Using a deep-learning approach to infer and forecast the Indonesian Throughflow transport from sea surface height

The Indonesian Throughflow (ITF) connects the tropical Pacific and Indian Oceans and is critical to the regional and global climate systems. Previous research indicates that the Indo-Pacific pressure gradient is a major driver of the ITF, implying the possibility of forecasting ITF transport by the sea surface height (SSH) of the Indo-Pacific Ocean. Here we used a deep-learning approach with the convolutional neural network (CNN) model to reproduce ITF transport. The CNN model was trained with a random selection of the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations and verified with residual components of the CMIP6 simulations. A test of the training results showed that the CNN model with SSH is able to reproduce approximately 90% of the total variance of ITF transport. The CNN model with CMIP6 was then transformed to the Simple Ocean Data Assimilation (SODA) dataset and this transformed model reproduced approximately 80% of the total variance of ITF transport in the SODA. A time series of ITF transport, verified by Monitoring the ITF (MITF) and International Nusantara Stratification and Transport (INSTANT) measurements of ITF, was then produced by the model using satellite observations from 1993 to 2021. We discovered that the CNN model can make a valid prediction with a lead time of 7 months, implying that the ITF transport can be predicted using the deep-learning approach with SSH data

Self-Supervised 3D Action Representation Learning with Skeleton Cloud Colorization

3D Skeleton-based human action recognition has attracted increasing attention in recent years. Most of the existing work focuses on supervised learning which requires a large number of labeled action sequences that are often expensive and time-consuming to annotate. In this paper, we address self-supervised 3D action representation learning for skeleton-based action recognition. We investigate self-supervised representation learning and design a novel skeleton cloud colorization technique that is capable of learning spatial and temporal skeleton representations from unlabeled skeleton sequence data. We represent a skeleton action sequence as a 3D skeleton cloud and colorize each point in the cloud according to its temporal and spatial orders in the original (unannotated) skeleton sequence. Leveraging the colorized skeleton point cloud, we design an auto-encoder framework that can learn spatial-temporal features from the artificial color labels of skeleton joints effectively. Specifically, we design a two-steam pretraining network that leverages fine-grained and coarse-grained colorization to learn multi-scale spatial-temporal features. In addition, we design a Masked Skeleton Cloud Repainting task that can pretrain the designed auto-encoder framework to learn informative representations. We evaluate our skeleton cloud colorization approach with linear classifiers trained under different configurations, including unsupervised, semi-supervised, fully-supervised, and transfer learning settings. Extensive experiments on NTU RGB+D, NTU RGB+D 120, PKU-MMD, NW-UCLA, and UWA3D datasets show that the proposed method outperforms existing unsupervised and semi-supervised 3D action recognition methods by large margins and achieves competitive performance in supervised 3D action recognition as well.Comment: This work is an extension of our ICCV 2021 paper [arXiv:2108.01959] https://openaccess.thecvf.com/content/ICCV2021/html/Yang_Skeleton_Cloud_Colorization_for_Unsupervised_3D_Action_Representation_Learning_ICCV_2021_paper.htm

GMLight: Lighting Estimation via Geometric Distribution Approximation

Lighting estimation from a single image is an essential yet challenging task in computer vision and computer graphics. Existing works estimate lighting by regressing representative illumination parameters or generating illumination maps directly. However, these methods often suffer from poor accuracy and generalization. This paper presents Geometric Mover's Light (GMLight), a lighting estimation framework that employs a regression network and a generative projector for effective illumination estimation. We parameterize illumination scenes in terms of the geometric light distribution, light intensity, ambient term, and auxiliary depth, and estimate them as a pure regression task. Inspired by the earth mover's distance, we design a novel geometric mover's loss to guide the accurate regression of light distribution parameters. With the estimated lighting parameters, the generative projector synthesizes panoramic illumination maps with realistic appearance and frequency. Extensive experiments show that GMLight achieves accurate illumination estimation and superior fidelity in relighting for 3D object insertion.Comment: 12 pages, 11 figures. arXiv admin note: text overlap with arXiv:2012.1111

A real-world study of anlotinib combined with GS regimen as first-line treatment for advanced pancreatic cancer

BackgroundAnlotinib may boost the efficacy of pancreatic cancer (PC) treatment if timely added to the GS regimen (Gemcitabine, Tegafur-gimeracil-oteracil potassium); however, no data has been published. This study evaluated the safety and efficacy of anlotinib in combination with the GS regimen(hereafter referred to as the A+GS regimen) in the first-line treatment of patients with unresectable or metastatic PC.MethodsPatients with unresectable or metastatic PC treated at Yueyang Central Hospital and Yueyang People’s Hospital between October 2018 and June 2022 were enrolled in this retrospective real-world investigation. Treatment efficacy was evaluated based on the overall survival (OS), progression-free survival (PFS), disease control rate (DCR), and objective response rate (ORR), while the treatment safety was assessed by the frequency of major adverse events (AEs).ResultsSeventy-one patients were included in this study, 41 in the GS group and 30 in the A+GS group. The A+GS group had a longer mPFS than the GS group (12.0 months (95% CI, 6.0–18.0) and 6.0 months (95% CI, 3.0–8.1)), respectively (P = 0.005). mOS was longer in the GS+A group) when compared with the GS group (17.0 months (95%CI, 14.0–20.0) and 10.0 months (95% CI, 7.5–12.5)), respectively (P = 0.018). The GS+A group had higher ORR (50.0% vs 26.8%, P = 0.045) and DCR (83.3% vs 58.5%, P = 0.026). Furthermore, there were no grade 4-5 AEs and no treatment-related deaths, and no discernible increase in AEs in the GS+A group when compared with the GS group.ConclusionThe A+GS regimen therapy holds great promise in managing treatment-naive advanced PC, except that future prospective studies with larger sample sizes and multiple centers are required to determine its efficacy and safety

