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QuanFuzz: Fuzz Testing of Quantum Program | Nowadays, quantum program is widely used and quickly developed. However, the
absence of testing methodology restricts their quality. Different input format
and operator from traditional program make this issue hard to resolve.
In this paper, we present QuanFuzz, a search-based test input generator for
quantum program. We define the quantum sensitive information to evaluate test
input for quantum program and use matrix generator to generate test cases with
higher coverage. First, we extract quantum sensitive information -- measurement
operations on those quantum registers and the sensitive branches associated
with those measurement results, from the quantum source code. Then, we use the
sensitive information guided algorithm to mutate the initial input matrix and
select those matrices which improve the probability weight for a value of the
quantum register to trigger the sensitive branch. The process keeps iterating
until the sensitive branch triggered. We tested QuanFuzz on benchmarks and
acquired 20% - 60% more coverage compared to traditional testing input
generation.
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Option Pricing Models Driven by the Space-Time Fractional Diffusion: Series Representation and Applications | 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 |
Anticipation: an effective evolutionary strategy for a sub-optimal population in a cyclic environment | 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.
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Unravelling Airbnb Predicting Price for New Listing | This paper analyzes Airbnb listings in the city of San Francisco to better
understand how different attributes such as bedrooms, location, house type
amongst others can be used to accurately predict the price of a new listing
that optimal in terms of the host's profitability yet affordable to their
guests. This model is intended to be helpful to the internal pricing tools that
Airbnb provides to its hosts. Furthermore, additional analysis is performed to
ascertain the likelihood of a listings availability for potential guests to
consider while making a booking. The analysis begins with exploring and
examining the data to make necessary transformations that can be conducive for
a better understanding of the problem at large while helping us make
hypothesis. Moving further, machine learning models are built that are
intuitive to use to validate the hypothesis on pricing and availability and run
experiments in that context to arrive at a viable solution. The paper then
concludes with a discussion on the business implications, associated risks and
future scope.
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Finding, Hitting and Packing Cycles in Subexponential Time on Unit Disk Graphs | 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 |
The self-referring DNA and protein: a remark on physical and geometrical aspects | All known life forms are based upon a hierarchy of interwoven feedback loops,
operating over a cascade of space, time and energy scales. Among the most basic
loops are those connecting DNA and proteins. For example, in genetic networks,
DNA genes are expressed as proteins, which may bind near the same genes and
thereby control their own expression. In this molecular type of self-reference,
information is mapped from the DNA sequence to the protein and back to DNA.
There is a variety of dynamic DNA-protein self-reference loops, and the purpose
of this remark is to discuss certain geometrical and physical aspects related
to the back and forth mapping between DNA and proteins. The discussion raises
basic questions regarding the nature of DNA and proteins as self-referring
matter, which are examined in a simple toy model.
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Optimal input design for system identification using spectral decomposition | The aim of this paper is to design a band-limited optimal input with power
constraints for identifying a linear multi-input multi-output system. It is
assumed that the nominal system parameters are specified. The key idea is to
use the spectral decomposition theorem and write the power spectrum as
$\phi_{u}(j\omega)=\frac{1}{2}H(j\omega)H^*(j\omega)$. The matrix $H(j\omega)$
is expressed in terms of a truncated basis for
$\mathcal{L}^2\left(\left[-\omega_{\mbox{cut-off}},\omega_{\mbox{cut-off}}\right]\right)$.
With this parameterization, the elements of the Fisher Information Matrix and
the power constraints turn out to be homogeneous quadratics in the basis
coefficients. The optimality criterion used are the well-known
$\mathcal{D}-$optimality, $\mathcal{A}-$optimality, $\mathcal{T}-$optimality
and $\mathcal{E}-$optimality. The resulting optimization problem is non-convex
in general. A lower bound on the optimum is obtained through a bi-linear
formulation of the problem, while an upper bound is obtained through a convex
relaxation. These bounds can be computed efficiently as the associated problems
are convex. The lower bound is used as a sub-optimal solution, the
sub-optimality of which is determined by the difference in the bounds.
Interestingly, the bounds match in many instances and thus, the global optimum
is achieved. A discussion on the non-convexity of the optimization problem is
also presented. Simulations are provided for corroboration.
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Creating a Web Analysis and Visualization Environment | Due to the rapid growth of the World Wide Web, resource discovery becomes an
increasing problem. As an answer to the demand for information management, a
third generation of World-Wide Web tools will evolve: information gathering and
processing agents. This paper describes WAVE (Web Analysis and Visualization
Environment), a 3D interface for World-Wide Web information visualization and
browsing. It uses the mathematical theory of concept analysis to conceptually
cluster objects, and to create a three-dimensional layout of information nodes.
So-called "conceptual scales" for attributes, such as location, title,
keywords, topic, size, or modification time, provide a formal mechanism that
automatically classifies and categorizes documents, creating a conceptual
information space. A visualization shell serves as an ergonomically sound user
interface for exploring this information space.
| 1 | 0 | 0 | 0 | 0 | 0 |
Transfer Learning by Asymmetric Image Weighting for Segmentation across Scanners | 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 |
Service Providers of the Sharing Economy: Who Joins and Who Benefits? | 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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The generalized Fermat equation with exponents 2, 3, n | 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 $p = 13$.
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On the image of the almost strict Morse n-category under almost strict n-functors | In an earlier work, we constructed the almost strict Morse $n$-category
$\mathcal X$ which extends Cohen $\&$ Jones $\&$ Segal's flow category. In this
article, we define two other almost strict $n$-categories $\mathcal V$ and
$\mathcal W$ where $\mathcal V$ is based on homomorphisms between real vector
spaces and $\mathcal W$ consists of tuples of positive integers. The Morse
index and the dimension of the Morse moduli spaces give rise to almost strict
$n$-category functors $\mathcal F : \mathcal X \to \mathcal V$ and $\mathcal G
: \mathcal X \to \mathcal W$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Short-term Memory of Deep RNN | The extension of deep learning towards temporal data processing is gaining an
increasing research interest. In this paper we investigate the properties of
state dynamics developed in successive levels of deep recurrent neural networks
(RNNs) in terms of short-term memory abilities. Our results reveal interesting
insights that shed light on the nature of layering as a factor of RNN design.
