IA Scholar Query: Decomposing non-manifold objects in arbitrary dimensions.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgWed, 30 Nov 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440The hidden symmetry of Kontsevich's graph flows on the spaces of Nambu-determinant Poisson brackets
https://scholar.archive.org/work/qmv6ipdowbdfrjaivt6ca2mrpu
Kontsevich's graph flows are – universally for all finite-dimensional affine Poisson manifolds – infinitesimal symmetries of the spaces of Poisson brackets. We show that the previously known tetrahedral flow and the recently obtained pentagon-wheel flow preserve the class of Nambu-determinant Poisson bi-vectors P=[[ ϱ(x) ∂_x∧∂_y∧∂_z,a]] on ℝ^3∋x=(x,y,z) and P=[[ [[ϱ(y) ∂_x^1∧...∧∂_x^4,a_1]],a_2]] on ℝ^4∋y, including the general case ϱ≢1. We detect that the Poisson bracket evolution Ṗ = Q_γ(P^⊗^# Vert(γ)) is trivial in the second Poisson cohomology, Q_γ = [[ P, X⃗([ϱ],[a]) ]], for the Nambu-determinant bi-vectors P(ϱ,[a]) on ℝ^3. For the global Casimirs 𝐚 = (a_1,...,a_d-2) and inverse density ϱ on ℝ^d, we analyse the combinatorics of their evolution induced by the Kontsevich graph flows, namely ϱ̇ = ϱ̇([ϱ], [𝐚]) and 𝐚̇ = 𝐚̇([ϱ],[𝐚]) with differential-polynomial right-hand sides. Besides the anticipated collapse of these formulas by using the Civita symbols (three for the tetrahedron γ_3 and five for the pentagon-wheel graph cocycle γ_5), as dictated by the behaviour ϱ(𝐱') = ϱ(𝐱) ·∂𝐱' / ∂𝐱 of the inverse density ϱ under reparametrizations 𝐱⇄𝐱', we discover another, so far hidden discrete symmetry in the construction of these evolution equations.Ricardo Buring, Dimitri Lipper, Arthemy V. Kiselevwork_qmv6ipdowbdfrjaivt6ca2mrpuWed, 30 Nov 2022 00:00:00 GMTHomologically area-minimizing surfaces with non-smoothable singularities
https://scholar.archive.org/work/dxh4mmpjyzekzo4czg2ifh2wfa
Let dimensions d≥ 3, and codimensions c≥ 3 be positive integers. Set the exceptional set E={3,4}. For d∉E,c arbitrary or d∈ E,c≤ d, we prove that for every d-dimensional integral homology class [Σ] on a compact (not necessarily orientable) d+c-dimensional smooth manifold M, there exist open sets Ω_[Σ] in the space of smooth Riemannian metrics so that all area-minimizing integral currents in [Σ] are singular for metrics in Ω_[Σ]. This settles a conjecture of White about the generic regularity of area-minimizing surfaces. The answer is sharp dimension-wise. As a byproduct, we determine the moduli space of area-minimizing currents near any area-minimizing transverse immersion of dimension d≥ 3 and codimension c≥ 3 satisfying an angle condition of asymptotically sharp order in d. Similar conclusions hold for mod 2 area-minimizing surfaces.Zhenhua Liuwork_dxh4mmpjyzekzo4czg2ifh2wfaWed, 30 Nov 2022 00:00:00 GMTNeural Integro-Differential Equations
https://scholar.archive.org/work/e5lbae6bw5db5oslocujqr6wsa
Modeling continuous dynamical systems from discretely sampled observations is a fundamental problem in data science. Often, such dynamics are the result of non-local processes that present an integral over time. As such, these systems are modeled with Integro-Differential Equations (IDEs); generalizations of differential equations that comprise both an integral and a differential component. For example, brain dynamics are not accurately modeled by differential equations since their behavior is non-Markovian, i.e. dynamics are in part dictated by history. Here, we introduce the Neural IDE (NIDE), a novel deep learning framework based on the theory of IDEs where integral operators are learned using neural networks. We test NIDE on several toy and brain activity datasets and demonstrate that NIDE outperforms other models. These tasks include time extrapolation as well as predicting dynamics from unseen initial conditions, which we test on whole-cortex activity recordings in freely behaving mice. Further, we show that NIDE can decompose dynamics into their Markovian and non-Markovian constituents via the learned integral operator, which we test on fMRI brain activity recordings of people on ketamine. Finally, the integrand of the integral operator provides a latent space that gives insight into the underlying dynamics, which we demonstrate on wide-field brain imaging recordings. Altogether, NIDE is a novel approach that enables modeling of complex non-local dynamics with neural networks.Emanuele Zappala, Antonio Henrique de Oliveira Fonseca, Andrew Henry Moberly, Michael James Higley, Chadi Abdallah, Jessica Cardin, David van Dijkwork_e5lbae6bw5db5oslocujqr6wsaWed, 30 Nov 2022 00:00:00 GMTOn Continuous 2-Category Symmetries and Yang-Mills Theory
