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The transport of water and protons in the cathode catalyst layer (CCL) of proton exchange membrane (PEM) fuel cells is critical for cell performance, but the underlying mechanism is still unclear. Herein, the ionomer structure and the distribution/transport characteristics of water and protons in CCLs are investigated via all-atom molecular dynamics simulations. The results show that at low water contents, isolated water clusters form in ionomer pores, while proton transport is mainly via the charged sites of the ionomer side chains and the Grotthuss mechanism. Moreover, with increasing water content, water clusters are interconnected to form continuous water channels, which provide effective paths for proton transfer via the vehicular and Grotthuss mechanisms. Increasing the ionomer mass content can enhance the dense arrangement of the ionomer, which in turn increases the density of charge sites and improves the proton transport efficiency. When the ionomer mass content is high, the clustering effect reduces the space for water diffusion, increases the proton transport path, and finally decreases the proton transport efficiency. By providing physics insights into the proton transport mechanism, this study is helpful for the structural design and performance improvement of CCLs of PEM fuel cells., Comment: 14 pages, 15 figures

We define a random analytic function $\varphi$ on the unit disc by letting a Gaussian multiplicative measure to be one of its Clark measures. We show that $\varphi$ is almost surely a Blaschke product and we provide rather sharp estimates for the density of its zeroes., Comment: 25 pages

We determine the exact threshold of the extended Seiberg bound for the existence of correlation functions in the boundary Liouville conformal field theory in the unit disk. In probabilistic terms, our result is a toolbox yielding the threshold for the existence of positive moments of Gaussian multiplicative chaos measure, appliable to the case where singularities of arbitrary (co-)dimension in the background metric are present. We improve previous results of this type for 0-dimensional singularities in [DKRV16] and a sufficient condition for the 1-dimensional singularity in an unpublished appendix of [HRV18]. In particular, we prove the optimality of the moment bound threshold for boundary Gaussian multiplicative chaos conjectured in [HRV18], which is equivalent to the so-called unit volume Seiberg bound of the boundary Liouville conformal field theory., Comment: 31 pages, 7 figures

For large-scale distributed systems, it's crucial to efficiently diagnose the root causes of incidents to maintain high system availability. The recent development of microservice architecture brings three major challenges (i.e., operation, system scale, and monitoring complexities) to root cause analysis (RCA) in industrial settings. To tackle these challenges, in this paper, we present Groot, an event-graph-based approach for RCA. Groot constructs a real-time causality graph based on events that summarize various types of metrics, logs, and activities in the system under analysis. Moreover, to incorporate domain knowledge from site reliability engineering (SRE) engineers, Groot can be customized with user-defined events and domain-specific rules. Currently, Groot supports RCA among 5,000 real production services and is actively used by the SRE teamin a global e-commerce system serving more than 185 million active buyers per year. Over 15 months, we collect a data setcontaining labeled root causes of 952 real production incidents for evaluation. The evaluation results show that Groot is able to achieve 95% top-3 accuracy and 78% top-1 accuracy. To share our experience in deploying and adopting RCA in industrial settings, we conduct survey to show that users of Grootfindit helpful and easy to use. We also share the lessons learnedfrom deploying and adopting Grootto solve RCA problems inproduction environments.

Visual information extraction (VIE) has attracted increasing attention in recent years. The existing methods usually first organized optical character recognition (OCR) results into plain texts and then utilized token-level entity annotations as supervision to train a sequence tagging model. However, it expends great annotation costs and may be exposed to label confusion, and the OCR errors will also significantly affect the final performance. In this paper, we propose a unified weakly-supervised learning framework called TCPN (Tag, Copy or Predict Network), which introduces 1) an efficient encoder to simultaneously model the semantic and layout information in 2D OCR results; 2) a weakly-supervised training strategy that utilizes only key information sequences as supervision; and 3) a flexible and switchable decoder which contains two inference modes: one (Copy or Predict Mode) is to output key information sequences of different categories by copying a token from the input or predicting one in each time step, and the other (Tag Mode) is to directly tag the input sequence in a single forward pass. Our method shows new state-of-the-art performance on several public benchmarks, which fully proves its effectiveness., Comment: IJCAI2021

