title: Disciplined Multi-Convex Programming

abstract:
  A multi-convex optimization problem is one in which the variables can be
partitioned into sets over which the problem is convex when the other variables
are fixed. Multi-convex problems are generally solved approximately using
variations on alternating or cyclic minimization. Multi-convex problems arise
in many applications, such as nonnegative matrix factorization, generalized low
rank models, and structured control synthesis, to name just a few. In most
applications to date the multi-convexity is simple to verify by hand. In this
paper we study the automatic detection and verification of multi-convexity
using the ideas of disciplined convex programming. We describe an
implementation of our proposed method that detects and verifies
multi-convexity, and then invokes one of the general solution methods.


title: Knowledge as a Teacher: Knowledge-Guided Structural Attention Networks

abstract:
  Natural language understanding (NLU) is a core component of a spoken dialogue
system. Recently recurrent neural networks (RNN) obtained strong results on NLU
due to their superior ability of preserving sequential information over time.
Traditionally, the NLU module tags semantic slots for utterances considering
their flat structures, as the underlying RNN structure is a linear chain.
However, natural language exhibits linguistic properties that provide rich,
structured information for better understanding. This paper introduces a novel
model, knowledge-guided structural attention networks (K-SAN), a generalization
of RNN to additionally incorporate non-flat network topologies guided by prior
knowledge. There are two characteristics: 1) important substructures can be
captured from small training data, allowing the model to generalize to
previously unseen test data; 2) the model automatically figures out the salient
substructures that are essential to predict the semantic tags of the given
sentences, so that the understanding performance can be improved. The
experiments on the benchmark Air Travel Information System (ATIS) data show
that the proposed K-SAN architecture can effectively extract salient knowledge
from substructures with an attention mechanism, and outperform the performance
of the state-of-the-art neural network based frameworks.


title:
Enhanced Performance Neutron Scattering Spectroscopy by Use of
  Correlation Techniques

abstract:
  Neutron correlation spectroscopy can exceed direct spectroscopy in the
incoming beam intensity by up to two orders of magnitude at the same energy
resolution. However, the propagation of the counting noise in the correlation
algorithm of data reduction is disadvantageous for the lowest intensity parts
of the observed spectrum. To mitigate this effect at pulsed neutron sources we
propose two dimensional time-of-flight recording of each neutron detection
event: with respect to both the neutron source pulses and to the rotation phase
of the pseudo-random beam modulation statistical chopper. We have identified a
formulation of the data reduction algorithm by matching the data processing
time channel width to the inherent time resolution of this chopper, which makes
the reconstruction of the direct time-of-flight spectra exact and independent
of all other contributions to instrumental resolution. Two ways are proposed
for most flexible choice of intensity vs. resolution without changing the
statistical chopper or its speed: changing the size of the beam window during
the experiment or varying intensity and resolution options in the data
reduction algorithm after the experiment. This latter is a unique and very
promising new potential offered by the correlation method. Furthermore, it
displays reduced sensitivity to external background, also due to the much
higher signal intensity. This is particularly advantageous for extending the
operational range to higher neutron energies. In hot and thermal neutron
vibrational spectroscopy, the statistical chopper approach allows us to achieve
very significant gains in spectrometer efficiency compared to using
conventional choppers. High intensity for the most intense features in the
spectra and the reduced sensitivity to sample independent background make
correlation spectroscopy a most powerful choice for studying small samples.


title: New class of LRS spacetimes with simultaneous rotation and spatial twist

abstract:
  We establish the existence and find the necessary and sufficient conditions
for a new class of solutions of Locally Rotationally Symmetric spacetimes that
have non vanishing rotation and spatial twist simultaneously. We transparently
show that the existence of such solutions demand non vanishing and bounded heat
flux and these solutions are self similar. We provide a brief algorithm
indicating how to solve the system of field equations with the given Cauchy
data on an initial spacelike Cauchy surface. Finally we argue that these
solutions can be used as a first approximation from spherical symmetry to study
rotating, inhomogeneous, dynamic and radiating astrophysical stars.


title:
Empirical Evaluation of the Heat-Diffusion Collection Protocol for
  Wireless Sensor Networks

abstract:
  Heat-Diffusion (HD) routing is our recently-developed queue-aware routing
policy for multi-hop wireless networks inspired by Thermodynamics. In the prior
theoretical studies, we have shown that HD routing guarantees throughput
optimality, minimizes a quadratic routing cost, minimizes queue congestion on
the network, and provides a trade-off between routing cost and queueing delay
that is Pareto-Optimal. While striking, these guarantees are based on idealized
assumptions (including global synchronization, centralized control, and
infinite buffers) and heretofore have only been evaluated through simplified
numerical simulations. We present here the first practical decentralized
version of HD algorithm, which we refer to as Heat-Diffusion Collection
Protocol (HDCP), for wireless sensor networks. We present a thorough evaluation
of HDCP based on real testbed experiments, including a comparative analysis of
its performance with respect to the state of the art Collection Tree Protocol
(CTP) and Backpressure Collection Protocol (BCP) for wireless sensor networks.
We find that HDCP has a significantly higher throughput region and greater
resilience to interference compared to CTP. However, we also find that the best
performance of HDCP is comparable to the best performance of BCP, due to the
similarity in their neighbor rankings, which we verify through a Kendall's-Tau
test.


title:
Weighted estimates for generalized steady Stokes systems in nonsmooth
  domains

abstract:
  We consider a generalized steady Stokes system with discontinuous
coefficients in a nonsmooth domain when the inhomogeneous term belongs to a
weighted $L^q$ space for $2<q<\infty$. We prove the global weighted
$L^q$-estimates for the gradient of the weak solution and an associated
pressure under the assumptions that the coefficients have small BMO (bounded
mean oscillation) semi-norms and the domain is sufficiently flat in the
Reifenberg sense. On the other hand, a given weight is assumed to belong to a
Muckenhoupt class. Our result generalizes the global $W^{1.q}$ estimate of
Calder\'{o}n-Zygmund with respect to the Lebesgue measure for the Stokes system
in a Lipschitz domain.


title: Clustering coefficient and periodic orbits in flow networks

abstract:
  We show that the clustering coefficient, a standard measure in network
theory, when applied to flow networks, i.e. graph representations of fluid
flows in which links between nodes represent fluid transport between spatial
regions, identifies approximate locations of periodic trajectories in the flow
system. This is true for steady flows and for periodic ones in which the time
interval $\tau$ used to construct the network is the period of the flow or a
multiple of it. In other situations the clustering coefficient still identifies
cyclic motion between regions of the fluid. Besides the fluid context, these
ideas apply equally well to general dynamical systems. By varying the value of
$\tau$ used to construct the network, a kind of spectroscopy can be performed
so that the observation of high values of mean clustering at a value of $\tau$
reveals the presence of periodic orbits of period $3\tau$ which impact phase
space significantly. These results are illustrated with examples of increasing
complexity, namely a steady and a periodically perturbed model two-dimensional
fluid flow, the three-dimensional Lorenz system, and the turbulent surface flow
obtained from a numerical model of circulation in the Mediterranean sea.


title: Classification of Rigid Irregular $G_2$-Connections

abstract:
  Using the Katz-Arinkin algorithm we give a classification of irreducible
rigid irregular connections on a punctured $\mathbb{P}^1_{\mathbb{C}}$ having
differential Galois group $G_2$, the exceptional simple algebraic group, and
slopes having numerator 1. In addition to hypergeometric systems and their
Kummer pull-backs we construct families of $G_2$-connections which are not of
these types.


title: Effective Lagrangian in de Sitter Spacetime

abstract:
  Scale invariant fluctuations of metric are universal feature of quantum
gravity in de Sitter spacetime. We construct an effective Lagrangian which
summarizes their implications on local physics by integrating super-horizon
metric fluctuations. It shows infrared quantum effects are local and render
fundamental couplings time dependent. We impose Lorenz invariance on the
effective Lagrangian as it is required by the principle of general covariance.
We show that such a requirement leads to unique physical predictions by fixing
the quantization ambiguities. We explain how the gauge parameter dependence of
observables is canceled. In particular the relative evolution speed of the
couplings are shown to be gauge invariant.


title:
Gamma Matrix Expansion of the Bethe-Salpeter Equation for
  Nucleon-Nucleon System

abstract:
  For the coefficients of the amplitude a set of simultaneous equations is
derived in momentum space. By the auxiliary conditions they are equivalent to
nonrelativistic equations and suitable for the investigation of two-nucleon
system.


title:
New results on the asymptotic and finite sample properties of the MaCML
  approach to multinomial probit model estimation

abstract:
  In this paper the properties of the maximum approximate composite marginal
likelihood (MaCML) approach to the estimation of multinomial probit models
(MNP) proposed by Chandra Bhat and coworkers is investigated in finite samples
as well as with respect to asymptotic properties. Using a small illustration
example it is proven that the approach does not necessarily lead to consistent
estimators for four different types of approximation of the Gaussian cumulative
distribution function (including the Solow-Joe approach proposed by Bhat). It
is shown that the bias of parameter estimates can be substantial (while
typically it is small) and the bias in the corresponding implied probabilities
is small but non-negligible. Furthermore in finite sample it is demonstrated by
simulation that between two versions of the Solow-Joe method and two versions
of the Mendell-Elston approximation no method dominates the others in terms of
accuracy and numerical speed. Moreover the system to be estimated, the ordering
of the components in the approximation method and even the tolerance used for
stopping the numerical optimization routine all have an influence on the
relative performance of the procedures corresponding to the various
approximation methods. Jointly the paper thus points towards eminent research
needs in order to decide on the method to use for a particular estimation
problem at hand.


title: A Neural Network Alternative to Non-Negative Audio Models

abstract:
  We present a neural network that can act as an equivalent to a Non-Negative
Matrix Factorization (NMF), and further show how it can be used to perform
supervised source separation. Due to the extensibility of this approach we show
how we can achieve better source separation performance as compared to
NMF-based methods, and propose a variety of derivative architectures that can
be used for further improvements.


