title:
Bayesian Non-parametric Simultaneous Quantile Regression for Complete
  and Grid Data

abstract:
  In this paper, we consider Bayesian methods for non-parametric quantile
regressions with multiple continuous predictors ranging values in the unit
interval. In the first method, the quantile function is assumed to be smooth
over the explanatory variable and is expanded in tensor product of B-spline
basis functions. While in the second method, the distribution function is
assumed to be smooth over the explanatory variable and is expanded in tensor
product of B-spline basis functions. Unlike other existing methods of
non-parametric quantile regressions, the proposed methods estimate the whole
quantile function instead of estimating on a grid of quantiles. Priors on the
B-spline coefficients are put in such a way that the monotonicity of the
estimated quantile levels are maintained unlike local polynomial quantile
regression methods. The proposed methods have also been modified for quantile
grid data where only the percentile range of each response observations are
known. Simulations studies have been provided for both complete and quantile
grid data. The proposed method has been used to estimate the quantiles of US
household income data and North Atlantic hurricane intensity data.


title:
Recommendations for the use of notebooks in upper-division physics lab
  courses

abstract:
  The use of lab notebooks for scientific documentation is a ubiquitous part of
physics research. However, it is common for undergraduate physics laboratory
courses not to emphasize the development of documentation skills, despite the
fact that such courses are some of the earliest opportunities for students to
start engaging in this practice. One potential impediment to the inclusion of
explicit documentation training is that it may be unclear to instructors which
features of authentic documentation practice are efficacious to teach and how
to incorporate these features into the lab class environment. In this work, we
outline some of the salient features of authentic documentation, informed by
interviews with physics researchers, and provide recommendations for how these
can be incorporated into the lab curriculum. We do not focus on structural
details or templates for notebooks. Instead, we address holistic considerations
for the purpose of scientific documentation that can guide students to develop
their own documentation style. Taking into consideration all the aspects that
can help improve students' documentation, it is also important to consider the
design of the lab activities themselves. Students should have experience with
implementing these authentic features of documentation during lab activities in
order for them to find practice with documentation beneficial.


title:
Physical Characterization of ~2-meter Diameter Near-Earth Asteroid 2015
  TC25: A possible boulder from E-type Asteroid (44) Nysa

abstract:
  Small near-Earth asteroids (>20 meters) are interesting because they are
progenitors for meteorites in our terrestrial collection. Crucial to our
understanding of the effectiveness of our atmosphere in filtering low-strength
impactors is the physical characteristics of these small near-Earth asteroids
(NEAs). In the past, characterization of small NEAs has been a challenge
because of the difficulty in detecting them prior to close Earth flyby. In this
study we physically characterized the 2-meter diameter near-Earth asteroid 2015
TC25 using ground-based optical, near-infrared and radar assets during a close
flyby of the Earth (distance 69,000 miles) in Oct. 2015. Our observations
suggest that its surface composition is similar to aubrites, a rare class of
high albedo differentiated meteorites. Aubrites make up only 0.14 % of all know
meteorites in our terrestrial meteorite collection. 2015 TC25 is also a very
fast rotator with a rotation period of 133 seconds. We compared spectral and
dynamical properties of 2015 TC25 and found the best candidate source body in
the inner main belt to be the 70-km diameter E-type asteroid (44) Nysa. We
attribute difference in spectral slope between the two objects to the lack of
regolith on the surface of 2015 TC25. Using the albedo of E-type asteroids
(50-60%) we refine the diameter of 2015 TC25 to 2-meters making it one of the
smallest NEA ever to be characterized.

categories: astro-ph.EP astro-ph.IM physics.geo-ph physics.space-ph


title: R\'enyi Mutual Information for Free Scalar in Even Dimensions

abstract:
  We compute the R\'enyi mutual information of two disjoint spheres in free
massless scalar theory in even dimensions higher than two. The spherical twist
operator in a conformal field theory can be expanded into the sum of local
primary operators and their descendants. We analyze the primary operators in
the replicated scalar theory and find the ones of the fewest dimensions and
spins. We study the one-point function of these operators in the conical
geometry and obtain their expansion coefficients in the OPE of spherical twist
operators. We show that the R\'enyi mutual information can be expressed in
terms of the conformal partial waves. We compute explicitly the R\'enyi mutual
information up to order $z^d$, where $z$ is the cross ratio and $d$ is the
spacetime dimension.


title:
Collective atom phase controls in photon echoes for quantum memory
  applications I: Population inversion removal

abstract:
  Photon echo-based quantum memories demonstrated in rare-earth doped solids
over the last decade have solved the major constraint of population inversion
in conventional photon echoes by using collective atom phase controls. Both
atomic frequency comb and gradient echoes have also made a breakthrough in
higher echo efficiency, where conventional photon echo efficiency remains at a
few per cent. Here we review, analyze, and discuss the collective atom phase
control applied to conventional photon echoes for quantum memory applications
to clarify fundamental physics of coherent transients in a three-level system
specifically for optical-spin coherence conversion in a controlled double
rephasing echoes. Some critical misunderstandings in various protocols are also
analyzed, and corrected for near unity echo efficiency under no population
inversion.


title:
A novel method for transient detection in high-cadence optical surveys:
  Its application for a systematic search for novae in M31

abstract:
  [abridged] In large-scale time-domain surveys, the processing of data, from
procurement up to the detection of sources, is generally automated. One of the
main challenges is contamination by artifacts, especially in regions of strong
unresolved emission. We present a novel method for identifying candidates for
variables and transients from the outputs of such surveys' data pipelines. We
use the method to systematically search for novae in iPTF observations of the
bulge of M31. We demonstrate that most artifacts produced by the iPTF pipeline
form a locally uniform background of false detections approximately obeying
Poissonian statistics, whereas genuine variables and transients as well as
artifacts associated with bright stars result in clusters of detections, whose
spread is determined by the source localization accuracy. This makes the
problem analogous to source detection on images produced by X-ray telescopes,
enabling one to utilize tools developed in X-ray astronomy. In particular, we
use a wavelet-based source detection algorithm from the Chandra data analysis
package CIAO. Starting from ~2.5x10^5 raw detections made by the iPTF data
pipeline, we obtain ~4000 unique source candidates. Cross-matching these
candidates with the source-catalog of a deep reference image, we find
counterparts for ~90% of them. These are either artifacts due to imperfect PSF
matching or genuine variable sources. The remaining ~400 detections are
transient sources. We identify novae among these candidates by applying
selection cuts based on the expected properties of nova lightcurves. Thus, we
recovered all 12 known novae registered during the time span of the survey and
discovered three nova candidates. Our method is generic and can be applied for
mining any target out of the artifacts in optical time-domain data. As it is
fully automated, its incompleteness can be accurately computed and corrected
for.


title: A universal angular momentum profile for dark matter haloes

abstract:
  The angular momentum distribution in dark matter haloes and galaxies is a key
ingredient in understanding their formation. Especially, the internal
distribution of angular momenta is closely related to the formation of disk
galaxies. In this article, we use haloes identified from a high-resolution
simulation, the Bolshoi simulation, to study the spatial distribution of
specific angular momenta, $j(r,\theta)$. We show that by stacking haloes with
similar masses to increase the signal-to-noise ratio, the profile can be fitted
as a simple function, $j(r,\theta)=j_s \sin^2(\theta/\theta_s)
(r/r_s)^2/(1+r/r_s)^4 $, with three free parameters, $j_s, r_s$, and
$\theta_s$. Specifically, $j_s$ correlates with the halo mass $M_\mathrm{vir}$
as $j_s\propto M_\mathrm{vir}^{2/3}$, $r_s$ has a weak dependence on the halo
mass as $r_s \propto M_\mathrm{vir}^{0.040}$, and $\theta_s$ is independent of
$M_\mathrm{vir}$. This profile agrees with that from a rigid shell model,
though its origin is unclear. Our universal specific angular momentum profile
$j(r,\theta)$ is useful in modelling haloes' angular momenta. Furthermore, by
using an empirical stellar mass - halo mass relation, we can infer the averaged
angular momentum distribution of a dark matter halo. The specific angular
momentum - stellar mass relation within a halo computed from our profile is
shown to share a similar shape as that from the observed disk galaxies.


title:
Two-weight and three-weight codes from trace codes over
  $\mathbb{F}_p+u\mathbb{F}_p+v\mathbb{F}_p+uv\mathbb{F}_p$

abstract:
  We construct an infinite family of two-Lee-weight and three-Lee-weight codes
over the non-chain ring
$\mathbb{F}_p+u\mathbb{F}_p+v\mathbb{F}_p+uv\mathbb{F}_p,$ where
$u^2=0,v^2=0,uv=vu.$ These codes are defined as trace codes. They have the
algebraic structure of abelian codes. Their Lee weight distribution is computed
by using Gauss sums. With a linear Gray map, we obtain a class of abelian
three-weight codes and two-weight codes over $\mathbb{F}_p$. In particular, the
two-weight codes we describe are shown to be optimal by application of the
Griesmer bound. We also discuss their dual Lee distance. Finally, an
application to secret sharing schemes is given.


title: Video Scene Parsing with Predictive Feature Learning

abstract:
  In this work, we address the challenging video scene parsing problem by
developing effective representation learning methods given limited parsing
annotations. In particular, we contribute two novel methods that constitute a
unified parsing framework. (1) \textbf{Predictive feature learning}} from
nearly unlimited unlabeled video data. Different from existing methods learning
features from single frame parsing, we learn spatiotemporal discriminative
features by enforcing a parsing network to predict future frames and their
parsing maps (if available) given only historical frames. In this way, the
network can effectively learn to capture video dynamics and temporal context,
which are critical clues for video scene parsing, without requiring extra
manual annotations. (2) \textbf{Prediction steering parsing}} architecture that
effectively adapts the learned spatiotemporal features to scene parsing tasks
and provides strong guidance for any off-the-shelf parsing model to achieve
better video scene parsing performance. Extensive experiments over two
challenging datasets, Cityscapes and Camvid, have demonstrated the
effectiveness of our methods by showing significant improvement over
well-established baselines.


