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Title: Simultaneous determination of the drift and diffusion coefficients in stochastic differential equations,
Abstract: In this work, we consider a one-dimensional It{ô} diffusion process X t
with possibly nonlinear drift and diffusion coefficients. We show that, when
the diffusion coefficient is known, the drift coefficient is uniquely
determined by an observation of the expectation of the process during a small
time interval, and starting from values X 0 in a given subset of R. With the
same type of observation, and given the drift coefficient, we also show that
the diffusion coefficient is uniquely determined. When both coefficients are
unknown, we show that they are simultaneously uniquely determined by the
observation of the expectation and variance of the process, during a small time
interval, and starting again from values X 0 in a given subset of R. To derive
these results, we apply the Feynman-Kac theorem which leads to a linear
parabolic equation with unknown coefficients in front of the first and second
order terms. We then solve the corresponding inverse problem with PDE technics
which are mainly based on the strong parabolic maximum principle. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Statistics"
] |
Title: Demand-Independent Optimal Tolls,
Abstract: Wardrop equilibria in nonatomic congestion games are in general inefficient
as they do not induce an optimal flow that minimizes the total travel time.
Network tolls are a prominent and popular way to induce an optimum flow in
equilibrium. The classical approach to find such tolls is marginal cost pricing
which requires the exact knowledge of the demand on the network. In this paper,
we investigate under which conditions demand-independent optimum tolls exist
that induce the system optimum flow for any travel demand in the network. We
give several characterizations for the existence of such tolls both in terms of
the cost structure and the network structure of the game. Specifically we show
that demand-independent optimum tolls exist if and only if the edge cost
functions are shifted monomials as used by the Bureau of Public Roads.
Moreover, non-negative demand-independent optimum tolls exist when the network
is a directed acyclic multi-graph. Finally, we show that any network with a
single origin-destination pair admits demand-independent optimum tolls that,
although not necessarily non-negative, satisfy a budget constraint. | [
1,
0,
0,
0,
0,
0
] | [
"Mathematics",
"Computer Science"
] |
Title: A Proof of the Conjecture of Lehmer and of the Conjecture of Schinzel-Zassenhaus,
Abstract: The conjecture of Lehmer is proved to be true. The proof mainly relies upon:
(i) the properties of the Parry Upper functions $f_{house(\alpha)}(z)$
associated with the dynamical zeta functions $\zeta_{house(\alpha)}(z)$ of the
Rényi--Parry arithmetical dynamical systems, for $\alpha$ an algebraic
integer $\alpha$ of house "$house(\alpha)$" greater than 1, (ii) the discovery
of lenticuli of poles of $\zeta_{house(\alpha)}(z)$ which uniformly
equidistribute at the limit on a limit "lenticular" arc of the unit circle,
when $house(\alpha)$ tends to $1^+$, giving rise to a continuous lenticular
minorant ${\rm M}_{r}(house(\alpha))$ of the Mahler measure ${\rm M}(\alpha)$,
(iii) the Poincaré asymptotic expansions of these poles and of this minorant
${\rm M}_{r}(house(\alpha))$ as a function of the dynamical degree. With the
same arguments the conjecture of Schinzel-Zassenhaus is proved to be true. An
inequality improving those of Dobrowolski and Voutier ones is obtained. The set
of Salem numbers is shown to be bounded from below by the Perron number
$\theta_{31}^{-1} = 1.08545\ldots$, dominant root of the trinomial $-1 - z^{30}
+ z^{31}$. Whether Lehmer's number is the smallest Salem number remains open. A
lower bound for the Weil height of nonzero totally real algebraic numbers,
$\neq \pm 1$, is obtained (Bogomolov property). For sequences of algebraic
integers of Mahler measure smaller than the smallest Pisot number, whose houses
have a dynamical degree tending to infinity, the Galois orbit measures of
conjugates are proved to converge towards the Haar measure on $|z|=1$ (limit
equidistribution). | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Magnetic field control of cycloidal domains and electric polarization in multiferroic BiFeO$_3$,
Abstract: The magnetic field induced rearrangement of the cycloidal spin structure in
ferroelectric mono-domain single crystals of the room-temperature multiferroic
BiFeO$_3$ is studied using small-angle neutron scattering (SANS). The cycloid
propagation vectors are observed to rotate when magnetic fields applied
perpendicular to the rhombohedral (polar) axis exceed a pinning threshold value
of $\sim$5\,T. In light of these experimental results, a phenomenological model
is proposed that captures the rearrangement of the cycloidal domains, and we
revisit the microscopic origin of the magnetoelectric effect. A new coupling
between the magnetic anisotropy and the polarization is proposed that explains
the recently discovered magnetoelectric polarization to the rhombohedral axis. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: DepQBF 6.0: A Search-Based QBF Solver Beyond Traditional QCDCL,
Abstract: We present the latest major release version 6.0 of the quantified Boolean
formula (QBF) solver DepQBF, which is based on QCDCL. QCDCL is an extension of
the conflict-driven clause learning (CDCL) paradigm implemented in state of the
art propositional satisfiability (SAT) solvers. The Q-resolution calculus
(QRES) is a QBF proof system which underlies QCDCL. QCDCL solvers can produce
QRES proofs of QBFs in prenex conjunctive normal form (PCNF) as a byproduct of
the solving process. In contrast to traditional QCDCL based on QRES, DepQBF 6.0
implements a variant of QCDCL which is based on a generalization of QRES. This
generalization is due to a set of additional axioms and leaves the original
Q-resolution rules unchanged. The generalization of QRES enables QCDCL to
potentially produce exponentially shorter proofs than the traditional variant.
We present an overview of the features implemented in DepQBF and report on
experimental results which demonstrate the effectiveness of generalized QRES in
QCDCL. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: A fixed point formula and Harish-Chandra's character formula,
Abstract: The main result in this paper is a fixed point formula for equivariant
indices of elliptic differential operators, for proper actions by connected
semisimple Lie groups on possibly noncompact manifolds, with compact quotients.
For compact groups and manifolds, this reduces to the Atiyah-Segal-Singer fixed
point formula. Other special cases include an index theorem by Connes and
Moscovici for homogeneous spaces, and an earlier index theorem by the second
author, both in cases where the group acting is connected and semisimple. As an
application of this fixed point formula, we give a new proof of
Harish-Chandra's character formula for discrete series representations. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: The phonon softening due to melting of the ferromagnetic order in elemental iron,
Abstract: We study the fundamental question of the lattice dynamics of a metallic
ferromagnet in the regime where the static long range magnetic order is
replaced by the fluctuating local moments embedded in a metallic host. We use
the \textit{ab initio} Density Functional Theory(DFT)+embedded Dynamical
Mean-Field Theory(eDMFT) functional approach to address the dynamic stability
of iron polymorphs and the phonon softening with increased temperature. We show
that the non-harmonic and inhomogeneous phonon softening measured in iron is a
result of the melting of the long range ferromagnetic order, and is unrelated
to the first order structural transition from the BCC to the FCC phase, as is
usually assumed. We predict that the BCC structure is dynamically stable at all
temperatures at normal pressure, and is only thermodynamically unstable between
the BCC-$\alpha$ and the BCC-$\delta$ phase of iron. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Mastering Heterogeneous Behavioural Models,
Abstract: Heterogeneity is one important feature of complex systems, leading to the
complexity of their construction and analysis. Moving the heterogeneity at
model level helps in mastering the difficulty of composing heterogeneous models
which constitute a large system. We propose a method made of an algebra and
structure morphisms to deal with the interaction of behavioural models,
provided that they are compatible. We prove that heterogeneous models can
interact in a safe way, and therefore complex heterogeneous systems can be
built and analysed incrementally. The Uppaal tool is targeted for
experimentations. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Approximation and Convergence Properties of Generative Adversarial Learning,
Abstract: Generative adversarial networks (GAN) approximate a target data distribution
by jointly optimizing an objective function through a "two-player game" between
a generator and a discriminator. Despite their empirical success, however, two
very basic questions on how well they can approximate the target distribution
remain unanswered. First, it is not known how restricting the discriminator
family affects the approximation quality. Second, while a number of different
objective functions have been proposed, we do not understand when convergence
to the global minima of the objective function leads to convergence to the
target distribution under various notions of distributional convergence.
In this paper, we address these questions in a broad and unified setting by
defining a notion of adversarial divergences that includes a number of recently
proposed objective functions. We show that if the objective function is an
adversarial divergence with some additional conditions, then using a restricted
discriminator family has a moment-matching effect. Additionally, we show that
for objective functions that are strict adversarial divergences, convergence in
the objective function implies weak convergence, thus generalizing previous
results. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics",
"Mathematics"
] |
Title: Driver Action Prediction Using Deep (Bidirectional) Recurrent Neural Network,
Abstract: Advanced driver assistance systems (ADAS) can be significantly improved with
effective driver action prediction (DAP). Predicting driver actions early and
accurately can help mitigate the effects of potentially unsafe driving
behaviors and avoid possible accidents. In this paper, we formulate driver
action prediction as a timeseries anomaly prediction problem. While the anomaly
(driver actions of interest) detection might be trivial in this context,
finding patterns that consistently precede an anomaly requires searching for or
extracting features across multi-modal sensory inputs. We present such a driver
action prediction system, including a real-time data acquisition, processing
and learning framework for predicting future or impending driver action. The
proposed system incorporates camera-based knowledge of the driving environment
and the driver themselves, in addition to traditional vehicle dynamics. It then
uses a deep bidirectional recurrent neural network (DBRNN) to learn the
correlation between sensory inputs and impending driver behavior achieving
accurate and high horizon action prediction. The proposed system performs
better than other existing systems on driver action prediction tasks and can
accurately predict key driver actions including acceleration, braking, lane
change and turning at durations of 5sec before the action is executed by the
driver. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science"
] |
Title: Computing low-rank approximations of large-scale matrices with the Tensor Network randomized SVD,
Abstract: We propose a new algorithm for the computation of a singular value
decomposition (SVD) low-rank approximation of a matrix in the Matrix Product
Operator (MPO) format, also called the Tensor Train Matrix format. Our tensor
network randomized SVD (TNrSVD) algorithm is an MPO implementation of the
randomized SVD algorithm that is able to compute dominant singular values and
their corresponding singular vectors. In contrast to the state-of-the-art
tensor-based alternating least squares SVD (ALS-SVD) and modified alternating
least squares SVD (MALS-SVD) matrix approximation methods, TNrSVD can be up to
17 times faster while achieving the same accuracy. In addition, our TNrSVD
algorithm also produces accurate approximations in particular cases where both
ALS-SVD and MALS-SVD fail to converge. We also propose a new algorithm for the
fast conversion of a sparse matrix into its corresponding MPO form, which is up
to 509 times faster than the standard Tensor Train SVD (TT-SVD) method while
achieving machine precision accuracy. The efficiency and accuracy of both
algorithms are demonstrated in numerical experiments. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Path Cover and Path Pack Inequalities for the Capacitated Fixed-Charge Network Flow Problem,
Abstract: Capacitated fixed-charge network flows are used to model a variety of
problems in telecommunication, facility location, production planning and
supply chain management. In this paper, we investigate capacitated path
substructures and derive strong and easy-to-compute \emph{path cover and path
pack inequalities}. These inequalities are based on an explicit
characterization of the submodular inequalities through a fast computation of
parametric minimum cuts on a path, and they generalize the well-known flow
cover and flow pack inequalities for the single-node relaxations of
fixed-charge flow models. We provide necessary and sufficient facet conditions.
