Simple Perturbation Research Articles

Distribution of Relaxation Times of Electrolysis Cells Using Time Domain Impedance Spectroscopy

Electrolysis cells are being applied to increasingly diverse applications. As these devices become more ubiquitous, the need to understand the underlying processes limiting performance or contributing to degradation becomes critical. The Distribution of Relaxation Times (DRT) has emerged as a powerful technique for resolving and identifying polarization processes without a priori characterization of the system. Existing impedance measurement techniques used are not well suited for large systems or use in the field due to hardware limitations, cost, and slow measurement speeds. In comparison, time domain impedance measurement methods are faster and easier to implement, overcoming many of the drawbacks associated with conventional measurement techniques and additionally offer direct access to the DRT without needing intermediary calculations like conventional impedance spectroscopy. In this work the impedance of both high temperature solid oxide electrolysis stacks and low temperature PEM based flow cells for wastewater treatment are measured using commercial FRA’s as well as with a custom-built time domain impedance spectrometer. Using a simple current step perturbation accomplished through the rapid removal of current the entire impedance spectrum can be measured, and the resulting DRT is compared against what is obtained from conventional techniques.

A joint lightly perturbation and synthesizing optimization attack framework for point cloud perturbation

Due to the inherent characteristic of deep learning models, prior researchers have focused on studying adversarial strategies aimed at uncovering their vulnerabilities. However, these strategies have been primarily been confined to the realm of image processing. When considering 3D vision structures, such as point clouds and meshes, the robustness of learning models also diminishes significantly in the presence of discernible outliers, and these strategies often fail to generalize across different DNN architectures. In this paper, a simple adversarial perturbation and optimization attack framework, Tangent Plane Perturbation and Synthesizing Optimization Attack (TPSOA) is presented. TPSOA provides a point cloud projection direction for point clouds, subtly inducing imperceptible perturbations on their tangent planes. Furthermore, acknowledging the geometry form of adversarial generation and its potential for 3D printing in physical scenarios, we reconstruct the adversarial mesh alongside the adversarial point cloud. A synthesized loss, incorporating both the Sobolev loss and the Chamfer loss, is utilized to optimize the distances between the adversarial generation and ground truth. Through a combination of visualization and quantitative analysis experiments, we validate the imperceptible nature of TPSOA, the necessity of the loss terms, and the magnitude of perturbations. These experiments that TPSOA enables targeted point cloud attacks that are imperceptible and of minor magnitude. In real-world adversarial attack and defense scenarios, as evidenced by both white-box and black-box experiments, the results show that TPSOA surpasses other state-of-the-art adversarial attack models, effectively manipulating a greater number of victim models while maintaining high efficiency, all without compromising its original structural integrity.

Probing Protein-Ligand Methyl-π Interaction Geometries through Chemical Shift Measurements of Selectively Labeled Methyl Groups.

Fragment-based drug design is heavily dependent on the optimization of initial low-affinity binders. Herein we introduce an approach that uses selective labeling of methyl groups in leucine and isoleucine side chains to directly probe methyl-π contacts, one of the most prominent forms of interaction between proteins and small molecules. Using simple NMR chemical shift perturbation experiments with selected BRD4-BD1 binders, we find good agreement with a commonly used model of the ring-current effect as well as the overall interaction geometries extracted from the Protein Data Bank. By combining both interaction geometries and chemical shift calculations as fit quality criteria, we can position dummy aromatic rings into an AlphaFold model of the protein of interest. The proposed method can therefore provide medicinal chemists with important information about binding geometries of small molecules in fast and iterative matter, even in the absence of high-resolution experimental structures.

Spreading of a viscoelastic drop on a solid substrate

We study the spreading of Newtonian viscous (aqueous glycerin solution) and viscoelastic (aqueous polymer solution) drops on solid substrates with different wettabilities. For drops of the same zero-shear viscosity, we find in the early stages of spreading that viscoelastic drops (i) spread faster and (ii) their contact radius shows a different power law vs time than Newtonian drops. We argue that the effect of viscoelasticity is only observable for experimental time scales of the order of or larger than the internal relaxation time of the viscoelastic polymer solution. We attribute this behaviour to the shear thinning of the viscoelastic polymer solution. When approaching the contact line, the shear rate increases and the steady-state viscosity of the viscoelastic drop is lower than that of the Newtonian drop. We support our experimental findings with a simple (first-order) perturbation model that qualitatively agrees with our findings.

