FAF: A novel multimodal emotion recognition approach integrating face, body and text

Multimodal emotion analysis performed better in emotion recognition depending on more comprehensive emotional clues and multimodal emotion dataset. In this paper, we developed a large multimodal emotion dataset, named "HED" dataset, to facilitate the emotion recognition task, and accordingly propose a multimodal emotion recognition method. To promote recognition accuracy, "Feature After Feature" framework was used to explore crucial emotional information from the aligned face, body and text samples. We employ various benchmarks to evaluate the "HED" dataset and compare the performance with our method. The results show that the five classification accuracy of the proposed multimodal fusion method is about 83.75%, and the performance is improved by 1.83%, 9.38%, and 21.62% respectively compared with that of individual modalities. The complementarity between each channel is effectively used to improve the performance of emotion recognition. We had also established a multimodal online emotion prediction platform, aiming to provide free emotion prediction to more users

Fourier Ptychography Reconstruction Based on Reweighted Amplitude Flow With Regularization by Denoising and Deep Decoder

Fourier ptychography (FP) is a computational imaging technique with the advantage that it can obtain large field-of-view (FOV) and high-resolution (HR) imaging. We propose an algorithm for Fourier ptychography based on reweighted amplitude flow (RAF) with regularization by denoising (RED) and deep decoder (DD), which is an untrained deep generative model. The proposed method includes two loops, using reweighted amplitude flow with regularization by denoising as an inner loop for phase retrieval and deep decoder for further denoising as an outer loop in the Fourier ptychography recovery system. The proposed method does not need any training dataset, just adds a little computer time during the image recovery process. The proposed method has no bias due to training images, which is different from other deep learning methods. The experimental results show that the proposed method can improve the reconstruction quality in both PSNR and SSIM

Modification of FA0.85MA0.15Pb(I0.85Br0.15)3 Films by NH2-POSS

The surface composition and morphology of FA0.85MA0.15Pb(I0.85Br0.15)3 films fabricated by the spin-coating method with different concentrations of NH2-POSS were investigated with atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), angle-resolved X-ray photoelectron spectroscopy (AR-XPS), and Fourier transform infrared spectroscopy (FTIR). It was found that the surface composition of the FA0.85MA0.15Pb(I0.85Br0.15)3 films was changed regularly through the interaction between NH2-POSS and the perovskite film. The corresponding surface morphological changes were also observed. When the concentration of NH2-POSS exceeded 10 mg/mL, a lot of cracks on the surface of the perovskite film were observed and the surface morphology was damaged. The surface composition and its distribution can be adjusted by changing the concentration of NH2-POSS and the proper concentration of NH2-POSS can substantially improve the quality of perovskite film

Modification of FA_0.85MA_0.15Pb(I_0.85Br_0.15)₃ Films by NH₂-POSS

The surface composition and morphology of FA0.85MA0.15Pb(I0.85Br0.15)3 films fabricated by the spin-coating method with different concentrations of NH2-POSS were investigated with atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), angle-resolved X-ray photoelectron spectroscopy (AR-XPS), and Fourier transform infrared spectroscopy (FTIR). It was found that the surface composition of the FA0.85MA0.15Pb(I0.85Br0.15)3 films was changed regularly through the interaction between NH2-POSS and the perovskite film. The corresponding surface morphological changes were also observed. When the concentration of NH2-POSS exceeded 10 mg/mL, a lot of cracks on the surface of the perovskite film were observed and the surface morphology was damaged. The surface composition and its distribution can be adjusted by changing the concentration of NH2-POSS and the proper concentration of NH2-POSS can substantially improve the quality of perovskite film

ET White Paper: To Find the First Earth 2.0

We propose to develop a wide-field and ultra-high-precision photometric survey mission, temporarily named "Earth 2.0 (ET)". This mission is designed to measure, for the first time, the occurrence rate and the orbital distributions of Earth-sized planets. ET consists of seven 30cm telescopes, to be launched to the Earth-Sun's L2 point. Six of these are transit telescopes with a field of view of 500 square degrees. Staring in the direction that encompasses the original Kepler field for four continuous years, this monitoring will return tens of thousands of transiting planets, including the elusive Earth twins orbiting solar-type stars. The seventh telescope is a 30cm microlensing telescope that will monitor an area of 4 square degrees toward the galactic bulge. This, combined with simultaneous ground-based KMTNet observations, will measure masses for hundreds of long-period and free-floating planets. Together, the transit and the microlensing telescopes will revolutionize our understandings of terrestrial planets across a large swath of orbital distances and free space. In addition, the survey data will also facilitate studies in the fields of asteroseismology, Galactic archeology, time-domain sciences, and black holes in binaries.Comment: 116 pages,79 figure

The Seventh Visual Object Tracking VOT2019 Challenge Results

The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2019 focused on long-term tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard short-term, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).Funding Agencies|Slovenian research agencySlovenian Research Agency - Slovenia [J2-8175, P2-0214, P2-0094]; Czech Science Foundation Project GACR [P103/12/G084]; MURI project - MoD/DstlMURI; EPSRCEngineering &amp; Physical Sciences Research Council (EPSRC) [EP/N019415/1]; WASP; VR (ELLIIT, LAST, and NCNN); SSF (SymbiCloud); AIT Strategic Research Programme; Faculty of Computer Science, University of Ljubljana, Slovenia</p