Latent Information Research Articles

Deep dual incomplete multi-view multi-label classification via label semantic-guided contrastive learning

Multi-view multi-label learning (MVML) aims to train a model that can explore the multi-view information of the input sample to obtain its accurate predictions of multiple labels. Unfortunately, a majority of existing MVML methods are based on the assumption of data completeness, making them useless in practical applications with partially missing views or some uncertain labels. Recently, many approaches have been proposed for incomplete data, but few of them can handle the case of both missing views and labels. Moreover, these few existing works commonly ignore potentially valuable information about unknown labels or do not sufficiently explore latent label information. Therefore, in this paper, we propose a label semantic-guided contrastive learning method named LSGC for the dual incomplete multi-view multi-label classification problem. Concretely, LSGC employs deep neural networks to extract high-level features of samples. Inspired by the observation of exploiting label correlations to improve the feature discriminability, we introduce a graph convolutional network to effectively capture label semantics. Furthermore, we introduce a new sample-label contrastive loss to explore the label semantic information and enhance the feature representation learning. For missing labels, we adopt a pseudo-label filling strategy and develop a weighting mechanism to explore the confidently recovered label information. We validate the framework on five standard datasets and the experimental results show that our method achieves superior performance in comparison with the state-of-the-art methods.

MaskRecon: High-quality human reconstruction via masked autoencoders using a single RGB-D image

In this paper, we explore reconstructing high-quality clothed 3D humans from a single RGB-D image, assuming that virtual humans can be represented by front-view and back-view depths. Due to the scarcity of captured real RGB-D human images, we employ rendered images to train our method. However, rendered images lack background with significant depth variation in silhouettes, leading to shape prediction inaccuracies and noise. To mitigate this issue, we introduce a pseudo-multi-task framework, which incorporates a Conditional Generative Adversarial Network (CGAN) to infer back-view RGB-D images and a self-supervised Masked Autoencoder (MAE) to capture latent structural information of the human body. Additionally, we propose a Multi-scale Feature Fusion (MFF) module to effectively merge structural information and conditional features at various scales. Our method surpasses many existing techniques, as demonstrated through evaluations on the Thuman, RenderPeople, and BUFF datasets. Notably, our approach excels in reconstructing high-quality human models, even under challenging conditions such as complex poses and loose clothing, both on rendered and real-world images. Codes are available at https://github.com/Archaic-Atom/MaskRecon.

Adaptive channel-weight dual-constrained semi-supervised EEG clustering

When processing Electroencephalography(EEG) for applications such as brain disease diagnosis, rehabilitation, and brain–computer interfaces, we encounter challenges related to missing labels or incorrect annotations. These challenges may arise from subjective factors, technical limitations, or data quality issues. In traditional EEG processing, unsupervised clustering algorithms are commonly used; however, due to the lack of labels, these methods may struggle to fully leverage latent information, resulting in uncertainty and instability in clustering outcomes. To address this issue, we propose a semi-supervised clustering method tailored for partially labeled EEG signals, named Adaptive Channel-weight Dual-constrained Semi-supervised EEG Clustering (ACDSEEGc). This method incorporates dual constraints: a connectivity constraint for the interconnections between EEG points and a neighborhood constraint for the spatial neighborhood relationships among EEG data points. Additionally, it integrates channel weights to emphasize information from important channels. The objective function of ACDSEEGc integrates cross-entropy evaluation, channel weight learning, pseudo-label learning, connectivity constraint, and least squares error minimization. Our aim is to maximize the use of limited prior knowledge to achieve better EEG clustering results. Experimental validation demonstrates that ACDSEEGc effectively produces superior clustering outcomes compared to state-of-the-art standard unsupervised and semi-supervised EEG/time-series clustering algorithms. Furthermore, we conducted sensitivity experiments and ablation studies to verify the performance of ACDSEEGc under different settings.

