education program and the Minangkabau culture in extracurricular activities was carried out through the integration of the values of the character and the Minangkabau culture in various extra-curricular activities such as muhadarah, An Nisa forum, Ar Rijal forum (studying Islamic sciences with the halaqah method), pesantren , and pray. While strengthening the values of Minangkabau cultural character can be seen in randai activities, traditional speeches, and Minangkabau cuisine. The results also showed that the development of school culture was carried out through strengthening the character of students such as Adiwiyata School, Healthy and Smoke-Free School Program. It is concluded that character education based surau and Minangkabau culture had helped educators more easily to develop students' character. The implication is that educators need to develop teaching materials that can help students improve their character.
SEJARAH PENDIDIKAN ISLAM DI MINANGKABAU
Authors: Saharman Saharman
Published: August 2019

This study aims to describe the development of Islamic education in Minangkabau. Library studies are used in collecting data and analyzing data qualitatively. The development of Islamic Education Institutions in the archipelago shows the influence of the development of Islamic education in the Middle East, especially Mecca and Cairo, although in detail there are differences in understanding. This influence was brought by natives who were initially to perform the pilgrimage, but then most of them settled there a few years to seek knowledge. Not much different from Minangkabau, at the end of the 17th century, it was influenced by Aceh by Syekh Burhanuddin Ulakan who went to study religious knowledge for several years, and then after returning home, established a mosque in the form of an educational institution with a halakah system. Furthermore, in the 20th century the madrasa was established as a sign of the beginning of the renewal of Islamic education by the best sons of Minangkabau by following the system carried out by previous ulama scholars, so that Islamic Education Institutions in Minangkabau continued to develop into universities. It is concluded that Syeh Burhanuddin has influence in the history of the development of Islamic education in Minangkabau.
Simulação de um Controle de Semáforos Utilizando Planilhas Eletrônicas: Uma Proposta para Fins Didáticos
Authors: Ancioto Junior Edgar Marcos , Pereira Dantas Maria José
Published: August 2019

Modelos de simulação de filas em planilhas eletrônicas têm sido utilizados com êxito para fins didáticos. Neste trabalho, simula-se um problema simplificado de trânsito, um cruzamento de duas ruas, controlado por um semáforo. Em ambas as ruas existem uma entrada, ao lado da qual há um desvio. Sempre que um veículo vai entrar na rua e ela se encontra cheia ele toma o desvio. No trecho da rua A compreendido entre a entrada e o cruzamento há lugar para 12 veículos, e na rua B para 8. O modelo de simulação foi usado para investigar o que ocorre com o fluxo de tráfego quando se fazem alterações nos tempos do semáforo. Diferentes cenários do cruzamento foram avaliados, alterando os tempos do semáforo para diversas combinações. Os resultados foram analisados e os cenários classificados de acordo com seus desempenhos, demonstrando as melhores e piores combinações de tempo. A modelagem proposta com o uso de fórmulas é simples e fácil de ser replicada. Na parte final, são sugeridas modificações para tornar o sistema mais realístico.
Resiliência na Cadeia de Suprimentos: O Caso de Uma Empresa de Cosméticos
Authors: Grandi Bruno Valença , Silva Andrea Lago , Moraes Camila Colombo , Lima Flávia Renata Pinho
Published: August 2019

A busca pela eficiência em cadeias de suprimentos pode aumentar sua exposição à riscos de ruptura e para evitá-las, alguns elementos estão apontados no contexto da resiliência. O objetivo do trabalho foi identificar elementos para construir resiliência, tendo como foco a área de compras de uma in­dústria de cosméticos. Na parte empírica, foi reali­zado um estudo de caso. Os elementos que con­tribuem para a área de compras são comunicação, compartilhamento de informação e integração externa, usados na negociação com fornecedores. Identificou-se que ações para reduzir os riscos do modelo de negócio e variações de demanda são importantes para construção de resiliência.
Desenvolvimento de competências com jogos de empresas: pesquisa de opinião em um experimento com alunos do ensino técnico
Authors: Jungles Bruna Ferreira , Rodrigues José de Souza , Garcia Sheila Farias Alves
Published: August 2019

