title: Bayesian Adaptive Data Analysis Guarantees from Subgaussianity

abstract:
  The new field of adaptive data analysis seeks to provide algorithms and
provable guarantees for models of machine learning that allow researchers to
reuse their data, which normally falls outside of the usual statistical
paradigm of static data analysis. In 2014, Dwork, Feldman, Hardt, Pitassi,
Reingold and Roth introduced one potential model and proposed several solutions
based on differential privacy. In previous work in 2016, we described a problem
with this model and instead proposed a Bayesian variant, but also found that
the analogous Bayesian methods cannot achieve the same statistical guarantees
as in the static case.
  In this paper, we prove the first positive results for the Bayesian model,
showing that with a Dirichlet prior, the posterior mean algorithm indeed
matches the statistical guarantees of the static case. The main ingredient is a
new theorem showing that the $\mathrm{Beta}(\alpha,\beta)$ distribution is
subgaussian with variance proxy $O(1/(\alpha+\beta+1))$, a concentration result
also of independent interest. We provide two proofs of this result: a
probabilistic proof utilizing a simple condition for the raw moments of a
positive random variable and a learning-theoretic proof based on considering
the beta distribution as a posterior, both of which have implications to other
related problems.


title: Many Haken Heegaard splittings

abstract:
  We give a simple criterion for a Heegaard splitting to yield a Haken
manifold. As a consequence, we construct many Haken manifolds, in particular
homology spheres, with prescribed properties, namely Heegaard genus, Heegaard
distance and Casson invariant.
  Along the way we give simpler and shorter proofs of the existence of
splittings with specified Heegaard distance, originally proven by
Ido-Jang-Kobayashi, of the existence of hyperbolic manifolds with prescribed
Casson invariant, originally due to Lubotzky-Maher-Wu, and of a result about
subsurface projections of disc sets (for which we even get better constants),
originally due to Masur-Schleimer.


title:
Subsumed homoclinic connections and infinitely many coexisting
  attractors in piecewise-linear maps

abstract:
  We establish an equivalence between infinitely many asymptotically stable
periodic solutions and subsumed homoclinic connections for $N$-dimensional
piecewise-linear continuous maps. These features arise as a codimension-three
phenomenon. The periodic solutions are single-round: they each involve one
excursion away from a central saddle-type periodic solution. The homoclinic
connection is subsumed in the sense that one branch of the unstable manifold of
the saddle solution is contained entirely within its stable manifold. The
results are proved by using exact expressions for the periodic solutions and
components of the stable and unstable manifolds which are available because the
maps are piecewise-linear. We also describe a practical approach for finding
this phenomenon in the parameter space of a map and illustrate the results with
the three-dimensional border-collision normal form.


title: RNN Approaches to Text Normalization: A Challenge

abstract:
  This paper presents a challenge to the community: given a large corpus of
written text aligned to its normalized spoken form, train an RNN to learn the
correct normalization function. We present a data set of general text where the
normalizations were generated using an existing text normalization component of
a text-to-speech system. This data set will be released open-source in the near
future.
  We also present our own experiments with this data set with a variety of
different RNN architectures. While some of the architectures do in fact produce
very good results when measured in terms of overall accuracy, the errors that
are produced are problematic, since they would convey completely the wrong
message if such a system were deployed in a speech application. On the other
hand, we show that a simple FST-based filter can mitigate those errors, and
achieve a level of accuracy not achievable by the RNN alone.
  Though our conclusions are largely negative on this point, we are actually
not arguing that the text normalization problem is intractable using an pure
RNN approach, merely that it is not going to be something that can be solved
merely by having huge amounts of annotated text data and feeding that to a
general RNN model. And when we open-source our data, we will be providing a
novel data set for sequence-to-sequence modeling in the hopes that the the
community can find better solutions.
  The data used in this work have been released and are available at:
https://github.com/rwsproat/text-normalization-data


title: On singular square metrics with vanishing Douglas curvature

abstract:
  Square metrics $F=\frac{(\alpha+\beta)^2}{\alpha}$ are a special class of
Finsler metrics. It is the rate kind of metric category to be of excellent
geometrical properties. In this paper, we discuss the so-called singular square
metrics $F=\frac{(b\alpha+\beta)^2}{\alpha}$. A characterization for such
metrics to be of vanishing Douglas curvature is provided. Moreover, many
analytical examples are achieved by using a special kinds of metrical
deformations called $\beta$-deformations.


title: Student project: Of spinning coins and merging black holes

abstract:
  For the past decade, the SAIL labs at the University of Sydney have been
challenging students with short research projects that elucidate basic
principles of physics. These include the development of instruments launched on
cubesats, balloons, on telescopes or placed out in the field. This experiment
is inspired by the spectacular 2015 discovery of merging black holes with the
Laser Interferometric Gravitational-Wave Observatory (LIGO). Students are
profoundly inspired by LIGO, and for good reason, but it is challenging to
construct a table top demonstration of a gravitational wave observatory.
Instead we consider chirps which are remarkable transient phenomena in nature
involving both frequency and amplitude modulation, as we can demonstrate with a
spinning coin. In the case of the LIGO event, orbital energy is being released
as gravitational radiation; for the spinning coin, its spin/orbit energy is
being released dissipatively (sound, heat, air viscosity). Our experiment
involves a simple device to spin a coin remotely. This aids repeatability and
allows us to spin the coin within a vacuum chamber to examine the contribution
of air viscosity.


title: Eigenvalues of rotations and braids in spherical fusion categories

abstract:
  We give formulae for the multiplicities of eigenvalues of generalized
rotation operators in terms of generalized Frobenius-Schur indicators in a
semisimple spherical tensor category $\mathcal{C}$. In particular, this implies
that the entire collection of rotation eigenvalues for a fusion category can be
computed from the fusion rules and the traces of rotation at finitely many
tensor powers. We also establish a rigidity property for FS indicators of
fusion categories with a given fusion ring via Jones's theory of planar
algebras. If $\mathcal{C}$ is also braided, these formulae yield the
multiplicities of eigenvalues for a large class of braids in the associated
braid group representations. When $\mathcal{C}$ is modular, this allows one to
determine the eigenvalues and multiplicities of braids in terms of just the $S$
and $T$ matrices.


title: The Isomorphism Conjecture for solvable groups in Waldhausen's A-theory

abstract:
  We prove the A-theoretic Isomorphism Conjecture with coefficients and finite
wreath products for solvable groups.


title:
Noctilucent Cloud Particle Size Determination based on Multi-Wavelength
  All-Sky Analysis

abstract:
  The article deals with the analysis of color distribution in noctilucent
clouds (NLC) in the sky based on multi-wavelength (RGB) CCD-photometry provided
with the all-sky camera in Lovozero in the north of Russia (68.0 deg N, 35.1
deg E) during the bright expanded NLC performance in the night of August 12,
2016. Small changes in the NLC color across the sky are interpreted as the
atmospheric absorption and extinction effects combined with the difference in
the Mie scattering functions of NLC particles for the three color channels of
the camera. The method described in this paper is used to find the effective
monodisperse radius of particles about 55 nm. The result of these simple and
cost-effective measurements is in good agreement with previous estimations of
comparable accuracy. Non-spherical particles, Gaussian and lognormal
distribution of the particle size are also considered.


title: On the Lee classes of locally conformally symplectic complex surfaces

abstract:
  We prove that the deRham cohomology classes of Lee forms of locally
conformally symplectic structures taming the complex structure of a compact
complex surface $S$ with first Betti number equal to $1$ is either a non-empty
open subset of $H^1_{dR}(S, \mathbb R)$, or a single point. In the latter case,
we show that $S$ must be biholomorphic to a blow-up of an Inoue-Bombieri
surface. Similarly, the deRham cohomology classes of Lee forms of locally
conformally K\"ahler structures of a compact complex surface $S$ with first
Betti number equal to $1$ is either a non-empty open subset of $H^1_{dR}(S,
\mathbb R)$, a single point or the empty set. We give a characterization of
Enoki surfaces in terms of the existence of a special foliation, and obtain a
vanishing result for the Lichnerowicz-Novikov cohomology groups on the class
${\rm VII}$ compact complex surfaces with infinite cyclic fundamental group.


title: A hybrid algorithm for parallel molecular dynamics simulations

abstract:
  This article describes algorithms for the hybrid parallelization and SIMD
vectorization of molecular dynamics simulations with short-range forces. The
parallelization method combines domain decomposition with a thread-based
parallelization approach. The goal of the work is to enable efficient
simulations of very large (tens of millions of atoms) and inhomogeneous systems
on many-core processors with hundreds or thousands of cores and SIMD units with
large vector sizes. In order to test the efficiency of the method, simulations
of a variety of configurations with up to 74 million atoms have been performed.
Results are shown that were obtained on multi-core systems with Sandy Bridge
and Haswell processors as well as systems with Xeon Phi many-core processors.


title: Gravitational lensing by compact objects within plasma

abstract:
  Frequency-dependent gravitational lens effects are found for trajectories of
electromagnetic rays passing through a distribution of plasma near a massive
object. Ray propagation through plasma adds extra terms to the equations of
motion that depend on the plasma refractive index. For low-frequency rays these
refractive effects can dominate, turning the gravitational lens into a mirror.
While light rays behave like particles with an effective mass given by the
plasma frequency in a medium with constant density, an inhomogeneous plasma
introduces more complicated behavior even for the spherically symmetric case.
As a physical example, the pulse profile of a compact object sheathed in a
dense plasma is examined, which introduces dramatic frequency-dependent shifts
from the behavior in vacuum.


title: Superlensing Microscope Objective Lens

abstract:
  Conventional microscope objective lenses are diffraction limited, which means
that they cannot resolve features smaller than half the illumination
wavelength. Under white light illumination, such resolution limit is about
250-300 nm for an ordinary microscope. In this paper, we demonstrate a new
superlensing objective lens which has a resolution of about 100 nm, offering at
least two times resolution improvement over conventional objectives in
resolution. This is achieved by integrating a conventional microscope objective
lens with a superlensing microsphere lens using a 3D printed lens adaptor. The
new objective lens was used for label-free super-resolution imaging of 100
nm-sized engineering and biological samples, including a Blu-ray disc sample,
semiconductor chip and adenoviruses. Our work creates a solid base for
developing a commercially-viable superlens prototype, which has potential to
transform the field of optical microscopy and imaging.


