In order to study the influence of time-varying delay and double flux control strategy on traffic flow under curved road, in view of Nagatani’s model, this article put forward a novel lattice hydrodynamic model with consideration of time-varying delay in perceiving density and flux. Moreover, a double flux control strategy is introduced in our model. The influences of time-varying delay, double flux control strategy and the angle of curved road on traffic stability are investigated by theoretical analysis and stability criterion is acquired by linear matrix inequality. Through numerical simulation, the propagation characteristics of disturbance and the state of final traffic density wave under the influence of time-varying delay, double flux control strategy and the angle of curved road are intuitively displayed. Also, the oscillation trend of instantaneous fuel consumption under the influence of time-varying delay, double flux control strategy and the angle of curved road are clearly displayed. The results reveal that the time-varying delay in the perception process of density and flux information between lattices count against traffic stability and instantaneous fuel consumption, double flux control strategy has positive influence on the stability and instantaneous fuel consumption of delayed traffic system. Also, the angle of the curved road has important role for traffic system, the greater angle of the curve road, the more conducive to alleviate traffic congestion and reduce instantaneous fuel consumption.

Compiler testing is an important task for assuring the quality of compilers. However, most mutation-based compiler testing approaches still suffer from the effectiveness issue due to the ineffective mutation strategies. In this paper, we propose CTHist, which leverages history-driven test program mutation to construct diverse bug-triggering test programs, aiming to discover more optimizing compiler bugs. Specifically, CTHist first examines the test programs that cause historical compiler bugs, and identifies four categories of bug-triggering structures to help generate new bug-triggering test programs. To generate diverse test programs, CTHist then iteratively conducts AST-level mutations to integrate the extracted structures into seed programs, and enhances the connections between the seed and the mutated structures by introducing new control- and data-dependencies. Finally, given the generated test programs, CTHist leverages differentially test based on local hash checksum to test compilers. The experiments on three popular compilers GCC, LLVM and ICX show that, CTHist outperforms four state-of-the-art approaches (i.e., GrayC, Clang-Fuzzer, universalmutator, and Csmith) in detecting more compiler bugs, and improves the Line Coverage of GCC and LLVM by up to 1.15% ∼ 17.77%. Moreover, CTHist has successfully detected 36 new bugs during the practical evaluation, of which 26 are miscompilation bugs (the most difficult to detect), and 27 have been confirmed/fixed by developers.

This paper considers the unique attributes of autonomous vehicles, the characteristics of expressways’ minor actual slope grade, and the speed update integer requirements of discrete cellular automata (CA). The continuous deceleration efficiency of the uphill is discretized by introducing variable parameters connected to the vehicle position and gradient, and the hybrid traffic flow CA model suitable for slope bottlenecks based on the principle of the safety distance is further improved. The simulation results show that the improved model can simulate the unique traffic flow phenomenon caused by the change in slope conditions. There is a critical mutation in the traffic flow phase on the slope road section. Secondly, according to the different main factors influencing traffic flow, free flow, terrain jams, and homogeneous congested flow coexist on the slope section. The traffic flow in the crowded terrain area is significantly negatively correlated with the length and longitudinal gradient of the slope, and increasing the longitudinal angle of the hill will extend the congested terrain area. In addition, the influence of the change in slope conditions on mixed traffic flow has a critical longitudinal gradient of 3% and a fixed critical slope length. In non-free flow areas, autonomous vehicles can significantly alleviate traffic congestion on upslopes.

