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

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

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.)

Phytoplakton and Zooplankton Standing Stocks and Downward Flux of Particulate Material around Fast Ice Edge of Lutzow-Holm Bay,Antarctica

Phyto- and zooplankton standing stocks in the fast ice and in the water column under the ice and downward flux of particulate material through the water column were investigated in Liitzow-Holm Bay, Antarctica, during the austral summers, i.e., in January 1977 and February 1979. Chlorophyll a standing stock integrated through the ice was 0.38-0.80 mg/m^2 and that in the water column beneath the ice down to 150 m was 3.06 mg/m^2. Microdistribution of zooplankton beneath the ice was observed by the pumping collections and the dense populations were found just beneath the ice. Zooplankton density was in a range of 12-60 indiv/m^3 and the zooplankton stocks integrated through the 150 m water column ranged from 6000 to 7675 indiv/m^2. By the sediment trap operation, the fecal materials were found to comprise a large proportion of the collected particles. The maximum daily vertical flux of particulate organic carbon (POC) was found at the 100m depth (103 mg C/m^2/day) and concentration of POC in the water column was in a range of 24-56 mg C/m^3. These data on standing stocks of phyto- and zooplankton and vertical flux of POC in the icecovered Liitzow-Holm Bay were compared with those in the other sea areas