Federated learning has emerged as a promising paradigm for collaborative model training while preserving data privacy. However, many existing FL methods implicitly assume that clients have sufficient computational and storage resources, making them less applicable in real-world scenarios with severe system heterogeneity. To address this, submodel extraction has recently gained attention as a promising strategy to tailor the global model to resource-constrained clients. Despite this progress, existing methods often suffer from noticeable performance gaps across clients and structural inconsistency in the extracted models, leading to degraded global performance and increased communication overhead. In this work, we propose FedLAGC, a novel federated framework that jointly tackles performance imbalance and communication inefficiency through Layer-Adaptive submodel extraction and Gradient Correction. Specifically, FedLAGC constructs client-specific submodels by selecting structurally important parameters according to layer-wise importance scores, ensuring both resource adaptiveness and architectural consistency. Additionally, we propose a lightweight correction mechanism that captures historical optimization drift, helping to align local updates with the global direction and reduce redundant communication. The rigorous convergence analysis of FedLAGC for system-heterogeneous federated learning under non-convex objectives is given. Extensive experiments on CIFAR-10 and CIFAR-100 with ResNet-18 and ResNet-34 under various system and data heterogeneity settings demonstrate the significant superiority of FedLAGC (up to 24% accuracy improvement and 3.66× communication efficiency) over state-of-the-art methods.

Scene Graph Generation (SGG) aims to detect all objects and identify their pairwise relationships existing in the scene. Considering the substantial human labor costs, existing scene graph annotations are often sparse and biased, which result in confusion training with low-frequency predicates. In this work, we design a Semi-Supervised Clustering framework for Scene Graph Generation (SSC-SGG) that uses the sparse labeled data to guide the generation of effective pseudo-labels from unlabeled object pairs, thus enriching the labeled sample space, especially for low-frequency interaction samples. We approach from the perspective of clustering, reducing the problem of confirmation bias in a self-training manner. Specifically, we first enhance the model's robustness to feature extraction via prototype-based clustering, aggregating different relationship augmented features onto the same prototype. Secondly, we design a dynamic pseudo-label assignment algorithm based on a mini-batch, which adjusts the detection sensitivity to different frequency samples from the historical assignment. Finally, we conduct joint training on the pseudo-labels and the labeled data. We conduct experiments on various SGG models and achieve substantial overall performance improvements, demonstrating the effectiveness of SSC-SGG.

MOTIVATION: The process of analyzing high throughput sequencing data often requires the identification and extraction of specific target sequences. This could include tasks, such as identifying cellular barcodes and UMIs in single-cell data, and specific genetic variants for genotyping. However, existing tools, which perform these functions are often task-specific, such as only demultiplexing barcodes for a dedicated type of experiment, or are not tolerant to noise in the sequencing data. RESULTS: To overcome these limitations, we developed Flexiplex, a versatile and fast sequence searching and demultiplexing tool for omics data, which is based on the Levenshtein distance and thus allows imperfect matches. We demonstrate Flexiplex's application on three use cases, identifying cell-line-specific sequences in Illumina short-read single-cell data, and discovering and demultiplexing cellular barcodes from noisy long-read single-cell RNA-seq data. We show that Flexiplex achieves an excellent balance of accuracy and computational efficiency compared to leading task-specific tools. AVAILABILITY AND IMPLEMENTATION: Flexiplex is available at https://davidsongroup.github.io/flexiplex/.

Partitioning links rather than nodes is effective in overlapping community detection (OCD) on complex networks. However, it consumes high CPU and memory overheads because the volume of links is huge especially when the network is rather complex. In this paper, we proposes a symmetric non-negative matrix factorization (SNMF) based link partition method called SNMF-Link to overcome this deficiency. In particular, SNMF-Link represents data in a lower-dimensional space spanned by the node-link incidence matrix. By solving a lighter SNMF problem, SNMF-Link learns the clustering indicators of each links. Since traditional multiplicative update rule (MUR) based optimization algorithm for SNMF suffers from slow convergence, we applied the augmented Lagrangian method (ALM) to efficiently optimize SNMF. Experimental results show that SNMF-Link is much more efficient than the representative clustering algorithms without reducing the OCD performance.