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With the growth of the Japanese economy in the 1970s and 1980s and the following rapid business globalization, many Japanese companies started to seek more business opportunities overseas (Kanno, 2003; Tamiya et al., 2014; Yoshida et al., 2009). This tendency was prominent, especially among Japanese major automotive companies, such as Nissan, Toyota, and Mazda. One of the most popular destinations targeted by these automotive companies was South East Michigan, which is the heart of the American automotive industry, particularly in cities such as Detroit, Farmington Hills, and Ann Arbor. Since then, many automotive companies and related manufacturing, machinery, electronics, and chemical companies started to open their American branches in South East Michigan (Consulate-General of Japan in Detroit, 2017). Accordingly, these Japanese companies sent many Japanese employees and their families to this area. According to the survey on the status of market entry of Japanese companies in Michigan, most Japanese populations are concentrated in South East Michigan, such as Novi (3,826), Ann Arbor (1,915), West Bloomfield (1,211), and Farmington Hills (711) (Consulate-General of Japan in Detroit, 2017). Among these cities, Novi has the largest Japanese population and functions as the center of Japanese communities in South East Michigan. Therefore, several Japanese schools and supplemental schools (called hoshuko) are also located in Novi for Japanese expatriate children living in South East Michigan. Responding to the growth of the Japanese population in South East Michigan, one of the crucial issues in these areas is to provide educational support for Japanese expatriate children.

One experimental phenomenon that can cause a threat to the internal validity of L2 teaching research is test-practice effects: the extent to which L2 learners improve by taking the same or similar tests for multiple occasions (e.g., at a pretest and posttests). This article reports a pilot meta-analysis within a larger study that investigated test-practice effects in L2 grammar instruction research. Specifically, I focused on how the test-only control group (i.e., participants who only receive tests without instructional treatments) generally improve from the pretest to the immediate posttest and the delayed posttest. I retrieved and coded 21 primary studies of L2 grammar instruction research, which included 23 independent samples contributing 100 effect sizes in the form of Cohen’s d. Using multilevel random-effects models, I estimated the overall population estimate of the effect sizes and investigated whether two moderator variables, the mode of language use (comprehension or production) and the type of outcome measures (explicit or implicit measures), changed the size of the test-practice effects. Results showed that test-practice effects indeed exist in L2 grammar instruction research, and their sizes lie between small and medium effects (d = 0.116−0.315). The effects also became more prominent when the test drew upon comprehension skills (d = 0.179−0.466) or used implicit measures of L2 knowledge (d = 0.173−0.597).

Geologic ontology

In this study water soluble compounds that form complexes with Rare Earth Elements (REE) and other metals were isolated from

Chinese named entity recognition in the geoscience domain based on BERT

Original GPM data can download from https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDF_06/summary?keywords="IMERG final"original SinGAN: https://github.com/tamarott/SinGAN

In analogy to higher plants, eukaryotic microalgae are thought to be incapable of utilizing green light for growth, due to the “green gap” in the absorbance profiles of their photosynthetic pigments. This study demonstrates, that the marine chlorophyte Picochlorum sp. is able to grow efficiently under green light emitting diode (LED) illumination. Picochlorum sp. growth and pigment profiles under blue, red, green and white LED illumination (light intensity: 50–200 μmol m−2 s−1) in bottom-lightened shake flask cultures were evaluated. Green light-treated cultures showed a prolonged initial growth lag phase of one to 2 days, which was subsequently compensated to obtain comparable biomass yields to red and white light controls (approx. 0.8 gDW L−1). Interestingly, growth and final biomass yields of the green light-treated sample were higher than under blue light with equivalent illumination energies. Further, pigment analysis indicated, that during green light illumination, Picochlorum sp. formed unknown pigments (X1-X4). Pigment concentrations increased with illumination intensity and were most abundant during the exponential growth phase. Mass spectrometry and nuclear magnetic resonance data indicated, that pigments X1-X2 and X3-X4 are derivatives of chlorophyll b and a, which harbor C=C bonds in the phytol side chain similar to geranylgeranylated chlorophylls. Thus, for the first time, the natural accumulation of large pools (approx. 12 mg gDW−1) of chlorophyll intermediates with incomplete hydrogenation of their phytyl chains is demonstrated for algae under monochromatic green light (Peak λ 510 nm, full width at half maximum 91 nm). The ability to utilize green light offers competitive advantages for enhancing biomass production, particularly under conditions of dense cultures, long light pathways and high light intensity. Green light acclimation for an eukaryotic microalgae in conjunction with the formation of new aberrant geranylgeranylated chlorophylls and high efficiency of growth rates are novel for eukaryotic microalgae. Illumination with green light could enhance productivity in industrial processes and trigger the formation of new metabolites–thus, underlying mechanisms require further investigation.

