title: A Synthetic Version of Lie's Second Theorem

abstract:
  We formulate and prove a twofold generalisation of Lie's second theorem that
integrates homomorphisms between formal group laws to homomorphisms between Lie
groups. Firstly we generalise classical Lie theory by replacing groups with
categories. Secondly we include categories whose underlying spaces are not
smooth manifolds. The main intended application is when we replace the category
of smooth manifolds with a well-adapted model of synthetic differential
geometry. In addition we provide an axiomatic system that specifies the
abstract structures that are required to prove Lie's second theorem. As a part
of this abstract structure we define the notion of enriched mono-coreflective
subcategory which makes precise the notion of a subcategory of local models.


title: Criteria for folding in structure-based models of proteins

abstract:
  In structure-based models of proteins, one often assumes that folding is
accomplished when all contacts are established. This assumption may frequently
lead to a conceptual problem that folding takes place in a temperature region
of very low thermodynamic stability, especially when the contact map used is
too sparse. We consider six different structure-based models and show that
allowing for a small, but model-dependent, percentage of the native contacts
not being established boosts the folding temperature substantially while
affecting the time scales of folding only in a minor way. We also compare other
properties of the six models. We show that the choice of the description of the
backbone stiffness has a substantial effect on the values of characteristic
temperatures that relate both to equilibrium and kinetic properties. Models
without any backbone stiffness (like the self-organized polymer) are found to
perform similar to those with the stiffness, including in the studies of
stretching.


title:
Vibrational renormalisation of the electronic band gap in hexagonal and
  cubic ice

abstract:
  Electron-phonon coupling in hexagonal and cubic water ice is studied using
first-principles quantum mechanical methods. We consider 29 distinct hexagonal
and cubic ice proton-orderings with up to 192 molecules in the simulation cell
to account for proton-disorder. We find quantum zero-point vibrational
corrections to the minimum electronic band gaps ranging from -1.5 to -1.7 eV,
which leads to improved agreement between calculated and experimental band
gaps. Anharmonic nuclear vibrations play a negligible role in determining the
gaps. Deuterated ice has a smaller band-gap correction at zero-temperature of
-1.2 to -1.4eV. Vibrations reduce the differences between the electronic band
gaps of different proton-orderings from around 0.17 eV to less than 0.05 eV, so
that the electronic band gaps of hexagonal and cubic ice are almost independent
of the proton-ordering when quantum nuclear vibrations are taken into account.
The comparatively small reduction in the band gap over the temperature range
0-240 K of around 0.1 eV does not depend on the proton ordering, or whether the
ice is protiated or deuterated, or hexagonal or cubic. We explain this in terms
of the atomistic origin of the strong electron-phonon coupling in ice.


title:
OPESCI-FD: Automatic Code Generation Package for Finite Difference
  Models

abstract:
  In this project, we introduce OPESCI-FD, a Python package built on symbolic
mathematics to automatically generate Finite Difference models from a
high-level description of the model equations. We investigate applying this
framework to generate the propagator program used in seismic imaging. We
implement the 3D velocity-stress FD scheme as an example and demonstrate the
advantages of usability, flexibility and accuracy of the framework. The design
of OPESCI-FD aims to allow rapid development, analysis and optimisation of
Finite Difference programs. OPESCI-FD is the foundation for continuing
development by the OPESCI project team, building on the research presented in
this report. This report concludes by reviewing the further developments that
are already under way, as well as the scope for extension to cater for other
equations and numerical schemes.


title: Sur les estimations du type sup+inf et sup*inf

abstract:
  We give some results on a priori estimates and on estimates of type sup+inf
and sup*inf.


title: Generalized geometric vacua with eight supercharges

abstract:
  We investigate compactifications of type II and M-theory down to $AdS_5$ with
generic fluxes that preserve eight supercharges, in the framework of
Exceptional Generalized Geometry. The geometric data and gauge fields on the
internal manifold are encoded in a pair of generalized structures corresponding
to the vector and hyper-multiplets of the reduced five-dimensional
supergravity. Supersymmetry translates into integrability conditions for these
structures, generalizing, in the case of type IIB, the Sasaki-Einstein
conditions. We show that the ten and eleven-dimensional type IIB and M-theory
Killing-spinor equations specialized to a warped $AdS_5$ background imply the
generalized integrability conditions.


title: On duality of algebraic quantum groupoids

abstract:
  Like quantum groups, quantum groupoids frequently appear in pairs of mutually
dual objects. We develop a general Pontrjagin duality theory for quantum
groupoids in the algebraic setting that extends Van Daele's duality theory for
multiplier Hopf algebras and overcomes the finiteness restrictions of the
approach of Kadison, Szlach\'anyi, B\"ohm and Schauenburg. Our construction is
based on the integration theory for multiplier Hopf algebroids and yields, as a
corollary, a duality theory for weak multiplier Hopf algebras with integrals.
We compute the duals in several examples and introduce morphisms of multiplier
Hopf algebroids to succinctly describe their structure. Moreover, we show that
such morphisms preserve the antipode.


title: Spinors, Lagrangians and rank 2 Higgs bundles

abstract:
  The paper considers the Dirac operator on a Riemann surface coupled to a
symplectic holomorphic vector bundle W. Each spinor in the null-space generates
through the moment map a Higgs bundle, and varying W one obtains a holomorphic
Lagrangian subvariety in the moduli space of Higgs bundles. Applying this to
the irreducible symplectic representations of SL(2) we obtain Lagrangian
submanifolds of the rank 2 moduli space which link up with m-period points on
the Prym variety of the spectral curve as well as Brill-Noether loci on the
moduli space of semistable bundles. The case of genus 2 is investigated in some
detail.


title:
$k$-core percolation on complex networks: Comparing random, localized
  and targeted attacks

abstract:
  The type of malicious attack inflicting on networks greatly influences their
stability under ordinary percolation in which a node fails when it becomes
disconnected from the giant component. Here we study its generalization,
$k$-core percolation, in which a node fails when it loses connection to a
threshold $k$ number of neighbors. We study and compare analytically and by
numerical simulations of $k$-core percolation the stability of networks under
random attacks (RA), localized attacks (LA) and targeted attacks (TA),
respectively. By mapping a network under LA or TA into an equivalent network
under RA, we find that in both single and interdependent networks, TA exerts
the greatest damage to the core structure of a network. We also find that for
Erd\H{o}s-R\'{e}nyi (ER) networks, LA and RA exert equal damage to the core
structure whereas for scale-free (SF) networks, LA exerts much more damage than
RA does to the core structure.


title: Cross-intersecting pairs of hypergraphs

abstract:
  Two hypergraphs $H_1,\ H_2$ are called {\em cross-intersecting} if $e_1 \cap
e_2 \neq \emptyset$ for every pair of edges $e_1 \in H_1,~e_2 \in H_2$. Each of
the hypergraphs is then said to {\em block} the other. Given parameters $n,r,m$
we determine the maximal size of a sub-hypergraph of $[n]^r$ (meaning that it
is $r$-partite, with all sides of size $n$) for which there exists a blocking
sub-hypergraph of $[n]^r$ of size $m$. The answer involves a fractal-like (that
is, self-similar) sequence, first studied by Knuth. We also study the same
question with $\binom{n}{r}$ replacing $[n]^r$.


title: Non-linear Higgs portal to Dark Matter

abstract:
  The Higgs portal to scalar Dark Matter is considered in the context of
non-linearly realised electroweak symmetry breaking. We determine the
interactions of gauge bosons and the physical Higgs particle $h$ to a scalar
singlet Dark Matter candidate $S$ in an effective description. The main
phenomenological differences with respect to the standard scenario can be seen
in the Dark Matter relic abundance, in direct/indirect searches and in signals
at colliders.


title:
On horizontal Hardy, Rellich, Caffarelli-Kohn-Nirenberg and
  $p$-sub-Laplacian inequalities on stratified groups

abstract:
  In this paper, we present a version of horizontal weighted Hardy-Rellich type
and Caffarelli-Kohn-Nirenberg type inequalities on stratified groups and study
some of their consequences. Our results reflect on many results previously
known in special cases. Moreover, a new simple proof of the Badiale-Tarantello
conjecture [2] on the best constant of a Hardy type inequality is provided. We
also show a family of Poincar\'e inequalities as well as inequalities involving
the weighted and unweighted $p$-sub-Laplacians.


title: Geodesic motion in a stationary dihole spacetime

abstract:
  The knowledge of the properties of the different exact solutions modeling
binary systems, is a necessary step towards the classification of physically
suitable solutions and its corresponding limits of applicability. In the
present paper, we perform an analysis of the geodesics around two
counter--rotating Kerr--Newman black holes endowed with opposite electric
charges, which achieve equilibrium by means of a strut between their
constituents. We find that bounded and unbounded orbits are possible. However,
test particles may cross between the black holes only if their angular momentum
equals zero, otherwise, there exist a repulsive potential, which prohibits such
orbits. Two important aspects are pointed out for these trajectories: ({\it i})
the motion of photons is affected once crossing the strut; and ({\it ii})
massive particles exhibit oscillatory motion, as a first analog of the Sitnikov
problem in general relativity. The radius of the innermost stable circular
orbit as a function of the physical parameters of the black holes is also
investigated.


title: Deep Multi-task Representation Learning: A Tensor Factorisation Approach

abstract:
  Most contemporary multi-task learning methods assume linear models. This
setting is considered shallow in the era of deep learning. In this paper, we
present a new deep multi-task representation learning framework that learns
cross-task sharing structure at every layer in a deep network. Our approach is
based on generalising the matrix factorisation techniques explicitly or
implicitly used by many conventional MTL algorithms to tensor factorisation, to
realise automatic learning of end-to-end knowledge sharing in deep networks.
This is in contrast to existing deep learning approaches that need a
user-defined multi-task sharing strategy. Our approach applies to both
homogeneous and heterogeneous MTL. Experiments demonstrate the efficacy of our
deep multi-task representation learning in terms of both higher accuracy and
fewer design choices.


