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Deep Depth From Focus | Depth from focus (DFF) is one of the classical ill-posed inverse problems in
computer vision. Most approaches recover the depth at each pixel based on the
focal setting which exhibits maximal sharpness. Yet, it is not obvious how to
reliably estimate the sharpness level, particularly in low-textured areas. In
this paper, we propose `Deep Depth From Focus (DDFF)' as the first end-to-end
learning approach to this problem. One of the main challenges we face is the
hunger for data of deep neural networks. In order to obtain a significant
amount of focal stacks with corresponding groundtruth depth, we propose to
leverage a light-field camera with a co-calibrated RGB-D sensor. This allows us
to digitally create focal stacks of varying sizes. Compared to existing
benchmarks our dataset is 25 times larger, enabling the use of machine learning
for this inverse problem. We compare our results with state-of-the-art DFF
methods and we also analyze the effect of several key deep architectural
components. These experiments show that our proposed method `DDFFNet' achieves
state-of-the-art performance in all scenes, reducing depth error by more than
75% compared to the classical DFF methods.
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Deep Text Classification Can be Fooled | In this paper, we present an effective method to craft text adversarial
samples, revealing one important yet underestimated fact that DNN-based text
classifiers are also prone to adversarial sample attack. Specifically,
confronted with different adversarial scenarios, the text items that are
important for classification are identified by computing the cost gradients of
the input (white-box attack) or generating a series of occluded test samples
(black-box attack). Based on these items, we design three perturbation
strategies, namely insertion, modification, and removal, to generate
adversarial samples. The experiment results show that the adversarial samples
generated by our method can successfully fool both state-of-the-art
character-level and word-level DNN-based text classifiers. The adversarial
samples can be perturbed to any desirable classes without compromising their
utilities. At the same time, the introduced perturbation is difficult to be
perceived.
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Two-Stream 3D Convolutional Neural Network for Skeleton-Based Action Recognition | It remains a challenge to efficiently extract spatialtemporal information
from skeleton sequences for 3D human action recognition. Although most recent
action recognition methods are based on Recurrent Neural Networks which present
outstanding performance, one of the shortcomings of these methods is the
tendency to overemphasize the temporal information. Since 3D convolutional
neural network(3D CNN) is a powerful tool to simultaneously learn features from
both spatial and temporal dimensions through capturing the correlations between
three dimensional signals, this paper proposes a novel two-stream model using
3D CNN. To our best knowledge, this is the first application of 3D CNN in
skeleton-based action recognition. Our method consists of three stages. First,
skeleton joints are mapped into a 3D coordinate space and then encoding the
spatial and temporal information, respectively. Second, 3D CNN models are
seperately adopted to extract deep features from two streams. Third, to enhance
the ability of deep features to capture global relationships, we extend every
stream into multitemporal version. Extensive experiments on the SmartHome
dataset and the large-scale NTU RGB-D dataset demonstrate that our method
outperforms most of RNN-based methods, which verify the complementary property
between spatial and temporal information and the robustness to noise.
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Strong Bayesian Evidence for the Normal Neutrino Hierarchy | The configuration of the three neutrino masses can take two forms, known as
the normal and inverted hierarchies. We compute the Bayesian evidence
associated with these two hierarchies. Previous studies found a mild preference
for the normal hierarchy, and this was driven by the asymmetric manner in which
cosmological data has confined the available parameter space. Here we identify
the presence of a second asymmetry, which is imposed by data from neutrino
oscillations. By combining constraints on the squared-mass splittings with the
limit on the sum of neutrino masses of $\Sigma m_\nu < 0.13$ eV, and using a
minimally informative prior on the masses, we infer odds of 42:1 in favour of
the normal hierarchy, which is classified as "strong" in the Jeffreys' scale.
We explore how these odds may evolve in light of higher precision cosmological
data, and discuss the implications of this finding with regards to the nature
of neutrinos. Finally the individual masses are inferred to be $m_1 =
3.80^{+26.2}_{-3.73} \, \text{meV}, m_2 = 8.8^{+18}_{-1.2} \, \text{meV}, m_3 =
50.4^{+5.8}_{-1.2} \, \text{meV}$ ($95\%$ credible intervals).
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A fast reconstruction algorithm for geometric inverse problems using topological sensitivity analysis and Dirichlet-Neumann cost functional approach | This paper is concerned with the detection of objects immersed in anisotropic
media from boundary measurements. We propose an accurate approach based on the
Kohn-Vogelius formulation and the topological sensitivity analysis method. The
inverse problem is formulated as a topology optimization one minimizing an
energy like functional. A topological asymptotic expansion is derived for the
anisotropic Laplace operator. The unknown object is reconstructed using a
level-set curve of the topological gradient. The efficiency and accuracy of the
proposed algorithm are illustrated by some numerical results. MOTS-CLÉS :
Problème inverse géométrique, Laplace anisotrope, formulation de
Kohn-Vogelius, analyse de sensibilité, optimisation topologique.
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Tomographic X-ray data of carved cheese | This is the documentation of the tomographic X-ray data of a carved cheese
slice. Data are available at www.fips.fi/dataset.php, and can be freely used
for scientific purposes with appropriate references to them, and to this
document in this http URL. The data set consists of (1) the X-ray sinogram
of a single 2D slice of the cheese slice with three different resolutions and
(2) the corresponding measurement matrices modeling the linear operation of the
X-ray transform. Each of these sinograms was obtained from a measured
360-projection fan-beam sinogram by down-sampling and taking logarithms. The
original (measured) sinogram is also provided in its original form and
resolution.
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SMARTies: Sentiment Models for Arabic Target Entities | We consider entity-level sentiment analysis in Arabic, a morphologically rich
language with increasing resources. We present a system that is applied to
complex posts written in response to Arabic newspaper articles. Our goal is to
identify important entity "targets" within the post along with the polarity
expressed about each target. We achieve significant improvements over multiple
baselines, demonstrating that the use of specific morphological representations
improves the performance of identifying both important targets and their
sentiment, and that the use of distributional semantic clusters further boosts
performances for these representations, especially when richer linguistic
resources are not available.
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Quantile Markov Decision Process | In this paper, we consider the problem of optimizing the quantiles of the
cumulative rewards of Markov Decision Processes (MDP), to which we refers as
Quantile Markov Decision Processes (QMDP). Traditionally, the goal of a Markov
Decision Process (MDP) is to maximize expected cumulative reward over a defined
horizon (possibly to be infinite). In many applications, however, a decision
maker may be interested in optimizing a specific quantile of the cumulative
reward instead of its expectation. Our framework of QMDP provides analytical
results characterizing the optimal QMDP solution and presents the algorithm for
solving the QMDP. We provide analytical results characterizing the optimal QMDP
solution and present the algorithms for solving the QMDP. We illustrate the
model with two experiments: a grid game and a HIV optimal treatment experiment.
| 1 | 0 | 0 | 0 | 0 | 0 |
Explaining the elongated shape of 'Oumuamua by the Eikonal abrasion model | The photometry of the minor body with extrasolar origin (1I/2017 U1)
'Oumuamua revealed an unprecedented shape: Meech et al. (2017) reported a shape
elongation b/a close to 1/10, which calls for theoretical explanation. Here we
show that the abrasion of a primordial asteroid by a huge number of tiny
particles ultimately leads to such elongated shape. The model (called the
Eikonal equation) predicting this outcome was already suggested in Domokos et
al. (2009) to play an important role in the evolution of asteroid shapes.
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Generation of $1/f$ noise motivated by a model for musical melodies | We present a model to generate power spectrum noise with intensity
proportional to 1/f as a function of frequency f. The model arises from a
broken-symmetry variable which corresponds to absolute pitch, where
fluctuations occur in an attempt to restore that symmetry, influenced by
interactions in the creation of musical melodies.
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On the lattice of the $σ$-permutable subgroups of a finite group | Let $\sigma =\{\sigma_{i} | i\in I\}$ be some partition of the set of all
primes $\Bbb{P}$, $G$ a finite group and $\sigma (G) =\{\sigma_{i}
|\sigma_{i}\cap \pi (G)\ne \emptyset \}$. A set ${\cal H}$ of subgroups of $G$
is said to be a complete Hall $\sigma $-set of $G$ if every member $\ne 1$ of
${\cal H}$ is a Hall $\sigma_{i}$-subgroup of $G$ for some $\sigma_{i}\in
\sigma $ and ${\cal H}$ contains exactly one Hall $\sigma_{i}$-subgroup of $G$
for every $\sigma_{i}\in \sigma (G)$. A subgroup $A$ of $G$ is said to be
${\sigma}$-permutable in $G$ if $G$ possesses a complete Hall $\sigma $-set and
$A$ permutes with each Hall $\sigma_{i}$-subgroup $H$ of $G$, that is, $AH=HA$
for all $i \in I$. We characterize finite groups with distributive lattice of
the ${\sigma}$-permutable subgroups.
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The geometry of the generalized algebraic Riccati equation and of the singular Hamiltonian system | This paper analyzes the properties of the solutions of the generalized
continuous algebraic Riccati equation from a geometric perspective. This
analysis reveals the presence of a subspace that may provide an appropriate
degree of freedom to stabilize the system in the related optimal control
problem even in cases where the Riccati equation does not admit a stabilizing
solution.
| 0 | 0 | 1 | 0 | 0 | 0 |
High Order Numerical Integrators for Relativistic Charged Particle Tracking | In this paper, we extend several time reversible numerical integrators to
solve the Lorentz force equations from second order accuracy to higher order
accuracy for relativistic charged particle tracking in electromagnetic fields.
A fourth order algorithm is given explicitly and tested with numerical
examples. Such high order numerical integrators can significantly save the
computational cost by using a larger step size in comparison to the second
order integrators.
| 0 | 1 | 0 | 0 | 0 | 0 |
Astrophotonics: molding the flow of light in astronomical instruments | Since its emergence two decades ago, astrophotonics has found broad
application in scientific instruments at many institutions worldwide. The case
for astrophotonics becomes more compelling as telescopes push for AO-assisted,
diffraction-limited performance, a mode of observing that is central to the
next-generation of extremely large telescopes (ELTs). Even AO systems are
beginning to incorporate advanced photonic principles as the community pushes
for higher performance and more complex guide-star configurations. Photonic
instruments like Gravity on the Very Large Telescope achieve milliarcsec
resolution at 2000 nm which would be very difficult to achieve with
conventional optics. While space photonics is not reviewed here, we foresee
that remote sensing platforms will become a major beneficiary of astrophotonic
components in the years ahead. The field has given back with the development of
new technologies (e.g. photonic lantern, large area multi-core fibres) already
finding widespread use in other fields; Google Scholar lists more than 400
research papers making reference to this technology. This short review covers
representative key developments since the 2009 Focus issue on Astrophotonics.
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Towards quantitative methods to assess network generative models | Assessing generative models is not an easy task. Generative models should
synthesize graphs which are not replicates of real networks but show
topological features similar to real graphs. We introduce an approach for
assessing graph generative models using graph classifiers. The inability of an
established graph classifier for distinguishing real and synthesized graphs
could be considered as a performance measurement for graph generators.