O presente estudo teve por objetivo compreender quais são os atributos de competência mais utilizados pelos participantes de simulações empresariais durante o processo de tomada de decisão, segundo a opinião dos alunos. A amostra foi formada por 33 estudantes do ensino técnico em Administração, que responderam, inicialmente, um questionário de mapeamento de competências (conhecimentos, habilidades e atitudes). Após 7 rodadas de um jogo de simulação, os participantes responderam novamente ao questionário, pontuando o quanto cada um dos atributos de competência havia sido importante durante o processo de tomada de decisão. Os resultados evidenciaram que as competências mais utilizadas pelos estudantes durante a simulação foram aquelas relacionadas às dimensões habilidades e atitudes. Além disso, observou-se que o instrumento levou o aluno a refletir sobre suas competências e habilidades, incentivando-o a prestar atenção em como pensa e toma decisão. É provável que isso o tenha estimulado a pensar o exercício com o jogo como uma forma de estimular e desenvolver as habilidades e atitudes presentes no instrumento, bem como adquirir novos conhecimentos sobre o processo gerencial das empresas. Como limitações do estudo, apontam-se a utilização de um grupo pequeno de participantes e o fato de que os dados obtidos no experimento resultaram de um processo de autoavaliação. Recomenda-se que, em estudos futuros, sejam ampliados os tamanhos das amostras e realizadas simulações e avaliações em grupo para efeito de comparação com os resultados obtidos nesta pesquisa.
Refining Coarse-Grained Spatial Data Using Auxiliary Spatial Data Sets with Various Granularities
Authors: Tanaka Yusuke , Iwata Tomoharu , Tanaka Toshiyuki , Kurashima Takeshi , Okawa Maya , Toda Hiroyuki
Published: August 2019

We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data sets with various granularities by hierarchically incorporating Gaussian processes. With the proposed model, a distribution for each auxiliary data set on the continuous space is modeled using a Gaussian process, where the representation of uncertainty considers the levels of granularity. The finegrained target data are modeled by another Gaussian process that considers both the spatial correlation and the auxiliary data sets with their uncertainty. We integrate the Gaussian process with a spatial aggregation process that transforms the fine-grained target data into the coarse-grained target data, by which we can infer the fine-grained target Gaussian process from the coarse-grained data. Our model is designed such that the inference of model parameters based on the exact marginal likelihood is possible, in which the variables of finegrained target and auxiliary data are analytically integrated out. Our experiments on real-world spatial data sets demonstrate the effectiveness of the proposed model.
An Integral Tag Recommendation Model for Textual Content
Authors: Tang Shijie , Yao Yuan , Zhang Suwei , Xu Feng , Gu Tianxiao , Tong Hanghang , Yan Xiaohui , Lu Jian
Published: August 2019

Recommending suitable tags for online textual content is a key building block for better content organization and consumption. In this paper, we identify three pillars that impact the accuracy of tag recommendation: (1) sequential text modeling meaning that the intrinsic sequential ordering as well as different areas of text might have an important implication on the corresponding tag(s) , (2) tag correlation meaning that the tags for a certain piece of textual content are often semantically correlated with each other, and (3) content-tag overlapping meaning that the vocabularies of content and tags are overlapped. However, none of the existing methods consider all these three aspects, leading to a suboptimal tag recommendation. In this paper, we propose an integral model to encode all the three aspects in a coherent encoder-decoder framework. In particular, (1) the encoder models the semantics of the textual content via Recurrent Neural Networks with the attention mechanism, (2) the decoder tackles the tag correlation with a prediction path, and (3) a shared embedding layer and an indicator function across encoder-decoder address the content-tag overlapping. Experimental results on three realworld datasets demonstrate that the proposed method significantly outperforms the existing methods in terms of recommendation accuracy.
Holographic Factorization Machines for Recommendation
Authors: Tay Yi , Zhang Shuai , Luu Anh Tuan , Hui Siu Cheung , Yao Lina , Quang Vinh Tran Dang
Published: August 2019

Factorization Machines (FMs) are a class of popular algorithms that have been widely adopted for collaborative filtering and recommendation tasks. FMs are characterized by its usage of the inner product of factorized parameters to model pairwise feature interactions, making it highly expressive and powerful. This paper proposes Holographic Factorization Machines (HFM), a new novel method of enhancing the representation capability of FMs without increasing its parameter size. Our approach replaces the inner product in FMs with holographic reduced representations (HRRs), which are theoretically motivated by associative retrieval and compressed outer products. Empirically, we found that this leads to consistent improvements over vanilla FMs by up to 4% improvement in terms of mean squared error, with improvements larger at smaller parameterization. Additionally, we propose a neural adaptation of HFM which enhances its capability to handle nonlinear structures. We conduct extensive experiments on nine publicly available datasets for collaborative filtering with explicit feedback. HFM achieves state-of-theart performance on all nine, outperforming strong competitors such as Attentional Factorization Machines (AFM) and Neural Matrix Factorization (NeuMF).
Natural Option Critic
Authors: Tiwari Saket , Thomas Philip S.
Published: August 2019

