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6. Competition between group interactions and nonlinearity in voter dynamics on hypergraphs

Author

Kim, Jihye, Lee, Deok-Sun, Min, Byungjoon, Porter, Mason A., Miguel, Maxi San, and Goh, K. -I.

Subjects
Physics - Physics and Society, Computer Science - Social and Information Networks, Mathematics - Dynamical Systems, Nonlinear Sciences - Adaptation and Self-Organizing Systems
Abstract

Social dynamics are often driven by both pairwise (i.e., dyadic) relationships and higher-order (i.e., polyadic) group relationships, which one can describe using hypergraphs. To gain insight into the impact of polyadic relationships on dynamical processes on networks, we formulate and study a polyadic voter process, which we call the group-driven voter model (GVM), in which we incorporate the effect of group interactions by nonlinear interactions that are subject to a group (i.e., hyperedge) constraint. By examining the competition between nonlinearity and group sizes, we show that the GVM achieves consensus faster than standard voter-model dynamics, with an optimum minimizing exit time {\tau} . We substantiate this finding by using mean-field theory on annealed uniform hypergraphs with N nodes, for which {\tau} scales as A ln N, where the prefactor A depends both on the nonlinearity and on group-constraint factors. Our results reveal how competition between group interactions and nonlinearity shapes GVM dynamics. We thereby highlight the importance of such competing effects in complex systems with polyadic interactions., Comment: 6 pages, 5 figures

