Real-Time Numerical Simulation for Accurate Soft Tissues Modeling during Haptic Interaction

The simulation of fabrics physics and its interaction with the human body has been largely studied in recent years to provide realistic-looking garments and wears specifically in the entertainment business. When the purpose of the simulation is to obtain scientific measures and detailed mechanical properties of the interaction, the underlying physical models should be enhanced to obtain better simulation accuracy increasing the modeling complexity and relaxing the simulation timing constraints to properly solve the set of equations under analysis. However, in the specific field of haptic interaction, the desiderata are to have both physical consistency and high frame rate to display stable and coherent stimuli as feedback to the user requiring a tradeoff between accuracy and real-time interaction. This work introduces a haptic system for the evaluation of the fabric hand of specific garments either existing or yet to be produced in a virtual reality simulation. The modeling is based on the co-rotational Finite Element approach that allows for large displacements but the small deformation of the elements. The proposed system can be beneficial for the fabrics industry both in the design phase or in the presentation phase, where a virtual fabric portfolio can be shown to customers around the world. Results exhibit the feasibility of high-frequency real-time simulation for haptic interaction with virtual garments employing realistic mechanical properties of the fabric materials

Estimation of Success in Collaborative Learning Based on Multimodal Learning Analytics Features

Multimodal learning analytics provides researchers new tools and techniques to capture different types of data from complex learning activities in dynamic learning environments. This paper investigates high-fidelity synchronised multimodal recordings of small groups of learners interacting from diverse sensors that include computer vision, user generated content, and data from the learning objects (like physical computing components or laboratory equipment). We processed and extracted different aspects of the students' interactions to answer the following question: which features of student group work are good predictors of team success in open-ended tasks with physical computing? The answer to the question provides ways to automatically identify the students' performance during the learning activities

Fast approximations of activation functions in deep neural networks when using posit arithmetic

With increasing real-time constraints being put on the use of Deep Neural Networks (DNNs) by real-time scenarios, there is the need to review information representation. A very challenging path is to employ an encoding that allows a fast processing and hardware-friendly representation of information. Among the proposed alternatives to the IEEE 754 standard regarding floating point representation of real numbers, the recently introduced Posit format has been theoretically proven to be really promising in satisfying the mentioned requirements. However, with the absence of proper hardware support for this novel type, this evaluation can be conducted only through a software emulation. While waiting for the widespread availability of the Posit Processing Units (the equivalent of the Floating Point Unit (FPU)), we can already exploit the Posit representation and the currently available Arithmetic-Logic Unit (ALU) to speed up DNNs by manipulating the low-level bit string representations of Posits. As a first step, in this paper, we present new arithmetic properties of the Posit number system with a focus on the configuration with 0 exponent bits. In particular, we propose a new class of Posit operators called L1 operators, which consists of fast and approximated versions of existing arithmetic operations or functions (e.g., hyperbolic tangent (TANH) and extended linear unit (ELU)) only using integer arithmetic. These operators introduce very interesting properties and results: (i) faster evaluation than the exact counterpart with a negligible accuracy degradation; (ii) an efficient ALU emulation of a number of Posits operations; and (iii) the possibility to vectorize operations in Posits, using existing ALU vectorized operations (such as the scalable vector extension of ARM CPUs or advanced vector extensions on Intel CPUs). As a second step, we test the proposed activation function on Posit-based DNNs, showing how 16-bit down to 10-bit Posits represent an exact replacement for 32-bit floats while 8-bit Posits could be an interesting alternative to 32-bit floats since their performances are a bit lower but their high speed and low storage properties are very appealing (leading to a lower bandwidth demand and more cache-friendly code). Finally, we point out how small Posits (i.e., up to 14 bits long) are very interesting while PPUs become widespread, since Posit operations can be tabulated in a very efficient way (see details in the text)

Vitality forms processing in the insula during action observation: a multivoxel pattern analysis

