By employing weak forms of annotation, weakly supervised segmentation (WSS) trains segmentation models, thereby reducing the annotation requirement. However, existing methods are dependent upon significant, centralized datasets, which are difficult to establish due to concerns about patient confidentiality regarding medical information. Federated learning (FL), a paradigm for cross-site training, holds great promise for overcoming this challenge. This paper introduces the first approach to federated weakly supervised segmentation (FedWSS) and details a novel Federated Drift Mitigation (FedDM) framework to train segmentation models in a multi-site setting, maintaining the privacy of the individual sites' data. FedDM is dedicated to mitigating two significant challenges arising from weak supervision signals in federated learning: the divergence of client-side optimizations (local drift) and the divergence of server-side aggregations (global drift). It accomplishes this through Collaborative Annotation Calibration (CAC) and Hierarchical Gradient De-conflicting (HGD). CAC addresses local drift by tailoring a distant peer and a close peer for each client through a Monte Carlo sampling process. Inter-client knowledge agreement and disagreement are then employed to identify and correct clean and noisy labels, respectively. linear median jitter sum Additionally, to counteract the global trend's divergence, HGD online establishes a client hierarchy, leveraging the global model's historical gradient in each interaction. Through the de-conflicting of clients under the same parent nodes, from lower layers to upper layers, HGD achieves a potent gradient aggregation at the server. Furthermore, we perform a theoretical analysis of FedDM, along with comprehensive experimental evaluations on publicly available datasets. Our approach, as validated by experimental results, demonstrates a superior performance compared to current state-of-the-art methods. At the following URL, one can access the source code: https//github.com/CityU-AIM-Group/FedDM.

The identification of unconstrained handwritten text is a demanding problem to solve within the realm of computer vision. This task is typically addressed through a two-stage procedure involving line segmentation and then text line recognition. We formulate a novel end-to-end, segmentation-free architecture, the Document Attention Network, for the first time, to address the task of handwritten document recognition. Furthermore, the model, in addition to text recognition, is trained to identify and label portions of text using start and end markers analogous to XML tags. check details An FCN encoder, responsible for feature extraction, is coupled with a stack of transformer decoder layers for a recurrent token-by-token prediction in this model. Inputting complete documents, the system generates characters and logical layout markers in sequence. Instead of relying on segmentation labels, the model is trained using an alternative methodology, distinct from segmentation-based approaches. Our competitive results on the READ 2016 dataset extend to both page and double-page levels, with character error rates of 343% and 370%, respectively. Concerning the RIMES 2009 dataset, we've achieved a page-specific CER of 454%. Our project's source code and pre-trained model weights are provided for free download at https//github.com/FactoDeepLearning/DAN.

While graph representation learning approaches have proven successful in several graph mining applications, the knowledge utilized in generating predictions deserves further consideration. AdaSNN, a novel Adaptive Subgraph Neural Network, is presented in this paper to identify critical substructures, i.e., subgraphs, in graph data which hold significant sway over prediction outcomes. Without reliance on subgraph-level annotations, AdaSNN employs a Reinforced Subgraph Detection Module to locate critical subgraphs of diverse shapes and sizes, performing adaptive subgraph searches free from heuristic assumptions and predetermined rules. placental pathology A novel Bi-Level Mutual Information Enhancement Mechanism is proposed to foster the subgraph's global predictive capabilities. This mechanism combines global and label-specific mutual information maximization for enhanced subgraph representations, drawing upon concepts from information theory. AdaSNN's methodology of mining critical subgraphs, reflecting the inherent structure of a graph, enables sufficient interpretability of its learned results. Extensive empirical findings on seven representative graph datasets highlight AdaSNN's substantial and consistent performance gains, yielding valuable insights.

