title:
Search for massive long-lived particles decaying semileptonically in the
  LHCb detector

abstract:
  A search is presented for massive long-lived particles decaying into a muon
and two quarks. The dataset consists of proton-proton interactions at
centre-of-mass energies of 7 and 8 TeV, corresponding to integrated
luminosities of 1 and 2 1/fb, respectively. The analysis is performed assuming
a set of production mechanisms with simple topologies, including the production
of a Higgs-like particle decaying into two long-lived particles. The mass range
from 20 to 80 GeV and lifetimes from 5 to 100 ps are explored. Results are also
interpreted in terms of neutralino production in different supersymmetric
models, with masses in the 23-198 GeV range. No excess above the background
expectation is observed and upper limits are set on the production
cross-section for various points in the parameter space of theoretical models.


title: Latest results from CMS

abstract:
  A summary of the latest results released by the CMS Collaboration during the
Summer of 2016 is presented.


title:
Disruptive innovations in RoboCup 2D Soccer Simulation League: from
  Cyberoos'98 to Gliders2016

abstract:
  We review disruptive innovations introduced in the RoboCup 2D Soccer
Simulation League over the twenty years since its inception, and trace the
progress of our champion team (Gliders). We conjecture that the League has been
developing as an ecosystem shaped by diverse approaches taken by participating
teams, increasing in its overall complexity. A common feature is that different
champion teams succeeded in finding a way to decompose the enormous
search-space of possible single- and multi-agent behaviours, by automating the
exploration of the problem space with various techniques which accelerated the
software development efforts. These methods included interactive debugging,
machine learning, automated planning, and opponent modelling. The winning
approach developed by Gliders is centred on human-based evolutionary
computation which optimised several components such as an action-dependent
evaluation function, dynamic tactics with Voronoi diagrams, information
dynamics, and bio-inspired collective behaviour.


title:
A statistical study of giant molecular clouds traced by $^{13}$CO,
  C$^{18}$O, CS, and CH$_3$OH in the disk of NGC 1068 based on ALMA
  observations

abstract:
  We present $1''.4$ (98 pc) resolution ALMA observations of
$^{13}$CO($J$=1-0), C$^{18}$O($J$=1-0), CS($J$=2-1) and
CH$_3$OH($J_K$=$2_K$-$1_K$) molecular lines in the central $1'$ (4.2 kpc)
region of NGC 1068 to study the physical and chemical properties of giant
molecular clouds (GMCs) and to test whether these GMC-scale properties are
linked to the larger-scale galactic environment. Using the derived $^{13}$CO
cube, we have identified 187 high-significance ($> 8\sigma$) GMCs by employing
the CLUMPFIND algorithm. The molecular gas masses of GMCs $M_{\rm ^{13}CO}$,
derived from the $^{13}$CO data, range from $1.8 \times 10^4 ~ M_\odot$ to $4.2
\times 10^7 ~M_\odot$.
  A mass function of GMCs in NGC 1068 has been obtained for the first time at
$\sim$100 pc resolution. We find the slope of the mass function $\gamma = -1.25
\pm 0.07$ for a mass range of $M_{\rm ^{13}CO} \geq 10^5M_\odot$. This is
shallower than the GMCs in the disk regions of the Milky Way, M51 and NGC 300.
Further, we find that the high mass cut-off of the GMC mass function occurs at
$M_{\rm ^{13}CO} \sim 6 \times 10^7 M_\odot$, which is an order of magnitude
larger than that in the nuclear bar region of M51, indicating that the more
massive clouds dominate the mass budget in NGC 1068. The observed
C$^{18}$O)/$^{13}$CO intensity ratios are found to be fairly uniform (0.27
$\pm$ 0.05) among the identified GMCs. In contrast, the CH$_3$OH/$^{13}$CO
ratios exhibit striking spatial variation across the disk, with the smallest
values around the bar-end ($<$ 0.03), and larger ratios along the spiral arms
($\sim$ 0.1-0.2). We find that GMCs with detectable methanol emission tend to
have systematically larger velocity widths than those without methanol
emission, suggesting that (relatively weak) shocks are responsible for the
enhancement of the CH$_3$OH/$^{13}$CO ratios of GMCs in the disk of NGC 1068.


title:
Finding the minimum energy conformation of protein-like heteropolymers
  by Greedy Neighborhood Search

abstract:
  A global optimization method called Greedy Neighborhood Search (GNS) and a
novel conformational sampling method using a spherical distribution is proposed
to find the minimum energy conformation of a protein-like heteropolymer model
called AB model. The AB model consists of hydrophobic (A) and hydrophilic (B)
monomers analogous to the real proteins. The AB model in three-dimensional
space is represented by simple bead-rod chain system which is identical to the
one-bead protein model. The minimum energy conformations of four different
sequences consisting of 13, 21, 34, and 55 monomers are obtained by the GNS
method. The minimum energies found are lower than those obtained by other
methods. Also the minimum energy conformations found have a similarity with the
real proteins forming a single hydrophobic core.


title:
A limiting absorption principle for the Helmholtz equation with variable
  coefficients

abstract:
  We prove a limiting absorption principle for a generalized Helmholtz equation
on an exterior domain with Dirichlet boundary conditions \begin{equation*}
  (L+\lambda)v=f,
  \qquad
  \lambda\in \mathbb{R}
  \end{equation*} under a Sommerfeld radiation condition at infinity. The
operator $L$ is a second order elliptic operator with variable coefficients,
the principal part is a small, long range perturbation of $-\Delta$, while
lower order terms can be singular and large. The main tool is a sharp uniform
resolvent estimate, which has independent applications to the problem of
embedded eigenvalues and to smoothing estimates for dispersive equations.


title: On the Pitfalls of Nested Monte Carlo

abstract:
  There is an increasing interest in estimating expectations outside of the
classical inference framework, such as for models expressed as probabilistic
programs. Many of these contexts call for some form of nested inference to be
applied. In this paper, we analyse the behaviour of nested Monte Carlo (NMC)
schemes, for which classical convergence proofs are insufficient. We give
conditions under which NMC will converge, establish a rate of convergence, and
provide empirical data that suggests that this rate is observable in practice.
Finally, we prove that general-purpose nested inference schemes are inherently
biased. Our results serve to warn of the dangers associated with naive
composition of inference and models.


title:
Role of magnetic dopants in the phase diagram of Sm1111 pnictides: The
  Mn case

abstract:
  The deliberate insertion of magnetic Mn dopants in the Fe sites of the
optimally-doped SmFeAsO0.88-F0.12 iron-based superconductor can modify in a
controlled way its electronic properties. The resulting phase diagram was
investigated across a wide range of manganese contents (x) by means of
muon-spin spectroscopy (muSR), both in zero- and in transverse fields,
respectively, to probe the magnetic and the superconducting order. The pure
superconducting phase (at x < 0.03) is replaced by a crossover region at
intermediate Mn values (0.03 =< x < 0.08), where superconductivity coexists
with static magnetic order. After completely suppressing superconductivity for
x = 0.08, a further increase in Mn content reinforces the natural tendency
towards antiferromagnetic correlations among the magnetic Mn ions. The sharp
drop of Tc and the induced magnetic order in the presence of magnetic
disorder/dopants, such as Mn, are both consistent with a recent theoretical
model of unconventional superconductors [M. Gastiasoro et al., ArXiv
1606.09495], which includes correlation-enhanced RKKY-couplings between the
impurity moments.


title:
Mass-scaling as a method to constrain outflows and particle acceleration
  from low-luminosity accreting black holes

abstract:
  The `fundamental plane of black hole accretion' (FP), a relation between the
radio luminosities ($L_R$), X-ray luminosities ($L_X$), and masses ($M_{BH}$)
of hard/quiescent state black hole binaries and low-luminosity active galactic
nuclei, suggests some aspects of black hole accretion may be scale invariant.
However, key questions still exist concerning the relationship between the
inflow/outflow behaviour in the `classic' hard state and quiescence, which may
impact this scaling. We show that the broadband spectra of A0620-00
and~\sgra~(the least luminous stellar mass/supermassive black holes on the FP)
can be modelled simultaneously with a physically-motivated outflow-dominated
model where the jet power and all distances are scaled by the black hole mass.
We find we can explain the data of both A0620-00 and~\sgra~(in its non-thermal
flaring state) in the context of two outflow-model scenarios: (1) a
synchrotron-self-Compton dominated state in which the jet plasma reaches highly
sub-equipartition conditions (for the magnetic field with respect to that of
the radiating particles), and (2) a synchrotron dominated state in the
fast-cooling regime in which particle acceleration occurs within the inner few
gravitational radii of the black hole and plasma is close to equipartition. We
show that it may be possible to further discriminate between models (1) and (2)
through future monitoring of its submm/IR/X-ray emission, in particular via
time lags between the variable emission in these bands.


title:
The formation of long-lived high-correlated states of light and
  polarization in high-quality semiconductor microcavity at the resonant laser
  excitation

abstract:
  The correlation function of radiation from a high-quality semiconductor
microcavity at the resonant laser excitation demonstrates oscillations with
surprisingly long-period and damping times of a nanosecond range. It was shown
that the oscillations are not attributed to weak Rabi interaction between
long-lived exciton states and intracavity electromagnetic field. The study of a
response with high spectral resolution had revealed that the oscillations arise
if a spectral position as well as a period of longitudinal laser modes is
similar to modulation components of a microcavity transmission spectrum.


title: The XMM Deep Survey in the CDFS

abstract:
  The Chandra Deep Field South (CDF-S) was observed by XMM-Newton for a total
of about 3 Ms in many periods over the past decade (2001-2002 and 2008-2009).
The main goal of the survey was to obtain good quality X-ray spectroscopy of
the AGN responsible for the bulk of the X-ray background. We will present the
scientific highlights of the XMM-Newton survey and briefly discuss the
perspectives of future observations to pursue XMM deep survey science with
current and forthcoming X-ray facilities.


title: Euler potentials and Magnetic fields

abstract:
  It is known that the cross product of gradients of two scalars, the Euler
potentials (EP), may represent magnetic fields lines. We examine the utility of
such potential in the broader magneto-genesis and dynamo theories, and find
that a reinterpretation of the potentials offer a new understanding of the role
EP may play in the evolution of magnetic fields.


