Publications

Manifold Alignment with Label Information
Andres F. Duque Correa
Myriam Lizotte
Guy Wolf
Kevin R. Moon
Multi-domain data is becoming increasingly common and presents both challenges and opportunities in the data science community. The integrat… (see more)ion of distinct data-views can be used for exploratory data analysis, and benefit downstream analysis including machine learning related tasks. With this in mind, we present a novel manifold alignment method called MALI (Manifold alignment with label information) that learns a correspondence between two distinct domains. MALI belongs to a middle ground between the more commonly addressed semi-supervised manifold alignment, where some known correspondences between the two domains are assumed to be known beforehand, and the purely unsupervised case, where no information linking both domains is available. To do this, MALI learns the manifold structure in both domains via a diffusion process and then leverages discrete class labels to guide the alignment. MALI recovers a pairing and a common representation that reveals related samples in both domains. We show that MALI outperforms the current state-of-the-art manifold alignment methods across multiple datasets.
2023-05-21
SampTA/2023/Conference (published)
doi.org
openreview.net
ToxBuster: In-game Chat Toxicity Buster with BERT
Zachary Yang
Yasmine Maricar
M. Davari
Nicolas Grenon-Godbout
Reihaneh Rabbany
Detecting toxicity in online spaces is challenging and an ever more pressing problem given the increase in social media and gaming consumpti… (see more)on. We introduce ToxBuster, a simple and scalable model trained on a relatively large dataset of 194k lines of game chat from Rainbow Six Siege and For Honor, carefully annotated for different kinds of toxicity. Compared to the existing state-of-the-art, ToxBuster achieves 82.95% (+7) in precision and 83.56% (+57) in recall. This improvement is obtained by leveraging past chat history and metadata. We also study the implication towards real-time and post-game moderation as well as the model transferability from one game to another.
2023-05-21
ArXiv (preprint)
doi.org
arxiv.org
Training Acceleration of Frequency Domain CNNs Using Activation Compression
Seyyed Hasan Mozafari
James J. Clark
Warren Gross
Brett Meyer
Reducing the complexity of training convolutional neural networks results in lower energy consumption expended during training, or higher ac… (see more)curacy by admitting a greater number of training epochs within a training time budget. During backpropagation, a considerable amount of temporary data is offloaded from GPU memory to CPU memory, increasing training time. In this paper, we address this training time overhead by introducing an activation compression technique for frequency domain convolutional neural networks. Applying this compression technique on frequency domain AlexNet results in activation compression of 57.7%, and a reduction of training time by 23%, with a negligible effect on classification accuracy.
2023-05-21
International Symposium on Circuits and Systems (published)
doi.org
Idiolect: A Reconfigurable Voice Coding Assistant
Breandan Considine
Nicholas Albion
Xujie Si
This paper presents Idiolect, an open source 1 IDE plugin for voice coding and a novel approach to building bots that allows for users to de… (see more)fine custom commands on-the-fly. Unlike traditional chatbots, Idiolect does not pretend to be an omniscient virtual assistant but rather a reconfigurable voice programming system that empowers users to create their own commands and actions dynamically, without rebuilding or restarting the application. We offer an experience report describing the tool itself, illustrate some example use cases, and reflect on several lessons learned during the tool’s development.
2023-05-20
2023 IEEE/ACM 5th International Workshop on Bots in Software Engineering (BotSE) (published)
doi.org
arxiv.org
Neural Bee Colony Optimization: A Case Study in Public Transit Network Design
Andrew Holliday
Gregory Dudek
In this work we explore the combination of metaheuristics and learned neural network solvers for combinatorial optimization. We do this in t… (see more)he context of the transit network design problem, a uniquely challenging combinatorial optimization problem with real-world importance. We train a neural network policy to perform single-shot planning of individual transit routes, and then incorporate it as one of several sub-heuristics in a modified Bee Colony Optimization (BCO) metaheuristic algorithm. Our experimental results demonstrate that this hybrid algorithm outperforms the learned policy alone by up to 20% and the original BCO algorithm by up to 53% on realistic problem instances. We perform a set of ablations to study the impact of each component of the modified algorithm.
2023-05-18
ArXiv (preprint)
doi.org
arxiv.org
NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification
Iyanuoluwa Shode
David Ifeoluwa Adelani
Jing Peng
Anna Feldman
Africa has over 2000 indigenous languages but they are under-represented in NLP research due to lack of datasets. In recent years, there hav… (see more)e been progress in developing labelled corpora for African languages. However, they are often available in a single domain and may not generalize to other domains. In this paper, we focus on the task of sentiment classification for cross-domain adaptation. We create a new dataset, Nollywood movie reviews for five languages widely spoken in Nigeria (English, Hausa, Igbo, Nigerian Pidgin, and Yoruba). We provide an extensive empirical evaluation using classical machine learning methods and pre-trained language models. By leveraging transfer learning, we compare the performance of cross-domain adaptation from Twitter domain, and cross-lingual adaptation from English language. Our evaluation shows that transfer from English in the same target domain leads to more than 5% improvement in accuracy compared to transfer from Twitter in the same language. To further mitigate the domain difference, we leverage machine translation from English to other Nigerian languages, which leads to a further improvement of 7% over cross-lingual evaluation. While machine translation to low-resource languages are often of low quality, our analysis shows that sentiment related words are often preserved.
