arXiv subjects

Lili Zhao

Publications and source records attributed to Lili Zhao.

At least 37 records · Page 2Linked to original sources

arXiv2024

Event Grounded Criminal Court View Generation with Cooperative (Large) Language Models

With the development of legal intelligence, Criminal Court View Generation has attracted much attention as a crucial task of legal intelligence, which aims to generate concise and coherent texts that summarize case facts and provide explanations for verdicts. Existing researches explore the key information in case facts to yield the court views. Most of them employ a coarse-grained approach that partitions the facts into broad segments (e.g., verdict-related sentences) to make predictions. However, this approach fails to capture the complex details present in the case facts, such as various criminal elements and legal events. To this end, in this paper, we propose an Event Grounded Generation (EGG) method for criminal court view generation with cooperative (Large) Language Models, which introduces the fine-grained event information into the generation. Specifically, we first design a LLMs-based extraction method that can extract events in case facts without massive annotated events. Then, we incorporate the extracted events into court view generation by merging case facts and events. Besides, considering the computational burden posed by the use of LLMs in the extraction phase of EGG, we propose a LLMs-free EGG method that can eliminate the requirement for event extraction using LLMs in the inference phase. Extensive experimental results on a real-world dataset clearly validate the effectiveness of our proposed method.

cs.CL↗

arXiv2024

Metastable Charge Distribution Between Degenerate Landau Levels

We study two dimensional electron systems confined in wide quantum wells whose subband separation is comparable with the Zeeman energy. Two N = 0 Landau levels from different subbands and with opposite spins are pinned in energy when they cross each other and electrons can freely transfer between them. When the disorder is strong, we observe clear hysteresis in our data corresponding to instability of the electron distribution in the two crossing levels. When the intra-layer interaction dominates, multiple minima appear when a Landau level is 1/3 or 2/3 filled and fractional quantum hall effect can be stabilized.

cond-mat.mes-hall↗

arXiv2023

Kropina metrics with isotropic scalar curvature

In this paper, we study Kropina metrics with isotropic scalar curvature. First, we obtain the expressions of Ricci curvature tensor and scalar curvature. Then, we characterize the Kropina metrics with isotropic scalar curvature on by tensor analysis.

math.DG↗

arXiv2023

Deep learning radiomics for assessment of gastroesophageal varices in people with compensated advanced chronic liver disease

Objective: Bleeding from gastroesophageal varices (GEV) is a medical emergency associated with high mortality. We aim to construct an artificial intelligence-based model of two-dimensional shear wave elastography (2D-SWE) of the liver and spleen to precisely assess the risk of GEV and high-risk gastroesophageal varices (HRV). Design: A prospective multicenter study was conducted in patients with compensated advanced chronic liver disease. 305 patients were enrolled from 12 hospitals, and finally 265 patients were included, with 1136 liver stiffness measurement (LSM) images and 1042 spleen stiffness measurement (SSM) images generated by 2D-SWE. We leveraged deep learning methods to uncover associations between image features and patient risk, and thus conducted models to predict GEV and HRV. Results: A multi-modality Deep Learning Risk Prediction model (DLRP) was constructed to assess GEV and HRV, based on LSM and SSM images, and clinical information. Validation analysis revealed that the AUCs of DLRP were 0.91 for GEV (95% CI 0.90 to 0.93, p < 0.05) and 0.88 for HRV (95% CI 0.86 to 0.89, p < 0.01), which were significantly and robustly better than canonical risk indicators, including the value of LSM and SSM. Moreover, DLPR was better than the model using individual parameters, including LSM and SSM images. In HRV prediction, the 2D-SWE images of SSM outperform LSM (p < 0.01). Conclusion: DLRP shows excellent performance in predicting GEV and HRV over canonical risk indicators LSM and SSM. Additionally, the 2D-SWE images of SSM provided more information for better accuracy in predicting HRV than the LSM.

q-bio.TO↗

arXiv2023

Non-parametric Bayesian mixture model to study adverse events of COVID-19 vaccines

