title: General relativistic polytropes with a repulsive cosmological constant

abstract:
  Spherically symmetric equilibrium configurations of perfect fluid obeying a
polytropic equation of state are studied in spacetimes with a repulsive
cosmological constant. The configurations are specified in terms of three
parameters---the polytropic index $n$, the ratio of central pressure and
central energy density of matter $\sigma$, and the ratio of energy density of
vacuum and central density of matter $\lambda$. The static equilibrium
configurations are determined by two coupled first-order nonlinear differential
equations that are solved by numerical methods with the exception of polytropes
with $n=0$ corresponding to the configurations with a uniform distribution of
energy density, when the solution is given in terms of elementary functions.
The geometry of the polytropes is conveniently represented by embedding
diagrams of both the ordinary space geometry and the optical reference geometry
reflecting some dynamical properties of the geodesic motion. The polytropes are
represented by radial profiles of energy density, pressure, mass, and metric
coefficients. For all tested values of $n>0$, the static equilibrium
configurations with fixed parameters $n$, $\sigma$, are allowed only up to a
critical value of the cosmological parameter
$\lambda_{\mathrm{c}}=\lambda_{\mathrm{c}}(n,\sigma)$. In the case of $n>3$,
the critical value $\lambda_{\mathrm{c}}$ tends to zero for special values of
$\sigma$. The gravitational potential energy and the binding energy of the
polytropes are determined and studied by numerical methods. We discuss in
detail the polytropes with an extension comparable to those of the dark matter
halos related to galaxies, i.e., with extension $\ell > 100\,\mathrm{kpc}$ and
mass $M > 10^{12}\,\mathrm{M}_{\odot}$. ...


title:
Image Credibility Analysis with Effective Domain Transferred Deep
  Networks

abstract:
  Numerous fake images spread on social media today and can severely jeopardize
the credibility of online content to public. In this paper, we employ deep
networks to learn distinct fake image related features. In contrast to
authentic images, fake images tend to be eye-catching and visually striking.
Compared with traditional visual recognition tasks, it is extremely challenging
to understand these psychologically triggered visual patterns in fake images.
Traditional general image classification datasets, such as ImageNet set, are
designed for feature learning at the object level but are not suitable for
learning the hyper-features that would be required by image credibility
analysis. In order to overcome the scarcity of training samples of fake images,
we first construct a large-scale auxiliary dataset indirectly related to this
task. This auxiliary dataset contains 0.6 million weakly-labeled fake and real
images collected automatically from social media. Through an AdaBoost-like
transfer learning algorithm, we train a CNN model with a few instances in the
target training set and 0.6 million images in the collected auxiliary set. This
learning algorithm is able to leverage knowledge from the auxiliary set and
gradually transfer it to the target task. Experiments on a real-world testing
set show that our proposed domain transferred CNN model outperforms several
competing baselines. It obtains superiror results over transfer learning
methods based on the general ImageNet set. Moreover, case studies show that our
proposed method reveals some interesting patterns for distinguishing fake and
authentic images.


title: P-V criticality in AdS black holes of massive gravity

abstract:
  In this paper we have studied the extended phase space thermodynamics in the
canonical ensemble of black holes in massive gravity in AdS space. The black
holes considered here belong to a theory of massive gravity where the graviton
gain a mass due to Lorentz symmetry breaking. We have computed various
thermodynamical quantities such as temperature, pressure, Gibbs free energy and
specific heat capacity. The local and the global thermodynamical stability of
the black holes are studied in detail. For a specific value of the parameter in
the theory, the black holes undergo a first order phase transition similar to
the Van der Waals phase transitions between gas and liquid under constant
temperature. This transition is between the small and the large black hole. The
critical exponents are computed at the critical values and shown to be the same
as for the Van der Waals critical exponents.


title: Phases of dense matter with holographic instantons

abstract:
  We discuss nuclear matter and the transition to quark matter in the
decompactified limit of the Sakai-Sugimoto model. Nuclear matter is included
through instantons on the flavor branes of the model. Our approximation is
based on the flat-space solution, but we allow for a dynamical instanton width
and deformation and compute the energetically preferred number of instanton
layers in the bulk as a function of the baryon chemical potential. We determine
the regions in parameter space where the binding energy of nuclear matter is
like in QCD, and compute the phase diagram in the plane of temperature and
chemical potential.


title: Wheeler-Feynman Absorbers on the Light Horizon

abstract:
  Early Wheeler-Feynman absorber theories invoke both retarded and advanced
electromagnetic waves for photon emission and absorption in order to remove
problems involving lack of radiative damping during electron acceleration.
Subsequent inquiries have suggested that only certain cosmologies would allow
such a retarded-advanced wave mechanism to exist. These include quasi-steady
state cosmologies and exclude flat, expanding Friedman-type cosmologies. Key to
the exclusion process is a diminishing density of future absorbers in an
ever-expanding universe. Such absorbers would be expected to be real
electromagnetically interacting particles. However future virtual absorber
sites, if they exist, would not be so diminished. Such sites would be plentiful
on the future light horizon, receding from the source at the speed of light.
The present treatment proposes that virtual absorption sites are present at
every point in spacetime, and are characterized by the Fresnel-Kirchhoff
diffraction integral. On the future light horizon, they can remove
electromagnetic energy from the local causal domain and provide advance wave
signals as Wheeler-Feynman absorbers.


title: Net Shape 3D Printed NdFeB Permanent Magnet

abstract:
  For two decades, NdFeB based magnets have been a critical component in a
range of electrical devices engaged in energy production and conversion. The
magnet shape and the internal microstructure of the selected NdFeB grade govern
their efficiency and size. However, stricter requirements on device efficiency
call for better performing magnets preferably with novel functionality not
achievable today. Here we use 3D metal printing by Selective Laser Melting to
fabricate dense net shape permanent magnets based on NdFeB that exhibit high
magnetic performance. Evidence is provided that the internal microstructure,
not achievable by traditional manufacturing means, is the origin of the solid
magnetic properties. The freedom in magnet body shape and size that ranges from
the millimeter to tens of centimeter scale opens up a design freedom that could
be a catalyzer for the next generation of electrical devices.


title:
Effects of dopant type and concentration on the femtosecond laser
  ablation threshold and incubation behaviour of silicon

abstract:
  In laser micromachining, the ablation threshold (minimum fluence required to
cause ablation) is a key performance parameter and overall indicator of the
efficiency of material removal. For pulsed laser micromachining, this important
observable depends upon material properties, pulse properties and the number of
pulses applied in a complex manner that is not yet well understood. The
incubation effect is one example. It manifests as a change in the ablation
threshold as a function of number of laser pulses applied and is driven by
photoinduced defect accumulation in the material. Here, we study femtosecond
(800 nm, 110 fs, 0.1-1 mJ/pulse) micromachining of a material with well-defined
initial defect concentrations: doped Si across a range of dopant types and
concentrations. The single-pulse ablation threshold (Fth,1) was observed to
decrease with increasing dopant concentration, from a maximum of 0.70 J/cm2
(+/-0.02) for undoped Si to 0.51 J/cm2 (+/-0.01) for highly N-type doped Si.
The effect was greater for N-type doped Si than for P-type, consistent with the
higher carrier mobility of electrons compared to holes. In contrast, the
infinite-pulse ablation threshold (Fth,inf) was the same for all doping levels
and types. We attribute this asymptotic behaviour to a maximum defect
concentration that is independent of the initial defect concentration and type.
These results lend insight into the mechanism of multipulse, femtosecond laser
ablation.


title: Reconstruction from Representations: Jacobi via Cohomology

abstract:
  A subalgebra of a Lie algebra $\mathfrak{h}\subset\mathfrak{g}$ determines
$\mathfrak{h}$-representation $\rho$ on
$\mathfrak{m}=\mathfrak{g}/\mathfrak{h}$. In this note we discuss how to
reconstruct $\mathfrak{g}$ from $(\mathfrak{h},\mathfrak{m},\rho)$. In other
words, we find all the ingredients for building non-reductive Klein geometries.
The Lie algebra cohomology plays a decisive role here.


title: Unsupervised Learning of Important Objects from First-Person Videos

abstract:
  A first-person camera, placed at a person's head, captures, which objects are
important to the camera wearer. Most prior methods for this task learn to
detect such important objects from the manually labeled first-person data in a
supervised fashion. However, important objects are strongly related to the
camera wearer's internal state such as his intentions and attention, and thus,
only the person wearing the camera can provide the importance labels. Such a
constraint makes the annotation process costly and limited in scalability.
  In this work, we show that we can detect important objects in first-person
images without the supervision by the camera wearer or even third-person
labelers. We formulate an important detection problem as an interplay between
the 1) segmentation and 2) recognition agents. The segmentation agent first
proposes a possible important object segmentation mask for each image, and then
feeds it to the recognition agent, which learns to predict an important object
mask using visual semantics and spatial features.
  We implement such an interplay between both agents via an alternating
cross-pathway supervision scheme inside our proposed Visual-Spatial Network
(VSN). Our VSN consists of spatial ("where") and visual ("what") pathways, one
of which learns common visual semantics while the other focuses on the spatial
location cues. Our unsupervised learning is accomplished via a cross-pathway
supervision, where one pathway feeds its predictions to a segmentation agent,
which proposes a candidate important object segmentation mask that is then used
by the other pathway as a supervisory signal. We show our method's success on
two different important object datasets, where our method achieves similar or
better results as the supervised methods.


title: Constraining CPT-odd nonminimal interactions in the Electroweak sector

abstract:
  In this work, we propose two possibilities of CPT-odd and Lorentz-violating
(LV) nonminimal couplings in the Electroweak sector. These terms are
gauge-invariant and couple a fixed 4-vector to the physical fields of the
theory. After determining the LV contributions to the electroweak currents, we
reassess the evaluation of the decay rate for the vector mediators $W$ and $Z$.
Using the experimental uncertainty in these decay rates, upper bounds of $1$
part in $10^{-15}$ (eV )$^{-1}$ and $10^{-14}$ (eV )$^{-1}$ are imposed on the
magnitude of the proposed nonmimal interactions.


