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7. Interpolated SelectionConv for Spherical Images and Surfaces

Author

Hart, David, Whitney, Michael, and Morse, Bryan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We present a new and general framework for convolutional neural network operations on spherical (or omnidirectional) images. Our approach represents the surface as a graph of connected points that doesn't rely on a particular sampling strategy. Additionally, by using an interpolated version of SelectionConv, we can operate on the sphere while using existing 2D CNNs and their weights. Since our method leverages existing graph implementations, it is also fast and can be fine-tuned efficiently. Our method is also general enough to be applied to any surface type, even those that are topologically non-simple. We demonstrate the effectiveness of our technique on the tasks of style transfer and segmentation for spheres as well as stylization for 3D meshes. We provide a thorough ablation study of the performance of various spherical sampling strategies., Comment: To be presented at WACV 2023

Published
2022

8. SelectionConv: Convolutional Neural Networks for Non-rectilinear Image Data

Author

Hart, David, Whitney, Michael, and Morse, Bryan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Convolutional Neural Networks have revolutionized vision applications. There are image domains and representations, however, that cannot be handled by standard CNNs (e.g., spherical images, superpixels). Such data are usually processed using networks and algorithms specialized for each type. In this work, we show that it may not always be necessary to use specialized neural networks to operate on such spaces. Instead, we introduce a new structured graph convolution operator that can copy 2D convolution weights, transferring the capabilities of already trained traditional CNNs to our new graph network. This network can then operate on any data that can be represented as a positional graph. By converting non-rectilinear data to a graph, we can apply these convolutions on these irregular image domains without requiring training on large domain-specific datasets. Results of transferring pre-trained image networks for segmentation, stylization, and depth prediction are demonstrated for a variety of such data forms., Comment: To be presented at ECCV 2022

Published
2022

10. Contributors

Author

Abd-Elsayed, Alaa, primary, Bawany, Mohammad H., additional, Busch, Clayton, additional, Chitneni, Ahish, additional, Chopra, Pooja, additional, Fiala, Kenneth J., additional, Frisenda, Gabrielle, additional, Habibi, Manuchehr, additional, Hussain, Nasir, additional, Jassal, Navdeep S., additional, Malik, Ahmed, additional, Malik, Tariq, additional, Marshall, Genevieve, additional, Martens, Joshua M., additional, Mazur-Hart, David J., additional, Pendem, Kailash, additional, Pride, Keth, additional, Raslan, Ahmed M., additional, Vannoy, Lucas, additional, Visnjevac, Ognjen, additional, and Yaghi, Nasser K., additional

Published
2024
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19. Use of accounting concepts to study research: return on investment in XSEDE, a US cyberinfrastructure service

Author

Stewart, Craig A., Costa, Claudia M., Wernert, Julie A., Snapp-Childs, Winona, Bland, Marques, Blood, Philip, Campbell, Terry, Couvares, Peter, Fischer, Jeremy, Hancock, David Y., Hart, David L., Jankowski, Harmony, Knepper, Richard, McMullen, Donald F., Mehringer, Susan, Pierce, Marlon, Rogers, Gary, Sinkovits, Robert S., and Towns, John

Published
2023
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20. Style Transfer for Light Field Photography

Author

Hart, David, Greenland, Jessica, and Morse, Bryan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

As light field images continue to increase in use and application, it becomes necessary to adapt existing image processing methods to this unique form of photography. In this paper we explore methods for applying neural style transfer to light field images. Feed-forward style transfer networks provide fast, high-quality results for monocular images, but no such networks exist for full light field images. Because of the size of these images, current light field data sets are small and are insufficient for training purely feed-forward style-transfer networks from scratch. Thus, it is necessary to adapt existing monocular style transfer networks in a way that allows for the stylization of each view of the light field while maintaining visual consistencies between views. Instead, the proposed method backpropagates the loss through the network, and the process is iterated to optimize (essentially overfit) the resulting stylization for a single light field image alone. The network architecture allows for the incorporation of pre-trained fast monocular stylization networks while avoiding the need for a large light field training set., Comment: To be presented at WACV 2020

Published
2020

22. Three Versions of Hawthorne

Author

Hart, David J.

Subjects
Mediation, Company business planning, Humanities, Literature/writing, Philosophy and religion
Abstract

I. PHILOSOPHER ... Nathaniel were verily William. (1) Among the curiosities of Professor Borges's cabinet is one Nathaniel Hawthorne,'tall, handsome, lean, [and] dark' ('Hawthorne' 49). Almost one hundred years exactly [...]