title: Dynamic Simulation of Random Packing of Polydispersive Fine Particles

abstract:
  In this paper, we perform molecular dynamics (MD) simulations to study the
two-dimensional packing process of both monosized and random size particles
with radii ranging from $1.0 \, \mu m$ to $7.0 \, \mu m$. The system was
allowed to settle under gravity towards the bottom of a $300 \, \mu m \times
500 \, \mu m$ rectangular box. The initial positions as well as the radii of
five thousand fine particles were defined along the box by using a random
number generator. Both the translational and the rotational movement of each
particle were considered in the simulations. In order to deal with interacting
fine particles, we take into account both the contact forces and the long-range
dispersive forces. We account for normal and static/sliding tangential friction
forces between particles and between particle and wall by means of a linear
model approach, while the long-range dispersive forces are computed by using a
Lennard-Jones like potential. The packing processes were studied assuming
different long- range interaction strengths. We carry out statistical
calculations of the different quantities studied such as packing density, mean
coordination number and time derivative of the kinetic energy as the system
evolves over time. A size spectral analysis was employed to obtain the radial
distribution function (RDF) of the random close-packed structures (RCPS) for
the case of random size particles. We find that the long-range dispersive
forces can strongly influence the packing process dynamics as they might form
large particle clusters, depending on the intensity of the long-range
interaction strength. However, the general shape of the RDFs for the RCPS is
seen to be more influenced by the hardness of the particles than by the
long-range dispersive forces.


title: Reconfiguring Ordered Bases of a Matroid

abstract:
  For a matroid with an ordered (or "labelled") basis, a basis exchange step
removes one element with label $l$ and replaces it by a new element that
results in a new basis, and with the new element assigned label $l$. We prove
that one labelled basis can be reconfigured to another if and only if for every
label, the initial and final elements with that label lie in the same connected
component of the matroid. Furthermore, we prove that when the reconfiguration
is possible, the number of basis exchange steps required is $O(r^{1.5})$ for a
rank $r$ matroid. For a graphic matroid we improve the bound to $O(r \log r)$.


title:
RecSys Challenge 2016: job recommendations based on preselection of
  offers and gradient boosting

abstract:
  We present the Mim-Solution's approach to the RecSys Challenge 2016, which
ranked 2nd. The goal of the competition was to prepare job recommendations for
the users of the website Xing.com.
  Our two phase algorithm consists of candidate selection followed by the
candidate ranking. We ranked the candidates by the predicted probability that
the user will positively interact with the job offer. We have used Gradient
Boosting Decision Trees as the regression tool.


title: Non-monotone DR-Submodular Function Maximization

abstract:
  We consider non-monotone DR-submodular function maximization, where
DR-submodularity (diminishing return submodularity) is an extension of
submodularity for functions over the integer lattice based on the concept of
the diminishing return property. Maximizing non-monotone DR-submodular
functions has many applications in machine learning that cannot be captured by
submodular set functions. In this paper, we present a
$\frac{1}{2+\epsilon}$-approximation algorithm with a running time of roughly
$O(\frac{n}{\epsilon}\log^2 B)$, where $n$ is the size of the ground set, $B$
is the maximum value of a coordinate, and $\epsilon > 0$ is a parameter. The
approximation ratio is almost tight and the dependency of running time on $B$
is exponentially smaller than the naive greedy algorithm. Experiments on
synthetic and real-world datasets demonstrate that our algorithm outputs almost
the best solution compared to other baseline algorithms, whereas its running
time is several orders of magnitude faster.


title:
Discontinuous change of shear modulus for frictional jammed granular
  materials

abstract:
  The shear modulus of jammed frictional granular materials with the harmonic
repulsive interaction under an oscillatory shear is numerically investigated.
It is confirmed that the storage modulus, the real part of the shear modulus,
for frictional grains with sufficiently small strain amplitude $\gamma_0$
discontinuously emerges at the jamming transition point. The storage modulus
for small $\gamma_0$ differs from that of frictionless grains even in the zero
friction limit, while they are almost identical with each other for
sufficiently large $\gamma_0$, where the transition becomes continuous. The
stress-strain curve exhibits a hysteresis loop even for a small strain, which
connects a linear region for sufficiently small strain to another linear region
for relatively larger strain. We propose a new scaling law to interpolate
between the states of small and large $\gamma_0$.


title:
Positive blood culture detection in time series data using a BiLSTM
  network

abstract:
  The presence of bacteria or fungi in the bloodstream of patients is abnormal
and can lead to life-threatening conditions. A computational model based on a
bidirectional long short-term memory artificial neural network, is explored to
assist doctors in the intensive care unit to predict whether examination of
blood cultures of patients will return positive. As input it uses nine
monitored clinical parameters, presented as time series data, collected from
2177 ICU admissions at the Ghent University Hospital. Our main goal is to
determine if general machine learning methods and more specific, temporal
models, can be used to create an early detection system. This preliminary
research obtains an area of 71.95% under the precision recall curve, proving
the potential of temporal neural networks in this context.


title:
Undefinability of Standard sequent calculi for Paraconsistent
  three-valued logics

abstract:
  In this paper we study the deductive properties of a family of 3-valued
paraconsistent logics. We define a notion of standard sequent calculus and
prove that there is no sound and complete standard sequent calculus for these
logics. Moreover, we provide non-standard sound, complete and cut free sequent
calculus for Paraconsistent Weak Kleene Logic using three-sided sequents.


title:
Optically thick envelopes around ULXs powered by accreating neutron
  stars

abstract:
  Magnetized neutron stars power at least some ultra-luminous X-ray sources.
The accretion flow in these cases is interrupted at the magnetospheric radius
and then reaches the surface of a neutron star following magnetic field lines.
Accreting matter moving along magnetic field lines forms the accretion envelope
around the central object. We show that, in case of high mass accretion rates
$\gtrsim 10^{19}\,{\rm g\,s^{-1}}$ the envelope becomes closed and optically
thick, which influences the dynamics of the accretion flow and the
observational manifestation of the neutron star hidden behind the envelope.
Particularly, the optically thick accretion envelope results in a multi-color
black-body spectrum originating from the magnetospheric surface. The spectrum
and photon energy flux vary with the viewing angle, which gives rise to
pulsations characterized by high pulsed fraction and typically smooth pulse
profiles. The reprocessing of radiation due to interaction with the envelope
leads to the disappearance of cyclotron scattering features from the spectrum.
We speculate that the super-orbital variability of ultra-luminous X-ray sources
powered by accreting neutron stars can be attributed to precession of the
neutron star due to interaction of magnetic dipole with the accretion disc.


title: A microprocessor based on a two-dimensional semiconductor

abstract:
  The advent of microcomputers in the 1970s has dramatically changed our
society. Since then, microprocessors have been made almost exclusively from
silicon, but the ever-increasing demand for higher integration density and
speed, lower power consumption and better integrability with everyday goods has
prompted the search for alternatives. Germanium and III-V compound
semiconductors are being considered promising candidates for future
high-performance processor generations and chips based on thin-film plastic
technology or carbon nanotubes could allow for embedding electronic
intelligence into arbitrary objects for the Internet-of-Things. Here, we
present a 1-bit implementation of a microprocessor using a two-dimensional
semiconductor - molybdenum disulfide. The device can execute user-defined
programs stored in an external memory, perform logical operations and
communicate with its periphery. Importantly, our 1-bit design is readily
scalable to multi-bit data. The device consists of 115 transistors and
constitutes the most complex circuitry so far made from a two-dimensional
material.


title: New Classes of $p$-ary Few Weights Codes

abstract:
  In this paper, several classes of three-weight codes and two-weight codes for
the homogeneous metric over the chain ring $R=\mathbb{F}_p+u\mathbb{F}_p+\cdots
+u^{k-1}\mathbb{F}_{p},$ with $u^k=0,$ are constructed, which generalises
\cite{SL}, the special case of $p=k=2.$ These codes are defined as trace codes.
In some cases of their defining sets, they are abelian. Their homogeneous
weight distributions are computed by using exponential sums. In particular, in
the two-weight case, we give some conditions of optimality of their Gray images
by using the Griesmer bound. Their dual homogeneous distance is also given. The
codewords of these codes are shown to be minimal for inclusion of supports, a
fact favorable to an application to secret sharing schemes.


title: Two new families of two-weight codes

abstract:
  We construct two new infinite families of trace codes of dimension $2m$, over
the ring $\mathbb{F}_p+u\mathbb{F}_p,$ when $p$ is an odd prime. They have the
algebraic structure of abelian codes. Their Lee weight distribution is computed
by using Gauss sums. By Gray mapping, we obtain two infinite families of linear
$p$-ary codes of respective lengths $(p^m-1)^2$ and $2(p^m-1)^2.$ When $m$ is
singly-even, the first family gives five-weight codes. When $m$ is odd, and
$p\equiv 3 \pmod{4},$ the first family yields $p$-ary two-weight codes, which
are shown to be optimal by application of the Griesmer bound. The second family
consists of two-weight codes that are shown to be optimal, by the Griesmer
bound, whenever $p=3$ and $m \ge 3,$ or $p\ge 5$ and $m\ge 4.$ Applications to
secret sharing schemes are given.


title:
Characterization of Lipschitz spaces via commutators of the
  Hardy-Littlewood maximal function

abstract:
  Let $M$ be the Hardy-Littlewood maximal function and $b$ be a locally
integrable function. Denote by $M_b$ and $[b,M]$ the maximal commutator and the
(nonlinear) commutator of $M$ with $b$. In this paper, the author consider the
boundedness of $M_b$ and $[b,M]$ on Lebesgue spaces and Morrey spaces when $b$
belongs to the Lipschitz space, by which some new characterizations of the
Lipschitz spaces are given.


title:
Unit Dependency Graph and its Application to Arithmetic Word Problem
  Solving

abstract:
  Math word problems provide a natural abstraction to a range of natural
language understanding problems that involve reasoning about quantities, such
as interpreting election results, news about casualties, and the financial
section of a newspaper. Units associated with the quantities often provide
information that is essential to support this reasoning. This paper proposes a
principled way to capture and reason about units and shows how it can benefit
an arithmetic word problem solver. This paper presents the concept of Unit
Dependency Graphs (UDGs), which provides a compact representation of the
dependencies between units of numbers mentioned in a given problem. Inducing
the UDG alleviates the brittleness of the unit extraction system and allows for
a natural way to leverage domain knowledge about unit compatibility, for word
problem solving. We introduce a decomposed model for inducing UDGs with minimal
additional annotations, and use it to augment the expressions used in the
arithmetic word problem solver of (Roy and Roth 2015) via a constrained
inference framework. We show that introduction of UDGs reduces the error of the
solver by over 10 %, surpassing all existing systems for solving arithmetic
word problems. In addition, it also makes the system more robust to adaptation
to new vocabulary and equation forms .


