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Title: End-to-End Multi-View Networks for Text Classification,
Abstract: We propose a multi-view network for text classification. Our method
automatically creates various views of its input text, each taking the form of
soft attention weights that distribute the classifier's focus among a set of
base features. For a bag-of-words representation, each view focuses on a
different subset of the text's words. Aggregating many such views results in a
more discriminative and robust representation. Through a novel architecture
that both stacks and concatenates views, we produce a network that emphasizes
both depth and width, allowing training to converge quickly. Using our
multi-view architecture, we establish new state-of-the-art accuracies on two
benchmark tasks. | [
1,
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] |
Title: Collaborative similarity analysis of multilayer developer-project bipartite network,
Abstract: To understand the multiple relations between developers and projects on
GitHub as a whole, we model them as a multilayer bipartite network and analyze
the degree distributions, the nearest neighbors' degree distributions and their
correlations with degree, and the collaborative similarity distributions and
their correlations with degree. Our results show that all degree distributions
have a power-law form, especially, the degree distribution of projects in
watching layer has double power-law form. Negative correlations between nearest
neighbors' degree and degree for both developers and projects are observed in
both layers, exhibiting a disassortative mixing pattern. The collaborative
similarity of both developers and projects negatively correlates with degree in
watching layer, while a positive correlations is observed for developers in
forking layer and no obvious correlation is observed for projects in forking
layer. | [
1,
1,
0,
0,
0,
0
] |
Title: Siamese Networks with Location Prior for Landmark Tracking in Liver Ultrasound Sequences,
Abstract: Image-guided radiation therapy can benefit from accurate motion tracking by
ultrasound imaging, in order to minimize treatment margins and radiate moving
anatomical targets, e.g., due to breathing. One way to formulate this tracking
problem is the automatic localization of given tracked anatomical landmarks
throughout a temporal ultrasound sequence. For this, we herein propose a
fully-convolutional Siamese network that learns the similarity between pairs of
image regions containing the same landmark. Accordingly, it learns to localize
and thus track arbitrary image features, not only predefined anatomical
structures. We employ a temporal consistency model as a location prior, which
we combine with the network-predicted location probability map to track a
target iteratively in ultrasound sequences. We applied this method on the
dataset of the Challenge on Liver Ultrasound Tracking (CLUST) with competitive
results, where our work is the first to effectively apply CNNs on this tracking
problem, thanks to our temporal regularization. | [
1,
0,
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0
] |
Title: Single-Shot 3D Diffractive Imaging of Core-Shell Nanoparticles with Elemental Specificity,
Abstract: We report 3D coherent diffractive imaging of Au/Pd core-shell nanoparticles
with 6 nm resolution on 5-6 femtosecond timescales. We measured single-shot
diffraction patterns of core-shell nanoparticles using very intense and short
x-ray free electron laser pulses. By taking advantage of the curvature of the
Ewald sphere and the symmetry of the nanoparticle, we reconstructed the 3D
electron density of 34 core-shell structures from single-shot diffraction
patterns. We determined the size of the Au core and the thickness of the Pd
shell to be 65.0 +/- 1.0 nm and 4.0 +/- 0.5 nm, respectively, and identified
the 3D elemental distribution inside the nanoparticles with an accuracy better
than 2%. We anticipate this method can be used for quantitative 3D imaging of
symmetrical nanostructures and virus particles. | [
0,
1,
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] |
Title: Option Pricing Models Driven by the Space-Time Fractional Diffusion: Series Representation and Applications,
Abstract: In this paper, we focus on option pricing models based on space-time
fractional diffusion. We briefly revise recent results which show that the
option price can be represented in the terms of rapidly converging
double-series and apply these results to the data from real markets. We focus
on estimation of model parameters from the market data and estimation of
implied volatility within the space-time fractional option pricing models. | [
0,
0,
0,
0,
0,
1
] |
Title: Anticipation: an effective evolutionary strategy for a sub-optimal population in a cyclic environment,
Abstract: We built a two-state model of an asexually reproducing organism in a periodic
environment endowed with the capability to anticipate an upcoming environmental
change and undergo pre-emptive switching. By virtue of these anticipatory
transitions, the organism oscillates between its two states that is a time
$\theta$ out of sync with the environmental oscillation. We show that an
anticipation-capable organism increases its long-term fitness over an organism
that oscillates in-sync with the environment, provided $\theta$ does not exceed
a threshold. We also show that the long-term fitness is maximized for an
optimal anticipation time that decreases approximately as $1/n$, $n$ being the
number of cell divisions in time $T$. Furthermore, we demonstrate that optimal
"anticipators" outperforms "bet-hedgers" in the range of parameters considered.
For a sub-optimal ensemble of anticipators, anticipation performs better to
bet-hedging only when the variance in anticipation is small compared to the
mean and the rate of pre-emptive transition is high. Taken together, our work
suggests that anticipation increases overall fitness of an organism in a
periodic environment and it is a viable alternative to bet-hedging provided the
error in anticipation is small. | [
0,
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0,
1,
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] |
Title: Finding, Hitting and Packing Cycles in Subexponential Time on Unit Disk Graphs,
Abstract: We give algorithms with running time $2^{O({\sqrt{k}\log{k}})} \cdot
n^{O(1)}$ for the following problems. Given an $n$-vertex unit disk graph $G$
and an integer $k$, decide whether $G$ contains (1) a path on exactly/at least
$k$ vertices, (2) a cycle on exactly $k$ vertices, (3) a cycle on at least $k$
vertices, (4) a feedback vertex set of size at most $k$, and (5) a set of $k$
pairwise vertex-disjoint cycles. For the first three problems, no
subexponential time parameterized algorithms were previously known. For the
remaining two problems, our algorithms significantly outperform the previously
best known parameterized algorithms that run in time $2^{O(k^{0.75}\log{k})}
\cdot n^{O(1)}$. Our algorithms are based on a new kind of tree decompositions
of unit disk graphs where the separators can have size up to $k^{O(1)}$ and
there exists a solution that crosses every separator at most $O(\sqrt{k})$
times. The running times of our algorithms are optimal up to the $\log{k}$
factor in the exponent, assuming the Exponential Time Hypothesis. | [
1,
0,
0,
0,
0,
0
] |
Title: Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners,
Abstract: Supervised learning has been very successful for automatic segmentation of
images from a single scanner. However, several papers report deteriorated
performances when using classifiers trained on images from one scanner to
segment images from other scanners. We propose a transfer learning classifier
that adapts to differences between training and test images. This method uses a
weighted ensemble of classifiers trained on individual images. The weight of
each classifier is determined by the similarity between its training image and
the test image.
We examine three unsupervised similarity measures, which can be used in
scenarios where no labeled data from a newly introduced scanner or scanning
protocol is available. The measures are based on a divergence, a bag distance,
and on estimating the labels with a clustering procedure. These measures are
asymmetric. We study whether the asymmetry can improve classification. Out of
the three similarity measures, the bag similarity measure is the most robust
across different studies and achieves excellent results on four brain tissue
segmentation datasets and three white matter lesion segmentation datasets,
acquired at different centers and with different scanners and scanning
protocols. We show that the asymmetry can indeed be informative, and that
computing the similarity from the test image to the training images is more
appropriate than the opposite direction. | [
1,
0,
0,
1,
0,
0
] |
Title: Service Providers of the Sharing Economy: Who Joins and Who Benefits?,
Abstract: Many "sharing economy" platforms, such as Uber and Airbnb, have become
increasingly popular, providing consumers with more choices and suppliers a
chance to make profit. They, however, have also brought about emerging issues
regarding regulation, tax obligation, and impact on urban environment, and have
generated heated debates from various interest groups. Empirical studies
regarding these issues are limited, partly due to the unavailability of
relevant data. Here we aim to understand service providers of the sharing
economy, investigating who joins and who benefits, using the Airbnb market in
the United States as a case study. We link more than 211 thousand Airbnb
listings owned by 188 thousand hosts with demographic, socio-economic status
(SES), housing, and tourism characteristics. We show that income and education
are consistently the two most influential factors that are linked to the
joining of Airbnb, regardless of the form of participation or year. Areas with
lower median household income, or higher fraction of residents who have
Bachelor's and higher degrees, tend to have more hosts. However, when
considering the performance of listings, as measured by number of newly
received reviews, we find that income has a positive effect for entire-home
listings; listings located in areas with higher median household income tend to
have more new reviews. Our findings demonstrate empirically that the
disadvantage of SES-disadvantaged areas and the advantage of SES-advantaged
areas may be present in the sharing economy. | [
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0
] |
Title: The generalized Fermat equation with exponents 2, 3, n,
Abstract: We study the Generalized Fermat Equation $x^2 + y^3 = z^p$, to be solved in
coprime integers, where $p \ge 7$ is prime. Using modularity and level lowering
techniques, the problem can be reduced to the determination of the sets of
rational points satisfying certain 2-adic and 3-adic conditions on a finite set
of twists of the modular curve $X(p)$.
We first develop new local criteria to decide if two elliptic curves with
certain types of potentially good reduction at 2 and 3 can have symplectically
or anti-symplectically isomorphic $p$-torsion modules. Using these criteria we
produce the minimal list of twists of $X(p)$ that have to be considered, based
on local information at 2 and 3; this list depends on $p \bmod 24$. Using
recent results on mod $p$ representations with image in the normalizer of a
split Cartan subgroup, the list can be further reduced in some cases.
Our second main result is the complete solution of the equation when $p =
11$, which previously was the smallest unresolved $p$. One relevant new
ingredient is the use of the `Selmer group Chabauty' method introduced by the
third author in a recent preprint, applied in an Elliptic Curve Chabauty
context, to determine relevant points on $X_0(11)$ defined over certain number
fields of degree 12. This result is conditional on GRH, which is needed to show
correctness of the computation of the class groups of five specific number
fields of degree 36.
We also give some partial results for the case $p = 13$. | [
0,
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1,
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] |
Title: Neutron Stars in Screened Modified Gravity: Chameleon vs Dilaton,
Abstract: We consider the scalar field profile around relativistic compact objects such
as neutron stars for a range of modified gravity models with screening
mechanisms of the chameleon and Damour-Polyakov types. We focus primarily on
inverse power law chameleons and the environmentally dependent dilaton as
examples of both mechanisms. We discuss the modified Tolman-Oppenheimer-Volkoff
equation and then implement a relaxation algorithm to solve for the scalar
profiles numerically. We find that chameleons and dilatons behave in a similar
manner and that there is a large degeneracy between the modified gravity
parameters and the neutron star equation of state. This is exemplified by the
modifications to the mass-radius relationship for a variety of model
parameters. | [
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1,
0,
0,
0,
0
] |
Title: Evidence of Eta Aquariid Outbursts Recorded in the Classic Maya Hieroglyphic Script Using Orbital Integrations,
Abstract: No firm evidence has existed that the ancient Maya civilization recorded
specific occurrences of meteor showers or outbursts in the corpus of Maya
hieroglyphic inscriptions. In fact, there has been no evidence of any
pre-Hispanic civilization in the Western Hemisphere recording any observations
of any meteor showers on any specific dates.