Published
2023

23. Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data

Author

Kaddurah-Daouk, Rima, Kueider-Paisley, Alexandra, Doraiswamy, P. Murali, Blach, Colette, Moseley, Arthur, Thompson, Will, St John-Williams, Lisa, Mahmoudiandehkhordi, Siamak, Tenenbaum, Jessica, Welsh-Balmer, Kathleen, Plassman, Brenda, Saykin, Andrew J., Nho, Kwangsik, Risacher, Shannon L., Kastenmüller, Gabi, Arnold, Matthias, Han, Xianlin, Baillie, Rebecca, Knight, Rob, Dorrestein, Pieter, Brewer, James, Mayer, Emeran, Labus, Jennifer, Baldi, Pierre, Gupta, Arpana, Fiehn, Oliver, Barupal, Dinesh, Meikle, Peter, Mazmanian, Sarkis, Rader, Dan, Kling, Mitchel, Shaw, Leslie, Trojanowski, John, van Duijin, Cornelia, Nevado-Holgado, Alejo, Bennett, David, Krishnan, Ranga, Keshavarzian, Ali, Vogt, Robin, Ikram, Arfan, Hankemeier, Thomas, Thiele, Ines, Price, Nathan, Funk, Cory, Baloni, Priyanka, Jia, Wei, Wishart, David, Brinton, Roberta, Chang, Rui, Farrer, Lindsay, Au, Rhoda, Qiu, Wendy, Würtz, Peter, Koal, Therese, Mangravite, Lara, Krumsiek, Jan, Suhre, Karsten, Newman, John, Moreno, Herman, Foroud, Tatania, Sacks, Frank, Jansson, Janet, Weiner, Michael W., Aisen, Paul, Petersen, Ronald, Jack, Clifford R., Jr., Jagust, William, Trojanowki, John Q., Toga, Arthur W., Beckett, Laurel, Green, Robert C., Morris, John C., Perrin, Richard J., Shaw, Leslie M., Khachaturian, Zaven, Carrillo, Maria, Potter, William, Barnes, Lisa, Bernard, Marie, Gonzalez, Hector, Ho, Carole, Hsiao, John K., Jackson, Jonathan, Masliah, Eliezer, Masterman, Donna, Okonkwo, Ozioma, Perrin, Richard, Ryan, Laurie, Silverberg, Nina, Fleisher, Adam, Sacrey, Diana Truran, Fockler, Juliet, Conti, Cat, Veitch, Dallas, Neuhaus, John, Jin, Chengshi, Nosheny, Rachel, Ashford, Miriam, Flenniken, Derek, Kormos, Adrienne, Montine, Tom, Rafii, Michael, Raman, Rema, Jimenez, Gustavo, Donohue, Michael, Gessert, Devon, Salazar, Jennifer, Zimmerman, Caileigh, Cabrera, Yuliana, Walter, Sarah, Miller, Garrett, Coker, Godfrey, Clanton, Taylor, Hergesheimer, Lindsey, Smith, Stephanie, Adegoke, Olusegun, Mahboubi, Payam, Moore, Shelley, Pizzola, Jeremy, Shaffer, Elizabeth, Sloan, Brittany, Harvey, Danielle, Forghanian-Arani, Arvin, Borowski, Bret, Ward, Chad, Schwarz, Christopher, Jones, David, Gunter, Jeff, Kantarci, Kejal, Senjem, Matthew, Vemuri, Prashanthi, Reid, Robert, Fox, Nick C., Malone, Ian, Thompson, Paul, Thomopoulos, Sophia I., Nir, Talia M., Jahanshad, Neda, DeCarli, Charles, Knaack, Alexander, Fletcher, Evan, Tosun-Turgut, Duygu, Chen, Stephanie Rossi, Choe, Mark, Crawford, Karen, Yushkevich, Paul A., Das, Sandhitsu, Koeppe, Robert A., Reiman, Eric M., Chen, Kewei, Mathis, Chet, Landau, Susan, Cairns, Nigel J., Householder, Erin, Franklin, Erin, Bernhardt, Haley, Taylor-Reinwald, Lisa, Korecka, Magdalena, Figurski, Michal, Neu, Scott, Apostolova, Liana G., Shen, Li, Foroud, Tatiana M., Nudelman, Kelly, Faber, Kelley, Wilmes, Kristi, Thal, Leon, Silbert, Lisa C., Lind, Betty, Crissey, Rachel, Kaye, Jeffrey A., Carter, Raina, Dolen, Sara, Quinn, Joseph, Schneider, Lon S., Pawluczyk, Sonia, Becerra, Mauricio, Teodoro, Liberty, Dagerman, Karen, Spann, Bryan M., Vanderswag, Helen, Ziolkowski, Jaimie, Heidebrink, Judith L., Zbizek-Nulph, Lisa, Lord, Joanne L., Mason, Sara S., Albers, Colleen S., Knopman, David, Johnson, Kris, Villanueva-Meyer, Javier, Pavlik, Valory, Pacini, Nathaniel, Lamb, Ashley, Kass, Joseph S., Doody, Rachelle S., Shibley, Victoria, Chowdhury, Munir, Rountree, Susan, Dang, Mimi, Stern, Yaakov, Honig, Lawrence S., Mintz, Akiva, Ances, Beau, Winkfield, David, Carroll, Maria, Stobbs-Cucchi, Georgia, Oliver, Angela, Creech, Mary L., Mintun, Mark A., Schneider, Stacy, Geldmacher, David, Love, Marissa Natelson, Griffith, Randall, Clark, David, Brockington, John, Marson, Daniel, Grossman, Hillel, Goldstein, Martin A., Greenberg, Jonathan, Mitsis, Effie, Shah, Raj C., Lamar, Melissa, Samuels, Patricia, Duara, Ranjan, Greig-Custo, Maria T., Rodriguez, Rosemarie, Albert, Marilyn, Onyike, Chiadi, Farrington, Leonie, Rudow, Scott, Brichko, Rottislav, Kielb, Stephanie, Smith, Amanda, Raj, Balebail Ashok, Fargher, Kristin, Sadowski, Martin, Wisniewski, Thomas, Shulman, Melanie, Faustin, Arline, Rao, Julia, Castro, Karen M., Ulysse, Anaztasia, Chen, Shannon, Sheikh, Mohammed