title: Fractal generalized Pascal matrices

abstract:
  Set of generalized Pascal matrices whose elements are generalized binomial
coefficients is considered as an integral object. The special system of
generalized Pascal matrices, based on which we are building fractal generalized
Pascal matrices, is introduced. Pascal matrix (Pascal triangle) is the Hadamard
product of the fractal generalized Pascal matrices. The concept of zero
generalized Pascal matrices, an example of which is the Pascal triangle modulo
2, arise in connection with the system of matrices introduced.


title: On the Distinguishing number of Functigraphs

abstract:
  Let $G_{1}$ and $G_{2}$ be disjoint copies of a graph $G$, and let
$g:V(G_{1})\rightarrow V(G_{2})$ be a function. A functigraph $F_{G}$ consists
of the vertex set $V(G_{1})\cup V(G_{2})$ and the edge set $E(G_{1})\cup
E(G_{2})\cup \{uv:g(u)=v\}$. In this paper, we extend the study of the
distinguishing number of a graph to its functigraph. We discuss the behavior of
the distinguishing number in passing from $G$ to $F_{G}$ and find its sharp
lower and upper bounds. We also discuss the distinguishing number of
functigraphs of complete graphs and join graphs.


title:
Impurity-limited quantum transport variability in magnetic tunnel
  junctions

abstract:
  We report an extensive first-principles investigation of impurity-induced
device-to-device variability of spin-polarized quantum tunneling through
Fe/MgO/Fe magnetic tunnel junctions (MTJ). In particular, we calculated the
tunnel magnetoresistance ratio (TMR) and the average values and variances of
the currents and spin transfer torque (STT) of an interfacially doped Fe/MgO/Fe
MTJ. Further, we predicted that N-doped MgO can improve the performance of a
doped Fe/MgO/Fe MTJ. Our first-principles calculations of the fluctuations of
the on/off currents and STT provide vital information for future predictions of
the long-term reliability of spintronic devices, which is imperative for
high-volume production.


title: On some nonlinear evolution equation of second order

abstract:
  Here we study the abstract nonlinear differential equation of second order
that in special case is the equation of the type of equation of traffic flow.
We prove the solvability theorem for the posed problem under the appropriate
conditions and also investigate the behaviour of the solution.


title: Growth factor in $f(T,\mathcal{T})$ gravity

abstract:
  We investigate the growth factor for sub-horizon modes during late times in
$f(T,\mathcal{T})$ gravity, where $T$ is the torsion scalar and $\mathcal{T}$
is the trace of the stress-energy tensor. This is achieved by obtaining the
modified M\'{e}sz\'{a}ros equation, which describes the evolution of the
perturbations of the matter energy density, and obtaining numerical results.
Such results are obtained by solving the modified continuity equation and
analysing the behaviour of the solutions of the latter using various
constraints on the integration constants. Furthermore, the role of the
anisotropic term $\pi^{S}$ is investigated.


title:
Manipulation of quantum states in memory cell: controllable Mach-Zehnder
  interferometer

abstract:
  In this article, we consider the possibility of manipulation of quantum
signals, ensured by the use of the tripod-type atomic memory cell. We show that
depending on a configuration of driving fields at the writing and reading, such
a cell allows both to store and transform the signal. It is possible to provide
the operation of the memory cell in a Mach-Zehnder interferometer mode passing
two successive pulses at the input. We proposed a procedure of partial signal
read out that provides entanglement between the retrieved light and the atomic
ensemble. Thus, we have shown that tripod atomic cell is a promising candidate
to implement of quantum logical operations, including two-qubit ones, that can
be performed on the basis of only one cell.


title: Many-body localization in incommensurate models with a mobility edge

abstract:
  We review the physics of many-body localization in models with incommensurate
potentials. In particular, we consider one-dimensional quasiperiodic models
with single-particle mobility edges. Although a conventional perspective
suggests that delocalized states act as a thermalizing bath for the localized
states in the presence of of interactions, there is evidence that such systems
can display non-ergodicity. This is in part due to the fact that the
delocalized states do not have any kind of protection due to symmetry or
topology and are thus susceptible to localization. A study of non-interacting
incommensurate models shows that they admit extended, partially extended, and
fully localized many-body states. These models cannot thermalize dynamically
and remain localized upon the introduction of interactions. In particular, for
a certain range of energy, the system can host a non-ergodic extended (i.e.
metallic) phase in which the energy eigenstates violate the eigenstate
thermalization hypothesis (ETH) but the entanglement entropy obeys volume-law
scaling. The level statistics and entanglement growth also indicate the lack of
ergodicity in these models. The phenomenon of localization and non-ergodicity
in a system with interactions despite the presence of single-particle
delocalized states is closely related to the so-called "many-body proximity
effect" and can also be observed in models with disorder coupled to systems
with delocalized degrees of freedom. Many-body localization in systems with
incommensurate potentials (without single-particle mobility edges) have been
realized experimentally, and we show how this can be modified to study the the
effects of such mobility edges. Demonstrating the failure of thermalization in
the presence of a single-particle mobility edge in the thermodynamic limit
would indicate a more robust violation of the ETH.


title:
Ubiquitous evaluation of layer potentials using Quadrature by
  Kernel-Independent Expansion

abstract:
  We introduce a quadrature scheme--QBKIX--for the high-order accurate
evaluation of layer potentials associated with general elliptic PDEs near to
and on the domain boundary. Relying solely on point evaluations of the
underlying kernel, our scheme is essentially PDE-independent; in particular, no
analytic expansion nor addition theorem is required. Moreover, it applies to
boundary integrals with singular, weakly singular, and hypersingular kernels.
  Our work builds upon Quadrature by Expansion (QBX), which approximates the
potential by an analytic expansion in the neighborhood of each expansion
center. In contrast, we use a sum of fundamental solutions lying on a ring
enclosing the neighborhood, and solve a small dense linear system for their
coefficients to match the potential on a smaller concentric ring.
  We test the new method with Laplace, Helmholtz, Yukawa, Stokes, and Navier
(elastostatic) kernels in two dimensions (2D) using adaptive, panel-based
boundary quadratures on smooth and corner domains. Advantages of the algorithm
include its relative simplicity of implementation, immediate extension to new
kernels, dimension-independence (allowing simple generalization to 3D), and
compatibility with fast algorithms such as the kernel-independent FMM.


title: Ultralight scalars and resonances in black-hole physics

abstract:
  Ultralight degrees of freedom coupled to matter lead to resonances, which can
be excited when the Compton wavelength of the field equals a dynamical scale in
the problem. For binaries composed of a star orbiting a supermassive black
hole, these resonances lead to a smoking-gun effect: a periastron distance
which {\it stalls}, even in the presence of gravitational-wave dissipation.
This effect, also called a {\it floating orbit}, occurs for generic equatorial
but eccentric orbits and we argue that finite-size effects are not enough to
suppress it.


title: Semi-supervised learning of deep metrics for stereo reconstruction

abstract:
  Deep-learning metrics have recently demonstrated extremely good performance
to match image patches for stereo reconstruction. However, training such
metrics requires large amount of labeled stereo images, which can be difficult
or costly to collect for certain applications. The main contribution of our
work is a new semi-supervised method for learning deep metrics from unlabeled
stereo images, given coarse information about the scenes and the optical
system. Our method alternatively optimizes the metric with a standard
stochastic gradient descent, and applies stereo constraints to regularize its
prediction. Experiments on reference data-sets show that, for a given network
architecture, training with this new method without ground-truth produces a
metric with performance as good as state-of-the-art baselines trained with the
said ground-truth. This work has three practical implications. Firstly, it
helps to overcome limitations of training sets, in particular noisy ground
truth. Secondly it allows to use much more training data during learning.
Thirdly, it allows to tune deep metric for a particular stereo system, even if
ground truth is not available.


title: Observables of QCD Diffraction

abstract:
  A new combinatorial vector space measurement model is introduced for soft QCD
diffraction. The model independent mathematical construction resolves
experimental complications; the theoretical framework of the approach includes
the Good-Walker view of diffraction, Regge phenomenology together with AGK
cutting rules and random fluctuations.


title: How much market making does a market need?

abstract:
  We consider a simple model for the evolution of a limit order book in which
limit orders of unit size arrive according to independent Poisson processes.
The frequencies of buy limit orders below a given price level, respectively
sell limit orders above a given level are described by fixed demand and supply
functions. Buy (resp. sell) limit orders that arrive above (resp. below) the
current ask (resp. bid) price are converted into market orders. There is no
cancellation of limit orders. This model has independently been reinvented by
several authors, including Stigler in 1964 and Luckock in 2003, who was able to
calculate the equilibrium distribution of the bid and ask prices. We extend the
model by introducing market makers that simultaneously place both a buy and
sell limit order at the current bid and ask price. We show how the introduction
of market makers reduces the spread, which in the original model is
unrealistically large. In particular, we are able to calculate the exact rate
at which market makers need to place orders in order to close the spread
completely. If this rate is exceeded, we show that the price settles at a
random level that in general does not correspond the Walrasian equilibrium
price.


title: Triangular Ramsey Numbers

abstract:
  The purpose of this paper is to introduce the idea of triangular Ramsey
numbers and provide values as well as upper and lower bounds for them. To do
this, the combinatorial game Mines is introduced; after some necessary theorems
about triangular sets are proved. This game is easy enough that young children
are able to play. The most basic variations of this game are analyzed and
theorems about winning strategies and the existence of draws are proved. The
game of Mines is then used to define triangular Ramsey numbers. Lower bounds
are found for these triangular Ramsey numbers using the probabilistic method
and the theorems about triangular sets.


title: Food Image Recognition by Using Convolutional Neural Networks (CNNs)

abstract:
  Food image recognition is one of the promising applications of visual object
recognition in computer vision. In this study, a small-scale dataset consisting
of 5822 images of ten categories and a five-layer CNN was constructed to
recognize these images. The bag-of-features (BoF) model coupled with support
vector machine (SVM) was first evaluated for image classification, resulting in
an overall accuracy of 56%; while the CNN model performed much better with an
overall accuracy of 74%. Data augmentation techniques based on geometric
transformation were applied to increase the size of training images, which
achieved a significantly improved accuracy of more than 90% while preventing
the overfitting issue that occurred to the CNN based on raw training data.
Further improvements can be expected by collecting more images and optimizing
the network architecture and hyper-parameters.


