General Computational Model Research Articles

Laser-sound: optoacoustic transduction from digital audio streams

This work presents a novel laser-based optoacoustic transducer capable of reproducing controlled and continuous sound of arbitrary complexity in the air or on solid targets. Light-to-sound transduction is achieved via laser-induced breakdown, leading to the formation of plasma acoustic sources in any desired spatial location. The acoustic signal is encoded into pulse streams via a discrete-time audio modulation and is reproduced by fast consecutive excitation of the target medium with appropriately modulated laser pulses. This results in the signal being directly reconstructed at the desired location of the target medium without the need for a receiver or demodulation device. In this work, the principles and evaluation results of such a novel laser-sound prototype system are presented. The performance of the prototype is evaluated by systematic experimental measurements of audio test signals, from which the basic acoustical response is derived. Moreover, a generic computational model is presented that allows for the simulation of laser-sound reproduction of 1-bit or multibit audio streams. The model evaluations are validated by comparison with the acoustic measurements, whereby a good agreement is found. Finally, the computational model is used to simulate an ideal optoacoustic transducer based on the specifications of state-of-the-art commercially available lasers.

Electrodynamic Computer Model of a Metal Rod in a Concrete Medium Detection

The main aim of the present work is to describe a generalized electrodynamic model of the specific device operation, which provides detection and measurement of the geometric characteristics of the reinforcement cage in a concrete structure. The result of this study is the design of the device’s receiving-transmitting path that is optimal in a number of parameters, as well as a signal processing algorithm based on the operation of an artificial neural network trained on the generalized computer model output data. In addition to the main functionality of the device being developed, which consists in structureroscopy by the method of radar holography, useful target characteristics may be obtained during its operation: electrophysical parameters of concrete, structural defects, visualization of an object, etc. To solve this problem, on the basis of general radar principles, frequency-modulated continuous wave was chosen as the operating mode of the device. To create an electrodynamic model, the computer-aided design environment Pathwave EM Design (EMPro) 2021 was used. The developed generalized model may be optimized for a large number of parameters. In addition to the position and number of receiving antennas, the list of optimization variables may include parameters of the transmitting antenna (ray width, directivity, near-field distance), their number (i.e., the capacity of the MIMO system), power on the transmitting side, etc. The proposed scheme of the device is presented as the main result.

Towards Emotion-aware Recommender Systems: an Affective Coherence Model based on Emotion-driven Behaviors

Decision making is the cognitive process of identifying and choosing alternatives based on preferences, beliefs, and degree of importance given by the decision maker to objects or actions. For instance, choosing which movie to watch is a simple, small-sized decision-making process. Recommender systems help people to make this kind of choices, usually by computing a short list of suggestions that reduces the space of possible options. These systems are strongly based on the knowledge of user preferences but, in order to fully support people, they should be grounded on a holistic view of the user behavior, that includes also how emotions, mood, and personality traits influence her choosing patterns.In this work, we investigate how to include emotional aspects in the recommendation process. We suggest that the affective state of the user, defined by a set of emotions (e.g., joy, surprise), constitutes part of choosing situation that should be taken into account when modeling user preferences.The main contribution of the paper is a general emotion-aware computational model based on affective user profiles in which each preference, such as a 5-star rating on a movie, is associated with the affective state felt by the user at the time when that preference was collected.The model estimates whether an unseen item is suitable for the current affective state of the user, by computing an affective coherence score that takes into account both the affective user profile and not-affective item features. The approach has been implemented into an Emotion-aware Music Recommender System, whose effectiveness has been assessed by performing in-vitro experiments on two benchmark datasets. The main outcome is that our system showed improved accuracy of recommendations compared to baselines which include no affective information in the recommendation model.

