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Informed Sub-Sampling MCMC: Approximate Bayesian Inference for Large Datasets | This paper introduces a framework for speeding up Bayesian inference
conducted in presence of large datasets. We design a Markov chain whose
transition kernel uses an (unknown) fraction of (fixed size) of the available
data that is randomly refreshed throughout the algorithm. Inspired by the
Approximate Bayesian Computation (ABC) literature, the subsampling process is
guided by the fidelity to the observed data, as measured by summary statistics.
The resulting algorithm, Informed Sub-Sampling MCMC (ISS-MCMC), is a generic
and flexible approach which, contrary to existing scalable methodologies,
preserves the simplicity of the Metropolis-Hastings algorithm. Even though
exactness is lost, i.e. the chain distribution approximates the posterior, we
study and quantify theoretically this bias and show on a diverse set of
examples that it yields excellent performances when the computational budget is
limited. If available and cheap to compute, we show that setting the summary
statistics as the maximum likelihood estimator is supported by theoretical
arguments.
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Mathematical Analysis of Anthropogenic Signatures: The Great Deceleration | Distributions of anthropogenic signatures (impacts and activities) are
mathematically analysed. The aim is to understand the Anthropocene and to see
whether anthropogenic signatures could be used to determine its beginning. A
total of 23 signatures were analysed and results are presented in 31 diagrams.
Some of these signatures contain undistinguishable natural components but most
of them are of purely anthropogenic origin. Great care was taken to identify
abrupt accelerations, which could be used to determine the beginning of the
Anthropocene. Results of the analysis can be summarised in three conclusions.
1. Anthropogenic signatures cannot be used to determine the beginning of the
Anthropocene. 2. There was no abrupt Great Acceleration around 1950 or around
any other time. 3. Anthropogenic signatures are characterised by the Great
Deceleration in the second half of the 20th century. The second half of the
20th century does not mark the beginning of the Anthropocene but most likely
the beginning of the end of the strong anthropogenic impacts, maybe even the
beginning of a transition to a sustainable future. The Anthropocene is a unique
stage in human experience but it has no clearly marked beginning and it is
probably not a new geological epoch.
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Efficient Regret Minimization in Non-Convex Games | We consider regret minimization in repeated games with non-convex loss
functions. Minimizing the standard notion of regret is computationally
intractable. Thus, we define a natural notion of regret which permits efficient
optimization and generalizes offline guarantees for convergence to an
approximate local optimum. We give gradient-based methods that achieve optimal
regret, which in turn guarantee convergence to equilibrium in this framework.
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Spectral Methods for Immunization of Large Networks | Given a network of nodes, minimizing the spread of a contagion using a
limited budget is a well-studied problem with applications in network security,
viral marketing, social networks, and public health. In real graphs, virus may
infect a node which in turn infects its neighbor nodes and this may trigger an
epidemic in the whole graph. The goal thus is to select the best k nodes
(budget constraint) that are immunized (vaccinated, screened, filtered) so as
the remaining graph is less prone to the epidemic. It is known that the problem
is, in all practical models, computationally intractable even for moderate
sized graphs. In this paper we employ ideas from spectral graph theory to
define relevance and importance of nodes. Using novel graph theoretic
techniques, we then design an efficient approximation algorithm to immunize the
graph. Theoretical guarantees on the running time of our algorithm show that it
is more efficient than any other known solution in the literature. We test the
performance of our algorithm on several real world graphs. Experiments show
that our algorithm scales well for large graphs and outperforms state of the
art algorithms both in quality (containment of epidemic) and efficiency
(runtime and space complexity).
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Long-time existence of nonlinear inhomogeneous compressible elastic waves | In this paper, we consider the nonlinear inhomogeneous compressible elastic
waves in three spatial dimensions when the density is a small disturbance
around a constant state. In homogeneous case, the almost global existence was
established by Klainerman-Sideris [1996_CPAM], and global existence was built
by Agemi [2000_Invent. Math.] and Sideris [1996_Invent. Math., 2000_Ann. Math.]
independently. Here we establish the corresponding almost global and global
existence theory in the inhomogeneous case.
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Deep Convolutional Denoising of Low-Light Images | Poisson distribution is used for modeling noise in photon-limited imaging.
While canonical examples include relatively exotic types of sensing like
spectral imaging or astronomy, the problem is relevant to regular photography
now more than ever due to the booming market for mobile cameras. Restricted
form factor limits the amount of absorbed light, thus computational
post-processing is called for. In this paper, we make use of the powerful
framework of deep convolutional neural networks for Poisson denoising. We
demonstrate how by training the same network with images having a specific peak
value, our denoiser outperforms previous state-of-the-art by a large margin
both visually and quantitatively. Being flexible and data-driven, our solution
resolves the heavy ad hoc engineering used in previous methods and is an order
of magnitude faster. We further show that by adding a reasonable prior on the
class of the image being processed, another significant boost in performance is
achieved.
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Towards a fractal cohomology: Spectra of Polya--Hilbert operators, regularized determinants and Riemann zeros | Emil Artin defined a zeta function for algebraic curves over finite fields
and made a conjecture about them analogous to the famous Riemann hypothesis.
This and other conjectures about these zeta functions would come to be called
the Weil conjectures, which were proved by Weil for curves and later, by
Deligne for varieties over finite fields. Much work was done in the search for
a proof of these conjectures, including the development in algebraic geometry
of a Weil cohomology theory for these varieties, which uses the Frobenius
operator on a finite field. The zeta function is then expressed as a
determinant, allowing the properties of the function to relate to those of the
operator. The search for a suitable cohomology theory and associated operator
to prove the Riemann hypothesis is still on. In this paper, we study the
properties of the derivative operator $D = \frac{d}{dz}$ on a particular
weighted Bergman space of entire functions. The operator $D$ can be naturally
viewed as the `infinitesimal shift of the complex plane'. Furthermore, this
operator is meant to be the replacement for the Frobenius operator in the
general case and is used to construct an operator associated to any suitable
meromorphic function. We then show that the meromorphic function can be
recovered by using a regularized determinant involving the above operator. This
is illustrated in some important special cases: rational functions, zeta
functions of curves over finite fields, the Riemann zeta function, and
culminating in a quantized version of the Hadamard factorization theorem that
applies to any entire function of finite order. Our construction is motivated
in part by [23] on the infinitesimal shift of the real line, as well as by
earlier work of Deninger [10] on cohomology in number theory and a conjectural
`fractal cohomology theory' envisioned in [25] and [28].
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F-index of graphs based on four operations related to the lexicographic product | The forgotten topological index or F-index of a graph is defined as the sum
of cubes of the degree of all the vertices of the graph. In this paper we study
the F-index of four operations related to the lexicographic product on graphs
which were introduced by Sarala et al. [D. Sarala, H. Deng, S.K. Ayyaswamya and
S. Balachandrana, The Zagreb indices of graphs based on four new operations
related to the lexicographic product, \textit{Applied Mathematics and
Computation}, 309 (2017) 156--169.].
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Deleting vertices to graphs of bounded genus | We show that a problem of deleting a minimum number of vertices from a graph
to obtain a graph embeddable on a surface of a given Euler genus is solvable in
time $2^{C_g \cdot k^2 \log k} n^{O(1)}$, where $k$ is the size of the deletion
set, $C_g$ is a constant depending on the Euler genus $g$ of the target
surface, and $n$ is the size of the input graph. On the way to this result, we
develop an algorithm solving the problem in question in time $2^{O((t+g) \log
(t+g))} n$, given a tree decomposition of the input graph of width $t$. The
results generalize previous algorithms for the surface being a sphere by Marx
and Schlotter [Algorithmica 2012], Kawarabayashi [FOCS 2009], and Jansen,
Lokshtanov, and Saurabh [SODA 2014].
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Axion dark matter search using the storage ring EDM method | We propose using the storage ring EDM method to search for the axion dark
matter induced EDM oscillation in nucleons. The method uses a combination of B
and E-fields to produce a resonance between the $g-2$ spin precession frequency
and the background axion field oscillation to greatly enhance sensitivity to
it. An axion frequency range from $10^{-9}$ Hz to 100 MHz can in principle be
scanned with high sensitivity, corresponding to an $f_a$ range of $10^{13} $
GeV $\leq f_a \leq 10^{30}$ GeV, the breakdown scale of the global symmetry
generating the axion or axion like particles (ALPs).
| 0 | 1 | 0 | 0 | 0 | 0 |
Time crystal platform: from quasi-crystal structures in time to systems with exotic interactions | Time crystals are quantum many-body systems which, due to interactions
between particles, are able to spontaneously self-organize their motion in a
periodic way in time by analogy with the formation of crystalline structures in
space in condensed matter physics. In solid state physics properties of space
crystals are often investigated with the help of external potentials that are
spatially periodic and reflect various crystalline structures. A similar
approach can be applied for time crystals, as periodically driven systems
constitute counterparts of spatially periodic systems, but in the time domain.
Here we show that condensed matter problems ranging from single particles in
potentials of quasi-crystal structure to many-body systems with exotic
long-range interactions can be realized in the time domain with an appropriate
periodic driving. Moreover, it is possible to create molecules where atoms are
bound together due to destructive interference if the atomic scattering length
is modulated in time.
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Fast and accurate classification of echocardiograms using deep learning | Echocardiography is essential to modern cardiology. However, human
interpretation limits high throughput analysis, limiting echocardiography from
reaching its full clinical and research potential for precision medicine. Deep
learning is a cutting-edge machine-learning technique that has been useful in
analyzing medical images but has not yet been widely applied to
echocardiography, partly due to the complexity of echocardiograms' multi view,
multi modality format. The essential first step toward comprehensive computer
assisted echocardiographic interpretation is determining whether computers can
learn to recognize standard views. To this end, we anonymized 834,267
transthoracic echocardiogram (TTE) images from 267 patients (20 to 96 years, 51
percent female, 26 percent obese) seen between 2000 and 2017 and labeled them
according to standard views. Images covered a range of real world clinical
variation. We built a multilayer convolutional neural network and used
supervised learning to simultaneously classify 15 standard views. Eighty
percent of data used was randomly chosen for training and 20 percent reserved
for validation and testing on never seen echocardiograms. Using multiple images
from each clip, the model classified among 12 video views with 97.8 percent
overall test accuracy without overfitting. Even on single low resolution
images, test accuracy among 15 views was 91.7 percent versus 70.2 to 83.5
percent for board-certified echocardiographers. Confusional matrices, occlusion
experiments, and saliency mapping showed that the model finds recognizable
similarities among related views and classifies using clinically relevant image
features. In conclusion, deep neural networks can classify essential
echocardiographic views simultaneously and with high accuracy. Our results
provide a foundation for more complex deep learning assisted echocardiographic
interpretation.
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Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis | Anomaly detection in database management systems (DBMSs) is difficult because
of increasing number of statistics (stat) and event metrics in big data system.
In this paper, I propose an automatic DBMS diagnosis system that detects
anomaly periods with abnormal DB stat metrics and finds causal events in the
periods. Reconstruction error from deep autoencoder and statistical process
control approach are applied to detect time period with anomalies. Related
events are found using time series similarity measures between events and
abnormal stat metrics. After training deep autoencoder with DBMS metric data,
efficacy of anomaly detection is investigated from other DBMSs containing
anomalies. Experiment results show effectiveness of proposed model, especially,
batch temporal normalization layer. Proposed model is used for publishing
automatic DBMS diagnosis reports in order to determine DBMS configuration and
SQL tuning.
