Distributed Processing Network Research Articles

New aspects of weaving K-frames: the excess and duality

Weaving frames in separable Hilbert spaces have been recently introduced by Bemrose et al. to deal with some problems in distributed signal processing and wireless sensor networks. Likewise weaving K -frames have been proved to be useful during signal reconstructions from the range of a bounded linear operator K. In this paper, we study the notion of weaving and its connection to the duality of K-frames and construct several pairs of woven K-frames. Also, we find a unique biorthogonal sequence for every K-Riesz basis and obtain a $K^*$-frame which is woven by its canonical dual. Moreover, we describe the excess for K-frames and prove that any two woven K-frames in a separable Hilbert space have the same excess. Finally, we introduce the necessary and sufficient condition under which a K-frame and its image under an invertible operator have the same excess.

Language and Aphasias.

This article reveals how it is possible for a brain composed of 100 billion highly interconnected, lipid-encased, reticular electrochemical devices to support complex functions such as language and how language disorders can be understood as a reflection of degradation of one or more domains of knowledge. Ongoing research, building on landmark work regarding parallel distributed processing (PDP), provides the basis for understanding cognitive functions as a manifestation of the activity of populations of millions or billions of neurons in various highly interconnected networks. Population encoding networks have the following intrinsic properties that provide an orderly explanation for normal and degraded language: (1) a capacity for settling into stable "attractor" states; (2) processing occurs in and knowledge (long-term memories) is stored in exactly the same network; (3) a capacity for incorporating statistical regularities of experience, frequency, and age of acquisition; (4) support of content-addressable memory; and (5) graceful degradation, such that lesions increase the probability of errors but do not fundamentally transform network operations. Knowledge in parallel distributed processing networks resides in the strength of connections between units (synapses in the brain). Aphasia, whether stemming from stroke or dementing disorders, can be understood in terms of the degradation of one or more domains of knowledge. Understanding the brain as a population encoding machine incorporating vast interconnectivity provides an orderly explanation for language function, both normal and abnormal.

Retraction: Machine Learning Based Classification Models for Heart Disease Prediction (J. Phys.: Conf. Ser 1916 012092)

This article has been retracted by IOP Publishing following an allegation that this article contains text overlap from multiple unreferenced sources [1, 2]. IOP Publishing has investigated and agree the article constitutes plagiarism. IOP Publishing also expresses concern regarding a number of nonsensical phrases used in the article, which suggests the article may have been created at least partly by artificial intelligence or translation software. IOP Publishing also notes sections of this article were published in multiple other journals at a similar time [3, 4, 5, 6], by different author groups. These issues all bring the legitimacy of this article into serious doubt.The authors have not responded to confirm whether they agree or disagree to this retraction.IOP Publishing wishes to credit Problematic Paper Screener [7] for bringing some of these issues to our attention.1. "Deep learning" Wikipedia, Wikimedia Foundation, https://en.wikipedia.org/wiki/Deep_learning 2. "Cardiovascular disease" Wikipedia, Wikimedia Foundation,https://en.wikipedia.org/wiki/Cardiovascular_disease 3. Sukanth, N. et al., 2021. Heart Disease Classification using Machine Learning Algorithm. International Journal of Innovative Research in Computer and Communication Engineering, 9(3), pp.1108-1114. 4. Siamala Devi, S., Harini Karthika, G. & Deepika, M., 2021. Machine learning based classification for heart disease identification. Journal of Physics: Conference Series, 1916. 5. Priyadharshini, K. et al., 2021. Coronary Infarction Prediction Using Correlation Analysis aspects based on Parallel Distributed Processing Network. Annals of the Romanian Society for Cell Biology, 25(4), pp.2864-2869. 6. Vennila, V. et al., 2021. Enhanced Deep Learning Assisted Convolutional Neural Network for Heart Disease Prediction. Annals of the Romanian Society for Cell Biology, 25(3), pp.8467-8474. 7. Cabanac G, Labbe C, Magazinov A, 2021, arXiv:2107.06751v1Retraction published: 17 December 2021

Retraction: Machine Learning based Classification for Heart Disease Identification (J. Phys.: Conf. Ser 1916 012174)

