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The O-type long-period binary HD 168112 and triple HD 167971 star systems have been known for several decades for their non-thermal synchrotron radio emission. This emission arises from relativistic electrons accelerated in the hydrodynamic shocks of the wind collisions in these systems. Such wind collisions are expected to produce a strong X-ray emission that varies as a function of orbital phase. In wide eccentric binaries, such as our targets, the X-ray emission arises from an adiabatic plasma and its intensity should scale as the inverse of the orbital separation. We present a set of XMM-Newton observations of these systems which help us gain insight into the properties of their wind interactions., Comment: in French language. Accepted for publication in the Bulletin de la Soci\'et\'e Royale des Sciences de Li\`ege, Proceedings of the 41st Li\`ege Astrophysical Colloquium, held in Li\`ege 15-19 July 2024, edited by Y. Naz\'e, & E. Bozzo

Deep learning models are effective, yet brittle. Even carefully trained, their behavior tends to be hard to predict when confronted with out-of-distribution samples. In this work, our goal is to propose a simple yet effective solution to predict and describe via natural language potential failure modes of computer vision models. Given a pretrained model and a set of samples, our aim is to find sentences that accurately describe the visual conditions in which the model underperforms. In order to study this important topic and foster future research on it, we formalize the problem of Language-Based Error Explainability (LBEE) and propose a set of metrics to evaluate and compare different methods for this task. We propose solutions that operate in a joint vision-and-language embedding space, and can characterize through language descriptions model failures caused, e.g., by objects unseen during training or adverse visual conditions. We experiment with different tasks, such as classification under the presence of dataset bias and semantic segmentation in unseen environments, and show that the proposed methodology isolates nontrivial sentences associated with specific error causes. We hope our work will help practitioners better understand the behavior of models, increasing their overall safety and interpretability., Comment: Preprint of the eXCV Workshop paper (ECCV'24)

The long-period O-star binary system HD 168112 and the triple O-star system HD 167971 are well-known sources of non-thermal radio emission that arises from a colliding wind interaction. The wind-wind collisions in these systems should result in phase-dependent X-ray emissions. The presence of a population of relativistic electrons in the wind interaction zone could affect the properties of the X-ray emission and make it deviate from the behaviour expected for adiabatic shocks. We investigate the X-ray emission of these systems with the goals of quantifying the fraction of the X-ray flux arising from wind interactions and determining whether these emissions follow the predictions for adiabatic wind-wind collisions. Six X-ray observations were collected with XMM-Newton. Three observations were scheduled around the most recent periastron passage of HD 168112. Spectra and light curves were analysed and compared with simple predictions of model calculations for X-ray emission from colliding wind systems. The X-ray emission of HD 168112 varies as the inverse of the orbital separation, as expected for an adiabatic wind interaction zone. The relative contribution of intrinsic X-ray emission from wind-embedded shocks varies between 38% at periastron to 81% at apastron. The wind-wind collision zone remains adiabatic even around periastron passage. The X-ray emission of HD 167971 displays variations on the orbital timescale of the inner eclipsing binary. The existing data of this system do not allow us to probe variations on the timescale of the outer orbit. Shock modification due to the action of relativistic electrons does not seem to be efficiently operating in the HD 168112 system. In the existing observations, a significant part of the emission of HD 167971 must arise in the inner eclipsing binary. The origin of this emission is as yet unclear., Comment: Accepted for publication in Astronomy & Astrophysics

Open-vocabulary object detection (OvOD) has transformed detection into a language-guided task, empowering users to freely define their class vocabularies of interest during inference. However, our initial investigation indicates that existing OvOD detectors exhibit significant variability when dealing with vocabularies across various semantic granularities, posing a concern for real-world deployment. To this end, we introduce Semantic Hierarchy Nexus (SHiNe), a novel classifier that uses semantic knowledge from class hierarchies. It runs offline in three steps: i) it retrieves relevant super-/sub-categories from a hierarchy for each target class; ii) it integrates these categories into hierarchy-aware sentences; iii) it fuses these sentence embeddings to generate the nexus classifier vector. Our evaluation on various detection benchmarks demonstrates that SHiNe enhances robustness across diverse vocabulary granularities, achieving up to +31.9% mAP50 with ground truth hierarchies, while retaining improvements using hierarchies generated by large language models. Moreover, when applied to open-vocabulary classification on ImageNet-1k, SHiNe improves the CLIP zero-shot baseline by +2.8% accuracy. SHiNe is training-free and can be seamlessly integrated with any off-the-shelf OvOD detector, without incurring additional computational overhead during inference. The code is open source., Comment: Accepted as a conference paper (highlight) at CVPR 2024

