Embellir pour mieux dire: des ornement sur la carte de l\u27amérique de jodocus hondiu (1606)

While a map is a representation of a territory, it often also reflects a particular worldview. This is the case with this map of the American continent, engraved by the Flemish cartographer Jodocus Hondius (1563–1612) in 1606 in Amsterdam, of which BAnQ has recently acquired a copy from an edition published in 1613 or 1616¹ by his wife, Coletta van den Keere². Although he was himself a cartographer, Hondius regarded himself first and foremost as the publisher of the work of Gerardus Mercator (1512–1594), whose plates he had acquired ten years after his death.³ In 1606, despite competition from Abraham Ortelius’s superb atlas, Hondius’s first edition of Mercator’s general atlas sold out within the year.⁴ In this work, until 1630, two maps of the whole of America coexisted: one engraved by Michael Mercator, Gerard’s grandson, the other, this *America*, a compilation by Hondius. But whilst the former is accompanied by a text describing the continent, Hondius’s *America* is not accompanied by any commentary. This absence makes the various ornaments all the more valuable, as they not only embellish it but also enhance its documentary richness: Native Americans busy around a cauldron or sailing in canoes, sea monsters, tropical birds, familiar or more exotic boats; none of these elements is purely decorative. On the contrary, this imagery is rich in meaning and helps to flesh out the vision of the New World conveyed by the map.Si une carte est la représentation d’un territoire, elle témoigne bien souvent aussi d’une vision du monde. C’est le cas de cette carte du continent américain gravée par le Flamand Jodocus Hondius (1563-1612) en 1606 à Amsterdam et dont BAnQ vient d’acquérir un exemplaire d’une édition publiée en 1613 ou 16161 par sa femme, Coletta van den Keere2. Bien qu’il fût lui-même cartographe, Hondius se considérait avant tout comme l’éditeur de l’œuvre de Gérard Mercator (1512-1594) dont il avait acquis les plaques dix ans après sa mort3. En 1606, en dépit de la concurrence du superbe atlas d’Abraham Ortelius, la première édition par Hondius de l’atlas général de Mercator est épuisée dans l’année4. Dans cet ouvrage, jusqu’en 1630, coexistent deux cartes de l’Amérique entière : l’une gravée par Michael Mercator, petit-fils de Gérard, l’autre, cette America, œuvre compilatoire de Hondius. Mais alors que la première est accompagnée d’un texte décrivant le continent, l’America de Hondius n’est associée à aucun commentaire5. Cette absence rend d’autant plus précieux les différents ornements qui non seulement l’embellissent mais aussi accroissent sa richesse documentaire : Amérindiens s’affairant autour d’un chaudron ou naviguant sur des canoës, monstres marins, oiseaux tropicaux, bateaux familiers ou plus exotiques ; aucun de ces éléments n’est purement décoratif. Au contraire, cette iconographie est chargée de sens et contribue à étoffer la vision du Nouveau Monde exprimé par la carte

Deep Sequence Model for Genome Wide Discovery of Coding and Regulatory Element Signatures

Interpreting deep neural networks for genomic sequence classification remains challenging despite strong predictive performance. We develop a lightweight CNN with post-training gradient-based analysis to identify which sequence positions drive coding versus intergenomic classification. Applied to the standardized demo coding vs ntergenomic dataset, our model achieves 91.7% validation accuracy while revealing interpretable patterns: nine hot spots with consistently high importance, clear class-specific separation at positions 20--100 (coding) and 150--190 (intergenomic), and a strong mean variance correlation r = 0.530 indicating robust discriminative features. Gradient-based importance analysis shows that the model implicitly learns biologically meaningful sequence distinctions without explicit annotations. This work demonstrates that neural network interpretability and accuracy can coexist, providing a framework for understanding genomic sequence classification and enabling biology-driven hypothesis generation

How does drought affect residential water demand and price elasticity?

Urban water scarcity is an important social and economic concern, particularly as the intensity, duration, and frequency of droughts is increasing in many regions. We consider whether drought induces changes to water demand and the price elasticity of demand for water that may last beyond a drought’s official end date. If drought shocks prompt long-term changes in water demand behavior, and these changes occur at broad geographic scale, they could have important implications for modeling adaptive responses to water scarcity. We assemble a novel dataset on residential water demand and pricing in the western United States to test empirically for effects of drought on water demand and price elasticity. We perform our analysis with aggregate quantity, price, and drought data, accounting for endogenous prices under increasing-block water tariffs and using both average and marginal water fees in estimating water demand functions. Results are consistent with the hypothesis that households may become less price-sensitive after exposure to drought. However, we find no systematic evidence of long-run, drought-related reductions in water demand, itself

