389 resultados para Markup Language for Manuscript Images
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This action research study investigated face-to-face and wiki technology collaboration to enhance students' English writing skills in a second language (L2) class in Vietnam. The thesis is underpinned by socio-cultural theory and argues that collaborative learning using wikis led to an enhancement in L2 writing skills. The findings show that collaborating via wikis challenged traditional L2 writing pedagogy in the following ways: increased student autonomy; understanding formative feedback; and awareness of process writing, genre and audiences. This study contributes practical knowledge about affordances and constraints of collaborative writing using wikis in Vietnam and other countries where traditional pedagogies are prevalent.
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The use of mobile digital devices, such as laptops and tablets, has implications for how teachers interact with young students within the institutional context of educational settings. This article examines language and participation in a digitally enabled preschool classroom as students engage with teachers and peers. Ethnomethodology, conversation analysis and membership categorization analysis are used to explicate video-recorded episodes of students (aged 3-5 years) interacting while using a laptop and a tablet. Attending to the sequential organization (when, how) and the context relevance (where) of talk and interaction, analysis shows how the intersection of interactions involving the teacher, students and digital devices, shape the ways that talk and interactions unfold. Analysis found that the teacher-student interactions were jointly arranged around a participation framework that included: 1) the teacher’s embodied action that mobilizes an accompanying action by a student, 2) allocation of turn-taking and participation while using a digital device and, 3) the affordances of the digital device in relation to the participants’ social organization. In this way, it is possible to understand not just what a digital device is or does, but the affordances of what it makes possible in constituting teachers’ and students’ social and learning relationships.
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Aerial surveys conducted using manned or unmanned aircraft with customized camera payloads can generate a large number of images. Manual review of these images to extract data is prohibitive in terms of time and financial resources, thus providing strong incentive to automate this process using computer vision systems. There are potential applications for these automated systems in areas such as surveillance and monitoring, precision agriculture, law enforcement, asset inspection, and wildlife assessment. In this paper, we present an efficient machine learning system for automating the detection of marine species in aerial imagery. The effectiveness of our approach can be credited to the combination of a well-suited region proposal method and the use of Deep Convolutional Neural Networks (DCNNs). In comparison to previous algorithms designed for the same purpose, we have been able to dramatically improve recall to more than 80% and improve precision to 27% by using DCNNs as the core approach.
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Self-organized Bi lines that are only 1.5 nm wide can be grown without kinks or breaks on Si(0 0 1) surfaces to lengths of up to 500 nm. Constant-current topographical images of the lines, obtained with the scanning tunneling microscope, have a striking bias dependence. Although the lines appear darker than the Si terraces at biases below ≈∣1.2∣ V, the contrast reverses at biases above ≈∣1.5∣ V. Between these two ranges the lines and terraces are of comparable brightness. It has been suggested that this bias dependence may be due to the presence of a semiconductor-like energy gap within the line. Using ab initio calculations it is demonstrated that the energy gap is too small to explain the experimentally observed bias dependence. Consequently, at this time, there is no compelling explanation for this phenomenon. An alternative explanation is proposed that arises naturally from calculations of the tunneling current, using the Tersoff–Hamann approximation, and an examination of the electronic structure of the line.
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For Adorno writing in 1953, Hollywood cinema was a medium of “regression” based on infantile wish fulfillment manufactured by the industrial repetition of the filmic image that he called a modern “hieroglyphics”—like the archaic language of pictures in Ancient Egypt, which guaranteed immortality after death in Egyptian burial rites. From that 1953 essay Prolog zum Fernsehen to Das Schema der Massenkultur in 1981, Adorno likened film frames to cultural ideograms: What he called the filmic “language of images” (Bildersprache) constituted a Hieroglyphenschrift that visualised forbidden sexual impulses and ideations of death and domination in the unconscious of the mass spectator. In his famous passage he writes, “As image, the image-writing (Bilderschrift) is a medium of regression, where the producer and consumer coincide; as writing, film resurrects the archaic images of modernity.” In other words, cinema takes the spectator on a journey into his unconscious in order to control him from within. It works, because the spectator begins to believe the film is speaking to him in his very own image-language (the unconscious), making him do and buy whatever capitalism demands. Modernity for Adorno is precisely the instrumentalisation of the collective unconscious through the mediatic images of the culture industry.