International audience; We compare disagreement in expectations and the frequency of forecast revisions among five categories of agents: households, firms, professional forecasters, policymakers and participants to laboratory experiments. We provide evidence of disagreement among all categories of agents. There is however a strong heterogeneity across categories: while policymakers and professional forecasters exhibit low disagreement, firms and households show strong disagreement. This translates into a heterogeneous frequency of forecast revision across categories of agents, with policymakers revising more frequently their forecasts than firms and professional forecasters. Households last revise less frequently. We are also able to explore the external validity of experimental expectations.

By correlating the speech act to its implementation within the boundaries of the university environment, this particular study orientates towards the interlinguistic phenotypes produced by students of the Turkish language during the learning process on the course of the curriculum of the Department of Turkish Studies & Modern Asian Studies. In a contemporary, evolving research program, launched on 2013, interlanguage issues of the students integrated into a system of multivariate analysis are being examined. The progress of the intaking capability on morphological and syntactical aspects is investigated, as well as the assimilation levels of the conditions leading to a successful pragmatic interaction between the inputting of the teaching process and the outputting of the language product, in generally. Our aim, within this paper, is to give some raw data received by preliminary reports regarding in an unusual reading comprehension test, the socio-linguistic dimensions and the pragmatic correlations on the B2-C1 learning levels.

Surface-enhanced Raman spectroscopy (SERS) has been increasingly used in the last decade for the identification of organic colourants in works of art. 'Surface-enhancement' is the giant increase in Raman scattering intensity experienced by organic molecules adsorbed on atomically rough metallic surfaces. In the case of organic colourants, which are in general strongly luminescent under laser excitation and therefore cannot usually be identified by conventional Raman microscopy (see AMCTB no. 67), SERS yields a strong and clearly resolved spectrum, allowing for easy identification. Unlike ordinary Raman spectroscopy, SERS often requires sampling, yet the sample size is considerably smaller (from 20 to 100 micrometres) than that required for high-performance liquid chromatography (HPLC), the benchmark technique for organic colourants analysis. For this reason, SERS has seen substantial use in the analysis of works of art such as prints, drawings, paintings, and polychrome sculpture. This technical brief focuses on the practical aspects of SERS and its application to the analysis of cultural heritage material, as well as on its current limitations.

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Inference and testing in general point process models such as the Hawkes model is predominantly based on asymptotic approximations for likelihoodbased estimators and tests, as originally developed in Ogata (1978). As an alternative, and to improve finite sample performance, this paper considers bootstrap-based inference for interval estimation and testing. Specifically, for a wide class of point process models we consider a novel bootstrap scheme labeled 'fixed intensity bootstrap' (FIB), where the conditional intensity is kept fixed across bootstrap repetitions. The FIB, which is very simple to implement and fast in practice, naturally extends previous ideas from the bootstrap literature on time series in discrete time, where the so-called 'fixed design' and 'fixed volatility' bootstrap schemes have shown to be particularly useful and effective. We compare the FIB with the classic recursive bootstrap, which is here labeled 'recursive intensity bootstrap' (RIB). In RIB algorithms, the intensity is stochastic in the bootstrap world and implementation of the bootstrap is more involved, due to its sequential structure. For both bootstrap schemes, no asymptotic theory is available; we therefore provide here a new bootstrap (asymptotic) theory, which allows to assess bootstrap validity. We also introduce novel 'nonparametric' FIB and RIB schemes, which are based on resampling time-changed transformations of the original waiting times. We show effectiveness of the different bootstrap schemes in finite samples through a set of detailed Monte Carlo experiments. As far as we are aware, this is the first detailed Monte Carlo study of bootstrap implementations for Hawkes-type processes. Finally, in order to illustrate, we provide applications of the bootstrap to both financial data and social media data.

