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Title: Optimal Input Design for Affine Model Discrimination with Applications in Intention-Aware Vehicles,
Abstract: This paper considers the optimal design of input signals for the purpose of
discriminating among a finite number of affine models with uncontrolled inputs
and noise. Each affine model represents a different system operating mode,
corresponding to unobserved intents of other drivers or robots, or to fault
types or attack strategies, etc. The input design problem aims to find optimal
separating/discriminating (controlled) inputs such that the output trajectories
of all the affine models are guaranteed to be distinguishable from each other,
despite uncertainty in the initial condition and uncontrolled inputs as well as
the presence of process and measurement noise. We propose a novel formulation
to solve this problem, with an emphasis on guarantees for model discrimination
and optimality, in contrast to a previously proposed conservative formulation
using robust optimization. This new formulation can be recast as a bilevel
optimization problem and further reformulated as a mixed-integer linear program
(MILP). Moreover, our fairly general problem setting allows the incorporation
of objectives and/or responsibilities among rational agents. For instance, each
driver has to obey traffic rules, while simultaneously optimizing for safety,
comfort and energy efficiency. Finally, we demonstrate the effectiveness of our
approach for identifying the intention of other vehicles in several driving
scenarios. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Stable Unitary Integrators for the Numerical Implementation of Continuous Unitary Transformations,
Abstract: The technique of continuous unitary transformations has recently been used to
provide physical insight into a diverse array of quantum mechanical systems.
However, the question of how to best numerically implement the flow equations
has received little attention. The most immediately apparent approach, using
standard Runge-Kutta numerical integration algorithms, suffers from both severe
inefficiency due to stiffness and the loss of unitarity. After reviewing the
formalism of continuous unitary transformations and Wegner's original choice
for the infinitesimal generator of the flow, we present a number of approaches
to resolving these issues including a choice of generator which induces what we
call the "uniform tangent decay flow" and three numerical integrators
specifically designed to perform continuous unitary transformations efficiently
while preserving the unitarity of flow. We conclude by applying one of the flow
algorithms to a simple calculation that visually demonstrates the many-body
localization transition. | [
1,
1,
0,
0,
0,
0
] | [
"Physics",
"Mathematics",
"Computer Science"
] |
Title: Deep Learning: Generalization Requires Deep Compositional Feature Space Design,
Abstract: Generalization error defines the discriminability and the representation
power of a deep model. In this work, we claim that feature space design using
deep compositional function plays a significant role in generalization along
with explicit and implicit regularizations. Our claims are being established
with several image classification experiments. We show that the information
loss due to convolution and max pooling can be marginalized with the
compositional design, improving generalization performance. Also, we will show
that learning rate decay acts as an implicit regularizer in deep model
training. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science"
] |
Title: First detection of sign-reversed linear polarization from the forbidden [O I] 630.03 nm line,
Abstract: We report on the detection of linear polarization of the forbidden [O i]
630.03 nm spectral line. The observations were carried out in the broader
context of the determination of the solar oxygen abundance, an important
problem in astrophysics that still remains unresolved. We obtained
spectro-polarimetric data of the forbidden [O i] line at 630.03 nm as well as
other neighboring permitted lines with the Solar Optical Telescope of the
Hinode satellite. A novel averaging technique was used, yielding very high
signal-to-noise ratios in excess of $10^5$. We confirm that the linear
polarization is sign-reversed compared to permitted lines as a result of the
line being dominated by a magnetic dipole transition. Our observations open a
new window for solar oxygen abundance studies, offering an alternative method
to disentangle the Ni i blend from the [O i] line at 630.03 nm that has the
advantage of simple LTE formation physics. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Exploiting Multi-layer Graph Factorization for Multi-attributed Graph Matching,
Abstract: Multi-attributed graph matching is a problem of finding correspondences
between two sets of data while considering their complex properties described
in multiple attributes. However, the information of multiple attributes is
likely to be oversimplified during a process that makes an integrated
attribute, and this degrades the matching accuracy. For that reason, a
multi-layer graph structure-based algorithm has been proposed recently. It can
effectively avoid the problem by separating attributes into multiple layers.
Nonetheless, there are several remaining issues such as a scalability problem
caused by the huge matrix to describe the multi-layer structure and a
back-projection problem caused by the continuous relaxation of the quadratic
assignment problem. In this work, we propose a novel multi-attributed graph
matching algorithm based on the multi-layer graph factorization. We reformulate
the problem to be solved with several small matrices that are obtained by
factorizing the multi-layer structure. Then, we solve the problem using a
convex-concave relaxation procedure for the multi-layer structure. The proposed
algorithm exhibits better performance than state-of-the-art algorithms based on
the single-layer structure. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: A general model for plane-based clustering with loss function,
Abstract: In this paper, we propose a general model for plane-based clustering. The
general model contains many existing plane-based clustering methods, e.g.,
k-plane clustering (kPC), proximal plane clustering (PPC), twin support vector
clustering (TWSVC) and its extensions. Under this general model, one may obtain
an appropriate clustering method for specific purpose. The general model is a
procedure corresponding to an optimization problem, where the optimization
problem minimizes the total loss of the samples. Thereinto, the loss of a
sample derives from both within-cluster and between-cluster. In theory, the
termination conditions are discussed, and we prove that the general model
terminates in a finite number of steps at a local or weak local optimal point.
Furthermore, based on this general model, we propose a plane-based clustering
method by introducing a new loss function to capture the data distribution
precisely. Experimental results on artificial and public available datasets
verify the effectiveness of the proposed method. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Mathematics",
"Statistics"
] |
Title: Renormalization of quasiparticle band gap in doped two-dimensional materials from many-body calculations,
Abstract: Doped free carriers can substantially renormalize electronic self-energy and
quasiparticle band gaps of two-dimensional (2D) materials. However, it is still
challenging to quantitatively calculate this many-electron effect, particularly
at the low doping density that is most relevant to realistic experiments and
devices. Here we develop a first-principles-based effective-mass model within
the GW approximation and show a dramatic band gap renormalization of a few
hundred meV for typical 2D semiconductors. Moreover, we reveal the roles of
different many-electron interactions: The Coulomb-hole contribution is dominant
for low doping densities while the screened-exchange contribution is dominant
for high doping densities. Three prototypical 2D materials are studied by this
method, h-BN, MoS2, and black phosphorus, covering insulators to
semiconductors. Especially, anisotropic black phosphorus exhibits a
surprisingly large band gap renormalization because of its smaller
density-of-state that enhances the screened-exchange interactions. Our work
demonstrates an efficient way to accurately calculate band gap renormalization
and provides quantitative understanding of doping-dependent many-electron
physics of general 2D semiconductors. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Computer Science"
] |
Title: Lasso ANOVA Decompositions for Matrix and Tensor Data,
Abstract: Consider the problem of estimating the entries of an unknown mean matrix or
tensor given a single noisy realization. In the matrix case, this problem can
be addressed by decomposing the mean matrix into a component that is additive
in the rows and columns, i.e.\ the additive ANOVA decomposition of the mean
matrix, plus a matrix of elementwise effects, and assuming that the elementwise
effects may be sparse. Accordingly, the mean matrix can be estimated by solving
a penalized regression problem, applying a lasso penalty to the elementwise
effects. Although solving this penalized regression problem is straightforward,
specifying appropriate values of the penalty parameters is not. Leveraging the
posterior mode interpretation of the penalized regression problem, moment-based
empirical Bayes estimators of the penalty parameters can be defined. Estimation
of the mean matrix using these these moment-based empirical Bayes estimators
can be called LANOVA penalization, and the corresponding estimate of the mean
matrix can be called the LANOVA estimate. The empirical Bayes estimators are
shown to be consistent. Additionally, LANOVA penalization is extended to
accommodate sparsity of row and column effects and to estimate an unknown mean
tensor. The behavior of the LANOVA estimate is examined under misspecification
of the distribution of the elementwise effects, and LANOVA penalization is
applied to several datasets, including a matrix of microarray data, a three-way
tensor of fMRI data and a three-way tensor of wheat infection data. | [
0,
0,
0,
1,
0,
0
] | [
"Statistics",
"Mathematics"
] |
Title: NetSciEd: Network Science and Education for the Interconnected World,
Abstract: This short article presents a summary of the NetSciEd (Network Science and
Education) initiative that aims to address the need for curricula, resources,
accessible materials, and tools for introducing K-12 students and the general
public to the concept of networks, a crucial framework in understanding
complexity. NetSciEd activities include (1) the NetSci High educational
outreach program (since 2010), which connects high school students and their
teachers with regional university research labs and provides them with the
opportunity to work on network science research projects; (2) the NetSciEd
symposium series (since 2012), which brings network science researchers and
educators together to discuss how network science can help and be integrated
into formal and informal education; and (3) the Network Literacy: Essential
Concepts and Core Ideas booklet (since 2014), which was created collaboratively
and subsequently translated into 18 languages by an extensive group of network
science researchers and educators worldwide. | [
1,
1,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: A cup product lemma for continuous plurisubharmonic functions,
Abstract: A version of Gromov's cup product lemma in which one factor is the (1,0)-part
of the differential of a continuous plurisubharmonic function is obtained. As
an application, it is shown that a connected noncompact complete Kaehler
manifold that has exactly one end and admits a continuous plurisubharmonic
function that is strictly plurisubharmonic along some germ of a 2-dimensional
complex analytic set at some point has the Bochner-Hartogs property; that is,
the first compactly supported cohomology with values in the structure sheaf
vanishes. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Search for magnetic inelastic dark matter with XENON100,
Abstract: We present the first search for dark matter-induced delayed coincidence
signals in a dual-phase xenon time projection chamber, using the 224.6 live
days of the XENON100 science run II. This very distinct signature is predicted
in the framework of magnetic inelastic dark matter which has been proposed to
reconcile the modulation signal reported by the DAMA/LIBRA collaboration with
the null results from other direct detection experiments. No candidate event
has been found in the region of interest and upper limits on the WIMP's
magnetic dipole moment are derived. The scenarios proposed to explain the
DAMA/LIBRA modulation signal by magnetic inelastic dark matter interactions of
WIMPs with masses of 58.0 GeV/c$^2$ and 122.7 GeV/c$^2$ are excluded at 3.3
$\sigma$ and 9.3 $\sigma$, respectively. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Multinomial Sum Formulas of Multiple Zeta Values,
Abstract: For a pair of positive integers $n,k$ with $n\geq 2$, in this paper we prove
that $$ \sum_{r=1}^k\sum_{|\bf\alpha|=k}{k\choose\bf\alpha}
\zeta(n\bf\alpha)=\zeta(n)^k =\sum^k_{r=1}\sum_{|\bf\alpha|=k}
{k\choose\bf\alpha}(-1)^{k-r}\zeta^\star(n\bf\alpha), $$ where
$\bf\alpha=(\alpha_1,\alpha_2,\ldots,\alpha_r)$ is a $r$-tuple of positive
integers. Moreover, we give an application to combinatorics and get the
following identity: $$ \sum^{2k}_{r=1}r!{2k\brace
r}=\sum^k_{p=1}\sum^k_{q=1}{k\brace p}{k\brace q} p!q!D(p,q), $$ where
${k\brace p}$ is the Stirling numbers of the second kind and $D(p,q)$ is the
Delannoy number. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Go with the Flow: Compositional Abstractions for Concurrent Data Structures (Extended Version),
Abstract: Concurrent separation logics have helped to significantly simplify
correctness proofs for concurrent data structures. However, a recurring problem
in such proofs is that data structure abstractions that work well in the
sequential setting are much harder to reason about in a concurrent setting due
to complex sharing and overlays. To solve this problem, we propose a novel
approach to abstracting regions in the heap by encoding the data structure
invariant into a local condition on each individual node. This condition may
depend on a quantity associated with the node that is computed as a fixpoint
over the entire heap graph. We refer to this quantity as a flow. Flows can
encode both structural properties of the heap (e.g. the reachable nodes from
the root form a tree) as well as data invariants (e.g. sortedness). We then
introduce the notion of a flow interface, which expresses the relies and
guarantees that a heap region imposes on its context to maintain the local flow
invariant with respect to the global heap. Our main technical result is that
this notion leads to a new semantic model of separation logic. In this model,
flow interfaces provide a general abstraction mechanism for describing complex
data structures. This abstraction mechanism admits proof rules that generalize
over a wide variety of data structures. To demonstrate the versatility of our
approach, we show how to extend the logic RGSep with flow interfaces. We have
used this new logic to prove linearizability and memory safety of nontrivial
concurrent data structures. In particular, we obtain parametric linearizability
proofs for concurrent dictionary algorithms that abstract from the details of
the underlying data structure representation. These proofs cannot be easily
expressed using the abstraction mechanisms provided by existing separation
logics. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: A Liouville Theorem for Mean Curvature Flow,
Abstract: Ancient solutions arise in the study of parabolic blow-ups. If we can
categorize ancient solutions, we can better understand blow-up limits. Based on
an argument of Giga and Kohn, we give a Liouville-type theorem restricting
ancient, type-I, non-collapsing two- dimensional mean curvature flows to either
spheres or cylinders. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: FeSe(en)0.3 - Separated FeSe layers with stripe-type crystal structure by intercalation of neutral spacer molecules,
Abstract: Solvothermal intercalation of ethylenediamine molecules into FeSe separates
the layers by 1078 pm and creates a different stacking. FeSe(en)0.3 is not
superconducting although each layer exhibits the stripe-type crystal structure
and the Fermi surface topology of superconducting FeSe. FeSe(en)0.3 requires
electron-doping for high-Tc similar to monolayers of FeSe@SrTiO3, whose much
higher Tc may arise from the proximity of the oxide surface. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Lions' formula for RKHSs of real harmonic functions on Lipschitz domains,
Abstract: Let $ \Omega$ be a bounded Lipschitz domain of $ \mathbb{R}^{d}.$ The purpose
of this paper is to establish Lions' formula for reproducing kernel Hilbert
spaces $\mathcal H^s(\Omega)$ of real harmonic functions elements of the usual
Sobolev space $H^s(\Omega)$ for $s\geq 0.$ To this end, we provide a functional
characterization of $\mathcal H^s(\Omega)$ via some new families of positive
self-adjoint operators, describe their trace data and discuss the values of $s$
for which they are RKHSs. Also a construction of an orthonormal basis of
$\mathcal H^s(\Omega)$ is established. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Optimization and Performance of Bifacial Solar Modules: A Global Perspective,
Abstract: With the rapidly growing interest in bifacial photovoltaics (PV), a worldwide
map of their potential performance can help assess and accelerate the global
deployment of this emerging technology. However, the existing literature only
highlights optimized bifacial PV for a few geographic locations or develops
worldwide performance maps for very specific configurations, such as the
vertical installation. It is still difficult to translate these location- and
configuration-specific conclusions to a general optimized performance of this
technology. In this paper, we present a global study and optimization of
bifacial solar modules using a rigorous and comprehensive modeling framework.
