3,455 research outputs found
SoK: Anti-Facial Recognition Technology
The rapid adoption of facial recognition (FR) technology by both government
and commercial entities in recent years has raised concerns about civil
liberties and privacy. In response, a broad suite of so-called "anti-facial
recognition" (AFR) tools has been developed to help users avoid unwanted facial
recognition. The set of AFR tools proposed in the last few years is
wide-ranging and rapidly evolving, necessitating a step back to consider the
broader design space of AFR systems and long-term challenges. This paper aims
to fill that gap and provides the first comprehensive analysis of the AFR
research landscape. Using the operational stages of FR systems as a starting
point, we create a systematic framework for analyzing the benefits and
tradeoffs of different AFR approaches. We then consider both technical and
social challenges facing AFR tools and propose directions for future research
in this field.Comment: Camera-ready version for Oakland S&P 202
High p_T Triggered Delta-eta,Delta-phi Correlations over a Broad Range in Delta-eta
The first measurement of pseudorapidity (Delta-eta) and azimuthal angle
(Delta-phi) correlations between high transverse momentum charged hadrons (p_T
> 2.5 GeV/c) and all associated particles is presented at both short- (small
Delta-eta) and long-range (large Delta-eta) over a continuous pseudorapidity
acceptance (-4<Delta-eta<2). In these proceedings, the various near- and
away-side features of the correlation structure are discussed as a function of
centrality in Au+Au collisions measured by PHOBOS at sqrt(s_NN)=200 GeV. In
particular, this measurement allows a much more complete determination of the
longitudinal extent of the ridge structure, first observed by the STAR
collaboration over a limited eta range. In central collisions the ridge
persists to at least Delta-eta=4, diminishing in magnitude as collisions become
more peripheral until it disappears around Npart=80.Comment: 5 pages, 2 figures, presented at the 20th International Conference on
Ultra-Relativistic Nucleus-Nucleus Collisions, "Quark Matter 2008", Jaipur,
India, February 4-10, 2008. Full author list included and typo corrected in
equation
Intracule Functional Models I. Angle-corrected correlation kernels
We explore the merits of applying a simple angle-dependent correction to the correlation kernel
within the framework of Hartree–Fock–Wigner theory. Based on numerical results for the first
eighteen atoms, we conclude that such a correction offers a significant improvement over the
action kernel that we and others have explored previously
Latest results from the PHOBOS experiment
Over the past years PHOBOS has continued to analyze the large datasets
obtained from the first five runs of the Relativistic Heavy Ion Collider (RHIC)
at Brookhaven National Laboratory. The two main analysis streams have been
pursued. The first one aims to obtain a broad and systematic survey of global
properties of particle production in heavy ion collisions. The second class
includes the study of fluctuations and correlations in particle production.
Both type of studies have been performed for a variety of the collision
systems, covering a wide range in collision energy and centrality. The uniquely
large angular coverage of the PHOBOS detector and its ability to measure
charged particles down to very low transverse momentum is exploited. The latest
physics results from PHOBOS, as presented at Quark Matter 2008 Conference, are
contained in this report.Comment: 9 pages, 9 figures, presented at the 20th International Conference on
Ultra-Relativistic Nucleus-Nucleus Collisions, "Quark Matter 2008", Jaipur,
India, Feb.4-10, 200
Towards a killer app for the Semantic Web
Killer apps are highly transformative technologies that create new markets and widespread patterns of behaviour. IT generally, and the Web in particular, has benefited from killer apps to create new networks of users and increase its value. The Semantic Web community on the other hand is still awaiting a killer app that proves the superiority of its technologies. There are certain features that distinguish killer apps from other ordinary applications. This paper examines those features in the context of the Semantic Web, in the hope that a better understanding of the characteristics of killer apps might encourage their consideration when developing Semantic Web applications
Novel Bose-Einstein Interference in the Passage of a Fast Particle in a Dense Medium
When an energetic particle collides coherently with many medium particles at
high energies, the Bose-Einstein symmetry with respect to the interchange of
the exchanged virtual bosons leads to a destructive interference of the Feynman
amplitudes in most regions of the phase space but a constructive interference
in some other regions of the phase space. As a consequence, the recoiling
medium particles have a tendency to come out collectively along the direction
of the incident fast particle, each carrying a substantial fraction of the
incident longitudinal momentum. Such an interference appearing as collective
recoils of scatterers along the incident particle direction may have been
observed in angular correlations of hadrons associated with a high-
trigger in high-energy AuAu collisions at RHIC.Comment: 10 pages, 2 figures, invited talk presented at the 35th Symposium on
Nuclear Physics, Cocoyoc, Mexico, January 3, 2012, to be published in IOP
Conference Serie
Filtered overlap: speedup, locality, kernel non-normality and Z_A~1
We investigate the overlap operator with a UV filtered Wilson kernel. The
filtering leads to a better localization of the operator even on coarse
lattices and with the untuned choice . Furthermore, the axial-vector
renormalization constant is much closer to 1, reducing the mismatch with
perturbation theory. We show that all these features persist over a wide range
of couplings and that the details of filtering prove immaterial. We investigate
the properties of the kernel spectrum and find that the kernel non-normality is
reduced. As a side effect we observe that for certain applications of the
filtered overlap a speed-up factor of 2-4 can be achieved.Comment: 30 pp, 23 fig
Blacklight: Defending Black-Box Adversarial Attacks on Deep Neural Networks
The vulnerability of deep neural networks (DNNs) to adversarial examples is
well documented. Under the strong white-box threat model, where attackers have
full access to DNN internals, recent work has produced continual advancements
in defenses, often followed by more powerful attacks that break them.
Meanwhile, research on the more realistic black-box threat model has focused
almost entirely on reducing the query-cost of attacks, making them increasingly
practical for ML models already deployed today.
This paper proposes and evaluates Blacklight, a new defense against black-box
adversarial attacks. Blacklight targets a key property of black-box attacks: to
compute adversarial examples, they produce sequences of highly similar images
while trying to minimize the distance from some initial benign input. To detect
an attack, Blacklight computes for each query image a compact set of one-way
hash values that form a probabilistic fingerprint. Variants of an image produce
nearly identical fingerprints, and fingerprint generation is robust against
manipulation. We evaluate Blacklight on 5 state-of-the-art black-box attacks,
across a variety of models and classification tasks. While the most efficient
attacks take thousands or tens of thousands of queries to complete, Blacklight
identifies them all, often after only a handful of queries. Blacklight is also
robust against several powerful countermeasures, including an optimal black-box
attack that approximates white-box attacks in efficiency. Finally, Blacklight
significantly outperforms the only known alternative in both detection coverage
of attack queries and resistance against persistent attackers
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