text
stringlengths 57
2.88k
| labels
sequencelengths 6
6
|
|---|---|
Title: Intense cross-tail field-aligned currents in the plasma sheet at lunar distances,
Abstract: Field-aligned currents in the Earth's magnetotail are traditionally
associated with transient plasma flows and strong plasma pressure gradients in
the near-Earth side. In this paper we demonstrate a new field-aligned current
system present at the lunar orbit tail. Using magnetotail current sheet
observations by two ARTEMIS probes at $\sim60 R_E$, we analyze statistically
the current sheet structure and current density distribution closest to the
neutral sheet. For about half of our 130 current sheet crossings, the
equatorial magnetic field component across-the tail (along the main, cross-tail
current) contributes significantly to the vertical pressure balance. This
magnetic field component peaks at the equator, near the cross-tail current
maximum. For those cases, a significant part of the tail current, having an
intensity in the range 1-10nA/m$^2$, flows along the magnetic field lines (it
is both field-aligned and cross-tail). We suggest that this current system
develops in order to compensate the thermal pressure by particles that on its
own is insufficient to fend off the lobe magnetic pressure. | [
0,
1,
0,
0,
0,
0
] |
Title: A Result of Uniqueness of Solutions of the Shigesada-Kawasaki-Teramoto Equations,
Abstract: We derive the uniqueness of weak solutions to the Shigesada-Kawasaki-Teramoto
(SKT) systems using the adjoint problem argument. Combining with [PT17] we then
derive the well-posedness for the SKT systems in space dimension $d\le 4$ | [
0,
0,
1,
0,
0,
0
] |
Title: Mind the Gap: A Well Log Data Analysis,
Abstract: The main task in oil and gas exploration is to gain an understanding of the
distribution and nature of rocks and fluids in the subsurface. Well logs are
records of petro-physical data acquired along a borehole, providing direct
information about what is in the subsurface. The data collected by logging
wells can have significant economic consequences, due to the costs inherent to
drilling wells, and the potential return of oil deposits. In this paper, we
describe preliminary work aimed at building a general framework for well log
prediction.
First, we perform a descriptive and exploratory analysis of the gaps in the
neutron porosity logs of more than a thousand wells in the North Sea. Then, we
generate artificial gaps in the neutron logs that reflect the statistics
collected before. Finally, we compare Artificial Neural Networks, Random
Forests, and three algorithms of Linear Regression in the prediction of missing
gaps on a well-by-well basis. | [
1,
0,
0,
1,
0,
0
] |
Title: All-optical switching and unidirectional plasmon launching with electron-hole plasma driven silicon nanoantennas,
Abstract: High-index dielectric nanoparticles have become a powerful platform for
modern light science, enabling various fascinating applications, especially in
nonlinear nanophotonics for which they enable special types of optical
nonlinearity, such as electron-hole plasma photoexcitation, which are not
inherent to plasmonic nanostructures. Here, we propose a novel geometry for
highly tunable all-dielectric nanoantennas, consisting of a chain of silicon
nanoparticles excited by an electric dipole source, which allows tuning their
radiation properties via electron-hole plasma photoexcitation. We show that the
slowly guided modes determining the Van Hove singularity of the nanoantenna are
very sensitive to the nanoparticle permittivity, opening up the ability to
utilize this effect for efficient all-optical modulation. We show that by
pumping several boundary nanoparticles with relatively low intensities may
cause dramatic variations in the nanoantenna radiation power patterns and
Purcell factor. We also demonstrate that ultrafast pumping of the designed
nanoantenna allows unidirectional launching of surface plasmon-polaritons, with
interesting implications for modern nonlinear nanophotonics. | [
0,
1,
0,
0,
0,
0
] |
Title: Group chasing tactics: how to catch a faster prey?,
Abstract: We propose a bio-inspired, agent-based approach to describe the natural
phenomenon of group chasing in both two and three dimensions. Using a set of
local interaction rules we created a continuous-space and discrete-time model
with time delay, external noise and limited acceleration. We implemented a
unique collective chasing strategy, optimized its parameters and studied its
properties when chasing a much faster, erratic escaper. We show that collective
chasing strategies can significantly enhance the chasers' success rate. Our
realistic approach handles group chasing within closed, soft boundaries -
contrasting most of those published in the literature with periodic ones -- and
resembles several properties of pursuits observed in nature, such as the
emergent encircling or the escaper's zigzag motion. | [
0,
1,
0,
1,
0,
0
] |
Title: Probing for sparse and fast variable selection with model-based boosting,
Abstract: We present a new variable selection method based on model-based gradient
boosting and randomly permuted variables. Model-based boosting is a tool to fit
a statistical model while performing variable selection at the same time. A
drawback of the fitting lies in the need of multiple model fits on slightly
altered data (e.g. cross-validation or bootstrap) to find the optimal number of
boosting iterations and prevent overfitting. In our proposed approach, we
augment the data set with randomly permuted versions of the true variables, so
called shadow variables, and stop the step-wise fitting as soon as such a
variable would be added to the model. This allows variable selection in a
single fit of the model without requiring further parameter tuning. We show
that our probing approach can compete with state-of-the-art selection methods
like stability selection in a high-dimensional classification benchmark and
apply it on gene expression data for the estimation of riboflavin production of
Bacillus subtilis. | [
0,
0,
0,
1,
0,
0
] |
Title: A surface-hopping method for semiclassical calculations of cross sections for radiative association with electronic transitions,
Abstract: A semicalssical method based on surface-hopping techniques is developed to
model the dynamics of radiative association with electronic transitions in
arbitrary polyatomic systems. It can be proven that our method is an extension
of the established semiclassical formula used in the characterization of
diatomic molecule- formation. Our model is tested for diatomic molecules. It
gives the same cross sections as the former semiclassical formula, but contrary
to the former method it allows us to follow the fate of the trajectories after
the emission of a photon. This means that we can characterize the rovibrational
states of the stabilized molecules: using semiclassial quantization we can
obtain quantum state resolved cross sections or emission spectra for the
radiative association process. The calculated semiclassical state resolved
spectra show good agreement with the result of quantum mechanical perturbation
theory. Furthermore our surface-hopping model is not only applicable for the
description of radiative association but it can be use for semiclassical
characterization of any molecular process where spontaneous emission occurs. | [
0,
1,
0,
0,
0,
0
] |
Title: Persistent Spread Measurement for Big Network Data Based on Register Intersection,
Abstract: Persistent spread measurement is to count the number of distinct elements
that persist in each network flow for predefined time periods. It has many
practical applications, including detecting long-term stealthy network
activities in the background of normal-user activities, such as stealthy DDoS
attack, stealthy network scan, or faked network trend, which cannot be detected
by traditional flow cardinality measurement. With big network data, one
challenge is to measure the persistent spreads of a massive number of flows
without incurring too much memory overhead as such measurement may be performed
at the line speed by network processors with fast but small on-chip memory. We
propose a highly compact Virtual Intersection HyperLogLog (VI-HLL) architecture
for this purpose. It achieves far better memory efficiency than the best prior
work of V-Bitmap, and in the meantime drastically extends the measurement
range. Theoretical analysis and extensive experiments demonstrate that VI-HLL
provides good measurement accuracy even in very tight memory space of less than
1 bit per flow. | [
1,
0,
0,
0,
0,
0
] |
Title: High-dimensional Linear Regression for Dependent Observations with Application to Nowcasting,
Abstract: In the last few years, an extensive literature has been focused on the
$\ell_1$ penalized least squares (Lasso) estimators of high dimensional linear
regression when the number of covariates $p$ is considerably larger than the
sample size $n$. However, there is limited attention paid to the properties of
the estimators when the errors or/and the covariates are serially dependent. In
this study, we investigate the theoretical properties of the Lasso estimators
for linear regression with random design under serially dependent and/or
non-sub-Gaussian errors and covariates. In contrast to the traditional case in
which the errors are i.i.d and have finite exponential moments, we show that
$p$ can at most be a power of $n$ if the errors have only polynomial moments.
In addition, the rate of convergence becomes slower due to the serial
dependencies in errors and the covariates. We also consider sign consistency
for model selection via Lasso when there are serial correlations in the errors
or the covariates or both. Adopting the framework of functional dependence
measure, we provide a detailed description on how the rates of convergence and
the selection consistencies of the estimators depend on the dependence measures
and moment conditions of the errors and the covariates. Simulation results show
that Lasso regression can be substantially more powerful than the mixed
frequency data sampling regression (MIDAS) in the presence of irrelevant
variables. We apply the results obtained for the Lasso method to nowcasting
mixing frequency data in which serially correlated errors and a large number of
covariates are common. In real examples, the Lasso procedure outperforms the
MIDAS in both forecasting and nowcasting. | [
0,
0,
1,
1,
0,
0
] |
Title: Dynamic control of the optical emission from GaN/InGaN nanowire quantum dots by surface acoustic waves,
Abstract: The optical emission of InGaN quantum dots embedded in GaN nanowires is
dynamically controlled by a surface acoustic wave (SAW). The emission energy of
both the exciton and biexciton lines is modulated over a 1.5 meV range at ~330
MHz. A small but systematic difference in the exciton and biexciton spectral
modulation reveals a linear change of the biexciton binding energy with the SAW
amplitude. The present results are relevant for the dynamic control of
individual single photon emitters based on nitride semiconductors. | [
0,
1,
0,
0,
0,
0
] |
Title: Deep Generative Networks For Sequence Prediction,
Abstract: This thesis investigates unsupervised time series representation learning for
sequence prediction problems, i.e. generating nice-looking input samples given
a previous history, for high dimensional input sequences by decoupling the
static input representation from the recurrent sequence representation. We
introduce three models based on Generative Stochastic Networks (GSN) for
unsupervised sequence learning and prediction. Experimental results for these
three models are presented on pixels of sequential handwritten digit (MNIST)
data, videos of low-resolution bouncing balls, and motion capture data. The
main contribution of this thesis is to provide evidence that GSNs are a viable
framework to learn useful representations of complex sequential input data, and
to suggest a new framework for deep generative models to learn complex
sequences by decoupling static input representations from dynamic time
dependency representations. | [
0,
0,
0,
1,
0,
0
] |
Title: Composite Behavioral Modeling for Identity Theft Detection in Online Social Networks,
Abstract: In this work, we aim at building a bridge from poor behavioral data to an
effective, quick-response, and robust behavior model for online identity theft
detection. We concentrate on this issue in online social networks (OSNs) where
users usually have composite behavioral records, consisting of
multi-dimensional low-quality data, e.g., offline check-ins and online user
generated content (UGC). As an insightful result, we find that there is a
complementary effect among different dimensions of records for modeling users'
behavioral patterns. To deeply exploit such a complementary effect, we propose
a joint model to capture both online and offline features of a user's composite
behavior. We evaluate the proposed joint model by comparing with some typical
models on two real-world datasets: Foursquare and Yelp. In the widely-used
setting of theft simulation (simulating thefts via behavioral replacement), the
experimental results show that our model outperforms the existing ones, with
the AUC values $0.956$ in Foursquare and $0.947$ in Yelp, respectively.
