88 research outputs found
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New approaches in analysis of the priority of fiscal income per GDP-a case of China
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Effectively Settling SMEs financing Difficulty with Warehouse Receipt-a Case from China
SMEs financing is a difficult problem in the world, and it is more difficult in China. The highlight of our paper is that it offers a route which could settle the difficult problem but loan on credit. There are three platforms which can effectively settle SMEs financing difficulty with warehouse receipt. Platform 1 is a warehouse receipt management platform which manages SMEs movable property. Platform 2 is a P2P platform, or warehouse receipt financing platform. Platform 3 is a transaction platform of warehouse receipt. Future works have been put in the end
On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs
Graph neural networks (GNNs) have been widely used in various graph-related
problems such as node classification and graph classification, where the
superior performance is mainly established when natural node features are
available. However, it is not well understood how GNNs work without natural
node features, especially regarding the various ways to construct artificial
ones. In this paper, we point out the two types of artificial node
features,i.e., positional and structural node features, and provide insights on
why each of them is more appropriate for certain tasks,i.e., positional node
classification, structural node classification, and graph classification.
Extensive experimental results on 10 benchmark datasets validate our insights,
thus leading to a practical guideline on the choices between different
artificial node features for GNNs on non-attributed graphs. The code is
available at https://github.com/zjzijielu/gnn-exp/.Comment: This paper has been accepted to the Sixth International Workshop on
Deep Learning on Graphs (DLG-KDD'21) (co-located with KDD'21
Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis
Human brains lie at the core of complex neurobiological systems, where the
neurons, circuits, and subsystems interact in enigmatic ways. Understanding the
structural and functional mechanisms of the brain has long been an intriguing
pursuit for neuroscience research and clinical disorder therapy. Mapping the
connections of the human brain as a network is one of the most pervasive
paradigms in neuroscience. Graph Neural Networks (GNNs) have recently emerged
as a potential method for modeling complex network data. Deep models, on the
other hand, have low interpretability, which prevents their usage in
decision-critical contexts like healthcare. To bridge this gap, we propose an
interpretable framework to analyze disorder-specific Regions of Interest (ROIs)
and prominent connections. The proposed framework consists of two modules: a
brain-network-oriented backbone model for disease prediction and a globally
shared explanation generator that highlights disorder-specific biomarkers
including salient ROIs and important connections. We conduct experiments on
three real-world datasets of brain disorders. The results verify that our
framework can obtain outstanding performance and also identify meaningful
biomarkers. All code for this work is available at
https://github.com/HennyJie/IBGNN.git.Comment: Previous version presented at icml-imlh 2021 (no proceedings,
archived at 2107.05097), this version is accepted to miccai 202
Weak Magnetic Field Accelerates Chromate Removal by Zero-Valent Iron
Weak magnetic field (WMF) was employed to improve the removal of Cr(VI) by zero-valent iron (ZVI) for the first time. The removal rate of Cr(VI) was elevated by a factor of 1.12-5.89 due to the application of a WMF, and the WMF-induced improvement was more remarkable at higher Cr(VI) concentration and higher pH. Fe2+ was not detected until Cr(VI) was exhausted, and there was a positive correlation between the WMF-induced promotion factor of Cr(VI) removal rate and that of Fe2+ release rate in the absence of Cr(VI) at pH 4.0-5.5. These phenomena imply that ZVI corrosion with Fe2+ release was the limiting step in the process of Cr(VI) removal. The superimposed WMF had negligible influence on the apparent activation energy of Cr(VI) removal by ZVI, indicating that WMF accelerated Cr(VI) removal by ZVI but did not change the mechanism. The passive layer formed with WMF was much more porous than without WMF, thereby facilitating mass transport. Therefore, WMF could accelerate ZVI corrosion and alleviate the detrimental effects of the passive layer, resulting in more rapid removal of Cr(VI) by ZVI. Exploiting the magnetic memory of ZVI, a two-stage process consisting of a small reactor with WMF for ZVI magnetization and a large reactor for removing contaminants by magnetized ZVI can be employed as a new method of ZVI-mediated remediation
Study on surface asperity flattening in cold quasi-static uniaxial planar compression by crystal plasticity finite element method
In order to study the surface asperity flattening in a quasi-static cold uniaxial planar compression, the experimental results of atomic force microscope and electron backscattered diffraction have been employed in a ratedependent crystal plasticity model to analyze this process. The simulation results show a good agreement with the experimental results: in this quasi-static deformation process, lubrication can hinder the surface asperity flattening process even under very low deformation rate. However, due to the limitation of the model and some parameters, the simulation results cannot predict all the properties in detail such as S orientation {123}and the maximum stress in sample compressed without lubrication. In addition, the experimental results show, with an increase in gauged reduction, the development of Taylor factor, and CSL boundaries show certain tendencies. Under the same gauged reduction, friction can increase the Taylor factor and ÎŁ = 7
PV2TEA: Patching Visual Modality to Textual-Established Information Extraction
Information extraction, e.g., attribute value extraction, has been
extensively studied and formulated based only on text. However, many attributes
can benefit from image-based extraction, like color, shape, pattern, among
others. The visual modality has long been underutilized, mainly due to
multimodal annotation difficulty. In this paper, we aim to patch the visual
modality to the textual-established attribute information extractor. The
cross-modality integration faces several unique challenges: (C1) images and
textual descriptions are loosely paired intra-sample and inter-samples; (C2)
images usually contain rich backgrounds that can mislead the prediction; (C3)
weakly supervised labels from textual-established extractors are biased for
multimodal training. We present PV2TEA, an encoder-decoder architecture
equipped with three bias reduction schemes: (S1) Augmented label-smoothed
contrast to improve the cross-modality alignment for loosely-paired image and
text; (S2) Attention-pruning that adaptively distinguishes the visual
foreground; (S3) Two-level neighborhood regularization that mitigates the label
textual bias via reliability estimation. Empirical results on real-world
e-Commerce datasets demonstrate up to 11.74% absolute (20.97% relatively) F1
increase over unimodal baselines.Comment: ACL 2023 Finding
From a long-term dynamic perspective: how should internal carbon pricing be implemented?
