Perturbative Constraints Research Articles

Environmentally and statistically robust matched-field source localization based on information geometry principles.

Matched-field processing (MFP) achieves underwater source localization by measuring the correlation between the array and replica signals, with traditional MFP being equivalent to estimating the Euclidean distance between the data cross-spectral density matrix (CSDM) and replica matrices. However, in practical applications, random inhomogeneities in the marine environment and inaccurate estimation of CSDM reduce MFP performance. The traditional minimum variance matched-field processor with environmental perturbation constraints perturbs a priori environment parameters to obtain linear constraints and yields the optimal weight vectors as the replica vectors. In this paper, within the framework of information geometry, the geometric properties of CSDMs as semi-positive definite and Hermitian enable CSDMs to be described as points in a Riemannian manifold. Source localization can be achieved by quantifying the similarity between the CSDMs as the geodesic distance between the points on the manifold. This paper introduces a constrained replica CSDM composed of perturbed replica vectors and proposes a robust matched-field processor based on two non-Euclidean distances: the Riemannian distance and the modified Jensen-Shannon distance. Simulations and experimental results demonstrate that the proposed processors are more robust against environmental and statistical mismatches than traditional processors and can also reduce sidelobe level and improve the resolution.

Triple Higgs boson production and electroweak phase transition in the two-real-singlet model

The production of three Higgs bosons at hadron colliders can be enhanced by a double-resonant effect in the ℤ2-symmetric two-real-singlet extension of the Standard Model, making it potentially observable in future LHC runs. The production rate is maximized for large scalar couplings, which prompts us to carefully reconsider the perturbativity constraints on the theory. This leads us to construct a new set of 140 benchmark points that have a triple Higgs boson production cross-section at least 100 times larger than the SM value.Furthermore, we study the dynamics of the electroweak phase transition, both analytically at leading order, and numerically without the high-temperature expansion. Both analyses indicate that a first-order phase transition is incompatible with the requirement that both singlets have a non-zero vev in the present-day vacuum, as required by doubly-enhanced triple Higgs boson production. Allowing instead one of the singlets to remain at zero field value opens up the possibility of a first-order phase transition, while di-Higgs boson production can still be enhanced by a (single) resonance.

Anomalous U(1) extension of the Standard Model

We present a set of example models in which the Standard Model (SM) symmetry group is extended by a new abelian symmetry. This additional symmetry appears anomalous in the effective low-energy theory; however, the anomalies cancel out when massive chiral fermions not present in the effective low-energy theory are taken into account. These chiral fermions under the new abelian gauge group, are chosen to be vector-like under the SM symmetries, and reside in the same representations as quarks and leptons. This allows us to quantitatively determine the magnitude of tree-level interactions between three vector bosons induced in low-energy effective field theory by the integration of chiral heavy fermions. We also examine the perturbativity constraints of the theory and the ultraviolet cut-off. We conclude by highlighting possible extensions of our work.

Perturbative quantum evolution of the gravitational state and dressing in general backgrounds

This paper sets up a perturbative treatment of the evolving quantum state of a gravitational system, in a Schrödinger-like picture, working about a general background. This connects gauge symmetry, the constraints, gravitational dressing, and evolution. Starting with a general time slicing, we give a simple derivation of the relation between the constraints, the Hamiltonian, and its well-known boundary term. Among different approaches to quantization with constraints, we focus on a “gauge-invariant canonical quantization,” which is developed perturbatively in the gravitational coupling. The leading-order solution of the constraints (including the Wheeler-DeWitt equation) for perturbations about the background is given in terms of an explicit construction of gravitational dressings built using certain generalized Green’s functions; different such dressings corresponding to adding propagating gravitational waves to a particular solution of the constraints. Dressed operators commute with the constraints, expressing their gauge invariance, and have an algebraic structure differing significantly from the undressed operators of the underlying field theory. These operators can act on the vacuum to create dressed states, and evolution of general such states is then generated by the boundary Hamiltonian, and alternately may be characterized using other relational observables. This provides a concrete approach to studying perturbative time evolution, including the leading gravitational backreaction, of quantum states of black holes with flat or anti–de-Sitter asymptotics, for example on horizon-crossing slices. This description of evolution in turn provides a starting point for investigating possibly important corrections to quantum evolution, that go beyond quantized general relativity. Published by the American Physical Society 2024

Bi-Level Inverse Robust Optimization Dispatch of Wind Power and Pumped Storage Hydropower Complementary Systems

