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Runout transition and clustering instability observed in binary-mixture avalanche deposits | Binary mixtures of dry grains avalanching down a slope are experimentally
studied in order to determine the interaction among coarse and fine grains and
their effect on the deposit morphology. The distance travelled by the massive
front of the avalanche over the horizontal plane of deposition area is measured
as a function of mass content of fine particles in the mixture, grain-size
ratio, and flume tilt. A sudden transition of the runout is detected at a
critical content of fine particles, with a dependence on the grain-size ratio
and flume tilt. This transition is explained as two simultaneous avalanches in
different flowing regimes (a viscous-like one and an inertial one) competing
against each other and provoking a full segregation and a split-off of the
deposit into two well-defined, separated deposits. The formation of the distal
deposit, in turn, depends on a critical amount of coarse particles. This allows
the condensation of the pure coarse deposit around a small, initial seed
cluster, which grows rapidly by braking and capturing subsequent colliding
coarse particles. For different grain-size ratios and keeping a constant total
mass, the change in the amount of fines needed for the transition to occur is
found to be always less than 7%. For avalanches with a total mass of 4 kg we
find that, most of the time, the runout of a binary avalanche is larger than
the runout of monodisperse avalanches of corresponding constituent particles,
due to lubrication on the coarse-dominated side or to drag by inertial
particles on the fine-dominated side.
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A Simple Convex Layers Algorithm | Given a set of $n$ points $P$ in the plane, the first layer $L_1$ of $P$ is
formed by the points that appear on $P$'s convex hull. In general, a point
belongs to layer $L_i$, if it lies on the convex hull of the set $P \setminus
\bigcup_{j<i}\{L_j\}$. The \emph{convex layers problem} is to compute the
convex layers $L_i$. Existing algorithms for this problem either do not achieve
the optimal $\mathcal{O}\left(n\log n\right)$ runtime and linear space, or are
overly complex and difficult to apply in practice. We propose a new algorithm
that is both optimal and simple. The simplicity is achieved by independently
computing four sets of monotone convex chains in $\mathcal{O}\left(n\log
n\right)$ time and linear space. These are then merged in
$\mathcal{O}\left(n\log n\right)$ time.
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Entire Solution in an Ignition Nonlocal Dispersal Equation: Asymmetric Kernel | This paper mainly focus on the front-like entire solution of a classical
nonlocal dispersal equation with ignition nonlinearity. Especially, the
dispersal kernel function $J$ may not be symmetric here. The asymmetry of $J$
has a great influence on the profile of the traveling waves and the sign of the
wave speeds, which further makes the properties of the entire solution more
diverse. We first investigate the asymptotic behavior of the traveling wave
solutions since it plays an essential role in obtaining the front-like entire
solution. Due to the impact of $f'(0)=0$, we can no longer use the common
method which mainly depending on Ikehara theorem and bilateral Laplace
transform to study the asymptotic rates of the nondecreasing traveling wave and
the nonincreasing one tending to 0, respectively, thus we adopt another method
to investigate them. Afterwards, we establish a new entire solution and obtain
its qualitative properties by constructing proper supersolution and subsolution
and by classifying the sign and size of the wave speeds.
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Fundamental solutions for Schrodinger operators with general inverse square potentials | In this paper, we classify the fundamental solutions for a class of
Schrodinger operators.
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Making up for the deficit in a marathon run | To predict the final result of an athlete in a marathon run thoroughly is the
eternal desire of each trainer. Usually, the achieved result is weaker than the
predicted one due to the objective (e.g., environmental conditions) as well as
subjective factors (e.g., athlete's malaise). Therefore, making up for the
deficit between predicted and achieved results is the main ingredient of the
analysis performed by trainers after the competition. In the analysis, they
search for parts of a marathon course where the athlete lost time. This paper
proposes an automatic making up for the deficit by using a Differential
Evolution algorithm. In this case study, the results that were obtained by a
wearable sports-watch by an athlete in a real marathon are analyzed. The first
experiments with Differential Evolution show the possibility of using this
method in the future.
| 1 | 0 | 0 | 0 | 0 | 0 |
An Efficient Load Balancing Method for Tree Algorithms | Nowadays, multiprocessing is mainstream with exponentially increasing number
of processors. Load balancing is, therefore, a critical operation for the
efficient execution of parallel algorithms. In this paper we consider the
fundamental class of tree-based algorithms that are notoriously irregular, and
hard to load-balance with existing static techniques. We propose a hybrid load
balancing method using the utility of statistical random sampling in estimating
the tree depth and node count distributions to uniformly partition an input
tree. To conduct an initial performance study, we implemented the method on an
Intel Xeon Phi accelerator system. We considered the tree traversal operation
on both regular and irregular unbalanced trees manifested by Fibonacci and
unbalanced (biased) randomly generated trees, respectively. The results show
scalable performance for up to the 60 physical processors of the accelerator,
as well as an extrapolated 128 processors case.
| 1 | 0 | 0 | 0 | 0 | 0 |
Dynamics of the spin-1/2 Heisenberg chain initialized in a domain-wall state | We study the dynamics of an isotropic spin-1/2 Heisenberg chain starting in a
domain-wall initial condition, where the spins are initially up on the left
half-line and down on the right half-line. We focus on the long-time behavior
of the magnetization profile. We perform extensive time-dependent
density-matrix renormalization group simulations (up to t=350) and find that
the data are compatible with a diffusive behavior. Subleading corrections decay
slowly blurring the emergence of the diffusive behavior. We also compare our
results with two alternative scenarios: superdiffusive behavior and enhanced
diffusion with a logarithmic correction. We finally discuss the evolution of
the entanglement entropy.
| 0 | 1 | 0 | 0 | 0 | 0 |
Multi-Erasure Locally Recoverable Codes Over Small Fields For Flash Memory Array | Erasure codes play an important role in storage systems to prevent data loss.
In this work, we study a class of erasure codes called Multi-Erasure Locally
Recoverable Codes (ME-LRCs) for flash memory array. Compared to previous
related works, we focus on the construction of ME-LRCs over small fields. We
first develop upper and lower bounds on the minimum distance of ME-LRCs. These
bounds explicitly take the field size into account. Our main contribution is to
propose a general construction of ME-LRCs based on generalized tensor product
codes, and study their erasure-correcting property. A decoding algorithm
tailored for erasure recovery is given. We then prove that our construction
yields optimal ME-LRCs with a wide range of code parameters. Finally, we
present several families of ME-LRCs over different fields.
| 1 | 0 | 0 | 0 | 0 | 0 |
NSML: A Machine Learning Platform That Enables You to Focus on Your Models | Machine learning libraries such as TensorFlow and PyTorch simplify model
implementation. However, researchers are still required to perform a
non-trivial amount of manual tasks such as GPU allocation, training status
tracking, and comparison of models with different hyperparameter settings. We
propose a system to handle these tasks and help researchers focus on models. We
present the requirements of the system based on a collection of discussions
from an online study group comprising 25k members. These include automatic GPU
allocation, learning status visualization, handling model parameter snapshots
as well as hyperparameter modification during learning, and comparison of
performance metrics between models via a leaderboard. We describe the system
architecture that fulfills these requirements and present a proof-of-concept
implementation, NAVER Smart Machine Learning (NSML). We test the system and
confirm substantial efficiency improvements for model development.
| 1 | 0 | 0 | 0 | 0 | 0 |
High order local absorbing boundary conditions for acoustic waves in terms of farfield expansions | We devise a new high order local absorbing boundary condition (ABC) for
radiating problems and scattering of time-harmonic acoustic waves from
obstacles of arbitrary shape. By introducing an artificial boundary $S$
enclosing the scatterer, the original unbounded domain $\Omega$ is decomposed
into a bounded computational domain $\Omega^{-}$ and an exterior unbounded
domain $\Omega^{+}$. Then, we define interface conditions at the artificial
boundary $S$, from truncated versions of the well-known Wilcox and Karp
farfield expansion representations of the exact solution in the exterior region
$\Omega^{+}$. As a result, we obtain a new local absorbing boundary condition
(ABC) for a bounded problem on $\Omega^{-}$, which effectively accounts for the
outgoing behavior of the scattered field. Contrary to the low order absorbing
conditions previously defined, the order of the error induced by this ABC can
easily match the order of the numerical method in $\Omega^{-}$. We accomplish
this by simply adding as many terms as needed to the truncated farfield
expansions of Wilcox or Karp. The convergence of these expansions guarantees
that the order of approximation of the new ABC can be increased arbitrarily
without having to enlarge the radius of the artificial boundary. We include
numerical results in two and three dimensions which demonstrate the improved
accuracy and simplicity of this new formulation when compared to other
absorbing boundary conditions.
| 0 | 1 | 1 | 0 | 0 | 0 |
Simple Necessary Conditions for the Existence of a Hamiltonian Path with Applications to Cactus Graphs | We describe some necessary conditions for the existence of a Hamiltonian path
in any graph (in other words, for a graph to be traceable). These conditions
result in a linear time algorithm to decide the Hamiltonian path problem for
cactus graphs. We apply this algorithm to several molecular databases to report
the numbers of graphs that are traceable cactus graphs.
| 1 | 0 | 0 | 0 | 0 | 0 |
Bootstrapping Exchangeable Random Graphs | We introduce two new bootstraps for exchangeable random graphs. One, the
"empirical graphon", is based purely on resampling, while the other, the
"histogram stochastic block model", is a model-based "sieve" bootstrap. We show
that both of them accurately approximate the sampling distributions of motif
densities, i.e., of the normalized counts of the number of times fixed
subgraphs appear in the network. These densities characterize the distribution
of (infinite) exchangeable networks. Our bootstraps therefore give, for the
first time, a valid quantification of uncertainty in inferences about
fundamental network statistics, and so of parameters identifiable from them.
| 0 | 0 | 0 | 1 | 0 | 0 |
A question proposed by K. Mahler on exceptional sets of transcendental functions with integer coefficients: solution of a Mahler's problem | In this paper, we shall prove that any subset of $\overline{\mathbb Q}\cap
B(0,1)$, which is closed under complex conjugation and which contains the
element $0$, is the exceptional set of uncountably many transcendental
functions, analytic in the unit ball, with integer coefficients. This solves a
strong version of an old question proposed by K. Mahler (1976).
