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BSDEs and SDEs with time-advanced and -delayed coefficients | This paper introduces a class of backward stochastic differential equations
(BSDEs), whose coefficients not only depend on the value of its solutions of
the present but also the past and the future. For a sufficiently small time
delay or a sufficiently small Lipschitz constant, the existence and uniqueness
of such BSDEs is obtained. As an adjoint process, a class of stochastic
differential equations (SDEs) is introduced, whose coefficients also depend on
the present, the past and the future of its solutions. The existence and
uniqueness of such SDEs is proved for a sufficiently small time advance or a
sufficiently small Lipschitz constant. A duality between such BSDEs and SDEs is
established.
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Characterizing information importance and the effect on the spread in various graph topologies | In this paper we present a thorough analysis of the nature of news in
different mediums across the ages, introducing a unique mathematical model to
fit the characteristics of information spread. This model enhances the
information diffusion model to account for conflicting information and the
topical distribution of news in terms of popularity for a given era. We
translate this information to a separate graphical node model to determine the
spread of a news item given a certain category and relevance factor. The two
models are used as a base for a simulation of information dissemination for
varying graph topoligies. The simulation is stress-tested and compared against
real-world data to prove its relevancy. We are then able to use these
simulations to deduce some conclusive statements about the optimization of
information spread.
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Quantum criticality at the superconductor to insulator transition revealed by specific heat measurements | The superconductor-insulator transition (SIT) is considered an excellent
example of a quantum phase transition which is driven by quantum fluctuations
at zero temperature. The quantum critical point is characterized by a diverging
correlation length and a vanishing energy scale. Low energy fluctuations near
quantum criticality may be experimentally detected by specific heat, $c_{\rm
p}$, measurements. Here, we use a unique highly sensitive experiment to measure
$c_{\rm p}$ of two-dimensional granular Pb films through the SIT. The specific
heat shows the usual jump at the mean field superconducting transition
temperature $T_{\rm c}^{\rm {mf}}$ marking the onset of Cooper pairs formation.
As the film thickness is tuned toward the SIT, $T_{\rm c}^{\rm {mf}}$ is
relatively unchanged, while the magnitude of the jump and low temperature
specific heat increase significantly. This behaviour is taken as the
thermodynamic fingerprint of quantum criticality in the vicinity of a quantum
phase transition.
| 0 | 1 | 0 | 0 | 0 | 0 |
Continuous-Time Gaussian Process Motion Planning via Probabilistic Inference | We introduce a novel formulation of motion planning, for continuous-time
trajectories, as probabilistic inference. We first show how smooth
continuous-time trajectories can be represented by a small number of states
using sparse Gaussian process (GP) models. We next develop an efficient
gradient-based optimization algorithm that exploits this sparsity and GP
interpolation. We call this algorithm the Gaussian Process Motion Planner
(GPMP). We then detail how motion planning problems can be formulated as
probabilistic inference on a factor graph. This forms the basis for GPMP2, a
very efficient algorithm that combines GP representations of trajectories with
fast, structure-exploiting inference via numerical optimization. Finally, we
extend GPMP2 to an incremental algorithm, iGPMP2, that can efficiently replan
when conditions change. We benchmark our algorithms against several
sampling-based and trajectory optimization-based motion planning algorithms on
planning problems in multiple environments. Our evaluation reveals that GPMP2
is several times faster than previous algorithms while retaining robustness. We
also benchmark iGPMP2 on replanning problems, and show that it can find
successful solutions in a fraction of the time required by GPMP2 to replan from
scratch.
| 1 | 0 | 0 | 0 | 0 | 0 |
Polynomially and Infinitesimally Injective Modules | The injective polynomial modules for a general linear group $G$ of degree $n$
are labelled by the partitions with at most $n$ parts. Working over an
algebraically closed field of characteristic $p$, we consider the question of
which partitions correspond to polynomially injective modules that are also
injective as modules for the restricted enveloping algebra of the Lie algebra
of $G$. The question is related to the "index of divisibility" of a polynomial
module in general, and an explicit answer is given for $n=2$.
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Nonstandard Methods in Ramsey Theory and Combinatorial Number Theory | The goal of this present manuscript is to introduce the reader to the
nonstandard method and to provide an overview of its most prominent
applications in Ramsey theory and combinatorial number theory.
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When is Network Lasso Accurate? | The "least absolute shrinkage and selection operator" (Lasso) method has been
adapted recently for networkstructured datasets. In particular, this network
Lasso method allows to learn graph signals from a small number of noisy signal
samples by using the total variation of a graph signal for regularization.
While efficient and scalable implementations of the network Lasso are
available, only little is known about the conditions on the underlying network
structure which ensure network Lasso to be accurate. By leveraging concepts of
compressed sensing, we address this gap and derive precise conditions on the
underlying network topology and sampling set which guarantee the network Lasso
for a particular loss function to deliver an accurate estimate of the entire
underlying graph signal. We also quantify the error incurred by network Lasso
in terms of two constants which reflect the connectivity of the sampled nodes.
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T-ROME: A Simple and Energy Efficient Tree Routing Protocol for Low-Power Wake-up Receivers | Wireless sensor networks are deployed in many monitoring applications but
still suffer from short lifetimes originating from limited energy sources and
storages. Due to their low-power consumption and their on-demand communication
ability, wake-up receivers represent an energy efficient and simple enhancement
to wireless sensor nodes and wireless sensor network protocols. In this
context, wake-up receivers have the ability to increase the network lifetime.
In this article, we present T-ROME, a simple and energy efficient cross-layer
routing protocol for wireless sensor nodes containing wake-up receivers. The
protocol makes use of the different transmission ranges of wake-up and main
radios in order to save energy by skipping nodes during data transfer. With
respect to energy consumption and latency, T-ROME outperforms existing
protocols in many scenarios. Here, we describe and analyze the cross layer
multi-hop protocol by means of a Markov chain model that we verify using a
laboratory test setup.
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Classifying the Correctness of Generated White-Box Tests: An Exploratory Study | White-box test generator tools rely only on the code under test to select
test inputs, and capture the implementation's output as assertions. If there is
a fault in the implementation, it could get encoded in the generated tests.
Tool evaluations usually measure fault-detection capability using the number of
such fault-encoding tests. However, these faults are only detected, if the
developer can recognize that the encoded behavior is faulty. We designed an
exploratory study to investigate how developers perform in classifying
generated white-box test as faulty or correct. We carried out the study in a
laboratory setting with 54 graduate students. The tests were generated for two
open-source projects with the help of the IntelliTest tool. The performance of
the participants were analyzed using binary classification metrics and by
coding their observed activities. The results showed that participants
incorrectly classified a large number of both fault-encoding and correct tests
(with median misclassification rate 33% and 25% respectively). Thus the real
fault-detection capability of test generators could be much lower than
typically reported, and we suggest to take this human factor into account when
evaluating generated white-box tests.
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Numerical Simulations of Saturn's B Ring: Granular Friction as a Mediator between Self-Gravity and Viscous Overstability | We study the B ring's complex optical depth structure. The source of this
structure may be the complex dynamics of the Keplerian shear and the
self-gravity of the ring particles. The outcome of these dynamic effects
depends sensitively on the collisional and physical properties of the
particles. Two mechanisms can emerge that dominate the macroscopic physical
structure of the ring: self-gravity wakes and viscous overstability. Here we
study the interplay between these two mechanisms by using our recently
developed particle collision method that allows us to better model the
inter-particle contact physics. We find that for a constant ring surface
density and particle internal density, particles with rough surfaces tend to
produce axisymmetric ring features associated with the viscous overstability,
while particles with smoother surfaces produce self-gravity wakes.
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Shock-darkening in ordinary chondrites: determination of the pressure-temperature conditions by shock physics mesoscale modeling | We determined the shock-darkening pressure range in ordinary chondrites using
the iSALE shock physics code. We simulated planar shock waves on a mesoscale in
a sample layer at different nominal pressures. Iron and troilite grains were
resolved in a porous olivine matrix in the sample layer. We used equations of
state (Tillotson EoS and ANEOS) and basic strength and thermal properties to
describe the material phases. We used Lagrangian tracers to record peak shock
pressures in each material unit. The post-shock temperatures (and the fractions
of tracers experiencing temperatures above the melting point) for each material
were estimated after the passage of the shock wave and after reflections of the
shock at grain boundaries in the heterogeneous materials. The results showed
that shock-darkening, associated with troilite melt and the onset of olivine
melt, happened between 40 and 50 GPa - with 52 GPa being the pressure at which
all tracers in the troilite material reach the melting point. We demonstrate
the difficulties of shock heating in iron and also the importance of porosity.
Material impedances, grain shapes and the porosity models available in the
iSALE code are discussed. We also discussed possible not-shock-related triggers
for iron melt.
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On the Global Fluctuations of Block Gaussian Matrices | In this paper we study the global fluctuations of block Gaussian matrices
within the framework of second-order free probability theory. In order to
compute the second-order Cauchy transform of these matrices, we introduce a
matricial second-order conditional expectation and compute the matricial
second-order Cauchy transform of a certain type of non-commutative random
variables. As a by-product, using the linearization technique, we obtain the
second-order Cauchy transform of non-commutative rational functions evaluated
on selfadjoint Gaussian matrices.
| 0 | 0 | 1 | 0 | 0 | 0 |
Carnot Efficiency of Publication | This paper analyzes publication efficiency in terms of Hirsch-index or
h-index and total citations, with an analogy to the Carnot efficiency used in
thermodynamics. Such publication efficiency, with typical value of 30%, can be
utilized to normalize the research output judgment, favoring quality outputs in
reduced quantity, which is currently lacking in many discipline.
| 1 | 0 | 0 | 0 | 0 | 0 |
EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras | Event-based cameras have shown great promise in a variety of situations where
frame based cameras suffer, such as high speed motions and high dynamic range
scenes. However, developing algorithms for event measurements requires a new
class of hand crafted algorithms. Deep learning has shown great success in
providing model free solutions to many problems in the vision community, but
existing networks have been developed with frame based images in mind, and
there does not exist the wealth of labeled data for events as there does for
images for supervised training. To these points, we present EV-FlowNet, a novel
self-supervised deep learning pipeline for optical flow estimation for event
based cameras. In particular, we introduce an image based representation of a
given event stream, which is fed into a self-supervised neural network as the
sole input. The corresponding grayscale images captured from the same camera at
the same time as the events are then used as a supervisory signal to provide a
loss function at training time, given the estimated flow from the network. We
show that the resulting network is able to accurately predict optical flow from
events only in a variety of different scenes, with performance competitive to
image based networks. This method not only allows for accurate estimation of
dense optical flow, but also provides a framework for the transfer of other
self-supervised methods to the event-based domain.
| 1 | 0 | 0 | 0 | 0 | 0 |
Deep Active Learning for Named Entity Recognition | Deep learning has yielded state-of-the-art performance on many natural
language processing tasks including named entity recognition (NER). However,
this typically requires large amounts of labeled data. In this work, we
demonstrate that the amount of labeled training data can be drastically reduced
when deep learning is combined with active learning. While active learning is
sample-efficient, it can be computationally expensive since it requires
iterative retraining. To speed this up, we introduce a lightweight architecture
for NER, viz., the CNN-CNN-LSTM model consisting of convolutional character and
word encoders and a long short term memory (LSTM) tag decoder. The model
achieves nearly state-of-the-art performance on standard datasets for the task
while being computationally much more efficient than best performing models. We
carry out incremental active learning, during the training process, and are
able to nearly match state-of-the-art performance with just 25\% of the
original training data.
| 1 | 0 | 0 | 0 | 0 | 0 |
Bipedal Walking with Corrective Actions in the Tilt Phase Space | Many methods exist for a bipedal robot to keep its balance while walking. In
addition to step size and timing, other strategies are possible that influence
the stability of the robot without interfering with the target direction and
speed of locomotion. This paper introduces a multifaceted feedback controller
that uses numerous different feedback mechanisms, collectively termed
corrective actions, to stabilise a core keypoint-based gait. The feedback
controller is experimentally effective, yet free of any physical model of the
robot, very computationally inexpensive, and requires only a single 6-axis IMU
sensor. Due to these low requirements, the approach is deemed to be highly
portable between robots, and was specifically also designed to target lower
cost robots that have suboptimal sensing, actuation and computational
resources. The IMU data is used to estimate the yaw-independent tilt
orientation of the robot, expressed in the so-called tilt phase space, and is
the source of all feedback provided by the controller. Experimental validation
is performed in simulation as well as on real robot hardware.
