arXiv subjects

Qiong Zhang

Publications and source records attributed to Qiong Zhang.

At least 73 records · Page 4Linked to original sources

arXiv2021

A Degradation Performance Model With Mixed-type Covariates and Latent Heterogeneity

Successful modeling of degradation performance data is essential for accurate reliability assessment and failure predictions of highly reliable product units. The degradation performance measurements over time are highly heterogeneous. Such heterogeneity can be partially attributed to external factors, such as accelerated/environmental conditions, and can also be attributed to internal factors, such as material microstructure characteristics of product units. The latent heterogeneity due to the unobserved/unknown factors shared within each product unit may also exists and need to be considered as well. Existing degradation models often fail to consider (i) the influence of both external accelerated/environmental conditions and internal material information, (ii) the influence of unobserved/unknown factors within each unit. In this work, we propose a generic degradation performance modeling framework with mixed-type covariates and latent heterogeneity to account for both influences of observed internal and external factors as well as unobserved factors. Effective estimation algorithm is also developed to jointly quantify the influences of mixed-type covariates and individual latent heterogeneity, and also to examine the potential interaction between mixed-type covariates. Functional data analysis and data augmentation techniques are employed to address a series of estimation issues. A real case study is further provided to demonstrate the superior performance of the proposed approach over several alternative modeling approaches. Besides, the proposed degradation performance modeling framework also provides interpretable findings.

stat.AP↗

arXiv2020

Classification Beats Regression: Counting of Cells from Greyscale Microscopic Images based on Annotation-free Training Samples

Modern methods often formulate the counting of cells from microscopic images as a regression problem and more or less rely on expensive, manually annotated training images (e.g., dot annotations indicating the centroids of cells or segmentation masks identifying the contours of cells). This work proposes a supervised learning framework based on classification-oriented convolutional neural networks (CNNs) to count cells from greyscale microscopic images without using annotated training images. In this framework, we formulate the cell counting task as an image classification problem, where the cell counts are taken as class labels. This formulation has its limitation when some cell counts in the test stage do not appear in the training data. Moreover, the ordinal relation among cell counts is not utilized. To deal with these limitations, we propose a simple but effective data augmentation (DA) method to synthesize images for the unseen cell counts. We also introduce an ensemble method, which can not only moderate the influence of unseen cell counts but also utilize the ordinal information to improve the prediction accuracy. This framework outperforms many modern cell counting methods and won the data analysis competition (Case Study 1: Counting Cells From Microscopic Images https://ssc.ca/en/case-study/case-study-1-counting-cells-microscopic-images) of the 47th Annual Meeting of the Statistical Society of Canada (SSC). Our code is available at https://github.com/anno2020/CellCount_TinyBBBC005.

eess.IV↗

arXiv2020

SemEval-2020 Task 5: Counterfactual Recognition

We present a counterfactual recognition (CR) task, the shared Task 5 of SemEval-2020. Counterfactuals describe potential outcomes (consequents) produced by actions or circumstances that did not happen or cannot happen and are counter to the facts (antecedent). Counterfactual thinking is an important characteristic of the human cognitive system; it connects antecedents and consequents with causal relations. Our task provides a benchmark for counterfactual recognition in natural language with two subtasks. Subtask-1 aims to determine whether a given sentence is a counterfactual statement or not. Subtask-2 requires the participating systems to extract the antecedent and consequent in a given counterfactual statement. During the SemEval-2020 official evaluation period, we received 27 submissions to Subtask-1 and 11 to Subtask-2. The data, baseline code, and leaderboard can be found at https://competitions.codalab.org/competitions/21691. The data and baseline code are also available at https://zenodo.org/record/3932442.

cs.CL↗

arXiv2020

Robust Layout-aware IE for Visually Rich Documents with Pre-trained Language Models

Many business documents processed in modern NLP and IR pipelines are visually rich: in addition to text, their semantics can also be captured by visual traits such as layout, format, and fonts. We study the problem of information extraction from visually rich documents (VRDs) and present a model that combines the power of large pre-trained language models and graph neural networks to efficiently encode both textual and visual information in business documents. We further introduce new fine-tuning objectives to improve in-domain unsupervised fine-tuning to better utilize large amount of unlabeled in-domain data. We experiment on real world invoice and resume data sets and show that the proposed method outperforms strong text-based RoBERTa baselines by 6.3% absolute F1 on invoices and 4.7% absolute F1 on resumes. When evaluated in a few-shot setting, our method requires up to 30x less annotation data than the baseline to achieve the same level of performance at ~90% F1.

