Multi-modal knowledge graph completion (MMKGC) aims to automatically discover
the unobserved factual knowledge from a given multi-modal knowledge graph by
collaboratively modeling the triple structure and multi-modal information from
entities. However, real-world MMKGs present challenges due to their diverse and
imbalanced nature, which means that the modality information can span various
types (e.g., image, text, numeric, audio, video) but its distribution among
entities is uneven, leading to missing modalities for certain entities.
Existing works usually focus on common modalities like image and text while
neglecting the imbalanced distribution phenomenon of modal information. To
address these issues, we propose a comprehensive framework NativE to achieve
MMKGC in the wild. NativE proposes a relation-guided dual adaptive fusion
module that enables adaptive fusion for any modalities and employs a
collaborative modality adversarial training framework to augment the imbalanced
modality information. We construct a new benchmark called WildKGC with five
datasets to evaluate our method. The empirical results compared with 21 recent
baselines confirm the superiority of our method, consistently achieving
state-of-the-art performance across different datasets and various scenarios
while keeping efficient and generalizable. Our code and data are released at
this https URL

提出了一种名为 NativE 的综合框架，用于实现野外多模态知识图谱补全，该框架通过关系引导的双重自适应融合模块实现任意模态的自适应融合，并采用协作模态对抗训练框架来增强不平衡的模态信息，实验结果表明，该方法在不同数据集和各种场景下始终保持高效且可推广的最先进性能。

NativE: 野外多模态知识图谱补全

NativE: Multi-modal Knowledge Graph Completion in the Wild

Heart failure (HF) is a critical condition in which the accurate prediction
of mortality plays a vital role in guiding patient management decisions.
However, clinical datasets used for mortality prediction in HF often suffer
from an imbalanced distribution of classes, posing significant challenges. In
this paper, we explore preprocessing methods for enhancing one-month mortality
prediction in HF patients. We present a comprehensive preprocessing framework
including scaling, outliers processing and resampling as key techniques. We
also employed an aware encoding approach to effectively handle missing values
in clinical datasets. Our study utilizes a comprehensive dataset from the
Persian Registry Of cardio Vascular disease (PROVE) with a significant class
imbalance. By leveraging appropriate preprocessing techniques and Machine
Learning (ML) algorithms, we aim to improve mortality prediction performance
for HF patients. The results reveal an average enhancement of approximately
3.6% in F1 score and 2.7% in MCC for tree-based models, specifically Random
Forest (RF) and XGBoost (XGB). This demonstrates the efficiency of our
preprocessing approach in effectively handling Imbalanced Clinical Datasets
(ICD). Our findings hold promise in guiding healthcare professionals to make
informed decisions and improve patient outcomes in HF management.

通过使用合适的预处理技术和机器学习算法，研究在预测心力衰竭患者的死亡率方面对不平衡的临床数据集进行预处理的方法，以提高预测性能。研究结果表明，相比树模型，尤其是随机森林（RF）和 XGBoost（XGB），在 F1 评分和 MCC 上平均提高了约 3.6% 和 2.7%，从而显示出预处理方法在处理不平衡的临床数据集方面的效率。该研究有望引导医疗专业人员做出明智决策，并改善心力衰竭管理中的患者预后。

增强心力衰竭患者的死亡预测：探索不平衡临床数据集的预处理方法

Enhancing Mortality Prediction in Heart Failure Patients: Exploring  Preprocessing Methods for Imbalanced Clinical Datasets

Conventional detectors suffer from performance degradation when dealing with
long-tailed data due to a classification bias towards the majority head
categories. In this paper, we contend that the learning bias originates from
two factors: 1) the unequal competition arising from the imbalanced
distribution of foreground categories, and 2) the lack of sample diversity in
tail categories. To tackle these issues, we introduce a unified framework
called BAlanced CLassification (BACL), which enables adaptive rectification of
inequalities caused by disparities in category distribution and dynamic
intensification of sample diversities in a synchronized manner. Specifically, a
novel foreground classification balance loss (FCBL) is developed to ameliorate
the domination of head categories and shift attention to
difficult-to-differentiate categories by introducing pairwise class-aware
margins and auto-adjusted weight terms, respectively. This loss prevents the
over-suppression of tail categories in the context of unequal competition.
Moreover, we propose a dynamic feature hallucination module (FHM), which
enhances the representation of tail categories in the feature space by
synthesizing hallucinated samples to introduce additional data variances. In
this divide-and-conquer approach, BACL sets a new state-of-the-art on the
challenging LVIS benchmark with a decoupled training pipeline, surpassing
vanilla Faster R-CNN with ResNet-50-FPN by 5.8% AP and 16.1% AP for overall and
tail categories. Extensive experiments demonstrate that BACL consistently
achieves performance improvements across various datasets with different
backbones and architectures. Code and models are available at
this https URL

本文介绍了一种名为 BAlanced CLassification（BACL）的统一框架，用于应对长尾数据分类偏差问题，该框架通过自适应校正类别分布中的不平等和动态增强样本多样性来解决这些问题。具体采用了前景分类平衡损失（FCBL）和动态特征幻化模块（FHM），在 LVIS 基准上取得了 5.8％ AP 和 16.1％ AP 的性能提升。