Link prediction for hypothesis generation: an active curriculum learning infused temporal graph-based approach

Over the last few years Literature-based Discovery (LBD) has regained popularity as a means to enhance the scientific research process. The resurgent interest has spurred the development of supervised and semi-supervised machine learning models aimed at making previously implicit connections between scientific concepts/entities within often extensive bodies of literature explicit—i.e., suggesting novel scientific hypotheses. In doing so, understanding the temporally evolving interactions between these entities can provide valuable information for predicting the future development of entity relationships. However, existing methods often underutilize the latent information embedded in the temporal aspects of the interaction data. Motivated by applications in the food domain—where we aim to connect nutritional information with health-related benefits—we address the hypothesis-generation problem using a temporal graph-based approach. Given that hypothesis generation involves predicting future (i.e., still to be discovered) entity connections, in our view the ability to capture the dynamic evolution of connections over time is pivotal for a robust model. To address this, we introduce THiGER, a novel batch contrastive temporal node-pair embedding method. THiGER excels in providing a more expressive node-pair encoding by effectively harnessing node-pair relationships. Furthermore, we present THiGER-A, an incremental training approach that incorporates an active curriculum learning strategy to mitigate label bias arising from unobserved connections. By progressively training on increasingly challenging and high-utility samples, our approach significantly enhances the performance of the embedding model. Empirical validation of our proposed method demonstrates its effectiveness on established temporal-graph benchmark datasets, as well as on real-world datasets within the food domain.

The Role of Latent Information

The pressure of climate change and policy guidance has attracted plenty of enterprises to expand their renewable energy business, presenting an opportunity to investigate whether latent information can contribute to improved performance in their transformation. This context also offers a broader application scenario for studying information management theory. Therefore, this paper explores the above topic using business data from listed Chinese renewable energy companies between 2003 and 2021, focusing on three aspects of latent information related to diversification. The findings reveal that: 1) Market power information plays a fundamental role in explaining the performance enhancement from renewable energy business expansion, and non-dedicated resource information is a significant factor for understanding the enhancement, whereas agency problems have no significant effect. 2) The latent information exhibits notable heterogeneity concerning the expansion of renewable energy business among enterprises with different transformation backgrounds.

Influence Maximization Based on Adaptive Graph Convolution Neural Network in Social Networks

The influence maximization problem is a hot issue in the research on social networks due to its wide application. The problem aims to find a small subset of influential nodes to maximize the influence spread. To tackle the challenge of striking a balance between efficiency and effectiveness in traditional influence maximization algorithms, deep learning-based influence maximization algorithms have been introduced and have achieved advancement. However, these algorithms still encounter two key problems: (1) Traditional deep learning models are not well-equipped to capture the latent topological information of networks with varying sizes and structures. (2) Many deep learning-based methods use the influence spread of individual nodes as labels to train a model, which can result in an overlap of influence among the seed nodes selected by the model. In this paper, we reframe the influence maximization problem as a regression task and introduce an innovative approach to influence maximization. The method adopts an adaptive graph convolution neural network which can explore the latent topology information of the network and can greatly improve the performance of the algorithm. In our approach, firstly, we integrate several network-level attributes and some centrality metrics into a vector as the presentation vector of nodes in the social network. Next, we propose a new label generation method to measure the influence of nodes by neighborhood discount strategy, which takes full account of the influence overlapping problem. Subsequently, labels and presentation vectors are fed into an adaptive graph convolution neural network model. Finally, we use the well-trained model to predict the importance of nodes and select top-K nodes as a seed set. Abundant experiments conducted on various real-world datasets have confirmed that the performance of our proposed algorithm surpasses that of several current state-of-the-art algorithms.

