We obtain external binary self-dual codes of parameters [64,32,12] as binary images of self-dual codes over R-1, R-2 and R-3 by employing different methods. We then apply the extension theorem to these codes to obtain...
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We obtain external binary self-dual codes of parameters [64,32,12] as binary images of self-dual codes over R-1, R-2 and R-3 by employing different methods. We then apply the extension theorem to these codes to obtain a number of extremal binary self-dual codes of length 66 with trivial automoiphism groups. Fifteen of the codes we obtain have new beta values in W-66,W-3, of which only three were known to exist before. We also find nine codes with new beta values in W-66,W-1, thus updating the list of such known codes. (C) 2013 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
Motivation: Fronto-temporal dementia (FTD) and amyotrophic lateral sclerosis (ALS, also called motor neuron disease, MND) are severe neurodegenerative diseases that show considerable overlap at the clinical and cellul...
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Motivation: Fronto-temporal dementia (FTD) and amyotrophic lateral sclerosis (ALS, also called motor neuron disease, MND) are severe neurodegenerative diseases that show considerable overlap at the clinical and cellular level. The most common single mutation in families with FTD or ALS has recently been mapped to a non-coding repeat expansion in the uncharacterized gene C9ORF72. Although a plausible mechanism for disease is that aberrant C9ORF72 mRNA poisons splicing, it is important to determine the cellular function of C9ORF72, about which nothing is known. Results: Sensitive homology searches showed that C9ORF72 is a full-length distant homologue of proteins related to Differentially Expressed in Normal and Neoplasia (DENN), which is a GDP/GTP exchange factor (GEF) that activates Rab-GTPases. Our results suggest that C9ORF72 is likely to regulate membrane traffic in conjunction with Rab-GTPase switches, and we propose to name the gene and its product DENN-like 72 (DENNL72).
The target article focuses on the predictive coding of "what" and "where" something happened and the "where" and "what" response to make. We extend that scope by addressing the ...
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The target article focuses on the predictive coding of "what" and "where" something happened and the "where" and "what" response to make. We extend that scope by addressing the "when" aspect of perception and action. Successful interaction with the environment requires predictions of everything from millisecond-accurate motor timing to far future events. The hierarchical framework seems appropriate for timing.
While the target article provides a glowing account for the excitement in the field, we stress that hierarchical predictive learning in the brain requires sparseness of the representation. We also question the relatio...
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While the target article provides a glowing account for the excitement in the field, we stress that hierarchical predictive learning in the brain requires sparseness of the representation. We also question the relation between Bayesian cognitive processes and hierarchical generative models as discussed by the target article.
Learning-dependent cortical encoding has been well described in single neurons. But behaviorally relevant sensory signals drive the coordinated activity of millions of cortical neurons;whether learning produces stimul...
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Learning-dependent cortical encoding has been well described in single neurons. But behaviorally relevant sensory signals drive the coordinated activity of millions of cortical neurons;whether learning produces stimulus-specific changes in population codes is unknown. Because the pattern of firing rate correlations between neurons-an emergent property of neural populations-can significantly impact encoding fidelity, we hypothesize that it is a target for learning. Using an associative learning procedure, we manipulated the behavioral relevance of natural acoustic signals and examined the evoked spiking activity in auditory cortical neurons in songbirds. We show that learning produces stimulus-specific changes in the pattern of interneuronal correlations that enhance the ability of neural populations to recognize signals relevant for behavior. This learning-dependent enhancement increases with population size. The results identify the pattern of interneuronal correlation in neural populations as a target of learning that can selectively enhance the representations of specific sensory signals.
We build a coding of the trajectories of billiards in regular 2n-gons, similar but different from the one in J. Smillie and C. Ulcigrai [in 'Beyond Sturmian sequences: coding linear trajectories in the regular oct...
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We build a coding of the trajectories of billiards in regular 2n-gons, similar but different from the one in J. Smillie and C. Ulcigrai [in 'Beyond Sturmian sequences: coding linear trajectories in the regular octagon', Proc. Lond. Math. Soc. (3) 102 (2011) 291-340], by applying the self-dual induction [S. Ferenczi and L.Q. Zamboni, 'Structure of K-interval exchange transformations: induction, trajectories, and distance theorems', J. Anal. Math. 112 (2010) 289-328] to the underlying one-parameter family of n-interval exchange transformations. This allows us to show that, in that family, for n=3 non-periodicity is enough to guarantee weak mixing, and in some cases minimal self-joinings, and for every n we can build examples of n-interval exchange transformations with weak mixing, which are the first known explicitly for n > 6.
Clark makes a convincing case for the merits of conceptualizing brains as hierarchical prediction machines. This perspective has the potential to provide an elegant and powerful general theory of brain function, but i...
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Clark makes a convincing case for the merits of conceptualizing brains as hierarchical prediction machines. This perspective has the potential to provide an elegant and powerful general theory of brain function, but it will ultimately stand or fall with evidence from basic neuroscience research. Here, we characterize the status quo of that evidence and highlight important avenues for future investigations.
GRN2SBML automatically encodes gene regulatory networks derived from several inference tools in systems biology markup language. Providing a graphical user interface, the networks can be annotated via the simple objec...
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GRN2SBML automatically encodes gene regulatory networks derived from several inference tools in systems biology markup language. Providing a graphical user interface, the networks can be annotated via the simple object access protocol (SOAP)-based application programming interface of BioMart Central Portal and minimum information required in the annotation of models registry. Additionally, we provide an R-package, which processes the output of supported inference algorithms and automatically passes all required parameters to GRN2SBML. Therefore, GRN2SBML closes a gap in the processing pipeline between the inference of gene regulatory networks and their subsequent analysis, visualization and storage.
It is essential to build good image representations for many computer vision tasks. In this study, the authors propose a hierarchical spatial pyramid max pooling method based on scale-invariant feature transform ( SIF...
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It is essential to build good image representations for many computer vision tasks. In this study, the authors propose a hierarchical spatial pyramid max pooling method based on scale-invariant feature transform ( SIFT) features and sparse coding, which builds image representations through a hierarchical network. It includes three parts: SIFT features' extraction, sparse coding and spatial pyramid max pooling. To mimic visual cortex, spatial pyramid max pooling is, firstly, performed on the original SIFT features in the image patches, which distils the features and extracts the most distinctive and significant feature, the SIFT-pooled feature, in each local patch, instead of using the original SIFT features as usual. Then, a dictionary is trained using some random SIFT-pooled features and sparse coding is performed using the trained dictionary for all SIFT-pooled features through K-singular value decomposition algorithm. Finally, on the sparse codes of all image patches, spatial pyramid max pooling is carried again on the image level. The image representations will be built by concatenating the pooling features of each level. The authors use the algorithm and simple linear support vector machine (SVM) for image classification on three datasets: Caltech-101, Caltech-256 and 15-Scenes and the experimental results show that the authors algorithm can reach a competitive performance compared with recently published results.
The paper characterizes the family of homomorphisms, under which the deterministic context-free languages, the LL context-free languages and unambiguous context-free languages are closed. The family of deterministic c...
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The paper characterizes the family of homomorphisms, under which the deterministic context-free languages, the LL context-free languages and unambiguous context-free languages are closed. The family of deterministic context-free languages is closed under a homomorphism h if and only if h is either a code of bounded deciphering delay, or the images of all symbols under h are powers of the same string. The same characterization holds for LL context-free languages. The unambiguous context-free languages are closed under h if and only if either h is a code, or the images of all symbols under h are powers of the same string.
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