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Relation: Chae, Soosang; Choi, Won Jin; Fotev, Ivan; Bittrich, Eva; Uhlmann, Petra; Schubert, Mathias; Makarov, Denys; Wagner, Jens; Pashkin, Alexej; Fery, Andreas (2021). "Stretchable Thin Film Mechanical‐Strain‐Gated Switches and Logic Gate Functions Based on a Soft Tunneling Barrier." Advanced Materials 33(41): n/a-n/a.; https://hdl.handle.net/2027.42/170849; Advanced Materials; J. Lee, S. Kim, J. Lee, D. Yang, B. C. Park, S. Ryu, I. Park, Nanoscale 2014, 6, 11932.; H. J. Kim, A. Thukral, C. Yu, ACS Appl. Mater. Interfaces 2018, 10, 5000.; S. Chen, Y. Wei, X. Yuan, Y. Lin, L. Liu, J. Mater. Chem. C 2016, 4, 4304.; H. Park, D. S. Kim, S. Y. Hong, C. Kim, J. Y. Yun, S. Y. Oh, S. W. Jin, Y. R. Jeong, G. T. Kim, J. S. Ha, Nanoscale 2017, 9, 7631.; D. Cho, J. Park, J. Kim, T. Kim, J. Kim, I. Park, S. Jeon, ACS Appl. Mater. Interfaces 2017, 9, 17369.; G. Cai, J. Wang, K. Qian, J. Chen, S. Li, P. S. Lee, Adv. Sci. 2017, 4, 1600190.; L. Nela, J. Tang, Q. Cao, G. Tulevski, S. J. Han, Nano Lett. 2018, 18, 2054.; S. Gong, L. W. Yap, B. Zhu, Q. Zhai, Y. Liu, Q. Lyu, K. Wang, M. Yang, Y. Ling, D. T. H. Lai, F. Marzbanrad, W. Cheng, Adv. Mater. 2019, 31, 1903789.; Y. Cheng, R. Wang, H. Zhai, J. Sun, Nanoscale 2017, 9, 3834.; Y. Kim, J. Zhu, B. Yeom, M. Di Prima, X. Su, J. G. Kim, S. J. Yoo, C. Uher, N. A. Kotov, Nature 2013, 500, 59.; W. F. Brinkman, D. E. Haggan, W. W. Troutman, IEEE J. Solid‐State Circuits 1997, 32, 1858.; S. H. Byun, J. Y. Sim, Z. Zhou, J. Lee, R. Qazi, M. C. Walicki, K. E. Parker, M. P. Haney, S. H. Choi, A. Shon, G. B. Gereau, J. Bilbily, S. Li, Y. Liu, W. H. Yeo, J. G. McCall, J. Xiao, J. W. Jeong, Sci. Adv. 2019, 5, eaay0418.; D. H. Kim, R. Ghaffari, N. Lu, J. A. Rogers, Annu. Rev. Biomed. Eng. 2012, 14, 113.; S. C. B. Mannsfeld, B. C. K. Tee, R. M. Stoltenberg, C. V. H. H. Chen, S. Barman, B. V. O. Muir, A. N. Sokolov, C. Reese, Z. Bao, Nat. Mater. 2010, 9, 859.; C. Wang, D. Hwang, Z. Yu, K. Takei, J. Park, T. Chen, B. Ma, A. Javey, Nat. Mater. 2013, 12, 899.; K. K. Kim, S. Hong, H. M. Cho, J. Lee, Y. D. Suh, J. Ham, S. H. Ko, Nano Lett. 2015, 15, 5240.; T. Q. Trung, N. E. Lee, Adv. Mater. 