Digital Implementation of Vibrato

Digital Audio Effects are often discussed in audio production circles by their acoustical effect on an input signal. As opposed to discussing what is happening acoustically, this report aims to delve deeper into the DSP (Digital Signal Processing) algorithms underlying an effect to explain how it is being created. The focus of this report is on the digital implementation of a vibrato effect.Architecture & Allied Art

Creating Vibrato Using Matlab

Report analysing a function in Matlab used to create vibrato.Architecture & Allied Art

Pot, atom and step economic (PASE) synthesis of highly functionalized piperidines: a five-component condensation

The diastereoselective pot, atom and step economic (PASE) synthesis of highly functionalized piperidines has been realized. The procedure simply involves mixing methyl acetoacetate, 2 equiv of aldehyde and 2 equiv of aniline together in the presence of InCl3. In most cases the piperidine precipitates out of solution. (c) 2007 Elsevier Ltd. All rights reserved

Improving Trust in Deep Neural Networks with Nearest Neighbors

Deep neural networks are used increasingly for perception and decision-making in UAVs. For example, they can be used to recognize objects from images and decide what actions the vehicle should take. While deep neural networks can perform very well at complex tasks, their decisions may be unintuitive to a human operator. When a human disagrees with a neural network prediction, due to the black box nature of deep neural networks, it can be unclear whether the system knows something the human does not or whether the system is malfunctioning. This uncertainty is problematic when it comes to ensuring safety. As a result, it is important to develop technologies for explaining neural network decisions for trust and safety. This paper explores a modification to the deep neural network classification layer to produce both a predicted label and an explanation to support its prediction. Specifically, at test time, we replace the final output layer of the neural network classifier by a k-nearest neighbor classifier. The nearest neighbor classifier produces 1) a predicted label through voting and 2) the nearest neighbors involved in the prediction, which represent the most similar examples from the training dataset. Because prediction and explanation are derived from the same underlying process, this approach guarantees that the explanations are always relevant to the predictions. We demonstrate the approach on a convolutional neural network for a UAV image classification task. We perform experiments using a forest trail image dataset and show empirically that the hybrid classifier can produce intuitive explanations without loss of predictive performance compared to the original neural network. We also show how the approach can be used to help identify potential issues in the network and training process

Modelling of light scattering by cirrus ice crystals using geometric optics combined with diffraction of facets

A new 3D model of light scattering applicable to dielectric faceted objects is presented. The model combines Geometric Optics with diffraction on individual facets yet maintains the low computational expense of standard Geometric Optics. The current implementation of the model is explained and then applied to the problem of light scattering by ice crystals in cirrus clouds. Accurate modelling of the scattering properties of such crystals is crucial to better understanding of cirrus radiative properties and hence to climate modelling and weather forecasting. Calculations using the new model are compared to a separation of variables method and the Improved Geometric Optics method with encouraging results. The model shows significant improvements over standard Geometric Optics. The size applicability of the new model is discussed. The model is applied to a range of crystal geometries that have been observed in cirrus including the hexagonal column, the hollow column, the droxtal and the bullet rosette. For each geometry the phase function and degree of linear polarization are presented and discussed. Ice analogue crystals grown at the University of Hertfordshire have optical properties very close to ice but are stable at room temperature. The geometries of three ice analogue crystals are reconstructed and the single scattering properties of the reconstructions are presented. 2D scattering patterns calculated using the model are compared to laboratory photographs of scattering patterns on a screen created by an ice analogue hexagonal column. The agreement is shown to be very good. By applying the model to a range of geometries, it is shown that the results in the form of 2D scattering patterns can potentially be used to aid particle characterization. By combining the model with a Monte Carlo radiative transfer code, comparisons are made with aircraft radiance measurements of cirrus provided by the Met Office. The improvements over standard Geometric Optics are found to persist following a radiative transfer treatment

Modeling association in microbial communities with clique loglinear models

There is a growing awareness of the important roles that microbial communities play in complex biological processes. Modern investigation of these often uses next generation sequencing of metagenomic samples to determine community composition. We propose a statistical technique based on clique loglinear models and Bayes model averaging to identify microbial components in a metagenomic sample at various taxonomic levels that have significant associations. We describe the model class, a stochastic search technique for model selection, and the calculation of estimates of posterior probabilities of interest. We demonstrate our approach using data from the Human Microbiome Project and from a study of the skin microbiome in chronic wound healing. Our technique also identifies significant dependencies among microbial components as evidence of possible microbial syntrophy. KEYWORDS: contingency tables, graphical models, model selection, microbiome, next generation sequencingComment: 30 pages, 17 figur

Modeling Association in Microbial Communities with Clique Loginear Models

There is a growing awareness of the important roles that microbial communities play in complex biological processes. Modern investigation of these often uses next generation sequencing of metagenomic samples to determine community composition. We propose a statistical technique based on clique loglinear models and Bayes model averaging to identify microbial components in a metagenomic sample at various taxonomic levels that have significant associations. We describe the model class, a stochastic search technique for model selection, and the calculation of estimates of posterior probabilities of interest. We demonstrate our approach using data from the Human Microbiome Project and from a study of the skin microbiome in chronic wound healing. Our technique also identifies significant dependencies among microbial components as evidence of possible microbial syntrophy

Modeling Association in Microbial Communities with Clique Loginear Models

There is a growing awareness of the important roles that microbial communities play in complex biological processes. Modern investigation of these often uses next generation sequencing of metagenomic samples to determine community composition. We propose a statistical technique based on clique loglinear models and Bayes model averaging to identify microbial components in a metagenomic sample at various taxonomic levels that have significant associations. We describe the model class, a stochastic search technique for model selection, and the calculation of estimates of posterior probabilities of interest. We demonstrate our approach using data from the Human Microbiome Project and from a study of the skin microbiome in chronic wound healing. Our technique also identifies significant dependencies among microbial components as evidence of possible microbial syntrophy

Funding grant proposals for scientific research: retrospective analysis of scores by members of grant review panel

Objective To quantify randomness and cost when choosing health and medical research projects for funding

The generation of an induced pluripotent stem cell line (DCGi001-A) from an individual with FOXG1 syndrome carrying the c.460dupG (p.Glu154fs) variation in the FOXG1 gene

FOXG1 syndrome is a neurodevelopmental disorder caused by mutations in the FOXG1 gene. Here, an induced pluripotent stem cell (iPSC) line was generated from human dermal fibroblasts of an individual with the c.490dupG (p.Glu154fs) mutation in the FOXG1 gene. Fibroblasts were reprogrammed using non-integrating episomal plasmids and pluripotency marker expression was confirmed by both immunocytochemistry and quantitative PCR in the resultant iPSC line. There were no karyotypic abnormalities and the cell line successfully differentiated into all three germ layers. This cell line may prove useful in the study of the pathogenic mechanisms that underpin FOXG1 syndrome