Complex Information Processing Research Articles

Characterization of the Core Determinants of Social Influence From a Computational and Cognitive Perspective.

Most human decisions are made among social others, and in what social context the choices are made is known to influence individuals' decisions. Social influence has been noted as an important factor that may nudge individuals to take more risks (e.g., initiation of substance use), but ironically also help individuals to take safer actions (e.g., successful abstinence). Such bi-directional impacts of social influence hint at the complexity of social information processing. Here, we first review the recent computational approaches that shed light on neural and behavioral mechanisms underlying social influence following basic computations involved in decision-making: valuation, action selection, and learning. We next review the studies on social influence from various fields including neuroeconomics, developmental psychology, social psychology, and cognitive neuroscience, and highlight three dimensions of determinants—who are the recipients, how the social contexts are presented, and to what domains and processes of decisions the influence is applied—that modulate the extent to which individuals are influenced by others. Throughout the review, we also introduce the brain regions that were suggested as neural instantiations of social influence from a large body of functional neuroimaging studies. Finally, we outline the remaining questions to be addressed in the translational application of computational and cognitive theories of social influence to psychopathology and health.

Multifunctional Logic‐in‐Memory Cell Based on Wafer‐Scale MoS2 Thin Films Prepared by Atomic Layer Deposition

Large‐volume data storage and high‐efficiency data processing have been greatly urged by the fast development in artificial intelligence and internet of things involving novel semiconductor materials. Logic‐in‐memory has provided a promising route to address the processing/storage bottleneck in advanced computing systems. Herein, a logic‐in‐memory design based on wafer‐scale MoS2 floating‐gate field‐effect transistor (FGFET) arrays is demonstrated. Multiple logic states and over 10 V memory window are achieved through electrical gate modulation, which further enables in‐memory computing including logic functions of inverter and two‐input NAND gates. Such robust and stable logic‐in‐memory is ascribed to the highly uniform wafer‐scale MoS2 thin film prepared by atomic layer deposition (ALD)‐based synthesis and the reproducible electrical performance of the device arrays. As both memory and computing units are based on floating‐gate FET architecture with area efficiency, the proposed logic‐in‐memory cell can accomplish multifunctional tasks with fewer transistors, suggesting a potential application in high‐density and complex information processing and data storage systems.

Intelligent escalator passenger safety management

This article addresses an approach to intelligent safety control of passengers on escalators. The aim is to improve the accuracy of detecting threatening situations on escalators in the subway to make decisions to prevent threats and eliminate the consequences. The novelty of the approach lies in the complex processing of information from three types of sources (video, audio, sensors) using machine learning methods and recurrent neural networks with controlled elements. The conditions and indicators of safety assurance efficiency are clarified. New methods and algorithms for managing the safety of passengers on escalators are proposed. The architecture of a promising safety software system is developed, and implementation of its components for cloud and fog computing environments is provided. Modeling results confirm the capabilities and advantages of the proposed technological solutions for enhancing the safety of escalator passengers, efficiency of control decision making, and system usability. Due to the proposed solutions, it has become possible to increase the speed of identifying situations 3.5 times and increase the accuracy of their determination by 26%. The efficiency of decision making has increased by almost 30%.

Brain connectivity at rest predicts individual differences in normative activity during movie watching

When exposed to the same sensory event, some individuals are bound to have less typical experiences than others. Previous research has investigated this phenomenon by showing that the typicality of one's sensory experience is associated with the typicality of their stimulus-evoked brain activity (as measured by intersubject correlation, or ISC). Individual differences in ISC have recently been attributed to variability in focal neural processing. However, the extent to which these differences reflect purely intra-regional variability versus variation in the brain's baseline ability to transmit information between regions has yet to be established. Here, we show that an individual's degree and spatial distribution of ISC are closely related to their brain's functional organization at rest. Using resting state and movie watching fMRI data from the Human Connectome Project, we reveal that resting state functional connectivity (RSFC) profiles can be used to predict cortex-wide ISC. Similar region-level analyses demonstrate that the levels of ISC exhibited by brain regions during movie watching are associated with their connectivity to other regions at rest, and that the nature of these connectivity-activity relationships varies as a function of regional roles in sensory information processing. Finally, we show that an individual's unique spatial distribution of ISC, independent of its magnitude, is also related to their RSFC profile. These findings contextualize reports of localized individual differences in ISC as potentially reflecting larger, network-level alterations in resting brain function and detail how the brain's ability to process complex sensory information is linked to its baseline functional organization.