title: Bardeen-de Sitter black holes

abstract:
  In this paper we present a regular black hole with a positive cosmological
constant. The regular black hole considered is the well known Bardeen black
hole and it is a solution to the Einstein equations coupled to non-linear
electrodynamics with a magnetic monopole. The paper discuss the properties of
the Bardeen-de Sitter black hole. We have computed the grey body factors and
partial absorption cross sections for massless scalar field impinges on this
black hole with the third order WKB approximation. A detailed discussion on how
the behavior of the grey body factors depend on the parameters of the theory
such as the mass, charge and the cosmological constant is given. Possible
extensions of the work is discussed at the end of the paper.


title:
Next-to-leading-order electroweak corrections to the production of four
  charged leptons at the LHC

abstract:
  We present a state-of-the-art calculation of the next-to-leading-order
electroweak corrections to ZZ production, including the leptonic decays of the
Z bosons into $\mu^+\mu^-\mathrm{e}^+\mathrm{e}^-$ or $\mu^+\mu^-\mu^+\mu^-$
final states. We use complete leading-order and next-to-leading-order matrix
elements for four-lepton production, including contributions of virtual photons
and all off-shell effects of Z bosons, where the finite Z-boson width is taken
into account using the complex-mass scheme. The matrix elements are implemented
into Monte Carlo programs allowing for the evaluation of arbitrary differential
distributions. We present integrated and differential cross sections for the
LHC at 13 TeV both for an inclusive setup where only lepton identification cuts
are applied, and for a setup motivated by Higgs-boson analyses in the
four-lepton decay channel. The electroweak corrections are divided into
photonic and purely weak contributions. The former show the well-known
pronounced tails near kinematical thresholds and resonances; the latter are
generically at the level of $\sim-5\%$ and reach several $-10\%$ in the
high-energy tails of distributions. Comparing the results for
$\mu^+\mu^-\mathrm{e}^+\mathrm{e}^-$ and $\mu^+\mu^-\mu^+\mu^-$ final states,
we find significant differences mainly in distributions that are sensitive to
the $\mu^+\mu^-$ pairing in the $\mu^+\mu^-\mu^+\mu^-$ final state. Differences
between $\mu^+\mu^-\mathrm{e}^+\mathrm{e}^-$ and $\mu^+\mu^-\mu^+\mu^-$
channels due to interferences of equal-flavour leptons in the final state can
reach up to $10\%$ in off-shell-sensitive regions. Contributions induced by
incoming photons, i.e. photon-photon and quark-photon channels, are included,
but turn out to be phenomenologically unimportant.


title: CareerMapper: An Automated Resume Evaluation Tool

abstract:
  The advent of the Web brought about major changes in the way people search
for jobs and companies look for suitable candidates. As more employers and
recruitment firms turn to the Web for job candidate search, an increasing
number of people turn to the Web for uploading and creating their online
resumes. Resumes are often the first source of information about candidates and
also the first item of evaluation in candidate selection. Thus, it is
imperative that resumes are complete, free of errors and well-organized. We
present an automated resume evaluation tool called "CareerMapper". Our tool is
designed to conduct a thorough review of a user's LinkedIn profile and provide
best recommendations for improved online resumes by analyzing a large number of
online user profiles.


title: Approximating Wisdom of Crowds using K-RBMs

abstract:
  An important way to make large training sets is to gather noisy labels from
crowds of non experts. We propose a method to aggregate noisy labels collected
from a crowd of workers or annotators. Eliciting labels is important in tasks
such as judging web search quality and rating products. Our method assumes that
labels are generated by a probability distribution over items and labels. We
formulate the method by drawing parallels between Gaussian Mixture Models
(GMMs) and Restricted Boltzmann Machines (RBMs) and show that the problem of
vote aggregation can be viewed as one of clustering. We use K-RBMs to perform
clustering. We finally show some empirical evaluations over real datasets.


title: Finite descent obstruction and non-abelian reciprocity

abstract:
  For a nice algebraic variety $X$ over a number field $F$, one of the central
problems of Diophantine Geometry is to locate precisely the set $X(F)$ inside
$X(\A_F)$, where $\A_F$ denotes the ring of ad\`eles of $F$. One approach to
this problem is provided by the finite descent obstruction, which is defined to
be the set of adelic points which can be lifted to twists of torsors for
(certain) finite \'etale group schemes over $F$ on $X$. More recently, Kim
proposed an iterative construction of another subset of $X(\A_F)$ which
contains the set of rational points. In this paper, we compare the two
constructions. Our main result shows that the two approaches are essentially
equivalent.


title: Approximately Efficient Two-Sided Combinatorial Auctions

abstract:
  Mechanism design for one-sided markets has been investigated for several
decades in economics and in computer science. More recently, there has been an
increased attention on mechanisms for two-sided markets, in which buyers and
sellers act strategically. For two-sided markets, an impossibility result of
Myerson and Satterthwaite states that no mechanism can simultaneously satisfy
individual rationality (IR), incentive compatibility (IC), strong
budget-balance (SBB), and be efficient. On the other hand, important
applications to web advertisement, stock exchange, and frequency spectrum
allocation, require us to consider two-sided combinatorial auctions in which
buyers have preferences on subsets of items, and sellers may offer multiple
heterogeneous items. No efficient mechanism was known so far for such two-sided
combinatorial markets. This work provides the first IR, IC and SBB mechanisms
that provides an O(1)-approximation to the optimal social welfare for two-sided
markets. An initial construction yields such a mechanism, but exposes a
conceptual problem in the traditional SBB notion. This leads us to define the
stronger notion of direct trade strong budget balance (DSBB). We then proceed
to design mechanisms that are IR, IC, DSBB, and again provide an
O(1)-approximation to the optimal social welfare. Our mechanisms work for any
number of buyers with XOS valuations - a class in between submodular and
subadditive functions - and any number of sellers. We provide a mechanism that
is dominant strategy incentive compatible (DSIC) if the sellers each have one
item for sale, and one that is bayesian incentive compatible (BIC) if sellers
hold multiple items and have additive valuations over them. Finally, we present
a DSIC mechanism for the case that the valuation functions of all buyers and
sellers are additive.


title:
Finite Element Approximation for the Dynamics of Fluidic Two-Phase
  Biomembranes

abstract:
  Biomembranes and vesicles consisting of multiple phases can attain a
multitude of shapes, undergoing complex shape transitions. We study a
Cahn--Hilliard model on an evolving hypersurface coupled to Navier--Stokes
equations on the surface and in the surrounding medium to model these
phenomena. The evolution is driven by a curvature energy, modelling the
elasticity of the membrane, and by a Cahn--Hilliard type energy, modelling line
energy effects. A stable semidiscrete finite element approximation is
introduced and, with the help of a fully discrete method, several phenomena
occurring for two-phase membranes are computed.


title:
On composite likelihood in bivariate meta-analysis of diagnostic test
  accuracy studies

abstract:
  The composite likelihood (CL) is amongst the computational methods used for
estimation of the generalized linear mixed model (GLMM) in the context of
bivariate meta-analysis of diagnostic test accuracy studies. Its advantage is
that the likelihood can be derived conveniently under the assumption of
independence between the random effects, but there has not been a clear
analysis of the merit or necessity of this method. For synthesis of diagnostic
test accuracy studies, a copula mixed model has been proposed in the
biostatistics literature. This general model includes the GLMM as a special
case and can also allow for flexible dependence modelling, different from
assuming simple linear correlation structures, normality and tail independence
in the joint tails. A maximum likelihood (ML) method, which is based on
evaluating the bi-dimensional integrals of the likelihood with quadrature
methods has been proposed, and in fact it eases any computational difficulty
that might be caused by the double integral in the likelihood function. Both
methods are thoroughly examined with extensive simulations and illustrated with
data of a published meta-analysis. It is shown that the ML method has no
non-convergence issues or computational difficulties and at the same time
allows estimation of the dependence between study-specific sensitivity and
specificity and thus prediction via summary receiver operating curves.


title:
Backtracking Spatial Pyramid Pooling (SPP)-based Image Classifier for
  Weakly Supervised Top-down Salient Object Detection

abstract:
  Top-down saliency models produce a probability map that peaks at target
locations specified by a task/goal such as object detection. They are usually
trained in a fully supervised setting involving pixel-level annotations of
objects. We propose a weakly supervised top-down saliency framework using only
binary labels that indicate the presence/absence of an object in an image.
First, the probabilistic contribution of each image region to the confidence of
a CNN-based image classifier is computed through a backtracking strategy to
produce top-down saliency. From a set of saliency maps of an image produced by
fast bottom-up saliency approaches, we select the best saliency map suitable
for the top-down task. The selected bottom-up saliency map is combined with the
top-down saliency map. Features having high combined saliency are used to train
a linear SVM classifier to estimate feature saliency. This is integrated with
combined saliency and further refined through a multi-scale
superpixel-averaging of saliency map. We evaluate the performance of the
proposed weakly supervised topdown saliency and achieve comparable performance
with fully supervised approaches. Experiments are carried out on seven
challenging datasets and quantitative results are compared with 40 closely
related approaches across 4 different applications.


title: File Synchronization Systems Survey

abstract:
  Several solutions exist for file storage, sharing, and synchronization. Many
of them involve a central server, or a collection of servers, that either store
the files, or act as a gateway for them to be shared. Some systems take a
decentralized approach, wherein interconnected users form a peer-to-peer (P2P)
network, and partake in the sharing process: they share the files they possess
with others, and can obtain the files owned by other peers. In this paper, we
survey various technologies, both cloud-based and P2P-based, that users use to
synchronize their files across the network, and discuss their strengths and
weaknesses.


title:
Energetic particle transport across the mean magnetic field: before
  diffusion

abstract:
  Current particle transport models describe the propagation of charged
particles across the mean field direction in turbulent plasmas as diffusion.
However, recent studies suggest that at short time-scales, such as soon after
solar energetic particle (SEP) injection, particles remain on turbulently
meandering field lines, which results in non-diffusive initial propagation
across the mean magnetic field. In this work, we use a new technique to
investigate how the particles are displaced from their original field lines,
and quantify the parameters of the transition from field-aligned particle
propagation along meandering field lines to particle diffusion across the mean
magnetic field. We show that the initial decoupling of the particles from the
field lines is slow, and particles remain within a Larmor radius from their
initial meandering field lines for tens to hundreds of Larmor periods, for
0.1-10 MeV protons in turbulence conditions typical of the solar wind at 1~AU.
Subsequently, particles decouple from their initial field lines and after
hundreds to thousands of Larmor periods reach time-asymptotic diffusive
behaviour consistent with particle diffusion across the mean field caused by
the meandering of the field lines. We show that the typical duration of the
pre-diffusive phase, hours to tens of hours for 10 MeV protons in 1~AU solar
wind turbulence conditions, is significant for SEP propagation to 1~AU and must
be taken into account when modelling SEP propagation in the interplanetary
space.


