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16,201 | An Ensemble Quadratic Echo State Network for Nonlinear Spatio-Temporal Forecasting | Spatio-temporal data and processes are prevalent across a wide variety of
scientific disciplines. These processes are often characterized by nonlinear
time dynamics that include interactions across multiple scales of spatial and
temporal variability. The data sets associated with many of these processes are
increasing in size due to advances in automated data measurement, management,
and numerical simulator output. Non- linear spatio-temporal models have only
recently seen interest in statistics, but there are many classes of such models
in the engineering and geophysical sciences. Tradi- tionally, these models are
more heuristic than those that have been presented in the statistics
literature, but are often intuitive and quite efficient computationally. We
show here that with fairly simple, but important, enhancements, the echo state
net- work (ESN) machine learning approach can be used to generate long-lead
forecasts of nonlinear spatio-temporal processes, with reasonable uncertainty
quantification, and at only a fraction of the computational expense of a
traditional parametric nonlinear spatio-temporal models.
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16,202 | Profit Maximization for Online Advertising Demand-Side Platforms | We develop an optimization model and corresponding algorithm for the
management of a demand-side platform (DSP), whereby the DSP aims to maximize
its own profit while acquiring valuable impressions for its advertiser clients.
We formulate the problem of profit maximization for a DSP interacting with ad
exchanges in a real-time bidding environment in a
cost-per-click/cost-per-action pricing model. Our proposed formulation leads to
a nonconvex optimization problem due to the joint optimization over both
impression allocation and bid price decisions. We use Lagrangian relaxation to
develop a tractable convex dual problem, which, due to the properties of
second-price auctions, may be solved efficiently with subgradient methods. We
propose a two-phase solution procedure, whereby in the first phase we solve the
convex dual problem using a subgradient algorithm, and in the second phase we
use the previously computed dual solution to set bid prices and then solve a
linear optimization problem to obtain the allocation probability variables. On
several synthetic examples, we demonstrate that our proposed solution approach
leads to superior performance over a baseline method that is used in practice.
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16,203 | Traces of surfactants can severely limit the drag reduction of superhydrophobic surfaces | Superhydrophobic surfaces (SHSs) have the potential to achieve large drag
reduction for internal and external flow applications. However, experiments
have shown inconsistent results, with many studies reporting significantly
reduced performance. Recently, it has been proposed that surfactants,
ubiquitous in flow applications, could be responsible, by creating adverse
Marangoni stresses. Yet, testing this hypothesis is challenging. Careful
experiments with purified water show large interfacial stresses and,
paradoxically, adding surfactants yields barely measurable drag increases. This
suggests that other physical processes, such as thermal Marangoni stresses or
interface deflection, could explain the lower performance. To test the
surfactant hypothesis, we perform the first numerical simulations of flows over
a SHS inclusive of surfactant kinetics. These simulations reveal that
surfactant-induced stresses are significant at extremely low concentrations,
potentially yielding a no-slip boundary condition on the air--water interface
(the "plastron") for surfactant amounts below typical environmental values.
These stresses decrease as the streamwise distance between plastron stagnation
points increases. We perform microchannel experiments with thermally-controlled
SHSs consisting of streamwise parallel gratings, which confirm this numerical
prediction. We introduce a new, unsteady test of surfactant effects. When we
rapidly remove the driving pressure following a loading phase, a backflow
develops at the plastron, which can only be explained by surfactant gradients
formed in the loading phase. This demonstrates the significance of surfactants
in deteriorating drag reduction, and thus the importance of including
surfactant stresses in SHS models. Our time-dependent protocol can assess the
impact of surfactants in SHS testing and guide future mitigating designs.
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16,204 | Non-Convex Rank/Sparsity Regularization and Local Minima | This paper considers the problem of recovering either a low rank matrix or a
sparse vector from observations of linear combinations of the vector or matrix
elements. Recent methods replace the non-convex regularization with $\ell_1$ or
nuclear norm relaxations. It is well known that this approach can be guaranteed
to recover a near optimal solutions if a so called restricted isometry property
(RIP) holds. On the other hand it is also known to perform soft thresholding
which results in a shrinking bias which can degrade the solution.
In this paper we study an alternative non-convex regularization term. This
formulation does not penalize elements that are larger than a certain threshold
making it much less prone to small solutions. Our main theoretical results show
that if a RIP holds then the stationary points are often well separated, in the
sense that their differences must be of high cardinality/rank. Thus, with a
suitable initial solution the approach is unlikely to fall into a bad local
minima. Our numerical tests show that the approach is likely to converge to a
better solution than standard $\ell_1$/nuclear-norm relaxation even when
starting from trivial initializations. In many cases our results can also be
used to verify global optimality of our method.
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16,205 | Supervised Speech Separation Based on Deep Learning: An Overview | Speech separation is the task of separating target speech from background
interference. Traditionally, speech separation is studied as a signal
processing problem. A more recent approach formulates speech separation as a
supervised learning problem, where the discriminative patterns of speech,
speakers, and background noise are learned from training data. Over the past
decade, many supervised separation algorithms have been put forward. In
particular, the recent introduction of deep learning to supervised speech
separation has dramatically accelerated progress and boosted separation
performance. This article provides a comprehensive overview of the research on
deep learning based supervised speech separation in the last several years. We
first introduce the background of speech separation and the formulation of
supervised separation. Then we discuss three main components of supervised
separation: learning machines, training targets, and acoustic features. Much of
the overview is on separation algorithms where we review monaural methods,
including speech enhancement (speech-nonspeech separation), speaker separation
(multi-talker separation), and speech dereverberation, as well as
multi-microphone techniques. The important issue of generalization, unique to
supervised learning, is discussed. This overview provides a historical
perspective on how advances are made. In addition, we discuss a number of
conceptual issues, including what constitutes the target source.
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16,206 | Sample Efficient Feature Selection for Factored MDPs | In reinforcement learning, the state of the real world is often represented
by feature vectors. However, not all of the features may be pertinent for
solving the current task. We propose Feature Selection Explore and Exploit
(FS-EE), an algorithm that automatically selects the necessary features while
learning a Factored Markov Decision Process, and prove that under mild
assumptions, its sample complexity scales with the in-degree of the dynamics of
just the necessary features, rather than the in-degree of all features. This
can result in a much better sample complexity when the in-degree of the
necessary features is smaller than the in-degree of all features.
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16,207 | Sgoldstino-less inflation and low energy SUSY breaking | We assess the range of validity of sgoldstino-less inflation in a scenario of
low energy supersymmetry breaking. We first analyze the consistency conditions
that an effective theory of the inflaton and goldstino superfields should
satisfy in order to be faithfully described by a sgoldstino-less model.
Enlarging the scope of previous studies, we investigate the case where the
effective field theory cut-off, and hence also the sgoldstino mass, are
inflaton-dependent. We then introduce a UV complete model where one can realize
successfully sgoldstino-less inflation and gauge mediation of supersymmetry
breaking, combining the alpha-attractor mechanism and a weakly coupled model of
spontaneous breaking of supersymmetry. In this class of models we find that,
given current limits on superpartner masses, the gravitino mass has a lower
bound of the order of the MeV, i.e. we cannot reach very low supersymmetry
breaking scales. On the plus side, we recognize that in this framework, one can
derive the complete superpartner spectrum as well as compute inflation
observables, the reheating temperature, and address the gravitino overabundance
problem. We then show that further constraints come from collider results and
inflation observables. Their non trivial interplay seems a staple feature of
phenomenological studies of supersymmetric inflationary models.
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16,208 | SideEye: A Generative Neural Network Based Simulator of Human Peripheral Vision | Foveal vision makes up less than 1% of the visual field. The other 99% is
peripheral vision. Precisely what human beings see in the periphery is both
obvious and mysterious in that we see it with our own eyes but can't visualize
what we see, except in controlled lab experiments. Degradation of information
in the periphery is far more complex than what might be mimicked with a radial
blur. Rather, behaviorally-validated models hypothesize that peripheral vision
measures a large number of local texture statistics in pooling regions that
overlap and grow with eccentricity. In this work, we develop a new method for
peripheral vision simulation by training a generative neural network on a
behaviorally-validated full-field synthesis model. By achieving a 21,000 fold
reduction in running time, our approach is the first to combine realism and
speed of peripheral vision simulation to a degree that provides a whole new way
to approach visual design: through peripheral visualization.
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16,209 | Carbon stars in the X-Shooter Spectral Library: II. Comparison with models | In a previous paper, we assembled a collection of medium-resolution spectra
of 35 carbon stars, covering optical and near-infrared wavelengths from 400 to
2400 nm. The sample includes stars from the Milky Way and the Magellanic
Clouds, with a variety of $(J-K_s)$ colors and pulsation properties. In the
present paper, we compare these observations to a new set of high-resolution
synthetic spectra, based on hydrostatic model atmospheres. We find that the
broad-band colors and the molecular-band strengths measured by
spectrophotometric indices match those of the models when $(J-K_s)$ is bluer
than about 1.6, while the redder stars require either additional reddening or
dust emission or both. Using a grid of models to fit the full observed spectra,
we estimate the most likely atmospheric parameters $T_\mathrm{eff}$, $\log(g)$,
$[\mathrm{Fe/H}]$ and C/O. These parameters derived independently in the
optical and near-infrared are generally consistent when $(J-K_s)<1.6$. The
temperatures found based on either wavelength range are typically within
$\pm$100K of each other, and $\log(g)$ and $[\mathrm{Fe/H}]$ are consistent
with the values expected for this sample. The reddest stars ($(J-K_s)$ $>$ 1.6)
are divided into two families, characterized by the presence or absence of an
absorption feature at 1.53\,$\mu$m, generally associated with HCN and
C$_2$H$_2$. Stars from the first family begin to be more affected by
circumstellar extinction. The parameters found using optical or near-infrared
wavelengths are still compatible with each other, but the error bars become
larger. In stars showing the 1.53\,$\mu$m feature, which are all
large-amplitude variables, the effects of pulsation are strong and the spectra
are poorly matched with hydrostatic models. For these, atmospheric parameters
could not be derived reliably, and dynamical models are needed for proper
interpretation.
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16,210 | Four-variable expanders over the prime fields | Let $\mathbb{F}_p$ be a prime field of order $p>2$, and $A$ be a set in
$\mathbb{F}_p$ with very small size in terms of $p$. In this note, we show that
the number of distinct cubic distances determined by points in $A\times A$
satisfies \[|(A-A)^3+(A-A)^3|\gg |A|^{8/7},\] which improves a result due to
Yazici, Murphy, Rudnev, and Shkredov. In addition, we investigate some new
families of expanders in four and five variables.
We also give an explicit exponent of a problem of Bukh and Tsimerman, namely,
we prove that \[\max \left\lbrace |A+A|, |f(A, A)|\right\rbrace\gg |A|^{6/5},\]
where $f(x, y)$ is a quadratic polynomial in $\mathbb{F}_p[x, y]$ that is not
of the form $g(\alpha x+\beta y)$ for some univariate polynomial $g$.
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16,211 | Limit multiplicities for ${\rm SL}_2(\mathcal{O}_F)$ in ${\rm SL}_2(\mathbb{R}^{r_1}\oplus\mathbb{C}^{r_2})$ | We prove that the family of lattices ${\rm SL}_2(\mathcal{O}_F)$, $F$ running
over number fields with fixed archimedean signature $(r_1, r_2)$, in ${\rm
SL}_2(\mathbb{R}^{r_1}\oplus\mathbb{C}^{r_2})$ has the limit multiplicity
property.
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16,212 | On comparing clusterings: an element-centric framework unifies overlaps and hierarchy | Clustering is one of the most universal approaches for understanding complex
data. A pivotal aspect of clustering analysis is quantitatively comparing
clusterings; clustering comparison is the basis for tasks such as clustering
evaluation, consensus clustering, and tracking the temporal evolution of
clusters. For example, the extrinsic evaluation of clustering methods requires
comparing the uncovered clusterings to planted clusterings or known metadata.