Decoding individual identity from brain activity elicited in imagining common experiences

Everyone experiences common events differently. This leads to personal memories that presumably provide neural signatures of individual identity when events are reimagined. We present initial evidence that these signatures can be read from brain activity. To do this, we progress beyond previous work that has deployed generic group-level computational semantic models to distinguish between neural representations of different events, but not revealed interpersonal differences in event representations. We scanned 26 participants’ brain activity using functional Magnetic Resonance Imaging as they vividly imagined themselves personally experiencing 20 common scenarios (e.g., dancing, shopping, wedding). Rather than adopting a one-size-fits-all approach to generically model scenarios, we constructed personal models from participants’ verbal descriptions and self-ratings of sensory/motor/cognitive/spatiotemporal and emotional characteristics of the imagined experiences. We demonstrate that participants’ neural representations are better predicted by their own models than other peoples’. This showcases how neuroimaging and personalized models can quantify individual-differences in imagined experiences.

Are You Still With Me? Continuous Engagement Assessment From a Robot's Point of View.

Continuously measuring the engagement of users with a robot in a Human-Robot Interaction (HRI) setting paves the way toward in-situ reinforcement learning, improve metrics of interaction quality, and can guide interaction design and behavior optimization. However, engagement is often considered very multi-faceted and difficult to capture in a workable and generic computational model that can serve as an overall measure of engagement. Building upon the intuitive ways humans successfully can assess situation for a degree of engagement when they see it, we propose a novel regression model (utilizing CNN and LSTM networks) enabling robots to compute a single scalar engagement during interactions with humans from standard video streams, obtained from the point of view of an interacting robot. The model is based on a long-term dataset from an autonomous tour guide robot deployed in a public museum, with continuous annotation of a numeric engagement assessment by three independent coders. We show that this model not only can predict engagement very well in our own application domain but show its successful transfer to an entirely different dataset (with different tasks, environment, camera, robot and people). The trained model and the software is available to the HRI community, at https://github.com/LCAS/engagement_detector, as a tool to measure engagement in a variety of settings.

In vivo estimates of axonal stretch and 3D brain deformation during mild head impact

The rapid deformation of brain tissue in response to head impact can lead to traumatic brain injury. In vivo measurements of brain deformation during non-injurious head impacts are necessary to understand the underlying mechanisms of traumatic brain injury and compare to computational models of brain biomechanics. Using tagged magnetic resonance imaging (MRI), we obtained measurements of three-dimensional strain tensors that resulted from a mild head impact after neck rotation or neck extension. Measurements of maximum principal strain (MPS) peaked shortly after impact, with maximal values of 0.019–0.053 that correlated strongly with peak angular velocity. Subject-specific patterns of MPS were spatially heterogeneous and consistent across subjects for the same motion, though regions of high deformation differed between motions. The largest MPS values were seen in the cortical gray matter and cerebral white matter for neck rotation and the brainstem and cerebellum for neck extension. Axonal fiber strain (Ef) was estimated by combining the strain tensor with diffusion tensor imaging data. As with MPS, patterns of Ef varied spatially within subjects, were similar across subjects within each motion, and showed group differences between motions. Values were highest and most strongly correlated with peak angular velocity in the corpus callosum for neck rotation and in the brainstem for neck extension. The different patterns of brain deformation between head motions highlight potential areas of greater risk of injury between motions at higher loading conditions. Additionally, these experimental measurements can be directly compared to predictions of generic or subject-specific computational models of traumatic brain injury. Statement of SignificanceTraumatic brain injury can result from the rapid acceleration of the skull, leading to deformation of brain tissue and elongation of axonal fibers. Because treatment options and prognostic models for patients are lacking, a better understanding of injury mechanisms is needed. Here, we use tagged magnetic resonance imaging to measure deformation throughout the live, human brain during non-injurious head accelerations. We present the first in vivo measurements of axonal stretch and compare MPS and axonal stretch experienced during neck rotation and neck extension. These results are important to elucidate brain regions at risk for injury. Additionally, they can be directly used to evaluate computational models of brain injury, which are used to predict risk of concussion during head impacts and design protective equipment.