title: On the origin of intrinsic randomness of Rayleigh-Benard turbulence

abstract:
  It is of broad interest to understand how the evolution of non-equilibrium
systems can be triggered and the role played by external perturbations. A
famous example is the origin of randomness in the laminar-turbulence
transition, which is raised in the pipe flow experiment by Reynolds as a
century old unresolved problem. Although there exist different hypotheses, it
is widely believed that the randomness is "intrinsic", which, however, remains
as an open question to be verified. Simulating the modeled Rayleigh-Benard
convection system by means of the so-called clean numerical simulation (CNS)
with negligible numerical noises that are smaller even than thermal
fluctuation, we verify that turbulence can be self-excited from the inherent
thermal fluctuation, without any external disturbances, i.e. out of nothing.
This reveals a relationship between microscopic physical uncertainty and
macroscopic randomness. It is found that in physics the system nonlinearity
functions as a channel for randomness information, and energy as well, to
transport microscopic uncertainty toward large scales. Such scenario can
generally be helpful to understand the various relevant phenomena. In
methodology, compared with direct numerical simulation (DNS), CNS opens a new
direction to investigate turbulent flows with largely improved accuracy and
reliability.


title:
Characteristic spectra of circuit quantum electrodynamics systems from
  the ultrastrong- to the deep-strong-coupling regime

abstract:
  We report on spectra of circuit-quantum-electrodynamics (QED) systems in an
intermediate regime that lies between the ultrastrong and deep-strong-coupling
regimes, which have been reported previously in the literature. Our
experimental results, along with numerical simulations, demonstrate that as the
coupling strength increases, the spectrum of a circuit-QED system undergoes
multiple qualitative transformations, such that several coupling regimes are
identified, each with its own unique spectral features. The different spectral
transformations can be related to crossings between energy level differences
and to changes in the symmetries of the energy eigenstates. These results allow
us to use qualitative spectral features to infer certain properties and
parameters of the system.


title:
Towards Hierarchical Mobile Edge Computing: An Auction-Based Profit
  Maximization Approach

abstract:
  The multi-tiered concept of Internet of Things (IoT) devices, cloudlets and
clouds is facilitating a user-centric IoT. However, in such three tier network,
it is still desirable to investigate efficient strategies to offer the
computing, storage and communications resources to the users. To this end, this
paper proposes a new hierarchical model by introducing the concept of field,
shallow, and deep cloudlets where the cloudlet tier itself is designed in three
hierarchical levels based on the principle of LTE-Advanced backhaul network.
Accordingly, we explore a two time scale approach in which the computing
resources are offered in an auction-based profit maximization manner and then
the communications resources are allocated to satisfy the users' QoS.


title: Optimal three-weight cubic codes

abstract:
  In this paper, we construct an infinite family of three-weight binary codes
from linear codes over the ring $R=\mathbb{F}_2+v\mathbb{F}_2+v^2\mathbb{F}_2$,
where $v^3=1.$ These codes are defined as trace codes. They have the algebraic
structure of abelian codes. Their Lee weight distributions are computed by
employing character sums. The three-weight binary linear codes which we
construct are shown to be optimal when $m$ is odd and $m>1$. They are cubic,
that is to say quasi-cyclic of co-index three. An application to secret sharing
schemes is given.


title: Unusually stable helical coil allotrope of phosphorus

abstract:
  We have identified an unusually stable helical coil allotrope of phosphorus.
Our ab initio Density Functional Theory calculations indicate that the
uncoiled, isolated straight 1D chain is equally stable as a monolayer of black
phosphorus dubbed phosphorene. The coiling tendency and the attraction between
adjacent coil segments add an extra stabilization energy of about 12 meV/atom
to the coil allotrope, similar in value to the approximately 16 meV/atom
inter-layer attraction in bulk black phosphorus. Thus, the helical coil
structure is essentially as stable as black phosphorus, the most stable
phosphorus allotrope known to date. With an optimum radius of 2.4 nm, the
helical coil of phosphorus may fit well and even form inside wide carbon
nanotubes.


title:
A Novel Artificial Fish Swarm Algorithm for Pattern Recognition with
  Convex Optimization

abstract:
  Image pattern recognition is an important area in digital image processing.
An efficient pattern recognition algorithm should be able to provide correct
recognition at a reduced computational time. Off late amongst the machine
learning pattern recognition algorithms, Artificial fish swarm algorithm is one
of the swarm intelligence optimization algorithms that works based on
population and stochastic search. In order to achieve acceptable result, there
are many parameters needs to be adjusted in AFSA. Among these parameters,
visual and step are very significant in view of the fact that artificial fish
basically move based on these parameters. In standard AFSA, these two
parameters remain constant until the algorithm termination. Large values of
these parameters increase the capability of algorithm in global search, while
small values improve the local search ability of the algorithm. In this paper,
we empirically study the performance of the AFSA and different approaches to
balance between local and global exploration have been tested based on the
adaptive modification of visual and step during algorithm execution. The
proposed approaches have been evaluated based on the four well-known benchmark
functions. Experimental results show considerable positive impact on the
performance of AFSA. A Convex optimization has been integrated into the
proposed work to have an ideal segmentation of the input image which is a MR
brain image.


title: Three-weight codes and the quintic construction

abstract:
  We construct a class of three-Lee-weight and two infinite families of
five-Lee-weight codes over the ring $R=\mathbb{F}_2 +v\mathbb{F}_2
+v^2\mathbb{F}_2 +v^3\mathbb{F}_2 +v^4\mathbb{F}_2,$ where $v^5=1.$ The same
ring occurs in the quintic construction of binary quasi-cyclic codes. %The
length of these codes depends on the degree $m$ of ring extension. They have
the algebraic structure of abelian codes. Their Lee weight distribution is
computed by using character sums. Given a linear Gray map, we obtain three
families of binary abelian codes with few weights. In particular, we obtain a
class of three-weight codes which are optimal. Finally, an application to
secret sharing schemes is given.


title:
A Simple Generalization of a Result for Random Matrices with Independent
  Sub-Gaussian Rows

abstract:
  In this short note, we give a very simple but useful generalization of a
result of Vershynin (Theorem 5.39 of [1]) for a random matrix with independent
sub-Gaussian rows. We also explain with an example where our generalization is
useful.


title: Trace Codes with Few Weights over $\mathbb{F}_p+u\mathbb{F}_p$

abstract:
  We construct an infinite family of two-Lee-weight and three-Lee-weight codes
over the chain ring $\mathbb{F}_p+u\mathbb{F}_p.$ They have the algebraic
structure of abelian codes. Their Lee weight distribution is computed by using
Gauss sums. Then by using a linear Gray map, we obtain an infinite family of
abelian codes with few weights over $\mathbb{F}_p$. In particular, we obtain an
infinite family of two-weight codes which meets the Griesmer bound with
equality. Finally, an application to secret sharing schemes is given.


title:
Predicting Long-term Outcomes of Educational Interventions Using the
  Evolutionary Causal Matrices and Markov Chain Based on Educational
  Neuroscience

abstract:
  We developed a prediction model based on the evolutionary causal matrices
(ECM) and the Markov Chain to predict long-term influences of educational
interventions on adolescents development. Particularly, we created a
computational model predicting longitudinal influences of different types of
stories of moral exemplars on adolescents voluntary service participation. We
tested whether the developed prediction model can properly predict a long-term
longitudinal trend of change in voluntary service participation rate by
comparing prediction results and surveyed data. Furthermore, we examined which
type of intervention would most effectively promote service engagement and what
is the minimum required frequency of intervention to produce a large effect. We
discussed the implications of the developed prediction model in educational
interventions based on educational neuroscience.


title: Secure Polar Coding for the Two-Way Wiretap Channel

abstract:
  We consider the problem of polar coding for secure communications over the
two-way wiretap channel, where two legitimate users communicate with each other
simultaneously while a passive eavesdropper overhears a combination of their
exchanged signals. The legitimate users wish to design a cooperative jamming
code such that the interference between their codewords can jam the
eavesdropper. In this paper, we design a polar coded cooperative jamming scheme
that achieves the whole secrecy rate region of the general two-way wiretap
channel under the strong secrecy criterion. The chaining method is used to make
proper alignment of polar indices. The randomness required to be shared between
two legitimate users is treated as a limited resource and we show that its rate
can be made negligible by increasing the blocklength and the number of chained
blocks. For the special case when the eavesdropper channel is degraded with
respect to the legitimate ones, a simplified scheme is proposed which can
simultaneously ensure reliability and weak secrecy within a single transmission
block. An example of the binary erasure channel case is given to demonstrate
the performance of our scheme.


title: Blind Estimation of Sparse Multi-User Massive MIMO Channels

abstract:
  We provide a maximum likelihood formulation for the blind estimation of
massive mmWave MIMO channels while taking into account their underlying sparse
structure. The main advantage of this approach is the fact that the overhead
due to pilot sequences can be reduced dramatically especially when operating at
low SNR per antenna. Thereby, the sparsity in the angular domain is exploited
as a key property to enable the unambiguous blind separation between user's
channels. On the other hand, as only the sparsity is assumed, the proposed
method is robust with respect to the statistical properties of the channel and
data and allows the estimation in rapidly time-varying scenarios and eventually
the separation of interfering users from adjacent base stations. Additionally,
a performance limit is derived based on the clairvoyant Cram\'er Rao lower
bound. Simulation results demonstrate that this maximum likelihood formulation
yields superior estimation accuracy with reasonable computational complexity
and limited model assumptions.


title:
CDVAE: Co-embedding Deep Variational Auto Encoder for Conditional
  Variational Generation

abstract:
  Problems such as predicting a new shading field (Y) for an image (X) are
ambiguous: many very distinct solutions are good. Representing this ambiguity
requires building a conditional model P(Y|X) of the prediction, conditioned on
the image. Such a model is difficult to train, because we do not usually have
training data containing many different shadings for the same image. As a
result, we need different training examples to share data to produce good
models. This presents a danger we call "code space collapse" - the training
procedure produces a model that has a very good loss score, but which
represents the conditional distribution poorly. We demonstrate an improved
method for building conditional models by exploiting a metric constraint on
training data that prevents code space collapse. We demonstrate our model on
two example tasks using real data: image saturation adjustment, image
relighting. We describe quantitative metrics to evaluate ambiguous generation
results. Our results quantitatively and qualitatively outperform different
strong baselines.