| 0 | 0 | 1 | 0 | 0 | 0 |
Implementation of the Bin Hierarchy Method for restoring a smooth function from a sampled histogram | We present $\texttt{BHM}$, a tool for restoring a smooth function from a
sampled histogram using the bin hierarchy method. The theoretical background of
the method is presented in [arXiv:1707.07625]. The code automatically generates
a smooth polynomial spline with the minimal acceptable number of knots from the
input data. It works universally for any sufficiently regular shaped
distribution and any level of data quality, requiring almost no external
parameter specification. It is particularly useful for large-scale numerical
data analysis. This paper explains the details of the implementation and the
use of the program.
| 0 | 1 | 0 | 1 | 0 | 0 |
The Discrete Stochastic Galerkin Method for Hyperbolic Equations with Non-smooth and Random Coefficients | We develop a general polynomial chaos (gPC) based stochastic Galerkin (SG)
for hyperbolic equations with random and singular coefficients. Due to the
singu- lar nature of the solution, the standard gPC-SG methods may suffer from
a poor or even non convergence. Taking advantage of the fact that the discrete
solution, by the central type finite difference or finite volume approximations
in space and time for example, is smoother, we first discretize the equation by
a smooth finite difference or finite volume scheme, and then use the gPC-SG
approximation to the discrete system. The jump condition at the interface is
treated using the immersed upwind methods introduced in [8, 12]. This yields a
method that converges with the spectral accuracy for finite mesh size and time
step. We use a linear hyperbolic equation with discontinuous and random
coefficient, and the Liouville equation with discontinuous and random
potential, to illustrate our idea, with both one and second order spatial
discretizations. Spectral convergence is established for the first equation,
and numerical examples for both equations show the desired accu- racy of the
method.
| 0 | 0 | 1 | 0 | 0 | 0 |
Seasonal evolution of $\mathrm{C_2N_2}$, $\mathrm{C_3H_4}$, and $\mathrm{C_4H_2}$ abundances in Titan's lower stratosphere | We study the seasonal evolution of Titan's lower stratosphere (around
15~mbar) in order to better understand the atmospheric dynamics and chemistry
in this part of the atmosphere. We analysed Cassini/CIRS far-IR observations
from 2006 to 2016 in order to measure the seasonal variations of three
photochemical by-products: $\mathrm{C_4H_2}$, $\mathrm{C_3H_4}$, and
$\mathrm{C_2N_2}$. We show that the abundances of these three gases have
evolved significantly at northern and southern high latitudes since 2006. We
measure a sudden and steep increase of the volume mixing ratios of
$\mathrm{C_4H_2}$, $\mathrm{C_3H_4}$, and $\mathrm{C_2N_2}$ at the south pole
from 2012 to 2013, whereas the abundances of these gases remained approximately
constant at the north pole over the same period. At northern mid-latitudes,
$\mathrm{C_2N_2}$ and $\mathrm{C_4H_2}$ abundances decrease after 2012 while
$\mathrm{C_3H_4}$ abundances stay constant. The comparison of these volume
mixing ratio variations with the predictions of photochemical and dynamical
models provides constraints on the seasonal evolution of atmospheric
circulation and chemical processes at play.
| 0 | 1 | 0 | 0 | 0 | 0 |
Systems, Actors and Agents: Operation in a multicomponent environment | Multi-agent approach has become popular in computer science and technology.
However, the conventional models of multi-agent and multicomponent systems
implicitly or explicitly assume existence of absolute time or even do not
include time in the set of defining parameters. At the same time, it is proved
theoretically and validated experimentally that there are different times and
time scales in a variety of real systems - physical, chemical, biological,
social, informational, etc. Thus, the goal of this work is construction of a
multi-agent multicomponent system models with concurrency of processes and
diversity of actions. To achieve this goal, a mathematical system actor model
is elaborated and its properties are studied.
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An invitation to model theory and C*-algebras | We present an introductory survey to first order logic for metric structures
and its applications to C*-algebras.
| 0 | 0 | 1 | 0 | 0 | 0 |
Random Close Packing and the Hard Sphere Percus-Yevick Theory | The Percus-Yevick theory for monodisperse hard spheres gives very good
results for the pressure and structure factor of the system in a whole range of
densities that lie within the liquid phase. However, the equation seems to lead
to a very unacceptable result beyond that region. Namely, the Percus-Yevick
theory predicts a smooth behavior of the pressure that diverges only when the
volume fraction $\eta$ approaches unity. Thus, within the theory there seems to
be no indication for the termination of the liquid phase and the transition to
a solid or to a glass. In the present article we study the Percus-Yevick hard
sphere pair distribution function, $g_2(r)$, for various spatial dimensions. We
find that beyond a certain critical volume fraction $\eta_c$, the pair
distribution function, $g_2(r)$, which should be positive definite, becomes
negative at some distances. We also present an intriguing observation that the
critical $\eta_c$ values we find are consistent with volume fractions where
onsets of random close packing (or maximally random jammed states) are reported
in the literature for various dimensions. That observation is supported by an
intuitive argument. This work may have important implications for other systems
for which a Percus-Yevick theory exists.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Painlevé property of $\mathbb{C}P^{N-1}$ sigma models | We test the $\mathbb{C}P^{N-1}$ sigma models for the Painlevé property.
While the construction of finite action solutions ensures their meromorphicity,
the general case requires testing. The test is performed for the equations in
the homogeneous variables, with their first component normalised to one. No
constraints are imposed on the dimensionality of the model or the values of the
initial exponents. This makes the test nontrivial, as the number of equations
and dependent variables are indefinite. A $\mathbb{C}P^{N-1}$ system proves to
have a $(4N-5)$-parameter family of solutions whose movable singularities are
only poles, while the order of the investigated system is $4N-4$. The remaining
degree of freedom, connected with an extra negative resonance, may correspond
to a branching movable essential singularity. An example of such a solution is
provided.
| 0 | 1 | 0 | 0 | 0 | 0 |
A comment on Stein's unbiased risk estimate for reduced rank estimators | In the framework of matrix valued observables with low rank means, Stein's
unbiased risk estimate (SURE) can be useful for risk estimation and for tuning
the amount of shrinkage towards low rank matrices. This was demonstrated by
Candès et al. (2013) for singular value soft thresholding, which is a
Lipschitz continuous estimator. SURE provides an unbiased risk estimate for an
estimator whenever the differentiability requirements for Stein's lemma are
satisfied. Lipschitz continuity of the estimator is sufficient, but it is
emphasized that differentiability Lebesgue almost everywhere isn't. The reduced
rank estimator, which gives the best approximation of the observation with a
fixed rank, is an example of a discontinuous estimator for which Stein's lemma
actually applies. This was observed by Mukherjee et al. (2015), but the proof
was incomplete. This brief note gives a sufficient condition for Stein's lemma
to hold for estimators with discontinuities, which is then shown to be
fulfilled for a class of spectral function estimators including the reduced
rank estimator. Singular value hard thresholding does, however, not satisfy the
condition, and Stein's lemma does not apply to this estimator.
| 0 | 0 | 1 | 1 | 0 | 0 |
An empirical study on evaluation metrics of generative adversarial networks | Evaluating generative adversarial networks (GANs) is inherently challenging.
In this paper, we revisit several representative sample-based evaluation
metrics for GANs, and address the problem of how to evaluate the evaluation
metrics. We start with a few necessary conditions for metrics to produce
meaningful scores, such as distinguishing real from generated samples,
identifying mode dropping and mode collapsing, and detecting overfitting. With
a series of carefully designed experiments, we comprehensively investigate
existing sample-based metrics and identify their strengths and limitations in
practical settings. Based on these results, we observe that kernel Maximum Mean
Discrepancy (MMD) and the 1-Nearest-Neighbor (1-NN) two-sample test seem to
satisfy most of the desirable properties, provided that the distances between
samples are computed in a suitable feature space. Our experiments also unveil
interesting properties about the behavior of several popular GAN models, such
as whether they are memorizing training samples, and how far they are from
learning the target distribution.
| 0 | 0 | 0 | 1 | 0 | 0 |
Analytical and simulation studies of pedestrian flow at a crossing with random update rule | The intersecting pedestrian flow on the 2D lattice with random update rule is
studied. Each pedestrian has three moving directions without the back step.
Under periodic boundary conditions, an intermediate phase has been found at
which some pedestrians could move along the border of jamming stripes. We have
performed mean field analysis for the moving and intermediate phase
respectively. The analytical results agree with the simulation results well.
The empty site moves along the interface of jamming stripes when the system
only has one empty site. The average movement of empty site in one Monte Carlo
step (MCS) has been analyzed through the master equation. Under open boundary
conditions, the system exhibits moving and jamming phases. The critical
injection probability $\alpha_c$ shows nontrivially against the forward moving
probability $q$. The analytical results of average velocity, the density and
the flow rate against the injection probability in the moving phase also agree
with simulation results well.
| 0 | 1 | 0 | 0 | 0 | 0 |
Static and Fluctuating Magnetic Moments in the Ferroelectric Metal LiOsO$_3$ | LiOsO$_3$ is the first example of a new class of material called a
ferroelectric metal. We performed zero-field and longitudinal-field $\mu$SR,
along with a combination of electronic structure and dipole field calculations,
to determine the magnetic ground state of LiOsO$_3$. We find that the sample
contains both static Li nuclear moments and dynamic Os electronic moments.
Below $\approx 0.7\,$K, the fluctuations of the Os moments slow down, though
remain dynamic down to 0.08$\,$K. We expect this could result in a frozen-out,
disordered ground state at even lower temperatures.
| 0 | 1 | 0 | 0 | 0 | 0 |
Compiling Deep Learning Models for Custom Hardware Accelerators | Convolutional neural networks (CNNs) are the core of most state-of-the-art
deep learning algorithms specialized for object detection and classification.