Fast Spectral Approximation of Structured Graphs with Applications to Graph Filtering

To analyze and synthesize signals on networks or graphs, Fourier theory has been extended to irregular domains, leading to a so-called graph Fourier transform. Unfortunately, different from the traditional Fourier transform, each graph exhibits a different graph Fourier transform. Therefore to analyze the graph-frequency domain properties of a graph signal, the graph Fourier modes and graph frequencies must be computed for the graph under study. Although to find these graph frequencies and modes, a computationally expensive, or even prohibitive, eigendecomposition of the graph is required, there exist families of graphs that have properties that could be exploited for an approximate fast graph spectrum computation. In this work, we aim to identify these families and to provide a divide-and-conquer approach for computing an approximate spectral decomposition of the graph. Using the same decomposition, results on reducing the complexity of graph filtering are derived. These results provide an attempt to leverage the underlying topological properties of graphs in order to devise general computational models for graph signal processing.

A generic computational model for three-dimensional fracture and fragmentation problems of quasi-brittle materials

Fracture and fragmentation in three dimensions are of great importance to understand the mechanical behaviour of quasi-brittle materials in failure stress states. In this paper, a generic computational model has been developed in an in-house C/C++ code using the combined finite-discrete element method, which is capable of modelling the entire three-dimensional fracturing process, including pre-peak hardening deformation, post-peak strain softening, transition from continuum to discontinuum, and explicit interaction between discrete fragments. The computational model is validated by Brazilian tests and polyaxial compression tests, and a realistic multi-layer rock model in an in situ stress condition is presented as an application example. The results show that the computational model can capture both continuum and discontinuum behaviour and therefore it provides an ideal numerical tool for fracture and fragmentation problems.

Massless Majorna bispinors and two-qubit entangled states

This is a pedagogical paper, where bispinors solutions to the four-dimensional massless Dirac equation are considered in relativistic quantum mechanics and in quantum computation, taking advantage of the common mathematical description of four-dimensional spaces. First, Weyl and massless Majorana bispinors are shown to be unitary equivalent, closing a gap in the literature regarding their equivalence. A discrepancy in the number of linearly independent solutions reported in the literature is also addressed. Then, it is shown that Weyl bispinors are algebraically equivalent to two-qubit direct product states, and that the massless Majorana bispinors are algebraically equivalent to maximally entangled sates (Bell states), with the transformations relating the two bispinors types acting as entangling gates in quantum computation. Different types of entangling gates are presented, highlighting a set that fulfills the required properties for Majorana zero mode operators in topological quantum computation. Based on this set, a general topological quantum computation model with four Majorana operators is presented, which exhibits all the required technical and physical properties to obtain entanglement of two logical qubits from tpological operations.

Modulation in phase and frequency of neural oscillations during epileptiform activity induced by neonatal Zika virus infection in mice

Modulation of brain activity is one of the main mechanisms capable of demonstrating the synchronization dynamics of neural oscillations. In epilepsy, modulation is a key concept since seizures essentially result from neural hypersynchronization and hyperexcitability. In this study, we have introduced a time-dependent index based on the Kullback-Leibler divergence to quantify the effects of phase and frequency modulations of neural oscillations in neonatal mice exhibiting epileptiform activity induced by Zika virus (ZIKV) infection. Through this index, we demonstrate that fast oscillations (gamma and beta 2) are the more susceptible modulated rhythms in terms of phase, during seizures, whereas slow waves (delta and theta) mainly undergo changes in frequency. The index also allowed detection of specific patterns associated with the interdependent modulation of phase and frequency in neural activity. Furthermore, by comparing ZIKV modulations with the general computational model Epileptors, we verify different signatures related to the brain rhythms modulation in phase and frequency. These findings instigate new studies on the effects of ZIKV infection on neuronal networks from electrophysiological activities, and how different mechanisms can trigger epilepsy.

Responses of vertical axis wind turbines to gusty winds

Gust is a common atmospheric condition encountered by wind turbines. Despite the presence of a vast amount of literature on this topic, few of them involve lateral gust influence. Due to this motivation, this paper presents an investigation on the effects of sinusoidal gusts on a three-bladed vertical axis wind turbine under varying gust parameters including gust direction, velocity amplitude, and frequency. The chimera mesh technique is used to model the real rotation of the rotor, as well as the resolved gust approach model in the DLR (German Aerospace Center) TAU code for gust simulation. Both the general aerodynamic computational fluid dynamics model and the gust model are validated before the following simulations. Numerous new flow phenomena and physics are revealed. The influences of gust on the rotor power output and flowfield characteristics are discussed and analyzed in detail. The findings in this study may be helpful for some practical wind engineering applications, such as atmospheric influence evaluation and field site selection.

