Laboratoire Ville Mobilité Transport

This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors' placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.

As long-term pool feeders, ticks have developed myriad strategies to remain discreetly but solidly attached to their hosts for the duration of their blood meal. The critical biological material that dampens host defenses and facilitates the flow of blood-thus assuring adequate feeding-is tick saliva. Saliva exhibits cytolytic, vasodilator, anticoagulant, anti-inflammatory, and immunosuppressive activity. This essential fluid is secreted by the salivary glands, which also mediate several other biological functions, including secretion of cement and hygroscopic components, as well as the watery component of blood as regards hard ticks. When salivary glands are invaded by tick-borne pathogens, pathogens may be transmitted via saliva, which is injected alternately with blood uptake during the tick bite. Both salivary glands and saliva thus play a key role in transmission of pathogenic microorganisms to vertebrate hosts. During their long co-evolution with ticks and vertebrate hosts, microorganisms have indeed developed various strategies to exploit tick salivary molecules to ensure both acquisition by ticks and transmission, local infection and systemic dissemination within the vertebrate host.

Ultrahigh-performance concrete (UHPC) offers significant potential to address a variety of needs in bridge design, construction, and performance enhancement. Bridge owners have shown willingness to embrace novel solutions that could address specific challenges related to the cost, speed of construction, durability, and service life of their projects. There are hundreds of bridges worldwide that, largely in the past decade, have utilized UHPC. These applications range from minor field-cast closures to precast segments for long-span bridges to kilometer-long bridge deck overlays on a signature structure. The objective of this paper is to promote the application of this class of cementitious material in bridge engineering by presenting the progress that has been made in different regions of the world in the past two decades. Today, UHPC is being widely used in Malaysia to design and construct many bridges of different types and spans as they build out their roadway network. In South Korea, the unique characteristics of UHPC are being utilized to advance the state-of-the-art in long-span bridges. The French were early adopters and pioneers in building a strong foundation for using UHPC in a variety of bridge applications. In Switzerland, UHPC is employed to address major bridge rehabilitation needs. The United States bridge sector has embraced UHPC for a variety of field-cast connections. Current research and development efforts are promoting the use of UHPC in major rehabilitation projects and construction of primary bridge components. The adoption of UHPC solutions into the bridge sector is progressing rapidly because of the unique opportunities provided by the strength and durability of the material. It is expected that additional innovations and refinements of solutions will occur as knowledge of the material proliferates.

Due to its capacity to store or supply energy with high power, the supercapacitor is becoming an attractive component. Because of the electrostatic nature of energy storage, the endurance of this component toward repetitive charge and discharge cycles is relatively high. The goal of this paper is to demonstrate that cycling has an impact on the degradation of the supercapacitor and, as a result, on its lifetime. Based on accelerated cycling tests, some supercapacitors were studied using a dedicated test bench. Temperature, voltage, and current, which are the parameters that accelerate aging, are monitored. In fact, observations during the cycling tests show an important acceleration in the degradation compared with a similar static test having the same voltage and core temperature but without cycling. This paper proposes a method to quantify the acceleration of aging during a cycling phase.

In this paper we propose a novel approach to the perceptual interpretation of building facades that combines shape grammars, supervised classification and random walks. Procedural modeling is used to model the geometric and the photometric variation of buildings. This is fused with visual classification techniques (randomized forests) that provide a crude probabilistic interpretation of the observation space in order to measure the appropriateness of a procedural generation with respect to the image. A random exploration of the grammar space is used to optimize the sequence of derivation rules towards a semantico-geometric interpretation of the observations. Experiments conducted on complex architecture facades with ground truth validate the approach.

We consider the mean‐variance hedging problem when the risky assets price process is a continuous semimartingale. The usual approach deals with self‐financed portfolios with respect to the primitive assets family. By adding a numéraire as an asset to trade in, we show how self‐financed portfolios may be expressed with respect to this extended assets family, without changing the set of attainable contingent claims. We introduce the hedging numéraire and relate it to the variance‐optimal martingale measure. Using this numéraire both as a deflator and to extend the primitive assets family, we are able to transform the original mean‐variance hedging problem into an equivalent and simpler one; this transformed quadratic optimization problem is solved by the Galtchouk–Kunita–Watanabe projection theorem under a martingale measure for the hedging numéraire extended assets family. This gives in turn an explicit description of the optimal hedging strategy for the original mean‐variance hedging problem.