title: Flexible Tweedie regression models for continuous data

abstract:
  Tweedie regression models provide a flexible family of distributions to deal
with non-negative highly right-skewed data as well as symmetric and heavy
tailed data and can handle continuous data with probability mass at zero. The
estimation and inference of Tweedie regression models based on the maximum
likelihood method are challenged by the presence of an infinity sum in the
probability function and non-trivial restrictions on the power parameter space.
In this paper, we propose two approaches for fitting Tweedie regression models,
namely, quasi- and pseudo-likelihood. We discuss the asymptotic properties of
the two approaches and perform simulation studies to compare our methods with
the maximum likelihood method. In particular, we show that the quasi-likelihood
method provides asymptotically efficient estimation for regression parameters.
The computational implementation of the alternative methods is faster and
easier than the orthodox maximum likelihood, relying on a simple Newton scoring
algorithm. Simulation studies showed that the quasi- and pseudo-likelihood
approaches present estimates, standard errors and coverage rates similar to the
maximum likelihood method. Furthermore, the second-moment assumptions required
by the quasi- and pseudo-likelihood methods enables us to extend the Tweedie
regression models to the class of quasi-Tweedie regression models in the
Wedderburn's style. Moreover, it allows to eliminate the non-trivial
restriction on the power parameter space, and thus provides a flexible
regression model to deal with continuous data. We provide \texttt{R}
implementation and illustrate the application of Tweedie regression models
using three data sets.


title:
A statistical perspective on particle-wave duality of matter: unlocking
  the quantum correlations

abstract:
  Here, the particle-wave duality principle is generalized to accommodate the
non-relativistic quantum mechanics of interacting (correlated) particles.
Through the creation and propagation of concurrent stochastic ensembles of
walkers in physical and in Hilbert spaces one can correctly predict the ground
state and the time evolution of a subsystem of a larger quantum system in a
quantum Monte Carlo manner. It is shown that such walker ensembles can be
constructed straightforwardly through a random sampling (windowing) applied to
the mean-field approximation. Our calculations reveal that although the
resulting individual quantum trajectories may differ significantly from those
predicted by the many-body Schr\"odinger equation, the evolution of the
particle distribution and the decoherence due to the Coulomb interaction can be
accounted for correctly using an explicitly parameterized quantum non-locality.


title: Emergence of Network Bifurcation Triggered by Entanglement

abstract:
  In many non-linear systems, such as plasma oscillation, boson condensation,
chemical reaction, and even predatory-prey oscillation, the coarse-grained
dynamics are governed by an equation containing anti-symmetric transitions,
known as the anti-symmetric Lotka-Volterra (ALV) equations. In this work, we
prove the existence of a novel bifurcation mechanism for the ALV equations,
where the equilibrium state can be drastically changed by flipping the
stability of a pair of fixed points. As an application, we focus on the
implications of the bifurcation mechanism for evolutionary networks; we found
that the bifurcation point can be determined quantitatively by the microscopic
quantum entanglement. The equilibrium state can be critically changed from one
type of global demographic condensation to another state that supports global
cooperation for homogeneous networks. In other words, our results indicate that
there exist a class of many-body systems where the macroscopic properties are
invariant with a certain amount of microscopic entanglement, but they can be
changed abruptly once the entanglement exceeds a critical value. Furthermore,
we provide numerical evidence showing that the emergence of bifurcation is
robust against the change of the network topologies, and the critical values
are in good agreement with our theoretical prediction. These results show that
the bifurcation mechanism could be ubiquitous in many physical systems, in
addition to evolutionary networks.

categories: quant-ph physics.bio-ph physics.data-an physics.soc-ph


title:
Influence of coherent adiabatic excitation on femtosecond transient
  signals

abstract:
  The transient signals derived from femtosecond pump-probe experiments are
analyzed in terms of the coherent evolution of the energy levels perturbed by
the excitation pulse. The model system is treated as the sum of independent
two-level subsystems that evolve adiabatically or are permanently excited,
depending on the detuning from the central wavelength of the excitation laser.
This approach will allow us to explain numerically and analytically the
convergence between the coherent and incoherent (rate equations) treatments for
complex multi-level systems. It will be also shown that the parameter that
determines the validity of the incoherent treatment is the distribution of
states outside and inside the laser bandwidth, rather than the density of
states as it is commonly accepted.


title:
Massive Streaming PMU Data Modeling and Analytics in Smart Grid State
  Evaluation Based on Multiple High-Dimensional Covariance Tests

abstract:
  The analogous deployment of phase measurement units (PMUs), the increase of
data quantum and the deregulation of energy market, all call for the robust
state evaluation in large scale power systems. Implementing model based
estimators is impractical because of the complexity scale of solving the high
dimension power flow equations. In this paper, we first represent massive
streaming PMU data as big random matrix flow. By exploiting the variations in
the covariance matrix of the massive streaming PMU data, a novel power state
evaluation algorithm is then developed based on the multiple high dimensional
covariance matrix tests. The proposed test statistic is flexible and
nonparametric, which assumes no specific parameter distribution or dimension
structure for the PMU data. Besides, it can jointly reveal the relative
magnitude, duration and location of a system event. For the sake of practical
application, we reduce the computation of the proposed test statistic from
$O(\varepsilon n_g^4)$ to $O(\eta n_g^2)$ by principal component calculation
and redundant computation elimination. The novel algorithm is numerically
evaluated utilizing the IEEE 30-, 118-bus system, a Polish 2383-bus system, and
a real 34-PMU system. The case studies illustrate and verify the superiority of
proposed state evaluation indicator.


title: Image denoising via group sparsity residual constraint

abstract:
  Group sparsity has shown great potential in various low-level vision tasks
(e.g, image denoising, deblurring and inpainting). In this paper, we propose a
new prior model for image denoising via group sparsity residual constraint
(GSRC). To enhance the performance of group sparse-based image denoising, the
concept of group sparsity residual is proposed, and thus, the problem of image
denoising is translated into one that reduces the group sparsity residual. To
reduce the residual, we first obtain some good estimation of the group sparse
coefficients of the original image by the first-pass estimation of noisy image,
and then centralize the group sparse coefficients of noisy image to the
estimation. Experimental results have demonstrated that the proposed method not
only outperforms many state-of-the-art denoising methods such as BM3D and WNNM,
but results in a faster speed.


title:
Strong ultra-regularity properties for positive elements in the twisted
  convolutions

abstract:
  We show that positive elements with respect to the twisted convolutions,
belonging to some ultra-test function space of certain order at origin, belong
to the ultra-test function space of the same order everywhere. We apply the
result to positive semi-definite Weyl operators.


title:
Side gate tunable Josephson junctions at the LaAlO$_3$/SrTiO$_3$
  interface

abstract:
  Novel physical phenomena arising at the interface of complex oxide
heterostructures offer exciting opportunities for the development of future
electronic devices. Using the prototypical LaAlO$_3$/SrTiO$_3$ interface as a
model system, we employ a single-step lithographic process to realize gate
tunable Josephson junctions through a combination of lateral confinement and
local side gating. The action of the side gates is found to be comparable to
that of a local back gate, constituting a robust and efficient way to control
the properties of the interface at the nanoscale. We demonstrate that the side
gates enable reliable tuning of both the normal-state resistance and the
critical (Josephson) current of the constrictions. The conductance and
Josephson current show mesoscopic fluctuations as a function of the applied
side gate voltage, and the analysis of their amplitude enables the extraction
of the phase coherence and thermal lengths. Finally, we realize a
superconducting quantum interference device in which the critical currents of
each of the constriction-type Josephson junctions can be controlled
independently via the side gates.


title: Predicting the elliptic curve congruential generator

abstract:
  Let $p$ be a prime and let $\mathbf{E}$ be an elliptic curve defined over the
finite field $\mathbb{F}_p$ of $p$ elements. For a point
$G\in\mathbf{E}(\mathbb{F}_p)$ the elliptic curve congruential generator (with
respect to the first coordinate) is a sequence $(x_n)$ defined by the relation
$x_n=x(W_n)=x(W_{n-1}\oplus G)=x(nG\oplus W_0)$, $n=1,2,\dots$, where $\oplus$
denotes the group operation in $\mathbf{E}$ and $W_0$ is an initial point. In
this paper, we show that if some consecutive elements of the sequence $(x_n)$
are given as integers, then one can compute in polynomial time an elliptic
curve congruential generator (where the curve possibly defined over the
rationals or over a residue ring) such that the generated sequence is identical
to $(x_n)$ in the revealed segment. It turns out that in practice, all the
secret parameters, and thus the whole sequence $(x_n)$, can be computed from
eight consecutive elements, even if the prime and the elliptic curve are
private.


title:
Coupling cavity model for circular cylindrical waveguide with uniform
  cross section

abstract:
  We developed the general approach that gives possibility to calculate the
coupling coefficients for arbitrary chain of resonators without using the great
number of eigen functions. For understanding this method and having possibility
to control the accuracy of obtained results, we applied this procedure for
simplest structure that has the analytical solutions - a circular cylindrical
waveguide with uniform cross section.


title: Semistable Higgs bundles over compact Gauduchon manifolds

abstract:
  In this paper, we consider the existence of approximate Hermitian-Einstein
structure and the semi-stability on Higgs bundles over compact Gauduchon
manifolds. By using the continuity method, we show that they are equivalent.


title:
Intramode correlations enhanced phase sensitivities in an SU(1,1)
  interferometer

abstract:
  We theoretically derive the lower and upper bounds of quantum Fisher
information (QFI) of an SU(1,1) interferometer whatever the input state chosen.
According to the QFI, the crucial resource for quantum enhancement is shown to
be large intramode correlations indicated by the Mandel $Q$-parameter. For a
photon-subtracted squeezed vacuum state with high super-Poissonian statistics
in one input port and a coherent state in the other input port, the quantum
Cram\'{e}r-Rao bound of the SU(1,1) interferometer can beat
$1/\langle\hat{N}\rangle$ scaling in presence of large fluctuations in the
number of photons, with a given fixed input mean number of photons. The
definition of the Heisenberg limit (HL) should take into account the amount of
fluctuations. The HL considering the number fluctuation effect may be the
ultimate phase limit.


title: Naturalness in see-saw mechanism and Bogoliubov transformation

abstract:
  We present an alternative perspective on the see-saw mechanism for the
neutrino mass, according to which the small neutrino mass is given as a
difference of two large masses. This view emerges when an analogue of the
Bogoliubov transformation is used to describe Majorana neutrinos in the
Lagrangian of the see-saw mechanism, which is analogous to the BCS theory. The
Bogoliubov transformation clarifies the natural appearance of Majorana fermions
when C is strongly violated by the right-handed neutrino mass term with good CP
in the single flavor model. Analyzing typical models with $m_{R}$= $10^{4}$ to
$10^{15}$ GeV, it is shown that a hitherto unrecognized fine tuning of the
order $m_{\nu}/m_{R}=10^{-15}$ to $10^{-26}$ is required to make the commonly
perceived see-saw mechanism work in a natural setting, namely, when none of
dimensionless coupling constants are very small.