The authors numerically integrated meteoroid-sized particles released by
Comet Halley as early as 1404 BC to identify years within the Maya Classic
Period, AD 250-909, when Eta Aquariid outbursts might have occurred. Outbursts
determined by computer model were then compared to specific events in the Maya
record to see if any correlation existed between the date of the event and the
date of the outburst. The model was validated by successfully explaining
several outbursts around the same epoch in the Chinese record. Some outbursts
observed by the Maya were due to recent revolutions of Comet Halley, within a
few centuries, and some to resonant behavior in older Halley trails, of the
order of a thousand years. Examples were found of several different Jovian mean
motion resonances as well as the 1:3 Saturnian resonance that have controlled
the dynamical evolution of meteoroids in apparently observed outbursts. | [
0,
1,
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0,
0
] |
Title: A Convex Parametrization of a New Class of Universal Kernel Functions for use in Kernel Learning,
Abstract: We propose a new class of universal kernel functions which admit a linear
parametrization using positive semidefinite matrices. These kernels are
generalizations of the Sobolev kernel and are defined by piecewise-polynomial
functions. The class of kernels is termed "tessellated" as the resulting
discriminant is defined piecewise with hyper-rectangular domains whose corners
are determined by the training data. The kernels have scalable complexity, but
each instance is universal in the sense that its hypothesis space is dense in
$L_2$. Using numerical testing, we show that for the soft margin SVM, this
class can eliminate the need for Gaussian kernels. Furthermore, we demonstrate
that when the ratio of the number of training data to features is high, this
method will significantly outperform other kernel learning algorithms. Finally,
to reduce the complexity associated with SDP-based kernel learning methods, we
use a randomized basis for the positive matrices to integrate with existing
multiple kernel learning algorithms such as SimpleMKL. | [
1,
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1,
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0
] |
Title: Hessian-based Analysis of Large Batch Training and Robustness to Adversaries,
Abstract: Large batch size training of Neural Networks has been shown to incur accuracy
loss when trained with the current methods. The exact underlying reasons for
this are still not completely understood. Here, we study large batch size
training through the lens of the Hessian operator and robust optimization. In
particular, we perform a Hessian based study to analyze exactly how the
landscape of the loss function changes when training with large batch size. We
compute the true Hessian spectrum, without approximation, by back-propagating
the second derivative. Extensive experiments on multiple networks show that
saddle-points are not the cause for generalization gap of large batch size
training, and the results consistently show that large batch converges to
points with noticeably higher Hessian spectrum. Furthermore, we show that
robust training allows one to favor flat areas, as points with large Hessian
spectrum show poor robustness to adversarial perturbation. We further study
this relationship, and provide empirical and theoretical proof that the inner
loop for robust training is a saddle-free optimization problem \textit{almost
everywhere}. We present detailed experiments with five different network
architectures, including a residual network, tested on MNIST, CIFAR-10, and
CIFAR-100 datasets. We have open sourced our method which can be accessed at
[1]. | [
0,
0,
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1,
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] |
Title: Design of Improved Quasi-Cyclic Protograph-Based Raptor-Like LDPC Codes for Short Block-Lengths,
Abstract: Protograph-based Raptor-like low-density parity-check codes (PBRL codes) are
a recently proposed family of easily encodable and decodable rate-compatible
LDPC (RC-LDPC) codes. These codes have an excellent iterative decoding
threshold and performance across all design rates. PBRL codes designed thus
far, for both long and short block-lengths, have been based on optimizing the
iterative decoding threshold of the protograph of the RC code family at various
design rates.
In this work, we propose a design method to obtain better quasi-cyclic (QC)
RC-LDPC codes with PBRL structure for short block-lengths (of a few hundred
bits). We achieve this by maximizing an upper bound on the minimum distance of
any QC-LDPC code that can be obtained from the protograph of a PBRL ensemble.
The obtained codes outperform the original PBRL codes at short block-lengths by
significantly improving the error floor behavior at all design rates.
Furthermore, we identify a reduction in complexity of the design procedure,
facilitated by the general structure of a PBRL ensemble. | [
1,
0,
0,
0,
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0
] |
Title: Comparing the Finite-Time Performance of Simulation-Optimization Algorithms,
Abstract: We empirically evaluate the finite-time performance of several
simulation-optimization algorithms on a testbed of problems with the goal of
motivating further development of algorithms with strong finite-time
performance. We investigate if the observed performance of the algorithms can
be explained by properties of the problems, e.g., the number of decision
variables, the topology of the objective function, or the magnitude of the
simulation error. | [
0,
0,
1,
1,
0,
0
] |
Title: Borcherds-Bozec algebras, root multiplicities and the Schofield construction,
Abstract: Using the twisted denominator identity, we derive a closed form root
multiplicity formula for all symmetrizable Borcherds-Bozec algebras and discuss
its applications including the case of Monster Borcherds-Bozec algebra. In the
second half of the paper, we provide the Schofield constuction of symmetric
Borcherds-Bozec algebras. | [
0,
0,
1,
0,
0,
0
] |
Title: Pressure-induced spin pairing transition of Fe$^{3+}$ in oxygen octahedra,
Abstract: High pressure can provoke spin transitions in transition metal-bearing
compounds. These transitions are of high interest not only for fundamental
physics and chemistry, but also may have important implications for
geochemistry and geophysics of the Earth and planetary interiors. Here we have
carried out a comparative study of the pressure-induced spin transition in
compounds with trivalent iron, octahedrally coordinated by oxygen.
High-pressure single-crystal Mössbauer spectroscopy data for FeBO$_3$,
Fe$_2$O$_3$ and Fe$_3$(Fe$_{1.766(2)}$Si$_{0.234(2)}$)(SiO$_4$)$_3$ are
presented together with detailed analysis of hyperfine parameter behavior. We
argue that $\zeta$-Fe$_2$O$_3$ is an intermediate phase in the reconstructive
phase transition between $\iota$-Fe$_2$O$_3$ and $\theta$-Fe$_2$O$_3$ and
question the proposed perovskite-type structure for $\zeta$-Fe$_2$O$_3$.The
structural data show that the spin transition is closely related to the volume
of the iron octahedron. The transition starts when volumes reach 8.9-9.3
\AA$^3$, which corresponds to pressures of 45-60 GPa, depending on the
compound. Based on phenomenological arguments we conclude that the spin
transition can proceed only as a first-order phase transition in
magnetically-ordered compounds. An empirical rule for prediction of cooperative
behavior at the spin transition is proposed. The instability of iron octahedra,
together with strong interactions between them in the vicinity of the critical
volume, may trigger a phase transition in the metastable phase. We find that
the isomer shift of high spin iron ions depends linearly on the octahedron
volume with approximately the same coefficient, independent of the particular
compounds and/or oxidation state. For eight-fold coordinated Fe$^{2+}$ we
observe a significantly weaker nonlinear volume dependence. | [
0,
1,
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] |
Title: LSH on the Hypercube Revisited,
Abstract: LSH (locality sensitive hashing) had emerged as a powerful technique in
nearest-neighbor search in high dimensions [IM98, HIM12]. Given a point set $P$
in a metric space, and given parameters $r$ and $\varepsilon > 0$, the task is
to preprocess the point set, such that given a query point $q$, one can quickly
decide if $q$ is in distance at most $\leq r$ or $\geq (1+\varepsilon)r$ from
the point set $P$. Once such a near-neighbor data-structure is available, one
can reduce the general nearest-neighbor search to logarithmic number of queries
in such structures [IM98, Har01, HIM12].
In this note, we revisit the most basic settings, where $P$ is a set of
points in the binary hypercube $\{0,1\}^d$, under the $L_1$/Hamming metric, and
present a short description of the LSH scheme in this case. We emphasize that
there is no new contribution in this note, except (maybe) the presentation
itself, which is inspired by the authors recent work [HM17]. | [
1,
0,
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] |
Title: A biofilm and organomineralisation model for the growth and limiting size of ooids,
Abstract: Ooids are typically spherical sediment grains characterised by concentric
layers encapsulating a core. There is no universally accepted explanation for
ooid genesis, though factors such as agitation, abiotic and/or microbial
mineralisation and size limitation have been variously invoked. We develop a
mathematical model for ooid growth, inspired by work on avascular brain
tumours, that assumes mineralisation in a biofilm to form a central core and
concentric growth of laminations. The model predicts a limiting size with the
sequential width variation of growth rings comparing favourably with those
observed in experimentally grown ooids generated from biomicrospheres. In
reality, this model pattern may be complicated during growth by syngenetic
aggrading neomorphism of the unstable mineral phase, followed by diagenetic
recrystallisation that further complicates the structure. Our model provides a
potential key to understanding the genetic archive preserved in the internal
structures of naturally occurring ooids. | [
0,
1,
0,
0,
0,
0
] |
Title: Spectral Filtering for General Linear Dynamical Systems,
Abstract: We give a polynomial-time algorithm for learning latent-state linear
dynamical systems without system identification, and without assumptions on the
spectral radius of the system's transition matrix. The algorithm extends the
recently introduced technique of spectral filtering, previously applied only to
systems with a symmetric transition matrix, using a novel convex relaxation to
allow for the efficient identification of phases. | [
0,
0,
0,
1,
0,
0
] |
Title: Evolution-Preserving Dense Trajectory Descriptors,
Abstract: Recently Trajectory-pooled Deep-learning Descriptors were shown to achieve
state-of-the-art human action recognition results on a number of datasets. This
paper improves their performance by applying rank pooling to each trajectory,
encoding the temporal evolution of deep learning features computed along the
trajectory. This leads to Evolution-Preserving Trajectory (EPT) descriptors, a
novel type of video descriptor that significantly outperforms Trajectory-pooled
Deep-learning Descriptors. EPT descriptors are defined based on dense
trajectories, and they provide complimentary benefits to video descriptors that
are not based on trajectories. In particular, we show that the combination of
EPT descriptors and VideoDarwin leads to state-of-the-art performance on
Hollywood2 and UCF101 datasets. | [
1,
0,
0,
0,
0,
0
] |
Title: Optimal segmentation of directed graph and the minimum number of feedback arcs,
Abstract: The minimum feedback arc set problem asks to delete a minimum number of arcs
(directed edges) from a digraph (directed graph) to make it free of any
directed cycles. In this work we approach this fundamental cycle-constrained
optimization problem by considering a generalized task of dividing the digraph
into D layers of equal size. We solve the D-segmentation problem by the
replica-symmetric mean field theory and belief-propagation heuristic
algorithms. The minimum feedback arc density of a given random digraph ensemble
is then obtained by extrapolating the theoretical results to the limit of large
D. A divide-and-conquer algorithm (nested-BPR) is devised to solve the minimum
feedback arc set problem with very good performance and high efficiency. | [
1,
1,
0,
0,
0,
0
] |
Title: Ensemble of Neural Classifiers for Scoring Knowledge Base Triples,
Abstract: This paper describes our approach for the triple scoring task at the WSDM Cup
2017. The task required participants to assign a relevance score for each pair
of entities and their types in a knowledge base in order to enhance the ranking
results in entity retrieval tasks. We propose an approach wherein the outputs
of multiple neural network classifiers are combined using a supervised machine
learning model. The experimental results showed that our proposed method
achieved the best performance in one out of three measures (i.e., Kendall's
tau), and performed competitively in the other two measures (i.e., accuracy and
average score difference). | [
1,
0,
0,
0,
0,
0
] |
Title: Automated Detection, Exploitation, and Elimination of Double-Fetch Bugs using Modern CPU Features,
Abstract: Double-fetch bugs are a special type of race condition, where an unprivileged
execution thread is able to change a memory location between the time-of-check
and time-of-use of a privileged execution thread. If an unprivileged attacker
changes the value at the right time, the privileged operation becomes
inconsistent, leading to a change in control flow, and thus an escalation of
privileges for the attacker. More severely, such double-fetch bugs can be
introduced by the compiler, entirely invisible on the source-code level.