Noticeably, higher layers in a hierarchically organized RNN architecture
results to be inherently biased towards longer memory spans even prior to
training of the recurrent connections. Moreover, in the context of Reservoir
Computing framework, our analysis also points out the benefit of a layered
recurrent organization as an efficient approach to improve the memory skills of
reservoir models.
| 0 | 0 | 0 | 1 | 0 | 0 |
Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge | We consider the use of Deep Learning methods for modeling complex phenomena
like those occurring in natural physical processes. With the large amount of
data gathered on these phenomena the data intensive paradigm could begin to
challenge more traditional approaches elaborated over the years in fields like
maths or physics. However, despite considerable successes in a variety of
application domains, the machine learning field is not yet ready to handle the
level of complexity required by such problems. Using an example application,
namely Sea Surface Temperature Prediction, we show how general background
knowledge gained from physics could be used as a guideline for designing
efficient Deep Learning models. In order to motivate the approach and to assess
its generality we demonstrate a formal link between the solution of a class of
differential equations underlying a large family of physical phenomena and the
proposed model. Experiments and comparison with series of baselines including a
state of the art numerical approach is then provided.
| 1 | 0 | 0 | 1 | 0 | 0 |
Neutron Stars in Screened Modified Gravity: Chameleon vs Dilaton | 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.
| 0 | 1 | 0 | 0 | 0 | 0 |
Spin pumping into superconductors: A new probe of spin dynamics in a superconducting thin film | Spin pumping refers to the microwave-driven spin current injection from a
ferromagnet into the adjacent target material. We theoretically investigate the
spin pumping into superconductors by fully taking account of impurity
spin-orbit scattering that is indispensable to describe diffusive spin
transport with finite spin diffusion length. We calculate temperature
dependence of the spin pumping signal and show that a pronounced coherence peak
appears immediately below the superconducting transition temperature Tc, which
survives even in the presence of the spin-orbit scattering. The phenomenon
provides us with a new way of studying the dynamic spin susceptibility in a
superconducting thin film. This is contrasted with the nuclear magnetic
resonance technique used to study a bulk superconductor.
| 0 | 1 | 0 | 0 | 0 | 0 |
Evidence of Eta Aquariid Outbursts Recorded in the Classic Maya Hieroglyphic Script Using Orbital Integrations | 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.
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Degenerations of NURBS curves while all of weights approaching infinity | NURBS curve is widely used in Computer Aided Design and Computer Aided
Geometric Design. When a single weight approaches infinity, the limit of a
NURBS curve tends to the corresponding control point. In this paper, a kind of
control structure of a NURBS curve, called regular control curve, is defined.
We prove that the limit of the NURBS curve is exactly its regular control curve
when all of weights approach infinity, where each weight is multiplied by a
certain one-parameter function tending to infinity, different for each control
point. Moreover, some representative examples are presented to show this
property and indicate its application for shape deformation.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Convex Parametrization of a New Class of Universal Kernel Functions for use in Kernel Learning | 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 | 0 | 0 | 1 | 0 | 0 |
Hessian-based Analysis of Large Batch Training and Robustness to Adversaries | 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 | 0 | 1 | 0 | 0 |
Sparse-Group Bayesian Feature Selection Using Expectation Propagation for Signal Recovery and Network Reconstruction | We present a Bayesian method for feature selection in the presence of
grouping information with sparsity on the between- and within group level.
Instead of using a stochastic algorithm for parameter inference, we employ
expectation propagation, which is a deterministic and fast algorithm. Available
methods for feature selection in the presence of grouping information have a
number of short-comings: on one hand, lasso methods, while being fast,
underestimate the regression coefficients and do not make good use of the
grouping information, and on the other hand, Bayesian approaches, while
accurate in parameter estimation, often rely on the stochastic and slow Gibbs
sampling procedure to recover the parameters, rendering them infeasible e.g.
for gene network reconstruction. Our approach of a Bayesian sparse-group
framework with expectation propagation enables us to not only recover accurate
parameter estimates in signal recovery problems, but also makes it possible to
apply this Bayesian framework to large-scale network reconstruction problems.
The presented method is generic but in terms of application we focus on gene
regulatory networks. We show on simulated and experimental data that the method
constitutes a good choice for network reconstruction regarding the number of
correctly selected features, prediction on new data and reasonable computing
time.
| 0 | 0 | 0 | 1 | 0 | 0 |
A Simple, Fast and Fully Automated Approach for Midline Shift Measurement on Brain Computed Tomography | Brain CT has become a standard imaging tool for emergent evaluation of brain
condition, and measurement of midline shift (MLS) is one of the most important
features to address for brain CT assessment. We present a simple method to
estimate MLS and propose a new alternative parameter to MLS: the ratio of MLS
over the maximal width of intracranial region (MLS/ICWMAX). Three neurosurgeons
and our automated system were asked to measure MLS and MLS/ICWMAX in the same
sets of axial CT images obtained from 41 patients admitted to ICU under
neurosurgical service. A weighted midline (WML) was plotted based on individual
pixel intensities, with higher weighted given to the darker portions. The MLS
could then be measured as the distance between the WML and ideal midline (IML)
near the foramen of Monro. The average processing time to output an automatic
MLS measurement was around 10 seconds. Our automated system achieved an overall
accuracy of 90.24% when the CT images were calibrated automatically, and
performed better when the calibrations of head rotation were done manually
(accuracy: 92.68%). MLS/ICWMAX and MLS both gave results in same confusion
matrices and produced similar ROC curve results. We demonstrated a simple, fast
and accurate automated system of MLS measurement and introduced a new parameter
(MLS/ICWMAX) as a good alternative to MLS in terms of estimating the degree of
brain deformation, especially when non-DICOM images (e.g. JPEG) are more easily
accessed.
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Anisotropic Dielectric Relaxation in Single Crystal H$_{2}$O Ice Ih from 80-250 K | Three properties of the dielectric relaxation in ultra-pure single
crystalline H$_{2}$O ice Ih were probed at temperatures between 80-250 K; the
thermally stimulated depolarization current, static electrical conductivity,
and dielectric relaxation time. The measurements were made with a guarded
parallel-plate capacitor constructed of fused quartz with Au electrodes. The
data agree with relaxation-based models and provide for the determination of
activation energies, which suggest that relaxation in ice is dominated by
Bjerrum defects below 140 K. Furthermore, anisotropy in the dielectric
relaxation data reveals that molecular reorientations along the
crystallographic $c$-axis are energetically favored over those along the
$a$-axis between 80-140 K. These results lend support for the postulate of a
shared origin between the dielectric relaxation dynamics and the thermodynamic
partial proton-ordering in ice near 100 K, and suggest a preference for
ordering along the $c$-axis.
