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31 results on '"Wang, Gloria"'

1. Physical Property Understanding from Language-Embedded Feature Fields

Author

Zhai, Albert J., Shen, Yuan, Chen, Emily Y., Wang, Gloria X., Wang, Xinlei, Wang, Sheng, Guan, Kaiyu, and Wang, Shenlong

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

Can computers perceive the physical properties of objects solely through vision? Research in cognitive science and vision science has shown that humans excel at identifying materials and estimating their physical properties based purely on visual appearance. In this paper, we present a novel approach for dense prediction of the physical properties of objects using a collection of images. Inspired by how humans reason about physics through vision, we leverage large language models to propose candidate materials for each object. We then construct a language-embedded point cloud and estimate the physical properties of each 3D point using a zero-shot kernel regression approach. Our method is accurate, annotation-free, and applicable to any object in the open world. Experiments demonstrate the effectiveness of the proposed approach in various physical property reasoning tasks, such as estimating the mass of common objects, as well as other properties like friction and hardness., Comment: CVPR 2024. Project page (with code): https://ajzhai.github.io/NeRF2Physics/

Published
2024

2. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Author

Srivastava, Aarohi, Rastogi, Abhinav, Rao, Abhishek, Shoeb, Abu Awal Md, Abid, Abubakar, Fisch, Adam, Brown, Adam R., Santoro, Adam, Gupta, Aditya, Garriga-Alonso, Adrià, Kluska, Agnieszka, Lewkowycz, Aitor, Agarwal, Akshat, Power, Alethea, Ray, Alex, Warstadt, Alex, Kocurek, Alexander W., Safaya, Ali, Tazarv, Ali, Xiang, Alice, Parrish, Alicia, Nie, Allen, Hussain, Aman, Askell, Amanda, Dsouza, Amanda, Slone, Ambrose, Rahane, Ameet, Iyer, Anantharaman S., Andreassen, Anders, Madotto, Andrea, Santilli, Andrea, Stuhlmüller, Andreas, Dai, Andrew, La, Andrew, Lampinen, Andrew, Zou, Andy, Jiang, Angela, Chen, Angelica, Vuong, Anh, Gupta, Animesh, Gottardi, Anna, Norelli, Antonio, Venkatesh, Anu, Gholamidavoodi, Arash, Tabassum, Arfa, Menezes, Arul, Kirubarajan, Arun, Mullokandov, Asher, Sabharwal, Ashish, Herrick, Austin, Efrat, Avia, Erdem, Aykut, Karakaş, Ayla, Roberts, B. Ryan, Loe, Bao Sheng, Zoph, Barret, Bojanowski, Bartłomiej, Özyurt, Batuhan, Hedayatnia, Behnam, Neyshabur, Behnam, Inden, Benjamin, Stein, Benno, Ekmekci, Berk, Lin, Bill Yuchen, Howald, Blake, Orinion, Bryan, Diao, Cameron, Dour, Cameron, Stinson, Catherine, Argueta, Cedrick, Ramírez, César Ferri, Singh, Chandan, Rathkopf, Charles, Meng, Chenlin, Baral, Chitta, Wu, Chiyu, Callison-Burch, Chris, Waites, Chris, Voigt, Christian, Manning, Christopher D., Potts, Christopher, Ramirez, Cindy, Rivera, Clara