1,666 research outputs found
Simulations of one-flavor QCD at finite temperature by RHMC
We simulate one-flavor QCD with standard Wilson fermions at finite
temperature by the rational hybrid Monte Carlo algorithm. In the heavy quark
region when we decrease the quark mass there is an endpoint which terminates
the first order phase transition. We try to locate it by calculating the Binder
cumulant of the Polyakov loop norm. We estimate the end-point to be kappa_c
\sim 0.07-0.08.Comment: 7 pages, Presented at the XXV International Symposium on Lattice
Field Theory, July 30 - August 4 2007, Regensburg, German
Equation of State at Finite Density from Imaginary Chemical Potential
We perform two flavor QCD simulations with an imaginary chemical potential
and measure derivatives of the pressure up to 4th order as a function of the
imaginary chemical potential and the temperature . For
temperatures , these derivatives are fitted by a Taylor series in
about . A fit limited to 4th order describes the data poorly at
all temperatures, showing that we are sensitive to 6th order contributions.
Similarly, a 6th order fit fails for temperatures ,
showing the need for 8th order terms. Thus, our method may offer a
computational advantage over the direct measurement of Taylor coefficients at
. At temperatures , we fit our data with a hadron resonance
gas ansatz. The fit starts to fail at . Using our fits, we
also reconstruct the equation of state as a function of real quark and isospin
chemical potentials.Comment: 8 pages, Lattice 2009 (non-zero temperature and density
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
Image-to-Graph Convolutional Network for 2D/3D Deformable Model Registration of Low-Contrast Organs
Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a three-dimensional (3D) organ mesh for a low-contrast two-dimensional (2D) projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiation therapy, the 2D/3D deformable registration performance is verified for multiple abdominal organs that have not been targeted to date, i.e., the liver, stomach, duodenum, and kidney, and for pancreatic cancer. The experimental results show shape prediction considering relationships among multiple organs can be used to predict respiratory motion and deformation from digitally reconstructed radiographs with clinically acceptable accuracy
Magnetic dichroism study on MnCoGa thin film using a combination of X-ray absorption and photoemission spectroscopy
Using circularly polarised radiation and a combination of bulk-sensitive hard
X-ray photoelectron spectroscopy and X-ray-absorption spectroscopy (XAS) we
studied the electronic and magnetic structure of epitaxial
MnCoGa thin films. Spin resolved Bloch spectral functions,
density of states as well as charge and magnetisation densities were
investigated by a first-principles analysis of full potential, fully
relativistic Korringa--Kohn--Rostoker calculations of the electronic structure.
The valence states were experimentally investigated by using linear dichroism
in the angular distribution and comparing the results to spin-resolved
densities of states. The linear dichroism in the valence band enabled a
symmetry analysis of the contributing states. The spectra were in good
agreement with the theoretical partial density of states. The element-specific,
spin-resolved, unoccupied densities of states for Co and Mn were analysed by
using XAS and X-ray magnetic circular dichroism (XMCD) at the edges.
The spectra were influenced by strong correlation effects. XMCD was used to
extract the site resolved magnetic moments. The experimental values of and agree very well with the
calculated magnetic moments. Magnetic circular dichroism in angle-resolved
photoelectron spectroscopy at the Mn and Co core level exhibited a
pronounced magnetic dichroism and confirmed the localised character of the Mn
valence states
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