arXiv subjects

Haoran Zhang

Publications and source records attributed to Haoran Zhang.

190 records · Page 11Linked to original sources

arXiv2020

Fault-tolerant and Transactional Stateful Serverless Workflows (extended version)

This paper introduces Beldi, a library and runtime system for writing and composing fault-tolerant and transactional stateful serverless functions. Beldi runs on existing providers and lets developers write complex stateful applications that require fault tolerance and transactional semantics without the need to deal with tasks such as load balancing or maintaining virtual machines. Beldi's contributions include extending the log-based fault-tolerant approach in Olive (OSDI 2016) with new data structures, transaction protocols, function invocations, and garbage collection. They also include adapting the resulting framework to work over a federated environment where each serverless function has sovereignty over its own data. We implement three applications on Beldi, including a movie review service, a travel reservation system, and a social media site. Our evaluation on 1,000 AWS Lambdas shows that Beldi's approach is effective and affordable.

cs.DC↗

arXiv2020

Unfolding-Model-Based Visualization: Theory, Method and Applications

Multidimensional unfolding methods are widely used for visualizing item response data. Such methods project respondents and items simultaneously onto a low-dimensional Euclidian space, in which respondents and items are represented by ideal points, with person-person, item-item, and person-item similarities being captured by the Euclidian distances between the points. In this paper, we study the visualization of multidimensional unfolding from a statistical perspective. We cast multidimensional unfolding into an estimation problem, where the respondent and item ideal points are treated as parameters to be estimated. An estimator is then proposed for the simultaneous estimation of these parameters. Asymptotic theory is provided for the recovery of the ideal points, shedding lights on the validity of model-based visualization. An alternating projected gradient descent algorithm is proposed for the parameter estimation. We provide two illustrative examples, one on users' movie rating and the other on senate roll call voting.

stat.ME↗

arXiv2020

Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring

While automated essay scoring (AES) can reliably grade essays at scale, automated writing evaluation (AWE) additionally provides formative feedback to guide essay revision. However, a neural AES typically does not provide useful feature representations for supporting AWE. This paper presents a method for linking AWE and neural AES, by extracting Topical Components (TCs) representing evidence from a source text using the intermediate output of attention layers. We evaluate performance using a feature-based AES requiring TCs. Results show that performance is comparable whether using automatically or manually constructed TCs for 1) representing essays as rubric-based features, 2) grading essays.

cs.CL↗

arXiv2020

Dose-response modeling in high-throughput cancer drug screenings: An end-to-end approach

Personalized cancer treatments based on the molecular profile of a patient's tumor are an emerging and exciting class of treatments in oncology. As genomic tumor profiling is becoming more common, targeted treatments to specific molecular alterations are gaining traction. To discover new potential therapeutics that may apply to broad classes of tumors matching some molecular pattern, experimentalists and pharmacologists rely on high-throughput, in-vitro screens of many compounds against many different cell lines. We propose a hierarchical Bayesian model of how cancer cell lines respond to drugs in these experiments and develop a method for fitting the model to real-world high-throughput screening data. Through a case study, the model is shown to capture nontrivial associations between molecular features and drug response, such as requiring both wild type TP53 and overexpression of MDM2 to be sensitive to Nutlin-3(a). In quantitative benchmarks, the model outperforms a standard approach in biology, with ~20% lower predictive error on held out data. When combined with a conditional randomization testing procedure, the model discovers biomarkers of therapeutic response that recapitulate known biology and suggest new avenues for investigation. All code for the paper is publicly available at https://github.com/tansey/deep-dose-response.

stat.AP↗

arXiv2020

A Note on Exploratory Item Factor Analysis by Singular Value Decomposition

We revisit a singular value decomposition (SVD) algorithm given in Chen et al. (2019b) for exploratory Item Factor Analysis (IFA). This algorithm estimates a multidimensional IFA model by SVD and was used to obtain a starting point for joint maximum likelihood estimation in Chen et al. (2019b). Thanks to the analytic and computational properties of SVD, this algorithm guarantees a unique solution and has computational advantage over other exploratory IFA methods. Its computational advantage becomes significant when the numbers of respondents, items, and factors are all large. This algorithm can be viewed as a generalization of principal component analysis (PCA) to binary data. In this note, we provide the statistical underpinning of the algorithm. In particular, we show its statistical consistency under the same double asymptotic setting as in Chen et al. (2019b). We also demonstrate how this algorithm provides a scree plot for investigating the number of factors and provide its asymptotic theory. Further extensions of the algorithm are discussed. Finally, simulation studies suggest that the algorithm has good finite sample performance.