title: Geoneutrinos at Jinping: Flux prediction and oscillation analysis

abstract:
  Geoneutrinos are electron antineutrinos ($\bar\nu_e$) generated by the
beta-decays of radionuclides naturally occurring inside the Earth, in
particular $^{238}$U, $^{232}$Th, and $^{40}$K. Measurement of these neutrinos
provides powerful constraints on the radiogenic heat of the Earth and tests on
the Earth models. Since the prediction of $\bar\nu_e$'s in geoneutrino flux is
subject to neutrino oscillation effects, we performed a calculation including
detailed oscillation analysis in the propagation of geoneutrinos and reactor
neutrinos generated around the Earth. The expected geoneutrino signal, the
reactor neutrino background rates and the systematic error budget are provided
for a proposed 3-kiloton neutrino detector at the Jinping underground lab in
Sichuan, China. In addition, we evaluated sensitivities for the geoneutrino
flux, Th/U ratio and power of a possible fission reactor in the interior of
Earth.


title: Non-elementary convergence groups are acylindrically hyperbolic

abstract:
  We proved that non-elementary discrete convergence groups are acylindrically
hyperbolic.


title: Hydrodynamic alignment of microswimmers in pipes

abstract:
  We investigate the dynamics of model microswimmers under confinement, in
cylindrical geometries, by means of three dimensional direct numerical
calculations with fully resolved hydrodynamics. Such swimmers are known to show
collective alignment in bulk, and we confirmed that similar alignment can be
observed even in pipes, although the volume fraction dependency shows
qualitative differences. By comparing the structural information, we
investigated the cause of such differences. We found an order/disorder phase
transition for a specific type of swimmers, as the size of the pipe becomes
comparable to the size of the swimmers. Such dynamics are found not to depend
on the geometry of confinement.


title:
Estimation and Model Identification of Locally Stationary
  Varying-Coefficient Additive Models

abstract:
  Nonparametric regression models with locally stationary covariates have
received increasing interest in recent years. As a nice relief of "curse of
dimensionality" induced by large dimension of covariates, additive regression
model is commonly used. However, in locally stationary context, to catch the
dynamic nature of regression function, we adopt a flexible varying-coefficient
additive model where the regression function has the form
$\alpha_{0}\left(u\right)+\sum_{k=1}^{p}\alpha_{k}\left(u\right)\beta_{k}\left(x_{k}\right).$
For this model, we propose a three-step spline estimation method for each
univariate nonparametric function, and show its consistency and $L_{2}$ rate of
convergence. Furthermore, based upon the three-step estimators, we develop a
two-stage penalty procedure to identify pure additive terms and
varying-coefficient terms in varying-coefficient additive model. As expected,
we demonstrate that the proposed identification procedure is consistent, and
the penalized estimators achieve the same $L_{2}$ rate of convergence as the
polynomial spline estimators. Simulation studies are presented to illustrate
the finite sample performance of the proposed three-step spline estimation
method and two-stage model selection procedure.


title: RMPE: Regional Multi-person Pose Estimation

abstract:
  Multi-person pose estimation in the wild is challenging. Although
state-of-the-art human detectors have demonstrated good performance, small
errors in localization and recognition are inevitable. These errors can cause
failures for a single-person pose estimator (SPPE), especially for methods that
solely depend on human detection results. In this paper, we propose a novel
regional multi-person pose estimation (RMPE) framework to facilitate pose
estimation in the presence of inaccurate human bounding boxes. Our framework
consists of three components: Symmetric Spatial Transformer Network (SSTN),
Parametric Pose Non-Maximum-Suppression (NMS), and Pose-Guided Proposals
Generator (PGPG). Our method is able to handle inaccurate bounding boxes and
redundant detections, allowing it to achieve a 17% increase in mAP over the
state-of-the-art methods on the MPII (multi person) dataset.Our model and
source codes are publicly available.


title: Towards Robust Deep Neural Networks with BANG

abstract:
  Machine learning models, including state-of-the-art deep neural networks, are
vulnerable to small perturbations that cause unexpected classification errors.
This unexpected lack of robustness raises fundamental questions about their
generalization properties and poses a serious concern for practical
deployments. As such perturbations can remain imperceptible - the formed
adversarial examples demonstrate an inherent inconsistency between vulnerable
machine learning models and human perception - some prior work casts this
problem as a security issue. Despite the significance of the discovered
instabilities and ensuing research, their cause is not well understood and no
effective method has been developed to address the problem. In this paper, we
present a novel theory to explain why this unpleasant phenomenon exists in deep
neural networks. Based on that theory, we introduce a simple, efficient, and
effective training approach, Batch Adjusted Network Gradients (BANG), which
significantly improves the robustness of machine learning models. While the
BANG technique does not rely on any form of data augmentation or the
utilization of adversarial images for training, the resultant classifiers are
more resistant to adversarial perturbations while maintaining or even enhancing
the overall classification performance.


title:
The Hausdorff measure version of Gallagher's theorem -- closing the gap
  and beyond

abstract:
  In this paper we prove an upper bound on the "size" of the set of
multiplicatively $\psi$-approximable points in $\mathbb R^d$ for $d>1$ in terms
of $f$-dimensional Hausdorff measure. This upper bound exactly complements the
known lower bound, providing a "zero-full" law which relates the Hausdorff
measure to the convergence/divergence of a certain series in both the
homogeneous and inhomogeneous settings. This zero-full law resolves a question
posed by Beresnevich and Velani (2015) regarding the "log factor" discrepancy
in the convergent/divergent sum conditions of their theorem. We further prove
the analogous result for the multiplicative doubly metric setup.


title: Semiholography for heavy ion collisions

abstract:
  The formation of QGP in heavy ion collisions gives us a great opportunity for
learning about nonperturbative dynamics of QCD. Semiholography provides a new
consistent framework to combine perturbative and non-perturbative effects in a
coherent way and can be applied to obtain an effective description for heavy
ion collisions. In particular, it allows us to include nonperturbative effects
in existing glasma effective theory and QCD kinetic theory for the weakly
coupled saturated degrees of freedom liberated by the collisions in the initial
stages in a consistent manner. We argue why the full framework should be able
to confront experiments with only a few phenomenological parameters and present
feasibility tests for the necessary numerical computations. Furthermore, we
discuss that semiholography leads to a new description of collective flow in
the form of a generalised non-Newtonian fluid. We discuss some open questions
which we hope to answer in the near future.


title: Understanding the holographic principle via RG flow

abstract:
  This is a review of some recent works which demonstrate how the classical
equations of gravity in AdS themselves hold the key to understanding their
holographic origin in the form of a strongly coupled large $N$ QFT whose
algebra of local operators can be generated by a few (single-trace) elements. I
discuss how this can be realised by reformulating Einstein's equations in AdS
in the form of a non-perturbative RG flow that further leads to a new approach
towards constructing strongly interacting QFTs. In particular, the RG flow can
self-determine the UV data that are otherwise obtained by solving classical
gravity equations and demanding that the solutions do not have naked
singularities. For a concrete demonstration, I focus on the hydrodynamic limit
in which case this RG flow connects the AdS/CFT correspondence with the
membrane paradigm, and also reproduces the known values of the dual QFT
transport coefficients.


title:
Strong Second-Order Karush--Kuhn--Tucker Optimality Conditions for
  Vector Optimization

abstract:
  In the present paper, we focus on the vector optimization problems with
inequality constraints, where objective functions and constrained functions are
Fr\'echet differentiable, and whose gradient mapping is locally Lipschitz on an
open set. By using the second-order symmetric subdifferential and the
second-order tangent set, we propose two types of second-order regularity
conditions in the sense of Abadie. Then we establish some strong second-order
Karush--Kuhn--Tucker necessary optimality conditions for Geoffrion properly
efficient solutions of the considered problem. Examples are given to illustrate
the obtained results.


title: Bounding the Dimension of Points on a Line

abstract:
  We use Kolmogorov complexity methods to give a lower bound on the effective
Hausdorff dimension of the point (x, ax+b), given real numbers a, b, and x. We
apply our main theorem to a problem in fractal geometry, giving an improved
lower bound on the (classical) Hausdorff dimension of generalized sets of
Furstenberg type.


title:
BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network
  for Hyperspectral Image Classification

abstract:
  Deep learning based landcover classification algorithms have recently been
proposed in literature. In hyperspectral images (HSI) they face the challenges
of large dimensionality, spatial variability of spectral signatures and
scarcity of labeled data. In this article we propose an end-to-end deep
learning architecture that extracts band specific spectral-spatial features and
performs landcover classification. The architecture has fewer independent
connection weights and thus requires lesser number of training data. The method
is found to outperform the highest reported accuracies on popular hyperspectral
image data sets.


title: Comments on Exchange Graphs in Cluster Algebras

abstract:
  An important problem in the theory of cluster algebras is to compute the
fundamental group of the exchange graph. A non-trivial closed loop in the
exchange graph, for example, generates a non-trivial identity for the classical
and quantum dilogarithm functions. An interesting conjecture, partly motivated
by dilogarithm functions, is that this fundamental group is generated by closed
loops of mutations involving only two of the cluster variables. We present
examples and counterexamples for this naive conjecture, and then formulate a
better version of the conjecture for acyclic seeds.


title:
Conductance fluctuations in disordered 2D topological insulator wires:
  From quantum spin-Hall to ordinary quantum phases

abstract:
  Impurities and defects are ubiquitous in topological insulators (TIs) and
thus understanding the effects of disorder on electronic transport is
important. We calculate the distribution of the random conductance fluctuations
$P(G)$ of disordered 2D TI wires modeled by the Bernevig-Hughes-Zhang (BHZ)
Hamiltonian with realistic parameters. As we show, the disorder drives the TIs
into different regimes: metal (M), quantum spin-Hall insulator (QSHI), and
ordinary insulator (OI). By varying the disorder strength and Fermi energy, we
calculate analytically and numerically $P(G)$ across the entire phase diagram.
The conductance fluctuations follow the statistics of the unitary universality
class $\beta=2$. At strong disorder and high energy, however, the size of the
fluctutations $\delta G$ reaches the universal value of the orthogonal symmetry
class ($\beta=1$). At the QSHI-M and QSHI-OI crossovers, the interplay between
edge and bulk states plays a key role in the statistical properties of the
conductance.