The recently proposed option-critic architecture (Bacon, Harb, and Precup 2017) provides a stochastic policy gradient approach to hierarchical reinforcement learning. Specifically, it provides a way to estimate the gradient of the expected discounted return with respect to parameters that define a finite number of temporally extended actions, called options. In this paper we show how the option-critic architecture can be extended to estimate the natural gradient (Amari 1998) of the expected discounted return. To this end, the central questions that we consider in this paper are: 1) what is the definition of the natural gradient in this context, 2) what is the Fisher information matrix associated with an option’s parameterized policy, 3) what is the Fisher information matrix associated with an option’s parameterized termination function, and 4) how can a compatible function approximation approach be leveraged to obtain natural gradient estimates for both the parameterized policy and parameterized termination functions of an option with per-time-step time and space complexity linear in the total number of parameters. Based on answers to these questions we introduce the natural option critic algorithm. Experimental results showcase improvement over the vanilla gradient approach.
Learning Triggers for Heterogeneous Treatment Effects
Authors: Tran Christopher , Zheleva Elena
Published: August 2019

The causal effect of a treatment can vary from person to person based on their individual characteristics and predispositions. Mining for patterns of individual-level effect differences, a problem known as heterogeneous treatment effect estimation, has many important applications, from precision medicine to recommender systems. In this paper we define and study a variant of this problem in which an individuallevel threshold in treatment needs to be reached, in order to trigger an effect. One of the main contributions of our work is that we do not only estimate heterogeneous treatment effects with fixed treatments but can also prescribe individualized treatments. We propose a tree-based learning method to find the heterogeneity in the treatment effects. Our experimental results on multiple datasets show that our approach can learn the triggers better than existing approaches.
Theoretical Analysis of Label Distribution Learning
Authors: Wang Jing , Geng Xin
Published: August 2019

As a novel learning paradigm, label distribution learning (LDL) explicitly models label ambiguity with the definition of label description degree. Although lots of work has been done to deal with real-world applications, theoretical results on LDL remain unexplored. In this paper, we rethink LDL from theoretical aspects, towards analyzing learnability of LDL. Firstly, risk bounds for three representative LDL algorithms (AA-kNN, AA-BP and SA-ME) are provided. For AA-kNN, Lipschitzness of the label distribution function is assumed to bound the risk, and for AA-BP and SA-ME, rademacher complexity is utilized to give data-dependent risk bounds. Secondly, a generalized plug-in decision theorem is proposed to understand the relation between LDL and classification, uncovering that approximation to the conditional probability distribution function in absolute loss guarantees approaching to the optimal classifier, and also data-dependent error probability bounds are presented for the corresponding LDL algorithms to perform classification. As far as we know, this is perhaps the first research on theory of LDL.
SVM-Based Deep Stacking Networks
Authors: Wang Jingyuan , Feng Kai , Wu Junjie
Published: August 2019

The deep network model, with the majority built on neural networks, has been proved to be a powerful framework to represent complex data for high performance machine learning. In recent years, more and more studies turn to nonneural network approaches to build diverse deep structures, and the Deep Stacking Network (DSN) model is one of such approaches that uses stacked easy-to-learn blocks to build a parameter-training-parallelizable deep network. In this paper, we propose a novel SVM-based Deep Stacking Network (SVM-DSN), which uses the DSN architecture to organize linear SVM classifiers for deep learning. A BP-like layer tuning scheme is also proposed to ensure holistic and local optimizations of stacked SVMs simultaneously. Some good math properties of SVM, such as the convex optimization, is introduced into the DSN framework by our model. From a global view, SVM-DSN can iteratively extract data representations layer by layer as a deep neural network but with parallelizability, and from a local view, each stacked SVM can converge to its optimal solution and obtain the support vectors, which compared with neural networks could lead to interesting improvements in anti-saturation and interpretability. Experimental results on both image and text data sets demonstrate the excellent performances of SVM-DSN compared with some competitive benchmark models.
A Sharper Generalization Bound for Divide-and-Conquer Ridge Regression
Authors: Wang Shusen
Published: August 2019

We study the distributed machine learning problem where the n feature-response pairs are partitioned among m machines uniformly at random. The goal is to approximately solve an empirical risk minimization (ERM) problem with the minimum amount of communication. The divide-and-conquer (DC) method, which was proposed several years ago, lets every worker machine independently solve the same ERM problem using its local feature-response pairs and the driver machine combine the solutions. This approach is in one-shot and thereby extremely communication-efficient. Although the DC method has been studied by many prior works, reasonable generalization bound has not been established before this work.For the ridge regression problem, we show that the prediction error of the DC method on unseen test samples is at most ε times larger than the optimal. There have been constantfactor bounds in the prior works, their sample complexities have a quadratic dependence on d, which does not match the setting of most real-world problems. In contrast, our bounds are much stronger. First, our 1 + ε error bound is much better than their constant-factor bounds. Second, our sample complexity is merely linear with d.
Deep Metric Learning by Online Soft Mining and Class-Aware Attention
Authors: Wang Xinshao , Hua Yang , Kodirov Elyor , Hu Guosheng , Robertson Neil M.
Published: August 2019

