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1,409 results on '"Cárdenas, Carlos"'

2. Manipulating the wavelength of single photons in insulating van der Waals heterostructures: theory and application to bilayer hexagonal boron nitride

Author

Pinilla, Fernanda, Muriel, Wilver A., Cabezas-Escares, Javiera, Chacon, Ignacio, Cardenas, Carlos, and Munoz, Francisco

Subjects
Condensed Matter - Materials Science
Abstract

Van der Waals heterostructures are promising for adding new functionalities to two-dimensional materials. In this study, we focus on single photon emitters hosted in one layer and adjacent to other insulating two-dimensional material. Specifically, we show how the emission energy is modified by such a heterostructure. We developed a general approach to elucidate the mechanisms affecting the emission energy and studied the particular case of carbon substitutions in the hexagonal boron nitride bilayer., Comment: ACS Appl. Nano Mater

Published
2023
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3. Floquet Engineering of a Diatomic Molecule Through a Bichromatic Radiation Field

Author

Barriga, Edgar, Torres, Luis E. F. Foa, and Cárdenas, Carlos

Subjects
Physics - Chemical Physics, Quantum Physics
Abstract

We report on a theoretical study of a Cs$_2$ molecule illuminated by two lasers and show how it can result in novel quantum dynamics. We reveal that these interactions facilitate the bypass of the non-crossing rule, forming Light-Induced Conical Intersections and modifiable avoided crossings. Our findings show how laser field orientation and strength, along with initial phase differences, can control molecular state transitions, especially on the micromotion scale. We also discuss extensively how the interaction of radiation with matter gives rise to the emergence of potential energy surfaces of hybrids of radiation and molecular states. This research advances a technique for manipulating photoassociation processes in Cs$_2$ molecules, offering potential new avenues in quantum control., Comment: 12 pages, 4 figures

Published
2023
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4. Evolving Horizons in Radiotherapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification

Author

Wahid, Kareem A., Cardenas, Carlos E., Marquez, Barbara, Netherton, Tucker J., Kann, Benjamin H., Court, Laurence E., He, Renjie, Naser, Mohamed A., Moreno, Amy C., Fuller, Clifton D., and Fuentes, David

Subjects
Physics - Medical Physics
Abstract

Deep learning has significantly advanced the potential for automated contouring in radiotherapy planning. In this manuscript, guided by contemporary literature, we underscore three key insights: (1) High-quality training data is essential for auto-contouring algorithms; (2) Auto-contouring models demonstrate commendable performance even with limited medical image data; (3) The quantitative performance of auto-contouring is reaching a plateau. Given these insights, we emphasize the need for the radiotherapy research community to embrace data-centric approaches to further foster clinical adoption of auto-contouring technologies., Comment: 13 pages, 4 figures

Published
2023
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7. Formation of $\text{H}_{2}$ on polycyclic aromatic hydrocarbons under conditions of the ISM: an ab initio molecular dynamics study

Author

Barrera, Nicolás F., Fuentealba, Patricio, Muñoz, Francisco, Gómez, Tatiana, and Cárdenas, Carlos

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

Understanding how the $\mathrm{H}_2$ molecule is formed under the chemical conditions of the interstellar media (ISM) is critical to the whole chemistry of it. Formation of $\mathrm{H}_2$ in the ISM requires a third body acting as a reservoir of energy. Polycyclic aromatic hydrocarbons (PAH's) are excellent candidates to play that role. In this work we simulated the collisions of hydrogen atoms with coronene to form $\mathrm{H}_2$ via the Eley-Rideal mechanism. To do so, we used Born-Oppenheimer (ab initio) Molecular Dynamics simulations. Our results show that that adsorption of H atoms and subsequent release of $\mathrm{H}_2$ readily happen on coronene for H atoms with kinetic energy as large as 1 eV. Special attention is paid to dissipation and partition of the energy released in the reactions. The capacity of coronene to dissipate collision and reaction energies depends varies with the reaction site. Inner sites dissipate energy easier and faster than edge sites, thus evidencing an interplay between the potential energy surface around the reaction center and its ability to cool the projectile. As for the the recombination of H atoms and the subsequent formation of $\mathrm{H}_{2}$, it is observed that $\sim 15\%$ of the energy is dissipated by the coronene molecule as vibrational energy and the remaining energy is carried by $\mathrm{H}_{2}$. The $\mathrm{H}_{2}$ molecules desorb from coronene with an excited vibrational state ($\upsilon \geq 3$), a large amount of translational kinetic energy ($\geq$ 0.4 eV) and with a small activation of the rotational degree of freedom., Comment: Accepted in MNRAS

Published
2023
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8. Portadilla, Portada, Página legal

Author

Polo Numpaque, Andrea C., Gil Gómez, Karen, Cortés Beltrán, Paola A., Bernal Cárdenas, Carlos Y., and Briñez Ariza, Karol J.