O., Singleton-Garvin, Jamika, Petrella, JeffreyR., James, Olga, Wong, Terence Z., Borges-Neto, Salvador, Karlawish, Jason H., Wolk, David A., Vaishnavi, Sanjeev, Clark, Christopher M., Arnold, Steven E., Smith, Charles D., Jicha, Gregory A., Raslau, Flavius D., Lopez, Oscar L., Oakley, MaryAnn, Simpson, Donna M., Porsteinsson, Anton P., Martin, Kim, Kowalski, Nancy, Keltz, Melanie, Goldstein, Bonnie S., Makino, Kelly M., Ismail, M. Saleem, Brand, Connie, Thai, Gaby, Pierce, Aimee, Yanez, Beatriz, Sosa, Elizabeth, Witbracht, Megan, Kelley, Brendan, Nguyen, Trung, Womack, Kyle, Mathews, Dana, Quiceno, Mary, Levey, Allan I., Lah, James J., Hajjar, Ihab, Cellar, Janet S., Burns, Jeffrey M., Swerdlow, Russell H., Brooks, William M., Silverman, Daniel H.S., Kremen, Sarah, Apostolova, Liana, Tingus, Kathleen, Lu, Po H., Bartzokis, George, Woo, Ellen, Teng, Edmond, Graff-Radford, Neill R., Parfitt, Francine, Poki-Walker, Kim, Farlow, Martin R., Hake, Ann Marie, Matthews, Brandy R., Brosch, Jared R., Herring, Scott, van, Christopher H., Mecca, Adam P., Good, Susan P., MacAvoy, Martha G., Carson, Richard E., Varma, Pradeep, Chertkow, Howard, Vaitekunis, Susan, Hosein, Chris, Black, Sandra, Stefanovic, Bojana, Heyn, Chris (Chinthaka), Robin Hsiung, Ging-Yuek, Kim, Ellen, Mudge, Benita, Sossi, Vesna, Feldman, Howard, Assaly, Michele, Finger, Elizabeth, Pasternak, Stephen, Rachinsky, Irina, Kertesz, Andrew, Drost, Dick, Rogers, John, Grant, Ian, Muse, Brittanie, Rogalski, Emily, Robson, Jordan, Mesulam, M.-Marsel, Kerwin, Diana, Wu, Chuang-Kuo, Johnson, Nancy, Lipowski, Kristine, Weintraub, Sandra, Bonakdarpour, Borna, Pomara, Nunzio, Hernando, Raymundo, Sarrael, Antero, Rosen, Howard J., Miller, Bruce L., Perry, David, Turner, Raymond Scott, Johnson, Kathleen, Reynolds, Brigid, MCCann, Kelly, Poe, Jessica, Sperling, Reisa A., Johnson, Keith A., Marshall, Gad A., Yesavage, Jerome, Taylor, Joy L., Chao, Steven, Coleman, Jaila, White, Jessica D., Lane, Barton, Rosen, Allyson, Tinklenberg, Jared, Belden, Christine M., Atri, Alireza, Clark, Kelly A., Zamrini, Edward, Sabbagh, Marwan, Killiany, Ronald, Stern, Robert, Mez, Jesse, Kowall, Neil, Budson, Andrew E., Obisesan, Thomas O., Ntekim, Oyonumo E., Wolday, Saba, Khan, Javed I., Nwulia, Evaristus, Nadarajah, Sheeba, Lerner, Alan, Ogrocki, Paula, Tatsuoka, Curtis, Fatica, Parianne, Maillard, Pauline, Olichney, John, Carmichael, Owen, Bates, Vernice, Capote, Horacio, Rainka, Michelle, Borrie, Michael, Lee, T.-Y., Bartha, Dr Rob, Johnson, Sterling, Asthana, Sanjay, Carlsson, Cynthia M., Perrin, Allison, Burke, Anna, Scharre, Douglas W., Kataki, Maria, Tarawneh, Rawan, Hart, David, Zimmerman, Earl A., Celmins, Dzintra, Miller, Delwyn D., BolesPonto, Laura L., Smith, Karen Ekstam, Koleva, Hristina, Shim, Hyungsub, Nam, Ki Won, Schultz, Susan K., Williamson, Jeff D., Craft, Suzanne, Cleveland, Jo, Yang, Mia, Sink, Kaycee M., Ott, Brian R., Drake, Jonathan, Tremont, Geoffrey, Daiello, Lori A., Drake, Jonathan D., Ritter, Aaron, Bernick, Charles, Munic, Donna, O'Connelll, Abigail, Mintzer, Jacobo, Wiliams, Arthur, Masdeu, Joseph, Shi, Jiong, Garcia, Angelica, Newhouse, Paul, Potkin, Steven, Salloway, Stephen, Malloy, Paul, Correia, Stephen, Kittur, Smita, Pearlson, Godfrey D., Blank, Karen, Anderson, Karen, Flashman, Laura A., Seltzer, Marc, Hynes, Mary L., Santulli, Robert B., Relkin, Norman, Chiang, Gloria, Lee, Athena, Lin, Michael, Ravdin, Lisa, Petersen, Ron, Neylan, Thomas, Grafman, Jordan, Danowski, Sarah, Nguyen-Barrera, Catherine, Hayes, Jacqueline, Finley, Shannon, Bernstein, Matthew, Senjem, Matt, Foster, Norm, Kim, Sungeun, Sood, Ajay, Blanchard, Kimberly S., Fleischman, Debra, Arfanakis, Konstantinos, Varon, Daniel, Greig, Maria T., Petrella, Jeffrey R., Goldstein, Bonnie, Martin, Kimberly S., Reist, Christopher, Sadowsky, Carl, Martinez, Walter, Villena, Teresa, Rosen, Howard, Marshall, Gad, Peskind, Elaine R., Petrie, Eric C., Li, Gail, Mackin, Scott, Jimenez-Maggiora, Gustavo, Drake, Erin, Donohue, Mike, Nelson, Craig, Bickford, David, Butters, Meryl, Zmuda, Michelle, Reyes, Denise, Faber, Kelley M., Nudelman, Kelly N., Au, Yiu Ho, Scherer, Kelly, Catalinotto, Daniel, Stark, Samuel, Ong, Elise, Fernandez, Dariella, Jo, Taeho, Kim, Junpyo, Bice, Paula, Huynh, Kevin, Wang, Tingting, Meikle, Peter J., and Giles, Corey

