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Title: Holomorphic differentials, thermostats and Anosov flows,
Abstract: We introduce a new family of thermostat flows on the unit tangent bundle of
an oriented Riemannian $2$-manifold. Suitably reparametrised, these flows
include the geodesic flow of metrics of negative Gauss curvature and the
geodesic flow induced by the Hilbert metric on the quotient surface of
divisible convex sets. We show that the family of flows can be parametrised in
terms of certain weighted holomorphic differentials and investigate their
properties. In particular, we prove that they admit a dominated splitting and
we identify special cases in which the flows are Anosov. In the latter case, we
study when they admit an invariant measure in the Lebesgue class and the
regularity of the weak foliations. | [
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] |
Title: Near-field coupling of gold plasmonic antennas for sub-100 nm magneto-thermal microscopy,
Abstract: The development of spintronic technology with increasingly dense, high-speed,
and complex devices will be accelerated by accessible microscopy techniques
capable of probing magnetic phenomena on picosecond time scales and at deeply
sub-micron length scales. A recently developed time-resolved magneto-thermal
microscope provides a path towards this goal if it is augmented with a
picosecond, nanoscale heat source. We theoretically study adiabatic
nanofocusing and near-field heat induction using conical gold plasmonic
antennas to generate sub-100 nm thermal gradients for time-resolved
magneto-thermal imaging. Finite element calculations of antenna-sample
interactions reveal focused electromagnetic loss profiles that are either
peaked directly under the antenna or are annular, depending on the sample's
conductivity, the antenna's apex radius, and the tip-sample separation. We find
that the thermal gradient is confined to 40 nm to 60 nm full width at half
maximum for realistic ranges of sample conductivity and apex radius. To
mitigate this variation, which is undesirable for microscopy, we investigate
the use of a platinum capping layer on top of the sample as a thermal
transduction layer to produce heat uniformly across different sample materials.
After determining the optimal capping layer thickness, we simulate the
evolution of the thermal gradient in the underlying sample layer, and find that
the temporal width is below 10 ps. These results lay a theoretical foundation
for nanoscale, time-resolved magneto-thermal imaging. | [
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] |
Title: A reproducible effect size is more useful than an irreproducible hypothesis test to analyze high throughput sequencing datasets,
Abstract: Motivation: P values derived from the null hypothesis significance testing
framework are strongly affected by sample size, and are known to be
irreproducible in underpowered studies, yet no suitable replacement has been
proposed. Results: Here we present implementations of non-parametric
standardized median effect size estimates, dNEF, for high-throughput sequencing
datasets. Case studies are shown for transcriptome and tag-sequencing datasets.
The dNEF measure is shown to be more repro- ducible and robust than P values
and requires sample sizes as small as 3 to reproducibly identify differentially
abundant features. Availability: Source code and binaries freely available at:
this https URL, omicplotR, and
this https URL. | [
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1,
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] |
Title: Laplace Beltrami operator in the Baran metric and pluripotential equilibrium measure: the ball, the simplex and the sphere,
Abstract: The Baran metric $\delta_E$ is a Finsler metric on the interior of $E\subset
\R^n$ arising from Pluripotential Theory. We consider the few instances, namely
$E$ being the ball, the simplex, or the sphere, where $\delta_E$ is known to be
Riemaniann and we prove that the eigenfunctions of the associated Laplace
Beltrami operator (with no boundary conditions) are the orthogonal polynomials
with respect to the pluripotential equilibrium measure $\mu_E$ of $E.$ We
conjecture that this may hold in a wider generality.
The considered differential operators have been already introduced in the
framework of orthogonal polynomials and studied in connection with certain
symmetry groups. In this work instead we highlight the relationships between
orthogonal polynomials with respect to $\mu_E$ and the Riemaniann structure
naturally arising from Pluripotential Theory | [
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1,
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0,
0
] |
Title: Magnetic polarons in a nonequilibrium polariton condensate,
Abstract: We consider a condensate of exciton-polaritons in a diluted magnetic
semiconductor microcavity. Such system may exhibit magnetic self-trapping in
the case of sufficiently strong coupling between polaritons and magnetic ions
embedded in the semiconductor. We investigate the effect of the nonequilibrium
nature of exciton-polaritons on the physics of the resulting self-trapped
magnetic polarons. We find that multiple polarons can exist at the same time,
and derive a critical condition for self-trapping which is different to the one
predicted previously in the equilibrium case. Using the Bogoliubov-de Gennes
approximation, we calculate the excitation spectrum and provide a physical
explanation in terms of the effective magnetic attraction between polaritons,
mediated by the ion subsystem. | [
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] |
Title: Oracle Importance Sampling for Stochastic Simulation Models,
Abstract: We consider the problem of estimating an expected outcome from a stochastic
simulation model using importance sampling. We propose a two-stage procedure
that involves a regression stage and a sampling stage to construct our
estimator. We introduce a parametric and a nonparametric regression estimator
in the first stage and study how the allocation between the two stages affects
the performance of final estimator. We derive the oracle property for both
approaches. We analyze the empirical performances of our approaches using two
simulated data and a case study on wind turbine reliability evaluation. | [
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0,
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] |
Title: The Generalized Cross Validation Filter,
Abstract: Generalized cross validation (GCV) is one of the most important approaches
used to estimate parameters in the context of inverse problems and
regularization techniques. A notable example is the determination of the
smoothness parameter in splines. When the data are generated by a state space
model, like in the spline case, efficient algorithms are available to evaluate
the GCV score with complexity that scales linearly in the data set size.
However, these methods are not amenable to on-line applications since they rely
on forward and backward recursions. Hence, if the objective has been evaluated
at time $t-1$ and new data arrive at time t, then O(t) operations are needed to
update the GCV score. In this paper we instead show that the update cost is
$O(1)$, thus paving the way to the on-line use of GCV. This result is obtained
by deriving the novel GCV filter which extends the classical Kalman filter
equations to efficiently propagate the GCV score over time. We also illustrate
applications of the new filter in the context of state estimation and on-line
regularized linear system identification. | [
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0,
0,
1,
0,
0
] |
Title: Of the People: Voting Is More Effective with Representative Candidates,
Abstract: In light of the classic impossibility results of Arrow and Gibbard and
Satterthwaite regarding voting with ordinal rules, there has been recent
interest in characterizing how well common voting rules approximate the social
optimum. In order to quantify the quality of approximation, it is natural to
consider the candidates and voters as embedded within a common metric space,
and to ask how much further the chosen candidate is from the population as
compared to the socially optimal one. We use this metric preference model to
explore a fundamental and timely question: does the social welfare of a
population improve when candidates are representative of the population? If so,
then by how much, and how does the answer depend on the complexity of the
metric space?
We restrict attention to the most fundamental and common social choice
setting: a population of voters, two independently drawn candidates, and a
majority rule election. When candidates are not representative of the
population, it is known that the candidate selected by the majority rule can be
thrice as far from the population as the socially optimal one. We examine how
this ratio improves when candidates are drawn independently from the population
of voters. Our results are two-fold: When the metric is a line, the ratio
improves from $3$ to $4-2\sqrt{2}$, roughly $1.1716$; this bound is tight. When
the metric is arbitrary, we show a lower bound of $1.5$ and a constant upper
bound strictly better than $2$ on the approximation ratio of the majority rule.
The positive result depends in part on the assumption that candidates are
independent and identically distributed. However, we show that independence
alone is not enough to achieve the upper bound: even when candidates are drawn
independently, if the population of candidates can be different from the
voters, then an upper bound of $2$ on the approximation is tight. | [
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] |
Title: Hidden Community Detection in Social Networks,
Abstract: We introduce a new paradigm that is important for community detection in the
realm of network analysis. Networks contain a set of strong, dominant
communities, which interfere with the detection of weak, natural community
structure. When most of the members of the weak communities also belong to
stronger communities, they are extremely hard to be uncovered. We call the weak
communities the hidden community structure.
We present a novel approach called HICODE (HIdden COmmunity DEtection) that
identifies the hidden community structure as well as the dominant community
structure. By weakening the strength of the dominant structure, one can uncover
the hidden structure beneath. Likewise, by reducing the strength of the hidden
structure, one can more accurately identify the dominant structure. In this
way, HICODE tackles both tasks simultaneously.
Extensive experiments on real-world networks demonstrate that HICODE
outperforms several state-of-the-art community detection methods in uncovering
both the dominant and the hidden structure. In the Facebook university social
networks, we find multiple non-redundant sets of communities that are strongly
associated with residential hall, year of registration or career position of
the faculties or students, while the state-of-the-art algorithms mainly locate
the dominant ground truth category. In the Due to the difficulty of labeling
all ground truth communities in real-world datasets, HICODE provides a
promising approach to pinpoint the existing latent communities and uncover
communities for which there is no ground truth. Finding this unknown structure
is an extremely important community detection problem. | [
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] |
Title: Two-photon exchange correction to the hyperfine splitting in muonic hydrogen,
Abstract: We reevaluate the Zemach, recoil and polarizability corrections to the
hyperfine splitting in muonic hydrogen expressing them through the low-energy
proton structure constants and obtain the precise values of the Zemach radius
and two-photon exchange (TPE) contribution. The uncertainty of TPE correction
to S energy levels in muonic hydrogen of 105 ppm exceeds the ppm accuracy level
of the forthcoming 1S hyperfine splitting measurements at PSI, J-PARC and
RIKEN-RAL. | [
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1,
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] |
Title: Generation and analysis of lamplighter programs,
Abstract: We consider a programming language based on the lamplighter group that uses
only composition and iteration as control structures. We derive generating
functions and counting formulas for this language and special subsets of it,
establishing lower and upper bounds on the growth rate of semantically distinct
programs. Finally, we show how to sample random programs and analyze the
distribution of runtimes induced by such sampling. | [
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0
] |
Title: Preduals for spaces of operators involving Hilbert spaces and trace-class operators,
Abstract: Continuing the study of preduals of spaces $\mathcal{L}(H,Y)$ of bounded,
linear maps, we consider the situation that $H$ is a Hilbert space. We
establish a natural correspondence between isometric preduals of
$\mathcal{L}(H,Y)$ and isometric preduals of $Y$.
The main ingredient is a Tomiyama-type result which shows that every
contractive projection that complements $\mathcal{L}(H,Y)$ in its bidual is
automatically a right $\mathcal{L}(H)$-module map.
