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Title: A Language for Function Signature Representations,
Abstract: Recent work by (Richardson and Kuhn, 2017a,b; Richardson et al., 2018) looks
at semantic parser induction and question answering in the domain of source
code libraries and APIs. In this brief note, we formalize the representations
being learned in these studies and introduce a simple domain specific language
and a systematic translation from this language to first-order logic. By
recasting the target representations in terms of classical logic, we aim to
broaden the applicability of existing code datasets for investigating more
complex natural language understanding and reasoning problems in the software
domain. | [
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] |
Title: Link Adaptation for Wireless Video Communication Systems,
Abstract: This PhD thesis considers the performance evaluation and enhancement of video
communication over wireless channels. The system model considers hybrid
automatic repeat request (HARQ) with Chase combining and turbo product codes
(TPC). The thesis proposes algorithms and techniques to optimize the
throughput, transmission power and complexity of HARQ-based wireless video
communication. A semi-analytical solution is developed to model the performance
of delay-constrained HARQ systems. The semi-analytical and Monte Carlo
simulation results reveal that significant complexity reduction can be achieved
by noting that the coding gain advantage of the soft over hard decoding is
reduced when Chase combining is used, and it actually vanishes completely for
particular codes. Moreover, the thesis proposes a novel power optimization
algorithm that achieves a significant power saving of up to 80%. Joint
throughput maximization and complexity reduction is considered as well. A CRC
(cyclic redundancy check)-free HARQ is proposed to improve the system
throughput when short packets are transmitted. In addition, the computational
complexity/delay is reduced when the packets transmitted are long. Finally, a
content-aware and occupancy-based HARQ scheme is proposed to ensure minimum
video quality distortion with continuous playback. | [
1,
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] |
Title: On the Complexity of Polytopes in $LI(2)$,
Abstract: In this paper we consider polytopes given by systems of $n$ inequalities in
$d$ variables, where every inequality has at most two variables with nonzero
coefficient. We denote this family by $LI(2)$. We show that despite of the easy
algebraic structure, polytopes in $LI(2)$ can have high complexity. We
construct a polytope in $LI(2)$, whose number of vertices is almost the number
of vertices of the dual cyclic polytope, the difference is a multiplicative
factor of depending on $d$ and in particular independent of $n$. Moreover we
show that the dual cyclic polytope can not be realized in $LI(2)$. | [
1,
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] |
Title: Dynamic Sensitivity Study of MEMS Capacitive Acceleration Transducer Based on Analytical Squeeze Film Damping and Mechanical Thermoelasticity Approaches,
Abstract: The dynamic behavior of a capacitive micro-electro-mechanical (MEMS)
accelerometer is evaluated by using a theoretical approach which makes use of a
squeeze film damping (SFD) model and ideal gas approach. The study investigates
the performance of the device as a function of the temperature, from 228 K to
398 K, and pressure, from 20 to 1000 Pa, observing the damping gas trapped
inside de mechanical transducer. Thermoelastic properties of the silicon bulk
are considered for the entire range of temperature. The damping gases
considered are Air, Helium and Argon. The global behavior of the system is
evaluated considering the electro-mechanical sensitivity (SEM) as the main
figure of merit in frequency domain. The results show the behavior of the main
mechanism losses of SFD, as well as the dynamic sensitivity of the MEMS
transducer system, and are in good agreement with experimental dynamic results
behavior. | [
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] |
Title: Nature of the electromagnetic force between classical magnetic dipoles,
Abstract: The Lorentz force law of classical electrodynamics states that the force F
exerted by the magnetic induction B on a particle of charge q moving with
velocity V is given by F=qVxB. Since this force is orthogonal to the direction
of motion, the magnetic field is said to be incapable of performing mechanical
work. Yet there is no denying that a permanent magnet can readily perform
mechanical work by pushing/pulling on another permanent magnet -- or by
attracting pieces of magnetizable material such as scrap iron or iron filings.
We explain this apparent contradiction by examining the magnetic Lorentz force
acting on an Amperian current loop, which is the model for a magnetic dipole.
We then extend the discussion by analyzing the Einstein-Laub model of magnetic
dipoles in the presence of external magnetic fields. | [
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] |
Title: Tractability of $\mathbb{L}_2$-approximation in hybrid function spaces,
Abstract: We consider multivariate $\mathbb{L}_2$-approximation in reproducing kernel
Hilbert spaces which are tensor products of weighted Walsh spaces and weighted
Korobov spaces. We study the minimal worst-case error
$e^{\mathbb{L}_2-\mathrm{app},\Lambda}(N,d)$ of all algorithms that use $N$
information evaluations from the class $\Lambda$ in the $d$-dimensional case.
The two classes $\Lambda$ considered in this paper are the class $\Lambda^{\rm
all}$ consisting of all linear functionals and the class $\Lambda^{\rm std}$
consisting only of function evaluations.
The focus lies on the dependence of
$e^{\mathbb{L}_2-\mathrm{app},\Lambda}(N,d)$ on the dimension $d$. The main
results are conditions for weak, polynomial, and strong polynomial
tractability. | [
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] |
Title: The Linearity of the Mitchell Order,
Abstract: We show from a weak comparison principle (the Ultrapower Axiom) that the
Mitchell order is linear on certain kinds of ultrafilters: normal ultrafilters,
Dodd solid ultrafilters, and assuming GCH, generalized normal ultrafilters. In
the process we prove a generalization of Solovay's lemma to singular cardinals. | [
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] |
Title: Atomic Order in Non-Equilibrium Silicon-Germanium-Tin Semiconductors,
Abstract: The precise knowledge of the atomic order in monocrystalline alloys is
fundamental to understand and predict their physical properties. With this
perspective, we utilized laser-assisted atom probe tomography to investigate
the three-dimensional distribution of atoms in non-equilibrium epitaxial
Sn-rich group IV SiGeSn ternary semiconductors. Different atom probe
statistical analysis tools including frequency distribution analysis, partial
radial distribution functions, and nearest neighbor analysis were employed in
order to evaluate and compare the behavior of the three elements to their
spatial distributions in an ideal solid solution. This atomistic-level analysis
provided clear evidence of an unexpected repulsive interaction between Sn and
Si leading to the deviation of Si atoms from the theoretical random
distribution. This departure from an ideal solid solution is supported by first
principal calculations and attributed to the tendency of the system to reduce
its mixing enthalpy throughout the layer-by-layer growth process. | [
0,
1,
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] |
Title: On the Laws of Large Numbers in Possibility Theory,
Abstract: In this paper we obtain some possibilistic variants of the probabilistic laws
of large numbers, different from those obtained by other authors, but very
natural extensions of the corresponding ones in probability theory. Our results
are based on the possibility measure and on the "maxitive" definitions for
possibility expectation and possibility variance. Also, we show that in this
frame, the weak form of the law of large numbers, implies the strong law of
large numbers. | [
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1,
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] |
Title: Finite Sample Inference for Targeted Learning,
Abstract: The Highly-Adaptive-Lasso(HAL)-TMLE is an efficient estimator of a pathwise
differentiable parameter in a statistical model that at minimal (and possibly
only) assumes that the sectional variation norm of the true nuisance parameters
are finite. It relies on an initial estimator (HAL-MLE) of the nuisance
parameters by minimizing the empirical risk over the parameter space under the
constraint that sectional variation norm is bounded by a constant, where this
constant can be selected with cross-validation. In the formulation of the
HALMLE this sectional variation norm corresponds with the sum of absolute value
of coefficients for an indicator basis. Due to its reliance on machine
learning, statistical inference for the TMLE has been based on its normal limit
distribution, thereby potentially ignoring a large second order remainder in
finite samples.
In this article, we present four methods for construction of a finite sample
0.95-confidence interval that use the nonparametric bootstrap to estimate the
finite sample distribution of the HAL-TMLE or a conservative distribution
dominating the true finite sample distribution. We prove that it consistently
estimates the optimal normal limit distribution, while its approximation error
is driven by the performance of the bootstrap for a well behaved empirical
process. We demonstrate our general inferential methods for 1) nonparametric
estimation of the average treatment effect based on observing on each unit a
covariate vector, binary treatment, and outcome, and for 2) nonparametric
estimation of the integral of the square of the multivariate density of the
data distribution. | [
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] |
Title: A Short and Elementary Proof of the Two-sidedness of the Matrix-Inverse,
Abstract: An elementary proof of the two-sidedness of the matrix-inverse is given using
only linear independence and the reduced row-echelon form of a matrix. In
addition, it is shown that a matrix is invertible if and only if it is
row-equivalent to the identity matrix without appealing to elementary matrices.
This proof underscores the importance of a basis and provides a proof of the
invertible matrix theorem. | [
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] |
Title: Rich-Club Ordering and the Dyadic Effect: Two Interrelated Phenomena,
Abstract: Rich-club ordering and the dyadic effect are two phenomena observed in
complex networks that are based on the presence of certain substructures
composed of specific nodes. Rich-club ordering represents the tendency of
highly connected and important elements to form tight communities with other
central elements. The dyadic effect denotes the tendency of nodes that share a
common property to be much more interconnected than expected. In this study, we
consider the interrelation between these two phenomena, which until now have
always been studied separately. We contribute with a new formulation of the
rich-club measures in terms of the dyadic effect. Moreover, we introduce
certain measures related to the analysis of the dyadic effect, which are useful
in confirming the presence and relevance of rich-clubs in complex networks. In
addition, certain computational experiences show the usefulness of the
introduced quantities with regard to different classes of real networks. | [
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] |
Title: The global dust modelling framework THEMIS (The Heterogeneous dust Evolution Model for Interstellar Solids),
Abstract: Here we introduce the interstellar dust modelling framework THEMIS (The
Heterogeneous dust Evolution Model for Interstellar Solids), which takes a
global view of dust and its evolution in response to the local conditions in
interstellar media. This approach is built upon a core model that was developed
to explain the dust extinction and emission in the diffuse interstellar medium.
