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5,337 results on '"Amid A"'

214. EXPLANTATION SITE MANAGEMENT: A DECISION ALGORITHM FOR REIMPLANTATION.

Author

Ardakani, Mohammadreza Talebi, Amid, Reza, and Moscowchi, Anahita

Subjects
DECISION trees, DENTAL implants, MEDICAL device removal, COSMETIC dentistry, TOOTH replantation, SURVIVAL rate, COMPLICATIONS of prosthesis
Abstract

Dental implants are currently the preferred choice to restore function and esthetics. Nonetheless, explantation is sometimes inevitable in cases with advanced bone loss, implant fracture, or improper implant position. This study aimed to propose an algorithm for reimplantation at sites of previous failure. There is limited evidence on the replacement of failed implants, most of which are case reports or clinical trials with a small number of patients. To the best of the authors' knowledge, this is the first study proposing a clinical-decision algorithm to help clinicians manage implant failures with new implants. There are a variety of reasons contributing to implant failure: The etiologic factor of failure and the morphology of the residual defect have paramount importance on implant removal techniques and subsequent treatment modalities. There is no consensus on a distinct protocol to replace failed implants. However, the clinicians and patients should know that placing a new implant in an area with a history of failure, regardless of early or late implant failure, may have a lower survival rate. [ABSTRACT FROM AUTHOR]

Published
2023
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217. Semi-supervised Kernel Metric Learning Using Relative Comparisons

Author

Amid, Ehsan, Gionis, Aristides, and Ukkonen, Antti

Subjects
Computer Science - Learning, Statistics - Machine Learning
Abstract

We consider the problem of metric learning subject to a set of constraints on relative-distance comparisons between the data items. Such constraints are meant to reflect side-information that is not expressed directly in the feature vectors of the data items. The relative-distance constraints used in this work are particularly effective in expressing structures at finer level of detail than must-link (ML) and cannot-link (CL) constraints, which are most commonly used for semi-supervised clustering. Relative-distance constraints are thus useful in settings where providing an ML or a CL constraint is difficult because the granularity of the true clustering is unknown. Our main contribution is an efficient algorithm for learning a kernel matrix using the log determinant divergence --- a variant of the Bregman divergence --- subject to a set of relative-distance constraints. The learned kernel matrix can then be employed by many different kernel methods in a wide range of applications. In our experimental evaluations, we consider a semi-supervised clustering setting and show empirically that kernels found by our algorithm yield clusterings of higher quality than existing approaches that either use ML/CL constraints or a different means to implement the supervision using relative comparisons.

Published
2016

218. Low-dimensional Data Embedding via Robust Ranking

Author

Amid, Ehsan, Vlassis, Nikos, and Warmuth, Manfred K.

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

We describe a new method called t-ETE for finding a low-dimensional embedding of a set of objects in Euclidean space. We formulate the embedding problem as a joint ranking problem over a set of triplets, where each triplet captures the relative similarities between three objects in the set. By exploiting recent advances in robust ranking, t-ETE produces high-quality embeddings even in the presence of a significant amount of noise and better preserves local scale than known methods, such as t-STE and t-SNE. In particular, our method produces significantly better results than t-SNE on signature datasets while also being faster to compute.

Published
2016

234. Optimizing the Information Retrieval Trade-off in Data Visualization Using $\alpha$-Divergence

Author

Amid, Ehsan, Dikmen, Onur, and Oja, Erkki

Subjects
Computer Science - Information Retrieval
Abstract

Data visualization is one of the major applications of nonlinear dimensionality reduction. From the information retrieval perspective, the quality of a visualization can be evaluated by considering the extent that the neighborhood relation of each data point is maintained while the number of unrelated points that are retrieved is minimized. This property can be quantified as a trade-off between the mean precision and mean recall of the visualization. While there have been some approaches to formulate the visualization objective directly as a weighted sum of the precision and recall, there is no systematic way to determine the optimal trade-off between these two nor a clear interpretation of the optimal value. In this paper, we investigate the properties of $\alpha$-divergence for information visualization, focusing our attention on a particular range of $\alpha$ values. We show that the minimization of the new cost function corresponds to maximizing a geometric mean between precision and recall, parameterized by $\alpha$. Contrary to some earlier methods, no hand-tuning is needed, but we can rigorously estimate the optimal value of $\alpha$ for a given input data. For this, we provide a statistical framework using a novel distribution called Exponential Divergence with Augmentation (EDA). By the extensive set of experiments, we show that the optimal value of $\alpha$, obtained by EDA corresponds to the optimal trade-off between the precision and recall for a given data distribution.

