Variable Latency Research Articles

Propagation dynamics for an epidemic patch model with variable incubation period

Abstract We study an epidemic patch model that describes the disease spread in population with variable latency due to the differences in immunologic tolerance between individuals. We focus on whether the disease can spread in space that leads to the emergence of epidemic wave, that is the travelling wave solution with constant speed. We first establish some properties of the linearized wave profile equations, which are helpful in obtaining the priori estimates of travelling waves and wave speeds. Then, applying the truncation method and limiting arguments, we can obtain threshold propagation dynamics of the epidemic model. Our result gives a complete characterization of the existence, nonexistence and minimal wave speed of travelling waves. To the best of our knowledge, this is the first time to characterize the propagation dynamics of epidemic patch model with variable latency, which contributes to the understanding of the transmission phenomenon of disease.

A framework for Interpretable deep learning in cross-subject detection of event-related potentials

Event-related potential-based Brain-Computer Interfaces are becoming widely popular due to their ability to send commands quickly with high accuracy. However, the stationary characteristics of electroencephalographic signals, coupled with their low signal-to-noise ratio, lead to variations in amplitude, time period, and latency in the patterns of event-related potentials across different trials, sessions, days and subjects. Conventional feature extraction and machine learning algorithms are not designed to handle these differences, requiring the development of methods that can address these variations. Here, we propose a novel lightweight deep neural network for event-related potential classification, consisting of three modules. In this model, we have a spatio-temporal module that learns local features simultaneously across channels and time points. Following this, there's a component extractor module comprising depthwise convolutions, inspired by mixed depthwise convolutions, to capture the event-related potential characteristics with different temporal durations. Lastly, an advanced temporal layer addresses event-related potential shape and scale variations using deformable convolutions. We conducted experiments on event-related potential detection in a subject-independent scenario using one error-related negativity potential dataset and three perturbation-evoked potential datasets. Comparisons were made with established methods including two conventional machine learning algorithms and three well-known deep learning architectures, demonstrating that our model outperformed them in terms of classification accuracy and parameter efficiency. In our analysis, we aimed to understand the model's performance using gradient-weighted class activation mapping and t-distributed stochastic neighbor embedding. These methods facilitated the visualization and interpretation of our model's effectiveness, providing insights into its relationship with the neuroscientific characteristics of event-related potentials.

Enhancing real-world far-field speech with supervised adversarial training

The generalization of speech enhancement models to real-world far-field speech encounters significant challenges, including low signal-to-noise ratio, high reverberation, and variable latency between far-field and near-field recordings. Additionally, using the non-ideal near-field recordings as the labeled desired output further reduces the effectiveness of commonly utilized predictive models. To tackle these challenges, we propose the Far-field to Near-field Speech Enhancement through Supervised Adversarial Training (FNSE-SAT) strategy. This approach leverages supervised adversarial learning via the Multi-Resolution Discriminator, leveraging diverse speech characteristics with different frequency resolutions. A temporal frame shift operation is also incorporated to mitigate alignment discrepancies observed in real-world data and its effectiveness is confirmed by counting the accuracy of Voice Activity Detection. Experimental validation in both causal and non-causal configurations demonstrates that FNSE-SAT significantly outperforms the state-of-the-art predictive model on real-world datasets. Furthermore, adopting the transfer learning strategy, where the model is initialized with a simulated dataset before fine-tuning with real-world data, strengthens the efficacy of FNSE-SAT, leading to superior outcomes. The results of character error rate show that FNSE-SAT generates fewer components that deviate from the textual content compared to the generative diffusion method. Reducing the Discriminator's resolution to a single version decreases the DNSMOS but has a slight effect on the character error rate.

Perceptual Response Training for Reduction of Injury Risk Among High School Girls' Soccer Players.

