With the development of information technology, the operation of power grid increasingly depends upon the support of strong communication network. The power system consisting of grids infrastructure and communication ...
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Typically, the deployment of face recognition models in the wild needs to identify low-resolution faces with extremely low computational cost. To address this problem, a feasible solution is compressing a complex face...
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Representing video events is an essential step for a wide range of visual applications. In this paper,we propose the event sketch, a high-level event representation, to depict the dynamic properties of video events co...
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Representing video events is an essential step for a wide range of visual applications. In this paper,we propose the event sketch, a high-level event representation, to depict the dynamic properties of video events composed of actions of semantic objects. We show that this representation can facilitate a novel sketch based video retrieval(SBVR) system, which has not been considered before to the best of our knowledge. In this system, users are allowed to draw the evolutions(e.g. spatiotemporal layouts and behaviors of semantic objects)on a board, and retrieve the events whose semantic objects have the similar evolutions from a database. To do this, event sketches are constructed on both the user queries and database videos, and compared under a novel graph-matching scheme based on data-driven Monta Carlo Markov chain(DDMCMC). To test our approach, we collect a novel dataset of goal events in real soccer videos, which consists actions of multiple players and shows large variability in the evolution process of the events. Experiments on this dataset and the publicly available dataset CAVIAR demonstrated the effectiveness of the proposed approach.
Kernel-level malwares are a serious threat to the integrity and security of the operating system. Current kernel integrity measurement methods have one-sidedness in selecting the measurement objects, and the character...
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We present a semiclassical formalism for antiferromagnetic (AFM) magnonics which promotes the central ingredient of spin wave chirality, encoded in a quantity called magnonic isospin, to a first-class citizen of the t...
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We present a semiclassical formalism for antiferromagnetic (AFM) magnonics which promotes the central ingredient of spin wave chirality, encoded in a quantity called magnonic isospin, to a first-class citizen of the theory. We use this formalism to unify results of interest from the field under a single chirality-centric formulation. Our main result is that the isospin is governed by unitary time evolution, through a Hamiltonian projected down from the full spin wave dynamics. Because isospin is SU(2) valued, its dynamics on the Bloch sphere are precisely rotations, which, in general, do not commute. Consequently, the induced group of operations on AFM spin waves is nonabelian. This is a paradigmatic departure from ferromagnetic magnonics, which operates purely within the abelian group generated by spin wave phase and amplitude. Our investigation of this nonabelian magnonics in AFM insulators focuses on studying several simple gate operations, and offering in broad strokes a program of study for interesting new logic families in antiferromagnetic spin wave systems.
Atomic many-body phase transitions and quantum criticality have recently attracted much attention in non-standard optical lattices. Here we perform an experimental study of finite-temperature superfluid transition of ...
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Due to the existence of various views or representations in many real-world data, multi-view learning has drawn much attention recently. Multi-view spectral clustering methods based on similarity matrixes or graphs ar...
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As a major threat to cyber security,malware has been increasingly damaging national *** paper proposes a malware classification model,*** model(Malware Classification Based on Static Malware Gene Sequences),that combi...
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As a major threat to cyber security,malware has been increasingly damaging national *** paper proposes a malware classification model,*** model(Malware Classification Based on Static Malware Gene Sequences),that combines the static malware genes with deep learning *** model extracts the malware gene sequences that have both material attribute and informational *** it makes distributed representation for each malware gene to represent the intrinsic correlation and ***,the SMGS_CNN(Static Malware Gene Sequences--Convolution Neural Network)module is used to construct the neural network to analyze the malware gene sequences and realize malware *** experimental results show that the classification accuracy is greatly improved and up to 98%with the MCSMGS *** model is more effective than the traditional SVM model.
With the advantages of extremely high access speed, low energy consumption, nonvolatility, and byte addressability, nonvolatile memory (NVM) device has already been setting off a revolution in storage field. Conventio...
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