A novel heat kernel based Monte Carlo localization (HK-MCL) algorithm is presented to solve the degeneracy problem of conventional Monte Carlo localization: real-time global localization requires the number of initial...
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A novel heat kernel based Monte Carlo localization (HK-MCL) algorithm is presented to solve the degeneracy problem of conventional Monte Carlo localization: real-time global localization requires the number of initial samples to be small, whereas global localization may fail if the number of initial samples is small. The degeneracy problem is solved by an optimization approach called heat kernel based perturbation (HK-perturbation), which moves the samples towards the high likelihood area. HK-perturbation integrates the average local density and importance weight of samples to determine each sample's perturbation probability. The strategy improves simulated annealing algorithm via an obvious form of temperature, both in time and space, with respect to average local density and importance weight of samples. Systematic empirical results in global localization based on sonar illustrate superior performance, when compared to other state-of-the-art updating of Monte Carlo localization.
In this paper, low-complexity block-constrained trellis coded quantization (BC-TCQ) structures are introduced, and a predictive BC-TCQ encoding method is developed for quantization of line spectrum frequencies (LSF) p...
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In this paper, low-complexity block-constrained trellis coded quantization (BC-TCQ) structures are introduced, and a predictive BC-TCQ encoding method is developed for quantization of line spectrum frequencies (LSF) parameters for wideband speech coding applications. The performance is compared to the linear predictive coding (LPC) vector quantizers used in the AMR-WB (ITUG.722.2) speech coding standard, demonstrating reduction in spectral distortion and significant reduction in encoding complexity.
Software development is a knowledge intensive activity and software developers are knowledge workers. Knowledge needed for software development is often distributed among different developers. Supporting efficient kno...
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Software development is a knowledge intensive activity and software developers are knowledge workers. Knowledge needed for software development is often distributed among different developers. Supporting efficient knowledge collaboration and transfer is thus essential for software development organizations to remain competitive. This paper proposes a new theory /sub y/namic community to support knowledge collaboration and discusses its application in software development. Dynamic community integrates the traditional knowledge management approach in which knowledge is formalized and accumulated in knowledge repositories, and the community-based knowledge collaboration approach in which knowledge is transferred through informal community participation and human contacts. The dynamic community theory takes into full consideration individual knowledge workers' ever-changing needs for new knowledge as well as the role of social relationship of knowledge workers in effective knowledge collaboration. This paper introduces the dynamic community theory, a general system architecture of sociotechnical environments in support of dynamic community, and its application in software development.
We describe a broadly-applicable conservative error correcting model, N-fold Templated Piped Correction (NTPC), that consistently improves the accuracy of existing high-accuracy base models. Under circumstances where ...
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We describe a broadly-applicable conservative error correcting model, N-fold Templated Piped Correction (NTPC), that consistently improves the accuracy of existing high-accuracy base models. Under circumstances where most obvious approaches actually reduce accuracy more than they improve it, NTPC nevertheless comes with little risk of accidentally degrading performance. NTPC is particularly well suited for natural language applications involving high-dimensional feature spaces, such as bracketing and disambiguation tasks, since its easily customizable templatedriven learner allows efficient search over the kind of complex feature combinations that have typically eluded the base models. We show empirically that NTPC yields small but consistent accuracy gains on top of even high-performing models like boosting. We also give evidence that the various extreme design parameters in NTPC are indeed necessary for the intended operating range, even though they diverge from usual practice.
In this paper, we propose a new method to generate modular structures. In the method, the number of elements, that is, the number of competitive units is gradually increased. To control a process of module generation,...
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ISBN:
(纸本)0780386442
In this paper, we propose a new method to generate modular structures. In the method, the number of elements, that is, the number of competitive units is gradually increased. To control a process of module generation, we introduce two kinds of information, that is, unit and modular information. Unit information represents information content obtained by individual elements in all modules. On the other hand, modular information is information content obtained by each module. We try to increase both types of information simultaneously. We applied our method to two classification problems: random data classification and web data classification. In both cases, we observed that modular structures were automatically generated.
