title: Learning from Maps: Visual Common Sense for Autonomous Driving

abstract:
  Today's autonomous vehicles rely extensively on high-definition 3D maps to
navigate the environment. While this approach works well when these maps are
completely up-to-date, safe autonomous vehicles must be able to corroborate the
map's information via a real time sensor-based system. Our goal in this work is
to develop a model for road layout inference given imagery from on-board
cameras, without any reliance on high-definition maps. However, no sufficient
dataset for training such a model exists. Here, we leverage the availability of
standard navigation maps and corresponding street view images to construct an
automatically labeled, large-scale dataset for this complex scene understanding
problem. By matching road vectors and metadata from navigation maps with Google
Street View images, we can assign ground truth road layout attributes (e.g.,
distance to an intersection, one-way vs. two-way street) to the images. We then
train deep convolutional networks to predict these road layout attributes given
a single monocular RGB image. Experimental evaluation demonstrates that our
model learns to correctly infer the road attributes using only panoramas
captured by car-mounted cameras as input. Additionally, our results indicate
that this method may be suitable to the novel application of recommending
safety improvements to infrastructure (e.g., suggesting an alternative speed
limit for a street).


title: Energy Extraction from the Einstein-Born-Infeld Black Hole

abstract:
  The energy extraction from a Einstein-Born-Infeld (EBI) black hole is
addressed determining the extension of the ergosphere as well as the
extractable energy using the irreducible mass concept. These results are
compared with the Reissner-Nordstrom (RN) ones; RN is the linear
electromagnetic counterpart of the BI black hole. It turns out that for a fixed
charge Q, more energy can be extracted from the RN black hole than from the EBI
one. The extreme case is investigated as well, presenting remarkable features,
for instance that more energy can be extracted from extreme EBI black holes
than from extreme RN, however, extreme EBI black holes lack of a linear
electromagnetic black hole limit.


title: The Goldbach Problem for Primes That Are Sums of Two Squares Plus One

abstract:
  We study the Goldbach problem for primes represented by the polynomial
$x^2+y^2+1$. The set of such primes is sparse in the set of all primes, but the
infinitude of such primes was established by Linnik. We prove that almost all
even integers $n$ satisfying certain necessary local conditions are
representable as the sum of two primes of the form $x^2+y^2+1$. This improves a
result of Matom\"aki, which tells that almost all even $n$ satisfying a local
condition are the sum of one prime of the form $x^2+y^2+1$ and one generic
prime. We also solve the analogous ternary Goldbach problem, stating that every
large odd $n$ is the sum of three primes represented by our polynomial. As a
byproduct of the proof, we show that the primes of the form $x^2+y^2+1$ contain
infinitely many three term arithmetic progressions, and that the numbers
$\alpha p \pmod 1$ with $\alpha$ irrational and $p$ running through primes of
the form $x^2+y^2+1$, are distributed rather uniformly.


title: ProFit: Bayesian Profile Fitting of Galaxy Images

abstract:
  We present ProFit, a new code for Bayesian two-dimensional photometric galaxy
profile modelling. ProFit consists of a low-level C++ library (libprofit),
accessible via a command-line interface and documented API, along with
high-level R (ProFit) and Python (PyProFit) interfaces (available at
github.com/ICRAR/ libprofit, github.com/ICRAR/ProFit, and
github.com/ICRAR/pyprofit respectively). R ProFit is also available pre-built
from CRAN, however this version will be slightly behind the latest GitHub
version. libprofit offers fast and accurate two- dimensional integration for a
useful number of profiles, including Sersic, Core-Sersic, broken-exponential,
Ferrer, Moffat, empirical King, point-source and sky, with a simple mechanism
for adding new profiles. We show detailed comparisons between libprofit and
GALFIT. libprofit is both faster and more accurate than GALFIT at integrating
the ubiquitous Serrsic profile for the most common values of the Serrsic index
n (0.5 < n < 8).
  The high-level fitting code ProFit is tested on a sample of galaxies with
both SDSS and deeper KiDS imaging. We find good agreement in the fit
parameters, with larger scatter in best-fit parameters from fitting images from
different sources (SDSS vs KiDS) than from using different codes (ProFit vs
GALFIT). A large suite of Monte Carlo-simulated images are used to assess
prospects for automated bulge-disc decomposition with ProFit on SDSS, KiDS and
future LSST imaging. We find that the biggest increases in fit quality come
from moving from SDSS- to KiDS-quality data, with less significant gains moving
from KiDS to LSST.


