In 2000, Wu presented two new types of generalized Ball curves, one of which is called an NB1 curve located between the Wang-Ball curve and the Said-Ball curve. In this article, the authors aim to discuss properties o...In 2000, Wu presented two new types of generalized Ball curves, one of which is called an NB1 curve located between the Wang-Ball curve and the Said-Ball curve. In this article, the authors aim to discuss properties of NB1 curves and surfaces, including the recursive algorithms, conversion algorithms between NB1 and Bezier curves and surfaces, etc. In addition the authors compare the computation efficiency of recursive algorithms for the NB1 and above mentioned two generalized Ball curves and surfaces.展开更多
How to compose existing web services automatically and to guarantee the correctness of the design(e.g.freeness of deadlock and unspecified reception,and temporal constraints)is an important and challenging problem in ...How to compose existing web services automatically and to guarantee the correctness of the design(e.g.freeness of deadlock and unspecified reception,and temporal constraints)is an important and challenging problem in web services.Most existing approaches require a detailed specification of the desired behaviors of a composite service beforehand and then perform certain formal verification to guarantee the correctness of the design,which makes the composition process both complex and time-consuming.In this paper,we propose a novel approach,referred to as AutoSyn to compose web services,where the correctness is guaranteed in the synthesis process.For a given set of services,a composite service is automatically constructed based on L*algorithm,which guarantees that the composite service is the most general way of coordinating services so that the correctness is ensured.We show the soundness and completeness of our solution and give a set of optimization techniques for reducing the time consumption.We have implemented a prototype system of AutoSyn and evaluated the effectiveness and efficiency of AutoSyn through an experimental study.展开更多
Quantization has emerged as an essential technique for deploying deep neural networks(DNNs)on devices with limited resources.However,quantized models exhibit vulnerabilities when exposed to various types of noise in r...Quantization has emerged as an essential technique for deploying deep neural networks(DNNs)on devices with limited resources.However,quantized models exhibit vulnerabilities when exposed to various types of noise in real-world applications.Despite the importance of evaluating the impact of quantization on robustness,existing research on this topic is limited and often disregards established principles of robustness evaluation,resulting in incomplete and inconclusivefindings.To address this gap,we thoroughly evaluated the robustness of quantized models against various types of noise(adversarial attacks,natural corruption,and systematic noise)on ImageNet.The comprehensive evaluation results empirically provide valuable insights into the robustness of quantized models in various scenarios.For example:1)quantized models exhibit higher adversarial robustness than theirfloating-point counterparts,but are more vulnerable to natural corruption and systematic noise;2)in general,increasing the quantization bit-width results in a decrease in adversarial robustness,an increase in natural robustness,and an increase in systematic robustness;3)among corruption methods,impulse noise and glass blur are the most harmful to quantized models,while brightness has the least impact;4)among different types of systematic noise,the nearest neighbor interpolation has the highest impact,while bilinear interpolation,cubic interpolation,and area interpolation are the three least harmful.Our research contributes to advancing the robust quantization of models and their deployment in real-world scenarios.展开更多
文摘In 2000, Wu presented two new types of generalized Ball curves, one of which is called an NB1 curve located between the Wang-Ball curve and the Said-Ball curve. In this article, the authors aim to discuss properties of NB1 curves and surfaces, including the recursive algorithms, conversion algorithms between NB1 and Bezier curves and surfaces, etc. In addition the authors compare the computation efficiency of recursive algorithms for the NB1 and above mentioned two generalized Ball curves and surfaces.
基金the National High-Tech Research&Development Program of China(Grant No.2007AA010301)the National Basic Research Program of China(Grant No.2005CB321803)+2 种基金the National Natural Science Foundation of China for Distinguished Young Scholar(Grant No.60525209)the National Natural Science Foundation of China(NSFC)/Research Grants Council(RGC)Joint Research Project(Grant No.60731160632)the Program for New Century Excellent Talents in University(Grant No.NCET-05-0186)
文摘How to compose existing web services automatically and to guarantee the correctness of the design(e.g.freeness of deadlock and unspecified reception,and temporal constraints)is an important and challenging problem in web services.Most existing approaches require a detailed specification of the desired behaviors of a composite service beforehand and then perform certain formal verification to guarantee the correctness of the design,which makes the composition process both complex and time-consuming.In this paper,we propose a novel approach,referred to as AutoSyn to compose web services,where the correctness is guaranteed in the synthesis process.For a given set of services,a composite service is automatically constructed based on L*algorithm,which guarantees that the composite service is the most general way of coordinating services so that the correctness is ensured.We show the soundness and completeness of our solution and give a set of optimization techniques for reducing the time consumption.We have implemented a prototype system of AutoSyn and evaluated the effectiveness and efficiency of AutoSyn through an experimental study.
基金supported by the National Key R&D Program of China(No.2022ZD0116310)the National Natural Science Foundation of China(Nos.62022009 and 62206009)the State Key Laboratory of Software Development Environment.
文摘Quantization has emerged as an essential technique for deploying deep neural networks(DNNs)on devices with limited resources.However,quantized models exhibit vulnerabilities when exposed to various types of noise in real-world applications.Despite the importance of evaluating the impact of quantization on robustness,existing research on this topic is limited and often disregards established principles of robustness evaluation,resulting in incomplete and inconclusivefindings.To address this gap,we thoroughly evaluated the robustness of quantized models against various types of noise(adversarial attacks,natural corruption,and systematic noise)on ImageNet.The comprehensive evaluation results empirically provide valuable insights into the robustness of quantized models in various scenarios.For example:1)quantized models exhibit higher adversarial robustness than theirfloating-point counterparts,but are more vulnerable to natural corruption and systematic noise;2)in general,increasing the quantization bit-width results in a decrease in adversarial robustness,an increase in natural robustness,and an increase in systematic robustness;3)among corruption methods,impulse noise and glass blur are the most harmful to quantized models,while brightness has the least impact;4)among different types of systematic noise,the nearest neighbor interpolation has the highest impact,while bilinear interpolation,cubic interpolation,and area interpolation are the three least harmful.Our research contributes to advancing the robust quantization of models and their deployment in real-world scenarios.
