Phonological Characteristics Shared By Questioner And Responder: A Comparison Between Individuals With And Without Autism Spectrum Disorder

How typically developed (TD) persons modulate their speech rhythm while talking to individuals with autism spectrum disorder (ASD) remains unclear. We aimed to elucidate the characteristics of phonological hierarchy in the verbal communication between ASD individuals and TD persons. TD and ASD respondents were asked by a TD questioner to share their recent experiences on 12 topics. We included 87 samples of ASD-directed speech (from TD questioner to ASD respondent), 72 of TD-directed speech (from TD questioner to TD respondent), 74 of ASD speech (from ASD respondent to TD questioner), and 55 of TD speech (from TD respondent to TD questioner). We analysed the amplitude modulation structures of speech waveforms using probabilistic amplitude demodulation based on Bayesian inference and found similarities between ASD speech and ASD-directed speech and between TD speech and TD-directed speech. Prosody and the interactions between prosodic, syllabic, and phonetic rhythms were significantly weaker in ASD-directed and ASD speech than those in TD-directed and TD speech, respectively. ASD speech showed weaker dynamic processing from higher to lower phonological bands (e.g. from prosody to syllable) than TD speech. The results indicate that TD individuals may spontaneously adapt their phonological characteristics to those of ASD speech.

A study of spatio-temporal dynamics of emotion processing usingDENS dataset

The emotion research with artificial stimuli does not represent the dynamic processing of emotions in real-life situations. The lack of data on emotion with the ecologically valid naturalistic paradigm hinders the knowledge of emotion mechanisms in a real-world interaction. To this aim, we collected the emotional multimedia clips, validated them with the university students, recorded the neuro-physiological activities and self-assessment ratings for these stimuli. Participants localized their emotional feelings (in time) and were free to choose the best emotion for describing their feelings with minimum distractions and cognitive load. The obtained electrophysiological and self-assessment responses were analyzed with functional connectivity, machine learning and source localization techniques. We observed that the connectivity patterns in the theta and beta band could differentiate emotions better. Using machine learning, we observed that the classification of affective self-assessment features, namely dominance, familiarity, and self-relevance, involves midline brain regions responsible for mentalization and event construction activity compared to valence and arousal, which were mainly associated with lateral brain regions. This finding advocates the need for more than two dimensions for emotion representation. The channels with high predictability were source localized to the brain regions in DMN, sensorimotor and salience networks. Hence, in this naturalistic study, we find that the domain-general systems contribute to emotion construction.

Development of a procedure for determination of damage to seeds and cotton fibers in cotton cleaning machines

Studies on the dynamic state of cotton raw materials when introducing working bodies of processing machines into it allow to draw the following proposition. Depending on the rate of penetration of the working body into the cotton medium and the density of the medium, in the formulas used to describe the state of the medium, the exponent у ρ can vary from 1.5 to 3. The exponent for density ρ is a measure of the compression and compaction of raw materials on the surface of the working body. The exponent of ρ is also related to the amount of damage to cotton fibers and seeds. For the first time, a cotton mass is considered as a compressible porous two-component medium consisting of a mixture of cotton fibers and air included in the composition of a porous medium, which is essential in dynamic processing processes, and it must be taken into account when planning technological modes. From experiments on the penetration of a splitter with a peripheral speed u=3.5 m/s into a cotton medium with a density of ρ=150–350 kg/m3, it can be seen that a locally located “air cushion” appears in the close vicinity of the split end. The pressure in it increases by 1.5–2 times in comparison with the pressure of statistical compression of cotton fibers alone, without taking into account the influence of the air located in the pores of the system. The forces of compression of cotton fibers from the action of the splitter and the force of volumetric action on the fibers are comparable in the area of the "air cushion". Using the general equations of the mechanics of the compressed medium, as well as experimental data, the fundamental equation of the dynamic state of the mass of raw cotton when the working body of the processing machine is introduced into it, such as the density of the medium, the speed of the working body, its external shape and the degree of surface treatment, is derived. The resulting equation can be used to describe the power stresses in a cotton environment in the technological processes of roller and saw ginning, and during cotton cleaning

Forecasting oil and gas saturation of the section based on the results of seismic survey interpretation using innovative methods on the territory of the Republic of Kazakhstan

Seismoacoustic entropy analysis (SAE-analysis) and the method of frequency compositions (MFC) are methods of seismic exploration aimed at solving problems of direct search for hydrocarbons based on the results of seismic exploration in promising areas. Both methods use the seismoacoustic response of a hydrocarbon deposit when interacting with the incident wave front as a search criterion. The location of the deposit is determined through dynamic processing and statistical analysis of the spectral characteristics of the wave field. The article presents the results of the forecast of oil saturation by the methods of MFC and SAE-analysis on the territory of Kazakhstan.

Undivided attention: The temporal effects of attention dissociated from decision, memory, and expectation

Selective attention prioritises relevant information amongst competing sensory input. Time-resolved electrophysiological studies have shown stronger representation of attended compared to unattended stimuli, which has been interpreted as an effect of attention on information coding. However, because attention is often manipulated by making only the attended stimulus a target to be remembered and/or responded to, many reported attention effects have been confounded with target-related processes such as visual short-term memory or decision-making. In addition, the effects of attention could be influenced by temporal expectation. The aim of this study was to investigate the dynamic effect of attention on visual processing using multivariate pattern analysis of electroencephalography (EEG) data, while 1) controlling for target-related confounds, and 2) directly investigating the influence of temporal expectation. Participants viewed rapid sequences of overlaid oriented grating pairs at fixation while detecting a "target" grating of a particular orientation. We manipulated attention, one grating was attended and the other ignored, and temporal expectation, with stimulus onset timing either predictable or not. We controlled for target-related processing confounds by only analysing non-target trials. Both attended and ignored gratings were initially coded equally in the pattern of responses across EEG sensors. An effect of attention, with preferential coding of the attended stimulus, emerged approximately 230ms after stimulus onset. This attention effect occurred even when controlling for target-related processing confounds, and regardless of stimulus onset predictability. These results provide insight into the effect of attention on the dynamic processing of competing visual information, presented at the same time and location.

In-sensor reservoir computing for language learning via two-dimensional memristors

The dynamic processing of optoelectronic signals carrying temporal and sequential information is critical to various machine learning applications including language processing and computer vision. Despite extensive efforts to emulate the visual cortex of human brain, large energy/time overhead and extra hardware costs are incurred by the physically separated sensing, memory, and processing units. The challenge is further intensified by the tedious training of conventional recurrent neural networks for edge deployment. Here, we report in-sensor reservoir computing for language learning. High dimensionality, nonlinearity, and fading memory for the in-sensor reservoir were achieved via two-dimensional memristors based on tin sulfide (SnS), uniquely having dual-type defect states associated with Sn and S vacancies. Our in-sensor reservoir computing demonstrates an accuracy of 91% to classify short sentences of language, thus shedding light on a low training cost and the real-time solution for processing temporal and sequential signals for machine learning applications at the edge.