As an application, we show that isometric preduals of
$\mathcal{L}(\mathcal{S}_1)$, the algebra of operators on the space of
trace-class operators, correspond to isometric preduals of $\mathcal{S}_1$
itself (and there is an abundance of them). On the other hand, the compact
operators are the unique predual of $\mathcal{S}_1$ making its multiplication
separately weak* continuous. | [
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] |
Title: Interactions between Health Searchers and Search Engines,
Abstract: The Web is an important resource for understanding and diagnosing medical
conditions. Based on exposure to online content, people may develop undue
health concerns, believing that common and benign symptoms are explained by
serious illnesses. In this paper, we investigate potential strategies to mine
queries and searcher histories for clues that could help search engines choose
the most appropriate information to present in response to exploratory medical
queries. To do this, we performed a longitudinal study of health search
behavior using the logs of a popular search engine. We found that query
variations which might appear innocuous (e.g. "bad headache" vs "severe
headache") may hold valuable information about the searcher which could be used
by search engines to improve performance. Furthermore, we investigated how
medically concerned users respond differently to search engine result pages
(SERPs) and find that their disposition for clicking on concerning pages is
pronounced, potentially leading to a self-reinforcement of concern. Finally, we
studied to which degree variations in the SERP impact future search and
real-world health-seeking behavior and obtained some surprising results (e.g.,
viewing concerning pages may lead to a short-term reduction of real-world
health seeking). | [
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] |
Title: Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices,
Abstract: In a previous work we have detailed the requirements to obtain a maximal
performance benefit by implementing fully connected deep neural networks (DNN)
in form of arrays of resistive devices for deep learning. This concept of
Resistive Processing Unit (RPU) devices we extend here towards convolutional
neural networks (CNNs). We show how to map the convolutional layers to RPU
arrays such that the parallelism of the hardware can be fully utilized in all
three cycles of the backpropagation algorithm. We find that the noise and bound
limitations imposed due to analog nature of the computations performed on the
arrays effect the training accuracy of the CNNs. Noise and bound management
techniques are presented that mitigate these problems without introducing any
additional complexity in the analog circuits and can be addressed by the
digital circuits. In addition, we discuss digitally programmable update
management and device variability reduction techniques that can be used
selectively for some of the layers in a CNN. We show that combination of all
those techniques enables a successful application of the RPU concept for
training CNNs. The techniques discussed here are more general and can be
applied beyond CNN architectures and therefore enables applicability of RPU
approach for large class of neural network architectures. | [
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] |
Title: Absolute versus convective helical magnetorotational instabilities in Taylor-Couette flows,
Abstract: We study magnetic Taylor-Couette flow in a system having nondimensional radii
$r_i=1$ and $r_o=2$, and periodic in the axial direction with wavelengths
$h\ge100$. The rotation ratio of the inner and outer cylinders is adjusted to
be slightly in the Rayleigh-stable regime, where magnetic fields are required
to destabilize the flow, in this case triggering the axisymmetric helical
magnetorotational instability (HMRI). Two choices of imposed magnetic field are
considered, both having the same azimuthal component $B_\phi=r^{-1}$, but
differing axial components. The first choice has $B_z=0.1$, and yields the
familiar HMRI, consisting of unidirectionally traveling waves. The second
choice has $B_z\approx0.1\sin(2\pi z/h)$, and yields HMRI waves that travel in
opposite directions depending on the sign of $B_z$. The first configuration
corresponds to a convective instability, the second to an absolute instability.
The two variants behave very similarly regarding both linear onset as well as
nonlinear equilibration. | [
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] |
Title: Symmetries and multipeakon solutions for the modified two-component Camassa-Holm system,
Abstract: Compared with the two-component Camassa-Holm system, the modified
two-component Camassa-Holm system introduces a regularized density which makes
possible the existence of solutions of lower regularity, and in particular of
multipeakon solutions. In this paper, we derive a new pointwise invariant for
the modified two-component Camassa-Holm system. The derivation of the invariant
uses directly the symmetry of the system, following the classical argument of
Noether's theorem. The existence of the multipeakon solutions can be directly
inferred from this pointwise invariant. This derivation shows the strong
connection between symmetries and the existence of special solutions. The
observation also holds for the scalar Camassa-Holm equation and, for
comparison, we have also included the corresponding derivation. Finally, we
compute explicitly the solutions obtained for the peakon-antipeakon case. We
observe the existence of a periodic solution which has not been reported in the
literature previously. This case shows the attractive effect that the
introduction of an elastic potential can have on the solutions. | [
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] |
Title: A pliable lasso for the Cox model,
Abstract: We introduce a pliable lasso method for estimation of interaction effects in
the Cox proportional hazards model framework. The pliable lasso is a linear
model that includes interactions between covariates X and a set of modifying
variables Z and assumes sparsity of the main effects and interaction effects.
The hierarchical penalty excludes interaction effects when the corresponding
main effects are zero: this avoids overfitting and an explosion of model
complexity. We extend this method to the Cox model for survival data,
incorporating modifiers that are either fixed or varying in time into the
partial likelihood. For example, this allows modeling of survival times that
differ based on interactions of genes with age, gender, or other demographic
information. The optimization is done by blockwise coordinate descent on a
second order approximation of the objective. | [
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] |
Title: Khintchine's Theorem with random fractions,
Abstract: We prove versions of Khintchine's Theorem (1924) for approximations by
rational numbers whose numerators lie in randomly chosen sets of integers, and
we explore the extent to which the monotonicity assumption can be removed.
Roughly speaking, we show that if the number of available fractions for each
denominator grows too fast, then the monotonicity assumption cannot be removed.
There are questions in this random setting which may be seen as cognates of the
Duffin-Schaeffer Conjecture (1941), and are likely to be more accessible. We
point out that the direct random analogue of the Duffin-Schaeffer Conjecture,
like the Duffin-Schaeffer Conjecture itself, implies Catlin's Conjecture
(1976). It is not obvious whether the Duffin-Schaeffer Conjecture and its
random version imply one another, and it is not known whether Catlin's
Conjecture implies either of them. The question of whether Catlin implies
Duffin-Schaeffer has been unsettled for decades. | [
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] |
Title: A Method of Generating Random Weights and Biases in Feedforward Neural Networks with Random Hidden Nodes,
Abstract: Neural networks with random hidden nodes have gained increasing interest from
researchers and practical applications. This is due to their unique features
such as very fast training and universal approximation property. In these
networks the weights and biases of hidden nodes determining the nonlinear
feature mapping are set randomly and are not learned. Appropriate selection of
the intervals from which weights and biases are selected is extremely
important. This topic has not yet been sufficiently explored in the literature.
In this work a method of generating random weights and biases is proposed. This
method generates the parameters of the hidden nodes in such a way that
nonlinear fragments of the activation functions are located in the input space
regions with data and can be used to construct the surface approximating a
nonlinear target function. The weights and biases are dependent on the input
data range and activation function type. The proposed methods allows us to
control the generalization degree of the model. These all lead to improvement
in approximation performance of the network. Several experiments show very
promising results. | [
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] |
Title: Representation of big data by dimension reduction,
Abstract: Suppose the data consist of a set $S$ of points $x_j, 1 \leq j \leq J$,
distributed in a bounded domain $D \subset R^N$, where $N$ and $J$ are large
numbers. In this paper an algorithm is proposed for checking whether there
exists a manifold $\mathbb{M}$ of low dimension near which many of the points
of $S$ lie and finding such $\mathbb{M}$ if it exists. There are many dimension
reduction algorithms, both linear and non-linear. Our algorithm is simple to
implement and has some advantages compared with the known algorithms. If there
is a manifold of low dimension near which most of the data points lie, the
proposed algorithm will find it. Some numerical results are presented
illustrating the algorithm and analyzing its performance compared to the
classical PCA (principal component analysis) and Isomap. | [
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] |
Title: Introduction to Plasma Physics,
Abstract: These notes are intended to provide a brief primer in plasma physics,
introducing common definitions, basic properties, and typical processes found
in plasmas. These concepts are inherent in contemporary plasma-based
accelerator schemes, and thus provide a foundation for the more advanced
expositions that follow in this volume. No prior knowledge of plasma physics is
required, but the reader is assumed to be familiar with basic electrodynamics
and fluid mechanics. | [
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] |
Title: Theoretical calculation of the fine-structure constant and the permittivity of the vacuum,
Abstract: Light traveling through the vacuum interacts with virtual particles similarly
to the way that light traveling through a dielectric interacts with ordinary
matter. And just as the permittivity of a dielectric can be calculated, the
permittivity $\epsilon_0$ of the vacuum can be calculated, yielding an equation
for the fine-structure constant $\alpha$. The most important contributions to
the value of $\alpha$ arise from interactions in the vacuum of photons with
virtual, bound states of charged lepton-antilepton pairs. Considering only
these contributions, the fully screened $\alpha \cong 1/(8^2\sqrt{3\pi/2})
\cong 1/139$. | [
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] |
Title: Calibrated Projection in MATLAB: Users' Manual,
Abstract: We present the calibrated-projection MATLAB package implementing the method
to construct confidence intervals proposed by Kaido, Molinari and Stoye (2017).
This manual provides details on how to use the package for inference on
projections of partially identified parameters. It also explains how to use the
MATLAB functions we developed to compute confidence intervals on solutions of
nonlinear optimization problems with estimated constraints. | [
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0,
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1,
0,
0
] |
Title: Atomic Clock Measurements of Quantum Scattering Phase Shifts Spanning Feshbach Resonances at Ultralow Fields,
Abstract: We use an atomic fountain clock to measure quantum scattering phase shifts
precisely through a series of narrow, low-field Feshbach resonances at average
collision energies below $1\,\mu$K. Our low spread in collision energy yields
phase variations of order $\pm \pi/2$ for target atoms in several $F,m_F$
states. We compare them to a theoretical model and establish the accuracy of
the measurements and the theoretical uncertainties from the fitted potential.
We find overall excellent agreement, with small statistically significant
differences that remain unexplained. | [
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1,
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] |
Title: Spin Distribution of Primordial Black Holes,
Abstract: We estimate the spin distribution of primordial black holes based on the
recent study of the critical phenomena in the gravitational collapse of a
rotating radiation fluid. We find that primordial black holes are mostly slowly
rotating. | [
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] |
Title: Automated flow for compressing convolution neural networks for efficient edge-computation with FPGA,
Abstract: Deep convolutional neural networks (CNN) based solutions are the current
state- of-the-art for computer vision tasks. Due to the large size of these
models, they are typically run on clusters of CPUs or GPUs. However, power
requirements and cost budgets can be a major hindrance in adoption of CNN for
IoT applications. Recent research highlights that CNN contain significant
redundancy in their structure and can be quantized to lower bit-width
parameters and activations, while maintaining acceptable accuracy. Low
bit-width and especially single bit-width (binary) CNN are particularly
suitable for mobile applications based on FPGA implementation, due to the
bitwise logic operations involved in binarized CNN. Moreover, the transition to
lower bit-widths opens new avenues for performance optimizations and model
improvement. In this paper, we present an automatic flow from trained
TensorFlow models to FPGA system on chip implementation of binarized CNN. This
flow involves quantization of model parameters and activations, generation of
network and model in embedded-C, followed by automatic generation of the FPGA
accelerator for binary convolutions. The automated flow is demonstrated through
implementation of binarized "YOLOV2" on the low cost, low power Cyclone- V FPGA
device. Experiments on object detection using binarized YOLOV2 demonstrate
significant performance benefit in terms of model size and inference speed on
FPGA as compared to CPU and mobile CPU platforms. Furthermore, the entire
automated flow from trained models to FPGA synthesis can be completed within
one hour. | [
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] |
Title: Foundation for a series of efficient simulation algorithms,
Abstract: Compute the coarsest simulation preorder included in an initial preorder is
used to reduce the resources needed to analyze a given transition system. This
technique is applied on many models like Kripke structures, labeled graphs,
labeled transition systems or even word and tree automata. Let (Q,
$\rightarrow$) be a given transition system and Rinit be an initial preorder
over Q. Until now, algorithms to compute Rsim , the coarsest simulation
included in Rinit , are either memory efficient or time efficient but not both.
