512 research outputs found
Multimodal Convolutional Neural Networks for Matching Image and Sentence
In this paper, we propose multimodal convolutional neural networks (m-CNNs)
for matching image and sentence. Our m-CNN provides an end-to-end framework
with convolutional architectures to exploit image representation, word
composition, and the matching relations between the two modalities. More
specifically, it consists of one image CNN encoding the image content, and one
matching CNN learning the joint representation of image and sentence. The
matching CNN composes words to different semantic fragments and learns the
inter-modal relations between image and the composed fragments at different
levels, thus fully exploit the matching relations between image and sentence.
Experimental results on benchmark databases of bidirectional image and sentence
retrieval demonstrate that the proposed m-CNNs can effectively capture the
information necessary for image and sentence matching. Specifically, our
proposed m-CNNs for bidirectional image and sentence retrieval on Flickr30K and
Microsoft COCO databases achieve the state-of-the-art performances.Comment: Accepted by ICCV 201
Auxiliary Factor Method to Remove ISI of Nyquist Filters
As has been known, the Nyquist first condition promises no intersymbol
interference (ISI) as derived in the frequency domain. However, the practical
implementation using the FIR filter truncates the Fourier transform by its
window and prevents the mathematical calculation from reaching the ideal
solution at zero-ISI. For obtaining better results, an increase in the window's
length is required in general. To address this problem, a new approach is
presented by using auxiliary factors (AFs) to compensate shortcomings of the
truncated Fourier transform and remove the ISI completely, regardless of the
window's length. In addition, the performance in the presence of the timing
jitter is also improved significantly. The closed-form solution of the AFs is
derived and the effectiveness is confirmed by the simulation results. Finally,
the problems of the transmission delay and additional calculation complexity
are analysed.Comment: This paper was accepted by IEEE Communications Letter
Statistical methods for meta-analysis
University of Minnesota Ph.D. dissertation. May 2017. Major: Biostatistics. Advisor: Haitao Chu. 1 computer file (PDF); xi, 166 pages.Meta-analysis has become a widely-used tool to combine findings from independent studies in various research areas. This thesis deals with several important statistical issues in systematic reviews and meta-analyses, such as assessing heterogeneity in the presence of outliers, quantifying publication bias, and simultaneously synthesizing multiple treatments and factors. The first part of this thesis focuses on univariate meta-analysis. We propose alternative measures to robustly describe between-study heterogeneity, which are shown to be less affected by outliers compared with traditional measures. Publication bias is another issue that can seriously affect the validity and generalizability of meta-analysis conclusions. We present the first work to empirically evaluate the performance of seven commonly-used publication bias tests based on a large collection of actual meta-analyses in the Cochrane Library. Our findings may guide researchers in properly assessing publication bias and interpreting test results for future systematic reviews. Moreover, instead of just testing for publication bias, we further consider quantifying it and propose an intuitive publication bias measure, called the skewness of standardized deviates, which effectively describes the asymmetry of the collected studies’ results. The measure’s theoretical properties are studied, and we show that it can also serve as a powerful test statistic. The second part of this thesis introduces novel ideas in multivariate meta-analysis. In medical sciences, a disease condition is typically associated with multiple risk and protective factors. Although many studies report results of multiple factors, nearly all meta-analyses separately synthesize the association between each factor and the disease condition of interest. We propose a new concept, multivariate meta-analysis of multiple factors, to synthesize all available factors simultaneously using a Bayesian hierarchical model. By borrowing information across factors, the multivariate method can improve statistical efficiency and reduce biases compared with separate analyses. In addition to synthesizing multiple factors, network meta-analysis has recently attracted much attention in evidence-based medicine because it simultaneously combines both direct and indirect evidence to compare multiple treatments and thus facilitates better decision making. First, we empirically compare two network meta-analysis models, contrast- and arm-based, with respect to their sensitivity to treatment exclusions. The arm-based method is shown to be more robust to such exclusions, mostly because it can use single-arm studies while the contrast-based method cannot. Then, focusing on the currently popular contrast-based method, we theoretically explore the key factors that make network meta-analysis outperform traditional pairwise meta-analyses. We prove that evidence cycles in the treatment network play critical roles in network meta-analysis. Specifically, network meta-analysis produces posterior distributions identical to separate pairwise meta-analyses for all treatment comparisons when a treatment network does not contain cycles. This equivalence is illustrated using simulations and a case study
Spatial-spectral Terahertz Networks
This paper focuses on the spatial-spectral terahertz (THz) networks, where
transmitters equipped with leaky-wave antennas send information to their
receivers at the THz frequency bands. As a directional and nearly planar
antenna, the leaky-wave antenna allows for information transmissions with
narrow beams and high antenna gains. The conventional large antenna arrays are
confronted with challenging issues such as scaling limits and path discovery in
the THz frequencies. Therefore, this work exploits the potential of leaky-wave
antennas in the dense THz networks, to establish low-complexity THz links. By
addressing the propagation angle-frequency coupling effects, the transmission
rate is analyzed. The results show that the leaky-wave antenna is efficient for
achieving the high-speed transmission rate. The co-channel interference
management is unnecessary when the THz transmitters with large subchannel
bandwidths are not extremely dense. A simple subchannel allocation solution is
proposed, which enhances the transmission rate compared with the same number of
subchannels with the equal allocation of the frequency band. After subchannel
allocation, a low-complexity power allocation method is proposed to improve the
energy efficiency.Comment: accepted by the IEEE Transactions on Wireless Communication
High-Precision Channel Estimation for Sub-Noise Self-Interference Cancellation
Self-interference cancellation plays a crucial role in achieving reliable
full-duplex communications. In general, it is essential to cancel the
self-interference signal below the thermal noise level, which necessitates
accurate reconstruction of the self-interference signal. In this paper, we
propose a high-precision channel estimation method specifically designed for
sub-noise self-interference cancellation. Exploiting the fact that all
transmitted symbols are known to their respective receivers, our method
utilizes all transmitted symbols for self-interference channel estimation.
Through analytical derivations and numerical simulations, we validate the
effectiveness of the proposed method. The results demonstrate the superior
performance of our approach in achieving sub-noise self-interference
cancellation
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