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Hermitian-Yang-Mills connections on collapsing elliptically fibered $K3$ surfaces | Let $X\rightarrow {\mathbb P}^1$ be an elliptically fibered $K3$ surface with
a section, admitting a sequence of Ricci-flat metrics collapsing the fibers.
Let $\mathcal E$ be a generic, holomoprhic $SU(n)$ bundle over $X$ such that
the restriction of $\mathcal E$ to each fiber is semi-stable. Given a sequence
$\Xi_i$ of Hermitian-Yang-Mills connections on $\mathcal E$ corresponding to
this degeneration, we prove that, if $E$ is a given fiber away from a finite
set, the restricted sequence $\Xi_i|_{E}$ converges to a flat connection
uniquely determined by the holomorphic structure on $\mathcal E$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Highly Efficient Human Action Recognition with Quantum Genetic Algorithm Optimized Support Vector Machine | 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 |
The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI | We present a vision-only model for gaming AI which uses a late integration
deep convolutional network architecture trained in a purely supervised
imitation learning context. Although state-of-the-art deep learning models for
video game tasks generally rely on more complex methods such as deep-Q
learning, we show that a supervised model which requires substantially fewer
resources and training time can already perform well at human reaction speeds
on the N64 classic game Super Smash Bros. We frame our learning task as a
30-class classification problem, and our CNN model achieves 80% top-1 and 95%
top-3 validation accuracy. With slight test-time fine-tuning, our model is also
competitive during live simulation with the highest-level AI built into the
game. We will further show evidence through network visualizations that the
network is successfully leveraging temporal information during inference to aid
in decision making. Our work demonstrates that supervised CNN models can
provide good performance in challenging policy prediction tasks while being
significantly simpler and more lightweight than alternatives.
| 1 | 0 | 0 | 0 | 0 | 0 |
Differential relations for almost Belyi maps | 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 |
Non-commutative holomorphic semicocycles | This paper studies holomorphic semicocycles over semigroups in the unit disk,
which take values in an arbitrary unital Banach algebra. We prove that every
such semicocycle is a solution to a corresponding evolution problem. We then
investigate the linearization problem: which semicocycles are cohomologous to
constant semicocycles? In contrast with the case of commutative semicocycles,
in the non-commutative case non-linearizable semicocycles are shown to exist.
Simple conditions for linearizability are derived and are shown to be sharp.
| 0 | 0 | 1 | 0 | 0 | 0 |
Comparative Study of Virtual Machines and Containers for DevOps Developers | 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 |
Developmental tendencies in the Academic Field of Intellectual Property through the Identification of Invisible Colleges | The emergence of intellectual property as an academic issue opens a big gate
to a cross-disciplinary field. Different disciplines start a dialogue in the
framework of the international multilateral treaties in the early 90's. As
global economy demands new knowledge on intellectual property, Science grows at
its own pace. However, the degree of consolidation of cross-disciplinary
academic communities is not clear. In order to know how closely related are
these communities, this paper proposes a mixed methodology to find invisible
colleges in the production about intellectual property. The articles examined
in this paper were extracted from Web of Science. The analyzed period was from
1994 to 2016, taking into account the signature of the agreement on
Trade-Related Aspects of Intellectual Property Rights in the early 90's. A
total amount of 1580 papers were processed through co-citation network
analysis. An especial technique, which combine algorithms of community
detection and defining population of articles through thresholds of shared
references, was applied. In order to contrast the invisible colleges that
emerged with the existence of formal institutional relations, it was made a
qualitative tracking of the authors with respect to their institutional
affiliation, lines of research and meeting places. Both methods show that the
subjects of interest can be grouped into 13 different issues related to
intellectual property field. Even though most of them are related to Laws and
Economics, there are weak linkages between disciplines which could indicate the
construction of a cross-disciplinary field.
| 1 | 1 | 0 | 0 | 0 | 0 |
Periodic fourth-order cubic NLS: Local well-posedness and Non-squeezing property | 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 |
Feature Learning for Meta-Paths in Knowledge Graphs | In this thesis, we study the problem of feature learning on heterogeneous
knowledge graphs. These features can be used to perform tasks such as link
prediction, classification and clustering on graphs. Knowledge graphs provide
rich semantics encoded in the edge and node types. Meta-paths consist of these
types and abstract paths in the graph. Until now, meta-paths can only be used
as categorical features with high redundancy and are therefore unsuitable for
machine learning models. We propose meta-path embeddings to solve this problem
by learning semantical and compact vector representations of them. Current
graph embedding methods only embed nodes and edge types and therefore miss
semantics encoded in the combination of them. Our method embeds meta-paths
using the skipgram model with an extension to deal with the redundancy and high
amount of meta-paths in big knowledge graphs. We critically evaluate our
embedding approach by predicting links on Wikidata. The experiments indicate
that we learn a sensible embedding of the meta-paths but can improve it
further.
| 1 | 0 | 0 | 1 | 0 | 0 |
Closed-Form Exact Inverses of the Weakly Singular and Hypersingular Operators On Disks | We introduce new boundary integral operators which are the exact inverses of
the weakly singular and hypersingular operators for the Laplacian on flat
disks. Moreover, we provide explicit closed forms for them and prove the
continuity and ellipticity of their corresponding bilinear forms in the natural
Sobolev trace spaces. This permit us to derive new Calderón-type identities
that can provide the foundation for optimal operator preconditioning in
Galerkin boundary element methods.
| 0 | 0 | 1 | 0 | 0 | 0 |
Focus on Imaging Methods in Granular Physics | Granular materials are complex multi-particle ensembles in which macroscopic
properties are largely determined by inter-particle interactions between their
numerous constituents. In order to understand and to predict their macroscopic
physical behavior, it is necessary to analyze the composition and interactions
at the level of individual contacts and grains. To do so requires the ability
to image individual particles and their local configurations to high precision.
A variety of competing and complementary imaging techniques have been developed
for that task. In this introductory paper accompanying the Focus Issue, we
provide an overview of these imaging methods and discuss their advantages and
drawbacks, as well as their limits of application.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Stochastic Matching Problem: Beating Half with a Non-Adaptive Algorithm | In the stochastic matching problem, we are given a general (not necessarily
bipartite) graph $G(V,E)$, where each edge in $E$ is realized with some
constant probability $p > 0$ and the goal is to compute a bounded-degree
(bounded by a function depending only on $p$) subgraph $H$ of $G$ such that the
expected maximum matching size in $H$ is close to the expected maximum matching
size in $G$. The algorithms in this setting are considered non-adaptive as they
have to choose the subgraph $H$ without knowing any information about the set
of realized edges in $G$. Originally motivated by an application to kidney
exchange, the stochastic matching problem and its variants have received
significant attention in recent years.
