The COVID-19 pandemic, as indicated by our benchmark dataset results, demonstrated a worrisome trend of previously non-depressed individuals exhibiting depressive symptoms.
In chronic glaucoma, the optic nerve suffers from progressive damage, a distressing aspect of the disease. While cataracts hold the title of the most prevalent cause of blindness, this condition is the primary driver of irreversible vision loss and second in the overall blindness-causing list. Historical fundus image analysis allows for predicting a patient's future glaucoma status, enabling early intervention and potentially avoiding blindness. Utilizing irregularly sampled fundus images, this paper presents GLIM-Net, a glaucoma forecasting transformer model that predicts future glaucoma probabilities. The significant hurdle involves the inconsistent intervals at which fundus images are taken, which complicates the precise visualization of the subtle progression of glaucoma over time. In order to address this problem, we introduce two new modules, namely, time positional encoding and a time-sensitive multi-head self-attention module. Unlike existing models which forecast for a future period without explicit specification, our model innovatively extends this framework to allow predictions tailored to particular points in the future. The SIGF benchmark dataset indicates that our method's accuracy exceeds that of the current state-of-the-art models. The ablation experiments, in addition, validate the effectiveness of our two proposed modules, which can serve as a valuable guide for enhancing Transformer models.
The capacity of autonomous agents to navigate to long-term spatial targets represents a challenging endeavor. Graph-based planning methods, focused on recent subgoals, tackle this difficulty by breaking down a goal into a series of shorter-term sub-objectives. These methods, nonetheless, employ arbitrary heuristics for sampling or unearthing subgoals, which may not align with the accumulative reward distribution. Their predisposition exists for learning incorrect connections (edges) among sub-goals, particularly those that extend across hindering elements. This article introduces a novel planning method, Learning Subgoal Graph using Value-based Subgoal Discovery and Automatic Pruning (LSGVP), to tackle these existing problems. The proposed method's heuristic for discovering subgoals is grounded in a cumulative reward metric, and it yields sparse subgoals, including those situated on higher cumulative reward paths. Moreover, the learned subgoal graph is automatically pruned by LSGVP to remove any flawed connections. These novel features contribute to the LSGVP agent's higher cumulative positive rewards compared to alternative subgoal sampling or discovery methods, while also yielding higher rates of goal attainment than other leading subgoal graph-based planning techniques.
In scientific and engineering disciplines, nonlinear inequalities are frequently employed, prompting considerable research interest. A novel jump-gain integral recurrent (JGIR) neural network is presented in this article for addressing noise-corrupted time-variant nonlinear inequality problems. Initially, an integral error function is formulated for this purpose. Following this, a neural dynamic methodology is implemented, resulting in the corresponding dynamic differential equation. SP 600125 negative control purchase Implementing a jump gain is the third step in the process for modifying the dynamic differential equation. The jump-gain dynamic differential equation is updated with the derivatives of errors in the fourth phase, and the relevant JGIR neural network is then implemented. Through rigorous theoretical analysis, global convergence and robustness theorems are demonstrated and proven. Noise-disturbed, time-varying nonlinear inequality problems are effectively handled by the proposed JGIR neural network, as substantiated by computer simulations. The JGIR method outperforms comparable advanced approaches, including modified zeroing neural networks (ZNNs), noise-tolerant ZNNs, and varying-parameter convergent-differential neural networks, by exhibiting lower computational error rates, faster convergence, and no overshoot under disturbance conditions. In addition, practical manipulator control experiments have shown the efficacy and superiority of the proposed JGIR neural network design.
