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Yangian Symmetry and Integrability of Planar N=4 Super-Yang-Mills Theory | In this letter we establish Yangian symmetry of planar N=4 super-Yang-Mills
theory. We prove that the classical equations of motion of the model close onto
themselves under the action of Yangian generators. Moreover we propose an
off-shell extension of our statement which is equivalent to the invariance of
the action and prove that it is exactly satisfied. We assert that our
relationship serves as a criterion for integrability in planar gauge theories
by explicitly checking that it applies to integrable ABJM theory but not to
non-integrable N=1 super-Yang-Mills theory.
| 0 | 0 | 1 | 0 | 0 | 0 |
DaMaSCUS: The Impact of Underground Scatterings on Direct Detection of Light Dark Matter | Conventional dark matter direct detection experiments set stringent
constraints on dark matter by looking for elastic scattering events between
dark matter particles and nuclei in underground detectors. However these
constraints weaken significantly in the sub-GeV mass region, simply because
light dark matter does not have enough energy to trigger detectors regardless
of the dark matter-nucleon scattering cross section. Even if future experiments
lower their energy thresholds, they will still be blind to parameter space
where dark matter particles interact with nuclei strongly enough that they lose
enough energy and become unable to cause a signal above the experimental
threshold by the time they reach the underground detector. Therefore in case
dark matter is in the sub-GeV region and strongly interacting, possible
underground scatterings of dark matter with terrestrial nuclei must be taken
into account because they affect significantly the recoil spectra and event
rates, regardless of whether the experiment probes DM via DM-nucleus or
DM-electron interaction. To quantify this effect we present the publicly
available Dark Matter Simulation Code for Underground Scatterings (DaMaSCUS), a
Monte Carlo simulator of DM trajectories through the Earth taking underground
scatterings into account. Our simulation allows the precise calculation of the
density and velocity distribution of dark matter at any detector of given depth
and location on Earth. The simulation can also provide the accurate recoil
spectrum in underground detectors as well as the phase and amplitude of the
diurnal modulation caused by this shadowing effect of the Earth, ultimately
relating the modulations expected in different detectors, which is important to
decisively conclude if a diurnal modulation is due to dark matter or an
irrelevant background.
| 0 | 1 | 0 | 0 | 0 | 0 |
Allocation strategies for high fidelity models in the multifidelity regime | We propose a novel approach to allocating resources for expensive simulations
of high fidelity models when used in a multifidelity framework. Allocation
decisions that distribute computational resources across several simulation
models become extremely important in situations where only a small number of
expensive high fidelity simulations can be run. We identify this allocation
decision as a problem in optimal subset selection, and subsequently regularize
this problem so that solutions can be computed. Our regularized formulation
yields a type of group lasso problem that has been studied in the literature to
accomplish subset selection. Our numerical results compare performance of
algorithms that solve the group lasso problem for algorithmic allocation
against a variety of other strategies, including those based on classical
linear algebraic pivoting routines and those derived from more modern machine
learning-based methods. We demonstrate on well known synthetic problems and
more difficult real-world simulations that this group lasso solution to the
relaxed optimal subset selection problem performs better than the alternatives.
| 1 | 0 | 0 | 0 | 0 | 0 |
Simplicial Closure and higher-order link prediction | Networks provide a powerful formalism for modeling complex systems by using a
model of pairwise interactions. But much of the structure within these systems
involves interactions that take place among more than two nodes at once; for
example, communication within a group rather than person-to person,
collaboration among a team rather than a pair of coauthors, or biological
interaction between a set of molecules rather than just two. Such higher-order
interactions are ubiquitous, but their empirical study has received limited
attention, and little is known about possible organizational principles of such
structures. Here we study the temporal evolution of 19 datasets with explicit
accounting for higher-order interactions. We show that there is a rich variety
of structure in our datasets but datasets from the same system types have
consistent patterns of higher-order structure. Furthermore, we find that tie
strength and edge density are competing positive indicators of higher-order
organization, and these trends are consistent across interactions involving
differing numbers of nodes. To systematically further the study of theories for
such higher-order structures, we propose higher-order link prediction as a
benchmark problem to assess models and algorithms that predict higher-order
structure. We find a fundamental differences from traditional pairwise link
prediction, with a greater role for local rather than long-range information in
predicting the appearance of new interactions.
| 1 | 0 | 0 | 1 | 0 | 0 |
Basic concepts and tools for the Toki Pona minimal and constructed language: description of the language and main issues; analysis of the vocabulary; text synthesis and syntax highlighting; Wordnet synsets | A minimal constructed language (conlang) is useful for experiments and
comfortable for making tools. The Toki Pona (TP) conlang is minimal both in the
vocabulary (with only 14 letters and 124 lemmas) and in the (about) 10 syntax
rules. The language is useful for being a used and somewhat established minimal
conlang with at least hundreds of fluent speakers. This article exposes current
concepts and resources for TP, and makes available Python (and Vim) scripted
routines for the analysis of the language, synthesis of texts, syntax
highlighting schemes, and the achievement of a preliminary TP Wordnet. Focus is
on the analysis of the basic vocabulary, as corpus analyses were found. The
synthesis is based on sentence templates, relates to context by keeping track
of used words, and renders larger texts by using a fixed number of phonemes
(e.g. for poems) and number of sentences, words and letters (e.g. for
paragraphs). Syntax highlighting reflects morphosyntactic classes given in the
official dictionary and different solutions are described and implemented in
the well-established Vim text editor. The tentative TP Wordnet is made
available in three patterns of relations between synsets and word lemmas. In
summary, this text holds potentially novel conceptualizations about, and tools
and results in analyzing, synthesizing and syntax highlighting the TP language.
| 1 | 0 | 0 | 0 | 0 | 0 |
Spectral Methods for Nonparametric Models | Nonparametric models are versatile, albeit computationally expensive, tool
for modeling mixture models. In this paper, we introduce spectral methods for
the two most popular nonparametric models: the Indian Buffet Process (IBP) and
the Hierarchical Dirichlet Process (HDP). We show that using spectral methods
for the inference of nonparametric models are computationally and statistically
efficient. In particular, we derive the lower-order moments of the IBP and the
HDP, propose spectral algorithms for both models, and provide reconstruction
guarantees for the algorithms. For the HDP, we further show that applying
hierarchical models on dataset with hierarchical structure, which can be solved
with the generalized spectral HDP, produces better solutions to that of flat
models regarding likelihood performance.
| 1 | 0 | 0 | 1 | 0 | 0 |
Setting the threshold for high throughput detectors: A mathematical approach for ensembles of dynamic, heterogeneous, probabilistic anomaly detectors | Anomaly detection (AD) has garnered ample attention in security research, as
such algorithms complement existing signature-based methods but promise
detection of never-before-seen attacks. Cyber operations manage a high volume
of heterogeneous log data; hence, AD in such operations involves multiple
(e.g., per IP, per data type) ensembles of detectors modeling heterogeneous
characteristics (e.g., rate, size, type) often with adaptive online models
producing alerts in near real time. Because of high data volume, setting the
threshold for each detector in such a system is an essential yet underdeveloped
configuration issue that, if slightly mistuned, can leave the system useless,
either producing a myriad of alerts and flooding downstream systems, or giving
none. In this work, we build on the foundations of Ferragut et al. to provide a
set of rigorous results for understanding the relationship between threshold
values and alert quantities, and we propose an algorithm for setting the
threshold in practice. Specifically, we give an algorithm for setting the
threshold of multiple, heterogeneous, possibly dynamic detectors completely a
priori, in principle. Indeed, if the underlying distribution of the incoming
data is known (closely estimated), the algorithm provides provably manageable
thresholds. If the distribution is unknown (e.g., has changed over time) our
analysis reveals how the model distribution differs from the actual
distribution, indicating a period of model refitting is necessary. We provide
empirical experiments showing the efficacy of the capability by regulating the
alert rate of a system with $\approx$2,500 adaptive detectors scoring over 1.5M
events in 5 hours. Further, we demonstrate on the real network data and
detection framework of Harshaw et al. the alternative case, showing how the
inability to regulate alerts indicates the detection model is a bad fit to the
data.
| 1 | 0 | 0 | 1 | 0 | 0 |
Photonic Loschmidt echo in binary waveguide lattices | Time reversal is one of the most intriguing yet elusive wave phenomenon of
major interest in different areas of classical and quantum physics. Time
reversal requires in principle to flip the sign of the Hamiltonian of the
system, leading to a revival of the initial state (Loschmidt echo). Here it is
shown that Loschmidt echo of photons can be observed in an optical setting
without resorting to reversal of the Hamiltonian. We consider photonic
propagation in a binary waveguide lattice and show that, by exchanging the two
sublattices after some propagation distance, a Loschmidt echo can be observed.
Examples of Loschmidt echoes for single photon and NOON states are given in
one- and two-dimensional waveguide lattices.
| 0 | 1 | 0 | 0 | 0 | 0 |
Proof Reduction of Fair Stuttering Refinement of Asynchronous Systems and Applications | We present a series of definitions and theorems demonstrating how to reduce
the requirements for proving system refinements ensuring containment of fair
stuttering runs. A primary result of the work is the ability to reduce the
requisite proofs on runs of a system of interacting state machines to a set of
definitions and checks on single steps of a small number of state machines
corresponding to the intuitive notions of freedom from starvation and deadlock.
We further refine the definitions to afford an efficient explicit-state
checking procedure in certain finite state cases. We demonstrate the proof
reduction on versions of the Bakery Algorithm.
| 1 | 0 | 0 | 0 | 0 | 0 |
Steady Galactic Dynamos and Observational Consequences I: Halo Magnetic Fields | We study the global consequences in the halos of spiral galaxies of the
steady, axially symmetric, mean field dynamo. We use the classical theory but
add the possibility of using the velocity field components as parameters in
addition to the helicity and diffusivity. The analysis is based on the simplest
version of the theory and uses scale-invariant solutions. The velocity field
(subject to restrictions) is a scale invariant field in a `pattern' frame, in
place of a full dynamical theory. The `pattern frame' of reference may either
be the systemic frame or some rigidly rotating spiral pattern frame. One type
of solution for the magnetic field yields off-axis, spirally wound, magnetic
field lines. These predict sign changes in the Faraday screen rotation measure
in every quadrant of the halo of an edge-on galaxy. Such rotation measure
oscillations have been observed in the CHANG-ES survey.
| 0 | 1 | 0 | 0 | 0 | 0 |
Efficient Decision Trees for Multi-class Support Vector Machines Using Entropy and Generalization Error Estimation | We propose new methods for Support Vector Machines (SVMs) using tree
architecture for multi-class classi- fication. In each node of the tree, we
select an appropriate binary classifier using entropy and generalization error
estimation, then group the examples into positive and negative classes based on
the selected classi- fier and train a new classifier for use in the
classification phase. The proposed methods can work in time complexity between
O(log2N) to O(N) where N is the number of classes. We compared the performance
of our proposed methods to the traditional techniques on the UCI machine
learning repository using 10-fold cross-validation. The experimental results
show that our proposed methods are very useful for the problems that need fast
classification time or problems with a large number of classes as the proposed
methods run much faster than the traditional techniques but still provide
comparable accuracy.
| 1 | 0 | 0 | 1 | 0 | 0 |
Semiclassical measures on hyperbolic surfaces have full support | We show that each limiting semiclassical measure obtained from a sequence of
eigenfunctions of the Laplacian on a compact hyperbolic surface is supported on
the entire cosphere bundle. The key new ingredient for the proof is the fractal
uncertainty principle, first formulated in [arXiv:1504.06589] and proved for
porous sets in [arXiv:1612.09040].
| 0 | 1 | 1 | 0 | 0 | 0 |
Assessment of algorithms for computing moist available potential energy | Atmospheric moist available potential energy (MAPE) has been traditionally
defined as the potential energy of a moist atmosphere relative to that of the
adiabatically sorted reference state defining a global potential energy
minimum. Finding such a reference state was recently shown to be a linear
assignment problem, and therefore exactly solvable. However, this is
computationally extremely expensive, so there has been much interest in
developing heuristic methods for computing MAPE in practice. Comparisons of the
accuracy of such approximate algorithms have so far been limited to a small
number of test cases; this work provides an assessment of the algorithms'
performance across a wide range of atmospheric soundings, in two different
locations. We determine that the divide-and-conquer algorithm is the best
suited to practical application, but suffers from the previously overlooked
shortcoming that it can produce a reference state with higher potential energy
than the actual state, resulting in a negative value of MAPE. Additionally, we
show that it is possible to construct an algorithm exploiting a theoretical
expression linking MAPE to Convective Available Potential Energy (CAPE)
previously derived by Kerry Emanuel. This approach has a similar accuracy to
existing approximate sorting algorithms, whilst providing greater insight into
the physical source of MAPE. In light of these results, we discuss how to make
progress towards constructing a satisfactory moist APE theory for the
atmosphere. We also outline a method for vectorising the adiabatic lifting of
moist air parcels, which increases the computational efficiency of algorithms
for calculating MAPE, and could be used for other applications such as
convection schemes.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Zero Knowledge Sumcheck and its Applications | Many seminal results in Interactive Proofs (IPs) use algebraic techniques
based on low-degree polynomials, the study of which is pervasive in theoretical
computer science. Unfortunately, known methods for endowing such proofs with
zero knowledge guarantees do not retain this rich algebraic structure.
