9,938 research outputs found
New Concurrent Modulus Algorithm and Soft Decision Directed Scheme for Blind Equalization
AbstractThe Constant Modulus Algorithm (CMA) is recognized as the most widely used algorithm in blind channel equalization practice. However, the CMA cost function exhibits local minima, which often leads to ill-convergence. This paper proposes a concurrent equalizer, in which a Soft Decision Directed (SDD) equalizer operates cooperatively with a CMA equalizer, controlled through a non-linear link that depends on the system a priory state. The simulation results show that the proposed equalizer has faster convergence rate and lower steady-state mean square error than the CMA equalizer
Joint Beamforming and Power Control in Coordinated Multicell: Max-Min Duality, Effective Network and Large System Transition
This paper studies joint beamforming and power control in a coordinated
multicell downlink system that serves multiple users per cell to maximize the
minimum weighted signal-to-interference-plus-noise ratio. The optimal solution
and distributed algorithm with geometrically fast convergence rate are derived
by employing the nonlinear Perron-Frobenius theory and the multicell network
duality. The iterative algorithm, though operating in a distributed manner,
still requires instantaneous power update within the coordinated cluster
through the backhaul. The backhaul information exchange and message passing may
become prohibitive with increasing number of transmit antennas and increasing
number of users. In order to derive asymptotically optimal solution, random
matrix theory is leveraged to design a distributed algorithm that only requires
statistical information. The advantage of our approach is that there is no
instantaneous power update through backhaul. Moreover, by using nonlinear
Perron-Frobenius theory and random matrix theory, an effective primal network
and an effective dual network are proposed to characterize and interpret the
asymptotic solution.Comment: Some typos in the version publised in the IEEE Transactions on
Wireless Communications are correcte
Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search
Despite substantial interest in applications of neural networks to
information retrieval, neural ranking models have only been applied to standard
ad hoc retrieval tasks over web pages and newswire documents. This paper
proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network)
a novel neural ranking model specifically designed for ranking short social
media posts. We identify document length, informal language, and heterogeneous
relevance signals as features that distinguish documents in our domain, and
present a model specifically designed with these characteristics in mind. Our
model uses hierarchical convolutional layers to learn latent semantic
soft-match relevance signals at the character, word, and phrase levels. A
pooling-based similarity measurement layer integrates evidence from multiple
types of matches between the query, the social media post, as well as URLs
contained in the post. Extensive experiments using Twitter data from the TREC
Microblog Tracks 2011--2014 show that our model significantly outperforms prior
feature-based as well and existing neural ranking models. To our best
knowledge, this paper presents the first substantial work tackling search over
social media posts using neural ranking models.Comment: AAAI 2019, 10 page
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Robust filtering for stochastic genetic regulatory networks with time-varying delay
This is the post print version of the article. The official published version can be obtained from the link - Copyright 2009 Elsevier LtdThis paper addresses the robust filtering problem for a class of linear genetic regulatory networks (GRNs) with stochastic disturbances, parameter uncertainties and time delays. The parameter uncertainties are assumed to reside in a polytopic region, the stochastic disturbance is state-dependent described by a scalar Brownian motion, and the time-varying delays enter into both the translation process and the feedback regulation process. We aim to estimate the true concentrations of mRNA and protein by designing a linear filter such that, for all admissible time delays, stochastic disturbances as well as polytopic uncertainties, the augmented state estimation dynamics is exponentially mean square stable with an expected decay rate. A delay-dependent linear matrix inequality (LMI) approach is first developed to derive sufficient conditions that guarantee the exponential stability of the augmented dynamics, and then the filter gains are parameterized in terms of the solution to a set of LMIs. Note that LMIs can be easily solved by using standard software packages. A simulation example is exploited in order to illustrate the effectiveness of the proposed design procedures.This work was supported in part by the Biotechnology and Biological Sciences Research Council (BBSRC) of the U.K. under Grants BB/C506264/1 and 100/EGM17735, an International Joint Project sponsored by the Royal Society of the U.K., the Research Grants Council of Hong Kong under Grant HKU 7031/06P, the National Natural Science Foundation of China under Grant 60804028, and the Alexander von Humboldt Foundation of Germany
Dynamic response of exchange bias in graphene nanoribbons
The dynamics of magnetic hysteresis, including the training effect and the
field sweep rate dependence of the exchange bias, is experimentally
investigated in exchange-coupled potassium split graphene nanoribbons (GNRs).
We find that, at low field sweep rate, the pronounced absolute training effect
is present over a large number of cycles. This is reflected in a gradual
decrease of the exchange bias with the sequential field cycling. However, at
high field sweep rate above 0.5 T/min, the training effect is not prominent.
With the increase in field sweep rate, the average value of exchange bias field
grows and is found to follow power law behavior. The response of the exchange
bias field to the field sweep rate variation is linked to the difference in the
time it takes to perform a hysteresis loop measurement compared with the
relaxation time of the anti-ferromagnetically aligned spins. The present
results may broaden our current understanding of magnetism of GNRs and would be
helpful in establishing the GNRs based spintronic devices.Comment: Accepted Applied Physics Letters (In press
HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE
Factor model is a fundamental investment tool in quantitative investment,
which can be empowered by deep learning to become more flexible and efficient
in practical complicated investing situations. However, it is still an open
question to build a factor model that can conduct stock prediction in an online
and adaptive setting, where the model can adapt itself to match the current
market regime identified based on only point-in-time market information. To
tackle this problem, we propose the first deep learning based online and
adaptive factor model, HireVAE, at the core of which is a hierarchical latent
space that embeds the underlying relationship between the market situation and
stock-wise latent factors, so that HireVAE can effectively estimate useful
latent factors given only historical market information and subsequently
predict accurate stock returns. Across four commonly used real stock market
benchmarks, the proposed HireVAE demonstrate superior performance in terms of
active returns over previous methods, verifying the potential of such online
and adaptive factor model.Comment: Accepted to IJCAI 202
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