Blind image quality assessment (BIQA) methods often incorporate auxiliary tasks to improve performance. However, existing approaches face limitations due to insufficient integration and a lack of flexible uncertainty estimation, leading to suboptimal performance. To address these challenges, we propose a multitasks-based Deep Evidential Fusion Network (DEFNet) for BIQA, which performs multitask optimization with the assistance of scene and distortion type classification tasks. To achieve a more robust and reliable representation, we design a novel trustworthy information fusion strategy. It first combines diverse features and patterns across sub-regions to enhance information richness, and then performs local-global information fusion by balancing fine-grained details with coarse-grained context. Moreover, DEFNet exploits advanced uncertainty estimation technique inspired by evidential learning with the help of normal-inverse gamma distribution mixture. Extensive experiments on both synthetic and authentic distortion datasets demonstrate the effectiveness and robustness of the proposed framework. Additional evaluation and analysis are carried out to highlight its strong generalization capability and adaptability to previously unseen scenarios.

Molecular assembly (MA) has long been a fundamental task in chemistry and biology, with the potential to create new materials and enable novel functions beyond the molecular scale. However, its vast conformational search space poses substantial challenges, and current generative models remain limited in capturing molecular flexibility and preventing non-physical poses. In this paper, we propose AssemUDB, a diffusion bridge–based framework that learns transport mappings between two distinct flexible domains for molecular assembly generation. We reformulate the marginal matching constraint of diffusion bridges as a coupling distribution governed by unbalanced transport rather than imposing strict conservation. Subsequently, we employ a progressive process from structural relaxation in Euclidean space to assembly on the SE(3) manifold. This relaxation of marginal conservation grants the generative model greater flexibility and leads to more physically plausible atom placements. Comprehensive experiments demonstrate the superior performance of AssemUDB. Notably, we find that the method demonstrates performance comparable to, or even better than, mature tools such as PackMol for packing tasks.

Coarse-grained (CG) molecular dynamics of proteins is a preferred approach to studying large molecules on extended time scales by condensing the entire atomic model into a limited number of pseudo-atoms and preserving the thermodynamic properties of the system. However, the significantly increased efficiency impedes the analysis of substantial physicochemical information, since high-resolution atomic details are sacrificed to accelerate simulation. In this paper, we propose LatCPB, a generative approach based on diffusion that enables high-resolution backmapping of CG proteins. Specifically, our model encodes an all-atom into discrete latent embeddings, aligned with learnable multimodal discrete priors for circumventing posterior collapse and maintaining the discrete properties of the protein sequence. During the generation, we further design a latent diffusion process within the continuous latent space due to the potential stochastics in the data. Moreover, LatCPB performs a contrastive learning strategy in latent space to separate feature representations of various molecules and conformations of the same molecule, thus enhancing the comprehension of molecular representational diversity. Experimental results demonstrate that LatCPB is able to backmap CG proteins effectively and achieve outstanding performance.

Existing large language model (LLM) agents for automating data science show promise, but they remain constrained by narrow task scopes, limited generalization across tasks and models, and over-reliance on state-of-the-art (SOTA) LLMs.We introduce DatawiseAgent 1 , a notebook-centric LLM agent framework for adaptive and robust data science automation.Inspired by how human data scientists work in computational notebooks, DatawiseAgent introduces a unified interaction representation and a multi-stage architecture based on finitestate transducers (FSTs).This design enables flexible long-horizon planning, progressive solution development, and robust recovery from execution failures.Extensive experiments across diverse data science scenarios and models show that DatawiseAgent consistently achieves SOTA performance by surpassing strong baselines such as AutoGen and TaskWeaver, demonstrating superior effectiveness and adaptability.Further evaluations reveal graceful performance degradation under weaker or smaller models, underscoring the robustness and scalability.

Medical Visual Question Answering (Medical VQA) plays an important role in medical informatics. However, the robustness of existing medical VQA models is severely challenged by adversarial attacks. Current methods (e.g. adversarial training and noise-based reasoning) heavily rely on additional data or complex procedures and often ignore model-level robustness. To address these issues, we propose Multimodal Variational Masked Autoencoder (MVMAE), a novel pre-training framework designed to enhance the robustness of the medical VQA task. MVMAE leverages masked modeling and variational inference to extract robust multimodal features. The framework introduces a low-cost multimodal bottleneck fusion module and employs reparameterization to sample robust latent representations, ensuring effective feature fusion and reconstruction. Extensive experiments on public medical VQA datasets demonstrate that MVMAE significantly improves resistance to various adversarial attacks and outperforms other medical multimodal pre-training methods.