The vaccine adverse event reporting system (VAERS) is a vital resource for post-licensure vaccine safety monitoring and has played a key role in assessing the safety of COVID-19 vaccines. However it is difficult to properly identify rare adverse events (AEs) associated with vaccines due to small or zero counts. We propose a Bayesian model with a Dirichlet Process Mixture prior to improve accuracy of the AE estimates with small counts by allowing data-guided information sharing between AE estimates. We also propose a negative control procedure embedded in our Bayesian model to mitigate the reporting bias due to the heightened awareness of COVID-19 vaccines, and use it to identify associated AEs as well as associated AE groups defined by the organ system in the Medical Dictionary for Regulatory Activities (MedDRA) ontology. The proposed model is evaluated using simulation studies, in which it outperforms baseline models without information sharing and is applied to study the safety of COVID-19 vaccines using VAERS data.

stat.ME↗

arXiv2023

Dynamic Response of Wigner Crystals

The Wigner crystal, an ordered array of electrons, is one of the very first proposed many-body phases stabilized by the electron-electron interaction. This electron solid phase has been reported in ultra-clean two-dimensional electron systems at extremely low temperatures, where the Coulomb interaction dominants over the kinetic energy, disorder potential and thermal fluctuation. We closely examine this quantum phase with capacitance measurements where the device length-scale is comparable with the crystal's correlation length. The extraordinarily high performance of our technique makes it possible to quantitatively study the dynamic response of the Wigner crystal within the single crystal regime. Our result will greatly boost the study of this inscrutable electron solid.

cond-mat.mes-hall↗

arXiv2022

CLIP2TV: Align, Match and Distill for Video-Text Retrieval

Modern video-text retrieval frameworks basically consist of three parts: video encoder, text encoder and the similarity head. With the success on both visual and textual representation learning, transformer based encoders and fusion methods have also been adopted in the field of video-text retrieval. In this report, we present CLIP2TV, aiming at exploring where the critical elements lie in transformer based methods. To achieve this, We first revisit some recent works on multi-modal learning, then introduce some techniques into video-text retrieval, finally evaluate them through extensive experiments in different configurations. Notably, CLIP2TV achieves 52.9@R1 on MSR-VTT dataset, outperforming the previous SOTA result by 4.1%.

cs.CV↗

arXiv2022

Bayesian learning of COVID-19 Vaccine safety while incorporating Adverse Events ontology

While vaccines are crucial to end the COVID-19 pandemic, public confidence in vaccine safety has always been vulnerable. Many statistical methods have been applied to VAERS (Vaccine Adverse Event Reporting System) database to study the safety of COVID-19 vaccines. However, all these methods ignored the adverse event (AE) ontology. AEs are naturally related; for example, events of retching, dysphagia, and reflux are all related to an abnormal digestive system. Explicitly bringing AE relationships into the model can aid in the detection of true AE signals amid the noise while reducing false positives. We propose a Bayesian graphical model to estimate all AEs while incorporating the AE ontology simultaneously. We proposed strategies to construct conjugate forms leading to an efficient Gibbs sampler. Built upon the posterior distributions, we proposed a negative control approach to mitigate reporting bias and an enrichment approach to detect AE groups of concern. The proposed methods were evaluated using simulation studies and were further illustrated on studying the safety of COVID-19 vaccines. The proposed methods were implemented in R package \textit{BGrass} and source code are available at https://github.com/BangyaoZhao/BGrass.

stat.ME↗

arXiv2022

High Precision, Low Excitation Capactiance Measurement Methods from 10 mK- to Room-Temperature

Capacitance measurement is a useful technique in studying quantum devices, as it directly probes the local particle charging properties, i.e. the system compressibility. Here we report one approach which can measure capacitance from mK to room temperature with excellent accuracy. Our experiments show that such a high-precision technique is able to reveal delicate and essential properties of high-mobility two-dimensional electron systems.

cond-mat.mes-hall↗

arXiv2022

Capacitive Response of Wigner Crystals at the Quantum Hall Plateau

In this report, we study ultra-high-mobility two-dimensional (2D) electron/hole systems with high precision capacitance measurement. We find that the capacitance charge appears only at the fringe of the gate at high magnetic field when the 2D conductivity decreases significantly. At integer quantum Hall effects, the capacitance vanishes and forms a plateau at high temperatures $T\gtrsim 300$ mK, which surprisingly, disappears at $T\lesssim 100$ mK. This anomalous behavior is likely a manifestation that the dilute particles/vacancies in the top-most Landau level form Wigner crystal, which has finite compressibility and can host polarization current.

