Weakly supervised segmentation (WSS) attempts to train segmentation models with weak annotation specifications, thereby lessening the annotation demand. Nonetheless, existing approaches depend on substantial, centralized data repositories, which pose challenges in their creation owing to privacy restrictions surrounding medical data. Federated learning (FL), a paradigm for cross-site training, holds great promise for overcoming this challenge. We demonstrate the first effort in federated weakly supervised segmentation (FedWSS) by proposing a new Federated Drift Mitigation (FedDM) framework, enabling the construction of segmentation models across multiple locations without necessitating the sharing of raw data. Through the application of Collaborative Annotation Calibration (CAC) and Hierarchical Gradient De-conflicting (HGD), FedDM seeks to resolve the two primary problems in federated learning environments—the local optimization drift on the client side and the global aggregation drift on the server side, both of which originate from weak supervision signals. Using a Monte Carlo sampling strategy, CAC tailors a distal and a proximal peer for each client to counteract local deviations. Subsequently, inter-client knowledge consistency and inconsistency are employed to detect accurate labels and correct inaccurate labels, respectively. Symbiont-harboring trypanosomatids Furthermore, to mitigate the global deviation, HGD online constructs a client hierarchy, guided by the global model's historical gradient, during each communication cycle. 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. Subsequently, we delve into the theoretical underpinnings of FedDM and conduct extensive experimentation using public datasets. Experimental results showcase that our method delivers superior performance in comparison to the prevailing state-of-the-art methodologies. Within the GitHub repository, https//github.com/CityU-AIM-Group/FedDM, the source code for FedDM is located.

Unconstrained handwritten text recognition poses a complex problem for computer vision systems. Following a two-step process, line segmentation is initially performed, which is then followed by text line recognition, in the traditional manner. In a pioneering effort, we propose the Document Attention Network, an end-to-end, segmentation-free architecture for the task of handwritten document recognition. The model's training incorporates text recognition, along with the task of assigning 'begin' and 'end' labels to specific portions of the text in an XML-esque style. learn more 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. Text documents are fed into the system, resulting in a sequential output stream of characters and logical layout tokens. Diverging from segmentation-based methodologies, the model is trained independently of segmentation labels. On the READ 2016 dataset, we demonstrate competitive performance, achieving character error rates of 343% and 370% for page and double-page recognition, respectively. We've calculated the RIMES 2009 dataset's CER, measured at the page level, and obtained a figure of 454%. For your convenience, all the source code and pre-trained model weights are hosted on GitHub 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. This paper introduces AdaSNN, a novel adaptive subgraph neural network, focusing on discerning critical subgraphs in graph data, the ones primarily responsible for prediction results. To identify critical subgraphs of any size or shape, in the absence of subgraph-level annotations, AdaSNN employs a Reinforced Subgraph Detection Module to discover subgraphs adaptively, avoiding any heuristic assumptions or established rules. Personality pathology A Bi-Level Mutual Information Enhancement Mechanism, incorporating both global and label-specific mutual information maximization, is designed to improve subgraph representations, enhancing their predictive power at a global level within an information-theoretic framework. By excavating critical subgraphs that accurately capture the graph's intrinsic characteristics, AdaSNN achieves sufficient interpretability in its learned results. AdaSNN's superior performance is consistent and notable, as demonstrated by exhaustive experimental results across seven typical graph datasets, producing insightful results.

Given a natural language expression referencing an object, the objective of referring video segmentation is to predict a segmentation mask denoting the object's presence 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. Although 3D convolutions are proficient in identifying the object performing the described actions, they introduce misaligned spatial information from adjacent frames, which ultimately obscures the target frame's features and results in inaccurate segmentation. To deal with this issue, we introduce a language-based spatial-temporal collaboration framework, possessing a 3D temporal encoder that processes the video clip to identify the actions in question, and a 2D spatial encoder analyzing the target frame to provide unobscured spatial information about the item in focus. We propose a Cross-Modal Adaptive Modulation (CMAM) module and its enhanced version, CMAM+, for extracting multimodal features. Adaptive cross-modal interaction in the encoders is achieved by incorporating spatial or temporal language features that are updated incrementally to enhance the broader linguistic context. In the decoder, a Language-Aware Semantic Propagation (LASP) module is implemented. It propagates semantic data from deep stages to superficial layers through language-aware sampling and allocation. This allows for the highlighting of language-coherent foreground visual elements and the downplaying of language-incoherent background visual elements, thereby improving the spatial-temporal synergy. Experiments employing four widely used benchmarks for reference video segmentation establish the surpassing performance of our method compared to the previous leading methodologies.

Multi-target brain-computer interfaces (BCIs) built using electroencephalogram (EEG) signals commonly depend on the steady-state visual evoked potential (SSVEP). Despite this, constructing precise SSVEP systems depends on training data for each specific target, which necessitates extended calibration. Data from only a portion of the targets was utilized in this study's training process, yet achieving a high rate of classification accuracy across all the targets. This research outlines a generalized zero-shot learning (GZSL) technique for classifying SSVEP signals. By dividing the target classes into seen and unseen groups, the classifier was trained using the seen classes alone. The search space during the test period contained both observed and unobserved categories. By means of convolutional neural networks (CNN), the proposed scheme achieves the embedding of EEG data and sine waves into a single latent space. We employ the correlation coefficient in the latent space to perform classification on the two outputs. Our method, assessed on two public datasets, showcased a 899% increment in classification accuracy compared to the most advanced data-driven method, which needs a complete dataset to train for all targets. Our method surpassed the state-of-the-art training-free approach by a multiple of improvement. A promising avenue for SSVEP classification system development is presented, one that does not necessitate training data for the complete set of targets.

This study investigates predefined-time bipartite consensus tracking control, targeting a class of nonlinear multi-agent systems with asymmetric full-state constraints. A predefined-time bipartite consensus tracking framework is constructed, implementing cooperative and adversarial communication strategies amongst neighbor agents. In contrast to conventional finite-time and fixed-time controller design techniques for multi-agent systems, the algorithm presented here provides a unique advantage: it enables followers to track either the leader's output or its negation within the user-defined timeframe. For optimal control performance, a newly developed time-varying nonlinear transform function is strategically implemented to manage the asymmetric constraints on all states, and radial basis function neural networks (RBF NNs) are employed to model the unknown nonlinearities. 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. The control algorithm's validity is demonstrated through simulated experiments on a practical case study.

Antiretroviral therapy (ART) has demonstrably increased the life expectancy of those living with HIV. This has resulted in an older population that is at increased risk for both non-AIDS-defining and AIDS-defining cancers. In Kenya, cancer patients are not routinely screened for HIV, thereby obscuring the true prevalence of the virus. This study investigated the proportion of HIV infection and the diversity of malignancies in HIV-positive and HIV-negative cancer patients treated at a Kenyan tertiary hospital.
The cross-sectional study which we undertook ran from February 2021 to September 2021. Patients who received a histologic cancer diagnosis were included in the study cohort.