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1. The ChatGPT Artificial Intelligence Chatbot: How Well Does It Answer Accounting Assessment Questions?

Author

Wood, David A., Achhpilia, M.P., Adams, Mollie T., Aghazadeh, Sanaz, Akinyele, Kazeem, Akpan, Mfon, Allee, Kristian D., Allen, Abigail M., Almer, Elizabeth D., Ames, Daniel, Arity, Viktor, Barr-Pulliam, Dereck, Basoglu, K. Asli, Belnap, Andrew, Bentley, Jeremiah W., Berg, Terje, Berglund, Nathan R., Berry, Erica, Bhandari, Avishek, Bhuyan, Md Nazmul Hasan, Black, Paul W., Blondeel, Eva, Bond, David, Bonrath, Annika, Borthick, A. Faye, Boyle, Erik S., Bradford, Marianne, Brandon, Duane M., Brazel, Joseph F., Brockbank, Bryan G., Burger, Marcus, Byzalov, Dmitri, Cannon, James N., Caro, Cecil, Carr, Abraham H., Cathey, Jack, Cating, Ryan, Charron, Kimberly, Chavez, Stacy, Chen, Jason, Chen, Jennifer C., Chen, Jennifer W., Cheng, Christine, Cheng, Xu, Christensen, Brant E., Church, Kimberly Swanson, Cicone, N. J., Constance, Patience, Cooper, Lauren A., Correia, Candice L., Coyne, Joshua, Cram, W. Alec, Curtis, Asher, Daigle, Ronald J., Dannemiller, Steve, Davenport, Stephan A., Dawson, Gregory S., Meyst, K.J.L. de, Dell, Scott, Demirkan, Sebahattin, Denison, Christine A., Desai, Hrishikesh, DeSimone, Steven, Diehl, Leah M., Dimes, Ruth, Dong, Bei, Donnelly, Amy, du Pon, Adam, Duan, Huijue Kelly, Duffey, Ada, Dunn, Ryan T., Durkin, Mary P., Dzuranin, Ann C., Eberle, Rachel M., Ege, Matthew S., El Mahdy, Dina, Esplin, Adam, Eulerich, Marc, Everaert, Patricia, Farah, Nusrat, Farish, Lauren, Favere-Marchesi, Michael, Fayard, Dutch, Filosa, Jessica R., Ford, Melinda, Franz, Diana R., Fulmer, Bachman P., Fulmer, Sarah, Furner, Zhan Z., Gantman, Sonia, Garner, Steve, Garrett, Jace, Geng, Xin, Golden, Joanna, Goldman, William, Gomez, Josue, Gooley, Mark, Granitto, Shawn P., Green, Karen Y., Greenman, Cindy L., Gupta, Gaurav, Guymon, Ronald N., Hale, Kevin, Harper, Christopher J., Hartt, S. Allen, Hawk, Holly, Hawkins, Steven R., Hawkins, Erin M., Hay, David C., Heinzelmann, Rafael, Henderson, Cassy D., Hendricks, Bradley E., Heninger, William G., Hill, Mary S., Holden, Nicole, Holderness, D. Kip, Jr., Holt, Travis P., Hoopes, Jeffrey L., Hsieh, Sheng-Feng, Huang, Feiqi, Huang, Hua-Wei, Huang, Ting-Chiao, Huels, Brian W., Hunter, Kara, Hurley, Patrick J., Inger, Kerry, Islam, Sharif, Ison, Isaac, Issa, Hussein, Jackson, Andrew B., Jackson, Scott C., Janvrin, Diane J., Jimenez, Peggy D., Johanson, Daniel, Judd, J. Scott, Kawada, Brett S., Kelton, Andrea Seaton, Kern, Sara, Kerr, Jon N., Keune, Marsha B., Kim, Mindy (Hyo Jung), Knox, Brian D., Kogan, Gregory, Kotb, Amr, Krane, Ronja, Kremin, Joleen, Krieg, Kimberly S., Kugel, Jonathan, Kulset, Ellen M., Kuruppu, Chamara, LaDuca, Garrison, Lamberton, Barbara A., Lamboy-Ruiz, Melvin A., Lang, Bradley, Larocque, Stephannie A., Larson, Melissa P., Lawson, Bradley P., Lawson, James G., Lee, Lorraine, Lenk, Margarita M., Li-Kuehne, Michelle, Liljegren, Jonathan, Lin, Yi-Hung, Liu, Wu-Po, Liu, Zishang, Lock, Brandon, Long, James H., Loraas, Tina, Lowensohn, Suzanne, Loy, Thomas R., Lyngstadaas, Hakim, Maas, Wim, MacGregor, Jason E., Madsen, Dag Øivind, Malone, Carissa L., Margolin, Maximilian, Marshall, Mary E., Martin, Rachel M., Mpofu, Colleen McClain, McCoy, Chris, McGuigan, Nicholas C., McSwain, Dwayne N., Meckfessel, Michele D., Mellon, Mark J., Melton, Olivia S., Mercado, Julie M., Mitsuda, Steven, Modugu, Kennedy, Moehrle, Stephen, Chaghervand, Amirali Moeini, Moffitt, Kevin, Moon, Joon Seok, Muehlmann, Brigitte, Murray, Johnna, Mwaungulu, Emmanuel S., Myers, Noah, Naegle, J. Conrad, Jr., Ndicu, Martin J., Nelson, Aaron S., Nguyen, Anh L., Niederkofler, N.T., Nikbakht, Ehsan, O'Brien, Ann D., Ogunade, Kehinde M., O'Leary, Daniel, Oler, Mitchell J., Oler, Derek K., Olsen, Kari Joseph, Otalor, John I., Outlaw, Kyle W., Ozlanski, Michael. E., Parlier, Jenny, Paterson, Jeffrey S., Pearson, Christopher A., Petersen, Michael J., Petra, Steven T., Pickard, Matthew D., Pickerd, Jeffrey, Pinsker, Robert, Plante, Catherine, Plečnik, James M., Price, Richard A., III, Quick, Linda A., Raedy, Jana, Raschke, Robyn, Ravenscraft, Julie, Richardson, Vernon, Rixom, Brett A., Robertson, John F., Rock, Iyad, Romney, Miles A., Rozario, Andrea, Ruff, Michael F., Rupley, Kathleen, Saeedi, Ali, Saiewitz, Aaron, Salzsieder, Leigh W., Sarkar, Sayan, Saulls, Michael, Scanlan, Tialei A., Schaefer, Tammie J., Schaupp, Daniel, Schneider, Gary P., Seebeck, Andreas, Sellers, R. Drew, Seto, Samantha C., Sevel, Romi-Lee, Shan, Yuxin, Sherwood, Matthew G., Singorahardjo, Maggie, Skaftadottir, Hanna Kristin, Skomra, Justyna, Smith, Jason L., Smith, Dallin O., Smith, James, Snow, Mason C., Sommerfeldt, Ryan D., Sorensen, Kate B., Sorensen, Trevor L., Spieler, Andrew C., Stallings, Matthew A., Stallings, Lesya, Stancill, Alan, Stanley, Jonathan D., Stefaniak, Chad M., Stephens, Nathaniel M., Stewart, Bryan W., Stratopoulos, Theophanis C., Street, Daniel A., Subedi, Meena, Summers, Scott L., Sundkvist, Charlotte H., Synn, Christina, Tadesse, Amanuel, Tapis, Gregory P., Tassin, Kerri, Taylor, Samantha, Teal, Mary, Teeter, Ryan, Tharapos, Meredith, Theis, Jochen C., Thomas, Jack, Thompson, Kristen S., Thornock, Todd A., Tietz, Wendy, Travalent, Anthony M., Trinkle, Brad S., Truelson, J. Mike, Turner, Michael C., Vagner, Brandon, Vakilzadeh, Hamid, van der Geest, Jesse, van Pelt, Victor, Vandervelde, Scott D., Vega, Jose, Vera-Muñoz, Sandra, Villanueva, Brigham, Vincent, Nishani Edirisinghe, Wagener, Martin, Walton, Stephanie, Warne, Rick C., Watanabe, Olena V., Watson, David, Watson, Marcia Weidenmier, Weber, Jill, Weirich, Thomas, West, Ashley N., Wilford, Amanda L., Wilson, Aaron B., Winrow, Brian, Winrow, Timothy, Winrow, Tasia S., Wiseman, Denise, Witte, Annie L., Wood, Bryan D., Wood, Jessica, Woolley, Darryl, Wright, Nicole S., Wu, Juan, Xiong, Xiaomei (Grazia), Yatsenko, Dimitri, Yazzie, Courtney