MOOCs video recommendation using low-rank and sparse matrix factorization with inter-entity relations and intra-entity affinity information

PurposeThe serious information overload problem of MOOCs videos decreases the learning efficiency of the students and the utilization rate of the videos. There are two problems worthy of attention for the matrix factorization (MF)-based video learning resource recommender systems. Those methods suffer from the sparsity problem of the user-item rating matrix, while side information about user and item is seldom used to guide the learning procedure of the MF. MethodTo address those two problems, we proposed a new MOOCs video resource recommender LSMFERLI based on Low-rank and Sparse Matrix Factorization (LSMF) with the guidance of the inter-Entity Relations and intra-entity Latent Information of the students and videos. Firstly, we construct the inter-entity relation matrices and intra-entity latent preference matrix for the students. Secondly, we construct the inter-entity relation matrices and intra-entity affinity matrix for the videos. Lastly, with the guidance of the inter-entity relation and intra-entity affinity matrices of the students and videos, the student-video rating matrix is factorized into a low-rank matrix and a sparse matrix by the alternative iteration optimization scheme. ConclusionsExperimental results on dataset MOOCcube indicate that LSMFERLI outperforms 7 state-of-the-art methods in terms of the HR@K and NDCG@K(K = 5,10,15) indicators increased by an average of 20.6 % and 21.0 %, respectively.

Hate, prejudice and conspiracy theories

Abstract The Internet serves as a dynamic and expansive space where numerous groups seek to exchange and disseminate their views. Some of those groups aim to promote hateful ideologies, bigotry, conspiracy theories while externalizing their anger. To achieve this, a spectrum of online platforms is utilized, ranging from mainstream social networks to underground forums. Notably, imageboards stand out as fertile ground where these ideologies flourish, due to anonymity, community engagement, and a plethora of hateful content. This study endeavors to examine the prevalence of bigotry in these spaces within the Brazilian context. Natural language processing algorithms provide the possibility to explore discursive features of massive text sets, extracting latent information and patterns in a relatively simple and automated way. This study shows how prejudice forms the basis of the different discourses found in Brazilian imageboards. Notably, these discourses not only echo historically rooted hateful ideologies but also assimilate conspiratorial perspectives and instances of bigotry emanating from non-Brazilian imageboards.

Effective type label-based synergistic representation learning for biomedical event trigger detection

BackgroundDetecting event triggers in biomedical texts, which contain domain knowledge and context-dependent terms, is more challenging than in general-domain texts. Most state-of-the-art models rely mainly on external resources such as linguistic tools and knowledge bases to improve system performance. However, they lack effective mechanisms to obtain semantic clues from label specification and sentence context. Given its success in image classification, label representation learning is a promising approach to enhancing biomedical event trigger detection models by leveraging the rich semantics of pre-defined event type labels.ResultsIn this paper, we propose the Biomedical Label-based Synergistic representation Learning (BioLSL) model, which effectively utilizes event type labels by learning their correlation with trigger words and enriches the representation contextually. The BioLSL model consists of three modules. Firstly, the Domain-specific Joint Encoding module employs a transformer-based, domain-specific pre-trained architecture to jointly encode input sentences and pre-defined event type labels. Secondly, the Label-based Synergistic Representation Learning module learns the semantic relationships between input texts and event type labels, and generates a Label-Trigger Aware Representation (LTAR) and a Label-Context Aware Representation (LCAR) for enhanced semantic representations. Finally, the Trigger Classification module makes structured predictions, where each label is predicted with respect to its neighbours. We conduct experiments on three benchmark BioNLP datasets, namely MLEE, GE09, and GE11, to evaluate our proposed BioLSL model. Results show that BioLSL has achieved state-of-the-art performance, outperforming the baseline models.ConclusionsThe proposed BioLSL model demonstrates good performance for biomedical event trigger detection without using any external resources. This suggests that label representation learning and context-aware enhancement are promising directions for improving the task. The key enhancement is that BioLSL effectively learns to construct semantic linkages between the event mentions and type labels, which provide the latent information of label-trigger and label-context relationships in biomedical texts. Moreover, additional experiments on BioLSL show that it performs exceptionally well with limited training data under the data-scarce scenarios.

