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52. Object Recognition and Identification Using ESM Data

Author

Taghavi, E., Song, D., Tharmarasa, R., Kirubarajan, T., Boury-Brisset, Anne-Claire, and Balaji, Bhashyam

Subjects
Statistics - Applications, Computer Science - Computer Vision and Pattern Recognition
Abstract

Recognition and identification of unknown targets is a crucial task in surveillance and security systems. Electronic Support Measures (ESM) are one of the most effective sensors for identification, especially for maritime and air--to--ground applications. In typical surveillance systems multiple ESM sensors are usually deployed along with kinematic sensors like radar. Different ESM sensors may produce different types of reports ready to be sent to the fusion center. The focus of this paper is to develop a new architecture for target recognition and identification when non--homogeneous ESM and possibly kinematic reports are received at the fusion center. The new fusion architecture is evaluated using simulations to show the benefit of utilizing different ESM reports such as attributes and signal level ESM data.

Published
2016

53. Multisensor--Multitarget Bearing--Only Sensor Registration

Author

Taghavi, Ehsan, Tharmarasa, R., Kirubarajan, T., and McDonald, Mike

Subjects
Statistics - Methodology, Computer Science - Systems and Control
Abstract

Bearing--only estimation is one of the fundamental and challenging problems in target tracking. As in the case of radar tracking, the presence of offset or position biases can exacerbate the challenges in bearing--only estimation. Modeling various sensor biases is not a trivial task and not much has been done in the literature specifically for bearing--only tracking. This paper addresses the modeling of offset biases in bearing--only sensors and the ensuing multitarget tracking with bias compensation. Bias estimation is handled at the fusion node to which individual sensors report their local tracks in the form of associated measurement reports (AMR) or angle-only tracks. The modeling is based on a multisensor approach that can effectively handle a time--varying number of targets in the surveillance region. The proposed algorithm leads to a maximum likelihood bias estimator. The corresponding Cram\'er--Rao Lower Bound to quantify the theoretical accuracy that can be achieved by the proposed method or any other algorithm is also derived. Finally, simulation results on different distributed tracking scenarios are presented to demonstrate the capabilities of the proposed approach. In order to show that the proposed method can work even with false alarms and missed detections, simulation results on a centralized tracking scenario where the local sensors send all their measurements (not AMRs or local tracks) are also presented.

Published
2016

54. A Practical Bias Estimation Algorithm for Multisensor--Multitarget Tracking

Author

Taghavi, Ehsan, Tharmarasa, R., Kirubarajan, T., Bar-Shalom, Yaakov, and McDonald, Mike

Subjects
Statistics - Methodology
Abstract

Bias estimation or sensor registration is an essential step in ensuring the accuracy of global tracks in multisensor-multitarget tracking. Most previously proposed algorithms for bias estimation rely on local measurements in centralized systems or tracks in distributed systems, along with additional information like covariances, filter gains or targets of opportunity. In addition, it is generally assumed that such data are made available to the fusion center at every sampling time. In practical distributed multisensor tracking systems, where each platform sends local tracks to the fusion center, only state estimates and, perhaps, their covariances are sent to the fusion center at non-consecutive sampling instants or scans. That is, not all the information required for exact bias estimation at the fusion center is available in practical distributed tracking systems. In this paper, a new algorithm that is capable of accurately estimating the biases even in the absence of filter gain information from local platforms is proposed for distributed tracking systems with intermittent track transmission. Through the calculation of the Posterior Cram\'er--Rao lower bound and various simulation results, it is shown that the performance of the new algorithm, which uses the tracklet idea and does not require track transmission at every sampling time or exchange of filter gains, can approach the performance of the exact bias estimation algorithm that requires local filter gains.

