the problem of determining the Worse Case Execution Time (WCET) of a piece of code is a fundamental one in the Real Time Systems community. Existing methods either try to gain this information by analysis of the progr...
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ISBN:
(纸本)9783540859277
the problem of determining the Worse Case Execution Time (WCET) of a piece of code is a fundamental one in the Real Time Systems community. Existing methods either try to gain this information by analysis of the program code or by running extensive timing analyses. this paper presents a new approach to the problem based on using Machine Learning in the form of ILP to infer program properties based on sample executions of the code. Additionally, significant improvements in the range of functions learnable and the time taken for learning can be made by the application of more advanced ILP techniques.
Markov logic Networks (MLNs) combine Markov networks and first-order logic by attaching weights to first-order formulas and viewing these as templates for features of Markov networks. Learning the structure of MLNs is...
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ISBN:
(纸本)9783540859277
Markov logic Networks (MLNs) combine Markov networks and first-order logic by attaching weights to first-order formulas and viewing these as templates for features of Markov networks. Learning the structure of MLNs is performed by state-of-the-art methods by maximizing the likelihood of a relational database. this can lead to suboptimal results given prediction tasks. On the other hand better results in prediction problems have been achieved by discriminative learning of MLNs weights given a certain structure. In this paper we propose an algorithm for learning the structure of MLNs discriminatively by maximimizing the conditional likelihood of the query predicates instead of the joint likelihood of all predicates. the algorithm chooses the structures by maximizing conditional likelihood and sets the parameters by maximum likelihood. Experiments in two real-world domains show that the proposed algorithm improves over the state-of-the-art discriminative weight learning algorithm for MLNs in terms of conditional likelihood. We also compare the proposed algorithm withthe state-of-the-art generative structure learning algorithm for MLNs and confirm the results ill [22] showing that for small datasets the generative algorithm is competitive, while for larger datasets the discriminative algorithm outperfoms the generative one.
We identify a shortcoming of a standard positive-only clause evaluation function within the context of learning biological grammars. To overcome this shortcoming we propose L-modification, a modification to this evalu...
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ISBN:
(纸本)9783540859277
We identify a shortcoming of a standard positive-only clause evaluation function within the context of learning biological grammars. To overcome this shortcoming we propose L-modification, a modification to this evaluation function such that the lengths of individual examples are considered. We use a set of bio-sequences known as neuropeptide precursor middles (NPP-middles). Using L-modification to learn from these NPP-middles results in induced grammars that have a better performance than that achieved when using the standard positive-only clause evaluation function. We also show that L-modification improves the performance of induced grammars when learning on short, medium or long NPPs-middles. A potential disadvantage of L-modification is discussed. Finally, we show that, as the limit on the search space size increases, the greater is the increase in predictive performance arising from L-modification.
In this paper we introduce preference rules which allow us to specify preferences as an ordering among the possible solutions of a problem. Our approach allow us to express preferences for general theories and we defi...
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In this paper we introduce preference rules which allow us to specify preferences as an ordering among the possible solutions of a problem. Our approach allow us to express preferences for general theories and we define a semantics for them. the formalism used to develop our work is Pstable semantics.
this paper explores an approach to design for verification in systems built atop a middleware framework which separates synchronization concerns from the "core-functional logic" of a program. the framework i...
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ISBN:
(纸本)9781627486606
this paper explores an approach to design for verification in systems built atop a middleware framework which separates synchronization concerns from the "core-functional logic" of a program. the framework is based on a language-independent compositional model of synchronization contracts, called Szumo, which integrates well with popular OO design artifacts and provides strong guarantees of non-interference for a class of strictly exclusive systems. An approach for extracting models from Szumo design artifacts and analyzing the generated models to detect deadlocks is described. A key decision was to use Constraint Handling Rules to express the semantics of synchronization contracts, which allowed a transparent model of the implementation logic.
Medial is an inference rule scheme that appears in various deductive systems based on deep inference. In this paper we investigate the properties of medial as rewriting rule independently from logic. We present a grap...
