310 research outputs found
The Inefficiencies of Legislative Centralization: Evidence from Provincial Tax Rate-Setting
Legislative power in China is centralized to an unusual degree. This arrangement is both positively and normatively significant, but has received little attention in prior scholarship. We devise a novel method for analyzing the consequence of centralization by examining provincial rate setting for the vehicle and vessel tax (VVT). Because all provinces have assigned VVT revenue and VVT administration to sub-provincial governments, provincial rate-setting represents centralized, not decentralized, decision-making. Using spatio-econometric analyses, we find that provincial tax rate choices fail to reflect local economic and demographic conditions and display traces of tax mimicking. Both support the hypothesis that provincial officials lack information and incentives to make effective policy
ADTR: Anomaly Detection Transformer with Feature Reconstruction
Anomaly detection with only prior knowledge from normal samples attracts more
attention because of the lack of anomaly samples. Existing CNN-based pixel
reconstruction approaches suffer from two concerns. First, the reconstruction
source and target are raw pixel values that contain indistinguishable semantic
information. Second, CNN tends to reconstruct both normal samples and anomalies
well, making them still hard to distinguish. In this paper, we propose Anomaly
Detection TRansformer (ADTR) to apply a transformer to reconstruct pre-trained
features. The pre-trained features contain distinguishable semantic
information. Also, the adoption of transformer limits to reconstruct anomalies
well such that anomalies could be detected easily once the reconstruction
fails. Moreover, we propose novel loss functions to make our approach
compatible with the normal-sample-only case and the anomaly-available case with
both image-level and pixel-level labeled anomalies. The performance could be
further improved by adding simple synthetic or external irrelevant anomalies.
Extensive experiments are conducted on anomaly detection datasets including
MVTec-AD and CIFAR-10. Our method achieves superior performance compared with
all baselines.Comment: Accepted by ICONIP 202
Arbuscular mycorrhizal fungal interactions bridge the support of root-associated microbiota for slope multifunctionality in an erosion-prone ecosystem
The role of diverse soil microbiota in restoring erosion-induced degraded lands is well recognized. Yet, the facilitative interactions among symbiotic arbuscular mycorrhizal (AM) fungi, rhizobia, and heterotrophic bacteria, which underpin multiple functions in eroded ecosystems, remain unclear. Here, we utilized quantitative microbiota profiling and ecological network analyses to explore the interplay between the diversity and biotic associations of root-associated microbiota and multifunctionality across an eroded slope of a Robinia pseudoacacia plantation on the Loess Plateau. We found explicit variations in slope multifunctionality across different slope positions, associated with shifts in limiting resources, including soil phosphorus (P) and moisture. To cope with P limitation, AM fungi were recruited by R. pseudoacacia, assuming pivotal roles as keystones and connectors within cross-kingdom networks. Furthermore, AM fungi facilitated the assembly and composition of bacterial and rhizobial communities, collectively driving slope multifunctionality. The symbiotic association among R. pseudoacacia, AM fungi, and rhizobia promoted slope multifunctionality through enhanced decomposition of recalcitrant compounds, improved P mineralization potential, and optimized microbial metabolism. Overall, our findings highlight the crucial role of AM fungal-centered microbiota associated with R. pseudoacacia in functional delivery within eroded landscapes, providing valuable insights for the sustainable restoration of degraded ecosystems in erosion-prone regions
Evaluating Modules in Graph Contrastive Learning
The recent emergence of contrastive learning approaches facilitates the
research on graph representation learning (GRL), introducing graph contrastive
learning (GCL) into the literature. These methods contrast semantically similar
and dissimilar sample pairs to encode the semantics into node or graph
embeddings. However, most existing works only performed model-level evaluation,
and did not explore the combination space of modules for more comprehensive and
systematic studies. For effective module-level evaluation, we propose a
framework that decomposes GCL models into four modules: (1) a sampler to
generate anchor, positive and negative data samples (nodes or graphs); (2) an
encoder and a readout function to get sample embeddings; (3) a discriminator to
score each sample pair (anchor-positive and anchor-negative); and (4) an
estimator to define the loss function. Based on this framework, we conduct
controlled experiments over a wide range of architectural designs and
hyperparameter settings on node and graph classification tasks. Specifically,
we manage to quantify the impact of a single module, investigate the
interaction between modules, and compare the overall performance with current
model architectures. Our key findings include a set of module-level guidelines
for GCL, e.g., simple samplers from LINE and DeepWalk are strong and robust; an
MLP encoder associated with Sum readout could achieve competitive performance
on graph classification. Finally, we release our implementations and results as
OpenGCL, a modularized toolkit that allows convenient reproduction, standard
model and module evaluation, and easy extension
Conceptual Text Region Network: Cognition-Inspired Accurate Scene Text Detection
Segmentation-based methods are widely used for scene text detection due to their superiority in describing arbitrary-shaped text instances. However, two major problems still exist: (1) current label generation techniques are mostly empirical and lack theoretical support, discouraging elaborate label design; and (2) as a result, most methods rely heavily on text kernel segmentation which is unstable and requires deliberate tuning. To address these challenges, we propose a human cognition-inspired framework, termed, Conceptual Text Region Network (CTRNet). The framework utilizes Conceptual Text Regions (CTRs), which is a class of cognition-based tools inheriting good mathematical properties, allowing for sophisticated label design. Another component of CTRNet is an inference pipeline that, with the help of CTRs, completely omits the need for text kernel segmentation. Compared with previous segmentation-based methods, our approach is not only more interpretable but also more accurate. Experimental results show that CTRNet achieves state-of-the-art performance on benchmark CTW1500, Total-Text, MSRA-TD500, and ICDAR 2015 datasets, yielding performance gains of up to 2.0%. Notably, to the best of our knowledge, CTRNet is among the first detection models to achieve F-measures higher than 85.0% on all four of the benchmarks, demonstrating remarkable consistency and stability
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