It has frequently been suggested in the literature that a polycentric distribution of employment and people shortens commuting distances because people locate within or close to their employment sub-centre (the co-location hypothesis). Having studied the three biggest French metropolitan areas over the past decade it has been established that co-location affects only a minority of inhabitants, of whom there are fewer in 1999 than there were nine years earlier. Indeed, the majority of people living in a sub-centre work outside their sub-centre of residence. This situation was even more marked in 1999 than it was in 1990. In addition to this, the majority of jobs located in sub-centres are held by non-residents who are generally living further and further from their place of work.

Abstract This paper presents a new method for estimating normals on unorganized point clouds that preserves sharp features. It is based on a robust version of the Randomized Hough Transform (RHT). We consider the filled Hough transform accumulator as an image of the discrete probability distribution of possible normals. The normals we estimate corresponds to the maximum of this distribution. We use a fixed‐size accumulator for speed, statistical exploration bounds for robustness, and randomized accumulators to prevent discretization effects. We also propose various sampling strategies to deal with anisotropy, as produced by laser scans due to differences of incidence. Our experiments show that our approach offers an ideal compromise between precision, speed, and robustness: it is at least as precise and noise‐resistant as state‐of‐the‐art methods that preserve sharp features, while being almost an order of magnitude faster. Besides, it can handle anisotropy with minor speed and precision losses.

Computation offloading has already shown itself to be successful for enabling resource-intensive applications on mobile devices. Moreover, in view of mobile edge computing (MEC) system, mobile devices can offload compute-intensive tasks to a nearby cloudlet, so as to save the energy and enhance the processing speed. However, due to the varying network conditions and limited computation resources of cloudlets, the offloading actions taken by a mobile user may not achieve the lowest cost. In this paper, we develop a dynamic offloading framework for mobile users, considering the local overhead in the mobile terminal side, as well as the limited communication and computation resources in the network side. We formulate the offloading decision problem as a multi-label classification problem and develop the Deep Supervised Learning (DSL) method to minimize the computation and offloading overhead. Simulation results show that our proposal can reduce system cost up to 49.24%, 23.87%, 15.69%, and 11.18% compared to the “no offloading” scheme, “random offloading” scheme, “total offloading” scheme and “multi-label linear classifier-based offloading” scheme, respectively.

Abstract We report the observation of a coalescing compact binary with component masses 2.5–4.5 M ⊙ and 1.2–2.0 M ⊙ (all measurements quoted at the 90% credible level). The gravitational-wave signal GW230529_181500 was observed during the fourth observing run of the LIGO–Virgo–KAGRA detector network on 2023 May 29 by the LIGO Livingston observatory. The primary component of the source has a mass less than 5 M ⊙ at 99% credibility. We cannot definitively determine from gravitational-wave data alone whether either component of the source is a neutron star or a black hole. However, given existing estimates of the maximum neutron star mass, we find the most probable interpretation of the source to be the coalescence of a neutron star with a black hole that has a mass between the most massive neutron stars and the least massive black holes observed in the Galaxy. We provisionally estimate a merger rate density of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msubsup> <mml:mrow> <mml:mn>55</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>47</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>127</mml:mn> </mml:mrow> </mml:msubsup> <mml:mspace width="0.25em"/> <mml:msup> <mml:mrow> <mml:mi>Gpc</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> <mml:mspace width="0.25em"/> <mml:msup> <mml:mrow> <mml:mi>yr</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:math> for compact binary coalescences with properties similar to the source of GW230529_181500; assuming that the source is a neutron star–black hole merger, GW230529_181500-like sources may make up the majority of neutron star–black hole coalescences. The discovery of this system implies an increase in the expected rate of neutron star–black hole mergers with electromagnetic counterparts and provides further evidence for compact objects existing within the purported lower mass gap.