Self-training, a prevalent semi-supervised learning technique, creates synthetic labels to mitigate the arduous and time-consuming annotation process in crowd counting, concurrently enhancing model efficacy with a limited labeled dataset and a vast unlabeled one. Unfortunately, the noise levels in the density map pseudo-labels dramatically impair the effectiveness of semi-supervised crowd counting. Auxiliary tasks, exemplified by binary segmentation, are used to support the learning of feature representation, but are separate from the main task of density map regression, leaving multi-task relationships unaddressed. Addressing the preceding issues, we formulate a multi-task, credible pseudo-label learning (MTCP) framework for crowd counting. This framework utilizes three multi-task branches, specifically, density regression as the primary task and binary segmentation and confidence prediction as auxiliary tasks. posttransplant infection By utilizing labeled data, multi-task learning executes through the application of a unified feature extractor for all three tasks, acknowledging and incorporating the relationships between these tasks. To diminish epistemic uncertainty, labeled data is augmented by employing a confidence map to identify and remove low-confidence regions, which constitutes an effective data enhancement strategy. Whereas existing methods for unlabeled data rely on pseudo-labels originating from binary segmentation, our technique generates direct pseudo-labels from density maps. This approach effectively reduces pseudo-label noise and thereby lessens aleatoric uncertainty. Our proposed model, as demonstrated by extensive comparisons across four crowd-counting datasets, outperformed all competing methods. The code for MTCP, as a project on GitHub, can be accessed at https://github.com/ljq2000/MTCP.
Generative models, such as variational autoencoders (VAEs), are commonly used to achieve disentangled representation learning. Existing variational autoencoder methods try to simultaneously disentangle all attributes in a unified hidden space, yet the intricacy of separating attribute-related information from irrelevant data displays variability. Hence, the operation should unfold in diverse hidden chambers. Accordingly, we propose to separate the disentanglement procedure by allocating the disentanglement of each attribute to distinct network layers. To accomplish this, we introduce a stair disentanglement network (STDNet), a network structured like a staircase, with each step representing the disentanglement of a specific attribute. An information-separation principle is implemented to remove extraneous data, producing a condensed representation of the target attribute at each stage. Consequently, the combined compact representations yield the ultimate disentangled representation. To obtain a compressed yet complete representation of the input data in the disentangled space, we propose a refined information bottleneck (IB) approach, the stair IB (SIB) principle, which carefully balances compression and expressive power. To assign attributes to network steps, we introduce an attribute complexity metric governed by the ascending complexity rule (CAR). This rule dictates the disentanglement of attributes in a sequence ordered by increasing complexity. Experimental results for STDNet showcase its superior capabilities in image generation and representation learning, outperforming prior methods on benchmark datasets including MNIST, dSprites, and CelebA. We also conduct thorough ablation studies to demonstrate how each element—neurons block, CARs, hierarchical structure, and the variational form of SIB—contributes to performance
While predictive coding is a highly influential theory in neuroscience, its widespread application in machine learning remains a relatively unexplored avenue. A new deep learning framework is developed based on the Rao and Ballard (1999) model, remaining consistent with the original schematic structure. We evaluate the PreCNet network on a frequently employed benchmark for next-frame video prediction. This benchmark showcases images from an urban environment, captured by a camera positioned on a vehicle, and the PreCNet network demonstrates industry-leading performance. The performance gains across MSE, PSNR, and SSIM metrics became more pronounced when transitioning to a larger training dataset (2 million images from BDD100k), which highlighted the deficiencies in the KITTI dataset. The study reveals that an architecture, meticulously based on a neuroscience model, without task-specific adjustments, can perform exceptionally well.
Few-shot learning (FSL) has the ambition to design a model which can identify novel classes while using only a few representative training instances for each class. In most FSL methods, evaluating the connection between a sample and a class relies on a manually-specified metric, a process generally requiring extensive effort and domain expertise. Appropriate antibiotic use In opposition, our novel approach, Automatic Metric Search (Auto-MS), defines an Auto-MS space to automatically discover metric functions pertinent to the specific task. This facilitates further development of a novel search strategy for automating FSL. The search strategy, which utilizes an episode-training component within a bilevel search framework, is particularly effective at optimizing the structural parameters and network weights of the few-shot model. The Auto-MS approach's superiority in few-shot learning problems is evident from the extensive experimental results obtained using the miniImageNet and tieredImageNet datasets.
Using reinforcement learning (RL), this article examines sliding mode control (SMC) for fuzzy fractional-order multi-agent systems (FOMAS) with time-varying delays on directed networks, (01).