Published
2024

12. Cubierta

Author

Polo Numpaque, Andrea C., Gil Gómez, Karen, Cortés Beltrán, Paola A., Bernal Cárdenas, Carlos Y., and Briñez Ariza, Karol J.

Published
2024

18. Prólogo

Author

Polo Numpaque, Andrea C., Gil Gómez, Karen, Cortés Beltrán, Paola A., Bernal Cárdenas, Carlos Y., and Briñez Ariza, Karol J.

Published
2024

20. Introducción

Author

Polo Numpaque, Andrea C., Gil Gómez, Karen, Cortés Beltrán, Paola A., Bernal Cárdenas, Carlos Y., and Briñez Ariza, Karol J.

Published
2024

21. An Empirical Investigation into the Use of Image Captioning for Automated Software Documentation

Author

Moran, Kevin, Yachnes, Ali, Purnell, George, Mahmud, Junayed, Tufano, Michele, Bernal-Cárdenas, Carlos, Poshyvanyk, Denys, and H'Doubler, Zach

Subjects
Computer Science - Software Engineering, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Existing automated techniques for software documentation typically attempt to reason between two main sources of information: code and natural language. However, this reasoning process is often complicated by the lexical gap between more abstract natural language and more structured programming languages. One potential bridge for this gap is the Graphical User Interface (GUI), as GUIs inherently encode salient information about underlying program functionality into rich, pixel-based data representations. This paper offers one of the first comprehensive empirical investigations into the connection between GUIs and functional, natural language descriptions of software. First, we collect, analyze, and open source a large dataset of functional GUI descriptions consisting of 45,998 descriptions for 10,204 screenshots from popular Android applications. The descriptions were obtained from human labelers and underwent several quality control mechanisms. To gain insight into the representational potential of GUIs, we investigate the ability of four Neural Image Captioning models to predict natural language descriptions of varying granularity when provided a screenshot as input. We evaluate these models quantitatively, using common machine translation metrics, and qualitatively through a large-scale user study. Finally, we offer learned lessons and a discussion of the potential shown by multimodal models to enhance future techniques for automated software documentation., Comment: Published in the Proceedings of the 29th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER'22), Honolulu, Hawaii, March 15-18, 2022, pp. 514-525

Published
2023
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22. Translating Video Recordings of Complex Mobile App UI Gestures into Replayable Scenarios

Author

Bernal-Cárdenas, Carlos, Cooper, Nathan, Havranek, Madeleine, Moran, Kevin, Chaparro, Oscar, Poshyvanyk, Denys, and Marcus, Andrian

Subjects
Computer Science - Software Engineering
Abstract

Screen recordings of mobile applications are easy to obtain and capture a wealth of information pertinent to software developers (e.g., bugs or feature requests), making them a popular mechanism for crowdsourced app feedback. Thus, these videos are becoming a common artifact that developers must manage. In light of unique mobile development constraints, including swift release cycles and rapidly evolving platforms, automated techniques for analyzing all types of rich software artifacts provide benefit to mobile developers. Unfortunately, automatically analyzing screen recordings presents serious challenges, due to their graphical nature, compared to other types of (textual) artifacts. To address these challenges, this paper introduces V2S+, an automated approach for translating video recordings of Android app usages into replayable scenarios. V2S+ is based primarily on computer vision techniques and adapts recent solutions for object detection and image classification to detect and classify user gestures captured in a video, and convert these into a replayable test scenario. Given that V2S+ takes a computer vision-based approach, it is applicable to both hybrid and native Android applications. We performed an extensive evaluation of V2S+ involving 243 videos depicting 4,028 GUI-based actions collected from users exercising features and reproducing bugs from a collection of over 90 popular native and hybrid Android apps. Our results illustrate that V2S+ can accurately replay scenarios from screen recordings, and is capable of reproducing $\approx$ 90.2% of sequential actions recorded in native application scenarios on physical devices, and $\approx$ 83% of sequential actions recorded in hybrid application scenarios on emulators, both with low overhead. A case study with three industrial partners illustrates the potential usefulness of V2S+ from the viewpoint of developers., Comment: Accepted to IEEE Transactions on Software Engineering. arXiv admin note: substantial text overlap with arXiv:2005.09057

Published
2023

23. Tungiasis

Author

Sánchez-Cárdenas, Carlos D., Moreno-Leiva, Cristhian, Vega-Memije, M. Elisa, Juarez-Duran, Eder R., Arenas, Roberto, Berth-Jones, John, Series Editor, Goh, Chee Leok, Series Editor, Maibach, Howard I., Series Editor, Lipner, Shari R., Series Editor, and Robles, Wanda, editor

Published
2024
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24. Resection cavity auto-contouring for patients with pediatric medulloblastoma using only CT information

Author

Hernandez, Soleil, Nguyen, Callistus, Gay, Skylar, Duryea, Jack, Howell, Rebecca, Fuentes, David, Parkes, Jeannette, Burger, Hester, Cardenas, Carlos, Paulino, Arnold C., Pollard-Larkin, Julianne, and Court, Laurence