Deep metric learning aims to learn a deep embedding that can capture the semantic similarity of data points. Given the availability of massive training samples, deep metric learning is known to suffer from slow convergence due to a large fraction of trivial samples. Therefore, most existing methods generally resort to sample mining strategies for selecting nontrivial samples to accelerate convergence and improve performance. In this work, we identify two critical limitations of the sample mining methods, and provide solutions for both of them. First, previous mining methods assign one binary score to each sample, i.e., dropping or keeping it, so they only selects a subset of relevant samples in a mini-batch. Therefore, we propose a novel sample mining method, called Online Soft Mining (OSM), which assigns one continuous score to each sample to make use of all samples in the mini-batch. OSM learns extended manifolds that preserve useful intraclass variances by focusing on more similar positives. Second, the existing methods are easily influenced by outliers as they are generally included in the mined subset. To address this, we introduce Class-Aware Attention (CAA) that assigns little attention to abnormal data samples. Furthermore, by combining OSM and CAA, we propose a novel weighted contrastive loss to learn discriminative embeddings. Extensive experiments on two fine-grained visual categorisation datasets and two video-based person re-identification benchmarks show that our method significantly outperforms the state-of-the-art.
Universal Approximation Property and Equivalence of Stochastic Computing-Based Neural Networks and Binary Neural Networks
Authors: Wang Yanzhi , Zhan Zheng , Zhao Liang , Tang Jian , Wang Siyue , Li Jiayu , Yuan Bo , Wen Wujie , Lin Xue
Published: August 2019

Large-scale deep neural networks are both memory and computation-intensive, thereby posing stringent requirements on the computing platforms. Hardware accelerations of deep neural networks have been extensively investigated. Specific forms of binary neural networks (BNNs) and stochastic computing-based neural networks (SCNNs) are particularly appealing to hardware implementations since they can be implemented almost entirely with binary operations.

Despite the obvious advantages in hardware implementation, these approximate computing techniques are questioned by researchers in terms of accuracy and universal applicability. Also it is important to understand the relative pros and cons of SCNNs and BNNs in theory and in actual hardware implementations. In order to address these concerns, in this paper we prove that the “ideal” SCNNs and BNNs satisfy the universal approximation property with probability 1 (due to the stochastic behavior), which is a new angle from the original approximation property. The proof is conducted by first proving the property for SCNNs from the strong law of large numbers, and then using SCNNs as a “bridge” to prove for BNNs. Besides the universal approximation property, we also derive an appropriate bound for bit length M in order to provide insights for the actual neural network implementations. Based on the universal approximation property, we further prove that SCNNs and BNNs exhibit the same energy complexity. In other words, they have the same asymptotic energy consumption with the growth of network size. We also provide a detailed analysis of the pros and cons of SCNNs and BNNs for hardware implementations and conclude that SCNNs are more suitable.
Non-Autoregressive Machine Translation with Auxiliary Regularization
Authors: Wang Yiren , Tian Fei , He Di , Qin Tao , Zhai ChengXiang , Liu Tie-Yan
Published: August 2019

As a new neural machine translation approach, NonAutoregressive machine Translation (NAT) has attracted attention recently due to its high efficiency in inference. However, the high efficiency has come at the cost of not capturing the sequential dependency on the target side of translation, which causes NAT to suffer from two kinds of translation errors: 1) repeated translations (due to indistinguishable adjacent decoder hidden states), and 2) incomplete translations (due to incomplete transfer of source side information via the decoder hidden states). In this paper, we propose to address these two problems by improving the quality of decoder hidden representations via two auxiliary regularization terms in the training process of an NAT model. First, to make the hidden states more distinguishable, we regularize the similarity between consecutive hidden states based on the corresponding target tokens. Second, to force the hidden states to contain all the information in the source sentence, we leverage the dual nature of translation tasks (e.g., English to German and German to English) and minimize a backward reconstruction error to ensure that the hidden states of the NAT decoder are able to recover the source side sentence. Extensive experiments conducted on several benchmark datasets show that both regularization strategies are effective and can alleviate the issues of repeated translations and incomplete translations in NAT models. The accuracy of NAT models is therefore improved significantly over the state-of-the-art NAT models with even better efficiency for inference.
Learning Compact Model for Large-Scale Multi-Label Data
Authors: Wei Tong , Li Yu-Feng
Published: August 2019

