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1. An Empirical Study of the Generalization Ability of Lidar 3D Object Detectors to Unseen Domains

Author

Eskandar, George, Zhang, Chongzhe, Kaushik, Abhishek, Guirguis, Karim, Sayed, Mohamed, and Yang, Bin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

3D Object Detectors (3D-OD) are crucial for understanding the environment in many robotic tasks, especially autonomous driving. Including 3D information via Lidar sensors improves accuracy greatly. However, such detectors perform poorly on domains they were not trained on, i.e. different locations, sensors, weather, etc., limiting their reliability in safety-critical applications. There exist methods to adapt 3D-ODs to these domains; however, these methods treat 3D-ODs as a black box, neglecting underlying architectural decisions and source-domain training strategies. Instead, we dive deep into the details of 3D-ODs, focusing our efforts on fundamental factors that influence robustness prior to domain adaptation. We systematically investigate four design choices (and the interplay between them) often overlooked in 3D-OD robustness and domain adaptation: architecture, voxel encoding, data augmentations, and anchor strategies. We assess their impact on the robustness of nine state-of-the-art 3D-ODs across six benchmarks encompassing three types of domain gaps - sensor type, weather, and location. Our main findings are: (1) transformer backbones with local point features are more robust than 3D CNNs, (2) test-time anchor size adjustment is crucial for adaptation across geographical locations, significantly boosting scores without retraining, (3) source-domain augmentations allow the model to generalize to low-resolution sensors, and (4) surprisingly, robustness to bad weather is improved when training directly on more clean weather data than on training with bad weather data. We outline our main conclusions and findings to provide practical guidance on developing more robust 3D-ODs.

Published
2024

2. Accelerating Process Development for 3D Printing of New Metal Alloys

Author

Guirguis, David, Tucker, Conrad, and Beuth, Jack

Subjects
Condensed Matter - Materials Science, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, J.2
Abstract

Addressing the uncertainty and variability in the quality of 3D printed metals can further the wide spread use of this technology. Process mapping for new alloys is crucial for determining optimal process parameters that consistently produce acceptable printing quality. Process mapping is typically performed by conventional methods and is used for the design of experiments and ex situ characterization of printed parts. On the other hand, in situ approaches are limited because their observable features are limited and they require complex high-cost setups to obtain temperature measurements to boost accuracy. Our method relaxes these limitations by incorporating the temporal features of molten metal dynamics during laser-metal interactions using video vision transformers and high-speed imaging. Our approach can be used in existing commercial machines and can provide in situ process maps for efficient defect and variability quantification. The generalizability of the approach is demonstrated by performing cross-dataset evaluations on alloys with different compositions and intrinsic thermofluid properties.

Published
2023

4. Telerehabilitation Program Impact on Overactive Bladder Symptoms and Metabolic Health in Obese Women: a Randomized Controlled Trial

Author

Salma I.A. Alghitany, Hend A. Abd El-Monaem, Marihan Z. Aziz, Nouran A. Ibrahim, and Sandra A. Guirguis

Subjects
telerehabilitation, obesity, lower urinary tract symptoms, videoconferencing, Medicine (General), R5-920, Sports medicine, RC1200-1245
Abstract

INTRODUCTION. Overactive bladder syndrome is caused by many factors including obesity, insulin resistance and poor dietary habits. Since it is a chronic disease and needs time to be treated, introducing telepilates in addition to Mediterranean diet would encourage better adherence and results to the treatment program. AIM. To assess the impact of a virtual group-based telerehabilitation program on overactive bladder symptoms and metabolic health in women with obesity. MATERIAL AND METHODS. Eighty obese women (BMI 30.0–34.9 kg/m2) between the ages of 35 and 45 were allocated into two equal groups, 40 for each: (A) supervised telepilates and (B) unsupervised telepilates. The supervised group participated in a 12-week Pilates workout program over videoconference platforms three times a week. Meanwhile, the unsupervised group only received four online meetings. The groups’ diet was the Mediterranean style. The Hemostatic Model of Insulin Resistance (HOMA-IR), body mass index (BMI), waist circumference (WC), Patient Perception of Urgency Scale (PPIUS), Overactive Bladder Questionnaire Short Form (OAB-q SF), and Telehealth Usability Scale (TUS) were measured. RESULTS AND DISCUSSION. The supervised telepilates group exhibited statistically significant amelioration of overactive bladder symptoms and a reduction in HOMA-IR (p 0.001), while the unsupervised telepilates group showed insignificant changes in these measures (p 0.05). Furthermore, the supervised telepilates group showed significantly greater reductions in BMI and WC (p 0.001) than the unsupervised telepilates group (p 0.05). Additionally, the supervised telegroup outperformed the unsupervised telegroup on all parameters of TUS (p 0.001) CONCLUSION. Women with obesity experienced decrease in overactive bladder symptoms and improved metabolic health after completing a 12-week telepilates training program.

Published
2024
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6. Multiparametric MRI–based radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer

Author

Mohamed, Rania M., Panthi, Bikash, Adrada, Beatriz E., Boge, Medine, Candelaria, Rosalind P., Chen, Huiqin, Guirguis, Mary S., Hunt, Kelly K., Huo, Lei, Hwang, Ken-Pin, Korkut, Anil, Litton, Jennifer K., Moseley, Tanya W., Pashapoor, Sanaz, Patel, Miral M., Reed, Brandy, Scoggins, Marion E., Son, Jong Bum, Thompson, Alastair, Tripathy, Debu, Valero, Vicente, Wei, Peng, White, Jason, Whitman, Gary J., Xu, Zhan, Yang, Wei, Yam, Clinton, Ma, Jingfei, and Rauch, Gaiane M.

Published
2024
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9. Towards Pragmatic Semantic Image Synthesis for Urban Scenes

Author

Eskandar, George, Guo, Diandian, Guirguis, Karim, and Yang, Bin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The need for large amounts of training and validation data is a huge concern in scaling AI algorithms for autonomous driving. Semantic Image Synthesis (SIS), or label-to-image translation, promises to address this issue by translating semantic layouts to images, providing a controllable generation of photorealistic data. However, they require a large amount of paired data, incurring extra costs. In this work, we present a new task: given a dataset with synthetic images and labels and a dataset with unlabeled real images, our goal is to learn a model that can generate images with the content of the input mask and the appearance of real images. This new task reframes the well-known unsupervised SIS task in a more practical setting, where we leverage cheaply available synthetic data from a driving simulator to learn how to generate photorealistic images of urban scenes. This stands in contrast to previous works, which assume that labels and images come from the same domain but are unpaired during training. We find that previous unsupervised works underperform on this task, as they do not handle distribution shifts between two different domains. To bypass these problems, we propose a novel framework with two main contributions. First, we leverage the synthetic image as a guide to the content of the generated image by penalizing the difference between their high-level features on a patch level. Second, in contrast to previous works which employ one discriminator that overfits the target domain semantic distribution, we employ a discriminator for the whole image and multiscale discriminators on the image patches. Extensive comparisons on the benchmarks GTA-V $\rightarrow$ Cityscapes and GTA-V $\rightarrow$ Mapillary show the superior performance of the proposed model against state-of-the-art on this task.

Published
2023

10. Urban-StyleGAN: Learning to Generate and Manipulate Images of Urban Scenes

Author

Eskandar, George, Farag, Youssef, Yenamandra, Tarun, Cremers, Daniel, Guirguis, Karim, and Yang, Bin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

A promise of Generative Adversarial Networks (GANs) is to provide cheap photorealistic data for training and validating AI models in autonomous driving. Despite their huge success, their performance on complex images featuring multiple objects is understudied. While some frameworks produce high-quality street scenes with little to no control over the image content, others offer more control at the expense of high-quality generation. A common limitation of both approaches is the use of global latent codes for the whole image, which hinders the learning of independent object distributions. Motivated by SemanticStyleGAN (SSG), a recent work on latent space disentanglement in human face generation, we propose a novel framework, Urban-StyleGAN, for urban scene generation and manipulation. We find that a straightforward application of SSG leads to poor results because urban scenes are more complex than human faces. To provide a more compact yet disentangled latent representation, we develop a class grouping strategy wherein individual classes are grouped into super-classes. Moreover, we employ an unsupervised latent exploration algorithm in the $\mathcal{S}$-space of the generator and show that it is more efficient than the conventional $\mathcal{W}^{+}$-space in controlling the image content. Results on the Cityscapes and Mapillary datasets show the proposed approach achieves significantly more controllability and improved image quality than previous approaches on urban scenes and is on par with general-purpose non-controllable generative models (like StyleGAN2) in terms of quality.

Published
2023

11. NIFF: Alleviating Forgetting in Generalized Few-Shot Object Detection via Neural Instance Feature Forging

Author

Guirguis, Karim, Meier, Johannes, Eskandar, George, Kayser, Matthias, Yang, Bin, and Beyerer, Juergen

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

Privacy and memory are two recurring themes in a broad conversation about the societal impact of AI. These concerns arise from the need for huge amounts of data to train deep neural networks. A promise of Generalized Few-shot Object Detection (G-FSOD), a learning paradigm in AI, is to alleviate the need for collecting abundant training samples of novel classes we wish to detect by leveraging prior knowledge from old classes (i.e., base classes). G-FSOD strives to learn these novel classes while alleviating catastrophic forgetting of the base classes. However, existing approaches assume that the base images are accessible, an assumption that does not hold when sharing and storing data is problematic. In this work, we propose the first data-free knowledge distillation (DFKD) approach for G-FSOD that leverages the statistics of the region of interest (RoI) features from the base model to forge instance-level features without accessing the base images. Our contribution is three-fold: (1) we design a standalone lightweight generator with (2) class-wise heads (3) to generate and replay diverse instance-level base features to the RoI head while finetuning on the novel data. This stands in contrast to standard DFKD approaches in image classification, which invert the entire network to generate base images. Moreover, we make careful design choices in the novel finetuning pipeline to regularize the model. We show that our approach can dramatically reduce the base memory requirements, all while setting a new standard for G-FSOD on the challenging MS-COCO and PASCAL-VOC benchmarks., Comment: Accepted at CVPR 2023

Published
2023

12. An entropy and machine learning based approach for DDoS attacks detection in software defined networks

Author

Amany I. Hassan, Eman Abd El Reheem, and Shawkat K. Guirguis

Subjects
Medicine, Science
Abstract

Abstract Software-defined networks (SDNs) have been growing rapidly due to their ability to provide an efficient network management approach compared to traditional methods. However, one of the major challenges facing SDNs is the threat of Distributed Denial of Service (DDoS) attacks, which can severely impact network availability. Detecting and mitigating such attacks is challenging, given the constantly evolving range of attack techniques. In this paper, a novel hybrid approach is proposed that combines statistical methods with machine-learning capabilities to address the detection and mitigation of DDoS attacks in SDN environments. The statistical phase of the approach utilizes an entropy-based detection mechanism, while the machine-learning phase employs a clustering mechanism to analyze the impact of active users on the entropy of the system. The k-means algorithm is used for clustering. The proposed approach was experimentally evaluated using three modern datasets, namely, CIC-IDS2017, CSE-CIC-2018, and CICIDS2019. The results demonstrate the effectiveness of the system in detecting and blocking sudden and rapid attacks, highlighting the potential of the proposed approach to significantly enhance security against DDoS attacks in SDN environments.

Published
2024
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13. Multiparametric MRI–based radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer

Author

Rania M. Mohamed, Bikash Panthi, Beatriz E. Adrada, Medine Boge, Rosalind P. Candelaria, Huiqin Chen, Mary S. Guirguis, Kelly K. Hunt, Lei Huo, Ken-Pin Hwang, Anil Korkut, Jennifer K. Litton, Tanya W. Moseley, Sanaz Pashapoor, Miral M. Patel, Brandy Reed, Marion E. Scoggins, Jong Bum Son, Alastair Thompson, Debu Tripathy, Vicente Valero, Peng Wei, Jason White, Gary J. Whitman, Zhan Xu, Wei Yang, Clinton Yam, Jingfei Ma, and Gaiane M. Rauch

Subjects
Triple-negative breast cancer, Dynamic contrast-enhanced breast MRI, Diffusion-weighted imaging, Neoadjuvant systemic therapy, Treatment response, Radiomic features, Medicine, Science
Abstract

Abstract Triple-negative breast cancer (TNBC) is often treated with neoadjuvant systemic therapy (NAST). We investigated if radiomic models based on multiparametric Magnetic Resonance Imaging (MRI) obtained early during NAST predict pathologic complete response (pCR). We included 163 patients with stage I-III TNBC with multiparametric MRI at baseline and after 2 (C2) and 4 cycles of NAST. Seventy-eight patients (48%) had pCR, and 85 (52%) had non-pCR. Thirty-six multivariate models combining radiomic features from dynamic contrast-enhanced MRI and diffusion-weighted imaging had an area under the receiver operating characteristics curve (AUC) > 0.7. The top-performing model combined 35 radiomic features of relative difference between C2 and baseline; had an AUC = 0.905 in the training and AUC = 0.802 in the testing set. There was high inter-reader agreement and very similar AUC values of the pCR prediction models for the 2 readers. Our data supports multiparametric MRI-based radiomic models for early prediction of NAST response in TNBC.

Published
2024
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14. Association of western US compound hydrometeorological extremes with Madden-Julian oscillation and ENSO interaction

Author

Jiabao Wang, Michael J. DeFlorio, Alexander Gershunov, Kristen Guirguis, Luca Delle Monache, and F. Martin Ralph

Subjects
Geology, QE1-996.5, Environmental sciences, GE1-350
Abstract

Abstract Extreme weather and climate events can have substantial impacts on society and the environment. Compound extremes (two or more extreme events occurring simultaneously or successively) may exert even larger impacts than individual events. Here we examine physical drivers behind variability in hydrometeorological (precipitation and temperature) compound extremes on subseasonal-to-seasonal timescales. Observational evidence presented here through composite analysis indicates that compound extreme frequency is linked to the Madden-Julian oscillation, a unique type of organized tropical convection varying primarily on subseasonal-to-seasonal timescales. The linkage between Madden-Julian oscillation and compound extremes is largely dependent on ENSO phases, which can be seen through different magnitudes or changes in sign of the canonical relationship conditioned on ENSO states. Similarly, the Madden-Julian oscillation can interrupt the canonical ENSO-compound extreme relationship. Our results suggest a potential route to improve subseasonal-to-seasonal prediction of western US compound extremes by considering the combined effect of both Madden-Julian oscillation and ENSO.

