title:
CER: Complementary Entity Recognition via Knowledge Expansion on Large
  Unlabeled Product Reviews

abstract:
  Product reviews contain a lot of useful information about product features
and customer opinions. One important product feature is the complementary
entity (products) that may potentially work together with the reviewed product.
Knowing complementary entities of the reviewed product is very important
because customers want to buy compatible products and avoid incompatible ones.
In this paper, we address the problem of Complementary Entity Recognition
(CER). Since no existing method can solve this problem, we first propose a
novel unsupervised method to utilize syntactic dependency paths to recognize
complementary entities. Then we expand category-level domain knowledge about
complementary entities using only a few general seed verbs on a large amount of
unlabeled reviews. The domain knowledge helps the unsupervised method to adapt
to different products and greatly improves the precision of the CER task. The
advantage of the proposed method is that it does not require any labeled data
for training. We conducted experiments on 7 popular products with about 1200
reviews in total to demonstrate that the proposed approach is effective.


title: Controlling False Discoveries During Interactive Data Exploration

abstract:
  Recent tools for interactive data exploration significantly increase the
chance that users make false discoveries. The crux is that these tools
implicitly allow the user to test a large body of different hypotheses with
just a few clicks thus incurring in the issue commonly known in statistics as
the multiple hypothesis testing error. In this paper, we propose solutions to
integrate multiple hypothesis testing control into interactive data exploration
tools. A key insight is that existing methods for controlling the false
discovery rate (such as FDR) are not directly applicable for interactive data
exploration. We therefore discuss a set of new control procedures that are
better suited and integrated them in our system called Aware. By means of
extensive experiments using both real-world and synthetic data sets we
demonstrate how Aware can help experts and novice users alike to efficiently
control false discoveries.


title: Optimality of Correlated Sampling Strategies

abstract:
  In the "correlated sampling" problem, two players are given probability
distributions $P$ and $Q$, respectively, over the same finite set, with access
to shared randomness. Without any communication, the two players are each
required to output an element sampled according to their respective
distributions, while trying to minimize the probability that their outputs
disagree. A well known strategy due to Kleinberg-Tardos and Holenstein, with a
close variant (for a similar problem) due to Broder, solves this task with
disagreement probability at most $2 \delta/(1+\delta)$, where $\delta$ is the
total variation distance between $P$ and $Q$. This strategy has been used in
several different contexts, including sketching algorithms, approximation
algorithms based on rounding linear programming relaxations, the study of
parallel repetition and cryptography.
  In this paper, we give a surprisingly simple proof that this strategy is
essentially optimal. Specifically, for every $\delta \in (0,1)$, we show that
any correlated sampling strategy incurs a disagreement probability of
essentially $2\delta/(1+\delta)$ on some inputs $P$ and $Q$ with total
variation distance at most $\delta$. This partially answers a recent question
of Rivest.
  Our proof is based on studying a new problem that we call "constrained
agreement". Here, the two players are given subsets $A \subseteq [n]$ and $B
\subseteq [n]$, respectively, and their goal is to output an element $i \in A$
and $j \in B$, respectively, while minimizing the probability that $i \neq j$.
We prove tight bounds for this question, which in turn imply tight bounds for
correlated sampling. Though we settle basic questions about the two problems,
our formulation leads to more fine-grained questions that remain open.


title: Very Thin Disc Galaxies in The SDSS Catalog of Edge-on Galaxies

abstract:
  We study the properties of galaxies with very thin discs using a sample of 85
objects whose stellar disc radial-to-vertical scale ratio determined from
photometric decomposition, exceeds nine. We present evidences of similarities
between the very thin disc galaxies (VTD galaxies) and low surface brightness
(LSB) disc galaxies, and conclude that both small and giant LSB galaxies may
reveal themselves as VTD, edge-on galaxies. Our VTD galaxies are mostly
bulgeless, and those with large radial scale length tend to have redder colors.
We performed spectral observations of 22 VTD galaxies with the Dual Imaging
Spectrograph on the 3.5m telescope at the Apache Point Observatory. The spectra
with good resolution (R ~ 5000) allow us to determine the distance and the
ionized gas rotation curve maximum for the galaxies. Our VTD galaxies have low
dust content, in contrast to regular disc galaxies. Apparently, VTD galaxies
reside in specific cosmological low-density environments and tend to have less
connection with filaments. Comparing a toy model that assumes marginally low
star formation in galactic discs with obtained gas kinematics data, we conclude
that there is a threshold central surface density of about 88 Mo/pc**2, which
we observe in the case of very thin, rotationally supported galactic discs.


title: Strong maximum principles for fractional Laplacians

abstract:
  We give a unified approach to strong maximum principles for a large class of
nonlocal operators of the order $s\in(0,1)$, that includes the Dirichlet, the
Neumann Restricted (or Regional) and the Neumann Semirestricted Laplacians.


title:
Dynamic Magnetometer Calibration and Alignment to Inertial Sensors by
  Kalman Filtering

abstract:
  Magnetometer and inertial sensors are widely used for orientation estimation.
Magnetometer usage is often troublesome, as it is prone to be interfered by
onboard or ambient magnetic disturbance. The onboard soft-iron material
distorts not only the magnetic field, but the magnetometer sensor frame
coordinate and the cross-sensor misalignment relative to inertial sensors. It
is desirable to conveniently put magnetic and inertial sensors information in a
common frame. Existing methods either split the problem into successive
intrinsic and cross-sensor calibrations, or rely on stationary accelerometer
measurements which is infeasible in dynamic conditions. This paper formulates
the magnetometer calibration and alignment to inertial sensors as a state
estimation problem, and collectively solves the magnetometer intrinsic and
cross-sensor calibrations, as well as the gyroscope bias estimation. Sufficient
conditions are derived for the problem to be globally observable, even when no
accelerometer information is used at all. An extended Kalman filter is designed
to implement the state estimation and comprehensive test data results show the
superior performance of the proposed approach. It is immune to acceleration
disturbance and applicable potentially in any dynamic conditions.


title: Quantum generalisation of feedforward neural networks

abstract:
  We propose a quantum generalisation of a classical neural network. The
classical neurons are firstly rendered reversible by adding ancillary bits.
Then they are generalised to being quantum reversible, i.e.\ unitary. (The
classical networks we generalise are called feedforward, and have step-function
activation functions.) The quantum network can be trained efficiently using
gradient descent on a cost function to perform quantum generalisations of
classical tasks. We demonstrate numerically that it can: (i) compress quantum
states onto a minimal number of qubits, creating a quantum autoencoder, and
(ii) discover quantum communication protocols such as teleportation. Our
general recipe is theoretical and implementation-independent. The quantum
neuron module can naturally be implemented photonically.


title: Stochastic Longshore Current Dynamics

abstract:
  We develop a stochastic parametrization, based on a `simple' deterministic
model for the dynamics of steady longshore currents, that produces ensembles
that are statistically consistent with field observations of these currents.
Unlike deterministic models, stochastic parameterization incorporates
randomness and hence can only match the observations in a statistical sense.
Unlike statistical emulators, in which the model is tuned to the statistical
structure of the observation, stochastic parametrization are not directly tuned
to match the statistics of the observations. Rather, stochastic
parameterization combines deterministic, i.e physics based models with
stochastic models for the "missing physics" to create hybrid models, that are
stochastic, but yet can be used for making predictions, especially in the
context of data assimilation. We introduce a novel measure of the utility of
stochastic models of complex processes, that we call {\em consistency of
sensitivity}. We show, in the context of data assimilation, the stochastic
parametrization of longshore currents achieves good results in capturing the
statistics of observation {\em that were not used} in tuning the model.

categories: physics.data-an math.ST physics.ao-ph stat.TH


title: Placement Optimization of UAV-Mounted Mobile Base Stations

abstract:
  In terrestrial communication networks without fixed infrastructure, unmanned
aerial vehicle (UAV)-mounted mobile base stations (MBSs) provide an efficient
solution to achieve wireless connectivity. This letter aims to minimize the
number of MBSs needed to provide wireless coverage for a group of distributed
ground terminals (GTs), ensuring that each GT is within the communication range
of at least one MBS. We propose a polynomial-time algorithm with successive MBS
placement, where the MBSs are placed sequentially starting on the area
perimeter of the uncovered GTs along a spiral path towards the center, until
all GTs are covered. Each MBS is placed to cover as many uncovered GTs as
possible, with higher priority given to the GTs on the boundary to reduce the
occurrence of outlier GTs that each may require one dedicated MBS for its
coverage. Numerical results show that the proposed algorithm performs favorably
compared to other schemes in terms of the total number of required MBSs and/or
time complexity.


title: Rationality of capped descendent vertex in $K$-theory

abstract:
  In this paper we analyze the fundamental solution of the \textit{quantum
difference equation} (qde) for the moduli space of instantons on
two-dimensional projective space. The qde is a $K$-theoretic generalization of
the quantum differential equation in quantum cohomology. As in the quantum
cohomology case, the fundamental solution of qde provides the capping operator
in $K$-theory (the rubber part of the capped vertex). We study the dependence
of the capping operator on the equivariant parameters $a_i$ of the torus acting
on the instanton moduli space by changing the framing. We prove that the
capping operator factorizes at $a_i\to 0$. The rationality of the $K$-theoretic
1-leg capped descendent vertex follows from factorization of the capping
operator as a simple corollary.


title:
Approximation properties of univalent mappings on the unit ball in
  $\mathbb{C}^n$

abstract:
  Let $n\geq 2$. In this paper, we obtain approximation properties of various
families of normalized univalent mappings $f$ on the Euclidean unit ball
$\mathbb{B}^n$ in $\mathbb{C}^n$ by automorphisms of $\mathbb{C}^n$ whose
restrictions to $\mathbb{B}^n$ have the same geometric property of $f$. First,
we obtain approximation properties of spirallike, convex and $g$-starlike
mappings $f$ on $\mathbb{B}^n$ by automorphisms of $\mathbb{C}^n$ whose
restrictions to $\mathbb{B}^n$ have the same geometric property of $f$,
respectively. Next, for a nonresonant operator $A$ with $m(A)>0$, we obtain an
approximation property ofmappings which have $A$-parametric representation by
automorphisms of $\mathbb{C}^n$ whose restrictions to $\mathbb{B}^n$ have
$A$-parametric representation. Certain questions will be also mentioned.
Finally, we obtain an approximation property by automorphisms of $\mathbb{C}^n$
for a subset of $S_{I_n}^0(\mathbb{B}^n)$ consisting of mappings $f$ which
satisfy the condition $\|Df(z)-I_n\|<1$, $z\in\mathbb{B}^n$. Related results
will be also obtained.