We propose novel techniques to efficiently detect, exploit, and eliminate
double-fetch bugs. We demonstrate the first combination of state-of-the-art
cache attacks with kernel-fuzzing techniques to allow fully automated
identification of double fetches. We demonstrate the first fully automated
reliable detection and exploitation of double-fetch bugs, making manual
analysis as in previous work superfluous. We show that cache-based triggers
outperform state-of-the-art exploitation techniques significantly, leading to
an exploitation success rate of up to 97%. Our modified fuzzer automatically
detects double fetches and automatically narrows down this candidate set for
double-fetch bugs to the exploitable ones. We present the first generic
technique based on hardware transactional memory, to eliminate double-fetch
bugs in a fully automated and transparent manner. We extend defensive
programming techniques by retrofitting arbitrary code with automated
double-fetch prevention, both in trusted execution environments as well as in
syscalls, with a performance overhead below 1%. | [
1,
0,
0,
0,
0,
0
] |
Title: Unoriented Spectral Triples,
Abstract: Any oriented Riemannian manifold with a Spin-structure defines a spectral
triple, so the spectral triple can be regarded as a noncommutative
Spin-manifold. Otherwise for any unoriented Riemannian manifold there is the
two-fold covering by oriented Riemannian manifold. Moreover there are
noncommutative generalizations of finite-fold coverings. This circumstances
yield a notion of unoriented spectral triple which is covered by oriented one. | [
0,
0,
1,
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0,
0
] |
Title: Contagion dynamics of extremist propaganda in social networks,
Abstract: Recent terrorist attacks carried out on behalf of ISIS on American and
European soil by lone wolf attackers or sleeper cells remind us of the
importance of understanding the dynamics of radicalization mediated by social
media communication channels. In this paper, we shed light on the social media
activity of a group of twenty-five thousand users whose association with ISIS
online radical propaganda has been manually verified. By using a computational
tool known as dynamical activity-connectivity maps, based on network and
temporal activity patterns, we investigate the dynamics of social influence
within ISIS supporters. We finally quantify the effectiveness of ISIS
propaganda by determining the adoption of extremist content in the general
population and draw a parallel between radical propaganda and epidemics
spreading, highlighting that information broadcasters and influential ISIS
supporters generate highly-infectious cascades of information contagion. Our
findings will help generate effective countermeasures to combat the group and
other forms of online extremism. | [
1,
1,
0,
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0
] |
Title: Estimate exponential memory decay in Hidden Markov Model and its applications,
Abstract: Inference in hidden Markov model has been challenging in terms of scalability
due to dependencies in the observation data. In this paper, we utilize the
inherent memory decay in hidden Markov models, such that the forward and
backward probabilities can be carried out with subsequences, enabling efficient
inference over long sequences of observations. We formulate this forward
filtering process in the setting of the random dynamical system and there exist
Lyapunov exponents in the i.i.d random matrices production. And the rate of the
memory decay is known as $\lambda_2-\lambda_1$, the gap of the top two Lyapunov
exponents almost surely. An efficient and accurate algorithm is proposed to
numerically estimate the gap after the soft-max parametrization. The length of
subsequences $B$ given the controlled error $\epsilon$ is
$B=\log(\epsilon)/(\lambda_2-\lambda_1)$. We theoretically prove the validity
of the algorithm and demonstrate the effectiveness with numerical examples. The
method developed here can be applied to widely used algorithms, such as
mini-batch stochastic gradient method. Moreover, the continuity of Lyapunov
spectrum ensures the estimated $B$ could be reused for the nearby parameter
during the inference. | [
0,
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1,
0,
0
] |
Title: Fabrication of antenna-coupled KID array for Cosmic Microwave Background detection,
Abstract: Kinetic Inductance Detectors (KIDs) have become an attractive alternative to
traditional bolometers in the sub-mm and mm observing community due to their
innate frequency multiplexing capabilities and simple lithographic processes.
These advantages make KIDs a viable option for the $O(500,000)$ detectors
needed for the upcoming Cosmic Microwave Background - Stage 4 (CMB-S4)
experiment. We have fabricated antenna-coupled MKID array in the 150GHz band
optimized for CMB detection. Our design uses a twin slot antenna coupled to
inverted microstrip made from a superconducting Nb/Al bilayer and SiN$_x$,
which is then coupled to an Al KID grown on high resistivity Si. We present the
fabrication process and measurements of SiN$_x$ microstrip resonators. | [
0,
1,
0,
0,
0,
0
] |
Title: Run-and-Inspect Method for Nonconvex Optimization and Global Optimality Bounds for R-Local Minimizers,
Abstract: Many optimization algorithms converge to stationary points. When the
underlying problem is nonconvex, they may get trapped at local minimizers and
occasionally stagnate near saddle points. We propose the Run-and-Inspect
Method, which adds an "inspect" phase to existing algorithms that helps escape
from non-global stationary points. The inspection samples a set of points in a
radius $R$ around the current point. When a sample point yields a sufficient
decrease in the objective, we move there and resume an existing algorithm. If
no sufficient decrease is found, the current point is called an approximate
$R$-local minimizer. We show that an $R$-local minimizer is globally optimal,
up to a specific error depending on $R$, if the objective function can be
implicitly decomposed into a smooth convex function plus a restricted function
that is possibly nonconvex, nonsmooth. For high-dimensional problems, we
introduce blockwise inspections to overcome the curse of dimensionality while
still maintaining optimality bounds up to a factor equal to the number of
blocks. Our method performs well on a set of artificial and realistic nonconvex
problems by coupling with gradient descent, coordinate descent, EM, and
prox-linear algorithms. | [
1,
0,
0,
1,
0,
0
] |
Title: Hierarchical Adversarially Learned Inference,
Abstract: We propose a novel hierarchical generative model with a simple Markovian
structure and a corresponding inference model. Both the generative and
inference model are trained using the adversarial learning paradigm. We
demonstrate that the hierarchical structure supports the learning of
progressively more abstract representations as well as providing semantically
meaningful reconstructions with different levels of fidelity. Furthermore, we
show that minimizing the Jensen-Shanon divergence between the generative and
inference network is enough to minimize the reconstruction error. The resulting
semantically meaningful hierarchical latent structure discovery is exemplified
on the CelebA dataset. There, we show that the features learned by our model in
an unsupervised way outperform the best handcrafted features. Furthermore, the
extracted features remain competitive when compared to several recent deep
supervised approaches on an attribute prediction task on CelebA. Finally, we
leverage the model's inference network to achieve state-of-the-art performance
on a semi-supervised variant of the MNIST digit classification task. | [
0,
0,
0,
1,
0,
0
] |
Title: UntrimmedNets for Weakly Supervised Action Recognition and Detection,
Abstract: Current action recognition methods heavily rely on trimmed videos for model
training. However, it is expensive and time-consuming to acquire a large-scale
trimmed video dataset. This paper presents a new weakly supervised
architecture, called UntrimmedNet, which is able to directly learn action
recognition models from untrimmed videos without the requirement of temporal
annotations of action instances. Our UntrimmedNet couples two important
components, the classification module and the selection module, to learn the
action models and reason about the temporal duration of action instances,
respectively. These two components are implemented with feed-forward networks,
and UntrimmedNet is therefore an end-to-end trainable architecture. We exploit
the learned models for action recognition (WSR) and detection (WSD) on the
untrimmed video datasets of THUMOS14 and ActivityNet. Although our UntrimmedNet
only employs weak supervision, our method achieves performance superior or
comparable to that of those strongly supervised approaches on these two
datasets. | [
1,
0,
0,
0,
0,
0
] |
Title: Deconvolutional Latent-Variable Model for Text Sequence Matching,
Abstract: A latent-variable model is introduced for text matching, inferring sentence
representations by jointly optimizing generative and discriminative objectives.
To alleviate typical optimization challenges in latent-variable models for
text, we employ deconvolutional networks as the sequence decoder (generator),
providing learned latent codes with more semantic information and better
generalization. Our model, trained in an unsupervised manner, yields stronger
empirical predictive performance than a decoder based on Long Short-Term Memory
(LSTM), with less parameters and considerably faster training. Further, we
apply it to text sequence-matching problems. The proposed model significantly
outperforms several strong sentence-encoding baselines, especially in the
semi-supervised setting. | [
1,
0,
0,
1,
0,
0
] |
Title: Magneto-elastic coupling model of deformable anisotropic superconductors,
Abstract: We develop a magneto-elastic (ME) coupling model for the interaction between
the vortex lattice and crystal elasticity. The theory extends the Kogan-Clem's
anisotropic Ginzburg-Landau (GL) model to include the elasticity effect. The
anisotropies in superconductivity and elasticity are simultaneously considered
in the GL theory frame. We compare the field and angular dependences of the
magnetization to the relevant experiments. The contribution of the ME
interaction to the magnetization is comparable to the vortex-lattice energy, in
materials with relatively strong pressure dependence of the critical
temperature. The theory can give the appropriate slope of the field dependence
of magnetization near the upper critical field. The magnetization ratio along
different vortex frame axes is independent with the ME interaction. The
theoretical description of the magnetization ratio is applicable only if the
applied field moderately close to the upper critical field. | [
0,
1,
0,
0,
0,
0
] |
Title: Matchability of heterogeneous networks pairs,
Abstract: We consider the problem of graph matchability in non-identically distributed
networks. In a general class of edge-independent networks, we demonstrate that
graph matchability is almost surely lost when matching the networks directly,
and is almost perfectly recovered when first centering the networks using
Universal Singular Value Thresholding before matching. These theoretical
results are then demonstrated in both real and synthetic simulation settings.
