The accurate prediction of drug absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties represents a crucial step in early drug development for reducing failure risk.Current deep learning approaches...The accurate prediction of drug absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties represents a crucial step in early drug development for reducing failure risk.Current deep learning approaches face challenges with data sparsity and information loss due to single-molecule representation limitations and isolated predictive tasks.This research proposes molecular properties prediction with parallel-view and collaborative learning(MolP-PC),a multi-view fusion and multi-task deep learning framework that integrates 1D molecular fingerprints(MFs),2D molecular graphs,and 3D geometric representations,incorporating an attention-gated fusion mechanism and multi-task adaptive learning strategy for precise ADMET property predictions.Experimental results demonstrate that MolP-PC achieves optimal performance in 27 of 54 tasks,with its multi-task learning(MTL)mechanism significantly enhancing predictive performance on small-scale datasets and surpassing single-task models in 41 of 54 tasks.Additional ablation studies and interpretability analyses confirm the significance of multi-view fusion in capturing multi-dimensional molecular information and enhancing model generalization.A case study examining the anticancer compound Oroxylin A demonstrates MolP-PC’s effective generalization in predicting key pharmacokinetic parameters such as half-life(T0.5)and clearance(CL),indicating its practical utility in drug modeling.However,the model exhibits a tendency to underestimate volume of distribution(VD),indicating potential for improvement in analyzing compounds with high tissue distribution.This study presents an efficient and interpretable approach for ADMET property prediction,establishing a novel framework for molecular optimization and risk assessment in drug development.展开更多
Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches ofte...Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches often rely on limited data sources and simplistic hypotheses,which restrict their ability to capture the multi-faceted nature of biological systems.This study introduces adaptive multi-view learning(AMVL),a novel methodology that integrates chemical-induced transcriptional profiles(CTPs),knowledge graph(KG)embeddings,and large language model(LLM)representations,to enhance drug repurposing predictions.AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning(MVL),matrix factorization,and ensemble optimization techniques to integrate heterogeneous multi-source data.Comprehensive evaluations on benchmark datasets(Fdata-set,Cdataset,and Ydataset)and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art(SOTA)methods,achieving superior accuracy in predicting drug-disease associations across multiple metrics.Literature-based validation further confirmed the model's predictive capabilities,with seven out of the top ten predictions corroborated by post-2011 evidence.To promote transparency and reproducibility,all data and codes used in this study were open-sourced,providing resources for pro-cessing CTPs,KG,and LLM-based similarity calculations,along with the complete AMVL algorithm and benchmarking procedures.By unifying diverse data modalities,AMVL offers a robust and scalable so-lution for accelerating drug discovery,fostering advancements in translational medicine and integrating multi-omics data.We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.展开更多
Epilepsy is a central nervous system disorder in which brain activity becomes abnormal.Electroencephalogram(EEG)signals,as recordings of brain activity,have been widely used for epilepsy recognition.To study epilep-ti...Epilepsy is a central nervous system disorder in which brain activity becomes abnormal.Electroencephalogram(EEG)signals,as recordings of brain activity,have been widely used for epilepsy recognition.To study epilep-tic EEG signals and develop artificial intelligence(AI)-assist recognition,a multi-view transfer learning(MVTL-LSR)algorithm based on least squares regression is proposed in this study.Compared with most existing multi-view transfer learning algorithms,MVTL-LSR has two merits:(1)Since traditional transfer learning algorithms leverage knowledge from different sources,which poses a significant risk to data privacy.Therefore,we develop a knowledge transfer mechanism that can protect the security of source domain data while guaranteeing performance.(2)When utilizing multi-view data,we embed view weighting and manifold regularization into the transfer framework to measure the views’strengths and weaknesses and improve generalization ability.In the experimental studies,12 different simulated multi-view&transfer scenarios are constructed from epileptic EEG signals licensed and provided by the Uni-versity of Bonn,Germany.Extensive experimental results show that MVTL-LSR outperforms baselines.The source code will be available on https://github.com/didid5/MVTL-LSR.展开更多
Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewpriv...Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewprivatemeaningless information or noise may interfere with the learning of self-expression, which may lead to thedegeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistencyand Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple viewsand fuses them based on their discrimination, so that it can effectively explore consistent and complementaryinformation for achieving precise clustering. Specifically, the view-specific self-expression is learned by a selfexpressionlayer embedded into the auto-encoder network for each view. To guarantee consistency across views andreduce the effect of view-private information or noise, we align all the view-specific self-expressions by contrastivelearning. The aligned self-expressions are assigned adaptive weights by channel attention mechanism according totheir discrimination. Then they are fused by convolution kernel to obtain consensus self-expression withmaximumcomplementarity ofmultiple views. Extensive experimental results on four benchmark datasets and one large-scaledataset of the CCAC method outperformother state-of-the-artmethods, demonstrating its clustering effectiveness.展开更多