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Soliton groups as the reason for extreme statistics of unidirectional sea waves | The results of the probabilistic analysis of the direct numerical simulations
of irregular unidirectional deep-water waves are discussed. It is shown that an
occurrence of large-amplitude soliton-like groups represents an extraordinary
case, which is able to increase noticeably the probability of high waves even
in moderately rough sea conditions. The ensemble of wave realizations should be
large enough to take these rare events into account. Hence we provide a
striking example when long-living coherent structures make the water wave
statistics extreme.
| 0 | 1 | 0 | 0 | 0 | 0 |
Stack Overflow: A Code Laundering Platform? | Developers use Question and Answer (Q&A) websites to exchange knowledge and
expertise. Stack Overflow is a popular Q&A website where developers discuss
coding problems and share code examples. Although all Stack Overflow posts are
free to access, code examples on Stack Overflow are governed by the Creative
Commons Attribute-ShareAlike 3.0 Unported license that developers should obey
when reusing code from Stack Overflow or posting code to Stack Overflow. In
this paper, we conduct a case study with 399 Android apps, to investigate
whether developers respect license terms when reusing code from Stack Overflow
posts (and the other way around). We found 232 code snippets in 62 Android apps
from our dataset that were potentially reused from Stack Overflow, and 1,226
Stack Overflow posts containing code examples that are clones of code released
in 68 Android apps, suggesting that developers may have copied the code of
these apps to answer Stack Overflow questions. We investigated the licenses of
these pieces of code and observed 1,279 cases of potential license violations
(related to code posting to Stack overflow or code reuse from Stack overflow).
This paper aims to raise the awareness of the software engineering community
about potential unethical code reuse activities taking place on Q&A websites
like Stack Overflow.
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New Abilities and Limitations of Spectral Graph Bisection | Spectral based heuristics belong to well-known commonly used methods which
determines provably minimal graph bisection or outputs "fail" when the
optimality cannot be certified. In this paper we focus on Boppana's algorithm
which belongs to one of the most prominent methods of this type. It is well
known that the algorithm works well in the random \emph{planted bisection
model} -- the standard class of graphs for analysis minimum bisection and
relevant problems. In 2001 Feige and Kilian posed the question if Boppana's
algorithm works well in the semirandom model by Blum and Spencer. In our paper
we answer this question affirmatively. We show also that the algorithm achieves
similar performance on graph classes which extend the semirandom model.
Since the behavior of Boppana's algorithm on the semirandom graphs remained
unknown, Feige and Kilian proposed a new semidefinite programming (SDP) based
approach and proved that it works on this model. The relationship between the
performance of the SDP based algorithm and Boppana's approach was left as an
open problem. In this paper we solve the problem in a complete way by proving
that the bisection algorithm of Feige and Kilian provides exactly the same
results as Boppana's algorithm. As a consequence we get that Boppana's
algorithm achieves the optimal threshold for exact cluster recovery in the
\emph{stochastic block model}. On the other hand we prove some limitations of
Boppana's approach: we show that if the density difference on the parameters of
the planted bisection model is too small then the algorithm fails with high
probability in the model.
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Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks | Accelerated magnetic resonance (MR) scan acquisition with compressed sensing
(CS) and parallel imaging is a powerful method to reduce MR imaging scan time.
However, many reconstruction algorithms have high computational costs. To
address this, we investigate deep residual learning networks to remove aliasing
artifacts from artifact corrupted images. The proposed deep residual learning
networks are composed of magnitude and phase networks that are separately
trained. If both phase and magnitude information are available, the proposed
algorithm can work as an iterative k-space interpolation algorithm using
framelet representation. When only magnitude data is available, the proposed
approach works as an image domain post-processing algorithm. Even with strong
coherent aliasing artifacts, the proposed network successfully learned and
removed the aliasing artifacts, whereas current parallel and CS reconstruction
methods were unable to remove these artifacts. Comparisons using single and
multiple coil show that the proposed residual network provides good
reconstruction results with orders of magnitude faster computational time than
existing compressed sensing methods. The proposed deep learning framework may
have a great potential for accelerated MR reconstruction by generating accurate
results immediately.
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Quantification of market efficiency based on informational-entropy | Since the 1960s, the question whether markets are efficient or not is
controversially discussed. One reason for the difficulty to overcome the
controversy is the lack of a universal, but also precise, quantitative
definition of efficiency that is able to graduate between different states of
efficiency. The main purpose of this article is to fill this gap by developing
a measure for the efficiency of markets that fulfill all the stated
requirements. It is shown that the new definition of efficiency, based on
informational-entropy, is equivalent to the two most used definitions of
efficiency from Fama and Jensen. The new measure therefore enables steps to
settle the dispute over the state of efficiency in markets. Moreover, it is
shown that inefficiency in a market can either arise from the possibility to
use information to predict an event with higher than chance level, or can
emerge from wrong pricing/ quotes that do not reflect the right probabilities
of possible events. Finally, the calculation of efficiency is demonstrated on a
simple game (of coin tossing), to show how one could exactly quantify the
efficiency in any market-like system, if all probabilities are known.
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A critical analysis of resampling strategies for the regularized particle filter | We analyze the performance of different resampling strategies for the
regularized particle filter regarding parameter estimation. We show in
particular, building on analytical insight obtained in the linear Gaussian
case, that resampling systematically can prevent the filtered density from
converging towards the true posterior distribution. We discuss several means to
overcome this limitation, including kernel bandwidth modulation, and provide
evidence that the resulting particle filter clearly outperforms traditional
bootstrap particle filters. Our results are supported by numerical simulations
on a linear textbook example, the logistic map and a non-linear plant growth
model.
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Geometry of the free-sliding Bernoulli beam | If a variational problem comes with no boundary conditions prescribed
beforehand, and yet these arise as a consequence of the variation process
itself, we speak of a free boundary values variational problem. Such is, for
instance, the problem of finding the shortest curve whose endpoints can slide
along two prescribed curves. There exists a rigorous geometric way to formulate
this sort of problems on smooth manifolds with boundary, which we review here
in a friendly self-contained way. As an application, we study a particular free
boundary values variational problem, the free-sliding Bernoulli beam.
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Note on equivalences for degenerations of Calabi-Yau manifolds | This note studies the equivalencies among convergences of Ricci-flat
Kähler-Einstein metrics on Calabi-Yau manifolds, cohomology classes and
potential functions.
| 0 | 0 | 1 | 0 | 0 | 0 |
Improving DNN-based Music Source Separation using Phase Features | Music source separation with deep neural networks typically relies only on
amplitude features. In this paper we show that additional phase features can
improve the separation performance. Using the theoretical relationship between
STFT phase and amplitude, we conjecture that derivatives of the phase are a
good feature representation opposed to the raw phase. We verify this conjecture
experimentally and propose a new DNN architecture which combines amplitude and
phase. This joint approach achieves a better signal-to distortion ratio on the
DSD100 dataset for all instruments compared to a network that uses only
amplitude features. Especially, the bass instrument benefits from the phase
information.
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Rapid, User-Transparent, and Trustworthy Device Pairing for D2D-Enabled Mobile Crowdsourcing | Mobile Crowdsourcing is a promising service paradigm utilizing ubiquitous
mobile devices to facilitate largescale crowdsourcing tasks (e.g. urban sensing
and collaborative computing). Many applications in this domain require
Device-to-Device (D2D) communications between participating devices for
interactive operations such as task collaborations and file transmissions.
Considering the private participating devices and their opportunistic
encountering behaviors, it is highly desired to establish secure and
trustworthy D2D connections in a fast and autonomous way, which is vital for
implementing practical Mobile Crowdsourcing Systems (MCSs). In this paper, we
develop an efficient scheme, Trustworthy Device Pairing (TDP), which achieves
user-transparent secure D2D connections and reliable peer device selections for
trustworthy D2D communications. Through rigorous analysis, we demonstrate the
effectiveness and security intensity of TDP in theory. The performance of TDP
is evaluated based on both real-world prototype experiments and extensive
trace-driven simulations. Evaluation results verify our theoretical analysis
and show that TDP significantly outperforms existing approaches in terms of
pairing speed, stability, and security.
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liquidSVM: A Fast and Versatile SVM package | liquidSVM is a package written in C++ that provides SVM-type solvers for
various classification and regression tasks. Because of a fully integrated
hyper-parameter selection, very carefully implemented solvers, multi-threading
and GPU support, and several built-in data decomposition strategies it provides
unprecedented speed for small training sizes as well as for data sets of tens
of millions of samples. Besides the C++ API and a command line interface,
bindings to R, MATLAB, Java, Python, and Spark are available. We present a
brief description of the package and report experimental comparisons to other
SVM packages.
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Thermalizing sterile neutrino dark matter | Sterile neutrinos produced through oscillations are a well motivated dark
matter candidate, but recent constraints from observations have ruled out most
of the parameter space. We analyze the impact of new interactions on the
evolution of keV sterile neutrino dark matter in the early Universe. Based on
general considerations we find a mechanism which thermalizes the sterile
neutrinos after an initial production by oscillations. The thermalization of
sterile neutrinos is accompanied by dark entropy production which increases the
yield of dark matter and leads to a lower characteristic momentum. This
resolves the growing tensions with structure formation and X-ray observations
and even revives simple non-resonant production as a viable way to produce
sterile neutrino dark matter. We investigate the parameters required for the
realization of the thermalization mechanism in a representative model and find
that a simple estimate based on energy- and entropy conservation describes the
mechanism well.
| 0 | 1 | 0 | 0 | 0 | 0 |
Information Elicitation for Bayesian Auctions | In this paper we design information elicitation mechanisms for Bayesian
auctions. While in Bayesian mechanism design the distributions of the players'
private types are often assumed to be common knowledge, information elicitation
considers the situation where the players know the distributions better than
the decision maker. To weaken the information assumption in Bayesian auctions,
we consider an information structure where the knowledge about the
distributions is arbitrarily scattered among the players. In such an
unstructured information setting, we design mechanisms for unit-demand auctions
and additive auctions that aggregate the players' knowledge, generating revenue
that are constant approximations to the optimal Bayesian mechanisms with a
common prior. Our mechanisms are 2-step dominant-strategy truthful and the
revenue increases gracefully with the amount of knowledge the players
collectively have.
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Partially chaotic orbits in a perturbed cubic force model | Three types of orbits are theoretically possible in autonomous Hamiltonian
systems with three degrees of freedom: fully chaotic (they only obey the energy
integral), partially chaotic (they obey an additional isolating integral
besides energy) and regular (they obey two isolating integrals besides energy).
The existence of partially chaotic orbits has been denied by several authors,
however, arguing either that there is a sudden transition from regularity to
full chaoticity, or that a long enough follow up of a supposedly partially
chaotic orbit would reveal a fully chaotic nature. This situation needs
clarification, because partially chaotic orbits might play a significant role
in the process of chaotic diffusion. Here we use numerically computed Lyapunov
exponents to explore the phase space of a perturbed three dimensional cubic
force toy model, and a generalization of the Poincaré maps to show that
partially chaotic orbits are actually present in that model. They turn out to
be double orbits joined by a bifurcation zone, which is the most likely source
of their chaos, and they are encapsulated in regions of phase space bounded by
regular orbits similar to each one of the components of the double orbit.
