With its omnidirectional spatial field of view, panoramic depth estimation has become a central subject in discussions surrounding 3D reconstruction techniques. The paucity of panoramic RGB-D cameras creates a significant obstacle in the creation of panoramic RGB-D datasets, consequently restricting the viability of supervised approaches for panoramic depth estimation. Due to its reduced reliance on training datasets, self-supervised learning using RGB stereo image pairs holds the potential to overcome this limitation. Employing a transformer and spherical geometry features, the SPDET network offers a self-supervised approach to edge-aware panoramic depth estimation. Our panoramic transformer is built with the inclusion of the panoramic geometry feature, allowing us to produce high-quality depth maps. selleck chemical We present, in addition, a method for pre-filtering depth images, rendering them to generate novel view images for self-supervision. Our parallel effort focuses on designing an edge-aware loss function to refine self-supervised depth estimation within panoramic image datasets. Ultimately, we showcase the efficacy of our SPDET through a series of comparative and ablation studies, achieving state-of-the-art self-supervised monocular panoramic depth estimation. Our models and code are located in the GitHub repository, accessible through the link https://github.com/zcq15/SPDET.
Generative quantization, a data-independent compression method, achieves low-bit-width for deep neural networks without requiring real-world data. Data generation is performed by quantizing the networks using batch normalization (BN) statistics sourced from the full-precision networks. Although this is the case, there remains the consistent problem of decreased accuracy during application. Our theoretical analysis emphasizes the necessity of a diverse synthetic dataset for successful data-free quantization. However, existing approaches, where synthetic data is experimentally restricted by batch normalization (BN) statistics, demonstrate pronounced homogenization across both the sample and the overall distribution. This paper's novel Diverse Sample Generation (DSG) scheme, generic in nature, tackles the issue of detrimental homogenization within generative data-free quantization. We commence by easing the alignment of statistics for features within the BN layer to lessen the constraint imposed on the distribution. In the generative process, the loss impact of unique batch normalization (BN) layers is accentuated for each sample to diversify them from both statistical and spatial viewpoints, while minimizing correlations between samples. Across a multitude of neural architectures, our DSG demonstrates a consistent advantage in quantization performance for large-scale image classification tasks, particularly under the stringent constraints of ultra-low bit-widths. Our DSG's effect on data diversification produces a consistent improvement in the performance of various quantization-aware training and post-training quantization techniques, confirming its general applicability and effectiveness.
Our approach to denoising Magnetic Resonance Images (MRI) in this paper incorporates nonlocal multidimensional low-rank tensor transformations (NLRT). Employing a non-local low-rank tensor recovery framework, we create a non-local MRI denoising method. selleck chemical In addition, a multidimensional low-rank tensor constraint is utilized to obtain low-rank prior information, incorporating the 3-dimensional structural features of MRI image data. Our NLRT method enhances image quality by preserving intricate details. The alternating direction method of multipliers (ADMM) algorithm provides a solution to the model's optimization and updating process. Several state-of-the-art denoising techniques are selected for detailed comparative testing. To assess the denoising method's efficacy, various levels of Rician noise were introduced into the experimental setup for subsequent result analysis. The experimental outcomes highlight the remarkable denoising capabilities of our NLTR, resulting in superior MRI image clarity.
Medication combination prediction (MCP) serves to assist medical professionals in a more complete apprehension of the multifaceted processes involved in health and disease. selleck chemical While many recent studies analyze patient information from historical medical documents, they often disregard the value of medical knowledge, including prior knowledge and medication insights. The medical-knowledge-based graph neural network (MK-GNN) model, detailed in this article, integrates both patient representations and medical knowledge within its framework. Specifically, features of patients are determined from the medical documentation, separated into diverse feature subspaces. The features from each patient are then linked together to develop their feature representation. From the established mapping of medications to diagnoses, prior knowledge determines heuristic medication characteristics corresponding to the diagnostic conclusions. Optimal parameter determination within the MK-GNN model is aided by these medicinal features in the medication. Furthermore, prescriptions' medication relationships are structured as a drug network, incorporating medication knowledge into medication vector representations. Across multiple evaluation metrics, the MK-GNN model outperforms competing state-of-the-art baselines, as the results clearly show. Through the case study, the MK-GNN model's practical applicability is revealed.
Event anticipation is intrinsically linked to event segmentation in humans, as highlighted in some cognitive research. Following this key discovery, we devise a simple yet effective end-to-end self-supervised learning framework for the delineation of events and the detection of their boundaries. Unlike conventional clustering-based methods, our system employs a transformer-based scheme for reconstructing features, thereby detecting event boundaries through the analysis of reconstruction errors. A hallmark of human event detection is the contrast between anticipated scenarios and the observed data. The semantic variability present in boundary frames significantly complicates their reconstruction (generally leading to substantial errors), a factor which facilitates event boundary detection. Because the reconstruction process is applied at the semantic feature level, instead of the pixel level, a temporal contrastive feature embedding (TCFE) module is developed to learn the semantic visual representation needed for frame feature reconstruction (FFR). This procedure, like human experience, functions by storing and utilizing long-term memory. Our endeavor aims at dissecting general events, in contrast to pinpointing specific ones. Establishing the precise timeframe of each event's occurrence is our key objective. Subsequently, we have chosen the F1 score (Precision divided by Recall) as the primary benchmark for a fair comparison with previous methods. We simultaneously determine the standard frame average over frames (MoF) and the intersection over union (IoU) metric. Employing four freely available datasets, we extensively benchmark our work, achieving considerably better results. One can access the CoSeg source code through the link: https://github.com/wang3702/CoSeg.
Industrial processes, especially those in chemical engineering, frequently experience issues with nonuniform running length in incomplete tracking control, which this article addresses, highlighting the influence of artificial and environmental changes. Strict repetition plays a critical role in defining and implementing iterative learning control (ILC) strategies, influencing its design and application. Subsequently, a dynamic neural network (NN) predictive compensation technique is devised for implementation within the point-to-point iterative learning control (ILC) system. Faced with the difficulty of developing an accurate mechanism model for practical process control, a data-driven approach is further explored. The iterative dynamic predictive data model (IDPDM), created using the iterative dynamic linearization (IDL) technique and radial basis function neural networks (RBFNN), depends on input-output (I/O) signals. The model further defines extended variables to adjust for partial or truncated operational lengths. Using multiple iterations of error analysis and an objective function, a novel learning algorithm is put forward. This learning gain is perpetually modified by the NN, ensuring its relevance to evolving system conditions. The composite energy function (CEF) and the compression mapping unequivocally demonstrate the system's convergence. Two numerical simulation demonstrations conclude this section.
GCNs, excelling in graph classification tasks, exhibit a structural similarity to encoder-decoder architectures. However, the prevailing methods often lack a holistic view of global and local considerations during decoding, causing the loss of global information or neglecting specific local features within large graphs. Essentially, the widely used cross-entropy loss is a global measure applied to the entire encoder-decoder system, neglecting to provide specific feedback on the training states of the encoder and decoder independently. We formulate a multichannel convolutional decoding network (MCCD) as a means of addressing the problems previously stated. Employing a multi-channel graph convolutional network encoder, MCCD exhibits superior generalization compared to single-channel GCN encoders; this is because different channels extract graph information from varying perspectives. A novel decoder, leveraging a global-to-local learning strategy, is proposed for decoding graph-based information, effectively capturing both global and local aspects. To ensure sufficient training of both the encoder and decoder, we incorporate a balanced regularization loss to supervise their training states. Experiments using standard datasets reveal the effectiveness of our MCCD in relation to accuracy, processing speed, and computational intricacy.