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On the complexity of non-orientable Seifert fibre spaces | In this paper we deal with Seifert fibre spaces, which are compact
3-manifolds admitting a foliation by circles. We give a combinatorial
description for these manifolds in all the possible cases: orientable,
non-orientable, closed, with boundary. Moreover, we compute a potentially sharp
upper bound for their complexity in terms of the invariants of the
combinatorial description, extending to the non-orientable case results by
Fominykh and Wiest for the orientable case with boundary and by Martelli and
Petronio for the closed orientable case.
| 0 | 0 | 1 | 0 | 0 | 0 |
MIMO-UFMC Transceiver Schemes for Millimeter Wave Wireless Communications | The UFMC modulation is among the most considered solutions for the
realization of beyond-OFDM air interfaces for future wireless networks. This
paper focuses on the design and analysis of an UFMC transceiver equipped with
multiple antennas and operating at millimeter wave carrier frequencies. The
paper provides the full mathematical model of a MIMO-UFMC transceiver, taking
into account the presence of hybrid analog/digital beamformers at both ends of
the communication links. Then, several detection structures are proposed, both
for the case of single-packet isolated transmission, and for the case of
multiple-packet continuous transmission. In the latter situation, the paper
also considers the case in which no guard time among adjacent packets is
inserted, trading off an increased level of interference with higher values of
spectral efficiency. At the analysis stage, the several considered detection
structures and transmission schemes are compared in terms of bit-error-rate,
root-mean-square-error, and system throughput. The numerical results show that
the proposed transceiver algorithms are effective and that the linear MMSE data
detector is capable of well managing the increased interference brought by the
removal of guard times among consecutive packets, thus yielding throughput
gains of about 10 - 13 $\%$. The effect of phase noise at the receiver is also
numerically assessed, and it is shown that the recursive implementation of the
linear MMSE exhibits some degree of robustness against this disturbance.
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Modeling human intuitions about liquid flow with particle-based simulation | Humans can easily describe, imagine, and, crucially, predict a wide variety
of behaviors of liquids--splashing, squirting, gushing, sloshing, soaking,
dripping, draining, trickling, pooling, and pouring--despite tremendous
variability in their material and dynamical properties. Here we propose and
test a computational model of how people perceive and predict these liquid
dynamics, based on coarse approximate simulations of fluids as collections of
interacting particles. Our model is analogous to a "game engine in the head",
drawing on techniques for interactive simulations (as in video games) that
optimize for efficiency and natural appearance rather than physical accuracy.
In two behavioral experiments, we found that the model accurately captured
people's predictions about how liquids flow among complex solid obstacles, and
was significantly better than two alternatives based on simple heuristics and
deep neural networks. Our model was also able to explain how people's
predictions varied as a function of the liquids' properties (e.g., viscosity
and stickiness). Together, the model and empirical results extend the recent
proposal that human physical scene understanding for the dynamics of rigid,
solid objects can be supported by approximate probabilistic simulation, to the
more complex and unexplored domain of fluid dynamics.
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Comment on "Spin-Orbit Coupling Induced Gap in Graphene on Pt(111) with Intercalated Pb Monolayer" | Recently a paper of Klimovskikh et al. was published presenting experimental
and theoretical analysis of the graphene/Pb/Pt(111) system. The authors
investigate the crystallographic and electronic structure of this
graphene-based system by means of LEED, ARPES, and spin-resolved PES of the
graphene $\pi$ states in the vicinity of the Dirac point of graphene. The
authors of this paper demonstrate that an energy gap of approx. 200 meV is
opened in the spectral function of graphene directly at the Dirac point of
graphene and spin-splitting of 100 meV is detected for the upper part of the
Dirac cone. On the basis of the spin-resolved photoelectron spectroscopy
measurements of the region around the gap the authors claim that these
splittings are of a spin-orbit nature and that the observed spin structure
confirms the observation of the quantum spin Hall state in graphene, proposed
in earlier theoretical works. Here we will show that careful systematic
analysis of the experimental data presented in this manuscript is needed and
their interpretation require more critical consideration for making such
conclusions. Our analysis demonstrates that the proposed effects and
interpretations are questionable and require further more careful experiments.
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Stochastic Non-convex Ordinal Embedding with Stabilized Barzilai-Borwein Step Size | Learning representation from relative similarity comparisons, often called
ordinal embedding, gains rising attention in recent years. Most of the existing
methods are batch methods designed mainly based on the convex optimization,
say, the projected gradient descent method. However, they are generally
time-consuming due to that the singular value decomposition (SVD) is commonly
adopted during the update, especially when the data size is very large. To
overcome this challenge, we propose a stochastic algorithm called SVRG-SBB,
which has the following features: (a) SVD-free via dropping convexity, with
good scalability by the use of stochastic algorithm, i.e., stochastic variance
reduced gradient (SVRG), and (b) adaptive step size choice via introducing a
new stabilized Barzilai-Borwein (SBB) method as the original version for convex
problems might fail for the considered stochastic \textit{non-convex}
optimization problem. Moreover, we show that the proposed algorithm converges
to a stationary point at a rate $\mathcal{O}(\frac{1}{T})$ in our setting,
where $T$ is the number of total iterations. Numerous simulations and
real-world data experiments are conducted to show the effectiveness of the
proposed algorithm via comparing with the state-of-the-art methods,
particularly, much lower computational cost with good prediction performance.
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The Mass-Metallicity Relation revisited with CALIFA | We present an updated version of the mass--metallicity relation (MZR) using
integral field spectroscopy data obtained from 734 galaxies observed by the
CALIFA survey. These unparalleled spatially resolved spectroscopic data allow
us to determine the metallicity at the same physical scale ($\mathrm{R_{e}}$)
for different calibrators. We obtain MZ relations with similar shapes for all
calibrators, once the scale factors among them are taken into account. We do
not find any significant secondary relation of the MZR with either the star
formation rate (SFR) or the specific SFR for any of the calibrators used in
this study, based on the analysis of the residuals of the best fitted relation.
However we do see a hint for a (s)SFR-dependent deviation of the MZ-relation at
low masses (M$<$10$^{9.5}$M$_\odot$), where our sample is not complete. We are
thus unable to confirm the results by Mannucci et al. (2010), although we
cannot exclude that this result is due to the differences in the analysed
datasets. In contrast, our results are inconsistent with the results by
Lara-Lopez et al. (2010), and we can exclude the presence of a SFR-Mass-Oxygen
abundance Fundamental Plane. These results agree with previous findings
suggesting that either (1) the secondary relation with the SFR could be induced
by an aperture effect in single fiber/aperture spectroscopic surveys, (2) it
could be related to a local effect confined to the central regions of galaxies,
or (3) it is just restricted to the low-mass regime, or a combination of the
three effects.
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(p,q)-webs of DIM representations, 5d N=1 instanton partition functions and qq-characters | Instanton partition functions of $\mathcal{N}=1$ 5d Super Yang-Mills reduced
on $S^1$ can be engineered in type IIB string theory from the $(p,q)$-branes
web diagram. To this diagram is superimposed a web of representations of the
Ding-Iohara-Miki (DIM) algebra that acts on the partition function. In this
correspondence, each segment is associated to a representation, and the
(topological string) vertex is identified with the intertwiner operator
constructed by Awata, Feigin and Shiraishi. We define a new intertwiner acting
on the representation spaces of levels $(1,n)\otimes(0,m)\to(1,n+m)$, thereby
generalizing to higher rank $m$ the original construction. It allows us to use
a folded version of the usual $(p,q)$-web diagram, bringing great
simplifications to actual computations. As a result, the characterization of
Gaiotto states and vertical intertwiners, previously obtained by some of the
authors, is uplifted to operator relations acting in the Fock space of
horizontal representations. We further develop a method to build qq-characters
of linear quivers based on the horizontal action of DIM elements. While
fundamental qq-characters can be built using the coproduct, higher ones require
the introduction of a (quantum) Weyl reflection acting on tensor products of
DIM generators.
| 0 | 0 | 1 | 0 | 0 | 0 |
Self-Motion of the 3-PPPS Parallel Robot with Delta-Shaped Base | This paper presents the kinematic analysis of the 3-PPPS parallel robot with
an equi-lateral mobile platform and an equilateral-shaped base. Like the other
3-PPPS robots studied in the literature, it is proved that the parallel
singularities depend only on the orientation of the end-effector. The
quaternion parameters are used to represent the singularity surfaces. The study
of the direct kinematic model shows that this robot admits a self-motion of the
Cardanic type. This explains why the direct kinematic model admits an infinite
number of solutions in the center of the workspace at the "home" position but
has never been studied until now.
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Scaling-Up Reasoning and Advanced Analytics on BigData | BigDatalog is an extension of Datalog that achieves performance and
scalability on both Apache Spark and multicore systems to the point that its
graph analytics outperform those written in GraphX. Looking back, we see how
this realizes the ambitious goal pursued by deductive database researchers
beginning forty years ago: this is the goal of combining the rigor and power of
logic in expressing queries and reasoning with the performance and scalability
by which relational databases managed Big Data. This goal led to Datalog which
is based on Horn Clauses like Prolog but employs implementation techniques,
such as Semi-naive Fixpoint and Magic Sets, that extend the bottom-up
computation model of relational systems, and thus obtain the performance and
scalability that relational systems had achieved, as far back as the 80s, using
data-parallelization on shared-nothing architectures. But this goal proved
difficult to achieve because of major issues at (i) the language level and (ii)
at the system level. The paper describes how (i) was addressed by simple rules
under which the fixpoint semantics extends to programs using count, sum and
extrema in recursion, and (ii) was tamed by parallel compilation techniques
that achieve scalability on multicore systems and Apache Spark. This paper is
under consideration for acceptance in Theory and Practice of Logic Programming
(TPLP).
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ALMA Observations of the Gravitational Lens SDP.9 | We present long-baseline ALMA observations of the strong gravitational lens
H-ATLAS J090740.0-004200 (SDP.9), which consists of an elliptical galaxy at
$z_{\mathrm{L}}=0.6129$ lensing a background submillimeter galaxy into two
extended arcs. The data include Band 6 continuum observations, as well as CO
$J$=6$-$5 molecular line observations, from which we measure an updated source
redshift of $z_{\mathrm{S}}=1.5747$. The image morphology in the ALMA data is
different from that of the HST data, indicating a spatial offset between the
stellar, gas, and dust component of the source galaxy. We model the lens as an
elliptical power law density profile with external shear using a combination of
archival HST data and conjugate points identified in the ALMA data. Our best
model has an Einstein radius of $\theta_{\mathrm{E}}=0.66\pm0.01$ and a
slightly steeper than isothermal mass profile slope. We search for the central
image of the lens, which can be used constrain the inner mass distribution of
the lens galaxy including the central supermassive black hole, but do not
detect it in the integrated CO image at a 3$\sigma$ rms level of 0.0471 Jy km
s$^{-1}$.