Published
2024
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15. Primary 3-Month Outcomes of a Double-Blind Randomized Prospective Study (The QUEST Study) Assessing Effectiveness and Safety of Novel High-Frequency Electric Nerve Block System for Treatment of Post-Amputation Pain

Author

Kapural L, Melton J, Kim B, Mehta P, Sigdel A, Bautista A, Petersen EA, Slavin KV, Eidt J, Wu J, Elshihabi S, Schwalb JM, Garrett HE Jr, Veizi E, Barolat G, Rajani RR, Rhee PC, Guirguis M, and Mekhail N

Subjects
post-amputation pain, phantom limb pain, neuromodulation, peripheral nerve stimulation, high-frequency nerve block, Medicine (General), R5-920
Abstract

Leonardo Kapural,1 Jim Melton,2 Billy Kim,3 Priyesh Mehta,4 Abindra Sigdel,5 Alexander Bautista,6 Erika A Petersen,7 Konstantin V Slavin,8,9 John Eidt,10 Jiang Wu,11 Said Elshihabi,12 Jason Matthew Schwalb,13 H Edward Garrett Jr,14 Elias Veizi,15 Giancarlo Barolat,16 Ravi R Rajani,17 Peter C Rhee,18 Maged Guirguis,19 Nagy Mekhail20 1Carolinas Pain Institute and Center for Clinical Research, Winston-Salem, NC, USA; 2Department of Vascular Surgery, Cardiovascular Health Clinic, Oklahoma City, OK, USA; 3Department of Vascular Surgery, The Surgical Clinic, Nashville, TN, USA; 4Department of Pain Medicine, Meta Medical Research Institute, Dayton, OH, USA; 5Department of Surgery, University of Louisville, Louisville, KY, USA; 6Department of Anesthesiology and Perioperative Medicine, University of Louisville, Louisville, KY, USA; 7Department of Neurosurgery, University of Arkansas, Little Rock, AR, USA; 8Department of Neurosurgery, University of Illinois at Chicago, Chicago, IL, USA; 9Department of Neurology, Jesse Brown VA Medical Center, Chicago, IL, USA; 10Department of Vascular Surgery, Baylor Scott and White Heart and Vascular Hospital Dallas, Dallas, TX, USA; 11Department of Anesthesiology & Pain Medicine, University of Washington Medical Center, Seattle, WA, USA; 12Department of Neurosurgery, Legacy Brain & Spine Surgical Center, Atlanta, GA, USA; 13Department of Neurosurgery, Henry Ford Medical Group, Detroit, MI, USA; 14Department of Vascular Surgery, University of Tennessee-Memphis, Memphis, TN, USA; 15Department of Pain Medicine, VA Northeast OH Healthcare System, Cleveland, OH, USA; 16Department of Neurosurgery, Barolat Neuroscience, Presbyterian/St Luke’s Medical Center, Denver, CO, USA; 17Department of Vascular Surgery, Emory University and Grady Memorial Hospital, Atlanta, GA, USA; 18Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN, USA; 19Department of Interventional Pain Management, Ochsner Health System, New Orleans, LA, USA; 20Department of Pain Management, Cleveland Clinic, Cleveland, OH, USACorrespondence: Leonardo Kapural, Carolinas Pain Institute and Center for Clinical Research, 145 Kimel Park Dr #330, Winston-Salem, NC, 27103, USA, Email lkapuralmd@gmail.comPurpose: This multicenter, randomized, double-blinded, active sham-controlled pivotal study was designed to assess the efficacy and safety of high-frequency nerve block treatment for chronic post-amputation and phantom limb pain.Patients and Methods: QUEST enrolled 180 unilateral lower-limb amputees with severe post-amputation pain, 170 of whom were implanted with the Altius device, were randomized 1:1 to active-sham or treatment groups and reached the primary endpoint. Responders were those subjects who received ≥ 50% pain relief 30 min after treatment in ≥ 50% of their self-initiated treatment sessions within the 3-month randomized period. Differences between the active treatment and sham control groups as well as numerous secondary outcomes were determined.Results: At 30-min, (primary outcome), 24.7% of the treatment group were responders compared to 7.1% of the control group (p=0.002). At 120-minutes following treatment, responder rates were 46.8% in the Treatment group and 22.2% in the Control group (p=0.001). Improvement in Brief Pain Inventory interference score of 2.3 ± 0.29 was significantly greater in treatment group than the 1.3 ± 0.26-point change in the Control group (p = 0.01). Opioid usage, although not significantly different, trended towards a greater reduction in the treatment group than in the control group. The incidence of adverse events did not differ significantly between the treatment and control groups.Conclusion: The primary outcomes of the study were met, and the majority of Treatment patients experienced a substantial improvement in PAP (regardless of meeting the study definition of a responder). The significant in PAP was associated with significantly improved QOL metrics, and a trend towards reduced opioid utilization compared to Control. These data indicate that Altius treatment represents a significant therapeutic advancement for lower-limb amputees suffering from chronic PAP.Keywords: post-amputation pain, phantom limb pain, neuromodulation, peripheral nerve stimulation, high-frequency nerve block

Published
2024

16. Methylphenidate abuse and misuse in patients affected with a psychiatric disorder and a substance use disorder: a systematic review

Author

Stefania Chiappini, Pietro Domenico Gramuglia, Alessio Mosca, Clara Cavallotto, Andrea Miuli, John Martin Corkery, Amira Guirguis, Fabrizio Schifano, and Giovanni Martinotti

Subjects
methylphenidate, MPH, ADHD, dual diagnosis, SUD, drug misuse, Psychiatry, RC435-571
Abstract

BackgroundMethylphenidate (MPH), a central nervous system stimulant primarily prescribed for attention-deficit/hyperactivity disorder (ADHD), has seen increasing rates of misuse and abuse, particularly in patients with dual diagnosis (co-occurring psychiatric disorders and substance use disorders/SUDs). The heightened risk of dependence and adverse effects in these vulnerable populations warrants a systematic review to assess the prevalence and pattern of abuse/misuse of MPH among patients within this population and to understand potential risk factors, patterns of misuse, and outcomes, including the impact on psychiatric symptoms and overall mental health, the effects on SUD (e.g., exacerbation or mitigation of symptoms), and the incidence of adverse events and complications (e.g., cardiovascular issues, psychological effects).MethodologyA systematic review was conducted in August-September 2024 using both PubMed and Scopus databases. The following search strategy was used: TITLE-ABS-KEY (methylphenidate OR Ritalin OR Concerta) AND TITLE-ABS-KEY (abuse OR misuse OR dependency OR addiction) AND TITLE-ABS-KEY (dual diagnosis OR comorbid psychiatric disorder OR psychiatric disorder AND substance use disorder). The systematic review was structured in accordance with the PRISMA guidelines and identified studies were assessed by title/abstract and full text screening against eligibility criteria.ResultsA total of 12 studies were selected for analysis after screening for relevance, quality, and adherence to inclusion criteria. Findings indicated that individuals with psychiatric disorders, particularly conduct disorder (N=593/1551 individuals), mood disorder (N=90/1551 individuals), anxiety disorder (N=66/1551 individuals), personality disorder (N=44/1551 individuals) and major depression disorder (N=40/1551 individuals), were more likely to misuse MPH. Co-occurring SUD, especially involving Alcohol Use Disorder (N=475/1551 individuals), Cannabis Use Disorder (N=371/1551 individuals), Nicotine Use Disorder (N=343/1551 individuals), Cocaine Use Disorder (N=68/1551 individuals), significantly elevated the risk. Misuse often involved higher doses than prescribed (N=84/1551 individuals) or using non-oral routes of administration (N=20/1551 individuals; e.g., snorting). Adverse outcomes included heightened risk of gastrointestinal events (N=201/1551 individuals), cardiovascular events (N=108/1551 individuals), psychosis (N=69/1551 individuals), and exacerbation of psychiatric symptoms (N=1082/1551 individuals).ConclusionMPH misuse and abuse are significant concerns in patients with psychiatric disorders and SUD. Risk factors include impulsivity, history of substance abuse, and access to prescription stimulants. Integrated therapeutic approaches and stricter prescription monitoring are recommended to mitigate misuse risks.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42024576724.

Published
2024
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17. Kerkouane 2022-2023: primo rapporto sulle campagne di scavi della missione archeologica tuniso-italiana (INP-UNISS)

Author

Mounir Fantar and Michele Guirguis

Subjects
Tunisia, Capo Bon, Mondo punico, Santuario, ceramica, Archaeology, CC1-960
Abstract

Les investigations archéologiques menées à Kerkouane, dans le cadre d’une collaboration scientifique entre l’Institut National du Patrimoine (Tunisie) et l’Université de Sassari (Italie), ont apporté de nouvelles perspectives à l’histoire de cette cité punique du Cap Bon. Dans cet exposé préliminaire, nous présentons les premiers résultats obtenus lors des recherches effectuées entre 2022 et 2023, et définissons quelques axes d’étude visant à replacer cette nouvelle documentation archéologique dans un contexte élargi, celui du Cap Bon entre les périodes archaïque et hellénistique (VIIe/VIe-IIIe siècles av. J.-C.). Nous tenons compte de l’influence orientale introduite et diffusée en Afrique par Carthage, ainsi que de l’élément autochtone libyque, qui prédomine et reste très actif. Ces nouvelles données, combinées aux découvertes antérieures, inciteront à des investigations archéologiques plus approfondies pour une meilleure compréhension de la ville de Kerkouane.

Published
2024
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18. Public Acceptance of Government Information Systems: Evidence From the Popular Vote on an Electronic Identity (e-ID) in Switzerland

Author

Lyn Ellen Pleger and Katharina Guirguis

Subjects
electronic identity, public information systems, public acceptance, e-government, e-service design, Political institutions and public administration (General), JF20-2112
Abstract

E-government describes governments’ efforts to use information technology to improve the efficiency of government services. While the positive aspects of e-government are evident from an organisational point of view, individuals can either accept or decline e-government solutions and governments aim to increase citizen acceptance of their e-government initiatives. There is a wide range of literature with so-called technology acceptance models to study population acceptance of e-government. However, these models have some shortcomings regarding the planned implementation of tangible systems in the public sector (ex-ante acceptance). We, therefore, propose a revised model of information system acceptance in the public sector, the GISAM, and apply it to the case of the 2021 ballot proposal on electronic identity (e-ID) introduction in Switzerland. Using the VOX dataset allows us to examine the survey data of 2,092 Swiss citizens, covering their voting decisions and reasons for them, and several socio-demographic and socio-economic variables. Our findings show that a broad range of factors influences voter acceptance of e-ID. Contrary to traditional technology acceptance models, the results of this study suggest that individual attitudes towards digitisation play a significant role in technology adoption in the e-ID context. Future research should investigate the case of ex-ante technology acceptance more deeply.

Published
2024
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19. Home-Based Digital Exercise Training Program to Improve Physical Function of Older Sepsis Survivors: Protocol of the HEAL Sepsis Randomized Clinical Trial

Author

Rola S Zeidan, Margaret K Ohama, Natalia Evripidou, Stephen D Anton, Laith L Hamed, Yi Lin, Christiaan Leeuwenburgh, Faheem W Guirguis, Philip A Efron, Sheryl Flynn, Barbara Smith, Rhonda Bacher, Naveen Bakarasan, Juan Sarmiento Delgado, and Robert T Mankowski

Subjects
Medicine, Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundWhile sepsis, an exaggerated response to infection, can affect people of all age groups, it is more prevalent in middle-aged and older adults. Older adults suffer worse short-term and long-term outcomes than younger patients. Older sepsis survivors are commonly discharged to long-term acute care facilities, where they often die within 1 year. Those who return home from the hospital lose the momentum of physical function improvement after early inpatient rehabilitation, and often face exacerbation of comorbidities and decline in physical function. Additionally, patients who are discharged home often live at distant locations and are not able to commute to rehabilitation centers due to their poor health status. Therefore, remotely delivered exercise interventions tailored to this population hold promise to improve physical function safely and effectively after sepsis. However, this type of intervention has yet to be tested in this population. ObjectiveThis study aims to assess the safety, feasibility, and ease of recruitment and retention of participants for a remotely delivered physical activity intervention for improving physical function in middle-aged and older sepsis survivors. MethodsThe proposed intervention will be delivered through a digital health platform that comprises a patient-facing mobile app and a 12-week physical activity program specifically designed for middle-aged and older sepsis survivors with poor health status who may face challenges participating in traditional out-patient or community-based exercise interventions. This study is ongoing and plans to enroll 40 sepsis survivors aged 55 years and older who will be randomized to either a remotely delivered exercise intervention group or a control group (electronic health diary). Both groups will use a tablet containing the Health in Motion app (Blue Marble Health). The intervention group will receive a clinician-designed personalized avatar-guided home exercise program and reminders while the control group will self-report daily activities using the in-app health diary feature. ResultsThis study is the first to use a home-based, remotely monitored 12-week exercise program to improve physical function in sepsis survivors. This study will evaluate the safety, feasibility, and efficacy, providing the necessary knowledge to design and calculate power for future larger trials. ConclusionsThis study will provide important information for planning a future randomized clinical trial to test the efficacy of a remotely delivered exercise intervention in this high-risk population. Trial RegistrationClinicalTrials.gov NCT05568511; https://clinicaltrials.gov/study/NCT05568511 International Registered Report Identifier (IRRID)DERR1-10.2196/60270

Published
2024
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20. Outcomes of Lower Extremity Total Joint Arthroplasty in Patients With Skeletal Dysplasia: A Systematic Review

Author

Paul Guirguis, BA, Lucas Fowler, MD, and Benjamin F. Ricciardi, MD

Subjects
Skeletal dysplasia, Total hip arthroplasty, Total knee arthroplasty, Achondroplasia, Spondyloepiphyseal dysplasia, Orthopedic surgery, RD701-811
Abstract

Background: Patients with genetic skeletal dysplasias often require lower extremity total joint arthroplasty (TJA) due to early joint degeneration; however, little data exists regarding the outcomes of TJA in this population. Our purpose was to review the literature to determine the complication rates, revision rates, implant survivorship, and patient-reported outcomes of total knee arthroplasty and total hip arthroplasty (THA) in those with genetic skeletal dysplasias. Methods: A systematic literature review of online databases (PubMed and Google Scholar) was conducted. Studies that reported the outcomes of THA or total knee arthroplasty in patients with genetically confirmed skeletal dysplasias were included. Case reports and studies that defined dysplasia based on height alone were excluded. Fourteen studies met the criteria for data extraction and analysis. Results: Our review yielded a sample of 596 skeletal dysplasia patients with a median follow-up of 6.01 years (1.7-15.9). Mean age was 54.04 years, and mean body mass index was 29.1 kg/m2. Cementless fixation was utilized in 65.7% of THAs, while all knees were cemented. Hip implant survivorship was 79% at 10 years and 56% at 20 years. Knee implant survivorship was 92% at 10 years and 46% at 20 years. Hip and knee revisions were 15.3% and 13.5%, respectively. The most common indication was aseptic loosening and polyethylene wear. Patient-reported outcomes improved across all domains. Conclusions: The literature regarding lower extremity TJA in those with genetic skeletal dysplasias demonstrates appropriate 10-year implant survivorship and improvement in patient-reported outcomes across all survey domains.

