284 research outputs found
Short- and long-term impact of remarkable economic events on the growth causes of China-Germany trade in agri-food products
This paper focuses on a systematic quantitative discussion of the short- and long-term impact of remarkable economic events on international trade in a two-stage framework. Firstly, procedures based on dummy variables are proposed to detect structural breaks, types and sizes of jumps caused by such events. Then we propose to apply a hierarchical CMS (Constant Market Share) model to all sub-periods defined by the detected change points to study the short- and long-term impact of those events on growth causes. Application to China-Germany trade in agri-food products shows that China’s accession to WTO had a negative short-term impact on corresponding series. But its long-term impact on China’s export competitiveness was definitely positive. The short-term impact of the EU’s CAP (Common Agricultural Policy) reform on Germany’s exports to China was also negative. Its long-term impact on export competitiveness was sometimes positive and sometimes negative. The financial crisis of 2008 caused a significant reduction of China’s agri-food exports to Germany. But Germany’s exports to China in 2009 were not affected by the financial crisis as much.Growth causes of agri-food trade; the CMS model; the EU’s CAP reform; China’s accession to WTO; financial crisis
Robust Graph Neural Networks via Unbiased Aggregation
The adversarial robustness of Graph Neural Networks (GNNs) has been
questioned due to the false sense of security uncovered by strong adaptive
attacks despite the existence of numerous defenses. In this work, we delve into
the robustness analysis of representative robust GNNs and provide a unified
robust estimation point of view to understand their robustness and limitations.
Our novel analysis of estimation bias motivates the design of a robust and
unbiased graph signal estimator. We then develop an efficient Quasi-Newton
iterative reweighted least squares algorithm to solve the estimation problem,
which unfolds as robust unbiased aggregation layers in GNNs with a theoretical
convergence guarantee. Our comprehensive experiments confirm the strong
robustness of our proposed model, and the ablation study provides a deep
understanding of its advantages
PACS: Prediction and analysis of cancer subtypes from multi-omics data based on a multi-head attention mechanism model
Due to the high heterogeneity and clinical characteristics of cancer, there
are significant differences in multi-omic data and clinical characteristics
among different cancer subtypes. Therefore, accurate classification of cancer
subtypes can help doctors choose the most appropriate treatment options,
improve treatment outcomes, and provide more accurate patient survival
predictions. In this study, we propose a supervised multi-head attention
mechanism model (SMA) to classify cancer subtypes successfully. The attention
mechanism and feature sharing module of the SMA model can successfully learn
the global and local feature information of multi-omics data. Second, it
enriches the parameters of the model by deeply fusing multi-head attention
encoders from Siamese through the fusion module. Validated by extensive
experiments, the SMA model achieves the highest accuracy, F1 macroscopic, F1
weighted, and accurate classification of cancer subtypes in simulated,
single-cell, and cancer multiomics datasets compared to AE, CNN, and GNN-based
models. Therefore, we contribute to future research on multiomics data using
our attention-based approach.Comment: Submitted to BIBM202
LDCSF: Local depth convolution-based Swim framework for classifying multi-label histopathology images
Histopathological images are the gold standard for diagnosing liver cancer.
However, the accuracy of fully digital diagnosis in computational pathology
needs to be improved. In this paper, in order to solve the problem of
multi-label and low classification accuracy of histopathology images, we
propose a locally deep convolutional Swim framework (LDCSF) to classify
multi-label histopathology images. In order to be able to provide local field
of view diagnostic results, we propose the LDCSF model, which consists of a
Swin transformer module, a local depth convolution (LDC) module, a feature
reconstruction (FR) module, and a ResNet module. The Swin transformer module
reduces the amount of computation generated by the attention mechanism by
limiting the attention to each window. The LDC then reconstructs the attention
map and performs convolution operations in multiple channels, passing the
resulting feature map to the next layer. The FR module uses the corresponding
weight coefficient vectors obtained from the channels to dot product with the
original feature map vector matrix to generate representative feature maps.
Finally, the residual network undertakes the final classification task. As a
result, the classification accuracy of LDCSF for interstitial area, necrosis,
non-tumor and tumor reached 0.9460, 0.9960, 0.9808, 0.9847, respectively.
Finally, we use the results of multi-label pathological image classification to
calculate the tumor-to-stromal ratio, which lays the foundation for the
analysis of the microenvironment of liver cancer histopathological images.
Second, we released a multilabel histopathology image of liver cancer, our code
and data are available at https://github.com/panliangrui/LSF.Comment: Submitted to BIBM202
CVFC: Attention-Based Cross-View Feature Consistency for Weakly Supervised Semantic Segmentation of Pathology Images
Histopathology image segmentation is the gold standard for diagnosing cancer,
and can indicate cancer prognosis. However, histopathology image segmentation
requires high-quality masks, so many studies now use imagelevel labels to
achieve pixel-level segmentation to reduce the need for fine-grained
annotation. To solve this problem, we propose an attention-based cross-view
feature consistency end-to-end pseudo-mask generation framework named CVFC
based on the attention mechanism. Specifically, CVFC is a three-branch joint
framework composed of two Resnet38 and one Resnet50, and the independent branch
multi-scale integrated feature map to generate a class activation map (CAM); in
each branch, through down-sampling and The expansion method adjusts the size of
the CAM; the middle branch projects the feature matrix to the query and key
feature spaces, and generates a feature space perception matrix through the
connection layer and inner product to adjust and refine the CAM of each branch;
finally, through the feature consistency loss and feature cross loss to
optimize the parameters of CVFC in co-training mode. After a large number of
experiments, An IoU of 0.7122 and a fwIoU of 0.7018 are obtained on the
WSSS4LUAD dataset, which outperforms HistoSegNet, SEAM, C-CAM, WSSS-Tissue, and
OEEM, respectively.Comment: Submitted to BIBM202
Interpretable Math Word Problem Solution Generation Via Step-by-step Planning
Solutions to math word problems (MWPs) with step-by-step explanations are
valuable, especially in education, to help students better comprehend
problem-solving strategies. Most existing approaches only focus on obtaining
the final correct answer. A few recent approaches leverage intermediate
solution steps to improve final answer correctness but often cannot generate
coherent steps with a clear solution strategy. Contrary to existing work, we
focus on improving the correctness and coherence of the intermediate solutions
steps. We propose a step-by-step planning approach for intermediate solution
generation, which strategically plans the generation of the next solution step
based on the MWP and the previous solution steps. Our approach first plans the
next step by predicting the necessary math operation needed to proceed, given
history steps, then generates the next step, token-by-token, by prompting a
language model with the predicted math operation. Experiments on the GSM8K
dataset demonstrate that our approach improves the accuracy and
interpretability of the solution on both automatic metrics and human
evaluation.Comment: Accepted to The 61st Annual Meeting of the Association for
Computational Linguistics (ACL 2023
Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM
The analysis and mining of user heterogeneous behavior are of paramount
importance in recommendation systems. However, the conventional approach of
incorporating various types of heterogeneous behavior into recommendation
models leads to feature sparsity and knowledge fragmentation issues. To address
this challenge, we propose a novel approach for personalized recommendation via
Large Language Model (LLM), by extracting and fusing heterogeneous knowledge
from user heterogeneous behavior information. In addition, by combining
heterogeneous knowledge and recommendation tasks, instruction tuning is
performed on LLM for personalized recommendations. The experimental results
demonstrate that our method can effectively integrate user heterogeneous
behavior and significantly improve recommendation performance.Comment: Accepted at RecSys 202
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