The ESR1 (6q25) locus is associated with calcaneal ultrasound parameters and radial volumetric bone mineral density in European men

<p><b>Purpose:</b> Genome-wide association studies (GWAS) have identified 6q25, which incorporates the oestrogen receptor alpha gene (ESR1), as a quantitative trait locus for areal bone mineral density (BMD(a)) of the hip and lumbar spine. The aim of this study was to determine the influence of this locus on other bone health outcomes; calcaneal ultrasound (QUS) parameters, radial peripheral quantitative computed tomography (pQCT) parameters and markers of bone turnover in a population sample of European men.</p> <p><b>Methods:</b> Eight single nucleotide polymorphisms (SNP) in the 6q25 locus were genotyped in men aged 40-79 years from 7 European countries, participating in the European Male Ageing Study (EMAS). The associations between SNPs and measured bone parameters were tested under an additive genetic model adjusting for centre using linear regression.</p> <p><b>Results:</b> 2468 men, mean (SD) aged 59.9 (11.1) years had QUS measurements performed and bone turnover marker levels measured. A subset of 628 men had DXA and pQCT measurements. Multiple independent SNPs showed significant associations with BMD using all three measurement techniques. Most notably, rs1999805 was associated with a 0.10 SD (95%CI 0.05, 0.16; p = 0.0001) lower estimated BMD at the calcaneus, a 0.14 SD (95%CI 0.05, 0.24; p = 0.004) lower total hip BMD(a), a 0.12 SD (95%CI 0.02, 0.23; p = 0.026) lower lumbar spine BMD(a) and a 0.18 SD (95%CI 0.06, 0.29; p = 0.003) lower trabecular BMD at the distal radius for each copy of the minor allele. There was no association with serum levels of bone turnover markers and a single SNP which was associated with cortical density was also associated with cortical BMC and thickness.</p> <p><b>Conclusions:</b> Our data replicate previous associations found between SNPs in the 6q25 locus and BMD(a) at the hip and extend these data to include associations with calcaneal ultrasound parameters and radial volumetric BMD.</p&gt

Multicast Capacity of Optical WDM Packet Ring for Hotspot Traffic

Packet-switching WDM ring networks with a hotspot transporting unicast, multicast, and broadcast traffic are important components of high-speed metropolitan area networks. For an arbitrary multicast fanout traffic model with uniform, hotspot destination, and hotspot source packet traffic, we analyze the maximum achievable long-run average packet throughput, which we refer to as \textit{multicast capacity}, of bi-directional shortest-path routed WDM rings. We identify three segments that can experience the maximum utilization, and thus, limit the multicast capacity. We characterize the segment utilization probabilities through bounds and approximations, which we verify through simulations. We discover that shortest-path routing can lead to utilization probabilities above one half for moderate to large portions of hotspot source multi- and broadcast traffic, and consequently multicast capacities of less than two simultaneous packet transmissions. We outline a one-copy routing strategy that guarantees a multicast capacity of at least two simultaneous packet transmissions for arbitrary hotspot source traffic

Optimisation du risque de sécurité pour l'apprentissage sur données de qualité hétérogène

