Natural Information Processing Research Articles

Experimental Demonstration of Reservoir Computing with Self-Assembled Percolating Networks of Nanoparticles.

The complex self-assembled network of neurons and synapses that comprises the biological brain enables natural information processing with remarkable efficiency. Percolating networks of nanoparticles (PNNs) are complex self-assembled nanoscale systems that have been shown to possess many promising brain-like attributes and which are therefore appealing systems for neuromorphic computation. Here experiments are performed that show that PNNs can be utilized as physical reservoirs within a nanoelectronic reservoir computing framework and demonstrate successful computation for several benchmark tasks (chaotic time series prediction, nonlinear transformation, and memory capacity). For each task, relevant literature results are compiled and it is shown that the performance of the PNNs compares favorably to that previously reported from nanoelectronic reservoirs. It is then demonstrated experimentally that PNNs can be used for spoken digit recognition with state-of-the-art accuracy. Finally, a parallel reservoir architecture is emulated, which increases the dimensionality and richness of the reservoir outputs and results in further improvements in performance across all tasks.

Neural population dynamics reveals disruption of spinal circuits’ responses to proprioceptive input during electrical stimulation of sensory afferents

While neurostimulation technologies are rapidly approaching clinical applications for sensorimotor disorders, the impact of electrical stimulation on network dynamics is still unknown. Given the high degree of shared processing in neural structures, it is critical to understand if neurostimulation affects functions that are related to, but not targeted by, the intervention. Here, we approach this question by studying the effects of electrical stimulation of cutaneous afferents on unrelated processing of proprioceptive inputs. We recorded intraspinal neural activity in four monkeys while generating proprioceptive inputs from the radial nerve. We then applied continuous stimulation to the radial nerve cutaneous branch and quantified the impact of the stimulation on spinal processing of proprioceptive inputs via neural population dynamics. Proprioceptive pulses consistently produce neural trajectories that are disrupted by concurrent cutaneous stimulation. This disruption propagates to the somatosensory cortex, suggesting that electrical stimulation can perturb natural information processing across the neural axis.

Biological evolution and human cognition are analogous information processing systems.

The mechanisms that govern biological evolution and human cognition are analogous, as both follow the same principles of natural information processing systems. In this article, we describe the following five principles that provide an analogy between biological evolution and human cognition: (a) Randomness as Genesis Principle and (b) Borrowing and Reorganizing Principle, which indicate how natural information processing systems obtain information; (c) Narrow Limits of Change Principle and (d) Information Store Principle, which indicate how information is processed and stored; and (e) Environmental Organizing and Linking Principle, which indicate how stored information is used to generate actions appropriate to an environment. In human cognition, these analogs only apply to cognitive processes associated with biologically secondary knowledge, the knowledge typically taught in educational institutions. Based on these five principles, cognitive load theory researchers have provided diverse prescriptions to optimize instructional activities and materials. We conclude by discussing general instructional implications and future research directions based on this analogy.

Ontology-Driven Computer Systems

This article delves into the evolving frontier of ontology-driven natural language information processing. Through an in-depth examination, we put forth a novel linguistic processor architecture, uniquely integrating linguistic and ontological paradigms during semantic analysis. Distancing from conventional methodologies, our approach showcases a profound merger of knowledge extraction and representation techniques. A central highlight of our research is the development of an ontology-driven information system, architected with an innate emphasis on self-enhancement and adaptability. The system’s salient capability lies in its adept handling of elementary knowledge, combined with its dynamic aptitude to foster innovative concepts and relationships. A particular focus is accorded to the system’s application in scientific information processing, signifying its potential in revolutionising knowledge-based applications within scientific domains. Through our endeavours, we aim to pave theway for more intuitive, precise, and expansive ontology-driven tools in the realm of knowledge extraction and representation.

An integrated three-way decision methodology for sustainability of wastewater circularity in thermal power plants

