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Article: Extending repair in peer interaction: A conversation analytic study.

Chen, Mia Huimin / Ye, Shelly Xueting

2022 Volume 13, Page(s) 926842

Abstract: Peer interaction constitutes a focal site for understanding learning orientations and autonomous learning behaviors. Based on 10 h of video-recorded data collected from small-size conversation-for-learning classes, this study, through the lens of ... ...

Abstract	Peer interaction constitutes a focal site for understanding learning orientations and autonomous learning behaviors. Based on 10 h of video-recorded data collected from small-size conversation-for-learning classes, this study, through the lens of Conversation Analysis, analyzes instances in which L2 learners spontaneously exploit learning opportunities from the on-task public talk and make them relevant for private learning in sequential private peer interaction. The analysis of extended negation-for-meaning practices in peer interaction displays how L2 learners orient to public repair for their learning opportunities in an immediate manner and in so doing, how different participation framework is being utilized to maximize their learning outcomes. As these extended repair practices are entirely managed by learners themselves, they yield both efficient and inefficient learning outcomes. Findings reveal that learners frequently resort to their peers to recycle the focal trouble words for learning opportunities, shifting their participating role from the on looking audience to active learners. By reporting the rather under-researched post-repair negotiation-for-meaning sequence in peer interactions, the study highlights the relevance between on-task classroom activities and private learning, contributing to understanding private learning behaviors in the language classroom and learning as a co-constructed activity locally situated in peer interaction.
Language	English
Publishing date	2022-08-29
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2563826-9
ISSN	1664-1078
ISSN	1664-1078
DOI	10.3389/fpsyg.2022.926842
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Evaluating a Targeted Minimum Loss-Based Estimator for Capture-Recapture Analysis: An Application to HIV Surveillance in San Francisco, California.

Wesson, Paul / Das, Manjari / Chen, Mia / Hsu, Ling / McFarland, Willi / Kennedy, Edward / Jewell, Nicholas P

American journal of epidemiology

2023 Volume 193, Issue 4, Page(s) 673–683

Abstract: The capture-recapture method is a common tool used in epidemiology to estimate the size of "hidden" populations and correct the underascertainment of cases, based on incomplete and overlapping lists of the target population. Log-linear models are often ... ...

Abstract	The capture-recapture method is a common tool used in epidemiology to estimate the size of "hidden" populations and correct the underascertainment of cases, based on incomplete and overlapping lists of the target population. Log-linear models are often used to estimate the population size yet may produce implausible and unreliable estimates due to model misspecification and small cell sizes. A novel targeted minimum loss-based estimation (TMLE) model developed for capture-recapture makes several notable improvements to conventional modeling: "targeting" the parameter of interest, flexibly fitting the data to alternative functional forms, and limiting bias from small cell sizes. Using simulations and empirical data from the San Francisco, California, Department of Public Health's human immunodeficiency virus (HIV) surveillance registry, we evaluated the performance of the TMLE model and compared results with those of other common models. Based on 2,584 people observed on 3 lists reportable to the surveillance registry, the TMLE model estimated the number of San Francisco residents living with HIV as of December 31, 2019, to be 13,523 (95% confidence interval: 12,222, 14,824). This estimate, compared with a "ground truth" of 12,507, was the most accurate and precise of all models examined. The TMLE model is a significant advancement in capture-recapture studies, leveraging modern statistical methods to improve estimation of the sizes of hidden populations.
MeSH term(s)	Humans ; HIV ; San Francisco/epidemiology ; Linear Models ; Bias ; HIV Infections/epidemiology
Language	English
Publishing date	2023-11-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	2937-3
ISSN	1476-6256 ; 0002-9262
ISSN (online)	1476-6256
ISSN	0002-9262
DOI	10.1093/aje/kwad231
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Book ; Online: Faster Transformer Decoding

Chelba, Ciprian / Chen, Mia / Bapna, Ankur / Shazeer, Noam

N-gram Masked Self-Attention

2020

Abstract: Motivated by the fact that most of the information relevant to the prediction of target tokens is drawn from the source sentence $S=s_1, \ldots, s_S$, we propose truncating the target-side window used for computing self-attention by making an $N$-gram ... ...