High-quality factor Quasi-BIC mode via selective symmetry-breaking approach in a terahertz metasurface

This study numerically and experimentally presents a novel approach to excite bound state in the continuum (BIC) mode with a high Q-factor in the THz meta-molecule (composition of meta-atoms) system, leveraging a unique method of selective symmetry breaking in a ring-shaped metamolecule system. Unlike conventional strategies that uniformly disrupt the symmetry across all resonators to excite a quasi-BIC mode, this innovative technique targets only half of the unit cell for symmetry perturbation. This selective symmetry breaking minimizes radiative losses and enhances the Q-factor of the quasi-bound states in continuum (quasi-BIC) modes. The selective symmetry breaking is achieved in a ring-shaped metamolecule system by simple radial perturbation. The results depict a notable improvement in the Q-factor, achieving values as high as 107 in simulation, a significant enhancement compared to the uniformly symmetry-breaking approach, which exhibits Q-factors around 25.80. The experimental transmission spectrum and the near-field scanning images firmly validate the existence of the high Q BIC mode under this strategic symmetry-breaking approach. This work may open new avenues for developing advanced THz devices with promising applications in sensing, filtering, and non-linearity in the THz domain.

CogCol: Code Graph-Based Contrastive Learning Model for Code Summarization

Summarizing source code by natural language aims to help developers better understand existing code, making software development more efficient. Since source code is highly structured, recent research uses code structure information like Abstract Semantic Tree (AST) to enhance the structure understanding rather than a normal translation task. However, AST can only represent the syntactic relationship of code snippets, which can not reflect high-level relationships like control and data dependency in the program dependency graph. Moreover, researchers treat the AST as the unique structure information of one code snippet corresponding to one summarization. It will be easily affected by simple perturbations as it lacks the understanding of code with similar structure. To handle the above problems, we build CogCol, a Code graph-based Contrastive learning model. CogCol is a Transformer-based model that converts code graphs into unique sequences to enhance the model’s structure learning. In detail, CogCol uses supervised contrastive learning by building several kinds of code graphs as positive samples to enhance the structural representation of code snippets and generalizability. Moreover, experiments on the widely used open-source dataset show that CogCol can significantly improve the state-of-the-art code summarization models under Meteor, BLEU, and ROUGE.

Adversarial Robust Safeguard for Evading Deep Facial Manipulation

The non-consensual exploitation of facial manipulation has emerged as a pressing societal concern. In tandem with the identification of such fake content, recent research endeavors have advocated countering manipulation techniques through proactive interventions, specifically the incorporation of adversarial noise to impede the manipulation in advance. Nevertheless, with insufficient consideration of robustness, we show that current methods falter in providing protection after simple perturbations, e.g., blur. In addition, traditional optimization-based methods face limitations in scalability as they struggle to accommodate the substantial expansion of data volume, a consequence of the time-intensive iterative pipeline. To solve these challenges, we propose a learning-based model, Adversarial Robust Safeguard (ARS), to generate desirable protection noise in a single forward process, concurrently exhibiting a heightened resistance against prevalent perturbations. Specifically, our method involves a two-way protection design, characterized by a basic protection component responsible for generating efficacious noise features, coupled with robust protection for further enhancement. In robust protection, we first fuse image features with spatially duplicated noise embedding, thereby accounting for inherent information redundancy. Subsequently, a combination comprising a differentiable perturbation module and an adversarial network is devised to simulate potential information degradation during the training process. To evaluate it, we conduct experiments on four manipulation methods and compare recent works comprehensively. The results of our method exhibit good visual effects with pronounced robustness against varied perturbations at different levels.

Lifting Klein-Gordon/Einstein solutions to general nonlinear sigma-models: the wormhole example

We describe a simple technique for generating solutions to the classical field equations for an arbitrary nonlinear sigma-model minimally coupled to gravity. The technique promotes an arbitrary solution to the coupled Einstein/Klein-Gordon field equations for a single scalar field σ to a solution of the nonlinear sigma-model for N scalar fields minimally coupled to gravity. This mapping between solutions does not require there to be any target-space isometries and exists for every choice of geodesic computed using the target-space metric. In some special situations — such as when the solution depends only on a single coordinate (e.g. for homogeneous time-dependent or static spherically symmetric configurations) — the general solution to the sigma-model equations can be obtained in this way. We illustrate the technique by applying it to generate Euclidean wormhole solutions for multi-field sigma models coupled to gravity starting from the simplest Giddings-Strominger wormhole, clarifying why in the wormhole case Minkowski-signature target-space geometries can arise. We reproduce in this way the well-known axio-dilaton string wormhole and we illustrate the power of the technique by generating simple perturbations to it, like those due to string or α′ corrections.