title:
Thickness dependent enhancement of the polar magneto-optic Kerr effect
  in Co magnetoplasmonic nanostructures

abstract:
  Large surface plasmon polariton assisted enhancement of the magneto-optical
activity has been observed in the past, through spectral measurements of the
polar Kerr rotation in Co hexagonal antidot arrays. Here, we report a strong
thickness dependence, which is unexpected given that the Kerr effect is
considered a surface sensitive phenomena. The maximum Kerr rotation was found
to be -0.66 degrees for a 100 nm thick sample. This thickness is far above the
typical optical penetration depth of a continuous Co film, demonstrating that
in the presence of plasmons the critical lengthscales are dramatically altered,
and in this case extended. We therefore establish that the plasmon enhanced
Kerr effect does not only depend on the in-plane structuring of the sample, but
also on the out-of-plane geometrical parameters, which is an important
consideration in magnetoplasmonic device design.


title: UHECR narrow clustering correlating IceCube through-going muons

abstract:
  The recent UHECR events by AUGER and the Telescope Array (TA) suggested that
wide clusterings as the North and South, named Hot Spot, are related to near
AGNs such as the one in M82 and Cen A. In the same frame since 2008 we assumed
that the UHECR are made by light and lightest nuclei to explain the otherwise
embarrassing absence of the huge nearby Virgo cluster, absence due to the
fragility and the opacity of lightest nuclei by photo-dissociation from Virgo
distances. Moreover UHECR map exhibits a few narrow clustering, some near the
galactic plane, as toward SS 433 and on the opposite side of the plane at
celestial horizons: we tagged them in 2014 suggesting possible near source
active also as a UHE neutrino. Indeed since last year, 2015, highest IceCube
trough-going muons, UHE up-going neutrino events at hundreds TeV energy, did
show (by two cases over three tagged in North sky) the expected overlapping of
UHE neutrinos signals with narrow crowding UHECR. New data with higher energy
threshold somehow re-confirmed our preliminary proposal; new possible sources
appear by a additional correlated UHE-neutrino versus UHE-neutrino and-or with
narrow UHECR clustering events. A possible role of relic neutrino mass
scattering by ZeV neutrino arised.


title:
KMS conditions, standard real subspaces and reflection positivity on the
  circle group

abstract:
  In the present paper we continue our investigations of the representation
theoretic side of reflection positivity by studying positive definite functions
\psi on the additive group (R,+) satisfying a suitably defined KMS condition.
These functions take values in the space Bil(V) of bilinear forms on a real
vector space V. As in quantum statistical mechanics, the KMS condition is
defined in terms of an analytic continuation of \psi to the strip { z \in C\: 0
\leq Im z \leq b} with a coupling condition \psi (ib + t) = \oline{\psi (t)} on
the boundary.
  Our first main result consists of a characterization of these functions in
terms of modular objects (\Delta, J) (J an antilinear involution and \Delta > 0
selfadjoint with J\Delta J = \Delta^{-1}) and an integral representation.
  Our second main result is the existence of a Bil(V)-valued positive definite
function f on the group R_\tau = R \rtimes {\id_\R,\tau} with \tau(t) = -t
satisfying f(t,\tau) = \psi(it) for t \in R. We thus obtain a 2b-periodic
unitary one-parameter group on the GNS space H_f for which the one-parameter
group on the GNS space H_\psi is obtained by Osterwalder--Schrader
quantization.
  Finally, we show that the building blocks of these representations arise from
bundle-valued Sobolev spaces corresponding to the kernels 1/(\lambda^2 -
(d^2)/(dt^2}) on the circle R/bZ of length b.


title: Interpolation for Brill-Noether space curves

abstract:
  In this note we compute the number of general points through which a general
Brill-Noether space curve passes.


title:
A free energy satisfying discontinuous Galerkin method for
  one-dimensional Poisson--Nernst--Planck systems

abstract:
  We design an arbitrary-order free energy satisfying discontinuous Galerkin
(DG) method for solving time-dependent Poisson-Nernst-Planck systems. Both the
semi-discrete and fully discrete DG methods are shown to satisfy the
corresponding discrete free energy dissipation law for positive numerical
solutions. Positivities of numerical solutions are enforced by an
accuracy-preserving limiter in reference to positive cell averages. Numerical
examples are presented to demonstrate the high resolution of the numerical
algorithm and to illustrate the proven properties of mass conservation, free
energy dissipation, as well as the preservation of steady states.


title:
Confronting Quasi-Separation in Logistic Mixed Effects for Linguistic
  Data: A Bayesian Approach

abstract:
  Mixed effects regression models are widely used by language researchers.
However, these regressions are implemented with an algorithm which may not
converge on a solution. While convergence issues in linear mixed effects models
can often be addressed with careful experiment design and model building,
logistic mixed effects models introduce the possibility of separation or
quasi-separation, which can cause problems for model estimation that result in
convergence errors or in unreasonable model estimates. These problems cannot be
solved by experiment or model design. In this paper, we discuss
(quasi-)separation with the language researcher in mind, explaining what it is,
how it causes problems for model estimation, and why it can be expected in
linguistic datasets. Using real linguistic datasets, we then show how Bayesian
models can be used to overcome convergence issues introduced by
quasi-separation, whereas frequentist approaches fail. On the basis of these
demonstrations, we advocate for the adoption of Bayesian models as a practical
solution to dealing with convergence issues when modeling binary linguistic
data.


title: The Largest Pure Partial Planes of Order 6 Have Size 25

abstract:
  In this paper, we prove that the largest pure partial plane of order 6 has
size 25. At the same time, we classify all pure partial planes of order 6 and
size 25 up to isomorphism. Our major approach is computer search. The search
space is very large so we use combinatorial arguments to rule out some of the
cases. For the remaining cases, we subdivide each search by phases and use
multiple checks to reduce search space via symmetry.


title:
Gravity-Driven Acceleration and Kinetic Inflation in Noncommutative
  Brans-Dicke Setting

abstract:
  By assuming the spatially flat~FLRW line-element and employing the
Hamiltonian formalism, a noncommutative (NC) setting of the Brans-Dicke (BD)
theory is introduced. We investigate gravity-driven acceleration and kinetic
inflation in this NC BD cosmology. Despite to the commutative case, in which
both the scale factor and BD scalar field are obtained in power-law forms (in
terms of the cosmic time), in our herein NC model, we see that the power-law
scalar factor is multiplied by a dynamical exponential warp factor. This warp
factor depends on not only the NC parameter but also the momentum conjugate
associated to the BD scalar field. For very small values of this parameter, we
obtain an appropriate inflationary solution, which can overcome the problems
within the standard BD cosmology in a more efficient manner. Moreover, we see
that a graceful exit from an early acceleration epoch towards a decelerating
radiation epoch is provided. For late times, due to the presence of the NC
parameter, we obtain a zero acceleration epoch, which can be interpreted as the
coarse-grained explanation.


title: Magnetoelectric Behavior from $S=1/2$ Asymmetric Square Cupolas

abstract:
  Magnetoelectric properties are studied by a combined experimental and
theoretical study of a quasi-two-dimensional material composed of square
cupolas, Ba(TiO)Cu$_4$(PO$_4$)$_4$. The magnetization is measured up to above
the saturation field, and several anomalies are observed depending on the field
directions. We propose a $S$=1/2 spin model with Dzyaloshinskii-Moriya
interactions, which well reproduces the full magnetization curves. Elaborating
the phase diagram of the model, we show that the anomalies are explained by
magnetoelectric phase transitions. Our theory also accounts for the scaling of
the dielectric anomaly observed in experiments. The results elucidate the
crucial role of the in-plane component of Dzyaloshinskii-Moriya interactions,
which is induced by the noncoplanar buckling of square cupola. We also predict
a `hidden' phase and another magnetoelectric response both of which appear in
nonzero magnetic field.


title:
Closed-form REML estimators and sample size determination for mixed
  effects models for repeated measures under monotone missingness

abstract:
  We derive the closed-form restricted maximum likelihood (REML) estimator and
Kenward-Roger's variance estimator for fixed effects in the mixed effects model
for repeated measures (MMRM) when the missing data pattern is monotone. As an
important application of the analytic result, we present the formula for
calculating the power of treatment comparison using the Wald t test with the
Kenward-Roger adjusted variance estimate in MMRM. It allows adjustment for
baseline covariates without the need to specify the covariate distribution in
randomized trials. A simple two-step procedure is proposed to determine the
sample size needed to achieve the targeted power. The proposed method performs
well for both normal and moderately nonnormal data even in small samples (n =
20) in simulations. An anti-depressant trial is analyzed for illustrative
purposes.


title: Hierarchical Cluster Assembly in Globally Collapsing Clouds

abstract:
  We discuss the mechanism of cluster formation in a numerical simulation of a
molecular cloud (MC) undergoing global hierarchical collapse (GHC). The global
nature of the collapse implies that the SFR increases over time. The
hierarchical nature of the collapse consists of small-scale collapses within
larger-scale ones. The large-scale collapses culminate a few Myr later than the
small-scale ones and consist of filamentary flows that accrete onto massive
central clumps. The small-scale collapses form clumps that are embedded in the
filaments and falling onto the large-scale collapse centers. The stars formed
in the early, small-scale collapses share the infall motion of their parent
clumps. Thus, the filaments feed both gaseous and stellar material to the
massive central clump. This leads to the presence of a few older stars in a
region where new protostars are forming, and also to a self-similar structure,
in which each unit is composed of smaller-scale sub-units that approach each
other and may merge. Because the older stars formed in the filaments share the
infall motion of the gas onto the central clump, they tend to have larger
velocities and to be distributed over larger areas than the younger stars
formed in the central clump. Finally, interpreting the IMF at face-value as a
probability distribution implies that massive stars only form once the {\it
local} SFR is large enough to sample the IMF up to high masses. In combination
with the increase of the SFR, this implies that massive stars tend to appear
late in the evolution of the MC, and only in the central massive clumps. We
discuss the correspondence of these features with observed properties of young
stellar clusters, finding very good qualitative agreement, thus providing
support to the scenario of global, hierarchical collapse of MCs, while
explaining the origin of the observed cluster structure.