The state-of-the-art non-adaptive algorithms for stochastic matching achieve
an approximation ratio of $\frac{1}{2}-\epsilon$ for any $\epsilon > 0$,
naturally raising the question that if $1/2$ is the limit of what can be
achieved with a non-adaptive algorithm. In this work, we resolve this question
by presenting the first algorithm for stochastic matching with an approximation
guarantee that is strictly better than $1/2$: the algorithm computes a subgraph
$H$ of $G$ with the maximum degree $O(\frac{\log{(1/ p)}}{p})$ such that the
ratio of expected size of a maximum matching in realizations of $H$ and $G$ is
at least $1/2+\delta_0$ for some absolute constant $\delta_0 > 0$. The degree
bound on $H$ achieved by our algorithm is essentially the best possible (up to
an $O(\log{(1/p)})$ factor) for any constant factor approximation algorithm,
since an $\Omega(\frac{1}{p})$ degree in $H$ is necessary for a vertex to
acquire at least one incident edge in a realization.
| 1 | 0 | 0 | 0 | 0 | 0 |
Measuring the unmeasurable - a project of domestic violence risk prediction and management | 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 |
Attaining Capacity with Algebraic Geometry Codes through the $(U|U+V)$ Construction and Koetter-Vardy Soft Decoding | In this paper we show how to attain the capacity of discrete symmetric
channels with polynomial time decoding complexity by considering iterated
$(U|U+V)$ constructions with Reed-Solomon code or algebraic geometry code
components. These codes are decoded with a recursive computation of the {\em a
posteriori} probabilities of the code symbols together with the Koetter-Vardy
soft decoder used for decoding the code components in polynomial time. We show
that when the number of levels of the iterated $(U|U+V)$ construction tends to
infinity, we attain the capacity of any discrete symmetric channel in this way.
This result follows from the polarization theorem together with a simple lemma
explaining how the Koetter-Vardy decoder behaves for Reed-Solomon codes of rate
close to $1$. However, even if this way of attaining the capacity of a
symmetric channel is essentially the Ar{\i}kan polarization theorem, there are
some differences with standard polar codes.
Indeed, with this strategy we can operate succesfully close to channel
capacity even with a small number of levels of the iterated $(U|U+V)$
construction and the probability of error decays quasi-exponentially with the
codelength in such a case (i.e. exponentially if we forget about the
logarithmic terms in the exponent). We can even improve on this result by
considering the algebraic geometry codes constructed in \cite{TVZ82}. In such a
case, the probability of error decays exponentially in the codelength for any
rate below the capacity of the channel. Moreover, when comparing this strategy
to Reed-Solomon codes (or more generally algebraic geometry codes) decoded with
the Koetter-Vardy decoding algorithm, it does not only improve the noise level
that the code can tolerate, it also results in a significant complexity gain.
| 1 | 0 | 0 | 0 | 0 | 0 |
Embedded real-time monitoring using SystemC in IMA network | 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 |
One pixel attack for fooling deep neural networks | 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 |
A computer simulation of the Volga River hydrological regime: a problem of water-retaining dam optimal location | We investigate of a special dam optimal location at the Volga river in area
of the Akhtuba left sleeve beginning (7 \, km to the south of the Volga
Hydroelectric Power Station dam). We claim that a new water-retaining dam can
resolve the key problem of the Volga-Akhtuba floodplain related to insufficient
water amount during the spring flooding due to the overregulation of the Lower
Volga. By using a numerical integration of Saint-Vacant equations we study the
water dynamics across the northern part of the Volga-Akhtuba floodplain with
taking into account its actual topography. As the result we found an amount of
water $V_A$ passing to the Akhtuba during spring period for a given water flow
through the Volga Hydroelectric Power Station (so-called hydrograph which
characterises the water flow per unit of time). By varying the location of the
water-retaining dam $ x_d, y_d $ we obtained various values of $V_A (x_d, y_d)
$ as well as various flow spatial structure on the territory during the flood
period. Gradient descent method provide us the dam coordinated with the maximum
value of ${V_A}$. Such approach to the dam location choice let us to find the
best solution, that the value $V_A$ increases by a factor of 2. Our analysis
demonstrate a good potential of the numerical simulations in the field of
hydraulic works.
| 1 | 0 | 0 | 0 | 0 | 0 |
Multi-proton bunch driven hollow plasma wakefield acceleration in the nonlinear regime | 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 |
Self corrective Perturbations for Semantic Segmentation and Classification | Convolutional Neural Networks have been a subject of great importance over
the past decade and great strides have been made in their utility for producing
state of the art performance in many computer vision problems. However, the
behavior of deep networks is yet to be fully understood and is still an active
area of research. In this work, we present an intriguing behavior: pre-trained
CNNs can be made to improve their predictions by structurally perturbing the
input. We observe that these perturbations - referred as Guided Perturbations -
enable a trained network to improve its prediction performance without any
learning or change in network weights. We perform various ablative experiments
to understand how these perturbations affect the local context and feature
representations. Furthermore, we demonstrate that this idea can improve
performance of several existing approaches on semantic segmentation and scene
labeling tasks on the PASCAL VOC dataset and supervised classification tasks on
MNIST and CIFAR10 datasets.
| 1 | 0 | 0 | 1 | 0 | 0 |
Large-Scale Mapping of Human Activity using Geo-Tagged Videos | 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 |
Structure of a Parabolic Partial Differential Equation on Graphs and Digital spaces. Solution of PDE on Digital Spaces: a Klein Bottle, a Projective Plane, a 4D Sphere and a Moebius Band | This paper studies the structure of a parabolic partial differential equation
on graphs and digital n-dimensional manifolds, which are digital models of
continuous n-manifolds. Conditions for the existence of solutions of equations
are determined and investigated. Numerical solutions of the equation on a Klein
bottle, a projective plane, a 4D sphere and a Moebius strip are presented.
| 1 | 0 | 1 | 0 | 0 | 0 |
GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks | In this paper, we generally formulate the dynamics prediction problem of
various network systems (e.g., the prediction of mobility, traffic and
topology) as the temporal link prediction task. Different from conventional
techniques of temporal link prediction that ignore the potential non-linear
characteristics and the informative link weights in the dynamic network, we
introduce a novel non-linear model GCN-GAN to tackle the challenging temporal
link prediction task of weighted dynamic networks. The proposed model leverages
the benefits of the graph convolutional network (GCN), long short-term memory
(LSTM) as well as the generative adversarial network (GAN). Thus, the dynamics,
topology structure and evolutionary patterns of weighted dynamic networks can
be fully exploited to improve the temporal link prediction performance.