This article has been retracted by IOP Publishing following an allegation that this article contains text overlap from multiple unreferenced sources [1, 2]. IOP Publishing has investigated and agree the article constitutes plagiarism. IOP Publishing also expresses concern regarding a number of nonsensical phrases used in the article, which suggests the article may have been created at least partly by artificial intelligence or translation software. IOP Publishing also notes sections of this article were published in multiple other journals at a similar time [3, 4, 5, 6], by different author groups. These issues all bring the legitimacy of this article into serious doubt.The authors have not responded to confirm whether they agree or disagree to this retraction.IOP Publishing wishes to credit Problematic Paper Screener [7] for bringing some of these issues to our attention. 1. "Machine learning" Wikipedia, Wikimedia Foundation,https://en.wikipedia.org/wiki/Machine_learning 2. "Cardiovascular disease" Wikipedia, Wikimedia Foundation, https://en.wikipedia.org/wiki/Cardiovascular_disease 3. Sukanth, N. et al., 2021. Heart Disease Classification using Machine Learning Algorithm. International Journal of Innovative Research in Computer and Communication Engineering, 9(3), pp.1108-1114. 4. Karthikeyan, N. et al., 2021. Machine learning based classification models for heart disease prediction. Journal of Physics: Conference Series, 1916. 5. Priyadharshini, K. et al., 2021. Coronary Infarction Prediction Using Correlation Analysis aspects based on Parallel Distributed Processing Network. Annals of the Romanian Society for Cell Biology, 25(4), pp.2864-2869. 6. Vennila, V. et al., 2021. Enhanced Deep Learning Assisted Convolutional Neural Network for Heart Disease Prediction. Annals of the Romanian Society for Cell Biology, 25(3), pp.8467-8474. 7. Cabanac G, Labbe C, Magazinov A, 2021, arXiv:2107.06751v1Retraction published: 17 December 2021

On Excesses and Duality in Woven Frames

Weaving frames in separable Hilbert spaces have been recently introduced by Bemrose et al. to deal with some problems in distributed signal processing and wireless sensor networks. In this paper, we study the notion of excess for woven frames and prove that any two frames in a separable Hilbert space that are woven have the same excess. We also show that every frame with a large class of duals is woven provided that its redundant elements have small enough norm. Also, we try to transfer the woven property from frames to their duals and vice versa. Finally, we look at which perturbations of dual frames preserve the woven property; moreover, it is shown that under some conditions, the canonical Parseval frame of two woven frames is also woven.

Hierarchical object recognition system in machine vision

Authors propose an efficient architecture of the hierarchical machine vision system and the multimedia transfer algorithm for a distributed client-server system which can adapt to mobile network speed variations. This algorithm is based on the wavelet transform of an input multimedia source and allows for data exchange using all the available bandwidth of the unstable mobile network. The main principle of the hierarchical recognition system is the distributed processing network utilizing the “coarse-fine” paradigm in computer vision. Each source of a video stream is processed in-place by the tiny SoC computer which acts as an Edge Computing Unit and detects the presence of an object fragment in a video frame and crops the bounds of the ROI. The resulting stream containing ROI is directed to the main server if the object is detected, reducing traffic and resource consumption.

Distributed fusion for nonlinear uncertain systems with multiplicative parameters and random delay

Abstract This paper proposes an information filtering-based distributed fusion for nonlinear uncertain systems with multiplicative random parameters and randomly delayed measurements in sensor networks. This is a typical nonlinear and non-Gaussian stochastic system with multiple uncertainties including nonlinearity, multiplicative random parameters and random delay, along with additive noises. In the criterion of minimizing the mean square error of state estimate, the centralized information-type filter via Gaussian mixture realization is put forward, by applying statistical linear regression to nonlinear measurement models to reconstruct a new and equivalent one with the linear form. Then, the distributed implementation is designed via average consensus with the corresponding weight of each component being updated distributively, in order to ensure that the Gaussian mixture distribution of the posterior probability density in each processing unit asymptotically approximates the corresponding one in the centralized fusion as closely as possible. Meanwhile, the d-step lag state smoothing is obtained to further improve the estimation precision, in terms of the proposed filtering method. An example with multiplicative random parameters and one-step/two-step random delay in the distributed processing network is simulated to validate the proposed method.

Do arbitrary input–output mappings in parallel distributed processing networks require localist coding?

ABSTRACTThe Parallel Distributed Processing (PDP) approach to cognitive modelling assumes that knowledge is distributed across multiple processing units. This view is typically justified on the basis of the computational advantages and biological plausibility of distributed representations. However, both these assumptions have been challenged. First, there is growing evidence that some neurons respond to information in a highly selective manner. Second, it has been demonstrated that localist representations are better suited for certain computational tasks. In this paper, we continue this line of research by investigating whether localist representations are learned in tasks involving arbitrary input–output mappings. The results imply that the pressure to learn local codes in such tasks is weak, but still there are conditions under which feed-forward PDP networks learn localist representation. Our findings further challenge the assumption that PDP modelling always goes hand in hand with distributed representations and provide directions for future research.