title: Particle Mesh Ewald's Method and Non-Interacting Dyon Gas

abstract:
  We study the free energy of a quark-antiquark pair near the deconfinement
temperature by particle mesh Ewald's method for non-interacting dyon ensemble.
We show that the free energy of the quark-antiquark pair increases linearly by
increasing the distance between them. The string tension decreases by
increasing the temperature, as expected.


title: The index of Rubin-Stark units

abstract:
  The aim of this paper is to compare the orders of the class groups and the
quotients of the $r$-th exterior power of units modulo Rubin-Stark units.


title: Traveling Solitons in Long-Range Oscillator Chains

abstract:
  We investigate the existence and propagation of solitons in a long-range
extension of the quartic Fermi-Pasta-Ulam (FPU) chain of anharmonic
oscillators. The coupling in the linear term decays as a power-law with an
exponent greater than 1 and less than 3. We obtain an analytic perturbative
expression of traveling envelope solitons by introducing a Non Linear
Schrodinger (NLS) equation for the slowly varying amplitude of short wavelength
modes. Due to the non analytic properties of the dispersion relation, it is
crucial to develop the theory using discrete difference operators. Those
properties are also the ultimate reason why kink-solitons may exist but are
unstable, at variance with the short-range FPU model. We successfully compare
these approximate analytic results with numerical simulations.


title: Strongly $\sigma$-metrizable spaces are super $\sigma$-metrizable

abstract:
  A topological space $X$ is called strongly $\sigma$-metrizable if
$X=\bigcup_{n\in\omega}X_n$ for an increasing sequence $(X_n)_{n\in\omega}$ of
closed metrizable subspaces such that every convergence sequence in $X$ is
contained in some $X_n$. If, in addition, every compact subset of $X$ is
contained in some $X_n$, $n\in\omega$, then $X$ is called super
$\sigma$-metrizable. Answering a question of V.K.Maslyuchenko and
O.I.Filipchuk, we prove that a topological space is strongly
$\sigma$-metrizable if and only if it is super $\sigma$-metrizable.


title:
Optimizing DF Cognitive Radio Networks with Full-Duplex-Enabled Energy
  Access Points

abstract:
  With the recent advances in radio frequency (RF) energy harvesting (EH)
technologies, wireless powered cooperative cognitive radio network (CCRN) has
drawn an upsurge of interest for improving the spectrum utilization with
incentive to motivate joint information and energy cooperation between the
primary and secondary systems. Dedicated energy beamforming (EB) is aimed for
remedying the low efficiency of wireless power transfer (WPT), which
nevertheless arouses out-of-band EH phases and thus low cooperation efficiency.
To address this issue, in this paper, we consider a novel RF EH CCRN aided by
full-duplex (FD)-enabled energy access points (EAPs) that can cooperate to
wireless charge the secondary transmitter (ST) while concurrently receiving
primary transmitter (PT)'s signal in the first transmission phase, and to
perform decode-and-forward (DF) relaying in the second transmission phase. We
investigate a weighted sum-rate maximization problem subject to the
transmitting power constraints as well as a total cost constraint using
successive convex approximation (SCA) techniques. A zero-forcing (ZF) based
suboptimal scheme that requires only local CSIs for the EAPs to obtain their
optimum receive beamforming is also derived. Various tradeoffs between the
weighted sum-rate and other system parameters are provided in numerical results
to corroborate the effectiveness of the proposed solutions against the
benchmark ones.


title: Context Awareness in Next Generation of Mobile Core Networks

abstract:
  Context awareness is an important enabler for next generation of Mobile Core
Networks (MCN). However there exist a number of challenges in this regard. For
example how to develop a framework which 1) is able to generate context richer
than what is available today; 2) allows reusability of context across the
network; 3) provides a mechanism for exposing context to third parties; and 4)
can bring together "big data" for mobile core network optimization. In this
work, we introduce a context awareness framework addressing the aforementioned
challenges but also taking into account the 3GPP standardization activities
related to context awareness in MCN. Within this framework we propose Context
Generation and Handling Function (CGHF) which generates rich context by
processing information from various sources and then handles its distribution
through an efficient publish subscribe mechanism. In addition we provide
examples where context can be used to optimize control plane decision making.
While the focus of this work is on the use of context for MCN, we still believe
such context can be also used by applications (at the edge as well as in data
centers) and third party services to improve their operations and providing new
unforeseen services.


title: Electroweak precision constraints at present and future colliders

abstract:
  We revisit the global fit to electroweak precision observables in the
Standard Model and present model-independent bounds on several general new
physics scenarios. We present a projection of the fit based on the expected
experimental improvements at future $e^+ e^-$ colliders, and compare the
constraining power of some of the different experiments that have been
proposed. All results have been obtained with the HEPfit code.


title: Weighted Fano varieties and infinitesimal Torelli problem

abstract:
  We solve the infinitesimal Torelli problem for $3$-dimensional quasi-smooth
${\mathbb{Q}}$-Fano hypersurfaces with at worst terminal singularities. We also
find infinite chains of double coverings of increasing dimension which
alternatively distribute themselves in examples and counterexamples for the
infinitesimal Torelli claim and which share the analogue, and in some cases the
same, Hodge-diagram properties as the length $3$ Gushel-Mukai chain of prime
smooth Fanos of coindex $3$ and degree $10$.


title:
Towards Interconnected Virtual Reality: Opportunities, Challenges and
  Enablers

abstract:
  Just recently, the concept of augmented and virtual reality (AR/VR) over
wireless has taken the entire 5G ecosystem by storm spurring an unprecedented
interest from both academia, industry and others. Yet, the success of an
immersive VR experience hinges on solving a plethora of grand challenges
cutting across multiple disciplines. This article underscores the importance of
VR technology as a disruptive use case of 5G (and beyond) harnessing the latest
development of storage/memory, fog/edge computing, computer vision, artificial
intelligence and others. In particular, the main requirements of wireless
interconnected VR are described followed by a selection of key enablers, then,
research avenues and their underlying grand challenges are presented.
Furthermore, we examine three VR case studies and provide numerical results
under various storage, computing and network configurations. Finally, this
article exposes the limitations of current networks and makes the case for more
theory, and innovations to spearhead VR for the masses.


title:
The light U boson as the mediator of a new force, coupled to a
  combination of Q, B, L and dark matter

abstract:
  A new light gauge boson $U$ may have both vector and axial couplings. In a
large class of theories however, the new $U(1)$ current $J^\mu_F$ naturally
combines with the weak neutral current $J^\mu_{Z_{\rm sm}}$, both
parity-violating, into a vectorial current $J^\mu_U$, combination of the
$B,\,L$ and electromagnetic currents with a possible dark matter current.
  $U^\mu $ may be expressed equivalently as $\cos\xi \,C^\mu\! + \sin \xi
\,Z^\mu_{\rm \,sm}$ ("mixing with the $Z$") or $(1/\cos\chi) \,\hat
C^\mu+\tan\chi \,A^\mu$ ("mixing with the photon"), with $\hat C$ coupled to
$B,\,L$ and dark matter. The $U$ boson may be viewed as a generalized dark
photon, coupled to SM particles through $Q_U=Q+\lambda_B B+\lambda_i L_i$, with
strength $\,g"\cos\xi\,\cos^2\theta=e\tan\chi$. "Kinetic mixing" terms, gauge
invariant or not, simply correspond to a description in a non-orthogonal field
basis (rather than to a new physical effect), with the dark photon in general
also coupled to $B$ and $L$.
  In a grand-unified theory $\,Q_U^{\rm gut}=Q-2\,(B-L)\,$ at the GUT scale for
SM particles, invariant under the $SU(4)$ electrostrong symmetry group, with a
non-vanishing $\epsilon = \tan\chi$ already present at the GUT scale, leading
to $Q_U\simeq \,Q-1.64\, (B-L)\,$ at low energy. This also applies, for a very
light or massless $U$ boson, to a new long-range force, which could show up
through apparent violations of the Equivalence Principle.


title: Lip Reading Sentences in the Wild

abstract:
  The goal of this work is to recognise phrases and sentences being spoken by a
talking face, with or without the audio. Unlike previous works that have
focussed on recognising a limited number of words or phrases, we tackle lip
reading as an open-world problem - unconstrained natural language sentences,
and in the wild videos.
  Our key contributions are: (1) a 'Watch, Listen, Attend and Spell' (WLAS)
network that learns to transcribe videos of mouth motion to characters; (2) a
curriculum learning strategy to accelerate training and to reduce overfitting;
(3) a 'Lip Reading Sentences' (LRS) dataset for visual speech recognition,
consisting of over 100,000 natural sentences from British television.
  The WLAS model trained on the LRS dataset surpasses the performance of all
previous work on standard lip reading benchmark datasets, often by a
significant margin. This lip reading performance beats a professional lip
reader on videos from BBC television, and we also demonstrate that visual
information helps to improve speech recognition performance even when the audio
is available.


title: Longest Common Extensions with Recompression

abstract:
  Given two positions $i$ and $j$ in a string $T$ of length $N$, a longest
common extension (LCE) query asks for the length of the longest common prefix
between suffixes beginning at $i$ and $j$. A compressed LCE data structure is a
data structure that stores $T$ in a compressed form while supporting fast LCE
queries. In this article we show that the recompression technique is a powerful
tool for compressed LCE data structures. We present a new compressed LCE data
structure of size $O(z \lg (N/z))$ that supports LCE queries in $O(\lg N)$
time, where $z$ is the size of Lempel-Ziv 77 factorization without
self-reference of $T$. Given $T$ as an uncompressed form, we show how to build
our data structure in $O(N)$ time and space. Given $T$ as a grammar compressed
form, i.e., an straight-line program of size n generating $T$, we show how to
build our data structure in $O(n \lg (N/n))$ time and $O(n + z \lg (N/z))$
space. Our algorithms are deterministic and always return correct answers.