title:
Geomagnetically Induced Currents in the Irish Power Network during
  Geomagnetic Storms

abstract:
  Geomagnetically induced currents (GICs) are a well-known terrestrial space
weather hazard. They occur in power transmission networks and are known to have
adverse effects in both high and mid-latitude countries. Here, we study GICs in
the Irish power transmission network (geomagnetic latitude 54.7--58.5$^{\circ}$
N) during five geomagnetic storms (06-07 March 2016, 20-21 December 2015, 17-18
March 2015, 29-31 October 2003 and 13-14 March 1989). We simulate electric
fields using a plane wave method together with two ground resistivity models,
one of which is derived from magnetotelluric measurements (MT model). We then
calculate GICs in the 220, 275 and 400~kV transmission network. During the
largest of the storm periods studied, the peak electric field was calculated to
be as large as 3.8~V~km\textsuperscript{-1}, with associated GICs of up to 23~A
using our MT model. Using our homogenous resistivity model, those peak values
were 1.46~V~km\textsuperscript{-1} and 25.8~A. We find that three 400 and
275~kV substations are the most likely locations for the Irish transformers to
experience large GICs.


title: PVANet: Lightweight Deep Neural Networks for Real-time Object Detection

abstract:
  In object detection, reducing computational cost is as important as improving
accuracy for most practical usages. This paper proposes a novel network
structure, which is an order of magnitude lighter than other state-of-the-art
networks while maintaining the accuracy. Based on the basic principle of more
layers with less channels, this new deep neural network minimizes its
redundancy by adopting recent innovations including C.ReLU and Inception
structure. We also show that this network can be trained efficiently to achieve
solid results on well-known object detection benchmarks: 84.9% and 84.2% mAP on
VOC2007 and VOC2012 while the required compute is less than 10% of the recent
ResNet-101.


title:
Qualitative observation of reversible phase change in astrochemical
  ethanethiol ices using infrared spectroscopy

abstract:
  Here we report the first evidence for a reversible phase change in an
ethanethiol ice prepared under astrochemical conditions. InfraRed (IR)
spectroscopy was used to monitor the morphology of the ice using the S-H
stretching vibration, a characteristic vibration of thiol molecules. The
deposited sample was able to switch between amorphous and crystalline phases
repeatedly under temperature cycles between 10 K and 130 K with subsequent loss
of molecules in every phase change. Such an effect is dependent upon the
original thickness of the ice. Further work on quantitative analysis is to be
carried out in due course whereas here we are reporting the first results
obtained.


title: Planetary Nebula Candidates Uncovered with the HASH Research Platform

abstract:
  A detailed examination of new high quality radio catalogues (e.g. Cornish) in
combination with available mid-infrared (MIR) satellite imagery (e.g. Glimpse)
has allowed us to find 70 new planetary nebula (PN) candidates based on
existing knowledge of their typical colors and fluxes. To further examine the
nature of these sources, multiple diagnostic tools have been applied to these
candidates based on published data and on available imagery in the HASH (Hong
Kong/ AAO/ Strasbourg H{\alpha} planetary nebula) research platform. Some
candidates have previously-missed optical counterparts allowing for
spectroscopic follow-up. Indeed, the single object spectroscopically observed
so far has turned out to be a bona fide PN.