We also recover analogous core-matchability results in a very general core-junk
network model, wherein some vertices do not correspond between the graph pair. | [
1,
0,
1,
1,
0,
0
] |
Title: Capacity Releasing Diffusion for Speed and Locality,
Abstract: Diffusions and related random walk procedures are of central importance in
many areas of machine learning, data analysis, and applied mathematics. Because
they spread mass agnostically at each step in an iterative manner, they can
sometimes spread mass "too aggressively," thereby failing to find the "right"
clusters. We introduce a novel Capacity Releasing Diffusion (CRD) Process,
which is both faster and stays more local than the classical spectral diffusion
process. As an application, we use our CRD Process to develop an improved local
algorithm for graph clustering. Our local graph clustering method can find
local clusters in a model of clustering where one begins the CRD Process in a
cluster whose vertices are connected better internally than externally by an
$O(\log^2 n)$ factor, where $n$ is the number of nodes in the cluster. Thus,
our CRD Process is the first local graph clustering algorithm that is not
subject to the well-known quadratic Cheeger barrier. Our result requires a
certain smoothness condition, which we expect to be an artifact of our
analysis. Our empirical evaluation demonstrates improved results, in particular
for realistic social graphs where there are moderately good---but not very
good---clusters. | [
1,
0,
0,
0,
0,
0
] |
Title: Two-term spectral asymptotics for the Dirichlet pseudo-relativistic kinetic energy operator on a bounded domain,
Abstract: Continuing the series of works following Weyl's one-term asymptotic formula
for the counting function $N(\lambda)=\sum_{n=1}^\infty(\lambda_n{-}\lambda)_-$
of the eigenvalues of the Dirichlet Laplacian and the much later found two-term
expansion on domains with highly regular boundary by Ivrii and Melrose, we
prove a two-term asymptotic expansion of the $N$-th Cesàro mean of the
eigenvalues of $\sqrt{-\Delta + m^2} - m$ for $m>0$ with Dirichlet boundary
condition on a bounded domain $\Omega\subset\mathbb R^d$ for $d\geq 2$,
extending a result by Frank and Geisinger for the fractional Laplacian ($m=0$)
and improving upon the small-time asymptotics of the heat trace $Z(t) =
\sum_{n=1}^\infty e^{-t \lambda_n}$ by Bañuelos et al. and Park and Song. | [
0,
0,
1,
0,
0,
0
] |
Title: Exact Good-Turing characterization of the two-parameter Poisson-Dirichlet superpopulation model,
Abstract: Large sample size equivalence between the celebrated {\it approximated}
Good-Turing estimator of the probability to discover a species already observed
a certain number of times (Good, 1953) and the modern Bayesian nonparametric
counterpart has been recently established by virtue of a particular smoothing
rule based on the two-parameter Poisson-Dirichlet model. Here we improve on
this result showing that, for any finite sample size, when the population
frequencies are assumed to be selected from a superpopulation with
two-parameter Poisson-Dirichlet distribution, then Bayesian nonparametric
estimation of the discovery probabilities corresponds to Good-Turing {\it
exact} estimation. Moreover under general superpopulation hypothesis the
Good-Turing solution admits an interpretation as a modern Bayesian
nonparametric estimator under partial information. | [
0,
0,
1,
1,
0,
0
] |
Title: Simplified Gating in Long Short-term Memory (LSTM) Recurrent Neural Networks,
Abstract: The standard LSTM recurrent neural networks while very powerful in long-range
dependency sequence applications have highly complex structure and relatively
large (adaptive) parameters. In this work, we present empirical comparison
between the standard LSTM recurrent neural network architecture and three new
parameter-reduced variants obtained by eliminating combinations of the input
signal, bias, and hidden unit signals from individual gating signals. The
experiments on two sequence datasets show that the three new variants, called
simply as LSTM1, LSTM2, and LSTM3, can achieve comparable performance to the
standard LSTM model with less (adaptive) parameters. | [
1,
0,
0,
1,
0,
0
] |
Title: Transition Jitter in Heat Assisted Magnetic Recording by Micromagnetic Simulation,
Abstract: In this paper we apply an extended Landau-Lifschitz equation, as introduced
by Baňas et al. for the simulation of heat-assisted magnetic recording.
This equation has similarities with the Landau-Lifshitz-Bloch equation. The
Baňas equation is supposed to be used in a continuum setting with sub-grain
discretization by the finite-element method. Thus, local geometric features and
nonuniform magnetic states during switching are taken into account. We
implement the Baňas model and test its capability for predicting the
recording performance in a realistic recording scenario. By performing
recording simulations on 100 media slabs with randomized granular structure and
consecutive read back calculation, the write position shift and transition
jitter for bit lengths of 10nm, 12nm, and 20nm are calculated. | [
0,
1,
0,
0,
0,
0
] |
Title: Complexity of human response delay in intermittent control: The case of virtual stick balancing,
Abstract: Response delay is an inherent and essential part of human actions. In the
context of human balance control, the response delay is traditionally modeled
using the formalism of delay-differential equations, which adopts the
approximation of fixed delay. However, experimental studies revealing
substantial variability, adaptive anticipation, and non-stationary dynamics of
response delay provide evidence against this approximation. In this paper, we
call for development of principally new mathematical formalism describing human
response delay. To support this, we present the experimental data from a simple
virtual stick balancing task. Our results demonstrate that human response delay
is a widely distributed random variable with complex properties, which can
exhibit oscillatory and adaptive dynamics characterized by long-range
correlations. Given this, we argue that the fixed-delay approximation ignores
essential properties of human response, and conclude with possible directions
for future developments of new mathematical notions describing human control. | [
0,
0,
0,
0,
1,
0
] |
Title: Algebraic cycles on some special hyperkähler varieties,
Abstract: This note contains some examples of hyperkähler varieties $X$ having a
group $G$ of non-symplectic automorphisms, and such that the action of $G$ on
certain Chow groups of $X$ is as predicted by Bloch's conjecture. The examples
range in dimension from $6$ to $132$. For each example, the quotient $Y=X/G$ is
a Calabi-Yau variety which has interesting Chow-theoretic properties; in
particular, the variety $Y$ satisfies (part of) a strong version of the
Beauville-Voisin conjecture. | [
0,
0,
1,
0,
0,
0
] |
Title: Strongly regular decompositions and symmetric association schemes of a power of two,
Abstract: For any positive integer $m$, the complete graph on $2^{2m}(2^m+2)$ vertices
is decomposed into $2^m+1$ commuting strongly regular graphs, which give rise
to a symmetric association scheme of class $2^{m+2}-2$. Furthermore, the
eigenmatrices of the symmetric association schemes are determined explicitly.
As an application, the eigenmatrix of the commutative strongly regular
decomposition obtained from the strongly regular graphs is derived. | [
0,
0,
1,
0,
0,
0
] |
Title: Resilient Non-Submodular Maximization over Matroid Constraints,
Abstract: The control and sensing of large-scale systems results in combinatorial
problems not only for sensor and actuator placement but also for scheduling or
observability/controllability. Such combinatorial constraints in system design
and implementation can be captured using a structure known as matroids. In
particular, the algebraic structure of matroids can be exploited to develop
scalable algorithms for sensor and actuator selection, along with quantifiable
approximation bounds. However, in large-scale systems, sensors and actuators
may fail or may be (cyber-)attacked. The objective of this paper is to focus on
resilient matroid-constrained problems arising in control and sensing but in
the presence of sensor and actuator failures. In general, resilient
matroid-constrained problems are computationally hard. Contrary to the
non-resilient case (with no failures), even though they often involve objective
functions that are monotone or submodular, no scalable approximation algorithms
are known for their solution. In this paper, we provide the first algorithm,
that also has the following properties: First, it achieves system-wide
resiliency, i.e., the algorithm is valid for any number of denial-of-service
attacks or failures. Second, it is scalable, as our algorithm terminates with
the same running time as state-of-the-art algorithms for (non-resilient)
matroid-constrained optimization. Third, it provides provable approximation
bounds on the system performance, since for monotone objective functions our
algorithm guarantees a solution close to the optimal. We quantify our
algorithm's approximation performance using a notion of curvature for monotone
(not necessarily submodular) set functions. Finally, we support our theoretical
analyses with numerical experiments, by considering a control-aware sensor
selection scenario, namely, sensing-constrained robot navigation. | [
1,
0,
0,
1,
0,
0
] |
Title: A Nash Type result for Divergence Parabolic Equation related to Hormander's vector fields,
Abstract: In this paper we consider the divergence parabolic equation with bounded and
measurable coefficients related to Hormander's vector fields and establish a
Nash type result, i.e., the local Holder regularity for weak solutions. After
deriving the parabolic Sobolev inequality, (1,1) type Poincaré inequality of
Hormander's vector fields and a De Giorgi type Lemma, the Holder regularity
of weak solutions to the equation is proved based on the estimates of
oscillations of solutions and the isomorphism between parabolic Campanato space
and parabolic Holder space. As a consequence, we give the Harnack inequality
of weak solutions by showing an extension property of positivity for functions
in the De Giorgi class. | [
0,
0,
1,
0,
0,
0
] |
Title: Visualizing Time-Varying Particle Flows with Diffusion Geometry,
Abstract: The tasks of identifying separation structures and clusters in flow data are
fundamental to flow visualization. Significant work has been devoted to these
tasks in flow represented by vector fields, but there are unique challenges in
addressing these tasks for time-varying particle data. The unstructured nature
of particle data, nonuniform and sparse sampling, and the inability to access
arbitrary particles in space-time make it difficult to define separation and
clustering for particle data. We observe that weaker notions of separation and
clustering through continuous measures of these structures are meaningful when
coupled with user exploration. We achieve this goal by defining a measure of
particle similarity between pairs of particles. More specifically, separation
occurs when spatially-localized particles are dissimilar, while clustering is
characterized by sets of particles that are similar to one another. To be
robust to imperfections in sampling we use diffusion geometry to compute
particle similarity. Diffusion geometry is parameterized by a scale that allows
a user to explore separation and clustering in a continuous manner. We
illustrate the benefits of our technique on a variety of 2D and 3D flow
datasets, from particles integrated in fluid simulations based on time-varying
vector fields, to particle-based simulations in astrophysics. | [
1,
0,
0,
0,
0,
0
] |
Title: Pure Rough Mereology and Counting,
Abstract: The study of mereology (parts and wholes) in the context of formal approaches
to vagueness can be approached in a number of ways. In the context of rough
sets, mereological concepts with a set-theoretic or valuation based ontology
acquire complex and diverse behavior. In this research a general rough set
framework called granular operator spaces is extended and the nature of
parthood in it is explored from a minimally intrusive point of view. This is
used to develop counting strategies that help in classifying the framework. The
developed methodologies would be useful for drawing involved conclusions about
the nature of data (and validity of assumptions about it) from antichains
derived from context. The problem addressed is also about whether counting
procedures help in confirming that the approximations involved in formation of
data are indeed rough approximations? | [
1,
0,
1,
0,
0,
0
] |
Title: Relaxation of nonlinear elastic energies involving deformed configuration and applications to nematic elastomers,
Abstract: We start from a variational model for nematic elastomers that involves two
energies: mechanical and nematic. The first one consists of a nonlinear elastic
energy which is influenced by the orientation of the molecules of the nematic
elastomer. The nematic energy is an Oseen--Frank energy in the deformed
configuration. The constraint of the positivity of the determinant of the
deformation gradient is imposed. The functionals are not assumed to have the
usual polyconvexity or quasiconvexity assumptions to be lower semicontinuous.
We instead compute its relaxation, that is, the lower semicontinuous envelope,
which turns out to be the quasiconvexification of the mechanical term plus the
tangential quasiconvexification of the nematic term. The main assumptions are
that the quasiconvexification of the mechanical term is polyconvex and that the
deformation is in the Sobolev space $W^{1,p}$ (with $p>n-1$ and $n$ the
dimension of the space) and does not present cavitation. | [
0,
0,
1,
0,
0,
0
] |
Title: Toward Incorporation of Relevant Documents in word2vec,
Abstract: Recent advances in neural word embedding provide significant benefit to
various information retrieval tasks. However as shown by recent studies,
adapting the embedding models for the needs of IR tasks can bring considerable
further improvements. The embedding models in general define the term
relatedness by exploiting the terms' co-occurrences in short-window contexts.
An alternative (and well-studied) approach in IR for related terms to a query
is using local information i.e. a set of top-retrieved documents. In view of
these two methods of term relatedness, in this work, we report our study on
incorporating the local information of the query in the word embeddings. One
main challenge in this direction is that the dense vectors of word embeddings
and their estimation of term-to-term relatedness remain difficult to interpret
and hard to analyze. As an alternative, explicit word representations propose
vectors whose dimensions are easily interpretable, and recent methods show
competitive performance to the dense vectors. We introduce a neural-based
explicit representation, rooted in the conceptual ideas of the word2vec
Skip-Gram model. The method provides interpretable explicit vectors while
keeping the effectiveness of the Skip-Gram model. The evaluation of various
explicit representations on word association collections shows that the newly
proposed method out- performs the state-of-the-art explicit representations
when tasked with ranking highly similar terms. Based on the introduced ex-
plicit representation, we discuss our approaches on integrating local documents
in globally-trained embedding models and discuss the preliminary results. | [
1,
0,
0,
0,
0,
0
] |
Title: Multi-Round Influence Maximization (Extended Version),
Abstract: In this paper, we study the Multi-Round Influence Maximization (MRIM)
problem, where influence propagates in multiple rounds independently from
possibly different seed sets, and the goal is to select seeds for each round to
maximize the expected number of nodes that are activated in at least one round.
