IA Scholar Query: Fast stochastic second-order method logarithmic in condition number.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgSat, 31 Dec 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440A Survey on Concept Drift in Process Mining
https://scholar.archive.org/work/hvmkupdorzf5df4tts42gzykjm
Concept drift in process mining (PM) is a challenge as classical methods assume processes are in a steady-state, i.e., events share the same process version. We conducted a systematic literature review on the intersection of these areas, and thus, we review concept drift in PM and bring forward a taxonomy of existing techniques for drift detection and online PM for evolving environments. Existing works depict that (i) PM still primarily focuses on offline analysis, and (ii) the assessment of concept drift techniques in processes is cumbersome due to the lack of common evaluation protocol, datasets, and metrics.Denise Maria Vecino Sato, Sheila Cristiana De Freitas, Jean Paul Barddal, Edson Emilio Scalabrinwork_hvmkupdorzf5df4tts42gzykjmSat, 31 Dec 2022 00:00:00 GMTAn integrated geological-geophysical approach to subsurface interface reconstruction of muon tomography measurements in high alpine regions
https://scholar.archive.org/work/ricux5erhrhe7dhkcevl5l3iuy
Muon tomography is an imaging technique that emerged in the last decades. The principal concept is similar to X-ray tomography, where one determines the spatial distribution of material densities by means of penetrating photons. It differs from this well-known technology only by the type of particle. Muons are continuously produced in the Earth's atmosphere when primary cosmic rays (mostly protons) interact with the atmosphere's molecules. Depending on their energies these muons can penetrate materials up to several hundreds of metres (or even kilometres). Consequently, they have been used for the imaging of larger objects, including large geological objects such as volcanoes, caves and fault systems. This research project aimed at applying this technology to an alpine glacier in Central Switzerland to determine its bedrock geometry, and if possible, to gain information on the bedrock erosion mechanism. To this end, two major experimental studies have been conducted with the aim to reconstruct bedrock geometries of two different glaciers. Given this framework, I present in this thesis my contribution to the project in which I worked for 5 years. Most of the technological know-how of muon tomography still lies within physics institutes who were the key drivers in the development of this method. As the geophysical/geological community is nowadays an important user of this technology, it is important that also non-physicists familiarise themselves with the theory and concepts behind muon tomography. This can be seen as an effective method to bring more geoscientists to utilize this new technology for their own research. The first part of this thesis is designed to tackle this problem with a review article on the principles of muon tomography and a guide to best practice. A second important aspect is the reconstruction of the bedrock topography given muon flux measurements at various locations. Many to-date reconstruction algorithms include supplementary geological information such as density and/or compositional me [...]Alessandro Diego Lechmannwork_ricux5erhrhe7dhkcevl5l3iuyThu, 29 Sep 2022 00:00:00 GMTFormation of crystalline bulk heterojunctions in organic solar cells: insights from phase-field simulations
https://scholar.archive.org/work/xkkgzcjezff7heglsqf2yjruhi
The performance of organic solar cells strongly depends on the bulk heterojunction (BHJ) morphology of the photoactive layer. This BHJ forms during the drying of the wet-deposited solution, because of physical processes such as crystallization and/or liquid liquid phase separation (LLPS). However, the process-structure relationship remains insufficiently understood. In this work, a recently developed, coupled phase field fluid mechanics framework is used to simulate the BHJ formation upon drying. For the first time, this allows to investigate the interplay between all the relevant physical processes (evaporation, crystal nucleation and growth, liquid demixing, composition-dependent kinetic properties), within a single coherent theoretical framework. Simulations for the model system P3HT-PCBM are presented. The comparison with previously reported in-situ characterization of the drying structure is very convincing: the morphology formation pathways, crystallization kinetics, and final morphology are in line with experimental results. The final BHJ morphology is a subtle mixture of pure crystalline donor and acceptor phases, pure and mixed amorphous domains, which depends on the process parameters and material properties. The expected benefit of such an approach is to identify physical design rules for ink formulation and processing conditions to optimize the cell performance. It could be applied to recent organic material systems in the future.Olivier Ronsin, Jens Hartingwork_xkkgzcjezff7heglsqf2yjruhiWed, 28 Sep 2022 00:00:00 GMTStudies of quantum chromodynamics with jets at the CMS experiment at the LHC
