Deep reinforcement learning(RL)has become one of the most popular topics in artificial intelligence *** has been widely used in various fields,such as end-to-end control,robotic control,recommendation systems,and natu...
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Deep reinforcement learning(RL)has become one of the most popular topics in artificial intelligence *** has been widely used in various fields,such as end-to-end control,robotic control,recommendation systems,and natural language dialogue *** this survey,we systematically categorize the deep RL algorithms and applications,and provide a detailed review over existing deep RL algorithms by dividing them into modelbased methods,model-free methods,and advanced RL *** thoroughly analyze the advances including exploration,inverse RL,and transfer ***,we outline the current representative applications,and analyze four open problems for future research.
Concurrency bugs widely exist in concurrent programs and have caused severe failures in the real world. Researchers have made significant progress in detecting concurrency bugs, which improves software reliability. In...
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Concurrency bugs widely exist in concurrent programs and have caused severe failures in the real world. Researchers have made significant progress in detecting concurrency bugs, which improves software reliability. In this paper, we survey the most up-to-date and well-known concurrency bug detectors. We categorize the existing detectors based on the types of concurrency bugs. Consequently, we analyze data race detectors, atomicity violation detectors, order violation detectors, and deadlock detectors, respectively. We also discuss some other techniques which are mostly related to concurrency bug detection, including schedule bounding techniques, interleaving optimizing techniques, path expanding techniques, and deterministic replay techniques. Additionally, we statistically analyze the reviewed detectors and get some interesting findings, for instance, nearly 86% of previous detectors focus on data races and atomicity violations, and dynamic approaches are popular(74%). We also discuss the limitations of previous detectors, finding that 91% of previous detectors suffer from false negatives and 64% of previous detectors suffer from runtime overhead. Based on the reviewed detectors and statistical analysis, we conclude some future research directions, including accuracy, performance,applicability, and integrality.
A data-driven method was proposed to realistically animate garments on human poses in reduced space. Firstly, a gradient based method was extended to generate motion sequences and garments were simulated on the sequen...
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A data-driven method was proposed to realistically animate garments on human poses in reduced space. Firstly, a gradient based method was extended to generate motion sequences and garments were simulated on the sequences as our training data. Based on the examples, the proposed method can fast output realistic garments on new poses. Our framework can be mainly divided into offline phase and online phase. During the offline phase, based on linear blend skinning(LBS), rigid bones and flex bones were estimated for human bodies and garments, respectively. Then, rigid bone weight maps on garment vertices were learned from examples. In the online phase, new human poses were treated as input to estimate rigid bone transformations. Then, both rigid bones and flex bones were used to drive garments to fit the new poses. Finally, a novel formulation was also proposed to efficiently deal with garment-body penetration. Experiments manifest that our method is fast and accurate. The intersection artifacts are fast removed and final garment results are quite realistic.
The contribution of parasitic bipolar amplification to SETs is experimentally verified using two P-hit target chains in the normal layout and in the special layout. For PMOSs in the normal layout, the single-event cha...
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The contribution of parasitic bipolar amplification to SETs is experimentally verified using two P-hit target chains in the normal layout and in the special layout. For PMOSs in the normal layout, the single-event charge collection is composed of diffusion, drift, and the parasitic bipolar effect, while for PMOSs in the special layout, the parasitic bipolar junction transistor cannot turn on. Heavy ion experimental results show that PMOSs without parasitic bipolar amplification have a 21.4% decrease in the average SET pulse width and roughly a 40.2% reduction in the SET cross-section.
Heavy ion experiments were performed on D flip-flop(DFF) and TMR flip-flop(TMRFF) fabricated in a 65-nm bulk CMOS process. The experiment results show that TMRFF has about 92% decrease in SEU crosssection compared to ...
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Heavy ion experiments were performed on D flip-flop(DFF) and TMR flip-flop(TMRFF) fabricated in a 65-nm bulk CMOS process. The experiment results show that TMRFF has about 92% decrease in SEU crosssection compared to the standard DFF design in static test mode. In dynamic test mode, TMRFF shows much stronger frequency dependency than the DFF design, which reduces its advantage over DFF at higher operation frequency. At 160 MHz, the TMRFF is only 3.2× harder than the standard DFF. Such small improvement in the SEU performance of the TMR design may warrant reconsideration for its use in hardening design.
To reduce the access latencies of end hosts,latency-sensitive applications need to choose suitably close service machines to answer the access requests from end *** K nearest neighbor search locates K service machines...
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To reduce the access latencies of end hosts,latency-sensitive applications need to choose suitably close service machines to answer the access requests from end *** K nearest neighbor search locates K service machines closest to end hosts,which can efficiently optimize the access latencies for end *** work has weakness in terms of the accuracy and *** to the scalable and accurate K nearest neighbor search problem,we propose a distributed K nearest neighbor search method called DKNNS in this *** machines are organized into a locality-aware multilevel *** first locates a service machine that starts the search process based on a farthest neighbor search scheme,then discovers K nearest service machines based on a backtracking approach within the proximity region containing the target in the latency *** analysis,simulation results and deployment experiments on the PlanetLab show that,DKNNS can determine K approximately optimal service machines,with modest completion time and query ***,DKNNS is also quite stable that can be used for reducing frequent searches by caching found nearest neighbors.