Particularly, the recall (True Positive Rate) can reach up to $65.3\%$ in
Foursquare and $72.2\%$ in Yelp with the corresponding disturbance rate (False
Positive Rate) below $1\%$. It is worth mentioning that these performances can
be achieved by examining only one composite behavior (visiting a place and
posting a tip online simultaneously) per authentication, which guarantees the
low response latency of our method. This study would give the cybersecurity
community new insights into whether and how a real-time online identity
authentication can be improved via modeling users' composite behavioral
patterns. | [
1,
0,
0,
0,
0,
0
] |
Title: Asymptotic properties of the set of systoles of arithmetic Riemann surfaces,
Abstract: The purpose this article is to try to understand the mysterious coincidence
between the asymptotic behavior of the volumes of the Moduli Space of closed
hyperbolic surfaces of genus $g$ with respect to the Weil-Petersson metric and
the asymptotic behavior of the number of arithmetic closed hyperbolic surfaces
of genus $g$. If the set of arithmetic surfaces is well distributed then its
image for any interesting function should be well distributed too. We
investigate the distribution of the function systole. We give several results
indicating that the systoles of arithmetic surfaces can not be concentrated,
consequently the same holds for the set of arithmetic surfaces. The proofs are
based in different techniques: combinatorics (obtaining regular graphs with any
girth from results of B. Bollobas and constructions with cages and Ramanujan
graphs), group theory (constructing finite index subgroups of surface groups
from finite index subgroups of free groups using results of G. Baumslag) and
geometric group theory (linking the geometry of graphs with the geometry of
coverings of a surface). | [
0,
0,
1,
0,
0,
0
] |
Title: Multiband Superconductivity in the time reversal symmetry broken superconductor Re6Zr,
Abstract: We report point contact Andreev Reflection (PCAR) measurements on a
high-quality single crystal of the non-centrosymmetric superconductor Re6Zr. We
observe that the PCAR spectra can be fitted by taking two isotropic
superconducting gaps with Delta_1 ~ 0.79 meV and Delta_2 ~ 0.22 meV
respectively, suggesting that there are at least two bands which contribute to
superconductivity. Combined with the observation of time reversal symmetry
breaking at the superconducting transition from muon spin relaxation
measurements (Phys. Rev. Lett. 112, 107002 (2014)), our results imply an
unconventional superconducting order in this compound: A multiband singlet
state that breaks time reversal symmetry or a triplet state dominated by
interband pairing. | [
0,
1,
0,
0,
0,
0
] |
Title: Comment on "Laser cooling of $^{173}$Yb for isotope separation and precision hyperfine spectroscopy",
Abstract: We present measurements of the hyperfine splitting in the Yb-173
$6s6p~^1P_1^{\rm o} (F^{\prime}=3/2,7/2)$ states that disagree significantly
with those measured previously by Das and Natarajan [Phys. Rev. A 76, 062505
(2007)]. We point out inconsistencies in their measurements and suggest that
their error is due to optical pumping and improper determination of the atomic
line center. Our measurements are made using an optical frequency comb. We use
an optical pumping scheme to improve the signal-to-background ratio for the
$F^{\prime}=3/2$ component. | [
0,
1,
0,
0,
0,
0
] |
Title: Training GANs with Optimism,
Abstract: We address the issue of limit cycling behavior in training Generative
Adversarial Networks and propose the use of Optimistic Mirror Decent (OMD) for
training Wasserstein GANs. Recent theoretical results have shown that
optimistic mirror decent (OMD) can enjoy faster regret rates in the context of
zero-sum games. WGANs is exactly a context of solving a zero-sum game with
simultaneous no-regret dynamics. Moreover, we show that optimistic mirror
decent addresses the limit cycling problem in training WGANs. We formally show
that in the case of bi-linear zero-sum games the last iterate of OMD dynamics
converges to an equilibrium, in contrast to GD dynamics which are bound to
cycle. We also portray the huge qualitative difference between GD and OMD
dynamics with toy examples, even when GD is modified with many adaptations
proposed in the recent literature, such as gradient penalty or momentum. We
apply OMD WGAN training to a bioinformatics problem of generating DNA
sequences. We observe that models trained with OMD achieve consistently smaller
KL divergence with respect to the true underlying distribution, than models
trained with GD variants. Finally, we introduce a new algorithm, Optimistic
Adam, which is an optimistic variant of Adam. We apply it to WGAN training on
CIFAR10 and observe improved performance in terms of inception score as
compared to Adam. | [
1,
0,
0,
1,
0,
0
] |
Title: A recipe for topological observables of density matrices,
Abstract: Meaningful topological invariants for mixed quantum states are challenging to
identify as there is no unique way to define them, and most choices do not
directly relate to physical observables. Here, we propose a simple pragmatic
approach to construct topological invariants of mixed states while preserving a
connection to physical observables, by continuously deforming known topological
invariants for pure (ground) states. Our approach relies on expectation values
of many-body operators, with no reference to single-particle (e.g., Bloch)
wavefunctions. To illustrate it, we examine extensions to mixed states of
$U(1)$ geometric (Berry) phases and their corresponding topological invariant
(winding or Chern number). We discuss measurement schemes, and provide a
detailed construction of invariants for thermal or more general mixed states of
quantum systems with (at least) $U(1)$ charge-conservation symmetry, such as
quantum Hall insulators. | [
0,
1,
0,
0,
0,
0
] |
Title: On a class of infinitely differentiable functions in ${\mathbb R}^n$ admitting holomorphic extension in ${\mathbb C}^n$,
Abstract: A space $G(M, \varPhi)$ of infinitely differentiable functions in ${\mathbb
R}^n$ constructed with a help of a family
$\varPhi=\{\varphi_m\}_{m=1}^{\infty}$ of real-valued functions $\varphi_m
\in~C({\mathbb R}^n)$ and a logarithmically convex sequence $M$ of positive
numbers is considered in the article. In view of conditions on $M$ each
function of $G(M, \varPhi)$ can be extended to an entire function in ${\mathbb
C}^n$. Imposed conditions on $M$ and $\varPhi$ allow to describe the space of
such extensions. | [
0,
0,
1,
0,
0,
0
] |
Title: Effects of Degree Correlations in Interdependent Security: Good or Bad?,
Abstract: We study the influence of degree correlations or network mixing in
interdependent security. We model the interdependence in security among agents
using a dependence graph and employ a population game model to capture the
interaction among many agents when they are strategic and have various security
measures they can choose to defend themselves. The overall network security is
measured by what we call the average risk exposure (ARE) from neighbors, which
is proportional to the total (expected) number of attacks in the network.
We first show that there exists a unique pure-strategy Nash equilibrium of a
population game. Then, we prove that as the agents with larger degrees in the
dependence graph see higher risks than those with smaller degrees, the overall
network security deteriorates in that the ARE experienced by agents increases
and there are more attacks in the network. Finally, using this finding, we
demonstrate that the effects of network mixing on ARE depend on the (cost)
effectiveness of security measures available to agents; if the security
measures are not effective, increasing assortativity of dependence graph
results in higher ARE. On the other hand, if the security measures are
effective at fending off the damages and losses from attacks, increasing
assortativity reduces the ARE experienced by agents. | [
1,
1,
0,
0,
0,
0
] |
Title: Forming short-period Wolf-Rayet X-ray binaries and double black holes through stable mass transfer,
Abstract: We show that black-hole High-Mass X-ray Binaries (HMXBs) with O- or B-type
donor stars and relatively short orbital periods, of order one week to several
months may survive spiral in, to then form Wolf-Rayet (WR) X-ray binaries with
orbital periods of order a day to a few days; while in systems where the
compact star is a neutron star, HMXBs with these orbital periods never survive
spiral-in. We therefore predict that WR X-ray binaries can only harbor black
holes. The reason why black-hole HMXBs with these orbital periods may survive
spiral in is: the combination of a radiative envelope of the donor star, and a
high mass of the compact star. In this case, when the donor begins to overflow
its Roche lobe, the systems are able to spiral in slowly with stable Roche-lobe
overflow, as is shown by the system SS433. In this case the transferred mass is
ejected from the vicinity of the compact star (so-called "isotropic
re-emission" mass loss mode, or "SS433-like mass loss"), leading to gradual
spiral-in. If the mass ratio of donor and black hole is $>3.5$, these systems
will go into CE evolution and are less likely to survive. If they survive, they
produce WR X-ray binaries with orbital periods of a few hours to one day.
Several of the well-known WR+O binaries in our Galaxy and the Magellanic
Clouds, with orbital periods in the range between a week and several months,
are expected to evolve into close WR-Black-Hole binaries,which may later
produce close double black holes. The galactic formation rate of double black
holes resulting from such systems is still uncertain, as it depends on several
poorly known factors in this evolutionary picture. It might possibly be as high
as $\sim 10^{-5}$ per year. | [
0,
1,
0,
0,
0,
0
] |
Title: Learning to Draw Samples with Amortized Stein Variational Gradient Descent,
Abstract: We propose a simple algorithm to train stochastic neural networks to draw
samples from given target distributions for probabilistic inference. Our method
is based on iteratively adjusting the neural network parameters so that the
output changes along a Stein variational gradient direction (Liu & Wang, 2016)
that maximally decreases the KL divergence with the target distribution. Our
method works for any target distribution specified by their unnormalized
density function, and can train any black-box architectures that are
differentiable in terms of the parameters we want to adapt. We demonstrate our
method with a number of applications, including variational autoencoder (VAE)
with expressive encoders to model complex latent space structures, and
hyper-parameter learning of MCMC samplers that allows Bayesian inference to
adaptively improve itself when seeing more data. | [
0,
0,
0,
1,
0,
0
] |
Title: Preconditioned dynamic mode decomposition and mode selection algorithms for large datasets using incremental proper orthogonal decomposition,
Abstract: This note proposes a simple and general framework of dynamic mode
decomposition (DMD) and a mode selection for large datasets. The proposed
framework explicitly introduces a preconditioning step using an incremental
proper orthogonal decomposition to DMD and mode selection algorithms. By
performing the preconditioning step, the DMD and the mode selection can be
performed with low memory consumption and small computational complexity and
can be applied to large datasets. In addition, a simple mode selection
algorithm based on a greedy method is proposed. The proposed framework is
applied to the analysis of a three-dimensional flows around a circular
cylinder. | [
0,
1,
0,
0,
0,
0
] |
Title: Variants of RMSProp and Adagrad with Logarithmic Regret Bounds,
Abstract: Adaptive gradient methods have become recently very popular, in particular as
they have been shown to be useful in the training of deep neural networks. In
this paper we have analyzed RMSProp, originally proposed for the training of
deep neural networks, in the context of online convex optimization and show
$\sqrt{T}$-type regret bounds. Moreover, we propose two variants SC-Adagrad and
SC-RMSProp for which we show logarithmic regret bounds for strongly convex
functions. Finally, we demonstrate in the experiments that these new variants
outperform other adaptive gradient techniques or stochastic gradient descent in
the optimization of strongly convex functions as well as in training of deep
neural networks. | [
1,
0,
0,
1,
0,
0
] |
Title: Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions,
Abstract: We study connections between Dykstra's algorithm for projecting onto an
intersection of convex sets, the augmented Lagrangian method of multipliers or
ADMM, and block coordinate descent. We prove that coordinate descent for a
regularized regression problem, in which the (separable) penalty functions are
seminorms, is exactly equivalent to Dykstra's algorithm applied to the dual
problem. ADMM on the dual problem is also seen to be equivalent, in the special
case of two sets, with one being a linear subspace. These connections, aside
from being interesting in their own right, suggest new ways of analyzing and
extending coordinate descent. For example, from existing convergence theory on
Dykstra's algorithm over polyhedra, we discern that coordinate descent for the
lasso problem converges at an (asymptotically) linear rate. We also develop two
parallel versions of coordinate descent, based on the Dykstra and ADMM
connections. | [
0,
0,
1,
1,
0,
0
] |
Title: A Topological Perspective on Interacting Algebraic Theories,
Abstract: Techniques from higher categories and higher-dimensional rewriting are
becoming increasingly important for understanding the finer, computational
properties of higher algebraic theories that arise, among other fields, in
quantum computation. These theories have often the property of containing
simpler sub-theories, whose interaction is regulated in a limited number of
ways, which reveals a topological substrate when pictured by string diagrams.
