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Title: A Graph Model with Indirect Co-location Links,
Abstract: Graph models are widely used to analyse diffusion processes embedded in
social contacts and to develop applications. A range of graph models are
available to replicate the underlying social structures and dynamics
realistically. However, most of the current graph models can only consider
concurrent interactions among individuals in the co-located interaction
networks. However, they do not account for indirect interactions that can
transmit spreading items to individuals who visit the same locations at
different times but within a certain time limit. The diffusion phenomena
occurring through direct and indirect interactions is called same place
different time (SPDT) diffusion. This paper introduces a model to synthesize
co-located interaction graphs capturing both direct interactions, where
individuals meet at a location, and indirect interactions, where individuals
visit the same location at different times within a set timeframe. We analyze
60 million location updates made by 2 million users from a social networking
application to characterize the graph properties, including the space-time
correlations and its time evolving characteristics, such as bursty or ongoing
behaviors. The generated synthetic graph reproduces diffusion dynamics of a
realistic contact graph, and reduces the prediction error by up to 82% when
compare to other contact graph models demonstrating its potential for
forecasting epidemic spread. | [
1,
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] |
Title: Homogenization of nonlinear elliptic systems in nonreflexive Musielak-Orlicz spaces,
Abstract: We study the homogenization process for families of strongly nonlinear
elliptic systems with the homogeneous Dirichlet boundary conditions. The growth
and the coercivity of the elliptic operator is assumed to be indicated by a
general inhomogeneous anisotropic $N-$function, which may be possibly also
dependent on the spatial variable, i.e., the homogenization process will change
the characteristic function spaces at each step. Such a problem is well known
and there exists many positive results for the function satisfying $\Delta_2$
and $\nabla_2$ conditions an being in addition Hölder continuous with
respect to the spatial variable. We shall show that cases these conditions can
be neglected and will deal with a rather general problem in general function
space setting. | [
0,
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1,
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] |
Title: A Generalization of Permanent Inequalities and Applications in Counting and Optimization,
Abstract: A polynomial $p\in\mathbb{R}[z_1,\dots,z_n]$ is real stable if it has no
roots in the upper-half complex plane. Gurvits's permanent inequality gives a
lower bound on the coefficient of the $z_1z_2\dots z_n$ monomial of a real
stable polynomial $p$ with nonnegative coefficients. This fundamental
inequality has been used to attack several counting and optimization problems.
Here, we study a more general question: Given a stable multilinear polynomial
$p$ with nonnegative coefficients and a set of monomials $S$, we show that if
the polynomial obtained by summing up all monomials in $S$ is real stable, then
we can lowerbound the sum of coefficients of monomials of $p$ that are in $S$.
We also prove generalizations of this theorem to (real stable) polynomials that
are not multilinear. We use our theorem to give a new proof of Schrijver's
inequality on the number of perfect matchings of a regular bipartite graph,
generalize a recent result of Nikolov and Singh, and give deterministic
polynomial time approximation algorithms for several counting problems. | [
1,
0,
1,
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0
] |
Title: H-infinity Filtering for Cloud-Aided Semi-active Suspension with Delayed Information,
Abstract: This chapter presents an H-infinity filtering framework for cloud-aided
semiactive suspension system with time-varying delays. In this system, road
profile information is downloaded from a cloud database to facilitate onboard
estimation of suspension states. Time-varying data transmission delays are
considered and assumed to be bounded. A quarter-car linear suspension model is
used and an H-infinity filter is designed with both onboard sensor measurements
and delayed road profile information from the cloud. The filter design
procedure is designed based on linear matrix inequalities (LMIs). Numerical
simulation results are reported that illustrates the fusion of cloud-based and
on-board information that can be achieved in Vehicleto- Cloud-to-Vehicle
(V2C2V) implementation. | [
1,
0,
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0,
0,
0
] |
Title: Deep Spatio-Temporal Random Fields for Efficient Video Segmentation,
Abstract: In this work we introduce a time- and memory-efficient method for structured
prediction that couples neuron decisions across both space at time. We show
that we are able to perform exact and efficient inference on a densely
connected spatio-temporal graph by capitalizing on recent advances on deep
Gaussian Conditional Random Fields (GCRFs). Our method, called VideoGCRF is (a)
efficient, (b) has a unique global minimum, and (c) can be trained end-to-end
alongside contemporary deep networks for video understanding. We experiment
with multiple connectivity patterns in the temporal domain, and present
empirical improvements over strong baselines on the tasks of both semantic and
instance segmentation of videos. | [
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1,
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] |
Title: A Coherent vorticity preserving eddy viscosity correction for Large-Eddy Simulation,
Abstract: This paper introduces a new approach to Large-Eddy Simulation (LES) where
subgrid-scale (SGS) dissipation is applied proportionally to the degree of
local spectral broadening, hence mitigated or deactivated in regions dominated
by large-scale and/or laminar vortical motion. The proposed Coherent vorticity
preserving (CvP) LES methodology is based on the evaluation of the ratio of the
test-filtered to resolved (or grid-filtered) enstrophy $\sigma$. Values of
$\sigma$ close to 1 indicate low sub-test-filter turbulent activity, justifying
local deactivation of the SGS dissipation. The intensity of the SGS dissipation
is progressively increased for $\sigma < 1$ which corresponds to a small-scale
spectral broadening. The SGS dissipation is then fully activated in developed
turbulence characterized by $\sigma \le \sigma_{eq}$, where the value
$\sigma_{eq}$ is derived assuming a Kolmogorov spectrum. The proposed approach
can be applied to any eddy-viscosity model, is algorithmically simple and
computationally inexpensive. LES of Taylor-Green vortex breakdown demonstrates
that the CvP methodology improves the performance of traditional, non-dynamic
dissipative SGS models, capturing the peak of total turbulent kinetic energy
dissipation during transition. Similar accuracy is obtained by adopting
Germano's dynamic procedure albeit at more than twice the computational
overhead. A CvP-LES of a pair of unstable periodic helical vortices is shown to
predict accurately the experimentally observed growth rate using coarse
resolutions. The ability of the CvP methodology to dynamically sort the
coherent, large-scale motion from the smaller, broadband scales during
transition is demonstrated via flow visualizations. LES of compressible channel
are carried out and show a good match with a reference DNS. | [
1,
1,
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] |
Title: A Universal Marginalizer for Amortized Inference in Generative Models,
Abstract: We consider the problem of inference in a causal generative model where the
set of available observations differs between data instances. We show how
combining samples drawn from the graphical model with an appropriate masking
function makes it possible to train a single neural network to approximate all
the corresponding conditional marginal distributions and thus amortize the cost
of inference. We further demonstrate that the efficiency of importance sampling
may be improved by basing proposals on the output of the neural network. We
also outline how the same network can be used to generate samples from an
approximate joint posterior via a chain decomposition of the graph. | [
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0
] |
Title: Fulde-Ferrell-Larkin-Ovchinnikov state in spin-orbit-coupled superconductors,
Abstract: We show that in the presence of magnetic field, two superconducting phases
with the center-of-mass momentum of Cooper pair parallel to the magnetic field
are induced in spin-orbit-coupled superconductor Li$_2$Pd$_3$B. Specifically,
at small magnetic field, the center-of-mass momentum is induced due to the
energy-spectrum distortion and no unpairing region with vanishing singlet
correlation appears. We refer to this superconducting state as the drift-BCS
state. By further increasing the magnetic field, the superconducting state
falls into the Fulde-Ferrell-Larkin-Ovchinnikov state with the emergence of the
unpairing regions. The observed abrupt enhancement of the center-of-mass
momenta and suppression on the order parameters during the crossover indicate
the first-order phase transition. Enhanced Pauli limit and hence enlarged
magnetic-field regime of the Fulde-Ferrell-Larkin-Ovchinnikov state, due to the
spin-flip terms of the spin-orbit coupling, are revealed. We also address the
triplet correlations induced by the spin-orbit coupling, and show that the
Cooper-pair spin polarizations, generated by the magnetic field and
center-of-mass momentum with the triplet correlations, exhibit totally
different magnetic-field dependences between the drift-BCS and
Fulde-Ferrell-Larkin-Ovchinnikov states. | [
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] |
Title: Smart Grids Data Analysis: A Systematic Mapping Study,
Abstract: Data analytics and data science play a significant role in nowadays society.
In the context of Smart Grids (SG), the collection of vast amounts of data has
seen the emergence of a plethora of data analysis approaches. In this paper, we
conduct a Systematic Mapping Study (SMS) aimed at getting insights about
different facets of SG data analysis: application sub-domains (e.g., power load
control), aspects covered (e.g., forecasting), used techniques (e.g.,
clustering), tool-support, research methods (e.g., experiments/simulations),
replicability/reproducibility of research. The final goal is to provide a view
of the current status of research. Overall, we found that each sub-domain has
its peculiarities in terms of techniques, approaches and research methodologies
applied. Simulations and experiments play a crucial role in many areas. The
replicability of studies is limited concerning the provided implemented
algorithms, and to a lower extent due to the usage of private datasets. | [
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0,
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0
] |
Title: On the K-theory of C*-algebras for substitution tilings (a pedestrian version),
Abstract: Under suitable conditions, a substitution tiling gives rise to a Smale space,
from which three equivalence relations can be constructed, namely the stable,
unstable, and asymptotic equivalence relations. We denote with $S$, $U$, and
$A$ their corresponding $C^*$-algebras in the sense of Renault. In this article
we show that the $K$-theories of $S$ and $U$ can be computed from the
cohomology and homology of a single cochain complex with connecting maps for
tilings of the line and of the plane. Moreover, we provide formulas to compute
the $K$-theory for these three $C^*$-algebras. Furthermore, we show that the
$K$-theory groups for tilings of dimension 1 are always torsion free. For
tilings of dimension 2, only $K_0(U)$ and $K_1(S)$ can contain torsion. | [
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1,
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0
] |
Title: Gorenstein homological properties of tensor rings,
Abstract: Let $R$ be a two-sided noetherian ring and $M$ be a nilpotent $R$-bimodule,
which is finitely generated on both sides. We study Gorenstein homological
properties of the tensor ring $T_R(M)$. Under certain conditions, the ring $R$
is Gorenstein if and only if so is $T_R(M)$. We characterize Gorenstein
projective $T_R(M)$-modules in terms of $R$-modules. | [
0,
0,
1,
0,
0,
0
] |
Title: Compositional Human Pose Regression,
Abstract: Regression based methods are not performing as well as detection based
methods for human pose estimation. A central problem is that the structural
information in the pose is not well exploited in the previous regression
methods. In this work, we propose a structure-aware regression approach. It
adopts a reparameterized pose representation using bones instead of joints. It
exploits the joint connection structure to define a compositional loss function
that encodes the long range interactions in the pose. It is simple, effective,
and general for both 2D and 3D pose estimation in a unified setting.
Comprehensive evaluation validates the effectiveness of our approach. It
significantly advances the state-of-the-art on Human3.6M and is competitive
with state-of-the-art results on MPII. | [
1,
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] |
Title: Remarks on Inner Functions and Optimal Approximants,
Abstract: We discuss the concept of inner function in reproducing kernel Hilbert spaces
with an orthogonal basis of monomials and examine connections between inner
functions and optimal polynomial approximants to $1/f$, where $f$ is a function
in the space. We revisit some classical examples from this perspective, and
show how a construction of Shapiro and Shields can be modified to produce inner
functions. | [
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0,
1,
0,
0,
0
] |
Title: Asymptotic Confidence Regions for High-dimensional Structured Sparsity,
Abstract: In the setting of high-dimensional linear regression models, we propose two
frameworks for constructing pointwise and group confidence sets for penalized
estimators which incorporate prior knowledge about the organization of the
non-zero coefficients. This is done by desparsifying the estimator as in van de
Geer et al. [18] and van de Geer and Stucky [17], then using an appropriate
estimator for the precision matrix $\Theta$. In order to estimate the precision
matrix a corresponding structured matrix norm penalty has to be introduced.