2016, 28, 4338.; S. Lee, A. Reuveny, J. Reeder, S. Lee, H. Jin, Q. Liu, T. Yokota, T. Sekitani, T. Isoyama, Y. Abe, Z. Suo, T. Someya, Nat. Nanotechnol. 2016, 11, 472.; S. I. Rich, R. J. Wood, C. Majidi, Nat. Electron. 2018, 1, 102.; C. Larson, B. Peele, S. Li, S. Robinson, M. Totaro, L. Beccai, B. Mazzolai, R. Shepherd, Science 2016, 351, 1071.; C. El Helou, P. R. Buskohl, C. E. Tabor, R. L. Harne, Nat. Commun. 2021, 12, 1633.; M. Park, J. Im, M. Shin, Y. Min, J. Park, H. Cho, S. Park, M. B. Shim, S. Jeon, D. Y. Chung, J. Bae, J. Park, U. Jeong, K. Kim, Nat. Nanotechnol. 2012, 7, 803.; N. Matsuhisa, D. Inoue, P. Zalar, H. Jin, Y. Matsuba, A. Itoh, T. Yokota, D. Hashizume, T. Someya, Nat. Mater. 2017, 16, 834.; N. Matsuhisa, M. Kaltenbrunner, T. Yokota, H. Jinno, K. Kuribara, T. Sekitani, T. Someya, Nat. Commun. 2015, 6, 7461.; H. Stoyanov, M. Kollosche, S. Risse, R. Waché, G. Kofod, Adv. Mater. 2013, 25, 578.; T. Sekitani, Y. Noguchi, K. Hata, T. Fukushima, T. Aida, T. Someya, Science 2008, 321, 1468.; K. Y. Chun, Y. Oh, J. Rho, J. H. Ahn, Y. J. Kim, H. R. Choi, S. Baik, Nat. Nanotechnol. 2010, 5, 853.; L. Duan, D. R. D’hooge, L. Cardon, Prog. Mater. Sci. 2020, 114, 100617.; S. Biccai, C. S. Boland, D. P. O’Driscoll, A. Harvey, C. Gabbett, D. R. O’Suilleabhain, A. J. Griffin, Z. Li, R. J. Young, J. N. Coleman, ACS Nano 2019, 13, 6845.; Z. Yang, Y. Pang, X. L. Han, Y. Yang, Y. Yang, J. Ling, M. Jian, Y. Zhang, T. L. Ren, ACS Nano 2018, 12, 9134.; K. Zhao, W. Niu, S. Zhang, J. Mater. Sci. 2020, 55, 2439.; S. Wang, D. D. L. Chung, J. Mater. Sci. 2007, 42, 4987.; T. Ouchi, R. C. Hayward, ACS Appl. Mater. Interfaces 2020, 12, 10031.; G. Lee, T. Lee, Y. W. Choi, P. V. Pikhitsa, S. J. Park, S. M. Kim, D. Kang, M. Choi, J. Mater. Chem. C 2017, 5, 10920.; B. Xu, D. Chen, R. C. Hayward, Adv. Mater. 2014, 26, 4381.; J. G. Simmons, J. Appl. Phys. 1963, 34, 1793.; M. Todeschini, A. Bastos da Silva Fanta, F. Jensen, J. B. Wagner, A. Han, ACS Appl. Mater. Interfaces 2017, 9, 37374.; T. Tsujioka, A. Nishimura, Appl. Phys. A 2021, 127, 228.; L. Tang, J. Shang, X. Jiang, Sci. Adv. 2021, 7, eabe3778.; Y. Wang, S. Lee, T. Yokota, H. Wang, Z. Jiang, J. Wang, T. Someya, Sci. Adv. 2020, 6, eabb7043.