title:
Residual finite dimensionality and representations of amenable operator
  algebras

abstract:
  We consider a version of a famous open problem formulated by Kadison, asking
whether bounded representations of operator algebras are automatically
completely bounded. We investigate this question in the context of amenable
operator algebras, and we provide an affirmative answer for representations
whose range is residually finite-dimensional. Furthermore, we show that
weak-${}^*$ closed, amenable, residually finite-dimensional operator algebras
are similar to $C^*$-algebras, and in particular have the property that all
their bounded representations are completely bounded. We prove our results for
operator algebras having the so-called total reduction property, which is known
to be weaker than amenability.


title:
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural
  Networks for Real-Time Object Detection for Autonomous Driving

abstract:
  Object detection is a crucial task for autonomous driving. In addition to
requiring high accuracy to ensure safety, object detection for autonomous
driving also requires real-time inference speed to guarantee prompt vehicle
control, as well as small model size and energy efficiency to enable embedded
system deployment.
  In this work, we propose SqueezeDet, a fully convolutional neural network for
object detection that aims to simultaneously satisfy all of the above
constraints. In our network, we use convolutional layers not only to extract
feature maps but also as the output layer to compute bounding boxes and class
probabilities. The detection pipeline of our model only contains a single
forward pass of a neural network, thus it is extremely fast. Our model is
fully-convolutional, which leads to a small model size and better energy
efficiency. While achieving the same accuracy as previous baselines, our model
is 30.4x smaller, 19.7x faster, and consumes 35.2x lower energy. The code is
open-sourced at \url{https://github.com/BichenWuUCB/squeezeDet}.


title:
Compact ideals and rigidity of representations for amenable operator
  algebras

abstract:
  We examine rigidity phenomena for representations of amenable operator
algebras which have an ideal of compact operators. We establish that a
generalized version of Kadison's conjecture on completely bounded homomorphisms
holds for the algebra if the associated quotient is abelian. We also prove that
injective completely bounded representations of the algebra are similar to
complete isometries. The main motivating example for these investigations is
the recent construction of Choi, Farah and Ozawa of an amenable operator
algebra that is not similar to a $C^*$-algebra, and we show how it fits into
our framework. All of our results hold in the presence of the total reduction
property, a property weaker than amenability.


title: Proceedings Second Graphs as Models Workshop

abstract:
  Graphs are used as models in all areas of computer science: examples are
state space graphs, control flow graphs, syntax graphs, UML-type models of all
kinds, network layouts, social networks, dependency graphs, and so forth. Once
such graphical models are constructed, they can be analysed and transformed to
verify their correctness within a domain, discover new properties, or produce
new equivalent and/or optimised versions.
  Graphs as Models' main focus is the exchange and collaboration of researchers
from different backgrounds. The workshop serves as platform to boost inter- and
transdisciplinary research and wants to serve as leeway for new ideas. Thus,
besides classical research presentations, the workshop is highly geared toward
numerous interactive sessions.
  The second edition of the Graphs as Models workshop was held on 2-3 June 2016
in Eindhoven, The Netherlands, colocated with the 19th European Joint
Conferences on Theory and Practice of Software (ETAPS 2016).


title: Chirality and Magnetic Configurations of Solar Filaments

abstract:
  It has been revealed that the magnetic topology in the solar atmosphere
displays hemispheric preference, i.e., negative/positive helicity in the
northern/southern hemisphere, respectively. However, the strength of the
hemispheric rule and its cyclic variation are controversial. In this paper, we
apply a new method based on filament drainage to 571 erupting filaments from
2010 May to 2015 December in order to determine the filament chirality and its
hemispheric preference. It is found that $91.6\%$ of our sample of erupting
filaments follow the hemispheric rule of helicity sign. It is found that the
strength of the hemispheric preference of the quiescent filaments decreases
slightly from $\sim$$97\%$ in the rising phase to $\sim$$85\%$ in the declining
phase of solar cycle 24, whereas the strength of the intermediate filaments
keeps a high value around $96\pm4\%$ all the time. Only the active-region
filaments show significant variations. Their strength of the hemispheric rule
rises from $\sim$$63\%$ to $\sim$$95\%$ in the rising phase, and keeps a high
value $82\pm5\%$ during the declining phase. Furthermore, during a half-year
period around the solar maximum, their hemispheric preference totally vanishes.
Besides, we also diagnose the magnetic configurations of the filaments based on
our indirect method, and found that in our sample of erupting events, $89\%$
are inverse-polarity filaments with a flux rope magnetic configuration, whereas
$11\%$ are normal-polarity filaments with a sheared arcade configuration.


title: Modeling trajectories of mental health: challenges and opportunities

abstract:
  More than two thirds of mental health problems have their onset during
childhood or adolescence. Identifying children at risk for mental illness later
in life and predicting the type of illness is not easy. We set out to develop a
platform to define subtypes of childhood social-emotional development using
longitudinal, multifactorial trait-based measures. Subtypes discovered through
this study could ultimately advance psychiatric knowledge of the early
behavioural signs of mental illness. To this extent we have examined two types
of models: latent class mixture models and GP-based models. Our findings
indicate that while GP models come close in accuracy of predicting future
trajectories, LCMMs predict the trajectories as well in a fraction of the time.
Unfortunately, neither of the models are currently accurate enough to lead to
immediate clinical impact. The available data related to the development of
childhood mental health is often sparse with only a few time points measured
and require novel methods with improved efficiency and accuracy.


title: An Endpoint Version of Uniform Sobolev Inequalities

abstract:
  We prove an endpoint version of the uniform Sobolev inequalities in
Kenig-Ruiz-Sogge [8]. It was known that strong type inequalities no longer hold
at the endpoints; however, we show that restricted weak type inequalities hold
there, which imply the earlier classical result by real interpolation. The key
ingredient in our proof is a type of interpolation first introduced by Bourgain
[2]. We also prove restricted weak type Stein-Tomas restriction inequalities on
some parts of the boundary of a pentagon, which completely characterizes the
range of exponents for which the inequalities hold.


title: Learning to Segment Object Candidates via Recursive Neural Networks

abstract:
  To avoid the exhaustive search over locations and scales, current
state-of-the-art object detection systems usually involve a crucial component
generating a batch of candidate object proposals from images. In this paper, we
present a simple yet effective approach for segmenting object proposals via a
deep architecture of recursive neural networks (ReNNs), which hierarchically
groups regions for detecting object candidates over scales. Unlike traditional
methods that mainly adopt fixed similarity measures for merging regions or
finding object proposals, our approach adaptively learns the region merging
similarity and the objectness measure during the process of hierarchical region
grouping. Specifically, guided by a structured loss, the ReNN model jointly
optimizes the cross-region similarity metric with the region merging process as
well as the objectness prediction. During inference of the object proposal
generation, we introduce randomness into the greedy search to cope with the
ambiguity of grouping regions. Extensive experiments on standard benchmarks,
e.g., PASCAL VOC and ImageNet, suggest that our approach is capable of
producing object proposals with high recall while well preserving the object
boundaries and outperforms other existing methods in both accuracy and
efficiency.


title: Algorithmic Songwriting with ALYSIA

abstract:
  This paper introduces ALYSIA: Automated LYrical SongwrIting Application.
ALYSIA is based on a machine learning model using Random Forests, and we
discuss its success at pitch and rhythm prediction. Next, we show how ALYSIA
was used to create original pop songs that were subsequently recorded and
produced. Finally, we discuss our vision for the future of Automated
Songwriting for both co-creative and autonomous systems.


title:
Short range correlations - The important role of few-body dynamics in
  many-body systems

abstract:
  For many-body systems with short range interaction a series of relations were
derived connecting many properties of the system to the dynamics of a closely
packed few-body subsystems. Some of these relations were experimentally
verified in ultra cold atomic gases. Here we shall review the implications of
these developments on our understanding of nuclear one and two-body momentum
distributions, and on the electron scattering Coulomb sum rule.


title: Simulation of Chua's Circuit by Means of Interval Analysis

abstract:
  The Chua's circuit is a paradigm for nonlinear scientific studies. It is
usually simulated by means of numerical methods under IEEE 754-2008 standard.
Although the error propagation problem is well known, little attention has been
given to the relationship between this error and inequalities presented in
Chua's circuit model. Taking the average of round mode towards $+\infty$ and
$-\infty$, we showed a qualitative change on the dynamics of Chua's circuit.


title: On spreading rumor in heterogeneous systems

abstract:
  In this paper we consider a model of spreading information in heterogeneous
systems wherein we have two kinds of objects. Some of them are active and
others are passive. Active objects can, if they possess information, share it
with an encountered passive object. We focus on a particular case such that
active objects communicate independently with randomly chosen passive objects.
Such model is motivated by two real-life scenarios. The first one is a very
dynamic system of mobile devices distributing information among stationary
devices. The second is an architecture wherein clients communicate with several
servers and can leave some information learnt from other servers. The main
question we investigate is how many rounds is needed to deliver the information
to all objects under the assumption that at the beginning exactly one object
has the information?
  In this paper we provide mathematical models of such process and show rigid
and very precise mathematical analysis for some special cases important from
practical point of view. Some mathematical results are quite surprising -- we
find relation of investigated process to both coupon collector's problem as
well as the birthday paradox. Additionally, we present simulations for showing
behaviour for general parameters


title: Anisotropic Pauli Spin Blockade of Holes in a GaAs Double Quantum Dot

abstract:
  Electrically defined semiconductor quantum dots are attractive systems for
spin manipulation and quantum information processing. Heavy-holes in both Si
and GaAs are promising candidates for all-electrical spin manipulation, owing
to the weak hyperfine interaction and strong spin-orbit interaction. However,
it has only recently become possible to make stable quantum dots in these
systems, mainly due to difficulties in device fabrication and stability. Here
we present electrical transport measurements on holes in a gate-defined double
quantum dot in a $\mathrm{GaAs/Al_xGa_{1-x}As}$ heterostructure. We observe
clear Pauli spin blockade and demonstrate that the lifting of this spin
blockade by an external magnetic field is highly anisotropic. Numerical
calculations of heavy-hole transport through a double quantum dot in the
presence of strong spin-orbit coupling show quantitative agreement with
experimental results and suggest that the observed anisotropy can be explained
by both the anisotropic effective hole g-factor and the surface Dresselhaus
spin-orbit interaction.


title:
Biharmonic homogeneous submanifolds in compact symmetric spaces and
  compact Lie groups

abstract:
  We give a necessary and sufficient condition for orbits of commutative
Hermann actions and actions of the direct product of two symmetric subgroups on
compact Lie groups to be biharmonic in terms of symmetric triad with
multiplicities. By this criterion, we determine all the proper biharmonic
submanifolds in irreducible symmetric spaces of compact type which are singular
orbits of commutative Hermann actions of cohomogeneity two. Also, in compact
simple Lie groups, we determine all the biharmonic hypersurfaces which are
regular orbits of actions of the direct product of two symmetric subgroups
which are associated to commutative Hermann actions of cohomogeneity one.