categories: hep-ph cond-mat.str-el cond-mat.supr-con hep-th


title: Formation, dissolution and properties of surface nanobubbles

abstract:
  Surface nanobubbles are stable gaseous phases in liquids that form on solid
substrates. While their existence has been confirmed, there are many open
questions related to their formation and dissolution processes along with their
structures and properties, which are difficult to investigate experimentally.
To address these issues, we carried out molecular dynamics simulations based on
atomistic force fields for systems comprised of water, air (N2 and O2), and a
Highly Oriented Pyrolytic Graphite (HOPG) substrate. Our results provide
insights into the formation/dissolution mechanisms of nanobubbles and estimates
for their density, contact angle, and surface tension. We found that the
formation of nanobubbles is driven by an initial nucleation process of air
molecules and the subsequent coalescence of the formed air clusters. The
clusters form favorably on the substrate, which provides an enhanced stability
to the clusters. In contrast, nanobubbles formed in the bulk either move
randomly to the substrate and spread or move to the water--air surface and pop
immediately. Moreover, nanobubbles consist of a condensed gaseous phase with a
surface tension smaller than that of an equivalent system under atmospheric
conditions, and contact angles larger than those in the equivalent nanodroplet
case. We anticipate that this study will provide useful insights into the
physics of nanobubbles and will stimulate further research in the field by
using all-atom simulations.


title: Metric Symplectic Lie Algebras

abstract:
  Every metric symplectic Lie algebra has the structure of a quadratic
extension. We give a standard model and describe the equivalence classes on the
level of corresponding quadratic cohomology sets. Finally, we give a scheme to
classify the isomorphism classes of metric symplectic Lie algebras and give a
complete list of all these Lie algebras in special cases.


title: Valorising the IoT Databox: Creating Value for Everyone

abstract:
  The Internet of Things (IoT) is expected to generate large amounts of
heterogeneous data from diverse sources including physical sensors, user
devices, and social media platforms. Over the last few years, significant
attention has been focused on personal data, particularly data generated by
smart wearable and smart home devices. Making personal data available for
access and trade is expected to become a part of the data driven digital
economy. In this position paper, we review the research challenges in building
personal Databoxes that hold personal data and enable data access by other
parties, and potentially thus sharing of data with other parties. These
Databoxes are expected to become a core part of future data marketplaces.


title:
Corrugation induced stacking solitons with topologically confined states
  in gapped bilayer graphene

abstract:
  Graphene, as an atomic-thick ultrasoft membrane, almost has no resistance
against out-of-plane deformations and, therefore, it is always wrinkled to a
certain degree. Recently, corrugated structures and their effects on the
electronic properties of monolayer graphene have been studied extensively.
However, similar experimental studies in bilayer graphene have yet to be
reported. Here, we show that corrugations in bilayer graphene can generate
incommensurate stacking solitons (domain walls) between commensurate
Bernal-stacked domains. By using scanning tunneling microscopy, we
microscopically study electronic structures of a corrugation-induced stacking
soliton that separates two adjacent AB and BA stacked bilayer regions with a
uniform interlayer potential difference. Both topological gapless edge states
and quasi-localized gapped quantum-well-like states are observed in the
nanoscale corrugation. Atomic resolution mapping of the topological edge states
along the stacking soliton reveals the existence of intervalley scattering
between them, which could explain recent experiments where the conductance
along domain walls of gapped graphene bilayer is smaller than a quantized value
4e2 h.


title: Symplectic Lie algebras with degenerate center

abstract:
  Every symplectic Lie algebra with degenerate (including non-abelian nilpotent
symplectic Lie algebras) has the structure of a quadratic extension. We give a
standard model and describe the equivalence classes on the level of
corresponding quadratic cohomology sets. Finally, we give a scheme to classify
the isomorphism classes of symplectic Lie algebras with degenerate center. We
also give a classification scheme for nilpotent symplectic Lie algebras and
compute a complete list of all 6-dimensional nilpotent symplectic Lie algebras,
which removes some little inaccuracies in an older list.


title: Aspects of the Doubled Worldsheet

abstract:
  We clarify the relation between various approaches to the manifestly
T-duality symmetric string. We explain in detail how the PST covariant doubled
string arises from an unusual gauge fixing. We pay careful attention to the
role of "spectator" fields in this process and also show how the T-duality
invariant doubled dilaton emerges naturally. We extend these ideas to
non-Abelian T-duality and show they give rise to the duality invariant
formalism based on the semi-Abelian Drinfeld Double. We then develop the
N=(0,1) supersymmetric duality invariant formalism.


title: Electronic excitations in $\gamma$-Li$_2$IrO$_3$

abstract:
  We investigate the electronic properties of the three-dimensional
stripyhoneycomb $\gamma$-Li$_2$IrO$_3$ via relativistic density functional
theory calculations as well as exact diagonalization of finite clusters and
explore the details of the optical conductivity. Our analysis of this quantity
reveals the microscopic origin of the experimentally observed (i) optical
transitions and (ii) anisotropic behavior along the various polarization
directions. In particular we find that the optical excitations are overall
dominated by transitions between $j_\text{eff}$ = 1/2 and 3/2 states and the
weight of transitions between $j_\text{eff}$ = 1/2 states at low frequencies
can be correlated to deviations from a pure Kitaev description. We furthermore
reanalyze within this approach the electronic excitations in the known
two-dimensional honeycomb systems $\alpha$-Li$_2$IrO$_3$ and Na$_2$IrO$_3$ and
discuss the results in comparison to $\gamma$-Li$_2$IrO$_3$.


title:
A data driven equivariant approach to constrained Gaussian mixture
  modeling

abstract:
  Maximum likelihood estimation of Gaussian mixture models with different
class-specific covariance matrices is known to be problematic. This is due to
the unboundedness of the likelihood, together with the presence of spurious
maximizers. Existing methods to bypass this obstacle are based on the fact that
unboundedness is avoided if the eigenvalues of the covariance matrices are
bounded away from zero. This can be done imposing some constraints on the
covariance matrices, i.e. by incorporating a priori information on the
covariance structure of the mixture components. The present work introduces a
constrained equivariant approach, where the class conditional covariance
matrices are shrunk towards a pre-specified matrix Psi. Data-driven choices of
the matrix Psi, when a priori information is not available, and the optimal
amount of shrinkage are investigated. The effectiveness of the proposal is
evaluated on the basis of a simulation study and an empirical example.


title: Sequential pulsed laser deposition of homoepitaxial SrTiO$_3$ thin films

abstract:
  Control of thin film stoichiometry is of primary relevance to achieve desired
functionality. Pulsed laser deposition ablating from binary-oxide targets
(sequential deposition) can be applied to precisely control the film
composition, offsetting the importance of growth conditions on the film
stoichiometry. In this work, we demonstrate that the cation stoichiometry of
SrTiO$_3$ thin films can be finely tuned by sequential deposition from SrO and
TiO$_2$ targets. Homoepitaxial SrTiO$_3$ films were deposited at different
substrate temperatures and Ti/Sr pulse ratios, allowing the establishment of a
growth window for stoichiometric SrTiO$_3$. The growth kinetics and nucleation
processes were studied by reflection high-energy electron diffraction and
atomic force microscopy, providing information about the growth mode and the
degree of off-stoichiometry. At the optimal (stoichiometric) growth conditions,
films exhibit atomically flat surfaces, whereas off-stoichiometry is
accommodated by crystal defects, 3D islands and/or surface precipitates
depending on the substrate temperature and the excess cation. This technique
opens the way to precisely control stoichiometry and doping of oxide thin
films.


title:
CompAdaGrad: A Compressed, Complementary, Computationally-Efficient
  Adaptive Gradient Method

abstract:
  The adaptive gradient online learning method known as AdaGrad has seen
widespread use in the machine learning community in stochastic and adversarial
online learning problems and more recently in deep learning methods. The
method's full-matrix incarnation offers much better theoretical guarantees and
potentially better empirical performance than its diagonal version; however,
this version is computationally prohibitive and so the simpler diagonal version
often is used in practice. We introduce a new method, CompAdaGrad, that
navigates the space between these two schemes and show that this method can
yield results much better than diagonal AdaGrad while avoiding the (effectively
intractable) $O(n^3)$ computational complexity of full-matrix AdaGrad for
dimension $n$. CompAdaGrad essentially performs full-matrix regularization in a
low-dimensional subspace while performing diagonal regularization in the
complementary subspace. We derive CompAdaGrad's updates for composite mirror
descent in case of the squared $\ell_2$ norm and the $\ell_1$ norm, demonstrate
that its complexity per iteration is linear in the dimension, and establish
guarantees for the method independent of the choice of composite regularizer.
Finally, we show preliminary results on several datasets.


title: Multiple Influential Point Detection in High-Dimensional Spaces

abstract:
  Influence diagnosis is an integrated component of data analysis, but is
severely under-investigated in a high-dimensional setting. One of the key
challenges, even in a fixed-dimensional setting, is how to deal with multiple
influential points giving rise to the masking and swamping effects. This paper
proposes a novel group deletion procedure referred to as MIP by studying two
extreme statistics based on a marginal correlation based influence measure.
Named the Min and Max statistics, they have complimentary properties in that
the Max statistic is effective for overcoming the masking effect while the Min
statistic is useful for overcoming the swamping effect. Combining their
strengths, we further propose an efficient algorithm that can detect
influential points with a prespecified false discovery rate. The proposed
influential point detection procedure is simple to implement, efficient to run,
and enjoys attractive theoretical properties. Its effectiveness is verified
empirically via extensive simulation study and data analysis. An R package
implementing the procedure is freely available.


