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Enumeration of Graphs and the Characteristic Polynomial of the Hyperplane Arrangements $\mathcal{J}_n$ | We give a complete formula for the characteristic polynomial of hyperplane
arrangements $\mathcal J_n$ consisting of the hyperplanes $x_i+x_j=1$, $x_k=0$,
$x_l=1$, $ 1\leq i, j, k, l\leq n$. The formula is obtained by associating
hyperplane arrangements with graphs, and then enumerating central graphs via
generating functions for the number of bipartite graphs of given order, size
and number of connected components.
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On the Scientific Value of Large-scale Testbeds for Wireless Multi-hop Networks | Large-scale wireless testbeds have been setup in the last years with the goal
to study wireless multi-hop networks in more realistic environments. Since the
setup and operation of such a testbed is expensive in terms of money, time, and
labor, the crucial question rises whether this effort is justified with the
scientific results the testbed generates.
In this paper, we give an answer to this question based on our experience
with the DES-Testbed, a large-scale wireless sensor network and wireless mesh
network testbed. The DES-Testbed has been operated for almost 5 years. Our
analysis comprises more than 1000 experiments that have been run on the testbed
in the years 2010 and 2011. We discuss the scientific value in respect to the
effort of experimentation.
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Brain Damage and Motor Cortex Impairment in Chronic Obstructive Pulmonary Disease: Implication of Nonrapid Eye Movement Sleep Desaturation | Nonrapid eye movement (NREM) sleep desaturation may cause neuronal damage due
to the withdrawal of cerebrovascular reactivity. The current study (1) assessed
the prevalence of NREM sleep desaturation in nonhypoxemic patients with chronic
obstructive pulmonary disease (COPD) and (2) compared a biological marker of
cerebral lesion and neuromuscular function in patients with and without NREM
sleep desaturation.
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FPT-algorithms for The Shortest Lattice Vector and Integer Linear Programming Problems | In this paper, we present FPT-algorithms for special cases of the shortest
vector problem (SVP) and the integer linear programming problem (ILP), when
matrices included to the problems' formulations are near square. The main
parameter is the maximal absolute value of rank minors of matrices included to
the problem formulation. Additionally, we present FPT-algorithms with respect
to the same main parameter for the problems, when the matrices have no singular
rank sub-matrices.
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A dynamic graph-cuts method with integrated multiple feature maps for segmenting kidneys in ultrasound images | Purpose: To improve kidney segmentation in clinical ultrasound (US) images,
we develop a new graph cuts based method to segment kidney US images by
integrating original image intensity information and texture feature maps
extracted using Gabor filters. Methods: To handle large appearance variation
within kidney images and improve computational efficiency, we build a graph of
image pixels close to kidney boundary instead of building a graph of the whole
image. To make the kidney segmentation robust to weak boundaries, we adopt
localized regional information to measure similarity between image pixels for
computing edge weights to build the graph of image pixels. The localized graph
is dynamically updated and the GC based segmentation iteratively progresses
until convergence. The proposed method has been evaluated and compared with
state of the art image segmentation methods based on clinical kidney US images
of 85 subjects. We randomly selected US images of 20 subjects as training data
for tuning the parameters, and validated the methods based on US images of the
remaining 65 subjects. The segmentation results have been quantitatively
analyzed using 3 metrics, including Dice Index, Jaccard Index, and Mean
Distance. Results: Experiment results demonstrated that the proposed method
obtained segmentation results for bilateral kidneys of 65 subjects with average
Dice index of 0.9581, Jaccard index of 0.9204, and Mean Distance of 1.7166,
better than other methods under comparison (p<10-19, paired Wilcoxon rank sum
tests). Conclusions: The proposed method achieved promising performance for
segmenting kidneys in US images, better than segmentation methods that built on
any single channel of image information. This method will facilitate extraction
of kidney characteristics that may predict important clinical outcomes such
progression chronic kidney disease.
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Natural Scales in Geographical Patterns | Human mobility is known to be distributed across several orders of magnitude
of physical distances , which makes it generally difficult to endogenously find
or define typical and meaningful scales. Relevant analyses, from movements to
geographical partitions, seem to be relative to some ad-hoc scale, or no scale
at all. Relying on geotagged data collected from photo-sharing social media, we
apply community detection to movement networks constrained by increasing
percentiles of the distance distribution. Using a simple parameter-free
discontinuity detection algorithm, we discover clear phase transitions in the
community partition space. The detection of these phases constitutes the first
objective method of characterising endogenous, natural scales of human
movement. Our study covers nine regions, ranging from cities to countries of
various sizes and a transnational area. For all regions, the number of natural
scales is remarkably low (2 or 3). Further, our results hint at scale-related
behaviours rather than scale-related users. The partitions of the natural
scales allow us to draw discrete multi-scale geographical boundaries,
potentially capable of providing key insights in fields such as epidemiology or
cultural contagion where the introduction of spatial boundaries is pivotal.
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Drawing materials studied by THz spectroscopy | THz time-domain spectroscopy in transmission mode was applied to study dry
and wet drawing inks. In specific, cochineal-, indigo- and iron-gall based inks
have been investigated; some prepared following ancient recipes and others by
using synthetic materials. The THz investigations have been realized on both
pellet samples, made by dried inks blended with polyethylene powder, and
layered inks, made by liquid deposition on polyethylene pellicles. We
implemented an improved THz spectroscopic technique that enabled the
measurement of the material optical parameters and thicknesses of the layered
ink samples on absolute scale. This experimental investigation shows that the
THz techniques have the potentiality to recognize drawing inks by their
spectroscopic features.
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A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning | With the advancement of treatment modalities in radiation therapy for cancer
patients, outcomes have improved, but at the cost of increased treatment plan
complexity and planning time. The accurate prediction of dose distributions
would alleviate this issue by guiding clinical plan optimization to save time
and maintain high quality plans. We have modified a convolutional deep network
model, U-net (originally designed for segmentation purposes), for predicting
dose from patient image contours of the planning target volume (PTV) and organs
at risk (OAR). We show that, as an example, we are able to accurately predict
the dose of intensity-modulated radiation therapy (IMRT) for prostate cancer
patients, where the average Dice similarity coefficient is 0.91 when comparing
the predicted vs. true isodose volumes between 0% and 100% of the prescription
dose. The average value of the absolute differences in [max, mean] dose is
found to be under 5% of the prescription dose, specifically for each structure
is [1.80%, 1.03%](PTV), [1.94%, 4.22%](Bladder), [1.80%, 0.48%](Body), [3.87%,
1.79%](L Femoral Head), [5.07%, 2.55%](R Femoral Head), and [1.26%,
1.62%](Rectum) of the prescription dose. We thus managed to map a desired
radiation dose distribution from a patient's PTV and OAR contours. As an
additional advantage, relatively little data was used in the techniques and
models described in this paper.
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Single-molecule imaging of DNA gyrase activity in living Escherichia coli | Bacterial DNA gyrase introduces negative supercoils into chromosomal DNA and
relaxes positive supercoils introduced by replication and transiently by
transcription. Removal of these positive supercoils is essential for
replication fork progression and for the overall unlinking of the two duplex
DNA strands, as well as for ongoing transcription. To address how gyrase copes
with these topological challenges, we used high-speed single-molecule
fluorescence imaging in live Escherichia coli cells. We demonstrate that at
least 300 gyrase molecules are stably bound to the chromosome at any time, with
~12 enzymes enriched near each replication fork. Trapping of reaction
intermediates with ciprofloxacin revealed complexes undergoing catalysis. Dwell
times of ~2 s were observed for the dispersed gyrase molecules, which we
propose maintain steady-state levels of negative supercoiling of the
chromosome. In contrast, the dwell time of replisome-proximal molecules was ~8
s, consistent with these catalyzing processive positive supercoil relaxation in
front of the progressing replisome.
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Shedding Light on Black Box Machine Learning Algorithms: Development of an Axiomatic Framework to Assess the Quality of Methods that Explain Individual Predictions | From self-driving vehicles and back-flipping robots to virtual assistants who
book our next appointment at the hair salon or at that restaurant for dinner -
machine learning systems are becoming increasingly ubiquitous. The main reason
for this is that these methods boast remarkable predictive capabilities.
However, most of these models remain black boxes, meaning that it is very
challenging for humans to follow and understand their intricate inner workings.
Consequently, interpretability has suffered under this ever-increasing
complexity of machine learning models. Especially with regards to new
regulations, such as the General Data Protection Regulation (GDPR), the
necessity for plausibility and verifiability of predictions made by these black
boxes is indispensable. Driven by the needs of industry and practice, the
research community has recognised this interpretability problem and focussed on
developing a growing number of so-called explanation methods over the past few
years. These methods explain individual predictions made by black box machine
learning models and help to recover some of the lost interpretability. With the
proliferation of these explanation methods, it is, however, often unclear,
which explanation method offers a higher explanation quality, or is generally
better-suited for the situation at hand. In this thesis, we thus propose an
axiomatic framework, which allows comparing the quality of different
explanation methods amongst each other. Through experimental validation, we
find that the developed framework is useful to assess the explanation quality
of different explanation methods and reach conclusions that are consistent with
independent research.
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Click-based porous cationic polymers for enhanced carbon dioxide capture | Imidazolium based porous cationic polymers were synthesized using an
innovative and facile approach, which takes advantage of the Debus Radziszewski
reaction to obtain meso- and microporous polymers following click chemistry
principles. In the obtained set of materials, click based porous cationic
polymers have the same cationic backbone, whereas they bear the commonly used
anions of imidazolium poly(ionic liquid)s. These materials show hierarchical
porosity and a good specific surface area. Furthermore, their chemical
structure was extensively characterized using ATR FTIR and SS NMR
spectroscopies, and HR MS. These polymers show good performance towards carbon
dioxide sorption, especially those possessing the acetate anion. This polymer
has an uptake of 2 mmol per g of CO2 at 1 bar and 273 K, a value which is among
the highest recorded for imidazolium poly(ionic liquid)s. These polymers were
also modified in order to introduce N-heterocyclic carbenes along the backbone.
Carbon dioxide loading in the carbene-containing polymer is in the same range
as that of the non-modified versions, but the nature of the interaction is
substantially different. The combined use of in situ FTIR spectroscopy and
microcalorimetry evidenced a chemisorption phenomenon that brings about the
formation of an imidazolium carboxylate zwitterion.
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Oscillons in the presence of external potential | We discuss similarity between oscillons and oscillational mode in perturbed
$\phi^4$. For small depths of the perturbing potential it is difficult to
distinguish between oscillons and the mode in moderately long time evolution,
moreover one can transform one into the other by adiabatically switching on and
off the potential. Basins of attraction are presented in the parameter space
describing the potential and initial conditions.
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Dynamical Tides in Highly Eccentric Binaries: Chaos, Dissipation and Quasi-Steady State | Highly eccentric binary systems appear in many astrophysical contexts,
ranging from tidal capture in dense star clusters, precursors of stellar
disruption by massive black holes, to high-eccentricity migration of giant
planets. In a highly eccentric binary, the tidal potential of one body can
excite oscillatory modes in the other during a pericenter passage, resulting in
energy exchange between the modes and the binary orbit. These modes exhibit one
of three behaviors over multiple passages: low-amplitude oscillations, large
amplitude oscillations corresponding to a resonance between the orbital
frequency and the mode frequency, and chaotic growth. We study these phenomena
with an iterative map, fully exploring how the mode evolution depends on the
pericenter distance and other parameters. In addition, we show that the
dissipation of mode energy results in a quasi-steady state, with gradual
orbital decay punctuated by resonances, even in systems where the mode
amplitude would initially grow stochastically. A newly captured star around a
black hole can experience significant orbital decay and heating due to the
chaotic growth of the mode amplitude and dissipation. A giant planet pushed
into a high-eccentricity orbit may experience a similar effect and become a hot
or warm Jupiter.