title: Trained Ternary Quantization

abstract:
  Deep neural networks are widely used in machine learning applications.
However, the deployment of large neural networks models can be difficult to
deploy on mobile devices with limited power budgets. To solve this problem, we
propose Trained Ternary Quantization (TTQ), a method that can reduce the
precision of weights in neural networks to ternary values. This method has very
little accuracy degradation and can even improve the accuracy of some models
(32, 44, 56-layer ResNet) on CIFAR-10 and AlexNet on ImageNet. And our AlexNet
model is trained from scratch, which means it's as easy as to train normal full
precision model. We highlight our trained quantization method that can learn
both ternary values and ternary assignment. During inference, only ternary
values (2-bit weights) and scaling factors are needed, therefore our models are
nearly 16x smaller than full-precision models. Our ternary models can also be
viewed as sparse binary weight networks, which can potentially be accelerated
with custom circuit. Experiments on CIFAR-10 show that the ternary models
obtained by trained quantization method outperform full-precision models of
ResNet-32,44,56 by 0.04%, 0.16%, 0.36%, respectively. On ImageNet, our model
outperforms full-precision AlexNet model by 0.3% of Top-1 accuracy and
outperforms previous ternary models by 3%.


title:
${\mathbf{{CFT}_6}}$ Bulk/Boundary ${\mathbf{{AdS}^Q_5}}$ Correspondence
  and Emergent Gravity

abstract:
  We revisit a non-perturbation theory of quantum gravity in $1.5$ order
underlying an emergent gravitational pair of $(4{\bar 4})$-brane with a renewed
interest. In particular the formulation is governed by a geometric torsion
${\cal H}_3$ in second order with an on-shell NS form in first order.
Interestingly the gravitational pair is sourced by a Kalb-Ramond two form CFT
on a $D_5$-brane in $d$$=$$10$ type IIB superstring theory. We show that a
generic form theory containing a CFT sector in $d=6$ bulk may be described by a
boundary ${\rm AdS}_5$ with a quintessence Q. Analysis reveals that the
bulk/boundary duality in emergent gravity can be a potential tool to explore
the quintessential cosmology.


title:
Systematic study of symmetric cumulants at $\sqrt{s_{NN}}$ = 200 GeV in
  Au+Au collision using transport approach

abstract:
  Measurement of 4-particle symmetric cumulants has been considered to be a
good tool to study the correlations between amplitudes of different orders of
anisotropic flow harmonics in the heavy-ion collision. These new observables
not yet been measured at RHIC. Using A Multi-Phase Transport model, a set of
predictions for the centrality dependence of the normalized 4-particle
symmetric cumulants in Au+Au collisions at $\sqrt{s_{NN}}$ = 200 GeV has been
given. In addition, the effects of shear viscosity and hadronic rescattering on
the magnitude of symmetric cumulants are discussed using AMPT model at
$\sqrt{s_{NN}}$ = 200 GeV. It is shown that $sc(2,3)$ is found to be more
sensitive to hadronic rescattering, whereas $sc(2,4)$ is more sensitive to the
shear viscosity. Rapidity dependence of symmetric cumulants is also shown. A
relation between symmetric cumulant and event plane correlation is investigated
using AMPT model.


title: Four-Dimensional Usability Investigation of Image CAPTCHA

abstract:
  Image CAPTCHA, aiming at effectively distinguishing human users from
malicious script attacks, has been an important mechanism to protect online
systems from spams and abuses. Despite the increasing interests in developing
and deploying image CAPTCHAs, the usability aspect of those CAPTCHAs has hardly
been explored systematically. In this paper, the universal design factors of
image CAPTCHAs, such as image layouts, quantities, sizes, tilting angles and
colors were experimentally evaluated through the following four dimensions:
eye-tracking, efficiency, effectiveness and satisfaction. The cognitive
processes revealed by eye-tracking indicate that the distribution of eye gaze
is equally assigned to each candidate image and irrelevant to the variation of
image contents. In addition, the gazing plot suggests that more than 70% of the
participants inspected CAPTCHA images row-by-row, which is more efficient than
scanning randomly. Those four-dimensional evaluations essentially suggest that
square and horizontal rectangle are the preferred layout; image quantities may
not exceed 16 while the image color is insignificant. Meanwhile, the image size
and tilting angle are suggested to be larger than 55 pixels x 55 pixels and
within -45~45 degrees, respectively. Basing on those usability experiment
results, we proposed a design guideline that is expected to be useful for
developing more usable image CAPTCHAs.


title:
Local well-posedness of the incompressible Euler equations in
  $B^1_{\infty,1}$ and the inviscid limit of the Navier-Stokes equations

abstract:
  We prove the inviscid limit of the incompressible Navier-Stokes equations in
the same topology of Besov spaces as the initial data. The proof is based on
proving the continuous dependence of the Navier-Stokes equations uniformly with
respect to the viscosity. To show the latter, we rely on some Bona-Smith type
method in the $L^p$ setting. Our obtained result implies a new result that the
Cauchy problem of the Euler equations is locally well-posed in the borderline
Besov space $B^{\frac dp+1}_{p,1}(\mathbb{R}^d)$, $1\leq p\leq \infty$, $d\geq
2$, in the sense of Hadmard, which is an open problem left in recent works by
Bourgain and Li in \cite{BL,BL1} and by Misio{\l}ek and Yoneda in \cite{MY,MY2,
MY3}.


title:
A Mathematical Proof of the Superiority of NOMA Compared to Conventional
  OMA

abstract:
  While existing works about non-orthogonal multiple access (NOMA) have
indicated that NOMA can yield a significant performance gain over orthogonal
multiple access (OMA) with fixed resource allocation, it is not clear whether
such a performance gain will diminish when optimal resource
(Time/Frequency/Power) allocation is carried out. In this paper, the
performance comparison between NOMA and conventional OMA systems is
investigated, from an optimization point of view. Firstly, by using the idea of
power splitting, a closed-form expression for the optimum sum rate of NOMA
systems is derived. Then, with rigorous mathematical proofs, we reveal the fact
that NOMA can always outperform conventional OMA systems, even if both are
equipped with the optimal resource allocation policies. Finally, computer
simulations are conducted to validate the accuracy of the analytical results.


title:
Usability Investigation on the Localization of Text CAPTCHAs: Take
  Chinese Characters as a Case Study

abstract:
  Text CAPTCHA has been an effective means to protect online systems from spams
and abuses caused by automatic scripts which pretend to be human beings.
However, nearly all the Text CAPTCHA designs in nowadays are based on English
characters, which may not be the most user-friendly option for non-English
speakers. Therefore, under the background of globalization, there is an
increasing interest in designing local-language CAPTCHA, which is expected to
be more usable for native speakers. However, systematic studies on the
usability of localized CAPTCHAs are rare, and a general procedure for the
design of usable localized CAPTCHA is still unavailable. Here, we
comprehensively explored the design of CAPTCHAs based on Chinese characters
from a usability perspective: cognitive processes of solving alphanumeric and
Chinese CAPTCHAs are analyzed, followed by a usability comparison of those two
types of CAPTCHAs and the evaluation of intrinsic design factors of Chinese
CAPTCHAs. It was found that Chinese CAPTCHAs could be equally usable comparing
with alphanumeric ones. Meanwhile, guidelines for the design of usable Chinese
CAPTCHAs were also presented. Moreover, those design practices were also
summarized as a general procedure which is expected to be applicable for the
design of CAPTCHAs based on other languages.


title:
Liouville theorem for the fractional Lane-Emden Equation in unbounded
  domain

abstract:
  Our purpose of this paper is to study the nonexistence of nonnegative very
weak solutions of \begin{equation}\label{eq 0.1}
  \displaystyle (-\Delta)^\alpha u = u^p+\nu\quad
  {\rm in}\quad \Omega,\qquad\ u=g\quad {\rm in}\quad \mathbb{ R}^N\setminus
\Omega,
  \end{equation} where $\alpha\in(0,1)$, $p>0$, $\Omega$ is a unbounded $C^2$
domain in $\mathbb{ R}^N$ with $N>2\alpha$, $g\in L^1(\mathbb{ R}^N\setminus
\Omega,\frac{dx}{1+|x|^{N+2\alpha}})$ nonnegative and $\nu$ is a nonnegative
Radon measure. We obtain that\smallskip
  $(i)$ if $\Omega\supseteq \left(\mathbb{R}^N\setminus
\overline{B_{r_0}(0)}\right)$ for some $r_0>0$ and $p<\frac{N}{N-2\alpha}$,
then fractional Lane-Emden equation has no weak solutions.
  $(ii)$ if $\Omega\supseteq \left\{x\in \mathbb{ R}^N:\, x\cdot a>r_0\right\}$
for some $r_0\ge 0$, $a\in \mathbb{ R}^N$ and $p<\frac{N+\alpha}{N-\alpha}$,
then fractional Lane-Emden equation has no weak solutions. Here
$\frac{N+\alpha}{N-\alpha}$ is sharp for the nonexistence in the half space.
\smallskip
  The above Liouville theorem could be applied to obtain nonexistence of
classical solution of the fractional Lane-Emden equations $$
  (-\Delta)^\alpha u = u^p\quad
  {\rm in}\quad \Omega, \qquad u\ge 0\quad {\rm in}\quad \mathbb{ R}^N\setminus
\Omega,
  $$ where $\Omega=\mathbb{R}^N\setminus B_{r_0}(0)$ with $r_0>0$ or
$\Omega=\mathbb{R}^{N-1}\times(0,+\infty)$.


title: Word Recognition with Deep Conditional Random Fields

abstract:
  Recognition of handwritten words continues to be an important problem in
document analysis and recognition. Existing approaches extract hand-engineered
features from word images--which can perform poorly with new data sets.
Recently, deep learning has attracted great attention because of the ability to
learn features from raw data. Moreover they have yielded state-of-the-art
results in classification tasks including character recognition and scene
recognition. On the other hand, word recognition is a sequential problem where
we need to model the correlation between characters. In this paper, we propose
using deep Conditional Random Fields (deep CRFs) for word recognition.
Basically, we combine CRFs with deep learning, in which deep features are
learned and sequences are labeled in a unified framework. We pre-train the deep
structure with stacked restricted Boltzmann machines (RBMs) for feature
learning and optimize the entire network with an online learning algorithm. The
proposed model was evaluated on two datasets, and seen to perform significantly
better than competitive baseline models. The source code is available at
https://github.com/ganggit/deepCRFs.