CNNs are both computationally complex and embarrassingly parallel. Two
properties that leave room for potential software and hardware optimizations
for embedded systems. Given a programmable hardware accelerator with a CNN
oriented custom instructions set, the compiler's task is to exploit the
hardware's full potential, while abiding with the hardware constraints and
maintaining generality to run different CNN models with varying workload
properties. Snowflake is an efficient and scalable hardware accelerator
implemented on programmable logic devices. It implements a control pipeline for
a custom instruction set. The goal of this paper is to present Snowflake's
compiler that generates machine level instructions from Torch7 model
description files. The main software design points explored in this work are:
model structure parsing, CNN workload breakdown, loop rearrangement for memory
bandwidth optimizations and memory access balancing. The performance achieved
by compiler generated instructions matches against hand optimized code for
convolution layers. Generated instructions also efficiently execute AlexNet and
ResNet18 inference on Snowflake. Snowflake with $256$ processing units was
synthesized on Xilinx's Zynq XC7Z045 FPGA. At $250$ MHz, AlexNet achieved in
$93.6$ frames/s and $1.2$ GB/s of off-chip memory bandwidth, and $21.4$
frames/s and $2.2$ GB/s for ResNet18. Total on-chip power is $5$ W.
| 1 | 0 | 0 | 0 | 0 | 0 |
Ranking with Adaptive Neighbors | Retrieving the most similar objects in a large-scale database for a given
query is a fundamental building block in many application domains, ranging from
web searches, visual, cross media, and document retrievals. State-of-the-art
approaches have mainly focused on capturing the underlying geometry of the data
manifolds. Graph-based approaches, in particular, define various diffusion
processes on weighted data graphs. Despite success, these approaches rely on
fixed-weight graphs, making ranking sensitive to the input affinity matrix. In
this study, we propose a new ranking algorithm that simultaneously learns the
data affinity matrix and the ranking scores. The proposed optimization
formulation assigns adaptive neighbors to each point in the data based on the
local connectivity, and the smoothness constraint assigns similar ranking
scores to similar data points. We develop a novel and efficient algorithm to
solve the optimization problem. Evaluations using synthetic and real datasets
suggest that the proposed algorithm can outperform the existing methods.
| 0 | 0 | 0 | 1 | 0 | 0 |
Identities and central polynomials of real graded division algebras | Let $A$ be a finite dimensional real algebra with a division grading by a
finite abelian group $G$. In this paper we provide finite basis for the
$T_G$-ideal of graded identities and for the $T_G$-space of graded central
polynomials for $A$.
| 0 | 0 | 1 | 0 | 0 | 0 |
VB-Courant algebroids, E-Courant algebroids and generalized geometry | In this paper, we first discuss the relation between VB-Courant algebroids
and E-Courant algebroids and construct some examples of E-Courant algebroids.
Then we introduce the notion of a generalized complex structure on an E-Courant
algebroid, unifying the usual generalized complex structures on
even-dimensional manifolds and generalized contact structures on
odd-dimensional manifolds. Moreover, we study generalized complex structures on
an omni-Lie algebroid in detail. In particular, we show that generalized
complex structures on an omni-Lie algebra $\gl(V)\oplus V$ correspond to
complex Lie algebra structures on V.
| 0 | 0 | 1 | 0 | 0 | 0 |
Majorana bound states in hybrid 2D Josephson junctions with ferromagnetic insulators | We consider a Josephson junction consisting of superconductor/ferromagnetic
insulator (S/FI) bilayers as electrodes which proximizes a nearby 2D electron
gas. By starting from a generic Josephson hybrid planar setup we present an
exhaustive analysis of the the interplay between the superconducting and
magnetic proximity effects and the conditions under which the structure
undergoes transitions to a non-trivial topological phase. We address the 2D
bound state problem using a general transfer matrix approach that reduces the
problem to an effective 1D Hamiltonian. This allows for straightforward study
of topological properties in different symmetry classes. As an example we
consider a narrow channel coupled with multiple ferromagnetic superconducting
fingers, and discuss how the Majorana bound states can be spatially controlled
by tuning the superconducting phases. Following our approach we also show the
energy spectrum, the free energy and finally the multiterminal Josephson
current of the setup.
| 0 | 1 | 0 | 0 | 0 | 0 |
Two-Dimensional Large Gap Topological Insulators with Large Rashba Spin-Orbit Coupling in Group-IV films | Rashba spin orbit coupling in topological insulators has attracted much
interest due to its exotic properties closely related to spintronic devices.
The coexistence of nontrivial topology and giant Rashba splitting, however, has
rare been observed in two-dimensional films, limiting severely its potential
applications at room temperature. Here, we propose a series of inversion
asymmetric group IV films, ABZ2, whose stability are confirmed by phonon
spectrum calculations. The analyses of electronic structures reveal that they
are intrinsic 2D TIs with a bulk gap as large as 0.74 eV, except for GeSiF2,
SnSiCl2, GeSiCl2 and GeSiBr2 monolayers which can transform from normal to
topological phases under appropriate tensile strains. Another prominent
intriguing feature is the giant Rashba spin splitting with a magnitude reaching
0.15 eV, the largest value reported in 2D films. These results present a
platform to explore 2D TIs for room temperature device applications.
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Topological boundary invariants for Floquet systems and quantum walks | A Floquet systems is a periodically driven quantum system. It can be
described by a Floquet operator. If this unitary operator has a gap in the
spectrum, then one can define associated topological bulk invariants which can
either only depend on the bands of the Floquet operator or also on the time as
a variable. It is shown how a K-theoretic result combined with the
bulk-boundary correspondence leads to edge invariants for the half-space
Floquet operators. These results also apply to topological quantum walks.
| 0 | 1 | 0 | 0 | 0 | 0 |
Assumption-Based Approaches to Reasoning with Priorities | This paper maps out the relation between different approaches for handling
preferences in argumentation with strict rules and defeasible assumptions by
offering translations between them. The systems we compare are: non-prioritized
defeats i.e. attacks, preference-based defeats, and preference-based defeats
extended with reverse defeat.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the Impact of Transposition Errors in Diffusion-Based Channels | In this work, we consider diffusion-based molecular communication with and
without drift between two static nano-machines. We employ type-based
information encoding, releasing a single molecule per information bit. At the
receiver, we consider an asynchronous detection algorithm which exploits the
arrival order of the molecules. In such systems, transposition errors
fundamentally undermine reliability and capacity. Thus, in this work we study
the impact of transpositions on the system performance. Towards this, we
present an analytical expression for the exact bit error probability (BEP)
caused by transpositions and derive computationally tractable approximations of
the BEP for diffusion-based channels with and without drift. Based on these
results, we analyze the BEP when background is not negligible and derive the
optimal bit interval that minimizes the BEP. Simulation results confirm the
theoretical results and show the error and goodput performance for different
parameters such as block size or noise generation rate.
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Uniform $L^p$-improving for weighted averages on curves | We define variable parameter analogues of the affine arclength measure on
curves and prove near-optimal $L^p$-improving estimates for associated
multilinear generalized Radon transforms. Some of our results are new even in
the convolution case.
| 0 | 0 | 1 | 0 | 0 | 0 |
Finite Sample Differentially Private Confidence Intervals | We study the problem of estimating finite sample confidence intervals of the
mean of a normal population under the constraint of differential privacy. We
consider both the known and unknown variance cases and construct differentially
private algorithms to estimate confidence intervals. Crucially, our algorithms
guarantee a finite sample coverage, as opposed to an asymptotic coverage.
Unlike most previous differentially private algorithms, we do not require the
domain of the samples to be bounded. We also prove lower bounds on the expected
size of any differentially private confidence set showing that our the
parameters are optimal up to polylogarithmic factors.
| 1 | 0 | 1 | 1 | 0 | 0 |
xSDK Foundations: Toward an Extreme-scale Scientific Software Development Kit | Extreme-scale computational science increasingly demands multiscale and
multiphysics formulations. Combining software developed by independent groups
is imperative: no single team has resources for all predictive science and
decision support capabilities. Scientific libraries provide high-quality,
reusable software components for constructing applications with improved
robustness and portability. However, without coordination, many libraries
cannot be easily composed. Namespace collisions, inconsistent arguments, lack
of third-party software versioning, and additional difficulties make
composition costly.
The Extreme-scale Scientific Software Development Kit (xSDK) defines
community policies to improve code quality and compatibility across
independently developed packages (hypre, PETSc, SuperLU, Trilinos, and
Alquimia) and provides a foundation for addressing broader issues in software
interoperability, performance portability, and sustainability. The xSDK
provides turnkey installation of member software and seamless combination of
aggregate capabilities, and it marks first steps toward extreme-scale
scientific software ecosystems from which future applications can be composed
rapidly with assured quality and scalability.
| 1 | 0 | 0 | 0 | 0 | 0 |
Muon Spin Rotation Analysis of the Internal Magnetic Field of Heavy Fermion System Uranium Beryllium-13 | Uranium beryllium-13 is a heavy fermion system whose anomalous behavior may
be explained by its poorly understood internal magnetic structure. Here,
uranium beryllium-13's magnetic distribution is probed via muon spin
spectroscopy ($\mu$SR)-a process where positive muons localize at magnetically
unique sites in the crystal lattice and precess at characteristic Larmor
frequencies, providing measurements of the internal field. Muon spin
experiments using the transverse-field technique conducted at varying
temperatures and external magnetic field strengths are analyzed via statistical
methods on ROOT. Two precession frequencies are observed at low temperatures
with an amplitude ratio in the Fourier transform of 2:1, enabling muon stopping
sites to be traced at the geometric centers of the edges of the crystal
lattice. Characteristic strong and weak magnetic sites are deduced,
additionally verified by mathematical relationships. Results can readily be
applied to other heavy fermion systems, and recent identification of quantum
critical points in a host of heavy fermion compounds show a promising future
for the application of these systems in quantum technology. Note that this
paper is an analysis of data, and all experiments mentioned here are conducted
by a third party.
| 0 | 1 | 0 | 0 | 0 | 0 |
The infinity-Fucik spectrum | In this article we study the behavior as $p \nearrow+\infty$ of the Fucik
spectrum for $p$-Laplace operator with zero Dirichlet boundary conditions in a
bounded domain $\Omega\subset \mathbb{R}^n$. We characterize the limit
equation, and we provide a description of the limit spectrum. Furthermore, we
show some explicit computations of the spectrum for certain configurations of
the domain.
| 0 | 0 | 1 | 0 | 0 | 0 |
Unitary Groups as Stabilizers of Orbits | We show that a finite unitary group which has orbits spanning the whole space
is necessarily the setwise stabilizer of a certain orbit.
