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544 results on '"Shah, Kinjal"'

9. The impact of perioperative stroke and delirium on outcomes after surgical aortic valve replacement

Author

Miller, Marissa A., Taddei-Peters, Wendy C., Jeffries, Neal O., Buxton, Dennis, Geller, Nancy L., Gordon, David, Burke, Catherine, Lee, Albert, Smith, Tyrone, Moy, Claudia S., Gombos, Ilana Kogan, Weisel, Richard, Gardner, Timothy J., O'Gara, Patrick T., Rose, Eric A., Gelijns, Annetine C., Parides, Michael K., Ascheim, Deborah D., Moskowitz, Alan J., Bagiella, Emilia, Moquete, Ellen, Shah, Kinjal, Overbey, Jessica R., Pan, Stephanie, Chang, Helena, Chase, Melissa, Goldfarb, Seth, Gupta, Lopa, Kirkwood, Katherine, Dobrev, Edlira, Levitan, Ron, O'Sullivan, Karen, Santos, Milerva, Ye, Xia, Mack, Michael, Winkle, Rachelle, Boswell, Haley, Fenlon, Amanda, Johnson, Melissa, Jones, Jessica, Kolb, Megan, Lam, Sarah, Miranda, Lucy, Ward, Jackie, Whitman, Renessa, Zingler, Brittany, Ryan, William, Smith, Robert L., Grayburn, Paul, Nosnik, Pedro, Gillinov, A. Marc, Blackstone, Eugene H., Moazami, Nader, Starling, Randall C., Barzilai, Benico, Grimm, Richard A., Soltesz, Edward G., Katzan, Irene, Strippy, Brian, Smith, Shoi, Garcia, Michelle, Alice bowman, Mary, Geither, Carrie, Wang, Robert, Argenziano, Michael, Borger, Michael, Takayama, Hiroo, Leon, Martin B., Goldsmith, Lyn, Schwartz, Allan, Sookraj, Nadia, McCright-Gill, Talaya, Sreekanth, Sowmya, McCullough, Jock N., Iribarne, Alexander, DeSimone, Joseph P., DiScipio, Anthony W., Stokes, Henry, Ivany, Amanda St., Petty, Gaylin, Smith, Peter K., Alexander, John H., Milano, Carmelo A., Glower, Donald D., Huber, Joel, Morganlander, Joel, Mathew, Joseph P., Welsh, Stacey, Casalinova, Sarah, Johnson, Victoria, Lane, Kathleen, Smith, Derek, Tipton, Greg, Berry, Mark F., Williams, Judson B., Englum, Brian, Hartwig, Matthew, Thourani, Vinod H., Guyton, Robert, Lattouf, Omar, Chen, Edward, Vega, J. David, Baer, Jefferson, Nguyen, Duc, Halkos, Michael, Baio, Kim, Prince, Tamara, Cook, Natascha, Neill, Alexis A., Voisine, Pierre, Senechal, Mario, Dagenais, François, Laforce, Robert, Jr., O'Connor, Kim, Dussault, Gladys, Caouette, Manon, Tremblay, Hugo, Gagne, Nathalie, Dumont, Julie, Landry, Patricia, Groh, Mark A., Trichon, Benjamin H., Binns, Oliver A., Ely, Stephen W., Johnson, Alan M., Hansen, Todd H., Short, John G., Taylor, Reid D., Mangusan, Ralph, Nanney, Tracy, Aubart, Holly, Cross, Kristin, McPeters, Leslie, Riggsbee, Christina, Rixey, Lucy, Michler, Robert E., DeRose, Joseph J., Jr., Goldstein, Daniel J., Bello, Ricardo A., Taub, Cynthia, Spevack, Daniel, Kirchoff, Kathryn, Meli, Rebecca, Garcia, Juan, Goldenberg, Jon, Kealy, Lauren, Perrault, Louis P., Bouchard, Denis, Tanguay, Jean François, O'Meara, Eileen, Lacharité, Jonathan, Robichaud, Sophie, Horvath, Keith A., Corcoran, Philip C., Siegenthaler, Michael P., Murphy, Mandy, Iraola, Margaret, Greenberg, Ann, Kumkumian, Greg, Milner, Mark, Nadareishvili, Zurab, Whitson, Bryan A., Hasan, Ayesha, McDavid, Asia, Fadorsen, Denise, Ouzounian, Maral, Yau, Terry, Farkouh, Michael, Woo, Anna, Cusimano, Robert James, David, Tirone, Feindel, Christopher, Fumakia, Nishit, Christie, Shakira, Mullen, John C., Bissonauth, Asvina, Hripko, Alexandra, Gammie, James S., Noor, Zahid, Mackowick, Kristen, Deasey, Stephanie, Al-Suqi, Manal, Collins, Julia, Acker, Michael A., Messé, Steven, Kirkpatrick, James, Mayer, Mary Lou, McDonald, Caitlin, Fok, Holley, Maffei, Breanna, Cresse, Stephen, Gepty, Christine, Bowdish, Michael, Starnes, Vaughn A., Shavalle, David, Heck, Christi, Hackmann, Amy, Baker, Craig, Fleischman, Fernando, Cunningham, Mark, Lozano, Edward, Hernandez, Michelle, Ailawadi, Gorav, Kron, Irving L., Johnston, Karen, Ghanta, Ravi K., Dent, John M., Kern, John, Yarboro, Leora, Ragosta, Michael, Annex, Brian, Bergin, Jim, Burks, Sandra, Cosner, Mike, Green, China, Loya, Samantha, Kim, Hye Ryun, Bull, David A., Desvigne-Nickens, Patrice, Dixon, Dennis O., Gottesman, Rebecca, Haigney, Mark, Holubkov, Richard, Iadecola, Constantino, Jacobs, Alice, Meslin, Eric M., Murkin, John M., Spertus, John A., Sellke, Frank, McDonald, Cheryl L., Canty, John, Dickert, Neal, Ikonomidis, John S., Kim, KyungMann, Williams, David O., Yancy, Clyde W., Chaturvedi, Seemant, Chimowitz, Marc, Fang, James C., Richenbacher, Wayne, Rao, Vivek, Furie, Karen L., Miller, Rachel, Cook, Jennifer, D'Alessandro, David, Han, Frederick, Pinney, Sean, Walsh, Mary N., Greer, David, Ishida, Koto, Stapf, Christian, Hung, Judy, Zeng, Xin, Hung, David, Satitthummanid, Sudarat, Billelo, Michel, Davatzikos, Christos, Erus, Guray, Karpf, Lauren, Desiderio, Lisa, Browndyke, Jeffrey N., James, Michael L., Toulgoat-Dubois, Yanne, Brassard, Rachele, Virmanu, Renu, Romero, Maria E., Braumann, Ryan, Messé, Steven R., Mack, Michael J., Southerland, Andrew M., Moy, Claudia Scala, and Bowdish, Michael E.

