Recoverable prevalence in growing scale-free networks and the effective immunization

We study the persistent recoverable prevalence and the extinction of computer viruses via e-mails on a growing scale-free network with new users, which structure is estimated form real data. The typical phenomenon is simulated in a realistic model with the probabilistic execution and detection of viruses. Moreover, the conditions of extinction by random and targeted immunizations for hubs are derived through bifurcation analysis for simpler models by using a mean-field approximation without the connectivity correlations. We can qualitatively understand the mechanisms of the spread in linearly growing scale-free networks.Comment: 9 pages, 9 figures, 1 table. Update version after helpful referee comment

Statistical mechanical evaluation of spread spectrum watermarking model with image restoration

In cases in which an original image is blind, a decoding method where both the image and the messages can be estimated simultaneously is desirable. We propose a spread spectrum watermarking model with image restoration based on Bayes estimation. We therefore need to assume some prior probabilities. The probability for estimating the messages is given by the uniform distribution, and the ones for the image are given by the infinite range model and 2D Ising model. Any attacks from unauthorized users can be represented by channel models. We can obtain the estimated messages and image by maximizing the posterior probability. We analyzed the performance of the proposed method by the replica method in the case of the infinite range model. We first calculated the theoretical values of the bit error rate from obtained saddle point equations and then verified them by computer simulations. For this purpose, we assumed that the image is binary and is generated from a given prior probability. We also assume that attacks can be represented by the Gaussian channel. The computer simulation retults agreed with the theoretical values. In the case of prior probability given by the 2D Ising model, in which each pixel is statically connected with four-neighbors, we evaluated the decoding performance by computer simulations, since the replica theory could not be applied. Results using the 2D Ising model showed that the proposed method with image restoration is as effective as the infinite range model for decoding messages. We compared the performances in a case in which the image was blind and one in which it was informed. The difference between these cases was small as long as the embedding and attack rates were small. This demonstrates that the proposed method with simultaneous estimation is effective as a watermarking decoder

A new 5d description of 6d D-type minimal conformal matter

We propose a new 5d description of the circle-compactified 6d $(D_{N+4}, D_{N+4})$ minimal conformal matter theory which can be approached by the 6d $\mathcal{N}=(1,0)$ $Sp(N)$ gauge theory with $N_f=2N+8$ flavors and one tensor multiplet. Compactifying the brane set-up for the 6d theory, we arrive at a 5-brane Tao diagram for 5d $\mathcal{N}=1$ $SU(N+2)$ theory of the vanishing Chern-Simons level with $2N+8$ flavors. We conjecture that the 6d theory is recovered as the UV fixed point of this 5d theory. We show that the global symmetry of this 5d theory is $SO(4N+16)$ identical to that of the 6d theory by analyzing the 7-brane monodromy. By using the Tao diagram, we also find the instanton fugacity is exactly given by the circle radius. By decoupling flavors in this 5d theory, one can obtain all the 5d $SU(N+2)$ gauge theories of various Chern-Simons levels and corresponding enhanced global symmetries at the 5d UV fixed point.Comment: v1: 21 pages, 10 figures. v2: mirror correction and references added. v3: published versio

Precocious Gauge Symmetry Breaking in SU(6)×SU(2)RSU(6) \times SU(2)_R Model

In the $SU(6) \times SU(2)_R$ string-inspired model, we evolve the couplings and the masses down from the string scale $M_S$ using the renormalization group equations and minimize the effective potential. This model has the flavor symmetry including the binary dihedral group $\tilde{D}_4$. We show that the scalar mass squared of the gauge non-singlet matter field possibly goes negative slightly below the string scale. As a consequence, the precocious radiative breaking of the gauge symmetry down to the standard model gauge group can occur. In the present model, the large Yukawa coupling which plays an important role in the symmetry breaking is identical with the colored Higgs coupling related to the longevity of the proton.Comment: 15 pages, 2 figure

Translating, adapting, and validating the medical student version of the patient care ownership scale for use in Japan

BackgroundPatient care ownership (PCO) among medical students is a growing area in the field of medical education. While PCO has received increasing attention, there are no instruments to assess PCO in the context of Japanese undergraduate medical education. This study aimed to translate, culturally adapt, and validate the PCO Scale – Medical students (PCOS-S) in the Japanese context.MethodsWe collected survey data from fifth- and sixth-grade medical students from five different universities varying in location and type. Structural validity, convergent validity, and internal consistency reliability were examined.ResultsData from 122 respondents were analyzed. Factor analysis of the Japanese PCOS-S revealed three factors with Cronbach’s alpha values exceeding the satisfactory criterion (0.70). A positive correlation was observed between the total Japanese PCOS-S scores and the global rating scores for the clinical department as a learning environment (Pearson’s correlation coefficient = 0.61).ConclusionsWe conducted the translation of the PCOS-S into Japanese and assessed its psychometric properties. The Japanese version has good reliability and validity. This instrument has potential value in assessing the development of medical students’ PCO

Robot arm system for automatic satellite capture and berthing

Load control is one of the most important technologies for capturing and berthing free flying satellites by a space robot arm because free flying satellites have different motion rates. The performance of active compliance control techniques depend on the location of the force sensor and the arm's structural compliance. A compliance control technique for the robot arm's structural elasticity and a consideration for an end-effector appropriate for it are presented in this paper

Quantitative Selection of Sample Structures in Small-Angle Scattering Using Bayesian Methods

Small-angle scattering (SAS) is a key experimental technique for analyzing nano-scale structures in various materials.In SAS data analysis, selecting an appropriate mathematical model for the scattering intensity is critical, as it generates a hypothesis of the structure of the experimental sample. Traditional model selection methods either rely on qualitative approaches or are prone to overfitting.This paper introduces an analytical method that applies Bayesian model selection to SAS measurement data, enabling a quantitative evaluation of the validity of mathematical models.We assess the performance of our method through numerical experiments using artificial data for multicomponent spherical materials, demonstrating that our proposed method analysis approach yields highly accurate and interpretable results.We also discuss the ability of our method to analyze a range of mixing ratios and particle size ratios for mixed components, along with its precision in model evaluation by the degree of fitting.Our proposed method effectively facilitates quantitative analysis of nano-scale sample structures in SAS, which has traditionally been challenging, and is expected to significantly contribute to advancements in a wide range of fields.Comment: 28 pages, 4 figure

Bayesian Inference for Small-Angle Scattering Data

In this paper, we propose a method for estimating model parameters using Small-Angle Scattering (SAS) data based on the Bayesian inference. Conventional SAS data analyses involve processes of manual parameter adjustment by analysts or optimization using gradient methods. These analysis processes tend to involve heuristic approaches and may lead to local solutions.Furthermore, it is difficult to evaluate the reliability of the results obtained by conventional analysis methods. Our method solves these problems by estimating model parameters as probability distributions from SAS data using the framework of the Bayesian inference. We evaluate the performance of our method through numerical experiments using artificial data of representative measurement target models.From the results of the numerical experiments, we show that our method provides not only high accuracy and reliability of estimation, but also perspectives on the transition point of estimability with respect to the measurement time and the lower bound of the angular domain of the measured data.Comment: 31 pages, 25 figure