2023-05-18
ArXiv (preprint)
doi.org
arxiv.org
The potential for co-operatives to mitigate AI ethics catastrophes: perspectives from media analysis
David Marino
AJung Moon
Would the world have seen less AI-related scandals if more AI companies operated as co-operatives? As a response to multiple high profile te… (see more)ch scandals within the last decade, there has been an increased call for introducing more accountability in the AI industry. However, it is unclear to what degree the proposed efforts have been or will be effective in practice. The question remains whether these incremental, multi-stakeholder AI ethics efforts are in fact trying to address a fundamentally systemic issue inherent to the existing corporate power structure. As an attempt to address this question, we gain an understanding of the major themes in high profile AI-related catastrophes in the last four years (2018–2021) through an inductive media analysis. We then investigate how the principle of democratic gov-ernance and distributive executive power - core to co-operative organization structure - could have prevented or mitigated the contributing factors of the reported events. We find that the vast majority (71%) of the recent AI ethics scandals are not the result of a lack of knowledge or tools, but attributed to power dynamics that hinder the ability of internal stakeholders from taking action. We present the co-operative governance structure as a possible mitigating solution to addressing future AI ethics catastrophes, and provide a critical look at practical challenges inherent to AI co-operatives.
2023-05-18
Ethics: An International Journal of Social, Political, and Legal Philosophy (published)
doi.org
Community-based Reconstruction and Simulation of a Full-scale Model of Region CA1 of Rat Hippocampus
Armando Romani
Alberto Antonietti
Davide Bella
Julian Budd
Elisabetta Giacalone
Kerem Kurban
Sára Sáray
Marwan Abdellah
Alexis Arnaudon
Elvis Boci
Cristina Colangelo
Jean-Denis Courcol
Thomas Delemontex
András Ecker
Joanne Falck
Cyrille Favreau
Michael Gevaert
Juan B. Hernando
Joni Herttuainen
Genrich Ivaska … (see 28 more)
Lida Kanari
Anna-Kristin Kaufmann
James King
Pramod Kumbhar
Sigrun Lange
Huanxiang Lu
Carmen Alina Lupascu
Rosanna Migliore
Fabien Petitjean
Judit Planas
Pranav Rai
Srikanth Ramaswamy
Michael W. Reimann
Juan Luis Riquelme
Nadir Román Guerrero
Ying Shi
Vishal Sood
Mohameth François Sy
Werner Van Geit
Liesbeth Vanherpe
Tamás F. Freund
Audrey Mercer
Eilif Benjamin Muller
Felix Schürmann
Alex M. Thomson
Michele Migliore
Szabolcs Káli
Henry Markram
The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive fun… (see more)ctions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, it has been challenging to reconcile information obtained from diverse experimental approaches. To address this challenge, we present a community-driven, full-scale in silico model of the rat CA1 that integrates a broad range of experimental data, from synapse to network, including the reconstruction of its principal afferents, the Schaffer collaterals, and a model of the effects that acetylcholine has on the system. We tested and validated each model component and the final network model, and made input data, assumptions, and strategies explicit and transparent. The unique flexibility of the model allows scientists to address a range of scientific questions. In this article, we describe the methods used to set up simulations that reproduce and extend in vitro and in vivo experiments. Among several applications in the article, we focus on theta rhythm, a prominent hippocampal oscillation associated with various behavioral correlates and use our computer model to reproduce and reconcile experimental findings. Finally, we make data, code and model available through the hippocampushub.eu portal, which also provides an extensive set of analyses of the model and a user-friendly interface to facilitate adoption and usage. This neuroscience community-driven model represents a valuable tool for integrating diverse experimental data and provides a foundation for further research into the complex workings of the hippocampal CA1 region.
2023-05-17
bioRxiv (preprint)
doi.org
A MC-based anthropomorphic test case for commissioning model-based dose calculation in interstitial breast 192-Ir HDR brachytherapy.