Published
2024
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11. PPL Bench: Evaluation Framework For Probabilistic Programming Languages

Author

Kulkarni, Sourabh, Shah, Kinjal Divesh, Arora, Nimar, Wang, Xiaoyan, Li, Yucen Lily, Tehrani, Nazanin Khosravani, Tingley, Michael, Noursi, David, Torabi, Narjes, Masouleh, Sepehr Akhavan, Lippert, Eric, and Meijer, Erik

Subjects
Computer Science - Programming Languages, Computer Science - Machine Learning
Abstract

We introduce PPL Bench, a new benchmark for evaluating Probabilistic Programming Languages (PPLs) on a variety of statistical models. The benchmark includes data generation and evaluation code for a number of models as well as implementations in some common PPLs. All of the benchmark code and PPL implementations are available on Github. We welcome contributions of new models and PPLs and as well as improvements in existing PPL implementations. The purpose of the benchmark is two-fold. First, we want researchers as well as conference reviewers to be able to evaluate improvements in PPLs in a standardized setting. Second, we want end users to be able to pick the PPL that is most suited for their modeling application. In particular, we are interested in evaluating the accuracy and speed of convergence of the inferred posterior. Each PPL only needs to provide posterior samples given a model and observation data. The framework automatically computes and plots growth in predictive log-likelihood on held out data in addition to reporting other common metrics such as effective sample size and $\hat{r}$., Comment: 6 pages, PROBPROG 2020

Published
2020

12. Uncertainty Estimation For Community Standards Violation In Online Social Networks

Author

Torabi, Narjes, Arora, Nimar S., Yu, Emma, Shah, Kinjal, Liu, Wenshun, and Tingley, Michael