In this work, we develop algebraic techniques for obtaining zero knowledge
variants of proof protocols in a way that leverages and preserves their
algebraic structure. Our constructions achieve unconditional (perfect) zero
knowledge in the Interactive Probabilistically Checkable Proof (IPCP) model of
Kalai and Raz [KR08] (the prover first sends a PCP oracle, then the prover and
verifier engage in an Interactive Proof in which the verifier may query the
PCP).
Our main result is a zero knowledge variant of the sumcheck protocol [LFKN92]
in the IPCP model. The sumcheck protocol is a key building block in many IPs,
including the protocol for polynomial-space computation due to Shamir [Sha92],
and the protocol for parallel computation due to Goldwasser, Kalai, and
Rothblum [GKR15]. A core component of our result is an algebraic commitment
scheme, whose hiding property is guaranteed by algebraic query complexity lower
bounds [AW09,JKRS09]. This commitment scheme can then be used to considerably
strengthen our previous work [BCFGRS16] that gives a sumcheck protocol with
much weaker zero knowledge guarantees, itself using algebraic techniques based
on algorithms for polynomial identity testing [RS05,BW04].
We demonstrate the applicability of our techniques by deriving zero knowledge
variants of well-known protocols based on algebraic techniques, including the
protocols of Shamir and of Goldwasser, Kalai, and Rothblum, as well as the
protocol of Babai, Fortnow, and Lund [BFL91].
| 1 | 0 | 0 | 0 | 0 | 0 |
Exact diagonalization and cluster mean-field study of triangular-lattice XXZ antiferromagnets near saturation | Quantum magnetic phases near the magnetic saturation of triangular-lattice
antiferromagnets with XXZ anisotropy have been attracting renewed interest
since it has been suggested that a nontrivial coplanar phase, called the
$\pi$-coplanar or $\Psi$ phase, could be stabilized by quantum effects in a
certain range of anisotropy parameter $J/J_z$ besides the well-known 0-coplanar
(known also as $V$) and umbrella phases. Recently, Sellmann $et$ $al$. [Phys.
Rev. B {\bf 91}, 081104(R) (2015)] claimed that the $\pi$-coplanar phase is
absent for $S=1/2$ from an exact-diagonalization analysis in the sector of the
Hilbert space with only three down-spins (three magnons). We first reconsider
and improve this analysis by taking into account several low-lying eigenvalues
and the associated eigenstates as a function of $J/J_z$ and by sensibly
increasing the system sizes (up to 1296 spins). A careful identification
analysis shows that the lowest eigenstate is a chirally antisymmetric
combination of finite-size umbrella states for $J/J_z\gtrsim 2.218$ while it
corresponds to a coplanar phase for $J/J_z\lesssim 2.218$. However, we
demonstrate that the distinction between 0-coplanar and $\pi$-coplanar phases
in the latter region is fundamentally impossible from the symmetry-preserving
finite-size calculations with fixed magnon number.} Therefore, we also perform
a cluster mean-field plus scaling analysis for small spins $S\leq 3/2$. The
obtained results, together with the previous large-$S$ analysis, indicate that
the $\pi$-coplanar phase exists for any $S$ except for the classical limit
($S\rightarrow \infty$) and the existence range in $J/J_z$ is largest in the
most quantum case of $S=1/2$.
| 0 | 1 | 0 | 0 | 0 | 0 |
Perturbed Proximal Descent to Escape Saddle Points for Non-convex and Non-smooth Objective Functions | We consider the problem of finding local minimizers in non-convex and
non-smooth optimization. Under the assumption of strict saddle points, positive
results have been derived for first-order methods. We present the first known
results for the non-smooth case, which requires different analysis and a
different algorithm.
| 1 | 0 | 0 | 1 | 0 | 0 |
Don't Panic! Better, Fewer, Syntax Errors for LR Parsers | Syntax errors are generally easy to fix for humans, but not for parsers, in
general, and LR parsers, in particular. Traditional 'panic mode' error
recovery, though easy to implement and applicable to any grammar, often leads
to a cascading chain of errors that drown out the original. More advanced error
recovery techniques suffer less from this problem but have seen little
practical use because their typical performance was seen as poor, their worst
case unbounded, and the repairs they reported arbitrary. In this paper we show
two generic error recovery algorithms that fix all three problems. First, our
algorithms are the first to report the complete set of possible repair
sequences for a given location, allowing programmers to select the one that
best fits their intention. Second, on a corpus of 200,000 real-world
syntactically invalid Java programs, we show that our best performing algorithm
is able to repair 98.71% of files within a cut-off of 0.5s. Furthermore, we are
also able to use the complete set of repair sequences to reduce the cascading
error problem even further than previous approaches. Our best performing
algorithm reports 442,252.0 error locations in the corpus to the user, while
the panic mode algorithm reports 980,848.0 error locations: in other words, our
algorithms reduce the cascading error problem by well over half.
| 1 | 0 | 0 | 0 | 0 | 0 |
Predicting Tomorrow's Headline using Today's Twitter Deliberations | Predicting the popularity of news article is a challenging task. Existing
literature mostly focused on article contents and polarity to predict
popularity. However, existing research has not considered the users' preference
towards a particular article. Understanding users' preference is an important
aspect for predicting the popularity of news articles. Hence, we consider the
social media data, from the Twitter platform, to address this research gap. In
our proposed model, we have considered the users' involvement as well as the
users' reaction towards an article to predict the popularity of the article. In
short, we are predicting tomorrow's headline by probing today's Twitter
discussion. We have considered 300 political news article from the New York
Post, and our proposed approach has outperformed other baseline models.
| 1 | 0 | 0 | 0 | 0 | 0 |
On a problem of Bharanedhar and Ponnusamy involving planar harmonic mappings | In this paper, we give a negative answer to a problem presented by
Bharanedhar and Ponnusamy (Rocky Mountain J. Math. 44: 753--777, 2014)
concerning univalency of a class of harmonic mappings. More precisely, we show
that for all values of the involved parameter, this class contains a
non-univalent function. Moreover, several results on a new subclass of
close-to-convex harmonic mappings, which is motivated by work of Ponnusamy and
Sairam Kaliraj (Mediterr. J. Math. 12: 647--665, 2015), are obtained.
| 0 | 0 | 1 | 0 | 0 | 0 |
Projecting UK Mortality using Bayesian Generalised Additive Models | Forecasts of mortality provide vital information about future populations,
with implications for pension and health-care policy as well as for decisions
made by private companies about life insurance and annuity pricing. Stochastic
mortality forecasts allow the uncertainty in mortality predictions to be taken
into consideration when making policy decisions and setting product prices.
Longer lifespans imply that forecasts of mortality at ages 90 and above will
become more important in such calculations.
This paper presents a Bayesian approach to the forecasting of mortality that
jointly estimates a Generalised Additive Model (GAM) for mortality for the
majority of the age-range and a parametric model for older ages where the data
are sparser. The GAM allows smooth components to be estimated for age, cohort
and age-specific improvement rates, together with a non-smoothed period effect.
Forecasts for the United Kingdom are produced using data from the Human
Mortality Database spanning the period 1961-2013. A metric that approximates
predictive accuracy under Leave-One-Out cross-validation is used to estimate
weights for the `stacking' of forecasts with different points of transition
between the GAM and parametric elements.
Mortality for males and females are estimated separately at first, but a
joint model allows the asymptotic limit of mortality at old ages to be shared
between sexes, and furthermore provides for forecasts accounting for
correlations in period innovations. The joint and single sex model forecasts
estimated using data from 1961-2003 are compared against observed data from
2004-2013 to facilitate model assessment.
| 0 | 0 | 0 | 1 | 0 | 0 |
The Case for Meta-Cognitive Machine Learning: On Model Entropy and Concept Formation in Deep Learning | Machine learning is usually defined in behaviourist terms, where external
validation is the primary mechanism of learning. In this paper, I argue for a
more holistic interpretation in which finding more probable, efficient and
abstract representations is as central to learning as performance. In other
words, machine learning should be extended with strategies to reason over its
own learning process, leading to so-called meta-cognitive machine learning. As
such, the de facto definition of machine learning should be reformulated in
these intrinsically multi-objective terms, taking into account not only the
task performance but also internal learning objectives. To this end, we suggest
a "model entropy function" to be defined that quantifies the efficiency of the
internal learning processes. It is conjured that the minimization of this model
entropy leads to concept formation. Besides philosophical aspects, some initial
illustrations are included to support the claims.
| 1 | 0 | 0 | 1 | 0 | 0 |
Bulk viscosity model for near-equilibrium acoustic wave attenuation | Acoustic wave attenuation due to vibrational and rotational molecular
relaxation, under simplifying assumptions of near-thermodynamic equilibrium and
absence of molecular dissociations, can be accounted for by specifying a bulk
viscosity coefficient $\mu_B$. In this paper, we propose a simple
frequency-dependent bulk viscosity model which, under such assumptions,
accurately captures wave attenuation rates from infrasonic to ultrasonic
frequencies in Navier--Stokes and lattice Boltzmann simulations. The proposed
model can be extended to any gas mixture for which molecular relaxation
timescales and attenuation measurements are available. The performance of the
model is assessed for air by varying the base temperature, pressure, relative
humidity $h_r$, and acoustic frequency. Since the vibrational relaxation
timescales of oxygen and nitrogen are a function of humidity, for certain
frequencies an intermediate value of $h_r$ can be found which maximizes
$\mu_B$. The contribution to bulk viscosity due to rotational relaxation is
verified to be a function of temperature, confirming recent findings in the
literature. While $\mu_B$ decreases with higher frequencies, its effects on
wave attenuation become more significant, as shown via a dimensionless
analysis. The proposed bulk viscosity model is designed for frequency-domain
linear acoustic formulations but is also extensible to time-domain simulations
of narrow-band frequency content flows.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Lagrangian fluctuation-dissipation relation for scalar turbulence, III. Turbulent Rayleigh-Bénard convection | A Lagrangian fluctuation-dissipation relation has been derived in a previous
work to describe the dissipation rate of advected scalars, both passive and
active, in wall-bounded flows. We apply this relation here to develop a
Lagrangian description of thermal dissipation in turbulent Rayleigh-Bénard
convection in a right-cylindrical cell of arbitrary cross-section, with either
imposed temperature difference or imposed heat-flux at the top and bottom
walls. We obtain an exact relation between the steady-state thermal dissipation
rate and the time for passive tracer particles released at the top or bottom
wall to mix to their final uniform value near those walls. We show that an
"ultimate regime" with the Nusselt-number scaling predicted by Spiegel (1971)
or, with a log-correction, by Kraichnan (1962) will occur at high Rayleigh
numbers, unless this near-wall mixing time is asymptotically much longer than
the free-fall time, or almost the large-scale circulation time. We suggest a
new criterion for an ultimate regime in terms of transition to turbulence of a
thermal "mixing zone", which is much wider than the standard thermal boundary
layer. Kraichnan-Spiegel scaling may, however, not hold if the intensity and
volume of thermal plumes decrease sufficiently rapidly with increasing Rayleigh
number. To help resolve this issue, we suggest a program to measure the
near-wall mixing time, which we argue is accessible both by laboratory
experiment and by numerical simulation.