Concretely, we first utilize GCN to explore the local topological
characteristics of each single snapshot and then employ LSTM to characterize
the evolving features of the dynamic networks. Moreover, GAN is used to enhance
the ability of the model to generate the next weighted network snapshot, which
can effectively tackle the sparsity and the wide-value-range problem of edge
weights in real-life dynamic networks. To verify the model's effectiveness, we
conduct extensive experiments on four datasets of different network systems and
application scenarios. The experimental results demonstrate that our model
achieves impressive results compared to the state-of-the-art competitors.
| 1 | 0 | 0 | 0 | 0 | 0 |
Efficient Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs | 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.
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A multi-instrument non-parametric reconstruction of the electron pressure profile in the galaxy cluster CLJ1226.9+3332 | Context: In the past decade, sensitive, resolved Sunyaev-Zel'dovich (SZ)
studies of galaxy clusters have become common. Whereas many previous SZ studies
have parameterized the pressure profiles of galaxy clusters, non-parametric
reconstructions will provide insights into the thermodynamic state of the
intracluster medium (ICM). Aims: We seek to recover the non-parametric pressure
profiles of the high redshift ($z=0.89$) galaxy cluster CLJ 1226.9+3332 as
inferred from SZ data from the MUSTANG, NIKA, Bolocam, and Planck instruments,
which all probe different angular scales. Methods: Our non-parametric algorithm
makes use of logarithmic interpolation, which under the assumption of
ellipsoidal symmetry is analytically integrable. For MUSTANG, NIKA, and Bolocam
we derive a non-parametric pressure profile independently and find good
agreement among the instruments. In particular, we find that the non-parametric
profiles are consistent with a fitted gNFW profile. Given the ability of Planck
to constrain the total signal, we include a prior on the integrated Compton Y
parameter as determined by Planck. Results: For a given instrument, constraints
on the pressure profile diminish rapidly beyond the field of view. The overlap
in spatial scales probed by these four datasets is therefore critical in
checking for consistency between instruments. By using multiple instruments,
our analysis of CLJ 1226.9+3332 covers a large radial range, from the central
regions to the cluster outskirts: $0.05 R_{500} < r < 1.1 R_{500}$. This is a
wider range of spatial scales than is typical recovered by SZ instruments.
Similar analyses will be possible with the new generation of SZ instruments
such as NIKA2 and MUSTANG2.
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Recognizing Union-Find trees built up using union-by-rank strategy is NP-complete | Disjoint-Set forests, consisting of Union-Find trees, are data structures
having a widespread practical application due to their efficiency. Despite them
being well-known, no exact structural characterization of these trees is known
(such a characterization exists for Union trees which are constructed without
using path compression) for the case assuming union-by-rank strategy for
merging. In this paper we provide such a characterization by means of a simple
push operation and show that the decision problem whether a given tree (along
with the rank info of its nodes) is a Union-Find tree is NP-complete,
complementing our earlier similar result for the union-by-size strategy.
| 1 | 0 | 0 | 0 | 0 | 0 |
A Competitive Algorithm for Online Multi-Robot Exploration of a Translating Plume | 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.
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Warped Riemannian metrics for location-scale models | 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 $n$.
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Admissibility of solution estimators for stochastic optimization | We look at stochastic optimization problems through the lens of statistical
decision theory. In particular, we address admissibility, in the statistical
decision theory sense, of the natural sample average estimator for a stochastic
optimization problem (which is also known as the empirical risk minimization
(ERM) rule in learning literature). It is well known that for general
stochastic optimization problems, the sample average estimator may not be
admissible. This is known as Stein's paradox in the statistics literature. We
show in this paper that for optimizing stochastic linear functions over compact
sets, the sample average estimator is admissible.
| 0 | 0 | 1 | 1 | 0 | 0 |
Min-Max Regret Scheduling To Minimize the Total Weight of Late Jobs With Interval Uncertainty | We study the single machine scheduling problem with the objective to minimize
the total weight of late jobs. It is assumed that the processing times of jobs
are not exactly known at the time when a complete schedule must be dispatched.
Instead, only interval bounds for these parameters are given. In contrast to
the stochastic optimization approach, we consider the problem of finding a
robust schedule, which minimizes the maximum regret of a solution. Heuristic
algorithm based on mixed-integer linear programming is presented and examined
through computational experiments.
| 1 | 0 | 0 | 0 | 0 | 0 |
Correcting rural building annotations in OpenStreetMap using convolutional neural networks | 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 |
Closed-form Harmonic Contrast Control with Surface Impedance Coatings for Conductive Objects | 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 |
Numerical methods to prevent pressure oscillations in transcritical flows | The accurate and robust simulation of transcritical real-fluid effects is
crucial for many engineering applications, such as fuel injection in internal
combustion engines, rocket engines and gas turbines. For example, in diesel
engines, the liquid fuel is injected into the ambient gas at a pressure that
exceeds its critical value, and the fuel jet will be heated to a supercritical
temperature before combustion takes place. This process is often referred to as
transcritical injection. The largest thermodynamic gradient in the
transcritical regime occurs as the fluid undergoes a liquid-like to a gas-like
transition when crossing the pseudo-boiling line (Yang 2000, Oschwald et al.
2006, Banuti 2015). The complex processes during transcritical injection are
still not well understood. Therefore, to provide insights into high-pressure
combustion systems, accurate and robust numerical simulation tools are required
for the characterization of supercritical and transcritical flows.
| 0 | 1 | 0 | 0 | 0 | 0 |
Shape Convergence for Aggregate Tiles in Conformal Tilings | 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 |
Performance and sensitivity of vortex coronagraphs on segmented space telescopes | 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 |
Classical Spacetime Structure | I discuss several issues related to "classical" spacetime structure. I review
Galilean, Newtonian, and Leibnizian spacetimes, and briefly describe more
recent developments. The target audience is undergraduates and early graduate
students in philosophy; the presentation avoids mathematical formalism as much
as possible.
| 0 | 1 | 0 | 0 | 0 | 0 |
Fractal curves from prime trigonometric series | We study the convergence of the parameter family of series
$$V_{\alpha,\beta}(t)=\sum_{p}p^{-\alpha}\exp(2\pi i p^{\beta}t),\quad
\alpha,\beta \in \mathbb{R}_{>0},\; t \in [0,1)$$ defined over prime numbers
$p$, and subsequently, their differentiability properties. The visible fractal
nature of the graphs as a function of $\alpha,\beta$ is analyzed in terms of
Hölder continuity, self similarity and fractal dimension, backed with
numerical results. We also discuss the link of this series to random walks and
consequently, explore numerically its random properties.
| 0 | 0 | 1 | 0 | 0 | 0 |
Facets on the convex hull of $d$-dimensional Brownian and Lévy motion | For stationary, homogeneous Markov processes (viz., Lévy processes,
including Brownian motion) in dimension $d\geq 3$, we establish an exact
formula for the average number of $(d-1)$-dimensional facets that can be
defined by $d$ points on the process's path. This formula defines a
universality class in that it is independent of the increments' distribution,
and it admits a closed form when $d=3$, a case which is of particular interest
for applications in biophysics, chemistry and polymer science.