Real-time collaborative editing(RTCE)can support a group of people collaboratively work from distributed locations at the same time.However,consistency maintenance is one key challenge when different types of conflict...Real-time collaborative editing(RTCE)can support a group of people collaboratively work from distributed locations at the same time.However,consistency maintenance is one key challenge when different types of conflicts happen.Therefore a common synchronous mechanism is proposed to support consistency maintenance in the process of multi-view business modeling.Based on operation analysis on different views of models in the real-time collaborative editing system,detection of potential conflicts is realized by means of a decision-making tree.Then consistency maintenance provides a comprehensive and applicable conflicts detection and resolution for collaborative business modeling.Finally,a prototype of collaborative multi-view business modeling system is introduced to verify the approach.The point is that the mechanism proposes a comprehensive solution for collaborative multi-view business modeling.展开更多
It is a vital step to find cocrystal formers of drugs in drug development.Dual-View Learning(DVL)has achieved inspiring progress in predicting cocrystals since compounds can be represented in a dual-source manner(i.e....It is a vital step to find cocrystal formers of drugs in drug development.Dual-View Learning(DVL)has achieved inspiring progress in predicting cocrystals since compounds can be represented in a dual-source manner(i.e.,sequence and 2D structures).Nonetheless,it is still an ongoing issue that the performance of existing DVL-based approaches depend on how appropriate the combination of dual view is.Furthermore,there is a need to elucidate what atoms are crucial to form a cocrystal of two compounds.This work holds an assumption that the orthogonal separation of view representations into view-shared representations and view-specific representations can eliminate the redundancy and irrelevant features among dual view.To address these issues,this work elaborates a novel DVL framework for predicting Compound Cocrystal(DVL-CC).The framework includes molecule encoders of dual view,a dual-view combinator,and a binary predictor.Especially,the dual-view combinator orthogonally disentangles view-shared and view-specific molecule representations from raw view representations by an elaborate Generative Adversarial Network(GAN)based consistency learner and a set of complementary constraints.The comparison with state-of-the-art DVL-based methods demonstrates the superiority of DVL-CC.Also,the comprehensive ablation studies validate and illustrate how its main components contribute to the cocrystal prediction,including individual-view representations,the dual-view combinator,the consistency learner,and the complementary constraints.Furthermore,a case study illustrates the interpretability of DVL-CC by indicating crucial atoms associated with cocrystal conformation patterns between compounds.It is anticipated that this work can boost drug development.The code and data underlying this article are available at https://github.com/savior-22/DVL-CC.展开更多
Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof ...Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof different types of features and domain shift problems are two of the critical issues in zero-shot learning. Toaddress both of these issues, this paper proposes a new modeling structure. The traditional approach mappedsemantic features and visual features into the same feature space;based on this, a dual discriminator approachis used in the proposed model. This dual discriminator approach can further enhance the consistency betweensemantic and visual features. At the same time, this approach can also align unseen class semantic features andtraining set samples, providing a portion of information about the unseen classes. In addition, a new feature fusionmethod is proposed in the model. This method is equivalent to adding perturbation to the seen class features,which can reduce the degree to which the classification results in the model are biased towards the seen classes.At the same time, this feature fusion method can provide part of the information of the unseen classes, improvingits classification accuracy in generalized zero-shot learning and reducing domain bias. The proposed method isvalidated and compared with othermethods on four datasets, and fromthe experimental results, it can be seen thatthe method proposed in this paper achieves promising results.展开更多
This paper presents a new method of using a convolutional neural network(CNN)in machine learning to identify brand consistency by product appearance variation.In Experiment 1,we collected fifty mouse devices from the ...This paper presents a new method of using a convolutional neural network(CNN)in machine learning to identify brand consistency by product appearance variation.In Experiment 1,we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions.Results show that it is a challenge to distinguish periods for the subtle evolution of themouse devices with such traditionalmethods as time series analysis and principal component analysis(PCA).In Experiment 2,we applied deep learning to predict the extent to which the product appearance variation ofmouse devices of various brands.The investigation collected 6,042 images ofmouse devices and divided theminto the Early Stage and the Late Stage.Results show the highest accuracy of 81.4%with the CNNmodel,and the evaluation score of brand style consistency is 0.36,implying that the brand consistency score converted by the CNN accuracy rate is not always perfect in the real world.The relationship between product appearance variation,brand style consistency,and evaluation score is beneficial for predicting new product styles and future product style roadmaps.In addition,the CNN heat maps highlight the critical areas of design features of different styles,providing alternative clues related to the blurred boundary.The study provides insights into practical problems for designers,manufacturers,and marketers in product design.It not only contributes to the scientific understanding of design development,but also provides industry professionals with practical tools and methods to improve the design process and maintain brand consistency.Designers can use these techniques to find features that influence brand style.Then,capture these features as innovative design elements and maintain core brand values.展开更多