Radio surveys of early-type stars have revealed a number of non-thermal emitters. Most of these have been shown to be binaries, where the collision between the two stellar winds is responsible for the non-thermal emission. HD 168112 is a non-thermal radio emitter, whose binary nature has only recently been confirmed spectroscopically. We obtained independent spectroscopic observations to determine its orbit, in addition to radio observations to see if the thermal or non-thermal nature of the emission changes during the periastron passage. We monitored HD 168112 spectroscopically for a 13 year time span. From these data, we determined the orbital parameters, which we compared to the previous results in the literature. From the spectral index of the radio observations, we found how the nature of the emission changes as the system goes through periastron. Combining our results with other literature data allowed us to further constrain the orbital and stellar parameters. We find HD 168112 to have an orbital period of P = 512.17+0.41-0.11 d, an eccentricity of e = 0.7533+0.0053-0.0124, and a mass ratio close to one. From our spectroscopic modelling, we derived the stellar parameters, but we had difficulty arriving at a spectroscopic mass ratio of one. The radio observations around periastron show only thermal emission, suggesting that most of the synchrotron photons are absorbed in the two stellar winds at that phase. Combining our data with the optical interferometry detection, we could constrain the inclination angle to i ~ 63 deg, and the mass of each component to ~ 26 Msun. We have provided an independent spectroscopic confirmation of the binary nature of HD 168112. Although detected as a non-thermal radio emitter, near periastron the radio emission of this highly eccentric system is thermal and is mainly formed in the colliding-wind region. [abridged], Comment: 13 pages, 7 figures, accepted for publication in Astronomy & Astrophysics. Corrected values of Omega on p. 9

Object detectors are typically trained once and for all on a fixed set of classes. However, this closed-world assumption is unrealistic in practice, as new classes will inevitably emerge after the detector is deployed in the wild. In this work, we look at ways to extend a detector trained for a set of base classes so it can i) spot the presence of novel classes, and ii) automatically enrich its repertoire to be able to detect those newly discovered classes together with the base ones. We propose PANDAS, a method for novel class discovery and detection. It discovers clusters representing novel classes from unlabeled data, and represents old and new classes with prototypes. During inference, a distance-based classifier uses these prototypes to assign a label to each detected object instance. The simplicity of our method makes it widely applicable. We experimentally demonstrate the effectiveness of PANDAS on the VOC 2012 and COCO-to-LVIS benchmarks. It performs favorably against the state of the art for this task while being computationally more affordable., Comment: Accepted to the Conference on Lifelong Learning Agents (CoLLAs 2024)

When deploying a semantic segmentation model into the real world, it will inevitably encounter semantic classes that were not seen during training. To ensure a safe deployment of such systems, it is crucial to accurately evaluate and improve their anomaly segmentation capabilities. However, acquiring and labelling semantic segmentation data is expensive and unanticipated conditions are long-tail and potentially hazardous. Indeed, existing anomaly segmentation datasets capture a limited number of anomalies, lack realism or have strong domain shifts. In this paper, we propose the Placing Objects in Context (POC) pipeline to realistically add any object into any image via diffusion models. POC can be used to easily extend any dataset with an arbitrary number of objects. In our experiments, we present different anomaly segmentation datasets based on POC-generated data and show that POC can improve the performance of recent state-of-the-art anomaly fine-tuning methods across several standardized benchmarks. POC is also effective for learning new classes. For example, we utilize it to augment Cityscapes samples by incorporating a subset of Pascal classes and demonstrate that models trained on such data achieve comparable performance to the Pascal-trained baseline. This corroborates the low synth2real gap of models trained on POC-generated images. Code: https://github.com/naver/poc, Comment: Accepted to ECCV 2024

RoboCup represents an International testbed for advancing research in AI and robotics, focusing on a definite goal: developing a robot team that can win against the human world soccer champion team by the year 2050. To achieve this goal, autonomous humanoid robots' coordination is crucial. This paper explores novel solutions within the RoboCup Standard Platform League (SPL), where a reduction in WiFi communication is imperative, leading to the development of new coordination paradigms. The SPL has experienced a substantial decrease in network packet rate, compelling the need for advanced coordination architectures to maintain optimal team functionality in dynamic environments. Inspired by market-based task assignment, we introduce a novel distributed coordination system to orchestrate autonomous robots' actions efficiently in low communication scenarios. This approach has been tested with NAO robots during official RoboCup competitions and in the SimRobot simulator, demonstrating a notable reduction in task overlaps in limited communication settings., Comment: International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023) - Italian Workshop on Artificial Intelligence and Robotics (AIRO) Rome, 6 - 9 November, 2023