Industrial product design method using feature expression and multi‐view learning in ICN

In order to realize multi‐layer semantic integration and innovation of complex product industrial design, promote multi‐disciplinary knowledge sharing and coordination, and improve product quality, the hierarchical semantic concept of product industrial design is put forward based on the theories of product semiotics and cognitive semantics. In addition, the hierarchical semantic industrial design process context model is constructed based on complex product development process and information processing theory using the Information Centric Networking (ICN) technology, since ICN architecture can be used to optimize the product quality. In the ICN environment, the scientific semantic expression methods and the artistic semantic expression method of product semantics are also used to extract features and construct the feature expression in the specific design situation. The icon semantic features, indicative semantic features, symbolic semantic features and their relevance of complex products are obtained. Experimental results have been achieved and shown that the proposed method has benefits than the other methods.

Who Is Credible? Government Popularity and the Catalytic Effect of IMF Lending

In this paper, I explain variations in international investors’ reactions to International Monetary Fund (IMF) programs. Investors react favorably if a borrowing government is credibly committed to implementing essential IMF conditionality. Instead of engaging complex information processing about economic reform, however, investors rely on a heuristic device to assess the borrower’s domestic political conditions. I argue that a borrowing government’s popularity is an important cue for investors to assess the prospect of an IMF program. Investors associate higher government popularity with better implementation of the program and react more favorably to more popular borrowers. Using annual data from up to 52 emerging market economies from 1998 to 2017, I find robust statistical evidence supporting these claims: an IMF program alone does not restore investor confidence. Rather, an IMF program carried out by a strong government does. My findings have important implications for the study of global financial governance and credible commitment.

The Phanerozoic aftermath of the Cambrian information revolution: sensory and cognitive complexity in marine faunas

AbstractThe Cambrian information revolution describes how biotically driven increases in signals, sensory abilities, behavioral interactions, and landscape spatial complexity drove a rapid increase in animal cognition concurrent with the Cambrian radiation. Here, we compare cognitive complexity in Cambrian and post-Cambrian marine ecosystems, documenting changes in animal cognition after the initial Cambrian increase. In a comparison of Cambrian and post-Cambrian Lagerstätten, we find no strong trend in the proportion of genera possessing two types of macroscopic sense organs (eyes and chemoreceptive organs such as antennae, feelers, or nostrils). There is also no trend in general nervous system complexity. These results suggest that sophisticated information processing was already common in early Phanerozoic ecosystems, comparable with behavioral evidence from the trace fossil record. Most taxa capable of complex information processing in Cambrian ecosystems were panarthropods, whereas mollusks and chordates made up larger proportions afterward. In both the Cambrian and the present day, ecological occupation of diverse habitat tiers and feeding modes is possible with even simple nervous systems, but ecological lifestyles requiring rapid, regular movement are almost exclusively associated within brain-bearing taxa, suggesting a connection with fast information-processing abilities and bodily responses. The overall rise in cognitive sophistication in the Cambrian was likely a unique event in the history of life, although some lineages subsequently developed more elaborate sensory systems and/or larger brains.

Ultralow‐Power Synaptic Transistors Based on Ta2O5/Al2O3 Bilayer Dielectric for Algebraic Arithmetic

AbstractMultifarious artificial synaptic devices are extensively proposed in the field of neuromorphic hardware systems for their applicability in promising parallel computer architecture, which is preferred to classical Von Neumann architecture in numerous and complex information processing. Besides the ability to mimic typical biological synaptic behaviors, low power consumption is critical for the synaptic devices in the neuromorphic hardware system.In this paper, ultralow‐power Ta2O5/Al2O3 bilayer‐gate‐dielectric synaptic transistors (TABSTs) with low‐temperature atomic layer deposited dielectric are proposed. The TABSTs show power consumption as low as 19.9 aJ per synaptic event successfully at a low drain voltage of 0.001 V and a short pulse width of 1 ms. Essential synaptic behaviors including excitatory postsynaptic current, inhibitory postsynaptic current, spike‐amplitude‐dependent plasticity, spike‐duration‐dependent plasticity, paired pulse facilitation, long‐term potentiation, long‐term depression, the transition from short‐term memory to long‐term memory as well as learning and forgetting abilities are well mimicked by the TABSTs. Moreover, algebraic arithmetic operations such as addition, subtraction, multiplication, and division are also implemented by the TABSTs. This work provides a promising approach to emerging neuromorphic systems.

