Publications

Dance of the Neurons: Unraveling Sex from Brain Signals (short paper).

Mohammad-Javad Darvishi Bayazi

Mohammad Sajjad Ghaemi

Jocelyn Faubert

Irina Rish

2023-12-31

ML4CMH@AAAI (publié)

dblp.uni-trier.de

Data-access performance anti-patterns in data-intensive systems

Biruk Asmare Muse

Kawser Wazed Nafi

Foutse Khomh

Giuliano Antoniol

Data-intensive systems handle variable, high volume, and high-velocity data generated by human and digital devices. Like traditional softwar… (voir plus)e, data-intensive systems are prone to technical debts introduced to cope-up with the pressure of time and resource constraints on developers. Data-access is a critical component of data-intensive systems as it determines the overall performance and functionality of such systems. While data access technical debts are getting attention from the research community, technical debts affecting the performance, are not well investigated. Objective: Identify, categorize, and validate data access performance issues in the context of NoSQL-based and polyglot persistence data-intensive systems using qualitative study. Method: We collect issues from NoSQL-based and polyglot persistence open-source data-intensive systems and identify data access performance issues using inductive coding and build a taxonomy of the root causes. Then, we validate the perceived relevance of the newly identified performance issues using a developer survey.

2023-12-31

Empir. Softw. Eng. (publié)

doi.org

arxiv.org

Deciphering lineage-relevant gene regulatory networks during endoderm formation by InPheRNo-ChIP

Chen Su

William A. Pastor

Amin Emad

Deciphering the underlying gene regulatory networks (GRNs) that govern early human embryogenesis is critical for understanding developmental… (voir plus) mechanisms yet remains challenging due to limited sample availability and the inherent complexity of the biological processes involved. To address this, we developed InPheRNo-ChIP, a computational framework that integrates multimodal data, including RNA-seq, transcription factor (TF)–specific ChIP-seq, and phenotypic labels, to reconstruct phenotype-relevant GRNs associated with endoderm development. The core of this method is a probabilistic graphical model that models the simultaneous effect of TFs on their putative target genes to influence a particular phenotypic outcome. Unlike the majority of existing GRN inference methods that are agnostic to the phenotypic outcomes, InPheRNo-ChIP directly incorporates phenotypic information during GRN inference, enabling the distinction between lineage-specific and general regulatory interactions. We integrated data from three experimental studies and applied InPheRNo-ChIP to infer the GRN governing the differentiation of human embryonic stem cells into definitive endoderm. Benchmarking against a scRNA-seq CRISPRi study demonstrated InPheRNo-ChIP’s ability to identify regulatory interactions involving endoderm markers FOXA2, SMAD2, and SOX17, outperforming other methods. This highlights the importance of incorporating the phenotypic context during network inference. Furthermore, an ablation study confirms the synergistic contribution of ChIP-seq, RNA-seq, and phenotypic data, highlighting the value of multimodal integration for accurate phenotype-relevant GRN reconstruction.

2023-12-31

Briefings Bioinform. (publié)

doi.org

DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations

Deep learning classifiers are prone to latching onto dominant confounders present in a dataset rather than on the causal markers associated … (voir plus)with the target class, leading to poor generalization and biased predictions. Although explainability via counterfactual image generation has been successful at exposing the problem, bias mitigation strategies that permit accurate explainability in the presence of dominant and diverse artifacts remain unsolved. In this work, we propose the DeCoDEx framework and show how an external, pre-trained binary artifact detector can be leveraged during inference to guide a diffusion-based counterfactual image generator towards accurate explainability. Experiments on the CheXpert dataset, using both synthetic artifacts and real visual artifacts (support devices), show that the proposed method successfully synthesizes the counterfactual images that change the causal pathology markers associated with Pleural Effusion while preserving or ignoring the visual artifacts. Augmentation of ERM and Group-DRO classifiers with the DeCoDEx generated images substantially improves the results across underrepresented groups that are out of distribution for each class. The code is made publicly available at https://github.com/NimaFathi/DeCoDEx.

2023-12-31

MIDL (publié)

doi.org

openreview.net

Decoding of Polar Codes Using Quadratic Unconstrained Binary Optimization

Huayi Zhou

Ryan Seah

Marwan Jalaleddine

Warren J. Gross

2023-12-31

IEEE Journal on Selected Areas in Communications (publié)

doi.org

Deep Learning Approach for Changepoint Detection: Penalty Parameter Optimization

Tung L. Nguyen

Toby Dylan Hocking

Changepoint detection, a technique for identifying significant shifts within data sequences, is crucial in various fields such as finance, g… (voir plus)enomics, medicine, etc. Dynamic programming changepoint detection algorithms are employed to identify the locations of changepoints within a sequence, which rely on a penalty parameter to regulate the number of changepoints. To estimate this penalty parameter, previous work uses simple models such as linear models or decision trees. This study introduces a novel deep learning method for predicting penalty parameters, leading to demonstrably improved changepoint detection accuracy on large benchmark supervised labeled datasets compared to previous methods.

