The atmospheric surface layer of the tropical coastal ocean is commonly very unstable and experiences weakwind conditions.How the latent(LE)and sensible(H)heat fluxes behave under such conditions are unclear because o...The atmospheric surface layer of the tropical coastal ocean is commonly very unstable and experiences weakwind conditions.How the latent(LE)and sensible(H)heat fluxes behave under such conditions are unclear because of the lack of observation stations in the tropics.Thus,this study aims to analyze LE and H and the microclimate parameters influencing them.The authors deployed an eddy covariance system in a tropical coastal region for seven months.The microclimate parameters investigated were wind speed(U),vapor pressure deficit(Δe),temperature difference(ΔT),wind-vapor pressure deficit(UΔe),wind-temperature difference(UΔT),and atmospheric stability(z/L),where z is height and L is the Monin–Obukhov length.On the daily time scale,the results show that LE was more associated with U thanΔe,while H was more related toΔT than U.Cross-wavelet analysis revealed the strong coherence in the LE-U relationship for periods between one and two days,and for H–ΔT,0.5 to 1 day.Correlation and regression analyses confirmed the time series analyses results,where strong positive correlation coefficients(r)were obtained between LE and U(r=0.494)and H andΔT(r=0.365).Compared to other water bodies,the transfer coefficient of moisture(CE N)was found to be small(=0.40×10^(-3))and independent of stability;conversely,the transfer coefficient of heat(CH N)was closer to literature values(=1.00×10^(-3))and a function of stability.展开更多
Objectives:SocialNetworkAddiction(SNA)is becoming increasingly prevalent among college students;however,there remains a lack of consensus regarding the measurement tools and their optimal cutoff score.This study aims ...Objectives:SocialNetworkAddiction(SNA)is becoming increasingly prevalent among college students;however,there remains a lack of consensus regarding the measurement tools and their optimal cutoff score.This study aims to validate the 21-item SocialNetwork Addiction Scale-Chinese(SNAS-C)in its Chinese version and to determine its optimal cutoff score for identifying potential SNA cases within the college student population.Methods:A crosssectional survey was conducted,recruiting 3387 college students.Latent profile analysis(LPA)and receiver operating characteristic(ROC)curve analysis were employed to establish the optimal cutoff score for the validated 21-item SNAS-C.Results:Three profile models were selected based on multiple statistical criteria,classifying participants into low-risk,moderate-risk,and high-risk groups.The highest-risk group was defined as“positive”for SNA,while the remaining groups were considered“negative”,serving as the reference standard for ROC analysis.The optimal cutoff score was determined to be 72(sensitivity:98.2%,specificity:96.86%),with an overall classification accuracy of 97.0%.The“positive”group reported significantly higher frequency of social network usage,greater digitalmedia dependence scores,and a higher incidence of network addiction.Conclusion:This study identified the optimal cutoff score for the SNAS-C as≥72,demonstrating high sensitivity,specificity,and diagnostic accuracy.This threshold effectively distinguishes between high-risk and low-risk SNA.展开更多
Objective:To assess aptamer-based assays for diagnosing latent tuberculosis infection(LTBI).Methods:Literature from Medline,ScienceDirect,and Scopus,covering publications from January 1,2012,to December 31,2023,was ex...Objective:To assess aptamer-based assays for diagnosing latent tuberculosis infection(LTBI).Methods:Literature from Medline,ScienceDirect,and Scopus,covering publications from January 1,2012,to December 31,2023,was examined.This review evaluates different aptamers,biomarkers,sample types,sample sizes,reference assays,and the assays'sensitivity and specificity.By using the Quality Assessment of Diagnostic Accuracy Studies 2,the risk of bias in each study was evaluated.Results:Aptamer-based assays generally showed a sensitivity of 90%(95%CI:75%-100%)and specificity of 90%(95%CI:50%-100%),where optical aptasensor showed the highest sensitivity and specificity at 100%.Serum samples were frequently used to enhance antigen detectability,improving the assay’s performance.Meanwhile,HspX was the most studied biomarker,followed by MPT64,and IFN-γ.Conclusions:Aptamer-based assays could be reliable alternatives to current LTBI detection methods,but further research is needed to validate their clinical efficacy.展开更多
In the field of organic synthesis,the core objective of retrosynthetic methods is to deduce possible synthetic routes and precursor molecules for complex target molecules.Traditional retrosynthetic methods,such as tem...In the field of organic synthesis,the core objective of retrosynthetic methods is to deduce possible synthetic routes and precursor molecules for complex target molecules.Traditional retrosynthetic methods,such as template-based retrosynthesis,have high accuracy and interpretability in specific types of reactions but are limited by the scope of the template library,making it difficult to adapt to new or uncommon reaction types.Moreover,sequence-to-sequence retrosynthetic prediction methods,although they enhance the flexibility of prediction,often overlook the complexity of molecular graph structures and the actual interactions between atoms,which limits the accuracy and reliability of the predictions.To address these limitations,this paper proposes a Molecular Retrosynthesis Top-k Prediction based on the Latent Generation Process(MRLGP)that uses latent variables from graph neural networks to model the generation process and produce diverse set of reactants.Utilising an encoding method based on Graphormer,the authors have also introduced topology-aware positional encoding to better capture the interactions between atomic nodes in the molecular graph structure,thereby more accurately simulating the retrosynthetic process.The MRLGP model significantly enhances the accuracy and diversity of predictions by correlating discrete latent variables with the reactant generation process and progressively constructing molecular graphs using a variational autoregressive decoder.Experimental results on benchmark datasets such as USPTO-50k,USPTO-Full,and USPTO-DIVERSE demonstrate that MRLGP outperforms baseline models on multiple Top-k evaluation metrics.Additionally,ablation experiments conducted on the USPTO-50K dataset further validate the effectiveness of the methods used in the encoder and decoder parts of the model.展开更多
Objectives:The study aimed to explore social alienation types in patients with inflammatorybowel diseases(IBD)and identify influencingfactors.Methods:This cross-sectional study was conducted using purposive sampling a...Objectives:The study aimed to explore social alienation types in patients with inflammatorybowel diseases(IBD)and identify influencingfactors.Methods:This cross-sectional study was conducted using purposive sampling among patients with IBD from July 2022 to July 2023.Patients were assessed using the Generalized Social Alienation Scale(GSAS),the Brief Illness Perception Questionnaire(B-IPQ),the Hospital Anxiety and Depression Scale(HADS),and the Medical Coping Modes Questionnaire(MCMQ).Demographic and disease-related characteristics were also collected.Latent profileanalysis(LPA)was used to identify potential subgroups of social alienation.Univariate analysis and multicollinearity analysis were conducted to explore the influencing factors,followed by multiple regression analysis to evaluate the effect of influencingfactors on social alienation.Results:Three distinct profilesof social alienation were identified:integrated-low alienation group(n=61,20.20%),accommodative-moderate alienation group(n=195,64.57%),and maladaptive-high alienation group(n=46,15.23%).Seven characteristics were associated with the profile’smembership:self-perceived financialstress,malnutrition risk,disease duration,illness comprehensibility,anxiety,depression,and acceptance-resignation coping mode.Conclusions:Patients with IBD were categorized into three subgroups based on social alienation levels.Financial stress,malnutrition risk,disease duration,illness comprehensibility,anxiety,depression,and acceptance-resignation coping mode were key predictors of the subgroup membership.Targeted interventions should be developed to mitigate the negative effects of social alienation,with a focus on improving illness perception,alleviating anxiety and depression,and promoting effective coping strategies.展开更多