Subjects
Physics - Medical Physics
Abstract

Purpose: Target delineation for radiation therapy is a time-consuming and complex task. Autocontouring gross tumor volumes (GTVs) has been shown to increase efficiency. However, there is limited literature on post-operative target delineation, particularly for CT-based studies. To this end, we trained a CT-based autocontouring model to contour the post-operative GTV of pediatric patients with medulloblastoma. Methods: 104 retrospective pediatric CT scans were used to train a GTV auto-contouring model. 80 patients were then preselected for contour visibility, continuity, and location to train an additional model. Each GTV was manually annotated with a visibility score based on the number of slices with a visible GTV (1 = <25%, 2 = 25%-50%, 3 = >50%-75%, and 4 = >75%-100%). Contrast and the contrast-to-noise ratio (CNR) were calculated for the GTV contour with respect to a cropped background image. Both models were tested on the original and pre-selected testing sets. The resulting surface and overlap metrics were calculated comparing the clinical and autocontoured GTVs and the corresponding clinical target volumes (CTVs). Results: 80 patients were pre-selected to have a continuous GTV within the posterior fossa. Of these, 7, 41, 21, and 11 were visibly scored as 4, 3, 2, and 1, respectively. The contrast and CNR removed an additional 11 and 20 patients from the dataset, respectively. The Dice similarity coefficients (DSC) were 0.61 +/- 0.29 and 0.67 +/- 0.22 on the models without pre-selected training data and 0.55 +/- 13.01 and 0.83 +/- 0.17 on the models with pre-selected data, respectively. The DSC on the CTV expansions were 0.90 +/- 0.13. Conclusion: We automatically contoured continuous GTVs within the posterior fossa on scans that had contrast >=10 HU. CT-Based auto-contouring algorithms have potential to positively impact centers with limited MRI access., Comment: 14 pages, 6 figures

Published
2022

25. Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans

Author

Gronberg, Mary P., Beadle, Beth M., Garden, Adam S., Skinner, Heath, Gay, Skylar, Netherton, Tucker, Cao, Wenhua, Cardenas, Carlos E., Chung, Christine, Fuentes, David, Fuller, Clifton D., Howell, Rebecca M., Jhingran, Anuja, Lim, Tze Yee, Marquez, Barbara, Mumme, Raymond, Olanrewaju, Adenike M., Peterson, Christine B., Vazquez, Ivan, Whitaker, Thomas J., Wooten, Zachary, Yang, Ming, and Court, Laurence E.

Subjects
Physics - Medical Physics
Abstract

Purpose: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans. Methods: A total of 245 VMAT HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included CT images, target prescriptions, and contours for targets and organs at risk (OARs). The model's performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared to manual flags by 3 HN radiation oncologists. Results: The predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D1%, D95%, and D99% across the targets were within -2.53%(SD=1.34%), -0.42%(SD=1.27%), and -0.12%(SD=1.97%), respectively, and the OAR mean and maximum doses were within -0.33Gy(SD=1.40Gy) and -0.96Gy(SD=2.08Gy). For the plan quality assessment study, radiation oncologists flagged 47 OARs for possible plan improvement. There was high interphysician variability; 83% of physician-flagged OARs were flagged by only one of 3 physicians. The comparative dose prediction model flagged 63 OARs, including 30 of 47 physician-flagged OARs. Conclusion: Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality., Comment: updated to reflect the published peer-reviewed article

Published
2022
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26. Carbon-based single photon emitters in hexagonal boron nitride with triplet ground state

Author

Pinilla, Fernanda, Vasquez, Nicolas, Maze, Jerónimo R., Cárdenas, Carlos, and Munoz, Francisco

Subjects
Condensed Matter - Materials Science
Abstract

Most single photon emitters in hexagonal boron nitride has been identified as carbon-based defects. These defects, when forming a donor-acceptor pair have spin $S\leq \frac{1}{2}$. By means of density functional calculations, we show that two non-adjacent carbon substitutional defects of the same type (i.e. C$_\mathrm{B}$-C$_\mathrm{B}$, and C$_\mathrm{N}$-C$_\mathrm{N}$), can have a triplet ground state. In particular, one of such defects has a zero phonon line energy of 2.5 eV, and its triplet state is nearly 0.5 eV more stable than its singlet.

Published
2022
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27. RADseq datasets of native beans from Mexico

Author

Aragón-Magadán Marco Aurelio, Cruz-Cárdenas Carlos Iván, Calvillo-Aguilar Francisco Fabián, Pichardo-González Juan Manuel, and Guzmán Luis Felipe

Subjects
Genotype-by-sequencing, Bioinformatics, Phaseolus, SNP, VCF, Computer applications to medicine. Medical informatics, R858-859.7, Science (General), Q1-390
Abstract

Forty-five accessions of the genus Phaseolus from the orthodox seed collection of the National Center for Genetic Resources (CNRG) of the National Institute of Forestry, Agricultural, and Livestock Research (INIFAP) of Mexico were sequenced using RADseq. The species utilized were: P. acutifolius (14), P. coccineus (12), P. lunatus (8), P. dumosus (6), P. leptostachyus (2), P. filiformis (2), and P. vulgaris (1). A variant call file (VCF) was generated using GATK with the P. vulgaris reference genome GCF_000499845.1, identifying 97,103 shared SNPs among the species. These data have the potential to be used for studies of genetic diversity intra and interspecies, phylogeny, evolution, genetic resource conservation, and agricultural improvement.