The model was then further developed to self-consistently include the effects
of dust evolution in the transition to denser regions. The THEMIS approach is
under continuous development and currently we are extending the framework to
explore the implications of dust evolution in HII regions and the
photon-dominated regions associated with star formation. We provide links to
the THEMIS, DustEM and DustPedia websites where more information about the
model, its input data and applications can be found. | [
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] |
Title: Variable Selection Methods for Model-based Clustering,
Abstract: Model-based clustering is a popular approach for clustering multivariate data
which has seen applications in numerous fields. Nowadays, high-dimensional data
are more and more common and the model-based clustering approach has adapted to
deal with the increasing dimensionality. In particular, the development of
variable selection techniques has received a lot of attention and research
effort in recent years. Even for small size problems, variable selection has
been advocated to facilitate the interpretation of the clustering results. This
review provides a summary of the methods developed for variable selection in
model-based clustering. Existing R packages implementing the different methods
are indicated and illustrated in application to two data analysis examples. | [
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] |
Title: Synergies between Asteroseismology and Exoplanetary Science,
Abstract: Over the past decade asteroseismology has become a powerful method to
systematically characterize host stars and dynamical architectures of exoplanet
systems. In this contribution I review current key synergies between
asteroseismology and exoplanetary science such as the precise determination of
planet radii and ages, the measurement of orbital eccentricities, stellar
obliquities and their impact on hot Jupiter formation theories, and the
importance of asteroseismology on spectroscopic analyses of exoplanet hosts. I
also give an outlook on future synergies such as the characterization of
sub-Neptune-size planets orbiting solar-type stars, the study of planet
populations orbiting evolved stars, and the determination of ages of
intermediate-mass stars hosting directly imaged planets. | [
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] |
Title: The stellar contents and star formation in the NGC 7538 region,
Abstract: Deep optical photometric data on the NGC 7538 region were collected and
combined with archival data sets from $Chandra$, 2MASS and {\it Spitzer}
surveys in order to generate a new catalog of young stellar objects (YSOs)
including those not showing IR excess emission. This new catalog is complete
down to 0.8 M$_\odot$. The nature of the YSOs associated with the NGC 7538
region and their spatial distribution are used to study the star formation
process and the resultant mass function (MF) in the region. Out of the 419
YSOs, $\sim$91\% have ages between 0.1 to 2.5 Myr and $\sim$86\% have masses
between 0.5 to 3.5 M$_\odot$, as derived by spectral energy distribution
fitting analysis. Around 24\%, 62\% and 2\% of these YSOs are classified to be
the Class I, Class II and Class III sources, respectively. The X-ray activity
in the Class I, Class II and Class III objects is not significantly different
from each other. This result implies that the enhanced X-ray surface flux due
to the increase in the rotation rate may be compensated by the decrease in the
stellar surface area during the pre-main sequence evolution. Our analysis shows
that the O3V type high mass star `IRS 6' might have triggered the formation of
young low mass stars up to a radial distance of 3 pc. The MF shows a turn-off
at around 1.5 M$_\odot$ and the value of its slope `$\Gamma$' in the mass range
$1.5 <$M/M$_\odot < 6$ comes out to be $-1.76\pm0.24$, which is steeper than
the Salpeter value. | [
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] |
Title: Ambiguity set and learning via Bregman and Wasserstein,
Abstract: Construction of ambiguity set in robust optimization relies on the choice of
divergences between probability distributions. In distribution learning,
choosing appropriate probability distributions based on observed data is
critical for approximating the true distribution. To improve the performance of
machine learning models, there has recently been interest in designing
objective functions based on Lp-Wasserstein distance rather than the classical
Kullback-Leibler (KL) divergence. In this paper, we derive concentration and
asymptotic results using Bregman divergence. We propose a novel asymmetric
statistical divergence called Wasserstein-Bregman divergence as a
generalization of L2-Wasserstein distance. We discuss how these results can be
applied to the construction of ambiguity set in robust optimization. | [
1,
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] |
Title: 1-bit Massive MU-MIMO Precoding in VLSI,
Abstract: Massive multiuser (MU) multiple-input multiple-output (MIMO) will be a core
technology in fifth-generation (5G) wireless systems as it offers significant
improvements in spectral efficiency compared to existing multi-antenna
technologies. The presence of hundreds of antenna elements at the base station
(BS), however, results in excessively high hardware costs and power
consumption, and requires high interconnect throughput between the
baseband-processing unit and the radio unit. Massive MU-MIMO that uses
low-resolution analog-to-digital and digital-to-analog converters (DACs) has
the potential to address all these issues. In this paper, we focus on downlink
precoding for massive MU-MIMO systems with 1-bit DACs at the BS. The objective
is to design precoders that simultaneously mitigate multi-user interference
(MUI) and quantization artifacts. We propose two nonlinear 1-bit precoding
algorithms and corresponding very-large scale integration (VLSI) designs. Our
algorithms rely on biconvex relaxation, which enables the design of efficient
1-bit precoding algorithms that achieve superior error-rate performance
compared to that of linear precoding algorithms followed by quantization. To
showcase the efficacy of our algorithms, we design VLSI architectures that
enable efficient 1-bit precoding for massive MU-MIMO systems in which hundreds
of antennas serve tens of user equipments. We present corresponding
field-programmable gate array (FPGA) implementations to demonstrate that 1-bit
precoding enables reliable and high-rate downlink data transmission in
practical systems. | [
1,
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] |
Title: Step evolution in two-dimensional diblock copolymer films,
Abstract: The formation and dynamics of free-surface structures, such as steps or
terraces and their interplay with the phase separation in the bulk are key
features of diblock copolymer films. We present a phase-field model with an
obstacle potential which follows naturally from derivations of the
Ohta-Kawasaki energy functional via self-consistent field theory. The free
surface of the film is incorporated into the phase-field model by including a
third phase for the void. The resulting model and its sharp interface limit are
shown to capture the energetics of films with steps in two dimensions. For this
model, we then develop a numerical approach that is capable of resolving the
long-time complex free-surface structures that arise in diblock copolymer
films. | [
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] |
Title: The univalence axiom in cubical sets,
Abstract: In this note we show that Voevodsky's univalence axiom holds in the model of
type theory based on symmetric cubical sets. We will also discuss Swan's
construction of the identity type in this variation of cubical sets. This
proves that we have a model of type theory supporting dependent products,
dependent sums, univalent universes, and identity types with the usual
judgmental equality, and this model is formulated in a constructive metatheory. | [
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] |
Title: Assessing the Privacy Cost in Centralized Event-Based Demand Response for Microgrids,
Abstract: Demand response (DR) programs have emerged as a potential key enabling
ingredient in the context of smart grid (SG). Nevertheless, the rising concerns
over privacy issues raised by customers subscribed to these programs constitute
a major threat towards their effective deployment and utilization. This has
driven extensive research to resolve the hindrance confronted, resulting in a
number of methods being proposed for preserving customers' privacy. While these
methods provide stringent privacy guarantees, only limited attention has been
paid to their computational efficiency and performance quality. Under the
paradigm of differential privacy, this paper initiates a systematic empirical
study on quantifying the trade-off between privacy and optimality in
centralized DR systems for maximizing cumulative customer utility. Aiming to
elucidate the factors governing this trade-off, we analyze the cost of privacy
in terms of the effect incurred on the objective value of the DR optimization
problem when applying the employed privacy-preserving strategy based on Laplace
mechanism. The theoretical results derived from the analysis are complemented
with empirical findings, corroborated extensively by simulations on a 4-bus MG
system with up to thousands of customers. By evaluating the impact of privacy,
this pilot study serves DR practitioners when considering the social and
economic implications of deploying privacy-preserving DR programs in practice.
Moreover, it stimulates further research on exploring more efficient approaches
with bounded performance guarantees for optimizing energy procurement of MGs
without infringing the privacy of customers on demand side. | [
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] |
Title: Threat Modeling Data Analysis in Socio-technical Systems,
Abstract: Our decision-making processes are becoming more data driven, based on data
from multiple sources, of different types, processed by a variety of
technologies. As technology becomes more relevant for decision processes, the
more likely they are to be subjects of attacks aimed at disrupting their
execution or changing their outcome. With the increasing complexity and
dependencies on technical components, such attempts grow more sophisticated and
their impact will be more severe. This is especially important in scenarios
with shared goals, which had to be previously agreed to, or decisions with
broad social impact. We need to think about our decisions-making and underlying
data analysis processes in a systemic way to correctly evaluate benefits and
risks of specific solutions and to design them to be resistant to attacks. To
reach these goals, we can apply experiences from threat modeling analysis used
in software security. We will need to adapt these practices to new types of
threats, protecting different assets and operating in socio-technical systems.
With these changes, threat modeling can become a foundation for implementing
detailed technical, organizational or legal mitigations and making our
decisions more reliable and trustworthy. | [
1,
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] |
Title: Nonlinear Instability of Half-Solitons on Star Graphs,
Abstract: We consider a half-soliton stationary state of the nonlinear Schrodinger
equation with the power nonlinearity on a star graph consisting of N edges and
a single vertex. For the subcritical power nonlinearity, the half-soliton state
is a degenerate critical point of the action functional under the mass
constraint such that the second variation is nonnegative. By using normal
forms, we prove that the degenerate critical point is a nonlinear saddle point,
for which the small perturbations to the half-soliton state grow slowly in time
resulting in the nonlinear instability of the half-soliton state. The result
holds for any $N \geq 3$ and arbitrary subcritical power nonlinearity. It gives
a precise dynamical characterization of the previous result of Adami {\em et
al.}, where the half-soliton state was shown to be a saddle point of the action
functional under the mass constraint for $N = 3$ and for cubic nonlinearity. | [
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] |
Title: Demographics and discussion influence views on algorithmic fairness,
Abstract: The field of algorithmic fairness has highlighted ethical questions which may
not have purely technical answers. For example, different algorithmic fairness
constraints are often impossible to satisfy simultaneously, and choosing
between them requires value judgments about which people may disagree.
Achieving consensus on algorithmic fairness will be difficult unless we
understand why people disagree in the first place. Here we use a series of
surveys to investigate how two factors affect disagreement: demographics and
discussion. First, we study whether disagreement on algorithmic fairness
questions is caused partially by differences in demographic backgrounds. This
is a question of interest because computer science is demographically
non-representative. If beliefs about algorithmic fairness correlate with
demographics, and algorithm designers are demographically non-representative,
decisions made about algorithmic fairness may not reflect the will of the
population as a whole. We show, using surveys of three separate populations,
that there are gender differences in beliefs about algorithmic fairness. For
example, women are less likely to favor including gender as a feature in an
algorithm which recommends courses to students if doing so would make female
students less likely to be recommended science courses. Second, we investigate
whether people's views on algorithmic fairness can be changed by discussion and
show, using longitudinal surveys of students in two computer science classes,
that they can. | [
1,
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] |
Title: The stifness of the supranuclear equation of state (once again),
Abstract: We revisit the present status of the stiffness of the supranuclear equations
of state, particularly the solutions that increase the stiffness in the
presence of hyperons, the putative transition to a quark matter phase and the
robustness of massive compact star observations. | [
0,
1,
0,
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] |
Title: Stable Desynchronization for Wireless Sensor Networks: (III) Stability Analysis,
Abstract: In this paper, we use dynamical systems to analyze stability of
desynchronization algorithms at equilibrium. We start by illustrating the
equilibrium of a dynamic systems and formalizing force components and time
phases. Then, we use Linear Approximation to obtain Jaconian (J) matrixes which
are used to find the eigenvalues. Next, we employ the Hirst and Macey theorem
and Gershgorins theorem to find the bounds of those eigenvalues. Finally, if
the number of nodes (n) is within such bounds, the systems are stable at
equilibrium. (This paper is the last part of the series Stable
Desynchronization for Wireless Sensor Networks - (I) Concepts and Algorithms
(II) Performance Evaluation (III) Stability Analysis) | [
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] |
Title: Optimizing Adiabatic Quantum Program Compilation using a Graph-Theoretic Framework,
Abstract: Adiabatic quantum computing has evolved in recent years from a theoretical
field into an immensely practical area, a change partially sparked by D-Wave
System's quantum annealing hardware. These multimillion-dollar quantum
annealers offer the potential to solve optimization problems millions of times
faster than classical heuristics, prompting researchers at Google, NASA and
Lockheed Martin to study how these computers can be applied to complex
real-world problems such as NASA rover missions. Unfortunately, compiling
(embedding) an optimization problem into the annealing hardware is itself a
difficult optimization problem and a major bottleneck currently preventing
widespread adoption. Additionally, while finding a single embedding is
difficult, no generalized method is known for tuning embeddings to use minimal
hardware resources. To address these barriers, we introduce a graph-theoretic
framework for developing structured embedding algorithms. Using this framework,
we introduce a biclique virtual hardware layer to provide a simplified
interface to the physical hardware. Additionally, we exploit bipartite
structure in quantum programs using odd cycle transversal (OCT) decompositions.