Abstract	Motivated by the fact that most of the information relevant to the prediction of target tokens is drawn from the source sentence $S=s_1, \ldots, s_S$, we propose truncating the target-side window used for computing self-attention by making an $N$-gram assumption. Experiments on WMT EnDe and EnFr data sets show that the $N$-gram masked self-attention model loses very little in BLEU score for $N$ values in the range $4, \ldots, 8$, depending on the task.
Keywords	Computer Science - Machine Learning ; Computer Science - Computation and Language ; Statistics - Machine Learning
Publishing date	2020-01-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Development of Authenticated Clients and Applications for ICICLE CI Services -- Final Report for the REHS Program, June-August, 2022

Samar, Sahil / Chen, Mia / Karpinski, Jack / Ray, Michael / Sarin, Archita / Garcia, Christian / Lange, Matthew / Stubbs, Joe / Thomas, Mary

2023

Abstract: The Artificial Intelligence (AI) institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE) is funded by the NSF to build the next generation of Cyberinfrastructure to render AI more accessible to everyone and ... ...

Abstract	The Artificial Intelligence (AI) institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE) is funded by the NSF to build the next generation of Cyberinfrastructure to render AI more accessible to everyone and drive its further democratization in the larger society. We describe our efforts to develop Jupyter Notebooks and Python command line clients that would access these ICICLE resources and services using ICICLE authentication mechanisms. To connect our clients, we used Tapis, which is a framework that supports computational research to enable scientists to access, utilize, and manage multi-institution resources and services. We used Neo4j to organize data into a knowledge graph (KG). We then hosted the KG on a Tapis Pod, which offers persistent data storage with a template made specifically for Neo4j KGs. In order to demonstrate the capabilities of our software, we developed several clients: Jupyter notebooks authentication, Neural Networks (NN) notebook, and command line applications that provide a convenient frontend to the Tapis API. In addition, we developed a data processing notebook that can manipulate KGs on the Tapis servers, including creations of a KG, data upload and modification. In this report we present the software architecture, design and approach, the successfulness of our client software, and future work.
Keywords	Computer Science - Cryptography and Security ; Computer Science - Artificial Intelligence ; I.2.8 ; F.2.2
Subject code	020
Publishing date	2023-04-16
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Rapid Domain Adaptation for Machine Translation with Monolingual Data

Mahdieh, Mahdis / Chen, Mia Xu / Cao, Yuan / Firat, Orhan

Abstract: One challenge of machine translation is how to quickly adapt to unseen domains in face of surging events like COVID-19, in which case timely and accurate translation of in-domain information into multiple languages is critical but little parallel data is ...

Abstract	One challenge of machine translation is how to quickly adapt to unseen domains in face of surging events like COVID-19, in which case timely and accurate translation of in-domain information into multiple languages is critical but little parallel data is available yet. In this paper, we propose an approach that enables rapid domain adaptation from the perspective of unsupervised translation. Our proposed approach only requires in-domain monolingual data and can be quickly applied to a preexisting translation system trained on general domain, reaching significant gains on in-domain translation quality with little or no drop on general-domain. We also propose an effective procedure of simultaneous adaptation for multiple domains and languages. To the best of our knowledge, this is the first attempt that aims to address unsupervised multilingual domain adaptation.
Keywords	covid19
Publisher	ArXiv
Document type	Article
Database	COVID19

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Book ; Online: Rapid Domain Adaptation for Machine Translation with Monolingual Data

Mahdieh, Mahdis / Chen, Mia Xu / Cao, Yuan / Firat, Orhan

2020

Abstract	One challenge of machine translation is how to quickly adapt to unseen domains in face of surging events like COVID-19, in which case timely and accurate translation of in-domain information into multiple languages is critical but little parallel data is available yet. In this paper, we propose an approach that enables rapid domain adaptation from the perspective of unsupervised translation. Our proposed approach only requires in-domain monolingual data and can be quickly applied to a preexisting translation system trained on general domain, reaching significant gains on in-domain translation quality with little or no drop on general-domain. We also propose an effective procedure of simultaneous adaptation for multiple domains and languages. To the best of our knowledge, this is the first attempt that aims to address unsupervised multilingual domain adaptation.
Keywords	Computer Science - Computation and Language
Publishing date	2020-10-23
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Results of a metabolic health clinic at a hostel for homeless men.