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Relation: Chae, Soosang; Choi, Won Jin; Fotev, Ivan; Bittrich, Eva; Uhlmann, Petra; Schubert, Mathias; Makarov, Denys; Wagner, Jens; Pashkin, Alexej; Fery, Andreas (2021). "Stretchable Thin Film Mechanical‐Strain‐Gated Switches and Logic Gate Functions Based on a Soft Tunneling Barrier (Adv. Mater. 41/2021)." Advanced Materials 33(41): n/a-n/a.; https://hdl.handle.net/2027.42/170848; Advanced Materials

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Relation: Ting, Paishun; Hayes, John P. (2019). "Removing constant‐induced errors in stochastic circuits." IET Computers & Digital Techniques 13(3): 187-197.; https://hdl.handle.net/2027.42/163790; IET Computers & Digital Techniques; Brown, B.D., Card, H.: ‘ Stochastic neural computation I ’, IEEE Trans. Comput., 2001, 50, pp. 891 – 905; Gaudet, V.C., Rapley, A.C.: ‘ Iterative decoding using stochastic computation ’, IET Electron Lett., 2003, 39, pp. 299 – 301; Alaghi, A., Li, C., Hayes, J.P.: ‘ Stochastic circuits for real‐time image‐processing applications ’. IEEE Proc. Design Automation Conf., Austin, TX, USA, 2013, pp. 1 – 6; Keener, R.W.: ‘ Theoretical statistics: topics for a core course ’ ( Springer, New York, NY, USA, 2010 ); Paler, A., Kinseher, J., Polian, I., et al.: ‘ Approximate simulation of circuits with probabilistic behavior ’. IEEE Proc. Defect and Fault Tolerance in VLSI and Nanotechnology Systems, New York, NY, USA, 2013, pp. 95 – 100; Li, P., Qian, W., Riedel, M.D.: ‘ The synthesis of linear finite state machine‐based stochastic computational elements ’. IEEE Proc. Asia and South Pacific Design Automation Conf., Sydney, NSW, Australia, 2012, pp. 757 – 762; Lee, V.T., Alaghi, A., Ceze, L.: ‘ Correlation manipulating circuits for stochastic computing ’. IEEE Proc. Conf. on Design, Automation & Test in Europe, Dresden, Germany, 2018, pp. 1417 – 1422; Tehrani, S.S., Gross, W.J., Mannor, S.: ‘ Stochastic decoding of LDPC codes ’, IEEE Commun. Lett., 2006, 10, ( 10 ), pp. 716 – 718; Ting, P.‐S., Hayes, J.P.: ‘ On the role of sequential circuits in stochastic computing ’. ACM Proc. 25th edition on Great Lakes Symp. on VLSI, Banff, Alberta, Canada, 2017, pp. 475 – 478; Golomb, S.W., Gong, G.: ‘ Signal design for good correlation ’ ( Cambridge University Press, New York, NY, USA, 2005 ); Gubner, J.A.: ‘ Probability and random processes for electrical and computer engineers ’ ( Cambridge University Press, New York, NY, USA, 2006 ); Lee, V.T., Alaghi, A., Hayes, J.P., et al.: ‘ Energy‐efficient hybrid stochastic‐binary neural networks for near‐sensor computing ’. IEEE Proc. Conf. on Design, Automation & Test in Europe, Lausanne, Switzerland, 2017, pp. 13 – 18; Chen, T.‐H., Hayes, J.P.: ‘ Equivalence among stochastic logic circuits and its applications ’, IEEE Proc. Design Automation Conf., San Francisco, CA, USA, 2015, pp. 131 – 136; Alaghi, A., Hayes, J.P.: ‘ On the functions realized by stochastic computing circuits ’. ACM Proc. 25th edition on Great Lakes Symp. on VLSI, Pittsburgh, PA, USA, 2015, pp. 331 – 336; Ting, P.‐S., Hayes, J.P.: ‘ Eliminating a hidden error source in stochastic circuits ’. IEEE Proc. Defect and Fault Tolerance, Cambridge, UK, 2017, pp. 44 – 49; Qian, W., Li, X., Riedel, M.D., et al.: ‘ An architecture for fault‐tolerant computation with stochastic logic ’, IEEE Trans. Comput., 2011, 60, pp. 93 – 105; Alaghi, A., Hayes, J.P.: ‘ STRAUSS: spectral transform use in stochastic circuit synthesis ’, IEEE Trans. Comput.‐Aided Des. Integr. Circuits Syst., 2015, 34, pp. 1770 – 1783; Alaghi, A., Hayes, J.P.: ‘ Fast and accurate computation using stochastic circuits ’. IEEE Proc. Conf. on Design, Automation & Test in Europe, Dresden, Germany, 2014, pp. 24 – 28; Vahapoglu, E., Altun, M.: ‘ Accurate synthesis of arithmetic operations with stochastic logic ’. 2016 IEEE Computer Society Annual Symp. on VLSI, Pittsburgh, PA, USA, 2016, pp. 415 – 420; Jenson, D., Riedel, M.: ‘ A deterministic approach to stochastic computation ’. IEEE Proc. 35th Int. Conf. on Computer‐Aided Design, Austin, TX, USA, 2016, pp. 1 – 8; Gupta, P.K., Kumaresan, R.: ‘ Binary multiplication with PN sequences ’, IEEE Trans. Acoust. Speech Signal Process., 1988, 36, pp. 603 – 606; Braendler, D., Hendtlass, T., O’Donoghue, P.: ‘ Deterministic bit‐stream digital neurons ’, IEEE Trans. Neural Netw., 2002, 13, pp. 1514 – 1525; Ting, P.‐S., Hayes, J.P.: ‘ Isolation‐based decorrelation of stochastic circuits ’. IEEE Proc. Int. Conf. of Stochastic Circuits, Scottsdale, AZ, USA, 2016, pp. 88 – 95