By coupling an OCT-based embedding algorithm with new, generalized reduction
methods, we develop a new baseline for embedding a wide range of optimization
problems into fault-free D-Wave annealing hardware. To encourage the reuse and
extension of these techniques, we provide an implementation of the framework
and embedding algorithms. | [
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] |
Title: Analysis of pedestrian behaviors through non-invasive Bluetooth monitoring,
Abstract: This paper analyzes pedestrians' behavioral patterns in the pedestrianized
shopping environment in the historical center of Barcelona, Spain. We employ a
Bluetooth detection technique to capture a large-scale dataset of pedestrians'
behavior over a one-month period, including during a key sales period. We
focused on comparing particular behaviors before, during, and after the
discount sales by analyzing this large-scale dataset, which is different but
complementary to the conventionally used small-scale samples. Our results
uncover pedestrians actively exploring a wider area of the district during a
discount period compared to weekdays, giving rise to strong underlying mobility
patterns. | [
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] |
Title: Open Gromov-Witten theory without Obstruction,
Abstract: We define Open Gromov-Witten invariants counting psudoholomorphic curves with
boundary of a fixed Euler characteristic. There is not obstruction in the
construction of the invariant. | [
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] |
Title: Resilience of Complex Networks,
Abstract: This article determines and characterizes the minimal number of actuators
needed to ensure structural controllability of a linear system under structural
alterations that can severe the connection between any two states. We assume
that initially the system is structurally controllable with respect to a given
set of controls, and propose an efficient system-synthesis mechanism to find
the minimal number of additional actuators required for resilience of the
system w.r.t such structural changes. The effectiveness of this approach is
demonstrated by using standard IEEE power networks. | [
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] |
Title: Forward Thinking: Building and Training Neural Networks One Layer at a Time,
Abstract: We present a general framework for training deep neural networks without
backpropagation. This substantially decreases training time and also allows for
construction of deep networks with many sorts of learners, including networks
whose layers are defined by functions that are not easily differentiated, like
decision trees. The main idea is that layers can be trained one at a time, and
once they are trained, the input data are mapped forward through the layer to
create a new learning problem. The process is repeated, transforming the data
through multiple layers, one at a time, rendering a new data set, which is
expected to be better behaved, and on which a final output layer can achieve
good performance. We call this forward thinking and demonstrate a proof of
concept by achieving state-of-the-art accuracy on the MNIST dataset for
convolutional neural networks. We also provide a general mathematical
formulation of forward thinking that allows for other types of deep learning
problems to be considered. | [
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] |
Title: Non-negative Tensor Factorization for Human Behavioral Pattern Mining in Online Games,
Abstract: Multiplayer online battle arena has become a popular game genre. It also
received increasing attention from our research community because they provide
a wealth of information about human interactions and behaviors. A major problem
is extracting meaningful patterns of activity from this type of data, in a way
that is also easy to interpret. Here, we propose to exploit tensor
decomposition techniques, and in particular Non-negative Tensor Factorization,
to discover hidden correlated behavioral patterns of play in a popular game:
League of Legends. We first collect the entire gaming history of a group of
about one thousand players, totaling roughly $100K$ matches. By applying our
methodological framework, we then separate players into groups that exhibit
similar features and playing strategies, as well as similar temporal
trajectories, i.e., behavioral progressions over the course of their gaming
history: this will allow us to investigate how players learn and improve their
skills. | [
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] |
Title: Graph-Cut RANSAC,
Abstract: A novel method for robust estimation, called Graph-Cut RANSAC, GC-RANSAC in
short, is introduced. To separate inliers and outliers, it runs the graph-cut
algorithm in the local optimization (LO) step which is applied when a
so-far-the-best model is found. The proposed LO step is conceptually simple,
easy to implement, globally optimal and efficient. GC-RANSAC is shown
experimentally, both on synthesized tests and real image pairs, to be more
geometrically accurate than state-of-the-art methods on a range of problems,
e.g. line fitting, homography, affine transformation, fundamental and essential
matrix estimation. It runs in real-time for many problems at a speed
approximately equal to that of the less accurate alternatives (in milliseconds
on standard CPU). | [
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] |
Title: Mixed measurements and the detection of phase synchronization in networks,
Abstract: Multivariate singular spectrum analysis (M-SSA), with a varimax rotation of
eigenvectors, was recently proposed to provide detailed information about phase
synchronization in networks of nonlinear oscillators without any a priori need
for phase estimation. The discriminatory power of M-SSA is often enhanced by
using only the time series of the variable that provides the best observability
of the node dynamics. In practice, however, diverse factors could prevent one
to have access to this variable in some nodes and other variables should be
used, resulting in a mixed set of variables. In the present work, the impact of
this mixed measurement approach on the M-SSA is numerically investigated in
networks of Rössler systems and cord oscillators. The results are threefold.
First, a node measured by a poor variable, in terms of observability, becomes
virtually invisible to the technique. Second, a side effect of using a poor
variable is that the characterization of phase synchronization clustering of
the {\it other}\, nodes is hindered by a small amount. This suggests that,
given a network, synchronization analysis with M-SSA could be more reliable by
not measuring those nodes that are accessible only through poor variables.
Third, global phase synchronization could be detected even using only poor
variables, given enough of them are measured. These insights could be useful in
defining measurement strategies for both experimental design and real world
applications for use with M-SSA. | [
0,
1,
0,
0,
0,
0
] |
Title: A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip,
Abstract: In building intelligent transportation systems such as taxi or rideshare
services, accurate prediction of travel time and distance is crucial for
customer experience and resource management. Using the NYC taxi dataset, which
contains taxi trips data collected from GPS-enabled taxis [23], this paper
investigates the use of deep neural networks to jointly predict taxi trip time
and distance. We propose a model, called ST-NN (Spatio-Temporal Neural
Network), which first predicts the travel distance between an origin and a
destination GPS coordinate, then combines this prediction with the time of day
to predict the travel time. The beauty of ST-NN is that it uses only the raw
trips data without requiring further feature engineering and provides a joint
estimate of travel time and distance. We compare the performance of ST-NN to
that of state-of-the-art travel time estimation methods, and we observe that
the proposed approach generalizes better than state-of-the-art methods. We show
that ST-NN approach significantly reduces the mean absolute error for both
predicted travel time and distance, about 17% for travel time prediction. We
also observe that the proposed approach is more robust to outliers present in
the dataset by testing the performance of ST-NN on the datasets with and
without outliers. | [
1,
0,
0,
1,
0,
0
] |
Title: Lion and man in non-metric spaces,
Abstract: A lion and a man move continuously in a space $X$. The aim of the lion is to
capture his prey while the man wants to escape forever. Which of them has a
strategy? This question has been studied for different metric domains. In this
article we consider the case of general topological spaces. | [
0,
0,
1,
0,
0,
0
] |
Title: Banach Algebra of Complex Bounded Radon Measures on Homogeneous Space,
Abstract: Let $ H $ be a compact subgroup of a locally compact group $G$. In this paper
we define a convolution on $ M(G/H) $, the space of all complex bounded Radon
measures on the homogeneous space G/H. Then we prove that the measure space $
M(G/H, *) $ is a non-unital Banach algebra that possesses an approximate
identity. Finally, it is shown that the Banach algebra $ M(G/H, *) $ is not
involutive and also $ L^1(G/H, *) $ is a two-sided ideal of it. | [
0,
0,
1,
0,
0,
0
] |
Title: A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification,
Abstract: In the past, Acoustic Scene Classification systems have been based on hand
crafting audio features that are input to a classifier. Nowadays, the common
trend is to adopt data driven techniques, e.g., deep learning, where audio
representations are learned from data. In this paper, we propose a system that
consists of a simple fusion of two methods of the aforementioned types: a deep
learning approach where log-scaled mel-spectrograms are input to a
convolutional neural network, and a feature engineering approach, where a
collection of hand-crafted features is input to a gradient boosting machine. We
first show that both methods provide complementary information to some extent.
Then, we use a simple late fusion strategy to combine both methods. We report
classification accuracy of each method individually and the combined system on
the TUT Acoustic Scenes 2017 dataset. The proposed fused system outperforms
each of the individual methods and attains a classification accuracy of 72.8%
on the evaluation set, improving the baseline system by 11.8%. | [
0,
0,
0,
1,
0,
0
] |
Title: Online Convex Optimization with Unconstrained Domains and Losses,
Abstract: We propose an online convex optimization algorithm (RescaledExp) that
achieves optimal regret in the unconstrained setting without prior knowledge of
any bounds on the loss functions. We prove a lower bound showing an exponential
separation between the regret of existing algorithms that require a known bound
on the loss functions and any algorithm that does not require such knowledge.
RescaledExp matches this lower bound asymptotically in the number of
iterations. RescaledExp is naturally hyperparameter-free and we demonstrate
empirically that it matches prior optimization algorithms that require
hyperparameter optimization. | [
1,
0,
0,
1,
0,
0
] |
Title: A Variational Feature Encoding Method of 3D Object for Probabilistic Semantic SLAM,
Abstract: This paper presents a feature encoding method of complex 3D objects for
high-level semantic features. Recent approaches to object recognition methods
become important for semantic simultaneous localization and mapping (SLAM).
