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Magnetisation Moment of a Bounded 3D Sample: Asymptotic Recovery from Planar Measurements on a Large Disk
No full textInternational audienceInverse magnetisation problem consists in inferring information about a magnetic source from measurements of its magnetic field.Unlike a general magnetisation distribution, the total magnetisation (net moment) of the source is a quantity that theoretically can be uniquely determined from the field. At the same time, it is often the most useful quantity for practical applications (on large and small scales) such as detection of a magnetic anomaly in magnetic prospection problem or finding the overall strength and mean direction of the magnetisation distribution of a magnetised rock sample.It is known that the net moment components can be explicitly estimated using the so-called Helbig's integrals which involve integration of the magnetic field data on the plane against simple polynomials. Evaluation of these integrals requires knowledge of the magnetic field data on a large region or the use of ad hoc methods to compensate for the lack thereof.In this paper, we derive higher-order analogs of Helbig's integrals which permit estimation of total magnetisation components in terms of measurement data available on a smaller region. Motivated by a concrete experimental setup for analysing remanent magnetisation of rock samples with a scanning microscope, we also extend Helbig's integrals to the situation when knowledge of only one field component is necessary. Moreover, apart from derivation of these novel formulas, we rigorously prove their accuracy.The presented approach, based on an appropriate splitting in the Fourier domain and estimates of oscillatory integrals (involving both small and large parameters), elucidates the derivation of asymptotic formulas for the net moment components to an arbitrary order, a possibility that was previously unclear.The obtained results are illustrated numerically and their robustness with respect to the noise is discussed
Towards Accurate Static Power Model on Multi-Core Operating Systems
No full textInternational audienceSoftware-based power meters, such as PowerAPI, provide a convenient way to monitor energy consumption in computing infrastructures. However, they often struggle to accurately account for static power consumption—the portion of power that remains constant regardless of system activity. This study aims to address these limitations by developing a robust static power estimation model for multi-core CPUs using offline data from controlled experiments.Our approach utilizes five key processor metrics: temperature, voltage, frequency, activity levels, and Running Average Power Limit (RAPL) measurements. We created a custom stress workload to gather data across diverse conditions, ensuring thorough core-specific activity. To address inaccuracies in thermal sensors, we implemented a temperature correction algorithm, refining raw data to enable analysis of core-specific parameters like frequency, voltage, and activity levels.Results show that the static power of all cores combined contributes as much as 12% of the total CPU package power.This methodology is designed to be reproducible and transparent, contributing to energy efficiency research by addressing the limitations of current software power meters and offering an offline analysis of static power consumption to be later integrated with a dynamic power consumption model
Strategic Input Selection for Deep Neural Networks Reliability Evaluation
No full textInternational audienc
Global weak solutions to the Quantum Navier-Stokes system in the whole space with a confining external potential
No full textInternational audienceWe consider a dissipative quantum fluid on the whole space R^d (d ≥ 1) confined by an external harmonic potential. The dynamics of the quantum fluid is described by the Quantum Navier-Stokes (QNS) system which is a particular case of the Navier-Stokes-Korteweg systems. The goal of this paper is to prove the existence of global weak solutions to the QNS system. To this end, we write the evolution equations with respect to a Gaussian reference measure and follow the general strategy of previous works [7, 11, 20, 24]. Nevertheless, several substantial modifications have to be done due to our choice of the reference measure
Signal processing for brain signals
No full textInternational audienceHow do we characterize and analyze brain signals? This chapter highlights best practices for brain signal processing and analysis. The first part of this chapter describes the type of information that constitutes the brain signals recorded via magnetoencephalography, electroencephalography, and intraelectroencephalography methods during cognitive tasks. The second part provides an overview of the common and specific preprocessing steps for each of these methods by explaining their implementation and their impact on the signal
Combining Open Data and Formal Reasoning for Autonomously Controlled Spreading near Water Bodies
No full textNational audienceApplying fertilizers and pesticides near bodies of water poses significant environmental risks, primarily due to the potential for chemical runoff to contaminate aquatic ecosystems. To avoid this, regulations establish prohibition zones based on environmental and application-specific parameters, such as terrain slope, wind speed, precipitation, and the type and composition of substances used. This paper presents an autonomous robotic system that was developed to comply with these regulations while maximizing usable agricultural land. The robot scans its environment with sensors, including LiDAR, to measure features such as the distance to nearby bodies of water and the slope of the ground underneath. The InteGraal reasoning framework uses samples of regulations encoded in machine-readable RDF formats (using PAM vocabularies) and sensor observations modeled by the Semantic Sensor Network Ontology (SSNO) to make real-time decisions about where to stop or resume spraying. We extend existing vocabularies to include fertilizer-specific regulations, ensuring a comprehensive, semantically rich decision-support system for autonomous farms
Large graph limits of local matching algorithms on Configuration model graphs
No full textIn this work, we propose a large-graph limit estimate of the matching coverage for several matching algorithms, on general graphs generated by the configuration model. For a wide class of local matching algorithms, namely, algorithms that only use information on the immediate neighborhood of the explored nodes, we propose a joint construction of the graph by the configuration model, and of the resulting matching on the latter graph. This leads to a generalization in infinite dimension of the differential equation method of Wormald: We keep track of the matching algorithm over time by a measure-valued CTMC, for which we prove the convergence, to the large-graph limit, to a deterministic hydrodynamic limit, identified as the unique solution of a system of ODE's in the space of integer measures. Then, the asymptotic proportion of nodes covered by the matching appears as a simple function of that solution. We then make this solution explicit for three particular local algorithms: the classical greedy algorithm, and then the uni-min and uni-max algorithms, two variants of the greedy algorithm that select, as neighbor of any explored node, its neighbor having the least (respectively largest) residual degree
Routing Quantum Control of Causal Order
No full textIn recent years, various frameworks have been proposed for the study of quantum processes with indefinite causal order. In particular, quantum circuits with quantum control of causal order (QC-QCs) form a broad class of physical supermaps obtained from a bottom-up construction and are believed to represent all quantum processes physically realisable in a fixed spacetime. Complementarily, the formalism of routed quantum circuits introduces quantum operations constrained by "routes" to represent processes in terms of a more fine-grained routed circuit decomposition. This decomposition, formalised using a so-called routed graph, represents the information flow within the respective process. However, the existence of routed circuit decompositions has only been established for a small set of processes so far, including both certain specific QC-QCs and more exotic processes as examples. In this work, we remedy this fact by connecting these two frameworks. We prove that for any given , one can use a single routed graph to systematically obtain a routed circuit decomposition for any QC-QC with parties. We detail this construction explicitly and contrast it with other routed circuit decompositions of QC-QCs, which we obtain from alternative routed graphs. We conclude by pointing out how this connection can be useful to tackle various open problems in the field of indefinite causal order, particularly establishing circuit representations of subclasses of QC-QCs
(Demo) MITIK Toolkit: A Privacy-Compliant Passive Collection of WiFi Probe Request Datasets
No full textInternational audienceThe ubiquity of WiFi-connected devices broadcasting unencrypted management frames enables identifying nearby devices, which can be beneficial for societal applications while raising significant privacy concerns. This demo paper introduces a unified toolkit, Mitik, for capturing, analyzing, and interpreting non-intrusive passive measurements of WiFi traces. The toolkit addresses several challenges, including the configuration of sniffers for synchronized data capture, privacy protection at the point of collection, and the association of randomized MAC addresses with individual smartphones. By systematically tackling these challenges, Mitik aims to advance our understanding of individual mobility patterns and uncover plausible links between distinct devices