Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation

While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we propose a novel deep learning model of Cross-Domain Representation Disentangler (CDRD). By observing fully annotated source-domain data and unlabeled target-domain data of interest, our model bridges the information across data domains and transfers the attribute information accordingly. Thus, cross-domain joint feature disentanglement and adaptation can be jointly performed. In the experiments, we provide qualitative results to verify our disentanglement capability. Moreover, we further confirm that our model can be applied for solving classification tasks of unsupervised domain adaptation, and performs favorably against state-of-the-art image disentanglement and translation methods.Comment: CVPR 2018 Spotligh

Study of Alternative GPS Network Meteorological Sensors in Taiwan: Case Studies of the Plum Rains and Typhoon Sinlaku

Plum rains and typhoons are important weather systems in the Taiwan region. They can cause huge economic losses, but they are also considered as important water resources as they strike Taiwan annually and fill the reservoirs around the island. There are many meteorological sensors available for investigating the characteristics of weather and climate systems. Recently, the use of GPS as an alternative meteorological sensor has become popular due to the catastrophic impact of global climate change. GPS provides meteorological parameters mainly from the atmosphere. Precise Point Positioning (PPP) is a proven algorithm that has attracted attention in GPS related studies. This study uses GPS measurements collected at more than fifty reference stations of the e-GPS network in Taiwan. The first data set was collected from June 1st 2008 to June 7th 2008, which corresponds to the middle of the plum rain season in Taiwan. The second data set was collected from September 11th to September 17th 2008 during the landfall of typhoon Sinlaku. The data processing strategy is to process the measurements collected at the reference stations of the e-GPS network using the PPP technique to estimate the zenith tropospheric delay (ZTD) values of the sites; thus, the correlations between the ZTD values and the variation of rainfall during the plum rains and typhoon are analyzed. In addition, several characteristics of the meteorological events are identified using spatial and temporal analyses of the ZTD values estimated with the GPS network PPP technique

Direct Large-Area Growth of Graphene on Silicon for Potential Ultra-Low-Friction Applications and Silicon-Based Technologies

Deposition of layers of graphene on silicon has the potential for a wide range of optoelectronic and mechanical applications. However, direct growth of graphene on silicon has been difficult due to the inert, oxidized silicon surfaces. Transferring graphene from metallic growth substrates to silicon is not a good solution either, because most transfer methods involve multiple steps that often lead to polymer residues or degradation of sample quality. Here we report a single-step method for large-area direct growth of continuous horizontal graphene sheets and vertical graphene nano-walls on silicon substrates by plasma-enhanced chemical vapor deposition (PECVD) without active heating. Comprehensive studies utilizing Raman spectroscopy, x-ray/ultraviolet photoelectron spectroscopy (XPS/UPS), atomic force microscopy (AFM), scanning electron microscopy (SEM) and optical transmission are carried out to characterize the quality and properties of these samples. Data gathered by the residual gas analyzer (RGA) during the growth process further provide information about the synthesis mechanism. Additionally, ultra-low friction (with a frictional coefficient ~0.015) on multilayer graphene-covered silicon surface is achieved, which is approaching the superlubricity limit (for frictional coefficients <0.01). Our growth method therefore opens up a new pathway towards scalable and direct integration of graphene into silicon technology for potential applications ranging from structural superlubricity to nanoelectronics, optoelectronics, and even the next-generation lithium-ion batteries

Direct growth of mm-size twisted bilayer graphene by plasma-enhanced chemical vapor deposition

Plasma enhanced chemical vapor deposition (PECVD) techniques have been shown to be an efficient method to achieve single-step synthesis of high-quality monolayer graphene (MLG) without the need of active heating. Here we report PECVD-growth of single-crystalline hexagonal bilayer graphene (BLG) flakes and mm-size BLG films with the interlayer twist angle controlled by the growth parameters. The twist angle has been determined by three experimental approaches, including direct measurement of the relative orientation of crystalline edges between two stacked monolayers by scanning electron microscopy, analysis of the twist angle-dependent Raman spectral characteristics, and measurement of the Moiré period with scanning tunneling microscopy. In mm-sized twisted BLG (tBLG) films, the average twist angle can be controlled from 0° to approximately 20°, and the angular spread for a given growth condition can be limited to < 7°. Different work functions between MLG and BLG have been verified by the Kelvin probe force microscopy and ultraviolet photoelectron spectroscopy. Electrical measurements of back-gated field-effect-transistor devices based on small-angle tBLG samples revealed high-quality electric characteristics at 300 K and insulating temperature dependence down to 100 K. This controlled PECVD-growth of tBLG thus provides an efficient approach to investigate the effect of varying Moiré potentials on tBLG

