Publications

Implementing automation in deep brain stimulation: has the time come?
Marco Bonizzato
Alfonso Fasano
2022-12-01
The Lancet Digital Health (publié)
doi.org
Improving Passage Retrieval with Zero-Shot Question Generation
Devendra Singh Sachan
Mike Lewis
Mandar Joshi
Armen Aghajanyan
Wen-tau Yih
Joelle Pineau
Luke Zettlemoyer
2022-12-01
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
arxiv.org
In-Processing Fairness Improvement Methods for Regression Data-Driven Building Models: Achieving Uniform Energy Prediction
Ying Sun
Benjamin Fung
Fariborz Haghighat
2022-12-01
Energy and Buildings (publié)
doi.org
A Multifaceted Framework to Evaluate Evasion, Content Preservation, and Misattribution in Authorship Obfuscation Techniques
Malik H. Altakrori
Thomas Scialom
Benjamin Fung
Jackie Cheung
2022-12-01
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance
Xiaoqiang Wang
Bang Liu
Siliang Tang
Lingfei Wu
Existing metrics for assessing question generation not only require costly human reference but also fail to take into account the input cont… (voir plus)ext of generation, rendering the lack of deep understanding of the relevance between the generated questions and input contexts. As a result, they may wrongly penalize a legitimate and reasonable candidate question when it (1) involves complicated reasoning with the context or (2) can be grounded by multiple evidences in the context.In this paper, we propose QRelScore, a context-aware Relevance evaluation metric for Question Generation.Based on off-the-shelf language models such as BERT and GPT2, QRelScore employs both word-level hierarchical matching and sentence-level prompt-based generation to cope with the complicated reasoning and diverse generation from multiple evidences, respectively.Compared with existing metrics, our experiments demonstrate that QRelScore is able to achieve a higher correlation with human judgments while being much more robust to adversarial samples.
2022-12-01
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
arxiv.org
Reference panel guided topological structure annotation of Hi-C data
Yanlin Zhang
Mathieu Blanchette
2022-12-01
Nature Communications (publié)
doi.org
Structure-Aware Reinforcement Learning for Node-Overload Protection in Mobile Edge Computing
Anirudha Jitani
Aditya Mahajan
Zhongwen Zhu
Hatem Abou-Zeid
Emmanuel Thepie Fapi
Hakimeh Purmehdi
Mobile Edge Computing (MEC) involves placing computational capability and applications at the edge of the network, providing benefits such a… (voir plus)s reduced latency, reduced network congestion, and improved performance of applications. The performance and reliability of MEC degrades significantly when the edge server(s) in the cluster are overloaded. In this work, an adaptive admission control policy to prevent edge node from getting overloaded is presented. This approach is based on a recently-proposed low complexity RL (Reinforcement Learning) algorithm called SALMUT (Structure-Aware Learning for Multiple Thresholds), which exploits the structure of the optimal admission control policy in multi-class queues for an average-cost setting. We extend the framework to work for node overload-protection problem in a discounted-cost setting. The proposed solution is validated using several scenarios mimicking real-world deployments in two different settings — computer simulations and a docker testbed. Our empirical evaluations show that the total discounted cost incurred by SALMUT is similar to state-of-the-art deep RL algorithms such as PPO (Proximal Policy Optimization) and A2C (Advantage Actor Critic) but requires an order of magnitude less time to train, outputs easily interpretable policy, and can be deployed in an online manner.
2022-12-01
IEEE Transactions on Cognitive Communications and Networking (publié)
doi.org
arxiv.org
The Emergence of Argument Structure in Artificial Languages
Tom Bosc
Pascal Vincent
Abstract Computational approaches to the study of language emergence can help us understand how natural languages are shaped by cognitive an… (voir plus)d sociocultural factors. Previous work focused on tasks where agents refer to a single entity. In contrast, we study how agents predicate, that is, how they express that some relation holds between several entities. We introduce a setup where agents talk about a variable number of entities that can be partially observed by the listener. In the presence of a least-effort pressure, they tend to discuss only entities that are not observed by the listener. Thus we can obtain artificial phrases that denote a single entity, as well as artificial sentences that denote several entities. In natural languages, if we ignore the verb, phrases are usually concatenated, either in a specific order or by adding case markers to form sentences. Our setup allows us to quantify how much this holds in emergent languages using a metric we call concatenability. We also measure transitivity, which quantifies the importance of word order. We demonstrate the usefulness of this new setup and metrics for studying factors that influence argument structure. We compare agents having access to input representations structured into pre-segmented objects with properties, versus unstructured representations. Our results indicate that the awareness of object structure yields a more natural sentence organization.