title: The Life of Lazarillo de Tormes and of His Machine Learning Adversities

abstract:
  Summit work of the Spanish Golden Age and forefather of the so-called
picaresque novel, The Life of Lazarillo de Tormes and of His Fortunes and
Adversities still remains an anonymous text. Although distinguished scholars
have tried to attribute it to different authors based on a variety of criteria,
a consensus has yet to be reached. The list of candidates is long and not all
of them enjoy the same support within the scholarly community. Analyzing their
works from a data-driven perspective and applying machine learning techniques
for style and text fingerprinting, we shed light on the authorship of the
Lazarillo. As in a state-of-the-art survey, we discuss the methods used and how
they perform in our specific case. According to our methodology, the most
likely author seems to be Juan Arce de Ot\'alora, closely followed by Alfonso
de Vald\'es. The method states that not certain attribution can be made with
the given corpus.


title:
Electronic Correlations in Vanadium Revealed by Electron-Positron
  Annihilation Measurements

abstract:
  The electronic structure of vanadium measured by Angular Correlation of
electron-positron Annihilation Radiation (ACAR) is compared with the
predictions of the combined Density Functional and Dynamical Mean-Field Theory
(DMFT). Reconstructing the momentum density from five 2D projections we were
able to determine the full Fermi surface and found excellent agreement with the
DMFT calculations. In particular, we show that the local, dynamic self-energy
corrections contribute to the anisotropy of the momentum density and need to be
included to explain the experimental results.


title: Outsmarting Network Security with SDN Teleportation

abstract:
  Software-defined networking is considered a promising new paradigm, enabling
more reliable and formally verifiable communication networks. However, this
paper shows that the separation of the control plane from the data plane, which
lies at the heart of Software-Defined Networks (SDNs), introduces a new
vulnerability which we call \emph{teleportation}. An attacker (e.g., a
malicious switch in the data plane or a host connected to the network) can use
teleportation to transmit information via the control plane and bypass critical
network functions in the data plane (e.g., a firewall), and to violate security
policies as well as logical and even physical separations. This paper
characterizes the design space for teleportation attacks theoretically, and
then identifies four different teleportation techniques. We demonstrate and
discuss how these techniques can be exploited for different attacks (e.g.,
exfiltrating confidential data at high rates), and also initiate the discussion
of possible countermeasures. Generally, and given today's trend toward more
intent-based networking, we believe that our findings are relevant beyond the
use cases considered in this paper.


title: Pointwise Bounds for Steklov Eigenfunctions

abstract:
  Let $(\Omega,g)$ be a compact, real-analytic Riemannian manifold with
real-analytic boundary $\partial \Omega.$ The harmonic extensions of the
boundary Dirchlet-to-Neumann eigenfunctions are called Steklov eigenfunctions.
We show that the Steklov eigenfuntions decay exponentially into the interior in
terms of the Dirichlet-to-Neumann eigenvalues and give a sharp rate of decay to
first order at the boundary. The proof uses the Poisson representation for the
Steklov eigenfunctions combined with sharp $h$-microlocal concentration
estimates for the boundary Dirichlet-to-Neumann eigenfunctions near the
cosphere bundle $S^*\partial \Omega.$ These estimates follow from sharp
estimates on the concentration of the FBI transforms of solutions to analytic
pseudodifferential equations $Pu=0$ near the characteristic set
$\{\sigma(P)=0\}$.


title: Isotropic self-consistent equations for mean-field random matrices

abstract:
  We present a simple and versatile method for deriving (an)isotropic local
laws for general random matrices constructed from independent random variables.
Our method is applicable to mean-field random matrices, where all independent
variables have comparable variances. It is entirely insensitive to the
expectation of the matrix. In this paper we focus on the probabilistic part of
the proof -- the derivation of the self-consistent equations. As a concrete
application, we settle in complete generality the local law for Wigner matrices
with arbitrary expectation.


title:
Am I a Baller? Basketball Performance Assessment from First-Person
  Videos

abstract:
  This paper presents a method to assess a basketball player's performance from
his/her first-person video. A key challenge lies in the fact that the
evaluation metric is highly subjective and specific to a particular evaluator.
We leverage the first-person camera to address this challenge. The
spatiotemporal visual semantics provided by a first-person view allows us to
reason about the camera wearer's actions while he/she is participating in an
unscripted basketball game. Our method takes a player's first-person video and
provides a player's performance measure that is specific to an evaluator's
preference.
  To achieve this goal, we first use a convolutional LSTM network to detect
atomic basketball events from first-person videos. Our network's ability to
zoom-in to the salient regions addresses the issue of a severe camera wearer's
head movement in first-person videos. The detected atomic events are then
passed through the Gaussian mixtures to construct a highly non-linear visual
spatiotemporal basketball assessment feature. Finally, we use this feature to
learn a basketball assessment model from pairs of labeled first-person
basketball videos, for which a basketball expert indicates, which of the two
players is better.
  We demonstrate that despite not knowing the basketball evaluator's criterion,
our model learns to accurately assess the players in real-world games.
Furthermore, our model can also discover basketball events that contribute
positively and negatively to a player's performance.


title:
Constraints on the formation of double neutron stars from the observed
  eccentricities and current limits on merger rates

abstract:
  We employ population synthesis method to model the double neutron star (DNS)
population and test various possibilities on natal kick velocities gained by
neutron stars after their formation. We first choose natal kicks after standard
core collapse SN from a Maxwellian distribution with velocity dispersion of
sigma=265 km/s as proposed by Hobbs et al. (2005) and then modify this
distribution by changing the velocity dispersion towards smaller and larger
kick values. We also take into account the possibility of NS formation through
electron capture supernova. In this case we test two scenarios: zero natal kick
or small natal kick, drawn from Maxwellian distribution with sigma = 26.5 km/s.
We calculate the present-day orbital parameters of binaries and compare the
resulting eccentricities with those known for observed DNSs. As an additional
test we calculate Galactic merger rates for our model populations and confront
them with observational limits. We do not find any model unequivocally
consistent with both observational constraints simultaneously.The models with
low kicks after CCSN for binaries with the second NS forming through core
collapse SN are marginally consistent with the observations. This means that
either 14 observed DNSs are not representative of the intrinsic Galactic
population, or that our modeling of DNS formation needs revision.


title: Holomorphic extendability in $\mathbf C^n$ as a rare phenomenon

abstract:
  We consider various notions of holomorphic extendability of complex valued
functions defined on subsets of $\mathbf C^n$, including one-sided
extendability. We show that in the relevant function spaces, these phenomena of
holomorphic extendability are rare in the topological sense, generalizing
several results of the article "One sided extendability and $p$-continuous
analytic capacities" by E. Bolkas, V. Nestoridis, C. Panagiotis and M.
Papadimitrakis, in dimensions $n\ge 2$.


title:
Neural Style Representations and the Large-Scale Classification of
  Artistic Style

abstract:
  The artistic style of a painting is a subtle aesthetic judgment used by art
historians for grouping and classifying artwork. The recently introduced
`neural-style' algorithm substantially succeeds in merging the perceived
artistic style of one image or set of images with the perceived content of
another. In light of this and other recent developments in image analysis via
convolutional neural networks, we investigate the effectiveness of a
`neural-style' representation for classifying the artistic style of paintings.


title: Fast On-Line Kernel Density Estimation for Active Object Localization

abstract:
  A major goal of computer vision is to enable computers to interpret visual
situations---abstract concepts (e.g., "a person walking a dog," "a crowd
waiting for a bus," "a picnic") whose image instantiations are linked more by
their common spatial and semantic structure than by low-level visual
similarity. In this paper, we propose a novel method for prior learning and
active object localization for this kind of knowledge-driven search in static
images. In our system, prior situation knowledge is captured by a set of
flexible, kernel-based density estimations---a situation model---that represent
the expected spatial structure of the given situation. These estimations are
efficiently updated by information gained as the system searches for relevant
objects, allowing the system to use context as it is discovered to narrow the
search.
  More specifically, at any given time in a run on a test image, our system
uses image features plus contextual information it has discovered to identify a
small subset of training images---an importance cluster---that is deemed most
similar to the given test image, given the context. This subset is used to
generate an updated situation model in an on-line fashion, using an efficient
multipole expansion technique.
  As a proof of concept, we apply our algorithm to a highly varied and
challenging dataset consisting of instances of a "dog-walking" situation. Our
results support the hypothesis that dynamically-rendered, context-based
probability models can support efficient object localization in visual
situations. Moreover, our approach is general enough to be applied to diverse
machine learning paradigms requiring interpretable, probabilistic
representations generated from partially observed data.


title: Juxtaposing Catalan permutation classes with monotone ones

abstract:
  This paper enumerates all juxtaposition classes of the form "Av($abc$) next
to Av($xy$)", where $abc$ is a permutation of length three and $xy$ is a
permutation of length two. We use Dyck paths decorated by sequences of points
to represent elements from such a juxtaposition class. Context-free grammars
are then used to enumerate these decorated Dyck paths.


title:
Probing low-energy hyperbolic polaritons in van der Waals crystals with
  an electron microscope

abstract:
  Van der Waals (vdW) materials exhibit intriguing structural, electronic and
photonic properties. Electron Energy Loss Spectroscopy (EELS) within Scanning
Transmission Electron Microscope (STEM) allows for nanoscale mapping of such
properties. However, typical STEM-EELS detection is limited to energy losses in
the eV range, which are too large for probing important low-energy excitation
such as phonons or mid-IR plasmons. Here we adapt a conventional STEM-EELS
system to probe energy loss down to 100 meV, and apply it to map phononic
states in h-BN, a representative van der Waals (vdW) material. The h-BN EELS
spectra, surprisingly, depend on the h-BN thickness and the distance of the
electron beam to h-BN flake edges. To explain this observation, we developed a
classical response theory describing the interaction of fast electrons with
(anisotropic) slabs of vdW materials. Theoretical and numerical calculations
perfectly match the experimental h-BN spectra, and reveal that the electron
loss is dominated by the excitation of hyperbolic phonon polaritons, and not of
bulk phonons as often reported. Thus, STEM-EELS offers the possibility to probe
low-energy polaritons in vdW materials, with our theory being of fundamental
importance for interpreting future experiments.


