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902 results on '"Yoo W"'

1. Patient web portals, disease management, and primary prevention

Author

Coughlin SS, Prochaska JJ, Williams LB, Besenyi GM, Heboyan V, Goggans DS, Yoo W, and De Leo G

Subjects
chronic diseases, diabetes, electronic health record, health disparities, hypertension, health information technology, immunization, patient web portals, screening, Public aspects of medicine, RA1-1270
Abstract

Steven S Coughlin,1 Judith J Prochaska,2 Lovoria B Williams,3 Gina M Besenyi,1 Vahé Heboyan,1 D Stephen Goggans,4 Wonsuk Yoo,5 Gianluca De Leo1 1Department of Clinical and Digital Health Sciences, College of Allied Health Sciences, Augusta University, Augusta, GA, 2Department of Medicine, Stanford Prevention Research Center, Stanford University, Stanford, CA, 3Department of Biobehavioral Nursing, College of Nursing, Augusta University, 4Department of Public Health, East Central Health District, 5Institute of Public and Preventive Health, Augusta University, Augusta, GA, USA Background: Efforts aimed at health care reform and continued advances in information technologies have prompted interest among providers and researchers in patient web portals. Patient web portals are password-protected online websites that offer the patients 24-hour access to personal health information from anywhere with an Internet connection.Methods: This article, which is based upon bibliographic searches in PubMed, reviews important developments in web portals for primary and secondary disease prevention, including patient web portals tethered to electronic medical records, disease-specific portals, health disparities, and health-related community web portals.Results: Although findings have not been uniformly positive, several studies of the effectiveness of health care system patient portals in chronic disease management have shown promising results with regard to patient outcomes. Patient web portals have also shown promising results in increasing adherence with screening recommendations. Racial and ethnic minorities, younger persons, and patients who are less educated or have lower health literacy have been found to be less likely to use patient portals.Conclusion: Additional studies are needed of the utility and effectiveness of different elements of web portals for different patient populations. This should include additional diseases and health topics such as smoking cessation and weight management. Keywords: chronic diseases, diabetes, electronic health record, health disparities, hypertension, health information technology, immunization, patient web portals, screening

Published
2017

2. UMAMI: A recipe for generating meaningful metrics through holistic I/O performance analysis

Author

Lockwood, GK, Yoo, W, Byna, S, Wright, NJ, Snyder, S, Harms, K, Nault, Z, and Carns, P

Subjects
Mental Health, I/O performance, TOKIO
Abstract

I/O efficiency is essential to productivity in scientific computing, especially as many scientific domains become more data-intensive. Many characterization tools have been used to elucidate specific aspects of parallel I/O performance, but analyzing components of complex I/O subsystems in isolation fails to provide insight into critical questions: how do the I/O components interact, what are reasonable expectations for application performance, and what are the underlying causes of I/O performance problems? To address these questions while capitalizing on existing component-level characterization tools, we propose an approach that combines on-demand, modular synthesis of I/O characterization data into a unified monitoring and metrics interface (UMAMI) to provide a normalized, holistic view of I/O behavior. We evaluate the feasibility of this approach by applying it to a month-long benchmarking study on two distinct largescale computing platforms. We present three case studies that highlight the importance of analyzing application I/O performance in context with both contemporaneous and historical component metrics, and we provide new insights into the factors affecting I/O performance. By demonstrating the generality of our approach, we lay the groundwork for a production-grade framework for holistic I/O analysis.

Published
2017

3. Parallel variable selection for effective performance prediction

Author

Wang, J, Yoo, W, Sim, A, Nugent, P, and Wu, K

Abstract

Large data analysis problems often involve a large number of variables, and the corresponding analysis algorithms may examine all variable combinations to find the optimal solution. For example, to model the time required to complete a scientific workflow, we need to consider the impact of dozens of parameters. To reduce the model building time and reduce the likelihood of overfitting, we look to variable selection methods to identify the critical variables for the performance model. In this work, we create a combination of variable selection and performance prediction methods that is as effective as the exhaustive search approach when the exhaustive search could be completed in a reasonable amount of time. To handle the cases where the exhaustive search is too time consuming, we develop the parallelized variable selection algorithm. Additionally, we develop a parallel grouping mechanism that further reduces the variable selection time by 70%.As a case study, we exercise the variable selection technique with the performance measurement data from the Palomar Transient Factory (PTF) workflow. The application scientists have determined that about 50 variables and parameters are important to the performance of the workflows. Our tests show that the Sequential Backward Selection algorithm is able to approximate the optimal subset relatively quickly. By reducing the number of variables used to build the model from 50 to 4, we are able to maintain the prediction quality while reducing the model building time by a factor of 6. Using the parallelization and grouping techniques we developed in this work, the variable selection process was reduced from over 18 hours to 15 minutes while ending up with the same variable subset.

Published
2017

4. A lightweight network anomaly detection technique

Author

Kim, J, Yoo, W, Sim, A, Suh, SC, and Kim, I

Abstract

While the network anomaly detection is essential in network operations and management, it becomes further challenging to perform the first line of detection against the exponentially increasing volume of network traffic. In this work, we develop a technique for the first line of online anomaly detection with two important considerations: (i) availability of traffic attributes during the monitoring time, and (ii) computational scalability for streaming data. The presented learning technique is lightweight and highly scalable with the beauty of approximation based on the grid partitioning of the given dimensional space. With the public traffic traces of KDD Cup 1999 and NSL-KDD, we show that our technique yields 98.5% and 83% of detection accuracy, respectively, only with a couple of readily available traffic attributes that can be obtained without the help of post-processing. The results are at least comparable with the classical learning methods including decision tree and random forest, with approximately two orders of magnitude faster learning performance.

Published
2017

5. Efficacy and Safety of Epratuzumab in Moderately to Severely Active Systemic Lupus Erythematosus: Results From Two Phase III Randomized, Double‐Blind, Placebo‐Controlled Trials

Author

Clowse, Megan EB, Wallace, Daniel J, Furie, Richard A, Petri, Michelle A, Pike, Marilyn C, Leszczyński, Piotr, Neuwelt, C Michael, Hobbs, Kathryn, Keiserman, Mauro, Duca, Liliana, Kalunian, Kenneth C, Galateanu, Catrinel, Bongardt, Sabine, Stach, Christian, Beaudot, Carolyn, Kilgallen, Brian, Gordon, Caroline, Batalov, A, Bojinca, M, Djerassi, R, Duca, L, Horak, P, Kolarov, Z, Milasiene, R, Monova, D, Otsa, K, Pileckyte, M, Popova, T, Radulescu, F, Rashkov, R, Rednic, S, Repin, M, Stoilov, R, Tegzova, D, Vezikova, N, Vitek, P, Zainea, C, East, Far, Baek, H, Chen, Y, Chiu, Y, Cho, C, Chou, C, Choe, J, Huang, C, Kang, Y, Kang, S, Lai, N, Lee, S, Park, W, Shim, S, Suh, C, Yoo, W, Armengol, H Avila, Zapata, F Avila, Santiago, M Barreto, Cavalcanti, F, Chahade, W, Costallat, L, Keiserman, M, Alcala, J Orozco, Remus, C Ramos, Roimicher, L, Abu‐Shakra, M, Agarwal, V, Agmon‐Levin, N, Kadel, J, Levy, Y, Mevorach, D, Paran, D, Reitblat, T, Rosner, I, Shobha, V, Sthoeger, Z, Zisman, D, Ayesu, K, Berney, S, Box, J, Busch, H, Buyon, J, Carter, J, Chi, J, Clowse, M, Collins, R, Dao, K, Diab, I, Dikranian, A, El‐Shahawy, M, Gaylis, N, Grossman, J, Halpert, E, Huff, J, Jarjour, W, Kao, A, Katz, R, Kennedy, A, Khan, M, Kivitz, A, Kohen, M, and Lawrence‐Ford, T

Subjects
Autoimmune Disease, Clinical Trials and Supportive Activities, Clinical Research, Lupus, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Inflammatory and immune system, Adult, Antibodies, Monoclonal, Humanized, Double-Blind Method, Female, Humans, Lupus Erythematosus, Systemic, Male, Severity of Illness Index, Treatment Outcome, EMBODY Investigator Group, Clinical Sciences, Immunology, Public Health and Health Services, Arthritis & Rheumatology
Abstract