Yet, as we demonstrate, existing clustering comparison measures have critical
biases which un- dermine their usefulness, and no measure accommodates both
overlapping and hierarchical clusterings. Here we unify the comparison of
disjoint, overlapping, and hierarchically struc- tured clusterings by proposing
a new element-centric framework: elements are compared based on the
relationships induced by the cluster structure, as opposed to the traditional
cluster-centric philosophy. We demonstrate that, in contrast to standard
clustering simi- larity measures, our framework does not suffer from critical
biases and naturally provides unique insights into how the clusterings differ.
We illustrate the strengths of our framework by revealing new insights into the
organization of clusters in two applications: the improved classification of
schizophrenia based on the overlapping and hierarchical community struc- ture
of fMRI brain networks, and the disentanglement of various social homophily
factors in Facebook social networks. The universality of clustering suggests
far-reaching impact of our framework throughout all areas of science.
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16,213 | Parametric Oscillatory Instability in a Fabry-Perot Cavity of the Einstein Telescope with different mirror's materials | We discuss the parametric oscillatory instability in a Fabry-Perot cavity of
the Einstein Telescope. Unstable combinations of elastic and optical modes for
two possible configurations of gravitational wave third-generation detector are
deduced. The results are compared with the results for gravita- tional wave
interferometers LIGO and LIGO Voyager.
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16,214 | Mapping properties of the Hilbert and Fubini--Study maps in Kähler geometry | Suppose that we have a compact Kähler manifold $X$ with a very ample line
bundle $\mathcal{L}$. We prove that any positive definite hermitian form on the
space $H^0 (X,\mathcal{L})$ of holomorphic sections can be written as an
$L^2$-inner product with respect to an appropriate hermitian metric on
$\mathcal{L}$. We apply this result to show that the Fubini--Study map, which
associates a hermitian metric on $\mathcal{L}$ to a hermitian form on $H^0
(X,\mathcal{L})$, is injective.
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16,215 | Space-time Constructivism vs. Modal Provincialism: Or, How Special Relativistic Theories Needn't Show Minkowski Chronogeometry | In 1835 Lobachevski entertained the possibility of multiple (rival)
geometries. This idea has reappeared on occasion (e.g., Poincaré) but
didn't become key in space-time foundations prior to Brown's \emph{Physical
Relativity} (at the end, the interpretive key to the book). A crucial
difference between his constructivism and orthodox "space-time realism" is
modal scope. Constructivism applies to all local classical field theories,
including those with multiple geometries. But the orthodox view provincially
assumes a unique geometry, as familiar theories (Newton, Special Relativity,
Nordström, and GR) have. They serve as the orthodox "canon." Their
historical roles suggest a story of inevitable progress. Physics literature
after c. 1920 is relevant to orthodoxy mostly as commentary on the canon, which
closed in the 1910s. The orthodox view explains the behavior of matter as the
manifestation of the real space-time geometry, which works within the canon.
The orthodox view, Whiggish history, and the canon relate symbiotically.
If one considers a theory outside the canon, space-time realism sheds little
light on matter's behavior. Worse, it gives the wrong answer when applied to an
example arguably in the canon, massive scalar gravity with universal coupling.
Which is the true geometry---the flat metric from the Poincaré symmetry,
the conformally flat metric exhibited by material rods and clocks, or both---or
is the question bad? How does space-time realism explain that all matter fields
see the same curved geometry, given so many ways to mix and match?
Constructivist attention to dynamical details is vindicated; geometrical
shortcuts disappoint. The more exhaustive exploration of relativistic field
theories (especially massive) in particle physics is an underused resource for
foundations.
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16,216 | On the representation of integers by binary quadratic forms | In this note we show that for a given irreducible binary quadratic form
$f(x,y)$ with integer coefficients, whenever we have $f(x,y) = f(u,v)$ for
integers $x,y,u,v$, there exists a rational automorphism of $f$ which sends
$(x,y)$ to $(u,v)$.
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16,217 | The First Planetary Microlensing Event with Two Microlensed Source Stars | We present the analysis of microlensing event MOA-2010-BLG-117, and show that
the light curve can only be explained by the gravitational lensing of a binary
source star system by a star with a Jupiter mass ratio planet. It was necessary
to modify standard microlensing modeling methods to find the correct light
curve solution for this binary-source, binary-lens event. We are able to
measure a strong microlensing parallax signal, which yields the masses of the
host star, $M_* = 0.58\pm 0.11 M_\odot$, and planet $m_p = 0.54\pm 0.10 M_{\rm
Jup}$ at a projected star-planet separation of $a_\perp = 2.42\pm 0.26\,$AU,
corresponding to a semi-major axis of $a = 2.9{+1.6\atop -0.6}\,$AU. Thus, the
system resembles a half-scale model of the Sun-Jupiter system with a
half-Jupiter mass planet orbiting a half-solar mass star at very roughly half
of Jupiter's orbital distance from the Sun. The source stars are slightly
evolved, and by requiring them to lie on the same isochrone, we can constrain
the source to lie in the near side of the bulge at a distance of $D_S = 6.9 \pm
0.7\,$kpc, which implies a distance to the planetary lens system of $D_L =
3.5\pm 0.4\,$kpc. The ability to model unusual planetary microlensing events,
like this one, will be necessary to extract precise statistical information
from the planned large exoplanet microlensing surveys, such as the WFIRST
microlensing survey.
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16,218 | A Learning-Based Approach for Lane Departure Warning Systems with a Personalized Driver Model | Misunderstanding of driver correction behaviors (DCB) is the primary reason
for false warnings of lane-departure-prediction systems. We propose a
learning-based approach to predicting unintended lane-departure behaviors (LDB)
and the chance for drivers to bring the vehicle back to the lane. First, in
this approach, a personalized driver model for lane-departure and lane-keeping
behavior is established by combining the Gaussian mixture model and the hidden
Markov model. Second, based on this model, we develop an online model-based
prediction algorithm to predict the forthcoming vehicle trajectory and judge
whether the driver will demonstrate an LDB or a DCB. We also develop a warning
strategy based on the model-based prediction algorithm that allows the
lane-departure warning system to be acceptable for drivers according to the
predicted trajectory. In addition, the naturalistic driving data of 10 drivers
is collected through the University of Michigan Safety Pilot Model Deployment
program to train the personalized driver model and validate this approach. We
compare the proposed method with a basic time-to-lane-crossing (TLC) method and
a TLC-directional sequence of piecewise lateral slopes (TLC-DSPLS) method. The
results show that the proposed approach can reduce the false-warning rate to
3.07\%.
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16,219 | Internet of Things: Survey on Security and Privacy | The Internet of Things (IoT) is intended for ubiquitous connectivity among
different entities or "things". While its purpose is to provide effective and
efficient solutions, security of the devices and network is a challenging
issue. The number of devices connected along with the ad-hoc nature of the
system further exacerbates the situation. Therefore, security and privacy has
emerged as a significant challenge for the IoT. In this paper,we aim to provide
a thorough survey related to the privacy and security challenges of the IoT.
This document addresses these challenges from the perspective of technologies
and architecture used. This work focuses also in IoT intrinsic vulnerabilities
as well as the security challenges of various layers based on the security
principles of data confidentiality, integrity and availability. This survey
analyzes articles published for the IoT at the time and relates it to the
security conjuncture of the field and its projection to the future.
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16,220 | A Connectedness Constraint for Learning Sparse Graphs | Graphs are naturally sparse objects that are used to study many problems
involving networks, for example, distributed learning and graph signal
processing. In some cases, the graph is not given, but must be learned from the
problem and available data. Often it is desirable to learn sparse graphs.
However, making a graph highly sparse can split the graph into several
disconnected components, leading to several separate networks. The main
difficulty is that connectedness is often treated as a combinatorial property,
making it hard to enforce in e.g. convex optimization problems. In this
article, we show how connectedness of undirected graphs can be formulated as an
analytical property and can be enforced as a convex constraint. We especially
show how the constraint relates to the distributed consensus problem and graph
Laplacian learning. Using simulated and real data, we perform experiments to
learn sparse and connected graphs from data.
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16,221 | Quotients of finite-dimensional operators by symmetry representations | A finite dimensional operator that commutes with some symmetry group admits
quotient operators, which are determined by the choice of associated
representation. Taking the quotient isolates the part of the spectrum
supporting the chosen representation and reduces the complexity of the problem,
however it is not uniquely defined. Here we present a computationally simple
way of choosing a special basis for the space of intertwiners, allowing us to
construct a quotient that reflects the structure of the original operator. This
quotient construction generalizes previous definitions for discrete graphs,
which either dealt with restricted group actions or only with the trivial
representation.
We also extend the method to quantum graphs, which simplifies previous
constructions within this context, answers an open question regarding
self-adjointness and offers alternative viewpoints in terms of a scattering
approach. Applications to isospectrality are discussed, together with numerous
examples and comparisons with previous results.
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16,222 | A probabilistic framework for the control of systems with discrete states and stochastic excitation | A probabilistic framework is proposed for the optimization of efficient
switched control strategies for physical systems dominated by stochastic
excitation. In this framework, the equation for the state trajectory is
replaced with an equivalent equation for its probability distribution function
in the constrained optimization setting. This allows for a large class of
control rules to be considered, including hysteresis and a mix of continuous
and discrete random variables. The problem of steering atmospheric balloons
within a stratified flowfield is a motivating application; the same approach
can be extended to a variety of mixed-variable stochastic systems and to new
classes of control rules.
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16,223 | Supersonic Flow onto Solid Wedges, Multidimensional Shock Waves and Free Boundary Problems | When an upstream steady uniform supersonic flow impinges onto a symmetric
straight-sided wedge, governed by the Euler equations, there are two possible
steady oblique shock configurations if the wedge angle is less than the
detachment angle -- the steady weak shock with supersonic or subsonic
downstream flow (determined by the wedge angle that is less or larger than the
sonic angle) and the steady strong shock with subsonic downstream flow, both of
which satisfy the entropy condition. The fundamental issue -- whether one or
both of the steady weak and strong shocks are physically admissible solutions
-- has been vigorously debated over the past eight decades. In this paper, we
survey some recent developments on the stability analysis of the steady shock
solutions in both the steady and dynamic regimes. For the static stability, we
first show how the stability problem can be formulated as an initial-boundary
value type problem and then reformulate it into a free boundary problem when
the perturbation of both the upstream steady supersonic flow and the wedge
boundary are suitably regular and small, and we finally present some recent
results on the static stability of the steady supersonic and transonic shocks.
For the dynamic stability for potential flow, we first show how the stability
problem can be formulated as an initial-boundary value problem and then use the
self-similarity of the problem to reduce it into a boundary value problem and
further reformulate it into a free boundary problem, and we finally survey some
recent developments in solving this free boundary problem for the existence of
the Prandtl-Meyer configurations that tend to the steady weak supersonic or
transonic oblique shock solutions as time goes to infinity. Some further
developments and mathematical challenges in this direction are also discussed.
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16,224 | A Zero-Shot Learning application in Deep Drawing process using Hyper-Process Model | One of the consequences of passing from mass production to mass customization
paradigm in the nowadays industrialized world is the need to increase
flexibility and responsiveness of manufacturing companies. The high-mix /
low-volume production forces constant accommodations of unknown product
variants, which ultimately leads to high periods of machine calibration. The
difficulty related with machine calibration is that experience is required
together with a set of experiments to meet the final product quality.
Unfortunately, all possible combinations of machine parameters is so high that
is difficult to build empirical knowledge. Due to this fact, normally trial and
error approaches are taken making one-of-a-kind products not viable. Therefore,
a Zero-Shot Learning (ZSL) based approach called hyper-process model (HPM) to
learn the relation among multiple tasks is used as a way to shorten the
calibration phase. Assuming each product variant is a task to solve, first, a
shape analysis on data to learn common modes of deformation between tasks is
made, and secondly, a mapping between these modes and task descriptions is
performed. Ultimately, the present work has two main contributions: 1)
Formulation of an industrial problem into a ZSL setting where new process
models can be generated for process optimization and 2) the definition of a
regression problem in the domain of ZSL. For that purpose, a 2-d deep drawing
simulated process was used based on data collected from the Abaqus simulator,
where a significant number of process models were collected to test the
effectiveness of the approach. The obtained results show that is possible to
learn new tasks without any available data (both labeled and unlabeled) by
leveraging information about already existing tasks, allowing to speed up the
calibration phase and make a quicker integration of new products into
manufacturing systems.
