Our analysis, both theoretical and empirical, indicates that task-specific supervision in the subsequent stages might not sufficiently facilitate the learning of both graph structure and GNN parameters, especially when the amount of labeled data is quite restricted. In order to bolster downstream supervision, we propose homophily-enhanced self-supervision for GSL (HES-GSL), a technique aimed at more effective learning of the underlying graph structure. Rigorous experimentation reveals that the HES-GSL method effectively scales across diverse datasets, significantly outperforming other prevailing methods. Discover our code at this GitHub link: https://github.com/LirongWu/Homophily-Enhanced-Self-supervision.
The distributed machine learning framework, federated learning (FL), permits resource-constrained clients to jointly train a global model, upholding data privacy. While FL enjoys broad acceptance, significant system and statistical heterogeneity persist as major challenges, leading to the possibility of divergence and non-convergence. Clustered federated learning (FL) directly confronts the challenge of statistical heterogeneity by discerning the geometric structure of clients utilizing different data generation processes, thereby generating multiple global models. Prior knowledge regarding the clustering structure, embedded within the number of clusters, substantially affects the performance of federated learning methods employing clustering. The existing framework for flexible clustering proves insufficient for dynamically estimating the optimal number of clusters within highly variable systems. To tackle this problem, we present an iterative clustered federated learning (ICFL) framework, wherein the central server dynamically identifies the clustering structure through successive rounds of incremental clustering and intra-iteration clustering. We concentrate on the average interconnectedness within each cluster, and present incremental clustering and clustering methodologies that align with ICFL, through rigorous mathematical analysis. We analyze the efficacy of ICFL through experimental investigations on datasets exhibiting substantial system and statistical heterogeneity, and encompassing both convex and nonconvex objectives. The results of our experiments corroborate our theoretical predictions, indicating that the ICFL method outperforms various clustered federated learning baseline techniques.
An image's object regions are identified for multiple classes via region-based detection. Recent advancements in deep learning and region proposal techniques have spurred the remarkable growth of convolutional neural network (CNN)-based object detectors, yielding promising detection outcomes. Unfortunately, the effectiveness of convolutional object detectors is often hampered by the reduced capacity for feature discrimination that originates from changes in an object's geometric properties or transformations. By proposing deformable part region (DPR) learning, we aim to allow decomposed part regions to be flexible in response to an object's geometric transformations. The non-availability of ground truth data for part models in numerous cases requires us to design specialized loss functions for part model detection and segmentation. The geometric parameters are then calculated by minimizing an integral loss incorporating these tailored part losses. Therefore, unsupervised training of our DPR network is achievable, allowing multi-part models to conform to the geometric variations of objects. vaginal infection We additionally propose a novel feature aggregation tree structure (FAT) for learning more discerning region-of-interest (RoI) features, utilizing a bottom-up tree construction algorithm. Semantic strengths within the FAT are learned through the aggregation of part RoI features, progressing bottom-up through the tree's pathways. Furthermore, a spatial and channel attention mechanism is presented to aggregate the features of various nodes. The DPR and FAT networks serve as blueprints for a new cascade architecture we develop, enabling iterative refinement of detection tasks. Our detection and segmentation on MSCOCO and PASCAL VOC datasets yields impressive results, even without bells and whistles. With the Swin-L backbone, our Cascade D-PRD model achieves a 579 box average precision. Furthermore, we conduct a thorough ablation study to establish the effectiveness and utility of the suggested methods for large-scale object detection.