Evaluation and of University Building Design Effect Based on Multisensor Perception and Data Security

The development of the smart cities with new and integrated information and communication technologies has changed the traditional industries’ processes. One of the domains is construction industry which plays an important supporting role for the economic development of a country, but at the same time, the construction industry is also an industry with high energy consumption and high pollution. Therefore, in order to alleviate the contradiction between economic development and resources and the environment, the construction industry must achieve sustainable development and take the road of green construction. In order to carry out the evaluation of the design effect of colleges and universities, this paper introduces the multisensor perception and fuzzy comprehensive evaluation methods. First, through the design and analysis of the sensor perception system used in the building environment, the collection, acquisition, analysis, and processing of complex information of heterogeneous multiterminals are obtained. Secondly, cluster analysis and genetic algorithms are used in the processing and analysis process of building multiterminal sensor data. The security aspect is also taking into account to design the methods. The system test verifies the performance of the university building design effect evaluation model, which can provide a reference for the sustainable development of the construction industry.

Evaluation of Surgical Complexity Considering Information Processing Complexity: A Case Study of Dacryocystorhinostomy

Evaluation of Surgical Complexity Considering Information Processing Complexity: A Case Study of Dacryocystorhinostomy

Integrated information as a common signature of dynamical and information-processing complexity.

The apparent dichotomy between information-processing and dynamical approaches to complexity science forces researchers to choose between two diverging sets of tools and explanations, creating conflict and often hindering scientific progress. Nonetheless, given the shared theoretical goals between both approaches, it is reasonable to conjecture the existence of underlying common signatures that capture interesting behavior in both dynamical and information-processing systems. Here, we argue that a pragmatic use of integrated information theory (IIT), originally conceived in theoretical neuroscience, can provide a potential unifying framework to study complexity in general multivariate systems. By leveraging metrics put forward by the integrated information decomposition framework, our results reveal that integrated information can effectively capture surprisingly heterogeneous signatures of complexity-including metastability and criticality in networks of coupled oscillators as well as distributed computation and emergent stable particles in cellular automata-without relying on idiosyncratic, ad hoc criteria. These results show how an agnostic use of IIT can provide important steps toward bridging the gap between informational and dynamical approaches to complex systems.

Dorsal-Ventral Visual Pathways and Object Characteristics: Beamformer Source Analysis of EEG

In performing a gaming task, mental rotation (MR) is one of the important aspects of visuospatial processing. MR involves dorsal-ventral pathways of the brain. Visual objects/models used in computer-games play a crucial role in gaming experience of the users. The visuospatial characteristics of the objects used in the computer-game influence the engagement of dorsal-ventral visual pathways. The current study investigates how the objects’ visuospatial characteristics (i.e., angular disparity and dimensionality) in an MR-based computer-game influence the cortical activities in dorsal-ventral visual pathways. Both the factors have two levels, angular disparity: convex angle (CA) vs. reflex angle (RA) and dimensionality: 2D vs. 3D. Sixty healthy adults, aged, 18–29 years (M = 21.6) were recruited for the study and randomly assigned to four gaming conditions i.e., 15 participants in each group. The multichannel electroencephalogram (EEG) data were recorded from 60 healthy adults while playing the game. The source reconstruction was done for ∼3000 sources inside the brain using the Dynamic Imaging of Coherent Sources (DICS) beamforming method for θ1(4–5.75), θ2(5.75–7.5), α1(7.5–9), α2(9–11), α3(11–13), β1(13–17.25), β2(17.25–21.5) Hz frequency sub-bands. The reconstructed neuronal sources were segmented into 68 functionally parcellated brain regions, and the percentage of active sources for each region was computed. Further, the differences across the 68 regions among the four gaming conditions were evaluated using the percentage of active sources. The differences in activation for the dorsal-ventral pathways and some additional brain regions were observed among the four groups. The game with 2D objects and CA showed higher activation than that with 3D objects and RA, respectively. The dorsal pathway was found to be more active in contrast to the ventral pathway. The findings suggest that angular disparity and dimensionality in MR influence the engagement of dorsal-ventral visual pathways in such a way that angular disparity has a greater impact on cortical activation across this region than dimensionality. Also, higher activation for CA as compared to RA irrespective of dimensionality reflects the complexity of spatial information processing under CA. Similarly, greater activation was seen for 2D objects than 3D, indicating difficulty in information processing due to deficient visual features.

