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Title: On Mimura's extension problem,
Abstract: We determine the group strucure of the $23$-rd homotopy group $\pi_{23}(G_2 :
2)$, where $G_2$ is the Lie group of exceptional type, which hasn't been
determined for $50$ years. | [
0,
0,
1,
0,
0,
0
] |
Title: On critical and supercritical pseudo-relativistic nonlinear Schrödinger equations,
Abstract: In this paper, we investigate existence and non-existence of a nontrivial
solution to the pseudo-relativistic nonlinear Schrödinger equation $$\left(
\sqrt{-c^2\Delta + m^2 c^4}-mc^2\right) u + \mu u = |u|^{p-1}u\quad
\textrm{in}~\mathbb{R}^n~(n \geq 2)$$ involving an
$H^{1/2}$-critical/supercritical power-type nonlinearity, i.e., $p \geq
\frac{n+1}{n-1}$. We prove that in the non-relativistic regime, there exists a
nontrivial solution provided that the nonlinearity is
$H^{1/2}$-critical/supercritical but it is $H^1$-subcritical. On the other
hand, we also show that there is no nontrivial bounded solution either $(i)$ if
the nonlinearity is $H^{1/2}$-critical/supercritical in the ultra-relativistic
regime or $(ii)$ if the nonlinearity is $H^1$-critical/supercritical in all
cases. | [
0,
0,
1,
0,
0,
0
] |
Title: Eye Tracker Accuracy: Quantitative Evaluation of the Invisible Eye Center Location,
Abstract: Purpose. We present a new method to evaluate the accuracy of an eye tracker
based eye localization system. Measuring the accuracy of an eye tracker's
primary intention, the estimated point of gaze, is usually done with volunteers
and a set of fixation points used as ground truth. However, verifying the
accuracy of the location estimate of a volunteer's eye center in 3D space is
not easily possible. This is because the eye center is an intangible point
hidden by the iris. Methods. We evaluate the eye location accuracy by using an
eye phantom instead of eyes of volunteers. For this, we developed a testing
stage with a realistic artificial eye and a corresponding kinematic model,
which we trained with {\mu}CT data. This enables us to precisely evaluate the
eye location estimate of an eye tracker. Results. We show that the proposed
testing stage with the corresponding kinematic model is suitable for such a
validation. Further, we evaluate a particular eye tracker based navigation
system and show that this system is able to successfully determine the eye
center with sub-millimeter accuracy. Conclusions. We show the suitability of
the evaluated eye tracker for eye interventions, using the proposed testing
stage and the corresponding kinematic model. The results further enable
specific enhancement of the navigation system to potentially get even better
results. | [
1,
0,
0,
0,
0,
0
] |
Title: Theoretical Foundations of Forward Feature Selection Methods based on Mutual Information,
Abstract: Feature selection problems arise in a variety of applications, such as
microarray analysis, clinical prediction, text categorization, image
classification and face recognition, multi-label learning, and classification
of internet traffic. Among the various classes of methods, forward feature
selection methods based on mutual information have become very popular and are
widely used in practice. However, comparative evaluations of these methods have
been limited by being based on specific datasets and classifiers. In this
paper, we develop a theoretical framework that allows evaluating the methods
based on their theoretical properties. Our framework is grounded on the
properties of the target objective function that the methods try to
approximate, and on a novel categorization of features, according to their
contribution to the explanation of the class; we derive upper and lower bounds
for the target objective function and relate these bounds with the feature
types. Then, we characterize the types of approximations taken by the methods,
and analyze how these approximations cope with the good properties of the
target objective function. Additionally, we develop a distributional setting
designed to illustrate the various deficiencies of the methods, and provide
several examples of wrong feature selections. Based on our work, we identify
clearly the methods that should be avoided, and the methods that currently have
the best performance. | [
1,
0,
0,
1,
0,
0
] |
Title: Robust quantum switch with Rydberg excitations,
Abstract: We develop an approach to realize a quantum switch for Rydberg excitation in
atoms with $Y$-typed level configuration. We find that the steady population on
two different Rydberg states can be reversibly exchanged in a controllable way
by properly tuning the Rydberg-Rydberg interaction. Moreover, our numerical
simulations verify that the switching scheme is robust against spontaneous
decay, environmental disturbance, as well as the duration of operation on the
interaction, and also a high switching efficiency is quite attainable, which
makes it have potential applications in quantum information processing and
other Rydberg-based quantum technologies. | [
0,
1,
0,
0,
0,
0
] |
Title: Non-robust phase transitions in the generalized clock model on trees,
Abstract: Pemantle and Steif provided a sharp threshold for the existence of a RPT
(robust phase transition) for the continuous rotator model and the Potts model
in terms of the branching number and the second eigenvalue of the transfer
operator, where a robust phase transition is said to occur if an arbitrarily
weak coupling with symmetry-breaking boundary conditions suffices to induce
symmetry breaking in the bulk. They further showed that for the Potts model RPT
occurs at a different threshold than PT (phase transition in the sense of
multiple Gibbs measures), and conjectured that RPT and PT should occur at the
same threshold in the continuous rotator model. We consider the class of 4- and
5-state rotation-invariant spin models with reflection symmetry on general
trees which contains the Potts model and the clock model with
scalarproduct-interaction as limiting cases. The clock model can be viewed as a
particular discretization which is obtained from the classical rotator model on
the continuous one-dimensional sphere. We analyze the transition between PT=RPT
and PT is unequal to RPT, in terms of the eigenvalues of the transfer matrix of
the model at the critical threshold value for the existence of RPT. The
transition between the two regimes depends sensitively on the third largest
eigenvalue. | [
0,
0,
1,
0,
0,
0
] |
Title: The Role of Gender in Social Network Organization,
Abstract: The digital traces we leave behind when engaging with the modern world offer
an interesting lens through which we study behavioral patterns as expression of
gender. Although gender differentiation has been observed in a number of
settings, the majority of studies focus on a single data stream in isolation.
Here we use a dataset of high resolution data collected using mobile phones, as
well as detailed questionnaires, to study gender differences in a large cohort.
We consider mobility behavior and individual personality traits among a group
of more than $800$ university students. We also investigate interactions among
them expressed via person-to-person contacts, interactions on online social
networks, and telecommunication. Thus, we are able to study the differences
between male and female behavior captured through a multitude of channels for a
single cohort. We find that while the two genders are similar in a number of
aspects, there are robust deviations that include multiple facets of social
interactions, suggesting the existence of inherent behavioral differences.
Finally, we quantify how aspects of an individual's characteristics and social
behavior reveals their gender by posing it as a classification problem. We ask:
How well can we distinguish between male and female study participants based on
behavior alone? Which behavioral features are most predictive? | [
1,
0,
0,
0,
0,
0
] |
Title: The Schur Lie-Multiplier of Leibinz Algebras,
Abstract: For a free presentation $0 \to R \to F \to G \to 0$ of a Leibniz algebra $G$,
the Baer invariant ${\cal M}^{\sf Lie}(G) = \frac{R \cap [F, F]_{Lie}}{[F,
R]_{Lie}}$ is called the Schur multiplier of $G$ relative to the Liezation
functor or Schur Lie-multiplier. For a two-sided ideal $N$ of a Leibniz algebra
$G$, we construct a four-term exact sequence relating the Schur Lie-multiplier
of $G$ and $G/N$, which is applied to study and characterize Lie-nilpotency,
Lie-stem covers and Lie-capability of Leibniz algebras. | [
0,
0,
1,
0,
0,
0
] |
Title: Did we learn from LLC Side Channel Attacks? A Cache Leakage Detection Tool for Crypto Libraries,
Abstract: This work presents a new tool to verify the correctness of cryptographic
implementations with respect to cache attacks. Our methodology discovers
vulnerabilities that are hard to find with other techniques, observed as
exploitable leakage. The methodology works by identifying secret dependent
memory and introducing forced evictions inside potentially vulnerable code to
obtain cache traces that are analyzed using Mutual Information. If dependence
is observed, the cryptographic implementation is classified as to leak
information.
We demonstrate the viability of our technique in the design of the three main
cryptographic primitives, i.e., AES, RSA and ECC, in eight popular up to date
cryptographic libraries, including OpenSSL, Libgcrypt, Intel IPP and NSS. Our
results show that cryptographic code designers are far away from incorporating
the appropriate countermeasures to avoid cache leakages, as we found that 50%
of the default implementations analyzed leaked information that lead to key
extraction. We responsibly notified the designers of all the leakages found and
suggested patches to solve these vulnerabilities. | [
1,
0,
0,
0,
0,
0
] |
Title: Effects of anisotropy in spin molecular-orbital coupling on effective spin models of trinuclear organometallic complexes,
Abstract: We consider layered decorated honeycomb lattices at two-thirds filling, as
realized in some trinuclear organometallic complexes. Localized $S=1$ moments
with a single-spin anisotropy emerge from the interplay of Coulomb repulsion
and spin molecular-orbit coupling (SMOC). Magnetic anisotropies with bond
dependent exchange couplings occur in the honeycomb layers when the direct
intracluster exchange and the spin molecular-orbital coupling are both present.
We find that the effective spin exchange model within the layers is an XXZ +
120$^\circ$ honeycomb quantum compass model. The intrinsic non-spherical
symmetry of the multinuclear complexes leads to very different transverse and
longitudinal spin molecular-orbital couplings, which greatly enhances the
single-spin and exchange coupling anisotropies. The interlayer coupling is
described by a XXZ model with anisotropic biquadratic terms. As the correlation
strength increases the systems becomes increasingly one-dimensional. Thus, if
the ratio of SMOC to the interlayer hopping is small this stabilizes the
Haldane phase. However, as the ratio increases there is a quantum phase
transition to the topologically trivial `$D$-phase'. We also predict a quantum
phase transition from a Haldane phase to a magnetically ordered phase at
sufficiently strong external magnetic fields. | [
0,
1,
0,
0,
0,
0
] |
Title: Design and experimental test of an optical vortex coronagraph,
Abstract: The optical vortex coronagraph (OVC) is one of the promising ways for direct
imaging exoplanets because of its small inner working angle and high
throughput. This paper presents the design and laboratory demonstration
performance at 633nm and 1520nm of the OVC based on liquid crystal polymers
(LCP). Two LCPs has been manufactured in partnership with a commercial vendor.
The OVC can deliver a good performance in laboratory test and achieve the
contrast of the order 10^-6 at angular distance 3{\lambda}/D, which is able to
image the giant exoplanets at a young stage in combination with extreme
adaptive optics. | [
0,
1,
0,
0,
0,
0
] |
Title: Symbolic Computation via Program Transformation,
Abstract: Symbolic computation is an important approach in automated program analysis.
Most state-of-the-art tools perform symbolic computation as interpreters and
directly maintain symbolic data. In this paper, we show that it is feasible,
and in fact practical, to use a compiler-based strategy instead. Using compiler
tooling, we propose and implement a transformation which takes a standard
program and outputs a program that performs semantically equivalent, but
partially symbolic, computation. The transformed program maintains symbolic
values internally and operates directly on them hence the program can be
processed by a tool without support for symbolic manipulation.
