title:
A practical approach to the sensitivity analysis for kinetic Monte Carlo
  simulation of heterogeneous catalysis

abstract:
  Lattice kinetic Monte Carlo simulations have become a vital tool for
predictive quality atomistic understanding of complex surface chemical reaction
kinetics over a wide range of reaction conditions. In order to expand their
practical value in terms of giving guidelines for atomic level design of
catalytic systems, it is very desirable to readily evaluate a sensitivity
analysis for a given model. The result of such a sensitivity analysis
quantitatively expresses the dependency of the turnover frequency, being the
main output variable, on the rate constants entering the model. In the past the
application of sensitivity analysis, such as Degree of Rate Control, has been
hampered by its exuberant computational effort required to accurately sample
numerical derivatives of a property that is obtained from a stochastic
simulation method. In this study we present an efficient and robust three stage
approach that is capable of reliably evaluating the sensitivity measures for
stiff microkinetic models as we demonstrate using CO oxidation on RuO2(110) as
a prototypical reaction. In a first step, we utilize the Fisher Information
Matrix for filtering out elementary processes which only yield negligible
sensitivity. Then we employ an estimator based on linear response theory for
calculating the sensitivity measure for non-critical conditions which covers
the majority of cases. Finally we adopt a method for sampling coupled finite
differences for evaluating the sensitivity measure of lattice based models.
This allows efficient evaluation even in critical regions near a second order
phase transition that are hitherto difficult to control. The combined approach
leads to significant computational savings over straightforward numerical
derivatives and should aid in accelerating the nano scale design of
heterogeneous catalysts.


title: Randomized Distributed Mean Estimation: Accuracy vs Communication

abstract:
  We consider the problem of estimating the arithmetic average of a finite
collection of real vectors stored in a distributed fashion across several
compute nodes subject to a communication budget constraint. Our analysis does
not rely on any statistical assumptions about the source of the vectors. This
problem arises as a subproblem in many applications, including reduce-all
operations within algorithms for distributed and federated optimization and
learning. We propose a flexible family of randomized algorithms exploring the
trade-off between expected communication cost and estimation error. Our family
contains the full-communication and zero-error method on one extreme, and an
$\epsilon$-bit communication and ${\cal O}\left(1/(\epsilon n)\right)$ error
method on the opposite extreme. In the special case where we communicate, in
expectation, a single bit per coordinate of each vector, we improve upon
existing results by obtaining $\mathcal{O}(r/n)$ error, where $r$ is the number
of bits used to represent a floating point value.


title: Cultivating Software Performance in Cloud Computing

abstract:
  There exist multitudes of cloud performance metrics, including workload
performance, application placement, software/hardware optimization,
scalability, capacity, reliability, agility and so on. In this paper, we
consider jointly optimizing the performance of the software applications in the
cloud. The challenges lie in bringing a diversity of raw data into tidy data
format, unifying performance data from multiple systems based on timestamps,
and assessing the quality of the processed performance data. Even after
verifying the quality of cloud performance data, additional challenges block
optimizing cloud computing. In this paper, we identify the challenges of cloud
computing from the perspectives of computing environment, data collection,
performance analytics and production environment.


title: Gauge-independence of tunneling rates

abstract:
  It is shown that tunneling rates can be defined in terms of a false-vacuum
effective action whose reality and convexity properties differ from those of
the corresponding groundstate functional. The tunneling rate is directly
related to the false-vacuum effective action evaluated at an extremal "quantum
bounce". The Nielsen identities of the false-vacuum functional ensure that the
rate remains independent of the choice of gauge-fixing. Our results are
nonperturbative and clarify issues related with convexity and radiative
corrections.


title:
On the linear quadratic problem for systems with time reversed Markov
  jump parameters and the duality with filtering of Markov jump linear systems

abstract:
  We study a class of systems whose parameters are driven by a Markov chain in
reverse time. A recursive characterization for the second moment matrix, a
spectral radius test for mean square stability and the formulas for optimal
control are given. Our results are determining for the question: is it possible
to extend the classical duality between filtering and control of linear systems
(whose matrices are transposed in the dual problem) by simply adding the jump
variable of a Markov jump linear system. The answer is positive provided the
jump process is reversed in time.


title: Sar image despeckling based on nonlocal similarity sparse decomposition

abstract:
  This letter presents a method of synthetic aperture radar (SAR) image
despeckling aimed to preserve the detail information while suppressing speckle
noise. This method combines the nonlocal self-similarity partition and a
proposed modified sparse decomposition. The nonlocal partition method groups a
series of structure-similarity data sets. Each data set has a good sparsity for
learning an over-complete dictionary in sparse representation. In the sparse
decomposition, we propose a novel method to identify principal atoms from
over-complete dictionary to form a principal dictionary. Despeckling is
performed on each data set over the principal dictionary with principal atoms.
Experimental results demonstrate that the proposed method can achieve high
performances in terms of both speckle noise reduction and structure details
preservation.


title:
Introduction of Static Quality Analysis in Small and Medium-Sized
  Software Enterprises: Experiences from Technology Transfer

abstract:
  Today, small and medium-sized enterprises (SMEs) in the software industry
face major challenges. Their resource constraints require high efficiency in
development. Furthermore, quality assurance (QA) measures need to be taken to
mitigate the risk of additional, expensive effort for bug fixes or
compensations. Automated static analysis (ASA) can reduce this risk because it
promises low application effort. SMEs seem to take little advantage of this
opportunity. Instead, they still mainly rely on the dynamic analysis approach
of software testing. In this article, we report on our experiences from a
technology transfer project. Our aim was to evaluate the results static
analysis can provide for SMEs as well as the problems that occur when
introducing and using static analysis in SMEs. We analysed five software
projects from five collaborating SMEs using three different ASA techniques:
code clone detection, bug pattern detection and architecture conformance
analysis. Following the analysis, we applied a quality model to aggregate and
evaluate the results. Our study shows that the effort required to introduce ASA
techniques in SMEs is small (mostly below one person-hour each). Furthermore,
we encountered only few technical problems. By means of the analyses, we could
detect multiple defects in production code. The participating companies
perceived the analysis results to be a helpful addition to their current QA and
will include the analyses in their QA process. With the help of the Quamoco
quality model, we could efficiently aggregate and rate static analysis results.
However, we also encountered a partial mismatch with the opinions of the SMEs.
We conclude, that ASA and quality models can be a valuable and affordable
addition to the QA process of SMEs.


title:
Multiband optical variability of the blazar OJ 287 during its outbursts
  in 2015 -- 2016

abstract:
  We present recent optical photometric observations of the blazar OJ 287 taken
during September 2015 -- May 2016. Our intense observations of the blazar
started in November 2015 and continued until May 2016 and included detection of
the large optical outburst in December 2016 that was predicted using the binary
black hole model for OJ 287. For our observing campaign, we used a total of 9
ground based optical telescopes of which one is in Japan, one is in India,
three are in Bulgaria, one is in Serbia, one is in Georgia, and two are in the
USA. These observations were carried out in 102 nights with a total of ~ 1000
image frames in BVRI bands, though the majority were in the R band. We detected
a second comparably strong flare in March 2016. In addition, we investigated
multi-band flux variations, colour variations, and spectral changes in the
blazar on diverse timescales as they are useful in understanding the emission
mechanisms. We briefly discuss the possible physical mechanisms most likely
responsible for the observed flux, colour and spectral variability.


title:
Neutron star kicks by the gravitational tug-boat mechanism in asymmetric
  supernova explosions: progenitor and explosion dependence

abstract:
  Asymmetric mass ejection in the early phase of supernova (SN) explosions can
impart a kick velocity to the new-born neutron star (NS). For neutrino-driven
explosions the NS acceleration was shown to be mainly caused by the
gravitational attraction of the anisotropically expelled inner ejecta, while
hydrodynamic forces contribute on a subdominant level, and asymmetric neutrino
emission plays only a secondary role. Two- and three-dimensional hydrodynamic
simulations demonstrated that this gravitational tug-boat mechanism can explain
the observed space velocities of young NSs up to more than 1000 km/s. Here, we
discuss how the NS kick depends on the energy, ejecta mass, and asymmetry of
the SN explosion, and which role the compactness of the pre-collapse stellar
core plays for the momentum transfer to the NS. We also provide simple analytic
expressions for the NS velocity in terms of these quantities. Referring to
results of hydrodynamic simulations in the literature, we argue why within the
discussed scenario of NS acceleration, electron-capture SNe, low-mass Fe-core
SNe and ultra-stripped SNe can be expected to have considerably lower intrinsic
NS kicks than core-collapse SNe of massive stellar cores. Our basic arguments
remain valid also if progenitor stars possess large-scale asymmetries in their
convective silicon and oxygen burning layers. Possible scenarios for spin-kick
alignment are sketched. Much of our discussion remains on a conceptual and
qualitative level and more work is needed on the numerical modeling side to
determine the dependences of involved parameters, whose prescriptions will be
needed for recipes that can be used to better describe NS kicks in binary
evolution and population synthesis studies.


title:
Analysis of Iterative Methods in Photoacoustic Tomography with Variable
  Sound Speed

abstract:
  In this article, we revisit iterative methods for solving the inverse problem
of photoacoustic tomography in free space. Recently, there have been
interesting developments on explicit formulations of the adjoint operator,
demonstrating that iterative methods are an attractive choice for photoacoustic
image reconstruction. In this work, we propose several modifications of current
formulations of the adjoint operator which help speed up the convergence and
yield improved error estimates. We establish a stability analysis and show
that, with our choices of the adjoint operator, the iterative methods can
achieve a linear rate of convergence, in the $L^2$-norm as well as in the
$H^1$-norm. In addition, we analyze the normal operator from the microlocal
analysis point of view. This gives insight into the convergence speed of the
iterative methods and choosing proper weights for the mapping spaces. Finally,
we present numerical results using various iterative reconstruction methods for
full as well as limited view data. Our results demonstrate that Nesterov's fast
gradient and the CG methods converge faster than Landweber's and iterative time
reversal methods in the visible as well as the invisible case.