| 0 | 0 | 1 | 0 | 0 | 0 |
m-TSNE: A Framework for Visualizing High-Dimensional Multivariate Time Series | Multivariate time series (MTS) have become increasingly common in healthcare
domains where human vital signs and laboratory results are collected for
predictive diagnosis. Recently, there have been increasing efforts to visualize
healthcare MTS data based on star charts or parallel coordinates. However, such
techniques might not be ideal for visualizing a large MTS dataset, since it is
difficult to obtain insights or interpretations due to the inherent high
dimensionality of MTS. In this paper, we propose 'm-TSNE': a simple and novel
framework to visualize high-dimensional MTS data by projecting them into a
low-dimensional (2-D or 3-D) space while capturing the underlying data
properties. Our framework is easy to use and provides interpretable insights
for healthcare professionals to understand MTS data. We evaluate our
visualization framework on two real-world datasets and demonstrate that the
results of our m-TSNE show patterns that are easy to understand while the other
methods' visualization may have limitations in interpretability.
| 1 | 0 | 0 | 1 | 0 | 0 |
On stabilization of solutions of nonlinear parabolic equations with a gradient term | For parabolic equations of the form $$ \frac{\partial u}{\partial t} -
\sum_{i,j=1}^n a_{ij} (x, u) \frac{\partial^2 u}{\partial x_i \partial x_j} + f
(x, u, D u) = 0 \quad \mbox{in } {\mathbb R}_+^{n+1}, $$ where ${\mathbb
R}_+^{n+1} = {\mathbb R}^n \times (0, \infty)$, $n \ge 1$, $D = (\partial /
\partial x_1, \ldots, \partial / \partial x_n)$ is the gradient operator, and
$f$ is some function, we obtain conditions guaranteeing that every solution
tends to zero as $t \to \infty$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Deep metric learning for multi-labelled radiographs | Many radiological studies can reveal the presence of several co-existing
abnormalities, each one represented by a distinct visual pattern. In this
article we address the problem of learning a distance metric for plain
radiographs that captures a notion of "radiological similarity": two chest
radiographs are considered to be similar if they share similar abnormalities.
Deep convolutional neural networks (DCNs) are used to learn a low-dimensional
embedding for the radiographs that is equipped with the desired metric. Two
loss functions are proposed to deal with multi-labelled images and potentially
noisy labels. We report on a large-scale study involving over 745,000 chest
radiographs whose labels were automatically extracted from free-text
radiological reports through a natural language processing system. Using 4,500
validated exams, we demonstrate that the methodology performs satisfactorily on
clustering and image retrieval tasks. Remarkably, the learned metric separates
normal exams from those having radiological abnormalities.
| 0 | 0 | 0 | 1 | 0 | 0 |
Algebras of generalized dihedral type | We provide a complete classification of all algebras of generalised dihedral
type, which are natural generalizations of algebras which occurred in the study
of blocks with dihedral defect groups. This gives a description by quivers and
relations coming from surface triangulations.
| 0 | 0 | 1 | 0 | 0 | 0 |
Resonant particle production during inflation: a full analytical study | We revisit the study of the phenomenology associated to a burst of particle
production of a field whose mass is controlled by the inflaton field and
vanishes at one given instance during inflation. This generates a bump in the
correlators of the primordial scalar curvature. We provide a unified formalism
to compute various effects that have been obtained in the literature and
confirm that the dominant effects are due to the rescattering of the produced
particles on the inflaton condensate. We improve over existing results (based
on numerical fits) by providing exact analytic expressions for the shape and
height of the bump, both in the power spectrum and the equilateral bispectrum.
We then study the regime of validity of the perturbative computations of this
signature. Finally, we extend these computations to the case of a burst of
particle production in a sector coupled only gravitationally to the inflaton.
| 0 | 1 | 0 | 0 | 0 | 0 |
Average whenever you meet: Opportunistic protocols for community detection | Consider the following asynchronous, opportunistic communication model over a
graph $G$: in each round, one edge is activated uniformly and independently at
random and (only) its two endpoints can exchange messages and perform local
computations. Under this model, we study the following random process: The
first time a vertex is an endpoint of an active edge, it chooses a random
number, say $\pm 1$ with probability $1/2$; then, in each round, the two
endpoints of the currently active edge update their values to their average. We
show that, if $G$ exhibits a two-community structure (for example, two
expanders connected by a sparse cut), the values held by the nodes will
collectively reflect the underlying community structure over a suitable phase
of the above process, allowing efficient and effective recovery in important
cases.
In more detail, we first provide a first-moment analysis showing that, for a
large class of almost-regular clustered graphs that includes the stochastic
block model, the expected values held by all but a negligible fraction of the
nodes eventually reflect the underlying cut signal. We prove this property
emerges after a mixing period of length $\mathcal O(n\log n)$. We further
provide a second-moment analysis for a more restricted class of regular
clustered graphs that includes the regular stochastic block model. For this
case, we are able to show that most nodes can efficiently and locally identify
their community of reference over a suitable time window. This results in the
first opportunistic protocols that approximately recover community structure
using only polylogarithmic work per node. Even for the above class of regular
graphs, our second moment analysis requires new concentration bounds on the
product of certain random matrices that are technically challenging and
possibly of independent interest.
| 1 | 0 | 0 | 0 | 0 | 0 |
A polynomial-time approximation algorithm for all-terminal network reliability | We give a fully polynomial-time randomized approximation scheme (FPRAS) for
the all-terminal network reliability problem, which is to determine the
probability that, in a undirected graph, assuming each edge fails
independently, the remaining graph is still connected. Our main contribution is
to confirm a conjecture by Gorodezky and Pak (Random Struct. Algorithms, 2014),
that the expected running time of the "cluster-popping" algorithm in
bi-directed graphs is bounded by a polynomial in the size of the input.
| 1 | 0 | 0 | 0 | 0 | 0 |
Provably Accurate Double-Sparse Coding | Sparse coding is a crucial subroutine in algorithms for various signal
processing, deep learning, and other machine learning applications. The central
goal is to learn an overcomplete dictionary that can sparsely represent a given
input dataset. However, a key challenge is that storage, transmission, and
processing of the learned dictionary can be untenably high if the data
dimension is high. In this paper, we consider the double-sparsity model
introduced by Rubinstein et al. (2010b) where the dictionary itself is the
product of a fixed, known basis and a data-adaptive sparse component. First, we
introduce a simple algorithm for double-sparse coding that can be amenable to
efficient implementation via neural architectures. Second, we theoretically
analyze its performance and demonstrate asymptotic sample complexity and
running time benefits over existing (provable) approaches for sparse coding. To
our knowledge, our work introduces the first computationally efficient
algorithm for double-sparse coding that enjoys rigorous statistical guarantees.
Finally, we support our analysis via several numerical experiments on simulated
data, confirming that our method can indeed be useful in problem sizes
encountered in practical applications.
| 1 | 0 | 0 | 1 | 0 | 0 |
Asymptotic theory for maximum likelihood estimates in reduced-rank multivariate generalised linear models | Reduced-rank regression is a dimensionality reduction method with many
applications. The asymptotic theory for reduced rank estimators of parameter
matrices in multivariate linear models has been studied extensively. In
contrast, few theoretical results are available for reduced-rank multivariate
generalised linear models. We develop M-estimation theory for concave criterion
functions that are maximised over parameters spaces that are neither convex nor
closed. These results are used to derive the consistency and asymptotic
distribution of maximum likelihood estimators in reduced-rank multivariate
generalised linear models, when the response and predictor vectors have a joint
distribution. We illustrate our results in a real data classification problem
with binary covariates.
| 0 | 0 | 1 | 1 | 0 | 0 |
Sequential two-fold Pearson chi-squared test and tails of the Bessel process distributions | We find asymptotic formulas for error probabilities of two-fold Pearson
goodness-of-fit test as functions of two critical levels. These results may be
reformulated in terms of tails of two-dimensional distributions of the Bessel
process. Necessary properties of the Infeld function are obtained.
| 0 | 0 | 1 | 1 | 0 | 0 |
Tracking Gaze and Visual Focus of Attention of People Involved in Social Interaction | The visual focus of attention (VFOA) has been recognized as a prominent
conversational cue. We are interested in estimating and tracking the VFOAs
associated with multi-party social interactions. We note that in this type of
situations the participants either look at each other or at an object of
interest; therefore their eyes are not always visible. Consequently both gaze
and VFOA estimation cannot be based on eye detection and tracking. We propose a
method that exploits the correlation between eye gaze and head movements. Both
VFOA and gaze are modeled as latent variables in a Bayesian switching
state-space model. The proposed formulation leads to a tractable learning
procedure and to an efficient algorithm that simultaneously tracks gaze and
visual focus. The method is tested and benchmarked using two publicly available
datasets that contain typical multi-party human-robot and human-human
interactions.
| 1 | 0 | 0 | 0 | 0 | 0 |
Fully-Dynamic and Kinetic Conflict-Free Coloring of Intervals with Respect to Points | We introduce the fully-dynamic conflict-free coloring problem for a set $S$
of intervals in $\mathbb{R}^1$ with respect to points, where the goal is to
maintain a conflict-free coloring for$S$ under insertions and deletions. A
coloring is conflict-free if for each point $p$ contained in some interval, $p$
is contained in an interval whose color is not shared with any other interval
containing $p$. We investigate trade-offs between the number of colors used and
the number of intervals that are recolored upon insertion or deletion of an
interval. Our results include:
- a lower bound on the number of recolorings as a function of the number of
colors, which implies that with $O(1)$ recolorings per update the worst-case
number of colors is $\Omega(\log n/\log\log n)$, and that any strategy using
$O(1/\varepsilon)$ colors needs $\Omega(\varepsilon n^{\varepsilon})$
recolorings;
- a coloring strategy that uses $O(\log n)$ colors at the cost of $O(\log n)$
recolorings, and another strategy that uses $O(1/\varepsilon)$ colors at the
cost of $O(n^{\varepsilon}/\varepsilon)$ recolorings;
- stronger upper and lower bounds for special cases.
We also consider the kinetic setting where the intervals move continuously
(but there are no insertions or deletions); here we show how to maintain a
coloring with only four colors at the cost of three recolorings per event and
show this is tight.
| 1 | 0 | 0 | 0 | 0 | 0 |
Magnetic properties in ultra-thin 3d transition metal alloys II: Experimental verification of quantitative theories of damping and spin-pumping | A systematic experimental study of Gilbert damping is performed via
ferromagnetic resonance for the disordered crystalline binary 3d transition
metal alloys Ni-Co, Ni-Fe and Co-Fe over the full range of alloy compositions.