However, there is a lack of consideration of the probabilistic observation
model for 3D objects, as the shape of a 3D object basically follows a complex
probability distribution. Furthermore, since the mobile robot equipped with a
range sensor observes only a single view, much information of the object shape
is discarded. These limitations are the major obstacles to semantic SLAM and
view-independent loop closure using 3D object shapes as features. In order to
enable the numerical analysis for the Bayesian inference, we approximate the
true observation model of 3D objects to tractable distributions. Since the
observation likelihood can be obtained from the generative model, we formulate
the true generative model for 3D object with the Bayesian networks. To capture
these complex distributions, we apply a variational auto-encoder. To analyze
the approximated distributions and encoded features, we perform classification
with maximum likelihood estimation and shape retrieval. | [
1,
0,
0,
0,
0,
0
] |
Title: Dimensionality reduction methods for molecular simulations,
Abstract: Molecular simulations produce very high-dimensional data-sets with millions
of data points. As analysis methods are often unable to cope with so many
dimensions, it is common to use dimensionality reduction and clustering methods
to reach a reduced representation of the data. Yet these methods often fail to
capture the most important features necessary for the construction of a Markov
model. Here we demonstrate the results of various dimensionality reduction
methods on two simulation data-sets, one of protein folding and another of
protein-ligand binding. The methods tested include a k-means clustering
variant, a non-linear auto encoder, principal component analysis and tICA. The
dimension-reduced data is then used to estimate the implied timescales of the
slowest process by a Markov state model analysis to assess the quality of the
projection. The projected dimensions learned from the data are visualized to
demonstrate which conformations the various methods choose to represent the
molecular process. | [
1,
0,
0,
1,
0,
0
] |
Title: Exploration--Exploitation in MDPs with Options,
Abstract: While a large body of empirical results show that temporally-extended actions
and options may significantly affect the learning performance of an agent, the
theoretical understanding of how and when options can be beneficial in online
reinforcement learning is relatively limited. In this paper, we derive an upper
and lower bound on the regret of a variant of UCRL using options. While we
first analyze the algorithm in the general case of semi-Markov decision
processes (SMDPs), we show how these results can be translated to the specific
case of MDPs with options and we illustrate simple scenarios in which the
regret of learning with options can be \textit{provably} much smaller than the
regret suffered when learning with primitive actions. | [
1,
0,
0,
1,
0,
0
] |
Title: Pressure induced spin crossover in disordered α-LiFeO2,
Abstract: Structural, magnetic and electrical-transport properties of {\alpha}-LiFeO2,
crystallizing in the rock salt structure with random distribution of Li and Fe
ions, have been studied by synchrotron X-ray diffraction, 57Fe Mössbauer
spectroscopy and electrical resistance measurements at pressures up to 100 GPa
using diamond anvil cells. It was found that the crystal structure is stable at
least to 82 GPa, though a significant change in compressibility has been
observed above 50 GPa. The changes in the structural properties are found to be
on a par with a sluggish Fe3+ high- to low-spin (HS-LS) transition (S=5/2 to
S=1/2) starting at 50 GPa and not completed even at ~100 GPa. The HS-LS
transition is accompanied by an appreciable resistance decrease remaining a
semiconductor up to 115 GPa and is not expected to be metallic even at about
200 GPa. The observed feature of the pressure-induced HS-LS transition is not
an ordinary behavior of ferric oxides at high pressures. The effect of Fe3+
nearest and next nearest neighbors on the features of the spin crossover is
discussed. | [
0,
1,
0,
0,
0,
0
] |
Title: Central Limit Theorem for empirical transportation cost in general dimension,
Abstract: We consider the problem of optimal transportation with quadratic cost between
a empirical measure and a general target probability on R d , with d $\ge$ 1.
We provide new results on the uniqueness and stability of the associated
optimal transportation potentials , namely, the minimizers in the dual
formulation of the optimal transportation problem. As a consequence, we show
that a CLT holds for the empirical transportation cost under mild moment and
smoothness requirements. The limiting distributions are Gaussian and admit a
simple description in terms of the optimal transportation potentials. | [
0,
0,
1,
1,
0,
0
] |
Title: Characterization of temperatures associated to Schrödinger operators with initial data in Morrey spaces,
Abstract: Let $\mathcal{L}$ be a Schrödinger operator of the form $\mathcal{L} =
-\Delta+V$ acting on $L^2(\mathbb R^n)$ where the nonnegative potential $V$
belongs to the reverse Hölder class $B_q$ for some $q\geq n.$ Let
$L^{p,\lambda}(\mathbb{R}^{n})$, $0\le \lambda<n$ denote the Morrey space on
$\mathbb{R}^{n}$. In this paper, we will show that a function $f\in
L^{2,\lambda}(\mathbb{R}^{n})$ is the trace of the solution of ${\mathbb
L}u=u_{t}+{\mathcal{L}}u=0, u(x,0)= f(x),$ where $u$ satisfies a Carleson-type
condition \begin{eqnarray*} \sup_{x_B, r_B}
r_B^{-\lambda}\int_0^{r_B^2}\int_{B(x_B, r_B)} |\nabla u(x,t)|^2 {dx dt} \leq C
<\infty. \end{eqnarray*} Conversely, this Carleson-type condition characterizes
all the ${\mathbb L}$-carolic functions whose traces belong to the Morrey space
$L^{2,\lambda}(\mathbb{R}^{n})$ for all $0\le \lambda<n$. This result extends
the analogous characterization founded by Fabes and Neri for the classical BMO
space of John and Nirenberg. | [
0,
0,
1,
0,
0,
0
] |
Title: Dynamic-sensitive cooperation in the presence of multiple strategy updating rules,
Abstract: The importance of microscopic details on cooperation level is an intensively
studied aspect of evolutionary game theory. Interestingly, these details become
crucial on heterogeneous populations where individuals may possess diverse
traits. By introducing a coevolutionary model in which not only strategies but
also individual dynamical features may evolve we revealed that the formerly
established conclusion is not necessarily true when different updating rules
are on stage. In particular, we apply two strategy updating rules, imitation
and Death-Birth rule, which allow local selection in a spatial system. Our
observation highlights that the microscopic feature of dynamics, like the level
of learning activity, could be a fundamental factor even if all players share
the same trait uniformly. | [
0,
0,
0,
0,
1,
0
] |
Title: Inference in Graphical Models via Semidefinite Programming Hierarchies,
Abstract: Maximum A posteriori Probability (MAP) inference in graphical models amounts
to solving a graph-structured combinatorial optimization problem. Popular
inference algorithms such as belief propagation (BP) and generalized belief
propagation (GBP) are intimately related to linear programming (LP) relaxation
within the Sherali-Adams hierarchy. Despite the popularity of these algorithms,
it is well understood that the Sum-of-Squares (SOS) hierarchy based on
semidefinite programming (SDP) can provide superior guarantees. Unfortunately,
SOS relaxations for a graph with $n$ vertices require solving an SDP with
$n^{\Theta(d)}$ variables where $d$ is the degree in the hierarchy. In
practice, for $d\ge 4$, this approach does not scale beyond a few tens of
variables. In this paper, we propose binary SDP relaxations for MAP inference
using the SOS hierarchy with two innovations focused on computational
efficiency. Firstly, in analogy to BP and its variants, we only introduce
decision variables corresponding to contiguous regions in the graphical model.
Secondly, we solve the resulting SDP using a non-convex Burer-Monteiro style
method, and develop a sequential rounding procedure. We demonstrate that the
resulting algorithm can solve problems with tens of thousands of variables
within minutes, and outperforms BP and GBP on practical problems such as image
denoising and Ising spin glasses. Finally, for specific graph types, we
establish a sufficient condition for the tightness of the proposed partial SOS
relaxation. | [
1,
0,
0,
1,
0,
0
] |
Title: A Hybrid Approach using Ontology Similarity and Fuzzy Logic for Semantic Question Answering,
Abstract: One of the challenges in information retrieval is providing accurate answers
to a user's question often expressed as uncertainty words. Most answers are
based on a Syntactic approach rather than a Semantic analysis of the query. In
this paper, our objective is to present a hybrid approach for a Semantic
question answering retrieval system using Ontology Similarity and Fuzzy logic.
We use a Fuzzy Co-clustering algorithm to retrieve the collection of documents
based on Ontology Similarity. The Fuzzy Scale uses Fuzzy type-1 for documents
and Fuzzy type-2 for words to prioritize answers. The objective of this work is
to provide retrieval system with more accurate answers than non-fuzzy Semantic
Ontology approach. | [
1,
0,
0,
0,
0,
0
] |
Title: Scalable multimodal convolutional networks for brain tumour segmentation,
Abstract: Brain tumour segmentation plays a key role in computer-assisted surgery. Deep
neural networks have increased the accuracy of automatic segmentation
significantly, however these models tend to generalise poorly to different
imaging modalities than those for which they have been designed, thereby
limiting their applications. For example, a network architecture initially
designed for brain parcellation of monomodal T1 MRI can not be easily
translated into an efficient tumour segmentation network that jointly utilises
T1, T1c, Flair and T2 MRI. To tackle this, we propose a novel scalable
multimodal deep learning architecture using new nested structures that
explicitly leverage deep features within or across modalities. This aims at
making the early layers of the architecture structured and sparse so that the
final architecture becomes scalable to the number of modalities. We evaluate
the scalable architecture for brain tumour segmentation and give evidence of
its regularisation effect compared to the conventional concatenation approach. | [
1,
0,
0,
0,
0,
0
] |
Title: Fermionic projected entangled-pair states and topological phases,
Abstract: We study fermionic matrix product operator algebras and identify the
associated algebraic data. Using this algebraic data we construct fermionic
tensor network states in two dimensions that have non-trivial
symmetry-protected or intrinsic topological order. The tensor network states
allow us to relate physical properties of the topological phases to the
underlying algebraic data. We illustrate this by calculating defect properties
and modular matrices of supercohomology phases. Our formalism also captures
Majorana defects as we show explicitly for a class of $\mathbb{Z}_2$
symmetry-protected and intrinsic topological phases. The tensor networks states
presented here are well-suited for numerical applications and hence open up new
possibilities for studying interacting fermionic topological phases. | [
0,
1,
0,
0,
0,
0
] |
Title: Exact collisional moments for plasma fluid theories,
Abstract: The velocity-space moments of the often troublesome nonlinear Landau
collision operator are expressed exactly in terms of multi-index
Hermite-polynomial moments of the distribution functions. The collisional
moments are shown to be generated by derivatives of two well-known functions,
namely the Rosenbluth-MacDonald-Judd-Trubnikov potentials for a Gaussian
distribution. The resulting formula has a nonlinear dependency on the relative
mean flow of the colliding species normalised to the root-mean-square of the
corresponding thermal velocities, and a bilinear dependency on densities and
higher-order velocity moments of the distribution functions, with no
restriction on temperature, flow or mass ratio of the species. The result can
be applied to both the classic transport theory of plasmas, that relies on the
Chapman-Enskog method, as well as to deriving collisional fluid equations that
follow Grad's moment approach. As an illustrative example, we provide the
collisional ten-moment equations with exact conservation laws for momentum- and
energy-transfer rate. | [
0,
1,
1,
0,
0,
0
] |
Title: What is Unique in Individual Gait Patterns? Understanding and Interpreting Deep Learning in Gait Analysis,
Abstract: Machine learning (ML) techniques such as (deep) artificial neural networks
(DNN) are solving very successfully a plethora of tasks and provide new
predictive models for complex physical, chemical, biological and social
systems. However, in most cases this comes with the disadvantage of acting as a
black box, rarely providing information about what made them arrive at a
particular prediction. This black box aspect of ML techniques can be
problematic especially in medical diagnoses, so far hampering a clinical
acceptance. The present paper studies the uniqueness of individual gait
patterns in clinical biomechanics using DNNs. By attributing portions of the
model predictions back to the input variables (ground reaction forces and
full-body joint angles), the Layer-Wise Relevance Propagation (LRP) technique
reliably demonstrates which variables at what time windows of the gait cycle
are most relevant for the characterisation of gait patterns from a certain
individual. By measuring the timeresolved contribution of each input variable
to the prediction of ML techniques such as DNNs, our method describes the first
general framework that enables to understand and interpret non-linear ML
methods in (biomechanical) gait analysis and thereby supplies a powerful tool
for analysis, diagnosis and treatment of human gait. | [
0,
0,
0,
1,
0,
0
] |
Title: On the ground state of spiking network activity in mammalian cortex,
Abstract: Electrophysiological recordings of spiking activity are limited to a small
number of neurons. This spatial subsampling has hindered characterizing even
most basic properties of collective spiking in cortical networks. In
particular, two contradictory hypotheses prevailed for over a decade: the first
proposed an asynchronous irregular state, the second a critical state. While
distinguishing them is straightforward in models, we show that in experiments
classical approaches fail to correctly infer network dynamics because of
subsampling. Deploying a novel, subsampling-invariant estimator, we find that
in vivo dynamics do not comply with either hypothesis, but instead occupy a
narrow "reverberating" state consistently across multiple mammalian species and
cortical areas. A generic model tuned to this reverberating state predicts
single neuron, pairwise, and population properties. With these predictions we
first validate the model and then deduce network properties that are
challenging to obtain experimentally, like the network timescale and strength
of cortical input. | [
0,
0,
0,
1,
1,
0
] |
Title: Inferring Information Flow in Spike-train Data Sets using a Trial-Shuffle Method,
Abstract: Understanding information processing in the brain requires the ability to
determine the functional connectivity between the different regions of the
brain. We present a method using transfer entropy to extract this flow of
information between brain regions from spike-train data commonly obtained in
neurological experiments. Transfer entropy is a statistical measure based in
information theory that attempts to quantify the information flow from one
process to another, and has been applied to find connectivity in simulated
spike-train data. Due to statistical error in the estimator, inferring
functional connectivity requires a method for determining significance in the
transfer entropy values. We discuss the issues with numerical estimation of
transfer entropy and resulting challenges in determining significance before
presenting the trial-shuffle method as a viable option. The trial-shuffle
method, for spike-train data that is split into multiple trials, determines
significant transfer entropy values independently for each individual pair of
neurons by comparing to a created baseline distribution using a rigorous
statistical test. This is in contrast to either globally comparing all neuron
transfer entropy values or comparing pairwise values to a single baseline
value.