After normalization the result is an asymptotic pivot.
The asymptotic behavior is studied and simulations are added to study the
differences between the two schemes. | [
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1,
1,
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] |
Title: Building a Structured Query Engine,
Abstract: Finding patterns in data and being able to retrieve information from those
patterns is an important task in Information retrieval. Complex search
requirements which are not fulfilled by simple string matching and require
exploring certain patterns in data demand a better query engine that can
support searching via structured queries. In this article, we built a
structured query engine which supports searching data through structured
queries on the lines of ElasticSearch. We will show how we achieved real time
indexing and retrieving of data through a RESTful API and how complex queries
can be created and processed using efficient data structures we created for
storing the data in structured way. Finally, we will conclude with an example
of movie recommendation system built on top of this query engine. | [
1,
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] |
Title: Optimal Timing of Decisions: A General Theory Based on Continuation Values,
Abstract: Building on insights of Jovanovic (1982) and subsequent authors, we develop a
comprehensive theory of optimal timing of decisions based around continuation
value functions and operators that act on them. Optimality results are provided
under general settings, with bounded or unbounded reward functions. This
approach has several intrinsic advantages that we exploit in developing the
theory. One is that continuation value functions are smoother than value
functions, allowing for sharper analysis of optimal policies and more efficient
computation. Another is that, for a range of problems, the continuation value
function exists in a lower dimensional space than the value function,
mitigating the curse of dimensionality. In one typical experiment, this reduces
the computation time from over a week to less than three minutes. | [
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] |
Title: OSIRIS-REx Contamination Control Strategy and Implementation,
Abstract: OSIRIS-REx will return pristine samples of carbonaceous asteroid Bennu. This
article describes how pristine was defined based on expectations of Bennu and
on a realistic understanding of what is achievable with a constrained schedule
and budget, and how that definition flowed to requirements and implementation.
To return a pristine sample, the OSIRIS- REx spacecraft sampling hardware was
maintained at level 100 A/2 and <180 ng/cm2 of amino acids and hydrazine on the
sampler head through precision cleaning, control of materials, and vigilance.
Contamination is further characterized via witness material exposed to the
spacecraft assembly and testing environment as well as in space. This
characterization provided knowledge of the expected background and will be used
in conjunction with archived spacecraft components for comparison with the
samples when they are delivered to Earth for analysis. Most of all, the
cleanliness of the OSIRIS-REx spacecraft was achieved through communication
among scientists, engineers, managers, and technicians. | [
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] |
Title: Linear Stochastic Approximation: Constant Step-Size and Iterate Averaging,
Abstract: We consider $d$-dimensional linear stochastic approximation algorithms (LSAs)
with a constant step-size and the so called Polyak-Ruppert (PR) averaging of
iterates. LSAs are widely applied in machine learning and reinforcement
learning (RL), where the aim is to compute an appropriate $\theta_{*} \in
\mathbb{R}^d$ (that is an optimum or a fixed point) using noisy data and $O(d)$
updates per iteration. In this paper, we are motivated by the problem (in RL)
of policy evaluation from experience replay using the \emph{temporal
difference} (TD) class of learning algorithms that are also LSAs. For LSAs with
a constant step-size, and PR averaging, we provide bounds for the mean squared
error (MSE) after $t$ iterations. We assume that data is \iid with finite
variance (underlying distribution being $P$) and that the expected dynamics is
Hurwitz. For a given LSA with PR averaging, and data distribution $P$
satisfying the said assumptions, we show that there exists a range of constant
step-sizes such that its MSE decays as $O(\frac{1}{t})$.
We examine the conditions under which a constant step-size can be chosen
uniformly for a class of data distributions $\mathcal{P}$, and show that not
all data distributions `admit' such a uniform constant step-size. We also
suggest a heuristic step-size tuning algorithm to choose a constant step-size
of a given LSA for a given data distribution $P$. We compare our results with
related work and also discuss the implication of our results in the context of
TD algorithms that are LSAs. | [
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] |
Title: Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval,
Abstract: We study the fundamental tradeoffs between statistical accuracy and
computational tractability in the analysis of high dimensional heterogeneous
data. As examples, we study sparse Gaussian mixture model, mixture of sparse
linear regressions, and sparse phase retrieval model. For these models, we
exploit an oracle-based computational model to establish conjecture-free
computationally feasible minimax lower bounds, which quantify the minimum
signal strength required for the existence of any algorithm that is both
computationally tractable and statistically accurate. Our analysis shows that
there exist significant gaps between computationally feasible minimax risks and
classical ones. These gaps quantify the statistical price we must pay to
achieve computational tractability in the presence of data heterogeneity. Our
results cover the problems of detection, estimation, support recovery, and
clustering, and moreover, resolve several conjectures of Azizyan et al. (2013,
2015); Verzelen and Arias-Castro (2017); Cai et al. (2016). Interestingly, our
results reveal a new but counter-intuitive phenomenon in heterogeneous data
analysis that more data might lead to less computation complexity. | [
0,
0,
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] |
Title: Single-trial P300 Classification using PCA with LDA, QDA and Neural Networks,
Abstract: The P300 event-related potential (ERP), evoked in scalp-recorded
electroencephalography (EEG) by external stimuli, has proven to be a reliable
response for controlling a BCI. The P300 component of an event related
potential is thus widely used in brain-computer interfaces to translate the
subjects' intent by mere thoughts into commands to control artificial devices.
The main challenge in the classification of P300 trials in
electroencephalographic (EEG) data is the low signal-to-noise ratio (SNR) of
the P300 response. To overcome the low SNR of individual trials, it is common
practice to average together many consecutive trials, which effectively
diminishes the random noise. Unfortunately, when more repeated trials are
required for applications such as the P300 speller, the communication rate is
greatly reduced. This has resulted in a need for better methods to improve
single-trial classification accuracy of P300 response. In this work, we use
Principal Component Analysis (PCA) as a preprocessing method and use Linear
Discriminant Analysis (LDA)and neural networks for classification. The results
show that a combination of PCA with these methods provided as high as 13\%
accuracy gain for single-trial classification while using only 3 to 4 principal
components. | [
1,
0,
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1,
0,
0
] |
Title: Automatic segmentation of trees in dynamic outdoor environments,
Abstract: Segmentation in dynamic outdoor environments can be difficult when the
illumination levels and other aspects of the scene cannot be controlled.
Specifically in orchard and vineyard automation contexts, a background material
is often used to shield a camera's field of view from other rows of crops. In
this paper, we describe a method that uses superpixels to determine low texture
regions of the image that correspond to the background material, and then show
how this information can be integrated with the color distribution of the image
to compute optimal segmentation parameters to segment objects of interest.
Quantitative and qualitative experiments demonstrate the suitability of this
approach for dynamic outdoor environments, specifically for tree reconstruction
and apple flower detection applications. | [
1,
0,
0,
0,
0,
0
] |
Title: Stochastic and Chance-Constrained Conic Distribution System Expansion Planning Using Bilinear Benders Decomposition,
Abstract: Second order conic programming (SOCP) has been used to model various
applications in power systems, such as operation and expansion planning. In
this paper, we present a two-stage stochastic mixed integer SOCP (MISOCP) model
for the distribution system expansion planning problem that considers
uncertainty and also captures the nonlinear AC power flow. To avoid costly
investment plans due to some extreme scenarios, we further present a
chance-constrained variant that could lead to cost-effective solutions. To
address the computational challenge, we extend the basic Benders decomposition
method and develop a bilinear variant to compute stochastic and
chance-constrained MISOCP formulations. A set of numerical experiments is
performed to illustrate the performance of our models and computational
methods. In particular, results show that our Benders decomposition algorithms
drastically outperform a professional MISOCP solver in handling stochastic
scenarios by orders of magnitude. | [
0,
0,
1,
0,
0,
0
] |
Title: Multi-Hop Extensions of Energy-Efficient Wireless Sensor Network Time Synchronization,
Abstract: We present the multi-hop extensions of the recently proposed energy-efficient
time synchronization scheme for wireless sensor networks, which is based on the
asynchronous source clock frequency recovery and reversed two-way message
exchanges. We consider two hierarchical extensions based on packet relaying and
time-translating gateways, respectively, and analyze their performance with
respect to the number of layers and the delay variations through simulations.
The simulation results demonstrate that the time synchronization performance of
the packet relaying, which has lower complexity, is close to that of
time-translating gateways. | [
1,
0,
0,
0,
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0
] |
Title: Value added or misattributed? A multi-institution study on the educational benefit of labs for reinforcing physics content,
Abstract: Instructional labs are widely seen as a unique, albeit expensive, way to
teach scientific content. We measured the effectiveness of introductory lab
courses at achieving this educational goal across nine different lab courses at
three very different institutions. These institutions and courses encompassed a
broad range of student populations and instructional styles. The nine courses
studied had two key things in common: the labs aimed to reinforce the content
presented in lectures, and the labs were optional. By comparing the performance
of students who did and did not take the labs (with careful normalization for
selection effects), we found universally and precisely no added value to
learning from taking the labs as measured by course exam performance. This work
should motivate institutions and departments to reexamine the goals and conduct
of their lab courses, given their resource-intensive nature. We show why these
results make sense when looking at the comparative mental processes of students
involved in research and instructional labs, and offer alternative goals and
instructional approaches that would make lab courses more educationally
valuable. | [
0,
1,
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0,
0,
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] |
Title: NeuroNER: an easy-to-use program for named-entity recognition based on neural networks,
Abstract: Named-entity recognition (NER) aims at identifying entities of interest in a
text. Artificial neural networks (ANNs) have recently been shown to outperform
existing NER systems. However, ANNs remain challenging to use for non-expert
users. In this paper, we present NeuroNER, an easy-to-use named-entity
recognition tool based on ANNs. Users can annotate entities using a graphical
web-based user interface (BRAT): the annotations are then used to train an ANN,
which in turn predict entities' locations and categories in new texts. NeuroNER
makes this annotation-training-prediction flow smooth and accessible to anyone. | [
1,
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] |
Title: Opinion dynamics model based on cognitive biases,
Abstract: We present an introduction to a novel model of an individual and group
opinion dynamics, taking into account different ways in which different sources
of information are filtered due to cognitive biases. The agent based model,
using Bayesian updating of the individual belief distribution, is based on the
recent psychology work by Dan Kahan. Open nature of the model allows to study
the effects of both static and time-dependent biases and information processing
filters. In particular, the paper compares the effects of two important
psychological mechanisms: the confirmation bias and the politically motivated
reasoning. Depending on the effectiveness of the information filtering (agent
bias), the agents confronted with an objective information source may either
reach a consensus based on the truth, or remain divided despite the evidence.
