14,643 research outputs found
Poly[[diaquadi-μ-dicyanamido-nickel(II)] bis(pyridinium-4-olate)]
The title compound, {[Ni(C2N3)2(H2O)2]·2C5H5NO}n, is a centrosymmetric two-dimensional coordination polymer with a layer (4,4) network structure. The asymmetric unit is compossed of an NiII atom, which sits on an inversion center, a μ-1,5-bridging dicyanamide anion, a water molecule, and a free 4-hydroxypyridine molecule present in the zwitterionic pyridinium-4-olate form. The NiII atom is coordinated in a slightly distorted N4O2 octahedral geometry by four bridging dicyanamide ligands and two trans water molecules. In the crystal, the two-dimensional networks are linked via N—H⋯O and O—H⋯O hydrogen bonds, forming a three-dimensional network
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
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
Analysis and Optimization of Cellular Network with Burst Traffic
In this paper, we analyze the performance of cellular networks and study the
optimal base station (BS) density to reduce the network power consumption. In
contrast to previous works with similar purpose, we consider Poisson traffic
for users' traffic model. In such situation, each BS can be viewed as M/G/1
queuing model. Based on theory of stochastic geometry, we analyze users'
signal-to-interference-plus-noise-ratio (SINR) and obtain the average
transmission time of each packet. While most of the previous works on SINR
analysis in academia considered full buffer traffic, our analysis provides a
basic framework to estimate the performance of cellular networks with burst
traffic. We find that the users' SINR depends on the average transmission
probability of BSs, which is defined by a nonlinear equation. As it is
difficult to obtain the closed-form solution, we solve this nonlinear equation
by bisection method. Besides, we formulate the optimization problem to minimize
the area power consumption. An iteration algorithm is proposed to derive the
local optimal BS density, and the numerical result shows that the proposed
algorithm can converge to the global optimal BS density. At the end, the impact
of BS density on users' SINR and average packet delay will be discussed.Comment: This paper has been withdrawn by the author due to missuse of queue
model in Section Fou
Applications of big knowledge summarization
Advanced technologies have resulted in the generation of large amounts of data ( Big Data ). The Big Knowledge derived from Big Data could be beyond humans\u27 ability of comprehension, which will limit the effective and innovative use of Big Knowledge repository. Biomedical ontologies, which play important roles in biomedical information systems, constitute one kind of Big Knowledge repository. Biomedical ontologies typically consist of domain knowledge assertions expressed by the semantic connections between tens of thousands of concepts. Without some high-level visual representation of Big Knowledge in biomedical ontologies, humans cannot grasp the big picture of those ontologies. Such Big Knowledge orientation is required for the proper maintenance of ontologies and their effective use. This dissertation is addressing the Big Knowledge challenge - How to enable humans to use Big Knowledge correctly and effectively (referred to as the Big Knowledge to Use (BK2U) problem) - with a focus on biomedical ontologies.
In previous work, Abstraction Networks (AbNs) have been demonstrated successful for the summarization, visualization and quality assurance (QA) of biomedical ontologies. Based on the previous research, this dissertation introduces new AbNs of various granularities for Big Knowledge summarization and extends the applications of AbNs. This dissertation consists of three main parts. The first part introduces two advanced AbNs. One is the weighted aggregate partial-area taxonomy with a parameter to flexibly control the summarization granularity. The second is the Ingredient Abstraction Network (IAbN) for the National Drug File - Reference Terminology (NDF-RT) Chemical Ingredients hierarchy, for which the previously developed AbNs for hierarchies with outgoing relationships, are not applicable. Since NDF-RT\u27s Chemical Ingredients hierarchy has no outgoing relationships.
The second part describes applications of the two advanced AbNs. A study utilizing the weighted aggregate partial-area taxonomy for the identification of major topics in SNOMED CT\u27s Specimen hierarchy is reported. A multi-layer interactive visualization system of required granularity for ontology comprehension, based on the weighted aggregate partial-area taxonomy, is demonstrated to comprehend the Neoplasm subhierarchy of National Cancer Institute thesaurus (NCIt). The IAbN is applied for drug-drug interaction (DDI) discovery.
The third part reports eight family-based QA studies on NCIt\u27s Neoplasm, Gene, and Biological Process hierarchies, SNOMED CT\u27s Infectious disease hierarchy, the Chemical Entities of Biological Interest ontology, and the Chemical Ingredients hierarchy in NDF-RT. There is no one-size-fits-all QA method and it is impossible to find a QA method for each individual ontology. Hence, family-based QA is an effective way, i.e., one QA technique could be applicable to a whole family of structurally similar ontologies. The results of these studies demonstrate that complex concepts and uncommonly modeled concepts are more likely to have errors. Furthermore, the three studies on overlapping concepts in partial-area taxonomies reported in this dissertation combined with previous three studies prove the success of overlapping concepts as a QA methodology for a whole family of 76 similar ontologies in BioPortal
Human Capital Development: A Look at the Impacts of Adolescent Fertility and Urbanization on Education Attainment
The paper examines the impact of adolescent fertility rate on tertiary educational attainment of populations aged 25 and above in 116 nations, divided into high income, upper-middle income and lower-middle income economies under the World Bank\u27s classification. It looks at this relationship while also studying the impact of urbanization on educational attainment. The reason why the relationship between adolescent fertility rate, urbanization and population with tertiary schooling is of interest is because most research associated with fertility rate and education focuses on the relationship between primary education and fertility, not on the level of impediments for higher education that relates to adolescent fertility rate and urbanization. It would be reasonable to think that higher education postpones parenthood and decreases the number of children a woman would have in their adolescent years. However, this research examines this relationship in the opposite direction to provide some observations on adolescent fertility and urbanization\u27s impact on educational attainment in a global perspective
Polar Coding for the Cognitive Interference Channel with Confidential Messages
In this paper, we propose a low-complexity, secrecy capacity achieving polar
coding scheme for the cognitive interference channel with confidential messages
(CICC) under the strong secrecy criterion. Existing polar coding schemes for
interference channels rely on the use of polar codes for the multiple access
channel, the code construction problem of which can be complicated. We show
that the whole secrecy capacity region of the CICC can be achieved by simple
point-to-point polar codes due to the cognitivity, and our proposed scheme
requires the minimum rate of randomness at the encoder
Markov Chain Monte Carlo Algorithms for Lattice Gaussian Sampling
Sampling from a lattice Gaussian distribution is emerging as an important
problem in various areas such as coding and cryptography. The default sampling
algorithm --- Klein's algorithm yields a distribution close to the lattice
Gaussian only if the standard deviation is sufficiently large. In this paper,
we propose the Markov chain Monte Carlo (MCMC) method for lattice Gaussian
sampling when this condition is not satisfied. In particular, we present a
sampling algorithm based on Gibbs sampling, which converges to the target
lattice Gaussian distribution for any value of the standard deviation. To
improve the convergence rate, a more efficient algorithm referred to as
Gibbs-Klein sampling is proposed, which samples block by block using Klein's
algorithm. We show that Gibbs-Klein sampling yields a distribution close to the
target lattice Gaussian, under a less stringent condition than that of the
original Klein algorithm.Comment: 5 pages, 1 figure, IEEE International Symposium on Information
Theory(ISIT) 201
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