| 0 | 1 | 0 | 0 | 0 | 0 |
Model Selection for Explosive Models | This paper examines the limit properties of information criteria (such as
AIC, BIC, HQIC) for distinguishing between the unit root model and the various
kinds of explosive models. The explosive models include the local-to-unit-root
model, the mildly explosive model and the regular explosive model. Initial
conditions with different order of magnitude are considered. Both the OLS
estimator and the indirect inference estimator are studied. It is found that
BIC and HQIC, but not AIC, consistently select the unit root model when data
come from the unit root model. When data come from the local-to-unit-root
model, both BIC and HQIC select the wrong model with probability approaching 1
while AIC has a positive probability of selecting the right model in the limit.
When data come from the regular explosive model or from the mildly explosive
model in the form of $1+n^{\alpha }/n$ with $\alpha \in (0,1)$, all three
information criteria consistently select the true model. Indirect inference
estimation can increase or decrease the probability for information criteria to
select the right model asymptotically relative to OLS, depending on the
information criteria and the true model. Simulation results confirm our
asymptotic results in finite sample.
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Deep Energy Estimator Networks | Density estimation is a fundamental problem in statistical learning. This
problem is especially challenging for complex high-dimensional data due to the
curse of dimensionality. A promising solution to this problem is given here in
an inference-free hierarchical framework that is built on score matching. We
revisit the Bayesian interpretation of the score function and the Parzen score
matching, and construct a multilayer perceptron with a scalable objective for
learning the energy (i.e. the unnormalized log-density), which is then
optimized with stochastic gradient descent. In addition, the resulting deep
energy estimator network (DEEN) is designed as products of experts. We present
the utility of DEEN in learning the energy, the score function, and in
single-step denoising experiments for synthetic and high-dimensional data. We
also diagnose stability problems in the direct estimation of the score function
that had been observed for denoising autoencoders.
| 0 | 0 | 0 | 1 | 0 | 0 |
A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis | Existing strategies for finite-armed stochastic bandits mostly depend on a
parameter of scale that must be known in advance. Sometimes this is in the form
of a bound on the payoffs, or the knowledge of a variance or subgaussian
parameter. The notable exceptions are the analysis of Gaussian bandits with
unknown mean and variance by Cowan and Katehakis [2015] and of uniform
distributions with unknown support [Cowan and Katehakis, 2015]. The results
derived in these specialised cases are generalised here to the non-parametric
setup, where the learner knows only a bound on the kurtosis of the noise, which
is a scale free measure of the extremity of outliers.
| 0 | 0 | 0 | 1 | 0 | 0 |
An Asynchronous Parallel Approach to Sparse Recovery | Asynchronous parallel computing and sparse recovery are two areas that have
received recent interest. Asynchronous algorithms are often studied to solve
optimization problems where the cost function takes the form $\sum_{i=1}^M
f_i(x)$, with a common assumption that each $f_i$ is sparse; that is, each
$f_i$ acts only on a small number of components of $x\in\mathbb{R}^n$. Sparse
recovery problems, such as compressed sensing, can be formulated as
optimization problems, however, the cost functions $f_i$ are dense with respect
to the components of $x$, and instead the signal $x$ is assumed to be sparse,
meaning that it has only $s$ non-zeros where $s\ll n$. Here we address how one
may use an asynchronous parallel architecture when the cost functions $f_i$ are
not sparse in $x$, but rather the signal $x$ is sparse. We propose an
asynchronous parallel approach to sparse recovery via a stochastic greedy
algorithm, where multiple processors asynchronously update a vector in shared
memory containing information on the estimated signal support. We include
numerical simulations that illustrate the potential benefits of our proposed
asynchronous method.
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Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples | State-of-the-art neural networks are vulnerable to adversarial examples; they
can easily misclassify inputs that are imperceptibly different than their
training and test data. In this work, we establish that the use of
cross-entropy loss function and the low-rank features of the training data have
responsibility for the existence of these inputs. Based on this observation, we
suggest that addressing adversarial examples requires rethinking the use of
cross-entropy loss function and looking for an alternative that is more suited
for minimization with low-rank features. In this direction, we present a
training scheme called differential training, which uses a loss function
defined on the differences between the features of points from opposite
classes. We show that differential training can ensure a large margin between
the decision boundary of the neural network and the points in the training
dataset. This larger margin increases the amount of perturbation needed to flip
the prediction of the classifier and makes it harder to find an adversarial
example with small perturbations. We test differential training on a binary
classification task with CIFAR-10 dataset and demonstrate that it radically
reduces the ratio of images for which an adversarial example could be found --
not only in the training dataset, but in the test dataset as well.
| 1 | 0 | 0 | 1 | 0 | 0 |
Parametrizing modified gravity for cosmological surveys | One of the challenges in testing gravity with cosmology is the vast freedom
opened when extending General Relativity. For linear perturbations, one
solution consists in using the Effective Field Theory of Dark Energy (EFT of
DE). Even then, the theory space is described in terms of a handful of free
functions of time. This needs to be reduced to a finite number of parameters to
be practical for cosmological surveys. We explore in this article how well
simple parametrizations, with a small number of parameters, can fit observables
computed from complex theories. Imposing the stability of linear perturbations
appreciably reduces the theory space we explore. We find that observables are
not extremely sensitive to short time-scale variations and that simple, smooth
parametrizations are usually sufficient to describe this theory space. Using
the Bayesian Information Criterion, we find that using two parameters for each
function (an amplitude and a power law index) is preferred over complex models
for 86% of our theory space.
| 0 | 1 | 0 | 0 | 0 | 0 |
Inductive Freeness of Ziegler's Canonical Multiderivations for Reflection Arrangements | Let $A$ be a free hyperplane arrangement. In 1989, Ziegler showed that the
restriction $A''$ of $A$ to any hyperplane endowed with the natural
multiplicity is then a free multiarrangement. We initiate a study of the
stronger freeness property of inductive freeness for these canonical free
multiarrangements and investigate them for the underlying class of reflection
arrangements.
More precisely, let $A = A(W)$ be the reflection arrangement of a complex
reflection group $W$. By work of Terao, each such reflection arrangement is
free. Thus so is Ziegler's canonical multiplicity on the restriction $A''$ of
$A$ to a hyperplane. We show that the latter is inductively free as a
multiarrangement if and only if $A''$ itself is inductively free.
| 0 | 0 | 1 | 0 | 0 | 0 |
Toward III-V/Si co-integration by controlling biatomic steps on hydrogenated Si(001) | The integration of III-V on silicon is still a hot topic as it will open up a
way to co-integrate Si CMOS logic with photonic vices. To reach this aim,
several hurdles should be solved, and more particularly the generation of
antiphase boundaries (APBs) at the III-V/Si(001) interface. Density functional
theory (DFT) has been used to demonstrate the existence of a double-layer steps
on nominal Si(001) which is formed during annealing under proper hydrogen
chemical potential. This phenomenon could be explained by the formation of
dimer vacancy lines which could be responsible for the preferential and
selective etching of one type of step leading to the double step surface
creation. To check this hypothesis, different experiments have been carried in
an industrial 300 mm MOCVD where the total pressure during the anneal step of
Si(001) surface has been varied. Under optimized conditions, an APBs-free GaAs
layer was grown on a nominal Si(001) surface paving the way for III-V
integration on silicon industrial platform.
| 0 | 1 | 0 | 0 | 0 | 0 |
Proceedings XVI Jornadas sobre Programación y Lenguajes | This volume contains a selection of the papers presented at the XVI Jornadas
sobre Programación y Lenguajes (PROLE 2016), held at Salamanca, Spain, during
September 14th-15th, 2016. Previous editions of the workshop were held in
Santander (2015), Cádiz (2014), Madrid (2013), Almería (2012), A Coruña
(2011), València (2010), San Sebastián (2009), Gijón (2008), Zaragoza
(2007), Sitges (2006), Granada (2005), Málaga (2004), Alicante (2003), El
Escorial (2002), and Almagro (2001). Programming languages provide a conceptual
framework which is necessary for the development, analysis, optimization and
understanding of programs and programming tasks. The aim of the PROLE series of
conferences (PROLE stems from PROgramación y LEnguajes) is to serve as a
meeting point for Spanish research groups which develop their work in the area
of programming and programming languages. The organization of this series of
events aims at fostering the exchange of ideas, experiences and results among
these groups. Promoting further collaboration is also one of its main goals.
| 1 | 0 | 0 | 0 | 0 | 0 |
The proximal point algorithm in geodesic spaces with curvature bounded above | We investigate the asymptotic behavior of sequences generated by the proximal
point algorithm for convex functions in complete geodesic spaces with curvature
bounded above. Using the notion of resolvents of such functions, which was
recently introduced by the authors, we show the existence of minimizers of
convex functions under the boundedness assumptions on such sequences as well as
the convergence of such sequences to minimizers of given functions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Spatio-temporal canards in neural field equations | Canards are special solutions to ordinary differential equations that follow
invariant repelling slow manifolds for long time intervals. In realistic
biophysical single cell models, canards are responsible for several complex
neural rhythms observed experimentally, but their existence and role in
spatially-extended systems is largely unexplored. We describe a novel type of
coherent structure in which a spatial pattern displays temporal canard
behaviour. Using interfacial dynamics and geometric singular perturbation
theory, we classify spatio-temporal canards and give conditions for the
existence of folded-saddle and folded-node canards. We find that
spatio-temporal canards are robust to changes in the synaptic connectivity and
firing rate. The theory correctly predicts the existence of spatio-temporal
canards with octahedral symmetries in a neural field model posed on the unit
sphere.
| 0 | 1 | 1 | 0 | 0 | 0 |
Counterfactuals, indicative conditionals, and negation under uncertainty: Are there cross-cultural differences? | In this paper we study selected argument forms involving counterfactuals and
indicative conditionals under uncertainty. We selected argument forms to
explore whether people with an Eastern cultural background reason differently
about conditionals compared to Westerners, because of the differences in the
location of negations. In a 2x2 between-participants design, 63 Japanese
university students were allocated to four groups, crossing indicative
conditionals and counterfactuals, and each presented in two random task orders.
The data show close agreement between the responses of Easterners and
Westerners. The modal responses provide strong support for the hypothesis that
conditional probability is the best predictor for counterfactuals and
indicative conditionals. Finally, the grand majority of the responses are
probabilistically coherent, which endorses the psychological plausibility of
choosing coherence-based probability logic as a rationality framework for
psychological reasoning research.
| 1 | 0 | 1 | 0 | 0 | 0 |
Efficient Convolutional Network Learning using Parametric Log based Dual-Tree Wavelet ScatterNet | We propose a DTCWT ScatterNet Convolutional Neural Network (DTSCNN) formed by
replacing the first few layers of a CNN network with a parametric log based
DTCWT ScatterNet. The ScatterNet extracts edge based invariant representations
that are used by the later layers of the CNN to learn high-level features. This
improves the training of the network as the later layers can learn more complex
patterns from the start of learning because the edge representations are
already present. The efficient learning of the DTSCNN network is demonstrated
on CIFAR-10 and Caltech-101 datasets. The generic nature of the ScatterNet
front-end is shown by an equivalent performance to pre-trained CNN front-ends.
