École Nationale Supérieure d’Informatique pour l’Industrie et l’Entreprise

Vehicular ad hoc networks (VANETs) have received considerable attention in recent times. Multihop data delivery between vehicles is an important aspect for the support of VANET-based applications. Although data dissemination and routing have extensively been addressed, many unique characteristics of VANETs, together with the diversity in promising applications, offer newer research challenges. This paper introduces the improved greedy traffic-aware routing protocol (GyTAR), which is an intersection-based geographical routing protocol that is capable of finding robust and optimal routes within urban environments. The main principle behind GyTAR is the dynamic and in-sequence selection of intersections through which data packets are forwarded to the destinations. The intersections are chosen considering parameters such as the remaining distance to the destination and the variation in vehicular traffic. Data forwarding between intersections in GyTAR adopts an improved greedy carry-and-forward mechanism. Evaluation of the proposed routing protocol shows significant performance improvement in comparison with other existing routing approaches. With the aid of extensive simulations, we also validate the optimality and sensitivity of significant GyTAR parameters.

ldquoAlways Best Connectedrdquo (ABC) is a fundamental and challenging dimension of the fourth generation heterogeneous wireless networks. In order to enable the ABC, access network selection is obviously the key issue. In this paper, we analyze, adapt and consolidate the utility theory to define an appropriate decision mechanism in the frame of the access network selection. A thorough study of the existing proposed utility models is carried out and the limits of these methods are highlighted. Subsequently, we propose new single-criterion and multi-criteria utility forms to best capture the user satisfaction and sensitivity facing up to a bundle of access network characteristics. Mathematical proofs and numerical analysis confirm the suitability and the effectiveness of our proposed models.

Ensuring the success of big graph processing for the next decade and beyond.

Many traditional methods for identifying changepoints can struggle in the presence of outliers, or when the noise is heavy-tailed. Often they will infer additional changepoints to fit the outliers. To overcome this problem, data often needs to be preprocessed to remove outliers, though this is difficult for applications where the data needs to be analyzed online. We present an approach to changepoint detection that is robust to the presence of outliers. The idea is to adapt existing penalized cost approaches for detecting changes so that they use loss functions that are less sensitive to outliers. We argue that loss functions that are bounded, such as the classical biweight loss, are particularly suitable—as we show that only bounded loss functions are robust to arbitrarily extreme outliers. We present an efficient dynamic programming algorithm that can find the optimal segmentation under our penalized cost criteria. Importantly, this algorithm can be used in settings where the data needs to be analyzed online. We show that we can consistently estimate the number of changepoints, and accurately estimate their locations, using the biweight loss function. We demonstrate the usefulness of our approach for applications such as analyzing well-log data, detecting copy number variation, and detecting tampering of wireless devices. Supplementary materials for this article are available online.

Gene duplication is an important evolutionary mechanism allowing to provide new genetic material and thus opportunities to acquire new gene functions for an organism, with major implications such as speciation events. Various processes are known to allow a gene to be duplicated and different models explain how duplicated genes can be maintained in genomes. Due to their particular importance, the identification of duplicated genes is essential when studying genome evolution but it can still be a challenge due to the various fates duplicated genes can encounter. In this review, we first describe the evolutionary processes allowing the formation of duplicated genes but also describe the various bioinformatic approaches that can be used to identify them in genome sequences. Indeed, these bioinformatic approaches differ according to the underlying duplication mechanism. Hence, understanding the specificity of the duplicated genes of interest is a great asset for tool selection and should be taken into account when exploring a biological question.

This article presents the formal semantics of a large subset of the C language called Clight. Clight includes pointer arithmetic, struct and union types, C loops and structured switch statements. Clight is the source language of the CompCert verified compiler. The formal semantics of Clight is a big-step operational semantics that observes both terminating and diverging executions and produces traces of input/output events. The formal semantics of Clight is mechanized using the Coq proof assistant. In addition to the semantics of Clight, this article describes its integration in the CompCert verified compiler and several ways by which the semantics was validated.

No abstract available.

Structural variation is a major source of genetic diversity and an important substrate for selection. In allopolyploids, homoeologous exchanges (i.e. between the constituent subgenomes) are a very frequent type of structural variant. However, their direct impact on gene content and gene expression had not been determined. Here, we used a tissue-specific mRNA-Seq dataset to measure the consequences of homoeologous exchanges (HE) on gene expression in Brassica napus, a representative allotetraploid crop. We demonstrate that expression changes are proportional to the change in gene copy number triggered by the HEs. Thus, when homoeologous gene pairs have unbalanced transcriptional contributions before the HE, duplication of one copy does not accurately compensate for loss of the other and combined homoeologue expression also changes. These effects are, however, mitigated over time. This study sheds light on the origins, timing and functional consequences of homeologous exchanges in allopolyploids. It demonstrates that the interplay between new structural variation and the resulting impacts on gene expression, influences allopolyploid genome evolution.

