Large-Batch, Neural Multi-Objective Bayesian Optimization

Bayesian optimization provides a powerful framework for global optimization of black-box, expensive-to-evaluate functions. However, it has a limited capacity in handling data-intensive problems, especially in multi-objective settings, due to the poor scalability of default Gaussian Process surrogates. We present a novel Bayesian optimization framework specifically tailored to address these limitations. Our method leverages a Bayesian neural networks approach for surrogate modeling. This enables efficient handling of large batches of data, modeling complex problems, and generating the uncertainty of the predictions. In addition, our method incorporates a scalable, uncertainty-aware acquisition strategy based on the well-known, easy-to-deploy NSGA-II. This fully parallelizable strategy promotes efficient exploration of uncharted regions. Our framework allows for effective optimization in data-intensive environments with a minimum number of iterations. We demonstrate the superiority of our method by comparing it with state-of-the-art multi-objective optimizations. We perform our evaluation on two real-world problems - airfoil design and color printing - showcasing the applicability and efficiency of our approach. Code is available at: https://github.com/an-on-ym-ous/lbn\_mob

Yule-Nielsen based multi-angle reflectance prediction of metallic halftones

Spectral prediction models are widely used for characterizing classical, almost transparent ink halftones printed on a diffuse substrate. Metallic-ink prints however reflect a significant portion of light in the specular direction. Due to their opaque nature, multi-color metallic halftones require juxtaposed halftoning methods where halftone dots of different colors are laid out side-by-side. In this work, we study the application of the Yule-Nielsen spectral Neugebauer (YNSN) model on metallic halftones in order to predict their reflectances. The model is calibrated separately at each considered illumination and observation angle. For each measuring geometry, there is a different Yule-Nielsen n-value. For traditional prints on paper, the n-value expresses the amount of optical dot gain. In the case of the metallic prints, the optical dot gain is much smaller than in paper prints. With the fitted n-values, we try to better understand the interaction of light and metallic halftones

Color Reproduction of Metallic-Ink Images

We study the color reproduction of full-color metallic-ink images. Full-color metallic-ink images are prints whose contributing colorants are exclusively made of colored metallic inks. Due to the presence of metallic particles, metallic inks show a metal-like luster. These particles are opaque and hide the underlying ink or substrate. In order to obtain predictable halftone colors, we need a juxtaposed halftoning method to create halftone dots of different colors side by side without overlapping. Juxtaposed halftoning invalidates many assumptions generally made for the color reproduction workflow. For printing metallic-ink images, one needs a color separation system creating surface coverages for the 8 metallic inks that correspond to the 8 Neugebauer primaries. For this purpose, we introduce a simple and fast method for N-color separation that relies either on Demichel’s or on a variant of Kueppers’ ink-to-colorant separations. Thanks to a unique set of ink-to-colorant formulas, pseudo-CMY ink values are separated into amounts of printable colorants. We also describe color separation procedures that are able to optimize different properties of the resulting metallic-ink images

Spectral Prediction of Juxtaposed Halftones Relying on the Two-by-two Dot Centering Model

In a color reproduction workflow, spectral prediction models are useful for establishing the correspondence between colorant sur- face coverages and resulting printed halftone color. Spectral predic- tion models enable calculation of the color gamut and establishment of the color separation tables. Discrete line juxtaposed halftoning, a recently proposed algorithm, is characterized by the fact that colorants formed by inks and ink superpositions are placed side by side. Juxtaposed halftoning is necessary when printing with special inks such as opaque or metallic inks. In order to predict the color of classical halftones, the Yule–Nielsen modified spectral Neugebauer model is generally used. However, this model may not predict the color of juxtaposed halftones, since the effective surface coverages of colorants and of possible colorant overlaps are unknown. In contrast, the two-by-two dot centering spectral prediction model developed by S. G. Wang enables the reflectance of slightly overlapping colorants to be captured and is therefore appropriate for predicting the color of juxtaposed halftones. Since this model requires a large calibration set, the authors use an estimation technique which predicts more than 90% of the two-by-two calibration pattern reflectances by measuring less than 10% of them. For juxtaposed halftoning, the two-by-two dot centering model offers high prediction accuracies and outperforms the different variants of the Yule–Nielsen spectral Neugebauer model for comparable setups