title:
Combining Deep Reinforcement Learning and Safety Based Control for
  Autonomous Driving

abstract:
  With the development of state-of-art deep reinforcement learning, we can
efficiently tackle continuous control problems. But the deep reinforcement
learning method for continuous control is based on historical data, which would
make unpredicted decisions in unfamiliar scenarios. Combining deep
reinforcement learning and safety based control can get good performance for
self-driving and collision avoidance. In this passage, we use the Deep
Deterministic Policy Gradient algorithm to implement autonomous driving without
vehicles around. The vehicle can learn the driving policy in a stable and
familiar environment, which is efficient and reliable. Then we use the
artificial potential field to design collision avoidance algorithm with
vehicles around. The path tracking method is also taken into consideration. The
combination of deep reinforcement learning and safety based control performs
well in most scenarios.


title:
Domain Adaptation for Named Entity Recognition in Online Media with Word
  Embeddings

abstract:
  Content on the Internet is heterogeneous and arises from various domains like
News, Entertainment, Finance and Technology. Understanding such content
requires identifying named entities (persons, places and organizations) as one
of the key steps. Traditionally Named Entity Recognition (NER) systems have
been built using available annotated datasets (like CoNLL, MUC) and demonstrate
excellent performance. However, these models fail to generalize onto other
domains like Sports and Finance where conventions and language use can differ
significantly. Furthermore, several domains do not have large amounts of
annotated labeled data for training robust Named Entity Recognition models. A
key step towards this challenge is to adapt models learned on domains where
large amounts of annotated training data are available to domains with scarce
annotated data.
  In this paper, we propose methods to effectively adapt models learned on one
domain onto other domains using distributed word representations. First we
analyze the linguistic variation present across domains to identify key
linguistic insights that can boost performance across domains. We propose
methods to capture domain specific semantics of word usage in addition to
global semantics. We then demonstrate how to effectively use such domain
specific knowledge to learn NER models that outperform previous baselines in
the domain adaptation setting.


title: Boundary and Corner Terms in the Action for General Relativity

abstract:
  We revisit the action principle for general relativity motivated by the path
integral approach to quantum gravity. We consider a spacetime region whose
boundary has piecewise $C^2$ components, each of which can be spacelike,
timelike or null and consider metric variations in which only the pullback of
the metric to the boundary is held fixed. Allowing all such metric variations
we present a unified treatment of the spacelike, timelike and null boundary
components using Cartan's tetrad formalism. Apart from its computational
simplicity, this formalism gives us a simple way of identifying corner terms.
We also discuss "creases" which occur when the boundary is the event horizon of
a black hole. Our treatment is geometric and intrinsic and we present our
results both in the computationally simpler tetrad formalism as well as the
more familiar metric formalism. We recover known results from a simpler and
more general point of view and find some new ones.


title: Decentralized Consensus Optimization with Asynchrony and Delays

abstract:
  We propose an asynchronous, decentralized algorithm for consensus
optimization. The algorithm runs over a network in which the agents communicate
with their neighbors and perform local computation. In the proposed algorithm,
each agent can compute and communicate independently at different times, for
different durations, with the information it has even if the latest information
from its neighbors is not yet available. Such an asynchronous algorithm reduces
the time that agents would otherwise waste idle because of communication delays
or because their neighbors are slower. It also eliminates the need for a global
clock for synchronization. Mathematically, the algorithm involves both primal
and dual variables, uses fixed step-size parameters, and provably converges to
the exact solution under a bounded delay assumption and a random agent
assumption. When running synchronously, the algorithm performs just as well as
existing competitive synchronous algorithms such as PG-EXTRA, which diverges
without synchronization. Numerical experiments confirm the theoretical findings
and illustrate the performance of the proposed algorithm.


title: A New Method for Classification of Datasets for Data Mining

abstract:
  Decision tree is an important method for both induction research and data
mining, which is mainly used for model classification and prediction. ID3
algorithm is the most widely used algorithm in the decision tree so far. In
this paper, the shortcoming of ID3's inclining to choose attributes with many
values is discussed, and then a new decision tree algorithm which is improved
version of ID3. In our proposed algorithm attributes are divided into groups
and then we apply the selection measure 5 for these groups. If information gain
is not good then again divide attributes values into groups. These steps are
done until we get good classification/misclassification ratio. The proposed
algorithms classify the data sets more accurately and efficiently.


title:
$1/f^\alpha$ power spectrum in the Kardar-Parisi-Zhang universality
  class

abstract:
  The power spectrum of interface fluctuations in the $(1+1)$-dimensional
Kardar-Parisi-Zhang (KPZ) universality class is studied both experimentally and
numerically. The $1/f^\alpha$-type spectrum is found and characterized through
a set of "critical exponents" for the power spectrum. The recently formulated
"aging Wiener-Khinchin theorem" accounts for the observed exponents.
Interestingly, the $1/f^\alpha$ spectrum in the KPZ class turns out to contain
information on a universal distribution function characterizing the asymptotic
state of the KPZ interfaces, namely the Baik-Rains universal variance. It is
indeed observed in the presented data, both experimental and numerical, and for
both circular and flat interfaces, in the long time limit.


title:
Quasi-localized Impurity State in Doped Topological Crystalline
  Insulator Sn$_{0.9}$In$_{0.1}$Te Probed by $^{125}$Te-NMR

abstract:
  The In-doped topological crystalline insulator Sn$_{1-x}$In$_x$Te is a
promising candidate for a topological superconductor, where it is theoretically
suggested that In creates an impurity state responsible for superconductivity.
We synthesized high purity Sn$_{1-x}$In$_x$Te samples and performed
$^{125}$Te-nuclear magnetic resonance (NMR) measurements. The NMR spectra under
a magnetic field of $H_0$ = 5 T show a broadening characteristic due to a
localized impurity state. The spin-lattice relaxation rate ($1/T_1$) divided by
temperature shows a Curie-Weiss like temperature-dependence under $H_0$ = 0.1 T
but is temperature-independent under $H_0$ = 5 T. These results indicate the
existence of quasi-localized impurity states due to In doping.


title: Representable Chow classes of a product of projective spaces

abstract:
  Inside a product of projective spaces, we try to understand which Chow
classes come from irreducible subvarieties. The answer is closely related to
the theory of integer polymatroids. The support of a representable class can be
(partially) characterized as some integer point inside a particular
polymatroid. If the class is multiplicity-free, we obtain a complete
characterization in terms of representable polymatroids. We also generalize
some of the results to the case of products of Grassmannians.


title: Adversarial Images for Variational Autoencoders

abstract:
  We investigate adversarial attacks for autoencoders. We propose a procedure
that distorts the input image to mislead the autoencoder in reconstructing a
completely different target image. We attack the internal latent
representations, attempting to make the adversarial input produce an internal
representation as similar as possible as the target's. We find that
autoencoders are much more robust to the attack than classifiers: while some
examples have tolerably small input distortion, and reasonable similarity to
the target image, there is a quasi-linear trade-off between those aims. We
report results on MNIST and SVHN datasets, and also test regular deterministic
autoencoders, reaching similar conclusions in all cases. Finally, we show that
the usual adversarial attack for classifiers, while being much easier, also
presents a direct proportion between distortion on the input, and misdirection
on the output. That proportionality however is hidden by the normalization of
the output, which maps a linear layer into non-linear probabilities.


title: Global and fixed-terminal cuts in digraphs

abstract:
  The computational complexity of multicut-like problems may vary significantly
depending on whether the terminals are fixed or not. In this work we present a
comprehensive study of this phenomenon in two types of cut problems in directed
graphs: double cut and bicut.
  1. The fixed-terminal edge-weighted double cut is known to be solvable
efficiently. We show a tight approximability factor of $2$ for the
fixed-terminal node-weighted double cut. We show that the global node-weighted
double cut cannot be approximated to a factor smaller than $3/2$ under the
Unique Games Conjecture (UGC).
  2. The fixed-terminal edge-weighted bicut is known to have a tight
approximability factor of $2$. We show that the global edge-weighted bicut is
approximable to a factor strictly better than $2$, and that the global
node-weighted bicut cannot be approximated to a factor smaller than $3/2$ under
UGC.
  3. In relation to these investigations, we also prove two results on
undirected graphs which are of independent interest. First, we show
NP-completeness and a tight inapproximability bound of $4/3$ for the
node-weighted $3$-cut problem. Second, we show that for constant $k$, there
exists an efficient algorithm to solve the minimum $\{s,t\}$-separating $k$-cut
problem.
  Our techniques for the algorithms are combinatorial, based on LPs and based
on enumeration of approximate min-cuts. Our hardness results are based on
combinatorial reductions and integrality gap instances.


title:
Fast 3D Variable-FOV Reconstruction for Parallel Imaging with Localized
  Sensitivities

abstract:
  Several successful iterative approaches have recently been proposed for
parallel-imaging reconstructions of variable-density (VD) acquisitions, but
they often induce substantial computational burden for non-Cartesian data. Here
we propose a generalized variable-FOV PILS reconstruction 3D VD Cartesian and
non-Cartesian data. The proposed method separates k-space into non-intersecting
annuli based on sampling density, and sets the 3D reconstruction FOV for each
annulus based on the respective sampling density. The variable-FOV method is
compared against conventional gridding, PILS, and ESPIRiT reconstructions.
Results indicate that the proposed method yields better artifact suppression
compared to gridding and PILS, and improves noise conditioning relative to
ESPIRiT, enabling fast and high-quality reconstructions of 3D datasets.


title: X-ray/GeV emissions from Crab-like pulsars in LMC

abstract:
  We discuss X-ray and gamma-ray emissions from Crab-like pulsars,
PSRs~J0537-6910 and~J0540-6919, in Large Magellanic Cloud. Fermi-LAT
observations have resolved the gamma-ray emissions from these two pulsars and
found the pulsed emissions from PSR~J0540-6919. The total pulsed radiation in
the X-ray/gamma-ray energy bands of PSR~J0540-6919 is observed with the
efficiency $\eta_{J0540}\sim 0.06$ (in 4$\pi$ sr), which is about a factor of
ten larger than $\eta_{Crab}\sim 0.006$ of the Crab pulsar. Although
PSR~J0537-6910 has the highest spin-down power among currently known pulsars,
the efficiency of the observed X-ray emissions is about two orders of magnitude
smaller than that of PSR~J0540-6919. This paper mainly discusses what causes
the difference in the radiation efficiencies of these three energetic Crab-like
pulsars. We discuss electron/positron acceleration and high-energy emission
processes within the outer gap model. By solving the outer gap structure with
the dipole magnetic field, we show that the radiation efficiency decreases as
the inclination angle between the magnetic axis and the rotation axis
increases. To explain the difference in the pulse profile and in the radiation
efficiency, our model suggests that PSR~J0540-6919 has an inclination angle
much smaller than the that of Crab pulsar (here we assume the inclination
angles of both pulsars are $\alpha<90^{\circ}$). On the other hand, we
speculate that the difference in the radiation efficiencies between
PSRs~J0537-6910 and J0549-6919 is mainly caused by the difference in the Earth
viewing angle, and that we see PSR~J0537-6910 with an Earth viewing angle
$\zeta>>90^{\circ}$ (or $<<90^{\circ}$) measured from the spin axis, while we
see PSR~J0540-6919 with $\zeta\sim 90^{\circ}$.


