Single charge sensing and transport in double quantum dots fabricated from commercially grown Si/SiGe heterostructures

We perform quantum Hall measurements on three types of commercially available modulation doped Si/SiGe heterostructures to determine their suitability for depletion gate defined quantum dot devices. By adjusting the growth parameters, we are able to achieve electron gases with charge densities 1-3 X 10^{11}/cm^2 and mobilities in excess of 100,000 cm^2/Vs. Double quantum dot devices fabricated on these heterostructures show clear evidence of single charge transitions as measured in dc transport and charge sensing and exhibit electron temperatures of 100 mK in the single quantum dot regime.Comment: Related papers at http://pettagroup.princeton.ed

MANF Is Neuroprotective in Early Stages of EAE, and Elevated in Spinal White Matter by Treatment With Dexamethasone

Multiple sclerosis (MS) is a progressive autoimmune disease characterized by T-cell mediated demyelination in central nervous system (CNS). Experimental autoimmune encephalomyelitis (EAE) is a widely used in vivo disease model of MS. Glucocorticoids such as dexamethasone (dex) function as immunosuppressants and are commonly used to treat acute exacerbations of MS. Dex is also often used as a positive control in EAE studies, as it has been shown to promote motor behavior, inhibit immune cell infiltration into the CNS and regulate the activation of glial cell in EAE. This study further validated the effects of intravenously administrated dex by time-dependent fashion in EAE. Dex postponed clinical signs and motor defects in early stages of EAE. Histological analysis revealed that the degeneration of myelin and axons, as well as the infiltration of peripheral immune cells into the white matter of spinal cord was inhibited by dex in early stages of EAE. Additionally, dex-treatment delayed the neuroinflammatory activation of microglia and astrocytes. Furthermore, this study analyzed the expression of the neurotrophic factor mesencephalic astrocyte-derived neurotrophic factor (MANF) in EAE, and the effect of treatment with dex on MANF-expression. We show that in dex-treated EAE mice expression MANF increased within myelinated areas of spinal cord white matter. We also show that intravenous administration with hMANF in EAE mice improved clinical signs and motor behavior in the early stage of EAE. Our report gives insight to the progression of EAE by providing a time-dependent analysis. Moreover, this study investigates the link between MANF and the EAE model, and shows that MANF is a potential drug candidate for MS.Peer reviewe

Positive Selection in East Asians for an EDAR Allele that Enhances NF-κB Activation

Genome-wide scans for positive selection in humans provide a promising approach to establish links between genetic variants and adaptive phenotypes. From this approach, lists of hundreds of candidate genomic regions for positive selection have been assembled. These candidate regions are expected to contain variants that contribute to adaptive phenotypes, but few of these regions have been associated with phenotypic effects. Here we present evidence that a derived nonsynonymous substitution (370A) in EDAR, a gene involved in ectodermal development, was driven to high frequency in East Asia by positive selection prior to 10,000 years ago. With an in vitro transfection assay, we demonstrate that 370A enhances NF-κB activity. Our results suggest that 370A is a positively selected functional genetic variant that underlies an adaptive human phenotype

Design, analysis and generalization of polynomial predictive filters

New methods for the design, analysis and generalization of polynomial predictive filters (polynomial predictors for short) are developed in this thesis. Polynomial predictors are a subclass of linear mathematical filters, i.e. linear transformations that transform an input sequence of numbers to an output sequence. Filters are in general used to modify or extract useful information from a signal. Examples of filters are echo cancellers in telephone networks, vocal tract filters in speech synthesizers and analysis filters for EEG signals. Polynomial predictors are useful in situations where the signal of interest changes relatively slowly. Examples of slowly varying signals that can be successfully modeled by polynomials include displacement curves of elevators, the received power level in mobile communication systems and angular accelaration in motor drives. Generally, the filtering of a signal causes delay. However, in many applications, particularly control systems, it is desirable to minimize the delay caused by filtering. Furthermore, the signals in any real-world application contain noise, which we also wish to minimize by filtering. For example, calculating the moving average of a signal generally reduces the noise in the signal, but also causes delay. Polynomial predictors are filters that can predict polynomial signals, and in addition reduce the noise in the signal. In this thesis we determine the precise class of IIR filters which are polynomial predictors, which unifies the various previously proposed predictor structures. Design methods are developed for optimal least-squares FIR polynomial predictors, which enable arbitrary prediction steps, error weighting and an arbitrary desired frequency response. The parameters of predictors based on the general IIR structure are optimized, yielding improvements over the best previously known IIR polynomial predictors. An analysis of the asymptotic properties of polynomial predictors as the filter length grows without bound is carried out, yielding insight into the characteristics of predictors in general, as well as proving a conjecture on the asymptotic behaviour of the noise gain. Predictors are generalized for more general signal models than polynomials, and the resulting signal model (corresponding to the solutions of linear homogenous finite difference equations) is shown to be the most general possible. The generalization of the signal model can be interpreted in terms of a z-plane diagram which specifies the complex exponential signals that are predicted. The design methods for FIR polynomial predictors are also carried over to this general signal model. The generalized signal model includes e.g. sinusoidal signals and the design methods are applied to finding improved filters for use in a 50Hz line frequency signal processing application. In addition to the signal model, the filter type is also generalized. This yields design methods for finding the optimal parameters of differentiators, predictive differentiators and integrators, among others. The asymptotic noise gain of generalized predictors is derived and found to depend on the largest modulus of the complex numbers which specify the signal model. Furthermore, an efficient implementation for generalized FIR predictors is derived