Recent state-of-the-art semi-supervised learning(SSL)methods usually use data augmentations as core components.Such methods,however,are limited to simple transformations such as the augmentations under the instance’s...Recent state-of-the-art semi-supervised learning(SSL)methods usually use data augmentations as core components.Such methods,however,are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic representations.To tackle this problem,we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method,called Attentive Neighborhood Feature Aug-mentation(ANFA).The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data,and further facilitating the classifier to distinguish the ambiguous features from the low-dense regions.Specially,we first project the labeled and unlabeled data points into an embedding space and then construct a neighbor graph that serves as a similarity measure based on the similar representations in the embedding space.Then,we employ an attention mechanism to transform the target features into augmented ones based on the neighbor graph.Finally,we formulate a novel semi-supervised loss by encouraging the predictions of the interpolations of augmented features to be consistent with the corresponding interpolations of the predictions of the target features.We carried out exper-iments on SVHN and CIFAR-10 benchmark datasets and the experimental results demonstrate that our method outperforms the state-of-the-art methods when the number of labeled examples is limited.展开更多
Unlike Indo-European languages,Mandarin relies heavily on lexical tones to distinguish word identity. Using the intermodal preferential looking paradigm, this study examined 3-year-old Mandarinspeakers＇ ability to us...Unlike Indo-European languages,Mandarin relies heavily on lexical tones to distinguish word identity. Using the intermodal preferential looking paradigm, this study examined 3-year-old Mandarinspeakers＇ ability to use Mandarin lexical tones in learning new words. Results showed that when children were presented with Tone 2（rising） and Tone 4（falling）pairs, children successfully learned both words.However, when children were presented with Tone 2and Tone 3（dipping） pairs, they learned the Tone 2word but not the Tone 3 one. Children were then divided into two groups based on their learning performance on the Tone 3 word. Successful learning of Tone 3 words was observed in the high performers but not in the low performers, who consistently misused Tone 3 as Tone 2. This study showed that Mandarinspeaking 3-year-olds could use lexical tones to learn words under experimental conditions, and that the difficulty of Tone 3 acquisition may be related to its lower level of perceptual distinctiveness compared with other tones.展开更多
The aim of this paper is to broaden the application of Stochastic Configuration Network (SCN) in the semi-supervised domain by utilizing common unlabeled data in daily life. It can enhance the classification accuracy ...The aim of this paper is to broaden the application of Stochastic Configuration Network (SCN) in the semi-supervised domain by utilizing common unlabeled data in daily life. It can enhance the classification accuracy of decentralized SCN algorithms while effectively protecting user privacy. To this end, we propose a decentralized semi-supervised learning algorithm for SCN, called DMT-SCN, which introduces teacher and student models by combining the idea of consistency regularization to improve the response speed of model iterations. In order to reduce the possible negative impact of unsupervised data on the model, we purposely change the way of adding noise to the unlabeled data. Simulation results show that the algorithm can effectively utilize unlabeled data to improve the classification accuracy of SCN training and is robust under different ground simulation environments.展开更多
The concept of language sense has never failed to arouse interest among scholars in recent decades at home and abroad.Many scholars point out that language sense is an important competence which helps facilitate learn...The concept of language sense has never failed to arouse interest among scholars in recent decades at home and abroad.Many scholars point out that language sense is an important competence which helps facilitate learning a language.It bears much connection with learners’acquisition of a language.Another concept,implicit learning,which is proved effective and has been applied in second language acquisition(SLA),is consistent with language sense in terms of its learning mechanism.In this sense,cultivation of English language sense can be theoretically supported by implicit learning and pedagogical implications can be derived accordingly.展开更多
As one of the three pillars of the tourism industry,hotel sales are influenced by a variety of factors.Particularly,with the exponential growth of the internet,user-generated images,text,and data presented by hotels i...As one of the three pillars of the tourism industry,hotel sales are influenced by a variety of factors.Particularly,with the exponential growth of the internet,user-generated images,text,and data presented by hotels impact hotel sales to varying degrees.This study attempts to explore how different factors affect hotel industry sales.Firstly,it examines review images,text data,and hotel base attribute data;secondly,it employs a deep learning-based approach to analyze the different types of data;finally,it uses random forest to calculate feature importance values and analyzes them based on different star ratings variance.The results show that image-text consistency influences all types of hotel sales.Furthermore,the consistency of image text also affects all hotel sales,and there are differences in the factors influencing sales across hotel types.The findings can be used to provide valuable advice to hotel managers in the sales field.展开更多
1 Introduction Current continual learning methods[1–4]can utilize labeled data to alleviate catastrophic forgetting effectively.However,obtaining labeled samples can be difficult and tedious as it may require expert ...1 Introduction Current continual learning methods[1–4]can utilize labeled data to alleviate catastrophic forgetting effectively.However,obtaining labeled samples can be difficult and tedious as it may require expert knowledge.In many practical application scenarios,labeled and unlabeled samples exist simultaneously,with more unlabeled than labeled samples in streaming data[5,6].Unfortunately,existing class-incremental learning methods face limitations in effectively utilizing unlabeled data,thereby impeding their performance in incremental learning scenarios.展开更多