In general, the model might provide an understanding into the increasingly
polarized modern societies, especially as it allows mixing of different types
of filters: psychological, social, and algorithmic. | [
1,
1,
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] |
Title: ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness,
Abstract: Convolutional Neural Networks (CNNs) are commonly thought to recognise
objects by learning increasingly complex representations of object shapes. Some
recent studies suggest a more important role of image textures. We here put
these conflicting hypotheses to a quantitative test by evaluating CNNs and
human observers on images with a texture-shape cue conflict. We show that
ImageNet-trained CNNs are strongly biased towards recognising textures rather
than shapes, which is in stark contrast to human behavioural evidence and
reveals fundamentally different classification strategies. We then demonstrate
that the same standard architecture (ResNet-50) that learns a texture-based
representation on ImageNet is able to learn a shape-based representation
instead when trained on "Stylized-ImageNet", a stylized version of ImageNet.
This provides a much better fit for human behavioural performance in our
well-controlled psychophysical lab setting (nine experiments totalling 48,560
psychophysical trials across 97 observers) and comes with a number of
unexpected emergent benefits such as improved object detection performance and
previously unseen robustness towards a wide range of image distortions,
highlighting advantages of a shape-based representation. | [
0,
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] |
Title: Learning Distributed Representations of Texts and Entities from Knowledge Base,
Abstract: We describe a neural network model that jointly learns distributed
representations of texts and knowledge base (KB) entities. Given a text in the
KB, we train our proposed model to predict entities that are relevant to the
text. Our model is designed to be generic with the ability to address various
NLP tasks with ease. We train the model using a large corpus of texts and their
entity annotations extracted from Wikipedia. We evaluated the model on three
important NLP tasks (i.e., sentence textual similarity, entity linking, and
factoid question answering) involving both unsupervised and supervised
settings. As a result, we achieved state-of-the-art results on all three of
these tasks. Our code and trained models are publicly available for further
academic research. | [
1,
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] |
Title: Moduli Spaces of Unordered $n\ge5$ Points on the Riemann Sphere and Their Singularities,
Abstract: For $n\ge5$, it is well known that the moduli space $\mathfrak{M_{0,\:n}}$ of
unordered $n$ points on the Riemann sphere is a quotient space of the Zariski
open set $K_n$ of $\mathbb C^{n-3}$ by an $S_n$ action. The stabilizers of this
$S_n$ action at certain points of this Zariski open set $K_n$ correspond to the
groups fixing the sets of $n$ points on the Riemann sphere. Let $\alpha$ be a
subset of $n$ distinct points on the Riemann sphere. We call the group of all
linear fractional transformations leaving $\alpha$ invariant the stabilizer of
$\alpha$, which is finite by observation. For each non-trivial finite subgroup
$G$ of the group ${\rm PSL}(2,{\Bbb C})$ of linear fractional transformations,
we give the necessary and sufficient condition for finite subsets of the
Riemann sphere under which the stabilizers of them are conjugate to $G$. We
also prove that there does exist some finite subset of the Riemann sphere whose
stabilizer coincides with $G$. Next we obtain the irreducible decompositions of
the representations of the stabilizers on the tangent spaces at the
singularities of $\mathfrak{M_{0,\:n}}$. At last, on $\mathfrak{M_{0,\:5}}$ and
$\mathfrak{M_{0,\:6}}$, we work out explicitly the singularities and the
representations of their stabilizers on the tangent spaces at them. | [
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] |
Title: A Framework for Time-Consistent, Risk-Sensitive Model Predictive Control: Theory and Algorithms,
Abstract: In this paper we present a framework for risk-sensitive model predictive
control (MPC) of linear systems affected by stochastic multiplicative
uncertainty. Our key innovation is to consider a time-consistent, dynamic risk
evaluation of the cumulative cost as the objective function to be minimized.
This framework is axiomatically justified in terms of time-consistency of risk
assessments, is amenable to dynamic optimization, and is unifying in the sense
that it captures a full range of risk preferences from risk-neutral (i.e.,
expectation) to worst case. Within this framework, we propose and analyze an
online risk-sensitive MPC algorithm that is provably stabilizing. Furthermore,
by exploiting the dual representation of time-consistent, dynamic risk
measures, we cast the computation of the MPC control law as a convex
optimization problem amenable to real-time implementation. Simulation results
are presented and discussed. | [
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] |
Title: Common Knowledge in a Logic of Gossips,
Abstract: Gossip protocols aim at arriving, by means of point-to-point or group
communications, at a situation in which all the agents know each other secrets.
Recently a number of authors studied distributed epistemic gossip protocols.
These protocols use as guards formulas from a simple epistemic logic, which
makes their analysis and verification substantially easier.
We study here common knowledge in the context of such a logic. First, we
analyze when it can be reduced to iterated knowledge. Then we show that the
semantics and truth for formulas without nested common knowledge operator are
decidable. This implies that implementability, partial correctness and
termination of distributed epistemic gossip protocols that use non-nested
common knowledge operator is decidable, as well. Given that common knowledge is
equivalent to an infinite conjunction of nested knowledge, these results are
non-trivial generalizations of the corresponding decidability results for the
original epistemic logic, established in (Apt & Wojtczak, 2016).
K. R. Apt & D. Wojtczak (2016): On Decidability of a Logic of Gossips. In
Proc. of JELIA 2016, pp. 18-33, doi:10.1007/ 978-3-319-48758-8_2. | [
1,
0,
0,
0,
0,
0
] |
Title: Network-theoretic approach to sparsified discrete vortex dynamics,
Abstract: We examine discrete vortex dynamics in two-dimensional flow through a
network-theoretic approach. The interaction of the vortices is represented with
a graph, which allows the use of network-theoretic approaches to identify key
vortex-to-vortex interactions. We employ sparsification techniques on these
graph representations based on spectral theory for constructing sparsified
models and evaluating the dynamics of vortices in the sparsified setup.
Identification of vortex structures based on graph sparsification and sparse
vortex dynamics are illustrated through an example of point-vortex clusters
interacting amongst themselves. We also evaluate the performance of
sparsification with increasing number of point vortices. The
sparsified-dynamics model developed with spectral graph theory requires reduced
number of vortex-to-vortex interactions but agrees well with the full nonlinear
dynamics. Furthermore, the sparsified model derived from the sparse graphs
conserves the invariants of discrete vortex dynamics. We highlight the
similarities and differences between the present sparsified-dynamics model and
the reduced-order models. | [
0,
1,
0,
0,
0,
0
] |
Title: Bäcklund Transformation and Quasi-Integrable Deformation of Mixed Fermi-Pasta-Ulam and Frenkel-Kontorova Models,
Abstract: In this paper we study a non-linear partial differential equation (PDE),
proposed by N. Kudryashov [arXiv:1611.06813v1[nlin.SI]], using continuum limit
approximation of mixed Fermi-Pasta-Ulam and Frenkel-Kontorova Models. This
generalized semi-discrete equation can be considered as a model for the
description of non-linear dislocation waves in crystal lattice and the
corresponding continuous system can be called mixed generalized potential KdV
and sine-Gordon equation. We obtain the Bäcklund transformation of this
equation in Riccati form in inverse method. We further study the
quasi-integrable deformation of this model. | [
0,
1,
1,
0,
0,
0
] |
Title: Symmetries of handlebodies and their fixed points: Dihedral extended Schottky groups,
Abstract: A Schottky structure on a handlebody $M$ of genus $g$ is provided by a
Schottky group of rank $g$. A symmetry (an orientation-reversing involution) of
$M$ is known to have at most $(g+1)$ connected components of fixed points. Each
of these components is either a point or a compact bordered surface (either
orientable or not) whose boundary is contained in the border of $M$. In this
paper, we derive sharp upper bounds for the total number of connected
components of the sets of fixed points of given two or three symmetries of $M$.
In order to obtain such an upper bound, we obtain a geometrical structure
description of those extended Kleinian groups $K$ containing a Schottky group
$\Gamma$ as finite index normal subgroup so that $K/\Gamma$ is a dihedral group
(called dihedral Schottky groups). Our upper bounds turn out to be different to
the corresponding ones at the level of closed Riemann surfaces. In contrast to
the case of Riemann surfaces, we observe that $M$ cannot have two different
maximal symmetries. | [
0,
0,
1,
0,
0,
0
] |
Title: A Robust Multi-Batch L-BFGS Method for Machine Learning,
Abstract: This paper describes an implementation of the L-BFGS method designed to deal
with two adversarial situations. The first occurs in distributed computing
environments where some of the computational nodes devoted to the evaluation of
the function and gradient are unable to return results on time. A similar
challenge occurs in a multi-batch approach in which the data points used to
compute function and gradients are purposely changed at each iteration to
accelerate the learning process. Difficulties arise because L-BFGS employs
gradient differences to update the Hessian approximations, and when these
gradients are computed using different data points the updating process can be
unstable. This paper shows how to perform stable quasi-Newton updating in the
multi-batch setting, studies the convergence properties for both convex and
nonconvex functions, and illustrates the behavior of the algorithm in a
distributed computing platform on binary classification logistic regression and
neural network training problems that arise in machine learning. | [
1,
0,
1,
1,
0,
0
] |
Title: Enhanced version of AdaBoostM1 with J48 Tree learning method,
Abstract: Machine Learning focuses on the construction and study of systems that can
learn from data. This is connected with the classification problem, which
usually is what Machine Learning algorithms are designed to solve. When a
machine learning method is used by people with no special expertise in machine
learning, it is important that the method be robust in classification, in the
sense that reasonable performance is obtained with minimal tuning of the
problem at hand. Algorithms are evaluated based on how robust they can classify
the given data. In this paper, we propose a quantifiable measure of robustness,
and describe a particular learning method that is robust according to this
measure in the context of classification problem. We proposed Adaptive Boosting
(AdaBoostM1) with J48(C4.5 tree) as a base learner with tuning weight threshold
(P) and number of iterations (I) for boosting algorithm. To benchmark the
performance, we used the baseline classifier, AdaBoostM1 with Decision Stump as
base learner without tuning parameters. By tuning parameters and using J48 as
base learner, we are able to reduce the overall average error rate ratio
(errorC/errorNB) from 2.4 to 0.9 for development sets of data and 2.1 to 1.2
for evaluation sets of data. | [
0,
0,
0,
1,
0,
0
] |
Title: A Game of Tax Evasion: evidences from an agent-based model,
Abstract: This paper presents a simple agent-based model of an economic system,
populated by agents playing different games according to their different view
about social cohesion and tax payment. After a first set of simulations,
correctly replicating results of existing literature, a wider analysis is
presented in order to study the effects of a dynamic-adaptation rule, in which
citizens may possibly decide to modify their individual tax compliance
according to individual criteria, such as, the strength of their ethical
commitment, the satisfaction gained by consumption of the public good and the
perceived opinion of neighbors. Results show the presence of thresholds levels
in the composition of society - between taxpayers and evaders - which explain
the extent of damages deriving from tax evasion. | [
0,
0,
0,
0,
0,
1
] |
Title: Variability response functions for statically determinate beams with arbitrary nonlinear constitutive laws,
Abstract: The variability response function (VRF) is generalized to statically
determinate Euler Bernoulli beams with arbitrary stress-strain laws following
Cauchy elastic behavior. The VRF is a Green's function that maps the spectral
density function (SDF) of a statistically homogeneous random field describing
the correlation structure of input uncertainty to the variance of a response
quantity. The appeal of such Green's functions is that the variance can be
determined for any correlation structure by a trivial computation of a
convolution integral. The method introduced in this work derives VRFs in closed
form for arbitrary nonlinear Cauchy-elastic constitutive laws and is
demonstrated through three examples. It is shown why and how higher order
spectra of the random field affect the response variance for nonlinear
constitutive laws. In the general sense, the VRF for a statically determinate
beam is found to be a matrix kernel whose inner product by a matrix of higher
order SDFs and statistical moments is integrated to give the response variance.