A comparison with the state-of-the-art on CIFAR-10 and Caltech-101 datasets is
also presented.
| 1 | 0 | 0 | 1 | 0 | 0 |
The $r$th moment of the divisor function: an elementary approach | Let $\tau(n)$ be the number of divisors of $n$. We give an elementary proof
of the fact that $$ \sum_{n\le x} \tau(n)^r =xC_{r} (\log x)^{2^r-1}+O(x(\log
x)^{2^r-2}), $$ for any integer $r\ge 2$. Here, $$ C_{r}=\frac{1}{(2^r-1)!}
\prod_{p\ge 2}\left( \left(1-\frac{1}{p}\right)^{2^r} \left(\sum_{\alpha\ge 0}
\frac{(\alpha+1)^r}{p^{\alpha}}\right)\right). $$
| 0 | 0 | 1 | 0 | 0 | 0 |
Liu-type Shrinkage Estimations in Linear Models | In this study, we present the preliminary test, Stein-type and positive part
Liu estimators in the linear models when the parameter vector
$\boldsymbol{\beta}$ is partitioned into two parts, namely, the main effects
$\boldsymbol{\beta}_1$ and the nuisance effects $\boldsymbol{\beta}_2$ such
that $\boldsymbol{\beta}=\left(\boldsymbol{\beta}_1, \boldsymbol{\beta}_2
\right)$. We consider the case that a priori known or suspected set of the
explanatory variables do not contribute to predict the response so that a
sub-model may be enough for this purpose. Thus, the main interest is to
estimate $\boldsymbol{\beta}_1$ when $\boldsymbol{\beta}_2$ is close to zero.
Therefore, we conduct a Monte Carlo simulation study to evaluate the relative
efficiency of the suggested estimators, where we demonstrate the superiority of
the proposed estimators.
| 0 | 0 | 1 | 1 | 0 | 0 |
Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds | We present an efficient coresets-based neural network compression algorithm
that provably sparsifies the parameters of a trained fully-connected neural
network in a manner that approximately preserves the network's output. Our
approach is based on an importance sampling scheme that judiciously defines a
sampling distribution over the neural network parameters, and as a result,
retains parameters of high importance while discarding redundant ones. We
leverage a novel, empirical notion of sensitivity and extend traditional
coreset constructions to the application of compressing parameters. Our
theoretical analysis establishes guarantees on the size and accuracy of the
resulting compressed neural network and gives rise to new generalization bounds
that may provide novel insights on the generalization properties of neural
networks. We demonstrate the practical effectiveness of our algorithm on a
variety of neural network configurations and real-world data sets.
| 0 | 0 | 0 | 1 | 0 | 0 |
Active Galactic Nuclei: what's in a name? | Active Galactic Nuclei (AGN) are energetic astrophysical sources powered by
accretion onto supermassive black holes in galaxies, and present unique
observational signatures that cover the full electromagnetic spectrum over more
than twenty orders of magnitude in frequency. The rich phenomenology of AGN has
resulted in a large number of different "flavours" in the literature that now
comprise a complex and confusing AGN "zoo". It is increasingly clear that these
classifications are only partially related to intrinsic differences between
AGN, and primarily reflect variations in a relatively small number of
astrophysical parameters as well the method by which each class of AGN is
selected. Taken together, observations in different electromagnetic bands as
well as variations over time provide complementary windows on the physics of
different sub-structures in the AGN. In this review, we present an overview of
AGN multi-wavelength properties with the aim of painting their "big picture"
through observations in each electromagnetic band from radio to gamma-rays as
well as AGN variability. We address what we can learn from each observational
method, the impact of selection effects, the physics behind the emission at
each wavelength, and the potential for future studies. To conclude we use these
observations to piece together the basic architecture of AGN, discuss our
current understanding of unification models, and highlight some open questions
that present opportunities for future observational and theoretical progress.
| 0 | 1 | 0 | 0 | 0 | 0 |
Fixed points of Legendre-Fenchel type transforms | A recent result characterizes the fully order reversing operators acting on
the class of lower semicontinuous proper convex functions in a real Banach
space as certain linear deformations of the Legendre-Fenchel transform.
Motivated by the Hilbert space version of this result and by the well-known
result saying that this convex conjugation transform has a unique fixed point
(namely, the normalized energy function), we investigate the fixed point
equation in which the involved operator is fully order reversing and acts on
the above-mentioned class of functions. It turns out that this nonlinear
equation is very sensitive to the involved parameters and can have no solution,
a unique solution, or several (possibly infinitely many) ones. Our analysis
yields a few by-products, such as results related to positive definite
operators, and to functional equations and inclusions involving monotone
operators.
| 0 | 0 | 1 | 0 | 0 | 0 |
High-Dimensional Materials and Process Optimization using Data-driven Experimental Design with Well-Calibrated Uncertainty Estimates | The optimization of composition and processing to obtain materials that
exhibit desirable characteristics has historically relied on a combination of
scientist intuition, trial and error, and luck. We propose a methodology that
can accelerate this process by fitting data-driven models to experimental data
as it is collected to suggest which experiment should be performed next. This
methodology can guide the scientist to test the most promising candidates
earlier, and can supplement scientific intuition and knowledge with data-driven
insights. A key strength of the proposed framework is that it scales to
high-dimensional parameter spaces, as are typical in materials discovery
applications. Importantly, the data-driven models incorporate uncertainty
analysis, so that new experiments are proposed based on a combination of
exploring high-uncertainty candidates and exploiting high-performing regions of
parameter space. Over four materials science test cases, our methodology led to
the optimal candidate being found with three times fewer required measurements
than random guessing on average.
| 0 | 1 | 0 | 1 | 0 | 0 |
Two Posets of Noncrossing Partitions Coming From Undesired Parking Spaces | Consider the noncrossing set partitions of an $n$-element set which either do
not contain the block $\{n-1,n\}$, or which do not contain the singleton block
$\{n\}$ whenever $1$ and $n-1$ are in the same block. In this article we study
the subposet of the noncrossing partition lattice induced by these elements,
and show that it is a supersolvable lattice, and therefore lexicographically
shellable. We give a combinatorial model for the NBB bases of this lattice and
derive an explicit formula for the value of its Möbius function between least
and greatest element. This work is motivated by a recent article by M. Bruce,
M. Dougherty, M. Hlavacek, R. Kudo, and I. Nicolas, in which they introduce a
subposet of the noncrossing partition lattice that is determined by parking
functions with certain forbidden entries. In particular, they conjecture that
the resulting poset always has a contractible order complex. We prove this
conjecture by embedding their poset into ours, and showing that it inherits the
lexicographic shellability.
| 0 | 0 | 1 | 0 | 0 | 0 |
Semi-supervised model-based clustering with controlled clusters leakage | In this paper, we focus on finding clusters in partially categorized data
sets. We propose a semi-supervised version of Gaussian mixture model, called
C3L, which retrieves natural subgroups of given categories. In contrast to
other semi-supervised models, C3L is parametrized by user-defined leakage
level, which controls maximal inconsistency between initial categorization and
resulting clustering. Our method can be implemented as a module in practical
expert systems to detect clusters, which combine expert knowledge with true
distribution of data. Moreover, it can be used for improving the results of
less flexible clustering techniques, such as projection pursuit clustering. The
paper presents extensive theoretical analysis of the model and fast algorithm
for its efficient optimization. Experimental results show that C3L finds high
quality clustering model, which can be applied in discovering meaningful groups
in partially classified data.
| 1 | 0 | 0 | 1 | 0 | 0 |
Local White Matter Architecture Defines Functional Brain Dynamics | Large bundles of myelinated axons, called white matter, anatomically connect
disparate brain regions together and compose the structural core of the human
connectome. We recently proposed a method of measuring the local integrity
along the length of each white matter fascicle, termed the local connectome. If
communication efficiency is fundamentally constrained by the integrity along
the entire length of a white matter bundle, then variability in the functional
dynamics of brain networks should be associated with variability in the local
connectome. We test this prediction using two statistical approaches that are
capable of handling the high dimensionality of data. First, by performing
statistical inference on distance-based correlations, we show that similarity
in the local connectome between individuals is significantly correlated with
similarity in their patterns of functional connectivity. Second, by employing
variable selection using sparse canonical correlation analysis and
cross-validation, we show that segments of the local connectome are predictive
of certain patterns of functional brain dynamics. These results are consistent
with the hypothesis that structural variability along axon bundles constrains
communication between disparate brain regions.
| 0 | 0 | 0 | 1 | 1 | 0 |
An upper bound on tricolored ordered sum-free sets | We present a strengthening of the lemma on the lower bound of the slice rank
by Tao (2016) motivated by the Croot-Lev-Pach-Ellenberg-Gijswijt bound on cap
sets (2017, 2017). The Croot-Lev-Pach-Ellenberg-Gijswijt method and the lemma
of Tao are based on the fact that the rank of a diagonal matrix is equal to the
number of non-zero diagonal entries. Our lemma is based on the rank of
upper-triangular matrices. This stronger lemma allows us to prove the following
extension of the Ellenberg-Gijswijt result (2017). A tricolored ordered
sum-free set in $\mathbb F_p^n$ is a collection
$\{(a_i,b_i,c_i):i=1,2,\ldots,m\}$ of ordered triples in $(\mathbb F_p^n )^3$
such that $a_i+b_i+c_i=0$ and if $a_i+b_j+c_k=0$, then $i\le j\le k$. By using
the new lemma, we present an upper bound on the size of a tricolored ordered
sum-free set in $\mathbb F_p^n$.
| 0 | 0 | 1 | 0 | 0 | 0 |
The effect of the spatial domain in FANOVA models with ARH(1) error term | Functional Analysis of Variance (FANOVA) from Hilbert-valued correlated data
with spatial rectangular or circular supports is analyzed, when Dirichlet
conditions are assumed on the boundary. Specifically, a Hilbert-valued fixed
effect model with error term defined from an Autoregressive Hilbertian process
of order one (ARH(1) process) is considered, extending the formulation given in
Ruiz-Medina (2016). A new statistical test is also derived to contrast the
significance of the functional fixed effect parameters. The Dirichlet
conditions established at the boundary affect the dependence range of the
correlated error term. While the rate of convergence to zero of the eigenvalues
of the covariance kernels, characterizing the Gaussian functional error
components, directly affects the stability of the generalized least-squares
parameter estimation problem. A simulation study and a real-data application
related to fMRI analysis are undertaken to illustrate the performance of the
parameter estimator and statistical test derived.
| 0 | 0 | 1 | 1 | 0 | 0 |
Audio to Body Dynamics | We present a method that gets as input an audio of violin or piano playing,
and outputs a video of skeleton predictions which are further used to animate
an avatar. The key idea is to create an animation of an avatar that moves their
hands similarly to how a pianist or violinist would do, just from audio. Aiming
for a fully detailed correct arms and fingers motion is a goal, however, it's
not clear if body movement can be predicted from music at all. In this paper,
we present the first result that shows that natural body dynamics can be
predicted at all. We built an LSTM network that is trained on violin and piano
recital videos uploaded to the Internet. The predicted points are applied onto
a rigged avatar to create the animation.