Our results demonstrate that with a low albedo of 0.25, the bifacial gain of
ground-mounted bifacial modules is less than 10% worldwide. However, increasing
the albedo to 0.5 and elevating modules 1 m above the ground can boost the
bifacial gain to 30%. Moreover, we derive a set of empirical design rules,
which optimize bifacial solar modules across the world, that provide the
groundwork for rapid assessment of the location-specific performance. We find
that ground-mounted, vertical, east-west-facing bifacial modules will
outperform their south-north-facing, optimally tilted counterparts by up to 15%
below the latitude of 30 degrees, for an albedo of 0.5. The relative energy
output is the reverse of this in latitudes above 30 degrees. A detailed and
systematic comparison with experimental data from Asia, Europe, and North
America validates the model presented in this paper. An online simulation tool
(this https URL) based on the model developed in this paper is
also available for a user to predict and optimize bifacial modules in any
arbitrary location across the globe. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settings,
Abstract: This article presents GuideR, a user-guided rule induction algorithm, which
overcomes the largest limitation of the existing methods-the lack of the
possibility to introduce user's preferences or domain knowledge to the rule
learning process. Automatic selection of attributes and attribute ranges often
leads to the situation in which resulting rules do not contain interesting
information. We propose an induction algorithm which takes into account user's
requirements. Our method uses the sequential covering approach and is suitable
for classification, regression, and survival analysis problems. The
effectiveness of the algorithm in all these tasks has been verified
experimentally, confirming guided rule induction to be a powerful data analysis
tool. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Frequency analysis and the representation of slowly diffusing planetary solutions,
Abstract: Over short time intervals planetary ephemerides have been traditionally
represented in analytical form as finite sums of periodic terms or sums of
Poisson terms that are periodic terms with polynomial amplitudes. Nevertheless,
this representation is not well adapted for the evolution of the planetary
orbits in the solar system over million of years as they present drifts in
their main frequencies, due to the chaotic nature of their dynamics. The aim of
the present paper is to develop a numerical algorithm for slowly diffusing
solutions of a perturbed integrable Hamiltonian system that will apply to the
representation of the chaotic planetary motions with varying frequencies. By
simple analytical considerations, we first argue that it is possible to recover
exactly a single varying frequency. Then, a function basis involving
time-dependent fundamental frequencies is formulated in a semi-analytical way.
Finally, starting from a numerical solution, a recursive algorithm is used to
numerically decompose the solution on the significant elements of the function
basis. Simple examples show that this algorithm can be used to give compact
representations of different types of slowly diffusing solutions. As a test
example, we show how this algorithm can be successfully applied to obtain a
very compact approximation of the La2004 solution of the orbital motion of the
Earth over 40 Myr ([-35Myr,5Myr]). This example has been chosen as this
solution is widely used for the reconstruction of the climates of the past. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Mathematics"
] |
Title: Spectrum Sharing for LTE-A Network in TV White Space,
Abstract: Rural areas in the developing countries are predominantly devoid of Internet
access as it is not viable for operators to provide broadband service in these
areas. To solve this problem, we propose a middle mile Long erm Evolution
Advanced (LTE-A) network operating in TV white space to connect villages to an
optical Point of Presence (PoP) located in the vicinity of a rural area. We
study the problem of spectrum sharing for the middle mile networks deployed by
multiple operators. A graph theory based Fairness Constrained Channel
Allocation (FCCA) algorithm is proposed, employing Carrier Aggregation (CA) and
Listen Before Talk (LBT) features of LTE-A. We perform extensive system level
simulations to demonstrate that FCCA not only increases spectral efficiency but
also improves system fairness. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Physics"
] |
Title: Instantons for 4-manifolds with periodic ends and an obstruction to embeddings of 3-manifolds,
Abstract: We construct an obstruction for the existence of embeddings of homology
$3$-sphere into homology $S^3\times S^1$ under some cohomological condition.
The obstruction is defined as an element in the filtered version of the
instanton Floer cohomology due to R.Fintushel-R.Stern. We make use of the
$\mathbb{Z}$-fold covering space of homology $S^3\times S^1$ and the instantons
on it. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: Dropping Convexity for More Efficient and Scalable Online Multiview Learning,
Abstract: Multiview representation learning is very popular for latent factor analysis.
It naturally arises in many data analysis, machine learning, and information
retrieval applications to model dependent structures among multiple data
sources. For computational convenience, existing approaches usually formulate
the multiview representation learning as convex optimization problems, where
global optima can be obtained by certain algorithms in polynomial time.
However, many pieces of evidence have corroborated that heuristic nonconvex
approaches also have good empirical computational performance and convergence
to the global optima, although there is a lack of theoretical justification.
Such a gap between theory and practice motivates us to study a nonconvex
formulation for multiview representation learning, which can be efficiently
solved by a simple stochastic gradient descent (SGD) algorithm. We first
illustrate the geometry of the nonconvex formulation; Then, we establish
asymptotic global rates of convergence to the global optima by diffusion
approximations. Numerical experiments are provided to support our theory. | [
0,
0,
1,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: A Deep Network Model for Paraphrase Detection in Short Text Messages,
Abstract: This paper is concerned with paraphrase detection. The ability to detect
similar sentences written in natural language is crucial for several
applications, such as text mining, text summarization, plagiarism detection,
authorship authentication and question answering. Given two sentences, the
objective is to detect whether they are semantically identical. An important
insight from this work is that existing paraphrase systems perform well when
applied on clean texts, but they do not necessarily deliver good performance
against noisy texts. Challenges with paraphrase detection on user generated
short texts, such as Twitter, include language irregularity and noise. To cope
with these challenges, we propose a novel deep neural network-based approach
that relies on coarse-grained sentence modeling using a convolutional neural
network and a long short-term memory model, combined with a specific
fine-grained word-level similarity matching model. Our experimental results
show that the proposed approach outperforms existing state-of-the-art
approaches on user-generated noisy social media data, such as Twitter texts,
and achieves highly competitive performance on a cleaner corpus. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Organic-inorganic Copper(II)-based Material: a Low-Toxic, Highly Stable Light Absorber beyond Organolead Perovskites,
Abstract: Lead halide perovskite solar cells have recently emerged as a very promising
photovoltaic technology due to their excellent power conversion efficiencies;
however, the toxicity of lead and the poor stability of perovskite materials
remain two main challenges that need to be addressed. Here, for the first time,
we report a lead-free, highly stable C6H4NH2CuBr2I compound. The C6H4NH2CuBr2I
films exhibit extraordinary hydrophobic behavior with a contact angle of
approximately 90 degree, and their X-ray diffraction patterns remain unchanged
even after four hours of water immersion. UV-Vis absorption spectrum shows that
C6H4NH2CuBr2I compound has an excellent optical absorption over the entire
visible spectrum. We applied this copper-based light absorber in printable
mesoscopic solar cell for the initial trial and achieved a power conversion
efficiency of 0.5%. Our study represents an alternative pathway to develop
low-toxic and highly stable organic-inorganic hybrid materials for photovoltaic
application. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Acyclic cluster algebras, reflection groups, and curves on a punctured disc,
Abstract: We establish a bijective correspondence between certain non-self-intersecting
curves in an $n$-punctured disc and positive ${\mathbf c}$-vectors of acyclic
cluster algebras whose quivers have multiple arrows between every pair of
vertices. As a corollary, we obtain a proof of a conjecture by K.-H. Lee and K.