Cropland maps are a core and critical component of remote-sensing-based agricultural monitoring, providing dense and up-to-date information about agricultural development. Machine learning is an effective tool for large-scale agricultural mapping, but relies on geo-referenced ground-truth data for model training and testing, which can be scarce or time-consuming to obtain. In this study, we explore the usefulness of combining a global cropland dataset and a hand-labeled dataset to train machine learning models for generating a new cropland map for Nigeria in 2020 at 10 m resolution. We provide the models with pixel-wise time series input data from remote sensing sources such as Sentinel-1 and 2, ERA5 climate data, and DEM data, in addition to binary labels indicating cropland presence. We manually labeled 1827 evenly distributed pixels across Nigeria, splitting them into 50\% training, 25\% validation, and 25\% test sets used to fit the models and test our output map. We evaluate and compare the performance of single- and multi-headed Long Short-Term Memory (LSTM) neural network classifiers, a Random Forest classifier, and three existing 10 m resolution global land cover maps (Google's Dynamic World, ESRI's Land Cover, and ESA's WorldCover) on our proposed test set. Given the regional variations in cropland appearance, we additionally experimented with excluding or sub-setting the global crowd-sourced Geowiki cropland dataset, to empirically assess the trade-off between data quantity and data quality in terms of the similarity to the target data distribution of Nigeria. We find that the existing WorldCover map performs the best with an F1-score of 0.825 and accuracy of 0.870 on the test set, followed by a single-headed LSTM model trained with our hand-labeled training samples and the Geowiki data points in Nigeria, with a F1-score of 0.814 and accuracy of 0.842.

Over the past decades, libraries of stellar spectra have been used in a large variety of science cases, including as sources of reference spectra for a given object or a given spectral type. Despite the existence of large libraries and the increasing number of projects of large-scale spectral surveys, there is to date only one very high-resolution spectral library offering spectra from a few hundred objects from the southern hemisphere (UVES-POP) . We aim to extend the sample, offering a finer coverage of effective temperatures and surface gravity with a uniform collection of spectra obtained in the northern hemisphere. Between 2010 and 2020, we acquired several thousand echelle spectra of bright stars with the Mercator-HERMES spectrograph located in the Roque de Los Muchachos Observatory in La Palma, whose pipeline offers high-quality data reduction products. We have also developed methods to correct for the instrumental response in order to approach the true shape of the spectral continuum. Additionally, we have devised a normalisation process to provide a homogeneous normalisation of the full spectral range for most of the objects. We present a new spectral library consisting of 3256 spectra covering 2043 stars. It combines high signal-to-noise and high spectral resolution over the entire range of effective temperatures and luminosity classes. The spectra are presented in four versions: raw, corrected from the instrumental response, with and without correction from the atmospheric molecular absorption, and normalised (including the telluric correction)., Comment: 17 pages, 18 figures Preview and access to the library: https://www.royer.se/melchiors.html

The presence of symmetries imposes a stringent set of constraints on a system. This constrained structure allows intelligent agents interacting with such a system to drastically improve the efficiency of learning and generalization, through the internalisation of the system's symmetries into their information-processing. In parallel, principled models of complexity-constrained learning and behaviour make increasing use of information-theoretic methods. Here, we wish to marry these two perspectives and understand whether and in which form the information-theoretic lens can "see" the effect of symmetries of a system. For this purpose, we propose a novel variant of the Information Bottleneck principle, which has served as a productive basis for many principled studies of learning and information-constrained adaptive behaviour. We show (in the discrete case and under a specific technical assumption) that our approach formalises a certain duality between symmetry and information parsimony: namely, channel equivariances can be characterised by the optimal mutual information-preserving joint compression of the channel's input and output. This information-theoretic treatment furthermore suggests a principled notion of "soft" equivariance, whose "coarseness" is measured by the amount of input-output mutual information preserved by the corresponding optimal compression. This new notion offers a bridge between the field of bounded rationality and the study of symmetries in neural representations. The framework may also allow (exact and soft) equivariances to be automatically discovered., Comment: 23 pages, 0 figures

Cities around the world face a critical shortage of affordable and decent housing. Despite its critical importance for policy, our ability to effectively monitor and track progress in urban housing is limited. Deep learning-based computer vision methods applied to street-level images have been successful in the measurement of socioeconomic and environmental inequalities but did not fully utilize temporal images to track urban change as time-varying labels are often unavailable. We used self-supervised methods to measure change in London using 15 million street images taken between 2008 and 2021. Our novel adaptation of Barlow Twins, Street2Vec, embeds urban structure while being invariant to seasonal and daily changes without manual annotations. It outperformed generic embeddings, successfully identified point-level change in London's housing supply from street-level images, and distinguished between major and minor change. This capability can provide timely information for urban planning and policy decisions toward more liveable, equitable, and sustainable cities., Comment: 16 pages, 5 figures

