Deep neural network model of haptic saliency

Haptic exploration usually involves stereotypical systematic movements that are adapted to the task. Here we tested whether exploration movements are also driven by physical stimulus features. We designed haptic stimuli, whose surface relief varied locally in spatial frequency, height, orientation, and anisotropy. In Experiment 1, participants subsequently explored two stimuli in order to decide whether they were same or different. We trained a variational autoencoder to predict the spatial distribution of touch duration from the surface relief of the haptic stimuli. The model successfully predicted where participants touched the stimuli. It could also predict participants’ touch distribution from the stimulus’ surface relief when tested with two new groups of participants, who performed a different task (Exp. 2) or explored different stimuli (Exp. 3). We further generated a large number of virtual surface reliefs (uniformly expressing a certain combination of features) and correlated the model’s responses with stimulus properties to understand the model’s preferences in order to infer which stimulus features were preferentially touched by participants. Our results indicate that haptic exploratory behavior is to some extent driven by the physical features of the stimuli, with e.g. edge-like structures, vertical and horizontal patterns, and rough regions being explored in more detail

SARAVÁ: data sharing for online communities in P2P

International audienceThis paper describes SARAVÁ, a research project that aims at investigating new challenges in P2P data sharing for online communities. The major advantage of P2P is a completely decentralized approach to data sharing which does not require centralized administration. Users may be in high numbers and interested in different kinds of collaboration and sharing their knowledge, ideas, experiences, etc. Data sources can be in high numbers, fairly autonomous, i.e. locally owned and controlled, and highly heterogeneous with different semantics and structures. Our project deals with new, decentralized data management techniques that scale up while addressing the autonomy, dynamic behavior and heterogeneity of both users and data sources. In this context, we focus on two major problems: query processing with uncertain data and management of scientific workflows

Heterogeneous nanofluids: natural convection heat transfer enhancement

Convective heat transfer using different nanofluid types is investigated. The domain is differentially heated and nanofluids are treated as heterogeneous mixtures with weak solutal diffusivity and possible Soret separation. Owing to the pronounced Soret effect of these materials in combination with a considerable solutal expansion, the resulting solutal buoyancy forces could be significant and interact with the initial thermal convection. A modified formulation taking into account the thermal conductivity, viscosity versus nanofluids type and concentration and the spatial heterogeneous concentration induced by the Soret effect is presented. The obtained results, by solving numerically the full governing equations, are found to be in good agreement with the developed solution based on the scale analysis approach. The resulting convective flows are found to be dependent on the local particle concentration φ and the corresponding solutal to thermal buoyancy ratio N. The induced nanofluid heterogeneity showed a significant heat transfer modification. The heat transfer in natural convection increases with nanoparticle concentration but remains less than the enhancement previously underlined in forced convection case

NICE : A Computational solution to close the gap from colour perception to colour categorization

The segmentation of visible electromagnetic radiation into chromatic categories by the human visual system has been extensively studied from a perceptual point of view, resulting in several colour appearance models. However, there is currently a void when it comes to relate these results to the physiological mechanisms that are known to shape the pre-cortical and cortical visual pathway. This work intends to begin to fill this void by proposing a new physiologically plausible model of colour categorization based on Neural Isoresponsive Colour Ellipsoids (NICE) in the cone-contrast space defined by the main directions of the visual signals entering the visual cortex. The model was adjusted to fit psychophysical measures that concentrate on the categorical boundaries and are consistent with the ellipsoidal isoresponse surfaces of visual cortical neurons. By revealing the shape of such categorical colour regions, our measures allow for a more precise and parsimonious description, connecting well-known early visual processing mechanisms to the less understood phenomenon of colour categorization. To test the feasibility of our method we applied it to exemplary images and a popular ground-truth chart obtaining labelling results that are better than those of current state-of-the-art algorithms

Critical Investigation of Heat Transfer Enhancement Using Nanofluids in Microchannels with Slip and Non-Slip Flow Regimes

International audienceThe Reynolds number in nanofluids studies depends on the inlet velocity and the kinematic viscosity of nanofluid. The nanofluid kinematic viscosity increases with an increasing in nanoparticles volume fraction while the inlet velocity should be increased to keep the Reynolds number constant. Therefore, it is not clear that either increasing the nanoparticles volume fraction or increasing the inlet velocity has major role on heat transfer enhancement in nanofluids flow studies which are done at constant Reynolds numbers. In this study, forced convection -water nanofluid flows in two-dimensional rectangular microchannels have been investigated to study heat transfer enhancement due to addition of the nanoparticles to the base fluid especially in microchannels at low Reynolds number. Three different cases are examined to evaluate proportion impact of increasing nanoparticles volume fraction () and the inlet velocity (u) on heat transfer enhancement. Two-dimensional Navier-Stokes and energy equations accompany with the slip velocity and the jump temperature boundary conditions expressions have been discretized using the Finite Volume Method (). The Brownian motions of nanoparticles have been considered to determine the thermal conductivity of nanofluids. The calculated results show good agreement with the previous numerical and analytical data. It is found that at a given Reynolds number, the major enhancement in the Nusselt number is not due to increasing the nanoparticle concentrations but it is due to the increasing the inlet velocity to reach a constant . Constant Reynolds number studies of nanofluids are not sufficient approach to evaluate the heat transfer and the skin friction factor due to the nanofluids usage

Continuous Timestamping for Efficient Replication Management in DHTs

International audienceDistributed Hash Tables (DHTs) provide an efficient solution for data location and lookup in large-scale P2P systems. However, it is up to the applications to deal with the availability of the data they store in the DHT, e.g. via replication. To improve data availability, most DHT applications rely on data replication. However, efficient replication management is quite challenging, in particular because of concurrent and missed updates. In this paper, we propose an efficient solution to data replication in DHTs. We propose a new service, called Continuous Timestamp based Replication Management (CTRM), which deals with the efficient storage, retrieval and updating of replicas in DHTs. To perform updates on replicas, we propose a new protocol that stamps update actions with timestamps generated in a distributed fashion. Timestamps are not only monotonically increasing but also continuous, i.e. without gap. The property of monotonically increasing allows applications to determine a total order on updates. The other property, i.e. continuity, enables applications to deal with missed updates. We evaluated the performance of our solution through simulation and experimentation. The results show its effectiveness for replication management in DHTs

Supporting Data Privacy in P2P Systems

Abstract. Peer-to-Peer (P2P) systems have been very successful for large-scale data sharing. However, sharing sensitive data, like in online social networks, without appropriate access control, can have undesirable impact on data privacy. Data can be accessed by everyone (by potentially untrusted peers) and used for everything (e.g., for marketing or activities against the owner’s preferences or ethics). Hippocratic databases (HDB) provide an effective solution to this problem, by integrating purposebased access control for privacy protection. However, the use of HDB has been restricted to centralized systems. This chapter gives an overview of current solutions for supporting data privacy in P2P systems, and develops in more details a complete solution based on HDB