Vasiliki Peppa
Rowan M. Thomson
Shirin A. Enger
Gabriel P. Fonseca
Choonik Lee
Joseph N. E. Lucero
Firas Mourtada
Frank‐André Siebert
Javier Vijande
Panagiotis Papagiannis
PURPOSE To provide the first clinical test case for commissioning of 192 Ir brachytherapy model-based dose calculation algorithms (MBDCAs) a… (see more)ccording to the AAPM TG-186 report workflow. ACQUISITION AND VALIDATION METHODS A computational patient phantom model was generated from a clinical multi-catheter 192 Ir HDR breast brachytherapy case. Regions of interest (ROIs) were contoured and digitized on the patient CT images and the model was written to a series of DICOM CT images using MATLAB. The model was imported into two commercial treatment planning systems (TPSs) currently incorporating an MBDCA. Identical treatment plans were prepared using a generic 192 Ir HDR source and the TG-43-based algorithm of each TPS. This was followed by dose to medium in medium calculations using the MBDCA option of each TPS. Monte Carlo (MC) simulation was performed in the model using three different codes and information parsed from the treatment plan exported in DICOM radiation therapy (RT) format. Results were found to agree within statistical uncertainty and the dataset with the lowest uncertainty was assigned as the reference MC dose distribution. DATA FORMAT AND USAGE NOTES The dataset is available online at http://irochouston.mdanderson.org/rpc/BrachySeeds/BrachySeeds/index.html,https://doi.org/10.52519/00005. Files include the treatment plan for each TPS in DICOM RT format, reference MC dose data in RT Dose format, as well as a guide for database users and all files necessary to repeat the MC simulations. POTENTIAL APPLICATIONS The dataset facilitates the commissioning of brachytherapy MBDCAs using TPS embedded tools and establishes a methodology for the development of future clinical test cases. It is also useful to non-MBDCA adopters for intercomparing MBDCAs and exploring their benefits and limitations, as well as to brachytherapy researchers in need of a dosimetric and/or a DICOM RT information parsing benchmark. Limitations include specificity in terms of radionuclide, source model, clinical scenario, and MBDCA version used for its preparation.
2023-05-17
Medical Physics (published)
doi.org
Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part II: Physiology and Experimentation
James B. Isbister
András Ecker
Christoph Pokorny
Sirio Bolaños-Puchet
Daniela Egas Santander
Alexis Arnaudon
Omar Awile
Natali Barros-Zulaica
Jorge Blanco Alonso
Elvis Boci
Giuseppe Chindemi
Jean-Denis Courcol
Tanguy Damart
Thomas Delemontex
Alexander Dietz
Gianluca Ficarelli
Michael Gevaert
Joni Herttuainen
Genrich Ivaska
Weina Ji … (see 22 more)
Daniel Keller
James King
Pramod Kumbhar
Samuel Lapere
Polina Litvak
Darshan Mandge
Eilif Benjamin Muller
Fernando Pereira
Judit Planas
Rajnish Ranjan
Maria Reva
Armando Romani
Christian Rössert
Felix Schürmann
Vishal Sood
Aleksandra Teska
Anil Tuncel
Werner Van Geit
Matthias Wolf
Henry Markram
Srikanth Ramaswamy
Michael W. Reimann
Cortical dynamics underlie many cognitive processes and emerge from complex multi-scale interactions, which can be studied in large-scale, b… (see more)iophysically detailed models. We present a model comprising eight somatosensory cortex subregions, 4.2 million morpho-logical and electrically-detailed neurons, and 13.2 billion local and long-range synapses. In silico tools enabled reproduction and extension of complex laboratory experiments under a single parameterization, providing strong validation. We reproduced millisecond-precise stimulus-responses, stimulus-encoding under targeted optogenetic activation, and selective propagation of stimulus-evoked activity to downstream areas. The model’s di-rect correspondence with biology generated predictions about how multiscale organisation shapes activity. We predict that structural and functional recurrency increases towards deeper layers and that stronger innervation by long-range connectivity increases local correlated activity. The model also predicts the role of inhibitory interneuron types in stimulus encoding, and of different layers in driving layer 2/3 stimulus responses. Simu-slation tools and a large subvolume of the model are made available.
2023-05-17
bioRxiv (preprint)
doi.org
Raising the Bar for Certified Adversarial Robustness with Diffusion Models
Thomas R. Altstidl
David Dobre
Björn M. Eskofier
Gauthier Gidel
Leo Schwinn
Certified defenses against adversarial attacks offer formal guarantees on the robustness of a model, making them more reliable than empirica… (see more)l methods such as adversarial training, whose effectiveness is often later reduced by unseen attacks. Still, the limited certified robustness that is currently achievable has been a bottleneck for their practical adoption. Gowal et al. and Wang et al. have shown that generating additional training data using state-of-the-art diffusion models can considerably improve the robustness of adversarial training. In this work, we demonstrate that a similar approach can substantially improve deterministic certified defenses. In addition, we provide a list of recommendations to scale the robustness of certified training approaches. One of our main insights is that the generalization gap, i.e., the difference between the training and test accuracy of the original model, is a good predictor of the magnitude of the robustness improvement when using additional generated data. Our approach achieves state-of-the-art deterministic robustness certificates on CIFAR-10 for the
2023-05-17
ArXiv (preprint)
doi.org
arxiv.org
Responses of pyramidal cell somata and apical dendrites in mouse visual cortex over multiple days
Colleen J Gillon
Jérôme A. Lecoq
Jason E. Pina
Ruweida Ahmed
Yazan N. Billeh
Shiella Caldejon
Peter Groblewski
Timothy M. Henley
India Kato
Eric Lee
Jennifer Luviano
Kyla Mace
Chelsea Nayan
Thuyanh V. Nguyen
Kat North
Jed Perkins
Sam Seid
Matthew T. Valley
Ali Williford
Yoshua Bengio … (see 3 more)
Timothy P. Lillicrap
Joel Zylberberg
Blake Richards
2023-05-17
Scientific Data (published)
doi.org