MRIM problem models the viral marketing scenarios in which advertisers conduct
multiple rounds of viral marketing to promote one product. We consider two
different settings: 1) the non-adaptive MRIM, where the advertiser needs to
determine the seed sets for all rounds at the very beginning, and 2) the
adaptive MRIM, where the advertiser can select seed sets adaptively based on
the propagation results in the previous rounds. For the non-adaptive setting,
we design two algorithms that exhibit an interesting tradeoff between
efficiency and effectiveness: a cross-round greedy algorithm that selects seeds
at a global level and achieves $1/2 - \varepsilon$ approximation ratio, and a
within-round greedy algorithm that selects seeds round by round and achieves
$1-e^{-(1-1/e)}-\varepsilon \approx 0.46 - \varepsilon$ approximation ratio but
saves running time by a factor related to the number of rounds. For the
adaptive setting, we design an adaptive algorithm that guarantees
$1-e^{-(1-1/e)}-\varepsilon$ approximation to the adaptive optimal solution. In
all cases, we further design scalable algorithms based on the reverse influence
sampling approach and achieve near-linear running time. We conduct experiments
on several real-world networks and demonstrate that our algorithms are
effective for the MRIM task. | [
1,
0,
0,
0,
0,
0
] |
Title: Generalisation dynamics of online learning in over-parameterised neural networks,
Abstract: Deep neural networks achieve stellar generalisation on a variety of problems,
despite often being large enough to easily fit all their training data. Here we
study the generalisation dynamics of two-layer neural networks in a
teacher-student setup, where one network, the student, is trained using
stochastic gradient descent (SGD) on data generated by another network, called
the teacher. We show how for this problem, the dynamics of SGD are captured by
a set of differential equations. In particular, we demonstrate analytically
that the generalisation error of the student increases linearly with the
network size, with other relevant parameters held constant. Our results
indicate that achieving good generalisation in neural networks depends on the
interplay of at least the algorithm, its learning rate, the model architecture,
and the data set. | [
1,
0,
0,
1,
0,
0
] |
Title: Nonparametric Testing for Differences in Electricity Prices: The Case of the Fukushima Nuclear Accident,
Abstract: This work is motivated by the problem of testing for differences in the mean
electricity prices before and after Germany's abrupt nuclear phaseout after the
nuclear disaster in Fukushima Daiichi, Japan, in mid-March 2011. Taking into
account the nature of the data and the auction design of the electricity
market, we approach this problem using a Local Linear Kernel (LLK) estimator
for the nonparametric mean function of sparse covariate-adjusted functional
data. We build upon recent theoretical work on the LLK estimator and propose a
two-sample test statistics using a finite sample correction to avoid size
distortions. Our nonparametric test results on the price differences point to a
Simpson's paradox explaining an unexpected result recently reported in the
literature. | [
0,
0,
0,
1,
0,
0
] |
Title: Dynamic coupling of ferromagnets via spin Hall magnetoresistance,
Abstract: The synchronized magnetization dynamics in ferromagnets on a nonmagnetic
heavy metal caused by the spin Hall effect is investigated theoretically. The
direct and inverse spin Hall effects near the ferromagnetic/nonmagnetic
interface generate longitudinal and transverse electric currents. The
phenomenon is known as the spin Hall magnetoresistance effect, whose magnitude
depends on the magnetization direction in the ferromagnet due to the spin
transfer effect. When another ferromagnet is placed onto the same nonmagnet,
these currents are again converted to the spin current by the spin Hall effect
and excite the spin torque to this additional ferromagnet, resulting in the
excitation of the coupled motions of the magnetizations. The in-phase or
antiphase synchronization of the magnetization oscillations, depending on the
value of the Gilbert damping constant and the field-like torque strength, is
found in the transverse geometry by solving the Landau-Lifshitz-Gilbert
equation numerically. On the other hand, in addition to these synchronizations,
the synchronization having a phase difference of a quarter of a period is also
found in the longitudinal geometry. The analytical theory clarifying the
relation among the current, frequency, and phase difference is also developed,
where it is shown that the phase differences observed in the numerical
simulations correspond to that giving the fixed points of the energy supplied
by the coupling torque. | [
0,
1,
0,
0,
0,
0
] |
Title: A symmetric monoidal and equivariant Segal infinite loop space machine,
Abstract: In [MMO] (arXiv:1704.03413), we reworked and generalized equivariant infinite
loop space theory, which shows how to construct $G$-spectra from $G$-spaces
with suitable structure. In this paper, we construct a new variant of the
equivariant Segal machine that starts from the category $\scr{F}$ of finite
sets rather than from the category ${\scr{F}}_G$ of finite $G$-sets and which
is equivalent to the machine studied by Shimakawa and in [MMO]. In contrast to
the machine in [MMO], the new machine gives a lax symmetric monoidal functor
from the symmetric monoidal category of $\scr{F}$-$G$-spaces to the symmetric
monoidal category of orthogonal $G$-spectra. We relate it multiplicatively to
suspension $G$-spectra and to Eilenberg-MacLane $G$-spectra via lax symmetric
monoidal functors from based $G$-spaces and from abelian groups to
$\scr{F}$-$G$-spaces. Even non-equivariantly, this gives an appealing new
variant of the Segal machine. This new variant makes the equivariant
generalization of the theory essentially formal, hence is likely to be
applicable in other contexts. | [
0,
0,
1,
0,
0,
0
] |
Title: Long-range proximity effect in Nb-based heterostructures induced by a magnetically inhomogeneous permalloy layer,
Abstract: Odd-frequency triplet Cooper pairs are believed to be the carriers of
long-range superconducting correlations in ferromagnets. Such triplet pairs are
generated by inhomogeneous magnetism at the interface between a superconductor
(S) and a ferromagnet (F). So far, reproducible long-range effects were
reported only in complex layered structures designed to provide the magnetic
inhomogeneity. Here we show that spin triplet pair formation can be found in
simple unstructured Nb/Permalloy (Py = Ni_0.8Fe_0.2)/Nb trilayers and Nb/Py
bilayers, but only when the thickness of the ferromagnetic layer ranges between
140 and 250 nm. The effect is related to the emergence of an intrinsically
inhomogeneous magnetic state, which is a precursor of the well-known stripe
regime in Py that in our samples sets in at thickness larger than 300 nm. | [
0,
1,
0,
0,
0,
0
] |
Title: Contracts as specifications for dynamical systems in driving variable form,
Abstract: This paper introduces assume/guarantee contracts on continuous-time control
systems, hereby extending contract theories for discrete systems to certain new
model classes and specifications. Contracts are regarded as formal
characterizations of control specifications, providing an alternative to
specifications in terms of dissipativity properties or set-invariance. The
framework has the potential to capture a richer class of specifications more
suitable for complex engineering systems. The proposed contracts are supported
by results that enable the verification of contract implementation and the
comparison of contracts. These results are illustrated by an example of a
vehicle following system. | [
1,
0,
0,
0,
0,
0
] |
Title: On decision regions of narrow deep neural networks,
Abstract: We show that for neural network functions that have width less or equal to
the input dimension all connected components of decision regions are unbounded.
The result holds for continuous and strictly monotonic activation functions as
well as for ReLU activation. This complements recent results on approximation
capabilities of [Hanin 2017 Approximating] and connectivity of decision regions
of [Nguyen 2018 Neural] for such narrow neural networks. Further, we give an
example that negatively answers the question posed in [Nguyen 2018 Neural]
whether one of their main results still holds for ReLU activation. Our results
are illustrated by means of numerical experiments. | [
0,
0,
0,
1,
0,
0
] |
Title: An adelic arithmeticity theorem for lattices in products,
Abstract: We prove that, under mild assumptions, a lattice in a product of semi-simple
Lie group and a totally disconnected locally compact group is, in a certain
sense, arithmetic. We do not assume the lattice to be finitely generated or the
ambient group to be compactly generated. | [
0,
0,
1,
0,
0,
0
] |
Title: Homotopy types of gauge groups related to $S^3$-bundles over $S^4$,
Abstract: Let $M_{l,m}$ be the total space of the $S^3$-bundle over $S^4$ classified by
the element $l\sigma+m\rho\in{\pi_4(SO(4))}$, $l,m\in\mathbb Z$. In this paper
we study the homotopy theory of gauge groups of principal $G$-bundles over
manifolds $M_{l,m}$ when $G$ is a simply connected simple compact Lie group
such that $\pi_6(G)=0$. That is, $G$ is one of the following groups: $SU(n)$
$(n\geq4)$, $Sp(n)$ $(n\geq2)$, $Spin(n)$ $(n\geq5)$, $F_4$, $E_6$, $E_7$,
$E_8$. If the integral homology of $M_{l,m}$ is torsion-free, we describe the
homotopy type of the gauge groups over $M_{l,m}$ as products of recognisable
spaces. For any manifold $M_{l,m}$ with non-torsion-free homology, we give a
$p$-local homotopy decomposition, for a prime $p\geq 5$, of the loop space of
the gauge groups. | [
0,
0,
1,
0,
0,
0
] |
Title: Optical quality assurance of GEM foils,
Abstract: An analysis software was developed for the high aspect ratio optical scanning
system in the Detec- tor Laboratory of the University of Helsinki and the
Helsinki Institute of Physics. The system is used e.g. in the quality assurance
of the GEM-TPC detectors being developed for the beam diagnostics system of the
SuperFRS at future FAIR facility. The software was tested by analyzing five
CERN standard GEM foils scanned with the optical scanning system. The
measurement uncertainty of the diameter of the GEM holes and the pitch of the
hole pattern was found to be 0.5 {\mu}m and 0.3 {\mu}m, respectively. The
software design and the performance are discussed. The correlation between the
GEM hole size distribution and the corresponding gain variation was studied by
comparing them against a detailed gain mapping of a foil and a set of six lower
precision control measurements. It can be seen that a qualitative estimation of
the behavior of the local variation in gain across the GEM foil can be made
based on the measured sizes of the outer and inner holes. | [
0,
1,
0,
0,
0,
0
] |
Title: On Number of Rich Words,
Abstract: Any finite word $w$ of length $n$ contains at most $n+1$ distinct palindromic
factors. If the bound $n+1$ is reached, the word $w$ is called rich. The number
of rich words of length $n$ over an alphabet of cardinality $q$ is denoted
$R_n(q)$. For binary alphabet, Rubinchik and Shur deduced that ${R_n(2)}\leq c
1.605^n $ for some constant $c$. We prove that $\lim\limits_{n\rightarrow
\infty }\sqrt[n]{R_n(q)}=1$ for any $q$, i.e. $R_n(q)$ has a subexponential
growth on any alphabet. | [
0,
0,
1,
0,
0,
0
] |
Title: A Geometric Analysis of Power System Loadability Regions,
Abstract: Understanding the feasible power flow region is of central importance to
power system analysis. In this paper, we propose a geometric view of the power
system loadability problem. By using rectangular coordinates for complex
voltages, we provide an integrated geometric understanding of active and
reactive power flow equations on loadability boundaries. Based on such an
understanding, we develop a linear programming framework to 1) verify if an
operating point is on the loadability boundary, 2) compute the margin of an
operating point to the loadability boundary, and 3) calculate a loadability
boundary point of any direction. The proposed method is computationally more
efficient than existing methods since it does not require solving nonlinear
optimization problems or calculating the eigenvalues of the power flow
Jacobian. Standard IEEE test cases demonstrate the capability of the new method
compared to the current state-of-the-art methods. | [
1,
0,
1,
0,
0,
0
] |
Title: Model Selection Confidence Sets by Likelihood Ratio Testing,
Abstract: The traditional activity of model selection aims at discovering a single
model superior to other candidate models. In the presence of pronounced noise,
however, multiple models are often found to explain the same data equally well.