Lightweight Range–Angle Imaging Based Algorithm for Quasi-Static Human Detection on Low-Cost FMCW Radar

Quasi-static human activities such as lying, standing or sitting produce very low Doppler shifts and highly spread radar signatures, making them difficult to detect with conventional constant–false–alarm rate (CFAR) detectors tuned for point targets. Moreover, privacy concerns and low lighting conditions limit the use of cameras in long–term care (LTC) facilities. This paper proposes a lightweight, non-visual image–based method for robust quasi-static human presence detection using a low–cost 60 GHz FMCW radar. On a dataset covering five semi-static activities, the proposed method improves average detection accuracy from 68.3% for Cell-Averaging CFAR (CA-CFAR) and 78.8% for Order-Statistics CFAR (OS-CFAR) to 85.4% for Subject 1, and from 51.3% and 68.3% to 79.5% for Subject 2. Finally, we benchmarked all three detectors across all activities on a Raspberry Pi 4B using a shared Range-Angle (RA) preprocessing pipeline. The proposed algorithm obtains an average 8.2 ms per frame, resulting in over 120 frames per second (FPS) and a 74 $\times$ speed-up over OS–CFAR. These results demonstrate that simple image–based processing can provide robust and deployable quasi-static human sensing in cluttered indoor environments

Object ReID in an office environment: An empirical study

Object Re-identification (ReID) is a fundamental task in computer vision, enabling systems to recognize and track the same object across different frames and viewpoints, lighting conditions, and environmental contexts. In robotic applications, reliable object ReID is essential for enabling robots to maintain persistent identity of objects over time. While person and vehicle ReID have been extensively studied, object-level ReID remains unexplored. In this work, we present an empirical comparative study of state-of-the art representation learning algorithms - DINO, DINOv2, Triplet, I-JEPA, and CLIP, which are applied to object ReID in an office environment. We construct a custom office dataset, capturing diverse office objects. Each image is cropped using Grounding DINO for object detection. We extract embeddings for each object instance and perform ReID by computing cosine similarity. Performance is assessed by measuring whether the top-matching image corresponds to the correct object, using Mean Average Precision, Top-1 and Top-5 metrics

Self-Supervised Learning by Curvature Alignment

Self-supervised learning (SSL) has recently advanced through non-contrastive methods that couple an invariance term with variance, covariance, or redundancy-reduction penalties. While such objectives shape first- and second-order statistics of the representation, they largely ignore the local geometry of the underlying data manifold. In this paper, we introduce CurvSSL, a curvature-regularized self-supervised learning framework, and its RKHS extension, kernel CurvSSL. Our approach retains a standard two-view encoder– projector architecture with a Barlow Twins-style redundancy-reduction loss on projected features, but augments it with a curvature-based regularizer. Each embedding is treated as a vertex whose k nearest neighbors define a discrete curvature score via cosine interactions on the unit hypersphere; in the kernel variant, curvature is computed from a normalized local Gram matrix in an RKHS. These scores are aligned and decorrelated across augmentations by a Barlow-style loss on a curvature-derived matrix, encouraging both view invariance and consistency of local manifold bending. Experiments on MNIST and CIFAR-10 datasets with a ResNet-18 backbone show that curvature-regularized SSL yields competitive or improved linear evaluation performance compared to Barlow Twins and VICReg. Our results indicate that explicitly shaping local geometry is a simple and effective complement to purely statistical SSL regularizers

Understanding vision transformer quantization robustness through the lens of out-of-distribution detection

Vision transformers have shown remarkable performance in computer vision tasks. Enabling powerful models for accessible, real-time use likely requires quantization to compress the model, opening the risk for loss in performance. Works typically seek to understand model behaviour at lower precision with respect to classification, but the attention mechanism leads us to believe that we can gain insight by including behaviour in out-of-distribution (OOD) situations. We investigate the behaviour of quantized small-variant popular vision transformers (DeiT, DeiT3, and ViT) using common OOD datasets such as OpenImage-O and iNaturalist. In-distribution (ID) analyses show the initial instabilities of 4-bit models, particularly of those trained on the larger ImageNet-22k, as the strongest FP32 model in our experiment, DeiT3, sharply drops 17% from 4-bit quantization error to be one of the weakest 4-bit models. While ViT shows reasonable quantization robustness for ID calibration, OOD detection reveals more: ViT and DeiT3 pretrained on ImageNet-22k respectively experienced a 15.0% and 19.2% average quantization delta in AUPR-out between full precision to 4-bit while the same models trained only on ImageNet-1k experienced a 9.5% and 12.0% delta. Overall, our results suggest pretraining on large scale datasets may hinder low-bit quantization robustness in OOD detection and that data augmentation may be a more beneficial option