title: Stride Length Estimation with Deep Learning

abstract:
  Accurate estimation of spatial gait characteristics is critical to assess
motor impairments resulting from neurological or musculoskeletal disease.
Currently, however, methodological constraints limit clinical applicability of
state-of-the-art double integration approaches to gait patterns with a clear
zero-velocity phase. We describe a novel approach to stride length estimation
that uses deep convolutional neural networks to map stride-specific inertial
sensor data to the resulting stride length. The model is trained on a publicly
available and clinically relevant benchmark dataset consisting of 1220 strides
from 101 geriatric patients. Evaluation is done in a 10-fold cross validation
and for three different stride definitions. Even though best results are
achieved with strides defined from mid-stance to mid-stance with average
accuracy and precision of 0.01 $\pm$ 5.37 cm, performance does not strongly
depend on stride definition. The achieved precision outperforms
state-of-the-art methods evaluated on this benchmark dataset by 3.0 cm (36%).
Due to the independence of stride definition, the proposed method is not
subject to the methodological constrains that limit applicability of
state-of-the-art double integration methods. Furthermore, precision on the
benchmark dataset could be improved. With more precise mobile stride length
estimation, new insights to the progression of neurological disease or early
indications might be gained. Due to the independence of stride definition,
previously uncharted diseases in terms of mobile gait analysis can now be
investigated by re-training and applying the proposed method.


title:
SecComp: Towards Practically Defending Against Component Hijacking in
  Android Applications

abstract:
  Cross-app collaboration via inter-component communication is a fundamental
mechanism on Android. Although it brings the benefits such as functionality
reuse and data sharing, a threat called component hijacking is also introduced.
By hijacking a vulnerable component in victim apps, an attack app can escalate
its privilege for originally prohibited operations. Many prior studies have
been performed to understand and mitigate this issue, but component hijacking
remains a serious open problem in the Android ecosystem due to no effective
defense deployed in the wild. In this paper, we present our vision on
practically defending against component hijacking in Android apps. First, we
argue that to fundamentally prevent component hijacking, we need to switch from
the previous mindset (i.e., performing system-level control or repackaging
vulnerable apps after they are already released) to a more proactive version
that aims to help security-inexperienced developers make secure components in
the first place. To this end, we propose to embed into apps a secure component
library (SecComp), which performs in-app mandatory access control on behalf of
app components. An important factor for SecComp to be effective is that we find
it is possible to devise a set of practical in-app policies to stop component
hijacking. Furthermore, we allow developers design custom policies, beyond our
by-default generic policies, to support more fine-grained access control. We
have overcome challenges to implement a preliminary SecComp prototype, which
stops component hijacking with very low performance overhead. We hope the
future research that fully implements our vision can eventually help real-world
apps get rid of component hijacking.


title:
Sensor-based Gait Parameter Extraction with Deep Convolutional Neural
  Networks

abstract:
  Measurement of stride-related, biomechanical parameters is the common
rationale for objective gait impairment scoring. State-of-the-art double
integration approaches to extract these parameters from inertial sensor data
are, however, limited in their clinical applicability due to the underlying
assumptions. To overcome this, we present a method to translate the abstract
information provided by wearable sensors to context-related expert features
based on deep convolutional neural networks. Regarding mobile gait analysis,
this enables integration-free and data-driven extraction of a set of 8
spatio-temporal stride parameters. To this end, two modelling approaches are
compared: A combined network estimating all parameters of interest and an
ensemble approach that spawns less complex networks for each parameter
individually. The ensemble approach is outperforming the combined modelling in
the current application. On a clinically relevant and publicly available
benchmark dataset, we estimate stride length, width and medio-lateral change in
foot angle up to ${-0.15\pm6.09}$ cm, ${-0.09\pm4.22}$ cm and ${0.13 \pm
3.78^\circ}$ respectively. Stride, swing and stance time as well as heel and
toe contact times are estimated up to ${\pm 0.07}$, ${\pm0.05}$, ${\pm 0.07}$,
${\pm0.07}$ and ${\pm0.12}$ s respectively. This is comparable to and in parts
outperforming or defining state-of-the-art. Our results further indicate that
the proposed change in methodology could substitute assumption-driven
double-integration methods and enable mobile assessment of spatio-temporal
stride parameters in clinically critical situations as e.g. in the case of
spastic gait impairments.


title:
Generation of Spatially Embedded Random Networks to Model Complex
  Transportation Networks

abstract:
  Random networks are increasingly used to analyse complex transportation
networks, such as airline routes, roads and rail networks. So far, this
research has been focused on describing the properties of the networks with the
help of random networks, often without considering their spatial properties. In
this article, a methodology is proposed to create random networks conserving
their spatial properties. The produced random networks are not intended to be
an accurate model of the real-world network being investigated, but are to be
used to gain insight into the functioning of the network taking into
consideration its spatial properties, which has potential to be useful in many
types of analysis, e.g. estimating the network related risk. The proposed
methodology combines a spatial non-homogeneous point process for vertex
creation, which accounts for the spatial distribution of vertices, considering
clustering effects of the network and a hybrid connection model for the edge
creation. To illustrate the ability of the proposed methodology to be used to
gain insight into a real world network, it is used to estimate standard
structural statistics for part of the Swiss road network, and these are then
compared with the known values.


title:
Measures with predetermined regularity and inhomogeneous self-similar
  sets

abstract:
  We show that if $X$ is a uniformly perfect complete metric space satisfying
the finite doubling property, then there exists a fully supported measure with
lower regularity dimension as close to the lower dimension of $X$ as we wish.
Furthermore, we show that, under the condensation open set condition, the lower
dimension of an inhomogeneous self-similar set $E_C$ coincides with the lower
dimension of the condensation set $C$, while the Assouad dimension of $E_C$ is
the maximum of the Assouad dimensions of the corresponding self-similar set $E$
and the condensation set $C$. If the Assouad dimension of $C$ is strictly
smaller than the Assouad dimension of $E$, then the upper regularity dimension
of any measure supported on $E_C$ is strictly larger than the Assouad dimension
of $E_C$. Surprisingly, the corresponding statement for the lower regularity
dimension fails.


title: Augmented Lagrangian finite element methods for contact problems

abstract:
  We propose two different Lagrange multiplier methods for contact problems
derived from the augmented Lagrangian variational formulation. Both the
obstacle problem, where a constraint on the solution is imposed in the bulk
domain and the Signorini problem, where a lateral contact condition is imposed
are considered. We consider both continuous and discontinuous approximation
spaces for the Lagrange multiplier. In the latter case the method is unstable
and a penalty on the jump of the multiplier must be applied for stability. We
prove the existence and uniqueness of discrete solutions, best approximation
estimates and convergence estimates that are optimal compared to the regularity
of the solution.


title: The new ABMP16 PDF

abstract:
  We present an update of the ABM12 PDF analysis including improved constraints
due to the final version of the inclusive DIS HERA data, the Tevatron and LHC
data on the W- and Z-production and those on heavy-quark production in the
electron- and neutrino-induced DIS at HERA and the fixed-target experiments
NOMAD and CHORUS. We also check the impact of the Tevatron and LHC top-quark
production data on the PDFs and the strong coupling constant. We obtain
$\alpha_s(M_Z)=0.1145(9)$ and $0.1147(8)$ with and without the top-quark data
included, respectively.


title:
How to get high order without loosing efficiency for the resolution of
  systems of nonlinear equations: A short review of Shamanskii's m method

abstract:
  We present relations between some recently proposed methods for the solution
of a nonlinear system of equations. In particular, we review the Shamanskii's m
method, that is an iterative method derived from Newton's method that converge
with order m+1. We discuss efficient implementation of this method via matrix
factorization and some relevant properties. We believe that recent developments
in the research of solutions of systems of equations did not take sufficiently
into account this method. The hope, with this paper, is to encourage the entire
community to remember this simple method and use it for comparison when new
methods are introduced.
  This work is dedicated to Prof. Elvira Russo: a very special teacher.


title: The HI/OH/Recombination line survey of the inner Milky Way (THOR)

abstract:
  Context: The past decade has witnessed a large number of Galactic plane
surveys at angular resolutions below 20". However, no comparable
high-resolution survey exists at long radio wavelengths around 21cm in line and
continuum emission. Methods: Employing the Very Large Array (VLA) in the
C-array configuration and a large program, we observe the HI 21cm line, four OH
lines, nineteen Halpha radio recombination lines as well as the continuum
emission from 1 to 2GHz in full polarization over a large part of the first
Galactic quadrant. Results: Covering Galactic longitudes from 14.5 to 67.4deg
and latitudes between +-1.25deg, we image all of these lines and the continuum
at ~20" resolution. These data allow us to study the various components of the
interstellar medium (ISM): from the atomic phase, traced by the HI line, to the
molecular phase, observed by the OH transitions, to the ionized medium,
revealed by the cm continuum and the Halpha radio recombination lines.
Furthermore, the polarized continuum emission enables magnetic field studies.
In this overview paper, we discuss the survey outline and present the first
data release as well as early results from the different datasets. We now
release the first half of the survey; the second half will follow later after
the ongoing data processing has been completed. The data in fits format
(continuum images and line data cubes) can be accessed through the project
web-page http://www.mpia.de/thor. Conclusions: The HI/OH/Recombination line
survey of the Milky Way (THOR) opens a new window to the different parts of the
ISM. It enables detailed studies of molecular cloud formation, conversion of
atomic to molecular gas, and feedback from HII regions as well as the magnetic
field in the Milky Way. It is highly complementary to other surveys of our
Galaxy, and comparing different datasets allows us to address many open
questions.


title: Hearables: Multimodal physiological in-ear sensing

abstract:
  Future health systems require the means to assess and track the neural and
physiological function of a user over long periods of time and in the
community. Human body responses are manifested through multiple modalities,
such as the mechanical, electrical and chemical; yet current physiological
monitors (actigraphy, heart rate) largely lack in both the desired cross-modal
and non-stigmatizing aspects. We address these challenges through an
inconspicuous and comfortable earpiece, equipped with miniature multimodal
sensors, which benefits from the relatively stable position of the ear canal
with respect to vital organs to robustly measure the brain, cardiac and
respiratory functions. Comprehensive experiments validate each modality within
the proposed earpiece, while its potential in health monitoring is illustrated
through case studies. We further demonstrate how combining data from multiple
sensors within such an integrated wearable device improves both the accuracy of
measurements and the ability to deal with artifacts in real-life scenarios.


title:
The spacetime outside a source of gravitational radiation: The axially
  symmetric null fluid

abstract:
  We carry out a study of the exterior of an axially and reflection symmetric
source of gravitational radiation. The exterior of such a source is filled with
a null fluid produced by the dissipative processes inherent to the emission of
gravitational radiation, thereby representing a generalization of the Vaidya
metric for axially and reflection symmetric spacetimes. The role of the
vorticity, and its relationship with the presence of gravitational radiation is
put in evidence. The spherically symmetric case (Vaidya) is, asymptotically,
recovered within the context of the $1+3$ formalism.