ObjectiveEpratuzumab, a monoclonal antibody that targets CD22, modulates B cell signaling without substantial reductions in the number of B cells. The aim of this study was to report the results of 2 phase III multicenter randomized, double-blind, placebo-controlled trials, the EMBODY 1 and EMBODY 2 trials, assessing the efficacy and safety of epratuzumab in patients with moderately to severely active systemic lupus erythematosus (SLE).MethodsPatients met ≥4 of the American College of Rheumatology revised classification criteria for SLE, were positive for antinuclear antibodies and/or anti-double-stranded DNA antibodies, had an SLE Disease Activity Index 2000 (SLEDAI-2K) score of ≥6 (increased disease activity), had British Isles Lupus Assessment Group 2004 index (BILAG-2004) scores of grade A (severe disease activity) in ≥1 body system or grade B (moderate disease activity) in ≥2 body systems (in the mucocutaneous, musculoskeletal, or cardiorespiratory domains), and were receiving standard therapy, including mandatory treatment with corticosteroids (5-60 mg/day). BILAG-2004 grade A scores in the renal and central nervous system domains were excluded. Patients were randomized 1:1:1 to receive either placebo, epratuzumab 600 mg every week, or epratuzumab 1,200 mg every other week, with infusions delivered for the first 4 weeks of each 12-week dosing cycle, for 4 cycles. Patients across all 3 treatment groups also continued with their standard therapy. The primary end point was the response rate at week 48 according to the BILAG-based Combined Lupus Assessment (BICLA) definition, requiring improvement in the BILAG-2004 score, no worsening in the BILAG-2004 score, SLEDAI-2K score, or physician's global assessment of disease activity, and no disallowed changes in concomitant medications. Patients who discontinued the study medication were classified as nonresponders.ResultsIn the EMBODY 1 and EMBODY 2 trials of epratuzumab, 793 patients and 791 patients, respectively, were randomized, 786 (99.1%) and 788 (99.6%), respectively, received study medication, and 528 (66.6%) and 533 (67.4%), respectively, completed the study. There was no statistically significant difference in the primary end point between the groups, with the week 48 BICLA response rates being similar between the epratuzumab groups and the placebo group (response rates ranging from 33.5% to 39.8%). No new safety signals were identified.ConclusionIn patients with moderate or severely active SLE, treatment with epratuzumab + standard therapy did not result in improvements in response rates over that observed in the placebo + standard therapy group.