title:
Predicted Extension of the Sagittarius Stream to the Milky Way Virial
  Radius

abstract:
  The extensive span of the Sagittarius (Sgr) stream makes it a promising tool
for studying the Milky Way gravitational potential. Characterizing its stellar
kinematics can constrain halo properties and provide a benchmark for the Cold
Dark Matter galaxy formation paradigm. Accurate models of the disruption
dynamics of the Sgr progenitor are necessary to employ this tool. Using a
combination of analytic modeling and N-body simulations, we build a new model
of the Sgr orbit and resulting stellar stream. In contrast to previous models,
we simulate the full infall trajectory of the Sgr progenitor from the time it
first crossed the Milky Way virial radius 8 Gyr ago. An exploration of the
parameter space of initial phase-space conditions yields tight constraints on
the angular momentum of the Sgr progenitor. Our best-fit model is the first to
reproduce accurately existing data on the 3D positions and radial velocities of
the debris detected 100 kpc away in the MW halo. In addition to replicating the
mapped stream, the simulation also predicts the existence of several arms of
the Sgr stream extending to hundreds of kiloparsecs. The two most distant stars
known in the Milky Way halo coincide with the predicted structure. Additional
stars in the newly predicted arms can be found with future data from the Large
Synoptic Survey Telescope. Detecting a statistical sample of stars in the most
distant Sgr arms would provide an opportunity to constrain the Milky Way
potential out to unprecedented Galactocentric radii.


title:
On the Linear Convergence of the Cauchy Algorithm for a Class of
  Restricted Strongly Convex Functions

abstract:
  In this short note, we extend the linear convergence result of the Cauchy
algorithm, derived recently by E. Klerk, F. Glineur, and A. Taylor, from the
case of smooth strongly convex functions to the case of restricted strongly
convex functions with certain form.


title:
The ESO UVES Advanced Data Products Quasar Sample-V. Identifying the
  Galaxy Counterpart to the sub-Damped Ly-alpha System towards Q2239-2949

abstract:
  Gas flows in and out of galaxies are one of the key unknowns in todays'
galaxy evolution studies. Because gas flows carry mass, energy and metals, they
are believed to be closely connected to the star formation history of galaxies.
Most of these processes take place in the circum-galactic medium (CGM) which
remains challenging to observe in emission. A powerful tool to study the CGM
gas is offered by combining observations of the gas traced by absorption lines
in quasar spectra with detection of the stellar component of the same
absorbing-galaxy. To this end, we have targeted the zabs=1.825 sub-Damped
Ly-alpha absorber (sub-DLA) towards the zem=2.102 quasar 2dF J 223941.8-294955
(hereafter Q2239-2949) with the ESO VLT/X-Shooter spectrograph. Our aim is to
investigate the relation between its properties in emission and in absorption.
The derived metallicity of the sub-DLA with log N(HI) = 19.84+/-0.14 cm-2 is
[M/H] >-0.75. Using the Voigt profile optical depth method, we measure
Delta_v90(FeII)=64 kms-1. The sub-DLA galaxy counterpart is located at an
impact parameter of 2."4+/-0."2 (20.8+/-1.7 kpc at z = 1.825). We have detected
Ly-alpha and marginal [OII] emissions. The mean measured flux of the Ly-alpha
line is F(Ly-alpha) ~ 5.7x10^-18 erg s-1 cm-2 A-1, corresponding to a dust
uncorrected SFR of ~ 0.13 M(solar) yr-1.


title:
The Wasserstein distance between stationary measures associated to
  iterated function schemes on the unit interval

abstract:
  We provide explicit formulaes for the first Kantorovich-Wasserstein distance
between stationary measures for iterated function scheme on the unit interval.
In particular, we consider two stationary measures with different
configurations of the weights associated to the same iterated function schemes
with disjoint images composed of: $k$ positive contractions or $2$ contractions
of different sign. We also study the case of two stationary measures associated
to different iterated function schemes.


title: Decentralized Stochastic Control of Distributed Energy Resources

abstract:
  We consider the decentralized control of radial distribution systems with
controllable photovoltaic inverters and energy storage resources. For such
systems, we investigate the problem of designing fully decentralized
controllers that minimize the expected cost of balancing demand, while
guaranteeing the satisfaction of individual resource and distribution system
voltage constraints. Employing a linear approximation of the branch flow model,
we formulate this problem as the design of a decentralized disturbance-feedback
controller that minimizes the expected value of a convex quadratic cost
function, subject to robust convex quadratic constraints on the system state
and input. As such problems are, in general, computationally intractable, we
derive a tractable inner approximation to this decentralized control problem,
which enables the efficient computation of an affine control policy via the
solution of a finite-dimensional conic program. As affine policies are, in
general, suboptimal for the family of systems considered, we provide an
efficient method to bound their suboptimality via the optimal solution of
another finite-dimensional conic program. A case study of a 12 kV radial
distribution system demonstrates that decentralized affine controllers can
perform close to optimal.


title: Learning recurrent representations for hierarchical behavior modeling

abstract:
  We propose a framework for detecting action patterns from motion sequences
and modeling the sensory-motor relationship of animals, using a generative
recurrent neural network. The network has a discriminative part (classifying
actions) and a generative part (predicting motion), whose recurrent cells are
laterally connected, allowing higher levels of the network to represent high
level phenomena. We test our framework on two types of data, fruit fly behavior
and online handwriting. Our results show that 1) taking advantage of unlabeled
sequences, by predicting future motion, significantly improves action detection
performance when training labels are scarce, 2) the network learns to represent
high level phenomena such as writer identity and fly gender, without
supervision, and 3) simulated motion trajectories, generated by treating motion
prediction as input to the network, look realistic and may be used to
qualitatively evaluate whether the model has learnt generative control rules.


title:
Bond formation kinetics affects self-assembly directed by
  ligand-receptor interactions

abstract:
  In this paper we study aggregation kinetics in systems of particles
functionalised by complementary linkers. Most of the coarse-grained models
currently employed to study large-scale self-assembly of these systems rely on
effective potentials between particles as calculated using equilibrium
statistical mechanics. In these approaches the kinetic aspects underlying the
formation of inter-particle linkages are neglected. We show how the rate at
which supramolecular linkages form drastically changes the self-assembly
pathway. In order to do this we develop a method that combines Brownian
dynamics simulations with a Gillespie algorithm accounting for the evolution of
inter-particle linkages. If compared with dynamics based on effective
potentials, an explicit description of inter-particle linkages results in
aggregates that in the early stages of self-assembly have a lower valency.
Relaxation towards equilibrium is hampered by the time required to break
existing linkages within one cluster and to reorient them toward free
particles. This effect is more important at low temperature and high particle
diffusion constant. Our results highlight the importance of including kinetic
rates into coarse-grained descriptions of ligand-receptor systems.


title:
LoRea: A Backscatter Architecture that Achieves a Long Communication
  Range

abstract:
  There is the long-standing assumption that radio communication in the range
of hundreds of meters needs to consume mWs of power at the transmitting device.
In this paper, we demonstrate that this is not necessarily the case for some
devices equipped with backscatter radios. We present LoRea an architecture
consisting of a tag, a reader and multiple carrier generators that overcomes
the power, cost and range limitations of existing systems such as Computational
Radio Frequency Identification~(CRFID). LoRea achieves this by: First,
generating narrow-band backscatter transmissions that improve receiver
sensitivity. Second, mitigating self-interference without the complex designs
employed on RFID readers by keeping carrier signal and backscattered signal
apart in frequency. Finally, decoupling carrier generation from the reader and
using devices such as WiFi routers and sensor nodes as a source of the carrier
signal. An off-the-shelf implementation of LoRea costs 70 USD, a drastic
reduction in price considering commercial RFID readers cost 2000 USD. LoRea's
range scales with the carrier strength, and proximity to the carrier source and
achieves a maximum range of 3.4 kilometre when the tag is located at 1 meter
distance from a 28 dBm carrier source while consuming 70 microwatts at the tag.
When the tag is equidistant from the carrier source and the receiver, we can
communicate upto 75 meter, a significant improvement over existing RFID
readers.


title: Scientific Literature Text Mining and the Case for Open Access

abstract:
  "Open access" has become a central theme of journal reform in academic
publishing. In this article, I examine the relationship between open access
publishing and an important infrastructural element of a modern research
enterprise, scientific literature text mining, or the use of data analytic
techniques to conduct meta-analyses and investigations into the scientific
corpus. I give a brief history of the open access movement, discuss novel
journalistic practices, and an overview of data-driven investigation of the
scientific corpus. I argue that particularly in an era where the veracity of
many research studies has been called into question, scientific literature text
mining should be one of the key motivations for open access publishing, not
only in the basic sciences, but in the engineering and applied sciences as
well. The enormous benefits of unrestricted access to the research literature
should prompt scholars from all disciplines to lend their vocal support to
enabling legal, wholesale access to the scientific literature as part of a data
science pipeline.


title: Semidualities from products of trees

abstract:
  Let $K$ be a global function field of characteristic $p$, and let $\Gamma$ be
a finite-index subgroup of an arithmetic group defined with respect to $K$ and
such that any torsion element of $\Gamma$ is a $p$-torsion element. We define
semiduality groups, and we show that $\Gamma$ is a
$\mathbb{Z}[1/p]$-semiduality group if $\Gamma$ acts as a lattice on a product
of trees. We also give other examples of semiduality groups, including
lamplighter groups, Diestel-Leader groups, and countable sums of finite groups.


title: Fermion-antifermion scattering via boson exchange in a trapped ion

abstract:
  Quantum field theories describe a wide variety of fundamental phenomena in
physics. However, their study often involves cumbersome numerical simulations.
Quantum simulators, on the other hand, may outperform classical computational
capacities due to their potential scalability. Here, we report an experimental
realization of a quantum simulation of fermion-antifermion scattering mediated
by bosonic modes, using a multilevel trapped ion, which is a simplified model
of fermion scattering in both perturbative and nonperturbative quantum
electrodynamics. The simulated model exhibits prototypical features in quantum
field theory including particle pair creation and annihilation, as well as
self-energy interactions. These are experimentally observed by manipulating
four internal levels of a $^{171}\mathrm{Yb}^{+}$ trapped ion, where we encode
the fermionic modes, and two motional degrees of freedom that simulate the
bosonic modes. Our experiment establishes an avenue towards the efficient
implementation of fermionic and bosonic quantum field modes, which may prove
useful in scalable studies of quantum field theories in perturbative and
nonperturbative regimes.


title: Spin-Mechanical Inertia in Antiferromagnet

abstract:
  Angular momentum conservation has served as a guiding principle in the
interplay between spin dynamics and mechanical rotations. However, in an
antiferromagnet with vanishing magnetization, new fundamental rules are
required to properly describe spin-mechanical phenomena. Here we show that the
N\'eel order dynamics affects the mechanical motion of a rigid body by
modifying its inertia tensor in the presence of strong magnetocrystalline
anisotropy. This effect depends on temperature when magnon excitations are
considered. Such a spin-mechanical inertia can produce measurable consequences
at small scales.


