ELECTRIC AND FERROELECTRIC CONTROL OF ELECTRONIC AND MAGNETIC PROPERTIES OF OXIDE HETEROSTRUCTURES

Ph.DDOCTOR OF PHILOSOPH

Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation

Recent studies have witnessed that self-supervised methods based on view synthesis obtain clear progress on multi-view stereo (MVS). However, existing methods rely on the assumption that the corresponding points among different views share the same color, which may not always be true in practice. This may lead to unreliable self-supervised signal and harm the final reconstruction performance. To address the issue, we propose a framework integrated with more reliable supervision guided by semantic co-segmentation and data-augmentation. Specially, we excavate mutual semantic from multi-view images to guide the semantic consistency. And we devise effective data-augmentation mechanism which ensures the transformation robustness by treating the prediction of regular samples as pseudo ground truth to regularize the prediction of augmented samples. Experimental results on DTU dataset show that our proposed methods achieve the state-of-the-art performance among unsupervised methods, and even compete on par with supervised methods. Furthermore, extensive experiments on Tanks&Temples dataset demonstrate the effective generalization ability of the proposed method.Comment: This paper is accepted by AAAI-21 with a Distinguished Paper Awar

Semi-supervised Deep Multi-view Stereo

Significant progress has been witnessed in learning-based Multi-view Stereo (MVS) under supervised and unsupervised settings. To combine their respective merits in accuracy and completeness, meantime reducing the demand for expensive labeled data, this paper explores the problem of learning-based MVS in a semi-supervised setting that only a tiny part of the MVS data is attached with dense depth ground truth. However, due to huge variation of scenarios and flexible settings in views, it may break the basic assumption in classic semi-supervised learning, that unlabeled data and labeled data share the same label space and data distribution, named as semi-supervised distribution-gap ambiguity in the MVS problem. To handle these issues, we propose a novel semi-supervised distribution-augmented MVS framework, namely SDA-MVS. For the simple case that the basic assumption works in MVS data, consistency regularization encourages the model predictions to be consistent between original sample and randomly augmented sample. For further troublesome case that the basic assumption is conflicted in MVS data, we propose a novel style consistency loss to alleviate the negative effect caused by the distribution gap. The visual style of unlabeled sample is transferred to labeled sample to shrink the gap, and the model prediction of generated sample is further supervised with the label in original labeled sample. The experimental results in semi-supervised settings of multiple MVS datasets show the superior performance of the proposed method. With the same settings in backbone network, our proposed SDA-MVS outperforms its fully-supervised and unsupervised baselines.Comment: This paper is accepted in ACMMM-2023. The code is released at: https://github.com/ToughStoneX/Semi-MV

Photometry of Variable Stars from THU-NAOC Transient Survey I: The First 2 Years

In this paper, we report the detections of stellar variabilities from the first 2-year observations of sky area of about 1300 square degrees from the Tsinghua University-NAOC Transient Survey (TNTS). A total of 1237 variable stars (including 299 new ones) were detected with brightness < 18.0 mag and magnitude variation >= 0.1 mag on a timescale from a few hours to few hundred days. Among such detections, we tentatively identified 661 RR Lyrae stars, 431 binaries, 72 Semiregular pulsators, 29 Mira stars, 11 slow irregular variables, 11 RS Canum Venaticorum stars, 7 Gamma Doradus stars, 5 long period variables, 3 W Virginis stars, 3 Delta Scuti stars, 2 Anomalous Cepheids, 1 Cepheid, and 1 nove-like star based on their time-series variability index Js and their phased diagrams. Moreover, we found that 14 RR Lyrae stars show the Blazhko effect and 67 contact eclipsing binaries exhibit the O'Connell effect. Since the period and amplitude of light variations of RR Lyrae variables depend on their chemical compositions, their photometric observations can be used to investigate distribution of metallicity along the direction perpendicular to the Galactic disk. We find that the metallicity of RR Lyrae stars shows large scatter at regions closer to the Galactic plane (e.g., -3.0 < [Fe/H] < 0) but tends to converge at [Fe/H]~ -1.7 at larger Galactic latitudes. This variation may be related to that the RRAB Lyrae stars in the Galactic halo come from globular clusters with different metallicity and vertical distances, i.e. OoI and OoII populations, favoring for the dual-halo model.Comment: 18 pages, 19 figures, published in AJ, 150, 10

A novel oligomer containing DOPO and ferrocene groups: Synthesis, characterization, and its application in fire retardant epoxy resin

A novel oligomer (PFDCHQ) based on 9,10-dihydro-9-oxa-10-phosphaphenanthrene −10-oxide (DOPO) and ferrocene groups was synthesized successfully, aiming at improving the flame retardant efficiency of diglycidyl ether of bisphenol A epoxy resin (DGEBA). FTIR, 1H NMR and 31P NMR were used to confirm the chemical structure of PFDCHQ. The high char yields of 60.3 wt% and 20.1 wt% were obtained for PFDCHQ from TGA results in nitrogen and air atmosphere, respectively. The thermal degradation mechanism of PFDCHQ was investigated by TG-FTIR and Py-GC/MS. The limiting oxygen index (LOI) of EP-5 with 5 wt% loading of PFDCHQ increased to 32.0% and the UL-94 V-0 rating was achieved, showing a notable blowing-out effect. In contrast to EP-0, the peak of the heat release rate (pHRR) and total heat release (THR) of EP-5 decreased by 18.0% and 10.3%. The flame retardant mechanism of PFDCHQ in epoxy resin was studied by TG-FTIR, SEM and Raman. SEM and Raman results indicated the formation of coherent and dense char residue with high degree of graphitization due to the incorporation of PFDCHQ. In UL-94, the blowing-out effect dominantly accounted for the enhanced flame retardancy in combination with optimized char structure. Furthermore, the addition of PFDCHQ improved the Young's modulus compared to EP-0