7,422 research outputs found
Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers
In image restoration tasks, like denoising and super resolution, continual
modulation of restoration levels is of great importance for real-world
applications, but has failed most of existing deep learning based image
restoration methods. Learning from discrete and fixed restoration levels, deep
models cannot be easily generalized to data of continuous and unseen levels.
This topic is rarely touched in literature, due to the difficulty of modulating
well-trained models with certain hyper-parameters. We make a step forward by
proposing a unified CNN framework that consists of few additional parameters
than a single-level model yet could handle arbitrary restoration levels between
a start and an end level. The additional module, namely AdaFM layer, performs
channel-wise feature modification, and can adapt a model to another restoration
level with high accuracy. By simply tweaking an interpolation coefficient, the
intermediate model - AdaFM-Net could generate smooth and continuous restoration
effects without artifacts. Extensive experiments on three image restoration
tasks demonstrate the effectiveness of both model training and modulation
testing. Besides, we carefully investigate the properties of AdaFM layers,
providing a detailed guidance on the usage of the proposed method.Comment: Accepted by CVPR 2019 (oral); code is available:
https://github.com/hejingwenhejingwen/AdaF
Characterizing the stabilization size for semi-implicit Fourier-spectral method to phase field equations
Recent results in the literature provide computational evidence that
stabilized semi-implicit time-stepping method can efficiently simulate phase
field problems involving fourth-order nonlinear dif- fusion, with typical
examples like the Cahn-Hilliard equation and the thin film type equation. The
up-to-date theoretical explanation of the numerical stability relies on the
assumption that the deriva- tive of the nonlinear potential function satisfies
a Lipschitz type condition, which in a rigorous sense, implies the boundedness
of the numerical solution. In this work we remove the Lipschitz assumption on
the nonlinearity and prove unconditional energy stability for the stabilized
semi-implicit time-stepping methods. It is shown that the size of stabilization
term depends on the initial energy and the perturba- tion parameter but is
independent of the time step. The corresponding error analysis is also
established under minimal nonlinearity and regularity assumptions
Gradient bounds for a thin film epitaxy equation
We consider a gradient flow modeling the epitaxial growth of thin films with
slope selection. The surface height profile satisfies a nonlinear diffusion
equation with biharmonic dissipation. We establish optimal local and global
wellposedness for initial data with critical regularity. To understand the
mechanism of slope selection and the dependence on the dissipation coefficient,
we exhibit several lower and upper bounds for the gradient of the solution in
physical dimensions
Single-particle machine for quantum thermalization
The long time accumulation of the \textit{random} actions of a single
particle "reservoir" on its coupled system can transfer some temperature
information of its initial state to the coupled system. This dynamic process
can be referred to as a quantum thermalization in the sense that the coupled
system can reach a stable thermal equilibrium with a temperature equal to that
of the reservoir. We illustrate this idea based on the usual micromaser model,
in which a series of initially prepared two-level atoms randomly pass through
an electromagnetic cavity. It is found that, when the randomly injected atoms
are initially prepared in a thermal equilibrium state with a given temperature,
the cavity field will reach a thermal equilibrium state with the same
temperature as that of the injected atoms. As in two limit cases, the cavity
field can be cooled and "coherently heated" as a maser process, respectively,
when the injected atoms are initially prepared in ground and excited states.
Especially, when the atoms in equilibrium are driven to possess some coherence,
the cavity field may reach a higher temperature in comparison with the injected
atoms. We also point out a possible experimental test for our theoretical
prediction based on a superconducting circuit QED system.Comment: 9 pages,4 figures
Enhancement of Pavement Maintenance Decision Making by Evaluating the Effectiveness of Pavement Maintenance Treatments
The performance of different pavement maintenance treatments were evaluated by investigating practical projects collected from Tennessee Pavement Management System (PMS) and Long Term Pavement Performance (LTPP) database. The influence of factors on the effectiveness, cost-effectiveness and cracking initiation of different treatment were evaluated by “Optime”, multiple linear regression and parametric survival analysis. Pavement roughness, pavement serviceability index (PSI) and the initiation time of cracking were used as pavement performance indicators.
Investigation on the pavement maintenance projects in Tennessee by Optime and multiple linear regression analysis indicated that HMA overlay had the highest effectiveness, followed by mill & fill and micro surfacing. Due to the relatively low cost, micro surfacing was the most cost-effective treatment, followed by HMA overlay and mill & fill. The effectiveness and cost-effectiveness decreased with the increase of traffic level and pre-treatment pavement condition.
Investigation on the LTPP resurfacing treatments indicated that thick overlay and milling reduced the roughness after rehabilitation. Thin overlay, high traffic level and poor pre-rehabilitation pavement condition increased the deterioration rate of new overlay. Using reclaimed asphalt material did not influence the treatment performance but was cost-effective in reducing the roughness of new overlay. For a certain deterioration rate, there was an optimized pre-rehabilitation roughness value or time for applying maintenance treatment.
Survival analysis on the crack initiation of asphalt overlay indicated that high traffic level accelerated the initiation of cracking. Thick overlay delayed the initiation of cracking except for the non-wheel path longitudinal crack. Mill retarded the occurrence of the non-fatigue cracks, whereas severe freeze thaw condition accelerated the occurrence of the two types of cracking. Using 30% RAP accelerated the initiation of longitudinal fatigue crack on wheel path but did not cause serious fatigue problem.
The performance curves of HMA resurfacing treatments used in Tennessee were calibrated by investigating the influence of different factors on the slopes and intercepts of post-treatment performance curves. The analysis indicated that pavement with high pre-treatment PSI, thick overlay and deep milling had low deterioration rate, whereas pavement with higher traffic level deteriorated faster
PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms
Mobile phones provide a powerful sensing platform that researchers may adopt
to understand proximity interactions among people and the diffusion, through
these interactions, of diseases, behaviors, and opinions. However, it remains a
challenge to track the proximity-based interactions of a whole community and
then model the social diffusion of diseases and behaviors starting from the
observations of a small fraction of the volunteer population. In this paper, we
propose a novel approach that tries to connect together these sparse
observations using a model of how individuals interact with each other and how
social interactions happen in terms of a sequence of proximity interactions. We
apply our approach to track the spreading of flu in the spatial-proximity
network of a 3000-people university campus by mobilizing 300 volunteers from
this population to monitor nearby mobile phones through Bluetooth scanning and
to daily report flu symptoms about and around them. Our aim is to predict the
likelihood for an individual to get flu based on how often her/his daily
routine intersects with those of the volunteers. Thus, we use the daily
routines of the volunteers to build a model of the volunteers as well as of the
non-volunteers. Our results show that we can predict flu infection two weeks
ahead of time with an average precision from 0.24 to 0.35 depending on the
amount of information. This precision is six to nine times higher than with a
random guess model. At the population level, we can predict infectious
population in a two-week window with an r-squared value of 0.95 (a random-guess
model obtains an r-squared value of 0.2). These results point to an innovative
approach for tracking individuals who have interacted with people showing
symptoms, allowing us to warn those in danger of infection and to inform health
researchers about the progression of contact-induced diseases
- …