Planetary influences on the solar cycle: A nonlinear dynamics approach.

We explore the effect of some simple perturbations on three nonlinear models proposed to describe large-scale solar behavior via the solar dynamo theory: the Lorenz and Rikitake systems and a Van der Pol-Duffing oscillator. Planetary magnetic fields affecting the solar dynamo activity have been simulated by using harmonic perturbations. These perturbations introduce cycle intermittency and amplitude irregularities revealed by the frequency spectra of the nonlinear signals. Furthermore, we have found that the perturbative intensity acts as an order parameter in the correlations between the system and the external forcing. Our findings suggest a promising avenue to study the sunspot activity by using nonlinear dynamics methods.

Unravelling diverse spatiotemporal orders in chlorine dioxide-iodine-malonic acid reaction-diffusion system through circularly polarized electric field and photo-illumination.

Designing and predicting self-organized pattern formation in out-of-equilibrium chemical and biochemical reactions holds fundamental significance. External perturbations like light and electric fields exert a crucial influence on reaction-diffusion systems involving ionic species. While the separate impacts of light and electric fields have been extensively studied, comprehending their combined effects on spatiotemporal dynamics is paramount for designing versatile spatial orders. Here, we theoretically investigate the spatiotemporal dynamics of chlorine dioxide-iodine-malonic acid reaction-diffusion system under photo-illumination and circularly polarized electric field (CPEF). By applying CPEF at varying intensities and frequencies, we observe the predominant emergence of oscillating hexagonal spot-like patterns from homogeneous stable steady states. Furthermore, our study unveils a spectrum of intriguing spatiotemporal instabilities, encompassing stripe-like patterns, oscillating dumbbell-shaped patterns, spot-like instabilities with square-based symmetry, and irregular chaotic patterns. However, when we introduce periodic photo-illumination to the hexagonal spot-like instabilities induced by CPEF in homogeneous steady states, we observe periodic size fluctuations. Additionally, the stripe-like instabilities undergo alternating transitions between hexagonal spots and stripes. Notably, within the Turing region, the interplay between these two external influences leads to the emergence of distinct superlattice patterns characterized by hexagonal-and square-based symmetry. These patterns include parallel lines of spots, target-like formations, black-eye patterns, and other captivating structures. Remarkably, the simple perturbation of the system through the application of these two external fields offers a versatile tool for generating a wide range of pattern-forming instabilities, thereby opening up exciting possibilities for future experimental validation.

Cavity-renormalized quantum criticality in a honeycomb bilayer antiferromagnet

Strong light-matter interactions as realized in an optical cavity provide a tantalizing opportunity to control the properties of condensed matter systems. Inspired by experimental advances in cavity quantum electrodynamics and the fabrication and control of two-dimensional magnets, we investigate the fate of a quantum critical antiferromagnet coupled to an optical cavity field. Using unbiased quantum Monte Carlo simulations, we compute the scaling behavior of the magnetic structure factor and other observables. While the position and universality class are not changed by a single cavity mode, the critical fluctuations themselves obtain a sizable enhancement, scaling with a fractional exponent that defies expectations based on simple perturbation theory. The scaling exponent can be understood using a generic scaling argument, based on which we predict that the effect may be even stronger in other universality classes. Our microscopic model is based on realistic parameters for two-dimensional magnetic quantum materials and the effect may be within the range of experimental detection.

