It’s a great honor for me to talk about ethics applied to artificial intelligence here.Most of the problems that we are facing today come from a strong misunderstanding of what ethics means.We tend to think ethics ar...It’s a great honor for me to talk about ethics applied to artificial intelligence here.Most of the problems that we are facing today come from a strong misunderstanding of what ethics means.We tend to think ethics are merely about establishing principles that would help us mitigate risks and secure benefits expected from AI systems.We are on the wrong path.Ethics are much more complex than that.Ethics are about philosophy,not about politics.Ethics are more about asking questions to enlighten decision-making processes,then to provide one-size-fit-all solutions.We are doing what I call cosmetics,which is a makeup using ethics-related vocabulary,notions and concepts to communicate and influence users and consumers,and to send messages to the market.展开更多
The current gait planning for legged robots is mostly based on human presets,which cannot match the flexible characteristics of natural mammals.This paper proposes a gait optimization framework for hexapod robots call...The current gait planning for legged robots is mostly based on human presets,which cannot match the flexible characteristics of natural mammals.This paper proposes a gait optimization framework for hexapod robots called Smart Gait.Smart Gait contains three modules:swing leg trajectory optimization,gait period&duty optimization,and gait sequence optimization.The full dynamics of a single leg,and the centroid dynamics of the overall robot are considered in the respective modules.The Smart Gait not only helps the robot to decrease the energy consumption when in locomotion,mostly,it enables the hexapod robot to determine its gait pattern transitions based on its current state,instead of repeating the formalistic clock-set step cycles.Our Smart Gait framework allows the hexapod robot to behave nimbly as a living animal when in 3D movements for the first time.The Smart Gait framework combines offline and online optimizations without any fussy data-driven training procedures,and it can run efficiently on board in real-time after deployment.Various experiments are carried out on the hexapod robot LittleStrong.The results show that the energy consumption is reduced by 15.9%when in locomotion.Adaptive gait patterns can be generated spontaneously both in regular and challenge environments,and when facing external interferences.展开更多
This paper explores the issue of secure synchronization control in piecewise-homogeneous Markovian jump delay neural networks affected by denial-of-service(DoS)attacks.Initially,a novel memory-based adaptive event-tri...This paper explores the issue of secure synchronization control in piecewise-homogeneous Markovian jump delay neural networks affected by denial-of-service(DoS)attacks.Initially,a novel memory-based adaptive event-triggered mechanism(MBAETM)is designed based on sequential growth rates,focusing on event-triggered conditions and thresholds.Subsequently,from the perspective of defenders,non-periodic DoS attacks are re-characterized,and a model of irregular DoS attacks with cyclic fluctuations within time series is further introduced to enhance the system's defense capabilities more effectively.Additionally,considering the unified demands of network security and communication efficiency,a resilient memory-based adaptive event-triggered mechanism(RMBAETM)is proposed.A unified Lyapunov-Krasovskii functional is then constructed,incorporating a loop functional to thoroughly consider information at trigger moments.The master-slave system achieves synchronization through the application of linear matrix inequality techniques.Finally,the proposed methods'effectiveness and superiority are confirmed through four numerical simulation examples.展开更多
The event-triggered mechanism serves as an effective discontinuous control strategy for addressing the consensus tracking problem in multiagent systems(MASs).This approach optimizes energy consumption by updating the ...The event-triggered mechanism serves as an effective discontinuous control strategy for addressing the consensus tracking problem in multiagent systems(MASs).This approach optimizes energy consumption by updating the controller only when some observed errors exceed a predefined threshold.Considering the influence of noise on agent dynamics in complex control environments,this study investigates an event-triggered control scheme for stochastic MASs,where noise is modeled as Brownian motion.Furthermore,the communication topology of the stochastic MASs is assumed to exhibit a Markovian switching mechanism.Analytical criteria are derived to guarantee consensus tracking in the mean square sense,and a numerical example is provided to validate the effectiveness of the proposed control methods.展开更多
As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitat...As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitations in handling nonlinear and high-dimensional data,while neural networks(NNs)have demonstrated great potential due to their powerful feature extraction and pattern recognition capabilities.Although several existing surveys discuss the applications of NNs in stock forecasting,they often lack a detailed examination of models that use time-series data as input and fail to cover the latest research developments.In response,this paper reviews relevant literature from 2015 to 2025 and classifies timeseriesbased stock forecasting methods into four categories:NNs,recurrent NNs(RNNs),convolutional NNs(CNNs),Transformers and other models.We analyze their performance under different market conditions,highlight strengths and limitations,and identify recent trends in model design.Our findings show that hybrid architectures and attention-based models consistently achieve superior forecasting stability and adaptability across volatile market scenarios.This survey offers a systematic reference for researchers and practitioners and outlines promising future research directions.展开更多
