Fast Time Scale Research Articles

State-dependent pupil dilation rapidly shifts visual feature selectivity.

To increase computational flexibility, the processing of sensory inputs changes with behavioural context. In the visual system, active behavioural states characterized by motor activity and pupil dilation1,2 enhance sensory responses, but typically leave the preferred stimuli of neurons unchanged2-9. Here we find that behavioural state also modulates stimulus selectivity in the mouse visual cortex in the context of coloured natural scenes. Using population imaging in behaving mice, pharmacology and deep neural network modelling, we identified a rapid shift in colour selectivity towards ultraviolet stimuli during an active behavioural state. This was exclusively caused by state-dependentpupil dilation, which resulted in a dynamic switch from rod to cone photoreceptors, thereby extending their role beyond night and day vision. The change in tuning facilitated the decoding of ethological stimuli, such as aerial predators against the twilight sky10. For decades, studies in neuroscience and cognitive sciencehave used pupil dilation as an indirect measure of brain state. Our data suggest that, in addition, state-dependent pupil dilation itself tunes visual representations to behavioural demands by differentially recruiting rods and cones on fast timescales.

Controlling atomic spin mixing via multiphoton transitions in a cavity

We propose to control spin-mixing dynamics in a gas of spinor atoms, via the combination of two off-resonant Raman transition pathways, enabled by a common cavity mode and a bichromatic pump laser. The mixing rate, which is proportional to the synthesized spin-exchange interaction strength, and the effective atomic quadratic Zeeman shift (QZS), can both be tuned by changing the pump laser parameters. Quench and driving dynamics of the atomic collective spin are shown to be controllable on a faster time scale than in existing experiments based on inherent spin-exchange collision interactions. The results we present open a promising avenue for exploring spin-mixing physics of atomic ensembles accessible in current experiments.

Spectral Broadening of a Single Er3+ Ion in a Si Nanotransistor

Single rare-earth ions in solids show great potential for quantum applications, including single photon emission, quantum computing, and high-precision sensing. However, homogeneous linewidths observed for single rare-earth ions are orders of magnitude larger than the sub-kilohertz linewidths observed for ensembles in bulk crystals. The spectral broadening creates a significant challenge for achieving entanglement generation and qubit operation with single rare-earth ions, so it is critical to investigate the broadening mechanisms. We report a spectral broadening study on a single Er$^{3+}$ ion in a Si nano-transistor. The Er-induced photoionisation rate is found to be an appropriate quantity to represent the optical transition probability for spectroscopic studies, and the single ion spectra display a Lorentzian lineshape at all optical powers in use. Spectral broadening is observed at relatively high optical powers and is caused by spectral diffusion on a fast time scale.

Burgers Turbulence in the Fermi-Pasta-Ulam-Tsingou Chain.

We prove analytically and show numerically that the dynamics of the Fermi-Pasta-Ulam-Tsingou chain is characterized by a transient Burgers turbulence regime on a wide range of time and energy scales. This regime is present at long wavelengths and energy per particle small enough that equipartition is not reached on a fast timescale. In this range, we prove that the driving mechanism to thermalization is the formation of a shock that can be predicted using a pair of generalized Burgers equations. We perform a perturbative calculation at small energy per particle, proving that the energy spectrum of the chain E_{k} decays as a power law, E_{k}∼k^{-ζ(t)}, on an extensive range of wave numbers k. We predict that ζ(t) takes first the value 8/3 at the Burgers shock time, and then reaches a value close to 2 within two shock times. The value of the exponent ζ=2 persists for several shock times before the system eventually relaxes to equipartition. During this wide time window, an exponential cutoff in the spectrum is observed at large k, in agreement with previous results. Such a scenario turns out to be universal, i.e., independent of the parameters characterizing the system and of the initial condition, once time is measured in units of the shock time.