Lee (arXiv:1703.09113) on the combinatorial description of real Schur roots for
acyclic quivers with multiple arrows, and give a combinatorial characterization
of seeds in terms of curves in an $n$-punctured disc. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: The connection between zero chromaticity and long in-plane polarization lifetime in a magnetic storage ring,
Abstract: In this paper, we demonstrate the connection between a magnetic storage ring
with additional sextupole fields set so that the x and y chromaticities vanish
and the maximizing of the lifetime of in-plane polarization (IPP) for a
0.97-GeV/c deuteron beam. The IPP magnitude was measured by continuously
monitoring the down-up scattering asymmetry (sensitive to sideways
polarization) in an in-beam, carbon-target polarimeter and unfolding the
precession of the IPP due to the magnetic anomaly of the deuteron. The optimum
operating conditions for a long IPP lifetime were made by scanning the field of
the storage ring sextupole magnet families while observing the rate of IPP loss
during storage of the beam. The beam was bunched and electron cooled. The IPP
losses appear to arise from the change of the orbit circumference, and
consequently the particle speed and spin tune, due to the transverse betatron
oscillations of individual particles in the beam. The effects of these changes
are canceled by an appropriate sextupole field setting. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Multi-agent Time-based Decision-making for the Search and Action Problem,
Abstract: Many robotic applications, such as search-and-rescue, require multiple agents
to search for and perform actions on targets. However, such missions present
several challenges, including cooperative exploration, task selection and
allocation, time limitations, and computational complexity. To address this, we
propose a decentralized multi-agent decision-making framework for the search
and action problem with time constraints. The main idea is to treat time as an
allocated budget in a setting where each agent action incurs a time cost and
yields a certain reward. Our approach leverages probabilistic reasoning to make
near-optimal decisions leading to maximized reward. We evaluate our method in
the search, pick, and place scenario of the Mohamed Bin Zayed International
Robotics Challenge (MBZIRC), by using a probability density map and reward
prediction function to assess actions. Extensive simulations show that our
algorithm outperforms benchmark strategies, and we demonstrate system
integration in a Gazebo-based environment, validating the framework's readiness
for field application. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Robotics"
] |
Title: Anisotropic twicing for single particle reconstruction using autocorrelation analysis,
Abstract: The missing phase problem in X-ray crystallography is commonly solved using
the technique of molecular replacement, which borrows phases from a previously
solved homologous structure, and appends them to the measured Fourier
magnitudes of the diffraction patterns of the unknown structure. More recently,
molecular replacement has been proposed for solving the missing orthogonal
matrices problem arising in Kam's autocorrelation analysis for single particle
reconstruction using X-ray free electron lasers and cryo-EM. In classical
molecular replacement, it is common to estimate the magnitudes of the unknown
structure as twice the measured magnitudes minus the magnitudes of the
homologous structure, a procedure known as `twicing'. Mathematically, this is
equivalent to finding an unbiased estimator for a complex-valued scalar. We
generalize this scheme for the case of estimating real or complex valued
matrices arising in single particle autocorrelation analysis. We name this
approach "Anisotropic Twicing" because unlike the scalar case, the unbiased
estimator is not obtained by a simple magnitude isotropic correction. We
compare the performance of the least squares, twicing and anisotropic twicing
estimators on synthetic and experimental datasets. We demonstrate 3D homology
modeling in cryo-EM directly from experimental data without iterative
refinement or class averaging, for the first time. | [
1,
0,
0,
1,
0,
0
] | [
"Physics",
"Quantitative Biology"
] |
Title: Dimensional reduction and its breakdown in the driven random field O(N) model,
Abstract: The critical behavior of the random field $O(N)$ model driven at a uniform
velocity is investigated at zero-temperature. From naive phenomenological
arguments, we introduce a dimensional reduction property, which relates the
large-scale behavior of the $D$-dimensional driven random field $O(N)$ model to
that of the $(D-1)$-dimensional pure $O(N)$ model. This is an analogue of the
dimensional reduction property in equilibrium cases, which states that the
large-scale behavior of $D$-dimensional random field models is identical to
that of $(D-2)$-dimensional pure models. However, the dimensional reduction
property breaks down in low enough dimensions due to the presence of multiple
meta-stable states. By employing the non-perturbative renormalization group
approach, we calculate the critical exponents of the driven random field $O(N)$
model near three-dimensions and determine the range of $N$ in which the
dimensional reduction breaks down. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Mathematics"
] |
Title: On The Communication Complexity of High-Dimensional Permutations,
Abstract: We study the multiparty communication complexity of high dimensional
permutations, in the Number On the Forehead (NOF) model. This model is due to
Chandra, Furst and Lipton (CFL) who also gave a nontrivial protocol for the
Exactly-n problem where three players receive integer inputs and need to decide
if their inputs sum to a given integer $n$. There is a considerable body of
literature dealing with the same problem, where $(\mathbb{N},+)$ is replaced by
some other abelian group. Our work can be viewed as a far-reaching extension of
this line of work.
We show that the known lower bounds for that group-theoretic problem apply to
all high dimensional permutations. We introduce new proof techniques that
appeal to recent advances in Additive Combinatorics and Ramsey theory. We
reveal new and unexpected connections between the NOF communication complexity
of high dimensional permutations and a variety of well known and thoroughly
studied problems in combinatorics.
Previous protocols for Exactly-n all rely on the construction of large sets
of integers without a 3-term arithmetic progression. No direct algorithmic
protocol was previously known for the problem, and we provide the first such
algorithm. This suggests new ways to significantly improve the CFL protocol.
Many new open questions are presented throughout. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: One-to-One Matching of RTT and Path Changes,
Abstract: Route selection based on performance measurements is an essential task in
inter-domain Traffic Engineering. It can benefit from the detection of
significant changes in RTT measurements and the understanding on potential
causes of change. Among the extensive works on change detection methods and
their applications in various domains, few focus on RTT measurements. It is
thus unclear which approach works the best on such data.
In this paper, we present an evaluation framework for change detection on RTT
times series, consisting of: 1) a carefully labelled 34,008-hour RTT dataset as
ground truth; 2) a scoring method specifically tailored for RTT measurements.
Furthermore, we proposed a data transformation that improves the detection
performance of existing methods. Path changes are as well attended to. We fix
shortcomings of previous works by distinguishing path changes due to routing
protocols (IGP and BGP) from those caused by load balancing.
Finally, we apply our change detection methods to a large set of measurements
from RIPE Atlas. The characteristics of both RTT and path changes are analyzed;
the correlation between the two are also illustrated. We identify extremely
frequent AS path changes yet with few consequences on RTT, which has not been
reported before. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Infinite horizon asymptotic average optimality for large-scale parallel server networks,
Abstract: We study infinite-horizon asymptotic average optimality for parallel server
network with multiple classes of jobs and multiple server pools in the
Halfin-Whitt regime. Three control formulations are considered: 1) minimizing
the queueing and idleness cost, 2) minimizing the queueing cost under a
constraints on idleness at each server pool, and 3) fairly allocating the idle
servers among different server pools. For the third problem, we consider a
class of bounded-queue, bounded-state (BQBS) stable networks, in which any
moment of the state is bounded by that of the queue only (for both the limiting
diffusion and diffusion-scaled state processes). We show that the optimal
values for the diffusion-scaled state processes converge to the corresponding
values of the ergodic control problems for the limiting diffusion. We present a
family of state-dependent Markov balanced saturation policies (BSPs) that
stabilize the controlled diffusion-scaled state processes. It is shown that
under these policies, the diffusion-scaled state process is exponentially
ergodic, provided that at least one class of jobs has a positive abandonment
rate. We also establish useful moment bounds, and study the ergodic properties
of the diffusion-scaled state processes, which play a crucial role in proving
the asymptotic optimality. | [
1,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Statistics"
] |
Title: Understanding low-temperature bulk transport in samarium hexaboride without relying on in-gap bulk states,
Abstract: We present a new model to explain the difference between the transport and
spectroscopy gaps in samarium hexaboride (SmB$_6$), which has been a mystery
for some time. We propose that SmB$_6$ can be modeled as an intrinsic
semiconductor with a depletion length that diverges at cryogenic temperatures.
In this model, we find a self-consistent solution to Poisson's equation in the
bulk, with boundary conditions based on Fermi energy pinning due to surface
charges. The solution yields band bending in the bulk; this explains the
difference between the two gaps because spectroscopic methods measure the gap
near the surface, while transport measures the average over the bulk. We also
connect the model to transport parameters, including the Hall coefficient and
thermopower, using semiclassical transport theory. The divergence of the
depletion length additionally explains the 10-12 K feature in data for these
parameters, demonstrating a crossover from bulk dominated transport above this
temperature to surface-dominated transport below this temperature. We find good
agreement between our model and a collection of transport data from 4-40 K.
This model can also be generalized to materials with similar band structure. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Towards Optimal Strategy for Adaptive Probing in Incomplete Networks,
Abstract: We investigate a graph probing problem in which an agent has only an
incomplete view $G' \subsetneq G$ of the network and wishes to explore the
network with least effort. In each step, the agent selects a node $u$ in $G'$
to probe. After probing $u$, the agent gains the information about $u$ and its
neighbors. All the neighbors of $u$ become \emph{observed} and are
\emph{probable} in the subsequent steps (if they have not been probed). What is
the best probing strategy to maximize the number of nodes explored in $k$
probes? This problem serves as a fundamental component for other
decision-making problems in incomplete networks such as information harvesting
in social networks, network crawling, network security, and viral marketing
with incomplete information.
While there are a few methods proposed for the problem, none can perform
consistently well across different network types. In this paper, we establish a
strong (in)approximability for the problem, proving that no algorithm can
guarantees finite approximation ratio unless P=NP. On the bright side, we
design learning frameworks to capture the best probing strategies for
individual network. Our extensive experiments suggest that our framework can
learn efficient probing strategies that \emph{consistently} outperform previous
heuristics and metric-based approaches. | [
1,
1,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Using Multiple Seasonal Holt-Winters Exponential Smoothing to Predict Cloud Resource Provisioning,
Abstract: Elasticity is one of the key features of cloud computing that attracts many
SaaS providers to minimize their services' cost. Cost is minimized by
automatically provision and release computational resources depend on actual
computational needs. However, delay of starting up new virtual resources can
cause Service Level Agreement violation. Consequently, predicting cloud
resources provisioning gains a lot of attention to scale computational
resources in advance. However, most of current approaches do not consider
multi-seasonality in cloud workloads. This paper proposes cloud resource
provisioning prediction algorithm based on Holt-Winters exponential smoothing
method. The proposed algorithm extends Holt-Winters exponential smoothing
method to model cloud workload with multi-seasonal cycles. Prediction accuracy
of the proposed algorithm has been improved by employing Artificial Bee Colony
algorithm to optimize its parameters. Performance of the proposed algorithm has
been evaluated and compared with double and triple exponential smoothing
methods. Our results have shown that the proposed algorithm outperforms other
methods. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Possible evidence for spin-transfer torque induced by spin-triplet supercurrent,
Abstract: Cooper pairs in superconductors are normally spin singlet. Nevertheless,
recent studies suggest that spin-triplet Cooper pairs can be created at
carefully engineered superconductor-ferromagnet interfaces. If Cooper pairs are
spin-polarized they would transport not only charge but also a net spin
component, but without dissipation, and therefore minimize the heating effects
associated with spintronic devices. Although it is now established that triplet
supercurrents exist, their most interesting property - spin - is only inferred
indirectly from transport measurements. In conventional spintronics, it is well
known that spin currents generate spin-transfer torques that alter
magnetization dynamics and switch magnetic moments. The observation of similar
effects due to spin-triplet supercurrents would not only confirm the net spin
of triplet pairs but also pave the way for applications of superconducting
spintronics. Here, we present a possible evidence for spin-transfer torques
induced by triplet supercurrents in superconductor/ferromagnet/superconductor
(S/F/S) Josephson junctions. Below the superconducting transition temperature
T_c, the ferromagnetic resonance (FMR) field at X-band (~ 9.0 GHz) shifts
rapidly to a lower field with decreasing temperature due to the spin-transfer
torques induced by triplet supercurrents. In contrast, this phenomenon is
absent in ferromagnet/superconductor (F/S) bilayers and
superconductor/insulator/ferromagnet/superconductor (S/I/F/S) multilayers where
no supercurrents pass through the ferromagnetic layer. These experimental
observations are discussed with theoretical predictions for ferromagnetic
Josephson junctions with precessing magnetization. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Evaluation of equity-based debt obligations,
Abstract: We consider a class of participation rights, i.e. obligations issued by a
company to investors who are interested in performance-based compensation.