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Measurable process selection theorem and non-autonomous inclusions | A semi-process is an analog of the semi-flow for non-autonomous differential
equations or inclusions. We prove an abstract result on the existence of
measurable semi-processes in the situations where there is no uniqueness. Also,
we allow solutions to blow up in finite time and then obtain local
semi-processes.
| 0 | 1 | 1 | 0 | 0 | 0 |
Just-infinite C*-algebras and their invariants | Just-infinite C*-algebras, i.e., infinite dimensional C*-algebras, whose
proper quotients are finite dimensional, were investigated in
[Grigorchuk-Musat-Rordam, 2016]. One particular example of a just-infinite
residually finite dimensional AF-algebras was constructed in that article. In
this paper we extend that construction by showing that each infinite
dimensional metrizable Choquet simplex is affinely homeomorphic to the trace
simplex of a just-infinite residually finite dimensional C*-algebras. The trace
simplex of any unital residually finite dimensional C*-algebra is hence
realized by a just-infinite one. We determine the trace simplex of the
particular residually finite dimensional AF-algebras constructed in the above
mentioned article, and we show that it has precisely one extremal trace of type
II_1.
We give a complete description of the Bratteli diagrams corresponding to
residually finite dimensional AF-algebras. We show that a modification of any
such Bratteli diagram, similar to the modification that makes an arbitrary
Bratteli diagram simple, will yield a just-infinite residually finite
dimensional AF-algebra.
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A Large-scale Dataset and Benchmark for Similar Trademark Retrieval | Trademark retrieval (TR) has become an important yet challenging problem due
to an ever increasing trend in trademark applications and infringement
incidents. There have been many promising attempts for the TR problem, which,
however, fell impracticable since they were evaluated with limited and mostly
trivial datasets. In this paper, we provide a large-scale dataset with
benchmark queries with which different TR approaches can be evaluated
systematically. Moreover, we provide a baseline on this benchmark using the
widely-used methods applied to TR in the literature. Furthermore, we identify
and correct two important issues in TR approaches that were not addressed
before: reversal of contrast, and presence of irrelevant text in trademarks
severely affect the TR methods. Lastly, we applied deep learning, namely,
several popular Convolutional Neural Network models, to the TR problem. To the
best of the authors, this is the first attempt to do so.
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Tracing Networks of Knowledge in the Digital Age | The emergence of new digital technologies has allowed the study of human
behaviour at a scale and at level of granularity that were unthinkable just a
decade ago. In particular, by analysing the digital traces left by people
interacting in the online and offline worlds, we are able to trace the
spreading of knowledge and ideas at both local and global scales.
In this article we will discuss how these digital traces can be used to map
knowledge across the world, outlining both the limitations and the challenges
in performing this type of analysis. We will focus on data collected from
social media platforms, large-scale digital repositories and mobile data.
Finally, we will provide an overview of the tools that are available to
scholars and practitioners for understanding these processes using these
emerging forms of data.
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Observational evidence of galaxy assembly bias | We analyze the spectra of 300,000 luminous red galaxies (LRGs) with stellar
masses $M_* \gtrsim 10^{11} M_{\odot}$ from the SDSS-III Baryon Oscillation
Spectroscopic Survey (BOSS). By studying their star-formation histories, we
find two main evolutionary paths converging into the same quiescent galaxy
population at $z\sim0.55$. Fast-growing LRGs assemble $80\%$ of their stellar
mass very early on ($z\sim5$), whereas slow-growing LRGs reach the same
evolutionary state at $z\sim1.5$. Further investigation reveals that their
clustering properties on scales of $\sim$1-30 Mpc are, at a high level of
significance, also different. Fast-growing LRGs are found to be more strongly
clustered and reside in overall denser large-scale structure environments than
slow-growing systems, for a given stellar-mass threshold. Our results imply a
dependence of clustering on stellar-mass assembly history (naturally connected
to the mass-formation history of the corresponding halos) for a homogeneous
population of similar mass and color, which constitutes a strong observational
evidence of galaxy assembly bias.
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Data-Driven Tree Transforms and Metrics | We consider the analysis of high dimensional data given in the form of a
matrix with columns consisting of observations and rows consisting of features.
Often the data is such that the observations do not reside on a regular grid,
and the given order of the features is arbitrary and does not convey a notion
of locality. Therefore, traditional transforms and metrics cannot be used for
data organization and analysis. In this paper, our goal is to organize the data
by defining an appropriate representation and metric such that they respect the
smoothness and structure underlying the data. We also aim to generalize the
joint clustering of observations and features in the case the data does not
fall into clear disjoint groups. For this purpose, we propose multiscale
data-driven transforms and metrics based on trees. Their construction is
implemented in an iterative refinement procedure that exploits the
co-dependencies between features and observations. Beyond the organization of a
single dataset, our approach enables us to transfer the organization learned
from one dataset to another and to integrate several datasets together. We
present an application to breast cancer gene expression analysis: learning
metrics on the genes to cluster the tumor samples into cancer sub-types and
validating the joint organization of both the genes and the samples. We
demonstrate that using our approach to combine information from multiple gene
expression cohorts, acquired by different profiling technologies, improves the
clustering of tumor samples.
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NotiMind: Utilizing Responses to Smart Phone Notifications as Affective sensors | Today's mobile phone users are faced with large numbers of notifications on
social media, ranging from new followers on Twitter and emails to messages
received from WhatsApp and Facebook. These digital alerts continuously disrupt
activities through instant calls for attention. This paper examines closely the
way everyday users interact with notifications and their impact on users'
emotion. Fifty users were recruited to download our application NotiMind and
use it over a five-week period. Users' phones collected thousands of social and
system notifications along with affect data collected via self-reported PANAS
tests three times a day. Results showed a noticeable correlation between
positive affective measures and keyboard activities. When large numbers of Post
and Remove notifications occur, a corresponding increase in negative affective
measures is detected. Our predictive model has achieved a good accuracy level
using three different classifiers "in the wild" (F-measure 74-78%
within-subject model, 72-76% global model). Our findings show that it is
possible to automatically predict when people are experiencing positive,
neutral or negative affective states based on interactions with notifications.
We also show how our findings open the door to a wide range of applications in
relation to emotion awareness on social and mobile communication.
| 1 | 0 | 0 | 0 | 0 | 0 |
Aktuelle Entwicklungen in der Automatischen Musikverfolgung | In this paper we present current trends in real-time music tracking (a.k.a.
score following). Casually speaking, these algorithms "listen" to a live
performance of music, compare the audio signal to an abstract representation of
the score, and "read" along in the sheet music. In this way at any given time
the exact position of the musician(s) in the sheet music is computed. Here, we
focus on the aspects of flexibility and usability of these algorithms. This
comprises work on automatic identification and flexible tracking of the piece
being played as well as current approaches based on Deep Learning. The latter
enables direct learning of correspondences between complex audio data and
images of the sheet music, avoiding the complicated and time-consuming
definition of a mid-level representation.
-----
Diese Arbeit befasst sich mit aktuellen Entwicklungen in der automatischen
Musikverfolgung durch den Computer. Es handelt sich dabei um Algorithmen, die
einer musikalischen Aufführung "zuhören", das aufgenommene Audiosignal mit
einer (abstrakten) Repräsentation des Notentextes vergleichen und sozusagen
in diesem mitlesen. Der Algorithmus kennt also zu jedem Zeitpunkt die Position
der Musiker im Notentext. Neben der Vermittlung eines generellen Überblicks,
liegt der Schwerpunkt dieser Arbeit auf der Beleuchtung des Aspekts der
Flexibilität und der einfacheren Nutzbarkeit dieser Algorithmen. Es wird
dargelegt, welche Schritte getätigt wurden (und aktuell getätigt werden) um
den Prozess der automatischen Musikverfolgung einfacher zugänglich zu machen.
Dies umfasst Arbeiten zur automatischen Identifikation von gespielten Stücken
und deren flexible Verfolgung ebenso wie aktuelle Ansätze mithilfe von Deep
Learning, die es erlauben Bild und Ton direkt zu verbinden, ohne Umwege über
abstrakte und nur unter gro{\ss}em Zeitaufwand zu erstellende
Zwischenrepräsentationen.
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Blind Demixing and Deconvolution at Near-Optimal Rate | We consider simultaneous blind deconvolution of r source signals from their
noisy superposition, a problem also referred to blind demixing and
deconvolution. This signal processing problem occurs in the context of the
Internet of Things where a massive number of sensors sporadically communicate
only short messages over unknown channels. We show that robust recovery of
message and channel vectors can be achieved via convex optimization when random
linear encoding using i.i.d. complex Gaussian matrices is used at the devices
and the number of required measurements at the receiver scales with the degrees
of freedom of the overall estimation problem. Since the scaling is linear in r
our result significantly improves over recent works.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Visualization of the Classical Musical Tradition | A study of around 13,000 musical compositions from the Western classical
tradition is carried out, spanning 33 major composers from the Baroque to the
Romantic, with a focus on the usage of major/minor key signatures. A
2-dimensional chromatic diagram is proposed to succinctly visualize the data.
The diagram is found to be useful not only in distinguishing style and period,
but also in tracking the career development of a particular composer.
| 0 | 0 | 0 | 1 | 0 | 0 |
Multivariate Locally Stationary Wavelet Process Analysis with the mvLSW R Package | This paper describes the R package mvLSW. The package contains a suite of
tools for the analysis of multivariate locally stationary wavelet (LSW) time
series. Key elements include: (i) the simulation of multivariate LSW time
series for a given multivariate evolutionary wavelet spectrum (EWS); (ii)
estimation of the time-dependent multivariate EWS for a given time series;
(iii) estimation of the time-dependent coherence and partial coherence between
time series channels; and, (iv) estimation of approximate confidence intervals
for multivariate EWS estimates. A demonstration of the package is presented via
both a simulated example and a case study with EuStockMarkets from the datasets
package. This paper has been accepted by the Journal of Statistical Software.
Presented code extracts demonstrating the mvLSW package is performed under
version 1.2.1.
| 0 | 0 | 0 | 1 | 0 | 0 |
Using Posters to Recommend Anime and Mangas in a Cold-Start Scenario | Item cold-start is a classical issue in recommender systems that affects
anime and manga recommendations as well. This problem can be framed as follows:
how to predict whether a user will like a manga that received few ratings from
the community? Content-based techniques can alleviate this issue but require
extra information, that is usually expensive to gather. In this paper, we use a
deep learning technique, Illustration2Vec, to easily extract tag information
from the manga and anime posters (e.g., sword, or ponytail). We propose BALSE
(Blended Alternate Least Squares with Explanation), a new model for
collaborative filtering, that benefits from this extra information to recommend
mangas. We show, using real data from an online manga recommender system called
Mangaki, that our model improves substantially the quality of recommendations,
especially for less-known manga, and is able to provide an interpretation of
the taste of the users.
| 1 | 0 | 0 | 1 | 0 | 0 |
Generalized least squares can overcome the critical threshold in respondent-driven sampling | In order to sample marginalized and/or hard-to-reach populations,
respondent-driven sampling (RDS) and similar techniques reach their
participants via peer referral. Under a Markov model for RDS, previous research
has shown that if the typical participant refers too many contacts, then the
variance of common estimators does not decay like $O(n^{-1})$, where $n$ is the
sample size. This implies that confidence intervals will be far wider than
under a typical sampling design. Here we show that generalized least squares
(GLS) can effectively reduce the variance of RDS estimates. In particular, a
theoretical analysis indicates that the variance of the GLS estimator is
$O(n^{-1})$. We then derive two classes of feasible GLS estimators. The first
class is based upon a Degree Corrected Stochastic Blockmodel for the underlying
social network. The second class is based upon a rank-two model. It might be of
independent interest that in both model classes, the theoretical results show
that it is possible to estimate the spectral properties of the population
network from the sampled observations. Simulations on empirical social networks
show that the feasible GLS (fGLS) estimators can have drastically smaller error
and rarely increase the error. A diagnostic plot helps to identify where fGLS
will aid estimation. The fGLS estimators continue to outperform standard
estimators even when they are built from a misspecified model and when there is
preferential recruitment.
| 0 | 0 | 1 | 1 | 0 | 0 |
Electron-Hole Symmetry Breaking in Charge Transport in Nitrogen-Doped Graphene | Graphitic nitrogen-doped graphene is an excellent platform to study
scattering processes of massless Dirac fermions by charged impurities, in which
high mobility can be preserved due to the absence of lattice defects through
direct substitution of carbon atoms in the graphene lattice by nitrogen atoms.