Computational results demonstrate the effectiveness of the inequalities when
used as cuts in a branch-and-cut algorithm. | [
1,
0,
1,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Chalcogenide Glass-on-Graphene Photonics,
Abstract: Two-dimensional (2-D) materials are of tremendous interest to integrated
photonics given their singular optical characteristics spanning light emission,
modulation, saturable absorption, and nonlinear optics. To harness their
optical properties, these atomically thin materials are usually attached onto
prefabricated devices via a transfer process. In this paper, we present a new
route for 2-D material integration with planar photonics. Central to this
approach is the use of chalcogenide glass, a multifunctional material which can
be directly deposited and patterned on a wide variety of 2-D materials and can
simultaneously function as the light guiding medium, a gate dielectric, and a
passivation layer for 2-D materials. Besides claiming improved fabrication
yield and throughput compared to the traditional transfer process, our
technique also enables unconventional multilayer device geometries optimally
designed for enhancing light-matter interactions in the 2-D layers.
Capitalizing on this facile integration method, we demonstrate a series of
high-performance glass-on-graphene devices including ultra-broadband on-chip
polarizers, energy-efficient thermo-optic switches, as well as graphene-based
mid-infrared (mid-IR) waveguide-integrated photodetectors and modulators. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Real elliptic curves and cevian geometry,
Abstract: We study the elliptic curve $E_a: (ax+1)y^2+(ax+1)(x-1)y+x^2-x=0$, which we
call the geometric normal form of an elliptic curve. We show that any elliptic
curve whose $j$-invariant is real is isomorphic to a curve $E_a$ in geometric
normal form, and show that for $a \notin \{0, -1, -9\}$, the points on $E_a$,
minus a set of $6$ points, can be characterized in terms of the cevian geometry
of a triangle. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Musical Instrument Recognition Using Their Distinctive Characteristics in Artificial Neural Networks,
Abstract: In this study an Artificial Neural Network was trained to classify musical
instruments, using audio samples transformed to the frequency domain. Different
features of the sound, in both time and frequency domain, were analyzed and
compared in relation to how much information that could be derived from that
limited data. The study concluded that in comparison with the base experiment,
that had an accuracy of 93.5%, using the attack only resulted in 80.2% and the
initial 100 Hz in 64.2%. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Revealing structure components of the retina by deep learning networks,
Abstract: Deep convolutional neural networks (CNNs) have demonstrated impressive
performance on visual object classification tasks. In addition, it is a useful
model for predication of neuronal responses recorded in visual system. However,
there is still no clear understanding of what CNNs learn in terms of visual
neuronal circuits. Visualizing CNN's features to obtain possible connections to
neuronscience underpinnings is not easy due to highly complex circuits from the
retina to higher visual cortex. Here we address this issue by focusing on
single retinal ganglion cells with a simple model and electrophysiological
recordings from salamanders. By training CNNs with white noise images to
predicate neural responses, we found that convolutional filters learned in the
end are resembling to biological components of the retinal circuit. Features
represented by these filters tile the space of conventional receptive field of
retinal ganglion cells. These results suggest that CNN could be used to reveal
structure components of neuronal circuits. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Quantitative Biology"
] |
Title: Generalized Yangians and their Poisson counterparts,
Abstract: By a generalized Yangian we mean a Yangian-like algebra of one of two
classes. One of these classes consists of the so-called braided Yangians,
introduced in our previous paper. The braided Yangians are in a sense similar
to the reflection equation algebra. The generalized Yangians of second class,
called the Yangians of RTT type, are defined by the same formulae as the usual
Yangians are but with other quantum $R$-matrices. If such an $R$-matrix is the
simplest trigonometrical $R$-matrix, the corresponding Yangian of RTT type is
the so-called q-Yangian. We claim that each generalized Yangian is a
deformation of the commutative algebra ${\rm Sym}(gl(m)[t^{-1}])$ provided that
the corresponding $R$-matrix is a deformation of the flip. Also, we exhibit the
corresponding Poisson brackets. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: Debt-Prone Bugs: Technical Debt in Software Maintenance,
Abstract: Fixing bugs is an important phase in software development and maintenance. In
practice, the process of bug fixing may conflict with the release schedule.
Such confliction leads to a trade-off between software quality and release
schedule, which is known as the technical debt metaphor. In this article, we
propose the concept of debt-prone bugs to model the technical debt in software
maintenance. We identify three types of debt-prone bugs, namely tag bugs,
reopened bugs, and duplicate bugs. A case study on Mozilla is conducted to
examine the impact of debt-prone bugs in software products. We investigate the
correlation between debt-prone bugs and the product quality. For a product
under development, we build prediction models based on historical products to
predict the time cost of fixing bugs. The result shows that identifying
debt-prone bugs can assist in monitoring and improving software quality. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Khovanov-Rozansky homology and higher Catalan sequences,
Abstract: We give a simple recursion which computes the triply graded Khovanov-Rozansky
homology of several infinite families of knots and links, including the
$(n,nm\pm 1)$ and $(n,nm)$ torus links for $n,m\geq 1$. We interpret our
results in terms of Catalan combinatorics, proving a conjecture of Gorsky's.
Our computations agree with predictions coming from Hilbert schemes and
rational DAHA, which also proves the Gorsky-Oblomkov-Rasmussen-Shende
conjectures in these cases. Additionally, our results suggest a topological
interpretation of the symmetric functions which appear in the context of the
$m$-shuffle conjecture of Haglund-Haiman-Loehr-Remmel-Ulyanov. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: New face of multifractality: Multi-branched left-sidedness and phase transitions in multifractality of interevent times,
Abstract: We develop an extended multifractal analysis based on the Legendre-Fenchel
transform rather than the routinely used Legendre transform. We apply this
analysis to studying time series consisting of inter-event times. As a result,
we discern the non-monotonic behavior of the generalized Hurst exponent - the
fundamental exponent studied by us - and hence a multi-branched left-sided
spectrum of dimensions. This kind of multifractality is a direct result of the
non-monotonic behavior of the generalized Hurst exponent and is not caused by
non-analytic behavior as has been previously suggested. We examine the main
thermodynamic consequences of the existence of this type of multifractality
related to the thermal stable, metastable, and unstable phases within a
hierarchy of fluctuations, and also to the first and second order phase
transitions between them. | [
0,
0,
0,
0,
0,
1
] | [
"Physics",
"Mathematics"
] |
Title: Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?,
Abstract: Binary neural networks (BNN) have been studied extensively since they run
dramatically faster at lower memory and power consumption than floating-point
networks, thanks to the efficiency of bit operations. However, contemporary
BNNs whose weights and activations are both single bits suffer from severe
accuracy degradation. To understand why, we investigate the representation
ability, speed and bias/variance of BNNs through extensive experiments. We
conclude that the error of BNNs is predominantly caused by the intrinsic
instability (training time) and non-robustness (train & test time). Inspired by
this investigation, we propose the Binary Ensemble Neural Network (BENN) which
leverages ensemble methods to improve the performance of BNNs with limited
efficiency cost. While ensemble techniques have been broadly believed to be
only marginally helpful for strong classifiers such as deep neural networks,
our analyses and experiments show that they are naturally a perfect fit to
boost BNNs. We find that our BENN, which is faster and much more robust than
state-of-the-art binary networks, can even surpass the accuracy of the
full-precision floating number network with the same architecture. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science"
] |
Title: Smart patterned surfaces with programmable thermal emissivity and their design through combinatorial strategies,
Abstract: The emissivity of common materials remains constant with temperature
variations, and cannot drastically change. However, it is possible to design
its entire behaviour as a function of temperature, and to significantly modify
the thermal emissivity of a surface through the combination of different
materials and patterns. Here, we show that smart patterned surfaces consisting
of smaller structures (motifs) may be designed to respond uniquely through
combinatorial design strategies by transforming themselves from 2D to 3D
complex structures with a two-way shape memory effect. The smart surfaces can
passively manipulate thermal radiation without-the use of controllers and power
supplies-because their modus operandi has already been programmed and
integrated into their intrinsic characteristics; the environment provides the
energy required for their activation. Each motif emits thermal radiation in a
certain manner, as it changes its geometry; however, the spatial distribution
of these motifs causes them to interact with each other. Therefore, their
combination and interaction determine the global behaviour of the surfaces,
thus enabling their a priori design. The emissivity behaviour is not random; it
is determined by two fundamental parameters, namely the combination of
orientations in which the motifs open (n-fold rotational symmetry (rn)) and the
combination of materials (colours) on the motifs; these generate functions
which fully determine the dependency of the emissivity on the temperature. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Materials Science"
] |
Title: Magnetization jump in one dimensional $J-Q_{2}$ model with anisotropic exchange,
Abstract: We investigate the adiabatic magnetization process of the one-dimensional
$J-Q_{2}$ model with XXZ anisotropy $g$ in an external magnetic field $h$ by
using density matrix renormalization group (DMRG) method. According to the
characteristic of the magnetization curves, we draw a magnetization phase
diagram consisting of four phases. For a fixed nonzero pair coupling $Q$, i)
when $g<-1$, the ground state is always ferromagnetic in spite of $h$; ii) when
$g>-1$ but still small, the whole magnetization curve is continuous and smooth;
iii) if further increasing $g$, there is a macroscopic magnetization jump from
partially- to fully-polarized state; iv) for a sufficiently large $g$, the
magnetization jump is from non- to fully-polarized state. By examining the
energy per magnon and the correlation function, we find that the origin of the
magnetization jump is the condensation of magnons and the formation of magnetic
domains. We also demonstrate that while the experienced states are
Heisenberg-like without long-range order, all the \textit{jumped-over} states
have antiferromagnetic or Néel long-range orders, or their mixing. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: On the Faithfulness of 1-dimensional Topological Quantum Field Theories,
Abstract: This paper explores 1-dimensional topological quantum field theories. We
separately deal with strict and strong 1-dimensional topological quantum field
theories. The strict one is regarded as a symmetric monoidal functor between
the category of 1-cobordisms and the category of matrices, and the strong one
is a symmetric monoidal functor between the category of 1-cobordisms and the
category of finite dimensional vector spaces. It has been proved that both
strict and strong 1-dimensional topological quantum field theories are
faithful. | [
0,
0,
1,
0,
0,
0
] | [
"Physics",
"Mathematics"
] |
Title: A Simplified Approach to Analyze Complementary Sensitivity Trade-offs in Continuous-Time and Discrete-Time Systems,
Abstract: A simplified approach is proposed to investigate the continuous-time and
discrete-time complementary sensitivity Bode integrals (CSBIs) in this note.