N-Ink Printer Characterization with Barycentric Subdivision

Printing with a large number of inks, also called N-ink printing, is a challenging task. The challenges comprise spectral modelling of the printer, color separation, halftoning, and limitations of the amount of inks. Juxtaposed halftoning, a perfectly dot-off-dot halftoning method, has proven to be useful to address some of these challenges. However, for juxtaposed halftones, prediction of colors as a function of ink area-coverages has not yet been fully investigated. The goal of this paper is to introduce a spectral prediction model for N-ink juxtaposed-halftone prints. As the area-coverage domain of juxtaposed inks forms a simplex, we propose a cellular subdivision of the area-coverage domain using barycentric subdivision of simplexes. The barycentric subdivision provides algorithmically straightforward means to design and implement an N-ink color prediction model. Within the subdomain cells, the Yule-Nielsen spectral Neugebauer model is used for the spectral prediction. Our proposed model is highly accurate for prints with a large number of inks while requiring a relatively low number of calibration samples

Reducing the number of calibration patterns for the two-by-two dot centering model

The two-by-two dot centering model enables predicting the spectral reflectance of color halftones and does not depend on a specific halftoning algorithm. It requires measuring the reflectances of a large number of two-by-two calibration tile patterns. Spectral measurement of hundreds or thousands of tile patterns is cumbersome and time consuming. In order to limit the number of measurements, we estimate the reflectances of a large majority of twoby- two calibration tile patterns from a small subset comprising less than 10% of all tile patterns. Using this subset of measured two-by-two calibration tile patterns, we perform a linear regression in the absorptance space and derive a transformation matrix converting tile pattern colorant surface coverages to absorptances. This transformation matrix enables calculating the absorptance of all remaining two-by-two tile patterns. For a cyan, magenta and yellow print, with 72 two-by-two measured calibration tile patterns, we are able to create a two-bytwo dot centering model having an accuracy only slightly below the accuracy of the model with the fully measured set of 1072 two-by-two tile patterns

Color Reproduction on Shrink Sleeves

Wrapping heat-deformable plastic labels around packages relies on a shrinking process. Shrinking plastic labels distorts not only the shape but also the color of the printed artwork. In this work, we analyze and model the color shifts induced by shrinkage. The ultimate goal is to generate full color images which after shrinking have colors as close as possible to the original colors. For this purpose, we present a thickness enhanced Clapper-Yule prediction model. Its calibration requires spectral measurements of original, non-shrunk samples as well as the measured shrinking factors. With the prediction model, we establish a table creating the correspondences between target colors after shrinking and ink area coverages. This enables creating color images which after shrinking match the original images

بررسی رژیم های قیمتی دو شاخص عمده بازار جهانی نفت(برنت و WTI) قبل و بعد از بحران مالی:کاربردی از رویکرد مارکف سوئیچینگ

This study has been investigated price regimes of two prime index in the world oil market(Brent and WTI) based on weekly data over the period 2003/1/3-2007/5/25 (before financial crisis) and 2009/3/6-2012/12/14(after financial crisis) by using marko regime switching model whit dynamic autoregressive coefficient. The results show that the model MSMAH(3)-AR(2) is the optimal model for description price regimes of Brent and WTI index before and after the financial crisis. Also the recent financial crisis causes a change in dominate price regime of Berent while WTI tends to stay in the its previous regime. This may lead to the abnormal price spreads between them after the financial crisis. in our opinions, the reasons for this are attributed to their difference fundamentals drivers and closely related whit the recent dynamics in crude oil market