The resulting VRF matrix is unique regardless of the random field's marginal
probability density function (PDF) and SDFs. | [
0,
1,
0,
0,
0,
0
] |
Title: Accurate Motion Estimation through Random Sample Aggregated Consensus,
Abstract: We reconsider the classic problem of estimating accurately a 2D
transformation from point matches between images containing outliers. RANSAC
discriminates outliers by randomly generating minimalistic sampled hypotheses
and verifying their consensus over the input data. Its response is based on the
single hypothesis that obtained the largest inlier support. In this article we
show that the resulting accuracy can be improved by aggregating all generated
hypotheses. This yields RANSAAC, a framework that improves systematically over
RANSAC and its state-of-the-art variants by statistically aggregating
hypotheses. To this end, we introduce a simple strategy that allows to rapidly
average 2D transformations, leading to an almost negligible extra computational
cost. We give practical applications on projective transforms and
homography+distortion models and demonstrate a significant performance gain in
both cases. | [
1,
0,
0,
0,
0,
0
] |
Title: A Topologist's View of Kinematic Maps and Manipulation Complexity,
Abstract: In this paper we combine a survey of the most important topological
properties of kinematic maps that appear in robotics, with the exposition of
some basic results regarding the topological complexity of a map. In
particular, we discuss mechanical devices that consist of rigid parts connected
by joints and show how the geometry of the joints determines the forward
kinematic map that relates the configuration of joints with the pose of the
end-effector of the device. We explain how to compute the dimension of the
joint space and describe topological obstructions for a kinematic map to be a
fibration or to admit a continuous section. In the second part of the paper we
define the complexity of a continuous map and show how the concept can be
viewed as a measure of the difficulty to find a robust manipulation plan for a
given mechanical device. We also derive some basic estimates for the complexity
and relate it to the degree of instability of a manipulation plan. | [
1,
0,
1,
0,
0,
0
] |
Title: Faster Tensor Canonicalization,
Abstract: The Butler-Portugal algorithm for obtaining the canonical form of a tensor
expression with respect to slot symmetries and dummy-index renaming suffers, in
certain cases with a high degree of symmetry, from $O(n!)$ explosion in both
computation time and memory. We present a modified algorithm which alleviates
this problem in the most common cases---tensor expressions with subsets of
indices which are totally symmetric or totally antisymmetric---in polynomial
time. We also present an implementation of the label-renaming mechanism which
improves upon that of the original Butler-Portugal algorithm, thus providing a
significant speed increase for the average case as well as the highly-symmetric
special case. The worst-case behavior remains $O(n!)$, although it occurs in
more limited situations unlikely to appear in actual computations. We comment
on possible strategies to take if the nature of a computation should make these
situations more likely. | [
1,
0,
0,
0,
0,
0
] |
Title: Mass Preconditioning for the Exact One-Flavor Action in Lattice QCD with Domain-Wall Fermion,
Abstract: The mass-preconditioning (MP) technique has become a standard tool to enhance
the efficiency of the hybrid Monte-Carlo simulation (HMC) of lattice QCD with
dynamical quarks, for 2-flavors QCD with degenerate quark masses, as well as
its extension to the case of one-flavor by taking the square-root of the
fermion determinant of 2-flavors with degenerate masses. However, for lattice
QCD with domain-wall fermion, the fermion determinant of any single fermion
flavor can be expressed as a functional integral with an exact pseudofermion
action $ \phi^\dagger H^{-1} \phi $, where $ H^{-1} $ is a positive-definite
Hermitian operator without taking square-root, and with the chiral structure
\cite{Chen:2014hyy}. Consequently, the mass-preconditioning for the exact
one-flavor action (EOFA) does not necessarily follow the conventional (old) MP
pattern. In this paper, we present a new mass-preconditioning for the EOFA,
which is more efficient than the old MP which we have used in Refs.
\cite{Chen:2014hyy,Chen:2014bbc}. We perform numerical tests in lattice QCD
with $ N_f = 1 $ and $ N_f = 1+1+1+1 $ optimal domain-wall quarks, with one
mass-preconditioner applied to one of the exact one-flavor actions, and we find
that the efficiency of the new MP is more than 20\% higher than that of the old
MP. | [
0,
1,
0,
0,
0,
0
] |
Title: Weighted Low Rank Approximation for Background Estimation Problems,
Abstract: Classical principal component analysis (PCA) is not robust to the presence of
sparse outliers in the data. The use of the $\ell_1$ norm in the Robust PCA
(RPCA) method successfully eliminates the weakness of PCA in separating the
sparse outliers. In this paper, by sticking a simple weight to the Frobenius
norm, we propose a weighted low rank (WLR) method to avoid the often
computationally expensive algorithms relying on the $\ell_1$ norm. As a proof
of concept, a background estimation model has been presented and compared with
two $\ell_1$ norm minimization algorithms. We illustrate that as long as a
simple weight matrix is inferred from the data, one can use the weighted
Frobenius norm and achieve the same or better performance. | [
0,
0,
1,
0,
0,
0
] |
Title: Controlling Chiral Domain Walls in Antiferromagnets Using Spin-Wave Helicity,
Abstract: In antiferromagnets, the Dzyaloshinskii-Moriya interaction lifts the
degeneracy of left- and right-circularly polarized spin waves. This
relativistic coupling increases the efficiency of spin-wave-induced domain wall
motion and leads to higher drift velocities. We show that in biaxial
antiferromagnets, the spin-wave helicity controls both the direction and
magnitude of the magnonic force on chiral domain walls. By contrast, in
uniaxial antiferromagnets, the magnonic force is propulsive with a helicity
dependent strength. | [
0,
1,
0,
0,
0,
0
] |
Title: Multiple Topological Electronic Phases in Superconductor MoC,
Abstract: The search for a superconductor with non-s-wave pairing is important not only
for understanding unconventional mechanisms of superconductivity but also for
finding new types of quasiparticles such as Majorana bound states. Materials
with both topological band structure and superconductivity are promising
candidates as $p+ip$ superconducting states can be generated through pairing
the spin-polarized topological surface states. In this work, the electronic and
phonon properties of the superconductor molybdenum carbide (MoC) are studied
with first-principles methods. Our calculations show that nontrivial band
topology and superconductivity coexist in both structural phases of MoC,
namely, the cubic $\alpha$ and hexagonal $\gamma$ phases. The $\alpha$ phase is
a strong topological insulator and the $\gamma$ phase is a topological nodal
line semimetal with drumhead surface states. In addition, hole doping can
stabilize the crystal structure of the $\alpha$ phase and elevate the
transition temperature in the $\gamma$ phase. Therefore, MoC in different
structural forms can be a practical material platform for studying topological
superconductivity and elusive Majorana fermions. | [
0,
1,
0,
0,
0,
0
] |
Title: Performance Scaling Law for Multi-Cell Multi-User Massive MIMO,
Abstract: This work provides a comprehensive scaling law based performance analysis for
multi-cell multi-user massive multiple-input-multiple-output (MIMO) downlink
systems. Imperfect channel state information (CSI), pilot contamination, and
channel spatial correlation are all considered. First, a sum- rate lower bound
is derived by exploiting the asymptotically deterministic property of the
received signal power, while keeping the random nature of other components in
the signal-to-interference-plus-noise-ratio (SINR) intact. Via a general
scaling model on important network parameters, including the number of users,
the channel training energy and the data transmission power, with respect to
the number of base station antennas, the asymptotic scaling law of the
effective SINR is obtained, which reveals quantitatively the tradeoff of the
network parameters. More importantly, pilot contamination and pilot
contamination elimination (PCE) are considered in the analytical framework. In
addition, the applicability of the derived asymptotic scaling law in practical
systems with large but finite antenna numbers are discussed. Finally,
sufficient conditions on the parameter scalings for the SINR to be
asymptotically deterministic in the sense of mean square convergence are
provided, which covers existing results on such analysis as special cases and
shows the effect of PCE explicitly. | [
1,
0,
0,
0,
0,
0
] |
Title: Pressure-tuning of bond-directional exchange interactions and magnetic frustration in hyperhoneycomb iridate $β$-$\mathrm{Li_2IrO_3}$,
Abstract: We explore the response of Ir $5d$ orbitals to pressure in
$\beta$-$\mathrm{Li_2IrO_3}$, a hyperhoneycomb iridate in proximity to a Kitaev
quantum spin liquid (QSL) ground state. X-ray absorption spectroscopy reveals a
reconstruction of the electronic ground state below 2 GPa, the same pressure
range where x-ray magnetic circular dichroism shows an apparent collapse of
magnetic order. The electronic reconstruction, which manifests a reduction in
the effective spin-orbit (SO) interaction in $5d$ orbitals, pushes
$\beta$-$\mathrm{Li_2IrO_3}$ further away from the pure $J_{\rm eff}=1/2$
limit. Although lattice symmetry is preserved across the electronic transition,
x-ray diffraction shows a highly anisotropic compression of the hyperhoneycomb
lattice which affects the balance of bond-directional Ir-Ir exchange
interactions driven by spin-orbit coupling at Ir sites. An enhancement of
symmetric anisotropic exchange over Kitaev and Heisenberg exchange interactions
seen in theoretical calculations that use precisely this anisotropic Ir-Ir bond
compression provides one possible route to realization of a QSL state in this
hyperhoneycomb iridate at high pressures. | [
0,
1,
0,
0,
0,
0
] |
Title: Computationally Efficient Estimation of the Spectral Gap of a Markov Chain,
Abstract: We consider the problem of estimating from sample paths the absolute spectral
gap $\gamma_*$ of a reversible, irreducible and aperiodic Markov chain
$(X_t)_{t \in \mathbb{N}}$ over a finite state $\Omega$. We propose the ${\tt
UCPI}$ (Upper Confidence Power Iteration) algorithm for this problem, a
low-complexity algorithm which estimates the spectral gap in time ${\cal O}(n)$
and memory space ${\cal O}((\ln n)^2)$ given $n$ samples. This is in stark
contrast with most known methods which require at least memory space ${\cal
O}(|\Omega|)$, so that they cannot be applied to large state spaces.
