Different Types Of Perturbations Research Articles

Homeostatic regulation of a motor circuit through temperature sensing rather than activity sensing.

Homeostasis is a driving principle in physiology. To achieve homeostatic control of neural activity, neurons monitor their activity levels and then initiate corrective adjustments in excitability when activity strays from a set point. However, fluctuations in the brain microenvironment, such as temperature, pH, and other ions represent some of the most common perturbations to neural function in animals. Therefore, it is unclear if activity sensing is a universal strategy for different types of perturbations or if stability may arise by sensing specific environmental cues. Here we show the respiratory network of amphibians mounts a fast homeostatic response to restore motor function following inactivity caused by cooling over the physiological range. This response was not initiated by inactivity, but rather, by temperature. Compensation involved cold-activation of the noradrenergic system via mechanisms that involve inhibition of the Na + /K + ATPase, causing β-adrenoceptor signaling that enhanced network excitability. Thus, acute cooling initiates a modulatory response that opposes inactivity and enhances network excitability. As the nervous system of all animals is subjected to changes in the microenvironment, some circuits may have selected regulatory systems tuned to environmental variables in place of, or in addition to, activity-dependent control mechanisms.

Quasinormal modes of a black hole surrounded by a fluid of strings in Rastall gravity* *Ming Zhang is Supported by the Natural Science Basic Research Program of Shaanxi Province, China (2023-JC-QN-0053). De-Cheng Zou is supported by the Natural Science Foundation of China (12365009) and the Natural Science Foundation of Jiangxi Province, China (20232BAB201039)

In this paper, we explore the quasinormal modes (QNMs) of a black hole surrounded by a fluid of strings within the framework of Rastall gravity. We analyze the behavior of scalar, electromagnetic, and gravitational perturbations, focusing on the influences of black hole charge Q and angular momentum l on the quasinormal frequencies. Our numerical results reveal a significant dependence on parameter ε. These trends are consistent across different types of perturbations, emphasizing the relationship between black hole parameters and QNM behavior.

Unveiling hidden scaling relations in dissipative relaxation dynamics of strongly correlated quantum impurity systems.

Understanding the time evolution of strongly correlated open quantum systems (OQSs) in response to perturbations (quenches) is of fundamental importance to the precise control of quantum devices. It is, however, rather challenging in multi-impurity quantum systems because such evolution often involves multiple intricate dynamical processes. In this work, we apply the numerically exact hierarchical equations of motion approach to explore the influence of two different types of perturbations, i.e., sudden swapping of the energy levels of impurity systems and activating the inter-impurity spin-exchange interaction, on the dissipation dynamics of the Kondo-correlated two-impurity Anderson model over a wide range of energetic parameters. By evaluating the time-dependent impurity spectral function and other system properties, we analyze the time evolution of the Kondo state in detail and conclude a phenomenologically scaling relation for Kondo dynamics driven by these perturbations. The evolutionary scaling relationship is not only related to the Kondo characteristic energy TK but also significantly affected by the simultaneous non-Kondo dynamic characteristic energy. We expect these results will inspire subsequent theoretical studies on the dynamics of strongly correlated OQSs.

Robust explainer recommendation for time series classification

Time series classification is a task which deals with temporal sequences, a prevalent data type common in domains such as human activity recognition, sports analytics and general sensing. In this area, interest in explanability has been growing as explanation is key to understand the data and the model better. Recently, a great variety of techniques (e.g., LIME, SHAP, CAM) have been proposed and adapted for time series to provide explanation in the form of saliency maps, where the importance of each data point in the time series is quantified with a numerical value. However, the saliency maps can and often disagree, so it is unclear which one to use. This paper provides a novel framework to quantitatively evaluate and rank explanation methods for time series classification. We show how to robustly evaluate the informativeness of a given explanation method (i.e., relevance for the classification task), and how to compare explanations side-by-side. The goal is to recommend the best explainer for a given time series classification dataset. We propose AMEE, a Model-Agnostic Explanation Evaluation framework, for recommending saliency-based explanations for time series classification. In this approach, data perturbation is added to the input time series guided by each explanation. Our results show that perturbing discriminative parts of the time series leads to significant changes in classification accuracy, which can be used to evaluate each explanation. To be robust to different types of perturbations and different types of classifiers, we aggregate the accuracy loss across perturbations and classifiers. This novel approach allows us to recommend the best explainer among a set of different explainers, including random and oracle explainers. We provide a quantitative and qualitative analysis for synthetic datasets, a variety of time-series datasets, as well as a real-world case study with known expert ground truth.