Published
2024
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21. Accelerating Transfer Learning with Near-Data Computation on Cloud Object Stores

Author

Petrescu, Diana, Guirguis, Arsany, Quoc, Do Le, Picorel, Javier, Guerraoui, Rachid, and Dinu, Florin

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

Storage disaggregation underlies today's cloud and is naturally complemented by pushing down some computation to storage, thus mitigating the potential network bottleneck between the storage and compute tiers. We show how ML training benefits from storage pushdowns by focusing on transfer learning (TL), the widespread technique that democratizes ML by reusing existing knowledge on related tasks. We propose HAPI, a new TL processing system centered around two complementary techniques that address challenges introduced by disaggregation. First, applications must carefully balance execution across tiers for performance. HAPI judiciously splits the TL computation during the feature extraction phase yielding pushdowns that not only improve network time but also improve total TL training time by overlapping the execution of consecutive training iterations across tiers. Second, operators want resource efficiency from the storage-side computational resources. HAPI employs storage-side batch size adaptation allowing increased storage-side pushdown concurrency without affecting training accuracy. HAPI yields up to 2.5x training speed-up while choosing in 86.8% of cases the best performing split point or one that is at most 5% off from the best., Comment: To appear in the proceedings of SoCC '24

Published
2022
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22. Towards Discriminative and Transferable One-Stage Few-Shot Object Detectors

Author

Guirguis, Karim, Abdelsamad, Mohamed, Eskandar, George, Hendawy, Ahmed, Kayser, Matthias, Yang, Bin, and Beyerer, Juergen

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

Recent object detection models require large amounts of annotated data for training a new classes of objects. Few-shot object detection (FSOD) aims to address this problem by learning novel classes given only a few samples. While competitive results have been achieved using two-stage FSOD detectors, typically one-stage FSODs underperform compared to them. We make the observation that the large gap in performance between two-stage and one-stage FSODs are mainly due to their weak discriminability, which is explained by a small post-fusion receptive field and a small number of foreground samples in the loss function. To address these limitations, we propose the Few-shot RetinaNet (FSRN) that consists of: a multi-way support training strategy to augment the number of foreground samples for dense meta-detectors, an early multi-level feature fusion providing a wide receptive field that covers the whole anchor area and two augmentation techniques on query and source images to enhance transferability. Extensive experiments show that the proposed approach addresses the limitations and boosts both discriminability and transferability. FSRN is almost two times faster than two-stage FSODs while remaining competitive in accuracy, and it outperforms the state-of-the-art of one-stage meta-detectors and also some two-stage FSODs on the MS-COCO and PASCAL VOC benchmarks.

Published
2022

24. Midwinter Dry Spells Amplify Post‐Fire Snowpack Decline

Author

Hatchett, Benjamin J, Koshkin, Arielle L, Guirguis, Kristen, Rittger, Karl, Nolin, Anne W, Heggli, Anne, Rhoades, Alan M, East, Amy E, Siirila‐Woodburn, Erica R, Brandt, W Tyler, Gershunov, Alexander, and Haleakala, Kayden

Subjects
Earth Sciences, Atmospheric Sciences, Physical Geography and Environmental Geoscience, Climate Action, Life on Land, snow, wildfire, drought, extreme events, compound events, Meteorology & Atmospheric Sciences
Abstract

Increasing wildfire and declining snowpacks in mountain regions threaten water availability. We combine satellite-based fire detections with snow seasonality classifications to examine fire activity in California's seasonal and ephemeral snow zones. We find a nearly tenfold increase in fire activity during 2020–2021 versus 2001–2019. Accumulation season broadband snow albedo declined 25%–71% at two burned sites (2021 and 2022) according to in-situ data relative to un-burned conditions, with greater declines associated with increased burn severity. By enhancing snowpack susceptibility to melt, both decreased snow albedo and canopy drove midwinter melt during a multi-week dry spell in 2022. Despite similar meteorological conditions in December–February 2013 and 2022–linked to persistent high pressure weather regimes–minimal melt occurred in 2013. Post-fire snowpack differences are confirmed with satellite measurements. With growing geographical overlap between wildfire and snow, our findings suggest California's snowpack is increasingly vulnerable to the compounding effects of dry spells and wildfire.

Published
2023

25. Pain Education and Knowledge (PEAK) Consensus Guidelines for Neuromodulation: A Proposal for Standardization in Fellowship and Training Programs.

Author

Goree, Johnathan, Hagedorn, Jonathan, Lee, David, Chapman, Kenneth, Christiansen, Sandy, Dudas, Andrew, Escobar, Alexander, Gilligan, Christopher, Guirguis, Maged, Gulati, Amitabh, Jameson, Jessica, Mallard, Christopher, Murphy, Melissa, Patel, Kiran, Patel, Raj, Sheth, Samir, Vanterpool, Stephanie, Singh, Vinita, Smith, Gregory, Strand, Natalie, Vu, Chau, Suvar, Tolga, Chakravarthy, Krishnan, Kapural, Leonardo, Leong, Michael, Lubenow, Timothy, Abd-Elsayed, Alaa, Pope, Jason, Sayed, Dawood, Deer, Timothy, and Pritzlaff, Scott

Subjects
dorsal root ganglion stimulation, fellowship training, neuromodulation, pain education, peripheral nerve stimulation, spinal cord stimulation
Abstract

The need to be competent in neuromodulation is and should be a prerequisite prior to completing a fellowship in interventional pain medicine. Unfortunately, many programs lack acceptable candidates for these advanced therapies, and fellows may not receive adequate exposure to neuromodulation procedures. The American Society of Pain and Neuroscience (ASPN) desires to create a consensus of experts to set a minimum standard of competence for neurostimulation procedures, including spinal cord stimulation (SCS), dorsal root ganglion stimulation (DRG-S), and peripheral nerve stimulation (PNS). The executive board of ASPN accepted nominations for colleagues with excellence in the subject matter of neuromodulation and physician education. This diverse group used peer-reviewed literature and, based on grading of evidence and expert opinion, developed critical consensus guides for training that all accredited fellowship programs should adopt. For each consensus point, transparency and recusal were used to eliminate bias, and an author was nominated for evidence grading oversight and bias control. Pain Education and Knowledge (PEAK) Consensus Guidelines for Neuromodulation sets a standard for neuromodulation training in pain fellowship training programs. The consensus panel has determined several recommendations to improve care in the United States for patients undergoing neuromodulation. As neuromodulation training in the United States has evolved dramatically, these therapies have become ubiquitous in pain medicine. Unfortunately, fellowship programs and the Accreditation Council for Graduate Medical Education (ACGME) pain program requirements have not progressed training to match the demands of modern advancements. PEAK sets a new standard for fellowship training and presents thirteen practice areas vital for physician competence in neuromodulation.

Published
2023

26. Reinterpreting ENSO's Role in Modulating Impactful Precipitation Events in California

Author

Kristen Guirguis, Benjamin Hatchett, Alexander Gershunov, Michael DeFlorio, Rachel Clemesha, W. Tyler Brandt, Kayden Haleakala, Christopher Castellano, Rosa Luna Niño, Alexander Tardy, Michael Anderson, and F. Martin Ralph

Subjects
ENSO teleconnections, California precipitation, seasonal predictability, internal atmospheric variability, weather regimes, Geophysics. Cosmic physics, QC801-809
Abstract

Abstract Water years (WY) 2017 and 2023 were anomalously wet for California, each alleviating multiyear drought. In both cases, this was unexpected given La Niña conditions, with most seasonal forecasts favoring drier‐than‐normal winters. We analyze over seven decades of precipitation and snow records along with mid‐tropospheric circulation to identify recurring weather patterns driving California precipitation and Sierra Nevada snowpack. Tropical forcing by ENSO causes subtle but important differences in these wet weather patterns, which largely drives the canonical seasonal ENSO‐precipitation relationship. However, the seasonal frequency of these weather patterns is not strongly modulated by ENSO and remains a primary source of uncertainty for seasonal forecasting. Seasonal frequency of ENSO‐independent weather patterns was a major cause of anomalous precipitation in WY2017, record‐setting snow in WY2023, and differences in precipitation outcome during recent El Niño winters 1983, 1998, and 2016. Improved understanding of recurrent atmospheric weather patterns could help to improve seasonal precipitation forecasts.

Published
2024
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27. A systematic assessment of the impact of rare canonical splice site variants on splicing using functional and in silico methods

Author

Rachel Y. Oh, Ali AlMail, David Cheerie, George Guirguis, Huayun Hou, Kyoko E. Yuki, Bushra Haque, Bhooma Thiruvahindrapuram, Christian R. Marshall, Roberto Mendoza-Londono, Adam Shlien, Lianna G. Kyriakopoulou, Susan Walker, James J. Dowling, Michael D. Wilson, and Gregory Costain

Subjects
transcriptomics, RNA sequencing, splicing, genetic testing, variant interpretation, genetic counseling, Genetics, QH426-470
Abstract

Summary: Canonical splice site variants (CSSVs) are often presumed to cause loss-of-function (LoF) and are assigned very strong evidence of pathogenicity (according to American College of Medical Genetics/Association for Molecular Pathology criterion PVS1). The exact nature and predictability of splicing effects of unselected rare CSSVs in blood-expressed genes are poorly understood. We identified 168 rare CSSVs in blood-expressed genes in 112 individuals using genome sequencing, and studied their impact on splicing using RNA sequencing (RNA-seq). There was no evidence of a frameshift, nor of reduced expression consistent with nonsense-mediated decay, for 25.6% of CSSVs: 17.9% had wildtype splicing only and normal junction depths, 3.6% resulted in cryptic splice site usage and in-frame insertions or deletions, 3.6% resulted in full exon skipping (in frame), and 0.6% resulted in full intron inclusion (in frame). Blind to these RNA-seq data, we attempted to predict the precise impact of CSSVs by applying in silico tools and the ClinGen Sequence Variant Interpretation Working Group 2018 guidelines for applying PVS1 criterion. The predicted impact on splicing using (1) SpliceAI, (2) MaxEntScan, and (3) AutoPVS1, an automatic classification tool for PVS1 interpretation of null variants that utilizes Ensembl Variant Effect Predictor and MaxEntScan, was concordant with RNA-seq analyses for 65%, 63%, and 61% of CSSVs, respectively. In summary, approximately one in four rare CSSVs did not show evidence for LoF based on analysis of RNA-seq data. Predictions from in silico methods were often discordant with findings from RNA-seq. More caution may be warranted in applying PVS1-level evidence to CSSVs in the absence of functional data.

Published
2024
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28. Rapid resolution of erythrodermic atopic dermatitis with upadacitinib: A case report

Author

Mysa Saad, Marina Guirguis, Camille Hamm, and Reetesh Bose

Subjects
Medicine (General), R5-920
Abstract

Atopic dermatitis is a chronic inflammatory skin disease that may progress to erythroderma in severe cases. Biologic agents such as dupilumab have recently become the mainstay of systemic treatment for moderate-to-severe cases, yet many patients remain refractory to therapy. Here, we present a case of erythrodermic atopic dermatitis, resistant to prednisone and dupilumab, with remarkably rapid achievement of remission following treatment with upadacitinib, an oral selective Janus kinase 1 inhibitor.

Published
2024
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29. CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection

Author

Guirguis, Karim, Hendawy, Ahmed, Eskandar, George, Abdelsamad, Mohamed, Kayser, Matthias, and Beyerer, Juergen

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Few-shot object detection (FSOD) seeks to detect novel categories with limited data by leveraging prior knowledge from abundant base data. Generalized few-shot object detection (G-FSOD) aims to tackle FSOD without forgetting previously seen base classes and, thus, accounts for a more realistic scenario, where both classes are encountered during test time. While current FSOD methods suffer from catastrophic forgetting, G-FSOD addresses this limitation yet exhibits a performance drop on novel tasks compared to the state-of-the-art FSOD. In this work, we propose a constraint-based finetuning approach (CFA) to alleviate catastrophic forgetting, while achieving competitive results on the novel task without increasing the model capacity. CFA adapts a continual learning method, namely Average Gradient Episodic Memory (A-GEM) to G-FSOD. Specifically, more constraints on the gradient search strategy are imposed from which a new gradient update rule is derived, allowing for better knowledge exchange between base and novel classes. To evaluate our method, we conduct extensive experiments on MS-COCO and PASCAL-VOC datasets. Our method outperforms current FSOD and G-FSOD approaches on the novel task with minor degeneration on the base task. Moreover, CFA is orthogonal to FSOD approaches and operates as a plug-and-play module without increasing the model capacity or inference time.