E., Siro, Clemencia, Raffel, Colin, Ashcraft, Courtney, Garbacea, Cristina, Sileo, Damien, Garrette, Dan, Hendrycks, Dan, Kilman, Dan, Roth, Dan, Freeman, Daniel, Khashabi, Daniel, Levy, Daniel, González, Daniel Moseguí, Perszyk, Danielle, Hernandez, Danny, Chen, Danqi, Ippolito, Daphne, Gilboa, Dar, Dohan, David, Drakard, David, Jurgens, David, Datta, Debajyoti, Ganguli, Deep, Emelin, Denis, Kleyko, Denis, Yuret, Deniz, Chen, Derek, Tam, Derek, Hupkes, Dieuwke, Misra, Diganta, Buzan, Dilyar, Mollo, Dimitri Coelho, Yang, Diyi, Lee, Dong-Ho, Schrader, Dylan, Shutova, Ekaterina, Cubuk, Ekin Dogus, Segal, Elad, Hagerman, Eleanor, Barnes, Elizabeth, Donoway, Elizabeth, Pavlick, Ellie, Rodola, Emanuele, Lam, Emma, Chu, Eric, Tang, Eric, Erdem, Erkut, Chang, Ernie, Chi, Ethan A., Dyer, Ethan, Jerzak, Ethan, Kim, Ethan, Manyasi, Eunice Engefu, Zheltonozhskii, Evgenii, Xia, Fanyue, Siar, Fatemeh, Martínez-Plumed, Fernando, Happé, Francesca, Chollet, Francois, Rong, Frieda, Mishra, Gaurav, Winata, Genta Indra, de Melo, Gerard, Kruszewski, Germán, Parascandolo, Giambattista, Mariani, Giorgio, Wang, Gloria, Jaimovitch-López, Gonzalo, Betz, Gregor, Gur-Ari, Guy, Galijasevic, Hana, Kim, Hannah, Rashkin, Hannah, Hajishirzi, Hannaneh, Mehta, Harsh, Bogar, Hayden, Shevlin, Henry, Schütze, Hinrich, Yakura, Hiromu, Zhang, Hongming, Wong, Hugh Mee, Ng, Ian, Noble, Isaac, Jumelet, Jaap, Geissinger, Jack, Kernion, Jackson, Hilton, Jacob, Lee, Jaehoon, Fisac, Jaime Fernández, Simon, James B., Koppel, James, Zheng, James, Zou, James, Kocoń, Jan, Thompson, Jana, Wingfield, Janelle, Kaplan, Jared, Radom, Jarema, Sohl-Dickstein, Jascha, Phang, Jason, Wei, Jason, Yosinski, Jason, Novikova, Jekaterina, Bosscher, Jelle, Marsh, Jennifer, Kim, Jeremy, Taal, Jeroen, Engel, Jesse, Alabi, Jesujoba, Xu, Jiacheng, Song, Jiaming, Tang, Jillian, Waweru, Joan, Burden, John, Miller, John, Balis, John U., Batchelder, Jonathan, Berant, Jonathan, Frohberg, Jörg, Rozen, Jos, Hernandez-Orallo, Jose, Boudeman, Joseph, Guerr, Joseph, Jones, Joseph, Tenenbaum, Joshua B., Rule, Joshua S., Chua, Joyce, Kanclerz, Kamil, Livescu, Karen, Krauth, Karl, Gopalakrishnan, Karthik, Ignatyeva, Katerina, Markert, Katja, Dhole, Kaustubh D., Gimpel, Kevin, Omondi, Kevin, Mathewson, Kory, Chiafullo, Kristen, Shkaruta, Ksenia, Shridhar, Kumar, McDonell, Kyle, Richardson, Kyle, Reynolds, Laria, Gao, Leo, Zhang, Li, Dugan, Liam, Qin, Lianhui, Contreras-Ochando, Lidia, Morency, Louis-Philippe, Moschella, Luca, Lam, Lucas, Noble, Lucy, Schmidt, Ludwig, He, Luheng, Colón, Luis Oliveros, Metz, Luke, Şenel, Lütfi Kerem, Bosma, Maarten, Sap, Maarten, ter Hoeve, Maartje, Farooqi, Maheen, Faruqui, Manaal, Mazeika, Mantas, Baturan, Marco, Marelli, Marco, Maru, Marco, Quintana, Maria Jose Ramírez, Tolkiehn, Marie, Giulianelli, Mario, Lewis, Martha, Potthast, Martin, Leavitt, Matthew L., Hagen, Matthias, Schubert, Mátyás, Baitemirova, Medina Orduna, Arnaud, Melody, McElrath, Melvin, Yee, Michael