The main motivation for the transformation is in symbolic verification, but
there are many other possible use-cases, including test generation and concolic
testing. Moreover using the transformation simplifies tools, since the symbolic
computation is handled by the program directly. We have implemented the
transformation at the level of LLVM bitcode. The paper includes an experimental
evaluation, based on an explicit-state software model checker as a verification
backend. | [
1,
0,
0,
0,
0,
0
] |
Title: Simulating Dirac models with ultracold atoms in optical lattices,
Abstract: We present a general model allowing "quantum simulation" of one-dimensional
Dirac models with 2- and 4-component spinors using ultracold atoms in driven 1D
tilted optical latices. The resulting Dirac physics is illustrated by one of
its well-known manifestations, Zitterbewegung. This general model can be
extended and applied with great flexibility to more complex situations. | [
0,
1,
0,
0,
0,
0
] |
Title: Network analysis of Japanese global business using quasi-exhaustive micro-data for Japanese overseas subsidiaries,
Abstract: Network analysis techniques remain rarely used for understanding
international management strategies. Our paper highlights their value as
research tool in this field of social science using a large set of micro-data
(20,000) to investigate the presence of networks of subsidiaries overseas. The
research question is the following: to what extent did/do global Japanese
business networks mirror organizational models existing in Japan? In
particular, we would like to assess how much the links building such business
networks are shaped by the structure of big-size industrial conglomerates of
firms headquartered in Japan, also described as HK. The major part of the
academic community in the fields of management and industrial organization
considers that formal links can be identified among firms belonging to HK. Miwa
and Ramseyer (Miwa and Ramseyer 2002; Ramseyer 2006) challenge this claim and
argue that the evidence supporting the existence of HK is weak. So far,
quantitative empirical investigation has been conducted exclusively using data
for firms incorporated in Japan. Our study tests the Miwa-Ramseyer hypothesis
(MRH) at the global level using information on the network of Japanese
subsidiaries overseas. The results obtained lead us to reject the MRH for the
global dataset, as well as for subsets restricted to the two main
regions/countries of destination of Japanese foreign investment. The results
are robust to the weighting of the links, with different specifications, and
are observed in most industrial sectors. The global Japanese network became
increasingly complex during the late 20th century as a consequence of increase
in the number of Japanese subsidiaries overseas but the key features of the
structure remained rather stable. We draw implications of these findings for
academic research in international business and for professionals involved in
corporate strategy. | [
1,
0,
0,
0,
0,
0
] |
Title: A Privacy-preserving Community-based P2P OSNs Using Broadcast Encryption Supporting Recommendation Mechanism,
Abstract: Online Social Networks (OSNs) have become one of the most important
activities on the Internet, such as Facebook and Google+. However, security and
privacy have become major concerns in existing C/S based OSNs. In this paper,
we propose a novel scheme called a Privacy-preserving Community-based P2P OSNs
Using Broadcast Encryption Supporting Recommendation Mechanism (PCBE) that
supports cross-platform availability in stringent privacy requirements. For the
first time, we introduce recommendation mechanism into a privacy-preserving P2P
based OSNs, in which we firstly employ the Open Directory Project to generate
user interest model. We firstly introduce broadcast encryption into P2P
community-based social networks together with reputation mechanism to decrease
the system overhead. We formulate the security requirements and design goals
for privacy- preserving P2P based OSNs supporting recommendation mechanism. The
RESTful web-services help to ensure cross-platform availability and
transmission security. As a result, thorough security analysis and performance
evaluation on experiments demonstrate that the PCBE scheme indeed accords with
our proposed design goals. | [
1,
0,
0,
0,
0,
0
] |
Title: Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop,
Abstract: The stochastic variance-reduced gradient method (SVRG) and its accelerated
variant (Katyusha) have attracted enormous attention in the machine learning
community in the last few years due to their superior theoretical properties
and empirical behaviour on training supervised machine learning models via the
empirical risk minimization paradigm. A key structural element in both of these
methods is the inclusion of an outer loop at the beginning of which a full pass
over the training data is made in order to compute the exact gradient, which is
then used to construct a variance-reduced estimator of the gradient. In this
work we design {\em loopless variants} of both of these methods. In particular,
we remove the outer loop and replace its function by a coin flip performed in
each iteration designed to trigger, with a small probability, the computation
of the gradient. We prove that the new methods enjoy the same superior
theoretical convergence properties as the original methods. However, we
demonstrate through numerical experiments that our methods have substantially
superior practical behavior. | [
1,
0,
0,
1,
0,
0
] |
Title: Hierarchical Temporal Representation in Linear Reservoir Computing,
Abstract: Recently, studies on deep Reservoir Computing (RC) highlighted the role of
layering in deep recurrent neural networks (RNNs). In this paper, the use of
linear recurrent units allows us to bring more evidence on the intrinsic
hierarchical temporal representation in deep RNNs through frequency analysis
applied to the state signals. The potentiality of our approach is assessed on
the class of Multiple Superimposed Oscillator tasks. Furthermore, our
investigation provides useful insights to open a discussion on the main aspects
that characterize the deep learning framework in the temporal domain. | [
1,
0,
0,
1,
0,
0
] |
Title: Bombieri-Vinogradov for multiplicative functions, and beyond the $x^{1/2}$-barrier,
Abstract: Part-and-parcel of the study of "multiplicative number theory" is the study
of the distribution of multiplicative functions in arithmetic progressions.
Although appropriate analogies to the Bombieri-Vingradov Theorem have been
proved for particular examples of multiplicative functions, there has not
previously been headway on a general theory; seemingly none of the different
proofs of the Bombieri-Vingradov Theorem for primes adapt well to this
situation. In this article we find out why such a result has been so elusive,
and discover what can be proved along these lines and develop some limitations.
For a fixed residue class $a$ we extend such averages out to moduli $\leq
x^{\frac {20}{39}-\delta}$. | [
0,
0,
1,
0,
0,
0
] |
Title: Three-component fermions with surface Fermi arcs in topological semimetal tungsten carbide,
Abstract: Topological Dirac and Weyl semimetals not only host quasiparticles analogous
to the elementary fermionic particles in high-energy physics, but also have
nontrivial band topology manifested by exotic Fermi arcs on the surface. Recent
advances suggest new types of topological semimetals, in which spatial
symmetries protect gapless electronic excitations without high-energy analogy.
Here we observe triply-degenerate nodal points (TPs) near the Fermi level of
WC, in which the low-energy quasiparticles are described as three-component
fermions distinct from Dirac and Weyl fermions. We further observe the surface
states whose constant energy contours are pairs of Fermi arcs connecting the
surface projection of the TPs, proving the nontrivial topology of the newly
identified semimetal state. | [
0,
1,
0,
0,
0,
0
] |
Title: Detecting stochastic inclusions in electrical impedance tomography,
Abstract: This work considers the inclusion detection problem of electrical impedance
tomography with stochastic conductivities. It is shown that a conductivity
anomaly with a random conductivity can be identified by applying the
Factorization Method or the Monotonicity Method to the mean value of the
corresponding Neumann-to-Dirichlet map provided that the anomaly has high
enough contrast in the sense of expectation. The theoretical results are
complemented by numerical examples in two spatial dimensions. | [
0,
0,
1,
0,
0,
0
] |
Title: On compact Hermitian manifolds with flat Gauduchon connections,
Abstract: Given a Hermitian manifold $(M^n,g)$, the Gauduchon connections are the one
parameter family of Hermitian connections joining the Chern connection and the
Bismut connection. We will call $\nabla^s = (1-\frac{s}{2})\nabla^c +
\frac{s}{2}\nabla^b$ the $s$-Gauduchon connection of $M$, where $\nabla^c$ and
$\nabla^b$ are respectively the Chern and Bismut connections. It is natural to
ask when a compact Hermitian manifold could admit a flat $s$-Gauduchon
connection. This is related to a question asked by Yau \cite{Yau}. The cases
with $s=0$ (a flat Chern connection) or $s=2$ (a flat Bismut connection) are
classified respectively by Boothby \cite{Boothby} in the 1950s or by Q. Wang
and the authors recently \cite{WYZ}. In this article, we observe that if either
$s\geq 4+2\sqrt{3} \approx 7.46$ or $s\leq 4-2\sqrt{3}\approx 0.54$ and $s\neq
0$, then $g$ is Kähler. We also show that, when $n=2$, $g$ is always Kähler
unless $s=2$. Note that non-Kähler compact Bismut flat surfaces are exactly
those isosceles Hopf surfaces by \cite{WYZ}. | [
0,
0,
1,
0,
0,
0
] |
Title: A Support Tensor Train Machine,
Abstract: There has been growing interest in extending traditional vector-based machine
learning techniques to their tensor forms. An example is the support tensor
machine (STM) that utilizes a rank-one tensor to capture the data structure,
thereby alleviating the overfitting and curse of dimensionality problems in the
conventional support vector machine (SVM). However, the expressive power of a
rank-one tensor is restrictive for many real-world data. To overcome this
limitation, we introduce a support tensor train machine (STTM) by replacing the
rank-one tensor in an STM with a tensor train. Experiments validate and confirm
the superiority of an STTM over the SVM and STM. | [
1,
0,
0,
1,
0,
0
] |
Title: Diffusion under confinement: hydrodynamic finite-size effects in simulation,
Abstract: We investigate finite-size effects on diffusion in confined fluids using
molecular dynamics simulations and hydrodynamic calculations. Specifically, we
consider a Lennard-Jones fluid in slit pores without slip at the interface and
show that the use of periodic boundary conditions in the directions along the
surfaces results in dramatic finite-size effects, in addition to that of the
physically relevant confining length. As in the simulation of bulk fluids,
these effects arise from spurious hydrodynamic interactions between periodic
images and from the constraint of total momentum conservation. We derive
analytical expressions for the correction to the diffusion coefficient in the
limits of both elongated and flat systems, which are in excellent agreement
with the molecular simulation results except for the narrowest pores, where the
discreteness of the fluid particles starts to play a role. The present work
implies that the diffusion coefficients for wide nanopores computed using
elongated boxes suffer from finite-size artifacts which had not been previously
appreciated. In addition, our analytical expression provides the correction to
be applied to the simulation results for finite (possibly small) systems. It
applies not only to molecular but also to all mesoscopic hydrodynamic
simulations, including Lattice-Boltzmann, Multi-Particle Collision Dynamics or
Dissipative Particle Dynamics, which are often used to investigate confined
soft matter involving colloidal particles and polymers. | [
0,
1,
0,
0,
0,
0
] |
Title: SPLBoost: An Improved Robust Boosting Algorithm Based on Self-paced Learning,
Abstract: It is known that Boosting can be interpreted as a gradient descent technique
to minimize an underlying loss function. Specifically, the underlying loss
being minimized by the traditional AdaBoost is the exponential loss, which is
proved to be very sensitive to random noise/outliers. Therefore, several
Boosting algorithms, e.g., LogitBoost and SavageBoost, have been proposed to
improve the robustness of AdaBoost by replacing the exponential loss with some
designed robust loss functions. In this work, we present a new way to robustify
AdaBoost, i.e., incorporating the robust learning idea of Self-paced Learning
(SPL) into Boosting framework. Specifically, we design a new robust Boosting
algorithm based on SPL regime, i.e., SPLBoost, which can be easily implemented
by slightly modifying off-the-shelf Boosting packages. Extensive experiments
and a theoretical characterization are also carried out to illustrate the
merits of the proposed SPLBoost. | [
1,
0,
0,
1,
0,
0
] |
Title: Influence of thermal boundary conditions on the current-driven resistive transition in $\mathbf{VO_2}$ microbridges,
Abstract: We investigate the resistive switching behaviour of $\mathrm{VO_2}$
microbridges under current bias as a function of temperature and thermal
coupling with the heat bath. Upon increasing the electrical current bias, the
formation of the metallic phase can progress smoothly or through sharp jumps.
The magnitude and threshold current values of these sharp resistance drops show
random behaviour and are dramatically influenced by thermal dissipation
conditions. Our results also evidence how the propagation of the metallic phase
induced by electrical current in $\mathrm{VO_2}$, and thus the shape of the
resulting high-conductivity path, are not predictable. We discuss the origin of
the switching events through a simple electro-thermal model based on the domain
structure of $\mathrm{VO_2}$ films that can be useful to improve the stability
and controllability of future $\mathrm{VO_2}$-based devices. | [
0,
1,
0,
0,
0,
0
] |
Title: Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction,
Abstract: This paper proposes a convolutional neural network (CNN)-based method that
learns traffic as images and predicts large-scale, network-wide traffic speed
with a high accuracy. Spatiotemporal traffic dynamics are converted to images
describing the time and space relations of traffic flow via a two-dimensional
time-space matrix. A CNN is applied to the image following two consecutive
steps: abstract traffic feature extraction and network-wide traffic speed
prediction. The effectiveness of the proposed method is evaluated by taking two
real-world transportation networks, the second ring road and north-east
transportation network in Beijing, as examples, and comparing the method with
four prevailing algorithms, namely, ordinary least squares, k-nearest
neighbors, artificial neural network, and random forest, and three deep
learning architectures, namely, stacked autoencoder, recurrent neural network,
and long-short-term memory network. The results show that the proposed method
outperforms other algorithms by an average accuracy improvement of 42.91%
within an acceptable execution time. The CNN can train the model in a
reasonable time and, thus, is suitable for large-scale transportation networks. | [
1,
0,
0,
1,
0,
0
] |
Title: The ZX calculus is a language for surface code lattice surgery,
Abstract: Quantum computing is moving rapidly to the point of deployment of technology.