title: Rigid constellations of closed Reeb orbits

abstract:
  We use Hamiltonian Floer theory to recover and generalize a classic rigidity
theorem of Ekelend and Lasry. That theorem can be rephrased as an assertion
about the existence of multiple closed Reeb orbits for certain tight contact
forms on the sphere that are close, in a suitable sense, to the standard
contact form. We first generalize this result to Reeb flows of contact forms on
prequantization spaces that are suitably close to Boothby-Wang forms. We then
establish, under an additional nondegeneracy assumption, the same rigidity
phenomenon for Reeb flows on any closed contact manifold.
  A natural obstruction to obtaining sharp multiplicity results for closed Reeb
orbits is the possible existence of fast closed orbits. To complement the
existence results established here, we also show that the existence of such
fast orbits can not be precluded by any condition which is invariant under
contactomorphisms, even for nearby contact forms.


title:
Skin Cancer Detection and Tracking using Data Synthesis and Deep
  Learning

abstract:
  Dense object detection and temporal tracking are needed across applications
domains ranging from people-tracking to analysis of satellite imagery over
time. The detection and tracking of malignant skin cancers and benign moles
poses a particularly challenging problem due to the general uniformity of large
skin patches, the fact that skin lesions vary little in their appearance, and
the relatively small amount of data available. Here we introduce a novel data
synthesis technique that merges images of individual skin lesions with
full-body images and heavily augments them to generate significant amounts of
data. We build a convolutional neural network (CNN) based system, trained on
this synthetic data, and demonstrate superior performance to traditional
detection and tracking techniques. Additionally, we compare our system to
humans trained with simple criteria. Our system is intended for potential
clinical use to augment the capabilities of healthcare providers. While
domain-specific, we believe the methods invoked in this work will be useful in
applying CNNs across domains that suffer from limited data availability.


title: Joint Visual Denoising and Classification using Deep Learning

abstract:
  Visual restoration and recognition are traditionally addressed in pipeline
fashion, i.e. denoising followed by classification. Instead, observing
correlations between the two tasks, for example clearer image will lead to
better categorization and vice visa, we propose a joint framework for visual
restoration and recognition for handwritten images, inspired by advances in
deep autoencoder and multi-modality learning. Our model is a 3-pathway deep
architecture with a hidden-layer representation which is shared by multi-inputs
and outputs, and each branch can be composed of a multi-layer deep model. Thus,
visual restoration and classification can be unified using shared
representation via non-linear mapping, and model parameters can be learnt via
backpropagation. Using MNIST and USPS data corrupted with structured noise, the
proposed framework performs at least 20\% better in classification than
separate pipelines, as well as clearer recovered images. The noise model and
the reproducible source code is available at
{\url{https://github.com/ganggit/jointmodel}}.


title: Distinguished representations for SL(n)

abstract:
  For $E/F$ a quadratic extension of local fields, and $\pi$ an irreducible
admissible generic representation of $SL_n(E)$, we calculate the dimension of
$Hom_{SL_n(F)}[\pi,C]$ and relate it to fibers of the base change map
corresponding to base change of representations of $SU_n(F)$ to $SL_n(E)$ as
suggested in a recent work of the second author. We also deal with finite
fields.


title:
Cyclic coverings of the projective line by Mumford curves in positive
  characteristic

abstract:
  We study the rigid analytic geometry of cyclic coverings of the projective
line. We determine the defining equation of a cyclic covering of degree $p$ of
the projective line by a Mumford curve over a complete discrete valuation field
of positive characteristic $p$. Previously, Bradley studied that of any degree
over a non-archimedean local field of characteristic zero.


title:
Enhancing Use Case Points Estimation Method Using Soft Computing
  Techniques

abstract:
  Software estimation is a crucial task in software engineering. Software
estimation encompasses cost, effort, schedule, and size. The importance of
software estimation becomes critical in the early stages of the software life
cycle when the details of software have not been revealed yet. Several
commercial and non-commercial tools exist to estimate software in the early
stages. Most software effort estimation methods require software size as one of
the important metric inputs and consequently, software size estimation in the
early stages becomes essential. One of the approaches that has been used for
about two decades in the early size and effort estimation is called use case
points. Use case points method relies on the use case diagram to estimate the
size and effort of software projects. Although the use case points method has
been widely used, it has some limitations that might adversely affect the
accuracy of estimation. This paper presents some techniques using fuzzy logic
and neural networks to improve the accuracy of the use case points method.
Results showed that an improvement up to 22% can be obtained using the proposed
approach.


title: End-to-end Learning of Driving Models from Large-scale Video Datasets

abstract:
  Robust perception-action models should be learned from training data with
diverse visual appearances and realistic behaviors, yet current approaches to
deep visuomotor policy learning have been generally limited to in-situ models
learned from a single vehicle or a simulation environment. We advocate learning
a generic vehicle motion model from large scale crowd-sourced video data, and
develop an end-to-end trainable architecture for learning to predict a
distribution over future vehicle egomotion from instantaneous monocular camera
observations and previous vehicle state. Our model incorporates a novel
FCN-LSTM architecture, which can be learned from large-scale crowd-sourced
vehicle action data, and leverages available scene segmentation side tasks to
improve performance under a privileged learning paradigm.


title: Fragmented Hofer's geometry

abstract:
  Hofer's norm (metric) is an important and interesting topic in symplectic
geometry. In the present paper, we define fragmented Hofer's norms which are
Hofer's norms controlled by fragmentation norms and give some observations on
fragmented Hofer's norms.


title:
Comparison between the Logotropic and $\Lambda$CDM models at the
  cosmological scale

abstract:
  We perform a detailed comparison between the Logotropic model [P.H. Chavanis,
Eur. Phys. J. Plus 130 (2015) 130] and the $\Lambda$CDM model. These two models
behave similarly at large (cosmological) scales up to the present. Differences
will appear only in the far future, in about $25\, {\rm Gyrs}$, when the
Logotropic Universe becomes phantom while the $\Lambda$CDM Universe enters in
the de Sitter era. However, the Logotropic model differs from the $\Lambda$CDM
model at small (galactic) scales, where the latter encounters serious problems.
Having a nonvanishing pressure, the Logotropic model can solve the cusp problem
and the missing satellite problem of the $\Lambda$CDM model. In addition, it
leads to dark matter halos with a constant surface density $\Sigma_0=\rho_0
r_h$, and can explain its observed value $\Sigma_0=141 \, M_{\odot}/{\rm pc}^2$
without adjustable parameter. This makes the logotropic model rather unique
among all the models attempting to unify dark matter and dark energy. In this
paper, we compare the Logotropic and $\Lambda$CDM models at the cosmological
scale where they are very close to each other in order to determine
quantitatively how much they differ. This comparison is facilitated by the fact
that these models depend on only two parameters, the Hubble constant $H_0$ and
the present fraction of dark matter $\Omega_{\rm m0}$. Using the latest
observational data from Planck 2015+Lensing+BAO+JLA+HST, we find that the best
fit values of $H_0$ and $\Omega_{\rm m0}$ are $H_0=68.30\, {\rm km}\, {\rm
s}^{-1}\,{\rm Mpc}^{-1}$ and $\Omega_{\rm m0}=0.3014$ for the Logotropic model,
and $H_0=68.02\, {\rm km}\, {\rm s}^{-1}\,{\rm Mpc}^{-1}$ and $\Omega_{\rm
m0}=0.3049$ for the $\Lambda$CDM model. The difference between the two models
appears at the percent level.


title:
Multi-Label Image Classification with Regional Latent Semantic
  Dependencies

abstract:
  Deep convolution neural networks (CNN) have demonstrated advanced performance
on single-label image classification, and various progress also have been made
to apply CNN methods on multi-label image classification, which requires to
annotate objects, attributes, scene categories etc. in a single shot. Recent
state-of-the-art approaches to multi-label image classification exploit the
label dependencies in an image, at global level, largely improving the labeling
capacity. However, predicting small objects and visual concepts is still
challenging due to the limited discrimination of the global visual features. In
this paper, we propose a Regional Latent Semantic Dependencies model (RLSD) to
address this problem. The utilized model includes a fully convolutional
localization architecture to localize the regions that may contain multiple
highly-dependent labels. The localized regions are further sent to the
recurrent neural networks (RNN) to characterize the latent semantic
dependencies at the regional level. Experimental results on several benchmark
datasets show that our proposed model achieves the best performance compared to
the state-of-the-art models, especially for predicting small objects occurred
in the images. In addition, we set up an upper bound model (RLSD+ft-RPN) using
bounding box coordinates during training, the experimental results also show
that our RLSD can approach the upper bound without using the bounding-box
annotations, which is more realistic in the real world.


title:
Exciton delocalization incorporated drift-diffusion model for
  bulk-heterojunction organic solar cells

abstract:
  Modeling the charge-generation process is highly important to understand
device physics and optimize power conversion efficiency of bulk-heterojunction
(BHJ) organic solar cells (OSCs). Free carriers are generated by both ultrafast
exciton delocalization and slow exciton diffusion and dissociation at the
heterojunction interface. In this work, we developed a systematic numerical
simulation to describe the charge-generation process by a modified
drift-diffusion model. The transport, recombination, and collection of free
carriers are incorporated to fully capture the device response. The theoretical
results match well with the state-of-the-art high-performance organic solar
cells. It is demonstrated that the increase of exciton delocalization ratio
reduces the energy loss in the exciton diffusion-dissociation process, and
thus, significantly improves the device efficiency especially for the
short-circuit current. By changing the exciton delocalization ratio, OSC
performances are comprehensively investigated under the conditions of
short-circuit and open-circuit. Particularly, bulk recombination dependent fill
factor saturation is unveiled and understood. As a fundamental electrical
analysis of the delocalization mechanism, our work is important to understand
and optimize the high-performance OSCs.

categories: cond-mat.mes-hall cond-mat.mtrl-sci physics.comp-ph physics.optics


title: Causal structures in cosmology

abstract:
  This article reviews the properties and limitations associated with the
existence of particle, visual, and event horizons in cosmology in general and
in inflationary universes in particular, carefully distinguishing them from
`Hubble horizons'. It explores to what extent one might be able to probe
conditions beyond the visual horizon (which is close in size to the present
Hubble radius), thereby showing that visual horizons place major limits on what
are observationally testable aspects of a multiverse, if such exists. Indeed
these limits largely prevent us from observationally proving a multiverse
either does or does not exist. We emphasize that event horizons play no role at
all in observational cosmology, even in the multiverse context, despite some
claims to the contrary in the literature.