Published
2024
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28. Automated WBRT Treatment Planning via Deep Learning Auto-Contouring and Customizable Landmark-Based Field Aperture Design

Author

Xiao, Yao, Cardenas, Carlos, Rhee, Dong Joo, Netherton, Tucker, Zhang, Lifei, Nguyen, Callistus, Douglas, Raphael, Mumme, Raymond, Skett, Stephen, Patel, Tina, Trauernicht, Chris, Chung, Caroline, Simonds, Hannah, Aggarwal, Ajay, and Court, Laurence

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Physics - Medical Physics
Abstract

In this work, we developed and evaluated a novel pipeline consisting of two landmark-based field aperture generation approaches for WBRT treatment planning; they are fully automated and customizable. The automation pipeline is beneficial for both clinicians and patients, where we can reduce clinician workload and reduce treatment planning time. The customizability of the field aperture design addresses different clinical requirements and allows the personalized design to become feasible. The performance results regarding quantitative and qualitative evaluations demonstrated that our plans were comparable with the original clinical plans. This technique has been deployed as part of a fully automated treatment planning tool for whole-brain cancer and could be translated to other treatment sites in the future., Comment: 10 pages, 7 figures, submitted to JACMP - Journal of Applied Clinical Medical Physics

Published
2022

29. Automation of Radiation Treatment Planning for Rectal Cancer

Author

Huang, Kai, Das, Prajnan, Olanrewaju, Adenike M., Cardenas, Carlos, Fuentes, David, Zhang, Lifei, Hancock, Donald, Simonds, Hannah, Rhee, Dong Joo, Beddar, Sam, Briere, Tina Marie, and Court, Laurence

Subjects
Physics - Medical Physics, Computer Science - Artificial Intelligence
Abstract

To develop an automated workflow for rectal cancer three-dimensional conformal radiotherapy treatment planning that combines deep-learning(DL) aperture predictions and forward-planning algorithms. We designed an algorithm to automate the clinical workflow for planning with field-in-field. DL models were trained, validated, and tested on 555 patients to automatically generate aperture shapes for primary and boost fields. Network inputs were digitally reconstructed radiography, gross tumor volume(GTV), and nodal GTV. A physician scored each aperture for 20 patients on a 5-point scale(>3 acceptable). A planning algorithm was then developed to create a homogeneous dose using a combination of wedges and subfields. The algorithm iteratively identifies a hotspot volume, creates a subfield, and optimizes beam weight all without user intervention. The algorithm was tested on 20 patients using clinical apertures with different settings, and the resulting plans(4 plans/patient) were scored by a physician. The end-to-end workflow was tested and scored by a physician on 39 patients using DL-generated apertures and planning algorithms. The predicted apertures had Dice scores of 0.95, 0.94, and 0.90 for posterior-anterior, laterals, and boost fields, respectively. 100%, 95%, and 87.5% of the posterior-anterior, laterals, and boost apertures were scored as clinically acceptable, respectively. Wedged and non-wedged plans were clinically acceptable for 85% and 50% of patients, respectively. The final plans hotspot dose percentage was reduced from 121%($\pm$ 14%) to 109%($\pm$ 5%) of prescription dose. The integrated end-to-end workflow of automatically generated apertures and optimized field-in-field planning gave clinically acceptable plans for 38/39(97%) of patients. We have successfully automated the clinical workflow for generating radiotherapy plans for rectal cancer for our institution.

Published
2022
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36. Enhancing Mobile App Bug Reporting via Real-time Understanding of Reproduction Steps

Author

Fazzini, Mattia, Moran, Kevin, Cardenas, Carlos Bernal, Wendland, Tyler, Orso, Alessandro, and Poshyvanyk, Denys

Subjects
Computer Science - Software Engineering
Abstract

One of the primary mechanisms by which developers receive feedback about in-field failures of software from users is through bug reports. Unfortunately, the quality of manually written bug reports can vary widely due to the effort required to include essential pieces of information, such as detailed reproduction steps (S2Rs). Despite the difficulty faced by reporters, few existing bug reporting systems attempt to offer automated assistance to users in crafting easily readable, and conveniently reproducible bug reports. To address the need for proactive bug reporting systems that actively aid the user in capturing crucial information, we introduce a novel bug reporting approach called EBug. EBug assists reporters in writing S2Rs for mobile applications by analyzing natural language information entered by reporters in real-time, and linking this data to information extracted via a combination of static and dynamic program analyses. As reporters write S2Rs, EBug is capable of automatically suggesting potential future steps using predictive models trained on realistic app usages. To evaluate EBug, we performed two user studies based on 20 failures from $11$ real-world apps. The empirical studies involved ten participants that submitted ten bug reports each and ten developers that reproduced the submitted bug reports. In the studies, we found that reporters were able to construct bug reports 31 faster with EBug as compared to the state-of-the-art bug reporting system used as a baseline. EBug's reports were also more reproducible with respect to the ones generated with the baseline. Furthermore, we compared EBug's prediction models to other predictive modeling approaches and found that, overall, the predictive models of our approach outperformed the baseline approaches. Our results are promising and demonstrate the potential benefits provided by proactively assistive bug reporting systems.