title:
Sensitivity Analysis for Convex Separable Optimization over Integral
  Polymatroids

abstract:
  We study the sensitivity of optimal solutions of convex separable
optimization problems over an integral polymatroid base polytope with respect
to parameters determining both the cost of each element and the polytope. Under
convexity and a regularity assumption on the functional dependency of the cost
function with respect to the parameters, we show that reoptimization after a
change in parameters can be done by elementary local operations. Applying this
result, we derive that starting from any optimal solution there is a new
optimal solution to new parameters such that the L1-norm of the difference of
the two solutions is at most two times the L1 norm of the difference of the
parameters. We apply these sensitivity results to a class of non-cooperative
polymatroid games and derive the existence of pure Nash equilibria. We
complement our results by showing that polymatroids are the maximal
combinatorial structure enabling these results. For any non-polymatroid region,
there is a corresponding optimization problem for which the sensitivity results
do not hold. In addition, there is a game where the players strategies are
isomorphic to the non-polymatroid region and that does not admit a pure Nash
equilibrium.


title: DeepCas: an End-to-end Predictor of Information Cascades

abstract:
  Information cascades, effectively facilitated by most social network
platforms, are recognized as a major factor in almost every social success and
disaster in these networks. Can cascades be predicted? While many believe that
they are inherently unpredictable, recent work has shown that some key
properties of information cascades, such as size, growth, and shape, can be
predicted by a machine learning algorithm that combines many features. These
predictors all depend on a bag of hand-crafting features to represent the
cascade network and the global network structure. Such features, always
carefully and sometimes mysteriously designed, are not easy to extend or to
generalize to a different platform or domain.
  Inspired by the recent successes of deep learning in multiple data mining
tasks, we investigate whether an end-to-end deep learning approach could
effectively predict the future size of cascades. Such a method automatically
learns the representation of individual cascade graphs in the context of the
global network structure, without hand-crafted features and heuristics. We find
that node embeddings fall short of predictive power, and it is critical to
learn the representation of a cascade graph as a whole. We present algorithms
that learn the representation of cascade graphs in an end-to-end manner, which
significantly improve the performance of cascade prediction over strong
baselines that include feature based methods, node embedding methods, and graph
kernel methods. Our results also provide interesting implications for cascade
prediction in general.


title:
An Automated Attendance System based on NFC & X-Bee Technologies with a
  Remote Database

abstract:
  The main aim of this research was to automate attendance registration,
thereby reducing human involvement in the whole process. Typically, the system
works by storing vital staff personable information, such as Name, Job
specification, etc. into a MySQL database upon engagement. The staff is
identified with a unique key associated with an NFC based ID card within the
database. So on typical work day, an employee scans his/her ID card on the
PN532 reader in close proximity. The exact time and date, together with the
unique identifier of the scanned card are stored locally on a storage media,
before the data is relayed via the XBee to the remote database. The captured
data is then authenticated by comparing with the pre-entered data to give
access or authorization to the corporate resources, as well as recorded for
attendance purposes. Our experiment shows that the automated attendance system
is more effective, efficient and reliable, due to its real time capability,
remote monitoring and attendance reports that it provides to the institution.


title:
Superconductivity without hole-pocket in electron-doped FeSe: Analysis
  beyond the Migdal-Eliashberg formalism

abstract:
  High-Tc pairing mechanism absent of hole-pockets in heavily electron-doped
FeSe is one of the key unsolved problems in Fe-based superconductors. Here,
this problem is attacked by focusing on the higher-order many-body effects
neglected in conventional Migdal-Eliashberg formalism. We uncover two
significant many-body effects for high-Tc superconductivity: (i) Due to the
"vertex correction", the dressed multiorbital Coulomb interaction acquires
prominent orbital dependence for low-energy electrons. The dressed Coulomb
interaction not only induces the orbital fluctuations, but also magnifies the
electron-boson coupling constant. Therefore, moderate orbital fluctuations give
strong attractive pairing interaction. (ii) The "multi-fluctuation-exchange
pairing process" causes large inter-pocket attractive force, which is as
important as usual single-fluctuation-exchange process. Due to these two
significant effects dropped in the Migdal-Eliashberg formalism, the anisotropic
$s_{++}$-wave state in heavily electron-doped FeSe is satisfactorily explained.
The proposed "inter-electron-pocket pairing mechanism" will enlarge Tc in other
Fe-based superconductors.


title: hMAC: Enabling Hybrid TDMA/CSMA on IEEE 802.11 Hardware

abstract:
  We present our current work-in-progress on the design and implementation of a
hybrid TDMA/CSMA medium access architecture, hereafter referred to as hMAC,
which can be used on top of commercial IEEE 802.11 off-the-shelf hardware. The
software only solution is based on the popular Linux ATH9K softMAC driver and
hence can be used with standard Linux systems using Atheros based wireless
network devices. The proposed hMAC exploits the standard 802.11 power saving
functionality present in the ATH9K device driver to enable control of the
software packet queues. This allows the assignment of TDMA time slots on
wireless link and traffic class basis. While the solution is placed only in the
device driver, the CSMA/CA functionality on hardware level is still active.
This enables inter-working with standard unmodified 802.11 devices. We tested
our prototypical hMAC implementation in a small test-bed. Therefore, we
implemented a centralized interference management scheme in which pairs of
links suffering from a hidden node problem are assigned to TDMA time slots on a
per-link basis. To show the benefits of the proposed hMAC approach we compared
the results with standard 802.11 DCF and classical, i.e. per-node, TDMA.
Finally, to enable collaboration with the research community, the hMAC source
code is provided as open-source.


title:
Fully-adaptive Feature Sharing in Multi-Task Networks with Applications
  in Person Attribute Classification

abstract:
  Multi-task learning aims to improve generalization performance of multiple
prediction tasks by appropriately sharing relevant information across them. In
the context of deep neural networks, this idea is often realized by
hand-designed network architectures with layers that are shared across tasks
and branches that encode task-specific features. However, the space of possible
multi-task deep architectures is combinatorially large and often the final
architecture is arrived at by manual exploration of this space subject to
designer's bias, which can be both error-prone and tedious. In this work, we
propose a principled approach for designing compact multi-task deep learning
architectures. Our approach starts with a thin network and dynamically widens
it in a greedy manner during training using a novel criterion that promotes
grouping of similar tasks together. Our Extensive evaluation on person
attributes classification tasks involving facial and clothing attributes
suggests that the models produced by the proposed method are fast, compact and
can closely match or exceed the state-of-the-art accuracy from strong baselines
by much more expensive models.


title: Spectral Convolution Networks

abstract:
  Previous research has shown that computation of convolution in the frequency
domain provides a significant speedup versus traditional convolution network
implementations. However, this performance increase comes at the expense of
repeatedly computing the transform and its inverse in order to apply other
network operations such as activation, pooling, and dropout. We show,
mathematically, how convolution and activation can both be implemented in the
frequency domain using either the Fourier or Laplace transformation. The main
contributions are a description of spectral activation under the Fourier
transform and a further description of an efficient algorithm for computing
both convolution and activation under the Laplace transform. By computing both
the convolution and activation functions in the frequency domain, we can reduce
the number of transforms required, as well as reducing overall complexity. Our
description of a spectral activation function, together with existing spectral
analogs of other network functions may then be used to compose a fully spectral
implementation of a convolution network.


title:
PCT and Beyond: Towards a Computational Framework for `Intelligent'
  Communicative Systems

abstract:
  Recent years have witnessed increasing interest in the potential benefits of
`intelligent' autonomous machines such as robots. Honda's Asimo humanoid robot,
iRobot's Roomba robot vacuum cleaner and Google's driverless cars have fired
the imagination of the general public, and social media buzz with speculation
about a utopian world of helpful robot assistants or the coming robot
apocalypse! However, there is a long way to go before autonomous systems reach
the level of capabilities required for even the simplest of tasks involving
human-robot interaction - especially if it involves communicative behaviour
such as speech and language. Of course the field of Artificial Intelligence
(AI) has made great strides in these areas, and has moved on from abstract
high-level rule-based paradigms to embodied architectures whose operations are
grounded in real physical environments. What is still missing, however, is an
overarching theory of intelligent communicative behaviour that informs
system-level design decisions in order to provide a more coherent approach to
system integration. This chapter introduces the beginnings of such a framework
inspired by the principles of Perceptual Control Theory (PCT). In particular,
it is observed that PCT has hitherto tended to view perceptual processes as a
relatively straightforward series of transformations from sensation to
perception, and has overlooked the potential of powerful generative model-based
solutions that have emerged in practical fields such as visual or auditory
scene analysis. Starting from first principles, a sequence of arguments is
presented which not only shows how these ideas might be integrated into PCT,
but which also extend PCT towards a remarkably symmetric architecture for a
needs-driven communicative agent. It is concluded that, if behaviour is the
control of perception, then perception is the simulation of behaviour.


title: Market Segmentation for Privacy Differentiated "Free" Services

abstract:
  The emerging marketplace for online free services in which service providers
earn revenue from using consumer data in direct and indirect ways has lead to
significant privacy concerns. This leads to the following question: can the
online marketplace sustain multiple service providers (SPs) that offer
privacy-differentiated free services? This paper studies the problem of market
segmentation for the free online services market by augmenting the classical
Hotelling model for market segmentation analysis to include the fact that for
the free services market, a consumer values service not in monetized terms but
by its quality of service (QoS) and that the differentiator of services is not
product price but the privacy risk advertised by a SP. Building upon the
Hotelling model, this paper presents a parametrized model for SP profit and
consumer valuation of service for both the two- and multi-SP problems to show
that: (i) when consumers place a high value on privacy, it leads to a lower use
of private data by SPs (i.e., their advertised privacy risk reduces), and thus,
SPs compete on the QoS; (ii) SPs that are capable of differentiating on
services that do not directly target consumers gain larger market share; and
(iii) a higher valuation of privacy by consumers forces SPs with smaller
untargeted revenue to offer lower privacy risk to attract more consumers. The
work also illustrates the market segmentation problem for more than two SPs and
highlights the instability of such markets.