In establishing the viability of this method by comparison to several
alternative approaches in the literature, we find evidence that preserving the
inter-spike-interval timing is important.
We then use the trial-shuffle method to investigate information flow within a
model network as we vary model parameters. This includes investigating the
global flow of information within a connectivity network divided into two
well-connected subnetworks, going beyond local transfer of information between
pairs of neurons. | [
0,
0,
0,
0,
1,
0
] |
Title: Correlated Components Analysis - Extracting Reliable Dimensions in Multivariate Data,
Abstract: How does one find dimensions in multivariate data that are reliably expressed
across repetitions? For example, in a brain imaging study one may want to
identify combinations of neural signals that are reliably expressed across
multiple trials or subjects. For a behavioral assessment with multiple ratings,
one may want to identify an aggregate score that is reliably reproduced across
raters. Correlated Components Analysis (CorrCA) addresses this problem by
identifying components that are maximally correlated between repetitions (e.g.
trials, subjects, raters). Here we formalize this as the maximization of the
ratio of between-repetition to within-repetition covariance. We show that this
criterion maximizes repeat-reliability, defined as mean over variance across
repeats, and that it leads to CorrCA or to multi-set Canonical Correlation
Analysis, depending on the constraints. Surprisingly, we also find that CorrCA
is equivalent to Linear Discriminant Analysis for zero-mean signals, which
provides an unexpected link between classic concepts of multivariate analysis.
We present an exact parametric test of statistical significance based on the
F-statistic for normally distributed independent samples, and present and
validate shuffle statistics for the case of dependent samples. Regularization
and extension to non-linear mappings using kernels are also presented. The
algorithms are demonstrated on a series of data analysis applications, and we
provide all code and data required to reproduce the results. | [
0,
0,
0,
1,
0,
0
] |
Title: Defining and estimating stochastic rate change in a dynamic general insurance portfolio,
Abstract: Rate change calculations in the literature involve deterministic methods that
measure the change in premium for a given policy. The definition of rate change
as a statistical parameter is proposed to address the stochastic nature of the
premium charged for a policy. It promotes the idea that rate change is a
property of an asymptotic population to be estimated, not just a property to
measure or monitor in the sample of observed policies that are written. Various
models and techniques are given for estimating this stochastic rate change and
quantifying the uncertainty in the estimates. The use of matched sampling is
emphasized for rate change estimation, as it adjusts for changes in policy
characteristics by directly searching for similar policies across policy years.
This avoids any of the assumptions and recipes that are required to re-rate
policies in years where they were not written, as is common with deterministic
methods. Such procedures can be subjective or implausible if the structure of
rating algorithms change or there are complex and heterogeneous exposure bases
and coverages. The methods discussed are applied to a motor premium database.
The application includes the use of a genetic algorithm with parallel
computations to automatically optimize the matched sampling. | [
0,
0,
0,
0,
0,
1
] |
Title: Teacher Improves Learning by Selecting a Training Subset,
Abstract: We call a learner super-teachable if a teacher can trim down an iid training
set while making the learner learn even better. We provide sharp super-teaching
guarantees on two learners: the maximum likelihood estimator for the mean of a
Gaussian, and the large margin classifier in 1D. For general learners, we
provide a mixed-integer nonlinear programming-based algorithm to find a super
teaching set. Empirical experiments show that our algorithm is able to find
good super-teaching sets for both regression and classification problems. | [
0,
0,
0,
1,
0,
0
] |
Title: Photospheric Emission of Gamma-Ray Bursts,
Abstract: We review the physics of GRB production by relativistic jets that start
highly opaque near the central source and then expand to transparency. We
discuss dissipative and radiative processes in the jet and how radiative
transfer shapes the observed nonthermal spectrum released at the photosphere. A
comparison of recent detailed models with observations gives estimates for
important parameters of GRB jets, such as the Lorentz factor and magnetization.
We also discuss predictions for GRB polarization and neutrino emission. | [
0,
1,
0,
0,
0,
0
] |
Title: GPU acceleration and performance of the particle-beam-dynamics code Elegant,
Abstract: Elegant is an accelerator physics and particle-beam dynamics code widely used
for modeling and design of a variety of high-energy particle accelerators and
accelerator-based systems. In this paper we discuss a recently developed
version of the code that can take advantage of CUDA-enabled graphics processing
units (GPUs) to achieve significantly improved performance for a large class of
simulations that are important in practice. The GPU version is largely defined
by a framework that simplifies implementations of the fundamental kernel types
that are used by Elegant: particle operations, reductions, particle loss,
histograms, array convolutions and random number generation. Accelerated
performance on the Titan Cray XK-7 supercomputer is approximately 6-10 times
better with the GPU than all the CPU cores associated with the same node count.
In addition to performance, the maintainability of the GPU-accelerated version
of the code was considered a key design objective. Accuracy with respect to the
CPU implementation is also a core consideration. Four different methods are
used to ensure that the accelerated code faithfully reproduces the CPU results. | [
0,
1,
0,
0,
0,
0
] |
Title: Short-distance breakdown of the Higgs mechanism and the robustness of the BCS theory for charged superconductors,
Abstract: Through the Higgs mechanism, the long-range Coulomb interaction eliminates
the low-energy Goldstone phase mode in superconductors and transfers spectral
weight all the way up to the plasma frequency. Here we show that the Higgs
mechanism breaks down for length scales shorter than the superconducting
coherence length while it stays intact, even at high energies, in the
long-wavelength limit. This effect is a consequence of the composite nature of
the Higgs field of superconductivity and the broken Lorentz invariance in a
solid. Most importantly, the breakdown of the Higgs mechanism inside the
superconducting coherence volume is crucial to ensure the stability of the BCS
mean-field theory in the weak-coupling limit. We also show that changes in the
gap equation due to plasmon-induced fluctuations can lead to significant
corrections to the mean-field theory and reveal that changes in the
density-fluctuation spectrum of a superconductor are not limited to the
vicinity of the gap. | [
0,
1,
0,
0,
0,
0
] |
Title: The Massey's method for sport rating: a network science perspective,
Abstract: We revisit the Massey's method for rating and ranking in sports and
contextualize it as a general centrality measure in network science. | [
1,
1,
0,
0,
0,
0
] |
Title: Pharmacokinetics Simulations for Studying Correlates of Prevention Efficacy of Passive HIV-1 Antibody Prophylaxis in the Antibody Mediated Prevention (AMP) Study,
Abstract: A key objective in two phase 2b AMP clinical trials of VRC01 is to evaluate
whether drug concentration over time, as estimated by non-linear mixed effects
pharmacokinetics (PK) models, is associated with HIV infection rate. We
conducted a simulation study of marker sampling designs, and evaluated the
effect of study adherence and sub-cohort sample size on PK model estimates in
multiple-dose studies. With m=120, even under low adherence (about half of
study visits missing per participant), reasonably unbiased and consistent
estimates of most fixed and random effect terms were obtained. Coarsened marker
sampling schedules were also studied. | [
0,
0,
0,
1,
1,
0
] |
Title: Magellan/M2FS Spectroscopy of Galaxy Clusters: Stellar Population Model and Application to Abell 267,
Abstract: We report the results of a pilot program to use the Magellan/M2FS
spectrograph to survey the galactic populations and internal kinematics of
galaxy clusters. For this initial study, we present spectroscopic measurements
for $223$ quiescent galaxies observed along the line of sight to the galaxy
cluster Abell 267 ($z\sim0.23$). We develop a Bayesian method for modeling the
integrated light from each galaxy as a simple stellar population, with free
parameters that specify redshift ($v_\mathrm{los}/c$) and characteristic age,
metallicity ($\mathrm{[Fe/H]}$), alpha-abundance ($[\alpha/\mathrm{Fe}]$), and
internal velocity dispersion ($\sigma_\mathrm{int}$) for individual galaxies.
Parameter estimates derived from our 1.5-hour observation of A267 have median
random errors of $\sigma_{v_\mathrm{los}}=20\ \mathrm{km\ s^{-1}}$,
$\sigma_{\mathrm{Age}}=1.2\ \mathrm{Gyr}$, $\sigma_{\mathrm{[Fe/H]}}=0.11\
\mathrm{dex}$, $\sigma_{[\alpha/\mathrm{Fe}]}=0.07\ \mathrm{dex}$, and
$\sigma_{\sigma_\mathrm{int}}=20\ \mathrm{km\ s^{-1}}$. In a companion paper,
we use these results to model the structure and internal kinematics of A267. | [
0,
1,
0,
0,
0,
0
] |
Title: Parametric Decay Instability and Dissipation of Low-frequency Alfvén Waves in Low-beta Turbulent Plasmas,
Abstract: Evolution of the parametric decay instability (PDI) of a circularly polarized
Aflvén wave in a turbulent low-beta plasma background is investigated using
3D hybrid simulations. It is shown that the turbulence reduces the growth rate
of PDI as compared to the linear theory predictions, but PDI can still exist.