By exploring the double nature of computads as presentations of higher
algebraic theories, and combinatorial descriptions of "directed spaces", we
develop a basic language of directed topology for the compositional study of
algebraic theories. We present constructions of computads, all with clear
analogues in standard topology, that capture in great generality such notions
as homomorphisms and actions, and the interactions of monoids and comonoids
that lead to the theory of Frobenius algebras and of bialgebras. After a number
of examples, we describe how a fragment of the ZX calculus can be reconstructed
in this framework. | [
1,
0,
1,
0,
0,
0
] |
Title: Dynamic nested sampling: an improved algorithm for parameter estimation and evidence calculation,
Abstract: We introduce dynamic nested sampling: a generalisation of the nested sampling
algorithm in which the number of "live points" varies to allocate samples more
efficiently. In empirical tests the new method significantly improves
calculation accuracy compared to standard nested sampling with the same number
of samples; this increase in accuracy is equivalent to speeding up the
computation by factors of up to ~72 for parameter estimation and ~7 for
evidence calculations. We also show that the accuracy of both parameter
estimation and evidence calculations can be improved simultaneously. In
addition, unlike in standard nested sampling, more accurate results can be
obtained by continuing the calculation for longer. Popular standard nested
sampling implementations can be easily adapted to perform dynamic nested
sampling, and several dynamic nested sampling software packages are now
publicly available. | [
0,
1,
0,
1,
0,
0
] |
Title: Bounds on poloidal kinetic energy in plane layer convection,
Abstract: A numerical method is presented which conveniently computes upper bounds on
heat transport and poloidal energy in plane layer convection for infinite and
finite Prandtl numbers. The bounds obtained for the heat transport coincide
with earlier results. These bounds imply upper bounds for the poloidal energy
which follow directly from the definitions of dissipation and energy. The same
constraints used for computing upper bounds on the heat transport lead to
improved bounds for the poloidal energy. | [
0,
1,
0,
0,
0,
0
] |
Title: Switching Isotropic and Directional Exploration with Parameter Space Noise in Deep Reinforcement Learning,
Abstract: This paper proposes an exploration method for deep reinforcement learning
based on parameter space noise. Recent studies have experimentally shown that
parameter space noise results in better exploration than the commonly used
action space noise. Previous methods devised a way to update the diagonal
covariance matrix of a noise distribution and did not consider the direction of
the noise vector and its correlation. In addition, fast updates of the noise
distribution are required to facilitate policy learning. We propose a method
that deforms the noise distribution according to the accumulated returns and
the noises that have led to the returns. Moreover, this method switches
isotropic exploration and directional exploration in parameter space with
regard to obtained rewards. We validate our exploration strategy in the OpenAI
Gym continuous environments and modified environments with sparse rewards. The
proposed method achieves results that are competitive with a previous method at
baseline tasks. Moreover, our approach exhibits better performance in sparse
reward environments by exploration with the switching strategy. | [
0,
0,
0,
1,
0,
0
] |
Title: The solution to the initial value problem for the ultradiscrete Somos-4 and 5 equations,
Abstract: We propose a method to solve the initial value problem for the ultradiscrete
Somos-4 and Somos-5 equations by expressing terms in the equations as convex
polygons and regarding max-plus algebras as those on polygons. | [
0,
1,
0,
0,
0,
0
] |
Title: Two variants of the Froiduire-Pin Algorithm for finite semigroups,
Abstract: In this paper, we present two algorithms based on the Froidure-Pin Algorithm
for computing the structure of a finite semigroup from a generating set. As was
the case with the original algorithm of Froidure and Pin, the algorithms
presented here produce the left and right Cayley graphs, a confluent
terminating rewriting system, and a reduced word of the rewriting system for
every element of the semigroup.
If $U$ is any semigroup, and $A$ is a subset of $U$, then we denote by
$\langle A\rangle$ the least subsemigroup of $U$ containing $A$. If $B$ is any
other subset of $U$, then, roughly speaking, the first algorithm we present
describes how to use any information about $\langle A\rangle$, that has been
found using the Froidure-Pin Algorithm, to compute the semigroup $\langle A\cup
B\rangle$. More precisely, we describe the data structure for a finite
semigroup $S$ given by Froidure and Pin, and how to obtain such a data
structure for $\langle A\cup B\rangle$ from that for $\langle A\rangle$. The
second algorithm is a lock-free concurrent version of the Froidure-Pin
Algorithm. | [
1,
0,
1,
0,
0,
0
] |
Title: Holon Wigner Crystal in a Lightly Doped Kagome Quantum Spin Liquid,
Abstract: We address the problem of a lightly doped spin-liquid through a large-scale
density-matrix renormalization group (DMRG) study of the $t$-$J$ model on a
Kagome lattice with a small but non-zero concentration, $\delta$, of doped
holes. It is now widely accepted that the undoped ($\delta=0$) spin 1/2
Heisenberg antiferromagnet has a spin-liquid groundstate. Theoretical arguments
have been presented that light doping of such a spin-liquid could give rise to
a high temperature superconductor or an exotic topological Fermi liquid metal
(FL$^\ast$). Instead, we infer that the doped holes form an insulating
charge-density wave state with one doped-hole per unit cell - i.e. a Wigner
crystal (WC). Spin correlations remain short-ranged, as in the spin-liquid
parent state, from which we infer that the state is a crystal of spinless
holons (WC$^\ast$), rather than of holes. Our results may be relevant to Kagome
lattice Herbertsmithite $\rm ZnCu_3(OH)_6Cl_2$ upon doping. | [
0,
1,
0,
0,
0,
0
] |
Title: Accelerated Block Coordinate Proximal Gradients with Applications in High Dimensional Statistics,
Abstract: Nonconvex optimization problems arise in different research fields and arouse
lots of attention in signal processing, statistics and machine learning. In
this work, we explore the accelerated proximal gradient method and some of its
variants which have been shown to converge under nonconvex context recently. We
show that a novel variant proposed here, which exploits adaptive momentum and
block coordinate update with specific update rules, further improves the
performance of a broad class of nonconvex problems. In applications to sparse
linear regression with regularizations like Lasso, grouped Lasso, capped
$\ell_1$ and SCAP, the proposed scheme enjoys provable local linear
convergence, with experimental justification. | [
1,
0,
0,
1,
0,
0
] |
Title: Static structure of chameleon dark Matter as an explanation of dwarf spheroidal galactic core,
Abstract: We propose a novel mechanism which explains cored dark matter density profile
in recently observed dark matter rich dwarf spheroidal galaxies. In our
scenario, dark matter particle mass decreases gradually as function of distance
towards the center of a dwarf galaxy due to its interaction with a chameleon
scalar. At closer distance towards galactic center the strength of attractive
scalar fifth force becomes much stronger than gravity and is balanced by the
Fermi pressure of dark matter cloud, thus an equilibrium static configuration
of dark matter halo is obtained. Like the case of soliton star or fermion
Q-star, the stability of the dark matter halo is obtained as the scalar
achieves a static profile and reaches an asymptotic value away from the
galactic center. For simple scalar-dark matter interaction and quadratic scalar
self interaction potential, we show that dark matter behaves exactly like cold
dark matter (CDM) beyond few $\rm{kpc}$ away from galactic center but at closer
distance it becomes lighter and fermi pressure cannot be ignored anymore. Using
Thomas-Fermi approximation, we numerically solve the radial static profile of
the scalar field, fermion mass and dark matter energy density as a function of
distance. We find that for fifth force mediated by an ultra light scalar, it is
possible to obtain a flattened dark matter density profile towards galactic
center. In our scenario, the fifth force can be neglected at distance $ r \geq
1\, \rm{kpc}$ from galactic center and dark matter can be simply treated as
heavy non-relativistic particles beyond this distance, thus reproducing the
success of CDM at large scales. | [
0,
1,
0,
0,
0,
0
] |
Title: Unbalancing Sets and an Almost Quadratic Lower Bound for Syntactically Multilinear Arithmetic Circuits,
Abstract: We prove a lower bound of $\Omega(n^2/\log^2 n)$ on the size of any
syntactically multilinear arithmetic circuit computing some explicit
multilinear polynomial $f(x_1, \ldots, x_n)$. Our approach expands and improves
upon a result of Raz, Shpilka and Yehudayoff ([RSY08]), who proved a lower
bound of $\Omega(n^{4/3}/\log^2 n)$ for the same polynomial. Our improvement
follows from an asymptotically optimal lower bound for a generalized version of
Galvin's problem in extremal set theory. | [
1,
0,
0,
0,
0,
0
] |
Title: Topological and non inertial effects on the interbank light absorption,
Abstract: In this work, we investigate the combined influence of the nontrivial
topology introduced by a disclination and non inertial effects due to rotation,
in the energy levels and the wave functions of a noninteracting electron gas
confined to a two-dimensional pseudoharmonic quantum dot, under the influence
of an external uniform magnetic field. The exact solutions for energy
eigenvalues and wave functions are computed as functions of the applied
magnetic field strength, the disclination topological charge, magnetic quantum
number and the rotation speed of the sample. We investigate the modifications
on the light interband absorption coefficient and absorption threshold
frequency. We observe novel features in the system, including a range of
magnetic field without corresponding absorption phenomena, which is due to a
tripartite term of the Hamiltonian, involving magnetic field, the topological
charge of the defect and the rotation frequency. | [
0,
1,
0,
0,
0,
0
] |
Title: Hyperbolic inverse mean curvature flow,
Abstract: In this paper, we prove the short-time existence of hyperbolic inverse (mean)
curvature flow (with or without the specified forcing term) under the
assumption that the initial compact smooth hypersurface of $\mathbb{R}^{n+1}$
($n\geqslant2$) is mean convex and star-shaped. Several interesting examples
and some hyperbolic evolution equations for geometric quantities of the
evolving hypersurfaces have been shown. Besides, under different assumptions
for the initial velocity, we can get the expansion and the convergence results
of a hyperbolic inverse mean curvature flow in the plane $\mathbb{R}^2$, whose
evolving curves move normally. | [
0,
0,
1,
0,
0,
0
] |
Title: A new charge reconstruction algorithm for the DAMPE silicon microstrip detector,
Abstract: The DArk Matter Particle Explorer (DAMPE) is one of the four satellites
within the Strategic Pioneer Research Program in Space Science of the Chinese
Academy of Science (CAS). The Silicon-Tungsten Tracker (STK), which is composed
of 768 singled-sided silicon microstrip detectors, is one of the four
subdetectors in DAMPE, providing track reconstruction and charge identification
for relativistic charged particles. The charge response of DAMPE silicon
microstrip detectors is complicated, depending on the incident angle and impact
position. A new charge reconstruction algorithm for the DAMPE silicon
microstrip detector is introduced in this paper. This algorithm can correct the
complicated charge response, and was proved applicable by the ion test beam. | [
0,
1,
0,
0,
0,
0
] |
Title: The Structure Transfer Machine Theory and Applications,
Abstract: Representation learning is a fundamental but challenging problem, especially
when the distribution of data is unknown. We propose a new representation
learning method, termed Structure Transfer Machine (STM), which enables feature
learning process to converge at the representation expectation in a
probabilistic way. We theoretically show that such an expected value of the
representation (mean) is achievable if the manifold structure can be
transferred from the data space to the feature space. The resulting structure
regularization term, named manifold loss, is incorporated into the loss
function of the typical deep learning pipeline. The STM architecture is
constructed to enforce the learned deep representation to satisfy the intrinsic
manifold structure from the data, which results in robust features that suit
various application scenarios, such as digit recognition, image classification
and object tracking. Compared to state-of-the-art CNN architectures, we achieve
the better results on several commonly used benchmarks\footnote{The source code
is available. this https URL }. | [
0,
0,
0,
1,
0,
0
] |
Title: Large dimensional analysis of general margin based classification methods,
Abstract: Margin-based classifiers have been popular in both machine learning and
statistics for classification problems. Since a large number of classifiers are
available, one natural question is which type of classifiers should be used
given a particular classification task. We aim to answering this question by
investigating the asymptotic performance of a family of large-margin
classifiers in situations where the data dimension $p$ and the sample $n$ are
both large. This family covers a broad range of classifiers including support
vector machine, distance weighted discrimination, penalized logistic
regression, and large-margin unified machine as special cases. The asymptotic
results are described by a set of nonlinear equations and we observe a close
match of them with Monte Carlo simulation on finite data samples. Our
analytical studies shed new light on how to select the best classifier among
various classification methods as well as on how to choose the optimal tuning
parameters for a given method. | [
1,
0,
0,
1,
0,
0
] |
Title: Covariance structure associated with an equality between two general ridge estimators,
Abstract: In a general linear model, this paper derives a necessary and sufficient
condition under which two general ridge estimators coincide with each other.
