High-quality garment representation is both a challenge and a key factor in constructing generalized 3D humans from a single-view image. Existing techniques often perform poorly when handling complex garments, primarily due to two critical challenges: (1) Single-view images lack complete information about the garments, limiting the completeness and realism of the reconstruction results. (2) The model’s generalization ability is insufficient, resulting in significant inconsistencies in garment texture and structure when rendered from different viewpoints, which severely impacts the quality of novel view images. To improve the quality of novel view images, we propose a three-stage garment-aware Neural Radiance Field (NeRF) method for generalizable 3D human digitization. To supplement the missing garment information in single-view images, the first garment prior awareness stage focuses on extracting prior knowledge of the garment’s shape, pose deformations, and style. To comprehensively eliminate ambiguities in rendered images across different viewpoints, we then introduce a set of prior-aware feature learning in the second stage to represent garment’s global texture, geometry, and fine details. Additionally, a garment-aware NeRF module with fusion and decoder is designed in the third stage to effectively fuse these prior features, and thus our model can render novel view clothed human and generate high-quality results. Experimental results on RenderPeople, Thuman, and HuMMan datasets demonstrate that our method achieves superior performance and robust generalization in garment representation over the existing methods, especially for synthesizing novel view images of garments without the human body.

Cancer treatments often yield different results from patient to patient due to the genomic heterogeneity of tumors. Accurately predicting a patient's response to an anticancer drug is challenging, especially when using traditional machine learning models trained on cell lines and applied to patient data. These models struggle with domain shift (out-of-distribution data due to differences between cell line and patient data), loss of tissue specificity, and data imbalance across domains. To address these issues, we developed the Tissue-Aware and Prototype Learning for Drug Response Prediction (TAPL-DRP) model. This model predicts anticancer drug responses using a two-stage process. At the stage of tissue-aware cross-domain feature extraction, we integrate a Variational Autoencoder (VAE) and a Generative Adversarial Network (GAN) to extract features from both cell line and patient data. This process incorporates tissue prototypes, InfoNCE loss, and class-balance loss to ensure the features are not only domain-invariant but also biologically meaningful and tissue-specific. At the drug response prediction stage, the model combines these extracted features with molecular drug graph features. It then uses the tissue prototypes to guide the training of the classifier, which further improves the prediction accuracy. We tested TAPL-DRP on the TCGA clinical dataset and the PDTC in vitro dataset. The results show that our model significantly outperforms other methods in key metrics like AUC, AUPRC, ACC, and MCC. This demonstrates its effectiveness in handling domain shift and maintaining tissue specificity. Further analysis confirmed that the tissue prototypes, mutual information loss, and class-balance strategies are all crucial components of the model. In summary, TAPL-DRP offers an effective and precise solution for predicting anticancer drug responses in personalized medicine.The source code is available at https://github.com/weiba/TAPL-DRP.

Local Differential Privacy (LDP) has garnered considerable attention in recent years because it does not rely on trusted third parties and has low interactivity and high operational efficiency. However, current LDP frequency estimation mechanisms aggregate data using different privacy budgets within the same domain of attribute values, overlooking the aggregation requirements across different domains of attribute values. This limits the potential for enhancing the data utility under fixed privacy budgets and meeting user preferences in multiple domains of attribute values and privacy budgets. To address this issue, we define a Multi-Domains Personalized Local Differential Privacy (MDPLDP) model that allows users to freely choose domains of attribute values and privacy budgets according to their privacy preferences. Furthermore, based on the MDPLDP model, two new frequency estimation mechanisms are proposed: MDPLDP-Generalized Randomized Response and MDPLDP-basic Randomized Aggregatable Privacy-Preserving Ordinal Response. These mechanisms support cross-domains data aggregation and optimize data utility by adjusting the domains of attribute values and increasing privacy budgets. Theoretical analysis reveals that these new mechanisms have lower estimation errors than the traditional LDP mechanisms. Experiments on real and synthetic datasets demonstrate that the proposed mechanisms effectively reduce estimation errors and enhance the utility of data-frequency estimation.