Full-Color Micro-LED Devices Based on Quantum Dots

Quantum dots (QDs) show remarkable optical and electrical characteristics. They offer the advantage of combining micro-LEDs (μLEDs) for full-color display devices due to their exceptional features. In addition, μLED used in conjunction with QDs as color-conversion layers also provide efficient white LEDs for high-speed visible light communication (VLC). In this article, we comprehensively review recent progress in QD-based μLED devices. It includes the research status of various QDs and white LEDs based on QDs’ color conversion layers. The fabrication of QD-based high-resolution full-color μLEDs is also discussed. Including charge-assisted layer-by-layer (LbL), aerosol jet printing, and super inkjet printing methods to fabricate QD-based μLEDs. The use of quantum dot photoresist in combination with semipolar μLEDs is also described. Finally, we discuss the research of QD-based μLEDs for visible light communication

Imperatorin ameliorates pulmonary fibrosis via GDF15 expression

Background: Pulmonary fibrosis features in damaged pulmonary structure or over-produced extracellular matrix and impaired lung function, leading to respiratory failure and eventually death. Fibrotic lungs are characterized by the secretion of pro-fibrotic factors, transformation of fibroblasts to myofibroblasts, and accumulation of matrix proteins.Hypothesis/purpose: Imperatorin shows anti-inflammatory effects on alveolar macrophages against acute lung injury. We attempt to evaluate the properties of imperatorin on the basis of fibroblasts.Methods: In in vitro, zymosan was introduced to provoke pro-fibrotic responses in NIH/3T3 or MRC-5 pulmonary fibroblasts. Imperatorin was given for examining its effects against fibrosis. The mice were stimulated by bleomycin, and imperatorin was administered to evaluate the prophylactic potential in vivo.Results: The upregulated expression of connective tissue growth factor (CTGF), α-smooth muscle actin (α-SMA), and collagen protein due to zymosan introduction was decreased by imperatorin in fibroblasts. Zymosan induced the activity of transglutaminase 2 (TGase2) and lysyl oxidase (LOX), which was also inhibited by the administration of imperatorin. Imperatorin alone enhanced sirtuin 1 (SIRT1) activity and growth differentiation factor 15 (GDF15) secretion in fibroblasts via LKB1/AMPK/CREB pathways. In addition, GDF15 exerted a beneficial effect by reducing the protein expression of CTGF, α-SMA, and collagen and the activities of TGase and LOX. Moreover, orally administered imperatorin showed prophylactic effects on bleomycin-induced pulmonary fibrosis in mice.Conclusion: Imperatorin reduces fibrotic marker expression in fibroblasts and also increases GDF15 secretion via the LKB1/AMPK/CREB pathway, attenuating pro-fibrotic responses in vitro. Imperatorin also alleviates pulmonary fibrosis induced by bleomycin in vivo

Bis(4,4′-methyl­enedicyclo­hexyl­aminium) μ-benzene-1,4-dicarboxyl­ato-bis­[tri­chlorido­zinc(II)] tetra­hydrate

The title compound, (C13H28N2)2[Zn2(C8H4O4)Cl6]·4H2O, was prepared by the reaction of ZnCl2·6H2O, benzene-1,4-dicarboxylic acid and 4,4′-diamino­dicyclo­hexyl­methane in methanol. The [Zn2Cl6(C8H4O4)]4− anions lie on centres of inversion and comprise two ZnCl3 groups bridged by benzene-1,4-dicarboxyl­ate. In addition to N—H⋯Cl and N—H⋯O hydrogen bonds between the cations and anions, solvent water mol­ecules form O—H⋯O and O—H⋯Cl hydrogen bonds to give a three-dimensional network

RVSL: Robust Vehicle Similarity Learning in Real Hazy Scenes Based on Semi-supervised Learning

Recently, vehicle similarity learning, also called re-identification (ReID), has attracted significant attention in computer vision. Several algorithms have been developed and obtained considerable success. However, most existing methods have unpleasant performance in the hazy scenario due to poor visibility. Though some strategies are possible to resolve this problem, they still have room to be improved due to the limited performance in real-world scenarios and the lack of real-world clear ground truth. Thus, to resolve this problem, inspired by CycleGAN, we construct a training paradigm called \textbf{RVSL} which integrates ReID and domain transformation techniques. The network is trained on semi-supervised fashion and does not require to employ the ID labels and the corresponding clear ground truths to learn hazy vehicle ReID mission in the real-world haze scenes. To further constrain the unsupervised learning process effectively, several losses are developed. Experimental results on synthetic and real-world datasets indicate that the proposed method can achieve state-of-the-art performance on hazy vehicle ReID problems. It is worth mentioning that although the proposed method is trained without real-world label information, it can achieve competitive performance compared to existing supervised methods trained on complete label information.Comment: Accepted by ECCV 202