Detecting change in the Indonesian Seas

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Sprintall, J., Gordon, A. L., Wijffels, S. E., Feng, M., Hu, S., Koch-Larrouy, A., Phillips, H., Nugroho, D., Napitu, A., Pujiana, K., Susanto, R. D., Sloyan, B., Yuan, D., Riama, N. F., Siswanto, S., Kuswardani, A., Arifin, Z., Wahyudi, A. J., Zhou, H., Nagai, T., Ansong, J. K., Bourdalle-Badie, R., Chanuts, J., Lyard, F., Arbic, B. K., Ramdhani, A., & Setiawan, A. Detecting change in the Indonesian Seas. Frontiers in Marine Science, 6, (2019):257, doi:10.3389/fmars.2019.00257.The Indonesian seas play a fundamental role in the coupled ocean and climate system with the Indonesian Throughflow (ITF) providing the only tropical pathway connecting the global oceans. Pacific warm pool waters passing through the Indonesian seas are cooled and freshened by strong air-sea fluxes and mixing from internal tides to form a unique water mass that can be tracked across the Indian Ocean basin and beyond. The Indonesian seas lie at the climatological center of the atmospheric deep convection associated with the ascending branch of the Walker Circulation. Regional SST variations cause changes in the surface winds that can shift the center of atmospheric deep convection, subsequently altering the precipitation and ocean circulation patterns within the entire Indo-Pacific region. Recent multi-decadal changes in the wind and buoyancy forcing over the tropical Indo-Pacific have directly affected the vertical profile, strength, and the heat and freshwater transports of the ITF. These changes influence the large-scale sea level, SST, precipitation and wind patterns. Observing long-term changes in mass, heat and freshwater within the Indonesian seas is central to understanding the variability and predictability of the global coupled climate system. Although substantial progress has been made over the past decade in measuring and modeling the physical and biogeochemical variability within the Indonesian seas, large uncertainties remain. A comprehensive strategy is needed for measuring the temporal and spatial scales of variability that govern the various water mass transport streams of the ITF, its connection with the circulation and heat and freshwater inventories and associated air-sea fluxes of the regional and global oceans. This white paper puts forward the design of an observational array using multi-platforms combined with high-resolution models aimed at increasing our quantitative understanding of water mass transformation rates and advection within the Indonesian seas and their impacts on the air-sea climate system. IntroductionJS acknowledges funding to support her effort by the National Science Foundation under Grant Number OCE-1736285 and NOAA’s Climate Program Office, Climate Variability and Predictability Program under Award Number NA17OAR4310257. SH was supported by the National Natural Science Foundation of China (Grant 41776018) and the Key Research Program of Frontier Sciences, CAS (QYZDB-SSW-SYS023). HP acknowledges support from the Australian Government’s National Environmental Science Programme. HZ acknowledges support from National Science Foundation under Grant No. 41876009. RS was supported by National Science Foundation Grant No. OCE-07-25935; Office of Naval Research Grant No. N00014-08-01-0618 and National Aeronautics and Space Administration Grant No. 80NSSC18K0777. SW, MF, and BS were supported by Center for Southern Hemisphere Oceans Research (CSHOR), which is a joint initiative between the Qingdao National Laboratory for Marine Science and Technology (QNLM), CSIRO, University of New South Wales and University of Tasmania

A New No-Equilibrium Chaotic System and Its Topological Horseshoe Chaos

A new no-equilibrium chaotic system is reported in this paper. Numerical simulation techniques, including phase portraits and Lyapunov exponents, are used to investigate its basic dynamical behavior. To confirm the chaotic behavior of this system, the existence of topological horseshoe is proven via the Poincaré map and topological horseshoe theory

Salient object detection by fusing local and global contexts

Benefiting from the powerful discriminative feature learning capability of convolutional neural networks (CNNs), deep learning techniques have achieved remarkable performance improvement for the task of salient object detection (SOD) in recent years. However, most existing deep SOD models do not fully exploit informative contextual features, which often leads to suboptimal detection performance in the presence of a cluttered background. This paper presents a context-aware attention module that detects salient objects by simultaneously constructing connections between each image pixel and its local and global contextual pixels. Specifically, each pixel and its neighbors bidirectionally exchange semantic information by computing their correlation coefficients, and this process aggregates contextual attention features both locally and globally. In addition, an attention-guided hierarchical network architecture is designed to capture fine-grained spatial details by transmitting contextual information from deeper to shallower network layers in a top-down manner. Extensive experiments on six public SOD datasets show that our proposed model demonstrates superior SOD performance against most of the current state-of-the-art models under different evaluation metrics.Nanyang Technological UniversitySubmitted/Accepted versionThis work was supported in part by the Scholarship from China Scholarship Council under Grant 201906090194, in part by the NTU Start-up under Grant M4082034, in part by the National Natural Science Fund of China under Grant 61703100, in part by the Natural Science Foundation of Jiangsu under Grant BK20170692, in part by the Fundamental Research Funds for the Central Universities, and in part by the Big Data Computing Center of Southeast University