title:
Estimating latent feature-feature interactions in large feature-rich
  graphs

abstract:
  Real-world complex networks describe connections between objects; in reality,
those objects are often endowed with some kind of features. How does the
presence or absence of such features interplay with the network link structure?
Although the situation here described is truly ubiquitous, there is a limited
body of research dealing with large graphs of this kind. Many previous works
considered homophily as the only possible transmission mechanism translating
node features into links. Other authors, instead, developed more sophisticated
models, that are able to handle complex feature interactions, but are unfit to
scale to very large networks. We expand on the MGJ model, where interactions
between pairs of features can foster or discourage link formation. In this
work, we will investigate how to estimate the latent feature-feature
interactions in this model. We shall propose two solutions: the first one
assumes feature independence and it is essentially based on Naive Bayes; the
second one, which relaxes the independence assumption assumption, is based on
perceptrons. In fact, we show it is possible to cast the model equation in
order to see it as the prediction rule of a perceptron. We analyze how
classical results for the perceptrons can be interpreted in this context; then,
we define a fast and simple perceptron-like algorithm for this task, which can
process $10^8$ links in minutes. We then compare these two techniques, first
with synthetic datasets that follows our model, gaining evidence that the Naive
independence assumptions are detrimental in practice. Secondly, we consider a
real, large-scale citation network where each node (i.e., paper) can be
described by different types of characteristics; there, our algorithm can
assess how well each set of features can explain the links, and thus finding
meaningful latent feature-feature interactions.


title:
Wikiwhere: An interactive tool for studying the geographical provenance
  of Wikipedia references

abstract:
  Wikipedia articles about the same topic in different language editions are
built around different sources of information. For example, one can find very
different news articles linked as references in the English Wikipedia article
titled "Annexation of Crimea by the Russian Federation" than in its German
counterpart (determined via Wikipedia's language links). Some of this
difference can of course be attributed to the different language proficiencies
of readers and editors in separate language editions, yet, although including
English-language news sources seems to be no issue in the German edition,
English references that are listed do not overlap highly with the ones in the
article's English version. Such patterns could be an indicator of bias towards
certain national contexts when referencing facts and statements in Wikipedia.
However, determining for each reference which national context it can be traced
back to, and comparing the link distributions to each other is infeasible for
casual readers or scientists with non-technical backgrounds. Wikiwhere answers
the question where Web references stem from by analyzing and visualizing the
geographic location of external reference links that are included in a given
Wikipedia article. Instead of relying solely on the IP location of a given URL
our machine learning models consider several features.


title: Deep Learning with Energy-efficient Binary Gradient Cameras

abstract:
  Power consumption is a critical factor for the deployment of embedded
computer vision systems. We explore the use of computational cameras that
directly output binary gradient images to reduce the portion of the power
consumption allocated to image sensing. We survey the accuracy of binary
gradient cameras on a number of computer vision tasks using deep learning.
These include object recognition, head pose regression, face detection, and
gesture recognition. We show that, for certain applications, accuracy can be on
par or even better than what can be achieved on traditional images. We are also
the first to recover intensity information from binary spatial gradient
images--useful for applications with a human observer in the loop, such as
surveillance. Our results, which we validate with a prototype binary gradient
camera, point to the potential of gradient-based computer vision systems.


title:
Tensile Strained Gray Tin: a New Dirac Semimetal for Observing Negative
  Magnetoresistance with Shubnikov-de-Haas Oscillation

abstract:
  The extremely stringent requirement on material quality has hindered the
investigation and potential applications of exotic chiral magnetic effect in
Dirac semimetals. Here, we propose that gray tin is a perfect candidate for
observing the chiral anomaly effect and Shubnikov-de-Haas (SdH) oscillation at
relatively low magnetic field. Based on effective $k.p$ analysis and
first-principles calculations, we discover that gray tin becomes a Dirac
semimetal under tensile uniaxial strain, in contrast to a topological insulator
under compressive uniaxial strain as known before. In this newly found Dirac
semimetal state, two Dirac points which are tunable by tensile [001] strains,
lie in the $k_z$ axis and Fermi arcs appear in the (100) surface. Duo the low
carrier concentration and high mobility of gray tin, a large chiral anomaly
induced negative magnetoresistance and a strong SdH oscillation are anticipated
in this half of strain spectrum. Comparing to other Dirac semimetals, the
proposed Dirac semimetal state in the nontoxic elemental gray tin can be more
easily manipulated and accurately controlled. We envision that gray tin
provides a perfect platform for strain engineering of chiral magnetic effects
by sweeping through the strain spectrum from positive to negative and vice
versa.


title: Majorana dc Josephson current mediated by a quantum dot

abstract:
  The Josephson supercurrent through the hybrid Majorana--quantum dot--Majorana
junction is investigated. We particularly analyze the effect of spin-selective
coupling between the Majorana and quantum dot states, which emerges only in the
topological phase and will influence the current through bent junctions and/or
in the presence of magnetic fields in the quantum dot. We find that the
characteristic behaviors of the supercurrent through this system are quite
counterintuitive, remarkably differing from the resonant tunneling, e.g.,
through the similar (normal phase) superconductor--quantum dot--superconductor
junction. Our analysis is carried out under the influence of full set-up
parameters and for both the $2\pi$ and $4\pi$ periodic currents. The present
study is expected to be relevant to future exploration of applications of the
Majorana-nanowire circuits.


title: Online Page Migration on Ring Networks in Uniform Model

abstract:
  This paper explores the problem of page migration in ring networks. A ring
network is a connected graph, in which each node is connected with exactly two
other nodes. In this problem, one of the nodes in a given network holds a page
of size D. This node is called the server and the page is a non-duplicable data
in the network. Requests are issued by nodes to access the page one after
another. Every time a new request is issued, the server must serve the request
and may migrate to another node before the next request arrives. A service
costs the distance between the server and the requesting node, and the
migration costs the distance of the migration multiplied by D. The problem is
to minimize the total costs of services and migrations. We study this problem
in uniform model, for which the page has a unit size, i.e. D=1. A
3.326-competitive algorithm improving the current best upper bound is designed.
We show that this ratio is tight for our algorithm.


title:
Nonequilibrium Generalised Langevin Equation for the calculation of heat
  transport properties in model 1D atomic chains coupled to two 3D thermal
  baths

abstract:
  We use a Generalised Langevin Equation (GLE) scheme to study the thermal
transport of low dimensional systems. In this approach, the central classical
region is connected to two realistic thermal baths kept at two different
temperatures [H. Ness et al., Phys. Rev. B {\bf 93}, 174303 (2016)]. We
consider model Al systems, i.e. one-dimensional atomic chains connected to
three-dimensional baths. The thermal transport properties are studied as a
function of the chain length $N$ and the temperature difference $\Delta T$
between the baths. We calculate the transport properties both in the linear
response regime and in the non-linear regime. Two different laws are obtained
for the linear conductance versus the length of the chains. For large
temperatures ($T \gtrsim 500$ K) and temperature differences ($\Delta T \gtrsim
500$ K), the chains, with $N > 18$ atoms, present a diffusive transport regime
with the presence of a temperature gradient across the system. For lower
temperatures($T \lesssim 500$ K) and temperature differences ($\Delta T
\lesssim 400$ K), a regime similar to the ballistic regime is observed. Such a
ballistic-like regime is also obtained for shorter chains ($N \le 15 $). Our
detailed analysis suggests that the behaviour at higher temperatures and
temperature differences is mainly due to anharmonic effects within the long
chains.


title: Ensembles of Generative Adversarial Networks

abstract:
  Ensembles are a popular way to improve results of discriminative CNNs. The
combination of several networks trained starting from different initializations
improves results significantly. In this paper we investigate the usage of
ensembles of GANs. The specific nature of GANs opens up several new ways to
construct ensembles. The first one is based on the fact that in the minimax
game which is played to optimize the GAN objective the generator network keeps
on changing even after the network can be considered optimal. As such ensembles
of GANs can be constructed based on the same network initialization but just
taking models which have different amount of iterations. These so-called self
ensembles are much faster to train than traditional ensembles. The second
method, called cascade GANs, redirects part of the training data which is badly
modeled by the first GAN to another GAN. In experiments on the CIFAR10 dataset
we show that ensembles of GANs obtain model probability distributions which
better model the data distribution. In addition, we show that these improved
results can be obtained at little additional computational cost.


title:
Mining Spatio-temporal Data on Industrialization from Historical
  Registries

abstract:
  Despite the growing availability of big data in many fields, historical data
on socioevironmental phenomena are often not available due to a lack of
automated and scalable approaches for collecting, digitizing, and assembling
them. We have developed a data-mining method for extracting tabulated, geocoded
data from printed directories. While scanning and optical character recognition
(OCR) can digitize printed text, these methods alone do not capture the
structure of the underlying data. Our pipeline integrates both page layout
analysis and OCR to extract tabular, geocoded data from structured text. We
demonstrate the utility of this method by applying it to scanned manufacturing
registries from Rhode Island that record 41 years of industrial land use. The
resulting spatio-temporal data can be used for socioenvironmental analyses of
industrialization at a resolution that was not previously possible. In
particular, we find strong evidence for the dispersion of manufacturing from
the urban core of Providence, the state's capital, along the Interstate 95
corridor to the north and south.


title:
A Protocol for a Secure Remote Keyless Entry System Applicable in
  Vehicles using Symmetric-Key Cryptography

abstract:
  In our modern society comfort became a standard. This comfort, especially in
cars can only be achieved by equipping the car with more electronic devices.
Some of the electronic devices must cooperate with each other and thus they
require a communication channel, which can be wired or wireless. In these days,
it would be hard to sell a new car operating with traditional keys. Almost all
modern cars can be locked or unlocked with a Remote Keyless System. A Remote
Keyless System consists of a key fob that communicates wirelessly with the car
transceiver that is responsible for locking and unlocking the car. However
there are several threats for wireless communication channels.
  This paper describes the possible attacks against a Remote Keyless System and
introduces a secure protocol as well as a lightweight Symmetric Encryption
Algorithm for a Remote Keyless Entry System applicable in vehicles.