E., Young, Glen M., Zhang, Chanyuan (Abigail), Zimmerman, Aleksandra B., Zoet, Emily, Wood, David A., Achhpilia, M.P., Adams, Mollie T., Aghazadeh, Sanaz, Akinyele, Kazeem, Akpan, Mfon, Allee, Kristian D., Allen, Abigail M., Almer, Elizabeth D., Ames, Daniel, Arity, Viktor, Barr-Pulliam, Dereck, Basoglu, K. Asli, Belnap, Andrew, Bentley, Jeremiah W., Berg, Terje, Berglund, Nathan R., Berry, Erica, Bhandari, Avishek, Bhuyan, Md Nazmul Hasan, Black, Paul W., Blondeel, Eva, Bond, David, Bonrath, Annika, Borthick, A. Faye, Boyle, Erik S., Bradford, Marianne, Brandon, Duane M., Brazel, Joseph F., Brockbank, Bryan G., Burger, Marcus, Byzalov, Dmitri, Cannon, James N., Caro, Cecil, Carr, Abraham H., Cathey, Jack, Cating, Ryan, Charron, Kimberly, Chavez, Stacy, Chen, Jason, Chen, Jennifer C., Chen, Jennifer W., Cheng, Christine, Cheng, Xu, Christensen, Brant E., Church, Kimberly Swanson, Cicone, N. J., Constance, Patience, Cooper, Lauren A., Correia, Candice L., Coyne, Joshua, Cram, W. Alec, Curtis, Asher, Daigle, Ronald J., Dannemiller, Steve, Davenport, Stephan A., Dawson, Gregory S., Meyst, K.J.L. de, Dell, Scott, Demirkan, Sebahattin, Denison, Christine A., Desai, Hrishikesh, DeSimone, Steven, Diehl, Leah M., Dimes, Ruth, Dong, Bei, Donnelly, Amy, du Pon, Adam, Duan, Huijue Kelly, Duffey, Ada, Dunn, Ryan T., Durkin, Mary P., Dzuranin, Ann C., Eberle, Rachel M., Ege, Matthew S., El Mahdy, Dina, Esplin, Adam, Eulerich, Marc, Everaert, Patricia, Farah, Nusrat, Farish, Lauren, Favere-Marchesi, Michael, Fayard, Dutch, Filosa, Jessica R., Ford, Melinda, Franz, Diana R., Fulmer, Bachman P., Fulmer, Sarah, Furner, Zhan Z., Gantman, Sonia, Garner, Steve, Garrett, Jace, Geng, Xin, Golden, Joanna, Goldman, William, Gomez, Josue, Gooley, Mark, Granitto, Shawn P., Green, Karen Y., Greenman, Cindy L., Gupta, Gaurav, Guymon, Ronald N., Hale, Kevin, Harper, Christopher J., Hartt, S. Allen, Hawk, Holly, Hawkins, Steven R., Hawkins, Erin M., Hay, David C., Heinzelmann, Rafael, Henderson, Cassy D., Hendricks, Bradley E., Heninger, William G., Hill, Mary S., Holden, Nicole, Holderness, D. Kip, Jr., Holt, Travis P., Hoopes, Jeffrey L., Hsieh, Sheng-Feng, Huang, Feiqi, Huang, Hua-Wei, Huang, Ting-Chiao, Huels, Brian W., Hunter, Kara, Hurley, Patrick J., Inger, Kerry, Islam, Sharif, Ison, Isaac, Issa, Hussein, Jackson, Andrew B., Jackson, Scott C., Janvrin, Diane J., Jimenez, Peggy D., Johanson, Daniel, Judd, J. Scott, Kawada, Brett S., Kelton, Andrea Seaton, Kern, Sara, Kerr, Jon N., Keune, Marsha B., Kim, Mindy (Hyo Jung), Knox, Brian D., Kogan, Gregory, Kotb, Amr, Krane, Ronja, Kremin, Joleen, Krieg, Kimberly S., Kugel, Jonathan, Kulset, Ellen M., Kuruppu, Chamara, LaDuca, Garrison, Lamberton, Barbara A., Lamboy-Ruiz, Melvin A., Lang, Bradley, Larocque, Stephannie A., Larson, Melissa P., Lawson, Bradley P., Lawson, James G., Lee, Lorraine, Lenk, Margarita M., Li-Kuehne, Michelle, Liljegren, Jonathan, Lin, Yi-Hung, Liu, Wu-Po, Liu, Zishang, Lock, Brandon, Long, James H., Loraas, Tina, Lowensohn, Suzanne, Loy, Thomas R., Lyngstadaas, Hakim, Maas, Wim, MacGregor, Jason E., Madsen, Dag Øivind, Malone, Carissa L., Margolin, Maximilian, Marshall, Mary E., Martin, Rachel M., Mpofu, Colleen McClain, McCoy, Chris, McGuigan, Nicholas C., McSwain, Dwayne N., Meckfessel, Michele D., Mellon, Mark J., Melton, Olivia S., Mercado, Julie M., Mitsuda, Steven, Modugu, Kennedy, Moehrle, Stephen, Chaghervand, Amirali Moeini, Moffitt, Kevin, Moon, Joon Seok, Muehlmann, Brigitte, Murray, Johnna, Mwaungulu, Emmanuel S., Myers, Noah, Naegle, J. Conrad, Jr., Ndicu, Martin J., Nelson, Aaron S., Nguyen, Anh L., Niederkofler, N.T., Nikbakht, Ehsan, O'Brien, Ann D., Ogunade, Kehinde M., O'Leary, Daniel, Oler, Mitchell J., Oler, Derek K., Olsen, Kari Joseph, Otalor, John I., Outlaw, Kyle W., Ozlanski, Michael. E., Parlier, Jenny, Paterson, Jeffrey S., Pearson, Christopher A., Petersen, Michael J., Petra, Steven T., Pickard, Matthew D., Pickerd, Jeffrey, Pinsker, Robert, Plante, Catherine, Plečnik, James M., Price, Richard A., III, Quick, Linda A., Raedy, Jana, Raschke, Robyn, Ravenscraft, Julie, Richardson, Vernon, Rixom, Brett A., Robertson, John F., Rock, Iyad, Romney, Miles A., Rozario, Andrea, Ruff, Michael F., Rupley, Kathleen, Saeedi, Ali, Saiewitz, Aaron, Salzsieder, Leigh W., Sarkar, Sayan, Saulls, Michael, Scanlan, Tialei A., Schaefer, Tammie J., Schaupp, Daniel, Schneider, Gary P., Seebeck, Andreas, Sellers, R. Drew, Seto, Samantha C., Sevel, Romi-Lee, Shan, Yuxin, Sherwood, Matthew G., Singorahardjo, Maggie, Skaftadottir, Hanna Kristin, Skomra, Justyna, Smith, Jason L., Smith, Dallin O., Smith, James, Snow, Mason C., Sommerfeldt, Ryan D., Sorensen, Kate B., Sorensen, Trevor L., Spieler, Andrew C., Stallings, Matthew A., Stallings, Lesya, Stancill, Alan, Stanley, Jonathan D., Stefaniak, Chad M., Stephens, Nathaniel M., Stewart, Bryan W., Stratopoulos, Theophanis C., Street, Daniel A., Subedi, Meena, Summers, Scott L., Sundkvist, Charlotte H., Synn, Christina, Tadesse, Amanuel, Tapis, Gregory P., Tassin, Kerri, Taylor, Samantha, Teal, Mary, Teeter, Ryan, Tharapos, Meredith, Theis, Jochen C., Thomas, Jack, Thompson, Kristen S., Thornock, Todd A., Tietz, Wendy, Travalent, Anthony M., Trinkle, Brad S., Truelson, J. Mike, Turner, Michael C., Vagner, Brandon, Vakilzadeh, Hamid, van der Geest, Jesse, van Pelt, Victor, Vandervelde, Scott D., Vega, Jose, Vera-Muñoz, Sandra, Villanueva, Brigham, Vincent, Nishani Edirisinghe, Wagener, Martin, Walton, Stephanie, Warne, Rick C., Watanabe, Olena V., Watson, David, Watson, Marcia Weidenmier, Weber, Jill, Weirich, Thomas, West, Ashley N., Wilford, Amanda L., Wilson, Aaron B., Winrow, Brian, Winrow, Timothy, Winrow, Tasia S., Wiseman, Denise, Witte, Annie L., Wood, Bryan D., Wood, Jessica, Woolley, Darryl, Wright, Nicole S., Wu, Juan, Xiong, Xiaomei (Grazia), Yatsenko, Dimitri, Yazzie, Courtney E., Young, Glen M., Zhang, Chanyuan (Abigail), Zimmerman, Aleksandra B., and Zoet, Emily