Objectives:Adolescents’aggression is widely studied,the underlying heterogeneity of aggression among rural Chinese boarding students remains unexplored.This study investigates the latent profiles of Chinese rural boa...Objectives:Adolescents’aggression is widely studied,the underlying heterogeneity of aggression among rural Chinese boarding students remains unexplored.This study investigates the latent profiles of Chinese rural boarding junior high school students’aggression and its correlations with moral disengagement and preference for solitude.Methods:A cross-sectional survey was conducted from 04–22 April 2022,using a convenient sampling method among 516 junior high school students from six Chinese rural boarding schools.The survey included the Aggression Questionnaire,the Moral Disengagement Scale(MDS),and the Preference for Solitude Scale(PSS).Results:Participants were divided into three latent profiles:low(36.0%),medium(50.9%),and high aggression levels(13.1%).Compared with low aggression,students who felt left-behind(minors who stay in the rural areas while one or both parents migrated to the urban areas for the work)accounted for a larger proportion in the medium aggression profile.The higher the grade level and the lower the educational level of the students’parents,the greater proportion of students in the medium and high aggression profiles.Additionally,students with high moral disengagement and preference for solitude showed a significant association with the medium aggression and high aggression profiles.Conclusions:The results demonstrate the significant group heterogeneity of aggression groups in Chinese rural boarding junior high school students.Targeted prevention and intervention measures can be carried out according to feeling left-behind,grade level,parents’education,and MDS and PSS scores.展开更多
Background:This study addresses the pressing need to understand the nuanced relationship between‘mattering’—the perception of being significant to others—and problematic internet use(PIU)among university students....Background:This study addresses the pressing need to understand the nuanced relationship between‘mattering’—the perception of being significant to others—and problematic internet use(PIU)among university students.Unlike previous research that has primarily employed variable-centered approaches,this study first adopts a person-centered approach using Latent Profile Analysis(LPA)to identify distinct mattering profiles.Subsequently,through variable-centered analyses,these profiles are examined in relation to different types of PIU—specifically problematic social media use(PSMU)and problematic gaming(PG)—as well as adaptability.Methods:Data were collected from 3587 university students across 19 universities in China.Participants completed three mattering-related scales(General Mattering Scale,Anti-Mattering Scale,and Fear of Not Mattering Inventory),along with the Bergen Social Media Addiction Scale,the Internet Gaming Disorder Scale-Short Form,and the Nine-item Adaptability Scale.Results:A four-class model identified by LPA was optimally selected:Class 1(high general mattering,low anti-mattering,low fear of not mattering),Class 2(moderate levels),Class 3(moderate general mattering,high antimattering,high fear of not mattering),and Class 4(low general mattering,low fear of not mattering,moderate anti-mattering).Significant differences were found among these classes in both PIU types(PSMU:F=139.66,p<0.001;PG:F=162.96,p<0.001).The pattern of mean differences consistently showed:Class 3>Class 2>Class 4>Class 1.Class 3 participants demonstrated the highest likelihood of meeting the addiction criteria,Class 2 showed moderate probability,while Classes 1 and 4 exhibited lower probabilities(χ^(2)=113.38 to 408.87,all p<0.001).Additionally,Class 3 reported the lowest adaptability(F=131.67,p<0.001).Conclusion:This study reveals that the unique influence of three ways of assessing feelings of mattering and the fear of not mattering on university students’PIU at the personal level,concluding that these factors are integral to understanding PIU among this demographic.展开更多
BACKGROUND Functional gastrointestinal disorders(FGIDs)are common gastrointestinal conditions that significantly impair patient quality of life.Current clinical treatment methods are relatively limited,making the sear...BACKGROUND Functional gastrointestinal disorders(FGIDs)are common gastrointestinal conditions that significantly impair patient quality of life.Current clinical treatment methods are relatively limited,making the search for more effective therapeutic strategies critically important.Latent myofascial trigger points(MTrPs)injection,as an emerging minimally invasive treatment method,has shown potential in alleviating muscle pain and improving function,but its application in FGIDs remains insufficiently validated.AIM To assess improvements in gastrointestinal symptom severity,quality of life indices,and treatment-related adverse events between the two therapeutic approaches.METHODS This single-blind randomized controlled study recruited 60 FGIDs patients from Qilu Hospital of Shandong University,randomly divided into an injection group(TI group)and an oral medication group(PO group)at a 1:1 ratio.The TI group received abdominal wall latent MTrPs injection therapy,while the PO group received oral symptomatic medication treatment.Primary outcome measures were gastrointestinal symptom severity scores(Gastrointestinal Symptom Rating Scale,Irritable Bowel Syndrome Severity Scoring System scales)at 2 weeks and 4 weeks after treatment completion.Secondary outcome measures included Gastrointestinal Quality of Life Index scores.Both groups underwent rigorous follow-up and assessment.RESULTS The TI group is anticipated to significantly outperform the PO group in gastrointestinal symptom relief and quality of life improvement.TI group patients are expected to show a notable decrease in symptom scores,increased quality of life index,and higher clinical effectiveness rate.Additionally,the TI group is projected to have a low adverse event rate and good safety profile.CONCLUSION Latent MTrPs injection therapy may represent an effective and safe new method for treating FGIDs.Compared to traditional oral medication treatment,this method demonstrates significant advantages in improving patient symptoms and quality of life.展开更多
Surface-latent heat(LE)and sensible heat(SH)fluxes play a pivotal role in governing hydrological,biological,geochemical,and ecological processes on the land surface in the Tibetan Plateau.However,to accurately assess ...Surface-latent heat(LE)and sensible heat(SH)fluxes play a pivotal role in governing hydrological,biological,geochemical,and ecological processes on the land surface in the Tibetan Plateau.However,to accurately assess and understand the spatial distribution of LE and SH fluxes across different underlying surfaces,it is crucial to verify the validity and reliability of ERA-5,GLDAS,and MODIS data against ground measurements obtained from the Flux Net micrometeorological tower network.This study analyzed the spatial patterns of LE and SH over the Tibetan Plateau using data from ERA-5,GLDAS,and MODIS.The results were compared with ground measurements from Flux Net tower observations on different underlying surfaces,and five statistical parameters(Pearson's r,LR slope,RMSE,MBE,and MAE)were used to validate the data.The results showed that:(1)MODIS LE data and ERA-5 SH data exhibited the closest agreement with ground observations,as indicated by their lowest root mean square error and mean bias area values.(2)The accuracy of ERA-5 SH was the highest in meadows and steppes,while GLDAS SH performed optimally in shrublands.Notably,MODIS LE consistently outperformed the other datasets across all vegetation types.(3)The spatial distribution of LE and SH displayed considerable heterogeneity,contingent upon the specific data sources and underlying surfaces.Notably,there was a contrasting trend between GLDAS and ERA-5,as well as MODIS,in terms of SH distribution in the shrubland.In shrublands and meadows,MODIS SH and LE exhibited more pronounced changes than ERA-5 and GLDAS.Additionally,ERA-5 SH demonstrated the opposite variation in meadow and steppe regions compared to GLDAS and MODIS.展开更多