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Calabi-Yau threefolds fibred by high rank lattice polarized K3 surfaces | We study threefolds fibred by K3 surfaces admitting a lattice polarization by
a certain class of rank 19 lattices. We begin by showing that any family of
such K3 surfaces is completely determined by a map from the base of the family
to the appropriate K3 moduli space, which we call the generalized functional
invariant. Then we show that if the threefold total space is a smooth
Calabi-Yau, there are only finitely many possibilities for the polarizing
lattice and the form of the generalized functional invariant. Finally, we
construct explicit examples of Calabi-Yau threefolds realizing each case and
compute their Hodge numbers.
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Lipschitz perturbations of Morse-Smale semigroups | In this paper we will deal with Lipschitz continuous perturbations of
Morse-Smale semigroups with only equilibrium points as critical elements. We
study the behavior of the structure of equilibrium points and their connections
when subjected to non-differentiable perturbations. To this end we define more
general notions of \emph{hyperbolicity} and \emph{transversality}, which do not
require differentiability.
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Semi-Supervised Learning for Detecting Human Trafficking | Human trafficking is one of the most atrocious crimes and among the
challenging problems facing law enforcement which demands attention of global
magnitude. In this study, we leverage textual data from the website "Backpage"-
used for classified advertisement- to discern potential patterns of human
trafficking activities which manifest online and identify advertisements of
high interest to law enforcement. Due to the lack of ground truth, we rely on a
human analyst from law enforcement, for hand-labeling a small portion of the
crawled data. We extend the existing Laplacian SVM and present S3VM-R, by
adding a regularization term to exploit exogenous information embedded in our
feature space in favor of the task at hand. We train the proposed method using
labeled and unlabeled data and evaluate it on a fraction of the unlabeled data,
herein referred to as unseen data, with our expert's further verification.
Results from comparisons between our method and other semi-supervised and
supervised approaches on the labeled data demonstrate that our learner is
effective in identifying advertisements of high interest to law enforcement
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Einstein's 1935 papers: EPR=ER? | In May of 1935, Einstein published with two co-authors the famous EPR-paper
about entangled particles, which questioned the completeness of Quantum
Mechanics by means of a gedankenexperiment. Only one month later, he published
a work that seems unconnected to the EPR-paper at first, the so called
Einstein-Rosen-paper, that presented a solution of the field equations for
particles in the framework of general relativity. Both papers ask for the
conception of completeness in a theory and, from a modern perspective, it is
easy to believe that there is a connection between these topics. We question
whether Einstein might have considered that a correlation between nonlocal
features of Quantum Mechanics and the Einstein-Rosen bridge can be used to
explain entanglement. We analyse this question by discussing the used
conceptions of "completeness," "atomistic structure of matter," and "quantum
phenomena." We discuss the historical embedding of the two works and the
context to modern research. Recent approaches are presented that formulate an
EPR=ER principle and claim an equivalence of the basic principles of these two
papers.
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An overview of process model quality literature - The Comprehensive Process Model Quality Framework | The rising interest in the construction and the quality of (business) process
models resulted in an abundancy of emerged research studies and different
findings about process model quality. The lack of overview and the lack of
consensus hinder the development of the research field. The research objective
is to collect, analyse, structure, and integrate the existing knowledge in a
comprehensive framework that strives to find a balance between completeness and
relevance without hindering the overview. The Systematic Literature Review
methodology was applied to collect the relevant studies. Because several
studies exist that each partially addresses this research objective, the review
was performed at a tertiary level. Based on a critical analysis of the
collected papers, a comprehensive, but structured overview of the state of the
art in the field was composed. The existing academic knowledge about process
model quality was carefully integrated and structured into the Comprehensive
Process Model Quality Framework (CPMQF). The framework summarizes 39 quality
dimensions, 21 quality metrics, 28 quality (sub)drivers, 44 (sub)driver
metrics, 64 realization initiatives and 15 concrete process model purposes
related to 4 types of organizational benefits, as well as the relations between
all of these. This overview is thus considered to form a valuable instrument
for both researchers and practitioners that are concerned about process model
quality. The framework is the first to address the concept of process model
quality in such a comprehensive way.
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Convergence rates of least squares regression estimators with heavy-tailed errors | We study the performance of the Least Squares Estimator (LSE) in a general
nonparametric regression model, when the errors are independent of the
covariates but may only have a $p$-th moment ($p\geq 1$). In such a
heavy-tailed regression setting, we show that if the model satisfies a standard
`entropy condition' with exponent $\alpha \in (0,2)$, then the $L_2$ loss of
the LSE converges at a rate \begin{align*}
\mathcal{O}_{\mathbf{P}}\big(n^{-\frac{1}{2+\alpha}} \vee
n^{-\frac{1}{2}+\frac{1}{2p}}\big). \end{align*} Such a rate cannot be improved
under the entropy condition alone.
This rate quantifies both some positive and negative aspects of the LSE in a
heavy-tailed regression setting. On the positive side, as long as the errors
have $p\geq 1+2/\alpha$ moments, the $L_2$ loss of the LSE converges at the
same rate as if the errors are Gaussian. On the negative side, if
$p<1+2/\alpha$, there are (many) hard models at any entropy level $\alpha$ for
which the $L_2$ loss of the LSE converges at a strictly slower rate than other
robust estimators.
The validity of the above rate relies crucially on the independence of the
covariates and the errors. In fact, the $L_2$ loss of the LSE can converge
arbitrarily slowly when the independence fails.
The key technical ingredient is a new multiplier inequality that gives sharp
bounds for the `multiplier empirical process' associated with the LSE. We
further give an application to the sparse linear regression model with
heavy-tailed covariates and errors to demonstrate the scope of this new
inequality.
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Some properties of nested Kriging predictors | Kriging is a widely employed technique, in particular for computer
experiments, in machine learning or in geostatistics. An important challenge
for Kriging is the computational burden when the data set is large. We focus on
a class of methods aiming at decreasing this computational cost, consisting in
aggregating Kriging predictors based on smaller data subsets. We prove that
aggregations based solely on the conditional variances provided by the
different Kriging predictors can yield an inconsistent final Kriging
prediction. In contrasts, we study theoretically the recent proposal by
[Rulli{è}re et al., 2017] and obtain additional attractive properties for it.
We prove that this predictor is consistent, we show that it can be interpreted
as an exact conditional distribution for a modified process and we provide
error bounds for it.
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Optimal Transport on Discrete Domains | Inspired by the matching of supply to demand in logistical problems, the
optimal transport (or Monge--Kantorovich) problem involves the matching of
probability distributions defined over a geometric domain such as a surface or
manifold. In its most obvious discretization, optimal transport becomes a
large-scale linear program, which typically is infeasible to solve efficiently
on triangle meshes, graphs, point clouds, and other domains encountered in
graphics and machine learning. Recent breakthroughs in numerical optimal
transport, however, enable scalability to orders-of-magnitude larger problems,
solvable in a fraction of a second. Here, we discuss advances in numerical
optimal transport that leverage understanding of both discrete and smooth
aspects of the problem. State-of-the-art techniques in discrete optimal
transport combine insight from partial differential equations (PDE) with convex
analysis to reformulate, discretize, and optimize transportation problems. The
end result is a set of theoretically-justified models suitable for domains with
thousands or millions of vertices. Since numerical optimal transport is a
relatively new discipline, special emphasis is placed on identifying and
explaining open problems in need of mathematical insight and additional
research.
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High-Resolution Altitude Profiles of the Atmospheric Turbulence with PML at the Sutherland Observatory | With the prospect of the next generation of ground-based telescopes, the
extremely large telescopes (ELTs), increasingly complex and demanding adaptive
optics (AO) systems are needed. This is to compensate for image distortion
caused by atmospheric turbulence and fully take advantage of mirrors with
diameters of 30 to 40 m. This requires a more precise characterization of the
turbulence. The PML (Profiler of Moon Limb) was developed within this context.
The PML aims to provide high-resolution altitude profiles of the turbulence
using differential measurements of the Moon limb position to calculate the
transverse spatio-angular covariance of the Angle of Arrival fluctuations. The
covariance of differential image motion for different separation angles is
sensitive to the altitude distribution of the seeing. The use of the continuous
Moon limb provides a large number of separation angles allowing for the
high-resolution altitude of the profiles. The method is presented and tested
with simulated data. Moreover a PML instrument was deployed at the Sutherland
Observatory in South Africa in August 2011. We present here the results of this
measurement campaign.
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Spice up Your Chat: The Intentions and Sentiment Effects of Using Emoji | Emojis, as a new way of conveying nonverbal cues, are widely adopted in
computer-mediated communications. In this paper, first from a message sender
perspective, we focus on people's motives in using four types of emojis --
positive, neutral, negative, and non-facial. We compare the willingness levels
of using these emoji types for seven typical intentions that people usually
apply nonverbal cues for in communication. The results of extensive statistical
hypothesis tests not only report the popularities of the intentions, but also
uncover the subtle differences between emoji types in terms of intended uses.
Second, from a perspective of message recipients, we further study the
sentiment effects of emojis, as well as their duplications, on verbal messages.
Different from previous studies in emoji sentiment, we study the sentiments of
emojis and their contexts as a whole. The experiment results indicate that the
powers of conveying sentiment are different between four emoji types, and the
sentiment effects of emojis vary in the contexts of different valences.
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Self-shielding of hydrogen in the IGM during the epoch of reionization | We investigate self-shielding of intergalactic hydrogen against ionizing
radiation in radiative transfer simulations of cosmic reionization carefully
calibrated with Lyman alpha forest data. While self-shielded regions manifest
as Lyman-limit systems in the post-reionization Universe, here we focus on
their evolution during reionization (redshifts z=6-10). At these redshifts, the
spatial distribution of hydrogen-ionizing radiation is highly inhomogeneous,
and some regions of the Universe are still neutral. After masking the neutral
regions and ionizing sources in the simulation, we find that the hydrogen
photoionization rate depends on the local hydrogen density in a manner very
similar to that in the post-reionization Universe. The characteristic physical
hydrogen density above which self-shielding becomes important at these
redshifts is about $\mathrm{n_H \sim 3 \times 10^{-3} cm^{-3}}$, or $\sim$ 20
times the mean hydrogen density, reflecting the fact that during reionization
photoionization rates are typically low enough that the filaments in the cosmic
web are often self-shielded. The value of the typical self-shielding density
decreases by a factor of 3 between redshifts z=3 and 10, and follows the
evolution of the average photoionization rate in ionized regions in a simple
fashion. We provide a simple parameterization of the photoionization rate as a
function of density in self-shielded regions during the epoch of reionization.
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Universal Conditional Machine | We propose a single neural probabilistic model based on variational
autoencoder that can be conditioned on an arbitrary subset of observed features
and then sample the remaining features in "one shot". The features may be both
real-valued and categorical. Training of the model is performed by stochastic
variational Bayes. The experimental evaluation on synthetic data, as well as
feature imputation and image inpainting problems, shows the effectiveness of
the proposed approach and diversity of the generated samples.
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A hybrid isogeometric approach on multi-patches with applications to Kirchhoff plates and eigenvalue problems | We present a systematic study on higher-order penalty techniques for
isogeometric mortar methods. In addition to the weak-continuity enforced by a
mortar method, normal derivatives across the interface are penalized. The
considered applications are fourth order problems as well as eigenvalue
problems for second and fourth order equations. The hybrid coupling enables the
discretization of fourth order problems in a multi-patch setting as well as a
convenient implementation of natural boundary conditions. For second order
eigenvalue problems, the pollution of the discrete spectrum - typically
referred to as 'outliers' - can be avoided.
Numerical results illustrate the good behaviour of the proposed method in
simple systematic studies as well as more complex multi-patch mapped geometries
for linear elasticity and Kirchhoff plates.
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Circumstellar discs: What will be next? | This prospective chapter gives our view on the evolution of the study of
circumstellar discs within the next 20 years from both observational and
theoretical sides. We first present the expected improvements in our knowledge
of protoplanetary discs as for their masses, sizes, chemistry, the presence of
planets as well as the evolutionary processes shaping these discs. We then
explore the older debris disc stage and explain what will be learnt concerning
their birth, the intrinsic links between these discs and planets, the hot dust
and the gas detected around main sequence stars as well as discs around white
dwarfs.
