228,368 research outputs found
CheckMATE 2: From the model to the limit
We present the latest developments to the CheckMATE program that allows
models of new physics to be easily tested against the recent LHC data. To
achieve this goal, the core of CheckMATE now contains over 60 LHC analyses of
which 12 are from the 13 TeV run. The main new feature is that CheckMATE 2 now
integrates the Monte Carlo event generation via Madgraph and Pythia 8. This
allows users to go directly from a SLHA file or UFO model to the result of
whether a model is allowed or not. In addition, the integration of the event
generation leads to a significant increase in the speed of the program. Many
other improvements have also been made, including the possibility to now
combine signal regions to give a total likelihood for a model.Comment: 53 pages, 6 figures; references updated, instructions slightly
change
GATE : a simulation toolkit for PET and SPECT
Monte Carlo simulation is an essential tool in emission tomography that can
assist in the design of new medical imaging devices, the optimization of
acquisition protocols, and the development or assessment of image
reconstruction algorithms and correction techniques. GATE, the Geant4
Application for Tomographic Emission, encapsulates the Geant4 libraries to
achieve a modular, versatile, scripted simulation toolkit adapted to the field
of nuclear medicine. In particular, GATE allows the description of
time-dependent phenomena such as source or detector movement, and source decay
kinetics. This feature makes it possible to simulate time curves under
realistic acquisition conditions and to test dynamic reconstruction algorithms.
A public release of GATE licensed under the GNU Lesser General Public License
can be downloaded at the address http://www-lphe.epfl.ch/GATE/
Event tracking for real-time unaware sensitivity analysis (EventTracker)
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This paper introduces a platform for online Sensitivity Analysis (SA) that is applicable in large scale real-time data acquisition (DAQ) systems. Here we use the term real-time in the context of a system that has to respond to externally generated input stimuli within a finite and specified period. Complex industrial systems such as manufacturing, healthcare, transport, and finance require high quality information on which to base timely responses to events occurring in their volatile environments. The motivation for the proposed EventTracker platform is the assumption that modern industrial systems are able to capture data in real-time and have the necessary technological flexibility to adjust to changing system requirements. The flexibility to adapt can only be assured if data is succinctly interpreted and translated into corrective actions in a timely manner. An important factor that facilitates data interpretation and information modelling is an appreciation of the affect system inputs have on each output at the time of occurrence. Many existing sensitivity analysis methods appear to hamper efficient and timely analysis due to a reliance on historical data, or sluggishness in providing a timely solution that would be of use in real-time applications. This inefficiency is further compounded by computational limitations and the complexity of some existing models. In dealing with real-time event driven systems, the underpinning logic of the proposed method is based on the assumption that in the vast majority of cases changes in input variables will trigger events. Every single or combination of events could subsequently result in a change to the system state. The proposed event tracking sensitivity analysis method describes variables and the system state as a collection of events. The higher the numeric occurrence of an input variable at the trigger level during an event monitoring interval, the greater is its impact on the final analysis of the system state. Experiments were designed to compare the proposed event tracking sensitivity analysis method with a comparable method (that of Entropy). An improvement of 10% in computational efficiency without loss in accuracy was observed. The comparison also showed that the time taken to perform the sensitivity analysis was 0.5% of that required when using the comparable Entropy based method.EPSR
Minimalist design of a robust real-time quantum random number generator
We present a simple and robust construction of a real-time quantum random
number generator (QRNG). Our minimalist approach ensures stable operation of
the device as well as its simple and straightforward hardware implementation as
a stand-alone module. As a source of randomness the device uses measurements of
time intervals between clicks of a single-photon detector. The obtained raw
sequence is then filtered and processed by a deterministic randomness
extractor, which is realized as a look-up table. This enables high speed
on-the-fly processing without the need of extensive computations. The overall
performance of the device is around 1 random bit per detector click, resulting
in 1.2 Mbit/s generation rate in our implementation
PS-Sim: A Framework for Scalable Simulation of Participatory Sensing Data
Emergence of smartphone and the participatory sensing (PS) paradigm have
paved the way for a new variant of pervasive computing. In PS, human user
performs sensing tasks and generates notifications, typically in lieu of
incentives. These notifications are real-time, large-volume, and multi-modal,
which are eventually fused by the PS platform to generate a summary. One major
limitation with PS is the sparsity of notifications owing to lack of active
participation, thus inhibiting large scale real-life experiments for the
research community. On the flip side, research community always needs ground
truth to validate the efficacy of the proposed models and algorithms. Most of
the PS applications involve human mobility and report generation following
sensing of any event of interest in the adjacent environment. This work is an
attempt to study and empirically model human participation behavior and event
occurrence distributions through development of a location-sensitive data
simulation framework, called PS-Sim. From extensive experiments it has been
observed that the synthetic data generated by PS-Sim replicates real
participation and event occurrence behaviors in PS applications, which may be
considered for validation purpose in absence of the groundtruth. As a
proof-of-concept, we have used real-life dataset from a vehicular traffic
management application to train the models in PS-Sim and cross-validated the
simulated data with other parts of the same dataset.Comment: Published and Appeared in Proceedings of IEEE International
Conference on Smart Computing (SMARTCOMP-2018
Detection of gravitational wave bursts by interferometric detectors
We study in this paper some filters for the detection of burst-like signals
in the data of interferometric gravitational-wave detectors. We present first
two general (non-linear) filters with no {\it a priori} assumption on the
waveforms to detect. A third filter, a peak correlator, is also introduced and
permits to estimate the gain, when some prior information is known about the
waveforms. We use the catalogue of supernova gravitational-wave signals built
by Zwerger and M\"uller in order to have a benchmark of the performance of each
filter and to compare to the performance of the optimal filter. The three
filters could be a part of an on-line triggering in interferometric
gravitational-wave detectors, specialised in the selection of burst events.Comment: 15 pages, 8 figure
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