Published
2016
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61. An Assignment Method for Multiple Extended Target Tracking With Azimuth Ambiguity Based on Pseudo Measurement Set

Author

Chen, Ming, Tharmarasa, Ratnasingham, Kirubarajan, Thia, and Chomal, Sunil

Abstract

Autonomous vehicle technology is rapidly becoming the driving force in the automobile industry. As such, the interest in high-resolution radio detection and ranging (radar) for autonomous vehicle applications is increasing due to its affordability and high angular resolution. However, for Advanced Driver Assistance Systems (ADAS), the challenge of azimuth ambiguity caused by a large physical distance between radar antennas is prevalent. This causes false measurements in a direction different from the target’s true angle due to grating lobes. This challenge increases when extended targets are considered. This paper proposes a Pseudo-3D Assignment (P3DA) method based on a Pseudo Measurement Set (PMS) to resolve azimuth ambiguity in multiple extended target tracking. The proposed method can resolve mono (single) and split (duplicated) azimuth ambiguities common in extended target tracking. The proposed solution uses Lagrangian Relaxation based on a Flexible Search (LR-FS) algorithm to solve the P3DA-PMS problem efficiently. The performance of the proposed algorithm in a typical traffic scenario simulated in Unreal Engine 4, with an ego vehicle mounted with both 2D (unambiguous) and 3D (ambiguous) radars, is evaluated. Simulation and experiment results suggest that the proposed P3DA-PMS-based tracking algorithm can perform better than conventional methods.

Published
2024
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73. Barriers and facilitators for cervical cancer screening among adolescents and young people: a systematic review

Author

Abirami Kirubarajan, Shannon Leung, Xinglin Li, Matthew Yau, and Mara Sobel

Subjects
Cervical cancer, Young people, Adolescent, Screening, Pap smear, HPV, Gynecology and obstetrics, RG1-991, Public aspects of medicine, RA1-1270
Abstract

Abstract Background Though cervical cancer is one of the leading causes of cancer-related death globally, its incidence is nearly entirely preventable. Young people have been an international priority for screening as this population has historically been under-screened. However, in both high-income and low-income countries, young people have not been screened appropriately according to country-specific guidelines. The aim of this systematic review was to systematically characterize the existing literature on barriers and facilitators for cervical cancer screening (CCS) among adolescents and young people globally. Methods We conducted a systematic review following PRISMA guidelines of three key databases: Medline-OVID, EMBASE, and CINAHL. Supplementary searches were done through ClinicialTrials.Gov and Scopus. Databases were examined from 1946 until the date of our literature searches on March 12th 2020. We only examined original, peer-reviewed literature. Articles were excluded if they did not specifically discuss CCS, were not specific to individuals under the age of 35, or did not report outcomes or evaluation. All screening, extraction, and synthesis was completed in duplicate with two independent reviewers. Outcomes were summarized descriptively. Risk of bias for individual studies was graded using an adapted rating scale based on the Risk of Bias Instrument for Cross-Sectional Surveys of Attitudes and Practices. Results Of the 2177 original database citations, we included 36 studies that met inclusion criteria. The 36 studies included a total of 14,362 participants, and around half (17/36, 47.2%) of studies specifically targeted students. The majority of studies (31/36, 86.1%) discussed barriers and facilitators to Pap testing specifically, while one study analyzed self-sampling (1/36, 2.8%), one study targeted HPV DNA testing (1/36, 2.8%), and the remainder (4/36, 11.1%) were not specified. Our systematic review found that there are three large categories of barriers for young people: lack of knowledge/awareness, negative perceptions of the test, and systemic barriers to testing. Facilitators included stronger relationships with healthcare providers, social norms, support from family, and self-efficacy. Conclusion There are unique barriers and facilitators that affect CCS rates in adolescents and young people. Health systems and healthcare providers worldwide should address the challenges for this unique population.

Published
2021
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96. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.