Background/Objectives: Neural processes involved in visual detection, decision-making, and motor plan execution are believed to play a key role in the avoidance of sport-related injuries, but very little evidence exists to guide the development of training activities for the optimization of brain function. Immersive virtual reality provides a means to precisely measure the amount of time that elapses from visual stimulus presentation to the initiation of a motor response (i.e., perceptual latency) or its completion (i.e., response time). Methods: The median value of a metric quantifying both the speed and accuracy (i.e., the rate correct per second of response time) of 50 high school female soccer players was used to assign those who exhibited suboptimal performance to a training program. Training sessions required less than 5 min and the number of sessions completed over a 7-week period ranged from 3 to 13 (median = 5). Results: Among 42 players available for follow-up assessment at 8 weeks after the first practice session (training n = 19; comparison n = 23), the results of regression-discontinuity analyses demonstrated statistically significant differences (p < 0.05) for metrics representing fast/accurate movement initiation (i.e., the rate correct score for perceptual latency, p = 0.016) and across-trial consistency (i.e., perceptual latency variability, p = 0.027). From the first practice session to the end of the soccer season, 12 injuries were sustained by 10 players (four concussions and eight musculoskeletal injuries). A time-to-event analysis demonstrated strong associations with perceptual latency variability ≥ 0.143 (Hazard Ratio = 15.43, p = 0.011) and a lifetime history of at least one concussion (Hazard Ratio = 8.84, p = 0.008). Conclusions: The strong association of movement initiation consistency with the avoidance of concussion or musculoskeletal injury suggests that the training program may have a highly beneficial far-transfer effect.

Dual-timescale resource management for multi-type caching placement and multi-user computation offloading in Internet of Vehicle

In Internet of Vehicle (IoV), edge computing can effectively reduce task processing delays and meet the real-time needs of connected-vehicle applications. However, since the requirements for caching and computing resources vary across heterogeneous vehicle requests, a new challenge is posed on the resource management in the three-tier cloud–edge–end architecture, particularly when multi users offload tasks in the same time. Our work comprehensively considers various scenarios involving the deployment of multiple caching types from multi-users and the distinct time scales of offloading and updating, then builds a joint optimization caching placement, computation offloading and computational resource allocation model, aiming to minimize overall latency. Meanwhile, to better solving the model, we propose the Multi-node Collaborative Caching, Offloading, and Resource Allocation Algorithm (MCCO-RAA). MCCO-RAA utilizes dual time scales to optimize the problem: employing a Bellman optimization idea-based multi-node collaborative greedy caching placement strategy at large time scales, and a computational offloading and resource allocation strategy based on a two-tier iterative Deep Deterministic Policy Gradient (DDPG) and cooperative game at small time scales. Experimental results demonstrate that our proposed scheme achieves a 28% reduction in overall system latency compared to the baseline scheme, with smoother latency variations under different parameters.

Assessing the influence of latency variability on EEG classifiers - a case study of face repetition priming

Data-driven strategies have been widely used to distinguish experimental effects on single-trial EEG signals. However, how latency variability, such as within-condition jitter or latency shifts between conditions, affects the performance of EEG classifiers has not been well investigated. Without explicitly considering and disentangling such attributes of single trials, neural network-based classifiers have limitations in measuring their contributions. Inspired by domain knowledge of subcomponent latency and amplitude from traditional cognitive neuroscience, this study applies a stepwise latency correction method on single trials to control for their contributions to classifier behavior. As a case study demonstrating the value of this method, we measure repetition priming effects of faces, which induce large reaction time differences, latency shifts, and amplitude effects in averaged event-related potentials. The results show that within-condition jitter negatively impacts classifier performance, but between-condition latency shifts improve accuracy, whereas genuine amplitude differences have no significant influence. While demonstrated in the case of priming effects, this methodology can be generalized to experiments involving many kinds of time-varying signals to account for the contributions of latency variability to classifier performance.

Diagnostic Value of Bereitschaftspotential in People With Functional Seizures.