title:
Semiconductor, topological semimetal, indirect semimetal, and
  topological Dirac semimetal phases of Ge$_{1-x}$Sn$_{x}$ alloys

abstract:
  Electronic structures of Ge$_{1-x}$Sn$_{x}$ alloys (0 $\leq$ $x$ $\leq$ 1)
are theoretically studied by nonlocal empirical pseudopotential method. For
relaxed Ge$_{1-x}$Sn$_{x}$, a topological semimetal is found for $\textit x$
$>$ 41$\%$ with gapless and band inversion at ${\Gamma}$ point, while there is
an indirect-direct bandgap transition at $x$ = 8.5$\%$. For strained
Ge$_{1-x}$Sn$_{x}$ on a Ge substrate, semimetals with a negative indirect
bandgap appear for $x$ $>$ 43$\%$, and the strained Ge$_{1-x}$Sn$_{x}$ on Ge is
always an indirect bandgap semiconductor for $x$ $<$ 43$\%$. With appropriate
biaxial compressive strains, a topological Dirac semimetal is found with band
inversion at ${\Gamma}$ and one pair of Dirac cones along the [001] direction.


title:
Maximum likelihood drift estimation for Gaussian process with stationary
  increments

abstract:
  The paper deals with the regression model $X_t = \theta t + B_t$, $t\in[0, T
]$, where $B=\{B_t, t\geq 0\}$ is a centered Gaussian process with stationary
increments. We study the estimation of the unknown parameter $\theta$ and
establish the formula for the likelihood function in terms of a solution to an
integral equation. Then we find the maximum likelihood estimator and prove its
strong consistency. The results obtained generalize the known results for
fractional and mixed fractional Brownian motion.


title:
On the critical branching random walk II: Branching capacity and
  branching recurrence

abstract:
  We continue our study of critical branching random walk and branching
capacity. In this paper we introduce branching recurrence and branching
transience and prove an analogous version of Wiener's Test.


title: Nonlinear wave interaction problems in three dimensional case

abstract:
  Three dimensional nonlinear wave interactions have been analytically
described. The procedure under interest can be applied to three dimensional
quasilinear systems of first order, whose hydrodynamic reductions are
homogeneous semi-Hamiltonian hydrodynamic type systems (i.e. possess a diagonal
form and infinitely many conservation laws). The interaction of N waves was
studied. In particular we prove that they behave like simple waves and they
distort after the collision region. The amount of the distortion can be
analytically computed.


title:
Preventing Incomplete/Hidden Requirements: Reflections on Survey Data
  from Austria and Brazil

abstract:
  Many software projects fail due to problems in requirements engineering (RE).
The goal of this paper is analyzing a specific and relevant RE problem in
detail: incomplete/hidden requirements. We replicated a global family of RE
surveys with representatives of software organizations in Austria and Brazil.
We used the data to (a) characterize the criticality of the selected RE
problem, and to (b) analyze the reported main causes and mitigation actions.
Based on the analysis, we discuss how to prevent the problem. The survey
includes 14 different organizations in Austria and 74 in Brazil, including
small, medium and large sized companies, conducting both, plan-driven and agile
development processes. Respondents from both countries cited the
incomplete/hidden requirements problem as one of the most critical RE problems.
We identified and graphically represented the main causes and documented
solution options to address these causes. Further, we compiled a list of
reported mitigation actions. From a practical point of view, this paper
provides further insights into common causes of incomplete/hidden requirements
and on how to prevent this problem.


title: Analysing Text in Software Projects

abstract:
  Most of the data produced in software projects is of textual nature: source
code, specifications, or documentations. The advances in quantitative analysis
methods drove a lot of data analytics in software engineering. This has
overshadowed to some degree the importance of texts and their qualitative
analysis. Such analysis has, however, merits for researchers and practitioners
as well.
  In this chapter, we describe the basics of analysing text in software
projects. We first describe how to manually analyse and code textual data.
Next, we give an overview of mixed methods to automatic text analysis including
N-Grams and clone detection as well as more sophisticated natural language
processing identifying syntax and contexts of words. Those methods and tools
are of critical importance to aid in the challenges in today's huge amounts of
textual data.
  We illustrate the introduced methods via a running example and conclude by
presenting two industrial studies.


title:
The dynamics of straight vortex filaments in a Bose-Einstein condensate
  with a Gaussian density profile

abstract:
  The dynamics of interacting quantized vortex filaments in a rotating trapped
Bose-Einstein condensate, which is in the Thomas-Fermi regime at zero
temperature and described by the Gross-Pitaevskii equation, is considered in
the hydrodynamic "anelastic" approximation. In the presence of a smoothly
inhomogeneous array of filaments (vortex lattice), a non-canonical Hamiltonian
equation of motion is derived for the macroscopically averaged vorticity, with
taking into account the spatial non-uniformity of the equilibrium condensate
density determined by the trap potential. A minimum of the corresponding
Hamiltonian describes a static configuration of deformed vortex lattice against
a given density background. The minimum condition is reduced to a vector
nonlinear partial differential equation of the second order, for which some
approximate and exact solutions are found. It is shown that if the condensate
density has an anisotropic Gaussian profile then equation of motion for the
averaged vorticity admits solutions in the form of a spatially uniform vector
with a nontrivial time dependence. An integral representation is obtained for
the matrix Green function determining the non-local Hamiltonian of a system of
arbitrary shaped vortex filaments in a condensate with Gaussian density. ... A
simple approximate expression for the two-dimensional Green function is
suggested at rather arbitrary density profile, and its successful comparison to
the exact result in the Gaussian case is done. Approximate equations of motion
are derived which describe a long-wave dynamics of interacting vortex filaments
in condensates with the density depending on the transverse coordinates only.


title:
Teleparallel Conformal Invariant Models induced by Kaluza-Klein
  Reduction

abstract:
  We study the extensions of teleparallism in the Kaluza-Klein (KK) scenario by
writing the analogous form to the torsion scalar $T_{\text{NGR}}$ in terms of
the corresponding antisymmetric tensors, given by $T_{\text{NGR}} = a\,T_{ijk}
\, T^{ijk} + b\,T_{ijk} \,T^{kji} + c\,T^{j}{}_{ji} \, T^{k}{}_{k}{}^{i}$, in
the four-dimensional New General Relativity (NGR) with arbitrary coefficients
$a$, $b$ and $c$. After the KK dimensional reduction, the Lagrangian in the
Einstein-frame can be realized by taking $2a+b+c=0$ with the ghost-free
condition $c\leq0$ for the one-parameter family of teleparallelism. We
demonstrate that the pure conformal invariant gravity models can be constructed
by the requirements of $2a+b=0$ and $c=0$. In particular, the torsion vector
can be identified as the conformal gauge field, while the conformal gauge
theory can be obtained by $2a+b+4c=0$ or $2a+b=0$, which is described on the
Weyl-Cartan geometry $Y_4$ with the ghost-free conditions $2a+b+c>0$ and
$c\neq0$. We also consider the weak field approximation and discuss the
non-minimal coupled term of the scalar current and torsion vector. For the
conformal invariant models with $2a+b=0$, we find that only the anti-symmetric
tensor field is allowed rather than the symmetric one.


title: Exploiting the HASH Planetary Nebula Research Platform

abstract:
  The HASH (Hong Kong/ AAO/ Strasbourg/ H{\alpha}) planetary nebula research
platform is a unique data repository with a graphical interface and SQL
capability that offers the community powerful, new ways to undertake Galactic
PN studies. HASH currently contains multi-wavelength images, spectra,
positions, sizes, morphologies and other data whenever available for 2401 true,
447 likely, and 692 possible Galactic PNe, for a total of 3540 objects. An
additional 620 Galactic post-AGB stars, pre-PNe, and PPN candidates are
included. All objects were classified and evaluated following the precepts and
procedures established and developed by our group over the last 15 years. The
complete database contains over 6,700 Galactic objects including the many
mimics and related phenomena previously mistaken or confused with PNe. Curation
and updating currently occurs on a weekly basis to keep the repository as up to
date as possible until the official release of HASH v1 planned in the near
future.


title:
Assessing the impacts of time to detection distribution assumptions on
  detection probability estimation

abstract:
  Abundance estimates from animal point-count surveys require accurate
estimates of detection probabilities. The standard model for estimating
detection from removal-sampled point-count surveys assumes that organisms at a
survey site are detected at a constant rate; however, this assumption is often
not justified. We consider a class of N-mixture models that allows for
detection heterogeneity over time through a flexibly defined time-to-detection
distribution (TTDD) and allows for fixed and random effects for both abundance
and detection. Our model is thus a combination of survival time-to-event
analysis with unknown-N, unknown-p abundance estimation. We specifically
explore two-parameter families of TTDDs, e.g. gamma, that can additionally
include a mixture component to model increased probability of detection in the
initial observation period. We find that modeling a TTDD by using a
two-parameter family is necessary when data have a chance of arising from a
distribution of this nature. In addition, models with a mixture component can
outperform non-mixture models even when the truth is non-mixture. Finally, we
analyze an Overbird data set from the Chippewa National Forest using mixed
effect models for both abundance and detection. We demonstrate that the effects
of explanatory variables on abundance and detection are consistent across
mixture TTDDs but that flexible TTDDs result in lower estimated probabilities
of detection and therefore higher estimates of abundance.