https://scholar.archive.org/work/tl6cqxvdijhwbnd4wxd3l6sbii
Several people played a decisive role in accomplishing this thesis and helped me in dierent aspects. In Hamburg, I would like to extend my deepest gratitude to Patrick L.S. Connor for his invaluable contribution to this work and for training me to consider scientic research as a "share, help, learn, cross-check, enjoy" cycle. Besides developing the overall analysis framework, he was always reachable for help and support, making the work with him a continuous upskilling process. I am also extremely grateful to Paolo Gunnellini for his contributions to the analysis, but mainly for his crucial guidance during my rst steps in high energy physics and his availability to help whenever I needed to. At DESY, I am deeply indebted to Hannes Jung for all his hospitality and support. Apart from that, he also gave me the opportunity to work with his wonderful team, to whom I am also grateful. In particular, many thanks toParaskevas Gianneios, University Of Ioanninawork_tl6cqxvdijhwbnd4wxd3l6sbiiWed, 28 Sep 2022 00:00:00 GMTConsensus Knowledge Graph Learning via Multi-view Sparse Low Rank Block Model
https://scholar.archive.org/work/yhy2qtpxrjfwdgytj7byxlsxii
Network analysis has been a powerful tool to unveil relationships and interactions among a large number of objects. Yet its effectiveness in accurately identifying important node-node interactions is challenged by the rapidly growing network size, with data being collected at an unprecedented granularity and scale. Common wisdom to overcome such high dimensionality is collapsing nodes into smaller groups and conducting connectivity analysis on the group level. Dividing efforts into two phases inevitably opens a gap in consistency and drives down efficiency. Consensus learning emerges as a new normal for common knowledge discovery with multiple data sources available. To this end, this paper features developing a unified framework of simultaneous grouping and connectivity analysis by combining multiple data sources. The algorithm also guarantees a statistically optimal estimator.Tianxi Cai, Dong Xia, Luwan Zhang, Doudou Zhouwork_yhy2qtpxrjfwdgytj7byxlsxiiWed, 28 Sep 2022 00:00:00 GMTEnergy Harvesting in a System with a Two-Stage Flexible Cantilever Beam
https://scholar.archive.org/work/dpdsccohf5d6rezlp5tazeirq4
The subject of the research contained in this paper is a new design solution for an energy harvesting system resulting from the combination of a quasi-zero-stiffness energy harvester and a two-stage flexible cantilever beam. Numerical tests were divided into two main parts-analysis of the dynamics of the system due to periodic, quasiperiodic, and chaotic solutions and the efficiency of energy generation. The results of numerical simulations were limited to zero initial conditions as they are the natural position of the static equilibrium. The article compares the energy efficiency for the selected range of the dimensionless excitation frequency. For this purpose, three cases of piezoelectric mounting were analyzed-only on the first stage of the beam, on the second and both stages. The analysis has been carried out with the use of diagrams showing difference of the effective values of the voltage induced on the piezoelectric electrodes. The results indicate that for effective energy harvesting, it is advisable to attach piezoelectric energy transducers to each step of the beam despite possible asynchronous vibrations.Jerzy Margielewicz, Damian Gąska, Grzegorz Litak, Piotr Wolszczak, Shengxi Zhouwork_dpdsccohf5d6rezlp5tazeirq4Wed, 28 Sep 2022 00:00:00 GMTReal Time Simulations of Quantum Spin Chains: Density-of-States and Reweighting approaches
https://scholar.archive.org/work/n4z24erzazc2finedtc73ycz2e