The condition is given as a structure of the dispersion matrix of the error
term. Since the class of estimators considered here contains linear unbiased
estimators such as the ordinary least squares estimator and the best linear
unbiased estimator, our result can be viewed as a generalization of the
well-known theorems on the equality between these two estimators, which have
been fully studied in the literature. Two related problems are also considered:
equality between two residual sums of squares, and classification of dispersion
matrices by a perturbation approach. | [
0,
0,
1,
1,
0,
0
] |
Title: Three natural subgroups of the Brauer-Picard group of a Hopf algebra with applications,
Abstract: In this article we construct three explicit natural subgroups of the
Brauer-Picard group of the category of representations of a finite-dimensional
Hopf algebra. In examples the Brauer Picard group decomposes into an ordered
product of these subgroups, somewhat similar to a Bruhat decomposition.
Our construction returns for any Hopf algebra three types of braided
autoequivalences and correspondingly three families of invertible bimodule
categories. This gives examples of so-called (2-)Morita equivalences and
defects in topological field theories. We have a closer look at the case of
quantum groups and Nichols algebras and give interesting applications. Finally,
we briefly discuss the three families of group-theoretic extensions. | [
0,
0,
1,
0,
0,
0
] |
Title: Towards a Deep Reinforcement Learning Approach for Tower Line Wars,
Abstract: There have been numerous breakthroughs with reinforcement learning in the
recent years, perhaps most notably on Deep Reinforcement Learning successfully
playing and winning relatively advanced computer games. There is undoubtedly an
anticipation that Deep Reinforcement Learning will play a major role when the
first AI masters the complicated game plays needed to beat a professional
Real-Time Strategy game player. For this to be possible, there needs to be a
game environment that targets and fosters AI research, and specifically Deep
Reinforcement Learning. Some game environments already exist, however, these
are either overly simplistic such as Atari 2600 or complex such as Starcraft II
from Blizzard Entertainment. We propose a game environment in between Atari
2600 and Starcraft II, particularly targeting Deep Reinforcement Learning
algorithm research. The environment is a variant of Tower Line Wars from
Warcraft III, Blizzard Entertainment. Further, as a proof of concept that the
environment can harbor Deep Reinforcement algorithms, we propose and apply a
Deep Q-Reinforcement architecture. The architecture simplifies the state space
so that it is applicable to Q-learning, and in turn improves performance
compared to current state-of-the-art methods. Our experiments show that the
proposed architecture can learn to play the environment well, and score 33%
better than standard Deep Q-learning which in turn proves the usefulness of the
game environment. | [
1,
0,
0,
0,
0,
0
] |
Title: UV Detector based on InAlN/GaN-on-Si HEMT Stack with Photo-to-Dark Current Ratio > 107,
Abstract: We demonstrate an InAlN/GaN-on-Si HEMT based UV detector with photo to dark
current ratio > 107. Ti/Al/Ni/Au metal stack was evaporated and rapid thermal
annealed for Ohmic contacts to the 2D electron gas (2DEG) at the InAlN/GaN
interface while the channel + barrier was recess etched to a depth of 20 nm to
pinch-off the 2DEG between Source-Drain pads. Spectral responsivity (SR) of 34
A/W at 367 nm was measured at 5 V in conjunction with very high photo to dark
current ratio of > 10^7. The photo to dark current ratio at a fixed bias was
found to be decreasing with increase in recess length of the PD. The fabricated
devices were found to exhibit a UV-to-visible rejection ratio of >103 with a
low dark current < 32 pA at 5 V. Transient measurements showed rise and fall
times in the range of 3-4 ms. The gain mechanism was investigated and carrier
lifetimes were estimated which matched well with those reported elsewhere. | [
0,
1,
0,
0,
0,
0
] |
Title: Deep Reinforcement Learning for Inquiry Dialog Policies with Logical Formula Embeddings,
Abstract: This paper is the first attempt to learn the policy of an inquiry dialog
system (IDS) by using deep reinforcement learning (DRL). Most IDS frameworks
represent dialog states and dialog acts with logical formulae. In order to make
learning inquiry dialog policies more effective, we introduce a logical formula
embedding framework based on a recursive neural network. The results of
experiments to evaluate the effect of 1) the DRL and 2) the logical formula
embedding framework show that the combination of the two are as effective or
even better than existing rule-based methods for inquiry dialog policies. | [
1,
0,
0,
0,
0,
0
] |
Title: A Lattice Model of Charge-Pattern-Dependent Polyampholyte Phase Separation,
Abstract: In view of recent intense experimental and theoretical interests in the
biophysics of liquid-liquid phase separation (LLPS) of intrinsically disordered
proteins (IDPs), heteropolymer models with chain molecules configured as
self-avoiding walks on the simple cubic lattice are constructed to study how
phase behaviors depend on the sequence of monomers along the chains. To address
pertinent general principles, we focus primarily on two fully charged
50-monomer sequences with significantly different charge patterns. Each monomer
in our models occupies a single lattice site and all monomers interact via a
screened pairwise Coulomb potential. Phase diagrams are obtained by extensive
Monte Carlo sampling performed at multiple temperatures on ensembles of 300
chains in boxes of sizes ranging from $52\times 52\times 52$ to $246\times
246\times 246$ to simulate a large number of different systems with the overall
polymer volume fraction $\phi$ in each system varying from $0.001$ to $0.1$.
Phase separation in the model systems is characterized by the emergence of a
large cluster connected by inter-monomer nearest-neighbor lattice contacts and
by large fluctuations in local polymer density. The simulated critical
temperatures, $T_{\rm cr}$, of phase separation for the two sequences differ
significantly, whereby the sequence with a more "blocky" charge pattern
exhibits a substantially higher propensity to phase separate. The trend is
consistent with our sequence-specific random-phase-approximation (RPA) polymer
theory, but the variation of the simulated $T_{\rm cr}$ with a previously
proposed "sequence charge decoration" pattern parameter is milder than that
predicted by RPA. Ramifications of our findings for the development of
analytical theory and simulation protocols of IDP LLPS are discussed. | [
0,
0,
0,
0,
1,
0
] |
Title: "Noiseless" thermal noise measurement of atomic force microscopy cantilevers,
Abstract: When measuring quadratic values representative of random fluctuations, such
as the thermal noise of Atomic Force Microscopy (AFM) cantilevers, the
background measurement noise cannot be averaged to zero. We present a signal
processing method that allows to get rid of this limitation using the
ubiquitous optical beam deflection sensor of standard AFMs. We demonstrate a
two orders of magnitude enhancement of the signal to noise ratio in our
experiment, allowing the calibration of stiff cantilevers or easy
identification of higher order modes from thermal noise measurements. | [
0,
1,
0,
0,
0,
0
] |
Title: A Forward Model at Purkinje Cell Synapses Facilitates Cerebellar Anticipatory Control,
Abstract: How does our motor system solve the problem of anticipatory control in spite
of a wide spectrum of response dynamics from different musculo-skeletal
systems, transport delays as well as response latencies throughout the central
nervous system? To a great extent, our highly-skilled motor responses are a
result of a reactive feedback system, originating in the brain-stem and spinal
cord, combined with a feed-forward anticipatory system, that is adaptively
fine-tuned by sensory experience and originates in the cerebellum. Based on
that interaction we design the counterfactual predictive control (CFPC)
architecture, an anticipatory adaptive motor control scheme in which a
feed-forward module, based on the cerebellum, steers an error feedback
controller with counterfactual error signals. Those are signals that trigger
reactions as actual errors would, but that do not code for any current or
forthcoming errors. In order to determine the optimal learning strategy, we
derive a novel learning rule for the feed-forward module that involves an
eligibility trace and operates at the synaptic level. In particular, our
eligibility trace provides a mechanism beyond co-incidence detection in that it
convolves a history of prior synaptic inputs with error signals. In the context
of cerebellar physiology, this solution implies that Purkinje cell synapses
should generate eligibility traces using a forward model of the system being
controlled. From an engineering perspective, CFPC provides a general-purpose
anticipatory control architecture equipped with a learning rule that exploits
the full dynamics of the closed-loop system. | [
1,
0,
1,
0,
0,
0
] |
Title: Episode-Based Active Learning with Bayesian Neural Networks,
Abstract: We investigate different strategies for active learning with Bayesian deep
neural networks. We focus our analysis on scenarios where new, unlabeled data
is obtained episodically, such as commonly encountered in mobile robotics
applications. An evaluation of different strategies for acquisition, updating,
and final training on the CIFAR-10 dataset shows that incremental network
updates with final training on the accumulated acquisition set are essential
for best performance, while limiting the amount of required human labeling
labor. | [
1,
0,
0,
1,
0,
0
] |
Title: Freeness and The Partial Transposes of Wishart Random Matrices,
Abstract: We show that the partial transposes of complex Wishart random matrices are
asymptotically free. We also investigate regimes where the number of blocks is
fixed but the size of the blocks increases. This gives a example where the
partial transpose produces freeness at the operator level. Finally we
investigate the case of real Wishart matrices. | [
0,
0,
1,
0,
0,
0
] |
Title: Fixed points of polarity type operators,
Abstract: A well-known result says that the Euclidean unit ball is the unique fixed
point of the polarity operator. This result implies that if, in $\mathbb{R}^n$,
the unit ball of some norm is equal to the unit ball of the dual norm, then the
norm must be Euclidean. Motivated by these results and by relatively recent
results in convex analysis and convex geometry regarding various properties of
order reversing operators, we consider, in a real Hilbert space setting, a more
general fixed point equation in which the polarity operator is composed with a
continuous invertible linear operator. We show that if the linear operator is
positive definite, then the considered equation is uniquely solvable by an
ellipsoid. Otherwise, the equation can have several (possibly infinitely many)
solutions or no solution at all. Our analysis yields a few by-products of
possible independent interest, among them results related to coercive bilinear
forms (essentially a quantitative convex analytic converse to the celebrated
Lax-Milgram theorem from partial differential equations) and a characterization
of real Hilbertian spaces. | [
0,
0,
1,
0,
0,
0
] |
Title: Implications of a wavelength dependent PSF for weak lensing measurements,
Abstract: The convolution of galaxy images by the point-spread function (PSF) is the
dominant source of bias for weak gravitational lensing studies, and an accurate
estimate of the PSF is required to obtain unbiased shape measurements. The PSF
estimate for a galaxy depends on its spectral energy distribution (SED),
because the instrumental PSF is generally a function of the wavelength. In this
paper we explore various approaches to determine the resulting `effective' PSF
using broad-band data. Considering the Euclid mission as a reference, we find
that standard SED template fitting methods result in biases that depend on
source redshift, although this may be remedied if the algorithms can be
optimised for this purpose. Using a machine-learning algorithm we show that, at
least in principle, the required accuracy can be achieved with the current
survey parameters. It is also possible to account for the correlations between
photometric redshift and PSF estimates that arise from the use of the same
photometry. We explore the impact of errors in photometric calibration, errors
in the assumed wavelength dependence of the PSF model and limitations of the
adopted template libraries. Our results indicate that the required accuracy for
Euclid can be achieved using the data that are planned to determine photometric
redshifts. | [
0,
1,
0,
0,
0,
0
] |
Title: Controlling Sources of Inaccuracy in Stochastic Kriging,
Abstract: Scientists and engineers commonly use simulation models to study real systems
for which actual experimentation is costly, difficult, or impossible. Many
simulations are stochastic in the sense that repeated runs with the same input
configuration will result in different outputs. For expensive or time-consuming
simulations, stochastic kriging \citep{ankenman} is commonly used to generate
predictions for simulation model outputs subject to uncertainty due to both
function approximation and stochastic variation. Here, we develop and justify a
few guidelines for experimental design, which ensure accuracy of stochastic
kriging emulators. We decompose error in stochastic kriging predictions into
nominal, numeric, parameter estimation and parameter estimation numeric
components and provide means to control each in terms of properties of the
underlying experimental design. The design properties implied for each source
of error are weakly conflicting and broad principles are proposed. In brief,
space-filling properties "small fill distance" and "large separation distance"
should balance with replication at distinct input configurations, with number
of replications depending on the relative magnitudes of stochastic and process
variability. Non-stationarity implies higher input density in more active
regions, while regression functions imply a balance with traditional design
properties. A few examples are presented to illustrate the results. | [
0,
0,
1,
1,
0,
0
] |
Title: Implications of Decentralized Q-learning Resource Allocation in Wireless Networks,
Abstract: Reinforcement Learning is gaining attention by the wireless networking
community due to its potential to learn good-performing configurations only
from the observed results. In this work we propose a stateless variation of
Q-learning, which we apply to exploit spatial reuse in a wireless network. In
particular, we allow networks to modify both their transmission power and the
channel used solely based on the experienced throughput. We concentrate in a
completely decentralized scenario in which no information about neighbouring
nodes is available to the learners. Our results show that although the
algorithm is able to find the best-performing actions to enhance aggregate
throughput, there is high variability in the throughput experienced by the
individual networks. We identify the cause of this variability as the
adversarial setting of our setup, in which the most played actions provide
intermittent good/poor performance depending on the neighbouring decisions. We
also evaluate the effect of the intrinsic learning parameters of the algorithm
on this variability. | [
1,
0,
0,
0,
0,
0
] |
Title: Exponential Ergodicity of the Bouncy Particle Sampler,
Abstract: Non-reversible Markov chain Monte Carlo schemes based on piecewise
deterministic Markov processes have been recently introduced in applied
probability, automatic control, physics and statistics. Although these
algorithms demonstrate experimentally good performance and are accordingly
increasingly used in a wide range of applications, geometric ergodicity results
for such schemes have only been established so far under very restrictive
assumptions. We give here verifiable conditions on the target distribution
under which the Bouncy Particle Sampler algorithm introduced in \cite{P_dW_12}
is geometrically ergodic. This holds whenever the target satisfies a curvature
condition and has tails decaying at least as fast as an exponential and at most
as fast as a Gaussian distribution. This allows us to provide a central limit
theorem for the associated ergodic averages. When the target has tails thinner
than a Gaussian distribution, we propose an original modification of this
scheme that is geometrically ergodic. For thick-tailed target distributions,
such as $t$-distributions, we extend the idea pioneered in \cite{J_G_12} in a
random walk Metropolis context. We apply a change of variable to obtain a
transformed target satisfying the tail conditions for geometric ergodicity. By
sampling the transformed target using the Bouncy Particle Sampler and mapping
back the Markov process to the original parameterization, we obtain a
geometrically ergodic algorithm. | [
0,
0,
0,
1,
0,
0
] |
Title: Analysis and X-ray tomography,
Abstract: These are lecture notes for the course "MATS4300 Analysis and X-ray
tomography" given at the University of Jyväskylä in Fall 2017. The course
is a broad overview of various tools in analysis that can be used to study
X-ray tomography. The focus is on tools and ideas, not so much on technical
details and minimal assumptions. Only very basic functional analysis is assumed
as background. Exercise problems are included. | [
0,
0,
1,
0,
0,
0
] |
Title: Differential-operator representations of Weyl group and singular vectors,
Abstract: Given a suitable ordering of the positive root system associated with a
semisimple Lie algebra, there exists a natural correspondence between Verma
modules and related polynomial algebras. With this, the Lie algebra action on a
Verma module can be interpreted as a differential operator action on
polynomials, and thus on the corresponding truncated formal power series. We
prove that the space of truncated formal power series is a
differential-operator representation of the Weyl group $W$. We also introduce a
system of partial differential equations to investigate singular vectors in the
Verma module. It is shown that the solution space of the system in the space of
truncated formal power series is the span of $\{w(1)\ |\ w\in W\}$. Those
$w(1)$ that are polynomials correspond to singular vectors in the Verma module.