Abnormalities in motor adaptation to different types of perturbations in schizophreniaperturbations in schizophrenia

Schizophrenia is a mental health disorder that often includes psychomotor disturbances, impacting how individuals adjust their motor output based on the cause of motor errors. While previous motor adaptation studies on individuals with schizophrenia have largely focused on large and consistent perturbations induced by abrupt experimental manipulations, such as donning prism goggles, the adaptation process to random perturbations, either caused by intrinsic motor noise or external disturbances, has not been examined – despite its ecological relevance. Here, we used a unified behavioral task paradigm to examine motor adaptation to perturbations of three causal structures among individuals in the remission stage of schizophrenia, youth with ultra-high risk of psychosis, adults with active symptoms, and age-matched controls. Results showed that individuals with schizophrenia had reduced trial-by-trial adaptation and large error variance when adapting to their own motor noise. When adapting to random but salient perturbations, they showed intact adaptation and normal causal inference of errors. This contrasted with reduced adaptation to large yet consistent perturbations, which could reflect difficulties in forming cognitive strategies rather than the often-assumed impairments in procedural learning or sense of agency. Furthermore, the observed adaptation effects were correlated with the severity of positive symptoms across the diagnosis groups. Our findings suggest that individuals with schizophrenia face challenges in accommodating intrinsic perturbations when motor errors are ambiguous but adapt with intact causal attribution when errors are salient.

Reactive dynamic balance in the geriatric setting : Possibilities for evaluation and quantification in functionally heterogeneous persons

Most falls in older persons occur during walking and are often due to maladaptation in response to gait perturbations. Therefore, the assessment of reactive dynamic balance is highly relevant for determining the individual risk of falling and could enable an early initiation of interventions. The methods available for perturbation of gait and for evaluating the corresponding reaction patterns are critically discussed in order to approach the assessment of reactive dynamic balance. Adiagnostic protocol for perturbation of gait on atreadmill was developed based on the literature. The application of the protocol to map reactive dynamic balance as comprehensively as possible is presented. After the initial determination of the individually preferred gait speed over ~6 min, the participant's gait is disrupted with 9 different types of perturbations over atime period of ~4:30 min. The evaluation options include spatiotemporal parameters and their variability, the margin of stability and the Lyapunov exponent. Dynamic reactive balance is apromising and specific parameter for quantifying the risk of falling in older persons. The comprehensive evaluation of the documented parameters is currently insufficient because there are no established methods or references. The development of aunified method for the sensitive determination of reactive dynamic balance is essential for its use in assessment of the risk of falling in the clinical context and for measuring the success of training.

Thermodynamics of Composition Graded Thermoelastic Solids.

We propose a thermodynamic model describing the thermoelastic behavior of composition graded materials. The compatibility of the model with the second law of thermodynamics is explored by applying a generalized Coleman-Noll procedure. For the material at hand, the specific entropy and the stress tensor may depend on the gradient of the unknown fields, resulting in a very general theory. We calculate the speeds of coupled first- and second-sound pulses, propagating either trough nonequilibrium or equilibrium states. We characterize several different types of perturbations depending on the value of the material coefficients. Under the assumption that the deformation of the body can produce changes in its stoichiometry, altering locally the material composition, the possibility of propagation of pure stoichiometric waves is pointed out. Thermoelastic perturbations generated by the coupling of stoichiometric and thermal effects are analyzed as well.