Design of multi‐actor distributed processing systems: A game‐theoretical approach

The manufacturing of a final product could be the result of a value chain involving several processing plants distributed across several distinct owners; a feature that may prevent the application of classical process design approaches that depend on a centralized treatment of the complete processing network. In this article we propose and develop a game‐theoretical framework and specific methodologies, which allow the optimal design of distributed processing systems, through the decentralized strategies of independent actors. The resulting process design corresponds to a Nash Equilibrium point among the interacting actors. Its optimality and the justification of the independent strategies that led to it, are theoretically based on (and constrained by) the properties of the 2‐level Lagrangian approach. The article also discusses the use of penalty‐term approaches, which can extend the applicability of the proposed framework and design methodologies to problems for which the underlying convexity assumptions of the 2‐level Lagrangian approach may not be possible to ascertain. A series of case studies illustrate the application of the proposed ideas to distributed processing networks of various structures. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3369–3391, 2016

Neural networks learn highly selective representations in order to overcome the superposition catastrophe.

A key insight from 50 years of neurophysiology is that some neurons in cortex respond to information in a highly selective manner. Why is this? We argue that selective representations support the coactivation of multiple "things" (e.g., words, objects, faces) in short-term memory, whereas nonselective codes are often unsuitable for this purpose. That is, the coactivation of nonselective codes often results in a blend pattern that is ambiguous; the so-called superposition catastrophe. We show that a recurrent parallel distributed processing network trained to code for multiple words at the same time over the same set of units learns localist letter and word codes, and the number of localist codes scales with the level of the superposition. Given that many cortical systems are required to coactivate multiple things in short-term memory, we suggest that the superposition constraint plays a role in explaining the existence of selective codes in cortex.

Time-optimized contextual information forwarding in mobile sensor networks

We study on the forwarding of quality contextual information in mobile sensor networks (MSNs). Mobile nodes form ad-hoc distributed processing networks that produce accessible and quality-stamped information about the surrounding environment. Due to the dynamic network topology of such networks the context quality indicators seen by the nodes vary over time. A node delays the context forwarding decision until context of better quality is attained. Moreover, nodes have limited resources, thus, they have to balance between energy conservation and quality of context. We propose a time-optimized, distributed decision making model for forwarding context in a MSN based on the theory of optimal stopping. We compare our findings with certain context forwarding schemes found in the literature and pinpoint the advantages of the proposed model.

Custom Digital Workflows: A New Framework for Design Analysis Integration

Flexible information exchange is critical to successful design-analysis integration, but current top-down, standards-based and model-oriented strategies impose restrictions that contradict this flexibility. In this article we present a bottom-up, user-controlled and process-oriented approach to linking design and analysis applications that is more responsive to the varied needs of designers and design teams. Drawing on research into scientific workflows, we present a framework for integration that capitalises on advances in cloud computing to connect discrete tools via flexible and distributed process networks. We then discuss how a shared mapping process that is flexible and user friendly supports non-programmers in creating these custom connections. Adopting a services-oriented system architecture, we propose a web-based platform that enables data, semantics and models to be shared on the fly. We then discuss potential challenges and opportunities for its development as a flexible, visual, collaborative, scalable and open system.

El desarrollo conceptual de la ciencia cognitiva. Parte I

IntroductionCognitive science has become the most influential mental paradigm of the late twentieth and early twenty-first centuries. Its concepts and approach to problems and solutions have changed significantly in the course of a few years. MethodThe fundamental concepts of cognitive science are presented and discussed, divided into four stages: The beginnings, classical cognitivism, connectionism, and embodiment-enaction. Development and ConclusionThe beginnings are marked by the construction of modern computers and the advent of information theory. Classical cognitivism began in 1956 with the notion that all information processing systems, including the human brain, share the same principles. From the analogy between computer and brain, it was considered appropriate to study the mind as if it were software. Connectionism, also called parallel distributed processing or neural networks get these names because of their underlying computational architecture. It helps explain the speed with which cognitive processes are performed and resistance to damage, being closer to biology. It does not work with representations, but with patterns of activation and deactivation of the component units and transmission of signals between them. Typical cases of tasks performed by neural networks are found in perception and memory, for example, pattern recognition (faces, words from letters, etc).

Towards Distributed Process Networks with CORBA

Process networks is a widely used model to describe highly concurrent applications. We present here a distributed implementation of a slightly restricted process network model realized using the CORBA middleware. This implementation allows the non computer science specialist to easily program heterogeneous meta-applications based on an assembly of components communicating through FIFO queues.

Effective networks for real-time distributed processing

This paper applies the concepts and methods of complex networks to the development of models and simulations of master-slave distributed real-time systems by introducing an upper bound in the allowable delivery time of the packets with computation results. Two representative interconnection models are taken into account: Uniformly random and scale free (Barabasi-Albert), including the presence of background traffic of packets. The obtained results include the identification of the uniformly random interconnectivity scheme as being largely more efficient than the scale-free counterpart. Also, increased latency tolerance of the application provides no help under congestion.

