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English for All: Advice for Students and Teachers

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Content-Based Language Instruction is popular in university and college academic settings for English language instruction. Content-Based Language Teaching is also used in primary and secondary educational settings in a variety of EFL and ESL settings, including full immersion environments and as supplements to content or foreign language learning in a bilingual setting. This article defines content-based instruction implemented as the foundation of an intensive English language program and how such a framework possibly helps accelerate language learning.

Look at today’s ESL/EFL textbooks. Most present English learning within topics, or themes. We could argue that this is content-based instruction. Many academic ESL classes use these content-based texts for learning English. Topics range from basic every day situations such as going to the grocery store, to more academic content such as environmental issues or psychology. So what makes this content-based instruction? Is it enough just to present language in a topic area or theme?

Content-Based Instruction can be viewed along a continuum, from focusing on the language and language learning itself to a focus on using language to navigate and learn a content ( Cristaseya split neck tunic dress Cheap Price Outlet X15dlR
). That continuum is often defined by the level of language skill of the students. What is important to note is that at each point on the continuum, it is essential to have support from an instructor with experience in language teaching and CBLI.

To illustrate the continuum of content-based instruction, let’s take a look at what typically happens in an academic university ESL program using content-based instruction from beginning level to advanced:

Beginning to intermediate levels – these courses are typically taught with themes as the content or context for learning English. In academic settings, these will include the use of textbook series which provide chapters around different themes. Some popular textbook series which are used in the United States include the following:

Beginning to intermediate levels

These types of textbooks provide relevant every day and academic content themes to promote development of English language skills and grammar through context. A class will consist of instruction around three to four different theme areas.

Advanced Courses – students who have a strong knowledge of the basics of English can benefit from courses designed as “shelter courses.” These are courses which focus on one content just as any university or academic course might. However, they are taught by experienced English teachers, who many times develop the content of the course themselves. They may use an unabridged college textbook as the core resource for the class. For example, in our program, Academic English Studies , instructors have designed a number of content-based courses for the intermediate to advanced levels. Instructors have developed content-based courses in sociology, technology and society, American culture through film, and leadership and organizations, just to name a few. These courses range from six to fifteen weeks in length. Students are able to test their English and critical thinking skills while developing language and knowledge of a subject area. Focus is on developing English skills within the academic content, integrating all aspects of language learning: reading, writing, speaking, listening, and grammar use. There is a heavy focus on developing English language to be able to express progressively more complex ideas and critical thinking. In sheltered courses there can be an emphasis on the content while also supporting development of language needed to be productive at an advanced level of English in the subject area.

Title: Generating 3D faces using Convolutional Mesh Autoencoders
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Journal-ref: European Conference on Computer Vision 2018
Subjects: Computer Vision and Pattern Recognition (cs.CV)

Learned 3D representations of human faces are useful for computer vision problems such as 3D face tracking and reconstruction from images, as well as graphics applications such as character generation and animation. Traditional models learn a latent representation of a face using linear subspaces or higher-order tensor generalizations. Due to this linearity, they can not capture extreme deformations and non-linear expressions. To address this, we introduce a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface. We introduce mesh sampling operations that enable a hierarchical mesh representation that captures non-linear variations in shape and expression at multiple scales within the model. In a variational setting, our model samples diverse realistic 3D faces from a multivariate Gaussian distribution. Our training data consists of 20,466 meshes of extreme expressions captured over 12 different subjects. Despite limited training data, our trained model outperforms state-of-the-art face models with 50% lower reconstruction error, while using 75% fewer parameters. We also show that, replacing the expression space of an existing state-of-the-art face model with our autoencoder, achieves a lower reconstruction error. Our data, model and code are available at this http URL

Title: Premise selection with neural networks and distributed representation of features
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Subjects: Artificial Intelligence (cs.AI) ; Machine Learning (cs.LG); Logic in Computer Science (cs.LO)

We present the problem of selecting relevant premises for a proof of a given statement. When stated as a binary classification task for pairs (conjecture, axiom), it can be efficiently solved using artificial neural networks. The key difference between our advance to solve this problem and previous approaches is the use of just functional signatures of premises. To further improve the performance of the model, we use dimensionality reduction technique, to replace long and sparse signature vectors with their compact and dense embedded versions. These are obtained by firstly defining the concept of a context for each functor symbol, and then training a simple neural network to predict the distribution of other functor symbols in the context of this functor. After training the network, the output of its hidden layer is used to construct a lower dimensional embedding of a functional signature (for each premise) with a distributed representation of features. This allows us to use 512-dimensional embeddings for conjecture-axiom pairs, containing enough information about the original statements to reach the accuracy of 76.45% in premise selection task, only with simple two-layer densely connected neural networks.

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