Design, analysis and generalization of polynomial predictive filters

New methods for the design, analysis and generalization of polynomial predictive filters (polynomial predictors for short) are developed in this thesis. Polynomial predictors are a subclass of linear mathematical filters, i.e. linear transformations that transform an input sequence of numbers to an output sequence. Filters are in general used to modify or extract useful information from a signal. Examples of filters are echo cancellers in telephone networks, vocal tract filters in speech synthesizers and analysis filters for EEG signals. Polynomial predictors are useful in situations where the signal of interest changes relatively slowly. Examples of slowly varying signals that can be successfully modeled by polynomials include displacement curves of elevators, the received power level in mobile communication systems and angular accelaration in motor drives. Generally, the filtering of a signal causes delay. However, in many applications, particularly control systems, it is desirable to minimize the delay caused by filtering. Furthermore, the signals in any real-world application contain noise, which we also wish to minimize by filtering. For example, calculating the moving average of a signal generally reduces the noise in the signal, but also causes delay. Polynomial predictors are filters that can predict polynomial signals, and in addition reduce the noise in the signal. In this thesis we determine the precise class of IIR filters which are polynomial predictors, which unifies the various previously proposed predictor structures. Design methods are developed for optimal least-squares FIR polynomial predictors, which enable arbitrary prediction steps, error weighting and an arbitrary desired frequency response. The parameters of predictors based on the general IIR structure are optimized, yielding improvements over the best previously known IIR polynomial predictors. An analysis of the asymptotic properties of polynomial predictors as the filter length grows without bound is carried out, yielding insight into the characteristics of predictors in general, as well as proving a conjecture on the asymptotic behaviour of the noise gain. Predictors are generalized for more general signal models than polynomials, and the resulting signal model (corresponding to the solutions of linear homogenous finite difference equations) is shown to be the most general possible. The generalization of the signal model can be interpreted in terms of a z-plane diagram which specifies the complex exponential signals that are predicted. The design methods for FIR polynomial predictors are also carried over to this general signal model. The generalized signal model includes e.g. sinusoidal signals and the design methods are applied to finding improved filters for use in a 50Hz line frequency signal processing application. In addition to the signal model, the filter type is also generalized. This yields design methods for finding the optimal parameters of differentiators, predictive differentiators and integrators, among others. The asymptotic noise gain of generalized predictors is derived and found to depend on the largest modulus of the complex numbers which specify the signal model. Furthermore, an efficient implementation for generalized FIR predictors is derived

Using robust Viterbi algorithm and HMM-modeling in unit selection TTS to replace units of poor quality

In hidden Markov model-based unit selection synthesis, the benefits of both unit selection and statistical parametric speech synthesis are combined. However, conventional Viterbi algorithm is forced to do a selection also when no suitable units are available. This can drift the search and decrease the overall quality. Consequently, we propose to use robust Viterbi algorithm that can simultaneously detect bad units and select the best sequence. The unsuitable units are replaced using hidden Markov model-based synthesis. Evaluations indicate that the use of robust Viterbi algorithm combined with unit replacement increases the quality compared to the traditional algorithm. © 2010 ISCA

Modelagem de requisitos de clientes de empreendimentos habitacionais de interesse social com o uso de BIM

O gerenciamento de requisitos do cliente visa a melhorar a geração de valor em empreendimentos de construção através de um processo sistemático de captura de requisitos, processamento desta informação, tornando-as explícitas para a equipe de desenvolvimento do produto, bem como controlar se os requisitos de diferentes clientes são atendidos de forma equilibrada. Isto é particularmente importante quando os recursos são limitados, como em empreendimentos habitacionais de interesse social (EHIS). A modelagem dos requisitos dos clientes é complexa, à medida que existe uma grande quantidade de informações qualitativas e é preciso considerar a diversidade de requisitos que normalmente existem entre os diferentes envolvidos no processo. O objetivo deste artigo é propor um método para modelar requisitos de clientes de EHIS com suporte de Building Information Modeling (BIM). Este método foi concebido para apoiar os processos de tomada de decisão durante as fases de desenvolvimento de projeto bem como a avaliação de projetos que já foram entregues. Este artigo está focado nas principais atividades envolvidas na aplicação do método: explorar diferentes abordagens de modelagem de requisitos, estruturar requisitos e utilizar o software dRofus para gerenciar requisitos em distintas etapas de projeto. Uma das principais contribuições do estudo refere-se à estruturação de requisitos genéricos que podem servir de base para o desenvolvimento de novos empreendimentos de habitação de interesse social