in English). Importantly, the findings provide a crucial constraint on, and resource for, future work that seeks to understand the effect of color on cognition and behavior, enabling stronger a priori predictions about universal as well as culturally relative effects of conceptual color associations on cognition and behavior to be systematically tested. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Modern Natural Language Processing (NLP) makes intensive use of deep learning methods because of the accuracy they offer for a variety of applications. Due to the significant environmental impact of deep learning, costbenefit analysis including carbon footprint as well as accuracy measures has been suggested to better document the use of NLP methods for research or deployment. In this paper, we review the tools that are available to measure energy use and CO 2 emissions of NLP methods. We describe the scope of the measures provided and compare the use of six tools (carbon tracker, experiment impact tracker, green algorithms, ML CO2 impact, energy usage and cumulator) on named entity recognition experiments performed on different computational set-ups (local server vs. computing facility). Based on these findings, we propose actionable recommendations to accurately measure the environmental impact of NLP experiments.

Vehicular networks are the major ingredients of the envisioned Intelligent Transportation Systems (ITS) concept. An important component of ITS which is currently attracting wider research focus is road traffic information processing. This has widespread applications in the context of vehicular networks. The existing centralized approaches for traffic estimation are characterized by longer response times. They are also subject to higher processing requirements and possess high deployment costs. In this paper, we propose a completely distributed and infrastructure-free mechanism for road density estimation. The proposed solution is adaptive and scalable and targets city traffic environments. The approach is based on the distributed exchange and maintenance of traffic information between vehicles traversing the routes. The performance analysis of the proposed mechanism shows the accuracy of the algorithm for different traffic densities. It also gives insights into the promptness of information delivery in the mechanism based on delay analysis at road intersections. This promptness is a necessary condition to various applications requiring reliable decision making based on road traffic awareness.

The coexistence of multiple access technologies deployed by different operators is fundamental for future fourth-generation mobile networks. In spite of this heterogeneity, seamless interoperator/intersystem mobility is a mandatory requirement. In this article we present a seamless mobility management approach that does not require changes to existing network infrastructure. The novelty of the proposed approach is that mobility management is fully controlled by the terminal, and network selection is user-centric, power-saving, cost-aware, and performance-aware. Total mobility management, including interface management, handover decision, and execution, is also detailed

TNBC is a highly heterogeneous and aggressive breast cancer subtype associated with high relapse rates, and for which no targeted therapy yet exists. Protein arginine methyltransferase 5 (PRMT5), an enzyme which catalyzes the methylation of arginines on histone and non-histone proteins, has recently emerged as a putative target for cancer therapy. Potent and specific PRMT5 inhibitors have been developed, but the therapeutic efficacy of PRMT5 targeting in TNBC has not yet been demonstrated. Here, we examine the expression of PRMT5 in a human breast cancer cohort obtained from the Institut Curie, and evaluate the therapeutic potential of pharmacological inhibition of PRMT5 in TNBC. We find that PRMT5 mRNA and protein are expressed at comparable levels in TNBC, luminal breast tumors, and healthy mammary tissues. However, immunohistochemistry analyses reveal that PRMT5 is differentially localized in TNBC compared to other breast cancer subtypes and to normal breast tissues. PRMT5 is heterogeneously expressed in TNBC and high PRMT5 expression correlates with poor prognosis within this breast cancer subtype. Using the small-molecule inhibitor EPZ015666, we show that PRMT5 inhibition impairs cell proliferation in a subset of TNBC cell lines. PRMT5 inhibition triggers apoptosis, regulates cell cycle progression and decreases mammosphere formation. Furthermore, EPZ015666 administration to a patient-derived xenograft model of TNBC significantly deters tumor progression. Finally, we reveal potentiation between EGFR and PRMT5 targeting, suggestive of a beneficial combination therapy. Our findings highlight a distinctive subcellular localization of PRMT5 in TNBC, and uphold PRMT5 targeting, alone or in combination, as a relevant treatment strategy for a subset of TNBC.

Fully consistent axially-symmetric deformed quasiparticle random-phase approximation (QRPA) calculations have been performed, using the same Gogny D1S effective force for both the Hartree-Fock-Bogolyubov mean field and QRPA matrix. New implementation of this approach leads to the applicability of QRPA to heavy deformed nuclei. Giant resonances and low-energy collective states for monopole, dipole, quadrupole, and octupole modes are predicted for the heavy deformed nucleus $^{238}\mathrm{U}$ and compared with experimental data.

