567 research outputs found
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
Decomposing complex time series into trend, seasonality, and remainder
components is an important task to facilitate time series anomaly detection and
forecasting. Although numerous methods have been proposed, there are still many
time series characteristics exhibiting in real-world data which are not
addressed properly, including 1) ability to handle seasonality fluctuation and
shift, and abrupt change in trend and reminder; 2) robustness on data with
anomalies; 3) applicability on time series with long seasonality period. In the
paper, we propose a novel and generic time series decomposition algorithm to
address these challenges. Specifically, we extract the trend component robustly
by solving a regression problem using the least absolute deviations loss with
sparse regularization. Based on the extracted trend, we apply the the non-local
seasonal filtering to extract the seasonality component. This process is
repeated until accurate decomposition is obtained. Experiments on different
synthetic and real-world time series datasets demonstrate that our method
outperforms existing solutions.Comment: Accepted to the thirty-third AAAI Conference on Artificial
Intelligence (AAAI 2019), 9 pages, 5 figure
Significant late Jurassic counterclockwise rotations of the Yanshiping region, east North Qiangtang terrane, implication on Lhasa - Qiangtang initial collision
Abstract HKT-ISTP 2013
A
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
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
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