Sparse Gr\"obner Bases: the Unmixed Case

Toric (or sparse) elimination theory is a framework developped during the last decades to exploit monomial structures in systems of Laurent polynomials. Roughly speaking, this amounts to computing in a \emph{semigroup algebra}, \emph{i.e.} an algebra generated by a subset of Laurent monomials. In order to solve symbolically sparse systems, we introduce \emph{sparse Gr\"obner bases}, an analog of classical Gr\"obner bases for semigroup algebras, and we propose sparse variants of the $F_5$ and FGLM algorithms to compute them. Our prototype "proof-of-concept" implementation shows large speed-ups (more than 100 for some examples) compared to optimized (classical) Gr\"obner bases software. Moreover, in the case where the generating subset of monomials corresponds to the points with integer coordinates in a normal lattice polytope $\mathcal P\subset\mathbb R^n$ and under regularity assumptions, we prove complexity bounds which depend on the combinatorial properties of $\mathcal P$. These bounds yield new estimates on the complexity of solving $0$-dim systems where all polynomials share the same Newton polytope (\emph{unmixed case}). For instance, we generalize the bound $\min(n_1,n_2)+1$ on the maximal degree in a Gr\"obner basis of a $0$-dim. bilinear system with blocks of variables of sizes $(n_1,n_2)$ to the multilinear case: $\sum n_i - \max(n_i)+1$. We also propose a variant of Fr\"oberg's conjecture which allows us to estimate the complexity of solving overdetermined sparse systems.Comment: 20 pages, Corollary 6.1 has been corrected, ISSAC 2014, Kobe : Japan (2014

Les privilèges et immunités humanitaires

Depuis nombre d'années, l'organisation non gouvernementale (ONG) humanitaire et ses volontaires se dévouent corps et âme en vue d'atténuer les effets néfastes des tragédies et catastrophes qui assaillent l'humanité. La présente étude s'inscrit dans une perspective de droit nouveau visant à leur accorder des statuts juridiques internationaux particuliers. L'exercice consiste d'abord à recenser les situations problématiques auxquelles se heurtent l'ONG humanitaire et ses volontaires sur le terrain. Celles-ci détermineront essentiellement l'étendue de la protection à leur offrir. S'ensuit une analyse du droit international, général et conventionnel, sous l'angle de la protection qu'il attribue à l'ONG humanitaire et à ses volontaires. Confronté au silence du droit international quant à la détermination de statuts juridiques internationaux particuliers conformes aux besoins ressentis par le milieu, nous proposons l'ébauche d'une convention sur les privilèges et immunités humanitaires.For many years now, the Humanitarian Non-Governmental Organization (NGO) and its volunteers have continuously devoted themselves to providing relief to lessen the distressing effects of calamities and disasters that afflict humankind. This study sheds light on a new legal approach that would recognize an international status for the Humanitarian NGO and its volunteers. First, the problematic situations that they faced on site are considered and they then serve to determine the scope of protection that must be granted. Attention is subsequently focused on International Law to highlight currently afforded protection. Since International Law remains silent as to the determination of an international status that would respond to the previously identified needs, a draft convention on humanitarian privileges and immunities will therefore be proposed

Terrestrial and fluvial carbon fluxes in a tropical watershed: Nyong basin, Cameroon

The Nyong watershed, with an area of 27 800 km2 and a mean annual discharge of 390 m3 s−1, is the second largest river in Cameroon. The Nyong watershed serves as an outstanding study area for the examination of carbon fluxes in humid tropical environments because of its limited anthropogenic impact and homogeneous silicate bedrock. Between April 2005 and April 2007, we sampled water at seven stations, from the small watershed of the Mengong (0.6 km2) to the Nyong at Edea (24 500 km2), and monitored temperature, pH, dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) contents, as well as the isotopic composition of DIC (δ13CDIC)andDOC(δ13CDOC).We estimated terrestrial net ecosystemproductivity in theNyong River watershed and measured fluvial fluxes of carbon to the ocean and the atmosphere. The Nyong River basin sequesters significant amounts of carbon on an annual basis: ~7 920 000t C year−1 (300 g C m−2 year−1). The combined dissolved organic, dissolved inorganic and atmospheric fluxes of carbon from the Nyong River only export 3% of this flux fromthe basin on an annual basis. This includes a minimumCO2 outgassing of 1487 g Cm−2 year−1, comparable to 115% of the annual flux of DOC and four times greater than the flux of DIC

Taking IT Artifacts Seriously: Developing a Mixed Determinants Model of Assimilation of Telehealth Systems

A number of healthcare authorities are considering the adoption of telehealth into mainstream clinical care, bringing telehealth technology out of experimental settings into real life settings. To fully reap the benefits from a technological innovation, the innovation must be assimilated into the organization\u27s work system. As most literature on telehealth adoption to date has focused on its evaluation (e.g., user acceptance), more work is warranted to understand how telehealth can be integrated into administrative and clinical practices and to identify factors that may impinge onto telehealth integration. Borrowing from institutional, structuration and organizational learning theories, we propose a research framework* to address limitations of past work and to guide research and managerial actions while integrating telehealth in the workplace

Domain generalization of 3D semantic segmentation in autonomous driving

Using deep learning, 3D autonomous driving semantic segmentation has become a well-studied subject, with methods that can reach very high performance. Nonetheless, because of the limited size of the training datasets, these models cannot see every type of object and scene found in real-world applications. The ability to be reliable in these various unknown environments is called \textup{domain generalization}. Despite its importance, domain generalization is relatively unexplored in the case of 3D autonomous driving semantic segmentation. To fill this gap, this paper presents the first benchmark for this application by testing state-of-the-art methods and discussing the difficulty of tackling Laser Imaging Detection and Ranging (LiDAR) domain shifts. We also propose the first method designed to address this domain generalization, which we call 3DLabelProp. This method relies on leveraging the geometry and sequentiality of the LiDAR data to enhance its generalization performances by working on partially accumulated point clouds. It reaches a mean Intersection over Union (mIoU) of 50.4% on SemanticPOSS and of 55.2% on PandaSet solid-state LiDAR while being trained only on SemanticKITTI, making it the state-of-the-art method for generalization (+5% and +33% better, respectively, than the second best method). The code for this method is available on GitHub: https://github.com/JulesSanchez/3DLabelProp