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TimeNet: Pre-trained deep recurrent neural network for time series classification | Inspired by the tremendous success of deep Convolutional Neural Networks as
generic feature extractors for images, we propose TimeNet: a deep recurrent
neural network (RNN) trained on diverse time series in an unsupervised manner
using sequence to sequence (seq2seq) models to extract features from time
series. Rather than relying on data from the problem domain, TimeNet attempts
to generalize time series representation across domains by ingesting time
series from several domains simultaneously. Once trained, TimeNet can be used
as a generic off-the-shelf feature extractor for time series. The
representations or embeddings given by a pre-trained TimeNet are found to be
useful for time series classification (TSC). For several publicly available
datasets from UCR TSC Archive and an industrial telematics sensor data from
vehicles, we observe that a classifier learned over the TimeNet embeddings
yields significantly better performance compared to (i) a classifier learned
over the embeddings given by a domain-specific RNN, as well as (ii) a nearest
neighbor classifier based on Dynamic Time Warping.
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Differentially Private Federated Learning: A Client Level Perspective | Federated learning is a recent advance in privacy protection. In this
context, a trusted curator aggregates parameters optimized in decentralized
fashion by multiple clients. The resulting model is then distributed back to
all clients, ultimately converging to a joint representative model without
explicitly having to share the data. However, the protocol is vulnerable to
differential attacks, which could originate from any party contributing during
federated optimization. In such an attack, a client's contribution during
training and information about their data set is revealed through analyzing the
distributed model. We tackle this problem and propose an algorithm for client
sided differential privacy preserving federated optimization. The aim is to
hide clients' contributions during training, balancing the trade-off between
privacy loss and model performance. Empirical studies suggest that given a
sufficiently large number of participating clients, our proposed procedure can
maintain client-level differential privacy at only a minor cost in model
performance.
| 1 | 0 | 0 | 1 | 0 | 0 |
Okapi: Causally Consistent Geo-Replication Made Faster, Cheaper and More Available | Okapi is a new causally consistent geo-replicated key- value store. Okapi
leverages two key design choices to achieve high performance. First, it relies
on hybrid logical/physical clocks to achieve low latency even in the presence
of clock skew. Second, Okapi achieves higher resource efficiency and better
availability, at the expense of a slight increase in update visibility latency.
To this end, Okapi implements a new stabilization protocol that uses a
combination of vector and scalar clocks and makes a remote update visible when
its delivery has been acknowledged by every data center. We evaluate Okapi with
different workloads on Amazon AWS, using three geographically distributed
regions and 96 nodes. We compare Okapi with two recent approaches to causal
consistency, Cure and GentleRain. We show that Okapi delivers up to two orders
of magnitude better performance than GentleRain and that Okapi achieves up to
3.5x lower latency and a 60% reduction of the meta-data overhead with respect
to Cure.
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Emergence of epithelial cell density waves | Epithelial cell monolayers exhibit traveling mechanical waves. We rationalize
this observation thanks to a hydrodynamic description of the monolayer as a
compressible, active and polar material. We show that propagating waves of the
cell density, polarity, velocity and stress fields may be due to a Hopf
bifurcation occurring above threshold values of active coupling coefficients.
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Complex Analysis of Real Functions IV: Non-Integrable Real Functions | In the context of the complex-analytic structure within the unit disk
centered at the origin of the complex plane, that was presented in a previous
paper, we show that a certain class of non-integrable real functions can be
represented within that same structure. In previous papers it was shown that
essentially all integrable real functions, as well as all singular Schwartz
distributions, can be represented within that same complex-analytic structure.
The large class of non-integrable real functions which we analyze here can
therefore be represented side by side with those other real objects, thus
allowing all these objects to be treated in a unified way.
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Convolution Aware Initialization | Initialization of parameters in deep neural networks has been shown to have a
big impact on the performance of the networks (Mishkin & Matas, 2015). The
initialization scheme devised by He et al, allowed convolution activations to
carry a constrained mean which allowed deep networks to be trained effectively
(He et al., 2015a). Orthogonal initializations and more generally orthogonal
matrices in standard recurrent networks have been proved to eradicate the
vanishing and exploding gradient problem (Pascanu et al., 2012). Majority of
current initialization schemes do not take fully into account the intrinsic
structure of the convolution operator. Using the duality of the Fourier
transform and the convolution operator, Convolution Aware Initialization builds
orthogonal filters in the Fourier space, and using the inverse Fourier
transform represents them in the standard space. With Convolution Aware
Initialization we noticed not only higher accuracy and lower loss, but faster
convergence. We achieve new state of the art on the CIFAR10 dataset, and
achieve close to state of the art on various other tasks.
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A Multitask Diffusion Strategy with Optimized Inter-Cluster Cooperation | We consider a multitask estimation problem where nodes in a network are
divided into several connected clusters, with each cluster performing a
least-mean-squares estimation of a different random parameter vector. Inspired
by the adapt-then-combine diffusion strategy, we propose a multitask diffusion
strategy whose mean stability can be ensured whenever individual nodes are
stable in the mean, regardless of the inter-cluster cooperation weights. In
addition, the proposed strategy is able to achieve an asymptotically unbiased
estimation, when the parameters have same mean. We also develop an
inter-cluster cooperation weights selection scheme that allows each node in the
network to locally optimize its inter-cluster cooperation weights. Numerical
results demonstrate that our approach leads to a lower average steady-state
network mean-square deviation, compared with using weights selected by various
other commonly adopted methods in the literature.
| 1 | 0 | 0 | 0 | 0 | 0 |
High Resolution Observations of the Massive Protostar in IRAS18566+0408 | We report 3 mm continuum, CH3CN(5-4) and 13CS(2-1) line observations with
CARMA, in conjunction with 6 and 1.3 cm continuum VLA data, and 12 and 25
micron broadband data from the Subaru Telescope toward the massive proto-star
IRAS18566+0408. The VLA data resolve the ionized jet into 4 components aligned
in the E-W direction. Radio components A, C, and D have flat cm SEDs indicative
of optically thin emission from ionized gas, and component B has a spectral
index alpha = 1.0, and a decreasing size with frequency proportional to
frequency to the -0.5 power. Emission from the CARMA 3 mm continuum, and from
the 13CS(2-1), and CH3CN(5-4) spectral lines is compact (i.e. < 6700 AU), and
peaks near the position of VLA cm source, component B. Analysis of these lines
indicates hot, and dense molecular gas, typical for HMCs. Our Subaru telescope
observations detect a single compact source, coincident with radio component B,
demonstrating that most of the energy in IRAS18566+0408 originates from a
region of size < 2400 AU. We also present UKIRT near-infrared archival data for
IRAS18566+0408 which show extended K-band emission along the jet direction. We
detect an E-W velocity shift of about 10 km/sec over the HMC in the CH3CN lines
possibly tracing the interface of the ionized jet with the surrounding core
gas. Our data demonstrate the presence of an ionized jet at the base of the
molecular outflow, and support the hypothesis that massive protostars with
O-type luminosity form with a mechanism similar to lower mass stars.
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MEDL and MEDLA: Methods for Assessment of Scaling by Medians of Log-Squared Nondecimated Wavelet Coefficients | High-frequency measurements and images acquired from various sources in the
real world often possess a degree of self-similarity and inherent regular
scaling. When data look like a noise, the scaling exponent may be the only
informative feature that summarizes such data. Methods for the assessment of
self-similarity by estimating Hurst exponent often involve analysis of rate of
decay in a spectrum defined in various multiresolution domains. When this
spectrum is calculated using discrete non-decimated wavelet transforms, due to
increased autocorrelation in wavelet coefficients, the estimators of $H$ show
increased bias compared to the estimators that use traditional orthogonal
transforms. At the same time, non-decimated transforms have a number of
advantages when employed for calculation of wavelet spectra and estimation of
Hurst exponents: the variance of the estimator is smaller, input signals and
images could be of arbitrary size, and due to the shift-invariance, the local
scaling can be assessed as well. We propose two methods based on robust
estimation and resampling that alleviate the effect of increased
autocorrelation while maintaining all advantages of non-decimated wavelet
transforms. The proposed methods extend the approaches in existing literature
where the logarithmic transformation and pairing of wavelet coefficients are
used for lowering the bias. In a simulation study we use fractional Brownian
motions with a range of theoretical Hurst exponents. For such signals for which
"true" $H$ is known, we demonstrate bias reduction and overall reduction of the
mean-squared error by the two proposed estimators. For fractional Brownian
motions, both proposed methods yield estimators of $H$ that are asymptotically
normal and unbiased.
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A Two-Level Graph Partitioning Problem Arising in Mobile Wireless Communications | In the k-partition problem (k-PP), one is given an edge-weighted undirected
graph, and one must partition the node set into at most k subsets, in order to
minimise (or maximise) the total weight of the edges that have their end-nodes
in the same cluster. Various hierarchical variants of this problem have been
studied in the context of data mining. We consider a 'two-level' variant that
arises in mobile wireless communications. We show that an exact algorithm based
on intelligent preprocessing, cutting planes and symmetry-breaking is capable
of solving small- and medium-size instances to proven optimality, and providing
strong lower bounds for larger instances.
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Emotion Specification from Musical Stimuli: An EEG Study with AFA and DFA | The present study reports interesting findings in regard to emotional arousal
based activities while listening to two Hindustani classical ragas of contrast
emotion. EEG data was taken on 5 naive listeners while they listened to two
ragas Bahar and Mia ki Malhar which are conventionally known to portray
contrast emotions. The EEG data were analyzed with the help of two robust non
linear tools viz. Adaptive Fractal Analysis (AFA) and Detrended Fluctuation
Analysis (DFA). A comparative study of the Hurst Exponents obtained from the
two methods have been shown which shows that DFA provides more rigorous results
compared to AFA when it comes to the scaling analysis of biosignal data. The
results and implications have been discussed in detail.