Nielssen, Olav / Chudleigh, Alan / Chen, Mia / Large, Matthew / Markovic, Tania / Cooper, Lucy

Australasian psychiatry : bulletin of Royal Australian and New Zealand College of Psychiatrists

2017 Volume 25, Issue 3, Page(s) 270–273

Abstract: Objectives: People who are homeless have high mortality and morbidity, including from metabolic disorder. The aim of this study was to report on the characteristics and progress of the metabolic health of people attending a metabolic clinic at a ... ...

Abstract	Objectives: People who are homeless have high mortality and morbidity, including from metabolic disorder. The aim of this study was to report on the characteristics and progress of the metabolic health of people attending a metabolic clinic at a homeless men's shelter. Methods: Homeless men attending the clinic were assessed by measuring their weight, height, body mass index (BMI), waist circumference, blood pressure, blood lipids, fasting blood glucose and, if indicated, HbA1c. The sample characteristics of people who attended once (one-off clients) were compared to those who attended on more than one occasion (returning clients). Changes in health status were examined among returning clients by comparing baseline results to those at their last clinic visit. Results: Baseline data were recorded on a total of 136 men, of whom 126 had a consultation with a general practitioner and at least one blood test. The 136 clients had a median BMI of 27.4 kg/m Conclusions: Homeless people in Sydney appear to be at a high risk of metabolic disease. The feasibility of a metabolic health clinic was demonstrated, and an encouraging improvement in some health indicators was found.
MeSH term(s)	Adult ; Aged ; Ambulatory Care Facilities/statistics & numerical data ; Diabetes Mellitus/diagnosis ; Diabetes Mellitus/epidemiology ; Homeless Persons/statistics & numerical data ; Housing/statistics & numerical data ; Humans ; Male ; Mental Disorders/epidemiology ; Metabolic Syndrome/diagnosis ; Metabolic Syndrome/epidemiology ; Middle Aged ; New South Wales/epidemiology ; Obesity/diagnosis ; Obesity/epidemiology
Language	English
Publishing date	2017-03-01
Publishing country	England
Document type	Journal Article
ZDB-ID	2213198-X
ISSN	1440-1665 ; 1039-8562
ISSN (online)	1440-1665
ISSN	1039-8562
DOI	10.1177/1039856217695705
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Article: Differential expression of VEGFR2 protein in HER2 positive primary human breast cancer: potential relevance to anti-angiogenic therapies.

Nasir, Aejaz / Holzer, Timothy R / Chen, Mia / Man, Michael Z / Schade, Andrew E

Cancer cell international

2017 Volume 17, Page(s) 56

Abstract: Background: Clinically relevant predictive biomarkers to tailor anti-angiogenic therapies to breast cancer (BRC) patient subpopulations are an unmet need.: Methods: We analyzed tumor vascular density and VEGFR2 protein expression in various subsets ... ...