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16,225 | Fracton topological phases from strongly coupled spin chains | We provide a new perspective on fracton topological phases, a class of
three-dimensional topologically ordered phases with unconventional
fractionalized excitations that are either completely immobile or only mobile
along particular lines or planes. We demonstrate that a wide range of these
fracton phases can be constructed by strongly coupling mutually intersecting
spin chains and explain via a concrete example how such a coupled-spin-chain
construction illuminates the generic properties of a fracton phase. In
particular, we describe a systematic translation from each coupled-spin-chain
construction into a parton construction where the partons correspond to the
excitations that are mobile along lines. Remarkably, our construction of
fracton phases is inherently based on spin models involving only two-spin
interactions and thus brings us closer to their experimental realization.
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16,226 | The Internet as Quantitative Social Science Platform: Insights from a Trillion Observations | With the large-scale penetration of the internet, for the first time,
humanity has become linked by a single, open, communications platform.
Harnessing this fact, we report insights arising from a unified internet
activity and location dataset of an unparalleled scope and accuracy drawn from
over a trillion (1.5$\times 10^{12}$) observations of end-user internet
connections, with temporal resolution of just 15min over 2006-2012. We first
apply this dataset to the expansion of the internet itself over 1,647 urban
agglomerations globally. We find that unique IP per capita counts reach
saturation at approximately one IP per three people, and take, on average, 16.1
years to achieve; eclipsing the estimated 100- and 60- year saturation times
for steam-power and electrification respectively. Next, we use intra-diurnal
internet activity features to up-scale traditional over-night sleep
observations, producing the first global estimate of over-night sleep duration
in 645 cities over 7 years. We find statistically significant variation between
continental, national and regional sleep durations including some evidence of
global sleep duration convergence. Finally, we estimate the relationship
between internet concentration and economic outcomes in 411 OECD regions and
find that the internet's expansion is associated with negative or positive
productivity gains, depending strongly on sectoral considerations. To our
knowledge, our study is the first of its kind to use online/offline activity of
the entire internet to infer social science insights, demonstrating the
unparalleled potential of the internet as a social data-science platform.
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16,227 | Deep Embedding Kernel | In this paper, we propose a novel supervised learning method that is called
Deep Embedding Kernel (DEK). DEK combines the advantages of deep learning and
kernel methods in a unified framework. More specifically, DEK is a learnable
kernel represented by a newly designed deep architecture. Compared with
pre-defined kernels, this kernel can be explicitly trained to map data to an
optimized high-level feature space where data may have favorable features
toward the application. Compared with typical deep learning using SoftMax or
logistic regression as the top layer, DEK is expected to be more generalizable
to new data. Experimental results show that DEK has superior performance than
typical machine learning methods in identity detection, classification,
regression, dimension reduction, and transfer learning.
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16,228 | A Radio-Inertial Localization and Tracking System with BLE Beacons Prior Maps | In this paper, we develop a system for the low-cost indoor localization and
tracking problem using radio signal strength indicator, Inertial Measurement
Unit (IMU), and magnetometer sensors. We develop a novel and simplified
probabilistic IMU motion model as the proposal distribution of the sequential
Monte-Carlo technique to track the robot trajectory. Our algorithm can globally
localize and track a robot with a priori unknown location, given an informative
prior map of the Bluetooth Low Energy (BLE) beacons. Also, we formulate the
problem as an optimization problem that serves as the Back-end of the algorithm
mentioned above (Front-end). Thus, by simultaneously solving for the robot
trajectory and the map of BLE beacons, we recover a continuous and smooth
trajectory of the robot, corrected locations of the BLE beacons, and the
time-varying IMU bias. The evaluations achieved using hardware show that
through the proposed closed-loop system the localization performance can be
improved; furthermore, the system becomes robust to the error in the map of
beacons by feeding back the optimized map to the Front-end.
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16,229 | Simple groups, generation and probabilistic methods | It is well known that every finite simple group can be generated by two
elements and this leads to a wide range of problems that have been the focus of
intensive research in recent years. In this survey article we discuss some of
the extraordinary generation properties of simple groups, focussing on topics
such as random generation, $(a,b)$-generation and spread, as well as
highlighting the application of probabilistic methods in the proofs of many of
the main results. We also present some recent work on the minimal generation of
maximal and second maximal subgroups of simple groups, which has applications
to the study of subgroup growth and the generation of primitive permutation
groups.
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16,230 | Large polaron evolution in anatase TiO2 due to carrier and temperature dependence of electron-phonon coupling | The electronic and magneto transport properties of reduced anatase TiO2
epitaxial thin films are analyzed considering various polaronic effects.
Unexpectedly, with increasing carrier concentration, the mobility increases,
which rarely happens in common metallic systems. We find that the screening of
the electron-phonon (e-ph) coupling by excess carriers is necessary to explain
this unusual dependence. We also find that the magnetoresistance (MR) could be
decomposed into a linear and a quadratic component, separately characterizing
the transport and trap behavior of carriers as a function of temperature. The
various transport behaviors could be organized into a single phase diagram
which clarifies the nature of large polaron in this material.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,231 | AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks | New types of machine learning hardware in development and entering the market
hold the promise of revolutionizing deep learning in a manner as profound as
GPUs. However, existing software frameworks and training algorithms for deep
learning have yet to evolve to fully leverage the capability of the new wave of
silicon. We already see the limitations of existing algorithms for models that
exploit structured input via complex and instance-dependent control flow, which
prohibits minibatching. We present an asynchronous model-parallel (AMP)
training algorithm that is specifically motivated by training on networks of
interconnected devices. Through an implementation on multi-core CPUs, we show
that AMP training converges to the same accuracy as conventional synchronous
training algorithms in a similar number of epochs, but utilizes the available
hardware more efficiently even for small minibatch sizes, resulting in
significantly shorter overall training times. Our framework opens the door for
scaling up a new class of deep learning models that cannot be efficiently
trained today.
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16,232 | Random data wave equations | Nowadays we have many methods allowing to exploit the regularising properties
of the linear part of a nonlinear dispersive equation (such as the KdV
equation, the nonlinear wave or the nonlinear Schroedinger equations) in order
to prove well-posedness in low regularity Sobolev spaces. By well-posedness in
low regularity Sobolev spaces we mean that less regularity than the one imposed
by the energy methods is required (the energy methods do not exploit the
dispersive properties of the linear part of the equation). In many cases these
methods to prove well-posedness in low regularity Sobolev spaces lead to
optimal results in terms of the regularity of the initial data. By optimal we
mean that if one requires slightly less regularity then the corresponding
Cauchy problem becomes ill-posed in the Hadamard sense. We call the Sobolev
spaces in which these ill-posedness results hold spaces of supercritical
regularity.
More recently, methods to prove probabilistic well-posedness in Sobolev
spaces of supercritical regularity were developed. More precisely, by
probabilistic well-posedness we mean that one endows the corresponding Sobolev
space of supercritical regularity with a non degenerate probability measure and
then one shows that almost surely with respect to this measure one can define a
(unique) global flow. However, in most of the cases when the methods to prove
probabilistic well-posedness apply, there is no information about the measure
transported by the flow. Very recently, a method to prove that the transported
measure is absolutely continuous with respect to the initial measure was
developed. In such a situation, we have a measure which is quasi-invariant
under the corresponding flow.
The aim of these lectures is to present all of the above described
developments in the context of the nonlinear wave equation.
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16,233 | The Persistent Homotopy Type Distance | We introduce the persistent homotopy type distance dHT to compare real valued
functions defined on possibly different homotopy equivalent topological spaces.
The underlying idea in the definition of dHT is to measure the minimal shift
that is necessary to apply to one of the two functions in order that the
sublevel sets of the two functions become homotopically equivalent. This
distance is interesting in connection with persistent homology. Indeed, our
main result states that dHT still provides an upper bound for the bottleneck
distance between the persistence diagrams of the intervening functions.
Moreover, because homotopy equivalences are weaker than homeomorphisms, this
implies a lifting of the standard stability results provided by the L-infty
distance and the natural pseudo-distance dNP. From a different standpoint, we
prove that dHT extends the L-infty distance and dNP in two ways. First, we show
that, appropriately restricting the category of objects to which dHT applies,
it can be made to coincide with the other two distances. Finally, we show that
dHT has an interpretation in terms of interleavings that naturally places it in
the family of distances used in persistence theory.
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16,234 | Optimal algorithms for smooth and strongly convex distributed optimization in networks | In this paper, we determine the optimal convergence rates for strongly convex
and smooth distributed optimization in two settings: centralized and
decentralized communications over a network. For centralized (i.e.
master/slave) algorithms, we show that distributing Nesterov's accelerated
gradient descent is optimal and achieves a precision $\varepsilon > 0$ in time
$O(\sqrt{\kappa_g}(1+\Delta\tau)\ln(1/\varepsilon))$, where $\kappa_g$ is the
condition number of the (global) function to optimize, $\Delta$ is the diameter
of the network, and $\tau$ (resp. $1$) is the time needed to communicate values
between two neighbors (resp. perform local computations). For decentralized
algorithms based on gossip, we provide the first optimal algorithm, called the
multi-step dual accelerated (MSDA) method, that achieves a precision
$\varepsilon > 0$ in time
$O(\sqrt{\kappa_l}(1+\frac{\tau}{\sqrt{\gamma}})\ln(1/\varepsilon))$, where
$\kappa_l$ is the condition number of the local functions and $\gamma$ is the
(normalized) eigengap of the gossip matrix used for communication between
nodes. We then verify the efficiency of MSDA against state-of-the-art methods
for two problems: least-squares regression and classification by logistic
regression.
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16,235 | Superconductivity in ultra-thin carbon nanotubes and carbyne-nanotube composites: an ab-initio approach | The superconductivity of the 4-angstrom single-walled carbon nanotubes
(SWCNTs) was discovered more than a decade ago, and marked the breakthrough of
finding superconductivity in pure elemental undoped carbon compounds. The van
Hove singularities in the electronic density of states at the Fermi level in
combination with a large Debye temperature of the SWCNTs are expected to cause
an impressively large superconducting gap. We have developed an innovative
computational algorithm specially tailored for the investigation of
superconductivity in ultrathin SWCNTs. We predict the superconducting
transition temperature of various thin carbon nanotubes resulting from
electron-phonon coupling by an ab-initio method, taking into account the effect
of radial pressure, symmetry, chirality (N,M) and bond lengths. By optimizing
the geometry of the carbon nanotubes, a maximum Tc of 60K is found. We also use
our method to calculate the Tc of a linear carbon chain embedded in the center
of (5,0) SWCNTs. The strong curvature in the (5,0) carbon nanotubes in the
presence of the inner carbon chain provides an alternative path to increase the
Tc of this carbon composite by a factor of 2.2 with respect to the empty (5,0)
SWCNTs.
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16,236 | Exponential lower bounds for history-based simplex pivot rules on abstract cubes | The behavior of the simplex algorithm is a widely studied subject.
Specifically, the question of the existence of a polynomial pivot rule for the
simplex algorithm is of major importance. Here, we give exponential lower
bounds for three history-based pivot rules. Those rules decide their next step
based on memory of the past steps. In particular, we study Zadeh's least
entered rule, Johnson's least-recently basic rule and Cunningham's
least-recently considered (or round-robin) rule. We give exponential lower
bounds on Acyclic Unique Sink Orientations (AUSO) of the abstract cube, for all
of these pivot rules. For Johnson's rule our bound is the first superpolynomial
one in any context; for Zadeh's it is the first one for AUSO. Those two are our
main results.
