Modeling and matching procedures, integral to atomic model creation, yield a product assessed through a variety of metrics. These metrics pinpoint areas for enhancement and refinement to ensure the model aligns with our current knowledge of molecular structures and their physical limitations. Model quality assessment is a fundamental component of the iterative modeling process in cryo-electron microscopy (cryo-EM), crucial to validation, particularly during the model's creation phase. A deficiency arises from the validation process and outcomes frequently failing to incorporate visual metaphors for communication. Molecular validation is visually framed in this work. Domain experts' involvement in a participatory design process was instrumental in the framework's development. 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. The user's attention is focused on more relevant regions through supplemental information, including local quality measurements of various types, sourced from the fundamental data. The three-dimensional molecular visualization, tied to the heatmap, contextualizes the structures and chosen metrics in space. Flavivirus infection The visual framework is enriched by the inclusion of the structure's statistical properties in graphical form. Cryo-EM serves as a source of illustrative examples to showcase the framework's usability and its guiding visualization.
K-means (KM) clustering, a widely used algorithm, is lauded for its simple implementation and consistently excellent clustering performance. Although widely adopted, the standard kilometer approach is computationally demanding and thus time-consuming. Consequently, a mini-batch (mbatch) k-means algorithm is suggested to substantially decrease computational expenses by updating centroids after distance calculations on only a mbatch, instead of the entirety, of the dataset's samples. Despite the accelerated convergence of the mbatch km algorithm, its quality suffers from the introduction of staleness during iterative processes. Within this article, we introduce the staleness-reduction minibatch k-means (srmbatch km) algorithm, which offers a balance between the computational efficiency of minibatch k-means and the superior clustering quality of standard k-means. Additionally, srmbatch's capabilities extend to the efficient implementation of massive parallelism on central processing units with multiple cores and graphic processing units with numerous cores. The experiments show srmbatch converges between 40 and 130 times faster than mbatch to reach the same loss target.
Input sentences, in the context of natural language processing, necessitate categorization, a crucial task assigned to an agent to select the most suitable category. Pretrained language models (PLMs), a subset of deep neural networks, have recently demonstrated exceptional performance within this specific area. Usually, these methodologies prioritize input sentences and their concomitant semantic vector generation. Even so, for another substantial component, namely labels, prevailing approaches frequently treat them as trivial one-hot vectors or utilize basic embedding techniques to learn label representations along with model training, thus underestimating the profound semantic insights and direction inherent 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 text classification algorithm is introduced, with the dual optimization goals of text categorization and R^2 classification. Furthermore, triplet loss is deployed to deepen the comprehension of divergences and interrelations 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. medical simulation Taking the process a step further, and aware of the potential for introducing noise with detailed descriptions, we develop a mutual interaction module. This module uses contrastive learning (CL) to simultaneously choose applicable segments from input sentences and labels, reducing noise. Comparative studies spanning various text classification problems show that this methodology demonstrably improves classification accuracy, effectively capitalizing on label data, thereby producing a notable performance improvement. Subsequently, the release of the codes is aimed at aiding similar research undertakings.
To swiftly and accurately grasp the sentiments and viewpoints individuals express regarding an event, multimodal sentiment analysis (MSA) is indispensable. Existing sentiment analysis methods, though present, encounter a constraint stemming from the prominent contribution of text within the dataset, which is termed text dominance. In the context of MSA, we emphasize the need to lessen the preeminent position of text-based approaches. Within the context of datasets, to resolve the above two problems, we initially introduce the Chinese multimodal opinion-level sentiment intensity dataset (CMOSI). Three separate versions of the dataset were created. The first involved the careful, manual review of subtitles. The second used machine speech transcription to generate subtitles. The third was created by having human translators provide cross-lingual translation for subtitles. The textual model's preponderant role is drastically lessened by the latter two iterations. 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. Employing network modeling principles, we present a multimodal semantic enhancement network (MSEN), incorporating a multi-headed attention mechanism and capitalizing on the various CMOSI dataset versions. According to CMOSI experiments, the text-unweakened dataset version results in optimal network performance. selleck products In each version of the text-weakened dataset, the diminished text component causes only minimal performance loss, indicating our network's capability to efficiently utilize latent semantics from non-textual patterns. 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.
Structured graph learning (SGL) coupled with multi-view clustering methods has garnered considerable attention within the field of graph-based multi-view clustering (GMC), demonstrating promising results. Although numerous SGL methods have been developed, a common limitation lies in the sparse graphs they utilize, often devoid of the insightful details typically seen in actual practice. We propose a novel multi-view and multi-order SGL (M²SGL) model to alleviate this problem, introducing multiple distinct order graphs into the SGL procedure. To be more specific, the M 2 SGL architecture incorporates a two-layered, weighted learning system. The initial layer selectively extracts portions of views from different orderings to maintain the most informative components. The final layer then assigns smooth weights to the retained multi-order graphs, allowing for a meticulous fusion process. Beyond this, an iterative optimization algorithm is designed for the optimization problem of M 2 SGL, coupled with the corresponding theoretical analyses. The M 2 SGL model's performance, as evidenced by extensive empirical results, surpasses all others in several benchmark situations.
Hyperspectral image (HSI) spatial enhancement is significantly improved by fusion with corresponding higher-resolution image sets. Recently, low-rank tensor-based methods have exhibited superior performance in comparison to other methodologies. Currently, these approaches either submit to the arbitrary, manual selection of the latent tensor rank, given the limited prior knowledge of tensor rank, or turn to regularization to impose low rank without probing the underlying low-dimensional structures, thereby neglecting the computational burden of parameter optimization. In order to address this, a novel tensor ring (TR) fusion model, employing Bayesian sparse learning, is proposed and named FuBay. The proposed method, leveraging a hierarchical sparsity-inducing prior distribution, presents itself as the first fully Bayesian probabilistic tensor framework for hyperspectral fusion. The well-researched connection between component sparseness and its corresponding hyperprior parameter motivates a component pruning segment, designed for asymptotic convergence towards the true latent rank. Moreover, a variational inference (VI) algorithm is developed to ascertain the posterior distribution of TR factors, thus sidestepping the non-convex optimization challenges frequently encountered by tensor decomposition-based fusion approaches. Our model, leveraging Bayesian learning methods, operates without the need for parameter adjustments. Ultimately, the results of extensive experiments demonstrate a superior performance compared to state-of-the-art methods.
The recent, remarkable expansion of mobile data traffic necessitates a pressing improvement in the transmission rate of the underlying wireless networks. Throughput enhancement has been pursued through network node deployment, yet this method often necessitates the resolution of highly complex and non-convex optimization problems. Although convex approximation solutions appear in the scholarly record, the accuracy of their throughput estimations can be limited, sometimes causing poor performance. Given this, we propose a novel graph neural network (GNN) technique within this article for the issue of network node deployment. Employing a GNN on the network throughput data, the gradients were used to iteratively refine the positions of the network nodes.