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Nonlinear Optical Properties of Fe(II) and Ru(II) Alkynyl-Functionalized 1,3,5-Triphenyl-1,3,5-triazine-2,4,6-triones and 1,3,5-Triphenylbenzenes: Syntheses, Second-Harmonic Generation and Two-Photon Absorption
No full textInternational audienceWe report the use of σ-alkynyl d6 electron-rich transition metal complexes as electron-releasing end-groups in octupolar molecules designed for nonlinear optical (NLO) applications, specifically, N,N′,N″-triarylisocyanurates (5,7,8,10,12) and 1,3,5-triarylbenzenes (6,9,11) functionalized by Fe(II) and Ru(II) organometallic moieties, and their NLO properties, as assessed by hyper-Rayleigh scattering (HRS) and Z-scan. The redox properties are briefly investigated through isolation of the corresponding Fe(III) trications 5[PF6]3 and 6[PF6]3. The second-harmonic generation (SHG) or two-photon absorption (2PA) performance of the Fe(II) and Ru(II) parents is compared with the help of TD-DFT calculations performed on models. Comparison with tris-ferrocenyl isocyanurate 4 reveals that the σ-connection of the metallic centers to the π-manifold is superior to the η5-connection for enhancing NLO properties. The positive effect of organometallic end-groups on NLO properties relative to purely organic electron-releasing substituents is established. The mechanism by which NLO enhancement occurs is complex and possibly connected to the polarizable π-electrons in the ligands surrounding the metal alkynyl units, but in most cases, the observed NLO enhancement must arise from the transition metal centers interacting with the central π-manifold
Polycontrolled PROPs for Qudit Circuits: A Uniform Complete Equational Theory For Arbitrary Finite Dimension
No full textWe present a finite schematic axiomatisation of quantum circuits over d-level systems (qudits), uniform in every finite dimension d >= 2. For each d we define a PROP equipped with a family of control functors, treating control as a primitive categorical constructor. Using a translation between qudit circuits and the LOPP calculus for linear optics based on d-ary Gray codes, we obtain for each d a finite set of local axiom schemata that is sound and complete for unitary d-level circuits: two circuits denote the same unitary if and only if they are inter-derivable using axioms involving at most three wires. The generators are compatible with standard universal qudit gate families, yielding a sound equational basis for circuit rewriting and optimisation-by-rewriting. Conceptually, this extends the qubit circuit completeness results of Cl\'ement et al.\ to arbitrary finite dimension, and instantiates the control-as-constructor approach of Delorme and Perdrix in this setting, while keeping the axiom shapes uniform in d
A Chase-based Approach to Consistent Answers of Analytic Queries in Star Schemas
No full textWe present an approach to computing consistent answers to queries possibly involving an aggregation operator in databases operating under a star schema and possibly containing missing values and inconsistent data. Our approach is based on earlier work concerning consistent query answering for standard queries (with no aggregate operator) in multi-table databases. In that work, we presented polynomial algorithms for computing either the exact consistent answer to a query or bounds of the exact answer, depending on whether the query involves a selection condition or not.In the present work, we consider databases operating under a star schema. Calling data warehouses such databases, we extend our previous work to queries involving aggregate operators, called analytic queries. In this context, we propose specific algorithms for computing exact consistent answers to queries, whether analytic or not, provided that the selection condition in the query satisfies the property of independency (i.e., the condition can be expressed as a conjunction of conditions each involving a single attribute). We show that the overall time complexity of these specific algorithms is in O(W.log(W)), where W is the size of the data warehouse. Moreover, the case of analytic queries involving a having clause associated with a group-by clause is discussed in the context of our approach
Graph Neural Networks for Graph-Level Regression on Heterogeneous Network Data: Use Case in Early-Stage Optimization of Software Mapping on Multicore Platforms
No full textInternational audienceAccurately predicting the power consumption and latencyof software applications deployed on multicore platforms is a criticalbottleneck for early-stage performance optimization, as it often relies onslow and costly simulations.To address this challenge, we introduce a graph-based methodology thatmodels the mappings of software applications onto multicore architecturesas heterogeneous graphs. We then investigate Graph Neural Networks(GNNs) to predict power consumption and latency for these mappings.Four classes of state-of-the-art GNNs were trained under variousconditions on 11 datasets, each containing multiple mappings of agiven Neural-Network application. The two best-performing GNN classesachieve mean absolute percentage errors of around 2% for power-consumptionprediction and 15% for latency. Inference requires only a few tens ofmilliseconds per prediction. This work demonstrates that GNNs offer afast and promising approach to performance prediction, opening the doorto AI-assisted optimization of software mapping on multicore platforms.The relevance of our work extends beyond the use case: we introduce anoperational framework for GNNs to support heterogeneous graph processingand multi-output regression at the graph level