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The Noether numbers and the Davenport constants of the groups of order less than 32 | The computation of the Noether numbers of all groups of order less than
thirty-two is completed. It turns out that for these groups in non-modular
characteristic the Noether number is attained on a multiplicity free
representation, it is strictly monotone on subgroups and factor groups, and it
does not depend on the characteristic. Algorithms are developed and used to
determine the small and large Davenport constants of these groups. For each of
these groups the Noether number is greater than the small Davenport constant,
whereas the first example of a group whose Noether number exceeds the large
Davenport constant is found, answering partially a question posed by
Geroldinger and Grynkiewicz.
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Multistability and coexisting soliton combs in ring resonators: the Lugiato-Lefever approach | We are reporting that the Lugiato-Lefever equation describing the frequency
comb generation in ring resonators with the localized pump and loss terms also
describes the simultaneous nonlinear resonances leading to the multistability
of nonlinear modes and coexisting solitons that are associated with the
spectrally distinct frequency combs.
| 0 | 1 | 0 | 0 | 0 | 0 |
Numerical assessment of the percolation threshold using complement networks | Models of percolation processes on networks currently assume locally
tree-like structures at low densities, and are derived exactly only in the
thermodynamic limit. Finite size effects and the presence of short loops in
real systems however cause a deviation between the empirical percolation
threshold $p_c$ and its model-predicted value $\pi_c$. Here we show the
existence of an empirical linear relation between $p_c$ and $\pi_c$ across a
large number of real and model networks. Such a putatively universal relation
can then be used to correct the estimated value of $\pi_c$. We further show how
to obtain a more precise relation using the concept of the complement graph, by
investigating on the connection between the percolation threshold of a network,
$p_c$, and that of its complement, $\bar{p}_c$.
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Planar graphs as L-intersection or L-contact graphs | The L-intersection graphs are the graphs that have a representation as
intersection graphs of axis parallel shapes in the plane. A subfamily of these
graphs are {L, |, --}-contact graphs which are the contact graphs of axis
parallel L, |, and -- shapes in the plane. We prove here two results that were
conjectured by Chaplick and Ueckerdt in 2013. We show that planar graphs are
L-intersection graphs, and that triangle-free planar graphs are {L, |,
--}-contact graphs. These results are obtained by a new and simple
decomposition technique for 4-connected triangulations. Our results also
provide a much simpler proof of the known fact that planar graphs are segment
intersection graphs.
| 1 | 0 | 0 | 0 | 0 | 0 |
An EPTAS for Scheduling on Unrelated Machines of Few Different Types | In the classical problem of scheduling on unrelated parallel machines, a set
of jobs has to be assigned to a set of machines. The jobs have a processing
time depending on the machine and the goal is to minimize the makespan, that is
the maximum machine load. It is well known that this problem is NP-hard and
does not allow polynomial time approximation algorithms with approximation
guarantees smaller than $1.5$ unless P$=$NP. We consider the case that there
are only a constant number $K$ of machine types. Two machines have the same
type if all jobs have the same processing time for them. This variant of the
problem is strongly NP-hard already for $K=1$. We present an efficient
polynomial time approximation scheme (EPTAS) for the problem, that is, for any
$\varepsilon > 0$ an assignment with makespan of length at most
$(1+\varepsilon)$ times the optimum can be found in polynomial time in the
input length and the exponent is independent of $1/\varepsilon$. In particular
we achieve a running time of $2^{\mathcal{O}(K\log(K)
\frac{1}{\varepsilon}\log^4 \frac{1}{\varepsilon})}+\mathrm{poly}(|I|)$, where
$|I|$ denotes the input length. Furthermore, we study three other problem
variants and present an EPTAS for each of them: The Santa Claus problem, where
the minimum machine load has to be maximized; the case of scheduling on
unrelated parallel machines with a constant number of uniform types, where
machines of the same type behave like uniformly related machines; and the
multidimensional vector scheduling variant of the problem where both the
dimension and the number of machine types are constant. For the Santa Claus
problem we achieve the same running time. The results are achieved, using mixed
integer linear programming and rounding techniques.
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An extensive impurity-scattering study on the pairing symmetry of monolayer FeSe films on SrTiO3 | Determination of the pairing symmetry in monolayer FeSe films on SrTiO3 is a
requisite for understanding the high superconducting transition temperature in
this system, which has attracted intense theoretical and experimental studies
but remains controversial. Here, by introducing several types of point defects
in FeSe monolayer films, we conduct a systematic investigation on the
impurity-induced electronic states by spatially resolved scanning tunneling
spectroscopy. Ranging from surface adsorption, chemical substitution to
intrinsic structural modification, these defects generate a variety of
scattering strength, which renders new insights on the pairing symmetry.
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Fast Depth Imaging Denoising with the Temporal Correlation of Photons | This paper proposes a novel method to filter out the false alarm of LiDAR
system by using the temporal correlation of target reflected photons. Because
of the inevitable noise, which is due to background light and dark counts of
the detector, the depth imaging of LiDAR system exists a large estimation
error. Our method combines the Poisson statistical model with the different
distribution feature of signal and noise in the time axis. Due to selecting a
proper threshold, our method can effectively filter out the false alarm of
system and use the ToFs of detected signal photons to rebuild the depth image
of the scene. The experimental results reveal that by our method it can fast
distinguish the distance between two close objects, which is confused due to
the high background noise, and acquire the accurate depth image of the scene.
Our method need not increase the complexity of the system and is useful in
power-limited depth imaging.
| 0 | 1 | 0 | 0 | 0 | 0 |
From Propositional Logic to Plausible Reasoning: A Uniqueness Theorem | We consider the question of extending propositional logic to a logic of
plausible reasoning, and posit four requirements that any such extension should
satisfy. Each is a requirement that some property of classical propositional
logic be preserved in the extended logic; as such, the requirements are simpler
and less problematic than those used in Cox's Theorem and its variants. As with
Cox's Theorem, our requirements imply that the extended logic must be
isomorphic to (finite-set) probability theory. We also obtain specific
numerical values for the probabilities, recovering the classical definition of
probability as a theorem, with truth assignments that satisfy the premise
playing the role of the "possible cases."
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Discovering objects and their relations from entangled scene representations | Our world can be succinctly and compactly described as structured scenes of
objects and relations. A typical room, for example, contains salient objects
such as tables, chairs and books, and these objects typically relate to each
other by their underlying causes and semantics. This gives rise to correlated
features, such as position, function and shape. Humans exploit knowledge of
objects and their relations for learning a wide spectrum of tasks, and more
generally when learning the structure underlying observed data. In this work,
we introduce relation networks (RNs) - a general purpose neural network
architecture for object-relation reasoning. We show that RNs are capable of
learning object relations from scene description data. Furthermore, we show
that RNs can act as a bottleneck that induces the factorization of objects from
entangled scene description inputs, and from distributed deep representations
of scene images provided by a variational autoencoder. The model can also be
used in conjunction with differentiable memory mechanisms for implicit relation
discovery in one-shot learning tasks. Our results suggest that relation
networks are a potentially powerful architecture for solving a variety of
problems that require object relation reasoning.
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Hall effect spintronics for gas detection | We present the concept of magnetic gas detection by the Extraordinary Hall
effect (EHE). The technique is compatible with the existing conductometric gas
detection technologies and allows simultaneous measurement of two independent
parameters: resistivity and magnetization affected by the target gas.
Feasibility of the approach is demonstrated by detecting low concentration
hydrogen using thin CoPd films as the sensor material. The Hall effect
sensitivity of the optimized samples exceeds 240% per 104 ppm at hydrogen
concentrations below 0.5% in the hydrogen/nitrogen atmosphere, which is more
than two orders of magnitude higher than the sensitivity of the conductance
detection.
| 0 | 1 | 0 | 0 | 0 | 0 |
An evolutionary strategy for DeltaE - E identification | In this article we present an automatic method for charge and mass
identification of charged nuclear fragments produced in heavy ion collisions at
intermediate energies. The algorithm combines a generative model of DeltaE - E
relation and a Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). The
CMA-ES is a stochastic and derivative-free method employed to search parameter
space of the model by means of a fitness function. The article describes
details of the method along with results of an application on simulated labeled
data.
| 1 | 1 | 0 | 0 | 0 | 0 |
Contrastive Training for Models of Information Cascades | This paper proposes a model of information cascades as directed spanning
trees (DSTs) over observed documents. In addition, we propose a contrastive
training procedure that exploits partial temporal ordering of node infections
in lieu of labeled training links. This combination of model and unsupervised
training makes it possible to improve on models that use infection times alone
and to exploit arbitrary features of the nodes and of the text content of
messages in information cascades. With only basic node and time lag features
similar to previous models, the DST model achieves performance with
unsupervised training comparable to strong baselines on a blog network
inference task. Unsupervised training with additional content features achieves
significantly better results, reaching half the accuracy of a fully supervised
model.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Structure of the Broad-Line Region In Active Galactic Nuclei. II. Dynamical Modeling of Data from the AGN10 Reverberation Mapping Campaign | We present inferences on the geometry and kinematics of the broad-Hbeta
line-emitting region in four active galactic nuclei monitored as a part of the
fall 2010 reverberation mapping campaign at MDM Observatory led by the Ohio
State University. From modeling the continuum variability and response in
emission-line profile changes as a function of time, we infer the geometry of
the Hbeta- emitting broad line regions to be thick disks that are close to
face-on to the observer with kinematics that are well-described by either
elliptical orbits or inflowing gas. We measure the black hole mass to be log
(MBH) = 7.25 (+/-0.10) for Mrk 335, 7.86 (+0.20, -0.17) for Mrk 1501, 7.84
(+0.14, -0.19) for 3C 120, and 6.92 (+0.24, -0.23) for PG 2130+099. These black
hole mass measurements are not based on a particular assumed value of the
virial scale factor f, allowing us to compute individual f factors for each
target. Our results nearly double the number of targets that have been modeled
in this manner, and investigate the properties of a more diverse sample by
including previously modeled objects. We measure an average scale factor f in
the entire sample to be log10(f) = 0.54 +/- 0.17 when the line dispersion is
used to characterize the line width, which is consistent with values derived
using the normalization of the MBH-sigma relation. We find that the scale
factor f for individual targets is likely correlated with the black hole mass,
inclination angle, and opening angle of the broad line region but we do not
find any correlation with the luminosity.
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A Unified Framework for Long Range and Cold Start Forecasting of Seasonal Profiles in Time Series | Providing long-range forecasts is a fundamental challenge in time series
modeling, which is only compounded by the challenge of having to form such
forecasts when a time series has never previously been observed. The latter
challenge is the time series version of the cold-start problem seen in
recommender systems which, to our knowledge, has not been addressed in previous
work. A similar problem occurs when a long range forecast is required after
only observing a small number of time points --- a warm start forecast. With
these aims in mind, we focus on forecasting seasonal profiles---or baseline
demand---for periods on the order of a year in three cases: the long range case
with multiple previously observed seasonal profiles, the cold start case with
no previous observed seasonal profiles, and the warm start case with only a
single partially observed profile. Classical time series approaches that
perform iterated step-ahead forecasts based on previous observations struggle
to provide accurate long range predictions; in settings with little to no
observed data, such approaches are simply not applicable. Instead, we present a
straightforward framework which combines ideas from high-dimensional regression
and matrix factorization on a carefully constructed data matrix. Key to our
formulation and resulting performance is leveraging (1) repeated patterns over
fixed periods of time and across series, and (2) metadata associated with the
individual series; without this additional data, the cold-start/warm-start
problems are nearly impossible to solve. We demonstrate that our framework can
accurately forecast an array of seasonal profiles on multiple large scale
datasets.