After accounting for inhomogeneous linewidth broadening, the damping shows
clear evidence of both interfacial damping enhancement (by spin pumping) and
radiative damping. We quantify these two extrinsic contributions and thereby
determine the intrinsic damping. The comparison of the intrinsic damping to
multiple theoretical calculations yields good qualitative and quantitative
agreement in most cases. Furthermore, the values of the damping obtained in
this study are in good agreement with a wide range of published experimental
and theoretical values. Additionally, we find a compositional dependence of the
spin mixing conductance.
| 0 | 1 | 0 | 0 | 0 | 0 |
Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks | Learning to detect fraud in large-scale accounting data is one of the
long-standing challenges in financial statement audits or fraud investigations.
Nowadays, the majority of applied techniques refer to handcrafted rules derived
from known fraud scenarios. While fairly successful, these rules exhibit the
drawback that they often fail to generalize beyond known fraud scenarios and
fraudsters gradually find ways to circumvent them. To overcome this
disadvantage and inspired by the recent success of deep learning we propose the
application of deep autoencoder neural networks to detect anomalous journal
entries. We demonstrate that the trained network's reconstruction error
obtainable for a journal entry and regularized by the entry's individual
attribute probabilities can be interpreted as a highly adaptive anomaly
assessment. Experiments on two real-world datasets of journal entries, show the
effectiveness of the approach resulting in high f1-scores of 32.93 (dataset A)
and 16.95 (dataset B) and less false positive alerts compared to state of the
art baseline methods. Initial feedback received by chartered accountants and
fraud examiners underpinned the quality of the approach in capturing highly
relevant accounting anomalies.
| 1 | 0 | 0 | 0 | 0 | 0 |
Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data | We consider a problem of learning a binary classifier only from positive data
and unlabeled data (PU learning) and estimating the class-prior in unlabeled
data under the case-control scenario. Most of the recent methods of PU learning
require an estimate of the class-prior probability in unlabeled data, and it is
estimated in advance with another method. However, such a two-step approach
which first estimates the class prior and then trains a classifier may not be
the optimal approach since the estimation error of the class-prior is not taken
into account when a classifier is trained. In this paper, we propose a novel
unified approach to estimating the class-prior and training a classifier
alternately. Our proposed method is simple to implement and computationally
efficient. Through experiments, we demonstrate the practical usefulness of the
proposed method.
| 0 | 0 | 0 | 1 | 0 | 0 |
Oscillating dipole with fractional quantum source in Aharonov-Bohm electrodynamics | We show, in the case of a special dipolar source, that electromagnetic fields
in fractional quantum mechanics have an unexpected space dependence:
propagating fields may have non-transverse components, and the distinction
between near-field zone and wave zone is blurred. We employ an extension of
Maxwell theory, Aharonov-Bohm electrodynamics, which is compatible with
currents $j^\nu$ conserved globally but not locally, we have derived in another
work the field equation $\partial_\mu F^{\mu \nu}=j^\nu+i^\nu$, where $i^\nu$
is a non-local function of $j^\nu$, called "secondary current". Y.\ Wei has
recently proved that the probability current in fractional quantum mechanics is
in general not locally conserved. We compute this current for a Gaussian wave
packet with fractional parameter $a=3/2$ and find that in a suitable limit it
can be approximated by our simplified dipolar source. Currents which are not
locally conserved may be present also in other quantum systems whose wave
functions satisfy non-local equations. The combined electromagnetic effects of
such sources and their secondary currents are very interesting both
theoretically and for potential applications.
| 0 | 1 | 0 | 0 | 0 | 0 |
Randomized Linear Programming Solves the Discounted Markov Decision Problem In Nearly-Linear (Sometimes Sublinear) Running Time | We propose a novel randomized linear programming algorithm for approximating
the optimal policy of the discounted Markov decision problem. By leveraging the
value-policy duality and binary-tree data structures, the algorithm adaptively
samples state-action-state transitions and makes exponentiated primal-dual
updates. We show that it finds an $\epsilon$-optimal policy using nearly-linear
run time in the worst case. When the Markov decision process is ergodic and
specified in some special data formats, the algorithm finds an
$\epsilon$-optimal policy using run time linear in the total number of
state-action pairs, which is sublinear in the input size. These results provide
a new venue and complexity benchmarks for solving stochastic dynamic programs.
| 1 | 0 | 1 | 0 | 0 | 0 |
SCAV'18: Report of the 2nd International Workshop on Safe Control of Autonomous Vehicles | This report summarizes the discussions, open issues, take-away messages, and
conclusions of the 2nd SCAV workshop.
| 1 | 0 | 0 | 0 | 0 | 0 |
Inference For High-Dimensional Split-Plot-Designs: A Unified Approach for Small to Large Numbers of Factor Levels | Statisticians increasingly face the problem to reconsider the adaptability of
classical inference techniques. In particular, divers types of high-dimensional
data structures are observed in various research areas; disclosing the
boundaries of conventional multivariate data analysis. Such situations occur,
e.g., frequently in life sciences whenever it is easier or cheaper to
repeatedly generate a large number $d$ of observations per subject than
recruiting many, say $N$, subjects. In this paper we discuss inference
procedures for such situations in general heteroscedastic split-plot designs
with $a$ independent groups of repeated measurements. These will, e.g., be able
to answer questions about the occurrence of certain time, group and
interactions effects or about particular profiles.
The test procedures are based on standardized quadratic forms involving
suitably symmetrized U-statistics-type estimators which are robust against an
increasing number of dimensions $d$ and/or groups $a$. We then discuss its
limit distributions in a general asymptotic framework and additionally propose
improved small sample approximations. Finally its small sample performance is
investigated in simulations and the applicability is illustrated by a real data
analysis.
| 0 | 0 | 1 | 1 | 0 | 0 |
Rate Optimal Binary Linear Locally Repairable Codes with Small Availability | A locally repairable code with availability has the property that every code
symbol can be recovered from multiple, disjoint subsets of other symbols of
small size. In particular, a code symbol is said to have $(r,t)$-availability
if it can be recovered from $t$ disjoint subsets, each of size at most $r$. A
code with availability is said to be 'rate-optimal', if its rate is maximum
among the class of codes with given locality, availability, and alphabet size.
This paper focuses on rate-optimal binary, linear codes with small
availability, and makes four contributions. First, it establishes tight upper
bounds on the rate of binary linear codes with $(r,2)$ and $(2,3)$
availability. Second, it establishes a uniqueness result for binary
rate-optimal codes, showing that for certain classes of binary linear codes
with $(r,2)$ and $(2,3)$-availability, any rate optimal code must be a direct
sum of shorter rate optimal codes. Third, it presents novel upper bounds on the
rates of binary linear codes with $(2,t)$ and $(r,3)$-availability. In
particular, the main contribution here is a new method for bounding the number
of cosets of the dual of a code with availability, using its covering
properties. Finally, it presents a class of locally repairable linear codes
associated with convex polyhedra, focusing on the codes associated with the
Platonic solids. It demonstrates that these codes are locally repairable with
$t = 2$, and that the codes associated with (geometric) dual polyhedra are
(coding theoretic) duals of each other.
| 1 | 0 | 1 | 0 | 0 | 0 |
A general renormalization procedure on the one-dimensional lattice and decay of correlations | We present a general form of Renormalization operator $\mathcal{R}$ acting on
potentials $V:\{0,1\}^\mathbb{N} \to \mathbb{R}$. We exhibit the analytical
expression of the fixed point potential $V$ for such operator $\mathcal{R}$.
This potential can be expressed in a naturally way in terms of a certain
integral over the Hausdorff probability on a Cantor type set on the interval
$[0,1]$. This result generalizes a previous one by A. Baraviera, R. Leplaideur
and A. Lopes where the fixed point potential $V$ was of Hofbauer type.
For the potentials of Hofbauer type (a well known case of phase transition)
the decay is like $n^{-\gamma}$, $\gamma>0$.
Among other things we present the estimation of the decay of correlation of
the equilibrium probability associated to the fixed potential $V$ of our
general renormalization procedure. In some cases we get polynomial decay like
$n^{-\gamma}$, $\gamma>0$, and in others a decay faster than $n \,e^{ -\,
\sqrt{n}}$, when $n \to \infty$.
The potentials $g$ we consider here are elements of the so called family of
Walters potentials on $\{0,1\}^\mathbb{N} $ which generalizes the potentials
considered initially by F. Hofbauer. For these potentials some explicit
expressions for the eigenfunctions are known.
In a final section we also show that given any choice $d_n \to 0$ of real
numbers varying with $n \in \mathbb{N}$ there exist a potential $g$ on the
class defined by Walters which has a invariant probability with such numbers as
the coefficients of correlation (for a certain explicit observable function).
| 0 | 1 | 1 | 0 | 0 | 0 |
Duality Spectral Sequences for Weierstrass Fibrations and Applications | We study duality spectral sequences for Weierstrass fibrations. Using these
spectral sequences, we show that on a K-trivial Weierstrass threefold over a
K-numerically trivial surface, any line bundle of nonzero fiber degree is taken
by a Fourier-Mukai transform to a slope stable locally free sheaf.
| 0 | 0 | 1 | 0 | 0 | 0 |
Occupation times for the finite buffer fluid queue with phase-type ON-times | In this short communication we study a fluid queue with a finite buffer. The
performance measure we are interested in is the occupation time over a finite
time period, i.e., the fraction of time the workload process is below some
fixed target level. We construct an alternating sequence of sojourn times
$D_1,U_1,...$ where the pairs $(D_i,U_i)_{i\in\mathbb{N}}$ are i.i.d. random
vectors. We use this sequence to determine the distribution function of the
occupation time in terms of its double transform.
| 0 | 0 | 1 | 0 | 0 | 0 |
Affine forward variance models | We introduce the class of affine forward variance (AFV) models of which both
the conventional Heston model and the rough Heston model are special cases. We
show that AFV models can be characterized by the affine form of their cumulant
generating function, which can be obtained as solution of a convolution Riccati
equation. We further introduce the class of affine forward order flow intensity
(AFI) models, which are structurally similar to AFV models, but driven by jump
processes, and which include Hawkes-type models. We show that the cumulant
generating function of an AFI model satisfies a generalized convolution Riccati
equation and that a high-frequency limit of AFI models converges in
distribution to the AFV model.
| 0 | 0 | 0 | 0 | 0 | 1 |
The cohomology of free loop spaces of homogeneous spaces | The free loops space $\Lambda X$ of a space $X$ has become an important
object of study particularly in the case when $X$ is a manifold.The study of
free loop spaces is motivated in particular by two main examples. The first is
their relation to geometrically distinct periodic geodesics on a manifold,
originally studied by Gromoll and Meyer in $1969$. More recently the study of
string topology and in particular the Chas-Sullivan loop product has been an
active area of research.
