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Title: A New Perspective on Robust $M$-Estimation: Finite Sample Theory and Applications to Dependence-Adjusted Multiple Testing,
Abstract: Heavy-tailed errors impair the accuracy of the least squares estimate, which
can be spoiled by a single grossly outlying observation. As argued in the
seminal work of Peter Huber in 1973 [{\it Ann. Statist.} {\bf 1} (1973)
799--821], robust alternatives to the method of least squares are sorely
needed. To achieve robustness against heavy-tailed sampling distributions, we
revisit the Huber estimator from a new perspective by letting the tuning
parameter involved diverge with the sample size. In this paper, we develop
nonasymptotic concentration results for such an adaptive Huber estimator,
namely, the Huber estimator with the tuning parameter adapted to sample size,
dimension, and the variance of the noise. Specifically, we obtain a
sub-Gaussian-type deviation inequality and a nonasymptotic Bahadur
representation when noise variables only have finite second moments. The
nonasymptotic results further yield two conventional normal approximation
results that are of independent interest, the Berry-Esseen inequality and
Cramér-type moderate deviation. As an important application to large-scale
simultaneous inference, we apply these robust normal approximation results to
analyze a dependence-adjusted multiple testing procedure for moderately
heavy-tailed data. It is shown that the robust dependence-adjusted procedure
asymptotically controls the overall false discovery proportion at the nominal
level under mild moment conditions. Thorough numerical results on both
simulated and real datasets are also provided to back up our theory. | [
0,
0,
1,
1,
0,
0
] |
Title: On the incorporation of interval-valued fuzzy sets into the Bousi-Prolog system: declarative semantics, implementation and applications,
Abstract: In this paper we analyse the benefits of incorporating interval-valued fuzzy
sets into the Bousi-Prolog system. A syntax, declarative semantics and im-
plementation for this extension is presented and formalised. We show, by using
potential applications, that fuzzy logic programming frameworks enhanced with
them can correctly work together with lexical resources and ontologies in order
to improve their capabilities for knowledge representation and reasoning. | [
1,
0,
0,
0,
0,
0
] |
Title: Pandeia: A Multi-mission Exposure Time Calculator for JWST and WFIRST,
Abstract: Pandeia is the exposure time calculator (ETC) system developed for the James
Webb Space Telescope (JWST) that will be used for creating JWST proposals. It
includes a simulation-hybrid Python engine that calculates the two-dimensional
pixel-by-pixel signal and noise properties of the JWST instruments. This allows
for appropriate handling of realistic point spread functions, MULTIACCUM
detector readouts, correlated detector readnoise, and multiple photometric and
spectral extraction strategies. Pandeia includes support for all the JWST
observing modes, including imaging, slitted/slitless spectroscopy, integral
field spectroscopy, and coronagraphy. Its highly modular, data-driven design
makes it easily adaptable to other observatories. An implementation for use
with WFIRST is also available. | [
0,
1,
0,
0,
0,
0
] |
Title: Fitch-Style Modal Lambda Calculi,
Abstract: Fitch-style modal deduction, in which modalities are eliminated by opening a
subordinate proof, and introduced by shutting one, were investigated in the
1990s as a basis for lambda calculi. We show that such calculi have good
computational properties for a variety of intuitionistic modal logics.
Semantics are given in cartesian closed categories equipped with an adjunction
of endofunctors, with the necessity modality interpreted by the right adjoint.
Where this functor is an idempotent comonad, a coherence result on the
semantics allows us to present a calculus for intuitionistic S4 that is simpler
than others in the literature. We show the calculi can be extended à la
tense logic with the left adjoint of necessity, and are then complete for the
categorical semantics. | [
1,
0,
0,
0,
0,
0
] |
Title: Asymptotic behaviour of ground states for mixtures of ferromagnetic and antiferromagnetic interactions in a dilute regime,
Abstract: We consider randomly distributed mixtures of bonds of ferromagnetic and
antiferromagnetic type in a two-dimensional square lattice with probability
$1-p$ and $p$, respectively, according to an i.i.d. random variable. We study
minimizers of the corresponding nearest-neighbour spin energy on large domains
in ${\mathbb Z}^2$. We prove that there exists $p_0$ such that for $p\le p_0$
such minimizers are characterized by a majority phase; i.e., they take
identically the value $1$ or $-1$ except for small disconnected sets. A
deterministic analogue is also proved. | [
0,
0,
1,
0,
0,
0
] |
Title: Extended Reduced-Form Framework for Non-Life Insurance,
Abstract: In this paper we propose a general framework for modeling an insurance
claims' information flow in continuous time, by generalizing the reduced-form
framework for credit risk and life insurance. In particular, we assume a
nontrivial dependence structure between the reference filtration and the
insurance internal filtration. We apply these results for pricing non-life
insurance liabilities in hybrid financial and insurance markets, while taking
into account the role of inflation under the benchmark approach. This framework
offers at the same time a general and flexible structure, and explicit and
treatable pricing formula. | [
0,
0,
0,
0,
0,
1
] |
Title: Overview of Recent Studies and Design Changes for the FNAL Magnetron Ion Source,
Abstract: This paper will cover several studies and design changes that will eventually
be implemented to the Fermi National Accelerator Laboratory (FNAL) magnetron
ion source. The topics include tungsten cathode insert, solenoid gas valves,
current controlled arc pulser, cesium boiler redesign, gas mixtures of hydrogen
and nitrogen, and duty factor reduction. The studies were performed on the FNAL
test stand, with the aim to improve source lifetime, stability, and reducing
the amount of tuning needed. | [
0,
1,
0,
0,
0,
0
] |
Title: Complete algebraic solution of multidimensional optimization problems in tropical semifield,
Abstract: We consider multidimensional optimization problems that are formulated in the
framework of tropical mathematics to minimize functions defined on vectors over
a tropical semifield (a semiring with idempotent addition and invertible
multiplication). The functions, given by a matrix and calculated through
multiplicative conjugate transposition, are nonlinear in the tropical
mathematics sense. We start with known results on the solution of the problems
with irreducible matrices. To solve the problems in the case of arbitrary
(reducible) matrices, we first derive the minimum value of the objective
function, and find a set of solutions. We show that all solutions of the
problem satisfy a system of vector inequalities, and then use these
inequalities to establish characteristic properties of the solution set.
Furthermore, all solutions of the problem are represented as a family of
subsets, each defined by a matrix that is obtained by using a matrix
sparsification technique. We describe a backtracking procedure that allows one
to reduce the brute-force generation of sparsified matrices by skipping those,
which cannot provide solutions, and thus offers an economical way to obtain all
subsets in the family. Finally, the characteristic properties of the solution
set are used to provide complete solutions in a closed form. We illustrate the
results obtained with simple numerical examples. | [
1,
0,
1,
0,
0,
0
] |
Title: Continuous Optimization of Adaptive Quadtree Structures,
Abstract: We present a novel continuous optimization method to the discrete problem of
quadtree optimization. The optimization aims at achieving a quadtree structure
with the highest mechanical stiffness, where the edges in the quadtree are
interpreted as structural elements carrying mechanical loads. We formulate
quadtree optimization as a continuous material distribution problem. The
discrete design variables (i.e., to refine or not to refine) are replaced by
continuous variables on multiple levels in the quadtree hierarchy. In discrete
quadtree optimization, a cell is only eligible for refinement if its parent
cell has been refined. We propose a continuous analogue to this dependency for
continuous multi-level design variables, and integrate it in the iterative
optimization process. Our results show that the continuously optimized quadtree
structures perform much stiffer than uniform patterns and the heuristically
optimized counterparts. We demonstrate the use of adaptive structures as
lightweight infill for 3D printed parts, where uniform geometric patterns have
been typically used in practice. | [
1,
0,
0,
0,
0,
0
] |
Title: Machine Learning Meets Microeconomics: The Case of Decision Trees and Discrete Choice,
Abstract: We provide a microeconomic framework for decision trees: a popular machine
learning method. Specifically, we show how decision trees represent a
non-compensatory decision protocol known as disjunctions-of-conjunctions and
how this protocol generalizes many of the non-compensatory rules used in the
discrete choice literature so far. Additionally, we show how existing decision
tree variants address many economic concerns that choice modelers might have.
Beyond theoretical interpretations, we contribute to the existing literature of
two-stage, semi-compensatory modeling and to the existing decision tree
literature. In particular, we formulate the first bayesian model tree, thereby
allowing for uncertainty in the estimated non-compensatory rules as well as for
context-dependent preference heterogeneity in one's second-stage choice model.
