arXiv subjects

Lixing Zhu

Publications and source records attributed to Lixing Zhu.

At least 37 records · Page 2Linked to original sources

arXiv2023

Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding

Narrative understanding involves capturing the author's cognitive processes, providing insights into their knowledge, intentions, beliefs, and desires. Although large language models (LLMs) excel in generating grammatically coherent text, their ability to comprehend the author's thoughts remains uncertain. This limitation hinders the practical applications of narrative understanding. In this paper, we conduct a comprehensive survey of narrative understanding tasks, thoroughly examining their key features, definitions, taxonomy, associated datasets, training objectives, evaluation metrics, and limitations. Furthermore, we explore the potential of expanding the capabilities of modularized LLMs to address novel narrative understanding tasks. By framing narrative understanding as the retrieval of the author's imaginative cues that outline the narrative structure, our study introduces a fresh perspective on enhancing narrative comprehension.

cs.CL↗

arXiv2023

NarrativePlay: Interactive Narrative Understanding

In this paper, we introduce NarrativePlay, a novel system that allows users to role-play a fictional character and interact with other characters in narratives such as novels in an immersive environment. We leverage Large Language Models (LLMs) to generate human-like responses, guided by personality traits extracted from narratives. The system incorporates auto-generated visual display of narrative settings, character portraits, and character speech, greatly enhancing user experience. Our approach eschews predefined sandboxes, focusing instead on main storyline events extracted from narratives from the perspective of a user-selected character. NarrativePlay has been evaluated on two types of narratives, detective and adventure stories, where users can either explore the world or improve their favorability with the narrative characters through conversations.

cs.CL↗

arXiv2023

Can Prompt Learning Benefit Radiology Report Generation?

Radiology report generation aims to automatically provide clinically meaningful descriptions of radiology images such as MRI and X-ray. Although great success has been achieved in natural scene image captioning tasks, radiology report generation remains challenging and requires prior medical knowledge. In this paper, we propose PromptRRG, a method that utilizes prompt learning to activate a pretrained model and incorporate prior knowledge. Since prompt learning for radiology report generation has not been explored before, we begin with investigating prompt designs and categorise them based on varying levels of knowledge: common, domain-specific and disease-enriched prompts. Additionally, we propose an automatic prompt learning mechanism to alleviate the burden of manual prompt engineering. This is the first work to systematically examine the effectiveness of prompt learning for radiology report generation. Experimental results on the largest radiology report generation benchmark, MIMIC-CXR, demonstrate that our proposed method achieves state-of-the-art performance. Code will be available upon the acceptance.

cs.CV↗

arXiv2023

The conditionally studentized test for high-dimensional parametric regressions

This paper studies model checking for general parametric regression models having no dimension reduction structures on the predictor vector. Using any U-statistic type test as an initial test, this paper combines the sample-splitting and conditional studentization approaches to construct a COnditionally Studentized Test (COST). Whether the initial test is global or local smoothing-based; the dimension of the predictor vector and the number of parameters are fixed or diverge at a certain rate, the proposed test always has a normal weak limit under the null hypothesis. When the dimension of the predictor vector diverges to infinity at faster rate than the number of parameters, even the sample size, these results are still available under some conditions. This shows the potential of our method to handle higher dimensional problems. Further, the test can detect the local alternatives distinct from the null hypothesis at the fastest possible rate of convergence in hypothesis testing. We also discuss the optimal sample splitting in power performance. The numerical studies offer information on its merits and limitations in finite sample cases including the setting where the dimension of predictor vector equals the sample size. As a generic methodology, it could be applied to other testing problems.

stat.ME↗

arXiv2023

Byzantine-robust distributed one-step estimation

This paper proposes a Robust One-Step Estimator(ROSE) to solve the Byzantine failure problem in distributed M-estimation when a moderate fraction of node machines experience Byzantine failures. To define ROSE, the algorithms use the robust Variance Reduced Median Of the Local(VRMOL) estimator to determine the initial parameter value for iteration, and communicate between the node machines and the central processor in the Newton-Raphson iteration procedure to derive the robust VRMOL estimator of the gradient, and the Hessian matrix so as to obtain the final estimator. ROSE has higher asymptotic relative efficiency than general median estimators without increasing the order of computational complexity. Moreover, this estimator can also cope with the problems involving anomalous or missing samples on the central processor. We prove the asymptotic normality when the parameter dimension p diverges as the sample size goes to infinity, and under weaker assumptions, derive the convergence rate. Numerical simulations and a real data application are conducted to evidence the effectiveness and robustness of ROSE.

math.ST↗

arXiv2023

Corrected kernel principal component analysis for model structural change detection

This paper develops a method to detect model structural changes by applying a Corrected Kernel Principal Component Analysis (CKPCA) to construct the so-called central distribution deviation subspaces. This approach can efficiently identify the mean and distribution changes in these dimension reduction subspaces. We derive that the locations and number changes in the dimension reduction data subspaces are identical to those in the original data spaces. Meanwhile, we also explain the necessity of using CKPCA as the classical KPCA fails to identify the central distribution deviation subspaces in these problems. Additionally, we extend this approach to clustering by embedding the original data with nonlinear lower dimensional spaces, providing enhanced capabilities for clustering analysis. The numerical studies on synthetic and real data sets suggest that the dimension reduction versions of existing methods for change point detection and clustering significantly improve the performances of existing approaches in finite sample scenarios.