For continuous-time feedback systems with unbounded frequency domain, the CSBI
weighted by $1/\omega^2$ is considered, where this simplified method reveals a
more explicit relationship between the value of CSBI and the structure of the
open-loop transfer function. With a minor modification of this method, the CSBI
of discrete-time system is derived, and illustrative examples are provided.
Compared with the existing results on CSBI, neither Cauchy integral theorem nor
Poisson integral formula are used throughout the analysis, and the analytic
constraint on the integrand is removed. | [
1,
0,
0,
0,
0,
0
] | [
"Mathematics",
"Computer Science"
] |
Title: Gradient Reversal Against Discrimination,
Abstract: No methods currently exist for making arbitrary neural networks fair. In this
work we introduce GRAD, a new and simplified method to producing fair neural
networks that can be used for auto-encoding fair representations or directly
with predictive networks. It is easy to implement and add to existing
architectures, has only one (insensitive) hyper-parameter, and provides
improved individual and group fairness. We use the flexibility of GRAD to
demonstrate multi-attribute protection. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Non-commutative crepant resolutions for some toric singularities I,
Abstract: We give a criterion for the existence of non-commutative crepant resolutions
(NCCR's) for certain toric singularities. In particular we recover Broomhead's
result that a 3-dimensional toric Gorenstein singularity has a NCCR. Our result
also yields the existence of a NCCR for a 4-dimensional toric Gorenstein
singularity which is known to have no toric NCCR. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: High temperature pairing in a strongly interacting two-dimensional Fermi gas,
Abstract: We observe many-body pairing in a two-dimensional gas of ultracold fermionic
atoms at temperatures far above the critical temperature for superfluidity. For
this, we use spatially resolved radio-frequency spectroscopy to measure pairing
energies spanning a wide range of temperatures and interaction strengths. In
the strongly interacting regime where the scattering length between fermions is
on the same order as the inter-particle spacing, the pairing energy in the
normal phase significantly exceeds the intrinsic two-body binding energy of the
system and shows a clear dependence on local density. This implies that pairing
in this regime is driven by many-body correlations, rather than two-body
physics. We find this effect to persist at temperatures close to the Fermi
temperature which demonstrates that pairing correlations in strongly
interacting two-dimensional fermionic systems are remarkably robust against
thermal fluctuations. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Centralities in Simplicial Complexes,
Abstract: Complex networks can be used to represent complex systems which originate in
the real world. Here we study a transformation of these complex networks into
simplicial complexes, where cliques represent the simplices of the complex. We
extend the concept of node centrality to that of simplicial centrality and
study several mathematical properties of degree, closeness, betweenness,
eigenvector, Katz, and subgraph centrality for simplicial complexes. We study
the degree distributions of these centralities at the different levels. We also
compare and describe the differences between the centralities at the different
levels. Using these centralities we study a method for detecting essential
proteins in PPI networks of cells and explain the varying abilities of the
centrality measures at the different levels in identifying these essential
proteins. The paper is written in a self-contained way, such that it can be
used by practitioners of network theory as a basis for further developments. | [
1,
1,
0,
0,
0,
0
] | [
"Mathematics",
"Quantitative Biology"
] |
Title: The Samuel realcompactification,
Abstract: For a uniform space (X, $\mu$), we introduce a realcompactification of X by
means of the family $U_{\mu}(X)$ of all the real-valued uniformly continuous
functions, in the same way that the known Samuel compactification is given by
$U^{*}_{\mu}(X)$ the set of all the bounded functions in $U_{\mu}(X)$. We will
call it "the Samuel realcompactification" by several resemblances to the Samuel
compactification. In this note, we present different ways to construct such
realcompactification as well as we study the corresponding problem of knowing
when a uniform space is Samuel realcompact, that is, it coincides with its
Samuel realcompactification. At this respect we obtain as main result a theorem
of Katětov-Shirota type, by means of a new property of completeness
recently introduced by the authors, called Bourbaki-completeness. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Segmented Terahertz Electron Accelerator and Manipulator (STEAM),
Abstract: Acceleration and manipulation of ultrashort electron bunches are the basis
behind electron and X-ray devices used for ultrafast, atomic-scale imaging and
spectroscopy. Using laser-generated THz drivers enables intrinsic
synchronization as well as dramatic gains in field strengths, field gradients
and component compactness, leading to shorter electron bunches, higher
spatio-temporal resolution and smaller infrastructures. We present a segmented
THz electron accelerator and manipulator (STEAM) with extended interaction
lengths capable of performing multiple high-field operations on the energy and
phase-space of ultrashort bunches with moderate charge. With this single
device, powered by few-microjoule, single-cycle, 0.3 THz pulses, we demonstrate
record THz-device acceleration of >30 keV, streaking with <10 fs resolution,
focusing with >2 kT/m strengths, compression to ~100 fs as well as real-time
switching between these modes of operation. The STEAM device demonstrates the
feasibility of future THz-based compact electron guns, accelerators, ultrafast
electron diffractometers and Free-Electron Lasers with transformative impact. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Learning graphs from data: A signal representation perspective,
Abstract: The construction of a meaningful graph topology plays a crucial role in the
effective representation, processing, analysis and visualization of structured
data. When a natural choice of the graph is not readily available from the data
sets, it is thus desirable to infer or learn a graph topology from the data. In
this tutorial overview, we survey solutions to the problem of graph learning,
including classical viewpoints from statistics and physics, and more recent
approaches that adopt a graph signal processing (GSP) perspective. We further
emphasize the conceptual similarities and differences between classical and
GSP-based graph inference methods, and highlight the potential advantage of the
latter in a number of theoretical and practical scenarios. We conclude with
several open issues and challenges that are keys to the design of future signal
processing and machine learning algorithms for learning graphs from data. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics",
"Mathematics"
] |
Title: Brownian dynamics of elongated particles in a quasi-2D isotropic liquid,
Abstract: We demonstrate experimentally that the long-range hydrodynamic interactions
in an incompressible quasi 2D isotropic fluid result in an anisotropic viscous
drag acting on elongated particles. The anisotropy of the drag is increasing
with increasing ratio of the particle length to the hydrodynamic scale given by
the Saffman-Delbrück length. The micro-rheology data for translational and
rotational drags collected over three orders of magnitude of the effective
particle length demonstrate the validity of the current theoretical approaches
to the hydrodynamics in restricted geometry. The results also demonstrate
crossovers between the hydrodynamical regimes determined by the characteristic
length scales. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Disproval of the validated planets K2-78b, K2-82b, and K2-92b,
Abstract: Transiting super-Earths orbiting bright stars in short orbital periods are
interesting targets for the study of planetary atmospheres. While selecting
super-Earths suitable for further characterization from the ground among a list
of confirmed and validated exoplanets detected by K2, we found some suspicious
cases that led to us re-assessing the nature of the detected transiting signal.
We did a photometric analysis of the K2 light curves and centroid motions of
the photometric barycenters. Our study shows that the validated planets K2-78b,
K2-82b, and K2-92b are actually not planets but background eclipsing binaries.
The eclipsing binaries are inside the Kepler photometric aperture, but outside
the ground-based high resolution images used for validation. We advise extreme
care on the validation of candidate planets discovered by space missions. It is
important that all the assumptions in the validation process are carefully
checked. An independent confirmation is mandatory in order to avoid wasting
valuable resources on further characterization of non-existent targets. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Quantitative Biology"
] |
Title: Measured Multiseries and Integration,
Abstract: A paper by Bruno Salvy and the author introduced measured multiseries and
gave an algorithm to compute these for a large class of elementary functions,
modulo a zero-equivalence method for constants. This gave a theoretical
background for the implementation that Salvy was developing at that time. The
main result of the present article is an algorithm to calculate measured
multiseries for integrals of functions of the form h*sin G, where h and G
belong to a Hardy field. The process can reiterated with the resulting algebra,
and also applied to solutions of a second order differential equation of a
particular form. | [
1,
0,
0,
0,
0,
0
] | [
"Mathematics",
"Computer Science"
] |
Title: Multi-Label Learning with Global and Local Label Correlation,
Abstract: It is well-known that exploiting label correlations is important to
multi-label learning. Existing approaches either assume that the label
correlations are global and shared by all instances; or that the label
correlations are local and shared only by a data subset. In fact, in the
real-world applications, both cases may occur that some label correlations are
globally applicable and some are shared only in a local group of instances.