Using an application of bicycle mode choice in the San Francisco Bay Area, we
estimate our bayesian model tree, and we find that it is over 1,000 times more
likely to be closer to the true data-generating process than a multinomial
logit model (MNL). Qualitatively, our bayesian model tree automatically finds
the effect of bicycle infrastructure investment to be moderated by travel
distance, socio-demographics and topography, and our model identifies
diminishing returns from bike lane investments. These qualitative differences
lead to bayesian model tree forecasts that directly align with the observed
bicycle mode shares in regions with abundant bicycle infrastructure such as
Davis, CA and the Netherlands. In comparison, MNL's forecasts are overly
optimistic. | [
0,
0,
0,
1,
0,
0
] |
Title: PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits,
Abstract: We consider the problem of identifying any $k$ out of the best $m$ arms in an
$n$-armed stochastic multi-armed bandit. Framed in the PAC setting, this
particular problem generalises both the problem of `best subset selection' and
that of selecting `one out of the best m' arms [arcsk 2017]. In applications
such as crowd-sourcing and drug-designing, identifying a single good solution
is often not sufficient. Moreover, finding the best subset might be hard due to
the presence of many indistinguishably close solutions. Our generalisation of
identifying exactly $k$ arms out of the best $m$, where $1 \leq k \leq m$,
serves as a more effective alternative. We present a lower bound on the
worst-case sample complexity for general $k$, and a fully sequential PAC
algorithm, \GLUCB, which is more sample-efficient on easy instances. Also,
extending our analysis to infinite-armed bandits, we present a PAC algorithm
that is independent of $n$, which identifies an arm from the best $\rho$
fraction of arms using at most an additive poly-log number of samples than
compared to the lower bound, thereby improving over [arcsk 2017] and
[Aziz+AKA:2018]. The problem of identifying $k > 1$ distinct arms from the best
$\rho$ fraction is not always well-defined; for a special class of this
problem, we present lower and upper bounds. Finally, through a reduction, we
establish a relation between upper bounds for the `one out of the best $\rho$'
problem for infinite instances and the `one out of the best $m$' problem for
finite instances. We conjecture that it is more efficient to solve `small'
finite instances using the latter formulation, rather than going through the
former. | [
1,
0,
0,
1,
0,
0
] |
Title: Stein's Method for Stationary Distributions of Markov Chains and Application to Ising Models,
Abstract: We develop a new technique, based on Stein's method, for comparing two
stationary distributions of irreducible Markov Chains whose update rules are
`close enough'. We apply this technique to compare Ising models on $d$-regular
expander graphs to the Curie-Weiss model (complete graph) in terms of pairwise
correlations and more generally $k$th order moments. Concretely, we show that
$d$-regular Ramanujan graphs approximate the $k$th order moments of the
Curie-Weiss model to within average error $k/\sqrt{d}$ (averaged over the size
$k$ subsets). The result applies even in the low-temperature regime; we also
derive some simpler approximation results for functionals of Ising models that
hold only at high enough temperatures. | [
0,
0,
1,
0,
0,
0
] |
Title: Enabling Multi-Source Neural Machine Translation By Concatenating Source Sentences In Multiple Languages,
Abstract: In this paper, we propose a novel and elegant solution to "Multi-Source
Neural Machine Translation" (MSNMT) which only relies on preprocessing a N-way
multilingual corpus without modifying the Neural Machine Translation (NMT)
architecture or training procedure. We simply concatenate the source sentences
to form a single long multi-source input sentence while keeping the target side
sentence as it is and train an NMT system using this preprocessed corpus. We
evaluate our method in resource poor as well as resource rich settings and show
its effectiveness (up to 4 BLEU using 2 source languages and up to 6 BLEU using
5 source languages) by comparing against existing methods for MSNMT. We also
provide some insights on how the NMT system leverages multilingual information
in such a scenario by visualizing attention. | [
1,
0,
0,
0,
0,
0
] |
Title: Model-based reinforcement learning in differential graphical games,
Abstract: This paper seeks to combine differential game theory with the
actor-critic-identifier architecture to determine forward-in-time, approximate
optimal controllers for formation tracking in multi-agent systems, where the
agents have uncertain heterogeneous nonlinear dynamics. A continuous control
strategy is proposed, using communication feedback from extended neighbors on a
communication topology that has a spanning tree. A model-based reinforcement
learning technique is developed to cooperatively control a group of agents to
track a trajectory in a desired formation. Simulation results are presented to
demonstrate the performance of the developed technique. | [
1,
0,
1,
0,
0,
0
] |
Title: Approximation of solutions of SDEs driven by a fractional Brownian motion, under pathwise uniqueness,
Abstract: Our aim in this paper is to establish some strong stability properties of a
solution of a stochastic differential equation driven by a fractional Brownian
motion for which the pathwise uniqueness holds. The results are obtained using
Skorokhod's selection theorem. | [
0,
0,
1,
0,
0,
0
] |
Title: Multi-district preference modelling,
Abstract: Generating realistic artificial preference distributions is an important part
of any simulation analysis of electoral systems. While this has been discussed
in some detail in the context of a single electoral district, many electoral
systems of interest are based on multiple districts. Neither treating
preferences between districts as independent nor ignoring the district
structure yields satisfactory results. We present a model based on an extension
of the classic Eggenberger-Pólya urn, in which each district is represented
by an urn and there is correlation between urns. We show in detail that this
procedure has a small number of tunable parameters, is computationally
efficient, and produces "realistic-looking" distributions. We intend to use it
in further studies of electoral systems. | [
1,
1,
0,
0,
0,
0
] |
Title: Neural Text Generation: A Practical Guide,
Abstract: Deep learning methods have recently achieved great empirical success on
machine translation, dialogue response generation, summarization, and other
text generation tasks. At a high level, the technique has been to train
end-to-end neural network models consisting of an encoder model to produce a
hidden representation of the source text, followed by a decoder model to
generate the target. While such models have significantly fewer pieces than
earlier systems, significant tuning is still required to achieve good
performance. For text generation models in particular, the decoder can behave
in undesired ways, such as by generating truncated or repetitive outputs,
outputting bland and generic responses, or in some cases producing
ungrammatical gibberish. This paper is intended as a practical guide for
resolving such undesired behavior in text generation models, with the aim of
helping enable real-world applications. | [
1,
0,
0,
1,
0,
0
] |
Title: On the Erasure Robustness Property of Random Matrices,
Abstract: The study of the restricted isometry property (RIP) for corrupted random
matrices is particularly important in the field of compressed sensing (CS) with
corruptions. If a matrix still satisfy RIP after a certain portion of rows are
erased, then we say that the matrix has the strong restricted isometry property
(SRIP. In the field of compressed sensing, random matrices satisfies certain
moment conditions are of particular interest. Among these matrices, those with
entries generated from i.i.d Gaussian or i.i.d $\pm1$ random variables are
often typically considered. Recent studies have shown that a matrix generated
from i.i.d Gaussian random variables satisfies the strong restricted isometry
property under arbitrary erasure of rows. In the first part of this paper we
will work on $\pm 1$ random matrices. We study the erasure robustness of $\pm
1$ random matrices show that with overwhelming probability the SRIP will still
hold. Moreover the analysis will also lead to the robust version of the
Johnson-Lindenstrauss Lemma for $\pm 1$ matrices. Then in the second part of
this paper we work on finite frames. The study of the stability of finite
frames under corruptions shares a lot of similarity to CS with corruption. We
will focus on the Gaussian finite frames as a starter. We will improve existing
results and confirm that a Gaussian random frame is numerically stable under
arbitrary erasure of rows. | [
0,
0,
1,
0,
0,
0
] |
Title: Synchronizing automata and the language of minimal reset words,
Abstract: We study a connection between synchronizing automata and its set $M$ of
minimal reset words, i.e., such that no proper factor is a reset word. We first
show that any synchronizing automaton having the set of minimal reset words
whose set of factors does not contain a word of length at most
$\frac{1}{4}\min\{|u|: u\in I\}+\frac{1}{16}$ has a reset word of length at
most $(n-\frac{1}{2})^{2}$ In the last part of the paper we focus on the
existence of synchronizing automata with a given ideal $I$ that serves as the
set of reset words. To this end, we introduce the notion of the tail structure
of the (not necessarily regular) ideal $I=\Sigma^{*}M\Sigma^{*}$. With this
tool, we first show the existence of an infinite strongly connected
synchronizing automaton $\mathcal{A}$ having $I$ as the set of reset words and
such that every other strongly connected synchronizing automaton having $I$ as
the set of reset words is an homomorphic image of $\mathcal{A}$. Finally, we
show that for any non-unary regular ideal $I$ there is a strongly connected
synchronizing automaton having $I$ as the set of reset words with at most
$(km^{k})2^{km^{k}n}$ states, where $k=|\Sigma|$, $m$ is the length of a
shortest word in $M$, and $n$ is the dimension of the smallest automaton
recognizing $M$ (state complexity of $M$). This automaton is computable and we
show an algorithm to compute it in time $\mathcal{O}((k^{2}m^{k})2^{km^{k}n})$. | [
1,
0,
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0,
0,
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] |
Title: Peephole: Predicting Network Performance Before Training,
Abstract: The quest for performant networks has been a significant force that drives
the advancements of deep learning in recent years. While rewarding, improving
network design has never been an easy journey. The large design space combined
with the tremendous cost required for network training poses a major obstacle
to this endeavor. In this work, we propose a new approach to this problem,
namely, predicting the performance of a network before training, based on its
architecture. Specifically, we develop a unified way to encode individual
layers into vectors and bring them together to form an integrated description
via LSTM. Taking advantage of the recurrent network's strong expressive power,
this method can reliably predict the performances of various network
architectures. Our empirical studies showed that it not only achieved accurate
predictions but also produced consistent rankings across datasets -- a key
desideratum in performance prediction. | [
1,
0,
0,
1,
0,
0
] |
Title: Quadratically Tight Relations for Randomized Query Complexity,
Abstract: Let $f:\{0,1\}^n \rightarrow \{0,1\}$ be a Boolean function. The certificate
complexity $C(f)$ is a complexity measure that is quadratically tight for the
zero-error randomized query complexity $R_0(f)$: $C(f) \leq R_0(f) \leq
C(f)^2$. In this paper we study a new complexity measure that we call
expectational certificate complexity $EC(f)$, which is also a quadratically
tight bound on $R_0(f)$: $EC(f) \leq R_0(f) = O(EC(f)^2)$. We prove that $EC(f)
\leq C(f) \leq EC(f)^2$ and show that there is a quadratic separation between
the two, thus $EC(f)$ gives a tighter upper bound for $R_0(f)$. The measure is
also related to the fractional certificate complexity $FC(f)$ as follows:
$FC(f) \leq EC(f) = O(FC(f)^{3/2})$. This also connects to an open question by
Aaronson whether $FC(f)$ is a quadratically tight bound for $R_0(f)$, as
$EC(f)$ is in fact a relaxation of $FC(f)$.
In the second part of the work, we upper bound the distributed query
complexity $D^\mu_\epsilon(f)$ for product distributions $\mu$ by the square of
the query corruption bound ($\mathrm{corr}_\epsilon(f)$) which improves upon a
result of Harsha, Jain and Radhakrishnan [2015]. A similar statement for
communication complexity is open. | [
1,
0,
0,
0,
0,
0
] |
Title: Parabolic subgroup orbits on finite root systems,
Abstract: Oshima's Lemma describes the orbits of parabolic subgroups of irreducible
finite Weyl groups on crystallographic root systems. This note generalises that
result to all root systems of finite Coxeter groups, and provides a self
contained proof, independent of the representation theory of semisimple complex
Lie algebras. | [
0,
0,
1,
0,
0,
0
] |
Title: User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction,
Abstract: In human-in-the-loop machine learning, the user provides information beyond
that in the training data. Many algorithms and user interfaces have been
designed to optimize and facilitate this human--machine interaction; however,
fewer studies have addressed the potential defects the designs can cause.
Effective interaction often requires exposing the user to the training data or
its statistics. The design of the system is then critical, as this can lead to
double use of data and overfitting, if the user reinforces noisy patterns in
the data. We propose a user modelling methodology, by assuming simple rational
behaviour, to correct the problem. We show, in a user study with 48
participants, that the method improves predictive performance in a sparse
linear regression sentiment analysis task, where graded user knowledge on
feature relevance is elicited. We believe that the key idea of inferring user
knowledge with probabilistic user models has general applicability in guarding
against overfitting and improving interactive machine learning. | [
1,
0,
0,
1,
0,
0
] |
Title: Advanced Bayesian Multilevel Modeling with the R Package brms,
Abstract: The brms package allows R users to easily specify a wide range of Bayesian
single-level and multilevel models, which are fitted with the probabilistic
programming language Stan behind the scenes. Several response distributions are
supported, of which all parameters (e.g., location, scale, and shape) can be
predicted at the same time thus allowing for distributional regression.
Non-linear relationships may be specified using non-linear predictor terms or
semi-parametric approaches such as splines or Gaussian processes. To make all
of these modeling options possible in a multilevel framework, brms provides an
intuitive and powerful formula syntax, which extends the well known formula
syntax of lme4. The purpose of the present paper is to introduce this syntax in
detail and to demonstrate its usefulness with four examples, each showing other
relevant aspects of the syntax. | [
0,
0,
0,
1,
0,
0
] |
Title: $W$-entropy, super Perelman Ricci flows and $(K, m)$-Ricci solitons,
Abstract: In this paper, we prove the characterization of the $(K, \infty)$-super
Perelman Ricci flows by various functional inequalities and gradient estimate
for the heat semigroup generated by the Witten Laplacian on manifolds equipped
with time dependent metrics and potentials. As a byproduct, we derive the
Hamilton type dimension free Harnack inequality on manifolds with $(K,
\infty)$-super Perelman Ricci flows. Based on a new second order differential
inequality on the Boltzmann-Shannon entropy for the heat equation of the Witten
Laplacian, we introduce a new $W$-entropy quantity and prove its monotonicity
for the heat equation of the Witten Laplacian on complete Riemannian manifolds
with the $CD(K, \infty)$-condition and on compact manifolds with $(K,
\infty)$-super Perelman Ricci flows. Our results characterize the $(K,
\infty)$-Ricci solitons and the $(K, \infty)$-Perelman Ricci flows. We also
prove a second order differential entropy inequality on $(K, m)$-super Ricci
flows, which can be used to characterize the $(K, m)$-Ricci solitons and the
$(K, m)$-Ricci flows. Finally, we give a probabilistic interpretation of the
$W$-entropy for the heat equation of the Witten Laplacian on manifolds with the
$CD(K, m)$-condition. | [
0,
0,
1,
0,
0,
0
] |
Title: Stellar energetic particle ionization in protoplanetary disks around T Tauri stars,
Abstract: Anomalies in the abundance measurements of short lived radionuclides in
meteorites indicate that the protosolar nebulae was irradiated by a high amount
of energetic particles (E$\gtrsim$10 MeV). The particle flux of the
contemporary Sun cannot explain these anomalies. However, similar to T Tauri
stars the young Sun was more active and probably produced enough high energy
particles to explain those anomalies. We want to study the interaction of
stellar energetic particles with the gas component of the disk and identify
possible observational tracers of this interaction. We use a 2D radiation
thermo-chemical protoplanetary disk code to model a disk representative for T
Tauri stars. We use a particle energy distribution derived from solar flare
observations and an enhanced stellar particle flux proposed for T Tauri stars.
