Sequential Information Processing Research Articles

Mind the information gap: How sampling and clustering impact the predictability of reach‐scale channel types in California (USA)

AbstractClustering and machine learning‐based predictions are increasingly used for environmental data analysis and management. In fluvial geomorphology, examples include predicting channel types throughout a river network and segmenting river networks into a series of channel types, or groups of channel forms. However, when relevant information is unevenly distributed throughout a river network, the discrepancy between data‐rich and data‐poor locations creates an information gap. Combining clustering and predictions addresses this information gap, but challenges and limitations remain poorly documented. This is especially true when considering that predictions are often achieved with two approaches that are meaningfully different in terms of information processing: decision trees (e.g., RF: random forest) and deep learning (e.g., DNNs: deep neural networks). This presents challenges for downstream management decisions and when comparing clusters and predictions within or across study areas. To address this, we investigate the performance of RF and DNN with respect to the information gap between clustering data and prediction data. We use nine regional examples of clustering and predicting river channel types, stemming from a single clustering methodology applied in California, USA. Our results show that prediction performance decreases when the information gap between field‐measured data and geospatial predictors increases. Furthermore, RF outperforms DNN, and their difference in performance decreases when the information gap between field‐measured and geospatial data decreases. This suggests that mismatched scales between field‐derived channel types and geospatial predictors hinder sequential information processing in DNN. Finally, our results highlight a sampling trade‐off between uniformly capturing geomorphic variability and ensuring robust generalisation.

Contingent magnetic variation and beta-band oscillations in sensorimotor temporal decision-making

The ability to accurately encode the temporal information of sensory events and hence to make prompt action is fundamental to humans’ prompt behavioral decision-making. Here we examined the ability of ensemble coding (averaging multiple inter-intervals in a sound sequence) and subsequent immediate reproduction of target duration at half, equal, or double that of the perceived mean interval in a sensorimotor loop. With magnetoencephalography (MEG), we found that the contingent magnetic variation (CMV) in the central scalp varied as a function of the averaging tasks, with a faster rate for buildup amplitudes and shorter peak latencies in the “half” condition as compared to the “double” condition. ERD (event-related desynchronization) -to-ERS (event-related synchronization) latency was shorter in the ”half” condition. A robust beta band (15–23 Hz) power suppression and recovery between the final tone and the action of key pressing was found for time reproduction. The beta modulation depth (i.e., the ERD-to-ERS power difference) was larger in motor areas than in primary auditory areas. Moreover, results of phase slope index (PSI) indicated that beta oscillations in the left supplementary motor area (SMA) led those in the right superior temporal gyrus (STG), showing SMA to STG directionality for the processing of sequential (temporal) auditory interval information. Our findings provide the first evidence to show that CMV and beta oscillations predict the coupling between perception and action in time averaging.

Controlled pathways and sequential information processing in serially coupled mechanical hysterons

The complex sequential response of frustrated materials results from the interactions between material bits called hysterons. Hence, a central challenge is to understand and control these interactions, so that materials with targeted pathways and functionalities can be realized. Here, we show that hysterons in serial configurations experience geometrically controllable antiferromagnetic-like interactions. We create hysteron-based metamaterials that leverage these interactions to realize targeted pathways, including those that break the return point memory property, characteristic of independent or weakly interacting hysterons. We uncover that the complex response to sequential driving of such strongly interacting hysteron-based materials can be described by finite state machines. We realize information processing operations such as string parsing in materia, and outline a general framework to uncover and characterize the FSMs for a given physical system. Our work provides a general strategy to understand and control hysteron interactions, and opens a broad avenue toward material-based information processing.