2022-12-01
Transactions of the Association for Computational Linguistics (publié)
doi.org
Using incorpoRATE to examine clinician willingness to engage in shared decision making: A study of Family Medicine residents.
Roland Grad
A. Sandhu
Michael Ferrante
Vinita D'souza
Lily Puterman-Salzman
Samira Abbasgholizadeh-Rahimi
Gabrielle Stevens
G. Elwyn
2022-12-01
Patient Education and Counseling (publié)
doi.org
VDGraph2Vec: Vulnerability Detection in Assembly Code using Message Passing Neural Networks
Ashita Diwan
Miles Q. Li
Benjamin Fung
Software vulnerability detection is one of the most challenging tasks faced by reverse engineers. Recently, vulnerability detection has rece… (voir plus)ived a lot of attention due to a drastic increase in the volume and complexity of software. Reverse engineering is a time-consuming and labor-intensive process for detecting malware and software vulnerabilities. However, with the advent of deep learning and machine learning, it has become possible for researchers to automate the process of identifying potential security breaches in software by developing more intelligent technologies. In this research, we propose VDGraph2Vec, an automated deep learning method to generate representations of assembly code for the task of vulnerability detection. Previous approaches failed to attend to topological characteristics of assembly code while discovering the weakness in the software. VDGraph2Vec embeds the control flow and semantic information of assembly code effectively using the expressive capabilities of message passing neural networks and the RoBERTa model. Our model is able to learn the important features that help distinguish between vulnerable and non-vulnerable software. We carry out our experimental analysis for performance benchmark on three of the most common weaknesses and demonstrate that our model can identify vulnerabilities with high accuracy and outperforms the current state-of-the-art binary vulnerability detection models.
2022-12-01
International Conference on Machine Learning and Applications (publié)
doi.org
CLIP-Mesh: Generating textured meshes from text using pretrained image-text models
Nasir M. Khalid
Tianhao Xie
Eugene Belilovsky
Tiberiu Popa
2022-11-30
SIGGRAPH Asia 2022 Conference Papers (publié)
doi.org
arxiv.org
Histology-informed automatic parcellation of white matter tracts in the rat spinal cord
Harris Nami
Christian S. Perone
Julien Cohen-Adad
The white matter is organized into “tracts” or “bundles,” which connect different parts of the central nervous system. Knowing where… (voir plus) these tracts are located in each individual is important for understanding the cause of potential sensorial, motor or cognitive deficits and for developing appropriate treatments. Traditionally, tracts are found using tracer injection, which is a difficult, slow and poorly scalable technique. However, axon populations from a given tract exhibit specific characteristics in terms of morphometrics and myelination. Hence, the delineation of tracts could, in principle, be done based on their morphometry. The objective of this study was to generate automatic parcellation of the rat spinal white matter tracts using the manifold information from scanning electron microscopy images of the entire spinal cord. The axon morphometrics (axon density, axon diameter, myelin thickness and g-ratio) were computed pixelwise following automatic axon segmentation using AxonSeg. The parcellation was based on an agglomerative clustering algorithm to group the tracts. Results show that axon morphometrics provide sufficient information to automatically identify some white matter tracts in the spinal cord, however, not all tracts were correctly identified. Future developments of microstructure quantitative MRI even bring hope for a personalized clustering of white matter tracts in each individual patient. The generated atlas and the associated code can be found at https://github.com/neuropoly/tract-clustering.
2022-11-29
Frontiers in Neuroanatomy (publié)
doi.org