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Algorithmic Verification of Linearizability for Ordinary Differential Equations | For a nonlinear ordinary differential equation solved with respect to the
highest order derivative and rational in the other derivatives and in the
independent variable, we devise two algorithms to check if the equation can be
reduced to a linear one by a point transformation of the dependent and
independent variables. The first algorithm is based on a construction of the
Lie point symmetry algebra and on the computation of its derived algebra. The
second algorithm exploits the differential Thomas decomposition and allows not
only to test the linearizability, but also to generate a system of nonlinear
partial differential equations that determines the point transformation and the
coefficients of the linearized equation. Both algorithms have been implemented
in Maple and their application is illustrated using several examples.
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Space weather challenges of the polar cap ionosphere | This paper presents research on polar cap ionosphere space weather phenomena
conducted during the European Cooperation in Science and Technology (COST)
action ES0803 from 2008 to 2012. The main part of the work has been directed
toward the study of plasma instabilities and scintillations in association with
cusp flow channels and polar cap electron density structures/patches,which is
considered as critical knowledge in order to develop forecast models for
scintillations in the polar cap. We have approached this problem by
multi-instrument techniques that comprise the EISCAT Svalbard Radar, SuperDARN
radars, in-situ rocket, and GPS scintillation measurements. The Discussion
section aims to unify the bits and pieces of highly specialized information
from several papers into a generalized picture. The cusp ionosphere appears as
a hot region in GPS scintillation climatology maps. Our results are consistent
with the existing view that scintillations in the cusp and the polar cap
ionosphere are mainly due to multi-scale structures generated by instability
processes associated with the cross-polar transport of polar cap patches. We
have demonstrated that the SuperDARN convection model can be used to track
these patches backward and forward in time. Hence, once a patch has been
detected in the cusp inflow region, SuperDARN can be used to forecast its
destination in the future. However, the high-density gradient of polar cap
patches is not the only prerequisite for high-latitude scintillations.
Unprecedented high resolution rocket measurements reveal that the cusp
ionosphere is associated with filamentary precipitation giving rise to
kilometer scale gradients onto which the gradient drift instability can operate
very efficiently... (continued)
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Unconventional Large Linear Magnetoresistance in Cu$_{2-x}$Te | We report a large linear magnetoresistance in Cu$_{2-x}$Te, reaching
$\Delta\rho/\rho(0)$ = 250\% at 2 K in a 9 T field. This is observed for
samples with $x$ in the range 0.13 to 0.22, and the results are comparable to
the effects observed in Ag$_2 X$ materials, although in this case the results
appear for a much wider range of bulk carrier density. Examining the magnitude
vs. crossover field from low-field quadratic to high-field linear behavior, we
show that models based on classical transport behavior best explain the
observed results. The effects are traced to misdirected currents due to
topologically inverted behavior in this system, such that stable surface states
provide the high mobility transport channels. The resistivity also crosses over
to a $T^2$ dependence in the temperature range where the large linear MR
appears, an indicator of electron-electron interaction effects within the
surface states. Thus this is an example of a system in which these interactions
dominate the low-temperature behavior of the surface states.
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Which Distribution Distances are Sublinearly Testable? | Given samples from an unknown distribution $p$ and a description of a
distribution $q$, are $p$ and $q$ close or far? This question of "identity
testing" has received significant attention in the case of testing whether $p$
and $q$ are equal or far in total variation distance. However, in recent work,
the following questions have been been critical to solving problems at the
frontiers of distribution testing:
-Alternative Distances: Can we test whether $p$ and $q$ are far in other
distances, say Hellinger?
-Tolerance: Can we test when $p$ and $q$ are close, rather than equal? And if
so, close in which distances?
Motivated by these questions, we characterize the complexity of distribution
testing under a variety of distances, including total variation, $\ell_2$,
Hellinger, Kullback-Leibler, and $\chi^2$. For each pair of distances $d_1$ and
$d_2$, we study the complexity of testing if $p$ and $q$ are close in $d_1$
versus far in $d_2$, with a focus on identifying which problems allow strongly
sublinear testers (i.e., those with complexity $O(n^{1 - \gamma})$ for some
$\gamma > 0$ where $n$ is the size of the support of the distributions $p$ and
$q$). We provide matching upper and lower bounds for each case. We also study
these questions in the case where we only have samples from $q$ (equivalence
testing), showing qualitative differences from identity testing in terms of
when tolerance can be achieved. Our algorithms fall into the classical paradigm
of $\chi^2$-statistics, but require crucial changes to handle the challenges
introduced by each distance we consider. Finally, we survey other recent
results in an attempt to serve as a reference for the complexity of various
distribution testing problems.
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Hund's coupling driven photo-carrier relaxation in the two-band Mott insulator | We study the relaxation dynamics of photo-carriers in the paramagnetic Mott
insulating phase of the half-filled two-band Hubbard model. Using
nonequilibrium dynamical mean field theory, we excite charge carriers across
the Mott gap by a short hopping modulation, and simulate the evolution of the
photo-doped population within the Hubbard bands. We observe an ultrafast
charge-carrier relaxation driven by emission of local spin excitations with an
inverse relaxation time proportional to the Hund's coupling. The photo-doping
generates additional side-bands in the spectral function, and for strong Hund's
coupling, the photo-doped population also splits into several resonances. The
dynamics of the local many-body states reveals two effects, thermal blocking
and kinetic freezing, which manifest themselves when the Hund's coupling
becomes of the order of the temperature or the bandwidth, respectively. These
effects, which are absent in the single-band Hubbard model, should be relevant
for the interpretation of experiments on correlated materials with multiple
active orbitals. In particular, the features revealed in the non-equilibrium
energy distribution of the photo-carriers are experimentally accessible, and
provide information on the role of the Hund's coupling in these materials.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Connection between Feed-Forward Neural Networks and Probabilistic Graphical Models | Two of the most popular modelling paradigms in computer vision are
feed-forward neural networks (FFNs) and probabilistic graphical models (GMs).
Various connections between the two have been studied in recent works, such as
e.g. expressing mean-field based inference in a GM as an FFN. This paper
establishes a new connection between FFNs and GMs. Our key observation is that
any FFN implements a certain approximation of a corresponding Bayesian network
(BN). We characterize various benefits of having this connection. In
particular, it results in a new learning algorithm for BNs. We validate the
proposed methods for a classification problem on CIFAR-10 dataset and for
binary image segmentation on Weizmann Horse dataset. We show that statistically
learned BNs improve performance, having at the same time essentially better
generalization capability, than their FFN counterparts.
| 1 | 0 | 0 | 1 | 0 | 0 |
On the digital representation of smooth numbers | Let $b \ge 2$ be an integer. Among other results, we establish, in a
quantitative form, that any sufficiently large integer which is not a multiple
of $b$ cannot have simultaneously only few distinct prime factors and only few
nonzero digits in its representation in base $b$.
| 0 | 0 | 1 | 0 | 0 | 0 |
On Constraint Qualifications of a Nonconvex Inequality | In this paper, we study constraint qualifications for the nonconvex
inequality defined by a proper lower semicontinuous function. These constraint
qualifications involve the generalized construction of normal cones and
subdifferentials. Several conditions for these constraint qualifications are
also provided therein. When restricted to the convex inequality, these
constraint qualifications reduce to basic constraint qualification (BCQ) and
strong BCQ studied in [SIAM J. Optim., 14(2004), 757-772] and [Math. Oper.
Res., 30 (2005), 956-965].
| 0 | 0 | 1 | 0 | 0 | 0 |
A Framework for Dynamic Stability Analysis of Power Systems with Volatile Wind Power | We propose a framework employing stochastic differential equations to
facilitate the long-term stability analysis of power grids with intermittent
wind power generations. This framework takes into account the discrete dynamics
which play a critical role in the long-term stability analysis, incorporates
the model of wind speed with different probability distributions, and also
develops an approximation methodology (by a deterministic hybrid model) for the
stochastic hybrid model to reduce the computational burden brought about by the
uncertainty of wind power. The theoretical and numerical studies show that a
deterministic hybrid model can provide an accurate trajectory approximation and
stability assessments for the stochastic hybrid model under mild conditions. In
addition, we discuss the critical cases that the deterministic hybrid model
fails and discover that these cases are caused by a violation of the proposed
sufficient conditions. Such discussion complements the proposed framework and
methodology and also reaffirms the importance of the stochastic hybrid model
when the system operates close to its stability limit.
| 1 | 0 | 0 | 0 | 0 | 0 |
Confidence interval for correlation estimator between latent processes | Kimura and Yoshida treated a model in which the finite variation part of a
two-dimensional semimartingale is expressed by time-integration of latent
processes. They proposed a correlation estimator between the latent processes
and proved its consistency and asymptotic mixed normality. In this paper, we
discuss the confidence interval of the correlation estimator to detect the
correlation. %between latent processes. We propose two types of estimators for
asymptotic variance of the correlation estimator and prove their consistency in
a high frequency setting. Our model includes doubly stochastic Poisson
processes whose intensity processes are correlated Itô processes. We compare
our estimators based on the simulation of the doubly stochastic Poisson
processes.
| 0 | 0 | 1 | 1 | 0 | 0 |
Novel event classification based on spectral analysis of scintillation waveforms in Double Chooz | Liquid scintillators are a common choice for neutrino physics experiments,
but their capabilities to perform background rejection by scintillation pulse
shape discrimination is generally limited in large detectors. This paper
describes a novel approach for a pulse shape based event classification
developed in the context of the Double Chooz reactor antineutrino experiment.
Unlike previous implementations, this method uses the Fourier power spectra of
the scintillation pulse shapes to obtain event-wise information. A
classification variable built from spectral information was able to achieve an
unprecedented performance, despite the lack of optimization at the detector
design level. Several examples of event classification are provided, ranging
from differentiation between the detector volumes and an efficient rejection of
instrumental light noise, to some sensitivity to the particle type, such as
stopping muons, ortho-positronium formation, alpha particles as well as
electrons and positrons. In combination with other techniques the method is
expected to allow for a versatile and more efficient background rejection in
the future, especially if detector optimization is taken into account at the
design level.
| 0 | 1 | 0 | 0 | 0 | 0 |
Symmetric Riemannian problem on the group of proper isometries of hyperbolic plane | We consider the Lie group PSL(2) (the group of orientation preserving
isometries of the hyperbolic plane) and a left-invariant Riemannian metric on
this group with two equal eigenvalues that correspond to space-like
eigenvectors (with respect to the Killing form). For such metrics we find a
parametrization of geodesics, the conjugate time, the cut time and the cut
locus. The injectivity radius is computed. We show that the cut time and the
cut locus in such Riemannian problem converge to the cut time and the cut locus
in the corresponding sub-Riemannian problem as the third eigenvalue of the
metric tends to infinity. Similar results are also obtained for SL(2).
| 0 | 0 | 1 | 0 | 0 | 0 |
Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach | Relation extraction is a fundamental task in information extraction. Most
existing methods have heavy reliance on annotations labeled by human experts,
which are costly and time-consuming. To overcome this drawback, we propose a
novel framework, REHession, to conduct relation extractor learning using
annotations from heterogeneous information source, e.g., knowledge base and
domain heuristics. These annotations, referred as heterogeneous supervision,
often conflict with each other, which brings a new challenge to the original
relation extraction task: how to infer the true label from noisy labels for a
given instance. Identifying context information as the backbone of both
relation extraction and true label discovery, we adopt embedding techniques to
learn the distributed representations of context, which bridges all components
with mutual enhancement in an iterative fashion. Extensive experimental results
demonstrate the superiority of REHession over the state-of-the-art.
| 1 | 0 | 0 | 0 | 0 | 0 |
Variational integrators for anelastic and pseudo-incompressible flows | The anelastic and pseudo-incompressible equations are two well-known
soundproof approximations of compressible flows useful for both theoretical and
numerical analysis in meteorology, atmospheric science, and ocean studies. In
this paper, we derive and test structure-preserving numerical schemes for these
two systems. The derivations are based on a discrete version of the
Euler-Poincaré variational method. This approach relies on a finite
dimensional approximation of the (Lie) group of diffeomorphisms that preserve
weighted-volume forms. These weights describe the background stratification of
the fluid and correspond to the weighed velocity fields for anelastic and
pseudo-incompressible approximations. In particular, we identify to these
discrete Lie group configurations the associated Lie algebras such that
elements of the latter correspond to weighted velocity fields that satisfy the
divergence-free conditions for both systems. Defining discrete Lagrangians in
terms of these Lie algebras, the discrete equations follow by means of
variational principles. Descending from variational principles, the schemes
exhibit further a discrete version of Kelvin circulation theorem, are
applicable to irregular meshes, and show excellent long term energy behavior.