Efficient Phasor-Based Dynamic Volt/VAr and Volt/Watt Analysis of Large Distribution Grid With High Penetration of Smart Inverters

As the penetration of power-electronics based smart inverters (SIs) is increasing in distribution grids, it adds computational challenges in solving dynamic models of large-scale distribution feeders. Voltage and reactive power (Volt/VAr), and voltage and active power (Volt/Watt) dynamics have been analyzed at slower time scales akin to the control of legacy grid devices. However, smart inverters, being power-electronics based devices, can provide dynamic active/reactive power support at a faster time scale, which necessitates Volt/VAr and Volt/Watt dynamics to be analyzed at a faster time scale. The existing dynamic models are overly detailed and computationally intractable for distribution feeders with a large number of inverters. In this context, this proposed work aims towards developing a computationally tractable, scalable, and accurate phasor-based model for dynamic Volt/VAr and Volt/Watt analyses of large distribution systems with high penetration of smart inverters. Case studies demonstrate that the proposed phasor-based model sufficiently captures the Volt/VAr and Volt/Watt dynamics, and is computationally faster by one order of magnitude compared to the average model and by two orders of magnitude compared to the detailed switching model. Case studies also demonstrate the efficacy and scalability of the proposed model in analyzing Volt/VAr and Volt/Watt dynamics of large-scale power networks with hundreds of SIs.

Mixtures of large-scale dynamic functional brain network modes

Accurate temporal modelling of functional brain networks is essential in the quest for understanding how such networks facilitate cognition. Researchers are beginning to adopt time-varying analyses for electrophysiological data that capture highly dynamic processes on the order of milliseconds. Typically, these approaches, such as clustering of functional connectivity profiles and Hidden Markov Modelling (HMM), assume mutual exclusivity of networks over time. Whilst a powerful constraint, this assumption may be compromising the ability of these approaches to describe the data effectively. Here, we propose a new generative model for functional connectivity as a time-varying linear mixture of spatially distributed statistical “modes”. The temporal evolution of this mixture is governed by a recurrent neural network, which enables the model to generate data with a rich temporal structure. We use a Bayesian framework known as amortised variational inference to learn model parameters from observed data. We call the approach DyNeMo (for Dynamic Network Modes), and show using simulations it outperforms the HMM when the assumption of mutual exclusivity is violated. In resting-state MEG, DyNeMo reveals a mixture of modes that activate on fast time scales of 100–150 ms, which is similar to state lifetimes found using an HMM. In task MEG data, DyNeMo finds modes with plausible, task-dependent evoked responses without any knowledge of the task timings. Overall, DyNeMo provides decompositions that are an approximate remapping of the HMM’s while showing improvements in overall explanatory power. However, the magnitude of the improvements suggests that the HMM’s assumption of mutual exclusivity can be reasonable in practice. Nonetheless, DyNeMo provides a flexible framework for implementing and assessing future modelling developments.

In vitro reconstitution of calcium-dependent recruitment of the human ESCRT machinery in lysosomal membrane repair

The endosomal sorting complex required for transport (ESCRT) machinery is centrally involved in the repair of damage to both the plasma and lysosome membranes. ESCRT recruitment to sites of damage occurs on a fast time scale, and Ca2+ has been proposed to play a key signaling role in the process. Here, we show that the Ca2+-binding regulatory protein ALG-2 binds directly to negatively charged membranes in a Ca2+-dependent manner. Next, by monitoring the colocalization of ALIX with ALG-2 on negatively charged membranes, we show that ALG-2 recruits ALIX to the membrane. Furthermore, we show that ALIX recruitment to the membrane orchestrates the downstream assembly of late-acting CHMP4B, CHMP3, and CHMP2A subunits along with the AAA+ ATPase VPS4B. Finally, we show that ALG-2 can also recruit the ESCRT-III machinery to the membrane via the canonical ESCRT-I/II pathway. Our reconstitution experiments delineate the minimal sets of components needed to assemble the entire membrane repair machinery and open an avenue for the mechanistic understanding of endolysosomal membrane repair.

