Recovering dense and uniformly distributed point clouds from sparse or noisy data remains a significant challenge. Recently, great progress has been made on these tasks, but usually at the cost of increasingly intricate modules or complicated network architectures, leading to long inference time and huge resource consumption. Instead, we embrace simplicity and present a simple yet efficient method for jointly upsampling and cleaning point clouds. Our method leverages an off-the-shelf octree-based 3D UNet (OUNet) with minor modifications, enabling both upsampling and cleaning within a single network. Our network directly processes each input point cloud as a whole instead of processing point cloud patches as in previous works, which significantly eases the implementation and brings at least 47 times faster inferencing. Extensive experiments demonstrate that our method achieves state-of-the-art performance with huge efficiency advantages on a series of benchmarks. We expect our method to serve as a simple baseline and inspire researchers to rethink method designs for point cloud upsampling and cleaning. Our code and trained models are available at https://github.com/octree-nn/upsample-clean.

Federated learning has emerged as a widely adopted distributed machine learning paradigm. In the field of medical diagnosis, it has become a research hotspot, enabling multiple institutions to collaboratively leverage medical data for accurate analysis. However, the distributed nature of federated learning also introduces new challenges in data security and privacy protection, i.e., a curious server might collude with the client to infer private data of honest clients. In this paper, we implement our own FHE library and integrate it with several widely used federated learning methods, providing a unified framework. Our framework employs multiple-party Fully Homomorphic Encryption (FHE) to remove the requirements of a trusted third party or central key servers and address data security and privacy concerns in federated learning, ensuring that the data of honest participants is never exposed while maintaining the accuracy of the final analysis. We deploy our Privacy-preserving Federated Learning framework in the context of Melanoma Diagnosis, PFL-MD, and across multiple types of widely used benchmark datasets, our method achieves accuracy comparable to that of the original federated learning framework, thereby enabling reliable melanoma diagnosis. Extensive experiments further demonstrate the effectiveness of our framework.

In regions with limited medical resources, detecting melanoma has long been a challenging problem, particularly due to its heterogeneity and rarity. However, with the recent advances in deep learning technologies, it has become increasingly feasible to enable melanoma detection in remote areas. By collecting melanoma datasets, researchers have focused on exploring more effective models to learn discriminative features and deploy automated detection systems, with the aim of assisting doctors in remote or under-resourced regions. In this paper, we explore the latest deep learning model, Vision Mamba, as an AI-based diagnostic approach for melanoma detection. Furthermore, we enhance the Vision Mamba model by analyzing the differences between benchmark datasets and real-world clinical data, aiming to further reduce the false positive rate and minimize misdiagnosis. We conduct experiments on multiple types of widely used datasets across three model architectures and compare our approach against several popular baseline methods. Extensive experimental results demonstrate that Vision Mamba achieves state-of-the-art performance and validates its effectiveness for melanoma diagnosis in diverse clinical scenarios.

Variable selection for genome-wide association studies (GWAS) has been a major research focus for decades. With the exponential growth of biological and biomedical data in the era of big data, scientists are confronted with the challenge of extracting meaningful information from vast datasets while managing the inherent heterogeneity in bioinformatics. To date, there are no highly effective tools that support high-dimensional datasets and achieve robust variable selection performance, all while accounting for the non-i.i.d. features and structured relatedness among explanatory and response variables.To address these challenges, we introduce the first high-performance computing framework for variable selection in GWAS. Our framework integrates various state-of-the-art methods, allowing researchers to easily combine different techniques and fully explore their potential. Additionally, our approach employs novel optimization strategies to solve the problem efficiently, even for high-dimensional data with sparse characteristics. By processing the data holistically, the framework delivers comprehensive analysis and accurate linkage mapping associations. Designed for ease of use, the framework is implemented in Python and offers seamless deployment, making it accessible to a wide range of researchers.

Variable selection for genome-wide association studies is a key focus for bioinformatics researchers in high-performance computing. The rapid growth of biological and biomedical data demands has led to high-dimensional, heterogeneous datasets characterized by non-i.i.d. properties and numerous response variables, often resulting in false negatives or positives in recovered results. Traditional methods, when nal̈ively applied, yield suboptimal performance due to confounding factors. To account for the complex interdependencies in heterogeneous data and enhance the practical outcomes of genome-wide association studies, we introduce two methods, TGsLMM and TTsLMM, which balance effects between response and explanatory variables for subpopulation inference. Our unified framework performs sparse variable selection using graph-structured or tree-guided structures in a low-rank linear mixed model. Additionally, we extend our approach to high-dimensional datasets and adaptively select the covariance structure for genomic data. Extensive experiments on synthetic and three real-world datasets emphasize the robustness and effectiveness of our proposed methods, achieving the highest ROC area compared to baselines and superior results for future potential.