Lightweight image super-resolution (SR) architectures, spurred by model compression techniques like neural architecture search and knowledge distillation, have experienced significant advancements. In spite of this, these methods exert substantial demands on resources or fail to fully eliminate network redundancy at the more precise level of convolution filters. A promising alternative to these drawbacks is network pruning. Structured pruning, while potentially effective, faces significant hurdles when applied to SR networks due to the requirement for consistent pruning indices across the extensive residual blocks. medical device Beyond that, establishing the proper layer-wise sparsity in a principled manner continues to be a difficult problem. This paper introduces Global Aligned Structured Sparsity Learning (GASSL) to address these issues. The architecture of GASSL is defined by two major modules: Hessian-Aided Regularization (HAIR) and Aligned Structured Sparsity Learning (ASSL). HAIR, an algorithm automatically selecting sparse representations, uses regularization, with the Hessian considered implicitly. A proposition already confirmed as true is used to explain the design. The technique of physically pruning SR networks is ASSL. Specifically, a novel penalty term, Sparsity Structure Alignment (SSA), is introduced to align the pruned indices across various layers. Employing GASSL, we craft two novel, high-performance single-image super-resolution networks, each with a distinct architectural style, thereby advancing the efficiency of SR models. The substantial findings solidify GASSL's prominence, outperforming all other recent models.
The optimization of deep convolutional neural networks for dense prediction tasks frequently employs synthetic data, as the manual creation of pixel-wise annotations from real-world data is a substantial undertaking. Despite being trained synthetically, these models exhibit poor generalization capabilities when confronted with real-world conditions. We dissect the poor generalization of synthetic data to real data (S2R) via the examination of shortcut learning. Deep convolutional networks' learning of feature representations is demonstrably affected by synthetic data artifacts, also known as shortcut attributes. To minimize this issue, we recommend an Information-Theoretic Shortcut Avoidance (ITSA) mechanism to automatically restrain the inclusion of shortcut-related information in the feature representations. The proposed method in synthetically trained models regularizes the learning of robust, shortcut-invariant features by minimizing the responsiveness of latent features to changes in input data. In light of the considerable computational cost associated with directly optimizing input sensitivity, a practical and viable algorithm to achieve robustness is presented here. The methodology presented here effectively improves S2R generalization capabilities in diverse dense prediction areas such as stereo matching, optical flow computation, and semantic segmentation. click here The proposed method's application strengthens the resilience of synthetically trained networks, leading to superior performance against fine-tuned counterparts in demanding out-of-domain tasks using real-world data.
Pathogen-associated molecular patterns (PAMPs) trigger an innate immune response through the activation of toll-like receptors (TLRs). A pathogen-associated molecular pattern (PAMP) is sensed directly by the ectodomain of a Toll-like receptor, resulting in the dimerization of the intracellular TIR domain and the activation of a signaling cascade. The TLR1 subfamily's TIR domains of TLR6 and TLR10 have been characterized structurally in a dimeric form, contrasting with the TLR15 and other subfamily members, which have not had similar structural or molecular investigation. In avian and reptilian species, TLR15 is a unique Toll-like receptor that reacts to fungal and bacterial proteases associated with pathogenicity. To identify the signaling cascade triggered by TLR15 TIR domain (TLR15TIR), its dimeric crystal structure was solved, and a mutational analysis was performed in parallel. TLR15TIR, like members of the TLR1 subfamily, exhibits a one-domain architecture comprising a five-stranded beta-sheet embellished by alpha-helices. The TLR15TIR's structure contrasts sharply with that of other TLRs, specifically within the BB and DD loops and the C2 helix, where dimerization is facilitated. Therefore, TLR15TIR is projected to assume a dimeric structure with a unique inter-subunit orientation, influenced by the distinctive roles of each dimerization domain. Further comparative investigation into TIR structures and sequences provides valuable information about the recruitment of a signaling adaptor protein by TLR15TIR.
Because of its antiviral characteristics, the weakly acidic flavonoid hesperetin (HES) is of topical interest. While dietary supplements frequently include HES, its bioavailability suffers from poor aqueous solubility (135gml-1) and a rapid initial metabolic process. Cocrystallization has established itself as a promising method for the creation of novel crystalline forms of bioactive compounds, improving their physicochemical properties without any need for covalent changes. Diverse crystal forms of HES were prepared and characterized in this work using crystal engineering principles. Two salts and six novel ionic cocrystals (ICCs) of HES, involving sodium or potassium salts of HES, were investigated using single-crystal X-ray diffraction (SCXRD) or powder X-ray diffraction methods, supplemented by thermal analyses.