Moreover, it is also a usual case that only partial labels are observed, which
makes the exploitation of the label correlations much more difficult. That is,
it is hard to estimate the label correlations when many labels are absent. In
this paper, we propose a new multi-label approach GLOCAL dealing with both the
full-label and the missing-label cases, exploiting global and local label
correlations simultaneously, through learning a latent label representation and
optimizing label manifolds. The extensive experimental studies validate the
effectiveness of our approach on both full-label and missing-label data. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Predicting signatures of anisotropic resonance energy transfer in dye-functionalized nanoparticles,
Abstract: Resonance energy transfer (RET) is an inherently anisotropic process. Even
the simplest, well-known Förster theory, based on the transition
dipole-dipole coupling, implicitly incorporates the anisotropic character of
RET. In this theoretical work, we study possible signatures of the fundamental
anisotropic character of RET in hybrid nanomaterials composed of a
semiconductor nanoparticle (NP) decorated with molecular dyes. In particular,
by means of a realistic kinetic model, we show that the analysis of the dye
photoluminescence difference for orthogonal input polarizations reveals the
anisotropic character of the dye-NP RET which arises from the intrinsic
anisotropy of the NP lattice. In a prototypical core/shell wurtzite CdSe/ZnS NP
functionalized with cyanine dyes (Cy3B), this difference is predicted to be as
large as 75\% and it is strongly dependent in amplitude and sign on the dye-NP
distance. We account for all the possible RET processes within the system,
together with competing decay pathways in the separate segments. In addition,
we show that the anisotropic signature of RET is persistent up to a large
number of dyes per NP. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Chemistry"
] |
Title: Matrix Completion and Performance Guarantees for Single Individual Haplotyping,
Abstract: Single individual haplotyping is an NP-hard problem that emerges when
attempting to reconstruct an organism's inherited genetic variations using data
typically generated by high-throughput DNA sequencing platforms. Genomes of
diploid organisms, including humans, are organized into homologous pairs of
chromosomes that differ from each other in a relatively small number of variant
positions. Haplotypes are ordered sequences of the nucleotides in the variant
positions of the chromosomes in a homologous pair; for diploids, haplotypes
associated with a pair of chromosomes may be conveniently represented by means
of complementary binary sequences. In this paper, we consider a binary matrix
factorization formulation of the single individual haplotyping problem and
efficiently solve it by means of alternating minimization. We analyze the
convergence properties of the alternating minimization algorithm and establish
theoretical bounds for the achievable haplotype reconstruction error. The
proposed technique is shown to outperform existing methods when applied to
synthetic as well as real-world Fosmid-based HapMap NA12878 datasets. | [
0,
0,
0,
1,
1,
0
] | [
"Computer Science",
"Quantitative Biology",
"Mathematics"
] |
Title: Localization of Extended Quantum Objects,
Abstract: A quantum system of particles can exist in a localized phase, exhibiting
ergodicity breaking and maintaining forever a local memory of its initial
conditions. We generalize this concept to a system of extended objects, such as
strings and membranes, arguing that such a system can also exhibit localization
in the presence of sufficiently strong disorder (randomness) in the
Hamiltonian. We show that localization of large extended objects can be mapped
to a lower-dimensional many-body localization problem. For example, motion of a
string involves propagation of point-like signals down its length to keep the
different segments in causal contact. For sufficiently strong disorder, all
such internal modes will exhibit many-body localization, resulting in the
localization of the entire string. The eigenstates of the system can then be
constructed perturbatively through a convergent 'string locator expansion.' We
propose a type of out-of-time-order string correlator as a diagnostic of such a
string localized phase. Localization of other higher-dimensional objects, such
as membranes, can also be studied through a hierarchical construction by
mapping onto localization of lower-dimensional objects. Our arguments are
'asymptotic' ($i.e.$ valid up to rare regions) but they extend the notion of
localization (and localization protected order) to a host of settings where
such ideas previously did not apply. These include high-dimensional
ferromagnets with domain wall excitations, three-dimensional topological phases
with loop-like excitations, and three-dimensional type-II superconductors with
flux line excitations. In type-II superconductors, localization of flux lines
could stabilize superconductivity at energy densities where a normal state
would arise in thermal equilibrium. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Big Data Classification Using Augmented Decision Trees,
Abstract: We present an algorithm for classification tasks on big data. Experiments
conducted as part of this study indicate that the algorithm can be as accurate
as ensemble methods such as random forests or gradient boosted trees. Unlike
ensemble methods, the models produced by the algorithm can be easily
interpreted. The algorithm is based on a divide and conquer strategy and
consists of two steps. The first step consists of using a decision tree to
segment the large dataset. By construction, decision trees attempt to create
homogeneous class distributions in their leaf nodes. However, non-homogeneous
leaf nodes are usually produced. The second step of the algorithm consists of
using a suitable classifier to determine the class labels for the
non-homogeneous leaf nodes. The decision tree segment provides a coarse segment
profile while the leaf level classifier can provide information about the
attributes that affect the label within a segment. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Special tilting modules for algebras with positive dominant dimension,
Abstract: We study a set of uniquely determined tilting and cotilting modules for an
algebra with positive dominant dimension, with the property that they are
generated or cogenerated (and usually both) by projective-injectives. These
modules have various interesting properties, for example that their
endomorphism algebras always have global dimension at most that of the original
algebra. We characterise d-Auslander-Gorenstein algebras and d-Auslander
algebras via the property that the relevant tilting and cotilting modules
coincide. By the Morita-Tachikawa correspondence, any algebra of dominant
dimension at least 2 may be expressed (essentially uniquely) as the
endomorphism algebra of a generator-cogenerator for another algebra, and we
also study our special tilting and cotilting modules from this point of view,
via the theory of recollements and intermediate extension functors. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Maxent-Stress Optimization of 3D Biomolecular Models,
Abstract: Knowing a biomolecule's structure is inherently linked to and a prerequisite
for any detailed understanding of its function. Significant effort has gone
into developing technologies for structural characterization. These
technologies do not directly provide 3D structures; instead they typically
yield noisy and erroneous distance information between specific entities such
as atoms or residues, which have to be translated into consistent 3D models.
Here we present an approach for this translation process based on
maxent-stress optimization. Our new approach extends the original graph drawing
method for the new application's specifics by introducing additional
constraints and confidence values as well as algorithmic components. Extensive
experiments demonstrate that our approach infers structural models (i. e.,
sensible 3D coordinates for the molecule's atoms) that correspond well to the
distance information, can handle noisy and error-prone data, and is
considerably faster than established tools. Our results promise to allow domain
scientists nearly-interactive structural modeling based on distance
constraints. | [
1,
1,
0,
0,
0,
0
] | [
"Quantitative Biology",
"Computer Science"
] |
Title: Enhanced Quantum Synchronization via Quantum Machine Learning,
Abstract: We study the quantum synchronization between a pair of two-level systems
inside two coupled cavities. By using a digital-analog decomposition of the
master equation that rules the system dynamics, we show that this approach
leads to quantum synchronization between both two-level systems. Moreover, we
can identify in this digital-analog block decomposition the fundamental
elements of a quantum machine learning protocol, in which the agent and the
environment (learning units) interact through a mediating system, namely, the
register. If we can additionally equip this algorithm with a classical feedback
mechanism, which consists of projective measurements in the register,
reinitialization of the register state and local conditional operations on the
agent and environment subspace, a powerful and flexible quantum machine
learning protocol emerges. Indeed, numerical simulations show that this
protocol enhances the synchronization process, even when every subsystem
experience different loss/decoherence mechanisms, and give us the flexibility
to choose the synchronization state. Finally, we propose an implementation
based on current technologies in superconducting circuits. | [
1,
0,
0,
1,
0,
0
] | [
"Physics",
"Computer Science"
] |
Title: Entropy generation and momentum transfer in the superconductor-normal and normal-superconductor phase transformations and the consistency of the conventional theory of superconductivity,
Abstract: Since the discovery of the Meissner effect the superconductor to normal (S-N)
phase transition in the presence of a magnetic field is understood to be a
first order phase transformation that is reversible under ideal conditions and
obeys the laws of thermodynamics. The reverse (N-S) transition is the Meissner
effect. This implies in particular that the kinetic energy of the supercurrent
is not dissipated as Joule heat in the process where the superconductor becomes
normal and the supercurrent stops. In this paper we analyze the entropy
generation and the momentum transfer between the supercurrent and the body in
the S-N transition and the N-S transition as described by the conventional
theory of superconductivity. We find that it is impossible to explain the
transition in a way that is consistent with the laws of thermodynamics unless
the momentum transfer between the supercurrent and the body occurs with zero
entropy generation, for which the conventional theory of superconductivity
provides no mechanism. Instead, we point out that the alternative theory of
hole superconductivity does not encounter such difficulties. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Étale groupoids and their $C^*$-algebras,
Abstract: These notes were written as supplementary material for a five-hour lecture
series presented at the Centre de Recerca Mathemàtica at the Universitat
Autònoma de Barcelona from the 13th to the 17th of March 2017. The intention
of these notes is to give a brief overview of some key topics in the area of
$C^*$-algebras associated to étale groupoids. The scope has been deliberately
contained to the case of étale groupoids with the intention that much of the
representation-theoretic technology and measure-theoretic analysis required to
handle general groupoids can be suppressed in this simpler setting.
A published version of these notes will appear in the volume tentatively
titled "Operator algebras and dynamics: groupoids, crossed products and Rokhlin
dimension" by Gabor Szabo, Dana P. Williams and myself, and edited by Francesc
Perera, in the series "Advanced Courses in Mathematics. CRM Barcelona." The
pagination of this arXiv version is not identical to Birkhäuser's style, but
I have tried to make it close. The theorem numbering should be correct. I'm
grateful to the CRM and Birkhäuser for allowing me to post a version on
arXiv. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: The Computer Science and Physics of Community Detection: Landscapes, Phase Transitions, and Hardness,
Abstract: Community detection in graphs is the problem of finding groups of vertices
which are more densely connected than they are to the rest of the graph. This
problem has a long history, but it is undergoing a resurgence of interest due
to the need to analyze social and biological networks. While there are many
ways to formalize it, one of the most popular is as an inference problem, where
there is a "ground truth" community structure built into the graph somehow. The
task is then to recover the ground truth knowing only the graph.
Recently it was discovered, first heuristically in physics and then
rigorously in probability and computer science, that this problem has a phase
transition at which it suddenly becomes impossible. Namely, if the graph is too
sparse, or the probabilistic process that generates it is too noisy, then no
algorithm can find a partition that is correlated with the planted one---or
even tell if there are communities, i.e., distinguish the graph from a purely
random one with high probability. Above this information-theoretic threshold,
there is a second threshold beyond which polynomial-time algorithms are known
to succeed; in between, there is a regime in which community detection is
possible, but conjectured to require exponential time.
For computer scientists, this field offers a wealth of new ideas and open
questions, with connections to probability and combinatorics, message-passing
algorithms, and random matrix theory. Perhaps more importantly, it provides a
window into the cultures of statistical physics and statistical inference, and
how those cultures think about distributions of instances, landscapes of
solutions, and hardness. | [
1,
1,
1,
0,
0,
0
] | [
"Computer Science",
"Physics",
"Statistics"
] |
Title: Characterizing the spread of exaggerated news content over social media,
Abstract: In this paper, we consider a dataset comprising press releases about health
research from different universities in the UK along with a corresponding set
of news articles. First, we do an exploratory analysis to understand how the
basic information published in the scientific journals get exaggerated as they
are reported in these press releases or news articles. This initial analysis
shows that some news agencies exaggerate almost 60\% of the articles they
publish in the health domain; more than 50\% of the press releases from certain
universities are exaggerated; articles in topics like lifestyle and childhood
are heavily exaggerated. Motivated by the above observation we set the central
objective of this paper to investigate how exaggerated news spreads over an
online social network like Twitter. The LIWC analysis points to a remarkable
observation these late tweets are essentially laden in words from opinion and
realize categories which indicates that, given sufficient time, the wisdom of
the crowd is actually able to tell apart the exaggerated news. As a second step
we study the characteristics of the users who never or rarely post exaggerated
news content and compare them with those who post exaggerated news content more
frequently. We observe that the latter class of users have less retweets or
mentions per tweet, have significantly more number of followers, use more slang
words, less hyperbolic words and less word contractions. We also observe that
the LIWC categories like bio, health, body and negative emotion are more
pronounced in the tweets posted by the users in the latter class. As a final
step we use these observations as features and automatically classify the two
groups achieving an F1 score of 0.83. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Tuning the effective spin-orbit coupling in molecular semiconductors,
Abstract: The control of spins and spin to charge conversion in organics requires
understanding the molecular spin-orbit coupling (SOC), and a means to tune its
strength. However, quantifying SOC strengths indirectly through spin relaxation
effects has proven diffi- cult due to competing relaxation mechanisms. Here we
present a systematic study of the g-tensor shift in molecular semiconductors
and link it directly to the SOC strength in a series of high mobility molecular
semiconductors with strong potential for future devices. The results
demonstrate a rich variability of the molecular g-shifts with the effective
SOC, depending on subtle aspects of molecular composition and structure. We
correlate the above g -shifts to spin-lattice relaxation times over four orders
of magnitude, from 200 {\mu}s to 0.15 {\mu}s, for isolated molecules in
solution and relate our findings for isolated molecules in solution to the spin
relaxation mechanisms that are likely to be relevant in solid state systems. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: How Deep Are Deep Gaussian Processes?,
Abstract: Recent research has shown the potential utility of Deep Gaussian Processes.