Generalized Computer Model Calibration for Radiation Transport Simulation

Model calibration uses outputs from a simulator and field data to build a predictive model for the physical system and to estimate unknown inputs. The conventional approach to model calibration assumes that the observations are continuous outcomes. In many applications this is not the case. The methodology proposed was motivated by an application in modeling photon counts at the Center for Exascale Radiation Transport. There, high performance computing is used for simulating the flow of neutrons through various materials. In this article, new Bayesian methodology for computer model calibration to handle the count structure of our observed data allows closer fidelity to the experimental system and provides flexibility for identifying different forms of model discrepancy between the simulator and experiment. Supplementary materials for this article are available online.

Comparing various influences on adhesive contact with friction

Abstract A general computational model covering many types of frictional contact interfaces between visco-elastic bodies is considered for some cases physically relevant in numerical analysis of contact in civil engineering structures. The relations between mechanical quantities and internal parameters of the model are illustrated in a couple of simplified examples including cohesive contact combined with Coulomb friction and/or interface plasticity. The computations are implemented a semi-implicit time discretisation, quadratic programming algorithms, and the boundary-element method.

Connecting the molecular function of microRNAs to cell differentiation dynamics.

MicroRNAs form a class of short, non-coding RNA molecules which are essential for proper development in tissue-based plants and animals. To help explain their role in gene regulation, a number of mathematical and computational studies have demonstrated the potential canalizing effects of microRNAs. However, such studies have typically focused on the effects of microRNAs on only one or a few target genes. Consequently, it remains unclear how these small-scale effects add up to the experimentally observed developmental outcomes resulting from microRNA perturbation at the whole-genome level. To answer this question, we built a general computational model of cell differentiation to study the effect of microRNAs in genome-scale gene regulatory networks. Our experiments show that in large gene regulatory networks, microRNAs can control differentiation time without significantly changing steady-state gene expression profiles. This temporal regulatory role cannot be naturally replicated using protein-based transcription factors alone. While several microRNAs have been shown to regulate differentiation time in vivo, our findings provide a new explanation of how the cumulative molecular actions of individual microRNAs influence genome-scale cellular dynamics. Taken together, these results may help explain why tissue-based organisms exclusively depend on miRNA-mediated regulation, while their more primitive counterparts do not.

Cell shape and intercellular adhesion regulate mitotic spindle orientation.

Cell division orientation plays an essential role in tissue morphogenesis and cell fate decision. Recent studies showed that either cell shape or adhesion geometry can regulate the orientation of mitotic spindles and thereby the cell division orientation. However, how they together regulate the spindle orientation remains largely unclear. In this work, we use a general computational model to investigate the competitive mechanism of determining the spindle orientation between cell shape and intercellular adhesion in epithelial cells. We find the spindle orientation is dominated by the intercellular adhesion when the cell shape anisotropy is small, but dominated by the cell shape when the shape anisotropy is large. A strong adhesion and moderate adhesive size can ensure the planar division of epithelial cells with large apico-basal elongation. We also find the spindle orientation could be perpendicular to the adhesive region when only one side of the cell is adhered to an E-cadherin–coated matrix. But after the cell is compressed, the spindle orientation is governed by the cell shape and the spindle will be parallel to the adhesive region when the cell shape anisotropy is large. Finally, we demonstrate the competition between cell shape and tricellular junctions can also effectively regulate the spindle orientation.

STDP Forms Associations between Memory Traces in Networks of Spiking Neurons.

Memory traces and associations between them are fundamental for cognitive brain function. Neuron recordings suggest that distributed assemblies of neurons in the brain serve as memory traces for spatial information, real-world items, and concepts. However, there is conflicting evidence regarding neural codes for associated memory traces. Some studies suggest the emergence of overlaps between assemblies during an association, while others suggest that the assemblies themselves remain largely unchanged and new assemblies emerge as neural codes for associated memory items. Here we study the emergence of neural codes for associated memory items in a generic computational model of recurrent networks of spiking neurons with a data-constrained rule for spike-timing-dependent plasticity. The model depends critically on 2 parameters, which control the excitability of neurons and the scale of initial synaptic weights. By modifying these 2 parameters, the model can reproduce both experimental data from the human brain on the fast formation of associations through emergent overlaps between assemblies, and rodent data where new neurons are recruited to encode the associated memories. Hence, our findings suggest that the brain can use both of these 2 neural codes for associations, and dynamically switch between them during consolidation.