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The light pollution as a surrogate for urban population of the US cities | We show that the definition of the city boundaries can have a dramatic
influence on the scaling behavior of the night-time light (NTL) as a function
of population (POP) in the US. Precisely, our results show that the arbitrary
geopolitical definition based on the Metropolitan/Consolidated Metropolitan
Statistical Areas (MSA/CMSA) leads to a sublinear power-law growth of NTL with
POP. On the other hand, when cities are defined according to a more natural
agglomeration criteria, namely, the City Clustering Algorithm (CCA), an
isometric relation emerges between NTL and population. This discrepancy is
compatible with results from previous works showing that the scaling behaviors
of various urban indicators with population can be substantially different for
distinct definitions of city boundaries. Moreover, considering the CCA
definition as more adequate than the MSA/CMSA one because the former does not
violate the expected extensivity between land population and area of their
generated clusters, we conclude that, without loss of generality, the CCA
measures of light pollution and population could be interchangeably utilized in
future studies.
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Analytic calculation of radio emission from parameterized extensive air showers, a tool to extract shower parameters | The radio intensity and polarization footprint of a cosmic-ray induced
extensive air shower is determined by the time-dependent structure of the
current distribution residing in the plasma cloud at the shower front. In turn,
the time dependence of the integrated charge-current distribution in the plasma
cloud, the longitudinal shower structure, is determined by interesting physics
which one would like to extract such as the location and multiplicity of the
primary cosmic-ray collision or the values of electric fields in the atmosphere
during thunderstorms. To extract the structure of a shower from its footprint
requires solving a complicated inverse problem. For this purpose we have
developed a code that semi-analytically calculates the radio footprint of an
extensive air shower given an arbitrary longitudinal structure. This code can
be used in a optimization procedure to extract the optimal longitudinal shower
structure given a radio footprint. On the basis of air-shower universality we
propose a simple parametrization of the structure of the plasma cloud. This
parametrization is based on the results of Monte-Carlo shower simulations.
Deriving the parametrization also teaches which aspects of the plasma cloud are
important for understanding the features seen in the radio-emission footprint.
The calculated radio footprints are compared with microscopic CoREAS
simulations.
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Akhmediev Breathers and Peregrine Solitary Waves in a Quadratic Medium | We investigate the formation of optical localized nonlinear structures,
evolving upon a non-zero background plane wave, in a dispersive quadratic
medium. We show the existence of quadratic Akhmediev breathers and Peregrine
solitary waves, in the regime of cascading second-harmonic generation. This
finding opens a novel path for the excitation of extreme rogue waves and for
the description of modulation instability in quadratic nonlinear optics.
| 0 | 1 | 0 | 0 | 0 | 0 |
Novel approaches to spectral properties of correlated electron materials: From generalized Kohn-Sham theory to screened exchange dynamical mean field theory | The most intriguing properties of emergent materials are typically
consequences of highly correlated quantum states of their electronic degrees of
freedom. Describing those materials from first principles remains a challenge
for modern condensed matter theory. Here, we review, apply and discuss novel
approaches to spectral properties of correlated electron materials, assessing
current day predictive capabilities of electronic structure calculations. In
particular, we focus on the recent Screened Exchange Dynamical Mean-Field
Theory scheme and its relation to generalized Kohn-Sham theory. These concepts
are illustrated on the transition metal pnictide BaCo$_2$As$_2$ and elemental
zinc and cadmium.
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Flux-Rope Twist in Eruptive Flares and CMEs: due to Zipper and Main-Phase Reconnection | The nature of three-dimensional reconnection when a twisted flux tube erupts
during an eruptive flare or coronal mass ejection is considered. The
reconnection has two phases: first of all, 3D "zipper reconnection" propagates
along the initial coronal arcade, parallel to the polarity inversion line
(PIL), then subsequent quasi-2D "main phase reconnection" in the low corona
around a flux rope during its eruption produces coronal loops and chromospheric
ribbons that propagate away from the PIL in a direction normal to it.
One scenario starts with a sheared arcade: the zipper reconnection creates a
twisted flux rope of roughly one turn ($2\pi$ radians of twist), and then main
phase reconnection builds up the bulk of the erupting flux rope with a
relatively uniform twist of a few turns. A second scenario starts with a
pre-existing flux rope under the arcade. Here the zipper phase can create a
core with many turns that depend on the ratio of the magnetic fluxes in the
newly formed flare ribbons and the new flux rope. Main phase reconnection then
adds a layer of roughly uniform twist to the twisted central core. Both phases
and scenarios are modeled in a simple way that assumes the initial magnetic
flux is fragmented along the PIL. The model uses conservation of magnetic
helicity and flux, together with equipartition of magnetic helicity, to deduce
the twist of the erupting flux rope in terms the geometry of the initial
configuration.
Interplanetary observations show some flux ropes have a fairly uniform twist,
which could be produced when the zipper phase and any pre-existing flux rope
possess small or moderate twist (up to one or two turns). Other interplanetary
flux ropes have highly twisted cores (up to five turns), which could be
produced when there is a pre-existing flux rope and an active zipper phase that
creates substantial extra twist.
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On the classification of Kantor pairs and structurable algebras in characteristic 5 | We observe that any finite-dimensional central simple 5-graded Lie algebra
over over a field k of characteristic not 2,3 is necessarily classical, i.e. a
twisted form of a Chevalley Lie algebra. Consequently, the classification of
central simple structurable algebras and Kantor pairs over fields of
characteristic 5 derives from the classification of simple algebraic groups.
Using the classification of nilpotent conjugacy classes, we list all 5-gradings
on Lie algebras of simple algebraic groups that give rise to simple
structurable algebras over algebraically closed fields.
| 0 | 0 | 1 | 0 | 0 | 0 |
Cross-lingual Distillation for Text Classification | Cross-lingual text classification(CLTC) is the task of classifying documents
written in different languages into the same taxonomy of categories. This paper
presents a novel approach to CLTC that builds on model distillation, which
adapts and extends a framework originally proposed for model compression. Using
soft probabilistic predictions for the documents in a label-rich language as
the (induced) supervisory labels in a parallel corpus of documents, we train
classifiers successfully for new languages in which labeled training data are
not available. An adversarial feature adaptation technique is also applied
during the model training to reduce distribution mismatch. We conducted
experiments on two benchmark CLTC datasets, treating English as the source
language and German, French, Japan and Chinese as the unlabeled target
languages. The proposed approach had the advantageous or comparable performance
of the other state-of-art methods.
| 1 | 0 | 0 | 0 | 0 | 0 |
Characterization of Majorana-Ising phase transition in a helical liquid system | We map an interacting helical liquid system, coupled to an external magnetic
field and s-wave superconductor, to an XYZ spin system, and it undergoes
Majorana-Ising transition by tuning of parameters. In the Majorana state,
lowest excitation gap decays exponentially with system size, and the system has
degenerate ground state in the thermodynamic limit. On the contrary, the gap
opens in the Ising phase even in the thermodynamic limit. We also study other
criteria to characterize the transition, such as edge spin correlation with its
neighbor $C(r=1)$, local susceptibility $\chi_i$, superconducting order
parameter of edge spin $P(r=1)$, and longitudinal structure factor $S(k)$. The
ground state degeneracy and three other criteria lead to the same critical
value of parameters for Majorana-Ising phase transition in the thermodynamic
limit. We study, for the first time, the entanglement spectrum of the reduced
density matrix of the helical liquid system. The system shows finite Schmidt
gap and non-degeneracy of the entanglement spectrum in the Ising limit. The
Schmidt gap closes in the Majorana state, and all the eigenvalues are either
doubly or multiply degenerate.
| 0 | 1 | 0 | 0 | 0 | 0 |
Monotonicity and symmetry of nonnegative solutions to $ -Δu=f(u) $ in half-planes and strips | We consider nonnegative solutions to $-\Delta u=f(u)$ in half-planes and
strips, under zero Dirichlet boundary condition. Exploiting a
rotating$\&$sliding line technique, we prove symmetry and monotonicity
properties of the solutions, under very general assumptions on the nonlinearity
$f$. In fact we provide a unified approach that works in all the cases
$f(0)<0$, $f(0)= 0$ or $f(0)> 0$. Furthermore we make the effort to deal with
nonlinearities $f$ that may be not locally-Lipschitz continuous. We also
provide explicite examples showing the sharpness of our assumptions on the
nonlinear function $f$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Gini-regularized Optimal Transport with an Application to Spatio-Temporal Forecasting | Rapidly growing product lines and services require a finer-granularity
forecast that considers geographic locales. However the open question remains,
how to assess the quality of a spatio-temporal forecast? In this manuscript we
introduce a metric to evaluate spatio-temporal forecasts. This metric is based
on an Opti- mal Transport (OT) problem. The metric we propose is a constrained
OT objec- tive function using the Gini impurity function as a regularizer. We
demonstrate through computer experiments both the qualitative and the
quantitative charac- teristics of the Gini regularized OT problem. Moreover, we
show that the Gini regularized OT problem converges to the classical OT
problem, when the Gini regularized problem is considered as a function of
{\lambda}, the regularization parame-ter. The convergence to the classical OT
solution is faster than the state-of-the-art Entropic-regularized OT[Cuturi,
2013] and results in a numerically more stable algorithm.
| 1 | 0 | 0 | 1 | 0 | 0 |
Indicators of Conformal Field Theory: entanglement entropy and multiple-point correlators | The entanglement entropy (EE) of quantum systems is often used as a test of
low-energy descriptions by conformal field theory (CFT). Here we point out that
this is not a reliable indicator, as the EE often shows the same behavior even
when a CFT description is not correct (as long as the system is asymptotically
scale-invariant). We use constraints on the scaling dimension given by the CFT
with SU(2) symmetry to provide alternative tests with two- and four-point
correlation functions, showing examples for quantum spin models in 1+1
dimensions. In the case of a critical amplitude-product state expressed in the
valence-bond basis (where the amplitudes decay as a power law of the bond
length and the wave function is the product of all bond amplitudes), we show
that even though the EE exhibits the expected CFT behavior, there is no CFT
description of this state. We provide numerical tests of the behavior predicted
by CFT for the correlation functions in the critical transverse-field Ising
chain and the $J$-$Q$ spin chain, where the conformal structure is well
understood. That behavior is not reproduced in the amplitude-product state.
| 0 | 1 | 0 | 0 | 0 | 0 |
Classical Entanglement Structure in the Wavefunction of Inflationary Fluctuations | We argue that the preferred classical variables that emerge from a pure
quantum state are determined by its entanglement structure in the form of
redundant records: information shared between many subsystems. Focusing on the
early universe, we ask how classical metric perturbations emerge from vacuum
fluctuations in an inflationary background. We show that the squeezing of the
quantum state for super-horizon modes, along with minimal gravitational
interactions, leads to decoherence and to an exponential number of records of
metric fluctuations on very large scales, $\lambda/\lambda_{\rm
Hubble}>\Delta_\zeta^{-2/3}$, where $\Delta_\zeta\lesssim 10^{-5}$ is the
amplitude of scalar metric fluctuations. This determines a preferred
decomposition of the inflationary wavefunction into orthogonal "branches"
corresponding to classical metric perturbations, which defines an inflationary
entropy production rate and accounts for the emergence of stochastic,
inhomogeneous spacetime geometry.