A complete flag manifold is the quotient of a Lie group by its maximal torus
and is one of the nicer examples of a homogeneous space. Both the cohomology
and Chas-Sullivan product structure are understood for spaces $S^n$,
$\mathbb{C}P^n$ and most simple Lie groups. Hence studying the topology of the
free loops space on homogeneous space is a natural next step.
In the thesis we compute the differentials in the integral Leray-Serre
spectral sequence associated to the free loops space fibrations in the cases of
$SU(n+1)/T^n$ and $Sp(n)/T^n$. Study in detail the structure of the third page
of the spectral sequence in the case of $SU(n)$ and give the module structure
of $H^*(\Lambda(SU(3)/T^2);\mathbb{Z})$ and
$H^*(\Lambda(Sp(2)/T^2);\mathbb{Z})$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Measurement of the muon-induced neutron seasonal modulation with LVD | Cosmic ray muons with the average energy of 280 GeV and neutrons produced by
muons are detected with the Large Volume Detector at LNGS. We present an
analysis of the seasonal variation of the neutron flux on the basis of the data
obtained during 15 years. The measurement of the seasonal variation of the
specific number of neutrons generated by muons allows to obtaine the variation
magnitude of of the average energy of the muon flux at the depth of the LVD
location. The source of the seasonal variation of the total neutron flux is a
change of the intensity and the average energy of the muon flux.
| 0 | 1 | 0 | 0 | 0 | 0 |
Trail-Mediated Self-Interaction | A number of microorganisms leave persistent trails while moving along
surfaces. For single-cell organisms, the trail-mediated self-interaction will
influence its dynamics. It has been discussed recently [Kranz \textit{et al.}
Phys. Rev. Lett. \textbf{117}, 8101 (2016)] that the self-interaction may
localize the organism above a critical coupling $\chi_c$ to the trail. Here we
will derive a generalized active particle model capturing the key features of
the self-interaction and analyze its behavior for smaller couplings $\chi <
\chi_c$. We find that fluctuations in propulsion speed shift the localization
transition to stronger couplings.
| 0 | 0 | 0 | 0 | 1 | 0 |
A sub-super solution method for a class of nonlocal problems involving the p(x)-Laplacian operator and applications | In the present paper we study the existence of solutions for some nonlocal
problems involving the p(x)-Laplacian operator. The approach is based on a new
sub-supersolution method
| 0 | 0 | 1 | 0 | 0 | 0 |
Summability properties of Gabor expansions | We show that there exist complete and minimal systems of time-frequency
shifts of Gaussians in $L^2(\mathbb{R})$ which are not strong Markushevich
basis (do not admit the spectral synthesis). In particular, it implies that
there is no linear summation method for general Gaussian Gabor expansions. On
the other hand we prove that the spectral synthesis for such Gabor systems
holds up to one dimensional defect.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Las Vegas algorithm to solve the elliptic curve discrete logarithm problem | In this paper, we describe a new Las Vegas algorithm to solve the elliptic
curve discrete logarithm problem. The algorithm depends on a property of the
group of rational points of an elliptic curve and is thus not a generic
algorithm. The algorithm that we describe has some similarities with the most
powerful index-calculus algorithm for the discrete logarithm problem over a
finite field.
| 1 | 0 | 1 | 0 | 0 | 0 |
Spectral sequences via examples | These are lecture notes for a short course about spectral sequences that was
held at Málaga, October 18--20 (2016), during the "Fifth Young Spanish
Topologists Meeting". The approach was to illustrate the basic notions via
fully computed examples arising from Algebraic Topology and Group Theory.
| 0 | 0 | 1 | 0 | 0 | 0 |
Coherent scattering from semi-infinite non-Hermitian potentials | When two identical (coherent) beams are injected at a semi-infinite
non-Hermitian medium from left and right, we show that both reflection
$(r_L,r_R)$ and transmission $(t_L,t_R)$ amplitudes are non-reciprocal. In a
parametric domain, there exists Spectral Singularity (SS) at a real energy
$E=E_*$ and the determinant of the time-reversed two port S-matrix i.e.,
$|\det(S)|=|t_L t_R-r_L r_R|$ vanishes sharply at $E=E_*$ displaying the
phenomenon of Coherent Perfect Absorption (CPA). In the complimentary
parametric domain, the potential becomes either left or right reflectionless at
$E=E_z$. But we rule out the existence of Invisibility despite $r_R(E_i)=0$ and
$t_R(E_i)=1$ in these new models. We present two simple exactly solvable models
where the expressions for $E_*$, $E_z$, $E_i$ and the parametric conditions on
the potential have been obtained in explicit and simple forms. Earlier, the
novel phenomena of SS and CPA have been found to occur only in the scattering
complex potentials which are spatially localized (vanish asymptotically) and
having $t_L=t_R$.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Higher Structure Identity Principle | We prove a Structure Identity Principle for theories defined on types of
$h$-level 3 by defining a general notion of saturation for a large class of
structures definable in the Univalent Foundations.
| 1 | 0 | 1 | 0 | 0 | 0 |
Two-Player Games for Efficient Non-Convex Constrained Optimization | In recent years, constrained optimization has become increasingly relevant to
the machine learning community, with applications including Neyman-Pearson
classification, robust optimization, and fair machine learning. A natural
approach to constrained optimization is to optimize the Lagrangian, but this is
not guaranteed to work in the non-convex setting, and, if using a first-order
method, cannot cope with non-differentiable constraints (e.g. constraints on
rates or proportions).
The Lagrangian can be interpreted as a two-player game played between a
player who seeks to optimize over the model parameters, and a player who wishes
to maximize over the Lagrange multipliers. We propose a non-zero-sum variant of
the Lagrangian formulation that can cope with non-differentiable--even
discontinuous--constraints, which we call the "proxy-Lagrangian". The first
player minimizes external regret in terms of easy-to-optimize "proxy
constraints", while the second player enforces the original constraints by
minimizing swap regret.
For this new formulation, as for the Lagrangian in the non-convex setting,
the result is a stochastic classifier. For both the proxy-Lagrangian and
Lagrangian formulations, however, we prove that this classifier, instead of
having unbounded size, can be taken to be a distribution over no more than m+1
models (where m is the number of constraints). This is a significant
improvement in practical terms.
| 0 | 0 | 0 | 1 | 0 | 0 |
On thin local sets of the Gaussian free field | We study how small a local set of the continuum Gaussian free field (GFF) in
dimension $d$ has to be to ensure that this set is thin, which loosely speaking
means that it captures no GFF mass on itself, in other words, that the field
restricted to it is zero. We provide a criterion on the size of the local set
for this to happen, and on the other hand, we show that this criterion is sharp
by constructing small local sets that are not thin.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Note on Prediction Markets | In a prediction market, individuals can sequentially place bets on the
outcome of a future event. This leaves a trail of personal probabilities for
the event, each being conditional on the current individual's private
background knowledge and on the previously announced probabilities of other
individuals, which give partial information about their private knowledge. By
means of theory and examples, we revisit some results in this area. In
particular, we consider the case of two individuals, who start with the same
overall probability distribution but different private information, and then
take turns in updating their probabilities. We note convergence of the
announced probabilities to a limiting value, which may or may not be the same
as that based on pooling their private information.
| 0 | 0 | 1 | 1 | 0 | 0 |
Dihedral angle prediction using generative adversarial networks | Several dihedral angles prediction methods were developed for protein
structure prediction and their other applications. However, distribution of
predicted angles would not be similar to that of real angles. To address this
we employed generative adversarial networks (GAN). Generative adversarial
networks are composed of two adversarially trained networks: a discriminator
and a generator. A discriminator distinguishes samples from a dataset and
generated samples while a generator generates realistic samples. Although the
discriminator of GANs is trained to estimate density, GAN model is intractable.
On the other hand, noise-contrastive estimation (NCE) was introduced to
estimate a normalization constant of an unnormalized statistical model and thus
the density function. In this thesis, we introduce noise-contrastive estimation
generative adversarial networks (NCE-GAN) which enables explicit density
estimation of a GAN model. And a new loss for the generator is proposed. We
also propose residue-wise variants of auxiliary classifier GAN (AC-GAN) and
Semi-supervised GAN to handle sequence information in a window. In our
experiment, the conditional generative adversarial network (C-GAN), AC-GAN and
Semi-supervised GAN were compared. And experiments done with improved
conditions were invested. We identified a phenomenon of AC-GAN that
distribution of its predicted angles is composed of unusual clusters. The
distribution of the predicted angles of Semi-supervised GAN was most similar to
the Ramachandran plot. We found that adding the output of the NCE as an
additional input of the discriminator is helpful to stabilize the training of
the GANs and to capture the detailed structures. Adding regression loss and
using predicted angles by regression loss only model could improve the
conditional generation performance of the C-GAN and AC-GAN.
| 0 | 0 | 0 | 1 | 1 | 0 |
A recurrence relation for the odd order moments of the Fabius function | A simple recurrence relation for the even order moments of the Fabius
function is proven. Also, a very similar formula for the odd order moments in
terms of the even order moments is proved. The matrices corresponding to these
formulas (and their inverses) are multiplied so as to obtain a matrix that
correspond to a recurrence relation for the odd order moments in terms of
themselves. The theorem at the end gives a closed-form for the coefficients.
| 0 | 0 | 1 | 0 | 0 | 0 |
Performance of Range Separated Hybrids: Study within BECKE88 family and Semilocal Exchange Hole based Range Separated Hybrid | A long range corrected range separated hybrid functional is developed based
on the density matrix expansion (DME) based semilocal exchange hole with
Lee-Yang-Parr (LYP) correlation. An extensive study involving the proposed
range separated hybrid for thermodynamic as well as properties related to the
fractional occupation number is compared with different BECKE88 family
semilocal, hybrid and range separated hybrids. It has been observed that using
Kohn-Sham kinetic energy dependent exchange hole several properties related to
the fractional occupation number can be improved without hindering the
thermochemical accuracy. The newly constructed range separated hybrid
accurately describe the hydrogen and non-hydrogen reaction barrier heights. The
present range separated functional has been constructed using full semilocal
meta-GGA type exchange hole having exact properties related to exchange hole
therefore, it has a strong physical basis.
| 0 | 1 | 0 | 0 | 0 | 0 |
Manifold Mixup: Learning Better Representations by Interpolating Hidden States | Deep networks often perform well on the data distribution on which they are
trained, yet give incorrect (and often very confident) answers when evaluated
on points from off of the training distribution. This is exemplified by the
adversarial examples phenomenon but can also be seen in terms of model
generalization and domain shift. Ideally, a model would assign lower confidence
to points unlike those from the training distribution. We propose a regularizer
which addresses this issue by training with interpolated hidden states and
encouraging the classifier to be less confident at these points. Because the
hidden states are learned, this has an important effect of encouraging the
hidden states for a class to be concentrated in such a way so that
interpolations within the same class or between two different classes do not
intersect with the real data points from other classes. This has a major
advantage in that it avoids the underfitting which can result from
interpolating in the input space. We prove that the exact condition for this
problem of underfitting to be avoided by Manifold Mixup is that the
dimensionality of the hidden states exceeds the number of classes, which is
often the case in practice. Additionally, this concentration can be seen as
making the features in earlier layers more discriminative. We show that despite
requiring no significant additional computation, Manifold Mixup achieves large
improvements over strong baselines in supervised learning, robustness to
single-step adversarial attacks, semi-supervised learning, and Negative
Log-Likelihood on held out samples.
| 0 | 0 | 0 | 1 | 0 | 0 |
Small Resolution Proofs for QBF using Dependency Treewidth | In spite of the close connection between the evaluation of quantified Boolean
formulas (QBF) and propositional satisfiability (SAT), tools and techniques
which exploit structural properties of SAT instances are known to fail for QBF.