In this work, we report on electrical and magnetotransport measurements of
high-quality graphitic nitrogen-doped graphene. We show that the substitutional
nitrogen dopants in graphene introduce atomically sharp scatters for electrons
but long-range Coulomb scatters for holes and, thus, graphitic nitrogen-doped
graphene exhibits clear electron-hole asymmetry in transport properties.
Dominant scattering processes of charge carriers in graphitic nitrogen-doped
graphene are analyzed. It is shown that the electron-hole asymmetry originates
from a distinct difference in intervalley scattering of electrons and holes. We
have also carried out the magnetotransport measurements of graphitic
nitrogen-doped graphene at different temperatures and the temperature
dependences of intervalley scattering, intravalley scattering and phase
coherent scattering rates are extracted and discussed. Our results provide an
evidence for the electron-hole asymmetry in the intervalley scattering induced
by substitutional nitrogen dopants in graphene and shine a light on versatile
and potential applications of graphitic nitrogen-doped graphene in electronic
and valleytronic devices.
| 0 | 1 | 0 | 0 | 0 | 0 |
Micro-sized cold atmospheric plasma source for brain and breast cancer treatment | Micro-sized cold atmospheric plasma (uCAP) has been developed to expand the
applications of CAP in cancer therapy. In this paper, uCAP devices with
different nozzle lengths were applied to investigate effects on both brain
(glioblastoma U87) and breast (MDA-MB-231) cancer cells. Various diagnostic
techniques were employed to evaluate the parameters of uCAP devices with
different lengths such as potential distribution, electron density, and optical
emission spectroscopy. The generation of short- and long-lived species (such as
hydroxyl radical (.OH), superoxide (O2-), hydrogen peroxide (H2O2), nitrite
(NO2-), et al) were studied. These data revealed that uCAP treatment with a 20
mm length tube has a stronger effect than that of the 60 mm tube due to the
synergetic effects of reactive species and free radicals. Reactive species
generated by uCAP enhanced tumor cell death in a dose-dependent fashion and was
not specific with regards to tumor cell type.
| 0 | 0 | 0 | 0 | 1 | 0 |
How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments | Consistently checking the statistical significance of experimental results is
one of the mandatory methodological steps to address the so-called
"reproducibility crisis" in deep reinforcement learning. In this tutorial
paper, we explain how the number of random seeds relates to the probabilities
of statistical errors. For both the t-test and the bootstrap confidence
interval test, we recall theoretical guidelines to determine the number of
random seeds one should use to provide a statistically significant comparison
of the performance of two algorithms. Finally, we discuss the influence of
deviations from the assumptions usually made by statistical tests. We show that
they can lead to inaccurate evaluations of statistical errors and provide
guidelines to counter these negative effects. We make our code available to
perform the tests.
| 0 | 0 | 0 | 1 | 0 | 0 |
Training Quantized Nets: A Deeper Understanding | Currently, deep neural networks are deployed on low-power portable devices by
first training a full-precision model using powerful hardware, and then
deriving a corresponding low-precision model for efficient inference on such
systems. However, training models directly with coarsely quantized weights is a
key step towards learning on embedded platforms that have limited computing
resources, memory capacity, and power consumption. Numerous recent publications
have studied methods for training quantized networks, but these studies have
mostly been empirical. In this work, we investigate training methods for
quantized neural networks from a theoretical viewpoint. We first explore
accuracy guarantees for training methods under convexity assumptions. We then
look at the behavior of these algorithms for non-convex problems, and show that
training algorithms that exploit high-precision representations have an
important greedy search phase that purely quantized training methods lack,
which explains the difficulty of training using low-precision arithmetic.
| 1 | 0 | 0 | 1 | 0 | 0 |
Some Sharpening and Generalizations of a result of T. J. Rivlin | Let $p(z)=a_0+a_1z+a_2z^2+a_3z^3+\cdots+a_nz^n$ be a polynomial of degree
$n$. Rivlin \cite{Rivlin} proved that if $p(z)\neq 0$ in the unit disk, then
for $0<r\leq 1$, $\displaystyle{\max_{|z| = r}|p(z)|} \geq
\Big(\dfrac{r+1}{2}\Big)^n \displaystyle{\max_{|z|=1} |p(z)|}.$ ~In this paper,
we prove a sharpening and generalization of this result, and show by means of
examples that for some polynomials our result can significantly improve the
bound obtained by the Rivlin's Theorem.
| 0 | 0 | 1 | 0 | 0 | 0 |
Computational Aided Design for Generating a Modular, Lightweight Car Concept | Developing an appropriate design process for a conceptual model is a stepping
stone toward designing car bodies. This paper presents a methodology to design
a lightweight and modular space frame chassis for a sedan electric car. The
dual phase high strength steel with improved mechanical properties is employed
to reduce the weight of the car body. Utilizing the finite element analysis
yields two models in order to predict the performance of each component. The
first model is a beam structure with a rapid response in structural stiffness
simulation. This model is used for performing the static tests including modal
frequency, bending stiffens and torsional stiffness evaluation. Whereas the
second model, i.e., a shell model, is proposed to illustrate every module's
mechanical behavior as well as its crashworthiness efficiency. In order to
perform the crashworthiness analysis, the explicit nonlinear dynamic solver
provided by ABAQUS, a commercial finite element software, is used. The results
of finite element beam and shell models are in line with the concept design
specifications. Implementation of this procedure leads to generate a
lightweight and modular concept for an electric car.
| 1 | 0 | 0 | 0 | 0 | 0 |
Reaction-Diffusion Systems in Epidemiology | A key problem in modelling the evolution dynamics of infectious diseases is
the mathematical representation of the mechanism of transmission of the
contagion. Models with a finite number of subpopulations can be described via
systems of ordinary differential equations. When dealing with populations with
space structure the relevant quantities are spatial densities, whose evolution
in time requires nonlinear partial differential equations, which are known as
reaction-diffusion systems. Here we present an (historical) outline of
mathematical epidemiology, with a particular attention to the role of spatial
heterogeneity and dispersal in the population dynamics of infectious diseases.
Two specific examples are discussed, which have been the subject of intensive
research by the authors, i.e. man-environment-man epidemics, and malaria. In
addition to the epidemiological relevance of these epidemics all over the
world, their treatment requires a large amount of different sophisticate
mathematical methods, and has even posed new non trivial mathematical problems,
as one can realize from the list of references. One of the most relevant
problems posed by the authors, i.e. regional control, has been emphasized here:
the public health concern consists of eradicating the disease in the relevant
population, as fast as possible. On the other hand, very often the entire
domain of interest for the epidemic, is either unknown, or difficult to manage
for an affordable implementation of suitable environmental programmes. For
regional control instead it might be sufficient to implement such programmes
only in a given subregion conveniently chosen so to lead to an effective
(exponentially fast) eradication of the epidemic in the whole habitat; it is
evident that this practice may have an enormous importance in real cases with
respect to both financial and practical affordability.
| 0 | 0 | 1 | 0 | 0 | 0 |
Is charge order induced near an antiferromagnetic quantum critical point? | We investigate the interplay between charge order and superconductivity near
an antiferromagnetic quantum critical point using sign-problem-free Quantum
Monte Carlo simulations. We establish that, when the electronic dispersion is
particle-hole symmetric, the system has an emergent SU(2) symmetry that implies
a degeneracy between $d$-wave superconductivity and charge order with $d$-wave
form factor. Deviations from particle-hole symmetry, however, rapidly lift this
degeneracy, despite the fact that the SU(2) symmetry is preserved at low
energies. As a result, we find a strong suppression of charge order caused by
the competing, leading superconducting instability. Across the
antiferromagnetic phase transition, we also observe a shift in the charge order
wave-vector from diagonal to axial. We discuss the implications of our results
to the universal phase diagram of antiferromagnetic quantum-critical metals and
to the elucidation of the charge order experimentally observed in the cuprates.
| 0 | 1 | 0 | 0 | 0 | 0 |
Commissioning and performance results of the WFIRST/PISCES integral field spectrograph | The Prototype Imaging Spectrograph for Coronagraphic Exoplanet Studies
(PISCES) is a high contrast integral field spectrograph (IFS) whose design was
driven by WFIRST coronagraph instrument requirements. We present commissioning
and operational results using PISCES as a camera on the High Contrast Imaging
Testbed at JPL. PISCES has demonstrated ability to achieve high contrast
spectral retrieval with flight-like data reduction and analysis techniques.
| 0 | 1 | 0 | 0 | 0 | 0 |
Metal nanospheres under intense continuous wave illumination - a unique case of non-perturbative nonlinear nanophotonics | We show that the standard perturbative (i.e., cubic) description of the
thermal nonlinear response of small metal nanospheres to intense continuous
wave illumination is insufficient already beyond temperature rises of a few
tens of degrees. In some cases, a cubic-quintic nonlinear response is
sufficient to describe accurately the intensity dependence of the temperature,
permittivity and field, while in other cases, a full non-perturbative
description is required. We further analyze the relative importance of the
various contributions to the thermal nonlinearity, identify a qualitative
difference between Au and Ag, and show that the thermo-optical nonlinearity of
the host typically plays a minor role, but its thermal conductivity is
important.
| 0 | 1 | 0 | 0 | 0 | 0 |
Revealing strong bias in common measures of galaxy properties using new inclination-independent structures | Accurate measurement of galaxy structures is a prerequisite for quantitative
investigation of galaxy properties or evolution. Yet, the impact of galaxy
inclination and dust on commonly used metrics of galaxy structure is poorly
quantified. We use infrared data sets to select inclination-independent samples
of disc and flattened elliptical galaxies. These samples show strong variation
in Sérsic index, concentration, and half-light radii with inclination. We
develop novel inclination-independent galaxy structures by collapsing the light
distribution in the near-infrared on to the major axis, yielding
inclination-independent `linear' measures of size and concentration. With these
new metrics we select a sample of Milky Way analogue galaxies with similar
stellar masses, star formation rates, sizes and concentrations. Optical
luminosities, light distributions, and spectral properties are all found to
vary strongly with inclination: When inclining to edge-on, $r$-band
luminosities dim by $>$1 magnitude, sizes decrease by a factor of 2,
`dust-corrected' estimates of star formation rate drop threefold, metallicities
decrease by 0.1 dex, and edge-on galaxies are half as likely to be classified
as star forming. These systematic effects should be accounted for in analyses
of galaxy properties.
| 0 | 1 | 0 | 0 | 0 | 0 |
Behind Every Great Tree is a Great (Phylogenetic) Network | In Francis and Steel (2015), it was shown that there exists non-trivial
networks on $4$ leaves upon which the distance metric affords a metric on a
tree which is not the base tree of the network. In this paper we extend this
result in two directions. We show that for any tree $T$ there exists a family
of non-trivial HGT networks $N$ for which the distance metric $d_N$ affords a
metric on $T$. We additionally provide a class of networks on any number of
leaves upon which the distance metric affords a metric on a tree which is not
the base tree of the network.
