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International audience; In this paper, we provide a necessary clarification of the good security properties that can be obtained from parallel implementations of masking schemes. For this purpose, we first argue that (i) the probing model is not straightforward to interpret, since it more naturally captures the intuitions of serial implementations, and (ii) the noisy leakage model is not always convenient, e.g. when combined with formal methods for the verification of cryptographic implementations. Therefore we introduce a new model, the bounded moment model, that formalizes a weaker notion of security order frequently used in the side-channel literature. Interestingly , we prove that probing security for a serial implementation implies bounded moment security for its parallel counterpart. This result therefore enables an accurate understanding of the links between formal security analyses of masking schemes and experimental security evaluations based on the estimation of statistical moments Besides its consolidating nature, our work also brings useful technical contributions. First, we describe and analyze refreshing and multiplication algorithms that are well suited for parallel implementations and improve security against multivariate side-channel attacks. Second, we show that simple refreshing algorithms (with linear complexity) that are not secure in the continuous probing model are secure in the continuous bounded moment model. Eventually, we discuss the independent leakage assumption required for masking to deliver its security promises, and its specificities related to the serial or parallel nature of an implementation.

Abstract The serotonergic system plays a key modulatory role in the brain. This system is critical in many drug developments for brain disorders via interactions with the 14 subtypes of 5-HT receptors or through reuptake blockade. Positron emission tomography (PET) is an efficient tool for in vivo studies of physiological and pathological processes. Because of its pertinent radiochemical properties, fluorine-18 is one of the most used radioisotopes in PET imaging. This chapter will propose an overview of the 18F-radioligands targeting serotonin receptors, which have been developed over the last few years. Both radiosyntheses and pharmacological properties of these radiotracers will be described, with a specific emphasis on their potential medical applications.

International audience; In this paper, we study the effect of smartphone camera exposure on the performance of optical camera communications (OCC) link. The exposure parameters of image sensor sensitivity (ISO), aperture and shutter speed are included. A static OCC link with a 8 × 8 red, green and blue (RGB) LED array employed as the transmitter and a smartphone camera as the receiver is demonstrated to verify the study. Signal-to-noise ratio (SNR) analysis at different ISO values, the effect of aperture and shutter speed on communication link quality is performed. While SNRs of 20.6 dB and 16.9 dB are measured at 1 m and 2 m transmission distance, respectively for a ISO value of 100, they are decreased to 17.4 dB and 13.32 dB for a ISO of 800. The bit error rate (BER) of a 1 m long OCC link with a camera's shutter speed of 1/6000 s is 1.3 × 10 −3 (i.e., below the forward error correction BER limit of 3.8 × 10 −3) and is dropped to 0.0125 at a shutter speed of 1/20 s. This study provides insight of the basic smartphone settings and the exposure adjustment for further complex OCC links.

Wildfire prediction from Earth Observation (EO) data has gained much attention in the past years, through the development of connected sensors and weather satellites. Nowadays, it is possible to extract knowledge from collected EO data and to learn from this knowledge without human intervention to trigger wildfire alerts. However, exploiting knowledge extracted from multiple EO data sources at run-time and predicting wildfire raise multiple challenges. One major challenge is to provide dynamic construction of service composition plans, according to the data obtained from sensors. In this paper, we present a knowledge-driven Machine Learning approach that relies on historical data related to wildfire observations to guide the collection of EO data and to automatically and dynamically compose services for triggering wildfire alerts.

Multiple theories of working memory are described in the chapters of this book and often these theories are viewed as being mutually incompatible, yet each is associated with a supporting body of empirical evidence. This chapter argues that many of these differences reflect different research questions, different levels of explanation, and differences in how participants perform their assigned tasks in different laboratories, rather than fundamental theoretical adversity. It describes a version of a multiple component working memory in which a range of specialized cognitive functions (or mental tools) act in concert, giving the impression, at a different level of explanation, of a unified cognitive system. The chapter argues that more rapid and more substantial scientific progress on the understanding of the concept of working memory would be achieved through identifying the levels of explanation explored within each theoretical framework, and attempting to integrate theoretical frameworks rather than perpetuating debate with no clear resolution in sight.

