arXiv subjects

Xiaojing Wang

Publications and source records attributed to Xiaojing Wang.

14 recordsLinked to original sources

arXiv2024

Humidity Sensing Properties of Different Atomic Layers of Graphene on SiO2/Si Substrate

Graphene has the great potential to be used for humidity sensing due to ultrahigh surface area and conductivity. However, the impact of different atomic layers of graphene on SiO2/Si substrate on the humidity sensing have not been studied yet. In this paper, we fabricated three types of humidity sensors on SiO2/Si substrate based on one to three atomic layers of graphene, in which the sensing areas of graphene are 75 μm * 72 μm and 45 μm * 72 μm, respectively. We studied the impact of both the number of atomic layers of graphene and the sensing areas of graphene on the responsivity and response/recovery time of the prepared graphene-based humidity sensors. We found the relative resistance change of the prepared devices decreased with the increase of number of atomic layers of graphene under the same change of relative humidity. Further, devices based on tri-layer graphene showed the fastest response/recovery time while devices based on double-layer graphene showed the slowest response/recovery time. Finally, we chose the devices based on double-layer graphene that have relatively good responsivity and stability for application in respiration monitoring and contact-free finger monitoring.

cond-mat.mes-hall↗

arXiv2024

UniTable: Towards a Unified Framework for Table Recognition via Self-Supervised Pretraining

Tables convey factual and quantitative data with implicit conventions created by humans that are often challenging for machines to parse. Prior work on table recognition (TR) has mainly centered around complex task-specific combinations of available inputs and tools. We present UniTable, a training framework that unifies both the training paradigm and training objective of TR. Its training paradigm combines the simplicity of purely pixel-level inputs with the effectiveness and scalability empowered by self-supervised pretraining from diverse unannotated tabular images. Our framework unifies the training objectives of all three TR tasks - extracting table structure, cell content, and cell bounding box - into a unified task-agnostic training objective: language modeling. Extensive quantitative and qualitative analyses highlight UniTable's state-of-the-art (SOTA) performance on four of the largest TR datasets. UniTable's table parsing capability has surpassed both existing TR methods and general large vision-language models, e.g., GPT-4o, GPT-4-turbo with vision, and LLaVA. Our code is publicly available at https://github.com/poloclub/unitable, featuring a Jupyter Notebook that includes the complete inference pipeline, fine-tuned across multiple TR datasets, supporting all three TR tasks.

cs.CV↗

arXiv2024

Self-Supervised Pre-Training for Table Structure Recognition Transformer

Table structure recognition (TSR) aims to convert tabular images into a machine-readable format. Although hybrid convolutional neural network (CNN)-transformer architecture is widely used in existing approaches, linear projection transformer has outperformed the hybrid architecture in numerous vision tasks due to its simplicity and efficiency. However, existing research has demonstrated that a direct replacement of CNN backbone with linear projection leads to a marked performance drop. In this work, we resolve the issue by proposing a self-supervised pre-training (SSP) method for TSR transformers. We discover that the performance gap between the linear projection transformer and the hybrid CNN-transformer can be mitigated by SSP of the visual encoder in the TSR model. We conducted reproducible ablation studies and open-sourced our code at https://github.com/poloclub/unitable to enhance transparency, inspire innovations, and facilitate fair comparisons in our domain as tables are a promising modality for representation learning.

cs.CV↗

arXiv2023

High-Performance Transformers for Table Structure Recognition Need Early Convolutions

Table structure recognition (TSR) aims to convert tabular images into a machine-readable format, where a visual encoder extracts image features and a textual decoder generates table-representing tokens. Existing approaches use classic convolutional neural network (CNN) backbones for the visual encoder and transformers for the textual decoder. However, this hybrid CNN-Transformer architecture introduces a complex visual encoder that accounts for nearly half of the total model parameters, markedly reduces both training and inference speed, and hinders the potential for self-supervised learning in TSR. In this work, we design a lightweight visual encoder for TSR without sacrificing expressive power. We discover that a convolutional stem can match classic CNN backbone performance, with a much simpler model. The convolutional stem strikes an optimal balance between two crucial factors for high-performance TSR: a higher receptive field (RF) ratio and a longer sequence length. This allows it to "see" an appropriate portion of the table and "store" the complex table structure within sufficient context length for the subsequent transformer. We conducted reproducible ablation studies and open-sourced our code at https://github.com/poloclub/tsr-convstem to enhance transparency, inspire innovations, and facilitate fair comparisons in our domain as tables are a promising modality for representation learning.

cs.CV↗

arXiv2023

Analysis of the current status of tuberculosis transmission in China based on a heterogeneity model