title:
A Cayley graph for $F_{2}\times F_{2}$ which is not minimally almost
  convex

abstract:
  We give an example of a Cayley graph $\Gamma$ for the group $F_{2}\times
F_{2}$ which is not minimally almost convex (MAC). On the other hand, the
standard Cayley graph for $F_{2}\times F_{2}$ does satisfy the falsification by
fellow traveler property (FFTP). As a result, we show that any Cayley graph
property $K$ which satisfies $\text{FFTP}\Rightarrow K\Rightarrow\text{MAC}$ is
dependent on the generating set. This includes the well known properties FFTP
and almost convexity, which were already known to depend on the generating set.
This also shows the new results that Poenaru's condition $P(2)$ and the
basepoint loop shortening property both depend on the generating set. We also
show that the Cayley graph $\Gamma$ does not have the loop shortening property,
so this property also depends on the generating set.


title:
On the penalty stabilization mechanism for upwind discontinuous Galerkin
  formulations of first order hyperbolic systems

abstract:
  Penalty fluxes are dissipative numerical fluxes for high order discontinuous
Galerkin (DG) methods which depend on a penalization parameter. We investigate
the dependence of the spectra of high order DG discretizations on this
parameter, and show that as its value increases, the spectra of the DG
discretization splits into two disjoint sets of eigenvalues. One set converges
to the eigenvalues of a conforming discretization, while the other set
corresponds to spurious eigenvalues which are damped proportionally to the
parameter. Numerical experiments also demonstrate that undamped spurious modes
present in both in the limit of zero and large penalization parameters are
damped for moderate values of the upwind parameter.


title: Geodesic curvature driven surface microdomain formation

abstract:
  Lipid bilayer membranes are not uniform and clusters of lipids in a more
ordered state exist within the generally disorder lipid milieu of the membrane.
These clusters of ordered lipids microdomains are now referred to as lipid
rafts. Recent reports attribute the formation of these microdomains to the
geometrical and molecular mechanical mismatch of lipids of different species on
the boundary. Here we introduce the geodesic curvature to characterize the
geometry of the domain boundary, and develop a geodesic curvature energy model
to describe the formation of these microdomains as a result of energy
minimization. Our model accepts the intrinsic geodesic curvature of any binary
lipid mixture as an input, and will produce microdomains of the given geodesic
curvature as demonstrated by three sets of numerical simulations. Our results
are in contrast to the surface phase separation predicted by the classical
surface Cahn-Hilliard equation, which tends to generate large domains as a
result of the minimizing line tension. Our model provides a direct and
quantified description of the structure inhomogeneity of lipid bilayer
membrane, and can be coupled to the investigations of biological processes on
membranes for which such inhomogeneity plays essential roles.


title:
Quantum control of photonic entanglement with a single sub-wavelength
  structure

abstract:
  Quantum entanglement is the basic resource for most quantum information
schemes. A fundamental problem of using photonic states as carriers of quantum
information is that they interact weakly with matter and that the interaction
volume is typically limited by the wavelength of light. The use of metallic
structures in quantum plasmonics has the potential to alleviate these problems.
Here, we present the first results showing that a single subwavelength
plasmonic nanoaperture can controllably modify the quantum state of light. In
particular, we experimentally demonstrate that two-photon entanglement can be
either completely preserved or completely lost after the interaction with the
nanoaperture solely depending on the relative phase between the quantum states.
We achieve this effect by using a specially engineered two photon state to
match the properties of the nanoaperture. The effect is fundamentally mediated
by quantum interference which occurs at scales smaller than the wavelength of
light. This connection between nano-photonics and quantum optics not only
demonstrates an unprecedented control over light-matter interaction in the
quantum limit, but also probes the fundamental limits of the phenomenon of
quantum interference.


title:
PANIC - A surface science package for the in situ characterization of a
  near-Earth asteroid

abstract:
  This paper presents the results of a mission concept study for an autonomous
micro-scale surface lander also referred to as PANIC - the Pico Autonomous
Near-Earth Asteroid In Situ Characterizer. The lander is based on the shape of
a regular tetrahedron with an edge length of 35 cm, has a total mass of
approximately 12 kg and utilizes hopping as a locomotion mechanism in
microgravity. PANIC houses four scientific instruments in its proposed baseline
configuration which enable the in situ characterization of an asteroid. It is
carried by an interplanetary probe to its target and released to the surface
after rendezvous. Detailed estimates of all critical subsystem parameters were
derived to demonstrate the feasibility of this concept. The study illustrates
that a small, simple landing element is a viable alternative to complex
traditional lander concepts, adding a significant science return to any
near-Earth asteroid (NEA) mission while meeting tight mass budget constraints.


title: Unfolding Genus-2 Orthogonal Polyhedra with Linear Refinement

abstract:
  We show that every orthogonal polyhedron of genus at most 2 can be unfolded
without overlap while using only a linear number of orthogonal cuts (parallel
to the polyhedron edges). This is the first result on unfolding general
orthogonal polyhedra beyond genus-0. Our unfolding algorithm relies on the
existence of at most 2 special leaves in what we call the "unfolding tree"
(which ties back to the genus), so unfolding polyhedra of genus 3 and beyond
requires new techniques.


title: Some Oscillatory Integral Estimates via Real Analysis

abstract:
  We study oscillatory integrals in several variables with analytic, smooth, or
$C^k$ phases satisfying a nondegeneracy condition attributed to Varchenko. With
only real analytic methods, Varchenko's estimates are rediscovered and
generalized. The same methods are pushed further to obtain full asymptotic
expansions of such integrals with analytic and smooth phases, and finite
expansions with error assuming the phase is only $C^k$. The Newton polyhedron
appears naturally in the estimates; in particular, we show precisely how the
exponents appearing in the asymptotic expansions depend only on the geometry of
the Newton polyhedron of the phase. All estimates proven hold for oscillatory
parameter real and nonzero, not just asymptotically.


title:
Observational Evidence of Particle Acceleration Associated with Plasmoid
  Motions

abstract:
  We report a strong association between the particle acceleration and plasma
motions found in the 2010 August 18 solar flare. The plasma motions are tracked
in the extreme-ultraviolet (EUV) images taken by the Atmospheric Imaging
Assembly (AIA) on board the Solar Dynamics Observatory and the Extreme
UltraViolet Imager (EUVI) on the Solar Terrestrial Relation Observatory
spacecraft Ahead, and the signature of particle acceleration was investigated
by using Nobeyama Radioheliograph data. In our previous paper, we reported that
in EUV images many plasma blobs appeared in the current sheet above the flare
arcade. They were ejected bidirectionally along the current sheet, and the
blobs that were ejected sunward collided with the flare arcade. Some of them
collided or merged with each other before they were ejected from the current
sheet. We discovered impulsive radio bursts associated with such plasma motions
(ejection, coalescence, and collision with the post flare loops). The radio
bursts are considered to be the gyrosynchrotron radiation by nonthermal high
energy electrons. In addition, the stereoscopic observation by AIA and EUVI
suggests that plasma blobs had a three-dimensionally elongated structure. We
consider that the plasma blobs were three-dimensional plasmoids (i.e. flux
ropes) moving in a current sheet. We believe that our observation provides
clear evidence of particle acceleration associated with the plasmoid motions.
We discuss possible acceleration mechanisms on the basis of our results.


title: Where are $\chi_{cJ}(3P)$?

abstract:
  In the present work, we propose $Y(4140)$ as the $\chi_{c1}(3P)$ state by
studying the $\chi_{c1} \pi^+ \pi^-$ invariant mass spectrum of the $B\to K
\chi_{c1} \pi^+ \pi^-$ process. In the $D\bar{D}$ invariant mass spectrum of
the $B\to K D\bar{D}$ process, we find a new resonance with the mass and width
to be $ (4083.0 \pm 5.0) $ and $ (24.1 \pm 15.4) $ MeV, respectively, which
could be a good candidate of the $\chi_{c0}(3P)$ state. The theoretical
investigations on the decay behaviors of the $\chi_{cJ}(3P)$ in the present
work support the assignments of the $Y(4140)$ and $Y(4080)$ as the
$\chi_{c1}(3P)$ and $\chi_{c0}(3P)$ states, respectively. In addition, the
$\chi_{c2}(3P)$ state is predicted to be a very narrow state. The results in
the present work could be tested by further experiments in the LHCb and
forthcoming Belle II.


title: Folding with a protein's native shortcut network

abstract:
  A complex network approach to protein folding is proposed. The graph object
is the network of shortcut edges present in a native-state protein (SCN0).
Although SCN0s are found via an intuitive message passing algorithm (S.
Milgram, Psychology Today, May 1967 pp. 61-67), they are meaningful enough that
the logarithm form of their contact order (SCN0_lnCO) correlates significantly
with protein kinetic rates, regardless of protein size. Further, the clustering
coefficient of a SCN0 (CSCN0) can be used to combine protein segments
iteratively within the Restricted Binary Collision model to form the whole
native structure. This simple yet surprisingly effective strategy identified
reasonable folding pathways for 12 small single-domain two-state folders, and
three non-canonical proteins: ACBP (non-two-state), Top7 (non-cooperative) and
DHFR (non-single-domain, > 100 residues). For two-state folders, CSCN0 is
relatable to folding rates, transition-state placement and stability. The
influence of CSCN0 on folding extends to non-native structures. Moreover, SCN
analysis of non-native structures could suggest three fold success factors for
the fast folding Villin headpiece peptide. These results support the view of
protein folding as a bottom-up hierarchical process guided from above by
native-state topology, and could facilitate future constructive demonstrations
of this long held hypothesis for larger proteins.


title:
Densification Strategies for Anytime Motion Planning over Large Dense
  Roadmaps

abstract:
  We consider the problem of computing shortest paths in a dense
motion-planning roadmap $\mathcal{G}$. We assume that~$n$, the number of
vertices of $\mathcal{G}$, is very large. Thus, using any path-planning
algorithm that directly searches $\mathcal{G}$, running in $O(V\textrm{log}V +
E) \approx O(n^2)$ time, becomes unacceptably expensive. We are therefore
interested in anytime search to obtain successively shorter feasible paths and
converge to the shortest path in $\mathcal{G}$. Our key insight is to provide
existing path-planning algorithms with a sequence of increasingly dense
subgraphs of $\mathcal{G}$. We study the space of all ($r$-disk) subgraphs of
$\mathcal{G}$. We then formulate and present two densification strategies for
traversing this space which exhibit complementary properties with respect to
problem difficulty. This inspires a third, hybrid strategy which has favourable
properties regardless of problem difficulty. This general approach is then
demonstrated and analyzed using the specific case where a low-dispersion
deterministic sequence is used to generate the samples used for $\mathcal{G}$.
Finally we empirically evaluate the performance of our strategies for random
scenarios in $\mathbb{R}^{2}$ and $\mathbb{R}^{4}$ and on manipulation planning
problems for a 7 DOF robot arm, and validate our analysis.