These deep structures are probability distributions, designed through
hierarchical construction, which are conditionally Gaussian. In this paper, the
current published body of work is placed in a common framework and, through
recursion, several classes of deep Gaussian processes are defined. The
resulting samples generated from a deep Gaussian process have a Markovian
structure with respect to the depth parameter, and the effective depth of the
resulting process is interpreted in terms of the ergodicity, or non-ergodicity,
of the resulting Markov chain. For the classes of deep Gaussian processes
introduced, we provide results concerning their ergodicity and hence their
effective depth. We also demonstrate how these processes may be used for
inference; in particular we show how a Metropolis-within-Gibbs construction
across the levels of the hierarchy can be used to derive sampling tools which
are robust to the level of resolution used to represent the functions on a
computer. For illustration, we consider the effect of ergodicity in some simple
numerical examples. | [
0,
0,
1,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: A quantum dynamic belief model to explain the interference effects of categorization on decision making,
Abstract: Categorization is necessary for many decision making tasks. However, the
categorization process may interfere the decision making result and the law of
total probability can be violated in some situations. To predict the
interference effect of categorization, some model based on quantum probability
has been proposed. In this paper, a new quantum dynamic belief (QDB) model is
proposed. Considering the precise decision may not be made during the process,
the concept of uncertainty is introduced in our model to simulate real human
thinking process. Then the interference effect categorization can be predicted
by handling the uncertain information. The proposed model is applied to a
categorization decision-making experiment to explain the interference effect of
categorization. Compared with other models, our model is relatively more
succinct and the result shows the correctness and effectiveness of our model. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Physics"
] |
Title: Behavioural Change Support Intelligent Transportation Applications,
Abstract: This workshop invites researchers and practitioners to participate in
exploring behavioral change support intelligent transportation applications. We
welcome submissions that explore intelligent transportation systems (ITS),
which interact with travelers in order to persuade them or nudge them towards
sustainable transportation behaviors and decisions. Emerging opportunities
including the use of data and information generated by ITS and users' mobile
devices in order to render personalized, contextualized and timely transport
behavioral change interventions are in our focus. We invite submissions and
ideas from domains of ITS including, but not limited to, multi-modal journey
planners, advanced traveler information systems and in-vehicle systems. The
expected outcome will be a deeper understanding of the challenges and future
research directions with respect to behavioral change support through ITS. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: SU(2) Pfaffian systems and gauge theory,
Abstract: Motivated by the description of Nurowski's conformal structure for maximally
symmetric homogeneous examples of bracket-generating rank 2 distributions in
dimension 5, aka $(2,3,5)$-distributions, we consider a rank $3$ Pfaffian
system in dimension 5 with $SU(2)$ symmetry. We find the conditions for which
this Pfaffian system has the maximal symmetry group (in the real case this is
the split real form of $G_2$), and give the associated Nurowski's conformal
classes. We also present a $SU(2)$ gauge-theoretic interpretation of the
results obtained. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: Correlation effects in superconducting quantum dot systems,
Abstract: We study the effect of electron correlations on a system consisting of a
single-level quantum dot with local Coulomb interaction attached to two
superconducting leads. We use the single-impurity Anderson model with BCS
superconducting baths to study the interplay between the proximity induced
electron pairing and the local Coulomb interaction. We show how to solve the
model using the continuous-time hybridization-expansion quantum Monte Carlo
method. The results obtained for experimentally relevant parameters are
compared with results of self-consistent second order perturbation theory as
well as with the numerical renormalization group method. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Computer Science"
] |
Title: Learning non-parametric Markov networks with mutual information,
Abstract: We propose a method for learning Markov network structures for continuous
data without invoking any assumptions about the distribution of the variables.
The method makes use of previous work on a non-parametric estimator for mutual
information which is used to create a non-parametric test for multivariate
conditional independence. This independence test is then combined with an
efficient constraint-based algorithm for learning the graph structure. The
performance of the method is evaluated on several synthetic data sets and it is
shown to learn considerably more accurate structures than competing methods
when the dependencies between the variables involve non-linearities. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms,
Abstract: We demonstrate that, for a range of state-of-the-art machine learning
algorithms, the differences in generalisation performance obtained using
default parameter settings and using parameters tuned via cross-validation can
be similar in magnitude to the differences in performance observed between
state-of-the-art and uncompetitive learning systems. This means that fair and
rigorous evaluation of new learning algorithms requires performance comparison
against benchmark methods with best-practice model selection procedures, rather
than using default parameter settings. We investigate the sensitivity of three
key machine learning algorithms (support vector machine, random forest and
rotation forest) to their default parameter settings, and provide guidance on
determining sensible default parameter values for implementations of these
algorithms. We also conduct an experimental comparison of these three
algorithms on 121 classification problems and find that, perhaps surprisingly,
rotation forest is significantly more accurate on average than both random
forest and a support vector machine. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Classification of Casimirs in 2D hydrodynamics,
Abstract: We describe a complete list of Casimirs for 2D Euler hydrodynamics on a
surface without boundary: we define generalized enstrophies which, along with
circulations, form a complete set of invariants for coadjoint orbits of
area-preserving diffeomorphisms on a surface. We also outline a possible
extension of main notions to the boundary case and formulate several open
questions in that setting. | [
0,
1,
1,
0,
0,
0
] | [
"Physics",
"Mathematics"
] |
Title: Novel Compliant omnicrawler-wheel transforming module,
Abstract: This paper presents a novel design of a crawler robot which is capable of
transforming its chassis from an Omni crawler mode to a large-sized wheel mode
using a novel mechanism. The transformation occurs without any additional
actuators. Interestingly the robot can transform into a large diameter and
small width wheel which enhances its maneuverability like small turning radius
and fast/efficient locomotion. This paper contributes on improving the
locomotion mode of previously developed hybrid compliant omnicrawler robot
CObRaSO. In addition to legged and tracked mechanism, CObRaSO can now display
large wheel mode which contributes to its locomotion capabilities. Mechanical
design of the robot has been explained in a detailed manner in this paper and
also the transforming experiment and torque analysis has been shown clearly | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Physics"
] |
Title: Bifurcation of solutions to Hamiltonian boundary value problems,
Abstract: A bifurcation is a qualitative change in a family of solutions to an equation
produced by varying parameters. In contrast to the local bifurcations of
dynamical systems that are often related to a change in the number or stability
of equilibria, bifurcations of boundary value problems are global in nature and
may not be related to any obvious change in dynamical behaviour. Catastrophe
theory is a well-developed framework which studies the bifurcations of critical
points of functions. In this paper we study the bifurcations of solutions of
boundary-value problems for symplectic maps, using the language of
(finite-dimensional) singularity theory. We associate certain such problems
with a geometric picture involving the intersection of Lagrangian submanifolds,
and hence with the critical points of a suitable generating function. Within
this framework, we then study the effect of three special cases: (i) some
common boundary conditions, such as Dirichlet boundary conditions for
second-order systems, restrict the possible types of bifurcations (for example,
in generic planar systems only the A-series beginning with folds and cusps can
occur); (ii) integrable systems, such as planar Hamiltonian systems, can
exhibit a novel periodic pitchfork bifurcation; and (iii) systems with
Hamiltonian symmetries or reversing symmetries can exhibit restricted
bifurcations associated with the symmetry. This approach offers an alternative
to the analysis of critical points in function spaces, typically used in the
study of bifurcation of variational problems, and opens the way to the
detection of more exotic bifurcations than the simple folds and cusps that are
often found in examples. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: Controlling thermal emission of phonon by magnetic metasurfaces,
Abstract: Our experiment shows that the thermal emission of phonon can be controlled by
magnetic resonance (MR) mode in a metasurface (MTS). Through changing the
structural parameter of metasurface, the MR wavelength can be tuned to the
phonon resonance wavelength. This introduces a strong coupling between phonon
and MR, which results in an anticrossing phonon-plasmons mode. In the process,
we can manipulate the polarization and angular radiation of thermal emission of
phonon. Such metasurface provides a new kind of thermal emission structures for
various thermal management applications. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Sharp Threshold of Blow-up and Scattering for the fractional Hartree equation,
Abstract: We consider the fractional Hartree equation in the $L^2$-supercritical case,
and we find a sharp threshold of the scattering versus blow-up dichotomy for
radial data: If $
M[u_{0}]^{\frac{s-s_c}{s_c}}E[u_{0}<M[Q]^{\frac{s-s_c}{s_c}}E[Q]$ and
$M[u_{0}]^{\frac{s-s_c}{s_c}}\|u_{0}\|^2_{\dot H^s}<M[Q]^{\frac{s-s_c}{s_c}}\|
Q\|^2_{\dot H^s}$, then the solution $u(t)$ is globally well-posed and
scatters; if $
M[u_{0}]^{\frac{s-s_c}{s_c}}E[u_{0}]<M[Q]^{\frac{s-s_c}{s_c}}E[Q]$ and
$M[u_{0}]^{\frac{s-s_c}{s_c}}\|u_{0}\|^2_{\dot H^s}>M[Q]^{\frac{s-s_c}{s_c}}\|
Q\|^2_{\dot H^s}$, the solution $u(t)$ blows up in finite time. This condition
is sharp in the sense that the solitary wave solution $e^{it}Q(x)$ is global
but not scattering, which satisfies the equality in the above conditions. Here,
$Q$ is the ground-state solution for the fractional Hartree equation. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs,
Abstract: This work studies the problem of stochastic dynamic filtering and state
propagation with complex beliefs. The main contribution is GP-SUM, a filtering
algorithm tailored to dynamic systems and observation models expressed as
Gaussian Processes (GP), and to states represented as a weighted sum of
Gaussians. The key attribute of GP-SUM is that it does not rely on
linearizations of the dynamic or observation models, or on unimodal Gaussian
approximations of the belief, hence enables tracking complex state
distributions. The algorithm can be seen as a combination of a sampling-based
filter with a probabilistic Bayes filter. On the one hand, GP-SUM operates by
sampling the state distribution and propagating each sample through the dynamic
system and observation models. On the other hand, it achieves effective
sampling and accurate probabilistic propagation by relying on the GP form of
the system, and the sum-of-Gaussian form of the belief. We show that GP-SUM
outperforms several GP-Bayes and Particle Filters on a standard benchmark. We
also demonstrate its use in a pushing task, predicting with experimental
accuracy the naturally occurring non-Gaussian distributions. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Spectral algebra models of unstable v_n-periodic homotopy theory,
Abstract: We give a survey of a generalization of Quillen-Sullivan rational homotopy
theory which gives spectral algebra models of unstable v_n-periodic homotopy
types. In addition to describing and contextualizing our original approach, we
sketch two other recent approaches which are of a more conceptual nature, due
to Arone-Ching and Heuts. In the process, we also survey many relevant concepts
which arise in the study of spectral algebra over operads, including
topological André-Quillen cohomology, Koszul duality, and Goodwillie
calculus. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: The Bane of Low-Dimensionality Clustering,
Abstract: In this paper, we give a conditional lower bound of $n^{\Omega(k)}$ on
running time for the classic k-median and k-means clustering objectives (where
n is the size of the input), even in low-dimensional Euclidean space of
dimension four, assuming the Exponential Time Hypothesis (ETH). We also
consider k-median (and k-means) with penalties where each point need not be
assigned to a center, in which case it must pay a penalty, and extend our lower
bound to at least three-dimensional Euclidean space.