Interestingly, the damping rate of ion acoustic mode (as the product of PDI) is
also reduced as compared to the linear Vlasov predictions. Nonetheless,
significant heating of ions in the direction parallel to the background
magnetic field is observed due to resonant Landau damping of the ion acoustic
waves. In low-beta turbulent plasmas, PDI can provide an important channel for
energy dissipation of low-frequency Alfvén waves at a scale much larger than
the ion kinetic scales, different from the traditional turbulence dissipation
models. | [
0,
1,
0,
0,
0,
0
] |
Title: Generalised model-independent characterisation of strong gravitational lenses II: Transformation matrix between multiple images,
Abstract: (shortened) We determine the transformation matrix T that maps multiple
images with resolved features onto one another and that is based on a
Taylor-expanded lensing potential close to a point on the critical curve within
our model-independent lens characterisation approach. From T, the same
information about the critical curve at fold and cusp points is derived as
determined by the quadrupole moment of the individual images as observables. In
addition, we read off the relative parities between the images, so that the
parity of all images is determined, when one is known. We compare all
retrievable ratios of potential derivatives to the actual ones and to those
obtained by using the quadrupole moment as observable for two and three image
configurations generated by a galaxy-cluster scale singular isothermal ellipse.
We conclude that using the quadrupole moments as observables, the properties of
the critical curve at the cusp points are retrieved to higher accuracy, at the
fold points to lower accuracy, and the ratios of second order potential
derivatives to comparable accuracy. We show that the approach using ratios of
convergences and reduced shear is equivalent to ours close to the critical
curve but yields more accurate results and is more robust because it does not
require a special coordinate system like the approach using potential
derivatives. T is determined by mapping manually assigned reference points in
the images onto each other. If the assignment of reference points is subject to
measurement uncertainties under noise, we find that the confidence intervals of
the lens parameters can be as large as the values, when the uncertainties are
larger than one pixel. Observed multiple images with resolved features are more
extended than unresolved ones, so that higher order moments should be taken
into account to improve the reconstruction. | [
0,
1,
0,
0,
0,
0
] |
Title: Properties of interaction networks, structure coefficients, and benefit-to-cost ratios,
Abstract: In structured populations the spatial arrangement of cooperators and
defectors on the interaction graph together with the structure of the graph
itself determines the game dynamics and particularly whether or not fixation of
cooperation (or defection) is favored. For a single cooperator (and a single
defector) and a network described by a regular graph the question of fixation
can be addressed by a single parameter, the structure coefficient. As this
quantity is generic for any regular graph, we may call it the generic structure
coefficient. For two and more cooperators (or several defectors) fixation
properties can also be assigned by structure coefficients. These structure
coefficients, however, depend on the arrangement of cooperators and defectors
which we may interpret as a configuration of the game. Moreover, the
coefficients are specific for a given interaction network modeled as regular
graph, which is why we may call them specific structure coefficients. In this
paper, we study how specific structure coefficients vary over interaction
graphs and link the distributions obtained over different graphs to spectral
properties of interaction networks. We also discuss implications for the
benefit-to-cost ratios of donation games. | [
0,
0,
0,
0,
1,
0
] |
Title: Optimized Household Demand Management with Local Solar PV Generation,
Abstract: Demand Side Management (DSM) strategies are of-ten associated with the
objectives of smoothing the load curve and reducing peak load. Although the
future of demand side manage-ment is technically dependent on remote and
automatic control of residential loads, the end-users play a significant role
by shifting the use of appliances to the off-peak hours when they are exposed
to Day-ahead market price. This paper proposes an optimum so-lution to the
problem of scheduling of household demand side management in the presence of PV
generation under a set of tech-nical constraints such as dynamic electricity
pricing and voltage deviation. The proposed solution is implemented based on
the Clonal Selection Algorithm (CSA). This solution is evaluated through a set
of scenarios and simulation results show that the proposed approach results in
the reduction of electricity bills and the import of energy from the grid. | [
1,
0,
0,
0,
0,
0
] |
Title: Interaction-Based Distributed Learning in Cyber-Physical and Social Networks,
Abstract: In this paper we consider a network scenario in which agents can evaluate
each other according to a score graph that models some physical or social
interaction. The goal is to design a distributed protocol, run by the agents,
allowing them to learn their unknown state among a finite set of possible
values. We propose a Bayesian framework in which scores and states are
associated to probabilistic events with unknown parameters and hyperparameters
respectively. We prove that each agent can learn its state by means of a local
Bayesian classifier and a (centralized) Maximum-Likelihood (ML) estimator of
the parameter-hyperparameter that combines plain ML and Empirical Bayes
approaches. By using tools from graphical models, which allow us to gain
insight on conditional dependences of scores and states, we provide two relaxed
probabilistic models that ultimately lead to ML parameter-hyperparameter
estimators amenable to distributed computation. In order to highlight the
appropriateness of the proposed relaxations, we demonstrate the distributed
estimators on a machine-to-machine testing set-up for anomaly detection and on
a social interaction set-up for user profiling. | [
1,
0,
1,
1,
0,
0
] |
Title: EC3: Combining Clustering and Classification for Ensemble Learning,
Abstract: Classification and clustering algorithms have been proved to be successful
individually in different contexts. Both of them have their own advantages and
limitations. For instance, although classification algorithms are more powerful
than clustering methods in predicting class labels of objects, they do not
perform well when there is a lack of sufficient manually labeled reliable data.
On the other hand, although clustering algorithms do not produce label
information for objects, they provide supplementary constraints (e.g., if two
objects are clustered together, it is more likely that the same label is
assigned to both of them) that one can leverage for label prediction of a set
of unknown objects. Therefore, systematic utilization of both these types of
algorithms together can lead to better prediction performance. In this paper,
We propose a novel algorithm, called EC3 that merges classification and
clustering together in order to support both binary and multi-class
classification. EC3 is based on a principled combination of multiple
classification and multiple clustering methods using an optimization function.
We theoretically show the convexity and optimality of the problem and solve it
by block coordinate descent method. We additionally propose iEC3, a variant of
EC3 that handles imbalanced training data. We perform an extensive experimental
analysis by comparing EC3 and iEC3 with 14 baseline methods (7 well-known
standalone classifiers, 5 ensemble classifiers, and 2 existing methods that
merge classification and clustering) on 13 standard benchmark datasets. We show
that our methods outperform other baselines for every single dataset, achieving
at most 10% higher AUC. Moreover our methods are faster (1.21 times faster than
the best baseline), more resilient to noise and class imbalance than the best
baseline method. | [
1,
0,
0,
1,
0,
0
] |
Title: A review of possible effects of cognitive biases on interpretation of rule-based machine learning models,
Abstract: This paper investigates to what extent cognitive biases may affect human
understanding of interpretable machine learning models, in particular of rules
discovered from data. Twenty cognitive biases are covered, as are possible
debiasing techniques that can be adopted by designers of machine learning
algorithms and software. Our review transfers results obtained in cognitive
psychology to the domain of machine learning, aiming to bridge the current gap
between these two areas. It needs to be followed by empirical studies
specifically aimed at the machine learning domain. | [
0,
0,
0,
1,
0,
0
] |
Title: High-Tc superconductivity up to 55 K under high pressure in the heavily electron doped Lix(NH3)yFe2Se2 single crystal,
Abstract: We report a high-pressure study on the heavily electron doped Lix(NH3)yFe2Se2
single crystal by using the cubic anvil cell apparatus. The superconducting
transition temperature Tc = 44 K at ambient pressure is first suppressed to
below 20 K upon increasing pressure to Pc = 2 GPa, above which the pressure
dependence of Tc(P) reverses and Tc increases steadily to ca. 55 K at 11 GPa.
These results thus evidenced a pressure-induced second high-Tc superconducting
(SC-II) phase in Lix(NH3)yFe2Se2 with the highest Tcmax = 55K among the
FeSe-based bulk materials. Hall data confirm that in the emergent SC-II phase
the dominant electron-type carrier density undergoes a fourfold enhancement and
tracks the same trend as Tc(P). Interesting, we find a nearly parallel scaling
behavior between Tc and the inverse Hall coefficient for the SC-II phases of
both Lix(NH3)yFe2Se2 and (Li,Fe)OHFeSe. The present work demonstrates that high
pressure offers a distinctive means to further raising the maximum Tc of
heavily electron doped FeSe-based materials by increasing the effective charge
carrier concentration via a plausible Fermi surface reconstruction at Pc. | [
0,
1,
0,
0,
0,
0
] |
Title: Existence theorems for a nonlinear second-order distributional differential equation,
Abstract: In this work, we are concerned with existence of solutions for a nonlinear
second-order distributional differential equation, which contains measure
differential equations and stochastic differential equations as special cases.
The proof is based on the Leray--Schauder nonlinear alternative and
Kurzweil--Henstock--Stieltjes integrals. Meanwhile, examples are worked out to
demonstrate that the main results are sharp. | [
0,
0,
1,
0,
0,
0
] |
Title: Spatial coherence measurement and partially coherent diffractive imaging,
Abstract: The complete characterization of spatial coherence is difficult because the
mutual coherence function is a complex-valued function of four independent
variables. This difficulty limits the ability of controlling and optimizing
spatial coherence in a broad range of key applications. Here we propose a
method for measuring the complete mutual coherence function, which does not
require any prior knowledge and can be scaled to measure arbitrary coherence
properties for any wavelength. Our method can also be used to retrieve objects
illuminated by partially coherent beam with unknown coherence properties. This
study is particularly useful for coherent diffractive imaging of nanoscale
structures in the X-ray or electron regime. Our method is not limited by any
assumption about the illumination and hence lays the foundation for a branch of
new diffractive imaging algorithms. | [
0,
1,
0,
0,
0,
0
] |
Title: SPECTRE: Seedless Network Alignment via Spectral Centralities,
Abstract: Network alignment consists of finding a correspondence between the nodes of
two networks. From aligning proteins in computational biology, to
de-anonymization of social networks, to recognition tasks in computer vision,
this problem has applications in many diverse areas. The current approaches to
network alignment mostly focus on the case where prior information is
available, either in the form of a seed set of correctly-matched nodes or
attributes on the nodes and/or edges. Moreover, those approaches which assume
no such prior information tend to be computationally expensive and do not scale
to large-scale networks. However, many real-world networks are very large in
size, and prior information can be expensive, if not impossible, to obtain. In
this paper we introduce SPECTRE, a scalable, accurate algorithm able to solve
the network alignment problem with no prior information. SPECTRE makes use of
spectral centrality measures and percolation techniques to robustly align nodes
across networks, even if those networks exhibit only moderate correlation.