Published
2017

6. Nonstandard Errors

Author

MENKVELD, AJ, DREBER, A, HOLZMEISTER, F, HUBER, J, JOHANNESSON, M, KIRCHLER, M, NEUSÜß, S, RAZEN, M, WEITZEL, U, ABAD‐DÍAZ, D, ABUDY, M, MANO, N, MARCHAL, A, MARTINEAU, C, MAZZOLA, F, MELOSO, D, MI, MG, MIHET, R, MOHAN, V, MOINAS, S, ANGEL, JJ, GEMAYEL, R, MOORE, D, MU, L, MURAVYEV, D, MURPHY, D, NESZVEDA, G, NEUMEIER, C, NIELSSON, U, NIMALENDRAN, M, NOLTE, S, NORDEN, LL, GERRITSEN, D, AVETIKIAN, AT, O'NEILL, P, OBAID, K, ØDEGAARD, BA, ÖSTBERG, P, PAGNOTTA, E, PAINTER, M, PALAN, S, PALIT, IJ, PARK, A, GIL‐BAZO, J, PASCUAL, R, BACH, A, PASQUARIELLO, P, PASTOR, L, PATEL, V, PATTON, AJ, PEARSON, ND, PELIZZON, L, PELLI, M, PELSTER, M, GILDER, D, PÉRIGNON, C, PFIFFER, C, BAIDOO, E, PHILIP, R, PLÍHAL, T, PRAKASH, P, PRESS, O, PRODROMOU, T, PROKOPCZUK, M, PUTNINS, T, GLOSTEN, LR, QIAN, Y, RAIZADA, G, RAKOWSKI, D, BAKALLI, G, RANALDO, A, REGIS, L, REITZ, S, RENAULT, T, RENJIE, RW, RENO, R, GOMEZ, T, RIDDIOUGH, SJ, RINNE, K, RINTAMÄKI, P, RIORDAN, R, BAO, L, RITTMANNSBERGER, T, LONGARELA, IR, ROESCH, D, ROGNONE, L, ROSEMAN, B, GORBENKO, A, ROŞU, I, ROY, S, RUDOLF, N, RUSH, SR, RZAYEV, K, BARBON, A, RZEŹNIK, AA, SANFORD, A, SANKARAN, H, SARKAR, A, GRAMMIG, J, SARNO, L, SCAILLET, O, SCHARNOWSKI, S, SCHENK‐HOPPÉ, KR, SCHERTLER, A, SCHNEIDER, M, BASHCHENKO, O, SCHROEDER, F, SCHÜRHOFF, N, SCHUSTER, P, GRÉGOIRE, V, SCHWARZ, MA, SEASHOLES, MS, SEEGER, NJ, SHACHAR, O, SHKILKO, A, SHUI, J, SIKIC, M, BINDRA, PC, SIMION, G, SMALES, LA, GÜÇBILMEZ, U, SÖDERLIND, P, SOJLI, E, SOKOLOV, K, SÖNKSEN, J, SPOKEVICIUTE, L, STEFANOVA, D, SUBRAHMANYAM, MG, SZASZI, B, BJØNNES, GH, TALAVERA, O, ADRIAN, T, TANG, Y, TAYLOR, N, THAM, W, THEISSEN, E, THIMME, J, TONKS, I, TRAN, H, TRAPIN, L, TROLLE, AB, BLACK, JR, HAGSTRÖMER, B, VADUVA, MA, VALENTE, G, VAN NESS, RA, VASQUEZ, A, VEROUSIS, T, VERWIJMEREN, P, VILHELMSSON, A, VILKOV, G, VLADIMIROV, V, VOGEL, S, HAMBUCKERS, J, BLACK, BS, VOIGT, S, WAGNER, W, WALTHER, T, WEISS, P, VAN DER WEL, M, WERNER, IM, WESTERHOLM, PJ, WESTHEIDE, C, WIKA, HC, HAPNES, E, WIPPLINGER, E, BOGOEV, D, WOLF, M, WOLFF, CCP, WOLK, L, WONG, W, WRAMPELMEYER, J, WU, Z, XIA, S, XIU, D, HARRIS, JH, XU, K, XU, C, CORREA, SB, YADAV, PK, YAGÜE, J, YAN, C, YANG, A, YOO, W, YU, W, YU, Y, HARRIS, L, YU, S, YUESHEN, BZ, YUFEROVA, D, BONDARENKO, O, ZAMOJSKI, M, ZAREEI, A, ZEISBERGER, SM, ZHANG, L, ZHANG, SS, ZHANG, X, HARTMANN, S, ZHAO, L, ZHONG, Z, ZHOU, ZI, ZHOU, C, BOS, CS, ZHU, XS, ZOICAN, M, ZWINKELS, R, BOSCH‐ROSA, C, BOURI, E, HASSE, J, BROWNLEES, C, CALAMIA, A, CAO, VN, CAPELLE‐BLANCARD, G, ROMERO, LMC, CAPORIN, M, CARRION, A, CASKURLU, T, CHAKRABARTY, B, CHEN, J, HAUTSCH, N, CHERNOV, M, CHEUNG, W, CHINCARINI, LB, CHORDIA, T, CHOW, S, CLAPHAM, B, COLLIARD, J, COMERTON‐FORDE, C, CURRAN, E, DAO, T, HE, X, DARE, W, DAVIES, RJ, DE BLASIS, R, DE NARD, GF, DECLERCK, F, DEEV, O, DEGRYSE, H, DEKU, SY, DESAGRE, C, VAN DIJK, MA, HEATH, D, DIM, C, DIMPFL, T, DONG, YJ, DRUMMOND, PA, DUDDA, T, DUEVSKI, T, DUMITRESCU, A, DYAKOV, T, DYHRBERG, AH, DZIELIŃSKI, M, AIT‐SAHALIA, Y, EKSI, A, KALAK, IE, ELLEN, ST, EUGSTER, N, EVANS, MDD, FARRELL, M, FELEZ‐VINAS, E, FERRARA, G, FERROUHI, EM, FLORI, A, HEDIGER, S, FLUHARTY‐JAIDEE, JT, FOLEY, SDV, FONG, KYL, FOUCAULT, T, FRANUS, T, FRANZONI, F, FRIJNS, B, FRÖMMEL, M, FU, SM, FÜLLBRUNN, SC, HENDERSHOTT, T, GAN, B, GAO, G, GEHRIG, TP, HIBBERT, AM, HJALMARSSON, E, HOELSCHER, SA, HOFFMANN, P, HOLDEN, CW, HORENSTEIN, AR, HUANG, W, HUANG, D, AKMANSOY, O, HURLIN, C, ILCZUK, K, IVASHCHENKO, A, IYER, SR, JAHANSHAHLOO, H, JALKH, N, JONES, CM, JURKATIS, S, JYLHÄ, P, KAECK, AT, ALCOCK, JT, KAISER, G, KARAM, A, KARMAZIENE, E, KASSNER, B, KAUSTIA, M, KAZAK, E, KEARNEY, F, VAN KERVEL, V, KHAN, SA, KHOMYN, MK, ALEXEEV, V, KLEIN, T, KLEIN, O, KLOS, A, KOETTER, M, KOLOKOLOV, A, KORAJCZYK, RA, KOZHAN, R, KRAHNEN, JP, KUHLE, P, KWAN, A, ALOOSH, A, LAJAUNIE, Q, LAM, FYEC, LAMBERT, M, LANGLOIS, H, LAUSEN, J, LAUTER, T, LEIPPOLD, M, LEVIN, V, LI, Y, LI, H, AMATO, L, LIEW, CY, LINDNER, T, LINTON, O, LIU, J, LIU, A, LLORENTE, G, LOF, M, LOHR, A, LONGSTAFF, F, LOPEZ‐LIRA, A, AMAYA, D, MANKAD, S, MENKVELD, AJ, DREBER, A, HOLZMEISTER, F, HUBER, J, JOHANNESSON, M, KIRCHLER, M, NEUSÜß, S, RAZEN, M, WEITZEL, U, ABAD‐DÍAZ, D, ABUDY, M, MANO, N, MARCHAL, A, MARTINEAU, C, MAZZOLA, F, MELOSO, D, MI, MG, MIHET, R, MOHAN, V, MOINAS, S, ANGEL, JJ, GEMAYEL, R, MOORE, D, MU, L, MURAVYEV, D, MURPHY, D, NESZVEDA, G, NEUMEIER, C, NIELSSON, U, NIMALENDRAN, M, NOLTE, S, NORDEN, LL, GERRITSEN, D, AVETIKIAN, AT, O'NEILL, P, OBAID, K, ØDEGAARD, BA, ÖSTBERG, P, PAGNOTTA, E, PAINTER, M, PALAN, S, PALIT, IJ, PARK, A, GIL‐BAZO, J, PASCUAL, R, BACH, A, PASQUARIELLO, P, PASTOR, L, PATEL, V, PATTON, AJ, PEARSON, ND, PELIZZON, L, PELLI, M, PELSTER, M, GILDER, D, PÉRIGNON, C, PFIFFER, C, BAIDOO, E, PHILIP, R, PLÍHAL, T, PRAKASH, P, PRESS, O, PRODROMOU, T, PROKOPCZUK, M, PUTNINS, T, GLOSTEN, LR, QIAN, Y, RAIZADA, G, RAKOWSKI, D, BAKALLI, G, RANALDO, A, REGIS, L, REITZ, S, RENAULT, T, RENJIE, RW, RENO, R, GOMEZ, T, RIDDIOUGH, SJ, RINNE, K, RINTAMÄKI, P, RIORDAN, R, BAO, L, RITTMANNSBERGER, T, LONGARELA, IR, ROESCH, D, ROGNONE, L, ROSEMAN, B, GORBENKO, A, ROŞU, I, ROY, S, RUDOLF, N, RUSH, SR, RZAYEV, K, BARBON, A, RZEŹNIK, AA, SANFORD, A, SANKARAN, H, SARKAR, A, GRAMMIG, J, SARNO, L, SCAILLET, O, SCHARNOWSKI, S, SCHENK‐HOPPÉ, KR, SCHERTLER, A, SCHNEIDER, M, BASHCHENKO, O, SCHROEDER, F, SCHÜRHOFF, N, SCHUSTER, P, GRÉGOIRE, V, SCHWARZ, MA, SEASHOLES, MS, SEEGER, NJ, SHACHAR, O, SHKILKO, A, SHUI, J, SIKIC, M, BINDRA, PC, SIMION, G, SMALES, LA, GÜÇBILMEZ, U, SÖDERLIND, P, SOJLI, E, SOKOLOV, K, SÖNKSEN, J, SPOKEVICIUTE, L, STEFANOVA, D, SUBRAHMANYAM, MG, SZASZI, B, BJØNNES, GH, TALAVERA, O, ADRIAN, T, TANG, Y, TAYLOR, N, THAM, W, THEISSEN, E, THIMME, J, TONKS, I, TRAN, H, TRAPIN, L, TROLLE, AB, BLACK, JR, HAGSTRÖMER, B, VADUVA, MA, VALENTE, G, VAN NESS, RA, VASQUEZ, A, VEROUSIS, T, VERWIJMEREN, P, VILHELMSSON, A, VILKOV, G, VLADIMIROV, V, VOGEL, S, HAMBUCKERS, J, BLACK, BS, VOIGT, S, WAGNER, W, WALTHER, T, WEISS, P, VAN DER WEL, M, WERNER, IM, WESTERHOLM, PJ, WESTHEIDE, C, WIKA, HC, HAPNES, E, WIPPLINGER, E, BOGOEV, D, WOLF, M, WOLFF, CCP, WOLK, L, WONG, W, WRAMPELMEYER, J, WU, Z, XIA, S, XIU, D, HARRIS, JH, XU, K, XU, C, CORREA, SB, YADAV, PK, YAGÜE, J, YAN, C, YANG, A, YOO, W, YU, W, YU, Y, HARRIS, L, YU, S, YUESHEN, BZ, YUFEROVA, D, BONDARENKO, O, ZAMOJSKI, M, ZAREEI, A, ZEISBERGER, SM, ZHANG, L, ZHANG, SS, ZHANG, X, HARTMANN, S, ZHAO, L, ZHONG, Z, ZHOU, ZI, ZHOU, C, BOS, CS, ZHU, XS, ZOICAN, M, ZWINKELS, R, BOSCH‐ROSA, C, BOURI, E, HASSE, J, BROWNLEES, C, CALAMIA, A, CAO, VN, CAPELLE‐BLANCARD, G, ROMERO, LMC, CAPORIN, M, CARRION, A, CASKURLU, T, CHAKRABARTY, B, CHEN, J, HAUTSCH, N, CHERNOV, M, CHEUNG, W, CHINCARINI, LB, CHORDIA, T, CHOW, S, CLAPHAM, B, COLLIARD, J, COMERTON‐FORDE, C, CURRAN, E, DAO, T, HE, X, DARE, W, DAVIES, RJ, DE BLASIS, R, DE NARD, GF, DECLERCK, F, DEEV, O, DEGRYSE, H, DEKU, SY, DESAGRE, C, VAN DIJK, MA, HEATH, D, DIM, C, DIMPFL, T, DONG, YJ, DRUMMOND, PA, DUDDA, T, DUEVSKI, T, DUMITRESCU, A, DYAKOV, T, DYHRBERG, AH, DZIELIŃSKI, M, AIT‐SAHALIA, Y, EKSI, A, KALAK, IE, ELLEN, ST, EUGSTER, N, EVANS, MDD, FARRELL, M, FELEZ‐VINAS, E, FERRARA, G, FERROUHI, EM, FLORI, A, HEDIGER, S, FLUHARTY‐JAIDEE, JT, FOLEY, SDV, FONG, KYL, FOUCAULT, T, FRANUS, T, FRANZONI, F, FRIJNS, B, FRÖMMEL, M, FU, SM, FÜLLBRUNN, SC, HENDERSHOTT, T, GAN, B, GAO, G, GEHRIG, TP, HIBBERT, AM, HJALMARSSON, E, HOELSCHER, SA, HOFFMANN, P, HOLDEN, CW, HORENSTEIN, AR, HUANG, W, HUANG, D, AKMANSOY, O, HURLIN, C, ILCZUK, K, IVASHCHENKO, A, IYER, SR, JAHANSHAHLOO, H, JALKH, N, JONES, CM, JURKATIS, S, JYLHÄ, P, KAECK, AT, ALCOCK, JT, KAISER, G, KARAM, A, KARMAZIENE, E, KASSNER, B, KAUSTIA, M, KAZAK, E, KEARNEY, F, VAN KERVEL, V, KHAN, SA, KHOMYN, MK, ALEXEEV, V, KLEIN, T, KLEIN, O, KLOS, A, KOETTER, M, KOLOKOLOV, A, KORAJCZYK, RA, KOZHAN, R, KRAHNEN, JP, KUHLE, P, KWAN, A, ALOOSH, A, LAJAUNIE, Q, LAM, FYEC, LAMBERT, M, LANGLOIS, H, LAUSEN, J, LAUTER, T, LEIPPOLD, M, LEVIN, V, LI, Y, LI, H, AMATO, L, LIEW, CY, LINDNER, T, LINTON, O, LIU, J, LIU, A, LLORENTE, G, LOF, M, LOHR, A, LONGSTAFF, F, LOPEZ‐LIRA, A, AMAYA, D, and MANKAD, S

Abstract

ABSTRACTIn statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.

