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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.

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 the use of diverse sensors, including computer vision, user-generated content, and data from the learning objects (physical computing components), to record high-fidelity synchronised multimodal recordings of small groups of learners interacting. 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? To answer this question, we have explored different supervised machine learning approaches (traditional and deep learning techniques) to analyse the data coming from multiple sources. The results illustrate that state-of-the-art computational techniques can be used to generate insights into the "black box" of learning in students' project-based activities. The features identified from the analysis show that distance between learners' hands and faces is a strong predictor of students' artefact quality, which can indicate the value of student collaboration. Our research shows that new and promising approaches such as neural networks, and more traditional regression approaches can both be used to classify multimodal learning analytics data, and both have advantages and disadvantages depending on the research questions and contexts being investigated. The work presented here is a significant contribution towards developing techniques to automatically identify the key aspects of students success in project-based learning environments, and to ultimately help teachers provide appropriate and timely support to students in these fundamental aspects.

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.

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 form is a term, originally introduced by Stern (2010), to describe “how” an action is performed. The capacity to perceive the vitality form of others’ actions is a fundamental element of social interactions and a basic way of relating to and understanding others’ behaviors. Although vitality forms characterize all human interactions, few studies have addressed their role in social and communicative disorders such as autism. The aim of the present study is to evaluate the ability to recognize different vitality forms during the observation of different motor actions in a group of children with autism spectrum disorders (ASD) compared to typically developing controls (TD). Results show a significant difference between children with ASD and TD in vitality forms recognition. This finding sheds new light on how children with ASD understand others’ actions providing new ideas on overall social understanding as well as useful insights for professionals and caregivers alike.

The Strength-Dexterity (SD) test measures the ability of the pulps of the thumb and index finger to compress a compliant and slender spring prone to buckling at low forces (

In this paper we report on the strategies adopted to model team rowing behavior for an in-door rowing training system. This system allows athletes to train at in-door location while preserving the main features of out-door rowing. This paper focuses on the dynamic models employed to simulate out-door rowing, as well as on the modeling of team behavior, in order to set up visual feedback for team rowing training. These models are employed for controlling the output in the virtual environment, in particular for training the inter-rower synchronization in a team. [ABSTRACT FROM AUTHOR]

The article examines a virtual reality (VR) based simulator used to bolster rowing skills by providing a medium for in-door skill training. This system incorporates a mechanical platform, sensors that capture a user’s performance, and a software system that sends data to an enhanced virtual environment (VE) that combines visual, haptic, and acoustic feedbacks.

In recent years, the frequency of mass mortality events in marine ecosystems has increased, and several populations of benthic organism have been affected, reducing their density and changing their size and age structure. Few details are known about the dynamics of these populations over long time intervals. In late summer of both 1999 and 2003 two drastic mass mortality events, co-occurring with anomalous temperature increases, affected the northwestern Mediterranean rocky coastal communities. Due to these events the Paramuricea clavata population living at the western edge of La Spezia Gulf (Italy) was stricken, and 78% of the colonies died. This population was monitored from 1998 (pre-mortality) until 2013. This paper deals with the photographic sampling of permanent plots carried out in 2013. The findings were compared with those from the previous sampling series. This long-term, non-destructive sampling highlights the demographic trajectory of the octocoral population there after two anomalous mortality events, indicating that some new drop-point between local extinction and complete recovery may be have been reached. Long-term monitoring (including pre-mortality data) could allow evaluating the effects of global climate change on the conservation of impacted populations.

This paper shows the selection and preliminary evaluation of rowing gesture timing training on the SPRINT platform. After the analysis of experts’ gestures and a literature review of rowing technique features, the selection of proper feedbacks and the development of the training protocol are investigated. The general problem discussed here is the learning of timing of complex motor pattern under the effect of load. Eight novice adults participated the experiment, half of them receiving vibrotactile feedback (VIB), both receiving knowledge of results (KR) after training blocks. Preliminary results show the difficulty people had to accomplish the task and to exploit feedback. There is in fact no evidence of feedback effectiveness when comparing VIB-KR and KR group. Some causes were hypothesized and a side effect of load condition arisen from data. Therefore timing training will be further investigated exploiting information gathered.

This study focused on the selection and preliminary evaluation of different types of modal and information feedback in virtual environment to facilitate acquisition and transfer of a complex motor-cognitive skill of rowing. Specifically, we addressed the effectiveness of immediate information feedback provided visually as compared to sensory haptic feedback on the improvement in hands kinematics and changes in cognitive load during the course of learning the basic rowing technique. Several pilot experiments described in this report lead to the evaluation and optimization of the training protocol, to enhance facilitatory effects of adding visual and haptic feedback during training.

This paper presents the training of juggling skills, a highly complex problem of coordination, under tight time and space constraints. This training is achieved with a simple training platform, a light weight juggling platform (LWJ), and is compared to training with real balls. The principle directing the design of the platform is to obtain a simple tool. The simplification, in comparison to real world juggling, is based on the identification of invariant properties in the spatiotemporal coordination of intermediate and expert jugglers. Two classes training solutions were added to the LWJ: An audio-tactile pacing or augmented multimodal environment, and the manipulation of cognitive components of the juggling skills. The transfer to juggling with real balls was evaluated in four different experiments.

Four expert rowers’ gestures were gathered on the SPRINT rowing platform with the aid of an optic motion tracking system. Data were analyzed in order to get a digital representation of the features involved in rowing. Moreover, these data provide a dataset for developing digital models for rowing motion synthesis. Rowers were modeled as kinematic chains, data were processed in order to get position and orientation of upper body limbs. This representation was combined with SPRINT data in order to evaluate features found in the literature, to find new ones and to build models for the generation of rowing motion. The analysis shows the effectiveness of the motion reconstruction and two examples of technique features: stroke timing and upper limbs orientation during the finish phase.

