An atomic model, the culmination of painstaking modeling and matching techniques, is judged through a series of metrics. These metrics enable further adjustments and refinement to ensure the model harmonizes with our knowledge of molecules and their physical parameters. Validation in cryo-electron microscopy (cryo-EM)'s iterative modeling process involves evaluating the quality of the model being constructed in parallel with the modeling procedure itself. The validation process and its findings are rarely depicted through the use of visual metaphors. A visual framework for molecular validation is introduced in this work. The framework's development, achieved through a participatory design process, benefited from close collaboration with domain experts. Central to its design is a novel visual representation, featuring 2D heatmaps, which sequentially displays all available validation metrics, offering a panoramic global perspective of the atomic model and enabling domain experts to engage in interactive analysis. In order to guide the user's focus towards regions of greater importance, the underlying data provides supplementary information, encompassing a range of localized quality metrics. A three-dimensional visualization of the molecules, coupled with the heatmap, displays the spatial relationships of the structures and the chosen metrics. plasma medicine The visual framework extends its depiction of the structure's properties to incorporate the statistical information. Cryo-EM serves as a source of illustrative examples to showcase the framework's usability and its guiding visualization.
The K-means (KM) clustering algorithm enjoys widespread adoption due to its straightforward implementation and the high quality of its resulting clusters. In spite of its widespread application, the standard kilometer method suffers from high computational complexity and is consequently time-consuming. A mini-batch (mbatch) k-means algorithm is proposed to effectively minimize computational costs. It updates centroids by processing only a mini-batch (mbatch) of samples after distance computations, unlike the complete dataset. While the mbatch km method converges more quickly, it compromises convergence quality by introducing a degree of staleness in the iterative procedure. Consequently, this paper introduces the staleness-reduction minibatch (srmbatch) k-means algorithm, which optimally balances low computational costs, akin to minibatch k-means, with high clustering quality, mirroring the standard k-means approach. In addition, srmbatch's architecture allows for significant parallelization on multiple CPU cores and numerous GPU cores. Experimental data reveals that srmbatch's convergence rate is up to 40 to 130 times faster than mbatch's when aiming for identical target loss.
Within the realm of natural language processing, sentence categorization is a fundamental requirement, calling for an agent to pinpoint the most suitable category for the input sentences. The impressive performance recently achieved in this area is largely attributable to pretrained language models (PLMs), a type of deep neural network. Frequently, these strategies are focused on input phrases and the creation of their associated semantic encodings. Nevertheless, for a vital component, namely labels, most existing research either treats them as meaningless one-hot vectors or uses rudimentary embedding methods to learn their representations alongside model training, failing to fully leverage the semantic richness and guidance implicit in these labels. To address this issue and maximize the value of label data, this paper incorporates self-supervised learning (SSL) into the model training process and introduces a novel self-supervised relation-of-relation (R²) classification task to leverage one-hot encoded labels. A novel approach to text classification is presented, aiming to optimize both text categorization and R^2 classification. Concurrently, triplet loss is applied to strengthen the interpretation of differences and associations between labels. Consequently, the one-hot encoding approach does not fully leverage label information, so we integrate WordNet's external knowledge to establish multi-faceted descriptions for label semantic learning and develop a novel label embedding strategy. see more To address the potential for unwanted noise from detailed descriptions, we implement a mutual interaction module that leverages contrastive learning (CL). This module selects appropriate parts from input sentences and labels in tandem to mitigate the effects of noise. Extensive tests performed on numerous text classification scenarios indicate that this method successfully enhances classification precision, better harnessing the utility of label information to further optimize performance. In parallel with our principal function, we have placed the codes at the disposal of other researchers.
