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Yichao Huang

Publications and source records attributed to Yichao Huang.

At least 19 recordsLinked to original sources

Royen's proof of the Gaussian correlation inequality as a supersymmetric dimensional reduction

We revisit Royen's proof of the Gaussian correlation inequality from a supersymmetric point of view. Many key elements in Royen's proof of this inequality have natural geometric interpretations in terms of supersymmetric dimensional reduction from $\mathbb{R}^{3|2}$ to $\mathbb{R}^{1|0}$. In particular, the auxiliary multivariate Gamma distributions appearing in Royen's Laplace-transform argument arise naturally as the body of a supersymmetric radial variable on $\mathbb{R}^{3|2}$. The generalization to the half-integer multivariate Gamma case also follows naturally as a dimensional reduction from $\mathbb{R}^{k+2|2}$ to $\mathbb{R}^{k|0}$, and we derive an exact interpolation identity for general smooth test functions. This provides an example in which the supersymmetric localization method is applied to prove correlation inequalities with continuous interpolation.

math.PR

The $H^{2|2}$ monotonicity theorem revisited

We use supersymmetric localization and integration by parts to derive variational and convex correlation inequalities in statistical physics. As a primary application, we give an alternative proof of the monotonicity theorem for the $H^{2|2}$ supersymmetric hyperbolic sigma model. This extends a result of Poudevigne to a stronger multivariate convex comparison inequality, without relying on probabilistic couplings.

math.PR

Poisoning effect of ammonia on the performance and transport process of proton exchange membrane fuel cells

Ammonia is a high-density hydrogen energy carrier and can be decomposed to produce hydrogen for use in fuel cells. However, a significant challenge in ammonia-decomposition-based fuel cell applications is the unavoidable presence of trace ammonia impurity, which can poison the fuel cell, but the poisoning mechanism remains unclear. To address this, a three-dimensional numerical model of proton exchange membrane (PEM) fuel cells with ammonia impurities is established to explore the transport process and underlying poisoning mechanism. The influences of key factors, including ammonia concentration, operating temperature, operating humidity, and membrane thickness, are studied. The poisoning mechanism is analyzed from the perspectives of the distributions of proton conductivity, current density, and dissolved water content. The results show that ammonia diminishes the cell performance by substantially reducing the proton conductivity of both the PEM and the anode catalyst layer. Higher operating temperatures and higher operating humidity can alleviate ammonia poisoning. Decreasing the membrane thickness can also help to mitigate ammonia poisoning, but may lead to less uniform current distribution.

physics.flu-dyn

Poisoning mechanism of ammonia on proton transport and ionomer structure in cathode catalyst layer of PEM fuel cells

Ammonia has strong poisoning effects on cathode catalyst layers of proton exchange membrane (PEM) fuel cells, but the poisoning mechanism is still unclear. In this study, all-atom molecular dynamics simulations are employed to investigate the poisoning mechanisms of ammonia. The results show that ammonium can replace the hydronium ions at the charged sites of sulfonic acid group of the ionomer side chain, and the adsorption of ammonium to sulfonic acid group can be attributed to van der Waals force and electrostatic interaction. Furthermore, other ammonia derivatives, amino and imino ions, can capture hydronium ions to form ion clusters. These ion clusters have strong capability to absorb hydronium ions, and their structures change with ammonia content and temperature. The main mechanism of formation of these clusters is due to the formation of relatively stable hydrogen bonds between ions within the clusters. These mechanisms significantly reduce the efficiency of proton transport, thereby decreasing the catalyst layer's performance in electrochemical reactions. We also discover that the increase in temperature leads to the dissociation of large ion clusters, the blockage in the ionomer layer can be alleviated, and the proton transport efficiency can be restored. The understanding of the poisoning mechanisms obtained in this study is helpful for subsequent research aimed at resolving ammonia poisoning and enhancing the anti-poisoning performance of catalyst layers.

physics.chem-ph

Reinforced Loop Soup via Wilson's Algorithm

The goal of this note is twofold: first, we explain the relation between the isomorphism theorems in the context of vertex reinforced jump process discovered in [BHS19, BHS21] and the standard Markovian isomorphism theorems for Markovian jump processes; second, we introduce the vertex reinforced counterpart of the standard Poissonian loop soup developed by Le Jan [LJ10]. To this end, we propose an algorithm that can be viewed as a variant of Wilson's algorithm with reinforcement. We establish the isomorphism theorems for the erased loops and the random walk from this algorithm, and in particular provide a concrete construction of the reinforced loop soup via a random process with a reinforcement mechanism.