title:
Evolution of High-Energy Particle Distribution in Mature Shell-Type
  Supernova Remnants

abstract:
  Multi-wavelength observations of mature supernova remnants (SNRs), especially
with recent advances in gamma-ray astronomy, make it possible to constrain
energy distribution of energetic particles within these remnants. In
consideration of the SNR origin of Galactic cosmic rays and physics related to
particle acceleration and radiative processes, we use a simple one-zone model
to fit the nonthermal emission spectra of three shell-type SNRs located within
2 degrees on the sky: RX J1713.7-3946, CTB 37B, and CTB 37A. Although radio
images of these three sources all show a shell (or half-shell) structure, their
radio, X-ray, and gamma-ray spectra are quite different, offering an ideal case
to explore evolution of energetic particle distribution in SNRs. Our spectral
fitting shows that 1) the particle distribution becomes harder with aging of
these SNRs, implying a continuous acceleration process, and the particle
distributions of CTB 37A and CTB 37B in the GeV range are harder than the
hardest distribution that can be produced at a shock via the linear diffusive
shock particle acceleration process, so spatial transport may play a role; 2)
the energy loss timescale of electrons at the high-energy cutoff due to
synchrotron radiation appears to be always a bit (within a factor of a few)
shorter than the age of the corresponding remnant, which also requires
continuous particle acceleration; 3) double power-law distributions are needed
to fit the spectra of CTB 37B and CTB 37A, which may be attributed to shock
interaction with molecular clouds.


title:
Timing of Transients: Quantifying Reaching Times and Transient Behavior
  in Complex Systems

abstract:
  When quantifying the time spent in the transient of a complex dynamical
system, the fundamental problem is that for a large class of systems the actual
time for reaching an attractor is infinite. Common methods for dealing with
this problem usually introduce three additional problems: non-invariance,
physical interpretation, and discontinuities, calling for carefully designed
methods for quantifying transients.
  In this article, we discuss how the aforementioned problems emerge and
propose two novel metrics, Regularized Reaching Time ($T_{RR}$) and Area under
Distance Curve (AUDIC), to solve them, capturing two complementary aspects of
the transient dynamics.
  $T_{RR}$ quantifies the additional time (positive or negative) that a
trajectory starting at a chosen initial condition needs to reach the attractor
after a reference trajectory has already arrived there. A positive or negative
value means that it arrives by this much earlier or later than the reference.
Because $T_{RR}$ is an analysis of return times after shocks, it is a
systematic approach to the concept of critical slowing down [1]; hence it is
naturally an early-warning signal [2] for bifurcations when central statistics
over distributions of initial conditions are used.
  AUDIC is the distance of the trajectory to the attractor integrated over
time. Complementary to $T_{RR}$, it measures which trajectories are reluctant,
i.e. stay away from the attractor for long, or eager to approach it right away.
  (... shortened for arxiv listing, full abstract in paper ...) New features in
these models can be uncovered, including the surprising regularity of the
Roessler system's basin of attraction even in the regime of a chaotic
attractor. Additionally, we demonstrate the critical slowing down
interpretation by presenting the metrics' sensitivity to prebifurcational
change and thus how they act as early-warning signals.


title: Totally asymmetric limit for models of heat conduction

abstract:
  We consider one dimensional weakly asymmetric boundary driven models of heat
conduction. In the cases of a constant diffusion coefficient and of a quadratic
mobility we compute the quasi-potential that is a non local functional obtained
by the solution of a variational problem. This is done using the dynamic
variational approach of the macroscopic fluctuation theory \cite{MFT}. The case
of a concave mobility corresponds essentially to the exclusion model that has
been discussed in \cite{Lag,CPAM,BGLa,ED}. We consider here the convex case
that includes for example the Kipnis-Marchioro-Presutti (KMP) model and its
dual (KMPd) \cite{KMP}. This extends to the weakly asymmetric regime the
computations in \cite{BGL}. We consider then, both microscopically and
macroscopically, the limit of large external fields. Microscopically we discuss
some possible totally asymmetric limits of the KMP model. In one case the
totally asymmetric dynamics has a product invariant measure. Another possible
limit dynamics has instead a non trivial invariant measure for which we give a
duality representation. Macroscopically we show that the quasi-potentials of
KMP and KMPd, that for any fixed external field are non local, become local in
the limit. Moreover the dependence on one of the external reservoirs
disappears. For models having strictly positive quadratic mobilities we obtain
instead in the limit a non local functional having a structure similar to the
one of the boundary driven asymmetric exclusion process.


title: Feature Importance Measure for Non-linear Learning Algorithms

abstract:
  Complex problems may require sophisticated, non-linear learning methods such
as kernel machines or deep neural networks to achieve state of the art
prediction accuracies. However, high prediction accuracies are not the only
objective to consider when solving problems using machine learning. Instead,
particular scientific applications require some explanation of the learned
prediction function. Unfortunately, most methods do not come with out of the
box straight forward interpretation. Even linear prediction functions are not
straight forward to explain if features exhibit complex correlation structure.
  In this paper, we propose the Measure of Feature Importance (MFI). MFI is
general and can be applied to any arbitrary learning machine (including kernel
machines and deep learning). MFI is intrinsically non-linear and can detect
features that by itself are inconspicuous and only impact the prediction
function through their interaction with other features. Lastly, MFI can be used
for both --- model-based feature importance and instance-based feature
importance (i.e, measuring the importance of a feature for a particular data
point).


title: Fission and fusion scenarios for magnetic microswimmer clusters

abstract:
  Fission and fusion processes of particles clusters occur in many areas of
physics and chemistry from subnuclear to astronomic length scales. Here we
study fission and fusion of magnetic microswimmer clusters as governed by their
hydrodynamic and dipolar interactions. Rich scenarios are found which depend
crucially on whether the swimmer is a pusher or a puller. In particular a
linear magnetic chain of pullers is stable while a pusher chain shows a cascade
of fission (or disassembly) processes as the self-propulsion velocity is
increased. Contrarily, magnetic ring clusters show fission for any type of
swimmer. Moreover, we find a plethora of possible fusion (or assembly)
scenarios if a single swimmer collides with a ringlike cluster and two rings
spontaneously collide. Our predictions are obtained by computer simulations and
verifiable in experiments on active colloidal Janus particles and magnetotactic
bacteria.


title:
Geometrical Clusterization in SU(2) gluodynamics and Liquid-gas Phase
  Transition

abstract:
  The liquid droplet formula is applied to an analysis of the properties of
geometrical (anti)clusters formed in SU(2) gluodynamics by the Polyakov loops
of the same sign. Using this approach, we explain the phase transition in SU(2)
gluodynamics as a transition between two liquids during which one of the liquid
droplets (the largest cluster of a certain Polyakov loop sign) experiences a
condensation, while another droplet (the next to the largest cluster of the
opposite sign of Polyakov loop) evaporates. The clusters of smaller sizes form
two accompanying gases, which behave oppositely to their liquids. The liquid
droplet formula is used to analyze the size distributions of the gas
(anti)clusters. The fit of these distributions allows us to extract the
temperature dependence of surface tension and the value of Fisher topological
exponent $\tau$ for both kinds of gaseous clusters. It is shown that the
surface tension coeficient of gaseous (anti)clusters can serve as an order
parameter of the deconfinement phase transition in SU(2) gluodynamics. The
Fisher topological exponent $\tau$ of (anti)clusters is found to have the same
value $1.806 \pm 0.008$. This value disagrees with the famous Fisher droplet
model, but it agrees well with an exactly solvable model of nuclear liquid-gas
phase transition. This finding may evidence for the fact that the SU(2)
gluodynamics and this exactly solvable model of nuclear liquid-gas phase
transition are in the same universality class.


title:
Schroedinger Equation in Rotating Frame by using Stochastic Variational
  Method

abstract:
  We give a pedagogical introduction of the stochastic variational method by
considering the quantization of a non-inertial particle system. We show that
the effects of fictitious forces are represented in the forms of vector fields
which behave analogous to the gauge fields in the electromagnetic interaction.
We further discuss that the operator expressions for observables can be defined
by applying the stochastic Noether theorem.


title:
Quad-networks: unsupervised learning to rank for interest point
  detection

abstract:
  Several machine learning tasks require to represent the data using only a
sparse set of interest points. An ideal detector is able to find the
corresponding interest points even if the data undergo a transformation typical
for a given domain. Since the task is of high practical interest in computer
vision, many hand-crafted solutions were proposed. In this paper, we ask a
fundamental question: can we learn such detectors from scratch? Since it is
often unclear what points are "interesting", human labelling cannot be used to
find a truly unbiased solution. Therefore, the task requires an unsupervised
formulation. We are the first to propose such a formulation: training a neural
network to rank points in a transformation-invariant manner. Interest points
are then extracted from the top/bottom quantiles of this ranking. We validate
our approach on two tasks: standard RGB image interest point detection and
challenging cross-modal interest point detection between RGB and depth images.
We quantitatively show that our unsupervised method performs better or on-par
with baselines.


title:
ADAM low- and medium-resolution spectrograph for 1.6-m AZT-33IK
  telescope

abstract:
  We describe the design of a low- and medium-resolution spectrograph (
R=300-1300) developed at the Special Astrophysical Observatory of the Russian
Academy of Sciences (SAO RAS) for the 1.6-m AZT-33IK telescope of Sayan
Observatory of the Institute of Solar-Terrestrial Physics of the Siberian
Branch of the Russian Academy of Sciences. We report the results of laboratory
measurements of the parameters of the instrument and tests performed on the SAO
RAS 1-m Zeiss-1000 telescope. We measured the total quantum efficiency of the
"spectrograph + telescope + detector" system on AZT-33IK telescope, which at
its maximum reaches 56%. Such a hight transparency of the spectrograph allows
it to be used with the 1.6-m telescope to determine the types and redshifts of
objects with magnitudes m_AB~20-21, that was confirmed by actual observations.