Author

Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ambrose Slone, Ameet Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Madotto, Andrea Santilli, Andreas Stuhlmüller, Andrew M. Dai, Andrew La, Andrew K. Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakas, B. Ryan Roberts, Bao Sheng Loe, Barret Zoph, Bartlomiej Bojanowski, Batuhan özyurt, Behnam Hedayatnia, Behnam Neyshabur, Benjamin Inden, Benno Stein 0001, Berk Ekmekci, Bill Yuchen Lin, Blake Howald, Bryan Orinion, Cameron Diao, Cameron Dour, Catherine Stinson, Cedrick Argueta, Cèsar Ferri Ramírez, Chandan Singh, Charles Rathkopf, Chenlin Meng, Chitta Baral, Chiyu Wu, Chris Callison-Burch, Chris Waites, Christian Voigt, Christopher D. Manning, Christopher Potts, Cindy Ramirez, Clara E. Rivera, Clemencia Siro, Colin Raffel, Courtney Ashcraft, Cristina Garbacea, Damien Sileo, Dan Garrette, Dan Hendrycks, Dan Kilman, Dan Roth, Daniel Freeman, Daniel Khashabi, Daniel Levy, Daniel Moseguí González, Danielle Perszyk, Danny Hernandez, Danqi Chen 0001, Daphne Ippolito, Dar Gilboa, David Dohan, David Drakard, David Jurgens, Debajyoti Datta, Deep Ganguli, Denis Emelin, Denis Kleyko, Deniz Yuret, Derek Chen, Derek Tam, Dieuwke Hupkes, Diganta Misra, Dilyar Buzan, Dimitri Coelho Mollo, Diyi Yang, Dong-Ho Lee, Dylan Schrader, Ekaterina Shutova, Ekin Dogus Cubuk, Elad Segal, Eleanor Hagerman, Elizabeth Barnes, Elizabeth Donoway, Ellie Pavlick, Emanuele Rodolà, Emma Lam, Eric Chu, Eric Tang, Erkut Erdem, Ernie Chang, Ethan A. Chi, Ethan Dyer, Ethan J. Jerzak, Ethan Kim, Eunice Engefu Manyasi, Evgenii Zheltonozhskii, Fanyue Xia, Fatemeh Siar, Fernando Martínez-Plumed, Francesca Happé, François Chollet, Frieda Rong, Gaurav Mishra, Genta Indra Winata, Gerard de Melo, Germán Kruszewski, Giambattista Parascandolo, Giorgio Mariani, Gloria Wang, Gonzalo Jaimovitch-López, Gregor Betz, Guy Gur-Ari, Hana Galijasevic, Hannah Kim 0010, Hannah Rashkin, Hannaneh Hajishirzi, Harsh Mehta, Hayden Bogar, Henry Shevlin, Hinrich Schütze, Hiromu Yakura, Hongming Zhang 0009, Hugh Mee Wong, Ian Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, Jackson Kernion, Jacob Hilton, Jaehoon Lee 0001, Jaime Fernández Fisac, James B. Simon, James Koppel, James Zheng, James Zou 0001, Jan Kocon, Jana Thompson, Janelle Wingfield, Jared Kaplan, Jarema Radom, Jascha Sohl-Dickstein, Jason Phang, Jason Wei, Jason Yosinski, Jekaterina Novikova, Jelle Bosscher, Jennifer Marsh, Jeremy Kim, Jeroen Taal, Jesse H. Engel, Jesujoba Alabi, Jiacheng Xu, Jiaming Song, Jillian Tang, Joan Waweru, John Burden, John Miller 0001, John U. Balis, Jonathan Batchelder, Jonathan Berant, Jörg Frohberg, Jos Rozen, José Hernández-Orallo, Joseph Boudeman, Joseph Guerr, Joseph Jones, Joshua B. Tenenbaum, Joshua S. Rule, Joyce Chua, Kamil Kanclerz, Karen Livescu, Karl Krauth, Karthik Gopalakrishnan 0001, Katerina Ignatyeva, Katja Markert, Kaustubh D. Dhole, Kevin Gimpel, Kevin Omondi, Kory Mathewson, Kristen Chiafullo, Ksenia Shkaruta, Kumar Shridhar, Kyle McDonell, Kyle Richardson 0001, Laria Reynolds, Leo Gao, Li Zhang 0039, Liam Dugan, Lianhui Qin, Lidia Contreras Ochando, Louis-Philippe Morency, Luca Moschella, Lucas Lam, Lucy Noble, Ludwig Schmidt, Luheng He, Luis Oliveros Colón, Luke Metz, Lütfi Kerem Senel, Maarten Bosma, Maarten Sap, Maartje ter