Tuberculosis (TB) is an infectious disease transmitted through the respiratory system. China is one of the countries with a high burden of TB. Since 2004, an average of more than 800,000 cases of active TB have been reported each year in China. Analyzing the case data from 2004-2018, we find significant differences in TB incidence by age group. Therefore, the effect of age heterogeneous structure on TB transmission needs further study. We develop a model of TB to explore the role of age heterogeneity as a factor in TB transmission. The model is fitted numerically using the nonlinear least squares method to obtain the key parameters in the model, and the basic reproduction number Rv 0.8017 is calculated and the sensitivity anal-ysis of Rv to the parameters is given. The simulation results show that reducing the number of new infections in the elderly population and increasing the recovery rate of elderly patients with the disease could significantly reduce the transmission of tuberculosis. Furthermore the feasibility of achieving the goals of the WHO End TB Strategy in China is assessed, and we obtain that with existing TB control measures it will take another 30 years for China to reach the WHO goal to reduce 90% of the number of new cases by year 2049. However, in theoretical it is feasible to reach the WHO strategic goal of ending tuberculosis by 2035 if the group contact rate in the elderly population can be reduced though it is difficulty to reduce the contact rate.

stat.AP↗

arXiv2023

A Flexible Bayesian Clustering of Dynamic Subpopulations in Neural Spiking Activity

With advances in neural recording techniques, neuroscientists are now able to record the spiking activity of many hundreds of neurons simultaneously, and new statistical methods are needed to understand the structure of this large-scale neural population activity. Although previous work has tried to summarize neural activity within and between known populations by extracting low-dimensional latent factors, in many cases what determines a unique population may be unclear. Neurons differ in their anatomical location, but also, in their cell types and response properties. To identify populations directly related to neural activity, we develop a clustering method based on a mixture of dynamic Poisson factor analyzers (mixDPFA) model, with the number of clusters and dimension of latent factors for each cluster treated as unknown parameters. To analyze the proposed mixDPFA model, we propose a Markov chain Monte Carlo (MCMC) algorithm to efficiently sample its posterior distribution. Validating our proposed MCMC algorithm through simulations, we find that it can accurately recover the unknown parameters and the true clustering in the model, and is insensitive to the initial cluster assignments. We then apply the proposed mixDPFA model to multi-region experimental recordings, where we find that the proposed method can identify novel, reliable clusters of neurons based on their activity, and may, thus, be a useful tool for neural data analysis.

q-bio.NC↗

arXiv2021

MHD analysis on the physical designs of CFETR and HFRC

The China Fusion Engineering Test Reactor (CFETR) and the Huazhong Field Reversed Configuration (HFRC), currently both under intensive physical and engineering designs in China, are the two major projects representative of the low-density steady-state and high-density pulsed pathways to fusion. One of the primary tasks of the physics designs for both CFETR and HFRC is the assessment and analysis of the magnetohydrodynamic (MHD) stability of the proposed design schemes. Comprehensive efforts on the assessment of MHD stability of CFETR and HFRC baseline scenarios have led to preliminary progresses that may further benefit engineering designs.

physics.plasm-ph↗

arXiv2021

Several topological indices of random caterpillars

In chemical graph theory, caterpillar trees have been an appealing model to represent the molecular structures of benzenoid hydrocarbon. Meanwhile, topological index has been thought of as a powerful tool for modeling quantitative structure-property relationship and quantitative structure-activity between molecules in chemical compounds. In this article, we consider a class of caterpillar trees that are incorporated with randomness, called random caterpillars, and investigate several popular topological indices of this random class, including Zagreb index, Randić index and Wiener index, etc. Especially, a central limit theorem is developed for the asymptotic distribution of the Zagreb index of random caterpillars.

math.PR↗

arXiv2021

A molecular dynamics simulation study on the frustrated Lewis pairs in ionic liquids

Steric hindered frustrated Lewis pairs (FLPs) have been shown to activate hydrogen molecules, and their reactivity is strongly determined by the geometric parameters of the Lewis acid s and bases. A recent experimental study showed that ionic liquids (ILs) could largely improve the effective configuration of FLPs. However, the detailed mechanistic profile is still unclear. Herein, we performed a molecular dynamics (MD) simulations, aimi ng to reveal the effects of ILs on the structures of FLPs, and to present a rule for selecting more efficient reaction media. For this purpose, mixture systems were adopt consisting of the ILs [Cnmim][NTf2] (n= 6, 10, 14), and the typical FLP (tBu)3P/B(C6F5)3 . Radial distribution function (RDF) results show that toluene competes with (tBu)3P to interact with B(C6F5)3 , resulting in a relatively low effective (tBu)3P/B(C6F5)3 complex. [Cnmim][NTf2] is more intended to form a solvated shell surrounding the (tBu)3P/B(C6F5)3 , which increases the amount of effective FLPs. Spatial distribution function (SDF) results show that toluene formed a continuum solvation shell, which hinders the interactions of (tBu)3P and B(C6F5)3 , while [Cnmim][NTf2] leave a relatively large empty space, which is accessible by (tBu3)P molecules, resulting in a higher probability of Lewis acids and bases interactions. Lastly, we find that the longer alkyl chain length of[Cnmim] cations, the higher probability of effective FLPs.