Recently, sequence anomaly detection has been widely used in many fields. Sequence data in these fields are usually multi-dimensional over the data stream. It is a challenge to design an anomaly detection method for a...
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Recently, sequence anomaly detection has been widely used in many fields. Sequence data in these fields are usually multi-dimensional over the data stream. It is a challenge to design an anomaly detection method for a multi-dimensional sequence over the data stream to satisfy the requirements of accuracy and high speed. It is because:(1) Redundant dimensions in sequence data and large state space lead to a poor ability for sequence modeling;(2) Anomaly detection cannot adapt to the high-speed nature of the data stream, especially when concept drift occurs, and it will reduce the detection rate. On one hand, most existing methods of sequence anomaly detection focus on the single-dimension sequence. On the other hand, some studies concerning multi-dimensional sequence concentrate mainly on the static database rather than the data stream. To improve the performance of anomaly detection for a multi-dimensional sequence over the data stream, we propose a novel unsupervised fast and accurate anomaly detection(FAAD) method which includes three algorithms. First, a method called "information calculation and minimum spanning tree cluster" is adopted to reduce redundant dimensions. Second, to speed up model construction and ensure the detection rate for the sequence over the data stream, we propose a method called"random sampling and subsequence partitioning based on the index probabilistic suffix tree." Last, the method called "anomaly buffer based on model dynamic adjustment" dramatically reduces the effects of concept drift in the data stream. FAAD is implemented on the streaming platform Storm to detect multi-dimensional log audit *** with the existing anomaly detection methods, FAAD has a good performance in detection rate and speed without being affected by concept drift.
It is widely believed that Shor's factoring algorithm provides a driving force to boost the quantum computing ***, a serious obstacle to its binary implementation is the large number of quantum gates. Non-binary quan...
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It is widely believed that Shor's factoring algorithm provides a driving force to boost the quantum computing ***, a serious obstacle to its binary implementation is the large number of quantum gates. Non-binary quantum computing is an efficient way to reduce the required number of elemental gates. Here, we propose optimization schemes for Shor's algorithm implementation and take a ternary version for factorizing 21 as an example. The optimized factorization is achieved by a two-qutrit quantum circuit, which consists of only two single qutrit gates and one ternary controlled-NOT gate. This two-qutrit quantum circuit is then encoded into the nine lower vibrational states of an ion trapped in a weakly anharmonic potential. Optimal control theory(OCT) is employed to derive the manipulation electric field for transferring the encoded states. The ternary Shor's algorithm can be implemented in one single step. Numerical simulation results show that the accuracy of the state transformations is about 0.9919.
Edge extraction is an indispensable task in digital image processing. With the sharp increase in the image data, real-time problem has become a limitation of the state of the art of edge extraction *** this paper, QSo...
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Edge extraction is an indispensable task in digital image processing. With the sharp increase in the image data, real-time problem has become a limitation of the state of the art of edge extraction *** this paper, QSobel, a novel quantum image edge extraction algorithm is designed based on the flexible representation of quantum image(FRQI) and the famous edge extraction algorithm Sobel. Because FRQI utilizes the superposition state of qubit sequence to store all the pixels of an image, QSobel can calculate the Sobel gradients of the image intensity of all the pixels simultaneously. It is the main reason that QSobel can extract edges quite fast. Through designing and analyzing the quantum circuit of QSobel, we demonstrate that QSobel can extract edges in the computational complexity of O(n2) for a FRQI quantum image with a size of2 n × 2n. Compared with all the classical edge extraction algorithms and the existing quantum edge extraction algorithms, QSobel can utilize quantum parallel computation to reach a significant and exponential ***, QSobel would resolve the real-time problem of image edge extraction.
The availability of computers and communication networks allows us to gather and analyse data on a far larger scale than previously. At present, it is believed that statistics is a suitable method to analyse networks ...
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The availability of computers and communication networks allows us to gather and analyse data on a far larger scale than previously. At present, it is believed that statistics is a suitable method to analyse networks with millions, or more, of vertices. The MATLAB language, with its mass of statistical functions, is a good choice to rapidly realize an algorithm prototype of complex networks. The performance of the MATLAB codes can be further improved by using graphic processor units (GPU). This paper presents the strategies and performance of the GPU implementation of a complex networks package, and the Jacket toolbox of MATLAB is used. Compared with some commercially available CPU implementations, GPU can achieve a speedup of, on average, 11.3x. The experimental result proves that the GPU platform combined with the MATLAB language is a good combination for complex network research.
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