In this paper we propose the foundation for a series of efficient simulation
algorithms with the introduction of the notion of maximal transitions and the
notion of stability of a preorder with respect to a coarser one. As an
illustration we solve an open problem by providing the first algorithm with the
best published time complexity, O(|Psim |.|$\rightarrow$|), and a bit space
complexity in O(|Psim |^2. log(|Psim |) + |Q|. log(|Q|)), with Psim the
partition induced by Rsim. | [
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] |
Title: A Review of Macroscopic Motion in Thermodynamic Equilibrium,
Abstract: A principle on the macroscopic motion of systems in thermodynamic
equilibrium, rarely discussed in texts, is reviewed: Very small but still
macroscopic parts of a fully isolated system in thermal equilibrium move as if
points of a rigid body, macroscopic energy being dissipated to increase
internal energy, and increase entropy along. It appears particularly important
in Space physics, when dissipation involves long-range fields at
Electromagnetism and Gravitation, rather than short-range contact forces. It is
shown how new physics, Special Relativity as regards Electromagnetism, first
Newtonian theory then General Relativity as regards Gravitation, determine
different dissipative processes involved in the approach to that equilibrium. | [
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] |
Title: Lord Kelvin's method of images approach to the Rotenberg model and its asymptotics,
Abstract: We study a mathematical model of cell populations dynamics proposed by M.
Rotenberg and investigated by M. Boulanouar. Here, a cell is characterized by
her maturity and speed of maturation. The growth of cell populations is
described by a partial differential equation with a boundary condition. In the
first part of the paper we exploit semigroup theory approach and apply Lord
Kelvin's method of images in order to give a new proof that the model is well
posed. Next, we use a semi-explicit formula for the semigroup related to the
model obtained by the method of images in order to give growth estimates for
the semigroup. The main part of the paper is devoted to the asymptotic
behaviour of the semigroup. We formulate conditions for the asymptotic
stability of the semigroup in the case in which the average number of viable
daughters per mitosis equals one. To this end we use methods developed by K.
Pichór and R. Rudnicki. | [
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] |
Title: Study of the Magnetizing Relationship of the Kickers for CSNS,
Abstract: The extraction system of CSNS mainly consists of two kinds of magnets: eight
kickers and one lambertson magnet. In this paper, firstly, the magnetic test
results of the eight kickers were introduced and then the filed uniformity and
magnetizing relationship of the kickers were given. Secondly, during the beam
commissioning in the future, in order to obtain more accurate magnetizing
relationship, a new method to measure the magnetizing coefficients of the
kickers by the real extraction beam was given and the data analysis would also
be processed. | [
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] |
Title: Episodic memory for continual model learning,
Abstract: Both the human brain and artificial learning agents operating in real-world
or comparably complex environments are faced with the challenge of online model
selection. In principle this challenge can be overcome: hierarchical Bayesian
inference provides a principled method for model selection and it converges on
the same posterior for both off-line (i.e. batch) and online learning. However,
maintaining a parameter posterior for each model in parallel has in general an
even higher memory cost than storing the entire data set and is consequently
clearly unfeasible. Alternatively, maintaining only a limited set of models in
memory could limit memory requirements. However, sufficient statistics for one
model will usually be insufficient for fitting a different kind of model,
meaning that the agent loses information with each model change. We propose
that episodic memory can circumvent the challenge of limited memory-capacity
online model selection by retaining a selected subset of data points. We design
a method to compute the quantities necessary for model selection even when the
data is discarded and only statistics of one (or few) learnt models are
available. We demonstrate on a simple model that a limited-sized episodic
memory buffer, when the content is optimised to retain data with statistics not
matching the current representation, can resolve the fundamental challenge of
online model selection. | [
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] |
Title: Upper-Bounding the Regularization Constant for Convex Sparse Signal Reconstruction,
Abstract: Consider reconstructing a signal $x$ by minimizing a weighted sum of a convex
differentiable negative log-likelihood (NLL) (data-fidelity) term and a convex
regularization term that imposes a convex-set constraint on $x$ and enforces
its sparsity using $\ell_1$-norm analysis regularization. We compute upper
bounds on the regularization tuning constant beyond which the regularization
term overwhelmingly dominates the NLL term so that the set of minimum points of
the objective function does not change. Necessary and sufficient conditions for
irrelevance of sparse signal regularization and a condition for the existence
of finite upper bounds are established. We formulate an optimization problem
for finding these bounds when the regularization term can be globally minimized
by a feasible $x$ and also develop an alternating direction method of
multipliers (ADMM) type method for their computation. Simulation examples show
that the derived and empirical bounds match. | [
0,
0,
1,
1,
0,
0
] |
Title: On the Privacy of the Opal Data Release: A Response,
Abstract: This document is a response to a report from the University of Melbourne on
the privacy of the Opal dataset release. The Opal dataset was released by
Data61 (CSIRO) in conjunction with the Transport for New South Wales (TfNSW).
The data consists of two separate weeks of "tap-on/tap-off" data of individuals
who used any of the four different modes of public transport from TfNSW: buses,
light rail, train and ferries. These taps are recorded through the smart
ticketing system, known as Opal, available in the state of New South Wales,
Australia. | [
1,
0,
0,
0,
0,
0
] |
Title: Long time behavior of Gross-Pitaevskii equation at positive temperature,
Abstract: The stochastic Gross-Pitaevskii equation is used as a model to describe
Bose-Einstein condensation at positive temperature. The equation is a complex
Ginzburg Landau equation with a trapping potential and an additive space-time
white noise. Two important questions for this system are the global existence
of solutions in the support of the Gibbs measure, and the convergence of those
solutions to the equilibrium for large time. In this paper, we give a proof of
these two results in one space dimension. In order to prove the convergence to
equilibrium, we use the associated purely dissipative equation as an auxiliary
equation, for which the convergence may be obtained using standard techniques.
Global existence is obtained for all initial data, and not almost surely with
respect to the invariant measure. | [
0,
0,
1,
0,
0,
0
] |
Title: On noncommutative geometry of the Standard Model: fermion multiplet as internal forms,
Abstract: We unveil the geometric nature of the multiplet of fundamental fermions in
the Standard Model of fundamental particles as a noncommutative analogue of de
Rham forms on the internal finite quantum space. | [
0,
0,
1,
0,
0,
0
] |
Title: A Review of Dynamic Network Models with Latent Variables,
Abstract: We present a selective review of statistical modeling of dynamic networks. We
focus on models with latent variables, specifically, the latent space models
and the latent class models (or stochastic blockmodels), which investigate both
the observed features and the unobserved structure of networks. We begin with
an overview of the static models, and then we introduce the dynamic extensions.
For each dynamic model, we also discuss its applications that have been studied
in the literature, with the data source listed in Appendix. Based on the
review, we summarize a list of open problems and challenges in dynamic network
modeling with latent variables. | [
0,
0,
0,
1,
0,
0
] |
Title: LevelHeaded: Making Worst-Case Optimal Joins Work in the Common Case,
Abstract: Pipelines combining SQL-style business intelligence (BI) queries and linear
algebra (LA) are becoming increasingly common in industry. As a result, there
is a growing need to unify these workloads in a single framework.
Unfortunately, existing solutions either sacrifice the inherent benefits of
exclusively using a relational database (e.g. logical and physical
independence) or incur orders of magnitude performance gaps compared to
specialized engines (or both). In this work we study applying a new type of
query processing architecture to standard BI and LA benchmarks. To do this we
present a new in-memory query processing engine called LevelHeaded. LevelHeaded
uses worst-case optimal joins as its core execution mechanism for both BI and
LA queries. With LevelHeaded, we show how crucial optimizations for BI and LA
queries can be captured in a worst-case optimal query architecture. Using these
optimizations, LevelHeaded outperforms other relational database engines
(LogicBlox, MonetDB, and HyPer) by orders of magnitude on standard LA
benchmarks, while performing on average within 31% of the best-of-breed BI
(HyPer) and LA (Intel MKL) solutions on their own benchmarks. Our results show
that such a single query processing architecture is capable of delivering
competitive performance on both BI and LA queries. | [
1,
0,
0,
0,
0,
0
] |
Title: Introduction to Delay Models and Their Wave Solutions,
Abstract: In this paper, a brief review of delay population models and their
applications in ecology is provided. The inclusion of diffusion and nonlocality
terms in delay models has given more capabilities to these models enabling them
to capture several ecological phenomena such as the Allee effect, waves of
invasive species and spatio-temporal competitions of interacting species.
Moreover, recent advances in the studies of traveling and stationary wave
solutions of delay models are outlined. In particular, the existence of
stationary and traveling wave solutions of delay models, stability of wave
solutions, formation of wavefronts in the special domain, and possible outcomes
of delay models are discussed. | [
0,
0,
1,
0,
0,
0
] |
Title: From a normal insulator to a topological insulator in plumbene,
Abstract: Plumbene, similar to silicene, has a buckled honeycomb structure with a large
band gap ($\sim 400$ meV). All previous studies have shown that it is a normal
insulator. Here, we perform first-principles calculations and employ a
sixteen-band tight-binding model with nearest-neighbor and
next-nearest-neighbor hopping terms to investigate electronic structures and
topological properties of the plumbene monolayer. We find that it can become a
topological insulator with a large bulk gap ($\sim 200$ meV) through electron
doping, and the nontrivial state is very robust with respect to external
strain. Plumbene can be an ideal candidate for realizing the quantum spin Hall
effect at room temperature. By investigating effects of external electric and
magnetic fields on electronic structures and transport properties of plumbene,
we present two rich phase diagrams with and without electron doping, and
propose a theoretical design for a four-state spin-valley filter. | [
0,
1,
0,
0,
0,
0
] |
Title: Bounding the composition length of primitive permutation groups and completely reducible linear groups,
Abstract: We obtain upper bounds on the composition length of a finite permutation
group in terms of the degree and the number of orbits, and analogous bounds for
primitive, quasiprimitive and semiprimitive groups. Similarly, we obtain upper
bounds on the composition length of a finite completely reducible linear group
in terms of some of its parameters. In almost all cases we show that the bounds
are sharp, and describe the extremal examples. | [
0,
0,
1,
0,
0,
0
] |
Title: Dispersive Regimes of the Dicke Model,
Abstract: We study two dispersive regimes in the dynamics of $N$ two-level atoms
interacting with a bosonic mode for long interaction times. Firstly, we analyze
the dispersive multiqubit quantum Rabi model for the regime in which the qubit
frequencies are equal and smaller than the mode frequency, and for values of
the coupling strength similar or larger than the mode frequency, namely, the
deep strong coupling regime. Secondly, we address an interaction that is
dependent on the photon number, where the coupling strength is comparable to
the geometric mean of the qubit and mode frequencies. We show that the
associated dynamics is analytically tractable and provide useful frameworks
with which to analyze the system behavior. In the deep strong coupling regime,
we unveil the structure of unexpected resonances for specific values of the
coupling, present for $N\ge2$, and in the photon-number-dependent regime we
demonstrate that all the nontrivial dynamical behavior occurs in the atomic
degrees of freedom for a given Fock state. We verify these assertions with
numerical simulations of the qubit population and photon-statistic dynamics. | [
0,
1,
0,
0,
0,
0
] |
Title: ZebraLancer: Crowdsource Knowledge atop Open Blockchain, Privately and Anonymously,
Abstract: We design and implement the first private and anonymous decentralized
crowdsourcing system ZebraLancer. It realizes the fair exchange (i.e. security
against malicious workers and dishonest requesters) without using any
third-party arbiter. More importantly, it overcomes two fundamental challenges
of decentralization, i.e. data leakage and identity breach.
First, our outsource-then-prove methodology resolves the critical tension
between blockchain transparency and data confidentiality without sacrificing
the fairness of exchange. ZebraLancer ensures: a requester will not pay more
than what data deserve, according to a policy announced when her task is
published through the blockchain; each worker indeed gets a payment based on
the policy, if submits data to the blockchain; the above properties are
realized not only without a central arbiter, but also without leaking the data
to blockchain network.
Furthermore, the blockchain transparency might allow one to infer private
information of workers/requesters through their participation history.