Published
2024

7. HyperCLOVA X Technical Report

Author

Yoo, Kang Min, Han, Jaegeun, In, Sookyo, Jeon, Heewon, Jeong, Jisu, Kang, Jaewook, Kim, Hyunwook, Kim, Kyung-Min, Kim, Munhyong, Kim, Sungju, Kwak, Donghyun, Kwak, Hanock, Kwon, Se Jung, Lee, Bado, Lee, Dongsoo, Lee, Gichang, Lee, Jooho, Park, Baeseong, Shin, Seongjin, Yu, Joonsang, Baek, Seolki, Byeon, Sumin, Cho, Eungsup, Choe, Dooseok, Han, Jeesung, Jin, Youngkyun, Jun, Hyein, Jung, Jaeseung, Kim, Chanwoong, Kim, Jinhong, Kim, Jinuk, Lee, Dokyeong, Park, Dongwook, Sohn, Jeong Min, Han, Sujung, Heo, Jiae, Hong, Sungju, Jeon, Mina, Jung, Hyunhoon, Jung, Jungeun, Jung, Wangkyo, Kim, Chungjoon, Kim, Hyeri, Kim, Jonghyun, Kim, Min Young, Lee, Soeun, Park, Joonhee, Shin, Jieun, Yang, Sojin, Yoon, Jungsoon, Lee, Hwaran, Bae, Sanghwan, Cha, Jeehwan, Gylleus, Karl, Ham, Donghoon, Hong, Mihak, Hong, Youngki, Hong, Yunki, Jang, Dahyun, Jeon, Hyojun, Jeon, Yujin, Jeong, Yeji, Ji, Myunggeun, Jin, Yeguk, Jo, Chansong, Joo, Shinyoung, Jung, Seunghwan, Kim, Adrian Jungmyung, Kim, Byoung Hoon, Kim, Hyomin, Kim, Jungwhan, Kim, Minkyoung, Kim, Minseung, Kim, Sungdong, Kim, Yonghee, Kim, Youngjun, Kim, Youngkwan, Ko, Donghyeon, Lee, Dughyun, Lee, Ha Young, Lee, Jaehong, Lee, Jieun, Lee, Jonghyun, Lee, Jongjin, Lee, Min Young, Lee, Yehbin, Min, Taehong, Min, Yuri, Moon, Kiyoon, Oh, Hyangnam, Park, Jaesun, Park, Kyuyon, Park, Younghun, Seo, Hanbae, Seo, Seunghyun, Sim, Mihyun, Son, Gyubin, Yeo, Matt, Yeom, Kyung Hoon, Yoo, Wonjoon, You, Myungin, Ahn, Doheon, Ahn, Homin, Ahn, Joohee, Ahn, Seongmin, An, Chanwoo, An, Hyeryun, An, Junho, An, Sang-Min, Byun, Boram, Byun, Eunbin, Cha, Jongho, Chang, Minji, Chang, Seunggyu, Cho, Haesong, Cho, Youngdo, Choi, Dalnim, Choi, Daseul, Choi, Hyoseok, Choi, Minseong, Choi, Sangho, Choi, Seongjae, Choi, Wooyong, Chun, Sewhan, Go, Dong Young, Ham, Chiheon, Han, Danbi, Han, Jaemin, Hong, Moonyoung, Hong, Sung Bum, Hwang, Dong-Hyun, Hwang, Seongchan, Im, Jinbae, Jang, Hyuk Jin, Jang, Jaehyung, Jang, Jaeni, Jang, Sihyeon, Jang, Sungwon, Jeon, Joonha, Jeong, Daun, Jeong, Joonhyun, Jeong, Kyeongseok, Jeong, Mini, Jin, Sol, Jo, Hanbyeol, Jo, Hanju, Jo, Minjung, Jung, Chaeyoon, Jung, Hyungsik, Jung, Jaeuk, Jung, Ju Hwan, Jung, Kwangsun, Jung, Seungjae, Ka, Soonwon, Kang, Donghan, Kang, Soyoung, Kil, Taeho, Kim, Areum, Kim, Beomyoung, Kim, Byeongwook, Kim, Daehee, Kim, Dong-Gyun, Kim, Donggook, Kim, Donghyun, Kim, Euna, Kim, Eunchul, Kim, Geewook, Kim, Gyu Ri, Kim, Hanbyul, Kim, Heesu, Kim, Isaac, Kim, Jeonghoon, Kim, Jihye, Kim, Joonghoon, Kim, Minjae, Kim, Minsub, Kim, Pil Hwan, Kim, Sammy, Kim, Seokhun, Kim, Seonghyeon, Kim, Soojin, Kim, Soong, Kim, Soyoon, Kim, Sunyoung, Kim, Taeho, Kim, Wonho, Kim, Yoonsik, Kim, You Jin, Kim, Yuri, Kwon, Beomseok, Kwon, Ohsung, Kwon, Yoo-Hwan, Lee, Anna, Lee, Byungwook, Lee, Changho, Lee, Daun, Lee, Dongjae, Lee, Ha-Ram, Lee, Hodong, Lee, Hwiyeong, Lee, Hyunmi, Lee, Injae, Lee, Jaeung, Lee, Jeongsang, Lee, Jisoo, Lee, Jongsoo, Lee, Joongjae, Lee, Juhan, Lee, Jung Hyun, Lee, Junghoon, Lee, Junwoo, Lee, Se Yun, Lee, Sujin, Lee, Sungjae, Lee, Sungwoo, Lee, Wonjae, Lee, Zoo Hyun, Lim, Jong Kun, Lim, Kun, Lim, Taemin, Na, Nuri, Nam, Jeongyeon, Nam, Kyeong-Min, Noh, Yeonseog, Oh, Biro, Oh, Jung-Sik, Oh, Solgil, Oh, Yeontaek, Park, Boyoun, Park, Cheonbok, Park, Dongju, Park, Hyeonjin, Park, Hyun Tae, Park, Hyunjung, Park, Jihye, Park, Jooseok, Park, Junghwan, Park, Jungsoo, Park, Miru, Park, Sang Hee, Park, Seunghyun, Park, Soyoung, Park, Taerim, Park, Wonkyeong, Ryu, Hyunjoon, Ryu, Jeonghun, Ryu, Nahyeon, Seo, Soonshin, Seo, Suk Min, Shim, Yoonjeong, Shin, Kyuyong, Shin, Wonkwang, Sim, Hyun, Sim, Woongseob, Soh, Hyejin, Son, Bokyong, Son, Hyunjun, Son, Seulah, Song, Chi-Yun, Song, Chiyoung, Song, Ka Yeon, Song, Minchul, Song, Seungmin, Wang, Jisung, Yeo, Yonggoo, Yi, Myeong Yeon, Yim, Moon Bin, Yoo, Taehwan, Yoo, Youngjoon, Yoon, Sungmin, Yoon, Young Jin, Yu, Hangyeol, Yu, Ui Seon, Zuo, Xingdong, Bae, Jeongin, Bae, Joungeun, Cho, Hyunsoo, Cho, Seonghyun, Cho, Yongjin, Choi, Taekyoon, Choi, Yera, Chung, Jiwan, Han, Zhenghui, Heo, Byeongho, Hong, Euisuk, Hwang, Taebaek, Im, Seonyeol, Jegal, Sumin, Jeon, Sumin, Jeong, Yelim, Jeong, Yonghyun, Jiang, Can, Jiang, Juyong, Jin, Jiho, Jo, Ara, Jo, Younghyun, Jung, Hoyoun, Jung, Juyoung, Kang, Seunghyeong, Kim, Dae Hee, Kim, Ginam, Kim, Hangyeol, Kim, Heeseung, Kim, Hyojin, Kim, Hyojun, Kim, Hyun-Ah, Kim, Jeehye, Kim, Jin-Hwa, Kim, Jiseon, Kim, Jonghak, Kim, Jung Yoon, Kim, Rak Yeong, Kim, Seongjin, Kim, Seoyoon, Kim, Sewon, Kim, Sooyoung, Kim, Sukyoung, Kim, Taeyong, Ko, Naeun, Koo, Bonseung, Kwak, Heeyoung, Kwon, Haena, Kwon, Youngjin, Lee, Boram, Lee, Bruce W., Lee, Dagyeong, Lee, Erin, Lee, Euijin, Lee, Ha Gyeong, Lee, Hyojin, Lee, Hyunjeong, Lee, Jeeyoon, Lee, Jeonghyun, Lee, Jongheok, Lee, Joonhyung, Lee, Junhyuk, Lee, Mingu, Lee, Nayeon, Lee, Sangkyu, Lee, Se Young, Lee, Seulgi, Lee, Seung Jin, Lee, Suhyeon, Lee, Yeonjae, Lee, Yesol, Lee, Youngbeom, Lee, Yujin, Li, Shaodong, Liu, Tianyu, Moon, Seong-Eun, Moon, Taehong, Nihlenramstroem, Max-Lasse, Oh, Wonseok, Oh, Yuri, Park, Hongbeen, Park, Hyekyung, Park, Jaeho, Park, Nohil, Park, Sangjin, Ryu, Jiwon, Ryu, Miru, Ryu, Simo, Seo, Ahreum, Seo, Hee, Seo, Kangdeok, Shin, Jamin, Shin, Seungyoun, Sin, Heetae, Wang, Jiangping, Wang, Lei, Xiang, Ning, Xiao, Longxiang, Xu, Jing, Yi, Seonyeong, Yoo, Haanju, Yoo, Haneul, Yoo, Hwanhee, Yu, Liang, Yu, Youngjae, Yuan, Weijie, Zeng, Bo, Zhou, Qian, Cho, Kyunghyun, Ha, Jung-Woo, Park, Joonsuk, Hwang, Jihyun, Kwon, Hyoung Jo, Kwon, Soonyong, Lee, Jungyeon, Lee, Seungho, Lim, Seonghyeon, Noh, Hyunkyung, Choi, Seungho, Lee, Sang-Woo, Lim, Jung Hwa, and Sung, Nako

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs., Comment: 44 pages; updated authors list and fixed author names

