Sudden Dynamic Changes Research Articles

Sudden change of genuine multipartite entanglement in non-Markovian dynamics

We investigate entanglement dynamics of bipartite as well multipartite systems beyond Markov approximation. We study two pairs of cavity–reservoir systems, modeled as four qubits and track the change of entanglement among cavity–cavity qubits, reservoir–reservoir qubits, and also for genuine entanglement of all four qubits. For cavity–cavity qubits, we find that non-Markovianity prolongs the life of entanglement besides collapse/revival phenomenon. For reservoir–reservoir qubits, entanglement sudden birth is delayed accordingly along with oscillations. For all four qubits, with a specific initial state, we find that genuine entanglement develops gradually, reaches to a maximum constant value and then freezes at this value for some time before decaying. This sudden change in dynamics of genuine entanglement occurs for a time window where neither cavities nor reservoirs are entangled. In contrast to Markov process, sudden change phenomenon may be recurrent in non-Markovian regime.

Reinforcement Learning for Autonomous Underwater Vehicles via Data-Informed Domain Randomization

Autonomous Underwater Vehicles (AUVs) or underwater vehicle-manipulator systems often have large model uncertainties from degenerated or damaged thrusters, varying payloads, disturbances from currents, etc. Other constraints, such as input dead zones and saturations, make the feedback controllers difficult to tune online. Model-free Reinforcement Learning (RL) has been applied to control AUVs, but most results were validated through numerical simulations. The trained controllers often perform unsatisfactorily on real AUVs; this is because the distributions of the AUV dynamics in numerical simulations and those of real AUVs are mismatched. This paper presents a model-free RL via Data-informed Domain Randomization (DDR) for controlling AUVs, where the mismatches between the trajectory data from numerical simulations and the real AUV were minimized by adjusting the parameters in the simulated AUVs. The DDR strategy extends the existing adaptive domain randomization technique by aggregating an input network to learn mappings between control signals across domains, enabling the controller to adapt to sudden changes in dynamics. The proposed RL via DDR was tested on the problems of AUV pose regulation through extensive numerical simulations and experiments in a lab tank with an underwater positioning system. These results have demonstrated the effectiveness of RL-DDR for transferring trained controllers to AUVs with different dynamics.

Feedback Gains modulate with Motor Memory Uncertainty

A sudden change in dynamics produces large errors leading to increases in muscle co-contraction and feedback gains during early adaptation. We previously proposed that internal model uncertainty drives these changes, whereby the sensorimotor system reacts to the change in dynamics by upregulating stiffness and feedback gains to reduce the effect of model errors. However, these feedback gain increases have also been suggested to represent part of the adaptation mechanism. Here, we investigate this by examining changes in visuomotor feedback gains during gradual or abrupt force field adaptation. Participants grasped a robotic manipulandum and reached while a curl force field was introduced gradually or abruptly. Abrupt introduction of dynamics elicited large initial increases in kinematic error, muscle co-contraction and visuomotor feedback gains, while gradual introduction showed little initial change in these measures despite evidence of adaptation. After adaptation had plateaued, there was a change in the co-contraction and visuomotor feedback gains relative to null field movements, but no differences between the abrupt and gradual introduction of dynamics. This suggests that the initial increase in feedback gains is not part of the adaptation process, but instead an automatic reactive response to internal model uncertainty. In contrast, the final level of feedback gains is a predictive tuning of the feedback gains to the external dynamics as part of the internal model adaptation. Together, the reactive and predictive feedback gains explain the wide variety of previous experimental results of feedback changes during adaptation.

Estimation of linear motion in dense crowd videos using Langevin model

Expert and intelligent systems are highly popular for designing cross-domain autonomous systems. Computer vision aided by expert decision-making systems are widely used to automate tasks that are normally carried out manually. For example, in the traditional way of traffic monitoring, signals are either controlled through predefined set-ups or with the help of visual observations. Even though some of the modern cities employ sensor-based surveillance at large, full automation is still far from the desirable accuracy. This is more challenging for monitoring high-density crowds that often cause unwanted situations due to sudden changes in dynamics. It has been shown in this paper that the movement of a dense crowd can be approximated using well-known physics-based models. Such a modeling can help to understand the overall crowd behavior. In accomplishing this, we have introduced a computer vision guided expert system with the help of a Langevin equation-based force model to represent the linear flow of the crowd, particularly in situations when the density is high. One of the primary contributions of our proposed model is its computational efficiency, particularly when a timely decision can help to avoid unwanted situations. Our proposed three-term force model is capable of predicting the positions of the group of key-points in a video frame leading to a significant computational gain. We have carried out several experiments on publicly available videos as well as our own videos to validate the claims in terms of accuracy as well as computational gain. It has been observed that the proposed physics-based model outperforms the existing systems with a 4−6% improvement in the segmentation accuracy. Moreover, we have achieved multi-fold computational gain. We believe the proposed work, when supported by appropriate post-processing, can be used to develop crowd monitoring applications.

