A nine point finite volume scheme for the simulation of diffusion in heterogeneous media

International audienceWe propose a cell-centred symmetric scheme which combines the advantages of MPFA (multi point ux approximation) schemes such as the L or the O scheme and of hybrid schemes: it may be used on general non conforming meshes, it yields a 9-point stencil on two-dimensional quadrangular meshes, it takes into account the heterogeneous diusion matrix, and it is coercive and convergent. The scheme relies on the use of special points, called harmonic averaging points, located at the interfaces of heterogeneity. 1

Distribution inference risks: Identifying and mitigating sources of leakage

A large body of work shows that machine learning (ML) models can leak sensitive or confidential information about their training data. Recently, leakage due to distribution inference (or property inference) attacks is gaining attention. In this attack, the goal of an adversary is to infer distributional information about the training data. So far, research on distribution inference has focused on demonstrating successful attacks, with little attention given to identifying the potential causes of the leakage and to proposing mitigations. To bridge this gap, as our main contribution, we theoretically and empirically analyze the sources of information leakage that allows an adversary to perpetrate distribution inference attacks. We identify three sources of leakage: (1) memorizing specific information about the $\mathbb{E}[Y|X]$ (expected label given the feature values) of interest to the adversary, (2) wrong inductive bias of the model, and (3) finiteness of the training data. Next, based on our analysis, we propose principled mitigation techniques against distribution inference attacks. Specifically, we demonstrate that causal learning techniques are more resilient to a particular type of distribution inference risk termed distributional membership inference than associative learning methods. And lastly, we present a formalization of distribution inference that allows for reasoning about more general adversaries than was previously possible.Comment: 14 pages, 8 figure

Mollusks of Candomblé: symbolic and ritualistic importance

Human societies utilize mollusks for myriad material and spiritual ends. An example of their use in a religious context is found in Brazil's African-derived belief systems. Candomblé, an Afro-Brazilian religion introduced during the 18th-19th centuries by enslaved Yoruba, includes various magical and liturgical uses of mollusks. This work inventoried the species utilized by adherents and to analyzed their symbolic and magical context. Data were obtained from Candomblé temples in two cities in the northeast of Brazil-Caruaru, in the state of Pernambuco, and Campina Grande, in the state of Paraíba. Questionnaires administered to eleven adepts revealed that at least nineteen mollusk species are being used. Shells from Monetaria moneta, M. annulus and Erosaria caputserpentis were cited by all of the interviewees. Three uses stood out: divination (jogo de búzios); utilization as ritual objects; and employment as sacrificial offerings (Igbin or Boi-de-Oxalá). The jogo de búzios (shell toss), employed in West Africa, Brazil and Cuba, is of fundamental importance to the cult, representing the means by which the faithful enter in contact with the divinities (Orixás) and consult people's futures (Odu). The utilization of mollusks in Candomblé is strongly influenced by ancient Yoruba myths (Itãs) which, having survived enslavement and generations of captive labor, continue to guide the lives of Brazil's African Diaspora

Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization

Domain shifts in the training data are common in practical applications of machine learning, they occur for instance when the data is coming from different sources. Ideally, a ML model should work well independently of these shifts, for example, by learning a domain-invariant representation. Moreover, privacy concerns regarding the source also require a domain-invariant representation. In this work, we provide theoretical results that link domain invariant representations -- measured by the Wasserstein distance on the joint distributions -- to a practical semi-supervised learning objective based on a cross-entropy classifier and a novel domain critic. Quantitative experiments demonstrate that the proposed approach is indeed able to practically learn such an invariant representation (between two domains), and the latter also supports models with higher predictive accuracy on both domains, comparing favorably to existing techniques.Comment: 20 pages including appendix. Under Revie

Stoichio-kinetic model discrimination and parameter identification in continuous microreactors