stat.ME↗

arXiv2023

Multiple change point detection in tensors

This paper proposes a criterion for detecting change structures in tensor data. To accommodate tensor structure with structural mode that is not suitable to be equally treated and summarized in a distance to measure the difference between any two adjacent tensors, we define a mode-based signal-screening Frobenius distance for the moving sums of slices of tensor data to handle both dense and sparse model structures of the tensors. As a general distance, it can also deal with the case without structural mode. Based on the distance, we then construct signal statistics using the ratios with adaptive-to-change ridge functions. The number of changes and their locations can then be consistently estimated in certain senses, and the confidence intervals of the locations of change points are constructed. The results hold when the size of the tensor and the number of change points diverge at certain rates, respectively. Numerical studies are conducted to examine the finite sample performances of the proposed method. We also analyze two real data examples for illustration.

stat.ME↗

arXiv2023

PANACEA: An Automated Misinformation Detection System on COVID-19

In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-of-the-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available.

cs.CL↗

arXiv2022

Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media

Building models to detect vaccine attitudes on social media is challenging because of the composite, often intricate aspects involved, and the limited availability of annotated data. Existing approaches have relied heavily on supervised training that requires abundant annotations and pre-defined aspect categories. Instead, with the aim of leveraging the large amount of unannotated data now available on vaccination, we propose a novel semi-supervised approach for vaccine attitude detection, called VADet. A variational autoencoding architecture based on language models is employed to learn from unlabelled data the topical information of the domain. Then, the model is fine-tuned with a few manually annotated examples of user attitudes. We validate the effectiveness of VADet on our annotated data and also on an existing vaccination corpus annotated with opinions on vaccines. Our results show that VADet is able to learn disentangled stance and aspect topics, and outperforms existing aspect-based sentiment analysis models on both stance detection and tweet clustering.

cs.CL↗

arXiv2022

A general Monte Carlo method for multivariate goodness-of-fit testing applied to elliptical families

A general and relatively simple method for construction of multivariate goodness-of-fit tests is introduced. The proposed test is applied to elliptical distributions. The method is based on a characterization of probability distributions via their characteristic function. The consistency and other limit properties of the new test statistics are studied. Also in a simulation study the proposed tests are compared with earlier as well as more recent competitors.

stat.ME↗

arXiv2021

Optimal prediction for kernel-based semi-functional linear regression

In this paper, we establish minimax optimal rates of convergence for prediction in a semi-functional linear model that consists of a functional component and a less smooth nonparametric component. Our results reveal that the smoother functional component can be learned with the minimax rate as if the nonparametric component were known. More specifically, a double-penalized least squares method is adopted to estimate both the functional and nonparametric components within the framework of reproducing kernel Hilbert spaces. By virtue of the representer theorem, an efficient algorithm that requires no iterations is proposed to solve the corresponding optimization problem, where the regularization parameters are selected by the generalized cross validation criterion. Numerical studies are provided to demonstrate the effectiveness of the method and to verify the theoretical analysis.

math.ST↗

arXiv2021

Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detection

Emotion detection in dialogues is challenging as it often requires the identification of thematic topics underlying a conversation, the relevant commonsense knowledge, and the intricate transition patterns between the affective states. In this paper, we propose a Topic-Driven Knowledge-Aware Transformer to handle the challenges above. We firstly design a topic-augmented language model (LM) with an additional layer specialized for topic detection. The topic-augmented LM is then combined with commonsense statements derived from a knowledge base based on the dialogue contextual information. Finally, a transformer-based encoder-decoder architecture fuses the topical and commonsense information, and performs the emotion label sequence prediction. The model has been experimented on four datasets in dialogue emotion detection, demonstrating its superiority empirically over the existing state-of-the-art approaches. Quantitative and qualitative results show that the model can discover topics which help in distinguishing emotion categories.

cs.CL↗

arXiv2020

The motion of respiratory droplets produced by coughing

Coronavirus disease 2019 (COVID-19) has become a global pandemic infectious respiratory disease with high mortality and infectiousness. This paper investigates respiratory droplet transmission, which is critical to understanding, modeling and controlling epidemics. In the present work, we implemented flow visualization, particle image velocimetry (PIV) and particle shadow tracking velocimetry (PSTV) to measure the velocity of the airflow and droplets involved in coughing and then constructed a physical model considering the evaporation effect to predict the motion of droplets under different weather conditions. The experimental results indicate that the convection velocity of cough airflow presents the relationship $t^{-0.7}$ with time; hence, the distance from the cougher increases by $t^{0.3}$ in the range of our measurement domain. Substituting these experimental results into the physical model reveals that the small droplets (initial diameter $D \leq$ 100 $μ$m) evaporate to droplet nuclei and that the large droplets with $D \geq$ 500 $μ$m and initial velocity $u_0 \geq$ 5 m/s travel more than 2 m. Winter conditions of low temperature and high relative humidity can cause more droplets to settle to the ground, which may be a possible driver of a second pandemic wave in the autumn and winter seasons.

