Information Processing Structure Research Articles

CyberUnits Bricks: An Implementation Study of a Class Library for Simulating Nonlinear Biological Feedback Loops

Feedback loops and other types of information processing structures play a pivotal role in maintaining the internal milieu of living organisms. Although methods of biomedical cybernetics and systems biology help to translate between the structure and function of processing structures, computer simulations are necessary for studying nonlinear systems and the full range of dynamic responses of feedback control systems. Currently, available approaches for modelling and simulation comprise basically domain-specific environments, toolkits for computer algebra systems and custom software written in universal programming languages for a specific purpose, respectively. All of these approaches are faced with certain weaknesses. We therefore developed a cross-platform class library that provides versatile building bricks for writing computer simulations in a universal programming language (CyberUnits Bricks). It supports the definition of models, the simulative analysis of linear and nonlinear systems in the time and frequency domain and the plotting of block diagrams. We compared several programming languages that are commonly used in biomedical research (S in the R implementation and Python) or that are optimized for speed (Swift, C++ and Object Pascal). In benchmarking experiments with two prototypical feedback loops, we found the implementations in Object Pascal to deliver the fastest results. CyberUnits Bricks is available as open-source software that has been optimised for Embarcadero Delphi and the Lazarus IDE for Free Pascal.

Amplitude and Phase Information Interaction for Speech Enhancement Method

In order to improve the speech enhancement ability of FullSubNet model, an improved method FullSubNet-pMix is proposed. Specifically, pMix module is added to the structure of full-band frequency domain information processing, which realizes the information interaction between amplitude spectrum and phase spectrum. At the same time, the hyperparameters used in training are optimized so that the full-band and sub-band structure of the system can play a better role. Experiments are carried out on selected test sets. The experimental results show that the proposed method can independently improve the speech enhancement effect of the model, and the effect on the four evaluation indicators of WB-PESQ, NB-PESQ, STOI, and SI-SDR is better than the original model. Therefore, the FullSubNet-pMix method proposed in this paper can effectively enhance the ability of the model to extract and use voice information. The impact of different loss functions on the training performance was also verified.

Evaluation of Anxiety State Discrimination by Recurrent Neural Network using the Connectivity of Brain Function Network by EEG

This study examined the differences in functional brain network over time between different anxiety states and evaluated their usefulness in neural networks (NN). Seventeen young adults with high-anxiety and 13 young adults with low-anxiety were examined. The subjects were given three stimulations: resting, pleasant, and unpleasant stimuli, and Electroencephalogram (EEG) was measured immediately after the stimuli. EEG was analyzed for the alpha band using coherence analysis and graph theory. We evaluated the classification accuracy of anxiety states by NN and recurrent neural networks (RNN). The results showed the information processing process and structure of the brain functional network to emotional stimuli differed over time depending on the anxiety state. The time series data of coherence and graph theoretical indicator by EEG would be considered to be useful for discriminating anxiety states using RNN.

Synthesis and analysis of the trace detector of air objects of an interrogating radar system

The paper considers the features of tracking air objects in existing information networks of radar systems for monitoring airspace. It is shown that the tracking is carried out according to the information of the primary radar surveillance systems, and the secondary radar surveillance systems are used as sources of additional radar information. But the transition to automatic dependent surveillance implies the mandatory use of only request radar surveillance systems. Therefore, the problems of formulating methods and algorithms for tracking air objects based on information from secondary radar surveillance systems are relevant. The specifics of the construction and operation of secondary radar surveillance systems differ significantly from primary radar surveillance systems. The work carried out the synthesis and analysis of the structure of the tracks detector of air objects by interrogating radar systems for monitoring the airspace, namely: a comparative analysis of the quality of identifying the tracks of air objects was carried out; the quality of information support to consumers of the airspace control system with the proposed structure was improved in comparison with the used information processing structure; it is shown that the quality of information support for consumers has more preferable indicators when using the signal processing method during acquisition with subsequent information merging; the influence of the readiness factor of aircraft transponders of interrogative radar systems on the quality of information support for consumers of the airspace control system was evaluated.