A., Cohen, Michael, Gu, Michael, Ivanitskiy, Michael, Starritt, Michael, Strube, Michael, Swędrowski, Michał, Bevilacqua, Michele, Yasunaga, Michihiro, Kale, Mihir, Cain, Mike, Xu, Mimee, Suzgun, Mirac, Walker, Mitch, Tiwari, Mo, Bansal, Mohit, Aminnaseri, Moin, Geva, Mor, Gheini, Mozhdeh, T, Mukund Varma, Peng, Nanyun, Chi, Nathan A., Lee, Nayeon, Krakover, Neta Gur-Ari, Cameron, Nicholas, Roberts, Nicholas, Doiron, Nick, Martinez, Nicole, Nangia, Nikita, Deckers, Niklas, Muennighoff, Niklas, Keskar, Nitish Shirish, Iyer, Niveditha S., Constant, Noah, Fiedel, Noah, Wen, Nuan, Zhang, Oliver, Agha, Omar, Elbaghdadi, Omar, Levy, Omer, Evans, Owain, Casares, Pablo Antonio Moreno, Doshi, Parth, Fung, Pascale, Liang, Paul Pu, Vicol, Paul, Alipoormolabashi, Pegah, Liao, Peiyuan, Liang, Percy, Chang, Peter, Eckersley, Peter, Htut, Phu Mon, Hwang, Pinyu, Miłkowski, Piotr, Patil, Piyush, Pezeshkpour, Pouya, Oli, Priti, Mei, Qiaozhu, Lyu, Qing, Chen, Qinlang, Banjade, Rabin, Rudolph, Rachel Etta, Gabriel, Raefer, Habacker, Rahel, Risco, Ramon, Millière, Raphaël, Garg, Rhythm, Barnes, Richard, Saurous, Rif A., Arakawa, Riku, Raymaekers, Robbe, Frank, Robert, Sikand, Rohan, Novak, Roman, Sitelew, Roman, LeBras, Ronan, Liu, Rosanne, Jacobs, Rowan, Zhang, Rui, Salakhutdinov, Ruslan, Chi, Ryan, Lee, Ryan, Stovall, Ryan, Teehan, Ryan, Yang, Rylan, Singh, Sahib, Mohammad, Saif M., Anand, Sajant, Dillavou, Sam, Shleifer, Sam, Wiseman, Sam, Gruetter, Samuel, Bowman, Samuel R., Schoenholz, Samuel S., Han, Sanghyun, Kwatra, Sanjeev, Rous, Sarah A., Ghazarian, Sarik, Ghosh, Sayan, Casey, Sean, Bischoff, Sebastian, Gehrmann, Sebastian, Schuster, Sebastian, Sadeghi, Sepideh, Hamdan, Shadi, Zhou, Sharon, Srivastava, Shashank, Shi, Sherry, Singh, Shikhar, Asaadi, Shima, Gu, Shixiang Shane, Pachchigar, Shubh, Toshniwal, Shubham, Upadhyay, Shyam, Shyamolima, Debnath, Shakeri, Siamak, Thormeyer, Simon, Melzi, Simone, Reddy, Siva, Makini, Sneha Priscilla, Lee, Soo-Hwan, Torene, Spencer, Hatwar, Sriharsha, Dehaene, Stanislas, Divic, Stefan, Ermon, Stefano, Biderman, Stella, Lin, Stephanie, Prasad, Stephen, Piantadosi, Steven T., Shieber, Stuart M., Misherghi, Summer, Kiritchenko, Svetlana, Mishra, Swaroop, Linzen, Tal, Schuster, Tal, Li, Tao, Yu, Tao, Ali, Tariq, Hashimoto, Tatsu, Wu, Te-Lin, Desbordes, Théo, Rothschild, Theodore, Phan, Thomas, Wang, Tianle, Nkinyili, Tiberius, Schick, Timo, Kornev, Timofei, Tunduny, Titus, Gerstenberg, Tobias, Chang, Trenton, Neeraj, Trishala, Khot, Tushar, Shultz, Tyler, Shaham, Uri, Misra, Vedant, Demberg, Vera, Nyamai, Victoria, Raunak, Vikas, Ramasesh, Vinay, Prabhu, Vinay Uday, Padmakumar, Vishakh, Srikumar, Vivek, Fedus, William, Saunders, William, Zhang, William, Vossen, Wout, Ren, Xiang, Tong, Xiaoyu, Zhao, Xinran, Wu, Xinyi, Shen, Xudong, Yaghoobzadeh, Yadollah, Lakretz, Yair, Song, Yangqiu, Bahri, Yasaman, Choi, Yejin, Yang, Yichi, Hao, Yiding, Chen, Yifu, Belinkov, Yonatan, Hou, Yu, Hou, Yufang, Bai, Yuntao, Seid, Zachary, Zhao, Zhuoye, Wang, Zijian, Wang, Zijie J., Wang, Zirui, and Wu, Ziyi