Few-shot point cloud 3D object detection(FS3D)aims to identify and locate objects of novel classes within point clouds using knowledge acquired from annotated base classes and a minimal number of samples from the nove...Few-shot point cloud 3D object detection(FS3D)aims to identify and locate objects of novel classes within point clouds using knowledge acquired from annotated base classes and a minimal number of samples from the novel classes.Due to imbalanced training data,existing FS3D methods based on fully supervised learning can lead to overfitting toward base classes,which impairs the network’s ability to generalize knowledge learned from base classes to novel classes and also prevents the network from extracting distinctive foreground and background representations for novel class objects.To address these issues,this thesis proposes a category-agnostic contrastive learning approach,enhancing the generalization and identification abilities for almost unseen categories through the construction of pseudo-labels and positive-negative sample pairs unrelated to specific classes.Firstly,this thesis designs a proposal-wise context contrastive module(CCM).By reducing the distance between foreground point features and increasing the distance between foreground and background point features within a region proposal,CCM aids the network in extracting more discriminative foreground and background feature representations without reliance on categorical annotations.Secondly,this thesis utilizes a geometric contrastive module(GCM),which enhances the network’s geometric perception capability by employing contrastive learning on the foreground point features associated with various basic geometric components,such as edges,corners,and surfaces,thereby enabling these geometric components to exhibit more distinguishable representations.This thesis also combines category-aware contrastive learning with former modules to maintain categorical distinctiveness.Extensive experimental results on FS-SUNRGBD and FS-ScanNet datasets demonstrate the effectiveness of this method with average precision exceeding the baseline by up to 8%.展开更多
In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with l...In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations.In particular,resource-intensive embeddings often lead to increased computational costs,and may limit scalability and adaptability in practical environ-ments,such as in low-resource settings or real-world applications.This paper explores an approach to knowledge graph representation learning that leverages small,reserved entities and relation sets for parameter-efficient embedding.We introduce a hierarchical attention network designed to refine and maximize the representational quality of embeddings by selectively focusing on these reserved sets,thereby reducing model complexity.Empirical assessments validate that our model achieves high performance on the benchmark dataset with fewer parameters and smaller embedding dimensions.The ablation studies further highlight the impact and contribution of each component in the proposed hierarchical attention structure.展开更多
In this paper we propose a robust watermarking algorithm for 3D mesh. The algorithm is based on spherical wavelet transform. Our basic idea is to decompose the original mesh into a series of details at different scale...In this paper we propose a robust watermarking algorithm for 3D mesh. The algorithm is based on spherical wavelet transform. Our basic idea is to decompose the original mesh into a series of details at different scales by using spherical wavelet transform; the watermark is then embedded into the different levels of details. The embedding process includes: global sphere parameterization, spherical uniform sampling, spherical wavelet forward transform, embedding watermark, spherical wavelet inverse transform, and at last resampling the mesh watermarked to recover the topological connectivity of the original model. Experiments showed that our algorithm can improve the capacity of the watermark and the robustness of watermarking against attacks.展开更多
Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique f...Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique for dimensionality reduction to search an optimal feature subset preserving the most relevant information. In this paper, we propose an effective feature evaluation criterion for multi-label feature selection, called neighborhood relationship preserving score. This criterion is inspired by similarity preservation, which is widely used in single-label feature selection. It evaluates each feature subset by measuring its capability in preserving neighborhood relationship among samples. Unlike similarity preservation, we address the order of sample similarities which can well express the neighborhood relationship among samples, not just the pairwise sample similarity. With this criterion, we also design one ranking algorithm and one greedy algorithm for feature selection problem. The proposed algorithms are validated in six publicly available data sets from machine learning repository. Experimental results demonstrate their superiorities over the compared state-of-the-art methods.展开更多
This survey provides a brief overview on the control Lyapunov function(CLF)and control barrier function(CBF)for general nonlinear-affine control systems.The problem of control is formulated as an optimization problem ...This survey provides a brief overview on the control Lyapunov function(CLF)and control barrier function(CBF)for general nonlinear-affine control systems.The problem of control is formulated as an optimization problem where the optimal control policy is derived by solving a constrained quadratic programming(QP)problem.The CLF and CBF respectively characterize the stability objective and the safety objective for the nonlinear control systems.These objectives imply important properties including controllability,convergence,and robustness of control problems.Under this framework,optimal control corresponds to the minimal solution to a constrained QP problem.When uncertainties are explicitly considered,the setting of the CLF and CBF is proposed to study the input-to-state stability and input-to-state safety and to analyze the effect of disturbances.The recent theoretic progress and novel applications of CLF and CBF are systematically reviewed and discussed in this paper.Finally,we provide research directions that are significant for the advance of knowledge in this area.展开更多
The cavitation in axial piston pumps threatens the reliability and safety of the overall hydraulic system.Vibration signal can reflect the cavitation conditions in axial piston pumps and it has been combined with mach...The cavitation in axial piston pumps threatens the reliability and safety of the overall hydraulic system.Vibration signal can reflect the cavitation conditions in axial piston pumps and it has been combined with machine learning to detect the pump cavitation.However,the vibration signal usually contains noise in real working conditions,which raises concerns about accurate recognition of cavitation in noisy environment.This paper presents an intelligent method to recognise the cavitation in axial piston pumps in noisy environment.First,we train a convolutional neural network(CNN)using the spectrogram images transformed from raw vibration data under different cavitation conditions.Second,we employ the technique of gradient-weighted class activation mapping(Grad-CAM)to visualise class-discriminative regions in the spectrogram image.Finally,we propose a novel image processing method based on Grad-CAM heatmap to automatically remove entrained noise and enhance class features in the spectrogram image.The experimental results show that the proposed method greatly improves the diagnostic performance of the CNN model in noisy environments.The classification accuracy of cavitation conditions increases from 0.50 to 0.89 and from 0.80 to 0.92 at signal-to-noise ratios of 4 and 6 dB,respectively.展开更多
