Learning directed acyclic graphs (DAGs) from observational data that involve a set of variables carrying intrinsic noise is a crucial yet challenging task. Recent approaches frame the DAG learning task as minimizing a reconstruction-based objective function, i.e., reconstructing observed data by learning a DAG, while adhering to an acyclic constraint. However, optimizing this objective does not always guarantee the correctness of the learned graphs. One reason for this is that the intrinsic noise entangled with the variables is inadvertently absorbed in the reconstruction process at the expense of inferring incorrect DAG structures. To address this issue, we propose a novel DAG learner that first injects artificial noise into observational variables that are contaminated by fixed intrinsic noise. The next step involves reconstructing these perturbed variables using a weighted structure estimator and a weighted noise estimator, instead of reconstructing the observational variables solely with the fixed intrinsic noise. This strategy effectively reduces the sensitivity of the structure estimator to the fixed intrinsic noise. Additionally, we observe a strong similarity between the proposed DAG learner and diffusion models. This similarity motivates us to replicate the well-known denoising capabilities of diffusion models in our DAG learner. We reformulate and adapt the denoising process in denoising diffusion probabilistic models (DDPMs), which allows us to derive a specific weight schedule for the weighted structure and noise estimators of our DAG learner. Extensive experiments conducted on synthetic and real datasets with varying scales demonstrate the outstanding performance of our proposed method.

Accurate segmentation of multimodal medical images with missing modalities remains a critical challenge due to incomplete data often encountered in clinical practice. Lack of modality-specific information often leads to significant performance degradation in scenarios with severely missing modalities. To address this problem, we focus on modeling the relationships between modality-specific features. We propose a joint representation learning framework, named as Cyclic Contrastive Latent Representation Segmentation (CLRS), which incorporates cyclic modality-specific representation generation and contrastive feature alignment for robust 3D medical image segmentation under missing modality conditions. CLRS first extracts feature from available modalities using a unified encoder, then generates missing latent representations conditioned on the encoded features via an elaborately designed synthesis strategy. Meanwhile, a channel-wise attention mechanism is introduced to enhance the specific features of the modality. In addition, modality-specific contrastive learning enforces cross-modal discrimination between the generated and encoded representations, which effectively disentangles modality-specific information from shared patterns and enhances the segmentation robustness in missing modality scenarios. Extensive experiments on three 3D multimodal datasets demonstrate the superior performance of CLRS, particularly in scenarios with severe modality absence. For instance, with only a single modality available on ProstateZS dataset, CLRS improves the state-of-the-art (SOTA) by over 4.06% for peripheral zone, 2.20% for central gland.

Exploiting multi-modal magnetic resonance imaging complementary information for brain tumor segmentation is still a challenging task. Existing methods are usually inclined to learn the joint representation of all tumor regions indiscriminately, thus salient sub-region or healthy tissue would be dominant during the training procedure, which leads to a biased and limited representation performance. In this study, a novel transformer-based multi-modal brain tumor segmentation approach is developed by decoupling and coupling strategy. First, Anatomy-induced Region Decoupler decouples the representation of the tumor scattered in different semantic sub-regions following anatomical view, which forces the model to fully learn intra-region representation separately with multiple modalities context. Additionally, we introduce the collaborative decoupling of the corresponding sub-region edge to serve auxiliary cues. We then design the Edge-supported Intra-region Coupler to separately couple edge and object learning within each anatomical sub-region structure. Lastly, the Mutual Cross-region Coupler is further applied to implement mutual improvement by coupling complementary gains among the above decoupled sub-regions. Extensive experiments clearly demonstrate that our method outperforms current state-of-the-arts for brain tumor segmentation on BRATS2018, BRATS2020, MSD, and BRATS2021 benchmarks while retaining high efficiency in the learning procedure. The code is available at https://github.com/mathwrx/Decouple-and-Couple_Learning_in_Multi-Modal_Brain_Tumor_Segmentation.

Protein–ligand binding affinity prediction plays a pivotal role in the field of drug discovery, with multimodalbased methods standing out. Existing approaches based on a multimodal framework typically rely on simple concatenation or pooling, which fail to identify and emphasize key features across heterogeneous sources. To tackle this bottleneck, we propose an adaptive fusion module, which injects sequence–structure embeddings into a functional annotation stream while incorporating functional annotation embeddings into the sequence–structure stream. Subsequently, learnable adaptive weights are employed to combine the outputs of these two pathways in a fully data-driven manner. Extensive experiments on the PDBBind benchmark demonstrate that our method achieves the best performance compared with state-of-the-art methods. Ablation study and hyperparameter analysis confirm that sequence, structure, and functional annotation each provide complementary information, and the joint optimization of these modalities via our adaptive fusion strategy yields the highest overall predictive accuracy. Our code is available at GitHub link https://github.com/Jessez2/AdaMM.

