Subsequently, we demonstrate, both theoretically and practically, that task-oriented supervision downstream may not be sufficient for learning both graph topology and GNN parameters, especially in scenarios where labeled data is limited to a minimal amount. To improve upon downstream supervision, we present homophily-enhanced self-supervision for GSL (HES-GSL), a methodology that leads to a more effective learning strategy for the underlying graph structure. A thorough empirical study validates HES-GSL's capability to effectively scale across different datasets, exceeding the performance of leading state-of-the-art methods. Our project's code is publicly available at the URL https://github.com/LirongWu/Homophily-Enhanced-Self-supervision.
Federated learning (FL), a distributed machine learning framework, enables clients with constrained resources to jointly train a global model, all while keeping data private. FL's prevalence notwithstanding, substantial system and statistical heterogeneity continue to pose key challenges, leading to a potential for divergence and non-convergence. Clustered FL addresses statistical heterogeneity effectively by extracting the geometric structure of clients, whose data originate from distinct generation processes, ultimately constructing multiple global models. Cluster count, a reflection of prior understanding of the underlying clustering structure, significantly impacts the effectiveness of federated learning techniques utilizing clustering. Existing flexible clustering procedures are not sufficient for dynamically ascertaining the ideal number of clusters in systems with substantial variations in characteristics. To resolve this matter, we introduce an iterative clustered federated learning (ICFL) methodology where the server dynamically identifies the clustering structure via consecutive incremental clustering and clustering procedures within a single iteration. We concentrate on the average interconnectedness within each cluster, and present incremental clustering and clustering methodologies that align with ICFL, through rigorous mathematical analysis. ICFL is evaluated through experiments that incorporate a variety of datasets, showcasing significant system and statistical heterogeneity, as well as both convex and nonconvex objectives. Empirical findings validate our theoretical framework, demonstrating that ICFL surpasses various clustered federated learning benchmarks.
The algorithm identifies regions of objects, belonging to various classes, present in an image, by using region-based object detection techniques. Object detectors based on convolutional neural networks (CNNs) are flourishing thanks to the recent strides in deep learning and region proposal methods, demonstrating promising detection results. The ability of convolutional object detectors to precisely identify objects can frequently suffer due to insufficient feature differentiation caused by object transformations or geometrical variations. We present a method for deformable part region (DPR) learning, which allows part regions to change shape according to object geometry. Given the scarcity of ground truth data for part models in many cases, we formulate specialized loss functions for part model detection and segmentation. Consequently, we calculate the geometric parameters by minimizing an integral loss encompassing these specific part model losses. Therefore, unsupervised training of our DPR network is achievable, allowing multi-part models to conform to the geometric variations of objects. read more Furthermore, a novel feature aggregation tree (FAT) is proposed to learn more distinctive region of interest (RoI) features through a bottom-up tree construction approach. Through bottom-up aggregation of part RoI features along the tree's paths, the FAT system develops a more robust semantic feature comprehension. We further incorporate a spatial and channel attention mechanism into the aggregation process of node features. From the established DPR and FAT networks, we conceive a new cascade architecture capable of iterative refinement in detection tasks. Using no bells and whistles, we consistently deliver impressive detection and segmentation outcomes on the MSCOCO and PASCAL VOC datasets. Through the application of the Swin-L backbone, our Cascade D-PRD model reaches a 579 box AP. The effectiveness and usefulness of our proposed methods for large-scale object detection are also demonstrated through a comprehensive ablation study.
Image super-resolution (SR) techniques have become more efficient, thanks to novel lightweight architectures, further facilitated by model compression strategies such as neural architecture search and knowledge distillation. 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. Network pruning is a promising alternative method for resolving these problems. The application of structured pruning to SR networks proves intricate, mainly because the extensive residual blocks dictate the need for uniform pruning indices across different layers. Technological mediation In addition, precisely defining the optimal sparsity for each layer proves to be a considerable obstacle. To tackle these problems, this paper proposes Global Aligned Structured Sparsity Learning (GASSL). Hessian-Aided Regularization (HAIR) and Aligned Structured Sparsity Learning (ASSL) are the two primary components of GASSL. Implicitly incorporating the Hessian, HAIR is a regularization-based sparsity auto-selection algorithm. A previously validated proposition is cited to explain the design's purpose. ASSL is the method employed for physically pruning SR networks. Specifically, a novel penalty term, Sparsity Structure Alignment (SSA), is introduced to align the pruned indices across various layers. GASSL's application results in the design of two innovative, efficient single image super-resolution networks, characterized by varied architectures, thereby boosting the efficiency of SR models. The extensive data showcases the significant benefits of GASSL in contrast to 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. Even though the models' training is based on synthetic data, they exhibit insufficient generalization to real-world environments. The problematic generalization of synthetic to real data (S2R) is explored through the theoretical lens of shortcut learning. Deep convolutional networks' acquisition of feature representations is profoundly shaped by synthetic data artifacts, which we demonstrate as shortcut attributes. To lessen the impact of this problem, we propose an Information-Theoretic Shortcut Avoidance (ITSA) system that automatically blocks the encoding of shortcut-related information into the feature representations. In synthetically trained models, our proposed method aims to regularize the learning of robust and shortcut-invariant features by mitigating the sensitivity of latent features to input variations. Given the computationally expensive nature of direct input sensitivity optimization, we propose a functional and attainable algorithm to ensure robustness. Our results affirm the considerable enhancement of S2R generalization through the implemented method, as demonstrated across distinct dense prediction applications like stereo matching, optical flow estimation, and semantic segmentation. Oncology research Remarkably, the proposed method improves the robustness of synthetically trained networks, showing better performance than fine-tuned counterparts when facing challenging out-of-domain applications on real-world data.
Toll-like receptors (TLRs) are responsible for activating the innate immune system in response to pathogen-associated molecular patterns (PAMPs). A TLR's extracellular portion, the ectodomain, directly recognizes and binds to a PAMP, triggering the dimerization of its intracellular TIR domain to activate a signaling cascade. Structural analysis of the dimeric TIR domains of TLR6 and TLR10, members of the TLR1 subfamily, has been undertaken; however, the structural and molecular exploration of corresponding domains in other subfamilies, notably TLR15, is not yet undertaken. Virulence-associated fungal and bacterial proteases specifically stimulate the unique Toll-like receptor, TLR15, present exclusively in birds and reptiles. The TLR15 TIR domain's (TLR15TIR) signalling was investigated by determining the crystal structure of its dimeric form and following it with a comprehensive mutational analysis. As observed in TLR1 subfamily members, TLR15TIR presents a one-domain structure where alpha-helices embellish a five-stranded beta-sheet. In comparison to other TLRs, the TLR15TIR exhibits significant structural variations in the BB and DD loops and the C2 helix, elements essential for dimer formation. Consequently, the TLR15TIR protein configuration is anticipated to be a dimer, distinguished by its distinctive inter-subunit alignment and the specific 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.
Topical application of hesperetin (HES), a weakly acidic flavonoid, is of interest due to its antiviral properties. HES, while sometimes present in dietary supplements, exhibits reduced bioavailability owing to its poor aqueous solubility (135gml-1) and a swift first-pass metabolic action. 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. To prepare and characterize various crystal forms of HES, the principles of crystal engineering were applied in this work. 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.