Terrestrial water storage anomalies emphasize interannual variations in global mean sea level during 1997-1998 and 2015-2016 El Nino Events

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Kuo, Y.-N., Lo, M.-H., Liang, Y.-C., Tseng, Y.-H., & Hsu, C.-W. Terrestrial water storage anomalies emphasize interannual variations in global mean sea level during 1997-1998 and 2015-2016 El Nino Events. Geophysical Research Letters, 48(18), (2021): e2021GL094104, https://doi.org/10.1029/2021GL094104.Interannual variations in global mean sea level (GMSL) closely correlate with the evolution of El Niño-Southern Oscillation. However, GMSL differences occur in extreme El Niños; for example, in the 2015–2016 and 1997–1998 El Niños, the peak GMSL during the mature stage of the former (9.00 mm) is almost 2.5 times higher than the latter (3.72 mm). Analyses from satellite and reanalysis data sets show that the disparity in GMSL is primarily due to barystatic (ocean mass) changes. We find that the 2015–2016 event developed not purely as an Eastern Pacific El Niño event but with Central Pacific (CP) El Niño forcing. CP El Niños contribute to a stronger negative anomaly of global terrestrial water storage and subsequent higher barystatic heights. Our results suggest that the mechanism of hydrology-related interannual variations of GMSL should be further emphasized, as more CP El Niño events are projected to occur.This study was supported by a grant of MOST 106-2111-M-002-010-MY4 to National Taiwan University

Decision Support System For Safety Warning Of Bridge – A Case Study In Central Taiwan

This study aims at developing the decision support system (DSS) for safety warning of bridge. In the DSS, real-time and forecasted radar rainfalls are used to predict flood stage, velocity and scouring depth around bridge piers for one to three hours ahead. The techniques adopted in the DSS include (1) measurement and correction models of radar rainfall, (2) a grid-based distributed rainfall-runoff model for simulating reservoir inflows, (3) models for predicting flood stages, velocities and scouring depths around bridge piers, and (4) ultimate analysis approaches for evaluating safety of pier foundation. The DSS can support the management department to decide whether they should close bridges or not during floods. The proposed DSS gave a test-run during Typhoon Morakot in 2009 in Dajia River Basin, central Taiwan. The results show the DSS has reasonable performances during floods

Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution

Implicit neural representation has recently shown a promising ability in representing images with arbitrary resolutions. In this paper, we present a Local Implicit Transformer (LIT), which integrates the attention mechanism and frequency encoding technique into a local implicit image function. We design a cross-scale local attention block to effectively aggregate local features. To further improve representative power, we propose a Cascaded LIT (CLIT) that exploits multi-scale features, along with a cumulative training strategy that gradually increases the upsampling scales during training. We have conducted extensive experiments to validate the effectiveness of these components and analyze various training strategies. The qualitative and quantitative results demonstrate that LIT and CLIT achieve favorable results and outperform the prior works in arbitrary super-resolution tasks

Comparing the outcomes of two strategies for colorectal tumor detection: Policy-promoted screening program versus health promotion service

AbstractBackgroundThe Taiwanese government has proposed a population-based colorectal tumor detection program for the average-risk population. This study's objectives were to understand the outcomes of these screening policies and to evaluate the effectiveness of the program.MethodsWe compared two databases compiled in one medical center. The “policy-promoted cancer screening” (PPS) database was built on the basis of the policy of the Taiwan Bureau of National Health Insurance for cancer screening. The “health promotion service” (HPS) database was built to provide health check-ups for self-paid volunteers. Both the PPS and HPS databases employ the immunochemical fecal occult blood test (iFOBT) and colonoscopy for colorectal tumor screening using different strategies. A comparison of outcomes between the PPS and HPS included: (1) quality indicators—compliance rate, cecum reaching rate, and tumor detection rate; and (2) validity indicators—sensitivity, specificity, positive, and negative predictive values for detecting colorectal neoplasms.ResultsA total of 10,563 and 1481 individuals were enrolled in PPS and HPS, respectively. Among quality indicators, there was no statistically significant difference in the cecum reaching rate between PPS and HPS. The compliance rates were 56.1% for PPS and 91.8% for HPS (p < 0.001). The advanced adenoma detection rates of PPS and HPS were 1.0% and 3.6%, respectively (p < 0.01). The carcinoma detection rates were 0.3% and 0.4%, respectively (p = 0.59). For validity indicators, PPS provides only a positive predictive value for colorectal tumor detection. HPS provides additional validity indicators, including sensitivity, specificity, positive predictive value, and negative predictive value, for colorectal tumor screening.ConclusionIn comparison with the outcomes of the HPS database, the screening efficacy of the PPS database is even for detecting colorectal carcinoma but is limited in detecting advanced adenoma. HPS may provide comprehensive validity indicators and will be helpful in adjusting current policies for improving screening performance

ImGeoNet: Image-induced Geometry-aware Voxel Representation for Multi-view 3D Object Detection

We propose ImGeoNet, a multi-view image-based 3D object detection framework that models a 3D space by an image-induced geometry-aware voxel representation. Unlike previous methods which aggregate 2D features into 3D voxels without considering geometry, ImGeoNet learns to induce geometry from multi-view images to alleviate the confusion arising from voxels of free space, and during the inference phase, only images from multiple views are required. Besides, a powerful pre-trained 2D feature extractor can be leveraged by our representation, leading to a more robust performance. To evaluate the effectiveness of ImGeoNet, we conduct quantitative and qualitative experiments on three indoor datasets, namely ARKitScenes, ScanNetV2, and ScanNet200. The results demonstrate that ImGeoNet outperforms the current state-of-the-art multi-view image-based method, ImVoxelNet, on all three datasets in terms of detection accuracy. In addition, ImGeoNet shows great data efficiency by achieving results comparable to ImVoxelNet with 100 views while utilizing only 40 views. Furthermore, our studies indicate that our proposed image-induced geometry-aware representation can enable image-based methods to attain superior detection accuracy than the seminal point cloud-based method, VoteNet, in two practical scenarios: (1) scenarios where point clouds are sparse and noisy, such as in ARKitScenes, and (2) scenarios involve diverse object classes, particularly classes of small objects, as in the case in ScanNet200.Comment: ICCV'23; project page: https://ttaoretw.github.io/imgeonet