Published
2024

7. Non-Standard Errors

Author

Menkveld, AJ, Dreber, A, Holzmeister, F, Huber, J, Johanneson, M, Kirchler, M, Razen, M, Weitzel, U, Abad, D, Abudy, MM, Adrian, T, Gregoire, V, Hagströmer, B, Hambuckers, J, Hapnes, E, Harris, JH, Harris, L, Hartmann, S, Hasse, J-B, Hautsch, N, He, X-ZT, Ait-Sahalia, Y, Heath, D, Hediger, S, Hendershott, TJ, Hibbert, AM, Hjalmarsson, E, Hoelscher, S, Hoffmann, P, Holden, CW, Horenstein, AR, Huang, W, Akmansoy, O, Huang, D, Hurlin, C, Ivashchenko, A, Iyer, SR, Jahanshahloo, H, Jalkh, N, Jones, CM, Jurkatis, S, Jylha, P, Kaeck, A, Alcock, J, Kaiser, G, Karam, A, Karmaziene, E, Kassner, B, Kaustia, M, Kazak, E, Kearney, F, van Kervel, V, Khan, S, Khomyn, M, Alexeev, V, Klein, T, Klein, O, Klos, A, Koetter, M, Krahnen, JP, Kolokolov, A, Korajczyk, RA, Kozhan, R, Kwan, A, Lajaunie, Q, Aloosh, A, Lam, FYEC, Lambert, M, Langlois, H, Lausen, J, Lauter, T, Leippold, M, Levin, V, Li, Y, Li, MH, Liew, CY, Amato, L, Lindner, T, Linton, OB, Liu, J, Liu, A, Llorente-Alvarez, J-G, Lof, M, Lohr, A, Longstaff, FA, Lopez-Lira, A, Mankad, S, Amaya, D, Mano, N, Marchal, A, Martineau, C, Mazzola, F, Meloso, DC, Mihet, R, Mohan, V, Moinas, S, Moore, D, Mu, L, Angel, JJ, Muravyev, D, Murphy, D, Neszveda, G, Neumeier, C, Nielsson, U, Nimalendran, M, Nolte, S, Nordén, LL, O'Neill, P, Obaid, K, Bach, A, Ødegaard, BA, Östberg, P, Painter, M, Palan, S, Palit, I, Park, A, Pascual Gascó, R, Pasquariello, P, Pastor, L, Patel, V, Baidoo, E, Patton, AJ, Pearson, ND, Pelizzon, L, Pelster, M, Pérignon, C, Pfiffer, C, Philip, R, Plíhal, T, Prakash, P, Press, O-A, Bakalli, G, Prodromou, T, Putnins, TJ, Raizada, G, Rakowski, DA, Ranaldo, A, Regis, L, Reitz, S, Renault, T, Wang, R, Renò, R, Barbon, A, Riddiough, S, Rinne, K, Rintamäki, P, Riordan, R, RITTMANNSBERGER, T, Rodríguez Longarela, I, Rösch, D, Rognone, L, Roseman, B, Rosu, I, Bashchenko, O, Roy, S, Rudolf, N, Rush, S, Rzayev, K, Rzeźnik, A, Sanford, A, Sankaran, H, Sarkar, A, Sarno, L, Scaillet, O, Bindra, PC, Scharnowski, S, Schenk-Hoppé, KR, Schertler, A, Schneider, M, Schroeder, F, Schürhoff, N, Schuster, P, Schwarz, MA, Seasholes, MS, Seeger, N, Bjonnes, GH, Shachar, O, Shkilko, A, Shui, J, Sikic, M, Simion, G, Smales, LA, Söderlind, P, Sojli, E, Sokolov, K, Spokeviciute, L, Black, JR, Stefanova, D, Subrahmanyam, MG, Neusüss, S, Szaszi, B, Talavera, O, Tang, Y, Taylor, N, Tham, WW, Theissen, E, Thimme, J, Black, BS, Tonks, I, Tran, H, Trapin, L, Trolle, AB, Vaduva, M, Valente, G, Van Ness, RA, Vasquez, A, Verousis, T, Verwijmeren, P, Bohorquez, S, Vilhelmsson, A, Vilkov, G, Vladimirov, V, Vogel, S, Voigt, S, Wagner, W, Walther, T, Weiss, P, van der Wel, M, Werner, IM, Bondarenko, O, Westerholm, PJ, Westheide, C, Wipplinger, E, Wolf, M, Wolff, CCP, Wolk, L, Wong, WK, Wrampelmeyer, J, Wu, Z-X, Xia, S, Bos, CS, Xiu, D, Xu, K, Xu, C, Yadav, PK, Yagüe, J, Yan, C, Yang, A, Yoo, W, Yu, W, Yu, S, Bosch-Rosa, C, Yueshen, BZ, Yuferova, D, Zamojski, M, Zareei, A, Zeisberger, S, Zhang, S, Zhang, X, Zhong, Z, Zhou, ZI, Zhou, C, Bouri, E, Zhu, X, Zoican, M, Zwinkels, RCJ, Chen, J, Duevski, T, Gao, G, Gemayel, R, Gilder, D, Kuhle, P, Pagnotta, E, Brownlees, CT, Pelli, M, Sönksen, J, Zhang, L, Ilczuk, K, Bogoev, D, Qian, Y, Wika, HC, Yu, Y, Zhao, L, Mi, M, Calamia, A, Bao, L, Cao, VN, Capelle-Blancard, G, Capera, L, Caporin, M, Carrion, A, Caskurlu, T, Chakrabarty, B, Chernov, M, Cheung, WMY, Chincarini, LB, Chordia, T, Chow, SC, Clapham, B, Colliard, J-E, Comerton-Forde, C, Curran, E, Dao, T, Dare, W, Davies, RJ, De Blasis, R, De Nard, G, Declerck, F, Deev, O, Degryse, H, Deku, S, Desagre, C, Van Dijk, MA, Dim, C, Dimpfl, T, Dong, YJ, Drummond, P, Dudda, T, Dumitrescu, A, Dyakov, T, Dyhrberg, AH, Dzieliński, M, Eksi, A, El Kalak, I, ter Ellen, S, Eugster, N, Evans, MDD, Farrell, M, Félez-Viñas, E, Ferrara, G, FERROUHI, EM, Flori, A, Fluharty-Jaidee, J, Foley, S, Fong, KYL, Foucault, T, Franus, T, Franzoni, FA, Frijns, B, Frömmel, M, Fu, S, Füllbrunn, S, Gan, B, Gehrig, T, Gerritsen, D, Gil-Bazo, J, Glosten, LR, Gomez, T, Gorbenko, A, Güçbilmez, U, Grammig, J, Menkveld, AJ, Dreber, A, Holzmeister, F, Huber, J, Johanneson, M, Kirchler, M, Razen, M, Weitzel, U, Abad, D, Abudy, MM, Adrian, T, Gregoire, V, Hagströmer, B, Hambuckers, J, Hapnes, E, Harris, JH, Harris, L, Hartmann, S, Hasse, J-B, Hautsch, N, He, X-ZT, Ait-Sahalia, Y, Heath, D, Hediger, S, Hendershott, TJ, Hibbert, AM, Hjalmarsson, E, Hoelscher, S, Hoffmann, P, Holden, CW, Horenstein, AR, Huang, W, Akmansoy, O, Huang, D, Hurlin, C, Ivashchenko, A, Iyer, SR, Jahanshahloo, H, Jalkh, N, Jones, CM, Jurkatis, S, Jylha, P, Kaeck, A, Alcock, J, Kaiser, G, Karam, A, Karmaziene, E, Kassner, B, Kaustia, M, Kazak, E, Kearney, F, van Kervel, V, Khan, S, Khomyn, M, Alexeev, V, Klein, T, Klein, O, Klos, A, Koetter, M, Krahnen, JP, Kolokolov, A, Korajczyk, RA, Kozhan, R, Kwan, A, Lajaunie, Q, Aloosh, A, Lam, FYEC, Lambert, M, Langlois, H, Lausen, J, Lauter, T, Leippold, M, Levin, V, Li, Y, Li, MH, Liew, CY, Amato, L, Lindner, T, Linton, OB, Liu, J, Liu, A, Llorente-Alvarez, J-G, Lof, M, Lohr, A, Longstaff, FA, Lopez-Lira, A, Mankad, S, Amaya, D, Mano, N, Marchal, A, Martineau, C, Mazzola, F, Meloso, DC, Mihet, R, Mohan, V, Moinas, S, Moore, D, Mu, L, Angel, JJ, Muravyev, D, Murphy, D, Neszveda, G, Neumeier, C, Nielsson, U, Nimalendran, M, Nolte, S, Nordén, LL, O'Neill, P, Obaid, K, Bach, A, Ødegaard, BA, Östberg, P, Painter, M, Palan, S, Palit, I, Park, A, Pascual Gascó, R, Pasquariello, P, Pastor, L, Patel, V, Baidoo, E, Patton, AJ, Pearson, ND, Pelizzon, L, Pelster, M, Pérignon, C, Pfiffer, C, Philip, R, Plíhal, T, Prakash, P, Press, O-A, Bakalli, G, Prodromou, T, Putnins, TJ, Raizada, G, Rakowski, DA, Ranaldo, A, Regis, L, Reitz, S, Renault, T, Wang, R, Renò, R, Barbon, A, Riddiough, S, Rinne, K, Rintamäki, P, Riordan, R, RITTMANNSBERGER, T, Rodríguez Longarela, I, Rösch, D, Rognone, L, Roseman, B, Rosu, I, Bashchenko, O, Roy, S, Rudolf, N, Rush, S, Rzayev, K, Rzeźnik, A, Sanford, A, Sankaran, H, Sarkar, A, Sarno, L, Scaillet, O, Bindra, PC, Scharnowski, S, Schenk-Hoppé, KR, Schertler, A, Schneider, M, Schroeder, F, Schürhoff, N, Schuster, P, Schwarz, MA, Seasholes, MS, Seeger, N, Bjonnes, GH, Shachar, O, Shkilko, A, Shui, J, Sikic, M, Simion, G, Smales, LA, Söderlind, P, Sojli, E, Sokolov, K, Spokeviciute, L, Black, JR, Stefanova, D, Subrahmanyam, MG, Neusüss, S, Szaszi, B, Talavera, O, Tang, Y, Taylor, N, Tham, WW, Theissen, E, Thimme, J, Black, BS, Tonks, I, Tran, H, Trapin, L, Trolle, AB, Vaduva, M, Valente, G, Van Ness, RA, Vasquez, A, Verousis, T, Verwijmeren, P, Bohorquez, S, Vilhelmsson, A, Vilkov, G, Vladimirov, V, Vogel, S, Voigt, S, Wagner, W, Walther, T, Weiss, P, van der Wel, M, Werner, IM, Bondarenko, O, Westerholm, PJ, Westheide, C, Wipplinger, E, Wolf, M, Wolff, CCP, Wolk, L, Wong, WK, Wrampelmeyer, J, Wu, Z-X, Xia, S, Bos, CS, Xiu, D, Xu, K, Xu, C, Yadav, PK, Yagüe, J, Yan, C, Yang, A, Yoo, W, Yu, W, Yu, S, Bosch-Rosa, C, Yueshen, BZ, Yuferova, D, Zamojski, M, Zareei, A, Zeisberger, S, Zhang, S, Zhang, X, Zhong, Z, Zhou, ZI, Zhou, C, Bouri, E, Zhu, X, Zoican, M, Zwinkels, RCJ, Chen, J, Duevski, T, Gao, G, Gemayel, R, Gilder, D, Kuhle, P, Pagnotta, E, Brownlees, CT, Pelli, M, Sönksen, J, Zhang, L, Ilczuk, K, Bogoev, D, Qian, Y, Wika, HC, Yu, Y, Zhao, L, Mi, M, Calamia, A, Bao, L, Cao, VN, Capelle-Blancard, G, Capera, L, Caporin, M, Carrion, A, Caskurlu, T, Chakrabarty, B, Chernov, M, Cheung, WMY, Chincarini, LB, Chordia, T, Chow, SC, Clapham, B, Colliard, J-E, Comerton-Forde, C, Curran, E, Dao, T, Dare, W, Davies, RJ, De Blasis, R, De Nard, G, Declerck, F, Deev, O, Degryse, H, Deku, S, Desagre, C, Van Dijk, MA, Dim, C, Dimpfl, T, Dong, YJ, Drummond, P, Dudda, T, Dumitrescu, A, Dyakov, T, Dyhrberg, AH, Dzieliński, M, Eksi, A, El Kalak, I, ter Ellen, S, Eugster, N, Evans, MDD, Farrell, M, Félez-Viñas, E, Ferrara, G, FERROUHI, EM, Flori, A, Fluharty-Jaidee, J, Foley, S, Fong, KYL, Foucault, T, Franus, T, Franzoni, FA, Frijns, B, Frömmel, M, Fu, S, Füllbrunn, S, Gan, B, Gehrig, T, Gerritsen, D, Gil-Bazo, J, Glosten, LR, Gomez, T, Gorbenko, A, Güçbilmez, U, and Grammig, J