| 1 | 0 | 0 | 0 | 0 | 0 |
Detecting causal associations in large nonlinear time series datasets | Identifying causal relationships from observational time series data is a key
problem in disciplines such as climate science or neuroscience, where
experiments are often not possible. Data-driven causal inference is challenging
since datasets are often high-dimensional and nonlinear with limited sample
sizes. Here we introduce a novel method that flexibly combines linear or
nonlinear conditional independence tests with a causal discovery algorithm that
allows to reconstruct causal networks from large-scale time series datasets. We
validate the method on a well-established climatic teleconnection connecting
the tropical Pacific with extra-tropical temperatures and using large-scale
synthetic datasets mimicking the typical properties of real data. The
experiments demonstrate that our method outperforms alternative techniques in
detection power from small to large-scale datasets and opens up entirely new
possibilities to discover causal networks from time series across a range of
research fields.
| 0 | 1 | 0 | 1 | 0 | 0 |
Controlling a remotely located Robot using Hand Gestures in real time: A DSP implementation | Telepresence is a necessity for present time as we can't reach everywhere and
also it is useful in saving human life at dangerous places. A robot, which
could be controlled from a distant location, can solve these problems. This
could be via communication waves or networking methods. Also controlling should
be in real time and smooth so that it can actuate on every minor signal in an
effective way. This paper discusses a method to control a robot over the
network from a distant location. The robot was controlled by hand gestures
which were captured by the live camera. A DSP board TMS320DM642EVM was used to
implement image pre-processing and fastening the whole system. PCA was used for
gesture classification and robot actuation was done according to predefined
procedures. Classification information was sent over the network in the
experiment. This method is robust and could be used to control any kind of
robot over distance.
| 1 | 0 | 0 | 0 | 0 | 0 |
Hybrid Machine Learning Approach to Popularity Prediction of Newly Released Contents for Online Video Streaming Service | In the industry of video content providers such as VOD and IPTV, predicting
the popularity of video contents in advance is critical not only from a
marketing perspective but also from a network optimization perspective. By
predicting whether the content will be successful or not in advance, the
content file, which is large, is efficiently deployed in the proper service
providing server, leading to network cost optimization. Many previous studies
have done view count prediction research to do this. However, the studies have
been making predictions based on historical view count data from users. In this
case, the contents had been published to the users and already deployed on a
service server. These approaches make possible to efficiently deploy a content
already published but are impossible to use for a content that is not be
published. To address the problems, this research proposes a hybrid machine
learning approach to the classification model for the popularity prediction of
newly video contents which is not published. In this paper, we create a new
variable based on the related content of the specific content and divide entire
dataset by the characteristics of the contents. Next, the prediction is
performed using XGBoosting and deep neural net based model according to the
data characteristics of the cluster. Our model uses metadata for contents for
prediction, so we use categorical embedding techniques to solve the sparsity of
categorical variables and make them learn efficiently for the deep neural net
model. As well, we use the FTRL-proximal algorithm to solve the problem of the
view-count volatility of video content. We achieve overall better performance
than the previous standalone method with a dataset from one of the top
streaming service company.
| 1 | 0 | 0 | 1 | 0 | 0 |
Vaught's Two-Cardinal Theorem and Quasi-Minimality in Continuous Logic | We prove the following continuous analogue of Vaught's Two-Cardinal Theorem:
if for some $\kappa>\lambda\geq \aleph_0$, a continuous theory $T$ has a model
with density character $\kappa$ which has a definable subset of density
character $\lambda$, then $T$ has a model with density character $\aleph_1$
which has a separable definable subset. We also show that if we assume that $T$
is $\omega$-stable, then if $T$ has a model of density character $\aleph_1$
with a separable definable set, then for any uncountable $\kappa$ we can find a
model of $T$ with density character $\kappa$ which has a separable definable
subset. In order to prove this, we develop an approximate notion of
quasi-minimality for the continuous setting. We apply these results to show a
continuous version of the forward direction of the Baldwin-Lachlan
characterization of uncountable categoricity: if a continuous theory $T$ is
uncountably categorical, then $T$ is $\omega$-stable and has no Vaughtian
pairs.
| 0 | 0 | 1 | 0 | 0 | 0 |
Spectral Calibration of the Fluorescence Telescopes of the Pierre Auger Observatory | We present a novel method to measure precisely the relative spectral response
of the fluorescence telescopes of the Pierre Auger Observatory. We used a
portable light source based on a xenon flasher and a monochromator to measure
the relative spectral efficiencies of eight telescopes in steps of 5 nm from
280 nm to 440 nm. Each point in a scan had approximately 2 nm FWHM out of the
monochromator. Different sets of telescopes in the observatory have different
optical components, and the eight telescopes measured represent two each of the
four combinations of components represented in the observatory. We made an
end-to-end measurement of the response from different combinations of optical
components, and the monochromator setup allowed for more precise and complete
measurements than our previous multi-wavelength calibrations. We find an
overall uncertainty in the calibration of the spectral response of most of the
telescopes of 1.5% for all wavelengths; the six oldest telescopes have larger
overall uncertainties of about 2.2%. We also report changes in physics
measureables due to the change in calibration, which are generally small.
| 0 | 1 | 0 | 0 | 0 | 0 |
Two properties of Müntz spaces | We show that Müntz spaces, as subspaces of $C[0,1]$, contain
asymptotically isometric copies of $c_0$ and that their dual spaces are
octahedral.
| 0 | 0 | 1 | 0 | 0 | 0 |
Rational approximations to the zeta function | This article describes a sequence of rational functions which converges
locally uniformly to the zeta function. The numerators (and denominators) of
these rational functions can be expressed as characteristic polynomials of
matrices that are on the face of it very simple. As a consequence, the Riemann
hypothesis can be restated as what looks like a rather conventional spectral
problem but which is related to the one found by Connes in his analysis of the
zeta function. However the point here is that the rational approximations look
to be susceptible of quantitative estimation.
| 0 | 0 | 1 | 0 | 0 | 0 |
The COS-Halos Survey: Metallicities in the Low-Redshift Circumgalactic Medium | We analyze new far-ultraviolet spectra of 13 quasars from the z~0.2 COS-Halos
survey that cover the HI Lyman limit of 14 circumgalactic medium (CGM) systems.
These data yield precise estimates or more constraining limits than previous
COS-Halos measurements on the HI column densities NHI. We then apply a
Monte-Carlo Markov Chain approach on 32 systems from COS-Halos to estimate the
metallicity of the cool (T~10^4K) CGM gas that gives rise to low-ionization
state metal lines, under the assumption of photoionization equilibrium with the
extragalactic UV background. The principle results are: (1) the CGM of field L*
galaxies exhibits a declining HI surface density with impact parameter Rperp
(at >99.5%$ confidence), (2) the transmission of ionizing radiation through CGM
gas alone is 70+/-7%; (3) the metallicity distribution function of the cool CGM
is unimodal with a median of 1/3 Z_Sun and a 95% interval from ~1/50 Z_Sun to
over 3x solar. The incidence of metal poor (<1/100 Z_Sun) gas is low, implying
any such gas discovered along quasar sightlines is typically unrelated to L*
galaxies; (4) we find an unexpected increase in gas metallicity with declining
NHI (at >99.9% confidence) and, therefore, also with increasing Rperp. The high
metallicity at large radii implies early enrichment; (5) A non-parametric
estimate of the cool CGM gas mass is M_CGM_cool = 9.2 +/- 4.3 10^10 Msun, which
together with new mass estimates for the hot CGM may resolve the galactic
missing baryons problem. Future analyses of halo gas should focus on the
underlying astrophysics governing the CGM, rather than processes that simply
expel the medium from the halo.
| 0 | 1 | 0 | 0 | 0 | 0 |
On Quaternionic Tori and their Moduli Spaces | Quaternionic tori are defined as quotients of the skew field $\mathbb{H}$ of
quaternions by rank-4 lattices. Using slice regular functions, these tori are
endowed with natural structures of quaternionic manifolds (in fact quaternionic
curves), and a fundamental region in a $12$-dimensional real subspace is then
constructed to classify them up to biregular diffeomorphisms. The points of the
moduli space correspond to suitable \emph{special} bases of rank-4 lattices,
which are studied with respect to the action of the group $GL(4, \mathbb{Z})$,
and up to biregular diffeomeorphisms. All tori with a non trivial group of
biregular automorphisms - and all possible groups of their biregular
automorphisms - are then identified, and recognized to correspond to five
different subsets of boundary points of the moduli space.
| 0 | 0 | 1 | 0 | 0 | 0 |
GIER: A Danish computer from 1961 with a role in the modern revolution of astronomy | A Danish computer, GIER, from 1961 played a vital role in the development of
a new method for astrometric measurement. This method, photon counting
astrometry, ultimately led to two satellites with a significant role in the
modern revolution of astronomy. A GIER was installed at the Hamburg Observatory
in 1964 where it was used to implement the entirely new method for the
measurement of stellar positions by means of a meridian circle, then the
fundamental instrument of astrometry. An expedition to Perth in Western
Australia with the instrument and the computer was a success. This method was
also implemented in space in the first ever astrometric satellite Hipparcos
launched by ESA in 1989. The Hipparcos results published in 1997 revolutionized
astrometry with an impact in all branches of astronomy from the solar system
and stellar structure to cosmic distances and the dynamics of the Milky Way. In
turn, the results paved the way for a successor, the one million times more
powerful Gaia astrometry satellite launched by ESA in 2013. Preparations for a
Gaia successor in twenty years are making progress.
| 0 | 1 | 0 | 0 | 0 | 0 |
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study | Several recent papers investigate Active Learning (AL) for mitigating the
data dependence of deep learning for natural language processing. However, the
applicability of AL to real-world problems remains an open question. While in
supervised learning, practitioners can try many different methods, evaluating
each against a validation set before selecting a model, AL affords no such
luxury. Over the course of one AL run, an agent annotates its dataset
exhausting its labeling budget. Thus, given a new task, an active learner has
no opportunity to compare models and acquisition functions. This paper provides
a large scale empirical study of deep active learning, addressing multiple
tasks and, for each, multiple datasets, multiple models, and a full suite of
acquisition functions. We find that across all settings, Bayesian active
learning by disagreement, using uncertainty estimates provided either by
Dropout or Bayes-by Backprop significantly improves over i.i.d. baselines and
usually outperforms classic uncertainty sampling.
| 0 | 0 | 0 | 1 | 0 | 0 |
Analogy and duality between random channel coding and lossy source coding | Here we write in a unified fashion (using "R(P, Q, D)") the random coding
exponents in channel coding and lossy source coding. We derive their explicit
forms and show, that, for a given random codebook distribution Q, the channel
decoding error exponent can be viewed as an encoding success exponent in lossy
source coding, and the channel correct-decoding exponent can be viewed as an
encoding failure exponent in lossy source coding. We then extend the channel
exponents to arbitrary D, which corresponds for D > 0 to erasure decoding and
for D < 0 to list decoding. For comparison, we also derive the exact random
coding exponent for Forney's optimum tradeoff decoder.
| 1 | 0 | 0 | 0 | 0 | 0 |
Towards Bursting Filter Bubble via Contextual Risks and Uncertainties | A rising topic in computational journalism is how to enhance the diversity in
news served to subscribers to foster exploration behavior in news reading.
Despite the success of preference learning in personalized news recommendation,
their over-exploitation causes filter bubble that isolates readers from
opposing viewpoints and hurts long-term user experiences with lack of
serendipity. Since news providers can recommend neither opposite nor
diversified opinions if unpopularity of these articles is surely predicted,
they can only bet on the articles whose forecasts of click-through rate involve
high variability (risks) or high estimation errors (uncertainties). We propose
a novel Bayesian model of uncertainty-aware scoring and ranking for news
articles. The Bayesian binary classifier models probability of success (defined
as a news click) as a Beta-distributed random variable conditional on a vector
of the context (user features, article features, and other contextual
features). The posterior of the contextual coefficients can be computed
efficiently using a low-rank version of Laplace's method via thin Singular
Value Decomposition. Efficiencies in personalized targeting of exceptional
articles, which are chosen by each subscriber in test period, are evaluated on
real-world news datasets. The proposed estimator slightly outperformed existing
training and scoring algorithms, in terms of efficiency in identifying
successful outliers.