Abstract

18 april 2023, Item does not contain fulltext

Published
2023

2. Modern Problems in Mathematical Signal Processing: Quantized Compressed Sensing and Randomized Neural Networks

Author

Nelson, Aaron Andrew, Saab, Rayan1, Nelson, Aaron Andrew, Nelson, Aaron Andrew, Saab, Rayan1, and Nelson, Aaron Andrew

Abstract

We study two problems from mathematical signal processing. First, we consider problem of approximately recovering signals on a smooth, compact manifold from one-bit linear measurements drawn from either a Gaussian ensemble, partial circulant ensemble, or bounded orthonormal ensemble and quantized using \(\Sigma\Delta\) or distributed noise-shaping schemes. We construct a convex optimization algorithm for signal recovery that, given a Geometric Multi-Resolution Analysis approximation of the manifold, guarantees signal recovery with high probability. We prove an upper bound on the recovery error which outperforms prior works that use memoryless scalar quantization, requires a simpler analysis, and extends the class of measurements beyond Gaussians.Second, we consider the problem of approximation continuous functions on compact domains using neural networks. The learning speed of feed-forward neural networks is notoriously slow and has presented a bottleneck in deep learning applications for several decades. For instance, gradient-based learning algorithms, which are used extensively to train neural networks, tend to work slowly when all of the network parameters must be iteratively tuned. To counter this, both researchers and practitioners have tried introducing randomness to reduce the learning requirement. Based on the original construction of B.~Igelnik and Y.H.~Pao, single layer neural-networks with random input-to-hidden layer weights and biases have seen success in practice, but the necessary theoretical justification is lacking. We begin to fill this theoretical gap by providing a (corrected) rigorous proof that the Igelnik and Pao construction is a universal approximator for continuous functions on compact domains, with \(\varepsilon\)-error convergence rate inversely proportional to the number of network nodes; we then extend this result to the non-asymptotic setting using a concentration inequality for Monte-Carlo integral approximations. We further adapt th

Published
2019

3. Modern Problems in Mathematical Signal Processing: Quantized Compressed Sensing and Randomized Neural Networks

Author

Nelson, Aaron Andrew, Saab, Rayan1, Nelson, Aaron Andrew, Nelson, Aaron Andrew, Saab, Rayan1, and Nelson, Aaron Andrew