Furthermore, ${\tt UCPI}$ is amenable to parallel implementation. | [
0,
0,
0,
1,
0,
0
] |
Title: Bounded solutions for a class of Hamiltonian systems,
Abstract: We obtain bounded for all $t$ solutions of ordinary differential equations as
limits of the solutions of the corresponding Dirichlet problems on $(-L,L)$,
with $L \rightarrow \infty$. We derive a priori estimates for the Dirichlet
problems, allowing passage to the limit, via a diagonal sequence. This approach
carries over to the PDE case. | [
0,
0,
1,
0,
0,
0
] |
Title: Cosmological perturbation effects on gravitational-wave luminosity distance estimates,
Abstract: Waveforms of gravitational waves provide information about a variety of
parameters for the binary system merging. However, standard calculations have
been performed assuming a FLRW universe with no perturbations. In reality this
assumption should be dropped: we show that the inclusion of cosmological
perturbations translates into corrections to the estimate of astrophysical
parameters derived for the merging binary systems. We compute corrections to
the estimate of the luminosity distance due to velocity, volume, lensing and
gravitational potential effects. Our results show that the amplitude of the
corrections will be negligible for current instruments, mildly important for
experiments like the planned DECIGO, and very important for future ones such as
the Big Bang Observer. | [
0,
1,
0,
0,
0,
0
] |
Title: Faster Clustering via Non-Backtracking Random Walks,
Abstract: This paper presents VEC-NBT, a variation on the unsupervised graph clustering
technique VEC, which improves upon the performance of the original algorithm
significantly for sparse graphs. VEC employs a novel application of the
state-of-the-art word2vec model to embed a graph in Euclidean space via random
walks on the nodes of the graph. In VEC-NBT, we modify the original algorithm
to use a non-backtracking random walk instead of the normal backtracking random
walk used in VEC. We introduce a modification to a non-backtracking random
walk, which we call a begrudgingly-backtracking random walk, and show
empirically that using this model of random walks for VEC-NBT requires shorter
walks on the graph to obtain results with comparable or greater accuracy than
VEC, especially for sparser graphs. | [
1,
0,
0,
1,
0,
0
] |
Title: Finding Submodularity Hidden in Symmetric Difference,
Abstract: A set function $f$ on a finite set $V$ is submodular if $f(X) + f(Y) \geq f(X
\cup Y) + f(X \cap Y)$ for any pair $X, Y \subseteq V$. The symmetric
difference transformation (SD-transformation) of $f$ by a canonical set $S
\subseteq V$ is a set function $g$ given by $g(X) = f(X \vartriangle S)$ for $X
\subseteq V$,where $X \vartriangle S = (X \setminus S) \cup (S \setminus X)$
denotes the symmetric difference between $X$ and $S$. Submodularity and
SD-transformations are regarded as the counterparts of convexity and affine
transformations in a discrete space, respectively. However, submodularity is
not preserved under SD-transformations, in contrast to the fact that convexity
is invariant under affine transformations. This paper presents a
characterization of SD-stransformations preserving submodularity. Then, we are
concerned with the problem of discovering a canonical set $S$, given the
SD-transformation $g$ of a submodular function $f$ by $S$, provided that $g(X)$
is given by a function value oracle. A submodular function $f$ on $V$ is said
to be strict if $f(X) + f(Y) > f(X \cup Y) + f(X \cap Y)$ holds whenever both
$X \setminus Y$ and $Y \setminus X$ are nonempty. We show that the problem is
solved by using ${\rm O}(|V|)$ oracle calls when $f$ is strictly submodular,
although it requires exponentially many oracle calls in general. | [
1,
0,
0,
0,
0,
0
] |
Title: Adaptive Quantization for Deep Neural Network,
Abstract: In recent years Deep Neural Networks (DNNs) have been rapidly developed in
various applications, together with increasingly complex architectures. The
performance gain of these DNNs generally comes with high computational costs
and large memory consumption, which may not be affordable for mobile platforms.
Deep model quantization can be used for reducing the computation and memory
costs of DNNs, and deploying complex DNNs on mobile equipment. In this work, we
propose an optimization framework for deep model quantization. First, we
propose a measurement to estimate the effect of parameter quantization errors
in individual layers on the overall model prediction accuracy. Then, we propose
an optimization process based on this measurement for finding optimal
quantization bit-width for each layer. This is the first work that
theoretically analyse the relationship between parameter quantization errors of
individual layers and model accuracy. Our new quantization algorithm
outperforms previous quantization optimization methods, and achieves 20-40%
higher compression rate compared to equal bit-width quantization at the same
model prediction accuracy. | [
1,
0,
0,
1,
0,
0
] |
Title: High-temperature terahertz optical diode effect without magnetic order in polar FeZnMo$_3$O$_8$,
Abstract: We present a terahertz spectroscopic study of polar ferrimagnet
FeZnMo$_3$O$_8$. Our main finding is a giant high-temperature optical diode
effect, or nonreciprocal directional dichroism, where the transmitted light
intensity in one direction is over 100 times lower than intensity transmitted
in the opposite direction. The effect takes place in the paramagnetic phase
with no long-range magnetic order in the crystal, which contrasts sharply with
all existing reports of the terahertz optical diode effect in other
magnetoelectric materials, where the long-range magnetic ordering is a
necessary prerequisite. In \fzmo, the effect occurs resonantly with a strong
magnetic dipole active transition centered at 1.27 THz and assigned as electron
spin resonance between the eigenstates of the single-ion anisotropy
Hamiltonian. We propose that the optical diode effect in paramagnetic
FeZnMo$_3$O$_8$ is driven by signle-ion terms in magnetoelectric free energy. | [
0,
1,
0,
0,
0,
0
] |
Title: Character Networks and Book Genre Classification,
Abstract: We compare the social character networks of biographical, legendary and
fictional texts, in search for marks of genre differentiation. We examine the
degree distribution of character appearance and find a power law that does not
depend on the literary genre or historical content. We also analyze local and
global complex networks measures, in particular, correlation plots between the
recently introduced Lobby (or Hirsh $H(1)$) index and Degree, Betweenness and
Closeness centralities. Assortativity plots, which previous literature claims
to separate fictional from real social networks, were also studied. We've found
no relevant differences in the books for these network measures and we give a
plausible explanation why the previous assortativity result is not correct. | [
1,
1,
0,
0,
0,
0
] |
Title: Forecasting the Impact of Stellar Activity on Transiting Exoplanet Spectra,
Abstract: Exoplanet host star activity, in the form of unocculted star spots or
faculae, alters the observed transmission and emission spectra of the
exoplanet. This effect can be exacerbated when combining data from different
epochs if the stellar photosphere varies between observations due to activity.
redHere we present a method to characterize and correct for relative changes
due to stellar activity by exploiting multi-epoch ($\ge$2 visits/transits)
observations to place them in a consistent reference frame. Using measurements
from portions of the planet's orbit where negligible planet transmission or
emission can be assumed, we determine changes to the stellar spectral
amplitude. With the analytical methods described here, we predict the impact of
stellar variability on transit observations. Supplementing these forecasts with
Kepler-measured stellar variabilities for F-, G-, K-, and M-dwarfs, and
predicted transit precisions by JWST's NIRISS, NIRCam, and MIRI, we conclude
that stellar activity does not impact infrared transiting exoplanet
observations of most presently-known or predicted TESS targets by current or
near-future platforms, such as JWST. | [
0,
1,
0,
0,
0,
0
] |
Title: Strong isomorphism in Marinatto-Weber type quantum games,
Abstract: Our purpose is to focus attention on a new criterion for quantum schemes by
bringing together the notions of quantum game and game isomorphism. A quantum
game scheme is required to generate the classical game as a special case. Now,
given a quantum game scheme and two isomorphic classical games, we additionally
require the resulting quantum games to be isomorphic as well. We show how this
isomorphism condition influences the players' strategy sets. We are concerned
with the Marinatto-Weber type quantum game scheme and the strong isomorphism
between games in strategic form. | [
1,
0,
0,
0,
0,
0
] |
Title: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,
Abstract: The recently proposed Temporal Ensembling has achieved state-of-the-art
results in several semi-supervised learning benchmarks. It maintains an
exponential moving average of label predictions on each training example, and
penalizes predictions that are inconsistent with this target. However, because
the targets change only once per epoch, Temporal Ensembling becomes unwieldy
when learning large datasets. To overcome this problem, we propose Mean
Teacher, a method that averages model weights instead of label predictions. As
an additional benefit, Mean Teacher improves test accuracy and enables training
with fewer labels than Temporal Ensembling. Without changing the network
architecture, Mean Teacher achieves an error rate of 4.35% on SVHN with 250
labels, outperforming Temporal Ensembling trained with 1000 labels. We also
show that a good network architecture is crucial to performance. Combining Mean
Teacher and Residual Networks, we improve the state of the art on CIFAR-10 with
4000 labels from 10.55% to 6.28%, and on ImageNet 2012 with 10% of the labels
from 35.24% to 9.11%. | [
1,
0,
0,
1,
0,
0
] |
Title: WOMBAT: A Scalable and High Performance Astrophysical MHD Code,
Abstract: We present a new code for astrophysical magneto-hydrodynamics specifically
designed and optimized for high performance and scaling on modern and future
supercomputers. We describe a novel hybrid OpenMP/MPI programming model that
emerged from a collaboration between Cray, Inc. and the University of
Minnesota. This design utilizes MPI-RMA optimized for thread scaling, which
allows the code to run extremely efficiently at very high thread counts ideal
for the latest generation of the multi-core and many-core architectures. Such
performance characteristics are needed in the era of "exascale" computing. We
describe and demonstrate our high-performance design in detail with the intent
that it may be used as a model for other, future astrophysical codes intended
for applications demanding exceptional performance. | [
0,
1,
0,
0,
0,
0
] |
Title: Securing Virtual Network Function Placement with High Availability Guarantees,
Abstract: Virtual Network Functions as a Service (VNFaaS) is currently under attentive
study by telecommunications and cloud stakeholders as a promising business and
technical direction consisting of providing network functions as a service on a
cloud (NFV Infrastructure), instead of delivering standalone network
appliances, in order to provide higher scalability and reduce maintenance
costs. However, the functioning of such NFVI hosting the VNFs is fundamental
for all the services and applications running on top of it, forcing to
guarantee a high availability level. Indeed the availability of an VNFaaS
relies on the failure rate of its single components, namely the servers, the
virtualization software, and the communication network. The proper assignment
of the virtual machines implementing network functions to NFVI servers and
their protection is essential to guarantee high availability. We model the High
Availability Virtual Network Function Placement (HA-VNFP) as the problem of
finding the best assignment of virtual machines to servers guaranteeing
protection by replication. We propose a probabilistic approach to measure the
real availability of a system and design both efficient and effective
algorithms that can be used by stakeholders for both online and offline
planning. | [
1,
0,
0,
0,
0,
0
] |
Title: A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates,
Abstract: This paper considers the problem of decentralized optimization with a
composite objective containing smooth and non-smooth terms. To solve the
problem, a proximal-gradient scheme is studied. Specifically, the smooth and
nonsmooth terms are dealt with by gradient update and proximal update,
respectively. The studied algorithm is closely related to a previous
decentralized optimization algorithm, PG-EXTRA [37], but has a few advantages.