The use of coaches in training provides several advantages to the skill transfer process. However, their use is limited because of cost reasons. Automatic recognition processes, on the other hand, have a vastly lower cost ratio and can replace or integrate many of the functions otherwise performed by a human. The paper presents an effective design of a real-time error recognition system for rowing in a simulator. It was designed in order to be easy to develop, be computationally efficient and offer an excellent classification ability.

Techniques for machine hearing are increasing their potentiality due to new application domains. In this work we are addressing the analysis of rowing sounds in natural context for the purpose of supporting a training system based on virtual environments. This paper presents the acquisition methodology and the evaluation of different machine learning techniques for classifying rowing-sound data. We see that a combination of principal component analysis and shallow networks perform equally well as deep architectures, while being much faster to train.

In this paper we analyze the concept of UnControlled Manifold (UCM), that consists in the kinematic variables that are not controlled by the user, being not relevant to the task. We proceed testing a set of controlled variables inspired by the literature about tracking task, then we propose a procedure to identify them on the basis of captured data. We are interested in the analysis of behavior in a Virtual Environment and in the real world. In particular we analyze the three ball cascade juggling and its simulation through a platform named Light Weight Juggling focusing on the task of ball tossing. Users arm kinematics is represented as a robotic manipulator with 7 degrees of freedom. Joint angles are retrieved through an optical tracking system. The variables controlled in the virtual environment are a subset of the ones controlled in the real world, that leads to an UM that differs from the one in the real world. A comparison between the statistics computed in the two cases is performed to explore behavioral differences in the two cases.

This study focused on one of the most important factors in rowing performance which is the ability demonstrated by elite rowers to regulate their effort during a 2000m race in order to produce their best performance. Experts are known to adopt a particular pacing strategy with a fast-start profile. The purpose of our study was to test by using virtual reality whether novice rowers were able to acquire this energy management skill during a 2000m race, with positive consequences for their rowing performance. Participants from the Avatar group were instructed to track a virtual boat, which velocity was previously calibrated to follow the appropriate to-be-learned velocity profile. This group was contrasted with a control group, which followed a classic training procedure. Our results indicate that the avatar group improved its performance (decrease in race duration), learned and maintained the expert profile. These effects were absent in the control group. Together, these results indicate that virtual reality can be used to accelerate the learning of energy-related skills.

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.

Differences between the signals captured at the entrances to the left and right ears are generally quantified by interaural time differences (ITDs), interaural level differences (ILDs) and interaural crosscorrelation (IC). Here, we attempt to gain knowledge on the changes of these interaural parameters during rowing and to evaluate their potential as source of information to determine rowing expertise exclusively from acoustic signals.

This paper presents the results of the design, implementation and evaluation of a platform for rowing training in Virtual Reality called SPRINT. The paper discusses how various aspects of the rowing skill can be analyzed and trained over a single common methodology and system platform. The result is a vision for new directions in the domain of sport training with Virtual Reality.

Background: Pollinosis is found more frequently in urban areas than in rural environments. This could be partly related to the different types of pollen exposure in these dissimilar areas. The objective of this study was to compare the distribution of pollen in these environments across an urbanization gradient., Methods: Daily pollen abundances were obtained in France using Hirst-type sensors. Sampling was conducted from January to June in 2003 and 2006 in a rural area, a semi-rural area and in two urban areas, which were characterized by several urbanization criteria., Results: Total allergenic pollen abundance was higher in rural and semi-rural areas than in urban areas irrespective of the sampling year. Multivariate analyses showed that pollen exposures differed according to the type of area and were strongly explained by the urbanization gradient. Grass, ash, birch, alder, hornbeam, hazel and plantain pollen quantities exceeded the allergy threshold more often in rural settings than in urban areas. In urban areas, only plane pollen quantities exceeded the allergy threshold more often than in rural areas., Conclusions: Allergenic pollen exposure is higher in rural areas than in urban areas, and the most abundant pollen in each area did not originated from the same taxa. This result should be taken into account in epidemiological studies comparing allergies in rural and urban areas to adapt the panel of pollen extracts for human environmental exposure. In addition, this study highlights that some ornamental trees produce a large number of allergenic pollens and provide new sources of aeroallergens., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Background: There is evidence of an allergy protective effect in children raised on farm. It has been assumed that microbial exposure may confer this protection. However in farm, little attention has been given to the pollen level and to concomitant microbiological exposure, and indoor pollen concentrations have never been precisely quantified., Methods: The kinetics of pollen in dairy farms have been studied in a pilot study (n = 9), and exposure in a sub-sample of the ongoing European birth cohort PASTURE (n = 106). Measurements of viable microorganisms and pollen were performed in air samples. To identify factors that modulate the pollen concentration multivariate regression analyses were run., Results: Indoor pollen (95% of Poaceae fragments and grains) were significantly higher in winter than in summer (P = 0.001) and ranged between 858 to 11 265 counts/m(3) during feeding in winter, thus exceeding typical outdoor levels during the pollen season. Geometric mean in French farms was significantly higher than in German and Swiss farms (7 534, 992 and 1 079 count/m(3), respectively). The presence of a ventilation system and loose housing systems significantly reduced indoor pollen levels. This pollen concentration rise after feeding was accompanied by an increase in fungal and actinomycetal levels, whereas the concentration of bacteria was not associated with feeding., Conclusions: Farmers and their children who attend cowsheds during the feeding sessions are exposed perennially to high pollen concentrations. It might be speculated that the combined permanent exposure to microbes from livestock and grass pollen may initiate tolerance in children living on a farm.