210 research outputs found
The Big Five:Addressing Recurrent Multimodal Learning Data Challenges
The analysis of multimodal data in learning is a growing field of research, which
has led to the development of different analytics solutions. However, there is no
standardised approach to handle multimodal data. In this paper, we describe and outline a
solution for five recurrent challenges in the analysis of multimodal data: the data collection,
storing, annotation, processing and exploitation. For each of these challenges, we envision
possible solutions. The prototypes for some of the proposed solutions will be discussed
during the Multimodal Challenge of the fourth Learning Analytics & Knowledge Hackathon, a
two-day hands-on workshop in which the authors will open up the prototypes for trials,
validation and feedback
Multimodal Challenge: Analytics Beyond User-computer Interaction Data
This contribution describes one the challenges explored in the Fourth LAK Hackathon. This challenge aims at shifting the focus from learning situations which can be easily traced through user-computer interactions data and concentrate more on user-world interactions events, typical of co-located and practice-based learning experiences. This mission, pursued by the multimodal learning analytics (MMLA) community, seeks to bridge
the gap between digital and physical learning spaces. The “multimodal” approach consists in combining learners’ motoric actions with physiological responses and data about the learning contexts. These data can be collected through multiple wearable sensors and Internet of Things (IoT) devices. This Hackathon table will confront with three main challenges arising from the analysis and valorisation of multimodal datasets: 1) the data
collection and storing, 2) the data annotation, 3) the data processing and exploitation. Some research questions which will be considered in this Hackathon challenge are the following: how to process the raw sensor data streams and extract relevant features? which data mining and machine learning techniques can be applied? how can we compare two action recordings? How to combine sensor data with Experience API (xAPI)? what are meaningful visualisations for these data
GeV-Class two-fold CW linac driven by an arc-compressor
We present a study of an innovative scheme to generate high repetition rate (MHz-class)
GeV electron beams by adopting a two-pass two-way acceleration in a super-conducting Linac
operated in Continuous Wave (CW) mode. The beam is accelerated twice in the Linac by being
re-injected, after the first pass, in opposite direction of propagation. The task of recirculating the
electron beam is performed by an arc compressor composed by 14 Double Bend Achromat (DBA).
In this paper, we study the main issues of the two-fold acceleration scheme, the electron beam
quality parameters preservation (emittance, energy spread), together with the bunch compression
performance of the arc compressor, aiming to operate an X-ray Free Electron Laser. The requested
power to supply the cryogenic plant and the RF sources is also significantly reduced w.r.t a
conventional one-pass SC Linac for the same final energy
The AMY experiment to measure GHz radiation for Ultra-High Energy Cosmic Ray detection
The Air Microwave Yield (AMY) project aims to measure the emission in the GHz regime from test-beam induced air-shower. The experiment is using the Beam Test Facility (BTF) of the Frascati INFN National Laboratories in Italy. The final purpose is to characterize a process to be used in a next generation of ultra-high energy cosmic rays (UHECRs) detectors. We describe the experimental apparatus and the first test performed in November 2011
Measurement of the cosmic ray spectrum above eV using inclined events detected with the Pierre Auger Observatory
A measurement of the cosmic-ray spectrum for energies exceeding
eV is presented, which is based on the analysis of showers
with zenith angles greater than detected with the Pierre Auger
Observatory between 1 January 2004 and 31 December 2013. The measured spectrum
confirms a flux suppression at the highest energies. Above
eV, the "ankle", the flux can be described by a power law with
index followed by
a smooth suppression region. For the energy () at which the
spectral flux has fallen to one-half of its extrapolated value in the absence
of suppression, we find
eV.Comment: Replaced with published version. Added journal reference and DO
Energy Estimation of Cosmic Rays with the Engineering Radio Array of the Pierre Auger Observatory
The Auger Engineering Radio Array (AERA) is part of the Pierre Auger
Observatory and is used to detect the radio emission of cosmic-ray air showers.
These observations are compared to the data of the surface detector stations of
the Observatory, which provide well-calibrated information on the cosmic-ray
energies and arrival directions. The response of the radio stations in the 30
to 80 MHz regime has been thoroughly calibrated to enable the reconstruction of
the incoming electric field. For the latter, the energy deposit per area is
determined from the radio pulses at each observer position and is interpolated
using a two-dimensional function that takes into account signal asymmetries due
to interference between the geomagnetic and charge-excess emission components.
The spatial integral over the signal distribution gives a direct measurement of
the energy transferred from the primary cosmic ray into radio emission in the
AERA frequency range. We measure 15.8 MeV of radiation energy for a 1 EeV air
shower arriving perpendicularly to the geomagnetic field. This radiation energy
-- corrected for geometrical effects -- is used as a cosmic-ray energy
estimator. Performing an absolute energy calibration against the
surface-detector information, we observe that this radio-energy estimator
scales quadratically with the cosmic-ray energy as expected for coherent
emission. We find an energy resolution of the radio reconstruction of 22% for
the data set and 17% for a high-quality subset containing only events with at
least five radio stations with signal.Comment: Replaced with published version. Added journal reference and DO
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