title:
Wave-mixing origin and optimization in single and compact aluminum
  nanoantennas

abstract:
  The outstanding optical properties for plasmon resonances in noble metal
nanoparticles enable the observation of non-linear optical processes such as
second-harmonic generation (SHG) at the nanoscale. Here, we investigate the SHG
process in single rectangular aluminum nanoantennas and demonstrate that i) a
doubly resonant regime can be achieved in very compact nanostructures, yielding
a 7.5 enhancement compared to singly resonant structures and ii) the
\(\chi_{\perp\perp\perp}\) local surface and \(\gamma_{bulk}\) nonlocal bulk
contributions can be separated while imaging resonant nanostructures excited by
a tightly focused beam, provided the \(\chi_{\perp\parallel\parallel}\) local
surface is assumed to be zero, as it is the case in all existing models for
metals. Thanks to the quantitative agreement between experimental and simulated
far-field SHG maps, taking into account the real experimental configuration
(focusing and substrate), we identify the physical origin of the SHG in
aluminum nanoantennas as arising mainly from \(\chi_{\perp\perp\perp}\) local
surface sources.


title:
Note on predictions for $c\bar{s}$ quarkonia using a three-loop static
  potential

abstract:
  We extend our treatment of the the spectroscopy and decays of the
charm-strange quarkonium system to include the effect of using the full
three-loop QCD correction to the static short distance potential. As before,
our potential model consists of the relativistic kinetic energy term, a scalar
linear confining term including its relativistic corrections and the
perturbative QCD spin-dependent terms. A set of unperturbed wave functions for
the various states is obtained using a variational technique that is further
constrained by requiring that the wave functions also satisfy the relativistic
virial theorem. These are then used in a perturbative treatment of the
potential to fit the mass spectrum of the $c\bar{s}$ system and calculate the
radiative decay widths. Our results accurately describe the $D_s$ spectrum and
are compatible with the little data that is available for the radiative decays
of the $D_s$ states.


title: Bayesian Hyperparameter Optimization for Ensemble Learning

abstract:
  In this paper, we bridge the gap between hyperparameter optimization and
ensemble learning by performing Bayesian optimization of an ensemble with
regards to its hyperparameters. Our method consists in building a fixed-size
ensemble, optimizing the configuration of one classifier of the ensemble at
each iteration of the hyperparameter optimization algorithm, taking into
consideration the interaction with the other models when evaluating potential
performances. We also consider the case where the ensemble is to be
reconstructed at the end of the hyperparameter optimization phase, through a
greedy selection over the pool of models generated during the optimization. We
study the performance of our proposed method on three different hyperparameter
spaces, showing that our approach is better than both the best single model and
a greedy ensemble construction over the models produced by a standard Bayesian
optimization.


title:
C*-simplicity of free products with amalgamation and radical classes of
  groups

abstract:
  We give new characterizations to ensure that a free product of groups with
amalgamation has a simple reduced group C*-algebra, and provide a concrete
example of an amalgam with trivial kernel, such that its reduced group
C*-algebra has a unique tracial state, but is not simple. Moreover, we show
that there is a radical class of groups for which the reduced group C*-algebra
of any group is simple precisely when the group has a trivial radical
corresponding to this class.


title: Soft Covering with High Probability

abstract:
  Wyner's soft-covering lemma is the central analysis step for achievability
proofs of information theoretic security, resolvability, and channel synthesis.
It can also be used for simple achievability proofs in lossy source coding.
This work sharpens the claim of soft-covering by moving away from an expected
value analysis. Instead, a random codebook is shown to achieve the
soft-covering phenomenon with high probability. The probability of failure is
super-exponentially small in the block-length, enabling many applications
through the union bound. This work gives bounds for both the exponential decay
rate of total variation and the second-order codebook rate for soft covering.


title: On weighted parametric tests

abstract:
  We describe a general framework for weighted parametric multiple test
procedures based on the closure principle. We utilize general weighting
strategies that can reflect complex study objectives and include many
procedures in the literature as special cases. The proposed weighted parametric
tests bridge the gap between rejection rules using either adjusted significance
levels or adjusted $p$-values. This connection is possible by allowing
intersection hypotheses to be tested at level smaller than $\alpha$, which may
be needed for certain study considerations. For such cases we introduce a
subclass of exact $\alpha$-level parametric tests which satisfy the consonance
property. When only subsets of test statistics are correlated, a new procedure
is proposed to fully utilize the parametric assumptions within each subset. We
illustrate the proposed weighted parametric tests using a clinical trial
example.


title: Stochastic Variance Reduction Methods for Saddle-Point Problems

abstract:
  We consider convex-concave saddle-point problems where the objective
functions may be split in many components, and extend recent stochastic
variance reduction methods (such as SVRG or SAGA) to provide the first
large-scale linearly convergent algorithms for this class of problems which is
common in machine learning. While the algorithmic extension is straightforward,
it comes with challenges and opportunities: (a) the convex minimization
analysis does not apply and we use the notion of monotone operators to prove
convergence, showing in particular that the same algorithm applies to a larger
class of problems, such as variational inequalities, (b) there are two notions
of splits, in terms of functions, or in terms of partial derivatives, (c) the
split does need to be done with convex-concave terms, (d) non-uniform sampling
is key to an efficient algorithm, both in theory and practice, and (e) these
incremental algorithms can be easily accelerated using a simple extension of
the "catalyst" framework, leading to an algorithm which is always superior to
accelerated batch algorithms.


title:
ImageCL: An Image Processing Language for Performance Portability on
  Heterogeneous Systems

abstract:
  Modern computer systems typically conbine multicore CPUs with accelerators
like GPUs for inproved performance and energy efficiency. However, these sys-
tems suffer from poor performance portability, code tuned for one device must
be retuned to achieve high performance on another. Image processing is increas-
ing in importance , with applications ranging from seismology and medicine to
Photoshop. Based on our experience with medical image processing, we propose
ImageCL, a high-level domain-specific language and source-to-source compiler,
targeting heterogeneous hardware. ImageCL resembles OpenCL, but abstracts away
per- formance optimization details, allowing the programmer to focus on
algorithm development, rather than performance tuning. The latter is left to
our source-to- source compiler and auto-tuner. From high-level ImageCL kernels,
our source- to-source compiler can generate multiple OpenCL implementations
with different optimizations applied. We rely on auto-tuning rather than
machine models or ex- pert programmer knowledge to determine which
optimizations to apply, making our tuning procedure highly robust. Furthermore,
we can generate high perform- ing implementations for different devices from a
single source code, thereby im- proving performance portability. We evaluate
our approach on three image processing benchmarks, on different GPU and CPU
devices, and are able to outperform other state of the art solutions in several
cases, achieving speedups of up to 4.57x.


title:
Properties of optimizers of the principal eigenvalue with indefinite
  weight and Robin conditions

abstract:
  In this paper, we are interested in the analysis of a well-known free
boundary/shape optimization problem motivated by some issues arising in
population dynamics. The question is to determine optimal spatial arrangements
of favorable and unfavorable regions for a species to survive. The mathematical
formulation of the model leads to an indefinite weight linear eigenvalueproblem
in a fixed box $\Omega$ and we consider the general case of Robin boundary
conditions on $\partial\Omega$. It is well known that it suffices to consider
{\it bang-bang} weights taking two values of different signs, that can be
parametrized by the characteristic function of the subset $E$ of $\Omega$ on
which resources are located. Therefore, the optimal spatial arrangement is
obtained by minimizing the positive principal eigenvalue with respect to $E$,
under a volume constraint. By using symmetrization techniques, as well as
necessary optimality conditions, we prove new qualitative results on the
solutions. Namely, we completely solve the problem in dimension 1, we prove the
counter-intuitive result that the ball is almost never a solution in dimension
2 or higher, despite what suggest the numerical simulations. We also introduce
a new rearrangement in the ball allowing to get a better candidate than the
ball for optimality when Neumann boundary conditions are imposed. We also
provide numerical illustrations of our results and of the optimal
configurations.


title: Sparse bilinear forms for Bochner Riesz multipliers and applications

abstract:
  We use the very recent approach developed by Lacey in [23] and extended by
Bernicot-Frey-Petermichl in [3], in order to control Bochner-Riesz operators by
a sparse bilinear form. In this way, new quantitative weighted estimates, as
well as vector-valued inequalities are deduced.


title:
Ristretto: Hardware-Oriented Approximation of Convolutional Neural
  Networks

abstract:
  Convolutional neural networks (CNN) have achieved major breakthroughs in
recent years. Their performance in computer vision have matched and in some
areas even surpassed human capabilities. Deep neural networks can capture
complex non-linear features; however this ability comes at the cost of high
computational and memory requirements. State-of-art networks require billions
of arithmetic operations and millions of parameters. To enable embedded devices
such as smartphones, Google glasses and monitoring cameras with the astonishing
power of deep learning, dedicated hardware accelerators can be used to decrease
both execution time and power consumption. In applications where fast
connection to the cloud is not guaranteed or where privacy is important,
computation needs to be done locally. Many hardware accelerators for deep
neural networks have been proposed recently. A first important step of
accelerator design is hardware-oriented approximation of deep networks, which
enables energy-efficient inference. We present Ristretto, a fast and automated
framework for CNN approximation. Ristretto simulates the hardware arithmetic of
a custom hardware accelerator. The framework reduces the bit-width of network
parameters and outputs of resource-intense layers, which reduces the chip area
for multiplication units significantly. Alternatively, Ristretto can remove the
need for multipliers altogether, resulting in an adder-only arithmetic. The
tool fine-tunes trimmed networks to achieve high classification accuracy. Since
training of deep neural networks can be time-consuming, Ristretto uses highly
optimized routines which run on the GPU. This enables fast compression of any
given network. Given a maximum tolerance of 1%, Ristretto can successfully
condense CaffeNet and SqueezeNet to 8-bit. The code for Ristretto is available.


title: Three supernova shells around a young star cluster in M33

abstract:
  Using a specialized technique sensitive to the presence of expanding ionized
gas we have detected a set of three concentric expanding shells in an HII
region in the nearby spiral galaxy M33. After mapping the kinematics in
H{\alpha} with Fabry-Perot spectroscopy we used slit spectra to measure the
intensities of the [SII] doublet at {\lambda}{\lambda} 671.9, 673.1 nm and the
[NII] doublet at {\lambda}{\lambda} 645.8, 658.3 nm to corroborate the
kinematics and apply diagnostic tests using line ratios. These showed that the
expanding shells are shock dominated as would be the case if they had
originated with supernova explosions. Estimating their kinetic energies we find
fairly low values, indicating a fairly advanced stage of evolution. We obtain
density, mass and parent star mass estimates, which, along with the kinetic
energies, are inconsistent with the simplest models of shock-interstellar
medium interaction. We propose that the presence and properties of an
inhomogeneous medium offer a scenario which can account for these observations,
and discuss the implications. Comparing our results with data from the
literature supports the combined presence of an HII region and supernova
remnant material at the observed position.