title: Hunting Dark Matter in ultra-compact structures within the Milky-Way

abstract:
  The local environment is ideal for searching out compact dark structures via
the microlensing and multi-frequency emissions as these objects are expected to
be faint and microlensing experiments have already hinted at their possibility.
In the case that these objects are composed of Dark Matter (DM) then there are
both few limits on their abundance but many consequences of their existence or
non-existence on both local and cosmic scales. In this work we examine the
possibility of Ultra-Compact Mini-Halos (UCMHs) formed in the early universe.
These objects can persist to the present epoch due to their large central
density inuring them to the worst effects of later tidal stripping. As such,
these objects could constitute probes of many details of early universe
physics, such as primordial phase-transitions, inflation, small scale
exploration of the primordial density perturbation field and non-Gaussianity of
these density perturbations. The fact that they are also highly dark
matter-dominated objects means that they are attractive objects of study in the
continuing hunt for the nature of Dark Matter (DM) through indirect detection.
Another reason to study such objects in the local environment is found in the
conjecture that encounters with UCMHs could induce catastrophic events on
planets within our solar system, e.g. mass-extinction events on Earth. We will
outline a strategy for multi-frequency UCMH searches within the region of the
Milky-Way in which Gaia can accurately reconstruct microlens masses. This
methodology ensures that any candidate UCMH DM emission should correlate to
some unidentified microlensing object with determinable mass and demonstrate
that large-scale volcanic extinctions on Earth due to UCMHs are unlikely but
that it is possible for loss of the Martian geodynamo to be driven encounters
with such compact objects.


title: Phase-Tunable Temperature Amplifier

abstract:
  Coherent caloritronics, the thermal counterpart of coherent electronics, has
drawn growing attention since the discovery of heat interference in 2012.
Thermal interferometers, diodes, transistors and nano-valves have been
theoretically proposed and experimentally demonstrated by exploiting the
quantum phase difference between two superconductors coupled through a
Josephson junction. So far, the quantum-phase modulator has been realized in
the form of a superconducting quantum interference device (SQUID) or a
superconducting quantum interference proximity transistor (SQUIPT). Thence, an
external magnetic field is necessary in order to manipulate the heat transport.
Here, we theoretically propose the first on-chip fully thermal caloritronic
device: the phase-tunable temperature amplifier. Taking advantage of a recent
thermoelectric effect discovered in spin-split superconductors coupled to a
spin-polarized system, by a temperature gradient we generate the magnetic flux
controlling the transport through a temperature biased SQUIPT. By employing
commonly used materials and a geometry compatible with state-of-the-art
nano-fabrication techniques, we simulate the behavior of the temperature
amplifier and define a number of figures of merit in full analogy with voltage
amplifiers. Notably, our architecture ensures infinite input thermal impedance,
maximum gain of about 11 and efficiency reaching the 95%. This device concept
could represent a breakthrough in coherent caloritronic devices, and paves the
way for applications in radiation sensing, thermal logics and quantum
information.


title:
A Non Linear Multifractal Study to Illustrate the Evolution of Tagore
  Songs Over a Century

abstract:
  The works of Rabindranath Tagore have been sung by various artistes over
generations spanning over almost 100 years. there are few songs which were
popular in the early years and have been able to retain their popularity over
the years while some others have faded away. In this study we look to find cues
for the singing style of these songs which have kept them alive for all these
years. For this we took 3 min clip of four Tagore songs which have been sung by
five generation of artistes over 100 years and analyze them with the help of
latest nonlinear techniques Multifractal Detrended Fluctuation Analysis
(MFDFA). The multifractal spectral width is a manifestation of the inherent
complexity of the signal and may prove to be an important parameter to identify
the singing style of particular generation of singers and how this style varies
over different generations. The results are discussed in detail.


title:
A Non Linear Approach towards Automated Emotion Analysis in Hindustani
  Music

abstract:
  In North Indian Classical Music, raga forms the basic structure over which
individual improvisations is performed by an artist based on his/her
creativity. The Alap is the opening section of a typical Hindustani Music (HM)
performance, where the raga is introduced and the paths of its development are
revealed using all the notes used in that particular raga and allowed
transitions between them with proper distribution over time. In India,
corresponding to each raga, several emotional flavors are listed, namely erotic
love, pathetic, devotional, comic, horrific, repugnant, heroic, fantastic,
furious, peaceful. The detection of emotional cues from Hindustani Classical
music is a demanding task due to the inherent ambiguity present in the
different ragas, which makes it difficult to identify any particular emotion
from a certain raga. In this study we took the help of a high resolution
mathematical microscope (MFDFA or Multifractal Detrended Fluctuation Analysis)
to procure information about the inherent complexities and time series
fluctuations that constitute an acoustic signal. With the help of this
technique, 3 min alap portion of six conventional ragas of Hindustani classical
music namely, Darbari Kanada, Yaman, Mian ki Malhar, Durga, Jay Jayanti and
Hamswadhani played in three different musical instruments were analyzed. The
results are discussed in detail.


title:
Proton-neutron random phase approximation studied by the
  Lipkin-Meshkov-Glick model in the SU(2) $\times$ SU(2)

abstract:
  We study the proton-neutron RPA with an extended Lipikin-Meshkov-Glick model.
We pay attention to the effect of correlated ground state and the case in which
neutron and proton numbers are different. The effect of the correlated ground
state are tested on the basis of quasi-boson approximation. We obtain the
result that RPA excitation energies and transition strengths are in a good
agreement with the exact solution up to a certain strength of the
particle-particle interaction. However, the transition strength becomes worse
if we consider the case in which neutron and proton numbers are different even
at a weak particle-particle interaction.


title:
Double-Cell Type Solar Meridional Circulation Based on Mean-Field
  Hydrodynamic Model

abstract:
  The main object of the paper is to present the condition of the non-diffusive
part of the Reynolds stress for driving the double-cell structure of the solar
meridional circulation, which has been revealed by recent helioseismic
observations. By conducting a set of mean-field hydrodynamic simulations, we
confirm for the first time that the double-cell meridional circulation can be
achieved along with the solar-like differential rotation when the Reynolds
stress transports the angular momentum upward in the lower part and downward in
the upper part of the convection zone. It is concluded that, in a stationary
state, the accumulated angular momentum via the Reynolds stress in the middle
layer is advected to both the upper and lower parts of the convection zone by
each of the two meridional circulation cells, respectively.


title:
A Convergent Crank-Nicolson Galerkin Scheme for the Benjamin-Ono
  Equation

abstract:
  In this paper we prove the convergence of a Crank-Nicolson type Galerkin
finite element scheme for the initial value problem associated to the
Benjamin-Ono equation. The proof is based on a recent result for a similar
discrete scheme for the Korteweg-de Vries equation and utilizes a local
smoothing effect to bound the $H^{1/2}$-norm of the approximations locally.
This enables us to show that the scheme converges strongly in
$L^{2}(0,T;L^{2}_{\text{loc}}(\mathbb{R}))$ to a weak solution of the equation
for initial data in $L^{2}(\mathbb{R})$ and some $T > 0$. Finally we illustrate
the method with some numerical examples.


title: Encoding orbital angular momentum of light in magnets

abstract:
  Breaking the diffraction limit and focusing laser beams to subwavelength
scale are becoming possible with the help of recent developments in plasmonics.
Such subwavelength focusing bridges different length scales of laser beams and
matter. Here we consider optical vortex, or laser beam carrying orbital angular
momentum (OAM) and discuss potential subwavelength magnetic phenomena induced
by such laser. On the basis of numerical calculations using
Landau-Lifshitz-Gilbert equation, we propose two OAM-dependent phenomena
induced by optical vortices, generation of radially anisotropic spin waves and
generation of topological defects in chiral magnets. The former could lead to
the transient topological Hall effect through the laser-induced scalar spin
chirality, and the latter reduces the timescale of generating skyrmionic
defects by several orders compared to other known means.


title: One sided conformal collars and the reflection principle

abstract:
  If a Jordan curve {\sigma} has a one-sided conformal collar with "good"
properties, then, using the Reflection principle, we show that any other
conformal collar of {\sigma} from the same side has the same "good" properties.
A particular use of this fact concerns analytic Jordan curves, but in general
the Jordan arcs we consider do not have to be analytic. We show that if an
one-sided conformal collar bounded by {\sigma} is of class A^p, then any other
collar bounded by {\sigma} and from the same side of {\sigma} is of class A^p.


title: Minimal clusters of four planar regions with the same area

abstract:
  We prove that the optimal way to enclose and separate four planar regions
with equal area using the less possible perimeter requires all regions to be
connected. Moreover, the topology of such optimal clusters is uniquely
determined.


title:
Online Offering Strategies for Storage-Assisted Renewable Power Producer
  in Hour-Ahead Market

abstract:
  A promising approach to hedge against the inherent uncertainty of renewable
generation is to equip the renewable plants with energy storage systems. This
paper focuses on designing profit maximization offering strategies, i.e., the
strategies that determine the offering price and volume, for a storage-assisted
renewable power producer that participates in hour-ahead electricity market.
Designing the strategies is challenging since (i) the underlying problem is
coupled across time due to the evolution of the storage level, and (ii) inputs
to the problem including the renewable output and market clearing price are
unknown when submitting offers. Following the competitive online algorithm
design approach, we first study a basic setting where the renewable output and
the clearing price are known for the next hour. We propose sOffer, a simple
online offering strategy that achieves the best possible competitive ratio of
O(log \theta), where $\theta$ is the ratio between the maximum and the minimum
clearing prices. Then, we consider the case where the clearing price is
unknown. By exploiting the idea of submitting multiple offers to combat price
uncertainty, we propose mOffer, and demonstrate that the competitive ratio of
mOffer converges to that of sOffer as the number of offers grows. Finally, we
extend our approach to the scenario where the renewable output has forecasting
error. We propose gOffer as the generalized offering strategy and characterize
its competitive ratio as a function of the forecasting error. Our trace-driven
experiments demonstrate that our algorithms achieve performance close to the
offline optimal and outperform a baseline alternative significantly.


title: Polarization and long-term variability of Sgr A* X-ray echo

abstract:
  We use a model of the molecular gas distribution within ~100 pc from the
center of the Milky Way (Kruijssen, Dale & Longmore) to simulate time evolution
and polarization properties of the reflected X-ray emission, associated with
the past outbursts from Sgr A*. While this model is too simple to describe the
complexity of the true gas distribution, it illustrates the importance and
power of long-term observations of the reflected emission. We show that the
variable part of X-ray emission observed by Chandra and XMM from prominent
molecular clouds is well described by a pure reflection model, providing strong
support of the reflection scenario. While the identification of Sgr A* as a
primary source for this reflected emission is already a very appealing
hypothesis, a decisive test of this model can be provided by future X-ray
polarimetric observations, that will allow placing constraints on the location
of the primary source. In addition, X-ray polarimeters (like, e.g., XIPE) have
sufficient sensitivity to constrain the line-of-sight positions of molecular
complexes, removing major uncertainty in the model.


