936 resultados para writing to learn


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The recent celebrations of the centenary of the publication of the Futurist manifesto led to a renewed discussion of the ideas and artworks of the Italian artists’ group. Jacques Rancière related the Futurist ethos with the modernist project of liberating art from representation. Franco ‘Bifo’ Berardi, in his post-Futurist manifesto, also identified a historical irony at play in the emptying out of Futurism’s promise: a liberated mechanical humanity did indeed materialize, in a global economic system premised on financial servitude to the future via debt. However, these models continue to assess Futurism against an unchallenged humanism, finding it either supporting ideals of freedom and human rights despite itself, or else lacking in these areas. But Futurism is potentially more relevant than ever not in spite of its anti-humanist agenda, precisely because of it. Tom McCarthy annexes not Futurist art but Futurist writing to an emerging object oriented ontology that seeks to challenge the primacy of the human. If Futurism is to be repurposed as a critical concept, it can only do so by countering the humanist myth the liberal subject that underlies the current cultural and political hegemony of neo-liberalism.

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A novel extension to Kohonen's self-organising map, called the plastic self organising map (PSOM), is presented. PSOM is unlike any other network because it only has one phase of operation. The PSOM does not go through a training cycle before testing, like the SOM does and its variants. Each pattern is thus treated identically for all time. The algorithm uses a graph structure to represent data and can add or remove neurons to learn dynamic nonstationary pattern sets. The network is tested on a real world radar application and an artificial nonstationary problem.

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While the construction industry is frequently encouraged to learn from other business sectors, the difficulties of transferring knowledge from one context to another are rarely acknowledged. The problematic nature of knowledge is addressed with particular emphasis on the concept of contextual embeddedness. From this point of view, the process of 'knowledge transfer' depends upon a prolonged process of socialization between actors from both the 'receiving' and 'sending' contexts networking. It is contended that a significant conceptual chasm exists between the exhortations of industry leaders to learn from other sectors and the theoretical complexities associated with knowledge transfer. An ongoing research project is described that seeks to facilitate knowledge sharing between construction and aerospace. A novel approach to knowledge sharing based upon soft systems methodology (SSM) (Mode 2) is described and justified. Initial findings from the first cycle of the research are discussed and used to highlight the importance of context in the implementation of supply chain management.

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The present study sets out to examine motivation to learn English by Chinese research students in an informal setting. Data were collected, using semi-structured interviews, from four research students at two points in time during their first year in the UK. The main findings are: they believed that learning English was important; their main goal orientations were instrumental and extrinsic; they set learning goals and persisted to attain them; they valued their current learning environment in general and saw it as supportive of their goals; they held both positive and negative attitudes towards the British, which had differential effects on their motivation; their self-perceived support seemed to have a positive impact on their motivation and the development of self-confidence; they tended to attribute their success to stable causes such as the environment and failure to unstable but controllable causes such as effort. It is concluded that qualitative data of this kind may complement insights from quantitative research. Implications for target country institutions in the provision of support are discussed.

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Although the curriculum subject of English is continually reviewed and revised in all English speaking countries, the status of literature is rarely questioned i.e. that it is of high cultural value and all students should be taught about it. The concerns of any review, in any country, are typically about what counts as literature, especially in terms of national heritage and then how much of the curriculum should it occupy. This article reports on three inter-related pieces of research that examine the views of in-service, and pre-service, English teachers about their experiences of teaching literature and their perceptions of its ‘status’ and significance at official level and in the actual classroom; it draws attention to how England compares to some other English speaking countries and draws attention to the need to learn from the negative outcomes of political policy in England. The findings suggest that the nature of engagement with literature for teachers and their students has been distorted by official rhetorics and assessment regimes and that English teachers are deeply concerned to reverse this pattern.

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Motivation: In order to enhance genome annotation, the fully automatic fold recognition method GenTHREADER has been improved and benchmarked. The previous version of GenTHREADER consisted of a simple neural network which was trained to combine sequence alignment score, length information and energy potentials derived from threading into a single score representing the relationship between two proteins, as designated by CATH. The improved version incorporates PSI-BLAST searches, which have been jumpstarted with structural alignment profiles from FSSP, and now also makes use of PSIPRED predicted secondary structure and bi-directional scoring in order to calculate the final alignment score. Pairwise potentials and solvation potentials are calculated from the given sequence alignment which are then used as inputs to a multi-layer, feed-forward neural network, along with the alignment score, alignment length and sequence length. The neural network has also been expanded to accommodate the secondary structure element alignment (SSEA) score as an extra input and it is now trained to learn the FSSP Z-score as a measurement of similarity between two proteins. Results: The improvements made to GenTHREADER increase the number of remote homologues that can be detected with a low error rate, implying higher reliability of score, whilst also increasing the quality of the models produced. We find that up to five times as many true positives can be detected with low error rate per query. Total MaxSub score is doubled at low false positive rates using the improved method.

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This study compared orthographic and semantic aspects of word learning in children who differed in reading comprehension skill. Poor comprehenders and controls matched for age (9-10 years), nonverbal ability and decoding skill were trained to pronounce 20 visually presented nonwords, 10 in a consistent way and 10 in an inconsistent way. They then had an opportunity to infer the meanings of the new words from story context. Orthographic learning was measured in three ways: the number of trials taken to learn to pronounce nonwords correctly, orthographic choice and spelling. Across all measures, consistent items were easier than inconsistent items and poor comprehenders did not differ from control children. Semantic learning was assessed on three occasions, using a nonword-picture matching task. While poor comprehenders showed equivalent semantic learning to controls immediately after exposure to nonword meaning, this knowledge was not well retained over time. Results are discussed in terms of the language and reading skills of poor comprehenders and in relation to current models of reading development.