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16,237 | Machine learning regression on hyperspectral data to estimate multiple water parameters | In this paper, we present a regression framework involving several machine
learning models to estimate water parameters based on hyperspectral data.
Measurements from a multi-sensor field campaign, conducted on the River Elbe,
Germany, represent the benchmark dataset. It contains hyperspectral data and
the five water parameters chlorophyll a, green algae, diatoms, CDOM and
turbidity. We apply a PCA for the high-dimensional data as a possible
preprocessing step. Then, we evaluate the performance of the regression
framework with and without this preprocessing step. The regression results of
the framework clearly reveal the potential of estimating water parameters based
on hyperspectral data with machine learning. The proposed framework provides
the basis for further investigations, such as adapting the framework to
estimate water parameters of different inland waters.
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16,238 | Critical factors and enablers of food quality and safety compliance risk management in the Vietnamese seafood supply chain | Recently, along with the emergence of food scandals, food supply chains have
to face with ever-increasing pressure from compliance with food quality and
safety regulations and standards. This paper aims to explore critical factors
of compliance risk in food supply chain with an illustrated case in Vietnamese
seafood industry. To this end, this study takes advantage of both primary and
secondary data sources through a comprehensive literature research of
industrial and scientific papers, combined with expert interview. Findings
showed that there are three main critical factor groups influencing on
compliance risk including challenges originating from Vietnamese food supply
chain itself, characteristics of regulation and standards, and business
environment. Furthermore, author proposed enablers to eliminate compliance
risks to food supply chain managers as well as recommendations to government
and other influencers and supporters.
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16,239 | Asymptotic behavior of semilinear parabolic equations on the circle with time almost-periodic/recurrent dependence | We study topological structure of the $\omega$-limit sets of the skew-product
semiflow generated by the following scalar reaction-diffusion equation
\begin{equation*} u_{t}=u_{xx}+f(t,u,u_{x}),\,\,t>0,\,x\in
S^{1}=\mathbb{R}/2\pi \mathbb{Z}, \end{equation*} where $f(t,u,u_x)$ is
$C^2$-admissible with time-recurrent structure including almost-periodicity and
almost-automorphy. Contrary to the time-periodic cases (for which any
$\omega$-limit set can be imbedded into a periodically forced circle flow), it
is shown that one cannot expect that any $\omega$-limit set can be imbedded
into an almost-periodically forced circle flow even if $f$ is uniformly
almost-periodic in $t$.
More precisely, we prove that, for a given $\omega$-limit set $\Omega$, if
${\rm dim}V^c(\Omega)\leq 1$ ($V^c(\Omega)$ is the center space associated with
$\Omega$), then $\Omega$ is either spatially-homogeneous or
spatially-inhomogeneous; and moreover, any spatially-inhomogeneous $\Omega$ can
be imbedded into a time-recurrently forced circle flow (resp. imbedded into an
almost periodically-forced circle flow if $f$ is uniformly almost-periodic in
$t$). On the other hand, when ${\rm dim}V^c(\Omega>1$, it is pointed out that
the above embedding property cannot hold anymore. Furthermore, we also show the
new phenomena of the residual imbedding into a time-recurrently forced circle
flow (resp. into an almost automorphically-forced circle flow if $f$ is
uniformly almost-periodic in $t$) provided that $\dim V^c(\Omega)=2$ and $\dim
V^u(\Omega)$ is odd. All these results reveal that for such system there are
essential differences between time-periodic cases and non-periodic cases.
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16,240 | Exploring 4D Quantum Hall Physics with a 2D Topological Charge Pump | The discovery of topological states of matter has profoundly augmented our
understanding of phase transitions in physical systems. Instead of local order
parameters, topological phases are described by global topological invariants
and are therefore robust against perturbations. A prominent example thereof is
the two-dimensional integer quantum Hall effect. It is characterized by the
first Chern number which manifests in the quantized Hall response induced by an
external electric field. Generalizing the quantum Hall effect to
four-dimensional systems leads to the appearance of a novel non-linear Hall
response that is quantized as well, but described by a 4D topological invariant
- the second Chern number. Here, we report on the first observation of a bulk
response with intrinsic 4D topology and the measurement of the associated
second Chern number. By implementing a 2D topological charge pump with
ultracold bosonic atoms in an angled optical superlattice, we realize a
dynamical version of the 4D integer quantum Hall effect. Using a small atom
cloud as a local probe, we fully characterize the non-linear response of the
system by in-situ imaging and site-resolved band mapping. Our findings pave the
way to experimentally probe higher-dimensional quantum Hall systems, where new
topological phases with exotic excitations are predicted.
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16,241 | Non-hermitian operator modelling of basic cancer cell dynamics | We propose a dynamical system of tumor cells proliferation based on
operatorial methods. The approach we propose is quantum-like: we use ladder and
number operators to describe healthy and tumor cells birth and death, and the
evolution is ruled by a non-hermitian Hamiltonian which includes, in a non
reversible way, the basic biological mechanisms we consider for the system. We
show that this approach is rather efficient in describing some processes of the
cells. We further add some medical treatment, described by adding a suitable
term in the Hamiltonian, which controls and limits the growth of tumor cells,
and we propose an optimal approach to stop, and reverse, this growth.
| 0 | 0 | 0 | 0 | 1 | 0 |
16,242 | Giant ripples on comet 67P/Churyumov-Gerasimenko sculpted by sunset thermal wind | Explaining the unexpected presence of dune-like patterns at the surface of
the comet 67P/Churyumov-Gerasimenko requires conceptual and quantitative
advances in the understanding of surface and outgassing processes. We show here
that vapor flow emitted by the comet around its perihelion spreads laterally in
a surface layer, due to the strong pressure difference between zones
illuminated by sunlight and those in shadow. For such thermal winds to be dense
enough to transport grains -- ten times greater than previous estimates --
outgassing must take place through a surface porous granular layer, and that
layer must be composed of grains whose roughness lowers cohesion consistently
with contact mechanics. The linear stability analysis of the problem, entirely
tested against laboratory experiments, quantitatively predicts the emergence of
bedforms in the observed wavelength range, and their propagation at the scale
of a comet revolution. Although generated by a rarefied atmosphere, they are
paradoxically analogous to ripples emerging on granular beds submitted to
viscous shear flows. This quantitative agreement shows that our understanding
of the coupling between hydrodynamics and sediment transport is able to account
for bedform emergence in extreme conditions and provides a reliable tool to
predict the erosion and accretion processes controlling the evolution of small
solar system bodies.
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16,243 | A ROS multi-ontology references services: OWL reasoners and application prototyping issues | The challenge of sharing and communicating information is crucial in complex
human-robot interaction (HRI) scenarios. Ontologies and symbolic reasoning are
the state-of-the-art approaches for a natural representation of knowledge,
especially within the Semantic Web domain. In such a context, scripted
paradigms have been adopted to achieve high expressiveness. Nevertheless, since
symbolic reasoning is a high complexity problem, optimizing its performance
requires a careful design of the knowledge. Specifically, a robot architecture
requires the integration of several components implementing different behaviors
and generating a series of beliefs. Most of the components are expected to
access, manipulate, and reason upon a run-time generated semantic
representation of knowledge grounding robot behaviors and perceptions through
formal axioms, with soft real-time requirements.
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16,244 | Efficient Covariance Approximations for Large Sparse Precision Matrices | The use of sparse precision (inverse covariance) matrices has become popular
because they allow for efficient algorithms for joint inference in
high-dimensional models. Many applications require the computation of certain
elements of the covariance matrix, such as the marginal variances, which may be
non-trivial to obtain when the dimension is large. This paper introduces a fast
Rao-Blackwellized Monte Carlo sampling based method for efficiently
approximating selected elements of the covariance matrix. The variance and
confidence bounds of the approximations can be precisely estimated without
additional computational costs. Furthermore, a method that iterates over
subdomains is introduced, and is shown to additionally reduce the approximation
errors to practically negligible levels in an application on functional
magnetic resonance imaging data. Both methods have low memory requirements,
which is typically the bottleneck for competing direct methods.
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16,245 | The Density of Numbers Represented by Diagonal Forms of Large Degree | Let $s \geq 3$ be a fixed positive integer and $a_1,\dots,a_s \in \mathbb{Z}$
be arbitrary. We show that, on average over $k$, the density of numbers
represented by the degree $k$ diagonal form \[ a_1 x_1^k + \cdots + a_s x_s^k
\] decays rapidly with respect to $k$.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,246 | Meta-Learning for Resampling Recommendation Systems | One possible approach to tackle the class imbalance in classification tasks
is to resample a training dataset, i.e., to drop some of its elements or to
synthesize new ones. There exist several widely-used resampling methods. Recent
research showed that the choice of resampling method significantly affects the
quality of classification, which raises resampling selection problem.
Exhaustive search for optimal resampling is time-consuming and hence it is of
limited use. In this paper, we describe an alternative approach to the
resampling selection. We follow the meta-learning concept to build resampling
recommendation systems, i.e., algorithms recommending resampling for datasets
on the basis of their properties.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,247 | Robustness Analysis of Systems' Safety through a New Notion of Input-to-State Safety | In this paper, we propose a new robustness notion that is applicable for
certifying systems' safety with respect to external disturbance signals. The
proposed input-to-state safety (ISSf) notion allows us to certify systems'
safety in the presence of the disturbances which is analogous to the notion of
input-to-state stability (ISS) for analyzing systems' stability.
| 1 | 0 | 1 | 0 | 0 | 0 |
16,248 | Estimating ground-level PM2.5 by fusing satellite and station observations: A geo-intelligent deep learning approach | Fusing satellite observations and station measurements to estimate
ground-level PM2.5 is promising for monitoring PM2.5 pollution. A
geo-intelligent approach, which incorporates geographical correlation into an
intelligent deep learning architecture, is developed to estimate PM2.5.
Specifically, it considers geographical distance and spatiotemporally
correlated PM2.5 in a deep belief network (denoted as Geoi-DBN). Geoi-DBN can
capture the essential features associated with PM2.5 from latent factors. It
was trained and tested with data from China in 2015. The results show that
Geoi-DBN performs significantly better than the traditional neural network. The
cross-validation R increases from 0.63 to 0.94, and RMSE decreases from 29.56
to 13.68${\mu}$g/m3. On the basis of the derived PM2.5 distribution, it is
predicted that over 80% of the Chinese population live in areas with an annual
mean PM2.5 of greater than 35${\mu}$g/m3. This study provides a new perspective
for air pollution monitoring in large geographic regions.
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16,249 | An Overview of Recent Progress in Laser Wakefield Acceleration Experiments | The goal of this paper is to examine experimental progress in laser wakefield
acceleration over the past decade (2004-2014), and to use trends in the data to
understand some of the important physical processes. By examining a set of over
50 experiments, various trends concerning the relationship between plasma
density, accelerator length, laser power and the final electron beam en- ergy
are revealed. The data suggest that current experiments are limited by
dephasing and that current experiments typically require some pulse evolution
to reach the trapping threshold.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,250 | MindID: Person Identification from Brain Waves through Attention-based Recurrent Neural Network | Person identification technology recognizes individuals by exploiting their
unique, measurable physiological and behavioral characteristics. However, the
state-of-the-art person identification systems have been shown to be
vulnerable, e.g., contact lenses can trick iris recognition and fingerprint
films can deceive fingerprint sensors. EEG (Electroencephalography)-based
identification, which utilizes the users brainwave signals for identification
and offers a more resilient solution, draw a lot of attention recently.
However, the accuracy still requires improvement and very little work is
focusing on the robustness and adaptability of the identification system. We
propose MindID, an EEG-based biometric identification approach, achieves higher
accuracy and better characteristics. At first, the EEG data patterns are
analyzed and the results show that the Delta pattern contains the most
distinctive information for user identification. Then the decomposed Delta
pattern is fed into an attention-based Encoder-Decoder RNNs (Recurrent Neural
Networks) structure which assigns varies attention weights to different EEG
channels based on the channels importance. The discriminative representations
learned from the attention-based RNN are used to recognize the user
identification through a boosting classifier. The proposed approach is
evaluated over 3 datasets (two local and one public). One local dataset (EID-M)
is used for performance assessment and the result illustrate that our model
achieves the accuracy of 0.982 which outperforms the baselines and the
state-of-the-art. Another local dataset (EID-S) and a public dataset (EEG-S)
are utilized to demonstrate the robustness and adaptability, respectively. The
results indicate that the proposed approach has the potential to be largely
deployment in practice environment.