We illustrate the properties of the schemes by performing preliminary test
cases.
| 0 | 1 | 1 | 0 | 0 | 0 |
The reparameterization trick for acquisition functions | Bayesian optimization is a sample-efficient approach to solving global
optimization problems. Along with a surrogate model, this approach relies on
theoretically motivated value heuristics (acquisition functions) to guide the
search process. Maximizing acquisition functions yields the best performance;
unfortunately, this ideal is difficult to achieve since optimizing acquisition
functions per se is frequently non-trivial. This statement is especially true
in the parallel setting, where acquisition functions are routinely non-convex,
high-dimensional, and intractable. Here, we demonstrate how many popular
acquisition functions can be formulated as Gaussian integrals amenable to the
reparameterization trick and, ensuingly, gradient-based optimization. Further,
we use this reparameterized representation to derive an efficient Monte Carlo
estimator for the upper confidence bound acquisition function in the context of
parallel selection.
| 1 | 0 | 0 | 1 | 0 | 0 |
Multi-model ensembles for ecosystem prediction | When making predictions about ecosystems, we often have available a number of
different ecosystem models that attempt to represent their dynamics in a
detailed mechanistic way. Each of these can be used as simulators of
large-scale experiments and make forecasts about the fate of ecosystems under
different scenarios in order to support the development of appropriate
management strategies. However, structural differences, systematic
discrepancies and uncertainties lead to different models giving different
predictions under these scenarios. This is further complicated by the fact that
the models may not be run with the same species or functional groups, spatial
structure or time scale. Rather than simply trying to select a 'best' model, or
taking some weighted average, it is important to exploit the strengths of each
of the available models, while learning from the differences between them. To
achieve this, we construct a flexible statistical model of the relationships
between a collection or 'ensemble' of mechanistic models and their biases,
allowing for structural and parameter uncertainty and for different ways of
representing reality. Using this statistical meta-model, we can combine prior
beliefs, model estimates and direct observations using Bayesian methods, and
make coherent predictions of future outcomes under different scenarios with
robust measures of uncertainty. In this paper we present the modelling
framework and discuss results obtained using a diverse ensemble of models in
scenarios involving future changes in fishing levels. These examples illustrate
the value of our approach in predicting outcomes for possible strategies
pertaining to climate and fisheries policy aimed at improving food security and
maintaining ecosystem integrity.
| 0 | 0 | 0 | 1 | 0 | 0 |
Nonlinear atomic vibrations and structural phase transitions in strained carbon chains | We consider longitudinal nonlinear atomic vibrations in uniformly strained
carbon chains with the cumulene structure ($=C=C=)_{n}$. With the aid of ab
initio simulations, based on the density functional theory, we have revealed
the phenomenon of the $\pi$-mode softening in a certain range of its amplitude
for the strain above the critical value $\eta_{c}\approx 11\,{\%}$.
Condensation of this soft mode induces the structural transformation of the
carbon chain with doubling of its unit cell. This is the Peierls phase
transition in the strained cumulene, which was previously revealed in [Nano
Lett. 14, 4224 (2014)]. The Peierls transition leads to appearance of the
energy gap in the electron spectrum of the strained carbyne, and this material
transforms from the conducting state to semiconducting or insulating states.
The authors of the above paper emphasize that such phenomenon can be used for
construction of various nanodevices. The $\pi$-mode softening occurs because
the old equilibrium positions (EQPs), around which carbon atoms vibrate at
small strains, lose their stability and these atoms begin to vibrate in the new
potential wells located near old EQPs. We study the stability of the new EQPs,
as well as stability of vibrations in their vicinity. In previous paper
[Physica D 203, 121(2005)], we proved that only three symmetry-determined
Rosenberg nonlinear normal modes can exist in monoatomic chains with arbitrary
interparticle interactions. They are the above-discussed $\pi$-mode and two
other modes, which we call $\sigma$-mode and $\tau$-mode. These modes
correspond to the multiplication of the unit cell of the vibrational state by
two, three or four times compared to that of the equilibrium state. We study
properties of these modes in the chain model with arbitrary pair potential of
interparticle interactions.
| 0 | 1 | 0 | 0 | 0 | 0 |
Homotopy classes of gauge fields and the lattice | For a smooth manifold $M$, possibly with boundary and corners, and a Lie
group $G$, we consider a suitable description of gauge fields in terms of
parallel transport, as groupoid homomorphisms from a certain path groupoid in
$M$ to $G$. Using a cotriangulation $\mathscr{C}$ of $M$, and collections of
finite-dimensional families of paths relative to $\mathscr{C}$, we define a
homotopical equivalence relation of parallel transport maps, leading to the
concept of an extended lattice gauge (ELG) field. A lattice gauge field, as
used in Lattice Gauge Theory, is part of the data contained in an ELG field,
but the latter contains further local topological information sufficient to
reconstruct a principal $G$-bundle on $M$ up to equivalence. The space of ELG
fields of a given pair $(M,\mathscr{C})$ is a covering for the space of fields
in Lattice Gauge Theory, whose connected components parametrize equivalence
classes of principal $G$-bundles on $M$. We give a criterion to determine when
ELG fields over different cotriangulations define equivalent bundles.
| 0 | 0 | 1 | 0 | 0 | 0 |
Classical System of Martin-Lof's Inductive Definitions is not Equivalent to Cyclic Proofs | A cyclic proof system, called CLKID-omega, gives us another way of
representing inductive definitions and effcient proof search. The 2011 paper by
Brotherston and Simpson showed that the provability of CLKID-omega includes the
provability of Martin-Lof's system of inductive definitions, called LKID, and
conjectured the equivalence. Since then, the equivalence has been left an open
question. This paper shows that CLKID-omega and LKID are indeed not equivalent.
This paper considers a statement called 2-Hydra in these two systems with the
first-order language formed by 0, the successor, the natural number predicate,
and a binary predicate symbol used to express 2-Hydra. This paper shows that
the 2-Hydra statement is provable in CLKID-omega, but the statement is not
provable in LKID, by constructing some Henkin model where the statement is
false.
| 1 | 0 | 0 | 0 | 0 | 0 |
Factorization and non-factorization theorems for pseudocontinuable functions | Let $\theta$ be an inner function on the unit disk, and let
$K^p_\theta:=H^p\cap\theta\overline{H^p_0}$ be the associated star-invariant
subspace of the Hardy space $H^p$, with $p\ge1$. While a nontrivial function
$f\in K^p_\theta$ is never divisible by $\theta$, it may have a factor $h$
which is "not too different" from $\theta$ in the sense that the ratio
$h/\theta$ (or just the anti-analytic part thereof) is smooth on the circle. In
this case, $f$ is shown to have additional integrability and/or smoothness
properties, much in the spirit of the Hardy--Littlewood--Sobolev embedding
theorem. The appropriate norm estimates are established, and their sharpness is
discussed.
| 0 | 0 | 1 | 0 | 0 | 0 |
Adaptively Detecting Malicious Queries in Web Attacks | Web request query strings (queries), which pass parameters to the referenced
resource, are always manipulated by attackers to retrieve sensitive data and
even take full control of victim web servers and web applications. However,
existing malicious query detection approaches in the current literature cannot
cope with changing web attacks with constant detection models. In this paper,
we propose AMODS, an adaptive system that periodically updates the detection
model to detect the latest unknown attacks. We also propose an adaptive
learning strategy, called SVM HYBRID, leveraged by our system to minimize
manual work. In the evaluation, an up-to-date detection model is trained on a
ten-day query dataset collected from an academic institute's web server logs.
Our system outperforms existing web attack detection methods, with an F-value
of 94.79% and FP rate of 0.09%. The total number of malicious queries obtained
by SVM HYBRID is 2.78 times that by the popular Support Vector Machine Adaptive
Learning (SVM AL) method. The malicious queries obtained can be used to update
the Web Application Firewall (WAF) signature library.
| 1 | 0 | 0 | 0 | 0 | 0 |
Derivatives pricing using signature payoffs | We introduce signature payoffs, a family of path-dependent derivatives that
are given in terms of the signature of the price path of the underlying asset.
We show that these derivatives are dense in the space of continuous payoffs, a
result that is exploited to quickly price arbitrary continuous payoffs. This
approach to pricing derivatives is then tested with European options, American
options, Asian options, lookback options and variance swaps. As we show,
signature payoffs can be used to price these derivatives with very high
accuracy.
| 0 | 0 | 0 | 0 | 0 | 1 |
ALMA constraints on star-forming gas in a prototypical z=1.5 clumpy galaxy: the dearth of CO(5-4) emission from UV-bright clumps | We present deep ALMA CO(5-4) observations of a main sequence, clumpy galaxy
at z=1.5 in the HUDF. Thanks to the ~0.5" resolution of the ALMA data, we can
link stellar population properties to the CO(5-4) emission on scales of a few
kpc. We detect strong CO(5-4) emission from the nuclear region of the galaxy,
consistent with the observed $L_{\rm IR}$-$L^{\prime}_{\rm CO(5-4)}$
correlation and indicating on-going nuclear star formation. The CO(5-4) gas
component appears more concentrated than other star formation tracers or the
dust distribution in this galaxy. We discuss possible implications of this
difference in terms of star formation efficiency and mass build-up at the
galaxy centre. Conversely, we do not detect any CO(5-4) emission from the
UV-bright clumps. This might imply that clumps have a high star formation
efficiency (although they do not display unusually high specific star formation
rates) and are not entirely gas dominated, with gas fractions no larger than
that of their host galaxy (~50%). Stellar feedback and disk instability torques
funnelling gas towards the galaxy centre could contribute to the relatively low
gas content. Alternatively, clumps could fall in a more standard star formation
efficiency regime if their actual star-formation rates are lower than generally
assumed. We find that clump star-formation rates derived with several
different, plausible methods can vary by up to an order of magnitude. The
lowest estimates would be compatible with a CO(5-4) non-detection even for
main-sequence like values of star formation efficiency and gas content.
| 0 | 1 | 0 | 0 | 0 | 0 |
Data-driven framework for real-time thermospheric density estimation | In this paper, we demonstrate a new data-driven framework for real-time
neutral density estimation via model-data fusion in quasi-physical
ionosphere-thermosphere models. The framework has two main components: (i) the
development of a quasi-physical dynamic reduced order model (ROM) that uses a
linear approximation of the underlying dynamics and effect of the drivers, and
(ii) dynamic calibration of the ROM through estimation of the ROM coefficients
that represent the model parameters. We have previously demonstrated the
development of a quasi-physical ROM using simulation output from a physical
model and assimilation of non-operational density estimates derived from
accelerometer measurements along a single orbit. In this paper, we demonstrate
the potential of the framework for use with operational measurements. We use
simulated GPS-derived orbit ephemerides with 5 minute resolution as
measurements. The framework is a first of its kind, simple yet robust and
accurate method with high potential for providing real-time operational updates
to the state of the upper atmosphere using quasi-physical models with inherent
forecasting/predictive capabilities.
| 1 | 0 | 0 | 0 | 0 | 0 |
Synergies between Asteroseismology and Three-dimensional Simulations of Stellar Turbulence | Turbulent mixing of chemical elements by convection has fundamental effects
on the evolution of stars. The standard algorithm at present, mixing-length
theory (MLT), is intrinsically local, and must be supplemented by extensions
with adjustable parameters. As a step toward reducing this arbitrariness, we
compare asteroseismically inferred internal structures of two Kepler slowly
pulsating B stars (SPB's; $M\sim 3.25 M_\odot$) to predictions of 321D
turbulence theory, based upon well-resolved, truly turbulent three-dimensional
simulations (Arnett , et al. 2015, Christini, et al. 2016) which include
boundary physics absent from MLT. We find promising agreement between the
steepness and shapes of the theoretically-predicted composition profile outside
the convective region in 3D simulations and in asteroseismically constrained
composition profiles in the best 1D models of the two SPBs. The structure and
motion of the boundary layer, and the generation of waves, are discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
A polyharmonic Maass form of depth 3/2 for SL_2(Z) | Duke, Imamoglu, and Toth constructed a polyharmonic Maass form of level 4
whose Fourier coefficients encode real quadratic class numbers. A more general
construction of such forms was subsequently given by Bruinier, Funke, and
Imamoglu. Here we give a direct construction of such a form for the full
modular group and study the properties of its coefficients. We give
interpretations of the coefficients of the holomorphic parts of each of these
polyharmonic Maass forms as inner products of certain weakly holomorphic
modular forms and harmonic Maass forms. The coefficients of square index are
particularly intractable; in order to address these, we develop various
extensions of the usual normalized Peterson inner product using a strategy of
Bringmann, Ehlen and Diamantis.
| 0 | 0 | 1 | 0 | 0 | 0 |
Systems of cubic forms in many variables | We consider a system of $R$ cubic forms in $n$ variables, with integer
coefficients, which define a smooth complete intersection in projective space.