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A Multi-Ringed, Modestly-Inclined Protoplanetary Disk around AA Tau | AA Tau is the archetype for a class of stars with a peculiar periodic
photometric variability thought to be related to a warped inner disk structure
with a nearly edge-on viewing geometry. We present high resolution ($\sim$0.2")
ALMA observations of the 0.87 and 1.3~mm dust continuum emission from the disk
around AA Tau. These data reveal an evenly spaced three-ringed emission
structure, with distinct peaks at 0.34", 0.66", and 0.99", all viewed at a
modest inclination of 59.1$^{\circ}\pm$0.3$^{\circ}$ (decidedly not edge-on).
In addition to this ringed substructure, we find non-axisymmetric features
including a `bridge' of emission that connects opposite sides of the innermost
ring. We speculate on the nature of this `bridge' in light of accompanying
observations of HCO$^+$ and $^{13}$CO (J=3--2) line emission. The HCO$^+$
emission is bright interior to the innermost dust ring, with a projected
velocity field that appears rotated with respect to the resolved disk geometry,
indicating the presence of a warp or inward radial flow. We suggest that the
continuum bridge and HCO$^+$ line kinematics could originate from gap-crossing
accretion streams, which may be responsible for the long-duration dimming of
optical light from AA Tau.
| 0 | 1 | 0 | 0 | 0 | 0 |
Verifying the Medical Specialty from User Profile of Online Community for Health-Related Advices | The paper describes the verifying methods of medical specialty from user
profile of online community for health-related advices. To avoid critical
situations with the proliferation of unverified and inaccurate information in
medical online community, it is necessary to develop a comprehensive software
solution for verifying the user medical specialty of online community for
health-related advices. The algorithm for forming the information profile of a
medical online community user is designed. The scheme systems of formation of
indicators of user specialization in the profession based on a training sample
is presented. The method of forming the user information profile of online
community for healthrelated advices by computer-linguistic analysis of the
information content is suggested. The system of indicators based on a training
sample of users in medical online communities is formed. The matrix of medical
specialties indicators and method of determining weight coefficients these
indicators is investigated. The proposed method of verifying the medical
specialty from user profile is tested in online medical community.
| 1 | 0 | 0 | 0 | 0 | 0 |
Stress-Based Navigation for Microscopic Robots in Viscous Fluids | Objects moving in fluids experience patterns of stress on their surfaces
determined by their motion and the geometry of nearby boundaries. Fish and
underwater robots can use these patterns for navigation. This paper extends
this stress-based navigation to microscopic robots in tiny vessels, where
robots can exploit the physics of fluids at low Reynolds number. This applies,
for instance, in vessels with sizes and flow speeds comparable to those of
capillaries in biological tissues. We describe how a robot can use simple
computations to estimate its motion, orientation and distance to nearby vessel
walls from fluid-induced stresses on its surface. Numerically evaluating these
estimates for a variety of vessel sizes and robot positions shows they are most
accurate when robots are close to vessel walls.
| 1 | 0 | 0 | 0 | 0 | 0 |
The $(-β)$-shift and associated Zeta Function | Given a real number $ \beta > 1$, we study the associated $ (-\beta)$-shift
introduced by S. Ito and T. Sadahiro. We compares some aspects of the
$(-\beta)$-shift to the $\beta$-shift. When the expansion in base $ -\beta $ of
$ -\frac{\beta}{\beta+1} $ is periodic with odd period or when $ \beta $ is
strictly less than the golden ratio, the $ (-\beta)$-shift, as defined by S.
Ito and T. Sadahiro cannot be coded because its language is not transitive.
This intransitivity of words explains the existence of gaps in the interval. We
observe that an intransitive word appears in the $(-\beta)$-expansion of a real
number taken in the gap. Furthermore, we determine the Zeta function
$\zeta_{-\beta}$ of the $(-\beta)$-transformation and the associated
lap-counting function $L_{T_{-\beta}}$. These two functions are related by $
\zeta_{-\beta}=(1-z^2)L_{T_{-\beta}}$. We observe some similarities with the
zeta function of the $\beta$-transformation. The function $\zeta_{-\beta}$ is
meromorphic in the unit disk, is holomorphic in the open disk $ \{z |z| <
\frac{1}{\beta} \}$, has a simple pole at $ \frac{1}{\beta}$ and no other
singularities $ z $ such that $\|z| = \frac{1}{\beta}$. We also note an
influence of gaps ($\beta$ less than the golden ratio) on the zeta function. In
factors of the denominator of $\zeta_{-\beta}$, the coefficients count the
words generating gaps.
| 0 | 0 | 1 | 0 | 0 | 0 |
Configurational forces in electronic structure calculations using Kohn-Sham density functional theory | We derive the expressions for configurational forces in Kohn-Sham density
functional theory, which correspond to the generalized variational force
computed as the derivative of the Kohn-Sham energy functional with respect to
the position of a material point $\textbf{x}$. These configurational forces
that result from the inner variations of the Kohn-Sham energy functional
provide a unified framework to compute atomic forces as well as stress tensor
for geometry optimization. Importantly, owing to the variational nature of the
formulation, these configurational forces inherently account for the Pulay
corrections. The formulation presented in this work treats both pseudopotential
and all-electron calculations in single framework, and employs a local
variational real-space formulation of Kohn-Sham DFT expressed in terms of the
non-orthogonal wavefunctions that is amenable to reduced-order scaling
techniques. We demonstrate the accuracy and performance of the proposed
configurational force approach on benchmark all-electron and pseudopotential
calculations conducted using higher-order finite-element discretization. To
this end, we examine the rates of convergence of the finite-element
discretization in the computed forces and stresses for various materials
systems, and, further, verify the accuracy from finite-differencing the energy.
Wherever applicable, we also compare the forces and stresses with those
obtained from Kohn-Sham DFT calculations employing plane-wave basis
(pseudopotential calculations) and Gaussian basis (all-electron calculations).
Finally, we verify the accuracy of the forces on large materials systems
involving a metallic aluminum nanocluster containing 666 atoms and an alkane
chain containing 902 atoms, where the Kohn-Sham electronic ground state is
computed using a reduced-order scaling subspace projection technique (P.
Motamarri and V. Gavini, Phys. Rev. B 90, 115127).
| 0 | 1 | 0 | 0 | 0 | 0 |
Utilizing Bluetooth and Adaptive Signal Control Data for Urban Arterials Safety Analysis | Real-time safety analysis has become a hot research topic as it can more
accurately reveal the relationships between real-time traffic characteristics
and crash occurrence, and these results could be applied to improve active
traffic management systems and enhance safety performance. Most of the previous
studies have been applied to freeways and seldom to arterials. This study
attempts to examine the relationship between crash occurrence and real-time
traffic and weather characteristics based on four urban arterials in Central
Florida. Considering the substantial difference between the interrupted urban
arterials and the access controlled freeways, the adaptive signal phasing data
was introduced in addition to the traditional traffic data. Bayesian
conditional logistic models were developed by incorporating the Bluetooth,
adaptive signal control, and weather data, which were extracted for a period of
20 minutes (four 5-minute intervals) before the time of crash occurrence. Model
comparison results indicated that the model based on 5-10 minute interval
dataset performs the best. It revealed that the average speed, upstream
left-turn volume, downstream green ratio, and rainy indicator were found to
have significant effects on crash occurrence. Furthermore, both Bayesian random
parameters logistic and Bayesian random parameters conditional logistic models
were developed to compare with the Bayesian conditional logistic model, and the
Bayesian random parameters conditional logistic model was found to have the
best model performance in terms of the AUC and DIC values. These results are
important in real-time safety applications in the context of Integrated Active
Traffic Management.
| 0 | 0 | 0 | 1 | 0 | 0 |
A Nernst current from the conformal anomaly in Dirac and Weyl semimetals | We show that a conformal anomaly in Weyl/Dirac semimetals generates a bulk
electric current perpendicular to a temperature gradient and the direction of a
background magnetic field. The associated conductivity of this novel
contribution to the Nernst effect is fixed by a beta function associated with
the electric charge renormalization in the material.
| 0 | 1 | 0 | 0 | 0 | 0 |
Employing both Gender and Emotion Cues to Enhance Speaker Identification Performance in Emotional Talking Environments | Speaker recognition performance in emotional talking environments is not as
high as it is in neutral talking environments. This work focuses on proposing,
implementing, and evaluating a new approach to enhance the performance in
emotional talking environments. The new proposed approach is based on
identifying the unknown speaker using both his/her gender and emotion cues.
Both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models
(SPHMMs) have been used as classifiers in this work. This approach has been
tested on our collected emotional speech database which is composed of six
emotions. The results of this work show that speaker identification performance
based on using both gender and emotion cues is higher than that based on using
gender cues only, emotion cues only, and neither gender nor emotion cues by
7.22%, 4.45%, and 19.56%, respectively. This work also shows that the optimum
speaker identification performance takes place when the classifiers are
completely biased towards suprasegmental models and no impact of acoustic
models in the emotional talking environments. The achieved average speaker
identification performance based on the new proposed approach falls within
2.35% of that obtained in subjective evaluation by human judges.
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Thermal Pressure in Diffuse H2 Gas Measured by Herschel [C II] Emission and FUSE UV H2 Absorption | UV absorption studies with FUSE have observed H2 molecular gas in translucent
and diffuse clouds. Observations of the 158 micron [C II] fine structure line
with Herschel also trace the same H2 molecular gas in emission. We present [C
II] observations along 27 lines of sight (LOSs) towards target stars of which
25 have FUSE H2 UV absorption. We detect [C II] emission features in all but
one target LOS. For three Target LOSs, which are close to the Galactic plane,
we also present position-velocity maps of [C II] emission observed by HIFI in
on-the-fly spectral line mapping. We use the velocity resolved [C II] spectra
towards the target LOSs observed by FUSE to identify C II] velocity components
associated with the H2 clouds. We analyze the observed velocity integrated [C
II] spectral line intensities in terms of the densities and thermal pressures
in the H2 gas using the H2 column densities and temperatures measured by the UV
absorption data. We present the H2 gas densities and thermal pressures for 26
target LOSs and from the [C II] intensities derive a mean thermal pressure in
the range 6100 to 7700 K cm^-3 in diffuse H2 clouds. We discuss the thermal
pressures and densities towards 14 targets, comparing them to results obtained
using the UV absorption data for two other tracers CI and CO.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Dictionary Approach to Identifying Transient RFI | As radio telescopes become more sensitive, the damaging effects of radio
frequency interference (RFI) become more apparent. Near radio telescope arrays,
RFI sources are often easily removed or replaced; the challenge lies in
identifying them. Transient (impulsive) RFI is particularly difficult to
identify. We propose a novel dictionary-based approach to transient RFI
identification. RFI events are treated as sequences of sub-events, drawn from
particular labelled classes. We demonstrate an automated method of extracting
and labelling sub-events using a dataset of transient RFI. A dictionary of
labels may be used in conjunction with hidden Markov models to identify the
sources of RFI events reliably. We attain improved classification accuracy over
traditional approaches such as SVMs or a naïve kNN classifier. Finally, we
investigate why transient RFI is difficult to classify. We show that cluster
separation in the principal components domain is influenced by the mains supply
phase for certain sources.
| 0 | 1 | 0 | 0 | 0 | 0 |
Eternal inflation and the quantum birth of cosmic structure | We consider the eternal inflation scenario of the slow-roll/chaotic type with
the additional element of an objective collapse of the wave function. The
incorporation of this new agent to the traditional inflationary setting might
represent a possible solution to the quantum measurement problem during
inflation, a subject that has not reached a consensus among the community.