Multi-view learning is an emerging field that aims to enhance learning performance by leveraging multiple views or sources of data across various domains.By integrating information from diverse perspectives,multi-view...Multi-view learning is an emerging field that aims to enhance learning performance by leveraging multiple views or sources of data across various domains.By integrating information from diverse perspectives,multi-view learning methods effectively enhance accuracy,robustness,and generalization capabilities.The existing research on multi-view learning can be broadly categorized into four groups in the survey based on the tasks it encompasses,namely multi-view classification approaches,multi-view semi-supervised classification approaches,multi-view clustering approaches,and multi-view semi-supervised clustering approaches.Despite its potential advantages,multi-view learning poses several challenges,including view inconsistency,view complementarity,optimal view fusion,the curse of dimensionality,scalability,limited labels,and generalization across domains.Nevertheless,these challenges have not discouraged researchers from exploring the potential of multiview learning.It continues to be an active and promising research area,capable of effectively addressing complex realworld problems.展开更多
The interactions between drugs and microbes affecting microbial abundance can lead to various diseases or reduce the effectiveness of pharmaceutical treatments.Traditional Microbe-Drug Association(MDA)determination th...The interactions between drugs and microbes affecting microbial abundance can lead to various diseases or reduce the effectiveness of pharmaceutical treatments.Traditional Microbe-Drug Association(MDA)determination through biological assays is time-consuming and costly.With the accumulation of MDA data,computational methods have become a promising approach to infer potential MDAs.Although existing methods focus on predicting whether a drug interacts with a microbe,they can rarely infer whether a drug promotes or inhibits the abundance of a given microbe.Moreover,the extreme imbalance among abundance-promoted,abundance-inhibited,and non-impacted cases remains a challenge for computational prediction methods.To address these issues,we propose a framework for predicting the imbalanced Impact of Drugs on Microbial Abundance by leveraging Multi-view Learning and Data Augmentation,named IDMA-MLDA.IDMA-MLDA employs a novel method of transforming a bipartite graph into a hypergraph,uses hypergraph convolutions to capture high-order vertex neighborhoods(macro-view),and employs graph neural networks to learn individual features of drugs and microbes(micro-view).It integrates features from both macro-view and micro-view to obtain more comprehensive representations,incorporates a data augmentation module to handle class imbalance,and uses a multilayer perceptron to predict the impact of drugs on microbial abundance.We demonstrate the superiority of IDMA-MLDA through comparisons with six baseline methods,and ablation studies affirm the contributions of each key module in IDMA-MLDA’s prediction.Furthermore,a comprehensive literature review verifies the abundance types of twelve MDAs predicted by IDMA-MLDA.展开更多
To improve the accuracy and robustness of rolling bearing fault diagnosis under complex conditions, a novel method based on multi-view feature fusion is proposed. Firstly, multi-view features from perspectives of the ...To improve the accuracy and robustness of rolling bearing fault diagnosis under complex conditions, a novel method based on multi-view feature fusion is proposed. Firstly, multi-view features from perspectives of the time domain, frequency domain and time-frequency domain are extracted through the Fourier transform, Hilbert transform and empirical mode decomposition (EMD).Then, the random forest model (RF) is applied to select features which are highly correlated with the bearing operating state. Subsequently, the selected features are fused via the autoencoder (AE) to further reduce the redundancy. Finally, the effectiveness of the fused features is evaluated by the support vector machine (SVM). The experimental results indicate that the proposed method based on the multi-view feature fusion can effectively reflect the difference in the state of the rolling bearing, and improve the accuracy of fault diagnosis.展开更多
Hashing technology has the advantages of reducing data storage and improving the efficiency of the learning system,making it more and more widely used in image retrieval.Multi-view data describes image information mor...Hashing technology has the advantages of reducing data storage and improving the efficiency of the learning system,making it more and more widely used in image retrieval.Multi-view data describes image information more comprehensively than traditional methods using a single-view.How to use hashing to combine multi-view data for image retrieval is still a challenge.In this paper,a multi-view fusion hashing method based on RKCCA(Random Kernel Canonical Correlation Analysis)is proposed.In order to describe image content more accurately,we use deep learning dense convolutional network feature DenseNet to construct multi-view by combining GIST feature or BoW_SIFT(Bag-of-Words model+SIFT feature)feature.This algorithm uses RKCCA method to fuse multi-view features to construct association features and apply them to image retrieval.The algorithm generates binary hash code with minimal distortion error by designing quantization regularization terms.A large number of experiments on benchmark datasets show that this method is superior to other multi-view hashing methods.展开更多
基金supported by the research on key technologies for monitoring and identifying drug abuse of anesthetic drugs and psychotropic drugs,and intervention for addiction(No.2023YFC3304200)the program of a study on the diagnosis of addiction to synthetic cannabinoids and methods of assessing the risk of abuse(No.2022YFC3300905)+1 种基金the program of Ab initio design and generation of AI models for small molecule ligands based on target structures(No.2022PE0AC03)ZHIJIANG LAB.