title: Rigid Band Doping of Large-Pore Semiconducting Graphene Nanomesh

abstract:
  Porous graphene structures, also termed graphene nanomeshes (GNMs), are
garnering increasing interest due to their potential application to important
technologies such as chemical sensing, ion-filtration, and nanoelectronics.
Semiconducting GNMs designed to have fractional eV band gaps are good
candidates for graphene-based electronics, provided that a mechanism for their
stable and controlled doping is developed. Recent work has shown that
controlled passivation of the edges of subnanometer pores and subsequent doping
by atoms or molecules gives rise to {\it p}- and {\it n}-doped GNM structures.
However, these structures are difficult to fabricate at the nanoscale. Here, we
use first principle calculations to study the effect of the pore size on the
doping physics of GNM structures with larger pores that can potentially host
more than a single dopant. We show that such doping mechanism is effective even
for pores with relatively large radii. We also study the effect of the number
of dopants per pore on doping stability. We find that stable rigid band {\it
n}- and {\it p}-doping emerges in such structures even if the dopants form a
nano-cluster in the pore - rigid band doping is achieved in all {\it n}- and
{\it p}-doping studied. Such doped large-pore GNM structures have potential
applications as field effect transistors, and as transparent conducting
electrodes.


title: Mass growth of objects and categorical entropy

abstract:
  In the pioneer work by Dimitrov-Haiden-Katzarkov-Kontsevich, they introduced
various categorical analogies from classical theory of dynamical systems. In
particular, they defined the entropy of an endofunctor on a triangulated
category with a split generator. In the connection between categorical theory
and classical theory, a stability condition on a triangulated category plays
the role of a measured foliation so that one can measure the "volume" of
objects, called the mass, via the stability condition. The aim of this paper is
to establish fundamental properties of the growth rate of mass of objects under
the mapping by the endofunctor and to clarify the relationship between the
entropy and that. We also show that they coincide under a certain condition.


title:
Center of the algebra of functions on the quantum group $SU_q(2)$ and
  related topics

abstract:
  The center of the algebra of continuous functions on the quantum group
$SU_q(2)$ is determined as well as centers of other related algebras. Several
other results concerning this quantum group are given with direct proofs based
on concrete realization of these algebras as algebras of operators on a Hilbert
space.


title:
Improving congestion control for Concurrent Multipath Transfer through
  bandwidth estimation based resource pooling

abstract:
  Stream Control Transmission Protocol (SCTP) was introduced in 2001 as a
multipath variant to traditional transport protocols, i.e. Transmission Control
Protocol (TCP) and User Datagram Protocol (UDP). Concurrent Multipath Transfer
(CMT) has been proposed as an extension for SCTP to support concurrent usage of
available multiple paths. In this paper, we propose a new congestion control
algorithm for CMT-SCTP based on the principle of resource pooling. We use the
connection bandwidth estimates to obtain the collection of the network
resources being used by different flows on multiple paths. Based on these
bandwidth estimates, we have used the bandwidth estimation based resource
pooling approach to adjust the congestion window of the respective paths. We
compare our proposed scheme with CMT-SCTP through ns-2 based simulations.


title: Note on Reheating in G-inflation

abstract:
  We study particle production at the end of inflation in kinetically driven
G-inflation model and show that, in spite of the fact that there are no
inflaton oscillations and hence no parametric resonance instabilities, the
production of matter particles due to a coupling to the evolving inflaton field
can be more efficient than pure gravitational Parker particle production.


title:
Search for single production of a heavy vector-like T quark decaying to
  a Higgs boson and a top quark with a lepton and jets in the final state

abstract:
  A search for single production of vector-like top quark partners (T) decaying
into a Higgs boson and a top quark is performed using data from pp collisions
at a centre-of-mass energy of 13 TeV collected by the CMS experiment at the
CERN LHC, corresponding to an integrated luminosity of 2.3 inverse femtobarns.
The top quark decay includes an electron or a muon while the Higgs boson decays
into a pair of b quarks. No significant excess over standard model backgrounds
is observed. Exclusion limits on the product of the production cross section
and the branching fraction are derived in the T quark mass range 700 to 1800
GeV. For a mass of 1000 GeV, values of the product of the production cross
section and the branching fraction greater than 0.8 and 0.7 pb are excluded at
95% confidence level, assuming left- and right-handed coupling of the T quark
to standard model particles, respectively. This is the first analysis setting
exclusion limits on the cross section of singly produced vector-like T quarks
at a centre-of-mass energy of 13 TeV.


title: All-optical band engineering of gapped Dirac materials

abstract:
  We demonstrate theoretically that the interaction of electrons in gapped
Dirac materials (gapped graphene and transition-metal dichalchogenide
monolayers) with a strong off-resonant electromagnetic field (dressing field)
substantially renormalizes the band gaps and the spin-orbit splitting.
Moreover, the renormalized electronic parameters drastically depend on the
field polarization. Namely, a linearly polarized dressing field always
decreases the band gap (and, particularly, can turn the gap into zero), whereas
a circularly polarized field breaks the equivalence of valleys in different
points of the Brillouin zone and can both increase and decrease corresponding
band gaps. As a consequence, the dressing field can serve as an effective tool
to control spin and valley properties of the materials and be potentially
exploited in optoelectronic applications.


title: On lengths on semisimple groups

abstract:
  We prove that every length on a simple group over a locally compact field, is
either bounded or proper.


title: Peculiar spectral statistics of ensembles of trees and star-like graphs

abstract:
  In this paper we investigate the eigenvalue statistics of exponentially
weighted ensembles of full binary trees and $p$-branching star graphs. We show
that spectral densities of corresponding adjacency matrices demonstrate
peculiar ultrametric structure inherent to sparse systems. In particular, the
tails of the distribution for binary trees share the "Lifshitz singularity"
emerging in the one-dimensional localization, while the spectral statistics of
$p$-branching star-like graphs is less universal, being strongly dependent on
$p$. The hierarchical structure of spectra of adjacency matrices is interpreted
as sets of resonance frequencies, that emerge in ensembles of fully branched
tree-like systems, known as dendrimers. However, the relaxational spectrum is
not determined by the cluster topology, but has rather the number-theoretic
origin, reflecting the peculiarities of the rare-event statistics typical for
one-dimensional systems with a quenched structural disorder. The similarity of
spectral densities of an individual dendrimer and of ensemble of linear chains
with exponential distribution in lengths, demonstrates that dendrimers could be
served as simple disorder-less toy models of one-dimensional systems with
quenched disorder.


title:
Interacting opinion and disease dynamics in multiplex networks:
  discontinuous phase transition and non-monotonic consensus times

abstract:
  Opinion formation and disease spreading are among the most studied dynamical
processes on complex networks. In real societies, it is expected that these two
processes depend on and affect each other. However, little is known about the
effects of opinion dynamics over disease dynamics and vice versa, since most
studies treat them separately. In this work we study the dynamics of the voter
model for opinion formation intertwined with that of the contact process for
disease spreading, in a population of agents that interact via two types of
connections, social and contact. These two interacting dynamics take place on
two layers of networks, coupled through a fraction $q$ of links present in both
networks. The probability that an agent updates its state depends on both, the
opinion and disease states of the interacting partner. We find that the opinion
dynamics has striking consequences on the properties of disease spreading. The
most important is that the smooth (continuous) transition from a healthy to an
endemic phase observed in the contact process, becomes abrupt (discontinuous)
in the two-layer system. Therefore, disregarding the effects of social dynamics
on epidemics propagation may lead to a misestimation of the real magnitude of
the spreading. Also, an endemic-healthy discontinuous transition is found when
the coupling $q$ overcomes a threshold value. Furthermore, we show that the
disease dynamics delays the opinion consensus, leading to a consensus time that
varies non-monotonically with $q$ in a large range of the model's parameters. A
mean-field approach reveals that the coupled dynamics of opinions and disease
can be approximately described by the dynamics of the voter model decoupled
from that of the contact process, with effective probabilities of opinion and
disease transmission.


title: Equilibrium fluctuations for the slow boundary exclusion process

abstract:
  We prove that the equilibrium fluctuations of the symmetric simple exclusion
process in contact with slow boundaries is given by an Ornstein-Uhlenbeck
process with Dirichlet, Robin or Neumann boundary conditions depending on the
range of the parameter that rules the slowness of the boundaries.


title: Mixed powerdomains for probability and nondeterminism

abstract:
  We consider mixed powerdomains combining ordinary nondeterminism and
probabilistic nondeterminism. We characterise them as free algebras for
suitable (in)equation-al theories; we establish functional representation
theorems; and we show equivalencies between state transformers and
appropriately healthy predicate transformers. The extended nonnegative reals
serve as `truth-values'. As usual with powerdomains, everything comes in three
flavours: lower, upper, and order-convex. The powerdomains are suitable convex
sets of subprobability valuations, corresponding to resolving nondeterministic
choice before probabilistic choice. Algebraically this corresponds to the
probabilistic choice operator distributing over the nondeterministic choice
operator. (An alternative approach to combining the two forms of nondeterminism
would be to resolve probabilistic choice first, arriving at a domain-theoretic
version of random sets. However, as we also show, the algebraic approach then
runs into difficulties.)
  Rather than working directly with valuations, we take a domain-theoretic
functional-analytic approach, employing domain-theoretic abstract convex sets
called Kegelspitzen; these are equivalent to the abstract probabilistic
algebras of Graham and Jones, but are more convenient to work with. So we
define power Kegelspitzen, and consider free algebras, functional
representations, and predicate transformers. To do so we make use of previous
work on domain-theoretic cones (d-cones), with the bridge between the two of
them being provided by a free d-cone construction on Kegelspitzen.