Published
2023
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31. Cartilage Lesions and Osteoarthritis: Cell Therapy

Author

Fernandes, Tiago Lazzaretti, Shimomura, Kazunori, Hart, David A., Boffa, Angelo, Nakamura, Norimasa, Filardo, Giuseppe, editor, Mandelbaum, Bert R., editor, Muschler, George F., editor, Rodeo, Scott A., editor, and Nakamura, Norimasa, editor

Published
2022
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32. Meniscal Lesions: Cell Therapy

Author

Shimomura, Kazunori, Hart, David A., Nakamura, Norimasa, Filardo, Giuseppe, editor, Mandelbaum, Bert R., editor, Muschler, George F., editor, Rodeo, Scott A., editor, and Nakamura, Norimasa, editor

Published
2022
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41. Advance the Distribution Grid with Measurements: Emerging challenges on the distribution grid are driving the need for new and improved solutions that use synchronized measurement technologies

Author

Jones, Kevin D., Hintos, Ludo, Hart, David G., Aguero, Julio Romero, Hunt, Rich, and Udren, Eric

Subjects
Dominion Energy Inc., Electric utilities, Global temperature changes, Alternative energy sources, Electronics and electrical industries
Abstract

Utilities are facing new distribution system challenges, including the growing adoption of distributed energy resources, transportation electrification, the increasing frequency of major weather events, and the evolving customer expectations of [...]