In this paper, we propose a new computational method for a network-growing method called greedy network-growing. We have so far introduced a network-growing algorithm called greedy network-growing based upon informati...
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ISBN:
(纸本)0780386442
In this paper, we propose a new computational method for a network-growing method called greedy network-growing. We have so far introduced a network-growing algorithm called greedy network-growing based upon information theoretic competitive learning. For competitive unit outputs, we have used the inverse of the squares of Euclidean distance between input patterns and connections. The algorithm has extracted very faithful representations of input patterns. However, one problem is that learning is very slow, and sometimes ambiguous final representations are obtained. To remedy these shortcomings, we introduce a new activation function, that is, Gaussian activation functions for competitive units. By changing a parameter for the Gaussian activation functions, we can build a network that does not focus on faithful representations of input patterns, but try to extract the main characteristics of input patterns. Because this method are not concerned with detailed parts of input patterns, learning is significantly accelerated and salient features should be extracted. We applied the method to a road classification problem. Experimental results confirmed that learning was significantly accelerated and salient features could be extracted.
Annotation of the rapidly accumulating body of sequence data relies heavily on the detection of remote homologues and functional motifs in protein families. The most popular methods rely on sequence alignment. These i...
Annotation of the rapidly accumulating body of sequence data relies heavily on the detection of remote homologues and functional motifs in protein families. The most popular methods rely on sequence alignment. These include programs that use a scoring matrix to compare the probability of a potential alignment with random chance and programs that use curated multiple alignments to train profile hidden Markov models (HMMs). Related approaches depend on bootstrapping multiple alignments from a single sequence. However, alignment-based programs have limitations. They make the assumption that contiguity is conserved between homologous segments, which may not be true in genetic recombination or horizontal transfer. Alignments also become ambiguous when sequence similarity drops below 40%. This has kindled interest in classification methods that do not rely on alignment. An approach to classification without alignment based on the distribution of contiguous sequences of four amino acids (4-grams) was developed. Interest in 4-grams stemmed from the observation that almost all theoretically possible 4-grams (204) occur in natural sequences and the majority of 4-grams are uniformly distributed. This implies that the probability of finding identical 4-grams by random chance in unrelated sequences is low. A Bayesian probabilistic model was developed to test this hypothesis. For each protein family in Pfam-A and PIR-PSD, a feature vector called a probe was constructed from the set of 4-grams that best characterised the family. In rigorous jackknife tests, unknown sequences from Pfam-A and PIR-PSD were compared with the probes for each family. A classification result was deemed a true positive if the probe match with the highest probability was in first place in a rank-ordered list. This was achieved in 70% of cases. Analysis of false positives suggested that the precision might approach 85% if selected families were clustered into subsets. Case studies indicated that
In certain spaces using some distance measures, the sum of any two distances is always bigger than the third one. Such a special property is called the tri-edge inequality (TEI). In this paper, the tri-edge inequality...
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In certain spaces using some distance measures, the sum of any two distances is always bigger than the third one. Such a special property is called the tri-edge inequality (TEI). In this paper, the tri-edge inequality characterizing several binary distance measures is mathematically proven and experimentally verified, and the implications of TEI are discussed as well.
Recent advances in intelligent signal processing have made it possible to capture high dynamic range images which are better represented as an array of real numbers rather than the current convention of an array of in...
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Recent advances in intelligent signal processing have made it possible to capture high dynamic range images which are better represented as an array of real numbers rather than the current convention of an array of integers. This paper proposes a solution to address the need for real, rather than just integer, image coding and file formats. Additionally, we propose that the real-valued data be linear in photoquantity (the quantity of light received by the camera) to avoid the image misrepresentation that occurs when a camera's non-linear dynamic range compressor and a display's dynamic range non-linear expander do not match. We present two novel image formats that achieve this: the portable lightspace map (PLM) and its compressed version the JPEG lightspace map (JLM), that builds upon the JPEG compression scheme. The results of various compression levels for real-valued data and their corresponding file sizes are reported.
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