Enhanced Text Classification with Label-Aware Graph Convolutional Networks

Text classification is an important research field in text mining and natural language processing, gaining momentum with the growth of social networks. Despite the accuracy advancements made by deep learning models, existing graph neural network-based methods often overlook the implicit class information within texts. To address this gap, we propose a graph neural network model named LaGCN to improve classification accuracy. LaGCN utilizes the latent class information in texts, treating it as explicit class labels. It refines the graph convolution process by adding label-aware nodes to capture document–word, word–word, and word–class correlations for text classification. Comparing LaGCN with leading-edge models like HDGCN and BERT, our experiments on Ohsumed, Movie Review, 20 Newsgroups, and R8 datasets demonstrate its superiority. LaGCN outperformed existing methods, showing average accuracy improvements of 19.47%, 10%, 4.67%, and 0.4%, respectively. This advancement underscores the importance of integrating class information into graph neural networks, setting a new benchmark for text classification tasks.

AssistDistil for Medical Image Segmentation

Deep learning models have demonstrated significant effectiveness in addressing intricate object segmentation and image classification tasks. Nevertheless, their widespread use is impeded by high computational demands, limiting their applicability on resource-constrained devices and in contexts like medical image segmentation. This paper proposes AssistDistil, a semi-knowledge distillation technique designed to facilitate the transfer of knowledge from a larger teacher network to a more compact student model. During the inference process, the student model works in conjunction with the teacher model by condensing the teacher model’s latent information into its own latent representation, thereby boosting its representational capacity. The effectiveness of the proposed approach is demonstrated for multiple case studies in medical image segmentation task of eye segmentation, skin lesion segmentation, and chest X-ray segmentation. Experimental results on the IIITD Cataract Surgery, HAM10000, PH2, Shenzhen and Montgomery chest X-ray datasets demonstrate the efficacy of the proposed approach both in terms of accuracy and computational cost. For example, in comparison to the AUNet-based teacher model, the proposed approach achieves a similar mIOU with only 0.5% of the model size. In the future, we plan to explore knowledge distillation approaches to improve the distillation process in case of large model capacity gap between teacher and student networks. With fewer parameters, we intend for the student model to attain performance comparable to that of the teacher model without additional assistance.

APPN: An Attention-based Pseudo-label Propagation Network for few-shot learning with noisy labels

Few-shot learning has garnered significant attention in deep learning as an effective approach for addressing the issue of data scarcity. Conventionally, training datasets in few-shot learning are clean. However, due to the occurrence of sensor malfunctions, data transmission anomalies, or inaccuracies in manual annotation, the accuracy of sample annotations cannot be guaranteed. Therefore, dealing with few-shot learning with noisy label becomes an urgent problem. To solve the above problem, we propose an Attention-based Pseudo-label Propagation Network (APPN). We make some technical contributions: (1) We propose an attention-based feature extraction method that can effectively capture the distinctions between clean and noisy samples. (2) We propose an improved graph-based pseudo-label propagation method that utilizes pseudo-labels with latent class information as initial labels, thereby enhancing the accuracy of label propagation. (3) We describe a comprehensive multi-step noise detection method which can accurately detect noisy samples from clean samples and effectively distinguish between out-of-domain (OOD) noise and in-domain (ID) noise. (4) Finally, the solid experiments verify the better performance on FC100, CIFAR-FS, miniImageNet, and tieredImageNet datasets. The results show that our method has high robustness and superiority in the few-shot learning with noisy labels. Code and models at: https://github.com/Typistchen/APPN.

Action-guided prompt tuning for video grounding

Video grounding aims to locate a moment-of-interest semantically corresponding to a given query. We claim that existing methods overlook two critical issues: (1) the sparsity of language, and (2) the human perception process of events. To be specific, previous studies forcibly map the video modality and language modality into a joint space for alignment, disregarding their inherent disparities. Verbs play a crucial role in queries, providing discriminative information for distinguishing different videos. However, in the video modality, actions especially salient ones, are typically manifested through a greater number of frames, encompassing a richer reservoir of informative details. At the query level, verbs are constrained to a single word representation,creating a disparity. This discrepancy highlights a significant sparsity in language features, resulting in the suboptimality of mapping the two modalities into a shared space naively. Furthermore, segmenting ongoing activity into meaningful events is integral to human perception and contributes to event memory. Preceding methods fail to account for this essential perception process. Considering the aforementioned issues, we propose a novel Action-Guided Prompt Tuning (AGPT) method for video grounding. Firstly, we design a Prompt Exploration module to explore latent expansion information of salient verbs in language,thereby reducing the language feature sparsity and facilitating cross-modal matching. Secondly, we design the auxiliary task of action temporal prediction for video grounding and introduce a temporal rank loss function to simulate the human perceptual system’s segmentation of events, rendering our AGPT to be temporal-aware. Our approach can be seamlessly integrated into any video grounding model with minimal additional parameters. Extensive ablation experiments on three backbones and three datasets demonstrate the superiority of our method.