Published
2024

9. Time-Series Forecast Modeling on High-Bandwidth Network Measurements

Author

Yoo, W and Sim, A

Subjects
Data modeling, Time series, Prediction model, Network measurements, Network traffic, Distributed Computing
Abstract

With the increasing number of geographically distributed scientific collaborations and the growing sizes of scientific data, it has become challenging for users to achieve the best possible network performance on a shared network. We have developed a model to forecast expected bandwidth utilization on high-bandwidth wide area networks. The forecast model can improve the efficiency of the resource utilization and scheduling of data movements on high-bandwidth networks to accommodate ever increasing data volume for large-scale scientific data applications. A univariate time-series forecast model is developed with the Seasonal decomposition of Time series by Loess (STL) and the AutoRegressive Integrated Moving Average (ARIMA) on Simple Network Management Protocol (SNMP) path utilization measurement data. Compared with the traditional approach such as Box-Jenkins methodology to train the ARIMA model, our forecast model reduces computation time up to 92.6 %. It also shows resilience against abrupt network usage changes. Our forecast model conducts the large number of multi-step forecast, and the forecast errors are within the mean absolute deviation (MAD) of the monitored measurements.

Published
2016

10. Machine learning based job status prediction in scientific clusters

Author

Yoo, W, Sim, A, and Wu, K

Subjects
Reliability, Job Log Analysis, Job Status Prediction
Abstract

Large high-performance computing systems are built with increasing number of components with more CPU cores, more memory, and more storage space. At the same time, scientific applications have been growing in complexity. Together, they are leading to more frequent unsuccessful job statuses on HPC systems. From measured job statuses, 23.4% of CPU time was spent to the unsuccessful jobs. We set out to study whether these unsuccessful job statuses could be anticipated from known job characteristics. To explore this possibility, we have developed a job status prediction method for the execution of jobs on scientific clusters. The Random Forests algorithm was applied to extract and characterize the patterns of unsuccessful job statuses. Experimental results show that our method can predict the unsuccessful job statuses from the monitored ongoing job executions in 99.8% the cases with 83.6% recall and 94.8% precision. This prediction accuracy can be sufficiently high that it can be used to mitigation procedures of predicted failures.

Published
2016

11. PATHA: Performance Analysis Tool for HPC Applications

Author

Yoo, W, Koo, M, Cao, Y, Sim, A, Nugent, P, and Wu, K

Subjects
Performance analysis, Performance evaluation, High performance computing
Abstract

Large science projects rely on complex workflows to analyze terabytes or petabytes of data. These jobs are often running over thousands of CPU cores and simultaneously performing data accesses, data movements, and computation. It is difficult to identify bottlenecks or to debug the performance issues in these large workflows. To address these challenges, we have developed Performance Analysis Tool for HPC Applications (PATHA) using the state-of-art open source big data processing tools. Our framework can ingest system logs to extract key performance measures, and apply the most sophisticated statistical tools and data mining methods on the performance data. It utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of PATHA, we conduct a case study on the workflows from an astronomy project known as the Palomar Transient Factory (PTF). Our study processed 1.6 TB of system logs collected on the NERSC supercomputer Edison. Using PATHA, we were able to identify performance bottlenecks, which reside in three tasks of PTF workflow with the dependency on the density of celestial objects.

Published
2016

12. Network bandwidth utilization forecast model on high bandwidth networks

Author

Yoo, W and Sim, A

Subjects
Forecasting, Network, Time series analysis
Abstract

With the increasing number of geographically distributed scientific collaborations and the growing sizes of scientific data, it has become challenging for users to achieve the best possible network performance on a shared network. We have developed a model to forecast expected bandwidth utilization on high-bandwidth wide area networks. The forecast model can improve the efficiency of resource utilization and scheduling of data movements on high-bandwidth networks to accommodate ever increasing data volume for large-scale scientific data applications. A univariate forecast model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology to train the ARIMA model, our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage changes. Its forecast errors are within the standard deviation of the monitored measurements.