Context awareness and embedding for biomedical event extraction.

Biomedical event extraction is fundamental for information extraction in molecular biology and biomedical research. The detected events form the central basis for comprehensive biomedical knowledge fusion, facilitating the digestion of massive information influx from the literature. Limited by the event context, the existing event detection models are mostly applicable for a single task. A general and scalable computational model is desiderated for biomedical knowledge management. We consider and propose a bottom-up detection framework to identify the events from recognized arguments. To capture the relations between the arguments, we trained a bidirectional long short-term memory network to model their context embedding. Leveraging the compositional attributes, we further derived the candidate samples for training event classifiers. We built our models on the datasets from BioNLP Shared Task for evaluations. Our method achieved the average F-scores of 0.81 and 0.92 on BioNLPST-BGI and BioNLPST-BB datasets, respectively. Comparing with seven state-of-the-art methods, our method nearly doubled the existing F-score performance (0.92 versus 0.56) on the BioNLPST-BB dataset. Case studies were conducted to reveal the underlying reasons. https://github.com/cskyan/evntextrc. Supplementary data are available at Bioinformatics online.

Development and research of the electric drive generalized computer model with pulse-phase regulation of angular speed

The article presents the developed generalized computer model of the electric drive with pulse-phase regulation of the angular speed. The model was created on the basis of a pulse frequency-phase detector (PFPD) model with additional functionality, that give a possibility to investigate the most effective ways to control this electric drive, from the point of view of dynamic indicators improving of regulation quality. On the basis of the known methods of phasing and synchronization, the model of precision electric drive was validated. The test showed that the work of the model meets theoretical expectations.

Optimizing Resources for Endovascular Clot Retrieval for Acute Ischemic Stroke, a Discrete Event Simulation.

Objective: Endovascular clot retrieval (ECR) is the standard of care for acute ischemic stroke due to large vessel occlusion. Performing ECR is a time critical and complex process involving many specialized care providers and resources. Maximizing patient benefit while minimizing service cost requires optimization of human and physical assets. The aim of this study is to develop a general computational model of an ECR service, which can be used to optimize resource allocation.Methods: Using a discrete event simulation approach, we examined ECR performance under a range of possible scenarios and resource use configurations.Results: The model demonstrated the impact of competing emergency interventional cases upon ECR treatment times and time impact of allocating more physical (more angiographic suites) or staff resources (extending work hours).Conclusion: Our DES model can be used to optimize resources for interventional treatment of acute ischemic stroke and large vessel occlusion. This proof-of-concept study of computational simulation of resource allocation for ECR can be easily extended. For example, center-specific cost data may be incorporated to optimize resource allocation and overall health care value.

Using a coupled geochemical/physical mass balance model to gain insights into the deposition of subrecent evaporites: Searles Valley, California, USA

Modern and ancient saline lakes are important sites for the deposition of carbonates and evaporites. Here we describe a generalized computer model of chemical sediment production in a closed-basin lake subject to: (1) variable inflow sources through time; (2) variable volumes of these inflows; (3) variable temperature, and; (4) variable evaporation rates through time. We then apply our model to gain insights into some aspects of late Pleistocene evaporite deposits beneath Searles Lake, California, a well-described chemical sedimentary deposit. We tuned our model to match the masses and proportions of the major saline minerals trona and halite deposited in layers circa 32 ka to 24 ka. However, we could not reproduce the thicknesses of mixed siliciclastic, carbonate and organic layers sandwiched between the evaporites. The model is useful for interpreting and constraining the environmental conditions in and around any ancient saline lakes during evaporite deposition where the amounts, mineralogies, and vertical distribution of chemical sediments are known.