Evolutionary branching under multi-dimensional evolutionary constraints.

The fitness of an existing phenotype and of a potential mutant should generally depend on the frequencies of other existing phenotypes. Adaptive evolution driven by such frequency-dependent fitness functions can be analyzed effectively using adaptive dynamics theory, assuming rare mutation and asexual reproduction. When possible mutations are restricted to certain directions due to developmental, physiological, or physical constraints, the resulting adaptive evolution may be restricted to subspaces (constraint surfaces) with fewer dimensionalities than the original trait spaces. To analyze such dynamics along constraint surfaces efficiently, we develop a Lagrange multiplier method in the framework of adaptive dynamics theory. On constraint surfaces of arbitrary dimensionalities described with equality constraints, our method efficiently finds local evolutionarily stable strategies, convergence stable points, and evolutionary branching points. We also derive the conditions for the existence of evolutionary branching points on constraint surfaces when the shapes of the surfaces can be chosen freely

Positioning of green information systems and technology from an ecosystem perspective

The aim of this paper is to define eco-focused information systems and technology (Green IS/IT) as a crossing between sustainable ecosystem (SE) and business ecosystem (BE) research. This paper examines 1,001 ecosystem studies covering sustainable, social, industrial, innovation, platform, and information technology ecosystems. Japanese newspaper entries on 833 ecosystem topics involving successful Green IS/IT implementation are also investigated. Our analysis suggests that Green IS/IT study can be positioned as an important ecosystem research agenda at the intersection of SE and BE. At present, SE and BE are studied separately, and empirical research is fragmented into subthemes

Image Diversification via Deep Learning based Generative Models

Machine learning driven pattern recognition from imagery such as object detection has been prevalenting among society due to the high demand for autonomy and the recent remarkable advances in such technology. The machine learning technologies acquire the abstraction of the existing data and enable inference of the pattern of the future inputs. However, such technologies require a sheer amount of images as a training dataset which well covers the distribution of the future inputs in order to predict the proper patterns whereas it is impracticable to prepare enough variety of images in many cases. To address this problem, this thesis pursues to discover the method to diversify image datasets for fully enabling the capability of machine learning driven applications. Focusing on the plausible image synthesis ability of generative models, we investigate a number of approaches to expand the variety of the output images using image-to-image translation, mixup and diffusion models along with the technique to enable a computation and training dataset efficient diffusion approach. First, we propose the combined use of unpaired image-to-image translation and mixup for data augmentation on limited non-visible imagery. Second, we propose diffusion image-to-image translation that generates greater quality images than other previous adversarial training based translation methods. Third, we propose a patch-wise and discrete conditional training of diffusion method enabling the reduction of the computation and the robustness on small training datasets. Subsequently, we discuss a remaining open challenge about evaluation and the direction of future work. Lastly, we make an overall conclusion after stating social impact of this research field

Chiral Symmetry Breaking in the Dual Ginzburg-Landau Theory

Confinement and chiral symmetry breaking are the most fundamental phenomena in Quark Nuclear Physics, where hadrons and nuclei are described in terms of quarks and gluons. The dual Ginzburg-Landau (DGL) theory, which contains monopole fields as the most essential degrees of freedom and their condensation in the vacuum, is modeled to describe quark confinement in strong connection with QCD. We then demonstrate that the DGL theory is able to describe the spontaneous break down of the chiral symmetry.Comment: Talk presented by H. Toki at the Joint Japan-Australia Workshop on ``Quarks, Hadrons and Nuclei'', 15 - 24 Nov. 1995, in Adelaide, Australia, 7 pages, Plain Latex, 4 postscript figures (included in a separate .uu file

Dual Higgs Mechanism for Quarks in Hadrons

We study nonperturbative features of QCD using the dual Ginzburg-Landau (DGL) theory, where the color confinement is realized through the dual Higgs mechanism brought by QCD-monopole condensation. The linear confinement potential appears in the QCD-monopole condensed vacuum. We study the infrared screening effect to the confinement potential by the light-quark pair creation, and derive a compact formula for the screened quark potential. We study the dynamical chiral-symmetry breaking (D$\chi$SB) in the DGL theory by solving the Schwinger-Dyson equation. QCD-monopole condensation plays an essential role to D$\chi$SB. The QCD phase transition at finite temperature is studied using the effective potential formalism in the DGL theory. We find the reduction of QCD-monopole condensation and the string tension at high temperatures. The surface tension is calculated using the effective potential at the critical temperature. The DGL theory predicts a large mass reduction of glueballs near the critical temperature. We apply the DGL theory to the quark-gluon-plasma (QGP) physics in the ultrarelativistic heavy-ion collisions. We propose a new scenario of the QGP formation via the annihilation of color-electric flux tubes based on the attractive force between them.Comment: Talk presented by H. Suganuma at the YITP Workshop on 'From Hadronic Matter to Quark Matter: Evolving View of Hadronic Matter', Oct. 30-Nov. 1, 1994, YITP Kyoto, Japan, 20 pages, uses PHYZZX (to be published in Prog. Theor. Phys. Suppl.)