Neurocognitive and psycho-emotional profile of children with disabilities

Objective Neurocognitive deficits in attention, short-term memory, and sequential information processing are present in children with a variety of disabilities, whereas language and visuospatial abilities vary. Method We compared the performance of 59 children (mean age, 15 years) with learning disabilities (n = 18), Down syndrome (n = 21), and intellectual disabilities (n = 20). A series of neuropsychological tests were used to evaluate the neurocognitive processes of memory, attention, visuospatial perception, and executive function. To better understand what emotions they experience, we assessed emotions like anxiety, depression, and positive and negative mood. Results The performance of children with Down syndrome was statistically significantly different from that of other groups, indicating lower performance (p = 0.001). In comparison to other groups, children with Down syndrome performed significantly worse across all cognitive domains. Additionally, there were no statistically significant differences between groups and low emotional functioning scores across the board for all children. People with DS frequently have distinctive neurocognitive and neurobehavioral profiles that appear during particular developmental phases and have many distinct strengths and weaknesses that should be respected as they mature over the course of their lives. The current findings have substantial consequences for interventions that are focused on achieving the best results.

Human intelligence for authors, reviewers and editors using artificial intelligence

We call artificial intelligence any machine that processes information with some purpose, complying with the logical rules of Turing's computation described more than 70 years ago. These machines work with instructions called algorithms, a finite and well-defined sequence of information processing implemented by automata (computers) or any digital technology to optimize a process. This means that the purpose of artificial intelligence is optimization

Theta Oscillations Support Prefrontal-hippocampal Interactions in Sequential Working Memory.

The prefrontal cortex and hippocampus may support sequential working memory beyond episodic memory and spatial navigation. This stereoelectroencephalography (SEEG) study investigated how the dorsolateral prefrontal cortex (DLPFC) interacts with the hippocampus in the online processing of sequential information. Twenty patients with epilepsy (eight women, age 27.6 ± 8.2 years) completed a line ordering task with SEEG recordings over the DLPFC and the hippocampus. Participants showed longer thinking times and more recall errors when asked to arrange random lines clockwise (random trials) than to maintain ordered lines (ordered trials) before recalling the orientation of a particular line. First, the ordering-related increase in thinking time and recall error was associated with a transient theta power increase in the hippocampus and a sustained theta power increase in the DLPFC (3-10 Hz). In particular, the hippocampal theta power increase correlated with the memory precision of line orientation. Second, theta phase coherences between the DLPFC and hippocampus were enhanced for ordering, especially for more precisely memorized lines. Third, the theta band DLPFC → hippocampus influence was selectively enhanced for ordering, especially for more precisely memorized lines. This study suggests that theta oscillations may support DLPFC-hippocampal interactions in the online processing of sequential information.

Counting and Sequential Information Processing in Mechanical Metamaterials.

Materials with an irreversible response to cyclic driving exhibit an evolving internal state which, in principle, encodes information on the driving history. Here we realize irreversible metamaterials that count mechanical driving cycles and store the result into easily interpretable internal states. We extend these designs to aperiodic metamaterials that are sensitive to the order of different driving magnitudes, and realize "lock and key" metamaterials that only reach a specific state for a given target driving sequence. Our metamaterials are robust, scalable, and extendable, give insight into the transient memories of complex media, and open new routes towards smart sensing, soft robotics, and mechanical information processing.

Classification tasks using input driven nonlinear magnetization dynamics in spin Hall oscillator

The inherent nonlinear magnetization dynamics in spintronic devices make them suitable candidates for neuromorphic hardware. Among spintronic devices, spin torque oscillators such as spin transfer torque oscillators and spin Hall oscillators have shown the capability to perform recognition tasks. In this paper, with the help of micromagnetic simulations, we model and demonstrate that the magnetization dynamics of a single spin Hall oscillator can be nonlinearly transformed by harnessing input pulse streams and can be utilized for classification tasks. The spin Hall oscillator utilizes the microwave spectral characteristics of its magnetization dynamics for processing a binary data input. The spectral change due to the nonlinear magnetization dynamics assists in real-time feature extraction and classification of 4-binary digit input patterns. The performance was tested for the classification of the standard MNIST handwritten digit data set and achieved an accuracy of 83.1% in a simple linear regression model. Our results suggest that modulating time-driven input data can generate diverse magnetization dynamics in the spin Hall oscillator that can be suitable for temporal or sequential information processing.