title:
A Non-Local Means Approach for Gaussian Noise Removal from Images using
  a Modified Weighting Kernel

abstract:
  Gaussian noise removal is an interesting area in digital image processing not
only to improve the visual quality, but for its impact on other post-processing
algorithms like image registration or segmentation. Many presented
state-of-the-art denoising methods are based on the self-similarity or
patch-based image processing. Specifically, Non-Local Means (NLM) as a
patch-based filter has gained increasing attention in recent years.
Essentially, this filter tends to obtain the noise-less signal value by
computing the Gaussian-weighted Euclidean distance between the patch
under-processing and other patches inside the image. However, the NLM filter is
sensitive to the outliers (pixels that their intensity values are far away from
other pixels) inside the patch, meaning that the pixels with the symmetric
locations in the patch are assigned the same weight. This can lead to
sub-optimal denoising performance when the destructive nature of noise
generates some outliers inside patches. In this paper, we propose a new
weighting approach to modify the Gaussian kernel of the NLM filter. Our
approach employs the geometric distance between image intensities to come up
with new weights for each pixel of a patch, lowering the impact of outliers on
the denoising performance. Experiments on a set of standard images and
different noise levels show that our proposed method outperforms the other
compared denoising filters.


title: Elastic sheets, phase surfaces and pattern universes

abstract:
  We connect the theories of the deformation of elastic surfaces and phase
surfaces arising in the description of almost periodic patterns. In particular,
we show parallels between asymptotic expansions for the energy of elastic
surfaces in powers of the thickness $h$ and the free energy for almost periodic
patterns expanded in powers of $\epsilon$, the inverse aspect ratio of the
pattern field. For sheets as well as patterns, the resulting energy can be
expressed in terms of natural geometric invariants, the first and second
fundamental forms of the elastic surface, respectively the phase surface. We
discuss various results for these energies and also address some of the
outstanding questions.
  We extend previous work on point (in 2D) and loop (in 3D) disclinations and
connect their topological indices with the condensation of Gaussian curvature
of the phase surface. Motivated by this connection with the charge and spin of
pattern quarks and leptons, we lay out an ambitious program to build a
multi-scale universe inspired by patterns in which the short (spatial and
temporal) scales are given by a nearly periodic microstructure and whose
macroscopic/slowly varying/averaged behaviors lead to a hierarchy of structures
and features on much longer scales including analogues to quarks and leptons,
dark matter, dark energy and inflationary cosmology. One of our new findings is
an interpretation of dark matter as the energy density in a pattern field. The
associated gravitational forces naturally result in galactic rotation curves
that are consistent with observations, while simultaneously avoiding some of
the small-scale difficulties of the standard $\Lambda$CDM paradigm in
cosmology.


title:
Energy current and its statistics in the nonequilibrium spin-boson
  model: Majorana fermion representation

abstract:
  We study the statistics of thermal energy transfer in the nonequilibrium
(two-bath) spin-boson model. This quantum many-body impurity system serves as a
canonical model for quantum energy transport. Our method makes use of the
Majorana fermion representation for the spin operators, in combination with the
Keldysh nonequilibrium Green's function approach. We derive an analytical
expression for the cumulant generating function of the model in the steady
state limit, and show that it satisfies the Gallavotti-Cohen fluctuation
symmetry. We obtain analytical expressions for the heat current and its noise,
valid beyond the sequential and the co-tunnelling regimes. Our results satisfy
the quantum mechanical bound for heat current in interacting nanojunctions.
Results are compared with other approximate theories, as well as with a
non-interacting model, a fully harmonic thermal junction.


title: Lectures on Symplectic Field Theory

abstract:
  This is the preliminary manuscript of a book on symplectic field theory based
on a lecture course for PhD students given in 2015-16. It covers the essentials
of the analytical theory of punctured pseudoholomorphic curves, taking the
opportunity to fill in gaps in the existing literature where necessary, and
then gives detailed explanations of a few of the standard applications in
contact topology such as distinguishing contact structures up to
contactomorphism and proving symplectic non-fillability.


title: DeepBach: a Steerable Model for Bach Chorales Generation

abstract:
  This paper introduces DeepBach, a graphical model aimed at modeling
polyphonic music and specifically hymn-like pieces. We claim that, after being
trained on the chorale harmonizations by Johann Sebastian Bach, our model is
capable of generating highly convincing chorales in the style of Bach.
DeepBach's strength comes from the use of pseudo-Gibbs sampling coupled with an
adapted representation of musical data. This is in contrast with many automatic
music composition approaches which tend to compose music sequentially. Our
model is also steerable in the sense that a user can constrain the generation
by imposing positional constraints such as notes, rhythms or cadences in the
generated score. We also provide a plugin on top of the MuseScore music editor
making the interaction with DeepBach easy to use.


title: Turning Gate Synthesis Errors into Incoherent Errors

abstract:
  Using error correcting codes and fault tolerant techniques, it is possible,
at least in theory, to produce logical qubits with significantly lower error
rates than the underlying physical qubits. Suppose, however, that the gates
that act on these logical qubits are only approximation of the desired gate.
This can arise, for example, in synthesizing a single qubit unitary from a set
of Clifford and $T$ gates; for a generic such unitary, any finite sequence of
gates only approximates the desired target. In this case, errors in the gate
can add coherently so that, roughly, the error $\epsilon$ in the unitary of
each gate must scale as $\epsilon \lesssim 1/N$, where $N$ is the number of
gates. If, however, one has the option of synthesizing one of several unitaries
near the desired target, and if an average of these options is closer to the
target, we give some elementary bounds showing cases in which the errors can be
made to add incoherently by averaging over random choices, so that, roughly,
one needs $\epsilon \lesssim 1/\sqrt{N}$. We remark on one particular
application to distilling magic states where this effect happens automatically
in the usual circuits.


title:
Shaping the solar wind temperature anisotropy by the interplay of
  electron and proton instabilities

abstract:
  A variety of nonthermal characteristics like kinetic, e.g., temperature,
anisotropies and suprathermal populations (enhancing the high energy tails of
the velocity distributions) are revealed by the in-situ observations in the
solar wind indicating quasistationary states of plasma particles out of thermal
equilibrium. Large deviations from isotropy generate kinetic instabilities and
growing fluctuating fields which should be more efficient than collisions in
limiting the anisotropy (below the instability threshold) and explain the
anisotropy limits reported by the observations. The present paper aims to
decode the principal instabilities driven by the temperature anisotropy of
electrons and protons in the solar wind, and contrast the instability
thresholds with the bounds observed at 1~AU for the temperature anisotropy. The
instabilities are characterized using linear kinetic theory to identify the
appropriate (fastest) instability in the relaxation of temperature anisotropies
$A_{e,p} = T_{e,p,\perp}/ T_{e,p,\parallel} \ne 1$. The analysis focuses on the
electromagnetic instabilities driven by the anisotropic protons ($A_p \lessgtr
1$) and invokes for the first time a dynamical model to capture the interplay
with the anisotropic electrons by correlating the effects of these two species
of plasma particles, dominant in the solar wind.


title:
Long-Term Growth Rate of Expected Utility for Leveraged ETFs: Martingale
  Extraction Approach

abstract:
  This paper studies the long-term growth rate of expected utility from holding
a leveraged exchanged-traded fund (LETF), which is a constant proportion
portfolio of the reference asset. Working with the power utility function, we
develop an analytical approach that employs martingale extraction and involves
finding the eigenpair associated with the infinitesimal generator of a
Markovian time-homogeneous diffusion. We derive explicitly the long-term growth
rates under a number of models for the reference asset, including the geometric
Brownian motion model, GARCH model, inverse GARCH model, extended CIR model,
3/2 model, quadratic model, as well as the Heston and 3/2 stochastic volatility
models. We also investigate the impact of stochastic interest rate such as the
Vasicek model and the inverse GARCH short rate model. We determine the optimal
leverage ratio for the long-term investor and examine the effects of model
parameters.


title: Nonparametric Bayes Models of Fiber Curves Connecting Brain Regions

abstract:
  In studying structural inter-connections in the human brain, it is common to
first estimate fiber bundles connecting different regions of the brain relying
on diffusion MRI. These fiber bundles act as highways for neural activity and
communication, snaking through the brain and connecting different regions.
Current statistical methods for analyzing these fibers reduce the rich
information into an adjacency matrix, with the elements containing a count of
the number of fibers or a mean diffusion feature (such as fractional
anisotropy) along the fibers. The goal of this article is to avoid discarding
the rich functional data on the shape, size and orientation of fibers,
developing flexible models for characterizing the population distribution of
fibers between brain regions of interest within and across different
individuals. We start by decomposing each fiber in each individual's brain into
a corresponding rotation matrix, shape and translation from a global reference
curve. These components can be viewed as data lying on a product space composed
of different Euclidean spaces and manifolds. To non-parametrically model the
distribution within and across individuals, we rely on a hierarchical mixture
of product kernels specific to the component spaces. Taking a Bayesian approach
to inference, we develop an efficient method for posterior sampling. The
approach automatically produces clusters of fibers within and across
individuals, and yields interesting new insights into variation in fiber
curves, while providing a useful starting point for more elaborate models
relating fibers to covariates and neuropsychiatric traits.


title: A Fast Algorithm for the Moments of Bingham Distribution

abstract:
  We propose a fast algorithm for evaluating the moments of Bingham
distribution. The calculation is done by piecewise rational approximation,
where interpolation and Gaussian integrals are utilized. Numerical test shows
that the algorithm reaches the maximal absolute error less than 5e-8 remarkably
faster than adaptive numerical quadrature. We apply the algorithm to a model
for liquid crystals with the Bingham distribution to examine the defect
patterns of rod-like molecules confined in a sphere, and find a different
pattern from the Landau-de Gennes theory.


title:
Wavelet-Based Characterization of Small-Scale Solar Emission Features at
  Low Radio Frequencies

abstract:
  Low radio frequency solar observations using the Murchison Widefield Array
have recently revealed the presence of numerous weak, short-lived and
narrow-band emission features, even during moderately quiet solar conditions.
These non-thermal features occur at rates of many thousands per hour in the
30.72 MHz observing bandwidth, and hence, necessarily require an automated
approach for their detection and characterization. Here, we employ continuous
wavelet transform using a mother Ricker wavelet for feature detection from the
dynamic spectrum. We establish the efficacy of this approach and present the
first statistically robust characterization of the properties of these
features. In particular, we examine distributions of their peak flux densities,
spectral spans, temporal spans and peak frequencies. We can reliably detect
features weaker than 1 SFU, making them, to the best of our knowledge, the
weakest bursts reported in literature. The distribution of their peak flux
densities follows a power law with an index of -2.23 in the 12-155 SFU range,
implying that they can provide an energetically significant contribution to
coronal and chromospheric heating. These features typically last for 1-2
seconds and possess bandwidths of about 4-5 MHz. Their occurrence rate remains
fairly flat in the 140-210 MHz frequency range. At the time resolution of the
data, they appear as stationary bursts, exhibiting no perceptible frequency
drift. These features also appear to ride on a broadband background continuum,
hinting at the likelihood of them being weak type-I bursts.