Domain Generalization (DG) aims to generalize a model trained on source domains to unseen target domains. Learning domain-invariant representations based on causal inference is one of the popular directions in DG. However, these methods would yield an inaccurate causal variable set due to the lack of heterogeneous domain data or a prior causal structure, which severely weakens their generalization capacity. To this end, we propose a novel DG method called Perturbing Confounders via Causal Disentanglement (PCCD), which explicitly disentangles latent features into causally relevant features and confounding features. The method perturbs the confounding features to improve generalization on unseen domains. Specifically, we first apply the causal disentanglement framework to separate causal features and confounding features. Then, we introduce a learnable perturbation initialized as Gaussian distribution on the batch-wise statistics for each dimension of the confounding features while constraining semantic consistency. Extensive experiments on several benchmarks indicate that our framework achieves state-of-the-art performance compared to other competing methods.

Recently, the expansion of Variance Reduction (VR) to Riemannian stochastic non-convex optimization has attracted increasing interest. Inspired by recursive momentum, we first introduce Stochastic Recursive Variance Reduced Gradient (SRVRG) algorithm and further present Stochastic Recursive Gradient Estimator (SRGE) in Euclidean spaces, which unifies the prevailing variance reduction estimators. We then extend SRGE to Riemannian spaces, resulting in a unified Stochastic rEcursive vaRiance reducEd gradieNt frAmework (SERENA) for Riemannian non-convex optimization. This framework includes the proposed R-SRVRG, R-SVRRM, and R-Hybrid-SGD methods, as well as other existing Riemannian VR methods. Furthermore, we establish a unified theoretical analysis for Riemannian non-convex optimization under retraction and vector transport. The IFO complexity of our proposed R-SRVRG and R-SVRRM to converge to $\varepsilon$-accurate solution is $\mathcal{O}\left(\min \{n^{1/2}{\varepsilon^{-2}}, \varepsilon^{-3}\}\right)$ in the finite-sum setting and ${\mathcal{O}\left( \varepsilon^{-3}\right)}$ for the online case, both of which align with the lower IFO complexity bound. Experimental results indicate that the proposed algorithms surpass other existing Riemannian optimization methods.

In many real-world scenarios, the goal is to identify variables whose causal mechanisms change across related datasets. For example, detecting abnormal root nodes in manufacturing, and identifying key genes that influence cancer by analyzing differences in gene regulatory mechanisms between healthy individuals and cancer patients. This can be done by recovering the causal structure for each dataset independently and then comparing them to identify differences, but the performance is often suboptimal. Typically, existing methods directly identify causal mechanism shifts based on linear additive noise models (ANMs) or by imposing restrictive assumptions on the noise distribution. In this paper, we introduce CMSI, a novel and more general algorithm based on nonlinear ANMs that identifies variables with shifting causal mechanisms under arbitrary noise distributions. Evaluated on various synthetic datasets, CMSI consistently outperforms existing baselines in terms of F1 score. Additionally, we demonstrate CMSI's applicability on gene expression datasets of ovarian cancer patients at different disease stages.

Multi-view clustering has attracted significant attention and application. Nonnegative matrix factorization is one popular feature of learning technology in pattern recognition. In recent years, many semi-supervised nonnegative matrix factorization algorithms were proposed by considering label information, which has achieved outstanding performance for multi-view clustering. However, most of these existing methods have either failed to consider discriminative information effectively or included too much hyper-parameters. Addressing these issues, a semi-supervised multi-view nonnegative matrix factorization with a novel fusion regularization (FRSMNMF) is developed in this article. In this work, we uniformly constrain alignment of multiple views and discriminative information among clusters with designed fusion regularization. Meanwhile, to align the multiple views effectively, two kinds of compensating matrices are used to normalize the feature scales of different views. Additionally, we preserve the geometry structure information of labeled and unlabeled samples by introducing the graph regularization simultaneously. Due to the proposed methods, two effective optimization strategies based on multiplicative update rules are designed. Experiments implemented on six real-world datasets have demonstrated the effectiveness of our FRSMNMF comparing with several state-of-the-art unsupervised and semi-supervised approaches.

