Publications

Asymmetry in the complexity of the multi-commodity network pricing problem

Quang Minh Bui

Margarida Carvalho

Jos'e Neto

2024-01-01

Math. Program. (publié)

doi.org

arxiv.org

BAND: Biomedical Alert News Dataset

Zihao Fu

Meiru Zhang

Zaiqiao Meng

Yannan Shen

Anya Okhmatovskaia

David Buckeridge

Nigel Collier

2024-01-01

AAAI (publié)

doi.org

arxiv.org

A benchmark of individual auto-regressive models in a massive fMRI dataset

Fraçois Paugam

Basile Pinsard

Guillaume Lajoie

Lune Bellec

Dense functional magnetic resonance imaging datasets open new avenues to create auto-regressive models of brain activity. Individual idiosyn… (voir plus)crasies are obscured by group models, but can be captured by purely individual models given sufficient amounts of training data. In this study, we compared several deep and shallow individual models on the temporal auto-regression of BOLD time series recorded during a natural video watching task. The best performing models were then analyzed in terms of their data requirements and scaling, subject specificity and the space-time structure of their predicted dynamics. We found the Chebnets, a type of graph convolutional neural network, to be best suited for temporal BOLD auto-regression, closely followed by linear models. Chebnets demonstrated an increase in performance with increasing amounts of data, with no complete saturation at 9 h of training data. Good generalization to other kinds of video stimuli and to resting state data marked the Chebnets’ ability to capture intrinsic brain dynamics rather than only stimulus-specific autocorrelation patterns. Significant subject specificity was found at short prediction time lags. The Chebnets were found to capture lower frequencies at longer prediction time lags, and the spatial correlations in predicted dynamics were found to match traditional functional connectivity networks. Overall, these results demonstrate that large individual fMRI datasets can be used to efficiently train purely individual auto-regressive models of brain activity, and that massive amounts of individual data are required to do so. The excellent performance of the Chebnets likely reflects their ability to combine spatial and temporal interactions on large time scales at a low complexity cost. The non-linearities of the models did not appear as a key advantage. In fact, surprisingly, linear versions of the Chebnets appeared to outperform the original nonlinear ones. Individual temporal auto-regressive models have the potential to improve the predictability of the BOLD signal. This study is based on a massive, publicly-available dataset, which can serve for future benchmarks of individual auto-regressive modeling.

2024-01-01

Imaging Neuroscience (publié)

doi.org

Benchmarking Vision Language Models for Cultural Understanding

Shravan Nayak

Kanishk Jain

Rabiul Awal

Siva Reddy

Sjoerd van Steenkiste

Lisa Anne Hendricks

Karolina Sta'nczak

Aishwarya Agrawal

Foundation models and vision-language pre-training have notably advanced Vision Language Models (VLMs), enabling multimodal processing of vi… (voir plus)sual and linguistic data. However, their performance has been typically assessed on general scene understanding - recognizing objects, attributes, and actions - rather than cultural comprehension. This study introduces CulturalVQA, a visual question-answering benchmark aimed at assessing VLM's geo-diverse cultural understanding. We curate a collection of 2,378 image-question pairs with 1-5 answers per question representing cultures from 11 countries across 5 continents. The questions probe understanding of various facets of culture such as clothing, food, drinks, rituals, and traditions. Benchmarking VLMs on CulturalVQA, including GPT-4V and Gemini, reveals disparity in their level of cultural understanding across regions, with strong cultural understanding capabilities for North America while significantly lower performance for Africa. We observe disparity in their performance across cultural facets too, with clothing, rituals, and traditions seeing higher performances than food and drink. These disparities help us identify areas where VLMs lack cultural understanding and demonstrate the potential of CulturalVQA as a comprehensive evaluation set for gauging VLM progress in understanding diverse cultures.

2024-01-01

EMNLP (publié)

doi.org

arxiv.org

BETAC: Bidirectional Encoder Transformer for Assembly Code Function Name Recovery

Guillaume Breyton

Mohd Saqib

Benjamin Fung

Philippe Charland

Recovering function names from stripped binaries is a crucial and time-consuming task for software reverse engineering’ particularly in en… (voir plus)hancing network reliability, resilience, and security. This paper tackles the challenge of recovering function names in stripped binaries, a fundamental step in reverse engineering. The absence of syntactic information and the possibility of different code producing identical behavior complicate this task. To overcome these challenges, we introduce a novel model, the Bidirectional Encoder Transformer for Assembly Code (BETAC), leveraging a transformer-based architecture known for effectively processing sequential data. BETAC utilizes self-attention mechanisms and feed-forward networks to discern complex relationships within assembly code for precise function name prediction. We evaluated BETAC against various existing encoder and decoder models in diverse binary datasets, including benign and malicious codes in multiple formats. Our model demonstrated superior performance over previous techniques in certain metrics and showed resilience against code obfuscation.

2024-01-01

DRCN (publié)

doi.org

Bidirectional Generative Pre-training for Improving Time Series Representation Learning

Ziyang Song

Qincheng Lu

He Zhu

David Buckeridge

Yue Li

2024-01-01

arXiv.org (prépublication)

doi.org

arxiv.org

Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities

Eslam G. Al-Sakkari

Ahmed Ragab

Hanane Dagdougui

Daria Camilla Boffito

Mouloud Amazouz

2024-01-01

Science of the Total Environment (publié)

doi.org

Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces

Ahmad-reza Ehyaei

Kiarash Mohammadi

Amir-Hossein Karimi