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The research will explore views on inclusive design policy implementation and learning strategy used in practice by Local Authorities’ planning, building control and policy departments in England. It reports emerging research findings. The research aim was developed from an extensive literature review, and informed by a pilot study with relevant Local Authority departments. The pilot study highlighted gaps within the process of policy implementation, a lack of awareness of the process and flaws in the design guidance policy. This has helped inform the development of a robust research design using both a survey and semi-structured interviews. The questionnaire targeted key employees within Local Authorities designed to establish how employees learn about inclusive design policy and to determine their views on current approaches of inclusive design policy implementation adopted by their Local Authorities. The questionnaire produces 117 responses. Interestingly approximately 9 out of 129 Local Authorities approached claimed that they were unable to participate either because an inclusive design policy was not adopted or they were faced with a high workload and thus unable to take part. An emerging finding is a lack of understanding of inclusive design problems, which may lead to problem with inclusive design policy implementation, and thus adversely affect how the built environment can be experienced. There is a strong indication from the survey respondents indicating that they are most likely to learn about inclusive design from policy guides produced by their Local Authorities and from their colleagues.

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In 1594, major decisions were made by the governors of London and the country about plays and playing. We need to learn what lay behind these events, such as what led James Burbage to build his Blackfriars theater in 1596. That initial fiasco might tell us much about what lay behind Shakespeare’s decision to join the new Chamberlain’s Men in 1594 and his subsequent commitment to them as a full-time playwright. When the Globe burned down in 1613, a majority of the shareholders decided to rebuild it at great cost, but Shakespeare withdrew. The rebuilding was old-fashioned thinking, reverting to the company’s desire, asserted in 1594, to play indoors in winter, which helps to clarify their decisions and Shakespeare’s own—to write plays rather than more long poems. The few surviving papers of the Privy Council and the London mayoralty from the time suggest that one of the two new companies of 1594 preferred to play indoors during the winter instead of at their allocated open playhouses in the suburbs. They tried to renew this traditional practice, first in 1594 and again in 1596 when James Burbage built the indoor Blackfriars playhouse for them. The renewal of the Globe in 1614 was part of the same thinking, although Shakespeare evidently opted out of the decision.

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The effective and efficient management of diversified business firms that supply multiple products and operate in multiple, dynamic markets, especially large multinational enterprises (MNEs), builds upon a number of specific governance principles. These governance principles allow the alignment of environmental characteristics, strategy and organization. Given the rising need tolearn from the world”, Doz et al., in their influential Harvard Business School Press book entitled From Global to Metanational, have proposed a new set of governance principles described under the “metanational” umbrella concept. This paper revisits the metanational, using a comparative institutional perspective; here we contrast multidivisional and metanational governance principles. A comparative institutional analysis suggests that the metanational's application potential in terms of actually improving the effectiveness and efficiency of MNE governance may be subject to more qualification than suggested by Doz et al. Senior MNE management must therefore reflect carefully before substituting metanational governance principles for the more conventional, multidivisional ones with established contributions to managerial effectiveness and efficiency.

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Often, firms have no information on the specification of the true demand model they are faced with. It is, however, a well established fact that trial-and-error algorithms may be used by them in order to learn how to make optimal decisions. Using experimental methods, we identify a property of the information on past actions which helps the seller of two asymmetric demand substitutes to reach the optimal prices more precisely and faster. The property concerns the possibility of disaggregating changes in each product’s demand into client exit/entry and shift from one product to the other.

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In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models.

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Neutral cues that predict emotional events (emotional harbingers) acquire emotional properties and attract attention. Given the importance of emotional harbingers for future survival, it is desirable to flexibly learn new facts about emotional harbingers when needed. However, recent research revealed that it is harder to learn new associations for emotional harbingers than cues that predict non-emotional events (neutral harbingers). In the current study, we addressed whether this impaired association learning for emotional harbingers is altered by one’s awareness of the contingencies between cues and emotional outcomes. Across 3 studies, we found that one’s awareness of the contingencies determines subsequent association learning of emotional harbingers. Emotional harbingers produced worse association learning than neutral harbingers when people were not aware of the contingencies between cues and emotional outcomes, but produced better association learning when people were aware of the contingencies. These results suggest that emotional harbingers do not always suffer from impaired association learning and can show facilitated learning depending on one’s contingency awareness.

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Traditional dictionary learning algorithms are used for finding a sparse representation on high dimensional data by transforming samples into a one-dimensional (1D) vector. This 1D model loses the inherent spatial structure property of data. An alternative solution is to employ Tensor Decomposition for dictionary learning on their original structural form —a tensor— by learning multiple dictionaries along each mode and the corresponding sparse representation in respect to the Kronecker product of these dictionaries. To learn tensor dictionaries along each mode, all the existing methods update each dictionary iteratively in an alternating manner. Because atoms from each mode dictionary jointly make contributions to the sparsity of tensor, existing works ignore atoms correlations between different mode dictionaries by treating each mode dictionary independently. In this paper, we propose a joint multiple dictionary learning method for tensor sparse coding, which explores atom correlations for sparse representation and updates multiple atoms from each mode dictionary simultaneously. In this algorithm, the Frequent-Pattern Tree (FP-tree) mining algorithm is employed to exploit frequent atom patterns in the sparse representation. Inspired by the idea of K-SVD, we develop a new dictionary update method that jointly updates elements in each pattern. Experimental results demonstrate our method outperforms other tensor based dictionary learning algorithms.