Les systèmes de détection d'intrusion (IDS) sont des composants essentiels dans l'infrastructure de sécurité des réseaux. Pour faire face aux problèmes de scalabilité des IDS utilisant des règles de détection artisanales, l'apprentissage automatique est utilisé pour concevoir des IDS formés sur des ensembles de données. Cependant, ils sont de plus en plus mis au défi par des méta-attaques, appelées attaques d'évasion adverses, qui modifient les attaques existantes pour améliorer leurs capacités d'évasion. Par exemple, ces approches utilisent les réseaux antagonistes génératifs (GAN) pour automatiser la modification. Différentes approches ont été proposées pour rendre ces IDS robustes : les solutions basées sur l'entraînement antagoniste se sont avérées assez réussies.Néanmoins, l'évasion des IDS demeure pertinente car de nombreuses contributions montrent également que les attaques d'évasion adverses restent efficaces malgré l'utilisation de l'entraînement antagoniste sur les IDS. Dans cette thèse, nous étudions cette situation et présentons des contributions qui améliorent la compréhension de l'une de ses causes profondes et des directives pour l'atténuer. La première étape est de mieux comprendre les sources possibles de variabilité dans les performances des IDS ou des attaques d'évasion. Trois sources potentielles sont considérées : les problèmes d'évaluation méthodologique, la course inhérente conduisant à dépenser davantage de ressources informatiques en attaque ou en défense, ainsi que les problèmes d'entraînement et d'acquisition de données lors de l'entraînement des IDS.La première contribution consiste en des directives pour mener des évaluations robustes des IDS au-delà de la simple recommandation pour une analyse empirique. Ces directives couvrent à la fois la conception d'une seule expérience mais aussi les campagnes d'analyse de sensibilité. La conséquence de l'application de ces directives est d'obtenir des résultats plus stables lors du changement de paramètres liés aux ressources d'entraînement. L'élimination des artefacts dus à des procédures d'évaluation inadéquates nous amène à enquêter sur les raisons pour lesquelles certaines parties sélectionnées de l'ensemble de données considérées tendent à n'être presque pas affectées par les attaques adverses.La deuxième contribution est la formalisation des milieux adverses en proposant une autre façon de caractériser les échantillons contradictoires. Cette formalisation nous permet de revisiter un critère de qualité des données, à savoir l'absence d'échantillons contradictoires, qui porte habituellement sur les échantillons non contradictoires, et de l'adapter aux ensembles de données d'échantillons contradictoires. À partir de cette démarche, quatre situations de menace ont été identifiées avec des impacts qualitatifs clairs soit sur l'entraînement d'un IDS robuste, soit sur la capacité de l'attaquant à trouver des attaques d'évasion plus réussies.Enfin, nous proposons des contre-mesures aux menaces mentionnées ci-dessus et effectuons ensuite une évaluation quantitative empirique de ces menaces et des contre-mesures proposées. Les résultats de ces expérimentations confirment l'importance de vérifier et d'atténuer de manière appropriée les menaces liées à un ensemble contradictoire étendu non vide. En effet, il s'agit d'une vulnérabilité non triviale qui peut être vérifiée et atténuée avant l'entraînement des IDS.Intrusion Detection Systems (IDSs) serve as critical components in network security infrastructure.In order to cope with the scalability issues of IDSs using handcrafted detection rules, machine learning is used to design IDSs trained on datasets.Yet, they are increasingly challenged by meta-attacks, called adversarial evasion attacks, that alter existing attacks to improve their evasion capabilities.These approaches, for instance, employ Generative Adversarial Networks (GANs) to automate the alteration process.Several strategies have been proposed to enhance the robustness of IDSs against such attacks, with significant success in strategies based on adversarial training.However, IDSs evasion remains relevant as many contributions also show that adversarial evasion attacks are still efficient despite using adversarial training on IDSs. In this thesis, we investigate this situation and present contributions that improve the understanding of one of its root causes and guidelines to mitigate it.The first step is to better understand the possible sources of variability in IDS or evasion attack performances. Three potential sources are considered: methodological assessment issues, the inherent race to spend more computational resources in attack or defense, or issues in training and dataset acquisition when training IDSs.The first contribution consists of guidelines to conduct robust IDSs assessments beyond the simple recommendation for empirical analysis. These guidelines cover both single experiment design but also sensitivity analysis campaigns.The consequence of applying such guidelines is to obtain more stable results when changing training resource related parameters. Removing artifacts due to inadequate assessment procedures leads us to investigate why some selected parts of the considered dataset tend to be almost not affected by adversarial attacks.The second contribution is the formalization of adversarial neighborhoods: an alternative way to characterize adversarial samples. This formalization allows us to adapt and evaluate data quality criteria used for non-adversarial samples, such as the absence of contradictory samples, and apply similar criteria to adversarial sample datasets. From this concept, four threat situations have been identified with clear qualitative impacts either on the training of a robust IDS or the attacker's ability to find more successful evasion attacks.Finally, we propose countermeasures to the identified threats and then perform an empirical quantitative assessment of both threats and countermeasures.The findings of these experiments highlight the need to identify and mitigate threats associated with a non-empty extended contradictory set. Indeed, this crucial vulnerability should be identified and addressed prior to IDS training