title:
Multiple positive solutions for Kirchhoff type problems involving
  concave and critical nonlinearities in ${R}^3$

abstract:
  In this paper, we consider the multiplicity of solutions for a class of
Kirchhoff type problems with sub-linear and critical terms on an unbounded
domain. With the aid of Ekeland's variational principle and the concentration
compactness principle we prove that the Kirchhoff problem has at least two
solutions.


title:
The Price of Fog: a Data-Driven Study on Caching Architectures in
  Vehicular Networks

abstract:
  Vehicular users are expected to consume large amounts of data, for both
entertainment and navigation purposes. This will put a strain on cellular
networks, which will be able to cope with such a load only if proper caching is
in place, this in turn begs the question of which caching architecture is the
best-suited to deal with vehicular content consumption. In this paper, we
leverage a large-scale, crowd-collected trace to (i) characterize the vehicular
traffic demand, in terms of overall magnitude and content breakup, (ii) assess
how different caching approaches perform against such a real-world load, (iii)
study the effect of recommendation systems and local contents. We define a
price-of-fog metric, expressing the additional caching capacity to deploy when
moving from traditional, centralized caching architectures to a "fog computing"
approach, where caches are closer to the network edge. We find that for
location-specific contents, such as the ones that vehicular users are most
likely to request, such a price almost disappears. Vehicular networks thus make
a strong case for the adoption of mobile-edge caching, as we are able to reap
the benefit thereof -- including a reduction in the distance traveled by data,
within the core network -- with little or no of the associated disadvantages.


title:
Excitation of surface waves on the interfaces of general bi-isotropic
  media

abstract:
  We study theoretically the characteristics of surface waves excited at the
interface between a metal and a general bi-isotropic medium, which includes
isotropic chiral media and Tellegen media as special cases. We derive an
analytical dispersion relation for surface waves, using which we calculate the
effective index and the propagation length numerically. We also calculate the
absorptance, the cross-polarized reflectance and the spatial distribution of
the electromagnetic fields for plane waves incident on a bilayer system
consisting of a metal layer and a bi-isotropic layer in the Kretschmann
configuration, using the invariant imbedding method. The results obtained using
the invariant imbedding method agree with those obtained from the dispersion
relation perfectly. In the case of chiral media, the effective index is an
increasing function of the chirality index, whereas in Tellegen media, it is a
decreasing function of the Tellegen parameter. The propagation length for
surface waves in both cases increase substantially as either the chirality
index or the Tellegen parameter increases. In Tellegen media, it diverges to
infinity when the effective index goes to zero, whereas in chiral media, it
does when the parameters approach the cutoff values where quasi surface waves
are excited. We investigate the characteristics of quasi surface waves excited
when the chirality index is sufficiently large.


title:
Another proof of Moon's theorem on generalised tournament score
  sequences

abstract:
  Landau \cite{Landau1953} showed that a sequence $(d_i)_{i=1}^n$ of integers
is the score sequence of some tournament if and only if $\sum_{i\in J}d_i \geq
\binom{|J|}{2}$ for all $J\subseteq \{1,2,\dots, n\}$, with equality if
$|J|=n$. Moon \cite{Moon63} extended this result to generalised tournaments. We
show how Moon's result can be derived from Landau's result.


title: Dispersing Nanoparticles in a Polymer Film via Solvent Evaporation

abstract:
  Large scale molecular dynamics simulations are used to study the dispersion
of nanoparticles (NPs) in a polymer film during solvent evaporation. As the
solvent evaporates, a dense polymer-rich skin layer forms at the liquid/vapor
interface, which is either NP rich or poor depending on the strength of the
NP/polymer interaction. When the NPs are strongly wet by the polymer, the NPs
accumulate at the interface and form layers. However when the NPs are only
partially wet by the polymer, most NPs are uniformly distributed in the bulk of
the polymer film with the dense skin layer serving as a barrier to prevent the
NPs from moving to the interface. Our results point to a possible route to
employ less favorable NP/polymer interactions and fast solvent evaporation to
uniformly disperse NPs in a polymer film, contrary to the common belief that
strong NP/polymer attractions are needed to make NPs well dispersed in polymer
nanocomposites.


title: R-FCN: Object Detection via Region-based Fully Convolutional Networks

abstract:
  We present region-based, fully convolutional networks for accurate and
efficient object detection. In contrast to previous region-based detectors such
as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of
times, our region-based detector is fully convolutional with almost all
computation shared on the entire image. To achieve this goal, we propose
position-sensitive score maps to address a dilemma between
translation-invariance in image classification and translation-variance in
object detection. Our method can thus naturally adopt fully convolutional image
classifier backbones, such as the latest Residual Networks (ResNets), for
object detection. We show competitive results on the PASCAL VOC datasets (e.g.,
83.6% mAP on the 2007 set) with the 101-layer ResNet. Meanwhile, our result is
achieved at a test-time speed of 170ms per image, 2.5-20x faster than the
Faster R-CNN counterpart. Code is made publicly available at:
https://github.com/daijifeng001/r-fcn


title:
Long-range orders, spin- and orbital-freezing in the two-band Hubbard
  model

abstract:
  We solve the orbitally degenerate two-band Hubbard model within dynamical
mean field theory and map out the instabilities to various symmetry-broken
phases based on an analysis of the corresponding lattice susceptibilities.
Phase diagrams as a function of the Hund coupling parameter J are obtained both
for the model with rotationally invariant interaction and for the model with
Ising-type anisotropy. For negative J, an intra-orbital spin-singlet
superconducting phase appears at low temperatures, while the normal state
properties are characterized by an orbital freezing phenomenon. This is the
negative-J analogue of the recently discovered fluctuating-moment induced
s-wave spin-triplet superconductivity in the spin-freezing regime of
multi-orbital models with J>0.


title: Virtual-detector approach to tunnel ionization and tunneling times

abstract:
  Tunneling times in atomic ionization are studied theoretically by a virtual
detector approach. A virtual detector is a hypothetical device that allows one
to monitor the wave function's density with spatial and temporal resolution
during the ionization process. With this theoretical approach, it becomes
possible to define unique moments when the electron enters and leaves with
highest probability the classically forbidden region from first principles and
a tunneling time can be specified unambiguously. It is shown that neither the
moment when the electron enters the tunneling barrier nor when it leaves the
tunneling barrier coincides with the moment when the external electric field
reaches its maximum. Under the tunneling barrier as well as at the exit the
electron has a nonzero velocity in the electric field direction. This nonzero
exit velocity has to be incorporated when the free motion of the electron is
modeled by classical equations of motion.


title: Fibonacci type presentations and 3-manifolds

abstract:
  We study the cyclic presentations with relators of the form
$x_ix_{i+m}x_{i+k}^{-1}$ and the groups they define. These "groups of Fibonacci
type" were introduced by Johnson and Mawdesley and they generalize the
Fibonacci groups $F(2,n)$ and the Sieradski groups $S(2,n)$. With the exception
of two groups, we classify when these groups are fundamental groups of
3-manifolds, and it turns out that only Fibonacci, Sieradski, and cyclic groups
arise. Using this classification, we completely classify the presentations that
are spines of 3-manifolds, answering a question of Cavicchioli, Hegenbarth, and
Repov\v{s}. When $n$ is even the groups $F(2,n),S(2,n)$ admit alternative
cyclic presentations on $n/2$ generators. We show that these alternative
presentations also arise as spines of 3-manifolds.


title:
Investigation of the bi-drifting subpulses of radio pulsar B1839-04
  utilising the open-source data-analysis project PSRSALSA

abstract:
  The usefulness and versatility of the PSRSALSA open-source pulsar
data-analysis project is demonstrated through an analysis of the radio pulsar
B1839-04. This study focuses on the phenomenon of bi-drifting, an effect where
the drift direction of subpulses is systematically different in different pulse
profile components. Bi-drifting is extremely rare in the pulsar population.
Various tools in PSRSALSA, including those allowing quantification of
periodicities in the subpulse modulation, their flux distribution, and
polarization properties, are exploited to obtain a comprehensive picture of the
radio properties of PSR B1839-04. In particular, the second harmonic in the
fluctuation spectra of the subpulse modulation is exploited to convincingly
demonstrate the existence of bi-drifting. Bi-drifting is confirmed with a
completely independent method allowing the average modulation cycle to be
determined. Polarization measurements were used to obtain a robust constraint
on the magnetic inclination angle of less than 35 deg. Two distinct emission
modes are discovered to be operating, with periodic subpulse modulation being
present only during the weaker mode. Despite the variability of the modulation
cycle and interruption by mode-changes, the modulation pattern responsible for
the bi-drifting is strictly phase locked over a timescale of years such that
the variability is identical in the different components. The phase locking
implies that a single physical origin is responsible for both drift directions.
Phase locking is hard to explain for many models, including those specifically
proposed in the literature to explain bi-drifting, and they are therefore shown
to be implausible. It is argued that within the framework of circulating
beamlets, bi-drifting could occur if the circulation were severely distorted,
possibly by distortions in the magnetic field.


title:
Recent reproducibility estimates indicate that negative evidence is
  observed over 30 times before publication

abstract:
  The Open Science Collaboration recently reported that 36% of published
findings from psychological studies were reproducible by independent
researchers. We can use this information together with Bayes theorem to
estimate the statistical power needed to produce these findings under various
assumptions and calculate the expected distribution of positive and negative
evidence for a range of prior probabilities of the tested hypotheses; and by
comparing this distribution to other findings indicating that >90% of
publications in the psychological literature are statistically significant in
support of the authors hypothesis, we can estimate the magnitude of publication
bias. The results indicate that negative evidence was observed 30--200 times
before one was published.


title:
Nanoimprint of a 3D structure on an optical fiber for light wavefront
  manipulation

abstract:
  Integration of complex photonic structures onto optical fiber facets enables
powerful platforms with unprecedented optical functionalities. Conventional
nanofabrication technologies, however, do not permit viable integration of
complex photonic devices onto optical fibers owing to their low throughput and
high cost. In this paper we report the fabrication of a three dimensional
structure achieved by direct Nanoimprint Lithography on the facet of an optical
fiber. Nanoimprint processes and tools were specifically developed to enable a
high lithographic accuracy and coaxial alignment of the optical device with
respect to the fiber core. To demonstrate the capability of this new approach,
a 3D beam splitter has been designed, imprinted and optically characterized.
Scanning electron microscopy and optical measurements confirmed the excellent
lithographic capabilities of the proposed approach as well as the desired
optical performance of the imprinted structure. The inexpensive solution
presented here should enable advancements in areas such as integrated optics
and sensing, achieving enhanced portability and versatility of fiber optic
components.