title: A Note on Vectorial AdS$_5$/CFT$_4$ Duality for Spin-$j$ Boundary Theory

abstract:
  The vectorial holographic correspondences between higher-spin theories in
AdS$_5$ and free vector models on the boundary are extended to the cases where
the latter is described by free massless spin-$j$ field. The dual higher-spin
theory in the bulk does not include gravity and can only be defined on rigid
AdS$_5$ background with $S^4$ boundary. We discuss various properties of these
rather special higher-spin theories and calculate their one-loop free energies.
We show that the result is proportional to the same quantity for spin-$j$
doubleton treated as if it is a AdS$_5$ field. Finally, we consider even more
special case where the boundary theory itself is given by an infinite tower of
massless higher-spin fields.


title: Checking for prior-data conflict using prior to posterior divergences

abstract:
  When using complex Bayesian models to combine information, the checking for
consistency of the information being combined is good statistical practice.
Here a new method is developed for detecting prior-data conflicts in Bayesian
models based on comparing the observed value of a prior to posterior divergence
to its distribution under the prior predictive distribution for the data. The
divergence measure used in our model check is a measure of how much beliefs
have changed from prior to posterior, and can be thought of as a measure of the
overall size of a relative belief function. It is shown that the proposed
method is intuitive, has desirable properties, can be extended to hierarchical
settings, and is related asymptotically to Jeffreys' and reference prior
distributions. In the case where calculations are difficult, the use of
variational approximations as a way of relieving the computational burden is
suggested. The methods are compared in a number of examples with an alternative
but closely related approach in the literature based on the prior predictive
distribution of a minimal sufficient statistic.


title: Faces of highest weight modules and the universal Weyl polyhedron

abstract:
  Let $V$ be a highest weight module over a Kac-Moody algebra $\mathfrak{g}$,
and let conv $V$ denote the convex hull of its weights. We determine the
combinatorial isomorphism type of conv $V$, i.e. we completely classify the
faces and their inclusions. In the special case where $\mathfrak{g}$ is
semisimple, this brings closure to a question studied by Cellini-Marietti [IMRN
2015] for the adjoint representation, and by Khare [J. Algebra 2016; Trans.
Amer. Math. Soc. 2017] for most modules. The determination of faces of
finite-dimensional modules up to the Weyl group action and some of their
inclusions also appears in previous work of Satake [Ann. of Math. 1960],
Borel-Tits [IHES Publ. Math. 1965], Vinberg [Izv. Akad. Nauk 1990], and
Casselman [Austral. Math. Soc. 1997].
  For any subset of the simple roots, we introduce a remarkable convex cone
which we call the universal Weyl polyhedron, which controls the convex hulls of
all modules parabolically induced from the corresponding Levi factor. Namely,
the combinatorial isomorphism type of the cone stores the classification of
faces for all such highest weight modules, as well as how faces degenerate as
the highest weight gets increasingly singular. To our knowledge, this cone is
new in finite and infinite type.
  We further answer a question of Michel Brion, by showing that the
localization of conv $V$ along a face is always the convex hull of the weights
of a parabolically induced module. Finally, as we determine the inclusion
relations between faces representation-theoretically from the set of weights,
without recourse to convexity, we answer a similar question for highest weight
modules over symmetrizable quantum groups.


title: Toral and spherical Aluthge transforms of $2$-variable weighted shifts

abstract:
  We introduce two natural notions of Aluthge transforms (toral and spherical)
for 2-variable weighted shifts and study their basic properties. Next, we study
the class of spherically quasinormal $2$-variable weighted shifts, which are
the fixed points for the spherical Aluthge transform. Finally, we briefly
discuss the relation between spherically quasinormal and spherically isometric
2-variable weighted shifts.


title: WES - Weihai Echelle Spectrograph

abstract:
  The Weihai Echelle Spectrograph (WES) is the first fiber-fed echelle
spectrograph for astronomical observation in China. It is primarily used for
chemical abundance and asteroseismology studies of nearby bright stars, as well
as radial velocity detections for exoplanets. The optical design of WES is
based on the widely demonstrated and well-established white-pupil concept. We
describe the WES in detail and present some examples of its performance. A
single exposure echelle image covers the spectral region 371-1,100 nm in 107
spectral orders over the rectangular CCD. The spectral resolution
$R=\lambda/\Delta\lambda$ changes from 40,600 to 57,000 through adjusting the
entrance slit width from full to 2.2 pixels sampling at the fiber-exit. The
limiting magnitude scales to $V=8$ with a signal-to-noise ratio (SNR) of more
than 100 in $V$ for an hour exposure, at the spectral resolution
R$\approx$40,000 in the median seeing of 1.7$^{\prime\prime}$ at Weihai
Observatory (WHO) for the 1-meter telescope. The radial velocity (RV)
measurement accuracy of WES is estimated to be $<$10 m/s in 10 months (302
days) and better than 15 m/s in 4.4 years (1,617 days) in the recent data
processing.


title:
Morphology of Open Clusters NGC 1857 and Czernik 20 using Clustering
  Algorithms

abstract:
  The morphology and cluster membership of the Galactic open clusters - Czernik
20 and NGC 1857 were analyzed using two different clustering algorithms. We
present the maiden use of density-based spatial clustering of applications with
noise (DBSCAN) to determine open cluster morphology from spatial distribution.
The region of analysis has also been spatially classified using a statistical
membership determination algorithm. We utilized near infrared (NIR) data for a
suitably large region around the clusters from the United Kingdom Infrared Deep
Sky Survey Galactic Plane Survey star catalogue database, and also from the Two
Micron All Sky Survey star catalogue database. The densest regions of the
cluster morphologies (1 for Czernik 20 and 2 for NGC 1857) thus identified were
analyzed with a K-band extinction map and color-magnitude diagrams (CMDs). To
address significant discrepancy in known distance and reddening parameters, we
carried out field decontamination of these CMDs and subsequent isochrone
fitting of the cleaned CMDs to obtain reliable distance and reddening
parameters for the clusters (Czernik 20: D = 2900 pc; E(J-K) = 0.33; NGC 1857:
D = 2400 pc; E(J-K) = 0.18-0.19). The isochrones were also used to convert the
luminosity functions for the densest regions of Czernik 20 and NGC 1857 into
mass function, to derive their slopes. Additionally, a previously unknown
over-density consistent with that of a star cluster is identified in the region
of analysis.


title:
Towards Self-Consistent Modelling of the Sgr A* Accretion Flow: Linking
  Theory and Observation

abstract:
  The interplay between supermassive black holes (SMBHs) and their environments
is believed to command an essential role in galaxy evolution. The majority of
these SMBHs are in the radiative inefficient accretion phase where this
interplay remains elusive, but suggestively important, due to few observational
constraints. To remedy this, we directly fit 2-D hydrodynamic simulations to
Chandra observations of Sgr A* with Markov Chain Monte Carlo sampling,
self-consistently modelling the 2-D inflow-outflow solution for the first time.
We find the temperature and density at flow onset are consistent with the
origin of the gas in the stellar winds of massive stars in the vicinity of Sgr
A*. We place the first observational constraints on the angular momentum of the
gas and estimate the centrifugal radius, r$_c$ $\approx$ 0.056 r$_b$
$\approx8\times10^{-3}$ pc, where r$_b$ is the Bondi radius. Less than 1\% of
the inflowing gas accretes onto the SMBH, the remainder being ejected in a
polar outflow. For the first time, we decouple the quiescent point-like
emission from the spatially extended flow. We find this point-like emission,
accounting for $\sim4$\% of the quiescent flux, is spectrally too steep to be
explained by unresolved flares, nor Bremsstrahlung, but is likely a combination
of a relatively steep synchrotron powerlaw and the high energy tail of
inverse-Compton emission. With this self-consistent model of the accretion flow
structure, we make predictions for the flow dynamics and discuss how future
X-ray spectroscopic observations can further our understanding of the Sgr A*
accretion flow.


title: Rethinking Sketching as Sampling: A Graph Signal Processing Approach

abstract:
  Sampling of signals belonging to a low-dimensional subspace has
well-documented merits for dimensionality reduction, limited memory storage,
and online processing of streaming network data. When the subspace is known,
these signals can be modeled as bandlimited graph signals. Most existing
sampling methods are designed to minimize the error incurred when
reconstructing the original signal from its samples. Oftentimes these
parsimonious signals serve as inputs to computationally-intensive linear
operators. Hence, interest shifts from reconstructing the signal itself towards
approximating the output of the prescribed linear operator efficiently. In this
context, we propose a novel sampling scheme that leverages graph signal
processing, exploiting the low-dimensional (bandlimited) structure of the input
as well as the transformation whose output we wish to approximate. We formulate
problems to jointly optimize sample selection and a sketch of the target linear
transformation, so when the latter is applied to the sampled input signal the
result is close to the desired output. Similar sketching as sampling ideas are
also shown effective in the context of linear inverse problems. Because these
designs are carried out off line, the resulting sampling plus
reduced-complexity processing pipeline is particularly useful for data that are
acquired or processed in a sequential fashion, where the linear operator has to
be applied fast and repeatedly to successive inputs or response signals.
Numerical tests showing the effectiveness of the proposed algorithms include
classification of handwritten digits from as few as 20 out of 784 pixels in the
input images and selection of sensors from a network deployed to carry out a
distributed parameter estimation task.


title: Heisenberg-limited Sagnac Interferometer with Multi-particle States

abstract:
  The Sagnac interferometry has been widely used to measure rotation frequency.
Beyond the conventional single-particle Sagnac interferometry, we propose an
atomic Sagnac interferometry via multi-particle entangled states. In our
scheme, an ensemble of entangled two-state Bose atoms are moved in a ring by a
state-dependent rotating potential and then are recombined for interference via
Ramsey pulses after a specific time determined by the state-dependent rotating
potential. The ultimate rotation sensitivity can be improved to the Heisenberg
limit if the initial internal degrees of freedom are entangled. By implementing
parity measurement, the ultimate measurement precision can be saturated and the
achieved measurement precisions approach to the Heisenberg limit. Our results
provide a promising way to exploit many-body quantum entanglement in precision
metrology of rotation sensing.