Physics-informed neural networks(PINNs)are promising to replace conventional mesh-based partial tial differen-equation(PDE)solvers by offering more accurate and flexible PDE solutions.However,PINNs are hampered by the...Physics-informed neural networks(PINNs)are promising to replace conventional mesh-based partial tial differen-equation(PDE)solvers by offering more accurate and flexible PDE solutions.However,PINNs are hampered by the relatively slow convergence and the need to perform additional,potentially expensive training for new PDE parameters.To solve this limitation,we introduce LatentPINN,a framework that utilizes latent representations of the PDE parameters as additional(to the coordinates)inputs into PINNs and allows for training over the distribution of these parameters.Motivated by the recent progress on generative models,we promote using latent diffusion models to learn compressed latent representations of the distribution of PDE parameters as they act as input parameters for NN functional solutions.We use a two-stage training scheme in which,in the first stage,we learn the latent representations for the distribution of PDE parameters.In the second stage,we train a physics-informed neural network over inputs given by randomly drawn samples from the coordinate space within the solution domain and samples from the learned latent representation of the PDE parameters.Considering their importance in capturing evolving interfaces and fronts in various fields,we test the approach on a class of level set equations given,for example,by the nonlinear Eikonal equation.We share results corresponding to three Eikonal parameters(velocity models)sets.The proposed method performs well on new phase velocity models without the need for any additional training.展开更多
The latent heat thermal energy storage system with solid-liquid phase-change material(SLPCM-LHTES)as energy storage medium provides outstanding advantages such as system simplicity,stable temperature control,and high ...The latent heat thermal energy storage system with solid-liquid phase-change material(SLPCM-LHTES)as energy storage medium provides outstanding advantages such as system simplicity,stable temperature control,and high energy storage density,showing great potential toward addressing the energy storage problems associated with decentralized,intermittent,and unstable renewable energy sources.Notably,effective heat transfer within the SLPCM-LHTES is crucial for extending its application potential.Therefore,a comprehensive understanding of the heat transfer processes in SLPCM-LHTES from a theoretical perspective is necessary.In this review,we propose a three-stage heat transfer pathway in SLPCM-LHTES,including external heating,interfacial heat transfer,and intrinsic phase transition processes.From the perspective of this three-stage pathway,the theoretical basis of heat transfer processes and typical efficiency enhancement strategies in SLPCM-LHTES are summarized.Moreover,an overview of the typical applications of SLPCM-LHTES in various fields,such as building energy efficiency,textiles and garments,and battery thermal management,is presented.Finally,the remaining challenges and possible avenues of research in this burgeoning field will also be discussed.展开更多
In modern forensic science,fingerprints are critical evidence due to their uniqueness and difficulty to replicate.However,it is challenging to observe and identify some latent fingerprints(LFP),because alternative pro...In modern forensic science,fingerprints are critical evidence due to their uniqueness and difficulty to replicate.However,it is challenging to observe and identify some latent fingerprints(LFP),because alternative processing methods for recognition are often required.The use of Eu^(3+)-doped phosphors,which emit red-orange light,presents an effective approach for enhancing the visibility of LFP.Eu^(3+)-doped Ba_(2)LuSbO_(6)(BLSO)phosphors were synthesized using a high-temperature solid-state method,aiming to improve the recognition of LFP for enhanced fingerprint identification.The phase structure of the fluorescent powder samples was analyzed through X-ray diffraction(XRD)combined with Rietveld refinement,as well as scanning electron microscopy(SEM)and X-ray photoelectron spectroscopy(XPS).Photoluminescence spectra of Ba_(2)LuSbO_(6):xEu^(3+)phosphor samples exhibit orange-red emission peaks at595 and 618 nm when excited by 250 nm deep ultraviolet light.Concentration quenching is observed at a doping concentration of 20 mol%.The Ba_(2)LuSbO_(6):0.2Eu^(3+)phosphor sample demonstrates exceptional thermal stability,with value of 92.50% at 423 K.The quantum efficiency of the Ba_(2)LuSbO_(6):0.2Eu^(3+)phosphor sample was evaluated using an integrating sphere,yielding an IQE of 79.34%.In order to visualize latent fingerprints(LFPs),hydrophilic BLSO:0.2Eu^(3+)phosphors were transformed into hydrophobic BLSO:0.2Eu^(3+)@OA phosphors through a coating of oleic acid(OA).The BLSO:0.2Eu^(3+)@OA phosphor proves capable of generating dependable LFP fluorescence images with superior contrast and resolution.These findings underscore the significant potential for application of BLSO:0.2Eu^(3+)products LFP visualization.展开更多
Infrared and visible image fusion technology integrates the thermal radiation information of infrared images with the texture details of visible images to generate more informative fused images.However,existing method...Infrared and visible image fusion technology integrates the thermal radiation information of infrared images with the texture details of visible images to generate more informative fused images.However,existing methods often fail to distinguish salient objects from background regions,leading to detail suppression in salient regions due to global fusion strategies.This study presents a mask-guided latent low-rank representation fusion method to address this issue.First,the GrabCut algorithm is employed to extract a saliency mask,distinguishing salient regions from background regions.Then,latent low-rank representation(LatLRR)is applied to extract deep image features,enhancing key information extraction.In the fusion stage,a weighted fusion strategy strengthens infrared thermal information and visible texture details in salient regions,while an average fusion strategy improves background smoothness and stability.Experimental results on the TNO dataset demonstrate that the proposed method achieves superior performance in SPI,MI,Qabf,PSNR,and EN metrics,effectively preserving salient target details while maintaining balanced background information.Compared to state-of-the-art fusion methods,our approach achieves more stable and visually consistent fusion results.The fusion code is available on GitHub at:https://github.com/joyzhen1/Image(accessed on 15 January 2025).展开更多
Efficient and accurate prediction of ocean surface latent heat fluxes is essential for understanding and modeling climate dynamics.Conventional estimation methods have low resolution and lack accuracy.The transformer ...Efficient and accurate prediction of ocean surface latent heat fluxes is essential for understanding and modeling climate dynamics.Conventional estimation methods have low resolution and lack accuracy.The transformer model,with its self-attention mechanism,effectively captures long-range dependencies,leading to a degradation of accuracy over time.Due to the non-linearity and uncertainty of physical processes,the transformer model encounters the problem of error accumulation,leading to a degradation of accuracy over time.To solve this problem,we combine the Data Assimilation(DA)technique with the transformer model and continuously modify the model state to make it closer to the actual observations.In this paper,we propose a deep learning model called TransNetDA,which integrates transformer,convolutional neural network and DA methods.By combining data-driven and DA methods for spatiotemporal prediction,TransNetDA effectively extracts multi-scale spatial features and significantly improves prediction accuracy.The experimental results indicate that the TransNetDA method surpasses traditional techniques in terms of root mean square error and R2 metrics,showcasing its superior performance in predicting latent heat fluxes at the ocean surface.展开更多
