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Discrete CMC surfaces in R^3 and discrete minimal surfaces in S^3. A discrete Lawson correspondence | The main result of this paper is a discrete Lawson correspondence between
discrete CMC surfaces in R^3 and discrete minimal surfaces in S^3. This is a
correspondence between two discrete isothermic surfaces. We show that this
correspondence is an isometry in the following sense: it preserves the metric
coefficients introduced previously by Bobenko and Suris for isothermic nets.
Exactly as in the smooth case, this is a correspondence between nets with the
same Lax matrices, and the immersion formulas also coincide with the smooth
case.
| 0 | 1 | 1 | 0 | 0 | 0 |
Network Model Selection Using Task-Focused Minimum Description Length | Networks are fundamental models for data used in practically every
application domain. In most instances, several implicit or explicit choices
about the network definition impact the translation of underlying data to a
network representation, and the subsequent question(s) about the underlying
system being represented. Users of downstream network data may not even be
aware of these choices or their impacts. We propose a task-focused network
model selection methodology which addresses several key challenges. Our
approach constructs network models from underlying data and uses minimum
description length (MDL) criteria for selection. Our methodology measures
efficiency, a general and comparable measure of the network's performance of a
local (i.e. node-level) predictive task of interest. Selection on efficiency
favors parsimonious (e.g. sparse) models to avoid overfitting and can be
applied across arbitrary tasks and representations. We show stability,
sensitivity, and significance testing in our methodology.
| 1 | 0 | 0 | 0 | 0 | 0 |
Concentration of curvature and Lipschitz invariants of holomorphic functions of two variables | By combining analytic and geometric viewpoints on the concentration of the
curvature of the Milnor fibre, we prove that Lipschitz homeomorphisms preserve
the zones of multi-scale curvature concentration as well as the gradient canyon
structure of holomorphic functions of two variables. This yields the first new
Lipschitz invariants after those discovered by Henry and Parusinski in 2003.
| 0 | 0 | 1 | 0 | 0 | 0 |
Beam tuning and bunch length measurement in the bunch compression operation at the cERL | Realization of a short bunch beam by manipulating the longitudinal phase
space distribution with a finite longitudinal dispersion following an off-crest
accelera- tion is a widely used technique. The technique was applied in a
compact test accelerator of an energy-recovery linac scheme for compressing the
bunch length at the return loop. A diagnostic system utilizing coherent
transition radiation was developed for the beam tuning and for estimating the
bunch length. By scanning the beam parameters, we experimentally found the best
condition for the bunch compression. The RMS bunch length of 250+-50 fs was
obtained at a bunch charge of 2 pC. This result confirmed the design and the
tuning pro- cedure of the bunch compression operation for the future
energy-recovery linac (ERL).
| 0 | 1 | 0 | 0 | 0 | 0 |
Long-Lived Ultracold Molecules with Electric and Magnetic Dipole Moments | We create fermionic dipolar $^{23}$Na$^6$Li molecules in their triplet ground
state from an ultracold mixture of $^{23}$Na and $^6$Li. Using
magneto-association across a narrow Feshbach resonance followed by a two-photon
STIRAP transfer to the triplet ground state, we produce $3\,{\times}\,10^4$
ground state molecules in a spin-polarized state. We observe a lifetime of
$4.6\,\text{s}$ in an isolated molecular sample, approaching the $p$-wave
universal rate limit. Electron spin resonance spectroscopy of the triplet state
was used to determine the hyperfine structure of this previously unobserved
molecular state.
| 0 | 1 | 0 | 0 | 0 | 0 |
Bilinear generalized Radon transforms in the plane | Let $\sigma$ be arc-length measure on $S^1\subset \mathbb R^2$ and $\Theta$
denote rotation by an angle $\theta \in (0, \pi]$. Define a model bilinear
generalized Radon transform, $$B_{\theta}(f,g)(x)=\int_{S^1} f(x-y)g(x-\Theta
y)\, d\sigma(y),$$ an analogue of the linear generalized Radon transforms of
Guillemin and Sternberg \cite{GS} and Phong and Stein (e.g.,
\cite{PhSt91,St93}). Operators such as $B_\theta$ are motivated by problems in
geometric measure theory and combinatorics. For $\theta<\pi$, we show that
$B_{\theta}: L^p({\Bbb R}^2) \times L^q({\Bbb R}^2) \to L^r({\Bbb R}^2)$ if
$\left(\frac{1}{p},\frac{1}{q},\frac{1}{r}\right)\in Q$, the polyhedron with
the vertices $(0,0,0)$, $(\frac{2}{3}, \frac{2}{3}, 1)$, $(0, \frac{2}{3},
\frac{1}{3})$, $(\frac{2}{3},0,\frac{1}{3})$, $(1,0,1)$, $(0,1,1)$ and
$(\frac{1}{2},\frac{1}{2},\frac{1}{2})$, except for $\left(
\frac{1}{2},\frac{1}{2},\frac{1}{2} \right)$, where we obtain a restricted
strong type estimate. For the degenerate case $\theta=\pi$, a more restrictive
set of exponents holds. In the scale of normed spaces, $p,q,r \ge 1$, the type
set $Q$ is sharp. Estimates for the same exponents are also proved for a class
of bilinear generalized Radon transforms in $\mathbb R^2$ of the form $$
B(f,g)(x)=\int \int \delta(\phi_1(x,y)-t_1)\delta(\phi_2(x,z)-t_2)
\delta(\phi_3(y,z)-t_3) f(y)g(z) \psi(y,z) \, dy\, dz, $$ where $\delta$
denotes the Dirac distribution, $t_1,t_2,t_3\in\mathbb R$, $\psi$ is a smooth
cut-off and the defining functions $\phi_j$ satisfy some natural geometric
assumptions.
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Diffusion of new products with recovering consumers | We consider the diffusion of new products in the discrete Bass-SIR model, in
which consumers who adopt the product can later "recover" and stop influencing
their peers to adopt the product. To gain insight into the effect of the social
network structure on the diffusion, we focus on two extreme cases. In the
"most-connected" configuration where all consumers are inter-connected
(complete network), averaging over all consumers leads to an aggregate model,
which combines the Bass model for diffusion of new products with the SIR model
for epidemics. In the "least-connected" configuration where consumers are
arranged on a circle and each consumer can only be influenced by his left
neighbor (one-sided 1D network), averaging over all consumers leads to a
different aggregate model which is linear, and can be solved explicitly. We
conjecture that for any other network, the diffusion is bounded from below and
from above by that on a one-sided 1D network and on a complete network,
respectively. When consumers are arranged on a circle and each consumer can be
influenced by his left and right neighbors (two-sided 1D network), the
diffusion is strictly faster than on a one-sided 1D network. This is different
from the case of non-recovering adopters, where the diffusion on one-sided and
on two-sided 1D networks is identical. We also propose a nonlinear model for
recoveries, and show that consumers' heterogeneity has a negligible effect on
the aggregate diffusion.
| 1 | 1 | 0 | 0 | 0 | 0 |
Answering Complex Questions Using Open Information Extraction | While there has been substantial progress in factoid question-answering (QA),
answering complex questions remains challenging, typically requiring both a
large body of knowledge and inference techniques. Open Information Extraction
(Open IE) provides a way to generate semi-structured knowledge for QA, but to
date such knowledge has only been used to answer simple questions with
retrieval-based methods. We overcome this limitation by presenting a method for
reasoning with Open IE knowledge, allowing more complex questions to be
handled. Using a recently proposed support graph optimization framework for QA,
we develop a new inference model for Open IE, in particular one that can work
effectively with multiple short facts, noise, and the relational structure of
tuples. Our model significantly outperforms a state-of-the-art structured
solver on complex questions of varying difficulty, while also removing the
reliance on manually curated knowledge.
| 1 | 0 | 0 | 0 | 0 | 0 |
On Testing Quantum Programs | A quantum computer (QC) can solve many computational problems more
efficiently than a classic one. The field of QCs is growing: companies (such as
DWave, IBM, Google, and Microsoft) are building QC offerings. We position that
software engineers should look into defining a set of software engineering
practices that apply to QC's software. To start this process, we give examples
of challenges associated with testing such software and sketch potential
solutions to some of these challenges.
| 1 | 0 | 0 | 0 | 0 | 0 |
Degree weighted recurrence networks for the analysis of time series data | Recurrence networks are powerful tools used effectively in the nonlinear
analysis of time series data. The analysis in this context is done mostly with
unweighted and undirected complex networks constructed with specific criteria
from the time series. In this work, we propose a novel method to construct
"weighted recurrence network"(WRN) from a time series and show how it can
reveal useful information regarding the structure of a chaotic attractor, which
the usual unweighted recurrence network cannot provide. Especially, we find the
node strength distribution of the WRN, from every chaotic attractor follows a
power law (with exponential tail) with the index characteristic to the fractal
structure of the attractor. This leads to a new class among complex networks,
to which networks from all standard chaotic attractors are found to belong. In
addition, we present generalized definitions for clustering coefficient and
characteristic path length and show that these measures can effectively
discriminate chaotic dynamics from white noise and $1/f$ colored noise. Our
results indicate that the WRN and the associated measures can become
potentially important tools for the analysis of short and noisy time series
from the real world systems as they are clearly demarked from that of noisy or
stochastic systems.
| 0 | 1 | 0 | 0 | 0 | 0 |
Parcels v0.9: prototyping a Lagrangian Ocean Analysis framework for the petascale age | As Ocean General Circulation Models (OGCMs) move into the petascale age,
where the output from global high-resolution model runs can be of the order of
hundreds of terabytes in size, tools to analyse the output of these models will
need to scale up too. Lagrangian Ocean Analysis, where virtual particles are
tracked through hydrodynamic fields, is an increasingly popular way to analyse
OGCM output, by mapping pathways and connectivity of biotic and abiotic
particulates. However, the current software stack of Lagrangian Ocean Analysis
codes is not dynamic enough to cope with the increasing complexity, scale and
need for customisation of use-cases. Furthermore, most community codes are
developed for stand-alone use, making it a nontrivial task to integrate virtual
particles at runtime of the OGCM. Here, we introduce the new Parcels code,
which was designed from the ground up to be sufficiently scalable to cope with
petascale computing. We highlight its API design that combines flexibility and
customisation with the ability to optimise for HPC workflows, following the
paradigm of domain-specific languages. Parcels is primarily written in Python,
utilising the wide range of tools available in the scientific Python ecosystem,
while generating low-level C-code and using Just-In-Time compilation for
performance-critical computation. We show a worked-out example of its API, and
validate the accuracy of the code against seven idealised test cases. This
version~0.9 of Parcels is focussed on laying out the API, with future work
concentrating on optimisation, efficiency and at-runtime coupling with OGCMs.
| 1 | 1 | 0 | 0 | 0 | 0 |
TFLMS: Large Model Support in TensorFlow by Graph Rewriting | While accelerators such as GPUs have limited memory, deep neural networks are
becoming larger and will not fit with the memory limitation of accelerators for
training. We propose an approach to tackle this problem by rewriting the
computational graph of a neural network, in which swap-out and swap-in
operations are inserted to temporarily store intermediate results on CPU
memory. In particular, we first revise the concept of a computational graph by
defining a concrete semantics for variables in a graph. We then formally show
how to derive swap-out and swap-in operations from an existing graph and
present rules to optimize the graph. To realize our approach, we developed a
module in TensorFlow, named TFLMS. TFLMS is published as a pull request in the
TensorFlow repository for contributing to the TensorFlow community. With TFLMS,
we were able to train ResNet-50 and 3DUnet with 4.7x and 2x larger batch size,
respectively. In particular, we were able to train 3DUNet using images of size
of $192^3$ for image segmentation, which, without TFLMS, had been done only by
dividing the images to smaller images, which affects the accuracy.
| 0 | 0 | 0 | 1 | 0 | 0 |
Machine Learning for Quantum Dynamics: Deep Learning of Excitation Energy Transfer Properties | Understanding the relationship between the structure of light-harvesting
systems and their excitation energy transfer properties is of fundamental
importance in many applications including the development of next generation
photovoltaics. Natural light harvesting in photosynthesis shows remarkable
excitation energy transfer properties, which suggests that pigment-protein
complexes could serve as blueprints for the design of nature inspired devices.