Pilot Study on the Visualization of Latent Fingerprints and Naked Eye Detection of Hg2+ and Zn2+ Ions in Aqueous Media Using Ninhydrin-Based Thiosemicarbazone.

Here, we described a cheap and effective chemosensor (NHPyTSC) that can distinguish Hg2+ and Zn2+ ions from other metal ions and evaluated this phenomenon using several spectroscopy techniques. With the addition of mercury and zinc ions, the proposed chemosensor in particular showed noticeable changes in color and absorption spectra. Additionally, by including EDTA in the NHPyTSC-Hg2+ and NHPyTSC-Zn2+ solutions, colorimetry readings can be reversed. We developed a molecular-scale sequential information processing circuit and presented the "writing-reading-erasing-reading" and "multiwrite" behaviors in the form of binary logic based on the great reversibility of this process. Moreover, by sequentially adding Hg2+, Zn2+, and EDTA, NHPyTSC imitates a molecular keypad lock and molecular logic gates. Density functional theory (DFT) investigations provided more evidence of the Hg2+ and Zn2+ ions' ability to attach to NHPyTSC. The most interesting part of this work is that a study on the latent fingerprint detection of the powder compound revealed that NHPyTSC exhibits good adherence and finger ridge features without background stains. When compared to black and white fingerprint powders, it is discovered that the NHPyTSC powder produces results that are remarkably clear on the majority of surfaces. This demonstrated their potential for real-world use, particularly in the area of criminal investigations.

Sequential information processing in persuasion.

We present a theory of sequential information processing in persuasion (SIP). It extends assumptions of the heuristic-systematic model, in particular the idea that information encountered early in a persuasion situation may affect the processing of subsequent information. SIP also builds on the abstraction from content-related dichotomies in accord with the parametric unimodel of social judgment. SIP features one constitutional axiom and three main postulates: (A) Persuasion is the sequential processing of information that is relevant to judgment formation. (1) Inferences drawn from initial information may bias the processing of subsequent information if they are either activated rules or valence expectations that are relevant to the subsequent information. (2) Inferences drawn from initial information are resistant to change. Thus, the interpretation of subsequent information is assimilated to inferences drawn from the initial information. Or, if assimilation is impossible, contrast effects occur. (3) The overall effect of a persuasion attempt corresponds to the recipient’s judgment at the moment the processing of information is terminated. We illustrate how our predictions for assimilation and contrast effects may be tested by presenting results from an experiment (N = 216) in which we presented exactly the same arguments but varied the processing sequence. We discuss theoretical and applied implications of sequence effects for persuasion phenomena, as well as challenges for further research developing and testing the theory.

An efficient 2-aminothiazolesalicylaldehyde fluorescent chemosensor for Fe2+ ion detection and a potential inhibitor of NUDT5 signaling hormone for breast cancer cell and molecular keypad lock application

A novel thiazole phenol conjugate, 2-aminothiazolesalicylaldehyde (receptor1) was designed and synthesized for the first time through a single step process via Schiff base condensation reaction. The formation of receptor1 was confirmed by FTIR, 13C NMR, and 1H NMR. The IR spectra confirmed the presence of the aldimine formation. It is further supported by the proton NMR, showing the disappearance of aldehyde peaks and the formation of a new imine peak. This is further corroborated by the 13C NMR. The receptor1 complexing with various metal ions were studied through fluorescence spectroscopy showed its selectivity toward Fe2+ ion following a reverse photoinduced electron transfer (PET) process compared to all other potentially competing ions. The receptor1 was applied as a sensor to sense Fe2+ ion in water samples. The detection limit for Fe2+ ion in drinking water was substantially lower (0.003 µM) than the EPA (environmental protection agency) recommendation (5.37 M). The capability of receptor1 in recovering Fe2+ ion in bore water, tap water, and drinking water was up to 99.5%. The receptor1 was also used as a chelating ligand (receptor1) in molecular docking and it was assessed as a potential inhibitor of NUDT5, a silence hormone signaling for breast cancer. The test compound (PDB: 5NWH) showed good affinity toward the target receptor1 with the binding energy of – 5.23 kcal mol−1. Furthermore, the receptor1 showed excellent reversibility property on adding EDTA solution. Due to the marvelous reversible property, a molecular-scale sequential information processing circuit is designed for the multi-task behavior such as ‘Writing-Reading-Erasing-Reading’ in the form of binary logic gate. The consecutive addition of Fe2+ ion and EDTA solution to receptor1 paves a way for the construction of INHIBIT logic gate. Additionally, the receptor1 showed the mimicking behavior of molecular keypad lock.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11696-022-02373-z.