Distributed processing of radar information in airspace surveillance systems

The work is dedicated to the analysis of the quality of combining assessments of the radar signals and airborne objects detections in the implementation of distributed processing of radar information of airspace surveillance systems. The main sources of radar information about the air situation in the airspace control system are primary surveillance radars, secondary radar systems and identification systems on the basis of "friend or foe". It should be noted that the analysis of the information security of single-position radars shows their vulnerability in a wide range of unintentional and intentional interference, as well as determining their location. This is due to the ease of detection of the emitting transmitter of the probing signal in single-position radars. It led to the main disadvantage of single-position radars – low noise immunity and low survivability. The transition to a network of radar systems can significantly reduce the impact of deliberately directed interference. It also allows the use of methods for distributed processing of radar information in airspace surveillance systems.
 Analysis of the effectiveness of information support algorithms based on distributed processing of radar information of airspace surveillance systems, taking into account the final result, makes it possible to detect airborne objects using a packet of binary-quantized signals, taking into account two algorithms for combining detection results: channel accumulation and combining results; association of channel solutions and accumulation. It shows following: – the quality of consumer information support based on the proposed structure is much higher compared to the used radar information processing structure; the quality of information support for consumers has the best performance when using the signal processing method based on the accumulation of signals with the subsequent combination of detection results; the availability factor of the aircraft transponder significantly affects the quality of information support, already at P0<0.9 the use of integer logic for combining detection information is undesirable.

MODENN: A Shallow Broad Neural Network Model Based on Multi-Order Descartes Expansion.

Deep neural networks have achieved great success in almost every field of artificial intelligence. However, several weaknesses keep bothering researchers due to its hierarchical structure, particularly when large-scale parallelism, faster learning, better performance, and high reliability are required. Inspired by the parallel and large-scale information processing structures in the human brain, a shallow broad neural network model is proposed on a specially designed multi-order Descartes expansion operation. Such Descartes expansion acts as an efficient feature extraction method for the network, improve the separability of the original pattern by transforming the raw data pattern into a high-dimensional feature space, the multi-order Descartes expansion space. As a result, a single-layer perceptron network will be able to accomplish the classification task. The multi-order Descartes expansion neural network (MODENN) is thus created by combining the multi-order Descartes expansion operation and the single-layer perceptron together, and its capacity is proved equivalent to the traditional multi-layer perceptron and the deep neural networks. Three kinds of experiments were implemented, the results showed that the proposed MODENN model retains great potentiality in many aspects, including implementability, parallelizability, performance, robustness, and interpretability, indicating MODENN would be an excellent alternative to mainstream neural networks.

Missing the Forest for the Trees: Modular Search and Systemic Inertia as a Response to Environmental Change

I develop and test a theory that explains why organizations may struggle to adapt in the face of change even when their members are aware of change, are motivated to adapt, and have the resources to do so. I build on complex-systems theory, which posits that organizations face a hierarchy of interdependent problems: they must choose how to fulfill different specialized tasks and choose processes to integrate the outputs of these tasks. Because these choices are interdependent, environmental change that directly affects only a few tasks in isolation can indirectly affect the viability of major organizational processes. Recognizing these ripple effects is difficult, however: understanding complex interdependencies is challenging for decision makers, and the division of labor within organizations can create an illusion of separability between tasks. As a result, organizations may respond to such change by engaging in “modular search” for new ways to fulfill specialized tasks, but they may fail to engage in “systemic search” for new processes integrating the outputs of specialized tasks unless they can rely on information-processing structures that help decision makers better understand interdependencies among choices. I test my theory by applying sequence analysis methods to micro-level behavioral data on competitive video gaming (esports) teams. Qualitative fieldwork and an online experiment provide additional evidence of my proposed mechanisms.

Visual Information Computing and Processing Model Based on Artificial Neural Network.

This paper analyzes the parallel and serial information processing structure of visual system and proposes a visual information processing model with three layers: visual receptor layer, visual information conduction and relay layer, and information processing layer of visual information computing and processing area. Based on the analysis, abstraction, and simplification of the biological prototype of each layer in the visual system, a framework model of an artificial neural system corresponding to the visual system is proposed. An artificial neural network model is proposed to simulate the mechanism of visual attention. A network model is formed by introducing the saliency mask map as additional information on the benchmark network, and the selective enhancement operation is performed on the extracted features in different regions according to the mask map. The experimental results show that the visual computing processing network model can effectively improve the classification performance of the network when the appropriate saliency mask is used. The visual information computing and processing model network can work effectively for different data sets and different structures of the benchmark network, which is a universal network model. The complexity of visual information computing and processing model network is very small, and the improvement of network performance is not at the cost of increasing model complexity, but in the way of improving network efficiency. The performance of artificial neural network visual information computation and processing model is directly related to the performance of saliency map used as mask map.