Albeit having desirable economic properties equity-based debt obligations
(EbDO) pose challenges in accounting and contract pricing. We formulate and
solve the associated mathematical problem in a discrete time, as well as a
continuous time setting. In the latter case the problem is reduced to a
forward-backward stochastic differential equation (FBSDE) and solved using the
method of decoupling fields. | [
0,
0,
0,
0,
0,
1
] | [
"Quantitative Finance",
"Mathematics"
] |
Title: Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP,
Abstract: Online sparse linear regression is an online problem where an algorithm
repeatedly chooses a subset of coordinates to observe in an adversarially
chosen feature vector, makes a real-valued prediction, receives the true label,
and incurs the squared loss. The goal is to design an online learning algorithm
with sublinear regret to the best sparse linear predictor in hindsight. Without
any assumptions, this problem is known to be computationally intractable. In
this paper, we make the assumption that data matrix satisfies restricted
isometry property, and show that this assumption leads to computationally
efficient algorithms with sublinear regret for two variants of the problem. In
the first variant, the true label is generated according to a sparse linear
model with additive Gaussian noise. In the second, the true label is chosen
adversarially. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics",
"Mathematics"
] |
Title: SEPIA - a new single pixel receiver at the APEX Telescope,
Abstract: Context: We describe the new SEPIA (Swedish-ESO PI Instrument for APEX)
receiver, which was designed and built by the Group for Advanced Receiver
Development (GARD), at Onsala Space Observatory (OSO) in collaboration with
ESO. It was installed and commissioned at the APEX telescope during 2015 with
an ALMA Band 5 receiver channel and updated with a new frequency channel (ALMA
Band 9) in February 2016. Aims: This manuscript aims to provide, for observers
who use the SEPIA receiver, a reference in terms of the hardware description,
optics and performance as well as the commissioning results. Methods: Out of
three available receiver cartridge positions in SEPIA, the two current
frequency channels, corresponding to ALMA Band 5, the RF band 158--211 GHz, and
Band 9, the RF band 600--722 GHz, provide state-of-the-art dual polarization
receivers. The Band 5 frequency channel uses 2SB SIS mixers with an average SSB
noise temperature around 45K with IF (intermediate frequency) band 4--8 GHz for
each sideband providing total 4x4 GHz IF band. The Band 9 frequency channel
uses DSB SIS mixers with a noise temperature of 75--125K with IF band 4--12 GHz
for each polarization. Results: Both current SEPIA receiver channels are
available to all APEX observers. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting,
Abstract: We present an algorithm to identify sparse dependence structure in continuous
and non-Gaussian probability distributions, given a corresponding set of data.
The conditional independence structure of an arbitrary distribution can be
represented as an undirected graph (or Markov random field), but most
algorithms for learning this structure are restricted to the discrete or
Gaussian cases. Our new approach allows for more realistic and accurate
descriptions of the distribution in question, and in turn better estimates of
its sparse Markov structure. Sparsity in the graph is of interest as it can
accelerate inference, improve sampling methods, and reveal important
dependencies between variables. The algorithm relies on exploiting the
connection between the sparsity of the graph and the sparsity of transport
maps, which deterministically couple one probability measure to another. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: QuanFuzz: Fuzz Testing of Quantum Program,
Abstract: Nowadays, quantum program is widely used and quickly developed. However, the
absence of testing methodology restricts their quality. Different input format
and operator from traditional program make this issue hard to resolve.
In this paper, we present QuanFuzz, a search-based test input generator for
quantum program. We define the quantum sensitive information to evaluate test
input for quantum program and use matrix generator to generate test cases with
higher coverage. First, we extract quantum sensitive information -- measurement
operations on those quantum registers and the sensitive branches associated
with those measurement results, from the quantum source code. Then, we use the
sensitive information guided algorithm to mutate the initial input matrix and
select those matrices which improve the probability weight for a value of the
quantum register to trigger the sensitive branch. The process keeps iterating
until the sensitive branch triggered. We tested QuanFuzz on benchmarks and
acquired 20% - 60% more coverage compared to traditional testing input
generation. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Physics"
] |
Title: Unravelling Airbnb Predicting Price for New Listing,
Abstract: This paper analyzes Airbnb listings in the city of San Francisco to better
understand how different attributes such as bedrooms, location, house type
amongst others can be used to accurately predict the price of a new listing
that optimal in terms of the host's profitability yet affordable to their
guests. This model is intended to be helpful to the internal pricing tools that
Airbnb provides to its hosts. Furthermore, additional analysis is performed to
ascertain the likelihood of a listings availability for potential guests to
consider while making a booking. The analysis begins with exploring and
examining the data to make necessary transformations that can be conducive for
a better understanding of the problem at large while helping us make
hypothesis. Moving further, machine learning models are built that are
intuitive to use to validate the hypothesis on pricing and availability and run
experiments in that context to arrive at a viable solution. The paper then
concludes with a discussion on the business implications, associated risks and
future scope. | [
0,
0,
0,
0,
0,
1
] | [
"Computer Science",
"Statistics",
"Quantitative Finance"
] |
Title: The self-referring DNA and protein: a remark on physical and geometrical aspects,
Abstract: All known life forms are based upon a hierarchy of interwoven feedback loops,
operating over a cascade of space, time and energy scales. Among the most basic
loops are those connecting DNA and proteins. For example, in genetic networks,
DNA genes are expressed as proteins, which may bind near the same genes and
thereby control their own expression. In this molecular type of self-reference,
information is mapped from the DNA sequence to the protein and back to DNA.
There is a variety of dynamic DNA-protein self-reference loops, and the purpose
of this remark is to discuss certain geometrical and physical aspects related
to the back and forth mapping between DNA and proteins. The discussion raises
basic questions regarding the nature of DNA and proteins as self-referring
matter, which are examined in a simple toy model. | [
0,
0,
0,
0,
1,
0
] | [
"Quantitative Biology",
"Physics"
] |
Title: Optimal input design for system identification using spectral decomposition,
Abstract: The aim of this paper is to design a band-limited optimal input with power
constraints for identifying a linear multi-input multi-output system. It is
assumed that the nominal system parameters are specified. The key idea is to
use the spectral decomposition theorem and write the power spectrum as
$\phi_{u}(j\omega)=\frac{1}{2}H(j\omega)H^*(j\omega)$. The matrix $H(j\omega)$
is expressed in terms of a truncated basis for
$\mathcal{L}^2\left(\left[-\omega_{\mbox{cut-off}},\omega_{\mbox{cut-off}}\right]\right)$.
With this parameterization, the elements of the Fisher Information Matrix and
the power constraints turn out to be homogeneous quadratics in the basis
coefficients. The optimality criterion used are the well-known
$\mathcal{D}-$optimality, $\mathcal{A}-$optimality, $\mathcal{T}-$optimality
and $\mathcal{E}-$optimality. The resulting optimization problem is non-convex
in general. A lower bound on the optimum is obtained through a bi-linear
formulation of the problem, while an upper bound is obtained through a convex
relaxation. These bounds can be computed efficiently as the associated problems
are convex. The lower bound is used as a sub-optimal solution, the
sub-optimality of which is determined by the difference in the bounds.
Interestingly, the bounds match in many instances and thus, the global optimum
is achieved. A discussion on the non-convexity of the optimization problem is
also presented. Simulations are provided for corroboration. | [
1,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Statistics",
"Computer Science"
] |
Title: Creating a Web Analysis and Visualization Environment,
Abstract: Due to the rapid growth of the World Wide Web, resource discovery becomes an
increasing problem. As an answer to the demand for information management, a
third generation of World-Wide Web tools will evolve: information gathering and
processing agents. This paper describes WAVE (Web Analysis and Visualization
Environment), a 3D interface for World-Wide Web information visualization and
browsing. It uses the mathematical theory of concept analysis to conceptually
cluster objects, and to create a three-dimensional layout of information nodes.
So-called "conceptual scales" for attributes, such as location, title,
keywords, topic, size, or modification time, provide a formal mechanism that
automatically classifies and categorizes documents, creating a conceptual
information space. A visualization shell serves as an ergonomically sound user
interface for exploring this information space. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: On the image of the almost strict Morse n-category under almost strict n-functors,
Abstract: In an earlier work, we constructed the almost strict Morse $n$-category
$\mathcal X$ which extends Cohen $\&$ Jones $\&$ Segal's flow category. In this
article, we define two other almost strict $n$-categories $\mathcal V$ and
$\mathcal W$ where $\mathcal V$ is based on homomorphisms between real vector
spaces and $\mathcal W$ consists of tuples of positive integers. The Morse
index and the dimension of the Morse moduli spaces give rise to almost strict
$n$-category functors $\mathcal F : \mathcal X \to \mathcal V$ and $\mathcal G
: \mathcal X \to \mathcal W$. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Short-term Memory of Deep RNN,
Abstract: The extension of deep learning towards temporal data processing is gaining an
increasing research interest. In this paper we investigate the properties of
state dynamics developed in successive levels of deep recurrent neural networks
(RNNs) in terms of short-term memory abilities. Our results reveal interesting
insights that shed light on the nature of layering as a factor of RNN design.
Noticeably, higher layers in a hierarchically organized RNN architecture
results to be inherently biased towards longer memory spans even prior to
training of the recurrent connections. Moreover, in the context of Reservoir
Computing framework, our analysis also points out the benefit of a layered
recurrent organization as an efficient approach to improve the memory skills of
reservoir models. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science"
] |
Title: Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge,
Abstract: We consider the use of Deep Learning methods for modeling complex phenomena
like those occurring in natural physical processes. With the large amount of
data gathered on these phenomena the data intensive paradigm could begin to
challenge more traditional approaches elaborated over the years in fields like
maths or physics. However, despite considerable successes in a variety of
application domains, the machine learning field is not yet ready to handle the
level of complexity required by such problems. Using an example application,
namely Sea Surface Temperature Prediction, we show how general background
knowledge gained from physics could be used as a guideline for designing
efficient Deep Learning models. In order to motivate the approach and to assess
its generality we demonstrate a formal link between the solution of a class of
differential equations underlying a large family of physical phenomena and the
proposed model. Experiments and comparison with series of baselines including a
state of the art numerical approach is then provided. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Physics"
] |
Title: Spin pumping into superconductors: A new probe of spin dynamics in a superconducting thin film,
Abstract: Spin pumping refers to the microwave-driven spin current injection from a
ferromagnet into the adjacent target material. We theoretically investigate the
spin pumping into superconductors by fully taking account of impurity
spin-orbit scattering that is indispensable to describe diffusive spin
transport with finite spin diffusion length. We calculate temperature
dependence of the spin pumping signal and show that a pronounced coherence peak
appears immediately below the superconducting transition temperature Tc, which
survives even in the presence of the spin-orbit scattering. The phenomenon
provides us with a new way of studying the dynamic spin susceptibility in a
superconducting thin film. This is contrasted with the nuclear magnetic
resonance technique used to study a bulk superconductor. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Degenerations of NURBS curves while all of weights approaching infinity,
Abstract: NURBS curve is widely used in Computer Aided Design and Computer Aided
Geometric Design. When a single weight approaches infinity, the limit of a
NURBS curve tends to the corresponding control point. In this paper, a kind of
control structure of a NURBS curve, called regular control curve, is defined.
We prove that the limit of the NURBS curve is exactly its regular control curve
when all of weights approach infinity, where each weight is multiplied by a
certain one-parameter function tending to infinity, different for each control
point. Moreover, some representative examples are presented to show this
property and indicate its application for shape deformation. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Sparse-Group Bayesian Feature Selection Using Expectation Propagation for Signal Recovery and Network Reconstruction,
Abstract: We present a Bayesian method for feature selection in the presence of
grouping information with sparsity on the between- and within group level.
