Wavelet Integrated CNNs for Noise-Robust Image Classification

Convolutional Neural Networks (CNNs) are generally prone to noise interruptions, i.e., small image noise can cause drastic changes in the output. To suppress the noise effect to the final predication, we enhance CNNs by replacing max-pooling, strided-convolution, and average-pooling with Discrete Wavelet Transform (DWT). We present general DWT and Inverse DWT (IDWT) layers applicable to various wavelets like Haar, Daubechies, and Cohen, etc., and design wavelet integrated CNNs (WaveCNets) using these layers for image classification. In WaveCNets, feature maps are decomposed into the low-frequency and high-frequency components during the down-sampling. The low-frequency component stores main information including the basic object structures, which is transmitted into the subsequent layers to extract robust high-level features. The high-frequency components, containing most of the data noise, are dropped during inference to improve the noise-robustness of the WaveCNets. Our experimental results on ImageNet and ImageNet-C (the noisy version of ImageNet) show that WaveCNets, the wavelet integrated versions of VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness than their vanilla versions.Comment: CVPR accepted pape

Educating the leaders of tomorrow : the library without walls

A description of library services offered to students in distance programs within Canada and Ecuador

Robustness analysis of a Maximum Correntropy framework for linear regression

In this paper we formulate a solution of the robust linear regression problem in a general framework of correntropy maximization. Our formulation yields a unified class of estimators which includes the Gaussian and Laplacian kernel-based correntropy estimators as special cases. An analysis of the robustness properties is then provided. The analysis includes a quantitative characterization of the informativity degree of the regression which is appropriate for studying the stability of the estimator. Using this tool, a sufficient condition is expressed under which the parametric estimation error is shown to be bounded. Explicit expression of the bound is given and discussion on its numerical computation is supplied. For illustration purpose, two special cases are numerically studied.Comment: 10 pages, 5 figures, To appear in Automatic

Lipoproteins of Mycobacterium tuberculosis : an abundant and functionally diverse class of cell envelope components

Mycobacterium tuberculosis remains the predominant bacterial scourge of mankind. Understanding of its biology and pathogenicity has been greatly advanced by the determination of whole genome sequences for this organism. Bacterial lipoproteins are a functionally diverse class of membrane-anchored proteins. The signal peptides of these proteins direct their export and post-translational lipid modification. These signal peptides are amenable to bioinformatic analysis, allowing the lipoproteins encoded in whole genomes to be catalogued. This review applies bioinformatic methods to the identification and functional characterisation of the lipoproteins encoded in the M. tuberculosis genomes. Ninety nine putative lipoproteins were identified and so this family of proteins represents ca. 2.5% of the M. tuberculosis predicted proteome. Thus, lipoproteins represent an important class of cell envelope proteins that may contribute to the virulence of this major pathogen

Blended Value Investing: Capital Opportunities for Social and Environmental Impact

This paper is offered not as a fully comprehensive survey of the emerging area of blended value investing, but rather as a set of examples of how such investing practices are being developed and applied around the world. The paper's intent is not to provide a single answer for all investment challenges, but to demonstrate how groups of investors are mobilizing capital on new terms to meet the challenges of emerging investment opportunities, as well as the demands of investors seeking out new asset classes in which to place their capital.This paper presents innovations in capital finance that promise to bridge market-rate interests with strategic opportunities to create blended value that benefits shareholder and stakeholder alike. The following examples speak to an evolving capital convergence wherein mainstream capital markets and investing will increasingly become drivers of new solutions to historic problems. Blended value investing funds and instruments offer financing strategies a set of tools that go beyond traditional philanthropy or market rate investing and which complement the vision we all share of a world with greater equity and opportunity for its members.This paper also identifies several areas of research that would help advance the field of blended value investing. Finally, the paper concludes with words of caution that suggest a prudent approach to developing blended value capital markets. It offers a critique of the state of the markets, presents a strategic vision for the blended value capital markets, and suggests specific steps that participants might take in moving toward the ideal

Visualizations for an Explainable Planning Agent

In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making. Imposing transparency and explainability requirements on such agents is especially important in order to establish trust and common ground with the end-to-end automated planning system. Visualizing the agent's internal decision-making processes is a crucial step towards achieving this. This may include externalizing the "brain" of the agent -- starting from its sensory inputs, to progressively higher order decisions made by it in order to drive its planning components. We also show how the planner can bootstrap on the latest techniques in explainable planning to cast plan visualization as a plan explanation problem, and thus provide concise model-based visualization of its plans. We demonstrate these functionalities in the context of the automated planning components of a smart assistant in an instrumented meeting space.Comment: PREVIOUSLY Mr. Jones -- Towards a Proactive Smart Room Orchestrator (appeared in AAAI 2017 Fall Symposium on Human-Agent Groups