291,801 research outputs found

    Extracting finite structure from infinite language

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    This paper presents a novel connectionist memory-rule based model capable of learning the finite-state properties of an input language from a set of positive examples. The model is based upon an unsupervised recurrent self-organizing map [T. McQueen, A. Hopgood, J. Tepper, T. Allen, A recurrent self-organizing map for temporal sequence processing, in: Proceedings of Fourth International Conference in Recent Advances in Soft Computing (RASC2002), Nottingham, 2002] with laterally interconnected neurons. A derivation of functionalequivalence theory [J. Hopcroft, J. Ullman, Introduction to Automata Theory, Languages and Computation, vol. 1, Addison-Wesley, Reading, MA, 1979] is used that allows the model to exploit similarities between the future context of previously memorized sequences and the future context of the current input sequence. This bottom-up learning algorithm binds functionally related neurons together to form states. Results show that the model is able to learn the Reber grammar [A. Cleeremans, D. Schreiber, J. McClelland, Finite state automata and simple recurrent networks, Neural Computation, 1 (1989) 372–381] perfectly from a randomly generated training set and to generalize to sequences beyond the length of those found in the training set

    2A - the "go-to" technology for transgene co-expression

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    In order to co-express multiple genes for biotechnological and biomedical applications, several approaches have been used with varying degrees of success. Currently, internal ribosome entry site (IRES) elements and “self-cleaving” 2A peptides are the most widely used. The length of the IRES can be prohibitive and IRES-dependent translation of the second open reading frame is often significantly reduced. 2A peptides have gained in popularity due to their small size and ability to consistently produce discrete proteins at an equal level. Here, we promote the use of these sequences as the “go-to” technology for co-expression of multiple proteins.Publisher PDFPeer reviewe

    USING VIDEO SUBTITLES TO IMPROVE STUDENTS’ READING COMPREHENSION IN NARRATIVE TEXTS

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    AbstractThe purpose of this research was to improve reading comprehension in narrative texts to the ninth grader at SMPN 1 Singkawang. The researcher applied four strategies taken from Westwood: carefully viewing and overviewing what to be read, self questioning, selectively reading and summarizing. These strategies were implemented through three sequences; pre-viewing, while viewing and post viewing. The study showed that the use of video subtitles gave positive implication which effected on learning improvement. A set of sequences in video subtitles assisted the students in understanding the text, interpreting words, and improve their vocabularies. The students’ attitude towards their reading comprehension have improved, which was shown engaged actively during the learning process and their responses towards the strategy used. Moreover, the students also improved their score. Thus, it indicated that the learning process have run well and have increased individual performance. Keywords: Video Subtitles, Reading Comprehension, and Narrative Texts

    Molecular access to multi-dimensionally encoded information

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    Polymer scientist have only recently realized that information storage on the molecular level is not only restricted to DNA-based systems. Similar encoding and decoding of data have been demonstrated on synthetic polymers that could overcome some of the drawbacks associated with DNA, such as the ability to make use of a larger monomer alphabet. This feature article describes some of the recent data storage strategies that were investigated, ranging from writing information on linear sequence-defined macromolecules up to layer-by-layer casted surfaces and QR codes. In addition, some strategies to increase storage density are elaborated and some trends regarding future perspectives on molecular data storage from the literature are critically evaluated. This work ends with highlighting the demand for new strategies setting up reliable solutions for future data management technologies

    Lawson on Veblen on Social Ontology

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    This paper discusses Lawson’s use of Veblen’s concept of ‘neoclassical economics’ and argument that the category of neoclassical economics should be jettisoned on the grounds that it obfuscates effective critique of mainstream economics. The paper links Lawson’s critique of closed systems and Veblen’s cumulative causation view by offering a reflexivity, feedback loop formulation of the latter aimed at overcoming the pre-Socratic dichotomy between Heraclitian and Parmenidean ontological thinking. The paper then reviews what this implies for three key social ontology doctrines: social reality as processual and highly transient; emergence and the appearance of novelty; the internal relatedness of social reality. Final remarks address the use of the ‘neoclassical economics’ concept
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