We put the Density-of-States (DoS) approach to Monte-Carlo (MC) simulations under a stress test by applying it to a physical problem with the worst possible sign problem: the real time evolution of a non-integrable quantum spin chain. Benchmarks against numerical exact diagonalisation and stochastic reweighting are presented. Both MC methods, the DoS approach and reweighting, allow for simulations of spin chains as long as L=40, far beyond exact diagonalisability, though only for short evolution times t≲ 1. We identify discontinuities of the density of states as one of the key problems in the MC simulations and propose to calculate some of the dominant contributions analytically, increasing the precision of our simulations by several orders of magnitude. Even after these improvements the density of states is found highly non-smooth and therefore the DoS approach cannot outperform reweighting. We prove this implication theoretically and provide numerical evidence, concluding that the DoS approach is not well suited for quantum real time simulations with discrete degrees of freedom.Pavel Buividovich, Johann Ostmeyerwork_n4z24erzazc2finedtc73ycz2eWed, 28 Sep 2022 00:00:00 GMTCompressing network populations with modal networks reveals structural diversity
https://scholar.archive.org/work/tkfiq2ubwvgmxlenlkn37imxzy
Analyzing relational data collected over time requires a critical decision: Is one network representation sufficient? Or are more networks needed to capture changing structures? While the choice may be evident in some cases, for example when analyzing a physical system going through abrupt changes between two known states, other datasets can pose more difficult modeling challenges. Here we describe efficient nonparametric methods derived from the minimum description length principle to construct these network representations automatically. The methods input a population of networks measured on the same set of nodes and output a small set of representative networks together with an assignment of each measurement to one of these representative networks. We show that these methods recover planted heterogeneity in synthetic network populations and effectively identify important structural heterogeneities in example network populations representing global trade and the fossil record.Alec Kirkley, Alexis Rojas, Martin Rosvall, Jean-Gabriel Youngwork_tkfiq2ubwvgmxlenlkn37imxzyWed, 28 Sep 2022 00:00:00 GMTNovelty detection and multiple timescale integration drive Drosophila orientation dynamics in temporally diverse olfactory environments
https://scholar.archive.org/work/mdpcrubobzfidpt54tgtk4ayua
To survive, insects must effectively navigate odors plumes to their source. In natural plumes, turbulent winds break up smooth odor regions into disconnected patches, so navigators encounter brief bursts of odor interrupted by bouts of clean air. The timing of these encounters plays a critical role in navigation, determining the direction, rate, and magnitude of insects' orientation and speed dynamics. Still, disambiguating the specific role of odor timing from other cues, such as spatial structure, is challenging due to natural correlations between plumes' temporal and spatial features. Here, we use optogenetics to isolate temporal features of odor signals, examining how the frequency and duration of odor encounters shape the navigational decisions of freely-walking Drosophila. We find that fly angular velocity depends on signal frequency and intermittency – fraction of time signal can be detected – but not directly on durations. Rather than switching strategies when signal statistics change, flies smoothly transition between signal regimes, by combining an odor offset response with a frequency-dependent novelty-like response. In the latter, flies are more likely to turn in response to each odor hit only when the hits are sparse. Finally, the upwind bias of individual turns relies on a filtering scheme with two distinct timescales, allowing rapid and sustained responses in a variety of signal statistics. A quantitative model incorporating these ingredients recapitulates fly orientation dynamics across a wide range of environments.Aarti Sehdev, Viraaj Jayaram, Nirag Kadakia, Ethan Brown, Thierry Emonetwork_mdpcrubobzfidpt54tgtk4ayuaWed, 28 Sep 2022 00:00:00 GMTGlobal Speed Limit for Finite-Time Dynamical Phase Transition in Nonequilibrium Relaxation
https://scholar.archive.org/work/yhbanm7nmbhujmpsexzcu4sjre