The figurative world of Florent Schmitt’s piano works (following the diptych “Mirages”)

The figurative world of Florent Schmitt’s piano works (following the diptych “Mirages”)

Dynamics of cortical activity eigenmodes including standing, traveling, and rotating waves

The eigenfrequencies of natural eigenmodes of cortical activity are calculated from corticothalamic neural field theory. These correspond to poles of a transcendental transfer function, which is accurately approximated here as a rational function. In this approximation, the least damped frequencies of the lowest order spatial eigenmodes dominate the dynamics and correspond to slow-wave ($\ensuremath{\lesssim}1$ Hz) and alpha ($\ensuremath{\sim}10$ Hz) resonances. The eigenmodes exhibit standing and rotating wave patterns in which eigenmode beating can give the appearance of sudden changes in dynamics. This provides a physical explanation of sudden transitions that have been argued to occur between artificially discretized brain activity patterns, sometimes termed microstates, and allows the dynamics to be analyzed by replacing these phenomenologically constructed activity patterns with well-defined eigenmodes. An eigenmode interpretation of large-scale brain activity can thus potentially supplant phenomenological approaches and place analysis on a systematic physical footing.

How bifurcations affect functional connectivity in finite-size neural networks

Understanding how the functional connectivity of a neural network (i.e. the statistical dependencies among different neurons) depends upon its anatomical connectivity and how it is modulated by other network parameters is a central topic in neuroscience [1]. However, only few analytical investigations of functional connectivity in network models have been reported [2]. A difficulty for such analytical studies is that they require in principle characterizing the bifurcation points (i.e. the parameter values at which the network undergoes a sudden change in dynamics) and how functional connectivity changes close to such points. Given that biological networks have a finite number of neurons, it is also important to develop such analytical studies without relying only on large network approximations that neglect finite-size effects. To overcome these difficulties, here we developed a new approach to investigate the dynamics of finite-size networks composed by interconnected inhibitory and excitatory neurons. We applied this approach to a network of neurons described by voltage-based differential equations, with an input to each neuron provided by a deterministic current superimposed to a weak normally distributed stochastic component. To understand the interplay between the deterministic dynamics of the network and the effects of random fluctuations in a finite-size network, we proceeded in two steps. First, we neglected the stochastic component of the input and we performed a complete numerical and analytical bifurcation analysis while varying the deterministic inputs and the strength of the connections. We did this using the MatCont continuation package [3] and the analytical expressions of the Jacobian matrix eigenvalues. We found that our finite-size network displayed standard bifurcations such as saddle-node and Hopf manifolds. Specifically, these manifolds describe the input currents values for which equilibria collide and annihilate to each other, and for which self-sustained oscillations arise, respectively. Moreover, strong inhibition gave rise to additional branch point bifurcations: for highly negative synaptic weights, the system exhibited special multiple solutions characterized by heterogeneous membrane potentials. These particular solutions represent new finite-size effects not captured by large network approximations. Second, we inserted stochastic components into the input and studied the behaviour of the network under the effect of noise. Specifically, we used a new first-order perturbative method where the perturbative parameter is the standard deviation of the noise in the input. This technique allowed the analytical calculation of the cross-correlation between firing rates, and of the Fisher information about the external input carried by the network. Our analytical solutions showed that the neurons become independent when the deterministic component of the input is strong, while they become correlated for smaller current values lying on the saddle-node manifold. The latter is due to a counter-intuitive finite-size effect [4] that can be fully captured by our formalism. Furthermore, in our network strong correlations led to large Fisher information values, thus enhancing the encoding precision of the network with respect to the input variables. This also shows that reducing correlations among neurons activity does not imply an enhancement of the information encoding, as suggested in some studies [5].

Sudden change in dynamics of genuine multipartite entanglement of cavity-reservoir qubits

We study the dynamics of genuine multipartite entanglement for a system of four qubits. Using a computable entanglement monotone for multipartite systems, we investigate the as yet unexplored aspects of a cavity-reservoir system of qubits. For one specific initial state, we observe a sudden transition in the dynamics of genuine entanglement for the four qubits. This sudden change occurs only during a time window where neither cavity-cavity qubits nor reservoir-reservoir qubits are entangled. We show that the sudden change in dynamics of this specific state is extremely sensitive to white noise.