title: Monge's Optimal Transport Distance for Image Classification

abstract:
  This paper focuses on a similarity measure, known as the Wasserstein
distance, with which to compare images. The Wasserstein distance results from a
partial differential equation (PDE) formulation of Monge's optimal transport
problem. We present an efficient numerical solution method for solving Monge's
problem. To demonstrate the measure's discriminatory power when comparing
images, we use a $1$-Nearest Neighbour ($1$-NN) machine learning algorithm to
illustrate the measure's potential benefits over other more traditional
distance metrics and also the Tangent Space distance, designed to perform
excellently on the well-known MNIST dataset. To our knowledge, the PDE
formulation of the Wasserstein metric has not been presented for dealing with
image comparison, nor has the Wasserstein distance been used within the
$1$-nearest neighbour architecture.


title: Hydrodynamics in kinetically-constrained lattice-gas models

abstract:
  Kinetically-constrained models are lattice-gas models that are used for
describing glassy systems. By construction, their equilibrium state is trivial
and there are no equal-time correlations between the occupancy of different
sites. We drive such models out of equilibrium by connecting them to two
reservoirs of different densities, and measure the response of the system to
this perturbation. We find that under the proper coarse-graining, the behavior
of these models may be expressed by a nonlinear diffusion equation, with a
model- and density-dependent diffusion coefficient. We find a simple
approximation for the diffusion coefficient, and show that the relatively mild
discrepancy between the approximation and our numerical results arises due to
non-negligible correlations that appear as the system is driven out of
equilibrium, even when the density gradient is infinitesimally small. Similar
correlations appear when such kinetically-constrained models are driven out of
equilibrium by applying a uniform external force. We suggest that these
correlations are the reason for the same discrepancy between the approximate
diffusion coefficient and the numerical results for a broader group of models,
non-gradient lattice-gas models, for which kinetically-constrained models are
arguably the simplest example thereof.


title:
The power of teaming up HST and Gaia: the first proper motion
  measurement of the distant cluster NGC 2419

abstract:
  Aims: We present the first measurement of the proper motion and orbit of the
very distant and intriguing globular cluster NCG 2419. Methods: We have
combined data from HST and Gaia DR1 to derive the relative proper motions of
stars in the direction to the cluster. To tie to an absolute reference frame we
have used a background galaxy located in the field. Results: We find the
absolute proper motion of NGC 2419 to be $(\mu_{\alpha}\cos(\delta)$,
$\mu_{\delta}$)=($-0.17\pm0.26,-0.49\pm0.17$) mas/yr. We have integrated the
orbit of the cluster in a Galactic potential and found it to oscillate between
$\sim$53 kpc and $\sim$98 kpc on a nearly polar orbit. This makes it very
likely that NGC 2419 is a former cluster of the Sagittarius dwarf spheroidal
galaxy, also because it shares the same sense of rotation around the Milky Way.


title:
Remarks on Kawamata's effective non-vanishing conjecture for manifolds
  with trivial first Chern classes

abstract:
  Kawamata proposed a conjecture predicting that every nef and big line bundle
on a smooth projective variety with trivial first Chern class has nontrivial
global sections. We verify this conjecture for several cases, including (i) all
hyperk\"{a}hler varieties of dimension $\leq 6$; (ii) all known hyperk\"{a}hler
varieties except for O'Grady's 10-dimensional example; (iii) general complete
intersection Calabi-Yau varieties in certain Fano manifolds (e.g. toric ones).
Moreover, we investigate the effectivity of Todd classes of hyperk\"{a}hler
varieties and Calabi-Yau varieties. We prove that the fourth Todd classes are
"fakely effective" for all hyperk\"{a}hler varieties and general complete
intersection Calabi-Yau varieties in products of projective spaces.


title: Detection of collaborative activity with Kinect depth cameras

abstract:
  The health status of elderly subjects is highly correlated to their
activities together with their social interactions. Thus, the long term
monitoring in home of their health status, shall also address the analysis of
collaborative activities. This paper proposes a preliminary approach of such a
system which can detect the simultaneous presence of several subjects in a
common area using Kinect depth cameras. Most areas in home being dedicated to
specific tasks, the localization enables the classification of tasks, whether
collaborative or not. A scenario of a 24 hours day shrunk into 24 minutes was
used to validate our approach. It pointed out the need of artifacts removal to
reach high specificity and good sensitivity.


title: An impossibility theorem for paired comparisons

abstract:
  In several decision-making problems, alternatives should be ranked on the
basis of paired comparisons between them. We present an axiomatic approach for
the universal ranking problem with arbitrary preference intensities, incomplete
and multiple comparisons. In particular, two basic properties -- independence
of irrelevant matches and self-consistency -- are considered. It is revealed
that there exists no ranking method satisfying both requirements at the same
time. The impossibility result holds under various restrictions on the set of
ranking problems, however, it does not emerge in the case of round-robin
tournaments. An interesting and more general possibility result is obtained by
restricting the domain of independence of irrelevant matches through the
concept of macrovertex.


title: A locally integrable multi-dimensional billiard system

abstract:
  We consider a multi-dimensional billiard system in an (n+1)-dimensional
Euclidean space, the direct product of the "horizontal" hyperplane and the
"vertical" line. The hypersurface that determines the system is assumed to be
smooth and symmetric in all coordinate hyperplanes. Hence there exists a
periodic orbit $\gamma$ of period 2 moving along the "vertical" coordinate
axis. The question we ask is as follows. Is it possible to choose such a system
to have the dynamics locally (near $\gamma$) conjugated to the dynamics of a
linear map?
  Since the problem is local, the billiard hypersurface can be determined as
the graphs of the functions $\pm f$, where $f$ is even and defined in a
neighborhood of the origin on the "horizontal" coordinate hyperplane. We prove
that $f$ exists as a formal Taylor series in the non-resonant case and give
numerical evidence for convergence of the series.


title:
Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using
  Householder Reflections

abstract:
  The problem of learning long-term dependencies in sequences using Recurrent
Neural Networks (RNNs) is still a major challenge. Recent methods have been
suggested to solve this problem by constraining the transition matrix to be
unitary during training which ensures that its norm is equal to one and
prevents exploding gradients. These methods either have limited expressiveness
or scale poorly with the size of the network when compared with the simple RNN
case, especially when using stochastic gradient descent with a small mini-batch
size. Our contributions are as follows; we first show that constraining the
transition matrix to be unitary is a special case of an orthogonal constraint.
Then we present a new parametrisation of the transition matrix which allows
efficient training of an RNN while ensuring that the matrix is always
orthogonal. Our results show that the orthogonal constraint on the transition
matrix applied through our parametrisation gives similar benefits to the
unitary constraint, without the time complexity limitations.


title:
Mechanisms of initial oxidation of 4H-SiC (0001) and (000$\bar{1}$)
  surfaces unraveled by first-principles calculations

abstract:
  We have performed electronic state calculations to clarify the initial stage
of the oxidation of the Si- and C-faces in 4H-SiC based on the
density-functional theory. We investigate how each Si and C atomic site is
oxidized on C- and Si-face, and explore most probable reaction pathways,
corresponding energy barriers, and possible defects generated during the
oxidation. We have found that carbon annihilation process is different between
on Si- and on C-face, and this difference causes different defects in
interface; In C-face case, (1), carbon atoms are dissociated directly from the
substrate as CO molecules. (2), after CO dissociation, 3-fold coordinated
oxygen atoms (called Y-lid) are observed at the interface. (3), high density of
C-dangling bonds can remain at the interface. In Si-face case, (1), C atoms
inevitably form carbon nano clusters (composed of a few atoms) in interface to
reduce the number of dangling bonds there. Moreover, we have found that the
carbon nano clusters are composed of not only single but also double chemical
bonds. (2), We have observed that CO molecules are dissociated from the carbon
nano clusters in MD simulations. Furthermore, we have investigated whether
H$_2$ and NO molecules react with the defects found in this study.


title: Equilibrium Computation in Resource Allocation Games

abstract:
  We study the equilibrium computation problem for two classical resource
allocation games: atomic splittable congestion games and multimarket Cournot
oligopolies. For atomic splittable congestion games with singleton strategies
and player-specific affine cost functions, we devise the first polynomial time
algorithm computing a pure Nash equilibrium. Our algorithm is combinatorial and
computes the exact equilibrium assuming rational input. The idea is to compute
an equilibrium for an associated integrally-splittable singleton congestion
game in which the players can only split their demands in integral multiples of
a common packet size. While integral games have been considered in the
literature before, no polynomial time algorithm computing an equilibrium was
known. Also for this class, we devise the first polynomial time algorithm and
use it as a building block for our main algorithm.
  We then develop a polynomial time computable transformation mapping a
multimarket Cournot competition game with firm-specific affine price functions
and quadratic costs to an associated atomic splittable congestion game as
described above. The transformation preserves equilibria in either games and,
thus, leads -- via our first algorithm -- to a polynomial time algorithm
computing Cournot equilibria. Finally, our analysis for integrally-splittable
games implies new bounds on the difference between real and integral Cournot
equilibria. The bounds can be seen as a generalization of the recent bounds for
single market oligopolies obtained by Todd [2016].


title: Infinite algebraic subgroups of the real Cremona group

abstract:
  We give the classification of the maximal infinite algebraic subgroups of the
real Cremona group of the plane up to conjugacy and present a parametrisation
space of each conjugacy class. Moreover, we show that the real plane Cremona
group is not generated by a countable union of its infinite algebraic
subgroups.


title: Flight Dynamics-based Recovery of a UAV Trajectory using Ground Cameras

abstract:
  We propose a new method to estimate the 6-dof trajectory of a flying object
such as a quadrotor UAV within a 3D airspace monitored using multiple fixed
ground cameras. It is based on a new structure from motion formulation for the
3D reconstruction of a single moving point with known motion dynamics. Our main
contribution is a new bundle adjustment procedure which in addition to
optimizing the camera poses, regularizes the point trajectory using a prior
based on motion dynamics (or specifically flight dynamics). Furthermore, we can
infer the underlying control input sent to the UAV's autopilot that determined
its flight trajectory.
  Our method requires neither perfect single-view tracking nor appearance
matching across views. For robustness, we allow the tracker to generate
multiple detections per frame in each video. The true detections and the data
association across videos is estimated using robust multi-view triangulation
and subsequently refined during our bundle adjustment procedure. Quantitative
evaluation on simulated data and experiments on real videos from indoor and
outdoor scenes demonstrates the effectiveness of our method.