Shared e-scooters refer to a micro-mobility service that enables the short rentals of e-scooters. The rapid growth of e-scooter sharing has sparked a heated discussion about its role in the urban mobility sector. This article presents a systematic review of the current knowledge on its uses and users, health and environmental impacts, and policy issues. The analysis is based on academic literature, identified with Google Scholar by using keywords and publication years from 2017, and relevant gray literature. Firstly, we highlight that the profiles of e-scooter renters seem to highly match the characteristics of other micro-mobility services users. Secondly, e-scooters are often associated with a high perception of risk from the public and an increasing occurrence of related road accidents. Thirdly, even if promoted as a green mobility option, the true environmental impact of shared e-scooters has only started to be investigated. Early studies point out negative impacts around their production, usage, and maintenance. Fourthly, the integration of shared e-scooters into the existing transport systems requires policy changes, both at the local and national level, including traffic regulations, safety rules, and physical infrastructure. Finally, this paper reveals the ambiguity of the term “e-scooter” and stresses the need for more research, as the future of cities is tied to the development of low-car lifestyles.

Computation offloading is a proven successful paradigm for enabling resource-intensive applications on mobile devices. Moreover, in view of emerging mobile collaborative application, the offloaded tasks can be duplicated when multiple users are in the same proximity. This motivates us to design a collaborative offloading scheme and cache the popular computation results that are likely to be reused by other mobile users. In this paper, we consider the scenario where multiple mobile users offload duplicated computation tasks to the network edge, and share the computation results among them. Our goal is to develop the optimal fine-grained collaborative offloading strategies with caching-enhancements to minimize the overall execution delay at the mobile terminal side. To this end, we propose an optimal offloading with caching-enhancement scheme (OOCS) for femto-cloud scenario and mobile edge computing scenario, respectively. Simulation results show that compared to six alternative solutions in literature, our single-user OOCS can reduce execution delay up to 42.83% and 33.28% for single-user femto-cloud and single-user mobile edge computing, respectively. Our multi-user OOCS can further reduce 11.71% delay compared to single-user OOCS through users' cooperation.

Although the Lasso has been extensively studied, the relationship between its prediction performance and the correlations of the covariates is not fully understood. In this paper, we give new insights into this relationship in the context of multiple linear regression. We show, in particular, that the incorporation of a simple correlation measure into the tuning parameter can lead to a nearly optimal prediction performance of the Lasso even for highly correlated covariates. However, we also reveal that for moderately correlated covariates, the prediction performance of the Lasso can be mediocre irrespective of the choice of the tuning parameter. We finally show that our results also lead to near-optimal rates for the least-squares estimator with total variation penalty.

In this paper, we propose a high-order graph matching formulation to address non-rigid surface matching. The singleton terms capture the geometric and appearance similarities (e.g., curvature and texture) while the high-order terms model the intrinsic embedding energy. The novelty of this paper includes: 1. casting 3D surface registration into a graph matching problem that combines both geometric and appearance similarities and intrinsic embedding information, 2. the first implementation of high-order graph matching algorithm that solves a non-convex optimization problem, and 3. an efficient two-stage optimization approach to constrain the search space for dense surface registration. Our method is validated through a series of experiments demonstrating its accuracy and efficiency, notably in challenging cases of large and/or non-isometric deformations, or meshes that are partially occluded.

The research presented in this paper explores, in the French context, the hypothesis that employment problems experienced by low-skilled jobseekers are partially caused by spatial urban factors. Many low-skilled workers live in poor neighbourhoods where they are exposed to a distressed social environment and/or weak job accessibility. For reasons discussed in this article, living in such neighbourhoods may increase the duration of unemployment for jobseekers. On the basis of an empirical study, this hypothesis is tested in the Paris-Ile-de-France metropolitan area and addresses the question: all other things being equal, are low-skilled workers living in high-poverty neighbourhoods and/or neighbourhoods with low job accessibility exposed to a greater risk of long-term unemployment?