Subjects
Computer Science - Artificial Intelligence
Abstract

Online Social Networks (OSNs) provide a platform for users to share their thoughts and opinions with their community of friends or to the general public. In order to keep the platform safe for all users, as well as to keep it compliant with local laws, OSNs typically create a set of community standards organized into policy groups, and use Machine Learning (ML) models to identify and remove content that violates any of the policies. However, out of the billions of content that is uploaded on a daily basis only a small fraction is so unambiguously violating that it can be removed by the automated models. Prevalence estimation is the task of estimating the fraction of violating content in the residual items by sending a small sample of these items to human labelers to get ground truth labels. This task is exceedingly hard because even though we can easily get the ML scores or features for all of the billions of items we can only get ground truth labels on a few thousands of these items due to practical considerations. Indeed the prevalence can be so low that even after a judicious choice of items to be labeled there can be many days in which not even a single item is labeled violating. A pragmatic choice for such low prevalence, $10^{-4}$ to $10^{-5}$, regimes is to report the upper bound, or $97.5\%$ confidence interval, prevalence (UBP) that takes the uncertainties of the sampling and labeling processes into account and gives a smoothed estimate. In this work we present two novel techniques Bucketed-Beta-Binomial and a Bucketed-Gaussian Process for this UBP task and demonstrate on real and simulated data that it has much better coverage than the commonly used bootstrapping technique.

Published
2020

13. A County-level Dataset for Informing the United States' Response to COVID-19

Author

Killeen, Benjamin D., Wu, Jie Ying, Shah, Kinjal, Zapaishchykova, Anna, Nikutta, Philipp, Tamhane, Aniruddha, Chakraborty, Shreya, Wei, Jinchi, Gao, Tiger, Thies, Mareike, and Unberath, Mathias

Subjects
Computer Science - Computers and Society, Computer Science - Databases, Physics - Physics and Society, Quantitative Biology - Populations and Evolution
Abstract

As the coronavirus disease 2019 (COVID-19) continues to be a global pandemic, policy makers have enacted and reversed non-pharmaceutical interventions with various levels of restrictions to limit its spread. Data driven approaches that analyze temporal characteristics of the pandemic and its dependence on regional conditions might supply information to support the implementation of mitigation and suppression strategies. To facilitate research in this direction on the example of the United States, we present a machine-readable dataset that aggregates relevant data from governmental, journalistic, and academic sources on the U.S. county level. In addition to county-level time-series data from the JHU CSSE COVID-19 Dashboard, our dataset contains more than 300 variables that summarize population estimates, demographics, ethnicity, housing, education, employment and income, climate, transit scores, and healthcare system-related metrics. Furthermore, we present aggregated out-of-home activity information for various points of interest for each county, including grocery stores and hospitals, summarizing data from SafeGraph and Google mobility reports. We compile information from IHME, state and county-level government, and newspapers for dates of the enactment and reversal of non-pharmaceutical interventions. By collecting these data, as well as providing tools to read them, we hope to accelerate research that investigates how the disease spreads and why spread may be different across regions. Our dataset and associated code are available at github.com/JieYingWu/COVID-19_US_County-level_Summaries., Comment: Updated 10 September 2020

Published
2020

17. Newtonian Monte Carlo: single-site MCMC meets second-order gradient methods

Author

Arora, Nimar S., Tehrani, Nazanin Khosravani, Shah, Kinjal Divesh, Tingley, Michael, Li, Yucen Lily, Torabi, Narjes, Noursi, David, Masouleh, Sepehr Akhavan, Lippert, Eric, and Meijer, Erik

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

Single-site Markov Chain Monte Carlo (MCMC) is a variant of MCMC in which a single coordinate in the state space is modified in each step. Structured relational models are a good candidate for this style of inference. In the single-site context, second order methods become feasible because the typical cubic costs associated with these methods is now restricted to the dimension of each coordinate. Our work, which we call Newtonian Monte Carlo (NMC), is a method to improve MCMC convergence by analyzing the first and second order gradients of the target density to determine a suitable proposal density at each point. Existing first order gradient-based methods suffer from the problem of determining an appropriate step size. Too small a step size and it will take a large number of steps to converge, while a very large step size will cause it to overshoot the high density region. NMC is similar to the Newton-Raphson update in optimization where the second order gradient is used to automatically scale the step size in each dimension. However, our objective is to find a parameterized proposal density rather than the maxima. As a further improvement on existing first and second order methods, we show that random variables with constrained supports don't need to be transformed before taking a gradient step. We demonstrate the efficiency of NMC on a number of different domains. For statistical models where the prior is conjugate to the likelihood, our method recovers the posterior quite trivially in one step. However, we also show results on fairly large non-conjugate models, where NMC performs better than adaptive first order methods such as NUTS or other inexact scalable inference methods such as Stochastic Variational Inference or bootstrapping., Comment: StarAI has a 6 page limit excluding references