Published
2015

236. Dabigatran etexilate for the treatment of acute venous thromboembolism in children (DIVERSITY): a randomised, controlled, open-label, phase 2b/3, non-inferiority trial

Author

Nurmeev, Ildar, Safina, Asiya, Zapletal, Ondrej, Brandão, Leonardo R, Kuhn, Tomas, Votava, Tomas, Felgenhauer, Judy, Sharathkumar, Anjali, Svirin, Pavel, Amid, Ali, Halton, Jacqueline, Gorbatikov, Kirill, Saracco, Paola, Kiss, Csongor, Halimeh, Susan, Reschke, Madlen, Wulff, Beate, David, Michele, Novak, Zbynek, Trunina, Inna, Albisetti, Manuela, Frisk, Tony, Glosli, Heidi, Groll, Andreas, Lvova, Olga, Sasmaz, Ilgen, Sosothikul, Darintr, Zilinskaite, Virginija, Cockrell, Erin, Digtyar, Valeriy, Hadacova, Ivana, Palmu, Sauli, Pawar, Anjali, Annichino Bizzacchi, Joyce Maria, Caliskan, Umran, Celkan, Tiraje, Dmytriiev, Dmytro, Druzgal, Colleen Harkins, Onelda Elena, Graciela, Kattamis, Antonis, Kavakli, Ramazan Kaan, Male, Christoph, Ozdemir, Nihal, Van Damme, An, Zvereva, Tatiana, Aarli, Aanen, Paredes Aguilera, Rogelio Alejandro, Aytac, Selin, Carneiro, Jorge, Chistolini, Antonio, Mazzucconi, Maria Gabriela, Corrales-Medina, Fernando, Couturaud, Francis, Croteau, Stacey E, Trenor III, Cameron, Damgaard, Michael, Dixon, Natalia, Galustyan, Anna, Hak, Jiri, Hoffmann, Marianne, Kupesiz, Alphan, Labarque, Veerle, van Geet, Christel, Lin, Ming-Chih, Fu, Yun-Ching, Loggetto, Sandra, Mondelaers, Veerle, Odri-Komazec, Irena, Revel-Vilk, Shoshana, Sevilla, Julian, Fuzzato Silva, Luciano, Kerr Saraiva, José, Montes Tapia, Fernando Felix, Woods-Swafford, Wendy, Luciani, Matteo, Bomgaars, Lisa, Chalmers, Elizabeth, Mitchell, Lesley G, Tartakovsky, Igor, Simetzberger, Monika, Huang, Fenglei, Sun, Zhichao, Kreuzer, Jörg, Gropper, Savion, Reilly, Paul, and Brueckmann, Martina

Published
2021
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239. Novel Experimental Modules to Introduce Students to Nanoparticle Characterization in a Chemical Engineering Course

Author

Vahedi, Amid and Farnoud, Amir M.

Abstract

The increasing industrial and biomedical applications of nanomaterials have enhanced the need to educate a well-trained nanotechnology workforce. This need has led to efforts to introduce hands-on, nanotechnology-based, experimental modules into high school and college level courses in science and engineering. However, the majority of such efforts have focused on nanoparticle synthesis techniques, and an equally important aspect of working with nanomaterials, nanoparticle characterization, has received less attention. Herein, we report a series of nanoparticle characterization experiments, as part of a newly developed "Nano and Biointerfaces" course, to familiarize upper undergraduate students as well as graduate students in chemical engineering with nanoparticle characterization techniques. An inquiry-based approach was used in that the composition and properties of nanoparticles were not revealed to the students beforehand and students were asked to perform experiments to characterize nanoparticle composition, size, morphology, and surface area. The results of these experiments were compared with certificates of analysis for particles, provided by the vendor, and the differences in measured properties were discussed. Assessment was performed through evaluation of laboratory memos and presentations, a question in the end-of-semester final exam, and a student survey. The modular nature of these experiments allows for them to be implemented, with modifications as needed, in other higher education institutions or in high schools to familiarize students with nanoparticle characterization.

Published
2019
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