To resolve this model selection ambiguity, we introduce the general approach of
model selection confidence sets (MSCSs) based on likelihood ratio testing. A
MSCS is defined as a list of models statistically indistinguishable from the
true model at a user-specified level of confidence, which extends the familiar
notion of confidence intervals to the model-selection framework. Our approach
guarantees asymptotically correct coverage probability of the true model when
both sample size and model dimension increase. We derive conditions under which
the MSCS contains all the relevant information about the true model structure.
In addition, we propose natural statistics based on the MSCS to measure
importance of variables in a principled way that accounts for the overall model
uncertainty. When the space of feasible models is large, MSCS is implemented by
an adaptive stochastic search algorithm which samples MSCS models with high
probability. The MSCS methodology is illustrated through numerical experiments
on synthetic data and real data examples. | [
0,
0,
1,
1,
0,
0
] |
Title: A Vietoris-Smale mapping theorem for the homotopy of hyperdefinable sets,
Abstract: Results of Smale (1957) and Dugundji (1969) allow to compare the homotopy
groups of two topological spaces $X$ and $Y$ whenever a map $f:X\to Y$ with
strong connectivity conditions on the fibers is given. We apply similar
techniques in o-minimal expansions of fields to compare the o-minimal homotopy
of a definable set $X$ with the homotopy of some of its bounded hyperdefinable
quotients $X/E$. Under suitable assumption, we show that $\pi_{n}(X)^{\rm
def}\cong\pi_{n}(X/E)$ and $\dim(X)=\dim_{\mathbb R}(X/E)$. As a special case,
given a definably compact group, we obtain a new proof of Pillay's group
conjecture "$\dim(G)=\dim_{\mathbb R}(G/G^{00}$)" largely independent of the
group structure of $G$. We also obtain different proofs of various comparison
results between classical and o-minimal homotopy. | [
0,
0,
1,
0,
0,
0
] |
Title: Privacy-Preserving Multi-Period Demand Response: A Game Theoretic Approach,
Abstract: We study a multi-period demand response problem in the smart grid with
multiple companies and their consumers. We model the interactions by a
Stackelberg game, where companies are the leaders and consumers are the
followers. It is shown that this game has a unique equilibrium at which the
companies set prices to maximize their revenues while the consumers respond
accordingly to maximize their utilities subject to their local constraints.
Billing minimization is achieved as an outcome of our method. Closed-form
expressions are provided for the strategies of all players. Based on these
solutions, a power allocation game has been formulated, and which is shown to
admit a unique pure-strategy Nash equilibrium, for which closed-form
expressions are provided. For privacy, we provide a distributed algorithm for
the computation of all strategies. We study the asymptotic behavior of
equilibrium strategies when the numbers of periods and consumers grow. We find
an appropriate company-to-user ratio for the large population regime.
Furthermore, it is shown, both analytically and numerically, that the
multi-period scheme, compared with the single-period one, provides more
incentives for energy consumers to participate in demand response. We have also
carried out case studies on real life data to demonstrate the benefits of our
approach, including billing savings of up to 30\%. | [
1,
0,
0,
0,
0,
0
] |
Title: Negative differential resistance and magnetoresistance in zigzag borophene nanoribbons,
Abstract: We investigate the transport properties of pristine zigzag-edged borophene
nanoribbons (ZBNRs) of different widths, using the fist-principles
calculations. We choose ZBNRs with widths of 5 and 6 as odd and even widths.
The differences of the quantum transport properties are found, where even-N
BNRs and odd-N BNRs have different current-voltage relationships. Moreover, the
negative differential resistance (NDR) can be observed within certain bias
range in 5-ZBNR, while 6-ZBNR behaves as metal whose current rises with the
increase of the voltage. The spin filter effect of 36% can be revealed when the
two electrodes have opposite magnetization direction. Furthermore, the
magnetoresistance effect appears to be in even-N ZBNRs, and the maximum value
can reach 70%. | [
0,
1,
0,
0,
0,
0
] |
Title: Incremental Eigenpair Computation for Graph Laplacian Matrices: Theory and Applications,
Abstract: The smallest eigenvalues and the associated eigenvectors (i.e., eigenpairs)
of a graph Laplacian matrix have been widely used in spectral clustering and
community detection. However, in real-life applications the number of clusters
or communities (say, $K$) is generally unknown a-priori. Consequently, the
majority of the existing methods either choose $K$ heuristically or they repeat
the clustering method with different choices of $K$ and accept the best
clustering result. The first option, more often, yields suboptimal result,
while the second option is computationally expensive. In this work, we propose
an incremental method for constructing the eigenspectrum of the graph Laplacian
matrix. This method leverages the eigenstructure of graph Laplacian matrix to
obtain the $K$-th smallest eigenpair of the Laplacian matrix given a collection
of all previously computed $K-1$ smallest eigenpairs. Our proposed method
adapts the Laplacian matrix such that the batch eigenvalue decomposition
problem transforms into an efficient sequential leading eigenpair computation
problem. As a practical application, we consider user-guided spectral
clustering. Specifically, we demonstrate that users can utilize the proposed
incremental method for effective eigenpair computation and for determining the
desired number of clusters based on multiple clustering metrics. | [
1,
0,
0,
1,
0,
0
] |
Title: Proceedings 15th International Conference on Automata and Formal Languages,
Abstract: The 15th International Conference on Automata and Formal Languages (AFL 2017)
was held in Debrecen, Hungary, from September 4 to 6, 2017. The conference was
organized by the Faculty of Informatics of the University of Debrecen and the
Faculty of Informatics of the Eötvös Loránd University of Budapest.
Topics of interest covered all aspects of automata and formal languages,
including theory and applications. | [
1,
0,
0,
0,
0,
0
] |
Title: Phase Transitions in Approximate Ranking,
Abstract: We study the problem of approximate ranking from observations of pairwise
interactions. The goal is to estimate the underlying ranks of $n$ objects from
data through interactions of comparison or collaboration. Under a general
framework of approximate ranking models, we characterize the exact optimal
statistical error rates of estimating the underlying ranks. We discover
important phase transition boundaries of the optimal error rates. Depending on
the value of the signal-to-noise ratio (SNR) parameter, the optimal rate, as a
function of SNR, is either trivial, polynomial, exponential or zero. The four
corresponding regimes thus have completely different error behaviors. To the
best of our knowledge, this phenomenon, especially the phase transition between
the polynomial and the exponential rates, has not been discovered before. | [
0,
0,
1,
1,
0,
0
] |
Title: Finding Influential Training Samples for Gradient Boosted Decision Trees,
Abstract: We address the problem of finding influential training samples for a
particular case of tree ensemble-based models, e.g., Random Forest (RF) or
Gradient Boosted Decision Trees (GBDT). A natural way of formalizing this
problem is studying how the model's predictions change upon leave-one-out
retraining, leaving out each individual training sample. Recent work has shown
that, for parametric models, this analysis can be conducted in a
computationally efficient way. We propose several ways of extending this
framework to non-parametric GBDT ensembles under the assumption that tree
structures remain fixed. Furthermore, we introduce a general scheme of
obtaining further approximations to our method that balance the trade-off
between performance and computational complexity. We evaluate our approaches on
various experimental setups and use-case scenarios and demonstrate both the
quality of our approach to finding influential training samples in comparison
to the baselines and its computational efficiency. | [
0,
0,
0,
1,
0,
0
] |
Title: Transfer learning for music classification and regression tasks,
Abstract: In this paper, we present a transfer learning approach for music
classification and regression tasks. We propose to use a pre-trained convnet
feature, a concatenated feature vector using the activations of feature maps of
multiple layers in a trained convolutional network. We show how this convnet
feature can serve as general-purpose music representation. In the experiments,
a convnet is trained for music tagging and then transferred to other
music-related classification and regression tasks. The convnet feature
outperforms the baseline MFCC feature in all the considered tasks and several
previous approaches that are aggregating MFCCs as well as low- and high-level
music features. | [
1,
0,
0,
0,
0,
0
] |
Title: Dynamic Transition in Symbiotic Evolution Induced by Growth Rate Variation,
Abstract: In a standard bifurcation of a dynamical system, the stationary points (or
more generally attractors) change qualitatively when varying a control
parameter. Here we describe a novel unusual effect, when the change of a
parameter, e.g. a growth rate, does not influence the stationary states, but
nevertheless leads to a qualitative change of dynamics. For instance, such a
dynamic transition can be between the convergence to a stationary state and a
strong increase without stationary states, or between the convergence to one
stationary state and that to a different state. This effect is illustrated for
a dynamical system describing two symbiotic populations, one of which exhibits
a growth rate larger than the other one. We show that, although the stationary
states of the dynamical system do not depend on the growth rates, the latter
influence the boundary of the basins of attraction. This change of the basins
of attraction explains this unusual effect of the quantitative change of
dynamics by growth rate variation. | [
0,
1,
0,
0,
0,
0
] |
Title: Thermoelectric Devices: Principles and Future Trends,
Abstract: The principles of the thermoelectric phenomenon, including Seebeck effect,
Peltier effect, and Thomson effect are discussed. The dependence of the
thermoelectric devices on the figure of merit, Seebeck coefficient, electrical
conductivity, and thermal conductivity are explained in details. The paper
provides an overview of the different types of thermoelectric materials,
explains the techniques used to grow thin films for these materials, and
discusses future research and development trends for this technology. | [
0,
1,
0,
0,
0,
0
] |
Title: On transient waves in linear viscoelasticity,
Abstract: The aim of this paper is to present a comprehensive review of method of the
wave-front expansion, also known in the literature as the Buchen-Mainardi
algorithm. In particular, many applications of this technique to the
fundamental models of both ordinary and fractional linear viscoelasticity are
thoroughly presented and discussed. | [
0,
1,
0,
0,
0,
0
] |
Title: Boosting the power factor with resonant states: a model study,
Abstract: A particularly promising pathway to enhance the efficiency of thermoelectric
materials lies in the use of resonant states, as suggested by experimentalists
and theorists alike. In this paper, we go over the mechanisms used in the
literature to explain how resonant levels affect the thermoelectric properties,
and we suggest that the effects of hybridization are crucial yet
ill-understood. In order to get a good grasp of the physical picture and to
draw guidelines for thermoelectric enhancement, we use a tight-binding model
containing a conduction band hybridized with a flat band. We find that the
conductivity is suppressed in a wide energy range near the resonance, but that
the Seebeck coefficient can be boosted for strong enough hybridization, thus
allowing for a significant increase of the power factor. The Seebeck
coefficient can also display a sign change as the Fermi level crosses the
resonance. Our results suggest that in order to boost the power factor, the
hybridization strength must not be too low, the resonant level must not be too
close to the conduction (or valence) band edge, and the Fermi level must be
located around, but not inside, the resonant peak. | [
0,
1,
0,
0,
0,
0
] |
Title: Deep Learning Based Large-Scale Automatic Satellite Crosswalk Classification,
Abstract: High-resolution satellite imagery have been increasingly used on remote
sensing classification problems. One of the main factors is the availability of
this kind of data. Even though, very little effort has been placed on the zebra
crossing classification problem. In this letter, crowdsourcing systems are
exploited in order to enable the automatic acquisition and annotation of a
large-scale satellite imagery database for crosswalks related tasks. Then, this
dataset is used to train deep-learning-based models in order to accurately
classify satellite images that contains or not zebra crossings. A novel dataset
with more than 240,000 images from 3 continents, 9 countries and more than 20
cities was used in the experiments. Experimental results showed that freely
available crowdsourcing data can be used to accurately (97.11%) train robust
models to perform crosswalk classification on a global scale. | [
1,
0,
0,
1,
0,
0
] |
Title: Complexity of the Regularized Newton Method,
Abstract: Newton's method for finding an unconstrained minimizer for strictly convex
functions, generally speaking, does not converge from any starting point.