ZebraLancer solves the problem by allowing anonymous participations without
surrendering user accountability. Specifically, workers cannot misuse anonymity
to submit multiple times to reap rewards, and an anonymous requester cannot
maliciously submit colluded answers to herself to repudiate payments. The idea
behind is a subtle linkability: if one authenticates twice in a task, everybody
can tell, or else staying anonymous. To realize such delicate linkability, we
put forth a novel cryptographic notion, the common-prefix-linkable anonymous
authentication.
Finally, we implement our protocol for a common image annotation task and
deploy it in a test net of Ethereum. The experiment results show the
applicability of our protocol and highlight subtleties of tailoring the
protocol to be compatible with the existing real-world open blockchain. | [
1,
0,
0,
0,
0,
0
] |
Title: Fast, Better Training Trick -- Random Gradient,
Abstract: In this paper, we will show an unprecedented method to accelerate training
and improve performance, which called random gradient (RG). This method can be
easier to the training of any model without extra calculation cost, we use
Image classification, Semantic segmentation, and GANs to confirm this method
can improve speed which is training model in computer vision. The central idea
is using the loss multiplied by a random number to random reduce the
back-propagation gradient. We can use this method to produce a better result in
Pascal VOC, Cifar, Cityscapes datasets. | [
0,
0,
0,
1,
0,
0
] |
Title: Multiple VLAD encoding of CNNs for image classification,
Abstract: Despite the effectiveness of convolutional neural networks (CNNs) especially
in image classification tasks, the effect of convolution features on learned
representations is still limited. It mostly focuses on the salient object of
the images, but ignores the variation information on clutter and local. In this
paper, we propose a special framework, which is the multiple VLAD encoding
method with the CNNs features for image classification. Furthermore, in order
to improve the performance of the VLAD coding method, we explore the
multiplicity of VLAD encoding with the extension of three kinds of encoding
algorithms, which are the VLAD-SA method, the VLAD-LSA and the VLAD-LLC method.
Finally, we equip the spatial pyramid patch (SPM) on VLAD encoding to add the
spatial information of CNNs feature. In particular, the power of SPM leads our
framework to yield better performance compared to the existing method. | [
1,
0,
0,
0,
0,
0
] |
Title: Centroid vetting of transiting planet candidates from the Next Generation Transit Survey,
Abstract: The Next Generation Transit Survey (NGTS), operating in Paranal since 2016,
is a wide-field survey to detect Neptunes and super-Earths transiting bright
stars, which are suitable for precise radial velocity follow-up and
characterisation. Thereby, its sub-mmag photometric precision and ability to
identify false positives are crucial. Particularly, variable background objects
blended in the photometric aperture frequently mimic Neptune-sized transits and
are costly in follow-up time. These objects can best be identified with the
centroiding technique: if the photometric flux is lost off-centre during an
eclipse, the flux centroid shifts towards the centre of the target star.
Although this method has successfully been employed by the Kepler mission, it
has previously not been implemented from the ground. We present a
fully-automated centroid vetting algorithm developed for NGTS, enabled by our
high-precision auto-guiding. Our method allows detecting centroid shifts with
an average precision of 0.75 milli-pixel, and down to 0.25 milli-pixel for
specific targets, for a pixel size of 4.97 arcsec. The algorithm is now part of
the NGTS candidate vetting pipeline and automatically employed for all detected
signals. Further, we develop a joint Bayesian fitting model for all photometric
and centroid data, allowing to disentangle which object (target or background)
is causing the signal, and what its astrophysical parameters are. We
demonstrate our method on two NGTS objects of interest. These achievements make
NGTS the first ground-based wide-field transit survey ever to successfully
apply the centroiding technique for automated candidate vetting, enabling the
production of a robust candidate list before follow-up. | [
0,
1,
0,
0,
0,
0
] |
Title: Large sums of Hecke eigenvalues of holomorphic cusp forms,
Abstract: Let $f$ be a Hecke cusp form of weight $k$ for the full modular group, and
let $\{\lambda_f(n)\}_{n\geq 1}$ be the sequence of its normalized Fourier
coefficients. Motivated by the problem of the first sign change of
$\lambda_f(n)$, we investigate the range of $x$ (in terms of $k$) for which
there are cancellations in the sum $S_f(x)=\sum_{n\leq x} \lambda_f(n)$. We
first show that $S_f(x)=o(x\log x)$ implies that $\lambda_f(n)<0$ for some
$n\leq x$. We also prove that $S_f(x)=o(x\log x)$ in the range $\log x/\log\log
k\to \infty$ assuming the Riemann hypothesis for $L(s, f)$, and furthermore
that this range is best possible unconditionally. More precisely, we establish
the existence of many Hecke cusp forms $f$ of large weight $k$, for which
$S_f(x)\gg_A x\log x$, when $x=(\log k)^A.$ Our results are $GL_2$ analogues of
work of Granville and Soundararajan for character sums, and could also be
generalized to other families of automorphic forms. | [
0,
0,
1,
0,
0,
0
] |
Title: Playtime Measurement with Survival Analysis,
Abstract: Maximizing product use is a central goal of many businesses, which makes
retention and monetization two central analytics metrics in games. Player
retention may refer to various duration variables quantifying product use:
total playtime or session playtime are popular research targets, and active
playtime is well-suited for subscription games. Such research often has the
goal of increasing player retention or conversely decreasing player churn.
Survival analysis is a framework of powerful tools well suited for retention
type data. This paper contributes new methods to game analytics on how playtime
can be analyzed using survival analysis without covariates. Survival and hazard
estimates provide both a visual and an analytic interpretation of the playtime
phenomena as a funnel type nonparametric estimate. Metrics based on the
survival curve can be used to aggregate this playtime information into a single
statistic. Comparison of survival curves between cohorts provides a scientific
AB-test. All these methods work on censored data and enable computation of
confidence intervals. This is especially important in time and sample limited
data which occurs during game development. Throughout this paper, we illustrate
the application of these methods to real world game development problems on the
Hipster Sheep mobile game. | [
1,
0,
0,
1,
0,
0
] |
Title: Invariant-based inverse engineering of crane control parameters,
Abstract: By applying invariant-based inverse engineering in the small-oscillations
regime, we design the time dependence of the control parameters of an overhead
crane (trolley displacement and rope length), to transport a load between two
positions at different heights with minimal final energy excitation for a
microcanonical ensemble of initial conditions. The analogies between ion
transport in multisegmented traps or neutral atom transport in moving optical
lattices and load manipulation by cranes opens a route for a useful transfer of
techniques among very different fields. | [
0,
1,
0,
0,
0,
0
] |
Title: Leaf Space Isometries of Singular Riemannian Foliations and Their Spectral Properties,
Abstract: In this paper, the authors consider leaf spaces of singular Riemannian
foliations $\mathcal{F}$ on compact manifolds $M$ and the associated
$\mathcal{F}$-basic spectrum on $M$, $spec_B(M, \mathcal{F}),$ counted with
multiplicities. Recently, a notion of smooth isometry $\varphi:
M_1/\mathcal{F}_1\rightarrow M_2/\mathcal{F}_2$ between the leaf spaces of such
singular Riemannian foliations $(M_1,\mathcal{F}_1)$ and $(M_2,\mathcal{F}_2)$
has appeared in the literature. In this paper, the authors provide an example
to show that the existence a smooth isometry of leaf spaces as above is not
sufficient to guarantee the equality of $spec_B(M_1,\mathcal{F}_1)$ and
$spec_B(M_2,\mathcal{F}_2).$ The authors then prove that if some additional
conditions involving the geometry of the leaves are satisfied, then the
equality of $spec_B(M_1,\mathcal{F}_1)$ and $spec_B(M_2,\mathcal{F}_2)$ is
guaranteed. Consequences and applications to orbifold spectral theory,
isometric group actions, and their reductions are also explored. | [
0,
0,
1,
0,
0,
0
] |
Title: Functional importance of noise in neuronal information processing,
Abstract: Noise is an inherent part of neuronal dynamics, and thus of the brain. It can
be observed in neuronal activity at different spatiotemporal scales, including
in neuronal membrane potentials, local field potentials,
electroencephalography, and magnetoencephalography. A central research topic in
contemporary neuroscience is to elucidate the functional role of noise in
neuronal information processing. Experimental studies have shown that a
suitable level of noise may enhance the detection of weak neuronal signals by
means of stochastic resonance. In response, theoretical research, based on the
theory of stochastic processes, nonlinear dynamics, and statistical physics,
has made great strides in elucidating the mechanism and the many benefits of
stochastic resonance in neuronal systems. In this perspective, we review recent
research dedicated to neuronal stochastic resonance in biophysical mathematical
models. We also explore the regulation of neuronal stochastic resonance, and we
outline important open questions and directions for future research. A deeper
understanding of neuronal stochastic resonance may afford us new insights into
the highly impressive information processing in the brain. | [
0,
0,
0,
0,
1,
0
] |
Title: Self-consistent dynamical model of the Broad Line Region,
Abstract: We develope a self-consistent description of the Broad Line Region based on
the concept of the failed wind powered by the radiation pressure acting on
dusty accretion disk atmosphere in Keplerian motion. The material raised high
above the disk is illuminated, dust evaportes, and the matter falls back
towards the disk. This material is the source of emission lines. The model
predicts the inner and outer radius of the region, the cloud dynamics under the
dust radiation pressure and, subsequently, just the gravitational field of the
central black hole, which results in assymetry between the rise and fall.
Knowledge of the dynamics allows to predict the shapes of the emission lines as
functions of the basic parameters of an active nucleus: black hole mass,
accretion rate, black hole spin (or accretion efficiency) and the viewing angle
with respect to the symmetry axis. Here we show preliminary results based on
analytical approximations to the cloud motion. | [
0,
1,
0,
0,
0,
0
] |
Title: Pixelwise Instance Segmentation with a Dynamically Instantiated Network,
Abstract: Semantic segmentation and object detection research have recently achieved
rapid progress. However, the former task has no notion of different instances
of the same object, and the latter operates at a coarse, bounding-box level. We
propose an Instance Segmentation system that produces a segmentation map where
each pixel is assigned an object class and instance identity label. Most
approaches adapt object detectors to produce segments instead of boxes. In
contrast, our method is based on an initial semantic segmentation module, which
feeds into an instance subnetwork. This subnetwork uses the initial
category-level segmentation, along with cues from the output of an object
detector, within an end-to-end CRF to predict instances. This part of our model
is dynamically instantiated to produce a variable number of instances per
image. Our end-to-end approach requires no post-processing and considers the
image holistically, instead of processing independent proposals. Therefore,
unlike some related work, a pixel cannot belong to multiple instances.
Furthermore, far more precise segmentations are achieved, as shown by our
state-of-the-art results (particularly at high IoU thresholds) on the Pascal
VOC and Cityscapes datasets. | [
1,
0,
0,
0,
0,
0
] |
Title: Strong homotopy types of acyclic categories and $Δ$-complexes,
Abstract: We extend the homotopy theories based on point reduction for finite spaces
and simplicial complexes to finite acyclic categories and $\Delta$-complexes,
respectively. The functors of classifying spaces and face posets are compatible
with these homotopy theories. In contrast with the classical settings of finite
spaces and simplicial complexes, the universality of morphisms and simplices
plays a central role in this paper. | [
0,
0,
1,
0,
0,
0
] |
Title: Bohm's approach to quantum mechanics: Alternative theory or practical picture?,
Abstract: Since its inception Bohmian mechanics has been generally regarded as a
hidden-variable theory aimed at providing an objective description of quantum
phenomena. To date, this rather narrow conception of Bohm's proposal has caused
it more rejection than acceptance. Now, after 65 years of Bohmian mechanics,
should still be such an interpretational aspect the prevailing appraisal? Why
not favoring a more pragmatic view, as a legitimate picture of quantum
mechanics, on equal footing in all respects with any other more conventional
quantum picture? These questions are used here to introduce a discussion on an
alternative way to deal with Bohmian mechanics at present, enhancing its aspect
as an efficient and useful picture or formulation to tackle, explore, describe
and explain quantum phenomena where phase and correlation (entanglement) are
key elements. This discussion is presented through two complementary blocks.