High pressure deformation induced precipitation in Al–Zn–Mg–Cu alloy (Al7075)

Precipitate-matrix interactions govern the mechanical behavior of precipitate strengthened Al-based alloys. These alloys find a wide range of applications ranging from aerospace to automobile and naval industries due to their low cost and high strength to weight ratio. Structures made from Al-based alloys undergo complex loading conditions such as impact and shock. Understanding the microstructural changes in Al-alloys under extreme conditions is challenging due to the fast time scale of the experiments. Here, we study the high-pressure behavior of Al7075 alloy separately from the high strain-rate behavior using quasi-static experiment in a diamond anvil cell. We combine in situ X-ray diffraction (XRD) and pre- and post-compression transmission electron microscopy (TEM) imaging to analyze microstructural changes and estimate high pressure strength. XRD showed that non-hydrostatic pressure leads to a significant increase in defect density and peak broadening with pressure cycling. XRD mapping under non-hydrostatic pressure revealed that the region with the highest local pressure had the greatest increase in defect nucleation, whereas the region with the largest local pressure gradient underwent texturing and had larger grains. TEM analysis showed that pressure cycling leads to the nucleation, growth and significant increase in density of precipitates. The significant increase in precipitate density was due to the destabilization of solid solution atoms under high pressure and led to an increase in strength for Al7075 alloy at high pressures.

All-optical valley switch and clock of electronic dephasing.

2D materials with broken inversion symmetry posses an extra degree of freedom, the valley pseudospin, that labels in which of the two energy-degenerate crystal momenta, K or K', the conducting carriers are located. It has been shown that shining circularly-polarized light allows to achieve close to 100% of valley polarization, opening the way to valley-based transistors. Yet, switching of the valley polarization is still a key challenge for the practical implementation of such devices due to the short valley lifetimes. Recent progress in ultrashort laser technology now allows to produce trains of attosecond pulses with controlled phase and polarization between the pulses. Taking advantage of such technology, we introduce a coherent control protocol to turn on, off and switch the valley polarization at faster timescales than electron-hole decoherence and valley depolarization, that is, an ultrafast optical valley switch. We theoretically demonstrate the protocol for hBN and MoS2 monolayers calculated from first principles. Additionally, using two time-delayed linearly-polarized pulses with perpendicular polarization, we show that we can extract the electronic dephasing time T2 from the valley Hall conductivity.

Inferring Insulin Secretion Rate from Sparse Patient Glucose and Insulin Measures.

The insulin secretion rate (ISR) contains information that can provide a personal, quantitative understanding of endocrine function. If the ISR can be reliably inferred from measurements, it could be used for understanding and clinically diagnosing problems with the glucose regulation system. Objective: This study aims to develop a model-based method for inferring a parametrization of the ISR and related physiological information among people with different glycemic conditions in a robust manner. The developed algorithm is applicable for both dense or sparsely sampled plasma glucose/insulin measurements, where sparseness is defined in terms of sampling time with respect to the fastest time scale of the dynamics. Methods: An algorithm for parametrizing and validating a functional form of the ISR for different compartmental models with unknown but estimable ISR function and absorption/decay rates describing the dynamics of insulin accumulation was developed. The method and modeling applies equally to c-peptide secretion rate (CSR) when c-peptide is measured. Accuracy of fit is reliant on reconstruction error of the measured trajectories, and when c-peptide is measured the relationship between CSR and ISR. The algorithm was applied to data from 17 subjects with normal glucose regulatory systems and 9 subjects with cystic fibrosis related diabetes (CFRD) in which glucose, insulin and c-peptide were measured in course of oral glucose tolerance tests (OGTT). Results: This model-based algorithm inferred parametrization of the ISR and CSR functional with relatively low reconstruction error for 12 of 17 control and 7 of 9 CFRD subjects. We demonstrate that when there are suspect measurements points, the validity of excluding them may be interrogated with this method. Significance: A new estimation method is available to infer the ISR and CSR functional profile along with plasma insulin and c-peptide absorption rates from sparse measurements of insulin, c-peptide, and plasma glucose concentrations. We propose a method to interrogate and exclude potentially erroneous OGTT measurement points based on reconstruction errors.

Fixed-Time Synchronization of Competitive Neural Networks With Multiple Time Scales.