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Quantifying the stiffness properties of soft tissues is essential for the diagnosis of many cardiovascular diseases such as atherosclerosis. In these pathologies it is widely agreed that the arterial wall stiffness is an indicator of vulnerability. The present paper focuses on the carotid artery and proposes a new inversion methodology for deriving the stiffness properties of the wall from cine-MRI (magnetic resonance imaging) data. We address this problem by setting-up a cost function defined as the distance between the modeled pixel signals and the measured ones. Minimizing this cost function yields the unknown stiffness properties of both the arterial wall and the surrounding tissues. The sensitivity of the identified properties to various sources of uncertainty is studied. Validation of the method is performed on a rubber phantom. The elastic modulus identified using the developed methodology lies within a mean error of 9.6%. It is then applied to two young healthy subjects as a proof of practical feasibility, with identified values of 625 kPa and 587 kPa for one of the carotid of each subject.
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The rupture of atherosclerotic plaques is known to be associated with the stresses that act on or within the arterial wall. The extreme wall tensile stress (WTS) is usually recognized as a primary trigger for the rupture of vulnerable plaque. The present study used the in-vivo high-resolution multi-spectral magnetic resonance imaging (MRI) for carotid arterial plaque morphology reconstruction. Image segmentation of different plaque components was based on the multi-spectral MRI and co-registered with different sequences for the patient. Stress analysis was performed on totally four subjects with different plaque burden by fluid-structure interaction (FSI) simulations. Wall shear stress distributions are highly related to the degree of stenosis, while the level of its magnitude is much lower than the WTS in the fibrous cap. WTS is higher in the luminal wall and lower at the outer wall, with the lowest stress at the lipid region. Local stress concentrations are well confined in the thinner fibrous cap region, and usually locating in the plaque shoulder; the introduction of relative stress variation during a cycle in the fibrous cap can be a potential indicator for plaque fatigue process in the thin fibrous cap. According to stress analysis of the four subjects, a risk assessment in terms of mechanical factors could be made, which may be helpful in clinical practice. However, more subjects with patient specific analysis are desirable for plaque-stability study.
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Language learning beyond the classroom is part of a growing body of literature focused on teaching and learning in contexts that are informal and unstructured. Areas include so-called shadow education and informal pedagogies. Shadow education refers to the privatised tutoring supplementing school curricular that is a pervasive feature of education in parts of Asia (Bray & Lykins, 2012) and increasingly evident in Australia. Informal pedagogies refers to teaching in informal contexts and was the focus of a Special Interest Group (SIG) at the recent American Educational Research Association (AERA) annual conference in Chicago. Presentations in the SIG included designing tools for supporting learning in science classes after school and in sites such as zoos...
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The prevalence and developmental course of supposed ‘secret language’ was examined in a cohort of twins and closely spaced singletons pairs, with systematic assessments at 20 months and again at 36 months. Two forms of apparent ‘secret language’ were examined: (1) shared understanding—speech directed generally but unintelligible to the parent, although apparently clearly understood within the child pair, and (2) private language directed exclusively to the other twin/sibling—not intelligible to the parent, but apparently clearly understood and used only within the child pair. Both occurred in singleton pairs, but the rate was much higher in twins. In most cases it seemed to be a developmental phenomenon occurring in the second year of life with the emergence of immature speech, and decreasing considerably over the next 16 months. A small group of children, primarily male twins, was reported to use a private language at 36 months. This group had poorer cognitive and language functioning, and was characterized by highly dependent relationships. Some aspects of the twins’ home environment were less stimulating and less responsive, most probably reflecting the abilities and relationships of the children. A follow-up of these children when they were ~6 years of age showed that language outcome was poor for the subgroup (n = 4) who did not develop normal language alongside the use of a private language.