Coupled effects of channels and synaptic dynamics in stochastic modelling of healthy and Parkinson's-disease-affected brains

<abstract> <p>Our brain is a complex information processing network in which the nervous system receives information from the environment to quickly react to incoming events or learns from experience to sharp our memory. In the nervous system, the brain states translate collective activities of neurons interconnected via synaptic connections. In this paper, we study coupled effects of channels and synaptic dynamics under the stochastic influence of healthy brain cells with applications to Parkinson's disease (PD). In particular, we investigate the effects of random inputs in a subthalamic nucleus (STN) cell membrane potential model. The STN bursting phenomena and parkinsonian hypokinetic motor symptoms are closely connected, as electrical and chemical maneuvers modulating STN bursts are sufficient to ameliorate or mimic parkinsonian motor deficits. Deep brain stimulation (DBS) of the STN is an important surgical technique used in the treatment to improve PD symptoms. Our numerical results show that the random inputs strongly affect the spiking activities of the STN neuron not only in the case of healthy cells but also in the case of PD cells in the presence of DBS treatment. Specifically, the existence of a random refractory period together with random input current in the system may substantially influence an increased irregularity of spike trains of the output neurons.</p> </abstract>

Understanding Differences in Wayfinding Strategies.

Navigating to goal locations in a known environment (wayfinding) can be accomplished by different strategies, notably by taking habitual, well-learned routes (response strategy) or by inferring novel paths, such as shortcuts, from spatial knowledge of the environment's layout (place strategy). Human and animal neuroscience studies reveal that these strategies reflect different brain systems, with response strategies relying more on activation of the striatum and place strategies associated with activation of the hippocampus. In addition to individual differences in strategy, recent behavioral studies show sex differences such that men use place strategies more than women, and age differences such that older adults use more response strategies than younger adults. This paper takes a comprehensive multilevel approach to understanding these differences, characterizing wayfinding as a complex information processing task. This analysis reveals factors that affect navigation strategy, including availability of the relevant type of environmental knowledge, momentary access to this knowledge, trade-offs between physical and mental effort in different navigation contexts, and risk taking. We consider how strategies are influenced by the computational demands of a navigation task and by factors that affect the neural circuits underlying navigation. We also discuss limitations of laboratory studies to date and outline priorities for future research, including relating wayfinding strategies to independent measures of spatial knowledge, and studying wayfinding strategies in naturalistic environments.

Complexing of information channels of UAV detection and observation systems from the statistic solutions theory standpoint

Currently, unmanned aerial vehicles (UAVs) provide a wide range of useful tasks for humanity, but, on the other hand, they pose a serious threat in economic, military and other areas of human activity. Difficulties in observing UAVs using modern technical means, as well as their relatively low cost, lead to an expansion of the scope of UAVs based illegal actions. Therefore, the protection of various objects against UAVs is a serious scientific and technical task of today.
 Since the possibilities of the known methods of UAV detection are different, the joint use of systems of different types is realized in practice nowadays, in order to increase the informativeness of the obtained data by their joint (complex) processing.
 The number of publications in this field is constantly increasing, and considerable attention is paid to integrated systems built on the basis of various physical sensors. However, the efficiency of multi-sensor systems with integrated processing of the output signals of the channels in practice remains insufficient.
 This article is devoted to the study of methods for the synthesis of new, more efficient algorithms for complexing radar, acoustic, optical and infrared information channels of integrated UAV detection and recognition systems, which are performed from the standpoint of statistical theory of radio system optimization.
 This approach allows synthesizing the optimal (according to the selected quality criterion) complex information processing system, which ensures obtaining the maximum amount of information from the vector process observed at the inputs of information channels. There shown the possibility of constructing an optimal UAV detector with the use of the late strategy of combining information at the level of decisions made in individual channels of the system.

Temporal distinguishability in Hong-Ou-Mandel interference for harnessing high-dimensional frequency entanglement

High-dimensional quantum entanglement is currently one of the most prolific fields in quantum information processing due to its high information capacity and error resilience. A versatile method for harnessing high-dimensional entanglement has long been hailed as an absolute necessity in the exploration of quantum science and technologies. Here we exploit Hong-Ou-Mandel interference to manipulate discrete frequency entanglement in arbitrary-dimensional Hilbert space. The generation and characterization of two-, four- and six-dimensional frequency entangled qudits are theoretically and experimentally investigated, allowing for the estimation of entanglement dimensionality in the whole state space. Additionally, our strategy can be generalized to engineer higher-dimensional entanglement in other photonic degrees of freedom. Our results may provide a more comprehensive understanding of frequency shaping and interference phenomena, and pave the way to more complex high-dimensional quantum information processing protocols.