In this brief, we investigate the fixed-time synchronization of competitive neural networks with multiple time scales. These neural networks play an important role in visual processing, pattern recognition, neural computing, and so on. Our main contribution is the design of a novel synchronizing controller, which does not depend on the ratio between the fast and slow time scales. This feature makes the controller easy to implement since it is designed through well-posed algebraic conditions (i.e., even when the ratio between the time scales goes to 0, the controller gain is well defined and does not go to infinity). Last but not least, the closed-loop dynamics is characterized by a high convergence speed with a settling time which is upper bounded, and the bound is independent of the initial conditions. A numerical simulation illustrates our results and emphasizes their effectiveness.

Modelling alternating current effects in a submerged arc furnace

Abstract Modelling the production of silicon in a submerged arc furnace (SAF) requires accounting for the wide range of timescales of the different physical and chemical processes: the electric current which is used to heat the furnace varies over a timescale of around $10^{-2}\,$ s, whereas the flow and chemical consumption of the raw materials in the furnace occurs over several hours. Models for the silicon furnace generally either include only the fast-timescale, or only the slow-timescale processes. In a prior work, we developed a model incorporating effects on both the fast and slow timescales, and used a multiple-timescales analysis to homogenise the fast variations, deriving an averaged model for the slow evolution of the raw materials. For simplicity, in the previous work we focussed on the electrical behaviour around the base of a single electrode, and prescribed the current in this electrode to be sinusoidal, with given amplitude. In this paper, we extend our previous analysis to include the full electrical system, modelled using an equivalent circuit system. In this way, we demonstrate how the two furnace-modelling approaches (on the fast and slow timescales) may be combined in a computationally efficient way. Our previously derived model for the arc resistance is based on the assumption that the dominant heat loss from the arc is by radiation (we will refer to this as the radiation model). Alternative arc models include the empirical Cassie and Mayr models, which are commonly used in the SAF literature. We compare these various arc models, explore the dependence of the solution of our model on the model parameters and compare our solutions with measurements from an operational silicon furnace. In particular, we show that only the radiation arc model has a rising current-voltage characteristic at high currents. Simulations of the model show that there is an upper limit on the length of the furnace arc, above which all the current bypasses the arc and flows through the surrounding material.

Measuring Molecular Diffusion in Dynamic Subcellular Nanostructures by Fast Raster Image Correlation Spectroscopy and 3D Orbital Tracking.

Here we provide demonstration that fast fluorescence fluctuation spectroscopy is a fast and robust approach to extract information on the dynamics of molecules enclosed within subcellular nanostructures (e.g., organelles or vesicles) which are also moving in the complex cellular environment. In more detail, Raster Image Correlation Spectroscopy (RICS) performed at fast timescales (i.e., microseconds) reveals the fast motion of fluorescently labeled molecules within two exemplary dynamic subcellular nanostructures of biomedical interest, the lysosome and the insulin secretory granule (ISG). The measurement of molecular diffusion is then used to extract information on the average properties of subcellular nanostructures, such as macromolecular crowding or molecular aggregation. Concerning the lysosome, fast RICS on a fluorescent tracer allowed us to quantitatively assess the increase in organelle viscosity in the pathological condition of Krabbe disease. In the case of ISGs, fast RICS on two ISG-specific secreting peptides unveiled their differential aggregation propensity depending on intragranular concentration. Finally, a combination of fast RICS and feedback-based 3D orbital tracking was used to subtract the slow movement of subcellular nanostructures from the fast diffusion of molecules contained within them and independently validate the results. Results presented here not only demonstrate the acquired ability to address the dynamic behavior of molecules in moving, nanoscopic reference systems, but prove the relevance of this approach to advance our knowledge on cell function at the subcellular scale.

Computational Proposal for Tracking Multiple Molecules in a Multifocus Confocal Setup.