Kinetics is essential for chemical reactor modelling, in particular to reduce the inherent risks of extrapolation going along with scaling-up. Pharmaceutical industries are especially concerned. However, when chemical systems are very complex, development of good models may lead to prohibitively expensive and time consuming experiments. The aim of this paper is to describe an efficient experimental design strategy for discrimination of stoichio-kinetic models. The proposed methodology is based on model-based experimental design(optimal design), which uses information already acquired on models to determine the best conditions to implement a new experiment with the highest discrimination potential. The combination with microreactor technology is also proposed in this work. The whole procedure for model discrimination is firstly described in detail and then, applied to a numerical study case, consisting of a chemical synthesis carried out in a microreactor. The discrimination procedure efficiently leads to the determination of the single adequate model among the various potential models proposed before the implementation of the designed experiments.It is verified that the procedure does not depend on the set of preliminary experiments and is time-saving when compared to a classical factorial plan

Density-dependence of reproductive success in a Houbara bustard population

Although density-dependent processes and their impacts on population dynamics are key issues in ecology and conservation biology, empirical evidence of density-dependence remains scarce for species or populations with low densities, scattered distributions, and especially for managed populations where densities may vary as a result of extrinsic factors (such as harvesting or releases). Here, we explore the presence of density-dependent processes in a reinforced population of North African Houbara bustard (Chlamydotis undulata undulata). We investigated the relationship between reproductive success and local density, and the possible variation of this relationship according to habitat suitability using three independent datasets. Based on eight years of nests monitoring (more than 7000 nests), we modeled the Daily Nest Survival Rate (DNSR) as a proxy of reproductive success. Our results indicate that DNSR was negatively impacted by local densities and that this relationship was approximately constant in space and time: (1) although DNSR strongly decreased over the breeding season, the negative relationship between DNSR and density remained constant over the breeding season; (2) this density-dependent relationship did not vary with the quality of the habitat associated with the nest location. Previous studies have shown that the demographic parameters and population dynamics of the reinforced North African Houbara bustard are strongly influenced by extrinsic environmental and management parameters. Our study further indicates the existence of density-dependent regulation in a low-density, managed population.The study was funded by Emirates Center for Wildlife Propagation (ECWP, Morocco), a project of the International Fund for Houbara Conservation (IFHC, United Arab Emirates)

Prohibition and the American dream: an analysis of the entrepreneurial life and times of Al Capone.

The iconic figure of Al Capone is arguably the most prominent figure of organised crime. Both biographers and scholars have analysed his life and behaviour. Hollywood has immortalised his character in film. Today, the name Capone remains synonymous with the word gangster. Although Capone owned businesses of a legitimate nature, illicit ventures and the spectre of the gangster-entrepreneur define his practice of entrepreneurship. In an attempt to understand Capone as an entrepreneur, this paper explores his entrepreneurial behaviour within an analysis of his resource profile, his Italian-American culture and the social context of the USA in the early 20th century

Privacy-preserving Attestation for Virtualized Network Infrastructures

In multi-tenant cloud environments, physical resources are shared between various parties (called tenants) through the use of virtual machines (VMs). Tenants can verify the state of their VMs by means of deep-attestation: a process by which a (physical or virtual) Trusted Platform Module --TPM -- generates attestation quotes about the integrity state of the VMs. Unfortunately, most existing deep-attestation solutions are either: limited to single-tenant environments, in which tenant {privacy is irrelevant; are inefficient in terms of {linking VM attestations to hypervisor attestations; or provide privacy and/or linking, but at the cost of modifying the TPM hardware. In this paper, we propose a privacy preserving TPM-based deep-attestation solution in multi-tenant environments, which provably guarantees: (i) Inter-tenant privacy: a tenant is unaware of whether or not the physical machine hosting its VMs also contains other VMs (belonging to other tenants); (ii) Configuration privacy: the hypervisor\u27s configuration, used in the attestation process, remains private with respect to the tenants requiring a hypervisor attestation; and (iii) Layer linking: our protocol enables tenants to link hypervisors with the VMs, thus obtaining a guarantee that their VMs are running on specific physical machines. Our solution relies on vector commitments and ZK-SNARKs. We build on the security model of Arfaoui et al. and provide both formalizations of the properties we require and proofs that our scheme does, in fact attain them. Our protocol is scalable, and our implementation results prove that it is viable, even for a large number of VMs hosted on a single platform