We introduce and study the damped regularized Newton's method (DRNM). It
converges globally for any strictly convex function, which has a minimizer in
$R^n$.
Locally DRNM converges with a quadratic rate. We characterize the
neighborhood of the minimizer, where the quadratic rate occurs. Based on it we
estimate the number of DRNM's steps required for finding an $\varepsilon$-
approximation for the minimizer. | [
0,
0,
1,
0,
0,
0
] |
Title: Quantifying Program Bias,
Abstract: With the range and sensitivity of algorithmic decisions expanding at a
break-neck speed, it is imperative that we aggressively investigate whether
programs are biased. We propose a novel probabilistic program analysis
technique and apply it to quantifying bias in decision-making programs.
Specifically, we (i) present a sound and complete automated verification
technique for proving quantitative properties of probabilistic programs; (ii)
show that certain notions of bias, recently proposed in the fairness
literature, can be phrased as quantitative correctness properties; and (iii)
present FairSquare, the first verification tool for quantifying program bias,
and evaluate it on a range of decision-making programs. | [
1,
0,
0,
0,
0,
0
] |
Title: Tuning Majorana zero modes with temperature in $π$-phase Josephson junctions,
Abstract: We study a superconductor-normal state-superconductor (SNS) Josephson
junction along the edge of a quantum spin Hall insulator (QSHI) with a
superconducting $\pi$-phase across the junction. We solve self-consistently for
the superconducting order parameter and find both real junctions, where the
order parameter is fully real throughout the system, and junctions where the
order parameter has a complex phase winding. Real junctions host two Majorana
zero modes (MZMs), while phase-winding junctions have no subgap states close to
zero energy. At zero temperature we find that the phase-winding solution always
has the lowest free energy, which we establish being due to a strong
proximity-effect into the N region. With increasing temperature this
proximity-effect is dramatically decreased and we find a phase transition into
a real junction with two MZMs. | [
0,
1,
0,
0,
0,
0
] |
Title: Generating the Log Law of the Wall with Superposition of Standing Waves,
Abstract: Turbulence remains an unsolved multidisciplinary science problem. As one of
the most well-known examples in turbulent flows, knowledge of the logarithmic
mean velocity profile (MVP), so called the log law of the wall, plays an
important role everywhere turbulent flow meets the solid wall, such as fluids
in any kind of channels, skin friction of all types of transportations, the
atmospheric wind on a planetary ground, and the oceanic current on the seabed.
However, the mechanism of how this log-law MVP is formed under the multiscale
nature of turbulent shears remains one of the greatest interests of turbulence
puzzles. To untangle the multiscale coupling of turbulent shear stresses, we
explore for a known fundamental tool in physics. Here we present how to
reproduce the log-law MVP with the even harmonic modes of fixed-end standing
waves. We find that when these harmonic waves of same magnitude are considered
as the multiscale turbulent shear stresses, the wave envelope of their
superposition simulates the mean shear stress profile of the wall-bounded flow.
It implies that the log-law MVP is not expectedly related to the turbulent
scales in the inertial subrange associated with the Kolmogorov energy cascade,
revealing the dissipative nature of all scales involved. The MVP with reduced
harmonic modes also shows promising connection to the understanding of flow
transition to turbulence. The finding here suggests the simple harmonic waves
as good agents to help unravel the complex turbulent dynamics in wall-bounded
flow. | [
0,
1,
0,
0,
0,
0
] |
Title: Topical homophily in online social systems,
Abstract: Understanding the dynamics of social interactions is crucial to comprehend
human behavior. The emergence of online social media has enabled access to data
regarding people relationships at a large scale. Twitter, specifically, is an
information oriented network, with users sharing and consuming information. In
this work, we study whether users tend to be in contact with people interested
in similar topics, i.e., topical homophily. To do so, we propose an approach
based on the use of hashtags to extract information topics from Twitter
messages and model users' interests. Our results show that, on average, users
are connected with other users similar to them and stronger relationships are
due to a higher topical similarity. Furthermore, we show that topical homophily
provides interesting information that can eventually allow inferring users'
connectivity. Our work, besides providing a way to assess the topical
similarity of users, quantifies topical homophily among individuals,
contributing to a better understanding of how complex social systems are
structured. | [
1,
0,
0,
0,
0,
0
] |
Title: Linear Optimal Power Flow Using Cycle Flows,
Abstract: Linear optimal power flow (LOPF) algorithms use a linearization of the
alternating current (AC) load flow equations to optimize generator dispatch in
a network subject to the loading constraints of the network branches. Common
algorithms use the voltage angles at the buses as optimization variables, but
alternatives can be computationally advantageous. In this article we provide a
review of existing methods and describe a new formulation that expresses the
loading constraints directly in terms of the flows themselves, using a
decomposition of the network graph into a spanning tree and closed cycles. We
provide a comprehensive study of the computational performance of the various
formulations, in settings that include computationally challenging applications
such as multi-period LOPF with storage dispatch and generation capacity
expansion. We show that the new formulation of the LOPF solves up to 7 times
faster than the angle formulation using a commercial linear programming solver,
while another existing cycle-based formulation solves up to 20 times faster,
with an average speed-up of factor 3 for the standard networks considered here.
If generation capacities are also optimized, the average speed-up rises to a
factor of 12, reaching up to factor 213 in a particular instance. The speed-up
is largest for networks with many buses and decentral generators throughout the
network, which is highly relevant given the rise of distributed renewable
generation and the computational challenge of operation and planning in such
networks. | [
1,
1,
0,
0,
0,
0
] |
Title: The Exact Solution to Rank-1 L1-norm TUCKER2 Decomposition,
Abstract: We study rank-1 {L1-norm-based TUCKER2} (L1-TUCKER2) decomposition of 3-way
tensors, treated as a collection of $N$ $D \times M$ matrices that are to be
jointly decomposed. Our contributions are as follows. i) We prove that the
problem is equivalent to combinatorial optimization over $N$ antipodal-binary
variables. ii) We derive the first two algorithms in the literature for its
exact solution. The first algorithm has cost exponential in $N$; the second one
has cost polynomial in $N$ (under a mild assumption). Our algorithms are
accompanied by formal complexity analysis. iii) We conduct numerical studies to
compare the performance of exact L1-TUCKER2 (proposed) with standard HOSVD,
HOOI, GLRAM, PCA, L1-PCA, and TPCA-L1. Our studies show that L1-TUCKER2
outperforms (in tensor approximation) all the above counterparts when the
processed data are outlier corrupted. | [
1,
0,
0,
1,
0,
0
] |
Title: A Statistical Comparative Planetology Approach to the Hunt for Habitable Exoplanets and Life Beyond the Solar System,
Abstract: The search for habitable exoplanets and life beyond the Solar System is one
of the most compelling scientific opportunities of our time. Nevertheless, the
high cost of building facilities that can address this topic and the keen
public interest in the results of such research requires the rigorous
development of experiments that can deliver a definitive advance in our
understanding. Most work to date in this area has focused on a "systems
science" approach of obtaining and interpreting comprehensive data for
individual planets to make statements about their habitability and the
possibility that they harbor life. This strategy is challenging because of the
diversity of exoplanets, both observed and expected, and the limited
information that can be obtained with astronomical instruments. Here we propose
a complementary approach that is based on performing surveys of key planetary
characteristics and using statistical marginalization to answer broader
questions than can be addressed with a small sample of objects. The fundamental
principle of this comparative planetology approach is maximizing what can be
learned from each type of measurement by applying it widely rather than
requiring that multiple kinds of observations be brought to bear on a single
object. As a proof of concept, we outline a survey of terrestrial exoplanet
atmospheric water and carbon dioxide abundances that would test the habitable
zone hypothesis and lead to a deeper understanding of the frequency of
habitable planets. We also discuss ideas for additional surveys that could be
developed to test other foundational hypotheses is this area. | [
0,
1,
0,
0,
0,
0
] |
Title: Periodic Airy process and equilibrium dynamics of edge fermions in a trap,
Abstract: We establish an exact mapping between (i) the equilibrium (imaginary time)
dynamics of non-interacting fermions trapped in a harmonic potential at
temperature $T=1/\beta$ and (ii) non-intersecting Ornstein-Uhlenbeck (OU)
particles constrained to return to their initial positions after time $\beta$.
Exploiting the determinantal structure of the process we compute the universal
correlation functions both in the bulk and at the edge of the trapped Fermi
gas. The latter corresponds to the top path of the non-intersecting OU
particles, and leads us to introduce and study the time-periodic Airy$_2$
process, ${\cal A}^b_2(u)$, depending on a single parameter, the period $b$.
The standard Airy$_2$ process is recovered for $b=+\infty$. We discuss
applications of our results to the real time quantum dynamics of trapped
fermions. | [
0,
1,
1,
0,
0,
0
] |
Title: Quantum machine learning: a classical perspective,
Abstract: Recently, increased computational power and data availability, as well as
algorithmic advances, have led machine learning techniques to impressive
results in regression, classification, data-generation and reinforcement
learning tasks. Despite these successes, the proximity to the physical limits
of chip fabrication alongside the increasing size of datasets are motivating a
growing number of researchers to explore the possibility of harnessing the
power of quantum computation to speed-up classical machine learning algorithms.
Here we review the literature in quantum machine learning and discuss
perspectives for a mixed readership of classical machine learning and quantum
computation experts. Particular emphasis will be placed on clarifying the
limitations of quantum algorithms, how they compare with their best classical
counterparts and why quantum resources are expected to provide advantages for
learning problems. Learning in the presence of noise and certain
computationally hard problems in machine learning are identified as promising
directions for the field. Practical questions, like how to upload classical
data into quantum form, will also be addressed. | [
1,
0,
0,
1,
0,
0
] |
Title: Robot Assisted Tower Construction - A Resource Distribution Task to Study Human-Robot Collaboration and Interaction with Groups of People,
Abstract: Research on human-robot collaboration or human-robot teaming, has focused
predominantly on understanding and enabling collaboration between a single
robot and a single human. Extending human-robot collaboration research beyond
the dyad, raises novel questions about how a robot should distribute resources
among group members and about what the social and task related consequences of
the distribution are. Methodological advances are needed to allow researchers
to collect data about human robot collaboration that involves multiple people.