Abstract	Background: Clinically relevant predictive biomarkers to tailor anti-angiogenic therapies to breast cancer (BRC) patient subpopulations are an unmet need. Methods: We analyzed tumor vascular density and VEGFR2 protein expression in various subsets of primary human BRCs (186 females; Mean age: 59 years; range 33-88 years), using a tissue microarray. Discrete VEGFR2+ and CD34+ tumor vessels were manually scored in invasive ductal, lobular, mixed ductal-lobular and colloid (N = 139, 22, 18, 7) BRC cores. Results: The observed CD34+ and VEGFR2+ tumor vascular counts in individual cases were heterogeneous. Mean CD34+ and VEGFR2+ tumor vessel counts were 11 and 3.4 per tumor TMA core respectively. Eighty-nine of 186 (48%) cases had >10 CD34+ tumor vessels, while 97/186 (52%) had fewer CD34+ vessels in each TMA core. Of 169 analyzable cores in the VEGFR2 stained TMA, 90 (53%) showed 1-5 VEGFR2+ tumor vessels/TMA core, while 42/169 (25%) cores had no detectable VEGFR2+ tumor vessels. Thirteen of 169 (8%) cases also showed tumor cell (cytoplasmic/membrane) expression of VEGFR2. Triple-negative breast cancers (TNBCs) appeared to be less vascular (Mean VD = 9.8, range 0-34) than other breast cancer subtypes. Overall, VEGFR2+ tumor vessel counts were significantly higher in HER2+ as compared to HR+ (p = 0.04) and TNBC (p = 0.02) tissues. Compared to HER2- cases, HER2+ breast cancers had higher VEGFR2+ tumor vessel counts (p = 0.007). Conclusion: Characterization of pathologic angiogenesis in HER2+ breast cancer provides scientific rationale for future investigation of clinical activity of agents targeting the VEGF/VEGFR2 axis in this clinically aggressive breast cancer subtype.
Language	English
Publishing date	2017-05-19
Publishing country	England
Document type	Journal Article
ISSN	1475-2867
ISSN	1475-2867
DOI	10.1186/s12935-017-0427-5
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation

Siddhant, Aditya / Bapna, Ankur / Cao, Yuan / Firat, Orhan / Chen, Mia / Kudugunta, Sneha / Arivazhagan, Naveen / Wu, Yonghui

2020

Abstract: Over the last few years two promising research directions in low-resource neural machine translation (NMT) have emerged. The first focuses on utilizing high-resource languages to improve the quality of low-resource languages via multilingual NMT. The ... ...

Abstract	Over the last few years two promising research directions in low-resource neural machine translation (NMT) have emerged. The first focuses on utilizing high-resource languages to improve the quality of low-resource languages via multilingual NMT. The second direction employs monolingual data with self-supervision to pre-train translation models, followed by fine-tuning on small amounts of supervised data. In this work, we join these two lines of research and demonstrate the efficacy of monolingual data with self-supervision in multilingual NMT. We offer three major results: (i) Using monolingual data significantly boosts the translation quality of low-resource languages in multilingual models. (ii) Self-supervision improves zero-shot translation quality in multilingual models. (iii) Leveraging monolingual data with self-supervision provides a viable path towards adding new languages to multilingual models, getting up to 33 BLEU on ro-en translation without any parallel data or back-translation.
Keywords	Computer Science - Computation and Language ; Computer Science - Machine Learning
Subject code	410
Publishing date	2020-05-10
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Building Machine Translation Systems for the Next Thousand Languages

2022

Abstract: In this paper we share findings from our effort to build practical machine translation (MT) systems capable of translating across over one thousand languages. We describe results in three research domains: (i) Building clean, web-mined datasets for 1500+ ...

Abstract	In this paper we share findings from our effort to build practical machine translation (MT) systems capable of translating across over one thousand languages. We describe results in three research domains: (i) Building clean, web-mined datasets for 1500+ languages by leveraging semi-supervised pre-training for language identification and developing data-driven filtering techniques; (ii) Developing practical MT models for under-served languages by leveraging massively multilingual models trained with supervised parallel data for over 100 high-resource languages and monolingual datasets for an additional 1000+ languages; and (iii) Studying the limitations of evaluation metrics for these languages and conducting qualitative analysis of the outputs from our MT models, highlighting several frequent error modes of these types of models. We hope that our work provides useful insights to practitioners working towards building MT systems for currently understudied languages, and highlights research directions that can complement the weaknesses of massively multilingual models in data-sparse settings. Comment: V2: updated with some details from 24-language Google Translate launch in May 2022 V3: spelling corrections, additional acknowledgements
Keywords	Computer Science - Computation and Language ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning
Subject code	410
Publishing date	2022-05-08
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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