19,613 research outputs found
Generating Predicate Callback Summaries for the Android Framework
One of the challenges of analyzing, testing and debugging Android apps is
that the potential execution orders of callbacks are missing from the apps'
source code. However, bugs, vulnerabilities and refactoring transformations
have been found to be related to callback sequences. Existing work on control
flow analysis of Android apps have mainly focused on analyzing GUI events. GUI
events, although being a key part of determining control flow of Android apps,
do not offer a complete picture. Our observation is that orthogonal to GUI
events, the Android API calls also play an important role in determining the
order of callbacks. In the past, such control flow information has been modeled
manually. This paper presents a complementary solution of constructing program
paths for Android apps. We proposed a specification technique, called Predicate
Callback Summary (PCS), that represents the callback control flow information
(including callback sequences as well as the conditions under which the
callbacks are invoked) in Android API methods and developed static analysis
techniques to automatically compute and apply such summaries to construct apps'
callback sequences. Our experiments show that by applying PCSs, we are able to
construct Android apps' control flow graphs, including inter-callback
relations, and also to detect infeasible paths involving multiple callbacks.
Such control flow information can help program analysis and testing tools to
report more precise results. Our detailed experimental data is available at:
http://goo.gl/NBPrKsComment: 11 page
Learning to Skim Text
Recurrent Neural Networks are showing much promise in many sub-areas of
natural language processing, ranging from document classification to machine
translation to automatic question answering. Despite their promise, many
recurrent models have to read the whole text word by word, making it slow to
handle long documents. For example, it is difficult to use a recurrent network
to read a book and answer questions about it. In this paper, we present an
approach of reading text while skipping irrelevant information if needed. The
underlying model is a recurrent network that learns how far to jump after
reading a few words of the input text. We employ a standard policy gradient
method to train the model to make discrete jumping decisions. In our benchmarks
on four different tasks, including number prediction, sentiment analysis, news
article classification and automatic Q\&A, our proposed model, a modified LSTM
with jumping, is up to 6 times faster than the standard sequential LSTM, while
maintaining the same or even better accuracy
Cristina Valdes: Pianist, in recital
Program listing performers and works performe
Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis \& Application
Spontaneous subtle emotions are expressed through micro-expressions, which
are tiny, sudden and short-lived dynamics of facial muscles; thus poses a great
challenge for visual recognition. The abrupt but significant dynamics for the
recognition task are temporally sparse while the rest, irrelevant dynamics, are
temporally redundant. In this work, we analyze and enforce sparsity constrains
to learn significant temporal and spectral structures while eliminate
irrelevant facial dynamics of micro-expressions, which would ease the challenge
in the visual recognition of spontaneous subtle emotions. The hypothesis is
confirmed through experimental results of automatic spontaneous subtle emotion
recognition with several sparsity levels on CASME II and SMIC, the only two
publicly available spontaneous subtle emotion databases. The overall
performances of the automatic subtle emotion recognition are boosted when only
significant dynamics are preserved from the original sequences.Comment: IEEE Transaction of Affective Computing (2016
Correlated Dirac Particles and Superconductivity on the Honeycomb Lattice
We investigate the properties of the nearest-neighbor singlet pairing and the
emergence of d-wave superconductivity in the doped honeycomb lattice
considering the limit of large interactions and the model. First,
by applying a renormalized mean-field procedure as well as slave-boson theories
which account for the proximity to the Mott insulating state, we confirm the
emergence of d-wave superconductivity in agreement with earlier works. We show
that a small but finite spin coupling between next-nearest neighbors
stabilizes d-wave symmetry compared to the extended s-wave scenario. At small
hole doping, to minimize energy and to gap the whole Fermi surface or all the
Dirac points, the superconducting ground state is characterized by a
singlet pairing assigned to one valley and a singlet pairing to the
other, which then preserves time-reversal symmetry. The slightly doped
situation is distinct from the heavily doped case (around 3/8 and 5/8 filling)
supporting a pure chiral symmetry and breaking time-reversal symmetry.
Then, we apply the functional Renormalization Group and we study in more detail
the competition between antiferromagnetism and superconductivity in the
vicinity of half-filling. We discuss possible applications to
strongly-correlated compounds with Copper hexagonal planes such as
InCuVO. Our findings are also relevant to the understanding of
exotic superfluidity with cold atoms.Comment: 13 pages, 8 figure
Quantum Spin Hall Insulators with Interactions and Lattice Anisotropy
We investigate the interplay between spin-orbit coupling and
electron-electron interactions on the honeycomb lattice combining the cellular
dynamical mean-field theory and its real space extension with analytical
approaches. We provide a thorough analysis of the phase diagram and temperature
effects at weak spin-orbit coupling. We systematically discuss the stability of
the quantum spin Hall phase toward interactions and lattice anisotropy
resulting in the plaquette-honeycomb model. We also show the evolution of the
helical edge states characteristic of quantum spin Hall insulators as a
function of Hubbard interaction and anisotropy. At very weak spin-orbit
coupling and intermediate electron-electron interactions, we substantiate the
existence of a quantum spin liquid phase.Comment: 7 pages, 9 figures, final versio
Bounds on the multipartite entanglement of superpositions
We derive the lower and upper bounds on the entanglement of a given
multipartite superposition state in terms of the entanglement of the states
being superposed. The first entanglement measure we use is the geometric
measure, and the second is the q-squashed entanglement. These bounds allow us
to estimate the amount of the multipartite entanglement of superpositions. We
also show that two states of high fidelity to one another do not necessarily
have nearly the same q-squashed entanglement.Comment: 4 pages, 2 figure. few typos correcte
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