EEG Signal Processing for Survey of Dynamic Auditory Verbal Learning and Memory Formation in Brain by Fractal Analysis

Learning and memory involve a complex cognitive process to acquire, retain, and retrieve information in the central nervous system. However, the brain mechanism still needs to be well understood. This study aimed to examine the dynamic auditory verbal learning model of the brain mechanism involved in cognitive learning using the scale-free approach by the fractal analysis of electroencephalogram (EEG) data. This illustrates how the complexity of information processing in the brain changes while auditory and verbal learning occurs. Therefore, a standard verbal-auditory cognitive assessment test was used to create a learning paradigm. Eighteen healthy male volunteers (19–23[Formula: see text]years old) were recruited and their verbal memories were assessed using the Rey auditory verbal learning test. Fifteen unrelated words were sequentially presented to the subjects and they were asked to recall the presented words as many as possible. The experiment was repeated five times with no stop in between. EEG recording was performed before, during and after each stage. Subsequently, the Hurst exponents of EEG were calculated and their associations with the recalled words and the learning rate were estimated. The approximate entropy was intended to confirm the Hurst exponent variations of signals. The statistical analysis of the data showed that the increase in the number of the recalled words was positively correlated with an increase in the Hurst exponents of EEG signals (more significant at the temporal channels) and a decrease in the approximate entropy of EEG signals during the learning of trials. These results denoted a reduced complexity pattern in EEG signals while rehearsing auditory and verbal memories.

Towards a Blending-Based Approach to Early Christian Characters: Nicodemus as a Test Case

Abstract This article presents a blending-based approach to characters in early Christian narratives. Even cognitive approaches to complex characters (including my previous work) tend to frame character development as a primarily linear unidirectional process. However, the human mind integrates incoming information through processes that are more recursive than linear and more synergistic than summative. I propose that Cognitive Blending Theory, pioneered by Gilles Fauconnier & Mark Turner, provides a heuristic approach that better accounts for the complexity of cognitive information processing. First, I articulate a blending-based approach to ancient characters. Next, I show the validity of such an approach by modeling the blends for each of Nicodemus’s appearances in John’s gospel, focusing on the novel insights only available through blending. As will become apparent, these blends are interrelated, building upon and challenging one another on the path toward a complete characterization of “Nicodemus” across John’s gospel. I conclude with brief reflections on the future prospects of blending-based character studies.

Basal Forebrain Cholinergic Neurons Selectively Drive Coordinated Motor Learning in Mice.

Motor control requires precise temporal and spatial encoding across distinct motor centers that is refined through the repetition of learning. The recruitment of motor regions requires modulatory input to shape circuit activity. Here, we identify a role for the baso-cortical cholinergic pathway in the acquisition of a coordinated motor skill in mice. Targeted depletion of basal forebrain cholinergic neurons results in significant impairments in training on the rotarod task of coordinated movement. Cholinergic neuromodulation is required during training sessions as chemogenetic inactivation of cholinergic neurons also impairs task acquisition. Rotarod learning is known to drive refinement of corticostriatal neurons arising in both medial prefrontal cortex (mPFC) and motor cortex, and we have found that cholinergic input to both motor regions is required for task acquisition. Critically, the effects of cholinergic neuromodulation are restricted to the acquisition stage, as depletion of basal forebrain cholinergic neurons after learning does not affect task execution. Our results indicate a critical role for cholinergic neuromodulation of distant cortical motor centers during coordinated motor learning.SIGNIFICANCE STATEMENT Acetylcholine release from basal forebrain cholinergic neuron terminals rapidly modulates neuronal excitability, circuit dynamics, and cortical coding; all processes required for processing complex sensory information, cognition, and attention. We found that depletion or transient silencing of cholinergic inputs to anatomically isolated motor areas, medial prefrontal cortex (mPFC) and motor cortex, selectively led to significant impairments on coordinated motor learning; disrupting this baso-cortical network after acquisition elicited no effect on task execution. Our results indicate a pivotal role for cholinergic neuromodulation of distant cortical motor centers during coordinated motor learning. These findings support the concept that cognitive components (such as attention) are indispensable in the adjustment of motor output and training-induced improvements in motor performance.

Research on automatic physical testing method of relay protection equipment through data fusion technology

Intelligent substations can accomplish deeper and more complex substation information exchange and processing by virtue of their equipment intelligence and data integration. In this paper, a relay protection equipment test system for intelligent substations is investigated for intelligent relay protection equipment. By applying data fusion technology in the testing process, functions such as automatically obtaining the action information of secondary devices and judging the response status of secondary devices can be accomplished. Applying the test system proposed in this paper to high-voltage line protection devices, the results show that the relay protection equipment test system can optimize the relay protection equipment test process, which further improves the accuracy and efficiency of relay protection equipment testing.