Land use intensification is widely considered to be an essential strategy for achieving global goals to eliminate poverty and to avoid damaging losses of ecosystem services. This chapter investigates whether current land use intensification activities are achieving these twin goals. To do so, it reviews a body of academic literature that reports on case studies in which both social and ecological outcomes of intensification are reported. There are two main findings. First, there are relatively few cases in which land use intensification is clearly succeeding in these twinned objectives. There are many more cases in which, for example, short-term income or productivity gains from land use intensification are resulting in long-term diminution of biodiversity and ecosystem services. Studies with longer-term perspectives are already seeing how such trade-offs are leading to negative feedbacks for human wellbeing, especially for marginalised social groups. Secondly, we learn most from those studies that a) go beyond measuring production and income to measure multiple dimensions of wellbeing and ecosystem services, b) monitor dynamics of outcomes across longer time periods and across landscapes and c) disaggregate outcome measures to identify outcomes for different social groups.

The interplay among the effects of dispersion, nonlinearity and gain/loss in optical fibre systems can be efficiently used to shape the pulses and manipulate and control the light dynamics and, hence, lead to different pulse-shaping regimes [1,2]. However, achieving a precise waveform with various prescribed characteristics is a complex issue that requires careful choice of the initial pulse conditions and system parameters. The general problem of optimisation towards a target operational regime in a complex multi-parameter space can be intelligently addressed by implementing machine-learning strategies. In this paper, we discuss a novel approach to the characterisation and optimisation of nonlinear shaping in fibre systems, which combines numerical simulations of the governing equations to identify the relevant parameters and the machine-learning method of neural networks (NNs) to make predictions across a larger range of the data domain. We illustrate this general method through application to two configurations.

The standardization effort in vibration’s field has unfolded the interests of industries. This research aimed at better understanding of the impact of vibration on human body. In the past years, healthcare centers and manufacturers started to equip the workplace with Anti-fatigue Mats (AFM) for the improvement of the working conditions and quality at work. This study analyzed the impact of AFM on the reduction of vibrations transmitted during the weekly activity of manufacturing workers. Seven AFM marketed by Notrax Premium Superior Manufacturing Group were tested. The laser vibrometry technique was used in order to measure vibratory displacements at various points. Under the effect of the vibrations, AFM behaved differently according to their structure and geometries. In comparison, the values obtained showed that AFM absorbed the vibration. It permitted to decrease the risks of ligamentous, or articular pathologies, which can affect employees at work. Taking into account, different results obtained showing that AFM could be used effectively in different areas of workplace, such as in medical or manufacturing settings, in order to improve working conditions.

International audience; Chemical synapses enable neurons to communicate rapidly, process and filter signals and to store information. However, studying their functional properties is difficult because synaptic connections typically consist of multiple synaptic contacts that release vesicles stochastically and exhibit time-dependent behavior. Moreover, most central synapses are small and inaccessible to direct measurements. Estimation of synaptic properties from responses recorded at the soma is complicated by the presence of nonuniform release probability and nonuniform quantal properties. The presence of multivesicular release and postsynaptic receptor saturation at some synapses can also complicate the interpretation of quantal parameters. Multiple-probability fluctuation analysis (MPFA; also known as variance-mean analysis) is a method that has been developed for estimating synaptic parameters from the variance and mean amplitude of synaptic responses recorded at different release probabilities. This statistical approach, which incorporates nonuniform synaptic properties, has become widely used for studying synaptic transmission. In this chapter, we describe the statistical models used to extract quantal parameters and discuss their interpretation when applying MPFA.

International audience; Synthetic biology is an emergent field of research whose aim is to make biology an engineering discipline, thus permitting to design, control, and standardize biological processes. Synthetic biology is therefore expected to boost the development of biotechnological processes such as protein production and enzyme engineering, which can be significantly relevant for lipases and esterases.