title: A remark on the mathematics of the seesaw mechanism

abstract:
  To demonstrate that matrices of seesaw type lead to a hieararchy in the
neutrino masses, i.e. that there is a large gap in the singular spectrum of
these matrices, one generally uses an approximate block-diagonalization
procedure. In this note we show that no approximation is required to prove this
gap property if the Courant-Fisher-Weyl theorem is used instead. This simple
observation might not be original, however it does not seem to show up in the
literature. We also sketch the proof of additional inequalities for the
singular values of matrices of seesaw type.


title: A conservation rule for constructing bibliometric network matrices

abstract:
  The social network analysis of bibliometric data needs matrices to be recast
in a network framework. In this paper we argue that a simple conservation rule
requires that this should be done only using fractional counting so that
conservation at the paper level will be faithfully reproduced at higher levels
ofaggregation (i.e. author, institute, country, journal etc.) of the complex
network.


title:
Electrical Detection of Spin Backflow from an Antiferromagnetic
  Insulator/Y3Fe5O12 Interface

abstract:
  Spin Hall magnetoresistance (SMR) has been observed in Pt/NiO/Y3Fe5O12 (YIG)
heterostructures with characteristics very different from those in Pt/YIG. We
show that the SMR in Pt/NiO/YIG strongly correlates with spin conductance, both
sharing very strong temperature dependence due to antiferromagnetic magnons and
spin fluctuation. This phenomenon indicates that spin current generated by spin
Hall effect in the Pt transmits through the insulating NiO and is reflected
from the NiO/YIG interface. Inverted SMR has been observed below a temperature
which increases with the NiO thickness, suggesting spin-flip reflection from
the antiferromagnetic NiO exchange coupled with the YIG.


title:
The impact of ionizing radiation on the formation of a supermassive star
  in the early Universe

abstract:
  A massive primordial halo near an intensely star-forming galaxy may collapse
into a supermassive star (SMS) and leave a massive black hole seed of about
$10^5~M_{\odot}$. To investigate the impact of ionizing radiation on the
formation of an SMS from a nearby galaxy, we perform three-dimensional
radiation hydrodynamical simulations by selecting a pair of massive dark matter
halos forming at $z >10$. We find that rich structures such as clumps and
filaments around the source galaxy shield the cloud from ionizing radiation. In
fact, in some cases, cloud collapse is accelerated under ionizing radiation.
This fact suggests that the ionization of the cloud's surroundings helps its
collapse. Only strong radiation at the early stage of structure formation can
halt the cloud collapse, but this is much stronger than observationally allowed
value. We also explored the effect of ionizing radiation on a sample of 68
halos by employing an analytical model and found that increase in the mean
density of the gas between the SMS forming cloud and the source galaxy protects
the gas cloud from ionizing radiation as they approach each other. Thus, we
conclude that ionizing radiation does not prevent the formation of an SMS in
most of the cases.


title:
Mass-Metallicity Relation for Local Analogs of High-Redshift galaxies:
  Implications for the Evolution of the Mass-Metallicity Relations

abstract:
  We revisit the evolution of the mass-metallicity relation of low- and
high-redshift galaxies by using a sample of local analogs of high-redshift
galaxies. These analogs share the same location of the UV-selected star-forming
galaxies at $z\sim2$ on the [OIII]/H$\beta$ versus [NII]/H$\alpha$ nebular
emission-line diagnostic (or BPT) diagram. Their physical properties closely
resemble those in $z\sim2$ UV-selected star-forming galaxies being
characterized in particular by high ionization parameters ($\log q\approx7.9$)
and high electron densities ($n_e\approx100~\rm{cm}^{-3}$). With the full set
of well-detected rest-frame optical diagnostic lines, we measure the gas-phase
oxygen abundance in the SDSS galaxies and these local analogs using the
empirical relations and the photoionization models. We find that the
metallicity difference between the SDSS galaxies and our local analogs in the
$8.5<log(M_*/M_{\odot})<9.0$ stellar mass bin varies from -0.09 to 0.39 dex,
depending on strong-line metallicity measurement methods. Due to this
discrepancy the evolution of mass-metallicity should be used to compare with
the cosmological simulations with caution. We use the [SII]/H$\alpha$ and
[OI]/H$\alpha$ BPT diagram to reduce the potential AGN and shock contamination
in our local analogs. We find that the AGN/shock influences are negligible on
the metallicity estimation.


