3,469 research outputs found
Low-frequency Gravitational Wave Detection via Double Optical Clocks in Space
We propose a Doppler tracking system for gravitational wave detection via
Double Optical Clocks in Space (DOCS). In this configuration two spacecrafts
(each containing an optical clock) are launched to space for Doppler shift
observations. Compared to the similar attempt of gravitational wave detection
in the Cassini mission, the radio signal of DOCS that contains the relative
frequency changes avoids completely noise effects due for instance to
troposphere, ionosphere, ground-based antenna and transponder. Given the high
stabilities of the two optical clocks (Allan deviation @ 1000 s), an overall estimated sensitivity of
could be achieved with an observation time of 2 years, and would allow to
detect gravitational waves in the frequency range from Hz to
Hz.Comment: 18 pages, 2 figure
MARKET INTEGRATION TEST FOR PACIFIC EGG MARKETS
This paper uses of Johansen's multivariate cointegration test to test for egg market integration of six Pacific states, Washington, Idaho, Oregon, California, Nevada, and Arizona. We conclude that eggs from these states substitute for each other to some degree, and arbitrage possibilities through trade bind the egg prices. In addition, the Law of One Price (LOP), the case of perfect integration, is examined by testing the linear combination of cointegration vectors. Test results show that the LOP is not satisfied even though the egg markets in the six Pacific states are highly integrated. Arizona egg prices, California egg prices, and Washington egg prices play dominant roles on the Pacific egg market in the long run.Demand and Price Analysis, Livestock Production/Industries,
A BP-MF-EP Based Iterative Receiver for Joint Phase Noise Estimation, Equalization and Decoding
In this work, with combined belief propagation (BP), mean field (MF) and
expectation propagation (EP), an iterative receiver is designed for joint phase
noise (PN) estimation, equalization and decoding in a coded communication
system. The presence of the PN results in a nonlinear observation model.
Conventionally, the nonlinear model is directly linearized by using the
first-order Taylor approximation, e.g., in the state-of-the-art soft-input
extended Kalman smoothing approach (soft-in EKS). In this work, MF is used to
handle the factor due to the nonlinear model, and a second-order Taylor
approximation is used to achieve Gaussian approximation to the MF messages,
which is crucial to the low-complexity implementation of the receiver with BP
and EP. It turns out that our approximation is more effective than the direct
linearization in the soft-in EKS with similar complexity, leading to
significant performance improvement as demonstrated by simulation results.Comment: 5 pages, 3 figures, Resubmitted to IEEE Signal Processing Letter
Multiagent model and mean field theory of complex auction dynamics
Acknowledgements We are grateful to Ms Yinan Zhao for providing the data and to Yuzhong Chen and Cancan Zhou for discussions and suggestions. This work was supported by ARO under Grant No. W911NF-14-1-0504 and by NSFC under Grants Nos. 11275003 and 61174165. The visit of QC to Arizona State University was partially sponsored by the State Scholarship Fund of China.Peer reviewedPublisher PD
Knowledge Pyramid: A Novel Hierarchical Reasoning Structure for Generalized Knowledge Augmentation and Inference
Knowledge graph (KG) based reasoning has been regarded as an effective means
for the analysis of semantic networks and is of great usefulness in areas of
information retrieval, recommendation, decision-making, and man-machine
interaction. It is widely used in recommendation, decision-making,
question-answering, search, and other fields. However, previous studies mainly
used low-level knowledge in the KG for reasoning, which may result in
insufficient generalization and poor robustness of reasoning. To this end, this
paper proposes a new inference approach using a novel knowledge augmentation
strategy to improve the generalization capability of KG. This framework
extracts high-level pyramidal knowledge from low-level knowledge and applies it
to reasoning in a multi-level hierarchical KG, called knowledge pyramid in this
paper. We tested some medical data sets using the proposed approach, and the
experimental results show that the proposed knowledge pyramid has improved the
knowledge inference performance with better generalization. Especially, when
there are fewer training samples, the inference accuracy can be significantly
improved.Comment: 10 pages,8 figure
Principles of microRNA regulation of a human cellular signaling network
MicroRNAs (miRNAs) are endogenous 22-nucleotide RNAs, which suppress gene
expression by selectively binding to the 3-noncoding region of specific message
RNAs through base-pairing. Given the diversity and abundance of miRNA targets,
miRNAs appear to functionally interact with various components of many cellular
networks. By analyzing the interactions between miRNAs and a human cellular
signaling network, we found that miRNAs predominantly target positive
regulatory motifs, highly connected scaffolds and most downstream network
components such as signaling transcription factors, but less frequently target
negative regulatory motifs, common components of basic cellular machines and
most upstream network components such as ligands. In addition, when an adaptor
has potential to recruit more downstream components, these components are more
frequently targeted by miRNAs. This work uncovers the principles of miRNA
regulation of signal transduction networks and implies a potential function of
miRNAs for facilitating robust transitions of cellular response to
extracellular signals and maintaining cellular homeostasis
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