The first block is aimed at briefly revisiting the historical context that gave
rise to the appearance of Bohmian mechanics, and how this approach or analogous
ones have been used in different physical contexts. This discussion is used to
emphasize a more pragmatic view to the detriment of the more conventional
hidden-variable (ontological) approach that has been a leitmotif within the
quantum foundations. The second block focuses on some particular formal aspects
of Bohmian mechanics supporting the view presented here, with special emphasis
on the physical meaning of the local phase field and the associated velocity
field encoded within the wave function. As an illustration, a simple model of
Young's two-slit experiment is considered. The simplicity of this model allows
to understand in an easy manner how the information conveyed by the Bohmian
formulation relates to other more conventional concepts in quantum mechanics.
This sort of pedagogical application is also aimed at ... | [
0,
1,
0,
0,
0,
0
] |
Title: Improved Algorithms for Computing the Cycle of Minimum Cost-to-Time Ratio in Directed Graphs,
Abstract: We study the problem of finding the cycle of minimum cost-to-time ratio in a
directed graph with $ n $ nodes and $ m $ edges. This problem has a long
history in combinatorial optimization and has recently seen interesting
applications in the context of quantitative verification. We focus on strongly
polynomial algorithms to cover the use-case where the weights are relatively
large compared to the size of the graph. Our main result is an algorithm with
running time $ \tilde O (m^{3/4} n^{3/2}) $, which gives the first improvement
over Megiddo's $ \tilde O (n^3) $ algorithm [JACM'83] for sparse graphs. We
further demonstrate how to obtain both an algorithm with running time $ n^3 /
2^{\Omega{(\sqrt{\log n})}} $ on general graphs and an algorithm with running
time $ \tilde O (n) $ on constant treewidth graphs. To obtain our main result,
we develop a parallel algorithm for negative cycle detection and single-source
shortest paths that might be of independent interest. | [
1,
0,
0,
0,
0,
0
] |
Title: Interplay of dust alignment, grain growth and magnetic fields in polarization: lessons from the emission-to-extinction ratio,
Abstract: Polarized extinction and emission from dust in the interstellar medium (ISM)
are hard to interpret, as they have a complex dependence on dust optical
properties, grain alignment and magnetic field orientation. This is
particularly true in molecular clouds. The data available today are not yet
used to their full potential.
The combination of emission and extinction, in particular, provides
information not available from either of them alone. We combine data from the
scientific literature on polarized dust extinction with Planck data on
polarized emission and we use them to constrain the possible variations in dust
and environmental conditions inside molecular clouds, and especially
translucent lines of sight, taking into account magnetic field orientation.
We focus on the dependence between \lambda_max -- the wavelength of maximum
polarization in extinction -- and other observables such as the extinction
polarization, the emission polarization and the ratio of the two. We set out to
reproduce these correlations using Monte-Carlo simulations where the relevant
quantities in a dust model -- grain alignment, size distribution and magnetic
field orientation -- vary to mimic the diverse conditions expected inside
molecular clouds.
None of the quantities chosen can explain the observational data on its own:
the best results are obtained when all quantities vary significantly across and
within clouds. However, some of the data -- most notably the stars with low
emission-to-extinction polarization ratio -- are not reproduced by our
simulation. Our results suggest not only that dust evolution is necessary to
explain polarization in molecular clouds, but that a simple change in size
distribution is not sufficient to explain the data, and point the way for
future and more sophisticated models. | [
0,
1,
0,
0,
0,
0
] |
Title: Asynchronous Distributed Variational Gaussian Processes for Regression,
Abstract: Gaussian processes (GPs) are powerful non-parametric function estimators.
However, their applications are largely limited by the expensive computational
cost of the inference procedures. Existing stochastic or distributed
synchronous variational inferences, although have alleviated this issue by
scaling up GPs to millions of samples, are still far from satisfactory for
real-world large applications, where the data sizes are often orders of
magnitudes larger, say, billions. To solve this problem, we propose ADVGP, the
first Asynchronous Distributed Variational Gaussian Process inference for
regression, on the recent large-scale machine learning platform,
PARAMETERSERVER. ADVGP uses a novel, flexible variational framework based on a
weight space augmentation, and implements the highly efficient, asynchronous
proximal gradient optimization. While maintaining comparable or better
predictive performance, ADVGP greatly improves upon the efficiency of the
existing variational methods. With ADVGP, we effortlessly scale up GP
regression to a real-world application with billions of samples and demonstrate
an excellent, superior prediction accuracy to the popular linear models. | [
0,
0,
0,
1,
0,
0
] |
Title: Stacco: Differentially Analyzing Side-Channel Traces for Detecting SSL/TLS Vulnerabilities in Secure Enclaves,
Abstract: Intel Software Guard Extension (SGX) offers software applications enclave to
protect their confidentiality and integrity from malicious operating systems.
The SSL/TLS protocol, which is the de facto standard for protecting
transport-layer network communications, has been broadly deployed for a secure
communication channel. However, in this paper, we show that the marriage
between SGX and SSL may not be smooth sailing.
Particularly, we consider a category of side-channel attacks against SSL/TLS
implementations in secure enclaves, which we call the control-flow inference
attacks. In these attacks, the malicious operating system kernel may perform a
powerful man-in-the-kernel attack to collect execution traces of the enclave
programs at page, cacheline, or branch level, while positioning itself in the
middle of the two communicating parties. At the center of our work is a
differential analysis framework, dubbed Stacco, to dynamically analyze the
SSL/TLS implementations and detect vulnerabilities that can be exploited as
decryption oracles. Surprisingly, we found exploitable vulnerabilities in the
latest versions of all the SSL/TLS libraries we have examined.
To validate the detected vulnerabilities, we developed a man-in-the-kernel
adversary to demonstrate Bleichenbacher attacks against the latest OpenSSL
library running in the SGX enclave (with the help of Graphene) and completely
broke the PreMasterSecret encrypted by a 4096-bit RSA public key with only
57286 queries. We also conducted CBC padding oracle attacks against the latest
GnuTLS running in Graphene-SGX and an open-source SGX-implementation of mbedTLS
(i.e., mbedTLS-SGX) that runs directly inside the enclave, and showed that it
only needs 48388 and 25717 queries, respectively, to break one block of AES
ciphertext. Empirical evaluation suggests these man-in-the-kernel attacks can
be completed within 1 or 2 hours. | [
1,
0,
0,
0,
0,
0
] |
Title: Representations of Super $W(2,2)$ algebra $\mathfrak{L}$,
Abstract: In paper, we study the representation theory of super $W(2,2)$ algebra
${\mathfrak{L}}$. We prove that ${\mathfrak{L}}$ has no mixed irreducible
modules and give the classification of irreducible modules of intermediate
series. We determinate the conjugate-linear anti-involution of ${\mathfrak{L}}$
and give the unitary modules of intermediate series. | [
0,
0,
1,
0,
0,
0
] |
Title: Effective Reformulation of Query for Code Search using Crowdsourced Knowledge and Extra-Large Data Analytics,
Abstract: Software developers frequently issue generic natural language queries for
code search while using code search engines (e.g., GitHub native search,
Krugle). Such queries often do not lead to any relevant results due to
vocabulary mismatch problems. In this paper, we propose a novel technique that
automatically identifies relevant and specific API classes from Stack Overflow
Q & A site for a programming task written as a natural language query, and then
reformulates the query for improved code search. We first collect candidate API
classes from Stack Overflow using pseudo-relevance feedback and two term
weighting algorithms, and then rank the candidates using Borda count and
semantic proximity between query keywords and the API classes. The semantic
proximity has been determined by an analysis of 1.3 million questions and
answers of Stack Overflow. Experiments using 310 code search queries report
that our technique suggests relevant API classes with 48% precision and 58%
recall which are 32% and 48% higher respectively than those of the
state-of-the-art. Comparisons with two state-of-the-art studies and three
popular search engines (e.g., Google, Stack Overflow, and GitHub native search)
report that our reformulated queries (1) outperform the queries of the
state-of-the-art, and (2) significantly improve the code search results
provided by these contemporary search engines. | [
1,
0,
0,
0,
0,
0
] |
Title: Superconductivity at 7.3 K in the 133-type Cr-based RbCr3As3 single crystals,
Abstract: Here we report the preparation and superconductivity of the 133-type Cr-based
quasi-one-dimensional (Q1D) RbCr3As3 single crystals. The samples were prepared
by the deintercalation of Rb+ ions from the 233-type Rb2Cr3As3 crystals which
were grown from a high-temperature solution growth method. The RbCr3As3
compound crystallizes in a centrosymmetric structure with the space group of
P63/m (No. 176) different with its non-centrosymmetric Rb2Cr3As3
superconducting precursor, and the refined lattice parameters are a = 9.373(3)
{\AA} and c = 4.203(7) {\AA}. Electrical resistivity and magnetic
susceptibility characterizations reveal the occurrence of superconductivity
with an interestingly higher onset Tc of 7.3 K than other Cr-based
superconductors, and a high upper critical field Hc2(0) near 70 T in this
133-type RbCr3As3 crystals. | [
0,
1,
0,
0,
0,
0
] |
Title: Neural-Network Quantum States, String-Bond States, and Chiral Topological States,
Abstract: Neural-Network Quantum States have been recently introduced as an Ansatz for
describing the wave function of quantum many-body systems. We show that there
are strong connections between Neural-Network Quantum States in the form of
Restricted Boltzmann Machines and some classes of Tensor-Network states in
arbitrary dimensions. In particular we demonstrate that short-range Restricted
Boltzmann Machines are Entangled Plaquette States, while fully connected
Restricted Boltzmann Machines are String-Bond States with a nonlocal geometry
and low bond dimension. These results shed light on the underlying architecture
of Restricted Boltzmann Machines and their efficiency at representing many-body
quantum states. String-Bond States also provide a generic way of enhancing the
power of Neural-Network Quantum States and a natural generalization to systems
with larger local Hilbert space. We compare the advantages and drawbacks of
these different classes of states and present a method to combine them
together. This allows us to benefit from both the entanglement structure of
Tensor Networks and the efficiency of Neural-Network Quantum States into a
single Ansatz capable of targeting the wave function of strongly correlated
systems. While it remains a challenge to describe states with chiral
topological order using traditional Tensor Networks, we show that
Neural-Network Quantum States and their String-Bond States extension can
describe a lattice Fractional Quantum Hall state exactly. In addition, we
provide numerical evidence that Neural-Network Quantum States can approximate a
chiral spin liquid with better accuracy than Entangled Plaquette States and
local String-Bond States. Our results demonstrate the efficiency of neural
networks to describe complex quantum wave functions and pave the way towards
the use of String-Bond States as a tool in more traditional machine-learning
applications. | [
0,
1,
0,
1,
0,
0
] |
Title: End-to-End Information Extraction without Token-Level Supervision,
Abstract: Most state-of-the-art information extraction approaches rely on token-level
labels to find the areas of interest in text. Unfortunately, these labels are
time-consuming and costly to create, and consequently, not available for many
real-life IE tasks. To make matters worse, token-level labels are usually not
the desired output, but just an intermediary step. End-to-end (E2E) models,
which take raw text as input and produce the desired output directly, need not
depend on token-level labels. We propose an E2E model based on pointer
networks, which can be trained directly on pairs of raw input and output text.