Provided $n\geq 25R$, we prove an asymptotic formula for the number of integer
points in an expanding box at which these forms simultaneously vanish. In
particular we can handle systems of forms in $O(R)$ variables, previous work
having required that $n \gg R^2$. One conjectures that $n \geq 6R+1$ should be
sufficient. We reduce the problem to an upper bound for the number of solutions
to a certain auxiliary inequality. To prove this bound we adapt a method of
Davenport.
| 0 | 0 | 1 | 0 | 0 | 0 |
Nitrogen-doped Nanoporous Carbon Membranes Functionalized with Co/CoP Janus-type nanocrystals as Hydrogen Evolution Electrode in Both Acid and Alkaline Environment | Self-supported electrocatalysts being generated and employed directly as
electrode for energy conversion has been intensively pursued in the fields of
materials chemistry and energy. Herein, we report a synthetic strategy to
prepare freestanding hierarchically structured, nitrogen-doped nanoporous
graphitic carbon membranes functionalized with Janus-type Co/CoP nanocrystals
(termed as HNDCM-Co/CoP), which were successfully applied as a
highly-efficient, binder-free electrode in hydrogen evolution reaction (HER).
Benefited from multiple structural merits, such as high degree of
graphitization, three-dimensionally interconnected micro-/meso-/macropores,
uniform nitrogen-doping, well-dispersed Co/CoP nanocrystals as well as the
confinement effect of the thin carbon layer on the nanocrystals, HNDCM-Co/CoP
exhibited superior electrocatalytic activity and long-term operation stability
for HER under both acid and alkaline conditions. As a proof-of-concept of
practical usage, a macroscopic piece of HNDCM-Co/CoP of 5.6 cm x 4 cm x 60 um
in size was prepared in our laboratory. Driven by a solar cell,
electroreduction of water in alkaline condition (pH 14) was performed, and H2
has been produced at a rate of 16 ml/min, demonstrating its potential as
real-life energy conversion systems.
| 0 | 1 | 0 | 0 | 0 | 0 |
Cyber-Physical Systems Security -- A Survey | With the exponential growth of cyber-physical systems (CPS), new security
challenges have emerged. Various vulnerabilities, threats, attacks, and
controls have been introduced for the new generation of CPS. However, there
lack a systematic study of CPS security issues. In particular, the
heterogeneity of CPS components and the diversity of CPS systems have made it
very difficult to study the problem with one generalized model.
In this paper, we capture and systematize existing research on CPS security
under a unified framework. The framework consists of three orthogonal
coordinates: (1) from the \emph{security} perspective, we follow the well-known
taxonomy of threats, vulnerabilities, attacks and controls; (2)from the
\emph{CPS components} perspective, we focus on cyber, physical, and
cyber-physical components; and (3) from the \emph{CPS systems} perspective, we
explore general CPS features as well as representative systems (e.g., smart
grids, medical CPS and smart cars). The model can be both abstract to show
general interactions of a CPS application and specific to capture any details
when needed. By doing so, we aim to build a model that is abstract enough to be
applicable to various heterogeneous CPS applications; and to gain a modular
view of the tightly coupled CPS components. Such abstract decoupling makes it
possible to gain a systematic understanding of CPS security, and to highlight
the potential sources of attacks and ways of protection.
| 1 | 0 | 0 | 0 | 0 | 0 |
WebPol: Fine-grained Information Flow Policies for Web Browsers | In the standard web browser programming model, third-party scripts included
in an application execute with the same privilege as the application's own
code. This leaves the application's confidential data vulnerable to theft and
leakage by malicious code and inadvertent bugs in the third-party scripts.
Security mechanisms in modern browsers (the same-origin policy, cross-origin
resource sharing and content security policies) are too coarse to suit this
programming model. All these mechanisms (and their extensions) describe whether
or not a script can access certain data, whereas the meaningful requirement is
to allow untrusted scripts access to confidential data that they need and to
prevent the scripts from leaking data on the side. Motivated by this gap, we
propose WebPol, a policy mechanism that allows a website developer to include
fine-grained policies on confidential application data in the familiar syntax
of the JavaScript programming language. The policies can be associated with any
webpage element, and specify what aspects of the element can be accessed by
which third-party domains. A script can access data that the policy allows it
to, but it cannot pass the data (or data derived from it) to other scripts or
remote hosts in contravention of the policy. To specify the policies, we expose
a small set of new native APIs in JavaScript. Our policies can be enforced
using any of the numerous existing proposals for information flow tracking in
web browsers. We have integrated our policies into one such proposal that we
use to evaluate performance overheads and to test our examples.
| 1 | 0 | 0 | 0 | 0 | 0 |
Large Sample Asymptotics of the Pseudo-Marginal Method | The pseudo-marginal algorithm is a variant of the Metropolis-Hastings
algorithm which samples asymptotically from a probability distribution when it
is only possible to estimate unbiasedly an unnormalized version of its density.
Practically, one has to trade-off the computational resources used to obtain
this estimator against the asymptotic variances of the ergodic averages
obtained by the pseudo-marginal algorithm. Recent works optimizing this
trade-off rely on some strong assumptions which can cast doubts over their
practical relevance. In particular, they all assume that the distribution of
the additive error in the log-likelihood estimator is independent of the
parameter value at which it is evaluated. Under weak regularity conditions we
show here that, as the number of data points tends to infinity, a
space-rescaled version of the pseudo-marginal chain converges weakly towards
another pseudo-marginal chain for which this assumption indeed holds. A study
of this limiting chain allows us to provide parameter dimension-dependent
guidelines on how to optimally scale a normal random walk proposal and the
number of Monte Carlo samples for the pseudo-marginal method in the large
sample regime. This complements and validates currently available results.
| 0 | 0 | 0 | 1 | 0 | 0 |
Large-time behavior of solutions to Vlasov-Poisson-Fokker-Planck equations: from evanescent collisions to diffusive limit | The present contribution investigates the dynamics generated by the
two-dimensional Vlasov-Poisson-Fokker-Planck equation for charged particles in
a steady inhomogeneous background of opposite charges. We provide global in
time estimates that are uniform with respect to initial data taken in a bounded
set of a weighted $L^2$ space, and where dependencies on the mean-free path
$\tau$ and the Debye length $\delta$ are made explicit. In our analysis the
mean free path covers the full range of possible values: from the regime of
evanescent collisions $\tau\to\infty$ to the strongly collisional regime
$\tau\to0$. As a counterpart, the largeness of the Debye length, that enforces
a weakly nonlinear regime, is used to close our nonlinear estimates.
Accordingly we pay a special attention to relax as much as possible the
$\tau$-dependent constraint on $\delta$ ensuring exponential decay with
explicit $\tau$-dependent rates towards the stationary solution. In the
strongly collisional limit $\tau\to0$, we also examine all possible asymptotic
regimes selected by a choice of observation time scale. Here also, our emphasis
is on strong convergence, uniformity with respect to time and to initial data
in bounded sets of a $L^2$ space. Our proofs rely on a detailed study of the
nonlinear elliptic equation defining stationary solutions and a careful
tracking and optimization of parameter dependencies of
hypocoercive/hypoelliptic estimates.
| 0 | 0 | 1 | 0 | 0 | 0 |
Towards Adaptive Resilience in High Performance Computing | Failure rates in high performance computers rapidly increase due to the
growth in system size and complexity. Hence, failures became the norm rather
than the exception. Different approaches on high performance computing (HPC)
systems have been introduced, to prevent failures (e. g., redundancy) or at
least minimize their impacts (e. g., checkpoint and restart). In most cases,
when these approaches are employed to increase the resilience of certain parts
of a system, energy consumption rapidly increases, or performance significantly
degrades. To address this challenge, we propose on-demand resilience as an
approach to achieve adaptive resilience in HPC systems. In this work, the HPC
system is considered in its entirety and resilience mechanisms such as
checkpointing, isolation, and migration, are activated on-demand. Using the
proposed approach, the unavoidable increase in total energy consumption and
system performance degradation is decreased compared to the typical
checkpoint/restart and redundant resilience mechanisms. Our work aims to
mitigate a large number of failures occurring at various layers in the system,
to prevent their propagation, and to minimize their impact, all of this in an
energy-saving manner. In the case of failures that are estimated to occur but
cannot be mitigated using the proposed on-demand resilience approach, the
system administrators will be notified in view of performing further
investigations into the causes of these failures and their impacts.
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Discrete Sequential Prediction of Continuous Actions for Deep RL | It has long been assumed that high dimensional continuous control problems
cannot be solved effectively by discretizing individual dimensions of the
action space due to the exponentially large number of bins over which policies
would have to be learned. In this paper, we draw inspiration from the recent
success of sequence-to-sequence models for structured prediction problems to
develop policies over discretized spaces. Central to this method is the
realization that complex functions over high dimensional spaces can be modeled
by neural networks that predict one dimension at a time. Specifically, we show
how Q-values and policies over continuous spaces can be modeled using a next
step prediction model over discretized dimensions. With this parameterization,
it is possible to both leverage the compositional structure of action spaces
during learning, as well as compute maxima over action spaces (approximately).
On a simple example task we demonstrate empirically that our method can perform
global search, which effectively gets around the local optimization issues that
plague DDPG. We apply the technique to off-policy (Q-learning) methods and show
that our method can achieve the state-of-the-art for off-policy methods on
several continuous control tasks.
| 1 | 0 | 0 | 1 | 0 | 0 |
Sampling Errors in Nested Sampling Parameter Estimation | Sampling errors in nested sampling parameter estimation differ from those in
Bayesian evidence calculation, but have been little studied in the literature.
This paper provides the first explanation of the two main sources of sampling
errors in nested sampling parameter estimation, and presents a new diagrammatic
representation for the process. We find no current method can accurately
measure the parameter estimation errors of a single nested sampling run, and
propose a method for doing so using a new algorithm for dividing nested
sampling runs. We empirically verify our conclusions and the accuracy of our
new method.
| 0 | 1 | 0 | 1 | 0 | 0 |
Towards a Generic Diver-Following Algorithm: Balancing Robustness and Efficiency in Deep Visual Detection | This paper explores the design and development of a class of robust
diver-following algorithms for autonomous underwater robots. By considering the
operational challenges for underwater visual tracking in diverse real-world
settings, we formulate a set of desired features of a generic diver following
algorithm. We attempt to accommodate these features and maximize general
tracking performance by exploiting the state-of-the-art deep object detection
models. We fine-tune the building blocks of these models with a goal of
balancing the trade-off between robustness and efficiency in an onboard setting
under real-time constraints. Subsequently, we design an architecturally simple
Convolutional Neural Network (CNN)-based diver-detection model that is much
faster than the state-of-the-art deep models yet provides comparable detection
performances. In addition, we validate the performance and effectiveness of the
proposed diver-following modules through a number of field experiments in
closed-water and open-water environments.
| 1 | 0 | 0 | 0 | 0 | 0 |
Blockchain: A Graph Primer | Bitcoin and its underlying technology Blockchain have become popular in
recent years. Designed to facilitate a secure distributed platform without
central authorities, Blockchain is heralded as a paradigm that will be as
powerful as Big Data, Cloud Computing and Machine learning. Blockchain
incorporates novel ideas from various fields such as public key encryption and
distributed systems. As such, a reader often comes across resources that
explain the Blockchain technology from a certain perspective only, leaving the
reader with more questions than before. We will offer a holistic view on
Blockchain. Starting with a brief history, we will give the building blocks of
Blockchain, and explain their interactions. As graph mining has become a major
part its analysis, we will elaborate on graph theoretical aspects of the
Blockchain technology. We also devote a section to the future of Blockchain and
explain how extensions like Smart Contracts and De-centralized Autonomous
Organizations will function. Without assuming any reader expertise, our aim is
to provide a concise but complete description of the Blockchain technology.
| 1 | 0 | 0 | 0 | 0 | 0 |
Comparison of forcing functions in magnetohydrodynamic turbulence | Results are presented of direct numerical simulations of incompressible,
homogeneous magnetohydrodynamic turbulence without a mean magnetic field,
subject to different mechanical forcing functions commonly used in the
literature. Specifically, the forces are negative damping (which uses the
large-scale velocity field as a forcing function), a nonhelical random force,
and a nonhelical static sinusoidal force (analogous to helical ABC forcing).