title:
Relaxed Earth Mover's Distances for Chain- and Tree-connected Spaces and
  their use as a Loss Function in Deep Learning

abstract:
  The Earth Mover's Distance (EMD) computes the optimal cost of transforming
one distribution into another, given a known transport metric between them. In
deep learning, the EMD loss allows us to embed information during training
about the output space structure like hierarchical or semantic relations. This
helps in achieving better output smoothness and generalization. However EMD is
computationally expensive.Moreover, solving EMD optimization problems usually
require complex techniques like lasso. These properties limit the applicability
of EMD-based approaches in large scale machine learning.
  We address in this work the difficulties facing incorporation of EMD-based
loss in deep learning frameworks. Additionally, we provide insight and novel
solutions on how to integrate such loss function in training deep neural
networks. Specifically, we make three main contributions: (i) we provide an
in-depth analysis of the fastest state-of-the-art EMD algorithm (Sinkhorn
Distance) and discuss its limitations in deep learning scenarios. (ii) we
derive fast and numerically stable closed-form solutions for the EMD gradient
in output spaces with chain- and tree- connectivity; and (iii) we propose a
relaxed form of the EMD gradient with equivalent computational complexity but
faster convergence rate. We support our claims with experiments on real
datasets. In a restricted data setting on the ImageNet dataset, we train a
model to classify 1000 categories using 50K images, and demonstrate that our
relaxed EMD loss achieves better Top-1 accuracy than the cross entropy loss.
Overall, we show that our relaxed EMD loss criterion is a powerful asset for
deep learning in the small data regime.


title: Boolean graphs are unmixed and vertex decomposable

abstract:
  For each Boolean graph $B_n$, it is proved that both $B_n$ and its complement
graph $\overline{B_n}$ are vertex decomposable. It is also proved that $B_n$ is
an unmixed graph, thus it is also Cohen-Macaulay.


title: Model of the best-of-N nest-site selection process in honeybees

abstract:
  The ability of a honeybee swarm to select the best nest site plays a
fundamental role in determining the future colony's fitness. To date, the
nest-site selection process has mostly been modelled and theoretically analysed
for the case of binary decisions. However, when the number of alternative nests
is larger than two, the decision process dynamics qualitatively change. In this
work, we extend previous analyses of a value-sensitive decision-making
mechanism to a decision process among N nests. First, we present the
decision-making dynamics in the symmetric case of N equal-quality nests. Then,
we generalise our findings to a best-of-N decision scenario with one superior
nest and N-1 inferior nests, previously studied empirically in bees and ants.
Whereas previous binary models highlighted the crucial role of inhibitory
stop-signalling, the key parameter in our new analysis is the relative time
invested by swarm members in individual discovery and in signalling behaviours.
Our new analysis reveals conflicting pressures on this ratio in symmetric and
best-of-N decisions, which could be solved through a time-dependent signalling
strategy. Additionally, our analysis suggests how ecological factors
determining the density of suitable nest sites may have led to selective
pressures for an optimal stable signalling ratio.


title: Normal forms of para-CR hypersurfaces

abstract:
  We consider hypersurfaces of finite type in a direct product space ${\mathbb
R}^2 \times {\mathbb R}^2$, which are analogues to real hypersurfaces of finite
type in ${\mathbb C}^2$. We shall consider separately the cases where such
hypersurfaces are regular and singular, in a sense that corresponds to Levi
degeneracy in hypersurfaces in ${\mathbb C}^2$. For the regular case, we study
formal normal forms and prove convergence by following Chern and Moser. The
normal form of such an hypersurface, considered as the solution manifold of a
2nd order ODE, gives rise to a normal form of the corresponding 2nd order ODE.
For the degenerate case, we study normal forms for weighted $\ell$-jets.
Furthermore, we study the automorphisms of finite type hypersurfaces.


title: Three-dimensional wave evolution on electrified falling films

abstract:
  We consider the full 3D dynamics of a thin falling liquid film on a flat
plate inclined at some non-zero angle to the horizontal. In addition to
gravitational effects, the flow is driven by an electric field, which is normal
to the substrate far from the flow. We study both the cases of overlying and
hanging films, where the liquid rests above and below the substrate
respectively. Starting with the Navier-Stokes equations coupled with
electrostatics, a fully nonlinear 2D Benney equation for the interfacial
dynamics is derived valid for waves that are long compared to the film
thickness. The weakly nonlinear evolution is governed by a Kuramoto-Sivashinsky
equation with a non-local term due to the electric field effect. The electric
field term is linearly destabilising and produces growth rates proportional to
the cube of the size of the wavenumber vector of the perturbations. It is found
that transverse gravitational instabilities are always present for hanging
films and lead to unboundedness of solutions even in the absence of electric
fields. For overlying films and a restriction on the strength of the electric
field, the equation possesses bounded solutions. This 2D equation is studied
numerically for the case of periodic boundary conditions in order to assess the
effects of inertia, electric field strength the dimensions of the periodic
domain. Rich dynamical behaviours are observed and classified in various
parameter windows. For subcritical Reynolds number flows, a sufficiently strong
electric field can promote non-trivial dynamics for some choices of domain
dimensions, leading to fully 2D evolutions for the interface. These dynamics
are also found to produce spatiotemporal chaos on sufficiently large domains.
For supercritical flows, such 2D chaotic dynamics emerge in the absence of a
field, and its presence enhances the amplitude of the fluctuations and broadens
their spectrum.


title:
2dFLenS and KiDS: Determining source redshift distributions with
  cross-correlations

abstract:
  We develop a statistical estimator to infer the redshift probability
distribution of a photometric sample of galaxies from its angular
cross-correlation in redshift bins with an overlapping spectroscopic sample.
This estimator is a minimum variance weighted quadratic function of the data: a
quadratic estimator. This extends and modifies the methodology presented by
McQuinn & White (2013). The derived source redshift distribution is degenerate
with the source galaxy bias, which must be constrained via additional
assumptions. We apply this estimator to constrain source galaxy redshift
distributions in the Kilo-Degree imaging survey through cross-correlation with
the spectroscopic 2-degree Field Lensing Survey, presenting results first as a
binned step-wise distribution in the range z < 0.8, and then building a
continuous distribution using a Gaussian process model. We demonstrate the
robustness of our methodology using mock catalogues constructed from N-body
simulations, and comparisons with other techniques for inferring the redshift
distribution.


title: Programs as Black-Box Explanations

abstract:
  Recent work in model-agnostic explanations of black-box machine learning has
demonstrated that interpretability of complex models does not have to come at
the cost of accuracy or model flexibility. However, it is not clear what kind
of explanations, such as linear models, decision trees, and rule lists, are the
appropriate family to consider, and different tasks and models may benefit from
different kinds of explanations. Instead of picking a single family of
representations, in this work we propose to use "programs" as model-agnostic
explanations. We show that small programs can be expressive yet intuitive as
explanations, and generalize over a number of existing interpretable families.
We propose a prototype program induction method based on simulated annealing
that approximates the local behavior of black-box classifiers around a specific
prediction using random perturbations. Finally, we present preliminary
application on small datasets and show that the generated explanations are
intuitive and accurate for a number of classifiers.


title:
Pricing Mechanisms for Crowd-Sensed Spatial-Statistics-Based Radio
  Mapping

abstract:
  Networking on white spaces (i.e., locally unused spectrum) relies on active
monitoring of spectrum usage. Spectrum databases based on empirical radio
propagation models are widely adopted but shown to be error-prone, since they
do not account for built environments like trees and man-made buildings. As an
economically viable option, crowd-sensed radio mapping acquires more accurate
local spectrum data from mobile users and constructs radio maps using spatial
models such as Kriging and Gaussian Process. Success of such crowd-sensing
systems presumes some incentive mechanisms to attract user participation. In
this work, we consider the scenario where the platform who constructs radio
environment maps makes one-time offers to selected users, and collects data
from those who accept the offers. We design pricing mechanisms based on
expected utility (EU) maximization, where EU captures the tradeoff between
radio mapping performance (location and data quality), crowd-sensing cost and
uncertainty in offer outcomes (i.e., possible expiration and rejection).
Specifically, we consider sequential offering, where one best price offer is
sent to the best user in each round, and batched offering, where a batch of
multiple offers are made in each round. For the later, we show that EU is
submodular in the discrete domain, and propose a mechanism that first fixes the
pricing rule and selects users based on Unconstrained Submodular Maximization
(USM); it then compares different pricing rules to find the best batch of
offers in each round. We show that USM-based user selection has provable
performance guarantee. Proposed mechanisms are evaluated and compared against
utility-maximization-based baseline mechanisms.


title:
Quantizations on Nilpotent Lie Groups and Algebras Having Flat Coadjoint
  Orbits

abstract:
  For a connected simply connected nilpotent Lie group $\G$ with Lie algebra
$\g$ and unitary dual $\wG$ one has (a) a global quantization of
operator-valued symbols defined on $\G\times\wG$, involving the representation
theory of the group, (b) a quantization of scalar-valued symbols defined on
$\G\times\g^*$, taking the group structure into account and (c) Weyl-type
quantizations of all the coadjoint orbits $\big\{\O_\xi\mid\xi\in\wG\big\}$. We
show how these quantizations are connected, in the case when flat coadjoint
orbits exist. This is done by a careful analysis of the composition of two
different types of Fourier transformations. We also describe the concrete form
of the operator-valued symbol quantization, by using Kirillov theory and the
Euclidean version of the unitary dual and Plancherel measure. In the case of
the Heisenberg group this corresponds to the known picture, presenting the
representation theoretical pseudo-differential operators in terms of families
of Weyl operators depending on a parameter. For illustration, we work out a
couple of examples and put into evidence some specific features of the case of
Lie algebras with one-dimensional center. When $\G$ is also graded, we make a
short presentation of the symbol classes $S^m_{\rho,\delta}$, transferred from
$\G\times\wG$ to $\G\times\g^*$ by means of the connection mentioned above.


title:
Exclusive open-charm near-threshold cross sections in a coupled-channel
  approach

abstract:
  Data on open-charm channels collected by the Belle Collaboration are analysed
simultaneously using a unitary approach based on a coupled-channel model in a
wide energy range $\sqrt{s}=3.7\div 4.7$ GeV. The resulting fit provides a
remarkably good overall description of the line shapes in all studied channels.
Parameters of 5 vector charmonium resonances are extracted from the fit.


title: Alternating Direction Graph Matching

abstract:
  In this paper, we introduce a graph matching method that can account for
constraints of arbitrary order, with arbitrary potential functions. Unlike
previous decomposition approaches that rely on the graph structures, we
introduce a decomposition of the matching constraints. Graph matching is then
reformulated as a non-convex non-separable optimization problem that can be
split into smaller and much-easier-to-solve subproblems, by means of the
alternating direction method of multipliers. The proposed framework is modular,
scalable, and can be instantiated into different variants. Two instantiations
are studied exploring pairwise and higher-order constraints. Experimental
results on widely adopted benchmarks involving synthetic and real examples
demonstrate that the proposed solutions outperform existing pairwise graph
matching methods, and competitive with the state of the art in higher-order
settings.