We evaluate our model on the ATIS data set, MIT restaurant corpus and the MIT
movie corpus and compare to neural baselines that do use token-level labels. We
achieve competitive results, within a few percentage points of the baselines,
showing the feasibility of E2E information extraction without the need for
token-level labels. This opens up new possibilities, as for many tasks
currently addressed by human extractors, raw input and output data are
available, but not token-level labels. | [
1,
0,
0,
0,
0,
0
] |
Title: Lipschitz regularity of solutions to two-phase free boundary problems,
Abstract: We prove Lipschitz continuity of viscosity solutions to a class of two-phase
free boundary problems governed by fully nonlinear operators. | [
0,
0,
1,
0,
0,
0
] |
Title: Efficient Localized Inference for Large Graphical Models,
Abstract: We propose a new localized inference algorithm for answering marginalization
queries in large graphical models with the correlation decay property. Given a
query variable and a large graphical model, we define a much smaller model in a
local region around the query variable in the target model so that the marginal
distribution of the query variable can be accurately approximated. We introduce
two approximation error bounds based on the Dobrushin's comparison theorem and
apply our bounds to derive a greedy expansion algorithm that efficiently guides
the selection of neighbor nodes for localized inference. We verify our
theoretical bounds on various datasets and demonstrate that our localized
inference algorithm can provide fast and accurate approximation for large
graphical models. | [
1,
0,
0,
1,
0,
0
] |
Title: Multi-kink collisions in the $ϕ^6$ model,
Abstract: We study simultaneous collisions of two, three, and four kinks and antikinks
of the $\phi^6$ model at the same spatial point. Unlike the $\phi^4$ kinks, the
$\phi^6$ kinks are asymmetric and this enriches the variety of the collision
scenarios. In our numerical simulations we observe both reflection and bound
state formation depending on the number of kinks and on their spatial ordering
in the initial configuration. We also analyze the extreme values of the energy
densities and the field gradient observed during the collisions. Our results
suggest that very high energy densities can be produced in multi-kink
collisions in a controllable manner. Appearance of high energy density spots in
multi-kink collisions can be important in various physical applications of the
Klein-Gordon model. | [
0,
1,
0,
0,
0,
0
] |
Title: Deep Multimodal Subspace Clustering Networks,
Abstract: We present convolutional neural network (CNN) based approaches for
unsupervised multimodal subspace clustering. The proposed framework consists of
three main stages - multimodal encoder, self-expressive layer, and multimodal
decoder. The encoder takes multimodal data as input and fuses them to a latent
space representation. The self-expressive layer is responsible for enforcing
the self-expressiveness property and acquiring an affinity matrix corresponding
to the data points. The decoder reconstructs the original input data. The
network uses the distance between the decoder's reconstruction and the original
input in its training. We investigate early, late and intermediate fusion
techniques and propose three different encoders corresponding to them for
spatial fusion. The self-expressive layers and multimodal decoders are
essentially the same for different spatial fusion-based approaches. In addition
to various spatial fusion-based methods, an affinity fusion-based network is
also proposed in which the self-expressive layer corresponding to different
modalities is enforced to be the same. Extensive experiments on three datasets
show that the proposed methods significantly outperform the state-of-the-art
multimodal subspace clustering methods. | [
0,
0,
0,
1,
0,
0
] |
Title: Recurrent Autoregressive Networks for Online Multi-Object Tracking,
Abstract: The main challenge of online multi-object tracking is to reliably associate
object trajectories with detections in each video frame based on their tracking
history. In this work, we propose the Recurrent Autoregressive Network (RAN), a
temporal generative modeling framework to characterize the appearance and
motion dynamics of multiple objects over time. The RAN couples an external
memory and an internal memory. The external memory explicitly stores previous
inputs of each trajectory in a time window, while the internal memory learns to
summarize long-term tracking history and associate detections by processing the
external memory. We conduct experiments on the MOT 2015 and 2016 datasets to
demonstrate the robustness of our tracking method in highly crowded and
occluded scenes. Our method achieves top-ranked results on the two benchmarks. | [
1,
0,
0,
0,
0,
0
] |
Title: The occurrence of transverse and longitudinal electric currents in the classical plasma under the action of N transverse electromagnetic waves,
Abstract: Classical plasma with arbitrary degree of degeneration of electronic gas is
considered. In plasma N (N>2) collinear electromagnatic waves are propagated.
It is required to find the response of plasma to these waves. Distribution
function in square-law approximation on quantities of two small parameters from
Vlasov equation is received. The formula for electric current calculation is
deduced. It is demonstrated that the nonlinearity account leads to occurrence
of the longitudinal electric current directed along a wave vector. This
longitudinal current is orthogonal to the known transversal current received at
the linear analysis. The case of small values of wave number is considered. | [
0,
1,
0,
0,
0,
0
] |
Title: Large deviation theorem for random covariance matrices,
Abstract: We establish a large deviation theorem for the empirical spectral
distribution of random covariance matrices whose entries are independent random
variables with mean 0, variance 1 and having controlled forth moments. Some new
properties of Laguerre polynomials are also given. | [
0,
0,
1,
0,
0,
0
] |
Title: Abundances in photoionized nebulae of the Local Group and nucleosynthesis of intermediate mass stars,
Abstract: Photoionized nebulae, comprising HII regions and planetary nebulae, are
excellent laboratories to investigate the nucleosynthesis and chemical
evolution of several elements in the Galaxy and other galaxies of the Local
Group. Our purpose in this investigation is threefold: (i) compare the
abundances of HII regions and planetary nebulae in each system in order to
investigate the differences derived from the age and origin of these objects,
(ii) compare the chemical evolution in different systems, such as the Milky
Way, the Magellanic Clouds, and other galaxies of the Local Group, and (iii)
investigate to what extent the nucleosynthesis contributions from the
progenitor stars affect the observed abundances in planetary nebulae, which
constrains the nucleosynthesis of intermediate mass stars. We show that all
objects in the samples present similar trends concerning distance-independent
correlations, and some constraints can be defined on the production of He and N
by the PN progenitor stars. | [
0,
1,
0,
0,
0,
0
] |
Title: Multi-scale analysis of lead-lag relationships in high-frequency financial markets,
Abstract: We propose a novel estimation procedure for scale-by-scale lead-lag
relationships of financial assets observed at a high-frequency in a
non-synchronous manner. The proposed estimation procedure does not require any
interpolation processing of the original data and is applicable to quite fine
resolution data. The validity of the proposed estimators is shown under the
continuous-time framework developed in our previous work Hayashi and Koike
(2016). An empirical application shows promising results of the proposed
approach. | [
0,
0,
0,
1,
0,
0
] |
Title: Anomaly Detection Using Optimally-Placed Micro-PMU Sensors in Distribution Grids,
Abstract: As the distribution grid moves toward a tightly-monitored network, it is
important to automate the analysis of the enormous amount of data produced by
the sensors to increase the operators situational awareness about the system.
In this paper, focusing on Micro-Phasor Measurement Unit ($\mu$PMU) data, we
propose a hierarchical architecture for monitoring the grid and establish a set
of analytics and sensor fusion primitives for the detection of abnormal
behavior in the control perimeter. Due to the key role of the $\mu$PMU devices
in our architecture, a source-constrained optimal $\mu$PMU placement is also
described that finds the best location of the devices with respect to our
rules. The effectiveness of the proposed methods are tested through the
synthetic and real $\mu$PMU data. | [
1,
0,
0,
0,
0,
0
] |
Title: Ultrahigh Magnetic Field Phases in Frustrated Triangular-lattice Magnet CuCrO$_2$,
Abstract: The magnetic phases of a triangular-lattice antiferromagnet, CuCrO$_2$, were
investigated in magnetic fields along to the $c$ axis, $H$ // [001], up to 120
T. Faraday rotation and magneto-absorption spectroscopy were used to unveil the
rich physics of magnetic phases. An up-up-down (UUD) magnetic structure phase
was observed around 90--105 T at temperatures around 10 K. Additional distinct
anomalies adjacent to the UUD phase were uncovered and the Y-shaped and the
V-shaped phases are proposed to be viable candidates. These ordered phases are
emerged as a result of the interplay of geometrical spin frustration, single
ion anisotropy and thermal fluctuations in an environment of extremely high
magnetic fields. | [
0,
1,
0,
0,
0,
0
] |
Title: Preserving Differential Privacy in Convolutional Deep Belief Networks,
Abstract: The remarkable development of deep learning in medicine and healthcare domain
presents obvious privacy issues, when deep neural networks are built on users'
personal and highly sensitive data, e.g., clinical records, user profiles,
biomedical images, etc. However, only a few scientific studies on preserving
privacy in deep learning have been conducted. In this paper, we focus on
developing a private convolutional deep belief network (pCDBN), which
essentially is a convolutional deep belief network (CDBN) under differential
privacy. Our main idea of enforcing epsilon-differential privacy is to leverage
the functional mechanism to perturb the energy-based objective functions of
traditional CDBNs, rather than their results. One key contribution of this work
is that we propose the use of Chebyshev expansion to derive the approximate
polynomial representation of objective functions. Our theoretical analysis
shows that we can further derive the sensitivity and error bounds of the
approximate polynomial representation. As a result, preserving differential
privacy in CDBNs is feasible. We applied our model in a health social network,
i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for
human behavior prediction, human behavior classification, and handwriting digit
recognition tasks. Theoretical analysis and rigorous experimental evaluations
show that the pCDBN is highly effective. It significantly outperforms existing
solutions. | [
1,
0,
0,
1,
0,
0
] |
Title: Injectivity almost everywhere and mappings with finite distortion in nonlinear elasticity,
Abstract: We show that a sufficient condition for the weak limit of a sequence of
$W^1_q$-homeomorphisms with finite distortion to be almost everywhere injective
for $q \geq n-1$, can be stated by means of composition operators. Applying
this result, we study nonlinear elasticity problems with respect to these new
classes of mappings. Furthermore, we impose loose growth conditions on the
stored-energy function for the class of $W^1_n$-homeomorphisms with finite
distortion and integrable inner as well as outer distortion coefficients. | [
0,
0,
1,
0,
0,
0
] |
Title: Achromatic super-oscillatory lenses with sub-wavelength focusing,
Abstract: Lenses are crucial to light-enabled technologies. Conventional lenses have
been perfected to achieve near-diffraction-limited resolution and minimal
chromatic aberrations. However, such lenses are bulky and cannot focus light
into a hotspot smaller than half wavelength of light. Pupil filters, initially
suggested by Toraldo di Francia, can overcome the resolution constraints of
conventional lenses, but are not intrinsically chromatically corrected. Here we
report single-element planar lenses that not only deliver sub-wavelength
focusing (beating the diffraction limit of conventional refractive lenses) but
also focus light of different colors into the same hotspot. Using the principle
of super-oscillations we designed and fabricated a range of binary dielectric
and metallic lenses for visible and infrared parts of the spectrum that are
manufactured on silicon wafers, silica substrates and optical fiber tips. Such
low cost, compact lenses could be useful in mobile devices, data storage,
surveillance, robotics, space applications, imaging, manufacturing with light,
and spatially resolved nonlinear microscopies. | [
0,
1,
0,
0,
0,
0
] |
Title: Wireless Power Transfer for Distributed Estimation in Sensor Networks,
Abstract: This paper studies power allocation for distributed estimation of an unknown
scalar random source in sensor networks with a multiple-antenna fusion center
(FC), where wireless sensors are equipped with radio-frequency based energy
harvesting technology. The sensors' observation is locally processed by using
an uncoded amplify-and-forward scheme. The processed signals are then sent to
the FC, and are coherently combined at the FC, at which the best linear
unbiased estimator (BLUE) is adopted for reliable estimation. We aim to solve
the following two power allocation problems: 1) minimizing distortion under
various power constraints; and 2) minimizing total transmit power under
distortion constraints, where the distortion is measured in terms of
mean-squared error of the BLUE. Two iterative algorithms are developed to solve
the non-convex problems, which converge at least to a local optimum. In
particular, the above algorithms are designed to jointly optimize the
amplification coefficients, energy beamforming, and receive filtering. For each
problem, a suboptimal design, a single-antenna FC scenario, and a common
harvester deployment for colocated sensors, are also studied. Using the
powerful semidefinite relaxation framework, our result is shown to be valid for
any number of sensors, each with different noise power, and for an arbitrarily
number of antennas at the FC. | [
1,
0,
1,
0,
0,
0
] |
Title: About a non-standard interpolation problem,
Abstract: Using algebraic methods, and motivated by the one variable case, we study a
multipoint interpolation problem in the setting of several complex variables.