We also show that the asymptotical average number of facets behaves as
$\langle \mathcal{F}_T^{(d)}\rangle \sim 2\left[\ln \left( T/\Delta
t\right)\right]^{d-1}$, where $T$ is the total duration of the motion and
$\Delta t$ is the minimum time lapse separating points that define a facet.
| 0 | 1 | 1 | 0 | 0 | 0 |
Local systems on complements of arrangements of smooth, complex algebraic hypersurfaces | 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 |
Some Open Problems in Random Matrix Theory and the Theory of Integrable Systems. II | 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 |
A semi-parametric estimation for max-mixture spatial processes | 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 |
Spectroscopic Observation and Analysis of HII regions in M33 with MMT: Temperatures and Oxygen Abundances | 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 |
Output-only parameter identification of a colored-noise-driven Van der Pol oscillator -- Thermoacoustic instabilities as an example | The problem of output-only parameter identification for nonlinear oscillators
forced by colored noise is considered. In this context, it is often assumed
that the forcing noise is white, since its actual spectral content is unknown.
The impact of this white noise forcing assumption upon parameter identification
is quantitatively analyzed. First, a Van der Pol oscillator forced by an
Ornstein-Uhlenbeck process is considered. Second, the practical case of
thermoacoustic limit cycles in combustion chambers with turbulence-induced
forcing is investigated. It is shown that in both cases, the system parameters
are accurately identified if time signals are appropriately band-pass filtered
around the oscillator eigenfrequency.
| 0 | 1 | 0 | 0 | 0 | 0 |
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy | 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 |
Anomaly Detection in Hierarchical Data Streams under Unknown Models | We consider the problem of detecting a few targets among a large number of
hierarchical data streams. The data streams are modeled as random processes
with unknown and potentially heavy-tailed distributions. The objective is an
active inference strategy that determines, sequentially, which data stream to
collect samples from in order to minimize the sample complexity under a
reliability constraint. We propose an active inference strategy that induces a
biased random walk on the tree-structured hierarchy based on confidence bounds
of sample statistics. We then establish its order optimality in terms of both
the size of the search space (i.e., the number of data streams) and the
reliability requirement. The results find applications in hierarchical heavy
hitter detection, noisy group testing, and adaptive sampling for active
learning, classification, and stochastic root finding.
| 1 | 0 | 0 | 0 | 0 | 0 |
Intelligent Parameter Tuning in Optimization-based Iterative CT Reconstruction via Deep Reinforcement Learning | 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 |
Adversarial Examples that Fool Detectors | 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 |
FLUX: Progressive State Estimation Based on Zakai-type Distributed Ordinary Differential Equations | We propose a homotopy continuation method called FLUX for approximating
complicated probability density functions. It is based on progressive
processing for smoothly morphing a given density into the desired one.
Distributed ordinary differential equations (DODEs) with an artificial time
$\gamma \in [0,1]$ are derived for describing the evolution from the initial
density to the desired final density. For a finite-dimensional parametrization,
the DODEs are converted to a system of ordinary differential equations (SODEs),
which are solved for $\gamma \in [0,1]$ and return the desired result for
$\gamma=1$. This includes parametric representations such as Gaussians or
Gaussian mixtures and nonparametric setups such as sample sets. In the latter
case, we obtain a particle flow between the two densities along the artificial
time.
FLUX is applied to state estimation in stochastic nonlinear dynamic systems
by gradual inclusion of measurement information. The proposed approximation
method (1) is fast, (2) can be applied to arbitrary nonlinear systems and is
not limited to additive noise, (3) allows for target densities that are only
known at certain points, (4) does not require optimization, (5) does not
require the solution of partial differential equations, and (6) works with
standard procedures for solving SODEs. This manuscript is limited to the
one-dimensional case and a fixed number of parameters during the progression.
Future extensions will include consideration of higher dimensions and on the
fly adaption of the number of parameters.
| 1 | 0 | 0 | 0 | 0 | 0 |
Direct and mediating influences of user-developer perception gaps in requirements understanding on user participation | 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 |
Equivariant Schrödinger maps from two dimensional hyperbolic space | In this article, we consider the equivariant Schrödinger map from $\Bbb
H^2$ to $\Bbb S^2$ which converges to the north pole of $\Bbb S^2$ at the
origin and spatial infinity of the hyperbolic space. If the energy of the data
is less than $4\pi$, we show that the local existence of Schrödinger map.
Furthermore, if the energy of the data sufficiently small, we prove the
solutions are global in time.
| 0 | 0 | 1 | 0 | 0 | 0 |
A fast numerical method for ideal fluid flow in domains with multiple stirrers | 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 |
A Short Survey on Probabilistic Reinforcement Learning | 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 |
Modification of low-temperature silicon dioxide films under the influence of technology factors | The structure, composition and electrophysical characteristics of
low-temperature silicon dioxide films under influence of various technology
factors, such as ion implantation, laser irradiation, thermal and photonic
annealing, have been studied. Silicon dioxide films have been obtained by
monosilane oxidation using plasma chemical method, reactive cathode sputtering,
and tetraethoxysilane pyrolysis. In the capacity of substrates, germanium,
silicon, gallium arsenide and gallium nitride were used. Structure and
composition of the dielectric films were analyzed by methods of infrared
transmission spectroscopy and frustrated internal reflectance spectroscopy.