Published
2015

13. A Combined Phase 0/2 "Trigger" Trial Evaluating Pamiparib or Olaparib with Concurrent Radiotherapy in Patients with Newly-Diagnosed or Recurrent Glioblastoma

Author

Kennedy, W.R., primary, Chang, Y.W., additional, Jiang, J., additional, Molloy, J., additional, Pennington-Krygier, C., additional, Harmon, J., additional, Hong, A., additional, Wanebo, J., additional, Braun, K., additional, Garcia, M.A., additional, Barani Jr, I.J., additional, Yoo, W., additional, Tovmasyan, A., additional, Tien, A.C., additional, Li, J., additional, Mehta, S., additional, and Sanai, N., additional

Published
2023
Full Text
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14. A Combined Phase 0/2 "Trigger" Trial of Niraparib in Combination with Radiation in Patients with Newly-Diagnosed Glioblastoma

Author

Kennedy, W.R., primary, Margaryan, T., additional, Molloy, J., additional, Knight, W., additional, Harmon, J., additional, Hong, A., additional, Wanebo, J., additional, Braun, K., additional, Garcia, M.A., additional, Barani, I.J., additional, Yoo, W., additional, Tien, A.C., additional, Tovmasyan, A., additional, Mehta, S., additional, and Sanai, N., additional

Published
2023
Full Text
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15. Feasibility of Integrated GNSS/OBD-II/IMU as a Prerequisite for Virtual Reality

Author

Lim, J. H., Choi, K. H., Yoo, W., Kim, L., Lee, Y., Lee, Hyung Keun, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, and Stephanidis, Constantine, editor

Published
2017
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16. Ballistic Transport and Andreev Resonances in Nb-In Superconducting Contacts to InAs and LTG-GaAs

Author

Rizk, T., Yulius, A., Yoo, W. I., Bagwell, P. F., McInturff, D., Chin, P., Woodall, J. M., Pekarek, T. M., and Jackson, T. N.

Subjects
Condensed Matter - Superconductivity
Abstract

We have formed superconducting contacts in which Cooper pairs incident from a thick In layer must move through a thin Nb layer to reach a semiconductor, either InAs or low temperature grown (LTG) GaAs. The effect of pair tunneling through the Nb layer can be seen by varying the temperature through the critical temperature of In. Several of the In/Nb-InAs devices display a peak in the differential conductance near zero-bias voltage, which is strong evidence of ballistic transport across the NS interface. The differential conductance of the In/Nb-(LTG) GaAs materials system displays conductance resonances of McMillan-Rowell type. These resonant levels exist within a band of conducting states inside the energy gap, formed from excess As incorporation into the (LTG) GaAs during growth. Electrons propagating in this band of states multiply reflect between the superconductor and a potential barrier in the GaAs conduction band to form the conductance resonances. A scattering state theory of the differential conductance, including Andreev reflections from the composite In/Nb contact, accounts for most qualitative features in the data.

Published
1999
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22. Non-Standard Errors