Published
2022

30. Few-Shot Object Detection in Unseen Domains

Author

Guirguis, Karim, Eskandar, George, Kayser, Matthias, Yang, Bin, and Beyerer, Juergen

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Few-shot object detection (FSOD) has thrived in recent years to learn novel object classes with limited data by transferring knowledge gained on abundant base classes. FSOD approaches commonly assume that both the scarcely provided examples of novel classes and test-time data belong to the same domain. However, this assumption does not hold in various industrial and robotics applications, where a model can learn novel classes from a source domain while inferring on classes from a target domain. In this work, we address the task of zero-shot domain adaptation, also known as domain generalization, for FSOD. Specifically, we assume that neither images nor labels of the novel classes in the target domain are available during training. Our approach for solving the domain gap is two-fold. First, we leverage a meta-training paradigm, where we learn the domain shift on the base classes, then transfer the domain knowledge to the novel classes. Second, we propose various data augmentations techniques on the few shots of novel classes to account for all possible domain-specific information. To constraint the network into encoding domain-agnostic class-specific representations only, a contrastive loss is proposed to maximize the mutual information between foreground proposals and class embeddings and reduce the network's bias to the background information from target domain. Our experiments on the T-LESS, PASCAL-VOC, and ExDark datasets show that the proposed approach succeeds in alleviating the domain gap considerably without utilizing labels or images of novel categories from the target domain.

Published
2022

31. An Unsupervised Domain Adaptive Approach for Multimodal 2D Object Detection in Adverse Weather Conditions

Author

Eskandar, George, Marsden, Robert A., Pandiyan, Pavithran, Döbler, Mario, Guirguis, Karim, and Yang, Bin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Integrating different representations from complementary sensing modalities is crucial for robust scene interpretation in autonomous driving. While deep learning architectures that fuse vision and range data for 2D object detection have thrived in recent years, the corresponding modalities can degrade in adverse weather or lighting conditions, ultimately leading to a drop in performance. Although domain adaptation methods attempt to bridge the domain gap between source and target domains, they do not readily extend to heterogeneous data distributions. In this work, we propose an unsupervised domain adaptation framework, which adapts a 2D object detector for RGB and lidar sensors to one or more target domains featuring adverse weather conditions. Our proposed approach consists of three components. First, a data augmentation scheme that simulates weather distortions is devised to add domain confusion and prevent overfitting on the source data. Second, to promote cross-domain foreground object alignment, we leverage the complementary features of multiple modalities through a multi-scale entropy-weighted domain discriminator. Finally, we use carefully designed pretext tasks to learn a more robust representation of the target domain data. Experiments performed on the DENSE dataset show that our method can substantially alleviate the domain gap under the single-target domain adaptation (STDA) setting and the less explored yet more general multi-target domain adaptation (MTDA) setting.

Published
2022

32. Play and Trauma in Young Children during a Pandemic

Author

Guirguis, Ruth V. and Longley, Jennifer M.

Abstract

Vygotsky (1978) describes play as having three main components, one being the ability for a child to create an imaginary situation, the second taking on and acting out roles, and the third, following a set of rules that were determined by the roles children took on during play during social or group settings. Hence, supporting much needed social skills and processes that foster a positive social development. The ambiguities of play, specifically the intricate functions between what play entails and the aligned developmental outcomes of play, makes defining play challenging. Research has revealed that children who are in isolated environments, with reduced physical contact among peers of their own age, tend to have lower levels of academic achievements, and are more susceptible to long term psychological stress as they get older (Ammermueller, 2012; Lacey, Kumari & Bartley, 2014). Specifically, the trauma of isolation affects both the social and cognitive domains of development among preschoolers. Isolation, also takes a toll on the type of play children can engage in. The lack of play during a pandemic can prevent children from feeling a sense of joy and familiarity. This article describes how play is not just a mechanism for supporting academic achievement in young children, but also a form of supporting emotional survival during a crisis.

Published
2021

34. Transformer-Based Deep Learning Strategies for Lithium-Ion Batteries SOX Estimation Using Regular and Inverted Embedding

Author

John Guirguis, Ahmed Abdulmaksoud, Mohanad Ismail, Phillip J. Kollmeyer, and Ryan Ahmed

Subjects
Convolutional neural networks, deep learning, electric vehicles, iTransformers, li-ion batteries, state estimation, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The accurate estimation of Li-ion battery (LIB) states such as State of Charge (SOC), State of Health (SOH), and State of Power (SOP) plays a pivotal role in the efficient operation of Electric Vehicles (EVs). These parameters can impact the battery’s health, driving range, and overall vehicle performance. Transformer-based artificial neural networks have shown impressive results in natural language processing (NLP) and estimation problems of many other domains. This paper presents an intensive study on the capabilities of various Transformer-based models in estimating the SOC and SOH of LIBs, the SOP is obtained based on the estimated SOC. This paper provides the following key original contributions: 1) the application of the Informer and Reformer variants of the Transformer model for the first time for SOH estimation of LIBs in EVs, 2) studying the effect of inverted embedding of iTransformers, a modified architecture of the transformers, on SOC and SOH estimation, inversion is performed on the Informer and Reformer as well; 3) applying a simple feature extraction method using partial discharge cycles for SOH estimation with Transformer-based models; 4) a new robust method is proposed for SOC estimation based on a 2-Encoder-Transformer with a one-dimensional convolutional neural network (1D-CNN) architecture; 5) the various architectures are trained, validated and tested on two real-world datasets comprising various driving scenarios and battery conditions. Comparative analysis with various deep learning architectures show impressive accuracy for estimating the SOC and SOH, leading to better SOP calculation.

Published
2024
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35. Deep Learning Algorithms for Cyber-Bulling Detection in Social Media Platforms

Author

Mohammed Hussein Obaida, Saleh Mesbah Elkaffas, and Shawkat Kamal Guirguis

Subjects
Cyberbullying, deep learning, LSTM, social networks, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

Social media platforms are among the most widely used means of communication. However, some individuals exploit these platforms for nefarious purposes, with “cyberbullying” being particularly prevalent. Cyberbullying, which involves using electronic means to harass or harm others, is especially common among young people. Consequently, this study aims to propose a model for detecting cyberbullying using a deep learning algorithm. Three datasets from Twitter, Instagram, and Facebook were utilized to predict instances of bullying using the Long Short-Term Memory (LSTM) method. The results obtained revealed the development of an effective model for detecting cyberbullying, addressing challenges faced by previous cyberbullying detection techniques. The model achieved accuracies of approximately 96.64%, 94.49%, and 91.26% for the Twitter, Instagram, and Facebook datasets, respectively.

Published
2024
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36. Accelerating process development for 3D printing of new metal alloys

Author

David Guirguis, Conrad Tucker, and Jack Beuth

Subjects
Science
Abstract

Abstract Addressing the uncertainty and variability in the quality of 3D printed metals can further the wide spread use of this technology. Process mapping for new alloys is crucial for determining optimal process parameters that consistently produce acceptable printing quality. Process mapping is typically performed by conventional methods and is used for the design of experiments and ex situ characterization of printed parts. On the other hand, in situ approaches are limited because their observable features are limited and they require complex high-cost setups to obtain temperature measurements to boost accuracy. Our method relaxes these limitations by incorporating the temporal features of molten metal dynamics during laser-metal interactions using video vision transformers and high-speed imaging. Our approach can be used in existing commercial machines and can provide in situ process maps for efficient defect and variability quantification. The generalizability of the approach is demonstrated by performing cross-dataset evaluations on alloys with different compositions and intrinsic thermofluid properties.

Published
2024
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37. HALS: A Height-Aware Lidar Super-Resolution Framework for Autonomous Driving

Author

Eskandar, George, Sudarsan, Sanjeev, Guirguis, Karim, Palaniswamy, Janaranjani, Somashekar, Bharath, and Yang, Bin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Lidar sensors are costly yet critical for understanding the 3D environment in autonomous driving. High-resolution sensors provide more details about the surroundings because they contain more vertical beams, but they come at a much higher cost, limiting their inclusion in autonomous vehicles. Upsampling lidar pointclouds is a promising approach to gain the benefits of high resolution while maintaining an affordable cost. Although there exist many pointcloud upsampling frameworks, a consistent comparison of these works against each other on the same dataset using unified metrics is still missing. In the first part of this paper, we propose to benchmark existing methods on the Kitti dataset. In the second part, we introduce a novel lidar upsampling model, HALS: Height-Aware Lidar Super-resolution. HALS exploits the observation that lidar scans exhibit a height-aware range distribution and adopts a generator architecture with multiple upsampling branches of different receptive fields. HALS regresses polar coordinates instead of spherical coordinates and uses a surface-normal loss. Extensive experiments show that HALS achieves state-of-the-art performance on 3 real-world lidar datasets.

Published
2022

38. A comparative analysis of using ensemble trees for botnet detection and classification in IoT

Author

Mohamed Saied, Shawkat Guirguis, and Magda Madbouly

Subjects
Medicine, Science
Abstract

Abstract Enhancing IoT security is a corner stone for building trust in its technology and driving its growth. Limited resources and diversified nature of IoT devices make them vulnerable to attacks. Botnet attacks compromise the IoT systems and can pose significant security challenges. Numerous investigations have utilized machine learning and deep learning techniques to identify botnet attacks in IoT. However, achieving high detection accuracy with reasonable computational requirements is still a challenging research considering the particularity of IoT. This paper aims to analytically study the performance of the tree based machine learning in detecting botnet attacks for IoT ecosystems. Through an empirical study performed on a public botnet dataset of IoT environment, basic decision tree algorithm in addition to ensemble learning of different bagging and boosting algorithms are compared. The comparison covers two perspectives: IoT botnet detection capability and computational performance. Results demonstrated that the significant potential for the tree based ML algorithms in detecting network intrusions in IoT environments. The RF algorithm achieved the best performance for multi-class classification with accuracy rate of 0.999991. It achieved also the highest results in all other measures.

Published
2023
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39. Planning Indicators: Non-Auto Mobility

Author

Mead-Newton, Miranda, Guirguis, Paul, Murshed, Antara, Phan, Karen, Schwartz, Ellen, and Silverstein, Ben

Subjects
transit, travel behavior, transit ridership, bicycle planning, Los Angeles, transit ridership, program evaluation
Abstract

Cities and transit agencies, including the Los Angeles County Metropolitan Transportation Authority (LA Metro), are interested in shifting travel from solo driving to more sustainable modes, including transit, biking, walking, and other non-auto modes. As LA Metro and other agencies collect more data about their system and its users, there are many opportunities to apply that data to productively improve the system and drive policy. In this project, our primary goal was to identify a set of specific indicators Metro and other agencies could use to measure their success in the creation and provision of a high-quality non-auto mobility system for both existing users reliant on these modes as well as future users who shift from driving.

Published
2022

40. Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text

Author

Shaaban, Mai A., Hassan, Yasser F., and Guirguis, Shawkat K.

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

The increase in people's use of mobile messaging services has led to the spread of social engineering attacks like phishing, considering that spam text is one of the main factors in the dissemination of phishing attacks to steal sensitive data such as credit cards and passwords. In addition, rumors and incorrect medical information regarding the COVID-19 pandemic are widely shared on social media leading to people's fear and confusion. Thus, filtering spam content is vital to reduce risks and threats. Previous studies relied on machine learning and deep learning approaches for spam classification, but these approaches have two limitations. Machine learning models require manual feature engineering, whereas deep neural networks require a high computational cost. This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically. The proposed model utilizes convolutional and pooling layers for feature extraction along with base classifiers such as random forests and extremely randomized trees for classifying texts into spam or legitimate ones. Moreover, the model employs ensemble learning procedures like boosting and bagging. As a result, the model achieved high precision, recall, f1-score and accuracy of 98.38\%.

Published
2021
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42. Access to and quality of elective care: a prospective cohort study using hernia surgery as a tracer condition in 83 countries