文摘The accurate prediction of drug absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties represents a crucial step in early drug development for reducing failure risk.Current deep learning approaches face challenges with data sparsity and information loss due to single-molecule representation limitations and isolated predictive tasks.This research proposes molecular properties prediction with parallel-view and collaborative learning(MolP-PC),a multi-view fusion and multi-task deep learning framework that integrates 1D molecular fingerprints(MFs),2D molecular graphs,and 3D geometric representations,incorporating an attention-gated fusion mechanism and multi-task adaptive learning strategy for precise ADMET property predictions.Experimental results demonstrate that MolP-PC achieves optimal performance in 27 of 54 tasks,with its multi-task learning(MTL)mechanism significantly enhancing predictive performance on small-scale datasets and surpassing single-task models in 41 of 54 tasks.Additional ablation studies and interpretability analyses confirm the significance of multi-view fusion in capturing multi-dimensional molecular information and enhancing model generalization.A case study examining the anticancer compound Oroxylin A demonstrates MolP-PC’s effective generalization in predicting key pharmacokinetic parameters such as half-life(T0.5)and clearance(CL),indicating its practical utility in drug modeling.However,the model exhibits a tendency to underestimate volume of distribution(VD),indicating potential for improvement in analyzing compounds with high tissue distribution.This study presents an efficient and interpretable approach for ADMET property prediction,establishing a novel framework for molecular optimization and risk assessment in drug development.
基金supported by the National Natural Science Foundation of China(Grant No.:62101087)the China Postdoctoral Science Foundation(Grant No.:2021MD703942)+2 种基金the Chongqing Postdoctoral Research Project Special Funding,China(Grant No.:2021XM2016)the Science Foundation of Chongqing Municipal Commission of Education,China(Grant No.:KJQN202100642)the Chongqing Natural Science Foundation,China(Grant No.:cstc2021jcyj-msxmX0834).
文摘Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces costs and timelines by identifying new therapeutic uses for existing drugs.However,the current approaches often rely on limited data sources and simplistic hypotheses,which restrict their ability to capture the multi-faceted nature of biological systems.This study introduces adaptive multi-view learning(AMVL),a novel methodology that integrates chemical-induced transcriptional profiles(CTPs),knowledge graph(KG)embeddings,and large language model(LLM)representations,to enhance drug repurposing predictions.AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning(MVL),matrix factorization,and ensemble optimization techniques to integrate heterogeneous multi-source data.Comprehensive evaluations on benchmark datasets(Fdata-set,Cdataset,and Ydataset)and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art(SOTA)methods,achieving superior accuracy in predicting drug-disease associations across multiple metrics.Literature-based validation further confirmed the model's predictive capabilities,with seven out of the top ten predictions corroborated by post-2011 evidence.To promote transparency and reproducibility,all data and codes used in this study were open-sourced,providing resources for pro-cessing CTPs,KG,and LLM-based similarity calculations,along with the complete AMVL algorithm and benchmarking procedures.By unifying diverse data modalities,AMVL offers a robust and scalable so-lution for accelerating drug discovery,fostering advancements in translational medicine and integrating multi-omics data.We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.
基金supported in part by the National Natural Science Foundation of China(Grant No.82072019)the Shenzhen Basic Research Program(JCYJ20210324130209023)of Shenzhen Science and Technology Innovation Committee+6 种基金the Shenzhen-Hong Kong-Macao S&T Program(Category C)(SGDX20201103095002019)the Natural Science Foundation of Jiangsu Province(No.BK20201441)the Provincial and Ministry Co-constructed Project of Henan Province Medical Science and Technology Research(SBGJ202103038 and SBGJ202102056)the Henan Province Key R&D and Promotion Project(Science and Technology Research)(222102310015)the Natural Science Foundation of Henan Province(222300420575)the Henan Province Science and Technology Research(222102310322)The Jiangsu Students’Innovation and Entrepreneurship Training Program(202110304096Y).
文摘Epilepsy is a central nervous system disorder in which brain activity becomes abnormal.Electroencephalogram(EEG)signals,as recordings of brain activity,have been widely used for epilepsy recognition.To study epilep-tic EEG signals and develop artificial intelligence(AI)-assist recognition,a multi-view transfer learning(MVTL-LSR)algorithm based on least squares regression is proposed in this study.Compared with most existing multi-view transfer learning algorithms,MVTL-LSR has two merits:(1)Since traditional transfer learning algorithms leverage knowledge from different sources,which poses a significant risk to data privacy.Therefore,we develop a knowledge transfer mechanism that can protect the security of source domain data while guaranteeing performance.(2)When utilizing multi-view data,we embed view weighting and manifold regularization into the transfer framework to measure the views’strengths and weaknesses and improve generalization ability.In the experimental studies,12 different simulated multi-view&transfer scenarios are constructed from epileptic EEG signals licensed and provided by the Uni-versity of Bonn,Germany.Extensive experimental results show that MVTL-LSR outperforms baselines.The source code will be available on https://github.com/didid5/MVTL-LSR.
