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 $T \in [0.83 T_c, 2 T_c]$. For temperatures $T \geq T_c$, these derivatives are fitted by a Taylor series in $\mu/T$ about $\mu=0$. 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 $T_c \leq T \leq 1.05 T_c$, showing the need for 8th order terms. Thus, our method may offer a computational advantage over the direct measurement of Taylor coefficients at $\mu=0$. At temperatures $T \leq T_c$, we fit our data with a hadron resonance gas ansatz. The fit starts to fail at $T \gtrsim 0.95 T_c$. 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

Gradient sensing limit of a cell when controlling the elongating direction

Eukaryotic cells perform chemotaxis by determining the direction of chemical gradients based on stochastic sensing of concentrations at the cell surface. To examine the efficiency of this process, previous studies have investigated the limit of estimation accuracy for gradients. However, these studies assume that the cell shape and gradient are constant, and do not consider how adaptive regulation of cell shape affects the estimation limit. Dynamics of cell shape during gradient sensing is biologically ubiquitous and can influence the estimation by altering the way the concentration is measured, and cells may strategically regulate their shape to improve estimation accuracy. To address this gap, we investigate the estimation limits in dynamic situations where cells change shape adaptively depending on the sensed signal. We approach this problem by analyzing the stationary solution of the Bayesian nonlinear filtering equation. By applying diffusion approximation to the ligand-receptor binding process and the Laplace method for the posterior expectation under a high signal-to-noise ratio regime, we obtain an analytical expression for the estimation limit. This expression indicates that estimation accuracy can be improved by elongating perpendicular to the estimated direction, which is also confirmed by numerical simulations. Our analysis provides a basis for clarifying the interplay between estimation and control in gradient sensing and sheds light on how cells optimize their shape to enhance chemotactic efficiency.Comment: 14 pages, 5 figure

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