title: Completely general bounds on Non-Unitary leptonic mixing

abstract:
  We derive constraints on the mixing of heavy right-handed neutrinos with the
SM fields in the most general Seesaw scenario where the heavy neutrinos are
integrated out. Among the electroweak and flavour observables included in the
global fit, $\mu\rightarrow e\gamma$ sets the present strongest bound on the
additional neutrino mixing, while in the future it will be dominated by $\mu-e$
conversion in nuclei. Increasing its sensitivity in future experiments could
probe Non-Unitarity in Lepton Flavour Violating processes. Nevertheless, in
order to determine completely model-independent constraints, we provide a
second set of bounds derived through a global fit that does not include LFV
observables. These indirect constraints on the off-diagonal elements come from
the diagonal bounds through the Schwarz inequality.


title:
The Highly Ionized Circumgalactic Medium is Kinematically Uniform Around
  Galaxies

abstract:
  The circumgalactic medium (CGM) traced by OVI $\lambda\lambda 1031, 1037$
doublet absorption has been found to concentrate along the projected major and
minor axes of the host galaxies. This suggests that OVI traces accreting and
outflowing gas, respectively, which are key components of the baryon cycle of
galaxies. We investigate this further by examining the kinematics of 29 OVI
absorbers associated with galaxies at $0.13 < z_{\rm gal} < 0.66$ as a function
of galaxy color, inclination, and azimuthal angle. Each galaxy was imaged with
HST and the absorption was detected in COS/HST spectra of nearby ($D<200$ kpc)
background quasars. We use the pixel-velocity two-point correlation function to
characterize the velocity spread of the absorbers, which is a method used
previously for a sample of MgII absorber--galaxy pairs. The absorption velocity
spread for OVI is more extended than MgII, which suggests that the two ions
trace differing components of the CGM. Also contrary to MgII, the OVI
absorption velocity spreads are similar regardless of galaxy color,
inclination, and azimuthal angle. This indicates that the kinematics of the
high ionization gas is not strongly influenced by the present star formation
activity in the galaxy. The kinematic homogeneity of OVI absorption and its
tendency to be observed mainly along the projected galaxy major and minor axes
is likely due to varying ionization conditions and gas densities about the
galaxy. Gas in intermediate azimuthal angles may be ionized out of the OVI
phase, possibly resulting in an azimuthal angle dependence on the distribution
of gas in higher ionization states.


title:
Integrability conditions of quasi-Newtonian cosmologies in modified
  gravity

abstract:
  We investigate the integrability conditions of a class of shear-free
perfect-fluid cosmological models within the framework of anisotropic fluid
sources, applying our results to f(R) dark energy models. Generalising earlier
general relativistic results for time-like geodesics, we extend the potential
and acceleration terms of the quasi-Newtonian formulation of integrable dust
cosmological models about a linearized Friedmann-Lemaitre-Robertson-Walker
background and derive the equations that describe their dynamical evolutions.
We show that in general, models with an anisotropic fluid source are not
consistent, but because of the particular form the anisotropic stress takes in
f(R) gravity, the general integrability conditions, in this case, are
satisfied.


title: Green's function representation of spin pumping effect

abstract:
  In this study, current pumping by an external potential is studied on the
basis of the Keldysh Green's function method, and a pumping formula written in
terms of retarded and advanced Green's functions is obtained. It is shown that
pumping is essentially driven by a change of particle distribution before and
after an external perturbation. The formula is used to study the spin pumping
effect in the case of strong sd exchange interaction, and the driving field is
identified to be the spin gauge field. At the lowest order in the precession
frequency of magnetization, the spin gauge field works as a constant potential,
and the system is shown to reduce to a static problem of spin current
generation by a time-independent potential with off-diagonal spin components.


title:
A Neural Network Model to Classify Liver Cancer Patients Using Data
  Expansion and Compression

abstract:
  We develop a neural network model to classify liver cancer patients into
high-risk and low-risk groups using genomic data. Our approach provides a novel
technique to classify big data sets using neural network models. We preprocess
the data before training the neural network models. We first expand the data
using wavelet analysis. We then compress the wavelet coefficients by mapping
them onto a new scaled orthonormal coordinate system. Then the data is used to
train a neural network model that enables us to classify cancer patients into
two different classes of high-risk and low-risk patients. We use the
leave-one-out approach to build a neural network model. This neural network
model enables us to classify a patient using genomic data as a high-risk or
low-risk patient without any information about the survival time of the
patient. The results from genomic data analysis are compared with survival time
analysis. It is shown that the expansion and compression of data using wavelet
analysis and singular value decomposition (SVD) is essential to train the
neural network model.


title: Effective long wavelength scalar dynamics in de Sitter

abstract:
  We discuss the effective infrared theory governing a light scalar's long
wavelength dynamics in de Sitter spacetime. We show how the separation of
scales around the physical curvature radius $k/a \sim H$ can be performed
consistently with a window function and how short wavelengths can be integrated
out in the Schwinger-Keldysh path integral formalism. At leading order, and for
time scales $\Delta t \gg H^{-1}$, this results in the well-known Starobinsky
stochastic evolution. However, our approach allows for the computation of
quantum UV corrections, generating an effective potential on which the
stochastic dynamics takes place. The long wavelength stochastic dynamical
equations are now second order in time, incorporating temporal scales $\Delta t
\sim H^{-1}$ and resulting in a Kramers equation for the probability
distribution - more precisely the Wigner function - in contrast to the more
usual Fokker-Planck equation. This feature allows us to non-perturbatively
evaluate, within the stochastic formalism, not only expectation values of field
correlators, but also the stress-energy tensor of $\phi$.


title: Symmetry enforced line nodes in unconventional superconductors

abstract:
  We classify line nodes in superconductors with strong spin-orbit interactions
and time-reversal symmetry, where the latter may include non-primitive
translations in the magnetic Brillouin zone to account for coexistence with
antiferromagnetic order. We find four possible combinations of irreducible
representations of the order parameter on high symmetry planes, two of which
allow for line nodes in pseudo-spin triplet pairs and two that exclude
conventional fully gapped pseudo-spin singlet pairs. We show that the former
can only be realized in the presence of band-sticking degeneracies, and verify
their topological stability using arguments based on Clifford algebra
extensions. Our classification exhausts all possible symmetry protected line
nodes in the presence of spin-orbit coupling and a (generalized) time-reversal
symmetry. Implications for existing non-symmorphic and antiferromagnetic
superconductors are discussed.


title: Categories in control: applied PROPs

abstract:
  Control theory uses `signal-flow diagrams' to describe processes where
real-valued functions of time are added, multiplied by scalars, differentiated
and integrated, duplicated and deleted. These diagrams can be seen as string
diagrams for the PROP FinRel_k, the strict version of the category of
finite-dimensional vector spaces over the field of rational functions k = R(s)
and linear relations, where the variable s acts as differentiation and the
monoidal structure is direct sum rather than the usual tensor product of vector
spaces. Control processes are also described by controllability and
observability---whether the input can drive the process to any state, and
whether any state can be determined from later outputs. For any field k we give
a presentation of FinRel_k in terms of generators of the free PROP of
signal-flow diagrams together with the equations that give FinRel_k its
structure. The `cap' and `cup' generators, missing when the morphisms are
linear maps, make it possible to model feedback. The relations say, among other
things, that the 1-dimensional vector space k has two special commutative
dagger-Frobenius structures, such that the multiplication and unit of either
one and the comultiplication and counit of the other fit together to form a
bimonoid. This sort of structure, but with tensor product replacing direct sum,
is familiar from the `ZX-calculus' obeyed by a finite-dimensional Hilbert space
with two mutually unbiased bases. In order to address controllability and
observability, we construct the PROP Stateful_k and relate it back to the PROP
of signal-flow diagrams. This provides a way to graphically express
controllability and observability for linear time-invariant processes.


title: The game of Overprescribed Cops and Robbers played on graphs

abstract:
  We consider the effect on the length of the game of Cops and Robbers when
more cops are added to the game play. In Overprescribed Cops and Robbers, as
more cops are added, the capture time (the minimum length of the game assuming
optimal play) monotonically decreases. We give the full range of capture times
for any number of cops on trees, and classify the capture time for an
asymptotic number of cops on grids, hypercubes, and binomial random graphs. The
capture time of planar graphs with a number of cops at and far above the cop
number is considered.


title: Learning Joint Feature Adaptation for Zero-Shot Recognition

abstract:
  Zero-shot recognition (ZSR) aims to recognize target-domain data instances of
unseen classes based on the models learned from associated pairs of seen-class
source and target domain data. One of the key challenges in ZSR is the relative
scarcity of source-domain features (e.g. one feature vector per class), which
do not fully account for wide variability in target-domain instances. In this
paper we propose a novel framework of learning data-dependent feature
transforms for scoring similarity between an arbitrary pair of source and
target data instances to account for the wide variability in target domain. Our
proposed approach is based on optimizing over a parameterized family of local
feature displacements that maximize the source-target adaptive similarity
functions. Accordingly we propose formulating zero-shot learning (ZSL) using
latent structural SVMs to learn our similarity functions from training data. As
demonstration we design a specific algorithm under the proposed framework
involving bilinear similarity functions and regularized least squares as
penalties for feature displacement. We test our approach on several benchmark
datasets for ZSR and show significant improvement over the state-of-the-art.
For instance, on aP&Y dataset we can achieve 80.89% in terms of recognition
accuracy, outperforming the state-of-the-art by 11.15%.


