5,083 research outputs found
On the origin of \gamma-ray emission in \eta\ Carina
\eta\ Car is the only colliding-wind binary for which high-energy \gamma\
rays are detected. Although the physical conditions in the shock region change
on timescales of hours to days, the variability seen at GeV energies is weak
and on significantly longer timescales. The \gamma-ray spectrum exhibits two
features that can be interpreted as emission from the shocks on either side of
the contact discontinuity. Here we report on the first time-dependent modelling
of the non-thermal emission in \eta\ Car. We find that emission from primary
electrons is likely not responsible for the \gamma-ray emission, but
accelerated protons interacting with the dense wind material can explain the
observations. In our model, efficient acceleration is required at both shocks,
with the primary side acting as a hadron calorimeter, whilst on the companion
side acceleration is limited by the flow time out of the system, resulting in
changing acceleration conditions. The system therefore represents a unique
laboratory for the exploration of hadronic particle acceleration in
non-relativistic shocks.Comment: 5 pages, 4 figures, 1 table, accepted for publication in MNRAS
Letter
Learning Temporal Transformations From Time-Lapse Videos
Based on life-long observations of physical, chemical, and biologic phenomena
in the natural world, humans can often easily picture in their minds what an
object will look like in the future. But, what about computers? In this paper,
we learn computational models of object transformations from time-lapse videos.
In particular, we explore the use of generative models to create depictions of
objects at future times. These models explore several different prediction
tasks: generating a future state given a single depiction of an object,
generating a future state given two depictions of an object at different times,
and generating future states recursively in a recurrent framework. We provide
both qualitative and quantitative evaluations of the generated results, and
also conduct a human evaluation to compare variations of our models.Comment: ECCV201
Response of finite-time particle detectors in non-inertial frames and curved spacetime
The response of the Unruh-DeWitt type monopole detectors which were coupled
to the quantum field only for a finite proper time interval is studied for
inertial and accelerated trajectories, in the Minkowski vacuum in (3+1)
dimensions. Such a detector will respond even while on an inertial trajctory
due to the transient effects. Further the response will also depend on the
manner in which the detector is switched on and off. We consider the response
in the case of smooth as well as abrupt switching of the detector. The former
case is achieved with the aid of smooth window functions whose width, ,
determines the effective time scale for which the detector is coupled to the
field. We obtain a general formula for the response of the detector when a
window function is specified, and work out the response in detail for the case
of gaussian and exponential window functions. A detailed discussion of both and limits are given and several
subtlities in the limiting procedure are clarified. The analysis is extended
for detector responses in Schwarzschild and de-Sitter spacetimes in (1+1)
dimensions.Comment: 29 pages, normal TeX, figures appended as postscript file, IUCAA
Preprint # 23/9
EDGE: a code to calculate diffusion of cosmic-ray electrons and their gamma-ray emission
The positron excess measured by PAMELA and AMS can only be explained if there
is one or several sources injecting them. Moreover, at the highest energies, it
requires the presence of nearby (hundreds of parsecs) and middle age
(maximum of hundreds of kyr) source. Pulsars, as factories of electrons
and positrons, are one of the proposed candidates to explain the origin of this
excess. To calculate the contribution of these sources to the electron and
positron flux at the Earth, we developed EDGE (Electron Diffusion and Gamma
rays to the Earth), a code to treat diffusion of electrons and compute their
diffusion from a central source with a flexible injection spectrum. We can
derive the source's gamma-ray spectrum, spatial extension, the all-electron
density in space and the electron and positron flux reaching the Earth. We
present in this contribution the fundamentals of the code and study how
different parameters affect the gamma-ray spectrum of a source and the electron
flux measured at the Earth.Comment: Presented at the 35th International Cosmic Ray Conference (ICRC2017),
Bexco, Busan, Kore
Signal processing for ION mobility spectrometers
Signal processing techniques for systems based upon Ion Mobility Spectrometry will be discussed in the light of 10 years of experience in the design of real-time IMS. Among the topics to be covered are compensation techniques for variations in the number density of the gas - the use of an internal standard (a reference peak) or pressure and temperature sensors. Sources of noise and methods for noise reduction will be discussed together with resolution limitations and the ability of deconvolution techniques to improve resolving power. The use of neural networks (either by themselves or as a component part of a processing system) will be reviewed
Interpolating between the Bose-Einstein and the Fermi-Dirac distributions in odd dimensions
We consider the response of a uniformly accelerated monopole detector that is
coupled to a superposition of an odd and an even power of a quantized, massless
scalar field in flat spacetime in arbitrary dimensions. We show that, when the
field is assumed to be in the Minkowski vacuum, the response of the detector is
characterized by a Bose-Einstein factor in even spacetime dimensions, whereas a
Bose-Einstein as well as a Fermi-Dirac factor appear in the detector response
when the dimension of spacetime is odd. Moreover, we find that, it is possible
to interpolate between the Bose-Einstein and the Fermi-Dirac distributions in
odd spacetime dimensions by suitably adjusting the relative strengths of the
detector's coupling to the odd and the even powers of the scalar field. We
point out that the response of the detector is always thermal and we, finally,
close by stressing the apparent nature of the appearance of the Fermi-Dirac
factor in the detector response.Comment: RevTeX, 7 page
CAPTCHaStar! A novel CAPTCHA based on interactive shape discovery
Over the last years, most websites on which users can register (e.g., email
providers and social networks) adopted CAPTCHAs (Completely Automated Public
Turing test to tell Computers and Humans Apart) as a countermeasure against
automated attacks. The battle of wits between designers and attackers of
CAPTCHAs led to current ones being annoying and hard to solve for users, while
still being vulnerable to automated attacks.
In this paper, we propose CAPTCHaStar, a new image-based CAPTCHA that relies
on user interaction. This novel CAPTCHA leverages the innate human ability to
recognize shapes in a confused environment. We assess the effectiveness of our
proposal for the two key aspects for CAPTCHAs, i.e., usability, and resiliency
to automated attacks. In particular, we evaluated the usability, carrying out a
thorough user study, and we tested the resiliency of our proposal against
several types of automated attacks: traditional ones; designed ad-hoc for our
proposal; and based on machine learning. Compared to the state of the art, our
proposal is more user friendly (e.g., only some 35% of the users prefer current
solutions, such as text-based CAPTCHAs) and more resilient to automated
attacks.Comment: 15 page
DeepWalk: Online Learning of Social Representations
We present DeepWalk, a novel approach for learning latent representations of
vertices in a network. These latent representations encode social relations in
a continuous vector space, which is easily exploited by statistical models.
DeepWalk generalizes recent advancements in language modeling and unsupervised
feature learning (or deep learning) from sequences of words to graphs. DeepWalk
uses local information obtained from truncated random walks to learn latent
representations by treating walks as the equivalent of sentences. We
demonstrate DeepWalk's latent representations on several multi-label network
classification tasks for social networks such as BlogCatalog, Flickr, and
YouTube. Our results show that DeepWalk outperforms challenging baselines which
are allowed a global view of the network, especially in the presence of missing
information. DeepWalk's representations can provide scores up to 10%
higher than competing methods when labeled data is sparse. In some experiments,
DeepWalk's representations are able to outperform all baseline methods while
using 60% less training data. DeepWalk is also scalable. It is an online
learning algorithm which builds useful incremental results, and is trivially
parallelizable. These qualities make it suitable for a broad class of real
world applications such as network classification, and anomaly detection.Comment: 10 pages, 5 figures, 4 table
- …