This stands in stark contrast to many other geometric problems such as the
traveling salesman problem, or computing an independent set of unit spheres.
While these problems benefit from the so-called (limited) blessing of
dimensionality, as they can be solved in time $n^{O(k^{1-1/d})}$ or
$2^{n^{1-1/d}}$ in d dimensions, our work shows that widely-used clustering
objectives have a lower bound of $n^{\Omega(k)}$, even in dimension four.
We complete the picture by considering the two-dimensional case: we show that
there is no algorithm that solves the penalized version in time less than
$n^{o(\sqrt{k})}$, and provide a matching upper bound of $n^{O(\sqrt{k})}$.
The main tool we use to establish these lower bounds is the placement of
points on the moment curve, which takes its inspiration from constructions of
point sets yielding Delaunay complexes of high complexity. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Blue-detuned magneto-optical trap,
Abstract: We present the properties and advantages of a new magneto-optical trap (MOT)
where blue-detuned light drives `type-II' transitions that have dark ground
states. Using $^{87}$Rb, we reach a radiation-pressure-limited density
exceeding $10^{11}$cm$^{-3}$ and a temperature below 30$\mu$K. The phase-space
density is higher than in normal atomic MOTs, and a million times higher than
comparable red-detuned type-II MOTs, making it particularly attractive for
molecular MOTs which rely on type-II transitions. The loss of atoms from the
trap is dominated by ultracold collisions between Rb atoms. For typical
trapping conditions, we measure a loss rate of
$1.8(4)\times10^{-10}$cm$^{3}$s$^{-1}$. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Polishness of some topologies related to word or tree automata,
Abstract: We prove that the Büchi topology and the automatic topology are Polish. We
also show that this cannot be fully extended to the case of a space of infinite
labelled binary trees; in particular the Büchi and the Muller topologies are
not Polish in this case. | [
1,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Computer Science"
] |
Title: Emulating satellite drag from large simulation experiments,
Abstract: Obtaining accurate estimates of satellite drag coefficients in low Earth
orbit is a crucial component in positioning and collision avoidance. Simulators
can produce accurate estimates, but their computational expense is much too
large for real-time application. A pilot study showed that Gaussian process
(GP) surrogate models could accurately emulate simulations. However, cubic
runtime for training GPs means that they could only be applied to a narrow
range of input configurations to achieve the desired level of accuracy. In this
paper we show how extensions to the local approximate Gaussian Process (laGP)
method allow accurate full-scale emulation. The new methodological
contributions, which involve a multi-level global/local modeling approach, and
a set-wise approach to local subset selection, are shown to perform well in
benchmark and synthetic data settings. We conclude by demonstrating that our
method achieves the desired level of accuracy, besting simpler viable (i.e.,
computationally tractable) global and local modeling approaches, when trained
on seventy thousand core hours of drag simulations for two real-world
satellites: the Hubble space telescope (HST) and the gravity recovery and
climate experiment (GRACE). | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics",
"Physics"
] |
Title: On the analysis of personalized medication response and classification of case vs control patients in mobile health studies: the mPower case study,
Abstract: In this work we provide a couple of contributions to the analysis of
longitudinal data collected by smartphones in mobile health applications.
First, we propose a novel statistical approach to disentangle personalized
treatment and "time-of-the-day" effects in observational studies. Under the
assumption of no unmeasured confounders, we show how to use conditional
independence relations in the data in order to determine if a difference in
performance between activity tasks performed before and after the participant
has taken medication, are potentially due to an effect of the medication or to
a "time-of-the-day" effect (or still to both). Second, we show that smartphone
data collected from a given study participant can represent a "digital
fingerprint" of the participant, and that classifiers of case/control labels,
constructed using longitudinal data, can show artificially improved performance
when data from each participant is included in both training and test sets. We
illustrate our contributions using data collected during the first 6 months of
the mPower study. | [
0,
0,
0,
1,
0,
0
] | [
"Statistics",
"Quantitative Biology"
] |
Title: A Distributed Scheduling Algorithm to Provide Quality-of-Service in Multihop Wireless Networks,
Abstract: Control of multihop Wireless networks in a distributed manner while providing
end-to-end delay requirements for different flows, is a challenging problem.
Using the notions of Draining Time and Discrete Review from the theory of fluid
limits of queues, an algorithm that meets delay requirements to various flows
in a network is constructed. The algorithm involves an optimization which is
implemented in a cyclic distributed manner across nodes by using the technique
of iterative gradient ascent, with minimal information exchange between nodes.
The algorithm uses time varying weights to give priority to flows. The
performance of the algorithm is studied in a network with interference modelled
by independent sets. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: Training Probabilistic Spiking Neural Networks with First-to-spike Decoding,
Abstract: Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at
harnessing the energy efficiency of spike-domain processing by building on
computing elements that operate on, and exchange, spikes. In this paper, the
problem of training a two-layer SNN is studied for the purpose of
classification, under a Generalized Linear Model (GLM) probabilistic neural
model that was previously considered within the computational neuroscience
literature. Conventional classification rules for SNNs operate offline based on
the number of output spikes at each output neuron. In contrast, a novel
training method is proposed here for a first-to-spike decoding rule, whereby
the SNN can perform an early classification decision once spike firing is
detected at an output neuron. Numerical results bring insights into the optimal
parameter selection for the GLM neuron and on the accuracy-complexity trade-off
performance of conventional and first-to-spike decoding. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Quantitative Biology"
] |
Title: A Multi-Layer K-means Approach for Multi-Sensor Data Pattern Recognition in Multi-Target Localization,
Abstract: Data-target association is an important step in multi-target localization for
the intelligent operation of un- manned systems in numerous applications such
as search and rescue, traffic management and surveillance. The objective of
this paper is to present an innovative data association learning approach named
multi-layer K-means (MLKM) based on leveraging the advantages of some existing
machine learning approaches, including K-means, K-means++, and deep neural
networks. To enable the accurate data association from different sensors for
efficient target localization, MLKM relies on the clustering capabilities of
K-means++ structured in a multi-layer framework with the error correction
feature that is motivated by the backpropogation that is well-known in deep
learning research. To show the effectiveness of the MLKM method, numerous
simulation examples are conducted to compare its performance with K-means,
K-means++, and deep neural networks. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Spatial localization for nonlinear dynamical stochastic models for excitable media,
Abstract: Nonlinear dynamical stochastic models are ubiquitous in different areas.
Excitable media models are typical examples with large state dimensions. Their
statistical properties are often of great interest but are also very
challenging to compute. In this article, a theoretical framework to understand
the spatial localization for a large class of stochastically coupled nonlinear
systems in high dimensions is developed. Rigorous mathematical theories show
the covariance decay behavior due to both local and nonlocal effects, which
result from the diffusion and the mean field interaction, respectively. The
analysis is based on a comparison with an appropriate linear surrogate model,
of which the covariance propagation can be computed explicitly. Two important
applications of these theoretical results are discussed. They are the spatial
averaging strategy for efficiently sampling the covariance matrix and the
localization technique in data assimilation. Test examples of a surrogate
linear model and a stochastically coupled FitzHugh-Nagumo model for excitable
media are adopted to validate the theoretical results. The latter is also used
for a systematical study of the spatial averaging strategy in efficiently
sampling the covariance matrix in different dynamical regimes. | [
0,
0,
1,
1,
0,
0
] | [
"Mathematics",
"Statistics",
"Quantitative Biology"
] |
Title: Nonlinear photoionization of transparent solids: a nonperturbative theory obeying selection rules,
Abstract: We provide a nonperturbative theory for photoionization of transparent
solids. By applying a particular steepest-descent method, we derive analytical
expressions for the photoionization rate within the two-band structure model,
which consistently account for the $selection$ $rules$ related to the parity of
the number of absorbed photons ($odd$ or $even$). We demonstrate the crucial
role of the interference of the transition amplitudes (saddle-points), which in
the semi-classical limit, can be interpreted in terms of interfering quantum
trajectories. Keldysh's foundational work of laser physics [Sov. Phys. JETP 20,
1307 (1965)] disregarded this interference, resulting in the violation of
$selection$ $rules$. We provide an improved Keldysh photoionization theory and
show its excellent agreement with measurements for the frequency dependence of
the two-photon absorption and nonlinear refractive index coefficients in
dielectrics. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Models,
Abstract: We focus on two particular aspects of model risk: the inability of a chosen
model to fit observed market prices at a given point in time (calibration
error) and the model risk due to recalibration of model parameters (in
contradiction to the model assumptions). In this context, we follow the
approach of Glasserman and Xu (2014) and use relative entropy as a pre-metric
in order to quantify these two sources of model risk in a common framework, and
consider the trade-offs between them when choosing a model and the frequency
with which to recalibrate to the market. We illustrate this approach applied to
the models of Black and Scholes (1973) and Heston (1993), using option data for
Apple (AAPL) and Google (GOOG). We find that recalibrating a model more
frequently simply shifts model risk from one type to another, without any
substantial reduction of aggregate model risk. Furthermore, moving to a more
complicated stochastic model is seen to be counterproductive if one requires a
high degree of robustness, for example as quantified by a 99 percent quantile
of aggregate model risk. | [
0,
0,
0,
0,
0,
1
] | [
"Quantitative Finance",
"Statistics"
] |
Title: A Trio Neural Model for Dynamic Entity Relatedness Ranking,
Abstract: Measuring entity relatedness is a fundamental task for many natural language
processing and information retrieval applications. Prior work often studies
entity relatedness in static settings and an unsupervised manner. However,
entities in real-world are often involved in many different relationships,
consequently entity-relations are very dynamic over time. In this work, we
propose a neural networkbased approach for dynamic entity relatedness,
leveraging the collective attention as supervision. Our model is capable of
learning rich and different entity representations in a joint framework.