| 0 | 1 | 0 | 0 | 0 | 0 |
Pulsejet engine dynamics in vertical motion using momentum conservation | The momentum conservation law is applied to analyse the dynamics of pulsejet
engine in vertical motion in a uniform gravitational field in the absence of
friction. The model predicts existence of a terminal speed given frequency of
the short pulses. The conditions that the engine does not return to the
starting position are identified. The number of short periodic pulses after
which the engine returns to the starting position is found to be independent of
the exhaust velocity and gravitational field intensity for certain frequency of
the pulses. The pulsejet engine and turbojet engine aircraft models of dynamics
are compared. Also the octopus dynamics is modelled. The paper is addressed to
intermediate undergraduate students of classical mechanics and aerospace
engineering.
| 0 | 1 | 0 | 0 | 0 | 0 |
Mitigating Blackout Risk via Maintenance : Inference from Simulation Data | Whereas maintenance has been recognized as an important and effective means
for risk management in power systems, it turns out to be intractable if
cascading blackout risk is considered due to the extremely high computational
complexity. In this paper, based on the inference from the blackout simulation
data, we propose a methodology to efficiently identify the most influential
component(s) for mitigating cascading blackout risk in a large power system. To
this end, we first establish an analytic relationship between maintenance
strategies and blackout risk estimation by inferring from the data of cascading
outage simulations. Then we formulate the component maintenance decision-making
problem as a nonlinear 0-1 programming. Afterwards, we quantify the credibility
of blackout risk estimation, leading to an adaptive method to determine the
least required number of simulations, which servers as a crucial parameter of
the optimization model. Finally, we devise two heuristic algorithms to find
approximate optimal solutions to the model with very high efficiency. Numerical
experiments well manifest the efficacy and high efficiency of our methodology.
| 0 | 0 | 0 | 1 | 0 | 0 |
Evolving to Non-round Weingarten Spheres: Integer Linear Hopf Flows | In the 1950's Hopf gave examples of non-round convex 2-spheres in Euclidean
3-space with rotational symmetry that satisfy a linear relationship between
their principal curvatures. In this paper we investigate conditions under which
evolving a smooth rotationally symmetric sphere by a linear combination of its
radii of curvature yields a Hopf sphere. When the coefficients of the flow have
certain integer values, the fate of an initial sphere is entirely determined by
the local geometry of its isolated umbilic points. A surprising variety of
behaviours is uncovered: convergence to round spheres and non-round Hopf
spheres, as well as divergence to infinity.
The critical quantity is the rate of vanishing of the astigmatism - the
difference of the radii of curvature - at the isolated umbilic points. It is
proven that the size of this quantity versus the coefficient in the flow
function determines the fate of the evolution.
The geometric setting for the equation is Radius of Curvature space, viewed
as a pair of hyperbolic/AdS half-planes joined along their boundary, the
umbilic horizon. A rotationally symmetric sphere determines a parameterized
curve in this plane with end-points on the umbilic horizon. The slope of the
curve at the umbilic horizon is linked by the Codazzi-Mainardi equations to the
rate of vanishing of astigmatism, and for generic initial conditions can be
used to determine the outcome of the flow.
The slope can jump during the flow, and a number of examples are given:
instant jumps of the initial slope, as well as umbilic circles that contract to
points in finite time and 'pop' the slope. Finally, we present soliton-like
solutions: curves that evolve under linear flows by mutual hyperbolic/AdS
isometries (dilation and translation) of Radius of Curvature space. A
forthcoming paper will apply these geometric ideas to non-linear curvature
flows.
| 0 | 0 | 1 | 0 | 0 | 0 |
On Infinite Linear Programming and the Moment Approach to Deterministic Infinite Horizon Discounted Optimal Control Problems | We revisit the linear programming approach to deterministic, continuous time,
infinite horizon discounted optimal control problems. In the first part, we
relax the original problem to an infinite-dimensional linear program over a
measure space and prove equivalence of the two formulations under mild
assumptions, significantly weaker than those found in the literature until now.
The proof is based on duality theory and mollification techniques for
constructing approximate smooth subsolutions to the associated
Hamilton-Jacobi-Bellman equation. In the second part, we assume polynomial data
and use Lasserre's hierarchy of primal-dual moment-sum-of-squares semidefinite
relaxations to approximate the value function and design an approximate optimal
feedback controller. We conclude with an illustrative example.
| 1 | 0 | 1 | 0 | 0 | 0 |
Granular permittivity representation in extremely near-field light-matter interactions processes | Light-matter interaction processes are significantly affected by surrounding
electromagnetic environment. Dielectric materials are usually introduced into
an interaction picture via their classical properties, e.g. permittivity,
appearing in constitutive relations. While this approach was proven to be
applicable in many occasions, it might face limitations when an emitter is
situated very close to a material boundary. In this case nonlocal extend of a
quantum wave function of an emitter becomes comparable with a distance to a
boundary and a lattice constant of a material. Here a semi-classical model,
taking into account material's granularity, is developed. In particular,
spontaneous emission process in the vicinity of flat boundaries is considered.
The material boundary is divided into a pair areas - far zone is modeled as a
continuous phase, while the near zone next to a nonlocal emitter is represented
with a discrete array of polarizable particles. This array resembles optical
properties of the continuous phase under the standard homogenization procedure.
Local field effects were shown to lead orders of magnitude corrections to
spontaneous emission rates in the case of sub-nanometer emitter-surface
separation distances. The developed mesoscopic model enables addressing few
aspects of local field corrections in quite complex scenarios, where quantum ab
initio techniques yet face challenges owing to involved computational
complexity. The developed method could be utilized for designs of quantum
sources and networks, enhanced with structured electromagnetic environment.
| 0 | 1 | 0 | 0 | 0 | 0 |
Application of Superhalogens in the Design of Organic Superconductors | Bechgaard salts, (TMTSF)2X (TMTSF = tetramethyl tetraselenafulvalene and X =
complex anion), form the most popular series of organic superconductors. In
these salts, TMTSF molecules act as super-electron donor and X as acceptor. We
computationally examine the electronic structure and properties of X in
commonly used (TMTSF)2X (X = NO3, BF4, ClO4, PF6) superconductors and notice
that they belong to the class of superhalogens due to their higher vertical
detachment energy than halogen anions. This prompted us to choose other
superhalogens such as X = BO2, BH4, B2F7, AuF6 and study their (TMTSF)2X
complexes. Our findings suggest that these complexes behave more or less
similar to those of established (TMTSF)2X superconductors, particularly for X =
BO2 and B2F7. We, therefore, believe that the concept of superhalogen can be
successfully applied in the design of novel organic superconductors.
| 0 | 1 | 0 | 0 | 0 | 0 |
Inhomogeneous hard-core bosonic mixture with checkerboard supersolid phase: Quantum and thermal phase diagram | We introduce an inhomogeneous bosonic mixture composed of two kinds of
hard-core and semi-hard-core bosons with different nilpotency conditions and
demonstrate that in contrast with the standard hard-core Bose-Hubbard model,
our bosonic mixture with nearest- and next-nearest-neighbor interactions on a
square lattice develops the checkerboard supersolid phase characterized by the
simultaneous superfluid and checkerboard solid orders. Our bosonic mixture is
created from a two-orbital Bose-Hubbard model including two kinds of bosons: a
single-orbital boson and a two-orbital boson. By mapping the bosonic mixture to
an anisotropic inhomogeneous spin model in the presence of a magnetic field, we
study the ground-state phase diagram of the model by means of cluster mean
field theory and linear spin-wave theory and show that various phases such as
solid, superfluid, supersolid, and Mott insulator appear in the phase diagram
of the mixture. Competition between the interactions and magnetic field causes
the mixture to undergo different kinds of first- and second-order phase
transitions. By studying the behavior of the spin-wave excitations, we find the
reasons of all first- and second-order phase transitions. We also obtain the
temperature phase diagram of the system using cluster mean field theory. We
show that the checkerboard supersolid phase persists at finite temperature
comparable with the interaction energies of bosons.
| 0 | 1 | 0 | 0 | 0 | 0 |
Width-$k$ Generalizations of Classical Permutation Statistics | We introduce new natural generalizations of the classical descent and
inversion statistics for permutations, called width-$k$ descents and width-$k$
inversions. These variations induce generalizations of the excedance and major
statistics, providing a framework in which the most well-known
equidistributivity results for classical statistics are paralleled. We explore
additional relationships among the statistics providing specific formulas in
certain special cases. Moreover, we explore the behavior of these width-$k$
statistics in the context of pattern avoidance.
| 0 | 0 | 1 | 0 | 0 | 0 |
Klout Topics for Modeling Interests and Expertise of Users Across Social Networks | This paper presents Klout Topics, a lightweight ontology to describe social
media users' topics of interest and expertise. Klout Topics is designed to: be
human-readable and consumer-friendly; cover multiple domains of knowledge in
depth; and promote data extensibility via knowledge base entities. We discuss
why this ontology is well-suited for text labeling and interest modeling
applications, and how it compares to available alternatives. We show its
coverage against common social media interest sets, and examples of how it is
used to model the interests of over 780M social media users on Klout.com.
Finally, we open the ontology for external use.
| 1 | 0 | 0 | 0 | 0 | 0 |
Set-Based Tests for Genetic Association Using the Generalized Berk-Jones Statistic | Studying the effects of groups of Single Nucleotide Polymorphisms (SNPs), as
in a gene, genetic pathway, or network, can provide novel insight into complex
diseases, above that which can be gleaned from studying SNPs individually.
Common challenges in set-based genetic association testing include weak effect
sizes, correlation between SNPs in a SNP-set, and scarcity of signals, with
single-SNP effects often ranging from extremely sparse to moderately sparse in
number. Motivated by these challenges, we propose the Generalized Berk-Jones
(GBJ) test for the association between a SNP-set and outcome. The GBJ extends
the Berk-Jones (BJ) statistic by accounting for correlation among SNPs, and it
provides advantages over the Generalized Higher Criticism (GHC) test when
signals in a SNP-set are moderately sparse. We also provide an analytic p-value
calculation procedure for SNP-sets of any finite size. Using this p-value
calculation, we illustrate that the rejection region for GBJ can be described
as a compromise of those for BJ and GHC. We develop an omnibus statistic as
well, and we show that this omnibus test is robust to the degree of signal
sparsity. An additional advantage of our method is the ability to conduct
inference using individual SNP summary statistics from a genome-wide
association study. We evaluate the finite sample performance of the GBJ though
simulation studies and application to gene-level association analysis of breast
cancer risk.
| 0 | 0 | 0 | 1 | 0 | 0 |
Comprehensive Modeling of Three-Phase Distribution Systems via the Bus Admittance Matrix | The theme of this paper is three-phase distribution system modeling suitable
for the Z-Bus load-flow. Detailed models of wye and delta constant-power,
constant-current, and constant-impedance loads are presented. Models of
transmission lines, voltage regulators, and transformers that build the bus
admittance matrix (Y-Bus) are laid out. The Z-Bus load-flow is then reviewed
and the singularity of the Y-Bus in case of certain transformer connections is
rigorously discussed. Based on realistic assumptions and conventional
modifications, the invertibility of the Y-Bus is proved. Last but not least,
the MATLAB scripts that construct the detailed component models for the IEEE
37-bus, IEEE 123-bus, and 8500-node feeders as well as the European 906-bus
low-voltage feeder are provided.
| 1 | 0 | 1 | 0 | 0 | 0 |
$c$-vectors of 2-Calabi--Yau categories and Borel subalgebras of ${\mathfrak{sl}}_{\infty}$ | We develop a general framework for $c$-vectors of 2-Calabi--Yau categories,
which deals with cluster tilting subcategories that are not reachable from each
other and contain infinitely many indecomposable objects. It does not rely on
iterative sequences of mutations.
