This paper presents a new low complexity multiple description coding (MDC) method that can generate any number of descriptions. For correlated sources, a special DPCM encoder is used in each description, such that it ...
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ISBN:
(纸本)9781424417650
This paper presents a new low complexity multiple description coding (MDC) method that can generate any number of descriptions. For correlated sources, a special DPCM encoder is used in each description, such that it carries higher rate information of a subset of the samples and lower rate information of the rest. The lower rate codings in different descriptions are designed to be mutually refinable using staggered scalar quantizers. The closed-form expression of the expected distortion is derived when an arbitrary subset of the descriptions are received. Experimental results on natural images using lapped transform show that the proposed method is competent with the state-of-the-art multiple description image coders.
In this paper, we propose a predictive coding approach to identify microvessel subimages using Single-Opponent (SO) signal. First, we simulate the signal from SO neurons in the positive red and negative cyan channel t...
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ISBN:
(纸本)9781479927630
In this paper, we propose a predictive coding approach to identify microvessel subimages using Single-Opponent (SO) signal. First, we simulate the signal from SO neurons in the positive red and negative cyan channel to segment microvessel regions. Second, the SO signal is convolved with a Laplacian of Gaussian (LoG) filter and then processed in a predictive coding and biased competition (PC/BC) neural network. The texture patterns from SO signal is learnt and represented by the PC/BC model. Finally, we employ Support Vector Machine (SVM) with Spatial Pyramid Matching (SPM) scheme to classify the microvessel subimages on the learnt texture patterns. We have carried out extensive experiments on the identification of microvessel subimages. The proposed method is about 10% superior in F1-measure compared with direct classification on subimages in RGB channel.
predictive coding, adapted from text categorization for litigation support, is an evolving process with identification of responsive documents and changing labeling decisions. The current state-of-art within predictiv...
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ISBN:
(纸本)9781538627150
predictive coding, adapted from text categorization for litigation support, is an evolving process with identification of responsive documents and changing labeling decisions. The current state-of-art within predictive coding workflow uses Active Learning, where a new model is periodically rebuilt with additional documents reviewed, to continuously revise a model and improve the identification of responsive documents. We propose an alternative approach to recursively update the model using the Unscented Kalman Filter for each additional labeled document. With synthetic text streaming data and induced concept drift, we show that our approach learns new patterns at a faster rate, renders better accuracy and recall, and requires a reduced labeling cost, which when combined makes it potentially a better alternative in updating the model in the setting of Active Learning for predictive coding.
Background: Delusions, a core symptom of schizophrenia, are thought to arise from an alteration in predictive coding mechanisms that underlie perceptual inference. Here, we aimed to empirically test the hypothesized l...
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This paper presents the preliminary results of a pilot project to design a predictive coding system for electronic discovery (e-discovery) that will be able to handle potentially relevant evidence in a myriad of forma...
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ISBN:
(纸本)9783319585246;9783319585239
This paper presents the preliminary results of a pilot project to design a predictive coding system for electronic discovery (e-discovery) that will be able to handle potentially relevant evidence in a myriad of formats and that will have the features and functionality that lawyers and members of the legal team will find most useful. We developed our predictive coding system to combine available software tools with particular emphasis on usability and in making the user interface as intuitive, attractive and user-friendly as possible. Future work will include a survey and interviews with potential users, testing the system with larger sets of files and documents, and continued refinement of the user interface and backend processing.
How can we rethink 'rationality' in the wake of animal and artificial intelligence studies? Can nonhuman systems be rational in any nontrivial sense? In this paper, we propose that all organisms, under certain...
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How can we rethink 'rationality' in the wake of animal and artificial intelligence studies? Can nonhuman systems be rational in any nontrivial sense? In this paper, we propose that all organisms, under certain circumstances, exhibit rationality to a diverse degree and aspect in the sense of the standard picture (SP): Their inferential processes conform to logic and probability rules. We first show that according to Calvo and Friston (J R Soc Interface 14(131):20170096, 2017) and Orlandi (2018), all biological systems must embody a top-down process (active inference) to minimize free energy. Next, based on Maddy's (Second philosophy, Oxford University Press, Oxford, 2007;The logical must: Wittgenstein on logic, Oxford University Press, Oxford, 2014) analysis, we argue that this inferential process conforms to logic and probability rules;thus, it satisfies the SP, which explains the rudimentary logic and arithmetic (e.g., categorizing and numbering) found among pigeons and mice. We also hold that the mammalian brain is only one among many ways of implementing rationality. Finally, we discuss data from microorganisms to support this view.
