Trading Query Complexity for Sample-Based Testing and Multi-testing Scalability

Testing Eulerianity and connectivity in directed sparse graphs

The traveling salesman problem (TSP) is an NP-hard problem being studied by many researchers. Metaheuristic algorithms generally depend on nature-inspired phenomena successfully applied to combinatorial optimization, such as routing, scheduling, assignment problems, engineering, optimization, genetics, robotics, nanotechnology, and various fields. In this paper, new versions of physarum-energy optimization algorithms (PEOs) which are population-based optimization algorithms were applied to the symmetric traveling salesman problems. PEOs use multiple solutions (population), multiple conductivities (dij) for the stochastic disturbance model and appropriate parameter strategies to update the solutions in each generation. To measure the effectiveness of the PEOs and the hybrids, the algorithms have been evaluated on several benchmark problems compared to the recent metaheuristics. The computational results show that the PEOs and their hybrids (k-NN, 2-opt, 3-opt, k-opt) can find high-quality results compared to the original physarum-energy optimization algorithm (PEO), ant colony optimization (ACO), black hole algorithm (BH), tabu search (TS), and other hybrid algorithms such as whale optimization algorithm + 4-opt heuristic (WOA + 4-opt), camel algorithm + 4-opt heuristic (CA + 4-opt), genetic algorithm + 2-opt heuristic (GA + 2-opt), and the k-NN, 2-opt, 3-opt, k-opt algorithms. Moreover, the population-based PEOs and their derived forms solve the optimization problem quite competitively in CPU time compared to other test algorithms.

This article initiates the study of testing properties of directed graphs. In particular, the article considers the most basic property of directed graphs--acyclicity. Because the choice of representation affects the choice of algorithm, the two main representations of graphs are studied. For the adjacency-matrix representation, most appropriate for dense graphs, a testing algorithm is developed that requires query and time complexity of O(1/e2), where e is a distance parameter independent of the size of the graph. The algorithm, which can probe the adjacency matrix of the graph, accepts every graph that is acyclic, and rejects, with probability at least 2/3, every graph whose adjacency matrix should be modified in at least e fraction of its entries so that it becomes acyclic. For the incidence list representation, most appropriate for sparse graphs, an Ω(|V|1/3) lower bound is proved on the number of queries and the time required for testing, where V is the set of vertices in the graph. Along with acyclicity, this article considers the property of strong connectivity. Contrasting upper and lower bounds are proved for the incidence list representation. In particular, if the testing algorithm can query on both incoming and outgoing edges at each vertex, then it is possible to test strong connectivity in O(1/e) time and query complexity. On the other hand, if the testing algorithm only has access to outgoing edges, then Ω(√N) queries are required to test for strong connectivity.

In this paper, a mathematical model of flight pattern and flight attitude is established and analyzed. The flight pattern of the power patrol flight is analyzed. Based on the original linear quadratic optimal control algorithm, the motion and optimal control model of the unmanned aerial vehicle (UAV) patrol process is established to meet the unmanned aerial vehicle (UAV) inspection constraints, then a linear quadratic optimization Control based optimal control algorithm is proposed. In this paper, four-rotor aircraft as an example for simulation research, different motion models with different optimization control, and add different interference signals and the corresponding filter algorithm for antijamming performance testing. The simulation results show that the UAV can obtain good control effect by the linear quadratic optimal control, and its stability and anti - jamming performance can be improved obviously.

Persistent homology is the mathematical core of recent work on shape, including reconstruction, recognition, and matching. Its pertinent information is encapsulated by a pairing of the critical values of a function, visualized by points forming a diagram in the plane. The original algorithm in [10] computes the pairs from an ordering of the simplices in a triangulation and takes worst-case time cubic in the number of simplices. The main result of this paper is an algorithm that maintains the pairing in worst-case linear time per transposition in the ordering. A side-effect of the algorithm's analysis is an elementary proof of the stability of persistence diagrams [7] in the special case of piecewise-linear functions. We use the algorithm to compute 1-parameter families of diagrams which we apply to the study of protein folding trajectories.

