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Fuzzy Answer Set Programming (FASP) extends the popular Answer Set Programming (ASP) paradigm to modeling and solving combinatorial search problems in continuous domains. The recent development of FASP solvers has turned FASP into a practical tool for solving real-world problems. In this paper, we propose the use of FASP for modeling the dynamics of Gene Regulatory Networks (GRNs), an important kind...
Biologically inspired episodic memory is able to store time sequential events, and to recall all of them from partial information. Because of the advantages of episodic memory, the biological concepts of episodic memory have been utilized to many applications. In this research, we propose a new memory model, called Deep ART (Adaptive Resonance Theory), to make a robust memory system for learning episodic...
We introduce Sparse Entropy Clustering (SEC) which uses minimum entropy criterion to split high dimensional binary vectors into groups. The idea is based on the analogy between clustering and data compression: every group is reflected by a single encoder which provides its optimal compression. Following the Minimum Description Length Principle the clustering criterion function includes the cost of...
Traditional routing mode is just responsible for receiving and forwarding data packets. The proposing of network coding offers a new research direction for the development of wireless sensor networks(WSN) [1]. Preliminary theoretical studies and experiments have shown the advantages of network coding which can improve throughput and reduce energy consumption in WSN [2,3]. By linear coding operation,...
Bidirectional Associative Memories (BAMs) are artificial neural networks that can learn and recall various types of associations. Although BAM models have shown great promise at modeling human cognitive processes, these models have often been investigated under conditions where stimuli are densely represented using a bipolar coding scheme. However, research has shown that dense representations are...
We explore the application of grammar-based Genetic Programming, specifically Grammatical Evolution, to the problem of modeling the outcome of Six Nations Rugby matches. A series of grammars are developed in attempts to generate different forms of predictive rules, which might be useful in pre-match and mid-match scenarios. A number of interesting models are generated and their utility discussed.
The responses of disparity selective complex cells in the mammalian visual system are often modeled by the disparity energy model. This model linearly combines inputs from binocular simple cell units, whose responses are computed by the combination of left and right eye inputs through linear binocular receptive fields, followed by half wave rectification and an expansive nonlinearity. While many complex...
Vehicle identification is one of the frequently studied problems in video surveillance. Commonly, identifying an unknown vehicle object requires a large amount of training instances. Unfortunately, in the large parking scenario, the cost may be prohibitively expensive because of the finitely waiting time from the car owners. In this paper, we show that it is possible to identify a registered vehicle...
Vector quantization is an essential tool for tasks involving large scale data, for example, large scale similarity search, which is crucial for content-based information retrieval and analysis. In this paper, we propose a novel vector quantization framework that iteratively minimizes quantization error. First, we provide a detailed review on a relevant vector quantization method named residual vector...
This paper proposes a novel methodology of rolling bearings remaining useful life (RUL) estimation based on sparse representation theory. By analyzing the inner relationships among monitored data, three particular properties of remaining useful life estimation tasks are concluded as the key prior knowledge: monotonicity and continuity of RUL evaluation show the relationships of monitored data and...
The key of water-flood management is how to extract useful information from the mass of data contained in the injection-production well pattern, and evaluation of injection-production well pattern is the main content of water-flood management. In this paper, the theory of computing with words(CWW) is applied to intelligent evaluation of injection-production well pattern in water-flood reservoir for...
The regularity of the spiking activity is closely related to the neural encoding and transmission of neural information. However, it is largely unknown how the inhibitory feedback, as a common interaction existed in cortex, contributes to the precise transmission of neural information. In order to explore the answer to this question, we construct a feed-forward network (FFN) model with lateral inhibitory...
Text messages are generally encoded by performing table look-up on fixed length code tables. In this paper, a lossless text compression algorithm which works on the principle of entropy reduction is proposed. Characters in a text message in any language are generally encoded using a binary string with a Unique Lexicographical Rank (ULR). A corresponding Maximum Rank(MR) for any binary string can be...
Coefficient-level rate distortion optimized quantization (RDOQ) is an efficient tool to improve rate-distortion performance with 6%–8% bit-rate saving. It has been widely adopted in video encoders such as JM, x264, HM and so on. However, software implementation of RDOQ suffers from high computation complexity due to intensive path search and from data dependency caused by context based entropy coding...
Model checking has proved to be a very useful formal verification technique in the design of communication and security protocols. Researches have used it to validate the communications protocols from the point of view of their functional and security specifications. Over the years, model checking has evolved from explicit encoding of the state space to symbolic encoding, thus overcoming the state...
The success of sparse representation, in face recognition and visual tracking, has attracted much attention in computer vision in spite of its computational complexity. However, these sparse representation-based methods often assume that the coding residual follows either Gaussian or Laplacian distribution, which may not be precise enough to describe the coding residuals in real tracking situations...
In this paper we present the computational study of one class of discrete models of collective behavior. In the context of these models a set of agents, that form a collective, is represented by a network. Each agent is assigned a special weight function. The behavior of a collective in discrete time moments is specified with a vector function, the coordinates of which are defined by values of agents...
The success of sparse representation, in face recognition and visual tracking, has attracted much attention in computer vision in spite of its computational complexity. These sparse representation-based methods assume that the coding residual follows either Gaussian or Laplacian distribution, which may not be accurate enough to describe the coding residuals in real scenarios. In order to deal with...
An electronic design and evaluation tool for quantum circuit design is presented. It allows easy implementation of quantum algorithms based on the circuit model of quantum computation. The layout of an ideal circuit network can be designed in the logical layer and then automatically get converted into an encoded form closer to the physical layer. The possibility to select and apply the desired quantum...
Bag-of-words (BoW) modeling has yielded successful results in document and image classification tasks. In this paper, we explore the use of BoW for cognitive state classification. We estimate a set of common patterns embedded in the fMRI time series recorded in three dimensional voxel coordinates by clustering the BOLD responses. We use these common patterns, called the code-words, to encode activities...
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