How Chinese provincial governments responded to the Delta and Omicron waves

How long will China continue to try to eliminate COVID? A change of strategy is not very likely, argue Hao Zha (Tsinghua), Yuxi Zhang (LSE), and Thomas Hale (Oxford) who collect and analyse China’s data for the Oxford COVID-19 Government Response Tracker

Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignment

Few-shot fine-grained recognition (FS-FGR) aims to recognize novel fine-grained categories with the help of limited available samples. Undoubtedly, this task inherits the main challenges from both few-shot learning and fine-grained recognition. First, the lack of labeled samples makes the learned model easy to overfit. Second, it also suffers from high intra-class variance and low inter-class difference in the datasets. To address this challenging task, we propose a two-stage background suppression and foreground alignment framework, which is composed of a background activation suppression (BAS) module, a foreground object alignment (FOA) module, and a local to local (L2L) similarity metric. Specifically, the BAS is introduced to generate a foreground mask for localization to weaken background disturbance and enhance dominative foreground objects. What's more, considering the lack of labeled samples, we compute the pairwise similarity of feature maps using both the raw image and the refined image. The FOA then reconstructs the feature map of each support sample according to its correction to the query ones, which addresses the problem of misalignment between support-query image pairs. To enable the proposed method to have the ability to capture subtle differences in confused samples, we present a novel L2L similarity metric to further measure the local similarity between a pair of aligned spatial features in the embedding space. Extensive experiments conducted on multiple popular fine-grained benchmarks demonstrate that our method outperforms the existing state-of-the-art by a large margin.Comment: Preprint under review in TCSVT Journa

Longitudinal compression of macro relativistic electron beam

We presented a novel concept of longitudinal bunch train compression capable of manipulating relativistic electron beam in range of hundreds of meters. This concept has the potential to compress the electron beam with a high ratio and raise its power to an ultrahigh level. The method utilizes the spiral motion of electrons in a uniform magnetic field to fold hundreds-of-meters-long trajectories into a compact set-up. The interval between bunches can be adjusted by modulating their sprial movement. The method is explored both analytically and numerically. Compared to set-up of similar size, such as chicane, this method can compress bunches at distinct larger scales and higher intensities, opening up new possibilities for generating beam with ultra-large energy storage.Comment: 6 pages, 6 figure

Magnetic γ-Fe2O3-Loaded Attapulgite Sorbent for Hg0 Removal in Coal-Fired Flue Gas

A magnetically recoverable composite mercury removal sorbent was produced by introducing magnetic γ-Fe2O3 into attapulgite (ATT) (xFe1ATT) via the co-precipitation method and used to remove Hg0 in the simulated coal-fired power plant flue gas. The as-prepared 0.5Fe1ATT sorbent was characterized by X-ray diffraction, Brunauer–Emmett–Teller, transmission electron microscopy, vibrating sample magnetometer, X-ray photoelectron spectroscopy, and Fourier transform infrared spectroscopy analyses. The results showed that the Hg0 removal performance of the composite of γ-Fe2O3 and ATT was significantly promoted in comparison to pure γ-Fe2O3 and ATT individually. A relatively high magnetization value and good Hg0 removal performance were obtained by the sample of 0.5Fe1ATT. O2 could enhance Hg0 removal activity via the Mars–Maessen mechanism. NO displayed a significant promotion effect on Hg0 removal as a result of the formation of active species, such as NO2 and NO+. SO2 inhibited the removal of Hg0 as a result of its competition adsorption against Hg0 for the active sites and the sulfation of the sorbent. However, the introduction of NO could obviously alleviate the adverse effect of SO2 on the Hg0 removal capability. H2O showed a prohibitive effect on Hg0 removal as a result of its competition with Hg0 for the active sites. The findings of this study are of fundamental importance to the development of efficient and economic magnetic mercury sorbents for Hg0 removal from coal-fired boiler flue gases

Tackling Diverse Minorities in Imbalanced Classification

Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. When working with large datasets, the imbalanced issue can be further exacerbated, making it exceptionally difficult to train classifiers effectively. To address the problem, over-sampling techniques have been developed to linearly interpolating data instances between minorities and their neighbors. However, in many real-world scenarios such as anomaly detection, minority instances are often dispersed diversely in the feature space rather than clustered together. Inspired by domain-agnostic data mix-up, we propose generating synthetic samples iteratively by mixing data samples from both minority and majority classes. It is non-trivial to develop such a framework, the challenges include source sample selection, mix-up strategy selection, and the coordination between the underlying model and mix-up strategies. To tackle these challenges, we formulate the problem of iterative data mix-up as a Markov decision process (MDP) that maps data attributes onto an augmentation strategy. To solve the MDP, we employ an actor-critic framework to adapt the discrete-continuous decision space. This framework is utilized to train a data augmentation policy and design a reward signal that explores classifier uncertainty and encourages performance improvement, irrespective of the classifier's convergence. We demonstrate the effectiveness of our proposed framework through extensive experiments conducted on seven publicly available benchmark datasets using three different types of classifiers. The results of these experiments showcase the potential and promise of our framework in addressing imbalanced datasets with diverse minorities