This paper analyzes the global competitive landscape of smartphone technological innovation capacity using the latent semantic indexing(LSI)and the vector space model(VSM).It integrates the theory of technological eco...This paper analyzes the global competitive landscape of smartphone technological innovation capacity using the latent semantic indexing(LSI)and the vector space model(VSM).It integrates the theory of technological ecological niches and evaluates four key dimensions:patent quality,energy efficiency engineering,technological modules,and intelligent computing power.The findings reveal that USA has established strong technological barriers through standard-essential patents(SEPs)in wireless communication and integrated circuits.In contrast,Chinese mainland firms rely heavily on fundamental technologies.Qualcomm Inc.in USA and Taiwan Semiconductor Manufacturing Company(TSMC)in Chineses Taiwan have built a comprehensive patent porfolio in energy efficiency engineering.While Chinese mainland faces challenges in advancing dynamic frequency modulation algorithms and high-end manufacturing processes.Huawei Inc.in Chinese mainland leads in 5G module technology but struggles with ecosystem collaboration.Semiconductor manufacturing and radio frequency(RF)components still rely on external suppliers.This highlights the urgent need for innovation in new materials and open'source architectures.To enhance intelligent computing power,Chinese mainland firms must address coordination challenges.They should adopt scenario-driven technological strategies and build a comprehensive ecosystem that includes hardware,operating systems,and developer networks.展开更多
Dear Editor,This letter presents a latent-factorization-of-tensors(LFT)-incorporated battery cycle life prediction framework.Data-driven prognosis and health management(PHM)for battery pack(BP)can boost the safety and...Dear Editor,This letter presents a latent-factorization-of-tensors(LFT)-incorporated battery cycle life prediction framework.Data-driven prognosis and health management(PHM)for battery pack(BP)can boost the safety and sustainability of a battery management system(BMS),which relies heavily on the quality of the measured BP data like the voltage(V),current(I),and temperature(T).展开更多
Aiming to resolve the problem that conventional sewage source heat pump systems cannot satisfy heat peak loads of buildings,a new idea that the freezing latent heat is exacted as the auxiliary heat source at the peak ...Aiming to resolve the problem that conventional sewage source heat pump systems cannot satisfy heat peak loads of buildings,a new idea that the freezing latent heat is exacted as the auxiliary heat source at the peak heat load is proposed.First,on the basis of sewage characteristics,a freezing latent heat exchanger is developed to safely eliminate ice,continuously extract heat and remove sewage soft-dirt.A reasonable form of the urban sewage source heat pump system with freezing latent heat collection is presented.Then,the feasibility of the system is theoretically analyzed.The calculation results under typical operating conditions show that the heating ability of the new system is higher than that of the conventional one and the ratio of these two highest heating rates is between 4.5 and 8.7,which proves that the new system has great application potential in cold regions.展开更多
Latent factor(LF)models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS)matrices which are commonly seen in various industrial applications.An LF model usually adopts iterativ...Latent factor(LF)models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS)matrices which are commonly seen in various industrial applications.An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost.Hence,determining how to accelerate the training process for LF models has become a significant issue.To address this,this work proposes a randomized latent factor(RLF)model.It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices,thereby greatly alleviating computational burden.It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly.Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models,RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices,which is especially desired for industrial applications demanding highly efficient models.展开更多
With the development of intelligent agents pursuing humanisation,artificial intelligence must consider emotion,the most basic spiritual need in human interaction.Traditional emotional dialogue systems usually use an e...With the development of intelligent agents pursuing humanisation,artificial intelligence must consider emotion,the most basic spiritual need in human interaction.Traditional emotional dialogue systems usually use an external emotional dictionary to select appropriate emotional words to add to the response or concatenate emotional tags and semantic features in the decoding step to generate appropriate responses.However,selecting emotional words from a fixed emotional dictionary may result in loss of the diversity and consistency of the response.We propose a semantic and emotion-based dual latent variable generation model(Dual-LVG)for dialogue systems,which is able to generate appropriate emotional responses without an emotional dictionary.Different from previous work,the conditional variational autoencoder(CVAE)adopts the standard transformer structure.Then,Dual-LVG regularises the CVAE latent space by introducing a dual latent space of semantics and emotion.The content diversity and emotional accuracy of the generated responses are improved by learning emotion and semantic features respectively.Moreover,the average attention mechanism is adopted to better extract semantic features at the sequence level,and the semi-supervised attention mechanism is used in the decoding step to strengthen the fusion of emotional features of the model.Experimental results show that Dual-LVG can successfully achieve the effect of generating different content by controlling emotional factors.展开更多
基金supported by a PETRONAS-Academia Collabora-tion Dialogue 2022 Grant[Grant number PACD 2022]from PETRONAS Research Sdn.Bhd。
文摘The atmospheric surface layer of the tropical coastal ocean is commonly very unstable and experiences weakwind conditions.How the latent(LE)and sensible(H)heat fluxes behave under such conditions are unclear because of the lack of observation stations in the tropics.Thus,this study aims to analyze LE and H and the microclimate parameters influencing them.The authors deployed an eddy covariance system in a tropical coastal region for seven months.The microclimate parameters investigated were wind speed(U),vapor pressure deficit(Δe),temperature difference(ΔT),wind-vapor pressure deficit(UΔe),wind-temperature difference(UΔT),and atmospheric stability(z/L),where z is height and L is the Monin–Obukhov length.On the daily time scale,the results show that LE was more associated with U thanΔe,while H was more related toΔT than U.Cross-wavelet analysis revealed the strong coherence in the LE-U relationship for periods between one and two days,and for H–ΔT,0.5 to 1 day.Correlation and regression analyses confirmed the time series analyses results,where strong positive correlation coefficients(r)were obtained between LE and U(r=0.494)and H andΔT(r=0.365).Compared to other water bodies,the transfer coefficient of moisture(CE N)was found to be small(=0.40×10^(-3))and independent of stability;conversely,the transfer coefficient of heat(CH N)was closer to literature values(=1.00×10^(-3))and a function of stability.