Mechanistic insights into energy transport dynamics can be gained by leveraging
numerically involved propagation schemes such as the hierarchical equations of
motion (HEOM). Solving these equations, however, is computationally costly due
to the adverse scaling with the number of pigments. Therefore virtual
high-throughput screening, which has become a powerful tool in material
discovery, is less readily applicable for the search of novel excitonic
devices. We propose the use of artificial neural networks to bypass the
computational limitations of established techniques for exploring the
structure-dynamics relation in excitonic systems. Once trained, our neural
networks reduce computational costs by several orders of magnitudes. Our
predicted transfer times and transfer efficiencies exhibit similar or even
higher accuracies than frequently used approximate methods such as secular
Redfield theory
| 0 | 1 | 0 | 1 | 0 | 0 |
Multi-Player Bandits: A Trekking Approach | We study stochastic multi-armed bandits with many players. The players do not
know the number of players, cannot communicate with each other and if multiple
players select a common arm they collide and none of them receive any reward.
We consider the static scenario, where the number of players remains fixed, and
the dynamic scenario, where the players enter and leave at any time. We provide
algorithms based on a novel `trekking approach' that guarantees constant regret
for the static case and sub-linear regret for the dynamic case with high
probability. The trekking approach eliminates the need to estimate the number
of players resulting in fewer collisions and improved regret performance
compared to the state-of-the-art algorithms. We also develop an epoch-less
algorithm that eliminates any requirement of time synchronization across the
players provided each player can detect the presence of other players on an
arm. We validate our theoretical guarantees using simulation based and real
test-bed based experiments.
| 0 | 0 | 0 | 1 | 0 | 0 |
High-resolution investigation of spinal cord and spine | High-resolution non-invasive 3D study of intact spine and spinal cord
morphology on the level of complex vascular and neuronal organization is a
crucial issue for the development of treatments for the injuries and
pathologies of central nervous system (CNS). X-ray phase contrast tomography
enables high quality 3D visualization in ex-vivo mouse model of both vascular
and neuronal network of the soft spinal cord tissue at the scale from
millimeters to hundreds of nanometers without any contrast agents and
sectioning. Until now, 3D high resolution visualization of spinal cord mostly
has been limited by imaging of organ extracted from vertebral column because
high absorbing boney tissue drastically reduces the morphological details of
soft tissue in image. However, the extremely destructive procedure of bones
removal leads to sample deterioration and, therefore, to the lack of
considerable part of information about the object. In this work we present the
data analysis procedure to get high resolution and high contrast 3D images of
intact mice spinal cord surrounded by vertebras, preserving all richness of
micro-details of the spinal cord inhabiting inside. Our results are the first
step forward to the difficult way toward the high- resolution investigation of
in-vivo model central nervous system.
| 0 | 1 | 0 | 0 | 0 | 0 |
Bohr--Rogosinski radius for analytic functions | There are a number of articles which deal with Bohr's phenomenon whereas only
a few papers appeared in the literature on Rogosinski's radii for analytic
functions defined on the unit disk $|z|<1$. In this article, we introduce and
investigate Bohr-Rogosinski's radii for analytic functions defined for $|z|<1$.
Also, we prove several different improved versions of the classical Bohr's
inequality. Finally, we also discuss the Bohr-Rogosinski's radius for a class
of subordinations. All the results are proved to be sharp.
| 0 | 0 | 1 | 0 | 0 | 0 |
Methods to locate Saddle Points in Complex Landscapes | We present a class of simple algorithms that allows to find the reaction path
in systems with a complex potential energy landscape. The approach does not
need any knowledge on the product state and does not require the calculation of
any second derivatives. The underlying idea is to use two nearby points in
configuration space to locate the path of slowest ascent. By introducing a weak
noise term, the algorithm is able to find even low-lying saddle points that are
not reachable by means of a slowest ascent path. Since the algorithm makes only
use of the value of the potential and its gradient, the computational effort to
find saddles is linear in the number of degrees of freedom, if the potential is
short-ranged. We test the performance of the algorithm for two potential energy
landscapes. For the Müller-Brown surface we find that the algorithm always
finds the correct saddle point. For the modified Müller-Brown surface, which
has a saddle point that is not reachable by means of a slowest ascent path, the
algorithm is still able to find this saddle point with high probability.
| 0 | 1 | 0 | 0 | 0 | 0 |
On the Complexity of Opinions and Online Discussions | In an increasingly polarized world, demagogues who reduce complexity down to
simple arguments based on emotion are gaining in popularity. Are opinions and
online discussions falling into demagoguery? In this work, we aim to provide
computational tools to investigate this question and, by doing so, explore the
nature and complexity of online discussions and their space of opinions,
uncovering where each participant lies.
More specifically, we present a modeling framework to construct latent
representations of opinions in online discussions which are consistent with
human judgements, as measured by online voting. If two opinions are close in
the resulting latent space of opinions, it is because humans think they are
similar. Our modeling framework is theoretically grounded and establishes a
surprising connection between opinions and voting models and the sign-rank of a
matrix. Moreover, it also provides a set of practical algorithms to both
estimate the dimension of the latent space of opinions and infer where opinions
expressed by the participants of an online discussion lie in this space.
Experiments on a large dataset from Yahoo! News, Yahoo! Finance, Yahoo! Sports,
and the Newsroom app suggest that unidimensional opinion models may often be
unable to accurately represent online discussions, provide insights into human
judgements and opinions, and show that our framework is able to circumvent
language nuances such as sarcasm or humor by relying on human judgements
instead of textual analysis.
| 1 | 0 | 0 | 1 | 0 | 0 |
Free energy distribution of the stationary O'Connell-Yor directed random polymer model | We study the semi-discrete directed polymer model introduced by O'Connell-Yor
in its stationary regime, based on our previous work on the stationary
$q$-totally asymmetric simple exclusion process ($q$-TASEP) using a two-sided
$q$-Whittaker process. We give a formula for the free energy distribution of
the polymer model in terms of Fredholm determinant and show that the universal
KPZ stationary distribution appears in the long time limit. We also consider
the limit to the stationary KPZ equation and discuss the connections with
previously found formulas.
| 0 | 1 | 1 | 0 | 0 | 0 |
Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13) | In this paper, we introduce the use of a personalized Gaussian Process model
(pGP) to predict per-patient changes in ADAS-Cog13 -- a significant predictor
of Alzheimer's Disease (AD) in the cognitive domain -- using data from each
patient's previous visits, and testing on future (held-out) data. We start by
learning a population-level model using multi-modal data from previously seen
patients using a base Gaussian Process (GP) regression. The personalized GP
(pGP) is formed by adapting the base GP sequentially over time to a new
(target) patient using domain adaptive GPs. We extend this personalized
approach to predict the values of ADAS-Cog13 over the future 6, 12, 18, and 24
months. We compare this approach to a GP model trained only on past data of the
target patients (tGP), as well as to a new approach that combines pGP with tGP.
We find that the new approach, combining pGP with tGP, leads to large
improvements in accurately forecasting future ADAS-Cog13 scores.
| 0 | 0 | 0 | 1 | 0 | 0 |
Analysis and Control of a Non-Standard Hyperbolic PDE Traffic Flow Model | The paper provides results for a non-standard, hyperbolic, 1-D, nonlinear
traffic flow model on a bounded domain. The model consists of two first-order
PDEs with a dynamic boundary condition that involves the time derivative of the
velocity. The proposed model has features that are important from a
traffic-theoretic point of view: is completely anisotropic and information
travels forward exactly at the same speed as traffic. It is shown that, for all
physically meaningful initial conditions, the model admits a globally defined,
unique, classical solution that remains positive and bounded for all times.
Moreover, it is shown that global stabilization can be achieved for arbitrary
equilibria by means of an explicit boundary feedback law. The stabilizing
feedback law depends only on the inlet velocity and consequently, the
measurement requirements for the implementation of the proposed boundary
feedback law are minimal. The efficiency of the proposed boundary feedback law
is demonstrated by means of a numerical example.
| 1 | 0 | 1 | 0 | 0 | 0 |
Learning Robust Representations for Computer Vision | Unsupervised learning techniques in computer vision often require learning
latent representations, such as low-dimensional linear and non-linear
subspaces. Noise and outliers in the data can frustrate these approaches by
obscuring the latent spaces.
Our main goal is deeper understanding and new development of robust
approaches for representation learning. We provide a new interpretation for
existing robust approaches and present two specific contributions: a new robust
PCA approach, which can separate foreground features from dynamic background,
and a novel robust spectral clustering method, that can cluster facial images
with high accuracy. Both contributions show superior performance to standard
methods on real-world test sets.
| 1 | 0 | 0 | 1 | 0 | 0 |
Detection Estimation and Grid matching of Multiple Targets with Single Snapshot Measurements | In this work, we explore the problems of detecting the number of narrow-band,
far-field targets and estimating their corresponding directions from single
snapshot measurements. The principles of sparse signal recovery (SSR) are used
for the single snapshot detection and estimation of multiple targets. In the
SSR framework, the DoA estimation problem is grid based and can be posed as the
lasso optimization problem. However, the SSR framework for DoA estimation gives
rise to the grid mismatch problem, when the unknown targets (sources) are not
matched with the estimation grid chosen for the construction of the array
steering matrix at the receiver. The block sparse recovery framework is known
to mitigate the grid mismatch problem by jointly estimating the targets and
their corresponding offsets from the estimation grid using the group lasso
estimator. The corresponding detection problem reduces to estimating the
optimal regularization parameter ($\tau$) of the lasso (in case of perfect
grid-matching) or group-lasso estimation problem for achieving the required
probability of correct detection ($P_c$). We propose asymptotic and finite
sample test statistics for detecting the number of sources with the required
$P_c$ at moderate to high signal to noise ratios. Once the number of sources
are detected, or equivalently the optimal $\hat{\tau}$ is estimated, the
corresponding estimation and grid matching of the DoAs can be performed by
solving the lasso or group-lasso problem at $\hat{\tau}$
| 0 | 0 | 0 | 1 | 0 | 0 |
Small-Variance Asymptotics for Nonparametric Bayesian Overlapping Stochastic Blockmodels | The latent feature relational model (LFRM) is a generative model for
graph-structured data to learn a binary vector representation for each node in
the graph. The binary vector denotes the node's membership in one or more
communities. At its core, the LFRM miller2009nonparametric is an overlapping
stochastic blockmodel, which defines the link probability between any pair of
nodes as a bilinear function of their community membership vectors. Moreover,
using a nonparametric Bayesian prior (Indian Buffet Process) enables learning
the number of communities automatically from the data. However, despite its
appealing properties, inference in LFRM remains a challenge and is typically
done via MCMC methods. This can be slow and may take a long time to converge.