Analysis of modification efficiency of low-temperature silicon dioxide films
has been made depending on the substrate type, structure and properties of the
films, their moisture permeability, dielectric deposition technique, type and
dose of implantation ions, temperature and kind of annealing.
| 0 | 1 | 0 | 0 | 0 | 0 |
GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger | This work describes the development of a high-resolution tactile-sensing
finger for robot grasping. This finger, inspired by previous GelSight sensing
techniques, features an integration that is slimmer, more robust, and with more
homogeneous output than previous vision-based tactile sensors. To achieve a
compact integration, we redesign the optical path from illumination source to
camera by combining light guides and an arrangement of mirror reflections. We
parameterize the optical path with geometric design variables and describe the
tradeoffs between the finger thickness, the depth of field of the camera, and
the size of the tactile sensing area. The sensor sustains the wear from
continuous use -- and abuse -- in grasping tasks by combining tougher materials
for the compliant soft gel, a textured fabric skin, a structurally rigid body,
and a calibration process that maintains homogeneous illumination and contrast
of the tactile images during use. Finally, we evaluate the sensor's durability
along four metrics that track the signal quality during more than 3000 grasping
experiments.
| 1 | 0 | 0 | 0 | 0 | 0 |
The Muon g-2 experiment at Fermilab | 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 |
Ringel duality as an instance of Koszul duality | In their previous work, S. Koenig, S. Ovsienko and the second author showed
that every quasi-hereditary algebra is Morita equivalent to the right algebra,
i.e. the opposite algebra of the left dual, of a coring. Let $A$ be an
associative algebra and $V$ an $A$-coring whose right algebra $R$ is
quasi-hereditary. In this paper, we give a combinatorial description of an
associative algebra $B$ and a $B$-coring $W$ whose right algebra is the Ringel
dual of $R$. We apply our results in small examples to obtain restrictions on
the $A_\infty$-structure of the $\textrm{Ext}$-algebra of standard modules over
a class of quasi-hereditary algebras related to birational morphisms of smooth
surfaces.
| 0 | 0 | 1 | 0 | 0 | 0 |
Bypass Fraud Detection: Artificial Intelligence Approach | 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 |
Absence of cyclotron resonance in the anomalous metallic phase in InO$_x$ | It is observed that many thin superconducting films with not too high
disorder level (generally R$_N/\Box \leq 2000 \Omega$) placed in magnetic field
show an anomalous metallic phase where the resistance is low but still finite
as temperature goes to zero. Here we report in weakly disordered amorphous
InO$_x$ thin films, that this "Bose metal" metal phase possesses no cyclotron
resonance and hence non-Drude electrodynamics. Its microwave dynamical
conductivity shows signatures of remaining short-range superconducting
correlations and strong phase fluctuations through the whole anomalous regime.
The absence of a finite frequency resonant mode can be associated with a
vanishing downstream component of the vortex current parallel to the
supercurrent and an emergent particle-hole symmetry of this anomalous metal,
which establishes its non-Fermi liquid character.
| 0 | 1 | 0 | 0 | 0 | 0 |
Scenario Reduction Revisited: Fundamental Limits and Guarantees | 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 |
Testing the science/technology relationship by analysis of patent citations of scientific papers after decomposition of both science and technology | 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 |
The Informativeness of $k$-Means and Dimensionality Reduction for Learning Mixture Models | The learning of mixture models can be viewed as a clustering problem. Indeed,
given data samples independently generated from a mixture of distributions, we
often would like to find the correct target clustering of the samples according
to which component distribution they were generated from. For a clustering
problem, practitioners often choose to use the simple k-means algorithm.
k-means attempts to find an optimal clustering which minimizes the
sum-of-squared distance between each point and its cluster center. In this
paper, we provide sufficient conditions for the closeness of any optimal
clustering and the correct target clustering assuming that the data samples are
generated from a mixture of log-concave distributions. Moreover, we show that
under similar or even weaker conditions on the mixture model, any optimal
clustering for the samples with reduced dimensionality is also close to the
correct target clustering. These results provide intuition for the
informativeness of k-means (with and without dimensionality reduction) as an
algorithm for learning mixture models. We verify the correctness of our
theorems using numerical experiments and demonstrate using datasets with
reduced dimensionality significant speed ups for the time required to perform
clustering.
| 1 | 0 | 0 | 1 | 0 | 0 |
A Mention-Ranking Model for Abstract Anaphora Resolution | 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 |
Harmonic density interpolation methods for high-order evaluation of Laplace layer potentials in 2D and 3D | 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 |
Stochastic Low-Rank Bandits | Many problems in computer vision and recommender systems involve low-rank
matrices. In this work, we study the problem of finding the maximum entry of a
stochastic low-rank matrix from sequential observations. At each step, a
learning agent chooses pairs of row and column arms, and receives the noisy
product of their latent values as a reward. The main challenge is that the
latent values are unobserved. We identify a class of non-negative matrices
whose maximum entry can be found statistically efficiently and propose an
algorithm for finding them, which we call LowRankElim. We derive a
$\DeclareMathOperator{\poly}{poly} O((K + L) \poly(d) \Delta^{-1} \log n)$
upper bound on its $n$-step regret, where $K$ is the number of rows, $L$ is the
number of columns, $d$ is the rank of the matrix, and $\Delta$ is the minimum
gap. The bound depends on other problem-specific constants that clearly do not
depend $K L$. To the best of our knowledge, this is the first such result in
the literature.
| 1 | 0 | 0 | 1 | 0 | 0 |
Temporal resolution of a pre-maximum halt in a Classical Nova: V5589 Sgr observed with STEREO HI-1B | Classical novae show a rapid rise in optical brightness over a few hours.
Until recently the rise phase, particularly the phenomenon of a pre-maximum
halt, was observed sporadically. Solar observation satellites observing Coronal
Mass Ejections enable us to observe the pre-maximum phase in unprecedented
temporal resolution. We present observations of V5589 Sgr with STEREO HI-1B at
a cadence of 40 min, the highest to date. We temporally resolve a pre-maximum
halt for the first time, with two examples each rising over 40 min then
declining within 80 min. Comparison with a grid of outburst models suggests
this double peak, and the overall rise timescale, are consistent with a white
dwarf mass, central temperature and accretion rate close to 1.0 solar mass,
5x10^7 K and 10^-10 solar masses per year respectively. The modelling formally
predicts mass loss onset at JD 2456038.2391+/-0.0139, 12 hrs before optical
maximum. The model assumes a main-sequence donor. Observational evidence is for
a subgiant companion; meaning the accretion rate is under-estimated.
Post-maximum we see erratic variations commonly associated with much slower
novae. Estimating the decline rate difficult, but we place the time to decline
two magnitudes as 2.1 < t_2(days) < 3.9 making V5589 Sgr a "very fast" nova.
The brightest point defines "day 0" as JD 2456038.8224+/-0.0139, although at
this high cadence the meaning of the observed maximum becomes difficult to
define. We suggest that such erratic variability normally goes undetected in
faster novae due to the low cadence of typical observations; implying erratic
behaviour is not necessarily related to the rate of decline.
| 0 | 1 | 0 | 0 | 0 | 0 |
The Thermophysical Properties of the Bagnold Dunes, Mars: Ground-truthing Orbital Data | 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).
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Entrywise Eigenvector Analysis of Random Matrices with Low Expected Rank | Recovering low-rank structures via eigenvector perturbation analysis is a
common problem in statistical machine learning, such as in factor analysis,
community detection, ranking, matrix completion, among others. While a large
variety of results provide tight bounds on the average errors between empirical
and population statistics of eigenvectors, fewer results are tight for
entrywise analyses, which are critical for a number of problems such as
community detection and ranking.