| 0 | 1 | 0 | 0 | 0 | 0 |
Design of Improved Quasi-Cyclic Protograph-Based Raptor-Like LDPC Codes for Short Block-Lengths | 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 | 0 | 0 |
Comparing the Finite-Time Performance of Simulation-Optimization Algorithms | 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 |
On the Constituent Attributes of Software and Organisational Resilience | Our societies are increasingly dependent on services supplied by computers &
their software. New technology only exacerbates this dependence by increasing
the number, performance, and degree of autonomy and inter-connectivity of
software-empowered computers and cyber-physical "things", which translates into
unprecedented scenarios of interdependence. As a consequence, guaranteeing the
persistence-of-identity of individual & collective software systems and
software-backed organisations becomes an important prerequisite toward
sustaining the safety, security, & quality of the computer services supporting
human societies. Resilience is the term used to refer to the ability of a
system to retain its functional and non-functional identity. In this article we
conjecture that a better understanding of resilience may be reached by
decomposing it into ancillary constituent properties, the same way as a better
insight in system dependability was obtained by breaking it down into
sub-properties. 3 of the main sub-properties of resilience proposed here refer
respectively to the ability to perceive environmental changes; understand the
implications introduced by those changes; and plan & enact adjustments intended
to improve the system-environment fit. A fourth property characterises the way
the above abilities manifest themselves in computer systems. The 4 properties
are then analyzed in 3 families of case studies, each consisting of 3 software
systems that embed different resilience methods. Our major conclusion is that
reasoning in terms of resilience sub-properties may help revealing the
characteristics and limitations of classic methods and tools meant to achieve
system and organisational resilience. We conclude by suggesting that our method
may prelude to meta-resilient systems -- systems, that is, able to adjust
optimally their own resilience with respect to changing environmental
conditions.
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Borcherds-Bozec algebras, root multiplicities and the Schofield construction | 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.
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Pressure-induced spin pairing transition of Fe$^{3+}$ in oxygen octahedra | 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.
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LSH on the Hypercube Revisited | 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 | 0 | 0 | 0 | 0 |
A Novel Metamaterial-Inspired RF-coil for Preclinical Dual-Nuclei MRI | In this paper we propose, design and test a new dual-nuclei RF-coil inspired
by wire metamaterial structures. The coil operates due to resonant excitation
of hybridized eigenmodes in multimode flat periodic structures comprising
several coupled thin metal strips. It was shown that the field distribution of
the coil (i.e. penetration depth) can be controlled independently at two
different Larmor frequencies by selecting a proper eigenmode in each of two
mutually orthogonal periodic structures. The proposed coil requires no lumped
capacitors for tuning and matching. In order to demonstrate the performance of
the new design, an experimental preclinical coil for $^{19}$F/$^{1}$H imaging
of small animals at 7.05T was engineered and tested on a homogeneous liquid
phantom and in-vivo. The presented results demonstrate that the coil was well
tuned and matched simultaneously at two Larmor frequencies and capable of image
acquisition with both the nuclei reaching large homogeneity area along with a
sufficient signal-to-noise ratio. In an in-vivo experiment it has been shown
that without retuning the setup it was possible to obtain anatomical $^{1}$H
images of a mouse under anesthesia consecutively with $^{19}$F images of a tiny
tube filled with a fluorine-containing liquid and attached to the body of the
mouse.
| 0 | 1 | 0 | 0 | 0 | 0 |
A biofilm and organomineralisation model for the growth and limiting size of ooids | 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 |
Spectral Filtering for General Linear Dynamical Systems | 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 |
Markov $L_2$-inequality with the Laguerre weight | Let $w_\alpha(t) := t^{\alpha}\,e^{-t}$, where $\alpha > -1$, be the Laguerre
weight function, and let $\|\cdot\|_{w_\alpha}$ be the associated $L_2$-norm,
$$ \|f\|_{w_\alpha} = \left\{\int_{0}^{\infty} |f(x)|^2
w_\alpha(x)\,dx\right\}^{1/2}\,. $$ By $\mathcal{P}_n$ we denote the set of
algebraic polynomials of degree $\le n$.
We study the best constant $c_n(\alpha)$ in the Markov inequality in this
norm $$ \|p_n'\|_{w_\alpha} \le c_n(\alpha) \|p_n\|_{w_\alpha}\,,\qquad p_n \in
\mathcal{P}_n\,, $$ namely the constant $$ c_n(\alpha) := \sup_{p_n \in
\mathcal{P}_n} \frac{\|p_n'\|_{w_\alpha}}{\|p_n\|_{w_\alpha}}\,. $$ We derive
explicit lower and upper bounds for the Markov constant $c_n(\alpha)$, as well
as for the asymptotic Markov constant $$
c(\alpha)=\lim_{n\rightarrow\infty}\frac{c_n(\alpha)}{n}\,. $$
| 0 | 0 | 1 | 0 | 0 | 0 |
Intrusion Prevention and Detection in Grid Computing - The ALICE Case | Grids allow users flexible on-demand usage of computing resources through
remote communication networks. A remarkable example of a Grid in High Energy
Physics (HEP) research is used in the ALICE experiment at European Organization
for Nuclear Research CERN. Physicists can submit jobs used to process the huge
amount of particle collision data produced by the Large Hadron Collider (LHC).
Grids face complex security challenges. They are interesting targets for
attackers seeking for huge computational resources. Since users can execute
arbitrary code in the worker nodes on the Grid sites, special care should be
put in this environment. Automatic tools to harden and monitor this scenario
are required. Currently, there is no integrated solution for such requirement.
This paper describes a new security framework to allow execution of job
payloads in a sandboxed context. It also allows process behavior monitoring to
detect intrusions, even when new attack methods or zero day vulnerabilities are
exploited, by a Machine Learning approach. We plan to implement the proposed
framework as a software prototype that will be tested as a component of the
ALICE Grid middleware.
| 1 | 0 | 0 | 0 | 0 | 0 |
Evolution-Preserving Dense Trajectory Descriptors | 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 |
Multilingual and Cross-lingual Timeline Extraction | In this paper we present an approach to extract ordered timelines of events,
their participants, locations and times from a set of multilingual and
cross-lingual data sources. Based on the assumption that event-related
information can be recovered from different documents written in different
languages, we extend the Cross-document Event Ordering task presented at
SemEval 2015 by specifying two new tasks for, respectively, Multilingual and
Cross-lingual Timeline Extraction. We then develop three deterministic
algorithms for timeline extraction based on two main ideas. First, we address
implicit temporal relations at document level since explicit time-anchors are
too scarce to build a wide coverage timeline extraction system. Second, we
leverage several multilingual resources to obtain a single, inter-operable,
semantic representation of events across documents and across languages. The
result is a highly competitive system that strongly outperforms the current
state-of-the-art. Nonetheless, further analysis of the results reveals that
linking the event mentions with their target entities and time-anchors remains
a difficult challenge. The systems, resources and scorers are freely available
to facilitate its use and guarantee the reproducibility of results.