In this paper, we address the beacon congestion issue in Vehicular Ad Hoc Networks (VANETs) due to its devastating impact on the performance of ITS applications. The periodic beacon broadcast may consume a large part of the available bandwidth leading to an increasing number of collisions among MAC frames, especially in case of high vehicular density. This will severely affect the performance of the Intelligent Transportation Systems (ITS) safety based applications that require timely and reliable dissemination of the event-driven warning messages. To deal with this problem, we propose an original solution that consists of three phases as follows; priority assignment to the messages to be transmitted /forwarded according to two different metrics, congestion detection phase, and finally transmit power and beacon transmission rate adjustment to facilitate emergency messages spread within VANETs. Our solution outperforms the existing works since it doesn't alter the performance of the running ITS applications unless a VANET congestion state is detected. Moreover, it ensures that the most critical and nearest dangers are advertised prior to the farther and less damaging events. The simulation results show promising results and validate our solution.

Property graphs have reached a high level of maturity, witnessed by multiple robust graph database systems as well as the ongoing ISO standardization effort aiming at creating a new standard Graph Query Language (GQL). Yet, despite documented demand, schema support is limited both in existing systems and in the first version of the GQL Standard. It is anticipated that the second version of the GQL Standard will include a rich DDL. Aiming to inspire the development of GQL and enhance the capabilities of graph database systems, we propose PG-Schema, a simple yet powerful formalism for specifying property graph schemas. It features PG-Schema with flexible type definitions supporting multi-inheritance, as well as expressive constraints based on the recently proposed PG-Keys formalism. We provide the formal syntax and semantics of PG-Schema, which meet principled design requirements grounded in contemporary property graph management scenarios, and offer a detailed comparison of its features with those of existing schema languages and graph database systems.

Abstract The scrutiny over the carbon footprint of research and higher education has increased rapidly in the last few years. This has resulted in a series of publications providing various estimates of the carbon footprint of one or several research activities, principally at the scale of a university or a research center or, more recently, a field of research. The variety of tools or methodologies on which these estimates rely unfortunately prevents any aggregation or direct comparison. This is because carbon footprint assessments are very sensitive to key parameters (e.g., emission factors) or hypotheses (e.g., scopes). Hence, it is impossible to address fundamental questions such as: is the carbon footprint of research structurally different between disciplines? Are plane trips a major source of carbon emissions in academic research? Massive collection and curation of carbon footprint data, across a large array of research situations and disciplines, is hence an important, timely and necessary challenge to answer these questions. This paper presents a framework to collect and analyse large amounts of homogeneous research carbon emission data in a network of research entities at the national scale. It relies on an open-source web application, GES 1point5 , designed to estimate the carbon footprint of a department, research lab or team in any country of the world. Importantly, GES 1point5 is also designed to aggregate all input data and corresponding GHG emissions estimates into a comprehensive database. GES 1point5 therefore enables (i) the identification of robust local or national determinants of the carbon footprint of research and (ii) the estimation of the carbon footprint of the entire research sector at national scale. A preliminary analysis of the carbon footprint of more than one hundred laboratories in France is presented to illustrate the potential of the framework. It shows that the average emissions are 479 t CO 2 e for a research lab and 3.6 t CO 2 e for an average lab member (respectively 404 and 3.1 t CO 2 e without accounting for the indirect radiative effects of aviation), with the current scope of GES 1point5 . Availability and implementation: GES 1point5 is available online at http://labos1point5.org/ges- 1point5 and its source code can be downloaded from the GitLab platform at https://framagit.org/ labos1point5/l1p5-vuejs .

The authors compare the performance of two-valued and m-valued ICs for MOS and bipolar technologies according to speed, chip area, and power dissipation. They present the basic concepts and the basic types of m-valued circuits that make a serious comparison possible. They cover Post algebra and m-valued circuitry, m-valued current-mode circuits, and m-valued memory cells.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A novel framework for multiple robot cooperation called "cooperation by observation" is presented. It introduces many interesting issues such as a viewpoint constraint and role interchange, as well as novel concepts like "attentional structure". The framework has the potential to realize a high level of task coordination by decentralized autonomous robots allowing minimum explicit communication. Its source of power lies in an advanced capability given to each robot for recognizing other agent's actions by (primarily visual) observation. This provides rich information about the current task situation around each robot which facilitates highly-structured task coordination. The basic visuo-motor routines are described. Concrete examples and experiments using real mobile robots are also presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Link prediction is addressed as an output kernel learning task through semi-supervised Output Kernel Regression. Working in the framework of RKHS theory with vectorvalued functions, we establish a new representer theorem devoted to semi-supervised least square regression. We then apply it to get a new model (POKR: Penalized Output Kernel Regression) and show its relevance using numerical experiments on artificial networks and two real applications using a very low percentage of labeled data in a transductive setting. 1.