The time evolution of the three ideal invariants (energy, magnetic helicity and
cross helicity), the time-averaged energy spectra, the energy ratios and the
dissipation ratios are examined. All three forcing functions produce
qualitatively similar steady states with regards to the time evolution of the
energy and magnetic helicity. However, differences in the cross helicity
evolution are observed, particularly in the case of the static sinusoidal
method of energy injection. Indeed, an ensemble of sinusoidally-forced
simulations with identical parameters shows significant variations in the cross
helicity over long time periods, casting some doubt on the validity of the
principle of ergodicity in systems in which the injection of helicity cannot be
controlled. Cross helicity can unexpectedly enter the system through the
forcing function and must be carefully monitored.
| 0 | 1 | 0 | 0 | 0 | 0 |
Chance-Constrained AC Optimal Power Flow Integrating HVDC Lines and Controllability | The integration of large-scale renewable generation has major implications on
the operation of power systems, two of which we address in this paper. First,
system operators have to deal with higher degrees of uncertainty. Second, with
abundant potential of renewable generation in remote locations, they need to
incorporate the operation of High Voltage Direct Current lines (HVDC). This
paper introduces an optimization tool that addresses both challenges by
incorporating; the full AC power flow equations and chance constraints to
address the uncertainty of renewable infeed, HVDC modeling for point-to-point
lines, and optimizing generator and HVDC corrective control policies in
reaction to uncertainty. The main contributions are twofold. First, we
introduce a HVDC line model and the corresponding HVDC participation factors in
a chance-constrained AC-OPF framework. Second, we modify an existing algorithm
for solving the chance-constrained AC optimal power flow to allow for
optimization of the generation and HVDC participation factors. Using realistic
wind forecast data, and a 10 bus system with one HVDC line and two wind farms,
we demonstrate the performance of our algorithm and show the benefit of
controllability.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Scalable, Linear-Time Dynamic Cutoff Algorithm for Molecular Dynamics | Recent results on supercomputers show that beyond 65K cores, the efficiency
of molecular dynamics simulations of interfacial systems decreases
significantly. In this paper, we introduce a dynamic cutoff method (DCM) for
interfacial systems of arbitrarily large size. The idea consists in adopting a
cutoff-based method in which the cutoff is cho- sen on a particle-by-particle
basis, according to the distance from the interface. Computationally, the
challenge is shifted from the long-range solvers to the detection of the
interfaces and to the computation of the particle-interface distances. For
these tasks, we present linear-time algorithms that do not rely on global
communication patterns. As a result, the DCM algorithm is suited for large
systems of particles and mas- sively parallel computers. To demonstrate its
potential, we integrated DCM into the LAMMPS open-source molecular dynamics
package, and simulated large liquid/vapor systems on two supercomputers:
SuperMuc and JUQUEEN. In all cases, the accuracy of DCM is comparable to the
traditional particle-particle particle-mesh (PPPM) algorithm, while the
performance is considerably superior for large numbers of particles. For
JUQUEEN, we provide timings for simulations running on the full system (458,
752 cores), and show nearly perfect strong and weak scaling.
| 1 | 1 | 0 | 0 | 0 | 0 |
Spiral arms and disc stability in the Andromeda galaxy | Aims: Density waves are often considered as the triggering mechanism of star
formation in spiral galaxies. Our aim is to study relations between different
star formation tracers (stellar UV and near-IR radiation and emission from HI,
CO and cold dust) in the spiral arms of M31, to calculate stability conditions
in the galaxy disc and to draw conclusions about possible star formation
triggering mechanisms.
Methods: We select fourteen spiral arm segments from the de-projected data
maps and compare emission distributions along the cross sections of the
segments in different datasets to each other, in order to detect spatial
offsets between young stellar populations and the star forming medium. By using
the disc stability condition as a function of perturbation wavelength and
distance from the galaxy centre we calculate the effective disc stability
parameters and the least stable wavelengths at different distances. For this we
utilise a mass distribution model of M31 with four disc components (old and
young stellar discs, cold and warm gaseous discs) embedded within the external
potential of the bulge, the stellar halo and the dark matter halo. Each
component is considered to have a realistic finite thickness.
Results: No systematic offsets between the observed UV and CO/far-IR emission
across the spiral segments are detected. The calculated effective stability
parameter has a minimal value Q_{eff} ~ 1.8 at galactocentric distances 12 - 13
kpc. The least stable wavelengths are rather long, with the minimal values
starting from ~ 3 kpc at distances R > 11 kpc.
Conclusions: The classical density wave theory is not a realistic explanation
for the spiral structure of M31. Instead, external causes should be considered,
e.g. interactions with massive gas clouds or dwarf companions of M31.
| 0 | 1 | 0 | 0 | 0 | 0 |
Anomalous Brownian motion via linear Fokker-Planck equations | According to a traditional point of view Boltzmann entropy is intimately
related to linear Fokker-Planck equations (Smoluchowski, Klein-Kramers, and
Rayleigh equations) that describe a well-known nonequilibrium phenomenon:
(normal) Brownian motion of a particle immersed in a thermal bath.
Nevertheless, current researches have claimed that non-Boltzmann entropies
(Tsallis and Renyi entropies, for instance) may give rise to anomalous Brownian
motion through nonlinear Fokker-Planck equations. The novelty of the present
article is to show that anomalous diffusion could be investigated within the
framework of non-Markovian linear Fokker-Planck equations. So on the ground of
this non-Markovian approach to Brownian motion, we find out anomalous diffusion
characterized by the mean square displacement of a free particle and a harmonic
oscillator in absence of inertial force as well as the mean square momentum of
a free particle in presence of inertial force.
| 0 | 1 | 0 | 0 | 0 | 0 |
An RKHS model for variable selection in functional regression | A mathematical model for variable selection in functional regression models
with scalar response is proposed. By "variable selection" we mean a procedure
to replace the whole trajectories of the functional explanatory variables with
their values at a finite number of carefully selected instants (or "impact
points"). The basic idea of our approach is to use the Reproducing Kernel
Hilbert Space (RKHS) associated with the underlying process, instead of the
more usual L2[0,1] space, in the definition of the linear model. This turns out
to be especially suitable for variable selection purposes, since the
finite-dimensional linear model based on the selected "impact points" can be
seen as a particular case of the RKHS-based linear functional model. In this
framework, we address the consistent estimation of the optimal design of impact
points and we check, via simulations and real data examples, the performance of
the proposed method.
| 0 | 0 | 0 | 1 | 0 | 0 |
Dual-LED-based multichannel microscopy for whole-slide multiplane, multispectral, and phase imaging | We report the development of a multichannel microscopy for whole-slide
multiplane, multispectral, and phase imaging. We use trinocular heads to split
the beam path into 6 independent channels and employ a camera array for
parallel data acquisition, achieving a maximum data throughput of ~1 gigapixel
per second. To perform single-frame rapid autofocusing, we place two
near-infrared LEDs at the back focal plane of the condenser lens to illuminate
the sample from two different incident angles. A hot mirror is used to direct
the near-infrared light to an autofocusing camera. For multiplane whole slide
imaging (WSI), we acquire 6 different focal planes of a thick specimen
simultaneously. For multispectral WSI, we relay the 6 independent image planes
to the same focal position and simultaneously acquire information at 6 spectral
bands. For whole-slide phase imaging, we acquire images at 3 focal positions
simultaneously and use the transport-of-intensity equation to recover the phase
information. We also provide an open-source design to further increase the
number of channels from 6 to 15. The reported platform provides a simple
solution for multiplexed fluorescence imaging and multimodal WSI. Acquiring an
instant focal stack without z-scanning may also enable fast 3D dynamic tracking
of various biological samples.
| 0 | 1 | 0 | 0 | 0 | 0 |
Revisiting the quest for a universal log-law and the role of pressure gradient in "canonical" wall-bounded turbulent flows | The trinity of so-called "canonical" wall-bounded turbulent flows, comprising
the zero pressure gradient turbulent boundary layer, abbreviated ZPG TBL,
turbulent pipe flow and channel/duct flows has continued to receive intense
attention as new and more reliable experimental data have become available.
Nevertheless, the debate on whether the logarithmic part of the mean velocity
profile, in particular the Kármán constant $\kappa$, is identical for these
three canonical flows or flow-dependent is still ongoing. In this paper, which
expands upon Monkewitz and Nagib (24th ICTAM Conf., Montreal, 2016), the
asymptotic matching requirement of equal $\kappa$ in the log-law and in the
expression for the centerline/free-stream velocity is reiterated and shown to
preclude a single universal log-law in the three canonical flows or at least
make it very unlikely. The current re-analysis of high quality mean velocity
profiles in ZPG TBL's, the Princeton "Superpipe" and in channels and ducts
leads to a coherent description of (almost) all seemingly contradictory data
interpretations in terms of TWO logarithmic regions in pipes and channels: A
universal interior, near-wall logarithmic region with the same parameters as in
the ZPG TBL, in particular $\kappa_{\mathrm{wall}} \cong 0.384$, but only
extending from around $150$ to around $10^3$ wall units, and shrinking with
increasing pressure gradient, followed by an exterior logarithmic region with a
flow specific $\kappa$ matching the logarithmic slope of the respective
free-stream or centerline velocity. The log-law parameters of the exterior
logarithmic region in channels and pipes are shown to depend monotonically on
the pressure gradient.
| 0 | 1 | 0 | 0 | 0 | 0 |
Teaching robots to imitate a human with no on-teacher sensors. What are the key challenges? | In this paper, we consider the problem of learning object manipulation tasks
from human demonstration using RGB or RGB-D cameras. We highlight the key
challenges in capturing sufficiently good data with no tracking devices -
starting from sensor selection and accurate 6DoF pose estimation to natural
language processing. In particular, we focus on two showcases: gluing task with
a glue gun and simple block-stacking with variable blocks. Furthermore, we
discuss how a linguistic description of the task could help to improve the
accuracy of task description. We also present the whole architecture of our
transfer of the imitated task to the simulated and real robot environment.
| 1 | 0 | 0 | 0 | 0 | 0 |
Control and Observability Aspects of Phase Synchronization | This paper addresses important control and observability aspects of the phase
synchronization of two oscillators. To this aim a feedback control framework is
proposed based on which issues related to master-slave synchronization are
analyzed. Comparing results using Cartesian and cylindrical coordinates in the
context of the proposed framework it is argued that: i)~observability does not
play a significant role in phase synchronization, although it is granted that
it might be relevant for complete synchronization; and ii)~a practical
difficulty is faced when phase synchronization is aimed at but the control
action is not a direct function of the phase error. A procedure for overcoming
such a problem is proposed. The only assumption made is that the phase can be
estimated using the arctangent function. The main aspects of the paper are
illustrated using the Poincaré equations, van der Pol and Rössler
oscillators in dynamical regimes for which the phase is well defined.
| 0 | 1 | 0 | 0 | 0 | 0 |
Dynamic density structure factor of a unitary Fermi gas at finite temperature | We present a theoretical investigation of the dynamic density structure
factor of a strongly interacting Fermi gas near a Feshbach resonance at finite
temperature. The study is based on a gauge invariant linear response theory.