| 1 | 0 | 0 | 0 | 0 | 0 |
Mixture modeling on related samples by $ψ$-stick breaking and kernel perturbation | There has been great interest recently in applying nonparametric kernel
mixtures in a hierarchical manner to model multiple related data samples
jointly. In such settings several data features are commonly present: (i) the
related samples often share some, if not all, of the mixture components but
with differing weights, (ii) only some, not all, of the mixture components vary
across the samples, and (iii) often the shared mixture components across
samples are not aligned perfectly in terms of their location and spread, but
rather display small misalignments either due to systematic cross-sample
difference or more often due to uncontrolled, extraneous causes. Properly
incorporating these features in mixture modeling will enhance the efficiency of
inference, whereas ignoring them not only reduces efficiency but can jeopardize
the validity of the inference due to issues such as confounding. We introduce
two techniques for incorporating these features in modeling related data
samples using kernel mixtures. The first technique, called $\psi$-stick
breaking, is a joint generative process for the mixing weights through the
breaking of both a stick shared by all the samples for the components that do
not vary in size across samples and an idiosyncratic stick for each sample for
those components that do vary in size. The second technique is to imbue random
perturbation into the kernels, thereby accounting for cross-sample
misalignment. These techniques can be used either separately or together in
both parametric and nonparametric kernel mixtures. We derive efficient Bayesian
inference recipes based on MCMC sampling for models featuring these techniques,
and illustrate their work through both simulated data and a real flow cytometry
data set in prediction/estimation, cross-sample calibration, and testing
multi-sample differences.
| 0 | 0 | 0 | 1 | 0 | 0 |
Optimal segmentation of directed graph and the minimum number of feedback arcs | 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 |
Ensemble of Neural Classifiers for Scoring Knowledge Base Triples | 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 |
A Game-Theoretic Data-Driven Approach for Pseudo-Measurement Generation in Distribution System State Estimation | In this paper, we present an efficient computational framework with the
purpose of generating weighted pseudo-measurements to improve the quality of
Distribution System State Estimation (DSSE) and provide observability with
Advanced Metering Infrastructure (AMI) against unobservable customers and
missing data. The proposed technique is based on a game-theoretic expansion of
Relevance Vector Machines (RVM). This platform is able to estimate the customer
power consumption data and quantify its uncertainty while reducing the
prohibitive computational burden of model training for large AMI datasets. To
achieve this objective, the large training set is decomposed and distributed
among multiple parallel learning entities. The resulting estimations from the
parallel RVMs are then combined using a game-theoretic model based on the idea
of repeated games with vector payoff. It is observed that through this approach
and by exploiting the seasonal changes in customers' behavior the accuracy of
pseudo-measurements can be considerably improved, while introducing robustness
against bad training data samples. The proposed pseudo-measurement generation
model is integrated into a DSSE using a closed-loop information system, which
takes advantage of a Branch Current State Estimator (BCSE) data to further
improve the performance of the designed machine learning framework. This method
has been tested on a practical distribution feeder model with smart meter data
for verification.
| 1 | 0 | 0 | 0 | 0 | 0 |
Nonsparse learning with latent variables | As a popular tool for producing meaningful and interpretable models,
large-scale sparse learning works efficiently when the underlying structures
are indeed or close to sparse. However, naively applying the existing
regularization methods can result in misleading outcomes due to model
misspecification. In particular, the direct sparsity assumption on coefficient
vectors has been questioned in real applications. Therefore, we consider
nonsparse learning with the conditional sparsity structure that the coefficient
vector becomes sparse after taking out the impacts of certain unobservable
latent variables. A new methodology of nonsparse learning with latent variables
(NSL) is proposed to simultaneously recover the significant observable
predictors and latent factors as well as their effects. We explore a common
latent family incorporating population principal components and derive the
convergence rates of both sample principal components and their score vectors
that hold for a wide class of distributions. With the properly estimated latent
variables, properties including model selection consistency and oracle
inequalities under various prediction and estimation losses are established for
the proposed methodology. Our new methodology and results are evidenced by
simulation and real data examples.
| 0 | 0 | 1 | 1 | 0 | 0 |
The Role of Network Analysis in Industrial and Applied Mathematics | Many problems in industry --- and in the social, natural, information, and
medical sciences --- involve discrete data and benefit from approaches from
subjects such as network science, information theory, optimization,
probability, and statistics. The study of networks is concerned explicitly with
connectivity between different entities, and it has become very prominent in
industrial settings, an importance that has intensified amidst the modern data
deluge. In this commentary, we discuss the role of network analysis in
industrial and applied mathematics, and we give several examples of network
science in industry. We focus, in particular, on discussing a
physical-applied-mathematics approach to the study of networks. We also discuss
several of our own collaborations with industry on projects in network
analysis.
| 1 | 1 | 0 | 0 | 0 | 0 |
Automated Detection, Exploitation, and Elimination of Double-Fetch Bugs using Modern CPU Features | 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 |
Fano resonances and fluorescence enhancement of a dipole emitter near a plasmonic nanoshell | We analytically study the spontaneous emission of a single optical dipole
emitter in the vicinity of a plasmonic nanoshell, based on the Lorenz-Mie
theory. We show that the fluorescence enhancement due to the coupling between
optical emitter and sphere can be tuned by the aspect ratio of the core-shell
nanosphere and by the distance between the quantum emitter and its surface. In
particular, we demonstrate that both the enhancement and quenching of the
fluorescence intensity are associated with plasmonic Fano resonances induced by
near- and far-field interactions. These Fano resonances have asymmetry
parameters whose signs depend on the orientation of the dipole with respect to
the spherical nanoshell. We also show that if the atomic dipole is oriented
tangentially to the nanoshell, the interaction exhibits saddle points in the
near-field energy flow. This results in a Lorentzian fluorescence enhancement
response in the near field and a Fano line-shape in the far field. The
signatures of this interaction may have interesting applications for sensing
the presence and the orientation of optical emitters in close proximity to
plasmonic nanoshells.
| 0 | 1 | 0 | 0 | 0 | 0 |
Unoriented Spectral Triples | 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 | 0 | 0 | 0 |
Gradient-enhanced kriging for high-dimensional problems | Surrogate models provide a low computational cost alternative to evaluating
expensive functions. The construction of accurate surrogate models with large
numbers of independent variables is currently prohibitive because it requires a
large number of function evaluations. Gradient-enhanced kriging has the
potential to reduce the number of function evaluations for the desired accuracy
when efficient gradient computation, such as an adjoint method, is available.