title: Periodically-driven Kondo impurity in nonequilibrium steady states

abstract:
  We study the nonequilibrium dynamics of a periodically-driven anisotropic
Kondo impurity model. The periodic time dependence is introduced for a local
magnetic field which couples to the impurity spin and also for an in-plane
exchange interaction. We obtain the exact results on the time evolution for
arbitrary periodic time dependence at the special point in the parameter space
known as the Toulouse limit. We first consider a specific case where the local
magnetic field is periodically switched on and off. When the driving period is
much shorter than the inverse of the Kondo temperature, an intriguing
oscillating behavior (resonance phenomenon) emerges in the time average of the
impurity spin polarization with increasing the local magnetic field intensity.
By taking the high frequency limit of the external driving, we elucidate that
the system recovers the translational invariance in time and can be described
by a mixture of the zero-temperature and infinite-temperature properties. In
certain cases, the system is governed by either the zero-temperature or
infinite-temperature properties, and therefore can be properly described by the
corresponding equilibrium state.


title: On Generation of Time-based Label Refinements

abstract:
  Process mining is a research field focused on the analysis of event data with
the aim of extracting insights in processes. Applying process mining techniques
on data from smart home environments has the potential to provide valuable
insights in (un)healthy habits and to contribute to ambient assisted living
solutions. Finding the right event labels to enable application of process
mining techniques is however far from trivial, as simply using the triggering
sensor as the label for sensor events results in uninformative models that
allow for too much behavior (overgeneralizing). Refinements of sensor level
event labels suggested by domain experts have shown to enable discovery of more
precise and insightful process models. However, there exist no automated
approach to generate refinements of event labels in the context of process
mining. In this paper we propose a framework for automated generation of label
refinements based on the time attribute of events. We show on a case study with
real life smart home event data that behaviorally more specific, and therefore
more insightful, process models can be found by using automatically generated
refined labels in process discovery.


title:
Neutral Kaon Mixing Beyond the Standard Model with $n_f=2+1$ Chiral
  Fermions Part 1: Bare Matrix Elements and Physical Results

abstract:
  We compute the hadronic matrix elements of the four-quark operators relevant
for $K^0-{\bar K^0}$ mixing beyond the Standard Model. Our results are from
lattice QCD simulations with $n_f=2+1$ flavours of domain-wall fermion, which
exhibit continuum-like chiral-flavour symmetry. The simulations are performed
at two different values of the lattice spacing ($a\sim0.08$ and $a\sim 0.11 \,
\fm $) and with lightest unitary pion mass $\sim 300\, \MeV$. For the first
time, the full set of relevant four-quark operators is renormalised
non-perturbatively through RI-SMOM schemes; a detailed description of the
renormalisation procedure is presented in a companion paper. We argue that the
intermediate renormalisation scheme is responsible for the discrepancies found
by different collaborations. We also study different normalisations and
determine the matrix elements of the relevant four-quark operators with a
precision of $\sim 5\%$ or better.


title: Spin excitations in hole-overdoped iron-based superconductors

abstract:
  Understanding the overall features of magnetic excitation is essential for
clarifying the mechanism of Cooper pair formation in iron-based
superconductors. In particular, clarifying the relationship between magnetism
and superconductivity is a central challenge because magnetism may play a key
role in their exotic superconductivity. BaFe2As2 is one of ideal systems for
such investigation because its superconductivity can be induced in several
ways, allowing a comparative examination. Here we report a study on the spin
fluctuations of the hole-overdoped iron-based superconductors Ba1-xKxFe2As2 (x
= 0.5 and 1.0; Tc = 36 K and 3.4 K, respectively) over the entire Brillouin
zone using inelastic neutron scattering. We find that their spin spectra
consist of spin wave and chimney-like dispersions. The chimney-like dispersion
can be attributed to the itinerant character of magnetism. The band width of
the spin wave-like dispersion is almost constant from the non-doped to
optimum-doped region, which is followed by a large reduction in the overdoped
region. This suggests that the superconductivity is suppressed by the reduction
of magnetic exchange couplings, indicating a strong relationship between
magnetism and superconductivity in iron-based superconductors.


title: Cosmogenic photons strongly constrain UHECR source models

abstract:
  With the newest version of our Monte Carlo code for ultra-high-energy cosmic
ray (UHECR) propagation, CRPropa 3, the flux of neutrinos and photons due to
interactions of UHECRs with extragalactic background light can be predicted.
Together with the recently updated data for the isotropic diffuse gamma-ray
background (IGRB) by Fermi LAT, it is now possible to severely constrain UHECR
source models. The evolution of the UHECR sources especially plays an important
role in the determination of the expected secondary photon spectrum. Pure
proton UHECR models are already strongly constrained, primarily by the highest
energy bins of Fermi LAT's IGRB, as long as their number density is not
strongly peaked at recent times.


title:
Identification of Human Proteins vulnerable to multiple Organisms and
  their disease associations

abstract:
  While most studies emphasize on certain aspects of Pathogen-Host Interactions
(PHI), such as the preferential attachment of bacteria or virus to its human
receptor homolog, studies have attempted to methodically classify interactions
among pathogenic proteins and their host proteins. Here we have analyzed 182
pathogens from The Pathogen-Host Interaction Search Tool (PHISTO) and could
identify the proteins/protein coding genes that act on both virus and bacteria.
Importantly there were few proteins viz. P53 (Tumor protein p53), NFKB1
(Nuclear factor of kappa light polypeptide gene enhancer in B-cells 1), GBLP
(Guanine nucleotide-binding protein subunit beta-2-like-1), TOX4 (TOX high
mobility group box family member 4), PDIA1 (Protein disulfide-isomerase
precursor), MHY9 (Myosin 9), RAC1 (Ras-related C3 botulinum toxin substrate 1),
CCAR2 (Cell cycle and apoptosis regulator protein 2) and ILF3 (Interleukin
enhancer binding factor 3) that were more susceptible to both bacterial and
viral pathogens. Identification of such important interacting proteins (IIPs)
can elicit significant insights for better understanding the molecular
mechanisms of such pathogens that interact with the human host.


title: Knowing Values and Public Inspection

abstract:
  We present a basic dynamic epistemic logic of "knowing the value". Analogous
to public announcement in standard DEL, we study "public inspection", a new
dynamic operator which updates the agents' knowledge about the values of
constants. We provide a sound and strongly complete axiomatization for the
single and multi-agent case, making use of the well-known Armstrong axioms for
dependencies in databases.


title: Electric-field tunable Dirac semimetal state in phosphorene thin films

abstract:
  We study the electric-field tunable electronic properties of phosphorene thin
films, using the framework of density functional theory. We show that
phosphorene thin films offer a versatile material platform to study two
dimensional Dirac fermions on application of a transverse electric field.
Increasing the strength of the transverse electric field beyond a certain
critical value in phosphorene leads to the formation of two symmetry protected
gapless Dirac fermions states with anisotropic energy dispersion. The
spin-orbit coupling splits each of these Dirac state into two spin- polarized
Dirac cones which are also protected by non-symmorphic crystal symmetries. Our
study shows that the position as well as the carrier velocity of the spin
polarized Dirac cone states can be controlled by the strength of the external
electric field.


title: Shadow couplings

abstract:
  A classical result of Strassen asserts that given probabilities $\mu, \nu$ on
the real line which are in convex order, there exists a \emph{martingale
coupling} with these marginals, i.e.\ a random vector $(X_1,X_2)$ such that
$X_1\sim \mu, X_2\sim \nu$ and $E[X_2|X_1]=X_1$. Remarkably, it is a non
trivial problem to construct particular solutions to this problem. In this
article, we introduce a family of such martingale couplings, each of which
admits several characterizations in terms of optimality properties / geometry
of the support set / representation through a Skorokhod embedding. As a
particular element of this family we recover the (left-) curtain martingale
transport, which has recently been studied \cite{BeJu16, HeTo13, CaLaMa14,
BeHeTo15} and which can be viewed as a martingale analogue of the classical
monotone rearrangement. As another canonical element of this family we identify
a martingale coupling that resembles the usual \emph{product coupling} and
appears as an optimizer in the general transport problem recently introduced by
Gozlan et al. In addition, this coupling provides an explicit example of a
Lipschitz-kernel, shedding new light on Kellerer's proof of the existence of
Markov martingales with specified marginals.


title:
Formalized Confluence of Quasi-Decreasing, Strongly Deterministic
  Conditional TRSs

abstract:
  We present an Isabelle/HOL formalization of a characterization of confluence
for quasi-reductive strongly deterministic conditional term rewrite systems,
due to Avenhaus and Lor\'ia-S\'aenz.


title:
Multiple resonances of a moving, oscillating surface disturbance on a
  shear current

abstract:
  We consider waves radiated by a disturbance of oscillating strength moving at
constant velocity along the free surface of a shear flow which, when
undisturbed, has uniform horizontal vorticity of magnitude $S$. When no current
is present the problem is a classical one and much studied, and in deep water a
resonance is known to occur when $\tau=|\boldsymbol{V}|\omega_0/g$ equals the
critical value $1/4$ ($\boldsymbol{V}$: velocity of disturbance, $\omega_0$:
oscillation frequency, $g$: gravitational acceleration). We show that the
presence of the sub-surface shear current can change this picture radically.
Not only does the resonant value of $\tau$ depend strongly on the angle between
$\boldsymbol{V}$ and the current's direction and the "shear-Froude number"
$\mathrm{Frs}=|\boldsymbol{V}|S/g$; when $\mathrm{Frs}>1/3$, multiple resonant
values --- as many as $4$ --- can occur for some directions of motion. At
sufficiently large values of $\mathrm{Frs}$, the smallest resonance frequency
tends to zero, representing the phenomenon of critical velocity for ship waves.
We provide a detailed analysis of the dispersion relation for the moving,
oscillating disturbance, in both finite and infinite water depth, including for
the latter case an overview of the different far-field waves which exist in
different sectors of wave vector space under different conditions. Owing to the
large number of parameters, a detailed discussion of the structure of
resonances is provided for infinite depth only, where analytical results are
available.


