In the search for single molecule magnets, metal ions are considered pivotal towards achieving large magnetic anisotropy barriers. Linear or near‐linear bi-coordinate mononuclear Co(II) complexes are studied as potential single‐molecule magnets owing to the strong spin-orbit coupling constant of Co(II) and its low coordination number, which results in the retention of the unquenched metal‐orbital angular momentum [1]. The spin-orbit coupled unquenched orbital angular momentum of the metal centre produces strong magnetic anisotropy through ligand‐field interactions. In this context, the influence of ligands with heavy elements, showing large spin–orbit coupling, on magnetic anisotropy barriers was investigated using a series of Mn(II)-based complexes, in which the metal ion did not have any orbital contribution. The mixing of metal and ligand orbitals was achieved by explicitly correlating the metal and ligand valence electrons with CASSCF calculations. The CASSCF wave functions were further used for evaluating spin–orbit coupling and zero-field splitting parameters for these complexes. For Mn(II) complexes with heavy ligand atoms, such as Br and I, several interesting inter-state mixings occur via the spin–orbit operator, which results in large magnetic anisotropy in these Mn(II) complexes [2].
[1] S. Roy Chowdhury and S. Mishra Eur. J. Inorg. Chem. (2017) 659-668.
[2] S. Roy Chowdhury and S. Mishra Phys. Chem. Chem. Phys. 19 (2017) 16914-16922.

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Various ocean reanalysis datasets have been released with the development of ocean data assimilation systems since the 2000s when global ocean interior observations by Argo profiling floats started. Geostationary satellites such as Himawari-8 recently have observed sea surface temperatures at much higher temporal and spatial resolutions than before (10 mins. and 2km, respectively). To construct three-dimensional ocean fields by extracting the most from both sparse in-situ data and dense satellite data, we have developed an ensemble Kalman filter system for ocean data assimilation. We have faced two major challenges: (i) exceedingly underdispersive ensemble because of atmospheric forcing without uncertainties considered and (ii) dynamical imbalance causing initial shocks. This presentation provides an overview of ocean data assimilation and presents our research about how to tackle the challenges.

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In this presentation, I introduce research that I worked on as a Ph.D. student. The research is for live migration of operating systems (OS) in Bare-metal clouds, which are IaaS clouds providing physical machines instead of virtual machines. In the IaaS clouds, OS live migration, a function to move running OS from a physical machine to another physical machine, is an important function for machine maintenance. Because commodity OS live migration is achieved by machine virtualization, current bare-metal clouds do not support live migration. Previous studies have proposed OS-level live migration; however, live migration should be OS-independent for preventing user intervention and broaden OS choices in IaaS clouds. To achieve OS-independent live migration without device virtualization, we introduce a very thin hypervisor that exposes physical hardware devices to the guest OS directly rather than virtualizing them. The hypervisor captures, transfers, and reconstructs physical device states by monitoring access from the OS and controlling the physical devices with some techniques. Whenever it does not perform live migration, the hypervisor is mostly idle to minimize performance degradation. A performance evaluation confirmed that the OS performance with the hypervisor is comparable to that of a bare-metal machine.

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Formation of the COVID19 infection cluster and effectiveness of contact trace to suppress it are analyzed with computer simulation using an agent-based model. Some parameters are reported from clinical studies like incubation period(lognormal distribution with mean 5.6 days and SD 3.9 days) [1,2]. For these parameters, Monte Carlo sampling using these distribution functions are done. Parameters unknown for the COVID19 infection are assumed appropriately, for example, time interval being infective after infection be one third of pre-symptomatic or asymptomatic period, and check its effect with sensitivity analysis.
Simulations are made for systems with N agents in a closed system contacting with each other randomly among agents except ones in symptomatic state. Initially, there are one just-infected agent in pre-symptomatic state and (N-1) susceptible agents. Systems with N=1000 are simulated, but ones with smaller (more than a few) or larger N show statistically the same behavior. For a given number of basic reproduction, R0, contacting probability and infection probability are determined so that they realize the given R0 in average. Typical value of R0 used in the simulations are 2.5. For the contact trace, a privacy-preserving proximity trace[3] is simulated. When an agent found to be COVID19 positive, send alerts to agents contacted previous some interval and ask to be isolated for some period.
Sensitivity analysis for the model parameters are made, and the followings are observed:
(1) Time delay between symptomatic to alert, and basic reproduction number influence strongly on peak value of infected rate.
(2) Rate of asymptomatic agent affects on it.
(3) Effect of contact alert does not change if alert interval, that is, time interval of contact alert, and isolating time of alert receivers are longer than about one week. Contact alert does not work if these times are less than four days.
(4) Effect of contact alert is proportional to square of rate of agent with contact alert application.