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Relation: Dragan, Irina F.; Walji, Muhammad; Vervoorn, Marjoke; Quinn, Barry; Johnson, Lynn; Davis, Joan; Garcia, Lily T.; Valachovic, Richard W. (2020). "ADEA‐ADEE Shaping the Future of Dental Education III." Journal of Dental Education 84(1): 111-116.; https://hdl.handle.net/2027.42/153630; Journal of Dental Education; Jones ML, Hobson RS, Plasschaert AJ, et al. Quality assurance and benchmarking: an approach for European dental schools. Eur J Dent Educ. 2007; 11: 137 ‐ 143.; Roski J, Bo‐Linn GW, Andrews TA. Creating value in health care through big data: Opportunities and policy implications. Health Affairs. 2014; 7: 1115 ‐ 1122.; Dragan I, Dalessandri D, Johnson LA, Tucker A, Walmsley AD. Impact of scientific and technological advances. Eur J Dent Educ. 2018; 22 ( Suppl 1 ): 17 ‐ 20.; Kurzweil R. The Law of Accelerating Returns. March 7, 2001.; Kurzweil R. The Age of Spiritual Machines. Viking, 1999.; Cooksy LJ, Gill P, Kelly A. The program logic model as an integrative framework for a multimethod evaluation. Evaluation and Program Planning. 2001; 24: 119 ‐ 128.; Palatta AM, Kassebaum DK, Gadbury‐Amyot CC, et al. Change is here: aDEA CCI 2.0—a learning community for the advancement of dental education. J Dent Educ. 2017; 81 ( 6 ): 640 ‐ 648.; Walji MF, Karimbux NY, Spielman AI. Person‐centered care: opportunities and challenges for academic dental institutions and programs. J Dent Educ. 2017; 81 ( 11 ): 1265 ‐ 1272.; Corrigan JM. Crossing the quality chasm. Building a better delivery system. 2005.; Kalenderian E, Obadan‐Udoh E, Yansane A, et al. Feasibility of electronic health record‐based triggers in detecting dental adverse events. Appl Clin Inform. 2018; 9 ( 3 ): 646 ‐ 653.; Kumar SV, Bangar S, Neumann A, et al. Assessing the validity of existing dental sealant quality measures. J Amer Den Assoc. 2018; 149 ( 9 ): 756 ‐ 764. e751.; Walji MF, Kalenderian E, Stark PC, et al. BigMouth: a multi‐institutional dental data repository. J Am Med Inform Assoc. 2014; 21 ( 6 ): 1136 ‐ 1140.; Institute for Healthcare Improvement. Science of Improvement: How to Improve. Accessed 23 October 2017 at: http://www.ihi.org/resources/Pages/HowtoImprove/ScienceofImprovementHowtoImprove.aspx.; Emanuel L, Berwick D, Conway J, et al. What exactly is patient safety? 2008.; Serrano CM, Wesselink PR, Vervoorn JM. Real patients in virtual reality: the link between phantom heads and clinical dentistry. Ned Tijdschr Tandheelkd. 2018; 125: 263 ‐ 267.; de Boer IR, Bakker DR, Serrano CM, Koopman P, Wesselink PR, Vervoorn JM. Innovation in dental education: The on‐the‐Fly approach to simultaneous development, implementation and evidence collection. Eur J Dent Educ. 2018; 22: 215 ‐ 222.; Chawla NV, Davis DA. Bringing big data to personalized healthcare: A patient‐centered framework. J Gen Internal Med. 2013; 28 ( Suppl 3 ): 660 ‐ 665.; Chen M, Hao Y, Hwang K, Wang L, Wang L. Disease prediction by machine learning over big data from healthcare communities. IEEE Access. 2017; 5: 8869 ‐ 8879.; Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S. Artificial intelligence in healthcare: past, present and future. Stroke Vascular Neuro. 2017: 230 ‐ 243.; Malon RS, Sadir S, Balakrishnan M, Córcoles EP, Saliva‐Based Biosensors: Noninvasive Monitoring Tool for Clinical Diagnostics. BioMed Research International. 2014.; Thaler RH, Sunstein C. Nudge: Improving Decisions about Health, Wealth, and Happiness. Yale University Press; 2008.; Olsen LA, Aisner D, McGinnis JM, eds. The Learning Healthcare System: Workshop Summary. Washington (DC): National Academies Press (US) National Academy of Sciences; 2007. Institute of Medicine Roundtable on Evidence‐Based M. The National Academies Collection: Reports funded by National Institutes of Health.; Card SK, Moran TP, Newell A. The Psychology of Human‐Computer Interaction. London: Lawrence Erbaum Associates; 1983.