Conventional methods for solving intersections between two offset parametric surfaces often include iteratively using computationally expensive SSI (surface/surface intersections) algorithm. In addition, these methods...Conventional methods for solving intersections between two offset parametric surfaces often include iteratively using computationally expensive SSI (surface/surface intersections) algorithm. In addition, these methods ignore the relations between the intersection curves of parametric surfaces with different offset distances. The algorithm presented in this paper, makes full use of the topological relations between different intersection loops and calculates intersection loops with the help of previously calculated intersection loops. It first pre-processes two parametric surfaces to obtain the characteristic points, called topology transition points (TTPs), which can help in the subsequent finding of the topologies of the intersection curves. Then these points are categorized into several distinct groups, and we can determine the calculation strategy for searching initial points by analyzing the properties of these TTPs on the surfaces. Hence, all intersection curves can be marched from initial points by the tracing algorithm. The proposed algorithm could calculate intersection curves robustly and effectively, and has been tested to be capable of overcoming the degenerate conditions such as loop and singularities leaking that occur frequently in conventional algorithms.展开更多
Objective image quality assessment(IQA)plays an important role in various visual communication systems,which can automatically and efficiently predict the perceived quality of images.The human eye is the ultimate eval...Objective image quality assessment(IQA)plays an important role in various visual communication systems,which can automatically and efficiently predict the perceived quality of images.The human eye is the ultimate evaluator for visual experience,thus the modeling of human visual system(HVS)is a core issue for objective IQA and visual experience optimization.The traditional model based on black box fitting has low interpretability and it is difficult to guide the experience optimization effectively,while the model based on physiological simulation is hard to integrate into practical visual communication services due to its high computational complexity.For bridging the gap between signal distortion and visual experience,in this paper,we propose a novel perceptual no-reference(NR)IQA algorithm based on structural computational modeling of HVS.According to the mechanism of the human brain,we divide the visual signal processing into a low-level visual layer,a middle-level visual layer and a high-level visual layer,which conduct pixel information processing,primitive information processing and global image information processing,respectively.The natural scene statistics(NSS)based features,deep features and free-energy based features are extracted from these three layers.The support vector regression(SVR)is employed to aggregate features to the final quality prediction.Extensive experimental comparisons on three widely used benchmark IQA databases(LIVE,CSIQ and TID2013)demonstrate that our proposed metric is highly competitive with or outperforms the state-of-the-art NR IQA measures.展开更多
A more recent branch of natural computing is DNA computing. At the theoretical level, DNA computing is powerful. This is due to the fact that DNA structure and processing suggest a series of new data structures and op...A more recent branch of natural computing is DNA computing. At the theoretical level, DNA computing is powerful. This is due to the fact that DNA structure and processing suggest a series of new data structures and operations, and to the fact of the massive parallelism. The insertion-deletion system （insdel system） is a DNA computing model based on two genetic operations： insertion and deletion which, working together, are very powerful, leading to characterizations of recursively enumerable lan- guages. When designing an insdel computer, it is natural to try to keep the underlying model as simple as possible. One idea is to use either only insertion operations or only deletion operations. By helping with a weak coding and a morphism, the family INS4^7DEL0^0 is equal to the family of recursively enumerable languages. It is an open problem proposed by Martin-Vide et al. on whether or not the parameters 4 and 7 appearing here can be replaced by smaller numbers. In this paper, our positive answer to this question is that INS2^4DEL0^0 can also play the same role as insertion and deletion. We suppose that the INS2^4DEL0^0 may be the least only-insertion insdel system in this situation. We will give some reasons supporting this conjecture in our paper.展开更多
Myocardial segmentation and classification play a major role in the diagnosis of cardiovascular disease.Dilated Cardiomyopathy(DCM)is a kind of common chronic and life-threatening cardiopathy.Early diagnostics signifi...Myocardial segmentation and classification play a major role in the diagnosis of cardiovascular disease.Dilated Cardiomyopathy(DCM)is a kind of common chronic and life-threatening cardiopathy.Early diagnostics significantly increases the chances of correct treatment and survival.However,accurate and rapid diagnosis of DCM is still challenge due to high variability of cardiac structure,low contrast cardiac magnetic resonance(CMR)images,and intrinsic noise in synthetic CMR images caused by motion artifact and cardiac dynamics.Moreover,visual assessment and empirical evaluation are widely used in routine clinical diagnosis,but they are subject to high inter-observer variability and are both subjective and non-reproducible.To solve this problem,we proposed an effective unified multi-task framework for dilated cardiomyopathy CMR segmentation and classification simultaneously,and we firstly update one independent encoder from both recovery decoder and parallel attention path sharing some partial weights.This can encode both task choices into good embedding,but each one can achieve significant improvements respectively from the given embedding.It consists of three branches:extraction path,attention path,and recovery path,which allows the model to learn more higher-level intermediate representations and makes a more accurate prediction.We validated our approach on a DCM dataset,which contains 1155 CMR LGE images.Experimental results show that our multi-task network has achieved accuracy of 97.63%,AUC of 98.32%,demonstrating effectively segmenting the myocardium,quickly and accurately diagnosing the presence or absence of dilation.展开更多
Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial memory.In this paper,we propose a lear...Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial memory.In this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory configurations.We introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity estimation.This tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual domains.Furthermore,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy generation.Results from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory structures.Comprehensive validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.展开更多
More than two decades have passed since the first gene therapy clinical trial was conducted.During this time,we have gained much knowledge regarding gene therapy in general,but also learned to understand the fear that...More than two decades have passed since the first gene therapy clinical trial was conducted.During this time,we have gained much knowledge regarding gene therapy in general,but also learned to understand the fear that persists in society.We have experienced drawbacks and successes.More than 1700 clinical trials have been conducted where gene therapy is used as a means for therapy.In the very first trial,patients with advanced melanoma were treated with tumor infiltrating lymphocytes genetically modified ex-vivo to express tumor necrosis factor.Around the same time the first gene therapy trial was conducted,the ethical aspects of performing gene therapy on humans was intensively discussed.What are the risks involved with gene therapy?Can we control the technology?What is ethically acceptable and what are the indications gene therapy can be used for?Initially,gene therapy was thought to be implemented mainly for the treatment of monogenetic diseases,such as adenosine deaminase deficiency.However,other therapeutic areas have become of interest and currently cancer is the most studied therapeutic area for gene therapy based medicines.In this review I will be giving a short introduction into gene therapy and will direct the discussion to where we should go from here.Furthermore,I will focus on the use of the Herpes simplex virus-thymidine kinase for gene therapy of malignant gliomas and highlight the efficacy of gene therapy for the treatment of malignant gliomas,but other strategies will also be mentioned.展开更多
Seven novel linear polyketides,talaketides A-G(1-7),were isolated from the rice media cultures of the mangrove sed-iment-derived fungus Talaromyces sp.SCSIO 41027.Among these,talaketides A-E(1-5)represented unpreceden...Seven novel linear polyketides,talaketides A-G(1-7),were isolated from the rice media cultures of the mangrove sed-iment-derived fungus Talaromyces sp.SCSIO 41027.Among these,talaketides A-E(1-5)represented unprecedented unsaturated lin-ear polyketides with an epoxy ring structure.The structures,including absolute configurations of these compounds,were elucidated through detailed analyses of nuclear magnetic resonance(NMR)and high-resolution mass spectrometry(HR-MS)data,as well as elec-tronic custom distributors(ECD)calculations.In the cytotoxicity screening against prostate cancer cell lines,talaketide E(5)demon-strated a dose-dependent inhibitory effect on prostate cancer PC-3 cell lines,with an IC50 value of 14.44 μmol·L-1.Moreover,com-pound 5 significantly inhibited the cloning formation of PC-3 cell lines and arrested the cell cycle in S-phase,ultimately inducing ap-optosis.These findings indicate that compound 5 may serve as a promising lead compound for the development of a potential treat-ment for prostate cancer.展开更多
Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes over...Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more complex.The existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s performance.We analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream classification.Precision, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority classes.The study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.展开更多
In August 2018, the Institute of Urban Meteorology(IUM) in Beijing co-organized with Sinovation Ventures a Weather Forecasting Contest(WFC)—one of the AI(artificial intelligence) Challenger Global Contests. The WFC a...In August 2018, the Institute of Urban Meteorology(IUM) in Beijing co-organized with Sinovation Ventures a Weather Forecasting Contest(WFC)—one of the AI(artificial intelligence) Challenger Global Contests. The WFC aims to take advantage of the AI techniques to improve the quality of weather forecast. Across the world, more than1000 teams enrolled in the WFC and about 250 teams completed real-time weather forecasts, among which top 5 teams were awarded in the final contest. The contest results show that the AI-based ensemble models exhibited improved skill for forecasts of surface air temperature and relative humidity at 2-m and wind speed at 10-m height.Compared to the IUM operational analog ensemble weather model forecast, the most notable improvements of 24.2%and 17.0% in forecast accuracy for surface 2-m air temperature are achieved by two teams using the AI techniques of time series model, gradient boosting tree, depth probability prediction, and so on. Meanwhile, it is found that reasonable data processing techniques and model composite structure are also important for obtaining better forecasts.展开更多
文摘It’s a great honor for me to talk about ethics applied to artificial intelligence here.Most of the problems that we are facing today come from a strong misunderstanding of what ethics means.We tend to think ethics are merely about establishing principles that would help us mitigate risks and secure benefits expected from AI systems.We are on the wrong path.Ethics are much more complex than that.Ethics are about philosophy,not about politics.Ethics are more about asking questions to enlighten decision-making processes,then to provide one-size-fit-all solutions.We are doing what I call cosmetics,which is a makeup using ethics-related vocabulary,notions and concepts to communicate and influence users and consumers,and to send messages to the market.