Author

Dönmez, A Eylül, Goswami, Aakansha Giri, Raheja, Aashna, Bhadani, Aayush, El Kady, Abd Elrahman Safwat, Alniemi, Abdalaziz, Awad, Abdalkarim, Aladl, Abdalla, Younis, Abdalla, Alwali, Abdallah, Khalil, Abdallah, Mansour, Abdallah, Elkhouly, Abdallah Mohamed, Rashad Temerik, Abdallah, Hasan, Abdallatef, Chied, Abdelhafid, Abdraba, Abdelhamid, Hrora, Abdelmalek, Abdelshafi, Abdelrahman, Alwali, Abdelrahman, Maraqa, Abdelrahman, Ghaffar, Abdul, Chekfa, Abdul Jabar, Al Balushi, Abdul Majeed, Quddus, Abdul, Hammadieh, Abdul Rahman, Muntaka, Abdul-Jalilu Mohammed, Asekun, Abdulazeez, Alshahrani, Abdulaziz, Jowharji, Abdulaziz, Alshukre, Abdulbaset, Khair Etareig, Abdulhafid, Abubakar Abdulkarim, Abdulhafiz, Ashiru, Abdulhafiz, Alailesh, Abdulhamid, Al-Mallah, Abdullah, Baydoun, Abdullah, Chaudhry, Abdullah, Askin, Abdullah Emre, Almutairi, Abdulmjeed, Belkhair, Abdulmunem, Abdllah, Abdulrahman, Alhammali, Abdulrahman, Basalim, Abdulrahman, Abouelnagah, Abdulrahman Galal Mohamed Mostafa, Ghazal, Abdulrahman, Mohammed, Abdulrahman, Qasem, Abdulrahman, Anadani, Abdulrazak, Jimoh, Abdulrazaq, Lawal, Abdulrazzaq Oluwagbemiga, Khaity, Abdulrhman, Lawal, Abdulwahab, Elzoubi, Abdurrahim Esseid, Sheshe, Abdurrahman Abba, Cetişli, Abdurrahman Furkan, Oyedele, Abeeb, Aljahdali, Abeer, Aboelnasr, Abeer Gamal Elsyed, Demessie, Abel, Bhargava, Abhilasha, Sonkar, Abhinav Arun, Bavaharan, Abhirame, Khan, Abid, Campbell, Abigail, Oyelekan, Abimbola Adeola Akintunde, Okunlola, Abiodun Idowu, Ojewuyi, Abiodun, Ekerin, Abiola, Saleh, Abobakr, Khairy, Abouelatta, Muthunayagam, Abraham Benjamin, Botha, Abraham, Teshome Sahilemariam, Abraham, Nawawi, Abrar, Muhammad, Abubakar Bala, Dahiru, Abubakar, Yahaya, Abubakar, Abdelmalik, Abubaker, Anuar, Abul Akmal Arif, Ousseini, Adakal, Leon -Del- Angel, Adalberto, Frankel, Adam, Gyedu, Adam, Lim, Adam, Lukáč, Adam, Peckham-Cooper, Adam, Sabbah, Adam, Varga, Adam, Sanou, Adama, Issaka, Adamu, Ningi, Adamu, Yeshitila, Addis, Yingess, Addisu, Olugbami, Adebayo, Moses, Damola-Okesiji, Adebola, Lawal, Adedayo, Ojo, Adedoyin, Hashmi, Adeel, Fakoya, Adegbolahan, Ishola, Adegoke, Abiodun, Adekunle, Ajayi, Adekunle, Mouffokes, Adel, Bonelli, Adele, Adekoya, Adeleke, Akeem Aderogba, Adeleke, Özcan, Adem, Ayoade, Ademidun, Adeyeye, Ademola, Agbaje, Ademola, Talabi, Ademola, Ademuyiwa, Adesoji, Adewumiya, Adesoji, Ademuyiwa, Adesoji O, Aderounmu, Adewale, Adisa, Adewale, Oke, Adewale, Ragab, Adham, Alzadjali, Adil, Baksi, Aditya, Jayapalan, Aditya, Md Yunos, Adnan, Pisanu, Adolfo, Salazar, Adolfo, Salinas Barragan, Adrian Alberto, Fox, Adrian, Stollberg, Adrian, Franco, Adriana, Neves, Adriana, Vricheva, Adriana, Hodonou, Adrien Montcho, Aghayeva, Afag, Liaquat, Affifa, Alsultan, Afnan, Morad, Afnan, Kalidis, Afroditi Angella, Gkouniaroudi, Agathi, Karakosta, Agathi, Lovi, Agbenya, Yevide, Agossou Barthélémy, Duro, Agustin, Rodriguez Gonzalez, Agustin, Tariq, Ahbala, Abaidalla, Ahmad, Abuhammad, Ahmad, Abuhantash, Ahmad, Mansour, Ahmad Adel, Ahmad Zaidi, Ahmad Al Hafeez, Alahmad Alismael, Ahmad, Alayed, Ahmad, Alfarwan, Ahmad, Alhaj, Ahmad, Ali, Ahmad, Alroobi, Ahmad, Alsheikh, Ahmad, Alusef, Ahmad, Alzu'Bi, Ahmad, Kayali, Ahmad Amir, Bouhuwaish, Ahmad, Doghaim, Ahmad, El-Sherbiney, Ahmad, Ghazal, Ahmad, Ramly, Ahmad Nasir, Raza, Ahmad, Qureshi, Ahmad Uzair, Arnaout, Ahmad Yamen, Abdelhamid, Ahmed, Abuthaher, Ahmed, Adel, Ahmed, Ahmayda, Ahmed, Al-Mouahhed, Ahmed, Alagha, Ahmed, Albalawi, Ahmed, Albhaisi, Ahmed, Alrifaee, Ahmed, Azam, Ahmed, Barakat, Ahmed, Al-Hajjaj, Ahmed Basim Abed, Dawoud, Ahmed, El Shamarka, Ahmed, El-Bastwesy, Ahmed, Elghrieb, Ahmed, Elkhalifa, Ahmed, Elmosalamy, Ahmed, Elshazli Mahmoud, Ahmed, Montaser, Ahmed G., Gbeadese, Ahmed, Haty, Ahmed, K. Ali, Ahmed, Awad, Ahmed K., Kedwany, Ahmed, Khalleefah, Ahmed, Abbas, Ahmed M., Allam, Ahmed M., Maher, Ahmed, Elsayed, Ahmed Mohamed, Mohamed Ibrahim Mohamed, Ahmed, Mostafa, Ahmed, Nabil, Ahmed, Nada, Ahmed, Ragheb, Ahmed, Abdelrahman, Ahmed Saber Mohamed, Sabry, Ahmed, Sakr, Ahmed, Elkhodary, Ahmed Salah, Shaheen, Ahmed, Ammar, Ahmed Siddique, Hameed, Ahmer, Akmercan, Ahmet, Aslan, Ahmet, Barcin, Ahmet, Ciftci, Ahmet Burak, Bozkurt, Ahmet Çağlar, Sarı, Ahmet Can, Yaşar, Ahmet Can, Develioğlu, Ahmet, Öztürk, Ahmet Mücteba, Sanli, Ahmet Necati, Rencuzogullari, Ahmet, Yildiz, Ahmet, Kaan, Ahmet Zahit, Toi, Ahouefa Chourika Sakinath, Aljamoudi, Aida, Cheung, Aiden Chak Kei, Gkoutoula, Aikaterini, Kalogeropoulou, Aikaterini, Sarafi, Aikaterini, Abo Al Shamat, Aiman, Sanon, Aimé Serge Titianma, Sanon, Aimée Florence, Hiller, Aimée, Costas-Chavarri, Ainhoa, Lombardero, Ainhoa, Fraile, Ainoa, Bojazyah, Aisha, Kamal, Aisha, Gill, Aisling, Hada, Aïssatou, Gulla, Aiste, Mačiulaitytė, Aistė, 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Daniel, Montwedi, Daniel, Muñoz, Daniel, Patterson, Daniel, Reim, Daniel, Agbley, Daniel Yao Dodzie, Patiño García, Daniela Cristina, Di Pietrantonio, Daniela, Pais, Daniela, Rosado, Daniela, Tavares, Daniela, Alberti, Daniele, Fusario, Daniele, Massaro, Daniele, Paglione, Daniele, Pironi, Daniele, Kurochka, Daniil, Centonze, Danilo Corrado, Hackner, Danilo, Herrera, Danilo, Amangaliyev, Daniyar, Silva-Cano, Danny Wladimir, Chaudhry, Daoud, Palmisano, Dario, Ribero, Dario, Vukosav, Dario, Misca, Darius, Lowen, Darren, Rattray, Darren, Walia, Darshanjit Singh, Maan, Darvesh, Muyenzi, Daryle, Verdi, Daunia, Bardsley, David, Reyes Rodríguez, David Estuardo, Fra Corral, David, Garcia López De Goicoechea, David, Gavit, David, Gómez, David, Idowu, David, Konkin, David, Labadah, David Kplorla Kekeli, Liu, David, Merlini, David, Mitchell, David, Moro-Valdezate, David, Osifo, David, Proud, David, Ridder, David, Gobatti, Davide, Muschitiello, Davide, Pertile, Davide, Zulian, Davide, Mengiste, Dawit, Mikami, Dean, Mohanty, Debajyoti, Chatterjee, Debarghya, Komolafe, Deborah Olaitan, Scotto Di Carlo, Deborah, Wright, Deborah, Rahme, Deema, Talreja, Deep Parkash, Jain, Deepak, Rajput, Deepak, Singh, Deepak, Anbu, Deepika, Ibrahim, Dena, Phom, Denchu, Hili, Denise, Osei, Dennis Broni, Daary, Dennis, Kyeremeh, Dennis, Shen, Dennis, Uymaz, Derya Salim, Cianflocca, Desiree, Ashitey, Desmond, Evripidou, Despo, Papageorgouli, Despoina, Kaushal, Devesh, Abeysirigunawardana, Dewmi, Dugar, Dharmendra, Mallik, Dhiraj, Ghosh, Dhruva, Houmran, Diaa, Abdulrahman, Diala, Pantoja Pachajoa, Diana Alejandra, Cardona Gomez, Diana Catalina, Fenner, Diana, Gonzalez, Diana Samantha, Siriwardena, Diana, Garcés Palacios, Diana Sofía, Stoian, Diana, Zuikyte, Diana, Sosa Méndez, Dianne Alitzá, Wickramarathna, Dickson, Mutter, Didier, Palma Portillo, Diego Andrés, Chavez Fernandez, Diego, Córdova García, Diego, Enjuto, Diego, Giulitti, Diego, Sasia, Diego, Visconti, Diego, Yiğit, Dilara, George, Dilip, Rabaia, Dima, Georgiev, Dimitar, Hadzhiev, Dimitar, Alexandrou, Dimitrios, Balalis, Dimitrios, Giakoustidis, Dimitrios, Manatakis, Dimitrios K., Korkolis, Dimitrios, Lytras, Dimitrios, Massaras, Dimitrios, Moris, Dimitrios, Politis, Dimitrios, Sampanis, Dimitrios, Schizas, Dimitrios, Tsiardas, Dimitrios, Vardakostas, Dimitrios, Kotsaris, Dimitris, Khattab, Dina Hesham, M. Awad, Dina, Alrokh, Dina Sami, Schippers, Dina, Waleed, Dina, Salinovic, Dinko, Gonçalves Múrias Gomes, Diogo Miguel, Silva, Diogo, Vaz Acosta, Diogo, Morton, Dion, Panagopoulos, Dionisios, Kelgiorgi, Dionysia, Dellaportas, Dionysios, Prevezanos, Dionysios, Joshi, Dipti, Ozal, Direm, Yigit, Direnc, Chimkaomasiri, Divine, Jabbar, Diya, Nepogodiev, Dmitri, Belekar, Dnyanesh, Abdalaziz, Doaa, Abunemer, Doaa, Attia, Doaa, Elkafrawy, Doaa, Dimitrov, Dobromir, Pignatelli, Domenica, Merlicco, Domenico, Soriero, Domenico, Zerbo, Domenico, Atouguia, Domingas, Fairbrass, Dominic, T. Enti, Donald, Venskutonis, Donatas, Rukavina, Dora, Caruso, Dorena, Herappe, Dorihela, Lee, Dorinda Norbetha, Ofosuhene, Doris, Hajela, Apoorva, Badwan, Basem, Kulkarni, Datta, Dhawan, Deep, Dhillon, Jeevanjit, Mehmood, Manzer, Mahmood, Namrah, Nikam, Narendra, Ramchandani, Radhakrishna, Thind, Ravinder Singh, Riazhussain, D Hafiz, Maqsood, Saliha, Alalawi, Yousef, Tyagi, Harshdeep, Kshirsagar, Shivani, Ahmed, Hisham, Juan Carlos, Luna Cydejko, Kone, Dramane, Omar, Duaa, Alves, Duarte Gil, Subasinghe, Duminda, Ommi, Durgesh, Joshiba, Durshney, Chakarov, Dzhevdet, Arkoh, Ebenezer Ameyaw, Frimpong-Manso, Ebenezer, Ada, Ece, Lincango, Eddy P., Lozada Hernandez, Edgard Efren, Hodžić, Edin, Di Marco, Edith, Baccalini, Edoardo Andrea, Baldini, Edoardo, González Marín, Eduardo, Gutierrez, Eduardo, Sepúlveda, Eduardo, Dainius, Edvinas, Zhang, Edward, Yenli, Edwin Mwintiereh Ta-Ang, Antoniou, Efstathios, Kapasakis, Efstathios, Baili, Efstratia, Poulios, Efthymios, Kubiliute, Egle, Osaze, Ehioghae, Afeikhena, Ehis, Elshennawy, Eiad, Athanasopoulou, Eirini, Synekidou, Eirini, Ekaladze, Eka, Tampaki, Ekaterini Christina, Adam, Elaf, Adel Hamdoun Aziz, Elaf, Yousuf, Elamin, Gialamas, Eleftherios, Spartalis, Eleftherios, Bedzhanyan, Elen, Toma, Elena Adelina, Daukšaitė, Elena, Desiato, Elena, Dvivedi, Elena, Hadzhieva, Elena, Martí Cuñat, Elena, Montalbán Martínez, Elena, Montanari, Elena, Signaroli, Elenasofia, Efremidou, Eleni, Gavriil, Eleni, Karakeke, Eleni, Mavrodimitraki, Eleni, Papamattheou, Eleni, Jose, Elenza, Cianci, Eleonora, Ciccioli, Eleonora, Daleva, Eleonora, Locci, Eleonora, Lori, Eleonora, Silanos, Eleonora, Ayala, Elí, Bankart, Eliana, Brolese, Eliane, Efstathiou, Elias, Salloum, Elias, Colak, Elif, Terzi, Elif Didem, Ametefe, Elikem, Cagigal Ortega, Elima Pilar, Donnarumma, Elio, Mina, Elisa, Reitano, Elisa, Wallner, Elisabeth, Moggia, Elisabetta, Betz, Elise, Kontis, Elissaios, Anestiadou, Elissavet, Gimson, Eliza, Lun, Elizabeth, Fraser, Elizabeth, Li, Elizabeth, Torres De Anda, Elizabeth Magaly, Murphy, Elizabeth Mary Ann, O'Connell, Elizabeth, Redondo, Elizabeth, Lun, Elizabeth Weng Yan, Francis, Ella, Haiden, Elmar, Hamed, Elmonzir, Alkhalifa, Elmustafa, Dexter, Eloise, Daketsey, Elorm, Setsoafia, Elorm, Mohamed, Elsagad, Mohammed, Elsamoual, Hegab, Elsayed, Ay, Elvin, Adinolfi, Elvira, Mbanzabugabo, Elvis, Chin, Elynn, Ahmed, Emad Ali, Younes, Eman, Basile, Emanuela, Gessa, Emanuela, Pontecorvi, Emanuele, Romairone, Emanuele, Hatangimana, Eméry, Ndizeye, Emile, Ciofic, Emilia, Dijan, Emilio, López-Negrete Cueto, Emilio, Baker, Emily, Reid, Emily, Schmidt, Emily, Alić, Emir, Bičakčić, Emir, Agbowada, Emma, Colton, Emma, Downing, Emma, Haege, Emma, O'Neill, Emma, Roussos, Emmanouel, Nachelleh, Emmanuel Aadereyir, Adebunmi, Emmanuel, Obiesie, Emmanuel Ahuizechukwu, Akakpo, Emmanuel Bismark, Konlan, Emmanuel Dammigu Francis, Nwangwu, Emmanuel Ifeanyi, Kafui Ayodeji, Emmanuel, Ofori, Emmanuel Owusu, Spanoudakis, Emmanuel, Abate, Emmanuele, Balik, Emre, Bozdağ, Emre, Bozkurt, Emre, Karabulut, Emre Cagri, Ozoran, Emre, Tunçcan, Emre, Tuzuner, Emre, Halilović, Emsad, Alabed, Enaam, Zidan, Enas, Hodžić, Enes, Aybar, Engin, Yildirim, Engin Okan, Wong, Enoch, Yhoshu, Enono, Bisogno, Enrica, Andolfi, Enrico, Ferri, Enrico, Potenza, Enrico, Spalice, Enrico, Volpin, Enrico, González, Enrique, Downes, Eoin, Gallo, Erdachew, Kamer, Erdinc, Naidoo, Eressa, Erginöz, Ergin, Bara, Eric, Irungu, Eric, García-Villegas, Eric Yusif, Mazzotta, Erica, Monati, Erica, Chan, Erick, Gómez Mejía, Erika, Dylja, Eris, Somuncu, Erkan, Yavuz, Erkan, Begunić, Ernes, Margelis, Ernest, Osei, Ernest Owusu, Saitoglou, Ertzan, Agastra, Ervis, Mazumdar, Eshan, Soyer Güldoğan, Esra, Abu Siam, Esra'A, Abdelbaset, Esraa, Abdu, Esraa, Adel Mahmod Sultan, Esraa, Nubi Mohamed, Esraa Gamal, Abuobaida Banaga Hag El Tayeb, Essam Eldien, Ali, Essraa, Page-Taylor, Estelle, Marra, Ester, Garcia Santos, Esther Pilar, Elsheikh, Ethar, Unal, Ethem, Tan, Eugene, De Leo, Eugenio, Borges, Eva, Gáspár, Éva, Ruiz-Daum, Eva, Apostolopoulos, Evaggelos, Samara, Evangelia, Barkolias, Evangelos, Fradelos, Evangelos, Hopping, Eve, Romo Ascencio, Evelia Veronica, Sosa Ferreira, Ever David, Shestakov, Evgenii, Tyurina, Evgenya, Hristova, Evguenia, Efthymiou, Evripidis, Tokidis, Evripidis, Harrison, Ewen, Dontoh, Ewurama Akroma, Mansour, Eyad, Yeshialem, Eyosiyas, Dabbagh, Ezeddin, Amer, Ezzeldin, Manasci, Fabiana, Gomes, Fabio, Medas, Fabio, d'Acapito, Fabrizio, Vassallo, Fabrizio, Haddad, Fadi, Elhabishi, Fadwa, Akhtar, Fahad, Tauheed, Fahad, Al Abbood, Fahd, Gareb, Fahed, Ghazali, Fairuz, Alnazawi, Faisal, Ondago, Faith, Rasoaherinomenjanahary, Fanjandrainy, Wiese, Fanny, González