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This paper presents a symbolic navigation system that uses spatial language descriptions to inform goal-directed exploration in unfamiliar office environments. An abstract map is created from a collection of natural language phrases describing the spatial layout of the environment. The spatial representation in the abstract map is controlled by a constraint based interpretation of each natural language phrase. In goal-directed exploration of an unseen office environment, the robot links the information in the abstract map to observed symbolic information and its grounded world representation. This paper demonstrates the ability of the system, in both simulated and real-world trials, to efficiently find target rooms in environments that it has never been to previously. In three unexplored environments, it is shown that on average the system travels only 8.42% further than the optimal path when using only natural language phrases to complete navigation tasks.
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Objective Death certificates provide an invaluable source for cancer mortality statistics; however, this value can only be realised if accurate, quantitative data can be extracted from certificates – an aim hampered by both the volume and variable nature of certificates written in natural language. This paper proposes an automatic classification system for identifying cancer related causes of death from death certificates. Methods Detailed features, including terms, n-grams and SNOMED CT concepts were extracted from a collection of 447,336 death certificates. These features were used to train Support Vector Machine classifiers (one classifier for each cancer type). The classifiers were deployed in a cascaded architecture: the first level identified the presence of cancer (i.e., binary cancer/nocancer) and the second level identified the type of cancer (according to the ICD-10 classification system). A held-out test set was used to evaluate the effectiveness of the classifiers according to precision, recall and F-measure. In addition, detailed feature analysis was performed to reveal the characteristics of a successful cancer classification model. Results The system was highly effective at identifying cancer as the underlying cause of death (F-measure 0.94). The system was also effective at determining the type of cancer for common cancers (F-measure 0.7). Rare cancers, for which there was little training data, were difficult to classify accurately (F-measure 0.12). Factors influencing performance were the amount of training data and certain ambiguous cancers (e.g., those in the stomach region). The feature analysis revealed a combination of features were important for cancer type classification, with SNOMED CT concept and oncology specific morphology features proving the most valuable. Conclusion The system proposed in this study provides automatic identification and characterisation of cancers from large collections of free-text death certificates. This allows organisations such as Cancer Registries to monitor and report on cancer mortality in a timely and accurate manner. In addition, the methods and findings are generally applicable beyond cancer classification and to other sources of medical text besides death certificates.
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Images from cell biology experiments often indicate the presence of cell clustering, which can provide insight into the mechanisms driving the collective cell behaviour. Pair-correlation functions provide quantitative information about the presence, or absence, of clustering in a spatial distribution of cells. This is because the pair-correlation function describes the ratio of the abundance of pairs of cells, separated by a particular distance, relative to a randomly distributed reference population. Pair-correlation functions are often presented as a kernel density estimate where the frequency of pairs of objects are grouped using a particular bandwidth (or bin width), Δ>0. The choice of bandwidth has a dramatic impact: choosing Δ too large produces a pair-correlation function that contains insufficient information, whereas choosing Δ too small produces a pair-correlation signal dominated by fluctuations. Presently, there is little guidance available regarding how to make an objective choice of Δ. We present a new technique to choose Δ by analysing the power spectrum of the discrete Fourier transform of the pair-correlation function. Using synthetic simulation data, we confirm that our approach allows us to objectively choose Δ such that the appropriately binned pair-correlation function captures known features in uniform and clustered synthetic images. We also apply our technique to images from two different cell biology assays. The first assay corresponds to an approximately uniform distribution of cells, while the second assay involves a time series of images of a cell population which forms aggregates over time. The appropriately binned pair-correlation function allows us to make quantitative inferences about the average aggregate size, as well as quantifying how the average aggregate size changes with time.