physics.flu-dyn↗

arXiv2020

On IPW-based estimation of conditional average treatment effect

The research in this paper gives a systematic investigation on the asymptotic behaviours of four inverse probability weighting (IPW)-based estimators for conditional average treatment effect, with nonparametrically, semiparametrically, parametrically estimated and true propensity score, respectively. To this end, we first pay a particular attention to semiparametric dimension reduction structure such that we can well study the semiparametric-based estimator that can well alleviate the curse of dimensionality and greatly avoid model misspecification. We also derive some further properties of existing estimator with nonparametrically estimated propensity score. According to their asymptotic variance functions, the studies reveal the general ranking of their asymptotic efficiencies; in which scenarios the asymptotic equivalence can hold; the critical roles of the affiliation of the given covariates in the set of arguments of propensity score, the bandwidth and kernel selections. The results show an essential difference from the IPW-based (unconditional) average treatment effect(ATE). The numerical studies indicate that for high-dimensional paradigms, the semiparametric-based estimator performs well in general {whereas nonparametric-based estimator, even sometimes, parametric-based estimator, is more affected by dimensionality. Some numerical studies are carried out to examine their performances. A real data example is analysed for illustration.

math.ST↗

arXiv2020

A projection-based model checking for heterogeneous treatment effect

In this paper, we investigate the hypothesis testing problem that checks whether part of covariates / confounders significantly affect the heterogeneous treatment effect given all covariates. This model checking is particularly useful in the case where there are many collected covariates such that we can possibly alleviate the typical curse of dimensionality. In the test construction procedure, we use a projection-based idea and a nonparametric estimation-based test procedure to construct an aggregated version over all projection directions. The resulting test statistic is then interestingly with no effect from slow convergence rate the nonparametric estimation usually suffers from. This feature makes the test behave like a global smoothing test to have ability to detect a broad class of local alternatives converging to the null at the fastest possible rate in hypothesis testing. Also, the test can inherit the merit of lobal smoothing tests to be sensitive to oscillating alternative models. The performance of the test is examined by numerical studies and the analysis for a real data example for illustration.

math.ST↗

arXiv2020

Limiting laws for extreme eigenvalues of large-dimensional spiked Fisher matrices with a divergent number of spikes

Consider the $p\times p$ matrix that is the product of a population covariance matrix and the inverse of another population covariance matrix. Suppose that their difference has a divergent rank with respect to $p$, when two samples of sizes $n$ and $T$ from the two populations are available, we construct its corresponding sample version. In the regime of high dimension where both $n$ and $T$ are proportional to $p$, we investigate the limiting laws for extreme (spiked) eigenvalues of the sample (spiked) Fisher matrix when the number of spikes is divergent and these spikes are unbounded.

math.ST↗

arXiv2020

The Role of Propensity Score Structure in Asymptotic Efficiency of Estimated Conditional Quantile Treatment Effect

When a strict subset of covariates are given, we propose conditional quantile treatment effect to capture the heterogeneity of treatment effects via the quantile sheet that is the function of the given covariates and quantile. We focus on deriving the asymptotic normality of probability score-based estimators under parametric, nonparametric and semiparametric structure. We make a systematic study on the estimation efficiency to check the importance of propensity score structure and the essential differences from the unconditional counterparts. The derived unique properties can answer: what is the general ranking of these estimators? how does the affiliation of the given covariates to the set of covariates of the propensity score affect the efficiency? how does the convergence rate of the estimated propensity score affect the efficiency? and why would semiparametric estimation be worth of recommendation in practice? We also give a brief discussion on the extension of the methods to handle large-dimensional scenarios and on the estimation for the asymptotic variances. The simulation studies are conducted to examine the performances of these estimators. A real data example is analyzed for illustration and some new findings are acquired.

math.ST↗

arXiv2020

Outcome regression-based estimation of conditional average treatment effect

The research is about a systematic investigation on the following issues. First, we construct different outcome regression-based estimators for conditional average treatment effect under, respectively, true (oracle), parametric, nonparametric and semiparametric dimension reduction structure. Second, according to the corresponding asymptotic variance functions, we answer the following questions when supposing the models are correctly specified: what is the asymptotic efficiency ranking about the four estimators in general? how is the efficiency related to the affiliation of the given covariates in the set of arguments of the regression functions? what do the roles of bandwidth and kernel function selections play for the estimation efficiency; and in which scenarios should the estimator under semiparametric dimension reduction regression structure be used in practice? As a by-product, the results show that any outcome regression-based estimation should be asymptotically more efficient than any inverse probability weighting-based estimation. All these results give a relatively complete picture of the outcome regression-based estimation such that the theoretical conclusions could provide guidance for practical use when more than one estimations can be applied to the same problem. Several simulation studies are conducted to examine the performances of these estimators in finite sample cases and a real dataset is analyzed for illustration.

math.ST↗