This paper presents a bi-level inverse robust economic dispatch optimization model consisting of wind turbines and pumped storage hydropower (PSH). The inner level model aims to minimize the total generation cost, while the outer level introduces the optimal inverse robust index (OIRI) for wind power output based on the ideal perturbation constraints of the objective function. The OIRI represents the maximum distance by which decision variables in the non-dominated frontier can be perturbed. Compared to traditional methods for quantifying the worst-case sensitivity region using polygons and ellipses, the OIRI can more accurately quantify parameter uncertainty. We integrate the grid multi-objective bacterial colony chemotaxis algorithm and the bisection method to solve the proposed model. The former is adopted to solve the inner level problem, while the latter is used to calculate the OIRI. The proposed approach establishes the relationship between the maximum forecast deviation and the minimum generation cost associated with each non-dominated solution in the optimal load allocation. To demonstrate its economic viability and effectiveness, we simulate the proposed approach using real power system operation data and conduct a comparative analysis.

Black-box attacks on dynamic graphs via adversarial topology perturbations

Research and analysis of attacks on dynamic graph is beneficial for information systems to investigate vulnerabilities and strength abilities in resisting malicious attacks. Existing attacks on dynamic graphs mainly focus on rewiring original graph structures, which are often infeasible in real-world scenarios. To address this issue, we adopt a novel strategy by injecting both fake nodes and links to attack dynamic graphs. Based on that, we present the first study on attacking dynamic graphs via adversarial topology perturbations in a restricted black-box setting, in which downstream graph learning tasks are unknown. Specifically, we first divide dynamic graph structure perturbations into three sub-tasks and transform them as a sequential decision making process. Then, we propose a hierarchical reinforcement learning based black-box attack (HRBBA) framework to model three sub-tasks as attack policies. In addition, an imperceptible perturbation constraint to guarantee the concealment of attacks is incorporated into HRBBA. Finally, HRBBA is optimized based on the actor-critic process. Extensive experiments on four real-world dynamic graphs show that the performance of diverse dynamic graph learning methods (victim methods) on tasks like link prediction, node classification and network clustering can be substantially degraded under HRBBA attack.

PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models against Adversarial Examples

Point cloud completion, as the upstream procedure of 3D recognition and segmentation, has become an essential part of many tasks such as navigation and scene understanding. While various point cloud completion models have demonstrated their powerful capabilities, their robustness against adversarial attacks, which have been proven to be fatally malicious towards deep neural networks, remains unknown. In addition, existing attack approaches towards point cloud classifiers cannot be applied to the completion models due to different output forms and attack purposes. In order to evaluate the robustness of the completion models, we propose PointCA, the first adversarial attack against 3D point cloud completion models. PointCA can generate adversarial point clouds that maintain high similarity with the original ones, while being completed as another object with totally different semantic information. Specifically, we minimize the representation discrepancy between the adversarial example and the target point set to jointly explore the adversarial point clouds in the geometry space and the feature space. Furthermore, to launch a stealthier attack, we innovatively employ the neighbourhood density information to tailor the perturbation constraint, leading to geometry-aware and distribution-adaptive modifications for each point. Extensive experiments against different premier point cloud completion networks show that PointCA can cause the performance degradation from 77.9% to 16.7%, with the structure chamfer distance kept below 0.01. We conclude that existing completion models are severely vulnerable to adversarial examples, and state-of-the-art defenses for point cloud classification will be partially invalid when applied to incomplete and uneven point cloud data.

Muon g−2 anomaly from a massive spin-2 particle

We investigate the possibility to interpret the muon $g\ensuremath{-}2$ anomaly in terms of a massive spin-2 particle, $G$, which can be identified as the first Kaluza-Klein graviton in the generalized Randall-Sundrum model. In particular, we obtain the leading-order contributions to the muon $g\ensuremath{-}2$ by calculating the relevant one-loop Feynman diagrams induced by $G$. The analytic expression is shown to keep the gauge invariance of the quantum electrodynamics and to be consistent with the expected UV divergence structure. Moreover, we impose the theoretical bounds from the perturbativity and the experimental constraints from LHC and LEP-II on our model. Especially, we derive novel perturbativity constraints on nonrenormalizable operators related to $G$, which are the natural generalization of the counterpart for the renormalizable operators. As a result, we show that there exists a substantial parameter space, which can accommodate the muon $g\ensuremath{-}2$ anomaly allowed by all constraints. Finally, we also make comments on the possible explanation of the electron $g\ensuremath{-}2$ anomalies with the massive spin-2 particle.