Through extensive experiments on large-scale datasets, we demonstrate that our
method achieves better results than competitive baselines. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science"
] |
Title: Dynamic k-Struve Sumudu Solutions for Fractional Kinetic Equations,
Abstract: In this present study, we investigate solutions for fractional kinetic
equations, involving k-Struve functions using Sumudu transform. The methodology
and results can be considered and applied to various related fractional
problems in mathematical physics. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: Topological Terms and Phases of Sigma Models,
Abstract: We study boundary conditions of topological sigma models with the goal of
generalizing the concepts of anomalous symmetry and symmetry protected
topological order. We find a version of 't Hooft's anomaly matching conditions
on the renormalization group flow of boundaries of invertible topological sigma
models and discuss several examples of anomalous boundary theories. We also
comment on bulk topological transitions in dynamical sigma models and argue
that one can, with care, use topological data to draw sigma model phase
diagrams. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Mathematics"
] |
Title: The connected countable spaces of Bing and Ritter are topologically homogeneous,
Abstract: Answering a problem posed by the second author on Mathoverflow, we prove that
the connected countable Hausdorff spaces constructed by Bing and Ritter are
topologically homogeneous. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Dynamic Graph Convolutional Networks,
Abstract: Many different classification tasks need to manage structured data, which are
usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that
the vertices/edges of each graph may change during time. Our goal is to jointly
exploit structured data and temporal information through the use of a neural
network model. To the best of our knowledge, this task has not been addressed
using these kind of architectures. For this reason, we propose two novel
approaches, which combine Long Short-Term Memory networks and Graph
Convolutional Networks to learn long short-term dependencies together with
graph structure. The quality of our methods is confirmed by the promising
results achieved. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science"
] |
Title: A Robotic Auto-Focus System based on Deep Reinforcement Learning,
Abstract: Considering its advantages in dealing with high-dimensional visual input and
learning control policies in discrete domain, Deep Q Network (DQN) could be an
alternative method of traditional auto-focus means in the future. In this
paper, based on Deep Reinforcement Learning, we propose an end-to-end approach
that can learn auto-focus policies from visual input and finish at a clear spot
automatically. We demonstrate that our method - discretizing the action space
with coarse to fine steps and applying DQN is not only a solution to auto-focus
but also a general approach towards vision-based control problems. Separate
phases of training in virtual and real environments are applied to obtain an
effective model. Virtual experiments, which are carried out after the virtual
training phase, indicates that our method could achieve 100% accuracy on a
certain view with different focus range. Further training on real robots could
eliminate the deviation between the simulator and real scenario, leading to
reliable performances in real applications. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Quantitative Biology"
] |
Title: A proof of Hilbert's theorem on ternary quartic forms with the ladder technique,
Abstract: This paper proposes a totally constructive approach for the proof of
Hilbert's theorem on ternary quartic forms. The main contribution is the ladder
technique, with which the Hilbert's theorem is proved vividly. | [
1,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: The universal DAHA of type $(C_1^\vee,C_1)$ and Leonard pairs of $q$-Racah type,
Abstract: A Leonard pair is a pair of diagonalizable linear transformations of a
finite-dimensional vector space, each of which acts in an irreducible
tridiagonal fashion on an eigenbasis for the other one. Let $\mathbb F$ denote
an algebraically closed field, and fix a nonzero $q \in \mathbb F$ that is not
a root of unity. The universal double affine Hecke algebra (DAHA) $\hat{H}_q$
of type $(C_1^\vee,C_1)$ is the associative $\mathbb F$-algebra defined by
generators $\lbrace t_i^{\pm 1}\rbrace_{i=0}^3$ and relations (i)
$t_it_i^{-1}=t_i^{-1}t_i=1$; (ii) $t_i+t_i^{-1}$ is central; (iii)
$t_0t_1t_2t_3 = q^{-1}$. We consider the elements $X=t_3t_0$ and $Y=t_0t_1$ of
$\hat{H}_q$. Let $\mathcal V$ denote a finite-dimensional irreducible
$\hat{H}_q$-module on which each of $X$, $Y$ is diagonalizable and $t_0$ has
two distinct eigenvalues. Then $\mathcal V$ is a direct sum of the two
eigenspaces of $t_0$. We show that the pair $X+X^{-1}$, $Y+Y^{-1}$ acts on each
eigenspace as a Leonard pair, and each of these Leonard pairs falls into a
class said to have $q$-Racah type. Thus from $\mathcal V$ we obtain a pair of
Leonard pairs of $q$-Racah type. It is known that a Leonard pair of $q$-Racah
type is determined up to isomorphism by a parameter sequence $(a,b,c,d)$ called
its Huang data. Given a pair of Leonard pairs of $q$-Racah type, we find
necessary and sufficient conditions on their Huang data for that pair to come
from the above construction. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Causal Interventions for Fairness,
Abstract: Most approaches in algorithmic fairness constrain machine learning methods so
the resulting predictions satisfy one of several intuitive notions of fairness.
While this may help private companies comply with non-discrimination laws or
avoid negative publicity, we believe it is often too little, too late. By the
time the training data is collected, individuals in disadvantaged groups have
already suffered from discrimination and lost opportunities due to factors out
of their control. In the present work we focus instead on interventions such as
a new public policy, and in particular, how to maximize their positive effects
while improving the fairness of the overall system. We use causal methods to
model the effects of interventions, allowing for potential interference--each
individual's outcome may depend on who else receives the intervention. We
demonstrate this with an example of allocating a budget of teaching resources
using a dataset of schools in New York City. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Role of the orbital degree of freedom in iron-based superconductors,
Abstract: Almost a decade has passed since the serendipitous discovery of the
iron-based high temperature superconductors (FeSCs) in 2008. The question of
how much similarity the FeSCs have with the copper oxide high temperature
superconductors emerged since the initial discovery of long-range
antiferromagnetism in the FeSCs in proximity to superconductivity. Despite the
great resemblance in their phase diagrams, there exist important disparities
between FeSCs and cuprates that need to be considered in order to paint a full
picture of these two families of high temperature superconductors. One of the
key differences lies in the multi-orbital multi-band nature of FeSCs, in
contrast to the effective single-band model for cuprates. Due to the complexity
of multi-orbital band structures, the orbital degree of freedom is often
neglected in formulating the theoretical models for FeSCs. On the experimental
side, systematic studies of the orbital related phenomena in FeSCs have been
largely lacking. In this review, we summarize angle-resolved photoemission
spectroscopy (ARPES) measurements across various FeSC families in literature,
focusing on the systematic trend of orbital dependent electron correlations and
the role of different Fe 3d orbitals in driving the nematic transition, the
spin-density-wave transition, and implications for superconductivity. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: When Slepian Meets Fiedler: Putting a Focus on the Graph Spectrum,
Abstract: The study of complex systems benefits from graph models and their analysis.
In particular, the eigendecomposition of the graph Laplacian lets emerge
properties of global organization from local interactions; e.g., the Fiedler
vector has the smallest non-zero eigenvalue and plays a key role for graph
clustering. Graph signal processing focusses on the analysis of signals that
are attributed to the graph nodes. The eigendecomposition of the graph
Laplacian allows to define the graph Fourier transform and extend conventional
signal-processing operations to graphs. Here, we introduce the design of
Slepian graph signals, by maximizing energy concentration in a predefined
subgraph for a graph spectral bandlimit. We establish a novel link with
classical Laplacian embedding and graph clustering, which provides a meaning to
localized graph frequencies. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: An aptamer-biosensor for azole class antifungal drugs,
Abstract: This report describes the development of an aptamer for sensing azole
antifungal drugs for therapeutic drug monitoring. Modified Synthetic Evolution
of Ligands through Exponential Enrichment (SELEX) was used to discover a DNA
aptamer recognizing azole class antifungal drugs. This aptamer undergoes a
secondary structural change upon binding to its target molecule as shown
through fluorescence anisotropy-based binding measurements. Experiments using
circular dichroism spectroscopy, revealed a unique double G-quadruplex
structure that was essential and specific for binding to the azole antifungal
target. Aptamer-functionalized Graphene Field Effect Transistor (GFET) devices
were created and used to measure the binding of strength of azole antifungals
to this surface. In total this aptamer and the supporting sensing platform
could provide a valuable tool for improving the treatment of patients with
invasive fungal infections. | [
0,
1,
0,
0,
0,
0
] | [
"Quantitative Biology"
] |
Title: Notes on "Einstein metrics on compact simple Lie groups attached to standard triples",
Abstract: In the paper "Einstein metrics on compact simple Lie groups attached to
standard triples", the authors introduced the definition of standard triples
and proved that every compact simple Lie group $G$ attached to a standard
triple $(G,K,H)$ admits a left-invariant Einstein metric which is not naturally
reductive except the standard triple $(\Sp(4),2\Sp(2),4\Sp(1))$. For the triple
$(\Sp(4),2\Sp(2),4\Sp(1))$, we find there exists an involution pair of $\sp(4)$
such that $4\sp(1)$ is the fixed point of the pair, and then give the
decomposition of $\sp(4)$ as a direct sum of irreducible
$\ad(4\sp(1))$-modules. But $\Sp(4)/4\Sp(1)$ is not a generalized Wallach
space. Furthermore we give left-invariant Einstein metrics on $\Sp(4)$ which
are non-naturally reductive and $\Ad(4\Sp(1))$-invariant. For the general case
$(\Sp(2n_1n_2),2\Sp(n_1n_2),2n_2\Sp(n_1))$, there exist $2n_2-1$ involutions of
$\sp(2n_1n_2)$ such that $2n_2\sp(n_1))$ is the fixed point of these $2n_2-1$
involutions, and it follows the decomposition of $\sp(2n_1n_2)$ as a direct sum
of irreducible $\ad(2n_2\sp(n_1))$-modules. In order to give new non-naturally
reductive and $\Ad(2n_2\Sp(n_1)))$-invariant Einstein metrics on
$\Sp(2n_1n_2)$, we prove a general result, i.e. $\Sp(2k+l)$ admits at least two
non-naturally reductive Einstein metrics which are
$\Ad(\Sp(k)\times\Sp(k)\times\Sp(l))$-invariant if $k<l$. It implies that every
compact simple Lie group $\Sp(n)$ for $n\geq 4$ admits at least
$2[\frac{n-1}{3}]$ non-naturally reductive left-invariant Einstein metrics. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics",
"Physics"
] |
Title: DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems,
Abstract: Deep learning (DL) defines a new data-driven programming paradigm that
constructs the internal system logic of a crafted neuron network through a set
of training data. We have seen wide adoption of DL in many safety-critical
scenarios. However, a plethora of studies have shown that the state-of-the-art
DL systems suffer from various vulnerabilities which can lead to severe
consequences when applied to real-world applications. Currently, the testing
adequacy of a DL system is usually measured by the accuracy of test data.
Considering the limitation of accessible high quality test data, good accuracy
performance on test data can hardly provide confidence to the testing adequacy
and generality of DL systems. Unlike traditional software systems that have
clear and controllable logic and functionality, the lack of interpretability in
a DL system makes system analysis and defect detection difficult, which could
potentially hinder its real-world deployment. In this paper, we propose
DeepGauge, a set of multi-granularity testing criteria for DL systems, which
aims at rendering a multi-faceted portrayal of the testbed. The in-depth
evaluation of our proposed testing criteria is demonstrated on two well-known
datasets, five DL systems, and with four state-of-the-art adversarial attack
techniques against DL. The potential usefulness of DeepGauge sheds light on the
construction of more generic and robust DL systems. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science"
] |
Title: Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields,
Abstract: This work investigates the training of conditional random fields (CRFs) via
the stochastic dual coordinate ascent (SDCA) algorithm of Shalev-Shwartz and
Zhang (2016). SDCA enjoys a linear convergence rate and a strong empirical
performance for binary classification problems. However, it has never been used
to train CRFs. Yet it benefits from an `exact' line search with a single
marginalization oracle call, unlike previous approaches. In this paper, we
adapt SDCA to train CRFs, and we enhance it with an adaptive non-uniform
sampling strategy based on block duality gaps. We perform experiments on four
standard sequence prediction tasks. SDCA demonstrates performances on par with
the state of the art, and improves over it on three of the four datasets, which
have in common the use of sparse features. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: $η$-Ricci solitons in $(\varepsilon)$-almost paracontact metric manifolds,
Abstract: The object of this paper is to study $\eta$-Ricci solitons on
$(\varepsilon)$-almost paracontact metric manifolds. We investigate
$\eta$-Ricci solitons in the case when its potential vector field is exactly
the characteristic vector field $\xi$ of the $(\varepsilon)$-almost paracontact
metric manifold and when the potential vector field is torse-forming. We also
study Einstein-like and $(\varepsilon)$-para Sasakian manifolds admitting
$\eta$-Ricci solitons. Finally we obtain some results for $\eta$-Ricci solitons
on $(\varepsilon)$-almost paracontact metric manifolds with a special view
towards parallel symmetric (0,2)-tensor fields. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: Entropy facilitated active transport,
Abstract: We show how active transport of ions can be interpreted as an entropy
facilitated process. In this interpretation, the pore geometry through which
substrates are transported can give rise to a driving force. This gives a
direct link between the geometry and the changes in Gibbs energy required.