First of all, in our new scheme, agents use uncoordinated step-sizes and the
stable upper bounds on step-sizes are independent from network topology. The
step-sizes depend on local objective functions, and they can be as large as
that of the gradient descent. Secondly, for the special case without non-smooth
terms, linear convergence can be achieved under the strong convexity
assumption. The dependence of the convergence rate on the objective functions
and the network are separated, and the convergence rate of our new scheme is as
good as one of the two convergence rates that match the typical rates for the
general gradient descent and the consensus averaging. We also provide some
numerical experiments to demonstrate the efficacy of the introduced algorithms
and validate our theoretical discoveries. | [
0,
0,
1,
1,
0,
0
] |
Title: Generative Models for Spear Phishing Posts on Social Media,
Abstract: Historically, machine learning in computer security has prioritized defense:
think intrusion detection systems, malware classification, and botnet traffic
identification. Offense can benefit from data just as well. Social networks,
with their access to extensive personal data, bot-friendly APIs, colloquial
syntax, and prevalence of shortened links, are the perfect venues for spreading
machine-generated malicious content. We aim to discover what capabilities an
adversary might utilize in such a domain. We present a long short-term memory
(LSTM) neural network that learns to socially engineer specific users into
clicking on deceptive URLs. The model is trained with word vector
representations of social media posts, and in order to make a click-through
more likely, it is dynamically seeded with topics extracted from the target's
timeline. We augment the model with clustering to triage high value targets
based on their level of social engagement, and measure success of the LSTM's
phishing expedition using click-rates of IP-tracked links. We achieve state of
the art success rates, tripling those of historic email attack campaigns, and
outperform humans manually performing the same task. | [
0,
0,
0,
1,
0,
0
] |
Title: SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring,
Abstract: Deep learning has demonstrated tremendous potential for Automatic Text
Scoring (ATS) tasks. In this paper, we describe a new neural architecture that
enhances vanilla neural network models with auxiliary neural coherence
features. Our new method proposes a new \textsc{SkipFlow} mechanism that models
relationships between snapshots of the hidden representations of a long
short-term memory (LSTM) network as it reads. Subsequently, the semantic
relationships between multiple snapshots are used as auxiliary features for
prediction. This has two main benefits. Firstly, essays are typically long
sequences and therefore the memorization capability of the LSTM network may be
insufficient. Implicit access to multiple snapshots can alleviate this problem
by acting as a protection against vanishing gradients. The parameters of the
\textsc{SkipFlow} mechanism also acts as an auxiliary memory. Secondly,
modeling relationships between multiple positions allows our model to learn
features that represent and approximate textual coherence. In our model, we
call this \textit{neural coherence} features. Overall, we present a unified
deep learning architecture that generates neural coherence features as it reads
in an end-to-end fashion. Our approach demonstrates state-of-the-art
performance on the benchmark ASAP dataset, outperforming not only feature
engineering baselines but also other deep learning models. | [
1,
0,
0,
0,
0,
0
] |
Title: Reduction of topological $\mathbb{Z}$ classification in cold atomic systems,
Abstract: One of the most challenging problems in correlated topological systems is a
realization of the reduction of topological classification, but very few
experimental platforms have been proposed so far. We here demonstrate that
ultracold dipolar fermions (e.g., $^{167}$Er, $^{161}$Dy, and $^{53}$Cr) loaded
in an optical lattice of two-leg ladder geometry can be the first promising
testbed for the reduction $\mathbb{Z}\to\mathbb{Z}_4$, where solid evidence for
the reduction is available thanks to their high controllability. We further
give a detailed account of how to experimentally access this phenomenon; around
the edges, the destruction of one-particle gapless excitations can be observed
by the local radio frequency spectroscopy, while that of gapless spin
excitations can be observed by a time-dependent spin expectation value of a
superposed state of the ground state and the first excited state. We clarify
that even when the reduction occurs, a gapless edge mode is recovered around a
dislocation, which can be another piece of evidence for the reduction. | [
0,
1,
0,
0,
0,
0
] |
Title: Characterizing the ionospheric current pattern response to southward and northward IMF turnings with dynamical SuperMAG correlation networks,
Abstract: We characterize the response of the quiet time (no substorms or storms)
large-scale ionospheric transient equivalent currents to north-south and
south-north IMF turnings by using a dynamical network of ground-based
magnetometers. Canonical correlation between all pairs of SuperMAG magnetometer
stations in the Northern Hemisphere (magnetic latitude (MLAT) 50-82$^{\circ}$)
is used to establish the extent of near-simultaneous magnetic response between
regions of magnetic local time-MLAT. Parameters and maps that describe
spatial-temporal correlation are used to characterize the system and its
response to the turnings aggregated over several hundred events. We find that
regions that experience large increases in correlation post turning coincide
with typical locations of a two-cell convection system and are influenced by
the interplanetary magnetic field $\mathit{B}_{y}$. The time between the
turnings reaching the magnetopause and a network response is found to be
$\sim$8-10 min and correlation in the dayside occurs 2-8 min before that in the
nightside. | [
0,
1,
0,
0,
0,
0
] |
Title: On a question of Buchweitz about ranks of syzygies of modules of finite length,
Abstract: Let R be a local ring of dimension d. Buchweitz asks if the rank of the d-th
syzygy of a module of finite lengh is greater than or equal to the rank of the
d-th syzygy of the residue field, unless the module has finite projective
dimension. Assuming that R is Gorenstein, we prove that if the question is
affrmative, then R is a hypersurface. If moreover R has dimension two, then we
show that the converse also holds true. | [
0,
0,
1,
0,
0,
0
] |
Title: Resolution and Relevance Trade-offs in Deep Learning,
Abstract: Deep learning has been successfully applied to various tasks, but its
underlying mechanism remains unclear. Neural networks associate similar inputs
in the visible layer to the same state of hidden variables in deep layers. The
fraction of inputs that are associated to the same state is a natural measure
of similarity and is simply related to the cost in bits required to represent
these inputs. The degeneracy of states with the same information cost provides
instead a natural measure of noise and is simply related the entropy of the
frequency of states, that we call relevance. Representations with minimal
noise, at a given level of similarity (resolution), are those that maximise the
relevance. A signature of such efficient representations is that frequency
distributions follow power laws. We show, in extensive numerical experiments,
that deep neural networks extract a hierarchy of efficient representations from
data, because they i) achieve low levels of noise (i.e. high relevance) and ii)
exhibit power law distributions. We also find that the layer that is most
efficient to reliably generate patterns of training data is the one for which
relevance and resolution are traded at the same price, which implies that
frequency distribution follows Zipf's law. | [
1,
0,
0,
0,
0,
0
] |
Title: Approches d'analyse distributionnelle pour améliorer la désambiguïsation sémantique,
Abstract: Word sense disambiguation (WSD) improves many Natural Language Processing
(NLP) applications such as Information Retrieval, Machine Translation or
Lexical Simplification. WSD is the ability of determining a word sense among
different ones within a polysemic lexical unit taking into account the context.
The most straightforward approach uses a semantic proximity measure between the
word sense candidates of the target word and those of its context. Such a
method very easily entails a combinatorial explosion. In this paper, we propose
two methods based on distributional analysis which enable to reduce the
exponential complexity without losing the coherence. We present a comparison
between the selection of distributional neighbors and the linearly nearest
neighbors. The figures obtained show that selecting distributional neighbors
leads to better results. | [
1,
0,
0,
0,
0,
0
] |
Title: Non-degenerate parametric resonance in tunable superconducting cavity,
Abstract: We develop a theory for non-degenerate parametric resonance in a tunable
superconducting cavity. We focus on nonlinear effects that are caused by
nonlinear Josephson elements connected to the cavity. We analyze parametric
amplification in a strong nonlinear regime at the parametric instability
threshold, and calculate maximum gain values. Above the threshold, in the
parametric oscillator regime the linear cavity response diverges at the
oscillator frequency at all pump strengths. We show that this divergence is
related to the continuous degeneracy of the free oscillator state with respect
to the phase. Applying on-resonance input lifts the degeneracy and removes the
divergence. We also investigate the quantum noise squeezing. It is shown that
in the strong amplification regime the noise undergoes four-mode squeezing, and
that in this regime the output signal to noise ratio can significantly exceed
the input value. We also analyze the intermode frequency conversion and
identify parameters at which full conversion is achieved. | [
0,
1,
0,
0,
0,
0
] |
Title: Bridging Semantic Gaps between Natural Languages and APIs with Word Embedding,
Abstract: Developers increasingly rely on text matching tools to analyze the relation
between natural language words and APIs. However, semantic gaps, namely textual
mismatches between words and APIs, negatively affect these tools. Previous
studies have transformed words or APIs into low-dimensional vectors for
matching; however, inaccurate results were obtained due to the failure of
modeling words and APIs simultaneously. To resolve this problem, two main
challenges are to be addressed: the acquisition of massive words and APIs for
mining and the alignment of words and APIs for modeling. Therefore, this study
proposes Word2API to effectively estimate relatedness of words and APIs.
Word2API collects millions of commonly used words and APIs from code
repositories to address the acquisition challenge. Then, a shuffling strategy
is used to transform related words and APIs into tuples to address the
alignment challenge. Using these tuples, Word2API models words and APIs
simultaneously. Word2API outperforms baselines by 10%-49.6% of relatedness
estimation in terms of precision and NDCG. Word2API is also effective on
solving typical software tasks, e.g., query expansion and API documents
linking. A simple system with Word2API-expanded queries recommends up to 21.4%
more related APIs for developers. Meanwhile, Word2API improves comparison
algorithms by 7.9%-17.4% in linking questions in Question&Answer communities to
API documents. | [
1,
0,
0,
0,
0,
0
] |
Title: Feedback optimal controllers for the Heston model,
Abstract: We prove the existence of an optimal feedback controller for a stochastic
optimization problem constituted by a variation of the Heston model, where a
stochastic input process is added in order to minimize a given performance
criterion. The stochastic feedback controller is searched by solving a
nonlinear backward parabolic equation for which one proves the existence of a
martingale solution. | [
0,
0,
1,
0,
0,
0
] |
Title: Total-positivity preservers,
Abstract: We prove that the only entrywise transforms of rectangular matrices which
preserve total positivity or total non-negativity are either constant or
linear. This follows from an extended classification of preservers of these two
properties for matrices of fixed dimension. We also prove that the same
assertions hold upon working only with symmetric matrices; for total-positivity
preservers our proofs proceed through solving two totally positive completion
problems. | [
0,
0,
1,
0,
0,
0
] |
Title: A revision of the subtract-with-borrow random number generators,
Abstract: The most popular and widely used subtract-with-borrow generator, also known
as RANLUX, is reimplemented as a linear congruential generator using large
integer arithmetic with the modulus size of 576 bits. Modern computers, as well
as the specific structure of the modulus inferred from RANLUX, allow for the
development of a fast modular multiplication -- the core of the procedure. This
was previously believed to be slow and have too high cost in terms of computing
resources. Our tests show a significant gain in generation speed which is
comparable with other fast, high quality random number generators. An
additional feature is the fast skipping of generator states leading to a
seeding scheme which guarantees the uniqueness of random number sequences. | [
1,
1,
0,
0,
0,
0
] |
Title: Bias voltage effects on tunneling magnetoresistance in Fe/MgAl${}_2$O${}_4$/Fe(001) junctions: Comparative study with Fe/MgO/Fe(001) junctions,
Abstract: We investigate bias voltage effects on the spin-dependent transport
properties of Fe/MgAl${}_2$O${}_4$/Fe(001) magnetic tunneling junctions (MTJs)
by comparing them with those of Fe/MgO/Fe(001) MTJs. By means of the
nonequilibrium Green's function method and the density functional theory, we
calculate bias voltage dependences of magnetoresistance (MR) ratios in both the
MTJs. We find that in both the MTJs, the MR ratio decreases as the bias voltage
increases and finally vanishes at a critical bias voltage $V_{\rm c}$. We also
find that the critical bias voltage $V_{\rm c}$ of the MgAl${}_2$O${}_4$-based
MTJ is clearly larger than that of the MgO-based MTJ. Since the in-plane
lattice constant of the Fe/MgAl${}_2$O${}_4$/Fe(001) supercell is twice that of
the Fe/MgO/Fe(001) one, the Fe electrodes in the MgAl${}_2$O${}_4$-based MTJs
have an identical band structure to that obtained by folding the Fe band
structure of the MgO-based MTJs in the Brillouin zone of the in-plane wave
vector. We show that such a difference in the Fe band structure is the origin
of the difference in the critical bias voltage $V_{\rm c}$ between the
MgAl${}_2$O${}_4$- and MgO-based MTJs. | [
0,
1,
0,
0,
0,
0
] |
Title: Listen to Your Face: Inferring Facial Action Units from Audio Channel,
Abstract: Extensive efforts have been devoted to recognizing facial action units (AUs).