Mécanismes d'agrégation multicast dans un MAN optique avec support de QoS

The explosive growth of the Internet Protocol (IP)-based traffic has accelerated the emergence of wavelength division multiplexing (WDM) technology. In order to provide a simple transport platform of IP traffic over WDM structure, optical packet switching (OPS), based on fixed-length packets and synchronous node operation, is regarded as a long term issue especially for metropolitan area networks (MANs) where the synchronization is easy to handle and relatively simple to maintain. In this context, this thesis presents a novel approach for efficiently supporting IP traffic with several quality of service (QoS) requirements into a synchronous WDM MAN layer. The claimed efficiency is achieved by aggregating IP packets regard-less of their final destinations which yields a multicast optical packet. To support QoS, a timer mechanism is used and a class-based scheme at the edge of optical network is adopted. Several analytical models have been developed to quantify the performance of different versions of the aggregation approach. The different versions correspond to different timer mechanisms and to the permission or the ban of IP packet segmentation by the aggregation process. This is because IP packets have variable size while optical packet is of fixed size. The length variability of IP traffic is included in the analytical models which represents an essential contribution of this thesis. We next investigate the impact of multicast on WDM slotted ring MANs. In particular we focus on two families of MANs. The first family enables destination stripping, while in the second one, ring nodes contain passive components and the stripping is attributed to the hub which separates two different sets of wave-length channels, one for transmission and one for reception. The capacity (maximum throughput) of each network is evaluated by means of an analytical model. The access delay is also investigated by using an approximate approach in the case of destination stripping and an exact approach in the case of hub stripping. Further-more, the impact of the optical packet format on the performance is depicted. We show the attractiveness of the multicast aggregation in MANs through a comparative study with the performance of unicast aggregation and no aggregation approaches therein. Multicast aggregation increases bandwidth efficiency due to the filling ratio improvement exhibited in optical packets. Furthermore, hub stripping networks match very well the multicast nature of the generated optical packets without the addition of any complexity in the node architecture. However, a small overhead complexity is added in the case of destination stripping networks. Note that all approximative analytical models have been validated by using extensive simulations, where two traffic profiles were investigated: Poisson and Self-Similar.EVRY-BU (912282101) / SudocEVRY-INT (912282302) / SudocSudocFranceF

Exergetic optimization of a thermoacoustic engine using the particle swarm optimization method

International audienceThermoacoustic engines convert heat energy into acoustic energy. Then, the acoustic energy can be used to pump heat or to generate electricity. It is well-known that the acoustic energy and therefore the exergetic efficiency depend on parameters such as the stack’s hydraulic radius, the stack’s position in the resonator and the traveling–standing-wave ratio. In this paper, these three parameters are investigated in order to study and analyze the best value of the produced acoustic energy, the exergetic efficiency and the product of the acoustic energy by the exergetic efficiency of a thermoacoustic engine with a parallel-plate stack. The dimensionless expressions of the thermoacoustic equations are derived and calculated. Then, the Particle Swarm Optimization method (PSO) is introduced and used for the first time in the thermoacoustic research. The use of the PSO method and the optimization of the acoustic energy multipliedby the exergetic efficiency are novel contributions to this domain of research. This paper discusses some significant conclusions which are useful for the design of new thermoacoustic engines

Threats to Adversarial Training for IDSs and Mitigation

International audienceIntrusion Detection Systems (IDS) are essential tools to protect network security from malicious traffic. IDS have recently made significant advancements in their detection capabilities through deep learning algorithms compared to conventional approaches. However, these algorithms are susceptible to new types of adversarial evasion attacks. Deep learning-based IDS, in particular, are vulnerable to adversarial attacks based on Generative Adversarial Networks (GAN).First, this paper identifies the main threats to the robustness of IDS against adversarial sample attacks that aim at evading IDS detection by focusing on potential weaknesses in the structure and content of the dataset rather than on its representativeness.In addition, we propose an approach to improve the performance of adversarial training by driving it to focus on the best evasion candidates samples in the dataset.We find that GAN adversarial attack evasion capabilities are significantly reduced when our method is used to strengthen the IDS