Published
2022

37. OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines

Author

Babier, Aaron, Mahmood, Rafid, Zhang, Binghao, Alves, Victor G. L., Barragán-Montero, Ana Maria, Beaudry, Joel, Cardenas, Carlos E., Chang, Yankui, Chen, Zijie, Chun, Jaehee, Diaz, Kelly, Eraso, Harold David, Faustmann, Erik, Gaj, Sibaji, Gay, Skylar, Gronberg, Mary, Guo, Bingqi, He, Junjun, Heilemann, Gerd, Hira, Sanchit, Huang, Yuliang, Ji, Fuxin, Jiang, Dashan, Giraldo, Jean Carlo Jimenez, Lee, Hoyeon, Lian, Jun, Liu, Shuolin, Liu, Keng-Chi, Marrugo, José, Miki, Kentaro, Nakamura, Kunio, Netherton, Tucker, Nguyen, Dan, Nourzadeh, Hamidreza, Osman, Alexander F. I., Peng, Zhao, Muñoz, José Darío Quinto, Ramsl, Christian, Rhee, Dong Joo, Rodriguez, Juan David, Shan, Hongming, Siebers, Jeffrey V., Soomro, Mumtaz H., Sun, Kay, Hoyos, Andrés Usuga, Valderrama, Carlos, Verbeek, Rob, Wang, Enpei, Willems, Siri, Wu, Qi, Xu, Xuanang, Yang, Sen, Yuan, Lulin, Zhu, Simeng, Zimmermann, Lukas, Moore, Kevin L., Purdie, Thomas G., McNiven, Andrea L., and Chan, Timothy C. Y.

Subjects
Physics - Medical Physics, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients with head-and-neck cancer and high-quality dose predictions from 19 KBP models that were developed by different research groups during the OpenKBP Grand Challenge. The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans. The predictions and plans were compared to the reference plans via: dose score, which is the average mean absolute voxel-by-voxel difference in dose a model achieved; the deviation in dose-volume histogram (DVH) criterion; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50 to 0.62, which indicates that the quality of the predictions is generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P<0.05; one-sided Wilcoxon test) on 18 of 23 DVH criteria. Similarly, each optimization model generated plans that satisfied a higher percentage of criteria than the reference plans. Lastly, our theoretical investigation demonstrated that the dose mimicking models generated plans that are also optimal for a conventional planning model. This was the largest international effort to date for evaluating the combination of KBP prediction and optimization models. In the interest of reproducibility, our data and code is freely available at https://github.com/ababier/open-kbp-opt., Comment: 19 pages, 7 tables, 6 figures

Published
2022
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40. Shift-invariant waveform learning on epileptic ECoG

Author

Mendoza-Cardenas, Carlos H. and Brockmeier, Austin J.

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

Seizure detection algorithms must discriminate abnormal neuronal activity associated with a seizure from normal neural activity in a variety of conditions. Our approach is to seek spatiotemporal waveforms with distinct morphology in electrocorticographic (ECoG) recordings of epileptic patients that are indicative of a subsequent seizure (preictal) versus non-seizure segments (interictal). To find these waveforms we apply a shift-invariant k-means algorithm to segments of spatially filtered signals to learn codebooks of prototypical waveforms. The frequency of the cluster labels from the codebooks is then used to train a binary classifier that predicts the class (preictal or interictal) of a test ECoG segment. We use the Matthews correlation coefficient to evaluate the performance of the classifier and the quality of the codebooks. We found that our method finds recurrent non-sinusoidal waveforms that could be used to build interpretable features for seizure prediction and that are also physiologically meaningful., Comment: To be published in the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Corrected band number for patient Study012 in Table IV