Published
2024

9. Chapter Media art creation process using digitized archetype of Korean traditional dance movement

Author

Kim, Jihye and Yoon, Woolahm

Subjects
Korean Culture, Traditional Dance Movement, Use of Archetype, Media Art Creation Process, bic Book Industry Communication::W Lifestyle, sport & leisure::WJ Lifestyle & personal style guides::WJK Interior design, decor & style guides, bic Book Industry Communication::G Reference, information & interdisciplinary subjects::GM Museology & heritage studies
Abstract

Transport: general interest

Published
2023
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10. ATF7IP2/MCAF2 directs H3K9 methylation and meiotic gene regulation in the male germline

Author

Alavattam, Kris G, Esparza, Jasmine M, Hu, Mengwen, Shimada, Ryuki, Kohrs, Anna R, Abe, Hironori, Munakata, Yasuhisa, Otsuka, Kai, Yoshimura, Saori, Kitamura, Yuka, Yeh, Yu-Han, Hu, Yueh-Chiang, Kim, Jihye, Andreassen, Paul R, Ishiguro, Kei-Ichiro, and Namekawa, Satoshi H

Subjects
Biochemistry and Cell Biology, Genetics, Biological Sciences, Biotechnology, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, Generic health relevance, Male, Germ Cells, Heterochromatin, Histones, Meiosis, Methylation, Animals, Mice, ATF7IP2/MCAF2, H3K9me3, constitutive heterochromatin, gene activation, meiosis, meiotic sex chromosome inactivation, Medical and Health Sciences, Psychology and Cognitive Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences, Psychology
Abstract

H3K9 trimethylation (H3K9me3) plays emerging roles in gene regulation, beyond its accumulation on pericentric constitutive heterochromatin. It remains a mystery why and how H3K9me3 undergoes dynamic regulation in male meiosis. Here, we identify a novel, critical regulator of H3K9 methylation and spermatogenic heterochromatin organization: the germline-specific protein ATF7IP2 (MCAF2). We show that in male meiosis, ATF7IP2 amasses on autosomal and X-pericentric heterochromatin, spreads through the entirety of the sex chromosomes, and accumulates on thousands of autosomal promoters and retrotransposon loci. On the sex chromosomes, which undergo meiotic sex chromosome inactivation (MSCI), the DNA damage response pathway recruits ATF7IP2 to X-pericentric heterochromatin, where it facilitates the recruitment of SETDB1, a histone methyltransferase that catalyzes H3K9me3. In the absence of ATF7IP2, male germ cells are arrested in meiotic prophase I. Analyses of ATF7IP2-deficient meiosis reveal the protein's essential roles in the maintenance of MSCI, suppression of retrotransposons, and global up-regulation of autosomal genes. We propose that ATF7IP2 is a downstream effector of the DDR pathway in meiosis that coordinates the organization of heterochromatin and gene regulation through the spatial regulation of SETDB1-mediated H3K9me3 deposition.

Published
2024

12. Sex-Specific Associations between Adiponectin and Leptin Signaling and Pancreatic Cancer Survival.

Author

Babic, Ana, Wang, Qiao-Li, Lee, Alice, Yuan, Chen, Rifai, Nader, Luo, Juhua, Tabung, Fred, Wactawski-Wende, Jean, Saquib, Nazmus, Kim, Jihye, Kraft, Peter, Sesso, Howard, Buring, Julie, Giovannucci, Edward, Manson, JoAnn, Stampfer, Meir, Ng, Kimmie, Fuchs, Charles, Wolpin, Brian, and Shadyab, Aladdin

Subjects
Male, Humans, Female, Leptin, Adiponectin, Adipokines, Receptors, Adiponectin, Pancreatic Neoplasms, Polymorphism, Single Nucleotide, Receptors, Leptin
Abstract

BACKGROUND: Circulating adiponectin and leptin have been associated with risk of pancreatic cancer. However, the relationship between long-term exposure to these adipokines in the prediagnostic period with patient survival has not been investigated. METHODS: Adipokine levels were measured in prospectively collected samples from 472 patients with pancreatic cancer. Because of sex-specific differences in adipokine levels, associations were evaluated separately for men and women. In a subset of 415 patients, we genotyped 23 SNPs in adiponectin receptor genes (ADIPOR1 and ADIPOR2) and 30 SNPs in the leptin receptor gene (LEPR). RESULTS: Adiponectin levels were inversely associated with survival in women [HR, 1.71; 95% confidence interval (CI), 1.15-2.54]; comparing top with bottom quartile but not in men (HR, 0.89; 95% CI, 0.46-1.70). The SNPs rs10753929 and rs1418445 in ADIPOR1 were associated with survival in the combined population (per minor allele HR, 0.66; 95% CI, 0.51-0.84, and HR, 1.33; 95% CI, 1.12-1.58, respectively). Among SNPs in LEPR, rs12025906, rs3790431, and rs17127601 were associated with survival in the combined population [HRs, 1.54 (95% CI, 1.25-1.90), 0.72 (95% CI, 0.59-0.88), and 0.70 (95% CI, 0.56-0.89), respectively], whereas rs11585329 was associated with survival in men only (HR, 0.39; 95% CI, 0.23-0.66; Pinteraction = 0.0002). CONCLUSIONS: High levels of adiponectin in the prediagnostic period were associated with shorter survival among women, but not among men with pancreatic cancer. Several polymorphisms in ADIPOR1 and LEPR are associated with patient survival. IMPACT: Our findings reveal the association between adipokine signaling and pancreatic cancer survival and demonstrate the importance of examining obesity-associated pathways in relation to pancreatic cancer in a sex-specific manner.