We prove a categorical (infinite-rank) generalization of the
Nakanishi--Zelevinsky duality for $c$-vectors and establish two formulae for
the effective computation of $c$-vectors -- one in terms of indices and the
other in terms of dimension vectors for cluster tilted algebras.
In this framework, we construct a correspondence between the $c$-vectors of
the cluster categories ${\mathscr{C}}(A_{\infty})$ of type $A_{\infty}$ due to
Igusa--Todorov and the roots of the Borel subalgebras of
${\mathfrak{sl}}_{\infty}$. Contrary to the finite dimensional case, the Borel
subalgebras of ${\mathfrak{sl}}_{\infty}$ are not conjugate to each other. On
the categorical side, the cluster tilting subcategories of
${\mathscr{C}}(A_{\infty})$ exhibit different homological properties. The
correspondence builds a bridge between the two classes of objects.
| 0 | 0 | 1 | 0 | 0 | 0 |
Electrostatic interaction between non-identical charged particles at an electrolyte interface | In this thesis we study the lateral electrostatic interaction between a pair
of non-identical, moderately charged colloidal particles trapped at an
electrolyte interface in the limit of short inter-particle separations. Using a
simplified model system we solve the problem analytically within the framework
of linearised Poisson-Boltzmann theory and classical density functional theory.
In the first step, we calculate the electrostatic potential inside the system
exactly as well as within the widely used superposition approximation. Then
these results are used to calculate the surface and line interaction energy
densities between the particles. Contrary to the case of identical particles,
depending upon the parameters of the system, we obtain that both the surface
and the line interaction can vary non-monotonically with varying separation
between the particles and the superposition approximation fails to predict the
correct qualitative behaviours in most cases. Additionally, the superposition
approximation is unable to predict the energy contributions quantitatively even
at large distances. We also provide expression for the constant (independent of
the inter-particle separation) interaction parameters, i.e., the surface
tension, the line tension and the interfacial tension. Our results are expected
to be of use for modelling particle-interaction at fluid interfaces and, in
particular, for emulsion stabilization using oppositely charged particles.
| 0 | 1 | 0 | 0 | 0 | 0 |
The weighted connection and sectional curvature for manifolds with density | In this paper we study sectional curvature bounds for Riemannian manifolds
with density from the perspective of a weighted torsion free connection
introduced recently by the last two authors. We develop two new tools for
studying weighted sectional curvature bounds: a new weighted Rauch comparison
theorem and a modified notion of convexity for distance functions. As
applications we prove generalizations of theorems of Preissman and Byers for
negative curvature, the (homeomorphic) quarter-pinched sphere theorem, and
Cheeger's finiteness theorem. We also improve results of the first two authors
for spaces of positive weighted sectional curvature and symmetry.
| 0 | 0 | 1 | 0 | 0 | 0 |
Control and Limit Enforcements for VSC Multi-Terminal HVDC in Newton Power Flow | This paper proposes a novel method to automatically enforce controls and
limits for Voltage Source Converter (VSC) based multi-terminal HVDC in the
Newton power flow iteration process. A general VSC MT-HVDC model with primary
PQ or PV control and secondary voltage control is formulated. Both the
dependent and independent variables are included in the propose formulation so
that the algebraic variables of the VSC MT-HVDC are adjusted simultaneously.
The proposed method also maintains the number of equations and the dimension of
the Jacobian matrix unchanged so that, when a limit is reached and a control is
released, the Jacobian needs no re-factorization. Simulations on the IEEE
14-bus and Polish 9241-bus systems are performed to demonstrate the
effectiveness of the method.
| 1 | 0 | 0 | 0 | 0 | 0 |
A.Ya. Khintchine's Work in Probability Theory | The paper is devoted to the contribution in the Probability Theory of the
well-known Soviet mathematician Alexander Yakovlevich Khintchine (1894-1959).
Several of his results are described, in particular those fundamental results
on the infinitely divisible distributions. Attention is paid also to his
interaction with Paul Levy. The content of the paper is related to our joint
book The Legacy of A.Ya. Khintchine's Work in Probability Theory, published in
2010 by Cambridge Scientific Publishers.
| 0 | 0 | 1 | 0 | 0 | 0 |
Dynamic Complexity under Definable Changes | This paper studies dynamic complexity under definable change operations in
the DynFO framework by Patnaik and Immerman. It is shown that for changes
definable by parameter-free first-order formulas, all (uniform) $AC^1$ queries
can be maintained by first-order dynamic programs. Furthermore, many
maintenance results for single-tuple changes are extended to more powerful
change operations: (1) The reachability query for undirected graphs is
first-order maintainable under single tuple changes and first-order defined
insertions, likewise the reachability query for directed acyclic graphs under
quantifier-free insertions. (2) Context-free languages are first-order
maintainable under $\Sigma_1$-defined changes. These results are complemented
by several inexpressibility results, for example, that the reachability query
cannot be maintained by quantifier-free programs under definable,
quantifier-free deletions.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Planar Sandwich and Other 1D Planar Heat Flow Test Problems in ExactPack | This report documents the implementation of several related 1D heat flow
problems in the verification package ExactPack. In particular, the planar
sandwich class defined by Dawes et al., as well as the classes
PlanarSandwichHot, PlanarSandwichHalf, and other generalizations of the planar
sandwich problem, are defined and documented here. A rather general treatment
of 1D heat flow is presented, whose main results have been implemented in the
class Rod1D. All planar sandwich classes are derived from the parent class
Rod1D.
| 0 | 1 | 0 | 0 | 0 | 0 |
Difficulties of Timestamping Archived Web Pages | We show that state-of-the-art services for creating trusted timestamps in
blockchain-based networks do not adequately allow for timestamping of web
pages. They accept data by value (e.g., images and text), but not by reference
(e.g., URIs of web pages). Also, we discuss difficulties in repeatedly
generating the same cryptographic hash value of an archived web page. We then
introduce several requirements to be fulfilled in order to produce repeatable
hash values for archived web pages.
| 1 | 0 | 0 | 0 | 0 | 0 |
Cops and robber on grids and tori | This paper is a contribution to the classical cops and robber problem on a
graph, directed to two-dimensional grids and toroidal grids. These studies are
generally aimed at determining the minimum number of cops needed to capture the
robber and proposing algorithms for the capture. We apply some new concepts to
propose a new solution to the problem on grids that was already solved under a
different approach, and apply these concepts to give efficient algorithms for
the capture on toroidal grids. As for grids, we show that two cops suffice even
in a semi-torus (i.e. a grid with toroidal closure in one dimension) and three
cops are necessary and sufficient in a torus. Then we treat the problem in
function of any number k of cops, giving efficient algorithms for grids and
tori and computing lower and upper bounds on the capture time. Conversely we
determine the minimum value of k needed for any given capture time and study a
possible speed-up phenomenon.
| 1 | 0 | 0 | 0 | 0 | 0 |
Measuring Gender Inequalities of German Professions on Wikipedia | Wikipedia is a community-created online encyclopedia; arguably, it is the
most popular and largest knowledge resource on the Internet. Thus, reliability
and neutrality are of high importance for Wikipedia. Previous research [3]
reveals gender bias in Google search results for many professions and
occupations. Also, Wikipedia was criticized for existing gender bias in
biographies [4] and gender gap in the editor community [5, 6]. Thus, one could
expect that gender bias related to professions and occupations may be present
in Wikipedia. The term gender bias is used here in the sense of conscious or
unconscious favoritism towards one gender over another [47] with respect to
professions and occupations. The objective of this work is to identify and
assess gender bias. To this end, the German Wikipedia articles about
professions and occupations were analyzed on three dimensions: redirections,
images, and people mentioned in the articles. This work provides evidence for
systematic overrepresentation of men in all three dimensions; female bias is
only present for a few professions.
| 1 | 0 | 0 | 0 | 0 | 0 |
Contact Adaption during Epidemics: A Multilayer Network Formulation Approach | People change their physical contacts as a preventive response to infectious
disease propagations. Yet, only a few mathematical models consider the coupled
dynamics of the disease propagation and the contact adaptation process. This
paper presents a model where each agent has a default contact neighborhood set,
and switches to a different contact set once she becomes alert about infection
among her default contacts. Since each agent can adopt either of two possible
neighborhood sets, the overall contact network switches among 2^N possible
configurations. Notably, a two-layer network representation can fully model the
underlying adaptive, state-dependent contact network. Contact adaptation
influences the size of the disease prevalence and the epidemic threshold---a
characteristic measure of a contact network robustness against epidemics---in a
nonlinear fashion. Particularly, the epidemic threshold for the presented
adaptive contact network belongs to the solution of a nonlinear
Perron-Frobenius (NPF) problem, which does not depend on the contact adaptation
rate monotonically. Furthermore, the network adaptation model predicts a
counter-intuitive scenario where adaptively changing contacts may adversely
lead to lower network robustness against epidemic spreading if the contact
adaptation is not fast enough. An original result for a class of NPF problems
facilitate the analytical developments in this paper.
| 1 | 0 | 0 | 0 | 0 | 0 |
Estimation in the convolution structure density model. Part I: oracle inequalities | We study the problem of nonparametric estimation under $\bL_p$-loss, $p\in
[1,\infty)$, in the framework of the convolution structure density model on
$\bR^d$. This observation scheme is a generalization of two classical
statistical models, namely density estimation under direct and indirect
observations. In Part I the original pointwise selection rule from a family of
"kernel-type" estimators is proposed. For the selected estimator, we prove an
$\bL_p$-norm oracle inequality and several of its consequences. In Part II the
problem of adaptive minimax estimation under $\bL_p$--loss over the scale of
anisotropic Nikol'skii classes is addressed. We fully characterize the behavior
of the minimax risk for different relationships between regularity parameters
and norm indexes in the definitions of the functional class and of the risk. We
prove that the selection rule proposed in Part I leads to the construction of
an optimally or nearly optimally (up to logarithmic factor) adaptive estimator.
