Objective: To report a scala tympani drill-out technique for managing malformed facial nerve covering the entire oval window(OW).Methods: Data from three cases with OW atresia, malformed stapes and abnormal facial ner...Objective: To report a scala tympani drill-out technique for managing malformed facial nerve covering the entire oval window(OW).Methods: Data from three cases with OW atresia, malformed stapes and abnormal facial nerve courses were reported, in which a scala tympani drill-out technique was employed with a TORP between the tympanic membrane and scala tympani fenestration for hearing reconstruction.Results: Air conduction hearing improved in two of the three cases following surgery. In the third case, there was no improvement in air conduction hearing following a canal wall up mastoidectomy and tympanoplasty. There were no vertigo, tinnitus or sensorineural hearing loss in the three cases.Conclusion: The scala tympani drill-out technique, which is basically fenestration at the initial part of the basal turn, provides a choice in hearing reconstruction when the OW is completely covered by abarrently coursed facial nerve.展开更多
For the rapid development of internetware, functional programming languages, such as Haskell and Scala, can be used to implement complex domain-specific applications. In functional programming languages, a higher-orde...For the rapid development of internetware, functional programming languages, such as Haskell and Scala, can be used to implement complex domain-specific applications. In functional programming languages, a higher-order function is a function that takes functions as parameters or returns a function. Using higher-order functions in programs can increase the generality and reduce the redundancy of source code. To test a higher-order function, a tester needs to check the requirements and write another function as the test input. However, due to the complex structure of higher-order functions, testing higher-order functions is a time-consuming and labor-intensive task. Testers have to spend an amount of manual effort in testing all higher-order functions. Such testing is infeasible if the time budget is limited, such as a period before a project release. In practice, not every higher-order function is actually called. We refer to higher-order functions that are about to be called as calling-prone ones. Calling-prone higher-order functions should be tested first. In this paper, we propose an automatic approach, namely PHOF, which predicts whether a higher-order function of Scala programs will be called in the future, i.e., identifying calling-prone higher-order functions. Our approach can assist testers to reduce the number of higher-order functions of Scala programs under test. In PHOF, we extracted 24 features from source code and logs to train a predictive model based on known higher-order function calls. We empirically evaluated our approach on 4832 higher-order functions from 27 real-world Scala projects. Experimental results show that PHOF based on the random forest algorithm and the Synthetic Minority Oversampling Technique Processing strategy (SMOTE) performs well in the prediction of calls of higher-order functions. Our work can be used to support the scheduling of limited test resources.展开更多
ScalaLab is a MATLAB-like environment for the Java Virtual Machine(JVM).ScalaLab is based on the Scala programming language.It utilizes an extensive set of Java and Scala scientific libraries and also has access to ma...ScalaLab is a MATLAB-like environment for the Java Virtual Machine(JVM).ScalaLab is based on the Scala programming language.It utilizes an extensive set of Java and Scala scientific libraries and also has access to many native C/C++scientific libraries by using mainly the Java Native Interface(JNI).The performance of the JVM platform is continuously improved at a fast pace.Today JVM can effectively support demanding high-performance computing and scales well on multicore platforms.However,sometimes optimized native C/C++code can yield even better performance,by exploiting low-level programming issues,such as optimization of caches and architecture-dependent instruction sets.The present work reports some of the experiences that we gained with experiments with both Just in Time(JIT)JVM code and native code.We compare some aspects of Scala and C++that concern the requirements of scientific computing and highlight some strong features of the Scala language that facilitate the implementation of scientific scripting.This paper describes how ScalaLab tries to combine the best features of the JVM with those of the C/C++technology,in order to implement an effective scientific computing environment.展开更多
文摘Objective: To report a scala tympani drill-out technique for managing malformed facial nerve covering the entire oval window(OW).Methods: Data from three cases with OW atresia, malformed stapes and abnormal facial nerve courses were reported, in which a scala tympani drill-out technique was employed with a TORP between the tympanic membrane and scala tympani fenestration for hearing reconstruction.Results: Air conduction hearing improved in two of the three cases following surgery. In the third case, there was no improvement in air conduction hearing following a canal wall up mastoidectomy and tympanoplasty. There were no vertigo, tinnitus or sensorineural hearing loss in the three cases.Conclusion: The scala tympani drill-out technique, which is basically fenestration at the initial part of the basal turn, provides a choice in hearing reconstruction when the OW is completely covered by abarrently coursed facial nerve.
基金This work is supported by the National Key Research and Development Program of China under Grant No.2018YFB1003901the National Natural Science Foundation of China under Grant No.61872273Advanced Research Projects of the 13th Five-Year Plan of Civil Aerospace Technology,Intelligent Distribution Technology of Domestic Satellite Information under Grant No.B0301.
文摘For the rapid development of internetware, functional programming languages, such as Haskell and Scala, can be used to implement complex domain-specific applications. In functional programming languages, a higher-order function is a function that takes functions as parameters or returns a function. Using higher-order functions in programs can increase the generality and reduce the redundancy of source code. To test a higher-order function, a tester needs to check the requirements and write another function as the test input. However, due to the complex structure of higher-order functions, testing higher-order functions is a time-consuming and labor-intensive task. Testers have to spend an amount of manual effort in testing all higher-order functions. Such testing is infeasible if the time budget is limited, such as a period before a project release. In practice, not every higher-order function is actually called. We refer to higher-order functions that are about to be called as calling-prone ones. Calling-prone higher-order functions should be tested first. In this paper, we propose an automatic approach, namely PHOF, which predicts whether a higher-order function of Scala programs will be called in the future, i.e., identifying calling-prone higher-order functions. Our approach can assist testers to reduce the number of higher-order functions of Scala programs under test. In PHOF, we extracted 24 features from source code and logs to train a predictive model based on known higher-order function calls. We empirically evaluated our approach on 4832 higher-order functions from 27 real-world Scala projects. Experimental results show that PHOF based on the random forest algorithm and the Synthetic Minority Oversampling Technique Processing strategy (SMOTE) performs well in the prediction of calls of higher-order functions. Our work can be used to support the scheduling of limited test resources.
文摘ScalaLab is a MATLAB-like environment for the Java Virtual Machine(JVM).ScalaLab is based on the Scala programming language.It utilizes an extensive set of Java and Scala scientific libraries and also has access to many native C/C++scientific libraries by using mainly the Java Native Interface(JNI).The performance of the JVM platform is continuously improved at a fast pace.Today JVM can effectively support demanding high-performance computing and scales well on multicore platforms.However,sometimes optimized native C/C++code can yield even better performance,by exploiting low-level programming issues,such as optimization of caches and architecture-dependent instruction sets.The present work reports some of the experiences that we gained with experiments with both Just in Time(JIT)JVM code and native code.We compare some aspects of Scala and C++that concern the requirements of scientific computing and highlight some strong features of the Scala language that facilitate the implementation of scientific scripting.This paper describes how ScalaLab tries to combine the best features of the JVM with those of the C/C++technology,in order to implement an effective scientific computing environment.