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16,251 | Beyond black-boxes in Bayesian inverse problems and model validation: applications in solid mechanics of elastography | The present paper is motivated by one of the most fundamental challenges in
inverse problems, that of quantifying model discrepancies and errors. While
significant strides have been made in calibrating model parameters, the
overwhelming majority of pertinent methods is based on the assumption of a
perfect model. Motivated by problems in solid mechanics which, as all problems
in continuum thermodynamics, are described by conservation laws and
phenomenological constitutive closures, we argue that in order to quantify
model uncertainty in a physically meaningful manner, one should break open the
black-box forward model. In particular we propose formulating an undirected
probabilistic model that explicitly accounts for the governing equations and
their validity. This recasts the solution of both forward and inverse problems
as probabilistic inference tasks where the problem's state variables should not
only be compatible with the data but also with the governing equations as well.
Even though the probability densities involved do not contain any black-box
terms, they live in much higher-dimensional spaces. In combination with the
intractability of the normalization constant of the undirected model employed,
this poses significant challenges which we propose to address with a
linearly-scaling, double-layer of Stochastic Variational Inference. We
demonstrate the capabilities and efficacy of the proposed model in synthetic
forward and inverse problems (with and without model error) in elastography.
| 0 | 0 | 0 | 1 | 0 | 0 |
16,252 | On the Spectral Properties of Symmetric Functions | We characterize the approximate monomial complexity, sign monomial complexity
, and the approximate L 1 norm of symmetric functions in terms of simple
combinatorial measures of the functions. Our characterization of the
approximate L 1 norm solves the main conjecture in [AFH12]. As an application
of the characterization of the sign monomial complexity, we prove a conjecture
in [ZS09] and provide a characterization for the unbounded-error communication
complexity of symmetric-xor functions.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,253 | Strong light shifts from near-resonant and polychromatic fields: comparison of Floquet theory and experiment | We present a non-perturbative numerical technique for calculating strong
light shifts in atoms under the influence of multiple optical fields with
arbitrary polarization. We confirm our technique experimentally by performing
spectroscopy of a cloud of cold $^{87}$Rb atoms subjected to $\sim$ kW/cm$^2$
intensities of light at 1560.492 nm simultaneous with 1529.269 nm or 1529.282
nm. In these conditions the excited state resonances at 1529.26 nm and 1529.36
nm induce strong level mixing and the shifts are highly nonlinear. By
absorption spectroscopy, we observe that the induced shifts of the 5P3/2
hyperfine Zeeman sublevels agree well with our theoretical predictions.. We
propose the application of our theory and experiment to accurate measurements
of excited-state electric-dipole matrix elements.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,254 | Positive-rank elliptic curves arising pythagorean triples | In the present paper, we introduce some new families of elliptic curves with
positive rank arrising from Pythagorean triples.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,255 | Modeling Relational Data with Graph Convolutional Networks | Knowledge graphs enable a wide variety of applications, including question
answering and information retrieval. Despite the great effort invested in their
creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata)
remain incomplete. We introduce Relational Graph Convolutional Networks
(R-GCNs) and apply them to two standard knowledge base completion tasks: Link
prediction (recovery of missing facts, i.e. subject-predicate-object triples)
and entity classification (recovery of missing entity attributes). R-GCNs are
related to a recent class of neural networks operating on graphs, and are
developed specifically to deal with the highly multi-relational data
characteristic of realistic knowledge bases. We demonstrate the effectiveness
of R-GCNs as a stand-alone model for entity classification. We further show
that factorization models for link prediction such as DistMult can be
significantly improved by enriching them with an encoder model to accumulate
evidence over multiple inference steps in the relational graph, demonstrating a
large improvement of 29.8% on FB15k-237 over a decoder-only baseline.
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16,256 | Application of shifted-Laplace preconditioners for heterogenous Helmholtz equation- part 1: Data modelling | In several geophysical applications, such as full waveform inversion and data
modelling, we are facing the solution of inhomogeneous Helmholtz equation. The
difficulties of solving the Helmholtz equa- tion are two fold. Firstly, in the
case of large scale problems we cannot calculate the inverse of the Helmholtz
operator directly. Hence, iterative algorithms should be implemented. Secondly,
the Helmholtz operator is non-unitary and non-diagonalizable which in turn
deteriorates the performances of the iterative algorithms (especially for high
wavenumbers). To overcome this issue, we need to im- plement proper
preconditioners for a Krylov subspace method to solve the problem efficiently.
In this paper we incorporated shifted-Laplace operators to precondition the
system of equations and then generalized minimal residual (GMRES) method used
to solve the problem iteratively. The numerical results show the performance of
the preconditioning operator in improving the convergence rate of the GMRES
algorithm for data modelling case. In the companion paper we discussed the
application of preconditioned data modelling algorithm in the context of
frequency domain full waveform inversion. However, the analysis of the degree
of suitability of the preconditioners in the solution of Helmholtz equation is
an ongoing field of study.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,257 | Algorithms For Longest Chains In Pseudo- Transitive Graphs | A directed acyclic graph G = (V, E) is pseudo-transitive with respect to a
given subset of edges E1, if for any edge ab in E1 and any edge bc in E, we
have ac in E. We give algorithms for computing longest chains and demonstrate
geometric applications that unify and improves some important past results.
(For specific applications see the introduction.)
| 1 | 0 | 1 | 0 | 0 | 0 |
16,258 | From time-series to complex networks: Application to the cerebrovascular flow patterns in atrial fibrillation | A network-based approach is presented to investigate the cerebrovascular flow
patterns during atrial fibrillation (AF) with respect to normal sinus rhythm
(NSR). AF, the most common cardiac arrhythmia with faster and irregular
beating, has been recently and independently associated with the increased risk
of dementia. However, the underlying hemodynamic mechanisms relating the two
pathologies remain mainly undetermined so far; thus the contribution of
modeling and refined statistical tools is valuable. Pressure and flow rate
temporal series in NSR and AF are here evaluated along representative cerebral
sites (from carotid arteries to capillary brain circulation), exploiting
reliable artificially built signals recently obtained from an in silico
approach. The complex network analysis evidences, in a synthetic and original
way, a dramatic signal variation towards the distal/capillary cerebral regions
during AF, which has no counterpart in NSR conditions. At the large artery
level, networks obtained from both AF and NSR hemodynamic signals exhibit
elongated and chained features, which are typical of pseudo-periodic series.
These aspects are almost completely lost towards the microcirculation during
AF, where the networks are topologically more circular and present random-like
characteristics. As a consequence, all the physiological phenomena at
microcerebral level ruled by periodicity - such as regular perfusion, mean
pressure per beat, and average nutrient supply at cellular level - can be
strongly compromised, since the AF hemodynamic signals assume irregular
behaviour and random-like features. Through a powerful approach which is
complementary to the classical statistical tools, the present findings further
strengthen the potential link between AF hemodynamic and cognitive decline.
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16,259 | On the matrix $pth$ root functions and generalized Fibonacci sequences | This study is devoted to the polynomial representation of the matrix $p$th
root functions. The Fibonacci-Hörner decomposition of the matrix powers and
some techniques arisen from properties of generalized Fibonacci sequences,
notably the Binet formula, serves as a triggering factor to provide explicit
formulas for the matrix $p$th roots. Special cases and illustrative numerical
examples are given.
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16,260 | Investigation and Automating Extraction of Thumbnails Produced by Image viewers | Today, in digital forensics, images normally provide important information
within an investigation. However, not all images may still be available within
a forensic digital investigation as they were all deleted for example. Data
carving can be used in this case to retrieve deleted images but the carving
time is normally significant and these images can be moreover overwritten by
other data. One of the solutions is to look at thumbnails of images that are no
longer available. These thumbnails can often be found within databases created
by either operating systems or image viewers. In literature, most research and
practical focus on the extraction of thumbnails from databases created by the
operating system. There is a little research working on the thumbnails created
by the image reviewers as these thumbnails are application-driven in terms of
pre-defined sizes, adjustments and storage location. Eventually, thumbnail
databases from image viewers are significant forensic artefacts for
investigators as these programs deal with large amounts of images. However,
investigating these databases so far is still manual or semi-automatic task
that leads to the huge amount of forensic time. Therefore, in this paper we
propose a new approach of automating extraction of thumbnails produced by image
viewers. We also test our approach with popular image viewers in different
storage structures and locations to show its robustness.
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16,261 | Weighted network estimation by the use of topological graph metrics | Topological metrics of graphs provide a natural way to describe the prominent
features of various types of networks. Graph metrics describe the structure and
interplay of graph edges and have found applications in many scientific fields.
In this work, graph metrics are used in network estimation by developing
optimisation methods that incorporate prior knowledge of a network's topology.
The derivatives of graph metrics are used in gradient descent schemes for
weighted undirected network denoising, network completion, and network
decomposition. The successful performance of our methodology is shown in a
number of toy examples and real-world datasets. Most notably, our work
establishes a new link between graph theory, network science and optimisation.
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16,262 | Petrophysical property estimation from seismic data using recurrent neural networks | Reservoir characterization involves the estimation petrophysical properties
from well-log data and seismic data. Estimating such properties is a
challenging task due to the non-linearity and heterogeneity of the subsurface.
Various attempts have been made to estimate petrophysical properties using
machine learning techniques such as feed-forward neural networks and support
vector regression (SVR). Recent advances in machine learning have shown
promising results for recurrent neural networks (RNN) in modeling complex
sequential data such as videos and speech signals. In this work, we propose an
algorithm for property estimation from seismic data using recurrent neural
networks. An applications of the proposed workflow to estimate density and
p-wave impedance using seismic data shows promising results compared to
feed-forward neural networks.
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16,263 | Deep Learning for Spatio-Temporal Modeling: Dynamic Traffic Flows and High Frequency Trading | Deep learning applies hierarchical layers of hidden variables to construct
nonlinear high dimensional predictors. Our goal is to develop and train deep
learning architectures for spatio-temporal modeling. Training a deep
architecture is achieved by stochastic gradient descent (SGD) and drop-out (DO)
for parameter regularization with a goal of minimizing out-of-sample predictive
mean squared error. To illustrate our methodology, we predict the sharp
discontinuities in traffic flow data, and secondly, we develop a classification
rule to predict short-term futures market prices as a function of the order
book depth. Finally, we conclude with directions for future research.
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16,264 | Multi-objective Bandits: Optimizing the Generalized Gini Index | We study the multi-armed bandit (MAB) problem where the agent receives a
vectorial feedback that encodes many possibly competing objectives to be
optimized. The goal of the agent is to find a policy, which can optimize these
objectives simultaneously in a fair way. This multi-objective online
optimization problem is formalized by using the Generalized Gini Index (GGI)
aggregation function. We propose an online gradient descent algorithm which
exploits the convexity of the GGI aggregation function, and controls the
exploration in a careful way achieving a distribution-free regret
$\tilde{\bigO} (T^{-1/2} )$ with high probability. We test our algorithm on
synthetic data as well as on an electric battery control problem where the goal
is to trade off the use of the different cells of a battery in order to balance
their respective degradation rates.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,265 | Floquet multi-Weyl points in crossing-nodal-line semimetals | Weyl points with monopole charge $\pm 1$ have been extensively studied,
however, real materials of multi-Weyl points, whose monopole charges are higher
than $1$, have yet to be found. In this Rapid Communication, we show that
nodal-line semimetals with nontrivial line connectivity provide natural
platforms for realizing Floquet multi-Weyl points. In particular, we show that
driving crossing nodal lines by circularly polarized light generates
double-Weyl points. Furthermore, we show that monopole combination and
annihilation can be observed in crossing-nodal-line semimetals and nodal-chain
semimetals. These proposals can be experimentally verified in pump-probe
angle-resolved photoemission spectroscopy.