This elementary approach by partial differential equations also gives a new
proof of the well-known BGG-Verma Theorem. | [
0,
0,
1,
0,
0,
0
] |
Title: On Approximation Guarantees for Greedy Low Rank Optimization,
Abstract: We provide new approximation guarantees for greedy low rank matrix estimation
under standard assumptions of restricted strong convexity and smoothness. Our
novel analysis also uncovers previously unknown connections between the low
rank estimation and combinatorial optimization, so much so that our bounds are
reminiscent of corresponding approximation bounds in submodular maximization.
Additionally, we also provide statistical recovery guarantees. Finally, we
present empirical comparison of greedy estimation with established baselines on
two important real-world problems. | [
1,
0,
0,
1,
0,
0
] |
Title: Wasserstein Introspective Neural Networks,
Abstract: We present Wasserstein introspective neural networks (WINN) that are both a
generator and a discriminator within a single model. WINN provides a
significant improvement over the recent introspective neural networks (INN)
method by enhancing INN's generative modeling capability. WINN has three
interesting properties: (1) A mathematical connection between the formulation
of the INN algorithm and that of Wasserstein generative adversarial networks
(WGAN) is made. (2) The explicit adoption of the Wasserstein distance into INN
results in a large enhancement to INN, achieving compelling results even with a
single classifier --- e.g., providing nearly a 20 times reduction in model size
over INN for unsupervised generative modeling. (3) When applied to supervised
classification, WINN also gives rise to improved robustness against adversarial
examples in terms of the error reduction. In the experiments, we report
encouraging results on unsupervised learning problems including texture, face,
and object modeling, as well as a supervised classification task against
adversarial attacks. | [
1,
0,
0,
0,
0,
0
] |
Title: New skein invariants of links,
Abstract: We introduce new skein invariants of links based on a procedure where we
first apply the skein relation only to crossings of distinct components, so as
to produce collections of unlinked knots. We then evaluate the resulting knots
using a given invariant. A skein invariant can be computed on each link solely
by the use of skein relations and a set of initial conditions. The new
procedure, remarkably, leads to generalizations of the known skein invariants.
We make skein invariants of classical links, $H[R]$, $K[Q]$ and $D[T]$, based
on the invariants of knots, $R$, $Q$ and $T$, denoting the regular isotopy
version of the Homflypt polynomial, the Kauffman polynomial and the Dubrovnik
polynomial. We provide skein theoretic proofs of the well-definedness of these
invariants. These invariants are also reformulated into summations of the
generating invariants ($R$, $Q$, $T$) on sublinks of a given link $L$, obtained
by partitioning $L$ into collections of sublinks. | [
0,
0,
1,
0,
0,
0
] |
Title: Faddeev-Jackiw approach of the noncommutative spacetime Podolsky electromagnetic theory,
Abstract: The interest in higher derivatives field theories has its origin mainly in
their influence concerning the renormalization properties of physical models
and to remove ultraviolet divergences. The noncommutative Podolsky theory is a
constrained system that cannot by directly quantized by the canonical way. In
this work we have used the Faddeev-Jackiw method in order to obtain the Dirac
brackets of the NC Podolsky theory. | [
0,
1,
0,
0,
0,
0
] |
Title: Model-Based Control Using Koopman Operators,
Abstract: This paper explores the application of Koopman operator theory to the control
of robotic systems. The operator is introduced as a method to generate
data-driven models that have utility for model-based control methods. We then
motivate the use of the Koopman operator towards augmenting model-based
control. Specifically, we illustrate how the operator can be used to obtain a
linearizable data-driven model for an unknown dynamical process that is useful
for model-based control synthesis. Simulated results show that with increasing
complexity in the choice of the basis functions, a closed-loop controller is
able to invert and stabilize a cart- and VTOL-pendulum systems. Furthermore,
the specification of the basis function are shown to be of importance when
generating a Koopman operator for specific robotic systems. Experimental
results with the Sphero SPRK robot explore the utility of the Koopman operator
in a reduced state representation setting where increased complexity in the
basis function improve open- and closed-loop controller performance in various
terrains, including sand. | [
1,
0,
0,
0,
0,
0
] |
Title: Turbulence Hierarchy in a Random Fibre Laser,
Abstract: Turbulence is a challenging feature common to a wide range of complex
phenomena. Random fibre lasers are a special class of lasers in which the
feedback arises from multiple scattering in a one-dimensional disordered
cavity-less medium. Here, we report on statistical signatures of turbulence in
the distribution of intensity fluctuations in a continuous-wave-pumped
erbium-based random fibre laser, with random Bragg grating scatterers. The
distribution of intensity fluctuations in an extensive data set exhibits three
qualitatively distinct behaviours: a Gaussian regime below threshold, a mixture
of two distributions with exponentially decaying tails near the threshold, and
a mixture of distributions with stretched-exponential tails above threshold.
All distributions are well described by a hierarchical stochastic model that
incorporates Kolmogorov's theory of turbulence, which includes energy cascade
and the intermittence phenomenon. Our findings have implications for explaining
the remarkably challenging turbulent behaviour in photonics, using a random
fibre laser as the experimental platform. | [
0,
1,
0,
0,
0,
0
] |
Title: Optimal Rates for Learning with Nyström Stochastic Gradient Methods,
Abstract: In the setting of nonparametric regression, we propose and study a
combination of stochastic gradient methods with Nyström subsampling, allowing
multiple passes over the data and mini-batches. Generalization error bounds for
the studied algorithm are provided. Particularly, optimal learning rates are
derived considering different possible choices of the step-size, the mini-batch
size, the number of iterations/passes, and the subsampling level. In comparison
with state-of-the-art algorithms such as the classic stochastic gradient
methods and kernel ridge regression with Nyström, the studied algorithm has
advantages on the computational complexity, while achieving the same optimal
learning rates. Moreover, our results indicate that using mini-batches can
reduce the total computational cost while achieving the same optimal
statistical results. | [
1,
0,
1,
1,
0,
0
] |
Title: Run Procrustes, Run! On the convergence of accelerated Procrustes Flow,
Abstract: In this work, we present theoretical results on the convergence of non-convex
accelerated gradient descent in matrix factorization models. The technique is
applied to matrix sensing problems with squared loss, for the estimation of a
rank $r$ optimal solution $X^\star \in \mathbb{R}^{n \times n}$. We show that
the acceleration leads to linear convergence rate, even under non-convex
settings where the variable $X$ is represented as $U U^\top$ for $U \in
\mathbb{R}^{n \times r}$. Our result has the same dependence on the condition
number of the objective --and the optimal solution-- as that of the recent
results on non-accelerated algorithms. However, acceleration is observed in
practice, both in synthetic examples and in two real applications: neuronal
multi-unit activities recovery from single electrode recordings, and quantum
state tomography on quantum computing simulators. | [
0,
0,
0,
1,
0,
0
] |
Title: On the presentation of Hecke-Hopf algebras for non-simply-laced type,
Abstract: Hecke-Hopf algebras were defined by A. Berenstein and D. Kazhdan. We give an
explicit presentation of an Hecke-Hopf algebra when the parameter $m_{ij},$
associated to any two distinct vertices $i$ and $j$ in the presentation of a
Coxeter group, equals $4,$ $5$ or $6$. As an application, we give a proof of a
conjecture of Berenstein and Kazhdan when the Coxeter group is crystallographic
and non-simply-laced. As another application, we show that another conjecture
of Berenstein and Kazhdan holds when $m_{ij},$ associated to any two distinct
vertices $i$ and $j,$ equals $4$ and that the conjecture does not hold when
some $m_{ij}$ equals $6$ by giving a counterexample to it. | [
0,
0,
1,
0,
0,
0
] |
Title: ILP-based Alleviation of Dense Meander Segments with Prioritized Shifting and Progressive Fixing in PCB Routing,
Abstract: Length-matching is an important technique to bal- ance delays of bus signals
in high-performance PCB routing. Existing routers, however, may generate very
dense meander segments. Signals propagating along these meander segments
exhibit a speedup effect due to crosstalk between the segments of the same
wire, thus leading to mismatch of arrival times even under the same physical
wire length. In this paper, we present a post-processing method to enlarge the
width and the distance of meander segments and hence distribute them more
evenly on the board so that crosstalk can be reduced. In the proposed
framework, we model the sharing of available routing areas after removing dense
meander segments from the initial routing, as well as the generation of relaxed
meander segments and their groups for wire length compensation. This model is
transformed into an ILP problem and solved for a balanced distribution of wire
patterns. In addition, we adjust the locations of long wire segments according
to wire priorities to swap free spaces toward critical wires that need much
length compensation. To reduce the problem space of the ILP model, we also
introduce a progressive fixing technique so that wire patterns are grown
gradually from the edge of the routing toward the center area. Experimental
results show that the proposed method can expand meander segments significantly
even under very tight area constraints, so that the speedup effect can be
alleviated effectively in high- performance PCB designs. | [
1,
0,
0,
0,
0,
0
] |
Title: Smoothing with Couplings of Conditional Particle Filters,
Abstract: In state space models, smoothing refers to the task of estimating a latent
stochastic process given noisy measurements related to the process. We propose
an unbiased estimator of smoothing expectations. The lack-of-bias property has
methodological benefits: independent estimators can be generated in parallel,
and confidence intervals can be constructed from the central limit theorem to
quantify the approximation error. To design unbiased estimators, we combine a
generic debiasing technique for Markov chains with a Markov chain Monte Carlo
algorithm for smoothing. The resulting procedure is widely applicable and we
show in numerical experiments that the removal of the bias comes at a
manageable increase in variance. We establish the validity of the proposed
estimators under mild assumptions. Numerical experiments are provided on toy
models, including a setting of highly-informative observations, and a realistic
Lotka-Volterra model with an intractable transition density. | [
0,
0,
0,
1,
0,
0
] |
Title: Formation of High Pressure Gradients at the Free Surface of a Liquid Dielectric in a Tangential Electric Field,
Abstract: Nonlinear dynamics of the free surface of an ideal incompressible
non-conducting fluid with high dielectric constant subjected by strong
horizontal electric field is simulated on the base of the method of conformal
transformations. It is demonstrated that interaction of counter-propagating
waves leads to formation of regions with steep wave front at the fluid surface;
angles of the boundary inclination tend to {\pi}/2, and the curvature of
surface extremely increases. A significant concentration of the energy of the
system occurs at these points. From the physical point of view, the appearance
of these singularities corresponds to formation of regions at the fluid surface
where pressure exerted by electric field undergoes a discontinuity and
dynamical pressure increases almost an order of magnitude. | [
0,
1,
0,
0,
0,
0
] |
Title: Outliers and related problems,
Abstract: We define outliers as a set of observations which contradicts the proposed
mathematical (statistical) model and we discuss the frequently observed types
of the outliers. Further we explore what changes in the model have to be made
in order to avoid the occurance of the outliers. We observe that some variants
of the outliers lead to classical results in probability, such as the law of
large numbers and the concept of heavy tailed distributions.