title: The electron energy-loss rate due to radiative recombination

abstract:
  For photoionized plasmas, electron energy-loss rates due to radiative
recombination (RR) are required for thermal equilib- rium calculations, which
assume a local balance between the energy gain and loss. While many
calculations of total and/or partial RR rates are available from literature,
specific calculations of associated RR electron energy-loss rates are lacking.
Here we focus on electron energy-loss rates due to radiative recombination of
H-like to Ne-like ions for all the elements up to and including zinc (Z = 30),
over a wide temperature range. We use the AUTOSTRUCTURE code to calculate the
level-resolved photoionization cross section and modify the ADASRR code so that
we can simultaneously obtain level-resolved RR rate coefficients and associated
RR electron energy-loss rate coefficients. The total RR rates and electron
energy-loss rates of H i and He i are compared with those found in literature.
Furthermore, we utilize and parameterize the weighted electron energy-loss
factors (dimensionless) to characterize total electron energy-loss rates due to
RR. The RR electron energy-loss data are archived according to the Atomic Data
and Analysis Structure (ADAS) data class adf48. The RR electron energy-loss
data are also incorporated into the SPEX code for detailed modelling of
photoionized plamsas.


title: Quantisation of Paths in Space-Time and Non-Perturbative Quantum Gravity

abstract:
  In previous work we discussed the quantization of paths in spacetime.
Building on these ideas we have developed a mathematically coherent theory
addressing a number of open questions concerning Loop Quantum Gravity. Our
approach develops a discrete spacetime and shows that macroscopic spacetime is
a renormalization limiting form. Weaving together a number of our previous
results we then prove that quantum states invariant under either an external
group of local diffeomorphisms of spacetime or by contrast quantum states
invariant under the internal action of a compact Lie group are common in a
well-defined sense. These form the building blocks of invariant fields and
Lagrangians. A form of supersymmetry and noncommutative spacetime naturally
emerges, which predicts a massless graviton and its companion gravitino.


title:
Experimental signatures of phase interference and sub-femtosecond time
  dynamics on the incident energy axis of resonant inelastic X-ray scattering

abstract:
  Core hole resonance is used in X-ray spectroscopy to incisively probe the
local electronic states of many-body systems. Here, resonant inelastic X-ray
scattering (RIXS) is studied as a function of incident photon energy on Mott
insulators SrCuO2 and NiO to examine how resonance states decay into different
excitation symmetries at the transition metal M-, L- and K-edges. Quantum
interference patterns characteristic of the two major RIXS mechanisms are
identified within the data, and used to distinguish the attosecond scale
scattering dynamics by which fundamental excitations of a many-body system are
created. A function is proposed to experimentally evaluate whether a particular
excitation has constructive or destructive interference in the RIXS
cross-section, and corroborates other evidence that an anomalous excitation is
present at the leading edge of the Mott gap in quasi-one dimensional SrCuO2.


title: Hypothesis Transfer Learning via Transformation Functions

abstract:
  We consider the Hypothesis Transfer Learning (HTL) problem where one
incorporates a hypothesis trained on the source domain into the learning
procedure of the target domain. Existing theoretical analysis either only
studies specific algorithms or only presents upper bounds on the generalization
error but not on the excess risk. In this paper, we propose a unified
algorithm-dependent framework for HTL through a novel notion of transformation
function, which characterizes the relation between the source and the target
domains. We conduct a general risk analysis of this framework and in
particular, we show for the first time, if two domains are related, HTL enjoys
faster convergence rates of excess risks for Kernel Smoothing and Kernel Ridge
Regression than those of the classical non-transfer learning settings.
Experiments on real world data demonstrate the effectiveness of our framework.


title:
Attosecond control of spin polarization in electron-ion recollision
  driven by intense tailored fields

abstract:
  We show that electrons recolliding with the ionic core upon tunnel ionization
of noble gas atoms driven by a strong circularly polarized laser field in
combination with a counter-rotating second harmonic are spin polarized and that
their degree of polarization depends strongly on the recollision time. Spin
polarization arises as a consequence of (1) entanglement between the
recolliding electron and the ion, and (2) sensitivity of ionization to the
sense of electron rotation in the initial state. We demonstrate that one can
engineer the degree of spin polarization as a function of time by tuning the
relative intensities of the counter-rotating fields, opening the door for
attosecond control of spin-resolved dynamics.


title:
Short-term traffic flow forecasting with spatial-temporal correlation in
  a hybrid deep learning framework

abstract:
  Deep learning approaches have reached a celebrity status in artificial
intelligence field, its success have mostly relied on Convolutional Networks
(CNN) and Recurrent Networks. By exploiting fundamental spatial properties of
images and videos, the CNN always achieves dominant performance on visual
tasks. And the Recurrent Networks (RNN) especially long short-term memory
methods (LSTM) can successfully characterize the temporal correlation, thus
exhibits superior capability for time series tasks. Traffic flow data have
plentiful characteristics on both time and space domain. However, applications
of CNN and LSTM approaches on traffic flow are limited. In this paper, we
propose a novel deep architecture combined CNN and LSTM to forecast future
traffic flow (CLTFP). An 1-dimension CNN is exploited to capture spatial
features of traffic flow, and two LSTMs are utilized to mine the short-term
variability and periodicities of traffic flow. Given those meaningful features,
the feature-level fusion is performed to achieve short-term forecasting. The
proposed CLTFP is compared with other popular forecasting methods on an open
datasets. Experimental results indicate that the CLTFP has considerable
advantages in traffic flow forecasting. in additional, the proposed CLTFP is
analyzed from the view of Granger Causality, and several interesting properties
of CLTFP are discovered and discussed .


title:
Femtosecond laser induced structural dynamics and melting of Cu (111)
  single crystal. An ultrafast time-resolved x-ray diffraction study

abstract:
  Femtosecond, 8.04 KeV x-ray pulses are used to probe the lattice dynamics of
150 nm Cu (111) single crystal on mica substrate irradiated with 400 nm, 100 fs
laser pulses. For pump fluencies below the damage and melting threshold, we
observed lattice contraction due to the formation of a blast force, and
coherent acoustic phonons with a period of ~69 ps. At larger pump fluence,
solid to liquid phase transition, annealing, and recrystallization were
measured in real time by monitoring the intensity evolution of the probing fs
x-ray rocking curves and agreed with theoretical simulation results. The
experimental data suggest the melting process is a purely thermal phase
transition. This study provides, in real time, an ultrafast time-resolved
detailed description of the significant processes that occurs as a result of a
femtosecond light-pulse interacts with the Cu (111) crystal surface.


title: Rescaling Limits in Non-Archimedean Dynamics

abstract:
  Suppose $\{f_t\}$ is an analytic one-parameter family of rational maps
defined over a non-Archimedean field $K$. We prove a finiteness theorem for the
set of rescalings for $\{f_t\}$. This complements results of J. Kiwi.


title: Women of the Future in the Royal Astronomical Society

abstract:
  In this article we wonder what the next 100 years will bring for women in
astronomy in the UK. After this year of looking back and celebrating 100 years
of women in the Royal Astronomical Society (RAS), we now ask: what might the
future hold? Extrapolating current trends, when might we expect equality in the
genders of RAS members, speakers at meetings, award winners and more?
Ultimately, when might we stop needing to talk about women in astronomy at all
- when it will be as irrelevant to the conversation about astronomy as being a
male astronomer is?


title: Syzygies of Prym and paracanonical curves of genus 8

abstract:
  By analogy with Green's Conjecture on syzygies of canonical curves, the
Prym-Green conjecture predicts that the resolution of a general level p
paracanonical curve of genus g is natural. The Prym-Green Conjecture is known
to hold in odd genus for almost all levels. Probabilistic arguments strongly
suggested that the conjecture might fail for level 2 and genus 8 or 16. In this
paper, we present three geometric proofs of the surprising failure of the
Prym-Green Conjecture in genus 8, hoping that the methods introduced here will
shed light on all the exceptions to the Prym-Green Conjecture for genera with
high divisibility by 2.


title:
Removing biases in resolved stellar mass-maps of galaxy disks through
  successive Bayesian marginalization

abstract:
  Stellar masses of galaxies are frequently obtained by fitting stellar
population synthesis models to galaxy photometry or spectra. The state of the
art method resolves spatial structures within a galaxy to assess the total
stellar mass content. In comparison to unresolved studies, resolved methods
yield, on average, higher fractions of stellar mass for galaxies. In this work
we improve the current method in order to mitigate a bias related to the
resolved spatial distribution derived for the mass. The bias consists in an
apparent filamentary mass distribution, and a spatial coincidence between mass
structures and dust lanes near spiral arms. The improved method is based on
iterative Bayesian marginalization, through a new algorithm we have named
Bayesian Successive Priors (BSP). We have applied BSP to M 51, and to a pilot
sample of 90 spiral galaxies from the Ohio State University Bright Spiral
Galaxy Survey. By comparing quantitatively both methods, we find that the
average fraction of stellar mass missed by unresolved studies is only half than
previously thought. In contrast with the previous method, the output BSP
mass-maps bear a better resemblance to near infrared images.


title:
Multigap superconductivity at extremely high temperature: a model for
  the case of pressurized H2S

abstract:
  It is known that in pressurized H2S the complex electronic structure in the
energy range of 200 meV near the chemical potential can be separated into two
electronic components, the first characterized by steep bands with a high Fermi
velocity and the second by flat bands with a vanishing Fermi velocity. Also the
phonon modes interacting with electrons at the Fermi energy can be separated
into two components: hard modes with high energy around 150 meV and soft modes
with energies around 60 meV. Therefore we discuss here a multiband scenario in
the standard BCS approximation where the effective BCS coupling coefficient is
in the range 0.1- 0.32. We consider a first (second) BCS condensate in the
strong (weak) coupling regime 0.32 (0.15). We discuss different scenario
segregated in different portions of the material. The results show the
phenomenology of unconventional superconducting phases in this two-gap
superconductivity scenario where there are two electronic components in two
Fermi surface spots, the pairing is mediated by either by a soft or a hard
phonon branch where the inter-band exchange term, also if small, plays a key
role for the emergence of high temperature superconductivity in pressurized
sulfur hydride.