math.PR

Fine-Graining and Continuous Space Scaling Limit of the $H^{2|2}$ Model on the Hierarchical Lattice

We extend the exact coarse-graining result of Disertori, Merkl and Rolles~\cite{MR4517733} for the random field of $H^{2|2}$-model to the random Schrödinger operator representation of the $H^{2|2}$-model. We also introduce a fine-graining procedure as the reverse operation, and establish an associated exponential martingale property. Applying this fine-graining procedure to the $H^{2|2}$-model on the Dyson hierarchical lattice, we establish its continuous space scaling limit as a non-trivial random measure on $[0,1]$. This random measure is almost surely singular with respect to the Lebesgue measure if and only if the Vertex Reinforced Jump Process on the Dyson hierarchical lattice is recurrent. If the process is transient, the random measure almost surely has an absolutely continuous component. The density of this component is everywhere non-trivial and can be identified with the pointwise limit of an exponential martingale associated with the $H^{2|2}$-model on the Dyson hierarchical lattice.

math.PR

Bulk/boundary quotients of Gaussian multiplicative chaos measures II: Tail profile of bulk Gaussian multiplicative chaos measures in the exact scale-invariant case

This is the second part of a series of papers where we consider questions related to the tail profile of the bulk/boundary quotients of Gaussian multiplicative chaos measures appearing in boundary Liouville conformal field theory. In this part, we study of the right tail profile of a Gaussian multiplicative chaos measure with uniform singularity on the boundary, especially in the case of an exact scale-invariant kernel for the underlying log-correlated Gaussian field. This extends previous results by Rhodes-Vargas and Wong, where the case with flat background geometry (i.e. no boundary singularity) is studied using either the localization trick or some suitable versions of Tauberian theorems. Our generalization is non-trivial in the sense that we don't apply the localization trick to the bulk measure in question, but rather to an auxiliary Gaussian multiplicative chaos measure located at the boundary, on which the original bulk measure is not directly defined. We show that this modified localization scheme correctly captures the behavior of the right tail of the bulk Gaussian multiplicative chaos measure. The resulting tail profile coefficient is expressed in terms of a variant of bulk/boundary quotient of respective Gaussian multiplicative chaos measures, for which the well-definedness follows from preliminary joint moment bounds established previously in a companion paper.

math.PR

Tail Profile of Bulk Gaussian Multiplicative Chaos Measures I: Bulk/Boundary Quotients

This is the first part of a series of papers devoted to studying the right tail profile of a bulk Gaussian multiplicative chaos measure with uniform singularity on the boundary. We investigate the bulk/boundary quotients of Gaussian multiplicative chaos measures appearing in boundary Liouville conformal field theory, for which we establish preliminary joint moment bounds. These moment bounds will be a crucial ingredient in establishing the right tail profile of the bulk Gaussian multiplicative chaos measure in subsequent papers. The main idea is to implement the so-called localization trick at the boundary, and we also record a useful generalization of Kahane's convexity inequality, which is of independent interest. The study of the universal tail profiles of general bulk measures and bulk/boundary quotients as well as connections to integrability results of boundary Liouville conformal field theory will be pursued in subsequent papers.

math.PR

Ionomer structure and component transport in the cathode catalyst layer of PEM fuel cells: A molecular dynamics study

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.

physics.chem-ph

Random analytic functions via Gaussian multiplicative chaos

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

math.PR

Moment bounds for Gaussian multiplicative chaos with higher-dimensional singularities

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.

math.PR

Ward identities in the $\mathfrak{sl}_3$ Toda conformal field theory

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.

math.PR

Groot: An Event-graph-based Approach for Root Cause Analysis in Industrial Settings

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.

cs.SE

Tag, Copy or Predict: A Unified Weakly-Supervised Learning Framework for Visual Information Extraction using Sequences

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.

cs.CV

Towards an efficient framework for Data Extraction from Chart Images

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.

cs.CV

Another probabilistic construction of $Φ^{2n}$ in dimension 2

The main input of this note is to provide an alternative probabilistic approach to the $Φ^{2n}$ 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.

math.PR

A Simulation Approach to Multi-station Solar Irradiance Data Considering Temporal Correlations

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.

eess.SP