Published
2020

20. The Labeling Distribution Matrix (LDM): A Tool for Estimating Machine Learning Algorithm Capacity

Author

Segura, Pedro Sandoval, Lauw, Julius, Bashir, Daniel, Shah, Kinjal, Sehra, Sonia, Macias, Dominique, and Montanez, George

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

Algorithm performance in supervised learning is a combination of memorization, generalization, and luck. By estimating how much information an algorithm can memorize from a dataset, we can set a lower bound on the amount of performance due to other factors such as generalization and luck. With this goal in mind, we introduce the Labeling Distribution Matrix (LDM) as a tool for estimating the capacity of learning algorithms. The method attempts to characterize the diversity of possible outputs by an algorithm for different training datasets, using this to measure algorithm flexibility and responsiveness to data. We test the method on several supervised learning algorithms, and find that while the results are not conclusive, the LDM does allow us to gain potentially valuable insight into the prediction behavior of algorithms. We also introduce the Label Recorder as an additional tool for estimating algorithm capacity, with more promising initial results., Comment: Accepted to 12th International Conference on Agents and Artificial Intelligence (ICAART 2020), 7 pages including references

Published
2019
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23. Organophosphate compound poisoning - An unusual presentation as guillain barre syndrome

Author

Aishwarya, K., Zanzmera, Paresh, Patel, Jay, Mendpara, Vaidehi, Lapsiwala, Boney, Jain, Shruti, Porwal, Vishma, Modi, Nishi, and Shah, Kinjal

Subjects
Guillain-Barre syndrome, Poisoning, Levetiracetam, Nervous system diseases, Health
Abstract

Byline: K. Aishwarya, Paresh. Zanzmera, Jay. Patel, Vaidehi. Mendpara, Boney. Lapsiwala, Shruti. Jain, Vishma. Porwal, Nishi. Modi, Kinjal. Shah Sir, Organophosphate (OP) poisoning can present with a variety of clinical [...]

Published
2023

25. Essential laboratory tests for medical education

Author

Deyrup, Andrea, Knollmann-Ritschel, Barbara, D'Ambrosio, Danielle, Muir, Jeannie, Scordino, Teresa, Kraswoski, Matthew, Cao, Liyun, Shah, Kinjal, Zepf, Jennifer, Grindstaff, Samuel, Inman, Ashley, Moser, Karen, Olson, Kristin, Parker, Lynette, Shmookler, Aaron, Ou, Joyce, Putnam, Angelica, Watts, Luisa, Betts, Elham Vali, Lovitch, Scott, Stashek, Kristen, Flanagan, Melina, Singh, Nirupama, Suarez, Eric, Dudrey, Ellen, Furlong, Mary, Margeta, Marta, Wilberger, Adam, Chan, Joanna, Lin, Amy, and Deyrup, Andrea T.

Published
2022
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28. KRAS as a Prognostic and Predictive Marker in Metastatic Non-Small Cell Lung Carcinoma: A Systematic Review

Author

Fatima, Sheereen, primary, Pansuriya, Nirav, additional, Lakhani, Alisha, additional, Madhuri, Sai, additional, Ajmal, Reshma, additional, Clementina, Ruchira, additional, Lakdawala, Zahabiya, additional, Shah, Kinjal, additional, Dilshana, Husna, additional, Andrea, Maya, additional, Mathew, Bejoi, additional, and Raheja, Aashna, additional

Published
2024
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35. Preparation of CTAB-ATP/CTS Composite Adsorbent and Removal Performance of Norfloxacin in Water.

