Perceiving Mass in Mixed Reality through Pseudo-Haptic Rendering of Newton's Third Law

In mixed reality, real objects can be used to interact with virtual objects. However, unlike in the real world, real objects do not encounter any opposite reaction force when pushing against virtual objects. The lack of reaction force during manipulation prevents users from perceiving the mass of virtual objects. Although this could be addressed by equipping real objects with force-feedback devices, such a solution remains complex and impractical.In this work, we present a technique to produce an illusion of mass without any active force-feedback mechanism. This is achieved by simulating the effects of this reaction force in a purely visual way. A first study demonstrates that our technique indeed allows users to differentiate light virtual objects from heavy virtual objects. In addition, it shows that the illusion is immediately effective, with no prior training. In a second study, we measure the lowest mass difference (JND) that can be perceived with this technique. The effectiveness and ease of implementation of our solution provides an opportunity to enhance mixed reality interaction at no additional cost

A dynamic mode decomposition approach for large and arbitrarily sampled systems

Detection of coherent structures is of crucial importance for understanding the dynamics of a fluid flow. In this regard, the recently introduced Dynamic Mode Decomposition (DMD) has raised an increasing interest in the community. It allows to efficiently determine the dominant spatial modes, and their associated growth rate andfrequencyintime,responsiblefordescribingthetime-evolutionofanobservation ofthephysicalsystemathand.However,theunderlyingalgorithmrequiresuniformly sampled and time-resolved data, which may limit its usability in practical situations. Further, the computational cost associated with the DMD analysis of a large dataset is high, both in terms of central processing unit and memory. In this contribution, we present an alternative algorithm to achieve this decomposition, overcoming the above-mentioned limitations. A synthetic case, a two-dimensional restriction of an experimental flow over an open cavity, and a large-scale three-dimensional simulation, provide examples to illustrate the method

Investigating mode competition and three-dimensional features from two-dimensional velocity fields in an open cavity flow by modal decompositions

Shear-layer driven open cavity flows are known to exhibit strong self-sustained oscillations of the shear-layer. Over some range of the control parameters, a competition between two modes of oscillations of the shear layer can occur. We apply both Proper Orthogonal Decomposition and Dynamic Mode Decomposition to experimental two-dimensional two-components time and spaced velocity fields of an incompressible open cavity flow, in a regime of mode competition. We show that, although proper orthogonal decomposition successes in identifying salient features of the flow, it fails at identifying the spatial coherent structures associated with dominant frequencies of the shear-layer oscillations. On the contrary, we show that, as dynamic mode decomposition is devoted to identify spatial coherent structures associated with clearly defined frequency channels, it is well suited for investigating coherentstructuresinintermittentregimes.Weconsiderthevelocitydivergencefield, inordertoidentifyspanwisecoherentfeaturesoftheflow.Finally,weshowthatboth coherent structures in the inner-flow and in the shear-layer exhibit strong spanwise velocitygradients,andarethereforethree-dimensiona

Irregular dynamics of cellular blood flow in a model microvessel

The flow of red blood cells within cylindrical vessels is complex and irregular, so long as the vessel diameter is somewhat larger than the nominal cell size. Long-time-series simulations, in which cells flow 105 vessel diameters, are used to characterize the chaotic kinematics, particularly to inform reduced-order models. The simulation model used includes full coupling between the elastic red blood cell membranes and surrounding viscous fluid, providing a faithful representation of the cell-scale dynamics. Results show that the flow has neither classifiable recurrent features nor a dominant frequency. Instead, its kinematics are sensitive to the initial flow configuration in a way consistent with chaos and Lagrangian turbulence. Phase-space reconstructions show that a low-dimensional attractor does not exist, so the observed long-time dynamics are effectively stochastic. Based on this, a simple Markov chain model for the dynamics is introduced and shown to reproduce the statistics of the cell positions

A comprehensive and policy-oriented model of the hydrogen vehicle fleet composition, applied to the UK market

Road vehicles play an important role in the UK’s energy systems and are a critical component in reducing the reliance on fossil fuels and mitigating emissions. A dynamic model of light-duty vehicle fleet, based on predator-prey concepts, is presented. This model is designed to be comprehensive but captures the important features of the competition between types of vehicles on the car market. It allows to predict the evolution of the hydrogen based vehicle’s role in the UK’s vehicle fleet. The model allows to forecast effects of policies, hence to inform policy makers. In particular, it is shown that the transition happens only if the hydrogen supply can absorb at least 350,000 new vehicles per year. In addition to this, the model is used to predict the demand for hydrogen for the passenger vehicle fleet for various scenarios. A key finding of the policy-oriented model is that a successful transition to a clean fleet before 2050 is unlikely without policies designed to fully support the supply chain development. It also shows that the amount of hydrogen required to support a full hydrogen based vehicle fleet is currently not economically viable; the needed infrastructure requires yearly investment larger than £2.5 billions. In order to mitigate these costs, the policy focus should shift from hydrogen based vehicles to hybrid vehicles and range extenders in the transport energy system

CS-Embed at SemEval-2020 Task 9: The effectiveness of code-switched word embeddings for sentiment analysis