Recent works unraveled an intriguing finite-time dynamical phase transition in the thermal relaxation of the mean field Curie-Weiss model. The phase transition reflects a sudden switch in the dynamics. Its existence in systems with a finite range of interaction, however, remained unclear. Employing the Bethe-Guggenheim approximation, which is exact on Bethe lattices, we here demonstrate the finite-time dynamical phase transition in nearest-neighbor Ising systems for arbitrary quenches, including those within the two-phase region. Strikingly, for any given initial condition we prove and explain the existence of non-trivial speed limits for the dynamical phase transition and the relaxation of magnetization, which are absent in the mean field setting. Pair correlations, which are neglected in mean field theory and trivial in the Curie-Weiss model, account for kinetic constraints due to frustrated local configurations that give rise to a global speed limit.Kristian Blom, Aljaž Godecwork_yhbanm7nmbhujmpsexzcu4sjreWed, 28 Sep 2022 00:00:00 GMTImproved Generalization Bound and Learning of Sparsity Patterns for Data-Driven Low-Rank Approximation
https://scholar.archive.org/work/ox2q7qanyfeuvjevhufmcfajee
Learning sketching matrices for fast and accurate low-rank approximation (LRA) has gained increasing attention. Recently, Bartlett, Indyk, and Wagner (COLT 2022) presented a generalization bound for the learning-based LRA. Specifically, for rank-k approximation using an m × n learned sketching matrix with s non-zeros in each column, they proved an Õ(nsm) bound on the fat shattering dimension (Õ hides logarithmic factors). We build on their work and make two contributions. 1. We present a better Õ(nsk) bound (k ≤ m). En route to obtaining the bound, we give a low-complexity Goldberg–Jerrum algorithm for computing pseudo-inverse matrices, which would be of independent interest. 2. We alleviate an assumption of the previous study that the sparsity pattern of sketching matrices is fixed. We prove that learning positions of non-zeros increases the fat shattering dimension only by O(nslog n). Also, experiments confirm the practical benefit of learning sparsity patterns.Shinsaku Sakaue, Taihei Okiwork_ox2q7qanyfeuvjevhufmcfajeeTue, 27 Sep 2022 00:00:00 GMTControlling mean exit time of stochastic dynamical systems based on quasipotential and machine learning
https://scholar.archive.org/work/mebap7yhincmpov32vvx2vwj4i
The mean exit time escaping basin of attraction in the presence of white noise is of practical importance in various scientific fields. In this work, we propose a strategy to control mean exit time of general stochastic dynamical systems to achieve a desired value based on the quasipotential concept and machine learning. Specifically, we develop a neural network architecture to compute the global quasipotential function. Then we design a systematic iterated numerical algorithm to calculate the controller for a given mean exit time. Moreover, we identify the most probable path between metastable attractors with help of the effective Hamilton-Jacobi scheme and the trained neural network. Numerical experiments demonstrate that our control strategy is effective and sufficiently accurate.Yang Li, Shenglan Yuan, Shengyuan Xuwork_mebap7yhincmpov32vvx2vwj4iTue, 27 Sep 2022 00:00:00 GMTJoint Learning of Linear Time-Invariant Dynamical Systems
https://scholar.archive.org/work/itteex23yrchnhekmegyyujlve
Linear time-invariant systems are very popular models in system theory and applications. A fundamental problem in system identification that remains rather unaddressed in extant literature is to leverage commonalities amongst related linear systems to estimate their transition matrices more accurately. To address this problem, the current paper investigates methods for jointly estimating the transition matrices of multiple systems. It is assumed that the transition matrices are unknown linear functions of some unknown shared basis matrices. We establish finite-time estimation error rates that fully reflect the roles of trajectory lengths, dimension, and number of systems under consideration. The presented results are fairly general and show the significant gains that can be achieved by pooling data across systems in comparison to learning each system individually. Further, they are shown to be robust against model misspecifications. To obtain the results, we develop novel techniques that are of interest for addressing similar joint-learning problems. They include tightly bounding estimation errors in terms of the eigen-structures of transition matrices, establishing sharp high probability bounds for singular values of dependent random matrices, and capturing effects of misspecified transition matrices as the systems evolve over time.Aditya Modi, Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidiswork_itteex23yrchnhekmegyyujlveTue, 27 Sep 2022 00:00:00 GMTCollapsing Molecular Clouds with Tracer Particles: Part I, What Collapses?