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Relation: Foo, Maw‐der; Vissa, Balagopal; Wu, Brian (2020). "Entrepreneurship in emerging economies." Strategic Entrepreneurship Journal 14(3): 289-301.; https://hdl.handle.net/2027.42/162809; Strategic Entrepreneurship Journal; Shane, S., & Venkataraman, S. ( 2000 ). The promise of entrepreneurship as a field of research. Academy of Management Review, 25 ( 1 ), 217 - 226.; Keh, H. T., Foo, M. D., & Lim, B. C. ( 2002 ). Opportunity evaluation under risky conditions: The cognitive processes of entrepreneurs. Entrepreneurship Theory & Practice, 27 ( 2 ), 125 - 148.; Keister, L. A. ( 2005 ). Getting rich: America’s new rich and how they got that way. New York, NY: Cambridge University Press.; Luo, Y., & Junkunc, M. ( 2008 ). How private enterprises respond to government bureaucracy in emerging economies: the effects of entrepreneurial type and governance. Strategic Entrepreneurship Journal, 2 ( 2 ), 133 - 153. http://dx.doi.org/10.1002/sej.46.; March, J. G. ( 1994 ). 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Relation: Han, Wei; Wang, Wenshuo; Li, Xiaohan; Xi, Junqiang (2019). "Statistical‐based approach for driving style recognition using Bayesian probability with kernel density estimation." IET Intelligent Transport Systems 13(1): 22-30.; https://hdl.handle.net/2027.42/166283; https://dx.doi.org/10.7302/206; IET Intelligent Transport Systems; Wang W. Xi J. Zhao D.: ‘ Driving style analysis using primitive driving patterns with Bayesian nonparametric approaches ’. 2017, arXiv:1703.09744; Zhou M. Jin H. Wang W.: ‘ A review of vehicle fuel consumption models to evaluate eco‐driving and eco‐routing ’, Transp. Res. D, Transp. Environ., 2016, 49, pp. 203 – 218; Martinez C.M. Heucke M. Wang F. et al.: ‘ Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey ’, IEEE Trans. Intell. Transp. Syst., 2017, doi:10.1109/TITS.2017.2706978; Li Y. Wang J. 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Relation: Rees, Laura; Kopelman, Shirli (2019). "Logics and Logistics for Future Research: Appropriately Interpreting the Emotional Landscape of Multicultural Negotiation." Negotiation and Conflict Management Research 12(2): 131-145.; https://hdl.handle.net/2027.42/149334; Negotiation and Conflict Management Research; Perron, M., Roy‐Charland, A., Chamberland, J. A., Bleach, C., & Pelot, A. ( 2016 ). Differences between traces of negative emotions in smile judgment. Motivation and Emotion, 40, 478 – 488. https://doi.org/10.1007/s11031-016-9546-x; Oatley, K., & Jenkins, J. M. ( 1992 ). Human emotions: Function and dysfunction. Annual Review of Psychology, 43 ( 1 ), 55 – 85. https://doi.org/10.1146/annurev.ps.43.020192.000415; Rees, L., Rothman, N., Lehavy, R., & Sanchez‐Burks, J. ( 2013 ). The ambivalent mind can be a wise mind: Emotional ambivalence increases judgment accuracy. 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Contributors: Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, Clinical Sciences Section, National Institute of Arthritis, Musculoskeletal, and Skin Diseases, National Institutes of Health, Bethesda, MD, Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany, Statistical Genetics Section, National Human Genome Research Institute, National Institutes of Health, Baltimore, MD, Center for Information Technology, National Institutes of Health, Bethesda, MD, 333 Cassell Drive, Suite 1200, National Institute of Health/NHGRI, Baltimore, MD 21224