Key words: outlier; the law of large numbers; heavy tailed distributions;
model rejection. | [
0,
0,
1,
1,
0,
0
] |
Title: On Quadratic Convergence of DC Proximal Newton Algorithm for Nonconvex Sparse Learning in High Dimensions,
Abstract: We propose a DC proximal Newton algorithm for solving nonconvex regularized
sparse learning problems in high dimensions. Our proposed algorithm integrates
the proximal Newton algorithm with multi-stage convex relaxation based on the
difference of convex (DC) programming, and enjoys both strong computational and
statistical guarantees. Specifically, by leveraging a sophisticated
characterization of sparse modeling structures/assumptions (i.e., local
restricted strong convexity and Hessian smoothness), we prove that within each
stage of convex relaxation, our proposed algorithm achieves (local) quadratic
convergence, and eventually obtains a sparse approximate local optimum with
optimal statistical properties after only a few convex relaxations. Numerical
experiments are provided to support our theory. | [
1,
0,
1,
1,
0,
0
] |
Title: Heuristic Optimization for Automated Distribution System Planning in Network Integration Studies,
Abstract: Network integration studies try to assess the impact of future developments,
such as the increase of Renewable Energy Sources or the introduction of Smart
Grid Technologies, on large-scale network areas. Goals can be to support
strategic alignment in the regulatory framework or to adapt the network
planning principles of Distribution System Operators. This study outlines an
approach for the automated distribution system planning that can calculate
network reconfiguration, reinforcement and extension plans in a fully automated
fashion. This allows the estimation of the expected cost in massive
probabilistic simulations of large numbers of real networks and constitutes a
core component of a framework for large-scale network integration studies.
Exemplary case study results are presented that were performed in cooperation
with different major distribution system operators. The case studies cover the
estimation of expected network reinforcement costs, technical and economical
assessment of smart grid technologies and structural network optimisation. | [
1,
0,
0,
0,
0,
0
] |
Title: The Sizes and Depletions of the Dust and Gas Cavities in the Transitional Disk J160421.7-213028,
Abstract: We report ALMA Cycle 2 observations of 230 GHz (1.3 mm) dust continuum
emission, and $^{12}$CO, $^{13}$CO, and C$^{18}$O J = 2-1 line emission, from
the Upper Scorpius transitional disk [PZ99] J160421.7-213028, with an angular
resolution of ~0".25 (35 AU). Armed with these data and existing H-band
scattered light observations, we measure the size and depth of the disk's
central cavity, and the sharpness of its outer edge, in three components:
sub-$\mu$m-sized "small" dust traced by scattered light, millimeter-sized "big"
dust traced by the millimeter continuum, and gas traced by line emission. Both
dust populations feature a cavity of radius $\sim$70 AU that is depleted by
factors of at least 1000 relative to the dust density just outside. The
millimeter continuum data are well explained by a cavity with a sharp edge.
Scattered light observations can be fitted with a cavity in small dust that has
either a sharp edge at 60 AU, or an edge that transitions smoothly over an
annular width of 10 AU near 60 AU. In gas, the data are consistent with a
cavity that is smaller, about 15 AU in radius, and whose surface density at 15
AU is $10^{3\pm1}$ times smaller than the surface density at 70 AU; the gas
density grades smoothly between these two radii. The CO isotopologue
observations rule out a sharp drop in gas surface density at 30 AU or a
double-drop model as found by previous modeling. Future observations are needed
to assess the nature of these gas and dust cavities, e.g., whether they are
opened by multiple as-yet-unseen planets or photoevaporation. | [
0,
1,
0,
0,
0,
0
] |
Title: Demystifying AlphaGo Zero as AlphaGo GAN,
Abstract: The astonishing success of AlphaGo Zero\cite{Silver_AlphaGo} invokes a
worldwide discussion of the future of our human society with a mixed mood of
hope, anxiousness, excitement and fear. We try to dymystify AlphaGo Zero by a
qualitative analysis to indicate that AlphaGo Zero can be understood as a
specially structured GAN system which is expected to possess an inherent good
convergence property. Thus we deduct the success of AlphaGo Zero may not be a
sign of a new generation of AI. | [
1,
0,
0,
1,
0,
0
] |
Title: Commissioning and Operation,
Abstract: Chapter 16 in High-Luminosity Large Hadron Collider (HL-LHC) : Preliminary
Design Report. The Large Hadron Collider (LHC) is one of the largest scientific
instruments ever built. Since opening up a new energy frontier for exploration
in 2010, it has gathered a global user community of about 7,000 scientists
working in fundamental particle physics and the physics of hadronic matter at
extreme temperature and density. To sustain and extend its discovery potential,
the LHC will need a major upgrade in the 2020s. This will increase its
luminosity (rate of collisions) by a factor of five beyond the original design
value and the integrated luminosity (total collisions created) by a factor ten.
The LHC is already a highly complex and exquisitely optimised machine so this
upgrade must be carefully conceived and will require about ten years to
implement. The new configuration, known as High Luminosity LHC (HL-LHC), will
rely on a number of key innovations that push accelerator technology beyond its
present limits. Among these are cutting-edge 11-12 tesla superconducting
magnets, compact superconducting cavities for beam rotation with ultra-precise
phase control, new technology and physical processes for beam collimation and
300 metre-long high-power superconducting links with negligible energy
dissipation. The present document describes the technologies and components
that will be used to realise the project and is intended to serve as the basis
for the detailed engineering design of HL-LHC. | [
0,
1,
0,
0,
0,
0
] |
Title: Stable absorbing boundary conditions for molecular dynamics in general domains,
Abstract: A new type of absorbing boundary conditions for molecular dynamics
simulations are presented. The exact boundary conditions for crystalline solids
with harmonic approximation are expressed as a dynamic Dirichlet- to-Neumann
(DtN) map. It connects the displacement of the atoms at the boundary to the
traction on these atoms. The DtN map is valid for a domain with general
geometry. To avoid evaluating the time convo- lution of the dynamic DtN map, we
approximate the associated kernel function by rational functions in the Laplace
domain. The parameters in the approximations are determined by interpolations.
The explicit forms of the zeroth, first, and second order approximations will
be presented. The stability of the molecular dynamics model, supplemented with
these absorbing boundary conditions is established. Two numerical simulations
are performed to demonstrate the effectiveness of the methods. | [
0,
1,
0,
0,
0,
0
] |
Title: Algebraic operads up to homotopy,
Abstract: This paper deals with the homotopy theory of differential graded operads. We
endow the Koszul dual category of curved conilpotent cooperads, where the
notion of quasi-isomorphism barely makes sense, with a model category structure
Quillen equivalent to that of operads. This allows us to describe the homotopy
properties of differential graded operads in a simpler and richer way, using
obstruction methods. | [
0,
0,
1,
0,
0,
0
] |
Title: Functional data analysis in the Banach space of continuous functions,
Abstract: Functional data analysis is typically conducted within the $L^2$-Hilbert
space framework. There is by now a fully developed statistical toolbox allowing
for the principled application of the functional data machinery to real-world
problems, often based on dimension reduction techniques such as functional
principal component analysis. At the same time, there have recently been a
number of publications that sidestep dimension reduction steps and focus on a
fully functional $L^2$-methodology. This paper goes one step further and
develops data analysis methodology for functional time series in the space of
all continuous functions. The work is motivated by the fact that objects with
rather different shapes may still have a small $L^2$-distance and are therefore
identified as similar when using an $L^2$-metric. However, in applications it
is often desirable to use metrics reflecting the visualization of the curves in
the statistical analysis. The methodological contributions are focused on
developing two-sample and change-point tests as well as confidence bands, as
these procedures appear do be conducive to the proposed setting. Particular
interest is put on relevant differences; that is, on not trying to test for
exact equality, but rather for pre-specified deviations under the null
hypothesis.
