An automatic approach to detecting and eliminating lazy classes based on abstract syntax trees

Abstract To detect and eliminate lazy classes in source code, an automatic approach based on abstract syntax trees (ASTs) is proposed. Source code files transform to ASTs at first, then the relationships between classes are extracted from the ASTs. Three common relationships are considered, which are generalization, association and dependency. Some definitions are proposed to represent the classes set of different kinds of relationships. After carrying out several set operations on these sets, the candidate lazy classes set is obtained. By further manual examination, the true lazy classes are acquired. Finally, a specific lazy class will be removed automatically from the project. Four projects are tested to detect and eliminate the lazy classes. The experimental results show that the proposed detection algorithm has high precision rate. In addition, this approach has good efficiency, and its execution time has a linear relationship to the size of a system

Leveraging Uncertainty Quantification for Picking Robust First Break Times

In seismic exploration, the selection of first break times is a crucial aspect in the determination of subsurface velocity models, which in turn significantly influences the placement of wells. Many deep neural network (DNN)-based automatic first break picking methods have been proposed to speed up this picking processing. However, there has been no work on the uncertainty of the first picking results of the output of DNN. In this paper, we propose a new framework for first break picking based on a Bayesian neural network to further explain the uncertainty of the output. In a large number of experiments, we evaluate that the proposed method has better accuracy and robustness than the deterministic DNN-based model. In addition, we also verify that the uncertainty of measurement is meaningful, which can provide a reference for human decision-making

Dynamic surface tension of the pure liquid-vapor interface subjected to the cyclic loads

We demonstrate a methodology for computationally investigating the mechanical response of a pure molten lead surface system to the lateral mechanical cyclic loads and try to answer the question: how dose the dynamically driven liquid surface system follow the classical physics of the elastic-driven oscillation? The steady-state oscillation of the dynamic surface tension under cyclic load, including the excitation of high frequency vibration mode at different driving frequencies and amplitudes, was compared with the classical theory of single-body driven damped oscillator. Under the highest studied frequency (50 GHz) and amplitude (5%) of the load, the increase of the (mean value) dynamic surface tension could reach ~5%. The peak and trough values of the instantaneous dynamic surface tension could reach (up to) 40% increase and (up to) 20% decrease compared to the equilibrium surface tension, respectively. The extracted generalized natural frequencies and the generalized damping constants seem to be intimately related to the intrinsic timescales of the atomic temporal-spatial correlation functions of the liquids both in the bulk region and in the outermost surface layers. These insights uncovered could be helpful for quantitative manipulation of the liquid surface tension using ultrafast shockwaves or laser pulses

A Kind of Risk-Sensitive Group Decision-Making Based on MDP

Abstract. One-switch utility function is used to describe how the risk attitude of a decision maker changes with his wealth level. In this paper additive decision rule is used for the aggregation of decision member's utility which is represented by one-switch utility function. Based on Markov decision processes (MDP) and group utility, a dynamic, multi-stages and risk sensitive group decision model is proposed. The proposed model augments the state of MDP with wealth level, so the policy of the model is defined as an action executed in a state and a wealth level interval. A backward-induction algorithm is given to solve the optimal policy for the model. Numerical examples show that personal risk attitude has a great influence on group decision-making when personal risk attitudes of members are different, while the weights of members play a critical role when personal risk attitudes of members are similar

MSSPN: Automatic First Arrival Picking using Multi-Stage Segmentation Picking Network

Picking the first arrival times of prestack gathers is called First Arrival Time (FAT) picking, which is an indispensable step in seismic data processing, and is mainly solved manually in the past. With the current increasing density of seismic data collection, the efficiency of manual picking has been unable to meet the actual needs. Therefore, automatic picking methods have been greatly developed in recent decades, especially those based on deep learning. However, few of the current supervised deep learning-based method can avoid the dependence on labeled samples. Besides, since the gather data is a set of signals which are greatly different from the natural images, it is difficult for the current method to solve the FAT picking problem in case of a low Signal to Noise Ratio (SNR). In this paper, for hard rock seismic gather data, we propose a Multi-Stage Segmentation Pickup Network (MSSPN), which solves the generalization problem across worksites and the picking problem in the case of low SNR. In MSSPN, there are four sub-models to simulate the manually picking processing, which is assumed to four stages from coarse to fine. Experiments on seven field datasets with different qualities show that our MSSPN outperforms benchmarks by a large margin.Particularly, our method can achieve more than 90\% accurate picking across worksites in the case of medium and high SNRs, and even fine-tuned model can achieve 88\% accurate picking of the dataset with low SNR