基金Supported by National Key Research and Development Program of China(Grant No.2021YFF0306202).
文摘The current gait planning for legged robots is mostly based on human presets,which cannot match the flexible characteristics of natural mammals.This paper proposes a gait optimization framework for hexapod robots called Smart Gait.Smart Gait contains three modules:swing leg trajectory optimization,gait period&duty optimization,and gait sequence optimization.The full dynamics of a single leg,and the centroid dynamics of the overall robot are considered in the respective modules.The Smart Gait not only helps the robot to decrease the energy consumption when in locomotion,mostly,it enables the hexapod robot to determine its gait pattern transitions based on its current state,instead of repeating the formalistic clock-set step cycles.Our Smart Gait framework allows the hexapod robot to behave nimbly as a living animal when in 3D movements for the first time.The Smart Gait framework combines offline and online optimizations without any fussy data-driven training procedures,and it can run efficiently on board in real-time after deployment.Various experiments are carried out on the hexapod robot LittleStrong.The results show that the energy consumption is reduced by 15.9%when in locomotion.Adaptive gait patterns can be generated spontaneously both in regular and challenge environments,and when facing external interferences.
文摘This paper explores the issue of secure synchronization control in piecewise-homogeneous Markovian jump delay neural networks affected by denial-of-service(DoS)attacks.Initially,a novel memory-based adaptive event-triggered mechanism(MBAETM)is designed based on sequential growth rates,focusing on event-triggered conditions and thresholds.Subsequently,from the perspective of defenders,non-periodic DoS attacks are re-characterized,and a model of irregular DoS attacks with cyclic fluctuations within time series is further introduced to enhance the system's defense capabilities more effectively.Additionally,considering the unified demands of network security and communication efficiency,a resilient memory-based adaptive event-triggered mechanism(RMBAETM)is proposed.A unified Lyapunov-Krasovskii functional is then constructed,incorporating a loop functional to thoroughly consider information at trigger moments.The master-slave system achieves synchronization through the application of linear matrix inequality techniques.Finally,the proposed methods'effectiveness and superiority are confirmed through four numerical simulation examples.
文摘The event-triggered mechanism serves as an effective discontinuous control strategy for addressing the consensus tracking problem in multiagent systems(MASs).This approach optimizes energy consumption by updating the controller only when some observed errors exceed a predefined threshold.Considering the influence of noise on agent dynamics in complex control environments,this study investigates an event-triggered control scheme for stochastic MASs,where noise is modeled as Brownian motion.Furthermore,the communication topology of the stochastic MASs is assumed to exhibit a Markovian switching mechanism.Analytical criteria are derived to guarantee consensus tracking in the mean square sense,and a numerical example is provided to validate the effectiveness of the proposed control methods.
文摘As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitations in handling nonlinear and high-dimensional data,while neural networks(NNs)have demonstrated great potential due to their powerful feature extraction and pattern recognition capabilities.Although several existing surveys discuss the applications of NNs in stock forecasting,they often lack a detailed examination of models that use time-series data as input and fail to cover the latest research developments.In response,this paper reviews relevant literature from 2015 to 2025 and classifies timeseriesbased stock forecasting methods into four categories:NNs,recurrent NNs(RNNs),convolutional NNs(CNNs),Transformers and other models.We analyze their performance under different market conditions,highlight strengths and limitations,and identify recent trends in model design.Our findings show that hybrid architectures and attention-based models consistently achieve superior forecasting stability and adaptability across volatile market scenarios.This survey offers a systematic reference for researchers and practitioners and outlines promising future research directions.