Author

Du, Hongxia, Wang, Zihan, Shah, Kinjal J., and Sun, Yongjun

Subjects
LANGMUIR isotherms, FOURIER transform infrared spectroscopy, ADSORPTION (Chemistry), SCANNING electron microscopy, ADSORPTION capacity
Abstract

In this article, attapulgite (ATP) as a raw material, hexadecyltrimethylammonium bromide (CTAB) as a modifier, and chitosan (CTS) as a composite are used to prepare a natural mineral-based composite adsorbent CTAB-ATP/CTS for the adsorption of norfloxacin in water. Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and specific surface area (BET) were used for characterization. When the initial pH is 7, the dosage of adsorbent is 3 g/L, and the initial concentration of norfloxacin is 50 mg/L, the reaction temperature is 25 °C, the shaking time is 4 h, and the maximum removal rate and adsorption capacity of CTAB-ATP/CTS for norfloxacin reached 94.62% and 23.66 mg/g, respectively. The adsorption of norfloxacin by CTAB-ATP/CTS is more consistent with the pseudo-second-order kinetic model (R2 = 0.9999, 0.9998, 0.9999 at initial concentrations of 25 mg/L, 50 mg/L, 75 mg/L), and the Langmuir isotherm adsorption model(R2 = 0.9903, 0.9935, 0.9933 at temperatures of 25 °C, 30 °C, 35 °C), indicating that the adsorption process of the composite material is controlled by chemical adsorption and its adsorption behavior is mainly single-layer adsorption. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Research Progress of Magnetic Flocculation in Water Treatment.

Author

Hu, Zhihao, Wu, Kun, Wang, Zihan, Shah, Kinjal J., and Sun, Yongjun

Subjects
BODIES of water, FLOCCULANTS, FLOCCULATION, WATER purification, MAGNETIC particles
Abstract

As people's material quality of life continues to improve, water resources become subjected to varying degrees of contamination. As one of the most commonly utilised agents in water treatment, a flocculant exhibits a diverse range of forms and a vast scope of applications. However, the application of flocculants gives rise to a series of issues, including the use of large doses, the formation of sludge, the difficulty of recycling flocculants, and other concerns. The development of new flocculation technology has become a crucial step in enhancing the purification of wastewater and reducing environmental pollution. Magnetic flocculation can be classified into two main categories: magnetic seeds flocculation and magnetic flocculation. This paper presents an overview of the factors influencing magnetic flocculation, including the type of magnetic seeds, magnetic seeds particle size, and other pertinent considerations. Furthermore, the classification of magnetic flocculants in the process of magnetic flocculation is discussed. This includes the types of magnetic flocculant, namely, inorganic composite magnetic flocculants, organic composite magnetic flocculants, and biological composite magnetic flocculants. Inorganic composite magnetic flocculants are inexpensive and simple to produce; however, their dosage is considerable, and the resulting floc is not tightly formed, which impairs the efficacy of flocculation. The use of organic composite magnetic flocculants requires a smaller dosage and exhibits a strong flocculating ability; however, it may possess toxic properties and potentially cause harm to the water body. The biological composite magnetic flocculant exhibits high efficiency and no pollution, yet it is subject to stringent environmental conditions, displays poor stability, and is applicable to a relatively limited range of treatment scenarios. Furthermore, the integration of magnetic flocculation technology with other techniques is classified and summarised in diverse contexts, and the prospective research focus and direction of magnetic flocculants are proposed. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Global epidemiology of cannabis use disorders and its trend from 1990 to 2019: Benchmarking analysis of the global burden of disease study

Author

Shah, Kinjal, primary, Farwa, Umm E., additional, Vanaparti, Ankitha, additional, Patel, Savan, additional, Kanumuri, Manisha, additional, Vashishth, Oshin, additional, Hossain, Nadia, additional, Dahiya, Roopali, additional, Banala, Mallareddy, additional, Enamorado, Fredy Reynaldo Pavon, additional, Patel, Abhiraj S., additional, Nadeem, Huzaifa, additional, Kanani, Ravi, additional, Desai, Hardik D., additional, Sharma, Kamal H., additional, and Tripathi, Sharanya, additional

Published
2024
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39. A survey on Human Computer Interaction Mechanism Using Finger Tracking

Author

Shah, Kinjal N., Rathod, Kirit R., and Agravat, Shardul J.

Subjects
Computer Science - Human-Computer Interaction
Abstract

Human Computer Interaction (HCI) is a field in which developer makes a user friendly system. User can interact with a computer system without using any conventional peripheral devices. Marker is used to recognize hand movement accurately & successfully. Researchers establish the mechanism to interact with computer system using computer vision. The interaction is better than normal static keyboard and mouse. This paper represents most of innovative mechanisms of the finger tracking used to interact with a computer system using computer vision., Comment: 4 pages, 8 figures, International Journal of Computer Trends and Technology (IJCTT)

Published
2014
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