An analytic solution of Navier–Stokes flow past a sphere in the region of intermediate Reynolds number

We study an analytical solution of steady, laminar, and incompressible flow past a sphere in the region of intermediate Reynolds number. The flow is governed by the Navier–Stokes (N–S) equation and the continuity equation. By applying a simple perturbation method to solve the equations, a second-order approximation cannot be obtained, as well-known (Whitehead’s paradox). Many analytical studies, such as Oseen approximation, matching technique, the homotopy analysis method, etc, have been conducted to resolve the paradox. The drag coefficients of these solutions are valid in the region of Reynolds number (R d is the diameter-based Reynolds number) However, the solution cannot express the flow separation behind a sphere observed in experiments. We also develop a perturbation technique to construct a solution of the N–S equation asymptotically to solve the paradox. The solution consists of power series of , where h is an arbitrary constant . By setting the value of h to avoid divergence of the solution in the all-region where steady flow exists, the solution expresses the flow separation behind a sphere, coinciding with experiments in the region of intermediate Reynolds number (), although the existing analytical solutions could not express. Also, the present solution gives the drag coefficient which agrees with experimental and numerical values in the region of .

Axisymmetric Peeling of Thin Elastic Films: A Perturbation Solution

Abstract We study the mechanical behavior of a thin elastic film that is affixed to a rigid substrate and subjected to a transverse force using a shaft with a finite radius. This scenario, also referred to as axisymmetric peeling, is encountered frequently in conventional blister tests as well as in our daily lives when removing an adhesive film from a substrate. Our primary objective is to gain a quantitative understanding of how the shaft’s radius influences the relationships between force and displacement, as well as between force and delamination areas. These relationships can serve as a dependable method to determine both the film’s elastic modulus and the adhesion strength between the film and its substrate. In this work, we provide a simple perturbation solution to this geometrically nonlinear problem while avoiding any use of ad hoc assumptions that were previously required. As a result, our results are in excellent agreement with numerical simulations and offer improved accuracy compared to analytical solutions available in the literature.

Stability of PPT in equilibrium states

We use simple spectral perturbation theory to show that the positive partial transpose property is stable under bounded perturbations of the Hamiltonian, for equilibrium states in infinite dimensions. The result holds provided the temperature is high enough, or equivalently, provided the perturbation is small enough.

A Scalable Method to Exploit Screening in Gaussian Process Models with Noise

A common approach to approximating Gaussian log-likelihoods at scale exploits the fact that precision matrices can be well-approximated by sparse matrices in some circumstances. This strategy is motivated by the screening effect, which refers to the phenomenon in which the linear prediction of a process Z at a point x 0 depends primarily on measurements nearest to x 0 . But simple perturbations, such as iid measurement noise, can significantly reduce the degree to which this exploitable phenomenon occurs. While strategies to cope with this issue already exist and are certainly improvements over ignoring the problem, in this work we present a new one based on the EM algorithm that offers several advantages. While in this work we focus on the application to Vecchia’s approximation (Vecchia), a particularly popular and powerful framework in which we can demonstrate true second-order optimization of M steps, the method can also be applied using entirely matrix-vector products, making it applicable to a very wide class of precision matrix-based approximation methods. Supplementary materials for this article are available online.

Prospects for control methods in engineering systems

The article discusses the prerequisites and natural consequences of the control methods development in engineering systems: (1) a simple deviation and perturbation controller, (2) a fuzzy logic controller with a fuzzifier and a rule base, (3) a neural network controller for dynamically adjusting the coefficients of the corresponding links, (4) a discrete neural network controller with a neural approximator and controller. The experience gained by researchers and engineers since the first description of the principles of regulation in 1910 and the level of information technologies design, in particular the neural network method of machine learning and the enormous computing potential of computer devices, today can be integrated into a fundamentally new method of discrete neural network regulation.
 The review carried out in the article is aimed at identifying and demonstrating the significance of experimental and operational data, which must be properly structured and marked up at the stage of their collection and archiving. It is this approach that will allow us to quickly implement neural network controllers in engineering systems, since the most important stage for their creation is the process of learning and optimizing the architecture of neural networks.
 The principle of operation, advantages and disadvantages, the optimal stages of a neural network controller improvement based on two neural networks for the formation of a control strategy, taking into account the most probable state of the system at the next point in time, are given.