文摘Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewprivatemeaningless information or noise may interfere with the learning of self-expression, which may lead to thedegeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistencyand Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple viewsand fuses them based on their discrimination, so that it can effectively explore consistent and complementaryinformation for achieving precise clustering. Specifically, the view-specific self-expression is learned by a selfexpressionlayer embedded into the auto-encoder network for each view. To guarantee consistency across views andreduce the effect of view-private information or noise, we align all the view-specific self-expressions by contrastivelearning. The aligned self-expressions are assigned adaptive weights by channel attention mechanism according totheir discrimination. Then they are fused by convolution kernel to obtain consensus self-expression withmaximumcomplementarity ofmultiple views. Extensive experimental results on four benchmark datasets and one large-scaledataset of the CCAC method outperformother state-of-the-artmethods, demonstrating its clustering effectiveness.
基金the National Natural Science Foundation of China(Nos.61373030 and 71171132)
文摘Real-time collaborative editing(RTCE)can support a group of people collaboratively work from distributed locations at the same time.However,consistency maintenance is one key challenge when different types of conflicts happen.Therefore a common synchronous mechanism is proposed to support consistency maintenance in the process of multi-view business modeling.Based on operation analysis on different views of models in the real-time collaborative editing system,detection of potential conflicts is realized by means of a decision-making tree.Then consistency maintenance provides a comprehensive and applicable conflicts detection and resolution for collaborative business modeling.Finally,a prototype of collaborative multi-view business modeling system is introduced to verify the approach.The point is that the mechanism proposes a comprehensive solution for collaborative multi-view business modeling.
基金supported by the National Natural Science Foundation of China(No.62372375)and the Shaanxi Province Key R&D Program(No.2023-YBSF-114).
文摘It is a vital step to find cocrystal formers of drugs in drug development.Dual-View Learning(DVL)has achieved inspiring progress in predicting cocrystals since compounds can be represented in a dual-source manner(i.e.,sequence and 2D structures).Nonetheless,it is still an ongoing issue that the performance of existing DVL-based approaches depend on how appropriate the combination of dual view is.Furthermore,there is a need to elucidate what atoms are crucial to form a cocrystal of two compounds.This work holds an assumption that the orthogonal separation of view representations into view-shared representations and view-specific representations can eliminate the redundancy and irrelevant features among dual view.To address these issues,this work elaborates a novel DVL framework for predicting Compound Cocrystal(DVL-CC).The framework includes molecule encoders of dual view,a dual-view combinator,and a binary predictor.Especially,the dual-view combinator orthogonally disentangles view-shared and view-specific molecule representations from raw view representations by an elaborate Generative Adversarial Network(GAN)based consistency learner and a set of complementary constraints.The comparison with state-of-the-art DVL-based methods demonstrates the superiority of DVL-CC.Also,the comprehensive ablation studies validate and illustrate how its main components contribute to the cocrystal prediction,including individual-view representations,the dual-view combinator,the consistency learner,and the complementary constraints.Furthermore,a case study illustrates the interpretability of DVL-CC by indicating crucial atoms associated with cocrystal conformation patterns between compounds.It is anticipated that this work can boost drug development.The code and data underlying this article are available at https://github.com/savior-22/DVL-CC.
文摘Zero-shot learning enables the recognition of new class samples by migrating models learned from semanticfeatures and existing sample features to things that have never been seen before. The problems of consistencyof different types of features and domain shift problems are two of the critical issues in zero-shot learning. Toaddress both of these issues, this paper proposes a new modeling structure. The traditional approach mappedsemantic features and visual features into the same feature space;based on this, a dual discriminator approachis used in the proposed model. This dual discriminator approach can further enhance the consistency betweensemantic and visual features. At the same time, this approach can also align unseen class semantic features andtraining set samples, providing a portion of information about the unseen classes. In addition, a new feature fusionmethod is proposed in the model. This method is equivalent to adding perturbation to the seen class features,which can reduce the degree to which the classification results in the model are biased towards the seen classes.At the same time, this feature fusion method can provide part of the information of the unseen classes, improvingits classification accuracy in generalized zero-shot learning and reducing domain bias. The proposed method isvalidated and compared with othermethods on four datasets, and fromthe experimental results, it can be seen thatthe method proposed in this paper achieves promising results.
基金supported in part by a grant,PHA1110214,from MOE,Taiwan.
文摘This paper presents a new method of using a convolutional neural network(CNN)in machine learning to identify brand consistency by product appearance variation.In Experiment 1,we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions.Results show that it is a challenge to distinguish periods for the subtle evolution of themouse devices with such traditionalmethods as time series analysis and principal component analysis(PCA).In Experiment 2,we applied deep learning to predict the extent to which the product appearance variation ofmouse devices of various brands.The investigation collected 6,042 images ofmouse devices and divided theminto the Early Stage and the Late Stage.Results show the highest accuracy of 81.4%with the CNNmodel,and the evaluation score of brand style consistency is 0.36,implying that the brand consistency score converted by the CNN accuracy rate is not always perfect in the real world.The relationship between product appearance variation,brand style consistency,and evaluation score is beneficial for predicting new product styles and future product style roadmaps.In addition,the CNN heat maps highlight the critical areas of design features of different styles,providing alternative clues related to the blurred boundary.The study provides insights into practical problems for designers,manufacturers,and marketers in product design.It not only contributes to the scientific understanding of design development,but also provides industry professionals with practical tools and methods to improve the design process and maintain brand consistency.Designers can use these techniques to find features that influence brand style.Then,capture these features as innovative design elements and maintain core brand values.