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Contributors: Department of Radiology, The University of Michigan, Ann Arbor, Michigan 48109â 5842

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11
Dissertation/ Thesis

Authors: Agrawal, Mayank

Contributors: Glotzer, Sharon C, Horowitz, Jordan Michael, Solomon, Michael J, Ziff, Robert M

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Relation: https://hdl.handle.net/2027.42/155257; orcid:0000-0001-9563-9736; Agrawal, Mayank; 0000-0001-9563-9736

12
Dissertation/ Thesis

Authors: Sahin, Yunus

Contributors: Ozay, Necmiye, Panagou, Dimitra, Lafortune, Stephane, Tripakis, Stavros

File Description: application/pdf

Relation: https://hdl.handle.net/2027.42/162921; orcid:0000-0003-3585-608X; Sahin, Yunus; 0000-0003-3585-608X

13
Dissertation/ Thesis

Contributors: Kateeb, Ali El, Awad, Selim, Shaout, Adnan

File Description: application/pdf

Relation: https://hdl.handle.net/2027.42/155349; 7165 1976; orcid:0000-0002-7465-6446

14
Academic Journal

Contributors: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109

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Relation: Sahiner, Berkman; Chan, Heang‐ping; Hadjiiski, Lubomir (2008). "Classifier performance prediction for computerâ aided diagnosis using a limited dataset." Medical Physics 35(4): 1559-1570.; http://hdl.handle.net/2027.42/134979; Medical Physics; C. E. Metz, B. A. Herman, and J. H. Shen, â Maximumâ likelihood estimation of receiver operating characteristic (ROC) curves from continuouslyâ distributed data,â Stat. Med. 0277‐6715 --> 17, 1033 â 1053 ( 1998 ).; K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. ( Academic Press, New York, 1990 ).; A. P. Bradley, â The use of the area under the ROC curve in the evaluation of machine learning algorithms,â Pattern Recogn. PTNRA8 --> 0031‐3203 --> 10.1016/S0031â 3203(96)00142â 2 30, 1145 â 1159 ( 1997 ).; K. O. Hajianâ Tilaki, J. A. Hanley, L. Joseph, and J. P. Collet, â A comparison of parametric and nonparametric approaches to ROC analysis of quantitative diagnostic tests,â Med. Decis Making 0272‐989X --> 17, 94 â 102 ( 1997 ).; S. Wu and P. Flach, â A scored AUC metric for classifier evaluation and selection,â Proceedings of the ICML 2005 Workshop on ROC Analysis in Machine Learning ( International Machine Learning Society, Bonn, Germany, 2005 ).; W. A. Yousef, R. F. Wagner, and M. H. Loew, â Estimating the uncertainty in the estimated mean area under the ROC curve of a classifier,â Pattern Recogn. Lett. PRLEDG --> 0167‐8655 --> 10.1016/j.patrec.2005.06.006 26, 2600 â 2610 ( 2005 ).; G. W. Brier, â Verification of forecasts expressed in terms of probability,â Mon. Weather Rev. MWREAB --> 0027‐0644 --> 75, 1 â 3 ( 1950 ).; H. P. Chan, B. Sahiner, R. F. Wagner, and N. Petrick, â