The presence of multiple criteria for evaluating wastewater reuse applications indicates the potential usage of Multi-Criteria Decision-Making (MCDM) methods for this purpose. However, there is currently a scarcity of studies in the domain literature that utilize MCDM approaches in this application topic. This paper therefore advances the domain literature in two distinctive ways. Firstly, it analyzes and advances the reuse agenda of wastewater from thermal power plants, recognized as large-scale users of water, thus promoting greater water circularity. Secondly, it provides a methodological advance by integrating the notion of Three-Way Decision (3WD) into the computational structure of MCDM methods by introducing a middle reference point. Such an initiative results in a novel 3WD extension of the Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) method. Additionally, this work provides a proof that the MARCOS method utilizes a compromise solution in identifying priority alternatives, along with the integration of a Weighted Aggregated Sum Product ASsessment (WASPAS) metric. An initial hypothetical example illustrates how the proposed approach augments the canonical MARCOS method, particularly in promoting the “thinking in threes” as a more natural information processing approach and the high degree of distinguishability of priorities between decision alternatives. An actual case study in a thermal power plant then demonstrates the contributions of this work. With the best-worst method used to determine the priorities of the decision attributes, the findings reveal that wastewater reuse applications achieving reduced costs for needed infrastructures, operational simplicity, technological compatibility, consumer safety and household savings are preferred by stakeholders. The 3WD-MARCOS approach identifies industrial and commercial use, municipal use, environmental restoration, and household use as the high-priority alternatives, with cooking and drinking as least preferred. These insights guide stakeholders in their design of initiatives that allocate resources for greater wastewater reuse. A comparative analysis yields high consistency of these findings with similar MCDM methods. In addition, the efficacy of the novel 3WD-MARCOS method highlights its potential in handling MCDM problems, including those promoting water circularity.

Evolutionary Perspective on Human Cognitive Architecture in Cognitive Load Theory: a Dynamic, Emerging Principle Approach

Adopting an evolutionary approach to substantiate major characteristics of human cognitive architecture has been one of the major recent developments in cognitive load theory. According to this approach, human cognitive architecture is a natural information processing system which can be described by five general principles. This paper attempts to (1) identify the scope of applicability of these principles in natural information processing systems of different levels of complexity, (2) reconcile the coexistence of implicit (primary) and controlled (secondary) processes within the same human cognitive architecture, and (3) incorporate motivational factors into the evolutionary approach to human cognitive architecture. The paper suggests two principal modifications to the traditional formulation of the evolutionary approach. Firstly, natural information processing systems are viewed as dynamically evolving systems with new principles added with increasing levels of complexity of the systems. Secondly, a new (the explicit intention to learn) principle is added at the level of human cognition. This sixth principle is expected to address (1) the emergence of controlled mechanisms dealing with biologically secondary information as expressed by conscious processing in working memory and (2) the role of learner motivation in such processes from an evolutionary perspective. The paper concludes with discussion of theoretical and practical instructional implications of the proposed modifications.

Classification at the accuracy limit: facing the problem of data ambiguity

Data classification, the process of analyzing data and organizing it into categories or clusters, is a fundamental computing task of natural and artificial information processing systems. Both supervised classification and unsupervised clustering work best when the input vectors are distributed over the data space in a highly non-uniform way. These tasks become however challenging in weakly structured data sets, where a significant fraction of data points is located in between the regions of high point density. We derive the theoretical limit for classification accuracy that arises from this overlap of data categories. By using a surrogate data generation model with adjustable statistical properties, we show that sufficiently powerful classifiers based on completely different principles, such as perceptrons and Bayesian models, all perform at this universal accuracy limit under ideal training conditions. Remarkably, the accuracy limit is not affected by certain non-linear transformations of the data, even if these transformations are non-reversible and drastically reduce the information content of the input data. We further compare the data embeddings that emerge by supervised and unsupervised training, using the MNIST data set and human EEG recordings during sleep. We find for MNIST that categories are significantly separated not only after supervised training with back-propagation, but also after unsupervised dimensionality reduction. A qualitatively similar cluster enhancement by unsupervised compression is observed for the EEG sleep data, but with a very small overall degree of cluster separation. We conclude that the handwritten letters in MNIST can be considered as ’natural kinds’, whereas EEG sleep recordings are a relatively weakly structured data set, so that unsupervised clustering will not necessarily re-cover the human-defined sleep stages.

Drivers and outcomes of circular economy implementation: evidence from China

PurposeAlthough the circular economy is widely regarded as an essential strategy for firms, the present understanding of the drivers and outcomes of circular economy implementation is underdeveloped. In this paper, the authors draw on the natural resource-based view and information processing theory to explore how an environmental orientation and digital supply chain platforms promote circular economy implementation, and increase the impact of circular economy implementation on financial performance.Design/methodology/approachThe authors surveyed 249 Chinese firms and used hierarchical regression analysis to test hypotheses.FindingsThe empirical results reveal that three primary modes of circular economy implementation—reinvent and rethink (INV), restore, reduce and avoid (RRA), and recirculate (REC)—are all driven by environmental orientation and digital supply chain platforms. More interestingly, digital supply chain platforms have an inverted U-shaped moderating effect on the relationships between environmental orientation and INV and between environmental orientation and RRA. Most importantly, INV and RRA are positively related to financial performance, whereas REC does not have a significant relationship with financial performance.Originality/valueThe authors contribute to the literature on the circular economy by revealing new drivers and outcomes of different modes of its implementation. Additionally, the findings have implications for how firms should frame their circular economy initiatives in the context of the digital revolution.