Abstract

We study two problems from mathematical signal processing. First, we consider problem of approximately recovering signals on a smooth, compact manifold from one-bit linear measurements drawn from either a Gaussian ensemble, partial circulant ensemble, or bounded orthonormal ensemble and quantized using \(\Sigma\Delta\) or distributed noise-shaping schemes. We construct a convex optimization algorithm for signal recovery that, given a Geometric Multi-Resolution Analysis approximation of the manifold, guarantees signal recovery with high probability. We prove an upper bound on the recovery error which outperforms prior works that use memoryless scalar quantization, requires a simpler analysis, and extends the class of measurements beyond Gaussians.Second, we consider the problem of approximation continuous functions on compact domains using neural networks. The learning speed of feed-forward neural networks is notoriously slow and has presented a bottleneck in deep learning applications for several decades. For instance, gradient-based learning algorithms, which are used extensively to train neural networks, tend to work slowly when all of the network parameters must be iteratively tuned. To counter this, both researchers and practitioners have tried introducing randomness to reduce the learning requirement. Based on the original construction of B.~Igelnik and Y.H.~Pao, single layer neural-networks with random input-to-hidden layer weights and biases have seen success in practice, but the necessary theoretical justification is lacking. We begin to fill this theoretical gap by providing a (corrected) rigorous proof that the Igelnik and Pao construction is a universal approximator for continuous functions on compact domains, with \(\varepsilon\)-error convergence rate inversely proportional to the number of network nodes; we then extend this result to the non-asymptotic setting using a concentration inequality for Monte-Carlo integral approximations. We further adapt th

Published
2019

4. Random Vector Functional Link Networks for Function Approximation on Manifolds

Author

Needell, Deanna, Nelson, Aaron A., Saab, Rayan, Salanevich, Palina, Needell, Deanna, Nelson, Aaron A., Saab, Rayan, and Salanevich, Palina

Abstract

The learning speed of feed-forward neural networks is notoriously slow and has presented a bottleneck in deep learning applications for several decades. For instance, gradient-based learning algorithms, which are used extensively to train neural networks, tend to work slowly when all of the network parameters must be iteratively tuned. To counter this, both researchers and practitioners have tried introducing randomness to reduce the learning requirement. Based on the original construction of Igelnik and Pao, single layer neural-networks with random input-to-hidden layer weights and biases have seen success in practice, but the necessary theoretical justification is lacking. In this paper, we begin to fill this theoretical gap. We provide a (corrected) rigorous proof that the Igelnik and Pao construction is a universal approximator for continuous functions on compact domains, with approximation error decaying asymptotically like $O(1/\sqrt{n})$ for the number $n$ of network nodes. We then extend this result to the non-asymptotic setting, proving that one can achieve any desired approximation error with high probability provided $n$ is sufficiently large. We further adapt this randomized neural network architecture to approximate functions on smooth, compact submanifolds of Euclidean space, providing theoretical guarantees in both the asymptotic and non-asymptotic forms. Finally, we illustrate our results on manifolds with numerical experiments.

Published
2020

8. Random Vector Functional Link Networks for Function Approximation on Manifolds

Author

Needell, Deanna, Nelson, Aaron A., Saab, Rayan, Salanevich, Palina, Schavemaker, Olov, Needell, Deanna, Nelson, Aaron A., Saab, Rayan, Salanevich, Palina, and Schavemaker, Olov

Abstract

The learning speed of feed-forward neural networks is notoriously slow and has presented a bottleneck in deep learning applications for several decades. For instance, gradient-based learning algorithms, which are used extensively to train neural networks, tend to work slowly when all of the network parameters must be iteratively tuned. To counter this, both researchers and practitioners have tried introducing randomness to reduce the learning requirement. Based on the original construction of Igelnik and Pao, single layer neural-networks with random input-to-hidden layer weights and biases have seen success in practice, but the necessary theoretical justification is lacking. In this paper, we begin to fill this theoretical gap. We provide a (corrected) rigorous proof that the Igelnik and Pao construction is a universal approximator for continuous functions on compact domains, with approximation error decaying asymptotically like $O(1/\sqrt{n})$ for the number $n$ of network nodes. We then extend this result to the non-asymptotic setting, proving that one can achieve any desired approximation error with high probability provided $n$ is sufficiently large. We further adapt this randomized neural network architecture to approximate functions on smooth, compact submanifolds of Euclidean space, providing theoretical guarantees in both the asymptotic and non-asymptotic forms. Finally, we illustrate our results on manifolds with numerical experiments., Comment: 37 pages, 1 figure

Published
2020

9. Classification Scheme for Binary Data with Extensions

Author

Molitor, Denali, Needell, Deanna, Nelson, Aaron, Saab, Rayan, Salanevich, Palina, Boche, Holger, Caire, Giuseppe, Calderbank, Robert, Kutyniok, Gitta, Mathar, Rudolf, Petersen, Philipp, Molitor, Denali, Needell, Deanna, Nelson, Aaron, Saab, Rayan, Salanevich, Palina, Boche, Holger, Caire, Giuseppe, Calderbank, Robert, Kutyniok, Gitta, Mathar, Rudolf, and Petersen, Philipp

Published
2019

10. Classification Scheme for Binary Data with Extensions

Author

Molitor, Denali, Needell, Deanna, Nelson, Aaron, Saab, Rayan, Salanevich, Palina, Boche, Holger, Caire, Giuseppe, Calderbank, Robert, Kutyniok, Gitta, Mathar, Rudolf, Petersen, Philipp, Molitor, Denali, Needell, Deanna, Nelson, Aaron, Saab, Rayan, Salanevich, Palina, Boche, Holger, Caire, Giuseppe, Calderbank, Robert, Kutyniok, Gitta, Mathar, Rudolf, and Petersen, Philipp

Published
2019

11. Classification Scheme for Binary Data with Extensions

Author

Boche, Holger, Caire, Giuseppe, Calderbank, Robert, Kutyniok, Gitta, Mathar, Rudolf, Petersen, Philipp, Molitor, Denali, Needell, Deanna, Nelson, Aaron, Saab, Rayan, Salanevich, Palina, Boche, Holger, Caire, Giuseppe, Calderbank, Robert, Kutyniok, Gitta, Mathar, Rudolf, Petersen, Philipp, Molitor, Denali, Needell, Deanna, Nelson, Aaron, Saab, Rayan, and Salanevich, Palina

Published
2019

12. New Algorithms and Improved Guarantees for One-Bit Compressed Sensing on Manifolds

Author

Iwen, Mark, Lybrand, Eric, Nelson, Aaron, Saab, Rayan, Iwen, Mark, Lybrand, Eric, Nelson, Aaron, and Saab, Rayan

Abstract

We study the problem of approximately recovering signals on a manifold from one-bit linear measurements drawn from either a Gaussian ensemble, partial circulant ensemble, or bounded orthonormal ensemble and quantized using Sigma-Delta or distributed noise shaping schemes. We assume we are given a Geometric Multi-Resolution Analysis, which approximates the manifold, and we propose a convex optimization algorithm for signal recovery. We prove an upper bound on the recovery error which outperforms prior works that use memoryless scalar quantization, requires a simpler analysis, and extends the class of measurements beyond Gaussians. Finally, we illustrate our results with numerical experiments.