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 450 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting., Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-bench

Published
2022

3. Nimble Navigators: Recollections of Parental Influence on Girls' Activism

Author

Wang, Gloria, Scotto-Lavino, Elisabeth, and Baily, Supriya

Abstract

During adolescence, activism provides an outlet for ideas, visions, and concerns about societal issues as teens stake their claim in society. Girls often engage in activism as they begin to form their independent identities and explore their communities. However, the extent of parental influence on activist engagement is unclear, as gendered differences exist in how parents interact with youth activism. In this study, we explore how women, as they move away from their teenage years, recollect parental influence on their journeys as activists and the impact of their work on their relationships with parents. Using phenomenological inquiry and bracketing of the data, we examine the pivotal memories of teen activists and how they contributed to activists' agency and relationships. Uncovering how women perceive the effects of parental influences on their teen activism will provide insight into how girls navigate the challenges of independence, activism, and family during formative teenage years.

Published
2023
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4. NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation

Author

Dhole, Kaustubh D., Gangal, Varun, Gehrmann, Sebastian, Gupta, Aadesh, Li, Zhenhao, Mahamood, Saad, Mahendiran, Abinaya, Mille, Simon, Shrivastava, Ashish, Tan, Samson, Wu, Tongshuang, Sohl-Dickstein, Jascha, Choi, Jinho D., Hovy, Eduard, Dusek, Ondrej, Ruder, Sebastian, Anand, Sajant, Aneja, Nagender, Banjade, Rabin, Barthe, Lisa, Behnke, Hanna, Berlot-Attwell, Ian, Boyle, Connor, Brun, Caroline, Cabezudo, Marco Antonio Sobrevilla, Cahyawijaya, Samuel, Chapuis, Emile, Che, Wanxiang, Choudhary, Mukund, Clauss, Christian, Colombo, Pierre, Cornell, Filip, Dagan, Gautier, Das, Mayukh, Dixit, Tanay, Dopierre, Thomas, Dray, Paul-Alexis, Dubey, Suchitra, Ekeinhor, Tatiana, Di Giovanni, Marco, Goyal, Tanya, Gupta, Rishabh, Hamla, Louanes, Han, Sang, Harel-Canada, Fabrice, Honore, Antoine, Jindal, Ishan, Joniak, Przemyslaw K., Kleyko, Denis, Kovatchev, Venelin, Krishna, Kalpesh, Kumar, Ashutosh, Langer, Stefan, Lee, Seungjae Ryan, Levinson, Corey James, Liang, Hualou, Liang, Kaizhao, Liu, Zhexiong, Lukyanenko, Andrey, Marivate, Vukosi, de Melo, Gerard, Meoni, Simon, Meyer, Maxime, Mir, Afnan, Moosavi, Nafise Sadat, Muennighoff, Niklas, Mun, Timothy Sum Hon, Murray, Kenton, Namysl, Marcin, Obedkova, Maria, Oli, Priti, Pasricha, Nivranshu, Pfister, Jan, Plant, Richard, Prabhu, Vinay, Pais, Vasile, Qin, Libo, Raji, Shahab, Rajpoot, Pawan Kumar, Raunak, Vikas, Rinberg, Roy, Roberts, Nicolas, Rodriguez, Juan Diego, Roux, Claude, S., Vasconcellos P. H., Sai, Ananya B., Schmidt, Robin M., Scialom, Thomas, Sefara, Tshephisho, Shamsi, Saqib N., Shen, Xudong, Shi, Haoyue, Shi, Yiwen, Shvets, Anna, Siegel, Nick, Sileo, Damien, Simon, Jamie, Singh, Chandan, Sitelew, Roman, Soni, Priyank, Sorensen, Taylor, Soto, William, Srivastava, Aman, Srivatsa, KV Aditya, Sun, Tony, T, Mukund Varma, Tabassum, A, Tan, Fiona Anting, Teehan, Ryan, Tiwari, Mo, Tolkiehn, Marie, Wang, Athena, Wang, Zijian, Wang, Gloria, Wang, Zijie J., Wei, Fuxuan, Wilie, Bryan, Winata, Genta Indra, Wu, Xinyi, Wydmański, Witold, Xie, Tianbao, Yaseen, Usama, Yee, Michael A., Zhang, Jing, and Zhang, Yue

