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
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
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
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
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
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
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
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
Unsupervised learning algorithms can effectively solve sample imbalance.To address battery consistency anomalies in new energy vehicles,we adopt a variety of unsupervised learning algorithms to evaluate and predict th...Unsupervised learning algorithms can effectively solve sample imbalance.To address battery consistency anomalies in new energy vehicles,we adopt a variety of unsupervised learning algorithms to evaluate and predict the battery consistency of three vehicles using charging fragment data from actual operating conditions.We extract battery-related features,such as the mean of maximum difference,standard deviation,and entropy of batteries and then apply principal component analysis to reduce the dimensionality and record the amount of preserved information.We then build models through a collection of unsupervised learning algorithms for the anomaly detection of cell consistency faults.We also determine whether unsupervised and supervised learning algorithms can address the battery consistency problem and document the parameter tuning process.In addition,we compare the prediction effectiveness of charging and discharging features modeled individually and in combination,determine the choice of charging and discharging features to be modeled in combination,and visualize the multidimensional data for fault detection.Experimental results show that the unsupervised learning algorithm is effective in visualizing and predicting vehicle core conformance faults,and can accurately predict faults in real time.The“distance+boxplot”algorithm shows the best performance with a prediction accuracy of 80%,a recall rate of 100%,and an F1 of 0.89.The proposed approach can be applied to monitor battery consistency faults in real time and reduce the possibility of disasters arising from consistency faults.展开更多
The existing multi-view subspace clustering algorithms based on tensor singular value decomposition(t-SVD)predominantly utilize tensor nuclear norm to explore the intra view correlation between views of the same sampl...The existing multi-view subspace clustering algorithms based on tensor singular value decomposition(t-SVD)predominantly utilize tensor nuclear norm to explore the intra view correlation between views of the same samples,while neglecting the correlation among the samples within different views.Moreover,the tensor nuclear norm is not fully considered as a convex approximation of the tensor rank function.Treating different singular values equally may result in suboptimal tensor representation.A hypergraph regularized multi-view subspace clustering algorithm with dual tensor log-determinant(HRMSC-DTL)was proposed.The algorithm used subspace learning in each view to learn a specific set of affinity matrices,and introduced a non-convex tensor log-determinant function to replace the tensor nuclear norm to better improve global low-rankness.It also introduced hyper-Laplacian regularization to preserve the local geometric structure embedded in the high-dimensional space.Furthermore,it rotated the original tensor and incorporated a dual tensor mechanism to fully exploit the intra view correlation of the original tensor and the inter view correlation of the rotated tensor.At the same time,an alternating direction of multipliers method(ADMM)was also designed to solve non-convex optimization model.Experimental evaluations on seven widely used datasets,along with comparisons to several state-of-the-art algorithms,demonstrated the superiority and effectiveness of the HRMSC-DTL algorithm in terms of clustering performance.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
Literature shows that both market data and financial media impact stock prices;however,using only one kind of data may lead to information bias.Therefore,this study uses market data and news to investigate their joint...Literature shows that both market data and financial media impact stock prices;however,using only one kind of data may lead to information bias.Therefore,this study uses market data and news to investigate their joint impact on stock price trends.However,combining these two types of information is difficult because of their completely different characteristics.This study develops a hybrid model called MVL-SVM for stock price trend prediction by integrating multi-view learning with a support vector machine(SVM).It works by simply inputting heterogeneous multi-view data simultaneously,which may reduce information loss.Compared with the ARIMA and classic SVM models based on single-and multi-view data,our hybrid model shows statistically significant advantages.In the robustness test,our model outperforms the others by at least 10%accuracy when the sliding windows of news and market data are set to 1–5 days,which confirms our model’s effectiveness.Finally,trading strategies based on single stock and investment portfolios are constructed separately,and the simulations show that MVL-SVM has better profitability and risk control performance than the benchmarks.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.
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.
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.
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.
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.
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 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Unsupervised learning algorithms can effectively solve sample imbalance.To address battery consistency anomalies in new energy vehicles,we adopt a variety of unsupervised learning algorithms to evaluate and predict the battery consistency of three vehicles using charging fragment data from actual operating conditions.We extract battery-related features,such as the mean of maximum difference,standard deviation,and entropy of batteries and then apply principal component analysis to reduce the dimensionality and record the amount of preserved information.We then build models through a collection of unsupervised learning algorithms for the anomaly detection of cell consistency faults.We also determine whether unsupervised and supervised learning algorithms can address the battery consistency problem and document the parameter tuning process.In addition,we compare the prediction effectiveness of charging and discharging features modeled individually and in combination,determine the choice of charging and discharging features to be modeled in combination,and visualize the multidimensional data for fault detection.Experimental results show that the unsupervised learning algorithm is effective in visualizing and predicting vehicle core conformance faults,and can accurately predict faults in real time.The“distance+boxplot”algorithm shows the best performance with a prediction accuracy of 80%,a recall rate of 100%,and an F1 of 0.89.The proposed approach can be applied to monitor battery consistency faults in real time and reduce the possibility of disasters arising from consistency faults.
基金supported by National Natural Science Foundation of China(No.61806006)Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘The existing multi-view subspace clustering algorithms based on tensor singular value decomposition(t-SVD)predominantly utilize tensor nuclear norm to explore the intra view correlation between views of the same samples,while neglecting the correlation among the samples within different views.Moreover,the tensor nuclear norm is not fully considered as a convex approximation of the tensor rank function.Treating different singular values equally may result in suboptimal tensor representation.A hypergraph regularized multi-view subspace clustering algorithm with dual tensor log-determinant(HRMSC-DTL)was proposed.The algorithm used subspace learning in each view to learn a specific set of affinity matrices,and introduced a non-convex tensor log-determinant function to replace the tensor nuclear norm to better improve global low-rankness.It also introduced hyper-Laplacian regularization to preserve the local geometric structure embedded in the high-dimensional space.Furthermore,it rotated the original tensor and incorporated a dual tensor mechanism to fully exploit the intra view correlation of the original tensor and the inter view correlation of the rotated tensor.At the same time,an alternating direction of multipliers method(ADMM)was also designed to solve non-convex optimization model.Experimental evaluations on seven widely used datasets,along with comparisons to several state-of-the-art algorithms,demonstrated the superiority and effectiveness of the HRMSC-DTL algorithm in terms of clustering performance.
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partly supported by National Natural Science Foundation of China(No.71771204,72231010)the Fundamental Research Funds for the Central Universities(No.E0E48946X2).
文摘Literature shows that both market data and financial media impact stock prices;however,using only one kind of data may lead to information bias.Therefore,this study uses market data and news to investigate their joint impact on stock price trends.However,combining these two types of information is difficult because of their completely different characteristics.This study develops a hybrid model called MVL-SVM for stock price trend prediction by integrating multi-view learning with a support vector machine(SVM).It works by simply inputting heterogeneous multi-view data simultaneously,which may reduce information loss.Compared with the ARIMA and classic SVM models based on single-and multi-view data,our hybrid model shows statistically significant advantages.In the robustness test,our model outperforms the others by at least 10%accuracy when the sliding windows of news and market data are set to 1–5 days,which confirms our model’s effectiveness.Finally,trading strategies based on single stock and investment portfolios are constructed separately,and the simulations show that MVL-SVM has better profitability and risk control performance than the benchmarks.