Class-incremental semantic image segmentation assumes multiple model updates, each enriching the model to segment new categories. This is typically carried out by providing expensive pixel-level annotations to the training algorithm for all new objects, limiting the adoption of such methods in practical applications. Approaches that solely require image-level labels offer an attractive alternative, yet, such coarse annotations lack precise information about the location and boundary of the new objects. In this paper we argue that, since classes represent not just indices but semantic entities, the conceptual relationships between them can provide valuable information that should be leveraged. We propose a weakly supervised approach that exploits such semantic relations to transfer objectness prior from the previously learned classes into the new ones, complementing the supervisory signal from image-level labels. We validate our approach on a number of continual learning tasks, and show how even a simple pairwise interaction between classes can significantly improve the segmentation mask quality of both old and new classes. We show these conclusions still hold for longer and, hence, more realistic sequences of tasks and for a challenging few-shot scenario., Comment: Accepted to CoLLAs 2023

There is compelling evidence that charge carriers in organic semiconductors (OSs) self-localize in nano-scale space because of dynamic disorder. Yet, some OSs, in particular recently emerged high-mobility organic molecular crystals, feature reduced mobility at increasing temperature, a hallmark for delocalized band transport. Here we present the temperature-dependent mobility in two record-mobility OSs: DNTT (dinaphtho[2,3-b:2',3'-f]thieno[3,2-b]-thiophene), and its alkylated derivative, C8-DNTT-C8. By combining terahertz photoconductivity measurements with fully atomistic non-adiabatic molecular dynamics simulations, we show that while both crystals display a power-law decrease of the mobility (\mu) with temperature (T, following: \mu \propto T^(-n)), the exponent n differs substantially. Modelling provides n values in good agreement with experiments and reveals that the differences in the falloff parameter between the two chemically closely related semiconductors can be traced to the delocalization of the different states thermally accessible by charge carriers, which in turn depends on the specific electronic band structure of the two systems. The emerging picture is that of holes surfing on a dynamic manifold of vibrationally-dressed extended states with a temperature-dependent mobility that provides a sensitive fingerprint for the underlying density of states., Comment: 28 pages, 5 Figures

Motivated by the increasing popularity of transformers in computer vision, in recent times there has been a rapid development of novel architectures. While in-domain performance follows a constant, upward trend, properties like robustness or uncertainty estimation are less explored -leaving doubts about advances in model reliability. Studies along these axes exist, but they are mainly limited to classification models. In contrast, we carry out a study on semantic segmentation, a relevant task for many real-world applications where model reliability is paramount. We analyze a broad variety of models, spanning from older ResNet-based architectures to novel transformers and assess their reliability based on four metrics: robustness, calibration, misclassification detection and out-of-distribution (OOD) detection. We find that while recent models are significantly more robust, they are not overall more reliable in terms of uncertainty estimation. We further explore methods that can come to the rescue and show that improving calibration can also help with other uncertainty metrics such as misclassification or OOD detection. This is the first study on modern segmentation models focused on both robustness and uncertainty estimation and we hope it will help practitioners and researchers interested in this fundamental vision task. Code available at https://github.com/naver/relis., Comment: Accepted to CVPR 2023

Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer vision applications, providing key information for the global understanding of an image. This survey is an effort to summarize two decades of research in the field of SiS, where we propose a literature review of solutions starting from early historical methods followed by an overview of more recent deep learning methods including the latest trend of using transformers. We complement the review by discussing particular cases of the weak supervision and side machine learning techniques that can be used to improve the semantic segmentation such as curriculum, incremental or self-supervised learning. State-of-the-art SiS models rely on a large amount of annotated samples, which are more expensive to obtain than labels for tasks such as image classification. Since unlabeled data is instead significantly cheaper to obtain, it is not surprising that Unsupervised Domain Adaptation (UDA) reached a broad success within the semantic segmentation community. Therefore, a second core contribution of this book is to summarize five years of a rapidly growing field, Domain Adaptation for Semantic Image Segmentation (DASiS) which embraces the importance of semantic segmentation itself and a critical need of adapting segmentation models to new environments. In addition to providing a comprehensive survey on DASiS techniques, we unveil also newer trends such as multi-domain learning, domain generalization, domain incremental learning, test-time adaptation and source-free domain adaptation. Finally, we conclude this survey by describing datasets and benchmarks most widely used in SiS and DASiS and briefly discuss related tasks such as instance and panoptic image segmentation, as well as applications such as medical image segmentation., Comment: Pre-print of the book: G. Csurka, R. Volpi and B. Chidlovski: Semantic Image Segmentation: Two Decades of Research, FTCGV (14): No. 1-2, http://dx.doi.org/10.1561/0600000095. The authors retained the copyright and are allowed to post it on arXiv. Research only use, commercial use or systematic downloading (by robots or other automatic processes) is prohibited