Bereitschaftspotential (BP) or readiness potential in people with functional movement disorders can aid diagnostic workup. We evaluated the diagnostic value of BP as an interictal EEG marker in people with functional seizures (FS). We recorded and analyzed BP interictal before intended movements in 17 adults with FS and 17 controls with alternative diagnoses. We evaluated the signals for the presence of BP, latency, amplitude, and early versus late BP. Bereitschaftspotential was present in all except one person with FS. We found no significant differences in the latency and amplitude of BP between participants with FS and controls. The early BP showed the most significant variance in amplitude, latency, and presence. We found interictal typical BP values in participants with FS and variable semiology, while earlier research found interictal no BP in functional movement disorders. These findings do not support the use of BP as an interictal diagnostic tool for FS. Differences in early BP and focus on FS with pure motor semiology are starting points for further research evaluating potential interictal markers in people with FS.

Variability of cortico-cortical evoked potentials in the epileptogenic zone is related to seizure occurrence.

Cortico-cortical evoked potentials (CCEPs) were described as reproducible during trains of single-pulse electrical stimulations (SPES). Still, few studies described a variability of CCEPs that was higher within the epileptogenic zone (EZ). This study aimed at characterizing the relationship of CCEP variability with the occurrence of interictal/ictal epileptiform discharges at the temporal vicinity of the stimulation, but not during the stimulation, by effective connectivity modifications. We retrospectively included 20 patients who underwent SPES during their stereo-electroencephalography (SEEG). We analyzed the variability of CCEPs by using the post-stimulation time course of intertrial standard deviation (amplitude) and the timing of peak amplitude signal of CCEP epochs (latency). Values were corrected for the Euclidian distance between stimulating/recording electrodes. Receiver operating characteristics curves were used to assess the relationship with the EZ. The link between CCEP variability and interictal discharges occurrence, seizure frequency prior to the SEEG recording, and number of seizures during SEEG recording was assessed with Spearman's correlations. A relationship was demonstrated between the EZ and both the distance-corrected latency variation (area under the curve (AUC): 0.73-0.74) and the distance-corrected amplitude variation (AUC: 0.71-0.72) and both were related with the occurrence of seizures. Seizures before/during SEEG impact the dynamics of effective connectivity within the epileptogenic network by reducing the variability of CCEP latency/amplitude when the seizure frequency increases. It suggests a strengthening of the epileptogenic network with the occurrence of many seizures. These findings stress the importance of early epilepsy surgery at a time when the network organization has not yet been complete.

Distraction impact of concurrent conversation on event-related potential based brain-computer interfaces

Objective.This study investigates the impact of conversation on the performance of visual event-related potential (ERP)-based brain-computer interfaces (BCIs), considering distractions in real life environment. The research aims to understand how cognitive distractions from speaking and listening activities affect ERP-BCI performance.Approach.The experiment employs a dual-task paradigm where participants control a smart light using visual ERP-BCIs while simultaneously conducting speaking or listening tasks.Main results.The findings reveal that speaking notably degrades BCI accuracy and the amplitude of ERP components, while increases the latency variability of ERP components and occipital alpha power. In contrast, listening and simple syllable repetition tasks have a lesser impact on these variables. The results suggest that speaking activity significantly distracts visual attentional processes critical for BCI operationSignificance. This study highlights the need to take distractions by daily conversation into account of the design and implementation of ERP-BCIs.

A method for calculating the grand average of a set of auditory brain-stem responses.

To calculate a grand average waveform for a set of auditory brain-stem responses (ABRs), no generally accepted method exists. Here, we evaluate a new method using temporal adjustment of the underlying ABRs. Compared to a method without temporal adjustment, the new method results in higher amplitudes of the individual waves in the grand average. The grand average produced by the method better represents the group mean wave-amplitudes because it reduces smearing of the individual waves caused by inter-subject latency variability.

The "what" and "when" of language input to children: Linguistic and temporal predictors of vocabulary in 3-year-olds.