Instead of using a stochastic algorithm for parameter inference, we employ
expectation propagation, which is a deterministic and fast algorithm. Available
methods for feature selection in the presence of grouping information have a
number of short-comings: on one hand, lasso methods, while being fast,
underestimate the regression coefficients and do not make good use of the
grouping information, and on the other hand, Bayesian approaches, while
accurate in parameter estimation, often rely on the stochastic and slow Gibbs
sampling procedure to recover the parameters, rendering them infeasible e.g.
for gene network reconstruction. Our approach of a Bayesian sparse-group
framework with expectation propagation enables us to not only recover accurate
parameter estimates in signal recovery problems, but also makes it possible to
apply this Bayesian framework to large-scale network reconstruction problems.
The presented method is generic but in terms of application we focus on gene
regulatory networks. We show on simulated and experimental data that the method
constitutes a good choice for network reconstruction regarding the number of
correctly selected features, prediction on new data and reasonable computing
time. | [
0,
0,
0,
1,
0,
0
] | [
"Statistics",
"Computer Science",
"Quantitative Biology"
] |
Title: A Simple, Fast and Fully Automated Approach for Midline Shift Measurement on Brain Computed Tomography,
Abstract: Brain CT has become a standard imaging tool for emergent evaluation of brain
condition, and measurement of midline shift (MLS) is one of the most important
features to address for brain CT assessment. We present a simple method to
estimate MLS and propose a new alternative parameter to MLS: the ratio of MLS
over the maximal width of intracranial region (MLS/ICWMAX). Three neurosurgeons
and our automated system were asked to measure MLS and MLS/ICWMAX in the same
sets of axial CT images obtained from 41 patients admitted to ICU under
neurosurgical service. A weighted midline (WML) was plotted based on individual
pixel intensities, with higher weighted given to the darker portions. The MLS
could then be measured as the distance between the WML and ideal midline (IML)
near the foramen of Monro. The average processing time to output an automatic
MLS measurement was around 10 seconds. Our automated system achieved an overall
accuracy of 90.24% when the CT images were calibrated automatically, and
performed better when the calibrations of head rotation were done manually
(accuracy: 92.68%). MLS/ICWMAX and MLS both gave results in same confusion
matrices and produced similar ROC curve results. We demonstrated a simple, fast
and accurate automated system of MLS measurement and introduced a new parameter
(MLS/ICWMAX) as a good alternative to MLS in terms of estimating the degree of
brain deformation, especially when non-DICOM images (e.g. JPEG) are more easily
accessed. | [
1,
1,
0,
0,
0,
0
] | [
"Computer Science",
"Quantitative Biology"
] |
Title: Anisotropic Dielectric Relaxation in Single Crystal H$_{2}$O Ice Ih from 80-250 K,
Abstract: Three properties of the dielectric relaxation in ultra-pure single
crystalline H$_{2}$O ice Ih were probed at temperatures between 80-250 K; the
thermally stimulated depolarization current, static electrical conductivity,
and dielectric relaxation time. The measurements were made with a guarded
parallel-plate capacitor constructed of fused quartz with Au electrodes. The
data agree with relaxation-based models and provide for the determination of
activation energies, which suggest that relaxation in ice is dominated by
Bjerrum defects below 140 K. Furthermore, anisotropy in the dielectric
relaxation data reveals that molecular reorientations along the
crystallographic $c$-axis are energetically favored over those along the
$a$-axis between 80-140 K. These results lend support for the postulate of a
shared origin between the dielectric relaxation dynamics and the thermodynamic
partial proton-ordering in ice near 100 K, and suggest a preference for
ordering along the $c$-axis. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: On the Constituent Attributes of Software and Organisational Resilience,
Abstract: Our societies are increasingly dependent on services supplied by computers &
their software. New technology only exacerbates this dependence by increasing
the number, performance, and degree of autonomy and inter-connectivity of
software-empowered computers and cyber-physical "things", which translates into
unprecedented scenarios of interdependence. As a consequence, guaranteeing the
persistence-of-identity of individual & collective software systems and
software-backed organisations becomes an important prerequisite toward
sustaining the safety, security, & quality of the computer services supporting
human societies. Resilience is the term used to refer to the ability of a
system to retain its functional and non-functional identity. In this article we
conjecture that a better understanding of resilience may be reached by
decomposing it into ancillary constituent properties, the same way as a better
insight in system dependability was obtained by breaking it down into
sub-properties. 3 of the main sub-properties of resilience proposed here refer
respectively to the ability to perceive environmental changes; understand the
implications introduced by those changes; and plan & enact adjustments intended
to improve the system-environment fit. A fourth property characterises the way
the above abilities manifest themselves in computer systems. The 4 properties
are then analyzed in 3 families of case studies, each consisting of 3 software
systems that embed different resilience methods. Our major conclusion is that
reasoning in terms of resilience sub-properties may help revealing the
characteristics and limitations of classic methods and tools meant to achieve
system and organisational resilience. We conclude by suggesting that our method
may prelude to meta-resilient systems -- systems, that is, able to adjust
optimally their own resilience with respect to changing environmental
conditions. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: A Novel Metamaterial-Inspired RF-coil for Preclinical Dual-Nuclei MRI,
Abstract: In this paper we propose, design and test a new dual-nuclei RF-coil inspired
by wire metamaterial structures. The coil operates due to resonant excitation
of hybridized eigenmodes in multimode flat periodic structures comprising
several coupled thin metal strips. It was shown that the field distribution of
the coil (i.e. penetration depth) can be controlled independently at two
different Larmor frequencies by selecting a proper eigenmode in each of two
mutually orthogonal periodic structures. The proposed coil requires no lumped
capacitors for tuning and matching. In order to demonstrate the performance of
the new design, an experimental preclinical coil for $^{19}$F/$^{1}$H imaging
of small animals at 7.05T was engineered and tested on a homogeneous liquid
phantom and in-vivo. The presented results demonstrate that the coil was well
tuned and matched simultaneously at two Larmor frequencies and capable of image
acquisition with both the nuclei reaching large homogeneity area along with a
sufficient signal-to-noise ratio. In an in-vivo experiment it has been shown
that without retuning the setup it was possible to obtain anatomical $^{1}$H
images of a mouse under anesthesia consecutively with $^{19}$F images of a tiny
tube filled with a fluorine-containing liquid and attached to the body of the
mouse. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Markov $L_2$-inequality with the Laguerre weight,
Abstract: Let $w_\alpha(t) := t^{\alpha}\,e^{-t}$, where $\alpha > -1$, be the Laguerre
weight function, and let $\|\cdot\|_{w_\alpha}$ be the associated $L_2$-norm,
$$ \|f\|_{w_\alpha} = \left\{\int_{0}^{\infty} |f(x)|^2
w_\alpha(x)\,dx\right\}^{1/2}\,. $$ By $\mathcal{P}_n$ we denote the set of
algebraic polynomials of degree $\le n$.
We study the best constant $c_n(\alpha)$ in the Markov inequality in this
norm $$ \|p_n'\|_{w_\alpha} \le c_n(\alpha) \|p_n\|_{w_\alpha}\,,\qquad p_n \in
\mathcal{P}_n\,, $$ namely the constant $$ c_n(\alpha) := \sup_{p_n \in
\mathcal{P}_n} \frac{\|p_n'\|_{w_\alpha}}{\|p_n\|_{w_\alpha}}\,. $$ We derive
explicit lower and upper bounds for the Markov constant $c_n(\alpha)$, as well
as for the asymptotic Markov constant $$
c(\alpha)=\lim_{n\rightarrow\infty}\frac{c_n(\alpha)}{n}\,. $$ | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Intrusion Prevention and Detection in Grid Computing - The ALICE Case,
Abstract: Grids allow users flexible on-demand usage of computing resources through
remote communication networks. A remarkable example of a Grid in High Energy
Physics (HEP) research is used in the ALICE experiment at European Organization
for Nuclear Research CERN. Physicists can submit jobs used to process the huge
amount of particle collision data produced by the Large Hadron Collider (LHC).
Grids face complex security challenges. They are interesting targets for
attackers seeking for huge computational resources. Since users can execute
arbitrary code in the worker nodes on the Grid sites, special care should be
put in this environment. Automatic tools to harden and monitor this scenario
are required. Currently, there is no integrated solution for such requirement.
This paper describes a new security framework to allow execution of job
payloads in a sandboxed context. It also allows process behavior monitoring to
detect intrusions, even when new attack methods or zero day vulnerabilities are
exploited, by a Machine Learning approach. We plan to implement the proposed
framework as a software prototype that will be tested as a component of the
ALICE Grid middleware. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Physics"
] |
Title: Multilingual and Cross-lingual Timeline Extraction,
Abstract: In this paper we present an approach to extract ordered timelines of events,
their participants, locations and times from a set of multilingual and
cross-lingual data sources. Based on the assumption that event-related
information can be recovered from different documents written in different
languages, we extend the Cross-document Event Ordering task presented at
SemEval 2015 by specifying two new tasks for, respectively, Multilingual and
Cross-lingual Timeline Extraction. We then develop three deterministic
algorithms for timeline extraction based on two main ideas. First, we address
implicit temporal relations at document level since explicit time-anchors are
too scarce to build a wide coverage timeline extraction system. Second, we
leverage several multilingual resources to obtain a single, inter-operable,
semantic representation of events across documents and across languages. The
result is a highly competitive system that strongly outperforms the current
state-of-the-art. Nonetheless, further analysis of the results reveals that
linking the event mentions with their target entities and time-anchors remains
a difficult challenge. The systems, resources and scorers are freely available
to facilitate its use and guarantee the reproducibility of results. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Mixture modeling on related samples by $ψ$-stick breaking and kernel perturbation,
Abstract: There has been great interest recently in applying nonparametric kernel
mixtures in a hierarchical manner to model multiple related data samples
jointly. In such settings several data features are commonly present: (i) the
related samples often share some, if not all, of the mixture components but
with differing weights, (ii) only some, not all, of the mixture components vary
across the samples, and (iii) often the shared mixture components across
samples are not aligned perfectly in terms of their location and spread, but
rather display small misalignments either due to systematic cross-sample
difference or more often due to uncontrolled, extraneous causes. Properly
incorporating these features in mixture modeling will enhance the efficiency of
inference, whereas ignoring them not only reduces efficiency but can jeopardize
the validity of the inference due to issues such as confounding. We introduce
two techniques for incorporating these features in modeling related data
samples using kernel mixtures. The first technique, called $\psi$-stick
breaking, is a joint generative process for the mixing weights through the
breaking of both a stick shared by all the samples for the components that do
not vary in size across samples and an idiosyncratic stick for each sample for
those components that do vary in size. The second technique is to imbue random
perturbation into the kernels, thereby accounting for cross-sample
misalignment. These techniques can be used either separately or together in
both parametric and nonparametric kernel mixtures. We derive efficient Bayesian
inference recipes based on MCMC sampling for models featuring these techniques,
and illustrate their work through both simulated data and a real flow cytometry
data set in prediction/estimation, cross-sample calibration, and testing
multi-sample differences. | [
0,
0,
0,
1,
0,
0
] | [
"Statistics",
"Mathematics"
] |
Title: A Game-Theoretic Data-Driven Approach for Pseudo-Measurement Generation in Distribution System State Estimation,
Abstract: In this paper, we present an efficient computational framework with the
purpose of generating weighted pseudo-measurements to improve the quality of
Distribution System State Estimation (DSSE) and provide observability with
Advanced Metering Infrastructure (AMI) against unobservable customers and
missing data. The proposed technique is based on a game-theoretic expansion of
Relevance Vector Machines (RVM). This platform is able to estimate the customer
power consumption data and quantify its uncertainty while reducing the
prohibitive computational burden of model training for large AMI datasets. To
achieve this objective, the large training set is decomposed and distributed
among multiple parallel learning entities. The resulting estimations from the
parallel RVMs are then combined using a game-theoretic model based on the idea
of repeated games with vector payoff. It is observed that through this approach
and by exploiting the seasonal changes in customers' behavior the accuracy of
pseudo-measurements can be considerably improved, while introducing robustness
against bad training data samples. The proposed pseudo-measurement generation
model is integrated into a DSSE using a closed-loop information system, which
takes advantage of a Branch Current State Estimator (BCSE) data to further
improve the performance of the designed machine learning framework. This method
has been tested on a practical distribution feeder model with smart meter data
for verification. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Nonsparse learning with latent variables,
Abstract: As a popular tool for producing meaningful and interpretable models,
large-scale sparse learning works efficiently when the underlying structures
