32,388 research outputs found
Cluster-based reduced-order modelling of a mixing layer
We propose a novel cluster-based reduced-order modelling (CROM) strategy of
unsteady flows. CROM combines the cluster analysis pioneered in Gunzburger's
group (Burkardt et al. 2006) and and transition matrix models introduced in
fluid dynamics in Eckhardt's group (Schneider et al. 2007). CROM constitutes a
potential alternative to POD models and generalises the Ulam-Galerkin method
classically used in dynamical systems to determine a finite-rank approximation
of the Perron-Frobenius operator. The proposed strategy processes a
time-resolved sequence of flow snapshots in two steps. First, the snapshot data
are clustered into a small number of representative states, called centroids,
in the state space. These centroids partition the state space in complementary
non-overlapping regions (centroidal Voronoi cells). Departing from the standard
algorithm, the probabilities of the clusters are determined, and the states are
sorted by analysis of the transition matrix. Secondly, the transitions between
the states are dynamically modelled using a Markov process. Physical mechanisms
are then distilled by a refined analysis of the Markov process, e.g. using
finite-time Lyapunov exponent and entropic methods. This CROM framework is
applied to the Lorenz attractor (as illustrative example), to velocity fields
of the spatially evolving incompressible mixing layer and the three-dimensional
turbulent wake of a bluff body. For these examples, CROM is shown to identify
non-trivial quasi-attractors and transition processes in an unsupervised
manner. CROM has numerous potential applications for the systematic
identification of physical mechanisms of complex dynamics, for comparison of
flow evolution models, for the identification of precursors to desirable and
undesirable events, and for flow control applications exploiting nonlinear
actuation dynamics.Comment: 48 pages, 30 figures. Revised version with additional material.
Accepted for publication in Journal of Fluid Mechanic
Trajectory and Policy Aware Sender Anonymity in Location Based Services
We consider Location-based Service (LBS) settings, where a LBS provider logs
the requests sent by mobile device users over a period of time and later wants
to publish/share these logs. Log sharing can be extremely valuable for
advertising, data mining research and network management, but it poses a
serious threat to the privacy of LBS users. Sender anonymity solutions prevent
a malicious attacker from inferring the interests of LBS users by associating
them with their service requests after gaining access to the anonymized logs.
With the fast-increasing adoption of smartphones and the concern that historic
user trajectories are becoming more accessible, it becomes necessary for any
sender anonymity solution to protect against attackers that are
trajectory-aware (i.e. have access to historic user trajectories) as well as
policy-aware (i.e they know the log anonymization policy). We call such
attackers TP-aware.
This paper introduces a first privacy guarantee against TP-aware attackers,
called TP-aware sender k-anonymity. It turns out that there are many possible
TP-aware anonymizations for the same LBS log, each with a different utility to
the consumer of the anonymized log. The problem of finding the optimal TP-aware
anonymization is investigated. We show that trajectory-awareness renders the
problem computationally harder than the trajectory-unaware variants found in
the literature (NP-complete in the size of the log, versus PTIME). We describe
a PTIME l-approximation algorithm for trajectories of length l and empirically
show that it scales to large LBS logs (up to 2 million users)
Deep Predictive Policy Training using Reinforcement Learning
Skilled robot task learning is best implemented by predictive action policies
due to the inherent latency of sensorimotor processes. However, training such
predictive policies is challenging as it involves finding a trajectory of motor
activations for the full duration of the action. We propose a data-efficient
deep predictive policy training (DPPT) framework with a deep neural network
policy architecture which maps an image observation to a sequence of motor
activations. The architecture consists of three sub-networks referred to as the
perception, policy and behavior super-layers. The perception and behavior
super-layers force an abstraction of visual and motor data trained with
synthetic and simulated training samples, respectively. The policy super-layer
is a small sub-network with fewer parameters that maps data in-between the
abstracted manifolds. It is trained for each task using methods for policy
search reinforcement learning. We demonstrate the suitability of the proposed
architecture and learning framework by training predictive policies for skilled
object grasping and ball throwing on a PR2 robot. The effectiveness of the
method is illustrated by the fact that these tasks are trained using only about
180 real robot attempts with qualitative terminal rewards.Comment: This work is submitted to IEEE/RSJ International Conference on
Intelligent Robots and Systems 2017 (IROS2017
Simulated single molecule microscopy with SMeagol
SMeagol is a software tool to simulate highly realistic microscopy data based
on spatial systems biology models, in order to facilitate development,
validation, and optimization of advanced analysis methods for live cell single
molecule microscopy data. Availability and Implementation: SMeagol runs on
Matlab R2014 and later, and uses compiled binaries in C for reaction-diffusion
simulations. Documentation, source code, and binaries for recent versions of
Mac OS, Windows, and Ubuntu Linux can be downloaded from
http://smeagol.sourceforge.net.Comment: v2: 14 pages including supplementary text. Pre-copyedited,
author-produced version of an application note published in Bioinformatics
following peer review. The version of record, and additional supplementary
material is available online at:
https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btw10
Verifying black hole orbits with gravitational spectroscopy
Gravitational waves from test masses bound to geodesic orbits of rotating
black holes are simulated, using Teukolsky's black hole perturbation formalism,
for about ten thousand generic orbital configurations. Each binary radiates
power exclusively in modes with frequencies that are
integer-linear-combinations of the orbit's three fundamental frequencies. The
following general spectral properties are found with a survey of orbits: (i)
99% of the radiated power is typically carried by a few hundred modes, and at
most by about a thousand modes, (ii) the dominant frequencies can be grouped
into a small number of families defined by fixing two of the three integer
frequency multipliers, and (iii) the specifics of these trends can be
qualitatively inferred from the geometry of the orbit under consideration.
Detections using triperiodic analytic templates modeled on these general
properties would constitute a verification of radiation from an adiabatic
sequence of black hole orbits and would recover the evolution of the
fundamental orbital frequencies. In an analogy with ordinary spectroscopy, this
would compare to observing the Bohr model's atomic hydrogen spectrum without
being able to rule out alternative atomic theories or nuclei. The suitability
of such a detection technique is demonstrated using snapshots computed at
12-hour intervals throughout the last three years before merger of a kludged
inspiral. Because of circularization, the number of excited modes decreases as
the binary evolves. A hypothetical detection algorithm that tracks mode
families dominating the first 12 hours of the inspiral would capture 98% of the
total power over the remaining three years.Comment: 18 pages, expanded section on detection algorithms and made minor
edits. Final published versio
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