In this work, we develop a small-variance asymptotics based framework for the
non-parametric Bayesian LFRM. This leads to an objective function that retains
the nonparametric Bayesian flavor of LFRM, while enabling us to design
deterministic inference algorithms for this model, that are easy to implement
(using generic or specialized optimization routines) and are fast in practice.
Our results on several benchmark datasets demonstrate that our algorithm is
competitive to methods such as MCMC, while being much faster.
| 0 | 0 | 0 | 1 | 0 | 0 |
Towards a Science of Mind | The ancient mind/body problem continues to be one of deepest mysteries of
science and of the human spirit. Despite major advances in many fields, there
is still no plausible link between subjective experience (qualia) and its
realization in the body. This paper outlines some of the elements of a rigorous
science of mind (SoM) - key ideas include scientific realism of mind, agnostic
mysterianism, careful attention to language, and a focus on concrete
(touchstone) questions and results.
| 1 | 0 | 0 | 0 | 1 | 0 |
Resistance distance criterion for optimal slack bus selection | We investigate the dependence of transmission losses on the choice of a slack
bus in high voltage AC transmission networks. We formulate a transmission loss
minimization problem in terms of slack variables representing the additional
power injection that each generator provides to compensate the transmission
losses. We show analytically that for transmission lines having small,
homogeneous resistance over reactance ratios ${r/x\ll1}$, transmission losses
are generically minimal in the case of a unique \textit{slack bus} instead of a
distributed slack bus. For the unique slack bus scenario, to lowest order in
${r/x}$, transmission losses depend linearly on a resistance distance based
indicator measuring the separation of the slack bus candidate from the rest of
the network. We confirm these results numerically for several IEEE and Pegase
testcases, and show that our predictions qualitatively hold also in the case of
lines having inhomogeneous ${r/x}$ ratios, with optimal slack bus choices
reducing transmission losses by ${10}\%$ typically.
| 1 | 1 | 0 | 0 | 0 | 0 |
Interpolation in the Presence of Domain Inhomogeneity | Standard interpolation techniques are implicitly based on the assumption that
the signal lies on a homogeneous domain. In this letter, the proposed
interpolation method instead exploits prior information about domain
inhomogeneity, characterized by different, potentially overlapping, subdomains.
By introducing a domain-similarity metric for each sample, the interpolation
process is then based on a domain-informed consistency principle. We illustrate
and demonstrate the feasibility of domain-informed linear interpolation in 1D,
and also, on a real fMRI image in 2D. The results show the benefit of
incorporating domain knowledge so that, for example, sharp domain boundaries
can be recovered by the interpolation, if such information is available.
| 0 | 0 | 1 | 0 | 0 | 0 |
Lectures on the mean values of functionals -- An elementary introduction to infinite-dimensional probability | This is an elementary introduction to infinite-dimensional probability. In
the lectures, we compute the exact mean values of some functionals on C[0,1]
and L[0,1] by considering these functionals as infinite-dimensional random
variables. The results show that there exist the complete concentration of
measure phenomenon for these mean values since the variances are all zeroes.
| 0 | 1 | 1 | 1 | 0 | 0 |
Synthesis of Optimal Resilient Control Strategies | Repair mechanisms are important within resilient systems to maintain the
system in an operational state after an error occurred. Usually, constraints on
the repair mechanisms are imposed, e.g., concerning the time or resources
required (such as energy consumption or other kinds of costs). For systems
modeled by Markov decision processes (MDPs), we introduce the concept of
resilient schedulers, which represent control strategies guaranteeing that
these constraints are always met within some given probability. Assigning
rewards to the operational states of the system, we then aim towards resilient
schedulers which maximize the long-run average reward, i.e., the expected mean
payoff. We present a pseudo-polynomial algorithm that decides whether a
resilient scheduler exists and if so, yields an optimal resilient scheduler. We
show also that already the decision problem asking whether there exists a
resilient scheduler is PSPACE-hard.
| 1 | 0 | 0 | 0 | 0 | 0 |
Limits of the Kucera-Gacs coding method | Every real is computable from a Martin-Loef random real. This well known
result in algorithmic randomness was proved by Kucera and Gacs. In this survey
article we discuss various approaches to the problem of coding an arbitrary
real into a Martin-Loef random real,and also describe new results concerning
optimal methods of coding. We start with a simple presentation of the original
methods of Kucera and Gacs and then rigorously demonstrate their limitations in
terms of the size of the redundancy in the codes that they produce. Armed with
a deeper understanding of these methods, we then proceed to motivate and
illustrate aspects of the new coding method that was recently introduced by
Barmpalias and Lewis-Pye and which achieves optimal logarithmic redundancy, an
exponential improvement over the original redundancy bounds.
| 0 | 0 | 1 | 0 | 0 | 0 |
Subspace Tracking Algorithms for Millimeter Wave MIMO Channel Estimation with Hybrid Beamforming | This paper proposes the use of subspace tracking algorithms for performing
MIMO channel estimation at millimeter wave (mmWave) frequencies. Using a
subspace approach, we develop a protocol enabling the estimation of the right
(resp. left) singular vectors at the transmitter (resp. receiver) side; then,
we adapt the projection approximation subspace tracking with deflation (PASTd)
and the orthogonal Oja (OOJA) algorithms to our framework and obtain two
channel estimation algorithms. The hybrid analog/digital nature of the
beamformer is also explicitly taken into account at the algorithm design stage.
Numerical results show that the proposed estimation algorithms are effective,
and that they perform better than two relevant competing alternatives available
in the open literature.
| 1 | 0 | 0 | 0 | 0 | 0 |
Thomas Precession for Dressed Particles | We consider a particle dressed with boundary gravitons in three-dimensional
Minkowski space. The existence of BMS transformations implies that the
particle's wavefunction picks up a Berry phase when subjected to changes of
reference frames that trace a closed path in the asymptotic symmetry group. We
evaluate this phase and show that, for BMS superrotations, it provides a
gravitational generalization of Thomas precession. In principle, such phases
are observable signatures of asymptotic symmetries.
| 0 | 1 | 1 | 0 | 0 | 0 |
Exponential random graphs behave like mixtures of stochastic block models | We study the behavior of exponential random graphs in both the sparse and the
dense regime. We show that exponential random graphs are approximate mixtures
of graphs with independent edges whose probability matrices are critical points
of an associated functional, thereby satisfying a certain matrix equation. In
the dense regime, every solution to this equation is close to a block matrix,
concluding that the exponential random graph behaves roughly like a mixture of
stochastic block models. We also show existence and uniqueness of solutions to
this equation for several families of exponential random graphs, including the
case where the subgraphs are counted with positive weights and the case where
all weights are small in absolute value. In particular, this generalizes some
of the results in a paper by Chatterjee and Diaconis from the dense regime to
the sparse regime and strengthens their bounds from the cut-metric to the
one-metric.
| 1 | 0 | 1 | 1 | 0 | 0 |
Certificate Enhanced Data-Flow Analysis | Proof-carrying-code was proposed as a solution to ensure a trust relationship
between two parties: a (heavyweight) analyzer and a (lightweight) checker. The
analyzer verifies the conformance of a given application to a specified
property and generates a certificate attesting the validity of the analysis
result. It suffices then for the checker just to test the consistency of the
proof instead of constructing it. We set out to study the applicability of this
technique in the context of data- flow analysis. In particular, we want to know
if there is a significant performance difference between the analyzer and the
checker. Therefore, we developed a tool, called DCert, implementing an
inter-procedural context and flow-sensitive data-flow analyzer and checker for
Android. Applying our tool to real-world large applications, we found out that
checking can be up to 8 times faster than verification. This important gain in
time suggests a potential for equipping applications on app stores with
certificates that can be checked on mobile devices which are limited in
computation and storage resources. We describe our implementation and report on
experimental results.
| 1 | 0 | 0 | 0 | 0 | 0 |
Small cells in a Poisson hyperplane tessellation | Until now, little was known about properties of small cells in a Poisson
hyperplane tessellation. The few existing results were either heuristic or
applying only to the two dimensional case and for very specific size
functionals and directional distributions. This paper fills this gap by
providing a systematic study of small cells in a Poisson hyperplane
tessellation of arbitrary dimension, arbitrary directional distribution
$\varphi$ and with respect to an arbitrary size functional $\Sigma$. More
precisely, we investigate the distribution of the typical cell $Z$, conditioned
on the event $\{\Sigma(Z)<a\}$, where $a\to0$ and $\Sigma$ is a size
functional, i.e. a functional on the set of convex bodies which is continuous,
not identically zero, homogeneous of degree $k>0$, and increasing with respect
to set inclusion. We focus on the number of facets and the shape of such small
cells. We show in various general settings that small cells tend to minimize
the number of facets and that they have a non degenerated limit shape
distribution which depends on the size $\Sigma$ and the directional
distribution. We also exhibit a class of directional distribution for which
cells with small inradius do not tend to minimize the number of facets.
| 0 | 0 | 1 | 0 | 0 | 0 |
Privacy-Aware Guessing Efficiency | We investigate the problem of guessing a discrete random variable $Y$ under a
privacy constraint dictated by another correlated discrete random variable $X$,
where both guessing efficiency and privacy are assessed in terms of the
probability of correct guessing. We define $h(P_{XY}, \epsilon)$ as the maximum
probability of correctly guessing $Y$ given an auxiliary random variable $Z$,
where the maximization is taken over all $P_{Z|Y}$ ensuring that the
probability of correctly guessing $X$ given $Z$ does not exceed $\epsilon$. We
show that the map $\epsilon\mapsto h(P_{XY}, \epsilon)$ is strictly increasing,
concave, and piecewise linear, which allows us to derive a closed form
expression for $h(P_{XY}, \epsilon)$ when $X$ and $Y$ are connected via a
binary-input binary-output channel. For $(X^n, Y^n)$ being pairs of independent
and identically distributed binary random vectors, we similarly define
$\underline{h}_n(P_{X^nY^n}, \epsilon)$ under the assumption that $Z^n$ is also
a binary vector. Then we obtain a closed form expression for
$\underline{h}_n(P_{X^nY^n}, \epsilon)$ for sufficiently large, but nontrivial
values of $\epsilon$.
| 1 | 0 | 1 | 1 | 0 | 0 |
Positioning services of a travel agency in social networks | In this paper the methods of forming a travel company customer base by means
of social networks are observed. These methods are made to involve web-users of
the social networks (VK.com and Facebook) for positioning of the service of the
travel agency "New Europe" on the Internet. The methods of applying the
maintenance activities and interests of web-users are also used. So, the main
method of information exchanging in modern network society is on-line social
networks. The rapid development and improvement of such information and
communication technologies is a key factor in the positioning of the travel
agency brand in the global information space. The absence of time and space
restrictions and the speed of spreading of the information among an aim
audience of social networks create all the conditions for effective
popularization of the travel agency "New Europe" and its service in the
Internet.