Ponce, Fanny Yesenia, Georgiades, Fanourios, Ali, Farah, Galley, Fareeda, Huda, Farhanul, Amir, Farhat Naaz, Ashraf, Fariha, Alfarsi, Faris, Ayasra, Faris, Ali, Farman, Mahmoud, Faten, Aliskander, Fater, Tahir Lwdie, Fathea, Buyuker, Fatih, Aguilar Del Castillo, Fátima, Mohammed Daoud, Fatima Ahmed, Alowjaly, Fatima, Oni, Fatima, Rauf, Fatima, Elkhafeefi, Fatimah, Hourri, Fatine, Monib, Fatma A., Alshreef, Fatma, Benghalbon, Fatma, Ntirenganya, Faustin, Ashraf, Fauziyah, Chahrour, Fayez, Ahmad, Fazal, Parray, Fazlul Qadir, Philips, Febi, Campus, Federica, Di Marco, Federica, Ferraina, Federica, Frongia, Federica, Maffeis, Federica, Natali, Federica, Pederiva, Federica, Scolari, Federica, Tofani, Federica, Marin, Federico Alberto Niccolò, Biolchini, Federico, Cappellacci, Federico, Corronca, Federico, Mazzola, Federico, Suldrup, Federico, Nyarko, Felicia, Casas J, Felipe, Zapata, Felipe, Aigner, Felix, Alakaloko, Felix, Amoako, Felix, Camacho, Felix, Hammett, Felix, Kuubetersob B. N, Felix, Alahmad, Feras, Alnimri, Feras, Haddad, Feras, Gool, Ferhana, Castro, Fernanda, Diaz, Fernanda, Lucero, Fernanda, Vovola, Fernanda, Mahnic, Fernando, Mercado Sanchez, Fernando, Prieto La Noire, Fernando, Soggiu, Fiammetta, Tsige, Fikreab, Brzeszczyński, Filip, Rudisch, Filip, Valente Costa Pinto, Filipa, Ramalho De Almeida, Filipe, Banchini, Filippo, Palomino Escalante, Fiorella, Fadzlullah, Fitri, Terefe, Fitsum, Sulo, Fjorenta, Ciccarone, Flavia, Tirelli, Flavio, Krämer, Florian, Grama, Florin, Spanos, Foivos, Solimene, Folco, Akinwande, Foluso, Osman, Forzia, Stefou, Foteini, Kyramargios, Fotios, Stavratis, Fotios, Alassani, Fousseni, Ascari, Francesca, Cammelli, Francesca, D'Agostino, Francesca, Tropeano, Francesca Paola, Peters, Francesca, Ré, Francesca, Brucchi, Francesco, Casti, Francesco, Coratti, Francesco, Ferrara, Francesco, Floris, Francesco, Izzo, Francesco, Maffei, Francesco, Palmieri, Francesco, Pata, Francesco, Porcelli, Francesco, Selvaggi, Francesco, Velluti, Francesco, Mulita, Francesk, Bello, Francis, Caiquo, Francis, Dossou, Francis, Eshun, Francis Junior, Opoku Twene, Francis, Tierenye, Francis, Quayson, Francis William, Romo Rosales, Francisco David, Ibañez Ortiz, Francisco, León Frutos, Francisco Javier, Redondo Calvo, Francisco Javier, Silva Rivera, Francisco Javier, Barbosa Camacho, Francisco José, Bujalance Cabrera, Francisco Manuel, Ruescas, Francisco, Irakiza, Franck Fanon, Corvatta, Franco, Pascual, Franco Raúl, Roviello, Franco, Roodt, Francois, Gyamfi, Frank Enoch, Olaniru, Frank, Owusu, Frank, Bader, Franz G., Anzinger, Franz-Xaver, Agbodo, Fred David Anku, Ris, Frederic, Agbemafoh, Frederick Mawuli, Afonso, Frederico, Nazareth, Frederico, Berrevoet, Frederik, Hounde, Fréjus, Demiral, Furkan, Hassooni, Furqan Ridha Hassooni, Kafa, Gabi, Cárdenas Rivera, Gabriel, Ntwari, Gabriel, Szydlo Shein, Gabriel, Ambriz González, Gabriela, Nita, Gabriela Elisa, Bellio, Gabriele, Moitzi, Gabriele, Urdang, Gabriella, Marom, Gad, Gallo, Gaetano, Luglio, Gaetano, Poillucci, Gaetano, Singh, Gagandeep, Saletta, Gaia, Abouelnagah, Galal, Yarovenko, Galina, Kazobinka, Gallina, Ahmed, Gamal Mutwakil Gamal, Rahman, Ganiyu Adebisi, Smith, Garett, Pillai, Gargi, Saxena, Garima, Gbessi, Gaspard, Nkunguzi, Gaston, Jalal, Gaurav, Chillarge, Gauri, Singh, Gautam, Mevognon, Gedeon, Getachew, Gedion, George, Gejoe, De La Peña González, Gema, Martínez Izquierdo, Gema, Grande, Gennaro, Birikorang, Geoffrey, Weber, Georg F., Singer, Georg, Ansong, George, Bashour, George, Brown, George Darko, Hneino, George, Kavalieratos, George, Kenchadze, George, Nagy, George, Pappas Gogos, George, Skroubis, George, Waiyaki, George, Cox, Georgia, Zeringa, Georgia, De Wee, Georgin, Hart, Georgina, Bekakos, Georgios, Fragulidis, Georgios, Kapogiannatos, Georgios, Karakaidos, Georgios, Kokkinos, Georgios, Kostoulas, Georgios, Koukoulis, Georgios, Peros, Georgios, Rados, Georgios, Tsoulfas, Georgios, Verras, Georgios-Ioannis, Berger, Geraint, Adoro, Gerard Ayuka, Chamoso Mialdea, Gerardo, Palmieri, Gerardo, Procaccini, Gerardo, Steenkamp, Gerhard, Viscido, Germán Rodrigo, Dajti, Gerti, Alemayehu, Getachew, Seyfu, Gezachew, Birqeeq, Ghada, Alsheikh, Ghadeer, Alrayyes, Ghaith, El Hunjul, Ghalib Nashaat, Zaza, Ghayath, Matroud, Ghazlah, Alhadwah, Ghena, Maltinti, Gherardo, Awais, Ghina, Shamsi, Ghina, Calini, Giacomo, Carganico, Giacomo, Zanus, Giacomo, Canu, Gian Luigi, Mendiola, Gian, Ibero Casadiego, Giana Pamela, Cocorullo, Gianfranco, Baronio, Gianluca, Pellino, Gianluca, Zanni, Gianluca, Poto, Gianmario Edoardo, Gravante, Gianpiero, Aduroja, Gideon, Almogy, Gidon, Bond-Smith, Giles, Natchagande, Gilles, Bechara, Gio, Brachini, Gioia, Riccioli, Giordana, Ercolani, Giorgio, Farris, Giorgio, Giraudo, Giorgio, Mammolo, Giovanna, Pavone, Giovanna, Sgarzini, Giovanna, Aprea, Giovanni, Fonsi, Giovanni Battista, Boroni, Giovanni, De Angeli, Giovanni, De Palma, Giovanni Domenico, Emiliani, Giovanni, Grava, Giovanni, Guercio, Giovanni, De Carlo, Giulia, Del Corpo, Giulia, Duranti, Giulia, Galatioto, Giulia, Grassi, Giulia, Graziano, Giulia, Marini, Giulia Maria Francesca, Munzi, Giulia, Tarantino, Giulia, Borda-Luque, Giuliano, Pellicano', Giuseppe Antonino, Canonico, Giuseppe, Carollo, Giuseppe, Ietto, Giuseppe, Palomba, Giuseppe, Pascarella, Giuseppe, Salamone, Giuseppe, Sica, Giuseppe, Sorrentino, Giuseppe, Orlando, Giuseppina, Pignata, Giusto, Pisarevi, Givi, Akcakoca, Gizem, Owusu, Godfred, Yohanna Abrak, Godwin, Kurtoglu, Gokalp Kagan, Kurtoglugk, Gokalp Kagan, Alıcı, Gökhan, Kurtoglu, Goktug Mert, Delgado Hernandez, Gonzalo, Augustin, Goran, Bhat, Gowhar Aziz, Sundaram Venkatesan, Gowtham, Chang, Grace, Eke, Grace, Lee, Grace, Massarra, Grace, Travaglini, Grazia, Scialandrone, Grazisa, Stavrou, Gregor A., Di Franco, Gregorio, Charalambous, Gregory, Christodoulidis, Gregory, Kouraklis, Gregory, Goodwin, Gregory Richard, Devidze, Grigol, Fialho, Guilherme, Santos, Guilherme, Cabezudo, Guillermo, García-Santos, Guillermo, Pou, Guillermo, Romero Reyna, Guillermo, Yanowsky-Reyes, Guillermo, Ozyuksel, Gul, Karadeniz Cakmak, Guldeniz, Kucuk, Gultekin Ozan, Bulbuloglu, Guney Cem, Sarp, Guray, Kaur, Gurleen, Nibogora, Guy Darcy, Hamdan, Gymar, Ogundipe, Habeeb, Yunes, Habib, Ayesh, Hadeel, Al Atassi, Hadi, Jalilehvand, Hadis, Masalma, Hafez, Jafarkhan, Hafis, Shahid, Hafiz Huzaifa, Younus, Hafsa, Al-Derume, Hail, Shames, Haitam, Mimouni, Haithem Ahmed, Aldare, Hajar, Odah Bashi, Hajar Omar, Abusnina, Hajer, Altounsi, Hajir, Qayum, Hajra, Baysal, Hakan, Bolukbasi, Hakan, Yigitbas, Hakan, Alkhatib, Hala, Afzal, Haleema, Mftah, Halima, Oyinlola, Halimah, Tümer, Haluk, Dolo, Hamadoun, Braham, Hamdi, Ozsahin, Hamdi, Abu-Arish, Hamdoon, Al-Aamri, Hamed, Fadlalmola, Hammad Ali, Abu Obead, Hamza, Al-Fahel, Hamza, Al-Zoubi, Hamza, Asaad, Hamza, Ejaz, Hamza, Jaber, Hamza, Bhatti, Hamza Waqar, Mohammadi Sardoo, Hamzeh, Farhat, Hana, Idheiraj, Hanadi, Zwaraa, Hanady, Al Sohabi, Hanan, Basharat, Hanan, Said, Hanan, Al-Abdallat, Haneen, Foad, Haneen, Daaboul, Hanen, Al Qadhi, Hani, Elhallaq, Hani, Alhusaini, Hanin, Lidbetter, Hannah, Reilly, Hannah, Moya- Ambriz, Hannia Betzabeth, Lederhuber, Hans, Fehrer, Hans-Jörg, Xiao, Haotian, Iswariah, Harish, Kaur, Harmeena, Cheema, Harnoor Kaur, Foda, Haroun, Chauhan, Harpreet, Kaur, Harpreet, Sagar, Harry, Unwin, Harry, Kaur, Harsharan, Panga, Harshavardhan, Gupta, Harshit, Prabhakar, Harshit Kumar, Anand, Harshitha, Pahwa, Harvinder Singh, Abbadi, Hasan, Amoudi, Hasan, Zaini, Hasan Hadi Zaini, Škiljo, Hasan, Miyasika, Hasangi, Altabbaa, Hashem, Haj Freej, Hasn, Abdelazim, Hassan, Habiba, Hassan, Kamal, Hassan, Sabir, Hassan, Zaigham, Hassan, Pehlivan, Hatice, Telci, Hatice, Abuobaida, Hayat, Tetley, Heather, Taher, Heba, Aguado López, Héctor, Askarpour, Hedyeh, Sherif Farouk Ahmed Hassan, Heidi, Devesa, Helena, Arbogast, Helmut, Nuñez Del Barrio, Henar, Abiyere, Henry, Delacave, Henry, Rice, Henry E., Pleass, Henry, Bradly, Hera, 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Rugambwa, Jean Paul, Jordaan, Jean- Jacques, Razafindrahita, Jeannie Ba, Do, Jee Eun, Senavirathna, Jeewantha, Kalyanapu, Jeffrey A, Chan, Jeffrey, Ofori, Jeffrey, Otote, Jeffrey, Akiely, Jehad, Cassiano Neves, Jelena, Gillingham, Jemima, Herrera Batres, Jenifer Olga María, Ezeh, Jennifer, Okei, Jennifer, Vu, Jennifer, Guevara, Jenny, Siu, Jenny, Rolinger, Jens, Skaff, Jeolle, Uys, Jernelle, Makama, Jerry Godfrey, Barklimore, Jessica, Dei-Asamoa, Jessica, Jahjah, Jessica, Jones, Jessica, Ng, Jessica, Ricardo, Jéssica, Schembri Higgans, Jessica, You, Jessica, Annan, Jessie, Aguilar, Jesus Antonio, Pizarro Lozano, Jesús, Lim, Jia Hui, Zhu, Jiang, Wang, Jianxue, Abbasy, Jibran, Ugwu, Jideofor, Rathod, Jignesh, Albalushi, Jihan, Najajra, Jinan, Fu, Jing Hui, Tefay, Joan Sia Tan, Domingues, Joana Cristina, Simoes, Joana FF, Figueiredo, Joana, Fontaínhas, Joana, Frazão, Joana, Marques Antunes, Joana, Simoes, Joana, Marx, Joanna Naemi, Dias-Ferreira, João, Fernandes, João, Mendes, João, Pinho, João, Ribeiro, João, Vaz, João, Dominguez, Joaquina, Abutu John, Job, Chen, Joe Jun Qiao, Enaholo, Joel, Alaji, Joelle, Miller, Johanna, Binder, Johannes, Daleku, John, Katogiritis, John, Khoury, John, Anyorigiya, John Noah, Reilly, John, Abebrese, John Tabiri, Ahale, Johnny Lordson, Tabora-Zepeda, Johnny, Barrutia Leonardo, Jon, Demma, Jonathan Abraham, Hong, Jonathan, Chejfec-Ciociano, Jonathan Matias, Oppong, Jonathan, Quansah, Jonathan, Sutcliffe, Jonathan, Taiwo, Jones, Nyamekye - Baidoo, Joojo, Prat-Ortells, Jordi, Dongmo, Jordy, Kleeff, Jorg, Jimenez Flores, Jorge Alberto, Pellecer Cano, Jorge Bernardo, Chóliz, Jorge, Giubi Bobeda, Jorge Eduardo, Escartin, Jorge, Febré, Jorge, Gass, Jörn-Markus, Calvache, Jose A, Guzman Barba, Jose Aldo, Gutiérrez Gómez, José Alonso, Calvache, Jose Andres, Acevedo Parrales, José Danilo, Orozco Navarro, José Esteban, Viñas, José Federico, Gomez, José, Goncalves-Nobre, Jose Guilherme, Valenzuela, José Ignacio, Garcia Galocha, Jose Luis, 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Marcella, Pisano, Marcello, Doniquian, Marcelo Alejandro, Martínez Bareiro, Marcelo Nicolás, Pagani, Marcelo, Scortechini, Marcelo, Carvalho, Márcia, Sánchez Audelo, Marco Antonio, Brizzolari, Marco, Ceresoli, Marco, De Prizio, Marco, Amisano, Marco Francesco, Gramellini, Marco, Marcianò, Marco, Mariani, Marco, Milone, Marco, Montagna, Marco, Piccino, Marco, Santos, Marco, Dyer, Marcus, Kang, Marcus, Mahadi, Mardhiana, Zawadzki, Marek, Yinusa, Margaret, Bernardo, Margarida, Nunes Luís, Margarida, Gambelli, Margherita, Flint, Margot, Aggelidou, Maria, Bequis, Maria Agustina, Sotiriou, Maria Aikaterini, Ñañez, Maria Alejandra, Tahir, Maria Ambreen, Gascón Domínguez, María Ángeles, Asimakidou, Maria, Mourato, Maria Beatriz, Blanco, Maria, Christou, Maria, Coronas Soucheiron, Maria, Pignanelli, Maria Costanza, Kalaydjian, María Cristina, Martí-Ejarque, Maria del Mar, Olmedo Reinoso, María Del Mar, Cebollero, María Del Pilar, Derattani, Maria, Gama Caldeira, Maria Do Carmo, Drogouti, Maria, Gonzalez-Gonzalez, Maria Elena, Muñoz Fernández, María Elena, Muriel, María Emilia, Duran, María Eugenia, Zarate Casas, María Fernanda, Florou, Maria, Piacentini, Maria Gaia, Seixo, Maria Inês, Bellini, Maria Irene, Peña Soria, Maria Jesús, Martínez, María José, Medina, Maria José, Rueda Medécigo, María José, Karakeke, Maria, Kausar, Maria, Kyriazi, Maria, Marqueta De Salas, Maria, Modolo, Maria Marta, Mateu, Maria, Mavri, Maria, El Ghoul Miliotou, Maria Melina, Moreno Gijon, Maria, Papadoliopoulou, Maria, Proclamà, Maria Pia, Picciochi, Maria, Reis, Maria, Rennis, Maria, D'Anna, Maria Rosa, Ruffoli, Maria, Ruiz Soriano, María, Sánchez Ramirez, Maria, Sánchez-Rubio, María, Schüler, Maria, Sotiropoulou, Maria, Barrio Renteria, Maria Teresa, Tsopozidi, Maria, Vasconcelos, Maria, Facchino, Maria Vittoria, Wikar, Maria, Aguilera-Arevalo, Maria-Lorena, Abdu, Mariam, Asarbakhsh, Mariam, Elmiesiry, Mariam, Ashong, Marian Esi, Gomes, Mariana, Capuano, Marianna, Lill, Marianne, Nieto Galvan, 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Nathan, Michael, Maclean, Michael, Nortey, Michael, Pellen, Michael, Pham, Michael, Pitiakoudis, Michael, Singh, Michael Prodip, Spartalis, Michael, Van Der