title:
Realization of a "Two Relaxation Rates" in the Hubbard-Falicov-Kimball
  Model

abstract:
  A single transport relaxation rate governs the decay of both, longitudinal
and Hall currents in Landau Fermi Liquids (LFL). Breakdown of this fundamental
feature, first observed in cuprates and subsequently in other three-dimensional
correlated systems close to (partial or complete) Mott metal-insulator
transitions, played a pivotal role in emergence of a non-Landau Fermi liquid
paradigm in higher dimensions $D(>1)$. Motivated hereby, we explore the
emergence of this "two relaxation rates" scenario in the
Hubbard-Falicov-Kimball model (HFKM) using the dynamical mean-field theory
(DMFT). Specializing to $D=3$, we find, beyond a critical FK interaction, that
two distinct relaxation rates governing distinct temperature ($T$) dependence
of the longitudinal and Hall currents naturally emerges in the non-LFL metal.
We rationalize this surprising finding by an analytical analysis of the
structure of charge and spin correlations in the underlying impurity problem,
and point out good accord with observations in the famed case of V$_{2-y}$O$_3$
near the MIT.


title:
A note on entanglement entropy and regularization in holographic
  interface theories

abstract:
  We discuss the computation of holographic entanglement entropy for interface
conformal field theories. The fact that globally well defined Fefferman-Graham
coordinates are difficult to construct makes the regularization of the
holographic theory challenging. We introduce a simple new cut-off procedure,
which we call "double cut-off" regularization. We test the new cut-off
procedure by comparing the results for holographic entanglement entropies using
other cut-off procedures and find agreement. We also study three dimensional
conformal field theories with a two dimensional interface. In that case the
dual bulk geometry is constructed using warped geometry with an $AdS_3$ factor.
We define an effective central charge to the interface through the
Brown-Henneaux formula for the $AdS_3$ factor. We investigate two concrete
examples, showing that the same effective central charge appears in the
computation of entanglement entropy and governs the conformal anomaly.


title: Fast Fourier Color Constancy

abstract:
  We present Fast Fourier Color Constancy (FFCC), a color constancy algorithm
which solves illuminant estimation by reducing it to a spatial localization
task on a torus. By operating in the frequency domain, FFCC produces lower
error rates than the previous state-of-the-art by 13-20% while being 250-3000
times faster. This unconventional approach introduces challenges regarding
aliasing, directional statistics, and preconditioning, which we address. By
producing a complete posterior distribution over illuminants instead of a
single illuminant estimate, FFCC enables better training techniques, an
effective temporal smoothing technique, and richer methods for error analysis.
Our implementation of FFCC runs at ~700 frames per second on a mobile device,
allowing it to be used as an accurate, real-time, temporally-coherent automatic
white balance algorithm.


title: Self-similar solutions of $\sigma_k^{\alpha}$-curvature flow

abstract:
  In this paper, employing a new inequality, we show that under certain
curvature pinching condition, the strictly convex closed smooth self-similar
solution of $\sigma_k^{\alpha}$-flow must be a round sphere. We also obtain a
similar result for the solutions of $F=-\langle X, e_{n+1}\rangle \, (*)$ with
a non-homogeneous function $F$. At last, we prove that if $F$ can be compared
with $\frac{(n-k+1)\sigma_{k-1}}{k\sigma_{k}}$, then a closed strictly
$k$-convex solution of $(*)$ must be a round sphere.


title:
Single-Parameter Scaling and Maximum Entropy inside Disordered
  One-Dimensional Systems: Theory and Experiment

abstract:
  The single-parameter scaling hypothesis relating the average and variance of
the logarithm of the conductance is a pillar of the theory of electronic
transport. We use a maximum-entropy ansatz to explore the logarithm of the
energy density, $\ln {\cal W}(x)$, at a depth $x$ into a random one-dimensional
system. Single-parameter scaling would be the special case in which $x=L$ (the
system length). We find the result, confirmed in microwave measurements and
computer simulations, that the average of $\ln {\cal W}(x)$ is independent of
$L$ and equal to $-x/\ell$, with $\ell$ the mean free path. At the beginning of
the sample, ${\rm var}[\ln {\cal W}(x)]$ rises linearly with $x$ and is also
independent of $L$, with a sublinear increase near the sample output. At $x=L$
we find a correction to the value of ${\rm var}[\ln T]$ predicted by
single-parameter scaling.


title:
Efficient Delivery Policy to Minimize User Traffic Consumption in
  Guaranteed Advertising

abstract:
  In this work, we study the guaranteed delivery model which is widely used in
online display advertising. In the guaranteed delivery scenario, ad exposures
(which are also called impressions in some works) to users are guaranteed by
contracts signed in advance between advertisers and publishers. A crucial
problem for the advertising platform is how to fully utilize the valuable user
traffic to generate as much as possible revenue.
  Different from previous works which usually minimize the penalty of
unsatisfied contracts and some other cost (e.g. representativeness), we propose
the novel consumption minimization model, in which the primary objective is to
minimize the user traffic consumed to satisfy all contracts. Under this model,
we develop a near optimal method to deliver ads for users. The main advantage
of our method lies in that it consumes nearly as least as possible user traffic
to satisfy all contracts, therefore more contracts can be accepted to produce
more revenue. It also enables the publishers to estimate how much user traffic
is redundant or short so that they can sell or buy this part of traffic in bulk
in the exchange market. Furthermore, it is robust with regard to priori
knowledge of user type distribution. Finally, the simulation shows that our
method outperforms the traditional state-of-the-art methods.


title:
Coherent structures and extreme events in rotating multiphase turbulent
  flows

abstract:
  By using direct numerical simulations (DNS) at unprecedented resolution we
study turbulence under rotation in the presence of simultaneous direct and
inverse cascades. The accumulation of energy at large scale leads to the
formation of vertical coherent regions with high vorticity oriented along the
rotation axis. By seeding the flow with millions of inertial particles, we
quantify -for the first time- the effects of those coherent vertical structures
on the preferential concentration of light and heavy particles. Furthermore, we
quantitatively show that extreme fluctuations, leading to deviations from a
normal-distributed statistics, result from the entangled interaction of the
vertical structures with the turbulent background. Finally, we present the
first-ever measurement of the relative importance between Stokes drag, Coriolis
force and centripetal forces along the trajectories of inertial particles. We
discover that vortical coherent structures lead to unexpected diffusion
properties for heavy and light particles in the directions parallel and
perpendicular to the rotation axis.

categories: physics.flu-dyn cond-mat.stat-mech nlin.CD physics.geo-ph


title: Future Wireless Network: MyNET Platform and End-to-End Network Slicing

abstract:
  Future wireless networks are facing new challenges. These new challenges
require new solutions and strategies of the network deployment, management, and
operation. Many driving factors are decisive in the re-definition and re-design
of the future wireless network architecture. In the previously published paper
"5G Wireless Network - MyNET and SONAC", MyNET and SONAC, a future network
architecture, are described. This paper elaborates MyNET platform with more
details. The design principles of MyNET architecture, the development of MyNET
platform, the functions of MyNET platform, the automation of the creation and
the management of end-to-end slices by MyNET, and the new capabilities enabled
by MyNET are described in details.


title:
Dynamics of extended bodies with spin-induced quadrupole in Kerr
  spacetime: generic orbits

abstract:
  We discuss motions of extended bodies in Kerr spacetime by using
Mathisson-Papapetrou-Dixon equations. We firstly solve the conditions for
circular orbits, and calculate the orbital frequency shift due to the mass
quadrupoles. The results show that we need not consider the spin-induced
quadrupoles in extreme-mass-ratio inspirals for spatial gravitational wave
detectors. We quantitatively investigate the temporal variation of rotational
velocity of the extended body due to the coupling of quadrupole and background
gravitational field. For generic orbits, we numerically integrate the
Mathisson-Papapetrou-Dixon equations for evolving the motion of an extended
body orbiting a Kerr black hole. By comparing with the monopole-dipole
approximation, we reveal the influences of quadrupole moments of extended
bodies on the orbital motion and chaotic dynamics of extreme-mass-ratio
systems. We do not find any chaotic orbits for the extended bodies with
physical spins and spin-induced quadrupoles. Possible implications for
gravitational wave detection and pulsar timing observation are outlined.


title:
GIARPS: the unique VIS-NIR high precision radial velocity facility in
  this world

abstract:
  GIARPS (GIAno & haRPS) is a project devoted to have on the same focal station
of the Telescopio Nazionale Galileo (TNG) both the high resolution
spectrographs HARPS-N (VIS) and GIANO (NIR) working simultaneously. This could
be considered the first and unique worldwide instrument providing
cross-dispersed echelle spectroscopy at a high resolution (R=115,000 in the
visual and R=50,000 in the IR) and over in a wide spectral range (0.383 - 2.45
micron) in a single exposure. The science case is very broad, given the
versatility of such an instrument and the large wavelength range. A number of
outstanding science cases encompassing mainly extra-solar planet science
starting from rocky planet search and hot Jupiters, atmosphere characterization
can be considered. Furthermore both instrument can measure high precision
radial velocity by means the simultaneous thorium technique (HARPS - N) and
absorbing cell technique (GIANO) in a single exposure. Other science cases are
also possible. Young stars and proto-planetary disks, cool stars and stellar
populations, moving minor bodies in the solar system, bursting young stellar
objects, cataclysmic variables and X-ray binary transients in our Galaxy,
supernovae up to gamma-ray bursts in the very distant and young Universe, can
take advantage of the unicity of this facility both in terms of contemporaneous
wide wavelength range and high resolution spectroscopy.


title: A BCool survey of the magnetic fields of planet-hosting solar-type stars

abstract:
  We present a spectropolarimetric snapshot survey of solar-type planet hosting
stars. In addition to 14 planet-hosting stars observed as part of the BCool
magnetic snapshot survey, we obtained magnetic observations of a further 19
planet-hosting solar-type stars in order to see if the presence of close-in
planets had an effect on the measured surface magnetic field (|B$_{\ell}$|).
Our results indicate that the magnetic activity of this sample is congruent
with that of the overall BCool sample. The effects of the planetary systems on
the magnetic activity of the parent star, if any, are too subtle to detect
compared to the intrinsic dispersion and correlations with rotation, age and
stellar activity proxies in our sample. Four of the 19 newly observed stars,
two of which are subgiants, have unambiguously detected magnetic fields and are
future targets for Zeeman Doppler Mapping.