基金supported by the National Natural Science Foundation of China(Grant No.72364006).
文摘Objectives:SocialNetworkAddiction(SNA)is becoming increasingly prevalent among college students;however,there remains a lack of consensus regarding the measurement tools and their optimal cutoff score.This study aims to validate the 21-item SocialNetwork Addiction Scale-Chinese(SNAS-C)in its Chinese version and to determine its optimal cutoff score for identifying potential SNA cases within the college student population.Methods:A crosssectional survey was conducted,recruiting 3387 college students.Latent profile analysis(LPA)and receiver operating characteristic(ROC)curve analysis were employed to establish the optimal cutoff score for the validated 21-item SNAS-C.Results:Three profile models were selected based on multiple statistical criteria,classifying participants into low-risk,moderate-risk,and high-risk groups.The highest-risk group was defined as“positive”for SNA,while the remaining groups were considered“negative”,serving as the reference standard for ROC analysis.The optimal cutoff score was determined to be 72(sensitivity:98.2%,specificity:96.86%),with an overall classification accuracy of 97.0%.The“positive”group reported significantly higher frequency of social network usage,greater digitalmedia dependence scores,and a higher incidence of network addiction.Conclusion:This study identified the optimal cutoff score for the SNAS-C as≥72,demonstrating high sensitivity,specificity,and diagnostic accuracy.This threshold effectively distinguishes between high-risk and low-risk SNA.
基金supported by Higher Institution Centre of Excellence(HICoE)Grant(A305-KR-AKH002-0000000278-K134)from the Ministry of Higher Education,Malaysia.
文摘Objective:To assess aptamer-based assays for diagnosing latent tuberculosis infection(LTBI).Methods:Literature from Medline,ScienceDirect,and Scopus,covering publications from January 1,2012,to December 31,2023,was examined.This review evaluates different aptamers,biomarkers,sample types,sample sizes,reference assays,and the assays'sensitivity and specificity.By using the Quality Assessment of Diagnostic Accuracy Studies 2,the risk of bias in each study was evaluated.Results:Aptamer-based assays generally showed a sensitivity of 90%(95%CI:75%-100%)and specificity of 90%(95%CI:50%-100%),where optical aptasensor showed the highest sensitivity and specificity at 100%.Serum samples were frequently used to enhance antigen detectability,improving the assay’s performance.Meanwhile,HspX was the most studied biomarker,followed by MPT64,and IFN-γ.Conclusions:Aptamer-based assays could be reliable alternatives to current LTBI detection methods,but further research is needed to validate their clinical efficacy.
基金supported by the China Postdoctoral Science Foundation(No.2014m561331)Science and Technology Research Project of Heilongjiang Provincial Education Department(No.12521073)National Natural Science Youth Fund(No.61300115).
文摘In the field of organic synthesis,the core objective of retrosynthetic methods is to deduce possible synthetic routes and precursor molecules for complex target molecules.Traditional retrosynthetic methods,such as template-based retrosynthesis,have high accuracy and interpretability in specific types of reactions but are limited by the scope of the template library,making it difficult to adapt to new or uncommon reaction types.Moreover,sequence-to-sequence retrosynthetic prediction methods,although they enhance the flexibility of prediction,often overlook the complexity of molecular graph structures and the actual interactions between atoms,which limits the accuracy and reliability of the predictions.To address these limitations,this paper proposes a Molecular Retrosynthesis Top-k Prediction based on the Latent Generation Process(MRLGP)that uses latent variables from graph neural networks to model the generation process and produce diverse set of reactants.Utilising an encoding method based on Graphormer,the authors have also introduced topology-aware positional encoding to better capture the interactions between atomic nodes in the molecular graph structure,thereby more accurately simulating the retrosynthetic process.The MRLGP model significantly enhances the accuracy and diversity of predictions by correlating discrete latent variables with the reactant generation process and progressively constructing molecular graphs using a variational autoregressive decoder.Experimental results on benchmark datasets such as USPTO-50k,USPTO-Full,and USPTO-DIVERSE demonstrate that MRLGP outperforms baseline models on multiple Top-k evaluation metrics.Additionally,ablation experiments conducted on the USPTO-50K dataset further validate the effectiveness of the methods used in the encoder and decoder parts of the model.
基金supported by the“333 High-Level Talents Training Project”of Jiangsu province(No.BRA2020069)。
文摘Objectives:The study aimed to explore social alienation types in patients with inflammatorybowel diseases(IBD)and identify influencingfactors.Methods:This cross-sectional study was conducted using purposive sampling among patients with IBD from July 2022 to July 2023.Patients were assessed using the Generalized Social Alienation Scale(GSAS),the Brief Illness Perception Questionnaire(B-IPQ),the Hospital Anxiety and Depression Scale(HADS),and the Medical Coping Modes Questionnaire(MCMQ).Demographic and disease-related characteristics were also collected.Latent profileanalysis(LPA)was used to identify potential subgroups of social alienation.Univariate analysis and multicollinearity analysis were conducted to explore the influencing factors,followed by multiple regression analysis to evaluate the effect of influencingfactors on social alienation.Results:Three distinct profilesof social alienation were identified:integrated-low alienation group(n=61,20.20%),accommodative-moderate alienation group(n=195,64.57%),and maladaptive-high alienation group(n=46,15.23%).Seven characteristics were associated with the profile’smembership:self-perceived financialstress,malnutrition risk,disease duration,illness comprehensibility,anxiety,depression,and acceptance-resignation coping mode.Conclusions:Patients with IBD were categorized into three subgroups based on social alienation levels.Financial stress,malnutrition risk,disease duration,illness comprehensibility,anxiety,depression,and acceptance-resignation coping mode were key predictors of the subgroup membership.Targeted interventions should be developed to mitigate the negative effects of social alienation,with a focus on improving illness perception,alleviating anxiety and depression,and promoting effective coping strategies.