The family of networks are all "floating" networks, a subclass of a novel
family of networks introduced in this paper, and referred to as "versatile"
networks. Versatile networks are then characterised.
Additionally, we find a lower bound for the number of `useful' HGT arcs in
such networks, in a sense explained in the paper. This lower bound is equal to
the number of HGT arcs required for each floating network in the main results,
and thus our networks are minimal in this sense.
| 0 | 0 | 1 | 0 | 0 | 0 |
Cosmic-ray induced destruction of CO in star-forming galaxies | We explore the effects of the expected higher cosmic ray (CR) ionization
rates $\zeta_{\rm CR}$ on the abundances of carbon monoxide (CO), atomic carbon
(C), and ionized carbon (C$^+$) in the H$_2$ clouds of star-forming galaxies.
The study of Bisbas et al. (2015) is expanded by: a) using realistic
inhomogeneous Giant Molecular Cloud (GMC) structures, b) a detailed chemical
analysis behind the CR-induced destruction of CO, and c) exploring the thermal
state of CR-irradiated molecular gas. CRs permeating the interstellar medium
with $\zeta_{\rm CR}$$\gtrsim 10\times$(Galactic) are found to significantly
reduce the [CO]/[H$_2$] abundance ratios throughout the mass of a GMC. CO
rotational line imaging will then show much clumpier structures than the actual
ones. For $\zeta_{\rm CR}$$\gtrsim 100\times$(Galactic) this bias becomes
severe, limiting the utility of CO lines for recovering structural and
dynamical characteristics of H$_2$-rich galaxies throughout the Universe,
including many of the so-called Main Sequence (MS) galaxies where the bulk of
cosmic star formation occurs. Both C$^+$ and C abundances increase with rising
$\zeta_{\rm CR}$, with C remaining the most abundant of the two throughout
H$_2$ clouds, when $\zeta_{\rm CR}\sim (1-100)\times$(Galactic). C$^+$ starts
to dominate for $\zeta_{\rm CR}$$\gtrsim 10^3\times$(Galactic). The thermal
state of the gas in the inner and denser regions of GMCs is invariant with
$T_{\rm gas}\sim 10\,{\rm K}$ for $\zeta_{\rm CR}\sim (1-10)\times$(Galactic).
For $\zeta_{\rm CR}$$\sim 10^3\times$(Galactic) this is no longer the case and
$T_{\rm gas}\sim 30-50\,{\rm K}$ are reached. Finally we identify OH as the key
species whose $T_{\rm gas}-$sensitive abundance could mitigate the destruction
of CO at high temperatures.
| 0 | 1 | 0 | 0 | 0 | 0 |
News Session-Based Recommendations using Deep Neural Networks | News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods.
| 0 | 0 | 0 | 1 | 0 | 0 |
Online Nonparametric Anomaly Detection based on Geometric Entropy Minimization | We consider the online and nonparametric detection of abrupt and persistent
anomalies, such as a change in the regular system dynamics at a time instance
due to an anomalous event (e.g., a failure, a malicious activity). Combining
the simplicity of the nonparametric Geometric Entropy Minimization (GEM) method
with the timely detection capability of the Cumulative Sum (CUSUM) algorithm we
propose a computationally efficient online anomaly detection method that is
applicable to high-dimensional datasets, and at the same time achieve a
near-optimum average detection delay performance for a given false alarm
constraint. We provide new insights to both GEM and CUSUM, including new
asymptotic analysis for GEM, which enables soft decisions for outlier
detection, and a novel interpretation of CUSUM in terms of the discrepancy
theory, which helps us generalize it to the nonparametric GEM statistic. We
numerically show, using both simulated and real datasets, that the proposed
nonparametric algorithm attains a close performance to the clairvoyant
parametric CUSUM test.
| 0 | 0 | 0 | 1 | 0 | 0 |
Learning latent structure of large random graphs | In this paper, we estimate the distribution of hidden nodes weights in large
random graphs from the observation of very few edges weights. In this very
sparse setting, the first non-asymptotic risk bounds for maximum likelihood
estimators (MLE) are established. The proof relies on the construction of a
graphical model encoding conditional dependencies that is extremely efficient
to study n-regular graphs obtained using a round-robin scheduling. This
graphical model allows to prove geometric loss of memory properties and deduce
the asymp-totic behavior of the likelihood function. Following a classical
construction in learning theory, the asymptotic likelihood is used to define a
measure of performance for the MLE. Risk bounds for the MLE are finally
obtained by subgaussian deviation results derived from concentration
inequalities for Markov chains applied to our graphical model.
| 0 | 0 | 1 | 1 | 0 | 0 |
Loop Tiling in Large-Scale Stencil Codes at Run-time with OPS | The key common bottleneck in most stencil codes is data movement, and prior
research has shown that improving data locality through optimisations that
schedule across loops do particularly well. However, in many large PDE
applications it is not possible to apply such optimisations through compilers
because there are many options, execution paths and data per grid point, many
dependent on run-time parameters, and the code is distributed across different
compilation units. In this paper, we adapt the data locality improving
optimisation called iteration space slicing for use in large OPS applications
both in shared-memory and distributed-memory systems, relying on run-time
analysis and delayed execution. We evaluate our approach on a number of
applications, observing speedups of 2$\times$ on the Cloverleaf 2D/3D proxy
application, which contain 83/141 loops respectively, $3.5\times$ on the linear
solver TeaLeaf, and $1.7\times$ on the compressible Navier-Stokes solver
OpenSBLI. We demonstrate strong and weak scalability up to 4608 cores of
CINECA's Marconi supercomputer. We also evaluate our algorithms on Intel's
Knights Landing, demonstrating maintained throughput as the problem size grows
beyond 16GB, and we do scaling studies up to 8704 cores. The approach is
generally applicable to any stencil DSL that provides per loop data access
information.
| 1 | 0 | 0 | 0 | 0 | 0 |
Second differentials in the Quillen spectral sequence | For an algebraic variety $X$ we introduce generalized first Chern classes,
which are defined for coherent sheaves on $X$ with support in codimension $p$
and take values in $CH^p(X)$. We use them to provide an explicit formula for
the differentials ${d_2^p: E_2^{p,-p-1} \to E_2^{p+2, -p-2} \cong CH^{p+2}(X)}$
in the Quillen spectral sequence.
| 0 | 0 | 1 | 0 | 0 | 0 |
General Dynamics of Spinors | In this paper, we consider a general twisted-curved space-time hosting Dirac
spinors and we take into account the Lorentz covariant polar decomposition of
the Dirac spinor field: the corresponding decomposition of the Dirac spinor
field equation leads to a set of field equations that are real and where
spinorial components have disappeared while still maintaining Lorentz
covariance. We will see that the Dirac spinor will contain two real scalar
degrees of freedom, the module and the so-called Yvon-Takabayashi angle, and we
will display their field equations. This will permit us to study the coupling
of curvature and torsion respectively to the module and the YT angle.
| 0 | 1 | 0 | 0 | 0 | 0 |
PT-Spike: A Precise-Time-Dependent Single Spike Neuromorphic Architecture with Efficient Supervised Learning | One of the most exciting advancements in AI over the last decade is the wide
adoption of ANNs, such as DNN and CNN, in many real-world applications.
However, the underlying massive amounts of computation and storage requirement
greatly challenge their applicability in resource-limited platforms like the
drone, mobile phone, and IoT devices etc. The third generation of neural
network model--Spiking Neural Network (SNN), inspired by the working mechanism
and efficiency of human brain, has emerged as a promising solution for
achieving more impressive computing and power efficiency within light-weighted
devices (e.g. single chip). However, the relevant research activities have been
narrowly carried out on conventional rate-based spiking system designs for
fulfilling the practical cognitive tasks, underestimating SNN's energy
efficiency, throughput, and system flexibility. Although the time-based SNN can
be more attractive conceptually, its potentials are not unleashed in realistic
applications due to lack of efficient coding and practical learning schemes. In
this work, a Precise-Time-Dependent Single Spike Neuromorphic Architecture,
namely "PT-Spike", is developed to bridge this gap. Three constituent
hardware-favorable techniques: precise single-spike temporal encoding,
efficient supervised temporal learning, and fast asymmetric decoding are
proposed accordingly to boost the energy efficiency and data processing
capability of the time-based SNN at a more compact neural network model size
when executing real cognitive tasks. Simulation results show that "PT-Spike"
demonstrates significant improvements in network size, processing efficiency
and power consumption with marginal classification accuracy degradation when
compared with the rate-based SNN and ANN under the similar network
configuration.
| 0 | 0 | 0 | 0 | 1 | 0 |
Learning to Represent Edits | We introduce the problem of learning distributed representations of edits. By
combining a "neural editor" with an "edit encoder", our models learn to
represent the salient information of an edit and can be used to apply edits to
new inputs. We experiment on natural language and source code edit data. Our
evaluation yields promising results that suggest that our neural network models
learn to capture the structure and semantics of edits. We hope that this
interesting task and data source will inspire other researchers to work further
on this problem.
| 1 | 0 | 0 | 0 | 0 | 0 |
Semiclassical Prediction of Large Spectral Fluctuations in Interacting Kicked Spin Chains | While plenty of results have been obtained for single-particle quantum
systems with chaotic dynamics through a semiclassical theory, much less is
known about quantum chaos in the many-body setting. We contribute to recent
efforts to make a semiclassical analysis of many-body systems feasible. This is
nontrivial due to both the enormous density of states and the exponential
proliferation of periodic orbits with the number of particles. As a model
system we study kicked interacting spin chains employing semiclassical methods
supplemented by a newly developed duality approach. We show that for this model
the line between integrability and chaos becomes blurred. Due to the
interaction structure the system features (non-isolated) manifolds of periodic
orbits possessing highly correlated, collective dynamics. As with the invariant
tori in integrable systems, their presence lead to significantly enhanced
spectral fluctuations, which by order of magnitude lie in-between integrable
and chaotic cases.
| 0 | 1 | 0 | 0 | 0 | 0 |
If it ain't broke, don't fix it: Sparse metric repair | Many modern data-intensive computational problems either require, or benefit
from distance or similarity data that adhere to a metric. The algorithms run
faster or have better performance guarantees. Unfortunately, in real
applications, the data are messy and values are noisy. The distances between
the data points are far from satisfying a metric. Indeed, there are a number of
different algorithms for finding the closest set of distances to the given ones
that also satisfy a metric (sometimes with the extra condition of being
Euclidean). These algorithms can have unintended consequences, they can change
a large number of the original data points, and alter many other features of
the data.
