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Go provides a new concurrency mechanism based on message-passing, which brings a series of new concurrency bugs. The existing tools are highly dependent on model-checking methods in which they take the source program as the input of model-checking tools to detect the potential concurrent bugs. However, these methods can only qualitatively obtain the concurrency of the program but not obtain the exact number of concurrency bugs in the program. Meanwhile, these approaches are not sensitive to dead codes, which are prone to false negatives. Furthermore, few works have been done on detecting WaitGroup-related bugs in Go concurrent programming, which may cause deadlock. To this end, this paper proposes a novel approach GoDetector to detect concurrent bugs. GoDetector uses a detecting algorithm and a pushdown automaton to verify the channel-related concurrent bugs and WaitGroup-related concurrent bugs separately. To support the evaluation of the tool, 30 benchmarks are collected from the existing tools. GoDetector reports 2 channel safety errors, 29 deadlocks, and 11 WaitGroup-related errors. Compared with the existing tool Godel, GoDetector has the capacity to report more concurrency bugs and fewer false positives. GoDetector also offers additional support for the detection of WaitGroup variables. The experimental results indicate that GoDetector is more efficient at detecting the concurrency in regards to accuracy and diversity.

The lithium niobate (LiNbO3) metasurface is an attractive platform for nonlinear frequency conversion due to its excellent nonlinearity, high damage threshold, and strong enhancement of the driving field. Here, we demonstrated the nonlinear metasurface for terahertz (THz) generation from LiNbO3 on an insulator. The THz electric field from the LiNbO3 metasurface of 300 nm thickness is enhanced by more than one order of magnitude compared to the unstructured sample. The enhanced terahertz electric field is very selective in the excitation wavelength due to its resonant feature and is highly anisotropic with respect to the excitation polarization. The polarization direction of the THz electric field can be controlled by rotating the optical axis of the LiNbO3 metasurface. By combining the nanofabrication technology of the LiNbO3 metasurface and ultrafast nonlinear optics, our work paves the way for the design of new compact terahertz photonic devices that integrate THz emitters into LiNbO3-based chips with multifaceted capabilities.

To improve the correctness of data race detection, an approach to the data race detection based on the context-sensitive analysis in multithreaded programs was proposed. Firstly, control flow analysis was used to construct context-sensitive call graphs, and then escape analysis was employed to find thread-escaped objects that may cause data race. Secondly, context-sensitive alias analysis was conducted to reduce false positives and false negatives. Finally, the happens-before analysis was performed to remove false positives caused by ignoring thread interactions. A data race detection tool ConRacer was implemented in WALA framework based on this approach and was compared with the existing tools SRD and RVPredict. The experimental results show that ConRacer is the most precise tool compared with SRD and RVPredict and it can not only detect data races, but also reduce false positives and false negatives effectively. ConRacer improves the detection accuracy by combining context-sensitive with static detection methods, which has certain reference value for discovering concurrent errors and optimizing software performance.

With the rapid development of urbanization and industrialization, the problem of air pollution has become increas-ingly prominent, and air quality prediction is particularly important. Some representative studies currently monitor and forecast air quality in real time. For example, ZHOU Guangqiang et al. Used numerical prediction to analyze air quality in eastern China. However, experimental results show that this method is difficult to predict and is very important. SANKAR et al. Used multiple linear regression to predict air quality, but the experimental results showed that the linear model had low prediction accuracy and slow efficiency;PREZ et al. Used statistical methods to predict air quality, and the experimental results proved the prediction accuracy of the statistical method relatively low; WANG et al. Used an improved BP neural network to establish a prediction model for the air quality index, and their experiments verified that the BP neural network has a slow convergence rate and is prone to fall into the local optimal solution problem; YANG et al. Air quality concentration effect, a PM2.5 concentration prediction model based on random forests was established, and the empirical process proved that the meshing program weakened the quality and efficiency of subsequent air quality analysis; these methods are difficult to model from a time perspective, and the prediction accuracy is low is a more important issue. Because low prediction accuracy may lead to large errors in air quality prediction results. 河北科技大学学报 2020年 第1期 张冬雯，等:基于长短期记忆神经网络模型的空气质量预测 In this paper, a neural network model based on long -term memory (LSTM) is proposed to solve the problem of low prediction accuracy in air quality research.MAPE, RMSE, R, IA and MAE were used to test the predictive performance of LSTM neural network and the comparison model.Since Delhi and Houston are cities with high levels of air pollution, the experimental data sets used in this paper were from the air quality data of Punjabi Bagh monitoring station in Delhi from 2014 to 2016 and the air quality data of Harris County monitoring station in Houston from 2010 to 2016.By comparing LSTM neural network with multiple linear regression and regression model (SVR), the experimental results show that LSTM neural network is suitable for time series prediction with multiple variables or multiple inputs LSTM neural network has the advantages of high prediction accuracy, high speed and strong robustness.