基金the General project of the“14th Five-Year Plan”of Education Science of Gansu Province in 2023:“Research on the Integration Strategy of Rural Primary School Labor Education and Other Disciplines under the Background of Rural Revitalization”(Project number:GS[2023]GHB1420)Gansu Provincial University Curriculum Ideological and Political Demonstration Project“Exploration and Practice of Ideological and Political Courses in Pre-School EducationMajors from the Perspective of Three Educations:A Case study of Pre-School EducationHistory”(Project No.:GSkcsz-2021-094).
文摘Objectives:Adolescents’aggression is widely studied,the underlying heterogeneity of aggression among rural Chinese boarding students remains unexplored.This study investigates the latent profiles of Chinese rural boarding junior high school students’aggression and its correlations with moral disengagement and preference for solitude.Methods:A cross-sectional survey was conducted from 04–22 April 2022,using a convenient sampling method among 516 junior high school students from six Chinese rural boarding schools.The survey included the Aggression Questionnaire,the Moral Disengagement Scale(MDS),and the Preference for Solitude Scale(PSS).Results:Participants were divided into three latent profiles:low(36.0%),medium(50.9%),and high aggression levels(13.1%).Compared with low aggression,students who felt left-behind(minors who stay in the rural areas while one or both parents migrated to the urban areas for the work)accounted for a larger proportion in the medium aggression profile.The higher the grade level and the lower the educational level of the students’parents,the greater proportion of students in the medium and high aggression profiles.Additionally,students with high moral disengagement and preference for solitude showed a significant association with the medium aggression and high aggression profiles.Conclusions:The results demonstrate the significant group heterogeneity of aggression groups in Chinese rural boarding junior high school students.Targeted prevention and intervention measures can be carried out according to feeling left-behind,grade level,parents’education,and MDS and PSS scores.
基金supported by a special grant from the Taishan Scholars Project(Project No.tsqn202211130).
文摘Background:This study addresses the pressing need to understand the nuanced relationship between‘mattering’—the perception of being significant to others—and problematic internet use(PIU)among university students.Unlike previous research that has primarily employed variable-centered approaches,this study first adopts a person-centered approach using Latent Profile Analysis(LPA)to identify distinct mattering profiles.Subsequently,through variable-centered analyses,these profiles are examined in relation to different types of PIU—specifically problematic social media use(PSMU)and problematic gaming(PG)—as well as adaptability.Methods:Data were collected from 3587 university students across 19 universities in China.Participants completed three mattering-related scales(General Mattering Scale,Anti-Mattering Scale,and Fear of Not Mattering Inventory),along with the Bergen Social Media Addiction Scale,the Internet Gaming Disorder Scale-Short Form,and the Nine-item Adaptability Scale.Results:A four-class model identified by LPA was optimally selected:Class 1(high general mattering,low anti-mattering,low fear of not mattering),Class 2(moderate levels),Class 3(moderate general mattering,high antimattering,high fear of not mattering),and Class 4(low general mattering,low fear of not mattering,moderate anti-mattering).Significant differences were found among these classes in both PIU types(PSMU:F=139.66,p<0.001;PG:F=162.96,p<0.001).The pattern of mean differences consistently showed:Class 3>Class 2>Class 4>Class 1.Class 3 participants demonstrated the highest likelihood of meeting the addiction criteria,Class 2 showed moderate probability,while Classes 1 and 4 exhibited lower probabilities(χ^(2)=113.38 to 408.87,all p<0.001).Additionally,Class 3 reported the lowest adaptability(F=131.67,p<0.001).Conclusion:This study reveals that the unique influence of three ways of assessing feelings of mattering and the fear of not mattering on university students’PIU at the personal level,concluding that these factors are integral to understanding PIU among this demographic.
文摘BACKGROUND Functional gastrointestinal disorders(FGIDs)are common gastrointestinal conditions that significantly impair patient quality of life.Current clinical treatment methods are relatively limited,making the search for more effective therapeutic strategies critically important.Latent myofascial trigger points(MTrPs)injection,as an emerging minimally invasive treatment method,has shown potential in alleviating muscle pain and improving function,but its application in FGIDs remains insufficiently validated.AIM To assess improvements in gastrointestinal symptom severity,quality of life indices,and treatment-related adverse events between the two therapeutic approaches.METHODS This single-blind randomized controlled study recruited 60 FGIDs patients from Qilu Hospital of Shandong University,randomly divided into an injection group(TI group)and an oral medication group(PO group)at a 1:1 ratio.The TI group received abdominal wall latent MTrPs injection therapy,while the PO group received oral symptomatic medication treatment.Primary outcome measures were gastrointestinal symptom severity scores(Gastrointestinal Symptom Rating Scale,Irritable Bowel Syndrome Severity Scoring System scales)at 2 weeks and 4 weeks after treatment completion.Secondary outcome measures included Gastrointestinal Quality of Life Index scores.Both groups underwent rigorous follow-up and assessment.RESULTS The TI group is anticipated to significantly outperform the PO group in gastrointestinal symptom relief and quality of life improvement.TI group patients are expected to show a notable decrease in symptom scores,increased quality of life index,and higher clinical effectiveness rate.Additionally,the TI group is projected to have a low adverse event rate and good safety profile.CONCLUSION Latent MTrPs injection therapy may represent an effective and safe new method for treating FGIDs.Compared to traditional oral medication treatment,this method demonstrates significant advantages in improving patient symptoms and quality of life.