Functional quantum devices will require the ability to correct error in order
to be scalable and effective. A leading choice of error correction, in
particular for modular or distributed architectures, is the surface code with
logical two-qubit operations realised via "lattice surgery". These operations
consist of "merges" and "splits" acting non-unitarily on the logical states and
are not easily captured by standard circuit notation. This raises the question
of how best to reason about lattice surgery in order efficiently to use quantum
states and operations in architectures with complex resource management issues.
In this paper we demonstrate that the operations of the ZX calculus, a form of
quantum diagrammatic reasoning designed using category theory, match exactly
the operations of lattice surgery. Red and green "spider" nodes match rough and
smooth merges and splits, and follow the axioms of a dagger special associative
Frobenius algebra. Some lattice surgery operations can require non-trivial
correction operations, which are captured natively in the use of the ZX
calculus in the form of ensembles of diagrams. We give a first taste of the
power of the calculus as a language for surgery by considering two operations
(magic state use and producing a CNOT) and show how ZX diagram re-write rules
give lattice surgery procedures for these operations that are novel, efficient,
and highly configurable. | [
1,
0,
0,
0,
0,
0
] |
Title: Learning One-hidden-layer Neural Networks with Landscape Design,
Abstract: We consider the problem of learning a one-hidden-layer neural network: we
assume the input $x\in \mathbb{R}^d$ is from Gaussian distribution and the
label $y = a^\top \sigma(Bx) + \xi$, where $a$ is a nonnegative vector in
$\mathbb{R}^m$ with $m\le d$, $B\in \mathbb{R}^{m\times d}$ is a full-rank
weight matrix, and $\xi$ is a noise vector. We first give an analytic formula
for the population risk of the standard squared loss and demonstrate that it
implicitly attempts to decompose a sequence of low-rank tensors simultaneously.
Inspired by the formula, we design a non-convex objective function $G(\cdot)$
whose landscape is guaranteed to have the following properties: 1. All local
minima of $G$ are also global minima.
2. All global minima of $G$ correspond to the ground truth parameters.
3. The value and gradient of $G$ can be estimated using samples.
With these properties, stochastic gradient descent on $G$ provably converges
to the global minimum and learn the ground-truth parameters. We also prove
finite sample complexity result and validate the results by simulations. | [
1,
0,
0,
1,
0,
0
] |
Title: Growth and electronic structure of graphene on semiconducting Ge(110),
Abstract: The direct growth of graphene on semiconducting or insulating substrates
might help to overcome main drawbacks of metal-based synthesis, like metal-atom
contaminations of graphene, transfer issues, etc. Here we present the growth of
graphene on n-doped semiconducting Ge(110) by using an atomic carbon source and
the study of the structural and electronic properties of the obtained
interface. We found that graphene interacts weakly with the underlying Ge(110)
substrate that keeps graphene's electronic structure almost intact promoting
this interface for future graphene-semiconductor applications. The effect of
dopants in Ge on the electronic properties of graphene is also discussed. | [
0,
1,
0,
0,
0,
0
] |
Title: Alternative Lagrangians obtained by scalar deformations,
Abstract: We study non-conservative like SODEs admitting explicit Lagrangian
descriptions. Such systems are equivalent to the system of Lagrange equations
of some Lagrangian $L$, including a covariant force field which represents
non-conservative forces. We find necessary and sufficient conditions for the
existence of a differentiable function $\Phi:\mathbb{R}\rightarrow\mathbb{R}$
such that the initial system is equivalent to the system of Euler-Lagrange
equations of the deformed Lagrangian $\Phi(L)$. We give various examples of
such deformations. | [
0,
0,
1,
0,
0,
0
] |
Title: Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning,
Abstract: Constrained Markov Decision Process (CMDP) is a natural framework for
reinforcement learning tasks with safety constraints, where agents learn a
policy that maximizes the long-term reward while satisfying the constraints on
the long-term cost. A canonical approach for solving CMDPs is the primal-dual
method which updates parameters in primal and dual spaces in turn. Existing
methods for CMDPs only use on-policy data for dual updates, which results in
sample inefficiency and slow convergence. In this paper, we propose a policy
search method for CMDPs called Accelerated Primal-Dual Optimization (APDO),
which incorporates an off-policy trained dual variable in the dual update
procedure while updating the policy in primal space with on-policy likelihood
ratio gradient. Experimental results on a simulated robot locomotion task show
that APDO achieves better sample efficiency and faster convergence than
state-of-the-art approaches for CMDPs. | [
0,
0,
0,
1,
0,
0
] |
Title: Toward Common Components for Open Workflow Systems,
Abstract: The role of scalable high-performance workflows and flexible workflow
management systems that can support multiple simulations will continue to
increase in importance. For example, with the end of Dennard scaling, there is
a need to substitute a single long running simulation with multiple repeats of
shorter simulations, or concurrent replicas. Further, many scientific problems
involve ensembles of simulations in order to solve a higher-level problem or
produce statistically meaningful results. However most supercomputing software
development and performance enhancements have focused on optimizing single-
simulation performance. On the other hand, there is a strong inconsistency in
the definition and practice of workflows and workflow management systems. This
inconsistency often centers around the difference between several different
types of workflows, including modeling and simulation, grid, uncertainty
quantification, and purely conceptual workflows. This work explores this
phenomenon by examining the different types of workflows and workflow
management systems, reviewing the perspective of a large supercomputing
facility, examining the common features and problems of workflow management
systems, and finally presenting a proposed solution based on the concept of
common building blocks. The implications of the continuing proliferation of
workflow management systems and the lack of interoperability between these
systems are discussed from a practical perspective. In doing so, we have begun
an investigation of the design and implementation of open workflow systems for
supercomputers based upon common components. | [
1,
0,
0,
0,
0,
0
] |
Title: On the Sampling Problem for Kernel Quadrature,
Abstract: The standard Kernel Quadrature method for numerical integration with random
point sets (also called Bayesian Monte Carlo) is known to converge in root mean
square error at a rate determined by the ratio $s/d$, where $s$ and $d$ encode
the smoothness and dimension of the integrand. However, an empirical
investigation reveals that the rate constant $C$ is highly sensitive to the
distribution of the random points. In contrast to standard Monte Carlo
integration, for which optimal importance sampling is well-understood, the
sampling distribution that minimises $C$ for Kernel Quadrature does not admit a
closed form. This paper argues that the practical choice of sampling
distribution is an important open problem. One solution is considered; a novel
automatic approach based on adaptive tempering and sequential Monte Carlo.
Empirical results demonstrate a dramatic reduction in integration error of up
to 4 orders of magnitude can be achieved with the proposed method. | [
1,
0,
0,
1,
0,
0
] |
Title: Zeroth order regular approximation approach to electric dipole moment interactions of the electron,
Abstract: A quasi-relativistic two-component approach for an efficient calculation of
$\mathcal{P,T}$-odd interactions caused by a permanent electric dipole moment
of the electron (eEDM) is presented. The approach uses a (two-component)
complex generalized Hartree-Fock (cGHF) and a complex generalized Kohn-Sham
(cGKS) scheme within the zeroth order regular approximation (ZORA). In
applications to select heavy-elemental polar diatomic molecular radicals, which
are promising candidates for an eEDM experiment, the method is compared to
relativistic four-component electron-correlation calculations and confirms
values for the effective electrical field acting on the unpaired electron for
RaF, BaF, YbF and HgF. The calculations show that purely relativistic effects,
involving only the lower component of the Dirac bi-spinor, are well described
by treating only the upper component explicitly. | [
0,
1,
0,
0,
0,
0
] |
Title: Trace your sources in large-scale data: one ring to find them all,
Abstract: An important preprocessing step in most data analysis pipelines aims to
extract a small set of sources that explain most of the data. Currently used
algorithms for blind source separation (BSS), however, often fail to extract
the desired sources and need extensive cross-validation. In contrast, their
rarely used probabilistic counterparts can get away with little
cross-validation and are more accurate and reliable but no simple and scalable
implementations are available. Here we present a novel probabilistic BSS
framework (DECOMPOSE) that can be flexibly adjusted to the data, is extensible
and easy to use, adapts to individual sources and handles large-scale data
through algorithmic efficiency. DECOMPOSE encompasses and generalises many
traditional BSS algorithms such as PCA, ICA and NMF and we demonstrate
substantial improvements in accuracy and robustness on artificial and real
data. | [
0,
0,
0,
1,
0,
0
] |
Title: The rigorous derivation of the linear Landau equation from a particle system in a weak-coupling limit,
Abstract: We consider a system of N particles interacting via a short-range smooth
potential, in a intermediate regime between the weak-coupling and the
low-density. We provide a rigorous derivation of the Linear Landau equation
from this particle system. The strategy of the proof consists in showing the
asymptotic equivalence between the one-particle marginal and the solution of
the linear Boltzmann equation with vanishing mean free path.Then, following the
ideas of Landau, we prove the asympotic equivalence between the solutions of
the Boltzmann and Landau linear equation in the grazing collision limit. | [
0,
0,
1,
0,
0,
0
] |
Title: Fractal dimension and lower bounds for geometric problems,
Abstract: We study the complexity of geometric problems on spaces of low fractal
dimension. It was recently shown by [Sidiropoulos & Sridhar, SoCG 2017] that
several problems admit improved solutions when the input is a pointset in
Euclidean space with fractal dimension smaller than the ambient dimension. In
this paper we prove nearly-matching lower bounds, thus establishing
nearly-optimal bounds for various problems as a function of the fractal
dimension.
More specifically, we show that for any set of $n$ points in $d$-dimensional
Euclidean space, of fractal dimension $\delta\in (1,d)$, for any $\epsilon >0$
and $c\geq 1$, any $c$-spanner must have treewidth at least $\Omega \left(
\frac{n^{1-1/(\delta - \epsilon)}}{c^{d-1}} \right)$, matching the previous
upper bound. The construction used to prove this lower bound on the treewidth
of spanners can also be used to derive lower bounds on the running time of
algorithms for various problems, assuming the Exponential Time Hypothesis. We
provide two prototypical results of this type. For any $\delta \in (1,d)$ and
any $\epsilon >0$ we show that:
1) $d$-dimensional Euclidean TSP on $n$ points with fractal dimension at most
$\delta$ cannot be solved in time $2^{O\left(n^{1-1/(\delta - \epsilon)}
\right)}$. The best-known upper bound is $2^{O(n^{1-1/\delta} \log n)}$.
2) The problem of finding $k$-pairwise non-intersecting $d$-dimensional unit
balls/axis parallel unit cubes with centers having fractal dimension at most
$\delta$ cannot be solved in time $f(k)n^{O \left(k^{1-1/(\delta -
\epsilon)}\right)}$ for any computable function $f$. The best-known upper bound
is $n^{O(k^{1-1/\delta} \log n)}$.