| 1 | 0 | 0 | 0 | 0 | 0 |
Inverse Mapping for Rainfall-Runoff Models using History Matching Approach | In this paper, we consider two rainfall-runoff computer models. The first
model is Matlab-Simulink model which simulates runoff from windrow compost pad
(located at the Bioconversion Center in Athens, GA) over a period of time based
on rainfall events. The second model is Soil Water Assessment Tool (SWAT) which
estimates surface runoff in the Middle Oconee River in Athens, GA. The input
parameter spaces of both models are sensitive and high dimensional, the model
output for every input combination is a time-series of runoff, and these two
computer models generate a wide spectrum of outputs including some that are far
from reality. In order to improve the prediction accuracy, in this paper we
propose to apply a history matching approach for calibrating these hydrological
models, which also gives better insights for improved management of these
systems.
| 0 | 0 | 0 | 1 | 0 | 0 |
Geometric SMOTE: Effective oversampling for imbalanced learning through a geometric extension of SMOTE | Classification of imbalanced datasets is a challenging task for standard
algorithms. Although many methods exist to address this problem in different
ways, generating artificial data for the minority class is a more general
approach compared to algorithmic modifications. SMOTE algorithm and its
variations generate synthetic samples along a line segment that joins minority
class instances. In this paper we propose Geometric SMOTE (G-SMOTE) as a
generalization of the SMOTE data generation mechanism. G-SMOTE generates
synthetic samples in a geometric region of the input space, around each
selected minority instance. While in the basic configuration this region is a
hyper-sphere, G-SMOTE allows its deformation to a hyper-spheroid and finally to
a line segment, emulating, in the last case, the SMOTE mechanism. The
performance of G-SMOTE is compared against multiple standard oversampling
algorithms. We present empirical results that show a significant improvement in
the quality of the generated data when G-SMOTE is used as an oversampling
algorithm.
| 1 | 0 | 0 | 0 | 0 | 0 |
Liveness Verification and Synthesis: New Algorithms for Recursive Programs | We consider the problems of liveness verification and liveness synthesis for
recursive programs. The liveness verification problem (LVP) is to decide
whether a given omega-context-free language is contained in a given
omega-regular language. The liveness synthesis problem (LSP) is to compute a
strategy so that a given omega-context-free game, when played along the
strategy, is guaranteed to derive a word in a given omega-regular language. The
problems are known to be EXPTIME-complete and EXPTIME-complete, respectively.
Our contributions are new algorithms with optimal time complexity. For LVP, we
generalize recent lasso-finding algorithms (also known as Ramsey-based
algorithms) from finite to recursive programs. For LSP, we generalize a recent
summary-based algorithm from finite to infinite words. Lasso finding and
summaries have proven to be efficient in a number of implementations for the
finite state and finite word setting.
| 1 | 0 | 0 | 0 | 0 | 0 |
A semiparametric approach for bivariate extreme exceedances | Inference over tails is performed by applying only the results of extreme
value theory. Whilst such theory is well defined and flexible enough in the
univariate case, multivariate inferential methods often require the imposition
of arbitrary constraints not fully justifed by the underlying theory. In
contrast, our approach uses only the constraints imposed by theory. We build on
previous, theoretically justified work for marginal exceedances over a high,
unknown threshold, by combining it with flexible, semiparametric copulae
specifications to investigate extreme dependence. Whilst giving probabilistic
judgements about the extreme regime of all marginal variables, our approach
formally uses the full dataset and allows for a variety of patterns of
dependence, be them extremal or not. A new probabilistic criterion quantifying
the possibility that the data exhibits asymptotic independence is introduced
and its robustness empirically studied. Estimation of functions of interest in
extreme value analyses is performed via MCMC algorithms. Attention is also
devoted to the prediction of new extreme observations. Our approach is
evaluated through a series of simulations, applied to real data sets and
assessed against competing approaches. Evidence demonstrates that the bulk of
the data does not bias and improves the inferential process for the extremal
dependence.
| 0 | 0 | 0 | 1 | 0 | 0 |
Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network | The Soil Moisture Active Passive (SMAP) mission has delivered valuable
sensing of surface soil moisture since 2015. However, it has a short time span
and irregular revisit schedule. Utilizing a state-of-the-art time-series deep
learning neural network, Long Short-Term Memory (LSTM), we created a system
that predicts SMAP level-3 soil moisture data with atmospheric forcing,
model-simulated moisture, and static physiographic attributes as inputs. The
system removes most of the bias with model simulations and improves predicted
moisture climatology, achieving small test root-mean-squared error (<0.035) and
high correlation coefficient >0.87 for over 75\% of Continental United States,
including the forested Southeast. As the first application of LSTM in
hydrology, we show the proposed network avoids overfitting and is robust for
both temporal and spatial extrapolation tests. LSTM generalizes well across
regions with distinct climates and physiography. With high fidelity to SMAP,
LSTM shows great potential for hindcasting, data assimilation, and weather
forecasting.
| 0 | 0 | 0 | 1 | 0 | 0 |
Note on Green Function Formalism and Topological Invariants | It has been discovered previously that the topological order parameter could
be identified from the topological data of the Green function, namely the
(generalized) TKNN invariant in general dimensions, for both non-interacting
and interacting systems. In this note, we show that this phenomena has a clear
geometric derivation. This proposal could be regarded as an alternative proof
for the identification of the corresponding topological invariant and
topological order parameter.
| 0 | 0 | 1 | 0 | 0 | 0 |
Unsupervised Document Embedding With CNNs | We propose a new model for unsupervised document embedding. Leading existing
approaches either require complex inference or use recurrent neural networks
(RNN) that are difficult to parallelize. We take a different route and develop
a convolutional neural network (CNN) embedding model. Our CNN architecture is
fully parallelizable resulting in over 10x speedup in inference time over RNN
models. Parallelizable architecture enables to train deeper models where each
successive layer has increasingly larger receptive field and models longer
range semantic structure within the document. We additionally propose a fully
unsupervised learning algorithm to train this model based on stochastic forward
prediction. Empirical results on two public benchmarks show that our approach
produces comparable to state-of-the-art accuracy at a fraction of computational
cost.
| 1 | 0 | 0 | 1 | 0 | 0 |
A metric model for the functional architecture of the visual cortex | The purpose of this work is to construct a model for the functional
architecture of the primary visual cortex (V1), based on a structure of metric
measure space induced by the underlying organization of receptive profiles
(RPs) of visual cells. In order to account for the horizontal connectivity of
V1 in such a context, a diffusion process compatible with the geometry of the
space is defined following the classical approach of K.-T. Sturm. The
construction of our distance function does neither require any group
parameterization of the family of RPs, nor involve any differential structure.
As such, it adapts to non-parameterized sets of RPs, possibly obtained through
numerical procedures; it also allows to model the lateral connectivity arising
from non-differential metrics such as the one induced on a pinwheel surface by
a family of filters of vanishing scale. On the other hand, when applied to the
classical framework of Gabor filters, this construction yields a distance
approximating the sub-Riemannian structure proposed as a model for V1 by G.
Citti and A. Sarti [J Math Imaging Vis 24: 307 (2006)], thus showing itself to
be consistent with existing cortex models.
| 0 | 0 | 0 | 0 | 1 | 0 |
Bayesian parameter identification in Cahn-Hilliard models for biological growth | We consider the inverse problem of parameter estimation in a diffuse
interface model for tumour growth. The model consists of a fourth-order
Cahn--Hilliard system and contains three phenomenological parameters: the
tumour proliferation rate, the nutrient consumption rate, and the chemotactic
sensitivity. We study the inverse problem within the Bayesian framework and
construct the likelihood and noise for two typical observation settings. One
setting involves an infinite-dimensional data space where we observe the full
tumour. In the second setting we observe only the tumour volume, hence the data
space is finite-dimensional. We show the well-posedness of the posterior
measure for both settings, building upon and improving the analytical results
in [C. Kahle and K.F. Lam, Appl. Math. Optim. (2018)]. A numerical example
involving synthetic data is presented in which the posterior measure is
numerically approximated by the Sequential Monte Carlo approach with tempering.
| 0 | 0 | 0 | 1 | 0 | 0 |
Transport Phase Diagram and Anderson Localization in Hyperuniform Disordered Photonic Materials | Hyperuniform disordered photonic materials (HDPM) are spatially correlated
dielectric structures with unconventional optical properties. They can be
transparent to long-wavelength radiation while at the same time have isotropic
band gaps in another frequency range. This phenomenon raises fundamental
questions concerning photon transport through disordered media. While optical
transparency is robust against recurrent multiple scattering, little is known
about other transport regimes like diffusive multiple scattering or Anderson
localization. Here we investigate band gaps, and we report Anderson
localization in two-dimensional stealthy HDPM using numerical simulations of
the density of states and optical transport statistics. To establish a unified
view, we propose a transport phase diagram. Our results show that, depending
only on the degree of correlation, a dielectric material can transition from
localization behavior to a bandgap crossing an intermediate regime dominated by
tunneling between weakly coupled states.
| 0 | 1 | 0 | 0 | 0 | 0 |
Nearly Maximally Predictive Features and Their Dimensions | Scientific explanation often requires inferring maximally predictive features
from a given data set. Unfortunately, the collection of minimal maximally
predictive features for most stochastic processes is uncountably infinite. In
such cases, one compromises and instead seeks nearly maximally predictive
features. Here, we derive upper-bounds on the rates at which the number and the
coding cost of nearly maximally predictive features scales with desired
predictive power. The rates are determined by the fractal dimensions of a
process' mixed-state distribution. These results, in turn, show how widely-used
finite-order Markov models can fail as predictors and that mixed-state
predictive features offer a substantial improvement.
| 1 | 1 | 0 | 1 | 0 | 0 |
A Survey Of Cross-lingual Word Embedding Models | Cross-lingual representations of words enable us to reason about word meaning
in multilingual contexts and are a key facilitator of cross-lingual transfer
when developing natural language processing models for low-resource languages.
In this survey, we provide a comprehensive typology of cross-lingual word
embedding models. We compare their data requirements and objective functions.