文摘Few-shot point cloud 3D object detection(FS3D)aims to identify and locate objects of novel classes within point clouds using knowledge acquired from annotated base classes and a minimal number of samples from the novel classes.Due to imbalanced training data,existing FS3D methods based on fully supervised learning can lead to overfitting toward base classes,which impairs the network’s ability to generalize knowledge learned from base classes to novel classes and also prevents the network from extracting distinctive foreground and background representations for novel class objects.To address these issues,this thesis proposes a category-agnostic contrastive learning approach,enhancing the generalization and identification abilities for almost unseen categories through the construction of pseudo-labels and positive-negative sample pairs unrelated to specific classes.Firstly,this thesis designs a proposal-wise context contrastive module(CCM).By reducing the distance between foreground point features and increasing the distance between foreground and background point features within a region proposal,CCM aids the network in extracting more discriminative foreground and background feature representations without reliance on categorical annotations.Secondly,this thesis utilizes a geometric contrastive module(GCM),which enhances the network’s geometric perception capability by employing contrastive learning on the foreground point features associated with various basic geometric components,such as edges,corners,and surfaces,thereby enabling these geometric components to exhibit more distinguishable representations.This thesis also combines category-aware contrastive learning with former modules to maintain categorical distinctiveness.Extensive experimental results on FS-SUNRGBD and FS-ScanNet datasets demonstrate the effectiveness of this method with average precision exceeding the baseline by up to 8%.
基金supported by the National Science and Technology Council(NSTC),Taiwan,under Grants Numbers 112-2622-E-029-009 and 112-2221-E-029-019.
文摘In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations.In particular,resource-intensive embeddings often lead to increased computational costs,and may limit scalability and adaptability in practical environ-ments,such as in low-resource settings or real-world applications.This paper explores an approach to knowledge graph representation learning that leverages small,reserved entities and relation sets for parameter-efficient embedding.We introduce a hierarchical attention network designed to refine and maximize the representational quality of embeddings by selectively focusing on these reserved sets,thereby reducing model complexity.Empirical assessments validate that our model achieves high performance on the benchmark dataset with fewer parameters and smaller embedding dimensions.The ablation studies further highlight the impact and contribution of each component in the proposed hierarchical attention structure.
文摘In this paper we propose a robust watermarking algorithm for 3D mesh. The algorithm is based on spherical wavelet transform. Our basic idea is to decompose the original mesh into a series of details at different scales by using spherical wavelet transform; the watermark is then embedded into the different levels of details. The embedding process includes: global sphere parameterization, spherical uniform sampling, spherical wavelet forward transform, embedding watermark, spherical wavelet inverse transform, and at last resampling the mesh watermarked to recover the topological connectivity of the original model. Experiments showed that our algorithm can improve the capacity of the watermark and the robustness of watermarking against attacks.
基金supported in part by the National Natural Science Foundation of China(61379049,61772120)
文摘Multi-label learning deals with data associated with a set of labels simultaneously. Dimensionality reduction is an important but challenging task in multi-label learning. Feature selection is an efficient technique for dimensionality reduction to search an optimal feature subset preserving the most relevant information. In this paper, we propose an effective feature evaluation criterion for multi-label feature selection, called neighborhood relationship preserving score. This criterion is inspired by similarity preservation, which is widely used in single-label feature selection. It evaluates each feature subset by measuring its capability in preserving neighborhood relationship among samples. Unlike similarity preservation, we address the order of sample similarities which can well express the neighborhood relationship among samples, not just the pairwise sample similarity. With this criterion, we also design one ranking algorithm and one greedy algorithm for feature selection problem. The proposed algorithms are validated in six publicly available data sets from machine learning repository. Experimental results demonstrate their superiorities over the compared state-of-the-art methods.
基金supported in part by the National Natural Science Foundation of China(U22B2046,62073079,62088101)in part by the General Joint Fund of the Equipment Advance Research Program of Ministry of Education(8091B022114)in part by NPRP(NPRP 9-466-1-103)from Qatar National Research Fund。
文摘This survey provides a brief overview on the control Lyapunov function(CLF)and control barrier function(CBF)for general nonlinear-affine control systems.The problem of control is formulated as an optimization problem where the optimal control policy is derived by solving a constrained quadratic programming(QP)problem.The CLF and CBF respectively characterize the stability objective and the safety objective for the nonlinear control systems.These objectives imply important properties including controllability,convergence,and robustness of control problems.Under this framework,optimal control corresponds to the minimal solution to a constrained QP problem.When uncertainties are explicitly considered,the setting of the CLF and CBF is proposed to study the input-to-state stability and input-to-state safety and to analyze the effect of disturbances.The recent theoretic progress and novel applications of CLF and CBF are systematically reviewed and discussed in this paper.Finally,we provide research directions that are significant for the advance of knowledge in this area.
基金National Key R&D Program of China,Grant/Award Number:2018YFB1702503Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems,Grant/Award Number:GZKF-202108+2 种基金Open Foundation of the Guangdong Provincial Key Laboratory of Electronic Information Products Reliability TechnologyChina National Postdoctoral Program for Innovative Talents,Grant/Award Number:BX20200210China Postdoctoral Science Foundation,Grant/Award Number:2019M660086。
文摘The cavitation in axial piston pumps threatens the reliability and safety of the overall hydraulic system.Vibration signal can reflect the cavitation conditions in axial piston pumps and it has been combined with machine learning to detect the pump cavitation.However,the vibration signal usually contains noise in real working conditions,which raises concerns about accurate recognition of cavitation in noisy environment.This paper presents an intelligent method to recognise the cavitation in axial piston pumps in noisy environment.First,we train a convolutional neural network(CNN)using the spectrogram images transformed from raw vibration data under different cavitation conditions.Second,we employ the technique of gradient-weighted class activation mapping(Grad-CAM)to visualise class-discriminative regions in the spectrogram image.Finally,we propose a novel image processing method based on Grad-CAM heatmap to automatically remove entrained noise and enhance class features in the spectrogram image.The experimental results show that the proposed method greatly improves the diagnostic performance of the CNN model in noisy environments.The classification accuracy of cavitation conditions increases from 0.50 to 0.89 and from 0.80 to 0.92 at signal-to-noise ratios of 4 and 6 dB,respectively.