cs.CL↗

arXiv2020

Review-based Question Generation with Adaptive Instance Transfer and Augmentation

Online reviews provide rich information about products and service, while it remains inefficient for potential consumers to exploit the reviews for fulfilling their specific information need. We propose to explore question generation as a new way of exploiting review information. One major challenge of this task is the lack of review-question pairs for training a neural generation model. We propose an iterative learning framework for handling this challenge via adaptive transfer and augmentation of the training instances with the help of the available user-posed question-answer data. To capture the aspect characteristics in reviews, the augmentation and generation procedures incorporate related features extracted via unsupervised learning. Experiments on data from 10 categories of a popular E-commerce site demonstrate the effectiveness of the framework, as well as the usefulness of the new task.

cs.IR↗

arXiv2020

Robust Optimal Design of Two-Armed Trials with Side Information

Significant evidence has become available that emphasizes the importance of personalization in medicine. In fact, it has become a common belief that personalized medicine is the future of medicine. The core of personalized medicine is the ability to design clinical trials that investigate the role of patient covariates on treatment effects. In this work, we study the optimal design of two-armed clinical trials to maximize accuracy of statistical models where the interaction between patient covariates and treatment effect are incorporated to enable precision medication. Such a modeling extension leads to significant complexities for the produced optimization problems because they include optimization over design and covariates concurrently. We take a robust optimization approach and minimize (over design) the maximum (over population) variance of interaction effect between treatment and patient covariates. This results in a min-max bi-level mixed integer nonlinear programming problem, which is notably challenging to solve. To address this challenge, we introduce a surrogate model by approximating the objective function for which we propose two solution approaches. The first approach provides an exact solution based on reformulation and decomposition techniques. In the second approach, we provide a lower bound for the inner optimization problem and solve the outer optimization problem over the lower bound. We test our proposed algorithms with synthetic and real-world data sets and compare it with standard (re-)randomization methods. Our numerical analysis suggests that the lower bounding approach provides high-quality solutions across a variety of settings.

stat.ME↗

arXiv2020

Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue Representation Learning

Multi-role dialogue understanding comprises a wide range of diverse tasks such as question answering, act classification, dialogue summarization etc. While dialogue corpora are abundantly available, labeled data, for specific learning tasks, can be highly scarce and expensive. In this work, we investigate dialogue context representation learning with various types unsupervised pretraining tasks where the training objectives are given naturally according to the nature of the utterance and the structure of the multi-role conversation. Meanwhile, in order to locate essential information for dialogue summarization/extraction, the pretraining process enables external knowledge integration. The proposed fine-tuned pretraining mechanism is comprehensively evaluated via three different dialogue datasets along with a number of downstream dialogue-mining tasks. Result shows that the proposed pretraining mechanism significantly contributes to all the downstream tasks without discrimination to different encoders.

cs.CL↗

arXiv2020

Sequential Selection for Accelerated Life Testing via Approximate Bayesian Inference

Accelerated life testing (ALT) is typically used to assess the reliability of material's lifetime under desired stress levels. Recent advances in material engineering have made a variety of material alternatives readily available. To identify the most reliable material setting with efficient experimental design, a sequential test planning strategy is preferred. To guarantee a tractable statistical mechanism for information collection and update, we develop explicit model parameter update formulas via approximate Bayesian inference. Theories show that our explicit update formulas give consistent parameter estimates. Simulation study and a case study show that the proposed sequential selection approach can significantly improve the probability of identifying the material alternative with best reliability performance over other design approaches.

stat.ME↗

arXiv2019

DEEM, a versatile platform of FRD measurement for highly multiplexed fibre systems in astronomy

We present a new method of DEEM, the direct energy encircling method, for characterising the performance of fibres in most astronomical spectroscopic applications. It's a versatile platform to measure focal ratio degradation (FRD), throughput, and point spread function (PSF). The principle of DEEM and the relation between the encircled energy (EE) and the spot size were derived and simulated based on the power distribution model (PDM). We analysed the errors of DEEM and pointed out the major error source for better understanding and optimisation. The validation of DEEM has been confirmed by comparing the results with conventional method which shows that DEEM has good robustness with high accuracy in both stable and complex experiment environments. Applications on the integral field unit (IFU) show that the FRD of 50$μ$m core fibre is substandard for the requirement which requires the output focal ratio to be slower than 4.5. The homogeneity of throughput is acceptable and higher than 85 per cent. The prototype IFU of the first generation helps to find out the imperfections to optimise the new design of the next generation based on the staggered structure with 35$μ$m core fibres of $N.A.$=0.12, which can improve the FRD performance. The FRD dependence on wavelength and core size is revealed that higher output focal ratio occurs at shorter wavelengths for large core fibres, which is in agreement with the prediction of PDM. But the dependence of the observed data is weaker than the prediction.