title: Finite indentation of highly curved elastic shells

abstract:
  Experimentally measuring the elastic properties of thin biological surfaces
is non-trivial, particularly when they are curved. One technique that may be
used is the indentation of a thin sheet of material by a rigid indenter, whilst
measuring the applied force and displacement. This gives immediate information
on the fracture strength of the material (from the force required to puncture),
but it is also theoretically possible to determine the elastic properties by
comparing the resulting force-displacement curves with a mathematical model.
Existing mathematical studies generally assume that the elastic surface is
initially flat, which is often not the case for biological membranes. We
previously outlined a theory for the indentation of curved isotropic,
incompressible, hyperelastic membranes (with no bending stiffness) which breaks
down for highly curved surfaces, as the entire membrane becomes wrinkled. Here
we introduce the effect of bending stiffness, ensuring that energy is required
to change the shell shape without stretching, and find that commonly neglected
terms in the shell equilibrium equation must be included. The theory presented
here allows for the estimation of shape- and size-independent elastic
properties of highly curved surfaces via indentation experiments, and is
particularly relevant for biological surfaces.


title:
Deep Learning of Robotic Tasks without a Simulator using Strong and Weak
  Human Supervision

abstract:
  We propose a scheme for training a computerized agent to perform complex
human tasks such as highway steering. The scheme is designed to follow a
natural learning process whereby a human instructor teaches a computerized
trainee. The learning process consists of five elements: (i) unsupervised
feature learning; (ii) supervised imitation learning; (iii) supervised reward
induction; (iv) supervised safety module construction; and (v) reinforcement
learning. We implemented the last four elements of the scheme using deep
convolutional networks and applied it to successfully create a computerized
agent capable of autonomous highway steering over the well-known racing game
Assetto Corsa. We demonstrate that the use of the last four elements is
essential to effectively carry out the steering task using vision alone,
without access to a driving simulator internals, and operating in wall-clock
time. This is made possible also through the introduction of a safety network,
a novel way for preventing the agent from performing catastrophic mistakes
during the reinforcement learning stage.


title: Acceleration in a nonplanar time-dependent billiard

abstract:
  We study the dynamical properties of a particle in a non-planar square
billiard. The plane of the billiard has a sinusoidal shape. We consider both
the static and time-dependent plane. We study the affect of different
parameters that control the geometry of the billiard in this model. We consider
variations of different parameters of the model and describe how the particle
trajectory is affected by these parameters. We also investigate the dynamical
behavior of the system in the static condition using its reduced phase plot and
show that the dynamics of the particle inside the billiard may be regular,
mixed or chaotic. Finally, the problem of the particle energy growth is studied
in the billiard with the time-dependent plane. We show that when in the static
case, the billiard is chaotic, then the particle energy in the time-dependent
billiard grows for small number of collisions, and then it starts to saturate.
But when the dynamics of the static case is regular, then the particle average
energy in the time-dependent situation stays constant.


title:
Clustering of infrared-bright dust-obscured galaxies revealed by the
  Hyper Suprime-Cam and WISE

abstract:
  We present measurements of the clustering properties of a sample of infrared
(IR) bright dust-obscured galaxies (DOGs). Combining 125 deg$^2$ of wide and
deep optical images obtained with the Hyper Suprime-Cam on the Subaru Telescope
and all-sky mid-IR (MIR) images taken with Wide-Field Infrared Survey Explorer,
we have discovered 4,367 IR-bright DOGs with $(i - [22])_{\rm AB}$ $>$ 7.0 and
flux density at 22 $\mu$m $>$ 1.0 mJy. We calculate the angular autocorrelation
function (ACF) for a uniform subsample of 1411 DOGs with 3.0 mJy $<$ flux (22
$mu$m) $<$ 5.0 mJy and $i_{\rm AB}$ $<$ 24.0. The ACF of our DOG subsample is
well-fit with a single power-law, $\omega (\theta)$ = (0.010 $\pm$ 0.003)
$\theta^{-0.9}$, where $\theta$ in degrees. The correlation amplitude of
IR-bright DOGs is larger than that of IR-faint DOGs, which reflects a
flux-dependence of the DOG clustering, as suggested by Brodwin et al. (2008).
We assume that the redshift distribution for our DOG sample is Gaussian, and
consider 2 cases: (1) the redshift distribution is the same as IR-faint DOGs
with flux at 22 $\mu$m $<$ 1.0 mJy, mean and sigma $z$ = 1.99 $\pm$ 0.45, and
(2) $z$ = 1.19 $\pm$ 0.30, as inferred from their photometric redshifts. The
inferred correlation length of IR-bright DOGs is $r_0$ = 12.0 $\pm$ 2.0 and
10.3 $\pm$ 1.7 $h^{-1}$ Mpc, respectively. IR-bright DOGs reside in massive
dark matter halos with a mass of $\log [\langle M_{\mathrm{h}} \rangle /
(h^{-1} M_{\odot})]$ = 13.57$_{-0.55}^{+0.50}$ and 13.65$_{-0.52}^{+0.45}$ in
the two cases, respectively.


title:
A Novel Approach for Big Data Analytics in Future Grids Based on Free
  Probability

abstract:
  Based on the random matrix model, we can build statistical models using
massive datasets across the power grid, and employ hypothesis testing for
anomaly detection. First, the aim of this paper is to make the first attempt to
apply the recent free probability result in extracting big data analytics, in
particular data fusion. The nature of this work is basic in that new algorithms
and analytics tools are proposed to pave the way for the future's research.
Second, using the new analytic tool, we are able to make some discovery related
to anomaly detection that is very difficult for other approaches. To our best
knowledge, there is no similar report in the literature. Third, both linear and
nonlinear polynomials of large random matrices can be handled in this new
framework. Simulations demonstrate the following: Compared with the linearity,
nonlinearity is more flexible in problem modeling and closer to the nature of
the reality. In some sense, some other nonlinear matrix polynomials may be more
effective for the power grid


title:
Studying Academic Indicators within Virtual Learning Environment Using
  Educational Data Mining

abstract:
  Our main goal is to discover the main factors influencing students' academic
trajectory and students' academic evolution within such environment. Our
results indicate strong correlation in this virtual learning environment
between student average and some factors like: student's English level (despite
the fact that Arabic language is the teaching language), student's age,
student's gender, student's over-stay and student's place of residence (inside
or outside Syria). Our results indicate also a need to modify the academic
trajectory of students by changing the prerequisites of few courses delivered
as a part of BIT diploma like Advanced DBA II, Data Security. In this research,
the results also highlight the effect of the Syrian Crisis on students.
Finally, we've suggested some future recommendations based on our observations
and results to develop the current information system in SVU in order to help
us to deduce some indicators more easily.


title:
A new rule for almost-certain termination of probabilistic- and demonic
  programs

abstract:
  Extending our own and others' earlier approaches to reasoning about
termination of probabilistic programs, we propose and prove a new rule for
termination with probability one, also known as "almost-certain termination".
The rule uses both (non-strict) super martingales and guarantees of progress,
together, and it seems to cover significant cases that earlier methods do not.
In particular, it suffices for termination of the unbounded symmetric random
walk in both one- and two dimensions: for the first, we give a proof; for the
second, we use a theorem of Foster to argue that a proof exists.
Non-determinism (i.e. demonic choice) is supported; but we do currently
restrict to discrete distributions.


title:
Constructing separable states in infinite-dimensional systems by
  operator matrices

abstract:
  We introduce a class of states so-called semi-SSPPT (semi super strong
positive partial transposition) states in infinite-dimensional bipartite
systems by the Cholesky decomposition in terms of operator matrices and show
that every semi-SSPPT state is separable. This gives a method of constructing
separable states and generalizes the corresponding results in [Phys. Rev. A
\textbf{77}, 022113(2008), J. Phys. A: Math. Theor. 45 505303 (2012)]. This
criterion is specially convenient to be applied when one of the subsystem is a
qubit system.


title: Globular: an online proof assistant for higher-dimensional rewriting

abstract:
  This article introduces Globular, an online proof assistant for the
formalization and verification of proofs in higher-dimensional category theory.
The tool produces graphical visualizations of higher-dimensional proofs,
assists in their construction with a point-and- click interface, and performs
type checking to prevent incorrect rewrites. Hosted on the web, it has a low
barrier to use, and allows hyperlinking of formalized proofs directly from
research papers. It allows the formalization of proofs from logic, topology and
algebra which are not formalizable by other methods, and we give several
examples.


title: Learning to superoptimize programs - Workshop Version

abstract:
  Superoptimization requires the estimation of the best program for a given
computational task. In order to deal with large programs, superoptimization
techniques perform a stochastic search. This involves proposing a modification
of the current program, which is accepted or rejected based on the improvement
achieved. The state of the art method uses uniform proposal distributions,
which fails to exploit the problem structure to the fullest. To alleviate this
deficiency, we learn a proposal distribution over possible modifications using
Reinforcement Learning. We provide convincing results on the superoptimization
of "Hacker's Delight" programs.


title: Representing Independence Models with Elementary Triplets

abstract:
  In an independence model, the triplets that represent conditional
independences between singletons are called elementary. It is known that the
elementary triplets represent the independence model unambiguously under some
conditions. In this paper, we show how this representation helps performing
some operations with independence models, such as finding the dominant triplets
or a minimal independence map of an independence model, or computing the union
or intersection of a pair of independence models, or performing causal
reasoning. For the latter, we rephrase in terms of conditional independences
some of Pearl's results for computing causal effects.


title:
Linear Codes over $\mathbb{F}_{q}[x]/(x^2)$ and $GR(p^2,m)$ Reaching the
  Griesmer Bound

abstract:
  We construct two series of linear codes over finite ring
$\mathbb{F}_{q}[x]/(x^2)$ and Galois ring $GR(p^2,m)$ respectively reaching the
Griesmer bound. They derive two series of codes over finite field
$\mathbb{F}_{q}$ by Gray map. The first series of codes over $\mathbb{F}_{q}$
derived from $\mathbb{F}_{q}[x]/(x^2)$ are linear and also reach the Griesmer
bound in some cases. Many of linear codes over finite field we constructed have
two Hamming (non-zero) weights.


title: Quantum dynamics of bound states under spacetime fluctuations

abstract:
  With recent developments in high-precision quantum measurements, the question
of whether observations of decoherence from spacetime fluctuations are
accessible experimentally arises. Here we investigate the dynamics of bound
states interacting with an environment of gravitons under the Markov
approximation. The corresponding Lindblad master equation is presented that
enables gravitational decoherence and dissipation due to zero-point spacetime
fluctuations to be analyzed. Specifically, we consider a one-dimensional cavity
of massless scalar particles that models a light beam with negligible spin
polarizations being reflected between two free masses. Numerical simulations
have been performed to illustrate the wave-modal dependent decoherence and
dissipation of such a configuration. We further demonstrate the existence of
nontrivial collective effects akin to superradiance, providing amplifications
of gravitational decoherence for a large number of identical bosonic particles.


