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, $T$, 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 $T \rightarrow 0$ and $T \rightarrow \infty$ 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 ($\sim$hundreds of parsecs) and middle age (maximum of $\sim$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

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

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

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 $F_1$ 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