https://scholar.archive.org/work/fnmiwqgicncsxdrojiamlszqjq
To understand the formation of stars from clouds of molecular gas, one essentially needs to know two things: What gas collapses, and how long it takes to do so. We address these questions by embedding pseudo-Lagrangian tracer particles in three simulations of self-gravitating turbulence. We identify prestellar cores at the end of the collapse, and use the tracer particles to rewind the simulations to identify the preimage gas for each core at the beginning of each simulation. This is the first of a series of papers, wherein we present the technique and examine the first question: What gas collapses? For the preimage gas at the t=0, we examine a number of quantities; the probability distribution function (PDF) for several quantities, the structure function for velocity, several length scales, the volume filling fraction, the overlap between different preimages, and fractal dimension of the preimage gas. Analytic descriptions are found for the PDFs of density and velocity for the preimage gas. We find that the preimage of a core is large and sparse, and we show that gas for one core comes from many turbulent density fluctuations and a few velocity fluctuations. We find that binary systems have preimages that overlap in a fractal manner. Finally, we use the density distribution to derive a novel prediction of the star formation rate.David C. Collins, Dan K. Le, Luz L. Jimenez Velawork_fnmiwqgicncsxdrojiamlszqjqTue, 27 Sep 2022 00:00:00 GMTQuantifying and visualizing model similarities for multi-model methods
https://scholar.archive.org/work/hakolbblw5azlk7osjx5afunim
Modeling environmental systems is typically limited by an incomplete system understanding due to scarce and imprecise measurements. This leads to different types of uncertainties, among which conceptual uncertainty plays a key role, but is difficult to address. Conceptual uncertainty refers to the problem of finding the most appropriate model representation of the physical system. This includes the problem of choosing from several plausible model hypotheses, but also the problem that the true system description might not even be among this set of hypotheses. In this thesis, I address the first of these issues, the uncertainty of choosing a model from a finite set. To account for this uncertainty of model choice, modelers typically use multi-model methods. This means that they consider not only one but several models and apply statistical methods to either combine them or select the most appropriate one. For any of these methods, it is crucial to know how similar the individual models are. But even though multi-model methods have become increasingly popular, no methods were available that quantify the similarities between models and visualize them intuitively. This dissertation aims at closing these gaps. In particular, it tackles the challenges of judging whether simplified models are a suitable replacement for a more detailed model, and of visualizing model similarities in a way that helps modelers to gain an intuitive understanding of the model set. I defined three research questions that address these challenges and form the basis of this thesis. 1. How can we systematically assess how similar conceptually simplified model versions are compared to an original, more detailed model? 2. How can we extend the similarity analysis so it is suitable for computationally expensive models? 3. How can we visualize the similarities between probabilistic model predictions? With the first contribution, I show that the so-called model confusion matrix can be used to quantify model similarities and thus identify the best conc [...]Aline Schäfer Rodrigues Silva, Universität Stuttgartwork_hakolbblw5azlk7osjx5afunimTue, 27 Sep 2022 00:00:00 GMTNeural parameter calibration for large-scale multi-agent models
https://scholar.archive.org/work/ja3na4qy3rftrknlt35ays3lqm
Computational models have become a powerful tool in the quantitative sciences to understand the behaviour of complex systems that evolve in time. However, they often contain a potentially large number of free parameters whose values cannot be obtained from theory but need to be inferred from data. This is especially the case for models in the social sciences, economics, or computational epidemiology. Yet many current parameter estimation methods are mathematically involved and computationally slow to run. In this paper we present a computationally simple and fast method to retrieve accurate probability densities for model parameters using neural differential equations. We present a pipeline comprising multi-agent models acting as forward solvers for systems of ordinary or stochastic differential equations, and a neural network to then extract parameters from the data generated by the model. The two combined create a powerful tool that can quickly estimate densities on model parameters, even for very large systems. We demonstrate the method on synthetic time series data of the SIR model of the spread of infection, and perform an in-depth analysis of the Harris-Wilson model of economic activity on a network, representing a non-convex problem. For the latter, we apply our method both to synthetic data and to data of economic activity across Greater London. We find that our method calibrates the model orders of magnitude more accurately than a previous study of the same dataset using classical techniques, while running between 195 and 390 times faster.Thomas Gaskin, Grigorios A. Pavliotis, Mark Girolamiwork_ja3na4qy3rftrknlt35ays3lqmTue, 27 Sep 2022 00:00:00 GMTA Doubly Optimistic Strategy for Safe Linear Bandits