astro-ph.IM↗

arXiv2019

Blockchain-Assisted Spectrum Trading between Elastic Virtual Optical Networks

In communication networks, network virtualization can usually provide better capacity utilization and quality of service (QoS) than what can be achieved otherwise. However, conventional resource allocation for virtualized networks would still follow a fixed pattern based on the predicted capacity needs of the users, even though, in reality, the actual traffic demand of a user will always tend to fluctuate. The mismatch between the fixed capacity allocation and the actual fluctuating traffic would lead to degradation of provisioned network services and inefficiency in the assigned network capacity. To overcome this, we propose a new spectrum trading (ST) scheme between virtual optical networks (VONs) in the context of an elastic optical network (EON). The key idea here is to allow different VONs to trade their spectrum resources according to the actual capacity they need at different time instants. A VON with unused spectra can then trade away its unused spectra to other VONs that are short of spectrum resources at that time. In exchange, it is rewarded with a certain amount of credit for its contribution to the ST community, which it can then use later to get extra bandwidth, if needed. The trust-worthiness of the trading records between the VONs is ensured in a distributed fashion through a blockchain-assisted account book that is updated whenever a new trade occurs. For this, we develop a software-defined control plane to enable spectrum trading in an EON. The performance of the ST scheme is evaluated and compared with a scenario without such trading. Our results show that the proposed ST scheme is effective in improving the QoS of each VON and significantly improves the overall network capacity utilization.

cs.NI↗

arXiv2019

Rumor Detection on Social Media: Datasets, Methods and Opportunities

Social media platforms have been used for information and news gathering, and they are very valuable in many applications. However, they also lead to the spreading of rumors and fake news. Many efforts have been taken to detect and debunk rumors on social media by analyzing their content and social context using machine learning techniques. This paper gives an overview of the recent studies in the rumor detection field. It provides a comprehensive list of datasets used for rumor detection, and reviews the important studies based on what types of information they exploit and the approaches they take. And more importantly, we also present several new directions for future research.

cs.IR↗

arXiv2019

Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization

The number of personal stories about sexual harassment shared online has increased exponentially in recent years. This is in part inspired by the \#MeToo and \#TimesUp movements. Safecity is an online forum for people who experienced or witnessed sexual harassment to share their personal experiences. It has collected \textgreater 10,000 stories so far. Sexual harassment occurred in a variety of situations, and categorization of the stories and extraction of their key elements will provide great help for the related parties to understand and address sexual harassment. In this study, we manually annotated those stories with labels in the dimensions of location, time, and harassers' characteristics, and marked the key elements related to these dimensions. Furthermore, we applied natural language processing technologies with joint learning schemes to automatically categorize these stories in those dimensions and extract key elements at the same time. We also uncovered significant patterns from the categorized sexual harassment stories. We believe our annotated data set, proposed algorithms, and analysis will help people who have been harassed, authorities, researchers and other related parties in various ways, such as automatically filling reports, enlightening the public in order to prevent future harassment, and enabling more effective, faster action to be taken.

cs.CL↗

arXiv2019

A Pooled Quantile Estimator for Parallel Simulations

Quantile is an important risk measure quantifying the stochastic system random behaviors. This paper studies a pooled quantile estimator, which is the sample quantile of detailed simulation outputs after directly pooling independent sample paths together. We derive the asymptotic representation of the pooled quantile estimator and further prove its normality. By comparing with the classical quantile estimator used in stochastic simulation, both theoretical and empirical studies demonstrate the advantages of the proposal under the context of parallel simulation.