Specifically, it could provide an explanation for the generation of the
primordial anisotropies and inhomogeneities, starting from a perfectly
symmetric background and invoking symmetric dynamics. We adopt the continuous
spontaneous localization model, in the context of inflation, as the dynamical
reduction mechanism that generates the primordial inhomogeneities. Furthermore,
when enforcing the objective reduction mechanism, the condition for eternal
inflation can be bypassed. In particular, the collapse mechanism incites the
wave function, corresponding to the inflaton, to localize itself around the
zero mode of the field. Then the zero mode will evolve essentially unperturbed,
driving inflation to an end in any region of the Universe where inflation
occurred. Also, our approach achieves a primordial spectrum with an amplitude
and shape consistent with the one that best fits the observational data.
| 0 | 1 | 0 | 0 | 0 | 0 |
Exact time dependence of causal correlations and nonequilibrium density matrices in holographic systems | We present the first exact calculations of the time dependence of causal
correlations in driven nonequilibrium states in (2+1)-dimensional systems using
holography. Comparing exact results with those obtained from simple prototype
geometries that are parametrized only by a time dependent temperature, we find
that the universal slowly varying features are controlled just by the pump
duration and the initial and final temperatures only. We provide numerical
evidence that the locations of the event and apparent horizons in the dual
geometries can be deduced from the nonequilibrium causal correlations without
any prior knowledge of the dual gravity theory.
| 0 | 1 | 0 | 0 | 0 | 0 |
Discrete-time construction of nonequilibrium path integrals on the Kostantinov-Perel' time contour | Rigorous nonequilibrium actions for the many-body problem are usually derived
by means of path integrals combined with a discrete temporal mesh on the
Schwinger-Keldysh time contour. The latter suffers from a fundamental
limitation: the initial state on this contour cannot be arbitrary, but
necessarily needs to be described by a non-interacting density matrix, while
interactions are switched on adiabatically. The Kostantinov-Perel' contour
overcomes these and other limitations, allowing generic initial-state
preparations. In this Article, we apply the technique of the discrete temporal
mesh to rigorously build the nonequilibrium path integral on the
Kostantinov-Perel' time contour.
| 0 | 1 | 0 | 0 | 0 | 0 |
Estimation of Component Reliability in Coherent Systems | The first step in statistical reliability studies of coherent systems is the
estimation of the reliability of each system component. For the cases of
parallel and series systems the literature is abundant. It seems that the
present paper is the first that presents the general case of component
inferences in coherent systems. The failure time model considered here is the
three-parameter Weibull distribution. Furthermore, neither independence nor
identically distributed failure times are required restrictions. The proposed
model is general in the sense that it can be used for any coherent system, from
the simplest to the more complex structures. It can be considered for all kinds
of censored data; including interval-censored data. An important property
obtained for the Weibull model is the fact that the posterior distributions are
proper, even for non-informative priors. Using several simulations, the
excellent performance of the model is illustrated. As a real example, boys
first use of marijuana is considered to show the efficiency of the solution
even when censored data occurs.
| 0 | 0 | 0 | 1 | 0 | 0 |
Cell-Probe Lower Bounds from Online Communication Complexity | In this work, we introduce an online model for communication complexity.
Analogous to how online algorithms receive their input piece-by-piece, our
model presents one of the players, Bob, his input piece-by-piece, and has the
players Alice and Bob cooperate to compute a result each time before the next
piece is revealed to Bob. This model has a closer and more natural
correspondence to dynamic data structures than classic communication models do,
and hence presents a new perspective on data structures.
We first present a tight lower bound for the online set intersection problem
in the online communication model, demonstrating a general approach for proving
online communication lower bounds. The online communication model prevents a
batching trick that classic communication complexity allows, and yields a
stronger lower bound. We then apply the online communication model to prove
data structure lower bounds for two dynamic data structure problems: the Group
Range problem and the Dynamic Connectivity problem for forests. Both of the
problems admit a worst case $O(\log n)$-time data structure. Using online
communication complexity, we prove a tight cell-probe lower bound for each:
spending $o(\log n)$ (even amortized) time per operation results in at best an
$\exp(-\delta^2 n)$ probability of correctly answering a
$(1/2+\delta)$-fraction of the $n$ queries.
| 1 | 0 | 0 | 0 | 0 | 0 |
On Machine Learning and Structure for Mobile Robots | Due to recent advances - compute, data, models - the role of learning in
autonomous systems has expanded significantly, rendering new applications
possible for the first time. While some of the most significant benefits are
obtained in the perception modules of the software stack, other aspects
continue to rely on known manual procedures based on prior knowledge on
geometry, dynamics, kinematics etc. Nonetheless, learning gains relevance in
these modules when data collection and curation become easier than manual rule
design. Building on this coarse and broad survey of current research, the final
sections aim to provide insights into future potentials and challenges as well
as the necessity of structure in current practical applications.
| 1 | 0 | 0 | 1 | 0 | 0 |
On the degree of incompleteness of an incomplete financial market | In order to find a way of measuring the degree of incompleteness of an
incomplete financial market, the rank of the vector price process of the traded
assets and the dimension of the associated acceptance set are introduced. We
show that they are equal and state a variety of consequences.
| 0 | 0 | 0 | 0 | 0 | 1 |
Sparsity information and regularization in the horseshoe and other shrinkage priors | The horseshoe prior has proven to be a noteworthy alternative for sparse
Bayesian estimation, but has previously suffered from two problems. First,
there has been no systematic way of specifying a prior for the global shrinkage
hyperparameter based on the prior information about the degree of sparsity in
the parameter vector. Second, the horseshoe prior has the undesired property
that there is no possibility of specifying separately information about
sparsity and the amount of regularization for the largest coefficients, which
can be problematic with weakly identified parameters, such as the logistic
regression coefficients in the case of data separation. This paper proposes
solutions to both of these problems. We introduce a concept of effective number
of nonzero parameters, show an intuitive way of formulating the prior for the
global hyperparameter based on the sparsity assumptions, and argue that the
previous default choices are dubious based on their tendency to favor solutions
with more unshrunk parameters than we typically expect a priori. Moreover, we
introduce a generalization to the horseshoe prior, called the regularized
horseshoe, that allows us to specify a minimum level of regularization to the
largest values. We show that the new prior can be considered as the continuous
counterpart of the spike-and-slab prior with a finite slab width, whereas the
original horseshoe resembles the spike-and-slab with an infinitely wide slab.
Numerical experiments on synthetic and real world data illustrate the benefit
of both of these theoretical advances.
| 0 | 0 | 0 | 1 | 0 | 0 |
Learning an internal representation of the end-effector configuration space | Current machine learning techniques proposed to automatically discover a
robot kinematics usually rely on a priori information about the robot's
structure, sensors properties or end-effector position. This paper proposes a
method to estimate a certain aspect of the forward kinematics model with no
such information. An internal representation of the end-effector configuration
is generated from unstructured proprioceptive and exteroceptive data flow under
very limited assumptions. A mapping from the proprioceptive space to this
representational space can then be used to control the robot.
| 1 | 0 | 0 | 0 | 0 | 0 |
Correlation plots of the Siberian radioheliograph | The Siberian Solar Radio Telescope is now being upgraded. The upgrading is
aimed at providing the aperture synthesis imaging in the 4-8 GHz frequency
range, instead of the single-frequency direct imaging due to the Earth
rotation. The first phase of the upgrading is a 48-antenna array - the Siberian
Radioheliograph. One type of radioheliograph data represents correlation plots.
In evaluating the covariance of two-level signals, these plots are sums of
complex correlations, obtained for different antenna pairs. Bearing in mind
that correlation of signals from an antenna pair is related to a spatial
frequency, we can say that each value of the plot is an integral over a spatial
spectrum. Limits of the integration are defined by the task. Only high spatial
frequencies are integrated to obtain dynamics of compact sources. The whole
spectrum is integrated to reach maximum sensitivity. We show that the
covariance of two-level variables up to Van Vleck correction is a correlation
coefficient of these variables.
| 0 | 1 | 0 | 0 | 0 | 0 |
Anomalous Thermal Expansion, Negative Linear Compressibility and High-Pressure Phase Transition in ZnAu2(CN)4: Neutron Inelastic Scattering and Lattice Dynamics Studies | We present temperature dependent inelastic neutron scattering measurments,
accompanied byab-initio calculations of phonon spectra and elastic properties
as a function of pressure to understand anharmonicity of phonons and to study
the mechanism of negative thermal expansion and negative linear compressibility
behaviour of ZnAu2(CN)4. The mechanism is identified in terms of specific
anharmonic modes that involve bending of the Zn(CN)4-Au- Zn(CN)4 linkage. The
high-pressure phase transition at about 2 GPa is also investigated and found to
be related to softening of a phonon mode at the L-point at the Brillouin zone
boundary and its coupling with a zone-centre phonon and an M-point phonon in
the ambient pressure phase. Although the phase transition is primarily driven
by a L-point soft phonon mode, which usually leads to a second order transition
with a 2 x 2 x 2 supercell, in the present case the structure is close to an
elastic instability that leads to a weakly first order transition.
| 0 | 1 | 0 | 0 | 0 | 0 |
Comparing Computing Platforms for Deep Learning on a Humanoid Robot | The goal of this study is to test two different computing platforms with
respect to their suitability for running deep networks as part of a humanoid
robot software system. One of the platforms is the CPU-centered Intel NUC7i7BNH
and the other is a NVIDIA Jetson TX2 system that puts more emphasis on GPU
processing. The experiments addressed a number of benchmarking tasks including
pedestrian detection using deep neural networks. Some of the results were
unexpected but demonstrate that platforms exhibit both advantages and
disadvantages when taking computational performance and electrical power
requirements of such a system into account.
| 1 | 0 | 0 | 0 | 0 | 0 |
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy | Super-resolution fluorescence microscopy, with a resolution beyond the
diffraction limit of light, has become an indispensable tool to directly
visualize biological structures in living cells at a nanometer-scale
resolution. Despite advances in high-density super-resolution fluorescent
techniques, existing methods still have bottlenecks, including extremely long
execution time, artificial thinning and thickening of structures, and lack of
ability to capture latent structures. Here we propose a novel deep learning
guided Bayesian inference approach, DLBI, for the time-series analysis of
high-density fluorescent images. Our method combines the strength of deep
learning and statistical inference, where deep learning captures the underlying
distribution of the fluorophores that are consistent with the observed
time-series fluorescent images by exploring local features and correlation
along time-axis, and statistical inference further refines the ultrastructure
extracted by deep learning and endues physical meaning to the final image.