title: Optimal Pricing for Submodular Valuations with Bounded Curvature

abstract:
  The optimal pricing problem is a fundamental problem that arises in
combinatorial auctions. Suppose that there is one seller who has indivisible
items and multiple buyers who want to purchase a combination of the items. The
seller wants to sell his items for the highest possible prices, and each buyer
wants to maximize his utility (i.e., valuation minus payment) as long as his
payment does not exceed his budget. The optimal pricing problem seeks a price
of each item and an assignment of items to buyers such that every buyer
achieves the maximum utility under the prices. The goal of the problem is to
maximize the total payment from buyers. In this paper, we consider the case
that the valuations are submodular. We show that the problem is computationally
hard even if there exists only one buyer. Then we propose approximation
algorithms for the unlimited budget case. We also extend the algorithm for the
limited budget case when there exists one buyer and multiple buyers collaborate
with each other.


title:
On the global solution problem of semilinear generalized Tricomi
  equations, II

abstract: This paper is a continuation of our recent work in [9].


title:
Inviscid limit for the compressible Euler system with non-local
  interactions

abstract:
  The collective behavior of animals can be modeled by a system of equations of
continuum mechanics endowed with extra terms describing repulsive and
attractive forces between the individuals. This system can be viewed as a
generalization of the compressible Euler equations with all of its unpleasant
consequences, e.g., the non-uniqueness of solutions. In this paper, we analyze
the equations describing a viscous approximation of a generalized compressible
Euler system and we show that its dissipative measure-valued solutions tend to
a strong solution of the Euler system as viscosity tends to 0, provided that
the strong solution exists.


title:
A Framework for Collision-Tolerant Optimal Trajectory Planning of
  Autonomous Vehicles

abstract:
  Collision-tolerant trajectory planning is the consideration that collisions,
if they are planned appropriately, enable more effective path planning for
robots capable of handling them. A mixed integer programming (MIP) optimization
formulation demonstrates the computational practicality of optimizing
trajectories that comprise planned collisions. A damage quantification function
is proposed, and the influence of damage functions constraints on the
trajectory are studied in simulation. Using a simple example, an increase in
performance is achieved under this schema as compared to collision-free optimal
trajectories.


title:
Adaptive Accelerated Gradient Converging Methods under Holderian Error
  Bound Condition

abstract:
  Recent studies have shown that proximal gradient (PG) method and accelerated
gradient method (APG) with restarting can enjoy a linear convergence under a
weaker condition than strong convexity, namely a quadratic growth condition
(QGC). However, the faster convergence of restarting APG method relies on the
potentially unknown constant in QGC to appropriately restart APG, which
restricts its applicability. We address this issue by developing a novel
adaptive gradient converging methods, i.e., leveraging the magnitude of
proximal gradient as a criterion for restart and termination. Our analysis
extends to a much more general condition beyond the QGC, namely the
H\"{o}lderian error bound (HEB) condition. {\it The key technique} for our
development is a novel synthesis of {\it adaptive regularization and a
conditional restarting scheme}, which extends previous work focusing on
strongly convex problems to a much broader family of problems. Furthermore, we
demonstrate that our results have important implication and applications in
machine learning: (i) if the objective function is coercive and semi-algebraic,
PG's convergence speed is essentially $o(\frac{1}{t})$, where $t$ is the total
number of iterations; (ii) if the objective function consists of an $\ell_1$,
$\ell_\infty$, $\ell_{1,\infty}$, or huber norm regularization and a convex
smooth piecewise quadratic loss (e.g., squares loss, squared hinge loss and
huber loss), the proposed algorithm is parameter-free and enjoys a {\it faster
linear convergence} than PG without any other assumptions (e.g., restricted
eigen-value condition). It is notable that our linear convergence results for
the aforementioned problems are global instead of local. To the best of our
knowledge, these improved results are the first shown in this work.


title: Mutable WadlerFest DOT

abstract:
  The Dependent Object Types (DOT) calculus aims to model the essence of Scala,
with a focus on abstract type members, path-dependent types, and subtyping.
Other Scala features could be defined by translation to DOT. Mutation is a
fundamental feature of Scala currently missing in DOT. Mutation in DOT is
needed not only to model effectful computation and mutation in Scala programs,
but even to precisely specify how Scala initializes immutable variables and
fields (vals). We present an extension to DOT that adds typed mutable reference
cells. We have proven the extension sound with a mechanized proof in Coq. We
present the key features of our extended calculus and its soundness proof, and
discuss the challenges that we encountered in our search for a sound design and
the alternative solutions that we considered.


title: Time-delayed electromagnetic radiation reaction

abstract:
  The Lorentz-Abraham-Dirac (LAD) equation has proved valuable in describing
the motion of radiating electric charges but suffers from runaway,
pre-acceleration and other ambiguities. The usual scheme is problematic because
of locality, which leads to self-interaction with the propagating radiation
(i.e. real photons). Instead, the present heuristic model relies on an
infinitesimal time delay between the action of external forces and the inertial
reaction by the charge. This yields a new and pathology-free equation of motion
whereas the radiated energy-momentum is expressed as an infinite series that
generalises Larmor's formula and leads to testable predictions using current
and near future ultra-intense lasers. The time-delay hypothesis is to be put in
parallel with recently observed delays of order $10^{-18}$s (attosecond) in
photoemission by atoms and small molecules. Such behaviour is extended here to
elementary charges which are supposed to exhibit delays given by the time taken
by light to cross the charge's classical radius, which gives $\approx
10^{-23}$s for an electron.


title: Faster Population Counts Using AVX2 Instructions

abstract:
  Counting the number of ones in a binary stream is a common operation in
database, information-retrieval, cryptographic and machine-learning
applications. Most processors have dedicated instructions to count the number
of ones in a word (e.g., popcnt on x64 processors). Maybe surprisingly, we show
that a vectorized approach using SIMD instructions can be twice as fast as
using the dedicated instructions on recent Intel processors. The benefits can
be even greater for applications such as similarity measures (e.g., the Jaccard
index) that require additional Boolean operations. Our approach has been
adopted by LLVM: it is used by its popular C compiler (clang).


title: On the steady non-Newtonian fluids in domains with noncompact boundaries

abstract:
  In this paper, we study non-Newtonian fluids in a class of unbounded domains
with noncompact boundaries. With respect to the resulting mathematical
problems, we establish the global existence of solutions with arbitrary large
flux under some suitable conditions, and meanwhile, show the uniqueness of the
solutions when the flux is sufficiently small. Our results are an extension or
an improvement of those obtained in some previous references.


title: K2-113b: A dense hot-Jupiter transiting a solar analogue

abstract:
  We present the discovery of K2-113b, a dense hot-Jupiter discovered using
photometry from Campaign 8 of the Kepler-2 (K2) mission and high-resolution
spectroscopic follow up obtained with the FEROS spectrograph. The planet orbits
a $V=13.68$ solar analogue in a $P=5.81760^{+0.00003}_{-0.00003}$ day orbit,
has a radius of $0.93^{+0.10}_{-0.07}R_J$ and a mass of
$1.29^{+0.13}_{-0.14}M_J$. With a density of $1.97^{+0.60}_{-0.53}$ gr/cm$^3$,
the planet is among the densest systems known having masses below 2 $M_J$ and
$T_{eq} > 1000$, and is just above the temperature limit at which inflation
mechanisms are believed to start being important. Based on its mass and radius,
we estimate that K2-113b should have a heavy element content on the order of
$\sim$ 110 $M_{\oplus}$ or greater.


title: Experimental Study of Boron-coated Straws with a Neutron Source

abstract:
  Multiple types of high quality neutron detectors are proposed for the first
phase of the China Spallation Neutron Source (CSNS), which will be commissioned
in 2018. Considering the shortage of 3He supply, a detector module composed of
49 boron-coated straws (BCS) was developed by Proportional Technologies Inc.
(PTI). Each straw has a length of 1000 mm and a diameter of 7.5 mm. Seven
straws are tightly packed in a tube, and seven tubes are organized in a row to
form a detector module. The charge division method is used for longitudinal
positioning. A specific readout system was utilized to output the signal and
simultaneously encode each straw. The performance of this detector module was
studied using a moderated 252Cf source at the Institute of High Energy Physics
(IHEP). The signal amplitude spectrum indicates its n-gamma discrimination
capability. Despite the complex readout method, a longitudinal resolution of
6.1-0.5 mm was obtained. The three-dimensional positioning ability qualifies
this BCS detector module as a promising neutron scattering spectrometer
detector.


title: Ultra high energy cosmic rays from non-relativistic quasar outflows

abstract:
  It has been suggested that non-relativistic outflows from quasars can
naturally account for the missing component of the extragalactic $\gamma$-ray
background and explain the cumulative neutrino background through pion decay in
collisions between protons accelerated by the outflow shock and interstellar
protons. Here we show that the same quasar outflows are capable of accelerating
protons to energies of $\sim 10^{20}$ eV during the early phase of their
propagation. The overall quasar population is expected to produce a cumulative
ultra high energy cosmic ray flux of $\sim10^{-7}\,\rm
GeV\,cm^{-2}s^{-1}sr^{-1}$ at $E_{\rm CR}\gtrsim10^{18}$ eV. The spectral shape
and amplitude is consistent with recent observations for outflow parameters
constrained to fit secondary $\gamma$-rays and neutrinos without any additional
parameter tuning. This indicates that quasar outflows simultaneously account
for all three messengers at their observed levels.


title: A New Framework for Ranking Vulnerabilities in the Clouds

abstract:
  Qualifying and ranking threat degrees of vulnerabilities in cloud service are
known to be full of challenges. Although there have been several efforts aiming
to address this problem, most of them are too simple or cannot be applied into
cloud infrastructure. This paper aims to propose a novel framework to qualify
and rank the vulnerabilities based on their threat degrees in cloud service.
Through inputting or constructing service dependency graph, our framework is
able to generate the importance degree of each service and the ranking list of
all the vulnerabilities in cloud service. Moreover, our framework can be
adopted not only into various cloud infrastructures, but also different
categories of algorithms according to concrete requirements. To evaluate our
framework, we adopt AssetRank algorithm into the framework, and present the
whole design of our work. Comprehensive experiments prove the effectiveness of
our framework on qualifying and ranking vulnerabilities in cloud service.