| 0 | 0 | 1 | 1 | 0 | 0 |
Candidate exoplanet host HD131399A: a nascent Am star | Direct imaging suggests that there is a Jovian exoplanet around the primary
A-star in the triple-star system HD131399. We investigate a high-quality
spectrum of the primary component HD131399A obtained with FEROS on the ESO/MPG
2.2m telescope, aiming to characterise the star's atmospheric and fundamental
parameters, and to determine elemental abundances at high precision and
accuracy. The aim is to constrain the chemical composition of the birth cloud
of the system and therefore the bulk composition of the putative planet. A
hybrid non-local thermal equilibrium (non-LTE) model atmosphere technique is
adopted for the quantitative spectral analysis. Comparison with the most recent
stellar evolution models yields the fundamental parameters. The atmospheric and
fundamental stellar parameters of HD131399A are constrained to Teff=9200+-100
K, log g=4.37+-0.10, M=1.95+0.08-0.06 Msun, R=1.51+0.13-0.10 Rsun, and log
L/Lsun=1.17+-0.07, locating the star on the zero-age main sequence. Non-LTE
effects on the derived metal abundances are often smaller than 0.1dex, but can
reach up to ~0.8dex for individual lines. The observed lighter elements up to
calcium are overall consistent with present-day cosmic abundances, with a C/O
ratio of 0.45$\pm$0.07 by number, while the heavier elements show mild
overabundances. We conclude that the birth cloud of the system had a standard
chemical composition, but we witness the onset of the Am phenomenon in the
slowly rotating star. We furthermore show that non-LTE analyses have the
potential to solve the remaining discrepancies between observed abundances and
predictions by diffusion models for Am stars. Moreover, the present case allows
mass loss, not turbulent mixing, to be identified as the main transport process
competing with diffusion in very young Am stars.
| 0 | 1 | 0 | 0 | 0 | 0 |
Toward Quantum Combinatorial Games | In this paper, we propose a Quantum variation of combinatorial games,
generalizing the Quantum Tic-Tac-Toe proposed by Allan Goff. A combinatorial
game is a two-player game with no chance and no hidden information, such as Go
or Chess. In this paper, we consider the possibility of playing superpositions
of moves in such games. We propose different rulesets depending on when
superposed moves should be played, and prove that all these rulesets may lead
similar games to different outcomes. We then consider Quantum variations of the
game of Nim. We conclude with some discussion on the relative interest of the
different rulesets.
| 1 | 0 | 0 | 0 | 0 | 0 |
Markov $L_2$ inequality with the Gegenbauer weight | For the Gegenbauer weight function $w_{\lambda}(t)=(1-t^2)^{\lambda-1/2}$,
$\lambda>-1/2$, we denote by $\Vert\cdot\Vert_{w_{\lambda}}$ the associated
$L_2$-norm, $$ \Vert
f\Vert_{w_{\lambda}}:=\Big(\int_{-1}^{1}w_{\lambda}(t)f^2(t)\,dt\Big)^{1/2}. $$
We study the Markov inequality $$ \Vert p^{\prime}\Vert_{w_{\lambda}}\leq
c_{n}(\lambda)\,\Vert p\Vert_{w_{\lambda}},\qquad p\in \mathcal{P}_n, $$ where
$\mathcal{P}_n$ is the class of algebraic polynomials of degree not exceeding
$n$. Upper and lower bounds for the best Markov constant $c_{n}(\lambda)$ are
obtained, which are valid for all $n\in \mathbb{N}$ and $\lambda>-\frac{1}{2}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
A Note on Some Approximation Kernels on the Sphere | We produce precise estimates for the Kogbetliantz kernel for the
approximation of functions on the sphere. Furthermore, we propose and study a
new approximation kernel, which has slightly better properties.
| 0 | 0 | 1 | 0 | 0 | 0 |
Machine Learning as Statistical Data Assimilation | We identify a strong equivalence between neural network based machine
learning (ML) methods and the formulation of statistical data assimilation
(DA), known to be a problem in statistical physics. DA, as used widely in
physical and biological sciences, systematically transfers information in
observations to a model of the processes producing the observations. The
correspondence is that layer label in the ML setting is the analog of time in
the data assimilation setting. Utilizing aspects of this equivalence we discuss
how to establish the global minimum of the cost functions in the ML context,
using a variational annealing method from DA. This provides a design method for
optimal networks for ML applications and may serve as the basis for
understanding the success of "deep learning". Results from an ML example are
presented.
When the layer label is taken to be continuous, the Euler-Lagrange equation
for the ML optimization problem is an ordinary differential equation, and we
see that the problem being solved is a two point boundary value problem. The
use of continuous layers is denoted "deepest learning". The Hamiltonian version
provides a direct rationale for back propagation as a solution method for the
canonical momentum; however, it suggests other solution methods are to be
preferred.
| 1 | 0 | 0 | 1 | 0 | 0 |
Nonparametric estimation of the kernel function of symmetric stable moving average random functions | We use the empirical normalized (smoothed) periodogram of a $S\alpha S$
moving average random function to estimate its kernel function from high
frequency observation data. The weak consistency of the estimator is shown. A
simulation study of the performance of the estimates rounds up the paper.
| 0 | 0 | 1 | 1 | 0 | 0 |
Fermions in Two Dimensions: Scattering and Many-Body Properties | Ultracold atomic Fermi gases in two-dimensions (2D) are an increasingly
popular topic of research. The interaction strength between spin-up and
spin-down particles in two-component Fermi gases can be tuned in experiments,
allowing for a strongly interacting regime where the gas properties are yet to
be fully understood. We have probed this regime for 2D Fermi gases by
performing T=0 ab initio diffusion Monte Carlo calculations. The many-body
dynamics are largely dependent on the two-body interactions, therefore we start
with an in-depth look at scattering theory in 2D. We show the partial-wave
expansion and its relation to the scattering length and effective range. Then
we discuss our numerical methods for determining these scattering parameters.
We close out this discussion by illustrating the details of bound states in 2D.
Transitioning to the many-body system, we use variationally optimized wave
functions to calculate ground-state properties of the gas over a range of
interaction strengths. We show results for the energy per particle and
parametrize an equation of state. We then proceed to determine the chemical
potential for the strongly interacting gas.
| 0 | 1 | 0 | 0 | 0 | 0 |
Moving Horizon Estimation for ARMAX process with t-Distribution Noise | In this paper, instead of the usual Gaussian noise assumption,
$t$-distribution noise is assumed. A Maximum Likelihood Estimator using the
most recent N measurements is proposed for the Auto-Regressive-Moving-Average
with eXogenous input (ARMAX) process with this assumption. The proposed
estimator is robust to outliers because the `thick tail' of the t-distribution
reduces the effect of large errors in the likelihood function. Instead of
solving the resulting nonlinear estimator numerically, the Influence Function
is used to formulate a computationally efficient recursive solution, which
reduces to the traditional Moving Horizon Estimator when the noise is Gaussian.
The formula for the variance of the estimate is derived. This formula shows
explicitly how the variance of the estimate is affected by the number of
measurements and noise variance. The simulation results show that the proposed
estimator has smaller variance and is more robust to outliers than the Moving
Window Least-Squares Estimator. For the same accuracy, the proposed estimator
is an order of magnitude faster than the particle filter.
| 1 | 0 | 0 | 0 | 0 | 0 |
Chimera states in brain networks: empirical neural vs. modular fractal connectivity | Complex spatiotemporal patterns, called chimera states, consist of coexisting
coherent and incoherent domains and can be observed in networks of coupled
oscillators. The interplay of synchrony and asynchrony in complex brain
networks is an important aspect in studies of both brain function and disease.
We analyse the collective dynamics of FitzHugh-Nagumo neurons in complex
networks motivated by its potential application to epileptology and epilepsy
surgery. We compare two topologies: an empirical structural neural connectivity
derived from diffusion-weighted magnetic resonance imaging and a mathematically
constructed network with modular fractal connectivity. We analyse the
properties of chimeras and partially synchronized states, and obtain regions of
their stability in the parameter planes. Furthermore, we qualitatively simulate
the dynamics of epileptic seizures and study the influence of the removal of
nodes on the network synchronizability, which can be useful for applications to
epileptic surgery.
| 0 | 1 | 0 | 0 | 0 | 0 |
Bayesian surrogate learning in dynamic simulator-based regression problems | The estimation of unknown values of parameters (or hidden variables, control
variables) that characterise a physical system often relies on the comparison
of measured data with synthetic data produced by some numerical simulator of
the system as the parameter values are varied. This process often encounters
two major difficulties: the generation of synthetic data for each considered
set of parameter values can be computationally expensive if the system model is
complicated; and the exploration of the parameter space can be inefficient
and/or incomplete, a typical example being when the exploration becomes trapped
in a local optimum of the objection function that characterises the mismatch
between the measured and synthetic data. A method to address both these issues
is presented, whereby: a surrogate model (or proxy), which emulates the
computationally expensive system simulator, is constructed using deep recurrent
networks (DRN); and a nested sampling (NS) algorithm is employed to perform
efficient and robust exploration of the parameter space. The analysis is
performed in a Bayesian context, in which the samples characterise the full
joint posterior distribution of the parameters, from which parameter estimates
and uncertainties are easily derived. The proposed approach is compared with
conventional methods in some numerical examples, for which the results
demonstrate that one can accelerate the parameter estimation process by at
least an order of magnitude.
| 1 | 0 | 0 | 1 | 0 | 0 |
Stationary solutions for the ellipsoidal BGK model in a slab | We address the boundary value problem for the ellipsoidal BGK model of the
Boltzmann equation posed in a bounded interval. The existence of a unique mild
solution is established under the assumption that the inflow boundary data does
not concentrate too much around the zero velocity, and the gas is sufficiently
rarefied.
| 0 | 0 | 1 | 0 | 0 | 0 |
Raman signatures of monoclinic distortion in (Ba$_{1-x}$Sr$_{x}$)$_{3}$CaNb$_{2}$O$_{9}$ complex perovskites | Octahedral tilting is most common distortion process observed in
centrosymmetric perovskite compounds (ABO$_{3}$). Indeed, crucial physical
properties of this oxide stem from the tilts of BO$_{6}$ rigid octahedra. In
microwave ceramics with perovskite-type structure, there is a close relation
between the temperature coefficient of resonant frequency and tilt system of
the perovskite structure. However, in many cases, limited access facilities are
needed to assign correctly the space group, including neutron scattering and
transmission electron microscopy. Here, we combine the Raman scattering and
group-theory calculations to probe the structural distortion in the perovskite
(Ba$_{1-x}$Sr$_{x}$)$_{3}$CaNb$_{2}$O$_{9}$ solid solution, which exhibits a
structural phase transition at $x$ $\geq$ 0.7, from D$_{3d}^{3}$ trigonal to
C$_{2h}^{3}$ monoclinic cell. Both phases are related by an octahedral tilting
distortion ($a^{0}b^{-}b^{-}$ in Glazer notation). Low temperature Raman
spectra corroborate the group-theoretical predictions for
Sr$_{3}$CaNb$_{2}$O$_{9}$ compound, since 36 modes detected at 25 K agree well
with those 42 (25A$_{g}$ $\oplus$ 17B$_{g}$) predicted ones.
| 0 | 1 | 0 | 0 | 0 | 0 |
On Quadratic Penalties in Elastic Weight Consolidation | Elastic weight consolidation (EWC, Kirkpatrick et al, 2017) is a novel
algorithm designed to safeguard against catastrophic forgetting in neural
networks. EWC can be seen as an approximation to Laplace propagation (Eskin et
al, 2004), and this view is consistent with the motivation given by Kirkpatrick
et al (2017). In this note, I present an extended derivation that covers the
case when there are more than two tasks. I show that the quadratic penalties in
EWC are inconsistent with this derivation and might lead to double-counting
data from earlier tasks.
| 1 | 0 | 0 | 1 | 0 | 0 |
The design of the ILD forward region | Following the decision to reduce the L* from 4.4 m to 4.1 m, the BeamCal had
to be moved closer to the interaction point. Results of a study of how this
affects the beamstrahlung and backward scattering backgrounds show that the
e+e- pair background depositions from beamstrahlung at the BeamCal rises by
20%. The background from backscattered electrons and positrons in the inner
pixel layers rises almost by a factor two, so does the number of photons in the
tracker.
| 0 | 1 | 0 | 0 | 0 | 0 |
Conduction Channel Formation and Dissolution Due to Oxygen Thermophoresis/Diffusion in Hafnium Oxide Memristors | Transition metal oxide memristors, or resistive random-access memory (RRAM)
switches, are under intense development for storage-class memory because of
their favorable operating power, endurance, speed, and density. Their
commercial deployment critically depends on predictive compact models based on
understanding nanoscale physico-chemical forces, which remains elusive and
controversial owing to the difficulties in directly observing atomic motions
during resistive switching, Here, using scanning transmission synchrotron x-ray
spectromicroscopy to study in-situ switching of hafnium oxide memristors, we
directly observed the formation of a localized oxygen-deficiency-derived
conductive channel surrounded by a low-conductivity ring of excess oxygen.
