We present a novel temporal logic for expressing properties of behaviour in context. The logic is applied to models of continuous processes, specifically using the continuous pi-calculus as a modelling language for bi...
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We present a novel temporal logic for expressing properties of behaviour in context. The logic is applied to models of continuous processes, specifically using the continuous pi-calculus as a modelling language for biochemical systems. The logic allows the expression of the temporal behaviour of a system when placed in the context of another system. Here we study this in terms of biochemical reactions and the expression of temporal behaviour in the context of other biochemical processes. We present the syntax and semantics of the logic and study the model-checking problem over continuous time and continuous state-space process models, using the continuous pi-calculus. We present a succinct, but naive, model-checking algorithm and then show how this can be improved. We investigate the complexity of model-checking, where repeated ODE solving emerges as a particular cost;assess some limitations of the technique;and identify potential routes to overcome these. (c) 2014 Elsevier Inc. All rights reserved.
Many algorithms for computing minimal coverability sets for Petri nets prune futures. That is, if a new marking strictly covers an old one, then not just the old marking but also some subset of its successor markings ...
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Many algorithms for computing minimal coverability sets for Petri nets prune futures. That is, if a new marking strictly covers an old one, then not just the old marking but also some subset of its successor markings is discarded from search. In this publication, a simpler algorithm that lacks future pruning is presented and proven correct. Its performance is compared with future pruning. It is demonstrated, using examples, that neither approach is systematically better than the other. However, the simple algorithm has some attractive features. It never needs to re-construct pruned parts of the minimal coverability set. It automatically gives most of the advantage of future pruning, if the minimal coverability set is constructed in depth-first or most tokens first order, and if so-called history merging is applied. Some implementation aspects of minimal coverability set construction are also discussed. Some measurements are given to demonstrate the effect of construction order and other implementation aspects.
The ten-year anniversary of TOPLAP presents a unique opportunity for reflection and introspection. In this essay we ask the question, what is the meaning of live coding? Our goal is not to answer this question, in abs...
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The ten-year anniversary of TOPLAP presents a unique opportunity for reflection and introspection. In this essay we ask the question, what is the meaning of live coding? Our goal is not to answer this question, in absolute terms, but rather to attempt to unpack some of live coding's many meanings. Our hope is that by exploring some of the formal, embodied, and cultural meanings surrounding live-coding practice, we may help to stimulate a conversation that will resonate within the live-coding community for the next ten years.
Vibration signals captured from faulty mechanical components are often associated with transients which are significant for machinery fault diagnosis. However, the existence of strong background noise makes the detect...
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Vibration signals captured from faulty mechanical components are often associated with transients which are significant for machinery fault diagnosis. However, the existence of strong background noise makes the detection of transients a basis pursuit denoising (BPD) problem, which is hard to be solved in explicit form. With sparse representation theory, this paper proposes a novel method for machinery fault diagnosis by combining the wavelet basis and majorization-minimization (MM) algorithm. This method converts transients hidden in the noisy signal into sparse coefficients;thus the transients can be detected sparsely. Simulated study concerning cyclic transient signals with different signal-to-noise ratio (SNR) shows that the effectiveness of this method. The comparison in the simulated study shows that the proposed method outperforms the method based on split augmented Lagrangian shrinkage algorithm (SALSA) in convergence and detection effect. Application in defective gearbox fault diagnosis shows the fault feature of gearbox can be sparsely and effectively detected. A further comparison between this method and the method based on SALSA shows the superiority of the proposed method in machinery fault diagnosis.
One of the challenges in image search is to learn with few labeled examples. Existing solutions mainly focus on leveraging either unlabeled data or query logs to address this issue, but little is known in taking both ...
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One of the challenges in image search is to learn with few labeled examples. Existing solutions mainly focus on leveraging either unlabeled data or query logs to address this issue, but little is known in taking both into account. This work presents a novel learning scheme that exploits both unlabeled data and query logs through a unified Manifold Ranking (MR) framework. In particular, we propose a local scaling technique to facilitate MR by self-tuning the scale parameter, and a soft label propagation strategy to enhance the robustness of MR against erroneous query logs. Further, within the proposed MR framework, a hybrid active learning method is developed, which is effective and efficient to select the informative and representative unlabeled examples, so as to maximally reduce users' labeling effort. An empirical study shows that the proposed scheme is significantly more effective than the state-of-the-art approaches. (C) 2013 Elsevier Ltd. All rights reserved.
Multi-dimensional mean-payoff and energy games provide the mathematical foundation for the quantitative study of reactive systems, and play a central role in the emerging quantitative theory of verification and synthe...