Tracking single molecules continues to provide new insights into the fundamental rules governing biological function. Despite continued technical advances in fluorescent and non-fluorescent labeling as well as data analysis, direct observations of trajectories and interactions of multiple molecules in dense environments remain aspirational goals. While confocal methods provide a means to deduce dynamical parameters with high temporal resolution, such as diffusion coefficients, they do so at the expense of spatial resolution. Indeed, on account of a confocal volume’s symmetry, typically only distances from the center of the confocal spot can be deduced. Motivated by the need for true three dimensional high speed tracking in densely labeled environments, we propose a computational tool for tracking many fluorescent molecules traversing multiple, closely spaced, confocal measurement volumes providing independent observations. Various realizations of this multiple confocal volumes strategy have previously been used for long term, large area, tracking of one fluorescent molecule in three dimensions. What is more, we achieve tracking by directly using single photon arrival times to inform our likelihood and exploit Hamiltonian Monte Carlo to efficiently sample trajectories from our posterior within a Bayesian nonparametric paradigm. A nonparametric paradigm here is warranted as the number of molecules present are, themselves, a priori unknown. Taken together, we provide a computational framework to infer trajectories of multiple molecules at once, below the diffraction limit (the width of a confocal spot), in three dimensions at sub-millisecond or faster time scales.

Neural net modeling of equilibria in NSTX-U

Neural networks (NNs) offer a path towards synthesizing and interpreting data on faster timescales than traditional physics-informed computational models. In this work we develop two NNs relevant to equilibrium and shape control modeling, which are part of a suite of tools being developed for the National Spherical Torus Experiment-Upgrade for fast prediction, optimization, and visualization of plasma scenarios. The networks include Eqnet, a free-boundary equilibrium solver trained on the EFIT01 (Equilibrium FITtting 01) reconstruction algorithm, and Pertnet, which is trained on the Gspert code and predicts the non-rigid plasma response, a nonlinear term that arises in shape control modeling. The NNs are trained with different combinations of inputs and outputs in order to offer flexibility in use cases. In particular, Eqnet can use magnetic diagnostics as inputs and act as an EFIT-like reconstruction algorithm, or, by using pressure and current profile information the NN can act as a forward Grad–Shafranov equilibrium solver. This forward-mode version is envisioned to be implemented in the suite of tools for simulation of plasma scenarios. The reconstruction-mode version gives some performance improvements compared to the online reconstruction code real-time EFIT, especially when vessel eddy currents are significant. We report strong performance for all NNs indicating that the models could reliably be used within closed-loop simulations or other applications. Some limitations are discussed.

Hybrid Experimental Designs for Intervention Development: What, Why, and How.

Advances in mobile and wireless technologies offer tremendous opportunities for extending the reach and impact of psychological interventions and for adapting interventions to the unique and changing needs of individuals. However, insufficient engagement remains a critical barrier to the effectiveness of digital interventions. Human delivery of interventions (e.g., by clinical staff) can be more engaging but potentially more expensive and burdensome. Hence, the integration of digital and human-delivered components is critical to building effective and scalable psychological interventions. Existing experimental designs can be used to answer questions either about human-delivered components that are typically sequenced and adapted at relatively slow timescales (e.g., monthly) or about digital components that are typically sequenced and adapted at much faster timescales (e.g., daily). However, these methodologies do not accommodate sequencing and adaptation of components at multiple timescales and hence cannot be used to empirically inform the joint sequencing and adaptation of human-delivered and digital components. Here, we introduce the hybrid experimental design (HED)-a new experimental approach that can be used to answer scientific questions about building psychological interventions in which human-delivered and digital components are integrated and adapted at multiple timescales. We describe the key characteristics of HEDs (i.e., what they are), explain their scientific rationale (i.e., why they are needed), and provide guidelines for their design and corresponding data analysis (i.e., how can data arising from HEDs be used to inform effective and scalable psychological interventions).

Fixation in the stochastic Lotka-Volterra model with small fitness trade-offs.

We study the probability of fixation in a stochastic two-species competition model. By identifying a naturally occurring fast timescale, we derive an approximation to the associated backward Kolmogorov equation that allows us to obtain an explicit closed form solution for the probability of fixation of either species. We use our result to study fitness tradeoff strategies and show that, despite some tradeoffs having nearly negligible effects on the corresponding deterministic dynamics, they can have large implications for the outcome of the stochastic system.

