Late Miocene-Quaternary Synchronous-But-Magnitude- Differentiated Episodic Rapid Uplifts of The NE Tibetan Plateau: A Synthesis From Flexural Basins

Abstract HKT-ISTP 2013 A

Significant late Jurassic counterclockwise rotations of the Yanshiping region, east North Qiangtang terrane, implication on Lhasa - Qiangtang initial collision

Abstract HKT-ISTP 2013 A

Gain Improvement of Er-doped Amplifiers for the Feedback Filters

The combination of the arsenic trisulfide (As2S3) waveguide and titanium diffused lithium niobate (Ti:LiNbO3) waveguide provide us compact and versatile means for transmitting and processing optical signals, which benefits from the high index contrast between these two materials and the electro-optical properties of Ti: LiNbO3. Furthermore, waveguide gain is introduced through selective surface erbium (Er) doping which yields high quality loss-compensated or even amplifying waveguides without disturbing the excellent electrooptical, acoustooptical and nonlinear properties of the waveguide substrate LiNbO3. The integration of these waveguides allows the development of a whole class of new waveguide devices of higher functionality and complexity. As one kind of the hybrid waveguide devices, a new configuration consisting of an As2S3 channel waveguide on top of an Er doped titanium diffused x-cut lithium niobate waveguide has been investigated by simultaneous analytical expressions, numerical simulations, and experimentation. Both simulation and experimental results have shown that this structure can enhance the optical gain, as predicted by the analytical expressions. An As2S3 channel waveguide has been fabricated on top of a conventional Er:Ti:LiNbO3 waveguide, where the higher refractive index As2S3 waveguide is used to pull the optical mode towards the substrate surface where the higher Er concentration yields an improved propagation gain. The relationship between the gain and As2S3 layer thickness has been evaluated and the optimal As2S3 thickness was found by simulation and experimentation. Side integration was applied to reduce the extra propagation loss caused by the titanium diffusion bump. The propagation gain (dB/cm) has been improved from 1.1 to 2 dB/cm. Another hybrid device which combines the As2S3 and LiNbO3 is to make an As2S3 racetrack ring resonator on top of an x-cut y-propagation Er:Ti:LiNbO3 waveguide which is the potential structure for integrated lossless all-path filter. The ring was side-coupled with the Ti:LiNbO3 waveguide and the optical gain was achieved when the 5mm long coupling region where has been diffused with Er in advance pumped by 144mW pump laser. The free spectral range (FSR) of the measured ring response for TM mode is 0.0587nm (7.33GHz) at 1550nm. The roundtrip loss are 4.4dB (2.60dB/cm) when pump on and 5.8dB (3.44dB/cm) when pump off. The optical gain in the Er diffused area is 0.72dB/cm

Self-supervised phase unwrapping in fringe projection profilometry

Fast-speed and high-accuracy three-dimensional (3D) shape measurement has been the goal all along in fringe projection profilometry (FPP). The dual-frequency temporal phase unwrapping method (DF-TPU) is one of the prominent technologies to achieve this goal. However, the period number of the high-frequency pattern of existing DF-TPU approaches is usually limited by the inevitable phase errors, setting a limit to measurement accuracy. Deep-learning-based phase unwrapping methods for single-camera FPP usually require labeled data for training. In this letter, a novel self-supervised phase unwrapping method for single-camera FPP systems is proposed. The trained network can retrieve the absolute fringe order from one phase map of 64-period and overperform DF-TPU approaches in terms of depth accuracy. Experimental results demonstrate the validation of the proposed method on real scenes of motion blur, isolated objects, low reflectivity, and phase discontinuity

Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection

Dynamic time warping (DTW) is an effective dissimilarity measure in many time series applications. Despite its popularity, it is prone to noises and outliers, which leads to singularity problem and bias in the measurement. The time complexity of DTW is quadratic to the length of time series, making it inapplicable in real-time applications. In this paper, we propose a novel time series dissimilarity measure named RobustDTW to reduce the effects of noises and outliers. Specifically, the RobustDTW estimates the trend and optimizes the time warp in an alternating manner by utilizing our designed temporal graph trend filtering. To improve efficiency, we propose a multi-level framework that estimates the trend and the warp function at a lower resolution, and then repeatedly refines them at a higher resolution. Based on the proposed RobustDTW, we further extend it to periodicity detection and outlier time series detection. Experiments on real-world datasets demonstrate the superior performance of RobustDTW compared to DTW variants in both outlier time series detection and periodicity detection

MFI2-AS1 enhances the survival of esophageal cancer cell via regulation of miR-331-3p/SOX4

Purpose: To investigate the specific role of melanotransferrin antisense RNA (MFI2-AS1) in esophageal cancer (EC) progression. Methods: The differential expression of MFI2-AS1 in EC tissues and cells was determined using quantitative reverse transcription–polymerase chain reaction (qRT-PCR). Silencing MFI2-AS1 was performed by transfection with specific short hairpin RNAs targeting MFI2-AS1. The 3-(4,5- dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay (MTT) and flow cytometry (FC) were used to assess cell viability and apoptosis of EC cells, respectively. The sponging microRNA (miRNA) of MFI2-AS1 was validated using luciferase activity and RNA immunoprecipitation assays while the downstream target gene of the sponging miRNA was evaluated by luciferase activity assay. Results: MFI2-AS1 was significantly enhanced in EC tissues (p < 0.01) and indicated a poor prognosis in EC patients. Knockdown of MFI2-AS1 in EC cells decreased cell viability and promoted cell apoptosis of EC cells. Functionally, MFI2-AS1 targeted miR-331-3p, and sex-determining region on Ychromosome-related high-mobility-group box4 (SOX4) was identified as a target gene of miR-331-3p. Ectopic expression of SOX4  counteracted the suppressive effect of MFI2-AS1 knockdown on EC cell viability and stimulative effect on EC cell apoptosis. Conclusion: The pro-oncogenic effect of MFI2-AS1 on EC progression occurs via the regulation of the miR-331-3p/SOX4 axis, providing a new potential therapeutic target for EC

Similarity-Aware Multimodal Prompt Learning for Fake News Detection

The standard paradigm for fake news detection mainly utilizes text information to model the truthfulness of news. However, the discourse of online fake news is typically subtle and it requires expert knowledge to use textual information to debunk fake news. Recently, studies focusing on multimodal fake news detection have outperformed text-only methods. Recent approaches utilizing the pre-trained model to extract unimodal features, or fine-tuning the pre-trained model directly, have become a new paradigm for detecting fake news. Again, this paradigm either requires a large number of training instances, or updates the entire set of pre-trained model parameters, making real-world fake news detection impractical. Furthermore, traditional multimodal methods fuse the cross-modal features directly without considering that the uncorrelated semantic representation might inject noise into the multimodal features. This paper proposes a Similarity-Aware Multimodal Prompt Learning (SAMPLE) framework. First, we incorporate prompt learning into multimodal fake news detection. Prompt learning, which only tunes prompts with a frozen language model, can reduce memory usage significantly and achieve comparable performances, compared with fine-tuning. We analyse three prompt templates with a soft verbalizer to detect fake news. In addition, we introduce the similarity-aware fusing method to adaptively fuse the intensity of multimodal representation and mitigate the noise injection via uncorrelated cross-modal features. For evaluation, SAMPLE surpasses the F1 and the accuracies of previous works on two benchmark multimodal datasets, demonstrating the effectiveness of the proposed method in detecting fake news. In addition, SAMPLE also is superior to other approaches regardless of few-shot and data-rich settings