The recurring theme of the survey is that many of the models presented in the
literature optimize for the same objectives, and that seemingly different
models are often equivalent modulo optimization strategies, hyper-parameters,
and such. We also discuss the different ways cross-lingual word embeddings are
evaluated, as well as future challenges and research horizons.
| 1 | 0 | 0 | 0 | 0 | 0 |
Space-efficient classical and quantum algorithms for the shortest vector problem | A lattice is the integer span of some linearly independent vectors. Lattice
problems have many significant applications in coding theory and cryptographic
systems for their conjectured hardness. The Shortest Vector Problem (SVP),
which is to find the shortest non-zero vector in a lattice, is one of the
well-known problems that are believed to be hard to solve, even with a quantum
computer. In this paper we propose space-efficient classical and quantum
algorithms for solving SVP. Currently the best time-efficient algorithm for
solving SVP takes $2^{n+o(n)}$ time and $2^{n+o(n)}$ space. Our classical
algorithm takes $2^{2.05n+o(n)}$ time to solve SVP with only $2^{0.5n+o(n)}$
space. We then modify our classical algorithm to a quantum version, which can
solve SVP in time $2^{1.2553n+o(n)}$ with $2^{0.5n+o(n)}$ classical space and
only poly(n) qubits.
| 1 | 0 | 0 | 0 | 0 | 0 |
Low Power SI Class E Power Amplifier and RF Switch For Health Care | This research was to design a 2.4 GHz class E Power Amplifier (PA) for health
care, with 0.18um Semiconductor Manufacturing International Corporation CMOS
technology by using Cadence software. And also RF switch was designed at
cadence software with power Jazz 180nm SOI process. The ultimate goal for such
application is to reach high performance and low cost, and between high
performance and low power consumption design. This paper introduces the design
of a 2.4GHz class E power amplifier and RF switch design. PA consists of
cascade stage with negative capacitance. This power amplifier can transmit
16dBm output power to a 50{\Omega} load. The performance of the power amplifier
and switch meet the specification requirements of the desired.
| 1 | 0 | 0 | 0 | 0 | 0 |
Improved approximation algorithm for the Dense-3-Subhypergraph Problem | The study of Dense-$3$-Subhypergraph problem was initiated in Chlamt{á}c
et al. [Approx'16]. The input is a universe $U$ and collection ${\cal S}$ of
subsets of $U$, each of size $3$, and a number $k$. The goal is to choose a set
$W$ of $k$ elements from the universe, and maximize the number of sets, $S\in
{\cal S}$ so that $S\subseteq W$. The members in $U$ are called {\em vertices}
and the sets of ${\cal S}$ are called the {\em hyperedges}. This is the
simplest extension into hyperedges of the case of sets of size $2$ which is the
well known Dense $k$-subgraph problem.
The best known ratio for the Dense-$3$-Subhypergraph is $O(n^{0.69783..})$ by
Chlamt{á}c et al. We improve this ratio to $n^{0.61802..}$. More
importantly, we give a new algorithm that approximates Dense-$3$-Subhypergraph
within a ratio of $\tilde O(n/k)$, which improves the ratio of $O(n^2/k^2)$ of
Chlamt{á}c et al.
We prove that under the {\em log density conjecture} (see Bhaskara et al.
[STOC'10]) the ratio cannot be better than $\Omega(\sqrt{n})$ and demonstrate
some cases in which this optimum can be attained.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Survey of Security Assessment Ontologies | A literature survey on ontologies concerning the Security Assessment domain
has been carried out to uncover initiatives that aim at formalizing concepts
from the Security Assessment field of research. A preliminary analysis and a
discussion on the selected works are presented. Our main contribution is an
updated literature review, describing key characteristics, results, research
issues, and application domains of the papers. We have also detected gaps in
the Security Assessment literature that could be the subject of further studies
in the field. This work is meant to be useful for security researchers who wish
to adopt a formal approach in their methods.
| 1 | 0 | 0 | 0 | 0 | 0 |
Distributed Online Learning of Event Definitions | Logic-based event recognition systems infer occurrences of events in time
using a set of event definitions in the form of first-order rules. The Event
Calculus is a temporal logic that has been used as a basis in event recognition
applications, providing among others, direct connections to machine learning,
via Inductive Logic Programming (ILP). OLED is a recently proposed ILP system
that learns event definitions in the form of Event Calculus theories, in a
single pass over a data stream. In this work we present a version of OLED that
allows for distributed, online learning. We evaluate our approach on a
benchmark activity recognition dataset and show that we can significantly
reduce training times, exchanging minimal information between processing nodes.
| 1 | 0 | 0 | 0 | 0 | 0 |
Solving Graph Isomorphism Problem for a Special case | Graph isomorphism is an important computer science problem. The problem for
the general case is unknown to be in polynomial time. The base algorithm for
the general case works in quasi-polynomial time. The solutions in polynomial
time for some special type of classes are known. In this work, we have worked
with a special type of graphs. We have proposed a method to represent these
graphs and finding isomorphism between these graphs. The method uses a modified
version of the degree list of a graph and neighbourhood degree list. These
special type of graphs have a property that neighbourhood degree list of any
two immediate neighbours is different for every vertex.The representation
becomes invariant to the order in which the node was selected for giving the
representation making the isomorphism problem trivial for this case. The
algorithm works in $O(n^4)$ time, where n is the number of vertices present in
the graph. The proposed algorithm runs faster than quasi-polynomial time for
the graphs used in the study.
| 1 | 0 | 0 | 0 | 0 | 0 |
Selective inference for effect modification via the lasso | Effect modification occurs when the effect of the treatment on an outcome
varies according to the level of other covariates and often has important
implications in decision making. When there are tens or hundreds of covariates,
it becomes necessary to use the observed data to select a simpler model for
effect modification and then make valid statistical inference. We propose a two
stage procedure to solve this problem. First, we use Robinson's transformation
to decouple the nuisance parameters from the treatment effect of interest and
use machine learning algorithms to estimate the nuisance parameters. Next,
after plugging in the estimates of the nuisance parameters, we use the Lasso to
choose a low-complexity model for effect modification. Compared to a full model
consisting of all the covariates, the selected model is much more
interpretable. Compared to the univariate subgroup analyses, the selected model
greatly reduces the number of false discoveries. We show that the conditional
selective inference for the selected model is asymptotically valid given the
rate assumptions in classical semiparametric regression. Extensive simulation
studies are conducted to verify the asymptotic results and an epidemiological
application is used to demonstrate the method.
| 0 | 0 | 1 | 1 | 0 | 0 |
Is Climate Change Controversial? Modeling Controversy as Contention Within Populations | A growing body of research focuses on computationally detecting controversial
topics and understanding the stances people hold on them. Yet gaps remain in
our theoretical and practical understanding of how to define controversy, how
it manifests, and how to measure it. In this paper, we introduce a novel
measure we call "contention", defined with respect to a topic and a population.
We model contention from a mathematical standpoint. We validate our model by
examining a diverse set of sources: real-world polling data sets, actual voter
data, and Twitter coverage on several topics. In our publicly-released Twitter
data set of nearly 100M tweets, we examine several topics such as Brexit, the
2016 U.S. Elections, and "The Dress", and cross-reference them with other
sources. We demonstrate that the contention measure holds explanatory power for
a wide variety of observed phenomena, such as controversies over climate change
and other topics that are well within scientific consensus. Finally, we
re-examine the notion of controversy, and present a theoretical framework that
defines it in terms of population. We present preliminary evidence suggesting
that contention is one dimension of controversy, along with others, such as
"importance". Our new contention measure, along with the hypothesized model of
controversy, suggest several avenues for future work in this emerging
interdisciplinary research area.
| 1 | 1 | 0 | 0 | 0 | 0 |
Defending Against Adversarial Attacks by Leveraging an Entire GAN | Recent work has shown that state-of-the-art models are highly vulnerable to
adversarial perturbations of the input. We propose cowboy, an approach to
detecting and defending against adversarial attacks by using both the
discriminator and generator of a GAN trained on the same dataset. We show that
the discriminator consistently scores the adversarial samples lower than the
real samples across multiple attacks and datasets. We provide empirical
evidence that adversarial samples lie outside of the data manifold learned by
the GAN. Based on this, we propose a cleaning method which uses both the
discriminator and generator of the GAN to project the samples back onto the
data manifold. This cleaning procedure is independent of the classifier and
type of attack and thus can be deployed in existing systems.
| 0 | 0 | 0 | 1 | 0 | 0 |
Spectral Graph Convolutions for Population-based Disease Prediction | Exploiting the wealth of imaging and non-imaging information for disease
prediction tasks requires models capable of representing, at the same time,
individual features as well as data associations between subjects from
potentially large populations. Graphs provide a natural framework for such
tasks, yet previous graph-based approaches focus on pairwise similarities
without modelling the subjects' individual characteristics and features. On the
other hand, relying solely on subject-specific imaging feature vectors fails to
model the interaction and similarity between subjects, which can reduce
performance. In this paper, we introduce the novel concept of Graph
Convolutional Networks (GCN) for brain analysis in populations, combining
imaging and non-imaging data. We represent populations as a sparse graph where
its vertices are associated with image-based feature vectors and the edges
encode phenotypic information. This structure was used to train a GCN model on
partially labelled graphs, aiming to infer the classes of unlabelled nodes from
the node features and pairwise associations between subjects. We demonstrate
the potential of the method on the challenging ADNI and ABIDE databases, as a
proof of concept of the benefit from integrating contextual information in
classification tasks. This has a clear impact on the quality of the
predictions, leading to 69.5% accuracy for ABIDE (outperforming the current
state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion,
significantly outperforming standard linear classifiers where only individual
features are considered.
| 1 | 0 | 0 | 1 | 0 | 0 |
An independence system as knot invariant | An independence system (with respect to the unknotting number) is defined for
a classical knot diagram. It is proved that the independence system is a knot
invariant for alternating knots. The exchange property for minimal unknotting
sets are also discussed. It is shown that there exists an infinite family of
knot diagrams whose corresponding independence systems are matroids. In
contrast, infinite families of knot diagrams exist whose independence systems
are not matroids.
| 0 | 0 | 1 | 0 | 0 | 0 |
A cavity-induced artificial gauge field in a Bose-Hubbard ladder | We consider theoretically ultracold interacting bosonic atoms confined to
quasi-one-dimensional ladder structures formed by optical lattices and coupled
to the field of an optical cavity. The atoms can collect a spatial phase
imprint during a cavity-assisted tunneling along a rung via Raman transitions
employing a cavity mode and a transverse running wave pump beam. By adiabatic
elimination of the cavity field we obtain an effective Hamiltonian for the
bosonic atoms, with a self-consistency condition. Using the numerical density
matrix renormalization group method, we obtain a rich steady state diagram of
self-organized steady states. Transitions between superfluid to Mott-insulating
states occur, on top of which we can have Meissner, vortex liquid, and vortex
lattice phases. Also a state that explicitly breaks the symmetry between the
two legs of the ladder, namely the biased-ladder phase is dynamically
stabilized.
| 0 | 1 | 0 | 0 | 0 | 0 |
A Review of Laser-Plasma Ion Acceleration | An overview of research on laser-plasma based acceleration of ions is given.