Matrix factorization has demonstrated promising performance in the incomplete multiview clustering (IMC) tasks. However, many algorithms require feature normalization operations to ensure the stability of model results, so either the convergence is unstable, or the objective function cannot fit the data well. Addressing these issues, we propose a novel IMC algorithm using a normalizing alignment strategy (IMCNAS) based on nonnegative matrix factorization. Specifically, the columns of the basis matrices are constrained into unit vector space, which integrates the feature normalization and the optimizing process, and makes the model converge fast and stable. On the other hand, this enables the model to fit the data better and produce more reasonable factorization results. Further, we develop a novel pairwise co-regularization to align incomplete multiple views more directly, without introducing a common consensus matrix like traditional centroid-based co-regularization. Graph regularization is also incorporated in the proposed model to utilize the geometrical information of data. We implement IMCNAS with a centroid-based regularization and a pairwise co-regularization respectively, and leads to two variants, i.e., IMCNAS-1 and IMCNAS-2. Both variants are optimized with multiplicative updating rules. Extensive experiments conducted on various real-world datasets comparing several state-of-the-art IMC methods verified the effectiveness of the proposed methods. The source code is available at:https://github.com/GuoshengCui/IMCNAS.

Molecular optimization, which transforms a given input molecule X into another Y with desired properties, is essential in molecular drug discovery. The traditional approaches either suffer from sample-inefficient learning or ignore information that can be captured with the supervised learning of optimized molecule pairs. In this study, we present a novel molecular optimization paradigm, Graph Polish. In this paradigm, with the guidance of the source and target molecule pairs of the desired properties, a heuristic optimization solution can be derived: given an input molecule, we first predict which atom can be viewed as the optimization center, and then the nearby regions are optimized around this center. We then propose an effective and efficient learning framework, Teacher and Student polish, to capture the dependencies in the optimization steps. A teacher component automatically identifies and annotates the optimization centers and the preservation, removal, and addition of some parts of the molecules; a student component learns these knowledges and applies them to a new molecule. The proposed paradigm can offer an intuitive interpretation for the molecular optimization result. Experiments with multiple optimization tasks are conducted on several benchmark datasets. The proposed approach achieves a significant advantage over the six state-of-the-art baseline methods. Also, extensive studies are conducted to validate the effectiveness, explainability, and time savings of the novel optimization paradigm.

Clustering techniques attempt to group objects with similar properties into a cluster. Clustering the nodes of an attributed graph, in which each node is associated with a set of feature attributes, has attracted significant attention. Graph convolutional networks (GCNs) represent an effective approach for integrating the two complementary factors of node attributes and structural information for attributed graph clustering. Smoothness is an indicator for assessing the degree of similarity of feature representations among nearby nodes in a graph. Oversmoothing in GCNs, caused by unnecessarily high orders of graph convolution, produces indistinguishable representations of nodes, such that the nodes in a graph tend to be grouped into fewer clusters, and pose a challenge due to the resulting performance drop. In this study, we propose a smoothness sensor for attributed graph clustering based on adaptive smoothness-transition graph convolutions, which senses the smoothness of a graph and adaptively terminates the current convolution once the smoothness is saturated to prevent oversmoothing. Furthermore, as an alternative to graph-level smoothness, a novel fine-grained nodewise-level assessment of smoothness is proposed, in which smoothness is computed in accordance with the neighborhood conditions of a given node at a certain order of graph convolution. In addition, a self-supervision criterion is designed considering both the tightness within clusters and the separation between clusters to guide the entire neural network training process. The experiments show that the proposed methods significantly outperform 13 other state-of-the-art baselines in terms of different metrics across five benchmark datasets. In addition, an extensive study reveals the reasons for their effectiveness and efficiency.

The coronavirus disease 2019 (COVID-19) pandemic is spreading worldwide. Considering the limited clinicians and resources and the evidence that computed tomography (CT) analysis can achieve comparable sensitivity, specificity, and accuracy with reverse-transcription polymerase chain reaction, the automatic segmentation of lung infection from CT scans supplies a rapid and effective strategy for COVID-19 diagnosis, treatment, and follow-up. It is challenging because the infection appearance has high intraclass variation and interclass indistinction in CT slices. Therefore, a new context-aware neural network is proposed for lung infection segmentation. Specifically, the autofocus and panorama modules are designed for extracting fine details and semantic knowledge and capturing the long-range dependencies of the context from both peer level and cross level. Also, a novel structure consistency rectification is proposed for calibration by depicting the structural relationship between foreground and background. Experimental results on multiclass and single-class COVID-19 CT images demonstrate the effectiveness of our work. In particular, our method obtains the mean intersection over union (mIoU) score of 64.8%, 65.2%, and 73.8% on three benchmark datasets for COVID-19 infection segmentation.