For this particle spectrum we calculate the stellar particle ionization rate
throughout the disk with an accurate particle transport model. We study the
impact of stellar particles for models with varying X-ray and cosmic-ray
ionization rates. We find that stellar particle ionization has a significant
impact on the abundances of the common disk ionization tracers HCO$^+$ and
N$_2$H$^+$, especially in models with low cosmic-ray ionization rates. In
contrast to cosmic rays and X-rays, stellar particles cannot reach the midplane
of the disk. Therefore molecular ions residing in the disk surface layers are
more affected by stellar particle ionization than molecular ions tracing the
cold layers/midplane of the disk. Spatially resolved observations of molecular
ions tracing different vertical layers of the disk allow to disentangle the
contribution of stellar particle ionization from other competing ionization
sources. Modeling such observations with a model like the one presented here
allows to constrain the stellar particle flux in disks around T Tauri stars. | [
0,
1,
0,
0,
0,
0
] |
Title: Truncation-free Hybrid Inference for DPMM,
Abstract: Dirichlet process mixture models (DPMM) are a cornerstone of Bayesian
non-parametrics. While these models free from choosing the number of components
a-priori, computationally attractive variational inference often reintroduces
the need to do so, via a truncation on the variational distribution. In this
paper we present a truncation-free hybrid inference for DPMM, combining the
advantages of sampling-based MCMC and variational methods. The proposed
hybridization enables more efficient variational updates, while increasing
model complexity only if needed. We evaluate the properties of the hybrid
updates and their empirical performance in single- as well as mixed-membership
models. Our method is easy to implement and performs favorably compared to
existing schemas. | [
1,
0,
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1,
0,
0
] |
Title: Functors and morphisms determined by subcategories,
Abstract: We study the existence and uniqueness of minimal right determiners in various
categories. Particularly in a Hom-finite hereditary abelian category with
enough projectives, we prove that the Auslander-Reiten-Smal{\o}-Ringel formula
of the minimal right determiner still holds. As an application, we give a
formula of minimal right determiners in the category of finitely presented
representations of strongly locally finite quivers. | [
0,
0,
1,
0,
0,
0
] |
Title: The Risk of Machine Learning,
Abstract: Many applied settings in empirical economics involve simultaneous estimation
of a large number of parameters. In particular, applied economists are often
interested in estimating the effects of many-valued treatments (like teacher
effects or location effects), treatment effects for many groups, and prediction
models with many regressors. In these settings, machine learning methods that
combine regularized estimation and data-driven choices of regularization
parameters are useful to avoid over-fitting. In this article, we analyze the
performance of a class of machine learning estimators that includes ridge,
lasso and pretest in contexts that require simultaneous estimation of many
parameters. Our analysis aims to provide guidance to applied researchers on (i)
the choice between regularized estimators in practice and (ii) data-driven
selection of regularization parameters. To address (i), we characterize the
risk (mean squared error) of regularized estimators and derive their relative
performance as a function of simple features of the data generating process. To
address (ii), we show that data-driven choices of regularization parameters,
based on Stein's unbiased risk estimate or on cross-validation, yield
estimators with risk uniformly close to the risk attained under the optimal
(unfeasible) choice of regularization parameters. We use data from recent
examples in the empirical economics literature to illustrate the practical
applicability of our results. | [
0,
0,
0,
1,
0,
0
] |
Title: Agatha: disentangling periodic signals from correlated noise in a periodogram framework,
Abstract: Periodograms are used as a key significance assessment and visualisation tool
to display the significant periodicities in unevenly sampled time series. We
introduce a framework of periodograms, called "Agatha", to disentangle periodic
signals from correlated noise and to solve the 2-dimensional model selection
problem: signal dimension and noise model dimension. These periodograms are
calculated by applying likelihood maximization and marginalization and combined
in a self-consistent way. We compare Agatha with other periodograms for the
detection of Keplerian signals in synthetic radial velocity data produced for
the Radial Velocity Challenge as well as in radial velocity datasets of several
Sun-like stars. In our tests we find Agatha is able to recover signals to the
adopted detection limit of the radial velocity challenge. Applied to real
radial velocity, we use Agatha to confirm previous analysis of CoRoT-7 and to
find two new planet candidates with minimum masses of 15.1 $M_\oplus$ and 7.08
$M_\oplus$ orbiting HD177565 and HD41248, with periods of 44.5 d and 13.4 d,
respectively. We find that Agatha outperforms other periodograms in terms of
removing correlated noise and assessing the significances of signals with more
robust metrics. Moreover, it can be used to select the optimal noise model and
to test the consistency of signals in time. Agatha is intended to be flexible
enough to be applied to time series analyses in other astronomical and
scientific disciplines. Agatha is available at this http URL. | [
0,
1,
0,
1,
0,
0
] |
Title: On the generation of the quarks through spontaneous symmetry breaking,
Abstract: In this paper we present the state of the art about the quarks: group SU(3),
Lie algebra, the electric charge and mass. The quarks masses are generated in
the same way as the lepton masses. It is constructed a term in the Lagrangian
that couples the Higgs doublet to the fermion fields. | [
0,
1,
0,
0,
0,
0
] |
Title: Distributed Holistic Clustering on Linked Data,
Abstract: Link discovery is an active field of research to support data integration in
the Web of Data. Due to the huge size and number of available data sources,
efficient and effective link discovery is a very challenging task. Common
pairwise link discovery approaches do not scale to many sources with very large
entity sets. We here propose a distributed holistic approach to link many data
sources based on a clustering of entities that represent the same real-world
object. Our clustering approach provides a compact and fused representation of
entities, and can identify errors in existing links as well as many new links.
We support a distributed execution of the clustering approach to achieve faster
execution times and scalability for large real-world data sets. We provide a
novel gold standard for multi-source clustering, and evaluate our methods with
respect to effectiveness and efficiency for large data sets from the geographic
and music domains. | [
1,
0,
0,
0,
0,
0
] |
Title: A branch-and-bound algorithm for the minimum radius $k$-enclosing ball problem,
Abstract: The minimum $k$-enclosing ball problem seeks the ball with smallest radius
that contains at least~$k$ of~$m$ given points in a general $n$-dimensional
Euclidean space. This problem is NP-hard. We present a branch-and-bound
algorithm on the tree of the subsets of~$k$ points to solve this problem. The
nodes on the tree are ordered in a suitable way, which, complemented with a
last-in-first-out search strategy, allows for only a small fraction of nodes to
be explored. Additionally, an efficient dual algorithm to solve the subproblems
at each node is employed. | [
1,
0,
1,
0,
0,
0
] |
Title: Motion Planning in Irreducible Path Spaces,
Abstract: The motion of a mechanical system can be defined as a path through its
configuration space. Computing such a path has a computational complexity
scaling exponentially with the dimensionality of the configuration space. We
propose to reduce the dimensionality of the configuration space by introducing
the irreducible path --- a path having a minimal swept volume. The paper
consists of three parts: In part I, we define the space of all irreducible
paths and show that planning a path in the irreducible path space preserves
completeness of any motion planning algorithm. In part II, we construct an
approximation to the irreducible path space of a serial kinematic chain under
certain assumptions. In part III, we conduct motion planning using the
irreducible path space for a mechanical snake in a turbine environment, for a
mechanical octopus with eight arms in a pipe system and for the sideways motion
of a humanoid robot moving through a room with doors and through a hole in a
wall. We demonstrate that the concept of an irreducible path can be applied to
any motion planning algorithm taking curvature constraints into account. | [
1,
0,
0,
0,
0,
0
] |
Title: Sample-Derived Disjunctive Rules for Secure Power System Operation,
Abstract: Machine learning techniques have been used in the past using Monte Carlo
samples to construct predictors of the dynamic stability of power systems. In
this paper we move beyond the task of prediction and propose a comprehensive
approach to use predictors, such as Decision Trees (DT), within a standard
optimization framework for pre- and post-fault control purposes. In particular,
we present a generalizable method for embedding rules derived from DTs in an
operation decision-making model. We begin by pointing out the specific
challenges entailed when moving from a prediction to a control framework. We
proceed with introducing the solution strategy based on generalized disjunctive
programming (GDP) as well as a two-step search method for identifying optimal
hyper-parameters for balancing cost and control accuracy. We showcase how the
proposed approach constructs security proxies that cover multiple contingencies
while facing high-dimensional uncertainty with respect to operating conditions
with the use of a case study on the IEEE 39-bus system. The method is shown to
achieve efficient system control at a marginal increase in system price
compared to an oracle model. | [
0,
0,
0,
1,
0,
0
] |
Title: Zeeman interaction and Jahn-Teller effect in $Γ_8$ multiplet,
Abstract: We present a thorough analysis of the interplay of magnetic moment and the
Jahn-Teller effect in the $\Gamma_8$ cubic multiplet. We find that in the
presence of dynamical Jahn-Teller effect, the Zeeman interaction remains
isotropic, whereas the $g$ and $G$ factors can change their signs. The static
Jahn-Teller distortion also can change the sign of these $g$ factors as well as
the nature of the magnetic anisotropy. Combining the theory with
state-of-the-art {\it ab initio} calculations, we analyzed the magnetic
properties of Np$^{4+}$ and Ir$^{4+}$ impurity ions in cubic environment. The
calculated $g$ factors of Np$^{4+}$ impurity agree well with experimental data.
The {\it ab initio} calculation predicts strong Jahn-Teller effect in Ir$^{4+}$
ion in cubic environment and the strong vibronic reduction of $g$ and $G$
factors. | [
0,
1,
0,
0,
0,
0
] |
Title: The Design and Implementation of Modern Online Programming Competitions,
Abstract: This paper presents a framework for the implementation of online programming
competitions, including a set of principles for the design of the multiplayer
game and a practical framework for the construction of the competition
environment. The paper presents a successful example competition, the 2016-17
Halite challenge, and briefly mentions a second competition, the Halite II
challenge, which launched in October 2017. | [
1,
0,
0,
0,
0,
0
] |
Title: Emergence of a spectral gap in a class of random matrices associated with split graphs,
Abstract: Motivated by the intriguing behavior displayed in a dynamic network that
models a population of extreme introverts and extroverts (XIE), we consider the
spectral properties of ensembles of random split graph adjacency matrices. We
discover that, in general, a gap emerges in the bulk spectrum between -1 and 0
that contains a single eigenvalue. An analytic expression for the bulk
distribution is derived and verified with numerical analysis. We also examine
their relation to chiral ensembles, which are associated with bipartite graphs. | [
0,
1,
1,
0,
0,
0
] |
Title: ParaGraphE: A Library for Parallel Knowledge Graph Embedding,
Abstract: Knowledge graph embedding aims at translating the knowledge graph into
numerical representations by transforming the entities and relations into
continuous low-dimensional vectors. Recently, many methods [1, 5, 3, 2, 6] have
been proposed to deal with this problem, but existing single-thread
implementations of them are time-consuming for large-scale knowledge graphs.
Here, we design a unified parallel framework to parallelize these methods,
which achieves a significant time reduction without influencing the accuracy.