To improve the recognition model accuracy of crop disease leaves and locating diseased leaves, this paper proposes an improved Faster RCNN to detect healthy tomato leaves and four diseases: powdery mildew, blight, leaf mold fungus and ToMV. First, we use a depth residual network to replace VGG16 for image feature extraction so we can obtain deeper disease features. Second, the k-means clustering algorithm is used to cluster the bounding boxes. We improve the anchoring according to the clustering results. The improved anchor frame tends toward the real bounding box of the dataset. Finally, we carry out a k-means experiment with three kinds of different feature extraction networks. The experimental results show that the improved method for crop leaf disease detection had 2.71% higher recognition accuracy and a faster detection speed than the original Faster RCNN.

Traffic dynamics on multi-layer networks are of great importance to alleviate traffic congestion of real networks. In this work, we focus on information transport in multi-layer networks comprised of two coupled subnetworks. We investigate the relationship between correlated inter-layer couplings and resource allocation strategies on the traffic dynamics of multi-layer networks. Simulation results show that assortative coupling (AC) structure can improve the multi-layer networks traffic capacity more better than disassortative coupling (DC) and random coupling (RC) with degree-based (DE) efficient resources allocation strategy, and DC structure is better in enhancing the traffic capacity with the simplest average resources allocation (SA) strategy. Our work may shed some light on the traffic dynamics on multi-layer networks.

Two entirely different Cauchy functional equations have been investigated in the literature with a couple of unknown operators f and g mapping a unitary ring X into a domain of integrity Y satisfying the elimination law and keeping the operations of addition about operator f and multiplication about operator g, respectively. A total of four studying syzygies achieved by multiplying these two fundamental Cauchy functional equations side by side are investigated. The novelty of the literature is to find out sufficient conditions upon [Formula: see text] deriving the model to be produced a strongly alienation phenomenon by using some new computation techniques.

Abstract Ferroelectric materials have been a key research topic owing to their wide variety of modern electronic and photonic applications. For the quick exploration of higher operating speed, smaller size, and superior efficiencies of novel ferroelectric devices, the ultrafast dynamics of ferroelectrics that directly reflect their respond time and lifetimes have drawn considerable attention. Driven by time‐resolved pump‐probe spectroscopy that allows for probing, controlling, and modulating dynamic processes of ferroelectrics in real‐time, much research efforts have been made to understand and exploit the ultrafast dynamics of ferroelectric. Herein, the current state of ultrafast dynamic features of ferroelectrics tracked by time‐resolved pump‐probe spectroscopy is reviewed, which includes ferroelectrics order parameters of polarization, lattice, spin, electronic excitation, and their coupling. Several potential perspectives and possible further applications combining ultrafast pump‐probe spectroscopy and ferroelectrics are also presented. This review offers a clear guidance of ultrafast dynamics of ferroelectric orders, which may promote the rapid development of next‐generation devices.

More than ten years after Justyna Sikorska [8] attempted to solve the Heyers-Ulam stability of a single variable equation by using direct method. In this paper, we will improve the results of Justyna Sikorska by using a more efficient approach. Relations between the generalized functional equation, the dependence of their different parameters and several properties are also further explored. To achieve the problem, we try to develop some new techniques to overcome the fundamental difficulties caused by the different properties of the function and the presence of several variables in the equation. Furthermore, we continue to construct and study a couple of functional equations by making a new direct method.