Incorporating multi-perspective information into reinforcement learning to address multi-hop knowledge graph question answering

Knowledge graph question answering (KGQA) aims to answer natural language questions from structured knowledge graphs (KGs). Traditional KQGA methods are usually limited to single-hop queries and cannot handle complex questions involving multi-hop reasoning well. To overcome this issue, multi-hop KGQA based on reinforcement learning (RL) has been proposed. However, multi-hop KGQA based on RL still faces some challenges. Firstly, due to the insufficient availability of latent environmental information during the reasoning process, the agent finds it challenging to make coherent and correct decisions. Secondly, the agent only receives rewards from the environment upon reaching the answer entity during the exploration, leading to slow or even obstructed learning. To address these shortcomings, we construct multi-perspective information based on the state of the environment, and integrate multi-perspective information with RL framework, thereby creating the Multi-Perspective Information Fusion Reasoning Network (MPIFRN). MPIFRN achieves the goal via three steps. (1) We construct three different views of information, i.e., expectation embedding, instruction-guided embedding, and path-aware embedding. These environmental cues provide more reliable support for decision-making. (2) We still adopt the method of mapping entities and relations into the knowledge graph embedding space to answer multi-hop questions. At each step of reasoning, we use a scoring function to measure the plausibility of each “triple” ¡topic entity, question, candidate entity¿ in the embedding space. (3) Furthermore, we employ the asynchronous advantage actor-critic (A3C) algorithm to guide the agent in selecting the most promising entities and to expand the reasoning paths in parallel by updating policy and value network parameters, thereby facilitating multi-hop knowledge graph question answering. We conduct extensive experiments on KGQA benchmark datasets, providing substantial evidence to demonstrate the effectiveness of our approach.

Joint mining of fluid knowledge and multi-sensor data for gas–water two-phase flow status monitoring and evolution analysis

Gas-water two-phase flow process widely exists in petroleum, chemical and other industrial processes, which is intricate, manifested by dynamic evolution of fluid structures and real-time fluctuation of flow parameters. Detailed description and real-time monitoring of flow status are of great significance to safe operation of industry processes. In this paper, a monitoring framework jointly mining the fluid knowledge and latent property behind multi-sensor data is proposed to address the issues of uncertainty and sample scarcity in transitional flow statuses, which enables real-time monitoring on structural evolution and parameter change of flow process. Firstly, the shared iterative algorithm with multi-task weight discovery is proposed to establish the relative semantic description system to characterize flow process knowledge by mining the relationship between multi-sensor data and different fluid semantic properties, which is helpful for interpretable monitoring of flow status structure and parameter information. Secondly, latent properties mined from multi-sensor data by sparse exponential discriminant analysis contribute to flow characterization completeness and discrimination. Thirdly, the process knowledge and latent information of flow status is transformed into different degrees of describable semantic and latent properties, which obtain relative representations of flow status in the joint description space realized by rank support vector machine. The results demonstrate that the method enables precise monitoring and detailed description of typical and uncertain flow statuses in the absence of transitional flow status modeling samples.