Comprehensive experimental results on both real and simulated datasets
demonstrate that our method provides more accurate and realistic local patch
and large-field reconstruction than the state-of-the-art method, the 3B
analysis, while our method is more than two orders of magnitude faster. The
main program is available at this https URL
| 0 | 0 | 0 | 1 | 0 | 0 |
A Universal Ordinary Differential Equation | An astonishing fact was established by Lee A. Rubel (1981): there exists a
fixed non-trivial fourth-order polynomial differential algebraic equation (DAE)
such that for any positive continuous function $\varphi$ on the reals, and for
any positive continuous function $\epsilon(t)$, it has a $\mathcal{C}^\infty$
solution with $| y(t) - \varphi(t) | < \epsilon(t)$ for all $t$. Lee A. Rubel
provided an explicit example of such a polynomial DAE. Other examples of
universal DAE have later been proposed by other authors. However, Rubel's DAE
\emph{never} has a unique solution, even with a finite number of conditions of
the form $y^{(k_i)}(a_i)=b_i$.
The question whether one can require the solution that approximates $\varphi$
to be the unique solution for a given initial data is a well known open problem
[Rubel 1981, page 2], [Boshernitzan 1986, Conjecture 6.2]. In this article, we
solve it and show that Rubel's statement holds for polynomial ordinary
differential equations (ODEs), and since polynomial ODEs have a unique solution
given an initial data, this positively answers Rubel's open problem. More
precisely, we show that there exists a \textbf{fixed} polynomial ODE such that
for any $\varphi$ and $\epsilon(t)$ there exists some initial condition that
yields a solution that is $\epsilon$-close to $\varphi$ at all times.
In particular, the solution to the ODE is necessarily analytic, and we show
that the initial condition is computable from the target function and error
function.
| 1 | 0 | 1 | 0 | 0 | 0 |
Sun/Moon photometer for the Cherenkov Telescope Array - first results | Determination of the energy and flux of the gamma photons by Imaging
Atmospheric Cherenkov Technique is strongly dependent on optical properties of
the atmosphere. Therefore, atmospheric monitoring during the future
observations of the Cherenkov Telescope Array (CTA) as well as anticipated
long-term monitoring in order to characterize overal properties and annual
variation of atmospheric conditions are very important. Several instruments are
already installed at the CTA sites in order to monitor atmospheric conditions
on long-term. One of them is a Sun/Moon photometer CE318-T, installed at the
Southern CTA site. Since the photometer is installed at a place with very
stable atmospheric conditions, it can be also used for characterization of its
performance and testing of new methods of aerosol optical depth (AOD)
retrieval, cloud-screening and calibration. In this work, we describe our
calibration method for nocturnal measurements and the modification of
cloud-screening for purposes of nocturnal AOD retrieval. We applied these
methods on two months of observations and present the distribution of AODs in
four photometric passbands together with their uncertainties.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Connectome Based Hexagonal Lattice Convolutional Network Model of the Drosophila Visual System | What can we learn from a connectome? We constructed a simplified model of the
first two stages of the fly visual system, the lamina and medulla. The
resulting hexagonal lattice convolutional network was trained using
backpropagation through time to perform object tracking in natural scene
videos. Networks initialized with weights from connectome reconstructions
automatically discovered well-known orientation and direction selectivity
properties in T4 neurons and their inputs, while networks initialized at random
did not. Our work is the first demonstration, that knowledge of the connectome
can enable in silico predictions of the functional properties of individual
neurons in a circuit, leading to an understanding of circuit function from
structure alone.
| 0 | 0 | 0 | 0 | 1 | 0 |
Improved Kernels and Algorithms for Claw and Diamond Free Edge Deletion Based on Refined Observations | In the {claw, diamond}-free edge deletion problem, we are given a graph $G$
and an integer $k>0$, the question is whether there are at most $k$ edges whose
deletion results in a graph without claws and diamonds as induced graphs. Based
on some refined observations, we propose a kernel of $O(k^3)$ vertices and
$O(k^4)$ edges, significantly improving the previous kernel of $O(k^{12})$
vertices and $O(k^{24})$ edges. In addition, we derive an $O^*(3.792^k)$-time
algorithm for the {claw, diamond}-free edge deletion problem.
| 1 | 0 | 0 | 0 | 0 | 0 |
Distributed Edge Caching Scheme Considering the Tradeoff Between the Diversity and Redundancy of Cached Content | Caching popular contents at the edge of cellular networks has been proposed
to reduce the load, and hence the cost of backhaul links. It is significant to
decide which files should be cached and where to cache them. In this paper, we
propose a distributed caching scheme considering the tradeoff between the
diversity and redundancy of base stations' cached contents. Whether it is
better to cache the same or different contents in different base stations? To
find out this, we formulate an optimal redundancy caching problem. Our goal is
to minimize the total transmission cost of the network, including cost within
the radio access network (RAN) and cost incurred by transmission to the core
network via backhaul links. The optimal redundancy ratio under given system
configuration is obtained with adapted particle swarm optimization (PSO)
algorithm. We analyze the impact of important system parameters through
Monte-Carlo simulation. Results show that the optimal redundancy ratio is
mainly influenced by two parameters, which are the backhaul to RAN unit cost
ratio and the steepness of file popularity distribution. The total cost can be
reduced by up to 54% at given unit cost ratio of backhaul to RAN when the
optimal redundancy ratio is selected. Under typical file request pattern, the
reduction amount can be up to 57%.
| 1 | 0 | 0 | 0 | 0 | 0 |
Orbital Graphs | We introduce orbital graphs and discuss some of their basic properties. Then
we focus on their usefulness for search algorithms for permutation groups,
including finding the intersection of groups and the stabilizer of sets in a
group.
| 1 | 0 | 1 | 0 | 0 | 0 |
On the existence of harmonic $\mathbf{Z}_2$ spinors | We prove the existence of singular harmonic ${\bf Z}_2$ spinors on
$3$-manifolds with $b_1 > 1$. The proof relies on a wall-crossing formula for
solutions to the Seiberg-Witten equation with two spinors. The existence of
singular harmonic ${\bf Z}_2$ spinors and the shape of our wall-crossing
formula shed new light on recent observations made by Joyce regarding Donaldson
and Segal's proposal for counting $G_2$-instantons.
| 0 | 0 | 1 | 0 | 0 | 0 |
Penalty-based spatial smoothing and outlier detection for childhood obesity surveillance from electronic health records | Childhood obesity is associated with increased morbidity and mortality in
adulthood, leading to substantial healthcare cost. There is an urgent need to
promote early prevention and develop an accompanying surveillance system. In
this paper, we make use of electronic health records (EHRs) and construct a
penalized multi-level generalized linear model. The model provides regular
trend and outlier information simultaneously, both of which may be useful to
raise public awareness and facilitate targeted intervention. Our strategy is to
decompose the regional contribution in the model into smooth and sparse
signals, where the characteristics of the signals are encouraged by the
combination of fusion and sparse penalties imposed on the likelihood function.
In addition, we introduce a weighting scheme to account for the missingness and
potential non-representativeness arising from the EHRs data. We propose a novel
alternating minimization algorithm, which is computationally efficient, easy to
implement, and guarantees convergence. Simulation shows that the proposed
method has a superior performance compared with traditional counterparts.
Finally, we apply our method to the University of Wisconsin Population Health
Information Exchange database.
| 0 | 0 | 0 | 1 | 0 | 0 |
Some simple rules for estimating reproduction numbers in the presence of reservoir exposure or imported cases | The basic reproduction number ($R_0$) is a threshold parameter for disease
extinction or survival in isolated populations. However no human population is
fully isolated from other human or animal populations. We use compartmental
models to derive simple rules for the basic reproduction number for populations
with local person-to-person transmission and exposure from some other source:
either a reservoir exposure or imported cases. We introduce the idea of a
reservoir-driven or importation-driven disease: diseases that would become
extinct in the population of interest without reservoir exposure or imported
cases (since $R_0<1$), but nevertheless may be sufficiently transmissible that
many or most infections are acquired from humans in that population. We show
that in the simplest case, $R_0<1$ if and only if the proportion of infections
acquired from the external source exceeds the disease prevalence and explore
how population heterogeneity and the interactions of multiple strains affect
this rule. We apply these rules in two cases studies of Clostridium difficile
infection and colonisation: C. difficile in the hospital setting accounting for
imported cases, and C. difficile in the general human population accounting for
exposure to animal reservoirs. We demonstrate that even the hospital-adapted,
highly-transmissible NAP1/RT027 strain of C. difficile had a reproduction
number <1 in a landmark study of hospitalised patients and therefore was
sustained by colonised and infected admissions to the study hospital. We argue
that C. difficile should be considered reservoir-driven if as little as 13.0%
of transmission can be attributed to animal reservoirs.
| 0 | 0 | 0 | 0 | 1 | 0 |
Self-Assembled Monolayer Piezoelectrics: Electric-Field Driven Conformational Changes | We demonstrate that an applied electric field causes piezoelectric distortion
across single molecular monolayers of oligopeptides. We deposited
self-assembled monolayers ~1.5 nm high onto smooth gold surfaces. These
monolayers exhibit strong piezoelectric response that varies linearly with
applied bias (1-3V), measured using piezoresponse force microscopy (PFM). The
response is markedly greater than control experiments with rigid alkanethiols
and correlates with surface spectroscopy and theoretical predictions of
conformational change from applied electric fields. Unlike existing
piezoelectric oxides, our peptide monolayers are intrinsically flexible, easily
fabricated, aligned and patterned without poling.
| 0 | 1 | 0 | 0 | 0 | 0 |
Matrix divisors on Riemann surfaces and Lax operator algebras | Matrix divisors are introduced in the work by A.Weil (1938) which is
considered as a starting point of the theory of holomorphic vector bundles on
Riemann surfaces. In this theory matrix divisors play the role similar to the
role of usual divisors in the theory of line bundles. Moreover, they provide
explicit coordinates (Tyurin parameters) in an open subset of the moduli space
of stable vector bundles. These coordinates turned out to be helpful in
integration of soliton equations.