title:
Elastic scattering, polarization and absorption of relativistic
  antiprotons on nuclei

abstract:
  We perform Glauber model calculations of the antiproton-nucleus elastic and
quasielastic scattering and absorption in the beam momentum range $\sim
0.5\div10$ GeV/c. A good agreement of our calculations with available LEAR data
and with earlier Glauber model studies of the $\bar p A$ elastic scattering
allows us to make predictions at the beam momenta of $\sim 10$ GeV/c, i.e. at
the regime of the PANDA experiment at FAIR. The comparison with the
proton-nucleus elastic scattering cross sections shows that the diffractive
minima are much deeper in the $\bar p A$ case due to smaller absolute value of
the ratio of the real-to-imaginary part of the elementary elastic amplitude.
Significant polarization signal for $\bar p A$ elastic scattering at 10 GeV/c
is expected. We have also revealed a strong dependence of the $\bar p A$
absorption cross section on the slope parameter of the transverse momentum
dependence of the elementary $\bar pN$ amplitude. The $\bar p A$ optical
potential is discussed.


title:
Finite-sample and asymptotic analysis of generalization ability with an
  application to penalized regression

abstract:
  In this paper, we study the performance of extremum estimators from the
perspective of generalization ability (GA): the ability of a model to predict
outcomes in new samples from the same population. By adapting the classical
concentration inequalities, we derive upper bounds on the empirical
out-of-sample prediction errors as a function of the in-sample errors,
in-sample data size, heaviness in the tails of the error distribution, and
model complexity. We show that the error bounds may be used for tuning key
estimation hyper-parameters, such as the number of folds $K$ in
cross-validation. We also show how $K$ affects the bias-variance trade-off for
cross-validation. We demonstrate that the $\mathcal{L}_2$-norm difference
between penalized and the corresponding un-penalized regression estimates is
directly explained by the GA of the estimates and the GA of empirical moment
conditions. Lastly, we prove that all penalized regression estimates are
$L_2$-consistent for both the $n \geqslant p$ and the $n < p$ cases.
Simulations are used to demonstrate key results.
  Keywords: generalization ability, upper bound of generalization error,
penalized regression, cross-validation, bias-variance trade-off,
$\mathcal{L}_2$ difference between penalized and unpenalized regression, lasso,
high-dimensional data.

categories: stat.ML cs.LG math.ST q-fin.EC stat.CO stat.TH


title: A Characterization of Quasi-Decreasingness

abstract:
  In 2010 Schernhammer and Gramlich showed that quasi-decreasingness of a DCTRS
R is equivalent to \mu-termination of its context-sensitive unraveling Ucs(R)
on original terms. While the direction that quasi-decreasingness of R implies
\mu-termination of Ucs(R) on original terms is shown directly; the converse -
facilitating the use of context-sensitive termination tools like MU-TERM and
VMTL - employs the additional notion of context-sensitive quasi-reductivity of
R. In the following, we give a direct proof of the fact that \mu-termination of
Ucs(R) on original terms implies quasi-decreasingness of R. Moreover, we report
our experimental findings on DCTRSs from the confluence problems database
(Cops), extending the experiments of Schernhammer and Gramlich.


title: A Meaning-oriented Approach to Semantic Data Modeling

abstract:
  Semantic information is often represented as the entities and the
relationships among them with conventional semantic models. This approach is
straightforward but is not suitable for many posteriori requests in semantic
data modeling. In this paper, we propose a meaning-oriented approach to
modeling semantic data and establish a graph-based semantic data model. In this
approach we use the meanings, i.e., the subjective views of the entities and
relationships, to describe the semantic information, and use the semantic
graphs containing the meaning nodes and the meta-meaning relations to specify
the taxonomy and the compound construction of the semantic concepts. We
demonstrate how this meaning-oriented approach can address many important
semantic representation issues, including dynamic specialization and natural
join.


title:
An ECM-based energy-efficiency optimization approach for
  bandwidth-limited streaming kernels on recent Intel Xeon processors

abstract:
  We investigate an approach that uses low-level analysis and the
execution-cache-memory (ECM) performance model in combination with tuning of
hardware parameters to lower energy requirements of memory-bound applications.
The ECM model is extended appropriately to deal with software optimizations
such as non-temporal stores. Using incremental steps and the ECM model, we
analytically quantify the impact of various single-core optimizations and
pinpoint microarchitectural improvements that are relevant to energy
consumption. Using a 2D Jacobi solver as example that can serve as a blueprint
for other memory-bound applications, we evaluate our approach on the four most
recent Intel Xeon E5 processors (Sandy Bridge-EP, Ivy Bridge-EP, Haswell-EP,
and Broadwell-EP). We find that chip energy consumption can be reduced in the
range of 2.0-2.4$\times$ on the examined processors.


title: A Threshold-based Scheme for Reinforcement Learning in Neural Networks

abstract:
  A generic and scalable Reinforcement Learning scheme for Artificial Neural
Networks is presented, providing a general purpose learning machine. By
reference to a node threshold three features are described 1) A mechanism for
Primary Reinforcement, capable of solving linearly inseparable problems 2) The
learning scheme is extended to include a mechanism for Conditioned
Reinforcement, capable of forming long term strategy 3) The learning scheme is
modified to use a threshold-based deep learning algorithm, providing a robust
and biologically inspired alternative to backpropagation. The model may be used
for supervised as well as unsupervised training regimes.


title: Upper bounds for some Brill-Noether loci over a finite field

abstract:
  Let C be a smooth projective algebraic curve of genus g over the finite field
F_q. A classical result of H. Martens states that the Brill-Noether locus of
line bundles L in Pic^d C with deg L = d and h^0(L) >= i is of dimension at
most d-2i+2, under conditions that hold when such an L is both effective and
special. We show that the number of such L that are rational over F_q is
bounded above by K_g q^(d-2i+2), with an explicit constant K_g that grows
exponentially with g. Our proof uses the Weil estimates for function fields,
and is independent of Martens' theorem. We apply this bound to give a precise
lower bound of the form 1 - K'_g/q for the probability that a line bundle in
(Pic^(g+1) C)(F_q) is base point free. This gives an effective version over
finite fields of the usual statement that a general line bundle of degree g+1
is base point free. This is applicable to the author's work on fast Jacobian
group arithmetic for typical divisors on curves.


title:
Gamma-ray opacity of the anisotropic stratified broad-line regions in
  blazars

abstract:
  The GeV-range spectra of blazars are shaped not only by non-thermal emission
processes internal to the relativistic jet but also by external pair-production
absorption on the thermal emission of the accretion disc and the broad-line
region (BLR). For the first time, we compute here the pair-production opacities
in the GeV range produced by a realistic BLR accounting for the radial
stratification and radiation anisotropy. Using photoionization modelling with
the CLOUDY code, we calculate a series of BLR models of different sizes,
geometries, cloud densities, column densities and metallicities. The strongest
emission features in the model BLR are Ly$\alpha$ and HeII Ly$\alpha$.
Contribution of recombination continua is smaller, especially for hydrogen,
because Ly continuum is efficiently trapped inside the large optical depth BLR
clouds and converted to Lyman emission lines and higher-order recombination
continua. The largest effects on the gamma-ray opacity are produced by the BLR
geometry and localization of the gamma-ray source. We show that when the
gamma-ray source moves further from the central source, all the absorption
details move to higher energies and the overall level of absorption drops
because of decreasing incidence angles between the gamma-rays and BLR photons.
The observed positions of the spectral breaks can be used to measure the
geometry and the location of the gamma-ray emitting region relative to the BLR.
Strong dependence on geometry means that the soft photons dominating the
pair-production opacity may be actually produced by a different population of
BLR clouds than the bulk of the observed broad line emission.


title: XYZ-like Spectra from Laplace Sum Rule at N2LO in the Chiral Limit

abstract:
  We present new compact integrated expressions of QCD spectral functions of
heavy-light molecules and four-quark $XYZ$-like states at lowest order (LO) of
perturbative (PT) QCD and up to $d=8$ condensates of the Operator Product
Expansion (OPE). Then, by including up to next-to-next leading order (N2LO) PT
QCD corrections, which we have estimated by assuming the factorization of the
four-quark spectral functions, we improve previous LO results from QCD spectral
sum rules (QSSR), on the $XYZ$-like masses and decay constants which suffer
from the ill-defined heavy quark mass. PT N3LO corrections are estimated using
a geometric growth of the PT series and are included in the systematic errors.
Our optimal results based on stability criteria are summarized in Tables 11 to
14 and compared, in Section 10, with experimental candidates and some LO QSSR
results. We conclude that the masses of the $XZ$ observed states are compatible
with (almost) pure $J^{PC}=1^{+\pm}, 0^{++}$ molecule or/and four-quark states.
The ones of the $1^{-\pm}, 0^{-\pm}$ molecule / four-quark states are about 1.5
GeV above the $Y_{c,b}$ mesons experimental candidates and hadronic thresholds.
We also find that the couplings of these exotics to the associated
interpolating currents are weaker than that of ordinary $D,B$ mesons
($f_{DD}\approx 10^{-3}f_D$) and may behave numerically as $1/ \bar m_b^{3/2}$
(resp. $1/ \bar m_b$) for the $1^{+},0^{+}$ (resp. $1^{-}, 0^{-}$) states which
can stimulate further theoretical studies of these decay constants.


title: Formation of a molecular ion by photoassociative Raman processes

abstract:
  We show theoretically that it is possible to form a cold molecular ion from a
pair of colliding atom and ion at low energy by photoassociative two-photon
Raman processes. We explore the possibility of stimulated Raman adiabatic
passage (STIRAP) from the continuum of ion-atom scattering states to an ionic
molecular state. We provide physical conditions under which coherent population
transfer is possible in stimulated Raman photoassociation. Our results are
important for experimental realization of PA in ion-atom cold collisions.