However, current gradient-enhanced kriging methods do not scale well with the
number of sampling points due to the rapid growth in the size of the
correlation matrix where new information is added for each sampling point in
each direction of the design space. They do not scale well with the number of
independent variables either due to the increase in the number of
hyperparameters that needs to be estimated. To address this issue, we develop a
new gradient-enhanced surrogate model approach that drastically reduced the
number of hyperparameters through the use of the partial-least squares method
that maintains accuracy. In addition, this method is able to control the size
of the correlation matrix by adding only relevant points defined through the
information provided by the partial-least squares method. To validate our
method, we compare the global accuracy of the proposed method with conventional
kriging surrogate models on two analytic functions with up to 100 dimensions,
as well as engineering problems of varied complexity with up to 15 dimensions.
We show that the proposed method requires fewer sampling points than
conventional methods to obtain the desired accuracy, or provides more accuracy
for a fixed budget of sampling points. In some cases, we get over 3 times more
accurate models than a bench of surrogate models from the literature, and also
over 3200 times faster than standard gradient-enhanced kriging models.
| 1 | 0 | 0 | 1 | 0 | 0 |
Contagion dynamics of extremist propaganda in social networks | 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 | 0 | 0 | 0 |
Estimate exponential memory decay in Hidden Markov Model and its applications | 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 | 0 | 0 | 1 | 0 | 0 |
Fabrication of antenna-coupled KID array for Cosmic Microwave Background detection | 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 |
The biglasso Package: A Memory- and Computation-Efficient Solver for Lasso Model Fitting with Big Data in R | Penalized regression models such as the lasso have been extensively applied
to analyzing high-dimensional data sets. However, due to memory limitations,
existing R packages like glmnet and ncvreg are not capable of fitting
lasso-type models for ultrahigh-dimensional, multi-gigabyte data sets that are
increasingly seen in many areas such as genetics, genomics, biomedical imaging,
and high-frequency finance. In this research, we implement an R package called
biglasso that tackles this challenge. biglasso utilizes memory-mapped files to
store the massive data on the disk, only reading data into memory when
necessary during model fitting, and is thus able to handle out-of-core
computation seamlessly. Moreover, it's equipped with newly proposed, more
efficient feature screening rules, which substantially accelerate the
computation. Benchmarking experiments show that our biglasso package, as
compared to existing popular ones like glmnet, is much more memory- and
computation-efficient. We further analyze a 31 GB real data set on a laptop
with only 16 GB RAM to demonstrate the out-of-core computation capability of
biglasso in analyzing massive data sets that cannot be accommodated by existing
R packages.
| 0 | 0 | 0 | 1 | 0 | 0 |
Run-and-Inspect Method for Nonconvex Optimization and Global Optimality Bounds for R-Local Minimizers | 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 |
Hierarchical Adversarially Learned Inference | 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 |
Demonstration of a quantum key distribution network in urban fibre-optic communication lines | We report the results of the implementation of a quantum key distribution
(QKD) network using standard fibre communication lines in Moscow. The developed
QKD network is based on the paradigm of trusted repeaters and allows a common
secret key to be generated between users via an intermediate trusted node. The
main feature of the network is the integration of the setups using two types of
encoding, i.e. polarisation encoding and phase encoding. One of the possible
applications of the developed QKD network is the continuous key renewal in
existing symmetric encryption devices with a key refresh time of up to 14 s.
| 1 | 0 | 0 | 0 | 0 | 0 |
ZhuSuan: A Library for Bayesian Deep Learning | In this paper we introduce ZhuSuan, a python probabilistic programming
library for Bayesian deep learning, which conjoins the complimentary advantages
of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike
existing deep learning libraries, which are mainly designed for deterministic
neural networks and supervised tasks, ZhuSuan is featured for its deep root
into Bayesian inference, thus supporting various kinds of probabilistic models,
including both the traditional hierarchical Bayesian models and recent deep
generative models. We use running examples to illustrate the probabilistic
programming on ZhuSuan, including Bayesian logistic regression, variational
auto-encoders, deep sigmoid belief networks and Bayesian recurrent neural
networks.
| 1 | 0 | 0 | 1 | 0 | 0 |
UntrimmedNets for Weakly Supervised Action Recognition and Detection | 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 |
Flatness of Minima in Random Inflationary Landscapes | We study the likelihood which relative minima of random polynomial potentials
support the slow-roll conditions for inflation. Consistent with
renormalizability and boundedness, the coefficients that appear in the
potential are chosen to be order one with respect to the energy scale at which
inflation transpires. Investigation of the single field case illustrates a
window in which the potentials satisfy the slow-roll conditions. When there are
two scalar fields, we find that the probability depends on the choice of
distribution for the coefficients. A uniform distribution yields a $0.05\%$
probability of finding a suitable minimum in the random potential whereas a
maximum entropy distribution yields a $0.1\%$ probability.
| 0 | 1 | 0 | 0 | 0 | 0 |
Deconvolutional Latent-Variable Model for Text Sequence Matching | 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 |
Magneto-elastic coupling model of deformable anisotropic superconductors | 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 |
Sparse Phase Retrieval via Sparse PCA Despite Model Misspecification: A Simplified and Extended Analysis | We consider the problem of high-dimensional misspecified phase retrieval.
This is where we have an $s$-sparse signal vector $\mathbf{x}_*$ in
$\mathbb{R}^n$, which we wish to recover using sampling vectors
$\textbf{a}_1,\ldots,\textbf{a}_m$, and measurements $y_1,\ldots,y_m$, which
are related by the equation $f(\left<\textbf{a}_i,\textbf{x}_*\right>) = y_i$.
Here, $f$ is an unknown link function satisfying a positive correlation with
the quadratic function. This problem was analyzed in a recent paper by Neykov,
Wang and Liu, who provided recovery guarantees for a two-stage algorithm with
sample complexity $m = O(s^2\log n)$. In this paper, we show that the first
stage of their algorithm suffices for signal recovery with the same sample
complexity, and extend the analysis to non-Gaussian measurements. Furthermore,
we show how the algorithm can be generalized to recover a signal vector
$\textbf{x}_*$ efficiently given geometric prior information other than
sparsity.
| 1 | 0 | 1 | 0 | 0 | 0 |
Convex Optimization with Unbounded Nonconvex Oracles using Simulated Annealing | We consider the problem of minimizing a convex objective function $F$ when
one can only evaluate its noisy approximation $\hat{F}$. Unless one assumes
some structure on the noise, $\hat{F}$ may be an arbitrary nonconvex function,
making the task of minimizing $F$ intractable. To overcome this, prior work has
often focused on the case when $F(x)-\hat{F}(x)$ is uniformly-bounded. In this
paper we study the more general case when the noise has magnitude $\alpha F(x)
+ \beta$ for some $\alpha, \beta > 0$, and present a polynomial time algorithm
that finds an approximate minimizer of $F$ for this noise model. Previously,
Markov chains, such as the stochastic gradient Langevin dynamics, have been
used to arrive at approximate solutions to these optimization problems.