This is especially true for the structural parameter treewidth, which has
allowed the design of successful algorithms for SAT but cannot be
straightforwardly applied to QBF since it does not take into account the
interdependencies between quantified variables.
In this work we introduce and develop dependency treewidth, a new structural
parameter based on treewidth which allows the efficient solution of QBF
instances. Dependency treewidth pushes the frontiers of tractability for QBF by
overcoming the limitations of previously introduced variants of treewidth for
QBF. We augment our results by developing algorithms for computing the
decompositions that are required to use the parameter.
| 1 | 0 | 0 | 0 | 0 | 0 |
Theoretical Analysis of Generalized Sagnac Effect in the Standard Synchronization | The Sagnac effect has been shown in inertial frames as well as rotating
frames. We solve the problem of the generalized Sagnac effect in the standard
synchronization of clocks. The speed of a light beam that traverses an optical
fiber loop is measured with respect to the proper time of the light detector,
and is shown to be other than the constant c, though it appears to be c if
measured by the time standard-synchronized. The fiber loop, which can have an
arbitrary shape, is described by an infinite number of straight lines such that
it can be handled by the general framework of Mansouri and Sexl (MS). For a
complete analysis of the Sagnac effect, the motion of the laboratory should be
taken into account. The MS framework is introduced to deal with its motion
relative to a preferred reference frame. Though the one-way speed of light is
other than c, its two-way speed is shown to be c with respect to the proper
time. The theoretical analysis of the generalized Sagnac effect corresponds to
the experimental results, and shows the usefulness of the standard
synchronization. The introduction of the standard synchrony can make
mathematical manipulation easy and can allow us to deal with relative motions
between inertial frames without information on their velocities relative to the
preferred frame.
| 0 | 1 | 0 | 0 | 0 | 0 |
Lattice thermal expansion and anisotropic displacements in urea, bromomalonic aldehyde, pentachloropyridine and naphthalene | Anisotropic displacement parameters (ADPs) are commonly used in
crystallography, chemistry and related fields to describe and quantify thermal
motion of atoms. Within the very recent years, these ADPs have become
predictable by lattice dynamics in combination with first-principles theory.
Here, we study four very different molecular crystals, namely urea,
bromomalonic aldehyde, pentachloropyridine, and naphthalene, by
first-principles theory to assess the quality of ADPs calculated in the
quasi-harmonic approximation. In addition, we predict both thermal expansion
and thermal motion within the quasi-harmonic approximation and compare the
predictions with experimental data. Very reliable ADPs are calculated within
the quasi-harmonic approximation for all four cases up to at least 200 K, and
they turn out to be in better agreement with experiment than the harmonic ones.
In one particular case, ADPs can even reliably be predicted up to room
temperature. Our results also hint at the importance of normal-mode
anharmonicity in the calculation of ADPs.
| 0 | 1 | 0 | 0 | 0 | 0 |
Learning Heuristic Search via Imitation | Robotic motion planning problems are typically solved by constructing a
search tree of valid maneuvers from a start to a goal configuration. Limited
onboard computation and real-time planning constraints impose a limit on how
large this search tree can grow. Heuristics play a crucial role in such
situations by guiding the search towards potentially good directions and
consequently minimizing search effort. Moreover, it must infer such directions
in an efficient manner using only the information uncovered by the search up
until that time. However, state of the art methods do not address the problem
of computing a heuristic that explicitly minimizes search effort. In this
paper, we do so by training a heuristic policy that maps the partial
information from the search to decide which node of the search tree to expand.
Unfortunately, naively training such policies leads to slow convergence and
poor local minima. We present SaIL, an efficient algorithm that trains
heuristic policies by imitating "clairvoyant oracles" - oracles that have full
information about the world and demonstrate decisions that minimize search
effort. We leverage the fact that such oracles can be efficiently computed
using dynamic programming and derive performance guarantees for the learnt
heuristic. We validate the approach on a spectrum of environments which show
that SaIL consistently outperforms state of the art algorithms. Our approach
paves the way forward for learning heuristics that demonstrate an anytime
nature - finding feasible solutions quickly and incrementally refining it over
time.
| 1 | 0 | 0 | 0 | 0 | 0 |
Hook removal operators on the odd Young graph | In this article we consider hook removal operators on odd partitions, i.e.,
partitions labelling odd-degree irreducible characters of finite symmetric
groups. In particular we complete the discussion, started by Isaacs, Navarro,
Olsson and Tiep in 2016, concerning the commutativity of such operators.
| 0 | 0 | 1 | 0 | 0 | 0 |
Modal operators and toric ideals | In the present paper we consider modal propositional logic and look for the
constraints that are imposed to the propositions of the special type $\Box a$
by the structure of the relevant finite Kripke frame. We translate the usual
language of modal propositional logic in terms of notions of commutative
algebra, namely polynomial rings, ideals, and bases of ideals. We use
extensively the perspective obtained in previous works in Algebraic Statistics.
We prove that the constraints on $\Box a$ can be derived through a binomial
ideal containing a toric ideal and we give sufficient conditions under which
the toric ideal fully describes the constraints.
| 0 | 0 | 1 | 0 | 0 | 0 |
Metadynamics for Training Neural Network Model Chemistries: a Competitive Assessment | Neural network (NN) model chemistries (MCs) promise to facilitate the
accurate exploration of chemical space and simulation of large reactive
systems. One important path to improving these models is to add layers of
physical detail, especially long-range forces. At short range, however, these
models are data driven and data limited. Little is systematically known about
how data should be sampled, and `test data' chosen randomly from some sampling
techniques can provide poor information about generality. If the sampling
method is narrow `test error' can appear encouragingly tiny while the model
fails catastrophically elsewhere. In this manuscript we competitively evaluate
two common sampling methods: molecular dynamics (MD), normal-mode sampling
(NMS) and one uncommon alternative, Metadynamics (MetaMD), for preparing
training geometries. We show that MD is an inefficient sampling method in the
sense that additional samples do not improve generality. We also show MetaMD is
easily implemented in any NNMC software package with cost that scales linearly
with the number of atoms in a sample molecule. MetaMD is a black-box way to
ensure samples always reach out to new regions of chemical space, while
remaining relevant to chemistry near $k_bT$. It is one cheap tool to address
the issue of generalization.
| 0 | 1 | 0 | 1 | 0 | 0 |
ServeNet: A Deep Neural Network for Web Service Classification | Automated service classification plays a crucial role in service management
such as service discovery, selection, and composition. In recent years, machine
learning techniques have been used for service classification. However, they
can only predict around 10 to 20 service categories due to the quality of
feature engineering and the imbalance problem of service dataset. In this
paper, we present a deep neural network ServeNet with a novel dataset splitting
algorithm to deal with these issues. ServeNet can automatically abstract
low-level representation to high-level features, and then predict service
classification based on the service datasets produced by the proposed splitting
algorithm. To demonstrate the effectiveness of our approach, we conducted a
comprehensive experimental study on 10,000 real-world services in 50
categories. The result shows that ServeNet can achieve higher accuracy than
other machine learning methods.
| 0 | 0 | 0 | 1 | 0 | 0 |
Photoinduced Hund excitons in the breakdown of a two-orbital Mott insulator | We study the photoinduced breakdown of a two-orbital Mott insulator and
resulting metallic state. Using time-dependent density matrix renormalization
group, we scrutinize the real-time dynamics of the half-filled two-orbital
Hubbard model interacting with a resonant radiation field pulse. The breakdown,
caused by production of doublon-holon pairs, is enhanced by Hund's exchange,
which dynamically activates large orbital fluctuations. The melting of the Mott
insulator is accompanied by a high to low spin transition with a concomitant
reduction of antiferromagnetic spin fluctuations. Most notably, the overall
time response is driven by the photogeneration of excitons with orbital
character that are stabilized by Hund's coupling. These unconventional "Hund
excitons" correspond to bound spin-singlet orbital-triplet doublon-holon pairs.