We name our framework as ParaGraphE, which provides a library for parallel
knowledge graph embedding. The source code can be downloaded from
this https URL . | [
1,
0,
0,
0,
0,
0
] |
Title: Platooning in the Presence of a Speed Drop: A Generalized Control Model,
Abstract: The positive impacts of platooning on travel time reliability, congestion,
emissions, and energy consumption have been shown for homogeneous roadway
segments. However, speed limit changes frequently throughout the transportation
network, due to either safety-related considerations (e.g., workzone
operations) or congestion management schemes (e.g., speed harmonization
systems). These abrupt changes in speed limit can result in shock- wave
formation and cause travel time unreliability. Therefore, designing a
platooning strategy for tracking a reference velocity profile is critical to
enabling end-to-end platooning. Accordingly, this study introduces a
generalized control model to track a desired velocity profile, while ensuring
safety in the platoon of autonomous vehicles. We define appropriate natural
error terms and the target curve in the state space of the control system,
which is the set of points where all error terms vanish and corresponds to the
case when all vehicles move with the desired velocities and in the minimum safe
distance between them. In this way, we change the tracking velocity profile
problem into a state- feedback stabilization problem with respect to the target
curve. Under certain mild assumptions on the Lipschitz constant of the speed
drop profile, we show that the stabilizing feedback can be obtained via
introducing a natural dynamics for the maximum of the error terms for each
vehicle. Moreover, we show that with this stabilizing feedback collisions will
not occur if the initial state of the system of vehicles is sufficiently close
to the target curve. We also show that the error terms remain bounded
throughout the time and space. Two scenarios were simulated, with and without
initial perturbations, and results confirmed the effectiveness of the proposed
control model in tracking the speed drop while ensuring safety and string
stability. | [
1,
0,
0,
0,
0,
0
] |
Title: Direct visualization of vortex ice in a nanostructured superconductor,
Abstract: Artificial ice systems have unique physical properties promising for
potential applications. One of the most challenging issues in this field is to
find novel ice systems that allows a precise control over the geometries and
many-body interactions. Superconducting vortex matter has been proposed as a
very suitable candidate to study artificial ice, mainly due to availability of
tunable vortex-vortex interactions and the possibility to fabricate a variety
of nanoscale pinning potential geometries. So far, a detailed imaging of the
local configurations in a vortex-based artificial ice system is still lacking.
Here we present a direct visualization of the vortex ice state in a
nanostructured superconductor. By using the scanning Hall probe microscopy, a
large area with the vortex ice ground state configuration has been detected,
which confirms the recent theoretical predictions for this new ice system.
Besides the defects analogous to artificial spin ice systems, other types of
defects have been visualized and identified. We also demonstrate the
possibility to realize different types of defects by varying the magnetic
field. | [
0,
1,
0,
0,
0,
0
] |
Title: Statistical Mechanics of Node-perturbation Learning with Noisy Baseline,
Abstract: Node-perturbation learning is a type of statistical gradient descent
algorithm that can be applied to problems where the objective function is not
explicitly formulated, including reinforcement learning. It estimates the
gradient of an objective function by using the change in the object function in
response to the perturbation. The value of the objective function for an
unperturbed output is called a baseline. Cho et al. proposed node-perturbation
learning with a noisy baseline. In this paper, we report on building the
statistical mechanics of Cho's model and on deriving coupled differential
equations of order parameters that depict learning dynamics. We also show how
to derive the generalization error by solving the differential equations of
order parameters. On the basis of the results, we show that Cho's results are
also apply in general cases and show some general performances of Cho's model. | [
1,
0,
0,
1,
0,
0
] |
Title: New Generalized Fixed Point Results on $S_{b}$-Metric Spaces,
Abstract: Recently $S_{b}$-metric spaces have been introduced as the generalizations of
metric and $S$-metric spaces. In this paper we investigate some basic
properties of this new space. We generalize the classical Banach's contraction
principle using the theory of a complete $S_{b}$-metric space. Also we give an
application to linear equation systems using the $S_{b}$-metric which is
generated by a metric. | [
0,
0,
1,
0,
0,
0
] |
Title: Ternary and $n$-ary $f$-distributive Structures,
Abstract: We introduce and study ternary $f$-distributive structures, Ternary
$f$-quandles and more generally their higher $n$-ary analogues. A
classification of ternary $f$-quandles is provided in low dimensions. Moreover,
we study extension theory and introduce a cohomology theory for ternary, and
more generally $n$-ary, $f$-quandles. Furthermore, we give some computational
examples. | [
0,
0,
1,
0,
0,
0
] |
Title: Tunable high-harmonic generation by chromatic focusing of few-cycle laser pulses,
Abstract: In this work we study the impact of chromatic focusing of few-cycle laser
pulses on high-order harmonic generation (HHG) through analysis of the emitted
extreme ultraviolet (XUV) radiation. Chromatic focusing is usually avoided in
the few-cycle regime, as the pulse spatio-temporal structure may be highly
distorted by the spatiotemporal aberrations. Here, however, we demonstrate it
as an additional control parameter to modify the generated XUV radiation. We
present experiments where few-cycle pulses are focused by a singlet lens in a
Kr gas jet. The chromatic distribution of focal lengths allows us to tune HHG
spectra by changing the relative singlet-target distance. Interestingly, we
also show that the degree of chromatic aberration needed to this control does
not degrade substantially the harmonic conversion efficiency, still allowing
for the generation of supercontinua with the chirped-pulse scheme, demonstrated
previously for achromatic focussing. We back up our experiments with
theoretical simulations reproducing the experimental HHG results depending on
diverse parameters (input pulse spectral phase, pulse duration, focus position)
and proving that, under the considered parameters, the attosecond pulse train
remains very similar to the achromatic case, even showing cases of isolated
attosecond pulse generation for near single-cycle driving pulses. | [
0,
1,
0,
0,
0,
0
] |
Title: Settling the query complexity of non-adaptive junta testing,
Abstract: We prove that any non-adaptive algorithm that tests whether an unknown
Boolean function $f: \{0, 1\}^n\to \{0, 1\}$ is a $k$-junta or $\epsilon$-far
from every $k$-junta must make $\widetilde{\Omega}(k^{3/2} / \epsilon)$ many
queries for a wide range of parameters $k$ and $\epsilon$. Our result
dramatically improves previous lower bounds from [BGSMdW13, STW15], and is
essentially optimal given Blais's non-adaptive junta tester from [Blais08],
which makes $\widetilde{O}(k^{3/2})/\epsilon$ queries. Combined with the
adaptive tester of [Blais09] which makes $O(k\log k + k /\epsilon)$ queries,
our result shows that adaptivity enables polynomial savings in query complexity
for junta testing. | [
1,
0,
0,
0,
0,
0
] |
Title: Cross-layer Optimization for Ultra-reliable and Low-latency Radio Access Networks,
Abstract: In this paper, we propose a framework for cross-layer optimization to ensure
ultra-high reliability and ultra-low latency in radio access networks, where
both transmission delay and queueing delay are considered. With short
transmission time, the blocklength of channel codes is finite, and the Shannon
Capacity cannot be used to characterize the maximal achievable rate with given
transmission error probability. With randomly arrived packets, some packets may
violate the queueing delay. Moreover, since the queueing delay is shorter than
the channel coherence time in typical scenarios, the required transmit power to
guarantee the queueing delay and transmission error probability will become
unbounded even with spatial diversity. To ensure the required
quality-of-service (QoS) with finite transmit power, a proactive packet
dropping mechanism is introduced. Then, the overall packet loss probability
includes transmission error probability, queueing delay violation probability,
and packet dropping probability. We optimize the packet dropping policy, power
allocation policy, and bandwidth allocation policy to minimize the transmit
power under the QoS constraint. The optimal solution is obtained, which depends
on both channel and queue state information. Simulation and numerical results
validate our analysis, and show that setting packet loss probabilities equal is
a near optimal solution. | [
1,
0,
0,
0,
0,
0
] |
Title: Network Inference via the Time-Varying Graphical Lasso,
Abstract: Many important problems can be modeled as a system of interconnected
entities, where each entity is recording time-dependent observations or
measurements. In order to spot trends, detect anomalies, and interpret the
temporal dynamics of such data, it is essential to understand the relationships
between the different entities and how these relationships evolve over time. In
this paper, we introduce the time-varying graphical lasso (TVGL), a method of
inferring time-varying networks from raw time series data. We cast the problem
in terms of estimating a sparse time-varying inverse covariance matrix, which
reveals a dynamic network of interdependencies between the entities. Since
dynamic network inference is a computationally expensive task, we derive a
scalable message-passing algorithm based on the Alternating Direction Method of
Multipliers (ADMM) to solve this problem in an efficient way. We also discuss
several extensions, including a streaming algorithm to update the model and
incorporate new observations in real time. Finally, we evaluate our TVGL
algorithm on both real and synthetic datasets, obtaining interpretable results
and outperforming state-of-the-art baselines in terms of both accuracy and
scalability. | [
1,
0,
1,
0,
0,
0
] |
Title: Construction of dynamical semigroups by a functional regularisation à la Kato,
Abstract: A functional version of the Kato one-parametric regularisation for the
construction of a dynamical semigroup generator of a relative bound one
perturbation is introduced. It does not require that the minus generator of the
unperturbed semigroup is a positivity preserving operator. The regularisation
is illustrated by an example of a boson-number cut-off regularisation. | [
0,
0,
1,
0,
0,
0
] |
Title: Visual Reasoning with Multi-hop Feature Modulation,
Abstract: Recent breakthroughs in computer vision and natural language processing have
spurred interest in challenging multi-modal tasks such as visual
question-answering and visual dialogue. For such tasks, one successful approach
is to condition image-based convolutional network computation on language via
Feature-wise Linear Modulation (FiLM) layers, i.e., per-channel scaling and
shifting. We propose to generate the parameters of FiLM layers going up the
hierarchy of a convolutional network in a multi-hop fashion rather than all at
once, as in prior work. By alternating between attending to the language input
and generating FiLM layer parameters, this approach is better able to scale to
settings with longer input sequences such as dialogue. We demonstrate that
multi-hop FiLM generation achieves state-of-the-art for the short input
sequence task ReferIt --- on-par with single-hop FiLM generation --- while also
significantly outperforming prior state-of-the-art and single-hop FiLM
generation on the GuessWhat?! visual dialogue task. | [
0,
0,
0,
1,
0,
0
] |
Title: Ultrahigh capacitive energy storage in highly oriented BaZr(x)Ti(1-x)O3 thin films prepared by pulsed laser deposition,
Abstract: We report structural, optical, temperature and frequency dependent
dielectric, and energy storage properties of pulsed laser deposited (100)
highly textured BaZr(x)Ti(1-x)O3 (x = 0.3, 0.4 and 0.5) relaxor ferroelectric
thin films on La0.7Sr0.3MnO3/MgO substrates which make this compound as a
potential lead-free capacitive energy storage material for scalable electronic
devices. A high dielectric constant of ~1400 - 3500 and a low dielectric loss
of <0.025 were achieved at 10 kHz for all three compositions at ambient
conditions. Ultrahigh stored and recoverable electrostatic energy densities as
high as 214 +/- 1 and 156 +/- 1 J/cm3, respectively, were demonstrated at a
sustained high electric field of ~3 MV/cm with an efficiency of 72.8 +/- 0.6 %
in optimum 30% Zr substituted BaTiO3 composition. | [
0,
1,
0,
0,
0,
0
] |
Title: Highly Efficient Human Action Recognition with Quantum Genetic Algorithm Optimized Support Vector Machine,
Abstract: In this paper we propose the use of quantum genetic algorithm to optimize the
support vector machine (SVM) for human action recognition. The Microsoft Kinect
sensor can be used for skeleton tracking, which provides the joints' position
data. However, how to extract the motion features for representing the dynamics
of a human skeleton is still a challenge due to the complexity of human motion.