However, for the noise model considered in this paper, no single temperature
allows such a Markov chain to both mix quickly and concentrate near the global
minimizer. We bypass this by combining "simulated annealing" with the
stochastic gradient Langevin dynamics, and gradually decreasing the temperature
of the chain in order to approach the global minimizer. As a corollary one can
approximately minimize a nonconvex function that is close to a convex function;
however, the closeness can deteriorate as one moves away from the optimum.
| 1 | 0 | 0 | 1 | 0 | 0 |
Online $^{222}$Rn removal by cryogenic distillation in the XENON100 experiment | We describe the purification of xenon from traces of the radioactive noble
gas radon using a cryogenic distillation column. The distillation column is
integrated into the gas purification loop of the XENON100 detector for online
radon removal. This enabled us to significantly reduce the constant $^{222}$Rn
background originating from radon emanation. After inserting an auxiliary
$^{222}$Rn emanation source in the gas loop, we determined a radon reduction
factor of R > 27 (95% C.L.) for the distillation column by monitoring the
$^{222}$Rn activity concentration inside the XENON100 detector.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Kite Graph is Determined by Its Adjacency Spectrum | The Kite graph $Kite_{p}^{q}$ is obtained by appending the complete graph
$K_{p}$ to a pendant vertex of the path $P_{q}$. In this paper, the kite graph
is proved to be determined by the spectrum of its adjacency matrix.
| 0 | 0 | 1 | 0 | 0 | 0 |
Matchability of heterogeneous networks pairs | 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 |
Visual Progression Analysis of Student Records Data | University curriculum, both on a campus level and on a per-major level, are
affected in a complex way by many decisions of many administrators and faculty
over time. As universities across the United States share an urgency to
significantly improve student success and success retention, there is a
pressing need to better understand how the student population is progressing
through the curriculum, and how to provide better supporting infrastructure and
refine the curriculum for the purpose of improving student outcomes. This work
has developed a visual knowledge discovery system called eCamp that pulls
together a variety of populationscale data products, including student grades,
major descriptions, and graduation records. These datasets were previously
disconnected and only available to and maintained by independent campus
offices. The framework models and analyzes the multi-level relationships hidden
within these data products, and visualizes the student flow patterns through
individual majors as well as through a hierarchy of majors. These results
support analytical tasks involving student outcomes, student retention, and
curriculum design. It is shown how eCamp has revealed student progression
information that was previously unavailable.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with Confounding Correction | While linear mixed model (LMM) has shown a competitive performance in
correcting spurious associations raised by population stratification, family
structures, and cryptic relatedness, more challenges are still to be addressed
regarding the complex structure of genotypic and phenotypic data. For example,
geneticists have discovered that some clusters of phenotypes are more
co-expressed than others. Hence, a joint analysis that can utilize such
relatedness information in a heterogeneous data set is crucial for genetic
modeling.
We proposed the sparse graph-structured linear mixed model (sGLMM) that can
incorporate the relatedness information from traits in a dataset with
confounding correction. Our method is capable of uncovering the genetic
associations of a large number of phenotypes together while considering the
relatedness of these phenotypes. Through extensive simulation experiments, we
show that the proposed model outperforms other existing approaches and can
model correlation from both population structure and shared signals. Further,
we validate the effectiveness of sGLMM in the real-world genomic dataset on two
different species from plants and humans. In Arabidopsis thaliana data, sGLMM
behaves better than all other baseline models for 63.4% traits. We also discuss
the potential causal genetic variation of Human Alzheimer's disease discovered
by our model and justify some of the most important genetic loci.
| 1 | 0 | 0 | 1 | 0 | 0 |
Capacity Releasing Diffusion for Speed and Locality | 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 |
Two-term spectral asymptotics for the Dirichlet pseudo-relativistic kinetic energy operator on a bounded domain | 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 |
Exact Good-Turing characterization of the two-parameter Poisson-Dirichlet superpopulation model | 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 |
Uniform deviation and moment inequalities for random polytopes with general densities in arbitrary convex bodies | We prove an exponential deviation inequality for the convex hull of a finite
sample of i.i.d. random points with a density supported on an arbitrary convex
body in $\R^d$, $d\geq 2$. When the density is uniform, our result yields rate
optimal upper bounds for all the moments of the missing volume of the convex
hull, uniformly over all convex bodies of $\R^d$: We make no restrictions on
their volume, location in the space or smoothness of their boundary. After
extending an identity due to Efron, we also prove upper bounds for the moments
of the number of vertices of the random polytope. Surprisingly, these bounds do
not depend on the underlying density and we prove that the growth rates that we
obtain are tight in a certain sense.
| 0 | 0 | 1 | 1 | 0 | 0 |
On the Efficiency of Connection Charges---Part II: Integration of Distributed Energy Resources | This two-part paper addresses the design of retail electricity tariffs for
distribution systems with distributed energy resources (DERs). Part I presents
a framework to optimize an ex-ante two-part tariff for a regulated monopolistic
retailer who faces stochastic wholesale prices on the one hand and stochastic
demand on the other. In Part II, the integration of DERs is addressed by
analyzing their endogenous effect on the optimal two-part tariff and the
induced welfare gains. Two DER integration models are considered: (i) a
decentralized model involving behind-the-meter DERs in a net metering setting,
and (ii) a centralized model involving DERs integrated by the retailer. It is
shown that DERs integrated under either model can achieve the same social
welfare and the net-metering tariff structure is optimal. The retail prices
under both integration models are equal and reflect the expected wholesale
prices. The connection charges differ and are affected by the retailer's fixed
costs as well as the statistical dependencies between wholesale prices and
behind-the-meter DERs. In particular, the connection charge of the
decentralized model is generally higher than that of the centralized model. An
empirical analysis is presented to estimate the impact of DER on welfare
distribution and inter-class cross-subsidies using real price and demand data
and simulations. The analysis shows that, with the prevailing retail pricing
and net-metering, consumer welfare decreases with the level of DER integration.