Through extensive numerical experiments, we show that SPECTRE is able to
recover high-accuracy alignments on both synthetic and real-world networks, and
outperforms other algorithms in the seedless case. | [
1,
0,
0,
0,
0,
0
] |
Title: An efficient genetic algorithm for large-scale planning of robust industrial wireless networks,
Abstract: An industrial indoor environment is harsh for wireless communications
compared to an office environment, because the prevalent metal easily causes
shadowing effects and affects the availability of an industrial wireless local
area network (IWLAN). On the one hand, it is costly, time-consuming, and
ineffective to perform trial-and-error manual deployment of wireless nodes. On
the other hand, the existing wireless planning tools only focus on office
environments such that it is hard to plan IWLANs due to the larger problem size
and the deployed IWLANs are vulnerable to prevalent shadowing effects in harsh
industrial indoor environments. To fill this gap, this paper proposes an
overdimensioning model and a genetic algorithm based over-dimensioning (GAOD)
algorithm for deploying large-scale robust IWLANs. As a progress beyond the
state-of-the-art wireless planning, two full coverage layers are created. The
second coverage layer serves as redundancy in case of shadowing. Meanwhile, the
deployment cost is reduced by minimizing the number of access points (APs); the
hard constraint of minimal inter-AP spatial paration avoids multiple APs
covering the same area to be simultaneously shadowed by the same obstacle. The
computation time and occupied memory are dedicatedly considered in the design
of GAOD for large-scale optimization. A greedy heuristic based
over-dimensioning (GHOD) algorithm and a random OD algorithm are taken as
benchmarks. In two vehicle manufacturers with a small and large indoor
environment, GAOD outperformed GHOD with up to 20% less APs, while GHOD
outputted up to 25% less APs than a random OD algorithm. Furthermore, the
effectiveness of this model and GAOD was experimentally validated with a real
deployment system. | [
1,
0,
0,
0,
0,
0
] |
Title: Feature selection algorithm based on Catastrophe model to improve the performance of regression analysis,
Abstract: In this paper we introduce a new feature selection algorithm to remove the
irrelevant or redundant features in the data sets. In this algorithm the
importance of a feature is based on its fitting to the Catastrophe model.
Akaike information crite- rion value is used for ranking the features in the
data set. The proposed algorithm is compared with well-known RELIEF feature
selection algorithm. Breast Cancer, Parkinson Telemonitoring data and Slice
locality data sets are used to evaluate the model. | [
1,
0,
0,
1,
0,
0
] |
Title: Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model,
Abstract: This paper describes a neural-network model which performed competitively
(top 6) at the SemEval 2017 cross-lingual Semantic Textual Similarity (STS)
task. Our system employs an attention-based recurrent neural network model that
optimizes the sentence similarity. In this paper, we describe our participation
in the multilingual STS task which measures similarity across English, Spanish,
and Arabic. | [
1,
0,
0,
0,
0,
0
] |
Title: Coherent extension of partial automorphisms, free amalgamation, and automorphism groups,
Abstract: We give strengthened versions of the Herwig-Lascar and Hodkinson-Otto
extension theorems for partial automorphisms of finite structures. Such
strengthenings yield several combinatorial and group-theoretic consequences for
homogeneous structures. For instance, we establish a coherent form of the
extension property for partial automorphisms for certain Fraisse classes. We
deduce from these results that the isometry group of the rational Urysohn
space, the automorphism group of the Fraisse limit of any Fraisse class that is
the class of all $\mathcal{F}$-free structures (in the Herwig--Lascar sense),
and the automorphism group of any free homogeneous structure over a finite
relational language, all contain a dense locally finite subgroup. We also show
that any free homogeneous structure admits ample generics. | [
0,
0,
1,
0,
0,
0
] |
Title: Homological indices of collections of 1-forms,
Abstract: Homological index of a holomorphic 1-form on a complex analytic variety with
an isolated singular point is an analogue of the usual index of a 1-form on a
non-singular manifold. One can say that it corresponds to the top Chern number
of a manifold. We offer a definition of homological indices for collections of
1-forms on a (purely dimensional) complex analytic variety with an isolated
singular point corresponding to other Chern numbers. We also define new
invariants of germs of complex analytic varieties with isolated singular points
related to "vanishing Chern numbers" at them. | [
0,
0,
1,
0,
0,
0
] |
Title: An age-structured continuum model for myxobacteria,
Abstract: Myxobacteria are social bacteria, that can glide in 2D and form
counter-propagating, interacting waves. Here we present a novel age-structured,
continuous macroscopic model for the movement of myxobacteria. The derivation
is based on microscopic interaction rules that can be formulated as a
particle-based model and set within the SOH (Self-Organized Hydrodynamics)
framework. The strength of this combined approach is that microscopic knowledge
or data can be incorporated easily into the particle model, whilst the
continuous model allows for easy numerical analysis of the different effects.
However we found that the derived macroscopic model lacks a diffusion term in
the density equations, which is necessary to control the number of waves,
indicating that a higher order approximation during the derivation is crucial.
Upon ad-hoc addition of the diffusion term, we found very good agreement
between the age-structured model and the biology. In particular we analyzed the
influence of a refractory (insensitivity) period following a reversal of
movement. Our analysis reveals that the refractory period is not necessary for
wave formation, but essential to wave synchronization, indicating separate
molecular mechanisms. | [
0,
1,
1,
0,
0,
0
] |
Title: Smoothing Properties of Bilinear Operators and Leibniz-Type Rules in Lebesgue and Mixed Lebesgue Spaces,
Abstract: We prove that bilinear fractional integral operators and similar multipliers
are smoothing in the sense that they improve the regularity of functions. We
also treat bilinear singular multiplier operators which preserve regularity and
obtain several Leibniz-type rules in the contexts of Lebesgue and mixed
Lebesgue spaces. | [
0,
0,
1,
0,
0,
0
] |
Title: Local equilibrium in the Bak-Sneppen model,
Abstract: The Bak Sneppen (BS) model is a very simple model that exhibits all the
richness of self-organized criticality theory. At the thermodynamic limit, the
BS model converges to a situation where all particles have a fitness that is
uniformly distributed between a critical value $p_c$ and 1. The $p_c$ value is
unknown, as are the variables that influence and determine this value. Here, we
study the Bak Sneppen model in the case in which the lowest fitness particle
interacts with an arbitrary even number of $m$ nearest neighbors. We show that
$p_{c,m}$ verifies a simple local equilibrium relationship. Based on this
relationship, we can determine bounds for $p_{c,m}$. | [
0,
1,
0,
0,
0,
0
] |
Title: Ribbon structures of the Drinfeld center of a finite tensor category,
Abstract: We classify the ribbon structures of the Drinfeld center
$\mathcal{Z}(\mathcal{C})$ of a finite tensor category $\mathcal{C}$. Our
result generalizes Kauffman and Radford's classification result of the ribbon
elements of the Drinfeld double of a finite-dimensional Hopf algebra. As a
consequence, we see that $\mathcal{Z}(\mathcal{C})$ is a modular tensor
category in the sense of Lyubashenko if $\mathcal{C}$ is a spherical finite
tensor category in the sense of Douglas, Schommer-Pries and Snyder. | [
0,
0,
1,
0,
0,
0
] |
Title: X-ray luminescence computed tomography using a focused X-ray beam,
Abstract: Due to the low X-ray photon utilization efficiency and low measurement
sensitivity of the electron multiplying charge coupled device (EMCCD) camera
setup, the collimator based narrow beam X-ray luminescence computed tomography
(XLCT) usually requires a long measurement time. In this paper, we, for the
first time, report a focused X-ray beam based XLCT imaging system with
measurements by a single optical fiber bundle and a photomultiplier tube (PMT).
An X-ray tube with a polycapillary lens was used to generate a focused X-ray
beam whose X-ray photon density is 1200 times larger than a collimated X-ray
beam. An optical fiber bundle was employed to collect and deliver the emitted
photons on the phantom surface to the PMT. The total measurement time was
reduced to 12.5 minutes. For numerical simulations of both single and six fiber
bundle cases, we were able to reconstruct six targets successfully. For the
phantom experiment, two targets with an edge-to-edge distance of 0.4 mm and a
center-to-center distance of 0.8 mm were successfully reconstructed by the
measurement setup with a single fiber bundle and a PMT. | [
0,
1,
0,
0,
0,
0
] |
Title: Perturbed Kitaev model: excitation spectrum and long-ranged spin correlations,
Abstract: We developed general approach to the calculation of power-law infrared
asymptotics of spin-spin correlation functions in the Kitaev honeycomb model
with different types of perturbations. We have shown that in order to find
these correlation functions, one can perform averaging of some bilinear forms
composed out of free Majorana fermions, and we presented the method for
explicit calculation of these fermionic densities. We demonstrated how to
derive an effective Hamiltonian for the Majorana fermions, including the
effects of perturbations. For specific application of the general theory, we
have studied the effect of the Dzyaloshinskii-Moriya anisotropic spin-spin
interaction; we demonstrated that it leads, already in the second order over
its relative magnitude $D/K$, to a power-law spin correlation functions, and
calculated dynamical spin structure factor of the system. We have shown that an
external magnetic field $h$ in presence of the DM interaction, opens a gap in
the excitation spectrum of magnitude $\Delta \propto D h$. | [
0,
1,
0,
0,
0,
0
] |
Title: FreezeOut: Accelerate Training by Progressively Freezing Layers,
Abstract: The early layers of a deep neural net have the fewest parameters, but take up
the most computation. In this extended abstract, we propose to only train the
hidden layers for a set portion of the training run, freezing them out
one-by-one and excluding them from the backward pass. Through experiments on
CIFAR, we empirically demonstrate that FreezeOut yields savings of up to 20%
wall-clock time during training with 3% loss in accuracy for DenseNets, a 20%
speedup without loss of accuracy for ResNets, and no improvement for VGG
networks. Our code is publicly available at
this https URL | [
1,
0,
0,
1,
0,
0
] |
Title: Stochastic Subsampling for Factorizing Huge Matrices,
Abstract: We present a matrix-factorization algorithm that scales to input matrices
with both huge number of rows and columns. Learned factors may be sparse or
dense and/or non-negative, which makes our algorithm suitable for dictionary
learning, sparse component analysis, and non-negative matrix factorization. Our
algorithm streams matrix columns while subsampling them to iteratively learn
the matrix factors. At each iteration, the row dimension of a new sample is
reduced by subsampling, resulting in lower time complexity compared to a simple
streaming algorithm. Our method comes with convergence guarantees to reach a
stationary point of the matrix-factorization problem. We demonstrate its
efficiency on massive functional Magnetic Resonance Imaging data (2 TB), and on
patches extracted from hyperspectral images (103 GB). For both problems, which
involve different penalties on rows and columns, we obtain significant
speed-ups compared to state-of-the-art algorithms. | [
1,
0,
1,
1,
0,
0
] |
Title: Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding,
Abstract: This paper presents the design of the machine learning architecture that
underlies the Alexa Skills Kit (ASK) a large scale Spoken Language
Understanding (SLU) Software Development Kit (SDK) that enables developers to
extend the capabilities of Amazon's virtual assistant, Alexa. At Amazon, the
infrastructure powers over 25,000 skills deployed through the ASK, as well as
AWS's Amazon Lex SLU Service. The ASK emphasizes flexibility, predictability
and a rapid iteration cycle for third party developers. It imposes inductive
biases that allow it to learn robust SLU models from extremely small and sparse
datasets and, in doing so, removes significant barriers to entry for software
developers and dialogue systems researchers. | [
1,
0,
0,
0,
0,
0
] |
Title: Scaling, Scattering, and Blackbody Radiation in Classical Physics,
Abstract: Here we discuss blackbody radiation within the context of classical theory.