文摘Conventional methods for solving intersections between two offset parametric surfaces often include iteratively using computationally expensive SSI (surface/surface intersections) algorithm. In addition, these methods ignore the relations between the intersection curves of parametric surfaces with different offset distances. The algorithm presented in this paper, makes full use of the topological relations between different intersection loops and calculates intersection loops with the help of previously calculated intersection loops. It first pre-processes two parametric surfaces to obtain the characteristic points, called topology transition points (TTPs), which can help in the subsequent finding of the topologies of the intersection curves. Then these points are categorized into several distinct groups, and we can determine the calculation strategy for searching initial points by analyzing the properties of these TTPs on the surfaces. Hence, all intersection curves can be marched from initial points by the tracing algorithm. The proposed algorithm could calculate intersection curves robustly and effectively, and has been tested to be capable of overcoming the degenerate conditions such as loop and singularities leaking that occur frequently in conventional algorithms.
基金This work was supported by National Natural Science Foundation of China(Nos.61831015 and 61901260)Key Research and Development Program of China(No.2019YFB1405902).
文摘Objective image quality assessment(IQA)plays an important role in various visual communication systems,which can automatically and efficiently predict the perceived quality of images.The human eye is the ultimate evaluator for visual experience,thus the modeling of human visual system(HVS)is a core issue for objective IQA and visual experience optimization.The traditional model based on black box fitting has low interpretability and it is difficult to guide the experience optimization effectively,while the model based on physiological simulation is hard to integrate into practical visual communication services due to its high computational complexity.For bridging the gap between signal distortion and visual experience,in this paper,we propose a novel perceptual no-reference(NR)IQA algorithm based on structural computational modeling of HVS.According to the mechanism of the human brain,we divide the visual signal processing into a low-level visual layer,a middle-level visual layer and a high-level visual layer,which conduct pixel information processing,primitive information processing and global image information processing,respectively.The natural scene statistics(NSS)based features,deep features and free-energy based features are extracted from these three layers.The support vector regression(SVR)is employed to aggregate features to the final quality prediction.Extensive experimental comparisons on three widely used benchmark IQA databases(LIVE,CSIQ and TID2013)demonstrate that our proposed metric is highly competitive with or outperforms the state-of-the-art NR IQA measures.
文摘A more recent branch of natural computing is DNA computing. At the theoretical level, DNA computing is powerful. This is due to the fact that DNA structure and processing suggest a series of new data structures and operations, and to the fact of the massive parallelism. The insertion-deletion system （insdel system） is a DNA computing model based on two genetic operations： insertion and deletion which, working together, are very powerful, leading to characterizations of recursively enumerable lan- guages. When designing an insdel computer, it is natural to try to keep the underlying model as simple as possible. One idea is to use either only insertion operations or only deletion operations. By helping with a weak coding and a morphism, the family INS4^7DEL0^0 is equal to the family of recursively enumerable languages. It is an open problem proposed by Martin-Vide et al. on whether or not the parameters 4 and 7 appearing here can be replaced by smaller numbers. In this paper, our positive answer to this question is that INS2^4DEL0^0 can also play the same role as insertion and deletion. We suppose that the INS2^4DEL0^0 may be the least only-insertion insdel system in this situation. We will give some reasons supporting this conjecture in our paper.
基金This work was supported by the National Natural Science Foundation of China(61602066)the Project of Sichuan Outstanding Young Scientific and Technological Talents(19JCQN0003)+2 种基金the major Project of Education Department in Sichuan(17ZA0063 and 2017JQ0030)in part by the Natural Science Foundation for Young Scientists of CUIT(J201704)the Sichuan Science and Technology Program(2019JDRC0077).