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16,266 | Impact Ionization in $β-Ga_2O_3$ | A theoretical investigation of extremely high field transport in an emerging
wide-bandgap material $\beta-Ga_2O_3$ is reported from first principles. The
signature high-field effect explored here is impact ionization. Interaction
between a valence-band electron and an excited electron is computed from the
matrix elements of a screened Coulomb operator. Maximally localized Wannier
functions (MLWF) are utilized in computing the impact ionization rates. A
full-band Monte Carlo (FBMC) simulation is carried out incorporating the impact
ionization rates, and electron-phonon scattering rates. This work brings out
valuable insights on the impact ionization coefficient (IIC) of electrons in
$\beta-Ga_2O_3$. The isolation of the $\Gamma$ point conduction band minimum by
a significantly high energy from other satellite band pockets play a vital role
in determining ionization co-efficients. IICs are calculated for electric
fields ranging up to 8 MV/cm for different crystal directions. A Chynoweth
fitting of the computed IICs is done to calibrate ionization models in device
simulators.
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16,267 | Entombed: An archaeological examination of an Atari 2600 game | The act and experience of programming is, at its heart, a fundamentally human
activity that results in the production of artifacts. When considering
programming, therefore, it would be a glaring omission to not involve people
who specialize in studying artifacts and the human activity that yields them:
archaeologists. Here we consider this with respect to computer games, the focus
of archaeology's nascent subarea of archaeogaming.
One type of archaeogaming research is digital excavation, a technical
examination of the code and techniques used in old games' implementation. We
apply that in a case study of Entombed, an Atari 2600 game released in 1982 by
US Games. The player in this game is, appropriately, an archaeologist who must
make their way through a zombie-infested maze. Maze generation is a fruitful
area for comparative retrogame archaeology, because a number of early games on
different platforms featured mazes, and their variety of approaches can be
compared. The maze in Entombed is particularly interesting: it is shaped in
part by the extensive real-time constraints of the Atari 2600 platform, and
also had to be generated efficiently and use next to no memory. We reverse
engineered key areas of the game's code to uncover its unusual maze-generation
algorithm, which we have also built a reconstruction of, and analyzed the
mysterious table that drives it. In addition, we discovered what appears to be
a 35-year-old bug in the code, as well as direct evidence of code-reuse
practices amongst game developers.
What further makes this game's development interesting is that, in an era
where video games were typically solo projects, a total of five people were
involved in various ways with Entombed. We piece together some of the backstory
of the game's development and intoxicant-fueled design using interviews to
complement our technical work.
Finally, we contextualize this example in archaeology and lay the groundwork
for a broader interdisciplinary discussion about programming, one that includes
both computer scientists and archaeologists.
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16,268 | Particle Filters for Partially-Observed Boolean Dynamical Systems | Partially-observed Boolean dynamical systems (POBDS) are a general class of
nonlinear models with application in estimation and control of Boolean
processes based on noisy and incomplete measurements. The optimal minimum mean
square error (MMSE) algorithms for POBDS state estimation, namely, the Boolean
Kalman filter (BKF) and Boolean Kalman smoother (BKS), are intractable in the
case of large systems, due to computational and memory requirements. To address
this, we propose approximate MMSE filtering and smoothing algorithms based on
the auxiliary particle filter (APF) method from sequential Monte-Carlo theory.
These algorithms are used jointly with maximum-likelihood (ML) methods for
simultaneous state and parameter estimation in POBDS models. In the presence of
continuous parameters, ML estimation is performed using the
expectation-maximization (EM) algorithm; we develop for this purpose a special
smoother which reduces the computational complexity of the EM algorithm. The
resulting particle-based adaptive filter is applied to a POBDS model of Boolean
gene regulatory networks observed through noisy RNA-Seq time series data, and
performance is assessed through a series of numerical experiments using the
well-known cell cycle gene regulatory model.
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16,269 | Cross-National Measurement of Polarization in Political Discourse: Analyzing floor debate in the U.S. and the Japanese legislatures | Political polarization in public space can seriously hamper the function and
the integrity of contemporary democratic societies. In this paper, we propose a
novel measure of such polarization, which, by way of simple topic modelling,
quantifies differences in collective articulation of public agendas among
relevant political actors. Unlike most other polarization measures, our measure
allows cross-national comparison. Analyzing a large amount of speech records of
legislative debate in the United States Congress and the Japanese Diet over a
long period of time, we have reached two intriguing findings. First, on
average, Japanese political actors are far more polarized in their issue
articulation than their counterparts in the U.S., which is somewhat surprising
given the recent notion of U.S. politics as highly polarized. Second, the
polarization in each country shows its own temporal dynamics in response to a
different set of factors. In Japan, structural factors such as the roles of the
ruling party and the opposition often dominate such dynamics, whereas the U.S.
legislature suffers from persistent ideological differences over particular
issues between major political parties. The analysis confirms a strong
influence of institutional differences on legislative debate in parliamentary
democracies.
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16,270 | Size-Change Termination as a Contract | Program termination is an undecidable, yet important, property relevant to
program verification, optimization, debugging, partial evaluation, and
dependently-typed programming, among many other topics. This has given rise to
a large body of work on static methods for conservatively predicting or
enforcing termination. A simple effective approach is the size-change
termination (SCT) method, which operates in two-phases: (1) abstract programs
into "size-change graphs," and (2) check these graphs for the size-change
property: the existence of paths that lead to infinitely decreasing value
sequences.
This paper explores the termination problem starting from a different vantage
point: we propose transposing the two phases of the SCT analysis by developing
an operational semantics that accounts for the run time checking of the
size-change property, postponing program abstraction or avoiding it entirely.
This choice has two important consequences: SCT can be monitored and enforced
at run-time and termination analysis can be rephrased as a traditional safety
property and computed using existing abstract interpretation methods.
We formulate this run-time size-change check as a contract. This contributes
the first run-time mechanism for checking termination in a general-purporse
programming language. The result nicely compliments existing contracts that
enforce partial correctness to obtain the first contracts for total
correctness. Our approach combines the robustness of SCT with precise
information available at run-time. To obtain a sound and computable analysis,
it is possible to apply existing abstract interpretation techniques directly to
the operational semantics; there is no need for an abstraction tailored to
size-change graphs. We apply higher-order symbolic execution to obtain a novel
termination analysis that is competitive with existing, purpose-built
termination analyzers.
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16,271 | Disorder Dependent Valley Properties in Monolayer WSe2 | We investigate the effect on disorder potential on exciton valley
polarization and valley coherence in monolayer WSe2. By analyzing polarization
properties of photoluminescence, the valley coherence (VC) and valley
polarization (VP) is quantified across the inhomogeneously broadened exciton
resonance. We find that disorder plays a critical role in the exciton VC, while
minimally affecting VP. For different monolayer samples with disorder
characterized by their Stokes Shift (SS), VC decreases in samples with higher
SS while VP again remains unchanged. These two methods consistently demonstrate
that VC as defined by the degree of linearly polarized photoluminescence is
more sensitive to disorder potential, motivating further theoretical studies.
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16,272 | Results on the Hilbert coefficients and reduction numbers | Let $(R,\frak{m})$ be a $d$-dimensional Cohen-Macaulay local ring, $I$ an
$\frak{m}$-primary ideal and $J$ a minimal reduction of $I$. In this paper we
study the independence of reduction ideals and the behavior of the higher
Hilbert coefficients. In addition, we give some examples in this regards.
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16,273 | Progress on Experiments towards LWFA-driven Transverse Gradient Undulator-Based FELs | Free Electron Lasers (FEL) are commonly regarded as the potential key
application of laser wakefield accelerators (LWFA). It has been found that
electron bunches exiting from state-of-the-art LWFAs exhibit a normalized
6-dimensional beam brightness comparable to those in conventional linear
accelerators. Effectively exploiting this beneficial beam property for
LWFA-based FELs is challenging due to the extreme initial conditions
particularly in terms of beam divergence and energy spread. Several different
approaches for capturing, reshaping and matching LWFA beams to suited
undulators, such as bunch decompression or transverse-gradient undulator
schemes, are currently being explored. In this article the transverse gradient
undulator concept will be discussed with a focus on recent experimental
achievements.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,274 | Non-escaping endpoints do not explode | The family of exponential maps $f_a(z)= e^z+a$ is of fundamental importance
in the study of transcendental dynamics. Here we consider the topological
structure of certain subsets of the Julia set $J(f_a)$. When $a\in
(-\infty,-1)$, and more generally when $a$ belongs to the Fatou set of $f_a$,
it is known that $J(f_a)$ can be written as a union of "hairs" and "endpoints"
of these hairs. In 1990, Mayer proved for $a\in (-\infty,-1)$ that, while the
set of endpoints is totally separated, its union with infinity is a connected
set. Recently, Alhabib and the second author extended this result to the case
where $a \in F(f_a)$, and showed that it holds even for the smaller set of all
escaping endpoints.
We show that, in contrast, the set of non-escaping endpoints together with
infinity is totally separated. It turns out that this property is closely
related to a topological structure known as a `spider's web'; in particular we
give a new topological characterisation of spiders' webs that may be of
independent interest. We also show how our results can be applied to Fatou's
function, $z\mapsto z + 1 + e^{-z}$.
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16,275 | The Fornax Deep Survey with VST. II. Fornax A: a two-phase assembly caught on act | As part of the Fornax Deep Survey with the ESO VLT Survey Telescope, we
present new $g$ and $r$ bands mosaics of the SW group of the Fornax cluster. It
covers an area of $3 \times 2$ square degrees around the central galaxy
NGC1316. The deep photometry, the high spatial resolution of OmegaCam and the
large covered area allow us to study the galaxy structure, to trace stellar
halo formation and look at the galaxy environment. We map the surface
brightness profile out to 33arcmin ($\sim 200$kpc $\sim15R_e$) from the galaxy
centre, down to $\mu_g \sim 31$ mag arcsec$^{-2}$ and $\mu_r \sim 29$ mag
arcsec$^{-2}$. This allow us to estimate the scales of the main components
dominating the light distribution, which are the central spheroid, inside 5.5
arcmin ($\sim33$ kpc), and the outer stellar envelope. Data analysis suggests
that we are catching in act the second phase of the mass assembly in this
galaxy, since the accretion of smaller satellites is going on in both
components. The outer envelope of NGC1316 still hosts the remnants of the
accreted satellite galaxies that are forming the stellar halo. We discuss the
possible formation scenarios for NGC1316, by comparing the observed properties
(morphology, colors, gas content, kinematics and dynamics) with predictions
from cosmological simulations of galaxy formation. We find that {\it i)} the
central spheroid could result from at least one merging event, it could be a
pre-existing early-type disk galaxy with a lower mass companion, and {\it ii)}
the stellar envelope comes from the gradual accretion of small satellites.
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16,276 | Local decoding and testing of polynomials over grids | The well-known DeMillo-Lipton-Schwartz-Zippel lemma says that $n$-variate
polynomials of total degree at most $d$ over grids, i.e. sets of the form $A_1
\times A_2 \times \cdots \times A_n$, form error-correcting codes (of distance
at least $2^{-d}$ provided $\min_i\{|A_i|\}\geq 2$). In this work we explore
their local decodability and (tolerant) local testability. While these aspects
have been studied extensively when $A_1 = \cdots = A_n = \mathbb{F}_q$ are the
same finite field, the setting when $A_i$'s are not the full field does not
seem to have been explored before.
In this work we focus on the case $A_i = \{0,1\}$ for every $i$. We show that
for every field (finite or otherwise) there is a test whose query complexity
depends only on the degree (and not on the number of variables). In contrast we
show that decodability is possible over fields of positive characteristic (with
query complexity growing with the degree of the polynomial and the
characteristic), but not over the reals, where the query complexity must grow
with $n$. As a consequence we get a natural example of a code (one with a
transitive group of symmetries) that is locally testable but not locally
decodable.