The goal of sparse metric repair is to make as few changes as possible to the
original data set or underlying distances so as to ensure the resulting
distances satisfy the properties of a metric. In other words, we seek to
minimize the sparsity (or the $\ell_0$ "norm") of the changes we make to the
distances subject to the new distances satisfying a metric. We give three
different combinatorial algorithms to repair a metric sparsely. In one setting
the algorithm is guaranteed to return the sparsest solution and in the other
settings, the algorithms repair the metric. Without prior information, the
algorithms run in time proportional to the cube of the number of input data
points and, with prior information we can reduce the running time considerably.
| 1 | 0 | 0 | 1 | 0 | 0 |
Optimal one-shot quantum algorithm for EQUALITY and AND | We study the computation complexity of Boolean functions in the quantum black
box model. In this model our task is to compute a function
$f:\{0,1\}\to\{0,1\}$ on an input $x\in\{0,1\}^n$ that can be accessed by
querying the black box. Quantum algorithms are inherently probabilistic; we are
interested in the lowest possible probability that the algorithm outputs
incorrect answer (the error probability) for a fixed number of queries. We show
that the lowest possible error probability for $AND_n$ and $EQUALITY_{n+1}$ is
$1/2-n/(n^2+1)$.
| 1 | 0 | 0 | 0 | 0 | 0 |
Photon-gated spin transistor | Spin-polarized field-effect transistor (spin-FET), where a dielectric layer
is generally employed for the electrical gating as the traditional FET, stands
out as a seminal spintronic device under the miniaturization trend of
electronics. It would be fundamentally transformative if optical gating was
used for spin-FET. We report a new type of spin-polarized field-effect
transistor (spin-FET) with optical gating, which is fabricated by partial
exposure of the (La,Sr)MnO3 channel to light-emitting diode (LED) light. The
manipulation of the channel conductivity is ascribed to the enhanced scattering
of the spin-polarized current by photon-excited antiparallel aligned spins. And
the photon-gated spin-FET shows strong light power dependence and reproducible
enhancement of resistance under light illumination, indicting well-defined
conductivity cycling features. Our finding would enrich the concept of spin-FET
and promote the use of optical means in spintronics for low power consumption
and ultrafast data processing.
| 0 | 1 | 0 | 0 | 0 | 0 |
Polarisation of submillimetre lines from interstellar medium | Magnetic fields play important roles in many astrophysical processes.
However, there is no universal diagnostic for the magnetic fields in the
interstellar medium (ISM) and each magnetic tracer has its limitation. Any new
detection method is thus valuable. Theoretical studies have shown that
submillimetre fine-structure lines are polarised due to atomic alignment by
Ultraviolet (UV) photon-excitation, which opens up a new avenue to probe
interstellar magnetic fields. We will, for the first time, perform synthetic
observations on the simulated three-dimensional ISM to demonstrate the
measurability of the polarisation of submillimetre atomic lines. The maximum
polarisation for different absorption and emission lines expected from various
sources, including Star-Forming Regions (SFRs) are provided. Our results
demonstrate that the polarisation of submillimetre atomic lines is a powerful
magnetic tracer and add great value to the observational studies of the
submilimetre astronomy.
| 0 | 1 | 0 | 0 | 0 | 0 |
Two bosonic quantum walkers in one-dimensional optical lattices | Dynamical properties of two bosonic quantum walkers in a one-dimensional
lattice are studied theoretically. Depending on the initial state,
interactions, lattice tilting, and lattice disorder, whole plethora of
different behaviors are observed. Particularly, it is shown that two bosons
system manifests the many-body localization like behavior in the presence of a
quenched disorder. The whole analysis is based on a specific decomposition of
the temporal density profile into different contributions from singly and
doubly occupied sites. In this way, the role of interactions is extracted.
Since the contributions can be directly measured in experiments with ultra-cold
atoms in optical lattices, the predictions presented may have some importance
for upcoming experiment.
| 0 | 1 | 0 | 0 | 0 | 0 |
Photometric Redshifts with the LSST: Evaluating Survey Observing Strategies | In this paper we present and characterize a nearest-neighbors color-matching
photometric redshift estimator that features a direct relationship between the
precision and accuracy of the input magnitudes and the output photometric
redshifts. This aspect makes our estimator an ideal tool for evaluating the
impact of changes to LSST survey parameters that affect the measurement errors
of the photometry, which is the main motivation of our work (i.e., it is not
intended to provide the "best" photometric redshifts for LSST data). We show
how the photometric redshifts will improve with time over the 10-year LSST
survey and confirm that the nominal distribution of visits per filter provides
the most accurate photo-$z$ results. The LSST survey strategy naturally
produces observations over a range of airmass, which offers the opportunity of
using an SED- and $z$-dependent atmospheric affect on the observed photometry
as a color-independent redshift indicator. We show that measuring this airmass
effect and including it as a prior has the potential to improve the photometric
redshifts and can ameliorate extreme outliers, but that it will only be
adequately measured for the brightest galaxies, which limits its overall impact
on LSST photometric redshifts. We furthermore demonstrate how this airmass
effect can induce a bias in the photo-$z$ results, and caution against survey
strategies that prioritize high-airmass observations for the purpose of
improving this prior. Ultimately, we intend for this work to serve as a guide
for the expectations and preparations of the LSST science community with
regards to the minimum quality of photo-$z$ as the survey progresses.
| 0 | 1 | 0 | 0 | 0 | 0 |
Modelling diverse sources of Clostridium difficile in the community: importance of animals, infants and asymptomatic carriers | Clostridium difficile infections (CDIs) affect patients in hospitals and in
the community, but the relative importance of transmission in each setting is
unknown. We developed a mathematical model of C. difficile transmission in a
hospital and surrounding community that included infants, adults, and
transmission from animal reservoirs. We assessed the role of these transmission
routes in maintaining disease and evaluated the recommended classification
system for hospital and community-acquired CDIs. The reproduction number in the
hospital was <1 (range: 0.16-0.46) for all scenarios. Outside the hospital, the
reproduction number was >1 for nearly all scenarios without transmission from
animal reservoirs (range: 1.0-1.34). However, the reproduction number for the
human population was <1 if a minority (>3.5-26.0%) of human exposures
originated from animal reservoirs. Symptomatic adults accounted for <10%
transmission in the community. Under conservative assumptions, infants
accounted for 17% of community transmission. An estimated 33-40% of
community-acquired cases were reported but 28-39% of these reported cases were
misclassified as hospital-acquired by recommended definitions. Transmission
could be plausibly sustained by asymptomatically colonized adults and infants
in the community or exposure to animal reservoirs, but not hospital
transmission alone. Underreporting of community-onset cases and systematic
misclassification underplays the role of community transmission.
| 0 | 0 | 0 | 0 | 1 | 0 |
Quivers with potentials for cluster varieties associated to braid semigroups | Let $C$ be a simply laced generalized Cartan matrix. Given an element $b$ of
the generalized braid semigroup related to $C$, we construct a collection of
mutation-equivalent quivers with potentials. A quiver with potential in such a
collection corresponds to an expression of $b$ in terms of the standard
generators. For two expressions that differ by a braid relation, the
corresponding quivers with potentials are related by a mutation.
The main application of this result is a construction of a family of $CY_3$
$A_\infty$-categories associated to elements of the braid semigroup related to
$C$. In particular, we construct a canonical up to equivalence $CY_3$
$A_\infty$-category associated to quotient of any Double Bruhat cell
$G^{u,v}/{\rm Ad} H$ in a simply laced reductive Lie group $G$.
We describe the full set of parameters these categories depend on by defining
a 2-dimensional CW-complex and proving that the set of parameters is identified
with second cohomology group of this complex.
| 0 | 0 | 1 | 0 | 0 | 0 |
Effect of stellar flares on the upper atmospheres of HD 189733b and HD 209458b | Stellar flares are a frequent occurrence on young low-mass stars around which
many detected exoplanets orbit. Flares are energetic, impulsive events, and
their impact on exoplanetary atmospheres needs to be taken into account when
interpreting transit observations. We have developed a model to describe the
upper atmosphere of Extrasolar Giant Planets (EGPs) orbiting flaring stars. The
model simulates thermal escape from the upper atmospheres of close-in EGPs.
Ionisation by solar radiation and electron impact is included and photochemical
and diffusive transport processes are simulated. This model is used to study
the effect of stellar flares from the solar-like G star HD209458 and the young
K star HD189733 on their respective planets. A hypothetical HD209458b-like
planet orbiting the active M star AU Mic is also simulated. We find that the
neutral upper atmosphere of EGPs is not significantly affected by typical
flares. Therefore, stellar flares alone would not cause large enough changes in
planetary mass loss to explain the variations in HD189733b transit depth seen
in previous studies, although we show that it may be possible that an extreme
stellar proton event could result in the required mass loss. Our simulations do
however reveal an enhancement in electron number density in the ionosphere of
these planets, the peak of which is located in the layer where stellar X-rays
are absorbed. Electron densities are found to reach 2.2 to 3.5 times pre-flare
levels and enhanced electron densities last from about 3 to 10 hours after the
onset of the flare. The strength of the flare and the width of its spectral
energy distribution affect the range of altitudes that see enhancements in
ionisation. A large broadband continuum component in the XUV portion of the
flaring spectrum in very young flare stars, such as AU Mic, results in a broad
range of altitudes affected in planets orbiting this star.
| 0 | 1 | 0 | 0 | 0 | 0 |
Gross-Hopkins Duals of Higher Real K-theory Spectra | We determine the Gross-Hopkins duals of certain higher real K-theory spectra.
More specifically, let p be an odd prime, and consider the Morava E-theory
spectrum of height n=p-1. It is known, in the expert circles, that for certain
finite subgroups G of the Morava stabilizer group, the homotopy fixed point
spectra E_n^{hG} are Gross-Hopkins self-dual up to a shift. In this paper, we
determine the shift for those finite subgroups G which contain p-torsion. This
generalizes previous results for n=2 and p=3.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Projection Method for Metric-Constrained Optimization | We outline a new approach for solving optimization problems which enforce
triangle inequalities on output variables. We refer to this as
metric-constrained optimization, and give several examples where problems of
this form arise in machine learning applications and theoretical approximation
algorithms for graph clustering. Although these problem are interesting from a
theoretical perspective, they are challenging to solve in practice due to the
high memory requirement of black-box solvers. In order to address this
challenge we first prove that the metric-constrained linear program relaxation
of correlation clustering is equivalent to a special case of the metric
nearness problem. We then developed a general solver for metric-constrained
linear and quadratic programs by generalizing and improving a simple projection
algorithm originally developed for metric nearness. We give several novel
approximation guarantees for using our framework to find lower bounds for
optimal solutions to several challenging graph clustering problems. We also
demonstrate the power of our framework by solving optimizing problems involving
up to 10^{8} variables and 10^{11} constraints.
| 1 | 0 | 0 | 1 | 0 | 0 |
Calibrating the Planck Cluster Mass Scale with Cluster Velocity Dispersions | We measure the Planck cluster mass bias using dynamical mass measurements
based on velocity dispersions of a subsample of 17 Planck-detected clusters.
The velocity dispersions were calculated using redshifts determined from
spectra obtained at Gemini observatory with the GMOS multi-object spectrograph.