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Multi-dimensional mean-payoff and energy games provide the mathematical foundation for the quantitative study of reactive systems, and play a central role in the emerging quantitative theory of verification and synthesis. In this work, we study the strategy synthesis problem for games with such multi-dimensional objectives along with a parity condition, a canonical way to express -regular conditions. While in general, the winning strategies in such games may require infinite memory, for synthesis the most relevant problem is the construction of a finite-memory winning strategy (if one exists). Our main contributions are as follows. First, we show a tight exponential bound (matching upper and lower bounds) on the memory required for finite-memory winning strategies in both multi-dimensional mean-payoff and energy games along with parity objectives. This significantly improves the triple exponential upper bound for multi energy games (without parity) that could be derived from results in literature for games on vector addition systems with states. Second, we present an optimal symbolic and incremental algorithm to compute a finite-memory winning strategy (if one exists) in such games. Finally, we give a complete characterization of when finite memory of strategies can be traded off for randomness. In particular, we show that for one-dimension mean-payoff parity games, randomized memoryless strategies are as powerful as their pure finite-memory counterparts.
Artificial neural networks (ANNs) method is widely used in reliability analysis. However, the performance of ANNs cannot be guaranteed due to the fitting problems because there is no efficient constructive method for ...
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Artificial neural networks (ANNs) method is widely used in reliability analysis. However, the performance of ANNs cannot be guaranteed due to the fitting problems because there is no efficient constructive method for choosing the structure and the learning parameters of the network. To mitigate these difficulties, this article presents a new adaptive wavelet frame neural network method for reliability analysis of structures. The new method uses the single-scaling multidimensional wavelet frame as the activation function in the network to deal with the multidimensional problems in reliability analysis. Because the wavelet frame is highly redundant, the time-frequency localization and matching pursuit algorithm are respectively utilized to eliminate the superfluous wavelets, thus the obtained wavelet frame neural network can be implemented efficiently. Five examples are given to demonstrate the application and effectiveness of the proposed method. Comparisons of the new method and the classical radial basis function network method are made.
Factorable Laplace operators of the form L = a, (x) a, (y) + aa, (x) + ba, (y) + c, where the coefficients a, b, c are not necessarily constants, are considered. For these operators, the Darboux transformations , M a ...
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Factorable Laplace operators of the form L = a, (x) a, (y) + aa, (x) + ba, (y) + c, where the coefficients a, b, c are not necessarily constants, are considered. For these operators, the Darboux transformations , M a K[a, (x) ], defined by the intertwining relation NL = L (1) M are considered. It is shown that only the following cases are possible: either (1) M a (c) kera, (x) + b = {0} and L (1) is also factorable or (2) M a (c) kera, (x) + b contains a nonzero element. We prove that, in both cases, the Darboux transformation can be represented as a product of first-order Darboux transformations. For case (2), the proof is based on the fact that the Darboux transformation of operator L can be reduced to Darboux transformations of first-order operators.
The ability to identify the fault type and to locate the fault in extra high voltage transmission lines is very important for the economic operation of modern power systems. Accurate algorithms for fault classificatio...
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The ability to identify the fault type and to locate the fault in extra high voltage transmission lines is very important for the economic operation of modern power systems. Accurate algorithms for fault classification and location based on artificial neural network are suggested in this paper. Two fault classification algorithms are presented;the first one uses the single ANN approach and the second one uses the modular ANN approach. A comparative study of two classifiers is done in order to choose which ANN fault classifier structure leads to the best performance. Design and implementation of modular ANN-based fault locator are presented. Three fault locators are proposed and a comparative study of the three fault locators is carried out in order to determine which fault locator architecture leads to the accurate fault location. Instantaneous current and/or voltage samples were used as inputs to ANNs. For fault classification, only the pre-fault and post-fault samples of three-phase currents were used. For fault location, pre-fault and post-fault samples of three-phase currents and/or voltages were used. The proposed algorithms were evaluated under different fault scenarios. Studied simulation results which are presented confirm the effectiveness of the proposed algorithms.
This paper introduces an efficient and simple algorithm for constructing Multiple Reference (MR) octrees on a GPU in application to Photon Mapping and Irradiance Caching techniques. Although MR-octrees are hierarchica...
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This paper introduces an efficient and simple algorithm for constructing Multiple Reference (MR) octrees on a GPU in application to Photon Mapping and Irradiance Caching techniques. Although MR-octrees are hierarchical structures, we successfully ignore their hierarchical nature and present an approach with plain construction, compact data layout and stack-less traversal. Our algorithm uses only 2 parallel primitives (parallel append and parallel sort) and can be expressed in several lines of pseudo-code.
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