title: Statistical Inference for Cluster Trees

abstract:
  A cluster tree provides a highly-interpretable summary of a density function
by representing the hierarchy of its high-density clusters. It is estimated
using the empirical tree, which is the cluster tree constructed from a density
estimator. This paper addresses the basic question of quantifying our
uncertainty by assessing the statistical significance of topological features
of an empirical cluster tree. We first study a variety of metrics that can be
used to compare different trees, analyze their properties and assess their
suitability for inference. We then propose methods to construct and summarize
confidence sets for the unknown true cluster tree. We introduce a partial
ordering on cluster trees which we use to prune some of the statistically
insignificant features of the empirical tree, yielding interpretable and
parsimonious cluster trees. Finally, we illustrate the proposed methods on a
variety of synthetic examples and furthermore demonstrate their utility in the
analysis of a Graft-versus-Host Disease (GvHD) data set.


title: Shape Recognition by Bag of Skeleton-associated Contour Parts

abstract:
  Contour and skeleton are two complementary representations for shape
recognition. However combining them in a principal way is nontrivial, as they
are generally abstracted by different structures (closed string vs graph),
respectively. This paper aims at addressing the shape recognition problem by
combining contour and skeleton according to the correspondence between them.
The correspondence provides a straightforward way to associate skeletal
information with a shape contour. More specifically, we propose a new shape
descriptor. named Skeleton-associated Shape Context (SSC), which captures the
features of a contour fragment associated with skeletal information. Benefited
from the association, the proposed shape descriptor provides the complementary
geometric information from both contour and skeleton parts, including the
spatial distribution and the thickness change along the shape part. To form a
meaningful shape feature vector for an overall shape, the Bag of Features
framework is applied to the SSC descriptors extracted from it. Finally, the
shape feature vector is fed into a linear SVM classifier to recognize the
shape. The encouraging experimental results demonstrate that the proposed way
to combine contour and skeleton is effective for shape recognition, which
achieves the state-of-the-art performances on several standard shape
benchmarks.


title: Is the DBI scalar field as fragile as other $k$-essence fields?

abstract:
  Caustic singularity formations in shift-symmetric $k$-essence and Horndeski
theories on a fixed Minkowski spacetime were recently argued. In $n$
dimensions, this singularity is the $(n-2)$-dimensional plane in spacetime at
which second derivatives of a field diverge and the field loses single-valued
description for its evolution. This does not necessarily imply a pathological
behavior of the system but rather invalidates the effective description. The
effective theory would thus have to be replaced by another to describe the
evolution thereafter. In this paper, adopting the planar-symmetric
$1$+$1$-dimensional approach employed in the original analysis, we seek all
$k$-essence theories in which generic simple wave solutions are free from such
caustic singularities. Contrary to the previous claim, we find that not only
the standard canonical scalar but also the DBI scalar are free from caustics,
as far as planar-symmetric simple wave solutions are concerned. Addition of
shift-symmetric Horndeski terms does not change the conclusion.


title:
Multiple Actions of the Monodromy Matrix in
  $\mathfrak{gl}(2|1)$-Invariant Integrable Models

abstract:
  We study $\mathfrak{gl}(2|1)$ symmetric integrable models solvable by the
nested algebraic Bethe ansatz. Using explicit formulas for the Bethe vectors we
derive the actions of the monodromy matrix entries onto these vectors. We show
that the result of these actions is a finite linear combination of Bethe
vectors. The obtained formulas open a way for studying scalar products of Bethe
vectors.


title: Quantifying the accuracy of approximate diffusions and Markov chains

abstract:
  Markov chains and diffusion processes are indispensable tools in machine
learning and statistics that are used for inference, sampling, and modeling.
With the growth of large-scale datasets, the computational cost associated with
simulating these stochastic processes can be considerable, and many algorithms
have been proposed to approximate the underlying Markov chain or diffusion. A
fundamental question is how the computational savings trade off against the
statistical error incurred due to approximations. This paper develops general
results that address this question. We bound the Wasserstein distance between
the equilibrium distributions of two diffusions as a function of their mixing
rates and the deviation in their drifts. We show that this error bound is tight
in simple Gaussian settings. Our general result on continuous diffusions can be
discretized to provide insights into the computational-statistical trade-off of
Markov chains. As an illustration, we apply our framework to derive
finite-sample error bounds of approximate unadjusted Langevin dynamics. We
characterize computation-constrained settings where, by using fast-to-compute
approximate gradients in the Langevin dynamics, we obtain more accurate samples
compared to using the exact gradients. Finally, as an additional application of
our approach, we quantify the accuracy of approximate zig-zag sampling. Our
theoretical analyses are supported by simulation experiments.


title: Data-driven root-cause analysis for distributed system anomalies

abstract:
  Modern distributed cyber-physical systems encounter a large variety of
anomalies and in many cases, they are vulnerable to catastrophic fault
propagation scenarios due to strong connectivity among the sub-systems. In this
regard, root-cause analysis becomes highly intractable due to complex fault
propagation mechanisms in combination with diverse operating modes. This paper
presents a new data-driven framework for root-cause analysis for addressing
such issues. The framework is based on a spatiotemporal feature extraction
scheme for distributed cyber-physical systems built on the concept of symbolic
dynamics for discovering and representing causal interactions among subsystems
of a complex system. We present two approaches for root-cause analysis, namely
the sequential state switching ($S^3$, based on free energy concept of a
Restricted Boltzmann Machine, RBM) and artificial anomaly association ($A^3$, a
multi-class classification framework using deep neural networks, DNN).
Synthetic data from cases with failed pattern(s) and anomalous node are
simulated to validate the proposed approaches, then compared with the
performance of vector autoregressive (VAR) model-based root-cause analysis.
Real dataset based on Tennessee Eastman process (TEP) is also used for
validation. The results show that: (1) $S^3$ and $A^3$ approaches can obtain
high accuracy in root-cause analysis and successfully handle multiple nominal
operation modes, and (2) the proposed tool-chain is shown to be scalable while
maintaining high accuracy.


title: Fast Randomized Semi-Supervised Clustering

abstract:
  We consider the problem of clustering partially labeled data from a minimal
number of randomly chosen pairwise comparisons between the items. We introduce
an efficient local algorithm based on a power iteration of the non-backtracking
operator and study its performance on a simple model. For the case of two
clusters, we give bounds on the classification error and show that a small
error can be achieved from $O(n)$ randomly chosen measurements, where $n$ is
the number of items in the dataset. Our algorithm is therefore efficient both
in terms of time and space complexities. We also investigate numerically the
performance of the algorithm on synthetic and real world data.


title: Coresets for Scalable Bayesian Logistic Regression

abstract:
  The use of Bayesian methods in large-scale data settings is attractive
because of the rich hierarchical models, uncertainty quantification, and prior
specification they provide. Standard Bayesian inference algorithms are
computationally expensive, however, making their direct application to large
datasets difficult or infeasible. Recent work on scaling Bayesian inference has
focused on modifying the underlying algorithms to, for example, use only a
random data subsample at each iteration. We leverage the insight that data is
often redundant to instead obtain a weighted subset of the data (called a
coreset) that is much smaller than the original dataset. We can then use this
small coreset in any number of existing posterior inference algorithms without
modification. In this paper, we develop an efficient coreset construction
algorithm for Bayesian logistic regression models. We provide theoretical
guarantees on the size and approximation quality of the coreset -- both for
fixed, known datasets, and in expectation for a wide class of data generative
models. Crucially, the proposed approach also permits efficient construction of
the coreset in both streaming and parallel settings, with minimal additional
effort. We demonstrate the efficacy of our approach on a number of synthetic
and real-world datasets, and find that, in practice, the size of the coreset is
independent of the original dataset size. Furthermore, constructing the coreset
takes a negligible amount of time compared to that required to run MCMC on it.


title: Big(ger) Sets: decomposed delta CRDT Sets in Riak

abstract:
  CRDT[24] Sets as implemented in Riak[6] perform poorly for writes, both as
cardinality grows, and for sets larger than 500KB[25]. Riak users wish to
create high cardinality CRDT sets, and expect better than O(n) performance for
individual insert and remove operations. By decomposing a CRDT set on disk, and
employing delta-replication[2], we can achieve far better performance than just
delta replication alone: relative to the size of causal metadata, not the
cardinality of the set, and we can support sets that are 100s times the size of
Riak sets, while still providing the same level of consistency. There is a
trade-off in read performance but we expect it is mitigated by enabling queries
on sets.


title: Extension of valuations in characteristic one

abstract:
  We develop an extension of valuations theorem for suitable extensions of
idempotent semirings. As an application, we give a new proof for the classical
case of fields. Along the way, we develop characteristic one analogues of some
central results in the theory of valuation rings.


title: Lifting Primordial Non-Gaussianity Above the Noise

abstract:
  Primordial non-Gaussianity (PNG) in Large Scale Structures is obfuscated by
the many additional sources of non-linearity. Within the Effective Field Theory
approach to Standard Perturbation Theory, we show that matter non-linearities
in the bispectrum can be modeled sufficiently well to strengthen current bounds
with near future surveys, such as Euclid. We find that the EFT corrections are
crucial to this improvement in sensitivity. Yet, our understanding of
non-linearities is still insufficient to reach important theoretical benchmarks
for equilateral PNG, while, for local PNG, our forecast is more optimistic. We
consistently account for the theoretical error intrinsic to the perturbative
approach and discuss the details of its implementation in Fisher forecasts.


title:
The effect of gravitational spin-orbit coupling on the circular photon
  orbit in the Schwarzschild geometry

abstract:
  The (1, 0)+(0, 1) representation of the group SL(2, C) provides a
six-component spinor equivalent to the electromagnetic field tensor. By means
of the (1, 0)+(0, 1) description, one can treat the photon field in curved
spacetime via spin connection and the tetrad formalism, which is of great
advantage to study the gravitational spin-orbit coupling of photons. Once the
gravitational spin-orbit coupling is taken into account, the traditional radius
of the circular photon orbit in the Schwarzschild geometry should be replaced
with two different radiuses corresponding to the photons with the helicities of
+1 and -1, respectively. Owing to the splitting of energy levels induced by the
spin-orbit coupling, photons (from Hawking radiations, say) escaping from a
Schwarzschild black hole are partially polarized, provided that their initial
velocities possess nonzero tangential components.