title:
On the study of stochastic fractional-order differential equation
  systems

abstract:
  In this article, the existence and uniqueness about the solution for a class
of stochastic fractional-order differential equation systems are investigated,
where the fractional derivative is described in Caputo sense. The fractional
calculus, stochastic analysis techniques and the standard Picard's iteration
are used to obtain the required results, the nonlinear term is satisfied with
some non-Lipschitz conditions (where the classical Lipschitz conditions are
special cases). The stochastic fractional-order Newton-Leipnik and Lorenz
systems are provided to illustrate the obtained theory, and numerical
simulation results are also given by the modified Adams predictor-corrector
scheme.


title:
A Truthful $(1-\epsilon)$-Optimal Mechanism for On-demand Cloud Resource
  Provisioning

abstract:
  On-demand resource provisioning in cloud computing provides tailor-made
resource packages (typically in the form of VMs) to meet users' demands. Public
clouds nowadays provide more and more elaborated types of VMs, but have yet to
offer the most flexible dynamic VM assembly, which is partly due to the lack of
a mature mechanism for pricing tailor-made VMs on the spot. This work proposes
an efficient randomized auction mechanism based on a novel application of
smoothed analysis and randomized reduction, for dynamic VM provisioning and
pricing in geo-distributed cloud data centers. This auction, to the best of our
knowledge, is the first one in literature that achieves (i) truthfulness in
expectation, (ii) polynomial running time in expectation, and (iii)
$(1-\epsilon)$-optimal social welfare in expectation for resource allocation,
where $\epsilon$ can be arbitrarily close to 0. Our mechanism consists of three
modules: (1) an exact algorithm to solve the NP-hard social welfare
maximization problem, which runs in polynomial time in expectation, (2) a
perturbation-based randomized resource allocation scheme which produces a VM
provisioning solution that is $(1-\epsilon)$-optimal, and (3) an auction
mechanism that applies the perturbation-based scheme for dynamic VM
provisioning and prices the customized VMs using a randomized VCG payment, with
a guarantee in truthfulness in expectation. We validate the efficacy of the
mechanism through careful theoretical analysis and trace-driven simulations.


title: Using SyGuS to Synthesize Reactive Motion Plans

abstract:
  We present an approach for synthesizing reactive robot motion plans, based on
compilation to Syntax-Guided Synthesis (SyGuS) specifications. Our method
reduces the motion planning problem to the problem of synthesizing a function
that can choose the next robot action in response to the current state of the
system. This technique offers reactivity not by generating new motion plans
throughout deployment, but by synthesizing a single program that causes the
robot to reach its target from any system state that is consistent with the
system model. This approach allows our tool to handle environments with
adversarial obstacles. This work represents the first use of the SyGuS
formalism to solve robot motion planning problems. We investigate whether using
SyGuS for a bounded two-player reachability game is practical at this point in
time.


title: What You Really Need To Know About Your Neighbor

abstract:
  A fundamental question in system design is to decide how much of the design
of one component must be known in order to successfully design another
component of the system. We study this question in the setting of reactive
synthesis, where one constructs a system implementation from a specification
given in temporal logic. In previous work, we have shown that the system can be
constructed compositionally, one component at a time, if the specification
admits a "dominant" (as explained in Introduction) strategy for each component.
In this paper, we generalize the approach to settings where dominant strategies
only exist under certain assumptions about the future behavior of the other
components. We present an incremental synthesis method based on the automatic
construction of such assumptions.


title: Symbolic BDD and ADD Algorithms for Energy Games

abstract:
  Energy games, which model quantitative consumption of a limited resource,
e.g., time or energy, play a central role in quantitative models for reactive
systems. Reactive synthesis constructs a controller which satisfies a given
specification, if one exists. For energy games a synthesized controller ensures
to satisfy not only the safety constraints of the specification but also the
quantitative constraints expressed in the energy game. A symbolic algorithm for
energy games, recently presented by Chatterjee et al., is symbolic in its
representation of quantitative values but concrete in the representation of
game states and transitions. In this paper we present an algorithm that is
symbolic both in the quantitative values and in the underlying game
representation. We have implemented our algorithm using two different symbolic
representations for reactive games, Binary Decision Diagrams (BDD) and
Algebraic Decision Diagrams (ADD). We investigate the commonalities and
differences of the two implementations and compare their running times on
specifications of energy games.


title: Leveraging Parallel Data Processing Frameworks with Verified Lifting

abstract:
  Many parallel data frameworks have been proposed in recent years that let
sequential programs access parallel processing. To capitalize on the benefits
of such frameworks, existing code must often be rewritten to the
domain-specific languages that each framework supports. This rewriting-tedious
and error-prone-also requires developers to choose the framework that best
optimizes performance given a specific workload.
  This paper describes Casper, a novel compiler that automatically retargets
sequential Java code for execution on Hadoop, a parallel data processing
framework that implements the MapReduce paradigm. Given a sequential code
fragment, Casper uses verified lifting to infer a high-level summary expressed
in our program specification language that is then compiled for execution on
Hadoop. We demonstrate that Casper automatically translates Java benchmarks
into Hadoop. The translated results execute on average 3.3x faster than the
sequential implementations and scale better, as well, to larger datasets.


title:
Developing a Practical Reactive Synthesis Tool: Experience and Lessons
  Learned

abstract:
  We summarise our experience developing and using Termite, the first reactive
synthesis tool intended for use by software development practitioners. We
identify the main barriers to making reactive synthesis accessible to software
developers and describe the key features of Termite designed to overcome these
barriers, including an imperative C-like specification language, an interactive
source-level debugger, and a user-guided code generator. Based on our
experience applying Termite to synthesising real-world reactive software, we
identify several caveats of the practical use of the reactive synthesis
technology. We hope that these findings will help define the agenda for future
research on practical reactive synthesis.


title: An Update on Deductive Synthesis and Repair in the Leon Tool

abstract:
  We report our progress in scaling deductive synthesis and repair of recursive
functional Scala programs in the Leon tool. We describe new techniques,
including a more precise mechanism for encoding the space of meaningful
candidate programs. Our techniques increase the scope of synthesis by expanding
the space of programs we can synthesize and by reducing the synthesis time in
many cases. As a new example, we present a run-length encoding function for a
list of values, which Leon can now automatically synthesize from specification
consisting of the decoding function and the local minimality property of the
encoded value.


title: The Reactive Synthesis Competition: SYNTCOMP 2016 and Beyond

abstract:
  We report on the design of the third reactive synthesis competition (SYNTCOMP
2016), including a major extension of the competition to specifications in full
linear temporal logic. We give a brief overview of the synthesis problem as
considered in SYNTCOMP, and present the rules of the competition in 2016, as
well as the ideas behind our design choices. Furthermore, we evaluate the
recent changes to the competition based on the experiences with SYNTCOMP 2016.
Finally, we give an outlook on further changes and extensions of the
competition that are planned for the future.


title: SyGuS-Comp 2016: Results and Analysis

abstract:
  Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an
implementation f that meets both a semantic constraint given by a logical
formula $\varphi$ in a background theory T, and a syntactic constraint given by
a grammar G, which specifies the allowed set of candidate implementations. Such
a synthesis problem can be formally defined in SyGuS-IF, a language that is
built on top of SMT-LIB.
  The Syntax-Guided Synthesis Competition (SyGuS-Comp) is an effort to
facilitate, bring together and accelerate research and development of efficient
solvers for SyGuS by providing a platform for evaluating different synthesis
techniques on a comprehensive set of benchmarks. In this year's competition we
added a new track devoted to programming by examples. This track consisted of
two categories, one using the theory of bit-vectors and one using the theory of
strings. This paper presents and analyses the results of SyGuS-Comp'16.


title: Nearly Optimal Bounds for Orthogonal Least Squares

abstract:
  In this paper, we study the orthogonal least squares (OLS) algorithm for
sparse recovery. On the one hand, we show that if the sampling matrix
$\mathbf{A}$ satisfies the restricted isometry property (RIP) of order $K + 1$
with isometry constant $$ \delta_{K + 1} < \frac{1}{\sqrt{K+1}}, $$ then OLS
exactly recovers the support of any $K$-sparse vector $\mathbf{x}$ from its
samples $\mathbf{y} = \mathbf{A} \mathbf{x}$ in $K$ iterations. On the other
hand, we show that OLS may not be able to recover the support of a $K$-sparse
vector $\mathbf{x}$ in $K$ iterations for some $K$ if $$ \delta_{K + 1} \geq
\frac{1}{\sqrt{K+\frac{1}{4}}}. $$


title:
Approaching Symbolic Parallelization by Synthesis of Recurrence
  Decompositions

abstract:
  We present GraSSP, a novel approach to perform automated parallelization
relying on recent advances in formal verification and synthesis. GraSSP
augments an existing sequential program with an additional functionality to
decompose data dependencies in loop iterations, to compute partial results, and
to compose them together. We show that for some classes of the sequential
prefix sum problems, such parallelization can be performed efficiently.


title: Degrees of Freedom of the Bursty MIMO X Channel without Feedback

abstract:
  We study the sum degrees of freedom (DoF) of the bursty MIMO X channel
without feedback, where the four transmitter-receiver links are intermittently
on-and-off, controlled by four Bernoulli random sequences which may be
arbitrarily correlated, subject to a symmetry assumption: The two direct-links
have the same level of burstiness, modeled by $\mathrm{Ber}(p_d)$, and so do
the cross-links, modeled by $\mathrm{Ber}(p_c)$. The sum DoF is fully
characterized in the regime where $\frac{p_c}{p_d}$ is small, i.e. below a
certain threshold, and is partially characterized in the other regime where
$\frac{p_c}{p_d}$ is above the threshold. The achievability is proved with a
combination of Han-Kobayashi strategy and interference alignment, which can
achieve strictly higher DoF than interference alignment alone. The converse
proof employs a channel-state-sequence pairing technique. We highlight that
burstiness of the channel disrupts the network topology, turning the MIMO X
channel into a network with time-varying topology. This fundamental difference
has striking ramifications. In particular, various interference alignment
schemes that achieve the DoF of non-bursty X channels are found to be
suboptimal when the channels become bursty. The reciprocity between the forward
and the reverse links is lost, and the sum DoF does not saturate when the ratio
of the transmitter and the receiver antennas exceeds $\frac{2}{3}$.