A computational design of a programmable biological processor

Basic synthetic information processing structures, such as logic gates, oscillators and flip-flops, have already been implemented in living organisms. Current implementations of these structures have yet to be extended to more complex processing structures that would constitute a biological computer. We make a step forward towards the construction of a biological computer. We describe a model-based computational design of a biological processor that uses transcription and translation resources of the host cell to perform its operations. The proposed processor is composed of an instruction memory containing a biological program, a program counter that is used to address this memory, and a biological oscillator that triggers the execution of the next instruction in the memory. We additionally describe the implementation of a biological compiler that compiles a sequence of human-readable instructions into ordinary differential equation-based models, which can be used to simulate and analyse the dynamics of the processor. The proposed implementation presents the first programmable biological processor that exploits cellular resources to execute the specified instructions. We demonstrate the application of the described processor on a set of simple yet scalable biological programs. Biological descriptions of these programs can be produced manually or automatically using the provided compiler.

Pathogen disgust sensitivity: Individual differences in pathogen perception or pathogen avoidance?

The emotion disgust motivates the avoidance of pathogens and contaminants. Individuals differ in their tendency to experience disgust and this is referred to as pathogen disgust sensitivity. Yet, it remains unclear which differences in psychological processes are captured by pathogen disgust sensitivity. We tested two hypotheses about how the information processing structure underlying pathogen avoidance might give rise to individual differences in pathogen disgust sensitivity. Participants (n = 998) rated the perceived health of individuals with or without facial blemishes and indicated how comfortable they would feel about having physical contact with them. For participants with high disgust sensitivity, facial blemishes were more indicative of poor health and perceived health was more strongly related to comfort with physical contact. These findings suggest that pathogen disgust sensitivity reflects individual differences in the tendency to interpret stimuli as an infection risk and the weight given to estimated infection risk when deciding who should be approached or avoided.

Improved strategies of relation extraction based on graph convolutional model on tree structure for web information processing

In the Industry 4.0/5.0 era, information integration is employed to fuse information from different companies to facilitate interoperation. However, information extraction is an important preprocessing phase that must be performed prior to integrating data from different contexts. Relation extraction, which is an element of information extraction, is typically the basis of many upper-level applications, e.g., information visualization and inference. Some current models may not fully consider the complementary effect of information at different levels of granularity featured by different neural networks. In this paper, two improvement relation extraction strategies based on the graph convolutional model on tree structure (GCNTree) are proposed. The first strategy integrates a hierarchical attention mechanism and correlation analysis between subjects and objects to generate sentence and entity vectors, respectively. The second strategy merges a named-entity recognition subnetwork with GCNTree to realize joint learning of relation and entity extraction. Experimental results demonstrate that the proposed strategies are comparable to state-of-the-art methods.11Our code will be available at https://github.com/yangshuodelove/.

Control Graph Based Development of Problem Solvers for Systems with Knowledge Bases

Reducing the complexity of developing software systems that solve complex problems of science and practice, as well as ensuring their viability are among urgent tasks of software engineering. For systems with knowledge bases, as noted in many literature sources, this problem is most acute, despite the fact that significant results have been achieved in its solving. This problem is especially critical for those systems in which one of the requirements is a time limit for decision-making. Given that the used solution methods belong to the class of exhaustive ones that have a large computational complexity, parallelization is required to reduce the execution time. A methodology for using declaratively defined control graph structures for developing problem solvers for systems with knowledge bases is proposed. Special attention is paid to the processing of knowledge base fragments and the possibility of placing architectural components of the solver on different computing devices. The structure of such an information-and-control graph includes related constructs (operators), one of which is marked as "initial". These structures can be divided into two main types: computing control structures and information processing structures. The developer can group individual operators into blocks — for their repeated usage (including organization of recursive calculations). The structure of the graph also includes a description of the data.

Microfabricated Device to Record Axonal Conduction Under Pharmacological Treatment for Functional Evaluation of Axon Ion Channel.

Neuronal networks are fundamental structures for information processing in the central nervous system. This processing function is severely impaired by abnormal axonal conduction from changes in functional ion channel expression. The evaluation of axonal conduction properties can be effective in the early diagnosis of information-processing abnormalities. However, little is known about functional ion channel expression in axons owing to lack of an appropriate method. In this study, we developed a device to measure changes in axonal conduction properties by selective pharmacological stimulation for the functional evaluation of Na channels expressed in axons. Axons of rat cortical neurons were guided across a pair of electrodes through microtunnel structures by employing surface patterning. The developed device detected more than 50 axons while recording for 10 min. The conduction delay along the axons decreased by 22.5% with neuron maturation. Tetrodotoxin and lidocaine (Na channel blockers) increased the conduction delay in a concentration-dependent manner depending on their working concentrations, indicating the effectiveness of the device. Finally, selective Na channel blockers for various Na channel subtypes were used. Phrixotoxin, a Nav1.2 blocker, markedly increased the conduction delay, suggesting that Nav1.2 is functionally expressed in the unmyelinated axons of the cerebral cortex. These results show that our device is feasible for the high-throughput functional evaluation of Na channel subtypes in axons. The results obtained can contribute to the understanding of the pathogenic mechanisms of neurological diseases that involve changes in the functional expression states of ion channels in axons.