title: On quantum groups associated to a pair of preregular forms

abstract:
  We define the universal quantum group $\mathcal{H}$ that preserves a pair of
Hopf comodule maps, whose underlying vector space maps are preregular forms
defined on dual vector spaces. This generalizes the construction of Bichon and
Dubois-Violette (2013), where the target of these comodule maps are the ground
field. We also recover the quantum groups introduced by Dubois-Violette and
Launer (1990), by Takeuchi (1990), by Artin, Schelter, and Tate (1991), and by
Mrozinski (2014), via our construction. As a consequence, we obtain an explicit
presentation of a universal quantum group that coacts simultaneously on a pair
of $N$-Koszul Artin-Schelter regular algebras with arbitrary quantum
determinant.


title: Hedging with Small Uncertainty Aversion

abstract:
  We study the pricing and hedging of derivative securities with uncertainty
about the volatility of the underlying asset. Rather than taking all models
from a prespecified class equally seriously, we penalise less plausible ones
based on their "distance" to a reference local volatility model. In the limit
for small uncertainty aversion, this leads to explicit formulas for prices and
hedging strategies in terms of the security's cash gamma.


title:
Seasonality on the life cycle of Aedes aegypti mosquito and its effects
  on dengue outbreaks

abstract:
  Dengue is a vector-borne disease transmitted by the mosquito Aedes aegypti.
It has been observed that its incidence is strongly influenced by temperature
and other abiotic factors like rainfall and humidity. In this work we compare
the effects of seasonality in temperature that affect the entomological
parameters of the mosquito with precipitation that affects the hatchery
capacity. We also analyze its joint action using a dynamical model for the life
cycle of Aedes aegypti and historical weather data from 8 regions of Mexico. We
found that the joint action of different mechanisms can enhance the prevalence
of the disease, but also inhibit it when they act in an asynchronous way. We
found that for the studied regions, the seasonality of the low temperature
rather than mean temperature is the main driving force of Dengue outbreaks. We
also analyzed the role of the diapause in these kinds of outbreaks. The
methodology developed here can be used to discover the underlying mechanism of
Dengue outbreaks in different regions and thus help to apply targeted control
measures.


title: Residual Networks Behave Like Ensembles of Relatively Shallow Networks

abstract:
  In this work we propose a novel interpretation of residual networks showing
that they can be seen as a collection of many paths of differing length.
Moreover, residual networks seem to enable very deep networks by leveraging
only the short paths during training. To support this observation, we rewrite
residual networks as an explicit collection of paths. Unlike traditional
models, paths through residual networks vary in length. Further, a lesion study
reveals that these paths show ensemble-like behavior in the sense that they do
not strongly depend on each other. Finally, and most surprising, most paths are
shorter than one might expect, and only the short paths are needed during
training, as longer paths do not contribute any gradient. For example, most of
the gradient in a residual network with 110 layers comes from paths that are
only 10-34 layers deep. Our results reveal one of the key characteristics that
seem to enable the training of very deep networks: Residual networks avoid the
vanishing gradient problem by introducing short paths which can carry gradient
throughout the extent of very deep networks.


title:
Deep Variational Bayes Filters: Unsupervised Learning of State Space
  Models from Raw Data

abstract:
  We introduce Deep Variational Bayes Filters (DVBF), a new method for
unsupervised learning and identification of latent Markovian state space
models. Leveraging recent advances in Stochastic Gradient Variational Bayes,
DVBF can overcome intractable inference distributions via variational
inference. Thus, it can handle highly nonlinear input data with temporal and
spatial dependencies such as image sequences without domain knowledge. Our
experiments show that enabling backpropagation through transitions enforces
state space assumptions and significantly improves information content of the
latent embedding. This also enables realistic long-term prediction.


title:
H-ATLAS: A Candidate High Redshift Cluster/Protocluster of Star-Forming
  Galaxies

abstract:
  We investigate the region around the Planck-detected z=3.26 gravitationally
lensed galaxy HATLAS J114637.9-001132 (hereinafter HATLAS12-00) using both
archival Herschel data from the H-ATLAS survey and using submm data obtained
with both LABOCA and SCUBA2. The lensed source is found to be surrounded by a
strong overdensity of both Herschel-SPIRE sources and submm sources. We detect
17 bright (S_870 >~7 mJy) sources at >4sigma closer than 5 arcmin to the lensed
object at 850/870 microns. Ten of these sources have good cross-identifications
with objects detected by Herschel-SPIRE which have redder colours than other
sources in the field, with 350 micron flux > 250 micron flux, suggesting that
they lie at high redshift. Submillimeter Array (SMA) observations localise one
of these companions to ~1 arcsecond, allowing unambiguous cross identification
with a 3.6 and 4.5 micron Spitzer source. The optical/near-IR spectral energy
distribution (SED) of this source is measured by further observations and found
to be consistent with z>2, but incompatible with lower redshifts. We conclude
that this system may be a galaxy cluster/protocluster or larger scale structure
that contains a number of galaxies undergoing starbursts at the same time.


title:
Molecular Dynamics Simulations of Compression-Tension Asymmetry in
  Plasticity of Fe Nanopillars

abstract:
  Tension-compression asymmetry is a notable feature of plasticity in bcc
single crystals. Recent experiments reveal striking differences in the
plasticity of bcc nanopillars for tension and compression. Here we present
results from molecular dynamics simulations of nanopillars of bcc Fe in tension
and compression. We find that a totally different deformation mechanism applies
in each cases: dislocation glide in compression and twinning in tension. This
difference explains experimentally-observed asymmetry in the nanopillar
morphology.


title: Accuracy and Precision in Collider Event Shapes

abstract:
  In this thesis, we discuss and investigate the use of Soft-Collinear
Effective Theory (SCET) for high precision calculations. In the first part of
the work, we present an extraction of the strong coupling, $\alpha_s(m_Z)$ and
the leading nonperturbative parameter $\Omega_1$, using the event shape
C-parameter. By comparing lepton collider data to the C-parameter cross
section, we extract values $\alpha_s(m_Z)=0.1123\pm 0.0015$ and
$\Omega_1=0.421\pm 0.063\,{\rm GeV}$ with $\chi^2/\rm{dof}=0.988$ for $404$
bins of data. The later parts of this thesis are dedicated to using on-shell
helicity methods to extend SCET beyond leading power. We present a complete set
of scalar helicity building blocks and describe an interesting angular momentum
selection rule that restricts how these building blocks can be assembled.


title:
Transverse momentum, rapidity, and centrality dependence of inclusive
  charged-particle production in $\sqrt{s_{NN}}=5.02$ TeV p+Pb collisions
  measured by the ATLAS experiment

abstract:
  Measurements of the per-event charged-particle yield as a function of the
charged-particle transverse momentum and rapidity are performed using $p+$Pb
collision data collected by the ATLAS experiment at the LHC at a centre-of-mass
energy of $\sqrt{s_{NN}}=5.02$ TeV. Charged particles are reconstructed over
pseudorapidity $|\eta|<2.3$ and transverse momentum between $0.1$ GeV and $22$
GeV in a dataset corresponding to an integrated luminosity of $1$ $\mu b^{-1}$.
The results are presented in the form of charged-particle nuclear modification
factors, where the $p+$Pb charged-particle multiplicities are compared between
central and peripheral $p+$Pb collisions as well as to charged-particle cross
sections measured in pp collisions. The $p+$Pb collision centrality is
characterized by the total transverse energy measured in $-4.9<\eta<-3.1$,
which is in the direction of the outgoing lead beam. Three different
estimations of the number of nucleons participating in the $p+$Pb collision are
carried out using the Glauber model and two Glauber-Gribov colour-fluctuation
extensions to the Glauber model. The values of the nuclear modification factors
are found to vary significantly as a function of rapidity and transverse
momentum. A broad peak is observed for all centralities and rapidities in the
nuclear modification factors for charged-particle transverse momentum values
around $3$ GeV. The magnitude of the peak increases for more central collisions
as well as rapidity ranges closer to the direction of the outgoing lead
nucleus.


title:
Learning shape correspondence with anisotropic convolutional neural
  networks

abstract:
  Establishing correspondence between shapes is a fundamental problem in
geometry processing, arising in a wide variety of applications. The problem is
especially difficult in the setting of non-isometric deformations, as well as
in the presence of topological noise and missing parts, mainly due to the
limited capability to model such deformations axiomatically. Several recent
works showed that invariance to complex shape transformations can be learned
from examples. In this paper, we introduce an intrinsic convolutional neural
network architecture based on anisotropic diffusion kernels, which we term
Anisotropic Convolutional Neural Network (ACNN). In our construction, we
generalize convolutions to non-Euclidean domains by constructing a set of
oriented anisotropic diffusion kernels, creating in this way a local intrinsic
polar representation of the data (`patch'), which is then correlated with a
filter. Several cascades of such filters, linear, and non-linear operators are
stacked to form a deep neural network whose parameters are learned by
minimizing a task-specific cost. We use ACNNs to effectively learn intrinsic
dense correspondences between deformable shapes in very challenging settings,
achieving state-of-the-art results on some of the most difficult recent
correspondence benchmarks.


title: Smoothed Analysis for the Conjugate Gradient Algorithm

abstract:
  The purpose of this paper is to establish bounds on the rate of convergence
of the conjugate gradient algorithm when the underlying matrix is a random
positive definite perturbation of a deterministic positive definite matrix. We
estimate all finite moments of a natural halting time when the random
perturbation is drawn from the Laguerre unitary ensemble in a critical scaling
regime explored in Deift et al. (2016). These estimates are used to analyze the
expected iteration count in the framework of smoothed analysis, introduced by
Spielman and Teng (2001). The rigorous results are compared with numerical
calculations in several cases of interest.


title:
Combining Adversarial Guarantees and Stochastic Fast Rates in Online
  Learning

abstract:
  We consider online learning algorithms that guarantee worst-case regret rates
in adversarial environments (so they can be deployed safely and will perform
robustly), yet adapt optimally to favorable stochastic environments (so they
will perform well in a variety of settings of practical importance). We
quantify the friendliness of stochastic environments by means of the well-known
Bernstein (a.k.a. generalized Tsybakov margin) condition. For two recent
algorithms (Squint for the Hedge setting and MetaGrad for online convex
optimization) we show that the particular form of their data-dependent
individual-sequence regret guarantees implies that they adapt automatically to
the Bernstein parameters of the stochastic environment. We prove that these
algorithms attain fast rates in their respective settings both in expectation
and with high probability.