Published
2019

13. About Phase: Synthetic Aperture Radar and the Phase Retrieval

Author

AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH GRADUATE SCHOOL OF ENGINEERING AND MANAGEMENT, Nelson, Aaron A, AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH GRADUATE SCHOOL OF ENGINEERING AND MANAGEMENT, and Nelson, Aaron A

Abstract

Synthetic aperture radar (SAR) uses relative motion to produce ne resolution images from microwave frequencies and is a useful tool for regular monitoring and mapping applications. Unfortunately, if target distance is estimated poorly, then phase errors are incurred in the data, producing a blurry reconstruction of the image. In this thesis, we introduce a new multistatic methodology for determining these phase errors from interferometry-inspired combinations of signals. To motivate this, we rst consider a more general problem called phase retrieval, in which a signal is reconstructed from linear measurements whose phases are either unreliable or unavailable. We make signi cant theoretical progress on the phase retrieval problem, to include characterizing injectivity in the complex case, devising the theory of almost injectivity, and performing a stability analysis. We then apply certain ideas from phase retrieval to resolve phase errors in SAR. Speci cally, we use bistatic techniques to measure relative phases, and then we apply a graph-theoretic phase retrieval algorithm to recover the phase errors. We conclude by devising an image reconstruction procedure based on this algorithm, and we provide simulations that demonstrate stability to noise., The original document contains color images.

Published
2014

14. Phase retrieval from very few measurements

Author

Fickus, Matthew C., Mixon, Dustin G., Nelson, Aaron A., Wang, Yang, Fickus, Matthew C., Mixon, Dustin G., Nelson, Aaron A., and Wang, Yang

Abstract

In many applications, signals are measured according to a linear process, but the phases of these measurements are often unreliable or not available. To reconstruct the signal, one must perform a process known as phase retrieval. This paper focuses on completely determining signals with as few intensity measurements as possible, and on efficient phase retrieval algorithms from such measurements. For the case of complex M-dimensional signals, we construct a measurement ensemble of size 4M-4 which yields injective intensity measurements; this is conjectured to be the smallest such ensemble. For the case of real signals, we devise a theory of "almost" injective intensity measurements, and we characterize such ensembles. Later, we show that phase retrieval from M+1 almost injective intensity measurements is NP-hard, indicating that computationally efficient phase retrieval must come at the price of measurement redundancy.

Published
2014

15. Phase retrieval from very few measurements

Author

Fickus, Matthew C., Mixon, Dustin G., Nelson, Aaron A., Wang, Yang, Fickus, Matthew C., Mixon, Dustin G., Nelson, Aaron A., and Wang, Yang

Abstract

In many applications, signals are measured according to a linear process, but the phases of these measurements are often unreliable or not available. To reconstruct the signal, one must perform a process known as phase retrieval. This paper focuses on completely determining signals with as few intensity measurements as possible, and on efficient phase retrieval algorithms from such measurements. For the case of complex M-dimensional signals, we construct a measurement ensemble of size 4M-4 which yields injective intensity measurements; this is conjectured to be the smallest such ensemble. For the case of real signals, we devise a theory of "almost" injective intensity measurements, and we characterize such ensembles. Later, we show that phase retrieval from M+1 almost injective intensity measurements is NP-hard, indicating that computationally efficient phase retrieval must come at the price of measurement redundancy.

Published
2014

16. Phase retrieval from very few measurements

Author

Fickus, Matthew C., Mixon, Dustin G., Nelson, Aaron A., Wang, Yang, Fickus, Matthew C., Mixon, Dustin G., Nelson, Aaron A., and Wang, Yang

Abstract

In many applications, signals are measured according to a linear process, but the phases of these measurements are often unreliable or not available. To reconstruct the signal, one must perform a process known as phase retrieval. This paper focuses on completely determining signals with as few intensity measurements as possible, and on efficient phase retrieval algorithms from such measurements. For the case of complex M-dimensional signals, we construct a measurement ensemble of size 4M-4 which yields injective intensity measurements; this is conjectured to be the smallest such ensemble. For the case of real signals, we devise a theory of "almost" injective intensity measurements, and we characterize such ensembles. Later, we show that phase retrieval from M+1 almost injective intensity measurements is NP-hard, indicating that computationally efficient phase retrieval must come at the price of measurement redundancy.

Published
2014

17. Ethical decision-making for homeland security

Author

Nelson, Aaron G. and Nelson, Aaron G.

Subjects
National security Decision making Moral and ethical aspects United States., Civil defense Decision making Moral and ethical aspects United States., Professional ethics Study and teaching United States., Ethics Study and teaching United States., Decision making Case studies. Moral and ethical aspects, Protection civile Prise de décision Aspect moral États-Unis., Déontologie professionnelle Étude et enseignement États-Unis., Morale Étude et enseignement États-Unis., Prise de décision Études de cas. Aspect moral, Decision making Moral and ethical aspects, Ethics Study and teaching, Professional ethics Study and teaching, United States
Abstract

"The thesis suggests that homeland security personnel lack a uniform method to make sound and defensible ethical decisions. Building on a foundation of classical ethical thought, it is established that ethics are essential to the work of homeland security. Philosophical underpinnings include virtue ethics, deontology, utilitarianism, decision-making practices, and values common to the homeland security enterprise. Real-world case studies were examined in an attempt to understand and demonstrate what can happen if ethics are neglected, considered incompletely or incorrectly, or thoughtfully applied. Case studies include the response to Hurricane Katrina, motivation and thought behind terrorism, and the discussion on torture. Examples of good ethics programs were analyzed, including the Canadian Defense Ethics Program and the Wildfire Fire Leadership Development Program. From this research, a conceptual framework for understanding was developed. The DRIVE framework (Duty, Respect, Integrity, Vision, Ends/Expected outcomes) is proposed to give homeland security personnel the tools necessary to evaluate a situation, make a decision, and review it retrospectively. The framework is easy to remember, flexible to allow for individual differences, yet comprehensive enough to encompass classical ethical thought, common values, and decision-making. The thesis recommends developing an ethics-training program for homeland security, using DRIVE as a foundation"--Abstract.