This paper investigates the entrywise perturbation analysis for a large class
of random matrices whose expectations are low-rank, including community
detection, synchronization ($\mathbb{Z}_2$-spiked Wigner model) and matrix
completion models. Denoting by $\{u_k\}$, respectively $\{u_k^*\}$, the
eigenvectors of a random matrix $A$, respectively $\mathbb{E} A$, the paper
characterizes cases for which $$u_k \approx \frac{A u_k^*}{\lambda_k^*}$$
serves as a first-order approximation under the $\ell_\infty$ norm. The fact
that the approximation is both tight and linear in the random matrix $A$ allows
for sharp comparisons of $u_k$ and $u_k^*$. In particular, it allows to compare
the signs of $u_k$ and $u_k^*$ even when $\| u_k - u_k^*\|_{\infty}$ is large,
which in turn allows to settle the conjecture in Abbe et al. (2016) that the
spectral algorithm achieves exact recovery in the stochastic block model
without any trimming or cleaning steps. The results are further extended to the
perturbation of eigenspaces, providing new bounds for $\ell_\infty$-type errors
in noisy matrix completion.
| 0 | 0 | 1 | 1 | 0 | 0 |
Algorithmic Trading with Fitted Q Iteration and Heston Model | 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 |
Superrigidity of actions on finite rank median spaces | Finite rank median spaces are a simultaneous generalisation of finite
dimensional CAT(0) cube complexes and real trees. If $\Gamma$ is an irreducible
lattice in a product of rank one simple Lie groups, we show that every action
of $\Gamma$ on a complete, finite rank median space has a global fixed point.
This is in sharp contrast with the behaviour of actions on infinite rank median
spaces.
The fixed point property is obtained as corollary to a superrigidity result;
the latter holds for irreducible lattices in arbitrary products of compactly
generated groups.
In previous work, we introduced "Roller compactifications" of median spaces;
these generalise a well-known construction in the case of cube complexes. We
provide a reduced $1$-cohomology class that detects group actions with a finite
orbit in the Roller compactification. Even for CAT(0) cube complexes, only
second bounded cohomology classes were known with this property, due to
Chatterji-Fernós-Iozzi. As a corollary, we observe that, in Gromov's density
model, random groups at low density do not have Shalom's property $H_{FD}$.
| 0 | 0 | 1 | 0 | 0 | 0 |
Detecting and Explaining Causes From Text For a Time Series Event | Explaining underlying causes or effects about events is a challenging but
valuable task. We define a novel problem of generating explanations of a time
series event by (1) searching cause and effect relationships of the time series
with textual data and (2) constructing a connecting chain between them to
generate an explanation. To detect causal features from text, we propose a
novel method based on the Granger causality of time series between features
extracted from text such as N-grams, topics, sentiments, and their composition.
The generation of the sequence of causal entities requires a commonsense
causative knowledge base with efficient reasoning. To ensure good
interpretability and appropriate lexical usage we combine symbolic and neural
representations, using a neural reasoning algorithm trained on commonsense
causal tuples to predict the next cause step. Our quantitative and human
analysis show empirical evidence that our method successfully extracts
meaningful causality relationships between time series with textual features
and generates appropriate explanation between them.
| 1 | 0 | 0 | 0 | 0 | 0 |
Mailbox Types for Unordered Interactions | 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 |
The Complexity of Counting Surjective Homomorphisms and Compactions | A homomorphism from a graph G to a graph H is a function from the vertices of
G to the vertices of H that preserves edges. A homomorphism is surjective if it
uses all of the vertices of H and it is a compaction if it uses all of the
vertices of H and all of the non-loop edges of H. Hell and Nesetril gave a
complete characterisation of the complexity of deciding whether there is a
homomorphism from an input graph G to a fixed graph H. A complete
characterisation is not known for surjective homomorphisms or for compactions,
though there are many interesting results. Dyer and Greenhill gave a complete
characterisation of the complexity of counting homomorphisms from an input
graph G to a fixed graph H. In this paper, we give a complete characterisation
of the complexity of counting surjective homomorphisms from an input graph G to
a fixed graph H and we also give a complete characterisation of the complexity
of counting compactions from an input graph G to a fixed graph H. In an
addendum we use our characterisations to point out a dichotomy for the
complexity of the respective approximate counting problems (in the connected
case).
| 1 | 0 | 0 | 0 | 0 | 0 |
A recursive algorithm and a series expansion related to the homogeneous Boltzmann equation for hard potentials with angular cutoff | 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 |
Periodic auxetics: Structure and design | Materials science has adopted the term of auxetic behavior for structural
deformations where stretching in some direction entails lateral widening,
rather than lateral shrinking. Most studies, in the last three decades, have
explored repetitive or cellular structures and used the notion of negative
Poisson's ratio as the hallmark of auxetic behavior. However, no general
auxetic principle has been established from this perspective. In the present
paper, we show that a purely geometric approach to periodic auxetics is apt to
identify essential characteristics of frameworks with auxetic deformations and
can generate a systematic and endless series of periodic auxetic designs. The
critical features refer to convexity properties expressed through families of
homothetic ellipsoids.
| 0 | 0 | 1 | 0 | 0 | 0 |
Asymptotics of maximum likelihood estimation for stable law with $(M)$ parameterization | 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 |
Enabling Massive Deep Neural Networks with the GraphBLAS | 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 |
Univariate and Bivariate Geometric Discrete Generalized Exponential Distributions | Marshall and Olkin (1997, Biometrika, 84, 641 - 652) introduced a very
powerful method to introduce an additional parameter to a class of continuous
distribution functions and hence it brings more flexibility to the model. They
have demonstrated their method for the exponential and Weibull classes. In the
same paper they have briefly indicated regarding its bivariate extension. The
main aim of this paper is to introduce the same method, for the first time, to
the class of discrete generalized exponential distributions both for the
univariate and bivariate cases. We investigate several properties of the
proposed univariate and bivariate classes. The univariate class has three
parameters, whereas the bivariate class has five parameters. It is observed
that depending on the parameter values the univariate class can be both zero
inflated as well as heavy tailed. We propose to use EM algorithm to estimate
the unknown parameters. Small simulation experiments have been performed to see
the effectiveness of the proposed EM algorithm, and a bivariate data set has
been analyzed and it is observed that the proposed models and the EM algorithm
work quite well in practice.