are indeed or close to sparse. However, naively applying the existing
regularization methods can result in misleading outcomes due to model
misspecification. In particular, the direct sparsity assumption on coefficient
vectors has been questioned in real applications. Therefore, we consider
nonsparse learning with the conditional sparsity structure that the coefficient
vector becomes sparse after taking out the impacts of certain unobservable
latent variables. A new methodology of nonsparse learning with latent variables
(NSL) is proposed to simultaneously recover the significant observable
predictors and latent factors as well as their effects. We explore a common
latent family incorporating population principal components and derive the
convergence rates of both sample principal components and their score vectors
that hold for a wide class of distributions. With the properly estimated latent
variables, properties including model selection consistency and oracle
inequalities under various prediction and estimation losses are established for
the proposed methodology. Our new methodology and results are evidenced by
simulation and real data examples. | [
0,
0,
1,
1,
0,
0
] | [
"Statistics",
"Mathematics"
] |
Title: The Role of Network Analysis in Industrial and Applied Mathematics,
Abstract: Many problems in industry --- and in the social, natural, information, and
medical sciences --- involve discrete data and benefit from approaches from
subjects such as network science, information theory, optimization,
probability, and statistics. The study of networks is concerned explicitly with
connectivity between different entities, and it has become very prominent in
industrial settings, an importance that has intensified amidst the modern data
deluge. In this commentary, we discuss the role of network analysis in
industrial and applied mathematics, and we give several examples of network
science in industry. We focus, in particular, on discussing a
physical-applied-mathematics approach to the study of networks. We also discuss
several of our own collaborations with industry on projects in network
analysis. | [
1,
1,
0,
0,
0,
0
] | [
"Mathematics",
"Computer Science"
] |
Title: Fano resonances and fluorescence enhancement of a dipole emitter near a plasmonic nanoshell,
Abstract: We analytically study the spontaneous emission of a single optical dipole
emitter in the vicinity of a plasmonic nanoshell, based on the Lorenz-Mie
theory. We show that the fluorescence enhancement due to the coupling between
optical emitter and sphere can be tuned by the aspect ratio of the core-shell
nanosphere and by the distance between the quantum emitter and its surface. In
particular, we demonstrate that both the enhancement and quenching of the
fluorescence intensity are associated with plasmonic Fano resonances induced by
near- and far-field interactions. These Fano resonances have asymmetry
parameters whose signs depend on the orientation of the dipole with respect to
the spherical nanoshell. We also show that if the atomic dipole is oriented
tangentially to the nanoshell, the interaction exhibits saddle points in the
near-field energy flow. This results in a Lorentzian fluorescence enhancement
response in the near field and a Fano line-shape in the far field. The
signatures of this interaction may have interesting applications for sensing
the presence and the orientation of optical emitters in close proximity to
plasmonic nanoshells. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Gradient-enhanced kriging for high-dimensional problems,
Abstract: Surrogate models provide a low computational cost alternative to evaluating
expensive functions. The construction of accurate surrogate models with large
numbers of independent variables is currently prohibitive because it requires a
large number of function evaluations. Gradient-enhanced kriging has the
potential to reduce the number of function evaluations for the desired accuracy
when efficient gradient computation, such as an adjoint method, is available.
However, current gradient-enhanced kriging methods do not scale well with the
number of sampling points due to the rapid growth in the size of the
correlation matrix where new information is added for each sampling point in
each direction of the design space. They do not scale well with the number of
independent variables either due to the increase in the number of
hyperparameters that needs to be estimated. To address this issue, we develop a
new gradient-enhanced surrogate model approach that drastically reduced the
number of hyperparameters through the use of the partial-least squares method
that maintains accuracy. In addition, this method is able to control the size
of the correlation matrix by adding only relevant points defined through the
information provided by the partial-least squares method. To validate our
method, we compare the global accuracy of the proposed method with conventional
kriging surrogate models on two analytic functions with up to 100 dimensions,
as well as engineering problems of varied complexity with up to 15 dimensions.
We show that the proposed method requires fewer sampling points than
conventional methods to obtain the desired accuracy, or provides more accuracy
for a fixed budget of sampling points. In some cases, we get over 3 times more
accurate models than a bench of surrogate models from the literature, and also
over 3200 times faster than standard gradient-enhanced kriging models. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: The biglasso Package: A Memory- and Computation-Efficient Solver for Lasso Model Fitting with Big Data in R,
Abstract: Penalized regression models such as the lasso have been extensively applied
to analyzing high-dimensional data sets. However, due to memory limitations,
existing R packages like glmnet and ncvreg are not capable of fitting
lasso-type models for ultrahigh-dimensional, multi-gigabyte data sets that are
increasingly seen in many areas such as genetics, genomics, biomedical imaging,
and high-frequency finance. In this research, we implement an R package called
biglasso that tackles this challenge. biglasso utilizes memory-mapped files to
store the massive data on the disk, only reading data into memory when
necessary during model fitting, and is thus able to handle out-of-core
computation seamlessly. Moreover, it's equipped with newly proposed, more
efficient feature screening rules, which substantially accelerate the
computation. Benchmarking experiments show that our biglasso package, as
compared to existing popular ones like glmnet, is much more memory- and
computation-efficient. We further analyze a 31 GB real data set on a laptop
with only 16 GB RAM to demonstrate the out-of-core computation capability of
biglasso in analyzing massive data sets that cannot be accommodated by existing
R packages. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Demonstration of a quantum key distribution network in urban fibre-optic communication lines,
Abstract: We report the results of the implementation of a quantum key distribution
(QKD) network using standard fibre communication lines in Moscow. The developed
QKD network is based on the paradigm of trusted repeaters and allows a common
secret key to be generated between users via an intermediate trusted node. The
main feature of the network is the integration of the setups using two types of
encoding, i.e. polarisation encoding and phase encoding. One of the possible
applications of the developed QKD network is the continuous key renewal in
existing symmetric encryption devices with a key refresh time of up to 14 s. | [
1,
0,
0,
0,
0,
0
] | [
"Physics",
"Computer Science"
] |
Title: ZhuSuan: A Library for Bayesian Deep Learning,
Abstract: In this paper we introduce ZhuSuan, a python probabilistic programming
library for Bayesian deep learning, which conjoins the complimentary advantages
of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike
existing deep learning libraries, which are mainly designed for deterministic
neural networks and supervised tasks, ZhuSuan is featured for its deep root
into Bayesian inference, thus supporting various kinds of probabilistic models,
including both the traditional hierarchical Bayesian models and recent deep
generative models. We use running examples to illustrate the probabilistic
programming on ZhuSuan, including Bayesian logistic regression, variational
auto-encoders, deep sigmoid belief networks and Bayesian recurrent neural
networks. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Flatness of Minima in Random Inflationary Landscapes,
Abstract: We study the likelihood which relative minima of random polynomial potentials
support the slow-roll conditions for inflation. Consistent with
renormalizability and boundedness, the coefficients that appear in the
potential are chosen to be order one with respect to the energy scale at which
inflation transpires. Investigation of the single field case illustrates a
window in which the potentials satisfy the slow-roll conditions. When there are
two scalar fields, we find that the probability depends on the choice of
distribution for the coefficients. A uniform distribution yields a $0.05\%$
probability of finding a suitable minimum in the random potential whereas a
maximum entropy distribution yields a $0.1\%$ probability. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Sparse Phase Retrieval via Sparse PCA Despite Model Misspecification: A Simplified and Extended Analysis,
Abstract: We consider the problem of high-dimensional misspecified phase retrieval.
This is where we have an $s$-sparse signal vector $\mathbf{x}_*$ in
$\mathbb{R}^n$, which we wish to recover using sampling vectors
$\textbf{a}_1,\ldots,\textbf{a}_m$, and measurements $y_1,\ldots,y_m$, which
are related by the equation $f(\left<\textbf{a}_i,\textbf{x}_*\right>) = y_i$.
Here, $f$ is an unknown link function satisfying a positive correlation with
the quadratic function. This problem was analyzed in a recent paper by Neykov,
Wang and Liu, who provided recovery guarantees for a two-stage algorithm with
sample complexity $m = O(s^2\log n)$. In this paper, we show that the first
stage of their algorithm suffices for signal recovery with the same sample
complexity, and extend the analysis to non-Gaussian measurements. Furthermore,
we show how the algorithm can be generalized to recover a signal vector
$\textbf{x}_*$ efficiently given geometric prior information other than
sparsity. | [
1,
0,
1,
0,
0,
0
] | [
"Computer Science",
"Mathematics",
"Statistics"
] |
Title: Convex Optimization with Unbounded Nonconvex Oracles using Simulated Annealing,
Abstract: We consider the problem of minimizing a convex objective function $F$ when
one can only evaluate its noisy approximation $\hat{F}$. Unless one assumes
some structure on the noise, $\hat{F}$ may be an arbitrary nonconvex function,
making the task of minimizing $F$ intractable. To overcome this, prior work has
often focused on the case when $F(x)-\hat{F}(x)$ is uniformly-bounded. In this
paper we study the more general case when the noise has magnitude $\alpha F(x)
+ \beta$ for some $\alpha, \beta > 0$, and present a polynomial time algorithm
that finds an approximate minimizer of $F$ for this noise model. Previously,
Markov chains, such as the stochastic gradient Langevin dynamics, have been
used to arrive at approximate solutions to these optimization problems.
However, for the noise model considered in this paper, no single temperature
allows such a Markov chain to both mix quickly and concentrate near the global
minimizer. We bypass this by combining "simulated annealing" with the
stochastic gradient Langevin dynamics, and gradually decreasing the temperature
of the chain in order to approach the global minimizer. As a corollary one can
approximately minimize a nonconvex function that is close to a convex function;
however, the closeness can deteriorate as one moves away from the optimum. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Online $^{222}$Rn removal by cryogenic distillation in the XENON100 experiment,
Abstract: We describe the purification of xenon from traces of the radioactive noble
gas radon using a cryogenic distillation column. The distillation column is
integrated into the gas purification loop of the XENON100 detector for online
radon removal. This enabled us to significantly reduce the constant $^{222}$Rn
background originating from radon emanation. After inserting an auxiliary
$^{222}$Rn emanation source in the gas loop, we determined a radon reduction
factor of R > 27 (95% C.L.) for the distillation column by monitoring the
$^{222}$Rn activity concentration inside the XENON100 detector. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: The Kite Graph is Determined by Its Adjacency Spectrum,
Abstract: The Kite graph $Kite_{p}^{q}$ is obtained by appending the complete graph
$K_{p}$ to a pendant vertex of the path $P_{q}$. In this paper, the kite graph
is proved to be determined by the spectrum of its adjacency matrix. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Visual Progression Analysis of Student Records Data,
Abstract: University curriculum, both on a campus level and on a per-major level, are
affected in a complex way by many decisions of many administrators and faculty
over time. As universities across the United States share an urgency to
significantly improve student success and success retention, there is a
pressing need to better understand how the student population is progressing
through the curriculum, and how to provide better supporting infrastructure and
refine the curriculum for the purpose of improving student outcomes. This work
has developed a visual knowledge discovery system called eCamp that pulls
together a variety of populationscale data products, including student grades,
major descriptions, and graduation records. These datasets were previously
disconnected and only available to and maintained by independent campus
offices. The framework models and analyzes the multi-level relationships hidden
within these data products, and visualizes the student flow patterns through
individual majors as well as through a hierarchy of majors. These results
support analytical tasks involving student outcomes, student retention, and
curriculum design. It is shown how eCamp has revealed student progression
information that was previously unavailable. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with Confounding Correction,
Abstract: While linear mixed model (LMM) has shown a competitive performance in
correcting spurious associations raised by population stratification, family
structures, and cryptic relatedness, more challenges are still to be addressed
regarding the complex structure of genotypic and phenotypic data. For example,
geneticists have discovered that some clusters of phenotypes are more
co-expressed than others. Hence, a joint analysis that can utilize such
relatedness information in a heterogeneous data set is crucial for genetic
modeling.