title:
Machine-Learned Identification of RR Lyrae Stars from Sparse, Multi-band
  Data: the PS1 Sample

abstract:
  RR Lyrae stars may be the best practical tracers of Galactic halo
(sub-)structure and kinematics. The PanSTARRS1 (PS1) $3\pi$ survey offers
multi-band, multi-epoch, precise photometry across much of the sky, but a
robust identification of RR Lyrae stars in this data set poses a challenge,
given PS1's sparse, asynchronous multi-band light curves ($\lesssim 12$ epochs
in each of five bands, taken over a 4.5-year period). We present a novel
template fitting technique that uses well-defined and physically motivated
multi-band light curves of RR Lyrae stars, and demonstrate that we get accurate
period estimates, precise to 2~sec in $>80\%$ of cases. We augment these light
curve fits with other {\em features} from photometric time-series and provide
them to progressively more detailed machine-learned classification models. From
these models we are able to select the widest ($3/4$ of the sky) and deepest
(reaching 120 kpc) sample of RR Lyrae stars to date. The PS1 sample of $\sim
45,000$ RRab stars is pure (90\%), and complete (80\% at 80 kpc) at high
galactic latitudes. It also provides distances precise to 3\%, measured with
newly derived period-luminosity relations for optical/near-infrared PS1 bands.
With the addition of proper motions from {\em Gaia} and radial velocity
measurements from multi-object spectroscopic surveys, we expect the PS1 sample
of RR Lyrae stars to become the premier source for studying the structure,
kinematics, and the gravitational potential of the Galactic halo. The
techniques presented in this study should translate well to other sparse,
multi-band data sets, such as those produced by the Dark Energy Survey and the
upcoming Large Synoptic Survey Telescope Galactic plane sub-survey.


title:
Modeling the Anomalous Microwave Emission with Spinning Nanoparticles:
  No PAHs Required

abstract:
  In light of recent observational results indicating an apparent lack of
correlation between the Anomalous Microwave Emission (AME) and mid-infrared
emission from polycyclic aromatic hydrocarbons (PAHs), we assess whether
rotational emission from spinning silicate and/or iron nanoparticles could
account for the observed AME without violating observational constraints on
interstellar abundances, ultraviolet extinction, and infrared emission. By
modifying the SpDust code to compute the rotational emission from these grains,
we find that nanosilicate grains could account for the entirety of the observed
AME, whereas iron grains could be responsible for only a fraction, even for
extreme assumptions on the amount of interstellar iron concentrated in
ultrasmall iron nanoparticles. Given the added complexity of contributions from
multiple grain populations to the total spinning dust emission, as well as
existing uncertainties due to the poorly-constrained grain size, charge, and
dipole moment distributions, we discuss generic, carrier-independent
predictions of spinning dust theory and observational tests that could help
identify the AME carrier(s).