https://scholar.archive.org/work/udt7zonnpzbuzngxqmf6h3j5hm
We study a 4d gauge theory U(1)^N-1⋊ S_N obtained from a U(1)^N-1 theory by gauging a 0-form symmetry S_N. We show that this theory has a global continuous 2-category symmetry, whose structure is particularly rich for N>2. This example allows us to draw a connection between the higher gauging procedure and the difference between local and global fusion, which turns out to be a key feature of higher category symmetries. By studying the spectrum of local and extended operators, we find a mapping with gauge invariant operators of 4d SU(N) Yang-Mills theory. The largest group-like subcategory of the non-invertible symmetries of our theory is a ℤ_N^(1) 1-form symmetry, acting on the Wilson lines in the same way as the center symmetry of Yang-Mills theory does. Supported by a path-integral argument, we propose that the U(1)^N-1⋊ S_N gauge theory has a relation with the ultraviolet limit of SU(N) Yang-Mills theory in which all Gukov-Witten operators become topological, and form a continuous non-invertible 2-category symmetry, broken down to the center symmetry by the RG flow.Andrea Antinucci, Giovanni Galati, Giovanni Riziwork_udt7zonnpzbuzngxqmf6h3j5hmWed, 30 Nov 2022 00:00:00 GMTOpen r-spin theory II: The analogue of Witten's conjecture for r-spin disks
https://scholar.archive.org/work/auaxfqsmjjeblnehr7hxl65l24
We conclude the construction of r-spin theory in genus zero for Riemann surfaces with boundary. In particular, we define open r-spin intersection numbers, and we prove that their generating function is closely related to the wave function of the rth Gelfand-Dickey integrable hierarchy. This provides an analogue of Witten's r-spin conjecture in the open setting and a first step toward the construction of an open version of Fan-Jarvis-Ruan-Witten theory. As an unexpected consequence, we establish a mysterious relationship between open r-spin theory and an extension of Witten's closed theory.Alexandr Buryak and Emily Clader and Ran J. Tesslerwork_auaxfqsmjjeblnehr7hxl65l24Wed, 30 Nov 2022 00:00:00 GMTThe classification of surfaces via normal curves
https://scholar.archive.org/work/or3gcd6oefeyzm7uulk7y4drbu
We present a simple proof of the surface classification theorem using normal curves. This proof is analogous to Kneser's and Milnor's proof of the existence and uniqueness of the prime decomposition of 3-manifolds. In particular, we do not need any invariants from algebraic topology to distinguish surfaces.Fethi Ayaz, Marc Kegel, Klaus Mohnkework_or3gcd6oefeyzm7uulk7y4drbuWed, 30 Nov 2022 00:00:00 GMTChiral spectrum of the universal tuned (SU(3) ×SU(2) ×U(1))/ℤ_6 4D F-theory model
https://scholar.archive.org/work/2a6cs5p3hnhpvnl3cibpklxac4
We use the recently developed methods of 2108.07810 to analyze vertical flux backgrounds and associated chiral matter spectra in the 4D universal (SU(3) ×SU(2) ×U(1))/ℤ_6 model introduced in 1912.10991, which is believed to describe the most general generic family of F-theory vacua with tuned (SU(3) ×SU(2) ×U(1))/ℤ_6 gauge symmetry. Our analysis focuses on a resolution of a particular presentation of the (SU(3) ×SU(2) ×U(1))/ℤ_6 model in which the elliptic fiber is realized as a cubic in ℙ^2 fibered over an arbitrary smooth threefold base. We show that vertical fluxes can produce nonzero multiplicities for all chiral matter families that satisfy 4D anomaly cancellation, which include as a special case the chiral matter families of the Minimal Supersymmetric Standard Model.Patrick Jefferson, Washington Taylor, Andrew P. Turnerwork_2a6cs5p3hnhpvnl3cibpklxac4Wed, 30 Nov 2022 00:00:00 GMTDevelopment and validation of a panel of volatile biomarkers of airway eosinophilia in severe asthma
https://scholar.archive.org/work/spqz4nfca5grlehlgc5cegvfqm