title: Uncertainty principle for discrete Schr\"odinger evolution on graphs

abstract:
  We consider the Schr\"odinger evolution on graph, i.e. solution to the
equation
$\partial_tu(t,\alpha)=i\sum_{\beta\in\mathcal{A}}L(\alpha,\beta)u(t,\beta)$,
here $\mathcal{A}$ is the set of vertices of the graph and the matrix
$(L(\alpha,\beta))_{\alpha,\beta\in\mathcal{A}}$ describes interaction between
the vertices, in particular two vertices $\alpha$ and $\beta$ are connected if
$L(\alpha,\beta)\neq0$. We assume that the graph has a "web-like" structure,
i.e, it consists of an inner part, formed by a finite number of vertices, and
some threads attach to it. We prove that such solution $u(t,\alpha)$ cannot
decay too fast along one thread at two different times, unless it vanishes at
this thread. We also give a characterization of the dimension of the vector
space formed by all the solutions of
$\partial_tu(t,\alpha)=i\sum_{\beta\in\mathcal{A}}L(\alpha,\beta)u(t,\beta)$
when $\mathcal{A}$ is a finite set, in terms of the number of the different
eigenvalues of the matrix $L(\cdot,\cdot)$


title: Modified gravity (MOG) and the cluster Abell 1689 acceleration data

abstract:
  The galaxy cluster system Abell 1689 has been well studied and yields good
lensing and X-ray gas data. Modified gravity (MOG) is applied to the cluster
Abell 1689 and the acceleration data is well fitted without assuming dark
matter. Newtonian dynamics and Modified Newtonian dynamics (MOND) are shown not
to fit the acceleration data, while a dark matter model based on the
Navarro-Frenk-White (NFW) mass profile is shown to fit the acceleration data
for the radial range $r > 200$ kpc.


title: Revising the $f_1(1420)$ resonance

abstract:
  We have studied the production and decay of the $f_1(1285)$ into $\pi
a_0(980)$ and $K^* \bar K$ as a function of the mass of the resonance and find
a shoulder around 1400 MeV, tied to a triangle singularity, for the $\pi
a_0(980)$ mode, and a peak around 1420 MeV with about 60 MeV width for the $K^*
\bar K$ mode. Both these features agree with the experimental information on
which the $f_1(1420)$ resonance is based. In addition, we find that if the
$f_1(1420)$ is a genuine resonance, coupling mostly to $K^* \bar K$ as seen
experimentally, one finds unavoidably about a 20\% fraction for $\pi a_0(980)$
decay of this resonance, in drastic contradiction with all experiments.
Altogether, we conclude that the $f_1(1420)$ is not a genuine resonance, but
the manifestation of the $\pi a_0(980)$ and $K^* \bar K$ decay modes of the
$f_1(1285)$ at higher energies than the nominal one.


title:
A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and
  Part-of-Speech Tagging

abstract:
  Recently, neural network models for natural language processing tasks have
been increasingly focused on for their ability of alleviating the burden of
manual feature engineering. However, the previous neural models cannot extract
the complicated feature compositions as the traditional methods with discrete
features. In this work, we propose a feature-enriched neural model for joint
Chinese word segmentation and part-of-speech tagging task. Specifically, to
simulate the feature templates of traditional discrete feature based models, we
use different filters to model the complex compositional features with
convolutional and pooling layer, and then utilize long distance dependency
information with recurrent layer. Experimental results on five different
datasets show the effectiveness of our proposed model.


title:
On two-parameter solutions of simultaneous ultradiscrete Painleve II
  equation with parity variables

abstract:
  We introduce a simultaneous ultradiscrete Painleve II equation with parity
variables, which is shown to be more suitable for studying two-parameter
solutions than the single second-order ultradiscrete Painleve II equation with
parity variables. We investigate several types of two-parameter solutions and
the solutions which are related with the ultradiscrete limit of determinant
type solutions of q-Painleve II equation.


title:
Boundedness of derivatives and anti-derivatives of holomorphic functions
  as a rare phenomenon

abstract:
  In this article we prove a general result which in particular suggests that,
on a simply connected domain in C, all the derivatives and anti-derivatives of
the generic holomorphic function are unbounded. A similar result holds for the
operator of partial sums of the Taylor expansion with center z at 0, seen as
functions of the center z. We also discuss a universality result of these
operators.


title: Approximate Inertial Manifold Approach to Non-Equilibrium Thermodynamics

abstract:
  In this paper a reaction-diffusion type equation is the starting point for
setting up a genuine thermodynamic reduction, i.e. involving a finite number of
parameters or collective variables, of the initial system. This program is
carried over by firstly operating a finite Lyapunov-Schmidt reduction of the
cited reaction-diffusion equation when reformulated as a variational problem.
In this way we gain an approximate finite-dimensional o.d.e. description of the
initial system which preserves the gradient structure of the original one and
that is similar to the approximate inertial manifold description of a p.d.e.
introduced by Temam and coworkers. Secondly, we resort to the stochastic
version of the o.d.e., taking into account in this way the uncertainty (loss of
information) introduced with the above mentioned reduction. We study this
reduced stochastic system using classical tools from large deviations,
viscosity solutions and weak KAM Hamilton-Jacobi theory. In the last part we
highlight some essential similarities existing between our approach and the
comprehensive treatment non equilibrium thermodynamics given by Jona-Lasinio
and coworkers. The starting point of their axiomatic theory --motivated by
large deviations description of lattice gas models-- of systems in a stationary
non equilibrium state is precisely a conservation/balance law which is akin to
our simple model of reaction-diffusion equation.


title: Filling-dependent doublon dynamics in the one-dimensional Hubbard model

abstract:
  The fate of a local two-hole doublon excitation in the one-dimensional
Fermi-Hubbard model is systematically studied for strong Hubbard interaction U
in the entire filling range using the density-matrix renormalization group
(DMRG) and the Bethe ansatz. For strong U, two holes at the same site form a
compound object whose decay is impeded by the lack of phase space. Still, a
partial decay is possible on an extremely short time scale where phase-space
arguments do not yet apply. We argue that the initial decay and the resulting
intermediate state are relevant for experiments performed with ultracold atoms
loaded into an optical lattice as well as for (time-resolved) CVV
Auger-electron spectroscopy. The detailed discussion comprises the mixed
ballistic-diffusive real-time propagation of the doublon through the lattice,
its partial decay on the short time scale as a function of filling and
interaction strength, as well as the analysis of the decay products, which are
metastable on the intermediate time scale that is numerically accessible and
which show up in the two-hole excitation (Auger) spectrum. The ambivalent role
of singly occupied sites is key to understanding the doublon physics: For high
fillings, ground-state configurations with single occupancies are recognized to
strongly relax the kinematic constraints and to open up decay channels. For
fillings close to half filling, however, their presence actually blocks the
doublon decay. Finally, the analysis of the continua in the two-hole spectrum
excludes a picture where the doublon decays into unbound electron holes for
generic fillings, different from the limiting case of the completely filled
band. We demonstrate that the decay products as well as the doublon propagation
should rather be understood in terms of Bethe ansatz eigenstates.


title:
Probing the Anisotropy of the Milky Way Gaseous Halo-II: sightline
  toward Mrk509

abstract:
  Hot, million degree gas appears to pervade the Milky way halo, containing a
large fraction of the Galactic missing baryons. This circumgalactic medium
(CGM) is probed effectively in X-rays, both in absorption and in emission. The
CGM also appears to be anisotropic, so we have started a program to determine
CGM properties along several sightlines by combining absorption and emission
measurements. Here we present the emission measure close to the \mrk509
sightline using new \suzaku and \xmm observations. We also present new analysis
and modeling of \chandra HETG spectra to constrain the absorption parameters.
The emission measure in this sightline is high,
EM$=0.0165\pm0.0008\pm0.0006~$cm$^{-6}~$pc, five times larger than the average.
The observed \ovii column density N(\ovii)$= 2.35\pm0.4 \times
10^{16}$cm$^{-2}$, however, is close to the average. We find that the
temperature of the emitting and absorbing gas is the same: $\log T (\rm K) =
6.33\pm0.01$ and $\log T (\rm K)=6.33\pm0.16$ respectively. We fit the observed
column density and emission measure with a $\beta-$model density profile. The
the central density is constrained to be between $n_0=2.8$--$6.0\times 10^{-4}$
cm$^{-3}$ and the core radius of the density profile has a lower limit of 40
kpc. This shows that the hot gas is mostly in the CGM of the galaxy, not in the
Galactic disk. Our derived density profile is close to the \citet{Maller2004}
profile for adiabatic gas in hydrostatic equilibrium with an NFW dark matter
potential well. Assuming this density profile, the minimum mass of the hot CGM
is $3.2 \times 10^{10}~$M$_{\odot}$.


title: Non-Abelian symmetries of the half-infinite XXZ spin chain

abstract:
  The non-Abelian symmetries of the half-infinite XXZ spin chain for all
possible types of integrable boundary conditions are classified. For each type
of boundary conditions, an analog of the Chevalley-type presentation is given
for the corresponding symmetry algebra. In particular, two new algebras arise
that are, respectively, generated by the symmetry operators of the model with
triangular and special $U_q(gl_2)-$invariant integrable boundary conditions.


title:
From gluon topology to chiral anomaly: Emergent phenomena in quark-gluon
  plasma

abstract:
  Heavy-ion collision experiments at RHIC and the LHC have found a new emergent
phase of QCD, a strongly coupled quark-gluon plasma (sQGP) that is
distinctively different from either the low temperature hadron phase or the
very high temperature weakly coupled plasma phase. Highly nontrivial emergent
phenomena occur in such sQGP and two examples will be discussed in this
contribution: the magnetic component of sQGP that stems from topologically
nontrivial configurations in the gluon sector; and the anomalous chiral
transport that arises as macroscopic manifestation of microscopic chiral
anomaly in the quark sector. For both examples, their important roles in
explaining pertinent heavy-ion data will be emphasized.


title:
Atoms of None of the Elements Ionize While Atoms of Inert Behavior Split
  by Photonic Current

abstract:
  As studied, atoms deal with the positive or negative charge by losing or
gaining an electron. However, the gaseous and solid atoms can execute
interstate electron dynamics. They can also deal with transition states. Solid
atoms can elongate from the east-west poles at the ground surface level. Under
suitable energy, solid atoms can expand, and gaseous atoms can contract. When
the excessive field is intact, flowing inert gas atoms can split. The splitting
inert gas atoms convert into electron streams. Those electron streams carrying
the photons when impinging on the naturally-elongated solid atoms, further
elongation of the atoms takes place. If not, elongated atoms at least deform.
Gaseous atoms can squeeze by the suffering of their lattices. Such behaviors of
the atoms validate that they cannot ionize. On splitting the flowing inert gas
atoms, characteristics of the photons become apparent. Those photons that are
not carried by the electron streams can enter the air medium directly. On
traveling photons in the air medium, their energy dissipates in heat, and their
force confines in the form of a field. On confinement of the field of traveling
photons with the field of air-medium, a glow of light is appeared, which is
better known in plasma. The splitting of inert gas atoms, the carrying of
photons by the electron streams, and the lighting of traveling photons validate
that an electric current is photonic. In various microscopes, the magnification
of an image is based on the resolving power of photons. Photonic current is due
to the propagation of the photons in the structure of the interstate electron
gap. Some well-known principles are also discussed, validating that an electric
current is a photonic current. Indeed, this study brings about profound changes
in science.