Subsequent thermal annealing homogenized the segregated oxygen, resetting the
cells towards their as-grown resistance state. We show that the formation and
dissolution of the conduction channel are successfully modeled by radial
thermophoresis and Fick diffusion of oxygen atoms driven by Joule heating. This
confirmation and quantification of two opposing nanoscale radial forces that
affect bipolar memristor switching are important components for any future
physics-based compact model for the electronic switching of these devices.
| 0 | 1 | 0 | 0 | 0 | 0 |
Improving Massive MIMO Belief Propagation Detector with Deep Neural Network | In this paper, deep neural network (DNN) is utilized to improve the belief
propagation (BP) detection for massive multiple-input multiple-output (MIMO)
systems. A neural network architecture suitable for detection task is firstly
introduced by unfolding BP algorithms. DNN MIMO detectors are then proposed
based on two modified BP detectors, damped BP and max-sum BP. The correction
factors in these algorithms are optimized through deep learning techniques,
aiming at improved detection performance. Numerical results are presented to
demonstrate the performance of the DNN detectors in comparison with various BP
modifications. The neural network is trained once and can be used for multiple
online detections. The results show that, compared to other state-of-the-art
detectors, the DNN detectors can achieve lower bit error rate (BER) with
improved robustness against various antenna configurations and channel
conditions at the same level of complexity.
| 0 | 0 | 0 | 1 | 0 | 0 |
Trend Analysis on the Metadata of Program Comprehension Papers | As program comprehension is a vast research area, it is necessary to get an
overview of its rising and falling trends. We performed an n-gram frequency
analysis on titles, abstracts and keywords of 1885 articles about program
comprehension from the years 2000-2014. According to this analysis, the most
rising trends are feature location and open source systems, the most falling
ones are program slicing and legacy systems.
| 1 | 0 | 0 | 0 | 0 | 0 |
Combinatorial views on persistent characters in phylogenetics | The so-called binary perfect phylogeny with persistent characters has
recently been thoroughly studied in computational biology as it is less
restrictive than the well known binary perfect phylogeny. Here, we focus on the
notion of (binary) persistent characters, i.e. characters that can be realized
on a phylogenetic tree by at most one $0 \rightarrow 1$ transition followed by
at most one $1 \rightarrow 0$ transition in the tree, and analyze these
characters under different aspects. First, we illustrate the connection between
persistent characters and Maximum Parsimony, where we characterize persistent
characters in terms of the first phase of the famous Fitch algorithm.
Afterwards we focus on the number of persistent characters for a given
phylogenetic tree. We show that this number solely depends on the balance of
the tree. To be precise, we develop a formula for counting the number of
persistent characters for a given phylogenetic tree based on an index of tree
balance, namely the Sackin index. Lastly, we consider the question of how many
(carefully chosen) binary characters together with their persistence status are
needed to uniquely determine a phylogenetic tree and provide an upper bound for
the number of characters needed.
| 0 | 0 | 0 | 0 | 1 | 0 |
Distribution of residuals in the nonparametric IV model with application to separability testing | We develop a uniform asymptotic expansion for the empirical distribution
function of residuals in the nonparametric IV regression. Such expansion opens
a door for construction of a broad range of residual-based specification tests
in nonparametric IV models. Building on obtained result, we develop a test for
the separability of unobservables in econometric models with endogeneity. The
test is based on verifying the independence condition between residuals of the
NPIV estimator and the instrument and can distinguish between the non-separable
and the separable specification under endogeneity.
| 0 | 0 | 1 | 1 | 0 | 0 |
Scene Graph Generation by Iterative Message Passing | Understanding a visual scene goes beyond recognizing individual objects in
isolation. Relationships between objects also constitute rich semantic
information about the scene. In this work, we explicitly model the objects and
their relationships using scene graphs, a visually-grounded graphical structure
of an image. We propose a novel end-to-end model that generates such structured
scene representation from an input image. The model solves the scene graph
inference problem using standard RNNs and learns to iteratively improves its
predictions via message passing. Our joint inference model can take advantage
of contextual cues to make better predictions on objects and their
relationships. The experiments show that our model significantly outperforms
previous methods for generating scene graphs using Visual Genome dataset and
inferring support relations with NYU Depth v2 dataset.
| 1 | 0 | 0 | 0 | 0 | 0 |
On the Apparent Power Law in CDM Halo Pseudo Phase Space Density Profiles | We investigate the apparent power-law scaling of the pseudo phase space
density (PPSD) in CDM halos. We study fluid collapse, using the close analogy
between the gas entropy and the PPSD in the fluid approximation. Our
hydrodynamic calculations allow for a precise evaluation of logarithmic
derivatives. For scale-free initial conditions, entropy is a power law in
Lagrangian (mass) coordinates, but not in Eulerian (radial) coordinates. The
deviation from a radial power law arises from incomplete hydrostatic
equilibrium (HSE), linked to bulk inflow and mass accretion, and the
convergence to the asymptotic central power-law slope is very slow. For more
realistic collapse, entropy is not a power law with either radius or mass due
to deviations from HSE and scale-dependent initial conditions. Instead, it is a
slowly rolling power law that appears approximately linear on a log-log plot.
Our fluid calculations recover PPSD power-law slopes and residual amplitudes
similar to N-body simulations, indicating that deviations from a power law are
not numerical artefacts. In addition, we find that realistic collapse is not
self-similar: scale lengths such as the shock radius and the turnaround radius
are not power-law functions of time. We therefore argue that the apparent
power-law PPSD cannot be used to make detailed dynamical inferences or
extrapolate halo profiles inward, and that it does not indicate any hidden
integrals of motion. We also suggest that the apparent agreement between the
PPSD and the asymptotic Bertschinger slope is purely coincidental.
| 0 | 1 | 0 | 0 | 0 | 0 |
Heavy tailed approximate identities and $σ$-stable Markov kernels | The aim of this paper is to present some results relating the properties of
stability, concentration and approximation to the identity of convolution
through not necessarily mollification type families of heavy tailed Markov
kernels. A particular case is provided by the kernels $K_t$ obtained as the $t$
mollification of $L^{\sigma(t)}$ selected from the family
$\mathcal{L}=\{L^{\sigma}:
\widehat{L^{\sigma}}(\xi)=e^{-|\xi|^\sigma},0<\sigma<2\}$, by a given function
$\sigma$ with values in the interval $(0,2)$. We show that a basic Harnack type
inequality, introduced by C.~Calderón in the convolution case, becomes at
once natural to the setting and useful to connect the concepts of stability,
concentration and approximation of the identity. Some of the general results
are extended to spaces of homogeneous type since most of the concepts involved
in the theory are given in terms of metric and measure.
| 0 | 0 | 1 | 0 | 0 | 0 |
Spreading of correlations in the Falicov-Kimball model | We study dynamical properties of the one- and two-dimensional Falicov-Kimball
model using lattice Monte Carlo simulations. In particular, we calculate the
spreading of charge correlations in the equilibrium model and after an
interaction quench. The results show a reduction of the light-cone velocity
with interaction strength at low temperature, while the phase velocity
increases. At higher temperature, the initial spreading is determined by the
Fermi velocity of the noninteracting system and the maximum range of the
correlations decreases with increasing interaction strength. Charge order
correlations in the disorder potential enhance the range of the correlations.
We also use the numerically exact lattice Monte Carlo results to benchmark the
accuracy of equilibrium and nonequilibrium dynamical cluster approximation
calculations. It is shown that the bias introduced by the mapping to a
periodized cluster is substantial, and that from a numerical point of view, it
is more efficient to simulate the lattice model directly.
| 0 | 1 | 0 | 0 | 0 | 0 |
Direct Estimation of Information Divergence Using Nearest Neighbor Ratios | We propose a direct estimation method for Rényi and f-divergence measures
based on a new graph theoretical interpretation. Suppose that we are given two
sample sets $X$ and $Y$, respectively with $N$ and $M$ samples, where
$\eta:=M/N$ is a constant value. Considering the $k$-nearest neighbor ($k$-NN)
graph of $Y$ in the joint data set $(X,Y)$, we show that the average powered
ratio of the number of $X$ points to the number of $Y$ points among all $k$-NN
points is proportional to Rényi divergence of $X$ and $Y$ densities. A
similar method can also be used to estimate f-divergence measures. We derive
bias and variance rates, and show that for the class of $\gamma$-Hölder
smooth functions, the estimator achieves the MSE rate of
$O(N^{-2\gamma/(\gamma+d)})$. Furthermore, by using a weighted ensemble
estimation technique, for density functions with continuous and bounded
derivatives of up to the order $d$, and some extra conditions at the support
set boundary, we derive an ensemble estimator that achieves the parametric MSE
rate of $O(1/N)$. Our estimators are more computationally tractable than other
competing estimators, which makes them appealing in many practical
applications.
| 1 | 0 | 0 | 1 | 0 | 0 |
Efficiently Learning Mixtures of Mallows Models | Mixtures of Mallows models are a popular generative model for ranking data
coming from a heterogeneous population. They have a variety of applications
including social choice, recommendation systems and natural language
processing. Here we give the first polynomial time algorithm for provably
learning the parameters of a mixture of Mallows models with any constant number
of components. Prior to our work, only the two component case had been settled.
Our analysis revolves around a determinantal identity of Zagier which was
proven in the context of mathematical physics, which we use to show polynomial
identifiability and ultimately to construct test functions to peel off one
component at a time.
To complement our upper bounds, we show information-theoretic lower bounds on
the sample complexity as well as lower bounds against restricted families of
algorithms that make only local queries. Together, these results demonstrate
various impediments to improving the dependence on the number of components.