title: On the galaxy-halo connection in the EAGLE simulation

abstract:
  Empirical models of galaxy formation require assumptions about the
correlations between galaxy and halo properties. These may be calibrated
against observations or inferred from physical models such as hydrodynamical
simulations. In this Letter, we use the EAGLE simulation to investigate the
correlation of galaxy size with halo properties. We motivate this analysis by
noting that the common assumption of angular momentum partition between baryons
and dark matter in rotationally supported galaxies overpredicts both the spread
in the stellar mass-size relation and the anticorrelation of size and velocity
residuals, indicating a problem with the galaxy-halo connection it implies. We
find the EAGLE galaxy population to perform significantly better on both
statistics, and trace this success to the weakness of the correlations of
galaxy size with halo mass, concentration and spin at fixed stellar mass. Using
these correlations in empirical models will enable fine-grained aspects of
galaxy scalings to be matched.


title:
Large scale modeling of antimicrobial resistance with interpretable
  classifiers

abstract:
  Antimicrobial resistance is an important public health concern that has
implications in the practice of medicine worldwide. Accurately predicting
resistance phenotypes from genome sequences shows great promise in promoting
better use of antimicrobial agents, by determining which antibiotics are likely
to be effective in specific clinical cases. In healthcare, this would allow for
the design of treatment plans tailored for specific individuals, likely
resulting in better clinical outcomes for patients with bacterial infections.
In this work, we present the recent work of Drouin et al. (2016) on using Set
Covering Machines to learn highly interpretable models of antibiotic resistance
and complement it by providing a large scale application of their method to the
entire PATRIC database. We report prediction results for 36 new datasets and
present the Kover AMR platform, a new web-based tool allowing the visualization
and interpretation of the generated models.


title: Invariant derivations and differential forms for reflection groups

abstract:
  Classical invariant theory of a complex reflection group $W$ highlights three
beautiful structures:
  -- the $W$-invariant polynomials constitute a polynomial algebra, over which
  -- the $W$-invariant differential forms with polynomial coefficients
constitute an exterior algebra, and
  -- the relative invariants of any $W$-representation constitute a free
module.
  When $W$ is a duality (or well-generated) group, we give an explicit
description of the isotypic component within the differential forms of the
irreducible reflection representation. This resolves a conjecture of Armstrong,
Rhoades and the first author, and relates to Lie-theoretic conjectures and
results of Bazlov, Broer, Joseph, Reeder, and Stembridge, and also Deconcini,
Papi, and Procesi. We establish this result by examining the space of
$W$-invariant differential derivations; these are derivations whose
coefficients are not just polynomials, but differential forms with polynomial
coefficients.
  For every complex reflection group $W$, we show that the space of invariant
differential derivations is finitely generated as a module over the invariant
differential forms by the basic derivations together with their exterior
derivatives. When $W$ is a duality group, we show that the space of invariant
differential derivations is free as a module over the exterior subalgebra of
$W$-invariant forms generated by all but the top-degree exterior generator.
(The basic invariant of highest degree is omitted.)
  Our arguments for duality groups are case-free, i.e., they do not rely on any
reflection group classification.


title:
Impact of spatially correlated pore-scale heterogeneity on drying porous
  media

abstract:
  We study the effect of spatially-correlated heterogeneity on isothermal
drying of porous media. We combine a minimal pore-scale model with microfluidic
experiments with the same pore geometry. Our simulated drying behavior compare
favorably with experiments, considering the large sensitivity of the emergent
behavior to the uncertainty associated with even small manufacturing errors. We
show that increasing the correlation length in particle sizes promotes
preferential drying of clusters of large pores, prolonging liquid connectivity
and surface wetness and thus higher drying rates for longer periods. Our
findings improve our quantitative understanding of how pore-scale heterogeneity
impacts drying, which plays a role in a wide range of processes ranging from
fuel cells to curing of paints and cements to global budgets of energy, water
and solutes in soils.


title: Areas of Attention for Image Captioning

abstract:
  We propose "Areas of Attention", a novel attention-based model for automatic
image captioning. Our approach models the dependencies between image regions,
caption words, and the state of an RNN language model, using three pairwise
interactions. In contrast to previous attention-based approaches that associate
image regions only to the RNN state, our method allows a direct association
between caption words and image regions. During training these associations are
inferred from image-level captions, akin to weakly-supervised object detector
training. These associations help to improve captioning by localizing the
corresponding regions during testing. We also propose and compare different
ways of generating attention areas: CNN activation grids, object proposals, and
spatial transformers nets applied in a convolutional fashion. Spatial
transformers give the best results. They allow for image specific attention
areas, and can be trained jointly with the rest of the network. Our attention
mechanism and spatial transformer attention areas together yield
state-of-the-art results on the MSCOCO dataset.o meaningful latent semantic
structure in the generated captions.


title:
Localization of networked robot systems subject to random delay and
  packet loss

abstract:
  This paper deals with the localization problem of mobile robot subject to
communication delay and packet loss. The delay and loss may appear in a random
fashion in both control inputs and observation measurements. A unified
state-space representation is constructed to describe these mixed
uncertainties. Based on it, the optimal linear estimator is developed. The main
idea is the derivation of a relevance factor to incorporate delayed
measurements to the being estimate. The estimator is then extended for
nonlinear systems. The performance of this method is tested within the
simulations in MATLAB and the experiments in a real robot system. The good
localization results prove the efficiency of the method for the purpose of
localization of networked mobile robot.


title: Semi-Automated Annotation of Discrete States in Large Video Datasets

abstract:
  We propose a framework for semi-automated annotation of video frames where
the video is of an object that at any point in time can be labeled as being in
one of a finite number of discrete states. A Hidden Markov Model (HMM) is used
to model (1) the behavior of the underlying object and (2) the noisy
observation of its state through an image processing algorithm. The key insight
of this approach is that the annotation of frame-by-frame video can be reduced
from a problem of labeling every single image to a problem of detecting a
transition between states of the underlying objected being recording on video.
The performance of the framework is evaluated on a driver gaze classification
dataset composed of 16,000,000 images that were fully annotated over 6,000
hours of direct manual annotation labor. On this dataset, we achieve a 13x
reduction in manual annotation for an average accuracy of 99.1% and a 84x
reduction for an average accuracy of 91.2%.


title: The Role of $\alpha$-Scaling for Cartoon Approximation

abstract:
  The class of cartoon-like functions, classicly defined as piecewise $C^2$
functions consisting of smooth regions separated by $C^2$ discontinuity curves,
is a well-established model for image data. The quest for optimal approximation
of this class has among others led to the development of curvelets,
contourlets, and shearlets. Due to parabolic scaling, these systems are able to
provide a quasi-optimal $N$-term approximation rate of order $N^{-2}$.
Replacing parabolic scaling by $\alpha$-scaling, one obtains $\alpha$-curvelets
and $\alpha$-shearlets, which interpolate between wavelet-type systems
($\alpha=1$), parabolically scaled systems ($\alpha=\frac12$), and
ridgelet-type systems ($\alpha=0$). Previous research shows that in the range
$\alpha\in[\frac{1}{2},1)$ they provide quasi-optimal approximation for
cartoons of regularity $C^{1/\alpha}$ with a rate of order $N^{-1/\alpha}$.
  In this work we continue to explore $\alpha$-scaled representation systems,
with the aim to better understand the role of the parameter $\alpha$ for
approximation. Concerning $\alpha$-curvelets with $\alpha<1$, we prove that the
best possible $N$-term approximation rate achievable for cartoons with curved
edges is limited to at most $N^{-1/(1-\alpha)}$, independent of the smoothness
of the cartoons. The maximal rate achievable by simple thresholding of the
frame coefficients is even bounded by $N^{-1/\max\{\alpha,1-\alpha\}}$. If the
edges of the cartoons are straight the approximation performance of
$\alpha$-curvelets is different: Assuming $C^\beta$ regularity, we establish an
approximation rate of order $N^{-\min\{\alpha^{-1},\beta\}}$, which is
quasi-optimal if $\alpha\in [0,\beta^{-1}]$. Finally, via the framework of
$\alpha$-molecules, the obtained results are extended to other $\alpha$-scaled
systems including in particular $\alpha$-shearlets.


title:
Conducting interface in oxide homojunction: understanding of superior
  properties in black TiO2

abstract:
  Black TiO2 nanoparticles with a crystalline-core and amorphous-shell
structure exhibit superior optoelectronic properties in comparison with
pristine TiO2. The fundamental mechanisms underlying these enhancements,
however, remain unclear, largely due to the inherent complexities and
limitations of powder materials. Here, we fabricate TiO2 homojunction films
consisting of an oxygen-deficient amorphous layer on top of a highly
crystalline layer, to simulate the structural/functional configuration of black
TiO2 nanoparticles. Metallic conduction is achieved at the
crystalline-amorphous homointerface via electronic interface reconstruction,
which we show to be the main reason for the superior properties of black TiO2.
This work not only achieves an unprecedented understanding of black TiO2, but
also provides a new perspective for investigating carrier generation and
transport behavior at oxide interfaces, which are of tremendous fundamental and
technological interest.


title:
Approximation Algorithms for the Maximum Profit Pick-up Problem with
  Time Windows and Capacity Constraint

abstract:
  In this paper, we study the Maximum Profit Pick-up Problem with Time Windows
and Capacity Constraint (MP-PPTWC). Our main results are 3 polynomial time
algorithms, all having constant approximation factors. The first algorithm has
an approximation ratio of $~46 (1 + (71/60 + \frac{\alpha}{\sqrt{10+p}})
\epsilon) \log T$, where: (i) $\epsilon > 0$ and $T$ are constants; (ii) The
maximum quantity supplied is $q_{max} = O(n^p) q_{min}$, for some $p > 0$,
where $q_{min}$ is the minimum quantity supplied; (iii) $\alpha > 0$ is a
constant such that the optimal number of vehicles is always at least $\sqrt{10
+ p} / \alpha$. The second algorithm has an approximation ratio of $\simeq 46
(1 + \epsilon + \frac{(2 + \alpha) \epsilon}{\sqrt{10 + p}}) \log T$. Finally,
the third algorithm has an approximation ratio of $\simeq 11 (1 + 2 \epsilon)
\log T$. While our algorithms may seem to have quite high approximation ratios,
in practice they work well and, in the majority of cases, the profit obtained
is at least 1/2 of the optimum.