A inserçao no comércio internacional do Brasil, da India e da China (BIC): notas acerca do comércio exterior e política comercial

Incluye bibliografía

Clathrin heavy chain plays multiple roles in polarizing the Drosophila oocyte downstream of Bic-D

Bicaudal-D (Bic-D), Egalitarian (Egl), microtubules and their motors form a transport machinery that localizes a remarkable diversity of mRNAs to specific cellular regions during oogenesis and embryogenesis. Bic-D family proteins also promote dynein-dependent transport of Golgi vesicles, lipid droplets, synaptic vesicles and nuclei. However, the transport of these different cargoes is still poorly understood. We searched for novel proteins that either mediate Bic-Ddependent transport processes or are transported by them. Clathrin heavy chain (Chc) co-immunopurifies with Bic-D in embryos and ovaries, and a fraction of Chc colocalizes with Bic-D. Both proteins control posterior patterning of the Drosophila oocyte and endocytosis. Although the role of Chc in endocytosis is well established, our results show that Bic-D is also needed for the elevated endocytic activity at the posterior of the oocyte. Apart fromaffecting endocytosis indirectly by its role in osk mRNA localization, Bic-D is also required to transport Chc mRNA into the oocyte and for transport and proper localization of Chc protein to the oocyte cortex, pointing to an additional,more direct role of Bic-D in the endocytic pathway. Furthermore, similar to Bic-D, Chc also contributes to proper localization of osk mRNA and to oocyte growth. However, in contrast to other endocytic components and factors of the endocytic recycling pathway, such as Rabenosyn-5 (Rbsn-5) and Rab11, Chc is needed during early stages of oogenesis (from stage 6 onwards) to localize oskmRNA correctly.Moreover,we also uncovered a novel, presumably endocytosis-independent, role of Chc in the establishment of microtubule polarity in stage 6 oocytes.

Edad y crecimiento de Genypterus maculatus “congrio” capturado frente a Chimbote a bordo del BIC IMARPE V de abril a diciembre del 2014

La investigación tuvo como objetivo determinar la edad y crecimiento de Genypterus maculatus “congrio negro” capturado frente a Chimbote durante el año 2014. La muestra estuvo constituida por 705 pares de otolitos. Del análisis de microincrementos se comprobó que la periodicidad de formación de los anillos de crecimiento fue semestral. Se elaboró una clave talla-edad, al no encontrar diferencias significativas entre sexos. Asimismo, se obtuvo 4 edades (1, 2, 3 y 4 años), siendo el más representativo la edad de 1 año. Se obtuvo la distribución de frecuencias por edad. La relación entre la longitud total del pez y el radio total del otolito fue lineal y la relación longitud total entre el peso total y la longitud fue potencial con un b= 2.89, presentando un crecimiento alométrico negativo. Se estimó los parámetros de crecimiento de von Bertalanffy mediante el programa Table Curve 2D V5. 01, siendo estos L∞ = 101.74 cm; K = 0.124/año; t0 = -0.1997 años. La ecuación de la curva de crecimiento en longitud fue Lt = 101.74*(1-e(- 0.124*(t-0.1997))).

Bayes factor based speaker clustering for speaker diarization

This paper proposes the use of the Bayes Factor to replace the Bayesian Information Criterion (BIC) as a criterion for speaker clustering within a speaker diarization system. The BIC is one of the most popular decision criteria used in speaker diarization systems today. However, it will be shown in this paper that the BIC is only an approximation to the Bayes factor of marginal likelihoods of the data given each hypothesis. This paper uses the Bayes factor directly as a decision criterion for speaker clustering, thus removing the error introduced by the BIC approximation. Results obtained on the 2002 Rich Transcription (RT-02) Evaluation dataset show an improved clustering performance, leading to a 14.7% relative improvement in the overall Diarization Error Rate (DER) compared to the baseline system.

Automatic Audio Segmentation Using the Generalized Likelihood Ratio

This paper presents a novel technique for segmenting an audio stream into homogeneous regions according to speaker identities, background noise, music, environmental and channel conditions. Audio segmentation is useful in audio diarization systems, which aim to annotate an input audio stream with information that attributes temporal regions of the audio into their specific sources. The segmentation method introduced in this paper is performed using the Generalized Likelihood Ratio (GLR), computed between two adjacent sliding windows over preprocessed speech. This approach is inspired by the popular segmentation method proposed by the pioneering work of Chen and Gopalakrishnan, using the Bayesian Information Criterion (BIC) with an expanding search window. This paper will aim to identify and address the shortcomings associated with such an approach. The result obtained by the proposed segmentation strategy is evaluated on the 2002 Rich Transcription (RT-02) Evaluation dataset, and a miss rate of 19.47% and a false alarm rate of 16.94% is achieved at the optimal threshold.

Digital Copyright Exchange Feasibility Study : Response to the Hooper Consultation from the Big Innovation Centre

In this submission, we provide evidence for our view that copyright policy in the UK must encourage new digital business models which meet the changing needs of consumers and foster innovation in the UK both within, and beyond, the creative industries. We illustrate our arguments using evidence from the music industry. However, we believe that our key points on the relationship between the copyright system and innovative digital business models apply across the UK creative industries.