Is there an emotionality effect in older adults’ source memory?

ABSTRACT The goal of our research was to investigate whether older adults show a source memory enhancement for emotionally valenced sources. Additionally, building on research on the socioemotional selectivity theory and the age-related positivity effect, we tested whether older adults show a larger enhancement for positive compared to negative (and neutral) sources than younger adults. In Experiment 1 (nold = 25, nyoung = 27), we used one positive, one negative, and one neutral picture to manipulate source valence (many-to-one mapping of items to sources), whereas, in Experiment 2 (nold = 62, nyoung = 62), we used multiple pictures per source valence category (one-to-one mapping of items to sources) to counteract potential habituation effects. In both experiments, sources had medium and matching arousal levels. Items were neutral words superimposed on the source pictures. To support an implicit, natural information processing, participants rated the words in terms of pleasantness. We analyzed memory data with a multinomial processing tree model to disentangle memory processes from guessing bias. Across both experiments, an age-related positivity effect occurred in participants’ pleasantness ratings. This effect, however, did not carry over to older adults’ source memory. That is, in source memory, we found a general emotionality effect for younger but not for older adults and no age-related positivity effect. We propose that due to older adults’ pronounced difficulties in remembering the item-to-source link (i.e., associative deficit), even a greater focus on an inherently emotional source might be insufficient to boost source memory.

Constant gap between conventional strategies and those based on C*-dynamics for self-embezzlement

We consider a bipartite transformation that we call self-embezzlement and use it to prove a constant gap between the capabilities of two models of quantum information: the conventional model, where bipartite systems are represented by tensor products of Hilbert spaces; and a natural model of quantum information processing for abstract states on C*-algebras, where joint systems are represented by tensor products of C*-algebras. We call this the C*-circuit model and show that it is a special case of the commuting-operator model (in that it can be translated into such a model). For the conventional model, we show that there exists a constant ϵ0>0 such that self-embezzlement cannot be achieved with precision parameter less than ϵ0 (i.e., the fidelity cannot be greater than 1−ϵ0); whereas, in the C*-circuit model---as well as in a commuting-operator model---the precision can be 0 (i.e., fidelity 1).Self-embezzlement is not a non-local game, hence our results do not impact the celebrated Connes Embedding conjecture. Instead, the significance of these results is to exhibit a reasonably natural quantum information processing problem for which there is a constant gap between the capabilities of the conventional Hilbert space model and the commuting-operator or C*-circuit model.

A molecular paradigm: “Plug-and-play” chemical sensing and crypto-steganography based on molecular recognition and selective response

Inspired by information processing and communication in nature based on molecular recognition and structural diversity, ongoing efforts aim to development of artificial molecular or nano-systems for sensing, logic computing, and even data storage and safety. However, due to their preparation/functionalization shortcomings (laborious and time-consuming), poor flexibility and compatibility, and limited paradigm, it is still a big challenge whether simple molecules can be used to achieve comprehensive and universal applications from sensing to information storage and protection. Herein, we for the first time demonstrated a molecular paradigm—computer-like “basic input output system (BIOS)” which can realize “plug-and-play” sensing, information encoding, molecular cryptography, and steganography based on a simple artificial molecule (p-nitrophenol, PNP). Based on its molecular recognition and inherent chemical identities, PNP was utilized for colorimetric detection of multiple metal ions (Hg2+, Fe3+, Al3+, Cr3+) and distinguishing their valence (like Cr3+/Cr6+, Fe3+/Fe2+). Interestingly, PNP can achieve the “plug-and-play” fluorescent expansion of detection channels by directly mixing with fluorescent molecules, indicating that PNP molecule can be served as a molecular BIOS with flexibility and compatibility. Impressively, the selectivity embedded in PNP-based BIOS sensing system can be developed as molecular-level double cryptographic steganography to encode, encrypt and hide specific information (like the content of classical literature). This research not only provides a basic idea for building a molecular paradigm with “plug-and-play” flexibility and compatibility, but also provides ideas for the use of molecular sensing and informatization to open up the digitalization of the molecular world.