This paper presents Tower Construction, a novel resource distribution task that
allows researchers to examine collaboration between a robot and groups of
people. By focusing on the question of whether and how a robot's distribution
of resources (wooden blocks required for a building task) affects collaboration
dynamics and outcomes, we provide a case of how this task can be applied in a
laboratory study with 124 participants to collect data about human robot
collaboration that involves multiple humans. We highlight the kinds of insights
the task can yield. In particular we find that the distribution of resources
affects perceptions of performance, and interpersonal dynamics between human
team-members. | [
1,
0,
0,
0,
0,
0
] |
Title: Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals,
Abstract: An electroencephalography (EEG) based Brain Computer Interface (BCI) enables
people to communicate with the outside world by interpreting the EEG signals of
their brains to interact with devices such as wheelchairs and intelligent
robots. More specifically, motor imagery EEG (MI-EEG), which reflects a
subjects active intent, is attracting increasing attention for a variety of BCI
applications. Accurate classification of MI-EEG signals while essential for
effective operation of BCI systems, is challenging due to the significant noise
inherent in the signals and the lack of informative correlation between the
signals and brain activities. In this paper, we propose a novel deep neural
network based learning framework that affords perceptive insights into the
relationship between the MI-EEG data and brain activities. We design a joint
convolutional recurrent neural network that simultaneously learns robust
high-level feature presentations through low-dimensional dense embeddings from
raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various
artifacts such as background activities. The proposed approach has been
evaluated extensively on a large- scale public MI-EEG dataset and a limited but
easy-to-deploy dataset collected in our lab. The results show that our approach
outperforms a series of baselines and the competitive state-of-the- art
methods, yielding a classification accuracy of 95.53%. The applicability of our
proposed approach is further demonstrated with a practical BCI system for
typing. | [
1,
0,
0,
0,
0,
0
] |
Title: An Information-Theoretic Analysis for Thompson Sampling with Many Actions,
Abstract: Information-theoretic Bayesian regret bounds of Russo and Van Roy capture the
dependence of regret on prior uncertainty. However, this dependence is through
entropy, which can become arbitrarily large as the number of actions increases.
We establish new bounds that depend instead on a notion of rate-distortion.
Among other things, this allows us to recover through information-theoretic
arguments a near-optimal bound for the linear bandit. We also offer a bound for
the logistic bandit that dramatically improves on the best previously
available, though this bound depends on an information-theoretic statistic that
we have only been able to quantify via computation. | [
0,
0,
0,
1,
0,
0
] |
Title: Optimal portfolio selection in an Itô-Markov additive market,
Abstract: We study a portfolio selection problem in a continuous-time Itô-Markov
additive market with prices of financial assets described by Markov additive
processes which combine Lévy processes and regime switching models. Thus the
model takes into account two sources of risk: the jump diffusion risk and the
regime switching risk. For this reason the market is incomplete. We complete
the market by enlarging it with the use of a set of Markovian jump securities,
Markovian power-jump securities and impulse regime switching securities.
Moreover, we give conditions under which the market is
asymptotic-arbitrage-free. We solve the portfolio selection problem in the
Itô-Markov additive market for the power utility and the logarithmic utility. | [
0,
0,
0,
0,
0,
1
] |
Title: SPIRITS: Uncovering Unusual Infrared Transients With Spitzer,
Abstract: We present an ongoing, systematic search for extragalactic infrared
transients, dubbed SPIRITS --- SPitzer InfraRed Intensive Transients Survey. In
the first year, using Spitzer/IRAC, we searched 190 nearby galaxies with
cadence baselines of one month and six months. We discovered over 1958
variables and 43 transients. Here, we describe the survey design and highlight
14 unusual infrared transients with no optical counterparts to deep limits,
which we refer to as SPRITEs (eSPecially Red Intermediate Luminosity Transient
Events). SPRITEs are in the infrared luminosity gap between novae and
supernovae, with [4.5] absolute magnitudes between -11 and -14 (Vega-mag) and
[3.6]-[4.5] colors between 0.3 mag and 1.6 mag. The photometric evolution of
SPRITEs is diverse, ranging from < 0.1 mag/yr to > 7 mag/yr. SPRITEs occur in
star-forming galaxies. We present an in-depth study of one of them, SPIRITS
14ajc in Messier 83, which shows shock-excited molecular hydrogen emission.
This shock may have been triggered by the dynamic decay of a non-hierarchical
system of massive stars that led to either the formation of a binary or a
proto-stellar merger. | [
0,
1,
0,
0,
0,
0
] |
Title: Adversarial classification: An adversarial risk analysis approach,
Abstract: Classification problems in security settings are usually contemplated as
confrontations in which one or more adversaries try to fool a classifier to
obtain a benefit. Most approaches to such adversarial classification problems
have focused on game theoretical ideas with strong underlying common knowledge
assumptions, which are actually not realistic in security domains. We provide
an alternative framework to such problem based on adversarial risk analysis,
which we illustrate with several examples. Computational and implementation
issues are discussed. | [
0,
0,
0,
1,
0,
0
] |
Title: Volume growth in the component of fibered twists,
Abstract: For a Liouville domain $W$ whose boundary admits a periodic Reeb flow, we can
consider the connected component $[\tau] \in \pi_0(\text{Symp}^c(\widehat W))$
of fibered twists. In this paper, we investigate an entropy-type invariant,
called the slow volume growth, of the component $[\tau]$ and give a uniform
lower bound of the growth using wrapped Floer homology. We also show that
$[\tau]$ has infinite order in $\pi_0(\text{Symp}^c(\widehat W))$ if there is
an admissible Lagrangian $L$ in $W$ whose wrapped Floer homology is infinite
dimensional. We apply our results to fibered twists coming from the Milnor
fibers of $A_k$-type singularities and complements of a symplectic hypersurface
in a real symplectic manifold. They admit so-called real Lagrangians, and we
can explicitly compute wrapped Floer homology groups using a version of
Morse-Bott spectral sequences. | [
0,
0,
1,
0,
0,
0
] |
Title: Scalar Reduction of a Neural Field Model with Spike Frequency Adaptation,
Abstract: We study a deterministic version of a one- and two-dimensional attractor
neural network model of hippocampal activity first studied by Itskov et al
2011. We analyze the dynamics of the system on the ring and torus domain with
an even periodized weight matrix, assum- ing weak and slow spike frequency
adaptation and a weak stationary input current. On these domains, we find
transitions from spatially localized stationary solutions ("bumps") to
(periodically modulated) solutions ("sloshers"), as well as constant and
non-constant velocity traveling bumps depending on the relative strength of
external input current and adaptation. The weak and slow adaptation allows for
a reduction of the system from a distributed partial integro-differential
equation to a system of scalar Volterra integro-differential equations
describing the movement of the centroid of the bump solution. Using this
reduction, we show that on both domains, sloshing solutions arise through an
Andronov-Hopf bifurcation and derive a normal form for the Hopf bifurcation on
the ring. We also show existence and stability of constant velocity solutions
on both domains using Evans functions. In contrast to existing studies, we
assume a general weight matrix of Mexican-hat type in addition to a smooth
firing rate function. | [
0,
0,
0,
0,
1,
0
] |
Title: Revisiting Elementary Denotational Semantics,
Abstract: Operational semantics have been enormously successful, in large part due to
its flexibility and simplicity, but they are not compositional. Denotational
semantics, on the other hand, are compositional but the lattice-theoretic
models are complex and difficult to scale to large languages. However, there
are elementary models of the $\lambda$-calculus that are much less complex: by
Coppo, Dezani-Ciancaglini, and Salle (1979), Engeler (1981), and Plotkin
(1993).
This paper takes first steps toward answering the question: can elementary
models be good for the day-to-day work of language specification,
mechanization, and compiler correctness? The elementary models in the
literature are simple, but they are not as intuitive as they could be. To
remedy this, we create a new model that represents functions literally as
finite graphs. Regarding mechanization, we give the first machine-checked proof
of soundness and completeness of an elementary model with respect to an
operational semantics. Regarding compiler correctness, we define a polyvariant
inliner for the call-by-value $\lambda$-calculus and prove that its output is
contextually equivalent to its input. Toward scaling elementary models to
larger languages, we formulate our semantics in a monadic style, give a
semantics for System F with general recursion, and mechanize the proof of type
soundness. | [
1,
0,
0,
0,
0,
0
] |
Title: Secure Grouping Protocol Using a Deck of Cards,
Abstract: We consider a problem, which we call secure grouping, of dividing a number of
parties into some subsets (groups) in the following manner: Each party has to
know the other members of his/her group, while he/she may not know anything
about how the remaining parties are divided (except for certain public
predetermined constraints, such as the number of parties in each group). In
this paper, we construct an information-theoretically secure protocol using a
deck of physical cards to solve the problem, which is jointly executable by the
parties themselves without a trusted third party. Despite the non-triviality
and the potential usefulness of the secure grouping, our proposed protocol is
fairly simple to describe and execute. Our protocol is based on algebraic
properties of conjugate permutations. A key ingredient of our protocol is our
new techniques to apply multiplication and inverse operations to hidden
permutations (i.e., those encoded by using face-down cards), which would be of
independent interest and would have various potential applications. | [
1,
0,
0,
0,
0,
0
] |
Title: Annealed Generative Adversarial Networks,
Abstract: We introduce a novel framework for adversarial training where the target
distribution is annealed between the uniform distribution and the data
distribution. We posited a conjecture that learning under continuous annealing
in the nonparametric regime is stable irrespective of the divergence measures
in the objective function and proposed an algorithm, dubbed {\ss}-GAN, in
corollary. In this framework, the fact that the initial support of the
generative network is the whole ambient space combined with annealing are key
to balancing the minimax game. In our experiments on synthetic data, MNIST, and
CelebA, {\ss}-GAN with a fixed annealing schedule was stable and did not suffer
from mode collapse. | [
1,
0,
0,
1,
0,
0
] |
Title: Attitude Control of Spacecraft Formations Subject To Distributed Communication Delays,
Abstract: This paper considers the problem of achieving attitude consensus in
spacecraft formations with bounded, time-varying communication delays between
spacecraft connected as specified by a strongly connected topology. A state
feedback con- troller is proposed and investigated using a time domain approach
(via LMIs) and a frequency domain approach (via the small-gain theorem) to
obtain delay depen- dent stability criteria to achieve the desired consensus.
Simulations are presented to demonstrate the application of the strategy in a
specific scenario. | [
1,
0,
0,
0,
0,
0
] |
Title: Instability of pulses in gradient reaction-diffusion systems: A symplectic approach,
Abstract: In a scalar reaction-diffusion equation, it is known that the stability of a
steady state can be determined from the Maslov index, a topological invariant
that counts the state's critical points. In particular, this implies that pulse
solutions are unstable. We extend this picture to pulses in reaction-diffusion
systems with gradient nonlinearity. In particular, we associate a Maslov index
to any asymptotically constant state, generalizing existing definitions of the
Maslov index for homoclinic orbits. It is shown that this index equals the
number of unstable eigenvalues for the linearized evolution equation. Finally,
we use a symmetry argument to show that any pulse solution must have nonzero
Maslov index, and hence be unstable. | [
0,
0,
1,
0,
0,
0
] |
Title: Consistency of the plug-in functional predictor of the Ornstein-Uhlenbeck process in Hilbert and Banach spaces,
Abstract: New results on functional prediction of the Ornstein-Uhlenbeck process in an
autoregressive Hilbert-valued and Banach-valued frameworks are derived.
Specifically, consistency of the maximum likelihood estimator of the
autocorrelation operator, and of the associated plug-in predictor is obtained
in both frameworks. | [
0,
0,
1,
1,
0,
0
] |
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