The above results nearly match previously known upper bounds from
[Sidiropoulos & Sridhar, SoCG 2017], and generalize analogous lower bounds for
the case of ambient dimension due to [Marx & Sidiropoulos, SoCG 2014]. | [
1,
0,
0,
0,
0,
0
] |
Title: Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization,
Abstract: We present a unified framework to analyze the global convergence of Langevin
dynamics based algorithms for nonconvex finite-sum optimization with $n$
component functions. At the core of our analysis is a direct analysis of the
ergodicity of the numerical approximations to Langevin dynamics, which leads to
faster convergence rates. Specifically, we show that gradient Langevin dynamics
(GLD) and stochastic gradient Langevin dynamics (SGLD) converge to the almost
minimizer within $\tilde O\big(nd/(\lambda\epsilon) \big)$ and $\tilde
O\big(d^7/(\lambda^5\epsilon^5) \big)$ stochastic gradient evaluations
respectively, where $d$ is the problem dimension, and $\lambda$ is the spectral
gap of the Markov chain generated by GLD. Both of the results improve upon the
best known gradient complexity results. Furthermore, for the first time we
prove the global convergence guarantee for variance reduced stochastic gradient
Langevin dynamics (VR-SGLD) to the almost minimizer after $\tilde
O\big(\sqrt{n}d^5/(\lambda^4\epsilon^{5/2})\big)$ stochastic gradient
evaluations, which outperforms the gradient complexities of GLD and SGLD in a
wide regime. Our theoretical analyses shed some light on using Langevin
dynamics based algorithms for nonconvex optimization with provable guarantees. | [
1,
0,
1,
1,
0,
0
] |
Title: Eigenvalue Analysis via Kernel Density Estimation,
Abstract: In this paper, we propose an eigenvalue analysis -- of system dynamics models
-- based on the Mutual Information measure, which in turn will be estimated via
the Kernel Density Estimation method. We postulate that the proposed approach
represents a novel and efficient multivariate eigenvalue sensitivity analysis. | [
1,
0,
0,
0,
0,
0
] |
Title: The Saga of KPR: Theoretical and Experimental developments,
Abstract: In this article, we present a brief narration of the origin and the overview
of the recent developments done on the Kolkata Paise Restaurant (KPR) problem,
which can serve as a prototype for a broader class of resource allocation
problems in the presence of a large number of competing agents, typically
studied using coordination and anti-coordination games. We discuss the KPR and
its several extensions, as well as its applications in many economic and social
phenomena. We end the article with some discussions on our ongoing experimental
analysis of the same problem. We demonstrate that this provides an interesting
picture of how people analyze complex situations, and design their strategies
or react to them. | [
1,
0,
0,
0,
0,
0
] |
Title: The computational complexity of integer programming with alternations,
Abstract: We prove that integer programming with three quantifier alternations is
$NP$-complete, even for a fixed number of variables. This complements earlier
results by Lenstra and Kannan, which together say that integer programming with
at most two quantifier alternations can be done in polynomial time for a fixed
number of variables. As a byproduct of the proof, we show that for two
polytopes $P,Q \subset \mathbb{R}^4$ , counting the projection of integer
points in $Q \backslash P$ is $\#P$-complete. This contrasts the 2003 result by
Barvinok and Woods, which allows counting in polynomial time the projection of
integer points in $P$ and $Q$ separately. | [
1,
0,
1,
0,
0,
0
] |
Title: Robust Kronecker-Decomposable Component Analysis for Low-Rank Modeling,
Abstract: Dictionary learning and component analysis are part of one of the most
well-studied and active research fields, at the intersection of signal and
image processing, computer vision, and statistical machine learning. In
dictionary learning, the current methods of choice are arguably K-SVD and its
variants, which learn a dictionary (i.e., a decomposition) for sparse coding
via Singular Value Decomposition. In robust component analysis, leading methods
derive from Principal Component Pursuit (PCP), which recovers a low-rank matrix
from sparse corruptions of unknown magnitude and support. However, K-SVD is
sensitive to the presence of noise and outliers in the training set.
Additionally, PCP does not provide a dictionary that respects the structure of
the data (e.g., images), and requires expensive SVD computations when solved by
convex relaxation. In this paper, we introduce a new robust decomposition of
images by combining ideas from sparse dictionary learning and PCP. We propose a
novel Kronecker-decomposable component analysis which is robust to gross
corruption, can be used for low-rank modeling, and leverages separability to
solve significantly smaller problems. We design an efficient learning algorithm
by drawing links with a restricted form of tensor factorization. The
effectiveness of the proposed approach is demonstrated on real-world
applications, namely background subtraction and image denoising, by performing
a thorough comparison with the current state of the art. | [
1,
0,
0,
1,
0,
0
] |
Title: Adequacy of the Gradient-Descent Method for Classifier Evasion Attacks,
Abstract: Despite the wide use of machine learning in adversarial settings including
computer security, recent studies have demonstrated vulnerabilities to evasion
attacks---carefully crafted adversarial samples that closely resemble
legitimate instances, but cause misclassification. In this paper, we examine
the adequacy of the leading approach to generating adversarial samples---the
gradient descent approach. In particular (1) we perform extensive experiments
on three datasets, MNIST, USPS and Spambase, in order to analyse the
effectiveness of the gradient-descent method against non-linear support vector
machines, and conclude that carefully reduced kernel smoothness can
significantly increase robustness to the attack; (2) we demonstrate that
separated inter-class support vectors lead to more secure models, and propose a
quantity similar to margin that can efficiently predict potential
susceptibility to gradient-descent attacks, before the attack is launched; and
(3) we design a new adversarial sample construction algorithm based on
optimising the multiplicative ratio of class decision functions. | [
1,
0,
0,
1,
0,
0
] |
Title: The way to uncover community structure with core and diversity,
Abstract: Communities are ubiquitous in nature and society. Individuals that share
common properties often self-organize to form communities. Avoiding the
shortages of computation complexity, pre-given information and unstable results
in different run, in this paper, we propose a simple and effcient method to
deepen our understanding of the emergence and diversity of communities in
complex systems. By introducing the rational random selection, our method
reveals the hidden deterministic and normal diverse community states of
community structure. To demonstrate this method, we test it with real-world
systems. The results show that our method could not only detect community
structure with high sensitivity and reliability, but also provide instructional
information about the hidden deterministic community world and our normal
diverse community world by giving out the core-community, the real-community,
the tide and the diversity. This is of paramount importance in understanding,
predicting, and controlling a variety of collective behaviors in complex
systems. | [
1,
1,
0,
0,
0,
0
] |
Title: Stochastic comparisons of the largest claim amounts from two sets of interdependent heterogeneous portfolios,
Abstract: Let $ X_{\lambda_1},\ldots,X_{\lambda_n}$ be dependent non-negative random
variables and $Y_i=I_{p_i} X_{\lambda_i}$, $i=1,\ldots,n$, where
$I_{p_1},\ldots,I_{p_n}$ are independent Bernoulli random variables independent
of $X_{\lambda_i}$'s, with ${\rm E}[I_{p_i}]=p_i$, $i=1,\ldots,n$. In actuarial
sciences, $Y_i$ corresponds to the claim amount in a portfolio of risks. In
this paper, we compare the largest claim amounts of two sets of interdependent
portfolios, in the sense of usual stochastic order, when the variables in one
set have the parameters $\lambda_1,\ldots,\lambda_n$ and $p_1,\ldots,p_n$ and
the variables in the other set have the parameters
$\lambda^{*}_1,\ldots,\lambda^{*}_n$ and $p^*_1,\ldots,p^*_n$. For
illustration, we apply the results to some important models in actuary. | [
0,
0,
0,
0,
0,
1
] |
Title: An Achilles' Heel of Term-Resolution,
Abstract: Term-resolution provides an elegant mechanism to prove that a quantified
Boolean formula (QBF) is true. It is a dual to Q-resolution (also referred to
as clause-resolution) and is practically highly important as it enables
certifying answers of DPLL-based QBF solvers. While term-resolution and
Q-resolution are very similar, they're not completely symmetric. In particular,
Q-resolution operates on clauses and term-resolution operates on models of the
matrix. This paper investigates what impact this asymmetry has. We'll see that
there is a large class of formulas (formulas with "big models") whose
term-resolution proofs are exponential. As a possible remedy, the paper
suggests to prove true QBFs by refuting their negation ({\em negate-refute}),
rather than proving them by term-resolution. The paper shows that from the
theoretical perspective this is indeed a favorable approach. In particular,
negation-refutation can p-simulates term-resolution and there is an exponential
separation between the two calculi. These observations further our
understanding of proof systems for QBFs and provide a strong theoretical
underpinning for the effort towards non-CNF QBF solvers. | [
1,
0,
0,
0,
0,
0
] |
Title: Tunneling estimates and approximate controllability for hypoelliptic equations,
Abstract: This article is concerned with quantitative unique continuation estimates for
equations involving a "sum of squares" operator $\mathcal{L}$ on a compact
manifold $\mathcal{M}$ assuming: $(i)$ the Chow-Rashevski-Hörmander condition
ensuring the hypoellipticity of $\mathcal{L}$, and $(ii)$ the analyticity of
$\mathcal{M}$ and the coefficients of $\mathcal{L}$.
The first result is the tunneling estimate $\|\varphi\|_{L^2(\omega)} \geq
Ce^{- \lambda^{\frac{k}{2}}}$ for normalized eigenfunctions $\varphi$ of
$\mathcal{L}$ from a nonempty open set $\omega\subset \mathcal{M}$, where $k$
is the hypoellipticity index of $\mathcal{L}$ and $\lambda$ the eigenvalue.
The main result is a stability estimate for solutions to the hypoelliptic
wave equation $(\partial_t^2+\mathcal{L})u=0$: for $T>2 \sup_{x \in
\mathcal{M}}(dist(x,\omega))$ (here, $dist$ is the sub-Riemannian distance),
the observation of the solution on $(0,T)\times \omega$ determines the data.
The constant involved in the estimate is $Ce^{c\Lambda^k}$ where $\Lambda$ is
the typical frequency of the data.
We then prove the approximate controllability of the hypoelliptic heat
equation $(\partial_t+\mathcal{L})v=1_\omega f$ in any time, with appropriate
(exponential) cost, depending on $k$. In case $k=2$ (Grushin, Heisenberg...),
we further show approximate controllability to trajectories with polynomial
cost in large time.
We also explain how the analyticity assumption can be relaxed, and a boundary
$\partial \mathcal{M}$ can be added in some situations.
Most results turn out to be optimal on a family of Grushin-type operators.
The main proof relies on the general strategy developed by the authors in
arXiv:1506.04254. | [
0,
0,
1,
0,
0,
0
] |
Title: Optimal Resonant Beam Charging for Electronic Vehicles in Internet of Intelligent Vehicles,
Abstract: To enable electric vehicles (EVs) to access to the internet of intelligent
vehicles (IoIV), charging EVs wirelessly anytime and anywhere becomes an urgent
need. The resonant beam charging (RBC) technology can provide high-power and
long-range wireless energy for EVs. However, the RBC system is unefficient. To
improve the RBC power transmission efficiency, the adaptive resonant beam
charging (ARBC) technology was introduced. In this paper, after analyzing the
modular model of the ARBC system, we obtain the closed-form formula of the
end-to-end power transmission efficiency. Then, we prove that the optimal power
transmission efficiency uniquely exists. Moreover, we analyze the relationships
among the optimal power transmission efficiency, the source power, the output
power, and the beam transmission efficiency, which provide the guidelines for
the optimal ARBC system design and implementation. Hence, perpetual energy can
be supplied to EVs in IoIV virtually. | [
1,
0,
0,
0,
0,
0
] |
Title: An Adaptive Version of Brandes' Algorithm for Betweenness Centrality,
Abstract: Betweenness centrality---measuring how many shortest paths pass through a
vertex---is one of the most important network analysis concepts for assessing
the relative importance of a vertex. The well-known algorithm of Brandes [2001]
computes, on an $n$-vertex and $m$-edge graph, the betweenness centrality of
all vertices in $O(nm)$ worst-case time. In follow-up work, significant
empirical speedups were achieved by preprocessing degree-one vertices and by
graph partitioning based on cut vertices. We further contribute an algorithmic
treatment of degree-two vertices, which turns out to be much richer in
mathematical structure than the case of degree-one vertices. Based on these
three algorithmic ingredients, we provide a strengthened worst-case running
time analysis for betweenness centrality algorithms. More specifically, we
prove an adaptive running time bound $O(kn)$, where $k < m$ is the size of a
minimum feedback edge set of the input graph. | [
1,
0,
0,
0,
0,
0
] |
Title: Kinetic cascade in solar-wind turbulence: 3D3V hybrid-kinetic simulations with electron inertia,
Abstract: Understanding the nature of the turbulent fluctuations below the ion
gyroradius in solar-wind turbulence is a great challenge. Recent studies have
been mostly in favor of kinetic Alfvén wave (KAW) type of fluctuations, but
other kinds of fluctuations with characteristics typical of magnetosonic,
whistler and ion Bernstein modes, could also play a role depending on the
plasma parameters. Here we investigate the properties of the sub-proton-scale
cascade with high-resolution hybrid-kinetic simulations of freely-decaying
turbulence in 3D3V phase space, including electron inertia effects. Two proton
plasma beta are explored: the "intermediate" $\beta_p=1$ and "low"
$\beta_p=0.2$ regimes, both typically observed in solar wind and corona. The
magnetic energy spectum exhibits $k_\perp^{-8/3}$ and $k_\|^{-7/2}$ power laws
at $\beta_p=1$, while they are slightly steeper at $\beta_p=0.2$. Nevertheless,
both regimes develop a spectral anisotropy consistent with $k_\|\sim
k_\perp^{2/3}$ at $k_\perp\rho_p>1$, and pronounced small-scale intermittency.