We proposed the sparse graph-structured linear mixed model (sGLMM) that can
incorporate the relatedness information from traits in a dataset with
confounding correction. Our method is capable of uncovering the genetic
associations of a large number of phenotypes together while considering the
relatedness of these phenotypes. Through extensive simulation experiments, we
show that the proposed model outperforms other existing approaches and can
model correlation from both population structure and shared signals. Further,
we validate the effectiveness of sGLMM in the real-world genomic dataset on two
different species from plants and humans. In Arabidopsis thaliana data, sGLMM
behaves better than all other baseline models for 63.4% traits. We also discuss
the potential causal genetic variation of Human Alzheimer's disease discovered
by our model and justify some of the most important genetic loci. | [
1,
0,
0,
1,
0,
0
] | [
"Statistics",
"Quantitative Biology"
] |
Title: Uniform deviation and moment inequalities for random polytopes with general densities in arbitrary convex bodies,
Abstract: We prove an exponential deviation inequality for the convex hull of a finite
sample of i.i.d. random points with a density supported on an arbitrary convex
body in $\R^d$, $d\geq 2$. When the density is uniform, our result yields rate
optimal upper bounds for all the moments of the missing volume of the convex
hull, uniformly over all convex bodies of $\R^d$: We make no restrictions on
their volume, location in the space or smoothness of their boundary. After
extending an identity due to Efron, we also prove upper bounds for the moments
of the number of vertices of the random polytope. Surprisingly, these bounds do
not depend on the underlying density and we prove that the growth rates that we
obtain are tight in a certain sense. | [
0,
0,
1,
1,
0,
0
] | [
"Mathematics",
"Statistics"
] |
Title: On the Efficiency of Connection Charges---Part II: Integration of Distributed Energy Resources,
Abstract: This two-part paper addresses the design of retail electricity tariffs for
distribution systems with distributed energy resources (DERs). Part I presents
a framework to optimize an ex-ante two-part tariff for a regulated monopolistic
retailer who faces stochastic wholesale prices on the one hand and stochastic
demand on the other. In Part II, the integration of DERs is addressed by
analyzing their endogenous effect on the optimal two-part tariff and the
induced welfare gains. Two DER integration models are considered: (i) a
decentralized model involving behind-the-meter DERs in a net metering setting,
and (ii) a centralized model involving DERs integrated by the retailer. It is
shown that DERs integrated under either model can achieve the same social
welfare and the net-metering tariff structure is optimal. The retail prices
under both integration models are equal and reflect the expected wholesale
prices. The connection charges differ and are affected by the retailer's fixed
costs as well as the statistical dependencies between wholesale prices and
behind-the-meter DERs. In particular, the connection charge of the
decentralized model is generally higher than that of the centralized model. An
empirical analysis is presented to estimate the impact of DER on welfare
distribution and inter-class cross-subsidies using real price and demand data
and simulations. The analysis shows that, with the prevailing retail pricing
and net-metering, consumer welfare decreases with the level of DER integration.
Issues of cross-subsidy and practical drawbacks of decentralized integration
are also discussed. | [
0,
0,
1,
0,
0,
0
] | [
"Quantitative Finance",
"Statistics"
] |
Title: On recurrence in G-spaces,
Abstract: We introduce and analyze the following general concept of recurrence. Let $G$
be a group and let $X$ be a G-space with the action $G\times X\longrightarrow
X$, $(g,x)\longmapsto gx$. For a family $\mathfrak{F}$ of subset of $X$ and
$A\in \mathfrak{F}$, we denote $\Delta_{\mathfrak{F}}(A)=\{g\in G: gB\subseteq
A$ for some $B\in \mathfrak{F}, \ B\subseteq A\}$, and say that a subset $R$ of
$G$ is $\mathfrak{F}$-recurrent if $R\bigcap \Delta_{\mathfrak{F}}
(A)\neq\emptyset$ for each $A\in \mathfrak{F}$. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Deep adversarial neural decoding,
Abstract: Here, we present a novel approach to solve the problem of reconstructing
perceived stimuli from brain responses by combining probabilistic inference
with deep learning. Our approach first inverts the linear transformation from
latent features to brain responses with maximum a posteriori estimation and
then inverts the nonlinear transformation from perceived stimuli to latent
features with adversarial training of convolutional neural networks. We test
our approach with a functional magnetic resonance imaging experiment and show
that it can generate state-of-the-art reconstructions of perceived faces from
brain activations. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Quantitative Biology"
] |
Title: Optimally Guarding 2-Reflex Orthogonal Polyhedra by Reflex Edge Guards,
Abstract: We study the problem of guarding an orthogonal polyhedron having reflex edges
in just two directions (as opposed to three) by placing guards on reflex edges
only.
We show that (r - g)/2 + 1 reflex edge guards are sufficient, where r is the
number of reflex edges in a given polyhedron and g is its genus. This bound is
tight for g=0. We thereby generalize a classic planar Art Gallery theorem of
O'Rourke, which states that the same upper bound holds for vertex guards in an
orthogonal polygon with r reflex vertices and g holes.
Then we give a similar upper bound in terms of m, the total number of edges
in the polyhedron. We prove that (m - 4)/8 + g reflex edge guards are
sufficient, whereas the previous best known bound was 11m/72 + g/6 - 1 edge
guards (not necessarily reflex).
We also discuss the setting in which guards are open (i.e., they are segments
without the endpoints), proving that the same results hold even in this more
challenging case.
Finally, we show how to compute guard locations in O(n log n) time. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Tests for comparing time-invariant and time-varying spectra based on the Anderson-Darling statistic,
Abstract: Based on periodogram-ratios of two univariate time series at different
frequency points, two tests are proposed for comparing their spectra. One is an
Anderson-Darling-like statistic for testing the equality of two time-invariant
spectra. The other is the maximum of Anderson-Darling-like statistics for
testing the equality of two spectra no matter that they are time-invariant and
time-varying. Both of two tests are applicable for independent or dependent
time series. Several simulation examples show that the proposed statistics
outperform those that are also based on periodogram-ratios but constructed by
the Pearson-like statistics. | [
0,
0,
0,
1,
0,
0
] | [
"Statistics",
"Mathematics"
] |
Title: Temperley-Lieb and Birman-Murakami-Wenzl like relations from multiplicity free semi-simple tensor system,
Abstract: In this article we consider conditions under which projection operators in
multiplicity free semi-simple tensor categories satisfy Temperley-Lieb like
relations. This is then used as a stepping stone to prove sufficient conditions
for obtaining a representation of the Birman-Murakami-Wenzl algebra from a
braided multiplicity free semi-simple tensor category. The results are found by
utalising the data of the categories. There is considerable overlap with the
results found in arXiv:1607.08908, where proofs are shown by manipulating
diagrams. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: Wick order, spreadability and exchangeability for monotone commutation relations,
Abstract: We exhibit a Hamel basis for the concrete $*$-algebra $\mathfrak{M}_o$
associated to monotone commutation relations realised on the monotone Fock
space, mainly composed by Wick ordered words of annihilators and creators. We
apply such a result to investigate spreadability and exchangeability of the
stochastic processes arising from such commutation relations. In particular, we
show that spreadability comes from a monoidal action implementing a dissipative
dynamics on the norm closure $C^*$-algebra $\mathfrak{M} =
\overline{\mathfrak{M}_o}$. Moreover, we determine the structure of spreadable
and exchangeable monotone stochastic processes using their correspondence with
sp\-reading invariant and symmetric monotone states, respectively. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: Factorization tricks for LSTM networks,
Abstract: We present two simple ways of reducing the number of parameters and
accelerating the training of large Long Short-Term Memory (LSTM) networks: the
first one is "matrix factorization by design" of LSTM matrix into the product
of two smaller matrices, and the second one is partitioning of LSTM matrix, its
inputs and states into the independent groups. Both approaches allow us to
train large LSTM networks significantly faster to the near state-of the art
perplexity while using significantly less RNN parameters. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science"
] |
Title: A Scalable Framework for Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters with Weight and Workload Balancing,
Abstract: Deep Neural Networks (DNNs) have revolutionized numerous applications, but
the demand for ever more performance remains unabated. Scaling DNN computations
to larger clusters is generally done by distributing tasks in batch mode using
methods such as distributed synchronous SGD. Among the issues with this
approach is that to make the distributed cluster work with high utilization,
the workload distributed to each node must be large, which implies nontrivial
growth in the SGD mini-batch size.
In this paper, we propose a framework called FPDeep, which uses a hybrid of
model and layer parallelism to configure distributed reconfigurable clusters to
train DNNs. This approach has numerous benefits. First, the design does not
suffer from batch size growth. Second, novel workload and weight partitioning
leads to balanced loads of both among nodes. And third, the entire system is a
fine-grained pipeline. This leads to high parallelism and utilization and also
minimizes the time features need to be cached while waiting for
back-propagation. As a result, storage demand is reduced to the point where
only on-chip memory is used for the convolution layers. We evaluate FPDeep with
the Alexnet, VGG-16, and VGG-19 benchmarks. Experimental results show that
FPDeep has good scalability to a large number of FPGAs, with the limiting
factor being the FPGA-to-FPGA bandwidth. With 6 transceivers per FPGA, FPDeep
shows linearity up to 83 FPGAs. Energy efficiency is evaluated with respect to
GOPs/J. FPDeep provides, on average, 6.36x higher energy efficiency than
comparable GPU servers. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Randomized Load Balancing on Networks with Stochastic Inputs,
Abstract: Iterative load balancing algorithms for indivisible tokens have been studied
intensively in the past. Complementing previous worst-case analyses, we study
an average-case scenario where the load inputs are drawn from a fixed
probability distribution. For cycles, tori, hypercubes and expanders, we obtain
almost matching upper and lower bounds on the discrepancy, the difference
between the maximum and the minimum load. Our bounds hold for a variety of
probability distributions including the uniform and binomial distribution but
also distributions with unbounded range such as the Poisson and geometric
distribution. For graphs with slow convergence like cycles and tori, our
results demonstrate a substantial difference between the convergence in the
worst- and average-case. An important ingredient in our analysis is new upper
bound on the t-step transition probability of a general Markov chain, which is
derived by invoking the evolving set process. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: The classification of Lagrangians nearby the Whitney immersion,
Abstract: The Whitney immersion is a Lagrangian sphere inside the four-dimensional
symplectic vector space which has a single transverse double point of
self-intersection index $+1.$ This Lagrangian also arises as the Weinstein
skeleton of the complement of a binodal cubic curve inside the projective
plane, and the latter Weinstein manifold is thus the `standard' neighbourhood
of Lagrangian immersions of this type. We classify the Lagrangians inside such
a neighbourhood which are homologous to the Whitney immersion, and which either
are embedded or immersed with a single double point; they are shown to be
Hamiltonian isotopic to either product tori, Chekanov tori, or rescalings of
the Whitney immersion. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: Simulation study of energy resolution, position resolution and $π^0$-$γ$ separation of a sampling electromagnetic calorimeter at high energies,
Abstract: A simulation study of energy resolution, position resolution, and
$\pi^0$-$\gamma$ separation using multivariate methods of a sampling
calorimeter is presented. As a realistic example, the geometry of the
calorimeter is taken from the design geometry of the Shashlik calorimeter which
was considered as a candidate for CMS endcap for the phase II of LHC running.