Published
2023

15. Efficient Transparent Polynomial Commitments for zk-SNARKs

Author

Kim, Sungwook, Kim, Sungju, Shin, Yulim, Kim, Sunmi, Kim, Jihye, Oh, Hyunok, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tsudik, Gene, editor, Conti, Mauro, editor, Liang, Kaitai, editor, and Smaragdakis, Georgios, editor

Published
2024
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21. Contagion dynamics on hypergraphs with nested hyperedges

Author

Kim, Jihye, Lee, Deok-Sun, and Goh, Kwang-Il

Subjects
Physics - Physics and Society
Abstract

In complex social systems encoded as hypergraphs, higher-order (i.e., group) interactions taking place among more than two individuals are represented by hyperedges. One of the higher-order correlation structures native to hypergraphs is the nestedness: Some hyperedges can be entirely contained (that is, nested) within another larger hyperedge, which itself can also be nested further in a hierarchical manner. Yet the effect of such hierarchical structure of hyperedges on the dynamics has remained unexplored. In this context, here we propose a random nested-hypergraph model with a tunable level of nestedness and investigate the effects of nestedness on a higher-order susceptible-infected-susceptible process. By developing an analytic framework called the facet approximation, we obtain the steady-state fraction of infected nodes on the random nested-hypergraph model more accurately than existing methods. Our results show that the hyperedge-nestedness affects the phase diagram significantly. Monte Carlo simulations support the analytical results., Comment: 8 pages, 6 figures

Published
2023
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22. Early Detection of Mental Health through Universal Screening at Schools

Author

Kim, Jihye, Kim, Dong-gook, and Kamphaus, Randy

Abstract

Depression, anxiety, and stress are common mental health problems among adolescents. Cross-sectional and longitudinal studies have found that students who suffer from mental health problems (e.g., ADHD, anxiety, or depression) tend to manifest school and social problems. It is urgent to identify and intervene early to help children with mental health problems to improve their life outcomes. Unfortunately, research has shown that a significant proportion of children who suffer from behavioral or emotional problems remain unidentified because their symptoms are too mild to be noticed through casual observation by caregivers and teachers. As a result, their symptoms continue to develop gradually and eventually become mental illnesses. When mild to moderate symptoms become a noticeable mental illness, treatment becomes long-term, more invasive, and expensive. Early detection of children who are at risk of behavioral or emotional problems is possible through universal mental health screening in the school setting. The present study focused on the Behavioral and Emotional Screening System (BESS; Kamphaus & Reynolds, 2007) to identify students who were at mild risk of mental health problems and to examine their academic trajectories over four years. The findings suggest that students at mild risk of behavioral and emotional problems showed similar academic performance as other students. As a result, students with mild to moderate symptoms can be easily overlooked if academic performance were relied on as an indicator of mental health risk. The current results lend support for conducting universal behavioral and emotional risk screening at schools. A brief screener, applied universally, appears to be an effective solution for identifying students at mild risk of behavioral and emotional problems and acting early to prevent these problems from worsening.

Published
2022

25. CrossSplit: Mitigating Label Noise Memorization through Data Splitting

Author

Kim, Jihye, Baratin, Aristide, Zhang, Yan, and Lacoste-Julien, Simon

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

We approach the problem of improving robustness of deep learning algorithms in the presence of label noise. Building upon existing label correction and co-teaching methods, we propose a novel training procedure to mitigate the memorization of noisy labels, called CrossSplit, which uses a pair of neural networks trained on two disjoint parts of the labelled dataset. CrossSplit combines two main ingredients: (i) Cross-split label correction. The idea is that, since the model trained on one part of the data cannot memorize example-label pairs from the other part, the training labels presented to each network can be smoothly adjusted by using the predictions of its peer network; (ii) Cross-split semi-supervised training. A network trained on one part of the data also uses the unlabeled inputs of the other part. Extensive experiments on CIFAR-10, CIFAR-100, Tiny-ImageNet and mini-WebVision datasets demonstrate that our method can outperform the current state-of-the-art in a wide range of noise ratios., Comment: Accepted to ICML 2023

Published
2022

26. Transcriptome-wide association study and Mendelian randomization in pancreatic cancer identifies susceptibility genes and causal relationships with type 2 diabetes and venous thromboembolism

Author

Lindstrom, Sara, Wang, Lu, Smith, Erin, Gordon, William, Van Hylckama Vlieg, Astrid, De Andrade, Mariza, Brody, Jennifer, Pattee, Jack, Haessler, Jeffrey, Brumpton, Ben, Chasman, Daniel, Suchon, Pierre, Chen, Ming-Huei, Turman, Constance, Germain, Marine, Wiggins, Kerri, MacDonald, James, Braekkan, Sigrid, Armasu, Sebastian, Pankratz, Nathan, Jackson, Rabecca, Nielsen, Jonas, Giulianini, Franco, Puurunen, Marja, Ibrahim, Manal, Heckbert, Susan, Bammler, Theo, Frazer, Kelly, McCauley, Bryan, Taylor, Kent, Pankow, James, Reiner, Alexander, Gabrielsen, Maiken, Deleuze, Jean-François, O'Donnell, Chris, Kim, Jihye, McKnight, Barbara, Kraft, Peter, Hansen, John-Bjarne, Rosendaal, Frits, Heit, John, Psaty, Bruce, Tang, Weihong, Kooperberg, Charles, Hveem, Kristian, Ridker, Paul, Morange, Pierre-Emmanuel, Johnson, Andrew, Kabrhel, Christopher, Trégouët, David-Alexandre, Smith, Nicholas, Tan, Marcus C.B., Isom, Chelsea A., Liu, Yangzi, Wu, Lang, Zhou, Dan, and Gamazon, Eric R.

