Sub- and Supra-Second Timing in Auditory Perception: Evidence for Cross-Domain Relationships

Previous studies indicate that there are at least two levels of temporal processing: the sub- and supra-second domains. The relationship between these domains remains unclear. The aim of this study was to test whether performance on the sub-second level is related to that on the supra-second one, or whether these two domains operate independently. Participants were 118 healthy adults (mean age = 23 years). The sub-second level was studied with a temporal-order judgment task and indexed by the Temporal Order Threshold (TOT), on which lower values corresponded to better performance. On the basis of TOT results, the initial sample was classified into two groups characterized by either higher temporal efficiency (HTE) or lower temporal efficiency (LTE). Next, the efficiency of performance on the supra-second level was studied in these two groups using the subjective accentuation task, in which participants listened to monotonous sequences of beats and were asked to mentally accentuate every n-th beat to create individual rhythmic patterns. The extent of temporal integration was assessed on the basis of the number of beats being united and better performance corresponded to longer units. The novel results are differences between groups in this temporal integration. The HTE group integrated beats in significantly longer units than did the LTE group. Moreover, for tasks with higher mental load, the HTE group relied more on a constant time strategy, whereas the LTE group relied more on mental counting, probably because of less efficient temporal integration. These findings provide insight into associations between sub- and supra-second levels of processing and point to a common time keeping system, which is active independently of temporal domain.

Computing top-k temporal closeness in temporal networks

The closeness centrality of a vertex in a classical static graph is the reciprocal of the sum of the distances to all other vertices. However, networks are often dynamic and change over time. Temporal distances take these dynamics into account. In this work, we consider the harmonic temporal closeness with respect to the shortest duration distance. We introduce an efficient algorithm for computing the exact top-k temporal closeness values and the corresponding vertices. The algorithm can be generalized to the task of computing all closeness values. Furthermore, we derive heuristic modifications that perform well on real-world data sets and drastically reduce the running times. For the case that edge traversal takes an equal amount of time for all edges, we lift two approximation algorithms to the temporal domain. The algorithms approximate the transitive closure of a temporal graph (which is an essential ingredient for the top-k algorithm) and the temporal closeness for all vertices, respectively, with high probability. We experimentally evaluate all our new approaches on real-world data sets and show that they lead to drastically reduced running times while keeping high quality in many cases. Moreover, we demonstrate that the top-k temporal and static closeness vertex sets differ quite largely in the considered temporal networks.

Southwest monsoon rainfall in India : Part II - Principal components in temporal domain

An attempt has been made towards objective identification of phases of the southwest monsoon by principal component analysis (PCA) in temporal domain (T-mode). The method utilizes the relationship of weekly rainfall activities with principal components (PCs) of southwest monsoon. Based on the relationships, subgroup of weeks with similar spatial patterns have been identified. Synoptic features of these subgroups have been brought out with the help of synoptic charts. The first four significant PCs are associated with four kinds of active phases of the southwest monsoon when the low pressure systems have typical characteristics corresponding to each PC. Thus, the study suggests a method of interpretation of PCs with the help of synoptic charts by objective identification of phases of southwest monsoon.

Development of the Approximating Functions Method for Problems in a Planar Waveguide with Constant Polarization

Article introduces an extension of the approximating functions method, a particular case of the finite element method (FEM) with interpolating functions in the form of Lagrange polynomials of a special form, to solve electrodynamics problems in a planar waveguide with constant polarization in the spatial-temporal domain using the Volterra integral equation method. The main goal of the article is to expand the area of ​​applicability of this method to three-dimensional problems in a planar waveguide with constant polarization, as well as to obtain general interpolation expressions in analytical form, which will be used to construct a system of nonlinear equations for solving specific problems.

Smoothing spatio-temporal data with complex missing data patterns

We consider spatio-temporal data and functional data with spatial dependence, characterized by complicated missing data patterns. We propose a new method capable to efficiently handle these data structures, including the case where data are missing over large portions of the spatio-temporal domain. The method is based on regression with partial differential equation regularization. The proposed model can accurately deal with data scattered over domains with irregular shapes and can accurately estimate fields exhibiting complicated local features. We demonstrate the consistency and asymptotic normality of the estimators. Moreover, we illustrate the good performances of the method in simulations studies, considering different missing data scenarios, from sparse data to more challenging scenarios where the data are missing over large portions of the spatial and temporal domains and the missing data are clustered in space and/or in time. The proposed method is compared to competing techniques, considering predictive accuracy and uncertainty quantification measures. Finally, we show an application to the analysis of lake surface water temperature data, that further illustrates the ability of the method to handle data featuring complicated patterns of missingness and highlights its potentiality for environmental studies.