Aiming to solve the problems of false detection, missed detection, and insufficient detection ability of infrared vehicle images, an infrared vehicle target detection algorithm based on the improved YOLOv5 is proposed. The article analyzes the image characteristics of infrared vehicle detection, and then discusses the improved YOLOv5 algorithm in detail. The algorithm uses the DenseBlock module to increase the ability of shallow feature extraction. The Ghost convolution layer is used to replace the ordinary convolution layer, which increases the redundant feature graph based on linear calculation, improves the network feature extraction ability, and increases the amount of information from the original image. The detection accuracy of the whole network is enhanced by adding a channel attention mechanism and modifying loss function. Finally, the improved performance and comprehensive improved performance of each module are compared with common algorithms. Experimental results show that the detection accuracy of the DenseBlock and EIOU module added alone are improved by 2.5% and 3% compared with the original YOLOv5 algorithm, respectively, and the addition of the Ghost convolution module and SE module alone does not increase significantly. By using the EIOU module as the loss function, the three modules of DenseBlock, Ghost convolution and SE Layer are added to the YOLOv5 algorithm for comparative analysis, of which the combination of DenseBlock and Ghost convolution has the best effect. When adding three modules at the same time, the mAP fluctuation is smaller, which can reach 73.1%, which is 4.6% higher than the original YOLOv5 algorithm.

The all-pairs shortest path problem is a classic problem to study characteristics of the given graphs. Though many efficient all-pairs shortest path algorithms have been published, it is still a very expensive computing task, especially with large graph datasets. In this paper, we propose an efficient parallel all-pairs shortest path algorithm based on Peng et al.'s fast sequential algorithm on shared-memory parallel environments to achieve faster and more efficient calculation for large-scale real-world networks. Peng et al.'s algorithm needs to sort vertices with respect to their degrees. However, it turns out the original algorithm uses less efficient sorting method, which is a significant portion of parallel overhead. Therefore, we also propose an efficient parallel method to sort data within a fixed range, in order to minimize the parallel overhead in our parallel algorithm. The optimized efficient sorting method can be used for general sorting purposes. Our experimental analysis shows that our proposed parallel algorithm achieves very high parallel speedup, even hyper-linear speedup, with real-world test datasets on two different shared-memory multi-core systems.

The intelligent transportation system under the big data environment is the development direction of the future transportation system. It effectively integrates advanced information technology, data communication transmission technology, electronic sensing technology, control technology, and computer technology and applies them to the entire ground transportation management system to establish a real‐time, accurate, and efficient comprehensive transportation management system that works on a large scale and in all directions. Intelligent video analysis is an important part of smart transportation. In order to improve the accuracy and time efficiency of video retrieval schemes and recognition schemes, this article firstly proposes a segmentation and key frame extraction method for video behavior recognition, using a multi‐time scale dual‐stream network to extract video features, improving the efficiency and efficiency of video behavior detection. On this basis, an improved algorithm for vehicle detection based on Faster R‐CNN is proposed, and the Faster R‐CNN network feature extraction layer is improved by using the principle of residual network, and a hole convolution is added to the network to filter out the redundant features of high‐resolution video images to improve the problem of vehicle missed detection in the original algorithm. The experimental results show that the key frame extraction technology combined with the optimized Faster R‐CNN algorithm model greatly improves the accuracy of detection and reduces the leakage. The detection rate is satisfactory.

The noncircular fast independent vector analysis (nc-FastIVA) algorithm is an effective approach to achieve the joint blind source separation (JBSS) problem. However, the original nc-FastIVA algorithm does not take noise into account. In this letter, a new fixed-point algorithm is proposed for more realistic cases when noise is present. Simulations show that the proposed method can have better performance than the original nc-FastIVA algorithm for solving the noisy JBSS problem.

