Publications

Spectroscopy of CASSOWARY gravitationally-lensed galaxies in SDSS: characterisation of an extremely bright reionization-era analog at z = 1.42
Ramesh Mainali
Daniel P Stark
Tucker Jones
Richard S Ellis
Yashar Hezaveh
Jane R Rigby
We present new observations of sixteen bright (r = 19 − 21) gravitationally lensed galaxies at z ≃ 1 − 3 selected from the CASSOWARY s… (see more)urvey. Included in our sample is the z = 1.42 galaxy CSWA-141, one of the brightest known reionization-era analogs at high redshift (g=20.5), with a large sSFR (31.2 Gyr−1) and an [OIII]+Hβ equivalent width (EW[OIII] + Hβ=730 Å) that is nearly identical to the average value expected at z ≃ 7 − 8. In this paper, we investigate the rest-frame UV nebular line emission in our sample with the goal of understanding the factors that regulate strong CIII] emission. Whereas most of the sources in our sample show weak UV line emission, we find elevated CIII] in the spectrum of CSWA-141 (EWCIII]=4.6±1.9 Å) together with detections of other prominent emission lines (OIII], Si III], Fe II⋆, Mg II). We compare the rest-optical line properties of high redshift galaxies with strong and weak CIII] emission, and find that systems with the strongest UV line emission tend to have young stellar populations and nebular gas that is moderately metal-poor and highly ionized, consistent with trends seen at low and high redshift. The brightness of CSWA-141 enables detailed investigation of the extreme emission line galaxies which become common at z > 6. We find that gas traced by the CIII] doublet likely probes higher densities than that traced by [OII] and [SII]. Characterisation of the spectrally resolved Mg II emission line and several low ionization absorption lines suggests neutral gas around the young stars is likely optically thin, potentially facilitating the escape of ionizing radiation.
2023-02-07
Monthly Notices of the Royal Astronomical Society (published)
doi.org
arxiv.org
A Minimax Approach Against Multi-Armed Adversarial Attacks Detection
Federica Granese
Marco Romanelli
Siddharth Garg
Pablo Piantanida
2023-02-03
ArXiv (preprint)
doi.org
arxiv.org
AmbieGen tool at the SBST 2022 Tool Competition
Dmytro Humeniuk
Giuliano Antoniol
Foutse Khomh
AmbieGen is a tool for generating test cases for cyber-physical systems (CPS). In the context of SBST 2022 CPS tool competition, it has been… (see more) adapted to generating virtual roads to test a car lane keeping assist system. AmbieGen leverages a two objective NSGA-II algorithm to produce the test cases. It has achieved the highest final score, accounting for the test case efficiency, effectiveness and diversity in both testing configurations.
2023-02-02
Proceedings of the 15th Workshop on Search-Based Software Testing (published)
doi.org
Challenges in machine learning application development
Md Saidur Rahman
Foutse Khomh
Emilio Rivera
Yann-Gaël Guéhéneuc
Bernd Lehnert
SAP is the market leader in enterprise application software offering an end-to-end suite of applications and services to enable their custom… (see more)ers worldwide to operate their business. Especially, retail customers of SAP deal with millions of sales transactions for their day-to-day business. Transactions are created during retail sales at the point of sale (POS) terminals and those transactions are then sent to some central servers for validations and other business operations. A considerable proportion of the retail transactions may have inconsistencies or anomalies due to many technical and human errors. SAP provides an automated process for error detection but still requires a manual process by dedicated employees using workbench software for correction. However, manual corrections of these errors are time-consuming, labor-intensive, and might be prone to further errors due to incorrect modifications. Thus, automated detection and correction of transaction errors are very important regarding their potential business values and the improvement in the business workflow. In this paper, we report on our experience from a project where we develop an AI-based system to automatically detect transaction errors and propose corrections. We identify and discuss the challenges that we faced during this collaborative research and development project, from two distinct perspectives: Software Engineering and Machine Learning. We report on our experience and insights from the project with guidelines for the identified challenges. We collect developers’ feedback for qualitative analysis of our findings. We believe that our findings and recommendations can help other researchers and practitioners embarking into similar endeavours. CCS CONCEPTS • Software and its engineering → Programming teams.