基金supported by the National Natural Science Foundation of China (Nos.62072127,62002076,61906049)Natural Science Foundation of Guangdong Province (Nos.2023A1515011774,2020A1515010423)+4 种基金Project 6142111180404 supported by CNKLSTISS,Science and Technology Program of Guangzhou,China (No.202002030131)Guangdong basic and applied basic research fund joint fund Youth Fund (No.2019A1515110213)Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (No.MJUKF-IPIC202101)Natural Science Foundation of Guangdong Province No.2020A1515010423)Scientific research project for Guangzhou University (No.RP2022003).
文摘Recent state-of-the-art semi-supervised learning(SSL)methods usually use data augmentations as core components.Such methods,however,are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic representations.To tackle this problem,we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method,called Attentive Neighborhood Feature Aug-mentation(ANFA).The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data,and further facilitating the classifier to distinguish the ambiguous features from the low-dense regions.Specially,we first project the labeled and unlabeled data points into an embedding space and then construct a neighbor graph that serves as a similarity measure based on the similar representations in the embedding space.Then,we employ an attention mechanism to transform the target features into augmented ones based on the neighbor graph.Finally,we formulate a novel semi-supervised loss by encouraging the predictions of the interpolations of augmented features to be consistent with the corresponding interpolations of the predictions of the target features.We carried out exper-iments on SVHN and CIFAR-10 benchmark datasets and the experimental results demonstrate that our method outperforms the state-of-the-art methods when the number of labeled examples is limited.
基金supported by Macquarie University ARC Centre of Excellence in Cognition and its Disorders(CCD)through a CCD Research Fellowshipa National Educational Research Key Project under Grant No.GPA115005National Social Science Foundation of China under Grant No.16BYY076
文摘Unlike Indo-European languages,Mandarin relies heavily on lexical tones to distinguish word identity. Using the intermodal preferential looking paradigm, this study examined 3-year-old Mandarinspeakers＇ ability to use Mandarin lexical tones in learning new words. Results showed that when children were presented with Tone 2（rising） and Tone 4（falling）pairs, children successfully learned both words.However, when children were presented with Tone 2and Tone 3（dipping） pairs, they learned the Tone 2word but not the Tone 3 one. Children were then divided into two groups based on their learning performance on the Tone 3 word. Successful learning of Tone 3 words was observed in the high performers but not in the low performers, who consistently misused Tone 3 as Tone 2. This study showed that Mandarinspeaking 3-year-olds could use lexical tones to learn words under experimental conditions, and that the difficulty of Tone 3 acquisition may be related to its lower level of perceptual distinctiveness compared with other tones.
文摘The aim of this paper is to broaden the application of Stochastic Configuration Network (SCN) in the semi-supervised domain by utilizing common unlabeled data in daily life. It can enhance the classification accuracy of decentralized SCN algorithms while effectively protecting user privacy. To this end, we propose a decentralized semi-supervised learning algorithm for SCN, called DMT-SCN, which introduces teacher and student models by combining the idea of consistency regularization to improve the response speed of model iterations. In order to reduce the possible negative impact of unsupervised data on the model, we purposely change the way of adding noise to the unlabeled data. Simulation results show that the algorithm can effectively utilize unlabeled data to improve the classification accuracy of SCN training and is robust under different ground simulation environments.
文摘The concept of language sense has never failed to arouse interest among scholars in recent decades at home and abroad.Many scholars point out that language sense is an important competence which helps facilitate learning a language.It bears much connection with learners’acquisition of a language.Another concept,implicit learning,which is proved effective and has been applied in second language acquisition(SLA),is consistent with language sense in terms of its learning mechanism.In this sense,cultivation of English language sense can be theoretically supported by implicit learning and pedagogical implications can be derived accordingly.
基金Supported by Shaanxi Province Natural Science Basic Research Program(2025JC-YBQN-1003)National Natural Science Foundation of China(72101197,71988101)。
文摘As one of the three pillars of the tourism industry,hotel sales are influenced by a variety of factors.Particularly,with the exponential growth of the internet,user-generated images,text,and data presented by hotels impact hotel sales to varying degrees.This study attempts to explore how different factors affect hotel industry sales.Firstly,it examines review images,text data,and hotel base attribute data;secondly,it employs a deep learning-based approach to analyze the different types of data;finally,it uses random forest to calculate feature importance values and analyzes them based on different star ratings variance.The results show that image-text consistency influences all types of hotel sales.Furthermore,the consistency of image text also affects all hotel sales,and there are differences in the factors influencing sales across hotel types.The findings can be used to provide valuable advice to hotel managers in the sales field.
文摘1 Introduction Current continual learning methods[1–4]can utilize labeled data to alleviate catastrophic forgetting effectively.However,obtaining labeled samples can be difficult and tedious as it may require expert knowledge.In many practical application scenarios,labeled and unlabeled samples exist simultaneously,with more unlabeled than labeled samples in streaming data[5,6].Unfortunately,existing class-incremental learning methods face limitations in effectively utilizing unlabeled data,thereby impeding their performance in incremental learning scenarios.