We would like to gain attention to one more relationship between matrix
divisors of vector G-bundles (where G is a complex semi-simple Lie group) and
the theory of integrable systems, namely to the relationship with Lax operator
algebras. The result we obtain can be briefly formulated as follows: the moduli
space of matrix divisors with certain discrete invariants and fixed support is
a homogeneous space. Its tangent space at the unit is naturally isomorphic to
the quotient space of M-operators by L-operators, both spaces essentially
defined by the same invariants (the result goes back to Krichever, 2001). We
give one more description of the same space in terms of root systems.
| 0 | 0 | 1 | 0 | 0 | 0 |
Observation of pseudogap in MgB2 | Pseudogap phase in superconductors continues to be an outstanding puzzle that
differentiates unconventional superconductors from the conventional ones
(BCS-superconductors). Employing high resolution photoemission spectroscopy on
a highly dense conventional superconductor, MgB2, we discover an interesting
scenario. While the spectral evolution close to the Fermi energy is
commensurate to BCS descriptions as expected, the spectra in the wider energy
range reveal emergence of a pseudogap much above the superconducting transition
temperature indicating apparent departure from the BCS scenario. The energy
scale of the pseudogap is comparable to the energy of E2g phonon mode
responsible for superconductivity in MgB2 and the pseudogap can be attributed
to the effect of electron-phonon coupling on the electronic structure. These
results reveal a scenario of the emergence of the superconducting gap within an
electron-phonon coupling induced pseudogap.
| 0 | 1 | 0 | 0 | 0 | 0 |
Critical fields and fluctuations determined from specific heat and magnetoresistance in the same nanogram SmFeAs(O,F) single crystal | Through a direct comparison of specific heat and magneto-resistance we
critically asses the nature of superconducting fluctuations in the same
nano-gram crystal of SmFeAs(O, F). We show that although the superconducting
fluctuation contribution to conductivity scales well within the 2D-LLL scheme
its predictions contrast the inherently 3D nature of SmFeAs(O, F) in the
vicinity T_{c}. Furthermore the transition seen in specific heat cannot be
satisfactory described either by the LLL or the XY scaling. Additionally we
have validated, through comparing Hc2 values obtained from the entropy
conservation construction (Hab=-19.5 T/K and Hab=-2.9 T/K), the analysis of
fluctuation contribution to conductivity as a reasonable method for estimating
the Hc2 slope.
| 0 | 1 | 0 | 0 | 0 | 0 |
Determining the vortex tilt relative to a superconductor surface | It is of interest to determine the exit angle of a vortex from a
superconducting surface, since this affects the intervortex interactions and
their consequences. Two ways to determine this angle are to image the vortex
magnetic fields above the surface, or the vortex core shape at the surface. In
this work we evaluate the field h(x, y, z) above a flat superconducting surface
x, y and the currents J(x,y) at that surface for a straight vortex tilted
relative to the normal to the surface, for both the isotropic and anisotropic
cases. In principle, these results can be used to determine the vortex exit
tilt angle from analyses of magnetic field imaging or density of states data.
| 0 | 1 | 0 | 0 | 0 | 0 |
Onset of nonlinear structures due to eigenmode destabilization in tokamak plasmas | A general methodology is proposed to differentiate the likelihood of
energetic-particle-driven instabilities to produce frequency chirping or
fixed-frequency oscillations. The method employs numerically calculated
eigenstructures and multiple resonance surfaces of a given mode in the presence
of energetic ion drag and stochasticity (due to collisions and
micro-turbulence). Toroidicity-induced, reversed-shear and beta-induced
Alfven-acoustic eigenmodes are used as examples. Waves measured in experiments
are characterized and compatibility is found between the proposed criterion
predictions and the experimental observation or lack of observation of chirping
behavior of Alfvenic modes in different tokamaks. It is found that the
stochastic diffusion due to micro-turbulence can be the dominant energetic
particle detuning mechanism near the resonances in many plasma experiments, and
its strength is the key as to whether chirping solutions are likely to arise.
The proposed criterion constitutes a useful predictive tool in assessing
whether the nature of the transport for fast ion losses in fusion devices will
be dominated by convective or diffusive processes.
| 0 | 1 | 0 | 0 | 0 | 0 |
Self-consistent semi-analytic models of the first stars | We have developed a semi-analytic framework to model the large-scale
evolution of the first Population III (Pop III) stars and the transition to
metal-enriched star formation. Our model follows dark matter halos from
cosmological N-body simulations, utilizing their individual merger histories
and three-dimensional positions, and applies physically motivated prescriptions
for star formation and feedback from Lyman-Werner (LW) radiation, hydrogen
ionizing radiation, and external metal enrichment due to supernovae winds. This
method is intended to complement analytic studies, which do not include
clustering or individual merger histories, and hydrodynamical cosmological
simulations, which include detailed physics, but are computationally expensive
and have limited dynamic range. Utilizing this technique, we compute the
cumulative Pop III and metal-enriched star formation rate density (SFRD) as a
function of redshift at $z \geq 20$. We find that varying the model parameters
leads to significant qualitative changes in the global star formation history.
The Pop III star formation efficiency and the delay time between Pop III and
subsequent metal-enriched star formation are found to have the largest impact.
The effect of clustering (i.e. including the three-dimensional positions of
individual halos) on various feedback mechanisms is also investigated. The
impact of clustering on LW and ionization feedback is found to be relatively
mild in our fiducial model, but can be larger if external metal enrichment can
promote metal-enriched star formation over large distances.
| 0 | 1 | 0 | 0 | 0 | 0 |
SoK: Taxonomy and Challenges of Out-of-Band Signal Injection Attacks and Defenses | Research on how hardware imperfections impact security has primarily focused
on side-channel leakage mechanisms produced by power consumption,
electromagnetic emanations, acoustic vibrations, and optical emissions.
However, with the proliferation of sensors in security-critical devices, the
impact of attacks on sensor-to-microcontroller and microcontroller-to-actuator
interfaces using the same channels is starting to become more than an academic
curiosity. These out-of-band signal injection attacks target connections which
transform physical quantities to analog properties and fundamentally cannot be
authenticated, posing previously unexplored security risks. This paper contains
the first survey of such out-of-band signal injection attacks, with a focus on
unifying their terminology, and identifying commonalities in their causes and
effects. The taxonomy presented contains a chronological, evolutionary, and
thematic view of out-of-band signal injection attacks which highlights the
cross-influences that exist and underscores the need for a common language
irrespective of the method of injection. By placing attack and defense
mechanisms in the wider context of their dual counterparts of side-channel
leakage and electromagnetic interference, our paper identifies common threads
and gaps that can help guide and inform future research. Overall, the
ever-increasing reliance on sensors embedded in everyday commodity devices
necessitates that a stronger focus be placed on improving the security of such
systems against out-of-band signal injection attacks.
| 1 | 0 | 0 | 0 | 0 | 0 |
Identifying Harm Events in Clinical Care through Medical Narratives | Preventable medical errors are estimated to be among the leading causes of
injury and death in the United States. To prevent such errors, healthcare
systems have implemented patient safety and incident reporting systems. These
systems enable clinicians to report unsafe conditions and cases where patients
have been harmed due to errors in medical care. These reports are narratives in
natural language and while they provide detailed information about the
situation, it is non-trivial to perform large scale analysis for identifying
common causes of errors and harm to the patients. In this work, we present a
method based on attentive convolutional and recurrent networks for identifying
harm events in patient care and categorize the harm based on its severity
level. We demonstrate that our methods can significantly improve the
performance over existing methods in identifying harm in clinical care.
| 1 | 0 | 0 | 0 | 0 | 0 |
Deep Self-Paced Learning for Person Re-Identification | Person re-identification (Re-ID) usually suffers from noisy samples with
background clutter and mutual occlusion, which makes it extremely difficult to
distinguish different individuals across the disjoint camera views. In this
paper, we propose a novel deep self-paced learning (DSPL) algorithm to
alleviate this problem, in which we apply a self-paced constraint and symmetric
regularization to help the relative distance metric training the deep neural
network, so as to learn the stable and discriminative features for person
Re-ID. Firstly, we propose a soft polynomial regularizer term which can derive
the adaptive weights to samples based on both the training loss and model age.
As a result, the high-confidence fidelity samples will be emphasized and the
low-confidence noisy samples will be suppressed at early stage of the whole
training process. Such a learning regime is naturally implemented under a
self-paced learning (SPL) framework, in which samples weights are adaptively
updated based on both model age and sample loss using an alternative
optimization method. Secondly, we introduce a symmetric regularizer term to
revise the asymmetric gradient back-propagation derived by the relative
distance metric, so as to simultaneously minimize the intra-class distance and
maximize the inter-class distance in each triplet unit. Finally, we build a
part-based deep neural network, in which the features of different body parts
are first discriminately learned in the lower convolutional layers and then
fused in the higher fully connected layers. Experiments on several benchmark
datasets have demonstrated the superior performance of our method as compared
with the state-of-the-art approaches.
| 1 | 0 | 0 | 0 | 0 | 0 |
Moments and Cumulants of The Two-Stage Mann-Whitney Statistic | This paper illustrates how to calculate the moments and cumulants of the
two-stage Mann-Whitney statistic. These results may be used to calculate the
asymptotic critical values of the two-stage Mann-Whitney test. In this paper, a
large amount of deductions will be showed.
| 0 | 0 | 0 | 1 | 0 | 0 |
OGLE Cepheids and RR Lyrae Stars in the Milky Way | We present new large samples of Galactic Cepheids and RR Lyrae stars from the
OGLE Galaxy Variability Survey.
| 0 | 1 | 0 | 0 | 0 | 0 |
Closed-form approximations in derivatives pricing: The Kristensen-Mele approach | Kristensen and Mele (2011) developed a new approach to obtain closed-form
approximations to continuous-time derivatives pricing models. The approach uses
a power series expansion of the pricing bias between an intractable model and
some known auxiliary model. Since the resulting approximation formula has
closed-form it is straightforward to obtain approximations of greeks. In this
thesis I will introduce Kristensen and Mele's methods and apply it to a variety
of stochastic volatility models of European style options as well as a model
for commodity futures. The focus of this thesis is the effect of different
model choices and different model parameter values on the numerical stability
of Kristensen and Mele's approximation.
| 0 | 0 | 0 | 0 | 0 | 1 |
On the magnitude function of domains in Euclidean space | We study Leinster's notion of magnitude for a compact metric space. For a
smooth, compact domain $X\subset \mathbb{R}^{2m-1}$, we find geometric
significance in the function $\mathcal{M}_X(R) = \mathrm{mag}(R\cdot X)$. The
function $\mathcal{M}_X$ extends from the positive half-line to a meromorphic
function in the complex plane. Its poles are generalized scattering resonances.
In the semiclassical limit $R \to \infty$, $\mathcal{M}_X$ admits an asymptotic
expansion. The three leading terms of $\mathcal{M}_X$ at $R=+\infty$ are
proportional to the volume, surface area and integral of the mean curvature. In
particular, for convex $X$ the leading terms are proportional to the intrinsic
volumes, and we obtain an asymptotic variant of the convex magnitude conjecture
by Leinster and Willerton, with corrected coefficients.
| 0 | 0 | 1 | 0 | 0 | 0 |
Minimum edge cuts of distance-regular and strongly regular digraphs | In this paper, we show that the edge connectivity of a distance-regular
digraph $\Gamma$ with valency $k$ is $k$ and for $k>2$, any minimum edge cut of
$\Gamma$ is the set of all edges going into (or coming out of) a single vertex.
Moreover we show that the same result holds for strongly regular digraphs.
These results extend the same known results for undirected case with quite
different proofs.
| 0 | 0 | 1 | 0 | 0 | 0 |
Quality Enhancement by Weighted Rank Aggregation of Crowd Opinion | Expertise of annotators has a major role in crowdsourcing based opinion
aggregation models. In such frameworks, accuracy and biasness of annotators are
occasionally taken as important features and based on them priority of the
annotators are assigned. But instead of relying on a single feature, multiple
features can be considered and separate rankings can be produced to judge the
annotators properly. Finally, the aggregation of those rankings with perfect
weightage can be done with an aim to produce better ground truth prediction.