Large-scale multi-label learning (LMLL) aims to annotate relevant labels from a large number of candidates for unseen data. Due to the high dimensionality in both feature and label spaces in LMLL, the storage overheads of LMLL models are often costly. This paper proposes a POP (joint label and feature Parameter OPtimization) method. It tries to filter out redundant model parameters to facilitate compact models. Our key insights are as follows. First, we investigate labels that have little impact on the commonly used LMLL performance metrics and only preserve a small number of dominant parameters for these labels. Second, for the remaining influential labels, we reduce spurious feature parameters that have little contribution to the generalization capability of models, and preserve parameters for only discriminative features. The overall problem is formulated as a constrained optimization problem pursuing minimal model size. In order to solve the resultant difficult optimization, we show that a relaxation of the optimization can be efficiently solved using binary search and greedy strategies. Experiments verify that the proposed method clearly reduces the model size compared to state-of-the-art LMLL approaches, in addition, achieves highly competitive performance.
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
Authors: Wen Qingsong , Gao Jingkun , Song Xiaomin , Sun Liang , Xu Huan , Zhu Shenghuo
Published: August 2019

Decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting. Although numerous methods have been proposed, there are still many time series characteristics exhibiting in real-world data which are not addressed properly, including 1) ability to handle seasonality fluctuation and shift, and abrupt change in trend and reminder; 2) robustness on data with anomalies; 3) applicability on time series with long seasonality period. In the paper, we propose a novel and generic time series decomposition algorithm to address these challenges. Specifically, we extract the trend component robustly by solving a regression problem using the least absolute deviations loss with sparse regularization. Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component. This process is repeated until accurate decomposition is obtained. Experiments on different synthetic and real-world time series datasets demonstrate that our method outperforms existing solutions.
Properties of biocemented, basalt-fibre-reinforced calcareous sand
Authors: Lin Shengqiang , Lei Xuewen , Meng Qingshan , Xu Jianping
Published: August 2019

Basalt fibres and biocement were used to improve the mechanical properties of calcareous sand. The biocement was produced through a microbial-induced calcium carbonate precipitation (MICP) process. The unconfined compressive strength (UCS), tensile strength and permeability of the biocemented and fibre-reinforced calcareous sand were evaluated. The calcium carbonate content of each sample was also measured. The microstructure of the biocemented calcareous sand was observed using scanning electron microscopy (SEM). The SEM results indicated that the fibre facilitated the MICP process by bridging the pores in the calcareous sand. As a result, the fibre reduced the brittleness and increased the failure strains of the biocemented calcareous sand. Compared with biocemented calcareous sand without fibre, for all fibre lengths except 4 mm, the UCS, tensile strength and calcium carbonate content of the biocemented calcareous sand increased gradually with an increase in fibre content up to a fibre content of 1%; the optimum fibre content and fibre length were found to be 1% and 8 mm, respectively. The UCS and tensile strength of the biocemented calcareous sand at the optimum fibre content and fibre length increased by 717% and 115%, respectively.
Regulating Financial Networks Under Uncertainty
Authors: Ramírez Carlos
Published: August 2019

I study the problem of regulating a network of interdependent financial institutions that is prone to contagion when there is uncertainty regarding its precise structure. I show that such uncertainty reduces the scope for welfare-improving interventions. While improving network transparency potentially reduces this uncertainty, it does not always lead to welfare improvements. Under certain conditions, regulation that reduces the risk-taking incentives of a small set of institutions can improve welfare. The size and composition of such a set crucially depend on the interplay between (i) the (expected) susceptibility of the network to contagion, (ii) the cost of improving network transparency, (iii) the cost of regulating institutions, and (iv) investors' preferences.
From Transactions Data to Economic Statistics: Constructing Real-time, High-frequency, Geographic Measures of Consumer Spending
Authors: Aladangady Aditya , Aron-Dine Shifrah , Dunn Wendy , Feiveson Laura , Lengermann Paul , Sahm Claudia
Published: August 2019

Access to timely information on consumer spending is important to economic policymakers. The Census Bureau's monthly retail trade survey is a primary source for monitoring consumer spending nationally, but it is not well suited to study localized or short-lived economic shocks. Moreover, lags in the publication of the Census estimates and subsequent, sometimes large, revisions diminish its usefulness for real-time analysis. Expanding the Census survey to include higher frequencies and subnational detail would be costly and would add substantially to respondent burden. We take an alternative approach to fill these information gaps. Using anonymized transactions data from a large electronic payments technology company, we create daily estimates of retail spending at detailed geographies. Our daily estimates are available only a few days after the transactions occur, and the historical time series are available from 2010 to the present. When aggregated to the national leve l, the pattern of monthly growth rates is similar to the official Census statistics. We discuss two applications of these new data for economic analysis: First, we describe how our monthly spending estimates are useful for real-time monitoring of aggregate spending, especially during the government shutdown in 2019, when Census data were delayed and concerns about the economy spiked. Second, we show how the geographic detail allowed us quantify in real time the spending effects of Hurricanes Harvey and Irma in 2017.
Parental Proximity and Earnings After Job Displacements
Authors: Zabek Mike , Coate Patrick , Krolikowski Pawel
Published: August 2019