We present a highly efficient features extraction method for action
classification, that is, using the joint angles to represent a human skeleton
and calculating the variance of each angle during an action time window. Using
the proposed representation, we compared the human action classification
accuracy of two approaches, including the optimized SVM based on quantum
genetic algorithm and the conventional SVM with grid search. Experimental
results on the MSR-12 dataset show that the conventional SVM achieved an
accuracy of $ 93.85\% $. The proposed approach outperforms the conventional
method with an accuracy of $ 96.15\% $. | [
1,
0,
0,
1,
0,
0
] |
Title: Differential relations for almost Belyi maps,
Abstract: Several kinds of differential relations for polynomial components of almost
Belyi maps are presented. Saito's theory of free divisors give particularly
interesting (yet conjectural) logarithmic action of vector fields. The
differential relations implied by Kitaev's construction of algebraic Painleve
VI solutions through pull-back transformations are used to compute almost Belyi
maps for the pull-backs giving all genus 0 and 1 Painleve VI solutions in the
Lisovyy-Tykhyy classification. | [
0,
0,
1,
0,
0,
0
] |
Title: Comparative Study of Virtual Machines and Containers for DevOps Developers,
Abstract: In this work, we plan to develop a system to compare virtual machines with
container technology. We would devise ways to measure the administrator effort
of containers vs. Virtual Machines (VMs). Metrics that will be tested against
include human efforts required, ease of migration, resource utilization and
ease of use using containers and virtual machines. | [
1,
0,
0,
0,
0,
0
] |
Title: Periodic fourth-order cubic NLS: Local well-posedness and Non-squeezing property,
Abstract: In this paper, we consider the cubic fourth-order nonlinear Schrödinger
equation (4NLS) under the periodic boundary condition. We prove two results.
One is the local well-posedness in $H^s$ with $-1/3 \le s < 0$ for the Cauchy
problem of the Wick ordered 4NLS. The other one is the non-squeezing property
for the flow map of 4NLS in the symplectic phase space $L^2(\mathbb{T})$. To
prove the former we used the ideas introduced in [Takaoka and Tsutsumi 2004]
and [Nakanish et al 2010], and to prove the latter we used the ideas in
[Colliander et al 2005]. | [
0,
0,
1,
0,
0,
0
] |
Title: Measuring the unmeasurable - a project of domestic violence risk prediction and management,
Abstract: The prevention of domestic violence (DV) have aroused serious concerns in
Taiwan because of the disparity between the increasing amount of reported DV
cases that doubled over the past decade and the scarcity of social workers.
Additionally, a large amount of data was collected when social workers use the
predominant case management approach to document case reports information.
However, these data were not properly stored or organized.
To improve the efficiency of DV prevention and risk management, we worked
with Taipei City Government and utilized the 2015 data from its DV database to
perform a spatial pattern analysis of the reports of DV cases to build a DV
risk map. However, during our map building process, the issue of confounding
bias arose because we were not able to verify if reported cases truly reflected
real violence occurrence or were simply false reports from potential victim's
neighbors. Therefore, we used the random forest method to build a repeat
victimization risk prediction model. The accuracy and F1-measure of our model
were 96.3% and 62.8%. This model helped social workers differentiate the risk
level of new cases, which further reduced their major workload significantly.
To our knowledge, this is the first project that utilized machine learning in
DV prevention. The research approach and results of this project not only can
improve DV prevention process, but also be applied to other social work or
criminal prevention areas. | [
1,
0,
0,
0,
0,
0
] |
Title: Embedded real-time monitoring using SystemC in IMA network,
Abstract: Avionics is one kind of domain where prevention prevails. Nonetheless fails
occur. Sometimes due to pilot misreacting, flooded in information. Sometimes
information itself would be better verified than trusted. To avoid some kind of
failure, it has been thought to add,in midst of the ARINC664 aircraft data
network, a new kind of monitoring. | [
1,
0,
0,
0,
0,
0
] |
Title: One pixel attack for fooling deep neural networks,
Abstract: Recent research has revealed that the output of Deep Neural Networks (DNN)
can be easily altered by adding relatively small perturbations to the input
vector. In this paper, we analyze an attack in an extremely limited scenario
where only one pixel can be modified. For that we propose a novel method for
generating one-pixel adversarial perturbations based on differential
evolution(DE). It requires less adversarial information(a black-box attack) and
can fool more types of networks due to the inherent features of DE. The results
show that 68.36% of the natural images in CIFAR-10 test dataset and 41.22% of
the ImageNet (ILSVRC 2012) validation images can be perturbed to at least one
target class by modifying just one pixel with 73.22% and 5.52% confidence on
average. Thus, the proposed attack explores a different take on adversarial
machine learning in an extreme limited scenario, showing that current DNNs are
also vulnerable to such low dimension attacks. Besides, we also illustrate an
important application of DE (or broadly speaking, evolutionary computation) in
the domain of adversarial machine learning: creating tools that can effectively
generate low-cost adversarial attacks against neural networks for evaluating
robustness. The code is available on:
this https URL | [
1,
0,
0,
1,
0,
0
] |
Title: Multi-proton bunch driven hollow plasma wakefield acceleration in the nonlinear regime,
Abstract: Proton-driven plasma wakefield acceleration has been demonstrated in
simulations to be capable of accelerating particles to the energy frontier in a
single stage, but its potential is hindered by the fact that currently
available proton bunches are orders of magnitude longer than the plasma
wavelength. Fortunately, proton micro-bunching allows driving plasma waves
resonantly. In this paper, we propose using a hollow plasma channel for
multiple proton bunch driven plasma wakefield acceleration and demonstrate that
it enables the operation in the nonlinear regime and resonant excitation of
strong plasma waves. This new regime also involves beneficial features of
hollow channels for the accelerated beam (such as emittance preservation and
uniform accelerating field) and long buckets of stable deceleration for the
drive beam. The regime is attained at a proper ratio among plasma skin depth,
driver radius, hollow channel radius, and micro-bunch period. | [
0,
1,
0,
0,
0,
0
] |
Title: Large-Scale Mapping of Human Activity using Geo-Tagged Videos,
Abstract: This paper is the first work to perform spatio-temporal mapping of human
activity using the visual content of geo-tagged videos. We utilize a recent
deep-learning based video analysis framework, termed hidden two-stream
networks, to recognize a range of activities in YouTube videos. This framework
is efficient and can run in real time or faster which is important for
recognizing events as they occur in streaming video or for reducing latency in
analyzing already captured video. This is, in turn, important for using video
in smart-city applications. We perform a series of experiments to show our
approach is able to accurately map activities both spatially and temporally. We
also demonstrate the advantages of using the visual content over the
tags/titles. | [
1,
0,
0,
0,
0,
0
] |
Title: Efficient Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs,
Abstract: Graphs are an important tool to model data in different domains, including
social networks, bioinformatics and the world wide web. Most of the networks
formed in these domains are directed graphs, where all the edges have a
direction and they are not symmetric. Betweenness centrality is an important
index widely used to analyze networks. In this paper, first given a directed
network $G$ and a vertex $r \in V(G)$, we propose a new exact algorithm to
compute betweenness score of $r$. Our algorithm pre-computes a set
$\mathcal{RV}(r)$, which is used to prune a huge amount of computations that do
not contribute in the betweenness score of $r$. Time complexity of our exact
algorithm depends on $|\mathcal{RV}(r)|$ and it is respectively
$\Theta(|\mathcal{RV}(r)|\cdot|E(G)|)$ and
$\Theta(|\mathcal{RV}(r)|\cdot|E(G)|+|\mathcal{RV}(r)|\cdot|V(G)|\log |V(G)|)$
for unweighted graphs and weighted graphs with positive weights.
$|\mathcal{RV}(r)|$ is bounded from above by $|V(G)|-1$ and in most cases, it
is a small constant. Then, for the cases where $\mathcal{RV}(r)$ is large, we
present a simple randomized algorithm that samples from $\mathcal{RV}(r)$ and
performs computations for only the sampled elements. We show that this
algorithm provides an $(\epsilon,\delta)$-approximation of the betweenness
score of $r$. Finally, we perform extensive experiments over several real-world
datasets from different domains for several randomly chosen vertices as well as
for the vertices with the highest betweenness scores. Our experiments reveal
that in most cases, our algorithm significantly outperforms the most efficient
existing randomized algorithms, in terms of both running time and accuracy. Our
experiments also show that our proposed algorithm computes betweenness scores
of all vertices in the sets of sizes 5, 10 and 15, much faster and more
accurate than the most efficient existing algorithms. | [
1,
0,
0,
0,
0,
0
] |
Title: A Competitive Algorithm for Online Multi-Robot Exploration of a Translating Plume,
Abstract: In this paper, we study the problem of exploring a translating plume with a
team of aerial robots. The shape and the size of the plume are unknown to the
robots. The objective is to find a tour for each robot such that they
collectively explore the plume. Specifically, the tours must be such that each
point in the plume must be visible from the field-of-view of some robot along
its tour. We propose a recursive Depth-First Search (DFS)-based algorithm that
yields a constant competitive ratio for the exploration problem. The
competitive ratio is
$\frac{2(S_r+S_p)(R+\lfloor\log{R}\rfloor)}{(S_r-S_p)(1+\lfloor\log{R}\rfloor)}$
where $R$ is the number of robots, and $S_r$ and $S_p$ are the robot speed and
the plume speed, respectively. We also consider a more realistic scenario where
the plume shape is not restricted to grid cells but an arbitrary shape. We show
our algorithm has
$\frac{2(S_r+S_p)(18R+\lfloor\log{R}\rfloor)}{(S_r-S_p)(1+\lfloor\log{R}\rfloor)}$
competitive ratio under the fat condition. We empirically verify our algorithm
using simulations. | [
1,
0,
0,
0,
0,
0
] |
Title: Warped Riemannian metrics for location-scale models,
Abstract: The present paper shows that warped Riemannian metrics, a class of Riemannian
metrics which play a prominent role in Riemannian geometry, are also of
fundamental importance in information geometry. Precisely, the paper features a
new theorem, which states that the Rao-Fisher information metric of any
location-scale model, defined on a Riemannian manifold, is a warped Riemannian
metric, whenever this model is invariant under the action of some Lie group.
This theorem is a valuable tool in finding the expression of the Rao-Fisher
information metric of location-scale models defined on high-dimensional
Riemannian manifolds. Indeed, a warped Riemannian metric is fully determined by
only two functions of a single variable, irrespective of the dimension of the
underlying Riemannian manifold. Starting from this theorem, several original
contributions are made. The expression of the Rao-Fisher information metric of
the Riemannian Gaussian model is provided, for the first time in the
literature. A generalised definition of the Mahalanobis distance is introduced,
which is applicable to any location-scale model defined on a Riemannian
manifold. The solution of the geodesic equation is obtained, for any Rao-Fisher
information metric defined in terms of warped Riemannian metrics. Finally,
using a mixture of analytical and numerical computations, it is shown that the
parameter space of the von Mises-Fisher model of $n$-dimensional directional
data, when equipped with its Rao-Fisher information metric, becomes a Hadamard
manifold, a simply-connected complete Riemannian manifold of negative sectional
curvature, for $n = 2,\ldots,8$. Hopefully, in upcoming work, this will be
proved for any value of $n$. | [
0,
0,
1,
1,
0,
0
] |
Title: Correcting rural building annotations in OpenStreetMap using convolutional neural networks,
Abstract: Rural building mapping is paramount to support demographic studies and plan
actions in response to crisis that affect those areas. Rural building
annotations exist in OpenStreetMap (OSM), but their quality and quantity are
not sufficient for training models that can create accurate rural building
maps. The problems with these annotations essentially fall into three
categories: (i) most commonly, many annotations are geometrically misaligned
with the updated imagery; (ii) some annotations do not correspond to buildings
in the images (they are misannotations or the buildings have been destroyed);
and (iii) some annotations are missing for buildings in the images (the
buildings were never annotated or were built between subsequent image
acquisitions). First, we propose a method based on Markov Random Field (MRF) to
align the buildings with their annotations. The method maximizes the
correlation between annotations and a building probability map while enforcing
that nearby buildings have similar alignment vectors. Second, the annotations
with no evidence in the building probability map are removed. Third, we present
a method to detect non-annotated buildings with predefined shapes and add their
annotation. The proposed methodology shows considerable improvement in accuracy
of the OSM annotations for two regions of Tanzania and Zimbabwe, being more
accurate than state-of-the-art baselines. | [
1,
0,
0,
0,
0,
0
] |
Title: Closed-form Harmonic Contrast Control with Surface Impedance Coatings for Conductive Objects,
Abstract: The problem of suppressing the scattering from conductive objects is
addressed in terms of harmonic contrast reduction. A unique compact closed-form
solution for a surface impedance $Z_s(m,kr)$ is found in a straightforward
manner and without any approximation as a function of the harmonic index $m$
(scattering mode to suppress) and of the frequency regime $kr$ (product of
wavenumber $k$ and radius $r$ of the cloaked system) at any frequency regime.
