A flexible mandatory access control policy for XML databases

A flexible mandatory access control policy (MAC) for XML databases is presented in this paper. The label type and label access policy can be defined according to the requirements of applications. In order to preserve the integrity of data in XML databases, a constraint between a read access rule and a write access rule in label access policy is introduced. Rules for label assignment and propagation are proposed to alleviate the workload of label assignment. Also, a solution for resolving conflicts of label assignments is proposed. At last, operations for implementation of the MAC policy in a XML database are illustrated

A practical mandatory access control model for XML databases

A practical mandatory access control (MAC) model for XML databases is presented in this paper. The label type and label access policy can be defined according to the requirements of different applications. In order to preserve the integrity of data in XML databases, a constraint between a read-access rule and a write-access rule in label access policy is introduced. Rules for label assignment and propagation are presented to alleviate the workload of label assignments. Furthermore, a solution for resolving conflicts in label assignments is proposed. Rules for update-related operations, rules for exceptional privileges of ordinary users and the administrator are also proposed to preserve the security of operations in XML databases. The MAC model, we proposed in this study, has been implemented in an XML database. Test results demonstrated that our approach provides rational and scalable performance

A Quantum Convolutional Neural Network for Image Classification

Artificial neural networks have achieved great success in many fields ranging from image recognition to video understanding. However, its high requirements for computing and memory resources have limited further development on processing big data with high dimensions. In recent years, advances in quantum computing show that building neural networks on quantum processors is a potential solution to this problem. In this paper, we propose a novel neural network model named Quantum Convolutional Neural Network (QCNN), aiming at utilizing the computing power of quantum systems to accelerate classical machine learning tasks. The designed QCNN is based on implementable quantum circuits and has a similar structure as classical convolutional neural networks. Numerical simulation results on the MNIST dataset demonstrate the effectiveness of our model.Comment: 7 pages, 7 figure

D-type Minimal Conformal Matter: Quantum Curves, Elliptic Garnier Systems, and the 5d Descendants

We study the quantization of the 6d Seiberg-Witten curve for D-type minimal conformal matter theories compactified on a two-torus. The quantized 6d curve turns out to be a difference equation established via introducing codimension two and four surface defects. We show that, in the Nekrasov-Shatashvili limit, the 6d partition function with insertions of codimension two and four defects serve as the eigenfunction and eigenvalues of the difference equation, respectively. We further identify the quantum curve of D-type minimal conformal matters with an elliptic Garnier system recently studied in the integrability community. At last, as a concrete consequence of our elliptic quantum curve, we study its RG flows to obtain various quantum curves of 5d ${\rm Sp}(N)+N_f \mathsf{F},N_f\leq 2N+5$ theories.Comment: 36+6 page