title:
Effective faithful tropicalizations associated to linear systems on
  curves

abstract:
  For a connected smooth projective curve $X$ of genus $g$, global sections of
any line bundle $L$ with $\deg(L) \geq 2g+ 1$ give an embedding of the curve
into projective space. We consider an analogous statement for a Berkovich
skeleton in nonarchimedean geometry, in which projective space is replaced by
tropical projective space, and an embedding is replaced by a homeomorphism onto
its image preserving integral structures (called a faithful tropicalization).
Let $K$ be an algebraically closed field which is complete with respect to a
non-trivial nonarchimedean value. Suppose that $X$ is defined over $K$ and has
genus $g \geq 2$ and that $\Gamma$ is a skeleton (that is allowed to have ends)
of the analytification $X^{\mathrm{an}}$ of $X$ in the sense of Berkovich. We
show that if $\deg(L) \geq 3g-1$, then global sections of $L$ give a faithful
tropicalization of $\Gamma$ into tropical projective space. As an application,
when $Y$ is a suitable affine curve, we describe the analytification
$Y^{\mathrm{an}}$ as the limit of tropicalizations of an effectively bounded
degree.


title: Effective faithful tropicalizations associated to adjoint linear systems

abstract:
  Let $R$ be a complete discrete valuation ring of equi-characteristic zero
with fractional field $K$. Let $X$ be a connected, smooth projective variety of
dimension $d$ over $K$, and let $L$ be an ample line bundle over $X$. We assume
that there exist a regular strictly semistable model $\mathscr{X}$ of $X$ over
$R$ and a relatively ample line bundle $\mathscr{L}$ over $\mathscr{X}$ with
$\mathscr{L}|_{X} \cong L$. Let $S(\mathscr{X})$ be the skeleton associated to
$\mathscr{X}$ in the Berkovich analytification $X^{\mathrm{an}}$ of $X$. In
this article, we study when $S(\mathscr{X})$ is faithfully tropicalized into
tropical projective space by the adjoint linear system $|L^{\otimes m} \otimes
\omega_X|$. Roughly speaking, our results show that, if $m$ is an integer such
that the adjoint bundle is basepoint free, then the adjoint linear system
admits a faithful tropicalization of $S(\mathscr{X})$.


title: Composing generic linearly perturbed mappings and immersions/injections

abstract:
  Let $N$ (resp., $U$) be a manifold (resp., an open subset of $\mathbb{R}^m$).
Let $f:N\to U$ and $F:U\to \mathbb{R}^\ell$ be an immersion and a $C^{\infty}$
mapping, respectively. Generally, the composition $F\circ f$ does not
necessarily yield a mapping transverse to a given subfiber-bundle of
$J^1(N,\mathbb{R}^\ell)$. Nevertheless, in this paper, for any
$\mathcal{A}^1$-invariant fiber, we show that composing generic linearly
perturbed mappings of $F$ and the given immersion $f$ yields a mapping
transverse to the subfiber-bundle of $J^1(N,\mathbb{R}^\ell)$ with the given
fiber. Moreover, we show a specialized transversality theorem on crossings of
compositions of generic linearly perturbed mappings of a given mapping $F:U\to
\mathbb{R}^\ell$ and a given injection $f:N\to U$. Furthermore, applications of
the two main theorems are given.


title:
Deep CFHT Y-band imaging of VVDS-F22 field: I. data products and
  photometric redshifts

abstract:
  We present our deep $Y$-band imaging data of a two square degree field within
the F22 region of the VIMOS VLT Deep Survey. The observations were conducted
using the WIRCam instrument mounted at the Canada--France--Hawaii Telescope
(CFHT). The total on-sky time was 9 hours, distributed uniformly over 18 tiles.
The scientific goals of the project are to select faint quasar candidates at
redshift $z>2.2$, and constrain the photometric redshifts for quasars and
galaxies. In this paper, we present the observation and the image reduction, as
well as the photometric redshifts that we derived by combining our $Y$-band
data with the CFHTLenS $u^*g'r'i'z'$ optical data and UKIDSS DXS $JHK$
near-infrared data. With $J$-band image as reference total $\sim$80,000
galaxies are detected in the final mosaic down to $Y$-band $5\sigma$ point
source limiting depth of 22.86 mag. Compared with the $\sim$3500 spectroscopic
redshifts, our photometric redshifts for galaxies with $z<1.5$ and
$i'\lesssim24.0$ mag have a small systematic offset of
$|\Delta{z}|\lesssim0.2$, 1$\sigma$ scatter $0.03<\sigma_{\Delta z} < 0.06$,
and less than 4.0% of catastrophic failures. We also compare to the CFHTLenS
photometric redshifts, and find that ours are more reliable at $z\gtrsim0.6$
because of the inclusion of the near-infrared bands. In particular, including
the $Y$-band data can improve the accuracy at $z\sim 1.0-2.0$ because the
location of the 4000\AA-break is better constrained. The $Y$-band images, the
multi-band photometry catalog and the photometric redshifts are released at
\url{http://astro.pku.edu.cn/astro/data/DYI.html}.


title:
Globally coherent short duration magnetic field transients and their
  effect on ground based gravitational-wave detectors

abstract:
  It has been recognized that the magnetic fields from the Schumann resonances
could affect the search for a stochastic gravitational-wave background by LIGO
and Virgo. Presented here are the observations of short duration magnetic field
transients that are coincident in the magnetometers at the LIGO and Virgo
sites. Data from low-noise magnetometers in Poland and Colorado, USA, are also
used and show short duration magnetic transients of global extent. We measure
at least 2.3 coincident (between Poland and Colorado) magnetic transient events
per day where one of the pulses exceeds 200 pT. Given the recently measured
values of the magnetic coupling to differential arm motion for Advanced LIGO,
there would be a few events per day that would appear simultaneously at the
gravitational-wave detector sites and could move the test masses of order
$10^{-18}$ m. We confirm that in the advanced detector era short duration
transient gravitational-wave searches must account for correlated magnetic
field noise in the global detector network.


title: Robust nonparametric nearest neighbor random process clustering

abstract:
  We consider the problem of clustering noisy finite-length observations of
stationary ergodic random processes according to their generative models
without prior knowledge of the model statistics and the number of generative
models. Two algorithms, both using the $L^1$-distance between estimated power
spectral densities (PSDs) as a measure of dissimilarity, are analyzed. The
first one, termed nearest neighbor process clustering (NNPC), relies on
partitioning the nearest neighbor graph of the observations via spectral
clustering. The second algorithm, simply referred to as $k$-means (KM),
consists of a single $k$-means iteration with farthest point initialization and
was considered before in the literature, albeit with a different dissimilarity
measure. We prove that both algorithms succeed with high probability in the
presence of noise and missing entries, and even when the generative process
PSDs overlap significantly, all provided that the observation length is
sufficiently large. Our results quantify the tradeoff between the overlap of
the generative process PSDs, the observation length, the fraction of missing
entries, and the noise variance. Finally, we provide extensive numerical
results for synthetic and real data and find that NNPC outperforms
state-of-the-art algorithms in human motion sequence clustering.


title: Regime shifts driven by dynamic correlations in gene expression noise

abstract:
  Gene expression is a noisy process that leads to regime shift between
alternative steady states among individual living cells, inducing phenotypic
variability. The effects of white noise on the regime shift in bistable systems
have been well characterized, however little is known about such effects of
colored noise (noise with non-zero correlation time). Here, we show that noise
correlation time, by considering a genetic circuit of autoactivation, can have
significant effect on the regime shift in gene expression. We demonstrate this
theoretically, using stochastic potential, stationary probability density
function and first-passage time based on the Fokker-Planck description, where
the Ornstein-Uhlenbeck process is used to model colored noise. We find that
increase in noise correlation time in degradation rate can induce a regime
shift from low to high protein concentration state and enhance the bistable
regime, while increase in noise correlation time in basal rate retain the
bimodal distribution. We then show how cross-correlated colored noises in basal
and degradation rates can induce regime shifts from low to high protein
concentration state, but reduce the bistable regime. In addition, we show that
early warning indicators can also be used to predict shifts between distinct
phenotypic states in gene expression. Predictions that a cell is about to shift
to a harmful phenotype could improve early therapeutic intervention in complex
human diseases.


title: Pyramid Scene Parsing Network

abstract:
  Scene parsing is challenging for unrestricted open vocabulary and diverse
scenes. In this paper, we exploit the capability of global context information
by different-region-based context aggregation through our pyramid pooling
module together with the proposed pyramid scene parsing network (PSPNet). Our
global prior representation is effective to produce good quality results on the
scene parsing task, while PSPNet provides a superior framework for pixel-level
prediction tasks. The proposed approach achieves state-of-the-art performance
on various datasets. It came first in ImageNet scene parsing challenge 2016,
PASCAL VOC 2012 benchmark and Cityscapes benchmark. A single PSPNet yields new
record of mIoU accuracy 85.4% on PASCAL VOC 2012 and accuracy 80.2% on
Cityscapes.


title: Historical Auroras in the 990s: Evidence for Great Magnetic Storms

abstract:
  Recently, a significant carbon-14 enhancement in the year 994 in tree rings
has been found, suggesting an extremely large cosmic ray flux event during a
short period. The origin of this particular cosmic ray event has not been
confirmed, but one possibility is that it is of solar origin. Contemporary
historical records of low latitude auroras can be used as supporting evidence
for intense solar activity around that time. We investigated the previously
reported as well as the new records found in contemporary observations from the
990s to determine potential auroras. Records of potential red auroras in the
late 992 and early 993 were found around the world, i.e. in the Korean
Peninsula, Germany, and the Island of Ireland, suggesting the occurrence of an
intense geomagnetic storm driven by solar activity.


title: Fragmented Hofer's geometry on Hameomorphism groups and its application

abstract:
  Stefan M$\ddot{\mathrm{u}}$ller posed the problem "Do Hofer's metrics on the
group of Hamiltonian diffeomorphism and the one of Hamiltonian homeomorphisms
(Hameomorphisms) correspond?". Let $(M,\omega)$ be a compact exact symplectic
manifold. We prove that the group of Hamiltonian homeomorphisms is not a simple
group if the positive answer of M$\ddot{\mathrm{u}}$ller's question holds . To
prove this results, we introduce fragmented Hofer's norms on a normal subgroup
of the group of Hamiltonian homeomorphisms.