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Nonlinear Loewy Factorizable Algebraic ODEs and Hayman's Conjecture | In this paper, we introduce certain $n$-th order nonlinear Loewy factorizable
algebraic ordinary differential equations for the first time and study the
growth of their meromorphic solutions in terms of the Nevanlinna characteristic
function. It is shown that for generic cases all their meromorphic solutions
are elliptic functions or their degenerations and hence their order of growth
are at most two. Moreover, for the second order factorizable algebraic ODEs,
all the meromorphic solutions of them (except for one case) are found
explicitly. This allows us to show that a conjecture proposed by Hayman in 1996
holds for these second order ODEs.
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Grounding Symbols in Multi-Modal Instructions | As robots begin to cohabit with humans in semi-structured environments, the
need arises to understand instructions involving rich variability---for
instance, learning to ground symbols in the physical world. Realistically, this
task must cope with small datasets consisting of a particular users' contextual
assignment of meaning to terms. We present a method for processing a raw stream
of cross-modal input---i.e., linguistic instructions, visual perception of a
scene and a concurrent trace of 3D eye tracking fixations---to produce the
segmentation of objects with a correspondent association to high-level
concepts. To test our framework we present experiments in a table-top object
manipulation scenario. Our results show our model learns the user's notion of
colour and shape from a small number of physical demonstrations, generalising
to identifying physical referents for novel combinations of the words.
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Morphometric analysis in gamma-ray astronomy using Minkowski functionals: II. Joint structure quantification | We pursue a novel morphometric analysis to detect sources in very-high-energy
gamma-ray counts maps by structural deviations from the background noise.
Because the Minkowski functionals from integral geometry quantify the shape of
the counts map itself, the morphometric analysis includes unbiased structure
information without prior knowledge about the source. Their distribution
provides access to intricate geometric information about the background. We
combine techniques from stochastic geometry and statistical physics to
determine the joint distribution of all Minkowski functionals. We achieve an
accurate characterization of the background structure for large scan windows
(with up to $15\times15$ pixels), where the number of microstates varies over
up to 64 orders of magnitude. Moreover, in a detailed simulation study, we
confirm the statistical significance of features in the background noise and
discuss how to correct for trial effects. We also present a local correction of
detector effects that can considerably enhance the sensitivity of the analysis.
In the third paper of this series, we will use the here derived refined
structure characterization for a more sensitive data analysis that can detect
formerly undetected sources.
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An Event-based Fast Movement Detection Algorithm for a Positioning Robot Using POWERLINK Communication | This work develops a tracking system based on an event-based camera. A
bioinspired filtering algorithm to reduce noise and transmitted data while
keeping the main features at the scene is implemented in FPGA which also serves
as a network node. POWERLINK IEEE 61158 industrial network is used to
communicate the FPGA with a controller connected to a self-developed two axis
servo-controlled robot. The FPGA includes the network protocol to integrate the
event-based camera as any other existing network node. The inverse kinematics
for the robot is included in the controller. In addition, another network node
is used to control pneumatic valves blowing the ball at different speed and
trajectories. To complete the system and provide a comparison, a traditional
frame-based camera is also connected to the controller. The imaging data for
the tracking system are obtained either from the event-based or frame-based
camera. Results show that the robot can accurately follow the ball using fast
image recognition, with the intrinsic advantages of the event-based system
(size, price, power). This works shows how the development of new equipment and
algorithms can be efficiently integrated in an industrial system, merging
commercial industrial equipment with the new devices so that new technologies
can rapidly enter into the industrial field.
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$\textsf{S}^3T$: An Efficient Score-Statistic for Spatio-Temporal Surveillance | We present an efficient score statistic, called the $\textsf{S}^3 \textsf{T}$
statistic, to detect the emergence of a spatially and temporally correlated
signal from either fixed-sample or sequential data. The signal may cause a men
shift and/or a change in the covariance structure. The score statistic can
capture both spatial and temporal structures of the change and hence is
particularly powerful in detecting weak signals. The score statistic is
computationally efficient and statistically powerful. Our main theoretical
contribution are accurate analytical approximations on the false alarm rate of
the detection procedures, which can be used to calibrate the threshold
analytically. Numerical experiments on simulated and real data demonstrate the
good performance of our procedure for solar flame detection and water quality
monitoring.
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Relaxing Integrity Requirements for Attack-Resilient Cyber-Physical Systems | The increase in network connectivity has also resulted in several
high-profile attacks on cyber-physical systems. An attacker that manages to
access a local network could remotely affect control performance by tampering
with sensor measurements delivered to the controller. Recent results have shown
that with network-based attacks, such as Man-in-the-Middle attacks, the
attacker can introduce an unbounded state estimation error if measurements from
a suitable subset of sensors contain false data when delivered to the
controller. While these attacks can be addressed with the standard
cryptographic tools that ensure data integrity, their continuous use would
introduce significant communication and computation overhead. Consequently, we
study effects of intermittent data integrity guarantees on system performance
under stealthy attacks. We consider linear estimators equipped with a general
type of residual-based intrusion detectors (including $\chi^2$ and SPRT
detectors), and show that even when integrity of sensor measurements is
enforced only intermittently, the attack impact is significantly limited;
specifically, the state estimation error is bounded or the attacker cannot
remain stealthy. Furthermore, we present methods to: (1) evaluate the effects
of any given integrity enforcement policy in terms of reachable
state-estimation errors for any type of stealthy attacks, and (2) design an
enforcement policy that provides the desired estimation error guarantees under
attack. Finally, on three automotive case studies we show that even with less
than 10% of authenticated messages we can ensure satisfiable control
performance in the presence of attacks.
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Cross-Lingual Cross-Platform Rumor Verification Pivoting on Multimedia Content | With the increasing popularity of smart devices, rumors with multimedia
content become more and more common on social networks. The multimedia
information usually makes rumors look more convincing. Therefore, finding an
automatic approach to verify rumors with multimedia content is a pressing task.
Previous rumor verification research only utilizes multimedia as input
features. We propose not to use the multimedia content but to find external
information in other news platforms pivoting on it. We introduce a new features
set, cross-lingual cross-platform features that leverage the semantic
similarity between the rumors and the external information. When implemented,
machine learning methods utilizing such features achieved the state-of-the-art
rumor verification results.
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Convergence of the Kähler-Ricci iteration | The Ricci iteration is a discrete analogue of the Ricci flow. According to
Perelman, the Ricci flow converges to a Kahler-Einstein metric whenever one
exists, and it has been conjectured that the Ricci iteration should behave
similarly. This article confirms this conjecture. As a special case, this gives
a new method of uniformization of the Riemann sphere.
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Joint estimation of genetic and parent-of-origin effects using RNA-seq data from human | RNA sequencing allows one to study allelic imbalance of gene expression,
which may be due to genetic factors or genomic imprinting. It is desirable to
model both genetic and parent-of-origin effects simultaneously to avoid
confounding and to improve the power to detect either effect. In a study of
experimental cross, separation of genetic and parent-of-origin effects can be
achieved by studying reciprocal cross of two inbred strains. In contrast, this
task is much more challenging for an outbred population such as human
population. To address this challenge, we propose a new framework to combine
experimental strategies and novel statistical methods. Specifically, we propose
to collect genotype data from family trios as well as RNA-seq data from the
children of family trios. We have developed a new statistical method to
estimate both genetic and parent-of-origin effects from such data sets. We
demonstrated this approach by studying 30 trios of HapMap samples. Our results
support some of previous finding of imprinted genes and also recover new
candidate imprinted genes.
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Consistency Analysis for Massively Inconsistent Datasets in Bound-to-Bound Data Collaboration | Bound-to-Bound Data Collaboration (B2BDC) provides a natural framework for
addressing both forward and inverse uncertainty quantification problems. In
this approach, QOI (quantity of interest) models are constrained by related
experimental observations with interval uncertainty. A collection of such
models and observations is termed a dataset and carves out a feasible region in
the parameter space. If a dataset has a nonempty feasible set, it is said to be
consistent. In real-world applications, it is often the case that collections
of experiments and observations are inconsistent. Revealing the source of this
inconsistency, i.e., identifying which models and/or observations are
problematic, is essential before a dataset can be used for prediction. To
address this issue, we introduce a constraint relaxation-based approach,
entitled the vector consistency measure, for investigating datasets with
numerous sources of inconsistency. The benefits of this vector consistency
measure over a previous method of consistency analysis are demonstrated in two
realistic gas combustion examples.
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Hydra: a C++11 framework for data analysis in massively parallel platforms | Hydra is a header-only, templated and C++11-compliant framework designed to
perform the typical bottleneck calculations found in common HEP data analyses
on massively parallel platforms. The framework is implemented on top of the
C++11 Standard Library and a variadic version of the Thrust library and is
designed to run on Linux systems, using OpenMP, CUDA and TBB enabled devices.
This contribution summarizes the main features of Hydra. A basic description of
the overall design, functionality and user interface is provided, along with
some code examples and measurements of performance.
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Analysis of dropout learning regarded as ensemble learning | Deep learning is the state-of-the-art in fields such as visual object
recognition and speech recognition. This learning uses a large number of
layers, huge number of units, and connections. Therefore, overfitting is a
serious problem. To avoid this problem, dropout learning is proposed. Dropout
learning neglects some inputs and hidden units in the learning process with a
probability, p, and then, the neglected inputs and hidden units are combined
with the learned network to express the final output. We find that the process
of combining the neglected hidden units with the learned network can be
regarded as ensemble learning, so we analyze dropout learning from this point
of view.
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Spatially-resolved Brillouin spectroscopy reveals biomechanical changes in early ectatic corneal disease and post-crosslinking in vivo | Mounting evidence connects the biomechanical properties of tissues to the
development of eye diseases such as keratoconus, a common disease in which the
cornea thins and bulges into a conical shape. However, measuring biomechanical
changes in vivo with sufficient sensitivity for disease detection has proved
challenging. Here, we present a first large-scale study (~200 subjects,
including normal and keratoconus patients) using Brillouin light-scattering
microscopy to measure longitudinal modulus in corneal tissues with high
sensitivity and spatial resolution. Our results in vivo provide evidence of
biomechanical inhomogeneity at the onset of keratoconus and suggest that
biomechanical asymmetry between the left and right eyes may presage disease
development. We additionally measure the stiffening effect of corneal
crosslinking treatment in vivo for the first time. Our results demonstrate the
promise of Brillouin microscopy for diagnosis and treatment of keratoconus, and
potentially other diseases.
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Combining Symbolic Execution and Model Checking to Verify MPI Programs | Message Passing Interface (MPI) is the standard paradigm of programming in
high performance computing. MPI programming takes significant effort, and is
error-prone. Thus, effective tools for analyzing MPI programs are much needed.
On the other hand, analyzing MPI programs itself is challenging because of
non-determinism caused by program inputs and non-deterministic operations.
Existing approaches for analyzing MPI programs either do not handle inputs or
fail to support programs with mixed blocking and non-blocking operations.
This paper presents MPI symbolic verifier (MPI-SV), the first symbolic
execution based tool for verifying MPI programs having both blocking and
non-blocking operations. To ensure soundness, we propose a blockingdriven
matching algorithm to safely handle non-deterministic operations, and a method
to soundly and completely model the equivalent behavior of a program execution
path. The models of MPI program paths are generated on-the-fly during symbolic
execution, and verified w.r.t. the expected properties by model checking. To
improve scalability, MPI-SV uses the results of model checking to prune
redundant paths.