In the quasi-static limit, mantle cloaking is obtained as a particular case for
$kr \ll 1$ and $m=0$. In addition, beyond quasi-static regime, impedance
coatings for a selected dominant harmonic wave can be designed with proper
dispersive behaviour, resulting in improved reduction levels and harmonic
filtering capability. | [
0,
1,
0,
0,
0,
0
] |
Title: Shape Convergence for Aggregate Tiles in Conformal Tilings,
Abstract: Given a substitution tiling $T$ of the plane with subdivision operator
$\tau$, we study the conformal tilings $\mathcal{T}_n$ associated with $\tau^n
T$. We prove that aggregate tiles within $\mathcal{T}_n$ converge in shape as
$n\rightarrow \infty$ to their associated Euclidean tiles in $T$. | [
0,
0,
1,
0,
0,
0
] |
Title: Performance and sensitivity of vortex coronagraphs on segmented space telescopes,
Abstract: The detection of molecular species in the atmospheres of earth-like
exoplanets orbiting nearby stars requires an optical system that suppresses
starlight and maximizes the sensitivity to the weak planet signals at small
angular separations. Achieving sufficient contrast performance on a segmented
aperture space telescope is particularly challenging due to unwanted
diffraction within the telescope from amplitude and phase discontinuities in
the pupil. Apodized vortex coronagraphs are a promising solution that
theoretically meet the performance needs for high contrast imaging with future
segmented space telescopes. We investigate the sensitivity of apodized vortex
coronagraphs to the expected aberrations, including segment co-phasing errors
in piston and tip/tilt as well as other low-order and mid-spatial frequency
aberrations. Coronagraph designs and their associated telescope requirements
are identified for conceptual HabEx and LUVOIR telescope designs. | [
0,
1,
0,
0,
0,
0
] |
Title: Local systems on complements of arrangements of smooth, complex algebraic hypersurfaces,
Abstract: We consider smooth, complex quasi-projective varieties $U$ which admit a
compactification with a boundary which is an arrangement of smooth algebraic
hypersurfaces. If the hypersurfaces intersect locally like hyperplanes, and the
relative interiors of the hypersurfaces are Stein manifolds, we prove that the
cohomology of certain local systems on $U$ vanishes. As an application, we show
that complements of linear, toric, and elliptic arrangements are both duality
and abelian duality spaces. | [
0,
0,
1,
0,
0,
0
] |
Title: Some Open Problems in Random Matrix Theory and the Theory of Integrable Systems. II,
Abstract: We describe a list of open problems in random matrix theory and the theory of
integrable systems that was presented at the conference Asymptotics in
Integrable Systems, Random Matrices and Random Processes and Universality,
Centre de Recherches Mathematiques, Montreal, June 7-11, 2015. We also describe
progress that has been made on problems in an earlier list presented by the
author on the occasion of his 60th birthday in 2005 (see [Deift P., Contemp.
Math., Vol. 458, Amer. Math. Soc., Providence, RI, 2008, 419-430,
arXiv:0712.0849]). | [
0,
1,
1,
0,
0,
0
] |
Title: A semi-parametric estimation for max-mixture spatial processes,
Abstract: We proposed a semi-parametric estimation procedure in order to estimate the
parameters of a max-mixture model and also of a max-stable model (inverse
max-stable model) as an alternative to composite likelihood. A good estimation
by the proposed estimator required the dependence measure to detect all
dependence structures in the model, especially when dealing with the
max-mixture model. We overcame this challenge by using the F-madogram. The
semi-parametric estimation was then based on a quasi least square method, by
minimizing the square difference between the theoretical F-madogram and an
empirical one. We evaluated the performance of this estimator through a
simulation study. It was shown that on an average, the estimation is performed
well, although in some cases, it encountered some difficulties. We apply our
estimation procedure to model the daily rainfalls over the East Australia. | [
0,
0,
1,
1,
0,
0
] |
Title: Spectroscopic Observation and Analysis of HII regions in M33 with MMT: Temperatures and Oxygen Abundances,
Abstract: The spectra of 413 star-forming (or HII) regions in M33 (NGC 598) were
observed by using the multifiber spectrograph of Hectospec at the 6.5-m
Multiple Mirror Telescope (MMT). By using this homogeneous spectra sample, we
measured the intensities of emission lines and some physical parameters, such
as electron temperatures, electron densities, and metallicities. Oxygen
abundances were derived via the direct method (when available) and two
empirical strong-line methods, namely, O3N2 and N2. In the high-metallicity
end, oxygen abundances derived from O3N2 calibration were higher than those
derived from N2 index, indicating an inconsistency between O3N2 and N2
calibrations. We presented a detailed analysis of the spatial distribution of
gas-phase oxygen abundances in M33 and confirmed the existence of the
axisymmetric global metallicity distribution widely assumed in literature.
Local variations were also observed and subsequently associated with spiral
structures to provide evidence of radial migration driven by arms. Our O/H
gradient fitted out to 1.1 $R_{25}$ resulted in slopes of $-0.17\pm0.03$,
$-0.19\pm0.01$, and $-0.16\pm0.17$ dex $R_{25}^{-1}$ utilizing abundances from
O3N2, N2 diagnostics, and direct method, respectively. | [
0,
1,
0,
0,
0,
0
] |
Title: Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy,
Abstract: Over half a million individuals are diagnosed with head and neck cancer each
year worldwide. Radiotherapy is an important curative treatment for this
disease, but it requires manually intensive delineation of radiosensitive
organs at risk (OARs). This planning process can delay treatment commencement.
While auto-segmentation algorithms offer a potentially time-saving solution,
the challenges in defining, quantifying and achieving expert performance
remain. Adopting a deep learning approach, we demonstrate a 3D U-Net
architecture that achieves performance similar to experts in delineating a wide
range of head and neck OARs. The model was trained on a dataset of 663
deidentified computed tomography (CT) scans acquired in routine clinical
practice and segmented according to consensus OAR definitions. We demonstrate
its generalisability through application to an independent test set of 24 CT
scans available from The Cancer Imaging Archive collected at multiple
international sites previously unseen to the model, each segmented by two
independent experts and consisting of 21 OARs commonly segmented in clinical
practice. With appropriate validation studies and regulatory approvals, this
system could improve the effectiveness of radiotherapy pathways. | [
0,
0,
0,
1,
0,
0
] |
Title: Intelligent Parameter Tuning in Optimization-based Iterative CT Reconstruction via Deep Reinforcement Learning,
Abstract: A number of image-processing problems can be formulated as optimization
problems. The objective function typically contains several terms specifically
designed for different purposes. Parameters in front of these terms are used to
control the relative weights among them. It is of critical importance to tune
these parameters, as quality of the solution depends on their values. Tuning
parameter is a relatively straightforward task for a human, as one can
intelligently determine the direction of parameter adjustment based on the
solution quality. Yet manual parameter tuning is not only tedious in many
cases, but becomes impractical when a number of parameters exist in a problem.
Aiming at solving this problem, this paper proposes an approach that employs
deep reinforcement learning to train a system that can automatically adjust
parameters in a human-like manner. We demonstrate our idea in an example
problem of optimization-based iterative CT reconstruction with a pixel-wise
total-variation regularization term. We set up a parameter tuning policy
network (PTPN), which maps an CT image patch to an output that specifies the
direction and amplitude by which the parameter at the patch center is adjusted.
We train the PTPN via an end-to-end reinforcement learning procedure. We
demonstrate that under the guidance of the trained PTPN for parameter tuning at
each pixel, reconstructed CT images attain quality similar or better than in
those reconstructed with manually tuned parameters. | [
0,
1,
0,
0,
0,
0
] |
Title: Adversarial Examples that Fool Detectors,
Abstract: An adversarial example is an example that has been adjusted to produce a
wrong label when presented to a system at test time. To date, adversarial
example constructions have been demonstrated for classifiers, but not for
detectors. If adversarial examples that could fool a detector exist, they could
be used to (for example) maliciously create security hazards on roads populated
with smart vehicles. In this paper, we demonstrate a construction that
successfully fools two standard detectors, Faster RCNN and YOLO. The existence
of such examples is surprising, as attacking a classifier is very different
from attacking a detector, and that the structure of detectors - which must
search for their own bounding box, and which cannot estimate that box very
accurately - makes it quite likely that adversarial patterns are strongly
disrupted. We show that our construction produces adversarial examples that
generalize well across sequences digitally, even though large perturbations are
needed. We also show that our construction yields physical objects that are
adversarial. | [
1,
0,
0,
0,
0,
0
] |
Title: Direct and mediating influences of user-developer perception gaps in requirements understanding on user participation,
Abstract: User participation is considered an effective way to conduct requirements
engineering, but user-developer perception gaps in requirements understanding
occur frequently. Since user participation in practice is not as active as we
expect and the requirements perception gap has been recognized as a risk that
negatively affects projects, exploring whether user-developer perception gaps
in requirements understanding will hinder user participation is worthwhile.
This will help develop a greater comprehension of the intertwined relationship
between user participation and perception gap, a topic that has not yet been
extensively examined. This study investigates the direct and mediating
influences of user-developer requirements perception gaps on user participation
by integrating requirements uncertainty and top management support. Survey data
collected from 140 subjects were examined and analyzed using structural
equation modeling. The results indicate that perception gaps have a direct
negative effect on user participation and negate completely the positive effect
of top management support on user participation. Additionally, perception gaps
do not have a mediating effect between requirements uncertainty and user
participation because requirements uncertainty does not significantly and
directly affect user participation, but requirements uncertainty indirectly
influences user participation due to its significant direct effect on
perception gaps. The theoretical and practical implications are discussed, and
limitations and possible future research areas are identified. | [
1,
0,
0,
0,
0,
0
] |
Title: A fast numerical method for ideal fluid flow in domains with multiple stirrers,
Abstract: A collection of arbitrarily-shaped solid objects, each moving at a constant
speed, can be used to mix or stir ideal fluid, and can give rise to interesting
flow patterns. Assuming these systems of fluid stirrers are two-dimensional,
the mathematical problem of resolving the flow field - given a particular
distribution of any finite number of stirrers of specified shape and speed -
can be formulated as a Riemann-Hilbert problem. We show that this
Riemann-Hilbert problem can be solved numerically using a fast and accurate
algorithm for any finite number of stirrers based around a boundary integral
equation with the generalized Neumann kernel. Various systems of fluid stirrers
are considered, and our numerical scheme is shown to handle highly multiply
connected domains (i.e. systems of many fluid stirrers) with minimal
computational expense. | [
0,
0,
1,
0,
0,
0
] |
Title: A Short Survey on Probabilistic Reinforcement Learning,
Abstract: A reinforcement learning agent tries to maximize its cumulative payoff by
interacting in an unknown environment. It is important for the agent to explore
suboptimal actions as well as to pick actions with highest known rewards. Yet,
in sensitive domains, collecting more data with exploration is not always
possible, but it is important to find a policy with a certain performance
guaranty. In this paper, we present a brief survey of methods available in the
literature for balancing exploration-exploitation trade off and computing
robust solutions from fixed samples in reinforcement learning. | [
1,
0,
0,
1,
0,
0
] |
Title: The Muon g-2 experiment at Fermilab,
Abstract: The upcoming Fermilab E989 experiment will measure the muon anomalous
magnetic moment $a_{\mu}$ . This measurement is motivated by the previous
measurement performed in 2001 by the BNL E821 experiment that reported a 3-4
standard deviation discrepancy between the measured value and the Standard
Model prediction. The new measurement at Fermilab aims to improve the precision
by a factor of four reducing the total uncertainty from 540 parts per billion
(BNL E821) to 140 parts per billion (Fermilab E989). This paper gives the
status of the experiment. | [
0,
1,
0,
0,
0,
0
] |
Title: Bypass Fraud Detection: Artificial Intelligence Approach,
Abstract: Telecom companies are severely damaged by bypass fraud or SIM boxing.