Developers of parallel programming are faced with choices of using various Java locks. Choosing the best lock is a challenging task because a multithreaded application implemented using different locks may end up with uncertain performance. There is a strong need for automated tool support that helps Java programmers choose the best lock for each specific application. This paper presents an automated transformation approach to convert an implementation using a synchronized lock to a ReentrantLock or a ReentrantReadWriteLock at the bytecode level. For the ReentrantLock, our approach runs analysis for global monitors using the visitor pattern in the Joeq compiler. For the ReentrantReadWriteLock, a read or write lock is chosen after a side-effect analysis. Then, the proposed system validates the consistency of the analysis results to ensure a correct sequence of lock usage. Finally, lock operations and related thread communication operations are transformed into a ReentrantLock or a ReentrantReadWriteLock using a bytecode transformation framework Javassist. We validate our approach on several benchmarks including RBTree, PC, SPECjbb2005, and HSQLDB. The experimental results show that the proposed automated transformation approach can transform these benchmarks successfully in a timely fashion.

StampedLock, proposed in JDK 1.8, provides many interesting features, such as optimistic read locks and upgrading/downgrading locks to improve the design of concurrent programs instead of employing pure read/write locks. Existing refactorings have proposed algorithms to convert locks, but there are a few refactorings that use these promising features of StampedLock. To illustrate a possible refactoring, this paper first shows three code transformations based on StampedLock. Then, this paper presents CLOCK, an automated refactoring tool that helps developers convert the synchronized lock into the StampedLock. An algorithm for reentrance analysis is proposed for the precondition validation. The write lock, read lock, optimistic read lock, and upgrading/downgrading lock are inferred and refactored. CLOCK is evaluated with the SPECjbb2005 benchmark and two real-world applications, Xalan and FOP. A total of 66 classes are modified by searching approximately 395KSLOC and applying the refactoring, achieving an average of 22 classes per benchmark. The experimental results show that CLOCK can help developers with refactoring for StampedLock and save developer effort.

Understanding the evolution of molecular electronic structures is the key to explore and control photochemical reactions and photobiological processes. Subjected to strong laser fields, electronic holes are formed upon ionization and evolve in the attosecond timescale. It is crucial to probe the electronic dynamics in real time with attosecond-temporal and atomic-spatial precision. Here, we present molecular attosecond interferometry that enables the in situ manipulation of holes in carbon dioxide molecules via the interferometry of the phase-locked electrons (propagating in opposite directions) of a laser-triggered rotational wave packet. The joint measurement on high-harmonic and terahertz spectroscopy (HATS) provides a unique tool for understanding electron dynamics from picoseconds to attoseconds. The optimum phases of two-color pulses for controlling the electron wave packet are precisely determined owing to the robust reference provided with the terahertz pulse generation. It is noteworthy that the contribution of HOMO-1 and HOMO-2 increases reflecting the deformation of the hole as the harmonic order increases. Our method can be applied to study hole dynamics of complex molecules and electron correlations during the strong-field process. The threefold control through molecular alignment, laser polarization, and the two-color pulse phase delay allows the precise manipulation of the transient hole paving the way for new advances in attochemistry.

We investigate the Hyers–Ulam stability of an equation involving a single variable of the form ∥f(x)−αf(kn(x))−βf(kn+1(x))∥⩽u(x) where f is an unknown operator from a nonempty set X into a Banach space Y, and it preserves the addition operation, besides other certain conditions. The theory is employed and stability theorems are proven for various functional equations involving several variables. By comparing this method with the available techniques, it was noticed that this method does not require any restriction on the parity, on the domain, and on the range of the function. Our findings suggest that it is very much easy and more appropriate to apply the proposed method while investigating the stability of functional equations, in particular for several variables.