In this context, we find that the kinetic-scale cascade is dominated by
KAW-like fluctuations at $\beta_p=1$, whereas the low-$\beta$ case presents a
more complex scenario suggesting the simultaneous presence of different types
of fluctuations. In both regimes, however, a non-negligible role of ion
Bernstein type of fluctuations at the smallest scales seems to emerge. | [
0,
1,
0,
0,
0,
0
] |
Title: Gamma-Band Correlations in Primary Visual Cortex,
Abstract: Neural field theory is used to quantitatively analyze the two-dimensional
spatiotemporal correlation properties of gamma-band (30 -- 70 Hz) oscillations
evoked by stimuli arriving at the primary visual cortex (V1), and modulated by
patchy connectivities that depend on orientation preference (OP). Correlation
functions are derived analytically under different stimulus and measurement
conditions. The predictions reproduce a range of published experimental
results, including the existence of two-point oscillatory temporal
cross-correlations with zero time-lag between neurons with similar OP, the
influence of spatial separation of neurons on the strength of the correlations,
and the effects of differing stimulus orientations. | [
0,
0,
0,
0,
1,
0
] |
Title: A Branch-and-Bound Algorithm for Checkerboard Extraction in Camera-Laser Calibration,
Abstract: We address the problem of camera-to-laser-scanner calibration using a
checkerboard and multiple image-laser scan pairs. Distinguishing which laser
points measure the checkerboard and which lie on the background is essential to
any such system. We formulate the checkerboard extraction as a combinatorial
optimization problem with a clear cut objective function. We propose a
branch-and-bound technique that deterministically and globally optimizes the
objective. Unlike what is available in the literature, the proposed method is
not heuristic and does not require assumptions such as constraints on the
background or relying on discontinuity of the range measurements to partition
the data into line segments. The proposed approach is generic and can be
applied to both 3D or 2D laser scanners as well as the cases where multiple
checkerboards are present. We demonstrate the effectiveness of the proposed
approach by providing numerical simulations as well as experimental results. | [
1,
0,
0,
0,
0,
0
] |
Title: On the mapping of Points of Interest through StreetView imagery and paid crowdsourcing,
Abstract: The use of volunteers has emerged as low-cost alternative to generate
accurate geographical information, an approach known as Volunteered Geographic
Information (VGI). However, VGI is limited by the number and availability of
volunteers in the area to be mapped, hindering scalability for large areas and
making difficult to map within a time-frame. Fortunately, the availability of
street-view imagery enables the virtual exploration of urban environments,
making possible the recruitment of contributors not necessarily located in the
area to be mapped. In this paper, we describe the design, implementation, and
evaluation of the Virtual City Explorer (VCE), a system to collect the
coordinates of Points of Interest within a bounded area on top of a street view
service with the use of paid crowdworkers. Our evaluation suggests that paid
crowdworkers are effective for finding PoIs, and cover almost all the area.
With respect to completeness, our approach does not find all PoIs found by
experts or VGI communities, but is able to find PoIs that were not found by
them, suggesting complementarity. We also studied the impact of making PoIs
already discovered by a certain number of workers \emph{taboo} for incoming
workers, finding that it encourages more exploration from workers , increase
the number of detected PoIs , and reduce costs. | [
1,
0,
0,
0,
0,
0
] |
Title: Complex tensor factorisation with PARAFAC2 for the estimation of brain connectivity from the EEG,
Abstract: Objective: The coupling between neuronal populations and its magnitude have
been shown to be informative for various clinical applications. One method to
estimate brain connectivity is with electroencephalography (EEG) from which the
cross-spectrum between different sensor locations is derived. We wish to test
the efficacy of tensor factorisation in the estimation of brain connectivity.
Methods: Complex tensor factorisation based on PARAFAC2 is used to decompose
the EEG into scalp components described by the spatial, spectral, and complex
trial profiles. An EEG model in the complex domain was derived that shows the
suitability of PARAFAC2. A connectivity metric was also derived on the complex
trial profiles of the extracted components. Results: Results on a benchmark EEG
dataset confirmed that PARAFAC2 can estimate connectivity better than
traditional tensor analysis such as PARAFAC within a range of signal-to-noise
ratios. The analysis of EEG from patients with mild cognitive impairment or
Alzheimer's disease showed that PARAFAC2 identifies loss of brain connectivity
better than traditional approaches and agreeing with prior pathological
knowledge. Conclusion: The complex PARAFAC2 algorithm is suitable for EEG
connectivity estimation since it allows to extract meaningful coupled sources
and provides better estimates than complex PARAFAC. Significance: A new
paradigm that employs complex tensor factorisation has demonstrated to be
successful in identifying brain connectivity and the location of couples
sources for both a benchmark and a real-world EEG dataset. This can enable
future applications and has the potential to solve some the issues that
deteriorate the performance of traditional connectivity metrics. | [
1,
0,
0,
0,
0,
0
] |
Title: A New Approach of Exploiting Self-Adjoint Matrix Polynomials of Large Random Matrices for Anomaly Detection and Fault Location,
Abstract: Synchronized measurements of a large power grid enable an unprecedented
opportunity to study the spatialtemporal correlations. Statistical analytics
for those massive datasets start with high-dimensional data matrices.
Uncertainty is ubiquitous in a future's power grid. These data matrices are
recognized as random matrices. This new point of view is fundamental in our
theoretical analysis since true covariance matrices cannot be estimated
accurately in a high-dimensional regime. As an alternative, we consider
large-dimensional sample covariance matrices in the asymptotic regime to
replace the true covariance matrices. The self-adjoint polynomials of
large-dimensional random matrices are studied as statistics for big data
analytics. The calculation of the asymptotic spectrum distribution (ASD) for
such a matrix polynomial is understandably challenging. This task is made
possible by a recent breakthrough in free probability, an active research
branch in random matrix theory. This is the very reason why the work of this
paper is inspired initially. The new approach is interesting in many aspects.
The mathematical reason may be most critical. The real-world problems can be
solved using this approach, however. | [
0,
0,
0,
1,
0,
0
] |
Title: Bayesian Pool-based Active Learning With Abstention Feedbacks,
Abstract: We study pool-based active learning with abstention feedbacks, where a
labeler can abstain from labeling a queried example with some unknown
abstention rate. This is an important problem with many useful applications. We
take a Bayesian approach to the problem and develop two new greedy algorithms
that learn both the classification problem and the unknown abstention rate at
the same time. These are achieved by simply incorporating the estimated
abstention rate into the greedy criteria. We prove that both of our algorithms
have near-optimality guarantees: they respectively achieve a
${(1-\frac{1}{e})}$ constant factor approximation of the optimal expected or
worst-case value of a useful utility function. Our experiments show the
algorithms perform well in various practical scenarios. | [
1,
0,
0,
1,
0,
0
] |
Title: Multiconfigurational Short-Range Density-Functional Theory for Open-Shell Systems,
Abstract: Many chemical systems cannot be described by quantum chemistry methods based
on a singlereference wave function. Accurate predictions of energetic and
spectroscopic properties require a delicate balance between describing the most
important configurations (static correlation) and obtaining dynamical
correlation efficiently. The former is most naturally done through a
multiconfigurational (MC) wave function, whereas the latter can be done by,
e.g., perturbation theory. We have employed a different strategy, namely, a
hybrid between multiconfigurational wave functions and density-functional
theory (DFT) based on range separation. The method is denoted by MC short-range
(sr) DFT and is more efficient than perturbative approaches as it capitalizes
on the efficient treatment of the (short-range) dynamical correlation by DFT
approximations. In turn, the method also improves DFT with standard
approximations through the ability of multiconfigurational wave functions to
recover large parts of the static correlation. Until now, our implementation
was restricted to closed-shell systems, and to lift this restriction, we
present here the generalization of MC-srDFT to open-shell cases. The additional
terms required to treat open-shell systems are derived and implemented in the
DALTON program. This new method for open-shell systems is illustrated on
dioxygen and [Fe(H2O)6]3+. | [
0,
1,
0,
0,
0,
0
] |
Title: Shape recognition of volcanic ash by simple convolutional neural network,
Abstract: Shape analyses of tephra grains result in understanding eruption mechanism of
volcanoes. However, we have to define and select parameter set such as
convexity for the precise discrimination of tephra grains. Selection of the
best parameter set for the recognition of tephra shapes is complicated.
Actually, many shape parameters have been suggested. Recently, neural network
has made a great success in the field of machine learning. Convolutional neural
network can recognize the shape of images without human bias and shape
parameters. We applied the simple convolutional neural network developed for
the handwritten digits to the recognition of tephra shapes. The network was
trained by Morphologi tephra images, and it can recognize the tephra shapes
with approximately 90% of accuracy. | [
1,
1,
0,
0,
0,
0
] |
Title: The Uranie platform: an Open-source software for optimisation, meta-modelling and uncertainty analysis,
Abstract: The high-performance computing resources and the constant improvement of both
numerical simulation accuracy and the experimental measurements with which they
are confronted, bring a new compulsory step to strengthen the credence given to
the simulation results: uncertainty quantification. This can have different
meanings, according to the requested goals (rank uncertainty sources, reduce
them, estimate precisely a critical threshold or an optimal working point) and
it could request mathematical methods with greater or lesser complexity. This
paper introduces the Uranie platform, an Open-source framework which is
currently developed at the Alternative Energies and Atomic Energy Commission
(CEA), in the nuclear energy division, in order to deal with uncertainty
propagation, surrogate models, optimisation issues, code calibration... This
platform benefits from both its dependencies, but also from personal
developments, to offer an efficient data handling model, a C++ and Python
interpreter, advanced graphical tools, several parallelisation solutions...
These methods are very generic and can then be applied to many kinds of code
(as Uranie considers them as black boxes) so to many fields of physics as well.
In this paper, the example of thermal exchange between a plate-sheet and a
fluid is introduced to show how Uranie can be used to perform a large range of
analysis. The code used to produce the figures of this paper can be found in
this https URL along with the sources of the
platform. | [
0,
0,
0,
1,
0,
0
] |
Title: Learning Convolutional Text Representations for Visual Question Answering,
Abstract: Visual question answering is a recently proposed artificial intelligence task
that requires a deep understanding of both images and texts. In deep learning,
images are typically modeled through convolutional neural networks, and texts
are typically modeled through recurrent neural networks. While the requirement
for modeling images is similar to traditional computer vision tasks, such as
object recognition and image classification, visual question answering raises a
different need for textual representation as compared to other natural language
processing tasks. In this work, we perform a detailed analysis on natural
language questions in visual question answering. Based on the analysis, we
propose to rely on convolutional neural networks for learning textual
representations. By exploring the various properties of convolutional neural
networks specialized for text data, such as width and depth, we present our
"CNN Inception + Gate" model. We show that our model improves question
representations and thus the overall accuracy of visual question answering
models. We also show that the text representation requirement in visual
question answering is more complicated and comprehensive than that in
conventional natural language processing tasks, making it a better task to
evaluate textual representation methods. Shallow models like fastText, which
can obtain comparable results with deep learning models in tasks like text
classification, are not suitable in visual question answering. | [
1,
0,
0,
1,
0,
0
] |
Title: Cross-stream migration of active particles,
Abstract: For natural microswimmers, the interplay of swimming activity and external
flow can promote robust motion, e.g. propulsion against ("upstream rheotaxis")
or perpendicular to the direction of flow. These effects are generally
attributed to their complex body shapes and flagellar beat patterns. Here,
using catalytic Janus particles as a model experimental system, we report on a
strong directional response that occurs for spherical active particles in a
channel flow. The particles align their propulsion axes to be nearly
perpendicular to both the direction of flow and the normal vector of a nearby
bounding surface. We develop a deterministic theoretical model of spherical
microswimmers near a planar wall that captures the experimental observations.
We show how the directional response emerges from the interplay of shear flow
and near-surface swimming activity. Finally, adding the effect of thermal
noise, we obtain probability distributions for the swimmer orientation that
semi-quantitatively agree with the experimental distributions. | [
0,
1,
0,
0,
0,
0
] |
Title: Analysis of the Polya-Gamma block Gibbs sampler for Bayesian logistic linear mixed models,
Abstract: In this article, we construct a two-block Gibbs sampler using Polson et al.
