202 research outputs found
Feature Selection and Overlapping Clustering-Based Multilabel Classification Model
Multilabel classification (MLC) learning, which is widely applied in real-world applications, is a very important problem in machine learning. Some studies show that a clustering-based MLC framework performs effectively compared to a nonclustering framework. In this paper, we explore the clustering-based MLC problem. Multilabel feature selection also plays an important role in classification learning because many redundant and irrelevant features can degrade performance and a good feature selection algorithm can reduce computational complexity and improve classification accuracy. In this study, we consider feature dependence and feature interaction simultaneously, and we propose a multilabel feature selection algorithm as a preprocessing stage before MLC. Typically, existing cluster-based MLC frameworks employ a hard cluster method. In practice, the instances of multilabel datasets are distinguished in a single cluster by such frameworks; however, the overlapping nature of multilabel instances is such that, in real-life applications, instances may not belong to only a single class. Therefore, we propose a MLC model that combines feature selection with an overlapping clustering algorithm. Experimental results demonstrate that various clustering algorithms show different performance for MLC, and the proposed overlapping clustering-based MLC model may be more suitable
Gravitation Theory Based Model for Multi-Label Classification
The past decade has witnessed the growing popularity in multi-label classification algorithms in the fields like text categorization, music information retrieval, and the classification of videos and medical proteins. In the meantime, the methods based on the principle of universal gravitation have been extensively used in the classification of machine learning owing to simplicity and high performance. In light of the above, this paper proposes a novel multi-label classification algorithm called the interaction and data gravitation-based model for multi-label classification (ITDGM). The algorithm replaces the interaction between two objects with the attraction between two particles. The author carries out a series of experiments on five multi-label datasets. The experimental results show that the ITDGM performs better than some well-known multi-label classification algorithms. The effect of the proposed model is assessed by the example-based F1-Measure and Label-based micro F1-measure
A Study on Intercultural Pragmatic Failure and Development of Intercultural Interactive Competence based on International Chinese Education
[ES] La interacción transcultural es un fenómeno universal en la educación China
internacional. Este fenómeno siempre conduce al fracaso pragmático transcultural y al error de la
interacción, que se ven afectados por diferentes factores. Con base en las prácticas de la educación
China internacional y el análisis de estos fenómenos, así como de sus factores, se ilustra que el factor
psicológico cognitivo es la causa raíz de este fenómeno. Luego se exploran las estrategias y métodos
para el cultivo de la competencia de interacción intercultural y así tener éxito en lugar de error en la
interacción intercultural.[EN] : Inter-cultural interaction is prevalent within international Chinese education. However,
a variety of factors often lead to cross-cultural pragmatic failure and failed interactions. This article
studies the practices of international Chinese education and intercultural interaction failure and
its factors, aiming to find out how cognitive psychological factor decides and explore strategies of
developing students’ cross-cultural interactive competence to reduce failure.[ZH] :跨文化互动是国际中文教育中普遍存在的现象。由于受不同因素的影响,互动主
体经常出现跨文化语用失误并导致互动失败。结合国际中文教学实践,通过分析跨文化语用
失误现象和原因,进一步揭示出互动主体的认知心理是根本因素,并由此进一步提出培养跨
文化互动能力的策略方法,以期减少语用失误,促成跨文化互动的成功
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
Experimental Study of Cohesive Embankment Dam Breach Formation due to Overtopping
The recurrent floods in recent decades have imposed a challenge of embankment dam breaching, which needs great attention through improved design methods that are based on risk approach, the evacuation plans for people at risk, etc. In this study, based on the small-scale model tests a series of experiments were conducted to determine the breaching process of cohesive embankment dam using a simplified physical based breach model due to overtopping; the breach process observed during tests in the laboratory and the results from analyzed parameters are described. Five dam models, three of which were constructed with homogenous clay soil while two were sandy-clay mixture tested. The heights of the embankments dam were 0.45 m, and the widths at the crest were 0.20 m. The data from these examinations indicated that headcut erosion played an important role in the process of breach development. Initiation of erosion, flow shear erosion, sidewall bottom erosion, and distinct soil mechanical slope mass failure from the headcut vertically and laterally were all observed during these tests. In this physical based experimental model, the initial scouring position calculated by applying a hydraulic method, the broad crested weir formula used for breaching flow discharge and flow velocity computed based on breach flow discharge. The stability of the side slope failures was estimated by comparing the resisting and deriving force. Further, using data from laboratory experiments, the calculated peak breach discharge, breach characteristics times, breach widths, and breach flow velocity generally agreed well with the measured data and also the knowledge acquired from observed breach process at several stages. Finally, the accuracy of model was checked by root-mean-square-error
Membrane Potential Batch Normalization for Spiking Neural Networks
As one of the energy-efficient alternatives of conventional neural networks
(CNNs), spiking neural networks (SNNs) have gained more and more interest
recently. To train the deep models, some effective batch normalization (BN)
techniques are proposed in SNNs. All these BNs are suggested to be used after
the convolution layer as usually doing in CNNs. However, the spiking neuron is
much more complex with the spatio-temporal dynamics. The regulated data flow
after the BN layer will be disturbed again by the membrane potential updating
operation before the firing function, i.e., the nonlinear activation.
