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Due to COVID-19, many countries including Japan have implemented a suspension of economic activities for infection control. It has contributed to reduce the transmission of COVID-19 but caused severe economic losses. Today, several promising vaccines have been developed and are already being distributed in some countries. Therefore, we evaluated various vaccine and intensive countermeasure strategies with constraint of economic loss using SEIR model to obtain knowledge of how to balance economy with infection control in Japan. Our main results were that the vaccination strategy that prioritized younger generation was better in terms of deaths when a linear relationship between lockdown intensity and acceptable economic loss was assumed. On the other hand, when a non-linearity relationship was introduced, implying that the strong lockdown with small economic loss was possible, the old first strategies were best in the settings of small basic reproduction number. These results indicated a high potential of remote work when prioritizing vaccination for the old generation. When focusing on only the old first strategies as the Japanese government has decided to do, the strategy vaccinating the young next to the old was superior to the others when a non-linear relationship was assumed due to sufficient reduction of contact with small economic loss.

We use methods from Riemann geometry to investigate transformations between the color spaces of color-normal and color-weak observers. The two main applications are the simulation of the perception of a color weak observer for a color-normal observer, and the compensation of color images in a way that a color-weak observer has approximately the same perception as a color-normal observer. The metrics in the color spaces of interest are characterized with the help of ellipsoids defined by the just-noticeable-differences between the colors which are measured with the help of color-matching experiments. The constructed mappings are the isometries of Riemann spaces that preserve the perceived color differences for both observers. Among the two approaches to build such an isometry, we introduce normal coordinates in Riemann spaces as a tool to construct a global color-weak compensation map. Compared with the previously used methods, this method is free from approximation errors due to local linearizations, and it avoids the problem of shifting locations of the origin of the local coordinate system. We analyze the variations of the Riemann metrics for different observers obtained from new color-matching experiments and describe three variations of the basic method. The performance of the methods is evaluated with the help of semantic differential tests., Funding Agencies|Japan Society for the Promotion of Science (JSPS) [23500156]; Institute of Science and Engineering, Chuo University; Swedish Research Council through a framework grant for the project Energy Minimization for Computational Cameras [2014-6227]; Swedish Foundation for Strategic Research [IIS11-0081]

We introduce normal coordinates in Riemannspaces as a tool to construct color-weak compensation methods.We use them to compute color stimuli for a color weakobservers that result in the same color perception as theoriginal image presented to a color normal observer in the sensethat perceived color-differences are identical for both. Thecompensation is obtained through a color-difference-preservingmap, i.e. an isometry between the 3D color spaces of a colornormaland any given color-weak observer. This approach usesdiscrimination threshold data and is free from approximationerrors due to local linearization. The performance is evaluatedwith the help of semantic differential (SD) tests., Virtual Photo Set (VPS), European Community’s Seventh Framework Programme FP7/2007-2013 - Challenge 2 Cognitive Systems, Interaction, Robotics - under grant agreement No 247947 - GARNICS.

In this paper we use tools from Riemann geometry to construct color reproduction methods that take into account the varying color perception properties of observers. We summarize the different steps in the processing: the estimation of the discrimination thresholds, the estimation of the Riemann metric, the construction of the metric-preserving or color-difference-preserving mapping and the usage of Semantic Differentiation (SD) techniques in the evaluation. We tested the method by measuring the discrimination data for 45 observers. We illustrate the geometric properties of the color spaces obtained and show the result of the metric-preserving maps between the color space of color-normal observers and a color-weak observer. We then apply the obtained maps to produce color corrected reproductions of two natural images.

We introduce an invariant metric in the space of symmetric,positive definite matrices and illustrate the usage of thisspace together with this metric in color processing. For thismetric closed-form expressions for the distances and thegeodesics, (ie. the straight lines in this metric) are availableand we show how to implement them in the case of matricesof size 2x2. In the first illustration we use the framework toinvestigate an interpolation problem related to the ellipsesobtained in the measurements of just-noticeable-distances.For two such ellipses we use the metric to construct an interpolatingsequence of ellipses between them. In the secondapplication construct a texture descriptor for chromaticitydistributions. We describe the probability distributions ofchromaticity vectors by their matrices of second order moments.The distance between these matrices is independentunder linear changes of the coordinate system in the chromaticityspace and can therefore be used to define a distancebetween probability distributions that is independentof the coordinate system used. We illustrate this invariance,by way of an example, in the case of different white pointcorrections., Visuella Världar, The Knowledge Foundation, Sweden, GMIP: Groups and Manifolds for Information Processing; Vetenskapsråd