End-to-End Differentiable Learning to HDR Image Synthesis for Multi-exposure Images

Recently, high dynamic range (HDR) image reconstruction based on the multiple exposure stack from a given single exposure utilizes a deep learning framework to generate high-quality HDR images. These conventional networks focus on the exposure transfer task to reconstruct the multi-exposure stack. Therefore, they often fail to fuse the multi-exposure stack into a perceptually pleasant HDR image as the inversion artifacts occur. We tackle the problem in stack reconstruction-based methods by proposing a novel framework with a fully differentiable high dynamic range imaging (HDRI) process. By explicitly using the loss, which compares the network's output with the ground truth HDR image, our framework enables a neural network that generates the multiple exposure stack for HDRI to train stably. In other words, our differentiable HDR synthesis layer helps the deep neural network to train to create multi-exposure stacks while reflecting the precise correlations between multi-exposure images in the HDRI process. In addition, our network uses the image decomposition and the recursive process to facilitate the exposure transfer task and to adaptively respond to recursion frequency. The experimental results show that the proposed network outperforms the state-of-the-art quantitative and qualitative results in terms of both the exposure transfer tasks and the whole HDRI process

Analytical Modeling of Rheological Postbuckling Behavior of Wood-Based Composite Panels Under Cyclic Hygro-Loading

This study was conducted to develop analytical models to predict postbuckling behavior of woodbased composite panels under cyclic humidity conditions. Both the Rayleigh method and von Karman theory of nonlinear plate with imperfection were used to obtain a closed form solution to the hygrobuckling and postbuckling. In addition, mechano-sorptive creep effects were also taken into account for the derivation of analytical models. The closed-form solutions derived for both isotropic and orthotropic materials showed a good agreement with the experimental results in terms of the center deformation of hardboard, especially in the case of the edge movements. The unrecovery deformation was much greater at the first cycle and then decreased as the number of cyclic hygro-loading increased

Resonance of Domain Wall in a Ferromagnetic Nanostrip: Relation Between Distortion and Velocity

The resonance of the magnetic domain wall under the applied field amplifies its velocity compared to the one-dimensional model. To quantify the amplification, we define the distortion variation rate of the domain wall that can represent how fast and severely the wall shape is variated. Introducing that rate gives a way to bring the resonance into the one-dimensional domain wall dynamics model. We obtain the dissipated energy and domain wall velocity amplification by calculating the distortion variation rate. The relationship between velocity and distortion variation rate agrees well with micromagnetic simulation.Comment: 15 pages, 4 figure

Myricetin: A Naturally Occurring Regulator of Metal-Induced Amyloid-β Aggregation and Neurotoxicity

No AbstractPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/84385/1/1198_ftp.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/84385/2/cbic_201000790_sm_miscellaneous_information.pd

Digital Workflow for Retrofitting a Surveyed Crown Using a Removable Partial Denture as an Antagonist

Digital workflow expedites the procedure of retrofitting a surveyed crown against an existing removable partial denture (RPD). This article describes a simple and straightforward technique of digital workflow where an existing RPD is scanned as an antagonist to design the rest seat, guide plane, and height of contour of a surveyed crown.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156192/2/jopr13187_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156192/1/jopr13187.pd

Novel Architecture of OneM2M-Based Convergence Platform for Mixed Reality and IoT

There have been numerous works proposed to merge augmented reality/mixed reality (AR/MR) and Internet of Things (IoT) in various ways. However, they have focused on their specific target applications and have limitations on interoperability or reusability when utilizing them to different domains or adding other devices to the system. This paper proposes a novel architecture of a convergence platform for AR/MR and IoT systems and services. The proposed architecture adopts the oneM2M IoT standard as the basic framework that converges AR/MR and IoT systems and enables the development of application services used in general-purpose environments without being subordinate to specific systems, domains, and device manufacturers. We implement the proposed architecture utilizing the open-source oneM2M-based IoT server and device platforms released by the open alliance for IoT standards (OCEAN) and Microsoft HoloLens as an MR device platform. We also suggest and demonstrate the practical use cases and discuss the advantages of the proposed architecture

Comparative Studies on Toluene Removal and Pressure Drop in Biofilters Using Different Packing Materials.

Abstract: To select the best available packing material for malodorous organic gases such as toluene and benzene, biofilter performance was compared in biofilters employed different packing materials including porous ceramic (celite), Jeju scoria (lava), a mixture of granular activated carbon (GAC) and celite (GAC/celite), and cubic polyurethane foam (PU). A toluene-degrading bacterium, Stenotrophomonas maltophilia T3-c, was used as the inoculum. The maximum elimination capacities in the celite, lava, and GAC/celite biofilters were 100, 130, and 110 g m , which was 2 to 3.5 times higher than for the other biofilters. The pressure drop gradually increased in the GAC/ celite, celite and lava biofilters after 23 day due to bacterial over-growth, and the toluene removal efficiency remarkably decreased with increasing pressure drop. Backwashing method was not effective for the control of biomass in these biofilters. In the PU biofilter, however, backwashing allowed maintenance of a pressure drop of 1 to 3 mm H 2 O m -1 and a removal efficiency of > 80%, indicating that the PU was the best packing material for toluene removal among the packing materials tested

Recent Development of Bifunctional Small Molecules to Study Metal-Amyloid-β Species in Alzheimer's Disease

Alzheimer's disease (AD) is a multifactorial neurodegenerative disease related to the deposition of aggregated amyloid-β (Aβ) peptides in the brain. It has been proposed that metal ion dyshomeostasis and miscompartmentalization contribute to AD progression, especially as metal ions (e.g., Cu(II) and Zn(II)) found in Aβ plaques of the diseased brain can bind to Aβ and be linked to aggregation and neurotoxicity. The role of metal ions in AD pathogenesis, however, is uncertain. To accelerate understanding in this area and contribute to therapeutic development, recent efforts to devise suitable chemical reagents that can target metal ions associated with Aβ have been made using rational structure-based design that combines two functions (metal chelation and Aβ interaction) in the same molecule. This paper presents bifunctional compounds developed by two different design strategies (linkage or incorporation) and discusses progress in their applications as chemical tools and/or potential therapeutics