44493552
Adaptation of the temporal receptive fields of macaque V1 neurons
Abstract Traditional view of macaque V1 neurons is that they are static spatiotemporal filters. This study shows that V1 neurons’ temporal receptive fields (kernels) are dynamic and adaptive. In order to study the adaptation of the kernels to a variety of stimuli, we developed a regression approach to recover kernels using naturalistic stimuli. Using this technique, we studied how the kernels of the V1 neurons in macaque monkeys adapt to the frequency bandwidth of naturalistic signals. We found that the temporal frequency tunings and the gains of the receptive fields of the neurons change according to the statistical context of the stimulus environment.
8163714
Scaling Laws in Natural Scenes and the Inference of 3D Shape
This paper explores the statistical relationship between natural images and their underlying range (depth) images. We look at how this relationship changes over scale, and how this information can be used to enhance low resolution range data using a full resolution intensity image. Based on our findings, we propose an extension to an existing technique known as shape recipes [3], and the success of the two methods are compared using images and laser scans of real scenes. Our extension is shown to provide a two-fold improvement over the current method. Furthermore, we demonstrate that ideal linear shape-from-shading filters, when learned from natural scenes, may derive even more strength from shadow cues than from the traditional linear-Lambertian shading cues.
17578293
Representational strategy in the visual cortex
We generalized Daubechies' (see IEEE Transactions on Information Theory, vol.36, no.5, p.961-1004, 1990) results on 1D wavelets to 2D, and elucidated the conditions in which a family of Gabor wavelets will provide a complete representation of an image. We found that the phase space (scale, orientation, x, y) sampling density of the striate cortical simple cells is sufficiently dense to form a tight wavelet frame. This tight frame approximates the continuous wavelet transform, and its redundancy allows high resolution information to be represented in the low-resolution firings of the simple cells. We presented some experimental results to demonstrate this idea.<<ETX>>
6741849
Blind Separation of Delayed and Convolved Sources
We address the difficult problem of separating multiple speakers with multiple microphones in a real room. We combine the work of Torkkola and Amari, Cichocki and Yang, to give Natural Gradient information maximisation rules for recurrent (IIR) networks, blindly adjusting delays, separating and deconvolving mixed signals. While they work well on simulated data, these rules fail in real rooms which usually involve non-minimum phase transfer functions, not-invertible using stable IIR filters. An approach that sidesteps this problem is to perform infomax on a feedforward architecture in the frequency domain (Lambert 1996). We demonstrate real-room separation of two natural signals using this approach.
35157
A cooperative coevolutionary algorithm for multiobjective optimization
This paper presents a kind of cooperative coevolutionary algorithm (CCEA) for multi-objective optimization (MOO). In this algorithm, solutions evolve in the form of cooperative subpopulations. An archive stores non-dominated solutions and helps to evaluate individuals in the subpopulations. The mechanism of niching is applied to maintain the diversity of solutions in the archive. Meanwhile, an extending operator is designed to mine information on solution distribution from the archive and guide the search to regions that are not explored enough. Extensive simulations are performed on different benchmark problems for various multi-objective evolutionary algorithms (MOEAs) and indicate that CCEA is strongly competitive with five recent well-known MOEAs in finding a good non-dominated solution set.
14075649
Nonparametric log spectrum estimation using disconnected regression splines and genetic algorithms
This article proposes a new nonparametric procedure for estimating log spectra. This procedure consists of three major components: (1) a novel statistical model for modelling the unknown target log spectrum, (2) an AIC-based model selection criterion for choosing a 'best' fitting model, and (3) a genetic algorithm for effectively searching the 'best' fitting model. Numerical experiments are conducted to evaluate and compare the practical performance of the proposed procedure with some other common log spectral estimation procedures appearing in the literature. These other procedures include wavelet techniques, kernel smoothing and regression spline fitting. Empirical results suggest that the proposed procedure compares favourably against all these procedures, especially when the unknown log spectrum contains inhomogeneous structures.
9084974
Isolated word recognition using modular recurrent neural networks
Abstract This paper describes a novel method of using recurrent neural networks (RNN) for isolated word recognition. Each word in the target vocabulary is modeled by a fully connected recurrent network. To recognize an input utterance, the best matching word is determined based on its temporal output response. The system is trained in two stages. First, the RNN speech models (RSM) are trained independently to capture the essential static and temporal characteristics of individual words. This is performed by using an iterative re-segmentation training algorithm which gives the optimal phonetic segmentation automatically for each training utterance. The second-stage involves mutually discriminative training among the RSMs, aiming at minimizing the probability of misclassification. A series of simulation experiments have been performed to demonstrate the effectiveness of the proposed recognition method. For the recognition of (A) 20 English words, (B) 11 Cantonese digits and (C) 58 Cantonese CV syllables, the top-1 accuracy are 91.9, 93.6 and 87.1%, respectively.
44753947
Constraint-based wrapper specification and verification for cooperative information systems
In this paper, we propose the use of semistructured constraints in wrappers to mitigate the impact of poor extraction accuracy on Cooperative Information System (CIS) data quality. Wrappers are a critical element of CISs whenever the constituent information systems publish semistructured text such as forms, reports, and memos rather than structured databases. The accuracy of CIS data that stem from text depends upon the wrappers as well as the accuracy of the underlying sources. Wrapper specification is the process of defining patterns (i.e. regular expressions) to extract information from semistructured text. Wrapper verification is the process of ensuring extraction accuracy--that the extracted information faithfully reflects the underlying source. We focus on the problem of extraction accuracy. We use constraints on semistructured data for both wrapper specification and verification. Consequently, we perform extraction and verification simultaneously. We apply the concept to wrappers for a Uniform Domain Name Dispute Resolution Policy (UDRP) CIS of arbitration decisions. UDRP decisions are currently distributed across arbitration authorities on three continents. The accuracy of data extracted using constraint-based specification and verification is measured by Type I and Type II errors.