In this supplementary material, we will provide more analyses of mask prior in Section 1 and similarity transfer in Section 2. We will show the visualization results in Section 3 and the performance variance with iteration in Section 4. We will also conduct experiments to mine base categories in the target dataset in Section 5. Besides, the hyper-parameters analyses will be provided in Section 6. Finally, we will discuss the limitations in Section 7. As mentioned in Section 3.2 in the main paper, mask prior provides coarse pixel-wise category information to improve the ability of the object detection network to locate and identify objects. Our ablation studies (Table 3 in the main paper) have already proved the advantage of mask prior. To further evaluate the effectiveness of mask prior, we evaluate object detection network with/without mask generator on VOC test set. Considering that the target dataset may contain both base categories and novel categories, in which only novel categories have ground-truth bounding boxes, we evaluate our method on novel categories.

Our ablation studies (Table3in the main paper) havealready proved the advantage of mask prior. From Figure 2, we can see that the coarse masks indicate the rough locations of objects which can help the object detection network predicttheboundingboxes. Tovalidate the transferability ofour similarity transfer,we evaluate our similarity network trained on COCO-60 trainval set. Wetreat the similarity prediction task as abinary classification task, in which the binary label 1 (resp., 0) means that two bounding boxes belong to the same category (resp.,different The precision, recall and F1 scores are summarized in Table 1. We observe that the gap between the performance of similarity network on base categories and novel categories is negligible (e.g., F1 Scores 84.9% v.s.

Decision trees remain central for tabular prediction but struggle with (i) capturing spatial dependence and (ii) producing locally stable (robust) explanations. We present SX-GeoTree, a self-explaining geospatial regression tree that integrates three coupled objectives during recursive splitting: impurity reduction (MSE), spatial residual control (global Moran's I), and explanation robustness via modularity maximization on a consensus similarity network formed from (a) geographically weighted regression (GWR) coefficient distances (stimulus-response similarity) and (b) SHAP attribution distances (explanatory similarity). We recast local Lipschitz continuity of feature attributions as a network community preservation problem, enabling scalable enforcement of spatially coherent explanations without per-sample neighborhood searches. Experiments on two exemplar tasks (county-level GDP in Fujian, n=83; point-wise housing prices in Seattle, n=21,613) show SX-GeoTree maintains competitive predictive accuracy (within 0.01 $R^{2}$ of decision trees) while improving residual spatial evenness and doubling attribution consensus (modularity: Fujian 0.19 vs 0.09; Seattle 0.10 vs 0.05). Ablation confirms Moran's I and modularity terms are complementary; removing either degrades both spatial residual structure and explanation stability. The framework demonstrates how spatial similarity - extended beyond geometric proximity through GWR-derived local relationships - can be embedded in interpretable models, advancing trustworthy geospatial machine learning and offering a transferable template for domain-aware explainability.

Multimodal learning aims to integrate complementary signals from diverse data types, yet in practice one modality often dominates training when information content, data quality, or sample size are imbalanced. This modality imbalance suppresses the benefits of integration and is especially problematic in biomedical applications such as multi-omics disease subtyping, where cohorts are small and assays vary in noise and coverage. Foundational syntheses emphasize fusion, alignment, and coordination as core challenges, but principled mechanisms that explicitly counter modality imbalance while preserving useful cross-modal structure remain limited [Baltruˇ saitis et al., 2018]. We propose a balanced multimodal framework for multi-omics classification that combines three ideas: (i) graph-based encoders that exploit cross-sample structure; (ii) cross-modal knowledge transfer to strengthen weaker modalities; and (iii) a multitask-style optimization procedure that adaptively reweights unimodal and multimodal losses based on performance signals and cross-modal dependence. Concretely, we employ a revised graph convolutional encoder in which node features may derive from a single modality, while edges are constructed from a fused similarity network across modalities. We then pretrain weaker modalities via knowledge distillation from a stronger teacher to transfer predictive structure without overfitting [Hinton et al., 2015, Furlanello et al., 2018]. Finally, we train the joint model with dynamic loss balancing so that no single modality dictates the gradients, leveraging advances in multitask optimization [Chen et al., 2018, Kendall et al., 2018]. 1

Malware proliferation is increasing at a tremendous rate, with hundreds of thousands of new samples identified daily. Manual investigation of such a vast amount of malware is an unrealistic, time-consuming, and overwhelming task. To cope with this volume, there is a clear need to develop specialized techniques and efficient tools for preliminary filtering that can group malware based on semantic similarity. In this paper, we propose TrapNet, a novel, scalable, and unsupervised framework for malware fingerprinting and grouping. TrapNet employs graph community detection techniques for malware fingerprinting and family attribution based on static analysis, as follows: (1) TrapNet detects packed binaries and unpacks them using known generic packer tools. (2) From each malware sample, it generates a digest that captures the underlying semantics. Since the digest must be dense, efficient, and suitable for similarity checking, we designed FloatHash (FH), a novel numerical fuzzy hashing technique that produces a short real-valued vector summarizing the underlying assembly items and their order. FH is based on applying Principal Component Analysis (PCA) to ordered assembly items (e.g., opcodes, function calls) extracted from the malware's assembly code. (3) Representing malware with short numerical vectors enables high-performance, large-scale similarity computation, which allows TrapNet to build a malware similarity network. (4) Finally, TrapNet employs state-of-the-art community detection algorithms to identify dense communities, which represent groups of malware with similar semantics. Our extensive evaluation of TrapNet demonstrates its effectiveness in terms of the coverage and purity of the detected communities, while also highlighting its runtime efficiency, which outperforms other state-of-the-art solutions.