Quantifying the size of this effect for several proteins we find that the
entropic contribution from the pore geometry is significant and we discuss how
the effect can be used to interpret variations in the affinity at the binding
site. | [
0,
1,
0,
0,
0,
0
] | [
"Physics",
"Quantitative Biology"
] |
Title: Consistency of the Predicative Calculus of Cumulative Inductive Constructions (pCuIC),
Abstract: In order to avoid well-know paradoxes associated with self-referential
definitions, higher-order dependent type theories stratify the theory using a
countably infinite hierarchy of universes (also known as sorts), Type$_0$ :
Type$_1$ : $\cdots$ . Such type systems are called cumulative if for any type
$A$ we have that $A$ : Type$_{i}$ implies $A$ : Type$_{i+1}$. The predicative
calculus of inductive constructions (pCIC) which forms the basis of the Coq
proof assistant, is one such system.
In this paper we present and establish the soundness of the predicative
calculus of cumulative inductive constructions (pCuIC) which extends the
cumulativity relation to inductive types. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Mathematics"
] |
Title: ADaPTION: Toolbox and Benchmark for Training Convolutional Neural Networks with Reduced Numerical Precision Weights and Activation,
Abstract: Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) are
useful for many practical tasks in machine learning. Synaptic weights, as well
as neuron activation functions within the deep network are typically stored
with high-precision formats, e.g. 32 bit floating point. However, since storage
capacity is limited and each memory access consumes power, both storage
capacity and memory access are two crucial factors in these networks. Here we
present a method and present the ADaPTION toolbox to extend the popular deep
learning library Caffe to support training of deep CNNs with reduced numerical
precision of weights and activations using fixed point notation. ADaPTION
includes tools to measure the dynamic range of weights and activations. Using
the ADaPTION tools, we quantized several CNNs including VGG16 down to 16-bit
weights and activations with only 0.8% drop in Top-1 accuracy. The
quantization, especially of the activations, leads to increase of up to 50% of
sparsity especially in early and intermediate layers, which we exploit to skip
multiplications with zero, thus performing faster and computationally cheaper
inference. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Graph Attention Networks,
Abstract: We present graph attention networks (GATs), novel neural network
architectures that operate on graph-structured data, leveraging masked
self-attentional layers to address the shortcomings of prior methods based on
graph convolutions or their approximations. By stacking layers in which nodes
are able to attend over their neighborhoods' features, we enable (implicitly)
specifying different weights to different nodes in a neighborhood, without
requiring any kind of costly matrix operation (such as inversion) or depending
on knowing the graph structure upfront. In this way, we address several key
challenges of spectral-based graph neural networks simultaneously, and make our
model readily applicable to inductive as well as transductive problems. Our GAT
models have achieved or matched state-of-the-art results across four
established transductive and inductive graph benchmarks: the Cora, Citeseer and
Pubmed citation network datasets, as well as a protein-protein interaction
dataset (wherein test graphs remain unseen during training). | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science"
] |
Title: An approach to Griffiths conjecture,
Abstract: The Griffiths conjecture asserts that every ample vector bundle $E$ over a
compact complex manifold $S$ admits a hermitian metric with positive curvature
in the sense of Griffiths. In this article we give a sufficient condition for a
positive hermitian metric on $\mathcal{O}_{\mathbb{P}(E^*)}(1)$ to induce a
Griffiths positive $L^2$-metric on the vector bundle $E$. This result suggests
to study the relative Kähler-Ricci flow on $\mathcal{O}_{\mathbb{P}(E^*)}(1)$
for the fibration $\mathbb{P}(E^*)\to S$. We define a flow and give arguments
for the convergence. | [
0,
0,
1,
0,
0,
0
] | [
"Mathematics"
] |
Title: On Detecting Adversarial Perturbations,
Abstract: Machine learning and deep learning in particular has advanced tremendously on
perceptual tasks in recent years. However, it remains vulnerable against
adversarial perturbations of the input that have been crafted specifically to
fool the system while being quasi-imperceptible to a human. In this work, we
propose to augment deep neural networks with a small "detector" subnetwork
which is trained on the binary classification task of distinguishing genuine
data from data containing adversarial perturbations. Our method is orthogonal
to prior work on addressing adversarial perturbations, which has mostly focused
on making the classification network itself more robust. We show empirically
that adversarial perturbations can be detected surprisingly well even though
they are quasi-imperceptible to humans. Moreover, while the detectors have been
trained to detect only a specific adversary, they generalize to similar and
weaker adversaries. In addition, we propose an adversarial attack that fools
both the classifier and the detector and a novel training procedure for the
detector that counteracts this attack. | [
1,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Energy Optimization of Automatic Hybrid Sailboat,
Abstract: Autonomous Surface Vehicles (ASVs) provide an effective way to actualize
applications such as environment monitoring, search and rescue, and scientific
researches. However, the conventional ASVs depends overly on the stored energy.
Hybrid Sailboat, mainly powered by the wind, can solve this problem by using an
auxiliary propulsion system. The electric energy cost of Hybrid Sailboat needs
to be optimized to achieve the ocean automatic cruise mission. Based on
adjusted setting on sails and rudders, this paper seeks the optimal trajectory
for autonomic cruising to reduce the energy cost by changing the heading angle
of sailing upwind. The experiment results validate the heading angle accounts
for energy cost and the trajectory with the best heading angle saves up to
23.7% than other conditions. Furthermore, the energy-time line can be used to
predict the energy cost for long-time sailing. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science",
"Physics"
] |
Title: Estimating the Operating Characteristics of Ensemble Methods,
Abstract: In this paper we present a technique for using the bootstrap to estimate the
operating characteristics and their variability for certain types of ensemble
methods. Bootstrapping a model can require a huge amount of work if the
training data set is large. Fortunately in many cases the technique lets us
determine the effect of infinite resampling without actually refitting a single
model. We apply the technique to the study of meta-parameter selection for
random forests. We demonstrate that alternatives to bootstrap aggregation and
to considering \sqrt{d} features to split each node, where d is the number of
features, can produce improvements in predictive accuracy. | [
0,
0,
0,
1,
0,
0
] | [
"Computer Science",
"Statistics"
] |
Title: Plasma turbulence at ion scales: a comparison between PIC and Eulerian hybrid-kinetic approaches,
Abstract: Kinetic-range turbulence in magnetized plasmas and, in particular, in the
context of solar-wind turbulence has been extensively investigated over the
past decades via numerical simulations. Among others, one of the widely adopted
reduced plasma model is the so-called hybrid-kinetic model, where the ions are
fully kinetic and the electrons are treated as a neutralizing (inertial or
massless) fluid. Within the same model, different numerical methods and/or
approaches to turbulence development have been employed. In the present work,
we present a comparison between two-dimensional hybrid-kinetic simulations of
plasma turbulence obtained with two complementary approaches spanning about two
decades in wavenumber - from MHD inertial range to scales well below the ion
gyroradius - with a state-of-the-art accuracy. One approach employs hybrid
particle-in-cell (HPIC) simulations of freely-decaying Alfvénic turbulence,
whereas the other consists of Eulerian hybrid Vlasov-Maxwell (HVM) simulations
of turbulence continuously driven with partially-compressible large-scale
fluctuations. Despite the completely different initialization and
injection/drive at large scales, the same properties of turbulent fluctuations
at $k_\perp\rho_i\gtrsim1$ are observed. The system indeed self-consistently
"reprocesses" the turbulent fluctuations while they are cascading towards
smaller and smaller scales, in a way which actually depends on the plasma beta
parameter. Small-scale turbulence has been found to be mainly populated by
kinetic Alfvén wave (KAW) fluctuations for $\beta\geq1$, whereas KAW
fluctuations are only sub-dominant for low-$\beta$. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
Title: Lock-Free Parallel Perceptron for Graph-based Dependency Parsing,
Abstract: Dependency parsing is an important NLP task. A popular approach for
dependency parsing is structured perceptron. Still, graph-based dependency
parsing has the time complexity of $O(n^3)$, and it suffers from slow training.
To deal with this problem, we propose a parallel algorithm called parallel
perceptron. The parallel algorithm can make full use of a multi-core computer
which saves a lot of training time. Based on experiments we observe that
dependency parsing with parallel perceptron can achieve 8-fold faster training
speed than traditional structured perceptron methods when using 10 threads, and
with no loss at all in accuracy. | [
1,
0,
0,
0,
0,
0
] | [
"Computer Science"
] |
Title: Partitioning the Outburst Energy of a Low Eddington Accretion Rate AGN at the Center of an Elliptical Galaxy: the Recent 12 Myr History of the Supermassive Black Hole in M87,
Abstract: M87, the active galaxy at the center of the Virgo cluster, is ideal for
studying the interaction of a supermassive black hole (SMBH) with a hot,
gas-rich environment. A deep Chandra observation of M87 exhibits an
approximately circular shock front (13 kpc radius, in projection) driven by the
expansion of the central cavity (filled by the SMBH with relativistic
radio-emitting plasma) with projected radius $\sim$1.9 kpc. We combine
constraints from X-ray and radio observations of M87 with a shock model to
derive the properties of the outburst that created the 13 kpc shock. Principal
constraints for the model are 1) the measured Mach number ($M$$\sim$1.2), 2)
the radius of the 13 kpc shock, and 3) the observed size of the central
cavity/bubble (the radio-bright cocoon) that serves as the piston to drive the
shock. We find an outburst of $\sim$5$\times$$10^{57}$ ergs that began about 12
Myr ago and lasted $\sim$2 Myr matches all the constraints. In this model,
$\sim$22% of the energy is carried by the shock as it expands. The remaining
$\sim$80% of the outburst energy is available to heat the core gas. More than
half the total outburst energy initially goes into the enthalpy of the central
bubble, the radio cocoon. As the buoyant bubble rises, much of its energy is
transferred to the ambient thermal gas. For an outburst repetition rate of
about 12 Myrs (the age of the outburst), 80% of the outburst energy is
sufficient to balance the radiative cooling. | [
0,
1,
0,
0,
0,
0
] | [
"Physics"
] |
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