The growing popularity and applications of sentiment analysis of social media posts has naturally led to sentiment analysis of posts written in multiple languages, a practice known as code-switching. While recent research into code-switched posts has focused on the use of multilingual word embeddings, these embeddings were not trained on code-switched data. In this work, we present word-embeddings trained on code-switched tweets, specifically those that make use of Spanish and English, known as Spanglish. We explore the embedding space to discover how they capture the meanings of words in both languages. We test the effectiveness of these embeddings by participating in SemEval 2020 Task 9: ~\emph{Sentiment Analysis on Code-Mixed Social Media Text}. We utilised them to train a sentiment classifier that achieves an F-1 score of 0.722. This is higher than the baseline for the competition of 0.656, with our team (codalab username \emph{francesita}) ranking 14 out of 29 participating teams, beating the baseline.Comment: Accepted at SemEval-2020, COLIN

Enhanced Data-driven LoRa LP-WAN Channel Model in Birmingham

Innovative solutions providing better coverage and minimized power consumption by end nodes such as Low Power Wide Area Networks (LP-WAN) have facilitated the advances towards improved IoT connectivity. Long Range Wide Area Net-work (LoRaWAN) technology stands out as one leading platform of LP-WANs receiving vast attention from both industry and academia. Performance evaluation of LoRaWAN is promising, in particular in the field of outdoor localization and object tracking. Limitations of node ranging and tracking without the need of energy-draining solutions like GPS, however, has not been tackled thoroughly. In this work, we explore the performance of the LoRa LP-WAN technology using real-life measurements in Birmingham, UK, using commercially available equipment. We present a channel attenuation model that can be utilized to estimate the path loss in 868 MHz ISM band in urban-similar areas. The proposed channel model is then compared to previously well-identified empirical path loss models and enhanced by detecting and eliminating outlier data from the obtained real measurements for an optimal fitted model. We, further, propose a novel RSSI distribution-based and k-means clustering to enhance the power-to-distance prediction accuracy that improves absolute errors by 4% and 18%

Pertinence des champs bidimensionnels dans l'analyse des phénomènes instationnaires tridimensionnels

Pertinence des champs bidimensionnels dans l’analyse des phénomènes instationnaires tridimensionnels. F. Lusseyran, J. Basley, F. Gueniat , L. Pastur L’identification de structures cohérentes dans les écoulements de fluide constitue l’un des objectifs de nombreuses études actuelles en mécanique des fluides. L’évaluation de la cohérence spatiale a été longtemps réservée à l’approche numérique, l’expérimentation devant se limiter à des corrélations temporelles du fait des moyens métrologiques disponibles. Depuis 20 ans le développement des techniques de vélocimétrie par images de particules (PIV) donne accès à des champs de vitesse tout d’abord bidimensionnels (2D) et bicomposantes coplanaires (2C), pour actuellement aborder la mesure de champs tridimensionnels complets (3D,3C). Cette évolution est motivée par le caractère le plus souvent intrinsèquement 3D des tourbillons structurant la dynamique spatio-temporelle des sillages, des jets, des écoulements impactant ou même des couches limites et des couches de mélanges. Cependant, les contraintes et les limites imposées par les techniques 3D, justifient encore largement l’exploration 2D. Dans cet exposé nous abordons la validité et les possibilités offertes par différentes décompositions modales des itérés 2D d’un champ de vitesse, résolus en temps (ou non résolus), prélevés expérimentalement ou numériquement dans un champ de vitesse 3D fortement instationnaire. Trois décompositions modales sont appliquées à l’étude d’un écoulement de référence, constitué par une cavité ouverte en interaction avec une couche limite laminaire : - la décomposition en modes propres orthogonaux (POD), la décomposition en modes de Fourier globaux, la décomposition en modes dynamiques (DMD). De plus, on peut ajouter aux propriétés propres à chacune de ces décompositions modales des propriétés physiques, comme l’incompressibilité (transmise aux modes spatiaux) ou la propagation non dispersive de modes transverses au plan de mesure. L’information apportée par l’approche 2D permet alors une incursion pertinente dans la troisième dimension

Analysis of Locally Coupled 3D Manipulation Mappings Based on Mobile Device Motion

We examine a class of techniques for 3D object manipulation on mobile devices, in which the device's physical motion is applied to 3D objects displayed on the device itself. This "local coupling" between input and display creates specific challenges compared to manipulation techniques designed for monitor-based or immersive virtual environments. Our work focuses specifically on the mapping between device motion and object motion. We review existing manipulation techniques and introduce a formal description of the main mappings under a common notation. Based on this notation, we analyze these mappings and their properties in order to answer crucial usability questions. We first investigate how the 3D objects should move on the screen, since the screen also moves with the mobile device during manipulation. We then investigate the effects of a limited range of manipulation and present a number of solutions to overcome this constraint. This work provides a theoretical framework to better understand the properties of locally-coupled 3D manipulation mappings based on mobile device motion

A statistical learning strategy for closed-loop control of fluid flows

This work discusses a closed-loop control strategy for complex systems utilizing scarce and streaming data. A discrete embedding space is first built using hash functions applied to the sensor measurements from which a Markov process model is derived, approximating the complex system’s dynamics. A control strategy is then learned using reinforcement learning once rewards relevant with respect to the control objective are identified. This method is designed for experimental configurations, requiring no computations nor prior knowledge of the system, and enjoys intrinsic robustness. It is illustrated on two systems: the control of the transitions of a Lorenz’63 dynamical system, and the control of the drag of a cylinder flow. The method is shown to perform well