362 research outputs found
Image Restoration using Total Variation Regularized Deep Image Prior
In the past decade, sparsity-driven regularization has led to significant
improvements in image reconstruction. Traditional regularizers, such as total
variation (TV), rely on analytical models of sparsity. However, increasingly
the field is moving towards trainable models, inspired from deep learning. Deep
image prior (DIP) is a recent regularization framework that uses a
convolutional neural network (CNN) architecture without data-driven training.
This paper extends the DIP framework by combining it with the traditional TV
regularization. We show that the inclusion of TV leads to considerable
performance gains when tested on several traditional restoration tasks such as
image denoising and deblurring
LKB1 tumor suppressor protein regulates actin filament assembly through Rho and its exchange factor Dbl independently of kinase activity
<p>Abstract</p> <p>Background</p> <p>Germline mutations in <it>LKB1 </it>result in Peutz-Jeghers Syndrome characterized by intestinal hamartomas and increased incidence of epithelial cancers. <it>LKB1 </it>encodes a serine/threonine kinase that plays an important role in regulating energy metabolism through the AMPK/mTOR signaling pathway. In addition, LKB1 is homologous to PAR-4, a polarity protein first described in <it>C. elegans</it>, while activation of LKB1 in mammalian epithelial cells induces the polarized assembly of actin filaments.</p> <p>Results</p> <p>To explore the mechanism by which LKB1 interacts with the actin cytoskeleton, we introduced LKB1 into HeLa cells that lack endogenous LKB1. This results in activation of the small GTPase Rho and the assembly of linear actin filaments associated with focal adhesions. These effects on the actin cytoskeleton are attenuated by siRNA-mediated depletion of the guanine nucleotide exchange factor Dbl. Co-expression of the LKB1 with the adaptor protein STRAD induces actin filament puncta associated with phospho-ezrin.</p> <p>Conclusions</p> <p>This study reveals that LKB1 regulates the actin cytoskeleton through a Dbl/Rho pathway.</p
HOW TO ADVERTISE APPROAPRIATELY ON THE WORLD WIDE WEB? A MULTI-CONGRUITY ANALYSIS APPROACH
As a popular and important advertising style, Internet advertising has drawn substantial amount of scholarly attention. Previous studies focus on the independent effects of various factors, such as product, consumer, website and ad per se, but few studies consider the impacts of the congruities between these factors on consumerās attitude toward the ads. In this paper, we propose an integrative model, product-consumer-website-ad model, to articulate how the congruity between factors exerts its effect. We propose that ad appeal (emotional vs. informational) should be designed consistent with the nature of the advertised product (hedonic vs. utilitarian), the nature of the website (hedonic vs. utilitarian) and the thinking styles of consumer (intuitive vs. rational). Personalization plays an important role in the process to achieve the congruity. We also propose that the ad on the website with high reputation will generate more favourable attitude toward it. Implications and future research are also discussed in the paper
Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning
The growing significance of ridesourcing services in recent years suggests a
need to examine the key determinants of ridesourcing demand. However, little is
known regarding the nonlinear effects and spatial heterogeneity of ridesourcing
demand determinants. This study applies an explainable-machine-learning-based
analytical framework to identify the key factors that shape ridesourcing demand
and to explore their nonlinear associations across various spatial contexts
(airport, downtown, and neighborhood). We use the ridesourcing-trip data in
Chicago for empirical analysis. The results reveal that the importance of built
environment varies across spatial contexts, and it collectively contributes the
largest importance in predicting ridesourcing demand for airport trips.
Additionally, the nonlinear effects of built environment on ridesourcing demand
show strong spatial variations. Ridesourcing demand is usually most responsive
to the built environment changes for downtown trips, followed by neighborhood
trips and airport trips. These findings offer transportation professionals
nuanced insights for managing ridesourcing services
Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN) for Travel Demand Forecasting During Wildfires
Real-time forecasting of travel demand during wildfire evacuations is crucial
for emergency managers and transportation planners to make timely and
better-informed decisions. However, few studies focus on accurate travel demand
forecasting in large-scale emergency evacuations. Therefore, this study
develops and tests a new methodological framework for modeling trip generation
in wildfire evacuations by using (a) large-scale GPS data generated by mobile
devices and (b) state-of-the-art AI technologies. The proposed methodology aims
at forecasting evacuation trips and other types of trips. Based on the travel
demand inferred from the GPS data, we develop a new deep learning model, i.e.,
Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN), along
with a model updating scheme to achieve real-time forecasting of travel demand
during wildfire evacuations. The proposed methodological framework is tested in
this study for a real-world case study: the 2019 Kincade Fire in Sonoma County,
CA. The results show that SA-MGCRN significantly outperforms all the selected
state-of-the-art benchmarks in terms of prediction performance. Our finding
suggests that the most important model components of SA-MGCRN are evacuation
order/warning information, proximity to fire, and population change, which are
consistent with behavioral theories and empirical findings
Provable Convergence of Plug-and-Play Priors with MMSE denoisers
Plug-and-play priors (PnP) is a methodology for regularized image
reconstruction that specifies the prior through an image denoiser. While PnP
algorithms are well understood for denoisers performing maximum a posteriori
probability (MAP) estimation, they have not been analyzed for the minimum mean
squared error (MMSE) denoisers. This letter addresses this gap by establishing
the first theoretical convergence result for the iterative
shrinkage/thresholding algorithm (ISTA) variant of PnP for MMSE denoisers. We
show that the iterates produced by PnP-ISTA with an MMSE denoiser converge to a
stationary point of some global cost function. We validate our analysis on
sparse signal recovery in compressive sensing by comparing two types of
denoisers, namely the exact MMSE denoiser and the approximate MMSE denoiser
obtained by training a deep neural net
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