cond-mat.mes-hall↗

arXiv2021

BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values

There has been increasing interest in modeling survival data using deep learning methods in medical research. In this paper, we proposed a Bayesian hierarchical deep neural networks model for modeling and prediction of survival data. Compared with previously studied methods, the new proposal can provide not only point estimate of survival probability but also quantification of the corresponding uncertainty, which can be of crucial importance in predictive modeling and subsequent decision making. The favorable statistical properties of point and uncertainty estimates were demonstrated by simulation studies and real data analysis. The Python code implementing the proposed approach was provided.

stat.ME↗

arXiv2021

Lossless Point Cloud Attribute Compression with Normal-based Intra Prediction

The sparse LiDAR point clouds become more and more popular in various applications, e.g., the autonomous driving. However, for this type of data, there exists much under-explored space in the corresponding compression framework proposed by MPEG, i.e., geometry-based point cloud compression (G-PCC). In G-PCC, only the distance-based similarity is considered in the intra prediction for the attribute compression. In this paper, we propose a normal-based intra prediction scheme, which provides a more efficient lossless attribute compression by introducing the normals of point clouds. The angle between normals is used to further explore accurate local similarity, which optimizes the selection of predictors. We implement our method into the G-PCC reference software. Experimental results over LiDAR acquired datasets demonstrate that our proposed method is able to deliver better compression performance than the G-PCC anchor, with $2.1\%$ gains on average for lossless attribute coding.

eess.IV↗

arXiv2021

RAI-Net: Range-Adaptive LiDAR Point Cloud Frame Interpolation Network

LiDAR point cloud frame interpolation, which synthesizes the intermediate frame between the captured frames, has emerged as an important issue for many applications. Especially for reducing the amounts of point cloud transmission, it is by predicting the intermediate frame based on the reference frames to upsample data to high frame rate ones. However, due to high-dimensional and sparse characteristics of point clouds, it is more difficult to predict the intermediate frame for LiDAR point clouds than videos. In this paper, we propose a novel LiDAR point cloud frame interpolation method, which exploits range images (RIs) as an intermediate representation with CNNs to conduct the frame interpolation process. Considering the inherited characteristics of RIs differ from that of color images, we introduce spatially adaptive convolutions to extract range features adaptively, while a high-efficient flow estimation method is presented to generate optical flows. The proposed model then warps the input frames and range features, based on the optical flows to synthesize the interpolated frame. Extensive experiments on the KITTI dataset have clearly demonstrated that our method consistently achieves superior frame interpolation results with better perceptual quality to that of using state-of-the-art video frame interpolation methods. The proposed method could be integrated into any LiDAR point cloud compression systems for inter prediction.

eess.IV↗

arXiv2021

An Unsupervised Optical Flow Estimation For LiDAR Image Sequences

In recent years, the LiDAR images, as a 2D compact representation of 3D LiDAR point clouds, are widely applied in various tasks, e.g., 3D semantic segmentation, LiDAR point cloud compression (PCC). Among these works, the optical flow estimation for LiDAR image sequences has become a key issue, especially for the motion estimation of the inter prediction in PCC. However, the existing optical flow estimation models are likely to be unreliable for LiDAR images. In this work, we first propose a light-weight flow estimation model for LiDAR image sequences. The key novelty of our method lies in two aspects. One is that for the different characteristics (with the spatial-variation feature distribution) of the LiDAR images w.r.t. the normal color images, we introduce the attention mechanism into our model to improve the quality of the estimated flow. The other one is that to tackle the lack of large-scale LiDAR-image annotations, we present an unsupervised method, which directly minimizes the inconsistency between the reference image and the reconstructed image based on the estimated optical flow. Extensive experimental results have shown that our proposed model outperforms other mainstream models on the KITTI dataset, with much fewer parameters.

eess.IV↗

arXiv2020

Data Mining in Large Frequency Tables With Ontology, with an Application to the Vaccine Adverse Event Reporting System