Accurate prediction of drug responses is critical for advancing personalized cancer therapies. Although current graph neural network (GNN)-based approaches predominantly focus on pairwise interactions between cell lines and drugs, they often neglect the potential of higher-order interactions. In this study, we present HRLCDR, a novel computational framework that utilizes Hypergraph Representation Learning to predict Cancer Drug Responses. HRLCDR begins by constructing hypergraphs for both cell lines and drugs and then processes through low-pass and high-pass hypergraph convolutions, allowing the model to extract both common and different features from the complex higher-order interactions between cell lines and drugs. After that, HRLCDR constructs a heterogeneous graph using known cell line responses to drugs. Parallel heterogeneous graph convolution operations are then employed to extract primary interaction features between cell lines and drugs from these associations. Finally, HRLCDR integrates the features learned from both the hypergraphs and the heterogeneous graph, predicting drug response via Classifiers. We evaluated HRLCDR's performance on two major cancer drug response datasets: the Cancer Drug Sensitivity Data (GDSC) and the Cancer Cell Line Encyclopedia (CCLE). The results demonstrate that HRLCDR outperforms current state-of-the-art methods, underscoring its potential to enhance the accuracy and reliability of cancer drug response predictions.

This paper proposes a unified 3D Gaussian splatting framework consisting of three key components for motion and defocus blur reconstruction. First, a dual-blur perception module is designed to generate pixel-wise masks and predict the types of motion and defocus blur, guiding structural feature extraction. Second, a blur-aware Gaussian splatting integrates blur-aware features into the splatting process for accurate modeling of the global and local scene structure. Third, an Unoptimized Gaussian Ratio (UGR)-opacity joint optimization strategy is proposed to refine under-optimized regions, improving reconstruction accuracy under complex blur conditions. Experiments on a newly constructed motion and defocus blur dataset demonstrate the effectiveness of the proposed method for novel view synthesis. Compared with state-of-the-art methods, our framework achieves improvements of 0.28 dB, 2.46% and 39.88% on PSNR, SSIM, and LPIPS, respectively. For deblurring tasks, it achieves improvements of 0.36 dB, 3.24% and 28.96% on the same metrics. These results highlight the robustness and effectiveness of this approach. Additional visual results and video renderings are available on our project webpage: https://sunbeam-217.github.io/Dual-blur-reconstruction/ .

Using deep learning models on spatial transcriptomics data to identify the spatial domain is crucial for uncovering the spatial distribution of cells and gene expression patterns within tissues, essential for understanding complex biological processes and disease mechanisms. Existing methods for spatial domain partitioning often rely on predefined adjacency relationships at a single scale, overlooking the hierarchical structure and functional characteristics of biological tissues. In this paper, we propose SpaNFM, a novel method that leverages sparse attention-based hierarchical node representation and multi-view contrastive learning for spatial domain identification in spatial transcriptomics data. The SpaNFM first treats each spot as a node and constructs two views using different data augmentation techniques based on tissue image information, gene expression profiles, and spatial coordinates of cells. Subsequently, SpaNFM utilizes a sparse attention-based hierarchical node fusion module to generate coarse-grained node representations. This fine-to-coarse hierarchical structure integrates complementary information from multi-granularity node features and reduces model complexity due to the decreased node size. The model parameters are updated using gene expression reconstruction loss and contrastive loss on the coarse-grained node representations from the two views. Finally, the learned node features are subjected to downstream clustering using the Leiden algorithm. We tested SpaNFM on the human dorsolateral prefrontal cortex dataset. The results demonstrate that SpaNFM outperforms other state-of-the-art methods in most cases. The data and code are available at: https://github.com/weiba/SpaNFM

