Fast calibration of the Libor Market Model with Stochastic Volatility and Displaced Diffusion

This paper demonstrates the efficiency of using Edgeworth and Gram-Charlier expansions in the calibration of the Libor Market Model with Stochastic Volatility and Displaced Diffusion (DD-SV-LMM). Our approach brings together two research areas; first, the results regarding the SV-LMM since the work of Wu and Zhang (2006), especially on the moment generating function, and second the approximation of density distributions based on Edgeworth or Gram-Charlier expansions. By exploring the analytical tractability of moments up to fourth order, we are able to perform an adjustment of the reference Bachelier model with normal volatilities for skewness and kurtosis, and as a by-product to derive a smile formula relating the volatility to the moneyness with interpretable parameters. As a main conclusion, our numerical results show a 98% reduction in computational time for the DD-SV-LMM calibration process compared to the classical numerical integration method developed by Heston (1993)

Nickel-Mediated Hydrogenolysis of C–O Bonds of Aryl Ethers: What Is the Source of the Hydrogen?

Mechanistic studies of the hydrogenolysis of aryl ethers by nickel were undertaken with (diphosphine)aryl methyl ethers. A Ni(0) complex containing Ni–arene interactions adjacent to the aryl–O bond was isolated. Heating led to aryl–O bond activation and generation of a nickel aryl methoxide complex. Formal β-H elimination from this species produced a nickel aryl hydride which can undergo reductive elimination in the presence of formaldehyde to generate a carbon monoxide adduct of Ni(0). The reported complexes map out a plausible mechanism of aryl ether hydrogenolysis catalyzed by nickel. Investigations of a previously reported catalytic system using isotopically labeled substrates are consistent with the mechanism proposed in the stoichiometric system, involving β-H elimination from a nickel alkoxide rather than cleavage of the Ni–O bond by H_2

Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization

Many robotics applications require precise pose estimates despite operating in large and changing environments. This can be addressed by visual localization, using a pre-computed 3D model of the surroundings. The pose estimation then amounts to finding correspondences between 2D keypoints in a query image and 3D points in the model using local descriptors. However, computational power is often limited on robotic platforms, making this task challenging in large-scale environments. Binary feature descriptors significantly speed up this 2D-3D matching, and have become popular in the robotics community, but also strongly impair the robustness to perceptual aliasing and changes in viewpoint, illumination and scene structure. In this work, we propose to leverage recent advances in deep learning to perform an efficient hierarchical localization. We first localize at the map level using learned image-wide global descriptors, and subsequently estimate a precise pose from 2D-3D matches computed in the candidate places only. This restricts the local search and thus allows to efficiently exploit powerful non-binary descriptors usually dismissed on resource-constrained devices. Our approach results in state-of-the-art localization performance while running in real-time on a popular mobile platform, enabling new prospects for robotics research.Comment: CoRL 2018 Camera-ready (fix typos and update citations

Integration of Biological Sources: Exploring the Case of Protein Homology

Data integration is a key issue in the domain of bioin- formatics, which deals with huge amounts of heteroge- neous biological data that grows and changes rapidly. This paper serves as an introduction in the field of bioinformatics and the biological concepts it deals with, and an exploration of the integration problems a bioinformatics scientist faces. We examine ProGMap, an integrated protein homology system used by bioin- formatics scientists at Wageningen University, and several use cases related to protein homology. A key issue we identify is the huge manual effort required to unify source databases into a single resource. Un- certain databases are able to contain several possi- ble worlds, and it has been proposed that they can be used to significantly reduce initial integration efforts. We propose several directions for future work where uncertain databases can be applied to bioinformatics, with the goal of furthering the cause of bioinformatics integration

