A Generalization of the Doubling Construction for Sums of Squares Identities

The doubling construction is a fast and important way to generate new solutions to the Hurwitz problem on sums of squares identities from any known ones. In this short note, we generalize the doubling construction and obtain from any given admissible triple $[r,s,n]$ a series of new ones $[r+\rho(2^{m-1}),2^ms,2^mn]$ for all positive integer $m$, where $\rho$ is the Hurwitz-Radon function

"Can the neuro fuzzy model predict stock indexes better than its rivals?"

This paper develops a model of a trading system by using neuro fuzzy framework in order to better predict the stock index. Thirty well-known stock indexes are analyzed with the help of the model developed here. The empirical results show strong evidence of nonlinearity in the stock index by using KD technical indexes. The trading point analysis and the sensitivity analysis of trading costs show the robustness and opportunity for making further profits through using the proposed nonlinear neuro fuzzy system. The scenario analysis also shows that the proposed neuro fuzzy system performs consistently over time.

Leber's Hereditary Optic Neuropathy: A Case Report

Leber's hereditary optic neuropathy (LHON) is a maternally inherited mitochondrial disease that primarily affects the optic nerve, causing bilateral vision loss in juveniles and young adults. A 12-year-old boy had complained of blurred vision in both eyes for more than 1 year. His best-corrected visual acuity was 0.08 in the right eye and 0.1 in the left. Ophthalmologic examination showed bilateral optic disc hyperemia and margin blurring, peripapillary telangiectasis, and a relative afferent pupil defect in his right eye. Fluorescein angiography showed no stain or leakage around the optic disc in the late phase. Visual field analysis showed central scotoma in the left eye and a near-total defect in the right. Upon examination of the patient's mitochondrial DNA, a point mutation at nucleotide position 11778 was found, and the diagnosis of LHON was confirmed. Coenzyme Q10 was used to treat the patient

Hidden Trends in 90 Years of Harvard Business Review

In this paper, we demonstrate and discuss results of our mining the abstracts of the publications in Harvard Business Review between 1922 and 2012. Techniques for computing n-grams, collocations, basic sentiment analysis, and named-entity recognition were employed to uncover trends hidden in the abstracts. We present findings about international relationships, sentiment in HBR's abstracts, important international companies, influential technological inventions, renown researchers in management theories, US presidents via chronological analyses.Comment: 6 pages, 14 figures, Proceedings of 2012 International Conference on Technologies and Applications of Artificial Intelligenc

Three Tramp Dacetine Ants in Taiwan

Trabalho de projeto do mestrado em Economia (Economia Financeira), apresentado à Faculdade de Economia da Universidade de Coimbra.Neste trabalho, as taxas forward foram utilizadas para prever os valores futuros da Estrutura de Prazo das Taxas de Juro, em diferentes pontos desta estrutura, e em diferentes contextos do sistema financeiro, e abrange o período que vai do final de 2004 ao final de 2014. As taxas spot e forward foram construidas a partir do modelo de Nelson, Siegel e Svensson (1994), e para a anlisar a relação existente entre estes dois tipos de taxas, recorreu-se o método de cointegração proposto por Johansen (1988, 1991). Para períodos mais curtos, foram construídas taxas forward instantâneas, que antecipam as taxas spot instantâneas a distâncias que vão de 1 a 10 dias. Para períodos mais longos, foram construídas taxas forward com prazo de 1 mês, que antecipam as taxas spot com o mesmo prazo, a distâncias que vão de 1 a 12 meses. Nas taxas instantâneas, verificou-se que existe cointegração entre todas as taxas forward e as taxas spot que antecipam, nas estimações que abrangem a totalidade da amostra, e para alguns casos quando se divide a amostra em sub-períodos. Nas taxas mensais, pelo contrário, apenas em alguns casos foi constatada a existência de cointegração, quer para a totalidade do período quer para os sub-períodos. De seguida, foi estimado o Modelo de Correção dos Erros proposto por Johansen (1988, 1991), e recorreu-se à analise da função impulso-resposta, para as taxas cointegradas. As taxas mensais apresentaram sempre um comportamento mais instável, quando comparadas com as taxas instantâneas. Entretanto, com a divisão do período, as taxas instantâneas apresentaram um comportamento instável, principalmente para o sub-período 2012-2014

Domain Conditioned Adaptation Network

Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-invariant features. Most existing deep DA models only focus on aligning feature representations of task-specific layers across domains while integrating a totally shared convolutional architecture for source and target. However, we argue that such strongly-shared convolutional layers might be harmful for domain-specific feature learning when source and target data distribution differs to a large extent. In this paper, we relax a shared-convnets assumption made by previous DA methods and propose a Domain Conditioned Adaptation Network (DCAN), which aims to excite distinct convolutional channels with a domain conditioned channel attention mechanism. As a result, the critical low-level domain-dependent knowledge could be explored appropriately. As far as we know, this is the first work to explore the domain-wise convolutional channel activation for deep DA networks. Moreover, to effectively align high-level feature distributions across two domains, we further deploy domain conditioned feature correction blocks after task-specific layers, which will explicitly correct the domain discrepancy. Extensive experiments on three cross-domain benchmarks demonstrate the proposed approach outperforms existing methods by a large margin, especially on very tough cross-domain learning tasks.Comment: Accepted by AAAI 202

miRExpress: Analyzing high-throughput sequencing data for profiling microRNA expression

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs), small non-coding RNAs of 19 to 25 nt, play important roles in gene regulation in both animals and plants. In the last few years, the oligonucleotide microarray is one high-throughput and robust method for detecting miRNA expression. However, the approach is restricted to detecting the expression of known miRNAs. Second-generation sequencing is an inexpensive and high-throughput sequencing method. This new method is a promising tool with high sensitivity and specificity and can be used to measure the abundance of small-RNA sequences in a sample. Hence, the expression profiling of miRNAs can involve use of sequencing rather than an oligonucleotide array. Additionally, this method can be adopted to discover novel miRNAs.</p> <p>Results</p> <p>This work presents a systematic approach, miRExpress, for extracting miRNA expression profiles from sequencing reads obtained by second-generation sequencing technology. A stand-alone software package is implemented for generating miRNA expression profiles from high-throughput sequencing of RNA without the need for sequenced genomes. The software is also a database-supported, efficient and flexible tool for investigating miRNA regulation. Moreover, we demonstrate the utility of miRExpress in extracting miRNA expression profiles from two Illumina data sets constructed for the human and a plant species.</p> <p>Conclusion</p> <p>We develop miRExpress, which is a database-supported, efficient and flexible tool for detecting miRNA expression profile. The analysis of two Illumina data sets constructed from human and plant demonstrate the effectiveness of miRExpress to obtain miRNA expression profiles and show the usability in finding novel miRNAs.</p