We have implemented MPI-SV and evaluated it on the verification of deadlock
freedom for 108 real-world MPI tasks. The pure symbolic execution based
technique can successfully verify 61 out of the 108 tasks (56%) within one
hour, while in comparison, MPI-SV can verify 94 tasks (87%), a 31% improvement.
On average, MPI-SV also achieves 7.25X speedup on verifying deadlock freedom
and 2.64X speedup on finding deadlocks. These experimental results are
promising, and demonstrate MPI-SV's effectiveness and efficiency.
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An Improved Modified Cholesky Decomposition Method for Inverse Covariance Matrix Estimation | The modified Cholesky decomposition is commonly used for inverse covariance
matrix estimation given a specified order of random variables. However, the
order of variables is often not available or cannot be pre-determined. Hence,
we propose a novel estimator to address the variable order issue in the
modified Cholesky decomposition to estimate the sparse inverse covariance
matrix. The key idea is to effectively combine a set of estimates obtained from
multiple permutations of variable orders, and to efficiently encourage the
sparse structure for the resultant estimate by the use of thresholding
technique on the combined Cholesky factor matrix. The consistent property of
the proposed estimate is established under some weak regularity conditions.
Simulation studies show the superior performance of the proposed method in
comparison with several existing approaches. We also apply the proposed method
into the linear discriminant analysis for analyzing real-data examples for
classification.
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Self-exciting Point Processes: Infections and Implementations | This is a comment on Reinhart's "Review of Self-Exciting Spatio-Temporal
Point Processes and Their Applications" (arXiv:1708.02647v1). I contribute some
experiences from modelling the spread of infectious diseases. Furthermore, I
try to complement the review with regard to the availability of software for
the described models, which I think is essential in "paving the way for new
uses".
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Magnetism and charge density waves in RNiC$_2$ (R = Ce, Pr, Nd) | We have compared the magnetic, transport, galvanomagnetic and specific heat
properties of CeNiC$_2$, PrNiC$_2$ and NdNiC$_2$ to study the interplay between
charge density waves and magnetism in these compounds. The negative
magnetoresistance in NdNiC$_2$ is discussed in terms of the partial destruction
of charge density waves and an irreversible phase transition stabilized by the
field induced ferromagnetic transformation is reported. For PrNiC$_2$ we
demonstrate that the magnetic field initially weakens the CDW state, due to the
Zeeman splitting of conduction bands. However, the Fermi surface nesting is
enhanced at a temperature related to the magnetic anomaly.
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Conjoined constraints on modified gravity from the expansion history and cosmic growth | In this paper we present conjoined constraints on several cosmological models
from the expansion history $H(z)$ and cosmic growth $f\sigma_8(z)$. The models
we study include the CPL $w_0w_a$ parametrization, the Holographic Dark Energy
(HDE) model, the Time varying vacuum ($\Lambda_t$CDM) model, the Dvali,
Gabadadze and Porrati (DGP) and Finsler-Randers (FRDE) models, a power law
$f(T)$ model and finally the Hu-Sawicki $f(R)$ model. In all cases we perform a
simultaneous fit to the SnIa, CMB, BAO, $H(z)$ and growth data, while also
following the conjoined visualization of $H(z)$ and $f\sigma_8(z)$ as in Linder
(2017). Furthermore, we introduce the Figure of Merit (FoM) in the
$H(z)-f\sigma_8(z)$ parameter space as a way to constrain models that jointly
fit both probes well. We use both the latest $H(z)$ and $f\sigma_8(z)$ data,
but also LSST-like mocks with $1\%$ measurements and we find that the conjoined
method of constraining the expansion history and cosmic growth simultaneously
is able not only to place stringent constraints on these parameters but also to
provide an easy visual way to discriminate cosmological models. Finally, we
confirm the existence of a tension between the growth rate and Planck CMB data
and we find that the FoM in the conjoined parameter space of
$H(z)-f\sigma_8(z)$ can be used to discriminate between the $\Lambda$CDM model
and certain classes of modified gravity models, namely the DGP and $f(T)$.
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Coaxial collisions of a vortex ring and a sphere in an inviscid incompressible fluid | The dynamics of a circular thin vortex ring and a sphere moving along the
symmetry axis of the ring in an inviscid incompressible fluid is studied on the
basis of Euler's equations of motion. The equations of motion for position and
radius of the vortex ring and those for position and velocity of the sphere are
coupled by hydrodynamic interactions. The equations are cast in Hamiltonian
form, from which it is seen that total energy and momentum are conserved. The
four Hamiltonian equations of motion are solved numerically for a variety of
initial conditions.
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Cosmic viscosity as a remedy for tension between PLANCK and LSS data | Measurements of $\sigma_8$ from large scale structure observations show a
discordance with the extrapolated $\sigma_8$ from Planck CMB parameters using
$\Lambda$CDM cosmology. Similar discordance is found in the value of $H_0$ and
$\Omega_m$. In this paper, we show that the presence of viscosity in cold dark
matter, shear or bulk or combination of both, can remove the above mentioned
conflicts simultaneously. This indicates that the data from Planck CMB
observation and different LSS observations prefer small but non-zero amount of
viscosity in cold dark matter fluid.
| 0 | 1 | 0 | 0 | 0 | 0 |
A probabilistic approach to emission-line galaxy classification | We invoke a Gaussian mixture model (GMM) to jointly analyse two traditional
emission-line classification schemes of galaxy ionization sources: the
Baldwin-Phillips-Terlevich (BPT) and $\rm W_{H\alpha}$ vs. [NII]/H$\alpha$
(WHAN) diagrams, using spectroscopic data from the Sloan Digital Sky Survey
Data Release 7 and SEAGal/STARLIGHT datasets. We apply a GMM to empirically
define classes of galaxies in a three-dimensional space spanned by the $\log$
[OIII]/H$\beta$, $\log$ [NII]/H$\alpha$, and $\log$ EW(H${\alpha}$), optical
parameters. The best-fit GMM based on several statistical criteria suggests a
solution around four Gaussian components (GCs), which are capable to explain up
to 97 per cent of the data variance. Using elements of information theory, we
compare each GC to their respective astronomical counterpart. GC1 and GC4 are
associated with star-forming galaxies, suggesting the need to define a new
starburst subgroup. GC2 is associated with BPT's Active Galaxy Nuclei (AGN)
class and WHAN's weak AGN class. GC3 is associated with BPT's composite class
and WHAN's strong AGN class. Conversely, there is no statistical evidence --
based on four GCs -- for the existence of a Seyfert/LINER dichotomy in our
sample. Notwithstanding, the inclusion of an additional GC5 unravels it. The
GC5 appears associated to the LINER and Passive galaxies on the BPT and WHAN
diagrams respectively. Subtleties aside, we demonstrate the potential of our
methodology to recover/unravel different objects inside the wilderness of
astronomical datasets, without lacking the ability to convey physically
interpretable results. The probabilistic classifications from the GMM analysis
are publicly available within the COINtoolbox
(this https URL\_Catalogue/).
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Quantum repeaters with individual rare-earth ions at telecommunication wavelengths | We present a quantum repeater scheme that is based on individual erbium and
europium ions. Erbium ions are attractive because they emit photons at
telecommunication wavelength, while europium ions offer exceptional spin
coherence for long-term storage. Entanglement between distant erbium ions is
created by photon detection. The photon emission rate of each erbium ion is
enhanced by a microcavity with high Purcell factor, as has recently been
demonstrated. Entanglement is then transferred to nearby europium ions for
storage. Gate operations between nearby ions are performed using dynamically
controlled electric-dipole coupling. These gate operations allow entanglement
swapping to be employed in order to extend the distance over which entanglement
is distributed. The deterministic character of the gate operations allows
improved entanglement distribution rates in comparison to atomic ensemble-based
protocols. We also propose an approach that utilizes multiplexing in order to
enhance the entanglement distribution rate.
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Questions and dependency in intuitionistic logic | In recent years, the logic of questions and dependencies has been
investigated in the closely related frameworks of inquisitive logic and
dependence logic. These investigations have assumed classical logic as the
background logic of statements, and added formulas expressing questions and
dependencies to this classical core. In this paper, we broaden the scope of
these investigations by studying questions and dependency in the context of
intuitionistic logic. We propose an intuitionistic team semantics, where teams
are embedded within intuitionistic Kripke models. The associated logic is a
conservative extension of intuitionistic logic with questions and dependence
formulas. We establish a number of results about this logic, including a normal
form result, a completeness result, and translations to classical inquisitive
logic and modal dependence logic.
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Translations: generalizing relative expressiveness between logics | There is a strong demand for precise means for the comparison of logics in
terms of expressiveness both from theoretical and from application areas. The
aim of this paper is to propose a sufficiently general and reasonable formal
criterion for expressiveness, so as to apply not only to model-theoretic
logics, but also to Tarskian and proof-theoretic logics. For model-theoretic
logics there is a standard framework of relative expressiveness, based on the
capacity of characterizing structures, and a straightforward formal criterion
issuing from it. The problem is that it only allows the comparison of those
logics defined within the same class of models. The urge for a broader
framework of expressiveness is not new. Nevertheless, the enterprise is complex
and a reasonable model-theoretic formal criterion is still wanting. Recently
there appeared two criteria in this wider framework, one from García-Matos &
Väänänen and other from L. Kuijer. We argue that they are not adequate.
Their limitations are analyzed and we propose to move to an even broader
framework lacking model-theoretic notions, which we call "translational
expressiveness". There is already a criterion in this later framework by
Mossakowski et al., however it turned out to be too lax. We propose some
adequacy criteria for expressiveness and a formal criterion of translational
expressiveness complying with them is given.
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Learning Aided Optimization for Energy Harvesting Devices with Outdated State Information | This paper considers utility optimal power control for energy harvesting
wireless devices with a finite capacity battery. The distribution information
of the underlying wireless environment and harvestable energy is unknown and
only outdated system state information is known at the device controller. This
scenario shares similarity with Lyapunov opportunistic optimization and online
learning but is different from both. By a novel combination of Zinkevich's
online gradient learning technique and the drift-plus-penalty technique from
Lyapunov opportunistic optimization, this paper proposes a learning-aided
algorithm that achieves utility within $O(\epsilon)$ of the optimal, for any
desired $\epsilon>0$, by using a battery with an $O(1/\epsilon)$ capacity. The
proposed algorithm has low complexity and makes power investment decisions
based on system history, without requiring knowledge of the system state or its
probability distribution.
| 1 | 0 | 0 | 0 | 0 | 0 |
Absence of long range order in the frustrated magnet SrDy$_2$O$_4$ due to trapped defects from a dimensionality crossover | Magnetic frustration and low dimensionality can prevent long range magnetic
order and lead to exotic correlated ground states. SrDy$_2$O$_4$ consists of
magnetic Dy$^{3+}$ ions forming magnetically frustrated zig-zag chains along
the c-axis and shows no long range order to temperatures as low as $T=60$ mK.
We carried out neutron scattering and AC magnetic susceptibility measurements
using powder and single crystals of SrDy$_2$O$_4$. Diffuse neutron scattering
indicates strong one-dimensional (1D) magnetic correlations along the chain
direction that can be qualitatively accounted for by the axial next-nearest
neighbour Ising (ANNNI) model with nearest-neighbor and next-nearest-neighbor
exchange $J_1=0.3$ meV and $J_2=0.2$ meV, respectively. Three-dimensional (3D)
correlations become important below $T^*\approx0.7$ K. At $T=60$ mK, the short
range correlations are characterized by a putative propagation vector
$\textbf{k}_{1/2}=(0,\frac{1}{2},\frac{1}{2})$. We argue that the absence of
long range order arises from the presence of slowly decaying 1D domain walls
that are trapped due to 3D correlations. This stabilizes a low-temperature
phase without long range magnetic order, but with well-ordered chain segments
separated by slowly-moving domain walls.