Particle Swarm Optimization Method in Thermoacoustic Problems

International audienceThermoacoustic engine systems convert heat power into acoustic power which is useful to pump heat or to generate electricity. To construct a robust and useful thermoacoustic device, both the acoustic power produced and the exergetic efficiency of this device should have acceptable and meaningful values. In order to attain this objective, an optimization study is strongly recommended and required. In the literature of thermoacoustic research, we found only some limited synthetic optimization methods. This paper presents a new study that incorporates the Particle Swarm Optimization (PSO) method for the first time in the thermoacoustic researchin order to optimize the two objective functions, i.e. the acoustic power and the exergetic efficiency. The importance of using the PSO method in thermoacoustic research is highlighted and extensively investigated. In addition, significant conclusions, which are useful for the design of new thermoacoustic engines, are discussed

Optical MAN ring performance with traffic aggregations

International audienceWe propose to study end to end delays in slotted optical MAN (Metropolitan Area Networks) ring networks. Edge nodes interconnect the fibre of the MAN and the client layer. We propose a mechanism based on IP traffic aggregations with QoS requirements in order to build optical networks. IP packet aggregation is performed in a loop manner by always beginning the aggregation cycle with the highest priority class. In order to overcome the drawbacks of the strict priority discipline, an algorithm depicting a probabilistic priority discipline is presented. The aggregation technique leads to increasing the filling ratio of an optical packet, and consequently, the bandwidth efficiency of an optical network. We present an analytical model in order to evaluate the performance of the aggregation mechanism. Secondly, we study the performance of the optical MAN ring with different packet formats. The MAC protocol is based on the "empty slot" procedure. Different queueing models based on the GI/G/1 queues with server interruptions are presented in order to compute mean packet delays according to the rank nodes

A new numerical optimization approach for standing-wave thermoacoustic engines

International audienceThis paper develops a novel method to optimize standing-wave thermoacoustic engines with a parallel-plate stack using the particle swarm optimization method. The aim of the present work is to understand the effect of geometric, thermal and pressure parameters on the performance of a thermoacoustic engine. In particular, the studied parameters include: the resonators' length and diameter, stacks' length, hydraulic radius, porosity and position in the resonator, hot and cold temperature, frequency, mean pressure and drive ratio. To attain this objective, the Particle Swarm Optimization (PSO) method is highlighted and used in order to optimize this high number of parameters in one thermoacoustic problem which is, for the best of our knowledge, investigated for the first time in the literature. In this paper, the linear theory is applied to calculate the acoustics' pressure and velocity of a numerical model which consists of three sections, hot resonator, stack and cold resonator sections. Both the exergetic efficiency and the acoustic power produced are the two objective functions to be optimized. Results show that when exergetic efficiency is high the acoustic power produced is low andvice-versa. So, a third function that combines the two functions is optimized in order to have acceptable and meaningful values of both exergetic efficiency and acoustic power produced. Finally, significant results, which are useful to design any new thermoacoustic devices, are showed and discussed

A novel issue for the design of access interfaces in all optical slotted networks

International audience; In this contribution, we consider a novel approach for efficiently supporting IP packets directly into a slotted optical wavelength- division-multiplexing (WDM) layer with several quality of service (QoS) requirements. The approach is based on aggregating IP packets, regardless of their final destinations, at fixed time intervals before the optical conversion phase. A QoS support access mechanism based on the strict priority discipline is evaluated by means of an analytical model. In this case, IP packet aggregation is performed in a loop manner by always beginning the aggregation cycle with the highest priority class. The aggregation cycle ends if the aggregate packet cannot accommodate more IP packets, or if the lowest priority class is reached. In order to overcome the drawbacks of the strict priority discipline, an algorithm depicting a probabilistic priority discipline is presented. The aggregation technique leads to increasing the filling ratio of an optical packet, and consequently, the bandwidth efficiency of an optical network, due to the multicast nature of the aggregation technique. In this context, the support of multicast in metropolitan area networks (MANs), with ring architectures, is presented. Furthermore, a new architecture is proposed in order to support the multicast aggregation technique in wide area networks (WANs