However, it is still challenging to recognize AUs from spontaneous facial
displays especially when they are accompanied with speech. Different from all
prior work that utilized visual observations for facial AU recognition, this
paper presents a novel approach that recognizes speech-related AUs exclusively
from audio signals based on the fact that facial activities are highly
correlated with voice during speech. Specifically, dynamic and physiological
relationships between AUs and phonemes are modeled through a continuous time
Bayesian network (CTBN); then AU recognition is performed by probabilistic
inference via the CTBN model.
A pilot audiovisual AU-coded database has been constructed to evaluate the
proposed audio-based AU recognition framework. The database consists of a
"clean" subset with frontal and neutral faces and a challenging subset
collected with large head movements and occlusions. Experimental results on
this database show that the proposed CTBN model achieves promising recognition
performance for 7 speech-related AUs and outperforms the state-of-the-art
visual-based methods especially for those AUs that are activated at low
intensities or "hardly visible" in the visual channel. Furthermore, the CTBN
model yields more impressive recognition performance on the challenging subset,
where the visual-based approaches suffer significantly. | [
1,
0,
0,
0,
0,
0
] |
Title: Icing on the Cake: An Easy and Quick Post-Learnig Method You Can Try After Deep Learning,
Abstract: We found an easy and quick post-learning method named "Icing on the Cake" to
enhance a classification performance in deep learning. The method is that we
train only the final classifier again after an ordinary training is done. | [
0,
0,
0,
1,
0,
0
] |
Title: Minimal Effort Back Propagation for Convolutional Neural Networks,
Abstract: As traditional neural network consumes a significant amount of computing
resources during back propagation, \citet{Sun2017mePropSB} propose a simple yet
effective technique to alleviate this problem. In this technique, only a small
subset of the full gradients are computed to update the model parameters. In
this paper we extend this technique into the Convolutional Neural Network(CNN)
to reduce calculation in back propagation, and the surprising results verify
its validity in CNN: only 5\% of the gradients are passed back but the model
still achieves the same effect as the traditional CNN, or even better. We also
show that the top-$k$ selection of gradients leads to a sparse calculation in
back propagation, which may bring significant computational benefits for high
computational complexity of convolution operation in CNN. | [
1,
0,
0,
1,
0,
0
] |
Title: Hölder continuous solutions of the Monge-Ampère equation on compact Hermitian manifolds,
Abstract: We show that a positive Borel measure of positive finite total mass, on
compact Hermitian manifolds, admits a Holder continuous quasi-plurisubharmonic
solution to the Monge-Ampere equation if and only if it is dominated locally by
Monge-Ampere measures of Holder continuous plurisubharmonic functions. | [
0,
0,
1,
0,
0,
0
] |
Title: Comparative Climates of TRAPPIST-1 planetary system: results from a simple climate-vegetation model,
Abstract: The recent discovery of the planetary system hosted by the ultracool dwarf
star TRAPPIST-1 could open new perspectives into the investigation of planetary
climates of Earth-sized exoplanets, their atmospheres and their possible
habitability. In this paper, we use a simple climate-vegetation energy-balance
model to study the climate of the seven TRAPPIST-1 planets and the climate
dependence on the global albedo, on the fraction of vegetation that could cover
their surfaces and on the different greenhouse conditions. The model allows us
to investigate whether liquid water could be maintained on the planetary
surfaces (i.e., by defining a "surface water zone") in different planetary
conditions, with or without the presence of greenhouse effect.
It is shown that planet TRAPPIST-1d seems to be the most stable from an
Earth-like perspective, since it resides in the surface water zone for a wide
range of reasonable values of the model parameters. Moreover, according to the
model outer planets (f, g and h) cannot host liquid water on their surfaces,
even for Earth-like conditions, entering a snowball state. Although very
simple, the model allows to extract the main features of the TRAPPIST-1
planetary climates. | [
0,
1,
0,
0,
0,
0
] |
Title: Width Hierarchies for Quantum and Classical Ordered Binary Decision Diagrams with Repeated Test,
Abstract: We consider quantum, nondterministic and probabilistic versions of known
computational model Ordered Read-$k$-times Branching Programs or Ordered Binary
Decision Diagrams with repeated test ($k$-QOBDD, $k$-NOBDD and $k$-POBDD). We
show width hierarchy for complexity classes of Boolean function computed by
these models and discuss relation between different variants of $k$-OBDD. | [
1,
0,
0,
0,
0,
0
] |
Title: Stall force of a cargo driven by N interacting motor proteins,
Abstract: We study a generic one-dimensional model for an intracellular cargo driven by
N motor proteins against an external applied force. The model includes
motor-cargo and motor-motor interactions. The cargo motion is described by an
over-damped Langevin equation, while motor dynamics is specified by hopping
rates which follow a local detailed balance condition with respect to change in
energy per hopping event. Based on this model, we show that the stall force,
the mean external force corresponding to zero mean cargo velocity, is
completely independent of the details of the interactions and is, therefore,
always equal to the sum of the stall forces of the individual motors. This
exact result is arrived on the basis of a simple assumption: the (macroscopic)
state of stall of the cargo is analogous to a state of thermodynamic
equilibrium, and is characterized by vanishing net probability current between
any two microstates, with the latter specified by motor positions relative to
the cargo. The corresponding probability distribution of the microstates under
stall is also determined. These predictions are in complete agreement with
numerical simulations, carried out using specific forms of interaction
potentials. | [
0,
1,
0,
0,
0,
0
] |
Title: Separation of time scales and direct computation of weights in deep neural networks,
Abstract: Artificial intelligence is revolutionizing our lives at an ever increasing
pace. At the heart of this revolution is the recent advancements in deep neural
networks (DNN), learning to perform sophisticated, high-level tasks. However,
training DNNs requires massive amounts of data and is very computationally
intensive. Gaining analytical understanding of the solutions found by DNNs can
help us devise more efficient training algorithms, replacing the commonly used
mthod of stochastic gradient descent (SGD). We analyze the dynamics of SGD and
show that, indeed, direct computation of the solutions is possible in many
cases. We show that a high performing setup used in DNNs introduces a
separation of time-scales in the training dynamics, allowing SGD to train
layers from the lowest (closest to input) to the highest. We then show that for
each layer, the distribution of solutions found by SGD can be estimated using a
class-based principal component analysis (PCA) of the layer's input. This
finding allows us to forgo SGD entirely and directly derive the DNN parameters
using this class-based PCA, which can be well estimated using significantly
less data than SGD. We implement these results on image datasets MNIST, CIFAR10
and CIFAR100 and find that, in fact, layers derived using our class-based PCA
perform comparable or superior to neural networks of the same size and
architecture trained using SGD. We also confirm that the class-based PCA often
converges using a fraction of the data required for SGD. Thus, using our method
training time can be reduced both by requiring less training data than SGD, and
by eliminating layers in the costly backpropagation step of the training. | [
1,
0,
0,
1,
0,
0
] |
Title: $q$-deformed quadrature operator and optical tomogram,
Abstract: In this letter, we define the homodyne $q$-deformed quadrature operator.
Analytic expression for the wavefunctions of $q$-deformed oscillator in the
quadrature basis are found. Furthermore, we compute the explicit analytical
expression for the tomogram of the $q$-deformed coherent states by finding the
eigenstates of the $q$-deformed quadrature operator. | [
0,
1,
1,
0,
0,
0
] |
Title: Asymptotic measures and links in simplicial complexes,
Abstract: We introduce canonical measures on a locally finite simplicial complex $K$
and study their asymptotic behavior under infinitely many barycentric
subdivisions. We also compute the face polynomial of the asymptotic link and
dual block of a simplex in the $d^{th}$ barycentric subdivision $Sd^d(K)$ of
$K$, $d\gg0$. It is almost everywhere constant. When $K$ is finite, we study
the limit face polynomial of $Sd^d(K)$ after F.Brenti-V.Welker and
E.Delucchi-A.Pixton-L.Sabalka. | [
0,
0,
1,
0,
0,
0
] |
Title: Usability of Humanly Computable Passwords,
Abstract: Reusing passwords across multiple websites is a common practice that
compromises security. Recently, Blum and Vempala have proposed password
strategies to help people calculate, in their heads, passwords for different
sites without dependence on third-party tools or external devices. Thus far,
the security and efficiency of these "mental algorithms" has been analyzed only
theoretically. But are such methods usable? We present the first usability
study of humanly computable password strategies, involving a learning phase (to
learn a password strategy), then a rehearsal phase (to login to a few
websites), and multiple follow-up tests. In our user study, with training,
participants were able to calculate a deterministic eight-character password
for an arbitrary new website in under 20 seconds. | [
1,
0,
0,
0,
0,
0
] |
Title: Performance analysis of smart digital signage system based on software-defined IoT and invisible image sensor communication,
Abstract: Everything in the world is being connected, and things are becoming
interactive. The future of the interactive world depends on the future Internet
of Things (IoT). Software-defined networking (SDN) technology, a new paradigm
in the networking area, can be useful in creating an IoT because it can handle
interactivity by controlling physical devices, transmission of data among them,
and data acquisition. However, digital signage can be one of the promising
technologies in this era of technology that is progressing toward the
interactive world, connecting users to the IoT network through device-to-device
communication technology. This article illustrates a novel prototype that is
mainly focused on a smart digital signage system comprised of software-defined
IoT (SD-IoT) and invisible image sensor communication technology. We have
proposed an SDN scheme with a view to initiating its flexibility and
compatibility for an IoT network-based smart digital signage system. The idea
of invisible communication can make the users of the technology trendier to it,
and the usage of unused resources such as images and videos can be ensured. In
addition, this communication has paved the way for interactivity between the
user and digital signage, where the digital signage and the camera of a
smartphone can be operated as a transmitter and a receiver, respectively. The
proposed scheme might be applicable to real-world applications because SDN has
the flexibility to adapt with the alteration of network status without any
hardware modifications while displays and smartphones are available everywhere.