title:
The Proper Motion of Pyxis: the first use of Adaptive Optics in tandem
  with HST on a faint halo object

abstract:
  We present a proper motion measurement for the halo globular cluster Pyxis,
using HST/ACS data as the first epoch, and GeMS/GSAOI Adaptive Optics data as
the second, separated by a baseline of about 5 years. This is both the first
measurement of the proper motion of Pyxis and the first calibration and use of
Multi-Conjugate Adaptive Optics data to measure an absolute proper motion for a
faint, distant halo object. Consequently, we present our analysis of the
Adaptive Optics data in detail. We obtain a proper motion of mu_alpha
cos(delta)=1.09+/-0.31 mas/yr and mu_delta=0.68+/-0.29 mas/yr. From the proper
motion and the line-of-sight velocity we find the orbit of Pyxis is rather
eccentric with its apocenter at more than 100 kpc and its pericenter at about
30 kpc. We also investigate two literature-proposed associations for Pyxis with
the recently discovered ATLAS stream and the Magellanic system. Combining our
measurements with dynamical modeling and cosmological numerical simulations we
find it unlikely Pyxis is associated with either system. We examine other Milky
Way satellites for possible association using the orbit, eccentricity,
metallicity, and age as constraints and find no likely matches in satellites
down to the mass of Leo II. We propose that Pyxis probably originated in an
unknown galaxy, which today is fully disrupted. Assuming that Pyxis is bound
and not on a first approach, we derive a 68% lower limit on the mass of the
Milky Way of 0.95*10^12 M_sun.


title: On Form Factors and Correlation Functions in Twistor Space

abstract:
  In this paper, we continue our study of form factors and correlation
functions of gauge-invariant local composite operators in the twistor-space
formulation of N=4 super Yang-Mills theory. Using the vertices for these
operators obtained in our recent papers arXiv:1603.04471 and arXiv:1604.00012,
we show how to calculate the twistor-space diagrams for general N^kMHV form
factors via the inverse soft limit, in analogy to the amplitude case. For
general operators without $\dot\alpha$ indices, we then reexpress the NMHV form
factors from the position-twistor calculation in terms of momentum twistors,
deriving and expanding on a relation between the two twistor formalisms
previously observed in the case of amplitudes. Furthermore, we discuss the
calculation of generalized form factors and correlation functions as well as
the extension to loop level, in particular providing an argument promised in
arXiv:1410.6310.


title:
The Unusually High Halo Concentration of the Fossil Group NGC 6482:
  Evidence for Weak Adiabatic Contraction

abstract:
  We revisit the massive isolated elliptical galaxy / fossil group NGC 6482 for
which previous X-ray studies of a modest Chandra observation obtained a very
uncertain, but also possibly very high, halo concentration. We present new
measurements of the hot gas surface brightness, temperature, and iron abundance
using the modest Chandra observation and a previously unpublished Suzaku
observation, the latter of which allows measurements of the gas properties to
be extended out to ~r_2500. By constructing hydrostatic equilibrium models of
the gas with separate components for the gas, BCG stellar mass, and the dark
matter (DM), we measure c_200 = 32.2 +/- 7.1 and M_200 = (4.5 +/- 0.6 x 10^12
M_sun using an NFW DM profile. For a halo of this mass, c_200 exceeds the mean
value (7.1) expected for relaxed LCDM halos by $3.5 \sigma$ in terms of the
observational error, and by $6 \sigma$ considering the intrinsic scatter in the
LCDM c-M relation, which situates NGC 6482 as the most extreme outlier known
for a fossil system. We explored several variants of adiabatic contraction (AC)
models and, while the AC models provide fits of the same quality as the
un-contracted models, they do have the following advantages: (1) smaller c_200
that is less of an outlier in the LCDM c-M relation, and (2) baryon fractions
that agree better with the mean cosmic value. While the standard AC
prescriptions yield a BCG stellar mass that is uncomfortably small compared to
results from stellar population synthesis (SPS) models, a weaker AC variant
that artificially shuts off cooling and star formation at z=2 yields the same
stellar mass as the un-contracted models. For these reasons, we believe our
X-ray analysis prefers this weaker AC variant applied to either an NFW or
Einasto DM halo. Finally, the BCG stellar mass strongly favors SPS models with
a Chabrier or Kroupa IMF over a Salpeter IMF. (Abridged)


title: Sub-AdS Scale Locality in AdS$_3$/CFT$_2$

abstract:
  We investigate sub-AdS scale locality in a weakly coupled toy model of the
AdS$_3$/CFT$_2$ correspondence. We find that this simple model has the correct
density of states at low and high energies to be dual to Einstein gravity
coupled to matter in AdS$_3$. Bulk correlation functions also have the correct
behavior at leading order in the large $N$ expansion, but non-local effects
emerge at order $1/N$. Our analysis leads to the conjecture that any large $N$
CFT$_2$ that is modular invariant and has the right low-energy density of
states is dual to a gravitational theory with sub-AdS scale locality.