Author

Menkveld, A.J., Dreber, A., Holzmeister, F., Huber, J., Johannesson, M., Kirchler, M., Razen, M., Weitzel, G.U., Abad, D., Abudy, M., Adrian, T., Ait-Sahalia, Y., Akmansoy, O., Alcock, J., Alexeev, V., Aloosh, A., Amato, L., Amaya, D., Angel, J., Bach, A., Baidoo, E., Bakalli, G., Barbon, A., Bashchenko, O., Bindra, P.C., Bjonnes, G.H., Black, J., Black, B.S., Bohorquez, S., Bondarenko, O., Bos, C.S., Bosch-Rosa, C., Bouri, E., Brownlees, C.T., Calamia, A., Cao, V.N., Capelle-Blancard, G., Capera, L., Caporin, M., Carrion, A., Caskurlu, T., Chakrabarty, B., Chernov, M., Cheung, W.M., Chincarini, L.B., Chordia, T., Chow, S.C., Clapham, B., Colliard, J.-E., Comerton-Forde, C., Curran, E., Dao, T., Dare, W., Davies, R.J., Blasis, R. De, Nard, G. De, DeClerck, F., Deev, O., Degryse, H., Deku, S., Desagre, C., Dijk, M.A van, Dim, C., Dimpfl, T., Dong, Y., Drummond, P., Dudda, T., Dumitrescu, A., Dyakov, T., Dyhrberg, A.H., Dzieliński, M., Eksi, A., Kalak, I. El, Ellen, S. ter, Eugster, N., Evans, M.D.D., Farrell, M., Félez-Viñas, E., Ferrara, G., Ferrouhi, E.M., Flori, A., Fluharty-Jaidee, J., Foley, S., Fong, K.Y.L., Foucault, T., Franus, T., Franzoni, F.A., Frijns, B., Frömmel, M., Fu, S., Füllbrunn, S.C., Gan, B., Gehrig, T., Gerritsen, D., Gil-Bazo, J., Glosten, L.R., Gomez, T., Gorbenko, A., Güçbilmez, U., Grammig, J., Gregoire, V., Hagströmer, B., Hambuckers, J., Hapnes, E., Harris, J.H., Harris, L., Hartmann, S., Hasse, J.-B., Hautsch, N., He, X., Heath, D., Hediger, S., Hendershott, T., Hibbert, A.M., Hjalmarsson, E., Hoelscher, S.A., Hoffmann, P., Holden, C.W., Horenstein, A.R., Huang, W., Huang, D., Hurlin, C., Ivashchenko, A., Iyer, S.R., Jahanshahloo, H., Jalkh, N., Jones, C.M., Jurkatis, S., Jylha, P., Kaeck, A., Kaiser, G., Karam, A., Karmaziene, E., Kassner, B., Kaustia, M., Kazak, E., Kearney, F., Kervel, V. van, Khan, S., Khomyn, M., Klein, T., Klein, O., Klos, A., Koetter, M., Krahnen, J.P., Kolokolov, A., Korajczyk, R.A., Kozhan, R., Kwan, A., Lajaunie, Q., Lam, F.E.C., Lambert, M., Langlois, H., Lausen, J., Lauter, T., Leippold, M., Levin, V., Li, Y., Li, H., Liew, C.Y., Lindner, T., Linton, O.B., Liu, J., Liu, A., Llorente, G., Lof, M., Lohr, A., Longstaff, F.A., Lopez-Lira, A., Mankad, S., Mano, N., Marchal, A., Martineau, C., Mazzola, F., Meloso, D., Mihet, R., Mohan, V., Moinas, S., Moore, D., Mu, L., Muravyev, D., Murphy, D., Neszveda, G., Neumeier, C., Nielsson, U., Nimalendran, M., Nolte, S., Norden, L.L., O'Neill, P., Obaid, K., Ødegaard, B.A., Östberg, P., Painter, M., Palan, S., Palit, I., Park, A., Pascual, R., Pasquariello, P., Pastor, L., Patel, V., Patton, A.J., Pearson, N.D., Pelizzon, L., Pelster, M., Pérignon, C., Pfiffer, C., Philip, R., Plíhal, T., Prakash, P., Press, O.-A., Prodromou, T., Putniņš, T.J., Raizada, G., Rakowski, D.A., Ranaldo, A., Regis, L., Reitz, S., Renault, T., Renjie, R.W., Renò, R., Riddiough, S., Rinne, K., Rintamäki, P., Riordan, R., Rittmannsberger, T., Rodríguez-Longarela, I., Rösch, D., Rognone, L., Roseman, B., Rosu, I., Roy, S., Rudolf, N., Rush, S., Rzayev, K., Rzeźnik, A., Sanford, A., Sankaran, H., Sarkar, A., Sarno, L., Scaillet, O., Scharnowski, S., Schenk-Hoppé, K.R., Schertler, A., Schneider, M., Schroeder, F., Schürhoff, N., Schuster, P., Schwarz, M.A., Seasholes, M.S., Seeger, N., Shachar, O., Shkilko, A., Shui, J., Sikic, M., Simion, G., Smales, L.A., Söderlind, P., Sojli, E., Sokolov, K., Spokeviciute, L., Stefanova, D., Subrahmanyam, M.G., Neusüss, S., Szaszi, B., Talavera, O., Tang, Y., Taylor, N., Tham, W.W., Theissen, E., Thimme, J., Tonks, I., Tran, H., Trapin, L., Trolle, A.B., Valente, G., Ness, R.A. Van, Vasquez, A., Verousis, T., Verwijmeren, P., Vilhelmsson, A., Vilkov, G., Vladimirov, V., Vogel, S., Voigt, S., Wagner, W., Walther, T., Weiss, P., Wel, M. van der, Werner, I.M., Westerholm, P.J., Westheide, C., Wipplinger, E., Wolf, M., Wolff, C.C.P., Wolk, L., Wong, W.-K., Wrampelmeyer, J., Xia, S., Xiu, D., Xu, K., Xu, C., Yadav, P.K., Yagüe, J., Yan, C., Yang, A., Yoo, W., Yu, W., Yu, S., Yueshen, B.Z., Yuferova, D., Zamojski, M., Zareei, A., Zeisberger, S.M., Zhang, S.S., Zhang, X., Zhong, Z., Zhou, Z.I., Zhou, C., Zhu, X.S., Zoican, M., Zwinkels, R.C.J., Chen, J., Duevski, T., Gao, G., Gemayel, R., Gilder, D. de, Kuhle, P., Pagnotta, E., Pelli, M., Sönksen, J., Zhang, L., Ilczuk, K., Bogoev, D., Qian, Y., Wika, H.C., Yu, Y., Zhao, L., Mi, M., Bao, L., Vaduva, A., Prokopczuk, P., Avetikian, A., Wu, Z.-X., Menkveld, A.J., Dreber, A., Holzmeister, F., Huber, J., Johannesson, M., Kirchler, M., Razen, M., Weitzel, G.U., Abad, D., Abudy, M., Adrian, T., Ait-Sahalia, Y., Akmansoy, O., Alcock, J., Alexeev, V., Aloosh, A., Amato, L., Amaya, D., Angel, J., Bach, A., Baidoo, E., Bakalli, G., Barbon, A., Bashchenko, O., Bindra, P.C., Bjonnes, G.H., Black, J., Black, B.S., Bohorquez, S., Bondarenko, O., Bos, C.S., Bosch-Rosa, C., Bouri, E., Brownlees, C.T., Calamia, A., Cao, V.N., Capelle-Blancard, G., Capera, L., Caporin, M., Carrion, A., Caskurlu, T., Chakrabarty, B., Chernov, M., Cheung, W.M., Chincarini, L.B., Chordia, T., Chow, S.C., Clapham, B., Colliard, J.-E., Comerton-Forde, C., Curran, E., Dao, T., Dare, W., Davies, R.J., Blasis, R. De, Nard, G. De, DeClerck, F., Deev, O., Degryse, H., Deku, S., Desagre, C., Dijk, M.A van, Dim, C., Dimpfl, T., Dong, Y., Drummond, P., Dudda, T., Dumitrescu, A., Dyakov, T., Dyhrberg, A.H., Dzieliński, M., Eksi, A., Kalak, I. El, Ellen, S. ter, Eugster, N., Evans, M.D.D., Farrell, M., Félez-Viñas, E., Ferrara, G., Ferrouhi, E.M., Flori, A., Fluharty-Jaidee, J., Foley, S., Fong, K.Y.L., Foucault, T., Franus, T., Franzoni, F.A., Frijns, B., Frömmel, M., Fu, S., Füllbrunn, S.C., Gan, B., Gehrig, T., Gerritsen, D., Gil-Bazo, J., Glosten, L.R., Gomez, T., Gorbenko, A., Güçbilmez, U., Grammig, J., Gregoire, V., Hagströmer, B., Hambuckers, J., Hapnes, E., Harris, J.H., Harris, L., Hartmann, S., Hasse, J.-B., Hautsch, N., He, X., Heath, D., Hediger, S., Hendershott, T., Hibbert, A.M., Hjalmarsson, E., Hoelscher, S.A., Hoffmann, P., Holden, C.W., Horenstein, A.R., Huang, W., Huang, D., Hurlin, C., Ivashchenko, A., Iyer, S.R., Jahanshahloo, H., Jalkh, N., Jones, C.M., Jurkatis, S., Jylha, P., Kaeck, A., Kaiser, G., Karam, A., Karmaziene, E., Kassner, B., Kaustia, M., Kazak, E., Kearney, F., Kervel, V. van, Khan, S., Khomyn, M., Klein, T., Klein, O., Klos, A., Koetter, M., Krahnen, J.P., Kolokolov, A., Korajczyk, R.A., Kozhan, R., Kwan, A., Lajaunie, Q., Lam, F.E.C., Lambert, M., Langlois, H., Lausen, J., Lauter, T., Leippold, M., Levin, V., Li, Y., Li, H., Liew, C.Y., Lindner, T., Linton, O.B., Liu, J., Liu, A., Llorente, G., Lof, M., Lohr, A., Longstaff, F.A., Lopez-Lira, A., Mankad, S., Mano, N., Marchal, A., Martineau, C., Mazzola, F., Meloso, D., Mihet, R., Mohan, V., Moinas, S., Moore, D., Mu, L., Muravyev, D., Murphy, D., Neszveda, G., Neumeier, C., Nielsson, U., Nimalendran, M., Nolte, S., Norden, L.L., O'Neill, P., Obaid, K., Ødegaard, B.A., Östberg, P., Painter, M., Palan, S., Palit, I., Park, A., Pascual, R., Pasquariello, P., Pastor, L., Patel, V., Patton, A.J., Pearson, N.D., Pelizzon, L., Pelster, M., Pérignon, C., Pfiffer, C., Philip, R., Plíhal, T., Prakash, P., Press, O.-A., Prodromou, T., Putniņš, T.J., Raizada, G., Rakowski, D.A., Ranaldo, A., Regis, L., Reitz, S., Renault, T., Renjie, R.W., Renò, R., Riddiough, S., Rinne, K., Rintamäki, P., Riordan, R., Rittmannsberger, T., Rodríguez-Longarela, I., Rösch, D., Rognone, L., Roseman, B., Rosu, I., Roy, S., Rudolf, N., Rush, S., Rzayev, K., Rzeźnik, A., Sanford, A., Sankaran, H., Sarkar, A., Sarno, L., Scaillet, O., Scharnowski, S., Schenk-Hoppé, K.R., Schertler, A., Schneider, M., Schroeder, F., Schürhoff, N., Schuster, P., Schwarz, M.A., Seasholes, M.S., Seeger, N., Shachar, O., Shkilko, A., Shui, J., Sikic, M., Simion, G., Smales, L.A., Söderlind, P., Sojli, E., Sokolov, K., Spokeviciute, L., Stefanova, D., Subrahmanyam, M.G., Neusüss, S., Szaszi, B., Talavera, O., Tang, Y., Taylor, N., Tham, W.W., Theissen, E., Thimme, J., Tonks, I., Tran, H., Trapin, L., Trolle, A.B., Valente, G., Ness, R.A. Van, Vasquez, A., Verousis, T., Verwijmeren, P., Vilhelmsson, A., Vilkov, G., Vladimirov, V., Vogel, S., Voigt, S., Wagner, W., Walther, T., Weiss, P., Wel, M. van der, Werner, I.M., Westerholm, P.J., Westheide, C., Wipplinger, E., Wolf, M., Wolff, C.C.P., Wolk, L., Wong, W.-K., Wrampelmeyer, J., Xia, S., Xiu, D., Xu, K., Xu, C., Yadav, P.K., Yagüe, J., Yan, C., Yang, A., Yoo, W., Yu, W., Yu, S., Yueshen, B.Z., Yuferova, D., Zamojski, M., Zareei, A., Zeisberger, S.M., Zhang, S.S., Zhang, X., Zhong, Z., Zhou, Z.I., Zhou, C., Zhu, X.S., Zoican, M., Zwinkels, R.C.J., Chen, J., Duevski, T., Gao, G., Gemayel, R., Gilder, D. de, Kuhle, P., Pagnotta, E., Pelli, M., Sönksen, J., Zhang, L., Ilczuk, K., Bogoev, D., Qian, Y., Wika, H.C., Yu, Y., Zhao, L., Mi, M., Bao, L., Vaduva, A., Prokopczuk, P., Avetikian, A., and Wu, Z.-X.

Abstract

31 mei 2023, Contains fulltext : 240328.pdf (Author’s version preprint ) (Open Access), In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.

Published
2023

27. 287MO A phase 0 ‘trigger’ trial of pamiparib in newly diagnosed and recurrent glioblastoma patients

Author

Sanai, N., primary, Mehta, S.N., additional, Chang, Y-W., additional, Jiang, J., additional, Molloy, J., additional, Pennington-Krygier, C., additional, Harmon, J., additional, Hong, A., additional, Wanebo, J., additional, Kennedy, W., additional, Garcia, M., additional, Barani, I., additional, Yoo, W., additional, Tovmasyan, A., additional, Tien, A-C., additional, and Li, J., additional

Published
2022
Full Text
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31. Non-Standard Errors