(2013) data augmentation technique with Polya-Gamma latent variables for
Bayesian logistic linear mixed models under proper priors. Furthermore, we
prove the uniform ergodicity of this Gibbs sampler, which guarantees the
existence of the central limit theorems for MCMC based estimators. | [
0,
0,
1,
1,
0,
0
] |
Title: Direct and Simultaneous Observation of Ultrafast Electron and Hole Dynamics in Germanium,
Abstract: Understanding excited carrier dynamics in semiconductors is crucial for the
development of photovoltaics and efficient photonic devices. However,
overlapping spectral features in optical/NIR pump-probe spectroscopy often
render assignments of separate electron and hole carrier dynamics ambiguous.
Here, ultrafast electron and hole dynamics in germanium nanocrystalline thin
films are directly and simultaneously observed by attosecond transient
absorption spectroscopy (ATAS) in the extreme ultraviolet at the germanium
M_{4,5}-edge (~30 eV). We decompose the ATAS spectra into contributions of
electronic state blocking and photo-induced band shifts at a carrier density of
8*10^{20}cm^{-3}. Separate electron and hole relaxation times are observed as a
function of hot carrier energies. A first order electron and hole decay of ~1
ps suggests a Shockley-Read-Hall recombination mechanism. The simultaneous
observation of electrons and holes with ATAS paves the way for investigating
few to sub-femtosecond dynamics of both holes and electrons in complex
semiconductor materials and across junctions. | [
0,
1,
0,
0,
0,
0
] |
Title: Introducing the Robot Security Framework (RSF), a standardized methodology to perform security assessments in robotics,
Abstract: Robots have gained relevance in society, increasingly performing critical
tasks. Nonetheless, robot security is being underestimated. Robotics security
is a complex landscape, which often requires a cross-disciplinar perspective to
which classical security lags behind. To address this issue, we present the
Robot Security Framework (RSF), a methodology to perform systematic security
assessments in robots. We propose, adapt and develop specific terminology and
provide guidelines to enable a holistic security assessment following four main
layers (Physical, Network, Firmware and Application). We argue that modern
robotics should regard as equally relevant internal and external communication
security. Finally, we advocate against "security by obscurity". We conclude
that the field of security in robotics deserves further research efforts. | [
1,
0,
0,
0,
0,
0
] |
Title: Statistical Verification of Computational Rapport Model,
Abstract: Rapport plays an important role during communication because it can help
people understand each other's feelings or ideas and leads to a smooth
communication. Computational rapport model has been proposed based on theory in
previous work. But there lacks solid verification. In this paper, we apply
structural equation model (SEM) to the theoretical model on both dyads of
friend and stranger. The results indicate some unfavorable paths. Based on the
results and more literature, we modify the original model to integrate more
nonverbal behaviors, including gaze and smile. Fit indices and other
examination show the goodness of our new models, which can give us more insight
into rapport management during conversation. | [
1,
0,
0,
0,
0,
0
] |
Title: Least models of second-order set theories,
Abstract: The main theorems of this paper are (1) there is no least transitive model of
Kelley--Morse set theory $\mathsf{KM}$ and (2) there is a least
$\beta$-model---that is, a transitive model which is correct about which of its
classes are well-founded---of Gödel--Bernays set theory $\mathsf{GBC}$ +
Elementary Transfinite Recursion. Along the way I characterize when a countable
model of $\mathsf{ZFC}$ has a least $\mathsf{GBC}$-realization and show that no
countable model of $\mathsf{ZFC}$ has a least $\mathsf{KM}$-realization. I also
show that fragments of Elementary Transfinite Recursion have least
$\beta$-models and, for sufficiently weak fragments, least transitive models.
These fragments can be separated from each other and from the full principle of
Elementary Transfinite Recursion by consistency strength. The main question
left unanswered by this article is whether there is a least transitive model of
$\mathsf{GBC}$ + Elementary Transfinite Recursion. | [
0,
0,
1,
0,
0,
0
] |
Title: Characterizing K2 Candidate Planetary Systems Orbiting Low-Mass Stars I: Classifying Low-mass Host Stars Observed During Campaigns 1-7,
Abstract: We present near-infrared spectra for 144 candidate planetary systems
identified during Campaigns 1-7 of the NASA K2 Mission. The goal of the survey
was to characterize planets orbiting low-mass stars, but our IRTF/SpeX and
Palomar/TripleSpec spectroscopic observations revealed that 49% of our targets
were actually giant stars or hotter dwarfs reddened by interstellar extinction.
For the 72 stars with spectra consistent with classification as cool dwarfs
(spectral types K3 - M4), we refined their stellar properties by applying
empirical relations based on stars with interferometric radius measurements.
Although our revised temperatures are generally consistent with those reported
in the Ecliptic Plane Input Catalog (EPIC), our revised stellar radii are
typically 0.13 solar radii (39%) larger than the EPIC values, which were based
on model isochrones that have been shown to underestimate the radii of cool
dwarfs. Our improved stellar characterizations will enable more efficient
prioritization of K2 targets for follow-up studies. | [
0,
1,
0,
0,
0,
0
] |
Title: Binary orbits from combined astrometric and spectroscopic data,
Abstract: An efficient Bayesian technique for estimation problems in fundamental
stellar astronomy is tested on simulated data for a binary observed both
astrometrically and spectroscopically. Posterior distributions are computed for
the components' masses and for the binary's parallax. One thousand independent
repetitions of the simulation demonstrate that the 1- and 2-$\!\sigma$
credibility intervals for these fundamental quantities have close to the
correct coverage fractions. In addition, the simulations allow the
investigation of the statistical properties of a Bayesian goodness-of-fit
criterion and of the corresponding p-value. The criterion has closely similar
properties to the traditional chi^{2} test for minimum-chi^{2} solutions. | [
0,
1,
0,
0,
0,
0
] |
Title: Gyrotropic Zener tunneling and nonlinear IV curves in the zero-energy Landau level of graphene in a strong magnetic field,
Abstract: We have investigated tunneling current through a suspended graphene Corbino
disk in high magnetic fields at the Dirac point, i.e. at filling factor $\nu$ =
0. At the onset of the dielectric breakdown the current through the disk grows
exponentially before ohmic behaviour, but in a manner distinct from thermal
activation. We find that Zener tunneling between Landau sublevels dominates,
facilitated by tilting of the source-drain bias potential. According to our
analytic modelling, the Zener tunneling is strongly affected by the gyrotropic
force (Lorentz force) due to the high magnetic field | [
0,
1,
0,
0,
0,
0
] |
Title: Accurate, Efficient and Scalable Graph Embedding,
Abstract: The Graph Convolutional Network (GCN) model and its variants are powerful
graph embedding tools for facilitating classification and clustering on graphs.
However, a major challenge is to reduce the complexity of layered GCNs and make
them parallelizable and scalable on very large graphs --- state-of the art
techniques are unable to achieve scalability without losing accuracy and
efficiency. In this paper, we propose novel parallelization techniques for
graph sampling-based GCNs that achieve superior scalable performance on very
large graphs without compromising accuracy. Specifically, our GCN guarantees
work-efficient training and produces order of magnitude savings in computation
and communication. To scale GCN training on tightly-coupled shared memory
systems, we develop parallelization strategies for the key steps in training:
For the graph sampling step, we exploit parallelism within and across multiple
sampling instances, and devise an efficient data structure for concurrent
accesses that provides theoretical guarantee of near-linear speedup with number
of processing units. For the feature propagation step within the sampled graph,
we improve cache utilization and reduce DRAM communication by data
partitioning. We prove that our partitioning strategy is a 2-approximation for
minimizing the communication time compared to the optimal strategy. We
demonstrate that our parallel graph embedding outperforms state-of-the-art
methods in scalability (with respect to number of processors, graph size and
GCN model size), efficiency and accuracy on several large datasets. On a
40-core Xeon platform, our parallel training achieves 64$\times$ speedup (with
AVX) in the sampling step and 25$\times$ speedup in the feature propagation
step, compared to the serial implementation, resulting in a net speedup of
21$\times$. | [
1,
0,
0,
0,
0,
0
] |
Title: A principled methodology for comparing relatedness measures for clustering publications,
Abstract: There are many different relatedness measures, based for instance on citation
relations or textual similarity, that can be used to cluster scientific
publications. We propose a principled methodology for evaluating the accuracy
of clustering solutions obtained using these relatedness measures. We formally
show that the proposed methodology has an important consistency property. The
empirical analyses that we present are based on publications in the fields of
cell biology, condensed matter physics, and economics. Using the BM25
text-based relatedness measure as evaluation criterion, we find that
bibliographic coupling relations yield more accurate clustering solutions than
direct citation relations and co-citation relations. The so-called extended
direct citation approach performs similarly to or slightly better than
bibliographic coupling in terms of the accuracy of the resulting clustering
solutions. The other way around, using a citation-based relatedness measure as
evaluation criterion, BM25 turns out to yield more accurate clustering
solutions than other text-based relatedness measures. | [
1,
0,
0,
0,
0,
0
] |
Title: A Game-Theoretic Approach for Runtime Capacity Allocation in MapReduce,
Abstract: Nowadays many companies have available large amounts of raw, unstructured
data. Among Big Data enabling technologies, a central place is held by the
MapReduce framework and, in particular, by its open source implementation,
Apache Hadoop. For cost effectiveness considerations, a common approach entails
sharing server clusters among multiple users. The underlying infrastructure
should provide every user with a fair share of computational resources,
ensuring that Service Level Agreements (SLAs) are met and avoiding wastes. In
this paper we consider two mathematical programming problems that model the
optimal allocation of computational resources in a Hadoop 2.x cluster with the
aim to develop new capacity allocation techniques that guarantee better
performance in shared data centers. Our goal is to get a substantial reduction
of power consumption while respecting the deadlines stated in the SLAs and
avoiding penalties associated with job rejections. The core of this approach is
a distributed algorithm for runtime capacity allocation, based on Game Theory
models and techniques, that mimics the MapReduce dynamics by means of
interacting players, namely the central Resource Manager and Class Managers. | [
1,
0,
0,
0,
0,
0
] |
Title: Holographic Butterfly Effect and Diffusion in Quantum Critical Region,
Abstract: We investigate the butterfly effect and charge diffusion near the quantum
phase transition in holographic approach. We argue that their criticality is
controlled by the holographic scaling geometry with deformations induced by a
relevant operator at finite temperature. Specifically, in the quantum critical
region controlled by a single fixed point, the butterfly velocity decreases
when deviating from the critical point. While, in the non-critical region, the
behavior of the butterfly velocity depends on the specific phase at low
temperature. Moreover, in the holographic Berezinskii-Kosterlitz-Thouless
transition, the universal behavior of the butterfly velocity is absent.
Finally, the tendency of our holographic results matches with the numerical
results of Bose-Hubbard model. A comparison between our result and that in the
$O(N)$ nonlinear sigma model is also given. | [
0,
1,
0,
0,
0,
0
] |
Title: High-temperature charge density wave correlations in La$_{1.875}$Ba$_{0.125}$CuO$_{4}$ without spin-charge locking,
Abstract: Although all superconducting cuprates display charge-ordering tendencies,
their low-temperature properties are distinct, impeding efforts to understand
the phenomena within a single conceptual framework. While some systems exhibit
stripes of charge and spin, with a locked periodicity, others host charge
density waves (CDWs) without any obviously related spin order. Here we use
resonant inelastic x-ray scattering (RIXS) to follow the evolution of charge
correlations in the canonical stripe ordered cuprate
La$_{1.875}$Ba$_{0.125}$CuO$_{4}$ (LBCO~$1/8$) across its ordering transition.
We find that high-temperature charge correlations are unlocked from the
wavevector of the spin correlations, signaling analogies to CDW phases in
various other cuprates. This indicates that stripe order at low temperatures is
stabilized by the coupling of otherwise independent charge and spin density
waves, with important implications for the relation between charge and spin
correlations in the cuprates. | [
0,
1,
0,
0,
0,
0
] |
Title: A unified deep artificial neural network approach to partial differential equations in complex geometries,
Abstract: In this paper we use deep feedforward artificial neural networks to
approximate solutions to partial differential equations in complex geometries.
We show how to modify the backpropagation algorithm to compute the partial
derivatives of the network output with respect to the space variables which is
needed to approximate the differential operator. The method is based on an
ansatz for the solution which requires nothing but feedforward neural networks
and an unconstrained gradient based optimization method such as gradient
descent or a quasi-Newton method.
