The last few years have seen a renewed interest in continuations for expressing advanced control structures in programming languages, and new models such as Abstract Continuations have been proposed to capture these dimensions. This article investigates an alternative formulation, exploiting the latent expressive power of the standard continuation-passing style (CPS) instead of introducing yet other new concepts. We build on a single foundation: abstracting control as a hierarchy of continuations, each one modeling a specific language feature as acting on nested evaluation contexts. We show how iterating the continuation-passing conversion allows us to specify a wide range of control behavior. For example, two conversions yield an abstraction of Prolog-style backtracking. A number of other constructs can likewise be expressed in this framework; each is defined independently of the others, but all are arranged in a hierarchy making any interactions between them explicit. This approach preserves all the traditional results about CPS, e.g., its evaluation order independence. Accordingly, our semantics is directly implementable in a call-by-value language such as Scheme or ML. Furthermore, because the control operators denote simple, typable lambda-terms in CPS, they themselves can be statically typed. Contrary to intuition, the iterated CPS transformation does not yield huge results: except where explicitly needed, all continuations beyond the first one disappear due to the extensionality principle (&eegr;-reduction). Besides presenting a new motivation for control operators, this paper also describes an improved conversion into applicative-order CPS. The conversion operates in one pass by performing all administrative reductions at translation time; interestingly, it can be expressed very concisely using the new control operators. The paper also presents some examples of nondeterministic programming in direct style.
{"title":"Abstracting control","authors":"O. Danvy, Andrzej Filinski","doi":"10.1145/91556.91622","DOIUrl":"https://doi.org/10.1145/91556.91622","url":null,"abstract":"The last few years have seen a renewed interest in continuations for expressing advanced control structures in programming languages, and new models such as Abstract Continuations have been proposed to capture these dimensions. This article investigates an alternative formulation, exploiting the latent expressive power of the standard continuation-passing style (CPS) instead of introducing yet other new concepts. We build on a single foundation: abstracting control as a hierarchy of continuations, each one modeling a specific language feature as acting on nested evaluation contexts.\u0000We show how iterating the continuation-passing conversion allows us to specify a wide range of control behavior. For example, two conversions yield an abstraction of Prolog-style backtracking. A number of other constructs can likewise be expressed in this framework; each is defined independently of the others, but all are arranged in a hierarchy making any interactions between them explicit.\u0000This approach preserves all the traditional results about CPS, e.g., its evaluation order independence. Accordingly, our semantics is directly implementable in a call-by-value language such as Scheme or ML. Furthermore, because the control operators denote simple, typable lambda-terms in CPS, they themselves can be statically typed. Contrary to intuition, the iterated CPS transformation does not yield huge results: except where explicitly needed, all continuations beyond the first one disappear due to the extensionality principle (&eegr;-reduction).\u0000Besides presenting a new motivation for control operators, this paper also describes an improved conversion into applicative-order CPS. The conversion operates in one pass by performing all administrative reductions at translation time; interestingly, it can be expressed very concisely using the new control operators. The paper also presents some examples of nondeterministic programming in direct style.","PeriodicalId":409945,"journal":{"name":"LISP and Functional Programming","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1990-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126291111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
PCF, as considered in this paper, is a lazy typed lambda calculus with functions, pairing, fixed-point operators and arbitrary algebraic data types. The natural equational axioms for PCF include &eegr;-equivalence and the so-called “surjective pairing” axiom for pairs. However, the reduction system pcf&eegr;,sp defined by directing each equational axiom is not confluent, for virtually any choice of algebraic data types. Moreover, neither &eegr;-reduction nor surjective pairing seems to have a counterpart in ordinary execution. Therefore, we consider a smaller reduction system pcf without &eegr;-reduction or surjective pairing. The system pcf is confluent when combined with any linear, confluent algebraic rewrite rules. The system is also computationally adequate, in the sense that whenever a closed term of “observable” type has a pcf&eegr;,sp normal form, this is also the unique pcf normal form. Moreover, the equational axioms for PCF, including (&eegr;) and surjective pairing, are sound for pcf observational equivalence. These results suggest that if we take the equational axioms as defining the language, the smaller reduction system gives an appropriate operational semantics.