CGMM-Based Sound Zone Generation Using Robust Pressure Matching With ATF Perturbation Constraints

CGMM-Based Sound Zone Generation Using Robust Pressure Matching With ATF Perturbation Constraints

Multi-layer noise reshaping and perceptual optimization for effective adversarial attack of images

Adversarial attack aims to fail the deep neural network by adding a small amount of perturbation to the input image, in which the attack success rate and resulting image quality are maximized under the lp norm perturbation constraint. However, the lp norm is not accurately correlated to human perception of image quality. Attack methods based on l0 norm constraint usually suffer from the high computational cost due to the iterative search for candidate pixels to modify. In this work, we explore how perceptual quality optimization can be incorporated into the adversarial attack design and propose a two-stage attack method to reshape the adversarial noise by an initial attack and optimize the visual quality of the attacked images without sacrificing the attack success rate. Specifically, we construct a visual attention network to generate a perceptual attention map to modulate the adversarial noise generated by a base attack method. The network is trained to maximize the visual quality in Structural Similarity Index Metric (SSIM) while achieving the same attack success rate. To improve the image perceptual quality further, we propose a fast search algorithm to perform an iterative block-wise pruning of the adversarial noise. We evaluate our method on the mini-ImageNet dataset against three different defense schemes. The results have demonstrated that our method can achieve better attack performance in image quality, attack success rate, and efficiency than the state-of-the-art attack methods.

Adversarial Attack and Defense Strategies of Speaker Recognition Systems: A Survey

Speaker recognition is a task that identifies the speaker from multiple audios. Recently, advances in deep learning have considerably boosted the development of speech signal processing techniques. Speaker or speech recognition has been widely adopted in such applications as smart locks, smart vehicle-mounted systems, and financial services. However, deep neural network-based speaker recognition systems (SRSs) are susceptible to adversarial attacks, which fool the system to make wrong decisions by small perturbations, and this has drawn the attention of researchers to the security of SRSs. Unfortunately, there is no systematic review work in this domain. In this work, we conduct a comprehensive survey to fill this gap, which includes the development of SRSs, adversarial attacks and defenses against SRSs. Specifically, we first introduce the mainstream frameworks of SRSs and some commonly used datasets. Then, from the perspectives of adversarial example generation and evaluation, we introduce different attack tasks, the prior knowledge of attacks, perturbation objects, perturbation constraints, and attack effect evaluation indicators. Next, we focus on some effective defense strategies, including adversarial training, attack detection, and input refactoring against existing attacks, and analyze their strengths and weaknesses in terms of fidelity and robustness. Finally, we discuss the challenges posed by audio adversarial examples in SRSs and some valuable research topics in the future.

TextHoaxer: Budgeted Hard-Label Adversarial Attacks on Text

This paper focuses on a newly challenging setting in hard-label adversarial attacks on text data by taking the budget information into account. Although existing approaches can successfully generate adversarial examples in the hard-label setting, they follow an ideal assumption that the victim model does not restrict the number of queries. However, in real-world applications the query budget is usually tight or limited. Moreover, existing hard-label adversarial attack techniques use the genetic algorithm to optimize discrete text data by maintaining a number of adversarial candidates during optimization, which can lead to the problem of generating low-quality adversarial examples in the tight-budget setting. To solve this problem, in this paper, we propose a new method named TextHoaxer by formulating the budgeted hard-label adversarial attack task on text data as a gradient-based optimization problem of perturbation matrix in the continuous word embedding space. Compared with the genetic algorithm-based optimization, our solution only uses a single initialized adversarial example as the adversarial candidate for optimization, which significantly reduces the number of queries. The optimization is guided by a new objective function consisting of three terms, i.e., semantic similarity term, pair-wise perturbation constraint, and sparsity constraint. Semantic similarity term and pair-wise perturbation constraint can ensure the high semantic similarity of adversarial examples from both comprehensive text-level and individual word-level, while the sparsity constraint explicitly restricts the number of perturbed words, which is also helpful for enhancing the quality of generated text. We conduct extensive experiments on eight text datasets against three representative natural language models, and experimental results show that TextHoaxer can generate high-quality adversarial examples with higher semantic similarity and lower perturbation rate under the tight-budget setting.

Adversarial Attacks on Neural Network with Batch Dimensions Perturbation and Manhattan-Distance Constraints

Adversarial Attacks on Neural Network with Batch Dimensions Perturbation and Manhattan-Distance Constraints

A perturbation constraint related weak perceptual adversarial example generation method

A perturbation constraint related weak perceptual adversarial example generation method

How heavy can dark matter be? Constraining colourful unitarity with SARAH

We describe the automation of the calculation of perturbative unitarity constraints including scalars that have colour charges, and its release in SARAH4.14.4. We apply this, along with vacuum stability constraints, to a simple dark matter model with colourful mediators and interesting decays, and show how it leads to a bound on a thermal relic dark matter mass well below the classic Griest-Kamionkowski limit.