Multimodal sentiment analysis (MSA) is a key component in accurately and expeditiously comprehending the views and feelings individuals hold about an event. Current sentiment analysis methods, however, are challenged by the dominant presence of textual input in the dataset, a condition frequently described as text dominance. Concerning MSA assignments, attenuating the significant impact of text modalities is paramount. Our dataset-focused solution to the above two problems commences with the introduction of the Chinese multimodal opinion-level sentiment intensity (CMOSI) dataset. Three versions of the dataset were formed through three processes: human experts proofread subtitles manually; machine speech transcriptions generated alternative subtitles; and human translators performed cross-lingual translations for the last variation. Subsequent versions of two, notably, undermine the text-based model's prevailing status. A collection of 144 authentic Bilibili videos formed the basis of our study, from which we manually extracted and edited 2557 segments showcasing diverse emotions. In the field of network modeling, we introduce a multimodal semantic enhancement network (MSEN), structured by a multi-headed attention mechanism, taking advantage of the diverse CMOSI dataset versions. Our CMOSI experiments show that the network consistently achieves superior performance with the text-unweakened dataset form. biogenic nanoparticles In both versions of the text-weakened dataset, the loss of performance is insignificant, confirming the network's ability to comprehensively analyze latent semantics in patterns not based on text. Our model's generalization capabilities were tested on MOSI, MOSEI, and CH-SIMS datasets with MSEN; results indicated robust performance and impressive cross-language adaptability.
In recent research, graph-based multi-view clustering (GMC) has seen significant attention, and the application of structured graph learning (SGL) within multi-view clustering methods has emerged as a particularly promising direction, showcasing compelling performance. While many existing SGL methods exist, they often encounter issues due to sparse graphs, which are typically absent of the rich information found in practical applications. To address this issue, we present a novel multi-view and multi-order SGL (M²SGL) model, which thoughtfully incorporates multiple distinct order graphs into the SGL framework. In more detail, M 2 SGL employs a two-layered weighted learning strategy. The first layer selectively chooses portions of views in diverse orders, focusing on preserving the most pertinent information. The second layer then applies smooth weighting to the retained multi-order graphs to effectively fuse them. Likewise, an iterative optimization algorithm is developed for the optimization problem within M 2 SGL, with associated theoretical analyses provided. Extensive experimentation reveals that the proposed M 2 SGL model attains leading performance across multiple benchmarks.
Hyperspectral image (HSI) spatial improvement has been achieved through a successful approach of fusion with corresponding high-resolution images. Recently, low-rank tensor-based techniques have proven more effective than other similar methods. Nonetheless, present techniques either succumb to the arbitrary, manual selection of latent tensor rank, given the surprisingly limited prior knowledge of tensor rank, or rely on regularization to enforce low rank without investigating the underlying low-dimensional factors, both of which neglect the computational burden of parameter tuning. To tackle this issue, a novel Bayesian sparse learning-based tensor ring (TR) fusion model, dubbed FuBay, is presented. By virtue of its hierarchical sparsity-inducing prior distribution, the proposed method marks the first fully Bayesian probabilistic tensor framework for hyperspectral data fusion. With the established relationship between the sparsity of components and the corresponding hyperprior parameter, a component pruning element is incorporated, driving the model toward asymptotic convergence with the true latent rank. Subsequently, a variational inference (VI) approach is formulated to infer the posterior distribution of TR factors, thereby obviating the non-convex optimization problems that typically hamper tensor decomposition-based fusion methods. The parameter-tuning-free nature of our model stems from its Bayesian learning methodology. In conclusion, exhaustive trials highlight its superior functionality when measured against the best methods available.
An impressive increase in mobile data traffic necessitates a crucial enhancement in the efficiency and capacity of wireless communications networks. In pursuit of enhanced throughput, the deployment of network nodes is an often-considered strategy; however, it commonly results in highly intricate and non-convex optimization procedures. Though convex approximation solutions are acknowledged in the literature, their estimated throughput values may be inaccurate, occasionally resulting in disappointing performance. Due to this consideration, we present in this article a new graph neural network (GNN) approach to solving the network node deployment problem. A GNN was fitted to the network's throughput, and the gradients of this GNN were leveraged to iteratively adjust the positions of the network nodes.