Published
2021

44. Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Author

Schumacher, Austin E, Kyu, Hmwe Hmwe, Aali, Amirali, Abbafati, Cristiana, Abbas, Jaffar, Abbasgholizadeh, Rouzbeh, Abbasi, Madineh Akram, Abbasian, Mohammadreza, Abd ElHafeez, Samar, Abdelmasseh, Michael, Abd-Elsalam, Sherief, Abdelwahab, Ahmed, Abdollahi, Mohammad, Abdoun, Meriem, Abdullahi, Auwal, Abdurehman, Ame Mehadi, Abebe, Mesfin, Abedi, Aidin, Abedi, Armita, Abegaz, Tadesse M, Abeldaño Zuñiga, Roberto Ariel, Abhilash, E S, Abiodun, Olugbenga Olusola, Aboagye, Richard Gyan, Abolhassani, Hassan, Abouzid, Mohamed, Abreu, Lucas Guimarães, Abrha, Woldu Aberhe, Abrigo, Michael R M, Abtahi, Dariush, Abu Rumeileh, Samir, Abu-Rmeileh, Niveen ME, Aburuz, Salahdein, Abu-Zaid, Ahmed, Acuna, Juan Manuel, Adair, Tim, Addo, Isaac Yeboah, Adebayo, Oladimeji M, Adegboye, Oyelola A, Adekanmbi, Victor, Aden, Bashir, Adepoju, Abiola Victor, Adetunji, Charles Oluwaseun, Adeyeoluwa, Temitayo Esther, Adeyomoye, Olorunsola Israel, Adha, Rishan, Adibi, Amin, Adikusuma, Wirawan, Adnani, Qorinah Estiningtyas Sakilah, Adra, Saryia, Afework, Abel, Afolabi, Aanuoluwapo Adeyimika, Afraz, Ali, Afyouni, Shadi, Afzal, Saira, Agasthi, Pradyumna, Aghamiri, Shahin, Agodi, Antonella, Agyemang-Duah, Williams, Ahinkorah, Bright Opoku, Ahmad, Aqeel, Ahmad, Danish, Ahmad, Firdos, Ahmad, Muayyad M, Ahmad, Tauseef, Ahmadi, Keivan, Ahmadzade, Amir Mahmoud, Ahmadzade, Mohadese, Ahmed, Ayman, Ahmed, Haroon, Ahmed, Luai A, Ahmed, Muktar Beshir, Ahmed, Syed Anees, Ajami, Marjan, Aji, Budi, Ajumobi, Olufemi, Akalu, Gizachew Taddesse, Akara, Essona Matatom, Akinosoglou, Karolina, Akkala, Sreelatha, Akyirem, Samuel, Al Hamad, Hanadi, Al Hasan, Syed Mahfuz, Al Homsi, Ammar, Al Qadire, Mohammad, Ala, Moein, Aladelusi, Timothy Olukunle, AL-Ahdal, Tareq Mohammed Ali, Alalalmeh, Samer O, Al-Aly, Ziyad, Alam, Khurshid, Alam, Manjurul, Alam, Zufishan, Al-amer, Rasmieh Mustafa, Alanezi, Fahad Mashhour, Alanzi, Turki M, Albashtawy, Mohammed, AlBataineh, Mohammad T, Aldridge, Robert W, Alemi, Sharifullah, Al-Eyadhy, Ayman, Al-Gheethi, Adel Ali Saeed, Alhabib, Khalid F, Alhalaiqa, Fadwa Alhalaiqa Naji, Al-Hanawi, Mohammed Khaled, Ali, Abid, Ali, Akhtar, Ali, Beriwan Abdulqadir, Ali, Hassam, Ali, Mohammed Usman, Ali, Rafat, Ali, Syed Shujait Shujait, Ali, Zahid, Alian Samakkhah, Shohreh, Alicandro, Gianfranco, Alif, Sheikh Mohammad, Aligol, Mohammad, Alimi, Rasoul, Aliyi, Ahmednur Adem, Al-Jumaily, Adel, Aljunid, Syed Mohamed, Almahmeed, Wael, Al-Marwani, Sabah, Al-Maweri, Sadeq Ali Ali, Almazan, Joseph Uy, Al-Mekhlafi, Hesham M, Almidani, Omar, Alomari, Mahmoud A, Alonso, Nivaldo, Alqahtani, Jaber S, Alqutaibi, Ahmed Yaseen, Al-Sabah, Salman Khalifah, Altaf, Awais, Al-Tawfiq, Jaffar A, Altirkawi, Khalid A, Alvi, Farrukh Jawad, Alwafi, Hassan, Al-Worafi, Yaser Mohammed, Aly, Hany, Alzoubi, Karem H, Amare, Azmeraw T, Ameyaw, Edward Kwabena, Amhare, Abebe Feyissa, Amin, Tarek Tawfik, Amindarolzarbi, Alireza, Aminian Dehkordi, Javad, Amiri, Sohrab, Amu, Hubert, Amugsi, Dickson A, Amzat, 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L, Ayyoubzadeh, Seyed Mohammad, Azadnajafabad, Sina, Azhar, Gulrez Shah, Aziz, Shahkaar, Azzam, Ahmed Y, Babashahi, Mina, Babu, Abraham Samuel, Badar, Muhammad, Badawi, Alaa, Badiye, Ashish D, Baghdadi, Soroush, Bagheri, Nasser, Bagherieh, Sara, Bah, Sulaiman, Bahadorikhalili, Saeed, Bai, Jianjun, Bai, Ruhai, Baker, Jennifer L, Bakkannavar, Shankar M, Bako, Abdulaziz T, Balakrishnan, Senthilkumar, Balogun, Saliu A, Baltatu, Ovidiu Constantin, Bam, Kiran, Banach, Maciej, Bandyopadhyay, Soham, Banik, Biswajit, Banik, Palash