stat.ME↗

arXiv2020

Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings

In this work, we examine the extent to which embeddings may encode marginalized populations differently, and how this may lead to a perpetuation of biases and worsened performance on clinical tasks. We pretrain deep embedding models (BERT) on medical notes from the MIMIC-III hospital dataset, and quantify potential disparities using two approaches. First, we identify dangerous latent relationships that are captured by the contextual word embeddings using a fill-in-the-blank method with text from real clinical notes and a log probability bias score quantification. Second, we evaluate performance gaps across different definitions of fairness on over 50 downstream clinical prediction tasks that include detection of acute and chronic conditions. We find that classifiers trained from BERT representations exhibit statistically significant differences in performance, often favoring the majority group with regards to gender, language, ethnicity, and insurance status. Finally, we explore shortcomings of using adversarial debiasing to obfuscate subgroup information in contextual word embeddings, and recommend best practices for such deep embedding models in clinical settings.

cs.CL↗

arXiv2019

eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing

Writing a good essay typically involves students revising an initial paper draft after receiving feedback. We present eRevise, a web-based writing and revising environment that uses natural language processing features generated for rubric-based essay scoring to trigger formative feedback messages regarding students' use of evidence in response-to-text writing. By helping students understand the criteria for using text evidence during writing, eRevise empowers students to better revise their paper drafts. In a pilot deployment of eRevise in 7 classrooms spanning grades 5 and 6, the quality of text evidence usage in writing improved after students received formative feedback then engaged in paper revision.

cs.CL↗

arXiv2019

Word Embedding for Response-To-Text Assessment of Evidence

Manually grading the Response to Text Assessment (RTA) is labor intensive. Therefore, an automatic method is being developed for scoring analytical writing when the RTA is administered in large numbers of classrooms. Our long-term goal is to also use this scoring method to provide formative feedback to students and teachers about students' writing quality. As a first step towards this goal, interpretable features for automatically scoring the evidence rubric of the RTA have been developed. In this paper, we present a simple but promising method for improving evidence scoring by employing the word embedding model. We evaluate our method on corpora of responses written by upper elementary students.

cs.CL↗

arXiv2019

Co-Attention Based Neural Network for Source-Dependent Essay Scoring

This paper presents an investigation of using a co-attention based neural network for source-dependent essay scoring. We use a co-attention mechanism to help the model learn the importance of each part of the essay more accurately. Also, this paper shows that the co-attention based neural network model provides reliable score prediction of source-dependent responses. We evaluate our model on two source-dependent response corpora. Results show that our model outperforms the baseline on both corpora. We also show that the attention of the model is similar to the expert opinions with examples.

cs.CL↗

arXiv2014

High-T_c superconductivity in ultrathin Bi_2Sr_2CaCu_2O_8+x down to halfunit-cell thickness by protection with graphene

High-T_c superconductors confined to two dimension exhibit novel physical phenomena, such as superconductor-insulator transition. In the Bi_2Sr_2CaCu_2O_8+x (Bi2212) model system, despite extensive studies, the intrinsic superconducting properties at the thinness limit have been difficult to determine. Here we report a method to fabricate high quality single-crystal Bi2212 films down to half-unit-cell thickness in the form of graphene/Bi2212 van der Waals heterostructure, in which sharp superconducting transitions are observed. The heterostructure also exhibits a nonlinear current-voltage characteristic due to the Dirac nature of the graphene band structure. More interestingly, although the critical temperature remains essentially the same with reduced thickness of Bi2212, the slope of the normal state T-linear resistivity varies by a factor of 4-5, and the sheet resistance increases by three orders of magnitude, indicating a surprising decoupling of the normal state resistance and superconductivity. The developed technique is versatile, applicable to investigate other two-dimensional (2D) superconducting materials.

cond-mat.supr-con↗