Published
2013

18. Phase retrieval from very few measurements

Author

Fickus, Matthew, Mixon, Dustin G., Nelson, Aaron A., Wang, Yang, Fickus, Matthew, Mixon, Dustin G., Nelson, Aaron A., and Wang, Yang

Abstract

In many applications, signals are measured according to a linear process, but the phases of these measurements are often unreliable or not available. To reconstruct the signal, one must perform a process known as phase retrieval. This paper focuses on completely determining signals with as few intensity measurements as possible, and on efficient phase retrieval algorithms from such measurements. For the case of complex M-dimensional signals, we construct a measurement ensemble of size 4M-4 which yields injective intensity measurements; this is conjectured to be the smallest such ensemble. For the case of real signals, we devise a theory of "almost" injective intensity measurements, and we characterize such ensembles. Later, we show that phase retrieval from M+1 almost injective intensity measurements is NP-hard, indicating that computationally efficient phase retrieval must come at the price of measurement redundancy., Comment: 18 pages, 1 figure

Published
2013

19. Local responses of the respiratory mucosa to bacterial colonization

Author

Nelson, Aaron L and Nelson, Aaron L

Abstract

The respiratory mucosa of mammals possesses sophisticated systems to regulate microbial activities and populations in the adjacent lumen. This tissue is a highly coordinated unit with epithelial, glandular and structural components that can respond to diverse microbial signals. While many mucosal interactions have been characterized, this report describes a number of novel microbial stimuli and mucosal responses that occur in the respiratory tract. It is shown that colonization of mouse nasal mucosa induces MIP2 secretion, an important chemotactic factor, that recruits neutrophils into the mucosal lumen. These neutrophils interact intimately with bacteria, but are effective in clearing the microbes only under certain conditions. Colonization also stimulates submucosal glands to produce and secrete host defenses such as siderocalin and mucus. Respiratory epithelial cell culture models of these in vivo processes demonstrate that bacterial toxins and siderophores induce pro-inflammatory responses via novel pattern recognition systems. This work suggests that there is a great deal left to learn about the impact of microbial ecology and physiology on host tissue responses, and, conversely, also a great deal to learn about the impact of the host innate immune response on microbial physiology, lifestyle and ecology. It is clear that colonizing microbes and their hosts are co-evolving partners, responding to each others' physiology and biochemistry, frequently achieving stable interactions in the long run. Further, one can speculate, based on these novel systems, that the calculus by which the respiratory mucosa determines an appropriate response involves integration of a broad array of signals.