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

Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on. In this paper, we present NL-Augmenter, a new participatory Python-based natural language augmentation framework which supports the creation of both transformations (modifications to the data) and filters (data splits according to specific features). We describe the framework and an initial set of 117 transformations and 23 filters for a variety of natural language tasks. We demonstrate the efficacy of NL-Augmenter by using several of its transformations to analyze the robustness of popular natural language models. The infrastructure, datacards and robustness analysis results are available publicly on the NL-Augmenter repository (https://github.com/GEM-benchmark/NL-Augmenter)., Comment: 39 pages, repository at https://github.com/GEM-benchmark/NL-Augmenter

Published
2021

6. Anomaly Detection with SDAE

Author

Smith, Benjamin, Cant, Kevin, and Wang, Gloria

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Anomaly detection is a prominent data preprocessing step in learning applications for correction and/or removal of faulty data. Automating this data type with the use of autoencoders could increase the quality of the dataset by isolating anomalies that were missed through manual or basic statistical analysis. A Simple, Deep, and Supervised Deep Autoencoder were trained and compared for anomaly detection over the ASHRAE building energy dataset. Given the restricted parameters under which the models were trained, the Deep Autoencoder perfoms the best, however, the Supervised Deep Autoencoder outperforms the other models in total anomalies detected when considerations for the test datasets are given., Comment: 9 pages, 20 figures

Published
2020

8. Persistent Fetal SVT in a COVID-19 Positive Pregnancy

Author

Wang, Gloria, Stapley, Eric, Peterson, Sara, Parrott, Jessica, and Clark-Ganheart, Cecily

Subjects
Article Subject
Abstract

Background. Rapid introduction and spread of SARS-CoV-2 have posed unique challenges in understanding the disease, role in vertical transmission, and in developing management. We present a case of a patient with COVID-19 infection and fetus with new-onset fetal SVT. Case. A 26-year-old gravida 4 para 2012 with third trimester COVID-19 infection was diagnosed with new onset fetal SVT. Successful cardioversion was achieved with flecainide. The patient was followed outpatient until induction of labor at 39 and 3/7 weeks of gestational age resulting in an uncomplicated vaginal delivery. Postpartum course was uncomplicated. Conclusion. Fetal SVT is a potential complication of maternal COVID-19 infection. The use of transplacental therapy with flecainide is an appropriate alternative to digoxin in these cases.