The experimental state of the art is summarized and recent progress is
discussed. The basic acceleration processes are briefly reviewed with an
outlook on hybrid mechanisms and novel concepts. Finally, we put focus on the
development of engineered targets for enhanced acceleration and of all-optical
methods for beam post-acceleration and control.
| 0 | 1 | 0 | 0 | 0 | 0 |
Minimax Optimal Estimators for Additive Scalar Functionals of Discrete Distributions | In this paper, we consider estimators for an additive functional of $\phi$,
which is defined as $\theta(P;\phi)=\sum_{i=1}^k\phi(p_i)$, from $n$ i.i.d.
random samples drawn from a discrete distribution $P=(p_1,...,p_k)$ with
alphabet size $k$. We propose a minimax optimal estimator for the estimation
problem of the additive functional. We reveal that the minimax optimal rate is
characterized by the divergence speed of the fourth derivative of $\phi$ if the
divergence speed is high. As a result, we show there is no consistent estimator
if the divergence speed of the fourth derivative of $\phi$ is larger than
$p^{-4}$. Furthermore, if the divergence speed of the fourth derivative of
$\phi$ is $p^{4-\alpha}$ for $\alpha \in (0,1)$, the minimax optimal rate is
obtained within a universal multiplicative constant as $\frac{k^2}{(n\ln
n)^{2\alpha}} + \frac{k^{2-2\alpha}}{n}$.
| 1 | 0 | 1 | 1 | 0 | 0 |
Van der Waals Heterostructures Based on Allotropes of Phosphorene and MoSe2 | The van der Waals heterostructures of allotropes of phosphorene (${\alpha}$-
and $\beta-P$) with MoSe2 (H-, T-, ZT- and SO-MoSe2) are investigated in the
framework of state-of-the-art density functional theory. The semiconducting
heterostructures, $\beta$-P /H-MoSe2 and ${\alpha}$-P / H-MoSe2, forms
anti-type structures with type I and type II band alignments, respectively,
whose bands are tunable with external electric field. ${\alpha}$-P / ZT-MoSe2
and ${\alpha}$-P / SO-MoSe2 form ohmic semiconductor-metal contacts while
Schottky barrier in $\beta$-P / T-MoSe2 can be reduced to zero by external
electric field to form ohmic contact which is useful to realize
high-performance devices. Simulated STM images of given heterostructures reveal
that ${\alpha}$-P can be used as a capping layer to differentiate between
various allotropes of underlying MoSe2. The dielectric response of considered
heterostructures is highly anisotropic in terms of lateral and vertical
polarization. The tunable electronic and dielectric response of van der Waals
phosphorene/MoSe2 heterostructure may find potentials applications in the
fabrication of optoelectronic devices.
| 0 | 1 | 0 | 0 | 0 | 0 |
On separated solutions of logistic population equation with harvesting | We provide a surprising answer to a question raised in S. Ahmad and A.C.
Lazer [2], and extend the results of that paper.
| 0 | 0 | 1 | 0 | 0 | 0 |
Nonreciprocal Electromagnetic Scattering from a Periodically Space-Time Modulated Slab and Application to a Quasisonic Isolator | Scattering of obliquely incident electromagnetic waves from periodically
space-time modulated slabs is investigated. It is shown that such structures
operate as nonreciprocal harmonic generators and spatial-frequency filters. For
oblique incidences, low-frequency harmonics are filtered out in the form of
surface waves, while high-frequency harmonics are transmitted as space waves.
In the quasisonic regime, where the velocity of the space-time modulation is
close to the velocity of the electromagnetic waves in the background medium,
the incident wave is strongly coupled to space-time harmonics in the forward
direction, while in the backward direction it exhibits low coupling to other
harmonics. This nonreciprocity is leveraged for the realization of an
electromagnetic isolator in the quasisonic regime and is experimentally
demonstrated at microwave frequencies.
| 0 | 1 | 0 | 0 | 0 | 0 |
Story Cloze Ending Selection Baselines and Data Examination | This paper describes two supervised baseline systems for the Story Cloze Test
Shared Task (Mostafazadeh et al., 2016a). We first build a classifier using
features based on word embeddings and semantic similarity computation. We
further implement a neural LSTM system with different encoding strategies that
try to model the relation between the story and the provided endings. Our
experiments show that a model using representation features based on average
word embedding vectors over the given story words and the candidate ending
sentences words, joint with similarity features between the story and candidate
ending representations performed better than the neural models. Our best model
achieves an accuracy of 72.42, ranking 3rd in the official evaluation.
| 1 | 0 | 0 | 0 | 0 | 0 |
Linear Quadratic Optimal Control Problems with Fixed Terminal States and Integral Quadratic Constraints | This paper is concerned with a linear quadratic (LQ, for short) optimal
control problem with fixed terminal states and integral quadratic constraints.
A Riccati equation with infinite terminal value is introduced, which is
uniquely solvable and whose solution can be approximated by the solution for a
suitable unconstrained LQ problem with penalized terminal state. Using results
from duality theory, the optimal control is explicitly derived by solving the
Riccati equation together with an optimal parameter selection problem. It turns
out that the optimal control is not only a feedback of the current state, but
also a feedback of the target (terminal state). Some examples are presented to
illustrate the theory developed.
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A new sampling density condition for shift-invariant spaces | Let $X=\{x_i:i\in\mathbb{Z}\}$, $\dots<x_{i-1}<x_i<x_{i+1}<\dots$, be a
sampling set which is separated by a constant $\gamma>0$. Under certain
conditions on $\phi$, it is proved that if there exists a positive integer
$\nu$ such that
$$\delta_\nu:=\sup\limits_{i\in\mathbb{Z}}(x_{i+\nu}-x_i)<\dfrac{\nu}{2\pi}\left(\dfrac{c_{k}^2}{M_{2k}}\right)^{\frac{1}{4k}},$$
then every function belonging to a shift-invariant space $V(\phi)$ can be
reconstructed stably from its nonuniform sample values
$\{f^{(j)}(x_i):j=0,1,\dots, k-1, i\in\mathbb{Z}\}$, where $c_k$ is a
Wirtinger-Sobolev constant and $M_{2k}$ is a constant in Bernstein-type
inequality of $V(\phi)$. Further, when $k=1$, the maximum gap $\delta_\nu<\nu$
is sharp for certain shift-invariant spaces.
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Experimental Design via Generalized Mean Objective Cost of Uncertainty | The mean objective cost of uncertainty (MOCU) quantifies the performance cost
of using an operator that is optimal across an uncertainty class of systems as
opposed to using an operator that is optimal for a particular system.
MOCU-based experimental design selects an experiment to maximally reduce MOCU,
thereby gaining the greatest reduction of uncertainty impacting the operational
objective. The original formulation applied to finding optimal system
operators, where optimality is with respect to a cost function, such as
mean-square error; and the prior distribution governing the uncertainty class
relates directly to the underlying physical system. Here we provide a
generalized MOCU and the corresponding experimental design. We then demonstrate
how this new formulation includes as special cases MOCU-based experimental
design methods developed for materials science and genomic networks when there
is experimental error. Most importantly, we show that the classical Knowledge
Gradient and Efficient Global Optimization experimental design procedures are
actually implementations of MOCU-based experimental design under their modeling
assumptions.
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Parallelizing Over Artificial Neural Network Training Runs with Multigrid | Artificial neural networks are a popular and effective machine learning
technique. Great progress has been made parallelizing the expensive training
phase of an individual network, leading to highly specialized pieces of
hardware, many based on GPU-type architectures, and more concurrent algorithms
such as synthetic gradients. However, the training phase continues to be a
bottleneck, where the training data must be processed serially over thousands
of individual training runs. This work considers a multigrid reduction in time
(MGRIT) algorithm that is able to parallelize over the thousands of training
runs and converge to the exact same solution as traditional training would
provide. MGRIT was originally developed to provide parallelism for time
evolution problems that serially step through a finite number of time-steps.
This work recasts the training of a neural network similarly, treating neural
network training as an evolution equation that evolves the network weights from
one step to the next. Thus, this work concerns distributed computing approaches
for neural networks, but is distinct from other approaches which seek to
parallelize only over individual training runs. The work concludes with
supporting numerical results for two model problems.
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Learning the Structure of Generative Models without Labeled Data | Curating labeled training data has become the primary bottleneck in machine
learning. Recent frameworks address this bottleneck with generative models to
synthesize labels at scale from weak supervision sources. The generative
model's dependency structure directly affects the quality of the estimated
labels, but selecting a structure automatically without any labeled data is a
distinct challenge. We propose a structure estimation method that maximizes the
$\ell_1$-regularized marginal pseudolikelihood of the observed data. Our
analysis shows that the amount of unlabeled data required to identify the true
structure scales sublinearly in the number of possible dependencies for a broad
class of models. Simulations show that our method is 100$\times$ faster than a
maximum likelihood approach and selects $1/4$ as many extraneous dependencies.
We also show that our method provides an average of 1.5 F1 points of
improvement over existing, user-developed information extraction applications
on real-world data such as PubMed journal abstracts.
| 1 | 0 | 0 | 1 | 0 | 0 |
Robust Loss Functions under Label Noise for Deep Neural Networks | In many applications of classifier learning, training data suffers from label
noise. Deep networks are learned using huge training data where the problem of
noisy labels is particularly relevant. The current techniques proposed for
learning deep networks under label noise focus on modifying the network
architecture and on algorithms for estimating true labels from noisy labels. An
alternate approach would be to look for loss functions that are inherently
noise-tolerant. For binary classification there exist theoretical results on
loss functions that are robust to label noise. In this paper, we provide some
sufficient conditions on a loss function so that risk minimization under that
loss function would be inherently tolerant to label noise for multiclass
classification problems. These results generalize the existing results on
noise-tolerant loss functions for binary classification. We study some of the
widely used loss functions in deep networks and show that the loss function
based on mean absolute value of error is inherently robust to label noise. Thus
standard back propagation is enough to learn the true classifier even under
label noise. Through experiments, we illustrate the robustness of risk
minimization with such loss functions for learning neural networks.
| 1 | 0 | 0 | 1 | 0 | 0 |
A quality model for evaluating and choosing a stream processing framework architecture | Today, we have to deal with many data (Big data) and we need to make
decisions by choosing an architectural framework to analyze these data coming
from different area. Due to this, it become problematic when we want to process
these data, and even more, when it is continuous data. When you want to process
some data, you have to first receive it, store it, and then query it. This is
what we call Batch Processing. It works well when you process big amount of
data, but it finds its limits when you want to get fast (or real-time)
processing results, such as financial trades, sensors, user session activity,
etc. The solution to this problem is stream processing. Stream processing
approach consists of data arriving record by record and rather than storing it,
the processing should be done directly. Therefore, direct results are needed
with a latency that may vary in real-time.
In this paper, we propose an assessment quality model to evaluate and choose
stream processing frameworks. We describe briefly different architectural
frameworks such as Kafka, Spark Streaming and Flink that address the stream
processing. Using our quality model, we present a decision tree to support
engineers to choose a framework following the quality aspects. Finally, we
evaluate our model doing a case study to Twitter and Netflix streaming.
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On the Hardness of Inventory Management with Censored Demand Data | We consider a repeated newsvendor problem where the inventory manager has no
prior information about the demand, and can access only censored/sales data. In
analogy to multi-armed bandit problems, the manager needs to simultaneously
"explore" and "exploit" with her inventory decisions, in order to minimize the
cumulative cost. We make no probabilistic assumptions---importantly,
independence or time stationarity---regarding the mechanism that creates the
demand sequence. Our goal is to shed light on the hardness of the problem, and
to develop policies that perform well with respect to the regret criterion,
that is, the difference between the cumulative cost of a policy and that of the
best fixed action/static inventory decision in hindsight, uniformly over all
feasible demand sequences. We show that a simple randomized policy, termed the
Exponentially Weighted Forecaster, combined with a carefully designed cost
estimator, achieves optimal scaling of the expected regret (up to logarithmic
factors) with respect to all three key primitives: the number of time periods,
the number of inventory decisions available, and the demand support. Through
this result, we derive an important insight: the benefit from "information
stalking" as well as the cost of censoring are both negligible in this dynamic
learning problem, at least with respect to the regret criterion. Furthermore,
we modify the proposed policy in order to perform well in terms of the tracking
regret, that is, using as benchmark the best sequence of inventory decisions
that switches a limited number of times. Numerical experiments suggest that the
proposed approach outperforms existing ones (that are tailored to, or
facilitated by, time stationarity) on nonstationary demand models. Finally, we
extend the proposed approach and its analysis to a "combinatorial" version of
the repeated newsvendor problem.
| 1 | 0 | 0 | 1 | 0 | 0 |
Social Media Analysis For Organizations: Us Northeastern Public And State Libraries Case Study | Social networking sites such as Twitter have provided a great opportunity for
organizations such as public libraries to disseminate information for public
relations purposes. However, there is a need to analyze vast amounts of social
media data. This study presents a computational approach to explore the content
of tweets posted by nine public libraries in the northeastern United States of
America. In December 2017, this study extracted more than 19,000 tweets from
the Twitter accounts of seven state libraries and two urban public libraries.