We study exciton properties such as bandwidth, binding potential, and size
within a semiclassical approach. The photometallic state results from a
coexistence of Hund excitons and doublon-holon plasma.
| 0 | 1 | 0 | 0 | 0 | 0 |
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning | We draw a formal connection between using synthetic training data to optimize
neural network parameters and approximate, Bayesian, model-based reasoning. In
particular, training a neural network using synthetic data can be viewed as
learning a proposal distribution generator for approximate inference in the
synthetic-data generative model. We demonstrate this connection in a
recognition task where we develop a novel Captcha-breaking architecture and
train it using synthetic data, demonstrating both state-of-the-art performance
and a way of computing task-specific posterior uncertainty. Using a neural
network trained this way, we also demonstrate successful breaking of real-world
Captchas currently used by Facebook and Wikipedia. Reasoning from these
empirical results and drawing connections with Bayesian modeling, we discuss
the robustness of synthetic data results and suggest important considerations
for ensuring good neural network generalization when training with synthetic
data.
| 1 | 0 | 0 | 1 | 0 | 0 |
Multi-timescale memory dynamics in a reinforcement learning network with attention-gated memory | Learning and memory are intertwined in our brain and their relationship is at
the core of several recent neural network models. In particular, the
Attention-Gated MEmory Tagging model (AuGMEnT) is a reinforcement learning
network with an emphasis on biological plausibility of memory dynamics and
learning. We find that the AuGMEnT network does not solve some hierarchical
tasks, where higher-level stimuli have to be maintained over a long time, while
lower-level stimuli need to be remembered and forgotten over a shorter
timescale. To overcome this limitation, we introduce hybrid AuGMEnT, with leaky
or short-timescale and non-leaky or long-timescale units in memory, that allow
to exchange lower-level information while maintaining higher-level one, thus
solving both hierarchical and distractor tasks.
| 1 | 0 | 0 | 1 | 0 | 0 |
Dynamical structure of entangled polymers simulated under shear flow | The non-linear response of entangled polymers to shear flow is complicated.
Its current understanding is framed mainly as a rheological description in
terms of the complex viscosity. However, the full picture requires an
assessment of the dynamical structure of individual polymer chains which give
rise to the macroscopic observables. Here we shed new light on this problem,
using a computer simulation based on a blob model, extended to describe shear
flow in polymer melts and semi-dilute solutions. We examine the diffusion and
the intermediate scattering spectra during a steady shear flow. The relaxation
dynamics are found to speed up along the flow direction, but slow down along
the shear gradient direction. The third axis, vorticity, shows a slowdown at
the short scale of a tube, but reaches a net speedup at the large scale of the
chain radius of gyration.
| 0 | 1 | 0 | 0 | 0 | 0 |
Coherent modulation up to 100 GBd 16QAM using silicon-organic hybrid (SOH) devices | We demonstrate the generation of higher-order modulation formats using
silicon-based inphase/quadrature (IQ) modulators at symbol rates of up to 100
GBd. Our devices exploit the advantages of silicon-organic hybrid (SOH)
integration, which combines silicon-on-insulator waveguides with highly
efficient organic electro-optic (EO) cladding materials to enable small drive
voltages and sub-millimeter device lengths. In our experiments, we use an SOH
IQ modulator with a {\pi}-voltage of 1.6 V to generate 100 GBd 16QAM signals.
This is the first time that the 100 GBd mark is reached with an IQ modulator
realized on a semiconductor substrate, leading to a single-polarization line
rate of 400 Gbit/s. The peak-to-peak drive voltages amount to 1.5 Vpp,
corresponding to an electrical energy dissipation in the modulator of only 25
fJ/bit.
| 0 | 1 | 0 | 0 | 0 | 0 |
Current induced magnetization switching in PtCoCr structures with enhanced perpendicular magnetic anisotropy and spin-orbit torques | Magnetic trilayers having large perpendicular magnetic anisotropy (PMA) and
high spin-orbit torques (SOTs) efficiency are the key to fabricate nonvolatile
magnetic memory and logic devices. In this work, PMA and SOTs are
systematically studied in Pt/Co/Cr stacks as a function of Cr thickness. An
enhanced perpendicular anisotropy field around 10189 Oe is obtained and is
related to the interface between Co and Cr layers. In addition, an effective
spin Hall angle up to 0.19 is observed due to the improved antidamping-like
torque by employing dissimilar metals Pt and Cr with opposite signs of spin
Hall angles on opposite sides of Co layer. Finally, we observed a nearly linear
dependence between spin Hall angle and longitudinal resistivity from their
temperature dependent properties, suggesting that the spin Hall effect may
arise from extrinsic skew scattering mechanism. Our results indicate that 3d
transition metal Cr with a large negative spin Hall angle could be used to
engineer the interfaces of trilayers to enhance PMA and SOTs.
| 0 | 1 | 0 | 0 | 0 | 0 |
Grain boundary diffusion in severely deformed Al-based alloy | Grain boundary diffusion in severely deformed Al-based AA5024 alloy is
investigated. Different states are prepared by combination of equal channel
angular processing and heat treatments, with the radioisotope $^{57}$Co being
employed as a sensitive probe of a given grain boundary state. Its diffusion
rates near room temperature (320~K) are utilized to quantify the effects of
severe plastic deformation and a presumed formation of a previously reported
deformation-modified state of grain boundaries, solute segregation at the
interfaces, increased dislocation content after deformation and of the
precipitation behavior on the transport phenomena along grain boundaries. The
dominant effect of nano-sized Al$_3$Sc-based precipitates is evaluated using
density functional theory and the Eshelby model for the determination of
elastic stresses around the precipitates.
| 0 | 1 | 0 | 0 | 0 | 0 |
Quantum Black Holes and Atomic Nuclei are Hollow | The quantum Schrodinger-Newton equation is solved for a self-gravitating Bose
gas at zero temperature. It is derived that the density is non-uniform and a
central hollow cavity exists. The radial distribution of the particle momentum
is uniform. It is shown that a quantum black hole can be formed only above a
certain critical mass. The temperature effect is accounted for via the
Schrodinger-Poisson-Boltzmann equation, where low and high temperature
solutions are obtained. The theoretical analysis is extended to a strong
interacting gas via the Schrodinger-Yukawa equation, showing that the atomic
nuclei are also hollow. Hollow self-gravitating Fermi gases are described by
the Thomas-Fermi equation.
| 0 | 1 | 0 | 0 | 0 | 0 |
Learning Non-Discriminatory Predictors | We consider learning a predictor which is non-discriminatory with respect to
a "protected attribute" according to the notion of "equalized odds" proposed by
Hardt et al. [2016]. We study the problem of learning such a non-discriminatory
predictor from a finite training set, both statistically and computationally.
We show that a post-hoc correction approach, as suggested by Hardt et al, can
be highly suboptimal, present a nearly-optimal statistical procedure, argue
that the associated computational problem is intractable, and suggest a second
moment relaxation of the non-discrimination definition for which learning is
tractable.
| 1 | 0 | 0 | 0 | 0 | 0 |
Time-Resolved High Spectral Resolution Observation of 2MASSW J0746425+200032AB | Many brown dwarfs exhibit photometric variability at levels from tenths to
tens of percents. The photometric variability is related to magnetic activity
or patchy cloud coverage, characteristic of brown dwarfs near the L-T
transition. Time-resolved spectral monitoring of brown dwarfs provides
diagnostics of cloud distribution and condensate properties. However, current
time-resolved spectral studies of brown dwarfs are limited to low spectral
resolution (R$\sim$100) with the exception of the study of Luhman 16 AB at
resolution of 100,000 using the VLT$+$CRIRES. This work yielded the first map
of brown dwarf surface inhomogeneity, highlighting the importance and unique
contribution of high spectral resolution observations. Here, we report on the
time-resolved high spectral resolution observations of a nearby brown dwarf
binary, 2MASSW J0746425+200032AB. We find no coherent spectral variability that
is modulated with rotation. Based on simulations we conclude that the coverage
of a single spot on 2MASSW J0746425+200032AB is smaller than 1\% or 6.25\% if
spot contrast is 50\% or 80\% of its surrounding flux, respectively. Future
high spectral resolution observations aided by adaptive optics systems can put
tighter constraints on the spectral variability of 2MASSW J0746425+200032AB and
other nearby brown dwarfs.
| 0 | 1 | 0 | 0 | 0 | 0 |
Pinned, locked, pushed, and pulled traveling waves in structured environments | Traveling fronts describe the transition between two alternative states in a
great number of physical and biological systems. Examples include the spread of
beneficial mutations, chemical reactions, and the invasions by foreign species.
In homogeneous environments, the alternative states are separated by a smooth
front moving at a constant velocity. This simple picture can break down in
structured environments such as tissues, patchy landscapes, and microfluidic
devices. Habitat fragmentation can pin the front at a particular location or
lock invasion velocities into specific values. Locked velocities are not
sensitive to moderate changes in dispersal or growth and are determined by the
spatial and temporal periodicity of the environment. The synchronization with
the environment results in discontinuous fronts that propagate as periodic
pulses. We characterize the transition from continuous to locked invasions and
show that it is controlled by positive density-dependence in dispersal or
growth. We also demonstrate that velocity locking is robust to demographic and
environmental fluctuations and examine stochastic dynamics and evolution in
locked invasions.
| 0 | 0 | 0 | 0 | 1 | 0 |
Two-pixel polarimetric camera by compressive sensing | We propose an original concept of compressive sensing (CS) polarimetric
imaging based on a digital micro-mirror (DMD) array and two single-pixel
detectors. The polarimetric sensitivity of the proposed setup is due to an
experimental imperfection of reflecting mirrors which is exploited here to form
an original reconstruction problem, including a CS problem and a source
separation task. We show that a two-step approach tackling each problem
successively is outperformed by a dedicated combined reconstruction method,
which is explicited in this article and preferably implemented through a
reweighted FISTA algorithm. The combined reconstruction approach is then
further improved by including physical constraints specific to the polarimetric
imaging context considered, which are implemented in an original constrained
GFB algorithm. Numerical simulations demonstrate the efficiency of the 2-pixel
CS polarimetric imaging setup to retrieve polarimetric contrast data with
significant compression rate and good reconstruction quality. The influence of
experimental imperfections of the DMD are also analyzed through numerical
simulations, and 2D polarimetric imaging reconstruction results are finally
presented.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Stochastic Programming Approach for Electric Vehicle Charging Network Design | Advantages of electric vehicles (EV) include reduction of greenhouse gas and
other emissions, energy security, and fuel economy. The societal benefits of
large-scale adoption of EVs cannot be realized without adequate deployment of
publicly accessible charging stations. We propose a two-stage stochastic
programming model to determine the optimal network of charging stations for a
community considering uncertainties in arrival and dwell time of vehicles,
battery state of charge of arriving vehicles, walkable range and charging
preferences of drivers, demand during weekdays and weekends, and rate of
adoption of EVs within a community. We conducted studies using sample average
approximation (SAA) method which asymptotically converges to an optimal
solution for a two-stage stochastic problem, however it is computationally
expensive for large-scale instances. Therefore, we developed a heuristic to
produce near to optimal solutions quickly for our data instances. We conducted
computational experiments using various publicly available data sources, and
benefits of the solutions are evaluated both quantitatively and qualitatively
for a given community.
| 1 | 0 | 1 | 0 | 0 | 0 |
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