title: Higher Hasse--Witt matrices

abstract:
  We prove a number of p-adic congruences for the coefficients of powers of a
multivariate polynomial f(x) with coefficients in a ring R of characteristic
zero. If the Hasse--Witt operation is invertible, our congruences yield p-adic
limit formulas which conjecturally describe the Gauss--Manin connection and the
Frobenius operator on the unit-root crystal attached to f(x). As a second
application, we associate with f(x) formal group laws over R. Under certain
assumptions these formal group laws are coordinalizations of the Artin--Mazur
functors. (This is a final version which we send for a publication.)


title: Intrinsic character of Stokes matrices

abstract:
  Two germs of linear analytic differential systems $x^{k+1}Y^\prime=A(x)Y$
with a non resonant irregular singularity are analytically equivalent if and
only if they have the same eigenvalues and equivalent collections of Stokes
matrices. The Stokes matrices are the transition matrices between sectors on
which the system is analytically equivalent to its formal normal form. Each
sector contains exactly one separating ray for each pair of eigenvalues. A
rotation in $S$ allows supposing that $\mathbb R^+$ lies in the intersection of
two sectors. Reordering of the coordinates of $Y$ allows ordering the real
parts of the eigenvalues, thus yielding triangular Stokes matrices. However,
the choice of the rotation in $x$ is not canonical. In this paper we establish
how the collection of Stokes matrices depends on this rotation, and hence on a
chosen order of the projection of the eigenvalues on a line through the origin.


title:
Inter-Technology Coexistence in a Spectrum Commons: A Case Study of
  Wi-Fi and LTE in the 5 GHz Unlicensed Band

abstract:
  Spectrum sharing mechanisms need to be carefully designed to enable
inter-technology coexistence in the unlicensed bands, as these bands are an
instance of a spectrum commons where heterogeneous technologies and deployments
must coexist. Unlike in licensed bands, where multiple technologies could
coexist only in a primary-secondary DSA mode, a spectrum commons offers
competition opportunities between multiple dominant technologies, such as Wi-Fi
and the recently proposed LTE in the 5 GHz unlicensed band. In this paper we
systematically study the performance of different spectrum sharing schemes for
inter-technology coexistence in a spectrum commons. Our contributions are
threefold. First, we propose a general framework for comparative analysis of
spectrum sharing mechanisms in time and frequency, by studying the effect of
key constituent parameters. Second, we propose a novel throughput and
interference model for inter-technology coexistence, integrating different
distributed MAC sharing mechanisms at the same level of abstraction. Finally,
we present a case study of IEEE 802.11n Wi-Fi and LTE in the 5 GHz unlicensed
band, in order to obtain generalizable insight into coexistence in a spectrum
commons. Our extensive simulation results show that LTE/Wi-Fi coexistence in
the 5 GHz band can be ensured simply through channel selection schemes, such
that time-sharing MAC mechanisms are irrelevant. We also show that, in the
general co-channel case, the coexistence performance of MAC sharing mechanisms
strongly depends on the interference coupling in the network, as determined by
building shielding. We thus identify two regimes: (i) low interference
coupling, e.g. residential indoor scenarios, where duty cycle mechanisms
outperform sensing-based LBT mechanisms; and (ii) high interference coupling,
e.g. open-plan indoor or outdoor hotspot scenarios, where LBT outperforms duty
cycle mechanisms.


title: Structured Prediction Theory Based on Factor Graph Complexity

abstract:
  We present a general theoretical analysis of structured prediction with a
series of new results. We give new data-dependent margin guarantees for
structured prediction for a very wide family of loss functions and a general
family of hypotheses, with an arbitrary factor graph decomposition. These are
the tightest margin bounds known for both standard multi-class and general
structured prediction problems. Our guarantees are expressed in terms of a
data-dependent complexity measure, factor graph complexity, which we show can
be estimated from data and bounded in terms of familiar quantities. We further
extend our theory by leveraging the principle of Voted Risk Minimization (VRM)
and show that learning is possible even with complex factor graphs. We present
new learning bounds for this advanced setting, which we use to design two new
algorithms, Voted Conditional Random Field (VCRF) and Voted Structured Boosting
(StructBoost). These algorithms can make use of complex features and factor
graphs and yet benefit from favorable learning guarantees. We also report the
results of experiments with VCRF on several datasets to validate our theory.


title:
Unreasonable Effectiveness of Learning Neural Networks: From Accessible
  States and Robust Ensembles to Basic Algorithmic Schemes

abstract:
  In artificial neural networks, learning from data is a computationally
demanding task in which a large number of connection weights are iteratively
tuned through stochastic-gradient-based heuristic processes over a
cost-function. It is not well understood how learning occurs in these systems,
in particular how they avoid getting trapped in configurations with poor
computational performance. Here we study the difficult case of networks with
discrete weights, where the optimization landscape is very rough even for
simple architectures, and provide theoretical and numerical evidence of the
existence of rare - but extremely dense and accessible - regions of
configurations in the network weight space. We define a novel measure, which we
call the "robust ensemble" (RE), which suppresses trapping by isolated
configurations and amplifies the role of these dense regions. We analytically
compute the RE in some exactly solvable models, and also provide a general
algorithmic scheme which is straightforward to implement: define a
cost-function given by a sum of a finite number of replicas of the original
cost-function, with a constraint centering the replicas around a driving
assignment. To illustrate this, we derive several powerful new algorithms,
ranging from Markov Chains to message passing to gradient descent processes,
where the algorithms target the robust dense states, resulting in substantial
improvements in performance. The weak dependence on the number of precision
bits of the weights leads us to conjecture that very similar reasoning applies
to more conventional neural networks. Analogous algorithmic schemes can also be
applied to other optimization problems.


title: Characterizing quantum correlations in the nonsignaling framework

abstract:
  Quantum correlations forms a subset of the set of nonsignaling boxes. This
allows us to characterize quantum correlations as a convex combination of the
extremal boxes of the nonsignaling polytope which are Popescu-Rohrlich boxes
(maximally nonlocal boxes) and local deterministic boxes. There exists multiple
decomposition of quantum correlations in the context of the nonsignaling
polytope. I find that the existence of Popescu-Rohrlich box decomposition for
local boxes associates two notions of discord which capture nonclassicality of
quantum correlations originating from Bell nonlocality and EPR-steering. I
introduce, Bell and Mermin discord, and demonstrate that any bipartite
nonsignaling box admits a three-way decomposition. This decomposition allows us
to isolate the origin of nonclassicality into three disjoint sources: a
Popescu-Rohrlich box, a maximally local box that detects EPR-steering, and a
classical correlation. Interestingly, I show that all non-null quantum discord
states which are neither classical-quantum states nor quantum-classical states
can give rise to nonclassical correlations which have non-null Bell and/or
Mermin discord for suitable noncommuting measurements. I introduce two notions
of genuine discord, which are the generalizations of Bell and Mermin discord to
the multipartite boxes, to characterize the presence of genuine nonclassicality
in multipartite quantum correlations.


title: Deciphering the Dipole Anisotropy of Galactic Cosmic Rays

abstract:
  Recent measurements of the dipole anisotropy in the arrival directions of
Galactic cosmic rays (CRs) indicate a strong energy dependence of the dipole
amplitude and phase in the TeV-PeV range. We argue here that these observations
can be well understood within standard diffusion theory as a combined effect of
(i) one or more local sources at Galactic longitude 120deg < l < 300deg
dominating the CR gradient below 0.1-0.3 PeV, (ii) the presence of a strong
ordered magnetic field in our local environment, (iii) the relative motion of
the solar system, and (iv) the limited reconstruction capabilities of
ground-based observatories. We show that an excellent candidate of the local CR
source responsible for the dipole anisotropy at 1-100 TeV is the Vela supernova
remnant.


title:
Linearized Boltzmann transport model for jet propagation in the
  quark-gluon plasma: Heavy quark evolution

abstract:
  A Linearized Boltzmann Transport (LBT) model coupled with hydrodynamical
background is established to describe the evolution of jet shower partons and
medium excitations in high energy heavy-ion collisions. We extend the LBT model
to include both elastic and inelastic processes for light and heavy partons in
the quark-gluon plasma. A hybrid model of fragmentation and coalescence is
developed for the hadronization of heavy quarks. Within this framework, we
investigate how heavy flavor observables depend on various ingredients, such as
different energy loss and hadronization mechanisms, the momentum and
temperature dependences of the transport coefficients, and the radial flow of
the expanding fireball. Our model calculations show good descriptions of the
$D$ meson suppression and elliptic flow observed at the LHC and RHIC. The
prediction for the Pb-Pb collisions at $\sqrt{s_\mathrm{NN}}$=5.02~TeV is
provided.


title: Running vacuum versus the $\Lambda$CDM

abstract:
  It is well-known that a constant $\Lambda$-term is a traditional building
block of the concordance $\Lambda$CDM model. We show that this assumption is
not necessarily the optimal one from the phenomenological point of view. The
class of running vacuum models, with a possible running of the gravitational
coupling G, are capable to fit the overall cosmological data
SNIa+BAO+H(z)+LSS+BBN+CMB better than the $\Lambda$CDM, namely at a level of
$\sim 3\sigma$ and with Akaike and Bayesian information criteria supporting a
strong level of statistical evidence on this fact. Here we report on the
results of such analysis.


title: Observational Constraints on Monomial Warm Inflation

abstract:
  Warm inflation is, as of today, one of the best motivated mechanisms for
explaining an early inflationary period. In this paper, we derive and analyze
the current bounds on warm inflation with a monomial potential $U\propto
\phi^p$, using the constraints from the PLANCK mission. In particular, we
discuss the parameter space of the tensor-to-scalar ratio $r$ and the potential
coupling $\lambda$ of the monomial warm inflation in terms of the number of
e-folds. We obtain that the theoretical tensor-to-scalar ratio $r\sim 10^{-8}$
is much smaller than the current observational constrain $r \lesssim 0.12$,
despite a relatively large value of the field excursion $\Delta \phi \sim
0.1\MP$. Warm inflation thus eludes the Lyth bound set on the tensor-to-scalar
ratio by the field excursion.