Classical results on local decoding and testing of polynomials have relied on
the 2-transitive symmetries of the space of low-degree polynomials (under
affine transformations). Grids do not possess this symmetry: So we introduce
some new techniques to overcome this handicap and in particular use the
hypercontractivity of the (constant weight) noise operator on the Hamming cube.
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16,277 | A New Backpressure Algorithm for Joint Rate Control and Routing with Vanishing Utility Optimality Gaps and Finite Queue Lengths | The backpressure algorithm has been widely used as a distributed solution to
the problem of joint rate control and routing in multi-hop data networks. By
controlling a parameter $V$ in the algorithm, the backpressure algorithm can
achieve an arbitrarily small utility optimality gap. However, this in turn
brings in a large queue length at each node and hence causes large network
delay. This phenomenon is known as the fundamental utility-delay tradeoff. The
best known utility-delay tradeoff for general networks is $[O(1/V), O(V)]$ and
is attained by a backpressure algorithm based on a drift-plus-penalty
technique. This may suggest that to achieve an arbitrarily small utility
optimality gap, the existing backpressure algorithms necessarily yield an
arbitrarily large queue length. However, this paper proposes a new backpressure
algorithm that has a vanishing utility optimality gap, so utility converges to
exact optimality as the algorithm keeps running, while queue lengths are
bounded throughout by a finite constant. The technique uses backpressure and
drift concepts with a new method for convex programming.
| 1 | 0 | 1 | 0 | 0 | 0 |
16,278 | Lifshitz transition from valence fluctuations in YbAl3 | In Kondo lattice systems with mixed valence, such as YbAl3, interactions
between localized electrons in a partially filled f shell and delocalized
conduction electrons can lead to fluctuations between two different valence
configurations with changing temperature or pressure. The impact of this change
on the momentum-space electronic structure and Fermi surface topology is
essential for understanding their emergent properties, but has remained
enigmatic due to a lack of appropriate experimental probes. Here by employing a
combination of molecular beam epitaxy (MBE) and in situ angle-resolved
photoemission spectroscopy (ARPES) we show that valence fluctuations can lead
to dramatic changes in the Fermi surface topology, even resulting in a Lifshitz
transition. As the temperature is lowered, a small electron pocket in YbAl3
becomes completely unoccupied while the low-energy ytterbium (Yb) 4f states
become increasingly itinerant, acquiring additional spectral weight, longer
lifetimes, and well-defined dispersions. Our work presents the first unified
picture of how local valence fluctuations connect to momentum space concepts
including band filling and Fermi surface topology in the longstanding problem
of mixed-valence systems.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,279 | Diversity from the Topology of Citation Networks | We study transitivity in directed acyclic graphs and its usefulness in
capturing nodes that act as bridges between more densely interconnected parts
in such type of network. In transitively reduced citation networks degree
centrality could be used as a measure of interdisciplinarity or diversity. We
study the measure's ability to capture "diverse" nodes in random directed
acyclic graphs and citation networks. We show that transitively reduced degree
centrality is capable of capturing "diverse" nodes, thus this measure could be
a timely alternative to text analysis techniques for retrieving papers,
influential in a variety of research fields.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,280 | Superluminal transmission of phase modulation information by a long macroscopic pulse propagating through interstellar space | A method of transmitting information in interstellar space at superluminal,
or $> c$, speeds is proposed. The information is encoded as phase modulation of
an electromagnetic wave of constant intensity, i.e. fluctuations in the rate of
energy transport plays no role in the communication, and no energy is
transported at speed $>$ c. Of course, such a constant wave can ultimately last
only the duration of its enveloping wave packet. However, as a unique feature
of this paper, we assume the source is sufficiently steady to be capable of
emitting wave packets, or pulses, of size much larger than the separation
between sender and receiver. Therefore, if a pre-existing and enduring wave
envelope already connects the two sides, the subluminal nature of the
envelope's group velocity will no longer slow down the communication, which is
now limited by the speed at which information encoded as phase modulation
propagates through the plasma, i.e. the phase velocity $v_p > c$. The method
involves no sharp structure in either time or frequency. As a working example,
we considered two spaceships separated by 1 lt-s in the local hot bubble.
Provided the bandwidth of the extra Fourier modes generated by the phase
modulation is much smaller than the carrier wave frequency, the radio
communication of a message, encoded as a specific alignment between the carrier
wave phase and the anomalous (modulated) phase, can take place at a speed in
excess of light by a few parts in 10$^{11}$ at $\nu\approx 1$~GHz, and higher
at smaller $\nu$.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,281 | Causal Regularization | In application domains such as healthcare, we want accurate predictive models
that are also causally interpretable. In pursuit of such models, we propose a
causal regularizer to steer predictive models towards causally-interpretable
solutions and theoretically study its properties. In a large-scale analysis of
Electronic Health Records (EHR), our causally-regularized model outperforms its
L1-regularized counterpart in causal accuracy and is competitive in predictive
performance. We perform non-linear causality analysis by causally regularizing
a special neural network architecture. We also show that the proposed causal
regularizer can be used together with neural representation learning algorithms
to yield up to 20% improvement over multilayer perceptron in detecting
multivariate causation, a situation common in healthcare, where many causal
factors should occur simultaneously to have an effect on the target variable.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,282 | Voice Disorder Detection Using Long Short Term Memory (LSTM) Model | Automated detection of voice disorders with computational methods is a recent
research area in the medical domain since it requires a rigorous endoscopy for
the accurate diagnosis. Efficient screening methods are required for the
diagnosis of voice disorders so as to provide timely medical facilities in
minimal resources. Detecting Voice disorder using computational methods is a
challenging problem since audio data is continuous due to which extracting
relevant features and applying machine learning is hard and unreliable. This
paper proposes a Long short term memory model (LSTM) to detect pathological
voice disorders and evaluates its performance in a real 400 testing samples
without any labels. Different feature extraction methods are used to provide
the best set of features before applying LSTM model for classification. The
paper describes the approach and experiments that show promising results with
22% sensitivity, 97% specificity and 56% unweighted average recall.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,283 | Topology of two-dimensional turbulent flows of dust and gas | We perform direct numerical simulations (DNS) of passive heavy inertial
particles (dust) in homogeneous and isotropic two-dimensional turbulent flows
(gas) for a range of Stokes number, ${\rm St} < 1$, using both Lagrangian and
Eulerian approach (with a shock-capturing scheme). We find that: The
dust-density field in our Eulerian simulations have the same correlation
dimension $d_2$ as obtained from the clustering of particles in the Lagrangian
simulations for ${\rm St} < 1$; The cumulative probability distribution
function of the dust-density coarse-grained over a scale $r$ in the inertial
range has a left-tail with a power-law fall-off indicating presence of voids;
The energy spectrum of the dust-velocity has a power-law range with an exponent
that is same as the gas-velocity spectrum except at very high Fourier modes;
The compressibility of the dust-velocity field is proportional to ${\rm St}^2$.
We quantify the topological properties of the dust-velocity and the
gas-velocity through their gradient matrices, called $\mathcal{A}$ and
$\mathcal{B}$, respectively. The topological properties of $\mathcal{B}$ are
the same in Eulerian and Lagrangian frames only if the Eulerian data are
weighed by the dust-density -- a correspondence that we use to study Lagrangian
properties of $\mathcal{A}$. In the Lagrangian frame, the mean value of the
trace of $\mathcal{A} \sim - \exp(-C/{\rm St}$, with a constant $C\approx 0.1$.
The topology of the dust-velocity fields shows that as ${\rm St} increases the
contribution to negative divergence comes mostly from saddles and the
contribution to positive divergence comes from both vortices and saddles.
Compared to the Eulerian case, the density-weighed Eulerian case has less
inward spirals and more converging saddles. Outward spirals are the least
probable topological structures in both cases.
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16,284 | Trimming the Independent Fat: Sufficient Statistics, Mutual Information, and Predictability from Effective Channel States | One of the most fundamental questions one can ask about a pair of random
variables X and Y is the value of their mutual information. Unfortunately, this
task is often stymied by the extremely large dimension of the variables. We
might hope to replace each variable by a lower-dimensional representation that
preserves the relationship with the other variable. The theoretically ideal
implementation is the use of minimal sufficient statistics, where it is
well-known that either X or Y can be replaced by their minimal sufficient
statistic about the other while preserving the mutual information. While
intuitively reasonable, it is not obvious or straightforward that both
variables can be replaced simultaneously. We demonstrate that this is in fact
possible: the information X's minimal sufficient statistic preserves about Y is
exactly the information that Y's minimal sufficient statistic preserves about
X. As an important corollary, we consider the case where one variable is a
stochastic process' past and the other its future and the present is viewed as
a memoryful channel. In this case, the mutual information is the channel
transmission rate between the channel's effective states. That is, the
past-future mutual information (the excess entropy) is the amount of
information about the future that can be predicted using the past. Translating
our result about minimal sufficient statistics, this is equivalent to the
mutual information between the forward- and reverse-time causal states of
computational mechanics. We close by discussing multivariate extensions to this
use of minimal sufficient statistics.
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16,285 | Statistical Physics of the Symmetric Group | Ordered chains (such as chains of amino acids) are ubiquitous in biological
cells, and these chains perform specific functions contingent on the sequence
of their components. Using the existence and general properties of such
sequences as a theoretical motivation, we study the statistical physics of
systems whose state space is defined by the possible permutations of an ordered
list, i.e., the symmetric group, and whose energy is a function of how certain
permutations deviate from some chosen correct ordering. Such a non-factorizable
state space is quite different from the state spaces typically considered in
statistical physics systems and consequently has novel behavior in systems with
interacting and even non-interacting Hamiltonians. Various parameter choices of
a mean-field model reveal the system to contain five different physical regimes
defined by two transition temperatures, a triple point, and a quadruple point.
Finally, we conclude by discussing how the general analysis can be extended to
state spaces with more complex combinatorial properties and to other standard
questions of statistical mechanics models.
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16,286 | A New Phosphorus Allotrope with Direct Band Gap and High Mobility | Based on ab initio evolutionary crystal structure search computation, we
report a new phase of phosphorus called green phosphorus ({\lambda}-P), which
exhibits the direct band gaps ranging from 0.7 to 2.4 eV and the strong
anisotropy in optical and transport properties. Free energy calculations show
that a single-layer form, termed green phosphorene, is energetically more
stable than blue phosphorene and a phase transition from black to green
phosphorene can occur at temperatures above 87 K. Due to its buckled structure,
green phosphorene can be synthesized on corrugated metal surfaces rather than
clean surfaces.
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16,287 | An open shop approach in approximating optimal data transmission duration in WDM networks | In the past decade Optical WDM Networks (Wavelength Division Multiplexing)
are being used quite often and especially as far as broadband applications are
concerned. Message packets transmitted through such networks can be interrupted
using time slots in order to maximize network usage and minimize the time
required for all messages to reach their destination. However, preempting a
packet will result in time cost. The problem of scheduling message packets
through such a network is referred to as PBS and is known to be NP-Hard. In
this paper we have reduced PBS to Open Shop Scheduling and designed variations
of polynomially solvable instances of Open Shop to approximate PBS. We have
combined these variations and called the induced algorithm HSA (Hybridic
Scheduling Algorithm). We ran experiments to establish the efficiency of HSA
and found that in all datasets used it produces schedules very close to the
optimal. To further establish HSAs efficiency we ran tests to compare it to
SGA, another algorithm which when tested in the past has yielded excellent
results.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,288 | Optimal prediction in the linearly transformed spiked model | We consider the linearly transformed spiked model, where observations $Y_i$
are noisy linear transforms of unobserved signals of interest $X_i$:
\begin{align*}
Y_i = A_i X_i + \varepsilon_i, \end{align*} for $i=1,\ldots,n$. The transform
matrices $A_i$ are also observed. We model $X_i$ as random vectors lying on an
unknown low-dimensional space. How should we predict the unobserved signals
(regression coefficients) $X_i$?
