Energy-momentum tensor correlation function in Nf=2+1 full QCD at finite temperature

We measure correlation functions of the nonperturbatively renormalized energy-momentum tensor in $N_f=2+1$ full QCD at finite temperature by applying the gradient flow method both to the gauge and quark fields. Our main interest is to study the conservation law of the energy-momentum tensor and to test whether the linear response relation is properly realized for the entropy density. By using the linear response relation we calculate the specific heat from the correlation function. We adopt the nonperturbatively improved Wilson fermion and Iwasaki gauge action at a fine lattice spacing $=0.07$ fm. In this paper the temperature is limited to a single value $T=232$ MeV. The $u$, $d$ quark mass is rather heavy with $m_\pi/m_\rho=0.63$ while the $s$ quark mass is set to approximately its physical value.Comment: 9 pages, 4 figures. Talk presented at the 35th International Symposium on Lattice Field Theory (LATTICE 2017), 18-24 June 2017, Granada, Spai

A novel bioinformatics tool for phylogenetic classification of genomic sequence fragments derived from mixed genomes of uncultured environmental microbes

A Self-Organizing Map (SOM) is an effective tool for clustering and visualizing high-dimensional complex data on a two-dimensional map. We modified the conventional SOM to genome informatics, making the learning process and resulting map independent of the order of data input, and developed a novel bioinformatics tool for phylogenetic classification of sequence fragments obtained from pooled genome samples of microorganisms in environmental samples allowing visualization of microbial diversity and the relative abundance of microorganisms on a map. First we constructed SOMs of tri- and tetranucleotide frequencies from a total of 3.3-Gb of sequences derived using 113 prokaryotic and 13 eukaryotic genomes, for which complete genome sequences are available. SOMs classified the 330000 10-kb sequences from these genomes mainly according to species without information on the species. Importantly, classification was possible without orthologous sequence sets and thus was useful for studies of novel sequences from poorly characterized species such as those living only under extreme conditions and which have attracted wide scientific and industrial attention. Using the SOM method, sequences that were derived from a single genome but cloned independently in a metagenome library could be reassociated in silico. The usefulness of SOMs in metagenome studies was also discussed

Towards the QCD equation of state at the physical point using Wilson fermion

We study the (2+1)-flavor QCD at nonzero temperatures using nonperturbatively improved Wilson quarks of the physical masses by the fixed scale approach. We perform physical point simulations at finite temperatures with the coupling parameters which were adopted by the PACS-CS Collaboration in their studies using the reweighting technique. Zero temperature values are obtained on the PACS-CS configurations which are open to the public on the ILDG/JLDG. Finite temperature configurations are generated with the RHMC algorithm. The lattice sizes are 323×Nt with Nt=14, 13, ⋯, 4 which correspond to T≈160--550 MeV. We present results of some basic observables at these temperatures and the status of our calculation of the equation of state.This work is in part supported by JSPSKAKENHI Grant No. 26287040, No. 26400244, No. 26400251, and No. 15K05041. This work isin part supported also by the Large Scale Simulation Program of High Energy AcceleratorResearch Organization (KEK) No. 13/14-21 and No. 14/15-23.The 33rd International Symposium on Lattice Field Theory (LATTICE 2015) ; Place : Kobe International Conference Center, Kobe, Japan ; Date : July 14 -18, 201

Characterization of Genetic Signal Sequences with Batch-Learning SOM

An unsupervised clustering algorithm Kohonen's SOM is an effective tool for clustering and visualizing high-dimensional complex data on a single map. We previously modified the conventional SOM for genome informatics, making the learning process and resulting map independent of the order of data input on the basis of Batch Learning SOM (BL-SOM). We generated BL-SOMs for tetra- and pentanucleotide frequencies in 300,000 10-kb sequences from 13 eukaryotes for which almost complete genomic sequences are available. BL-SOM recognized species-specific characteristics of oligonucleotide frequencies in most 10-kb sequences, permitting species-specific classification of sequences without any information regarding the species. We next constructed BL-SOMs with tetra- and pentanucleotide frequencies in 37,086 full-length mouse cDNA sequences. With BL-SOM we also analyzed occurrence patterns of the oligonucleotides that are thought to be involved in transcriptional regulation on the human genome