title: Gas Kinematics in the HII regions G351.69-1.15 and G351.63-1.25

abstract:
  We probe the structure and kinematics of two neighbouring H II regions
identified as cometary and bipolar, using radio recombination lines (RRL). The
H172{\alpha} RRLs from these H II regions: G351.6-1.15 and G351.6-1.25, are
mapped using GMRT, India. We also detect carbon RRLs C172{\alpha} towards both
these regions. The hydrogen RRLs display the effects of pressure and dynamical
broadening in the line profiles, with the dynamical broadening (15 km/s)
playing a major role in the observed profile of G351.6-1.15. We investigate the
kinematics of molecular gas species towards this H II region from the MALT90
pilot survey. The molecular gas is mostly distributed towards the north and
north-west of the cometary head. The molecular line profiles indicate
signatures of turbulence and outflow in this region. The ionized gas at the
cometary tail is blue shifted by 8 km/s with respect to the ambient molecular
cloud, consistent with the earlier proposed champagne flow scenario. The
relative velocity of 5 km/s between the northern and southern lobes of the
bipolar H II region G351.6-1.25 is consistent with the premise that the bipolar
morphology is a result of the expanding ionized lobes within a flat molecular
cloud.


title:
Isospectral Hamiltonian for position-dependent mass for an arbitrary
  quantum system and coherent states

abstract:
  By means of the unitary transformation, a new way for discussing the ordering
prescription of Schrodinger equation with a position-dependent mass (PDM) for
isospectral Hamiltonian operators is presented. We show that the ambiguity
parameter choices in the kinetic part of the Hamiltonian can be explained
through an exact SUSY symmetry as well as a consequence of an accidental
symmetry under the Z2 action. By making use of the unitary transformation, we
construct coherent states for a family of PDM isospectral Hamiltonians from a
suitable choice of ladder operators. We show that these states preserve the
usual structure of Klauder-Perelomov s states and thus saturate and minimize
the generalized position-momentum uncertainty relation (GUR) under some special
restrictions. We show that GURs properties can be used to determine the sign of
the superpotential.


title: How thick are Mercury's polar water ice deposits?

abstract:
  An estimate is made of the thickness of the radar-bright deposits in craters
near to the north pole of Mercury. To construct an objective set of craters for
this measurement, an automated crater finding algorithm is developed and
applied to a digital elevation model based on data from the Mercury Laser
Altimeter on board the MESSENGER spacecraft. This produces a catalogue of 663
craters with diameters exceeding 4 km, northwards of latitude +55 degrees. A
subset of 12 larger, well-sampled and fresh polar craters are selected to
search for correlations between topography and radar same-sense backscatter
cross-section. It is found that the typical excess height associated with the
radar-bright regions within these fresh polar craters is (50+/-35)m. This puts
an approximate upper limit on the total polar water ice deposits on Mercury of
3e15 kg.


title:
Dynamic Attention-controlled Cascaded Shape Regression Exploiting
  Training Data Augmentation and Fuzzy-set Sample Weighting

abstract:
  We present a new Cascaded Shape Regression (CSR) architecture, namely Dynamic
Attention-Controlled CSR (DAC-CSR), for robust facial landmark detection on
unconstrained faces. Our DAC-CSR divides facial landmark detection into three
cascaded sub-tasks: face bounding box refinement, general CSR and
attention-controlled CSR. The first two stages refine initial face bounding
boxes and output intermediate facial landmarks. Then, an online dynamic model
selection method is used to choose appropriate domain-specific CSRs for further
landmark refinement. The key innovation of our DAC-CSR is the fault-tolerant
mechanism, using fuzzy set sample weighting for attention-controlled
domain-specific model training. Moreover, we advocate data augmentation with a
simple but effective 2D profile face generator, and context-aware feature
extraction for better facial feature representation. Experimental results
obtained on challenging datasets demonstrate the merits of our DAC-CSR over the
state-of-the-art.


title: Reinforcement Learning with Unsupervised Auxiliary Tasks

abstract:
  Deep reinforcement learning agents have achieved state-of-the-art results by
directly maximising cumulative reward. However, environments contain a much
wider variety of possible training signals. In this paper, we introduce an
agent that also maximises many other pseudo-reward functions simultaneously by
reinforcement learning. All of these tasks share a common representation that,
like unsupervised learning, continues to develop in the absence of extrinsic
rewards. We also introduce a novel mechanism for focusing this representation
upon extrinsic rewards, so that learning can rapidly adapt to the most relevant
aspects of the actual task. Our agent significantly outperforms the previous
state-of-the-art on Atari, averaging 880\% expert human performance, and a
challenging suite of first-person, three-dimensional \emph{Labyrinth} tasks
leading to a mean speedup in learning of 10$\times$ and averaging 87\% expert
human performance on Labyrinth.


title: On a problem of Kahane in higher dimensions

abstract:
  We characterise those real analytic mappings between any pair of tori which
carry absolutely convergent Fourier series to uniformly convergent Fourier
series via composition. We do this with respect to rectangular summation. We
also investigate uniform convergence with respect to square sums and highlight
the differences which arise.


title: Mode-matching for Optical Antennas

abstract:
  The emission rate of a point dipole can be strongly increased in presence of
a well-designed optical antenna. Yet, optical antenna design is largely based
on radio-frequency rules, ignoring e.g.~ohmic losses and non-negligible field
penetration in metals at optical frequencies. Here we combine reciprocity and
Poynting's theorem to derive a set of optical-frequency antenna design rules
for benchmarking and optimizing the performance of optical antennas driven by
single quantum emitters. Based on these findings a novel plasmonic cavity
antenna design is presented exhibiting a considerably improved performance
compared to a reference two-wire antenna. Our work will be useful for the
design of high-performance optical antennas and nanoresonators for diverse
applications ranging from quantum optics to antenna-enhanced single-emitter
spectroscopy and sensing.


title:
An Open Source, FPGA-based LeKID readout for BLAST-TNG: Pre-flight
  Results

abstract:
  We present a highly frequency multiplexed readout for large-format
superconducting detector arrays intended for use in the next generation of
balloon-borne and space-based sub-millimeter and far-infrared missions. We will
demonstrate this technology on the upcoming NASA Next Generation Balloon-borne
Large Aperture Sub-millimeter Telescope (BLAST-TNG) to measure the polarized
emission of Galactic dust at wavelengths of 250, 350 and 500 microns. The
BLAST-TNG receiver incorporates the first arrays of Lumped Element Kinetic
Inductance Detectors (LeKID) along with the first microwave multiplexing
readout electronics to fly in a space-like environment and will significantly
advance the TRL for these technologies. After the flight of BLAST-TNG, we will
continue to improve the performance of the detectors and readout electronics
for the next generation of balloon-borne instruments and for use in a future
FIR Surveyor.


title:
Bootstrapping and Sample Splitting For High-Dimensional, Assumption-Free
  Inference

abstract:
  Several new methods have been proposed for performing valid inference after
model selection. An older method is sampling splitting: use part of the data
for model selection and part for inference. In this paper we revisit sample
splitting combined with the bootstrap (or the Normal approximation). We show
that this leads to a simple, assumption-free approach to inference and we
establish results on the accuracy of the method. In fact, we find new bounds on
the accuracy of the bootstrap and the Normal approximation for general
nonlinear parameters with increasing dimension which we then use to assess the
accuracy of regression inference. We show that an alternative, called the image
bootstrap, has higher coverage accuracy at the cost of more computation. We
define new parameters that measure variable importance and that can be inferred
with greater accuracy than the usual regression coefficients. There is a
inference-prediction tradeoff: splitting increases the accuracy and robustness
of inference but can decrease the accuracy of the predictions.


title:
The ZipML Framework for Training Models with End-to-End Low Precision:
  The Cans, the Cannots, and a Little Bit of Deep Learning

abstract:
  Recently there has been significant interest in training machine-learning
models at low precision: by reducing precision, one can reduce computation and
communication by one order of magnitude. We examine training at reduced
precision, both from a theoretical and practical perspective, and ask: is it
possible to train models at end-to-end low precision with provable guarantees?
Can this lead to consistent order-of-magnitude speedups? We present a framework
called ZipML to answer these questions. For linear models, the answer is yes.
We develop a simple framework based on one simple but novel strategy called
double sampling. Our framework is able to execute training at low precision
with no bias, guaranteeing convergence, whereas naive quantization would
introduce significant bias. We validate our framework across a range of
applications, and show that it enables an FPGA prototype that is up to 6.5x
faster than an implementation using full 32-bit precision. We further develop a
variance-optimal stochastic quantization strategy and show that it can make a
significant difference in a variety of settings. When applied to linear models
together with double sampling, we save up to another 1.7x in data movement
compared with uniform quantization. When training deep networks with quantized
models, we achieve higher accuracy than the state-of-the-art XNOR-Net. Finally,
we extend our framework through approximation to non-linear models, such as
SVM. We show that, although using low-precision data induces bias, we can
appropriately bound and control the bias. We find in practice 8-bit precision
is often sufficient to converge to the correct solution. Interestingly,
however, in practice we notice that our framework does not always outperform
the naive rounding approach. We discuss this negative result in detail.


title:
Graphitic C3N4 Sensitized TiO2 Nanotube Layers: A Visible Light
  Activated Efficient Antimicrobial Platform

abstract:
  In this work, we introduce a facile procedure to graft a thin graphitic C3N4
(g-C3N4) layer on aligned TiO2 nanotube arrays (TiNT) by one-step chemical
vapor deposition (CVD) approach. This provides a platform to enhance the
visible-light response of TiO2 nanotubes for antimicrobial applications. The
formed g- C3N4/TiNT binary nanocomposite exhibits excellent bactericidal
efficiency against E. coli as a visiblelight activated antibacterial coating.