title: 4d $\mathcal{N}$=2 theories with disconnected gauge groups

abstract:
  In this paper we present a beautifully consistent web of evidence for the
existence of interacting 4d rank-1 $\mathcal{N}=2$ SCFTs obtained from gauging
discrete subgroups of global symmetries of other existing 4d rank-1
$\mathcal{N}=2$ SCFTs. The global symmetries that can be gauged involve a
non-trivial combination of discrete subgroups of the $U(1)_R$, low-energy EM
duality group $SL(2,\mathbb{Z})$, and the outer automorphism group of the
flavor symmetry algebra, Out($F$).
  The theories that we construct are remarkable in many ways: (i) two of them
have exceptional $F_4$ and $G_2$ flavor groups; (ii) they substantially
complete the picture of the landscape of rank-1 $\mathcal{N}=2$ SCFTs as they
realize all but one of the remaining consistent rank-1 Seiberg-Witten
geometries that we previously constructed but were not associated to known
SCFTs; and (iii) some of them have enlarged $\mathcal{N}=3$ SUSY, and have not
been previously constructed. They are also examples of SCFTs which violate the
Shapere-Tachikawa relation between the conformal central charges and the
scaling dimension of the Coulomb branch vev. We propose a modification of the
formulas computing these central charges from the topologically twisted Coulomb
branch partition function which correctly compute them for discretely gauged
theories.


title:
A global inversion-symmetry-broken phase inside the pseudogap region of
  YBa$_2$Cu$_3$O$_y$

abstract:
  The phase diagram of cuprate high-temperature superconductors features an
enigmatic pseudogap region that is characterized by a partial suppression of
low energy electronic excitations. Polarized neutron diffraction, Nernst
effect, THz polarimetery and ultrasound measurements on YBa$_2$Cu$_3$O$_y$
suggest that the pseudogap onset below a temperature T* coincides with a bona
fide thermodynamic phase transition that breaks time-reversal, four-fold
rotation and mirror symmetries respectively. However, the full point group
above and below T* has not been resolved and the fate of this transition as T*
approaches the superconducting critical temperature T$_c$ is poorly understood.
Here we reveal the point group of YBa$_2$Cu$_3$O$_y$ inside its pseudogap and
neighboring regions using high sensitivity linear and second harmonic optical
anisotropy measurements. We show that spatial inversion and two-fold rotational
symmetries are broken below T* while mirror symmetries perpendicular to the
Cu-O plane are absent at all temperatures. This transition occurs over a wide
doping range and persists inside the superconducting dome, with no detectable
coupling to either charge ordering or superconductivity. These results suggest
that the pseudogap region coincides with an odd-parity order that does not
arise from a competing Fermi surface instability and exhibits a quantum phase
transition inside the superconducting dome.


title: Schwinger-type parametrization of open string worldsheets

abstract:
  A parametrization of (super) moduli space near the corners corresponding to
bosonic or Neveu-Schwarz open string degenerations is introduced for
worldsheets of arbitrary topology. With this parametrization, Feynman graph
polynomials arise as the $\alpha ' \to 0$ limit of objects on moduli space.
Furthermore, the integration measures of string theory take on a very simple
and elegant form.