The software architecture of Internet of Things defines the component model and interconnection topology of Internet of Things systems. Refactoring is a systematic practice of improving a software structure without altering its external behaviors. When the Internet of Things software is refactored, it is necessary to detect the correctness of Internet of Things software to ensure its security. To this end, this article proposes a novel refactoring correction detection approach to ensure software security. Control flow analysis and data flow analysis are used to detect code changes before and after refactoring, and synchronization dependency analysis is used to detect changes in synchronization dependency. Three detection algorithms are designed to detect refactoring correctness. Four real-world benchmark applications are used to evaluate our approach. The experimental results show that our proposed approach can ensure correctness of Internet of Things software refactoring.

During the OPA (optical parametric amplifier) processes pumped by a high-energy laser, the phase distortion of pump light will transmit to the signal light inevitably. As a result, the beam quality of amplified signal will become poor. In this article, a hybrid seeding scheme with a paired two-stage OPA system is used, where the first and second stages used the signal and the idler as seed sources, respectively. The numerical results show that the hybrid seeding OPA scheme can suppress the phase distortion transformation from pump to signal in both spatial and temporal domains. Consequently, the phase of output signal is well restored to its input state. Because of the restriction removal of beam quality for pump light under the conventional birefringent phase-matching method, this scheme had a good application prospect in high-energy OPA or optical parametric chirped pulse amplification systems.

CoDA (Communities through Directed Affiliations) algorithm is a kind of community detection algorithm which can successfully detect 2-mode communities based on probability model. The F-measure criterion, for information retrieval, is adapted to the evaluation of CoDA algorithm in directed networks with overlapping communities or non-overlapping communities. The value of F1-measure in F-measure criterion can reflect whether CoDA algorithm performs well or not. The data sets used in the experiment is generated by the LFR Benchmark tool. The minimum number of nodes in data set is 100 and the maximum is 20 000, and evaluated experiment is conducted when every 100 nodes is added. The results show that CoDA algorithm performs well when the number of nodes is bellow 1 600. CoDA algorithm's performance becomes worse with the increase of the number of nodes, which proves the CoDA algorithm based on probability model is applicable to the community detection of small-scale networks.

In the optical parametric amplifier (OPA) process, the phase of the pump is transmitted to the amplified signal due to the spatial walk-off effect and/or a temporal group velocity mismatch (GVM) effect between coherent light waves. As a result, the quality of amplified signal will be reduced by the phase distortion and variation of beam equality of the pump pulse. By using a pair of OPAs, the signal and idler are chosen as seeding lights in the front and back stages, respectively, so that the phase of the amplified signal can be restored, and the influence of phase distortion and beam equality deterioration of pump pulse can be eliminated. The present article also reports other characteristics of this hybrid seeded scheme, including accepted bandwidth, spectral purity, and conversion efficiency.

Aiming at a class of discrete-time stochastic systems with Markov jump features, the state-feedback predictive control problem under probabilistic constraints of input variables is researched. On the basis of the concept and method of the multi-layer probabilistic sets, the predictive controller design algorithm with the soft constraints of different probabilities is presented. Under the control of the multi-step feedback laws, the system state moves to different ellipses with specified probabilities. The stability of the system is guaranteed, the feasible region of the control problem is enlarged, and the system performance is improved. Finally, a simulation example is given to prove the effectiveness of the proposed method.

Specific to the selective forwarding attack on routing in cognitive radio network, this paper proposes a trust-based secure routing model. Through monitoring nodes' forwarding behaviors, trusts of nodes are constructed to identify malicious nodes. In consideration of that routing selection-based model must be closely collaborative with spectrum allocation, a route request piggybacking available spectrum opportunities is sent to non-malicious nodes. In the routing decision phase, nodes' trusts are used to construct available path trusts and delay measurement is combined for making routing decisions. At the same time, according to the trust classification, different responses are made specific to their service requests. By adopting stricter punishment on malicious behaviors from non-trusted nodes, the cooperation of nodes in routing can be stimulated. Simulation results and analysis indicate that this model has good performance in network throughput and end-to-end delay under the selective forwarding attack.