Type 2 inflammation and airway eosinophilia have an incidence in 40-60% of severe asthmatics. Therefore, a number of biological therapies have been developed to target the type 2 inflammatory pathways involved in eosinophil activation, such as anti-IL5 and anti-IL5 receptor-α monoclonal antibodies. There is, therefore, the need to develop non-invasive biomarkers to assess airway eosinophilia, which seems to play an increasingly important role to improve severe asthmatics' stratification for eosinophil targeted therapies This study aimed to develop a panel of volatile biomarkers of airway eosinophilia identifying, by GC-MS analysis and statistical modelling, volatile organic compounds (VOCs) in severe asthmatics' sputum headspace samples, which were able to discriminate with a moderate accuracy eosinophil-enriched and non-eosinophil-enriched sputum headspaces. In order to optimise the headspace sampling method, the headspace background signal, generated within the sampling system in absence of sputum sample, was characterised, and its daily abundance variations were evaluated. Furthermore, the discovered panel of VOCs was validated in exhaled breath of severe asthmatics eligible for anti-IL5/Rα treatments by targeted GCxGC-FID/MS analysis. The statistical model developed showed a high accuracy to predict severe asthmatics' one-year response to anti-IL5/Rα therapies. The in vitro selected VOCs were also validated in exhaled breath of exacerbating eosinophilic asthmatics and exacerbating non-eosinophilic asthmatics - whose classification was based on the blood eosinophil count threshold of 0.5x109/L - and healthy volunteers. The panel of VOCs revealed a high discriminatory accuracy among acute eosinophilic asthmatics, acute non-eosinophilic asthmatics and healthy volunteers, suggesting that the selected VOCs represent a promising, non-invasive clinical tool for asthma exacerbation prediction. A future challenge will be to identify the metabolic pathways, in activated eosinophil cultures, which may be involved in the [...]Rosa Peltriniwork_spqz4nfca5grlehlgc5cegvfqmWed, 30 Nov 2022 00:00:00 GMTSingular Riemannian foliations and ℐ-Poisson manifolds
https://scholar.archive.org/work/yehk4pguojavhefhrdhhj2vjoa
We recall the notion of a singular foliation (SF) on a manifold M, viewed as an appropriate submodule of 𝔛(M), and adapt it to the presence of a Riemannian metric g, yielding a module version of a singular Riemannian foliation (SRF). Following Garmendia-Zambon on Hausdorff Morita equivalence of SFs, we define the Morita equivalence of SRFs (both in the module sense as well as in the more traditional geometric one of Molino) and show that the leaf spaces of Morita equivalent SRFs are isomrophic as pseudo-metric spaces. In a second part, we introduce the category of ℐ-Poisson manifolds. Its objects and morphisms generalize Poisson manifolds and morphisms in the presence of appropriate ideals ℐ of the smooth functions on the manifold such that two conditions are satisfied: (i) The category of Poisson manifolds becomes a full subcategory when choosing ℐ=0 and (ii) there is a reduction functor from this new category to the category of Poisson algebras, which generalizes coistropic reduction to the singular setting. Every SF on M gives rise to an ℐ-Poisson manifold on T^*M and g enhances this to an SRF if and only if the induced Hamiltonian lies in the normalizer of ℐ. This perspective provides, on the one hand, a simple proof of the fact that every module SRF is a geometric SRF and, on the other hand, a construction of an algebraic invariant of singular foliations: Hausdorff Morita equivalent SFs have isomorphic reduced Poisson algebras.Hadi Nahari, Thomas Stroblwork_yehk4pguojavhefhrdhhj2vjoaTue, 29 Nov 2022 00:00:00 GMTSignal Processing for Implicit Neural Representations
https://scholar.archive.org/work/6cevgqpzgncipm62jb3gqdgamm
Implicit Neural Representations (INRs) encoding continuous multi-media data via multi-layer perceptrons has shown undebatable promise in various computer vision tasks. Despite many successful applications, editing and processing an INR remains intractable as signals are represented by latent parameters of a neural network. Existing works manipulate such continuous representations via processing on their discretized instance, which breaks down the compactness and continuous nature of INR. In this work, we present a pilot study on the question: how to directly modify an INR without explicit decoding? We answer this question by proposing an implicit neural signal processing network, dubbed INSP-Net, via differential operators on INR. Our key insight is that spatial gradients of neural networks can be computed analytically and are invariant to translation, while