From data Processing to Knowledge Processing: Working with Operational Schemas by Autopoietic Machines

Knowledge processing is an important feature of intelligence in general and artificial intelligence in particular. To develop computing systems working with knowledge, it is necessary to elaborate the means of working with knowledge representations (as opposed to data), because knowledge is an abstract structure. There are different forms of knowledge representations derived from data. One of the basic forms is called a schema, which can belong to one of three classes: operational, descriptive, and representation schemas. The goal of this paper is the development of theoretical and practical tools for processing operational schemas. To achieve this goal, we use schema representations elaborated in the mathematical theory of schemas and use structural machines as a powerful theoretical tool for modeling parallel and concurrent computational processes. We describe the schema of autopoietic machines as physical realizations of structural machines. An autopoietic machine is a technical system capable of regenerating, reproducing, and maintaining itself by production, transformation, and destruction of its components and the networks of processes downstream contained in them. We present the theory and practice of designing and implementing autopoietic machines as information processing structures integrating both symbolic computing and neural networks. Autopoietic machines use knowledge structures containing the behavioral evolution of the system and its interactions with the environment to maintain stability by counteracting fluctuations.

Distributed biological computation: from oscillators, logic gates and switches to a multicellular processor and neural computing applications

Ever since its foundational years, synthetic biology has been focused on the implementation of biological computing structures. In the beginning, engineered biological computation has mainly been based on uncoupled monoclonal cellular populations. Implementations of such computing structures were mostly inspired by digital electronic circuits and revealed many constraints that limited the advance of the field to relatively simple information processing structures. The focus has recently shifted towards the implementation of biological computing structures within coupled intercellular circuits composed of engineered cellular modules. These circuits have, however, advanced only to a certain point, namely to consist of a few engineered bacterial strains, which perform the computation. It is now time to make a transition from modules and relatively simple systems of biological processing structures to networks composing different strains each presenting a designated computing structure. In such networks, each strain is analogous to a logic chip on a breadboard circuit and is connected to other strains by means of intercellular communication mechanisms rather than copper wires. This analogy can be driven further to use a set of engineered biological modules to construct a complex computing system, such as a multicellular biological processor. We review the state of the art of distributed cellular computation, communication mechanisms, and computational analysis and design approaches for distributed biological computing. We demonstrate the potential next step in engineered biological computation by a proposal of a design of a multicellular biological processor. We demonstrate an analysis of the proposed computing network using in silico simulation and optimisation approaches. Finally, we discuss the potential applications of the reviewed distributed cellular computing structures to the field of neural computing.

Cavity-enhanced light–matter interaction in Vogel-spiral devices as a platform for quantum photonics

Enhancing light–matter interactions on a chip is of paramount importance for classical and quantum photonics, sensing, and energy harvesting applications. Several photonic geometries have been developed, allowing high extraction efficiencies, enhanced light–matter interactions, and control over the spontaneous emission dynamics of solid-state quantum light sources. To this end, a device geometry resilient to nanofabrication imperfections, providing high-quality light confinement and control over the emitted light properties, would be desirable. We demonstrate that aperiodic arrangements, whose geometry is inspired by natural systems where scattering elements are arranged following Fibonacci series, represent a platform for enhancing the light–matter interaction in on-chip nanophotonic devices, allowing us to achieve efficient visible light confinement. We use optically active defect centers in silicon nitride as internal light sources to image and characterize, by means of microphotoluminescence spectroscopy, the individual optical modes confined by photonic membranes with Vogel-spiral geometry. By studying the statistics of the measured optical resonances, in combination with rigorous multiple scattering theory, we observe lognormal distributions and report quality factors with values as high as 2201 ± 443. Our findings improve the understanding of the fundamental physical properties of light-emitting Vogel-spiral systems and show their application to active nanophotonic devices. These results set the basis for further development of quantum devices that leverage the unique properties of aperiodic Vogel spiral order on a chip, including angular momentum states, thus producing mode structures for information processing and communications.