title: Query-Efficient Imitation Learning for End-to-End Autonomous Driving

abstract:
  One way to approach end-to-end autonomous driving is to learn a policy
function that maps from a sensory input, such as an image frame from a
front-facing camera, to a driving action, by imitating an expert driver, or a
reference policy. This can be done by supervised learning, where a policy
function is tuned to minimize the difference between the predicted and
ground-truth actions. A policy function trained in this way however is known to
suffer from unexpected behaviours due to the mismatch between the states
reachable by the reference policy and trained policy functions. More advanced
algorithms for imitation learning, such as DAgger, addresses this issue by
iteratively collecting training examples from both reference and trained
policies. These algorithms often requires a large number of queries to a
reference policy, which is undesirable as the reference policy is often
expensive. In this paper, we propose an extension of the DAgger, called
SafeDAgger, that is query-efficient and more suitable for end-to-end autonomous
driving. We evaluate the proposed SafeDAgger in a car racing simulator and show
that it indeed requires less queries to a reference policy. We observe a
significant speed up in convergence, which we conjecture to be due to the
effect of automated curriculum learning.


title:
Fixed Points of Belief Propagation -- An Analysis via Polynomial
  Homotopy Continuation

abstract:
  Belief propagation (BP) is an iterative method to perform approximate
inference on arbitrary graphical models. Whether BP converges and if the
solution is a unique fixed point depends on both the structure and the
parametrization of the model. To understand this dependence it is interesting
to find \emph{all} fixed points. In this work, we formulate a set of polynomial
equations, the solutions of which correspond to BP fixed points. To solve such
a nonlinear system we present the numerical polynomial-homotopy-continuation
(NPHC) method. Experiments on binary Ising models and on error-correcting codes
show how our method is capable of obtaining all BP fixed points. On Ising
models with fixed parameters we show how the structure influences both the
number of fixed points and the convergence properties. We further asses the
accuracy of the marginals and weighted combinations thereof. Weighting
marginals with their respective partition function increases the accuracy in
all experiments. Contrary to the conjecture that uniqueness of BP fixed points
implies convergence, we find graphs for which BP fails to converge, even though
a unique fixed point exists. Moreover, we show that this fixed point gives a
good approximation, and the NPHC method is able to obtain this fixed point.


title:
First-principles modeling of electromagnetic scattering by discrete and
  discretely heterogeneous random media

abstract:
  The main objective of this Report is to formulate the general theoretical
framework of electromagnetic scattering by discrete random media rooted in the
Maxwell-Lorentz electromagnetics and discuss its immediate analytical and
numerical consequences. Starting from the microscopic Maxwell-Lorentz
equations, we trace the development of the first-principles formalism enabling
accurate calculations of monochromatic and quasi-monochromatic scattering by
static and randomly varying multiparticle groups. We illustrate how this
general framework can be coupled with state-of-the-art computer solvers of the
Maxwell equations and applied to direct modeling of electromagnetic scattering
by representative random multi-particle groups with arbitrary packing
densities. This first-principles modeling yields general physical insights
unavailable with phenomenological approaches. We discuss how the
first-order-scattering approximation, the radiative transfer theory, and the
theory of weak localization of electromagnetic waves can be derived as
immediate corollaries of the Maxwell equations for very specific and
well-defined kinds of particulate medium. These recent developments confirm the
mesoscopic origin of the radiative transfer, weak localization, and
effective-medium regimes and help evaluate the numerical accuracy of widely
used approximate modeling methodologies.


title: The asymptotic dimension of quotients by finite groups

abstract:
  Let $X$ be a proper metric space and let $F$ be a finite group acting on $X$
by isometries. We show that the asymptotic dimension of $F\backslash X$ is the
same as the asymptotic dimension of $X$.


title: Measurement of a Vacuum-Induced Geometric Phase

abstract:
  Berry's geometric phase naturally appears when a quantum system is driven by
an external field whose parameters are slowly and cyclically changed. A
variation in the coupling between the system and the external field can also
give rise to a geometric phase, even when the field is in the vacuum state or
any other Fock state. Here we demonstrate the appearance of a vacuum-induced
Berry phase in an artificial atom, a superconducting transmon, interacting with
a single mode of a microwave cavity. As we vary the phase of the interaction,
the artificial atom acquires a geometric phase determined by the path traced
out in the combined Hilbert space of the atom and the quantum field. Our
ability to control this phase opens new possibilities for the geometric
manipulation of atom-cavity systems also in the context of quantum information
processing.


title:
Breathers in Hamiltonian ${\cal PT}$-symmetric chains of coupled pendula
  under a resonant periodic force

abstract:
  We derive a Hamiltonian version of the ${\cal PT}$-symmetric discrete
nonlinear Schr\"{o}dinger equation that describes synchronized dynamics of
coupled pendula driven by a periodic movement of their common strings. In the
limit of weak coupling between the pendula, we classify the existence and
spectral stability of breathers (time-periodic solutions localized in the
lattice) supported near one pair of coupled pendula. Orbital stability or
instability of breathers is proved in a subset of the existence region.


title: Constraints on Dbar Uplifts

abstract:
  We discuss constraints on KKLT/KKLMMT and LVS scenarios that use anti-branes
to get an uplift to a deSitter vacuum, coming from requiring the validity of an
effective field theory description of the physics. We find these are not always
satisfied or are hard to satisfy.


title: Virtual Worlds as Proxy for Multi-Object Tracking Analysis

abstract:
  Modern computer vision algorithms typically require expensive data
acquisition and accurate manual labeling. In this work, we instead leverage the
recent progress in computer graphics to generate fully labeled, dynamic, and
photo-realistic proxy virtual worlds. We propose an efficient real-to-virtual
world cloning method, and validate our approach by building and publicly
releasing a new video dataset, called Virtual KITTI (see
http://www.xrce.xerox.com/Research-Development/Computer-Vision/Proxy-Virtual-Worlds),
automatically labeled with accurate ground truth for object detection,
tracking, scene and instance segmentation, depth, and optical flow. We provide
quantitative experimental evidence suggesting that (i) modern deep learning
algorithms pre-trained on real data behave similarly in real and virtual
worlds, and (ii) pre-training on virtual data improves performance. As the gap
between real and virtual worlds is small, virtual worlds enable measuring the
impact of various weather and imaging conditions on recognition performance,
all other things being equal. We show these factors may affect drastically
otherwise high-performing deep models for tracking.


title:
An asteroseismic constraint on the mass of the axion from the period
  drift of the pulsating DA white dwarf star L19-2

abstract:
  We employ an asteroseismic model of L19-2, a relatively massive ($M_{\star}
\sim 0.75 M_{\sun}$) and hot ($T_{\rm eff} \sim 12\,100$ K) pulsating DA
(H-rich atmosphere) white dwarf star (DAV or ZZ Ceti variable), and use the
observed values of the temporal rates of period change of its dominant
pulsation modes ($\Pi \sim 113$ s and $\Pi \sim 192$ s), to derive a new
constraint on the mass of the axion, the hypothetical non-barionic particle
considered as a possible component of the dark matter of the Universe. If the
asteroseismic model employed is an accurate representation of L19-2, then our
results indicate hints of extra cooling in this star, compatible with emission
of axions of mass $m_{\rm a} \cos^2 \beta \lesssim 25$ meV or an axion-electron
coupling constant of $g_{\rm ae} \lesssim 7 \times 10^{-13}$.


title:
Two-Qubit Separability Probabilities as Joint Functions of the Bloch
  Radii of the Qubit Subsystems

abstract:
  We detect a certain pattern of behavior of separability probabilities
$p(r_A,r_B)$ for two-qubit systems endowed with Hilbert-Schmidt, and more
generally, random induced measures, where $r_A$ and $r_B$ are the Bloch radii
($0 \leq r_A,r_B \leq 1$) of the qubit reduced states ($A,B$). We observe a
relative repulsion of radii effect, that is $p(r_A,r_A) < p(r_A,1-r_A)$, except
for rather narrow "crossover" intervals $[\tilde{r}_A,\frac{1}{2}]$. Among the
seven specific cases we study are, firstly, the "toy" seven-dimensional
$X$-states model and, then, the fifteen-dimensional two-qubit states obtained
by tracing over the pure states in $4 \times K$-dimensions, for $K=3, 4, 5$,
with $K=4$ corresponding to Hilbert-Schmidt (flat/Euclidean) measure. We also
examine the real (two-rebit) $K=4$, the $X$-states $K=5$, and Bures (minimal
monotone)--for which no nontrivial crossover behavior is observed--instances.
In the two $X$-states cases, we derive analytical results, for $K=3, 4$, we
propose formulas that well-fit our numerical results, and for the other
scenarios, rely presently upon large numerical analyses. The separability
probability crossover regions found expand in length (lower $\tilde{r}_A$) as
$K$ increases. This report continues our efforts (arXiv:1506.08739) to extend
the recent work of Milz and Strunz (J. Phys. A: 48 [2015] 035306) from a
univariate ($r_A$) framework---in which they found separability probabilities
to hold constant with $r_A$---to a bivariate ($r_A,r_B$) one. We also analyze
the two-qutrit and qubit-qutrit counterparts reported in arXiv:1512.07210 in
this context, and study two-qubit separability probabilities of the form
$p(r_A,\frac{1}{2})$.


title: C*-algebras of labelled spaces and their diagonal C*-subalgebras

abstract:
  Motivated by Exel's inverse semigroup approach to combinatorial C*-algebras,
in a previous work the authors defined an inverse semigroup associated with a
labelled space. We construct a representation of the C*-algebra of a labelled
space, inspired by how one might cut or glue labelled paths together, that
proves that non-zero elements in the inverse semigroup correspond to non-zero
elements in the C*-algebra. We also show that the spectrum of its diagonal
C*-subalgebra is homeomorphic to the tight spectrum of the inverse semigroup
associated with the labelled space.


title:
No apparent superluminal motion in the first-known jetted tidal
  disruption event Swift J1644+5734

abstract:
  The first-known tidal disruption event (TDE) with strong evidence for a
relativistic jet -- based on extensive multi-wavelength campaigns -- is Swift
J1644+5734. In order to directly measure the apparent speed of the radio jet,
we performed very long baseline interferometry (VLBI) observations with the
European VLBI network (EVN) at 5 GHz. Our observing strategy was to identify a
very nearby and compact radio source with the real-time e-EVN, and then utilise
this source as a stationary astrometry reference point in the later five deep
EVN observations. With respect to the in-beam source FIRST J1644+5736, we have
achieved a statistical astrometric precision about 12 micro-arcsecond (68 %
confidence level) per epoch. This is one of the best phase-referencing
measurements available to date. No proper motion has been detected in the Swift
J1644+5734 radio ejecta. We conclude that the apparent average ejection speed
between 2012.2 and 2015.2 was less than 0.3c with a confidence level of 99 %.
This tight limit is direct observational evidence for either a very small
viewing angle or a strong jet deceleration due to interactions with a dense
circum-nuclear medium, in agreement with some recent theoretical studies.