However, there is a shortage of published research to tackle this problem. The
traditional method of Test Call Generating is easily overcome by fraudsters and
the need for more sophisticated ways is inevitable. In this work, we are
developing intelligent algorithms that mine a huge amount of mobile operator's
data and detect the SIMs that are used to bypass international calls. This
method will make it hard for fraudsters to generate revenue and hinder their
work. Also by reducing fraudulent activities, quality of service can be
increased as well as customer satisfaction. Our technique has been evaluated
and tested on real world mobile operator data, and proved to be very efficient. | [
1,
0,
0,
0,
0,
0
] |
Title: Scenario Reduction Revisited: Fundamental Limits and Guarantees,
Abstract: The goal of scenario reduction is to approximate a given discrete
distribution with another discrete distribution that has fewer atoms. We
distinguish continuous scenario reduction, where the new atoms may be chosen
freely, and discrete scenario reduction, where the new atoms must be chosen
from among the existing ones. Using the Wasserstein distance as measure of
proximity between distributions, we identify those $n$-point distributions on
the unit ball that are least susceptible to scenario reduction, i.e., that have
maximum Wasserstein distance to their closest $m$-point distributions for some
prescribed $m<n$. We also provide sharp bounds on the added benefit of
continuous over discrete scenario reduction. Finally, to our best knowledge, we
propose the first polynomial-time constant-factor approximations for both
discrete and continuous scenario reduction as well as the first exact
exponential-time algorithms for continuous scenario reduction. | [
0,
0,
1,
0,
0,
0
] |
Title: Testing the science/technology relationship by analysis of patent citations of scientific papers after decomposition of both science and technology,
Abstract: The relationship of scientific knowledge development to technological
development is widely recognized as one of the most important and complex
aspects of technological evolution. This paper adds to our understanding of the
relationship through use of a more rigorous structure for differentiating among
technologies based upon technological domains (defined as consisting of the
artifacts over time that fulfill a specific generic function using a specific
body of technical knowledge). | [
1,
1,
0,
0,
0,
0
] |
Title: A Mention-Ranking Model for Abstract Anaphora Resolution,
Abstract: Resolving abstract anaphora is an important, but difficult task for text
understanding. Yet, with recent advances in representation learning this task
becomes a more tangible aim. A central property of abstract anaphora is that it
establishes a relation between the anaphor embedded in the anaphoric sentence
and its (typically non-nominal) antecedent. We propose a mention-ranking model
that learns how abstract anaphors relate to their antecedents with an
LSTM-Siamese Net. We overcome the lack of training data by generating
artificial anaphoric sentence--antecedent pairs. Our model outperforms
state-of-the-art results on shell noun resolution. We also report first
benchmark results on an abstract anaphora subset of the ARRAU corpus. This
corpus presents a greater challenge due to a mixture of nominal and pronominal
anaphors and a greater range of confounders. We found model variants that
outperform the baselines for nominal anaphors, without training on individual
anaphor data, but still lag behind for pronominal anaphors. Our model selects
syntactically plausible candidates and -- if disregarding syntax --
discriminates candidates using deeper features. | [
1,
0,
0,
1,
0,
0
] |
Title: Harmonic density interpolation methods for high-order evaluation of Laplace layer potentials in 2D and 3D,
Abstract: We present an effective harmonic density interpolation method for the
numerical evaluation of singular and nearly singular Laplace boundary integral
operators and layer potentials in two and three spatial dimensions. The method
relies on the use of Green's third identity and local Taylor-like
interpolations of density functions in terms of harmonic polynomials. The
proposed technique effectively regularizes the singularities present in
boundary integral operators and layer potentials, and recasts the latter in
terms of integrands that are bounded or even more regular, depending on the
order of the density interpolation. The resulting boundary integrals can then
be easily, accurately, and inexpensively evaluated by means of standard
quadrature rules. A variety of numerical examples demonstrate the effectiveness
of the technique when used in conjunction with the classical trapezoidal rule
(to integrate over smooth curves) in two-dimensions, and with a Chebyshev-type
quadrature rule (to integrate over surfaces given as unions of non-overlapping
quadrilateral patches) in three-dimensions. | [
0,
1,
0,
0,
0,
0
] |
Title: The Thermophysical Properties of the Bagnold Dunes, Mars: Ground-truthing Orbital Data,
Abstract: In this work, we compare the thermophysical properties and particle sizes
derived from the Mars Science Laboratory (MSL) rover's Ground Temperature
Sensor (GTS) of the Bagnold dunes, specifically Namib dune, to those derived
orbitally from Thermal Emission Imaging System (THEMIS), ultimately linking
these measurements to ground-truth particle sizes determined from Mars Hand
Lens Imager (MAHLI) images. In general, we find that all three datasets report
consistent particle sizes for the Bagnold dunes (~110-350 microns, and are
within measurement and model uncertainties), indicating that particle sizes of
homogeneous materials determined from orbit are reliable. Furthermore, we
examine the effects of two physical characteristics that could influence the
modeled thermal inertia and particle sizes, including: 1) fine-scale (cm-m
scale) ripples, and 2) thin layering of indurated/armored materials. To first
order, we find small scale ripples and thin (approximately centimeter scale)
layers do not significantly affect the determination of bulk thermal inertia
from orbital thermal data determined from a single nighttime temperature.
Modeling of a layer of coarse or indurated material reveals that a thin layer
(< ~5 mm; similar to what was observed by the Curiosity rover) would not
significantly change the observed thermal properties of the surface and would
be dominated by the properties of the underlying material. Thermal inertia and
grain sizes of relatively homogeneous materials derived from nighttime orbital
data should be considered as reliable, as long as there are not significant
sub-pixel anisothermality effects (e.g. lateral mixing of multiple
thermophysically distinct materials). | [
0,
1,
0,
0,
0,
0
] |
Title: Algorithmic Trading with Fitted Q Iteration and Heston Model,
Abstract: We present the use of the fitted Q iteration in algorithmic trading. We show
that the fitted Q iteration helps alleviate the dimension problem that the
basic Q-learning algorithm faces in application to trading. Furthermore, we
introduce a procedure including model fitting and data simulation to enrich
training data as the lack of data is often a problem in realistic application.
We experiment our method on both simulated environment that permits arbitrage
opportunity and real-world environment by using prices of 450 stocks. In the
former environment, the method performs well, implying that our method works in
theory. To perform well in the real-world environment, the agents trained might
require more training (iteration) and more meaningful variables with predictive
value. | [
0,
0,
0,
0,
0,
1
] |
Title: Mailbox Types for Unordered Interactions,
Abstract: We propose a type system for reasoning on protocol conformance and deadlock
freedom in networks of processes that communicate through unordered mailboxes.
We model these networks in the mailbox calculus, a mild extension of the
asynchronous {\pi}-calculus with first-class mailboxes and selective input. The
calculus subsumes the actor model and allows us to analyze networks with
dynamic topologies and varying number of processes possibly mixing different
concurrency abstractions. Well-typed processes are deadlock free and never fail
because of unexpected messages. For a non-trivial class of them, junk freedom
is also guaranteed. We illustrate the expressiveness of the calculus and of the
type system by encoding instances of non-uniform, concurrent objects, binary
sessions extended with joins and forks, and some known actor benchmarks. | [
1,
0,
0,
0,
0,
0
] |
Title: A recursive algorithm and a series expansion related to the homogeneous Boltzmann equation for hard potentials with angular cutoff,
Abstract: We consider the spatially homogeneous Boltzmann equation for hard potentials
with angular cutoff. This equation has a unique conservative weak solution
$(f_t)_{t\geq 0}$, once the initial condition $f_0$ with finite mass and energy
is fixed. Taking advantage of the energy conservation, we propose a recursive
algorithm that produces a $(0,\infty)\times\mathbb{R}^3$ random variable
$(M_t,V_t)$ such that $E[M_t {\bf 1}_{\{V_t \in \cdot\}}]=f_t$. We also write
down a series expansion of $f_t$. Although both the algorithm and the series
expansion might be theoretically interesting in that they explicitly express
$f_t$ in terms of $f_0$, we believe that the algorithm is not very efficient in
practice and that the series expansion is rather intractable. This is a tedious
extension to non-Maxwellian molecules of Wild's sum and of its interpretation
by McKean. | [
0,
0,
1,
0,
0,
0
] |
Title: Asymptotics of maximum likelihood estimation for stable law with $(M)$ parameterization,
Abstract: Asymptotics of maximum likelihood estimation for $\alpha$-stable law are
analytically investigated with $(M)$ parameterization. The consistency and
asymptotic normality are shown on the interior of the whole parameter space.
Although these asymptotics have been proved with $(B)$ parameterization, there
are several gaps between. Especially in the latter, the density, so that scores
and their derivatives are discontinuous at $\alpha=1$ for $\beta\neq 0$ and
usual asymptotics are impossible, whereas in $(M)$ form these quantities are
shown to be continuous on the interior of the parameter space. We fill these
gaps and provide a convenient theory for applied people. We numerically
approximate the Fisher information matrix around the Cauchy law
$(\alpha,\beta)=(1,0)$. The results exhibit continuity at $\alpha=1,\,\beta\neq
0$ and this secures the accuracy of our calculations. | [
0,
0,
1,
1,
0,
0
] |
Title: Enabling Massive Deep Neural Networks with the GraphBLAS,
Abstract: Deep Neural Networks (DNNs) have emerged as a core tool for machine learning.