mathematically we show that any continuous convolution filter can be uniformly approximated by a linear combination of high-order differential operators. With these two knobs, INSP-Net instantiates the signal processing operator as a weighted composition of computational graphs corresponding to the high-order derivatives of INRs, where the weighting parameters can be data-driven learned. Based on our proposed INSP-Net, we further build the first Convolutional Neural Network (CNN) that implicitly runs on INRs, named INSP-ConvNet. Our experiments validate the expressiveness of INSP-Net and INSP-ConvNet in fitting low-level image and geometry processing kernels (e.g. blurring, deblurring, denoising, inpainting, and smoothening) as well as for high-level tasks on implicit fields such as image classification.Dejia Xu, Peihao Wang, Yifan Jiang, Zhiwen Fan, Zhangyang Wangwork_6cevgqpzgncipm62jb3gqdgammTue, 29 Nov 2022 00:00:00 GMTA non-lorentzian primer
https://scholar.archive.org/work/tw23zhjvoje3vkbaph6gwbva6a
We review both the kinematics and dynamics of non-lorentzian theories and their associated geometries. First, we introduce non-lorentzian kinematical spacetimes and their symmetry algebras. Next, we construct actions describing the particle dynamics in some of these kinematical spaces using the method of nonlinear realisations. We explain the relation with the coadjoint orbit method. We continue discussing three types of non-lorentzian gravity theories: Galilei gravity, Newton-Cartan gravity and Carroll gravity. Introducing matter, we discuss electric and magnetic non-lorentzian field theories for three different spins: spin-0, spin-1/2 and spin-1, as limits of relativistic theories.Eric Bergshoeff, José Figueroa-O'Farrill, Joaquim Gomiswork_tw23zhjvoje3vkbaph6gwbva6aTue, 29 Nov 2022 00:00:00 GMTWasserstein Stability for Persistence Diagrams
https://scholar.archive.org/work/z3jcni4xirdelcavaulfmyfmy4
The stability of persistence diagrams is among the most important results in applied and computational topology. Most results in the literature phrase stability in terms of the bottleneck distance between diagrams and the ∞-norm of perturbations. This has two main implications: it makes the space of persistence diagrams rather pathological and it is often provides very pessimistic bounds with respect to outliers. In this paper, we provide new stability results with respect to the p-Wasserstein distance between persistence diagrams. This includes an elementary proof for the setting of functions on sufficiently finite spaces in terms of the p-norm of the perturbations, along with an algebraic framework for p-Wasserstein distance which extends the results to wider class of modules. We also provide apply the results to a wide range of applications in topological data analysis (TDA) including topological summaries, persistence transforms and the special but important case of Vietoris-Rips complexes.Primoz Skraba, Katharine Turnerwork_z3jcni4xirdelcavaulfmyfmy4Tue, 29 Nov 2022 00:00:00 GMTExtremum Seeking Control for Fully Actuated Mechanical Systems on Lie Groups in the Absence of Dissipation
https://scholar.archive.org/work/njy234ejufhwbbetem5yjh5rdq
In this paper, we study the problem of extremum seeking control for mechanical systems in dissipation-free environments. This includes attitude control of satellites in space and displacement control of rigid bodies in ideal fluids. The configuration and the velocity of the mechanical system are treated as unknown quantities. The only source of information about the current system state is provided by real-time measurements of a scalar signal whose value has to be minimized. The signal is assumed to be given by a configuration-dependent objective function, which is not known analytically. Our goal is to asymptotically stabilize the mechanical system around states with vanishing velocity and a minimum value of the objective function. The proposed control law employs periodic perturbation signals to extract information about the gradient of the objective function and the velocity of the mechanical system from the response of the sensed signal. Under suitable assumptions, we prove local and non-local stability properties of the closed-loop system. The general results are illustrated by examples.Raik Suttner, Miroslav Krsticwork_njy234ejufhwbbetem5yjh5rdqTue, 29 Nov 2022 00:00:00 GMTSymmetric periodic Reeb orbits on the sphere