Modeling lens potentials with continuous neural fields in galaxy-scale strong lenses

Strong gravitational lensing is a unique observational tool for studying the dark and luminous mass distribution both within and between galaxies. Given the presence of substructures, current strong lensing observations demand more complex mass models than smooth analytical profiles, such as power-law ellipsoids. In this work, we introduce a continuous neural field to predict the lensing potential at any position throughout the image plane, allowing for a nearly model-independent description of the lensing mass. We applied our method to simulated Hubble Space Telescope imaging data containing different types of perturbations to a smooth mass distribution: a localized dark subhalo, a population of subhalos, and an external shear perturbation. Assuming knowledge of the source surface brightness, we used the continuous neural field to model either the perturbations alone or the full lensing potential. In both cases, the resulting model was able to fit the imaging data, and we were able to accurately recover the properties of both the smooth potential and the perturbations. Unlike many other deep-learning methods, ours explicitly retains lensing physics (i.e., the lens equation) and introduces high flexibility in the model only where required, namely, in the lens potential. Moreover, the neural network does not require pretraining on large sets of labeled data and predicts the potential from the single observed lensing image. Our model is implemented in the fully differentiable lens modeling code HERCULENS.

Optimal membership function based fuzzy proportional–integral controller for power control of molten salt breeder reactor core

The aim of this paper is to design an optimal membership function (MF)-based fuzzy PI (proportional integral) controller to control the core power of a nuclear reactor. The molten salt breeder reactor (MSBR) is extensively utilized for studying primary power management of nuclear reactors. Many challenges are associated with such systems, including mathematical modelling errors, parametric uncertainties, and external disturbances. Moreover, the liquid fuel used in MSBR systems makes it more challenging to design a suitable controller for the core relative power control of such systems. The conventional PI controller may not perform efficiently under such uncertainties in the MSBR system. Different types of perturbations can be handled by a fuzzy controller, if the coordination among fuzzy rules and membership functions of fuzzy variables is done accurately. The idea of this work is to optimize the settings (range and scale) of fuzzy MFs in a fuzzy base controller. The fuzzy controller will not be used directly in the system; rather, it will be used to tune a PI controller for the system where proper expert knowledge may not be available. A nonlinear dynamic acceleration coefficients-based class topper optimization (NDAC-CTO) algorithm is also developed to optimize the fuzzy membership functions. For load tracking with step disruption, the power control of the MSBR core is investigated. With the proposed controller, a significant improvement of 70 to 80 % in the settling time of the power profile of the MSBR system is achieved in comparison with the existing results.

Use of Assimilation Analysis in 4D-Var Source Inversion: Observing System Simulation Experiments (OSSEs) with GOSAT Methane and Hemispheric CMAQ

We previously introduced the parametric variance Kalman filter (PvKF) assimilation as a cost-efficient system to estimate the dynamics of methane analysis concentrations. As an extension of our development, this study demonstrates the linking of PvKF to a 4D-Var inversion aiming to improve on methane emissions estimation in comparison with the traditional 4D-Var. Using the proposed assimilation–inversion framework, we revisit fundamental assumptions of the perfect and already optimal model state that is typically made in the 4D-Var inversion algorithm. In addition, the new system objectively accounts for error correlations and the evolution of analysis error variances, which are non-trivial or computationally prohibitive to maintain otherwise. We perform observing system simulation experiments (OSSEs) aiming to isolate and explore various effects of the assimilation analysis on the source inversion. The effect of the initial field of analysis, forecast of analysis error covariance, and model error is examined through modified 4D-Var cost functions, while different types of perturbations of the prior emissions are considered. Our results show that using PvKF optimal analysis instead of the model forecast to initialize the inversion improves posterior emissions estimate (~35% reduction in the normalized mean bias, NMB) across the domain. The propagation of analysis error variance using the PvKF formulation also tends to retain the effect of background correlation structures within the observation space and, thus, results in a more reliable estimate of the posterior emissions in most cases (~50% reduction in the normalized mean error, NME). Our sectoral analysis of four main emission categories indicates how the additional information of assimilation analysis enhances the constraints of each emissions sector. Lastly, we found that adding the PvKF optimal analysis field to the cost function benefits the 4D-Var inversion by reducing its computational time (~65%), while including only the error covariance in the cost function has a negligible impact on the inversion time (10–20% reduction).