We show an example where classical mesh based methods cannot be used and
neural networks can be seen as an attractive alternative. Finally, we highlight
the benefits of deep compared to shallow neural networks and device some other
convergence enhancing techniques. | [
1,
0,
0,
1,
0,
0
] |
Title: Gradient Flows in Uncertainty Propagation and Filtering of Linear Gaussian Systems,
Abstract: The purpose of this work is mostly expository and aims to elucidate the
Jordan-Kinderlehrer-Otto (JKO) scheme for uncertainty propagation, and a
variant, the Laugesen-Mehta-Meyn-Raginsky (LMMR) scheme for filtering. We point
out that these variational schemes can be understood as proximal operators in
the space of density functions, realizing gradient flows. These schemes hold
the promise of leading to efficient ways for solving the Fokker-Planck equation
as well as the equations of non-linear filtering. Our aim in this paper is to
develop in detail the underlying ideas in the setting of linear stochastic
systems with Gaussian noise and recover known results. | [
1,
0,
1,
0,
0,
0
] |
Title: Investigating Simulation-Based Metrics for Characterizing Linear Iterative Reconstruction in Digital Breast Tomosynthesis,
Abstract: Simulation-based image quality metrics are adapted and investigated for
characterizing the parameter dependences of linear iterative image
reconstruction for DBT. Three metrics based on 2D DBT simulation are
investigated: (1) a root-mean-square-error (RMSE) between the test phantom and
reconstructed image, (2) a gradient RMSE where the comparison is made after
taking a spatial gradient of both image and phantom, and (3) a
region-of-interest (ROI) Hotelling observer (HO) for
signal-known-exactly/background-known-exactly (SKE/BKE) and
signal-known-exactly/background-known-statistically (SKE/BKS) detection tasks.
Two simulation studies are performed using the aforementioned metrics, varying
voxel aspect ratio and regularization strength for two types of Tikhonov
regularized least-squares optimization. The RMSE metrics are applied to a 2D
test phantom and the ROI-HO metric is applied to two tasks relevant to DBT:
large, low contrast lesion detection and small, high contrast
microcalcification detection. The RMSE metric trends are compared with visual
assessment of the reconstructed test phantom. The ROI-HO metric trends are
compared with 3D reconstructed images from ACR phantom data acquired with a
Hologic Selenia Dimensions DBT system. Sensitivity of image RMSE to mean pixel
value is found to limit its applicability to the assessment of DBT image
reconstruction. Image gradient RMSE is insensitive to mean pixel value and
appears to track better with subjective visualization of the reconstructed
bar-pattern phantom. The ROI-HO metric shows an increasing trend with
regularization strength for both forms of Tikhonov-regularized least-squares;
however, this metric saturates at intermediate regularization strength
indicating a point of diminishing returns for signal detection. Visualization
with reconstructed ACR phantom images appears to show a similar dependence with
regularization strength. | [
0,
1,
0,
0,
0,
0
] |
Title: Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem,
Abstract: Temporal-Difference learning (TD) [Sutton, 1988] with function approximation
can converge to solutions that are worse than those obtained by Monte-Carlo
regression, even in the simple case of on-policy evaluation. To increase our
understanding of the problem, we investigate the issue of approximation errors
in areas of sharp discontinuities of the value function being further
propagated by bootstrap updates. We show empirical evidence of this leakage
propagation, and show analytically that it must occur, in a simple Markov
chain, when function approximation errors are present. For reversible policies,
the result can be interpreted as the tension between two terms of the loss
function that TD minimises, as recently described by [Ollivier, 2018]. We show
that the upper bounds from [Tsitsiklis and Van Roy, 1997] hold, but they do not
imply that leakage propagation occurs and under what conditions. Finally, we
test whether the problem could be mitigated with a better state representation,
and whether it can be learned in an unsupervised manner, without rewards or
privileged information. | [
0,
0,
0,
1,
0,
0
] |
Title: First Order Theories of Some Lattices of Open Sets,
Abstract: We show that the first order theory of the lattice of open sets in some
natural topological spaces is $m$-equivalent to second order arithmetic. We
also show that for many natural computable metric spaces and computable domains
the first order theory of the lattice of effectively open sets is undecidable.
Moreover, for several important spaces (e.g., $\mathbb{R}^n$, $n\geq1$, and the
domain $P\omega$) this theory is $m$-equivalent to first order arithmetic. | [
1,
0,
1,
0,
0,
0
] |
Title: Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation,
Abstract: A cloud server spent a lot of time, energy and money to train a Viola-Jones
type object detector with high accuracy. Clients can upload their photos to the
cloud server to find objects. However, the client does not want the leakage of
the content of his/her photos. In the meanwhile, the cloud server is also
reluctant to leak any parameters of the trained object detectors. 10 years ago,
Avidan & Butman introduced Blind Vision, which is a method for securely
evaluating a Viola-Jones type object detector. Blind Vision uses standard
cryptographic tools and is painfully slow to compute, taking a couple of hours
to scan a single image. The purpose of this work is to explore an efficient
method that can speed up the process. We propose the Random Base Image (RBI)
Representation. The original image is divided into random base images. Only the
base images are submitted randomly to the cloud server. Thus, the content of
the image can not be leaked. In the meanwhile, a random vector and the secure
Millionaire protocol are leveraged to protect the parameters of the trained
object detector. The RBI makes the integral-image enable again for the great
acceleration. The experimental results reveal that our method can retain the
detection accuracy of that of the plain vision algorithm and is significantly
faster than the traditional blind vision, with only a very low probability of
the information leakage theoretically. | [
1,
0,
0,
0,
0,
0
] |
Title: Lagrangian Statistics for Navier-Stokes Turbulence under Fourier-mode reduction: Fractal and Homogeneous Decimations,
Abstract: We study small-scale and high-frequency turbulent fluctuations in
three-dimensional flows under Fourier-mode reduction. The Navier-Stokes
equations are evolved on a restricted set of modes, obtained as a projection on
a fractal or homogeneous Fourier set. We find a strong sensitivity (reduction)
of the high-frequency variability of the Lagrangian velocity fluctuations on
the degree of mode decimation, similarly to what is already reported for
Eulerian statistics. This is quantified by a tendency towards a quasi-Gaussian
statistics, i.e., to a reduction of intermittency, at all scales and
frequencies. This can be attributed to a strong depletion of vortex filaments
and of the vortex stretching mechanism. Nevertheless, we found that Eulerian
and Lagrangian ensembles are still connected by a dimensional bridge-relation
which is independent of the degree of Fourier-mode decimation. | [
0,
1,
0,
0,
0,
0
] |
Title: Conjunctive management of surface and groundwater under severe drought: A case study in southern Iran,
Abstract: Hormozgan Province, located in the south of Iran, faces several challenges
regarding water resources management. The first one is the discharge of a
massive volume of water to the Persian Gulf because of the concentration of the
annual rainfalls in a short period of time and the narrow distance between the
headwater and the coast. The second one is the unbalanced development of
economic sectors in comparison with distribution of fresh water resources.
Finally, long-term drought is also common in this area. The construction of a
carry-over dam (Esteghlal Dam) and several conveyance pipelines and withdrawing
of the surface water and groundwater resources were considered as the solution
to deal with those challenges. During recent drought, severe overdraft and
inefficient use of recourses confirmed the fact that all done before are not
enough. During this period, there was a tendency to store water in reservoir in
order to meet the demand of the urban sector. Therefore, the agricultural
demand was the first victim of water allocation policy. It caused over
exploitation of the groundwater resources (to meet the agricultural demand) and
considerable losses (evaporation and leakage) from the reservoir. All of the
above-mentioned problems confirm the necessity of the development of a
conjunctive use policy. In this paper, all demand related to the Esteghlal Dam
and the Minab Aquifer (Bandarabbas City and agriculture in the Minab Plain)
were considered as the case study. The main objective was to find the best
applicable conjunctive policy which as well guarantees the conservation of the
Minab Aquifer. Alternative water allocation policies have been developed based
on the present capacities and the experience of local operating staff. | [
0,
1,
0,
0,
0,
0
] |
Title: Classification in biological networks with hypergraphlet kernels,
Abstract: Biological and cellular systems are often modeled as graphs in which vertices
represent objects of interest (genes, proteins, drugs) and edges represent
relational ties among these objects (binds-to, interacts-with, regulates). This
approach has been highly successful owing to the theory, methodology and
software that support analysis and learning on graphs. Graphs, however, often
suffer from information loss when modeling physical systems due to their
inability to accurately represent multiobject relationships. Hypergraphs, a
generalization of graphs, provide a framework to mitigate information loss and
unify disparate graph-based methodologies. In this paper, we present a
hypergraph-based approach for modeling physical systems and formulate vertex
classification, edge classification and link prediction problems on
(hyper)graphs as instances of vertex classification on (extended, dual)
hypergraphs in a semi-supervised setting. We introduce a novel kernel method on
vertex- and edge-labeled (colored) hypergraphs for analysis and learning. The
method is based on exact and inexact (via hypergraph edit distances)
enumeration of small simple hypergraphs, referred to as hypergraphlets, rooted
at a vertex of interest. We extensively evaluate this method and show its
potential use in a positive-unlabeled setting to estimate the number of missing
and false positive links in protein-protein interaction networks. | [
1,
0,
0,
1,
0,
0
] |
Title: Pair Correlation and Gap Distributions for Substitution Tilings and Generalized Ulam Sets in the Plane,
Abstract: We study empirical statistical and gap distributions of several important
tilings of the plane. In particular, we consider the slope distributions, the
angle distributions, pair correlation, squared-distance pair correlation, angle
gap distributions, and slope gap distributions for the Ammann Chair tiling, the
recently discovered fifteenth pentagonal tiling, and a few pertinent tilings
related to these famous examples. We also consider the spatial statistics of
generalized Ulam sets in two dimensions. Additionally, we carefully prove a
tight asymptotic formula for the time steps in which Ulam set points at certain
prescribed geometric positions in their plots in the plane formally enter the
recursively-defined sets.
The software we have developed to these generate numerical approximations to
the distributions for the tilings we consider here is written in Python under
the Sage environment and is released as open-source software which is available
freely on our websites. In addition to the small subset of tilings and other
point sets in the plane we study within the article, our program supports many
other tiling variants and is easily extended for researchers to explore related
tilings and iterative sets. | [
0,
0,
1,
0,
0,
0
] |
Title: Pay-with-a-Selfie, a human-centred digital payment system,
Abstract: Mobile payment systems are increasingly used to simplify the way in which
money transfers and transactions can be performed. We argue that, to achieve
their full potential as economic boosters in developing countries, mobile
payment systems need to rely on new metaphors suitable for the business models,
lifestyle, and technology availability conditions of the targeted communities.
The Pay-with-a-Group-Selfie (PGS) project, funded by the Melinda & Bill Gates
Foundation, has developed a micro-payment system that supports everyday small
transactions by extending the reach of, rather than substituting, existing
payment frameworks. PGS is based on a simple gesture and a readily
understandable metaphor. The gesture - taking a selfie - has become part of the
lifestyle of mobile phone users worldwide, including non-technology-savvy ones.