2023-12-31

arXiv.org (prépublication)

doi.org

Deep reinforcement learning for continuous wood drying production line control

François-Alexandre Tremblay

Audrey Durand

Michael Morin

Philippe Marier

Jonathan Gaudreault

2023-12-31

Computers in Industry (publié)

doi.org

Designing and Evaluating Dialogue LLMs for Co-Creative Improvised Theatre

Boyd Branch

Piotr Mirowski

Kory Mathewson

Sophia Ppali

Alexandra Covaci

Social robotics researchers are increasingly interested in multi-party trained conversational agents. With a growing demand for real-world e… (voir plus)valuations, our study presents Large Language Models (LLMs) deployed in a month-long live show at the Edinburgh Festival Fringe. This case study investigates human improvisers co-creating with conversational agents in a professional theatre setting. We explore the technical capabilities and constraints of on-the-spot multi-party dialogue, providing comprehensive insights from both audience and performer experiences with AI on stage. Our human-in-the-loop methodology underlines the challenges of these LLMs in generating context-relevant responses, stressing the user interface's crucial role. Audience feedback indicates an evolving interest for AI-driven live entertainment, direct human-AI interaction, and a diverse range of expectations about AI's conversational competence and utility as a creativity support tool. Human performers express immense enthusiasm, varied satisfaction, and the evolving public opinion highlights mixed emotions about AI's role in arts.

2023-12-31

ICCC (publié)

doi.org

arxiv.org

Development of AI-assisted microscopy frameworks through realistic simulation with pySTED

Anthony Bilodeau

Albert Michaud-Gagnon

Julia Chabbert

Benoit Turcotte

Jörn Heine

Audrey Durand

Flavie Lavoie-Cardinal

The integration of artificial intelligence into microscopy systems significantly enhances performance, optimizing both image acquisition and… (voir plus) analysis phases. Development of artificial intelligence-assisted super-resolution microscopy is often limited by access to large biological datasets, as well as by difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic stimulated emission depletion microscopy simulation platform, pySTED, for the development and deployment of artificial intelligence strategies for super-resolution microscopy. pySTED integrates theoretically and empirically validated models for photobleaching and point spread function generation in stimulated emission depletion microscopy, as well as simulating realistic point-scanning dynamics and using a deep learning model to replicate the underlying structures of real images. This simulation environment can be used for data augmentation to train deep neural networks, for the development of online optimization strategies and to train reinforcement learning models. Using pySTED as a training environment allows the reinforcement learning models to bridge the gap between simulation and reality, as showcased by its successful deployment on a real microscope system without fine tuning.

2023-12-31

Nature Machine Intelligence (publié)

doi.org

Discovering modular solutions that generalize compositionally

Simon Schug

Seijin Kobayashi

Yassir Akram

Maciej Wolczyk

Alexandra Proca

Johannes Von Oswald

Razvan Pascanu

João Sacramento

Angelika Steger

Many complex tasks can be decomposed into simpler, independent parts. Discovering such underlying compositional structure has the potential … (voir plus)to enable compositional generalization. Despite progress, our most powerful systems struggle to compose flexibly. It therefore seems natural to make models more modular to help capture the compositional nature of many tasks. However, it is unclear under which circumstances modular systems can discover hidden compositional structure. To shed light on this question, we study a teacher-student setting with a modular teacher where we have full control over the composition of ground truth modules. This allows us to relate the problem of compositional generalization to that of identification of the underlying modules. In particular we study modularity in hypernetworks representing a general class of multiplicative interactions. We show theoretically that identification up to linear transformation purely from demonstrations is possible without having to learn an exponential number of module combinations. We further demonstrate empirically that under the theoretically identified conditions, meta-learning from finite data can discover modular policies that generalize compositionally in a number of complex environments.

2023-12-31

International Conference on Learning Representations (publié)

doi.org

arxiv.org

Disentangling the Causes of Plasticity Loss in Neural Networks

Clare Lyle

Zeyu Zheng

Khimya Khetarpal

Hado van Hasselt

Razvan Pascanu

James Martens

Will Dabney