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We have developed GENESIS software for biomolecular dynamics simulations for the last ten years. Originally, it was designed to utilize K computer, while it is also available on Intel CPU cluster, Intel KNL, and NVIDIA GPU clusters. Since Fugaku, the world fastest supercomputer in 2020, is composed of ARM CPU, we had to optimize GENESIS to Fugaku ARM architecture for the last few years. Co-design between system and application developers has worked well for this purpose. We could achieve significant acceleration of MD simulations using GENESIS on Fugaku compared to the same calculation on K computer. The accuracy in MD simulations has also been improved by introducing more accurate definitions of system temperature and pressure in MD simulations. This allows us to use a longer time step to integrate the equations of motion, suggesting a better performance than the standard simulation conditions. Using GENESIS 2.0 on Fugaku, protein dynamics in longer time scales and in realistic cellular environments is possible for better understanding of structure-dynamics-function relationships in biomacromolecules.

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The circadian rhythm is regulated by a multitude of proteins interacting with each others. We will describe the use of MD simulations, cryo-EM data fitting in conjecture with mutagenesis experimental data to shed the light on the effects of cryptochrome proteins.

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We have been developing FPGA Cluster in order to research new approaches for future HPC, where we rely on circuit-reconfigurable computing with data-flow based, and customized architectures. As a part of FS2020 project,we have investigated possibilities on enhancing Fugaku with other computing architectures. We focus on FPGAs, Field-Programmable Gate Arrays, which can be used to construct arbitrary circuit structure on a chip for processing elements, memory subsystems, on-chip interconnects, and external networks. A cluster of such FPGA chips gives us testbeds for new architecture researches by allowing us to construct various machines and conduct experiments with them. In this talk, I introduce the overview of this FPGA cluster project, as well as the technical details of the system from hardware to software developed very recently, what we can do with them, and the present trend of FPGAs and new CMOS-digital-based architectures for HPC and Big-data processing applications.

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A year later, Fugaku starts. Based on the results achieved so far, our team will continue R & D of climate/weather modeling and tackle more important issues for the climate system. For example, establishment of a reliable large-eddy simulation of moist flow to understand phenomenological, energy balance problem as a climate system in simple situations, and so on. To handle large amounts of data, we will employ not only traditional deduction method but also induction method using data science. In this talk, I will introduce the main achievements of our team so far, and clarify the team strategy toward next 5 or 10 years.

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Natural language processing is a fast-growing field, with a rapid evolution of approaches and models. In the last 6 years, we have come a long way from word embeddings to contextualized representations to pre-trained transformers, with numerous success stories for NLP system performance on question answering, text classification, machine translation and other tasks.
Despite the successes, we are still very far from reliable verbal reasoning, and one of the unresolved issues is semantic compositionality. It is not only a practical challenge, but also a theoretical one, as there is still no consensus on what a compositional representation of morphologically complex word, phrase or a sentence should be like.
This talk provides an introduction to our research endeavor in the field of NLP, briefly introduces basics of NLP models and aspects of linguistic theory in which they are explicitly or implicitly grounded, particularly compositionality.

Compared with all-atom force fields, early coarse-grained (CG) models were once considered as "toys" to study qualitative properties of biophsical processes such as protein folding. However, as people's interest increases in large-scale biological phenomenons, such as genome organization, liquid-liquid phase separation, and virus capsid assembly, CG models begin to show their advantage in lower computational cost. On the other hand, recent development of the CG models also gained great success in improving the accuracy. We first review the advances of the latest CG models for protein, DNA, and RNA. We then describe the currenet progress of the implementation of these models in the MD software GENESIS. At last, we show some simple applications of the CG simulations with GENESIS.