Estimating Illumination Chromaticity via Support Vector Regression

The technique of support vector regression is applied to the problem of estimating the chromaticity of the light illuminating a scene from a color histogram of an image of the scene. Illumination estimation is fundamental to white balancing digital color images and to understanding human color constancy. Under controlled experimental conditions, the support vector method is shown to perform better than the neural network and color by correlation methods

Nonlinear RGB-to-XYZ Mapping for Device Calibration

We introduce a new non-linear method for RGB-to-XYZ color calibration based on the technique of thin plate splines. Originally, thin plate splines were designed for deformable matching between 2-dimensional images for object recognition. We use 3-dimensional thin plate splines to map between sets of RGB device coordinates and corresponding sets of CIE XYZ coordinates. Tests calibrating several displays as well as a camera show thin plate spline calibration to be more accurate than existing linear or non-linear calibration methods

Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings

In neural machine translation, a source sequence of words is encoded into a vector from which a target sequence is generated in the decoding phase. Differently from statistical machine translation, the associations between source words and their possible target counterparts are not explicitly stored. Source and target words are at the two ends of a long information processing procedure, mediated by hidden states at both the source encoding and the target decoding phases. This makes it possible that a source word is incorrectly translated into a target word that is not any of its admissible equivalent counterparts in the target language. In this paper, we seek to somewhat shorten the distance between source and target words in that procedure, and thus strengthen their association, by means of a method we term bridging source and target word embeddings. We experiment with three strategies: (1) a source-side bridging model, where source word embeddings are moved one step closer to the output target sequence; (2) a target-side bridging model, which explores the more relevant source word embeddings for the prediction of the target sequence; and (3) a direct bridging model, which directly connects source and target word embeddings seeking to minimize errors in the translation of ones by the others. Experiments and analysis presented in this paper demonstrate that the proposed bridging models are able to significantly improve quality of both sentence translation, in general, and alignment and translation of individual source words with target words, in particular.Comment: 9 pages, 6 figures. Accepted by ACL201

Independent Component Analysis and Nonnegative Linear Model Analysis of Illuminant and Reflectance Spectra

Principal Component Analysis (PCA), Independent Component Analysis (ICA), Non-Negative Matrix Factorization (NNMF) and Non-Negative Independent Component Analysis (NNICA) are all techniques that can be used to compute basis vectors for finite-dimensional models of spectra. The two non-negative techniques turn out to be especially interesting because the pseudo-inverse of their basis vectors is also close to being non-negative. This means that after truncating any negative components of the pseudo-inverse vectors to zero, the resulting vectors become physically realizable sensors functions whose outputs map directly to the appropriate finite-dimensional weighting coefficients in terms of the associated (NNMF or NNICA) basis. Experiments show that truncating the negative values incurs only a very slight performance penalty in terms of the accuracy with which the input spectrum can be approximated using a finite-dimensional model

A Basis for Cones

Why do the human cones have the spectral sensitivities they do? We hypothesize that they may have evolved to their present form because their sensitivities are optimal in terms of their ability to recover the spectrum of incident light. As evidence in favor of this hypothesis, we compare the accuracy with which the incoming spectrum can be approximated by a three-dimensional linear model based on the cone responses and compare this to the optimal approximations defined by models based on principal components analysis, independent component analysis, non-negative matrix factorization and non-negative independent component analysis. We introduce a new method of reconstructing spectra from the cone responses and show that the cones are almost as good as these optimal methods in estimating the spectrum

The impact of information systems vulnerability announcements on firms’ market value

With the increasing deployment of IT systems, information systems vulnerabilities have led to a severe negative impact on firms and businesses. This paper aims to examine the impact of information system vulnerability announcements on the market value of Chinese firms. Using the collected security incidents in Chinese firms from 2015 to 2021, we study how characteristics of enterprises and vulnerabilities affect enterprises’ market value through event study and regression analysis. In particular, we find that state-owned enterprises suffer larger negative effects than other types of firms. This study also provides companies and managers with insights in decision-making and recommendations from a managerial perspective

Illumination Estimation based on Bilayer Sparse Coding

Abstract Computational color constancy is a very important topic in computer visio