Published
2024
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27. Genome-wide investigation of exogenous female hormones, genetic variation, and venous thromboembolism risk

Author

Hasser, Emily K., Brody, Jennifer A., Bartz, Traci M., Thibord, Florian, Li-Gao, Ruifang, Kauko, Anni, Wiggins, Kerri L., Teder-Laving, Maris, Kim, Jihye, Munsch, Gaëlle, Haile, Helen G., Deleuze, Jean-Francois, van Hylckama Vlieg, Astrid, Wolberg, Alisa S., Boland, Anne, Morange, Pierre-Emmanuel, Kraft, Peter, Lowenstein, Charles J., Emmerich, Joseph, Sitlani, Colleen M., Suchon, Pierre, Rosendaal, Frits R., Niiranen, Teemu, Kabrhel, Christopher, Trégouët, David-Alexandre, and Smith, Nicholas L.

Published
2024
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30. Quantum readout error mitigation via deep learning

Author

Kim, Jihye, Oh, Byungdu, Chong, Yonuk, Hwang, Euyheon, and Park, Daniel K.

Subjects
Quantum Physics, Physics - Data Analysis, Statistics and Probability
Abstract

Quantum computing devices are inevitably subject to errors. To leverage quantum technologies for computational benefits in practical applications, quantum algorithms and protocols must be implemented reliably under noise and imperfections. Since noise and imperfections limit the size of quantum circuits that can be realized on a quantum device, developing quantum error mitigation techniques that do not require extra qubits and gates is of critical importance. In this work, we present a deep learning-based protocol for reducing readout errors on quantum hardware. Our technique is based on training an artificial neural network with the measurement results obtained from experiments with simple quantum circuits consisting of singe-qubit gates only. With the neural network and deep learning, non-linear noise can be corrected, which is not possible with the existing linear inversion methods. The advantage of our method against the existing methods is demonstrated through quantum readout error mitigation experiments performed on IBM five-qubit quantum devices., Comment: 10 pages, 10 figures

Published
2021
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31. Do Advanced Degrees Matter? A Multiphase Mixed-Methods Study to Examine Teachers' Obtainment of Advanced Degrees and the Impact on Student and School Growth

Author

Chang, Mei-Lin, Jorrín Abellán, Ivan M., Wright, Jim, Kim, Jihye, and Gaines, Rachel E.

Abstract

Teacher quality has been found to offset the adverse effect of racial and socioeconomic differences in academic achievement; and teacher quality is often thought to be the product of a quality education. However, existing literature has produced mixed results regarding the relationship between student achievement and teachers' possession of advanced degrees (ADs). Despite these mixed results, ADs are often the most efficient (if not the only) way for teachers to earn certification and salary upgrades. A longitudinal, multiphase mixed-methods explanatory study aimed to bridge shortcomings of existing research on the effects of teachers obtaining ADs. Associations between teacher credentials and middle grades students' academic growth were examined by differentiating teachers' degree level (bachelor's, master's, specialist's) and field (content-related, non-content-related). Teachers and school leaders were also interviewed in order to broaden our understandings of the impact ADs make in areas besides student achievement. Findings suggest that inconsistency in associations between teacher ADs and student achievement may be attributable to variation in a number of individual, programmatic, and institutional factors.

Published
2020

32. State-Wide Implementation of EdTPA in Preparation for High-Stakes Testing: A Mixed-Methods Study of the Concerns of EdTPA Coordinators

Author

Many, Joyce E., Bhatnagar, Ruchi, Tanguay, Carla, Favors-Welch, Shaneeka, Thomas, Clarice, Cannon, Susan Ophelia, Ogletree, Tamra, Kim, Jihye, Howrey, Shannon, An, Sohyun, Kurz, Karen, and Wilson, Judi

Abstract

This study examined the implementation of high-stakes adoption of edTPA® in one state in the year prior to consequential use of edTPA scores for teacher licensure. Using a mixed methods design, we investigated concerns of coordinators who were responsible for edTPA implementation in their institutions. We utilized the Concerns Based Adoption Model (CBAM) to understand edTPA coordinators' "Stages of Concern," the nature of the challenges they faced, and the professional development opportunities that alleviated their concerns. Based on the CBAM survey, the most common "Stage of Concern" for edTPA coordinators was "Management." Coordinators' interviews revealed the nature of their concerns at different stages and how the size of their institution and supportive resources at particular times may have played a crucial role in shaping the edTPA roll-out in their institutions. The use of the CBAM framework enabled edTPA coordinators (a) to understand their own concerns about the high-stakes policy, (b) to articulate the complexities involved in implementing edTPA initiatives, and (c) to underscore the importance of relating concerns to appropriate professional development opportunities and support for themselves as well as their faculty.