Improved Plasma Vertical Position Control on TCV Using Model-Based Optimized Controller Synthesis

Elongated plasmas lead to improved performance in tokamaks but make the plasma prone to vertical instability, which requires active feedback control, a critical issue for future fusion reactors. Vertical control was optimized for the TCV tokamak by applying modern control theory to electromagnetic models for the plasma-vessel-coils dynamics. Two different optimal combinations of poloidal field coils for vertical control actuation are derived from linear plasma response models and used on different timescales for controlling the plasma vertical position. On fast timescales, the priority is input minimization, while on long timescales position control is designed to be compatible with shape control. A structured H-infinity design extending classical H-infinity to fixed-structure control systems was subsequently applied to obtain an optimized controller using all available coils for position control. Closed-loop performance improvement was demonstrated in dedicated TCV experiments, showing a reduction of input requirement for stabilizing the same plasma, thus reducing the risk of power supply saturation and consequent loss of vertical control. This novel algorithm is adaptable to different plasma equilibria as it is designed for model-based automated coil selection and controller tuning, thus avoiding extensive experimental gain scans when performing plasma discharges in TCV. The presented technique is general and can be applied to any present tokamak with independent coils or for the design of future tokamak magnetic control systems.

Multiple time scale state-of-charge and capacity-based equalisation strategy for lithium-ion battery pack with passive equaliser

The cycle life of a lithium-ion battery pack is much shorter than that of a single cell because of their different external operating environments and internal characteristic parameters. Equalisation management is essential for reducing the difference between battery cells, improving capacity, and prolonging the cycle life. Passive equalisation is also widely used in electric vehicles and energy storage systems because of its comparative advantages including the low cost, simple structure, and easy control. Compared with the mature development of the passive equalisation hardware circuits, the evolution of high-performance equalisation strategies is relatively backward. Along these lines, a multiple time scale state-of-charge (SOC) and capacity-based equalisation strategy for lithium-ion battery packs with passive equalisers was proposed in this work. Firstly, the minimum-capacity differential model (MCDM) consisting of a cell minimum-capacity model (CMCM) and a cell differential model (CDM) was established to improve the computational efficiency and accuracy of the dynamic behaviour of the minimum-capacity cell, as well as all the other cells in a battery pack. Secondly, the SOC and capacity of a battery pack were estimated on multiple time scales with the use of a dual extended Kalman filter and MCDM. The fast time scale based on CMCM and the slow time scale based on CDM can balance the efficiency and accuracy of the estimation algorithm. Based on the accurate estimation of both SOC and capacity, a SOC-and-capacity-based equalisation strategy was designed. Finally, a high-fidelity multicell model was used to simulate and verify the proposed equalisation strategy under the guidance of actual battery pack degradation data. The extracted experimental results show that the proposed method can efficiently and accurately estimate the SOC and capacity of new and aged batteries while a high-accuracy estimation in the entire life cycle is achieved. Compared with the traditional voltage-based and SOC-based strategies, the proposed SOC-and-capacity-based strategy can reconcile the maximization of battery pack capacity, the minimization of equalisation energy consumption, and the-equalisation duration to achieve high-efficiency balancing in the entire life cycle.

Experimental investigations of a vibro-impact absorber attached to a continuous structure

Vibro-impact (VI) phenomenon can be used in vibration absorbers to reduce low frequency vibrations on a fast time scale. VI absorbers are effective on a wide range of frequency, they are also known to be robust and insensitive to temperature changes, and they do not rely on visco-elastic materials. This solution offers very interesting and promising possibilities for vibration suppression in harsh environments where classical solutions can be pushed to their limits. If previous works have extensively studied VI based solutions numerically, mostly applied to single degree of freedom systems, experimental works dealing with continuous and multi-modal structures have received less attention. The work proposed in this paper focuses on the vibration reduction of a beam using a VI absorber with adjustable clearance. A parametric study on the clearance of the absorber has been performed. The response of the beam under harmonic excitation demonstrates the existence of an optimal clearance that leads to the lowest level of vibration of the beam. A relation between the gap value, the excitation amplitude and the response is found and can be translated into an attractive scalability property. The well-known regimes with two impacts per cycle and the Strongly Modulated Regime (SMR) have been clearly identified on the beam response, their relation with the optimal design of the absorber is highlighted, and their mechanism is explained. The present results open up new perspectives for vibro-impact absorbers, especially concerning their technological development and optimal design.