B(s)→SB_{(s)}\to S transitions in the light cone sum rules with the chiral current

$B_{(s)}$ semi-leptonic decays to the light scalar meson, $B_{(s)}\to S l\bar{\nu}_l, S l \bar{l}\,\,(l=e,\mu,\tau)$, 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 $q^2$, 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

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 $61.91\%$ on average. For backdoor removal tasks, CARE reduces the attack success rate from over $98\%$ to less than $1\%$. For safety property repair tasks, CARE reduces the property violation rate to less than $1\%$. 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 $T=0$ 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

Wave Run-Up Phenomenon on Offshore Platforms: Part 1. Tension Leg Platform

This study reports on an extensive experimental campaign carried out to evaluate non-linear waves applied to offshore structures in extreme marine environments. An offshore tension leg platform (TLP) model was used to observe the waves around a fixed-type offshore structure. The wave amplitude measured in the experiments of this study was indicated as a wave run-up ratio. Both the first-order analysis and the analysis of the entire wave amplitude were described. The experimental results were compared with the calculations from a potential-based code in order to verify the effectiveness of the developed technology