title: Aspect ratio effect on particle transport in turbulent duct flows

abstract:
  The dynamics of dilute micron-sized spherical inertial particles in turbulent
duct flows is studied by means of direct numerical simulations of the carrier
phase turbulence with one-way coupled Lagrangian particles. The geometries are
a square and a rectangular duct with width-to-height aspect ratio $AR$ of 3
operating at $Re_{\tau,c}=360$ . The present study is designed to determine the
effect of turbulence-driven secondary motion on the particle dynamics. Our
results show that a weak cross-flow secondary motion significantly changes the
cross-sectional map of the particle concentration, mean velocity and
fluctuations. As the geometry of the duct is widened from $AR=1$ to 3, the
secondary vortex on the horizontal wall significantly expands in the spanwise
direction, and although the kinetic energy of the secondary flow increases
close to the corner, it decays towards the duct centreplane in the $AR=3$ case
so as the turbulent carrier phase approaches the behavior in spanwise-periodic
channel flows, a fact that significantly affects the particle statistics. In
the square duct the particle concentration in the viscous sublayer is maximum
at the duct centreplane, whereas the maximum is found closer to the corner, at
a distance of $|z/h|\approx1.25$ from the centreplane, in the $AR=3$ case.
Interestingly the centreplane concentration in the rectangular duct is around 3
times lower than that in the square duct. Moreover, a second peak in the
accumulation distribution is found right at the corners for both ducts. At this
location the concentration increases with particle inertia. The secondary
motion changes also the cross-stream map of the particle velocities
significantly in comparison to the fluid flow statistics. These directly affect
the particle velocity fluctuations such that multiple peaks appear near the
duct walls for the particle streamwise and wall-normal velocity fluctuations.


title: The Ismagilov conjecture over a finite field ${\mathbb F}_p$

abstract:
  We construct the so-called quasiregular representations of the group
$B_0^{\mathbb N}({\mathbb F}_p)$ of infinite upper triangular matrices with
coefficients in a finite field and give the criteria of theirs irreducibility
in terms of the initial measure. These representations are particular case of
the Koopman representation hence, we find new conditions of its irreducibility.
Since the field ${\mathbb F}_p$ is compact some new operators in the commutant
emerges. Therefore, the Ismagilov conjecture in the case of the finite field
should be corrected.


title: Anomalous diffusion and diffusion anomaly in confined Janus dumbbells

abstract:
  Self-assembly and dynamical properties of Janus nanoparticles have been
studied by molecular dynamic simulations. The nanoparticles are modeled as
dimers and they are confined between two flat parallel plates to simulate a
thin film. One monomer from the dumbbells interacts by a standard Lennard Jones
potential and the other by a two-length scales shoulder potential, typically
used for anomalous fluids. Here, we study the effects of remove the Brownian
effects, typical from colloidal systems immersed in aqueous solution, and
consider a molecular system, without the drag force and the random collisions
from the Brownian motion. Self-assembly and diffusion anomaly are preserved in
relation to the Brownian system. Additionally, a superdiffusive regime
associated to a collective reorientation in a highly structured phase is
observed. Diffusion anomaly and anomalous diffusion are explained in the two
length scale framework.


title: Linearizability for third order evolution equations

abstract:
  The problem of linearization for third order evolution equations is
considered. Criteria for testing equations for linearity are presented. A class
of linearizable equations depending on arbitrary functions is obtained by
requiring presence of an infinite-dimensional symmetry group. Linearizing
transformations for this class are found using symmetry structure and local
conservation laws. A number of special cases as examples are discussed. Their
transformation to equations within the same class by differential substitutions
and connection with KdV and mKdV equations are also reviewed in this framework.


title: A simple wide range approximation of symmetric binomial distributions

abstract:
  The paper gives a wide range, uniform, local approximation of symmetric
binomial distribution. The result clearly shows how one has to modify the the
classical de Moivre--Laplace normal approximation in order to give an estimate
at the tail as well to minimize the relative error.


title: A novel Adaptive weighted Kronecker Compressive Sensing

abstract:
  Recently, multidimensional signal reconstruction using a low number of
measurements is of great interest. Therefore, an effective sampling scheme
which should acquire the most information of signal using a low number of
measurements is required. In this paper, we study a novel cube-based method for
sampling and reconstruction of multidimensional signals. First, inspired by the
block-based compressive sensing (BCS), we divide a group of pictures (GoP) in a
video sequence into cubes. By this way, we can easily store the measurement
matrix and also easily can generate the sparsifying basis. The reconstruction
process also can be done in parallel. Second, along with the Kronecker
structure of the sampling matrix, we design a weight matrix based on the human
visuality system, i.e. perceptually. We will also benefit from different
weighted $\ell_1$-minimization methods for reconstruction. Furthermore,
conventional methods for BCS consider an equal number of samples for all
blocks. However, the sparsity order of blocks in natural images could be
different and, therefore, a various number of samples could be required for
their reconstruction. Motivated by this point, we will adaptively allocate the
samples for each cube in a video sequence. Our aim is to show that our simple
linear sampling approach can be competitive with the other state-of-the-art
methods.


title:
On the Performance of Visible Light Communications Systems with
  Non-Orthogonal Multiple Access

abstract:
  Visible light communications (VLC) have been recently proposed as a promising
and efficient solution to indoor ubiquitous broadband connectivity. In this
paper, non-orthogonal multiple access, which has been recently proposed as an
effective scheme for fifth generation (5G) wireless networks, is considered in
the context of VLC systems, under different channel uncertainty models. To this
end, we first derive a novel closed-form expression for the bit-error-rate
(BER) under perfect channel state information (CSI). Capitalizing on this, we
quantify the effect of noisy and outdated CSI by deriving a simple approximated
expression for the former and a tight upper bound for the latter. The offered
results are corroborated by respective results from extensive Monte Carlo
simulations and are used to provide useful insights on the effect of imperfect
CSI knowledge on the system performance. It was shown that, while noisy CSI
leads to slight degradation in the BER performance, outdated CSI can cause
detrimental performance degradation if the order of the users' channel gains
change as a result of mobility


title:
How Does the Antarctic Circumpolar Current Affect the Southern Ocean
  Meridional Overturning Circulation?

abstract:
  The Meridional Overturning Circulation (MOC) in the Southern Ocean is
investigated using hydrographic observations combined with satellite
observations of sea-surface height. A three-dimensional (spatial and vertical)
estimate of the isopycnal eddy-diffusivity in the Southern Ocean is obtained
using the theory of Ferrari & Nikurashin (2010), that includes the influence of
suppression of the diffusivity by the strong, time-mean flows. It is found that
the eddy diffusivity is enhanced at depth, reaching a maximum at the "critical
layer" near 1000m. The estimate of diffusivity is used with a simple diffusive
parameterization to estimate the meridional eddy volume flux. Together with an
estimate of the meridional Ekman transport and the time-mean meridional
geostrophic transport, the eddy volume flux is used to reconstruct the
time-mean overturning circulation. By comparing the reconstruction with, and
without, suppression of the eddy diffusivity by the mean flow, the influence of
the suppression on the overturning is illuminated. It is shown that the
suppression of the eddy diffusivity results in a large reduction of interior
eddy transports, and a more realistic eddy induced overturning circulation.
Finally, a simple conceptual model is used to show that the MOC is influenced
not only by the existence of enhanced diffusivity at depth, but also by the
details of the vertical structure of the eddy diffusivity, such as the depth of
the critical layer.


title:
Characterization of CMO via compactness of the commutators of bilinear
  fractional integral operators

abstract:
  Let $I_{\alpha}$ be the bilinear fractional integral operator, $B_{\alpha}$
be a more singular family of bilinear fractional integral operators and
$\vec{b}=(b,b)$. B\'{e}nyi et al. in \cite{B1} showed that if $b\in {\rm CMO}$,
the {\rm BMO}-closure of $C^{\infty}_{c}(\mathbb{R}^n)$, the commutator
$[b,B_{\alpha}]_{i}(i=1,2)$ is a separately compact operator. In this paper, it
is proved that $b\in {\rm CMO}$ is necessary for $[b,B_{\alpha}]_{i}(i=1,2)$ is
a compact operator. Also, the authors characterize the compactness of the {\bf
iterated} commutator $[\Pi\vec{b},I_{\alpha}]$ of bilinear fractional integral
operator. More precisely, the commutator $[\Pi\vec{b},I_{\alpha}]$ is a compact
operator if and only if $b\in {\rm CMO}$.


title: Fibered Biset Functors

abstract:
  The theory of biset functors, introduced by Serge Bouc, gives a unified
treatment of operations in representation theory that are induced by
permutation bimodules. In this paper, by considering fibered bisets, we
introduce and describe the basic theory of fibered biset functors which is a
natural framework for operations induced by monomial bimodules. The main result
of this paper is the classification of simple fibered biset functors.


title: Quantum transport through MoS$_2$ constrictions defined by photodoping

abstract:
  We present a device scheme to explore mesoscopic transport through molybdenum
disulfide (MoS$_2$) constrictions using photodoping. The devices are based on
van-der-Waals heterostructures where few-layer MoS$_2$ flakes are partially
encapsulated by hexagonal boron nitride (hBN) and covered by a few-layer
graphene flake to fabricate electrical contacts. Since the as-fabricated
devices are insulating at low temperatures, we use photo-induced remote doping
in the hBN substrate to create free charge carriers in the MoS$_2$ layer. On
top of the device, we place additional metal structures, which define the shape
of the constriction and act as shadow masks during photodoping of the
underlying MoS$_2$/hBN heterostructure. Low temperature two- and four-terminal
transport measurements show evidence of quantum confinement effects.


title: Security Analysis of Encrypted Virtual Machines

abstract:
  Cloud computing has become indispensable in today's computer landscape. The
flexibility it offers for customers as well as for providers has become a
crucial factor for large parts of the computer industry. Virtualization is the
key technology that allows for sharing of hardware resources among different
customers. The controlling software component, called hypervisor, provides a
virtualized view of the computer resources and ensures separation of different
guest virtual machines. However, this important cornerstone of cloud computing
is not necessarily trustworthy. To mitigate this threat AMD introduced Secure
Encrypted Virtualization, short SEV. SEV is a processor extension that encrypts
guest memory in order to prevent a potentially malicious hypervisor from
accessing guest data. In this paper we analyse whether the proposed features
can resist a malicious hypervisor and discuss the trade-offs imposed by
additional protection mechanisms. To do so, we developed a model of SEV's
security capabilities based on the available documentation as actual silicon
implementations are not yet on the market. We found that the currently proposed
version of SEV is not up to the task owing to three design shortcomings. First,
as with standard AMD-V, under SEV, the virtual machine control block is not
encrypted and handled directly by the hypervisor, allowing him to bypass VM
memory encryption by executing conveniently chosen gadgets. Secondly, the
general purpose registers are not encrypted upon vmexit, leaking potentially
sensitive data. Finally, the control of the nested pagetables allows a
malicious hypervisor to closely control the execution of a VM and attack it
with memory replay attacks.