Dynamical systems theory in cognitive science and neuroscience

AbstractDynamical systems theory (DST) is a branch of mathematics that assesses abstract or physical systems that change over time. It has a quantitative part (mathematical equations) and a related qualitative part (plotting equations in a state space). Nonlinear dynamical systems theory applies the same tools in research involving phenomena such as chaos and hysteresis. These approaches have provided different ways of investigating and understanding cognitive systems in cognitive science and neuroscience. The ‘dynamical hypothesis’ claims that cognition is and can be understood as dynamical systems. Common consequences for such an approach include rejecting understanding cognition as information processing in nature, including eschewing explanatory roles for computation or representation. Contemporary applications of DST include mouse‐tracking studies in cognitive science and nonrepresentational perspectives on motor control in neuroscience. Such work has philosophical implications concerning the boundaries of cognition, explanation, and representations. DST offers powerful methodology and theories that raise many topics of philosophical significance.

In depth learning based method of denoising joint transform correlator optical image encryption system

There is serious noise interference in the decryption process of the joint transform correlator (JTC) optical encryption system, so the quality of the decrypted image cannot meet the accuracy requirements in most cases. The quality of decrypted image can be improved to a certain extent when the phase key is designed by the Gerchberg-Saxton algorithm and the iterative algorithm fuzzy control algorithm, but the complexity of the design process is inevitable and the quality of the decrypted image still needs improving. Recently, the in depth learning technology has attracted the attention of scholars in the fields of computer vision, natural language processing and optical information processing. In order to deal with the noise interference in the JTC optical encryption system, combining the current deep learning method, in this paper we propose a new denoising method for JTC optical image encryption system based on in depth learning, the dense modules are added into the generated network to enhance the reuse of feature information and improve the performance of the network. The latest self-attention mechanism area is added into the network to distinguish the weights of different channels and learn the relationship between channel and channel, so that the network can selectively strengthen the useful feature information but suppress useless feature information. The density module and the channel attention module are integrated into a DCAB synthesis module, which can effectively extract the image feature information and improve the performance of the network. The receptive field of the convolution kernel is enlarged by two down-sampling and the feature map is restored to its original size by two up-sampling. The VGG-19 is used to extract high-frequency details and texture features of images, meanwhile, the non-adversarial loss and mean-square error (MSE) loss are added into the loss function to reduce the difference among the image samples. The quality of noise-reduced images in this method are obviously better than that of the existing denoising algorithms by evaluating intuitive visual observation or SSIM (structural similarity), PSNR (peak signal to noise ratio) and MSE. The results of numerical calculation and simulation experiments show that this method can greatly eliminate the influence of noise on the JTC optical image encryption system, and effectively improve the effectiveness and feasibility of JTC optical image encryption system for high-quality image encryption.

The Deductibility of the Aristotelian Modal Syllogism ⼞IAI-4 from the Perspective of Natural Language Information Processing

The Deductibility of the Aristotelian Modal Syllogism ⼞IAI-4 from the Perspective of Natural Language Information Processing

Screening out All Valid Aristotelian Modal Syllogisms

It is easy to understand that whether a classical syllogism is valid. That whether a modal syllogism is valid is not so transparent. The prevailing view on Aristotelian modal syllogistic is that the syllogistic is incomprehensible due to its many faults and inconsistencies. Although adequate semantic analysis or reconstruction of the syllogistic have be given by many authors, it is far from obvious how to extend these results so as to consistently cover the whole modal syllogistic developed. The major aim of this paper is to overcome these difficulties, and screen out 384 Aristotelian valid modal syllogisms from 6656 Aristotelian modal syllogisms in natural language. They can be formalized by means of set theory and generalized quantifier theory, and their validity can be proved by possible world semantics and the truth definition of Aristotelian quantifiers defined in generalized quantifier theory. The basic steps of screening out all valid Aristotelian modal syllogisms are as follows: firstly one can get all possible modal syllogisms obtained by adding modal operators to 24 valid classical syllogisms, and secondly eliminate invalid modal syllogisms by characteristic rules of modal syllogisms. It is hoped that these innovative achievements will make contributions to promote the development of Aristotelian and generalized modal syllogistic, natural language information processing, and further research on knowledge representation and knowledge reasoning in computer science.

The Role of Information Acquisition and Processing in Decision‐Making by Individual within Insects Colonies

Insects usually present simple behaviours, but their information processing abilities result in complex collective behaviours, allowing them to perform task allocation and solve difficult problems. Biologists have invested efforts to better understand the mechanisms that govern the behaviour of social insects at the individual level and that allow the emergence of complex behaviours at the colony level. Based on biological researches, we identify the main mechanisms used to acquire different types of information and how this information is processed and used in decision‐making. We present Information Acquisition as an essential stage for Information Processing, focusing on external and internal information sources and exploring examples of information processing performed by insects. A better understanding of information processing and collective behavior in nature is the basis for the understanding of how computing is realized in insect societies, as well for new insights to develop more effective computational approaches inspired by social insects. © 2018 John Wiley & Sons, Ltd.