As a distributed machine learning paradigm, vertical federated learning enables multiple passive parties with distinct features and an active party with labels to train a model collaboratively. Although it has been widely applied for its ability to protect privacy to some extent, this paradigm still faces various threats, especially the label inference attack (LIA). In this paper, we present the first observation of the disparity in LIAs resulting from differences in feature distribution among passive parties. To substantiate this, we study four different types of LIAs across five benchmark datasets, investigating the potential influencing factors and their combined impact. The results show that attack performance disparities can vary up to 15 times among different passive parties. So, how to eliminate this disparity? We explore methods from both attack and defense perspectives, including learning rate adjustment and noise perturbation with differential privacy. Our findings indicate that a modest increase in the learning rate of the passive party effectively enhances the LIA performance. In light of these, we propose a novel defense strategy that identifies passive parties with important features and applies adaptive noise to their gradients. Experiments show that it effectively reduces both attack disparity among passive parties and overall attack accuracy, while maintaining low computational complexity and avoiding additional communication overhead. Our code is publicly accessible athttps://github.com/WWlnZSBMaXU/Attackers-Are-Not-the-Same.

Complementarity determining region (CDR) is a specific region in antibody molecules that binds to antigens, where a small portion of residues undergoes particularly pronounced variations. Generating CDRs with high affinity and specificity is a pivotal milestone in accelerating drug development for daunting and unresolved diseases. However, existing approaches predominantly center on characterizing the attributes of residues through sequential generation models, thus falling short in effectively modeling the intricate spatial correlations among residues and frequently succumbing to the trap of generating sequences that exhibit a high degree of arbitrariness. In this article, we propose a novel synergistic attention-guided cascaded graph diffusion model, termed GraphCas, which offers a pathway for optimized generation of high-affinity CDRs. Our approach is the first cascaded-based graph diffusion model for CDR synthesis. Specifically, we design a graph propagation algorithm with a relation-aware synergistic attention mechanism, enabling the targeted acquisition of structural insights from diverse protein sequences and bolstering the global information representation of the graph by precisely localizing to long-range key residue sites. We design a cascaded conditional enhanced diffusion approach, providing the capability to incorporate additional control constraints into the input. Experimental results demonstrate that GraphCas can generate photo-realistic CDRs and achieve performance comparable to top-tier approaches. In particular, GraphCas reduces the RMSD by nearly 0.42 units in the H1 region and improves the ERRAT by 9.36% points in the L1 region.

The actual collection of tabular data for sharing involves confidentiality and privacy constraints, leaving the potential risks of machine learning for interventional data analysis unsafely averted. Synthetic data has emerged recently as a privacy-protecting solution to address this challenge. However, existing approaches regard discrete and continuous modal features as separate entities, thus falling short in properly capturing their inherent correlations. In this paper, we propose a novel contrastive learning guided Gaussian Transformer autoencoder, termed GTCoder, to synthesize photo-realistic multimodal tabular data for scientific research. Our approach introduces a transformer-based fusion module that seamlessly integrates multimodal features, permitting for mining more informative latent representations. The attention within the fusion module directs the integrated output features to focus on critical components that facilitate the task of generating latent embeddings. Moreover, we formulate a contrastive learning strategy to implicitly constrain the embeddings from discrete features in the latent feature space by encouraging the similar discrete feature distributions closer while pushing the dissimilar further away, in order to better enhance the representation of the latent embedding. Experimental results indicate that GTCoder is effective to generate photo-realistic synthetic data, with interactive interpretation of latent embedding, and performs favorably against some baselines on most real-world and simulated datasets.

Magnetic resonance (MR) image quality assessment plays a crucial role in disease diagnosis and data analysis. Existing methods typically treat the data as images and process them with convolutional networks, thereby ignoring the sequential characteristics of MR data. In this paper, we propose a hybrid spatial-frequency network (HSFNet) for MR image quality assessment, which extracts MR image quality features in both spatial and frequency domains. Specifically, within each data domain, information from local images and global sequences is iteratively integrated by applying a Mamba-based cascading processing module for multiple times. Extensive experiments on both T1-weighting and T2-weighting MR datasets demonstrate the proposed method’s effectiveness and generalization ability in comparison with state-of-the-art MR image quality assessment methods.

Self-training with data augmentation emerges as an efficacious strategy for harnessing unlabeled data in the realm of semi-supervised medical image segmentation. Within the synthetic domain, existing models make a deliberate trade-off, sacrificing some of its absolute performance on labeled data to bolster its generalization capabilities on the predominantly abundant unlabeled data encompassed within the entire dataset. In this study, we find out the essence of employing data augmentation techniques to create a proxy data domain that serves as a bridge between labeled and unlabeled data. To this end, we optimize the aforementioned approach by incorporating the concept of curriculum learning, which encompasses two primary components: Dynamic Copy-Paste strategies and the Self-Iterative Segmentation Model. Concerning the former, the dynamic scaling of the copy-paste box guides the model in acquiring shared semantics, progressing from easier (labeled data) to more challenging (unlabeled data). In order to facilitate this incremental learning process, we have devised models that supports progressive iterative evolution throughout the training phase. Our approach has demonstrated remarkable efficacy through a comprehensive series of benchmarks, consistently outperforming existing methods and achieving state-of-the-art performance.