Published
2019

34. A genetic association study of circulating coagulation factor VIII and von Willebrand factor levels

Author

Abe, Namiko, Abecasis, Gonçalo, Aguet, Francois, Albert, Christine, Almasy, Laura, Alonso, Alvaro, Ament, Seth, Anderson, Peter, Anugu, Pramod, Applebaum-Bowden, Deborah, Ardlie, Kristin, Arking, Dan, Arnett, Donna K, Ashley-Koch, Allison, Aslibekyan, Stella, Assimes, Tim, Auer, Paul, Avramopoulos, Dimitrios, Ayas, Najib, Balasubramanian, Adithya, Barnard, John, Barnes, Kathleen, Barr, R. Graham, Barron-Casella, Emily, Barwick, Lucas, Beaty, Terri, Beck, Gerald, Becker, Diane, Becker, Lewis, Beer, Rebecca, Beitelshees, Amber, Benjamin, Emelia, Benos, Takis, Bezerra, Marcos, Bielak, Larry, Bis, Joshua, Blackwell, Thomas, Blangero, John, Blue, Nathan, Boerwinkle, Eric, Bowden, Donald W., Bowler, Russell, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Brown, Deborah, Bunting, Karen, Burchard, Esteban, Bustamante, Carlos, Buth, Erin, Cade, Brian, Cardwell, Jonathan, Carey, Vincent, Carrier, Julie, Carson, April P., Carty, Cara, Casaburi, Richard, Casas Romero, Juan P, Casella, James, Castaldi, Peter, Chaffin, Mark, Chang, Christy, Chang, Yi-Cheng, Chasman, Daniel, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Ida Chen, Yii-Der, Cho, Michael, Choi, Seung Hoan, Chuang, Lee-Ming, Chung, Mina, Chung, Ren-Hua, Clish, Clary, Comhair, Suzy, Conomos, Matthew, Cornell, Elaine, Correa, Adolfo, Crandall, Carolyn, Crapo, James, Cupples, L. Adrienne, Curran, Joanne, Curtis, Jeffrey, Custer, Brian, Damcott, Coleen, Darbar, Dawood, David, Sean, Davis, Colleen, Daya, Michelle, de Andrade, Mariza, de las Fuentes, Lisa, de Vries, Paul, DeBaun, Michael, Deka, Ranjan, DeMeo, Dawn, Devine, Scott, Dinh, Huyen, Doddapaneni, Harsha, Duan, Qing, Dugan-Perez, Shannon, Duggirala, Ravi, Durda, Jon Peter, Dutcher, Susan K., Eaton, Charles, Ekunwe, Lynette, El Boueiz, Adel, Ellinor, Patrick, Emery, Leslie, Erzurum, Serpil, Farber, Charles, Farek, Jesse, Fingerlin, Tasha, Flickinger, Matthew, Fornage, Myriam, Franceschini, Nora, Frazar, Chris, Fu, Mao, Fullerton, Stephanie M., Fulton, Lucinda, Gabriel, Stacey, Gan, Weiniu, Gao, Shanshan, Gao, Yan, Gass, Margery, Geiger, Heather, Gelb, Bruce, Geraci, Mark, Germer, Soren, Gerszten, Robert, Ghosh, Auyon, Gibbs, Richard, Gignoux, Chris, Gladwin, Mark, Glahn, David, Gogarten, Stephanie, Gong, Da-Wei, Goring, Harald, Graw, Sharon, Gray, Kathryn J., Grine, Daniel, Gross, Colin, Gu, C. Charles, Guan, Yue, Guo, Xiuqing, Gupta, Namrata, Haessler, Jeff, Hall, Michael, Han, Yi, Hanly, Patrick, Harris, Daniel, Hawley, Nicola L., He, Jiang, Heavner, Ben, Heckbert, Susan, Hernandez, Ryan, Herrington, David, Hersh, Craig, Hidalgo, Bertha, Hixson, James, Hobbs, Brian, Hokanson, John, Hong, Elliott, Hoth, Karin, Hsiung, Chao (Agnes), Hu, Jianhong, Hung, Yi-Jen, Huston, Haley, Hwu, Chii Min, Irvin, Marguerite Ryan, Jackson, Rebecca, Jain, Deepti, Jaquish, Cashell, Johnsen, Jill, Johnson, Andrew, Johnson, Craig, Johnston, Rich, Jones, Kimberly, Kang, Hyun Min, Kaplan, Robert, Kardia, Sharon, Kelly, Shannon, Kenny, Eimear, Kessler, Michael, Khan, Alyna, Khan, Ziad, Kim, Wonji, Kimoff, John, Kinney, Greg, Konkle, Barbara, Kooperberg, Charles, Kramer, Holly, Lange, Christoph, Lange, Ethan, Lange, Leslie, Laurie, Cathy, Laurie, Cecelia, LeBoff, Meryl, Lee, Jiwon, Lee, Sandra, Lee, Wen-Jane, LeFaive, Jonathon, Levine, David, Levy, Dan, Lewis, Joshua, Li, Xiaohui, Li, Yun, Lin, Henry, Lin, Honghuang, Lin, Xihong, Liu, Simin, Liu, Yongmei, Liu, Yu, Loos, Ruth J. F., Lubitz, Steven, Lunetta, Kathryn, Luo, James, Magalang, Ulysses, Mahaney, Michael, Make, Barry, Manichaikul, Ani, Manning, Alisa, Manson, JoAnn, Martin, Lisa, Marton, Melissa, Mathai, Susan, Mathias, Rasika, May, Susanne, McArdle, Patrick, McDonald, Merry-Lynn, McFarland, Sean, McGarvey, Stephen, McGoldrick, Daniel, McHugh, Caitlin, McNeil, Becky, Mei, Hao, Meigs, James, Menon, Vipin, Mestroni, Luisa, Metcalf, Ginger, Meyers, Deborah A, Mignot, Emmanuel, Mikulla, Julie, Min, Nancy, Minear, Mollie, Minster, Ryan L, Mitchell, Braxton D., Moll, Matt, Momin, Zeineen, Montasser, May E., Montgomery, Courtney, Muzny, Donna, Mychaleckyj, Josyf C, Nadkarni, Girish, Naik, Rakhi, Naseri, Take, Natarajan, Pradeep, Nekhai, Sergei, Nelson, Sarah C., Neltner, Bonnie, Nessner, Caitlin, Nickerson, Deborah, Nkechinyere, Osuji, North, Kari, O'Connell, Jeff, O'Connor, Tim, Ochs-Balcom, Heather, Okwuonu, Geoffrey, Pack, Allan, Paik, David T., Palmer, Nicholette, Pankow, James, Papanicolaou, George, Parker, Cora, Peloso, Gina, Peralta, Juan