Resistive random-access memory (ReRAM)-based crossbar array (RCA) is a promising platform to accelerate vector-matrix multiplication in deep neural networks (DNNs). There are, however, some practical issues, especially device variation, that hinder the versatile development of ReRAM in neural computing systems. The device variations include device-to-device variation (DDV) and cycle-to-cycle variation (CCV) that deviate the devise resistance in the RCA from their target state. Such resistance deviation seriously degrades the inference accuracy of DNNs. To address this issue, we propose a software-hardware compensation solution that includes compensation training based on scale factors (CTSF) and variation-aware compensation training based on scale factors (VACTSF) to protect the ReRAM-based DNN accelerator against device variation. The scale factors in CTSF can be flexibly set for reducing accuracy loss due to device variation when the weights programmed into RCA are determined. For effectively handling CCV, the scale factors are introduced into the training process for obtaining variation-tolerant weights by leveraging the inherent self-healing ability of DNNs. Simulation results based on our method confirm that the accuracy losses due to device variation on LeNet-5, ResNet, and VGG16 with different datasets are less than 5% under a large device variation by CTSF. More robust weights for conquering CCV are also obtained by VACTSF. The simulation results present that our method is competitive in comparison to other variation-tolerant methods.

Traditional CMOS-based von-Neumann computer architecture faces the issue of memory wall that the limitation of bus-bandwidth and the speed mismatch between processor and memory restrict the efficiency of data processing along with an irreducible energy consumption conducted by data movement, especially in some data-intensive applications. Recently, some novel in-memory computing (IMC) paradigms developed by utilizing the characteristics of different non-volatile memories provide promising ways to overcome the bottleneck of memory wall. Here, we propose a new IMC unit based on a memory array with the core element of magnetoelectric spin-orbit logic (MESO) device (AIMCU-MESO), in which the characteristics of the MESO device are exploited to achieve several in-memory logic operations with the functions of NAND, NOR, and XOR in the MESO-based memory array. With the aid of some transistor-based switches, these logic operations can be achieved between any two MESOs in the array. Furthermore, the computing process of a 1-bit full adder (FA) is achieved in AIMCU-MESO by the in-memory logic manner to demonstrate the ability of logic cascading. The result of SPICE simulation for achieving the 1-bit FA using MESO devices is demonstrated, and the performances are compared with other designs of spintronics-based devices. Compared to multilevel voltage-controlled spin-orbit torque–based magnetic memory, the proposed design demonstrates 71.4% and 49.2% reductions in terms of storage delay and logic delay, respectively.

State-of-the-art hashing methods, such as the kernelised locality-sensitive hashing and spectral hashing, have high algorithmic complexities to build the hash codes and tables. Our observation from the existing hashing method is that, putting two dissimilar data points into the same hash bucket only reduces the efficiency of the hash table, but it does not hurt the query accuracy. Whereas putting two similar data points into different hash buckets will reduce the correctness (i.e. query accuracy) of a hashing method. Therefore, it is much more important for a good hashing method to ensure that similar data points have high probabilities to be put to the same bucket, than considering those dissimilar data-point relations. On the other side, attracting similar data points to the same hash bucket will naturally suppress dissimilar data points to be put into the same hash bucket. With this locality-preserving observation, we naturally propose a new hashing method called the locality-preserving hashing, which builds the hash codes and tables with much lower algorithmic complexity. Experimental results show that the proposed method is very competitive in terms of the training time spent for large data-sets among the state of the arts, and with reasonable or even better query accuracy.

There are many methods to construct kernel with given data attribute information. Gaussian radial basis function (RBF) kernel is one of the most popular ways to construct a kernel. The key observation is that in real-world data, besides the data attribute information, data label information also exists, which indicates the data class. In order to make use of both data attribute information and data label information, in this work, we propose a supervised kernel construction method. Supervised information from training data is integrated into standard kernel construction process to improve the discriminative property of resulting kernel. A supervised Laplacian embedding cellular automaton model is another key application developed for two-lane heterogeneous traffic flow with the safe distance and large-scale truck. Based on the properties of traffic flow in China, we re-calibrate the cell length, velocity, random slowing mechanism and lane-change conditions and use simulation tests to study the relationships among the speed, density and flux. The numerical results show that the large-scale trucks will have great effects on the traffic flow, which are relevant to the proportion of the large-scale trucks, random slowing rate and the times of the lane space change.