title: Universal stability of two-dimensional traditional semiconductors

abstract:
  Interest in two dimensional materials has exploded in recent years. Not only
are they studied due to their novel electronic properties, such as the emergent
Dirac Fermion in graphene, but also as a new paradigm in which stacking layers
of distinct two dimensional materials may enable different functionality or
devices. Here, through first-principles theory, we reveal a large new class of
two dimensional materials which are derived from traditional III-V, II-VI, and
I-VII semiconductors. It is found that in the ultra-thin limit all of the
traditional binary semi-conductors studied (a series of 26 semiconductors)
stabilize in a two dimensional double layer honeycomb (DLHC) structure, as
opposed to the wurtzite or zinc-blende structures associated with three
dimensional bulk. Not only does this greatly increase the landscape of
two-dimensional materials, but it is shown that in the double layer honeycomb
form, even ordinary semiconductors, such as GaAs, can exhibit exotic
topological properties.


title:
Decomposition of Reachable Sets and Tubes for a Class of Nonlinear
  Systems

abstract:
  Reachability analysis provides formal guarantees for performance and safety
properties of nonlinear control systems. Here, one aims to compute the backward
reachable set (BRS) or tube (BRT) -- the set of states from which the system
can be driven into a target set at a particular time or within a time interval,
respectively. The computational complexity of current approaches scales
exponentially, making application to high-dimensional systems intractable. We
propose a technique that decomposes the dynamics of a general class of
nonlinear systems into subsystems which may be coupled through common states,
controls, and disturbances. Despite this coupling, BRSs and BRTs can be
computed efficiently using our technique without incurring additional
approximation errors and without the need for linearizing dynamics or
approximating sets as polytopes. Computations of BRSs and BRTs now become
orders of magnitude faster, and for the first time BRSs and BRTs for many
high-dimensional nonlinear control systems can be computed using the
Hamilton-Jacobi (HJ) formulation. In situations involving bounded adversarial
disturbances, our proposed method can obtain slightly conservative results. We
demonstrate our theory by numerically computing BRSs and BRTs using the HJ
formulation for several systems, including the 6D Acrobatic Quadrotor and the
10D Near-Hover Quadrotor.


title: Interference-Constrained Pricing for D2D Networks

abstract:
  The concept of device-to-device (D2D) communications underlaying cellular
networks opens up potential benefits for improving system performance but also
brings new challenges such as interference management. In this paper, we
propose a pricing framework for interference management from the D2D users to
the cellular system, where the base station (BS) protects itself (or its
serving cellular users) by pricing the crosstier interference caused from the
D2D users. A Stackelberg game is formulated to model the interactions between
the BS and D2D users. Specifically, the BS sets prices to a maximize its
revenue (or any desired utility) subject to an interference temperature
constraint. For given prices, the D2D users competitively adapt their power
allocation strategies for individual utility maximization. We first analyze the
competition among the D2D users by noncooperative game theory and an iterative
based distributed power allocation algorithm is proposed. Then, depending on
how much network information the BS knows, we develop two optimal algorithms,
one for uniform pricing with limited network information and the other for
differentiated pricing with global network information. The uniform pricing
algorithm can be implemented by a fully distributed manner and requires minimum
information exchange between the BS and D2D users, and the differentiated
pricing algorithm is partially distributed and requires no iteration between
the BS and D2D users. Then a suboptimal differentiated pricing scheme is
proposed to reduce complexity and it can be implemented in a fully distributed
fashion. Extensive simulations are conducted to verify the proposed framework
and algorithms.


title: Complementarity via error-free measurement in a two-path interferometer

abstract:
  We study both the wave-like behavior and particle-like behavior in a general
Mach-Zehnder interferometer with its asymmetric beam splitter. A error-free
measurement in the detector is used to extract the which-path information. The
fringe visibility V and the which-path information Ipath are derived: their
complementary relation V + Ipath less than or equal to 1 are found, and the
condition for the equality is also presented.


title: Gluon Sivers function in a light-cone spectator model

abstract:
  We calculate the gluon Sivers function of the proton in the valence-$x$
region using a light-cone spectator model with the presence of the gluon degree
of freedom. We obtain the values of the parameters by fitting the model
resulting gluon density distribution to the known parametrization. We find that
our results agree with the recent phenomenological extraction of the gluon
Sivers function after considering the evolution effect. We also estimate the
mean transverse momentum of the gluon in a transversely polarized proton and
find that it is within the range implied by the Burkardt sum rule.


title:
Improving Twitter Sentiment Classification via Multi-Level
  Sentiment-Enriched Word Embeddings

abstract:
  Most of existing work learn sentiment-specific word representation for
improving Twitter sentiment classification, which encoded both n-gram and
distant supervised tweet sentiment information in learning process. They assume
all words within a tweet have the same sentiment polarity as the whole tweet,
which ignores the word its own sentiment polarity. To address this problem, we
propose to learn sentiment-specific word embedding by exploiting both lexicon
resource and distant supervised information. We develop a multi-level
sentiment-enriched word embedding learning method, which uses parallel
asymmetric neural network to model n-gram, word level sentiment and tweet level
sentiment in learning process. Experiments on standard benchmarks show our
approach outperforms state-of-the-art methods.


title: Algebraic Multigrid Preconditioners for Multiphase Flow in Porous Media

abstract:
  Multiphase flow is a critical process in a wide range of applications,
including carbon sequestration, contaminant remediation, and groundwater
management. Typically, this process is modeled by a nonlinear system of partial
differential equations derived by considering the mass conservation of each
phase (e.g., oil, water), along with constitutive laws for the relationship of
phase velocity to phase pressure. In this study, we develop and study efficient
solution algorithms for solving the algebraic systems of equations derived from
a fully coupled and time-implicit treatment of models of multiphase flow. We
explore the performance of several preconditioners based on algebraic multigrid
(AMG) for solving the linearized problem, including "black-box" AMG applied
directly to the system, a new version of constrained pressure residual
multigrid (CPR-AMG) preconditioning, and a new preconditioner derived using an
approximate Schur complement arising from the block factorization of the
Jacobian. We show that the new methods are the most robust with respect to
problem character as determined by varying effects of capillary pressures, and
we show that the block factorization preconditioner is both efficient and
scales optimally with problem size.


title:
A Certifiably Correct Algorithm for Synchronization over the Special
  Euclidean Group

abstract:
  Many geometric estimation problems take the form of synchronization over the
special Euclidean group: estimate the values of a set of poses given noisy
measurements of a subset of their pairwise relative transforms. This problem is
typically formulated as a maximum-likelihood estimation that requires solving a
nonconvex nonlinear program, which is computationally intractable in general.
Nevertheless, in this paper we present an algorithm that is able to efficiently
recover certifiably globally optimal solutions of this estimation problem in a
non-adversarial noise regime. The crux of our approach is the development of a
semidefinite relaxation of the maximum-likelihood estimation whose minimizer
provides the exact MLE so long as the magnitude of the noise corrupting the
available measurements falls below a certain critical threshold; furthermore,
whenever exactness obtains, it is possible to verify this fact a posteriori,
thereby certifying the optimality of the recovered estimate. We develop a
specialized optimization scheme for solving large-scale instances of this
semidefinite relaxation by exploiting its low-rank, geometric, and
graph-theoretic structure to reduce it to an equivalent optimization problem on
a low-dimensional Riemannian manifold, and then design a Riemannian
truncated-Newton trust-region method to solve this reduction efficiently. We
combine this fast optimization approach with a simple rounding procedure to
produce our algorithm, SE-Sync. Experimental evaluation on a variety of
simulated and real-world pose-graph SLAM datasets shows that SE-Sync is capable
of recovering globally optimal solutions when the available measurements are
corrupted by noise up to an order of magnitude greater than that typically
encountered in robotics applications, and does so at a computational cost that
scales comparably with that of direct Newton-type local search techniques.


title: Submodular Maximization over Sliding Windows

abstract:
  In this paper we study the extraction of representative elements in the data
stream model in the form of submodular maximization. Different from the
previous work on streaming submodular maximization, we are interested only in
the recent data, and study the maximization problem over sliding windows. We
provide a general reduction from the sliding window model to the standard
streaming model, and thus our approach works for general constraints as long as
there is a corresponding streaming algorithm in the standard streaming model.
As a consequence, we obtain the first algorithms in the sliding window model
for maximizing a monotone/non-monotone submodular function under cardinality
and matroid constraints. We also propose several heuristics and show their
efficiency in real-world datasets.


title: Spin readout of trapped electron qubits

abstract:
  We propose a scheme to read out the spin of a single electron quantum bit in
a surface Paul trap using oscillating magnetic field gradients. The readout
sequence is composed of cooling, driving, amplification and detection of the
electron's motion. We study the scheme in the presence of noise and trap
anharmonicities at liquid helium temperatures. An analysis of the the four
procedures shows short measurement times ($25~\mu$s) and high fidelities
($99.7\%$) are achievable with realistic experimental parameters. Our scheme
performs the function of fluorescence detection in ion trapping schemes,
highlighting the potential to built all-electric quantum computers based on
trapped electron spin qubits.


title: Thermodynamics of a Sufficient Small Singly Spinning Kerr-AdS Black Hole

abstract:
  In this paper, we will analyze the thermodynamics of a small singly spinning
Kerr-AdS black hole. As the black hole will be sufficient small, its
temperature will be large and so we can not neglect the effects of thermal
fluctuations. We will demonstrate that these thermal fluctuations correct the
entropy of singly spinning Kerr-AdS black hole by a logarithmic correction
term. We will analyze the implications of the logarithmic correction on other
thermodynamic properties of this black hole, and analyze the stability of such
a black hole. We will observe that this form of correction becomes important
when the size of the black hole is sufficient small. We will also analyze the
effect of these thermal fluctuations on the critical phenomena for such a black
hole.