{"title":"Operational and axiomatic semantics of PCF","authors":"Brian T. Howard, John C. Mitchell","doi":"10.1145/91556.91677","DOIUrl":"https://doi.org/10.1145/91556.91677","url":null,"abstract":"PCF, as considered in this paper, is a lazy typed lambda calculus with functions, pairing, fixed-point operators and arbitrary algebraic data types. The natural equational axioms for PCF include <italic>&eegr;</italic>-equivalence and the so-called “surjective pairing” axiom for pairs. However, the reduction system <italic>pcf<subscrpt>&eegr;,sp</subscrpt></italic> defined by directing each equational axiom is not confluent, for virtually any choice of algebraic data types. Moreover, neither <italic>&eegr;</italic>-reduction nor surjective pairing seems to have a counterpart in ordinary execution. Therefore, we consider a smaller reduction system <italic>pcf</italic> without <italic>&eegr;</italic>-reduction or surjective pairing. The system <italic>pcf</italic> is confluent when combined with any linear, confluent algebraic rewrite rules. The system is also computationally adequate, in the sense that whenever a closed term of “observable” type has a <italic>pcf<subscrpt>&eegr;,sp</subscrpt></italic> normal form, this is also the unique <italic>pcf</italic> normal form. Moreover, the equational axioms for PCF, including (<italic>&eegr;</italic>) and surjective pairing, are sound for <italic>pcf</italic> observational equivalence. These results suggest that if we take the equational axioms as defining the language, the smaller reduction system gives an appropriate operational semantics.","PeriodicalId":409945,"journal":{"name":"LISP and Functional Programming","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1990-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131615376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Two abstract machines reducing terms to their full normal form are presented in this paper. They are based on Krivine's abstract machine [Kri85] which uses an environment to store arguments of function calls. A proof of their correctness is then stated in the abstract framework of λ&sgr;-calculus [Cur89].
{"title":"An abstract machine for Lambda-terms normalization","authors":"P. Crégut","doi":"10.1145/91556.91681","DOIUrl":"https://doi.org/10.1145/91556.91681","url":null,"abstract":"Two abstract machines reducing terms to their full normal form are presented in this paper. They are based on Krivine's abstract machine [Kri85] which uses an environment to store arguments of function calls. A proof of their correctness is then stated in the abstract framework of λ&sgr;-calculus [Cur89].","PeriodicalId":409945,"journal":{"name":"LISP and Functional Programming","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1990-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131812449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This extended abstract describes a way of inferring as much type information as possible about programs written in an untyped programming language. We present an algorithm that underlines the untypable parts of a program and assigns types to the rest. The algorithm is derived in a very simple manner from the well-known algorithm W of Damas & Milner [Damas and Milner 1982]. Our algorithm provides us with an easy solution to the problem of doing binding time analysis of the untyped higher order lambda calculus, and thereby of the wide range of programming languages based upon the lambda calculus. The techniques can also be used to eliminate superfluous runtime type checking in untyped functional languages, to produce better error messages from type analyzers for strongly typed languages, and to analyze feasibility of arity raising.