Spatio-Temporal Pattern Representation from AI Inspired Brain Model in Spiking Neural Network

Neuronal population activity in the brain is the combined response of information in the spatial domain and dynamics in the temporal domain. Modeling such Spatio-temporal mechanisms is a complex process because of the complexity of the brain and the limitations of the hardware. In this paper, we demonstrate how information processing principles adapted from the brain can be used to create a brain-inspired artificial intelligence (AI) model and represent Spatio-temporal patterns. The same is demonstrated by designing the tiny brain using spiking neural networks, where activated neuronal populations represent information in the spatial domain and transmitting signals represent dynamics in the temporal domain. Spatially located sensory neurons excited by input visual stimuli further activate motor neurons to trigger a motor response that causes behavior modification of the robotic agent. Initially, an isolated brain network is simulated to understand the excitation part from sensory to motor neurons while plotting waveform between membrane potential and time. The response of the network to stimulate robot body movements is also plotted to demonstrate representation. The simulation shows how the response of particular visual stimuli modifies behavior and helps us understand the body and brain synchronization. The perceived environment and resultant behavior response allow us to study body interaction with the environment.

Roadmap on multimode light shaping

Our ability to generate new distributions of light has been remarkably enhanced in recent years. At the most fundamental level, these light patterns are obtained by ingeniously combining different electromagnetic modes. Interestingly, the modal superposition occurs in the spatial, temporal as well as spatio-temporal domain. This generalized concept of structured light is being applied across the entire spectrum of optics: generating classical and quantum states of light, harnessing linear and nonlinear light-matter interactions, and advancing applications in microscopy, spectroscopy, holography, communication, and synchronization. This Roadmap highlights the common roots of these different techniques and thus establishes links between research areas that complement each other seamlessly. We provide an overview of all these areas, their backgrounds, current research, and future developments. We highlight the power of multimodal light manipulation and want to inspire new eclectic approaches in this vibrant research community.

Controlling phase synchrony in the mean field coupled Hindmarsh–Rose neurons

The phase synchronization in a network of mean field coupled Hindmarsh–Rose neurons and the control of phase synchrony by an external input has been analyzed in this work. The analysis of interspike interval, with varying coupling strength, reveals the dynamical change induced in each neuron in the network. The bursting phase lines depict that mean field coupling induces phase synchrony in excitatory mode and desynchrony in inhibitory mode. The coefficient of variability, in spatial and temporal domain, signifies the deviations in firing times of neurons, in a collective manner. The Kuramoto order parameter quantifies the intermittent and complete phase synchrony, induced by excitatory mean field coupling. The capability of external input, in the form of spikes, to control the intermittent and complete phase synchrony has been analyzed. The coefficient of variability and Kuramoto order parameter has been studied by varying the amplitude, pulse width and frequency of the input. The studies have shown that high-frequency spike input, with optimum amplitude and pulse width, has high desynchronizing ability, which is substantiated by the parameter space analysis. The control of synchrony in the network of neurons may find application in rectifying neural disorders.

Temporal saliency for motion direction may arise from visual stimulus-specific adaptation in avian midbrain inhibitory nucleus

Food and predators are the most noteworthy objects for the basic survival of wild animals. In nature, both of these are often rare or deviant in both spatial and temporal domains and would soon attract an animal's attention. Although stimulus-specific adaptation (SSA) is considered to be one neural basis of salient sound detection in the temporal domain, related research on visual SSA is lacking. The avian nucleus isthmi pars magnocellularis (Imc), which plays an extremely important role in the selective attention network, is one of the best models for investigating the neural correlate of visual stimulus-specific adaptation (SSA) and detection of salient stimulus in the temporal domain. Here, we used a constant order paradigm to test the existence of SSA in the pigeon's Imc. We found that the strength of response of Imc neurons significantly decreased after repetitive motion stimuli, but recovered when the motion was switched to a novel direction, leading to the saliency detection of the novel motion direction. These results suggest that the inhibitory nucleus Imc shows visual SSA to motion direction, allowing the Imc to implement temporal saliency mapping and to determine the spatial-temporal saliency of the current stimulus. This also implies that pigeons may detect novel spatial-temporal stimuli during the early stage of sensory processing.

Delineation of maize growing areas vis-à-vis climate and technology using statistical and geospatial techniques in Punjab

In the present investigation, maize growing areas in Punjab were delineated with respect toclimate and technology variables using statistical and geospatial techniques. The effect of the climate(maximum temperature, minimum temperature and rainfall) and technology variables (fertilizers, irrigation)on maize yield was studied in spatio-temporal domain in maize growing areas of Punjab. Long-termdata on climate and technology variables as well as maize productivity was collected for maize growingdistricts of the state. The maximum temperature during maize growing season was highest (35.7°C) inAmritsar, the minimum temperature was highest (25.3°C) in Ludhiana, whereas rainfall was highest (765.4 mm) in Gurdaspur. The results of Mann-Kendall test showed significant increase in maximum temperature @ 0.03°C year-1 in Hoshiarpur, Kapurthala, Patiala and Roopnagar and minimum temperature @ 0.04°C year-1in Gurdaspur, Hoshiarpur, Kapurthala and Roopnagar, @ 0.03°C year-1 in Jalandhar and @ 0.05°C year-1 in Ludhiana and Patiala districts. Analysis indicated that the maize yield was significantly higher in Ludhiana than other districts. Spatial variability in maize yield, climate and technology was studied using Geographic Information System (GIS). The integration of the layers of climate parameters with yield in GIS demarcated four major maize growing zones in Punjab.