The ongoing coronavirus disease 2019 (COVID-19) pandemic spread throughout China and worldwide since it was reported in Wuhan city, China in December 2019. 4 589 526 confirmed cases have been caused by the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), by May 18, 2020. At the early stage of the pandemic, the large-scale mobility of humans accelerated the spread of the pandemic. Rapidly and accurately tracking the population inflow from Wuhan and other cities in Hubei province is especially critical to assess the potential for sustained pandemic transmission in new areas. In this study, we first analyze the impact of related multisource urban data (such as local temperature, relative humidity, air quality, and inflow rate from Hubei province) on daily new confirmed cases at the early stage of the local pandemic transmission. The results show that the early trend of COVID-19 can be explained well by human mobility from Hubei province around the Chinese Lunar New Year. Different from the commonly-used pandemic models based on transmission dynamics, we propose a simple but effective short-term prediction model for COVID-19 cases, considering the human mobility from Hubei province to the target cities. The performance of our proposed model is validated by several major cities in Guangdong province. For cities like Shenzhen and Guangzhou with frequent population flow per day, the values of [Formula: see text] of daily prediction achieve 0.988 and 0.985. The proposed model has provided a reference for decision support of pandemic prevention and control in Shenzhen.

Recently, surface electromyogram (sEMG) has a trend with an increasing number of electrodes to compose a 2-dimension (D) electrode array, which is called high density sEMG (HD-sEMG). However, gesture recognition algorithm with HD-sEMG is still a challenge especially in real time recognition application. This paper researched several spatial attention modules and embedded them to the input layer of neural network. In this way, we can re-weight the input channel to get a better accuracy, robustness and interpretability. By utilizing the Group Convolution Neural Network (CNN), the gesture classification accuracy is improved by 4.44% and 2.71% in CapgMyo and CSL-HDEMG dataset respectively. This method is so efficient that it achieves only with 128 parameters, barely increasing the computational overhead. Meanwhile, we compared the performance in 1-D, 2-D and 3-D CNN, and found that our 1-D group CNN has great advantages in total computational overhead without the loss of accuracy. It provides a practical solution for real time gesture recognition application.

Automated electrocardiogram (ECG) analysis for arrhythmia detection plays a critical role in early prevention and diagnosis of cardiovascular diseases. Extracting powerful features from raw ECG signals for fine-grained diseases classification is still a challenging problem today due to variable abnormal rhythms and noise distribution. For ECG analysis, the previous research works depend mostly on heartbeat or single scale signal segments, which ignores underlying complementary information of different scales. In this paper, we formulate a novel end-to-end Deep Multi-Scale Fusion convolutional neural network (DMSFNet) architecture for multi-class arrhythmia detection. Our proposed approach can effectively capture abnormal patterns of diseases and suppress noise interference by multi-scale feature extraction and cross-scale information complementarity of ECG signals. The proposed method implements feature extraction for signal segments with different sizes by integrating multiple convolution kernels with different receptive fields. Meanwhile, joint optimization strategy with multiple losses of different scales is designed, which not only learns scale-specific features, but also realizes cumulatively multi-scale complementary feature learning during the learning process. In our work, we demonstrate our DMSFNet on two open datasets (CPSC_2018 and PhysioNet/CinC_2017) and deliver the state-of-art performance on them. Among them, CPSC_2018 is a 12-lead ECG dataset and CinC_2017 is a single-lead dataset. For these two datasets, we achieve the F1 score [Formula: see text] and [Formula: see text] which are higher than previous state-of-art approaches respectively. The results demonstrate that our end-to-end DMSFNet has outstanding performance for feature extraction from a broad range of distinct arrhythmias and elegant generalization ability for effectively handling ECG signals with different leads.

Automated electrocardiogram (ECG) analysis for arrhythmia detection plays a critical role in early prevention and diagnosis of cardiovascular diseases. In this paper, we proposed a novel end-to-end deep learning method for multiclass arrhythmia detection with multiple stage features fusion. The network is composed of multiple convolution and attention module. Specifically, we use skip connection operation to fuse different levels of features extracted at different stages for target task processing. And the channel-wise attention modules are adopted for effectively extracting the features learned at the different stages. By combining the attention module and convolutional neural network, the discrimination power of the network for ECG classification is improved. We demonstrate the proposed method for ECG classification on an open ECG dataset and compare it with some state-of-the-art methods, which achieves an average F1-score of 81.3% in classification of 8 types of arrhythmias and sinus rhythm. The experimental results convince the efficiency of the proposed method.