We correct our estimates for effects due to finite aperture, Eddington bias and
correlated scatter between velocity dispersion and the Planck mass proxy. The
result for the mass bias parameter, $(1-b)$, depends on the value of the galaxy
velocity bias $b_v$ adopted from simulations: $(1-b)=(0.51\pm0.09) b_v^3$.
Using a velocity bias of $b_v=1.08$ from Munari et al., we obtain
$(1-b)=0.64\pm 0.11$, i.e, an error of 17% on the mass bias measurement with 17
clusters. This mass bias value is consistent with most previous weak lensing
determinations. It lies within $1\sigma$ of the value needed to reconcile the
Planck cluster counts with the Planck primary CMB constraints. We emphasize
that uncertainty in the velocity bias severely hampers precision measurements
of the mass bias using velocity dispersions. On the other hand, when we fix the
Planck mass bias using the constraints from Penna-Lima et al., based on weak
lensing measurements, we obtain a positive velocity bias $b_v \gtrsim 0.9$ at
$3\sigma$.
| 0 | 1 | 0 | 0 | 0 | 0 |
Curie: Policy-based Secure Data Exchange | Data sharing among partners---users, organizations, companies---is crucial
for the advancement of data analytics in many domains. Sharing through secure
computation and differential privacy allows these partners to perform private
computations on their sensitive data in controlled ways. However, in reality,
there exist complex relationships among members. Politics, regulations,
interest, trust, data demands and needs are one of the many reasons. Thus,
there is a need for a mechanism to meet these conflicting relationships on data
sharing. This paper presents Curie, an approach to exchange data among members
whose membership has complex relationships. The CPL policy language that allows
members to define the specifications of data exchange requirements is
introduced. Members (partners) assert who and what to exchange through their
local policies and negotiate a global sharing agreement. The agreement is
implemented in a multi-party computation that guarantees sharing among members
will comply with the policy as negotiated. The use of Curie is validated
through an example of a health care application built on recently introduced
secure multi-party computation and differential privacy frameworks, and policy
and performance trade-offs are explored.
| 1 | 0 | 0 | 0 | 0 | 0 |
Boundedness in a fully parabolic chemotaxis system with nonlinear diffusion and sensitivity, and logistic source | In this paper we study the zero-flux chemotaxis-system \begin{equation*}
\begin{cases} u_{ t}=\nabla \cdot ((u+1)^{m-1} \nabla u-(u+1)^\alpha
\chi(v)\nabla v) + ku-\mu u^2 & x\in \Omega, t>0, \\ v_{t} = \Delta v-vu & x\in
\Omega, t>0,\\ \end{cases} \end{equation*} $\Omega$ being a bounded and smooth
domain of $\mathbb{R}^n$, $n\geq 1$, and where $m,k \in \mathbb{R}$, $\mu>0$
and $\alpha < \frac{m+1}{2}$. For any $v\geq 0$ the chemotactic sensitivity
function is assumed to behave as the prototype $\chi(v) =
\frac{\chi_0}{(1+av)^2}$, with $a\geq 0$ and $\chi_0>0$. We prove that for
nonnegative and sufficiently regular initial data $u(x,0)$ and $v(x,0),$ the
corresponding initial-boundary value problem admits a global bounded classical
solution provided $\mu$ is large enough.
| 0 | 0 | 1 | 0 | 0 | 0 |
Gemini/GMOS Transmission Spectral Survey: Complete Optical Transmission Spectrum of the hot Jupiter WASP-4b | We present the complete optical transmission spectrum of the hot Jupiter
WASP-4b from 440-940 nm at R ~ 400-1500 obtained with the Gemini Multi-Object
Spectrometers (GMOS); this is the first result from a comparative
exoplanetology survey program of close-in gas giants conducted with GMOS.
WASP-4b has an equilibrium temperature of 1700 K and is favorable to study in
transmission due to a large scale height (370 km). We derive the transmission
spectrum of WASP-4b using 4 transits observed with the MOS technique. We
demonstrate repeatable results across multiple epochs with GMOS, and derive a
combined transmission spectrum at a precision about twice above photon noise,
which is roughly equal to to one atmospheric scale height. The transmission
spectrum is well fitted with a uniform opacity as a function of wavelength. The
uniform opacity and absence of a Rayleigh slope from molecular hydrogen suggest
that the atmosphere is dominated by clouds with condensate grain size of ~1 um.
This result is consistent with previous observations of hot Jupiters since
clouds have been seen in planets with similar equilibrium temperatures to
WASP-4b. We describe a custom pipeline that we have written to reduce GMOS
time-series data of exoplanet transits, and present a thorough analysis of the
dominant noise sources in GMOS, which primarily consist of wavelength- and
time- dependent displacements of the spectra on the detector, mainly due to a
lack of atmospheric dispersion correction.
| 0 | 1 | 0 | 0 | 0 | 0 |
From Infinite to Finite Programs: Explicit Error Bounds with Applications to Approximate Dynamic Programming | We consider linear programming (LP) problems in infinite dimensional spaces
that are in general computationally intractable. Under suitable assumptions, we
develop an approximation bridge from the infinite-dimensional LP to tractable
finite convex programs in which the performance of the approximation is
quantified explicitly. To this end, we adopt the recent developments in two
areas of randomized optimization and first order methods, leading to a priori
as well as a posterior performance guarantees. We illustrate the generality and
implications of our theoretical results in the special case of the long-run
average cost and discounted cost optimal control problems for Markov decision
processes on Borel spaces. The applicability of the theoretical results is
demonstrated through a constrained linear quadratic optimal control problem and
a fisheries management problem.
| 1 | 0 | 1 | 0 | 0 | 0 |
An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting | The key component in forecasting demand and consumption of resources in a
supply network is an accurate prediction of real-valued time series. Indeed,
both service interruptions and resource waste can be reduced with the
implementation of an effective forecasting system. Significant research has
thus been devoted to the design and development of methodologies for short term
load forecasting over the past decades. A class of mathematical models, called
Recurrent Neural Networks, are nowadays gaining renewed interest among
researchers and they are replacing many practical implementation of the
forecasting systems, previously based on static methods. Despite the undeniable
expressive power of these architectures, their recurrent nature complicates
their understanding and poses challenges in the training procedures. Recently,
new important families of recurrent architectures have emerged and their
applicability in the context of load forecasting has not been investigated
completely yet. In this paper we perform a comparative study on the problem of
Short-Term Load Forecast, by using different classes of state-of-the-art
Recurrent Neural Networks. We test the reviewed models first on controlled
synthetic tasks and then on different real datasets, covering important
practical cases of study. We provide a general overview of the most important
architectures and we define guidelines for configuring the recurrent networks
to predict real-valued time series.
| 1 | 0 | 0 | 0 | 0 | 0 |
Periods of abelian differentials and dynamics | Given a closed oriented surface S we describe those cohomology classes which
appear as the period characters of abelian differentials for some choice of
complex structure on S consistent with the orientation. The proof is based upon
Ratner's solution of Raghunathan's conjecture.
| 0 | 0 | 1 | 0 | 0 | 0 |
Existence of regular solutions for a certain type of non-Newtonian Navier-Stokes equations | We are concerned with existence of regular solutions for non-Newtonian fluids
in dimension three. For a certain type of non-Newtonian fluids we prove local
existence of unique regular solutions, provided that the initial data are
sufficiently smooth. Moreover, if the $H^3$-norm of initial data is
sufficiently small, then the regular solution exists globally in time.
| 0 | 0 | 1 | 0 | 0 | 0 |
Multiferroic Quantum Criticality | The zero-temperature limit of a continuous phase transition is marked by a
quantum critical point, which can generate exotic physics that extends to
elevated temperatures. Magnetic quantum criticality is now well known, and has
been explored in systems ranging from heavy fermion metals to quantum Ising
materials. Ferroelectric quantum critical behaviour has also been recently
established, motivating a flurry of research investigating its consequences.
Here, we introduce the concept of multiferroic quantum criticality, in which
both magnetic and ferroelectric quantum criticality occur in the same system.
We develop the phenomenology of multiferroic quantum critical behaviour,
describe the associated experimental signatures, and propose material systems
and schemes to realize it.
| 0 | 1 | 0 | 0 | 0 | 0 |
Carbon Nanotube Wools Directly from CO2 By Molten Electrolysis Value Driven Pathways to Carbon Dioxide Greenhouse Gas Mitigation | A climate mitigation comprehensive solution is presented through the first
high yield, low energy synthesis of macroscopic length carbon nanotubes (CNT)
wool from CO2 by molten carbonate electrolysis, suitable for weaving into
carbon composites and textiles. Growing CO2 concentrations, the concurrent
climate change and species extinction can be addressed if CO2 becomes a sought
resource rather than a greenhouse pollutant. Inexpensive carbon composites
formed from carbon wool as a lighter metal, textiles and cement replacement
comprise a major market sink to compactly store transformed anthropogenic CO2.
100x-longer CNTs grow on Monel versus steel. Monel, electrolyte equilibration,
and a mixed metal nucleation facilitate the synthesis. CO2, the sole reactant
in this transformation, is directly extractable from dilute (atmospheric) or
concentrated sources, and is cost constrained only by the (low) cost of
electricity. Today's $100K per ton CNT valuation incentivizes CO2 removal.
| 0 | 1 | 0 | 0 | 0 | 0 |
Rock-Paper-Scissors Random Walks on Temporal Multilayer Networks | We study diffusion on a multilayer network where the contact dynamics between
the nodes is governed by a random process and where the waiting time
distribution differs for edges from different layers. We study the impact on a
random walk of the competition that naturally emerges between the edges of the
different layers. In opposition to previous studies which have imposed a priori
inter-layer competition, the competition is here induced by the heterogeneity
of the activity on the different layers. We first study the precedence relation
between different edges and by extension between different layers, and show
that it determines biased paths for the walker. We also discuss the emergence
of cyclic, rock-paper-scissors random walks, when the precedence between layers
is non-transitive. Finally, we numerically show the slowing-down effect due to
the competition on a heterogeneous multilayer as the walker is likely to be
trapped for a longer time either on a single layer, or on an oriented cycle .
Keywords: random walks; multilayer networks; dynamical systems on networks;
models of networks; simulations of networks; competition between layers.
| 1 | 0 | 0 | 0 | 0 | 0 |
Marginally compact fractal trees with semiflexibility | We study marginally compact macromolecular trees that are created by means of
two different fractal generators. In doing so, we assume Gaussian statistics
for the vectors connecting nodes of the trees. Moreover, we introduce bond-bond
correlations that make the trees locally semiflexible. The symmetry of the
structures allows an iterative construction of full sets of eigenmodes
(notwithstanding the additional interactions that are present due to
semiflexibility constraints), enabling us to get physical insights about the
trees' behavior and to consider larger structures. Due to the local stiffness
the self-contact density gets drastically reduced.
| 0 | 1 | 0 | 0 | 0 | 0 |
Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC | There has been considerable recent activity applying deep convolutional
neural nets (CNNs) to data from particle physics experiments. Current
approaches on ATLAS/CMS have largely focussed on a subset of the calorimeter,
and for identifying objects or particular particle types. We explore approaches
that use the entire calorimeter, combined with track information, for directly
conducting physics analyses: i.e. classifying events as known-physics
background or new-physics signals.
