5,467 research outputs found
transitions in the light cone sum rules with the chiral current
semi-leptonic decays to the light scalar meson, , are investigated in the QCD
light-cone sum rules (LCSR) with chiral current correlator. Having little
knowledge of ingredients of the scalar mesons, we confine ourself to the two
quark picture for them and work with the two possible Scenarios. The resulting
sum rules for the form factors receive no contributions from the twist-3
distribution amplitudes (DA's), in comparison with the calculation of the
conventional LCSR approach where the twist-3 parts play usually an important
role. We specify the range of the squared momentum transfer , in which the
operator product expansion (OPE) for the correlators remains valid
approximately. It is found that the form factors satisfy a relation consistent
with the prediction of soft collinear effective theory (SCET). In the effective
range we investigate behaviors of the form factors and differential decay
widthes and compare our calculations with the observations from other
approaches. The present findings can be beneficial to experimentally identify
physical properties of the scalar mesons.Comment: 22 pages,16 figure
Spectral Representation Theory for Dielectric Behavior of Nonspherical Cell Suspensions
Recent experiments revealed that the dielectric dispersion spectrum of
fission yeast cells in a suspension was mainly composed of two sub-dispersions.
The low-frequency sub-dispersion depended on the cell length, while the
high-frequency one was independent of it. The cell shape effect was simulated
by an ellipsoidal cell model but the comparison between theory and experiment
was far from being satisfactory. Prompted by the discrepancy, we proposed the
use of spectral representation to analyze more realistic cell models. We
adopted a shell-spheroidal model to analyze the effects of the cell membrane.
It is found that the dielectric property of the cell membrane has only a minor
effect on the dispersion magnitude ratio and the characteristic frequency
ratio. We further included the effect of rotation of dipole induced by an
external electric field, and solved the dipole-rotation spheroidal model in the
spectral representation. Good agreement between theory and experiment has been
obtained.Comment: 19 pages, 5 eps figure
Prediction for Irregular Ocean Wave and Floating Body Motion by Regularization: Part 1. Irregular Wave Prediction
Ocean waves can be explained in terms of many factors, including wave spectrum, which has the characteristics of wave height and periodicity, directional spreading function, which has a directional property, and random phase, which randomly represents a certain property. Under the assumption of a linear system, ocean waves show irregular behaviours, which can be observed in the forms of wave spectrum, directional spreading function, and complex phase calculations using the method of linear superposition. Ocean waves, which include a variety of periodic elements, exhibit direct proportionality between their period and propagation velocity. The purpose of this study was to understand the phase components of the period and to make exact calculations on the deterministic phase in order to make predictions on ocean waves. However, measurements of actual ocean waves exist only in the form of information on wave elevation, so we faced an inverse problem of having to analyse this information and calculate the deterministic phase. Regularization was used as part of the solution, and various methods were used to obtain stable values
Prediction for Irregular Ocean Wave and Floating Body Motion by Regularization: Part 2. Motion Prediction
In the analysis of the motion of a floating body, the domains can broadly be divided into the frequency domain and the time domain. The essence of the frequency domain analysis lies in calculating the hydrodynamic coefficient from the equation of motion, which has six degrees of freedom, by applying several methods. In this research, Bureau Veritas’s “HydroStar” software was used, and the comparison and the verification were carried out by experiments. For the time domain analysis, we used an existing method proposed by Cummins and made motion predictions by using deterministic random phases calculated in the time domain calculations of the excitation force. Lastly, the potential of wave and motion predictions was verified through the data obtained from a motion analysis experiment using a tension leg platform in the context of irregular waves
Causality-based Neural Network Repair
Neural networks have had discernible achievements in a wide range of
applications. The wide-spread adoption also raises the concern of their
dependability and reliability. Similar to traditional decision-making programs,
neural networks can have defects that need to be repaired. The defects may
cause unsafe behaviors, raise security concerns or unjust societal impacts. In
this work, we address the problem of repairing a neural network for desirable
properties such as fairness and the absence of backdoor. The goal is to
construct a neural network that satisfies the property by (minimally) adjusting
the given neural network's parameters (i.e., weights). Specifically, we propose
CARE (\textbf{CA}usality-based \textbf{RE}pair), a causality-based neural
network repair technique that 1) performs causality-based fault localization to
identify the `guilty' neurons and 2) optimizes the parameters of the identified
neurons to reduce the misbehavior. We have empirically evaluated CARE on
various tasks such as backdoor removal, neural network repair for fairness and
safety properties. Our experiment results show that CARE is able to repair all
neural networks efficiently and effectively. For fairness repair tasks, CARE
successfully improves fairness by on average. For backdoor removal
tasks, CARE reduces the attack success rate from over to less than
. For safety property repair tasks, CARE reduces the property violation
rate to less than . Results also show that thanks to the causality-based
fault localization, CARE's repair focuses on the misbehavior and preserves the
accuracy of the neural networks
A thermodynamically consistent quasi-particle model without density-dependent infinity of the vacuum zero point energy
In this paper, we generalize the improved quasi-particle model proposed in J.
Cao et al., [ Phys. Lett. B {\bf711}, 65 (2012)] from finite temperature and
zero chemical potential to the case of finite chemical potential and zero
temperature, and calculate the equation of state (EOS) for (2+1) flavor Quantum
Chromodynamics (QCD) at zero temperature and high density. We first calculate
the partition function at finite temperature and chemical potential, then go to
the limit and obtain the equation of state (EOS) for cold and dense QCD,
which is important for the study of neutron stars. Furthermore, we use this EOS
to calculate the quark-number density, the energy density, the quark-number
susceptibility and the speed of sound at zero temperature and finite chemical
potential and compare our results with the corresponding ones in the existing
literature
Comparison of Genetic Algorithm Based Support Vector Machine and Genetic Algorithm Based RBF Neural Network in Quantitative Structure-Property Relationship Models on Aqueous Solubility of Polycyclic Aromatic Hydrocarbons
AbstractA modified method to develop quantitative structure-property relationship (QSPR) models of organic contaminants was proposed based on genetic algorithm (GA) and support vector machine (SVM). GA was used to perform the variable selection and SVM was used to construct QSPR model. In this study, GA-SVM was applied to develop the QSPR model for aqueous solubility (Sw, mg•l-1) of polycyclic aromatic hydrocarbons (PAHs). The R2 (0.980), SSE (2.84), and RMSE (0.25) values of the model developed by GA-SVM indicated a good predictive capability for logSw values of PAHs. Based on leave-one-out cross validation, the results of GA-SVM were compared with those of genetic algorithm-radial based function neural network (GA-RBFNN). The comparison showed that the R2 (0.923) and RMSE (0.485) values of GA-SVM were higher and lower, respectively, which illustrated GA-SVM was more suitable to develop QSPR model for the logSw values of PAHs than GA-RBFNN
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