A novel colorimetric chemosensor for selective and highly sensitive determination of thiourea: An approach toward a molecular keypad lock

AbstractA novel simple reversible colorimetric chemosensor has been designed and developed using 4‐(2‐pridylazo) resorcinol (PAR). The colorimetric receptor PAR has been successfully applied for the determination of Hg2+ ions, and then the PAR‐Hg2+ complex has been used for thiourea (TU) measurements in aqueous‐buffer medium. This chemosensor exhibited an obvious color change from yellow to bright red in the presence of Hg2+ in aqueous solution. In addition, the resulting PAR‐Hg2+ complex sensed TU through naked‐eye, showing recovery from PAR‐Hg2+ to PAR, in aqueous solution. The detection limits reached up to 3.01 nmol/L for Hg2+ and 8.5 nmol/L for TU. The operational simplicity and cost‐effectiveness of colorimetric measurements using PAR and Hg2+ were achieved. Based on this, the designed method was well used to determine the analytes in industrial and environmental samples. The molecular switching between two chemical inputs (Hg2+, TU) consequences in the IMPLICATION molecular logic gate, which has been integrated sequentially to procedure a sequential information processing device.

Local Homeostatic Regulation of the Spectral Radius of Echo-State Networks.

Recurrent cortical networks provide reservoirs of states that are thought to play a crucial role for sequential information processing in the brain. However, classical reservoir computing requires manual adjustments of global network parameters, particularly of the spectral radius of the recurrent synaptic weight matrix. It is hence not clear if the spectral radius is accessible to biological neural networks. Using random matrix theory, we show that the spectral radius is related to local properties of the neuronal dynamics whenever the overall dynamical state is only weakly correlated. This result allows us to introduce two local homeostatic synaptic scaling mechanisms, termed flow control and variance control, that implicitly drive the spectral radius toward the desired value. For both mechanisms the spectral radius is autonomously adapted while the network receives and processes inputs under working conditions. We demonstrate the effectiveness of the two adaptation mechanisms under different external input protocols. Moreover, we evaluated the network performance after adaptation by training the network to perform a time-delayed XOR operation on binary sequences. As our main result, we found that flow control reliably regulates the spectral radius for different types of input statistics. Precise tuning is however negatively affected when interneural correlations are substantial. Furthermore, we found a consistent task performance over a wide range of input strengths/variances. Variance control did however not yield the desired spectral radii with the same precision, being less consistent across different input strengths. Given the effectiveness and remarkably simple mathematical form of flow control, we conclude that self-consistent local control of the spectral radius via an implicit adaptation scheme is an interesting and biological plausible alternative to conventional methods using set point homeostatic feedback controls of neural firing.

Analysis of the volatile compounds’ condensate exhaled air “electronic nose” based on piezoelectric sensor to assess the status of calves

The article discusses general principles of obtaining diagnostic information using eight chemical piezoelectric gas sensors with nanostructural coverings from exhaled breath condensate for health assessment of the upper respiratory tract of pre-month-old calves. Multidimensional information of an e-nose can be presented in various numeric and visualized matrices, characteristics of which are an integral analytical signal of the sensor array. The research devoted to the search for the techniques of extracting analytical information from multidimensional e-nose data to assess upper respiratory tract state from the appearance of the first sign of respiratory disease to tracheobronchitis and bronchopneumonia. The piezoelectric sensor array is characterized by high sorption activity with priority biomolecules (which are the markers of the abnormal metabolic processes), low cost along with reliable repeatability of sorption properties from batch to batch, the simplicity of application, and fast response and recovery time. Here we present the results of simple algorithms used for assessment of upper respiratory tract state by a 2-minute analysis of odour over 1-ml biosample without sample preparation. It was shown that traditional quantitative parameters of e-nose are not adequate for simultaneous sample grouping and volatile compounds qualitative composition establishing. The additionally calculated sorption parameters Aij are more informative in the analysis of biosamples volatile: using for identification of volatile biomarkers, describe the health state correlating with clinical diagnosis. The sequence of information processing: signals of each sensor, integral characteristic, “visual prints,” additional sorption parameters–allows assessing the calf health virtually in situ without transporting samples to specialized laboratories.