The quantity, quality, and complexity of language input are important for children's language development. This study examined how the detailed timing of this input relates to children's vocabulary at 3 years of age in 64 mother-child dyads (male = 28; female = 36; White = 69%, Black = 31%). Acoustical analysis of turn taking in mother-child dialogue found that more consistently timed maternal responses (lower response latency variability) were associated (r = .42, p < .001) with higher vocabulary (Peabody Picture Vocabulary Test, third edition) scores. In mothers with consistently timed responses, the complexity (mean length of utterance) of their child-directed speech significantly predicted (r = .53, p = .002) their children's vocabulary. This suggests that predictably timed contingent maternal responses provide an important learning cue that supports language development beyond the content of language input itself. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Deep reinforcement learning-based low-latency task offloading for mobile-edge computing networks

Wireless Mobile Edge Computing (MEC) is an emerging solution to facilitate users’ computing. However, there are still some drawbacks, such as slow learning speed and high task latency. To overcome these difficulties, a deep reinforcement learning (DRL)-based task offloading (DRTO) framework is proposed, taking into account the time-varying nature of wireless channels and the unpredictability of task arrivals in multi-user MEC systems. Many deep neural networks (DNNs) are also used as scalable solutions to quickly determine the best offloading strategy by combining the perceptual capabilities of deep learning with the decision making capabilities of reinforcement learning (RL). Additionally, the concept of a minimum upper bound on the target Q-value is utilized to address the value overestimation problem that occurs during offload policy updates. This approach aims to minimize the latency of offload decisions by considering different channel environments, variable latency constraints among many users, and limited computational resources. Simulation results show that the algorithm reduces computation time and latency while achieving near-optimal performance compared to traditional Deep Deterministic Policy Gradient (DDPG) and Q-learning optimization methods. In addition, DRTO achieves more than 99.6% computational speedup compared to current almost ideal benchmarking techniques. Thus, the DRTO computational offload approach offers great potential for reducing overall system latency and improving the user experience.

A 2 : Towards Accelerator Level Parallelism for Autonomous Micromobility Systems

Autonomous micromobility systems (AMS) such as low-speed minicabs and robots are thriving. In AMS, multiple Deep Neural Networks execute in parallel on heterogeneous AI accelerators. An emerging paradigm called Accelerator Level Parallelism (ALP) suggests managing accelerators holistically. However, there lacks a specialized and practical solution populating ALP for an AMS, where the varying real-time requirements under different working scenarios bring an opportunity to dynamically tradeoff between latency and efficiency. Furthermore, accelerator heterogeneity introduces enormous configuration space, and the shared-memory architecture results in dynamic bandwidth interference. In this paper, we propose A 2 , a novel AMS resource manager optimizing energy and memory space efficiency under variable latency constraints. We gain insight from prior Learn&amp;Control scheme to design an Analyze&amp;Adapt scheme specialized for heterogeneous AI accelerators under shared-memory architecture. It features analyzing the system thoroughly offline to support two-step adaptation online. We build a prototype of A 2 and evaluate it on a commercial edge platform. We show that A 2 achieves 32.8% improvements in power and 13.8% in memory compared with control-based methods. As for timeliness enhancement, A 2 reduces the deadline violation rate by 9.2 percentage points (12.8% → 3.6%) on average compared to directly porting Learn&amp;Control methods.

Functional and structural maturation of auditory cortex from 2 months to 2 years old