title: Enhanced Thermoelectric Properties of Dirac Semimetal Cd3As2

abstract:
  We report an investigation of temperature- and doping-dependent
thermoelectric behaviors of topological semimetal Cd3As2. The electrical
conductivity, thermal conductivity, Seebeck coefficient, and figure of merit
(ZT) are calculated by using Boltzmann transport theory. The calculated
thermoelectric properties of the pristine Cd3As2 match well the experimental
results. The electron or hole doping, especially the latter, is found improving
much the thermoelectric behaviors of the material. The optimum merit ZT of
Cd3As2 with electron doping is found to be about 0.5 at T=700 K with n=1x1020
cm-3, much larger than the maximum experimental value obtained for the pristine
Cd3As2 (~0.15). For the p-type Cd3As2, the maximal value of the Seebeck
coefficient as a function of temperature increases apparently with the increase
of the hole doping concentration and its position shifts drastically towards
the lower temperature region compared to that of the n-type Cd3As2, leading to
the optimum merit ZT of about 0.5 obtained at low temperature of 500K (p=1x1020
cm-3) in the p-type Cd3As2.


title:
Significantly super-Chandrasekhar limiting mass white dwarfs and their
  consequences

abstract:
  Since 2012, we have initiated a new idea showing that the mass of highly
magnetized or modified Einstein's gravity induced white dwarfs could be
significantly super-Chandrasekhar with a different mass-limit. This discovery
has several important consequences, including explanation of peculiar,
over-luminous type Ia supernovae, soft gamma-ray repeaters and anomalous X-ray
pulsars without invoking extraordinarily strong, yet unobserved, magnetic
fields. It further argues for a possible second standard candle. Based on
simpler calculations, these white dwarfs are also shown to be much less
luminous than their standard counter-parts (of low magnetic fields). This
discovery altogether initiates a new field of research.


title: An innovative concept for the AsteroidFinder/SSB focal plane assembly

abstract:
  This paper gives a summary on the system concept and design of the focal
plane assembly of AsteroidFinder/SSB, a small satellite mission which is
currently under development at the German Aerospace Center (DLR). An athermal
design concept has been developed in accordance to the requirements of the
instrument and spacecraft. Key aspects leading to this approach have been a
trade-off study of the mechanical telescope interface, the definition of
electrical and thermal interfaces and a material selection which minimizes
thermally induced stresses. As a novelty, the structure will be manufactured
from a machinable AlN-BN composite ceramic. To enable rapid design iterations
and development, an integrated modeling approach has been used to conduct a
thermo-mechanical analysis of the proposed concept in order to prove its
feasibility. The steady-state temperature distribution for various load cases
and the resulting stress and strain within the assembly have both been computed
using a finite element simulation.


title:
A Benchmark Dataset and Saliency-guided Stacked Autoencoders for
  Video-based Salient Object Detection

abstract:
  Image-based salient object detection (SOD) has been extensively studied in
the past decades. However, video-based SOD is much less explored since there
lack large-scale video datasets within which salient objects are unambiguously
defined and annotated. Toward this end, this paper proposes a video-based SOD
dataset that consists of 200 videos (64 minutes). In constructing the dataset,
we manually annotate all objects and regions over 7,650 uniformly sampled
keyframes and collect the eye-tracking data of 23 subjects that free-view all
videos. From the user data, we find salient objects in video can be defined as
objects that consistently pop-out throughout the video, and objects with such
attributes can be unambiguously annotated by combining manually annotated
object/region masks with eye-tracking data of multiple subjects. To the best of
our knowledge, it is currently the largest dataset for video-based salient
object detection.
  Based on this dataset, this paper proposes an unsupervised baseline approach
for video-based SOD by using saliency-guided stacked autoencoders. In the
proposed approach, multiple spatiotemporal saliency cues are first extracted at
pixel, superpixel and object levels. With these saliency cues, stacked
autoencoders are unsupervisedly constructed which automatically infer a
saliency score for each pixel by progressively encoding the high-dimensional
saliency cues gathered from the pixel and its spatiotemporal neighbors.
Experimental results show that the proposed unsupervised approach outperforms
30 state-of-the-art models on the proposed dataset, including 19 image-based &
classic (unsupervised or non-deep learning), 6 image-based & deep learning, and
5 video-based & unsupervised. Moreover, benchmarking results show that the
proposed dataset is very challenging and has the potential to boost the
development of video-based SOD.


title: Setting a disordered password on a photonic memory

abstract:
  Encryption is a vital tool of information technology protecting our data in
the world with ubiquitous computers. While photons are regarded as ideal
information carriers, it is a must to implement such data protection on
all-optical storage. However, the intrinsic risk of data breaches in existing
schemes of photonic memory was never addressed. We theoretically demonstrate
the first protocol using spatially disordered laser fields to encrypt data
stored on an optical memory, namely, encrypted photonic memory. Compare with a
digital key, a continuous disorder encrypts stored light pulses with a rather
long key length against brute-force attacks. To address the broadband storage,
we also investigate a novel scheme of disordered echo memory with a high
fidelity approaching unity. Our results pave novel ways to encrypt different
schemes of photonic memory based on quantum optics and raise the security level
of photonic information technology.


title:
Embedding Deep Metric for Person Re-identication A Study Against Large
  Variations

abstract:
  Person re-identification is challenging due to the large variations of pose,
illumination, occlusion and camera view. Owing to these variations, the
pedestrian data is distributed as highly-curved manifolds in the feature space,
despite the current convolutional neural networks (CNN)'s capability of feature
extraction. However, the distribution is unknown, so it is difficult to use the
geodesic distance when comparing two samples. In practice, the current deep
embedding methods use the Euclidean distance for the training and test. On the
other hand, the manifold learning methods suggest to use the Euclidean distance
in the local range, combining with the graphical relationship between samples,
for approximating the geodesic distance. From this point of view, selecting
suitable positive i.e. intra-class) training samples within a local range is
critical for training the CNN embedding, especially when the data has large
intra-class variations. In this paper, we propose a novel moderate positive
sample mining method to train robust CNN for person re-identification, dealing
with the problem of large variation. In addition, we improve the learning by a
metric weight constraint, so that the learned metric has a better
generalization ability. Experiments show that these two strategies are
effective in learning robust deep metrics for person re-identification, and
accordingly our deep model significantly outperforms the state-of-the-art
methods on several benchmarks of person re-identification. Therefore, the study
presented in this paper may be useful in inspiring new designs of deep models
for person re-identification.


title:
MusicMood: Predicting the mood of music from song lyrics using machine
  learning

abstract:
  Sentiment prediction of contemporary music can have a wide-range of
applications in modern society, for instance, selecting music for public
institutions such as hospitals or restaurants to potentially improve the
emotional well-being of personnel, patients, and customers, respectively. In
this project, music recommendation system built upon on a naive Bayes
classifier, trained to predict the sentiment of songs based on song lyrics
alone. The experimental results show that music corresponding to a happy mood
can be detected with high precision based on text features obtained from song
lyrics.


title:
Gravitational instability of filamentary molecular clouds, including
  ambipolar diffusion

abstract:
  The gravitational instability of a filamentary molecular cloud in non-ideal
magnetohydrodynamics is investigated. The filament is assumed to be in
hydrostatic equilibrium. We add the effect of ambipolar diffusion to the
filament which is threaded by an initial uniform axial magnetic field along its
axis. We write down the fluid equations in cylindrical coordinates and perform
linear perturbation analysis. We integrate the resultant differential equations
and then derive the numerical dispersion relation. We find that, a more
efficient ambipolar diffusion leads to an enhancement of the growth of the most
unstable mode, and to increase of the fragmentation scale of the filament.


title: On recovering solutions for SPDEs from their averages

abstract:
  We study linear stochastic partial differential equations of parabolic type.
We consider a new boundary value problem where a Cauchy condition is replaced
by a prescribed average of the solution either over time and probabilistic
space for forward SPDEs and over time for backward SPDEs. Well-posedness,
existence, uniqueness, and a regularity of the solution for this new problem
are obtained. In particular, this can be considered as a possibility to recover
a solution of a forward SPDE in a setting where its values at the initial time
are unknown, and where the average of the solution over time and probability
space is observable, as well as the input processes.


title:
Coulomb interactions induced perfect spin filtering effect in a
  quadruple quantum-dot cell

abstract:
  A quadruple quantum-dot (QQD) cell is proposed as a spin filter. The
transport properties of the QQD cell were studied in linear response regime on
the basis of the equations of motion for retarded Green's functions. The
developed approach allowed us to take into account the influence of both intra-
and interdot Coulomb interactions on carriers' spin polarization. It was shown
that the presence of the insulating bands in the conductance due to the Coulomb
correlations results in the emergence of spin-polarized windows (SPWs) in
magnetic field leading to the high spin polarization. We demonstrated the SPWs
can be effectively manipulated by gate fields and considering the hopping
between central dots in both isotropic and anisotropic regimes.


title: Deep fusion of visual signatures for client-server facial analysis

abstract:
  Facial analysis is a key technology for enabling human-machine interaction.
In this context, we present a client-server framework, where a client transmits
the signature of a face to be analyzed to the server, and, in return, the
server sends back various information describing the face e.g. is the person
male or female, is she/he bald, does he have a mustache, etc. We assume that a
client can compute one (or a combination) of visual features; from very simple
and efficient features, like Local Binary Patterns, to more complex and
computationally heavy, like Fisher Vectors and CNN based, depending on the
computing resources available. The challenge addressed in this paper is to
design a common universal representation such that a single merged signature is
transmitted to the server, whatever be the type and number of features computed
by the client, ensuring nonetheless an optimal performance. Our solution is
based on learning of a common optimal subspace for aligning the different face
features and merging them into a universal signature. We have validated the
proposed method on the challenging CelebA dataset, on which our method
outperforms existing state-of-the-art methods when rich representation is
available at test time, while giving competitive performance when only simple
signatures (like LBP) are available at test time due to resource constraints on
the client.


title:
Discrete linear Weingarten surfaces with singularities in Riemannian and
  Lorentzian spaceforms

abstract:
  In this paper we define and analyze singularities of discrete linear
Weingarten surfaces with Weierstrass-type representations in $3$-dimensional
Riemannian and Lorentzian spaceforms. In particular, we discuss singularities
of discrete surfaces with non-zero constant Gaussian curvature, and parallel
surfaces of discrete minimal and maximal surfaces, and discrete constant mean
curvature $1$ surfaces in de Sitter $3$-space, including comparisons with
different previously known definitions of such singularities.