Hoeve, Maheen Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, Marco Marelli, Marco Maru, María José Ramírez-Quintana, Marie Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew L. Leavitt, Matthias Hagen, Mátyás Schubert, Medina Baitemirova, Melody Arnaud, Melvin McElrath, Michael A. Yee, Michael Cohen, Michael Gu, Michael I. Ivanitskiy, Michael Starritt, Michael Strube 0001, Michal Swedrowski, Michele Bevilacqua, Michihiro Yasunaga, Mihir Kale, Mike Cain, Mimee Xu, Mirac Suzgun, Mitch Walker, Mo Tiwari, Mohit Bansal, Moin Aminnaseri, Mor Geva, Mozhdeh Gheini, Mukund Varma T., Nanyun Peng, Nathan A. Chi, Nayeon Lee, Neta Gur-Ari Krakover, Nicholas Cameron 0001, Nicholas Roberts, Nick Doiron, Nicole Martinez, Nikita Nangia, Niklas Deckers, Niklas Muennighoff, Nitish Shirish Keskar, Niveditha Iyer, Noah Constant, Noah Fiedel, Nuan Wen, Oliver Zhang, Omar Agha, Omar Elbaghdadi, Omer Levy, Owain Evans, Pablo Antonio Moreno Casares, Parth Doshi, Pascale Fung, Paul Pu Liang, Paul Vicol, Pegah Alipoormolabashi, Peiyuan Liao, Percy Liang, Peter Chang, Peter Eckersley, Phu Mon Htut, Pinyu Hwang, Piotr Milkowski, Piyush Patil, Pouya Pezeshkpour, Priti Oli, Qiaozhu Mei, Qing Lyu 0001, Qinlang Chen 0001, Rabin Banjade, Rachel Etta Rudolph, Raefer Gabriel, Rahel Habacker, Ramon Risco, Raphaël Millière, Rhythm Garg, Richard Barnes 0002, Rif A. Saurous, Riku Arakawa, Robbe Raymaekers, Robert Frank 0001, Rohan Sikand, Roman Novak, Roman Sitelew, Ronan LeBras, Rosanne Liu, Rowan Jacobs, Rui Zhang 0037, Ruslan Salakhutdinov, Ryan Chi, Ryan Lee, Ryan Stovall, Ryan Teehan, Rylan Yang, Sahib Singh, Saif M. Mohammad, Sajant Anand, Sam Dillavou, Sam Shleifer, Sam Wiseman, Samuel Gruetter, Samuel R. Bowman, Samuel S. Schoenholz, Sanghyun Han, Sanjeev Kwatra, Sarah A. Rous, Sarik Ghazarian, Sayan Ghosh 0004, Sean Casey, Sebastian Bischoff 0001, Sebastian Gehrmann, Sebastian Schuster, Sepideh Sadeghi, Shadi Hamdan, Sharon Zhou, Shashank Srivastava, Sherry Shi, Shikhar Singh, Shima Asaadi, Shixiang Shane Gu, Shubh Pachchigar, Shubham Toshniwal, Shyam Upadhyay, Shyamolima (Shammie) Debnath, Siamak Shakeri, Simon Thormeyer, Simone Melzi, Siva Reddy, Sneha Priscilla Makini, Soo-Hwan Lee, Spencer Torene, Sriharsha Hatwar, Stanislas Dehaene, Stefan Divic, Stefano Ermon, Stella Biderman, Stephanie Lin, Stephen Prasad, Steven T. Piantadosi, Stuart M. Shieber, Summer Misherghi, Svetlana Kiritchenko, Swaroop Mishra, Tal Linzen, Tal Schuster, Tao Li 0039, Tao Yu 0009, Tariq Ali, Tatsu Hashimoto, Te-Lin Wu, Théo Desbordes, Theodore Rothschild, Thomas Phan, Tianle Wang 0009, Tiberius Nkinyili, Timo Schick, Timofei Kornev, Titus Tunduny, Tobias Gerstenberg, Trenton Chang, Trishala Neeraj, Tushar Khot, Tyler Shultz, Uri Shaham 0002, Vedant Misra, Vera Demberg, Victoria Nyamai, Vikas Raunak, Vinay V. Ramasesh, Vinay Uday Prabhu, Vishakh Padmakumar, Vivek Srikumar, William Fedus, William Saunders, William Zhang, Wout Vossen, Xiang Ren 0001, Xiaoyu Tong, Xinran Zhao, Xinyi Wu, Xudong Shen, Yadollah Yaghoobzadeh, Yair Lakretz, Yangqiu Song, Yasaman Bahri, Yejin Choi 0001, Yichi Yang, Yiding Hao, Yifu Chen, Yonatan Belinkov, Yu Hou, Yufang Hou 0001, Yuntao Bai, Zachary Seid, Zhuoye Zhao, Zijian Wang 0002, Zijie J. Wang, Zirui Wang, and Ziyi Wu

Published
2022
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