{"title":"Partial type inference for untyped functional programs","authors":"C. K. Gomard","doi":"10.1145/91556.91672","DOIUrl":"https://doi.org/10.1145/91556.91672","url":null,"abstract":"This extended abstract describes a way of inferring as much type information as possible about programs written in an untyped programming language. We present an algorithm that underlines the untypable parts of a program and assigns types to the rest. The algorithm is derived in a very simple manner from the well-known algorithm W of Damas & Milner [Damas and Milner 1982].\u0000Our algorithm provides us with an easy solution to the problem of doing binding time analysis of the untyped higher order lambda calculus, and thereby of the wide range of programming languages based upon the lambda calculus. The techniques can also be used to eliminate superfluous runtime type checking in untyped functional languages, to produce better error messages from type analyzers for strongly typed languages, and to analyze feasibility of arity raising.","PeriodicalId":409945,"journal":{"name":"LISP and Functional Programming","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1990-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122389140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Static dependent types are the basis of a new type system that permits types and values to be packaged together into first class modules. Unlike other approaches to modules, static dependent types permit unrestricted access to the types and values in first class modules without sacrificing static type checking or data abstraction. Static dependent types are type safe in the presence of side effects because they rely on an effect system that can make deductions conventional type systems cannot. Experience with an implementation, built as an extension to the FX-87 programming language, shows that static dependent types can be used for building large systems.
{"title":"Static dependent types for first class modules","authors":"Mark A. Sheldon, D. Gifford","doi":"10.1145/91556.91577","DOIUrl":"https://doi.org/10.1145/91556.91577","url":null,"abstract":"Static dependent types are the basis of a new type system that permits types and values to be packaged together into first class modules. Unlike other approaches to modules, static dependent types permit unrestricted access to the types and values in first class modules without sacrificing static type checking or data abstraction. Static dependent types are type safe in the presence of side effects because they rely on an effect system that can make deductions conventional type systems cannot. Experience with an implementation, built as an extension to the FX-87 programming language, shows that static dependent types can be used for building large systems.","PeriodicalId":409945,"journal":{"name":"LISP and Functional Programming","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1990-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131364650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Type inference is the process by which an expression in an untyped computer language such as the lambda-calculus, Lisp, or a functional language can be assigned a static data type in order to improve the code generated by a compiler. Storage use inference is the process by which a program in a computer language can be statically analyzed to model its run-time behavior, particularly the containment and sharing relations among its run-time data structures. The information generated by storage use information can also be used to improve the code generated by a compiler, because knowledge of the containment and sharing relations of run-time data structures allows for methods of storage allocation and deallocation which are cheaper than garbage-collected heap storage and allows for the in-place updating of functional aggregates. Type inference and storage use inference have traditionally been considered orthogonal processes, with separate traditions and literature. However, we show in this paper than this separation may be a mistake, because the best-known and best-understood of the type inferencing algorithms—Milner's unification method for ML—already generates valuable sharing and containment information which is then unfortunately discarded. We show that this sharing information is already generated by standard unification algorithms with no additional overhead during unification; however, there is some additional work necessary to extract this information. We have not yet precisely characterized the resolving power of this sharing and containment information, but we believe that it is similar to that generated by researchers using other techniques. However, our scheme seems to only work for functional languages like pure Lisp. The unification of type and storage inferencing yields new insights into the meaning of “aggregate type”, which should prove valuable in the design of future type systems.