Fundamentals of Natural Representation

Our understanding of the natural universe is far from being comprehensive. The following questions bring to the fore some of the fundamental issues. Is there a reality of information associated with the states of matter based entirely on natural causation? If so, then what constitutes the mechanism of information exchange (processing) at each interaction of physical entities? Let the association of information with a state of matter be referred to as the representation of semantic value expressed by the information. We ask, can the semantic value be quantified, described, and operated upon with symbols, as mathematical symbols describe the material world? In this work, these questions are dealt with substantively to establish the fundamental principles of the mechanisms of representation and propagation of information with every physical interaction. A quantitative method of information processing is derived from the first principles to show how high level structured and abstract semantics may arise via physical interactions alone, without a need for an intelligent interpreter. It is further shown that the natural representation constitutes a basis for the description, and therefore, for comprehension, of all natural phenomena, creating a more holistic view of nature. A brief discussion underscores the natural information processing as the foundation for the genesis of language and mathematics. In addition to the derivation of theoretical basis from established observations, the method of information processing is further demonstrated by a computer simulation.

Metagenomic insights into the microbial diversity in manganese-contaminated mine tailings and their role in biogeochemical cycling of manganese

To extend the knowledge on the microbial diversity of manganese rich environments, we performed a clone library based study using metagenomic approach. Pyrosequencing based analysis of 16S rRNA genes were carried out on an Illumina platform to gain insights into the bacterial community inhabiting in a manganese mining site and the taxonomic profiles were correlated with the inherent capacities of these strains to solubilise manganese. The application of shot gun sequencing in this study yielded results which revealed the highest prevalence of Proteobacteria (42.47%), followed by Actinobacteria (23.99%) in the area of study. Cluster of orthologous group (COG) functional category has 85,066 predicted functions. Out of which 11% are involved in metabolism of amino acid, 9% are involved in production and conversion of energy while Keto Encyclopedia of Gene and Genomes (KEGG) functional category has 107,388 predicted functions, out of which 55% are involved in cellular metabolism, 15% are environmental and information processing and 12% are genetic information processing in nature. The isolated microbial consortia demonstrated visible growth in presence of high concentrations of Mn. Solubilisation studies resulted in 86% of manganese recovery after 20 days. The result presented in this study has important implications in understanding the microbial diversity in manganese contaminated mine tailings and their role in natural geochemical cycling of Mn.

Scene Classification Based on the Sparse Homogeneous–Heterogeneous Topic Feature Model

High spatial resolution (HSR) imagery scene classification has been the subject of increased interest in recent years, and has great potential for many applications, such as urban functional analysis. Rooted in natural information processing, the use of the probabilistic topic model (PTM) to capture latent topics to represent HSR images has been an effective way to bridge the semantic gap. However, how to effectively discover discriminative information to recognize the HSR scenes is a challenging task. In this paper, the sparse homogeneous–heterogeneous topic feature model (SHHTFM) is proposed for HSR image scene classification. Differing from the conventional PTM-based scene classification methods, which utilize only heterogeneous features, SHHTFM explores the effect of the homogeneous information. Based on the union of uniform grid sampling and simple linear iterative clustering superpixel sampling, SHHTFM exploits both the heterogeneous and homogeneous information. After separately mining different types of low-level features and latent topics, the sparse topic inference procedure of SHHTFM further improves the fusion of the sparse heterogeneous and homogeneous topics. In addition, multisource geographical data are effectively integrated, where the water and vegetation boundaries define a more accurate way to restrict the boundaries of different scenes, and are then combined with the road network data to further improve the scene annotation performance. This provides more reliable and applicable results for us to better understand the complex scenes. The experimental results obtained with two HSR image classification data sets and an HSR image annotation data set demonstrate that the proposed SHHTFM framework can solve the scene classification problem, with a high classification accuracy as well as a high time efficiency.

Reflex inhibition. Global manifestation of the local mechanism

The paper considers the issues of training and retraining in self-organizing natural information processing systems and shows the reasons for the rule “The one who comes first gets all” and the possibility of creating a technical device, which is an artificial intelligence carrier. Such a device is not filled with the necessary information at the stage of creation, but receives all the necessary information in the process of functioning, especially at the beginning of its activity and in the process of education, just as the intellect of a person is being raised since childhood.