Issues of cross-subsidy and practical drawbacks of decentralized integration
are also discussed.
| 0 | 0 | 1 | 0 | 0 | 0 |
Simplified Gating in Long Short-term Memory (LSTM) Recurrent Neural Networks | 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 |
Transition Jitter in Heat Assisted Magnetic Recording by Micromagnetic Simulation | 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 |
Complexity of human response delay in intermittent control: The case of virtual stick balancing | 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 |
Algebraic cycles on some special hyperkähler varieties | 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 |
On recurrence in G-spaces | We introduce and analyze the following general concept of recurrence. Let $G$
be a group and let $X$ be a G-space with the action $G\times X\longrightarrow
X$, $(g,x)\longmapsto gx$. For a family $\mathfrak{F}$ of subset of $X$ and
$A\in \mathfrak{F}$, we denote $\Delta_{\mathfrak{F}}(A)=\{g\in G: gB\subseteq
A$ for some $B\in \mathfrak{F}, \ B\subseteq A\}$, and say that a subset $R$ of
$G$ is $\mathfrak{F}$-recurrent if $R\bigcap \Delta_{\mathfrak{F}}
(A)\neq\emptyset$ for each $A\in \mathfrak{F}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Deep adversarial neural decoding | Here, we present a novel approach to solve the problem of reconstructing
perceived stimuli from brain responses by combining probabilistic inference
with deep learning. Our approach first inverts the linear transformation from
latent features to brain responses with maximum a posteriori estimation and
then inverts the nonlinear transformation from perceived stimuli to latent
features with adversarial training of convolutional neural networks. We test
our approach with a functional magnetic resonance imaging experiment and show
that it can generate state-of-the-art reconstructions of perceived faces from
brain activations.
| 1 | 0 | 0 | 1 | 0 | 0 |
Optimally Guarding 2-Reflex Orthogonal Polyhedra by Reflex Edge Guards | We study the problem of guarding an orthogonal polyhedron having reflex edges
in just two directions (as opposed to three) by placing guards on reflex edges
only.
We show that (r - g)/2 + 1 reflex edge guards are sufficient, where r is the
number of reflex edges in a given polyhedron and g is its genus. This bound is
tight for g=0. We thereby generalize a classic planar Art Gallery theorem of
O'Rourke, which states that the same upper bound holds for vertex guards in an
orthogonal polygon with r reflex vertices and g holes.
Then we give a similar upper bound in terms of m, the total number of edges
in the polyhedron. We prove that (m - 4)/8 + g reflex edge guards are
sufficient, whereas the previous best known bound was 11m/72 + g/6 - 1 edge
guards (not necessarily reflex).
We also discuss the setting in which guards are open (i.e., they are segments
without the endpoints), proving that the same results hold even in this more
challenging case.
Finally, we show how to compute guard locations in O(n log n) time.
| 1 | 0 | 0 | 0 | 0 | 0 |
Strongly regular decompositions and symmetric association schemes of a power of two | 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 |
Resilient Non-Submodular Maximization over Matroid Constraints | 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 |
Tests for comparing time-invariant and time-varying spectra based on the Anderson-Darling statistic | Based on periodogram-ratios of two univariate time series at different
frequency points, two tests are proposed for comparing their spectra. One is an
Anderson-Darling-like statistic for testing the equality of two time-invariant
spectra. The other is the maximum of Anderson-Darling-like statistics for
testing the equality of two spectra no matter that they are time-invariant and
time-varying. Both of two tests are applicable for independent or dependent
time series. Several simulation examples show that the proposed statistics
outperform those that are also based on periodogram-ratios but constructed by
the Pearson-like statistics.
| 0 | 0 | 0 | 1 | 0 | 0 |
Temperley-Lieb and Birman-Murakami-Wenzl like relations from multiplicity free semi-simple tensor system | In this article we consider conditions under which projection operators in
multiplicity free semi-simple tensor categories satisfy Temperley-Lieb like
relations. This is then used as a stepping stone to prove sufficient conditions
for obtaining a representation of the Birman-Murakami-Wenzl algebra from a
braided multiplicity free semi-simple tensor category. The results are found by
utalising the data of the categories. There is considerable overlap with the
results found in arXiv:1607.08908, where proofs are shown by manipulating
diagrams.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Nash Type result for Divergence Parabolic Equation related to Hormander's vector fields | 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 |
Wick order, spreadability and exchangeability for monotone commutation relations | We exhibit a Hamel basis for the concrete $*$-algebra $\mathfrak{M}_o$
associated to monotone commutation relations realised on the monotone Fock
space, mainly composed by Wick ordered words of annihilators and creators. We
apply such a result to investigate spreadability and exchangeability of the
stochastic processes arising from such commutation relations. In particular, we
show that spreadability comes from a monoidal action implementing a dissipative
dynamics on the norm closure $C^*$-algebra $\mathfrak{M} =
\overline{\mathfrak{M}_o}$. Moreover, we determine the structure of spreadable
and exchangeable monotone stochastic processes using their correspondence with
sp\-reading invariant and symmetric monotone states, respectively.
| 0 | 0 | 1 | 0 | 0 | 0 |
Visualizing Time-Varying Particle Flows with Diffusion Geometry | 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 |
Factorization tricks for LSTM networks | We present two simple ways of reducing the number of parameters and
accelerating the training of large Long Short-Term Memory (LSTM) networks: the
first one is "matrix factorization by design" of LSTM matrix into the product
of two smaller matrices, and the second one is partitioning of LSTM matrix, its
inputs and states into the independent groups. Both approaches allow us to
train large LSTM networks significantly faster to the near state-of the art
perplexity while using significantly less RNN parameters.
| 1 | 0 | 0 | 1 | 0 | 0 |
Pure Rough Mereology and Counting | 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 |
Relaxation of nonlinear elastic energies involving deformed configuration and applications to nematic elastomers | 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 |
A Scalable Framework for Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters with Weight and Workload Balancing | Deep Neural Networks (DNNs) have revolutionized numerous applications, but
the demand for ever more performance remains unabated. Scaling DNN computations
to larger clusters is generally done by distributing tasks in batch mode using
methods such as distributed synchronous SGD. Among the issues with this
approach is that to make the distributed cluster work with high utilization,
the workload distributed to each node must be large, which implies nontrivial
growth in the SGD mini-batch size.