Multi-view subspace clustering based on multi-order neighbor diffusion

Multi-view subspace clustering (MVC) intends to separate out samples via integrating the complementary information from diverse views. In MVC, since the structural information in the graph is crucial to the graph learning, most of the existing algorithms construct the superficial graph from the original data by directly measuring the similarity between the first-order complementary nearest neighbors. However, the information provided by the superficial graph structure would be influenced by contaminated or absent samples. To address this problem, in the proposed method, the higher-order complementary neighbor graphs are exploited to discover the latent structural information between the samples, and fusing the latent structural information across different orders to achieve the MVC. Specifically, the higher-order neighbor graphs under different views are leveraged to estimate the missing samples. Then, to integrate the neighbor graphs of different orders, the multi-order neighbor diffusion fusion is proposed. Nevertheless, the above problem of diffusion fusion is an intractable non-convex issue. Thus, to address it, the multi-order neighbor diffusion fusion is considered as a combination problem of the solution under different order, and the heuristic algorithm is leveraged to solve it. In this way, not only the data representation under different view and also the neighbor structure under different order can be diffused under a joint optimization framework, thus the consistency and integral information among various perspectives and orders can be utilized effectively and simultaneously. Experiments on both incomplete and complete multi-view dataset demonstrate the convincingness of the high-order neighborhood structure based subspace clustering scheme by comparing with the existing approaches.

CDTFAFN: A novel coarse-to-fine dual-scale time-frequency attention fusion network for machinery vibro-acoustic fault diagnosis

When the machinery device operates abnormally, it is not sufficient for fault detection only via extracting fault features from a single sensor due to the latent fault information may be scattered across multiple sensors. Multi-sensory fusion techniques with deep learning framework have attracted increasing attention from researchers due to the exploiting and integration of fault information between multiple sensors. Nevertheless, there are two remaining shortcomings in most existing multi-sensory fusion technologies. (1) Most existing fusion methods merely concentrate on conducting multi-sensory information fusion from time-domain or frequency-domain to achieve fault diagnosis, which are often unsatisfactory in the face of strong noise environments. (2) The collaborative fusion between several vibration sensors is generally considered in the past works, whereas the complementary information fusion between multi-sensory vibro-acoustic heterogeneous data are rarely studied. To address these deficiencies, this paper proposes a novel coarse-to-fine dual-scale time-frequency attention fusion network (CDTFAFN) for machinery fault diagnosis, which not only adequately considers the complementary information fusion of vibro-acoustic signal, but also has robust feature learning capabilities in a noisy scenario. Firstly, the signal-to-image encoding unit (SIEU) containing the improved constant-Q non-stationary Gabor transform (ICQ-NSGT) is introduced to convert the collected raw vibro-acoustic heterogeneous signal into time-frequency representation (TFR) and achieve the coarse-grained feature fusion. Secondly, the time-frequency attention feature fusion unit (TFA-FFU) is designed to concurrently learn the fine-grained features at two scales from the low-level fused features which are meaningful for fault diagnosis. Finally, the coarse-to-fine features are sequentially concatenated and fed into softmax classifier to preferably promote the network learning performance and automatically implement fault classification. The performance of the proposed approach is validated against those state-of-the-art results on two groups of multi-sensory vibro-acoustic data in different experimental platforms. Experiment results show that the proposed method with the diagnosis accuracy of 99 % above outperforms other several representative fusion technologies (i.e., 2MNet, MFF-GBFD, MSCNN-BiLSTM, MFAN-VAF, 1D-CNN-VAF, MI-CNN-TFT and TFFN-VAF) in the raw noise-free addition scenario. Moreover, the average testing accuracy of the proposed method can still reach 97 % above in the noisy scenarios with Gaussian white noises, which shows its competitive superiority and strong robustness against noises in machinery fault diagnosis. According to the five ensemble macro-average performance evaluation metrics (i.e., accuracy, precision, sensitivity, specificity and F1-score) and the receiver operator characteristic (ROC) analysis, our findings also emphasize the superiority of applying our method for machinery fault diagnosis under the colored noises compared with other fusion technologies (i.e., 2MNet, MFF-GBFD, MSCNN-BiLSTM, MFAN-VAF, 1D-CNN-VAF, MI-CNN-TFT and TFFN-VAF) reported in this paper.