Therefore, we advocate adding another BN layer before the firing function to
normalize the membrane potential again, called MPBN. To eliminate the induced
time cost of MPBN, we also propose a training-inference-decoupled
re-parameterization technique to fold the trained MPBN into the firing
threshold. With the re-parameterization technique, the MPBN will not introduce
any extra time burden in the inference. Furthermore, the MPBN can also adopt
the element-wised form, while these BNs after the convolution layer can only
use the channel-wised form. Experimental results show that the proposed MPBN
performs well on both popular non-spiking static and neuromorphic datasets. Our
code is open-sourced at \href{https://github.com/yfguo91/MPBN}{MPBN}.Comment: Accepted by ICCV202
Multi-Head Attention Mechanism Learning for Cancer New Subtypes and Treatment Based on Cancer Multi-Omics Data
Due to the high heterogeneity and clinical characteristics of cancer, there
are significant differences in multi-omics data and clinical features among
subtypes of different cancers. Therefore, the identification and discovery of
cancer subtypes are crucial for the diagnosis, treatment, and prognosis of
cancer. In this study, we proposed a generalization framework based on
attention mechanisms for unsupervised contrastive learning (AMUCL) to analyze
cancer multi-omics data for the identification and characterization of cancer
subtypes. AMUCL framework includes a unsupervised multi-head attention
mechanism, which deeply extracts multi-omics data features. Importantly, a
decoupled contrastive learning model (DMACL) based on a multi-head attention
mechanism is proposed to learn multi-omics data features and clusters and
identify new cancer subtypes. This unsupervised contrastive learning method
clusters subtypes by calculating the similarity between samples in the feature
space and sample space of multi-omics data. Compared to 11 other deep learning
models, the DMACL model achieved a C-index of 0.002, a Silhouette score of
0.801, and a Davies Bouldin Score of 0.38 on a single-cell multi-omics dataset.
On a cancer multi-omics dataset, the DMACL model obtained a C-index of 0.016, a
Silhouette score of 0.688, and a Davies Bouldin Score of 0.46, and obtained the
most reliable cancer subtype clustering results for each type of cancer.
Finally, we used the DMACL model in the AMUCL framework to reveal six cancer
subtypes of AML. By analyzing the GO functional enrichment, subtype-specific
biological functions, and GSEA of AML, we further enhanced the interpretability
of cancer subtype analysis based on the generalizable AMUCL framework
RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networks
Spiking Neural Networks (SNNs) as one of the biology-inspired models have
received much attention recently. It can significantly reduce energy
consumption since they quantize the real-valued membrane potentials to 0/1
spikes to transmit information thus the multiplications of activations and
weights can be replaced by additions when implemented on hardware. However,
this quantization mechanism will inevitably introduce quantization error, thus
causing catastrophic information loss. To address the quantization error
problem, we propose a regularizing membrane potential loss (RMP-Loss) to adjust
the distribution which is directly related to quantization error to a range
close to the spikes. Our method is extremely simple to implement and
straightforward to train an SNN. Furthermore, it is shown to consistently
outperform previous state-of-the-art methods over different network
architectures and datasets.Comment: Accepted by ICCV202
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