The procedures are justified through large-sample theory. To ensure
practicability, non-standard bootstrap procedures are developed and
investigated addressing particular features that arise in the problem of
testing relevant hypotheses. The finite sample properties are explored through
a simulation study and an application to annual temperature profiles. | [
0,
0,
1,
1,
0,
0
] |
Title: Cross-Correlation Redshift Calibration Without Spectroscopic Calibration Samples in DES Science Verification Data,
Abstract: Galaxy cross-correlations with high-fidelity redshift samples hold the
potential to precisely calibrate systematic photometric redshift uncertainties
arising from the unavailability of complete and representative training and
validation samples of galaxies. However, application of this technique in the
Dark Energy Survey (DES) is hampered by the relatively low number density,
small area, and modest redshift overlap between photometric and spectroscopic
samples. We propose instead using photometric catalogs with reliable
photometric redshifts for photo-z calibration via cross-correlations. We verify
the viability of our proposal using redMaPPer clusters from the Sloan Digital
Sky Survey (SDSS) to successfully recover the redshift distribution of SDSS
spectroscopic galaxies. We demonstrate how to combine photo-z with
cross-correlation data to calibrate photometric redshift biases while
marginalizing over possible clustering bias evolution in either the calibration
or unknown photometric samples. We apply our method to DES Science Verification
(DES SV) data in order to constrain the photometric redshift distribution of a
galaxy sample selected for weak lensing studies, constraining the mean of the
tomographic redshift distributions to a statistical uncertainty of $\Delta z
\sim \pm 0.01$. We forecast that our proposal can in principle control
photometric redshift uncertainties in DES weak lensing experiments at a level
near the intrinsic statistical noise of the experiment over the range of
redshifts where redMaPPer clusters are available. Our results provide strong
motivation to launch a program to fully characterize the systematic errors from
bias evolution and photo-z shapes in our calibration procedure. | [
0,
1,
0,
0,
0,
0
] |
Title: Completely bounded bimodule maps and spectral synthesis,
Abstract: We initiate the study of the completely bounded multipliers of the Haagerup
tensor product $A(G)\otimes_{\rm h} A(G)$ of two copies of the Fourier algebra
$A(G)$ of a locally compact group $G$. If $E$ is a closed subset of $G$ we let
$E^{\sharp} = \{(s,t) : st\in E\}$ and show that if $E^{\sharp}$ is a set of
spectral synthesis for $A(G)\otimes_{\rm h} A(G)$ then $E$ is a set of local
spectral synthesis for $A(G)$. Conversely, we prove that if $E$ is a set of
spectral synthesis for $A(G)$ and $G$ is a Moore group then $E^{\sharp}$ is a
set of spectral synthesis for $A(G)\otimes_{\rm h} A(G)$. Using the natural
identification of the space of all completely bounded weak* continuous
$VN(G)'$-bimodule maps with the dual of $A(G)\otimes_{\rm h} A(G)$, we show
that, in the case $G$ is weakly amenable, such a map leaves the multiplication
algebra of $L^{\infty}(G)$ invariant if and only if its support is contained in
the antidiagonal of $G$. | [
0,
0,
1,
0,
0,
0
] |
Title: Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation,
Abstract: Black-box risk scoring models permeate our lives, yet are typically
proprietary or opaque. We propose Distill-and-Compare, a model distillation and
comparison approach to audit such models. To gain insight into black-box
models, we treat them as teachers, training transparent student models to mimic
the risk scores assigned by black-box models. We compare the student model
trained with distillation to a second un-distilled transparent model trained on
ground-truth outcomes, and use differences between the two models to gain
insight into the black-box model. Our approach can be applied in a realistic
setting, without probing the black-box model API. We demonstrate the approach
on four public data sets: COMPAS, Stop-and-Frisk, Chicago Police, and Lending
Club. We also propose a statistical test to determine if a data set is missing
key features used to train the black-box model. Our test finds that the
ProPublica data is likely missing key feature(s) used in COMPAS. | [
1,
0,
0,
1,
0,
0
] |
Title: A GPU Accelerated Discontinuous Galerkin Incompressible Flow Solver,
Abstract: We present a GPU-accelerated version of a high-order discontinuous Galerkin
discretization of the unsteady incompressible Navier-Stokes equations. The
equations are discretized in time using a semi-implicit scheme with explicit
treatment of the nonlinear term and implicit treatment of the split Stokes
operators. The pressure system is solved with a conjugate gradient method
together with a fully GPU-accelerated multigrid preconditioner which is
designed to minimize memory requirements and to increase overall performance. A
semi-Lagrangian subcycling advection algorithm is used to shift the
computational load per timestep away from the pressure Poisson solve by
allowing larger timestep sizes in exchange for an increased number of advection
steps. Numerical results confirm we achieve the design order accuracy in time
and space. We optimize the performance of the most time-consuming kernels by
tuning the fine-grain parallelism, memory utilization, and maximizing
bandwidth. To assess overall performance we present an empirically calibrated
roofline performance model for a target GPU to explain the achieved efficiency.
We demonstrate that, in the most cases, the kernels used in the solver are
close to their empirically predicted roofline performance. | [
1,
0,
0,
0,
0,
0
] |
Title: Historic Emergence of Diversity in Painting: Heterogeneity in Chromatic Distance in Images and Characterization of Massive Painting Data Set,
Abstract: Painting is an art form that has long functioned as a major channel for the
creative expression and communication of humans, its evolution taking place
under an interplay with the science, technology, and social environments of the
times. Therefore, understanding the process based on comprehensive data could
shed light on how humans acted and manifested creatively under changing
conditions. Yet, there exist few systematic frameworks that characterize the
process for painting, which would require robust statistical methods for
defining painting characteristics and identifying human's creative
developments, and data of high quality and sufficient quantity. Here we propose
that the color contrast of a painting image signifying the heterogeneity in
inter-pixel chromatic distance can be a useful representation of its style,
integrating both the color and geometry. From the color contrasts of paintings
from a large-scale, comprehensive archive of 179,853 high-quality images
spanning several centuries we characterize the temporal evolutionary patterns
of paintings, and present a deep study of an extraordinary expansion in
creative diversity and individuality that came to define the modern era. | [
1,
1,
0,
0,
0,
0
] |
Title: Cage Size and Jump Precursors in Glass-Forming Liquids: Experiment and Simulations,
Abstract: Glassy dynamics is intermittent, as particles suddenly jump out of the cage
formed by their neighbours, and heterogeneous, as these jumps are not uniformly
distributed across the system. Relating these features of the dynamics to the
diverse local environments explored by the particles is essential to
rationalize the relaxation process. Here we investigate this issue
characterizing the local environment of a particle with the amplitude of its
short time vibrational motion, as determined by segmenting in cages and jumps
the particle trajectories. Both simulations of supercooled liquids and
experiments on colloidal suspensions show that particles in large cages are
likely to jump after a small time-lag, and that, on average, the cage enlarges
shortly before the particle jumps. At large time-lags, the cage has essentially
a constant value, which is smaller for longer-lasting cages. Finally, we
clarify how this coupling between cage size and duration controls the average
behaviour and opens the way to a better understanding of the relaxation process
in glass--forming liquids. | [
0,
1,
0,
0,
0,
0
] |
Title: A Floating Cylinder on An Unbounded Bath,
Abstract: In this paper, we reconsider a circular cylinder horizontally floating on an
unbounded reservoir in a gravitational field directed downwards, which was
studied by Bhatnargar and Finn in 2006. We follow their approach but with some
modifications. We establish the relation between the total energy relative to
the undisturbed state and the total force. There is a monotone relation between
the height of the centre and the wetting angle. We study the number of
equilibria, the floating configurations and their stability for all parameter
values. We find that the system admits at most two equilibrium points for
arbitrary contact angle, the one with smaller wetting angle is stable and the
one with larger wetting angle is unstable. The initial model has a limitation
that the fluid interfaces may intersect. We show that the stable equilibrium
point never lies in the intersection region, while the unstable equilibrium
point may lie in the intersection region. | [
0,
1,
1,
0,
0,
0
] |
Title: Switching between Limit Cycles in a Model of Running Using Exponentially Stabilizing Discrete Control Lyapunov Function,
Abstract: This paper considers the problem of switching between two periodic motions,
also known as limit cycles, to create agile running motions. For each limit
cycle, we use a control Lyapunov function to estimate the region of attraction
at the apex of the flight phase. We switch controllers at the apex, only if the
current state of the robot is within the region of attraction of the subsequent
limit cycle. If the intersection between two limit cycles is the null set, then
we construct additional limit cycles till we are able to achieve sufficient
overlap of the region of attraction between sequential limit cycles.
Additionally, we impose an exponential convergence condition on the control
Lyapunov function that allows us to rapidly transition between limit cycles.
Using the approach we demonstrate switching between 5 limit cycles in about 5
steps with the speed changing from 2 m/s to 5 m/s. | [
1,
0,
0,
0,
0,
0
] |
Title: Carrier Diffusion in Thin-Film CH3NH3PbI3 Perovskite Measured using Four-Wave Mixing,
Abstract: We report the application of femtosecond four-wave mixing (FWM) to the study
of carrier transport in solution-processed CH3NH3PbI3. The diffusion
coefficient was extracted through direct detection of the lateral diffusion of
carriers utilizing the transient grating technique, coupled with simultaneous
measurement of decay kinetics exploiting the versatility of the boxcar
excitation beam geometry. The observation of exponential decay of the transient
grating versus interpulse delay indicates diffusive transport with negligible
trapping within the first nanosecond following excitation. The in-plane
transport geometry in our experiments enabled the diffusion length to be
compared directly with the grain size, indicating that carriers move across
multiple grain boundaries prior to recombination. Our experiments illustrate
the broad utility of FWM spectroscopy for rapid characterization of macroscopic
film transport properties. | [
0,
1,
0,
0,
0,
0
] |
Title: On effective Birkhoff's ergodic theorem for computable actions of amenable groups,
Abstract: We introduce computable actions of computable groups and prove the following
versions of effective Birkhoff's ergodic theorem. Let $\Gamma$ be a computable
amenable group, then there always exists a canonically computable tempered
two-sided F{\o}lner sequence $(F_n)_{n \geq
1}$ in $\Gamma$. For a computable, measure-preserving, ergodic action of
$\Gamma$ on a Cantor space $\{0,1\}^{\mathbb N}$ endowed with a computable
probability measure $\mu$, it is shown that for every bounded lower
semicomputable function $f$ on $\{0,1\}^{\mathbb N}$ and for every Martin-Löf
random $\omega \in \{0,1\}^{\mathbb N}$ the equality \[ \lim\limits_{n \to
\infty} \frac{1}{|F_n|} \sum\limits_{g \in F_n} f(g \cdot \omega) = \int\limits
f d \mu \] holds, where the averages are taken with respect to a canonically
computable tempered two-sided F{\o}lner sequence $(F_n)_{n \geq
1}$. We also prove the same identity for all lower semicomputable $f$'s in
the special case when $\Gamma$ is a computable group of polynomial growth and
$F_n:=\mathrm{B}(n)$ is the F{\o}lner sequence of balls around the neutral
element of $\Gamma$. | [
0,
0,
1,
0,
0,
0
] |
Title: Minor-free graphs have light spanners,
Abstract: We show that every $H$-minor-free graph has a light $(1+\epsilon)$-spanner,
resolving an open problem of Grigni and Sissokho and proving a conjecture of
Grigni and Hung. Our lightness bound is
\[O\left(\frac{\sigma_H}{\epsilon^3}\log \frac{1}{\epsilon}\right)\] where
$\sigma_H = |V(H)|\sqrt{\log |V(H)|}$ is the sparsity coefficient of
$H$-minor-free graphs. That is, it has a practical dependency on the size of
the minor $H$. Our result also implies that the polynomial time approximation
scheme (PTAS) for the Travelling Salesperson Problem (TSP) in $H$-minor-free
graphs by Demaine, Hajiaghayi and Kawarabayashi is an efficient PTAS whose
running time is $2^{O_H\left(\frac{1}{\epsilon^4}\log
\frac{1}{\epsilon}\right)}n^{O(1)}$ where $O_H$ ignores dependencies on the
size of $H$. Our techniques significantly deviate from existing lines of
research on spanners for $H$-minor-free graphs, but build upon the work of
Chechik and Wulff-Nilsen for spanners of general graphs. | [
1,
0,
0,
0,
0,
0
] |
Title: Adversarial Generation of Natural Language,
Abstract: Generative Adversarial Networks (GANs) have gathered a lot of attention from
the computer vision community, yielding impressive results for image
generation. Advances in the adversarial generation of natural language from
noise however are not commensurate with the progress made in generating images,
and still lag far behind likelihood based methods. In this paper, we take a
step towards generating natural language with a GAN objective alone. We
introduce a simple baseline that addresses the discrete output space problem
without relying on gradient estimators and show that it is able to achieve
state-of-the-art results on a Chinese poem generation dataset. We present
quantitative results on generating sentences from context-free and
probabilistic context-free grammars, and qualitative language modeling results.
A conditional version is also described that can generate sequences conditioned
on sentence characteristics. | [
1,
0,
0,
1,
0,
0
] |
Title: Likely Transiting Exocomets Detected by Kepler,
Abstract: We present the first good evidence for exocomet transits of a host star in
continuum light in data from the Kepler mission. The Kepler star in question,
KIC 3542116, is of spectral type F2V and is quite bright at K_p = 10. The
transits have a distinct asymmetric shape with a steeper ingress and slower
egress that can be ascribed to objects with a trailing dust tail passing over
the stellar disk. There are three deeper transits with depths of ~0.1% that
last for about a day, and three that are several times more shallow and of
shorter duration. The transits were found via an exhaustive visual search of
the entire Kepler photometric data set, which we describe in some detail. We
review the methods we use to validate the Kepler data showing the comet
transits, and rule out instrumental artefacts as sources of the signals. We fit
the transits with a simple dust-tail model, and find that a transverse comet
speed of ~35-50 km/s and a minimum amount of dust present in the tail of ~10^16
g are required to explain the larger transits. For a dust replenishment time of
~10 days, and a comet lifetime of only ~300 days, this implies a total cometary
mass of > 3 x 10^17 g, or about the mass of Halley's comet. We also discuss the
number of comets and orbital geometry that would be necessary to explain the
six transits detected over the four years of Kepler prime-field observations.