The methods proposed in this paper can be easily adapted to various geometrical
layouts of a sampling calorimeter. Energy resolution is studied for different
layouts and different absorber-scintillator combinations of the Shashlik
detector. It is shown that a boosted decision tree using fine grained
information of the calorimeter can perform three times better than a cut-based
method for separation of $\pi^0$ from $\gamma$ over a large energy range of 20
GeV-200 GeV. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Computer Science"
] |
Title: Exact Combinatorial Inference for Brain Images,
Abstract: The permutation test is known as the exact test procedure in statistics.
However, often it is not exact in practice and only an approximate method since
only a small fraction of every possible permutation is generated. Even for a
small sample size, it often requires to generate tens of thousands
permutations, which can be a serious computational bottleneck. In this paper,
we propose a novel combinatorial inference procedure that enumerates all
possible permutations combinatorially without any resampling. The proposed
method is validated against the standard permutation test in simulation studies
with the ground truth. The method is further applied in twin DTI study in
determining the genetic contribution of the minimum spanning tree of the
structural brain connectivity. | [
0,
0,
0,
1,
1,
0
] | [
"Statistics",
"Quantitative Biology"
] |
Title: Laser annealing heals radiation damage in avalanche photodiodes,
Abstract: Avalanche photodiodes (APDs) are a practical option for space-based quantum
communications requiring single-photon detection. However, radiation damage to
APDs significantly increases their dark count rates and reduces their useful
lifetimes in orbit. We show that high-power laser annealing of irradiated APDs
of three different models (Excelitas C30902SH, Excelitas SLiK, and Laser
Components SAP500S2) heals the radiation damage and substantially restores low
dark count rates. Of nine samples, the maximum dark count rate reduction factor
varies between 5.3 and 758 when operating at minus 80 degrees Celsius. The
illumination power to reach these reduction factors ranges from 0.8 to 1.6 W.
Other photon detection characteristics, such as photon detection efficiency,
timing jitter, and afterpulsing probability, remain mostly unaffected. These
results herald a promising method to extend the lifetime of a quantum satellite
equipped with APDs. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification,
Abstract: We are interested in the development of surrogate models for uncertainty
quantification and propagation in problems governed by stochastic PDEs using a
deep convolutional encoder-decoder network in a similar fashion to approaches
considered in deep learning for image-to-image regression tasks. Since normal
neural networks are data intensive and cannot provide predictive uncertainty,
we propose a Bayesian approach to convolutional neural nets. A recently
introduced variational gradient descent algorithm based on Stein's method is
scaled to deep convolutional networks to perform approximate Bayesian inference
on millions of uncertain network parameters. This approach achieves state of
the art performance in terms of predictive accuracy and uncertainty
quantification in comparison to other approaches in Bayesian neural networks as
well as techniques that include Gaussian processes and ensemble methods even
when the training data size is relatively small. To evaluate the performance of
this approach, we consider standard uncertainty quantification benchmark
problems including flow in heterogeneous media defined in terms of limited
data-driven permeability realizations. The performance of the surrogate model
developed is very good even though there is no underlying structure shared
between the input (permeability) and output (flow/pressure) fields as is often
the case in the image-to-image regression models used in computer vision
problems. Studies are performed with an underlying stochastic input
dimensionality up to $4,225$ where most other uncertainty quantification
methods fail. Uncertainty propagation tasks are considered and the predictive
output Bayesian statistics are compared to those obtained with Monte Carlo
estimates. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics",
"Mathematics"
] |
Title: Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials,
Abstract: Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from
the parietal and occipital regions of the brain that are evoked from flickering
visual stimuli. SSVEPs are robust signals measurable in the
electroencephalogram (EEG) and are commonly used in brain-computer interfaces
(BCIs). However, methods for high-accuracy decoding of SSVEPs usually require
hand-crafted approaches that leverage domain-specific knowledge of the stimulus
signals, such as specific temporal frequencies in the visual stimuli and their
relative spatial arrangement. When this knowledge is unavailable, such as when
SSVEP signals are acquired asynchronously, such approaches tend to fail. In
this paper, we show how a compact convolutional neural network (Compact-CNN),
which only requires raw EEG signals for automatic feature extraction, can be
used to decode signals from a 12-class SSVEP dataset without the need for any
domain-specific knowledge or calibration data. We report across subject mean
accuracy of approximately 80% (chance being 8.3%) and show this is
substantially better than current state-of-the-art hand-crafted approaches
using canonical correlation analysis (CCA) and Combined-CCA. Furthermore, we
analyze our Compact-CNN to examine the underlying feature representation,
discovering that the deep learner extracts additional phase and amplitude
related features associated with the structure of the dataset. We discuss how
our Compact-CNN shows promise for BCI applications that allow users to freely
gaze/attend to any stimulus at any time (e.g., asynchronous BCI) as well as
provides a method for analyzing SSVEP signals in a way that might augment our
understanding about the basic processing in the visual cortex. | [
0,
0,
0,
1,
1,
0
] | [
"Computer Science",
"Quantitative Biology"
] |
Title: ASK/PSK-correspondence and the r-map,
Abstract: We formulate a correspondence between affine and projective special Kähler
manifolds of the same dimension. As an application, we show that, under this
correspondence, the affine special Kähler manifolds in the image of the rigid
r-map are mapped to one-parameter deformations of projective special Kähler
manifolds in the image of the supergravity r-map. The above one-parameter
deformations are interpreted as perturbative $\alpha'$-corrections in heterotic
and type-II string compactifications with $N=2$ supersymmetry. Also affine
special Kähler manifolds with quadratic prepotential are mapped to
one-parameter families of projective special Kähler manifolds with quadratic
prepotential. We show that the completeness of the deformed supergravity r-map
metric depends solely on the (well-understood) completeness of the undeformed
metric and the sign of the deformation parameter. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: Trust Region Value Optimization using Kalman Filtering,
Abstract: Policy evaluation is a key process in reinforcement learning. It assesses a
given policy using estimation of the corresponding value function. When using a
parameterized function to approximate the value, it is common to optimize the
set of parameters by minimizing the sum of squared Bellman Temporal Differences
errors. However, this approach ignores certain distributional properties of
both the errors and value parameters. Taking these distributions into account
in the optimization process can provide useful information on the amount of
confidence in value estimation. In this work we propose to optimize the value
by minimizing a regularized objective function which forms a trust region over
its parameters. We present a novel optimization method, the Kalman Optimization
for Value Approximation (KOVA), based on the Extended Kalman Filter. KOVA
minimizes the regularized objective function by adopting a Bayesian perspective
over both the value parameters and noisy observed returns. This distributional
property provides information on parameter uncertainty in addition to value
estimates. We provide theoretical results of our approach and analyze the
performance of our proposed optimizer on domains with large state and action
spaces. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Simplified Long Short-term Memory Recurrent Neural Networks: part I,
Abstract: We present five variants of the standard Long Short-term Memory (LSTM)
recurrent neural networks by uniformly reducing blocks of adaptive parameters
in the gating mechanisms. For simplicity, we refer to these models as LSTM1,
LSTM2, LSTM3, LSTM4, and LSTM5, respectively. Such parameter-reduced variants
enable speeding up data training computations and would be more suitable for
implementations onto constrained embedded platforms. We comparatively evaluate
and verify our five variant models on the classical MNIST dataset and
demonstrate that these variant models are comparable to a standard
implementation of the LSTM model while using less number of parameters.
Moreover, we observe that in some cases the standard LSTM's accuracy
performance will drop after a number of epochs when using the ReLU
nonlinearity; in contrast, however, LSTM3, LSTM4 and LSTM5 will retain their
performance. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Visualizing the Phase-Space Dynamics of an External Cavity Semiconductor Laser,
Abstract: We map the phase-space trajectories of an external-cavity semiconductor laser
using phase portraits. This is both a visualization tool as well as a
thoroughly quantitative approach enabling unprecedented insight into the
dynamical regimes, from continuous-wave through coherence collapse as feedback
is increased. Namely, the phase portraits in the intensity versus laser-diode
terminal-voltage (serving as a surrogate for inversion) plane are mapped out.
We observe a route to chaos interrupted by two types of limit cycles, a
subharmonic regime and period-doubled dynamics at the edge of chaos. The
transition of the dynamics are analyzed utilizing bifurcation diagrams for both
the optical intensity and the laser-diode terminal voltage. These observations
provide visual insight into the dynamics in these systems. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Quadratic automaton algebras and intermediate growth,
Abstract: We present an example of a quadratic algebra given by three generators and
three relations, which is automaton (the set of normal words forms a regular
language) and such that its ideal of relations does not possess a finite
Gröbner basis with respect to any choice of generators and any choice of a
well-ordering of monomials compatible with multiplication. This answers a
question of Ufnarovski.
Another result is a simple example (4 generators and 7 relations) of a
quadratic algebra of intermediate growth. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Computer Science"
] |
Title: On the Global Continuity of the Roots of Families of Monic Polynomials (in Russian),
Abstract: We raise a question on the existence of continuous roots of families of monic
polynomials (by the root of a family of polynomials we mean a function of the
coefficients of polynomials of a given family that maps each tuple of
coefficients to a root of the polynomial with these coefficients). We prove
that the family of monic second-degree polynomials with complex coefficients
and the families of monic fourth-degree and fifth-degree polynomials with real
coefficients have no continuous root. We also prove that the family of monic
second-degree polynomials with real coefficients has continuous roots and we
describe the set of all such roots. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Analysis of Dirichlet forms on graphs,
Abstract: In this thesis, we study connections between metric and combinatorial graphs
from a Dirichlet space point of view. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Designing and building the mlpack open-source machine learning library,
Abstract: mlpack is an open-source C++ machine learning library with an emphasis on
speed and flexibility. Since its original inception in 2007, it has grown to be
a large project implementing a wide variety of machine learning algorithms,
from standard techniques such as decision trees and logistic regression to
modern techniques such as deep neural networks as well as other
recently-published cutting-edge techniques not found in any other library.
mlpack is quite fast, with benchmarks showing mlpack outperforming other
libraries' implementations of the same methods. mlpack has an active community,
with contributors from around the world---including some from PUST. This short
paper describes the goals and design of mlpack, discusses how the open-source
community functions, and shows an example usage of mlpack for a simple data
science problem. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Characterization of multivariate Bernoulli distributions with given margins,
Abstract: We express each Fréchet class of multivariate Bernoulli distributions with
given margins as the convex hull of a set of densities, which belong to the
same Fréchet class. This characterisation allows us to establish whether a
given correlation matrix is compatible with the assigned margins and, if it is,
to easily construct one of the corresponding joint densities. % Such
%representation is based on a polynomial expression of the distributions of a
Fréchet class. We reduce the problem of finding a density belonging to a
Fréchet class and with given correlation matrix to the solution of a linear
system of equations. Our methodology also provides the bounds that each
correlation must satisfy to be compatible with the assigned margins. An
algorithm and its use in some examples is shown. | [
0,
0,
1,
1,
0,
0
] | [
"Mathematics",
"Statistics"
] |
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