| 0 | 0 | 0 | 1 | 0 | 0 |
What Can Machine Learning Teach Us about Communications? | 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 |
Global stability of a network-based SIRS epidemic model with nonmonotone incidence rate | This paper studies the dynamics of a network-based SIRS epidemic model with
vaccination and a nonmonotone incidence rate. This type of nonlinear incidence
can be used to describe the psychological or inhibitory effect from the
behavioral change of the susceptible individuals when the number of infective
individuals on heterogeneous networks is getting larger. Using the analytical
method, epidemic threshold $R_0$ is obtained. When $R_0$ is less than one, we
prove the disease-free equilibrium is globally asymptotically stable and the
disease dies out, while $R_0$ is greater than one, there exists a unique
endemic equilibrium. By constructing a suitable Lyapunov function, we also
prove the endemic equilibrium is globally asymptotically stable if the
inhibitory factor $\alpha$ is sufficiently large. Numerical experiments are
also given to support the theoretical results. It is shown both theoretically
and numerically a larger $\alpha$ can accelerate the extinction of the disease
and reduce the level of disease.
| 0 | 0 | 0 | 0 | 1 | 0 |
Slicewise definability in first-order logic with bounded quantifier rank | For every $q\in \mathbb N$ let $\textrm{FO}_q$ denote the class of sentences
of first-order logic FO of quantifier rank at most $q$. If a graph property can
be defined in $\textrm{FO}_q$, then it can be decided in time $O(n^q)$. Thus,
minimizing $q$ has favorable algorithmic consequences. Many graph properties
amount to the existence of a certain set of vertices of size $k$. Usually this
can only be expressed by a sentence of quantifier rank at least $k$. We use the
color-coding method to demonstrate that some (hyper)graph problems can be
defined in $\textrm{FO}_q$ where $q$ is independent of $k$. This property of a
graph problem is equivalent to the question of whether the corresponding
parameterized problem is in the class $\textrm{para-AC}^0$.
It is crucial for our results that the FO-sentences have access to built-in
addition and multiplication. It is known that then FO corresponds to the
circuit complexity class uniform $\textrm{AC}^0$. We explore the connection
between the quantifier rank of FO-sentences and the depth of
$\textrm{AC}^0$-circuits, and prove that $\textrm{FO}_q \subsetneq
\textrm{FO}_{q+1}$ for structures with built-in addition and multiplication.
| 1 | 0 | 0 | 0 | 0 | 0 |
The efficiency of community detection by most similar node pairs | 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 |
Composite Adaptive Control for Bilateral Teleoperation Systems without Persistency of Excitation | Composite adaptive control schemes, which use both the system tracking errors
and the prediction error to drive the update laws, have become widespread in
achieving an improvement of system performance. However, a strong
persistent-excitation (PE) condition should be satisfied to guarantee the
parameter convergence. This paper proposes a novel composite adaptive control
to guarantee parameter convergence without PE condition for nonlinear
teleoperation systems with dynamic uncertainties and time-varying communication
delays. The stability criteria of the closed-loop teleoperation system are
given in terms of linear matrix inequalities. New tracking performance measures
are proposed to evaluate the position tracking between the master and the
slave. Simulation studies are given to show the effectiveness of the proposed
method.
| 1 | 0 | 0 | 0 | 0 | 0 |
Infinitely many periodic orbits just above the Mañé critical value on the 2-sphere | 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 |
Decentralized Random Walk-Based Data Collection in Networks | 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 |
The Fan Region at 1.5 GHz. I: Polarized synchrotron emission extending beyond the Perseus Arm | The Fan Region is one of the dominant features in the polarized radio sky,
long thought to be a local (distance < 500 pc) synchrotron feature. We present
1.3-1.8 GHz polarized radio continuum observations of the region from the
Global Magneto-Ionic Medium Survey (GMIMS) and compare them to maps of Halpha
and polarized radio continuum intensity from 0.408-353 GHz. The high-frequency
(> 1 GHz) and low-frequency (< 600 MHz) emission have different morphologies,
suggesting a different physical origin. Portions of the 1.5 GHz Fan Region
emission are depolarized by about 30% by ionized gas structures in the Perseus
Arm, indicating that this fraction of the emission originates >2 kpc away. We
argue for the same conclusion based on the high polarization fraction at 1.5
GHz (about 40%). The Fan Region is offset with respect to the Galactic plane,
covering -5° < b < +10°; we attribute this offset to the warp in the
outer Galaxy. We discuss origins of the polarized emission, including the
spiral Galactic magnetic field. This idea is a plausible contributing factor
although no model to date readily reproduces all of the observations. We
conclude that models of the Galactic magnetic field should account for the > 1
GHz emission from the Fan Region as a Galactic-scale, not purely local,
feature.
| 0 | 1 | 0 | 0 | 0 | 0 |
The redshift distribution of cosmological samples: a forward modeling approach | 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 |
DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks | In this paper we develop a novel computational sensing framework for sensing
and recovering structured signals. When trained on a set of representative
signals, our framework learns to take undersampled measurements and recover
signals from them using a deep convolutional neural network. In other words, it
learns a transformation from the original signals to a near-optimal number of
undersampled measurements and the inverse transformation from measurements to
signals. This is in contrast to traditional compressive sensing (CS) systems
that use random linear measurements and convex optimization or iterative
algorithms for signal recovery. We compare our new framework with
$\ell_1$-minimization from the phase transition point of view and demonstrate
that it outperforms $\ell_1$-minimization in the regions of phase transition
plot where $\ell_1$-minimization cannot recover the exact solution. In
addition, we experimentally demonstrate how learning measurements enhances the
overall recovery performance, speeds up training of recovery framework, and
leads to having fewer parameters to learn.
| 1 | 0 | 0 | 1 | 0 | 0 |
Robotic frameworks, architectures and middleware comparison | Nowadays, the construction of a complex robotic system requires a high level
of specialization in a large number of diverse scientific areas. It is
reasonable that a single researcher cannot create from scratch the entirety of
this system, as it is impossible for him to have the necessary skills in the
necessary fields. This obstacle is being surpassed with the existent robotic
frameworks. This paper tries to give an extensive review of the most famous
robotic frameworks and middleware, as well as to provide the means to
effortlessly compare them. Additionally, we try to investigate the differences
between the definitions of a robotic framework, a robotic middleware and a
robotic architecture.
| 1 | 0 | 0 | 0 | 0 | 0 |
Analysis of equivalence relation in joint sparse recovery | 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 |
The Stochastic Firefighter Problem | 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 |
The phase space structure of the oligopoly dynamical system by means of Darboux integrability | We investigate the dynamical complexity of Cournot oligopoly dynamics of
three firms by using the qualitative methods of dynamical systems to study the
phase structure of this model. The phase space is organized with
one-dimensional and two-dimensional invariant submanifolds (for the monopoly
and duopoly) and unique stable node (global attractor) in the positive quadrant
of the phase space (Cournot equilibrium). We also study the integrability of
the system. We demonstrate the effectiveness of the method of the Darboux
polynomials in searching for first integrals of the oligopoly. The general
method as well as examples of adopting this method are presented. We study
Darboux non-integrability of the oligopoly for linear demand functions and find
first integrals of this system for special classes of the system, in
particular, rational integrals can be found for a quite general set of model
parameters. We show how first integral can be useful in lowering the dimension
of the system using the example of $n$ almost identical firms. This first
integral also gives information about the structure of the phase space and the
behaviour of trajectories in the neighbourhood of a Nash equilibrium
| 0 | 1 | 0 | 0 | 0 | 0 |
Generalizations of the 'Linear Chain Trick': Incorporating more flexible dwell time distributions into mean field ODE models | 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 |
Life and work of Egbert Brieskorn (1936 - 2013) | Egbert Brieskorn died on July 11, 2013, a few days after his 77th birthday.