Colf, Michael, Wilson, Michael, Billis, Michail, Karanikas, Michail, Papamichail, Michail, Vailas, Michail, Kisielewski, Michał, Kusiński, Michał, Nycz, Michał, Święch, Michał, Fagbayimu, Micheal, Adamina, Michel, Fiogbe, Michel, Prudhomme, Michel, Angelucci, Michela, Campanelli, Michela, Cricrì, Michele, Grande, Michele, Manigrasso, Michele, Scopelliti, Michele, George, Michelle, Mozel, Michelle, Galasyuk, Mick, Chevallay, Mickael, Giuliano, Micol, Dobón Rascón, Miguel Ángel, Fernandez Romero, Miguel Angel, García Ureña, Miguel Ángel, Zaragoza Mendieta, Miguel Ángel, Bejarano Serrano, Miguel, Brito, Miguel, Calderon, Miguel, Jurado Román, Miguel, Palas, Miguel, Li Valencia, Miguel Roberto, Sebastián-Mendoza, Miguel-Julián, Muresan, Mihai-Stefan, Slavchev, Mihail, Agapov, Mikhail, Milentijević, Milica, Catterall, Millie, 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Abosedra, Mohammed, Sarker, Mohammed Abu Kawsar, Abudabbous, Mohammed, Abuwarda, Mohammed, Elgak, Mohammed Ahmed, Al Hinai, Mohammed, Al Zebda, Mohammed, Al-Shehari, Mohammed, Alharthi, Mohammed, Ali, Mohammed, Ismael Alamin, Mohammed Ali, Alqadasi, Mohammed, Alshanwani, Mohammed, Alshehri, Mohammed, Asaad, Mohammed, Bashir, Mohammed, Bohlala, Mohammed, Meghaizerou, Mohammed El Amine, Albanna, Mohammed Emad Hashem, Ghunaim, Mohammed, Goga, Mohammed, Hajhamad, Mohammed, Hamada Takrouney, Mohammed, Hammoda, Mohammed, Hamoud Almahly, Mohammed, Muragi, Mohammed, Mohammed, Mohammed Mustafa Hassan, Obaid, Mohammed, Omar, Mohammed, Said, Mohammed, Salah, Mohammed, Saleh Khatab, Mohammed, Aliyu, Mohammed Salele, Shakhshir, Mohammed, Sheriff, Mohammed, Tageldin, Mohammed, Abdelfattah, Mohanad, Daher, Mohanad, Lotfy, Mohanad, Alqedrh, Mohannad, Nasani, Mohannad, Mohd Shah, Mohd Shahrulsalam, Madany, Mohie El-Din Mostafa, Mobolaji-Ojibara, Mojisola, Nkogatse, Moleseng, Alazabi, Mona, Gupta, 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Sedra, Bugren, Seeraj, Saudí-Moro, Sef, Ashini, Sehar, Aboseif, Seif, Abdul Rahman, Seif-Aldin, Karim, Seiver, Öztürk, Selen, Akuffo, Selma, Awad, Selmy, Srishankar, Selvaratnam, Tobome, Sèmèvo Romaric, Malek, Sepand, Quartarone, Serafino, Grimaldi, Serena, Guarriello, Serena, Kalenga, Serge, Katorkin, Sergei, Trujillo Ponce, Sergio Adrian, Villeda, Sergio Alejandro, Intini, Sergio Giuseppe, Vázquez-Sánchez, Sergio Jiram, Salido, Sergio, Valeri, Sergio, Yilmaz, Serhan, Meriç, Serhat, Bensalem, Serien, Sucu, Serkan, Yılmaz, Serkan, Uludağ, Server Sezgin, Torres Rodríguez, Servio Tulio, Ooi, Sett, Luciano, Severino, Houndji, Sèwou Francklin Albert, Agordjor, Seyram, Ward, Sezar, Samardali, Shaden, Altoume, Shadi, Mahfoud, Shafik, Razaq, Shahad, Khan, Shahbano, Husanov, Shahboz, Ahmed, Shaheer, Islam, Shahnoor, Waqar, Shahzad Hussain, Kumaran, Shairashree, Magrhi, Shamsa, Zhang, Shane, Kashyap, Shankarsai, Karandikar, Sharad, Sundaramurthy, Sharada, Chowdhury, Sharfuddin, Jayasekara, Sharitha, Laura, Sharon, Shanker, Shashwat, Ababneh, Shatha, Gilbert, Shaun, Kenworthy, Shaunna, Ng, Shawn Hong Ming, Mackenzie, Shawn, Salindera, Shehnarz, Khattak, Shehrbano, Loke, Sheih Nee, Manzoor, Sheikh Viqar, Alwardi, Sheikha, Allam, Shereen, Tawfik, Sherifa, Francis, Shikha, Cai, Shirley, Dindyal, Shiva, Pandya, Shivangi, Chopra, Shreya, Ingley, Shreya, Kulkarni, Shrirang, Kosna, Shuba, Nayak, Shubhalaxmi, Ahuja, Shubham, Hariharan, Shubhang, Karuppusamy Krishnasamy, Shyam, Khare, Siddhant, Handa, Siddhartha, Amin Omar Alsiddig, Siddig, Alqurashi, Siddiga, Al-Tahayneh, Sief-Addeen, Sibiya, Silindokuhle, Perretta, Silvana, Ibarra Camargo, Silvia Alejandra, Montisci, Silvia, Mungo, Silvia, Puddu, Silvia, Severi, Silvia, Agudelo Mendoza, Silvia Valentina, Makkai-Popa, Silviu Tiberiu, Boyes, Simon, Monkhouse, Simon, Stock, simon, Alberti, Simona, Meneghini, Simona, Giudici, Simone, Guadagni, Simone, Birolo, Simone Lorenzo, Novello, Simone, Oliveira, Simone, Gumede, Simphiwe, Dhole, Simran, Budhwani, Simran Saleem, Ramjit, Sinead, Yumurtacilar, Sinem, Mahendra, Sirigi Naga, Abeykoon, Sithira, Moh Pauzi, Siti Farhan, Rajendra, Sittampalam, Sundararajan, Siva, Gananadha, Sivakumar, Kamarajah, Sivesh K, Marawu, Siyasanga, Basu, Sk, Hassan, Sleman, Reddy, Snigdha Mettu, Anaya Sanchez, Sofia, Gaspar Reis, Sofia, Gomez, Sofía, Gamba, Sofia Lucia, Pimentel Morais, Sofia, Ashry, Soha, Abdullateef, Sohaila, Bandyopadhyay, Soham, Samadi, Soheil, Pathan, Sohilkhan R., Lakew, Solomon, Basu, Somprakas, Ohri, Sonali, Alonso Marcos, Sonia, Amoza Pais, Sonia, Fortuna Martins, Sónia, Gill, Sonia, Matthiess, Sonia, Sabbatini, Sonia, Soto Schütte, Sonia, Engel, Sophie, Eam, Sothseyhakmonique, Botaitis, Sotirios, Lawani, Souliath, Jamil, Soumaya, Kondpan, Sourav, Oum, Sovannarith, Govender, Sovishnee, Christodoulou, Spyridon, Davakis, Spyridon, Vernadakis, Spyridon, Gkogkos, Spyros, Gobishangar, Sreekanthan, Suresh, Sreelakshmi, Vembar, Sridhar, M, Srikanth, Abdelrasoul Elnour Ismail, Srraa, Zourntou, Stamatina-Emmanouela, Nwabuoku, Stanley Emeka, Ideh, Stanley, Charitonos, Stavria, Liapis, Stavros Chrysovalantis, Faber, Stefan, Piccioni, Stefania Angela, Romano, Stefania, Silvestre, Stefania, Jarmusch, Stefanie, Soelling, Stefanie, Cardelli, Stefano, Chaifouroosh Mamagany, Stefano, Corso, Stefano, Basso, Stefano Maria Massimiliano, Megna, Stefano, Bitsianis, Stefanos, Tsatsos, Stefanos, Morales, Steffanía, Gerdes, Stephan, Göller, Stephanie, Hanna, Stephanie, Tabiri, Stephen, Agana, Stephen, Theivendran, Stephen Arthur, Kache, Stephen, Knight, Stephen R, Smith, Stephen, Stonelake, Stephen, Wrenn, Stephen, Gopaul, Steven, Vederaki, Styliani, Kapiris, Stylianos, Kykalos, Stylianos, Hlaing, Su Su, Chia, Su Yin Denise, Abu Khousa, Suad, M. Udwan, Suad, Raul, Subrat, Chase, Suchita, Nair, Sudharrshan, Joseph, Sudhir, Singh, Sudhir Kumar, Singh, Sudhir, Timmalah, Suhaylah, Alsuwiyah, Suhir, Thalgaspitiya, Sujeewa Priyantha Bandara, Sellahewa, Sujeewa, Neogi, Sujoy, Burger, Sule, Polat, Süleyman, Alkadi, Sulman, Asiri, Sultan, Otho, Sumaira, Mewa Kinoo, Suman, Ebrahim, Sumayyah, Tontus, Sumeyya, Midya, Sumit, Thuraisamy Sarma, Sundaramoorthy Iyer, Payyanur Thotan, Sundeep, Kumar Venkatappa, Sunil, Techapongsatorn, Suphakarn, Kaur, Supreet, Jp, Supritha, Tan, Suqi, Djote, Surafel Mulatu, Surendran, Suraj, Singh, Surendra, Chilaka, Suresh, Lee, Susan, Henriques, Susana, Giridaran, Suseendirarajah, Achugatla, Sushma, Kumar, Suvam, Brincat, Svetlana, Deshpande, Swati, Batista, Sylvia, Akharaekpanya, Tachporn, Moges, Tadesse Girma, Türkmen, Taha Berkay, Alhaj Hasan, Taima, Alshawabkeh, Taima', Majed, Taima', Lawal, Taiwo Akeem, Ibiyeye, Taiye Taibat, Rashid, Tajamul, Košir Božič, Tajda, Mohammed, Tajudeen, Khewater, Talal, Abelman, Talia, Al Barhi, Tamadher, Mogne, Tamiris, Salazar-Lorenzana, Tania Abril, Akter, Tania, Gómez Sanz, Tania, Motiwala, Tanmay, Valizadeh Elizeh, Tanya, Almahdi, Tanzeel, Albushary, Tanzel Alrahman, Abdulrahman, Taofeeq, Finlay, Tara, Chouari, Tarak, Awad, Tarek, Sabra, Tarek, M. Abubasheer, Tareq, Alhusban, Tariq, Yılmaz, Tarık Emre, Aloqaili, Tasneem, Aldirawi, Tayef, Karahasanoğlu, Tayfun, Gülşen, Taygun, Jemal, Tebarek, Nadeem, Tehmina, Singh, Tejinder Paul, Alinger, Temsula, Longkumer, Temsutoshi, Lo, Terence, Gimenez Maurel, Teresa, Uprak, Tevfik Kıvılcım, Petropoulou, Thalia, Rawther, Thampi, Techapongsatorn, Thawin, Manickam, Thendral, Padioti, Theodora, Kozonis, Theodoros, Sidiropoulos, Theodoros, Tsirlis, Theodoros, Theodosopoulos, Theodosios, Hessou, Thierry Kinnongni, Uiyapat, Thitapon, Agyen, Thomas, Harris, Thomas, Hugh, Thomas J, Jorgensen, Thomas W., Theivendrampillai, Thrshegan, Pham, Thuy Vy, Branco, Tiago, Nagwamutse, Tijjani Nasiru, Tony V, Tina, Devabalan Koil, Titus, Pilia, Tiziana, Abdulmola, Tmador Mahmed Ali, Oyeyemi, Tobiloba, Kebede, Tolera, Ibarra-Hurtado, Tomás Ramón, Reichelt, Tomas, Gach, Tomasz, Stefura, Tomasz, Wojewoda, Tomasz, Soric, Tomislav, Risteski, Toni, Tridip, Tridip Dutta Baruah, Yotsov, Tsanko, Doma Bhutia, Tshering, Girma, Tsion, Kwan, Tsun Yu, Ivanov, Tsvetomir, Iahmo, TT, Ashkar, Tuleen, Mabogoane, Tumi, Olobatoke, Tunde, Sholadoye, Tunde T., Mishra, Tushar Subhadarshan, Parker, Tyla, Ezomike, Uchechukwu, Dilibe, Uchenna, Kumbhar, Uday, Una, Uduak-Abasi, Grossi, Ugo, Ezidiegwu, Ugochukwu, Edith, Ugwuoke, Daspal, Ujjwal, Kalu, Ukoha Agwu, Dietz, Ulrich, Ronellenfitsch, Ulrich, Wirth, Ulrich, Abdullah, Umar, Rivolta, Umberto, Jayarajah, Umesh, Kanyan Kassim, Umu-Hera, Govindan, Unnikrishnan, Fernandes, Urânia, Akram, Usman, Mohammed Bello, Usman, Omara, Usra, Naidoo, Uvarasen, Mahendravarman, Vaishnaivy, Negoita, Valentina Madalina, Mariottini, Valentina, Murzi, Valentina, Zucchini, Valentina, Agbakwuru, Valentine, Armenta Tapia, Valeria Estefania, Gentilino, Valerio, Borgogni, Vanessa, Ng, Vanessa, Wolfschluckner, Vanessa, Silvestri, Vania, Žufić, Vanja, Gunasaegaram, Varman, Kamalathevan, Vasan, Pillay, Vasanthan, Neykov, Vasil, Kalles, Vasileios, Mousafeiris, Vasileios, Papadopoulos, Vasileios, Tsaousis, Vasileios, Tselepidis, Vasileios, Vougas, Vasileios, Georgilaki, Vasiliki, Nikolaou, Vasiliki, Themelidi, Vasiliki, Manchev, Vassil, Ioannou, Veatriki, Jha, Vedant Shekhar, Dragisic, Vedran, Sutharshan, Vengadasalam, Alonso, Veronica, Brocco, Veronica, Ongil Rodríguez, Verónica, Schirinzi, Veronica, Budyakova, Veronika, Naunova, Vesna, Panduro-Correa, Vicky, Ruiz López, Víctor Andrés, Bill, Victor, Dassah, Victor, Gonzabay, Víctor Eduardo, Etwire, Victor, Modekwe, Victor, Serna-Alarcon, Victor, De Donato, Victor Ugo, Duque Mallén, Victoria, Liu, Victoria, Sharma, Vidushi, Kumar, Vijaya, Muralidharan, Vijayaragavan, Kakotkin, Viktor, Enemuo, Vincent, Kudoh, Vincent, Osoka, Vincent, Siepaal, Vincent, Granata, Vincenza, Dinuzzi, Vincenza Paola, Lizzi, Vincenzo, Ricchiuti, Vincenzo, Mosca, Vinicio, Kanna D, Vinoth, Nair, Vipin Venugopal, Ledda, Virginia, Michael, Vishal, Singh, Vishavdeep, Govindasamy, Vishendran, Kollengode, Viswanathan V, Giordano, Vittoria, Morinelli, Vittoria, Lin, Vivian, Cuk, Vladica, Cijan, Vladimir, Çalik, Volkan, Bakshi, Vrushank, Ten, Vyacheslav, Ratheesh, Vysakh, Alsado, Wael, Mayo, Wafik, Yang, Wah, Rehman, Wajeeh Ur, Abbas, Walaa, Al-Khyatt, Waleed, Mabood, Waleed, Santucci, Walter, Yew, Wan Ting, Tahir, Warda, De Silva, Warusha Dhammika Dulantha, Wijenayake, Wasantha, Almadhoun, Waseem J.N., Alhroub, Wasef, Ebrahim, Wesam, Jabłoński, Wiktor, Krawczyk, Wiktor, Bhatti, William, Lai, William Kwan Ho, Petrushnko, Wilson, Afedo, Wisdom, Kasetsermwiriya, Wisit, Serednicki, Wojciech, Wysocki, Wojciech, Zerefa, Wondwossen Amtataw, Vagena, Xanthoula, Tarrado, Xavier, Papazarkadas, Xenofon, Po, Xiang Yuen, Ouadi, Yacine, Myla, Yacob, Haido, Yacoub, Hasan, Yafea, Karataş, Yağmur, Carpio Colmenares, Yahaira, Galvañ Félix, Yaiza, Balogun, Yakub Lekan, Maktabi, Yaman, Nerabani, Yaman, Fulgence, Yamuremye, De Silva, Yannick, Asar, Yara, Samer Morsy, Yara, Ghaleb, Yasameen Saif Saeed Mohammed, Vashishth, Yash, M S, Yashas, Manickchund, Yashoda, Sakaray, Yashwant, Kara, Yasin, Tosun, Yasin, Zwain, Yasir, Farag, Yasmeen, Jedidi, Yasmine, Seada, Yasmine, Aldebasi, Yasser, Ashour, Yasser, Badr, Yassin, Sarpong, Yaw, Alawneh, Yazan, Aung, Yee Lay, Orabi, Yehia, Lam, Yick Ho, Şen, Yigit Kaan, Koyluoglu, Yilmaz Onat, Huang, Yining, Mintz, Yoav, Perez, Yocarli, Chamara, Yohan, Melkamu, Yohannes, Maestre González, Yolanda, Dean, Yomna, Metaferia, Yonas Yilma, Malki, Yosra, Mohamed, Younis, A. Omar, Yousef, Alhammoud, Yousef, Allaoua, Yousef, Tanas, Yousef, El Okazy, Youssef, Kerolous, Youssef, Mohamed, Youssef, Al-Junaidi, Youssuf, Lakdawala, Yousuf, Lu, Yuann, Asano, Yukio, Altinel, Yuksel, Aktimur, Yunus Emre, Fishman, Yuri, Kudryavcev, Yurii, Tandoğan, Yusuf Iskender, Razafimandimby, Yves Michaël, Adofo-Asamoah, Yvonne, Şenol, Zafer, Odeh, Zahra, Benamrouche, Zahra Rayane, Alkhaier, Zahraa, Al-Sheikh Ali, Zaid, Zaher, Zain Aldin, Alnajjar, Zaina, Al-Balushi, Zainab, Alkhuzaie, Zainab, Khraim, Zakaria, Toutounji, Zakaria, Djama, Zakarya, Farraj, Zamil, Shah, Zamir Ahmad, Khan, Zara, Khan, Zarnigar, Abo Alarous, Zayn Alabden, Asaad, Zaynab, Demetrashvili, Zaza, Lorenc, Zbigniew, Brekalo, Zdrinko, Hannouneh, Zein Alabdin, Robertson, Zelda, Bady, Zeyad, Durna, Zeynep, Wong, Zhen Yu, Fozo, Zien Alabdin, Klib, Zienab, Chin, Ziling, Tamou, Zime Farid, Johnson, Zina, Shabello, Ziyad, Orzeszko, Zofia, Aljalabi, Zohal, Al-Azher El-Hamel, Zohour, Karampotaki, Zoi Rafaila, Valera Sanchez, Zoraida, Matkovic, Zoran, Azhar, Zubair, Qureshi, Zubaria, Correa López, Zulema Margarita, Balciscueta, Zutoia, Musilová, Zuzana, Katusic, Zvonimir, Osama, Zyad, and Dauksa, Zygimantas