https://scholar.archive.org/work/ijwxq7tiwbbohpqw4ugqkd3f5m
A long standing conjecture in Hamiltonian Dynamics states that every contact form on the standard contact sphere S^2n+1 has at least n+1 simple periodic Reeb orbits. In this work, we consider a refinement of this problem when the contact form has a suitable symmetry and we ask if there are at least n+1 simple symmetric periodic orbits. We show that there is at least one symmetric periodic orbit for any contact form and at least two symmetric closed orbits whenever the contact form is dynamically convex.Miguel Abreu, Hui Liu, Leonardo Macariniwork_ijwxq7tiwbbohpqw4ugqkd3f5mTue, 29 Nov 2022 00:00:00 GMTNonconvex Matrix Factorization is Geodesically Convex: Global Landscape Analysis for Fixed-rank Matrix Optimization From a Riemannian Perspective
https://scholar.archive.org/work/m2tum4ngx5fv3ob2fa4laxzblq
We study a general matrix optimization problem with a fixed-rank positive semidefinite (PSD) constraint. We perform the Burer-Monteiro factorization and consider a particular Riemannian quotient geometry in a search space that has a total space equipped with the Euclidean metric. When the original objective f satisfies standard restricted strong convexity and smoothness properties, we characterize the global landscape of the factorized objective under the Riemannian quotient geometry. We show the entire search space can be divided into three regions: (R1) the region near the target parameter of interest, where the factorized objective is geodesically strongly convex and smooth; (R2) the region containing neighborhoods of all strict saddle points; (R3) the remaining regions, where the factorized objective has a large gradient. To our best knowledge, this is the first global landscape analysis of the Burer-Monteiro factorized objective under the Riemannian quotient geometry. Our results provide a fully geometric explanation for the superior performance of vanilla gradient descent under the Burer-Monteiro factorization. When f satisfies a weaker restricted strict convexity property, we show there exists a neighborhood near local minimizers such that the factorized objective is geodesically convex. To prove our results we provide a comprehensive landscape analysis of a matrix factorization problem with a least squares objective, which serves as a critical bridge. Our conclusions are also based on a result of independent interest stating that the geodesic ball centered at Y with a radius 1/3 of the least singular value of Y is a geodesically convex set under the Riemannian quotient geometry, which as a corollary, also implies a quantitative bound of the convexity radius in the Bures-Wasserstein space. The convexity radius obtained is sharp up to constants.Yuetian Luo, Nicolas Garcia Trilloswork_m2tum4ngx5fv3ob2fa4laxzblqTue, 29 Nov 2022 00:00:00 GMTLearning Antidote Data to Individual Unfairness
https://scholar.archive.org/work/yssdphnofbbydjcmj7pb74k25m
Fairness is an essential factor for machine learning systems deployed in high-stake applications. Among all fairness notions, individual fairness, following a consensus that 'similar individuals should be treated similarly,' is a vital notion to guarantee fair treatment for individual cases. Previous methods typically characterize individual fairness as a prediction-invariant problem when perturbing sensitive attributes, and solve it by adopting the Distributionally Robust Optimization (DRO) paradigm. However, adversarial perturbations along a direction covering sensitive information do not consider the inherent feature correlations or innate data constraints, and thus mislead the model to optimize at off-manifold and unrealistic samples. In light of this, we propose a method to learn and generate antidote data that approximately follows the data distribution to remedy individual unfairness. These on-manifold antidote data can be used through a generic optimization procedure with original training data, resulting in a pure pre-processing approach to individual unfairness, or can also fit well with the in-processing DRO paradigm. Through extensive experiments, we demonstrate our antidote data resists individual unfairness at a minimal or zero cost to the model's predictive utility.Peizhao Li, Ethan Xia, Hongfu Liuwork_yssdphnofbbydjcmj7pb74k25mTue, 29 Nov 2022 00:00:00 GMTN = 3 Poincare Supergravity in Four Dimensions