Published
2022
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18. Gordon: CRA May Not Fetter Discretion On Interest Relief Application

Author

Wang, Gloria

Subjects
Attorneys general -- Cases, Automobile dealers -- Cases, Excise taxes -- Cases, Tax collection -- Cases, Company legal issue, Business, international
Abstract

In Gordon v Canada (Attorney General) (2016 FC 643), the Federal Court granted the taxpayer's application for judicial review and reminded the CRA that it may not fetter its discretion [...]

Published
2016

19. Environmental variables associated with fish biodiversity in stormwater ponds in Ontario.

Author

Wang, Gloria and Jackson, Donald

Subjects
FISHES, PONDS
Abstract

Urbanization is associated with the ecological degradation of streams due to high inflows of stormwater runoff from impervious surfaces. Stormwater ponds (SWPs) are artificial ponds designed to divert runoff from streams by retaining stormwater before slowly releasing it into streams. In highly urbanized areas where aquatic habitat is otherwise scarce, SWPs can be biodiversity hotspots for many aquatic taxa. However, many SWPs can suffer from salinization, pollution, eutrophication, and hypoxia that affect aquatic species potentially using these ponds. While groups such as benthic invertebrates have been well studied, there is little existing research on fishes in SWPs despite reports of fish being found in them. We sampled fish species composition in 50 SWPs from Brampton, Ontario, along with the associated water quality characteristics, to determine how environmental conditions may impact the fish communities. We determine the relative importance of water quality, pond design, and landscape variables in predicting fish species richness and community composition. As SWPs release into the headwaters of adjacent streams, their contents are likely to impact adjoining streams, rivers, and ultimately Lake Ontario. The findings of this study can help inform SWP management to improve ecological function, as well as identify the potential effects of SWP outflow on downstream waters. [ABSTRACT FROM AUTHOR]

Published
2023

21. Synthesis of a FTO Inhibitor with Anticonvulsant Activity

Author

Zheng, Guanqun, primary, Cox, Thomas, additional, Tribbey, Leah, additional, Wang, Gloria Z., additional, Iacoban, Paulina, additional, Booher, Matthew E., additional, Gabriel, Gregory J., additional, Zhou, Lu, additional, Bae, Nancy, additional, Rowles, Joie, additional, He, Chuan, additional, and Olsen, Mark J., additional

Published
2014
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30. Reviewers