title: Learning molecular energies using localized graph kernels

abstract:
  Recent machine learning methods make it possible to model potential energy of
atomic configurations with chemical-level accuracy (as calculated from
ab-initio calculations) and at speeds suitable for molecular dynam- ics
simulation. Best performance is achieved when the known physical constraints
are encoded in the machine learning models. For example, the atomic energy is
invariant under global translations and rotations, it is also invariant to
permutations of same-species atoms. Although simple to state, these symmetries
are complicated to encode into machine learning algorithms. In this paper, we
present a machine learning approach based on graph theory that naturally
incorporates translation, rotation, and permutation symmetries. Specifically,
we use a random walk graph kernel to measure the similarity of two adjacency
matrices, each of which represents a local atomic environment. This Graph
Approximated Energy (GRAPE) approach is flexible and admits many possible
extensions. We benchmark a simple version of GRAPE by predicting atomization
energies on a standard dataset of organic molecules.


title: Field- and damping-like spin-transfer torque in magnetic multilayers

abstract:
  We investigate the spin-transfer torque in a magnetic multilayer structure by
means of a spin-diffusion model. The torque in the considered system,
consisting of two magnetic layers separated by a conducting layer, is caused by
a perpendicular-to-plane current. We compute the strength of the field-like and
the damping-like torque for different material parameters and geometries. Our
studies suggest that the field-like torque highly depends on the exchange
coupling strength of the itinerant electrons with the magnetization both in the
pinned and the free layer. While a low coupling leads to very high field-like
torques, a high coupling leads to low or even negative field-like torques. The
dependence of the different torque terms on system parameters is considered
very important for the development of applications such as STT MRAM and
spin-torque oscillators.


title:
Analysis of the mass and width of the $Y(4274)$ as axialvector
  molecule-like state

abstract:
  In this article, we assign the $Y(4274)$ to be the color octet-octet type
axialvector molecule-like state with $J^{PC}=1^{++}$ tentatively, and construct
the color octet-octet type axialvector current to study its mass and width with
the QCD sum rules in details. The predicted mass favors assigning the $Y(4274)$
to be the color octet-octet type molecule-like state, but the predicted width
disfavors assigning the $Y(4274)$ to be the color octet-octet type
molecule-like state strongly. The $Y(4274)$ may be the conventional charmonium
state $\chi_{c1}(\rm 3P)$, and it is important to observe the decay $Y(4274)\to
J/\psi \omega$ to diagnose the nature of the $Y(4274)$.


title: Estimating a monotone probability mass function with known flat regions

abstract:
  We propose a new estimator of a discrete monotone probability mass function
with known flat regions. We analyse its asymptotic properties and compare its
performance to the Grenander estimator and to the monotone rearrangement
estimator.


title:
Learning in an Uncertain World: Representing Ambiguity Through Multiple
  Hypotheses

abstract:
  Many prediction tasks contain uncertainty. In some cases, uncertainty is
inherent in the task itself. In future prediction, for example, many distinct
outcomes are equally valid. In other cases, uncertainty arises from the way
data is labeled. For example, in object detection, many objects of interest
often go unlabeled, and in human pose estimation, occluded joints are often
labeled with ambiguous values. In this work we focus on a principled approach
for handling such scenarios. In particular, we propose a framework for
reformulating existing single-prediction models as multiple hypothesis
prediction (MHP) models and an associated meta loss and optimization procedure
to train them. To demonstrate our approach, we consider four diverse
applications: human pose estimation, future prediction, image classification
and segmentation. We find that MHP models outperform their single-hypothesis
counterparts in all cases, and that MHP models simultaneously expose valuable
insights into the variability of predictions.


title: Pro-$p$ subgroups of profinite completions of 3-manifold groups

abstract:
  We completely describe the finitely generated pro-$p$ subgroups of the
profinite completion of the fundamental group of an arbitrary $3$-manifold. We
also prove a pro-$p$ analogue of the main theorem of Bass--Serre theory for
finitely generated pro-$p$ groups.


title:
Electromagnetic corrections to leptonic decay rates of charged
  pseudoscalar mesons: finite-volume effects

abstract:
  In Carrasco et al. we have recently proposed a method to calculate $O(e^2)$
electromagnetic corrections to leptonic decay widths of pseudoscalar mesons.
The method is based on the observation that the infrared divergent
contributions (that appear at intermediate stages of the calculation and that
cancel in physical quantities thanks to the Bloch-Nordsieck mechanism) are
universal, i.e. depend on the charge and the mass of the meson but not on its
internal structure. In this talk we perform a detailed analysis of the
finite-volume effects associated with our method. In particular we show that
also the leading $1/L$ finite-volume effects are universal and perform an
analytical calculation of the finite-volume leptonic decay rate for a
point-like meson.


title:
Neutron capture cross section measurement of 238U at the n TOF CERN
  facility with C6D6 scintillation detectors in the energy region from 1 eV to
  700 keV

abstract:
  The aim of this work is to provide a precise and accurate measurement of the
238U(n,g) reaction cross section in the energy region from 1 eV to 700 keV.
This reaction is of fundamental importance for the design calculations of
nuclear reactors, governing the behaviour of the reactor core. In particular,
fast reactors, which are experiencing a growing interest for their ability to
burn radioactive waste, operate in the high energy region of the neutron
spectrum. In this energy region most recent evaluations disagree due to
inconsistencies in the existing measurements of up to 15%. In addition, the
assessment of nuclear data uncertainty performed for innovative reactor systems
shows that the uncertainty in the radiative capture cross-section of 238U
should be further reduced to 1-3% in the energy region from 20 eV to 25 keV. To
this purpose, addressed by the Nuclear Energy Agency as a priority nuclear data
need, complementary experiments, one at the GELINA and two at the n_TOF
facility, were proposed and carried out within the 7th Framework Project ANDES
of the European Commission.
  The results of one of these 238U(n,g) measurements performed at the n_TOF
CERN facility are presented in this work. The gamma-ray cascade following the
radiative neutron capture has been detected exploiting a setup of two C6D6
liquid scintillators. Resonance parameters obtained from this work are on
average in excellent agreement with the ones reported in evaluated libraries.
In the unresolved resonance region, this work yields a cross section in
agreement with evaluated libraries up to 80 keV, while for higher energies our
results are significantly higher.


title: Towards a multigrid method for the minimum-cost flow problem

abstract:
  We present a first step towards a multigrid method for solving the min-cost
flow problem. Specifically, we present a strategy that takes advantage of
existing black-box fast iterative linear solvers, i.e. algebraic multigrid
methods. We show with standard benchmarks that, while less competitive than
combinatorial techniques on small problems that run on a single core, our
approach scales well with problem size, complexity, and number of processors,
allowing for tackling large-scale problems on modern parallel architectures.
Our approach is based on combining interior-point with multigrid methods for
solving the nonlinear KKT equations via Newton's method. However, the Jacobian
matrix arising in the Newton iteration is indefinite and its condition number
cannot be expected to be bounded. In fact, the eigenvalues of the Jacobian can
both vanish and blow up near the solution, leading to a significant slow-down
of the convergence speed of iterative solvers - or to the loss of convergence
at all. In order to allow for the application of multigrid methods, which have
been originally designed for elliptic problems, we furthermore show that the
occurring Jacobian can be interpreted as the stiffness matrix of a mixed
formulation of the weighted graph Laplacian of the network, whose metric
depends on the slack variables and the multipliers of the inequality
constraints. Together with our regularization, this allows for the application
of a black-box algebraic multigrid method on the Schur-complement of the
system.


title:
Measuring Diffuse Interstellar Bands with cool stars. An improved line
  list to model the background stellar spectra

abstract:
  DIBs are ubiquitous in stellar spectra. Traditionally, they have been studied
through their extraction from hot stars, because of their smooth continuum. In
an era where there are several going-on or planned massive Galactic surveys
using multi-object spectrographs, cool stars constitute an appealing set of
targets. From the technical point of view, the extraction of DIBs in their
spectra is more challenging due to the complexity of the continuum. In this
contribution we will provide the community with an improved set of stellar
lines in the spectral regions associated to the strong DIBs at l6196, l6269,
l6284, and l6379. These lines will allow for the creation of better stellar
synthetic spectra, reproducing the background emission and a more accurate
extraction of the magnitudes associated with a given DIB. The Sun and Arcturus
were used as representative examples of dwarf and giant stars, respectively. A
high quality spectrum for each of them was modeled using TURBOSPECTRUM and the
VALD stellar line list. The oscillator strength log(gf) and/or wavelength of
specific lines were modified to create synthetic spectra where the residuals in
both the Sun and Arcturus were minimized. The synthetic spectra based on the
improved line lists reproduce the observed spectra for the Sun and Arcturus in
the mentioned spectral ranges with greater accuracy. Residuals between the
synthetic and observed spectra are always <10%, much better than with
previously existing options. The new line list has been tested with some
characteristic spectra, from a variety of stars, including both giant and dwarf
stars, and under different degrees of extinction. As it happened with the Sun
and Arcturus residuals in the fits used to extract the DIB information are
smaller when using synthetic spectra made with the updated line lists. Tables
with the updated parameters are provided to the community.


title:
Analysis of the Human-Computer Interaction on the Example of Image-based
  CAPTCHA by Association Rule Mining

abstract:
  The paper analyzes the interaction between humans and computers in terms of
response time in solving the image-based CAPTCHA. In particular, the analysis
focuses on the attitude of the different Internet users in easily solving four
different types of image-based CAPTCHAs which include facial expressions like:
animated character, old woman, surprised face, worried face. To pursue this
goal, an experiment is realized involving 100 Internet users in solving the
four types of CAPTCHAs, differentiated by age, Internet experience, and
education level. The response times are collected for each user. Then,
association rules are extracted from user data, for evaluating the dependence
of the response time in solving the CAPTCHA from age, education level and
experience in internet usage by statistical analysis. The results implicitly
capture the users' psychological states showing in what states the users are
more sensible. It reveals to be a novelty and a meaningful analysis in the
state-of-the-art.