Modern disease classification often overlooks molecular commonalities hidden beneath divergent clinical presentations. This study introduces a transcriptomics-driven framework for discovering disease relationships by analyzing over 1300 disease-condition pairs using GenoMAS, a fully automated agentic AI system. Beyond identifying robust gene-level overlaps, we develop a novel pathway-based similarity framework that integrates multi-database enrichment analysis to quantify functional convergence across diseases. The resulting disease similarity network reveals both known comorbidities and previously undocumented cross-category links. By examining shared biological pathways, we explore potential molecular mechanisms underlying these connections-offering functional hypotheses that go beyond symptom-based taxonomies. We further show how background conditions such as obesity and hypertension modulate transcriptomic similarity, and identify therapeutic repurposing opportunities for rare diseases like autism spectrum disorder based on their molecular proximity to better-characterized conditions. In addition, this work demonstrates how biologically grounded agentic AI can scale transcriptomic analysis while enabling mechanistic interpretation across complex disease landscapes. All results are publicly accessible at github.com/KeeeeChen/Pathway_Similarity_Network.

In this paper, we propose an innovative approach to thoroughly explore dataset features that introduce bias in downstream machine-learning tasks. Depending on the data format, we use different techniques to map instances into a similarity feature space. Our method's ability to adjust the resolution of pairwise similarity provides clear insights into the relationship between the dataset classification complexity and model fairness. Experimental results confirm the promising applicability of the similarity network in promoting fair models. Moreover, leveraging our methodology not only seems promising in providing a fair downstream task such as classification, it also performs well in imputation and augmentation of the dataset satisfying the fairness criteria such as demographic parity and imbalanced classes.

The subject of features normalization plays an important central role in data representation, characterization, visualization, analysis, comparison, classification, and modeling, as it can substantially influence and be influenced by all of these activities and respective aspects. The selection of an appropriate normalization method needs to take into account the type and characteristics of the involved features, the methods to be used subsequently for the just mentioned data processing, as well as the specific questions being considered. After briefly considering how normalization constitutes one of the many interrelated parts typically involved in data analysis and modeling, the present work addressed the important issue of feature normalization from the perspective of uniform and proportional (right skewed) features and comparison operations. More general right skewed features are also considered in an approximated manner. Several concepts, properties, and results are described and discussed, including the description of a duality relationship between uniform and proportional feature spaces and respective comparisons, specifying conditions for consistency between comparisons in each of the two domains. Two normalization possibilities based on non-centralized dispersion of features are also presented, and also described is a modified version of the Jaccard similarity index which incorporates intrinsically normalization. Preliminary experiments are presented in order to illustrate the developed concepts and methods.

The increase in high-dimensional multiomics data demands advanced integration models to capture the complexity of human diseases. Graph-based deep learning integration models, despite their promise, struggle with small patient cohorts and high-dimensional features, often applying independent feature selection without modeling relationships among omics. Furthermore, conventional graph-based omics models focus on homogeneous graphs, lacking multiple types of nodes and edges to capture diverse structures. We introduce a Heterogeneous Graph ATtention network for omics integration (HeteroGATomics) to improve cancer diagnosis. HeteroGATomics performs joint feature selection through a multi-agent system, creating dedicated networks of feature and patient similarity for each omic modality. These networks are then combined into one heterogeneous graph for learning holistic omic-specific representations and integrating predictions across modalities. Experiments on three cancer multiomics datasets demonstrate HeteroGATomics' superior performance in cancer diagnosis. Moreover, HeteroGATomics enhances interpretability by identifying important biomarkers contributing to the diagnosis outcomes.

Recent advances in Graph Neural Networks (GNN) have led to a considerable growth in graph data modeling for multi-modal data which contains various types of nodes and edges. Although some integrative prediction solutions have been developed recently for network-structured data, these methods have some restrictions. For a node classification task involving multi-modal data, certain data modalities may perform better when predicting one class, while others might excel in predicting a different class. Thus, to obtain a better learning representation, advanced computational methodologies are required for the integrative analysis of multi-modal data. Moreover, existing integrative tools lack a comprehensive and cohesive understanding of the rationale behind their specific predictions, making them unsuitable for enhancing model interpretability. Addressing these restrictions, we introduce a novel integrative neural network approach for multi-modal data networks, named Integrative Graph Convolutional Networks (IGCN). IGCN learns node embeddings from multiple topologies and fuses the multiple node embeddings into a weighted form by assigning attention coefficients to the node embeddings. Our proposed attention mechanism helps identify which types of data receive more emphasis for each sample to predict a certain class. Therefore, IGCN has the potential to unravel previously unknown characteristics within different node classification tasks. We benchmarked IGCN on several datasets from different domains, including a multi-omics dataset to predict cancer subtypes and a multi-modal clinical dataset to predict the progression of Alzheimer's disease. Experimental results show that IGCN outperforms or is on par with the state-of-the-art and baseline methods.