Ensuring safety through set invariance has proven a useful method in a variety of applications in robotics and control. In this paper, we focus on the safe probabilistic invariance verification problem for discrete-ti...
Ensuring safety through set invariance has proven a useful method in a variety of applications in robotics and control. In this paper, we focus on the safe probabilistic invariance verification problem for discrete-time dynamical systems subject to stochastic disturbances over the infinite time horizon. Our goal is to compute the lower and upper bounds of the liveness probability for a given safe set and set of initial states. This probability represents the likelihood that the system will remain within the safe set for all time. To address this problem, we draw inspiration from stochastic barrier certificates for safety verification and build upon the findings in [21], where an equation was presented for exact probability analysis. We present two sets of optimizations and demonstrate their effectiveness through two examples, using semi-definite programming tools.
Functional dependencies (FDs) form a valuable ingredient for various data management tasks. However, existing methods can hardly discover practical and interpretable FDs, especially in large noisy real-life datasets. ...
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ISBN:
(数字)9798350317152
ISBN:
(纸本)9798350317169
Functional dependencies (FDs) form a valuable ingredient for various data management tasks. However, existing methods can hardly discover practical and interpretable FDs, especially in large noisy real-life datasets. This paper studies the problem of discovering meaningful functional dependencies (FDms) that utilize support and error parameters to capture interesting dependencies in such datasets and proposes an efficient discovery algorithm called FDMε. In order to scale with large datasets, FDM ε employs an efficient sampling method with accuracy guarantees to capture the differences between tuple pairs and to quantify the connection between support/error of dependencies on samples and those on the entire dataset. Moreover, it adopts a clustering-based correlated attributes extraction to divide the exponentially large search space into multiple small sub-spaces and proposes an easy-first traversal strategy with covariance-based guidance that quickly detects candidate dependencies and validates them. Additionally, we prove a covariance lower bound as an additional pruning criterion to reduce the search space. Extensive experiments on real-life and synthetic datasets demonstrate that FDM ε is 14 times faster than existing discovery algorithms on average, up to 31 times, and scales to larger datasets with the least memory cost.
Grasping a specified object from multi-object scenes is an essential ability for intelligent *** ability depends on the affiliation between the grasp position and the object category. Most existing multi-object grasp ...
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Grasping a specified object from multi-object scenes is an essential ability for intelligent *** ability depends on the affiliation between the grasp position and the object category. Most existing multi-object grasp detection methods considering the affiliation rely on object detection results, thus limiting the improvement of robotic grasp detection accuracy. This paper proposes a decoupled single-stage multitask robotic grasp detection method based on the Faster R-CNN framework for multi-object scenes. The designed network independently detects the category of an object and its possible grasp positions by using one loss function. A new grasp matching strategy is designed to determine the relationship between object categories and predicted grasp positions. The VMRD grasp dataset is used to test the performance of the proposed method. Compared with other grasp detection methods, the proposed method achieves higher object detection accuracy and grasp detection accuracy.
In this study,a commercial magnesium alloy AZ31(Mg-3Al-1Zn-0.3Mn)sheet through a short manufacturing process was found to be ductile and highly formable in *** possessing a strong basal texture,the short-processed she...
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Boson sampling is a promising candidate for demonstrating quantum supremacy. The validation that involves judging whether a quantum setup outputs photons following the boson sampling model is an essential task in the ...
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Boson sampling is a promising candidate for demonstrating quantum supremacy. The validation that involves judging whether a quantum setup outputs photons following the boson sampling model is an essential task in the experiments. However, the current validation methods may result in an incorrect conclusion being reached in realistic experiments, in which no ideally identical photons exist. Accordingly, this study proposes a slope-based approach, which is an extended Bayesian validation, to model the degree of photon indistinguishability. Through numerical simulations and performance evaluations, we demonstrate that the proposed approach can correctly validate boson sampling against the distribution of classical particles. In addition to offering a useful approach for validation, our research indicates that physicists should pay more attention to the quality of photon indistinguishability in boson sampling experiments.
With the development of emerging technologies such as cloud computing and large AI models (such as LLM), many applications have placed higher demands on the intensive read and write processing of massive data. Address...
With the development of emerging technologies such as cloud computing and large AI models (such as LLM), many applications have placed higher demands on the intensive read and write processing of massive data. Addressing the limitations of traditional file systems, we propose PancakeFS, an LSM-Tree-based file system. Leveraging the write-friendly characteristics of LSM-Tree, PancakeFS exhibits efficient write performance. We improve read performance through the design of an inode allocation algorithm based on subtree partitioning and optimization of the disk layout for hot data reconstruction. Our experimental results demonstrate that PancakeFS outperforms Ext4 and TableFS in terms of both read and write performance.
Directed fuzzing technology is one of the key technologies to quickly reach a specific location of software, and to conduct targeted testing or bug recurrence. However, directed fuzzing technology has some problems, s...
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ISBN:
(纸本)9781450397148
Directed fuzzing technology is one of the key technologies to quickly reach a specific location of software, and to conduct targeted testing or bug recurrence. However, directed fuzzing technology has some problems, such as unreasonable seed energy allocation, low code coverage and incomplete testing. To solve the above problems, this paper proposes an optimization method of directed fuzzing based on Rich-Branch nodes. In this method, the concept of Rich-Branch nodes is defined and the algorithm of extracting Rich-Branch nodes is given. The optimization method collects the coverage information of the target program in the running process, calculates the weights of covered functions and nodes in real time by combining CG and CFG of the target program, and generates a list of Rich-Branch nodes. According to the weights of Rich-Branch nodes, the seed energy allocation algorithm of AFLGo is optimized and improved. Compared with AFLGo, this optimization method improves the average code coverage of each targeted point by 56.79%, and has the same target reaching ability as AFLGo.
When it comes to the fifth generation, collaborative edge computing is preferred for offloading computation-intensive tasks of low-latency applications in Internet of Things. In this paper, we consider the partial tas...
When it comes to the fifth generation, collaborative edge computing is preferred for offloading computation-intensive tasks of low-latency applications in Internet of Things. In this paper, we consider the partial task offloading problem where tasks can be divided into subtasks and offloaded to nearby devices. The flow scheduling problem is integrated in the offloading process, i.e., multiple and conflicting route paths are considered. We propose the latency-aware partial task offloading framework (LaPTOF) for the considered problem. LaPTOF integrates a weighted priority ranking strategy (WPRS) which generates multiple solutions with different weights on task arrival time and the task processing time. A feasible solution generation method (FSGM) is designed where the best offloaded proportion of tasks are computed, and the appropriate offloaded devices and offload paths are determined. The proposed LaPTOF has an advantage in providing a scheduling plan that minimizes total completion time of task offloading in a shorter duration. The experimental results show that the proposal is suitable for the considered problem compared with JPOFH and its variants on both effectiveness and efficiency.
作者：
Hu, YangyiInstitute for Quantum Information
State Key Laboratory of High Performance Computing National University of Defense Technology College of Computer Science and Technology Changsha410073 China
It is well known that the minimum adversarial distortion associated with a specific sample x0 reflects the local robustness of neural networks. However, it is intractable to solve the optimization problem related to t...
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作者：
Hu, YangyiInstitute for Quantum Information
State Key Laboratory of High Performance Computing National University of Defense Technology College of Computer Science and Technology Changsha410073 China
Adversarial examples pose a great threat to the application of neural network as a classifier in areas with high security requirements. Intuitively, the adversarial property of neural network is closely related to the...
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