Publications

Low-Complexity Sphere Decoding for Polar-Coded MIMO Systems
Huayi Zhou
Jian Zheng
Minhua Yang
Warren Gross
Xiaohu You
Chuan Zhang
For polar-coded MIMO systems, separate detection and decoding (SDD) is the traditional scheme. In SDD systems, sphere decoding (SD) is one o… (voir plus)f the competitive MIMO detection schemes. However, SD may not utilize the coding information sufficiently in SDD systems, causing an error-correction performance loss. The existed joint detection and decoding using breadth-first SD (BSD) improves the performance than SDD, whereas the limited search space still causes a performance loss. In this paper, we propose joint detection and decoding based on SD (SD JDD) for polar-coded MIMO systems to reach maximum likelihood (ML) bound. Subsequently, two approaches are further proposed to reduce the computational complexity. The first approach reduces the layers of the SD search tree by exploiting symbol synchro sets, which could accelerate the convergence of SD JDD. The second efficient approach performs multiple tree searches. A small initial radius of the sphere for the first search is assigned to reduce the search space. The ML optimality could be preserved by the following multiple tree searches with increasing radius. It is shown from the numerical results that the proposed JDD outperforms SDD by 3.1 dB at FER
2023-05-01
IEEE Transactions on Vehicular Technology (publié)
doi.org
MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting
Oscar Mañas
Pau Rodriguez
Saba Ahmadi
Aida Nematzadeh
Yash Goyal
Aishwarya Agrawal
Large pre-trained models have proved to be remarkable zero- and (prompt-based) few-shot learners in unimodal vision and language tasks. We p… (voir plus)ropose MAPL, a simple and parameter-efficient method that reuses frozen pre-trained unimodal models and leverages their strong generalization capabilities in multimodal vision-language (VL) settings. MAPL learns a lightweight mapping between the representation spaces of unimodal models using aligned image-text data, and can generalize to unseen VL tasks from just a few in-context examples. The small number of trainable parameters makes MAPL effective at low-data and in-domain learning. Moreover, MAPL’s modularity enables easy extension to other pre-trained models. Extensive experiments on several visual question answering and image captioning benchmarks show that MAPL achieves superior or competitive performance compared to similar methods while training orders of magnitude fewer parameters. MAPL can be trained in just a few hours using modest computational resources and public datasets. We release our code and pre-trained model weights at https://github.com/oscmansan/mapl.
2023-05-01
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (publié)
doi.org
arxiv.org
OC-0290 Investigation of the feasibility of selenium-75 as a viable brachytherapy source
J. Reid
Jonathan Kalinowski
J. Munro
A. Armstrong
Shirin A. Enger
2023-05-01
Radiotherapy and Oncology (publié)
doi.org
PD-0334 Techniques to optimize auto-segmentation of small OARs in pediatric patients undergoing CSI
J. Tsui
M. Popovic
O. Ates
C. Hua
J. Schneider
S. Skamene
C. Freeman
Shirin A. Enger
2023-05-01
Radiotherapy and Oncology (publié)
doi.org
PD-0505 Monte Carlo simulated correction factors of a novel phantom for brachytherapy dosimetry audits
K. Chelminski
R. Abdulrahim
A. Dimitriadis
E. Granizo-Roman
Jonathan Kalinowski
Shirin A. Enger
G. Azangwe
J. Swamidas
2023-05-01
Radiotherapy and Oncology (publié)
doi.org
PD-0586 Design and assembly of a non-invasive radiation detector to measure the AIF in dynamic PET.
Liam Carroll
Y. Daoud
Shirin A. Enger
2023-05-01
Radiotherapy and Oncology (publié)
doi.org
PO-1632 deep learning-based automatic segmentation of rectal tumors in endoscopy images
A. Thibodeau-Antonacci
L. Weishaupt
Aurelie Garant
C. Miller
T. Vuong
P. Nicolaï
Shirin A. Enger
2023-05-01
Radiotherapy and Oncology (publié)
doi.org
PO-2166 Commissioning of new rectal applicator using electronic brachytherapy source
Nada Tomic
L. Liang
A. Esmaelbeigi
Jonathan Kalinowski
Shirin A. Enger
T. Vuong
S. Devic
2023-05-01
Radiotherapy and Oncology (publié)
doi.org
PO-2225 Characterization of the RBE of various photon radiation qualities on human cancer cell lines
N. Chabaytah
J. Babik
J. Li
B. Behmand
T. Connell
M. Evans
R. Ruo
H. Bekerat
Shirin A. Enger
T. Vuong
2023-05-01
Radiotherapy and Oncology (publié)
doi.org
Protective effectiveness of previous SARS-CoV-2 infection and hybrid immunity against the omicron variant and severe disease: a systematic review and meta-regression
Niklas Bobrovitz
Harriet Ware
Xiaomeng Ma
Zihan Li
Reza Hosseini
Christian Cao
Anabel Selemon
Mairead Whelan
Zahra Premji
Hanane Issa
Brianna Cheng
Laith J Abu Raddad
David Buckeridge
Maria D Van Kerkhove
Vanessa Piechotta
Melissa M Higdon
Annelies Wilder-Smith
Isabel Bergeri
Daniel R Feikin
Rahul K. Arora … (voir 2 de plus)
Minal K Patel
Lorenzo Subissi
2023-05-01
The Lancet Infectious Diseases (publié)
doi.org
Reassessing Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization
Aishwarya Agrawal
Ivana Kaji'c
Emanuele Bugliarello
Elnaz Davoodi
Anita Gergely
Phil Blunsom
Aida Nematzadeh
2023-05-01
Findings of the Association for Computational Linguistics: EACL 2023 (publié)
doi.org
arxiv.org
RIGAA at the SBFT 2023 Tool Competition - Cyber-Physical Systems Track
Dmytro Humeniuk
Foutse Khomh
Giuliano Antoniol
Testing and verification of autonomous systems is critically important. In the context of SBFT 2023 CPS testing tool competition, we present… (voir plus) our tool RIGAA for generating virtual roads to test an autonomous vehicle lane keeping assist system. RIGAA combines reinforcement learning as well as evolutionary search to generate test scenarios. It has achieved the second highest final score among 5 other submitted tools.
2023-05-01
2023 IEEE/ACM International Workshop on Search-Based and Fuzz Testing (SBFT) (publié)
doi.org