Observing how an action is done by others allows the observer to understand the cognitive and emotion state of the agent. This information, carried by the kinematics of the observed action, has been defined by Daniel Stern \u201cvitality forms\u201d. The expression and the capacity to understand the vitality forms is already present in infants, a finding indicating their importance for the development of social attunement. It has been proposed that, well before developing linguistic abilities, infants are actively engaged in non-verbal exchanges with their caregivers. This ability denotes a primordial way to relate to and understand others and presumably represents a constitutive element of interpersonal relations, namely intersubjectivity. In the present neuroimaging (fMRI) study we presented participants with videos showing hand actions performed with different velocities and asked them to judge their vitality form (gentle, neutral, rude) or their velocity (slow, medium, fast). Previous studies showed that the dorso-central insula is selectively active both during vitality form observation and execution. The aim of the present study was to assess, using multi-voxel pattern analysis (MVPA), whether in the insula there are voxels discriminating vitality form from velocity. Results showed that, consistently across subjects, in the dorso-central sector of the insula there are voxels selectively tuned to vitality forms. Supporting previous findings, these results confirm that the dorso-central insula is involved in processing the vitality forms of an action, both when carryied out in the first person and when observed in other individuals. This supports the idea that the understanding of others' behavior in terms of affective content is mediated by an automatic activation system that allows the recipient to tune in and respond to another individual's emotional state without necessarily having "formal" knowledge of what is being observed. As argued by Stern, this process would allow a synchronization with the behavior of others that underlies the first relational forms developing in early childhood

Experimental evaluation of vibrotactile training mappings for dual-joystick directional guidance

Two joystick-based teleoperation is a common method for controlling a remote machine or a robot. Their use could be counter-intuitive and could require a heavy mental workload. The goal of this paper is to investigate whether vibrotactile prompts could be used to trigger dual-joystick responses quickly and intuitively, so to possibly employ them for training. In particular, we investigate the effects of: (1) stimuli delivered either on the palm or on the back of the hand, (2) with attractive and repulsive mappings, (3) with single and sequential stimuli. We find that 38 participants responded quicker and more accurately when stimuli were delivered on the back of the hand, preferred to move towards the vibration. Sequential stimuli led to intermediate responses in terms of speed and accuracy

Testing the Effect of Relative Pollen Productivity on the REVEALS Model : A Validated Reconstruction of Europe-Wide Holocene Vegetation

Reliable quantitative vegetation reconstructions for Europe during the Holocene are crucial to improving our understanding of landscape dynamics, making it possible to assess the past effects of environmental variables and land-use change on ecosystems and biodiversity, and mitigating their effects in the future. We present here the most spatially extensive and temporally continuous pollen-based reconstructions of plant cover in Europe (at a spatial resolution of 1° × 1°) over the Holocene (last 11.7 ka BP) using the 'Regional Estimates of VEgetation Abundance from Large Sites' (REVEALS) model. This study has three main aims. First, to present the most accurate and reliable generation of REVEALS reconstructions across Europe so far. This has been achieved by including a larger number of pollen records compared to former analyses, in particular from the Mediterranean area. Second, to discuss methodological issues in the quantification of past land cover by using alternative datasets of relative pollen productivities (RPPs), one of the key input parameters of REVEALS, to test model sensitivity. Finally, to validate our reconstructions with the global forest change dataset. The results suggest that the RPPs.st1 (31 taxa) dataset is best suited to producing regional vegetation cover estimates for Europe. These reconstructions offer a long-term perspective providing unique possibilities to explore spatial-temporal changes in past land cover and biodiversity

Proceedings of the 5th International Conference on Enactive Interfaces

Enactive Interfaces and Systems are a new generation of Human- Computer Interfaces (HCI) that are based on the concept of Enactive knowledge, that is the knowledge acquired by doing. These new interfaces allow to make old applications more intelligent and responsive and to create new kinds of applications. The introduction of the Enaction concept in HCI has created a multidisciplinary research commu- nity capable of integrating theoretical model with interaction paradigms and advanced technologies like visualization systems, haptics and spatialized audio. The Enactive Conference is an important annual meeting occasion for researchers in the field of Enactive Interfaces. This conference series has been started by in 2004 by the European Network of Excellence. On the behalf of the Organization Committee we would like to welcome you to the proceedings of ENACTIVE08, the fifth edition of the International Conference on Enactive Interfaces. ENACTIVE08 is held in Pisa at Scuola Superiore Sant’Anna on 19th-21st November 2008. We would like to take this opportunity to thank to all the authors and the reviewers of the papers for their precious contribution and commitment. Furthermore we would like to thank all the people of the Committees for their efforts and the Scuola Superiore Sant’Anna for the support

Filtering Motion Data Through Piecewise Polynomial Approximation

In this work we propose a system to filter human movement data and store them into a compact representation. We are interested both in noise reduction and in segmentation. The method described in this paper relies on a iterative optimization and guarantee to converge to a local optimum: it proved anyway to produce stable results and to provide an accurate segmentation on the analyzed data . We analyze the Three ball cascade Juggling as case study: This provides us the challenge to represent both low-pass dynamics of human limbs and juggled balls and the discontinuities produced by contact forces