Published
2022

43. Size‐dependent effects of dams on river ecosystems and implications for dam removal outcomes.

Author

Brown, Rebecca L., Charles, Don, Horwitz, Richard J., Pizzuto, James E., Skalak, Katherine, Velinsky, David J., and Hart, David D.

Subjects
DAM retirement, FISH morphology, STREAM restoration, WATER chemistry, WATER quality
Abstract

Understanding the relationship between a dam's size and its ecological effects is important for prioritization of river restoration efforts based on dam removal. Although much is known about the effects of large storage dams, this information may not be applicable to small dams, which represent the vast majority of dams being considered for removal. To better understand how dam effects vary with size, we conducted a multidisciplinary study of the downstream effect of dams on a range of ecological characteristics including geomorphology, water chemistry, periphyton, riparian vegetation, benthic macroinvertebrates, and fish. We related dam size variables to the downstream–upstream fractional difference in measured ecological characteristics for 16 dams in the mid‐Atlantic region ranging from 0.9 to 57 m high, with hydraulic residence times (HRTs) ranging from 30 min to 1.5 years. For a range of physical attributes, larger dams had larger effects. For example, the water surface width below dams was greater below large dams. By contrast, there was no effect of dam size on sediment grain size, though the fraction of fine‐grained bed material was lower below dams independently of dam size. Larger dams tended to reduce water quality more, with decreased downstream dissolved oxygen and increased temperature. Larger dams decreased inorganic nutrients (N, P, Si), but increased particulate nutrients (N, P) in downstream reaches. Aquatic organisms tended to have greater dissimilarity in species composition below larger dams (for fish and periphyton), lower taxonomic diversity (for macroinvertebrates), and greater pollution tolerance (for periphyton and macroinvertebrates). Plants responded differently below large and small dams, with fewer invasive species below large dams, but more below small dams. Overall, these results demonstrate that larger dams have much greater impact on the ecosystem components we measured, and hence their removal has the greatest potential for restoring river ecosystems. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Collagen integrity of the annulus fibrosus in degenerative disc disease individuals quantified with collagen hybridizing peptide.

Author

Dhiman, Manmeet S., Bader, Taylor J., Ponjevic, Dragana, Salo, Paul T., Hart, David A., Swamy, Ganesh, Matyas, John R., and Duncan, Neil A.

Subjects
INTERVERTEBRAL disk, HEMATOXYLIN & eosin staining, PEPTIDES, DEGENERATION (Pathology), COLLAGEN
Abstract

Introduction: Degenerative disc disease (DDD) is accompanied by structural changes in the intervertebral discs (IVD). Extra‐cellular matrix degradation of the annulus fibrosus (AF) has been linked with degeneration of the IVD. Collagen is a vital component of the IVD. Collagen hybridizing peptide (CHP) is an engineered protein that binds to degraded collagen, which we used to quantify collagen damage in AF. This method was used to compare AF samples obtained from donors with no DDD to AF samples from patients undergoing surgery for symptomatic DDD. Methods: Fresh AF tissue was embedded in an optimal cutting temperature compound and cryosectioned at a thickness of 8 μm. Hematoxylin and Eosin staining was performed on sections for general histomorphological assessment. Serial sections were stained with Cy3‐conjugated CHP and the mean fluorescence intensity and areal fraction of Cy3‐positive staining were averaged for three regions of interest (ROI) on each CHP‐stained section. Results: Increases in mean fluorescence intensity (p = 0.0004) and percentage of positively stained area (p = 0.00008) with CHP were detected in DDD samples compared to the non‐DDD samples. Significant correlations were observed between mean fluorescence intensity and percentage of positively stained area for both non‐DDD (R = 0.98, p = 5E‐8) and DDD (R = 0.79, p = 0.0012) samples. No significant differences were detected between sex and the lumbar disc level subgroups of the non‐DDD and DDD groups. Only tissue pathology (non‐DDD versus DDD) influenced the measured parameters. No three‐way interactions between tissue pathology, sex, and lumbar disc level were observed. Discussion and Conclusions: These findings suggest that AF collagen degradation is greater in DDD samples compared to non‐DDD samples, as evidenced by the increased CHP staining. Strong positive correlations between the two measured parameters suggest that when collagen degradation occurs, it is detected by this technique and is widespread throughout the tissue. This study provides new insights into the structural alterations associated with collagen degradation in the AF that occur during DDD. [ABSTRACT FROM AUTHOR]

Published
2024
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