In this paper, we propose a framework called FPDeep, which uses a hybrid of
model and layer parallelism to configure distributed reconfigurable clusters to
train DNNs. This approach has numerous benefits. First, the design does not
suffer from batch size growth. Second, novel workload and weight partitioning
leads to balanced loads of both among nodes. And third, the entire system is a
fine-grained pipeline. This leads to high parallelism and utilization and also
minimizes the time features need to be cached while waiting for
back-propagation. As a result, storage demand is reduced to the point where
only on-chip memory is used for the convolution layers. We evaluate FPDeep with
the Alexnet, VGG-16, and VGG-19 benchmarks. Experimental results show that
FPDeep has good scalability to a large number of FPGAs, with the limiting
factor being the FPGA-to-FPGA bandwidth. With 6 transceivers per FPGA, FPDeep
shows linearity up to 83 FPGAs. Energy efficiency is evaluated with respect to
GOPs/J. FPDeep provides, on average, 6.36x higher energy efficiency than
comparable GPU servers.
| 1 | 0 | 0 | 0 | 0 | 0 |
Toward Incorporation of Relevant Documents in word2vec | 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 |
Randomized Load Balancing on Networks with Stochastic Inputs | Iterative load balancing algorithms for indivisible tokens have been studied
intensively in the past. Complementing previous worst-case analyses, we study
an average-case scenario where the load inputs are drawn from a fixed
probability distribution. For cycles, tori, hypercubes and expanders, we obtain
almost matching upper and lower bounds on the discrepancy, the difference
between the maximum and the minimum load. Our bounds hold for a variety of
probability distributions including the uniform and binomial distribution but
also distributions with unbounded range such as the Poisson and geometric
distribution. For graphs with slow convergence like cycles and tori, our
results demonstrate a substantial difference between the convergence in the
worst- and average-case. An important ingredient in our analysis is new upper
bound on the t-step transition probability of a general Markov chain, which is
derived by invoking the evolving set process.
| 1 | 0 | 0 | 0 | 0 | 0 |
The classification of Lagrangians nearby the Whitney immersion | The Whitney immersion is a Lagrangian sphere inside the four-dimensional
symplectic vector space which has a single transverse double point of
self-intersection index $+1.$ This Lagrangian also arises as the Weinstein
skeleton of the complement of a binodal cubic curve inside the projective
plane, and the latter Weinstein manifold is thus the `standard' neighbourhood
of Lagrangian immersions of this type. We classify the Lagrangians inside such
a neighbourhood which are homologous to the Whitney immersion, and which either
are embedded or immersed with a single double point; they are shown to be
Hamiltonian isotopic to either product tori, Chekanov tori, or rescalings of
the Whitney immersion.
| 0 | 0 | 1 | 0 | 0 | 0 |
Simulation study of energy resolution, position resolution and $π^0$-$γ$ separation of a sampling electromagnetic calorimeter at high energies | A simulation study of energy resolution, position resolution, and
$\pi^0$-$\gamma$ separation using multivariate methods of a sampling
calorimeter is presented. As a realistic example, the geometry of the
calorimeter is taken from the design geometry of the Shashlik calorimeter which
was considered as a candidate for CMS endcap for the phase II of LHC running.
The methods proposed in this paper can be easily adapted to various geometrical
layouts of a sampling calorimeter. Energy resolution is studied for different
layouts and different absorber-scintillator combinations of the Shashlik
detector. It is shown that a boosted decision tree using fine grained
information of the calorimeter can perform three times better than a cut-based
method for separation of $\pi^0$ from $\gamma$ over a large energy range of 20
GeV-200 GeV.
| 0 | 1 | 0 | 0 | 0 | 0 |
Multi-Round Influence Maximization (Extended Version) | 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 |
Generalisation dynamics of online learning in over-parameterised neural networks | 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 |
Nonparametric Testing for Differences in Electricity Prices: The Case of the Fukushima Nuclear Accident | 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 |
Dynamic coupling of ferromagnets via spin Hall magnetoresistance | 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 |
Exact Combinatorial Inference for Brain Images | The permutation test is known as the exact test procedure in statistics.
However, often it is not exact in practice and only an approximate method since
only a small fraction of every possible permutation is generated. Even for a
small sample size, it often requires to generate tens of thousands
permutations, which can be a serious computational bottleneck. In this paper,
we propose a novel combinatorial inference procedure that enumerates all
possible permutations combinatorially without any resampling. The proposed
method is validated against the standard permutation test in simulation studies
with the ground truth. The method is further applied in twin DTI study in
determining the genetic contribution of the minimum spanning tree of the
structural brain connectivity.
| 0 | 0 | 0 | 1 | 1 | 0 |
Laser annealing heals radiation damage in avalanche photodiodes | Avalanche photodiodes (APDs) are a practical option for space-based quantum
communications requiring single-photon detection. However, radiation damage to
APDs significantly increases their dark count rates and reduces their useful
lifetimes in orbit. We show that high-power laser annealing of irradiated APDs
of three different models (Excelitas C30902SH, Excelitas SLiK, and Laser
Components SAP500S2) heals the radiation damage and substantially restores low
dark count rates. Of nine samples, the maximum dark count rate reduction factor
varies between 5.3 and 758 when operating at minus 80 degrees Celsius. The
illumination power to reach these reduction factors ranges from 0.8 to 1.6 W.
Other photon detection characteristics, such as photon detection efficiency,
timing jitter, and afterpulsing probability, remain mostly unaffected. These
results herald a promising method to extend the lifetime of a quantum satellite
equipped with APDs.
| 0 | 1 | 0 | 0 | 0 | 0 |
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification | We are interested in the development of surrogate models for uncertainty
quantification and propagation in problems governed by stochastic PDEs using a
deep convolutional encoder-decoder network in a similar fashion to approaches
considered in deep learning for image-to-image regression tasks. Since normal
neural networks are data intensive and cannot provide predictive uncertainty,
we propose a Bayesian approach to convolutional neural nets. A recently
introduced variational gradient descent algorithm based on Stein's method is
scaled to deep convolutional networks to perform approximate Bayesian inference
on millions of uncertain network parameters. This approach achieves state of
the art performance in terms of predictive accuracy and uncertainty
quantification in comparison to other approaches in Bayesian neural networks as
well as techniques that include Gaussian processes and ensemble methods even
when the training data size is relatively small. To evaluate the performance of
this approach, we consider standard uncertainty quantification benchmark
problems including flow in heterogeneous media defined in terms of limited
data-driven permeability realizations. The performance of the surrogate model
developed is very good even though there is no underlying structure shared
between the input (permeability) and output (flow/pressure) fields as is often
the case in the image-to-image regression models used in computer vision
problems. Studies are performed with an underlying stochastic input
dimensionality up to $4,225$ where most other uncertainty quantification
methods fail. Uncertainty propagation tasks are considered and the predictive
output Bayesian statistics are compared to those obtained with Monte Carlo
estimates.
| 0 | 0 | 0 | 1 | 0 | 0 |
A symmetric monoidal and equivariant Segal infinite loop space machine | 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 |
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