{"title":"Unify and conquer","authors":"H. Baker","doi":"10.1145/91556.91652","DOIUrl":"https://doi.org/10.1145/91556.91652","url":null,"abstract":"Type inference is the process by which an expression in an untyped computer language such as the lambda-calculus, Lisp, or a functional language can be assigned a static data type in order to improve the code generated by a compiler. Storage use inference is the process by which a program in a computer language can be statically analyzed to model its run-time behavior, particularly the containment and sharing relations among its run-time data structures. The information generated by storage use information can also be used to improve the code generated by a compiler, because knowledge of the containment and sharing relations of run-time data structures allows for methods of storage allocation and deallocation which are cheaper than garbage-collected heap storage and allows for the in-place updating of functional aggregates.\u0000Type inference and storage use inference have traditionally been considered orthogonal processes, with separate traditions and literature. However, we show in this paper than this separation may be a mistake, because the best-known and best-understood of the type inferencing algorithms—Milner's unification method for ML—already generates valuable sharing and containment information which is then unfortunately discarded. We show that this sharing information is already generated by standard unification algorithms with no additional overhead during unification; however, there is some additional work necessary to extract this information. We have not yet precisely characterized the resolving power of this sharing and containment information, but we believe that it is similar to that generated by researchers using other techniques. However, our scheme seems to only work for functional languages like pure Lisp.\u0000The unification of type and storage inferencing yields new insights into the meaning of “aggregate type”, which should prove valuable in the design of future type systems.","PeriodicalId":409945,"journal":{"name":"LISP and Functional Programming","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1990-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130720114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Programs compiled by Gambit, our Scheme compiler, achieve performance as much as twice that of the fastest available Scheme compilers. Gambit is easily ported, while retaining its high performance, through the use of a simple virtual machine (PVM). PVM allows a wide variety of machine-independent optimizations and it supports parallel computation based on the future construct. PVM conveys high-level information bidirectionally between the machine-independent front end of the compiler and the machine-dependent back end, making it easy to implement a number of common back end optimizations that are difficult to achieve for other virtual machines. PVM is similar to many real computer architectures and has an option to efficiently gather dynamic measurements of virtual machine usage. These measurements can be used in performance prediction for ports to other architectures as well as design decisions related to proposed optimizations and object representations.
{"title":"A parallel virtual machine for efficient scheme compilation","authors":"M. Feeley, James S. Miller","doi":"10.1145/91556.91606","DOIUrl":"https://doi.org/10.1145/91556.91606","url":null,"abstract":"Programs compiled by Gambit, our Scheme compiler, achieve performance as much as twice that of the fastest available Scheme compilers. Gambit is easily ported, while retaining its high performance, through the use of a simple virtual machine (PVM). PVM allows a wide variety of machine-independent optimizations and it supports parallel computation based on the future construct. PVM conveys high-level information bidirectionally between the machine-independent front end of the compiler and the machine-dependent back end, making it easy to implement a number of common back end optimizations that are difficult to achieve for other virtual machines.\u0000PVM is similar to many real computer architectures and has an option to efficiently gather dynamic measurements of virtual machine usage. These measurements can be used in performance prediction for ports to other architectures as well as design decisions related to proposed optimizations and object representations.","PeriodicalId":409945,"journal":{"name":"LISP and Functional Programming","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1990-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134503832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When some inputs of a program are known at compile-time, certain expressions can be processed statically; this is the basis of the notion of partial evaluation. Identifying these early computations can be determined independently of the actual values of the input by a static analysis called binding time analysis. Then, to process a program, one simply follows the binding time information: evaluate compile-time expressions and defer the others to run-time. Using abstract interpretation, we present a binding time analysis for an untyped functional language which provides an effective treatment of both higher order functions and data structures. To our knowledge it is the first such analysis. It has been implemented and is used in a partial evaluator for a side-effect free dialect of Scheme. The analysis is general enough, however, to be valid for non-strict typed functional languages such as Haskell. Our approach and the system we have developed solve and go beyond the open problem of partially evaluating higher order functions described in [3] since we also provide a method to handle data structures. Our analysis improves on previous work [5, 15, 4] in that: (1) it treats both higher order functions and data structures, (2) it does not impose syntactic restrictions on the program being processed, and (3) it does not require a preliminary phase to collect the set of possible functions that may occur at each site of application.