BackgroundIn school-age children, the myelination of the auditory radiation thalamocortical pathway is associated with the latency of auditory evoked responses, with the myelination of thalamocortical axons facilitating the rapid propagation of acoustic information. Little is known regarding this auditory system function-structure association in infants and toddlers. Methods and ParticipantsThe present study tested the hypothesis that maturation of auditory radiation white-matter microstructure (e.g., fractional anisotropy (FA); measured using diffusion-weighted MRI) is associated with the latency of the infant auditory response (the P2m response, measured using magnetoencephalography, MEG) in a cross-sectional (N = 47, 2 to 24 months, 19 females) as well as longitudinal cohort (N = 18, 2 to 29 months, 8 females) of typically developing infants and toddlers. Of 18 longitudinal infants, 2 infants had data from 3 timepoints and 16 infants had data from 2 timepoints. ResultsIn the cross-sectional sample, non-linear maturation of P2m latency and auditory radiation diffusion measures were observed. Auditory radiation diffusion accounted for significant variance in P2m latency, even after removing the variance associated with age in both P2m latency and auditory radiation diffusion measures. In the longitudinal sample, latency and FA associations could be observed at the level of a single child. ConclusionsFindings provide strong support for the hypothesis that an increase in thalamocortical neural conduction velocity, due to increased axon diameter and/or myelin maturation, contributes to a decrease in the infant P2m auditory evoked response latency. SignificanceInfant multimodal brain imaging identifies brain mechanisms contributing to the rapid changes in neural circuit activity during the first two years of life.

Saccade size predicts onset time of object processing during visual search of an open world virtual environment

ObjectiveTo date the vast majority of research in the visual neurosciences have been forced to adopt a highly constrained perspective of the vision system in which stimuli are processed in an open-loop reactive fashion (i.e., abrupt stimulus presentation followed by an evoked neural response). While such constraints enable high construct validity for neuroscientific investigation, the primary outcomes have been a reductionistic approach to isolate the component processes of visual perception. In electrophysiology, of the many neural processes studied under this rubric, the most well-known is, arguably, the P300 evoked response. There is, however, relatively little known about the real-world corollary of this component in free-viewing paradigms where visual stimuli are connected to neural function in a closed-loop. While growing evidence suggests that neural activity analogous to the P300 does occur in such paradigms, it is an open question when this response occurs and what behavioral or environmental factors could be used to isolate this component. ApproachThe current work uses convolutional networks to decode neural signals during a free-viewing visual search task in a closed-loop paradigm within an open-world virtual environment. From the decoded activity we construct fixation-locked response profiles that enable estimations of the variable latency of any P300 analogue around the moment of fixation. We then use these estimates to investigate which factors best reduce variable latency and, thus, predict the onset time of the response. We consider measurable, search-related factors encompassing top-down (i.e., goal driven) and bottom-up (i.e., stimulus driven) processes, such as fixation duration and salience. We also consider saccade size as an intermediate factor reflecting the integration of these two systems. Main resultsThe results show that of these factors only saccade size reliably determines the onset time of P300 analogous activity for this task. Specifically, we find that for large saccades the variability in response onset is small enough to enable analysis using traditional ensemble averaging methods. SignificanceThe results show that P300 analogous activity does occur during closed-loop, free-viewing visual search while highlighting distinct differences between the open-loop version of this response and its real-world analogue. The results also further establish saccades, and saccade size, as a key factor in real-world visual processing.

What can cartographers learn from artistic paintings when stylizing maps? A preliminary synthesis from the perspective of visual neuroscience

ABSTRACT Creating a map style from scratch is challenging. Cartographers may find inspirations from artistic paintings and transfer their style to maps. However, the reference painting and the extracted style elements may not fit well with the content and function of maps, resulting in undesirable or even frustrating map stylization. The question of how to choose appropriate paintings as inspirations and utilize their essential visual elements to stylize maps remains unanswered. As neuroscience has developed, an increasing number of neural mechanisms underlying visual perception have been discovered, deepening our understanding of artistic styles and their adaptation to maps. In this paper, we draw connections between visual neurons and style elements of both artistic paintings and maps by examining seven neural mechanisms (i.e. central-peripheral vision, color-to-contour adhesion, double-opponent effects, visual filling-in, orientation selectivity, latency variation, and what-where separation) that are typical of artistic paintings. We also demonstrate that these mechanisms are transferable to maps. We then create a cartographic style syntactics to generally indicate how and to what degree these mechanisms can be reflected in four cartographic stylistic criteria (i.e. identification, expressiveness, aesthetic appeal and emotional impact). Our work contributes to the integration of principles from neuroscience with cartographic design to deepen our understanding of map perception and to facilitate map style transfer.

