19,759 research outputs found
Lattice Gaussian Sampling by Markov Chain Monte Carlo: Bounded Distance Decoding and Trapdoor Sampling
Sampling from the lattice Gaussian distribution plays an important role in
various research fields. In this paper, the Markov chain Monte Carlo
(MCMC)-based sampling technique is advanced in several fronts. Firstly, the
spectral gap for the independent Metropolis-Hastings-Klein (MHK) algorithm is
derived, which is then extended to Peikert's algorithm and rejection sampling;
we show that independent MHK exhibits faster convergence. Then, the performance
of bounded distance decoding using MCMC is analyzed, revealing a flexible
trade-off between the decoding radius and complexity. MCMC is further applied
to trapdoor sampling, again offering a trade-off between security and
complexity. Finally, the independent multiple-try Metropolis-Klein (MTMK)
algorithm is proposed to enhance the convergence rate. The proposed algorithms
allow parallel implementation, which is beneficial for practical applications.Comment: submitted to Transaction on Information Theor
Thermophysical properties of liquid carbon dioxide under shock compressions: Quantum molecular dynamic simulations
Quantum molecular dynamic simulations are introduced to study the dynamical,
electrical, and optical properties of carbon dioxide under dynamic
compressions. The principal Hugoniot derived from the calculated equation of
states is demonstrated to be well accordant with experimental results.
Molecular dissociation and recombination are investigated through pair
correlation functions, and decomposition of carbon dioxide is found to be
between 40 and 50 GPa along the Hugoniot, where nonmetal-metal transition is
observed. In addition, the optical properties of shock compressed carbon
dioxide are also theoretically predicted along the Hugoniot
On the Geometric Ergodicity of Metropolis-Hastings Algorithms for Lattice Gaussian Sampling
Sampling from the lattice Gaussian distribution is emerging as an important
problem in coding and cryptography. In this paper, the classic
Metropolis-Hastings (MH) algorithm from Markov chain Monte Carlo (MCMC) methods
is adapted for lattice Gaussian sampling. Two MH-based algorithms are proposed,
which overcome the restriction suffered by the default Klein's algorithm. The
first one, referred to as the independent Metropolis-Hastings-Klein (MHK)
algorithm, tries to establish a Markov chain through an independent proposal
distribution. We show that the Markov chain arising from the independent MHK
algorithm is uniformly ergodic, namely, it converges to the stationary
distribution exponentially fast regardless of the initial state. Moreover, the
rate of convergence is explicitly calculated in terms of the theta series,
leading to a predictable mixing time. In order to further exploit the
convergence potential, a symmetric Metropolis-Klein (SMK) algorithm is
proposed. It is proven that the Markov chain induced by the SMK algorithm is
geometrically ergodic, where a reasonable selection of the initial state is
capable to enhance the convergence performance.Comment: Submitted to IEEE Transactions on Information Theor
High performance deep packet inspection on multi-core platform
Deep packet inspection (DPI) provides the ability to perform quality of service (QoS) and Intrusion Detection on network packets. But since the explosive growth of Internet, performance and scalability issues have been raised due to the gap between network and end-system speeds. This article describles how a desirable DPI system with multi-gigabits throughput and good scalability should be like by exploiting parallelism on network interface card, network stack and user applications. Connection-based parallelism, affinity-based scheduling and lock-free data structure are the main technologies introduced to alleviate the performance and scalability issues. A common DPI application L7-Filter is used as an example to illustrate the applicaiton level parallelism
The equation of state and nonmetal-metal transition of benzene under shock compression
We employ quantum molecular dynamic simulations to investigate the behavior
of benzene under shock conditions. The principal Hugoniot derived from the
equation of state is determined. We compare our firs-principles results with
available experimental data and provide predictions of chemical reactions for
shocked benzene. The decomposition of benzene is found under the pressure of 11
GPa. The nonmetal-metal transition, which is associated with the rapid C-H bond
breaking and the formation of atomic and molecular hydrogen, occurs under the
pressure around 50 GPa. Additionally, optical properties are also studied.Comment: 12 pages, 5 figure
Scene Graph Generation via Conditional Random Fields
Despite the great success object detection and segmentation models have
achieved in recognizing individual objects in images, performance on cognitive
tasks such as image caption, semantic image retrieval, and visual QA is far
from satisfactory. To achieve better performance on these cognitive tasks,
merely recognizing individual object instances is insufficient. Instead, the
interactions between object instances need to be captured in order to
facilitate reasoning and understanding of the visual scenes in an image. Scene
graph, a graph representation of images that captures object instances and
their relationships, offers a comprehensive understanding of an image. However,
existing techniques on scene graph generation fail to distinguish subjects and
objects in the visual scenes of images and thus do not perform well with
real-world datasets where exist ambiguous object instances. In this work, we
propose a novel scene graph generation model for predicting object instances
and its corresponding relationships in an image. Our model, SG-CRF, learns the
sequential order of subject and object in a relationship triplet, and the
semantic compatibility of object instance nodes and relationship nodes in a
scene graph efficiently. Experiments empirically show that SG-CRF outperforms
the state-of-the-art methods, on three different datasets, i.e., CLEVR, VRD,
and Visual Genome, raising the Recall@100 from 24.99% to 49.95%, from 41.92% to
50.47%, and from 54.69% to 54.77%, respectively
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