2023-02-02
Proceedings of the 1st Workshop on Software Engineering for Responsible AI (published)
doi.org
Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty Modeling
Lucas Berry
David Meger
2023-02-01
ArXiv (preprint)
doi.org
arxiv.org
A Survey on Compositional Generalization in Applications
Baihan Lin
Djallel Bouneffouf
Irina Rish
2023-02-01
ArXiv (preprint)
doi.org
arxiv.org
Critic Sequential Monte Carlo
Vasileios Lioutas
Jonathan Wilder Lavington
Justice Sefas
Matthew Niedoba
Yunpeng Liu
Berend Zwartsenberg
Setareh Dabiri
Frank N. Wood
Adam Ścibior
We introduce CriticSMC, a new algorithm for planning as inference built from a composition of sequential Monte Carlo with learned Soft-Q fun… (see more)ction heuristic factors. These heuristic factors, obtained from parametric approximations of the marginal likelihood ahead, more effectively guide SMC towards the desired target distribution, which is particularly helpful for planning in environments with hard constraints placed sparsely in time. Compared with previous work, we modify the placement of such heuristic factors, which allows us to cheaply propose and evaluate large numbers of putative action particles, greatly increasing inference and planning efficiency. CriticSMC is compatible with informative priors, whose density function need not be known, and can be used as a model-free control algorithm. Our experiments on collision avoidance in a high-dimensional simulated driving task show that CriticSMC significantly reduces collision rates at a low computational cost while maintaining realism and diversity of driving behaviors across vehicles and environment scenarios.
2023-01-31
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net
Disentanglement of Correlated Factors via Hausdorff Factorized Support
Karsten Roth
Mark Ibrahim
Zeynep Akata
P Vincent
Diane Bouchacourt
A grand goal in deep learning research is to learn representations capable of generalizing across distribution shifts. Disentanglement is on… (see more)e promising direction aimed at aligning a model's representation with the underlying factors generating the data (e.g. color or background). Existing disentanglement methods, however, rely on an often unrealistic assumption: that factors are statistically independent. In reality, factors (like object color and shape) are correlated. To address this limitation, we consider the use of a relaxed disentanglement criterion -- the Hausdorff Factorized Support (HFS) criterion -- that encourages only pairwise factorized \emph{support}, rather than a factorial distribution, by minimizing a Hausdorff distance. This allows for arbitrary distributions of the factors over their support, including correlations between them. We show that the use of HFS consistently facilitates disentanglement and recovery of ground-truth factors across a variety of correlation settings and benchmarks, even under severe training correlations and correlation shifts, with in parts over
2023-01-31
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net
Fooling SHAP with Stealthily Biased Sampling
Gabriel Laberge
Ulrich Matchi Aïvodji
Satoshi Hara
Mario Marchand
Foutse Khomh
SHAP explanations aim at identifying which features contribute the most to the difference in model prediction at a specific input versus a … (see more)background distribution. Recent studies have shown that they can be manipulated by malicious adversaries to produce arbitrary desired explanations. However, existing attacks focus solely on altering the black-box model itself. In this paper, we propose a complementary family of attacks that leave the model intact and manipulate SHAP explanations using stealthily biased sampling of the data points used to approximate expectations w.r.t the background distribution. In the context of fairness audit, we show that our attack can reduce the importance of a sensitive feature when explaining the difference in outcomes between groups, while remaining undetected. These results highlight the manipulability of SHAP explanations and encourage auditors to treat post-hoc explanations with skepticism.
