95,790 research outputs found
Bayesian Inference Analysis of Unmodelled Gravitational-Wave Transients
We report the results of an in-depth analysis of the parameter estimation
capabilities of BayesWave, an algorithm for the reconstruction of
gravitational-wave signals without reference to a specific signal model. Using
binary black hole signals, we compare BayesWave's performance to the
theoretical best achievable performance in three key areas: sky localisation
accuracy, signal/noise discrimination, and waveform reconstruction accuracy.
BayesWave is most effective for signals that have very compact time-frequency
representations. For binaries, where the signal time-frequency volume decreases
with mass, we find that BayesWave's performance reaches or approaches
theoretical optimal limits for system masses above approximately 50 M_sun. For
such systems BayesWave is able to localise the source on the sky as well as
templated Bayesian analyses that rely on a precise signal model, and it is
better than timing-only triangulation in all cases. We also show that the
discrimination of signals against glitches and noise closely follow analytical
predictions, and that only a small fraction of signals are discarded as
glitches at a false alarm rate of 1/100 y. Finally, the match between
BayesWave- reconstructed signals and injected signals is broadly consistent
with first-principles estimates of the maximum possible accuracy, peaking at
about 0.95 for high mass systems and decreasing for lower-mass systems. These
results demonstrate the potential of unmodelled signal reconstruction
techniques for gravitational-wave astronomy.Comment: 10 pages, 7 figure
How Much Information is in a Jet?
Machine learning techniques are increasingly being applied toward data
analyses at the Large Hadron Collider, especially with applications for
discrimination of jets with different originating particles. Previous studies
of the power of machine learning to jet physics has typically employed image
recognition, natural language processing, or other algorithms that have been
extensively developed in computer science. While these studies have
demonstrated impressive discrimination power, often exceeding that of
widely-used observables, they have been formulated in a non-constructive manner
and it is not clear what additional information the machines are learning. In
this paper, we study machine learning for jet physics constructively,
expressing all of the information in a jet onto sets of observables that
completely and minimally span N-body phase space. For concreteness, we study
the application of machine learning for discrimination of boosted, hadronic
decays of Z bosons from jets initiated by QCD processes. Our results
demonstrate that the information in a jet that is useful for discrimination
power of QCD jets from Z bosons is saturated by only considering observables
that are sensitive to 4-body (8 dimensional) phase space.Comment: 14 pages + appendices, 10 figures; v2: JHEP version, updated neural
network, included deeper network and boosted decision tree result
Online advertising: analysis of privacy threats and protection approaches
Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft
1984 Is Still Fiction: Electronic Monitoring in the Workplace and U.S. Privacy Law
Electronic monitoring in the workplace has been the subject of relentless public criticism. Privacy advocates argue that technological advancements have given overbearing employers powerful tools to abuse employee dignity in the name of productivity and that new legislation should bolster workplace privacy rights. This iBrief contends that current U.S. legal doctrine governing electronic monitoring in the workplace is fair given the nature and purpose of the workplace, and potential employer liability for employee misconduct
Mechatronics & the cloud
Conventionally, the engineering design process has assumed that the design team is able to exercise control over all elements of the design, either directly or indirectly in the case of sub-systems through their specifications. The introduction of Cyber-Physical Systems (CPS) and the Internet of Things (IoT) means that a design team’s ability to have control over all elements of a system is no longer the case, particularly as the actual system configuration may well be being dynamically reconfigured in real-time according to user (and vendor) context and need. Additionally, the integration of the Internet of Things with elements of Big Data means that information becomes a commodity to be autonomously traded by and between systems, again according to context and need, all of which has implications for the privacy of system users. The paper therefore considers the relationship between mechatronics and cloud-basedtechnologies in relation to issues such as the distribution of functionality and user privacy
Oscar Pistorius and the Future Nature of Olympic, Paralympic, and Other Sports
Oscar Pistorius is a Paralympic bionic leg runner and record holder in the 100, 200, and 400 meters who wants to compete in the Olympics. This paper provides an analysis of a) his case; b) the impact of his case on the Olympics, the Paralympics and other -lympics and the relationships between the -lympics; c) the impact on other international and national sports; d) the applicability of the UN Convention on the Rights of Persons with Disabilities. It situates the evaluation of the Pistorius case within the broader doping discourse and the reality that new and emerging science and technology products increasingly generate internal and external human bodily enhancements that go beyond the species-typical, enabling more and more a culture of increasing demand for, and acceptance of modifications of the human body (structure, function, abilities) beyond its species-typical boundaries and the emergence of new social concepts such as transhumanism and the transhumanisation of ableism
Web Tracking: Mechanisms, Implications, and Defenses
This articles surveys the existing literature on the methods currently used
by web services to track the user online as well as their purposes,
implications, and possible user's defenses. A significant majority of reviewed
articles and web resources are from years 2012-2014. Privacy seems to be the
Achilles' heel of today's web. Web services make continuous efforts to obtain
as much information as they can about the things we search, the sites we visit,
the people with who we contact, and the products we buy. Tracking is usually
performed for commercial purposes. We present 5 main groups of methods used for
user tracking, which are based on sessions, client storage, client cache,
fingerprinting, or yet other approaches. A special focus is placed on
mechanisms that use web caches, operational caches, and fingerprinting, as they
are usually very rich in terms of using various creative methodologies. We also
show how the users can be identified on the web and associated with their real
names, e-mail addresses, phone numbers, or even street addresses. We show why
tracking is being used and its possible implications for the users (price
discrimination, assessing financial credibility, determining insurance
coverage, government surveillance, and identity theft). For each of the
tracking methods, we present possible defenses. Apart from describing the
methods and tools used for keeping the personal data away from being tracked,
we also present several tools that were used for research purposes - their main
goal is to discover how and by which entity the users are being tracked on
their desktop computers or smartphones, provide this information to the users,
and visualize it in an accessible and easy to follow way. Finally, we present
the currently proposed future approaches to track the user and show that they
can potentially pose significant threats to the users' privacy.Comment: 29 pages, 212 reference
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