https://scholar.archive.org/work/vjxxfxwwg5dvbcb47wk45viqhi
In this paper, we use the superconformal approach to derive the action for N = 3 Poincare supergravity in four space-time dimensions. We first study the coupling of N = 3 vector multiplets to conformal supergravity. Thereafter we combine it with the pure N = 3 conformal supergravity action and use a minimum of three vector multiplets as compensators to arrive at Poincare supergravity with higher derivative corrections. We give a general prescription on how to eliminate the auxiliary fields in an iterative manner and obtain the supergravity action order by order in derivatives. We also show that the truncation of the action at fourth order in derivatives is a consistent truncation.Subramanya Hegde, Madhu Mishra, Debangshu Mukherjee, Bindusar Sahoowork_vjxxfxwwg5dvbcb47wk45viqhiMon, 28 Nov 2022 00:00:00 GMTDifferentiable Dictionary Search: Integrating Linear Mixing with Deep Non-Linear Modelling for Audio Source Separation
https://scholar.archive.org/work/piiqpqxvk5cdpilg3rkijmpf34
This paper describes several improvements to a new method for signal decomposition that we recently formulated under the name of Differentiable Dictionary Search (DDS). The fundamental idea of DDS is to exploit a class of powerful deep invertible density estimators called normalizing flows, to model the dictionary in a linear decomposition method such as NMF, effectively creating a bijection between the space of dictionary elements and the associated probability space, allowing a differentiable search through the dictionary space, guided by the estimated densities. As the initial formulation was a proof of concept with some practical limitations, we will present several steps towards making it scalable, hoping to improve both the computational complexity of the method and its signal decomposition capabilities. As a testbed for experimental evaluation, we choose the task of frame-level piano transcription, where the signal is to be decomposed into sources whose activity is attributed to individual piano notes. To highlight the impact of improved non-linear modelling of sources, we compare variants of our method to a linear overcomplete NMF baseline. Experimental results will show that even in the absence of additional constraints, our models produce increasingly sparse and precise decompositions, according to two pertinent evaluation measures.Lukáš Samuel Marták, Rainer Kelz, Gerhard Widmerwork_piiqpqxvk5cdpilg3rkijmpf34Mon, 28 Nov 2022 00:00:00 GMTOn the Convergence of critical points of the Ambrosio-Tortorelli functional
https://scholar.archive.org/work/ddrcdoj7i5g7bhdo7e2xzxv7n4
This work is devoted to study the asymptotic behavior of critical points {(u_ε,v_ε)}_ε>0 of the Ambrosio-Tortorelli functional. Under a uniform energy bound assumption, the usual Γ-convergence theory ensures that (u_ε,v_ε) converges in the L^2-sense to some (u_*,1) as ε→ 0, where u_* is a special function of bounded variation. Assuming further the Ambrosio-Tortorelli energy of (u_ε,v_ε) to converge to the Mumford-Shah energy of u_*, the later is shown to be a critical point with respect to inner variations of the Mumford-Shah functional. As a byproduct, the second inner variation is also shown to pass to the limit. To establish these convergence results, interior (𝒞^∞) regularity and boundary regularity for Dirichlet boundary conditions are first obtained for a fixed parameter ε>0. The asymptotic analysis is then performed by means of varifold theory in the spirit of scalar phase transition problems.Jean-François Babadjian, Vincent Millot, Rémy Rodiacwork_ddrcdoj7i5g7bhdo7e2xzxv7n4Mon, 28 Nov 2022 00:00:00 GMTGibbs Manifolds
https://scholar.archive.org/work/3wgolh6qwjbevhmcdrty3f6zvq
Gibbs manifolds are images of affine spaces of symmetric matrices under the exponential map. They arise in applications such as optimization, statistics and quantum~physics, where they extend the ubiquitous role of toric geometry. The Gibbs variety is the zero locus of all polynomials that vanish on the Gibbs manifold. We compute these polynomials and show that the Gibbs variety is low-dimensional. Our theory is applied to a wide range of scenarios, including matrix pencils and quantum optimal transport.Dmitrii Pavlov, Bernd Sturmfels, Simon Telenwork_3wgolh6qwjbevhmcdrty3f6zvqMon, 28 Nov 2022 00:00:00 GMT