Stocks and cryptocurrencies: Antifragile or robust? A novel antifragility measure of the stock and cryptocurrency markets.

In contrast with robust systems that resist noise or fragile systems that break with noise, antifragility is defined as a property of complex systems that benefit from noise or disorder. Here we define and test a simple measure of antifragility for complex dynamical systems. In this work we use our antifragility measure to analyze real data from return prices in the stock and cryptocurrency markets. Our definition of antifragility is the product of the return price and a perturbation. We explore different types of perturbations that typically arise from within the system. Our results suggest that for both the stock market and the cryptocurrency market, the tendency among the 'top performers' is to be robust rather than antifragile. It would be important to explore other possible definitions of antifragility to understand its role in financial markets and in complex dynamical systems in general.

Abstract Layer for LeakyReLU for Neural Network Verification Based on Abstract Interpretation

Deep neural networks have been widely used in several complex tasks such as robotics, self-driving cars, medicine, etc. However, they have recently shown to be vulnerable in uncertain environments where inputs are noisy. As a consequence, the robustness of neural networks has become an essential property for their application in critical systems. Robustness is the capacity to take the same decision even when inputs are disturbed under different types of perturbations, including adversarial attacks. The great difficulty today is providing a formal guarantee of robustness, which is the context of this paper. To do so, abstract interpretation, a popular state-of-the-art method, consisting of converting the layers of the neural network into abstract layers, has been recently proposed. An abstract layer can act on a geometric abstract object or shape comprising implicitly an infinite number of inputs rather than an individual input. In this paper, we propose a new mathematical formulation of an abstract transformer to convert a LeakyReLU activation layer to an abstract layer. Moreover, we implement and integrate our transformer into the ERAN tool. For validation, we assess the performance of our transformer according to the LeakyReLU hyperparameter, and we study the robustness of the neural network according to the input perturbation intensity. Our approach is evaluated on three different datasets: MNIST, Fashion and a robotic dataset. The obtained results demonstrate the efficacy of our abstract transformer in terms of mathematical formulation and implementation.

Model and Training Method of the Resilient Image Classifier Considering Faults, Concept Drift, and Adversarial Attacks

Modern trainable image recognition models are vulnerable to different types of perturbations; hence, the development of resilient intelligent algorithms for safety-critical applications remains a relevant concern to reduce the impact of perturbation on model performance. This paper proposes a model and training method for a resilient image classifier capable of efficiently functioning despite various faults, adversarial attacks, and concept drifts. The proposed model has a multi-section structure with a hierarchy of optimized class prototypes and hyperspherical class boundaries, which provides adaptive computation, perturbation absorption, and graceful degradation. The proposed training method entails the application of a complex loss function assembled from its constituent parts in a particular way depending on the result of perturbation detection and the presence of new labeled and unlabeled data. The training method implements principles of self-knowledge distillation, the compactness maximization of class distribution and the interclass gap, the compression of feature representations, and consistency regularization. Consistency regularization makes it possible to utilize both labeled and unlabeled data to obtain a robust model and implement continuous adaptation. Experiments are performed on the publicly available CIFAR-10 and CIFAR-100 datasets using model backbones based on modules ResBlocks from the ResNet50 architecture and Swin transformer blocks. It is experimentally proven that the proposed prototype-based classifier head is characterized by a higher level of robustness and adaptability in comparison with the dense layer-based classifier head. It is also shown that multi-section structure and self-knowledge distillation feature conserve resources when processing simple samples under normal conditions and increase computational costs to improve the reliability of decisions when exposed to perturbations.