The earnings of young adults who live in the same neighborhoods as their parents completely recover after a job displacement, unlike the earnings of young adults who live farther away, which permanently decline. Nearby workers appear to benefit from help with childcare since grandmothers are less likely to be employed after their child's job displacement and since the earnings benefits are concentrated among young adults who have children. The result also suggests that parental employment networks improve earnings. Differences in job search durations, transfers of housing services, and geographic mobility, however, are too small to explain the result.
Combining long-lasting insecticidal nets and indoor residual spraying for malaria prevention in Ethiopia: study protocol for a cluster randomized controlled trial v1
Authors: Lindtjorn Bernt
Published: August 2019

Background: Long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) are the main malaria prevention interventions in Ethiopia. There is conflicting evidence that the combined application of both interventions is better than either LLINs or IRS used alone. This trial aims to investigate whether the combination of LLINs (PermaNet 2.0, Vestergaard Frandsen, Lausanne, Switzerland) with IRS using propoxur will enhance the protective benefits and cost-effectiveness of the interventions against malaria and its effect on mosquito behavior, as compared to each intervention alone.Methods/Design: This 2 x 2 factorial cluster randomized controlled trial is being carried out in the Adami Tullu district in south-central Ethiopia for about 116 weeks from September 2014 to December 2016. The trial is based on four arms: LLINs + IRS, LLINs alone, IRS alone and control. Villages (or clusters) will be the unit of randomization. The sample size includes 44 clusters per arm, with each cluster comprised of approximately 35 households (about 175 people). Prior to intervention, all households in the LLINs + IRS and LLINs alone arms will be provided with LLINs free of charge. Households in the LLINs + IRS and IRS alone arms will be sprayed with carbamate propoxur once a year just before the main malaria transmission season throughout the investigation. The primary outcome of this trial will be a malaria incidence based on the results of the rapid diagnostic tests in patients with a fever or history of fever attending health posts by passive case detection. Community-based surveys will be conducted each year to assess anemia among children 5–59 months old. In addition, community-based malaria prevalence surveys will be conducted each year on a representative sample of households during the main transmission season. The cost-effectiveness of the interventions and entomological studies will be simultaneously conducted. Analysis will be based on an intention-to-treat principle.Discussion: This trial aims to provide evidence on the combined use of LLINs and IRS for malaria prevention by answering the following research questions: Can the combined use of LLINs and IRS significantly reduce the incidence of malaria compared with the use of either LLINs or IRS alone? And is the reduced incidence justifiable compared to the added costs? Will the combined use of LLINs and IRS reduce vector density, infection, longevity and the entomological inoculation rate? These data are crucial in order to maximize the impact of vector control interventions on the morbidity and mortality of malaria.Trial registration: PACTR201411000882128 (8 September 2014).
PAS Staining of Fresh Frozen or Paraffin Embedded Human Kidney Tissue v1
Authors: Allen Jamie , Harvey Jennifer , Brewer Maya , De Caestecker Mark , Spraggins Jeff
Published: August 2019

Scope: The PAS stain is used to demonstrate polysaccharides such as glycogen, and mucosubstances such as glycoproteins, glycolipids and mucins in tissues. It is used as a replacement for the H&E in kidney pathology. Expected Outcome: Intermyofibrillar Network…………………………..Pink to Rose Glycogen………………………………………………….…..Pink to Rose Myofibrils……………………………………………………..Unstained Type I fibers………………………………………………….Lighter Type II fibers…………………………………………………Darker Blood Vessel walls & connective tissue…..Faintly Stained
Measure 40 worm feeding with bioluminescent bacteria v1
Authors: Ding Serena
Published: August 2019

Protocol uses bioluminescently labelled bacteria (Gregor et al, 2008, Addgene Plasmid #107879) to measure population feeding of 40 adult C. elegans worms.
Automated, Rapid Preparation of Tissue Sections for Proteomic Analysis v1
Authors: Allen Jamie , Spraggins Jeff , Gutierrez Danielle
Published: August 2019