LightGlue: Local Feature Matching at Light Speed

We introduce LightGlue, a deep neural network that learns to match local features across images. We revisit multiple design decisions of SuperGlue, the state of the art in sparse matching, and derive simple but effective improvements. Cumulatively, they make LightGlue more efficient - in terms of both memory and computation, more accurate, and much easier to train. One key property is that LightGlue is adaptive to the difficulty of the problem: the inference is much faster on image pairs that are intuitively easy to match, for example because of a larger visual overlap or limited appearance change. This opens up exciting prospects for deploying deep matchers in latency-sensitive applications like 3D reconstruction. The code and trained models are publicly available at https://github.com/cvg/LightGlue

Surgical management of metastatic disease to the adrenal gland

Metastatic disease to the adrenal glands can occur in a wide array of malignancies. With the increased use of abdominal imaging, these lesions are diagnosed with more frequency. Diagnostic and laboratory evaluation is essential for the differentiation of benign lesions from primary malignant adrenal tumors or extra-adrenal metastasis. Computed tomography (CT) and magnetic resonance imaging (MRI) characteristics, as well as the adjunctive use of immunocytochemical techniques on biopsy specimens, can allow accurate identification of metastatic lesions. Surgical management of metastastic lesions is appropriate in selected patients, primarily when representing the solitary site of metastatic disease. The surgical approach, while debatable, can de done either through open surgery or laparoscopically. Either approach appears comparable in terms of oncologic efficacy in the carefully selected patient, although laparoscopic adrenalectomy is associated with decreased pain and improved convalescence. The surgeon’s skill in laparoscopic technique, appropriate patient selection, and the ability to adhere to oncologic principles, including complete excision without tumor spillage, are of utmost importance when deciding the appropriate surgical intervention

Etude des méthodes éprouvées et innovantes pour la surveillance des digues

National audienceNowadays flood protection dikes surveillance is mostly ensured by visual inspection. There is only few instrumental monitoring whereas it could be used as a relevant approach, complementary to visual inspection. In particular instrumental monitoring can offer continuous and/or wide range surveillance, which is especially useful when dealing with dikes. Instrumental monitoring methods can also help optimizing visual surveillance during flood, when human resources can be scarcer than usual. An overview of the tried and tested surveillance techniques as well as the innovative ones has been made. The objective was to summarize the pros and cons of each technique toward the main dikes failure mechanisms. The techniques have been chosen regarding their range, efficiency and capacity of producing a relevant and precise diagnosis. The development level has also been evaluated through the feedbacks that could be identified. A specific focus is also given on the time and resources needed for data processing and interpretation.Aujourd'hui la surveillance des digues de protection contre les inondations est essentiellement assurée par examen visuel. La surveillance instrumentale est peu développée bien qu'elle puisse être considérée comme une approche complémentaire pertinente qui permettrait de pallier les limites pratiques de l'examen visuel : difficultés d'établir une surveillance continue dans le temps et sur des linéaires importants avec des ressources humaines limitées, notamment en période de crue. Afin d'évaluer les apports potentiels de méthodes instrumentale à la surveillance des digues, un état de l'art a été réalisé. Il a eu pour objectif de présenter de manière synthétique les avantages et limites de chaque méthode au regard des mécanismes de rupture susceptibles d'être détectés. Les méthodes ont été sélectionnées en fonction de critères opérationnels : - en premier lieu leur capacité à établir un diagnostic précis vis-à-vis d'un risque donné ; - leur grand rendement, caractéristique essentielle pour surveiller un objet étendu ; - les moyens et le temps nécessaires au traitement et à l'interprétation des données. Cet aspect deviendra notamment critique en période de crue. Ont été considérées à la fois les techniques éprouvées et les méthodes innovantes, parfois encore au stade de R&D. Dans ce cas la maturité de la technologie a été évaluée au regard des retours d'expérience identifiés. Enfin la notion de coût est abordée. Chaque technique retenue est décrite, la conclusion faisant la synthèse des constatations faites. Cet état de l'art pourra être consulté de manière plus complète au travers du rapport publique remis au MEDDE et disponible à la date de parution de cette communication