Détermination expérimentale de l’énergie de fissuration de bétons coquillers par corrélation d’images numériques
No full textInternational audienceDans le cadre de la réduction de l'empreinte environnementale des bétons, de récentes études ont permis de montrer qu'il était possible de substituer 50% des gravillons naturels par des co-produits coquilliers d'huîtres, tout en conservant des propriétés mécaniques suffisantes pour des bétons porteurs et en améliorant leurs propriétés de durabilité. Cependant, l'impact de ces granulats alternatifs sur la fissuration des bétons n'a pas encore été étudié. Le travail présenté ici s'intéresse à la fissuration et plus particulièrement à l'énergie de fissuration de bétons incluant des co-produits coquilliers. Des essais de flexion trois points sur éprouvettes entaillées ont été réalisés. L'utilisation de la corrélation d'images numériques (CIN) a permis de mesurer les champs de déplacement et d'observer la propagation de la fissure au cours de l'essai. En particulier, une méthode utilisant l'identification des séries de Williams par méthode intégrée a été utilisée. Elle permet de déterminer les facteurs d'intensité du matériau et de déduire l'énergie de fissuration pour chaque éprouvette
LivingFog: In-Situ Environmental Data Processing for Urgent Event Detection and Analysis
No full textInternational audienceMonitoring the natural environment requires collecting and processing data through a set of sensors placed in the relevant locations. To enable quick and reliable detection and analysis of events such as river floods and heat waves, fog computing technologies enable in-situ data processing and actuator activation without needing to rely on long-distance networks in remote regions. This chapter presents LivingFog, a fog computing platform designed to support the needs of environmental scientists. We first describe the challenges faced by environmental observatories and highlight the way fog computing addresses them. We then proceed to present LivingFog's data flow organization and the underlying system architecture. We motivate and illustrate the design and features of LivingFog with real-world scenarios from the Kaligandaki Observatory, a geohydrological observatory set in Nepal
OMP multidimensionnel déplié sous contraintes physiques pour les systèmes MIMO à grande échelle
No full textSparse recovery methods are essential for channel estimation and localization in modern communication systems, but their reliability relies on accurate physical models, which are rarely perfectly known. Their computational complexity also grows rapidly with the dictionary dimensions in large MIMO systems. In this paper, we propose MOMPnet, a novel unfolded sparse recovery framework that addresses both the reliability and complexity challenges of traditional methods. By integrating deep unfolding with data-driven dictionary learning, MOMPnet mitigates hardware impairments while preserving interpretability. Instead of a single large dictionary, multiple smaller, independent dictionaries are employed, enabling a low-complexity multidimensional Orthogonal Matching Pursuit algorithm. The proposed unfolded network is evaluated on realistic channel data against multiple baselines, demonstrating its strong performance and potential
LivingBench: an IoT/Edge Platform Benchmark Based on an Environmental Observation Use-Case
No full textInternational audienceIn recent years, a number of edge computing platforms have been proposed to process data produced by IoT sensors. Performing computation close to the sources of data allows faster insight and greater reliability at a lower cost compared to traditional cloud-based deployments. However, designers of IoT/edge platforms face difficult issues. In particular, exercising and testing a new platform in conditions that approach a real deployment requires a sufficient number of standard benchmarking systems capable of generating realistic workloads. In this paper, we propose LivingBench, a benchmarking tool with the capability of incorporating real or synthetic workload injection, developed to exercise edge computing systems. LivingBench integrates a real-world data trace captured in an environmental observatory, together with a collection of actual applications designed for processing these data, and a load injector tool capable of replaying a (possibly pre-processed) trace to benchmark an MQTT-based edge system. We describe the architecture of LivingBench and show how it may be used to evaluate the maximum data processing capacity of an edge system under test
A posteriori study of Thermal-Large Eddy Simulation in solar receiver operating conditions
No full textWe study Thermal-Large Eddy Simulations (T-LES) of anisothermal and turbulent channel flows. The studied physical condition represent solar receiver operating conditions. T-LES are compared to Direct Numerical Simulation (DNS) a posteriori. We solve the low-Mach number Navier-Stokes equations. 5 two-layered mixed models and 7 functional models are assessed. All models are based on the Anisotropic Minimum Dissipation (AMD) model. The effect of different numerical schemes is highlighted. A global error rate is shown for all 12 studied models. Four models are selected for a detailed analysis. The effect of the mesh, numerical scheme and model used is shown. Results show good agreement of a two-layered mixed model using the AMD---Bardina, AMD scalar---Bardina model. The mixed models based on the Gradient models show poor performance on all three meshes
Raman Spectroscopy Applied to Polymer Composites and Nanocomposites
No full textInternational audienc