Classifier design for computerâ aided diagnosis: Effects of finite sample size on the mean performance of classical and neural network classifiers,â Med. Phys. MPHYA6 --> 0094‐2405 --> 10.1118/1.598805 26, 2654 â 2668 ( 1999 ).; G. T. Toussaint, â Bibliography on estimation of misclassification,â IEEE Trans. Inf. Theory IETTAW --> 0018‐9448 --> IT20, 472 â 479 ( 1974 ).; B. Efron, â Estimating the error rate of a prediction rule: Improvement on crossâ validation,â J. Am. Stat. Assoc. JSTNAL --> 0162‐1459 --> 78, 316 â 331 ( 1983 ).; B. Efron and R. Tibshirani, â Improvements on crossâ validation: The 0.632 + bootstrap method,â J. Am. Stat. Assoc. JSTNAL --> 0162‐1459 --> 10.2307/2965703 92, 548 â 560 ( 1997 ).; D. J. Hand, â Recent advances in error rate estimation,â Pattern Recogn. Lett. PRLEDG --> 0167‐8655 --> 4, 335 â 346 ( 1986 ).; R. A. Schiavo and D. J. Hand, â Ten more years of error rate research,â Int. Statist. Rev. ISTRDP --> 0306‐7734 --> 68, 295 â 310 ( 2000 ).; G. D. Tourassi and C. E. Floyd, â The effect of data sampling on the performance evaluation of artificial neural networks in medical diagnosis,â Med. Decis Making 0272‐989X --> 17, 186 â 192 ( 1997 ).; E. Arana, P. Delicado, and L. Martiâ Bonmati, â Validation procedures in radiologic diagnostic models: Neural network and logistic regression,â Invest. Radiol. INVRAV --> 0020‐9996 --> 34, 636 â 642 ( 1999 ).; E. W. Steyerberg, F. E. Harrell, G. Borsboom, M. J. C. Eijkemans, Y. Vergouwe, and J. D. F. Habbema, â Internal validation of predictive models: Efficiency of some procedures for logistic regression analysis,â J. Clin. Epidemiol. 0895‐4356 --> 54, 774 â 781 ( 2001 ).; W. A. Yousef, R. F. Wagner, and M. H. Loew, â Comparison of nonparametric methods for assessing classifier performance in terms of ROC parameters,â in Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop ( IEEE, 2004 ), pp. 190 â 195.; B. Sahiner, H. P. Chan, N. Petrick, L. M. Hadjiiski, S. Paquerault, and M. N. Gurcan, â Resampling schemes for estimating the accuracy of a classifier designed with a limited data set,â Presented at the Medical Image Perception Conference IX, Airlie Conference Center, Warrenton, VA, September 20â 23, 2001.; D. D. Boos, â Introduction to the bootstrap world,â Stat. Sci. STSCEP --> 0883‐4237 --> 18, 168 â 174 ( 2003 ).; K. Fukunaga and R. R. Hayes, â Effects of sample size on classifier design,â IEEE Trans. Pattern Anal. Mach. Intell. ITPIDJ --> 0162‐8828 --> 10.1109/34.31448 11, 873 â 885 ( 1989 ).; P. A. Lachenbruch, â An almost unbiased method of obtaining confidence intervals for the probability of misclassification in discriminant analysis,â Biometrics BIOMB6 --> 0006‐341X --> 10.2307/2528418 23, 639 â 645 ( 1967 ).; P. A. Lachenbruch, Discriminant Analysis ( Hafner Press, New York, 1975 ).