title:
The Complexity of Bayesian Networks Specified by Propositional and
  Relational Languages

abstract:
  We examine the complexity of inference in Bayesian networks specified by
logical languages. We consider representations that range from fragments of
propositional logic to function-free first-order logic with equality; in doing
so we cover a variety of plate models and of probabilistic relational models.
We study the complexity of inferences when network, query and domain are the
input (the inferential and the combined complexity), when the network is fixed
and query and domain are the input (the query/data complexity), and when the
network and query are fixed and the domain is the input (the domain
complexity). We draw connections with probabilistic databases and liftability
results, and obtain complexity classes that range from polynomial to
exponential levels.


title:
Kaluza-Klein bulk viscous fluid cosmological models and the validity of
  the second law of thermodynamics in $f(R, T)$ gravity

abstract:
  The authors considered the bulk viscous fluid in $f(R, T)$ gravity within the
framework of Kaluza-Klein space time. The bulk viscous coefficient $(\xi)$
expressed as $\xi=\xi_0+\xi_1\frac{\dot{a}}{a}+\xi_2\frac{\ddot{a}}{\dot{a}}$,
where $\xi_0$, $\xi_1$ and $\xi_2$ are positive constants. We take
$p=(\gamma-1)\rho$, where $0\le\gamma\le2$ as an equation of state for perfect
fluid. The exact solutions to the corresponding field equations are given by
assuming a particular model of the form of $f(R, T)=R+2f(T)$, where
$f(T)=\lambda T$, $\lambda$ is constant. We studied the cosmological model in
two stages, in first stage: we studied the model with no viscosity, and in
second stage: we studied the model involve with viscosity. The cosmological
model involve with viscosity is studied by five possible scenarios for bulk
viscous fluid coefficient $(\xi)$. The total bulk viscous coefficient seems to
be negative, when the bulk viscous coefficient is proportional to
$\xi_2\frac{\ddot{a}}{\dot{a}}$, hence the second law of thermodynamics is not
valid, however, it is valid with the generalized second law of thermodynamics.
The total bulk viscous coefficient seems to be positive, when, the bulk viscous
coefficient is proportional to $\xi=\xi_1\frac{\dot{a}}{a}$,
$\xi=\xi_1\frac{\dot{a}}{a}+\xi_2\frac{\ddot{a}}{\dot{a}}$ and
$\xi=\xi_0+\xi_1\frac{\dot{a}}{a}+\xi_2\frac{\ddot{a}}{\dot{a}}$, so the second
law of thermodynamics and the generalized second law of thermodynamics is
satisfied throughout the evolution. We calculate statefinder parameters of the
model and observed that, it is different from the $\wedge$CDM model. Finally,
some physical and geometrical properties of the models are discussed.


title:
Polydisperse polymer brushes: internal structure, critical behavior, and
  interaction with flow

abstract:
  We study the effect of polydispersity on the structure of polymer brushes by
analytical theory, a numerical self-consistent field approach, and Monte Carlo
simulations. The polydispersity is represented by the Schulz-Zimm chain-length
distribution. We specifically focus on three different polydispersities
representing sharp, moderate and extremely wide chain length distributions and
derive explicit analytical expressions for the chain end distributions in these
brushes. The results are in very good agreement with numerical data obtained
with self-consistent field calculations and Monte Carlo simulations. With
increasing polydispersity, the brush density profile changes from convex to
concave, and for given average chain length $N_n$ and grafting density
$\sigma$, the brush height $H$ is found to scale as
$(H/H_{\mathrm{mono}}-1)\propto(N_w/N_n-1)^{1/2}$ over a wide range of
polydispersity indices $N_w/N_n$ (here $H_{\mathrm{mono}}$ is the height of the
corresponding monodisperse brush. Chain end fluctuations are found to be
strongly suppressed already at very small polydispersity. Based on this
observation, we introduce the concept of the brush as a near-critical system
with two parameters (scaling variables), $(N_{n}\sigma^{2/3})^{-1}$ and
$(N_w/N_n-1)^{1/2}$, controlling the distance from the critical point. This
approach provides a good description of the simulation data. Finally we study
the hydrodynamic penetration length $l_\mathrm{p}$ for brush-coated surfaces in
flow. We find that it generally increases with polydispersity. The scaling
behavior crosses over from $l_\mathrm{p}\sim N_\mathrm{n}^{1/2}\sigma^{-1/6}$
for monodisperse and weakly polydisperse brushes to $l_\mathrm{p}\sim
N_{n}^{2/3}$ for strongly polydisperse brushes.


title:
Level repulsion for Schroedinger operators with singular continuous
  spectrum

abstract:
  We describe a family of half-line continuum Schroedinger operators with
purely singular continuous essential spectrum, exhibiting asymptotic strong
level repulsion (known as clock behavior). This follows from the convergence of
the renormalized continuum Christoffel-Darboux kernel to the sine kernel.


title: A new expression for the Moore-Penrose inverse of a class of matrices

abstract:
  An expression for the Moore-Penrose inverse of a matrix of the form M = XNY ,
where X and Y are nonsingular, has been recently established by
Castro-Gonz\'alez et al. [1, Theorem 2.2]. The expression plays an essential
role in developing explicit expressions for the Moore-Penrose inverse of a
two-by-two block matrix. In this paper, we present a new expression for the
Moore-Penrose inverse of this class of matrices, which improves the result in
[1].


title:
Measurement of parameters of scintillating bars with wavelength-shifting
  fibres and silicon photomultiplier readout for the SHiP Muon Detector

abstract:
  The light yield and the time resolution of different types of 3 m long
scintillating bars instrumented with wavelength shifting fibres and read out by
different models of silicon photomultipliers have been measured at a test beam
at the T9 area at the CERN Proton Synchrotron. The results obtained with
different configurations are presented. A time resolution better than 800 ps,
constant along the bar length within 20%, and a light yield of ~ 140 (70)
photoelectrons are obtained for bars 3 m long, 4.5 (5) cm wide and 2 (0.7) cm
thick. These results nicely match the requirements for the Muon Detector of the
SHiP experiment.


title: Inflationary $\alpha$-attractors from $F(R)$ Gravity

abstract:
  In this paper we study some classes of $\alpha$-attractors models in the
Jordan frame and we find the corresponding $F(R)$ gravity theory. We study
analytically the problem at leading order and we investigate whether the
attractor picture persists in the $F(R)$ gravity equivalent theory. As we show,
if the slow-roll conditions are assumed in the Jordan frame, the spectral index
of primordial curvature perturbations and the scalar-to-tensor ratio are
identical to the corresponding observational indices of the $R^2$ model, a
result which indicates that the attractor property is also found in the
corresponding $F(R)$ gravity theories of the $\alpha$-attractors models.
Moreover, implicit and approximate forms of the $F(R)$ gravity inflationary
attractors are found.


title: Conformal mapping of rectangular heptagons II

abstract:
  A new analytical method for the conformal mapping of a rectangular heptagon
with a straight angle at infinity to a half plane and back is proposed. The
method is based on the observation that SC integral in this case is an abelian
integral on a genus two curve, so it may be represented in terms of Riemann
theta functions. The approach is illustrated by the computation of 2D flow of
ideal fluid above rectangular underlying surface and the computation of the
capacities of multi component rectangular condensers with axial symmetry.


title: Isotropic Measures and Maximizing Ellipsoids: Between John and Loewner

abstract:
  We define a one parameter family of positions of a convex body which
interpolates between the John position and the Loewner position: for $r>0$, we
say that $K$ is in maximal intersection position of radius $r$ if
$\textrm{Vol}_{n}(K\cap rB_{2}^{n})\geq \textrm{Vol}_{n}(K\cap rTB_{2}^{n})$
for all $T\in SL_{n}$. We show that under mild conditions on $K$, each such
position induces a corresponding isotropic measure on the sphere, which is
simply a normalized Lebesgue measure on $r^{-1}K\cap S^{n-1}$. In particular,
for $r_{M}$ satisfying $r_{M}^{n}\kappa_{n}=\textrm{Vol}_{n}(K)$, the maximal
intersection position of radius $r_{M}$ is an $M$-position, so we get an
$M$-position with an associated isotropic measure. Lastly, we give an
interpretation of John's theorem on contact points as a limit case of the
measures induced from the maximal intersection positions.


title: Algebraic Identifiability of Gaussian Mixtures

abstract:
  We prove that all moment varieties of univariate Gaussian mixtures have the
expected dimension. Our approach rests on intersection theory and Terracini's
classification of defective surfaces. The analogous identifiability result is
shown to be false for mixtures of Gaussians in dimension three and higher.
Their moments up to third order define projective varieties that are defective.
Our geometric study suggests an extension of the Alexander-Hirschowitz Theorem
for Veronese varieties to the Gaussian setting.


title:
Solutions of the Bogoliubov-de Gennes equation with position dependent
  Fermi--velocity and gap profiles

abstract:
  It is shown that bound state solutions of the one dimensional Bogoliubov-de
Gennes (BdG) equation may exist when the Fermi velocity becomes dependent on
the space coordinate. The existence of bound states in continuum (BIC) like
solutions has also been confirmed both in the normal phase as well as in the
super-conducting phase. We also show that a combination of Fermi velocity and
gap parameter step-like profiles provides scattering solutions with normal
reflection and transmission.


title:
A method for the segmentation of images based on thresholding and
  applied to vesicular textures

abstract:
  In image processing, a segmentation is a process of partitioning an image
into multiple sets of pixels, that are defined as super-pixels. Each
super-pixel is characterized by a label or parameter. Here, we are proposing a
method for determining the super-pixels based on the thresholding of the image.
This approach is quite useful for studying the images showing vesicular
textures.


title: A Model of Synchronization for Self-Organized Crowding Behavior

abstract:
  This paper proposes a general model for synchronized crowding behavior. An
order parameter is introduced to quantify the level of synchronization which is
shown a function of percentage of agents in reactive state. Further,
synchronization is shown to be driven by the most active agents with the
highest volatility. A tipping point is identified when crowd becomes
self-amplifying and unstable. By applying this model, financial bubbles, market
momentum and volatility patterns are simulated.


title:
Novel Delivery Schemes for Decentralized Coded Caching in the Finite
  File Size Regime

abstract:
  This paper analyzes the achievable tradeoff between cache~size and
download~rate in decentralized caching systems with the uncoded cache placement
originally proposed by Maddah-Ali and Niesen. It proposes two novel delivery
schemes that take advantage of the multicasting opportunities that arise when a
file is demanded by multiple users. These delivery schemes are extensions of
known ones to the regime where the file size is finite. Numerical evaluations
for the case of file uniform popularity show that the proposed schemes
outperform previous ones for all value of the cache size.


title: Distortion for Abelian Subgroups of $\mathrm{Out}(F_n)$

abstract:
  We prove that abelian subgroups of the outer automorphism group of a free
group are quasi-isometrically embedded. Our proof uses recent developments in
the theory of train track maps by Feighn-Handel. As an application, we prove
the rank conjecture for $\mathrm{Out}(F_n)$.