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ISBN:
(纸本)9783540734475
Medial is an inference rule scheme that appears in various deductive systems based on deep inference. In this paper we investigate the properties of medial as rewriting rule independently from logic. We present a graph theoretical criterion for checking whether there exists a medial rewriting path between two formulas. Finally, we return to logic and apply our criterion for giving a combinatorial proof for a decomposition theorem, i.e., proof theoretical statement about syntax.
this paper presents KOOL, a concurrent, dynamic, object-oriented language defined in rewriting logic. KOOL has been designed as an experimental language, with a focus on making the language easy to extend. this is don...
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ISBN:
(纸本)9783540734475
this paper presents KOOL, a concurrent, dynamic, object-oriented language defined in rewriting logic. KOOL has been designed as an experimental language, with a focus on making the language easy to extend. this is done by taking advantage of the flexibility provided by rewriting logic, which allows for the rapid prototyping of new language features. An example of this process is illustrated by sketching the addition of synchronized methods. KOOL also provides support for program analysis through language extensions and the underlying capabilities of rewriting logic. this support is illustrated with several examples.
this paper explores an approach to design for verification in systems built atop a middleware framework which separates synchronization concerns from the "core-functional logic" of a program. the framework i...
详细信息
this paper explores an approach to design for verification in systems built atop a middleware framework which separates synchronization concerns from the "core-functional logic" of a program. the framework is based on a language-independent compositional model of synchronization contracts, called Szumo, which integrates well with popular OO design artifacts and provides strong guarantees of non-interference for a class of strictly exclusive systems. An approach for extracting models from Szumo design artifacts and analyzing the generated models to detect deadlocks is described. A key decision was to use Constraint Handling Rules to express the semantics of synchronization contracts, which allowed a transparent model of the implementation logic.
the proceedings contain 15 papers. the topics discussed include: on optimising shape-generic array programs using symbolic structural information;index vector elimination-making index vectors affordable;functional-bas...
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ISBN:
(纸本)9783540741299
the proceedings contain 15 papers. the topics discussed include: on optimising shape-generic array programs using symbolic structural information;index vector elimination-making index vectors affordable;functional-based synthesis of a systolic array for GCD computation;comparing alternative evaluation strategies for stream-based parallel functional languages;parallel coordination made explicit in a functional setting;a conference management system based on the iData toolkit;a pattern logic for prompt lazy assertions in Haskell;IVOR,a proof engine;proving program properties specified with subtype marks;uniqueness typing redefined;heuristics for type error discovery and recovery;testing properties of generic functions;worst-case execution times for a purely functional language;and automatic partial inversion of inductively sequential functions.
Modelling is becoming a necessity in studying biological signalling pathways, because the combinatorial complexity of such systems rapidly overwhelms intuitive and qualitative forms of reasoning. Yet, this same combin...
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ISBN:
(纸本)9783540744061
Modelling is becoming a necessity in studying biological signalling pathways, because the combinatorial complexity of such systems rapidly overwhelms intuitive and qualitative forms of reasoning. Yet, this same combinatorial explosion makes the traditional modelling paradigm based on systems of differential equations impractical. In contrast, agent-based or concurrent languages, such as kappa. [1,2,3] or the closely related BioNetGen language [4,5,6,7,8,9,10], describe biological interactions in terms of rules, thereby avoiding the combinatorial explosion besetting differential equations. Rules are expressed in an intuitive graphical form that transparently represents biological knowledge. In this way, rules become a natural unit of model building, modification, and discussion. We illustrate this with a sizeable example obtained from refactoring two models of EGF receptor signalling that are based on differential equations [11,12]. An exciting aspect of the agent-based approach is that it naturally lends itself to the identification and analysis of the causal structures that deeply shape the dynamical, and perhaps even evolutionary, characteristics of complex distributed biological systems. In particular, one can adapt the notions of causality and conflict, familiar from con-currency theory, to kappa, our representation language of choice. Using the EGF receptor model as an example, we show how causality enables the formalization of the colloquial concept of pathway and, perhaps more surprisingly, how conflict can be used to dissect the signalling dynamics to obtain a qualitative handle on the range of system behaviours. By taming the combinatorial explosion, and exposing the causal structures and key kinetic junctures in a model, agent- and rule-based representations hold promise for making modelling more powerful, more perspicuous, and of appeal to a wider audience.
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