Equivalence of Discrete Fracture Network and Porous Media Models by Hydraulic Tomography

Hydraulic tomography (HT) has emerged as a potentially viable method for mapping fractures in geologic media as demonstrated by recent studies. However, most of the studies adopted equivalent porous media (EPM) models to generate and invert hydraulic interference test data for HT. While these models assign significant different hydraulic properties to fractures and matrix, they may not fully capture the discrete nature of the fractures in the rocks. As a result, HT performance may have been overrated. To explore this issue, this study employed a discrete fracture network (DFN) model to simulate hydraulic interference tests. HT with the EPM model was then applied to estimate the distributions of hydraulic conductivity (K) and specific storage (S-s) of the DFN. Afterward, the estimated fields were used to predict the observed heads from DFN models, not used in the HT analysis (i.e., validation). Additionally, this study defined the spatial representative elementary volume (REV) of the fracture connectivity probability for the entire DFN dominant. The study showed that if this spatial REV exists, the DFN is deemed equivalent to EPM and vice versa. The hydraulic properties estimated by HT with an EPM model can then predict head fields satisfactorily over the entire DFN domain with limited monitoring wells. For a sparse DFN without this spatial REV, a dense observation network is needed. Nevertheless, HT is able to capture the dominant fractures.National Science and Technology Major Project of China [2017ZX05008-003-021]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDB10030601]; Youth Innovation Promotion Association of the Chinese Academy of Sciences [2016063]; US Civilain Research and Development Foundation (CRDF) under the award: Hydraulic tomography in shallow alluvial sediments: Nile River Valley, Egypt [DAA2-15-61224-1]; Global Expert award through Tianjin Normal University from the Thousand Talents Plan of Tianjin City6 month embargo; published online: 23 April 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Revisiting Single Image Reflection Removal In the Wild

This research focuses on the issue of single-image reflection removal (SIRR) in real-world conditions, examining it from two angles: the collection pipeline of real reflection pairs and the perception of real reflection locations. We devise an advanced reflection collection pipeline that is highly adaptable to a wide range of real-world reflection scenarios and incurs reduced costs in collecting large-scale aligned reflection pairs. In the process, we develop a large-scale, high-quality reflection dataset named Reflection Removal in the Wild (RRW). RRW contains over 14,950 high-resolution real-world reflection pairs, a dataset forty-five times larger than its predecessors. Regarding perception of reflection locations, we identify that numerous virtual reflection objects visible in reflection images are not present in the corresponding ground-truth images. This observation, drawn from the aligned pairs, leads us to conceive the Maximum Reflection Filter (MaxRF). The MaxRF could accurately and explicitly characterize reflection locations from pairs of images. Building upon this, we design a reflection location-aware cascaded framework, specifically tailored for SIRR. Powered by these innovative techniques, our solution achieves superior performance than current leading methods across multiple real-world benchmarks. Codes and datasets will be publicly available

Evapotranspiration and its partitioning during and following a mountain pine beetle infestation of a lodgepole pine stand in the interior of British Columbia, Canada

IntroductionMassive tree mortality events in western Canada due to widespread infestation by mountain pine beetle (MPB) are expected to impact local-to-regional evapotranspiration (ET) dynamics during and after a disturbance. How ecosystem-level ET and its components may vary with canopy-tree mortality (treefall) and subsequent understory recovery remains unclear.MethodsWe used 10 years of continuous eddy-covariance and remote-sensing data (2007–2016) and machine-learning models based on random forest and xgboost to determine forest- and climate-driven effects at temporal scales appropriate for a lodgepole pine-dominated stand following a major, five-year MPB disturbance initiated in the summer of 2006.ResultsTotal annual ET over the 10 years ranged from 207.2 to 384.6 mm, with annual plant transpiration (T) contributing to 57 ± 5.4% (mean ± standard deviation) of annual ET. Annual ET initially declined (2007–2011) and then increased (2011–2016), with ET and T/ET increasing at statistically non-significant rates of approximately 3.2 and 1.2% per year from 2007 to 2016. Air temperature (Ta) and vapor pressure deficit (VPD) were the most important predictors of seasonal variation in ET and T/ET during the 10-year period, with high Ta, VPD, and photosynthetically active radiation (PAR) causing ET and T/ET to increase. Annual ET increased with both increasing spring Ta and decreasing VPD. Annual T/ET was shown to increase with increasing VPD and decrease with increasing volumetric soil water content at a 5-cm depth (VWC5). Enhanced vegetation index (EVI, an indicator of canopy greenness) lagged T and overstory tree mortality, whereas previous- and current-year values of EVI were shown to be poor predictors of annual ET and T/ET.Discussion and conclusionsThese findings suggest that the promotion of climate factors on forest ecosystem-level water vapor fluxes may offset reductions promoted by MPB outbreaks. Climate processes affected water vapor fluxes more than biotic factors, like stand greenness, highlighting the need to include climate-regulatory mechanisms in predictive models of ET dynamics during and subsequent to stand disturbance. Climate and forest-greenness effects on water vapor fluxes need to be explored at even longer time scales, e.g., at decadal scales, to capture long-drawn-out trends associated with stand disturbance and its subsequent recovery