A performance analysis of this system showed the advantages of an SD-IoT
network over an Internet protocol-based IoT network considering a queuing
analysis for a dynamic link allocation process in the case of user access to
the IoT network. | [
1,
0,
0,
0,
0,
0
] |
Title: What pebbles are made of: Interpretation of the V883 Ori disk,
Abstract: Recently, an Atacama Large Millimeter/submillimeter Array (ALMA) observation
of the water snow line in the protoplanetary disk around the FU Orionis star
V883 Ori was reported. The radial variation of the spectral index at
mm-wavelengths around the snow line was interpreted as being due to a pileup of
particles interior to the snow line. However, radial transport of solids in the
outer disk operates on timescales much longer than the typical timescale of an
FU Ori outburst ($10^{1}$--$10^{2}$ yr). Consequently, a steady-state pileup is
unlikely. We argue that it is only necessary to consider water evaporation and
re-coagulation of silicates to explain the recent ALMA observation of V883 Ori
because these processes are short enough to have had their impact since the
outburst. Our model requires the inner disk to have already been optically
thick before the outburst, and our results suggest that the carbon content of
pebbles is low. | [
0,
1,
0,
0,
0,
0
] |
Title: More on products of Baire spaces,
Abstract: New results on the Baire product problem are presented. It is shown that an
arbitrary product of almost locally ccc Baire spaces is Baire; moreover, the
product of a Baire space and a 1st countable space which is $\beta$-unfavorable
in the strong Choquet game is Baire. | [
0,
0,
1,
0,
0,
0
] |
Title: Social versus Moral preferences in the Ultimatum Game: A theoretical model and an experiment,
Abstract: In the Ultimatum Game (UG) one player, named "proposer", has to decide how to
allocate a certain amount of money between herself and a "responder". If the
offer is greater than or equal to the responder's minimum acceptable offer
(MAO), then the money is split as proposed, otherwise, neither the proposer nor
the responder get anything. The UG has intrigued generations of behavioral
scientists because people in experiments blatantly violate the equilibrium
predictions that self-interested proposers offer the minimum available non-zero
amount, and self-interested responders accept. Why are these predictions
violated? Previous research has mainly focused on the role of social
preferences. Little is known about the role of general moral preferences for
doing the right thing, preferences that have been shown to play a major role in
other social interactions (e.g., Dictator Game and Prisoner's Dilemma). Here I
develop a theoretical model and an experiment designed to pit social
preferences against moral preferences. I find that, although people recognize
that offering half and rejecting low offers are the morally right things to do,
moral preferences have no causal impact on UG behavior. The experimental data
are indeed well fit by a model according to which: (i) high UG offers are
motivated by inequity aversion and, to a lesser extent, self-interest; (ii)
high MAOs are motivated by inequity aversion. | [
0,
0,
0,
0,
1,
0
] |
Title: A simple recipe for making accurate parametric inference in finite sample,
Abstract: Constructing tests or confidence regions that control over the error rates in
the long-run is probably one of the most important problem in statistics. Yet,
the theoretical justification for most methods in statistics is asymptotic. The
bootstrap for example, despite its simplicity and its widespread usage, is an
asymptotic method. There are in general no claim about the exactness of
inferential procedures in finite sample. In this paper, we propose an
alternative to the parametric bootstrap. We setup general conditions to
demonstrate theoretically that accurate inference can be claimed in finite
sample. | [
0,
0,
1,
1,
0,
0
] |
Title: Panel collapse and its applications,
Abstract: We describe a procedure called panel collapse for replacing a CAT(0) cube
complex $\Psi$ by a "lower complexity" CAT(0) cube complex $\Psi_\bullet$
whenever $\Psi$ contains a codimension-$2$ hyperplane that is extremal in one
of the codimension-$1$ hyperplanes containing it. Although $\Psi_\bullet$ is
not in general a subcomplex of $\Psi$, it is a subspace consisting of a
subcomplex together with some cubes that sit inside $\Psi$ "diagonally". The
hyperplanes of $\Psi_\bullet$ extend to hyperplanes of $\Psi$. Applying this
procedure, we prove: if a group $G$ acts cocompactly on a CAT(0) cube complex
$\Psi$, then there is a CAT(0) cube complex $\Omega$ so that $G$ acts
cocompactly on $\Omega$ and for each hyperplane $H$ of $\Omega$, the stabiliser
in $G$ of $H$ acts on $H$ essentially.
Using panel collapse, we obtain a new proof of Stallings's theorem on groups
with more than one end. As another illustrative example, we show that panel
collapse applies to the exotic cubulations of free groups constructed by Wise.
Next, we show that the CAT(0) cube complexes constructed by Cashen-Macura can
be collapsed to trees while preserving all of the necessary group actions. (It
also illustrates that our result applies to actions of some non-discrete
groups.) We also discuss possible applications to quasi-isometric rigidity for
certain classes of graphs of free groups with cyclic edge groups. Panel
collapse is also used in forthcoming work of the first-named author and Wilton
to study fixed-point sets of finite subgroups of $\mathrm{Out}(F_n)$ on the
free splitting complex. | [
0,
0,
1,
0,
0,
0
] |
Title: Detecting Heavy Flows in the SDN Match and Action Model,
Abstract: Efficient algorithms and techniques to detect and identify large flows in a
high throughput traffic stream in the SDN match-and-action model are presented.
This is in contrast to previous work that either deviated from the match and
action model by requiring additional switch level capabilities or did not
exploit the SDN data plane. Our construction has two parts; (a) how to sample
in an SDN match and action model, (b) how to detect large flows efficiently and
in a scalable way, in the SDN model.
Our large flow detection methods provide high accuracy and present a good and
practical tradeoff between switch - controller traffic, and the number of
entries required in the switch flow table. Based on different parameters, we
differentiate between heavy flows, elephant flows and bulky flows and present
efficient algorithms to detect flows of the different types.
Additionally, as part of our heavy flow detection scheme, we present sampling
methods to sample packets with arbitrary probability $p$ per packet or per byte
that traverses an SDN switch.
Finally, we show how our algorithms can be adapted to a distributed
monitoring SDN setting with multiple switches, and easily scale with the number
of monitoring switches. | [
1,
0,
0,
0,
0,
0
] |
Title: Optimal Resource Allocation with Node and Link Capacity Constraints in Complex Networks,
Abstract: With the tremendous increase of the Internet traffic, achieving the best
performance with limited resources is becoming an extremely urgent problem. In
order to address this concern, in this paper, we build an optimization problem
which aims to maximize the total utility of traffic flows with the capacity
constraint of nodes and links in the network. Based on Duality Theory, we
propose an iterative algorithm which adjusts the rates of traffic flows and
capacity of nodes and links simultaneously to maximize the total utility.
Simulation results show that our algorithm performs better than the NUP
algorithm on BA and ER network models, which has shown to get the best
performance so far. Since our research combines the topology information with
capacity constraint, it may give some insights for resource allocation in real
communication networks. | [
1,
1,
0,
0,
0,
0
] |
Title: On the complexity of solving a decision problem with flow-depending costs: the case of the IJsselmeer dikes,
Abstract: We consider a fundamental integer programming (IP) model for cost-benefit
analysis flood protection through dike building in the Netherlands, due to
Verweij and Zwaneveld.
Experimental analysis with data for the Ijsselmeer lead to integral optimal
solution of the linear programming relaxation of the IP model.
This naturally led to the question of integrality of the polytope associated
with the IP model.
In this paper we first give a negative answer to this question by
establishing non-integrality of the polytope.
Second, we establish natural conditions that guarantee the linear programming
relaxation of the IP model to be integral.
We then test the most recent data on flood probabilities, damage and
investment costs of the IJsselmeer for these conditions.
Third, we show that the IP model can be solved in polynomial time when the
number of dike segments, or the number of feasible barrier heights, are
constant. | [
0,
0,
0,
0,
0,
1
] |
Title: Integrating Human-Provided Information Into Belief State Representation Using Dynamic Factorization,
Abstract: In partially observed environments, it can be useful for a human to provide
the robot with declarative information that represents probabilistic relational
constraints on properties of objects in the world, augmenting the robot's
sensory observations. For instance, a robot tasked with a search-and-rescue
mission may be informed by the human that two victims are probably in the same
room. An important question arises: how should we represent the robot's
internal knowledge so that this information is correctly processed and combined
with raw sensory information? In this paper, we provide an efficient belief
state representation that dynamically selects an appropriate factoring,
combining aspects of the belief when they are correlated through information
and separating them when they are not. This strategy works in open domains, in
which the set of possible objects is not known in advance, and provides
significant improvements in inference time over a static factoring, leading to
more efficient planning for complex partially observed tasks. We validate our
approach experimentally in two open-domain planning problems: a 2D discrete
gridworld task and a 3D continuous cooking task. A supplementary video can be
found at this http URL. | [
1,
0,
0,
0,
0,
0
] |
Title: On Integral Upper Limits Assuming Power Law Spectra and the Sensitivity in High-Energy Astronomy,
Abstract: The high-energy non-thermal universe is dominated by power law-like spectra.
Therefore results in high-energy astronomy are often reported as parameters of
power law fits, or, in the case of a non-detection, as an upper limit assuming
the underlying unseen spectrum behaves as a power law. In this paper I
demonstrate a simple and powerful one-to-one relation of the integral upper
limit in the two dimensional power law parameter space into the spectrum
parameter space and use this method to unravel the so far convoluted question
of the sensitivity of astroparticle telescopes. | [
0,
1,
0,
0,
0,
0
] |
Title: When a triangle is isosceles?,
Abstract: In 1840 Jacob Steiner on Christian Rudolf's request proved that a triangle
with two equal bisectors is isosceles. But what about changing the bisectors to
cevians? Cevian is any line segment in a triangle with one endpoint on a vertex
of the triangle and other endpoint on the opposite side. Not for any pairs of
equal cevians the triangle is isosceles. Theorem. If for a triangle ABC there
are equal cevians issuing from A and B, which intersect on the bisector or on
the median of the angle C, then AC=BC (so the triangle ABC is isosceles).
Proposition. Let ABC be an isosceles triangle. Define circle C to be the circle
symmetric relative to AB to the circumscribed circle of the triangle ABC. Then
the locus of intersection points of pairs of equal cevians is the union of the
base AB, the triangle's axis of symmetry, and the circle C. | [
0,
0,
1,
0,
0,
0
] |
Title: Anomaly detecting and ranking of the cloud computing platform by multi-view learning,
Abstract: Anomaly detecting as an important technical in cloud computing is applied to
support smooth running of the cloud platform. Traditional detecting methods
based on statistic, analysis, etc. lead to the high false-alarm rate due to
non-adaptive and sensitive parameters setting. We presented an online model for
anomaly detecting using machine learning theory. However, most existing methods
based on machine learning linked all features from difference sub-systems into
a long feature vector directly, which is difficult to both exploit the
complement information between sub-systems and ignore multi-view features
enhancing the classification performance. Aiming to this problem, the proposed
method automatic fuses multi-view features and optimize the discriminative
model to enhance the accuracy. This model takes advantage of extreme learning
machine (ELM) to improve detection efficiency. ELM is the single hidden layer
neural network, which is transforming iterative solution the output weights to
solution of linear equations and avoiding the local optimal solution. Moreover,
we rank anomies according to the relationship between samples and the
classification boundary, and then assigning weights for ranked anomalies,
retraining the classification model finally. Our method exploits the complement
information between sub-systems sufficiently, and avoids the influence from
imbalance dataset, therefore, deal with various challenges from the cloud
computing platform. We deploy the privately cloud platform by Openstack,
verifying the proposed model and comparing results to the state-of-the-art
methods with better efficiency and simplicity. | [
1,
0,
0,
1,
0,
0
] |
Title: A graph model of message passing processes,
Abstract: In the paper we consider a graph model of message passing processes and
present a method verification of message passing processes. The method is
illustrated by an example of a verification of sliding window protocol. | [
1,
0,
0,
0,
0,
0
] |
Title: Learning the Sparse and Low Rank PARAFAC Decomposition via the Elastic Net,
Abstract: In this article, we derive a Bayesian model to learning the sparse and low
rank PARAFAC decomposition for the observed tensor with missing values via the
elastic net, with property to find the true rank and sparse factor matrix which
is robust to the noise. We formulate efficient block coordinate descent
algorithm and admax stochastic block coordinate descent algorithm to solve it,
which can be used to solve the large scale problem. To choose the appropriate
rank and sparsity in PARAFAC decomposition, we will give a solution path by
gradually increasing the regularization to increase the sparsity and decrease
the rank. When we find the sparse structure of the factor matrix, we can fixed
the sparse structure, using a small to regularization to decreasing the
recovery error, and one can choose the proper decomposition from the solution
path with sufficient sparse factor matrix with low recovery error. We test the
power of our algorithm on the simulation data and real data, which show it is
powerful. | [
0,
0,
1,
1,
0,
0
] |
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