Manuel, Perez, Marco, Perry, James, Peters, Ulrike, Peyser, Patricia, Phillips, Lawrence S, Pleiness, Jacob, Pollin, Toni, Post, Wendy, Becker, Julia Powers, Boorgula, Meher Preethi, Preuss, Michael, Psaty, Bruce, Qasba, Pankaj, Qiao, Dandi, Qin, Zhaohui, Rafaels, Nicholas, Raffield, Laura, Rajendran, Mahitha, Ramachandran, Vasan S., Rao, D. C., Rasmussen-Torvik, Laura, Ratan, Aakrosh, Redline, Susan, Reed, Robert, Reeves, Catherine, Regan, Elizabeth, Reiner, Alex, Reupena, Muagututi‘a Sefuiva, Rice, Ken, Rich, Stephen, Robillard, Rebecca, Robine, Nicolas, Roden, Dan, Roselli, Carolina, Rotter, Jerome, Ruczinski, Ingo, Runnels, Alexi, Russell, Pamela, Ruuska, Sarah, Ryan, Kathleen, Sabino, Ester Cerdeira, Saleheen, Danish, Salimi, Shabnam, Salvi, Sejal, Salzberg, Steven, Sandow, Kevin, Sankaran, Vijay G., Santibanez, Jireh, Schwander, Karen, Schwartz, David, Sciurba, Frank, Seidman, Christine, Seidman, Jonathan, Sériès, Frédéric, Sheehan, Vivien, Sherman, Stephanie L., Shetty, Amol, Shetty, Aniket, Hui-Heng Sheu, Wayne, Shoemaker, M. Benjamin, Silver, Brian, Silverman, Edwin, Skomro, Robert, Smith, Albert Vernon, Smith, Jennifer, Smith, Josh, Smith, Nicholas, Smith, Tanja, Smoller, Sylvia, Snively, Beverly, Snyder, Michael, Sofer, Tamar, Sotoodehnia, Nona, Stilp, Adrienne M., Storm, Garrett, Streeten, Elizabeth, Su, Jessica Lasky, Sung, Yun Ju, Sylvia, Jody, Szpiro, Adam, Taliun, Daniel, Tang, Hua, Taub, Margaret, Taylor, Kent D., Taylor, Matthew, Taylor, Simeon, Telen, Marilyn, Thornton, Timothy A., Threlkeld, Machiko, Tinker, Lesley, Tirschwell, David, Tishkoff, Sarah, Tiwari, Hemant, Tong, Catherine, Tracy, Russell, Tsai, Michael, Vaidya, Dhananjay, Van Den Berg, David, VandeHaar, Peter, Vrieze, Scott, Walker, Tarik, Wallace, Robert, Walts, Avram, Wang, Fei Fei, Wang, Heming, Wang, Jiongming, Watson, Karol, Watt, Jennifer, Weeks, Daniel E., Weinstock, Joshua, Weir, Bruce, Weiss, Scott T, Weng, Lu-Chen, Wessel, Jennifer, Willer, Cristen, Williams, Kayleen, Williams, L. Keoki, Wilson, Carla, Wilson, James, Winterkorn, Lara, Wong, Quenna, Wu, Joseph, Xu, Huichun, Yanek, Lisa, Yang, Ivana, Yu, Ketian, Zekavat, Seyedeh Maryam, Zhang, Yingze, Zhao, Snow Xueyan, Zhao, Wei, Zhu, Xiaofeng, Ziv, Elad, Zody, Michael, Zoellner, Sebastian, Lindstrom, Sara, Wang, Lu, Smith, Erin N., Gordon, William, van Hylckama Vlieg, Astrid, Brody, Jennifer A., Pattee, Jack W., Haessler, Jeffrey, Brumpton, Ben M., Chasman, Daniel I., Suchon, Pierre, Chen, Ming-Huei, Turman, Constance, Germain, Marine, Wiggins, Kerri L., MacDonald, James, Braekkan, Sigrid K., Armasu, Sebastian M., Pankratz, Nathan, Jackson, Rabecca D., Nielsen, Jonas B., Giulianini, Franco, Puurunen, Marja K., Ibrahim, Manal, Heckbert, Susan R., Bammler, Theo K., Frazer, Kelly A., McCauley, Bryan M., Taylor, Kent, Pankow, James S., Reiner, Alexander P., Gabrielsen, Maiken E., Deleuze, Jean-François, O'Donnell, Chris J., Kim, Jihye, McKnight, Barbara, Kraft, Peter, Hansen, John-Bjarne, Rosendaal, Frits R., Heit, John A., Psaty, Bruce M., Tang, Weihong, Hveem, Kristian, Ridker, Paul M., Morange, Pierre-Emmanuel, Johnson, Andrew D., Kabrhel, Christopher, AlexandreTrégouët, David, Smith, Nicholas L., de Vries, Paul S., Reventun, Paula, Brown, Michael R., Heath, Adam S., Huffman, Jennifer E., Le, Ngoc-Quynh, Bebo, Allison, Temprano-Sagrera, Gerard, Raffield, Laura M., Ozel, Ayse Bilge, Thibord, Florian, Lewis, Joshua P., Rodriguez, Benjamin A. T., Polasek, Ozren, Yanek, Lisa R., Carrasquilla, German D., Marioni, Riccardo E., Kleber, Marcus E., Trégouët, David-Alexandre, Yao, Jie, Li-Gao, Ruifang, Joshi, Peter K., Trompet, Stella, Martinez-Perez, Angel, Ghanbari, Mohsen, Howard, Tom E., Reiner, Alex P., Arvanitis, Marios, Ryan, Kathleen A., Bartz, Traci M., Rudan, Igor, Faraday, Nauder, Linneberg, Allan, Davies, Gail, Delgado, Graciela E., Klaric, Lucija, Noordam, Raymond, van Rooij, Frank, Curran, Joanne E., Wheeler, Marsha M., Osburn, William O., O'Connell, Jeffrey R., Beswick, Andrew, Kolcic, Ivana, Souto, Juan Carlos, Becker, Lewis C., Hansen, Torben, Doyle, Margaret F., Harris, Sarah E., Moissl, Angela P., Rich, Stephen S., Campbell, Harry, Stott, David J., Soria, Jose Manuel, de Maat, Moniek P. M., Brody, Lawrence C., Auer, Paul L., Ben-Shlomo, Yoav, Hayward, Caroline, Mathias, Rasika A., Kilpeläinen, Tuomas O., Lange, Leslie A., Cox, Simon R., März, Winfried, Rotter, Jerome I., Mook-Kanamori, Dennis O., Wilson, James F., van der Harst, Pim, Jukema, J. Wouter, Ikram, M. Arfan, Desch, Karl C., Sabater-Lleal, Maria, Lowenstein, Charles J., and Morrison, Alanna C.