Network resonance and the auditory steady state response

The auditory steady state response (ASSR) arises when periodic sounds evoke stable responses in auditory networks that reflect the acoustic characteristics of the stimuli, such as the amplitude of the sound envelope. Larger for some stimulus rates than others, the ASSR in the human electroencephalogram (EEG) is notably maximal for sounds modulated in amplitude at 40 Hz. To investigate the local circuit underpinnings of the large ASSR to 40 Hz amplitude-modulated (AM) sounds, we acquired skull EEG and local field potential (LFP) recordings from primary auditory cortex (A1) in the rat during the presentation of 20, 30, 40, 50, and 80 Hz AM tones. 40 Hz AM tones elicited the largest ASSR from the EEG acquired above auditory cortex and the LFP acquired from each cortical layer in A1. The large ASSR in the EEG to 40 Hz AM tones was not due to larger instantaneous amplitude of the signals or to greater phase alignment of the LFP across the cortical layers. Instead, it resulted from decreased latency variability (or enhanced temporal consistency) of the 40 Hz response. Statistical models indicate the EEG signal was best predicted by LFPs in either the most superficial or deep cortical layers, suggesting deep layer coordinators of the ASSR. Overall, our results indicate that the recruitment of non-uniform but more temporally consistent responses across A1 layers underlie the larger ASSR to amplitude-modulated tones at 40 Hz.

Design, Implementation and Evaluation of a New Variable Latency Integer Division Scheme

Design, Implementation and Evaluation of a New Variable Latency Integer Division Scheme

Functional and structural maturation of auditory cortex from 2 months to 2 years old.

In school-age children, the myelination of the auditory radiation thalamocortical pathway is associated with the latency of auditory evoked responses, with the myelination of thalamocortical axons facilitating the rapid propagation of acoustic information. Little is known regarding this auditory system function-structure association in infants and toddlers. The present study tested the hypothesis that maturation of auditory radiation white-matter microstructure (e.g., fractional anisotropy (FA); measured using diffusion-weighted MRI) is associated with the latency of the infant auditory response (P2m measured using magnetoencephalography, MEG) in a cross-sectional (2 to 24 months) as well as longitudinal cohort (2 to 29 months) of typically developing infants and toddlers. In the cross-sectional sample, non-linear maturation of P2m latency and auditory radiation diffusion measures were observed. After removing the variance associated with age in both P2m latency and auditory radiation diffusion measures, auditory radiation still accounted for significant variance in P2m latency. In the longitudinal sample, latency and FA associations could be observed at the level of a single child. Findings provide strong support for a contribution of auditory radiation white matter to rapid cortical auditory encoding processes in infants.

Comparison of Thermal and Mechanical Pain Testing Modalities in Sprague Dawley and Fischer 344 Rats (Rattus norvegicus).

While rodents are used extensively for studying pain, there is a lack of reported direct comparisons of thermal and mechanical pain testing methods in rats of different genetic backgrounds. Understanding the range of interindividual variability of withdrawal thresholds and thermal latencies based on these testing methods and/or genetic background is important for appropriate experimental design. Testing was performed in two common rat genetic backgrounds: outbred Sprague-Dawley (SD) and inbred Fischer 344 (F344). Male and female, 10- to 14-wk-old F344 and SD rats were used to assess withdrawal thresholds in 3 different modalities: the Randall-Selitto test (RST), Hargreaves test (HT), and tail flick test (TFT). The RST was performed by using an operator-controlled handheld instrument to generate a noxious pressure stimulus to the left hind paw. The HT and the TFT used an electronically controlled light source to deliver a noxious thermal stimulus to the left hind paw or tail tip, respectively. Rats of each sex and genetic background underwent one type of test on day 0 and day 7. Withdrawal thresholds and thermal latencies were compared among tests. No significant differences were observed. Our findings can serve as a guide for researchers considering these nociceptive tests for their experiments.