Sign pattern, usability, representations and perturbation for the core-EP and weighted core-EP inverse

New properties and representations for the core-EP inverse are developed. Particularly, the core-EP inverse of an upper triangular matrix and its sign pattern are considered. Determinantal representations for the core-EP inverse and core-EP solution of linear systems are investigated. Corresponding representations of the weighted core-EP inverse are derived. The obtained results are illustrated by computational algorithms and illustrative examples. Certain results related to the perturbation constraints for the core-EP inverse are also provided. The results related with the core-EP inverse are a continuous of the results from [H. Ma and P.S. Stanimirović, Characterizations, approximation and perturbations of the Core-EP inverse, Appl. Math. Comput. 359 (2019), 404–417].

Active compensation for optimal RMS wavefront error in perturbed off-axis optical telescopes using nodal aberration theory.

This paper presents an active compensation strategy for RMS wavefront error of perturbed off-axis telescopes in the framework of nodal aberration theory. First, the orthogonalized expression of the wave aberration function in the vector form for perturbed off-axis telescopes is derived by using RMS normalization. The orthogonalized aberration function is applied to analytically describe the RMS wavefront error in perturbed off-axis telescopes with circular apertures. Then, the system compensation model for perturbed off-axis telescopes is established. The compensation model takes the weighted square sum of the RMS wavefront errors at representative field points as the objective function, which is minimized to obtain the optimal compensation solution of off-axis systems with perturbation constraints. The compensation model is solved by using a particle swarm optimization algorithm. Then, the off-axis three-mirror anastigmatic telescope is taken as an example, and the system compensations for the misaligned tertiary mirror and deformed primary mirror are discussed. After compensation, the average RMS wavefront errors in the perturbed off-axis systems are greatly reduced, which can well meet the system requirements. Finally, Monte Carlo simulations of the optimal compensation method and sensitivity table method are carried out to demonstrate the correctness and accuracy of the proposed method.

On perturbative constraints for vacuum f(R) gravity

Perturbative techniques are important for modified theories of gravity since they allow to calculate deviations from general relativity without recurring to exact solutions, which can be difficult to find. When applied to models such as f(R) gravity, these techniques introduce corrections in the field equations that involve higher order derivatives. Such corrections must be handled carefully to have a well defined perturbative scheme, and this can be achieved through the method of perturbative constraints, where the coefficient of the additional term in the action is used as expansion parameter for the quantities of interest. In this work, we implement a perturbative framework that compares solutions in modified theories of gravity with solutions of the Einstein field equations, by following the guidelines of perturbation theory constructed in general relativity together with the perturbative constraints rationale. By using this formalism, we demonstrate that a consistent f(R) perturbation theory in vacuum, for an important class of f(R) functions, produces no additional effects with respect to what is expected from the perturbation theory of general relativity. From this result, we argue that there are fundamental limitations that explain why the solutions of some f(R) models can be disconnected from their general relativistic counterparts, in the sense that the limit that leads from the f(R) action to general relativity does not transform the solutions accordingly.

Composite Higgs at high transverse momentum

In this paper we explore composite Higgs scenarios through the effects of light top-partners in Higgs+Jet production at the LHC. The pseudo-Goldstone boson nature of the Higgs field means that single-Higgs production via gluon fusion is insensitive to the mass spectrum of the top-partners. However in associated production this is not the case, and new physics scales may be probed. In the course of the work we consider scenarios with both one and two light top-partner multiplets in the spectrum of composite states. In compliance with perturbativity and experimental constraints, we study corrections to the Higgs couplings and the effects that the light top-partner multiplets have on the transverse momentum spectrum of the Higgs. Interestingly, we find that the corrections to the Standard Model expectation depend strongly on the representation of the top-partners in the global symmetry.

Anomaly-free dark matter models

We investigate the predictions of anomaly-free dark matter models for direct and indirect detection experiments. We focus on gauge theories where the existence of a fermionic dark matter candidate is predicted by anomaly cancellation, its mass is defined by the new symmetry breaking scale, and its stability is guaranteed by a remnant symmetry after the breaking of the gauge symmetry. We find an upper bound on the symmetry breaking scale by applying the relic density and perturbative constraints. The anomaly-free property of the theories allows us to perform a full study of the gamma lines from dark matter annihilation. We investigate the correlation between predictions for final radiation processes and gamma lines. Furthermore, we demonstrate that the latter can be distinguished from the continuum gamma ray spectrum.