文摘Myocardial segmentation and classification play a major role in the diagnosis of cardiovascular disease.Dilated Cardiomyopathy(DCM)is a kind of common chronic and life-threatening cardiopathy.Early diagnostics significantly increases the chances of correct treatment and survival.However,accurate and rapid diagnosis of DCM is still challenge due to high variability of cardiac structure,low contrast cardiac magnetic resonance(CMR)images,and intrinsic noise in synthetic CMR images caused by motion artifact and cardiac dynamics.Moreover,visual assessment and empirical evaluation are widely used in routine clinical diagnosis,but they are subject to high inter-observer variability and are both subjective and non-reproducible.To solve this problem,we proposed an effective unified multi-task framework for dilated cardiomyopathy CMR segmentation and classification simultaneously,and we firstly update one independent encoder from both recovery decoder and parallel attention path sharing some partial weights.This can encode both task choices into good embedding,but each one can achieve significant improvements respectively from the given embedding.It consists of three branches:extraction path,attention path,and recovery path,which allows the model to learn more higher-level intermediate representations and makes a more accurate prediction.We validated our approach on a DCM dataset,which contains 1155 CMR LGE images.Experimental results show that our multi-task network has achieved accuracy of 97.63%,AUC of 98.32%,demonstrating effectively segmenting the myocardium,quickly and accurately diagnosing the presence or absence of dilation.
基金supported in part by the National Natural Science Foundation of China (62225309,62073222,U21A20480,62361166632)。
文摘Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial memory.In this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory configurations.We introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity estimation.This tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual domains.Furthermore,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy generation.Results from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory structures.Comprehensive validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.
文摘More than two decades have passed since the first gene therapy clinical trial was conducted.During this time,we have gained much knowledge regarding gene therapy in general,but also learned to understand the fear that persists in society.We have experienced drawbacks and successes.More than 1700 clinical trials have been conducted where gene therapy is used as a means for therapy.In the very first trial,patients with advanced melanoma were treated with tumor infiltrating lymphocytes genetically modified ex-vivo to express tumor necrosis factor.Around the same time the first gene therapy trial was conducted,the ethical aspects of performing gene therapy on humans was intensively discussed.What are the risks involved with gene therapy?Can we control the technology?What is ethically acceptable and what are the indications gene therapy can be used for?Initially,gene therapy was thought to be implemented mainly for the treatment of monogenetic diseases,such as adenosine deaminase deficiency.However,other therapeutic areas have become of interest and currently cancer is the most studied therapeutic area for gene therapy based medicines.In this review I will be giving a short introduction into gene therapy and will direct the discussion to where we should go from here.Furthermore,I will focus on the use of the Herpes simplex virus-thymidine kinase for gene therapy of malignant gliomas and highlight the efficacy of gene therapy for the treatment of malignant gliomas,but other strategies will also be mentioned.
基金supported by the Key-Area Research and Development Program of Guangdong Province(No.2023B1111050008)the National Natural Science Foundation of China(Nos.U23A20528,U20A20101)+1 种基金Guangdong Local Innovation Team Program(No.2019BT02Y262)the Postdoctoral Fellowship Program of CPSF(No.GZC20232777).
文摘Seven novel linear polyketides,talaketides A-G(1-7),were isolated from the rice media cultures of the mangrove sed-iment-derived fungus Talaromyces sp.SCSIO 41027.Among these,talaketides A-E(1-5)represented unprecedented unsaturated lin-ear polyketides with an epoxy ring structure.The structures,including absolute configurations of these compounds,were elucidated through detailed analyses of nuclear magnetic resonance(NMR)and high-resolution mass spectrometry(HR-MS)data,as well as elec-tronic custom distributors(ECD)calculations.In the cytotoxicity screening against prostate cancer cell lines,talaketide E(5)demon-strated a dose-dependent inhibitory effect on prostate cancer PC-3 cell lines,with an IC50 value of 14.44 μmol·L-1.Moreover,com-pound 5 significantly inhibited the cloning formation of PC-3 cell lines and arrested the cell cycle in S-phase,ultimately inducing ap-optosis.These findings indicate that compound 5 may serve as a promising lead compound for the development of a potential treat-ment for prostate cancer.
基金The authors would like to extend their gratitude to Universiti Teknologi PETRONAS (Malaysia)for funding this research through grant number (015LA0-037).
文摘Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more complex.The existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s performance.We analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream classification.Precision, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority classes.The study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.
基金Supported by the National Key Research and Development Program of China(2018YFC1506801)National Natural Science Foundation of China(41505117)Special Funds for Basic Research and Operation in Government Level Research Institutes of Public Welfare Nature(IUMKY201904)
文摘In August 2018, the Institute of Urban Meteorology(IUM) in Beijing co-organized with Sinovation Ventures a Weather Forecasting Contest(WFC)—one of the AI(artificial intelligence) Challenger Global Contests. The WFC aims to take advantage of the AI techniques to improve the quality of weather forecast. Across the world, more than1000 teams enrolled in the WFC and about 250 teams completed real-time weather forecasts, among which top 5 teams were awarded in the final contest. The contest results show that the AI-based ensemble models exhibited improved skill for forecasts of surface air temperature and relative humidity at 2-m and wind speed at 10-m height.Compared to the IUM operational analog ensemble weather model forecast, the most notable improvements of 24.2%and 17.0% in forecast accuracy for surface 2-m air temperature are achieved by two teams using the AI techniques of time series model, gradient boosting tree, depth probability prediction, and so on. Meanwhile, it is found that reasonable data processing techniques and model composite structure are also important for obtaining better forecasts.
