Compensation Rights for Reduction in Property Values Due To Planning Decisions: The Case of France

This Article will describe the origins of the basic principle of non-compensation, the limits of its application, and the various ways in which landowners can receive limited compensation. The Article will conclude by commenting on the recent evolution of the European Court of Human Rights’ jurisprudence

A new blind adaptive antenna array for GNSS interference cancellation

This paper introduces a new blind adaptive antenna array as a possible solution to the interference cancellation problem. This new technique is compared to three classical ones over two different sensor radiation patterns. Special attention is paid to the array compatibility with a conventional GNSS receiver. A wide radiation pattern sensor is shown to improve the positioning accuracy by maximizing the satellite constellation visibility. Finally, the new processor demonstrates its superiority in term of positioning accuracy in presence of strong interferences. However, its phase response may make it incompatible with classical GNSS receivers. Some efforts must be done to stabilize it

Optical gratings induced by field-free alignment of molecules

We analyze the alignment of molecules generated by a pair of crossed ultra-short pump pulses of different polarizations by a technique based on the induced time-dependent gratings. Parallel polarizations yield an intensity grating, while perpendicular polarizations induce a polarization grating. We show that both configurations can be interpreted at moderate intensity as an alignment induced by a single polarized pump pulse. The advantage of the perpendicular polarizations is to give a signal of alignment that is free from the plasma contribution. Experiments on femtosecond transient gratings with aligned molecules were performed in CO2 at room temperature in a static cell and at 30 K in a molecular expansion jet.Comment: 10 pages, 5 figures, submitted to PR

Few Graphene layer/Carbon-Nanotube composite Grown at CMOS-compatible Temperature

We investigate the growth of the recently demonstrated composite material composed of vertically aligned carbon nanotubes capped by few graphene layers. We show that the carbon nanotubes grow epitaxially under the few graphene layers. By using a catalyst and gaseous carbon precursor different from those used originally we establish that such unconventional growth mode is not specific to a precise choice of catalyst-precursor couple. Furthermore, the composite can be grown using catalyst and temperatures compatible with CMOS processing (T < 450\degree C).Comment: 4 pages, 4 figure

Electron transport through antidot superlattices in Si/SiGeSi/SiGe heterostructures: new magnetoresistance resonances in lattices with large diameter antidots

In the present work we have investigated the transport properties in a number of Si/SiGe samples with square antidot lattices of different periods. In samples with lattice periods equal to 700 nm and 850 nm we have observed the conventional low-field commensurability magnetoresistance peaks consistent with the previous observations in GaAs/AlGaAs and Si/SiGe samples with antidot lattices. In samples with a 600 nm lattice period a new series of well-developed magnetoresistance oscillations has been found beyond the last commensurability peak which are supposed to originate from periodic skipping orbits encircling an antidot with a particular number of bounds.Comment: To appear in EuroPhys. Let

Corrections quantiques à la conductivité dans les systèmes\ud d'electrons bidimensionnels : effet de l'interaction électron-électron\ud

Ce mémoire présente l'étude des corrections quantiques à la conductivité d'un gaz d'électron\ud bidimensionnel. Les échantillons ont été choisis de façon à permettre une comparaison optimale avec les modèles théoriques disponibles à l'heure actuelle. La première partie de l'étude expérimentale porte sur des gaz d'électrons de haute densité et de faible mobilité obtenus dans des puits quantiques AlGaAs/GaAs/AlGaAs. Ces échantillons permettent une comparaison directe (sans paramètre d'ajustement) avec la théorie de liquide de Fermi des corrections quantiques au tenseur des conductivités. Un accord quantitatif est obtenu. La deuxième partie de ce travail porte sur des gaz d'électrons obtenus à l'interface Si/SiGe. Ce système est original en raison de la structure mixte du désordre qui y est présent et par la présence de contrainte modifiant le spectre énergétique. Une étude détaillée de la conductivité et de la magnéto-résistance est menée. Un accord qualitatif avec la théorie de liquide de Fermi est obtenu. Ces résultats s'inscrivent dans l'effort de recherche mené pour déterminer la nature de l'état fondamental d'un système bidimensionnel d'électrons en interaction. Ce sujet a vu son\ud intérêt renouvelé depuis la découverte en 1994 d'un état apparemment métallique dans ce type de systèmes.\ud \ud We present the experimental study of quantum corrections to the conductivity of a two dimensional electron system. The samples were chosen to allow the best possible comparison with the existing theories. The first part consists of the study of a high density low mobility electron gas in a narrow AlGaAs/GaAs/AlGaAs quantum well. These samples allow a parameter free comparison with the Fermi liquid theory of the quantum corrections to the conductivity tensor. A quantitative agreement is obtained. The second part of this work concerns the study of a two-dimensional electrons system realized at the Si/SiGe heterojuntion. The originality of this system consists in the structure of its disorder which is a mixture of short- and long-range. More over, constrain modifies strongly the energetic spectrum. A detailed analysis of the conductivity and the magnetoresistance is realized. A qualitative agreement with the Fermi liquid theory is obtained.. \ud Our results may help determining the nature of the fundamental state of a disordered two-dimensional electron system in presence of interaction. This subject has found a renewed interest after the discovery in 1994 of a metallic like state in this kind of system\u

A Comprehensive Python Library for Deep Learning-Based Event Detection in Multivariate Time Series Data and Information Retrieval in NLP

Event detection in time series data is crucial in various domains, including finance, healthcare, cybersecurity, and science. Accurately identifying events in time series data is vital for making informed decisions, detecting anomalies, and predicting future trends. Despite extensive research exploring diverse methods for event detection in time series, with deep learning approaches being among the most advanced, there is still room for improvement and innovation in this field. In this paper, we present a new deep learning supervised method for detecting events in multivariate time series data. Our method combines four distinct novelties compared to existing deep-learning supervised methods. Firstly, it is based on regression instead of binary classification. Secondly, it does not require labeled datasets where each point is labeled; instead, it only requires reference events defined as time points or intervals of time. Thirdly, it is designed to be robust by using a stacked ensemble learning meta-model that combines deep learning models, ranging from classic feed-forward neural networks (FFNs) to state-of-the-art architectures like transformers. This ensemble approach can mitigate individual model weaknesses and biases, resulting in more robust predictions. Finally, to facilitate practical implementation, we have developed a Python package to accompany our proposed method. The package, called eventdetector-ts, can be installed through the Python Package Index (PyPI). In this paper, we present our method and provide a comprehensive guide on the usage of the package. We showcase its versatility and effectiveness through different real-world use cases from natural language processing (NLP) to financial security domains.Comment: 2023 International Conference on Machine Learning and Applications (ICMLA

Event Detection in Time Series: Universal Deep Learning Approach

Event detection in time series is a challenging task due to the prevalence of imbalanced datasets, rare events, and time interval-defined events. Traditional supervised deep learning methods primarily employ binary classification, where each time step is assigned a binary label indicating the presence or absence of an event. However, these methods struggle to handle these specific scenarios effectively. To address these limitations, we propose a novel supervised regression-based deep learning approach that offers several advantages over classification-based methods. Our approach, with a limited number of parameters, can effectively handle various types of events within a unified framework, including rare events and imbalanced datasets. We provide theoretical justifications for its universality and precision and demonstrate its superior performance across diverse domains, particularly for rare events and imbalanced datasets.Comment: To be submitted to ICML 202

Comment faire émerger de la connaissance avec un algorithme d'intelligence artificielle et une visualisation de données pour la gestion du diabète

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