Internal carbon pricing has the potential to positively influence enterprises’ carbon emissions. However, the strategies for implementing internal carbon pricing for enterprises and internal organizations remain unclear. In this study, employing a differential game research methodology, we design three implementation strategies for internal carbon pricing from a dynamic time perspective. Through comparative research and numerical analysis of these three different strategies’ effects on the changes in enterprise carbon emission reduction and goodwill, we find that for both enterprises’ carbon emission reduction and goodwill, Model C (implementing secondary investment for internal carbon fee collection) is optimal when the proportion of internal organizational revenue allocation is high and the proportion coefficient of internal carbon fee collection is low. When the proportion coefficient of internal carbon fee collection meets certain conditions, it makes the total profit of system under model C (implementing secondary investment for internal carbon fee collection) larger than the other two strategies. Due to short-sighted behavior, both enterprises’ profits and carbon emissions gradually decrease, leading to the internal carbon prices of enterprises under the three strategies will approach a stable value
Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting
Images contain rich relational knowledge that can help machines understand
the world. Existing methods on visual knowledge extraction often rely on the
pre-defined format (e.g., sub-verb-obj tuples) or vocabulary (e.g., relation
types), restricting the expressiveness of the extracted knowledge. In this
work, we take a first exploration to a new paradigm of open visual knowledge
extraction. To achieve this, we present OpenVik which consists of an open
relational region detector to detect regions potentially containing relational
knowledge and a visual knowledge generator that generates format-free knowledge
by prompting the large multimodality model with the detected region of
interest. We also explore two data enhancement techniques for diversifying the
generated format-free visual knowledge. Extensive knowledge quality evaluations
highlight the correctness and uniqueness of the extracted open visual knowledge
by OpenVik. Moreover, integrating our extracted knowledge across various visual
reasoning applications shows consistent improvements, indicating the real-world
applicability of OpenVik.Comment: Accepted to NeurIPS 202
Characterization and comparisons of microbiota in different intestinal segments between adult Chinese Shanxi Black Pigs and Large White Pigs
The carcass weight of Chinese Shanxi Black Pigs is relatively lower in comparison with that of the counterparts, i.e., Large White Pigs, although the former are resistant to harsh conditions. Since gut microflora has been recognized to play a key role in pork production, it is of interest to explore the microbial communities in different intestinal segments of pigs and its potential relatedness with host features. In this study, 16S rRNA gene amplicon sequencing, accompanied by the inter- and intra-group comparisons, was implemented to investigate the structure composition and potential functions of microbial communities of four distinct intestinal segments [duodenum (D), jejunum (J), ileum (I), and cecum (C)] between adult Chinese Shanxi Black Pigs and Large White Pigs. Comparative survey revealed that the dominant phylum in both breeds was Firmicutes, followed by Proteobacteria, and Bacteroidetes. At the genus level, Lactobacillus was predominant in all samples, and Prevotella was specifically prevalent in the cecum. Further inspection showed the differences of dominant species in the same segments between these two groups. Notably, unique taxa in C and D segments were more than that in I and J segments. Additionally, each segment was characterized by specifically enriched genera, and distinctive pathways were predicted in certain intestinal segments. In short, the findings presented a coherent picture of structure composition and predicted functionalities of gut microbiota in diverse intestinal segments of adult Chinese Shanxi Black Pigs and Large White Pigs, and extend the understanding of potential link between intestinal microbiota and their hosts
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