Weakly supervised segmentation (WSS) seeks to leverage rudimentary annotation types for training segmentation models, thus mitigating the annotation effort. Yet, current methodologies are reliant on large-scale, centralized data sets, a creation process hampered by the privacy complications stemming from the use of medical records. This problem's solution can be approached with considerable potential by the cross-site training paradigm of federated learning (FL). This work pioneers federated weakly supervised segmentation (FedWSS) and introduces a novel Federated Drift Mitigation (FedDM) framework for learning segmentation models across disparate sites, preserving the privacy of their raw data. FedDM's approach to federated learning centers on addressing two key problems, local optimization drift on the client side and global aggregation drift on the server side, brought about by weak supervision signals, using Collaborative Annotation Calibration (CAC) and Hierarchical Gradient De-conflicting (HGD). For each client, CAC tailors a distant peer and a proximate peer via Monte Carlo sampling to diminish local bias. Subsequently, inter-client knowledge agreement and disagreement are used to pinpoint correct labels and rectify incorrect labels, respectively. ASN007 ERK inhibitor HGD online builds a client hierarchy within each communication phase to reduce the global deviation, informed by the global model's historical gradient. Robust gradient aggregation on the server side is facilitated by HGD's de-conflicting of clients situated under the same parent nodes, progressing from the bottom layers to the top layers. 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. Users can acquire the FedDM source code from the cited GitHub link: https//github.com/CityU-AIM-Group/FedDM.
The task of recognizing free-form handwritten text is a demanding one in the domain of computer vision. Line segmentation and subsequent text line recognition are combined in a customary two-part approach for handling this. We now unveil, for the very first time, the Document Attention Network, a segmentation-free, end-to-end architecture focused on the recognition of handwritten documents. Beyond text recognition, the model is also educated to mark up segments of text with start and end labels, employing a methodology akin to XML tagging. Western Blot Analysis The model architecture is designed with an FCN encoder for feature extraction and a stack of transformer decoder layers dedicated to the recurrent token-by-token prediction procedure. Input documents are parsed, resulting in a sequential output of characters and their corresponding logical layout tokens. The model's training process differs from segmentation-based approaches by not employing any segmentation labels. 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. We've calculated the RIMES 2009 dataset's CER, measured at the page level, and obtained a figure of 454%. Within the repository https//github.com/FactoDeepLearning/DAN, you can find the entire source code and pre-trained model weights.
Although graph representation learning techniques have yielded promising results in diverse graph mining applications, the underlying knowledge leveraged for predictions remains a relatively under-examined aspect. 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. To pinpoint critical subgraphs of any size or configuration, lacking explicit subgraph-level annotations, AdaSNN creates a Reinforced Subgraph Detection Module that adapts its search for subgraphs without heuristics or fixed rules. Hereditary diseases To ensure the subgraph exhibits predictive behavior at a global level, we design a Bi-Level Mutual Information Enhancement Mechanism. This mechanism leverages both label-specific and global insights for mutual information maximization, thereby refining the information content of subgraph representations. AdaSNN achieves sufficient interpretability of learned results by identifying and mining critical subgraphs that represent the intrinsic nature of the graph. Seven typical graph datasets provide comprehensive experimental evidence of AdaSNN's considerable and consistent performance enhancement, producing meaningful results.
A system for referring video segmentation takes a natural language description as input and outputs a segmentation mask of the described object within the video. Earlier methods leveraged 3D convolutional neural networks on the video clip as the sole encoder, creating a unified spatio-temporal feature representation for the target frame. 3D convolutions, while capable of determining which object performs the actions described, introduce misaligned spatial data from adjacent frames, ultimately causing a confusion of features in the target frame and inaccurate segmentation. To address this problem, we suggest a language-driven spatial-temporal collaboration framework, incorporating a 3D temporal encoder analyzing the video clip to identify the depicted actions, and a 2D spatial encoder processing the targeted frame to extract clear spatial details of the mentioned object. 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. 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 superior performance on four well-regarded reference video segmentation benchmarks, compared with preceding state-of-the-art techniques, is established through extensive experimentation.
Brain-computer interfaces (BCIs) focusing on multiple targets often utilize the steady-state visual evoked potential (SSVEP), measured through electroencephalogram (EEG). However, the development of high-accuracy SSVEP systems relies on training data unique to each target, requiring a substantial amount of calibration time. The research strategy of this study focused on training with a part of the target data set while ensuring high classification accuracy for all the targets. In this study, we developed a generalized zero-shot learning (GZSL) approach for classifying SSVEP signals. We allocated the target classes to seen and unseen groups, and the classifier's training was limited to the seen groups. The search space, during the testing timeframe, included both recognized and unrecognized classes. In the proposed scheme, a process using convolutional neural networks (CNN) embeds EEG data and sine waves into the same latent space. The correlation coefficient, calculated on the outputs in the latent space, is employed for the classification task. Our method's performance on two public datasets demonstrated an 899% increase in classification accuracy over the prevailing data-driven benchmark, demanding training data for all targets. Substantially exceeding the performance of the leading training-free method, our approach exhibited a multifold improvement. This study indicates that constructing an SSVEP classification system without requiring training data for every target is a promising approach.
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 framework for bipartite consensus tracking, adhering to a predetermined timeframe, is developed, encompassing cooperative and adversarial communication between neighboring agents. The controller design algorithm detailed in this paper stands apart from finite-time and fixed-time MAS control methods by enabling followers to track either the leader's output or its complementary value, all while adhering to pre-determined temporal constraints based on user specifications. An advanced time-varying nonlinear transformed function is meticulously applied to tackle the asymmetric full-state constraints, along with radial basis function neural networks (RBF NNs) to address the unknown nonlinear functions, enabling the desired control performance. The backstepping method is used to construct the predefined-time adaptive neural virtual control laws, their derivatives estimated by first-order sliding-mode differentiators. The theoretical framework validates that the proposed control algorithm maintains both bipartite consensus tracking performance for constrained nonlinear multi-agent systems within the stipulated time and the boundedness of all resulting closed-loop system signals. The simulation study with a practical example provides strong evidence for the validity of the presented control algorithm.
Thanks to antiretroviral therapy (ART), individuals living with HIV are now able to anticipate a longer lifespan. This trend has contributed to an aging populace, placing them at a considerable risk for both non-AIDS-defining cancers and AIDS-defining cancers. Prevalence of HIV in Kenyan cancer patients remains undefined due to the lack of routine testing procedures. This study, conducted at a Nairobi tertiary hospital, explored the rate of HIV infection and the spectrum of cancers affecting HIV-positive and HIV-negative cancer patients.
From February 2021 until September 2021, we executed a cross-sectional study design. Participants presenting a confirmed histologic cancer diagnosis were enrolled.