The duality realized by the residue generator associated with an underlying
Gorenstein algebra, using the Lagrange interpolation polynomial, plays a key
role in the arguments. | [
0,
0,
1,
0,
0,
0
] |
Title: Quantum spin liquid signatures in Kitaev-like frustrated magnets,
Abstract: Motivated by recent experiments on $\alpha$-RuCl$_3$, we investigate a
possible quantum spin liquid ground state of the honeycomb-lattice spin model
with bond-dependent interactions. We consider the $K-\Gamma$ model, where $K$
and $\Gamma$ represent the Kitaev and symmetric-anisotropic interactions
between spin-1/2 moments on the honeycomb lattice. Using the infinite density
matrix renormalization group (iDMRG), we provide compelling evidence for the
existence of quantum spin liquid phases in an extended region of the phase
diagram. In particular, we use transfer matrix spectra to show the evolution of
two-particle excitations with well-defined two-dimensional dispersion, which is
a strong signature of quantum spin liquid. These results are compared with
predictions from Majorana mean-field theory and used to infer the quasiparticle
excitation spectra. Further, we compute the dynamical structure factor using
finite size cluster computations and show that the results resemble the
scattering continuum seen in neutron scattering experiments on
$\alpha$-RuCl$_3$. We discuss these results in light of recent and future
experiments. | [
0,
1,
0,
0,
0,
0
] |
Title: Charge polarization effects on the optical response of blue-emitting superlattices,
Abstract: In the new approach to study the optical response of periodic structures,
successfully applied to study the optical properties of blue-emitting InGaN/GaN
superlattices, the spontaneous charge polarization was neglected. To search the
effect of this quantum confined Stark phenomenon we study the optical response,
assuming parabolic band edge modulations in the conduction and valence bands.
We discuss the consequences on the eigenfunction symmetries and the ensuing
optical transition selection rules. Using the new approach in the WKB
approximation of the finite periodic systems theory, we determine the energy
eigenvalues, their corresponding eigenfunctions and the subband structures in
the conduction and valence bands. We calculate the photoluminescence as a
function of the charge localization strength, and compare with the experimental
result. We show that for subbands close to the barrier edge the optical
response and the surface states are sensitive to charge polarization strength. | [
0,
1,
0,
0,
0,
0
] |
Title: System Description: Russell - A Logical Framework for Deductive Systems,
Abstract: Russell is a logical framework for the specification and implementation of
deductive systems. It is a high-level language with respect to Metamath
language, so inherently it uses a Metamath foundations, i.e. it doesn't rely on
any particular formal calculus, but rather is a pure logical framework. The
main difference with Metamath is in the proof language and approach to syntax:
the proofs have a declarative form, i.e. consist of actual expressions, which
are used in proofs, while syntactic grammar rules are separated from the
meaningful rules of inference.
Russell is implemented in c++14 and is distributed under GPL v3 license. The
repository contains translators from Metamath to Russell and back. Original
Metamath theorem base (almost 30 000 theorems) can be translated to Russell,
verified, translated back to Metamath and verified with the original Metamath
verifier. Russell can be downloaded from the repository
this https URL | [
1,
0,
1,
0,
0,
0
] |
Title: Short-Time Nonlinear Effects in the Exciton-Polariton System,
Abstract: In the exciton-polariton system, a linear dispersive photon field is coupled
to a nonlinear exciton field. Short-time analysis of the lossless system shows
that, when the photon field is excited, the time required for that field to
exhibit nonlinear effects is longer than the time required for the nonlinear
Schrödinger equation, in which the photon field itself is nonlinear. When the
initial condition is scaled by $\epsilon^\alpha$, it is found that the relative
error committed by omitting the nonlinear term in the exciton-polariton system
remains within $\epsilon$ for all times up to $t=C\epsilon^\beta$, where
$\beta=(1-\alpha(p-1))/(p+2)$. This is in contrast to $\beta=1-\alpha(p-1)$ for
the nonlinear Schrödinger equation. | [
0,
0,
1,
0,
0,
0
] |
Title: GTC Observations of an Overdense Region of LAEs at z=6.5,
Abstract: We present the results of our search for the faint galaxies near the end of
the Reionisation Epoch. This has been done using very deep OSIRIS images
obtained at the Gran Telescopio Canarias (GTC). Our observations focus around
two close, massive Lyman Alpha Emitters (LAEs) at redshift 6.5, discovered in
the SXDS field within a large-scale overdense region (Ouchi et al. 2010). The
total GTC observing time in three medium band filters (F883w35, F913w25 and
F941w33) is over 34 hours covering $7.0\times8.5$ arcmin$^2$ (or $\sim30,000$
Mpc$^3$ at $z=6.5$). In addition to the two spectroscopically confirmed LAEs in
the field, we have identified 45 other LAE candidates. The preliminary
luminosity function derived from our observations, assuming a spectroscopic
confirmation success rate of $\frac{2}{3}$ as in previous surveys, suggests
this area is about 2 times denser than the general field galaxy population at
$z=6.5$. If confirmed spectroscopically, our results will imply the discovery
of one of the earliest protoclusters in the universe, which will evolve to
resemble the most massive galaxy clusters today. | [
0,
1,
0,
0,
0,
0
] |
Title: Temporal Action Localization by Structured Maximal Sums,
Abstract: We address the problem of temporal action localization in videos. We pose
action localization as a structured prediction over arbitrary-length temporal
windows, where each window is scored as the sum of frame-wise classification
scores. Additionally, our model classifies the start, middle, and end of each
action as separate components, allowing our system to explicitly model each
action's temporal evolution and take advantage of informative temporal
dependencies present in this structure. In this framework, we localize actions
by searching for the structured maximal sum, a problem for which we develop a
novel, provably-efficient algorithmic solution. The frame-wise classification
scores are computed using features from a deep Convolutional Neural Network
(CNN), which are trained end-to-end to directly optimize for a novel structured
objective. We evaluate our system on the THUMOS 14 action detection benchmark
and achieve competitive performance. | [
1,
0,
0,
0,
0,
0
] |
Title: Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments,
Abstract: Ability to continuously learn and adapt from limited experience in
nonstationary environments is an important milestone on the path towards
general intelligence. In this paper, we cast the problem of continuous
adaptation into the learning-to-learn framework. We develop a simple
gradient-based meta-learning algorithm suitable for adaptation in dynamically
changing and adversarial scenarios. Additionally, we design a new multi-agent
competitive environment, RoboSumo, and define iterated adaptation games for
testing various aspects of continuous adaptation strategies. We demonstrate
that meta-learning enables significantly more efficient adaptation than
reactive baselines in the few-shot regime. Our experiments with a population of
agents that learn and compete suggest that meta-learners are the fittest. | [
1,
0,
0,
0,
0,
0
] |
Title: Alpha-Divergences in Variational Dropout,
Abstract: We investigate the use of alternative divergences to Kullback-Leibler (KL) in
variational inference(VI), based on the Variational Dropout \cite{kingma2015}.
Stochastic gradient variational Bayes (SGVB) \cite{aevb} is a general framework
for estimating the evidence lower bound (ELBO) in Variational Bayes. In this
work, we extend the SGVB estimator with using Alpha-Divergences, which are
alternative to divergences to VI' KL objective. The Gaussian dropout can be
seen as a local reparametrization trick of the SGVB objective. We extend the
Variational Dropout to use alpha divergences for variational inference. Our
results compare $\alpha$-divergence variational dropout with standard
variational dropout with correlated and uncorrelated weight noise. We show that
the $\alpha$-divergence with $\alpha \rightarrow 1$ (or KL divergence) is still
a good measure for use in variational inference, in spite of the efficient use
of Alpha-divergences for Dropout VI \cite{Li17}. $\alpha \rightarrow 1$ can
yield the lowest training error, and optimizes a good lower bound for the
evidence lower bound (ELBO) among all values of the parameter $\alpha \in
[0,\infty)$. | [
1,
0,
0,
1,
0,
0
] |
Title: Dehn invariant of flexible polyhedra,
Abstract: We prove that the Dehn invariant of any flexible polyhedron in Euclidean
space of dimension greater than or equal to 3 is constant during the flexion.