基金funded by the West Light Scholar of the Chinese Academy of Sciences(xbzg-zdsys-202202)the Natural Science Foundation of Henan(Grant No.232300420165)Integrated Scientific Investigation of the North-South Transitional Zone of China(2017FY100900)。
文摘Surface-latent heat(LE)and sensible heat(SH)fluxes play a pivotal role in governing hydrological,biological,geochemical,and ecological processes on the land surface in the Tibetan Plateau.However,to accurately assess and understand the spatial distribution of LE and SH fluxes across different underlying surfaces,it is crucial to verify the validity and reliability of ERA-5,GLDAS,and MODIS data against ground measurements obtained from the Flux Net micrometeorological tower network.This study analyzed the spatial patterns of LE and SH over the Tibetan Plateau using data from ERA-5,GLDAS,and MODIS.The results were compared with ground measurements from Flux Net tower observations on different underlying surfaces,and five statistical parameters(Pearson's r,LR slope,RMSE,MBE,and MAE)were used to validate the data.The results showed that:(1)MODIS LE data and ERA-5 SH data exhibited the closest agreement with ground observations,as indicated by their lowest root mean square error and mean bias area values.(2)The accuracy of ERA-5 SH was the highest in meadows and steppes,while GLDAS SH performed optimally in shrublands.Notably,MODIS LE consistently outperformed the other datasets across all vegetation types.(3)The spatial distribution of LE and SH displayed considerable heterogeneity,contingent upon the specific data sources and underlying surfaces.Notably,there was a contrasting trend between GLDAS and ERA-5,as well as MODIS,in terms of SH distribution in the shrubland.In shrublands and meadows,MODIS SH and LE exhibited more pronounced changes than ERA-5 and GLDAS.Additionally,ERA-5 SH demonstrated the opposite variation in meadow and steppe regions compared to GLDAS and MODIS.
基金King Abdullah University of Science and Technol-ogy(KAUST)for supporting this research and the Seismic Wave Anal-ysis group for the supportive and encouraging environment.
文摘Physics-informed neural networks(PINNs)are promising to replace conventional mesh-based partial tial differen-equation(PDE)solvers by offering more accurate and flexible PDE solutions.However,PINNs are hampered by the relatively slow convergence and the need to perform additional,potentially expensive training for new PDE parameters.To solve this limitation,we introduce LatentPINN,a framework that utilizes latent representations of the PDE parameters as additional(to the coordinates)inputs into PINNs and allows for training over the distribution of these parameters.Motivated by the recent progress on generative models,we promote using latent diffusion models to learn compressed latent representations of the distribution of PDE parameters as they act as input parameters for NN functional solutions.We use a two-stage training scheme in which,in the first stage,we learn the latent representations for the distribution of PDE parameters.In the second stage,we train a physics-informed neural network over inputs given by randomly drawn samples from the coordinate space within the solution domain and samples from the learned latent representation of the PDE parameters.Considering their importance in capturing evolving interfaces and fronts in various fields,we test the approach on a class of level set equations given,for example,by the nonlinear Eikonal equation.We share results corresponding to three Eikonal parameters(velocity models)sets.The proposed method performs well on new phase velocity models without the need for any additional training.
基金financial support was provided by the National Natural Science Foundation of China(Nos.52476146,52006008,and 52471219)the Guangdong Basic and Applied Basic Research Foundation(2023A1515140059 and 2025A1515011255)+2 种基金the Peking University Third Hospital Haidian transformation project(HDCXZHKC2023210)the National Foreign Expert Individual Human Project(Category H,No.H20240116)the State Key Laboratory of New Ceramic Materials Tsinghua University(No.KFZD202402).
文摘The latent heat thermal energy storage system with solid-liquid phase-change material(SLPCM-LHTES)as energy storage medium provides outstanding advantages such as system simplicity,stable temperature control,and high energy storage density,showing great potential toward addressing the energy storage problems associated with decentralized,intermittent,and unstable renewable energy sources.Notably,effective heat transfer within the SLPCM-LHTES is crucial for extending its application potential.Therefore,a comprehensive understanding of the heat transfer processes in SLPCM-LHTES from a theoretical perspective is necessary.In this review,we propose a three-stage heat transfer pathway in SLPCM-LHTES,including external heating,interfacial heat transfer,and intrinsic phase transition processes.From the perspective of this three-stage pathway,the theoretical basis of heat transfer processes and typical efficiency enhancement strategies in SLPCM-LHTES are summarized.Moreover,an overview of the typical applications of SLPCM-LHTES in various fields,such as building energy efficiency,textiles and garments,and battery thermal management,is presented.Finally,the remaining challenges and possible avenues of research in this burgeoning field will also be discussed.
基金Project supported by the Scientific Research Project of Jilin Provincial Department of Education(JJKH20230821KJ,JJKH20230823KJ,JJKH20230822KJ)。
文摘In modern forensic science,fingerprints are critical evidence due to their uniqueness and difficulty to replicate.However,it is challenging to observe and identify some latent fingerprints(LFP),because alternative processing methods for recognition are often required.The use of Eu^(3+)-doped phosphors,which emit red-orange light,presents an effective approach for enhancing the visibility of LFP.Eu^(3+)-doped Ba_(2)LuSbO_(6)(BLSO)phosphors were synthesized using a high-temperature solid-state method,aiming to improve the recognition of LFP for enhanced fingerprint identification.The phase structure of the fluorescent powder samples was analyzed through X-ray diffraction(XRD)combined with Rietveld refinement,as well as scanning electron microscopy(SEM)and X-ray photoelectron spectroscopy(XPS).Photoluminescence spectra of Ba_(2)LuSbO_(6):xEu^(3+)phosphor samples exhibit orange-red emission peaks at595 and 618 nm when excited by 250 nm deep ultraviolet light.Concentration quenching is observed at a doping concentration of 20 mol%.The Ba_(2)LuSbO_(6):0.2Eu^(3+)phosphor sample demonstrates exceptional thermal stability,with value of 92.50% at 423 K.The quantum efficiency of the Ba_(2)LuSbO_(6):0.2Eu^(3+)phosphor sample was evaluated using an integrating sphere,yielding an IQE of 79.34%.In order to visualize latent fingerprints(LFPs),hydrophilic BLSO:0.2Eu^(3+)phosphors were transformed into hydrophobic BLSO:0.2Eu^(3+)@OA phosphors through a coating of oleic acid(OA).The BLSO:0.2Eu^(3+)@OA phosphor proves capable of generating dependable LFP fluorescence images with superior contrast and resolution.These findings underscore the significant potential for application of BLSO:0.2Eu^(3+)products LFP visualization.
基金supported by Universiti Teknologi MARA through UiTM MyRA Research Grant,600-RMC 5/3/GPM(053/2022).