He was an impressive personality who has left a lasting impression on all who
knew him, whether inside or outside of mathematics. Brieskorn was a great
mathematician, but his interests, his knowledge, and activities ranged far
beyond mathematics. In this contribution, which is strongly influenced by many
years of personal connectedness of the authors with Brieskorn, we try to give a
deeper insight into the life and work of Brieskorn. We illuminate both his
personal commitment to peace and the environment as well as his long-term study
of the life and work of Felix Hausdorff and the publication of Hausdorff's
collected works. However, the main focus of the article is on the presentation
of his remarkable and influential mathematical work.
| 0 | 0 | 1 | 0 | 0 | 0 |
Resource Allocation for a Full-Duplex Base Station Aided OFDMA System | Exploiting full-duplex (FD) technology on base stations (BSs) is a promising
solution to enhancing the system performance. Motivated by this, we revisit a
full-duplex base station (FD-BS) aided OFDMA system, which consists of one BS,
several uplink/downlink users and multiple subcarriers. A joint 3-dimensional
(3D) mapping scheme among subcarriers, down-link users (DUEs), uplink users
(UUEs) is considered as well as an associated power allocation optimization. In
detail, we first decompose the complex 3D mapping problem into three
2-dimensional sub ones and solve them by using the iterative Hungarian method,
respectively. Then based on the Lagrange dual method, we sequentially solve the
power allocation and 3- dimensional mapping problem by fixing a dual point.
Finally, the optimal solution can be obtained by utilizing the sub-gradient
method. Unlike existing work that only solves either 3D mapping or power
allocation problem but with a high computation complexity, we tackle both of
them and have successfully reduced computation complexity from exponential to
polynomial order. Numerical simulations are conducted to verify the proposed
scheme.
| 1 | 0 | 0 | 0 | 0 | 0 |
Combining Neural Networks and Tree Search for Task and Motion Planning in Challenging Environments | 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 |
BT-Nets: Simplifying Deep Neural Networks via Block Term Decomposition | 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 |
Resampling Strategy in Sequential Monte Carlo for Constrained Sampling Problems | 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 |
Transverse Shift in Andreev Reflection | An incoming electron is reflected back as a hole at a
normal-metal-superconductor interface, a process known as Andreev reflection.
We predict that there exists a universal transverse shift in this process due
to the effect of spin-orbit coupling in the normal metal. Particularly, using
both the scattering approach and the argument of angular momentum conservation,
we demonstrate that the shifts are pronounced for lightly-doped Weyl
semimetals, and are opposite for incoming electrons with different chirality,
generating a chirality-dependent Hall effect for the reflected holes. The
predicted shift is not limited to Weyl systems, but exists for a general
three-dimensional spin-orbit- coupled metal interfaced with a superconductor.
| 0 | 1 | 0 | 0 | 0 | 0 |
Production of Entanglement Entropy by Decoherence | We examine the dynamics of entanglement entropy of all parts in an open
system consisting of a two-level dimer interacting with an environment of
oscillators. The dimer-environment interaction is almost energy conserving. We
find the precise link between decoherence and production of entanglement
entropy. We show that not all environment oscillators carry significant
entanglement entropy and we identify the oscillator frequency regions which
contribute to the production of entanglement entropy. Our results hold for
arbitrary strengths of the dimer-environment interaction, and they are
mathematically rigorous.
| 0 | 1 | 0 | 0 | 0 | 0 |
Aggressive Economic Incentives and Physical Activity: The Role of Choice and Technology Decision Aids | Aggressive incentive schemes that allow individuals to impose economic
punishment on themselves if they fail to meet health goals present a promising
approach for encouraging healthier behavior. However, the element of choice
inherent in these schemes introduces concerns that only non-representative
sectors of the population will select aggressive incentives, leaving value on
the table for those who don't opt in. In a field experiment conducted over a 29
week period on individuals wearing Fitbit activity trackers, we find modest and
short lived increases in physical activity for those provided the choice of
aggressive incentives. In contrast, we find significant and persistent
increases for those assigned (oftentimes against their stated preference) to
the same aggressive incentives. The modest benefits for those provided a choice
seems to emerge because those who benefited most from the aggressive incentives
were the least likely to choose them, and it was those who did not need them
who opted in. These results are confirmed in a follow up lab experiment. We
also find that benefits to individuals assigned to aggressive incentives were
pronounced if they also updated their step target in the Fitbit mobile
application to match the new activity goal we provided them. Our findings have
important implications for incentive based interventions to improve health
behavior. For firms and policy makers, our results suggest that one effective
strategy for encouraging sustained healthy behavior combines exposure to
aggressive incentive schemes to jolt individuals out of their comfort zones
with technology decision aids that help individuals sustain this behavior after
incentives end.
| 0 | 0 | 0 | 0 | 0 | 1 |
Dynamics of resonances and equilibria of Low Earth Objects | The nearby space surrounding the Earth is densely populated by artificial
satellites and instruments, whose orbits are distributed within the
Low-Earth-Orbit region (LEO), ranging between 90 and 2 000 $km$ of altitude. As
a consequence of collisions and fragmentations, many space debris of different
sizes are left in the LEO region. Given the threat raised by the possible
damages which a collision of debris can provoke with operational or manned
satellites, the study of their dynamics is nowadays mandatory. This work is
focused on the existence of equilibria and the dynamics of resonances in LEO.
We base our results on a simplified model which includes the geopotential and
the atmospheric drag. Using such model, we make a qualitative study of the
resonances and the equilibrium positions, including their location and
stability. The dissipative effect due to the atmosphere provokes a tidal decay,
but we give examples of different behaviors, precisely a straightforward
passage through the resonance or rather a temporary capture. We also
investigate the effect of the solar cycle which is responsible of fluctuations
of the atmospheric density and we analyze the influence of Sun and Moon on LEO
objects.
| 0 | 1 | 0 | 0 | 0 | 0 |
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