Federated learning provides privacy protection to the collaborative training of global model based on distributed private data. The local private data is often in the presence of long-tailed distribution in reality, which downgrades the performance and causes biased results. In this paper, we propose a dynamic adaptive federated learning optimization algorithm with the Grey Wolf Optimizer and Markov Chain, named FedWolf, to solve the problems of performance degradation and result bias caused by the local long-tailed data. FedWolf is launched with a set of randomly initialized parameters instead of a shared parameter employed by existing methods. Then multi-level participants are elected based on the F1 scores calculated from the uploaded parameters. A dynamic weighting strategy based on the participant level is used to adaptively update parameters without artificial control. The above parameter updating is modelled as a Markov Process. After all communication rounds are completed, the future performance (including the probability of each participant is elected as different participant level) of participants is predicted through the historical Markov states. Finally, the probability of each participant is elected as the level 1 is used as the contribution weight and the global model is obtained through dynamic contribution weight aggregating. We introduce the Gini index to evaluate the bias of classification results. Extensive experiments are conducted to validate the effectiveness of FedWolf in solving the problems of performance cracks and categorization result bias as well as the robustness of adaptive parameter updating in resisting outliers and malicious users.

Clustering individual cells or spots based on their gene expression profiles in a spatial context is a powerful approach to uncovering the underlying biological diversity and relationships among cells. The intricate information within spatial transcriptomics data demands sophisticated algorithms that effectively integrate gene expression, cell position, and tissue image data for accurate cell or spot clustering. This work proposes a Multi-View Comparative Learning method for clustering Spatial Transcriptomics data (MVCLST). MVCLST first builds on two data views using gene expression profiles, cell space coordinates, and image features. Then it employs four different encoders to capture the common and private features of the two views. The model employs a contrastive learning loss to encourage effective interaction between the two views and ensure feature consistency. The shared and private features from both views are fused using corresponding decoders. Finally, the model employs the Leiden algorithm for downstream clustering of the learned features. We test the MVCLST method on a human dorsolateral prefrontal cortex dataset. The results show that MVCLST outperforms other state-of-the-art methods in most cases. Additionally, the clusters identified by MVCLST align closely with manual annotations and established neuroscience definitions.

Abstract Detailed and accurate feature representation is essential for high‐resolution reconstruction of clothed human. Herein we introduce a unified feature representation for clothed human reconstruction, which can adapt to changeable posture and various clothing details. The whole method can be divided into two parts: the human shape feature representation and the details feature representation. Specifically, we firstly combine the voxel feature learned from semantic voxel with the pixel feature from input image as an implicit representation for human shape. Then, the details feature mixed with the clothed layer feature and the normal feature is used to guide the multi‐layer perceptron to capture geometric surface details. The key difference from existing methods is that we use the clothing semantics to infer clothed layer information, and further restore the layer details with geometric height. We qualitative and quantitative experience results demonstrate that proposed method outperforms existing methods in terms of handling limb swing and clothing details. Our method provides a new solution for clothed human reconstruction with high‐resolution details (style, wrinkles and clothed layers), and has good potential in three‐dimensional virtual try‐on and digital characters.

The correct identification of the driver genes that lead to cancer development is essential for understanding the mechanisms of cancer and developing drugs to treat it. Currently, most computational methods for identifying cancer driver genes are based on a cohort of patients. However, due to the heterogeneity of cancers, patients diagnosed with the same cancers may have different genomic characteristics and present varied clinical symptoms. It requires devising effective methods to identify personalized cancer driver genes in an individual. This work developed a novel method to predict personalized cancer driver genes of a single sample based on graph convolution networks, namely pDriverGCN. pDriverGCN constructed a mutant gene-sample heterogeneous network according to the known driver genes of samples. Then it employed two separate graph convolution network models to learn feature representations for genes and samples by gathering the features of themselves and their neighbors. Finally, pDriverGCN used the feature representations to reconstruct the association matrix between genes and samples through a linear correlation coefficient decoder. We apply our model to identify personalized driver genes of samples on the TCGA datasets. The experimental results show that our model outperforms state-of-the-art methods being evaluated at both population and individual levels.