title:
Energetics of point defects in aluminum via orbital-free density
  functional theory

abstract:
  The formation and migration energies for various point defects, including
vacancies and self-interstitials in aluminum are reinvestigated systematically
using the supercell approximation in the framework of orbital-free density
functional theory. In particular, the finite-size effects and the accuracy of
various kinetic energy density functionals are examined.The calculated results
suggest that the errors due to the finite-size effect decrease exponentially
upon enlarging the supercell. It is noteworthy that the formation energies of
self-interstitials converge much slower than that of vacancy. With carefully
chosen kinetic energy density functionals, the calculated results agree quite
well with the available experimental data and those obtained by Kohn-Sham
density functional theory which has exact kinetic term.


title:
Singularly perturbed fractional Schr\"{o}dinger equation involving a
  general critical nonlinearity

abstract:
  In this paper, we are concerned with the existence and concentration
phenomena of solutions for the following singularly perturbed fractional
Schr\"{o}dinger problem \begin{align*} \varepsilon^{2s}(-\Delta)^su+V(x)u=f(u)
\ \ \ \mbox{in} \ \ \ \mathbb{R}^N, \end{align*} where $N>2s$ and the
nonlinearity $f$ has critical growth. By using the variational approach, we
construct a localized bound-state solution concentrating around an isolated
component of the positive minimum point of $V$ as $\varepsilon\rightarrow 0$.
Our result improves the study made in X. He and W. Zou ({\it Calc. Var. Partial
Differential Equations}. 55-91(2016)), in the sense that, in the present paper,
the {\it Ambrosetti-Rabinowitz} condition and {\it monotonicity} condition on
$f(t)/t$ are not required.


title: Towards the Secure Storage of Images on Multi-Cloud System

abstract:
  With the rapidly changing technological realm, there is an urgent need to
provide and protect the confidentiality of confidential images when stored in a
cloud environment. To overcome the security risks associated with single cloud,
multiple clouds offered by unrelated cloud providers have to be used. This
paper outlines an integrated encryption scheme for the secure storage of
confidential images on multiple clouds based on DNA sequences.


title: Interpretation of Prediction Models Using the Input Gradient

abstract:
  State of the art machine learning algorithms are highly optimized to provide
the optimal prediction possible, naturally resulting in complex models. While
these models often outperform simpler more interpretable models by order of
magnitudes, in terms of understanding the way the model functions, we are often
facing a "black box".
  In this paper we suggest a simple method to interpret the behavior of any
predictive model, both for regression and classification. Given a particular
model, the information required to interpret it can be obtained by studying the
partial derivatives of the model with respect to the input. We exemplify this
insight by interpreting convolutional and multi-layer neural networks in the
field of natural language processing.


title:
T-CONV: A Convolutional Neural Network For Multi-scale Taxi Trajectory
  Prediction

abstract:
  Precise destination prediction of taxi trajectories can benefit many
intelligent location based services such as accurate ad for passengers.
Traditional prediction approaches, which treat trajectories as one-dimensional
sequences and process them in single scale, fail to capture the diverse
two-dimensional patterns of trajectories in different spatial scales. In this
paper, we propose T-CONV which models trajectories as two-dimensional images,
and adopts multi-layer convolutional neural networks to combine multi-scale
trajectory patterns to achieve precise prediction. Furthermore, we conduct
gradient analysis to visualize the multi-scale spatial patterns captured by
T-CONV and extract the areas with distinct influence on the ultimate
prediction. Finally, we integrate multiple local enhancement convolutional
fields to explore these important areas deeply for better prediction.
Comprehensive experiments based on real trajectory data show that T-CONV can
achieve higher accuracy than the state-of-the-art methods.


title: Mechanism Design for Multi-Type Housing Markets

abstract:
  We study multi-type housing markets, where there are $p\ge 2$ types of items,
each agent is initially endowed one item of each type, and the goal is to
design mechanisms without monetary transfer to (re)allocate items to the agents
based on their preferences over bundles of items, such that each agent gets one
item of each type. In sharp contrast to classical housing markets, previous
studies in multi-type housing markets have been hindered by the lack of natural
solution concepts, because the strict core might be empty.
  We break the barrier in the literature by leveraging AI techniques and making
natural assumptions on agents' preferences. We show that when agents'
preferences are lexicographic, even with different importance orders, the
classical top-trading-cycles mechanism can be extended while preserving most of
its nice properties. We also investigate computational complexity of checking
whether an allocation is in the strict core and checking whether the strict
core is empty. Our results convey an encouragingly positive message: it is
possible to design good mechanisms for multi-type housing markets under natural
assumptions on preferences.


title: Solar Active Longitudes From Kodaikanal White-light Digitized Data

abstract:
  The study of solar active longitudes has generated a great interest in the
recent years. In this work we have used an unique continuous sunspot data
series obtained from Kodaikanal observatory and revisited the problem. Analysis
of the data shows a persistent presence of the active longitude during the
whole 90 years of data duration. We compare two well studied analysis methods
and presented their respective results. The separation between the two most
active longitudes is found be roughly 180{\deg} for majority of time.
Additionally, we also find a comparatively weaker presence of separations at
90{\deg} and 270{\deg}. Migration pattern of these active longitudes as
revealed from our data is found to be consistent with the solar differential
rotation curve. We also study the periodicities in the active longitudes and
found two dominant periods of $\approx$1.3 years and $\approx$2.2 years. These
periods, also found in other solar proxies, indicate their relation with the
global solar dynamo mechanism.


title:
Secure detection in quantum key distribution by real-time calibration of
  receiver

abstract:
  The single photon detection efficiency of the detector unit is crucial for
the security of common quantum key distribution protocols like Bennett-Brassard
1984 (BB84). A low value for the efficiency indicates a possible eavesdropping
attack that exploits the photon receiver's imperfections. We present a method
for estimating the detection efficiency, and calculate the corresponding secure
key generation rate. The estimation is done by testing gated detectors using a
randomly activated photon source inside the receiver unit. This estimate gives
a secure rate for any detector with non-unity single photon detection
efficiency, both inherit or due to blinding. By adding extra optical components
to the receiver, we make sure that the key is extracted from photon states for
which our estimate is valid. The result is a quantum key distribution scheme
that is secure against any attack that exploits detector imperfections.


title:
Enabling photoemission electron microscopy in liquids via
  graphene-capped microchannel arrays

abstract:
  Photoelectron emission microscopy PEEM is a powerful tool to
spectroscopically image dynamic surface processes at the nanoscale but is
traditionally limited to ultra high or moderate vacuum conditions. Here, we
develop a novel grapheme capped multichannel array sample platform that extends
the capabilities of photoelectron spectromicroscopy to routine liquid and
atmospheric pressure studies with standard PEEM setups. Using this platform, we
show that graphene has only a minor influence on the electronic structure of
water in the first few layers and thus will allow for the examination of
minimally perturbed aqueous phase interfacial dynamics. Analogous to microarray
screening technology in biomedical research, our platform is highly suitable
for applications in tandem with large-scale data mining, pattern recognition,
and combinatorial methods for spectro temporal and spatiotemporal analyses at
solid liquid interfaces. Using Bayesian linear unmixing algorithm, we were able
to discriminate between different X-ray induced water radiolysis scenarios and
observe a metastable wetting intermediate water layer during the late stages of
bubble formationformation


title:
Solving multi-objective optimization problems in conservation with the
  reference point method

abstract:
  Managing biodiversity extinction crisis requires wise decision-making
processes able to account for the limited of resources available. In most
decision problems in conservation biology, several conflicting objectives have
to be taken into account. Most methods used in conservation either provide
suboptimal solutions or use strong assumptions about the decision-maker's
preferences. Our paper reviews some of the existing approaches to solve
multi-objective decision problems and presents new multi-objective linear
programming formulations of two multi-objective optimization problems in
conservation, allowing the use of a reference point approach. Reference point
approaches solve multi-objective optimization problems by interactively
representing the preferences of the decision-maker with a point in the criteria
space, called the reference point. We modelled and solved the following two
conservation problems: a dynamic multi-species management problem under
uncertainty and a spatial allocation resource management problem. Results show
that the reference point method outperforms classic methods while illustrating
the use of an interactive methodology for solving combinatorial problems with
multiple objectives. The method is general and can be adapted to a wide range
of ecological combinatorial problems.


title: Sparse Phase Retrieval via Truncated Amplitude Flow

abstract:
  This paper develops a novel algorithm, termed \emph{SPARse Truncated
Amplitude flow} (SPARTA), to reconstruct a sparse signal from a small number of
magnitude-only measurements. It deals with what is also known as sparse phase
retrieval (PR), which is \emph{NP-hard} in general and emerges in many science
and engineering applications. Upon formulating sparse PR as an amplitude-based
nonconvex optimization task, SPARTA works iteratively in two stages: In stage
one, the support of the underlying sparse signal is recovered using an
analytically well-justified rule, and subsequently, a sparse
orthogonality-promoting initialization is obtained via power iterations
restricted on the support; and, in the second stage, the initialization is
successively refined by means of hard thresholding based gradient-type
iterations. SPARTA is a simple yet effective, scalable, and fast sparse PR
solver. On the theoretical side, for any $n$-dimensional $k$-sparse ($k\ll n$)
signal $\bm{x}$ with minimum (in modulus) nonzero entries on the order of
$(1/\sqrt{k})\|\bm{x}\|_2$, SPARTA recovers the signal exactly (up to a global
unimodular constant) from about $k^2\log n$ random Gaussian measurements with
high probability. Furthermore, SPARTA incurs computational complexity on the
order of $k^2n\log n$ with total runtime proportional to the time required to
read the data, which improves upon the state-of-the-art by at least a factor of
$k$. Finally, SPARTA is robust against additive noise of bounded support.
Extensive numerical tests corroborate markedly improved recovery performance
and speedups of SPARTA relative to existing alternatives.


title: Wild Ramification and Restrictions to Curves

abstract:
  We prove that wild ramification of a constructible sheaf on a surface is
determined by that of the restrictions to all curves. We deduce from this
result that the Euler-Poincar\'e characteristic of a constructible sheaf on a
variety of arbitrary dimension over an algebraically closed field is determined
by wild ramification of the restrictions to all curves. We similarly deduce
from it that so is the alternating sum of the Swan conductors of the cohomology
groups, for a constructible sheaf on a variety over a local field.