They also motivate the study of learning mixtures of Mallows models from the
perspective of beyond worst-case analysis. In this direction, we show that when
the scaling parameters of the Mallows models have separation, there are much
faster learning algorithms.
| 0 | 0 | 0 | 1 | 0 | 0 |
Optimal Scheduling of Electrolyzer in Power Market with Dynamic Prices | Optimal scheduling of hydrogen production in dynamic pricing power market can
maximize the profit of hydrogen producer; however, it highly depends on the
accurate forecast of hydrogen consumption. In this paper, we propose a deep
leaning based forecasting approach for predicting hydrogen consumption of fuel
cell vehicles in future taxi industry. The cost of hydrogen production is
minimized by utilizing the proposed forecasting tool to reduce the hydrogen
produced during high cost on-peak hours and guide hydrogen producer to store
sufficient hydrogen during low cost off-peak hours.
| 0 | 0 | 0 | 1 | 0 | 0 |
Determination of the thermopower of microscale samples with an AC method | A modified AC method based on micro-fabricated heater and resistive
thermometers has been applied to measure the thermopower of microscale samples.
A sinusoidal current with frequency {\omega} is passed to the heater to
generate an oscillatory temperature difference across the sample at a frequency
2{\omega}, which simultaneously induces an AC thermoelectric voltage, also at
the frequency 2{\omega}. A key step of the method is to extract amplitude and
phase of the oscillatory temperature difference by probing the AC temperature
variation at each individual thermometer. The sign of the thermopower is
determined by examining the phase difference between the oscillatory
temperature difference and the AC thermoelectric voltage. The technique has
been compared with the popular DC method by testing both n-type and p-type thin
film samples. Both methods yielded consistent results, which verified the
reliability of the newly proposed AC method.
| 0 | 1 | 0 | 0 | 0 | 0 |
Food for Thought: Analyzing Public Opinion on the Supplemental Nutrition Assistance Program | This project explores public opinion on the Supplemental Nutrition Assistance
Program (SNAP) in news and social media outlets, and tracks elected
representatives' voting records on issues relating to SNAP and food insecurity.
We used machine learning, sentiment analysis, and text mining to analyze
national and state level coverage of SNAP in order to gauge perceptions of the
program over time across these outlets. Results indicate that the majority of
news coverage has negative sentiment, more partisan news outlets have more
extreme sentiment, and that clustering of negative reporting on SNAP occurs in
the Midwest. Our final results and tools will be displayed in an on-line
application that the ACFB Advocacy team can use to inform their communication
to relevant stakeholders.
| 1 | 0 | 0 | 0 | 0 | 0 |
Hasse diagrams of non-isomorphic posets with $n$ elements, $2\leq n \leq 7,$ and the number of posets with $10$ elements, without the aid of any computer program | Let $P(n)$ be the set of all posets with $n$ elements and $NIP(n)$ the set of
non-isomorphic posets with $n$ elements. Let $P^{(j)}(n)$, $1\leq j\leq 2^n,$
be the number of all posets with $n$ elements possessing exactly $j$
antichains. We have determined the numbers $P^{(j)}(7),$ $1\leq j\leq 128$, and
using a result of M. Erné \cite{EM4}, we compute $|P(10)|$ without the aid of
any computer program. We include the Hasse diagrams of all the non-isomorphic
posets of $P(7)$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Variable screening with multiple studies | Advancement in technology has generated abundant high-dimensional data that
allows integration of multiple relevant studies. Due to their huge
computational advantage, variable screening methods based on marginal
correlation have become promising alternatives to the popular regularization
methods for variable selection. However, all these screening methods are
limited to single study so far. In this paper, we consider a general framework
for variable screening with multiple related studies, and further propose a
novel two-step screening procedure using a self-normalized estimator for
high-dimensional regression analysis in this framework. Compared to the
one-step procedure and rank-based sure independence screening (SIS) procedure,
our procedure greatly reduces false negative errors while keeping a low false
positive rate. Theoretically, we show that our procedure possesses the sure
screening property with weaker assumptions on signal strengths and allows the
number of features to grow at an exponential rate of the sample size. In
addition, we relax the commonly used normality assumption and allow
sub-Gaussian distributions. Simulations and a real transcriptomic application
illustrate the advantage of our method as compared to the rank-based SIS
method.
| 0 | 0 | 1 | 1 | 0 | 0 |
Gaussian-Constrained training for speaker verification | Neural models, in particular the d-vector and x-vector architectures, have
produced state-of-the-art performance on many speaker verification tasks.
However, two potential problems of these neural models deserve more
investigation. Firstly, both models suffer from `information leak', which means
that some parameters participating in model training will be discarded during
inference, i.e, the layers that are used as the classifier. Secondly, both
models do not regulate the distribution of the derived speaker vectors. This
`unconstrained distribution' may degrade the performance of the subsequent
scoring component, e.g., PLDA. This paper proposes a Gaussian-constrained
training approach that (1) discards the parametric classifier, and (2) enforces
the distribution of the derived speaker vectors to be Gaussian. Our experiments
on the VoxCeleb and SITW databases demonstrated that this new training approach
produced more representative and regular speaker embeddings, leading to
consistent performance improvement.
| 1 | 0 | 0 | 0 | 0 | 0 |
Structural Change in (Economic) Time Series | Methods for detecting structural changes, or change points, in time series
data are widely used in many fields of science and engineering. This chapter
sketches some basic methods for the analysis of structural changes in time
series data. The exposition is confined to retrospective methods for univariate
time series. Several recent methods for dating structural changes are compared
using a time series of oil prices spanning more than 60 years. The methods
broadly agree for the first part of the series up to the mid-1980s, for which
changes are associated with major historical events, but provide somewhat
different solutions thereafter, reflecting a gradual increase in oil prices
that is not well described by a step function. As a further illustration, 1990s
data on the volatility of the Hang Seng stock market index are reanalyzed.
| 0 | 1 | 0 | 1 | 0 | 0 |
Exploiting Hierarchy in the Abstraction-Based Verification of Statecharts Using SMT Solvers | Statecharts are frequently used as a modeling formalism in the design of
state-based systems. Formal verification techniques are also often applied to
prove certain properties about the behavior of the system. One of the most
efficient techniques for formal verification is Counterexample-Guided
Abstraction Refinement (CEGAR), which reduces the complexity of systems by
automatically building and refining abstractions. In our paper we present a
novel adaptation of the CEGAR approach to hierarchical statechart models. First
we introduce an encoding of the statechart to logical formulas that preserves
information about the state hierarchy. Based on this encoding we propose
abstraction and refinement techniques that utilize the hierarchical structure
of statecharts and also handle variables in the model. The encoding allows us
to use SMT solvers for the systematic exploration and verification of the
abstract model, including also bounded model checking. We demonstrate the
applicability and efficiency of our abstraction techniques with measurements on
an industry-motivated example.
| 1 | 0 | 0 | 0 | 0 | 0 |
Modulus consensus in discrete-time signed networks and properties of special recurrent inequalities | Recently the dynamics of signed networks, where the ties among the agents can
be both positive (attractive) or negative (repulsive) have attracted
substantial attention of the research community. Examples of such networks are
models of opinion dynamics over signed graphs, recently introduced by Altafini
(2012,2013) and extended to discrete-time case by Meng et al. (2014). It has
been shown that under mild connectivity assumptions these protocols provide the
convergence of opinions in absolute value, whereas their signs may differ. This
"modulus consensus" may correspond to the polarization of the opinions (or
bipartite consensus, including the usual consensus as a special case), or their
convergence to zero. In this paper, we demonstrate that the phenomenon of
modulus consensus in the discrete-time Altafini model is a manifestation of a
more general and profound fact, regarding the solutions of a special recurrent
inequality. Although such a recurrent inequality does not provide the
uniqueness of a solution, it can be shown that, under some natural assumptions,
each of its bounded solutions has a limit and, moreover, converges to
consensus. A similar property has previously been established for special
continuous-time differential inequalities (Proskurnikov, Cao, 2016). Besides
analysis of signed networks, we link the consensus properties of recurrent
inequalities to the convergence analysis of distributed optimization algorithms
and the problems of Schur stability of substochastic matrices.
| 1 | 1 | 1 | 0 | 0 | 0 |
Mobility Transition at Grain Boundaries in Two-Step Sintered 8 mol% Yttria Stabilized Zirconia | Stagnation of grain growth is often attributed to impurity segregation.
Yttria-stabilized cubic zirconia does not evidence any segregation-induced
slowdown, as its grain growth obeys the parabolic law when the grain size
increases by more than one order of magnitude. However, lowering the
temperature below 1300 oC triggers an abrupt slowdown, constraining the average
grains to grow by less than 0.5 ${\mu}$m in 1000 h despite a relatively large
driving force imparted in the fine grains of ~0.5 ${\mu}$m. Yet isolated
pockets of abnormally large grains, along with pockets of abnormally small
grains, emerge in the same latter sample. Such microstructure bifurcation has
never been observed before, and can only be explained by an inhomogeneous
distribution of immobile four-grain junctions. The implications of these
findings for two-step sintering are discussed.
| 0 | 1 | 0 | 0 | 0 | 0 |
Sufficient conditions for the forcing theorem, and turning proper classes into sets | We present three natural combinatorial properties for class forcing notions,
which imply the forcing theorem to hold. We then show that all known sufficent
conditions for the forcing theorem (except for the forcing theorem itself),
including the three properties presented in this paper, imply yet another
regularity property for class forcing notions, namely that proper classes of
the ground model cannot become sets in a generic extension, that is they do not
have set-sized names in the ground model. We then show that over certain models
of Gödel-Bernays set theory without the power set axiom, there is a notion of
class forcing which turns a proper class into a set, however does not satisfy
the forcing theorem. Moreover, we show that the property of not turning proper
classes into sets can be used to characterize pretameness over such models of
Gödel-Bernays set theory.
| 0 | 0 | 1 | 0 | 0 | 0 |
Cauchy problem for effectively hyperbolic operators with triple characteristics | We study the Cauchy problem for effectively hyperbolic operators $P$ with
principal symbol $p(t, x,\tau,\xi)$ having triple characteristics on $t = 0$.
Under a condition (E) we show that such operators are strongly hyperbolic, that
is the Cauchy problem is well posed for $p(t, x,D_t, D_x) + Q(t, x, D_t, D_x)$
with arbitrary lower order term $Q$. The proof is based on energy estimates
with weight $t^{-N}$ for a first order pseudo-differential system, where $N$
depends on lower order terms. For our analysis we construct a non-negative
definite symmetrizer $S(t)$ and we prove a version of Fefferman-Phong type
inequality for ${\rm Re}\, (S(t)U, U)_{L^2({\mathbb R}^n)}$ with a lower bound
$-C t^{-1}\|\langle D \rangle^{-1}U\|_{L^2(\mathbb R^n)}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
\textit{Ab Initio} results for the Static Structure Factor of the Warm Dense Electron Gas | The uniform electron gas at finite temperature is of high current interest
for warm dense matter research. The complicated interplay of quantum degeneracy
and Coulomb coupling effects is fully contained in the pair distribution
function or, equivalently, the static strucutre factor. By combining exact
quantum Monte Carlo results for large wave vectors with the long-range behavior
from the Singwi-Tosi-Land-Sjölander approximation, we are able to obtain
highly accurate data for the static structure factor over the entire $k$-range.
This allows us to gauge the accuracy of previous approximations and discuss
their respective shortcomings. Further, our new data will serve as valuable
input for the computation of other quantities.
| 0 | 1 | 0 | 0 | 0 | 0 |
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