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A class of multi-resolution approximations for large spatial datasets | Gaussian processes are popular and flexible models for spatial, temporal, and
functional data, but they are computationally infeasible for large datasets. We
discuss Gaussian-process approximations that use basis functions at multiple
resolutions to achieve fast inference and that can (approximately) represent
any spatial covariance structure. We consider two special cases of this
multi-resolution-approximation framework, a taper version and a
domain-partitioning (block) version. We describe theoretical properties and
inference procedures, and study the computational complexity of the methods.
Numerical comparisons and an application to satellite data are also provided.
| 0 | 0 | 0 | 1 | 0 | 0 |
Origin of Operating Voltage Increase in InGaN-based Light-emitting Diodes under High Injection: Phase Space Filling Effect on Forward Voltage Characteristics | As an attempt to further elucidate the operating voltage increase in
InGaN-based light-emitting diodes (LEDs), the radiative and nonradiative
current components are separately analyzed in combination with the Shockley
diode equation. Through the analyses, we have shown that the increase in
operating voltage is caused by phase space filling effect in high injection. We
have also shown that the classical Shockley diode equation is insufficient to
comprehensively explain the I-V curve of the LED devices since the transport
and recombination characteristics of respective current components are
basically different. Hence, we have proposed a modified Shockley equation
suitable for modern LED devices. Our analysis gives a new insight on the cause
of the wall-plug-efficiency drop influenced by such factors as the efficiency
droop and the high operating voltage in InGaN LEDs.
| 0 | 1 | 0 | 0 | 0 | 0 |
Emergence of Selective Invariance in Hierarchical Feed Forward Networks | Many theories have emerged which investigate how in- variance is generated in
hierarchical networks through sim- ple schemes such as max and mean pooling.
The restriction to max/mean pooling in theoretical and empirical studies has
diverted attention away from a more general way of generating invariance to
nuisance transformations. We con- jecture that hierarchically building
selective invariance (i.e. carefully choosing the range of the transformation
to be in- variant to at each layer of a hierarchical network) is im- portant
for pattern recognition. We utilize a novel pooling layer called adaptive
pooling to find linear pooling weights within networks. These networks with the
learnt pooling weights have performances on object categorization tasks that
are comparable to max/mean pooling networks. In- terestingly, adaptive pooling
can converge to mean pooling (when initialized with random pooling weights),
find more general linear pooling schemes or even decide not to pool at all. We
illustrate the general notion of selective invari- ance through object
categorization experiments on large- scale datasets such as SVHN and ILSVRC
2012.
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Virtual retraction and Howson's theorem in pro-$p$ groups | We show that for every finitely generated closed subgroup $K$ of a
non-solvable Demushkin group $G$, there exists an open subgroup $U$ of $G$
containing $K$, and a continuous homomorphism $\tau \colon U \to K$ satisfying
$\tau(k) = k$ for every $k \in K$. We prove that the intersection of a pair of
finitely generated closed subgroups of a Demushkin group is finitely generated
(giving an explicit bound on the number of generators). Furthermore, we show
that these properties of Demushkin groups are preserved under free pro-$p$
products, and deduce that Howson's theorem holds for the Sylow subgroups of the
absolute Galois group of a number field. Finally, we confirm two conjectures of
Ribes, thus classifying the finitely generated pro-$p$ M. Hall groups.
| 0 | 0 | 1 | 0 | 0 | 0 |
Convolutional Dictionary Learning: A Comparative Review and New Algorithms | Convolutional sparse representations are a form of sparse representation with
a dictionary that has a structure that is equivalent to convolution with a set
of linear filters. While effective algorithms have recently been developed for
the convolutional sparse coding problem, the corresponding dictionary learning
problem is substantially more challenging. Furthermore, although a number of
different approaches have been proposed, the absence of thorough comparisons
between them makes it difficult to determine which of them represents the
current state of the art. The present work both addresses this deficiency and
proposes some new approaches that outperform existing ones in certain contexts.
A thorough set of performance comparisons indicates a very wide range of
performance differences among the existing and proposed methods, and clearly
identifies those that are the most effective.
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Replication Ethics | Suppose some future technology enables the same consciously experienced human
life to be repeated, identically or nearly so, N times, in series or in
parallel. Is this roughly N times as valuable as enabling the same life once,
because each life has value and values are additive? Or is it of roughly equal
value as enabling the life once, because only one life is enabled, albeit in a
physically unusual way? Does it matter whether the lives are contemporaneous or
successive? We argue that these questions highlight a hitherto neglected facet
of population ethics that may become relevant in the not necessarily far
distant future.
| 1 | 1 | 0 | 0 | 0 | 0 |
PSYM-WIDE: a survey for large-separation planetary-mass companions to late spectral type members of young moving groups | We present the results of a direct-imaging survey for very large separation
($>$100 au), companions around 95 nearby young K5-L5 stars and brown dwarfs.
They are high-likelihood candidates or confirmed members of the young
($\lessapprox$150 Myr) $\beta$ Pictoris and AB Doradus moving groups (ABDMG)
and the TW Hya, Tucana-Horologium, Columba, Carina, and Argus associations.
Images in $i'$ and $z'$ filters were obtained with the Gemini Multi-Object
Spectrograph (GMOS) on Gemini South to search for companions down to an
apparent magnitude of $z'\sim$22-24 at separations $\gtrapprox$20" from the
targets and in the remainder of the wide 5.5' $\times$ 5.5' GMOS field of view.
This allowed us to probe the most distant region where planetary-mass
companions could be gravitationally bound to the targets. This region was left
largely unstudied by past high-contrast imaging surveys, which probed much
closer-in separations. This survey led to the discovery of a planetary-mass
(9-13 $\,M_{\rm{Jup}}$) companion at 2000 au from the M3V star GU Psc, a highly
probable member of ABDMG. No other substellar companions were identified. These
results allowed us to constrain the frequency of distant planetary-mass
companions (5-13 $\,M_{\rm{Jup}}$) to 0.84$_{-0.66}^{+6.73}$% (95% confidence)
at semimajor axes between 500 and 5000 au around young K5-L5 stars and brown
dwarfs. This is consistent with other studies suggesting that gravitationally
bound planetary-mass companions at wide separations from low-mass stars are
relatively rare.
| 0 | 1 | 0 | 0 | 0 | 0 |
Boolean dimension and tree-width | The dimension is a key measure of complexity of partially ordered sets. Small
dimension allows succinct encoding. Indeed if $P$ has dimension $d$, then to
know whether $x \leq y$ in $P$ it is enough to check whether $x\leq y$ in each
of the $d$ linear extensions of a witnessing realizer. Focusing on the encoding
aspect Nešetřil and Pudlák defined a more expressive version of
dimension. A poset $P$ has boolean dimension at most $d$ if it is possible to
decide whether $x \leq y$ in $P$ by looking at the relative position of $x$ and
$y$ in only $d$ permutations of the elements of $P$. We prove that posets with
cover graphs of bounded tree-width have bounded boolean dimension. This stays
in contrast with the fact that there are posets with cover graphs of tree-width
three and arbitrarily large dimension. This result might be a step towards a
resolution of the long-standing open problem: Do planar posets have bounded
boolean dimension?
| 1 | 0 | 0 | 0 | 0 | 0 |
Drawing Planar Graphs with Few Geometric Primitives | We define the \emph{visual complexity} of a plane graph drawing to be the
number of basic geometric objects needed to represent all its edges. In
particular, one object may represent multiple edges (e.g., one needs only one
line segment to draw a path with an arbitrary number of edges). Let $n$ denote
the number of vertices of a graph. We show that trees can be drawn with $3n/4$
straight-line segments on a polynomial grid, and with $n/2$ straight-line
segments on a quasi-polynomial grid. Further, we present an algorithm for
drawing planar 3-trees with $(8n-17)/3$ segments on an $O(n)\times O(n^2)$
grid. This algorithm can also be used with a small modification to draw maximal
outerplanar graphs with $3n/2$ edges on an $O(n)\times O(n^2)$ grid. We also
study the problem of drawing maximal planar graphs with circular arcs and
provide an algorithm to draw such graphs using only $(5n - 11)/3$ arcs. This is
significantly smaller than the lower bound of $2n$ for line segments for a
nontrivial graph class.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the nature of the candidate T-Tauri star V501 Aurigae | We report new multi-colour photometry and high-resolution spectroscopic
observations of the long-period variable V501 Aur, previously considered to be
a weak-lined T-Tauri star belonging to the Taurus-Auriga star-forming region.
The spectroscopic observations reveal that V501 Aur is a single-lined
spectroscopic binary system with a 68.8-day orbital period, a slightly
eccentric orbit (e ~ 0.03), and a systemic velocity discrepant from the mean of
Taurus-Auriga. The photometry shows quasi-periodic variations on a different,
~55-day timescale that we attribute to rotational modulation by spots. No
eclipses are seen. The visible object is a rapidly rotating (vsini ~ 25 km/s)
early K star, which along with the rotation period implies it must be large (R
> 26.3 Rsun), as suggested also by spectroscopic estimates indicating a low
surface gravity. The parallax from the Gaia mission and other independent
estimates imply a distance much greater than the Taurus-Auriga region,
consistent with the giant interpretation. Taken together, this evidence
together with a re-evaluation of the LiI~$\lambda$6707 and H$\alpha$ lines
shows that V501 Aur is not a T-Tauri star, but is instead a field binary with a
giant primary far behind the Taurus-Auriga star-forming region. The large mass
function from the spectroscopic orbit and a comparison with stellar evolution
models suggest the secondary may be an early-type main-sequence star.
| 0 | 1 | 0 | 0 | 0 | 0 |
3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks | The success of various applications including robotics, digital content
creation, and visualization demand a structured and abstract representation of
the 3D world from limited sensor data. Inspired by the nature of human
perception of 3D shapes as a collection of simple parts, we explore such an
abstract shape representation based on primitives. Given a single depth image
of an object, we present 3D-PRNN, a generative recurrent neural network that
synthesizes multiple plausible shapes composed of a set of primitives. Our
generative model encodes symmetry characteristics of common man-made objects,
preserves long-range structural coherence, and describes objects of varying
complexity with a compact representation. We also propose a method based on
Gaussian Fields to generate a large scale dataset of primitive-based shape
representations to train our network. We evaluate our approach on a wide range
of examples and show that it outperforms nearest-neighbor based shape retrieval
methods and is on-par with voxel-based generative models while using a
significantly reduced parameter space.
| 1 | 0 | 0 | 1 | 0 | 0 |
Counting the number of distinct distances of elements in valued field extensions | The defect of valued field extensions is a major obstacle in open problems in
resolution of singularities and in the model theory of valued fields, whenever
positive characteristic is involved. We continue the detailed study of defect
extensions through the tool of distances, which measure how well an element in
an immediate extension can be approximated by elements from the base field. We
show that in several situations the number of essentially distinct distances in
fixed extensions, or even just over a fixed base field, is finite, and we
compute upper bounds. We apply this to the special case of valued functions
fields over perfect base fields. This provides important information used in
forthcoming research on relative resolution problems.
| 0 | 0 | 1 | 0 | 0 | 0 |
On the Robustness of the CVPR 2018 White-Box Adversarial Example Defenses | Neural networks are known to be vulnerable to adversarial examples. In this
note, we evaluate the two white-box defenses that appeared at CVPR 2018 and
find they are ineffective: when applying existing techniques, we can reduce the
accuracy of the defended models to 0%.
| 0 | 0 | 0 | 1 | 0 | 0 |
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