Comparing probability distributions is a fundamental problem in data\nsciences. Simple norms and divergences such as the total variation and the\nrelative entropy only compare densities in a point-wise manner and fail to\ncapture the geometric nature of the problem. In sharp contrast, Maximum Mean\nDiscrepancies (MMD) and Optimal Transport distances (OT) are two classes of\ndistances between measures that take into account the geometry of the\nunderlying space and metrize the convergence in law.\n This paper studies the Sinkhorn divergences, a family of geometric\ndivergences that interpolates between MMD and OT. Relying on a new notion of\ngeometric entropy, we provide theoretical guarantees for these divergences:\npositivity, convexity and metrization of the convergence in law. On the\npractical side, we detail a numerical scheme that enables the large scale\napplication of these divergences for machine learning: on the GPU, gradients of\nthe Sinkhorn loss can be computed for batches of a million samples.\n

Geospatial data users increasingly face the need to assess how datasets fit an intended use. However, information describing data quality is typically difficult to access and understand. Therefore, data quality is often neglected by users, leading to risks of misuse. Understanding data quality is a complex task that may involve thousands of partially related metadata. For complex cases where heterogeneous datasets have to be integrated, there is a need for tools supporting data quality analysis. This paper presents the design of such a tool that can manage heterogeneous data quality information and provide functions to support expert users in the assessment of the fitness for use of a given dataset. Combining concepts from GIS and Business Intelligence, this approach provides interactive, multi‐granularity and context‐sensitive spatial data quality indicators that help experts to build and justify their opinions. A prototype called the Multidimensional User Manual is presented to illustrate this approach.

Linear precoding consists in multiplying by an N/spl times/K matrix a K-dimensional vector obtained by serial-to-parallel conversion of a symbol sequence to be transmitted. In this paper, new tools, borrowed from the so-called free probability theory, are introduced for the purpose of analyzing the performance of minimum mean-square error (MMSE) receivers for certain large random isometric precoded systems on fading channels. The isometric condition represents the case of precoding matrices with orthonormal columns. It is shown in this contribution that the signal-to-interference-plus-noise ratio (SINR) at the equalizer output converges almost surely to a deterministic value depending on the probability distribution of the channel coefficients when N/spl rarr/+/spl infin/ and K/N/spl rarr//spl alpha//spl les/1. These asymptotic results are used to analyze the impact of orthogonal spreading as well as to optimally balance the redundancy introduced between linear precoding versus classical convolutional coding, while preserving a simple MMSE equalization scheme at the receiver.

This paper investigates how and to what extent changes in user behavior may mitigate the environmental benefits of urban ridesharing, a phenomenon commonly referred to as "rebound effect". Ridesharing reduces both the individual cost of car travel (through cost splitting) and road travel times (by decreasing congestion).

Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to alleviate symptoms during community-acquired pneumonia (CAP), while neither clinical data nor guidelines encourage this use. Experimental data suggest that NSAIDs impair neutrophil intrinsic functions, their recruitment to the inflammatory site, and the resolution of inflammatory processes after acute pulmonary bacterial challenge. During CAP, numerous observational data collected in hospitalized children, hospitalized adults, and adults admitted to intensive care units (ICUs) support a strong association between pre-hospital NSAID exposure and a delayed hospital referral, a delayed administration of antibiotic therapy, and the occurrence of pleuropulmonary complications, even in the only study that has accounted for a protopathic bias. Other endpoints have been described including a longer duration of antibiotic therapy and a greater hospital length of stay. In all adult series, patients exposed to NSAIDs were younger and had fewer comorbidities. The mechanisms by which NSAID use would entail a complicated course in pneumonia still remain uncertain. The temporal hypothesis and the immunological hypothesis are the two main emerging hypotheses. Current data strongly support an association between NSAID intake during the outpatient treatment of CAP and a complicated course. This should encourage experts and scientific societies to strongly advise against the use of NSAIDs in the management of lower respiratory tract infections.