基金supported in part by the Major Key Project of PCL,China(PCL2023AS7-1 and PCL2023A09)in part by the National Key R&D Program of China(2023YFA1011601)+1 种基金in part by the National Natural Science Foundation of China(Grant Nos.62106224 and U21A20478)in part by the Guangzhou Science and Technology Plan Project(2024A04J3749).
文摘Multi-view learning is an emerging field that aims to enhance learning performance by leveraging multiple views or sources of data across various domains.By integrating information from diverse perspectives,multi-view learning methods effectively enhance accuracy,robustness,and generalization capabilities.The existing research on multi-view learning can be broadly categorized into four groups in the survey based on the tasks it encompasses,namely multi-view classification approaches,multi-view semi-supervised classification approaches,multi-view clustering approaches,and multi-view semi-supervised clustering approaches.Despite its potential advantages,multi-view learning poses several challenges,including view inconsistency,view complementarity,optimal view fusion,the curse of dimensionality,scalability,limited labels,and generalization across domains.Nevertheless,these challenges have not discouraged researchers from exploring the potential of multiview learning.It continues to be an active and promising research area,capable of effectively addressing complex realworld problems.
基金supported by the National Natural Science Foundation of China(No.62372375)the Shaanxi Province Key R&D Program(No.2023-YBSF-114)the CAAI-Huawei MindSpore Open Fund(No.CAAIXSJLJJ-2022-035A).
文摘The interactions between drugs and microbes affecting microbial abundance can lead to various diseases or reduce the effectiveness of pharmaceutical treatments.Traditional Microbe-Drug Association(MDA)determination through biological assays is time-consuming and costly.With the accumulation of MDA data,computational methods have become a promising approach to infer potential MDAs.Although existing methods focus on predicting whether a drug interacts with a microbe,they can rarely infer whether a drug promotes or inhibits the abundance of a given microbe.Moreover,the extreme imbalance among abundance-promoted,abundance-inhibited,and non-impacted cases remains a challenge for computational prediction methods.To address these issues,we propose a framework for predicting the imbalanced Impact of Drugs on Microbial Abundance by leveraging Multi-view Learning and Data Augmentation,named IDMA-MLDA.IDMA-MLDA employs a novel method of transforming a bipartite graph into a hypergraph,uses hypergraph convolutions to capture high-order vertex neighborhoods(macro-view),and employs graph neural networks to learn individual features of drugs and microbes(micro-view).It integrates features from both macro-view and micro-view to obtain more comprehensive representations,incorporates a data augmentation module to handle class imbalance,and uses a multilayer perceptron to predict the impact of drugs on microbial abundance.We demonstrate the superiority of IDMA-MLDA through comparisons with six baseline methods,and ablation studies affirm the contributions of each key module in IDMA-MLDA’s prediction.Furthermore,a comprehensive literature review verifies the abundance types of twelve MDAs predicted by IDMA-MLDA.
基金The National Natural Science Foundation of China(No.51875100)
文摘To improve the accuracy and robustness of rolling bearing fault diagnosis under complex conditions, a novel method based on multi-view feature fusion is proposed. Firstly, multi-view features from perspectives of the time domain, frequency domain and time-frequency domain are extracted through the Fourier transform, Hilbert transform and empirical mode decomposition (EMD).Then, the random forest model (RF) is applied to select features which are highly correlated with the bearing operating state. Subsequently, the selected features are fused via the autoencoder (AE) to further reduce the redundancy. Finally, the effectiveness of the fused features is evaluated by the support vector machine (SVM). The experimental results indicate that the proposed method based on the multi-view feature fusion can effectively reflect the difference in the state of the rolling bearing, and improve the accuracy of fault diagnosis.
基金This work is supported by the National Natural Science Foundation of China(No.61772561)the Key Research&Development Plan of Hunan Province(No.2018NK2012)+1 种基金the Science Research Projects of Hunan Provincial Education Department(Nos.18A174,18C0262)the Science&Technology Innovation Platform and Talent Plan of Hunan Province(2017TP1022).
文摘Hashing technology has the advantages of reducing data storage and improving the efficiency of the learning system,making it more and more widely used in image retrieval.Multi-view data describes image information more comprehensively than traditional methods using a single-view.How to use hashing to combine multi-view data for image retrieval is still a challenge.In this paper,a multi-view fusion hashing method based on RKCCA(Random Kernel Canonical Correlation Analysis)is proposed.In order to describe image content more accurately,we use deep learning dense convolutional network feature DenseNet to construct multi-view by combining GIST feature or BoW_SIFT(Bag-of-Words model+SIFT feature)feature.This algorithm uses RKCCA method to fuse multi-view features to construct association features and apply them to image retrieval.The algorithm generates binary hash code with minimal distortion error by designing quantization regularization terms.A large number of experiments on benchmark datasets show that this method is superior to other multi-view hashing methods.