The theory is consistent with a diagrammatic approach for the equilibrium state
taking into account the pair fluctuation effects and respects some important
restrictions like the $f$-sum rule. Our numerical results show that the dynamic
density structure factor at large incoming momentum and at half recoil
frequency has a qualitatively similar behavior as the order parameter, which
can signify the appearance of the condensate. This qualitatively agrees with
the recent Bragg spectroscopy experiment results. We also present the results
at small incoming momentum.
| 0 | 1 | 0 | 0 | 0 | 0 |
Surjunctivity and topological rigidity of algebraic dynamical systems | Let $X$ be a compact metrizable group and $\Gamma$ a countable group acting
on $X$ by continuous group automorphisms. We give sufficient conditions under
which the dynamical system $(X,\Gamma)$ is surjunctive, i.e., every injective
continuous map $\tau \colon X \to X$ commuting with the action of $\Gamma$ is
surjective.
| 0 | 0 | 1 | 0 | 0 | 0 |
High Resilience Diverse Domain Multilevel Audio Watermarking with Adaptive Threshold | A novel diverse domain (DCT-SVD & DWT-SVD) watermarking scheme is proposed in
this paper. Here, the watermark is embedded simultaneously onto the two
domains. It is shown that an audio signal watermarked using this scheme has
better subjective and objective quality when compared with other watermarking
schemes. Also proposed are two novel watermark detection algorithms viz., AOT
(Adaptively Optimised Threshold) and AOTx (AOT eXtended). The fundamental idea
behind both is finding an optimum threshold for detecting a known character
embedded along with the actual watermarks in a known location, with the
constraint that the Bit Error Rate (BER) is minimum. This optimum threshold is
used for detecting the other characters in the watermarks. This approach is
shown to make the watermarking scheme less susceptible to various signal
processing attacks, thus making the watermarks more robust.
| 1 | 0 | 0 | 0 | 0 | 0 |
Multiprocessor Approximate Message Passing with Column-Wise Partitioning | Solving a large-scale regularized linear inverse problem using multiple
processors is important in various real-world applications due to the
limitations of individual processors and constraints on data sharing policies.
This paper focuses on the setting where the matrix is partitioned column-wise.
We extend the algorithmic framework and the theoretical analysis of approximate
message passing (AMP), an iterative algorithm for solving linear inverse
problems, whose asymptotic dynamics are characterized by state evolution (SE).
In particular, we show that column-wise multiprocessor AMP (C-MP-AMP) obeys an
SE under the same assumptions when the SE for AMP holds. The SE results imply
that (i) the SE of C-MP-AMP converges to a state that is no worse than that of
AMP and (ii) the asymptotic dynamics of C-MP-AMP and AMP can be identical.
Moreover, for a setting that is not covered by SE, numerical results show that
damping can improve the convergence performance of C-MP-AMP.
| 1 | 0 | 1 | 0 | 0 | 0 |
Qualitative robustness for bootstrap approximations | An important property of statistical estimators is qualitative robustness,
that is small changes in the distribution of the data only result in small
chances of the distribution of the estimator. Moreover, in practice, the
distribution of the data is commonly unknown, therefore bootstrap
approximations can be used to approximate the distribution of the estimator.
Hence qualitative robustness of the statistical estimator under the bootstrap
approximation is a desirable property. Currently most theoretical
investigations on qualitative robustness assume independent and identically
distributed pairs of random variables. However, in practice this assumption is
not fulfilled. Therefore, we examine the qualitative robustness of bootstrap
approximations for non-i.i.d. random variables, for example $\alpha$-mixing and
weakly dependent processes. In the i.i.d. case qualitative robustness is
ensured via the continuity of the statistical operator, representing the
estimator, see Hampel (1971) and Cuevas and Romo (1993). We show, that
qualitative robustness of the bootstrap approximation is still ensured under
the assumption that the statistical operator is continuous and under an
additional assumption on the stochastic process. In particular, we require a
convergence condition of the empirical measure of the underlying process, the
so called Varadarajan property.
| 0 | 0 | 1 | 1 | 0 | 0 |
On Asymptotic Properties of Hyperparameter Estimators for Kernel-based Regularization Methods | The kernel-based regularization method has two core issues: kernel design and
hyperparameter estimation. In this paper, we focus on the second issue and
study the properties of several hyperparameter estimators including the
empirical Bayes (EB) estimator, two Stein's unbiased risk estimators (SURE) and
their corresponding Oracle counterparts, with an emphasis on the asymptotic
properties of these hyperparameter estimators. To this goal, we first derive
and then rewrite the first order optimality conditions of these hyperparameter
estimators, leading to several insights on these hyperparameter estimators.
Then we show that as the number of data goes to infinity, the two SUREs
converge to the best hyperparameter minimizing the corresponding mean square
error, respectively, while the more widely used EB estimator converges to
another best hyperparameter minimizing the expectation of the EB estimation
criterion. This indicates that the two SUREs are asymptotically optimal but the
EB estimator is not. Surprisingly, the convergence rate of two SUREs is slower
than that of the EB estimator, and moreover, unlike the two SUREs, the EB
estimator is independent of the convergence rate of $\Phi^T\Phi/N$ to its
limit, where $\Phi$ is the regression matrix and $N$ is the number of data. A
Monte Carlo simulation is provided to demonstrate the theoretical results.
| 1 | 0 | 0 | 0 | 0 | 0 |
SECS: Efficient Deep Stream Processing via Class Skew Dichotomy | Despite that accelerating convolutional neural network (CNN) receives an
increasing research focus, the save on resource consumption always comes with a
decrease in accuracy. To both increase accuracy and decrease resource
consumption, we explore an environment information, called class skew, which is
easily available and exists widely in daily life. Since the class skew may
switch as time goes, we bring up probability layer to utilize class skew
without any overhead during the runtime. Further, we observe class skew
dichotomy that some class skew may appear frequently in the future, called hot
class skew, and others will never appear again or appear seldom, called cold
class skew. Inspired by techniques from source code optimization, two modes,
i.e., interpretation and compilation, are proposed. The interpretation mode
pursues efficient adaption during runtime for cold class skew and the
compilation mode aggressively optimize on hot ones for more efficient
deployment in the future. Aggressive optimization is processed by
class-specific pruning and provides extra benefit. Finally, we design a
systematic framework, SECS, to dynamically detect class skew, processing
interpretation and compilation, as well as select the most accurate
architectures under the runtime resource budget. Extensive evaluations show
that SECS can realize end-to-end classification speedups by a factor of 3x to
11x relative to state-of-the-art convolutional neural networks, at a higher
accuracy.
| 1 | 0 | 0 | 0 | 0 | 0 |
Implementation of infinite-range exterior complex scaling to the time-dependent complete-active-space self-consistent-field method | We present a numerical implementation of the infinite-range exterior complex
scaling (irECS) [Phys. Rev. A 81, 053845 (2010)] as an efficient absorbing
boundary to the time-dependent complete-active-space self-consistent field
(TD-CASSCF) method [Phys. Rev. A 94, 023405 (2016)] for multielectron atoms
subject to an intense laser pulse. We introduce Gauss-Laguerre-Radau quadrature
points to construct discrete variable representation basis functions in the
last radial finite element extending to infinity. This implementation is
applied to strong-field ionization and high-harmonic generation in He, Be, and
Ne atoms. It efficiently prevents unphysical reflection of photoelectron wave
packets at the simulation boundary, enabling accurate simulations with
substantially reduced computational cost, even under significant (~ 50%) double
ionization. For the case of a simulation of high-harmonic generation from Ne,
for example, 80% cost reduction is achieved, compared to a mask-function
absorption boundary.
| 0 | 1 | 0 | 0 | 0 | 0 |
A consistent measure of the merger histories of massive galaxies using close-pair statistics I: Major mergers at $z < 3.5$ | We use a large sample of $\sim 350,000$ galaxies constructed by combining the
UKIDSS UDS, VIDEO/CFHT-LS, UltraVISTA/COSMOS and GAMA survey regions to probe
the major merging histories of massive galaxies ($>10^{10}\ \mathrm{M}_\odot$)
at $0.005 < z < 3.5$. We use a method adapted from that presented in
Lopez-Sanjuan et al. (2014) using the full photometric redshift probability
distributions, to measure pair $\textit{fractions}$ of flux-limited, stellar
mass selected galaxy samples using close-pair statistics. The pair fraction is
found to weakly evolve as $\propto (1+z)^{0.8}$ with no dependence on stellar
mass. We subsequently derive major merger $\textit{rates}$ for galaxies at $>
10^{10}\ \mathrm{M}_\odot$ and at a constant number density of $n > 10^{-4}$
Mpc$^{-3}$, and find rates a factor of 2-3 smaller than previous works,
although this depends strongly on the assumed merger timescale and likelihood
of a close-pair merging. Galaxies undergo approximately 0.5 major mergers at $z
< 3.5$, accruing an additional 1-4 $\times 10^{10}\ \mathrm{M}_\odot$ in the
process. Major merger accretion rate densities of $\sim 2 \times 10^{-4}$
$\mathrm{M}_\odot$ yr$^{-1}$ Mpc$^{-3}$ are found for number density selected
samples, indicating that direct progenitors of local massive
($>10^{11}\mathrm{M}_\odot$) galaxies have experienced a steady supply of
stellar mass via major mergers throughout their evolution. While pair fractions
are found to agree with those predicted by the Henriques et al. (2014)
semi-analytic model, the Illustris hydrodynamical simulation fails to
quantitatively reproduce derived merger rates. Furthermore, we find major
mergers become a comparable source of stellar mass growth compared to
star-formation at $z < 1$, but is 10-100 times smaller than the SFR density at
higher redshifts.
| 0 | 1 | 0 | 0 | 0 | 0 |
EE-Grad: Exploration and Exploitation for Cost-Efficient Mini-Batch SGD | We present a generic framework for trading off fidelity and cost in computing
stochastic gradients when the costs of acquiring stochastic gradients of
different quality are not known a priori. We consider a mini-batch oracle that
distributes a limited query budget over a number of stochastic gradients and
aggregates them to estimate the true gradient. Since the optimal mini-batch
size depends on the unknown cost-fidelity function, we propose an algorithm,
{\it EE-Grad}, that sequentially explores the performance of mini-batch oracles
and exploits the accumulated knowledge to estimate the one achieving the best
performance in terms of cost-efficiency. We provide performance guarantees for
EE-Grad with respect to the optimal mini-batch oracle, and illustrate these
results in the case of strongly convex objectives. We also provide a simple
numerical example that corroborates our theoretical findings.
| 1 | 0 | 0 | 1 | 0 | 0 |
Test of special relativity using a fiber network of optical clocks | Phase compensated optical fiber links enable high accuracy atomic clocks
separated by thousands of kilometers to be compared with unprecedented
statistical resolution. By searching for a daily variation of the frequency
difference between four strontium optical lattice clocks in different locations
throughout Europe connected by such links, we improve upon previous tests of
time dilation predicted by special relativity. We obtain a constraint on the
Robertson--Mansouri--Sexl parameter $|\alpha|\lesssim 1.1 \times10^{-8}$
quantifying a violation of time dilation, thus improving by a factor of around
two the best known constraint obtained with Ives--Stilwell type experiments,
and by two orders of magnitude the best constraint obtained by comparing atomic
clocks. This work is the first of a new generation of tests of fundamental
physics using optical clocks and fiber links. As clocks improve, and as fiber
links are routinely operated, we expect that the tests initiated in this paper
will improve by orders of magnitude in the near future.