The impression of time continuity is a pervasive and given property of our subjective life. However, it appears to be compromised in patients with schizophrenia who experience what has been labelled 'self-disturba...
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The impression of time continuity is a pervasive and given property of our subjective life. However, it appears to be compromised in patients with schizophrenia who experience what has been labelled 'self-disturbances' (khstarungen). We propose that the gaps in the continuity of self-experience in schizophrenia reflect disruption of non-conscious levels of temporal processing and indicate how this view is supported by experimental, phenomenological, and predictive coding approaches. Both experimental data and the phenomenology of time support the same surprising findings, i.e. the unconscious, automatic processing of time is more accurate than conscious processing. These observations are also concordant with hierarchical predictive coding accounts of brain function, i.e. processing is more local or granular, with briefer timescales lower in the hierarchy. Automatic, or 'passive' integration of information over time at a high temporal resolution would ensure any gap arising when coding events consciously, i.e. at a larger timescale, are smoothed over. We suggest this to involve both retentional and protentional mechanisms.
Can what we know change what we see? Does language affect cognition and perception? The last few years have seen increased attention to these seemingly disparate questions, but with little theoretical advance. We argu...
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Can what we know change what we see? Does language affect cognition and perception? The last few years have seen increased attention to these seemingly disparate questions, but with little theoretical advance. We argue that substantial clarity can be gained by considering these questions through the lens of predictive processing, a framework in which mental representationsfrom the perceptual to the cognitivereflect an interplay between downward-flowing predictions and upward-flowing sensory signals. This framework provides a parsimonious account of how (and when) what we know ought to change what we see and helps us understand how a putatively high-level trait such as language can impact putatively low-level processes such as perception. Within this framework, language begins to take on a surprisingly central role in cognition by providing a uniquely focused and flexible means of constructing predictions against which sensory signals can be evaluated. predictive processing thus provides a plausible mechanism for many of the reported effects of language on perception, thought, and action, and new insights on how and when speakers of different languages construct the same reality in alternate ways.
Hybrid image compression system is discussed and analyzed for better objective fidelity metrics combining the advantages of Coiflet filter functions of wavelets, predictive coding (Differential Pulse Code Modulation-D...
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ISBN:
(纸本)9781479913565
Hybrid image compression system is discussed and analyzed for better objective fidelity metrics combining the advantages of Coiflet filter functions of wavelets, predictive coding (Differential Pulse Code Modulation-DPCM) and neural networks in addition to quantization and Huffman encoding techniques to eliminate the interpixel, psychovisual redundancy and coding redundancy. Artificial neural networks are self adaptive i.e. they can adjust themselves to data without any specification of the functional model they are fault tolerant by architecture. Wavelets (by choice) on the other hand are computationally simple and provide good compression ratios for high resolution images especially while DPCM removes redundancy in the information. Initially selected wavelet of choice (Coiflet5 in this case) is applied on the input image for two level decomposition generating seven bands of low frequency and high frequency coefficients. The low frequency band 1 coefficients are compressed with DPCM technique while the remaining bands of coefficients are compressed with artificial neural networks. Metrics obtained: Peak Signal to Noise Ratio (PSNR) Mean Square Error (MSE) and Compression Ratio (CR) are tabulated for comparative analysis.
Training documents have a significant impact on the performance of predictive models in the legal domain. Yet, there is limited research that explores the effectiveness of the training document selection strategy - in...
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ISBN:
(纸本)9781538650356
Training documents have a significant impact on the performance of predictive models in the legal domain. Yet, there is limited research that explores the effectiveness of the training document selection strategy - in particular, the strategy used to select the seed set, or the set of documents an attorney reviews first to establish an initial model. Since there is limited research on this important component of predictive coding, the authors of this paper set out to identify strategies that consistently perform well. Our research demonstrated that the seed set selection strategy can have a significant impact on the precision of a predictive model. Enabling attorneys with the results of this study will allow them to initiate the most effective predictive modeling process to comb through the terabytes of data typically present in modern litigation. This study used documents from four actual legal cases to evaluate eight different seed set selection strategies. Attorneys can use the results contained within this paper to enhance their approach to predictive coding.
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