In dimensions 3 and 4 this implies that any flexible polyhedron remains
scissors congruent to itself during the flexion. This proves the Strong Bellows
Conjecture posed by Connelly in 1979. It was believed that this conjecture was
disproved by Alexandrov and Connelly in 2009. However, we find an error in
their counterexample. Further, we show that the Dehn invariant of a flexible
polyhedron in either sphere or Lobachevsky space of dimension greater than or
equal to 3 is constant during the flexion if and only if this polyhedron
satisfies the usual Bellows Conjecture, i.e., its volume is constant during
every flexion of it. Using previous results due to the first listed author, we
deduce that the Dehn invariant is constant during the flexion for every bounded
flexible polyhedron in odd-dimensional Lobachevsky space and for every flexible
polyhedron with sufficiently small edge lengths in any space of constant
curvature of dimension greater than or equal to 3. | [
0,
0,
1,
0,
0,
0
] |
Title: Single Magnetic Impurity in Tilted Dirac Surface States,
Abstract: We utilize variational method to investigate the Kondo screening of a
spin-1/2 magnetic impurity in tilted Dirac surface states with the Dirac cone
tilted along the $k_y$-axis. We mainly study about the effect of the tilting
term on the binding energy and the spin-spin correlation between magnetic
impurity and conduction electrons, and compare the results with the
counterparts in a two dimensional helical metal. The binding energy has a
critical value while the Dirac cone is slightly tilted. However, as the tilting
term increases, the density of states around the Fermi surface becomes
significant, such that the impurity and the host material always favor a bound
state. The diagonal and the off-diagonal terms of the spin-spin correlation
between the magnetic impurity and conduction electrons are also studied. Due to
the spin-orbit coupling and the tilting of the spectra, various components of
spin-spin correlation show very strong anisotropy in coordinate space, and are
of power-law decay with respect to the spatial displacements. | [
0,
1,
0,
0,
0,
0
] |
Title: Leveraging the Path Signature for Skeleton-based Human Action Recognition,
Abstract: Human action recognition in videos is one of the most challenging tasks in
computer vision. One important issue is how to design discriminative features
for representing spatial context and temporal dynamics. Here, we introduce a
path signature feature to encode information from intra-frame and inter-frame
contexts. A key step towards leveraging this feature is to construct the proper
trajectories (paths) for the data steam. In each frame, the correlated
constraints of human joints are treated as small paths, then the spatial path
signature features are extracted from them. In video data, the evolution of
these spatial features over time can also be regarded as paths from which the
temporal path signature features are extracted. Eventually, all these features
are concatenated to constitute the input vector of a fully connected neural
network for action classification. Experimental results on four standard
benchmark action datasets, J-HMDB, SBU Dataset, Berkeley MHAD, and NTURGB+D
demonstrate that the proposed approach achieves state-of-the-art accuracy even
in comparison with recent deep learning based models. | [
1,
0,
0,
0,
0,
0
] |
Title: How Many Subpopulations is Too Many? Exponential Lower Bounds for Inferring Population Histories,
Abstract: Reconstruction of population histories is a central problem in population
genetics. Existing coalescent-based methods, like the seminal work of Li and
Durbin (Nature, 2011), attempt to solve this problem using sequence data but
have no rigorous guarantees. Determining the amount of data needed to correctly
reconstruct population histories is a major challenge. Using a variety of tools
from information theory, the theory of extremal polynomials, and approximation
theory, we prove new sharp information-theoretic lower bounds on the problem of
reconstructing population structure -- the history of multiple subpopulations
that merge, split and change sizes over time. Our lower bounds are exponential
in the number of subpopulations, even when reconstructing recent histories. We
demonstrate the sharpness of our lower bounds by providing algorithms for
distinguishing and learning population histories with matching dependence on
the number of subpopulations. | [
0,
0,
0,
0,
1,
0
] |
Title: Source localization in an ocean waveguide using supervised machine learning,
Abstract: Source localization in ocean acoustics is posed as a machine learning problem
in which data-driven methods learn source ranges directly from observed
acoustic data. The pressure received by a vertical linear array is preprocessed
by constructing a normalized sample covariance matrix (SCM) and used as the
input. Three machine learning methods (feed-forward neural networks (FNN),
support vector machines (SVM) and random forests (RF)) are investigated in this
paper, with focus on the FNN. The range estimation problem is solved both as a
classification problem and as a regression problem by these three machine
learning algorithms. The results of range estimation for the Noise09 experiment
are compared for FNN, SVM, RF and conventional matched-field processing and
demonstrate the potential of machine learning for underwater source
localization.. | [
1,
1,
0,
0,
0,
0
] |
Title: Mining Illegal Insider Trading of Stocks: A Proactive Approach,
Abstract: Illegal insider trading of stocks is based on releasing non-public
information (e.g., new product launch, quarterly financial report, acquisition
or merger plan) before the information is made public. Detecting illegal
insider trading is difficult due to the complex, nonlinear, and non-stationary
nature of the stock market. In this work, we present an approach that detects
and predicts illegal insider trading proactively from large heterogeneous
sources of structured and unstructured data using a deep-learning based
approach combined with discrete signal processing on the time series data. In
addition, we use a tree-based approach that visualizes events and actions to
aid analysts in their understanding of large amounts of unstructured data.
Using existing data, we have discovered that our approach has a good success
rate in detecting illegal insider trading patterns. | [
0,
0,
0,
1,
0,
1
] |
Title: Predictive Simulations for Tuning Electronic and Optical Properties of SubPc Derivatives,
Abstract: Boron subphthalocyanine chloride is an electron donor material used in small
molecule organic photovoltaics with an unusually large molecular dipole moment.
Using first-principles calculations, we investigate enhancing the electronic
and optical properties of boron subphthalocyanine chloride, by substituting the
boron and chlorine atoms with other trivalent and halogen atoms in order to
modify the molecular dipole moment. Gas phase molecular structures and
properties are predicted with hybrid functionals. Using positions and
orientations of the known compounds as the starting coordinates for these
molecules, stable crystalline structures are derived following a procedure that
involves perturbation and accurate total energy minimization. Electronic
structure and photonic properties of the predicted crystals are computed using
the GW method and the Bethe-Salpeter equation, respectively. Finally, a simple
transport model is use to demonstrate the importance of molecular dipole
moments on device performance. | [
0,
1,
0,
0,
0,
0
] |
Title: Free energy of formation of a crystal nucleus in incongruent solidification: Implication for modeling the crystallization of aqueous nitric acid droplets in type 1 polar stratospheric clouds,
Abstract: Using the formalism of the classical nucleation theory, we derive an
expression for the reversible work $W_*$ of formation of a binary crystal
nucleus in a liquid binary solution of non-stoichiometric composition
(incongruent crystallization). Applied to the crystallization of aqueous nitric
acid (NA) droplets, the new expression more adequately takes account of the
effect of nitric acid vapor compared to the conventional expression of
MacKenzie, Kulmala, Laaksonen, and Vesala (MKLV) [J.Geophys.Res. 102, 19729
(1997)]. The predictions of both MKLV and modified expressions for the average
liquid-solid interfacial tension $\sigma^{ls}$ of nitric acid dihydrate (NAD)
crystals are compared by using existing experimental data on the incongruent
crystallization of aqueous NA droplets of composition relevant to polar
stratospheric clouds (PSCs). The predictions based on the MKLV expression are
higher by about 5% compared to predictions based on our modified expression.
This results in similar differences between the predictions of both expressions
for the solid-vapor interfacial tension $\sigma^{sv}$ of NAD crystal nuclei.
The latter can be obtained by analyzing of experimental data on crystal
nucleation rates in aqueous NA droplets and exploiting the dominance of the
surface-stimulated mode of crystal nucleation in small droplets and its
negligibility in large ones. Applying that method, our expression for $W_*$
provides an estimate for $\sigma^{sv}$ of NAD in the range from 92 dyn/cm to
100 dyn/cm, while the MKLV expression predicts it in the range from 95 dyn/cm
to 105 dyn/cm. The predictions of both expressions for $W_*$ become identical
in the case of congruent crystallization; this was also demonstrated by
applying our method to the nucleation of nitric acid trihydrate (NAT) crystals
in PSC droplets of stoichiometric composition. | [
0,
1,
0,
0,
0,
0
] |
Title: Ensemble learning with Conformal Predictors: Targeting credible predictions of conversion from Mild Cognitive Impairment to Alzheimer's Disease,
Abstract: Most machine learning classifiers give predictions for new examples
accurately, yet without indicating how trustworthy predictions are. In the
medical domain, this hampers their integration in decision support systems,
which could be useful in the clinical practice. We use a supervised learning
approach that combines Ensemble learning with Conformal Predictors to predict
conversion from Mild Cognitive Impairment to Alzheimer's Disease. Our goal is
to enhance the classification performance (Ensemble learning) and complement
each prediction with a measure of credibility (Conformal Predictors). Our
results showed the superiority of the proposed approach over a similar ensemble
framework with standard classifiers. | [
0,
0,
0,
1,
0,
0
] |
Title: Repair Strategies for Storage on Mobile Clouds,
Abstract: We study the data reliability problem for a community of devices forming a
mobile cloud storage system. We consider the application of regenerating codes
for file maintenance within a geographically-limited area. Such codes require
lower bandwidth to regenerate lost data fragments compared to file replication
or reconstruction. We investigate threshold-based repair strategies where data
repair is initiated after a threshold number of data fragments have been lost
due to node mobility. We show that at a low departure-to-repair rate regime, a
lazy repair strategy in which repairs are initiated after several nodes have
left the system outperforms eager repair in which repairs are initiated after a
single departure. This optimality is reversed when nodes are highly mobile. We
further compare distributed and centralized repair strategies and derive the
optimal repair threshold for minimizing the average repair cost per unit of
time, as a function of underlying code parameters. In addition, we examine
cooperative repair strategies and show performance improvements compared to
non-cooperative codes. We investigate several models for the time needed for
node repair including a simple fixed time model that allows for the computation
of closed-form expressions and a more realistic model that takes into account
the number of repaired nodes. We derive the conditions under which the former
model approximates the latter. Finally, an extended model where additional
failures are allowed during the repair process is investigated. Overall, our
results establish the joint effect of code design and repair algorithms on the
maintenance cost of distributed storage systems. | [
1,
0,
0,
0,
0,
0
] |
Title: Learning from MOM's principles: Le Cam's approach,
Abstract: We obtain estimation error rates for estimators obtained by aggregation of
regularized median-of-means tests, following a construction of Le Cam. The
results hold with exponentially large probability -- as in the gaussian
framework with independent noise- under only weak moments assumptions on data
and without assuming independence between noise and design. Any norm may be
used for regularization. When it has some sparsity inducing power we recover
sparse rates of convergence.
The procedure is robust since a large part of data may be corrupted, these
outliers have nothing to do with the oracle we want to reconstruct. Our general
risk bound is of order \begin{equation*} \max\left(\mbox{minimax rate in the
i.i.d. setup}, \frac{\text{number of outliers}}{\text{number of
observations}}\right) \enspace. \end{equation*}In particular, the number of
outliers may be as large as (number of data) $\times$(minimax rate) without
affecting this rate. The other data do not have to be identically distributed
but should only have equivalent $L^1$ and $L^2$ moments.
For example, the minimax rate $s \log(ed/s)/N$ of recovery of a $s$-sparse
vector in $\mathbb{R}^d$ is achieved with exponentially large probability by a
median-of-means version of the LASSO when the noise has $q_0$ moments for some
$q_0>2$, the entries of the design matrix should have $C_0\log(ed)$ moments and
the dataset can be corrupted up to $C_1 s \log(ed/s)$ outliers. | [
0,
0,
1,
1,
0,
0
] |
Title: Generalized Log-sine integrals and Bell polynomials,
Abstract: In this paper, we investigate the integral of $x^n\log^m(\sin(x))$ for
natural numbers $m$ and $n$. In doing so, we recover some well-known results
and remark on some relations to the log-sine integral
$\operatorname{Ls}_{n+m+1}^{(n)}(\theta)$. Later, we use properties of Bell
polynomials to find a closed expression for the derivative of the central
binomial and shifted central binomial coefficients in terms of polygamma
functions and harmonic numbers. | [
0,
0,
1,
0,
0,
0
] |
Title: Towards a realistic NNLIF model: Analysis and numerical solver for excitatory-inhibitory networks with delay and refractory periods,
Abstract: The Network of Noisy Leaky Integrate and Fire (NNLIF) model describes the
behavior of a neural network at mesoscopic level. It is one of the simplest
self-contained mean-field models considered for that purpose. Even so, to study
the mathematical properties of the model some simplifications were necessary
Cáceres-Carrillo-Perthame(2011), Cáceres-Perthame(2014),
Cáceres-Schneider(2017), which disregard crucial phenomena. In this work we
deal with the general NNLIF model without simplifications. It involves a
network with two populations (excitatory and inhibitory), with transmission
delays between the neurons and where the neurons remain in a refractory state
for a certain time. We have studied the number of steady states in terms of the
model parameters, the long time behaviour via the entropy method and
Poincaré's inequality, blow-up phenomena, and the importance of transmission
delays between excitatory neurons to prevent blow-up and to give rise to
synchronous solutions. Besides analytical results, we have presented a
numerical resolutor for this model, based on high order flux-splitting WENO
schemes and an explicit third order TVD Runge-Kutta method, in order to
describe the wide range of phenomena exhibited by the network: blow-up,
asynchronous/synchronous solutions and instability/stability of the steady
states; the solver also allows us to observe the time evolution of the firing
rates, refractory states and the probability distributions of the excitatory
and inhibitory populations. | [
0,
0,
1,
0,
0,
0
] |
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