Functional connectivity (FC) graph analysis is an important method for diagnosing brain disorders using functional magnetic resonance imaging (fMRI). Existing FC graph diagnosis approaches preprocess the brain by dividing it into specific regions using atlases. However, relying on a single atlas exclusively for data preprocessing fails to fully harness the potential of the medical prior knowledge embedded within these atlases. To address this issue, this paper proposes a functional connectivity graph representation learning method that integrates multiple atlases and multiple views. This self-supervised approach to representation learning aligns features across different atlases through contrastive learning. Building upon this, we introduce an improved finite field- of-view multi-head attention mechanism for feature fusion. This mechanism is used to initialize a spectral graph convolutional network's nodes (GCN) with fusion features obtained through cross-atlas alignment. Meanwhile, edge initialization takes into account the inherent correlations among multiple independent atlas fusion features. The proposed method is validated on multi-site datasets and demonstrates superiority across various metrics. The experimental section provides visualizations of the learned feature alignment and consistency, evaluating the effectiveness of contrastive learning.

No-reference image quality assessment for magnetic resonance images (MRI) aims to generate quality evaluations closely aligned with human perception without the reliance on high-quality reference images. This faces challenges due to the limited availability of large-scale, publicly accessible datasets and the absence of rigorous scientific evaluation. To address these challenges, we underscore the importance of six quality indicators that are closely related to spatial and frequency domain features. Based on these indicators, three experts are employed to provide comprehensive quality ratings across eight public MRI datasets. In addition, we propose a novel approach to extract and combine quality feature representations from spatial and frequency domains. Within the spatial domain, we emphasize capturing anatomical details and structural integrity by applying two sets of data augmentation transformations, which produce data variants that facilitate spatial feature extraction via contrastive learning. Meanwhile, a parallel strategy is carried out in the frequency domain, focusing on signal variations and artifacts. Experimental results demonstrate the robustness of our approach in MRI quality assessment, effectively integrating insights from both spatial and frequency domain perspectives.

There is an urgent need to address the effective diagnosis of multiple diseases across various medical institutions while ensuring the privacy of medical data in IoT environments. This requires the model to have the ability of zero-shot generalization, which can not be satisfied by existing models. To address this issue, we propose a two-stage model for medical image diagnosis, based on decoupling and decorrelating. An adversarial architecture is built using a gradient reversal discriminator to improve the model’s robustness. To further address the mixed correlation within domain-invariant features achieved by disentanglement, we propose to mitigate feature dependency through sample weighting. The effectiveness of the model is validated using both the diabetic retinopathy and the skin lesion datasets. For cross-dataset experiment, we select two datasets for symmetric decoupling and reserve the remaining dataset as the test set. The test dataset is analogous to real-world scenarios, where all the samples and labels are completely unknown to the model. The experiments show that the model achieves excellent performance and outperforms baselines in most metrics, which demonstrate the effectiveness of our approach to address the issue of multi-center data privacy in IoT, with a focus on enhancing diagnostic accuracy while ensuring data security.

The automated generation of radiology reports has attracted significant attention in the field of bioinformatics. Currently, the main limitations of this task include insufficient utilization of prior medical knowledge, lack of efficient knowledge fusion algorithms, and less distinctiveness between different generated reports. To address these issues, we propose a novel algorithm for radiology report generation, which includes Structured Knowledge-Enhanced Multi-modal Attention (SKEMA) and Dual-Branch Contrastive Learning (DBCL) for the first time. SKEMA aims to effectively bridge the gap between visual and prior knowledge by leveraging the high-order adjacency matrix of the knowledge graph to weightedly fuse image features and knowledge features. We enhance both features through masking, and use the original features and augmented features as positive and negative samples in the dual-branch contrastive learning (DBCL). DBCL increases the differences between positive and negative samples to avoid generating templated results, and enhances the robustness of the model. Finally, we conducted experiments to demonstrate the effectiveness of our model on two public radiology datasets, IU-Xray and MIMIC-CXR. Our model outperformed previous baseline methods on both datasets and achieved excellent evaluation scores.

The inadequacy of labeled datasets for image quality assessment has led to the development and popularity of self-supervised approaches. However, most existing self-supervised methods primarily focus on content and fidelity features extracted with convolutional neural networks, overlooking the crucial importance of structural features in quality assessment. To address this problem, we present a novel self-supervised two-stream feature extraction and representation approach. In our approach, the first stream leverages a contrastive learning framework to extract image fidelity features, while the second stream emphasizes structural features by incorporating an attention mechanism. This innovative combination results in a comprehensive feature representation for quality assessment. Moreover, our proposed method facilitates transfer learning, allowing the pre-trained two-stream model in the source domain to be seamlessly applied to target domains for quality regression. This compatibility with transfer learning enhances the adaptability and generalization of the model. Extensive experiments are carried out on three synthetic distortion datasets to validate the effectiveness of our approach. The results demonstrate that our work not only competes with state-of-the-art self-supervised methods but also outperforms some supervised approaches.