We use an existing RPV-Supersymmetry analysis as a case study and explore
CNNs on multi-channel, high-resolution sparse images: applied on GPU and
multi-node CPU architectures (including Knights Landing (KNL) Xeon Phi nodes)
on the Cori supercomputer at NERSC.
| 1 | 0 | 0 | 0 | 0 | 0 |
Time-frequency analysis of ship wave patterns in shallow water: modelling and experiments | A spectrogram of a ship wake is a heat map that visualises the time-dependent
frequency spectrum of surface height measurements taken at a single point as
the ship travels by. Spectrograms are easy to compute and, if properly
interpreted, have the potential to provide crucial information about various
properties of the ship in question. Here we use geometrical arguments and
analysis of an idealised mathematical model to identify features of
spectrograms, concentrating on the effects of a finite-depth channel. Our
results depend heavily on whether the flow regime is subcritical or
supercritical. To support our theoretical predictions, we compare with data
taken from experiments we conducted in a model test basin using a variety of
realistic ship hulls. Finally, we note that vessels with a high aspect ratio
appear to produce spectrogram data that contains periodic patterns. We can
reproduce this behaviour in our mathematical model by using a so-called
two-point wavemaker. These results highlight the role of wave interference
effects in spectrograms of ship wakes.
| 0 | 1 | 0 | 0 | 0 | 0 |
HARE: Supporting efficient uplink multi-hop communications in self-organizing LPWANs | The emergence of low-power wide area networks (LPWANs) as a new agent in the
Internet of Things (IoT) will result in the incorporation into the digital
world of low-automated processes from a wide variety of sectors. The single-hop
conception of typical LPWAN deployments, though simple and robust, overlooks
the self-organization capabilities of network devices, suffers from lack of
scalability in crowded scenarios, and pays little attention to energy
consumption. Aimed to take the most out of devices' capabilities, the HARE
protocol stack is proposed in this paper as a new LPWAN technology flexible
enough to adopt uplink multi-hop communications when proving energetically more
efficient. In this way, results from a real testbed show energy savings of up
to 15% when using a multi-hop approach while keeping the same network
reliability. System's self-organizing capability and resilience have been also
validated after performing numerous iterations of the association mechanism and
deliberately switching off network devices.
| 1 | 0 | 0 | 0 | 0 | 0 |
Tensor tomography in periodic slabs | The X-ray transform on the periodic slab $[0,1]\times\mathbb T^n$, $n\geq0$,
has a non-trivial kernel due to the symmetry of the manifold and presence of
trapped geodesics. For tensor fields gauge freedom increases the kernel
further, and the X-ray transform is not solenoidally injective unless $n=0$. We
characterize the kernel of the geodesic X-ray transform for $L^2$-regular
$m$-tensors for any $m\geq0$. The characterization extends to more general
manifolds, twisted slabs, including the Möbius strip as the simplest example.
| 0 | 0 | 1 | 0 | 0 | 0 |
Central limit theorem for linear spectral statistics of general separable sample covariance matrices with applications | In this paper, we consider the separable covariance model, which plays an
important role in wireless communications and spatio-temporal statistics and
describes a process where the time correlation does not depend on the spatial
location and the spatial correlation does not depend on time. We established a
central limit theorem for linear spectral statistics of general separable
sample covariance matrices in the form of $\mathbf S_n=\frac1n\mathbf
T_{1n}\mathbf X_n\mathbf T_{2n}\mathbf X_n^*\mathbf T_{1n}^*$ where $\mathbf
X_n=(x_{jk})$ is of $m_1\times m_2$ dimension, the entries $\{x_{jk},
j=1,...,m_1, k=1,...,m_2\}$ are independent and identically distributed complex
variables with zero means and unit variances, $\mathbf T_{1n}$ is a $p\times
m_1 $ complex matrix and $\mathbf T_{2n}$ is an $m_2\times m_2$ Hermitian
matrix. We then apply this general central limit theorem to the problem of
testing white noise in time series.
| 0 | 0 | 1 | 1 | 0 | 0 |
Malnormality and join-free subgroups in right-angled Coxeter groups | In this paper, we prove that all finitely generated malnormal subgroups of
one-ended right-angled Coxeter groups are strongly quasiconvex and they are in
particular quasiconvex when the ambient groups are hyperbolic. The key idea is
to prove all infinite proper malnormal subgroups of one-ended right-angled
Coxeter groups are join-free and then prove the strong quasiconvexity and the
virtual freeness of these subgroups. We also study the subgroup divergence of
join-free subgroups in right-angled Coxeter groups and compare them with the
analogous subgroups in right-angled Artin groups. We characterize almost
malnormal parabolic subgroups in terms of their defining graphs and also
recognize them as strongly quasiconvex subgroups by the recent work of Genevois
and Russell-Spriano-Tran. Finally, we discuss some results on hyperbolically
embedded subgroups in right-angled Coxeter groups.
| 0 | 0 | 1 | 0 | 0 | 0 |
Mapping the Americanization of English in Space and Time | As global political preeminence gradually shifted from the United Kingdom to
the United States, so did the capacity to culturally influence the rest of the
world. In this work, we analyze how the world-wide varieties of written English
are evolving. We study both the spatial and temporal variations of vocabulary
and spelling of English using a large corpus of geolocated tweets and the
Google Books datasets corresponding to books published in the US and the UK.
The advantage of our approach is that we can address both standard written
language (Google Books) and the more colloquial forms of microblogging messages
(Twitter). We find that American English is the dominant form of English
outside the UK and that its influence is felt even within the UK borders.
Finally, we analyze how this trend has evolved over time and the impact that
some cultural events have had in shaping it.
| 1 | 0 | 0 | 1 | 0 | 0 |
Robot Localisation and 3D Position Estimation Using a Free-Moving Camera and Cascaded Convolutional Neural Networks | Many works in collaborative robotics and human-robot interaction focuses on
identifying and predicting human behaviour while considering the information
about the robot itself as given. This can be the case when sensors and the
robot are calibrated in relation to each other and often the reconfiguration of
the system is not possible, or extra manual work is required. We present a deep
learning based approach to remove the constraint of having the need for the
robot and the vision sensor to be fixed and calibrated in relation to each
other. The system learns the visual cues of the robot body and is able to
localise it, as well as estimate the position of robot joints in 3D space by
just using a 2D color image. The method uses a cascaded convolutional neural
network, and we present the structure of the network, describe our own
collected dataset, explain the network training and achieved results. A fully
trained system shows promising results in providing an accurate mask of where
the robot is located and a good estimate of its joints positions in 3D. The
accuracy is not good enough for visual servoing applications yet, however, it
can be sufficient for general safety and some collaborative tasks not requiring
very high precision. The main benefit of our method is the possibility of the
vision sensor to move freely. This allows it to be mounted on moving objects,
for example, a body of the person or a mobile robot working in the same
environment as the robots are operating in.
| 1 | 0 | 0 | 0 | 0 | 0 |
Meromorphic Jacobi Forms of Half-Integral Index and Umbral Moonshine Modules | In this work we consider an association of meromorphic Jacobi forms of
half-integral index to the pure D-type cases of umbral moonshine, and solve the
module problem for four of these cases by constructing vertex operator
superalgebras that realise the corresponding meromorphic Jacobi forms as graded
traces. We also present a general discussion of meromorphic Jacobi forms with
half-integral index and their relationship to mock modular forms.
| 0 | 0 | 1 | 0 | 0 | 0 |
Active Hypothesis Testing: Beyond Chernoff-Stein | An active hypothesis testing problem is formulated. In this problem, the
agent can perform a fixed number of experiments and then decide on one of the
hypotheses. The agent is also allowed to declare its experiments inconclusive
if needed. The objective is to minimize the probability of making an incorrect
inference (misclassification probability) while ensuring that the true
hypothesis is declared conclusively with moderately high probability. For this
problem, lower and upper bounds on the optimal misclassification probability
are derived and these bounds are shown to be asymptotically tight. In the
analysis, a sub-problem, which can be viewed as a generalization of the
Chernoff-Stein lemma, is formulated and analyzed. A heuristic approach to
strategy design is proposed and its relationship with existing heuristic
strategies is discussed.
| 1 | 0 | 1 | 1 | 0 | 0 |
Data-driven regularization of Wasserstein barycenters with an application to multivariate density registration | We present a framework to simultaneously align and smooth data in the form of
multiple point clouds sampled from unknown densities with support in a
$d$-dimensional Euclidean space. This work is motivated by applications in
bioinformatics where researchers aim to automatically homogenize large datasets
to compare and analyze characteristics within a same cell population.
Inconveniently, the information acquired is most certainly noisy due to
mis-alignment caused by technical variations of the environment. To overcome
this problem, we propose to register multiple point clouds by using the notion
of regularized barycenters (or Fréchet mean) of a set of probability
measures with respect to the Wasserstein metric. A first approach consists in
penalizing a Wasserstein barycenter with a convex functional as recently
proposed in Bigot and al. (2018). A second strategy is to transform the
Wasserstein metric itself into an entropy regularized transportation cost
between probability measures as introduced in Cuturi (2013). The main
contribution of this work is to propose data-driven choices for the
regularization parameters involved in each approach using the
Goldenshluger-Lepski's principle. Simulated data sampled from Gaussian mixtures
are used to illustrate each method, and an application to the analysis of flow
cytometry data is finally proposed.
| 0 | 0 | 0 | 1 | 0 | 0 |
BCS quantum critical phenomena | Theoretically, we recently showed that the scaling relation between the
transition temperature T_c and the superfluid density at zero temperature n_s
(0) might exhibit a parabolic pattern [Scientific Reports 6 (2016) 23863]. It
is significantly different from the linear scaling described by Homes' law,
which is well known as a mean-field result. More recently, Bozovic et al. have
observed such a parabolic scaling in the overdoped copper oxides with a
sufficiently low transition temperature T_c [Nature 536 (2016) 309-311]. They
further point out that this experimental finding is incompatible with the
standard Bardeen-Cooper-Schrieffer (BCS) description. Here we report that if
T_c is sufficiently low, applying the renormalization group approach into the
BCS action at zero temperature will naturally lead to the parabolic scaling.
Our result indicates that when T_c sufficiently approaches zero, quantum
fluctuations will be overwhelmingly amplified so that the mean-field
approximation may break down at zero temperature.
| 0 | 1 | 0 | 0 | 0 | 0 |
Action preserving (weak) topologies on the category of presheaves | Let $\mathcal{C}$ be a finitely complete small category. In this paper, first
we construct two weak (Lawvere-Tierney) topologies on the category of
presheaves. One of them is established by means of a subfunctor of the Yoneda
functor and the other one, is constructed by an admissible class on
$\mathcal{C}$ and the internal existential quantifier in the presheaf topos
$\widehat{\mathcal{C}}$. Moreover, by using an admissible class on
$\mathcal{C},$ we are able to define an action on the subobject classifier
$\Omega$ of $\widehat{\mathcal{C}}$. Then we find some necessary conditions for
that the two weak topologies and also the double negation topology $\neg\neg$
on $\widehat{\mathcal{C}}$ to be action preserving maps. Finally, among other
things, we constitute an action preserving weak topology on
$\widehat{\mathcal{C}}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
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