Author

Abdolghafoorian, Hamed, Ahmed, Bilawal, Amlani, Shehzad, Angouw, Karina, Apriliani, Adinda K., Aygin, Deren, Badhrinarayanan, Shreya, Baker, Christopher B., Balasoglu, Emalina, Bangero, Joener B., Barba, Carlos E., Beveridge, Tyler S., Bhatti, Usman, Blackburn, Kaitlyn Marie, Bodie, Brandon, Borges, Gonçalo, Brisson, Michael, Brooke, Dewey, Cahilog, Zhen, Carney, Rhonda, Cela, Bekim A., Charboneau, Alex J., Chen, Y.S., Cicani, Laura, Clark, Ryan, Cochran, Sam, Daniels, Natasha, Davies, Taylor, Dilaver, Nafi Mehmet, Dionisie, Repetchi, DuBois, Darcelle, Duenk, Larry L., Dufka, Faustine, Duralde, Erin, Ennes, Alexander Panayotou, Erb, Gregory M., Erdem, Muhammed Said, Fambiatos, Adam, Ferrando, Lisa, Fortin, Francis, Fourie, Helene, Franklin, Jameelah, Ganbat, Dariimaa, Valdez Garcia, Jose Angel, Zúñiga García, José Manuel, Figueroa Garcia, Martha Luisa, Garth, Lauren, Ghaly, Andrew R., Gol-Chambers, Alexandra, Gomes, Joseph, González, Patricia Téllez, Greco, Natalie, Hadjiandreou, Mr. Michalis, Hagar, Emily, Haire, Randolph D., Hamilton, Guy P., Hassen, Sara Seife, Hawks, Stephanie, Highton, Georgina, Hinchey, Sherri A., Hinds, Stephanie Anne, Hines, Adriane, Honeycutt, P. Barrett, Hricz, Craig, Huang, Warren, Hubeishy, Moudi, Ingram, Cameron M., Inouye, Aaron, Jaket, Courtney, Jefferies, Tolbert Dewayne, Jones, Benjamen, Jones, Hayden R., Kaminskaite, Viktorija, Klee, Alan, Krinock, Matthew, Krzywicki, Matthew, Law, Gemma Therese, Lawes, Caitlin M., Lorié, Natalia Soriano, Lowe, Melodie M., Lyon, Zachary, Kamil M., Macauley, Precious, Mahajan, Ankita, Malligan, Hugh J., Marcano, Yobanny Kafruni, Martin, Livingston, Martins, Maria Eduarda P.F., McClure, Amber, Mittal, Ramona, Monge, Eunice, Moran, Sean, Motosko, Catherine, Naidu, Nandita Natasha, Napaul, Gemma, Nayar, Nishchey, Newman, Ory S., Nitsch, Ann Margareth Wagner, Njoku, Eberechukwu G., Obaña, Caryl Joy C., Ogunseyila, Abimbola, Olea, Alain Garcia, Ong, Philip, Otniel, Boingeanu, Pacik, Deborah, Parfitt, Hollie, Paschal, Erika, Pearson, Emma, Phillips, D. Alexander, Pires-Ménard, Alexandra, Rainge-Campbell, Kristian, Ramsay, Joshua G., Randolph, Charles, Rassa, Nazanin, Richmond, Craig Stuart, Rogahn, Megan, Rowe, Aurielle E., Saberi-Movahed, Seena, Sabir, Sarah, Sanchez, Julienne Rowelie A., Santos, Andrea Lizeth Tarazona, Sarlo, Danielle N., Sarwal, Dhruv, Savelkoel, Jelmer, Shane, Matthew R., Shing, Leung, Ka, Shively, Jordan, Sleiman, Naama H., Smeaton, Jessica Zarathena, Smith, Valerie, Strange, Jehu, Stretton, Brandon, Sutton, Louisa, Switzer, Benjamin, Szablewski, Zachary, Tchaconas, Alexis, Thambirajah, Stella, Tompson, Jeffrey D., Tran, Tuyet Hong, Uhm, Tae Kyu, VanHam, Raymond, Varughese, Blessy, Verheyden, Matthew, Vinycomb, Tobias, Viqueira, Marta Costoya, Wang, Gloria Z., Weng, Ow Kok, Wike, Christopher, Wilson, Brent, Wisbeck, Alex, Woodard, Jennifer, Yang, Loo Li, Zaidi, Mohammad, Zhang, Chen, and Zovath, Andrew J.

Published
2017
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31. Wavefront-guided LASIK helpful tool for residual myopia.

Author

Wang, Gloria

Subjects
*LASIK, *REFRACTIVE errors, *REFRACTIVE lamellar keratoplasty, *MYOPIA treatment, *ASTIGMATISM, *THERAPEUTICS
Abstract

The article reports that wavefront-guided laser-assisted in situ keratomileusis retreatment with VISX CustomVue platform is effective in treating high myopia with astigmatism. Dr. Gloria Wang conducted and colleague conducted a study that evaluates effects of high-order root mean square (RMS) preoperatively and postoperatively. Analysis revealed RMS decreases, no complications developed and no cases of epithelial ingrowth. Wang claims longer follow-up is needed to assess refraction stability.

Published
2006

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