The computations performed during DNN training and inference are dominated by
operations on the weight matrices describing the DNN. As DNNs incorporate more
stages and more nodes per stage, these weight matrices may be required to be
sparse because of memory limitations. The GraphBLAS.org math library standard
was developed to provide high performance manipulation of sparse weight
matrices and input/output vectors. For sufficiently sparse matrices, a sparse
matrix library requires significantly less memory than the corresponding dense
matrix implementation. This paper provides a brief description of the
mathematics underlying the GraphBLAS. In addition, the equations of a typical
DNN are rewritten in a form designed to use the GraphBLAS. An implementation of
the DNN is given using a preliminary GraphBLAS C library. The performance of
the GraphBLAS implementation is measured relative to a standard dense linear
algebra library implementation. For various sizes of DNN weight matrices, it is
shown that the GraphBLAS sparse implementation outperforms a BLAS dense
implementation as the weight matrix becomes sparser. | [
1,
0,
0,
0,
0,
0
] |
Title: What Can Machine Learning Teach Us about Communications?,
Abstract: Rapid improvements in machine learning over the past decade are beginning to
have far-reaching effects. For communications, engineers with limited domain
expertise can now use off-the-shelf learning packages to design
high-performance systems based on simulations. Prior to the current revolution
in machine learning, the majority of communication engineers were quite aware
that system parameters (such as filter coefficients) could be learned using
stochastic gradient descent. It was not at all clear, however, that more
complicated parts of the system architecture could be learned as well. In this
paper, we discuss the application of machine-learning techniques to two
communications problems and focus on what can be learned from the resulting
systems. We were pleasantly surprised that the observed gains in one example
have a simple explanation that only became clear in hindsight. In essence, deep
learning discovered a simple and effective strategy that had not been
considered earlier. | [
1,
0,
0,
1,
0,
0
] |
Title: The efficiency of community detection by most similar node pairs,
Abstract: Community analysis is an important way to ascertain whether or not a complex
system consists of sub-structures with different properties. In this paper, we
give a two level community structure analysis for the SSCI journal system by
most similar co-citation pattern. Five different strategies for the selection
of most similar node (journal) pairs are introduced. The efficiency is checked
by the normalized mutual information technique. Statistical properties and
comparisons of the community results show that both of the two level detection
could give instructional information for the community structure of complex
systems. Further comparisons of the five strategies indicates that, the most
efficient strategy is to assign nodes with maximum similarity into the same
community whether the similarity information is complete or not, while random
selection generates small world local community with no inside order. These
results give valuable indication for efficient community detection by most
similar node pairs. | [
1,
0,
0,
0,
0,
0
] |
Title: Infinitely many periodic orbits just above the Mañé critical value on the 2-sphere,
Abstract: We introduce a new critical value $c_\infty(L)$ for Tonelli Lagrangians $L$
on the tangent bundle of the 2-sphere without minimizing measures supported on
a point. We show that $c_\infty(L)$ is strictly larger than the Mañé
critical value $c(L)$, and on every energy level $e\in(c(L),c_\infty(L))$ there
exist infinitely many periodic orbits of the Lagrangian system of $L$, one of
which is a local minimizer of the free-period action functional. This has
applications to Finsler metrics of Randers type on the 2-sphere. We show that,
under a suitable criticality assumption on a given Randers metric, after
rescaling its magnetic part with a sufficiently large multiplicative constant,
the new metric admits infinitely many closed geodesics, one of which is a
waist. Examples of critical Randers metrics include the celebrated Katok
metric. | [
0,
0,
1,
0,
0,
0
] |
Title: Decentralized Random Walk-Based Data Collection in Networks,
Abstract: We analyze a decentralized random walk-based algorithm for data collection at
the sink in a multi-hop sensor network. Our algorithm, Random-Collect, which
involves data packets being passed to random neighbors in the network according
to a random walk mechanism, requires no configuration and incurs no routing
overhead. To analyze this method, we model the data generation process as
independent Bernoulli arrivals at the source nodes. We analyze both latency and
throughput in this setting, providing a theoretical lower bound for the
throughput and a theoretical upper bound for the latency. The main contribution
of our paper, however, is the throughput result: we present a general lower
bound on the throughput achieved by our data collection method in terms of the
underlying network parameters. In particular, we show that the rate at which
our algorithm can collect data depends on the spectral gap of the given random
walk's transition matrix and if the random walk is simple then it also depends
on the maximum and minimum degrees of the graph modeling the network. For
latency, we show that the time taken to collect data not only depends on the
worst-case hitting time of the given random walk but also depends on the data
arrival rate. In fact, our latency bound reflects the data rate-latency
trade-off i.e., in order to achieve a higher data rate we need to compromise on
latency and vice-versa. We also discuss some examples that demonstrate that our
lower bound on the data rate is optimal up to constant factors, i.e., there
exists a network topology and sink placement for which the maximum stable data
rate is just a constant factor above our lower bound. | [
1,
0,
0,
0,
0,
0
] |
Title: The redshift distribution of cosmological samples: a forward modeling approach,
Abstract: Determining the redshift distribution $n(z)$ of galaxy samples is essential
for several cosmological probes including weak lensing. For imaging surveys,
this is usually done using photometric redshifts estimated on an
object-by-object basis. We present a new approach for directly measuring the
global $n(z)$ of cosmological galaxy samples, including uncertainties, using
forward modeling. Our method relies on image simulations produced using UFig
(Ultra Fast Image Generator) and on ABC (Approximate Bayesian Computation)
within the $MCCL$ (Monte-Carlo Control Loops) framework. The galaxy population
is modeled using parametric forms for the luminosity functions, spectral energy
distributions, sizes and radial profiles of both blue and red galaxies. We
apply exactly the same analysis to the real data and to the simulated images,
which also include instrumental and observational effects. By adjusting the
parameters of the simulations, we derive a set of acceptable models that are
statistically consistent with the data. We then apply the same cuts to the
simulations that were used to construct the target galaxy sample in the real
data. The redshifts of the galaxies in the resulting simulated samples yield a
set of $n(z)$ distributions for the acceptable models. We demonstrate the
method by determining $n(z)$ for a cosmic shear like galaxy sample from the
4-band Subaru Suprime-Cam data in the COSMOS field. We also complement this
imaging data with a spectroscopic calibration sample from the VVDS survey. We
compare our resulting posterior $n(z)$ distributions to the one derived from
photometric redshifts estimated using 36 photometric bands in COSMOS and find
good agreement. This offers good prospects for applying our approach to current
and future large imaging surveys. | [
0,
1,
0,
0,
0,
0
] |
Title: Analysis of equivalence relation in joint sparse recovery,
Abstract: The joint sparse recovery problem is a generalization of the single
measurement vector problem which is widely studied in Compressed Sensing and it
aims to recovery a set of jointly sparse vectors. i.e. have nonzero entries
concentrated at common location. Meanwhile l_p-minimization subject to matrices
is widely used in a large number of algorithms designed for this problem.
Therefore the main contribution in this paper is two theoretical results about
this technique. The first one is to prove that in every multiple systems of
linear equation, there exists a constant p* such that the original unique
sparse solution also can be recovered from a minimization in l_p quasi-norm
subject to matrices whenever 0< p<p*. The other one is to show an analysis
expression of such p*. Finally, we display the results of one example to
confirm the validity of our conclusions. | [
1,
0,
1,
0,
0,
0
] |
Title: The Stochastic Firefighter Problem,
Abstract: The dynamics of infectious diseases spread is crucial in determining their
risk and offering ways to contain them. We study sequential vaccination of
individuals in networks. In the original (deterministic) version of the
Firefighter problem, a fire breaks out at some node of a given graph. At each
time step, b nodes can be protected by a firefighter and then the fire spreads
to all unprotected neighbors of the nodes on fire. The process ends when the
fire can no longer spread. We extend the Firefighter problem to a probabilistic
setting, where the infection is stochastic. We devise a simple policy that only
vaccinates neighbors of infected nodes and is optimal on regular trees and on
general graphs for a sufficiently large budget. We derive methods for
calculating upper and lower bounds of the expected number of infected
individuals, as well as provide estimates on the budget needed for containment
in expectation. We calculate these explicitly on trees, d-dimensional grids,
and Erdős Rényi graphs. Finally, we construct a state-dependent budget
allocation strategy and demonstrate its superiority over constant budget
allocation on real networks following a first order acquaintance vaccination
policy. | [
1,
0,
0,
0,
0,
0
] |
Title: Generalizations of the 'Linear Chain Trick': Incorporating more flexible dwell time distributions into mean field ODE models,
Abstract: Mathematical modelers have long known of a "rule of thumb" referred to as the
Linear Chain Trick (LCT; aka the Gamma Chain Trick): a technique used to
construct mean field ODE models from continuous-time stochastic state
transition models where the time an individual spends in a given state (i.e.,
the dwell time) is Erlang distributed (i.e., gamma distributed with integer
shape parameter). Despite the LCT's widespread use, we lack general theory to
facilitate the easy application of this technique, especially for complex
models. This has forced modelers to choose between constructing ODE models
using heuristics with oversimplified dwell time assumptions, using time
consuming derivations from first principles, or to instead use non-ODE models
(like integro-differential equations or delay differential equations) which can
be cumbersome to derive and analyze. Here, we provide analytical results that
enable modelers to more efficiently construct ODE models using the LCT or
related extensions. Specifically, we 1) provide novel extensions of the LCT to
various scenarios found in applications; 2) provide formulations of the LCT and
it's extensions that bypass the need to derive ODEs from integral or stochastic
model equations; and 3) introduce a novel Generalized Linear Chain Trick (GLCT)
framework that extends the LCT to a much broader family of distributions,
including the flexible phase-type distributions which can approximate
distributions on $\mathbb{R}^+$ and be fit to data. These results give modelers
more flexibility to incorporate appropriate dwell time assumptions into mean
field ODEs, including conditional dwell time distributions, and these results
help clarify connections between individual-level stochastic model assumptions
and the structure of corresponding mean field ODEs. | [
0,
0,
0,
0,
1,
0
] |
Title: Combining Neural Networks and Tree Search for Task and Motion Planning in Challenging Environments,
Abstract: We consider task and motion planning in complex dynamic environments for
problems expressed in terms of a set of Linear Temporal Logic (LTL)
constraints, and a reward function. We propose a methodology based on
reinforcement learning that employs deep neural networks to learn low-level
control policies as well as task-level option policies. A major challenge in
this setting, both for neural network approaches and classical planning, is the
need to explore future worlds of a complex and interactive environment. To this
end, we integrate Monte Carlo Tree Search with hierarchical neural net control
policies trained on expressive LTL specifications. This paper investigates the
ability of neural networks to learn both LTL constraints and control policies
in order to generate task plans in complex environments. We demonstrate our
approach in a simulated autonomous driving setting, where a vehicle must drive
down a road in traffic, avoid collisions, and navigate an intersection, all
while obeying given rules of the road. | [
1,
0,
0,
0,
0,
0
] |
Title: BT-Nets: Simplifying Deep Neural Networks via Block Term Decomposition,
Abstract: Recently, deep neural networks (DNNs) have been regarded as the
state-of-the-art classification methods in a wide range of applications,
especially in image classification. Despite the success, the huge number of
parameters blocks its deployment to situations with light computing resources.
Researchers resort to the redundancy in the weights of DNNs and attempt to find
how fewer parameters can be chosen while preserving the accuracy at the same
time. Although several promising results have been shown along this research
line, most existing methods either fail to significantly compress a
well-trained deep network or require a heavy fine-tuning process for the
compressed network to regain the original performance. In this paper, we
propose the \textit{Block Term} networks (BT-nets) in which the commonly used
fully-connected layers (FC-layers) are replaced with block term layers
(BT-layers). In BT-layers, the inputs and the outputs are reshaped into two
low-dimensional high-order tensors, then block-term decomposition is applied as
tensor operators to connect them. We conduct extensive experiments on benchmark
datasets to demonstrate that BT-layers can achieve a very large compression
ratio on the number of parameters while preserving the representation power of
the original FC-layers as much as possible. Specifically, we can get a higher
performance while requiring fewer parameters compared with the tensor train
method. | [
1,
0,
0,
1,
0,
0
] |
Title: Resampling Strategy in Sequential Monte Carlo for Constrained Sampling Problems,
Abstract: Sequential Monte Carlo (SMC) methods are a class of Monte Carlo methods that
are used to obtain random samples of a high dimensional random variable in a
sequential fashion. Many problems encountered in applications often involve
different types of constraints. These constraints can make the problem much
more challenging. In this paper, we formulate a general framework of using SMC
for constrained sampling problems based on forward and backward pilot
resampling strategies. We review some existing methods under the framework and
develop several new algorithms. It is noted that all information observed or
imposed on the underlying system can be viewed as constraints. Hence the
approach outlined in this paper can be useful in many applications. | [
0,
0,
0,
1,
0,
0
] |
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