Strengthened linkage between November/December North Atlantic Oscillation and subsequent January european precipitation after the late 1980s

This work investigates the nonsynchronous relationship between the North Atlantic Oscillation (NAO) and winter European precipitation. The results indicate that the linkage between early-winter (November and December) NAO and the following January precipitation and atmospheric circulation over the North Atlantic and European sectors became statistically significant after the late 1980s. Before the late 1980s, January precipitation and atmospheric circulation are weakly correlated with early-winter NAO. After the late 1980s, by contrast, the positive phase of the early-winter NAO is generally followed by an anomalous meridional dipole pattern with barotropic structure over the North Atlantic, which provides conditions for more (less) precipitation south of Iceland (east of the Azores). Further analysis elucidates that this regime shift may be partly attributed to the change of early-winter NAO, which is concurrent with significant change in the intensity of the synoptic and low-frequency eddy interaction over the Atlantic–European sectors. Anomalous positive sea level pressure and geopotential height, along with zonal wind anomalies associated with a positive early-winter NAO over the North Atlantic, are more significant and extend more northeastward after the late 1980s, which may be induced by an intensified transient eddy feedback after the late 1980s, as well as the enhanced storm-track activity over the North Atlantic. Thus, early-winter NAO can induce significant ocean temperature anomalies in the North Atlantic after the late 1980s, which extend downward into the middle parts of the thermocline and persist until the following January to trigger NAO-like atmospheric circulation patterns. Analyses from the Community Earth System Model large ensemble simulations indicate the effects of internal climate variability on such a strengthened linkage.publishedVersio

Recent Intensified Influence of the Winter North Pacific Sea Surface Temperature on the Mei-Yu Withdrawal Date

Under embargo until: 2022-04-07The mei-yu withdrawal date (MWD) is a crucial indicator of flood/drought conditions over East Asia. It is characterized by a strong interannual variability, but its underlying mechanism remains unknown. We investigated the possible effects of the winter sea surface temperature (SST) in the North Pacific Ocean on the MWD on interannual to interdecadal time scales. Both our observations and model results suggest that the winter SST anomalies associated with the MWD are mainly contributed to by a combination of the first two leading modes of the winter SST in the North Pacific, which have a horseshoe shape (the NPSST). The statistical results indicate that the intimate linkage between the NPSST and the MWD has intensified since the early 1990s. During the time period 1990–2016, the NPSST-related SST anomalies persisted from winter to the following seasons and affected the SST over the tropical Pacific in July. Subsequently, the SST anomalies throughout the North Pacific strengthened the southward migration of the East Asian jet stream (EAJS) and the southward and westward displacement of the western North Pacific subtropical high (WPSH), leading to an increase in mei-yu rainfall from 1 to 20 July. More convincingly, the anomalous EAJS and WPSH induced by the SST anomalies can be reproduced well by numerical simulations. By contrast, the influence of the NPSST on the EASJ and WPSH were not clear between 1961 and 1985. This study further illustrates that the enhanced interannual variability of the NPSST may be attributed to the more persistent SST anomalies during the time period 1990–2016.publishedVersio

Light Field Saliency Detection with Deep Convolutional Networks

Light field imaging presents an attractive alternative to RGB imaging because of the recording of the direction of the incoming light. The detection of salient regions in a light field image benefits from the additional modeling of angular patterns. For RGB imaging, methods using CNNs have achieved excellent results on a range of tasks, including saliency detection. However, it is not trivial to use CNN-based methods for saliency detection on light field images because these methods are not specifically designed for processing light field inputs. In addition, current light field datasets are not sufficiently large to train CNNs. To overcome these issues, we present a new Lytro Illum dataset, which contains 640 light fields and their corresponding ground-truth saliency maps. Compared to current light field saliency datasets [1], [2], our new dataset is larger, of higher quality, contains more variation and more types of light field inputs. This makes our dataset suitable for training deeper networks and benchmarking. Furthermore, we propose a novel end-to-end CNN-based framework for light field saliency detection. Specifically, we propose three novel MAC (Model Angular Changes) blocks to process light field micro-lens images. We systematically study the impact of different architecture variants and compare light field saliency with regular 2D saliency. Our extensive comparisons indicate that our novel network significantly outperforms state-of-the-art methods on the proposed dataset and has desired generalization abilities on other existing datasets.Comment: 14 pages, 14 figure