Computational methods were applied to collect the tweets and discover
meaningful themes. This paper shows how the libraries have used Twitter to
represent their services and provides a starting point for different
organizations to evaluate the themes of their public tweets.
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Weakly tripotent rings | We study the class of rings $R$ with the property that for $x\in R$ at least
one of the elements $x$ and $1+x$ are tripotent.
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ML for Flood Forecasting at Scale | Effective riverine flood forecasting at scale is hindered by a multitude of
factors, most notably the need to rely on human calibration in current
methodology, the limited amount of data for a specific location, and the
computational difficulty of building continent/global level models that are
sufficiently accurate. Machine learning (ML) is primed to be useful in this
scenario: learned models often surpass human experts in complex
high-dimensional scenarios, and the framework of transfer or multitask learning
is an appealing solution for leveraging local signals to achieve improved
global performance. We propose to build on these strengths and develop ML
systems for timely and accurate riverine flood prediction.
| 1 | 0 | 0 | 1 | 0 | 0 |
Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis | We consider the frequency domain form of proper orthogonal decomposition
(POD) called spectral proper orthogonal decomposition (SPOD). Spectral POD is
derived from a space-time POD problem for statistically stationary flows and
leads to modes that each oscillate at a single frequency. This form of POD goes
back to the original work of Lumley (Stochastic tools in turbulence, Academic
Press, 1970), but has been overshadowed by a space-only form of POD since the
1990s. We clarify the relationship between these two forms of POD and show that
SPOD modes represent structures that evolve coherently in space and time while
space-only POD modes in general do not. We also establish a relationship
between SPOD and dynamic mode decomposition (DMD); we show that SPOD modes are
in fact optimally averaged DMD modes obtained from an ensemble DMD problem for
stationary flows. Accordingly, SPOD modes represent structures that are dynamic
in the same sense as DMD modes but also optimally account for the statistical
variability of turbulent flows. Finally, we establish a connection between SPOD
and resolvent analysis. The key observation is that the resolvent-mode
expansion coefficients must be regarded as statistical quantities to ensure
convergent approximations of the flow statistics. When the expansion
coefficients are uncorrelated, we show that SPOD and resolvent modes are
identical. Our theoretical results and the overall utility of SPOD are
demonstrated using two example problems: the complex Ginzburg-Landau equation
and a turbulent jet.
| 0 | 1 | 0 | 0 | 0 | 0 |
An optimal transportation approach for assessing almost stochastic order | When stochastic dominance $F\leq_{st}G$ does not hold, we can improve
agreement to stochastic order by suitably trimming both distributions. In this
work we consider the $L_2-$Wasserstein distance, $\mathcal W_2$, to stochastic
order of these trimmed versions. Our characterization for that distance
naturally leads to consider a $\mathcal W_2$-based index of disagreement with
stochastic order, $\varepsilon_{\mathcal W_2}(F,G)$. We provide asymptotic
results allowing to test $H_0: \varepsilon_{\mathcal W_2}(F,G)\geq
\varepsilon_0$ vs $H_a: \varepsilon_{\mathcal W_2}(F,G)<\varepsilon_0$, that,
under rejection, would give statistical guarantee of almost stochastic
dominance. We include a simulation study showing a good performance of the
index under the normal model.
| 0 | 0 | 0 | 1 | 0 | 0 |
The Effect of Electron Lens as Landau Damping Device on Single Particle Dynamics in HL-LHC | An electron lens can serve as an effective mechanism for suppressing coherent
instabilities in high intensity storage rings through nonlinear amplitude
dependent betatron tune shift. However, the addition of a strong localized
nonlinear focusing element to the accelerator lattice may lead to undesired
effects in particle dynamics. We evaluate the effect of a Gaussian electron
lens on single particle motion in HL-LHC using numerical tracking simulations,
and compare the results to the case when an equal tune spread is generated by
conventional octupole magnets.
| 0 | 1 | 0 | 0 | 0 | 0 |
Search for sterile neutrinos in holographic dark energy cosmology: Reconciling Planck observation with the local measurement of the Hubble constant | We search for sterile neutrinos in the holographic dark energy cosmology by
using the latest observational data. To perform the analysis, we employ the
current cosmological observations, including the cosmic microwave background
temperature power spectrum data from the Planck mission, the baryon acoustic
oscillation measurements, the type Ia supernova data, the redshift space
distortion measurements, the shear data of weak lensing observation, the Planck
lensing measurement, and the latest direct measurement of $H_0$ as well. We
show that, compared to the $\Lambda$CDM cosmology, the holographic dark energy
cosmology with sterile neutrinos can relieve the tension between the Planck
observation and the direct measurement of $H_0$ much better. Once we include
the $H_0$ measurement in the global fit, we find that the hint of the existence
of sterile neutrinos in the holographic dark energy cosmology can be given.
Under the constraint of the all-data combination, we obtain $N_{\rm eff}=
3.76\pm0.26$ and $m_{\nu,\rm sterile}^{\rm eff}< 0.215\,\rm eV$, indicating
that the detection of $\Delta N_{\rm eff}>0$ in the holographic dark energy
cosmology is at the $2.75\sigma$ level and the massless or very light sterile
neutrino is favored by the current observations.
| 0 | 1 | 0 | 0 | 0 | 0 |
Simulation of high temperature superconductors and experimental validation | In this work, we present a parallel, fully-distributed finite element
numerical framework to simulate the low-frequency electromagnetic response of
superconducting devices, which allows to efficiently exploit HPC platforms. We
select the so-called H-formulation, which uses the magnetic field as a state
variable. Nédélec elements (of arbitrary order) are required for an
accurate approximation of the H-formulation for modelling electromagnetic
fields along interfaces between regions with high contrast medium properties.
An h-adaptive mesh refinement technique customized for Nédélec elements
leads to a structured fine mesh in areas of interest whereas a smart coarsening
is obtained in other regions. The composition of a tailored, robust, parallel
nonlinear solver completes the exposition of the developed tools to tackle the
problem. First, a comparison against experimental data is performed to show the
availability of the finite element approximation to model the physical
phenomena. Then, a selected state-of-the-art 3D benchmark is reproduced,
focusing on the parallel performance of the algorithms.
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Hardware Translation Coherence for Virtualized Systems | To improve system performance, modern operating systems (OSes) often
undertake activities that require modification of virtual-to-physical page
translation mappings. For example, the OS may migrate data between physical
frames to defragment memory and enable superpages. The OS may migrate pages of
data between heterogeneous memory devices. We refer to all such activities as
page remappings. Unfortunately, page remappings are expensive. We show that
translation coherence is a major culprit and that systems employing
virtualization are especially badly affected by their overheads. In response,
we propose hardware translation invalidation and coherence or HATRIC, a readily
implementable hardware mechanism to piggyback translation coherence atop
existing cache coherence protocols. We perform detailed studies using KVM-based
virtualization, showing that HATRIC achieves up to 30% performance and 10%
energy benefits, for per-CPU area overheads of 2%. We also quantify HATRIC's
benefits on systems running Xen and find up to 33% performance improvements.
| 1 | 0 | 0 | 0 | 0 | 0 |
MotifMark: Finding Regulatory Motifs in DNA Sequences | The interaction between proteins and DNA is a key driving force in a
significant number of biological processes such as transcriptional regulation,
repair, recombination, splicing, and DNA modification. The identification of
DNA-binding sites and the specificity of target proteins in binding to these
regions are two important steps in understanding the mechanisms of these
biological activities. A number of high-throughput technologies have recently
emerged that try to quantify the affinity between proteins and DNA motifs.
Despite their success, these technologies have their own limitations and fall
short in precise characterization of motifs, and as a result, require further
downstream analysis to extract useful and interpretable information from a
haystack of noisy and inaccurate data. Here we propose MotifMark, a new
algorithm based on graph theory and machine learning, that can find binding
sites on candidate probes and rank their specificity in regard to the
underlying transcription factor. We developed a pipeline to analyze
experimental data derived from compact universal protein binding microarrays
and benchmarked it against two of the most accurate motif search methods. Our
results indicate that MotifMark can be a viable alternative technique for
prediction of motif from protein binding microarrays and possibly other related
high-throughput techniques.
| 1 | 0 | 0 | 0 | 0 | 0 |
Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning | This paper presents KeypointNet, an end-to-end geometric reasoning framework
to learn an optimal set of category-specific 3D keypoints, along with their
detectors. Given a single image, KeypointNet extracts 3D keypoints that are
optimized for a downstream task. We demonstrate this framework on 3D pose
estimation by proposing a differentiable objective that seeks the optimal set
of keypoints for recovering the relative pose between two views of an object.
Our model discovers geometrically and semantically consistent keypoints across
viewing angles and instances of an object category. Importantly, we find that
our end-to-end framework using no ground-truth keypoint annotations outperforms
a fully supervised baseline using the same neural network architecture on the
task of pose estimation. The discovered 3D keypoints on the car, chair, and
plane categories of ShapeNet are visualized at this http URL.
| 0 | 0 | 0 | 1 | 0 | 0 |
Multi-State Trajectory Approach to Non-Adiabatic Dynamics: General Formalism and the Active State Trajectory Approximation | A general theoretical framework is derived for the recently developed
multi-state trajectory (MST) approach from the time dependent Schrödinger
equation, resulting in equations of motion for coupled nuclear-electronic
dynamics equivalent to Hamilton dynamics or Heisenberg equation based on a new
multistate Meyer-Miller (MM) model. The derived MST formalism incorporates both
diabatic and adiabatic representations as limiting cases, and reduces to
Ehrenfest or Born-Oppenheimer dynamics in the mean field or the single state
limits, respectively. By quantizing nuclear dynamics to a particular active
state, the MST algorithm does not suffer from the instability caused by the
negative instant electronic population variables unlike the standard MM
dynamics. Furthermore the multistate representation for electron coupled
nuclear dynamics with each state associated with one individual trajectory
presumably captures single state dynamics better than the mean field
description. The coupled electronic-nuclear coherence is incorporated
consistently in the MST framework with no ad-hoc state switch and the
associated momentum adjustment or parameters for artificial decoherence, unlike
the original or modified surface hopping treatments. The implementation of the
MST approach to benchmark problems shows reasonably good agreement with exact
quantum calculations, and the results in both representations are similar in
accuracy. The active state trajectory (AST) approximation of the MST approach
provides a consistent interpretation to trajectory surface hopping, which
predicts the transition probabilities reasonably well for multiple nonadiabatic
transitions and conical intersection problems.
| 0 | 1 | 0 | 0 | 0 | 0 |
Combining Homotopy Methods and Numerical Optimal Control to Solve Motion Planning Problems | This paper presents a systematic approach for computing local solutions to
motion planning problems in non-convex environments using numerical optimal
control techniques. It extends the range of use of state-of-the-art numerical
optimal control tools to problem classes where these tools have previously not
been applicable. Today these problems are typically solved using motion
planners based on randomized or graph search. The general principle is to
define a homotopy that perturbs, or preferably relaxes, the original problem to
an easily solved problem. By combining a Sequential Quadratic Programming (SQP)
method with a homotopy approach that gradually transforms the problem from a
relaxed one to the original one, practically relevant locally optimal solutions
to the motion planning problem can be computed. The approach is demonstrated in
motion planning problems in challenging 2D and 3D environments, where the
presented method significantly outperforms a state-of-the-art open-source
optimizing sampled-based planner commonly used as benchmark.
| 0 | 0 | 1 | 0 | 0 | 0 |
High quality atomically thin PtSe2 films grown by molecular beam epitaxy | Atomically thin PtSe2 films have attracted extensive research interests for
potential applications in high-speed electronics, spintronics and
photodetectors. Obtaining high quality, single crystalline thin films with
large size is critical. Here we report the first successful layer-by-layer
growth of high quality PtSe2 films by molecular beam epitaxy. Atomically thin
films from 1 ML to 22 ML have been grown and characterized by low-energy
electron diffraction, Raman spectroscopy and X-ray photoemission spectroscopy.