Published
2024
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40. A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories

Author

Placido, Davide, Yuan, Bo, Hjaltelin, Jessica X., Zheng, Chunlei, Haue, Amalie D., Chmura, Piotr J., Yuan, Chen, Kim, Jihye, Umeton, Renato, Antell, Gregory, Chowdhury, Alexander, Franz, Alexandra, Brais, Lauren, Andrews, Elizabeth, Marks, Debora S., Regev, Aviv, Ayandeh, Siamack, Brophy, Mary T., Do, Nhan V., Kraft, Peter, Wolpin, Brian M., Rosenthal, Michael H., Fillmore, Nathanael R., Brunak, Søren, and Sander, Chris

Published
2023
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46. The Association of Urinary Sodium Excretion with Glaucoma and Related Traits in a Large United Kingdom Population

Author

Aschard, Hugues, Chia, Mark, Chua, Sharon, Do, Ron, Foster, Paul, Kang, Jae, Kastner, Alan, Khawaja, Anthony, Kim, Jihye, Lentjes, Marleen, Luben, Robert, Madjedi, Kian, Montesano, Giovanni, Pasquale, Louis, Stuart, Kelsey, Warwick, Alasdair, Wiggs, Janey, Allen, Naomi, Aslam, Tariq, Atan, Denize, Barman, Sarah, Barrett, Jenny, Bishop, Paul, Black, Graeme, Braithwaite, Tasanee, Carare, Roxana, Chakravarthy, Usha, Chan, Michelle, Day, Alexander, Desai, Parul, Dhillon, Bal, Dick, Andrew, Doney, Alexander, Egan, Cathy, Ennis, Sarah, Fruttiger, Marcus, Garway-Heath, David (Ted), Gibson, Jane, Guggenheim, Jeremy, Hammond, Chris, Hardcastle, Alison, Harding, Simon, Hogg, Ruth, Hysi, Pirro, Keane, Pearse, Khaw, Peng Tee, Lascaratos, Gerassimos, Littlejohns, Thomas, Lotery, Andrew, Luthert, Phil, MacGillivray, Tom, Mackie, Sarah, McGuinness, Bernadette, McKay, Gareth, McKibbin, Martin, Moore, Tony, Morgan, James, O'Sullivan, Eoin, Oram, Richard, Owen, Chris, Patel, Praveen, Paterson, Euan, Peto, Tunde, Petzold, Axel, Pontikos, Nikolas, Rahi, Jugnoo, Rudnicka, Alicja, Sattar, Naveed, Self, Jay, Sergouniotis, Panagiotis, Sivaprasad, Sobha, Steel, David, Stratton, Irene, Strouthidis, Nicholas, Sudlow, Cathie, Sun, Zihan, Tapp, Robyn, Thomas, Dhanes, Trucco, Emanuele, Tufail, Adnan, Viswanathan, Ananth, Vitart, Veronique, Weedon, Mike, Williams, Katie, Williams, Cathy, Woodside, Jayne, Yates, Max, Yip, Jennifer, Zheng, Yalin, Aung, Tin, Burdon, Kathryn, Chen, Li, Cheng, Ching-Yu, Craig, Jamie, Cree, Angela, de Vries, Victor, Driessen, Sjoerd, Fingert, John, Gharahkhani, Puya, Hammond, Christopher, Hayward, Caroline, Hewitt, Alex, Jansonius, Nomdo, Jonansson, Fridbert, Jonas, Jost, Kass, Michael, Khor, Chiea, Klaver, Caroline, Koh, Jacyline, MacGregor, Stuart, Mackey, David, Mitchell, Paul, Pang, Calvin, Pasutto, Francesca, Pfeiffer, Norbert, Polašek, Ozren, Ramdas, Wishal, Schuster, Alexander, Segrè, Ayellet, Stefansson, Einer, Stefánsson, Kári, Thorleifsson, Gudmar, Thorsteinsdottir, Unnur, van Duijn, Cornelia, Vergroesen, Joëlle, Vithana, Eranga, Wilson, James, Wojciechowski, Robert, Wong, Tien, Young, Terri, Stuart, Kelsey V., Biradar, Mahantesh I., Luben, Robert N., Dhaun, Neeraj, Wagner, Siegfried K., Warwick, Alasdair N., Madjedi, Kian M., Pasquale, Louis R., Wiggs, Janey L., Kang, Jae H., Lentjes, Marleen A.H., Foster, Paul J., and Khawaja, Anthony P.

Published
2024
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49. PAGE-B incorporating moderate HBV DNA levels predicts risk of HCC among patients entering into HBeAg-positive chronic hepatitis B

Author

Chun, Ho Soo, Papatheodoridis, George V., Lee, Minjong, Lee, Hye Ah, Kim, Yeong Hwa, Kim, Seo Hyun, Oh, Yun-Seo, Park, Su Jin, Kim, Jihye, Lee, Han Ah, Kim, Hwi Young, Kim, Tae Hun, Yoon, Eileen L., Jun, Dae Won, Ahn, Sang Hoon, Sypsa, Vana, Yurdaydin, Cihan, Lampertico, Pietro, Calleja, Jose Luis, Janssen, Harry LA., Dalekos, George N., Goulis, John, Berg, Thomas, Buti, Maria, Kim, Seung Up, and Kim, Yoon Jun

Published
2024
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