文摘Infrared and visible image fusion technology integrates the thermal radiation information of infrared images with the texture details of visible images to generate more informative fused images.However,existing methods often fail to distinguish salient objects from background regions,leading to detail suppression in salient regions due to global fusion strategies.This study presents a mask-guided latent low-rank representation fusion method to address this issue.First,the GrabCut algorithm is employed to extract a saliency mask,distinguishing salient regions from background regions.Then,latent low-rank representation(LatLRR)is applied to extract deep image features,enhancing key information extraction.In the fusion stage,a weighted fusion strategy strengthens infrared thermal information and visible texture details in salient regions,while an average fusion strategy improves background smoothness and stability.Experimental results on the TNO dataset demonstrate that the proposed method achieves superior performance in SPI,MI,Qabf,PSNR,and EN metrics,effectively preserving salient target details while maintaining balanced background information.Compared to state-of-the-art fusion methods,our approach achieves more stable and visually consistent fusion results.The fusion code is available on GitHub at:https://github.com/joyzhen1/Image(accessed on 15 January 2025).
基金The National Natural Science Foundation of China under contract Nos 42176011 and 61931025the Fundamental Research Funds for the Central Universities of China under contract No.24CX03001A.
文摘Efficient and accurate prediction of ocean surface latent heat fluxes is essential for understanding and modeling climate dynamics.Conventional estimation methods have low resolution and lack accuracy.The transformer model,with its self-attention mechanism,effectively captures long-range dependencies,leading to a degradation of accuracy over time.Due to the non-linearity and uncertainty of physical processes,the transformer model encounters the problem of error accumulation,leading to a degradation of accuracy over time.To solve this problem,we combine the Data Assimilation(DA)technique with the transformer model and continuously modify the model state to make it closer to the actual observations.In this paper,we propose a deep learning model called TransNetDA,which integrates transformer,convolutional neural network and DA methods.By combining data-driven and DA methods for spatiotemporal prediction,TransNetDA effectively extracts multi-scale spatial features and significantly improves prediction accuracy.The experimental results indicate that the TransNetDA method surpasses traditional techniques in terms of root mean square error and R2 metrics,showcasing its superior performance in predicting latent heat fluxes at the ocean surface.
基金supported in part by the National Social Science Foundation of China(No.20BGL203).
文摘This paper analyzes the global competitive landscape of smartphone technological innovation capacity using the latent semantic indexing(LSI)and the vector space model(VSM).It integrates the theory of technological ecological niches and evaluates four key dimensions:patent quality,energy efficiency engineering,technological modules,and intelligent computing power.The findings reveal that USA has established strong technological barriers through standard-essential patents(SEPs)in wireless communication and integrated circuits.In contrast,Chinese mainland firms rely heavily on fundamental technologies.Qualcomm Inc.in USA and Taiwan Semiconductor Manufacturing Company(TSMC)in Chineses Taiwan have built a comprehensive patent porfolio in energy efficiency engineering.While Chinese mainland faces challenges in advancing dynamic frequency modulation algorithms and high-end manufacturing processes.Huawei Inc.in Chinese mainland leads in 5G module technology but struggles with ecosystem collaboration.Semiconductor manufacturing and radio frequency(RF)components still rely on external suppliers.This highlights the urgent need for innovation in new materials and open'source architectures.To enhance intelligent computing power,Chinese mainland firms must address coordination challenges.They should adopt scenario-driven technological strategies and build a comprehensive ecosystem that includes hardware,operating systems,and developer networks.
文摘Dear Editor,This letter presents a latent-factorization-of-tensors(LFT)-incorporated battery cycle life prediction framework.Data-driven prognosis and health management(PHM)for battery pack(BP)can boost the safety and sustainability of a battery management system(BMS),which relies heavily on the quality of the measured BP data like the voltage(V),current(I),and temperature(T).
基金The National Key Technology R&D Program of Chinaduring the 11th Five-Year Plan Period(No.2008BAJ12B05-05)the Research Foundation of Education Bureau of Heilongjiang Province,China(No.11551114)the China Postdoctoral Science Foundation(No.20100471438).
文摘Aiming to resolve the problem that conventional sewage source heat pump systems cannot satisfy heat peak loads of buildings,a new idea that the freezing latent heat is exacted as the auxiliary heat source at the peak heat load is proposed.First,on the basis of sewage characteristics,a freezing latent heat exchanger is developed to safely eliminate ice,continuously extract heat and remove sewage soft-dirt.A reasonable form of the urban sewage source heat pump system with freezing latent heat collection is presented.Then,the feasibility of the system is theoretically analyzed.The calculation results under typical operating conditions show that the heating ability of the new system is higher than that of the conventional one and the ratio of these two highest heating rates is between 4.5 and 8.7,which proves that the new system has great application potential in cold regions.
基金supported in part by the National Natural Science Foundation of China (6177249391646114)+1 种基金Chongqing research program of technology innovation and application (cstc2017rgzn-zdyfX0020)in part by the Pioneer Hundred Talents Program of Chinese Academy of Sciences
文摘Latent factor(LF)models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS)matrices which are commonly seen in various industrial applications.An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost.Hence,determining how to accelerate the training process for LF models has become a significant issue.To address this,this work proposes a randomized latent factor(RLF)model.It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices,thereby greatly alleviating computational burden.It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly.Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models,RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices,which is especially desired for industrial applications demanding highly efficient models.
基金Fundamental Research Funds for the Central Universities of China,Grant/Award Number:CUC220B009National Natural Science Foundation of China,Grant/Award Numbers:62207029,62271454,72274182。
文摘With the development of intelligent agents pursuing humanisation,artificial intelligence must consider emotion,the most basic spiritual need in human interaction.Traditional emotional dialogue systems usually use an external emotional dictionary to select appropriate emotional words to add to the response or concatenate emotional tags and semantic features in the decoding step to generate appropriate responses.However,selecting emotional words from a fixed emotional dictionary may result in loss of the diversity and consistency of the response.We propose a semantic and emotion-based dual latent variable generation model(Dual-LVG)for dialogue systems,which is able to generate appropriate emotional responses without an emotional dictionary.Different from previous work,the conditional variational autoencoder(CVAE)adopts the standard transformer structure.Then,Dual-LVG regularises the CVAE latent space by introducing a dual latent space of semantics and emotion.The content diversity and emotional accuracy of the generated responses are improved by learning emotion and semantic features respectively.Moreover,the average attention mechanism is adopted to better extract semantic features at the sequence level,and the semi-supervised attention mechanism is used in the decoding step to strengthen the fusion of emotional features of the model.Experimental results show that Dual-LVG can successfully achieve the effect of generating different content by controlling emotional factors.