Moreover, a systematic thickness dependent study of the electronic structure is
revealed by angle-resolved photoemission spectroscopy (ARPES), and helical spin
texture is revealed by spin-ARPES. Our work provides new opportunities for
growing large size single crystalline films for investigating the physical
properties and potential applications of PtSe2.
| 0 | 1 | 0 | 0 | 0 | 0 |
NAVREN-RL: Learning to fly in real environment via end-to-end deep reinforcement learning using monocular images | We present NAVREN-RL, an approach to NAVigate an unmanned aerial vehicle in
an indoor Real ENvironment via end-to-end reinforcement learning RL. A suitable
reward function is designed keeping in mind the cost and weight constraints for
micro drone with minimum number of sensing modalities. Collection of small
number of expert data and knowledge based data aggregation is integrated into
the RL process to aid convergence. Experimentation is carried out on a Parrot
AR drone in different indoor arenas and the results are compared with other
baseline technologies. We demonstrate how the drone successfully avoids
obstacles and navigates across different arenas.
| 1 | 0 | 0 | 1 | 0 | 0 |
Fast, Accurate and Fully Parallelizable Digital Image Correlation | Digital image correlation (DIC) is a widely used optical metrology for
surface deformation measurements. DIC relies on nonlinear optimization method.
Thus an initial guess is quite important due to its influence on the converge
characteristics of the algorithm. In order to obtain a reliable, accurate
initial guess, a reliability-guided digital image correlation (RG-DIC) method,
which is able to intelligently obtain a reliable initial guess without using
time-consuming integer-pixel registration, was proposed. However, the RG-DIC
and its improved methods are path-dependent and cannot be fully parallelized.
Besides, it is highly possible that RG-DIC fails in the full-field analysis of
deformation without manual intervention if the deformation fields contain large
areas of discontinuous deformation. Feature-based initial guess is highly
robust while it is relatively time-consuming. Recently, path-independent
algorithm, fast Fourier transform-based cross correlation (FFT-CC) algorithm,
was proposed to estimate the initial guess. Complete parallelizability is the
major advantage of the FFT-CC algorithm, while it is sensitive to small
deformation. Wu et al proposed an efficient integer-pixel search scheme, but
the parameters of this algorithm are set by the users empirically. In this
technical note, a fully parallelizable DIC method is proposed. Different from
RG-DIC method, the proposed method divides DIC algorithm into two parts:
full-field initial guess estimation and sub-pixel registration. The proposed
method has the following benefits: 1) providing a pre-knowledge of deformation
fields; 2) saving computational time; 3) reducing error propagation; 4)
integratability with well-established DIC algorithms; 5) fully
parallelizability.
| 0 | 1 | 0 | 0 | 0 | 0 |
Discovering the effect of nonlocal payoff calculation on the stabilty of ESS: Spatial patterns of Hawk-Dove game in metapopulations | The classical idea of evolutionarily stable strategy (ESS) modeling animal
behavior does not involve any spatial dependence. We considered a spatial
Hawk-Dove game played by animals in a patchy environment with wrap around
boundaries. We posit that each site contains the same number of individuals. An
evolution equation for analyzing the stability of the ESS is found as the mean
dynamics of the classical frequency dependent Moran process coupled via
migration and nonlocal payoff calculation in 1D and 2D habitats. The linear
stability analysis of the model is performed and conditions to observe spatial
patterns are investigated. For the nearest neighbor interactions (including von
Neumann and Moore neighborhoods in 2D) we concluded that it is possible to
destabilize the ESS of the game and observe pattern formation when the
dispersal rate is small enough. We numerically investigate the spatial patterns
arising from the replicator equations coupled via nearest neighbor payoff
calculation and dispersal.
| 0 | 0 | 0 | 0 | 1 | 0 |
Analysis of the measurements of anisotropic a.c. vortex resistivity in tilted magnetic fields | Measurements of the high-frequency complex resistivity in superconductors are
a tool often used to obtain the vortex parameters, such as the vortex
viscosity, the pinning constant and the depinning frequency. In anisotropic
superconductors, the extraction of these quantities from the measurements faces
new difficulties due to the tensor nature of the electromagnetic problem. The
problem is specifically intricate when the magnetic field is tilted with
respect to the crystallographic axes. Partial solutions exist in the
free-flux-flow (no pinning) and Campbell (pinning dominated) regimes. In this
paper we develop a full tensor model for the vortex motion complex resistivity,
including flux-flow, pinning, and creep. We give explicit expressions for the
tensors involved. We obtain that, despite the complexity of the physics, some
parameters remain scalar in nature. We show that under specific circumstances
the directly measured quantities do not reflect the true vortex parameters, and
we give procedures to derive the true vortex parameters from measurements taken
with arbitrary field orientations. Finally, we discuss the applicability of the
angular scaling properties to the measured and transformed vortex parameters
and we exploit these properties as a tool to unveil the existence of
directional pinning.
| 0 | 1 | 0 | 0 | 0 | 0 |
Deep Convolutional Neural Network to Detect J-UNIWARD | This paper presents an empirical study on applying convolutional neural
networks (CNNs) to detecting J-UNIWARD, one of the most secure JPEG
steganographic method. Experiments guiding the architectural design of the CNNs
have been conducted on the JPEG compressed BOSSBase containing 10,000 covers of
size 512x512. Results have verified that both the pooling method and the depth
of the CNNs are critical for performance. Results have also proved that a
20-layer CNN, in general, outperforms the most sophisticated feature-based
methods, but its advantage gradually diminishes on hard-to-detect cases. To
show that the performance generalizes to large-scale databases and to different
cover sizes, one experiment has been conducted on the CLS-LOC dataset of
ImageNet containing more than one million covers cropped to unified size of
256x256. The proposed 20-layer CNN has cut the error achieved by a CNN recently
proposed for large-scale JPEG steganalysis by 35%. Source code is available via
GitHub: this https URL
| 1 | 0 | 0 | 0 | 0 | 0 |
Observing Power-Law Dynamics of Position-Velocity Correlation in Anomalous Diffusion | In this letter we present a measurement of the phase-space density
distribution (PSDD) of ultra-cold \Rb atoms performing 1D anomalous diffusion.
The PSDD is imaged using a direct tomographic method based on Raman velocity
selection. It reveals that the position-velocity correlation function
$C_{xv}(t)$ builds up on a timescale related to the initial conditions of the
ensemble and then decays asymptotically as a power-law. We show that the decay
follows a simple scaling theory involving the power-law asymptotic dynamics of
position and velocity. The generality of this scaling theory is confirmed using
Monte-Carlo simulations of two distinct models of anomalous diffusion.
| 0 | 1 | 0 | 0 | 0 | 0 |
Modular curves, invariant theory and $E_8$ | The $E_8$ root lattice can be constructed from the modular curve $X(13)$ by
the invariant theory for the simple group $\text{PSL}(2, 13)$. This gives a
different construction of the $E_8$ root lattice. It also gives an explicit
construction of the modular curve $X(13)$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Analysis of Approximate Stochastic Gradient Using Quadratic Constraints and Sequential Semidefinite Programs | We present convergence rate analysis for the approximate stochastic gradient
method, where individual gradient updates are corrupted by computation errors.
We develop stochastic quadratic constraints to formulate a small linear matrix
inequality (LMI) whose feasible set characterizes convergence properties of the
approximate stochastic gradient. Based on this LMI condition, we develop a
sequential minimization approach to analyze the intricate trade-offs that
couple stepsize selection, convergence rate, optimization accuracy, and
robustness to gradient inaccuracy. We also analytically solve this LMI
condition and obtain theoretical formulas that quantify the convergence
properties of the approximate stochastic gradient under various assumptions on
the loss functions.
| 0 | 0 | 0 | 1 | 0 | 0 |
Invariant holomorphic discs in some non-convex domains | We give a description of complex geodesics and we study the structure of
stationary discs in some non-convex domains for which complex geodesics are not
unique.
| 0 | 0 | 1 | 0 | 0 | 0 |
MIMIX: a Bayesian Mixed-Effects Model for Microbiome Data from Designed Experiments | Recent advances in bioinformatics have made high-throughput microbiome data
widely available, and new statistical tools are required to maximize the
information gained from these data. For example, analysis of high-dimensional
microbiome data from designed experiments remains an open area in microbiome
research. Contemporary analyses work on metrics that summarize collective
properties of the microbiome, but such reductions preclude inference on the
fine-scale effects of environmental stimuli on individual microbial taxa. Other
approaches model the proportions or counts of individual taxa as response
variables in mixed models, but these methods fail to account for complex
correlation patterns among microbial communities. In this paper, we propose a
novel Bayesian mixed-effects model that exploits cross-taxa correlations within
the microbiome, a model we call MIMIX (MIcrobiome MIXed model). MIMIX offers
global tests for treatment effects, local tests and estimation of treatment
effects on individual taxa, quantification of the relative contribution from
heterogeneous sources to microbiome variability, and identification of latent
ecological subcommunities in the microbiome. MIMIX is tailored to large
microbiome experiments using a combination of Bayesian factor analysis to
efficiently represent dependence between taxa and Bayesian variable selection
methods to achieve sparsity. We demonstrate the model using a simulation
experiment and on a 2x2 factorial experiment of the effects of nutrient
supplement and herbivore exclusion on the foliar fungal microbiome of
$\textit{Andropogon gerardii}$, a perennial bunchgrass, as part of the global
Nutrient Network research initiative.
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SpatEntropy: Spatial Entropy Measures in R | This article illustrates how to measure the heterogeneity of spatial data
presenting a finite number of categories via computation of spatial entropy.
The R package SpatEntropy contains functions for the computation of entropy and
spatial entropy measures. The extension to spatial entropy measures is a unique
feature of SpatEntropy. In addition to the traditional version of Shannon's
entropy, the package includes Batty's spatial entropy, O'Neill's entropy, Li
and Reynolds' contagion index, Karlstrom and Ceccato's entropy, Leibovici's
entropy, Parresol and Edwards' entropy and Altieri's entropy. The package is
able to work with both areal and point data. This paper is a general
description of SpatEntropy, as well as its necessary theoretical background,
and an introduction for new users.
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