LMTuner: An user-friendly and highly-integrable Training Framework for fine-tuning Large Language Models

With the burgeoning development in the realm of large language models (LLMs), the demand for efficient incremental training tailored to specific industries and domains continues to increase. Currently, the predominantly employed frameworks lack modular design, it often takes a lot of coding work to kickstart the training of LLM. To address this, we present "LMTuner", a highly usable, integrable, and scalable system for training LLMs expeditiously and with minimal user-input. LMTuner comprises three main modules - the Interaction, Training, and Inference Modules. We advocate that LMTuner's usability and integrality alleviate the complexities in training large language models. Remarkably, even a novice user could commence training large language models within five minutes. Furthermore, it integrates DeepSpeed frameworks and supports Efficient Fine-Tuning methodologies like Low Rank Adaptation (LoRA), Quantized LoRA (QLoRA), etc., enabling the training of language models scaling from 300M to a whopping 130B parameters using a single server. The LMTuner's homepage (https://wengsyx.github.io/LMTuner/)and screencast video (https://youtu.be/nsXmWOmN3rE) are now publicly available

Rodent hole detection in a typical steppe ecosystem using UAS and deep learning

IntroductionRodent outbreak is the main biological disaster in grassland ecosystems. Traditional rodent damage monitoring approaches mainly depend on costly field surveys, e.g., rodent trapping or hole counting. Integrating an unmanned aircraft system (UAS) image acquisition platform and deep learning (DL) provides a great opportunity to realize efficient large-scale rodent damage monitoring and early-stage diagnosis. As the major rodent species in Inner Mongolia, Brandt’s voles (BV) (Lasiopodomys brandtii) have markedly small holes, which are difficult to identify regarding various seasonal noises in this typical steppe ecosystem.MethodsIn this study, we proposed a novel UAS-DL-based framework for BV hole detection in two representative seasons. We also established the first bi-seasonal UAS image datasets for rodent hole detection. Three two-stage (Faster R-CNN, R-FCN, and Cascade R-CNN) and three one-stage (SSD, RetinaNet, and YOLOv4) object detection DL models were investigated from three perspectives: accuracy, running speed, and generalizability.ResultsExperimental results revealed that: 1) Faster R-CNN and YOLOv4 are the most accurate models; 2) SSD and YOLOv4 are the fastest; 3) Faster R-CNN and YOLOv4 have the most consistent performance across two different seasons.DiscussionThe integration of UAS and DL techniques was demonstrated to utilize automatic, accurate, and efficient BV hole detection in a typical steppe ecosystem. The proposed method has a great potential for large-scale multi-seasonal rodent damage monitoring

Rethinking Context Aggregation in Natural Image Matting

For natural image matting, context information plays a crucial role in estimating alpha mattes especially when it is challenging to distinguish foreground from its background. Exiting deep learning-based methods exploit specifically designed context aggregation modules to refine encoder features. However, the effectiveness of these modules has not been thoroughly explored. In this paper, we conduct extensive experiments to reveal that the context aggregation modules are actually not as effective as expected. We also demonstrate that when learned on large image patches, basic encoder-decoder networks with a larger receptive field can effectively aggregate context to achieve better performance.Upon the above findings, we propose a simple yet effective matting network, named AEMatter, which enlarges the receptive field by incorporating an appearance-enhanced axis-wise learning block into the encoder and adopting a hybrid-transformer decoder. Experimental results on four datasets demonstrate that our AEMatter significantly outperforms state-of-the-art matting methods (e.g., on the Adobe Composition-1K dataset, \textbf{25\%} and \textbf{40\%} reduction in terms of SAD and MSE, respectively, compared against MatteFormer). The code and model are available at \url{https://github.com/QLYoo/AEMatter}