title:
Instantaneous charge state of Uranium projectiles in fully ionized
  plasmas from energy loss experiments

abstract:
  The instantaneous charge state of uranium ions traveling through a fully
ionized hydrogen plasma has been theoretically studied and compared with one of
the first energy loss experiments in plasmas, carried out at GSI-Darmstadt by
Hoffmann \textit{et al.} in the 90's. For this purpose, two different methods
to estimate the instantaneous charge state of the projectile have been
employed: (1) rate equations using ionization and recombination cross sections,
and (2) equilibrium charge state formulas for plasmas. Also, the equilibrium
charge state has been obtained using these ionization and recombination cross
sections, and compared with the former equilibrium formulas. The equilibrium
charge state of projectiles in plasmas is not always reached, it depends mainly
on the projectile velocity and the plasma density. Therefore, a non-equilibrium
or an instantaneous description of the projectile charge is necessary. The
charge state of projectile ions cannot be measured, except after exiting the
target, and experimental data remain very scarce. Thus, the validity of our
charge state model is checked by comparing the theoretical predictions with an
energy loss experiment, as the energy loss has a generally quadratic dependence
on the projectile charge state. The dielectric formalism has been used to
calculate the plasma stopping power including the Brandt-Kitagawa (BK) model to
describe the charge distribution of the projectile. In this charge
distribution, the instantaneous number of bound electrons instead of the
equilibrium number has been taken into account. Comparing our theoretical
predictions with experiments, it is shown the necessity of including the
instantaneous charge state and the BK charge distribution for a correct energy
loss estimation. The results also show that the initial charge state has a
strong influence in order to estimate the energy loss of the uranium ions.


title:
Search for Two-Neutrino Double Electron Capture of $^{124}$Xe with
  XENON100

abstract:
  Two-neutrino double electron capture is a rare nuclear decay where two
electrons are simultaneously captured from the atomic shell. For $^{124}$Xe
this process has not yet been observed and its detection would provide a new
reference for nuclear matrix element calculations. We have conducted a search
for two-neutrino double electron capture from the K-shell of $^{124}$Xe using
7636 kg$\cdot$d of data from the XENON100 dark matter detector. Using a
Bayesian analysis we observed no significant excess above background, leading
to a lower 90 % credibility limit on the half-life $T_{1/2}>6.5\times10^{20}$
yr. We also evaluated the sensitivity of the XENON1T experiment, which is
currently being commissioned, and find a sensitivity of
$T_{1/2}>6.1\times10^{22}$ yr after an exposure of 2 t$\cdot$yr.


title:
Low-Rank Tensor Decomposition-Aided Channel Estimation for Millimeter
  Wave MIMO-OFDM Systems

abstract:
  We consider the problem of downlink channel estimation for millimeter wave
(mmWave) MIMO-OFDM systems, where both the base station (BS) and the mobile
station (MS) employ large antenna arrays for directional precoding/beamforming.
Hybrid analog and digital beamforming structures are employed in order to offer
a compromise between hardware complexity and system performance. Different from
most existing studies that are concerned with narrowband channels, we consider
estimation of wideband mmWave channels with frequency selectivity, which is
more appropriate for mmWave MIMO-OFDM systems. By exploiting the sparse
scattering nature of mmWave channels, we propose a CANDECOMP/PARAFAC (CP)
decomposition-based method for channel parameter estimation (including angles
of arrival/departure, time delays, and fading coefficients). In our proposed
method, the received signal at the BS is expressed as a third-order tensor. We
show that the tensor has the form of a low-rank CP decomposition, and the
channel parameters can be estimated from the associated factor matrices. Our
analysis reveals that the uniqueness of the CP decomposition can be guaranteed
even when the size of the tensor is small. Hence the proposed method has the
potential to achieve substantial training overhead reduction. We also develop
Cramer-Rao bound (CRB) results for channel parameters, and compare our proposed
method with a compressed sensing-based method. Simulation results show that the
proposed method attains mean square errors that are very close to their
associated CRBs, and presents a clear advantage over the compressed
sensing-based method in terms of both estimation accuracy and computational
complexity.


title:
H$\alpha$ Imaging spectroscopy of Balmer-dominated shocks in Tycho's
  supernova remnant

abstract:
  We present Fabry-P\'erot interferometric observations of the narrow H$\alpha$
component in the shock front of the historical supernova remnant Tycho (SN
1572). Using GH$\alpha$FaS (Galaxy H$\alpha$ Fabry-P\'erot Spectrometer) on the
William Herschel Telescope, we observed a great portion of the shock front in
the northeastern (NE) region of the remnant. The angular resolution of
$\sim$1$^{\prime\prime}$ and spectral resolving power of R$\sim$21 000 together
with the large field-of-view (3.4$^{\prime}$ $\times$ 3.4$^{\prime}$) of the
instrument allow us to measure the narrow H$\alpha$-line width in 73 bins
across individual parts of the shock simultaneously and thereby study the
indicators of several shock precursors in a large variety of shock front
conditions. Compared to previous studies, the detailed spatial resolution of
the filament also allows us to mitigate possible artificial broadening of the
line from unresolved differential motion and projection. Covering one quarter
of the remnant's shell, we confirm the broadening of the narrow H$\alpha$ line
beyond its intrinsic width of $\sim$20 km/s and report it to extend over most
of the filament, not only the previously investigated dense 'knot g'.
Similarly, we confirm and find additional strong evidence for wide-spread
intermediate-line ($\sim$150 km/s) emission. Our Bayesian analysis approach
allows us to quantify the evidence for this intermediate component as well as a
possible split in the narrow line. Suprathermal narrow line widths point toward
an additional heating mechanism in the form of a cosmic-ray precursor, while
the intermediate component, previously only qualitatively reported as a small
non-Gaussian contribution to the narrow component, reveals a broad-neutral
precursor.


title:
Modelling Creativity: Identifying Key Components through a Corpus-Based
  Approach

abstract:
  Creativity is a complex, multi-faceted concept encompassing a variety of
related aspects, abilities, properties and behaviours. If we wish to study
creativity scientifically, then a tractable and well-articulated model of
creativity is required. Such a model would be of great value to researchers
investigating the nature of creativity and in particular, those concerned with
the evaluation of creative practice. This paper describes a unique approach to
developing a suitable model of how creative behaviour emerges that is based on
the words people use to describe the concept. Using techniques from the field
of statistical natural language processing, we identify a collection of
fourteen key components of creativity through an analysis of a corpus of
academic papers on the topic. Words are identified which appear significantly
often in connection with discussions of the concept. Using a measure of lexical
similarity to help cluster these words, a number of distinct themes emerge,
which collectively contribute to a comprehensive and multi-perspective model of
creativity. The components provide an ontology of creativity: a set of building
blocks which can be used to model creative practice in a variety of domains.
The components have been employed in two case studies to evaluate the
creativity of computational systems and have proven useful in articulating
achievements of this work and directions for further research.


title: Gain and loss of esteem, direct reciprocity and Heider balance

abstract:
  The effect of gain and loss of esteem is introduced into the equations of
time evolution of social relations, hostile or friendly, in a group of actors.
The equations allow for asymmetric relations. We prove that in the presence of
this asymmetry, the majority of stable solutions are jammed states, i.e. the
Heider balance is not attained there. A phase diagram is constructed with three
phases: the jammed phase, the balanced phase with two mutually hostile groups,
and the phase of so-called paradise, where all relations are friendly.


title: Manipulating nanoscale atom-atom interactions with cavity QED

abstract:
  We theoretically explore manipulation of interactions between excited and
ground state atoms at nanoscale separations by cavity quantum electrodynamics
(CQED). We develop an adiabatic molecular dressed state formalism and show that
it is possible to generate Fano-Feshbach resonances between ground and
long-lived excited-state atoms inside a cavity. The resonances are shown to
arise due to non-adiabatic coupling near a pseudo-crossing between the dressed
state potentials. We illustrate our results with a model study using fermionic
$^{171}$Yb atoms in a two-modal cavity. Our study is important for manipulation
of interatomic interactions at low energy by cavity field.


title: Hermes: Global plasma edge fluid turbulence simulations

abstract:
  The transport of heat and particles in the relatively collisional edge
regions of magnetically confined plasmas is a scientifically challenging and
technologically important problem. Understanding and predicting this transport
requires the self-consistent evolution of plasma fluctuations, global profiles
and flows, but the numerical tools capable of doing this in realistic
(diverted) geometry are only now being developed. Here a 5-field reduced
2-fluid plasma model for the study of instabilities and turbulence in
magnetised plasmas is presented, built on the BOUT++ framework. This cold ion
model allows the evolution of global profiles, electric fields and flows on
transport timescales, with flux-driven cross-field transport determined
self-consistently by electromagnetic turbulence. Developments in the model
formulation and numerical implementation are described, and simulations are
performed in poloidally limited and diverted tokamak configurations.


title: Devito: Towards a generic Finite Difference DSL using Symbolic Python

abstract:
  Domain specific languages (DSL) have been used in a variety of fields to
express complex scientific problems in a concise manner and provide automated
performance optimization for a range of computational architectures. As such
DSLs provide a powerful mechanism to speed up scientific Python computation
that goes beyond traditional vectorization and pre-compilation approaches,
while allowing domain scientists to build applications within the comforts of
the Python software ecosystem. In this paper we present Devito, a new finite
difference DSL that provides optimized stencil computation from high-level
problem specifications based on symbolic Python expressions. We demonstrate
Devito's symbolic API and performance advantages over traditional Python
acceleration methods before highlighting its use in the scientific context of
seismic inversion problems.


title: Water Fibers

abstract:
  Fibers constitute the backbone of modern communication and are used in laser
surgeries; fibers also genarate coherent X-ray, guided-sound and
supercontinuum. In contrast, fibers for capillary oscillations, which are
unique to liquids, were rarely considered in optofluidics. Here we fabricate
fibers by water bridging an optical tapered-coupler to a microlensed coupler.
Our water fibers are held in air and their length can be longer than a
millimeter. These hybrid fibers co-confine two important oscillations in
nature: capillary- and electromagnetic-. We optically record vibrations in the
water fiber, including an audio-rate fundamental and its 3 overtones in a
harmonic series, that one can hear in soundtracks attached. Transforming
Micro-Electro-Mechanical-Systems [MEMS] to Micro-Electro-Capillary-Systems
[MECS], boosts the device softness by a million to accordingly improve its
response to minute forces. Furthermore, MECS are compatible with water, which
is a most important liquid in our world.