Referring video segmentation's purpose is to locate and delineate the area corresponding to the object mentioned in the natural language input, marking it as a segmentation mask within the video. Previous methodologies utilized 3D CNNs applied to the entire video clip as a singular encoder, deriving a combined spatio-temporal feature for the designated frame. Though 3D convolutions have the capacity to identify the object enacting the described actions, they nonetheless propagate misaligned spatial data from neighboring frames, inadvertently causing a mix-up of features in the target frame and inaccurate segmentation. For this concern, a language-integrated spatial-temporal collaboration framework is proposed, which contains a 3D temporal encoder interpreting the video clip to recognize the indicated actions, and a 2D spatial encoder extracting the clear spatial details of the designated item from the targeted frame. For multimodal feature extraction, we present a Cross-Modal Adaptive Modulation (CMAM) module, and its improved counterpart, CMAM+, designed for adaptive cross-modal interaction in encoders. Spatial or temporal language features are integrated and updated to progressively bolster the linguistic global context. To enhance spatial-temporal collaboration, the decoder now features a Language-Aware Semantic Propagation (LASP) module. This module utilizes language-aware sampling and assignment to propagate semantic information from deeper to shallower layers, highlighting language-aligned foreground features and minimizing language-incompatible background elements. Our method's greater effectiveness on reference video segmentation, as evidenced by extensive testing on four highly used benchmark datasets, surpasses all previously leading methods.

Utilizing the steady-state visual evoked potential (SSVEP), electroencephalogram (EEG) signals have been pivotal in the development of multi-target brain-computer interfaces (BCIs). However, the processes involved in designing precise SSVEP systems demand training data specific to each target, which involves a lengthy calibration stage. The aim of this study was to employ a portion of the target data for training, while achieving high classification accuracy on all target instances. We introduce a generalized zero-shot learning (GZSL) system dedicated to SSVEP classification in this work. We categorized the target classes into seen and unseen groups, and subsequently trained the classifier exclusively on the seen classes. The testing phase's search area involved both familiar and unfamiliar categories. Utilizing convolutional neural networks (CNNs), the proposed scheme integrates EEG data and sine waves into a shared latent space. For classification, we leverage the correlation coefficient between the two latent-space outputs. Two public datasets were used to benchmark our method, which achieved 899% of the classification accuracy of the current best data-driven approach, a method that requires training data for all targeted elements. Substantially exceeding the performance of the leading training-free method, our approach exhibited a multifold improvement. The research highlights the feasibility of developing an SSVEP classification system that circumvents the necessity of training data encompassing all possible targets.

The core of this research lies in developing a solution for the predefined-time bipartite consensus tracking control problem for a class of nonlinear multi-agent systems with asymmetric full-state constraints. A bipartite consensus tracking system, operating under a fixed time limit, is created, facilitating both cooperative and antagonistic communication between neighboring agents. This proposed controller design algorithm for multi-agent systems (MASs) offers a significant improvement over finite-time and fixed-time methods. Its strength lies in enabling followers to track either the leader's output or its reverse within a predefined duration, meeting the precise needs of the user. To achieve the desired control performance, a novel time-varying nonlinear transformation function is ingeniously incorporated to address the asymmetric full-state constraints, while radial basis function neural networks (RBF NNs) are utilized to approximate the unknown nonlinear functions. Employing first-order sliding-mode differentiators for the estimation of derivatives, predefined-time adaptive neural virtual control laws are subsequently constructed using the backstepping technique. Theoretical verification demonstrates that the suggested control algorithm not only guarantees bipartite consensus tracking performance of constrained nonlinear multi-agent systems within the predefined time frame, but also maintains the boundedness of all closed-loop system signals. Through simulation experiments on a practical example, the presented control algorithm proves its validity.

People living with HIV can now expect a greater lifespan, thanks to the efficacy of antiretroviral therapy (ART). A significant contributing factor has been the development of an aging population bearing the burden of heightened risk for both non-AIDS-defining cancers and AIDS-defining cancers. HIV testing isn't consistently conducted among cancer patients in Kenya, making the prevalence of HIV in this population difficult to determine. A tertiary hospital in Nairobi, Kenya, served as the setting for our study, which aimed to gauge the prevalence of HIV and the array of malignancies affecting HIV-positive and HIV-negative cancer patients.
From February 2021 until September 2021, we executed a cross-sectional study design. Individuals with a histologic cancer diagnosis were selected for participation.