Scope: To describe the procedure for the lysis, reduction/alkylation, trypsin digestion, and clean-up of tissue. Lysis will cover the lysing of tissue and protein concentration. Acetone precipitation will cover the precipitation of proteins. Digestion will cover the process for digesting 100 µg of protein using the Agilent AssayMap Bravo Robot and Promega Rapid Trypsin/LysC. The clean-up of the cells will cover the desalting process on the Agilent AssayMap Bravo and the subsequent preparation of the samples for LC/MS peptide analysis. Expected Outcome/Data: Cell samples lysed, digested, and desalted for analysis on MS instrument. Samples to be analyzed within one or two days of desalting.
Classes Duo Paris/Knoxville, an Integrative Learning Experience: A Case Study
Authors: Wells Monique Y. , Stein-Smith Kathleen
Published: August 2019

This case study examines the role of integration of the arts into learning within the framework of interdisciplinarity and integrative learning. Specifically, it describes and discusses the Classes Duo Paris/Knoxville program, which brings together students from Paris and Knoxville through the life and art of Knoxville-born, Paris-expatriate artist Beauford Delaney. The integration of the arts, languages (French and English), and travel abroad is the basis for a survey distributed to the adult participants in the program by the organizer, who is one of the authors of this study. Responses to the survey have been described and discussed in terms of arts education and international experiential learning, with implications for similar future initiatives and the current and potential role of philanthropy in educational programming.
Similarities in Textual Contents between Burung Simbangan Poetry and Siti Zubaidah Poetry
Authors: Effendi Rustam
Published: August 2019

One of the scripts stored in the Lambung Mangkurat Museum, Kalimantan Selatan Province, is Burung Simbangan Poetry. The interesting part is that this script has several similarities to Siti Zubaidah Poetry. This research is a philological study, of which the research aims to reveal the existence of a script amidst the owner’s ethnicity. The method applied to understand these scripts is a qualitative method using a content analysis technique. The data source comprises both of scripts documents. The findings of this study include several similarities in the narrative between Burung Simbangan Poetry and Siti Zubaidah Poetry. These similarities are the stories about (i) a protagonist who has multiple wives; (ii) a first wife (the oldest), who assists her husband in a war until victory; (iii) a first wife (the oldest), who disguises herself as a man; (iv) a protagonist who is imprisoned in a poisonous well; (v) a protagonist who is hit by a chained arrow; (vi) the oldest wife, who assists her husband in reclaiming a young wife kidnapped by an enemy; and (vii) a protagonist assisted by four loyal patih, or commanders.
A Strategy for English Reading Teaching—Based on Vocabulary, Grammar and Logic
Authors: Peng Yu
Published: August 2019

"English Reading" is a professional compulsory course combining theory with practice. This course covers English language knowledge, culture and literature knowledge, national knowledge, cross-cultural communication knowledge, basic knowledge of humanities, social sciences and natural sciences, etc., aiming to improve students' reading efficiency and ability to obtain main information, especially text appreciation, rhetoric and critical thinking ability. The task of the lesson is to cultivate students' ability of discourse analysis, semantic appreciation, logical thinking, independent thinking and proficient communication, so as to make them become "application-oriented talents” of high-quality and with innovative consciousness and creative ability". The study aimed to correlate the reading comprehension of English linguistic students, then inform some pedagogical implications for the teaching of reading comprehension (Cuc Thi Kim Pham, 2017). Through the data analysis of an authoritative reading test officially organized by the university, the author found that a large proportion of students in sophomore year failed to perform well in the test and did not demonstrate excellent reading skills. The reasons are as follows: firstly, the "teacher-led role" is neglected in reading teaching; Secondly, students ignore discourse analysis in reading; thirdly, the students' understanding of vocabulary is limited to the meaning of completely equivalent Chinese; Fourth: Grammatical awareness is not strong. The author finds out the problems that students have in reading, and combines relevant cases to conduct in-depth discussions on these issues.
A Comparative Study on English and Chinese Kinship Terms and Their Translation Strategies
Authors: Chen Chen
Published: August 2019

As an important part of addressing forms, kinship terms are frequently used in our daily life. Both Chinese and English languages have their unique cultural background, which determines the significant differences between English and Chinese kinship systems. This paper first makes a comparative analysis and a systematic induction of the English and Chinese kinship terms, then reveal the causes that the differences between the two kinship systems result from, and finally attempts to explores three translation methods of Chinese and English kinship terms, which will attach much more significance to cross-cultural communication as well.
Two-dimensional Plane Thermocapillary Flow of Two Immiscible Liquids
Authors: Lemeshkova Elena N.
Published: August 2019