The naive approach of performing regression for each observation separately
is inaccurate due to the large noise. Instead, we develop optimal linear
empirical Bayes methods for predicting $X_i$ by "borrowing strength" across the
different samples. Our methods are applicable to large datasets and rely on
weak moment assumptions. The analysis is based on random matrix theory.
We discuss applications to signal processing, deconvolution, cryo-electron
microscopy, and missing data in the high-noise regime. For missing data, we
show in simulations that our methods are faster, more robust to noise and to
unequal sampling than well-known matrix completion methods.
| 0 | 0 | 1 | 1 | 0 | 0 |
16,289 | Self-Normalizing Neural Networks | Deep Learning has revolutionized vision via convolutional neural networks
(CNNs) and natural language processing via recurrent neural networks (RNNs).
However, success stories of Deep Learning with standard feed-forward neural
networks (FNNs) are rare. FNNs that perform well are typically shallow and,
therefore cannot exploit many levels of abstract representations. We introduce
self-normalizing neural networks (SNNs) to enable high-level abstract
representations. While batch normalization requires explicit normalization,
neuron activations of SNNs automatically converge towards zero mean and unit
variance. The activation function of SNNs are "scaled exponential linear units"
(SELUs), which induce self-normalizing properties. Using the Banach fixed-point
theorem, we prove that activations close to zero mean and unit variance that
are propagated through many network layers will converge towards zero mean and
unit variance -- even under the presence of noise and perturbations. This
convergence property of SNNs allows to (1) train deep networks with many
layers, (2) employ strong regularization, and (3) to make learning highly
robust. Furthermore, for activations not close to unit variance, we prove an
upper and lower bound on the variance, thus, vanishing and exploding gradients
are impossible. We compared SNNs on (a) 121 tasks from the UCI machine learning
repository, on (b) drug discovery benchmarks, and on (c) astronomy tasks with
standard FNNs and other machine learning methods such as random forests and
support vector machines. SNNs significantly outperformed all competing FNN
methods at 121 UCI tasks, outperformed all competing methods at the Tox21
dataset, and set a new record at an astronomy data set. The winning SNN
architectures are often very deep. Implementations are available at:
github.com/bioinf-jku/SNNs.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,290 | Real-time Fault Localization in Power Grids With Convolutional Neural Networks | Diverse fault types, fast re-closures and complicated transient states after
a fault event make real-time fault location in power grids challenging.
Existing localization techniques in this area rely on simplistic assumptions,
such as static loads, or require much higher sampling rates or total
measurement availability. This paper proposes a data-driven localization method
based on a Convolutional Neural Network (CNN) classifier using bus voltages.
Unlike prior data-driven methods, the proposed classifier is based on features
with physical interpretations that are described in details. The accuracy of
our CNN based localization tool is demonstrably superior to other machine
learning classifiers in the literature. To further improve the location
performance, a novel phasor measurement units (PMU) placement strategy is
proposed and validated against other methods. A significant aspect of our
methodology is that under very low observability (7% of buses), the algorithm
is still able to localize the faulted line to a small neighborhood with high
probability. The performance of our scheme is validated through simulations of
faults of various types in the IEEE 68-bus power system under varying load
conditions, system observability and measurement quality.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,291 | Effective identifiability criteria for tensors and polynomials | A tensor $T$, in a given tensor space, is said to be $h$-identifiable if it
admits a unique decomposition as a sum of $h$ rank one tensors. A criterion for
$h$-identifiability is called effective if it is satisfied in a dense, open
subset of the set of rank $h$ tensors. In this paper we give effective
$h$-identifiability criteria for a large class of tensors. We then improve
these criteria for some symmetric tensors. For instance, this allows us to give
a complete set of effective identifiability criteria for ternary quintic
polynomial. Finally, we implement our identifiability algorithms in Macaulay2.
| 1 | 0 | 1 | 0 | 0 | 0 |
16,292 | Functoriality properties of the dual group | Let $G$ be a connected reductive group. In a previous paper,
arXiv:1702.08264, is was shown that the dual group $G^\vee_X$ attached to a
$G$-variety $X$ admits a natural homomorphism with finite kernel to the
Langlands dual group $G^\vee$ of $G$. Here, we prove that the dual group is
functorial in the following sense: if there is a dominant $G$-morphism $X\to Y$
or an injective $G$-morphism $Y\to X$ then there is a canonical homomorphism
$G^\vee_Y\to G^\vee_X$ which is compatible with the homomorphisms to $G^\vee$.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,293 | Chaotic dynamics around cometary nuclei | We apply a generalized Kepler map theory to describe the qualitative chaotic
dynamics around cometary nuclei, based on accessible observational data for
five comets whose nuclei are well-documented to resemble dumb-bells. The sizes
of chaotic zones around the nuclei and the Lyapunov times of the motion inside
these zones are estimated. In the case of Comet 1P/Halley, the circumnuclear
chaotic zone seems to engulf an essential part of the Hill sphere, at least for
orbits of moderate to high eccentricity.
| 0 | 1 | 0 | 0 | 0 | 0 |
16,294 | Riemannian curvature measures | A famous theorem of Weyl states that if $M$ is a compact submanifold of
euclidean space, then the volumes of small tubes about $M$ are given by a
polynomial in the radius $r$, with coefficients that are expressible as
integrals of certain scalar invariants of the curvature tensor of $M$ with
respect to the induced metric. It is natural to interpret this phenomenon in
terms of curvature measures and smooth valuations, in the sense of Alesker,
canonically associated to the Riemannian structure of $M$. This perspective
yields a fundamental new structure in Riemannian geometry, in the form of a
certain abstract module over the polynomial algebra $\mathbb R[t]$ that
reflects the behavior of Alesker multiplication. This module encodes a key
piece of the array of kinematic formulas of any Riemannian manifold on which a
group of isometries acts transitively on the sphere bundle. We illustrate this
principle in precise terms in the case where $M$ is a complex space form.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,295 | A new lower bound for the on-line coloring of intervals with bandwidth | The on-line interval coloring and its variants are important combinatorial
problems with many applications in network multiplexing, resource allocation
and job scheduling. In this paper we present a new lower bound of $4.1626$ for
the competitive ratio for the on-line coloring of intervals with bandwidth
which improves the best known lower bound of $\frac{24}{7}$. For the on-line
coloring of unit intervals with bandwidth we improve the lower bound of $1.831$
to $2$.
| 1 | 0 | 1 | 0 | 0 | 0 |
16,296 | Orbits of monomials and factorization into products of linear forms | This paper is devoted to the factorization of multivariate polynomials into
products of linear forms, a problem which has applications to differential
algebra, to the resolution of systems of polynomial equations and to Waring
decomposition (i.e., decomposition in sums of d-th powers of linear forms; this
problem is also known as symmetric tensor decomposition). We provide three
black box algorithms for this problem. Our main contribution is an algorithm
motivated by the application to Waring decomposition. This algorithm reduces
the corresponding factorization problem to simultaenous matrix diagonalization,
a standard task in linear algebra. The algorithm relies on ideas from invariant
theory, and more specifically on Lie algebras. Our second algorithm
reconstructs a factorization from several bi-variate projections. Our third
algorithm reconstructs it from the determination of the zero set of the input
polynomial, which is a union of hyperplanes.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,297 | Monodromy and Vinberg fusion for the principal degeneration of the space of G-bundles | We study the geometry and the singularities of the principal direction of the
Drinfeld-Lafforgue-Vinberg degeneration of the moduli space of G-bundles Bun_G
for an arbitrary reductive group G, and their relationship to the Langlands
dual group of G.
In the first part of the article we study the monodromy action on the nearby
cycles sheaf along the principal degeneration of Bun_G. We describe the
weight-monodromy filtration in terms of the combinatorics of the Langlands dual
group of G and generalizations of the Picard-Lefschetz oscillators found in
[Sch1]. Our proofs use certain local models for the principal degeneration
whose geometry is studied in the second part.
Our local models simultaneously provide two types of degenerations of the
Zastava spaces, which together equip the Zastava spaces with the geometric
analog of a Hopf algebra structure. The first degeneration corresponds to the
usual Beilinson-Drinfeld fusion of divisors on the curve. The second
degeneration is new and corresponds to what we call Vinberg fusion: It is
obtained not by degenerating divisors on the curve, but by degenerating the
group G via the Vinberg semigroup. On the level of cohomology the Vinberg
fusion gives rise to an algebra structure, while the Beilinson-Drinfeld fusion
gives rise to a coalgebra structure; the Hopf algebra axiom is a consequence of
the underlying geometry.
It is natural to conjecture that this Hopf algebra agrees with the universal
enveloping algebra of the positive part of the Langlands dual Lie algebra. The
above procedure would then yield a novel and highly geometric way to pass to
the Langlands dual side: Elements of the Langlands dual Lie algebra are
represented as cycles on the above moduli spaces, and the Lie bracket of two
elements is obtained by deforming the cartesian product cycle along the Vinberg
degeneration.
| 0 | 0 | 1 | 0 | 0 | 0 |
16,298 | Sketched Subspace Clustering | The immense amount of daily generated and communicated data presents unique
challenges in their processing. Clustering, the grouping of data without the
presence of ground-truth labels, is an important tool for drawing inferences
from data. Subspace clustering (SC) is a relatively recent method that is able
to successfully classify nonlinearly separable data in a multitude of settings.
In spite of their high clustering accuracy, SC methods incur prohibitively high
computational complexity when processing large volumes of high-dimensional
data. Inspired by random sketching approaches for dimensionality reduction, the
present paper introduces a randomized scheme for SC, termed Sketch-SC, tailored
for large volumes of high-dimensional data. Sketch-SC accelerates the
computationally heavy parts of state-of-the-art SC approaches by compressing
the data matrix across both dimensions using random projections, thus enabling
fast and accurate large-scale SC. Performance analysis as well as extensive
numerical tests on real data corroborate the potential of Sketch-SC and its
competitive performance relative to state-of-the-art scalable SC approaches.
| 1 | 0 | 0 | 1 | 0 | 0 |
16,299 | An infinite class of unsaturated rooted trees corresponding to designable RNA secondary structures | An RNA secondary structure is designable if there is an RNA sequence which
can attain its maximum number of base pairs only by adopting that structure.
The combinatorial RNA design problem, introduced by Haleš et al. in 2016,
is to determine whether or not a given RNA secondary structure is designable.
Haleš et al. identified certain classes of designable and non-designable
secondary structures by reference to their corresponding rooted trees. We
introduce an infinite class of rooted trees containing unpaired nucleotides at
the greatest height, and prove constructively that their corresponding
secondary structures are designable. This complements previous results for the
combinatorial RNA design problem.
| 1 | 0 | 0 | 0 | 0 | 0 |
16,300 | Any Baumslag-Solitar action on surfaces with a pseudo-Anosov element has a finite orbit | We consider $f, h$ homeomorphims generating a faithful $BS(1,n)$-action on a
closed surface $S$, that is, $h f h^{-1} = f^n$, for some $ n\geq 2$. According
to \cite{GL}, after replacing $f$ by a suitable iterate if necessary, we can
assume that there exists a minimal set $\Lambda$ of the action, included in
$Fix(f)$.
Here, we suppose that $f$ and $h$ are $C^1$ in neighbourhood of $\Lambda$ and
any point $x\in\Lambda$ admits an $h$-unstable manifold $W^u(x)$. Using
Bonatti's techniques, we prove that either there exists an integer $N$ such
that $W^u(x)$ is included in $Fix(f^N)$ or there is a lower bound for the norm
of the differential of $h$ only depending on $n$ and the Riemannian metric on
$S$.
Combining last statement with a result of \cite{AGX}, we show that any
faithful action of $BS(1, n)$ on $S$ with $h$ a pseudo-Anosov homeomorphism has
a finite orbit. As a consequence, there is no faithful $C^1$-action of $BS(1,
n)$ on the torus with $h$ an Anosov.
| 0 | 0 | 1 | 0 | 0 | 0 |
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