The main technical solutions for the creation of an automated control system for the technological process of thermal vortex enrichment

The main technical solutions for the creation of an automated control system for the technological process of thermal vortex enrichment were adopted for the creation of an integrated resource-saving technology and the organization of high-tech production of nanostructures based on carbon and silicon dioxide to improve the properties of building and structural materials using control digitalization techniques technological processes. The decisions were made taking into account the dependence of the process of obtaining a modifying additive containing nanostructures in the form of spherical silicon dioxide on a large number of interrelated variables, a variety of operating modes of the technological line, and instantaneous changes in parameters. Technical solutions have been developed for the structure, means and methods of communication for information exchange; interconnection with adjacent systems, ensuring its compatibility; modes of operation, diagnostics of system operation; number, qualifications and functions of personnel, modes of their work, the order of interaction. This includes also requirements for ensuring the given consumer characteristics of the system, which determine its quality; the composition of functions, complexes of tasks implemented by the system; a set of technical means, its placement at the facility. Besides, it concerns the composition of information, volume, methods of its organization, types of machine media, input and output documents and messages, information processing sequence and other components; the composition of software, languages of activity.

An Investigation of Cognitive Processing of Fear Appeal Messages Promoting HPV Vaccination: Predictors and Outcomes of Magnitude and Valence of Cognitive Responses

An improvement in HPV vaccination rates is one of the primary goals of public health organizations. Toward this end, fear appeal communication is commonly used in health interventions, warning individuals of threats of HPV infection and promoting vaccination. However, little is known about how threat-related emotions, such as fear and anxiety, influence the cognitive processing of vaccination information and how this processing is associated with vaccination intention. To address this void, this study tests a model drawing upon functional emotion theories and dual-process models of persuasion. Results from an experimental study showed that fear and anxiety, which arose from exposure to threat information, triggered motivation to process HPV protection-related information, which in turn, was positively associated with depth of HPV vaccination information processing. Subsequently, greater depth of processing led to a greater number of positive cognitive responses when participants were presented with information with a high (vs. low), level of response efficacy. Finally, greater positivity of cognitive responses predicted greater intention to obtain HPV vaccination. Collectively, our findings provide a theory-based explanation about how the sequential provision and processing of threat and efficacy information in fear appeals contribute to the promotion of HPV vaccination. Implications for designing fear appeal messages are discussed.

Brain-Inspired Active Learning Architecture for Procedural Knowledge Understanding Based on Human-Robot Interaction

Improving robots with self-learning ability is one of the critical challenges for the researchers in the area of cognitive robotics and artificial general intelligence. This robot will decide when, where, and what to learn in a continuous visual environment by itself. Here we focus on the procedural knowledge learning, which is sequential and considered harder to understand compared with declarative knowledge in the cognitive system. Inspired by the architecture of the human brain which has integrated well different kinds of cognitive functions, a Brain-inspired Active Learning Architecture (BALA) is proposed for procedural knowledge understanding based on Baxter robot and human interaction. The BALA model contains four main parts: inspired by Primary Visual Pathway, a Convolutional Neural Network (CNN) is constructed for spatial information abstraction; inspired by the Hippocampus Pathway (especially the recurrent loops in CA3 sub-region), a Recurrent Neural Network (RNN) is built for sequential information processing related with procedural knowledge; inspired by the Prefrontal Cortex, a Knowledge Graph based on Bag Of Words (BOW) is constructed for declarative knowledge generation and association; inspired by the Basal Ganglia Pathway, we select Q matrix for Reinforcement Learning (RL). The CNN and RNN parts will be firstly pre-trained on ImageNet dataset and standard Youtube Video-Scene dataset respectively. Then, the RNN, Knowledge Graph, and Q matrix will be dynamically updated in the Baxter robot’s interactive learning procedure with human cooperators. The BALA could actively and incrementally recognize different kinds of procedural knowledge. In 22-type daily-life videos with procedure knowledge (e.g., opening the door, wiping the table, or taking the phone), the BALA model gets the best performance compared with standard CNN, RNN, RL, and other integrative methods. The BALA model is a small step on integrative intelligence interaction between the Baxter robot and human cooperator.