Here, we propose a novel weighted rank aggregation method and its efficacy with
respect to other existing approaches is shown on artificial dataset. The
effectiveness of weighted rank aggregation to enhance quality prediction is
also shown by applying it on an Amazon Mechanical Turk (AMT) dataset.
| 1 | 0 | 0 | 0 | 0 | 0 |
Heterogeneous elastic plates with in-plane modulation of the target curvature and applications to thin gel sheets | We rigorously derive a Kirchhoff plate theory, via $\Gamma$-convergence, from
a three-di\-men\-sio\-nal model that describes the finite elasticity of an
elastically heterogeneous, thin sheet. The heterogeneity in the elastic
properties of the material results in a spontaneous strain that depends on both
the thickness and the plane variables $x'$. At the same time, the spontaneous
strain is $h$-close to the identity, where $h$ is the small parameter
quantifying the thickness. The 2D Kirchhoff limiting model is constrained to
the set of isometric immersions of the mid-plane of the plate into
$\mathbb{R}^3$, with a corresponding energy that penalizes deviations of the
curvature tensor associated with a deformation from a $x'$-dependent target
curvature tensor. A discussion on the 2D minimizers is provided in the case
where the target curvature tensor is piecewise constant. Finally, we apply the
derived plate theory to the modeling of swelling-induced shape changes in
heterogeneous thin gel sheets.
| 0 | 1 | 1 | 0 | 0 | 0 |
An educational distributed Cosmic Ray detector network based on ArduSiPM | The advent of microcontrollers with enough CPU power and with analog and
digital peripherals makes possible to design a complete particle detector with
relative acquisition system around one microcontroller chip. The existence of a
world wide data infrastructure as internet allows for devising a distributed
network of cheap detectors capable to elaborate and send data or respond to
settings commands. The internet infrastructure enables to distribute the
absolute time (with precision of few milliseconds), to the simple devices far
apart, with few milliseconds precision, from a few meters to thousands of
kilometres. So it is possible to create a crowdsourcing experiment of citizen
science that use small scintillation-based particle detectors to monitor the
high energetic cosmic ray and the radiation environment.
| 0 | 1 | 0 | 0 | 0 | 0 |
Irreducible characters with bounded root Artin conductor | In this work, we prove that the growth of the Artin conductor is at most,
exponential in the degree of the character.
| 0 | 0 | 1 | 0 | 0 | 0 |
Mapping Web Pages by Internet Protocol (IP) addresses: Analyzing Spatial and Temporal Characteristics of Web Search Engine Results | Internet Protocol (IP) addresses are frequently used as a method of locating
web users by researchers in several different fields. However, there are
competing reports concerning the accuracy of those locations, and little
research has been done in manually comparing the IP geolocation databases and
web page geographic information. This paper categorized web page from the Yahoo
search engine into twelve categories, ranging from 'Blog' and 'News' to
'Education' and 'Governmental'. Then we manually compared the mailing or street
address of the web page's content creator with the geolocation results by the
given IP address. We introduced a cartographic design method by creating kernel
density maps for visualizing the information landscape of web pages associated
with specific keywords.
| 1 | 0 | 0 | 0 | 0 | 0 |
A generalisation of Kani-Rosen decomposition theorem for Jacobian varieties | In this short paper we generalise a theorem due to Kani and Rosen on
decomposition of Jacobian varieties of Riemann surfaces with group action. This
generalisation extends the set of Jacobians for which it is possible to obtain
an isogeny decomposition where all the factors are Jacobians.
| 0 | 0 | 1 | 0 | 0 | 0 |
Optimal group testing designs for estimating prevalence with uncertain testing errors | We construct optimal designs for group testing experiments where the goal is
to estimate the prevalence of a trait by using a test with uncertain
sensitivity and specificity. Using optimal design theory for approximate
designs, we show that the most efficient design for simultaneously estimating
the prevalence, sensitivity and specificity requires three different group
sizes with equal frequencies. However, if estimating prevalence as accurately
as possible is the only focus, the optimal strategy is to have three group
sizes with unequal frequencies. On the basis of a chlamydia study in the
U.S.A., we compare performances of competing designs and provide insights into
how the unknown sensitivity and specificity of the test affect the performance
of the prevalence estimator. We demonstrate that the locally D- and Ds-optimal
designs proposed have high efficiencies even when the prespecified values of
the parameters are moderately misspecified.
| 0 | 0 | 1 | 1 | 0 | 0 |
Charge transfer driven emergent phenomena in oxide heterostructures | Complex oxides exhibit many intriguing phenomena, including metal-insulator
transition, ferroelectricity/multiferroicity, colossal magnetoresistance and
high transition temperature superconductivity. Advances in epitaxial thin film
growth techniques enable us to combine different complex oxides with atomic
precision and form an oxide heterostructure. Recent theoretical and
experimental work has shown that charge transfer across oxide interfaces
generally occurs and leads to a great diversity of emergent interfacial
properties which are not exhibited by bulk constituents. In this report, we
review mechanisms and physical consequence of charge transfer across interfaces
in oxide heterostructures. Both theoretical proposals and experimental
measurements of various oxide heterostructures are discussed and compared. We
also review the theoretical methods that are used to calculate charge transfer
across oxide interfaces and discuss the success and challenges in theory.
Finally, we present a summary and perspectives for future research.
| 0 | 1 | 0 | 0 | 0 | 0 |
Duality of Graphical Models and Tensor Networks | In this article we show the duality between tensor networks and undirected
graphical models with discrete variables. We study tensor networks on
hypergraphs, which we call tensor hypernetworks. We show that the tensor
hypernetwork on a hypergraph exactly corresponds to the graphical model given
by the dual hypergraph. We translate various notions under duality. For
example, marginalization in a graphical model is dual to contraction in the
tensor network. Algorithms also translate under duality. We show that belief
propagation corresponds to a known algorithm for tensor network contraction.
This article is a reminder that the research areas of graphical models and
tensor networks can benefit from interaction.
| 1 | 0 | 1 | 1 | 0 | 0 |
A further generalization of the Emden-Fowler equation | A generalization of the Emden-Fowler equation is presented and its solutions
are investigated. This paper is devoted to asymptotic behavior of its
solutions. The procedure is entirely based on a previous paper by the author.
| 0 | 0 | 1 | 0 | 0 | 0 |
Data Interpolations in Deep Generative Models under Non-Simply-Connected Manifold Topology | Exploiting the deep generative model's remarkable ability of learning the
data-manifold structure, some recent researches proposed a geometric data
interpolation method based on the geodesic curves on the learned data-manifold.
However, this interpolation method often gives poor results due to a
topological difference between the model and the dataset. The model defines a
family of simply-connected manifolds, whereas the dataset generally contains
disconnected regions or holes that make them non-simply-connected. To
compensate this difference, we propose a novel density regularizer that make
the interpolation path circumvent the holes denoted by low probability density.
We confirm that our method gives consistently better interpolation results from
the experiments with real-world image datasets.
| 1 | 0 | 0 | 1 | 0 | 0 |
Effect of Surfaces on Amyloid Fibril Formation | Using atomic force microscopy (AFM) we investigated the interaction of
amyloid beta (Ab) (1 42) peptide with chemically modified surfaces in order to
better understand the mechanism of amyloid toxicity, which involves interaction
of amyloid with cell membrane surfaces. We compared the structure and density
of Ab fibrils on positively and negatively charged as well as hydrophobic
chemically modified surfaces at physiologically relevant conditions.
| 0 | 1 | 0 | 0 | 0 | 0 |
Lie Transform Based Polynomial Neural Networks for Dynamical Systems Simulation and Identification | In the article, we discuss the architecture of the polynomial neural network
that corresponds to the matrix representation of Lie transform. The matrix form
of Lie transform is an approximation of general solution for the nonlinear
system of ordinary differential equations. Thus, it can be used for simulation
and modeling task. On the other hand, one can identify dynamical system from
time series data simply by optimization of the coefficient matrices of the Lie
transform. Representation of the approach by polynomial neural networks
integrates the strength of both neural networks and traditional model-based
methods for dynamical systems investigation. We provide a theoretical
explanation of learning dynamical systems from time series for the proposed
method, as well as demonstrate it in several applications. Namely, we show
results of modeling and identification for both well-known systems like
Lotka-Volterra equation and more complicated examples from retail,
biochemistry, and accelerator physics.
| 1 | 0 | 0 | 0 | 0 | 0 |
Large deformations of the Tracy-Widom distribution I. Non-oscillatory asymptotics | We analyze the left-tail asymptotics of deformed Tracy-Widom distribution
functions describing the fluctuations of the largest eigenvalue in invariant
random matrix ensembles after removing each soft edge eigenvalue independently
with probability $1-\gamma\in[0,1]$. As $\gamma$ varies, a transition from
Tracy-Widom statistics ($\gamma=1$) to classical Weibull statistics
($\gamma=0$) was observed in the physics literature by Bohigas, de Carvalho,
and Pato \cite{BohigasCP:2009}. We provide a description of this transition by
rigorously computing the leading-order left-tail asymptotics of the thinned
GOE, GUE and GSE Tracy-Widom distributions. In this paper, we obtain the
asymptotic behavior in the non-oscillatory region with $\gamma\in[0,1)$ fixed
(for the GOE, GUE, and GSE distributions) and $\gamma\uparrow 1$ at a
controlled rate (for the GUE distribution). This is the first step in an
ongoing program to completely describe the transition between Tracy-Widom and
Weibull statistics. As a corollary to our results, we obtain a new
total-integral formula involving the Ablowitz-Segur solution to the second
Painlevé equation.
| 0 | 1 | 1 | 0 | 0 | 0 |
Too Trivial To Test? An Inverse View on Defect Prediction to Identify Methods with Low Fault Risk | Background. Test resources are usually limited and therefore it is often not
possible to completely test an application before a release. To cope with the
problem of scarce resources, development teams can apply defect prediction to
identify fault-prone code regions. However, defect prediction tends to low
precision in cross-project prediction scenarios.
Aims. We take an inverse view on defect prediction and aim to identify
methods that can be deferred when testing because they contain hardly any
faults due to their code being "trivial". We expect that characteristics of
such methods might be project-independent, so that our approach could improve
cross-project predictions.
Method. We compute code metrics and apply association rule mining to create
rules for identifying methods with low fault risk. We conduct an empirical
study to assess our approach with six Java open-source projects containing
precise fault data at the method level.
Results. Our results show that inverse defect prediction can identify approx.
32-44% of the methods of a project to have a low fault risk; on average, they
are about six times less likely to contain a fault than other methods. In
cross-project predictions with larger, more diversified training sets,
identified methods are even eleven times less likely to contain a fault.
Conclusions. Inverse defect prediction supports the efficient allocation of
test resources by identifying methods that can be treated with less priority in
testing activities and is well applicable in cross-project prediction
scenarios.
| 1 | 0 | 0 | 0 | 0 | 0 |
Dynamical phase transitions in sampling complexity | We make the case for studying the complexity of approximately simulating
(sampling) quantum systems for reasons beyond that of quantum computational
supremacy, such as diagnosing phase transitions. We consider the sampling
complexity as a function of time $t$ due to evolution generated by spatially
local quadratic bosonic Hamiltonians. We obtain an upper bound on the scaling
of $t$ with the number of bosons $n$ for which approximate sampling is
classically efficient. We also obtain a lower bound on the scaling of $t$ with
$n$ for which any instance of the boson sampling problem reduces to this
problem and hence implies that the problem is hard, assuming the conjectures of
Aaronson and Arkhipov [Proc. 43rd Annu. ACM Symp. Theory Comput. STOC '11].
This establishes a dynamical phase transition in sampling complexity. Further,
we show that systems in the Anderson-localized phase are always easy to sample
from at arbitrarily long times. We view these results in the light of
classifying phases of physical systems based on parameters in the Hamiltonian.
In doing so, we combine ideas from mathematical physics and computational
complexity to gain insight into the behavior of condensed matter, atomic,
molecular and optical systems.
| 1 | 1 | 0 | 0 | 0 | 0 |
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