Single-Field Model of Gravitational-Scalar Instability. I. Evolution of Perturbations

On the basis of the previously formulated mathematical model of a statistical system with a scalar interaction of fermions and the theory of gravitational-scalar instability of a cosmological model based on a two-component statistical system of scalarly charged degenerate fermions, a numerical model of the cosmological evolution of gravitational-scalar perturbations for a one-component cosmological system with a canonical scalar interaction is constructed and studied. The influence of the magnitude of the scalar charge of fermions on the differential and integral parameters of the instability is revealed. It is shown that the gravitational-scalar instability in the early stages of expansion in the model under study arises at sufficiently small scalar charges. 4 fundamentally different types of perturbations are identified, as well as 4 types of gravitational-scalar instability, determined by the fundamental parameters of the model. Examples of numerical models are given that provide large values of the increments of the increase in the amplitude of perturbations.

DELE1 tracks perturbed protein import and processing in human mitochondria

Protein homeostatic control of mitochondria is key to age-related diseases and organismal decline. However, it is unknown how the diverse types of stress experienced by mitochondria can be integrated and appropriately responded to in human cells. Here we identify perturbations in the ancient conserved processes of mitochondrial protein import and processing as sources of DELE1 activation: DELE1 is continuously sorted across both mitochondrial membranes into the matrix and detects different types of perturbations along the way. DELE1 molecules in transit can become licensed for mitochondrial release and stress signaling through proteolytic removal of N-terminal sorting signals. Import defects that occur at the mitochondrial surface allow DELE1 precursors to bind and activate downstream factor HRI without the need for cleavage. Genome-wide genetics reveal that DELE1 additionally responds to compromised presequence processing by the matrix proteases PITRM1 and MPP, which are mutated in neurodegenerative diseases. These mechanisms rationalize DELE1-dependent mitochondrial stress integration in the human system and may inform future therapies of neuropathies.

Non-Singular Finite Time Tracking Control Approach Based on Disturbance Observers for Perturbed Quadrotor Unmanned Aerial Vehicles.

In this paper, a disturbance observer based on the non-singular terminal sliding mode control method was presented for the quadrotor in the presence of wind perturbation. First, the position and attitude dynamical equation of the quadrotor was introduced in the existence of windy perturbation. It was difficult to exactly determine the upper bound of the perturbations in the practical systems such as robot manipulators and quadrotor UAVs. Then, a disturbance observer was designed for the estimation of wind perturbation which was entered to the quadrotor system at any moment. Afterward, a non-singular terminal sliding surface was proposed based on the disturbance observer variable. Furthermore, finite time convergence of the closed-loop position and attitude models of the quadrotor was proved using Lyapunov theory concept. Unlike the existing methods, the new adaptive non-singular terminal sliding mode tracker for quadrotor unmanned aerial vehicles enabled accurate tracking control, robust performance, and parameter tuning. Through the combination of the finite time tracker and disturbance observer, the position and attitude tracking of quadrotor UAVs could be accurately performed not only in the nominal environment but also in the existence of different types of perturbations. Finally, simulation results based on the recommended method were provided to validate the proficiency of the suggested method. Moreover, comparison results with another existing study were presented to prove the success of the proposed method.

Oxidative Phosphorus Chemistry Perturbed by Minerals.

Life is a complex, open chemical system that must be supported with energy inputs. If one fathoms how simple early life must have been, the complexity of modern-day life is staggering by comparison. A minimally complex system that could plausibly provide pyrophosphates for early life could be the oxidation of reduced phosphorus sources such as hypophosphite and phosphite. Like all plausible prebiotic chemistries, this system would have been altered by minerals and rocks in close contact with the evolving solutions. This study addresses the different types of perturbations that minerals might have on this chemical system. This study finds that minerals may inhibit the total production of oxidized phosphorus from reduced phosphorus species, they may facilitate the production of phosphate, or they may facilitate the production of pyrophosphate. This study concludes with the idea that mineral perturbations from the environment increase the chemical complexity of this system.