We note that nonrelativistic classical mechanics and relativistic classical
electrodynamics have contrasting scaling symmetries which influence the
scattering of radiation. Also, nonrelativistic mechanical systems can be
accurately combined with relativistic electromagnetic radiation only provided
the nonrelativistic mechanical systems are the low-velocity limits of fully
relativistic systems. Application of the no-interaction theorem for
relativistic systems limits the scattering mechanical systems for thermal
radiation to relativistic classical electrodynamic systems, which involve the
Coulomb potential. Whereas the naive use of nonrelativistic scatterers or
nonrelativistic classical statistical mechanics leads to the Rayleigh-Jeans
spectrum, the use of fully relativistic scatterers leads to the Planck spectrum
for blackbody radiation within classical physics. | [
0,
1,
0,
0,
0,
0
] |
Title: Critical binomial ideals of Norhtcott type,
Abstract: In this paper, we study a family of binomial ideals defining monomial curves
in the $n-$dimensional affine space determined by $n$ hypersurfaces of the form
$x_i^{c_i} - x_1^{u_{i1}} \cdots x_n^{u_{1n}} \in k[x_1, \ldots, x_n]$ with
$u_{ii} = 0$, $i\in \{ 1, \ldots, n\}$. We prove that, the monomial curves in
that family are set-theoretic complete intersection. Moreover, if the monomial
curve is irreducible, we compute some invariants such as genus, type and
Fröbenius number of the corresponding numerical semigroup. We also describe a
method to produce set-theoretic complete intersection semigroup ideals of
arbitrary large height. | [
0,
0,
1,
0,
0,
0
] |
Title: Clustering of Magnetic Swimmers in a Poiseuille Flow,
Abstract: We investigate the collective behavior of magnetic swimmers, which are
suspended in a Poiseuille flow and placed under an external magnetic field,
using analytical techniques and Brownian dynamics simulations. We find that the
interplay between intrinsic activity, external alignment, and magnetic
dipole-dipole interactions leads to longitudinal structure formation. Our work
sheds light on a recent experimental observation of a clustering instability in
this system. | [
0,
1,
0,
0,
0,
0
] |
Title: A Time-Spectral Method for Initial-Value Problems Using a Novel Spatial Subdomain Scheme,
Abstract: We analyse a new subdomain scheme for a time-spectral method for solving
initial boundary value problems. Whilst spectral methods are commonplace for
spatially dependent systems, finite difference schemes are typically applied
for the temporal domain. The Generalized Weighted Residual Method (GWRM) is a
fully spectral method in that it spectrally decomposes all specified domains,
including the temporal domain, with multivariate Chebyshev polynomials. The
Common Boundary-Condition method (CBC) is a spatial subdomain scheme that
solves the physical equations independently from the global connection of
subdomains. It is here evaluated against two finite difference methods. For the
linearised Burger equation the CBC-GWRM is $\sim30\%$ faster and $\sim50\%$
more memory efficient than the semi implicit Crank-Nicolson method at a maximum
error $\sim10^{-5}$. For a forced wave equation the CBC-GWRM manages to average
efficiently over the small time-scale in the entire temporal domain. The
CBC-GWRM is also applied to the linearised ideal magnetohydrodynamic (MHD)
equations for a screw pinch equilibrium. The growth rate of the most unstable
mode was efficiently computed with an error $<0.1\%$. | [
0,
1,
0,
0,
0,
0
] |
Title: Stein Variational Adaptive Importance Sampling,
Abstract: We propose a novel adaptive importance sampling algorithm which incorporates
Stein variational gradient decent algorithm (SVGD) with importance sampling
(IS). Our algorithm leverages the nonparametric transforms in SVGD to
iteratively decrease the KL divergence between our importance proposal and the
target distribution. The advantages of this algorithm are twofold: first, our
algorithm turns SVGD into a standard IS algorithm, allowing us to use standard
diagnostic and analytic tools of IS to evaluate and interpret the results;
second, we do not restrict the choice of our importance proposal to predefined
distribution families like traditional (adaptive) IS methods. Empirical
experiments demonstrate that our algorithm performs well on evaluating
partition functions of restricted Boltzmann machines and testing likelihood of
variational auto-encoders. | [
0,
0,
0,
1,
0,
0
] |
Title: Closure operators on dcpos,
Abstract: We examine collective properties of closure operators on posets that are at
least dcpos. The first theorem sets the tone of the paper: it tells how a set
of preclosure maps on a dcpo determines the least closure operator above them,
and pronounces the related induction principle, and its companion, the obverse
induction principle. Using this theorem we prove that the poset of closure
operators on a dcpo is a complete lattice, and then provide a constructive
proof of the Tarski's theorem for dcpos. We go on to construct the joins in the
complete lattice of Scott-continuous closure operators on a dcpo, and to prove
that the complete lattice of nuclei on a preframe is a frame, giving some
constructions in the special case of the frame of all nuclei on a frame. In the
rather drawn-out proof if the Hofmann-Mislove-Johnstone theorem we show off the
utility of the obverse induction, applying it in the proof of the crucial
lemma. After that we shift a viewpoint and prove some results, analogous to
results about dcpos, for posets in which certain special subsets have enough
maximal elements; these results actually specialize to dcpos, but at the price
of using the axiom of choice. We conclude by pointing out two convex geometries
associated with closure operators on a dcpo. | [
0,
0,
1,
0,
0,
0
] |
Title: Multi-Agent Coverage Control with Energy Depletion and Repletion,
Abstract: We develop a hybrid system model to describe the behavior of multiple agents
cooperatively solving an optimal coverage problem under energy depletion and
repletion constraints. The model captures the controlled switching of agents
between coverage (when energy is depleted) and battery charging (when energy is
replenished) modes. It guarantees the feasibility of the coverage problem by
defining a guard function on each agent's battery level to prevent it from
dying on its way to a charging station. The charging station plays the role of
a centralized scheduler to solve the contention problem of agents competing for
the only charging resource in the mission space. The optimal coverage problem
is transformed into a parametric optimization problem to determine an optimal
recharging policy. This problem is solved through the use of Infinitesimal
Perturbation Analysis (IPA), with simulation results showing that a full
recharging policy is optimal. | [
1,
0,
0,
0,
0,
0
] |
Title: Self-injective commutative rings have no nontrivial rigid ideals,
Abstract: We establish a link between trace modules and rigidity in modules over
Noetherian rings. Using the theory of trace ideals we make partial progress on
a question of Dao, and on the Auslander-Reiten conjecture over Artinian
Gorenstein rings. | [
0,
0,
1,
0,
0,
0
] |
Title: Origin of the Drude peak and of zero sound in probe brane holography,
Abstract: At zero temperature, the charge current operator appears to be conserved,
within linear response, in certain holographic probe brane models of strange
metals. At small but finite temperature, we analytically show that the weak
non-conservation of this current leads to both a collective "zero sound" mode
and a Drude peak in the electrical conductivity. This simultaneously resolves
two outstanding puzzles about probe brane theories. The nonlinear dynamics of
the current operator itself appears qualitatively different. | [
0,
1,
0,
0,
0,
0
] |
Title: Multiscale dynamical network mechanisms underlying aging from birth to death,
Abstract: How self-organized networks develop, mature and degenerate is a key question
for sociotechnical, cyberphysical and biological systems with potential
applications from tackling violent extremism through to neurological diseases.
So far, it has proved impossible to measure the continuous-time evolution of
any in vivo organism network from birth to death. Here we provide such a study
which crosses all organizational and temporal scales, from individual
components (10^1) through to the mesoscopic (10^3) and entire system scale
(10^6). These continuous-time data reveal a lifespan driven by punctuated,
real-time co-evolution of the structural and functional networks. Aging sees
these structural and functional networks gradually diverge in terms of their
small-worldness and eventually their connectivity. Dying emerges as an extended
process associated with the formation of large but disjoint functional
sub-networks together with an increasingly detached core. Our mathematical
model quantifies the very different impacts that interventions will have on the
overall lifetime, period of initial growth, peak of potency, and duration of
old age, depending on when and how they are administered. In addition to their
direct relevance to online extremism, our findings offer fresh insight into
aging in any network system of comparable complexity for which extensive in
vivo data is not yet available. | [
0,
1,
0,
0,
0,
0
] |
Title: Theoretical calculations for precision polarimetry based on Mott scattering,
Abstract: Electron polarimeters based on Mott scattering are extensively used in
different fields in physics such as atomic, nuclear or particle physics. This
is because spin-dependent measurements gives additional information on the
physical processes under study. The main quantity that needs to be understood
in very much detail, both experimentally and theoretically, is the
spin-polarization function, so called analyzing power or Sherman function. A
detailed theoretical analysis on all the contributions to the effective
interaction potential that are relevant at the typical electron beam energies
and angles commonly used in the calibration of the experimental apparatus is
presented. The main contribution leading the theoretical error on the Sherman
function is found to correspond to radiative corrections that have been
qualitatively estimated to be below the 0.5% for the considered kinematical
conditions: unpolarized electron beams of few MeV elastically scattered from a
gold and silver targets at backward angles. | [
0,
1,
0,
0,
0,
0
] |
Title: An Online Hierarchical Algorithm for Extreme Clustering,
Abstract: Many modern clustering methods scale well to a large number of data items, N,
but not to a large number of clusters, K. This paper introduces PERCH, a new
non-greedy algorithm for online hierarchical clustering that scales to both
massive N and K--a problem setting we term extreme clustering. Our algorithm
efficiently routes new data points to the leaves of an incrementally-built
tree. Motivated by the desire for both accuracy and speed, our approach
performs tree rotations for the sake of enhancing subtree purity and
encouraging balancedness. We prove that, under a natural separability
assumption, our non-greedy algorithm will produce trees with perfect dendrogram
purity regardless of online data arrival order. Our experiments demonstrate
that PERCH constructs more accurate trees than other tree-building clustering
algorithms and scales well with both N and K, achieving a higher quality
clustering than the strongest flat clustering competitor in nearly half the
time. | [
1,
0,
0,
1,
0,
0
] |
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