Distributed Connectionist Models in Social Psychology

AbstractDistributed connectionist models of mental representation (also termed PDP or parallel distributed processing, or ANN or artificial neural networks) constitute a fundamental alternative to the associative or schematic models that have been much more prevalent in social psychology. A connectionist model is made up of a large number of very simple processing units, richly interconnected and able to send signals to each other depending on their momentary activation levels. No individual processing unit represents a meaningful concept; instead, overall patterns of activation hold representational meaning. This article emphasizes the novel properties of connectionist representation that might appeal to theorists and researchers in social psychology, including their context sensitivity and flexibility, ability to represent prototypes and exemplars within a single network, and ability to determine whether a stimulus is familiar even before the stimulus can be identified or categorized.

Using Parallel Distributed Processing Models to Simulate Phonological Dyslexia: The Key Role of Plasticity-related Recovery

PMSP96 [Plaut, D. C., McClelland, J. L., Seidenberg, M. S., & Patterson, K. Understanding normal and impaired word reading: Computational principles in quasi-regular domains. Psychological Review, 103, 56-115, 1996, Simulation 4] is an implementation of the triangle model of reading, which was able to simulate effects found in normal and surface dyslexic readers. This study replicated the original findings and explored the possibility that damage to the phonological portion of the model might produce symptoms of phonological dyslexia. The first simulation demonstrated that this implementation of PMSP96 was able to reproduce the standard effects of reading, and that when damaged by removal of the semantic input to phonology, it produced the kind of frequency/consistency interactions and regularization errors typical of surface dyslexia. The second simulation explored the effect of phonological damage. Phonological damage alone did not result in a convincing simulation of phonological dyslexia. However, when the damage was followed by a period of recovery, the network was able to simulate large lexicality and imageability effects characteristic of phonological dyslexia-the first time that both surface and phonological dyslexia have been simulated in the same parallel distributed processing network. This result supports the view that plasticity-related changes should be a significant factor in our understanding of chronic behavioral dissociations.

Automated instrument design and optimisation using a genetic algorithm running on a distributed processing network

Monte Carlo codes provide a means for accurately simulating nuclear and optical photon transport and are widely used to design and optimise new nuclear instruments. Typically, a number of design variations are simulated and the best variation is selected for construction and experimental testing. The process is limited by the time required to set up and run each design. Separate aspects of complex designs are often optimised sequentially rather than globally. A software package has been developed that allows users to create parameterised families of instrument designs, which are then searched by a genetic algorithm for the globally optimal design. The package has been interfaced to the MCNP, EGSnrc and PHOTON codes for solving neutron, gamma-ray, electron and optical photon design problems.

Using computational, parallel distributed processing networks to model rehabilitation in patients with acquired dyslexia: An initial investigation

Background : Traditional cognitive neuropsychological models are good at diagnosing deficits but are limited when it comes to studying recovery and rehabilitation. Parallel distributed processing (PDP) models have more potential in this regard as they are dynamic and can actually learn. However, to date very little work has been done in using PDP models to study recovery and rehabilitation. Aims : This study seeks to demonstrate how a PDP model of acquired dyslexia can be extended to provide a computational framework that is capable of making predictions about the relative effectiveness of therapeutic interventions. Methods & Procedures : A replication of Plaut, McClelland, Seidenberg, and Patterson's (1996, simulation 2) model of word reading was trained and then damaged. This damaged network was then retrained in a number of different ways designed to model both natural (spontaneous) recovery and recovery that can be attributed to a specific therapeutic intervention. Outcomes & Results : Interventions that used regular words were more effective than interventions based on inconsistent words. Early intervention (during the period of spontaneous recovery) was more effective than late intervention. Conclusions : These results suggest that this technique has the potential to provide a useful input to the therapeutic arena. The potential opportunities for further work are discussed.

A parallel distributed processing model of Wason’s selection task

Architectural accounts of cognitive performance are important to explore because they provide the infrastructure for algorithmic theories of cognition [Dawson, M.R.W. (1998). Understanding cognitive science. Malden, MA: Blackwell]. Three parallel distributed processing (PDP) networks were trained to generate the ‘p’, the ‘p and not-q’ and the ‘p and q’ responses, respectively, to the conditional rule used in Wason’s selection task [Wason, P.C. (1966). Reasoning. In: Foss, B.M. (Ed.), New Horizons in Psychology, London, Penguin]. Afterward, each trained network was analyzed for the algorithm it developed to learn the desired response to the task. Analyses of each network’s solution to the task suggested a ‘specialized’ algorithm that focused on card location. For example, if the desired response to the task was found at card 1, then a specific set of hidden units detected the response. In addition, we did not find support that selecting the ‘p’ and ‘q’ response is less difficult than selecting the ‘p’ and ‘not-q’ response. Human studies of the selection task usually find that participants fail to generate the latter response, whereas most easily generate the former. We discuss how our findings can be used to (a) extend our understanding of selection task performance, (b) understand existing algorithmic theories of selection task performance, and (c) generate new avenues of study of the selection task.