Author

Menkveld, A.J., Dreber, A., Holzmeister, F., Huber, J., Johannesson, M., Kirchler, M., Razen, M., Weitzel, G.U., Abad, D., Abudy, M., Adrian, T., Ait-Sahalia, Y., Akmansoy, O., Alcock, J., Alexeev, V., Aloosh, A., Amato, L., Amaya, D., Angel, J., Bach, A., Baidoo, E., Bakalli, G., Barbon, A., Bashchenko, O., Bindra, P.C., Bjonnes, G.H., Black, J., Black, B.S., Bohorquez, S., Bondarenko, O., Bos, C.S., Bosch-Rosa, C., Bouri, E., Brownlees, C.T., Calamia, A., Cao, V.N., Capelle-Blancard, G., Capera, L., Caporin, M., Carrion, A., Caskurlu, T., Chakrabarty, B., Chernov, M., Cheung, W.M., Chincarini, L.B., Chordia, T., Chow, S.C., Clapham, B., Colliard, J.-E., Comerton-Forde, C., Curran, E., Dao, T., Dare, W., Davies, R.J., Blasis, R. De, Nard, G. De, DeClerck, F., Deev, O., Degryse, H., Deku, S., Desagre, C., Dijk, M.A van, Dim, C., Dimpfl, T., Dong, Y., Drummond, P., Dudda, T., Dumitrescu, A., Dyakov, T., Dyhrberg, A.H., Dzieliński, M., Eksi, A., Kalak, I. El, Ellen, S. ter, Eugster, N., Evans, M.D.D., Farrell, M., Félez-Viñas, E., Ferrara, G., Ferrouhi, E.M., Flori, A., Fluharty-Jaidee, J., Foley, S., Fong, K.Y.L., Foucault, T., Franus, T., Franzoni, F.A., Frijns, B., Frömmel, M., Fu, S., Füllbrunn, S.C., Gan, B., Gehrig, T., Gerritsen, D., Gil-Bazo, J., Glosten, L.R., Gomez, T., Gorbenko, A., Güçbilmez, U., Grammig, J., Gregoire, V., Hagströmer, B., Hambuckers, J., Hapnes, E., Harris, J.H., Harris, L., Hartmann, S., Hasse, J.-B., Hautsch, N., He, X., Heath, D., Hediger, S., Hendershott, T., Hibbert, A.M., Hjalmarsson, E., Hoelscher, S.A., Hoffmann, P., Holden, C.W., Horenstein, A.R., Huang, W., Huang, D., Hurlin, C., Ivashchenko, A., Iyer, S.R., Jahanshahloo, H., Jalkh, N., Jones, C.M., Jurkatis, S., Jylha, P., Kaeck, A., Kaiser, G., Karam, A., Karmaziene, E., Kassner, B., Kaustia, M., Kazak, E., Kearney, F., Kervel, V. van, Khan, S., Khomyn, M., Klein, T., Klein, O., Klos, A., Koetter, M., Krahnen, J.P., Kolokolov, A., Korajczyk, R.A., Kozhan, R., Kwan, A., Lajaunie, Q., Lam, F.E.C., Lambert, M., Langlois, H., Lausen, J., Lauter, T., Leippold, M., Levin, V., Li, Y., Li, H., Liew, C.Y., Lindner, T., Linton, O.B., Liu, J., Liu, A., Llorente, G., Lof, M., Lohr, A., Longstaff, F.A., Lopez-Lira, A., Mankad, S., Mano, N., Marchal, A., Martineau, C., Mazzola, F., Meloso, D., Mihet, R., Mohan, V., Moinas, S., Moore, D., Mu, L., Muravyev, D., Murphy, D., Neszveda, G., Neumeier, C., Nielsson, U., Nimalendran, M., Nolte, S., Norden, L.L., O'Neill, P., Obaid, K., Ødegaard, B.A., Östberg, P., Painter, M., Palan, S., Palit, I., Park, A., Pascual, R., Pasquariello, P., Pastor, L., Patel, V., Patton, A.J., Pearson, N.D., Pelizzon, L., Pelster, M., Pérignon, C., Pfiffer, C., Philip, R., Plíhal, T., Prakash, P., Press, O.-A., Prodromou, T., Putniņš, T.J., Raizada, G., Rakowski, D.A., Ranaldo, A., Regis, L., Reitz, S., Renault, T., Renjie, R.W., Renò, R., Riddiough, S., Rinne, K., Rintamäki, P., Riordan, R., Rittmannsberger, T., Rodríguez-Longarela, I., Rösch, D., Rognone, L., Roseman, B., Rosu, I., Roy, S., Rudolf, N., Rush, S., Rzayev, K., Rzeźnik, A., Sanford, A., Sankaran, H., Sarkar, A., Sarno, L., Scaillet, O., Scharnowski, S., Schenk-Hoppé, K.R., Schertler, A., Schneider, M., Schroeder, F., Schürhoff, N., Schuster, P., Schwarz, M.A., Seasholes, M.S., Seeger, N., Shachar, O., Shkilko, A., Shui, J., Sikic, M., Simion, G., Smales, L.A., Söderlind, P., Sojli, E., Sokolov, K., Spokeviciute, L., Stefanova, D., Subrahmanyam, M.G., Neusüss, S., Szaszi, B., Talavera, O., Tang, Y., Taylor, N., Tham, W.W., Theissen, E., Thimme, J., Tonks, I., Tran, H., Trapin, L., Trolle, A.B., Valente, G., Ness, R.A. Van, Vasquez, A., Verousis, T., Verwijmeren, P., Vilhelmsson, A., Vilkov, G., Vladimirov, V., Vogel, S., Voigt, S., Wagner, W., Walther, T., Weiss, P., Wel, M. van der, Werner, I.M., Westerholm, P.J., Westheide, C., Wipplinger, E., Wolf, M., Wolff, C.C.P., Wolk, L., Wong, W.-K., Wrampelmeyer, J., Xia, S., Xiu, D., Xu, K., Xu, C., Yadav, P.K., Yagüe, J., Yan, C., Yang, A., Yoo, W., Yu, W., Yu, S., Yueshen, B.Z., Yuferova, D., Zamojski, M., Zareei, A., Zeisberger, S.M., Zhang, S.S., Zhang, X., Zhong, Z., Zhou, Z.I., Zhou, C., Zhu, X.S., Zoican, M., Zwinkels, R.C.J., Chen, J., Duevski, T., Gao, G., Gemayel, R., Gilder, D. de, Kuhle, P., Pagnotta, E., Pelli, M., Sönksen, J., Zhang, L., Ilczuk, K., Bogoev, D., Qian, Y., Wika, H.C., Yu, Y., Zhao, L., Mi, M., Bao, L., Vaduva, A., Prokopczuk, P., Avetikian, A., Wu, Z.-X., Menkveld, A.J., Dreber, A., Holzmeister, F., Huber, J., Johannesson, M., Kirchler, M., Razen, M., Weitzel, G.U., Abad, D., Abudy, M., Adrian, T., Ait-Sahalia, Y., Akmansoy, O., Alcock, J., Alexeev, V., Aloosh, A., Amato, L., Amaya, D., Angel, J., Bach, A., Baidoo, E., Bakalli, G., Barbon, A., Bashchenko, O., Bindra, P.C., Bjonnes, G.H., Black, J., Black, B.S., Bohorquez, S., Bondarenko, O., Bos, C.S., Bosch-Rosa, C., Bouri, E., Brownlees, C.T., Calamia, A., Cao, V.N., Capelle-Blancard, G., Capera, L., Caporin, M., Carrion, A., Caskurlu, T., Chakrabarty, B., Chernov, M., Cheung, W.M., Chincarini, L.B., Chordia, T., Chow, S.C., Clapham, B., Colliard, J.-E., Comerton-Forde, C., Curran, E., Dao, T., Dare, W., Davies, R.J., Blasis, R. De, Nard, G. De, DeClerck, F., Deev, O., Degryse, H., Deku, S., Desagre, C., Dijk, M.A van, Dim, C., Dimpfl, T., Dong, Y., Drummond, P., Dudda, T., Dumitrescu, A., Dyakov, T., Dyhrberg, A.H., Dzieliński, M., Eksi, A., Kalak, I. 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Abstract

Item does not contain fulltext, In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.

Published
2022

33. Performance analysis tool for HPC and big data applications on scientific clusters

Author

Yoo, W, Koo, M, Cao, Y, Sim, A, Nugent, P, and Wu, K

Subjects
Networking and Information Technology R&D
Abstract

Big data is prevalent in HPC computing. Many HPC projects rely on complex workflows to analyze terabytes or petabytes of data. These workflows often require running over thousands of CPU cores and performing simultaneous data accesses, data movements, and computation. It is challenging to analyze the performance involving terabytes or petabytes of workflow data or measurement data of the executions, from complex workflows over a large number of nodes and multiple parallel task executions. To help identify performance bottlenecks or debug the performance issues in large-scale scientific applications and scientific clusters, we have developed a performance analysis framework, using state-ofthe- art open-source big data processing tools. Our tool can ingest system logs and application performance measurements to extract key performance features, and apply the most sophisticated statistical tools and data mining methods on the performance data. It utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of the big data analysis framework, we conduct case studies on the workflows from an astronomy project known as the Palomar Transient Factory (PTF) and the job logs from the genome analysis scientific cluster.

Published
2016

39. 347MO 5-Aminolevulinic acid sonodynamic therapy in recurrent glioblastoma: A first-in-human phase 0/1 clinical trial

Author

Sanai, N., primary, Tien, A-C., additional, Tovmasyan, A., additional, Chang, Y-W., additional, Margaryan, T., additional, Hendrickson, K., additional, Eschbacher, J., additional, Yoo, W., additional, Harmon, J., additional, Quarles, C., additional, Alhilali, L., additional, Barani, I., additional, Mehta, S.N., additional, and Mirzadeh, Z., additional

Published
2021
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