Displacement motion prediction of a landing deck for recovery operations of rotary UAVs

This paper proposes a practical prediction procedure for vertical displacement of a Rotarywing Unmanned Aerial Vehicle (RUAV) landing deck in the presence of stochastic sea state disturbances. A proper time series model tending to capture characteristics of the dynamic relationship between an observer and a landing deck is constructed, with model orders determined by a novel principle based on Bayes Information Criterion (BIC) and coefficients identified using the Forgetting Factor Recursive Least Square (FFRLS) method. In addition, a fast-converging online multi-step predictor is developed, which can be implemented more rapidly than the Auto-Regressive (AR) predictor as it requires less memory allocations when updating coefficients. Simulation results demonstrate that the proposed prediction approach exhibits satisfactory prediction performance, making it suitable for integration into ship-helicopter approach and landing guidance systems in consideration of computational capacity of the flight computer.

Modelling survival data to account for model uncertainty: a single model or model averaging?

This study considered the problem of predicting survival, based on three alternative models: a single Weibull, a mixture of Weibulls and a cure model. Instead of the common procedure of choosing a single “best” model, where “best” is defined in terms of goodness of fit to the data, a Bayesian model averaging (BMA) approach was adopted to account for model uncertainty. This was illustrated using a case study in which the aim was the description of lymphoma cancer survival with covariates given by phenotypes and gene expression. The results of this study indicate that if the sample size is sufficiently large, one of the three models emerge as having highest probability given the data, as indicated by the goodness of fit measure; the Bayesian information criterion (BIC). However, when the sample size was reduced, no single model was revealed as “best”, suggesting that a BMA approach would be appropriate. Although a BMA approach can compromise on goodness of fit to the data (when compared to the true model), it can provide robust predictions and facilitate more detailed investigation of the relationships between gene expression and patient survival. Keywords: Bayesian modelling; Bayesian model averaging; Cure model; Markov Chain Monte Carlo; Mixture model; Survival analysis; Weibull distribution

Model selection with misspecified spatial covariance structure

Spatial data analysis has become more and more important in the studies of ecology and economics during the last decade. One focus of spatial data analysis is how to select predictors, variance functions and correlation functions. However, in general, the true covariance function is unknown and the working covariance structure is often misspecified. In this paper, our target is to find a good strategy to identify the best model from the candidate set using model selection criteria. This paper is to evaluate the ability of some information criteria (corrected Akaike information criterion, Bayesian information criterion (BIC) and residual information criterion (RIC)) for choosing the optimal model when the working correlation function, the working variance function and the working mean function are correct or misspecified. Simulations are carried out for small to moderate sample sizes. Four candidate covariance functions (exponential, Gaussian, Matern and rational quadratic) are used in simulation studies. With the summary in simulation results, we find that the misspecified working correlation structure can still capture some spatial correlation information in model fitting. When the sample size is large enough, BIC and RIC perform well even if the the working covariance is misspecified. Moreover, the performance of these information criteria is related to the average level of model fitting which can be indicated by the average adjusted R square ( [GRAPHICS] ), and overall RIC performs well.

Marker selection by Akaike information criterion and Bayesian information criterion

We carried out a discriminant analysis with identity by descent (IBD) at each marker as inputs, and the sib pair type (affected-affected versus affected-unaffected) as the output. Using simple logistic regression for this discriminant analysis, we illustrate the importance of comparing models with different number of parameters. Such model comparisons are best carried out using either the Akaike information criterion (AIC) or the Bayesian information criterion (BIC). When AIC (or BIC) stepwise variable selection was applied to the German Asthma data set, a group of markers were selected which provide the best fit to the data (assuming an additive effect). Interestingly, these 25-26 markers were not identical to those with the highest (in magnitude) single-locus lod scores.

A Bayesian incentive compatible mechanism for decentralized supply chain formation

In this paper we consider a decentralized supply chain formation problem for linear multi-echelon supply chains when the managers of the individual echelons are autonomous, rational, and intelligent. At each echelon, there is a choice of service providers and the specific problem we solve is that of determining a cost-optimal mix of service providers so as to achieve a desired level of end-to-end delivery performance. The problem can be broken up into two sub-problems following a mechanism design approach: (1) Design of an incentive compatible mechanism to elicit the true cost functions from the echelon managers; (2) Formulation and solution of an appropriate optimization problem using the true cost information. In this paper we propose a novel Bayesian incentive compatible mechanism for eliciting the true cost functions. This improves upon existing solutions in the literature which are all based on the classical Vickrey-Clarke-Groves mechanisms, requiring significant incentives to be paid to the echelon managers for achieving dominant strategy incentive compatibility. The proposed solution, which we call SCF-BIC (Supply Chain Formation with Bayesian Incentive Compatibility), significantly reduces the cost of supply chain formation. We illustrate the efficacy of the proposed methodology using the example of a three echelon manufacturing supply chain.