Chandra, Bansal, Hansi, Barati, Shirin, Barchitta, Martina, Bardhan, Mainak, Barker-Collo, Suzanne Lyn, Barone-Adesi, Francesco, Barqawi, Hiba Jawdat, Barr, Ronald D, Barrero, Lope H, Basharat, Zarrin, Bashir, Asma'u I J, Bashiru, Hameed Akande, Baskaran, Pritish, Basnyat, Buddha, Bassat, Quique, Basso, João Diogo, Basu, Saurav, Batra, Kavita, Batra, Ravi, Baune, Bernhard T, Bayati, Mohsen, Bayileyegn, Nebiyou Simegnew, Beaney, Thomas, Bedi, Neeraj, Begum, Tahmina, 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Shubha, Jeganathan, Jayakumar, Jema, Alelign Tasew, Jemere, Digisie Mequanint, Jeong, Wonjeong, Jha, Anil K, Jha, Ravi Prakash, Ji, John S, Jiang, Heng, Jin, Yingzhao, Jin, Yinzi, Johnson, Olatunji, Jomehzadeh, Nabi, Jones, Darwin Phan, Joo, Tamas, Joseph, Abel, Joseph, Nitin, Joshua, Charity Ehimwenma, Jozwiak, Jacek Jerzy, Jürisson, Mikk, Kaambwa, Billingsley, Kabir, Ali, Kabir, Hannaneh, Kabir, Zubair, Kadashetti, Vidya, Kahe, Farima, Kakodkar, Pradnya Vishal, Kalani, Rizwan, Kalankesh, Leila R, Kaliyadan, Feroze, Kalra, Sanjay, Kamath, Ashwin, Kamireddy, Arun, Kanagasabai, Thanigaivelan, Kandel, Himal, Kanmiki, Edmund Wedam, Kanmodi, Kehinde Kazeem, Kantar, Rami S, Kapoor, Neeti, Karajizadeh, Mehrdad, Karami Matin, Behzad, Karanth, Shama D, Karaye, Ibraheem M, Karim, Asima, Karimi, Hanie, Karimi, Salah Eddin, Karimi Behnagh, Arman, Karkhah, Samad, Karna, Ajit K, Kashoo, Faizan Zaffar, Kasraei, Hengameh, Kassaw, Nigussie Assefa, Kassebaum, Nicholas J, Kassel, Molly B, Katamreddy, 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Kasey E, Kiross, Girmay Tsegay, Kisa, Adnan, Kisa, Sezer, Kiss, Juniper Boroka, Kivimäki, Mika, Klu, Desmond, Knudsen, Ann Kristin Skrindo, Kolahi, Ali-Asghar, Kompani, Farzad, Koren, Gerbrand, Kosen, Soewarta, Kostev, Karel, Kotnis, Ashwin Laxmikant, Koul, Parvaiz A, Koulmane Laxminarayana, Sindhura Lakshmi, Koyanagi, Ai, Kravchenko, Michael A, Krishan, Kewal, Krishna, Hare, Krishnamoorthy, Vijay, Krishnamoorthy, Yuvaraj, Krohn, Kris J, Kuate Defo, Barthelemy, Kubeisy, Connor M, Kucuk Bicer, Burcu, Kuddus, Md Abdul, Kuddus, Mohammed, Kuitunen, Ilari, Kujan, Omar, Kulimbet, Mukhtar, Kulkarni, Vishnutheertha, Kumar, Ashish, Kumar, Harish, Kumar, Nithin, Kumar, Rahul, Kumar, Shiv, Kumari, Madhulata, Kurmanova, Almagul, Kurmi, Om P, Kusnali, Asep, Kusuma, Dian, Kutluk, Tezer, Kuttikkattu, Ambily, Kyei, Evans F, Kyriopoulos, Ilias, La Vecchia, Carlo, Ladan, Muhammad Awwal, Laflamme, Lucie, Lahariya, Chandrakant, Lahmar, Abdelilah, Lai, Daphne Teck Ching, Laksono, Tri, Lal, Dharmesh Kumar, 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Lin, Yano, Yuichiro, Yao, Yao, Ye, Pengpeng, Yesera, Gesila Endashaw, Yesodharan, Renjulal, Yesuf, Subah Abderehim, Yiğit, Arzu, Yiğit, Vahit, Yip, Paul, Yon, Dong Keon, Yonemoto, Naohiro, You, Yuyi, Younis, Mustafa Z, Yu, Chuanhua, Zadey, Siddhesh, Zadnik, Vesna, Zafari, Nima, Zahedi, Mohammad, Zahid, Muhammad Nauman, Zahir, Mazyar, Zakham, Fathiah, Zaki, Nazar, Zakzuk, Josefina, Zamagni, Giulia, Zaman, Burhan Abdullah, Zaman, Sojib Bin, Zamora, Nelson, Zand, Ramin, Zandi, Milad, Zandieh, Ghazal G Z, Zanghì, Aurora, Zare, Iman, Zastrozhin, Mikhail Sergeevich, Zeariya, Mohammed G M, Zeng, Youjie, Zhai, Chunxia, Zhang, Chen, Zhang, Haijun, Zhang, Hongwei, Zhang, Yunquan, Zhang, Zhaofeng, Zhang, Zhenyu, Zhao, Hanqing, Zhao, Yang, Zhao, Yong, Zheng, Peng, Zhong, Chenwen, Zhou, Juexiao, Zhu, Bin, Zhu, Zhaohua, Ziaeefar, Pardis, Zielińska, Magdalena, Zou, Zhiyong, Zumla, Alimuddin, Zweck, Elric, Zyoud, Samer H, Lim, Stephen S, and Murray, Christopher J L