2023-01-31
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net
Generative Augmented Flow Networks
Ling Pan
Dinghuai Zhang
Aaron Courville
Longbo Huang
Yoshua Bengio
The Generative Flow Network is a probabilistic framework where an agent learns a stochastic policy for object generation, such that the prob… (see more)ability of generating an object is proportional to a given reward function. Its effectiveness has been shown in discovering high-quality and diverse solutions, compared to reward-maximizing reinforcement learning-based methods. Nonetheless, GFlowNets only learn from rewards of the terminal states, which can limit its applicability. Indeed, intermediate rewards play a critical role in learning, for example from intrinsic motivation to provide intermediate feedback even in particularly challenging sparse reward tasks. Inspired by this, we propose Generative Augmented Flow Networks (GAFlowNets), a novel learning framework to incorporate intermediate rewards into GFlowNets. We specify intermediate rewards by intrinsic motivation to tackle the exploration problem in sparse reward environments. GAFlowNets can leverage edge-based and state-based intrinsic rewards in a joint way to improve exploration. Based on extensive experiments on the GridWorld task, we demonstrate the effectiveness and efficiency of GAFlowNet in terms of convergence, performance, and diversity of solutions. We further show that GAFlowNet is scalable to a more complex and large-scale molecule generation domain, where it achieves consistent and significant performance improvement.
2023-01-31
International Conference on Learning Representations (notable)
doi.org
openreview.net
ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations
Badr Youbi Idrissi
Diane Bouchacourt
Randall Balestriero
Ivan Evtimov
Caner Hazirbas
Nicolas Ballas
P Vincent
Michal Drozdzal
David Lopez-Paz
Mark Ibrahim
Deep learning vision systems are widely deployed across applications where reliability is critical. However, even today's best models can fa… (see more)il to recognize an object when its pose, lighting, or background varies. While existing benchmarks surface examples challenging for models, they do not explain why such mistakes arise. To address this need, we introduce ImageNet-X—a set of sixteen human annotations of factors such as pose, background, or lighting the entire ImageNet-1k validation set as well as a random subset of 12k training images. Equipped with ImageNet-X, we investigate 2,200 current recognition models and study the types of mistakes as a function of model’s (1) architecture, e.g. transformer vs. convolutional, (2) learning paradigm, e.g. supervised vs. self-supervised, and (3) training procedures, e.g., data augmentation. Regardless of these choices, we find models have consistent failure modes across ImageNet-X categories. We also find that while data augmentation can improve robustness to certain factors, they induce spill-over effects to other factors. For example, color-jitter augmentation improves robustness to color and brightness, but surprisingly hurts robustness to pose. Together, these insights suggest to advance the robustness of modern vision models, future research should focus on collecting additional data and understanding data augmentation schemes. Along with these insights, we release a toolkit based on ImageNet-X to spur further study into the mistakes image recognition systems make.
2023-01-31
ICLR.cc/2023/Conference (notable)
doi.org
openreview.net
Learning From FM Communications: Toward Accurate, Efficient, All-Terrain Vehicle Localization
X. T. Chen
Qiao Xiang
Linghe Kong
Huisan Xu
Xue Liu
Vehicle localization service is a fundamental component of intelligent transportation systems. The widely used satellite navigation systems … (see more)perform poorly in urban areas because the lines of sight to satellites are blocked by complex terrain characteristics, e.g., buildings, elevated streets and interchanges. In this paper, we design RadioLoc, a novel system achieving accurate, efficient, all-terrain vehicle localization with two key design points. First, RadioLoc harvests the frequency modulation (FM) signal, which has higher availability than satellite signal in complex terrains, as the signal source for localization. Second, RadioLoc integrates modern machine learning techniques into the processing of FM signals to efficiently learn the accurate vehicle localization in all-terrain environments. We validate the feasibility of FM-based vehicle localization and corresponding challenges and practical issues via field tests (e.g., signal distortion, signal inconsistency and limited in- vehicle radio bandwidth), and develop a series of advanced techniques in RadioLoc to address them, including adaptive batching, frequency sweeping, a novel multipath delay spread filter, a reconstructive PCA denoiser and a tailored FM feature extractor. We then develop a generic, modular localization module in RadioLoc, and design different learning-based 3D position identification algorithms for this module. We implement a prototype of RadioLoc and perform extensive field experiments to evaluate its efficiency and efficacy. Results show that (1) RadioLoc achieves a real-time localization latency of less than 100 milliseconds; (2) RadioLoc achieves a worst-case localization accuracy of 99.6% even in an underground parking lot, and (3) the horizontal error of RadioLoc is only one sixth of a dedicated GPS device even when the vehicle is moving at a high-speed (i.e., 80 km/h) in a complex highway scenario.
2023-01-31
IEEE/ACM Transactions on Networking (published)
doi.org