In this paper, we fill the research gap by adopting state-of-the-art computer vision techniques for the data extraction stage in a data mining system. As shown in Fig.1, this stage contains two subtasks, namely, plot element detection and data conversion. For building a robust box detector, we comprehensively compare different deep learning-based methods and find a suitable method to detect box with high precision. For building a robust point detector, a fully convolutional network with feature fusion module is adopted, which can distinguish close points compared to traditional methods. The proposed system can effectively handle various chart data without making heuristic assumptions. For data conversion, we translate the detected element into data with semantic value. A network is proposed to measure feature similarities between legends and detected elements in the legend matching phase. Furthermore, we provide a baseline on the competition of Harvesting raw tables from Infographics. Some key factors have been found to improve the performance of each stage. Experimental results demonstrate the effectiveness of the proposed system., Comment: accepted by ICDAR2021

Toda conformal field theories are natural generalizations of Liouville conformal field theory that enjoy an enhanced level of symmetry. In Toda conformal field theories this higher-spin symmetry can be made explicit, thanks to a path integral formulation of the model based on a Lie algebra structure. The purpose of the present document is to explain how this higher level of symmetry can manifest itself within the rigorous probabilistic framework introduced by R. Rhodes, V. Vargas and the first author. One of its features is the existence of holomorphic currents that are introduced via a rigorous derivation of the Miura transformation. More precisely, we prove that the spin-three Ward identities, that encode higher-spin symmetry, hold in the $\mathfrak{sl}_3$ Toda conformal field theory; as an original input we provide explicit expressions for the descendent fields which were left unidentified in the physics literature. This representation of the descendent fields provides a new systematic method to find the degenerate fields of the $\mathfrak{sl}_3$ Toda (and Liouville) conformal field theory, which in turn implies that certain four-point correlation functions are solutions of an hypergeometric differential equation of the third order., Comment: 51 pages. Presentation improved

The main input of this note is to provide an alternative probabilistic approach to the ${\Phi}^{2n}$ theory in dimension 2, based on concentration phenomenon of martingales associated to polynomials of Gaussian variables. This is based on an adaptation of the work of Lacoin-Rhodes-Vargas, in which exponential potentials associated to quantum Mabuchi K-energy are studied., Comment: 12 pages

Solar energy is one of important renewable energy sources and simulation of solar irradiance can be used as input for simulation of photovoltaic (PV) generation. This paper proposes a simulation algorithm of multi-station solar irradiance data considering temporal correlations. First of all, we group all the days of the observed data to k clusters for each station based on their daily features of solar irradiance and the daily states constitute Markov chain of days. Then, we reduce state permutations of different stations before getting Markov Transition Probability Matrix (MTPM). In terms of the observed data and MTPM, the simulation approach is proposed. Finally, we test our approach by applying to solar irradiance data of three stations and show that the properties of simulated data match those of the observed data., Comment: The 9th International Conference on Innovative Smart Grid Technologies (IEEE PES ISGT Asia 2019), Chengdu, China

We study the problem of analytic continuation of Liouville Conformal Field Theory using the probabilistic approach of David, Kupiainen, Rhodes and Vargas [DKRV16] based on the theory of Gaussian Multiplicative Chaos. The key idea is to apply stochastic calculus techniques to some Brownian motions associated to the Gaussian Free Field. We strengthen the results in [KRV17] and are able to provide rigorous justification of analytic continuation of Liouville Theory in an infinite trianglar region, which we call the pencil region. This is in accordance with the physics literature [HMW11]. Along the lines, we develop new tools and estimates of Gaussian measures and Liouville correlation functions using this probabilistic approach., Comment: 27 pages; comments are welcome