title:
CONDENSE: A Reconfigurable Knowledge Acquisition Architecture for Future
  5G IoT

abstract:
  In forthcoming years, the Internet of Things (IoT) will connect billions of
smart devices generating and uploading a deluge of data to the cloud. If
successfully extracted, the knowledge buried in the data can significantly
improve the quality of life and foster economic growth. However, a critical
bottleneck for realising the efficient IoT is the pressure it puts on the
existing communication infrastructures, requiring transfer of enormous data
volumes. Aiming at addressing this problem, we propose a novel architecture
dubbed Condense, which integrates the IoT-communication infrastructure into
data analysis. This is achieved via the generic concept of network function
computation: Instead of merely transferring data from the IoT sources to the
cloud, the communication infrastructure should actively participate in the data
analysis by carefully designed en-route processing. We define the Condense
architecture, its basic layers, and the interactions among its constituent
modules. Further, from the implementation side, we describe how Condense can be
integrated into the 3rd Generation Partnership Project (3GPP) Machine Type
Communications (MTC) architecture, as well as the prospects of making it a
practically viable technology in a short time frame, relying on Network
Function Virtualization (NFV) and Software Defined Networking (SDN). Finally,
from the theoretical side, we survey the relevant literature on computing
"atomic" functions in both analog and digital domains, as well as on function
decomposition over networks, highlighting challenges, insights, and future
directions for exploiting these techniques within practical 3GPP MTC
architecture.


title: Fermionic Dark Matter in a simple $t$-channel model

abstract:
  We consider a fermionic dark matter (DM) particle in renormalizable Standard
Model (SM) gauge interactions in a simple $t$-channel model. The DM particle
interactions with SM fermions is through the exchange of scalar and vector
mediators which carry colour or lepton number. In the case of coloured
mediators considered in this study, we find that if the DM is thermally
produced and accounts for the observed relic density almost the entire
parameter space is ruled out by the direct detection observations. The bounds
from the monojet plus missing energy searches at the Large Hadron Collider are
less stringent in this case. In contrast for the case of Majorana DM, we obtain
strong bounds from the monojet searches which rule out DM particles of mass
less than about a few hundred GeV for both the scalar and vector mediators.


title: Anisotropic XY antiferromagnets in a field

abstract:
  Classical anisotropic XY antiferromagnets in a field on square and simple
cubic lattices are studied using mainly Monte Carlo simulations. While in two
dimensions the ordered antiferromagnetic and spin--flop phases are observed to
be separated by a narrow disordered phase, a line of direct transitions of
first order between the two phases and a bicritical point are found in three
dimensions. Results are compared to previous findings.


title: The compression of a heavy floating elastic film

abstract:
  We study the effect of film density on the uniaxial compression of thin
elastic films at a liquid--fluid interface. Using a combination of experiments
and theory, we show that dense films first wrinkle and then fold as the
compression is increased, similarly to what has been reported when the film
density is neglected. However, we highlight the changes in the shape of the
fold induced by the film's own weight and extend the model of Diamant and
Witten [Phys.Rev.Lett. 2011, 107, 164302, arXiv:1107.5505] to understand these
changes. In particular, we suggest that it is the weight of the film that
breaks the up-down symmetry apparent from previous models, but elusive
experimentally. We then compress the film beyond the point of self-contact and
observe a new behaviour dependent on the film density: the single fold that
forms after wrinkling transitions into a closed loop after self-contact,
encapsulating a cylindrical droplet of the upper fluid. The encapsulated drop
either causes the loop to bend upward or to sink deeper as compression is
increased, depending on the relative buoyancy of the drop-film combination. We
propose a model to qualitatively explain this behaviour. Finally, we discuss
the relevance of the different buckling modes predicted in previous theoretical
studies and highlight the important role of surface tension in the shape of the
fold that is observed from the side -- an aspect that is usually neglected in
theoretical analyses.


title:
Using phase II data for the analysis of phase III studies: an
  application in rare diseases

abstract:
  Clinical research and drug development in orphan diseases is challenging,
since large-scale randomized studies are difficult to conduct. Formally
synthesizing the evidence is therefore of great value, yet this is rarely done
in the drug approval process. Phase III designs that make better use of phase
II data can facilitate drug development in orphan diseases.
  A Bayesian meta-analytic approach is used to inform the phase III study with
phase II data. It is particularly attractive, since uncertainty of
between-trial heterogeneity can be dealt with probabilistically, which is
critical if the number of studies is small. Furthermore, it allows quantifying
and discounting the phase II data through the predictive distribution relevant
for phase III. A phase III design is proposed which uses the phase II data and
considers approval based on a phase III interim analysis. The design is
illustrated with a non-inferiority case study from an FDA approval in herpetic
keratitis (an orphan disease). Design operating characteristics are compared to
those of a traditional design, which ignores the phase II data.
  An analysis of the phase II data reveals good but insufficient evidence for
non-inferiority, highlighting the need for a phase III study. For the phase III
study supported by phase II data, the interim analysis is based on half of the
patients. For this design, the meta-analytic interim results are conclusive and
would justify approval. In contrast, based on the phase III data only, interim
results are inconclusive and would require further evidence.
  To accelerate drug development for orphan diseases, innovative study designs
and appropriate methodology are needed. Taking advantage of randomized phase II
data when analyzing phase III studies looks promising because the evidence from
phase II supports informed decision making. The implementation of the Bayesian
design is straightforward.


title:
Ag colloids and arrays for plasmonic non-radiative energy transfer from
  quantum dots to a quantum well

abstract:
  Ag nanoparticles in the form of colloids and ordered arrays are used to
demonstrate plasmon-mediated non-radiative energy transfer from quantum dots to
quantum wells with varying top barrier thicknesses. Plasmon-mediated energy
transfer efficiencies of up to ~25% are observed with the Ag colloids. The
distance dependence of the plasmon-mediated energy transfer is found to follow
the same d^{-4} dependence as the direct quantum dot to quantum well energy
transfer. There is also evidence for an increase in the characteristic distance
of the interaction, thus indicating that it follows a F\"orster-like model with
the Ag nanoparticle-quantum dot acting as an enhanced donor dipole. Ordered Ag
nanoparticle arrays display plasmon-mediated energy transfer efficiencies up to
~21%. To explore the tunability of the array system, two arrays with different
geometries are presented. It is demonstrated that changing the geometry of the
array allows a transition from overall quenching of the acceptor quantum well
emission to enhancement, as well as control of the competition between the
quantum dot donor quenching and energy transfer rates.


title:
Inclusive $\psi$(2S) production at forward rapidity in pp, p-Pb and
  Pb-Pb collisions with ALICE at the LHC

abstract:
  The ALICE Collaboration has studied inclusive $\psi$(2S) production in pp,
p-Pb and Pb-Pb collisions with the ALICE Muon Spectrometer which covers the
rapidity range 2.5 $<$ $y_{\rm lab}$ $<$ 4. The $\psi$(2S) measurement was
performed in the dimuon decay channel. The $\psi$(2S) production cross-section
and $\psi$(2S) to J/$\psi$ cross-section ratio in pp collisions will be
presented, both integrated and differential in rapidity and in transverse
momentum. In p-Pb collisions, $\psi$(2S) results will be compared to the
J/$\psi$ ones by means of the production cross-section ratio and the double
ratio [$\psi$(2S)/J/$\psi$]$_{{\mathrm
{pPb}}}$/[$\psi$(2S)/J/$\psi$]$_{{\mathrm {pp}}}$ studied as a function of the
resonance transverse momentum and event activity of the collision. The
$\psi$(2S) nuclear modification factor, $R_{{\mathrm {pPb}}}$, will also be
presented. Theoretical models based on nuclear shadowing, coherent energy loss
or both cannot describe our results. Therefore other mechanisms must be invoked
in order to describe the $\psi$(2S) production. Finally, results on $\psi$(2S)
production in Pb-Pb collisions will be shown in two transverse momentum ranges
as a function of centrality.


title:
Anomalous hyperfine coupling and nuclear magnetic relaxation in Weyl
  semimetals

abstract:
  The electron-nuclear hyperfine interaction shows up in a variety of phenomena
including e.g. NMR studies of correlated states and spin decoherence effects in
quantum dots. Here we focus on the hyperfine coupling and the NMR spin
relaxation time, $T_1$ in Weyl semimetals. Since the density of states in Weyl
semimetals varies with the square of the energy around the Weyl point, a naive
power counting predicts a $1/T_1T\sim E^4$ scaling with $E$ the maximum of
temperature ($T$) and chemical potential. By carefully investigating the
hyperfine interaction between nuclear spins and Weyl fermions, we find that
while its spin part behaves conventionally, its orbital part diverges unusually
with the inverse of energy around the Weyl point. Consequently, the nuclear
spin relaxation rate scales in a graphene like manner as $1/T_1T\sim
E^2\ln(E/\omega_0)$ with $\omega_0$ the nuclear Larmor frequency. This allows
us to identify an effective hyperfine coupling constant, which is tunable by
gating or doping, which is relevant for decoherence effect in spintronics
devices and double quantum dots where hyperfine coupling is the dominant source
of spin-blockade lifting.


title:
Calibrating word problems of groups via the complexity of equivalence
  relations

abstract:
  (1) There is a finitely presented group with a word problem which is a
uniformly effectively inseparable equivalence relation. (2) There is a finitely
generated group of computable permutations with a word problem which is a
universal co-computably enumerable equivalence relation. (3) Each c.e.
truth-table degree contains the word problem of a finitely generated group of
computable permutations.


title:
Differentially Categorized Structural Connectome Hubs are Involved in
  Differential Microstructural Basis and Functional Implications and Contribute
  to Individual Identification

abstract:
  Human brain structural networks contain sets of centrally embedded hub
regions that enable efficient information communication. However, it remains
largely unknown about categories of structural brain hubs and their
microstructural, functional and cognitive characteristics as well as
contributions to individual identification. Here, we employed three multi-modal
imaging data sets with structural MRI, diffusion MRI and resting-state
functional MRI to construct individual structural brain networks, identify
brain hubs based on eight commonly used graph-nodal metrics, and perform
comprehensive validation analysis. We found three categories of structural hubs
in the brain networks, namely, aggregated, distributed and connector hubs.
Spatially, these distinct categories of hubs were primarily located in the
default-mode system and additionally in the visual and limbic systems for
aggregated hubs, in the frontoparietal system for distributed hubs, and in the
sensorimotor and ventral attention systems for connector hubs. Importantly,
these three categories of hubs exhibited various distinct characteristics, with
the highest level of microstructural organization in the aggregated hubs, the
largest wiring cost and topological vulnerability in the distributed hubs, and
the highest functional associations and cognitive flexibility in the connector
hubs, although they behaved better regarding these characteristics compared to
non-hubs. Finally, all three categories of hub indices displayed high
across-session spatial similarities and acted as a structural fingerprint with
high predictive rates (100%, 100% and 84.2%) for individual identification.
Collectively, our findings highlighted three categories of brain hubs with
differential microstructural, functional and cognitive associations, which may
shed light on the topological mechanisms of the human connectome.