Locally private estimation of conditional probability distribution for random forest in multimedia applications

The application of artificial intelligence models to raw multimedia data is susceptible to various data inference attacks, posing a significant risk in terms of sensitive input information leakage. Most of the existing studies on privacy-preserving multimedia applications based on artificial intelligence, focus on a single intelligent model and thus have various limitations. In this paper, we attempt to directly perturb the core component of many multimedia intelligent models in Bayesian networks and deep learning. That is, we apply conditional probability distribution estimation to guarantee the privacy of the models. At first, we present the formal problem formulation of private conditional probability distribution estimation and apply it to random forest for task classification in multimedia applications. Then, we design a simple perturbation approach called NaivePrivDistEst, to add noise to all the elements of probability estimation in random forest. Next, we present an improved approach called FastLRG, that utilizes the taxonomy tree to discretize the continuous attributes, thereby combining the attribute features to improve the prediction accuracy of random forest. Finally, we perform extensive experiments to evaluate the performance of random forest based on the proposed estimation algorithms. The experimental results indicate that the proposed models have better performance compared with existing private decision trees.

Exactly solvable model behind Bose-Hubbard dimers, Ince-Gauss beams, and aberrated optical cavities

The authors present a widely applicable, solvable model (an isotropic two-dimensional harmonic oscillator subject to two simple perturbations) and show that it underlies a broad range of physical phenomena displaying a topological transition, including aberrated optical cavities that support Ince-Gauss beams as their modes and the Bose-Hubbard dimer describing two coupled superfluids.

Capturing seismic velocity changes in receiver functions with optimal transport

SUMMARY Temporal changes in seismic velocities are an important tool for tracking structural changes within the crust during transient deformation. Although many geophysical processes span the crust, including volcanic unrest and large-magnitude earthquakes, existing methods for seismic monitoring are limited to the shallow subsurface. We present an approach for deep seismic monitoring based on teleseismic receiver functions, which illuminate the crustal velocity structure from the bottom-up. Using synthetic waveform modelling, we show that receiver functions are uniformly sensitive to velocity changes throughout the crust and can locate the depth of the perturbation. We introduce a novel method based on optimal transport for measuring the non-linear time–amplitude signal variations characteristic of receiver function monitoring. We show that optimal transport enables comparison of full waveform distributions rather than relying on representative stacked waveforms. We further study a linearized version of optimal transport that renders time-warping signal variations into simple Euclidean perturbations, and use this capability to perform blind source separation in the space of waveform variations. This disentangles the effects of changes in the source–receiver path from changes in subsurface velocities. Collectively, these methods extend the reach of seismic monitoring to deep geophysical processes, and provide a tool that can be used to study heterogeneous velocity changes with different spatial extents and temporal dynamics.

The Physical vs. Mathematical Problem of Navier-Stokes Equations (NSE)

The Navier-Stokes equations describing the motion of viscous/real fluids in Rn (n = 2 or 3) depend on a positive coefficient (the viscosity, ν) via the Reynolds number. The key of NSE problem is the Reynolds number, mathematically considered a simple small perturbation parameter without any physical explanation, or a vague physical Newtonian ratio of inertial to viscous forces, 𝑅𝑅𝑅𝑅=𝑈𝑈𝑈𝑈𝜈𝜈, in spite of its quantic physical meaning as the initial excitation to response ratio, at the beginning of motion (IC at t = 0). The paper deals with the thixotropic property of real viscosity which softens (ν ↓) when strained (Re↑), but it doesn’t tend to zero (ν → 0) as much as the Reynolds number increases, holding a finite value, corresponding to the new thermodynamic equilibrium state. The (ν → 0 for Re → ∞) false physical condition renders the NSE problem to a unique solution less one beyond a critical Reynolds number, Recr. The understanding of the wall-bounded viscous flows, at both small-scales (slow motion, small Re) and larger scale (turbulent motion, large Re) must be in conjunction with the more-subtle torsional buckling effect of the “wall” lag concept that the wall has on the inherent fluid dynamics during the starting phase. The limitations of the diathermal wall associated with the starting accelerations at the onset of motion, of the order of acr/g ≥ 2/3, create the physical conditions (thermomolecular changes) for the loss of the mathematical uniqueness of the NSE solutions. The physical limitations in conjunction with the validity area of NSE model are considered in the sequel. Because of the nonlinearity of the PDE differential equations, the variation of geometrical and physical properties can lead to bifurcations in the solution and thus, to multiple solutions. Considerations relative to laminar-turbulent transition as the main bifurcation source for the more complex structure of a solution, engendered by molecular structure changes of a flowing fluid in more or less contact with the walls, are given and illustrated for the canonical flows on flat plates and viscous decay of a starting/contact vortex (“vortex eye”).