{"title":"Binding time analysis for high order untyped functional languages","authors":"C. Consel","doi":"10.1145/91556.91668","DOIUrl":"https://doi.org/10.1145/91556.91668","url":null,"abstract":"When some inputs of a program are known at compile-time, certain expressions can be processed statically; this is the basis of the notion of partial evaluation. Identifying these early computations can be determined independently of the actual values of the input by a static analysis called binding time analysis. Then, to process a program, one simply follows the binding time information: evaluate compile-time expressions and defer the others to run-time.\u0000Using abstract interpretation, we present a binding time analysis for an untyped functional language which provides an effective treatment of both higher order functions and data structures. To our knowledge it is the first such analysis. It has been implemented and is used in a partial evaluator for a side-effect free dialect of Scheme. The analysis is general enough, however, to be valid for non-strict typed functional languages such as Haskell. Our approach and the system we have developed solve and go beyond the open problem of partially evaluating higher order functions described in [3] since we also provide a method to handle data structures.\u0000Our analysis improves on previous work [5, 15, 4] in that: (1) it treats both higher order functions and data structures, (2) it does not impose syntactic restrictions on the program being processed, and (3) it does not require a preliminary phase to collect the set of possible functions that may occur at each site of application.","PeriodicalId":409945,"journal":{"name":"LISP and Functional Programming","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1990-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130749343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The operational semantics of functional programming languages is frequently presented using inference rules within simple meta-logics. Such presentations of semantics can be high-level and perspicuous since meta-logics often handle numerous syntactic details in a declarative fashion. This is particularly true of the meta-logic we consider here, which includes simply typed λ-terms, quantification at higher types, and β-conversion. Evaluation of functional programming languages is also often presented using low-level descriptions based on abstract machines: simple term rewriting systems in which few high-level features are present. In this paper, we illustrate how a high-level description of evaluation using inference rules can be systematically transformed into a low-level abstract machine by removing dependencies on high-level features of the meta-logic until the resulting inference rules are so simple that they can be immediately identified as specifying an abstract machine. In particular, we present in detail the transformation of two inference rules specifying call-by-name evaluation of the untyped λ-calculus into the Krivine machine, a stack-based abstract machine that implements such evaluation. The initial specification uses the meta-logic's β-conversion to perform substitutions. The resulting machine uses de Bruijn numerals and closures instead of formal substitution. We also comment on a similar construction of a simplified SECD machine implementing call-by-value evaluation. This approach to abstract machine construction provides a semantics-directed method for motivating, proving correct, and extending such abstract machines.
{"title":"From operational semantics to abstract machines: preliminary results","authors":"J. Hannan, D. Miller","doi":"10.1145/91556.91680","DOIUrl":"https://doi.org/10.1145/91556.91680","url":null,"abstract":"The operational semantics of functional programming languages is frequently presented using inference rules within simple meta-logics. Such presentations of semantics can be high-level and perspicuous since meta-logics often handle numerous syntactic details in a declarative fashion. This is particularly true of the meta-logic we consider here, which includes simply typed λ-terms, quantification at higher types, and β-conversion. Evaluation of functional programming languages is also often presented using low-level descriptions based on abstract machines: simple term rewriting systems in which few high-level features are present. In this paper, we illustrate how a high-level description of evaluation using inference rules can be systematically transformed into a low-level abstract machine by removing dependencies on high-level features of the meta-logic until the resulting inference rules are so simple that they can be immediately identified as specifying an abstract machine. In particular, we present in detail the transformation of two inference rules specifying call-by-name evaluation of the untyped λ-calculus into the Krivine machine, a stack-based abstract machine that implements such evaluation. The initial specification uses the meta-logic's β-conversion to perform substitutions. The resulting machine uses de Bruijn numerals and closures instead of formal substitution. We also comment on a similar construction of a simplified SECD machine implementing call-by-value evaluation. This approach to abstract machine construction provides a semantics-directed method for motivating, proving correct, and extending such abstract machines.","PeriodicalId":409945,"journal":{"name":"LISP and Functional Programming","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1990-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129816210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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{"title":"Efficient method dispatch in PCL","authors":"G. Kiczales, Luis Rodriguez","doi":"10.1145/91556.91600","DOIUrl":"https://doi.org/10.1145/91556.91600","url":null,"abstract":"Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or permissions@acm.org.","PeriodicalId":409945,"journal":{"name":"LISP and Functional Programming","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1990-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122740305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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