We examined whether reduced perceptual specialization underlies atypical perception in autism spectrum disorder (ASD) testing classifications of stimuli that differ either along integral dimensions (prototypical integ...
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We examined whether reduced perceptual specialization underlies atypical perception in autism spectrum disorder (ASD) testing classifications of stimuli that differ either along integral dimensions (prototypical integral dimensions of value and chroma), or along separable dimensions (prototypical separable dimensions of value and size). Current models of the perception of individuals with an ASD would suggest that on these tasks, individuals with ASD would be as, or more, likely to process dimensions as separable, regardless of whether they represented separable or integrated dimensions. In contrast, reduced specialization would propose that individuals with ASD would respond in a more integral manner to stimuli that differ along separable dimensions, and at the same time, respond in a more separable manner to stimuli that differ along integral dimensions. A group of nineteen adults diagnosed with high functioning ASD and seventeen typically developing participants of similar age and IQ, were tested on speeded and restricted classifications tasks. Consistent with the reduced specialization account, results show that individuals with ASD do not always respond more analytically than typically developed (TD) observers: Dimensions identified as integral for TD individuals evoke less integral responding in individuals with ASD, while those identified as separable evoke less analytic responding. These results suggest that perceptual representations are more broadly tuned and more flexibly represented in ASD. Autism Res2017, 10: 1510-1522. (c) 2017 International Society for Autism Research, Wiley Periodicals, Inc.
New analytical formulas for quantization noise of two widely known adaptive delta modulation (ADM) systems, continuously variable slope delta modulation (CVSD) and Jayant's firstorder constant factor delta modulat...
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New analytical formulas for quantization noise of two widely known adaptive delta modulation (ADM) systems, continuously variable slope delta modulation (CVSD) and Jayant's firstorder constant factor delta modulation (CFDM), have been obtained. These formulas are derived based upon the previously obtained results for linear delta modulation (LDM) and adaptive differential pulse code modulation (ADPCM). To verify the results, computer simulation has been done using band-limited Gaussian signals. Close agreements between the formulas and the simulation results have been obtained in the wide range of the input signal level.
Experimental data suggests that a first hypothesis about the content of a complex visual scene is available as early as 150 ms after stimulus presentation. Other evidence suggests that recognition in the visual cortex...
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Experimental data suggests that a first hypothesis about the content of a complex visual scene is available as early as 150 ms after stimulus presentation. Other evidence suggests that recognition in the visual cortex of mammals is a bidirectional, often top-down driven process. Here, we present a spiking neural network model that demonstrates how the cortex can use both strategies: Faced with a new stimulus, the cortex first tries to catch the gist of the scene. The gist is then fed back as global hypothesis to influence and redirect further bottom-up processing. We propose that these two modes of processing are carried out in different layers of the cortex. A cortical column may, thus, be primarily defined by the specific connectivity that links neurons in different layers into a functional circuit. Given an input, our model generates an initial hypothesis after only a few milliseconds. The first wave of action potentials traveling up the hierarchy activates representations of features and feature combinations. In most cases, the correct feature representation is activated strongest and precedes all other candidates with millisecond precision. Thus, our model codes the reliability of a response in the relative latency of spikes. In the subsequent refinement stage where high-level activity modulates lower stages, this activation dominance is propagated back, influencing its own afferent activity to establish a unique decision. Thus, top-down influence de-activates representations that have contributed to the initial hypothesis about the current stimulus, comparable to predictive coding. Features that do not match the top-down prediction trigger an error signal that can be the basis for learning new representations. (C) 2009 Elsevier Ltd. All rights reserved.
A low complexity M-channel multiple description coding scheme is developed in this paper, in which each description carries one subset of the input with a higher bit rate and the rest with a lower bit rate. The lower-...
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A low complexity M-channel multiple description coding scheme is developed in this paper, in which each description carries one subset of the input with a higher bit rate and the rest with a lower bit rate. The lower-rate codings in different descriptions are designed to be mutually refinable using staggered scalar quantizers. For correlated sources, a two-rate predictive coding is used in each description. Closed-form expressions of the distortions are derived when different numbers of descriptions are received. The application of the proposed scheme in lapped transform based image coding is also investigated, and the optimal transform is obtained. Experimental results using both 1-D memoryless sources and 2-D images demonstrate the superior performance of the proposed scheme.
The principle of minimum entropy of error estimation (MEEE) is formulated for discrete random variables. In the case when the random variable to be estimated is binary, me show that the MEEE is given by a Neyman-Pears...
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The principle of minimum entropy of error estimation (MEEE) is formulated for discrete random variables. In the case when the random variable to be estimated is binary, me show that the MEEE is given by a Neyman-Pearson-type strictly monotonous test, In addition, the asymptotic behavior of the error probabilities is proved to be equivalent to that of the Bayesian test.