Published
2024
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47. Searching for waveforms on spatially-filtered epileptic ECoG

Author

Mendoza-Cardenas, Carlos H. and Brockmeier, Austin J.

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

Seizures are one of the defining symptoms in patients with epilepsy, and due to their unannounced occurrence, they can pose a severe risk for the individual that suffers it. New research efforts are showing a promising future for the prediction and preemption of imminent seizures, and with those efforts, a vast and diverse set of features have been proposed for seizure prediction algorithms. However, the data-driven discovery of nonsinusoidal waveforms for seizure prediction is lacking in the literature, which is in stark contrast with recent works that show the close connection between the waveform morphology of neural oscillations and the physiology and pathophysiology of the brain, and especially its use in effectively discriminating between normal and abnormal oscillations in electrocorticographic (ECoG) recordings of epileptic patients. Here, we explore a scalable, energy-guided waveform search strategy on spatially-projected continuous multi-day ECoG data sets. Our work shows that data-driven waveform learning methods have the potential to not only contribute features with predictive power for seizure prediction, but also to facilitate the discovery of oscillatory patterns that could contribute to our understanding of the pathophysiology and etiology of seizures., Comment: To be published in the 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER)

Published
2021
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48. V2S: A Tool for Translating Video Recordings of Mobile App Usages into Replayable Scenarios

Author

Havranek, Madeleine, Bernal-Cárdenas, Carlos, Cooper, Nathan, Chaparro, Oscar, Poshyvnayk, Denys, and Moran, Kevin

Subjects
Computer Science - Software Engineering
Abstract

Screen recordings are becoming increasingly important as rich software artifacts that inform mobile application development processes. However, the amount of manual effort required to extract information from these graphical artifacts can hinder resource-constrained mobile developers. This paper presents Video2Scenario (V2S), an automated tool that processes video recordings of Android app usages, utilizes neural object detection and image classification techniques to classify the depicted user actions, and translates these actions into a replayable scenario. We conducted a comprehensive evaluation to demonstrate V2S's ability to reproduce recorded scenarios across a range of devices and a diverse set of usage cases and applications. The results indicate that, based on its performance with 175 videos depicting 3,534 GUI-based actions, V2S is accurate in reproducing $\approx$89\% of actions from collected videos.

Published
2021

49. It Takes Two to Tango: Combining Visual and Textual Information for Detecting Duplicate Video-Based Bug Reports

Author

Cooper, Nathan, Bernal-Cárdenas, Carlos, Chaparro, Oscar, Moran, Kevin, and Poshyvanyk, Denys

Subjects
Computer Science - Software Engineering, Computer Science - Artificial Intelligence
Abstract

When a bug manifests in a user-facing application, it is likely to be exposed through the graphical user interface (GUI). Given the importance of visual information to the process of identifying and understanding such bugs, users are increasingly making use of screenshots and screen-recordings as a means to report issues to developers. However, when such information is reported en masse, such as during crowd-sourced testing, managing these artifacts can be a time-consuming process. As the reporting of screen-recordings in particular becomes more popular, developers are likely to face challenges related to manually identifying videos that depict duplicate bugs. Due to their graphical nature, screen-recordings present challenges for automated analysis that preclude the use of current duplicate bug report detection techniques. To overcome these challenges and aid developers in this task, this paper presents Tango, a duplicate detection technique that operates purely on video-based bug reports by leveraging both visual and textual information. Tango combines tailored computer vision techniques, optical character recognition, and text retrieval. We evaluated multiple configurations of Tango in a comprehensive empirical evaluation on 4,860 duplicate detection tasks that involved a total of 180 screen-recordings from six Android apps. Additionally, we conducted a user study investigating the effort required for developers to manually detect duplicate video-based bug reports and compared this to the effort required to use Tango. The results reveal that Tango's optimal configuration is highly effective at detecting duplicate video-based bug reports, accurately ranking target duplicate videos in the top-2 returned results in 83% of the tasks. Additionally, our user study shows that, on average, Tango can reduce developer effort by over 60%, illustrating its practicality., Comment: 13 pages and 1 figure. Published at ICSE'21

Published
2021

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