title: The Dilating Method for Cayley digraphs on finite Abelian groups

abstract:
  A geometric method for obtaining an infinite family of Cayley digraphs of
constant density on finite Abelian groups is presented. The method works for
any given degree and it is based on consecutive dilates of a minimum distance
diagram associated with a given initial Cayley digraph. The method is used to
obtain infinite families of dense or asymptotically dense Cayley digraphs. In
particular, for degree $d=3$, an infinite family of maximum known density is
proposed.


title: Correcting biased observation model error in data assimilation

abstract:
  While the formulation of most data assimilation schemes assumes an unbiased
observation model error, in real applications, model error with nontrivial
biases is unavoidable. A practical example is the error in the radiative
transfer model (which is used to assimilate satellite measurements) in the
presence of clouds. As a consequence, many (in fact 99\%) of the cloudy
observed measurements are not being used although they may contain useful
information. This paper presents a novel nonparametric Bayesian scheme which is
able to learn the observation model error distribution and correct the bias in
incoming observations. This scheme can be used in tandem with any data
assimilation forecasting system. The proposed model error estimator uses
nonparametric likelihood functions constructed with data-driven basis functions
based on the theory of kernel embeddings of conditional distributions developed
in the machine learning community. Numerically, we show positive results with
two examples. The first example is designed to produce a bimodality in the
observation model error (typical of "cloudy" observations) by introducing
obstructions to the observations which occur randomly in space and time. The
second example, which is physically more realistic, is to assimilate cloudy
satellite brightness temperature-like quantities, generated from a stochastic
cloud model for tropical convection and a simple radiative transfer model.


title: Synchronization in networks with multiple interaction layers

abstract:
  The structure of many real-world systems is best captured by networks
consisting of several interaction layers. Understanding how a multi-layered
structure of connections affects the synchronization properties of dynamical
systems evolving on top of it is a highly relevant endeavour in mathematics and
physics, and has potential applications to several societally relevant topics,
such as power grids engineering and neural dynamics. We propose a general
framework to assess stability of the synchronized state in networks with
multiple interaction layers, deriving a necessary condition that generalizes
the Master Stability Function approach. We validate our method applying it to a
network of R\"ossler oscillators with a double layer of interactions, and show
that highly rich phenomenology emerges. This includes cases where the stability
of synchronization can be induced even if both layers would have individually
induced unstable synchrony, an effect genuinely due to the true multi-layer
structure of the interactions amongst the units in the network.


title: A Semidefinite Program for Structured Blockmodels

abstract:
  Semidefinite programs have recently been developed for the problem of
community detection, which may be viewed as a special case of the stochastic
blockmodel. Here, we develop a semidefinite program that can be tailored to
other instances of the blockmodel, such as non-assortative networks and
overlapping communities. We establish label recovery in sparse settings, with
conditions that are analogous to recent results for community detection. In
settings where the data is not generated by a blockmodel, we give an oracle
inequality that bounds excess risk relative to the best blockmodel
approximation. Simulations are presented for community detection, for
overlapping communities, and for latent space models.


title: Thermally assisted ordering in Mott insulators

abstract:
  Ginzburg-Landau theory describes phase transitions as the competition between
energy and entropy: The ordered phase has lower energy, while the disordered
phase has larger entropy. When heating the system, ordering is reduced
entropically until it vanishes at the critical temperature. This established
picture implicitly assumes that the energy difference between ordered and
disordered phase does not change with temperature. We show that for the Mott
insulator KCuF3 this assumption is strongly violated: thermal expansion
energetically stabilizes the orbitally-ordered phase to such and extent that no
phase transition is observed. This new mechanism explains not only the absence
of a phase transition in KCuF3 but even suggests the possibility of an inverted
transition in closed-shell systems, where the ordered phase emerges only at
high temperatures.


title: Search for aluminium monoxide in the winds of oxygen-rich AGB stars

abstract:
  Aluminium monoxide, AlO, is likely efficiently depleted from the gas around
oxygen-rich evolved stars to form alumina clusters and dust seeds. Its presence
in the extended atmospheres of evolved stars has been derived from optical
spectroscopy. More recently, AlO gas was also detected at long wavelengths
around the supergiant VY CMa and the oxygen-rich asymptotic giant branch (AGB)
star o Cet (Mira A). In search of AlO, we mined data obtained with APEX, the
IRAM 30m telescope, Herschel/HIFI, SMA, and ALMA, which were primarily aimed at
studying other molecular species. We report here on observations of AlO towards
a sample of eight oxygen-rich AGB stars in different rotational transitions, up
to seven for some stars. We present definite detections of one rotational
transition of AlO for o Cet and R Aqr, and tentative detections of one
transition for R Dor and o Cet, and two for IK Tau and W Hya. The presented
spectra of WX Psc, R Cas, and TX Cam show no signature of AlO. For o Cet, R
Aqr, and IK Tau, we find that the AlO(N=9-8) emission likely traces the inner
parts of the wind, out to only a few tens of AU, where the gas has not yet
reached its terminal velocity. The conclusive detections of AlO emission in the
case of o Cet and R Aqr confirm the presence of AlO gas in outflows of AGB
stars. The tentative detections further support this. Since most of the
observations presented in this study were obtained with stronger emission from
other species than AlO in mind, observations with higher sensitivity in
combination with high angular resolution will improve our understanding of the
presence and behaviour of AlO. From the current data sets we cannot firmly
conclude whether there is a direct correlation between the wind properties and
the detection rate of AlO emission. We hope that this study can serve as a
stimulus to perform sample studies in search of AlO in oxygen-rich outflows.


title: Big Outliers Versus Heavy Tails: what to use?

abstract:
  A possibility to give strong mathematical definitions of outliers and heavy
tailed distributions or their modification is discussed. Some alternatives for
the notion of tail index are proposed. Key words: outliers, heavy tails, tail
index.


title:
Capacity estimation and verification of quantum channels with
  arbitrarily correlated errors

abstract:
  One of the main figures of merit for quantum memories and quantum
communication devices is their quantum capacity. It has been studied for
arbitrary kinds of quantum channels, but its practical estimation has so far
been limited to devices that implement independent and identically distributed
(i.i.d.) quantum channels, where each qubit is affected by the same noise
process. Real devices, however, typically exhibit correlated errors.
  Here, we overcome this limitation by presenting protocols that estimate a
channel's one-shot quantum capacity for the case where the device acts on (an
arbitrary number of) qubits. The one-shot quantum capacity quantifies a
device's ability to store or communicate quantum information, even if there are
correlated errors across the different qubits.
  We present a protocol which is easy to implement and which comes in two
versions. The first version estimates the one-shot quantum capacity by
preparing and measuring in two different bases, where all involved qubits are
used as test qubits. The second version verifies on-the-fly that a channel's
one-shot quantum capacity exceeds a minimal tolerated value while storing or
communicating data, therefore combining test qubits and data qubits in one
protocol. We discuss the performance of our method using simple examples, such
as the dephasing channel for which our method is asymptotically optimal.
Finally, we apply our method to estimate the one-shot capacity in an experiment
using a transmon qubit.


title: Resonant Floquet Scattering of Ultracold Atoms

abstract:
  In systems of ultracold atoms, pairwise interactions are resonantly enhanced
by the application of an oscillating magnetic field that is parallel to the
spin-quantization axis of the atoms. The resonance occurs when the frequency of
the applied field is precisely tuned near the transition frequency between the
scattering atoms and a diatomic molecule. The resulting cross section can be
made more than two orders of magnitude larger than the cross section in the
absence of the oscillating field. The low momentum resonance properties have a
universal description that is independent of the atomic species. To arrive at
these conclusions, we first develop a formal extension of Floquet theory to
describe scattering of atoms with time-periodic, short-range interaction
potentials. We then calculate the atomic scattering properties by modeling the
atomic interactions with a square well potential with oscillating depth and
then explicitly solving the time-dependent Schrodinger equation. We then apply
the Floquet formalism to the case of atoms scattering with a contact
interaction described by a time-periodic scattering length, obtaining analytic
results that agree with those obtained by solving the time-dependent
Schrodinger equation.


title:
On the Spectral Efficiency and Security Enhancements of NOMA Assisted
  Multicast-Unicast Streaming

abstract:
  This paper considers the application of non-orthogonal multiple access (NOMA)
to a multi-user network with mixed multicasting and unicasting traffic. The
proposed design of beamforming and power allocation ensures that the unicasting
performance is improved while maintaining the reception reliability of
multicasting. Both analytical and simulation results are provided to
demonstrate that the use of the NOMA assisted multicast-unicast scheme yields a
significant improvement in spectral efficiency compared to orthogonal multiple
access (OMA) schemes which realize multicasting and unicasting services
separately. Since unicasting messages are broadcasted to all the users, how the
use of NOMA can prevent those multicasting receivers intercepting the
unicasting messages is also investigated, where it is shown that the secrecy
unicasting rate achieved by NOMA is always larger than or equal to that of OMA.
This security gain is mainly due to the fact that the multicasting messages can
be used as jamming signals to prevent potential eavesdropping when the
multicasting and unicasting messages are superimposed together following the
NOMA principle.


title: Kalman-Takens filtering in the presence of dynamical noise

abstract:
  The use of data assimilation for the merging of observed data with dynamical
models is becoming standard in modern physics. If a parametric model is known,
methods such as Kalman filtering have been developed for this purpose. If no
model is known, a hybrid Kalman-Takens method has been recently introduced, in
order to exploit the advantages of optimal filtering in a nonparametric
setting. This procedure replaces the parametric model with dynamics
reconstructed from delay coordinates, while using the Kalman update formulation
to assimilate new observations. We find that this hybrid approach results in
comparable efficiency to parametric methods in identifying underlying dynamics,
even in the presence of dynamical noise. By combining the Kalman-Takens method
with an adaptive filtering procedure we are able to estimate the statistics of
the observational and dynamical noise. This solves a long standing problem of
separating dynamical and observational noise in time series data, which is
especially challenging when no dynamical model is specified.


title:
Multipliers: comparison of Fourier transformation based method and
  Synopsys design technique for up to 32 bits inputs in regular and saturation
  arithmetics

abstract:
  The technique for hardware multiplication based upon Fourier transformation
has been introduced. The technique has the highest efficiency on multiplication
units with up to 8 bit range. Each multiplication unit is realized on base of
the minimized Boolean functions. Experimental data showed that this technique
the multiplication process speed up to 20% higher for 2-8 bit range of input
operands and up to 3% higher for 8-32 bit range of input operands than
analogues designed by Synopsys technique.