The problem of two-dimensional stationary flow of two immiscible liquids in a plane channel with rigid walls is considered. A temperature distribution is specified on one of the walls and another wall is heat- insulated. The interfacial energy change is taken into account on the common interface. The temperature in liquids is distributed according to a quadratic law. It agrees with velocities field of the Hiemenz type. The corresponding conjugate boundary value problem is nonlinear and inverse with respect to pressure gradients along the channel. The Tau method is used for the solution of the problem . Three different solutions are obtained. It is established numerically that obtained solutions converge to the solutions of the slow flow problem with decreasing the Marangoni number. For each of the solutions the characteristic flow structures are constructed.
Global in Space Regularity Results for the Heat Equation with Robin-Neumann Type Boundary Conditions in Time-varying Domains
Authors: Boudjeriou Tahir , Kheloufi Arezki
Published: August 2019

This article deals with the heat equation @tu @2x u = f in D; D = {(t; x) 2 R2 : a < t < b; (t) < x < +1} with the function satisfying some conditions and the problem is supplemented with boundary conditions of Robin-Neumann type. We study the global regularity problem in a suitable parabolic Sobolev space. We prove in particular that for f 2 L2(D) there exists a unique solution u such that u; @tu; @jx u 2 L2 (D) ; j = 1; 2: The proof is based on the domain decomposition method. This work complements the results obtained in [10].
Ill-posed Boundary-value Problem for a System of Partial Differential Equations with Two Degenerate Lines
Authors: Fayazov Kudratillo S. , Khudayberganov Yashin K.
Published: August 2019

This paper is devoted to the investigation of ill-posed boundary-value problem for system of parabolic type equations with changing time direction with two degenerate lines. The problem under consideration is ill-posed in the sense of J. Hadamard, namely, there is no continuous dependence of the solution on the initial data. Such equations have many different applications, for example, describe the processes of heat propagation in inhomogeneous media, the interaction of filtration flows, mass transfer near the surface of an aircraft, and the description of complex viscous fluid flows. As possible applications should also indicate the task of calculating heat exchangers, in which the counter flow principle is used. Theorems on the uniqueness and conditional stability of a solution on a set of well-posedness are proved. We construct a sequence of approximate (regularized) solutions that are stable on the set of well-posedness
Gender normative and non-normative morphemes usages study: preference, tolerance and refuse on autoidentification
Authors: Rodríguez Iglesias Adrián
Published: August 2019

This study focuses on the use of a sociopragmatic methodology to research morphological phenomena. In particular, the study starts from the verification of new non-normative gender morphemes uses to make visible a part of the society that, according to the criticism of feminist linguistics to its normative usages, excludes them in its representation. So, the starting point of this study is to consider an associated perception of two phenomena: grammatical gender and (sociocultural) gender. This premise is treated as a sociocultural conditioning that enables the implementation of sociopragmatic research tools. The designed tool has enabled to determinate, on a scale of degrees, the preference, tolerance and refusal to these new gender morpheme uses in a given population segment, supporting the validity of sociopragmatic tools to study morphological phenomena.
Stereotypy – literatura – polityka. Jan (Iwan) Mazepa w polskim przekazie
Authors: Chynczewska-Hennel Teresa
Published: August 2019

Jan (Iwan) Mazepa-Kołodyński (około 1639–1709) to postać znana głównie pisarzom, poetom, malarzom i kompozytorom. Mazepa, początkowo związany z Janem Kazimierzem, a następnie z hetmanem prawobrzeżnej Ukrainy Doroszenką, zwrócił się do Piotra I. W latach 1687–1708 był hetmanem kozackim lewego brzegu Ukrainy. Powrót Mazepy do Polski nastąpił po wojnie Rosji ze Szwecją. Mazepa starał się odłączyć Ukrainę od Rosji. Nawiązał kontakt z obozem Stanisława Leszczyńskiego. Klęska Karola XII w Połtawie (8 sierpnia 1609 r.) Była końcem marzenia hetmana o wolnej Ukrainie. Dla Rosji i rosyjskiego Kościoła prawosławnego przez długi czas był symbolem zdrajcy, co miało wpływ na rosyjską historiografię. Dla współczesnej Ukrainy Iwan Mazepa jest bohaterem narodowym. W polskich mediach, z wyjątkiem relacji Jana Chryzostoma Paska i części starszej historiografii, obraz Mazepy jest obiektywny i raczej pozytywny. W artykule omówiono opinie i oceny w dzienniku Paska i dramacie Juliusza Słowackiego. Podano przykłady słowa „mazepa” w mowie potocznej. Z historiografii polskiej wspomniano polskich historyków, w tym: Józefa Feldmana, Jana Perdenia, Józefa Andrzeja Gierowskiego, Wiesława Majewskiego, Władysława Andrzeja Serczyka, Zbigniewa Wójcika i innych. W konkluzji autorka stwierdza, że nie ma książki biograficznej o Mazepie. Czeka on wciąż na właściwego historyka i należy mieć nadzieję, że uwzględni on zjawisko złego wpływu wszystkich tendencji politycznych na obiektywność historyczną.