| 0 | 1 | 0 | 0 | 0 | 0 |
Magnetic phases of spin-1 lattice gases with random interactions | A spin-1 atomic gas in an optical lattice, in the unit-filling Mott Insulator
(MI) phase and in the presence of disordered spin-dependent interaction, is
considered. In this regime, at zero temperature, the system is well described
by a disordered rotationally-invariant spin-1 bilinear-biquadratic model. We
study, via the density matrix renormalization group algorithm, a bounded
disorder model such that the spin interactions can be locally either
ferromagnetic or antiferromagnetic. Random interactions induce the appearance
of a disordered ferromagnetic phase characterized by a non-vanishing value of
spin-glass order parameter across the boundary between a ferromagnetic phase
and a dimer phase exhibiting random singlet order. The study of the
distribution of the block entanglement entropy reveals that in this region
there is no random singlet order.
| 0 | 1 | 0 | 0 | 0 | 0 |
On the second boundary value problem for Monge-Ampere type equations and geometric optics | In this paper, we prove the existence of classical solutions to second
boundary value prob- lems for generated prescribed Jacobian equations, as
recently developed by the second author, thereby obtaining extensions of
classical solvability of optimal transportation problems to problems arising in
near field geometric optics. Our results depend in particular on a priori
second derivative estimates recently established by the authors under weak
co-dimension one convexity hypotheses on the associated matrix functions with
respect to the gradient variables, (A3w). We also avoid domain deformations by
using the convexity theory of generating functions to construct unique initial
solutions for our homotopy family, thereby enabling application of the degree
theory for nonlinear oblique boundary value problems.
| 0 | 0 | 1 | 0 | 0 | 0 |
Is the kinetic equation for turbulent gas-particle flows ill-posed? | This paper is about well-posedness and realizability of the kinetic equation
for gas-particle flows and its relationship to the Generalized Langevin Model
(GLM) PDF equation. Previous analyses claim that this kinetic equation is
ill-posed, that in particular it has the properties of a backward heat equation
and as a consequence, its solutions will in the course of time exhibit
finite-time singularities. We show that the analysis leading to this conclusion
is fundamentally incorrect because it ignores the coupling between the phase
space variables in the kinetic equation and the time and particle inertia
dependence of the phase space diffusion tensor. This contributes an extra $+ve$
diffusion that always outweighs the contribution from the$-ve$ diffusion
associated with the dispersion along one of the principal axes of the phase
space diffusion tensor. This is confirmed by a numerical evaluation of analytic
solutions of these $+ve$ and $-ve$ contributions to the particle diffusion
coefficient along this principal axis. We also examine other erroneous claims
and assumptions made in previous studies that demonstrate the apparent
superiority of the GLM PDF approach over the kinetic approach. In so doing we
have drawn attention to the limitations of the GLM approach which these studies
have ignored or not properly considered, to give a more balanced appraisal of
the benefits of both PDF approaches.
| 0 | 1 | 0 | 0 | 0 | 0 |
On the construction of small subsets containing special elements in a finite field | In this note we construct a series of small subsets containing a non-d-th
power element in a finite field by applying certain bounds on incomplete
character sums.
Precisely, let $h=\lfloor q^{\delta}\rfloor>1$ and $d\mid q^h-1$. Let $r$ be
a prime divisor of $q-1$ such that the largest prime power part of $q-1$ has
the form $r^s$. Then there is a constant $0<\epsilon<1$ such that for a ratio
at least $ {q^{-\epsilon h}}$ of $\alpha\in \mathbb{F}_{q^{h}}
\backslash\mathbb{F}_{q}$, the set $S=\{ \alpha-x^t, x\in\mathbb{F}_{q}\}$ of
cardinality $1+\frac {q-1} {M(h)}$ contains a non-d-th power in
$\mathbb{F}_{q^{\lfloor q^\delta\rfloor}}$, where $t$ is the largest power of
$r$ such that $t<\sqrt{q}/h$ and $M(h)$ is defined as $$M(h)=\max_{r \mid
(q-1)} r^{\min\{v_r(q-1), \lfloor\log_r{q}/2-\log_r h\rfloor\}}.$$ Here $r$
runs thourgh prime divisors and $v_r(x)$ is the $r$-adic oder of $x$. For odd
$q$, the choice of $\delta=\frac 12-d, d=o(1)>0$ shows that there exists an
explicit subset of cardinality $q^{1-d}=O(\log^{2+\epsilon'}(q^h))$ containing
a non-quadratic element in the field $\mathbb{F}_{q^h}$. On the other hand, the
choice of $h=2$ shows that for any odd prime power $q$, there is an explicit
subset of cardinality $1+\frac {q-1}{M(2)}$ containing a non-quadratic element
in $\mathbb{F}_{q^2}$. This improves a $q-1$ construction by Coulter and Kosick
\cite{CK} since $\lfloor \log_2{(q-1)}\rfloor\leq M(2) < \sqrt{q}$.
In addition, we obtain a similar construction for small sets containing a
primitive element. The construction works well provided $\phi(q^h-1)$ is very
small, where $\phi$ is the Euler's totient function.
| 1 | 0 | 1 | 0 | 0 | 0 |
Information Potential Auto-Encoders | In this paper, we suggest a framework to make use of mutual information as a
regularization criterion to train Auto-Encoders (AEs). In the proposed
framework, AEs are regularized by minimization of the mutual information
between input and encoding variables of AEs during the training phase. In order
to estimate the entropy of the encoding variables and the mutual information,
we propose a non-parametric method. We also give an information theoretic view
of Variational AEs (VAEs), which suggests that VAEs can be considered as
parametric methods that estimate entropy. Experimental results show that the
proposed non-parametric models have more degree of freedom in terms of
representation learning of features drawn from complex distributions such as
Mixture of Gaussians, compared to methods which estimate entropy using
parametric approaches, such as Variational AEs.
| 1 | 0 | 0 | 1 | 0 | 0 |
Irreducible compositions of degree two polynomials over finite fields have regular structure | Let $q$ be an odd prime power and $D$ be the set of monic irreducible
polynomials in $\mathbb F_q[x]$ which can be written as a composition of monic
degree two polynomials. In this paper we prove that $D$ has a natural regular
structure by showing that there exists a finite automaton having $D$ as
accepted language. Our method is constructive.
| 1 | 0 | 1 | 0 | 0 | 0 |
Quantum interferometry in multi-mode systems | We consider the situation when the signal propagating through each arm of an
interferometer has a complicated multi-mode structure. We find the relation
between the particle-entanglement and the possibility to surpass the shot-noise
limit of the phase estimation. Our results are general---they apply to pure and
mixed states of identical and distinguishable particles (or combinations of
both), for a fixed and fluctuating number of particles. We also show that the
method for detecting the entanglement often used in two-mode system can give
misleading results when applied to the multi-mode case.
| 0 | 1 | 0 | 0 | 0 | 0 |
Glider representations of chains of semisimple Lie algebras | We start the study of glider representations in the setting of semisimple Lie
algebras. A glider representation is defined for some positively filtered ring
$FR$ and here we consider the right bounded algebra filtration
$FU(\mathfrak{g})$ on the universal enveloping algebra $U(\mathfrak{g})$ of
some semisimple Lie algebra $\mathfrak{g}$ given by a fixed chain of semisimple
sub Lie algebras $\mathfrak{g}_1 \subset \mathfrak{g}_2 \subset \ldots \subset
\mathfrak{g}_n = \mathfrak{g}$. Inspired by the classical representation
theory, we introduce so-called Verma glider representations. Their existence is
related to the relations between the root systems of the appearing Lie algebras
$\mathfrak{g}_i$. In particular, we consider chains of simple Lie algebras of
the same type $A,B,C$ and $D$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Equilibrium configurations of large nanostructures using the embedded saturated-fragments stochastic density functional theory | An \emph{ab initio} Langevin dynamics approach is developed based on
stochastic density functional theory (sDFT) within a new \emph{embedded
saturated } \emph{fragment }formalism, applicable to covalently bonded systems.
The forces on the nuclei generated by sDFT contain a random component natural
to Langevin dynamics and its standard deviation is used to estimate the
friction term on each atom by satisfying the fluctuation\textendash dissipation
relation. The overall approach scales linearly with system size even if the
density matrix is not local and is thus applicable to ordered as well as
disordered extended systems. We implement the approach for a series of silicon
nanocrystals (NCs) of varying size with a diameter of up to $3$nm corresponding
to $N_{e}=3000$ electrons and generate a set of configurations that are
distributed canonically at a fixed temperature, ranging from cryogenic to room
temperature. We also analyze the structure properties of the NCs and discuss
the reconstruction of the surface geometry.
| 0 | 1 | 0 | 0 | 0 | 0 |
Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks | Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented.
| 1 | 0 | 0 | 0 | 0 | 0 |
Hypergraph Convolution and Hypergraph Attention | Recently, graph neural networks have attracted great attention and achieved
prominent performance in various research fields. Most of those algorithms have
assumed pairwise relationships of objects of interest. However, in many real
applications, the relationships between objects are in higher-order, beyond a
pairwise formulation. To efficiently learn deep embeddings on the high-order
graph-structured data, we introduce two end-to-end trainable operators to the
family of graph neural networks, i.e., hypergraph convolution and hypergraph
attention. Whilst hypergraph convolution defines the basic formulation of
performing convolution on a hypergraph, hypergraph attention further enhances
the capacity of representation learning by leveraging an attention module. With
the two operators, a graph neural network is readily extended to a more
flexible model and applied to diverse applications where non-pairwise
relationships are observed. Extensive experimental results with semi-supervised
node classification demonstrate the effectiveness of hypergraph convolution and
hypergraph attention.
| 1 | 0 | 0 | 1 | 0 | 0 |
Network Capacity Bound for Personalized PageRank in Multimodal Networks | In a former paper the concept of Bipartite PageRank was introduced and a
theorem on the limit of authority flowing between nodes for personalized
PageRank has been generalized. In this paper we want to extend those results to
multimodal networks. In particular we introduce a hypergraph type that may be
used for describing multimodal network where a hyperlink connects nodes from
each of the modalities. We introduce a generalisation of PageRank for such
graphs and define the respective random walk model that can be used for
computations. we finally state and prove theorems on the limit of outflow of
authority for cases where individual modalities have identical and distinct
damping factors.
| 1 | 1 | 0 | 0 | 0 | 0 |
Hessian corrections to Hybrid Monte Carlo | A method for the introduction of second-order derivatives of the log
likelihood into HMC algorithms is introduced, which does not require the
Hessian to be evaluated at each leapfrog step but only at the start and end of
trajectories.
| 0 | 0 | 0 | 1 | 0 | 0 |
Are Over-massive Haloes of Ultra Diffuse Galaxies Consistent with Extended MOND? | A sample of Coma cluster ultra-diffuse galaxies (UDGs) are modelled in the
context of Extended Modified Newtonian Dynamics (EMOND) with the aim to explain
the large dark matter-like effect observed in these cluster galaxies.
We first build a model of the Coma cluster in the context of EMOND using gas
and galaxy mass profiles from the literature. Then assuming the dynamical mass
of the UDGs satisfies the fundamental manifold of other ellipticals, and that
the UDG stellar mass-to-light matches their colour, we can verify the EMOND
formulation by comparing two predictions of the baryonic mass of UDGs.
We find that EMOND can explain the UDG mass, within the expected modelling
errors, if they lie on the fundamental manifold of ellipsoids, however, given
that measurements show one UDG lying off the fundamental manifold, observations
of more UDGs are needed to confirm this assumption.
| 0 | 1 | 0 | 0 | 0 | 0 |
Magnetic-Visual Sensor Fusion-based Dense 3D Reconstruction and Localization for Endoscopic Capsule Robots | Reliable and real-time 3D reconstruction and localization functionality is a
crucial prerequisite for the navigation of actively controlled capsule
endoscopic robots as an emerging, minimally invasive diagnostic and therapeutic
technology for use in the gastrointestinal (GI) tract. In this study, we
propose a fully dense, non-rigidly deformable, strictly real-time,
intraoperative map fusion approach for actively controlled endoscopic capsule
robot applications which combines magnetic and vision-based localization, with
non-rigid deformations based frame-to-model map fusion. The performance of the
proposed method is demonstrated using four different ex-vivo porcine stomach
models. Across different trajectories of varying speed and complexity, and four
different endoscopic cameras, the root mean square surface reconstruction
errors 1.58 to 2.17 cm.
| 1 | 0 | 0 | 0 | 0 | 0 |
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