A review on empowering women Via women network education

Advanced education aims to foster networking, learning, or, as a result, the exchange of ideas about shared interests amongst teachers, students, and others. Because learning fires psychosocial information processes by linking observer cognition, network learning strengthens the structure, content, and types of information processing across groups and cohorts. When women connect with learning groups, hidden possibilities emerge to break down obstacles to development. Learning became a collaborative activity as a result of virtual research settings, cloud computing, social networking sites, including blogs. Piaget's constructivist cognitive learning, Jean Lava's Situated learning, as well as Salomon and Perkins' Transfer of learning all support the network instructional approach paradigm. This study attempts to demonstrate the value of girl's network learning in enhancing schooling. Women's network learning improves education quality, progress, and hope in the near future. In our overprotective social organization, the experts' research efforts supplement the knowledge to form and elevate the ladies network learning as “quality stroke to education.

Surface chemical trapping of optical cycling centers.

Quantum information processors based on trapped atoms utilize laser-induced optical cycling transitions for state preparation and measurement. These transitions consist of an electronic excitation from the ground to an excited state and a decay back to the initial ground state, associated with a photon emission. While this technique has been used primarily with atoms, it has also recently been shown to work for some divalent metal hydroxides (e.g. SrOH) and alkoxides (e.g. SrOCH3). This extension to molecules is possible because these molecules feature nearly isolated, atomic-like ground and first-excited electronic states centered on the radical metal atom. We theoretically investigate the extension of this idea to a larger scale by growing the alkyl group, R, beyond the initial methyl group, CH3, while preserving the isolated and highly vertical character of the electronic excitation on the radical metal atom, M. Theory suggests that in the limit as the size of the ligand carbon chain increases, it can be considered a functionalized diamond (or cubic boron nitride) surface. Several requirements must be observed for the cycling centers to function when bound to the surface. First, the surface must have a significant band gap that fully encapsulates both the ground and excited states of the cycling center. Second, while the surface lattice imposes strict limits on the achievable spacing between the SrO- groups, at high coverage, SrO- centers can interact, and show geometric changes and/or electronic state mixing. We show that the coverage of the diamond surface with SrO- cycling centers needs to be significantly sub-monolayer for the functionality of the cycling center to be preserved. Having the lattice-imposed spatial control of SrO- placements will allow nanometer-scale proximity between qubits and will eliminate the need for atom traps for localized cycling emitters. Our results also imply that a functionalization could be done on a scanning microscope tip for local quantum sensing or on photonic structures for optically-mediated quantum information processing.

Investigation of artificial quantum structures constructed by atom manipulation

The atom manipulation technique based on scanning tunneling microscope refers to a method of relocating single atoms or molecules on a certain surface at atomic accuracy by using an atomically sharp tip, which is a unique and powerful tool for studying the quantum physics and prototype quantum devices on a nanometer scale. This technique allows us to build artificial structure atom-by-atom, thus some desired interesting quantum structures which are difficult to grow or fabricate by conventional methods could be realized, and unique quantum states, spin order, band structure could be created by the fine tuning of the structural parameters like lattice constant, symmetry, periodicity, etc. Combined with nanosecond scale time domain electric measurement and autonomous control technique, the atom manipulation would be useful in exploring the atomic precision prototype quantum devices, and providing some valuable knowledge for future electronics. In this review, we introduce the atom manipulation technique and related milestone research achievements and latest progress of artificial quantum structures, including electronic lattices with exotic quantum states on Cu(111), quantum dots on III-V semiconductors, magnetic structures with tunable spin order, structures for quantum information storage and processing, prototype Boolean logic devices and single atom devices. The STM lithography and autonomous atom manipulation are discussed as well. With such improvements, this technique would play more important roles in developing the functional quantum devices in future.

Algebraic Neural Networks: Stability to Deformations

We study algebraic neural networks (AlgNNs) with commutative algebras which unify diverse architectures such as Euclidean convolutional neural networks, graph neural networks, and group neural networks under the umbrella of algebraic signal processing. An AlgNN is a stacked layered information processing structure where each layer is conformed by an algebra, a vector space and a homomorphism between the algebra and the space of endomorphisms of the vector space. Signals are modeled as elements of the vector space and are processed by convolutional filters that are defined as the images of the elements of the algebra under the action of the homomorphism. We analyze stability of algebraic filters and AlgNNs to deformations of the homomorphism and derive conditions on filters that lead to Lipschitz stable operators. We conclude that stable algebraic filters have frequency responses -- defined as eigenvalue domain representations -- whose derivative is inversely proportional to the frequency -- defined as eigenvalue magnitudes. It follows that for a given level of discriminability, AlgNNs are more stable than algebraic filters, thereby explaining their better empirical performance. This same phenomenon has been proven for Euclidean convolutional neural networks and graph neural networks. Our analysis shows that this is a deep algebraic property shared by a number of architectures.