https://scholar.archive.org/work/ts4l2pzjcvbxhhwzb6ifsappnq
We propose a doubly optimistic strategy for the safe-linear-bandit problem, DOSLB. The safe linear bandit problem is to optimise an unknown linear reward whilst satisfying unknown round-wise safety constraints on actions, using stochastic bandit feedback of reward and safety-risks of actions. In contrast to prior work on aggregated resource constraints, our formulation explicitly demands control on roundwise safety risks. Unlike existing optimistic-pessimistic paradigms for safe bandits, DOSLB exercises supreme optimism, using optimistic estimates of reward and safety scores to select actions. Yet, and surprisingly, we show that DOSLB rarely takes risky actions, and obtains Õ(d √(T)) regret, where our notion of regret accounts for both inefficiency and lack of safety of actions. Specialising to polytopal domains, we first notably show that the √(T)-regret bound cannot be improved even with large gaps, and then identify a slackened notion of regret for which we show tight instance-dependent O(log^2 T) bounds. We further argue that in such domains, the number of times an overly risky action is played is also bounded as O(log^2T).Tianrui Chen, Aditya Gangrade, Venkatesh Saligramawork_ts4l2pzjcvbxhhwzb6ifsappnqTue, 27 Sep 2022 00:00:00 GMTEgret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization
https://scholar.archive.org/work/ovp7i2yvhzajvgqz6sezzkvbci
A novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) is proposed in this paper, which is inspired by two egret species' hunting behavior (Great Egret and Snowy Egret). ESOA consists of three primary components: a sit-and-wait strategy, aggressive strategy as well as discriminant conditions. The learnable sit-and-wait strategy guides the egret to the most probable solution by applying a pseudo gradient estimator. The aggressive strategy uses random wandering and encirclement mechanisms to allow for optimal solution exploration. The discriminant model is utilized to balance the two strategies. The proposed approach provides a parallel framework and a strategy for parameter learning through historical information that can be adapted to most scenarios and has well stability. The performance of ESOA on 36 benchmark functions as well as 3 engineering problems are compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Harris Hawks Optimization (HHO). The result proves the superior effectiveness and robustness of ESOA. ESOA acquires the winner in all unimodal functions and reaches statistic scores all above 9.9, while the scores are better in complex functions as 10.96 and 11.92.Zuyan Chen, Adam Francis, Shuai Li, Bolin Liao, Dunhui Xiao, Tran Thu Ha, Jianfeng Li, Lei Ding, Xinwei Caowork_ovp7i2yvhzajvgqz6sezzkvbciTue, 27 Sep 2022 00:00:00 GMTProbing black-hole accretion through time variability
https://scholar.archive.org/work/g5kogdxwxjhsbdimjkvaeia7f4
Flux variability is a remarkable property of black hole (BH) accreting systems, and a powerful tool to investigate the multi-scale structure of the accretion flow. The X-ray band is where some of the most rapid variations occur, pointing to an origin in the innermost regions close to the BH. The study of fast time variability provides us with means to explore the accretion flow around compact objects in ways which are inaccessible via spectral analysis alone, and to peek at regions which cannot be imaged with the currently available instrumentation. In this chapter we will discuss fast X-ray variability in stellar-mass BH systems, namely binary systems containing a star and a BH, occasionally contrasting it with observations of supermassive BHs in active galactic nuclei. We will explore how rapid variations of the X-ray flux have been used in multiple studies as a diagnostic of the innermost regions of the accretion flow in these systems. To this aim we will provide an overview of the currently most used analysis approaches for the study of X-ray variability, describe observations of both aperiodic and quasi-periodic phenomena, and discuss some of the proposed models.Barbara De Marco, Sara E. Motta, Tomaso M. Belloniwork_g5kogdxwxjhsbdimjkvaeia7f4Tue, 27 Sep 2022 00:00:00 GMTEscaping saddle points in zeroth-order optimization: two function evaluations suffice
https://scholar.archive.org/work/punwbpldlveonczw2hpoiv4hdm
Zeroth-order methods are useful in solving black-box optimization and reinforcement learning problems in unknown environments. It uses function values to estimate the gradient. As optimization problems are often nonconvex, it is a natural question to understand how zeroth-order methods escape saddle points. In this paper, we consider zeroth-order methods, that at each iteration, may freely choose 2m function evaluations where m ranges from 1 to d, with d denoting the problem dimension. We show that by adding an appropriate isotropic perturbation at each iteration, a zeroth-order algorithm based on 2m function evaluations per iteration can not only find ϵ-second order stationary points polynomially fast, but do so using only Õ(d/ϵ^2.5) function evaluations.Zhaolin Ren, Yujie Tang, Na Liwork_punwbpldlveonczw2hpoiv4hdmTue, 27 Sep 2022 00:00:00 GMT