This paper proposes an encoding method for high-quality, low-delay audio communication that is robust to losses in packetized transmission. Robustness is provided by a multiple description vector quantization (MDVQ) t...
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This paper proposes an encoding method for high-quality, low-delay audio communication that is robust to losses in packetized transmission. Robustness is provided by a multiple description vector quantization (MDVQ) technique that is designed to minimize the mean-squared error (MSE). The key to applying this technique effectively is the use of psycho-acoustically controlled pre- and post-filters that make the mean-squared quantization error perceptually relevant. Experiments show that the MDVQ-based encoder yields better results-in both MSE and subjective audio quality-than simple alternative coders with the same low delay.
Animated meshes represented as sequences of static meshes sharing the same connectivity require efficient compression. Among the compression techniques, layered predictive coding methods efficiently encode the animate...
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Animated meshes represented as sequences of static meshes sharing the same connectivity require efficient compression. Among the compression techniques, layered predictive coding methods efficiently encode the animated meshes in a structured way such that the successive reconstruction with an adaptable quality can be performed. The decoding quality heavily depends on how well the prediction is performed in the encoder. Due to this fact, in this paper, three novel prediction structures are proposed and integrated into a state of the art layered predictive coder. The proposed structures are based on weighted spatial prediction with its weighted refinement and angular relations of triangles between current and previous frames. The experimental results show that compared to the state of the art scalable predictive coder, up to 30% bitrate reductions can be achieved with the combination of proposed prediction schemes depending on the content and quantization level. (C) 2011 Elsevier Inc. All rights reserved.
The brain function of prediction is fundamental for human beings to shape perceptions efficiently and successively. Through decades of effort, a valuable brain activation map has been obtained for prediction. However,...
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The brain function of prediction is fundamental for human beings to shape perceptions efficiently and successively. Through decades of effort, a valuable brain activation map has been obtained for prediction. However,much less is known about how the brain manages the prediction process over time using traditional neuropsychological paradigms. Here, we implemented an innovative paradigm for timing prediction to precisely study the temporal dynamics of neural oscillations. In the experiment recruiting 45 participants, expectation suppression was found for the overall electroencephalographic activity,consistent with previous hemodynamic studies. Notably,we found that N1 was positively associated with predictability while N2 showed a reversed relation to predictability. Furthermore, the matching prediction had a similar profile with no timing prediction, both showing an almost saturated N1 and an absence of N2. The results indicate that the N1 process showed a ‘sharpening' effect for predictable inputs, while the N2 process showed a‘dampening' effect. Therefore, these two paradoxical neural effects of prediction, which have provoked wide confusion in accounting for expectation suppression,actually co-exist in the procedure of timing prediction but work in separate time windows. These findings strongly support a recently-proposed opposing process theory.
This article examines the problem of 3D scalable mesh and texture coding. The triangular mesh topology is encoded by an edge-based conquest strategy, while positions are encoded using a multiple prediction method asso...
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This article examines the problem of 3D scalable mesh and texture coding. The triangular mesh topology is encoded by an edge-based conquest strategy, while positions are encoded using a multiple prediction method associated with an adaptive arithmetic coder. Moreover scalability is obtained on the positions via a bitplane coding technique, adapted to float numbers. Then, texture is approximated by a hybrid technique mixing affine interpolation and discrete cosinus transform applied to the triangles of a a hierarchical nested mesh.
Traditional halftoning usually drops colors when dithering images with binary dots, which makes it difficult to recover the original color information. We proposed a novel halftoning technique that converts a color im...
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Traditional halftoning usually drops colors when dithering images with binary dots, which makes it difficult to recover the original color information. We proposed a novel halftoning technique that converts a color image into a binary halftone with full restorability to its original version. Our novel base halftoning technique consists of two convolutional neural networks (CNNs) to produce the reversible halftone patterns, and a noise incentive block (NIB) to mitigate the flatness degradation issue of CNNs. Furthermore, to tackle the conflicts between the blue-noise quality and restoration accuracy in our novel base method, we proposed a predictor-embedded approach to offload predictable information from the network, which in our case is the luminance information resembling from the halftone pattern. Such an approach allows the network to gain more flexibility to produce halftones with better blue-noise quality without compromising the restoration quality. Detailed studies on the multiple-stage training method and loss weightings have been conducted. We have compared our predictor-embedded method and our novel method regarding spectrum analysis on halftone, halftone accuracy, restoration accuracy, and the data embedding studies. Our entropy evaluation evidences our halftone contains less encoding information than our novel base method. The experiments show our predictor-embedded method gains more flexibility to improve the blue-noise quality of halftones and maintains a comparable restoration quality with a higher tolerance for disturbances.
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