Published
2006

21. The ChatGPT Artificial Intelligence Chatbot: How Well Does It Answer Accounting Assessment Questions?

Author

Wood, David A., Achhpilia, Muskan P., Adams, Mollie T., Aghazadeh, Sanaz, Akinyele, Kazeem, Akpan, Mfon, Allee, Kristian D., Allen, Abigail M., Almer, Elizabeth D., Ames, Daniel, Arity, Viktor, Barr-Pulliam, Dereck, Basoglu, K. Asli, Belnap, Andrew, Bentley, Jeremiah W., Berg, Terje, Berglund, Nathan R., Berry, Erica, Bhandari, Avishek, Bhuyan, Md Nazmul Hasan, Black, Paul W., Blondeel, Eva, Bond, David, Bonrath, Annika, Borthick, A. Faye, Boyle, Erik S., Bradford, Marianne, Brandon, Duane M., Brazel, Joseph F., Brockbank, Bryan G., Burger, Marcus, Byzalov, Dmitri, Cannon, James N., Caro, Cecil, Carr, Abraham H., Cathey, Jack, Cating, Ryan, Charron, Kimberly, Chavez, Stacy, Chen, Jason, Chen, Jennifer C., Chen, Jennifer W., Cheng, Christine, Cheng, Xu, Christensen, Brant E., Church, Kimberly Swanson, Cicone, N. J., Constance, Patience, Cooper, Lauren A., Correia, Candice L., Coyne, Joshua, Cram, W. Alec, Curtis, Asher, Daigle, Ronald J., Dannemiller, Steve, Davenport, Stephan A., Dawson, Gregory S., De Meyst, Karen J. L., Dell, Scott, Demirkan, Sebahattin, Denison, Christine A., Desai, Hrishikesh, DeSimone, Steven, Diehl, Leah M., Dimes, Ruth, Dong, Bei, Donnelly, Amy, du Pon, Adam, Duan, Huijue Kelly, Duffey, Ada, Dunn, Ryan T., Durkin, Mary P., Dzuranin, Ann C., Eberle, Rachel M., Ege, Matthew S., El Mahdy, Dina, Esplin, Adam, Eulerich, Marc, Everaert, Patricia, Farah, Nusrat, Farish, Lauren, Favere-Marchesi, Michael, Fayard, Dutch, Filosa, Jessica R., Ford, Melinda, Franz, Diana R., Fulmer, Bachman P., Fulmer, Sarah, Furner, Zhan Z., Gantman, Sonia, Garner, Steve, Garrett, Jace, Geng, Xin, Golden, Joanna, Goldman, William, Gomez, Josue, Gooley, Mark, Granitto, Shawn P., Green, Karen Y., Greenman, Cindy L., Gupta, Gaurav, Guymon, Ronald N., Hale, Kevin, Harper, Christopher J., Hartt, S. Allen, Hawk, Holly, Hawkins, Steven R., Hawkins, Erin M., Hay, David C., Heinzelmann, Rafael, Henderson, Cassy D., Hendricks, Bradley E., Heninger, William G., Hill, Mary S., Holden, Nicole, Holderness, D. Kip, Holt, Travis P., Hoopes, Jeffrey L., Hsieh, Sheng-Feng, Huang, Feiqi, Huang, Hua-Wei, Huang, Ting-Chiao, Huels, Brian W., Hunter, Kara, Hurley, Patrick J., Inger, Kerry, Islam, Sharif, Ison, Isaac, Issa, Hussein, Jackson, Andrew B., Jackson, Scott C., Janvrin, Diane J., Jimenez, Peggy D., Johanson, Daniel, Judd, J. Scott, Kawada, Brett S., Kelton, Andrea Seaton, Kern, Sara, Kerr, Jon N., Keune, Marsha B., Kim, Mindy, Knox, Brian D., Kogan, Gregory, Kotb, Amr, Krane, Ronja, Kremin, Joleen, Krieg, Kimberly S., Kugel, Jonathan, Kulset, Ellen M., Kuruppu, Chamara, LaDuca, Garrison, Lamberton, Barbara A., Lamboy-Ruiz, Melvin A., Lang, Bradley, Larocque, Stephannie A., Larson, Melissa P., Lawson, Bradley P., Lawson, James G., Lee, Lorraine, Lenk, Margarita M., Li-Kuehne, Michelle, Liljegren, Jonathan, Lin, Yi-Hung, Liu, Wu-Po, Liu, Zishang, Lock, Brandon, Long, James H., Loraas, Tina, Lowensohn, Suzanne, Loy, Thomas R., Lyngstadaas, Hakim, Maas, Wim, MacGregor, Jason E., Madsen, Dag Øivind, Malone, Carissa L., Margolin, Maximilian, Marshall, Mary E., Martin, Rachel M., Mpofu, Colleen McClain, McCoy, Chris, McGuigan, Nicholas C., McSwain, Dwayne N., Meckfessel, Michele D., Mellon, Mark J., Melton, Olivia S., Mercado, Julie M., Mitsuda, Steven, Modugu, Kennedy, Moehrle, Stephen, Chaghervand, Amirali Moeini, Moffitt, Kevin, Moon, Joon Seok, Muehlmann, Brigitte, Murray, Johnna, Mwaungulu, Emmanuel S., Myers, Noah, Naegle, J. Conrad, Ndicu, Martin J., Nelson, Aaron S., Nguyen, Anh L., Niederkofler, Thomas, Nikbakht, Ehsan, O'Brien, Ann D., Ogunade, Kehinde M., O'Leary, Daniel, Oler, Mitchell J., Oler, Derek K., Olsen, Kari Joseph, Otalor, John I., Outlaw, Kyle W., Ozlanski, Michael. E., Parlier, Jenny, Paterson, Jeffrey S., Pearson, Christopher A., Petersen, Michael J., Petra, Steven T., Pickard, Matthew D., Pickerd, Jeffrey, Pinsker, Robert, Plante, Catherine, Plečnik, James M., Price, Richard A., Quick, Linda A., Raedy, Jana, Raschke, Robyn, Ravenscraft, Julie, Richardson, Vernon, Rixom, Brett A., Robertson, John F., Rock, Iyad, Romney, Miles A., Rozario, Andrea, Ruff, Michael F., Rupley, Kathleen, Saeedi, Ali, Saiewitz, Aaron, Salzsieder, Leigh W., Sarkar, Sayan, Saulls, Michael, Scanlan, Tialei A., Schaefer, Tammie J., Schaupp, Daniel, Schneider, Gary P., Seebeck, Andreas, Sellers, R. Drew, Seto, Samantha C., Sevel, Romi-Lee, Shan, Yuxin, Sherwood, Matthew G., Singorahardjo, Maggie, Skaftadottir, Hanna Kristin, Skomra, Justyna, Smith, Jason L., Smith, Dallin O., Smith, James, Snow, Mason C., Sommerfeldt, Ryan D., Sorensen, Kate B., Sorensen, Trevor L., Spieler, Andrew C., Stallings, Matthew A., Stallings, Lesya, Stancill, Alan, Stanley, Jonathan D., Stefaniak, Chad M., Stephens, Nathaniel M., Stewart, Bryan W., Stratopoulos, Theophanis C., Street, Daniel A., Subedi, Meena, Summers, Scott L., Sundkvist, Charlotte H., Synn, Christina, Tadesse, Amanuel, Tapis, Gregory P., Tassin, Kerri, Taylor, Samantha, Teal, Mary, Teeter, Ryan, Wood, David A., Achhpilia, Muskan P., Adams, Mollie T., Aghazadeh, Sanaz, Akinyele, Kazeem, Akpan, Mfon, Allee, Kristian D., Allen, Abigail M., Almer, Elizabeth D., Ames, Daniel, Arity, Viktor, Barr-Pulliam, Dereck, Basoglu, K. Asli, Belnap, Andrew, Bentley, Jeremiah W., Berg, Terje, Berglund, Nathan R., Berry, Erica, Bhandari, Avishek, Bhuyan, Md Nazmul Hasan, Black, Paul W., Blondeel, Eva, Bond, David, Bonrath, Annika, Borthick, A. Faye, Boyle, Erik S., Bradford, Marianne, Brandon, Duane M., Brazel, Joseph F., Brockbank, Bryan G., Burger, Marcus, Byzalov, Dmitri, Cannon, James N., Caro, Cecil, Carr, Abraham H., Cathey, Jack, Cating, Ryan, Charron, Kimberly, Chavez, Stacy, Chen, Jason, Chen, Jennifer C., Chen, Jennifer W., Cheng, Christine, Cheng, Xu, Christensen, Brant E., Church, Kimberly Swanson, Cicone, N. J., Constance, Patience, Cooper, Lauren A., Correia, Candice L., Coyne, Joshua, Cram, W. Alec, Curtis, Asher, Daigle, Ronald J., Dannemiller, Steve, Davenport, Stephan A., Dawson, Gregory S., De Meyst, Karen J. L., Dell, Scott, Demirkan, Sebahattin, Denison, Christine A., Desai, Hrishikesh, DeSimone, Steven, Diehl, Leah M., Dimes, Ruth, Dong, Bei, Donnelly, Amy, du Pon, Adam, Duan, Huijue Kelly, Duffey, Ada, Dunn, Ryan T., Durkin, Mary P., Dzuranin, Ann C., Eberle, Rachel M., Ege, Matthew S., El Mahdy, Dina, Esplin, Adam, Eulerich, Marc, Everaert, Patricia, Farah, Nusrat, Farish, Lauren, Favere-Marchesi, Michael, Fayard, Dutch, Filosa, Jessica R., Ford, Melinda, Franz, Diana R., Fulmer, Bachman P., Fulmer, Sarah, Furner, Zhan Z., Gantman, Sonia, Garner, Steve, Garrett, Jace, Geng, Xin, Golden, Joanna, Goldman, William, Gomez, Josue, Gooley, Mark, Granitto, Shawn P., Green, Karen Y., Greenman, Cindy L., Gupta, Gaurav, Guymon, Ronald N., Hale, Kevin, Harper, Christopher J., Hartt, S. Allen, Hawk, Holly, Hawkins, Steven R., Hawkins, Erin M., Hay, David C., Heinzelmann, Rafael, Henderson, Cassy D., Hendricks, Bradley E., Heninger, William G., Hill, Mary S., Holden, Nicole, Holderness, D. Kip, Holt, Travis P., Hoopes, Jeffrey L., Hsieh, Sheng-Feng, Huang, Feiqi, Huang, Hua-Wei, Huang, Ting-Chiao, Huels, Brian W., Hunter, Kara, Hurley, Patrick J., Inger, Kerry, Islam, Sharif, Ison, Isaac, Issa, Hussein, Jackson, Andrew B., Jackson, Scott C., Janvrin, Diane J., Jimenez, Peggy D., Johanson, Daniel, Judd, J. Scott, Kawada, Brett S., Kelton, Andrea Seaton, Kern, Sara, Kerr, Jon N., Keune, Marsha B., Kim, Mindy, Knox, Brian D., Kogan, Gregory, Kotb, Amr, Krane, Ronja, Kremin, Joleen, Krieg, Kimberly S., Kugel, Jonathan, Kulset, Ellen M., Kuruppu, Chamara, LaDuca, Garrison, Lamberton, Barbara A., Lamboy-Ruiz, Melvin A., Lang, Bradley, Larocque, Stephannie A., Larson, Melissa P., Lawson, Bradley P., Lawson, James G., Lee, Lorraine, Lenk, Margarita M., Li-Kuehne, Michelle, Liljegren, Jonathan, Lin, Yi-Hung, Liu, Wu-Po, Liu, Zishang, Lock, Brandon, Long, James H., Loraas, Tina, Lowensohn, Suzanne, Loy, Thomas R., Lyngstadaas, Hakim, Maas, Wim, MacGregor, Jason E., Madsen, Dag Øivind, Malone, Carissa L., Margolin, Maximilian, Marshall, Mary E., Martin, Rachel M., Mpofu, Colleen McClain, McCoy, Chris, McGuigan, Nicholas C., McSwain, Dwayne N., Meckfessel, Michele D., Mellon, Mark J., Melton, Olivia S., Mercado, Julie M., Mitsuda, Steven, Modugu, Kennedy, Moehrle, Stephen, Chaghervand, Amirali Moeini, Moffitt, Kevin, Moon, Joon Seok, Muehlmann, Brigitte, Murray, Johnna, Mwaungulu, Emmanuel S., Myers, Noah, Naegle, J. Conrad, Ndicu, Martin J., Nelson, Aaron S., Nguyen, Anh L., Niederkofler, Thomas, Nikbakht, Ehsan, O'Brien, Ann D., Ogunade, Kehinde M., O'Leary, Daniel, Oler, Mitchell J., Oler, Derek K., Olsen, Kari Joseph, Otalor, John I., Outlaw, Kyle W., Ozlanski, Michael. E., Parlier, Jenny, Paterson, Jeffrey S., Pearson, Christopher A., Petersen, Michael J., Petra, Steven T., Pickard, Matthew D., Pickerd, Jeffrey, Pinsker, Robert, Plante, Catherine, Plečnik, James M., Price, Richard A., Quick, Linda A., Raedy, Jana, Raschke, Robyn, Ravenscraft, Julie, Richardson, Vernon, Rixom, Brett A., Robertson, John F., Rock, Iyad, Romney, Miles A., Rozario, Andrea, Ruff, Michael F., Rupley, Kathleen, Saeedi, Ali, Saiewitz, Aaron, Salzsieder, Leigh W., Sarkar, Sayan, Saulls, Michael, Scanlan, Tialei A., Schaefer, Tammie J., Schaupp, Daniel, Schneider, Gary P., Seebeck, Andreas, Sellers, R. Drew, Seto, Samantha C., Sevel, Romi-Lee, Shan, Yuxin, Sherwood, Matthew G., Singorahardjo, Maggie, Skaftadottir, Hanna Kristin, Skomra, Justyna, Smith, Jason L., Smith, Dallin O., Smith, James, Snow, Mason C., Sommerfeldt, Ryan D., Sorensen, Kate B., Sorensen, Trevor L., Spieler, Andrew C., Stallings, Matthew A., Stallings, Lesya, Stancill, Alan, Stanley, Jonathan D., Stefaniak, Chad M., Stephens, Nathaniel M., Stewart, Bryan W., Stratopoulos, Theophanis C., Street, Daniel A., Subedi, Meena, Summers, Scott L., Sundkvist, Charlotte H., Synn, Christina, Tadesse, Amanuel, Tapis, Gregory P., Tassin, Kerri, Taylor, Samantha, Teal, Mary, and Teeter, Ryan