title:
Simulating the interaction of road users: A glance to complexity of
  Venezuelan traffic

abstract:
  Automotive traffic is a classical example of a complex system, being the
simplest case the homogeneous traffic where all vehicles are of the same kind,
and using different means of transportation increases complexity due to
different driving rules and interactions between each vehicle type. In
particular, when motorcyclists drive in between the lanes of stopped or
slow-moving vehicles. This later driving mode is a Venezuelan pervasive
practice of mobilization that clearly jeopardizes road safety. We developed a
minimalist agent-based model to analyze the interaction of road users with and
without motorcyclists on the way. The presence of motorcyclists dwindles
significantly the frequency of lane changes of motorists while increasing their
frequency of acceleration-deceleration maneuvers, without significantly
affecting their average speed. That is, motorcyclist "corralled" motorists in
their lanes limiting their ability to maneuver and increasing their
acceleration noise. Comparison of the simulations with real traffic videos
shows good agreement between model and observation. The implications of these
results regarding road safety concerns about the interaction between motorists
and motorcyclists are discussed.


title: Quantum Mechanical Corrections to the Schwarzschild Black Hole Metric

abstract:
  Motivated by quantum mechanical corrections to the Newtonian potential, which
can be translated into an $\hbar$-correction to the $g_{00}$ component of the
Schwarzschild metric, we construct a quantum mechanically corrected metric
assuming $-g_{00}=g^{rr}$. We show how the Bekenstein black hole entropy $S$
receives its logarithmic contribution provided the quantum mechanical
corrections to the metric are negative. In this case the standard horizon at
the Schwarzschild radius $r_S$ increases by small terms proportional to $\hbar$
and a remnant of the order of Planck mass emerges. We contrast these results
with a positive correction to the metric which, apart from a corrected
Schwarzschild horizon, leads to a new purely quantum mechanical horizon.


title:
Bichromatic magneto-optical trapping for $ J\rightarrow J,J-1 $
  configurations

abstract:
  A magneto-optical trap (MOT) of atoms or molecules is studied when two lasers
of different detunings and polarization are used. Especially for $J\rightarrow
J,J-1$ transitions, a scheme using more than one frequency per transition and
different polarization is required to create a significant force. Calculations
have been performed with the simplest forms of the $J\rightarrow J-1$ case
(i.e. $J''=1 \rightarrow J'=0$) and $J\rightarrow J$ case (i.e. $J''=1/2
\rightarrow J'=1/2$). A one dimensional (1D) model is presented and a complete
3D simulation using rate equations confirm the results. Even in the absence of
Zeeman effect in the excited state, where no force is expected in the single
laser field configuration, we show that efficient cooling and trapping forces
are restored in our configuration. We study this mechanism for the C$_2^-$
molecular anion as a typical example of the interplay between the two simple
transitions $J \rightarrow J,J-1$.


title: Swapout: Learning an ensemble of deep architectures

abstract:
  We describe Swapout, a new stochastic training method, that outperforms
ResNets of identical network structure yielding impressive results on CIFAR-10
and CIFAR-100. Swapout samples from a rich set of architectures including
dropout, stochastic depth and residual architectures as special cases. When
viewed as a regularization method swapout not only inhibits co-adaptation of
units in a layer, similar to dropout, but also across network layers. We
conjecture that swapout achieves strong regularization by implicitly tying the
parameters across layers. When viewed as an ensemble training method, it
samples a much richer set of architectures than existing methods such as
dropout or stochastic depth. We propose a parameterization that reveals
connections to exiting architectures and suggests a much richer set of
architectures to be explored. We show that our formulation suggests an
efficient training method and validate our conclusions on CIFAR-10 and
CIFAR-100 matching state of the art accuracy. Remarkably, our 32 layer wider
model performs similar to a 1001 layer ResNet model.


title:
Anomaly Detection in XML-Structured SOAP Messages Using Tree-Based
  Association Rule Mining

abstract:
  Web services are software systems designed for supporting interoperable
dynamic cross-enterprise interactions. The result of attacks to Web services
can be catastrophic and causing the disclosure of enterprises' confidential
data. As new approaches of attacking arise every day, anomaly detection systems
seem to be invaluable tools in this context. The aim of this work has been to
target the attacks that reside in the Web service layer and the extensible
markup language (XML)-structured simple object access protocol (SOAP) messages.
After studying the shortcomings of the existing solutions, a new approach for
detecting anomalies in Web services is outlined. More specifically, the
proposed technique illustrates how to identify anomalies by employing mining
methods on XML-structured SOAP messages. This technique also takes the
advantages of tree-based association rule mining to extract knowledge in the
training phase, which is used in the test phase to detect anomalies. In
addition, this novel composition of techniques brings nearly low false alarm
rate while maintaining the detection rate reasonably high, which is shown by a
case study.


title: Browser Feature Usage on the Modern Web

abstract:
  Modern web browsers are incredibly complex, with millions of lines of code
and over one thousand JavaScript functions and properties available to website
authors. This work investigates how these browser features are used on the
modern, open web. We find that JavaScript features differ wildly in popularity,
with over 50% of provided features never used in the Alexa 10k.
  We also look at how popular ad and tracking blockers change the distribution
of features used by sites, and identify a set of approximately 10% of features
that are disproportionately blocked (prevented from executing by these
extensions at least 90% of the time they are used). We additionally find that
in the presence of these blockers, over 83% of available features are executed
on less than 1% of the most popular 10,000 websites.
  We additionally measure a variety of aspects of browser feature usage on the
web, including how complex sites have become in terms of feature usage, how the
length of time a browser feature has been in the browser relates to its usage
on the web, and how many security vulnerabilities have been associated with
related browser features.


title: Interlayer interaction in multilayer CoPt/Co structures

abstract:
  We report a study of interlayer exchange interaction in multilayer CoPt/Co
structures consisting of periodic CoPt multilayer film with an "easy axis"
anisotropy and thick Co layer with an "easy plane" anisotropy separated by Pt
spacer with variable thickness. The magnetooptical Kerr effect (MOKE) and
ferromagnetic resonance (FMR) measurements show up the essentially
non-collinear state of magnetic moments of the layers and strong exchange
coupling between CoPt and Co subsystems. The estimation of effective anisotropy
and exchange coupling in a simple model based on the Landau-Lifshitz-Gilbert
equation describing magnetization dynamics was performed.


title:
Initial stage of cavitation in liquids and its observation by Rayleigh
  scattering

abstract:
  A theory is developed for the initial stage of cavitation in the framework of
Zel'dovich-Fisher theory of nucleation in the field of negative pressure, while
taking into account the surface tension dependence on the nanopore radius. A
saturation mechanism is proposed that limits the exponential dependence of the
nucleation rate on the energy required to create nanopores. An estimate of the
saturated density of nanopores at the nucleation stage is obtained. It is shown
that Rayleigh scattering can detect nanopores arising at the initial stage of
cavitation development.


title:
Topological defect launches 3D mound in the active nematic sheet of
  neural progenitors

abstract:
  Cultured stem cells have become a standard platform not only for regenerative
medicine and developmental biology but also for biophysical studies. Yet, the
characterization of cultured stem cells at the level of morphology and
macroscopic patterns resulting from cell-to-cell interactions remain largely
qualitative, even though they are the simplest features observed in everyday
experiments. Here we report that neural progenitor cells (NPCs), which are
multipotent stem cells that give rise to cells in the central nervous system,
rapidly glide and stochastically reverse its velocity while locally aligning
with neighboring cells, thus showing features of an active nematic system.
Within the two-dimensional nematic pattern, we find interspaced topological
defects with +1/2 and -1/2 charges. Remarkably, we identified rapid cell
accumulation leading to three-dimensional mounds at the +1/2 topological
defects. Single-cell level imaging around the defects allowed quantification of
the evolving cell density, clarifying that not only cells concentrate at +1/2
defects, but also escape from -1/2 defects. We propose the mechanism of
instability around the defects as the interplay between the anisotropic
friction and the active force field, thus addressing a novel universal
mechanism for local cell density control.


title: The six-vertex model and Schramm-Loewner evolution

abstract:
  Square ice is a statistical mechanics model for two-dimensional ice, widely
believed to have a conformally invariant scaling limit. We associate a Peano
(space filling) curve to a square ice configuration, and more generally to a
so-called 6-vertex model configuration, and argue that its scaling limit is a
space-filling version of the random fractal curve SLE$_\kappa$,
Schramm--Loewner evolution with parameter $\kappa$, where $4<\kappa\leq
12+8\sqrt{2}$. For square ice, $\kappa=12$. At the "free-fermion point" of the
6-vertex model, $\kappa=8+4\sqrt{3}$. These unusual values lie outside the
classical interval $2\le \kappa\le 8$.


title:
Reconfigurable swarms of colloidal particles electrophoretically
  drivenin nematic liquid crystals

abstract:
  We present experiments where anisometric colloidal microparticles dispersed
in a nematic liquid crystal cell with homeotropic anchoring conditions are
dynamically assembled by means of liquid-crystal-enabled electrophoresis
(LCEEP) using an AC electric field perpendicular to the confining plates. A
nematic host with negative dielectric anisotropy leads to a driving force
parallel to the cell plates. We take advantage of the resulting gliding
anchoring conditions and the degeneracy in the direction of particle motion to
design reconfigurable trajectories using a photosensitive anchoring layer
(azosilane self-assembled monolayer), as the particle trajectory follows the
local director orientation.


title:
Arbitrary n-Qubit State Transfer Implemented by Coherent Control and
  Simplest Switchable Local Noise

abstract:
  We study reachable sets of open n-qubit quantum systems, whose coherent parts
are under full unitary control, by adding as a further degree of incoherent
control switchable Markovian noise on a single qubit. In particular, adding
bang-bang control of amplitude damping noise (non-unital) allows the dynamic
system to act transitively on the entire set of density operators. Thus one can
transform any initial quantum state into any desired target state. Adding
switchable bit-flip noise (unital) instead suffices to get all states majorised
by the initial state. Our open-loop optimal control package DYNAMO is extended
by incoherent control to exploit these unprecedented reachable sets in
experiments. We propose implementation by a GMon, a superconducting device with
fast tunable coupling to an open transmission line, and illustrate how
open-loop control with noise switching achieves all state transfers without
measurement-based closed-loop feedback and resettable ancilla.