Vaccine safety is a concerning problem of the public, and many signal detecting methods have been developed to identify relative risks between vaccines and adverse events (AEs). Those methods usually focus on individual AEs, where the randomness of data is high. The results often turn out to be inaccurate and lack of clinical meaning. The AE ontology contains information about biological similarity of AEs. Based on this, we extend the concept of relative risks (RRs) to AE group level, which allows the possibility of more accurate and meaningful estimation by utilizing data from the whole group. In this paper, we propose the method zGPS.AO (Zero Inflated Gamma Poisson Shrinker with AE ontology) based on the zero inflated negative binomial distribution. This model has two purples: a regression model estimating group level RRs, and a empirical bayes framework to evaluate AE level RRs. The regression part can handle both excess zeros and over dispersion in the data, and the empirical method borrows information from both group level and AE level to reduce data noise and stabilize the AE level result. We have demonstrate the unbiaseness and low variance features of our model with simulated data, and obtained meaningful results coherent with previous studies on the VAERS (Vaccine Adverse Event Reporting System) database. The proposed methods are implemented in the R package zGPS.AO, which can be installed from the Comprehensive R Archive Network, CRAN. The results on VAERS data are visualized using the interactive web app Rshiny.

stat.AP↗

arXiv2020

A Bayesian Mixture Model for Changepoint Estimation Using Ordinal Predictors

In regression models, predictor variables with inherent ordering, such as tumor staging ranging and ECOG performance status, are commonly seen in medical settings. Statistically, it may be difficult to determine the functional form of an ordinal predictor variable. Often, such a variable is dichotomized based on whether it is above or below a certain cutoff. Other methods conveniently treat the ordinal predictor as a continuous variable and assume a linear relationship with the outcome. However, arbitrarily choosing a method may lead to inaccurate inference and treatment. In this paper, we propose a Bayesian mixture model to simultaneously assess the appropriate form of the predictor in regression models by considering the presence of a changepoint through the lens of a threshold detection problem. By using a mixture model framework to consider both dichotomous and linear forms for the variable, the estimate is a weighted average of linear and binary parameterizations. This method is applicable to continuous, binary, and survival outcomes, and easily amenable to penalized regression. We evaluated the proposed method using simulation studies and apply it to two real datasets. We provide JAGS code for easy implementation.

stat.ME↗

arXiv2020

Adverse event enrichment tests using VAERS

Vaccination safety is critical for individual and public health. Many existing methods have been used to conduct safety studies with the VAERS (Vaccine Adverse Event Reporting System) database. However, these methods frequently identify many adverse event (AE) signals and they are often hard to interpret in a biological context. The AE ontology introduces biologically meaningful structures to the VAERS database by connecting similar AEs, which provides meaningful interpretation for the underlying safety issues. In this paper, we develop rigorous statistical methods to identify "interesting" AE groups by performing AE enrichment analysis. We extend existing gene enrichment tests to perform AE enrichment analysis. Unlike the continuous gene expression data, AE data are counts. Therefore, AE data has many zeros and ties. We propose two enrichment tests, AEFisher and AEKS. AEFisher is a modified Fisher's exact test based on pre-selected significant AEs, while AEKS is based on a modified Kolmogorov-Smirnov statistic. Both tests incorporate the special features of the AE data. The proposed methods were evaluated using simulation studies and were further illustrated on two studies using VAERS data. By appropriately addressing the issues of ties and excessive zeros in AE count data, our enrichment tests performed well as demonstrated by simulation studies and analyses of VAERS data. The proposed methods were implemented in R package AEenrich and can be installed from the Comprehensive R Archive Network, CRAN.

stat.ME↗

arXiv2020

DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values

There has been increasing interest in modelling survival data using deep learning methods in medical research. Current approaches have focused on designing special cost functions to handle censored survival data. We propose a very different method with two steps. In the first step, we transform each subject's survival time into a series of jackknife pseudo conditional survival probabilities and then use these pseudo probabilities as a quantitative response variable in the deep neural network model. By using the pseudo values, we reduce a complex survival analysis to a standard regression problem, which greatly simplifies the neural network construction. Our two-step approach is simple, yet very flexible in making risk predictions for survival data, which is very appealing from the practice point of view. The source code is freely available at http://github.com/lilizhaoUM/DNNSurv.

stat.ML↗