One-way ticket to the moon? An NLP-based insight on the phenomenon of small-scale neo-broker trading

We present an Natural Language Processing based analysis on the phenomenon of “Meme Stocks”, which has emerged as a result of the proliferation of neo-brokers like Robinhood and the massive increase in the number of small-scale stock investors. Such investors often use specific Social Media channels to share short-term investment decisions and strategies, resulting in partial collusion and planning of investment decisions. The impact of online communities on the stock prices of affected companies has been considerable in the short term. This paper has two objectives. Firstly, we chronologically model the discourse on the most prominent platforms. Secondly, we examine the potential for using collaboratively made investment decisions as a means to assist in the selection of potential investments.. To understand the investment decision-making processes of small-scale investors, we analyze data from Social Media platforms like Reddit, Stocktwits and Seeking Alpha. Our methodology combines Sentiment Analysis and Topic Modelling. Sentiment Analysis is conducted using VADER and a fine-tuned BERT model. For Topic Modelling, we utilize LDA, NMF and the state-of-the-art BERTopic. We identify the topics and shapes of discussions over time and evaluate the potential for leveraging information of the decision-making process of investors for trading choices. We utilize Random Forest and Neural Network Models to show that latent information in discussions can be exploited for trend prediction of stocks affected by Social Network driven herd behavior. Our findings provide valuable insights into content and sentiment of discussions and are a vehicle to improve efficient trading decisions for stocks affected from short-term herd behavior.

LSDN Empowers the Metaverse Communication to Achieve High-Resolution Visuals with Lower Bandwidth

Deploying super-resolution models on metaverse terminal devices can enhance visual effects without increasing network bandwidth. However, deploying most current super-resolution networks on metaverse terminal devices with limited hardware resources poses a challenge due to their large volumes and high computing power consumption. In this paper, we present a lightweight separation and distillation network (LSDN) aimed at reducing the model complexity by prioritizing network structure. Specifically, we initially present the blueprint separable convolution (BSConv) to decrease model complexity and utilize the BSConv and information distillation mechanism building the channel separation distillation block (CSDB). Subsequently, we develop the enhanced spatial attention block (ESA) and Fused-MBConv (FMBConv) to explore latent information. In addition, we employ three CSDBs, an ESA, and an FMBConv to construct the residual attention unit (RAU). Finally, we concatenate several RAUs and amalgamate their hierarchical results, and transmit them to the upsampler for reconstructing the high-resolution images. We carried out comprehensive experiments on a range of datasets and found conclusive evidence that the LSDN outperforms state-of-the-art approaches, exhibiting notable enhancements in quantitative and qualitative terms.

LMFLS: A new fast local multi-factor node scoring and label selection-based algorithm for community detection

Community detection is still regarded as one of the most applicable approaches for discovering latent information in complex networks. To meet the needs of processing large networks in today's world, it is important to propose fast methods that have low execution time and fast convergence speed, while maintaining algorithmic accuracy. To overcome these issues, a fast local multi-factor node scoring and label selection-based (LMFLS) method with low time complexity and fast convergence is proposed. Node scoring step incorporates diverse metrics to better assess impact of nodes from different aspects and obtain more meaningful order of nodes. In second step, to construct and stabilize initial structure of communities, an efficient label assignment technique based on the selection of the most similar neighbor is suggested. Moreover, two label selection strategies are proposed to significantly enhance the accuracy and improve convergence of the algorithm. During the label selection step, each node in graph tends to choose the most appropriate label based on a multi-criteria label influence from its surrounding nodes. Finally, by utilizing a novel merge method, small group of nodes are merged to form the final communities. Meanwhile, since drug repositioning is one of the popular research fields in therapeutics, to extend the application of the proposed algorithm in practical context, the LMFLS algorithm is applied on Drug-Drug network to find potential repositioning for drugs. Thorough experiments are conducted on both actual real-world networks and synthetic networks to assess the algorithm's performance and accuracy. The findings demonstrate that the proposed method outperforms state-of-the-art algorithms in terms of both accuracy and execution time.