physics.chem-ph↗

arXiv2019

A Deep Probabilistic Model for Customer Lifetime Value Prediction

Accurate predictions of customers' future lifetime value (LTV) given their attributes and past purchase behavior enables a more customer-centric marketing strategy. Marketers can segment customers into various buckets based on the predicted LTV and, in turn, customize marketing messages or advertising copies to serve customers in different segments better. Furthermore, LTV predictions can directly inform marketing budget allocations and improve real-time targeting and bidding of ad impressions. One challenge of LTV modeling is that some customers never come back, and the distribution of LTV can be heavy-tailed. The commonly used mean squared error (MSE) loss does not accommodate the significant fraction of zero value LTV from one-time purchasers and can be sensitive to extremely large LTV's from top spenders. In this article, we model the distribution of LTV given associated features as a mixture of zero point mass and lognormal distribution, which we refer to as the zero-inflated lognormal (ZILN) distribution. This modeling approach allows us to capture the churn probability and account for the heavy-tailedness nature of LTV at the same time. It also yields straightforward uncertainty quantification of the point prediction. The ZILN loss can be used in both linear models and deep neural networks (DNN). For model evaluation, we recommend the normalized Gini coefficient to quantify model discrimination and decile charts to assess model calibration. Empirically, we demonstrate the predictive performance of our proposed model on two real-world public datasets.

stat.AP↗

arXiv2019

A Python Library For Empirical Calibration

Dealing with biased data samples is a common task across many statistical fields. In survey sampling, bias often occurs due to unrepresentative samples. In causal studies with observational data, the treated versus untreated group assignment is often correlated with covariates, i.e., not random. Empirical calibration is a generic weighting method that presents a unified view on correcting or reducing the data biases for the tasks mentioned above. We provide a Python library EC to compute the empirical calibration weights. The problem is formulated as convex optimization and solved efficiently in the dual form. Compared to existing software, EC is both more efficient and robust. EC also accommodates different optimization objectives, supports weight clipping, and allows inexact calibration, which improves usability. We demonstrate its usage across various experiments with both simulated and real-world data.

stat.CO↗

arXiv2019

Clustering-Based Codebook Design for MIMO Communication System

Codebook design is one of the core technologies in limited feedback multi-input multi-output (MIMO) communication systems. However, the conventional codebook designs usually assume MIMO vectors are uniformly distributed or isotropic. Motivated by the excellent classfication and analysis ability of clustering algorithms, we propose a K-means clustering based codebook design. First, large amounts of channel state information (CSI) is stored as the input data of the clustering, and finally divided into N clusters according to the minimal distance. The clustering centroids are used as the statistic channel information of the codebook construction which the sum distance is minimal to the real channel information. Simulation results consist with theoretical analysis in terms of the achievable rate, and demonstrate that the proposed codebook design outperforms conventional schemes, especially in the non-uniform distribution of channel scenarios.

cs.IT↗

arXiv2017

Robust Gaussian Stochastic Process Emulation

We consider estimation of the parameters of a Gaussian Stochastic Process (GaSP), in the context of emulation (approximation) of computer models for which the outcomes are real-valued scalars. The main focus is on estimation of the GaSP parameters through various generalized maximum likelihood methods, mostly involving finding posterior modes; this is because full Bayesian analysis in computer model emulation is typically prohibitively expensive. The posterior modes that are studied arise from objective priors, such as the reference prior. These priors have been studied in the literature for the situation of an isotropic covariance function or under the assumption of separability in the design of inputs for model runs used in the GaSP construction. In this paper, we consider more general designs (e.g., a Latin Hypercube Design) with a class of commonly used anisotropic correlation functions, which can be written as a product of isotropic correlation functions, each having an unknown range parameter and a fixed roughness parameter. We discuss properties of the objective priors and marginal likelihoods for the parameters of the GaSP and establish the posterior propriety of the GaSP parameters, but our main focus is to demonstrate that certain parameterizations result in more robust estimation of the GaSP parameters than others, and that some parameterizations that are in common use should clearly be avoided. These results are applicable to many frequently used covariance functions, e.g., power exponential, Mat{é}rn, rational quadratic and spherical covariance. We also generalize the results to the GaSP model with a nugget parameter. Both theoretical and numerical evidence is presented concerning the performance of the studied procedures.

math.ST↗

arXiv2013

Bayesian analysis of dynamic item response models in educational testing

Item response theory (IRT) models have been widely used in educational measurement testing. When there are repeated observations available for individuals through time, a dynamic structure for the latent trait of ability needs to be incorporated into the model, to accommodate changes in ability. Other complications that often arise in such settings include a violation of the common assumption that test results are conditionally independent, given ability and item difficulty, and that test item difficulties may be partially specified, but subject to uncertainty. Focusing on time series dichotomous response data, a new class of state space models, called Dynamic Item Response (DIR) models, is proposed. The models can be applied either retrospectively to the full data or on-line, in cases where real-time prediction is needed. The models are studied through simulated examples and applied to a large collection of reading test data obtained from MetaMetrics, Inc.

stat.AP↗