Span program is a linear-algebraic model of computation which can be used to design quantum algorithms. For any Boolean function there exists a span program that leads to a quantum algorithm with optimal quantum query complexity. In general, finding such span programs is not an easy task. In this work, given a query access to the adjacency matrix of a simple graph G with n vertices, we provide two new span-program-based quantum algorithms:an algorithm for testing if the graph is bipartite that uses $$On\sqrt{n}$$ quantum queries;an algorithm for testing if the graph is connected that uses $$On\sqrt{n}$$ quantum queries.

Clinical evaluation of the performance of a blind source separation algorithm combining beamforming and independent component analysis in hearing aid use

Wrapping machines usually consist of a two- or a four-column frame, supporting a huge rotating ring, connected to a pre-stretch unit with film coil carrier. Stiffness is a key point of packaging machines, since it is strictly related to the accuracy of the wrapping task. It depends on the stiffness of the frame, which can be achieved by the four-column architecture, and on the ring constraint system. As a consequence, the ring structures are usually highly statically indeterminate. Nowadays, there is an increasing demand for higher rotational speeds and more reduced operation times. Therefore, an accurate structural analysis of the ring, considering its actual loading and constraints is more and more important. The structural analysis of the rotating ring is treated by many references; however, such a statically indeterminate constraining makes this problem highly complicated. The goal of this paper consists in the development of a general and original computational algorithm for the structural analysis of rotating rings. The results are collected in a user-friendly way in terms of normalized internal loads, so that they can be of a great help even for not expert users. This model has been experimentally validated and easily applied to case studies and optimization tasks.

Spin Transfer Torque Magnetic Random Access Memory (STT-MRAM) is a promising alternative technology for on-chip memories due to several advantages such as high density, non-volatility, scalability, high endurance and CMOS compatibility. Due to the emerging fabrication processes of magnetic layers in the fabrication of STTMRAM devices, they are more susceptible to manufacturing defects and have different failure mechanisms compared to conventional CMOS memories. This mandates specific fault modeling and development of proper test algorithms for high coverage testing of these memories in order to ensure correct functionality in the field. In this paper, a detailed defect injection is performed based on the magnetic devices and layout characteristics of STT-MRAM and unique fault models are constructed for these memories. Based on the derived fault models and behaviors, efficient test algorithms are developed to fully cover these faults.

We consider the problem of distribution-free testing of the class of monotone monomials and the class of monomials over n variables.0 While there are very efficient algorithms for testing a variety of functions classes when the underlying distribution is uniform, designing distribution-free algorithms (which must work under any arbitrary and unknown distribution), tends to be a more challenging task. When the underlying distribution is uniform, Parnas et al. (SIAM Journal on Discrete Math, 2002) give an algorithm for testing (monotone) monomials whose query complexity does not depends on n, and whose dependence on the distance parameter is (inverse) linear. In contrast, Glasner and Servedio (in Proceedings of RANDOM, 2007) prove that every distribution-free testing algorithm for monotone monomials as well as for general monomials must have query complexity Ω(n1/5) (for a constant distance parameter e). In this paper we present distribution-free testing algorithms for these classes where the query complexity of the algorithms is O(n1/2/e). We note that as opposed to previous results for distribution-free testing, our algorithms do not build on the algorithms that work under the uniform distribution. Rather, we define and exploit certain structural properties of monomials (and functions that differ from them in a nonnegligible manner), which were not used in previous work on property testing.

Content addressable memories (CAMs) are gaining popularity with computer networks. Testing costs of CAMs are extremely high owing to their unique configuration. In this paper, we carried out a transistor-level fault analysis and devise a search path test algorithm. The proposed algorithm is of the order (nl / log2n) compared to the brute-force algorithm of complexity (nl). For the analyzed CAM, the search path test complexity is reduced by 30x.