Abstract

ChatGPT, a language-learning model chatbot, has garnered considerable attention for its ability to respond to users’ questions. Using data from 14 countries and 186 institutions, we compare ChatGPT and student performance for 28,085 questions from accounting assessments and textbook test banks. As of January 2023, ChatGPT provides correct answers for 56.5 percent of questions and partially correct answers for an additional 9.4 percent of questions. When considering point values for questions, students significantly outperform ChatGPT with a 76.7 percent average on assessments compared to 47.5 percent for ChatGPT if no partial credit is awarded and 56.5 percent if partial credit is awarded. Still, ChatGPT performs better than the student average for 15.8 percent of assessments when we include partial credit. We provide evidence of how ChatGPT performs on different question types, accounting topics, class levels, open/closed assessments, and test bank questions. We also discuss implications for accounting education and research.

23. Ethical decision-making for homeland security

Author

Brannan, David, Strindberg, Anders, National Security Affairs, Nelson, Aaron G., Brannan, David, Strindberg, Anders, National Security Affairs, and Nelson, Aaron G.

Abstract

CHDS State/Local, The thesis suggests that homeland security personnel lack a uniform method to make sound and defensible ethical decisions. Building on a foundation of classical ethical thought, it is established that ethics are essential to the work of homeland security. Philosophical underpinnings include virtue ethics, deontology, utilitarianism, decision-making practices, and values common to the homeland security enterprise. Real-world case studies were examined in an attempt to understand and demonstrate what can happen if ethics are neglected, considered incompletely or incorrectly, or thoughtfully applied. Case studies include the response to Hurricane Katrina, motivation and thought behind terrorism, and the discussion on torture. Examples of good ethics programs were analyzed, including the Canadian Defense Ethics Program and the Wildfire Fire Leadership Development Program. From this research, a conceptual framework for understanding was developed. The DRIVE framework (Duty, Respect, Integrity, Vision, Ends/Expected outcomes) is proposed to give homeland security personnel the tools necessary to evaluate a situation, make a decision, and review it retrospectively. The framework is easy to remember, flexible to allow for individual differences, yet comprehensive enough to encompass classical ethical thought, common values, and decision-making. The thesis recommends developing an ethics-training program for homeland security, using DRIVE as a foundation., http://archive.org/details/ethicaldecisionm1094537684, Captain, Unified Fire Authority of Greater Salt Lake, Salt Lake City, Utah

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