title: Product-based Neural Networks for User Response Prediction

abstract:
  Predicting user responses, such as clicks and conversions, is of great
importance and has found its usage in many Web applications including
recommender systems, web search and online advertising. The data in those
applications is mostly categorical and contains multiple fields; a typical
representation is to transform it into a high-dimensional sparse binary feature
representation via one-hot encoding. Facing with the extreme sparsity,
traditional models may limit their capacity of mining shallow patterns from the
data, i.e. low-order feature combinations. Deep models like deep neural
networks, on the other hand, cannot be directly applied for the
high-dimensional input because of the huge feature space. In this paper, we
propose a Product-based Neural Networks (PNN) with an embedding layer to learn
a distributed representation of the categorical data, a product layer to
capture interactive patterns between inter-field categories, and further fully
connected layers to explore high-order feature interactions. Our experimental
results on two large-scale real-world ad click datasets demonstrate that PNNs
consistently outperform the state-of-the-art models on various metrics.


title:
Excitonic pairing and insulating transition in two-dimensional
  semi-Dirac semimetals

abstract:
  A sufficiently strong long-range Coulomb interaction can induce excitonic
pairing in gapless Dirac semimetals, which generates a finite gap and drives
semimetal-insulator quantum phase transition. This phenomenon is in close
analogy to dynamical chiral symmetry breaking in high energy physics. In most
realistic Dirac semimetals, including suspended graphene, Coulomb interaction
is too weak to open an excitonic gap. The Coulomb interaction plays a more
important role at low energies in a two-dimensional semi-Dirac semimetal, in
which the fermion spectrum is linear in one component of momenta and quadratic
in the other, than a Dirac semimetal, and indeed leads to breakdown of Fermi
liquid theory. We study dynamical excitonic gap generation in a two-dimensional
semi-Dirac semimetal by solving the Dyson-Schwinger equation, and show that a
moderately strong Coulomb interaction suffices to induce excitonic pairing.
Additional short-range four-fermion coupling tends to promote excitonic
pairing. Among the available semi-Dirac semimetals, we find that
TiO$_{2}$/VO$_{2}$ nanostructure provides a promising candidate for the
realization of excitonic insulator. We also apply the renormalziation group
method to analyze the strong coupling between the massless semi-Dirac fermions
and the quantum critical fluctuation of excitonic order parameter at the
semimetal-insulator quantum critical point, and reveal non-Fermi liquid
behaviors of semi-Dirac fermions.


title:
Fixed Point Algorithm for Solving Nonmonotone Variational Inequalities
  in Nonnegative Matrix Factorization

abstract:
  Nonnegative matrix factorization (NMF), which is the approximation of a data
matrix as the product of two nonnegative matrices, is a key issue in machine
learning and data analysis. One approach to NMF is to formulate the problem as
a nonconvex optimization problem of minimizing the distance between a data
matrix and the product of two nonnegative matrices with nonnegativity
constraints and then solve the problem using an iterative algorithm. The
algorithms commonly used are the multiplicative update algorithm and the
alternating least-squares algorithm. Although both algorithms converge quickly,
they may not converge to a stationary point to the problem that is equal to the
solution to a nonmonotone variational inequality for the gradient of the
distance function. This paper presents an iterative algorithm for solving the
problem that is based on the Krasnosel'ski\u\i-Mann fixed point algorithm.
Convergence analysis showed that, under certain assumptions, any accumulation
point of the sequence generated by the proposed algorithm belongs to the
solution set of the variational inequality. Application of the {\tt 'mult'} and
{\tt'als'} algorithms in MATLAB and the proposed algorithm to various NMF
problems showed that the proposed algorithm had fast convergence and was
effective.


title: Compton scattering in the Endpoint Model

abstract:
  We use the Endpoint model for exclusive hadronic processes to study Compton
scattering of the proton. The parameters of the Endpoint model are fixed using
the data for $F_1$ and the ratio of Pauli and Dirac form factors ($F_2/F_1$)
and then used to get numerical predictions for the differential scattering
cross section. We studied the Compton scattering at fixed $\theta_{CM}$ in the
$s \sim t \gg \Lambda_{QCD}$ limit and at fixed $s$ much larger than $t$ limit.
We observed that the calculations in the Endpoint Model give a good fit with
experimental data in both regions.


title: Best-Buddies Tracking

abstract:
  Best-Buddies Tracking (BBT) applies the Best-Buddies Similarity measure (BBS)
to the problem of model-free online tracking. BBS was introduced as a
similarity measure between two point sets and was shown to be very effective
for template matching. Originally, BBS was designed to work with point sets of
equal size, and we propose a modification that lets it handle point sets of
different size. The modified BBS is better suited to handle scale changes in
the template size, as well as support a variable number of template images. We
embed the modified BBS in a particle filter framework and obtain good results
on a number of standard benchmarks.


title: Quantification of Concurrence via Weak Measurement

abstract:
  Since entanglement is not an observable per se, measuring its value in
practice is a difficult task. Here we propose a protocol for quantifying a
particular entanglement measure, namely concurrence, of an arbitrary two-qubit
pure state via a single fixed measurement set-up by exploiting so-called weak
measurements and the associated weak values together with the properties of the
Laguerre-Gaussian modes. The virtue of our technique is that it is generally
applicable for all two-qubit systems and does not involve simultaneous copies
of the entangled state. We also propose an explicit optical implementation of
the protocol.


title: Wall-Crossing Invariants from Spectral Networks

abstract:
  A new construction of BPS monodromies for 4d ${\mathcal N}=2$ theories of
class S is introduced. A novel feature of this construction is its manifest
invariance under Kontsevich-Soibelman wall crossing, in the sense that no
information on the 4d BPS spectrum is employed. The BPS monodromy is encoded by
topological data of a finite graph, embedded into the UV curve $C$ of the
theory. The graph arises from a degenerate limit of spectral networks,
constructed at maximal intersections of walls of marginal stability in the
Coulomb branch of the gauge theory. The topology of the graph, together with a
notion of framing, encode equations that determine the monodromy. We develop an
algorithmic technique for solving the equations, and compute the monodromy in
several examples. The graph manifestly encodes the symmetries of the monodromy,
providing some support for conjectural relations to specializations of the
superconformal index. For $A_1$-type theories, the graphs encoding the
monodromy are "dessins d'enfants" on $C$, the corresponding Strebel
differentials coincide with the quadratic differentials that characterize the
Seiberg-Witten curve.


title: Design and Test of Wire-Scanners for SwissFEL

abstract:
  The SwissFEL light-facility will provide coherent X-rays in the wavelength
region 7-0.7 nm and 0.7-0.1 nm. In SwissFEL, view-screens and wire-scanners
will be used to monitor the transverse profile of a 200/10pC electron beam with
a normalized emittance of 0.4/0.2 mm.mrad and a final energy of 5.8 GeV.
Compared to view screens, wire-scanners offer a quasi-non-destructive
monitoring of the beam transverse profile without suffering from possible
micro-bunching of the electron beam. The main aspects of the design, laboratory
characterization and beam-test of the SwissFEL wire-scanner prototype will be
discussed.


title:
New problems in universal algebraic geometry illustrated by boolean
  equations

abstract:
  We discuss new problems in universal algebraic geometry and explain them by
boolean equations.


title: Discovery of the secondary eclipse of HAT-P-11 b

abstract:
  We report the detection of the secondary eclipse of HAT-P-11 b, a
Neptune-sized planet orbiting an active K4 dwarf. Using all available
short-cadence data of the Kepler mission, we derive refined planetary ephemeris
increasing their precision by more than an order of magnitude. Our simultaneous
primary and secondary transit modeling results in improved transit and orbital
parameters. In particular, the precise timing of the secondary eclipse allows
to pin down the orbital eccentricity to $0.26459_{-0.00048}^{+0.00069}$. The
secondary eclipse depth of $6.09_{-1.11}^{+1.12}$ ppm corresponds to a
$5.5\sigma$ detection and results in a geometric albedo of $0.39\pm0.07$ for
HAT-P-11 b, close to Neptune's value, which may indicate further resemblances
between these two bodies. Due to the substantial orbital eccentricity, the
planetary equilibrium temperature is expected to change significantly with
orbital position and ought to vary between $630^\circ$ K and $950^\circ$ K,
depending on the details of heat redistribution in the atmosphere of HAT-P-11
b.


title:
Regular decomposition and a framework of order reducd methods for fourth
  order problems

abstract:
  This paper is devoted to the construction of order reduced method of fourth
order problems. A framework is presented such that a problem on a
high-regularity space can be deduced in a constructive way to an equivalent
problem on three low-regularity spaces which are connected by a regular
decomposition, which is corresponding to a decomposition of the figuration of
the regularity of the high order space. The framework is fit for various fourth
order problems, and the numerical schemes based on the deduced problems can be
of lower complicacy. Two fourth order problems in three dimensional are
discussed under the framework. They are each corresponding to a regular
decomposition, and thus are discretised based on the discretised analogues of
the regular decompositions constructed; optimal error estimates are given.


title: Trapping of molecular Oxygen together with Lithium atoms

abstract:
  We demonstrate simultaneous deceleration and trapping of a cold atomic and
molecular mixture. This is the first step towards studies of cold atom-molecule
collisions at low temperatures as well as application of sympathetic cooling.
Both atoms and molecules are cooled in a supersonic expansion and are loaded
into a moving magnetic trap which brings them to rest via the Zeeman
interaction from an initial velocity of 375 m/s. We use a beam seeded with
molecular Oxygen, and entrain it with Lithium atoms by laser ablation prior to
deceleration. The deceleration ends with loading of the mixture into a static
quadrupole trap, which is generated by two permanent magnets. We estimate
$10^9$ trapped O$_2$ molecules and $10^5$ Li atoms with background pressure
limited lifetime on the order of 1 second. With further improvements to Lithium
entrainment we expect that sympathetic cooling of molecules is within reach.


title:
Globalization Process in Emerging Capital Markets -- Lessons and
  Implications to China

abstract:
  Since 2002 when China first introduced QFII (Qualified Foreign Institutional
Investors) system, QFII has been developing in China for 14 years, during when
RQFII, Shanghai-Hongkong Stock Connect Program, Shanghai-London Stock Connect
Program furthur broadened the avenue for foreign capital to invest in Chinese
Security Market. As FTA (Free Trade Area) Financial Reform Program emerged, RMB
(CNY) Capital Project is likely to make the currency exchangeable. With the
success in QFII, RQFII and Shanghai-Hongkong Stock Connect Program, China's
long term advantage in interest rate, and the relatively low stock index value
after the recent stock market crashes in mid 2015 and early 2016, foreign
capitals' demand for Chinese market to loosen its restrictions continually
increases. This article picks the three most representative emerging capital
markets in the world, namely Taiwan, Korea and India, by comparing and
analyzing their paths of globalization, attempts to shed light on China's next
steps regarding globalization.