We introduce the basic concepts of deep learning. We use TensorFlow 2.x, the Google cloud service, and Google Drive interaction to make the concepts come alive with Python coding examples.

This paper investigates the effects of automation and offshoring on the dynamics of the occupational distribution of employment with a focus on Western Europe between 2000 and 2016. I use a general equilibrium model with three regions, three types of workers, ICT capital, trade in final goods and endogenous offshoring. Fed with exogenous measures of ICT-capital prices and trade costs, the model replicates key features of the data. It matches the observed dynamics of offshoring to Eastern Europe and Asian countries. It also reproduces accurately the observed polarization of the labor market: abstract and manual labor increase while routine labor falls. A counterfactual experiment reveals that automation is the main driver of polarization. Since it is also the only factor that drives individuals to become abstract (highskill) workers, it is welfare enhancing. The effects of falling trade costs on labor polarization are smaller, but imply welfare gains.

Neural style transfer (NST) is a computer vision technique that takes two images – a content image and a style reference image – and blends them together so that the resulting output image retains the core elements of the content image but appears to be painted in the style of the style reference image. The output image from a NST network is called a pastiche. A pastiche is a work of visual art, literature, theater or music that imitates the style (or character) of the work of one or more other artists. Unlike a parody, a pastiche celebrates rather than mocks the work it imitates.

Despite substantial efforts to develop foreign language learning pipelines, little is known about students’ experiences moving between levels of education. This is particularly true for less commonly taught languages, where pre-tertiary learning is considered crucial for graduating students at higher levels of proficiency. This qualitative case study examines how four L2 learners of Arabic transition from their high school classrooms to university programs. Adopting a grounded theory approach to analyze survey and focus group data, three key themes emerge: initial (dis)orientation in the tertiary setting, classroom (dis)harmony as students navigate new norms, and retrospective appreciation for their high school learning environments. The results illustrate that university programs seem to lack appropriate measures to accommodate pipeline learners. Likewise, high school programs are not doing enough to familiarize students with the learning opportunities ahead. The findings have implications for programs, teachers, and students on both sides of these periods of transition.

This chapter focuses on the inner rationale and consequences of four different archetypal positions regarding how ethical and political values are tackled in welfare economics. Welfare economics is standardly associated with the welfarist framework, for which social welfare is based on individual utility only. Beyond this, we distinguish the value-neutrality claim – for which ethical values should be and are out of the scope of welfare economics –, the value confinement ideal – for which ethical values are acceptable if they are minimal and consensual–, the transparency requirement – for which any ethical values may be acceptable in the welfare economics framework if explicit and formalized –, and the entanglement claim – which challenges the very possibility of demarcation between facts and values.

Index de les obres ressenyades: Ignacio FARÍAS ; José OSSANDÓN, Observando sistemas. Nuevas apropiaciones y usos de la teoría de Niklas Luhmann

We introduce you to TensorFlow input pipelines with the tf.data API, which enables you to build complex input pipelines from simple, reusable pieces. Input pipelines are the lifeblood of any deep learning experiment because learning models expect data in a TensorFlow consumable form. It is very easy to create high-performance pipelines with the tf.data.Dataset abstraction (a component of the tf.data API) because it represents a sequence of elements from a dataset in a simple format.

We introduce TensorFlow datasets (TFDS). We discuss many facets of a TFDS with code examples. We continue with a complete TFDS modeling example.

In the previous chapter, we demonstrated how to work with TFDS objects. In this chapter, we work through two end-to-end deep learning experiments with large and complex TFDS objects. The Fashion-MNIST and beans datasets are small with simple images.

Feedforward neural nets are generally great for classification and regression problems. CNNs are great for complex image classification. But activations for feedforward nets and CNNs flow only in one direction, from the input layers to the output layer. Since signals flow in only one direction, feedforward and convolutional nets are not ideal if patterns in data change over time. So we need a different network architecture to work with data impacted by time.