Towards an Analytic Framework for System Resilience Based on Reaction Networks

Reaction network is a promising framework for representing complex systems of diverse and even interdisciplinary types. In this approach, complex systems appear as self‐maintaining structures emerging from a multitude of interactions, similar to proposed scenarios for the origin of life out of autocatalytic networks. The formalism of chemical organization theory (COT) mathematically specifies under which conditions a reaction network is stable enough to be observed as a whole complex system. Such conditions specify the notion of organization, crucial in COT. In this paper, we show that the structure and operation of organizations can be advanced towards a formal framework of resilience in complex systems. That is, we show that there exist three fundamental types of change (state, process, and structural) defined for reaction networks, and that these perturbations not only provide a general representation of perturbations in the context of resilience but also pave the ground to formalize different forms of resilient responses. In particular, we show that decomposing the network’s operational structure into dynamically decoupled modules allows to formalize what is the impact of a perturbation and to what extent any potential compensation to that perturbation will be successful. We illustrate our approach with a toy model of a farm that operates in a sustainable way producing milk, eggs, and/or grains from other resources. With the help of simulations, we analyze the different types of perturbations and responses that the farm can undergo and how that affects its sustainable operation.

A Perturbation-Based Methodology to Estimate the Equivalent Inertia of an Area Monitored by PMUs

This paper proposes a novel methodology to estimate equivalent inertia of an area, observed from its boundary buses where Phasor Measurement Units (PMUs) are assumed to be installed. The areas are divided according to the measurement points, and the methodology proposed can obtain the equivalent dynamic response of the area dependent of or independent of coherency of the generators inside, which is the first contribution of this paper. The methodology is divided in three parts: estimating the frequency response, estimating the power imbalance and estimating inertia through the solution of the swing equation by Least-Squares Method (LSM). The estimation of the power imbalance is the second contribution of this paper, enabling the study of areas that contain perturbations and attending the limitation of methods of the literature that rely on assumptions of slow mechanical power. It can be further divided in three steps: accounting the total power injected, estimating an equivalent load behavior and estimating an equivalent mechanical power. The quality of results is proved with test systems of different sizes, simulating different types of perturbations.

Semi-supervised robust training with generalized perturbed neighborhood

Adversarial examples have been shown to be a severe threat to deep neural networks (DNNs). One of the most effective adversarial defense methods is adversarial training (AT) through minimizing the adversarial risk Radv, which encourages both the benign example x and its adversarially perturbed neighborhoods within the ℓp-ball to be predicted as the ground-truth label. In this paper, we propose a novel defense method, the robust training (RT), by jointly minimizing two separated risks (i.e., Rstand and Rrob), which are with respect to the benign example and its neighborhoods, respectively. The motivation is to explicitly and jointly enhance the accuracy and the adversarial robustness. We prove that Radv is upper-bounded by Rstand+Rrob, which implies that RT has similar effect as AT. Intuitively, minimizing the standard risk enforces the benign example to be correctly predicted, while the robust risk minimization encourages the predictions of the neighbor examples to be consistent with the prediction of the benign example. Besides, since Rrob is independent of the ground-truth label, RT is naturally extended to the semi-supervised mode (i.e., SRT), to further enhance its effectiveness. Moreover, we extend the ℓp-bounded neighborhood to a general case, which covers different types of perturbations, such as the pixel-wise (i.e., x+δ) or the spatial perturbation (i.e., Ax+b). Extensive experiments on benchmark datasets not only verify the superiority of the proposed SRT to state-of-the-art methods for defending pixel-wise or spatial perturbations separately but also demonstrate its robustness to both perturbations simultaneously. Our work may shed the light on the understanding of universal model robustness and the potential of unlabeled samples. The code for reproducing main results is available at https://github.com/THUYimingLi/Semi-supervised_Robust_Training.