Finally, we also report the discovery of a single comet-shaped transit in KIC
11084727 with very similar transit and host-star properties. | [
0,
1,
0,
0,
0,
0
] |
Title: Canonical Truth,
Abstract: We introduce and study a notion of canonical set theoretical truth, which
means truth in a `canonical model', i.e. a transitive class model that is
uniquely characterized by some $\in$-formula. We show that this notion of truth
is `informative', i.e. there are statements that hold in all canonical models
but do not follow from ZFC, such as Reitz' ground model axiom or the
nonexistence of measurable cardinals. We also show that ZF+$V=L[\mathbb{R}]$+AD
has no canonical models. On the other hand, we show that there are canonical
models for `every real has sharp'. Moreover, we consider `theory-canonical'
statements that only fix a transitive class model of ZFC up to elementary
equivalence and show that it is consistent relative to large cardinals that
there are theory-canonical models with measurable cardinals and that
theory-canonicity is still informative in the sense explained above. | [
0,
0,
1,
0,
0,
0
] |
Title: Effect of Composition Gradient on Magnetothermal Instability Modified by Shear and Rotation,
Abstract: We model the intracluster medium as a weakly collisional plasma that is a
binary mixture of the hydrogen and the helium ions, along with free electrons.
When, owing to the helium sedimentation, the gradient of the mean molecular
weight (or equivalently, composition or helium ions' concentration) of the
plasma is not negligible, it can have appreciable influence on the stability
criteria of the thermal convective instabilities, e.g., the heat-flux-buoyancy
instability and the magnetothermal instability (MTI). These instabilities are
consequences of the anisotropic heat conduction occurring preferentially along
the magnetic field lines. In this paper, without ignoring the magnetic tension,
we first present the mathematical criterion for the onset of composition
gradient modified MTI. Subsequently, we relax the commonly adopted equilibrium
state in which the plasma is at rest, and assume that the plasma is in a
sheared state which may be due to differential rotation. We discuss how the
concentration gradient affects the coupling between the Kelvin--Helmholtz
instability and the MTI in rendering the plasma unstable or stable. We derive
exact stability criterion by working with the sharp boundary case in which the
physical variables---temperature, mean molecular weight, density, and magnetic
field---change discontinuously from one constant value to another on crossing
the boundary. Finally, we perform the linear stability analysis for the case of
the differentially rotating plasma that is thermally and compositionally
stratified as well. By assuming axisymmetric perturbations, we find the
corresponding dispersion relation and the explicit mathematical expression
determining the onset of the modified MTI. | [
0,
1,
0,
0,
0,
0
] |
Title: A Verified Algorithm Enumerating Event Structures,
Abstract: An event structure is a mathematical abstraction modeling concepts as
causality, conflict and concurrency between events. While many other
mathematical structures, including groups, topological spaces, rings, abound
with algorithms and formulas to generate, enumerate and count particular sets
of their members, no algorithm or formulas are known to generate or count all
the possible event structures over a finite set of events. We present an
algorithm to generate such a family, along with a functional implementation
verified using Isabelle/HOL. As byproducts, we obtain a verified enumeration of
all possible preorders and partial orders. While the integer sequences counting
preorders and partial orders are already listed on OEIS (On-line Encyclopedia
of Integer Sequences), the one counting event structures is not. We therefore
used our algorithm to submit a formally verified addition, which has been
successfully reviewed and is now part of the OEIS. | [
1,
0,
0,
0,
0,
0
] |
Title: Missing Data as Part of the Social Behavior in Real-World Financial Complex Systems,
Abstract: Many real-world networks are known to exhibit facts that counter our
knowledge prescribed by the theories on network creation and communication
patterns. A common prerequisite in network analysis is that information on
nodes and links will be complete because network topologies are extremely
sensitive to missing information of this kind. Therefore, many real-world
networks that fail to meet this criterion under random sampling may be
discarded.
In this paper we offer a framework for interpreting the missing observations
in network data under the hypothesis that these observations are not missing at
random. We demonstrate the methodology with a case study of a financial trade
network, where the awareness of agents to the data collection procedure by a
self-interested observer may result in strategic revealing or withholding of
information. The non-random missingness has been overlooked despite the
possibility of this being an important feature of the processes by which the
network is generated. The analysis demonstrates that strategic information
withholding may be a valid general phenomenon in complex systems. The evidence
is sufficient to support the existence of an influential observer and to offer
a compelling dynamic mechanism for the creation of the network. | [
0,
0,
0,
1,
0,
0
] |
Title: Geometric Rescaling Algorithms for Submodular Function Minimization,
Abstract: We present a new class of polynomial-time algorithms for submodular function
minimization (SFM), as well as a unified framework to obtain strongly
polynomial SFM algorithms. Our new algorithms are based on simple iterative
methods for the minimum-norm problem, such as the conditional gradient and the
Fujishige-Wolfe algorithms. We exhibit two techniques to turn simple iterative
methods into polynomial-time algorithms.
Firstly, we use the geometric rescaling technique, which has recently gained
attention in linear programming. We adapt this technique to SFM and obtain a
weakly polynomial bound $O((n^4\cdot EO + n^5)\log (n L))$.
Secondly, we exhibit a general combinatorial black-box approach to turn any
strongly polynomial $\varepsilon L$-approximate SFM oracle into a strongly
polynomial exact SFM algorithm. This framework can be applied to a wide range
of combinatorial and continuous algorithms, including pseudo-polynomial ones.
In particular, we can obtain strongly polynomial algorithms by a repeated
application of the conditional gradient or of the Fujishige-Wolfe algorithm.
Combined with the geometric rescaling technique, the black-box approach
provides a $O((n^5\cdot EO + n^6)\log^2 n)$ algorithm. Finally, we show that
one of the techniques we develop in the paper can also be combined with the
cutting-plane method of Lee, Sidford, and Wong, yielding a simplified variant
of their $O(n^3 \log^2 n \cdot EO + n^4\log^{O(1)} n)$ algorithm. | [
1,
0,
1,
0,
0,
0
] |
Title: Statistical PT-symmetric lasing in an optical fiber network,
Abstract: PT-symmetry in optics is a condition whereby the real and imaginary parts of
the refractive index across a photonic structure are deliberately balanced.
This balance can lead to a host of novel optical phenomena, such as
unidirectional invisibility, loss-induced lasing, single-mode lasing from
multimode resonators, and non-reciprocal effects in conjunction with
nonlinearities. Because PT-symmetry has been thought of as fragile,
experimental realizations to date have been usually restricted to on-chip
micro-devices. Here, we demonstrate that certain features of PT-symmetry are
sufficiently robust to survive the statistical fluctuations associated with a
macroscopic optical cavity. We construct optical-fiber-based coupled-cavities
in excess of a kilometer in length (the free spectral range is less than 0.8
fm) with balanced gain and loss in two sub-cavities and examine the lasing
dynamics. In such a macroscopic system, fluctuations can lead to a
cavity-detuning exceeding the free spectral range. Nevertheless, by varying the
gain-loss contrast, we observe that both the lasing threshold and the growth of
the laser power follow the predicted behavior of a stable PT-symmetric
structure. Furthermore, a statistical symmetry-breaking point is observed upon
varying the cavity loss. These findings indicate that PT-symmetry is a more
robust optical phenomenon than previously expected, and points to potential
applications in optical fiber networks and fiber lasers. | [
0,
1,
0,
0,
0,
0
] |
Title: Probing the dusty stellar populations of the Local Volume Galaxies with JWST/MIRI,
Abstract: The Mid-Infrared Instrument (MIRI) for the {\em James Webb Space Telescope}
(JWST) will revolutionize our understanding of infrared stellar populations in
the Local Volume. Using the rich {\em Spitzer}-IRS spectroscopic data-set and
spectral classifications from the Surveying the Agents of Galaxy Evolution
(SAGE)-Spectroscopic survey of over a thousand objects in the Magellanic
Clouds, the Grid of Red supergiant and Asymptotic giant branch star ModelS
({\sc grams}), and the grid of YSO models by Robitaille et al. (2006), we
calculate the expected flux-densities and colors in the MIRI broadband filters
for prominent infrared stellar populations. We use these fluxes to explore the
{\em JWST}/MIRI colours and magnitudes for composite stellar population studies
of Local Volume galaxies. MIRI colour classification schemes are presented;
these diagrams provide a powerful means of identifying young stellar objects,
evolved stars and extragalactic background galaxies in Local Volume galaxies
with a high degree of confidence. Finally, we examine which filter combinations
are best for selecting populations of sources based on their JWST colours. | [
0,
1,
0,
0,
0,
0
] |
Title: Weak Versus Strong Disorder Superfluid-Bose Glass Transition in One Dimension,
Abstract: Using large-scale simulations based on matrix product state and quantum Monte
Carlo techniques, we study the superfluid to Bose glass-transition for
one-dimensional attractive hard-core bosons at zero temperature, across the
full regime from weak to strong disorder. As a function of interaction and
disorder strength, we identify a Berezinskii-Kosterlitz-Thouless critical line
with two different regimes. At small attraction where critical disorder is weak
compared to the bandwidth, the critical Luttinger parameter $K_c$ takes its
universal Giamarchi-Schulz value $K_{c}=3/2$. Conversely, a non-universal
$K_c>3/2$ emerges for stronger attraction where weak-link physics is relevant.
In this strong disorder regime, the transition is characterized by self-similar
power-law distributed weak links with a continuously varying characteristic
exponent $\alpha$. | [
0,
1,
0,
0,
0,
0
] |
Title: Fine-grained Event Learning of Human-Object Interaction with LSTM-CRF,
Abstract: Event learning is one of the most important problems in AI. However,
notwithstanding significant research efforts, it is still a very complex task,
especially when the events involve the interaction of humans or agents with
other objects, as it requires modeling human kinematics and object movements.
This study proposes a methodology for learning complex human-object interaction
(HOI) events, involving the recording, annotation and classification of event
interactions. For annotation, we allow multiple interpretations of a motion
capture by slicing over its temporal span, for classification, we use
Long-Short Term Memory (LSTM) sequential models with Conditional Randon Field
(CRF) for constraints of outputs. Using a setup involving captures of
human-object interaction as three dimensional inputs, we argue that this
approach could be used for event types involving complex spatio-temporal
dynamics. | [
1,
0,
0,
0,
0,
0
] |
Title: Carrier driven coupling in ferromagnetic oxide heterostructures,
Abstract: Transition metal oxides are well known for their complex magnetic and
electrical properties. When brought together in heterostructure geometries,
they show particular promise for spintronics and colossal magnetoresistance
applications. In this letter, we propose a new mechanism for the coupling
between layers of itinerant ferromagnetic materials in heterostructures. The
coupling is mediated by charge carriers that strive to maximally delocalize
through the heterostructure to gain kinetic energy. In doing so, they force a
ferromagnetic or antiferromagnetic coupling between the constituent layers. To
illustrate this, we focus on heterostructures composed of SrRuO$_3$ and
La$_{1-x}$A$_{x}$MnO$_3$ (A=Ca/Sr). Our mechanism is consistent with
antiferromagnetic alignment that is known to occur in multilayers of
SrRuO$_3$-La$_{1-x}$A$_{x}$MnO$_3$. To support our assertion, we present a
minimal Kondo-lattice model which reproduces the known magnetization properties
of such multilayers. In addition, we discuss a quantum well model for
heterostructures and argue that the spin-dependent density of states determines
the nature of the coupling. As a smoking gun signature, we propose that
bilayers with the same constituents will oscillate between ferromagnetic and
antiferromagnetic coupling upon tuning the relative thicknesses of the layers. | [
0,
1,
0,
0,
0,
0
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.