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Publications
The study of plasticity has always been about gradients
The video game industry is particularly rewarding as it represents a large portion of the software development market. However, working in t… (voir plus)his domain may be challenging for developers, not only because of the need for heterogeneous skills (from software design to computer graphics), but also for the limited body of knowledge in terms of good and bad design and development principles, and the lack of tool support to assist them. This tool demo proposes UnityLint, a tool able to detect 18 types of bad smells in Unity video games. UnityLint builds upon a previously-defined and validated catalog of bad smells for video games. The tool, developed in C# and available both as open-source and binary releases, is composed of (i) analyzers that extract facts from video game source code and metadata, and (ii) smell detectors that leverage detection rules to identify smells on top of the extracted facts.Tool: https://github.com/mdipenta/UnityCodeSmellAnalyzerTeaser Video: https://youtu.be/HooegxZ8H6g
2023-05-01
IEEE International Conference on Program Comprehension (publié)
Immunotherapies such as checkpoint blockade antibodies to block T cell exhaustion have been successful in several cancers such as non-small … (voir plus)cell lung cancer and melanoma, but limited in others (e.g., pancreatic or prostate carcinomas) owing to differences in tumor antigenicity. Therefore, quantifying tumor antigenicity is critical for successful immunotherapies. Our lab has shown that antigenicity can be encoded in a single parameter derived from bulk cytokine dynamics in ex vivo co-cultures between antigen presenting cells (APCs) and T cells. Here we built a model that can capture the antigenicity seen by individual cells. Using a custom robotic platform, we generated high-throughput kinetics of T cell activation in co-culture with APCs by analyzing cells at various timepoints across a large set of activation conditions. We performed spectral flow cytometry to measure the expression of up to 30 surface markers and intracellular signals per cell. To analyze our content-rich datasets, we designed a machine learning-based model that can classify the antigen seen by an individual cell using expression values from flow cytometry. The model performs well not only at classifying T cells (ROC-AUC > 0.91), but also APCs (ROC-AUC > 0.88), suggesting that each individual leukocyte may register the quality of antigen being presented. Blocking cytokine signaling disrupted this antigen classification. Our study demonstrates that every individual lymphocyte can bridge local and global response to achieve high discriminatory power of antigens.
Online sex trafficking is on the rise and a majority of trafficking victims report being advertised online. The use of OnlyFans as a platfor… (voir plus)m for adult content is also increasing, with Twitter as its main advertising tool. Furthermore, we know that traffickers usually work within a network and control multiple victims. Consequently, we suspect that there may be networks of traffickers promoting multiple OnlyFans accounts belonging to their victims. To this end, we present the first study of OnlyFans advertisements on Twitter in the context of finding organized activities. Preliminary analysis of this space shows that most tweets related to OnlyFans contain generic text, making text-based methods less reliable. Instead, focusing on what ties the authors of these tweets together, we propose a novel method for uncovering coordinated networks of users based on their behaviour. Our method, called Multi-Level Clustering (MLC), combines two levels of clustering that considers both the network structure as well as embedded node attribute information. It focuses jointly on user connections (through mentions) and content (through shared URLs). We apply MLC to real-world data of 2 million tweets pertaining to OnlyFans and analyse the detected groups. We also evaluate our method on synthetically generated data (with injected ground truth) and show its superior performance compared to competitive baselines. Finally, we discuss examples of organized clusters as case studies and provide interesting conclusions to our study.
2023-04-30
Proceedings of the 15th ACM Web Science Conference 2023 (publié)
The recent introduction of ChatGPT has drawn significant attention from both industry and academia due to its impressive capabilities in sol… (voir plus)ving a diverse range of tasks, including language translation, text summarization, and computer programming. Its capability for writing, modifying, and even correcting code together with its ease of use and access is already dramatically impacting computer science education. This paper aims to explore how well ChatGPT can perform in an introductory-level functional language programming course. In our systematic evaluation, we treated ChatGPT as one of our students and demonstrated that it can achieve a grade B- and its rank in the class is 155 out of 314 students overall. Our comprehensive evaluation provides valuable insights into ChatGPT's impact from both student and instructor perspectives. Additionally, we identify several potential benefits that ChatGPT can offer to both groups. Overall, we believe that this study significantly clarifies and advances our understanding of ChatGPT's capabilities and potential impact on computer science education.
Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to speci… (voir plus)fic symmetries. While graph neural networks (GNNs) have proven successful in such tasks, they enforce symmetries via the model architecture, which often reduces their expressivity, scalability and comprehensibility. In this paper, we introduce (1) a flexible framework relying on stochastic frame-averaging (SFA) to make any model E(3)-equivariant or invariant through data transformations. (2) FAENet: a simple, fast and expressive GNN, optimized for SFA, that processes geometric information without any symmetrypreserving design constraints. We prove the validity of our method theoretically and empirically demonstrate its superior accuracy and computational scalability in materials modeling on the OC20 dataset (S2EF, IS2RE) as well as common molecular modeling tasks (QM9, QM7-X). A package implementation is available at https://faenet.readthedocs.io.
Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to speci… (voir plus)fic symmetries. While graph neural networks (GNNs) have proven successful in such tasks, they enforce symmetries via the model architecture, which often reduces their expressivity, scalability and comprehensibility. In this paper, we introduce (1) a flexible framework relying on stochastic frame-averaging (SFA) to make any model E(3)-equivariant or invariant through data transformations. (2) FAENet: a simple, fast and expressive GNN, optimized for SFA, that processes geometric information without any symmetrypreserving design constraints. We prove the validity of our method theoretically and empirically demonstrate its superior accuracy and computational scalability in materials modeling on the OC20 dataset (S2EF, IS2RE) as well as common molecular modeling tasks (QM9, QM7-X). A package implementation is available at https://faenet.readthedocs.io.
Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to speci… (voir plus)fic symmetries. While graph neural networks (GNNs) have proven successful in such tasks, they enforce symmetries via the model architecture, which often reduces their expressivity, scalability and comprehensibility. In this paper, we introduce (1) a flexible framework relying on stochastic frame-averaging (SFA) to make any model E(3)-equivariant or invariant through data transformations. (2) FAENet: a simple, fast and expressive GNN, optimized for SFA, that processes geometric information without any symmetrypreserving design constraints. We prove the validity of our method theoretically and empirically demonstrate its superior accuracy and computational scalability in materials modeling on the OC20 dataset (S2EF, IS2RE) as well as common molecular modeling tasks (QM9, QM7-X). A package implementation is available at https://faenet.readthedocs.io.
We pose and study the problem of satisfying fairness in the online Reinforcement Learning (RL) setting. We focus on the group notions of fai… (voir plus)rness, according to which agents belonging to different groups should have similar performance based on some given measure. We consider the setting of maximizing return in an unknown environment (unknown transition and reward function) and show that it is possible to have RL algorithms that learn the best fair policies without violating the fairness requirements at any point in time during the learning process. In the tabular finite-horizon episodic setting, we provide an algorithm that combines the principle of optimism and pessimism under uncertainty to achieve zero fairness violation with arbitrarily high probability while also maintaining sub-linear regret guarantees. For the high-dimensional Deep-RL setting, we present algorithms based on the performance-difference style approximate policy improvement update step and we report encouraging empirical results on various traditional RL-inspired benchmarks showing that our algorithms display the desired behavior of learning the optimal policy while performing a fair learning process.
The Influence of Age, Sex, and Socioeconomic Status on Glycemic Control Among People With Type 1 and Type 2 Diabetes in Canada: Patient-Led Longitudinal Retrospective Cross-sectional Study With Multiple Time Points of Measurement