Reputation system is an important means to build trust, aid decision making of users, and sustain user loyalty in the context of online services. However, different users inherently have different preferences, and so it is impossible that all users rate services with the same criteria. Thus, aggregating cardinal ratings into reputation will potentially lead to unreliable and misleading result, which makes the impossibility of interpersonal utility comparisons should be considered in reputation systems. In this paper, we propose a novel reputation measurement mechanism that aggregates ordinal user preferences rather than cardinal ratings into reputation. By extending the majority rule naturally, dominance relationship between services is defined based on ordinal preferences. Then, reputation measurement is modelled as a problem to find a ranking that indicates the dominance relationships among services. A directed acyclic graph is constructed based on the dominance relationships of services pairs and then the ranking of services is found from the graph. We prove our method satisfies some basic criteria that a reasonable reputation measurement method should satisfy in the context of the impossibility of interpersonal utility comparisons. We also conduct a comprehensive experimental study and performance analysis to evaluate the effectiveness and efficiency of the proposed method.

A core source of raw information used as inputs to the reputation systems of online services is the feedback ratings provided by users. However, it is impossible that all users rate services with the same criteria and so ratings of different users are incommensurable. Meanwhile, users are not necessarily willing to provide honest feedbacks. Thus, aggregating dishonest cardinal ratings into reputation will potentially lead to unreliable and misleading reputation. In this paper, we propose a reputation model that aggregates ordinal user preferences rather than cardinal ratings for online services with user incentive. A distance metric is defined to measure the discrepancy between ordinal preferences. Then an optimal reputation model with the attributes of incentive compatible and individually rational is proposed. We design a B&B algorithm to solve the optimization problem so that a reputation vector that maximizes the total value of all users can be found efficiently. A comprehensive experimental study and performance analysis are conducted to evaluate the effectiveness and efficiency of the proposed method.

Reputation systems have become an important means to help users build trust, reduce information asymmetry and filter information in the context of online services provision. Different users cannot rate services under the same criteria due to the scale and dynamism of these systems. Thus, aggregating cardinal ratings into reputation will potentially lead to unreliable and misleading result, which makes reputation systems necessarily consider the impossibility of interpersonal utility comparisons. In this paper, we exploit the ordinal user preferences between services to compute reputation of services. A distance metric is defined to measure the discrepancy between two rating vectors and the reputation computation problem was formalized as an optimization problem. Then, genetic algorithm is used to solve the optimization problem to find a reputation vector that minimizes the total number of disagreements with the rating matrix. We conduct a comprehensive experimental study and performance analysis to evaluate the effectiveness and efficiency of the proposed method.

Reviews are contents written by users to express opinions on products or services. However, the number of reviews is always large and the quality of reviews is various. In order to assess the quality of reviews, we ought to know the factors that have influence on or are related to the quality of reviews. In this paper, we present an in-depth study of reviews on JD.com, one of the famous E-commerce sites in China. We observed the times when users buy goods are highly related to the time when they post reviews. Meanwhile, the level of users is an important factor affects the quality of reviews. On the other hand, users prefer to post short reviews containing the description of the quality and price of the product. Based on a lexical resource containing sentiment annotations, we found sentimental words expressing users' feelings and opinions is one of the key factors that can influence the quality of reviews. Finally, we evaluated the importance of more than 18 factors influencing the quality of reviews, and found these factors can be compressed into 5 principal factors.

Example-based color transfer is a critical operation in image editing but easily suffers from some corruptive artifacts in the mapping process. In this paper, we propose a novel unified color transfer framework with corruptive artifacts suppression, which performs iterative probabilistic color mapping with self-learning filtering scheme and multiscale detail manipulation scheme in minimizing the normalized Kullback-Leibler distance. First, an iterative probabilistic color mapping is applied to construct the mapping relationship between the reference and target images. Then, a self-learning filtering scheme is applied into the transfer process to prevent from artifacts and extract details. The transferred output and the extracted multi-levels details are integrated by the measurement minimization to yield the final result. Our framework achieves a sound grain suppression, color fidelity and detail appearance seamlessly. For demonstration, a series of objective and subjective measurements are used to evaluate the quality in color transfer. Finally, a few extended applications are implemented to show the applicability of this framework.