Published
2024
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49. Prediction of pathologic complete response to neoadjuvant systemic therapy in triple negative breast cancer using deep learning on multiparametric MRI

Author

Zhou, Zijian, Adrada, Beatriz E., Candelaria, Rosalind P., Elshafeey, Nabil A., Boge, Medine, Mohamed, Rania M., Pashapoor, Sanaz, Sun, Jia, Xu, Zhan, Panthi, Bikash, Son, Jong Bum, Guirguis, Mary S., Patel, Miral M., Whitman, Gary J., Moseley, Tanya W., Scoggins, Marion E., White, Jason B., Litton, Jennifer K., Valero, Vicente, Hunt, Kelly K., Tripathy, Debu, Yang, Wei, Wei, Peng, Yam, Clinton, Pagel, Mark D., Rauch, Gaiane M., and Ma, Jingfei

Published
2023
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50. Machine learning for real-time detection of local heat accumulation in metal additive manufacturing

Author

David Guirguis, Conrad Tucker, and Jack Beuth

Subjects
Powder bed fusion, Anomalies detection, Heat accumulation, Thermography, Additive manufacturing, IR imaging, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

Metal additive manufacturing is associated with thermal cycles of high rates of heating, melting, cooling, and solidification. Some areas within the build experience thermal cycles that depend on the paths of the energy source. Additionally, geometrical features, such as thin walls and overhangs, can lead to heat accumulation, potentially affecting the microstructure, fatigue life, and induced residual stresses that may lead to dimensional distortion and cracking. The identification of significant heat accumulation can be used for part quality monitoring to inform the design process, enhance the quality of printed parts, and optimize the process parameters. This study aims to efficiently identify heat accumulation with affordable in-situ infrared imaging for further characterization and mitigation to enhance the quality of printed parts. A computational framework employing machine learning is developed to identify zones of local heat accumulation in real time. The effectiveness of this approach is demonstrated by experiments conducted on a build with a wide variety of geometrical features. In addition, characterization and detailed analyses of detected local heat accumulation zones are provided.

Published
2024
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