Expectations about word stress modulate neural activity in speech-sensitive cortical areas

A recent dual-stream model of language processing proposed that the postero-dorsal stream performs predictive sequential processing of linguistic information via hierarchically organized internal models. However, it remains unexplored whether the prosodic segmentation of linguistic information involves predictive processes. Here, we addressed this question by investigating the processing of word stress, a major component of speech segmentation, using probabilistic repetition suppression (RS) modulation as a marker of predictive processing. In an event-related acoustic fMRI RS paradigm, we presented pairs of pseudowords having the same (Rep) or different (Alt) stress patterns, in blocks with varying Rep and Alt trial probabilities. We found that the BOLD signal was significantly lower for Rep than for Alt trials, indicating RS in the posterior and middle superior temporal gyrus (STG) bilaterally, and in the anterior STG in the left hemisphere. Importantly, the magnitude of RS was modulated by repetition probability in the posterior and middle STG. These results reveal the predictive processing of word stress in the STG areas and raise the possibility that words stress processing is related to the dorsal “where” auditory stream.

Congruent audio-visual stimulation during adaptation modulates the subsequently experienced visual motion aftereffect

Sensory information registered in one modality can influence perception associated with sensory information registered in another modality. The current work focuses on one particularly salient form of such multisensory interaction: audio-visual motion perception. Previous studies have shown that watching visual motion and listening to auditory motion influence each other, but results from those studies are mixed with regard to the nature of the interactions promoting that influence and where within the sequence of information processing those interactions transpire. To address these issues, we investigated whether (i) concurrent audio-visual motion stimulation during an adaptation phase impacts the strength of the visual motion aftereffect (MAE) during a subsequent test phase, and (ii) whether the magnitude of that impact was dependent on the congruence between auditory and visual motion experienced during adaptation. Results show that congruent direction of audio-visual motion during adaptation induced a stronger initial impression and a slower decay of the MAE than did the incongruent direction, which is not attributable to differential patterns of eye movements during adaptation. The audio-visual congruency effects measured here imply that visual motion perception emerges from integration of audio-visual motion information at a sensory neural stage of processing.

Biomedical word sense disambiguation with bidirectional long short-term memory and attention-based neural networks

BackgroundIn recent years, deep learning methods have been applied to many natural language processing tasks to achieve state-of-the-art performance. However, in the biomedical domain, they have not out-performed supervised word sense disambiguation (WSD) methods based on support vector machines or random forests, possibly due to inherent similarities of medical word senses.ResultsIn this paper, we propose two deep-learning-based models for supervised WSD: a model based on bi-directional long short-term memory (BiLSTM) network, and an attention model based on self-attention architecture. Our result shows that the BiLSTM neural network model with a suitable upper layer structure performs even better than the existing state-of-the-art models on the MSH WSD dataset, while our attention model was 3 or 4 times faster than our BiLSTM model with good accuracy. In addition, we trained “universal” models in order to disambiguate all ambiguous words together. That is, we concatenate the embedding of the target ambiguous word to the max-pooled vector in the universal models, acting as a “hint”. The result shows that our universal BiLSTM neural network model yielded about 90 percent accuracy.ConclusionDeep contextual models based on sequential information processing methods are able to capture the relative contextual information from pre-trained input word embeddings, in order to provide state-of-the-art results for supervised biomedical WSD tasks.