The metaphor likens computing two visual shares of the selfie to ripping a
banknote in two, a technique used for decades for delayed payment in cash-only
markets. PGS is designed to work with devices with limited computational power
and when connectivity is patchy or not always available. Thanks to visual
cryptography techniques PGS uses for computing the shares, the original selfie
can be recomposed simply by stacking the shares, preserving the analogy with
re-joining the two parts of the banknote. | [
1,
0,
0,
0,
0,
0
] |
Title: LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems,
Abstract: Deep convolutional Neural Networks (CNN) are the state-of-the-art performers
for object detection task. It is well known that object detection requires more
computation and memory than image classification. Thus the consolidation of a
CNN-based object detection for an embedded system is more challenging. In this
work, we propose LCDet, a fully-convolutional neural network for generic object
detection that aims to work in embedded systems. We design and develop an
end-to-end TensorFlow(TF)-based model. Additionally, we employ 8-bit
quantization on the learned weights. We use face detection as a use case. Our
TF-Slim based network can predict different faces of different shapes and sizes
in a single forward pass. Our experimental results show that the proposed
method achieves comparative accuracy comparing with state-of-the-art CNN-based
face detection methods, while reducing the model size by 3x and memory-BW by
~4x comparing with one of the best real-time CNN-based object detector such as
YOLO. TF 8-bit quantized model provides additional 4x memory reduction while
keeping the accuracy as good as the floating point model. The proposed model
thus becomes amenable for embedded implementations. | [
1,
0,
0,
0,
0,
0
] |
Title: Phase transitions of a 2D deformed-AKLT model,
Abstract: We study spin-2 deformed-AKLT models on the square lattice, specifically a
two-parameter family of $O(2)$-symmetric ground-state wavefunctions as defined
by Niggemann, Klümper, and Zittartz, who found previously that the phase
diagram consists of a Néel-ordered phase and a disordered phase which
contains the AKLT point. Using tensor-network methods, we not only confirm the
Néel phase but also find an XY phase with quasi-long-range order and a region
adjacent to it, within the AKLT phase, with very large correlation length, and
investigate the consequences of a perfectly factorizable point at the corner of
that phase. | [
0,
1,
0,
0,
0,
0
] |
Title: Statistical estimation of superhedging prices,
Abstract: We consider statistical estimation of superhedging prices using historical
stock returns in a frictionless market with d traded assets. We introduce a
simple plugin estimator based on empirical measures, show it is consistent but
lacks suitable robustness. This is addressed by our improved estimators which
use a larger set of martingale measures defined through a tradeoff between the
radius of Wasserstein balls around the empirical measure and the allowed norm
of martingale densities. We also study convergence rates, convergence of
superhedging strategies, and our study extends, in part, to the case of a
market with traded options and to a multiperiod setting. | [
0,
0,
0,
0,
0,
1
] |
Title: Ore's theorem on subfactor planar algebras,
Abstract: This paper proves that an irreducible subfactor planar algebra with a
distributive biprojection lattice admits a minimal 2-box projection generating
the identity biprojection. It is a generalization of a theorem of Ore on
intervals of finite groups, conjectured by the author since 2013. We deduce a
link between combinatorics and representations in finite groups theory, related
to an open problem of K.S. Brown in algebraic combinatorics. | [
0,
0,
1,
0,
0,
0
] |
Title: Machine Learning CICY Threefolds,
Abstract: The latest techniques from Neural Networks and Support Vector Machines (SVM)
are used to investigate geometric properties of Complete Intersection
Calabi-Yau (CICY) threefolds, a class of manifolds that facilitate string model
building. An advanced neural network classifier and SVM are employed to (1)
learn Hodge numbers and report a remarkable improvement over previous efforts,
(2) query for favourability, and (3) predict discrete symmetries, a highly
imbalanced problem to which both Synthetic Minority Oversampling Technique
(SMOTE) and permutations of the CICY matrix are used to decrease the class
imbalance and improve performance. In each case study, we employ a genetic
algorithm to optimise the hyperparameters of the neural network. We demonstrate
that our approach provides quick diagnostic tools capable of shortlisting
quasi-realistic string models based on compactification over smooth CICYs and
further supports the paradigm that classes of problems in algebraic geometry
can be machine learned. | [
0,
0,
0,
1,
0,
0
] |
Title: Weak lensing deflection of three-point correlation functions,
Abstract: Weak gravitational lensing alters the apparent separations between observed
sources, potentially affecting clustering statistics. We derive a general
expression for the lensing deflection which is valid for any three-point
statistic, and investigate its effect on the three-point clustering correlation
function. We find that deflection of the clustering correlation function is
greatest at around $z=2$. It is most prominent in regions where the correlation
function varies rapidly, in particular at the baryon acoustic oscillation scale
where it smooths out the peaks and troughs, reducing the peak-to-trough
difference by about 0.1 percent at $z=1$ and around 2.3 percent at $z=10$. The
modification due to lensing deflection is typically at the per cent level of
the expected errors in a Euclid-like survey and therefore undetectable. | [
0,
1,
0,
0,
0,
0
] |
Title: Solitonic dynamics and excitations of the nonlinear Schrodinger equation with third-order dispersion in non-Hermitian PT-symmetric potentials,
Abstract: Solitons are of the important significant in many fields of nonlinear science
such as nonlinear optics, Bose-Einstein condensates, plamas physics, biology,
fluid mechanics, and etc.. The stable solitons have been captured not only
theoretically and experimentally in both linear and nonlinear Schrodinger (NLS)
equations in the presence of non-Hermitian potentials since the concept of the
parity-time (PT)-symmetry was introduced in 1998. In this paper, we present
novel bright solitons of the NLS equation with third-order dispersion in some
complex PT-symmetric potentials (e.g., physically relevant PT-symmetric
Scarff-II-like and harmonic-Gaussian potentials). We find stable nonlinear
modes even if the respective linear PT-symmetric phases are broken. Moreover,
we also use the adiabatic changes of the control parameters to excite the
initial modes related to exact solitons to reach stable nonlinear modes. The
elastic interactions of two solitons are exhibited in the third-order NLS
equation with PT-symmetric potentials. Our results predict the dynamical
phenomena of soliton equations in the presence of third-order dispersion and
PT-symmetric potentials arising in nonlinear fiber optics and other physically
relevant fields. | [
0,
1,
1,
0,
0,
0
] |
Title: Supporting Ruled Polygons,
Abstract: We explore several problems related to ruled polygons. Given a ruling of a
polygon $P$, we consider the Reeb graph of $P$ induced by the ruling. We define
the Reeb complexity of $P$, which roughly equates to the minimum number of
points necessary to support $P$. We give asymptotically tight bounds on the
Reeb complexity that are also tight up to a small additive constant. When
restricted to the set of parallel rulings, we show that the Reeb complexity can
be computed in polynomial time. | [
1,
0,
0,
0,
0,
0
] |
Title: Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data,
Abstract: The recent Nobel-prize-winning detections of gravitational waves from merging
black holes and the subsequent detection of the collision of two neutron stars
in coincidence with electromagnetic observations have inaugurated a new era of
multimessenger astrophysics. To enhance the scope of this emergent field of
science, we pioneered the use of deep learning with convolutional neural
networks, that take time-series inputs, for rapid detection and
characterization of gravitational wave signals. This approach, Deep Filtering,
was initially demonstrated using simulated LIGO noise. In this article, we
present the extension of Deep Filtering using real data from LIGO, for both
detection and parameter estimation of gravitational waves from binary black
hole mergers using continuous data streams from multiple LIGO detectors. We
demonstrate for the first time that machine learning can detect and estimate
the true parameters of real events observed by LIGO. Our results show that Deep
Filtering achieves similar sensitivities and lower errors compared to
matched-filtering while being far more computationally efficient and more
resilient to glitches, allowing real-time processing of weak time-series
signals in non-stationary non-Gaussian noise with minimal resources, and also
enables the detection of new classes of gravitational wave sources that may go
unnoticed with existing detection algorithms. This unified framework for data
analysis is ideally suited to enable coincident detection campaigns of
gravitational waves and their multimessenger counterparts in real-time. | [
1,
1,
0,
0,
0,
0
] |
Title: Self-doping effect arising from electron correlations in multi-layer cuprates,
Abstract: A self-doping effect between outer and inner CuO$_2$ planes (OPs and IPs) in
multi-layer cuprate superconductors is studied. When one considers a
three-layer tight-binding model of the Hg-based three-layer cuprate derived
from the first principle calculations, the electron concentration gets to be
large in the OP compared to IP. This is inconsistent with the experimental fact
that more hole carriers tend to be introduced into the OP than IP.We
investigate a three-layer Hubbard model with the two-particle self-consistent
approach for multi-layer systems to incorporate electron correlations. We
observe that the double occupancy (antiferromagnetic instability) in the IP
decreases (increases) more than the OP, and also reveal that more electrons
tend to be introduced into the IP than OP to obtain the energy gain from the
on-site Hubbard interaction. These results are consistent with the experimental
facts, and this electron distribution between the OP and IP can be interpreted
as a self-doping effect arising from strong electron correlations. | [
0,
1,
0,
0,
0,
0
] |
Title: Increased Prediction Accuracy in the Game of Cricket using Machine Learning,
Abstract: Player selection is one the most important tasks for any sport and cricket is
no exception. The performance of the players depends on various factors such as
the opposition team, the venue, his current form etc. The team management, the
coach and the captain select 11 players for each match from a squad of 15 to 20
players. They analyze different characteristics and the statistics of the
players to select the best playing 11 for each match. Each batsman contributes
by scoring maximum runs possible and each bowler contributes by taking maximum
wickets and conceding minimum runs. This paper attempts to predict the
performance of players as how many runs will each batsman score and how many
wickets will each bowler take for both the teams. Both the problems are
targeted as classification problems where number of runs and number of wickets
are classified in different ranges. We used naïve bayes, random forest,
multiclass SVM and decision tree classifiers to generate the prediction models
for both the problems. Random Forest classifier was found to be the most
accurate for both the problems. | [
1,
0,
0,
0,
0,
0
] |
Title: Learning Probabilistic Programs Using Backpropagation,
Abstract: Probabilistic modeling enables combining domain knowledge with learning from
data, thereby supporting learning from fewer training instances than purely
data-driven methods. However, learning probabilistic models is difficult and
has not achieved the level of performance of methods such as deep neural
networks on many tasks. In this paper, we attempt to address this issue by
presenting a method for learning the parameters of a probabilistic program
using backpropagation. Our approach opens the possibility to building deep
probabilistic programming models that are trained in a similar way to neural
networks. | [
1,
0,
0,
1,
0,
0
] |
Title: Turbulent gas accretion between supermassive black holes and star-forming rings in the circumnuclear disk,
Abstract: While supermassive black holes are known to co-evolve with their host galaxy,
the precise nature and origin of this co-evolution is not clear. We here
explore the possible connection between star formation and black hole growth in
the circumnuclear disk (CND) to probe this connection in the vicinity close to
the black hole. We adopt here the circumnuclear disk model developed by
Kawakatu & Wada (2008) and Wutschik et al. (2013), and explore both the
dependence on the star formation recipe as well as the role of the
gravitational field, which can be dominated by the central black hole, the CND
itself or the host galaxy. A specific emphasis is put on the turbulence
regulated star formation model by Krumholz et al. (2005) to explore the impact
of a realistic star formation recipe. It is shown that this model helps to
introduce realistic fluctuations in the black hole and star formation rate,
without overestimating them. Consistent with previous works, we show that the
final black hole masses are rather insensitive to the masses of the initial
seeds, even for seed masses of up to 10^6 M_sol. In addition, we apply our
model to the formation of high-redshift quasars, as well as to the nearby
system NGC 6951, where a tentative comparison is made in spite of the presence
of a bar in the galaxy. We show that our model can reproduce the high black
hole masses of the high-redshift quasars within a sufficiently short time,
provided a high mass supply rate from the host galaxy. In addition, it
reproduces several of the properties observed in NGC 6951. With respect to the
latter system, our analysis suggests that supernova feedback may be important
to create the observed fluctuations in the star formation history as a result
of negative feedback effects. | [
0,
1,
0,
0,
0,
0
] |
Title: Grayscale Image Authentication using Neural Hashing,
Abstract: Many different approaches for neural network based hash functions have been
proposed. Statistical analysis must correlate security of them. This paper
proposes novel neural hashing approach for gray scale image authentication. The
suggested system is rapid, robust, useful and secure. Proposed hash function
generates hash values using neural network one-way property and non-linear
techniques. As a result security and performance analysis are performed and
satisfying results are achieved. These features are dominant reasons for
preferring against traditional ones. | [
1,
0,
0,
0,
0,
0
] |
Title: Binary Image Selection (BISON): Interpretable Evaluation of Visual Grounding,
Abstract: Providing systems the ability to relate linguistic and visual content is one
of the hallmarks of computer vision. Tasks such as image captioning and
retrieval were designed to test this ability, but come with complex evaluation
measures that gauge various other abilities and biases simultaneously. This
paper presents an alternative evaluation task for visual-grounding systems:
given a caption the system is asked to select the image that best matches the
caption from a pair of semantically similar images. The system's accuracy on
this Binary Image SelectiON (BISON) task is not only interpretable, but also
measures the ability to relate fine-grained text content in the caption to
visual content in the images. We gathered a BISON dataset that complements the
COCO Captions dataset and used this dataset in auxiliary evaluations of
captioning and caption-based retrieval systems. While captioning measures
suggest visual grounding systems outperform humans, BISON shows that these
systems are still far away from human performance. | [
1,
0,
0,
0,
0,
0
] |
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