15
Academic Journal

Authors: Gaskin, Richard

Contributors: University of Sussex University of Michigan, Arts Building, USA, Ann Arbor

File Description: 82189 bytes; 3115 bytes; application/pdf; text/plain

Relation: Gaskin, Richard; (1997). "Books Received." Studia Logica 58(3): 451-453.; https://hdl.handle.net/2027.42/43815; http://dx.doi.org/10.1023/A:1004934501474; Studia Logica

16
Academic Journal

Contributors: Ann Arbor

File Description: application/pdf

Relation: Nova Hedwigia, Beiheft, Volume 130, pp. 285-292; https://hdl.handle.net/2027.42/93636; Nova Hedwigia, Beiheft

17
Academic Journal

Contributors: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109â 0904

File Description: application/pdf

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18
Academic Journal

Contributors: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109

File Description: application/pdf

Relation: Wei, Jun; Chan, Heang‐ping; Sahiner, Berkman; Hadjiiski, Lubomir M.; Helvie, Mark A.; Roubidoux, Marilyn A.; Zhou, Chuan; Ge, Jun (2006). "Dual system approach to computerâ aided detection of breast masses on mammograms." Medical Physics 33(11): 4157-4168.; https://hdl.handle.net/2027.42/134813; Medical Physics; J. Y. Zhou and T. Pavlidis, â Discrimination of characters by a multiâ stage recognition process,â Pattern Recogn. PTNRA8 --> 0031‐3203 --> 27, 1539 â 1549 ( 1994 ).; H. C. Zuckerman, â The role of mammography in the diagnosis of breast cancer,â in Breast Cancer, Diagnosis and Treatment, edited by I. M. Ariel and J. B. Cleary ( McGrawâ Hill, New York, 1987 ).; F. Shtern, C. Stelling, B. Goldberg, and R. Hawkins, â Novel technologies in breast imaging: National Cancer Institute perspective,â Orlando, FL.; C. J. Vyborny, â Can computers help radiologists read mammograms?,â Radiology RADLAX --> 0033‐8419 --> 191, 315 â 317 ( 1994 ).; T. W. Freer and M. J. 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Contributors: Depertment of Philsophy, University of Michigan, 435 South state street, Ann Arbor, MI, 48109-1003, USA, Ann Arbor

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Relation: Railton, Peter; (2005). "Reply to Justin D’Arms." Philosophical Studies 126(3): 481-490.; https://hdl.handle.net/2027.42/43371; http://dx.doi.org/10.1007/s11098-005-2319-0; Philosophical Studies

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Academic Journal

Contributors: Department of Philosophy, University of Michigan, 435 South State Street, Ann Arbor, MI, 48109-1003, USA, Ann Arbor

File Description: 174869 bytes; 3115 bytes; application/pdf; text/plain

Relation: Railton, Peter; (2005). "Reply to Ralph Wedgwood." Philosophical Studies 126(3): 501-508.; https://hdl.handle.net/2027.42/43373; http://dx.doi.org/10.1007/s11098-005-2321-6; Philosophical Studies