stat.ME↗

arXiv2019

Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation

The task of Chinese text spam detection is very challenging due to both glyph and phonetic variations of Chinese characters. This paper proposes a novel framework to jointly model Chinese variational, semantic, and contextualized representations for Chinese text spam detection task. In particular, a Variation Family-enhanced Graph Embedding (VFGE) algorithm is designed based on a Chinese character variation graph. The VFGE can learn both the graph embeddings of the Chinese characters (local) and the latent variation families (global). Furthermore, an enhanced bidirectional language model, with a combination gate function and an aggregation learning function, is proposed to integrate the graph and text information while capturing the sequential information. Extensive experiments have been conducted on both SMS and review datasets, to show the proposed method outperforms a series of state-of-the-art models for Chinese spam detection.

cs.CL↗

arXiv2019

Remote sensing of angular scattering effect of aerosols in a North American megacity

The angle-dependent scattering effect of aerosols in the atmosphere can be used to infer their compositions, which in turn is important to understand their impacts of human health and Earth climate. The aerosol phase function, which characterizes the angular signature of scattering, has been continuously monitored from ground-based and space-borne observations. However, the range of scattering angles these instruments can sample is very limited. There is a dearth of research on the remote sensing of aerosol angular scattering effect at a city scale that analyzes diurnal variability and includes a wide range of scattering angles. Here, we quantify the aerosol angular scattering effect using measurements from a mountain-top remote sensing instrument: the California Laboratory for Atmospheric Remote Sensing Fourier Transform Spectrometer (CLARS-FTS). CLARS-FTS is located on top of the Mt. Wilson (1.67km above sea level) overlooking the Los Angeles (LA) megacity and receives reflected sunlight from targeted surface reflection points. The observational geometries of CLARS-FTS provide a wide range of scattering angles, from about 20 degrees (forward) to about 140 degrees (backward). The O2 ratio, which is the ratio of retrieved O2 Slant Column Density (SCD) to geometric O2 SCD, quantifies the aerosol transmission with a value of 1.0 represent aerosol-free and with a value closer to 0.0 represents stronger aerosol loadings. The aerosol transmission quantified by the O2 ratio from CLARS measurements provides an effective indicator of the aerosol scattering effect.

physics.ao-ph↗

arXiv2019

Graph Convolution for Multimodal Information Extraction from Visually Rich Documents

Visually rich documents (VRDs) are ubiquitous in daily business and life. Examples are purchase receipts, insurance policy documents, custom declaration forms and so on. In VRDs, visual and layout information is critical for document understanding, and texts in such documents cannot be serialized into the one-dimensional sequence without losing information. Classic information extraction models such as BiLSTM-CRF typically operate on text sequences and do not incorporate visual features. In this paper, we introduce a graph convolution based model to combine textual and visual information presented in VRDs. Graph embeddings are trained to summarize the context of a text segment in the document, and further combined with text embeddings for entity extraction. Extensive experiments have been conducted to show that our method outperforms BiLSTM-CRF baselines by significant margins, on two real-world datasets. Additionally, ablation studies are also performed to evaluate the effectiveness of each component of our model.

cs.IR↗

arXiv2019

Multi-Instance Learning for End-to-End Knowledge Base Question Answering

End-to-end training has been a popular approach for knowledge base question answering (KBQA). However, real world applications often contain answers of varied quality for users' questions. It is not appropriate to treat all available answers of a user question equally. This paper proposes a novel approach based on multiple instance learning to address the problem of noisy answers by exploring consensus among answers to the same question in training end-to-end KBQA models. In particular, the QA pairs are organized into bags with dynamic instance selection and different options of instance weighting. Curriculum learning is utilized to select instance bags during training. On the public CQA dataset, the new method significantly improves both entity accuracy and the Rouge-L score over a state-of-the-art end-to-end KBQA baseline.

cs.CL↗

arXiv2019

D-optimal Design for Network A/B Testing

A/B testing refers to the statistical procedure of conducting an experiment to compare two treatments, A and B, applied to different testing subjects. It is widely used by technology companies such as Facebook, LinkedIn, and Netflix, to compare different algorithms, web-designs, and other online products and services. The subjects participating these online A/B testing experiments are users who are connected in different scales of social networks. Two connected subjects are similar in terms of their social behaviors, education and financial background, and other demographic aspects. Hence, it is only natural to assume that their reactions to the online products and services are related to their network adjacency. In this paper, we propose to use the conditional auto-regressive model to present the network structure and include the network effects in the estimation and inference of the treatment effect. A D-optimal design criterion is developed based on the proposed model. Mixed integer programming formulations are developed to obtain the D-optimal designs. The effectiveness of the proposed method is shown through numerical results with synthetic networks and real social networks.

stat.ME↗