Robust Adaptive Control of an Uninhabited Surface Vehicle

A robust adaptive autopilot for uninhabited surface vehicles (USV) based on a model predictive controller (MPC) is presented in this paper. The novel autopilot is capable of handling sudden changes in system dynamics. In real life situations, very often a sudden change in dynamics results in missions being aborted and the uninhabited vehicles have to be rescued before they cause damage to other marine craft in the vicinity. This problem has been suitably dealt with by this innovative design. The MPC adopts an online adaptive nature by utilising three algorithms, individually: gradient descent, least squares and weighted least squares (WLS). Even with random initialisation, significant improvements over the other algorithmic approach were achieved by WLS by maintaining the intermittent continuous values of system parameters and periodically reinitialising the covariance matrix. Also, a time frame of 25 seconds appears to be the optimum to reinitialise the parameters in simulation studies. This novel approach enables the autopilot to cope well with significant changes in the system dynamics and empowers USVs to accomplish their desired missions.

Tip-over Prediction for Omnidirectional Mobile Robot

This paper proposes a holonomic omnidirectional mobile robot with active dual-wheel caster assemblies as a robotic transport vehicle. With concern to the sudden dynamic changes during maneuver, the tip-over stability monitoring is very important. This work presents the estimation technique of the tip-over direction and the stability measurement. The dynamical model of the omnidirectional mobile robot is derived to estimate the net force from the supporting reaction force at each wheel which is caus ed bythe inertial and external forces. The direction of tipping and the stability metric is estimated using the force-angle stability measure (FASM). The performance of the tip-over prediction for an omnidirectional mobile robot with active dual-wheel assemblies is shown by the conducted simulations.

Gyroxin and Its Biological Activity: Effects on CNS Basement Membranes and Endothelium and Protease-Activated Receptors

Gyroxin is a glycoprotein isolated from rattlesnake venom, with known thrombin-like serine protease properties and behavioral action in the CNS. The mechanism of the latter has eluded experimenters for three decades. In this paper about the in vitro chick retina we demonstrate an excitotoxic CNS action of Gyroxin by observing retinal Intrinsic Optical Signals (IOS). These show sudden dynamic changes in the intact tissue due to gyroxin action. The very fast kinetics of this response precludes deep tissue penetration by the protein, a mechanism of tissue response described here for the first time. At nanomolar concentrations, Gyroxin alters profoundly the optical profiles of retinal spreading depression waves (RSDs), suggesting modulation of ionic transport and metabolism. This effect is reversible in contrast with the acute cell lysis induced with gyroxin pulses at higher concentration. Because there may be more than one target of Gyroxine at the retinal inner limiting membrane, additional biochemical assays were performed to study a possible Na/K-ATPase blockade and PAR receptor activation. We conclude that the Gyroxin interaction with basement membranes of CNS and endothelium triggers conformational phase transitions at basement membranes, with multiple functional consequences.

Derivative-Free Model Reference Adaptive Control

A derivative-free, delayed weight update law is developed for model reference adaptive control of continuous-time uncertain systems, without assuming the existence of constant ideal weights. Using a Lyapunov―Krasovskii functional it is proven that the error dynamics are uniformly ultimately bounded, without the need for modification terms in the adaptive law. Estimates for the ultimate bound and the exponential rate of convergence to the ultimate bound are provided. Also discussed are employing various modification terms for further improving performance and robustness of the adaptively controlled system. Examples illustrate that the proposed derivative-free model reference adaptive control law is advantageous for applications to systems that can undergo a sudden change in dynamics.

The Ramp Archetype: A Score-Based Study of Musical Dynamics in 14 Piano Composers

A study of 435 piano works by 14 composers reveals a significant asymmetry between increasing and decreasing dynamics. Specificallv: (1) there is an association between incremental crescendos and abrupt diminuendos for successions of static dynamic indications, (2) crescendos are more frequent than diminuendos, (3) crescendos tend to last longer than diminuendos, (4) crescendos will more commonly follow low dynamic levels than will diminuen- dos follow high dynamic levels, and (5) sudden changes in dynamics are much more prone to be reductions of loudness than increases of loudness. These findings support the existence of a "ramp archetype" for musical dynamics in which the music tends to build in a gradual way, but tends to subside relatively quickly. The results replicate and extend a previous study of musical dynamics in Beethoven's 32 piano sonatas. The results are consistent with a well-adapted strategy for maintaining passive auditory attention/arousal.

Subarachnoid hemorrhage after preoperative embolization of a cystic meningioma

We present a case of cystic meningioma accompanied by hemorrhage in the cyst and adjacent subarachnoid space that occurred while preoperative embolization in feeders of the tumor was being applied. The possible reason for the hemorrhage was the sudden dynamic changes in blood flow triggered by the embolization. The changes could have caused multiple ruptures of pathologic small vessels. We recommend that preoperative embolization should be used cautiously in treating cystic meningiomas because of a possible increase in bleeding from pathologic weak vessels.