title:
The Many Faces of Heterogeneous Ice Nucleation: Interplay Between
  Surface Morphology and Hydrophobicity

abstract:
  What makes a material a good ice nucleating agent? Despite the importance of
heterogeneous ice nucleation to a variety of fields, from cloud science to
microbiology, major gaps in our understanding of this ubiquitous process still
prevent us from answering this question. In this work, we have examined the
ability of generic crystalline substrates to promote ice nucleation as a
function of the hydrophobicity and the morphology of the surface. Nucleation
rates have been obtained by brute-force molecular dynamics simulations of
coarse-grained water on top of different surfaces of a model fcc crystal,
varying the water-surface interaction and the surface lattice parameter. It
turns out that the lattice mismatch of the surface with respect to ice,
customarily regarded as the most important requirement for a good ice
nucleating agent, is at most desirable but not a requirement. On the other
hand, the balance between the morphology of the surface and its hydrophobicity
can significantly alter the ice nucleation rate and can also lead to the
formation of up to three different faces of ice on the same substrate. We have
pinpointed three circumstances where heterogeneous ice nucleation can be
promoted by the crystalline surface: (i) the formation of a water overlayer
that acts as an in-plane template; (ii) the emergence of a contact layer
buckled in an ice-like manner; and (iii) nucleation on compact surfaces with
very high interaction strength. We hope that this extensive systematic study
will foster future experimental work aimed at testing the physiochemical
understanding presented herein.


title: Lensing is Low: Cosmology, Galaxy Formation, or New Physics?

abstract:
  We present high signal-to-noise galaxy-galaxy lensing measurements of the
BOSS CMASS sample using 250 square degrees of weak lensing data from CFHTLenS
and CS82. We compare this signal with predictions from mock catalogs trained to
match observables including the stellar mass function and the projected and two
dimensional clustering of CMASS. We show that the clustering of CMASS, together
with standard models of the galaxy-halo connection, robustly predicts a lensing
signal that is 20-40% larger than observed. Detailed tests show that our
results are robust to a variety of systematic effects. Lowering the value of
$S_{\rm 8}=\sigma_{\rm 8} \sqrt{\Omega_{\rm m}/0.3}$ compared to Planck2015
reconciles the lensing with clustering. However, given the scale of our
measurement ($r<10$ $h^{-1}$ Mpc), other effects may also be at play and need
to be taken into consideration. We explore the impact of baryon physics,
assembly bias, massive neutrinos, and modifications to general relativity on
$\Delta\Sigma$ and show that several of these effects may be non-negligible
given the precision of our measurement. Disentangling cosmological effects from
the details of the galaxy-halo connection, the effects of baryons, and massive
neutrinos, is the next challenge facing joint lensing and clustering analyses.
This is especially true in the context of large galaxy samples from Baryon
Acoustic Oscillation surveys with precise measurements but complex selection
functions.


title: Thermodynamics and Charging of Interstellar Iron Nanoparticles

abstract:
  Interstellar iron in the form of metallic iron nanoparticles may constitute a
component of the interstellar dust. We compute the stability of iron
nanoparticles to sublimation in the interstellar radiation field, finding that
iron clusters can persist down to a radius of $\simeq 4.5\,$\AA, and perhaps
smaller. We employ laboratory data on small iron clusters to compute the
photoelectric yields as a function of grain size and the resulting grain charge
distribution in various interstellar environments, finding that iron
nanoparticles can acquire negative charges particularly in regions with high
gas temperatures and ionization fractions. If $\gtrsim 10\%$ of the
interstellar iron is in the form of ultrasmall iron clusters, the photoelectric
heating rate from dust may be increased by up to tens of percent relative to
dust models with only carbonaceous and silicate grains.


title:
Observing transiting planets with JWST -- Prime targets and their
  synthetic spectral observations

abstract:
  The James Webb Space Telescope will enable astronomers to obtain exoplanet
spectra of unprecedented precision. Especially the MIRI instrument may shed
light on the nature of the cloud particles obscuring planetary transmission
spectra in the optical and near-infrared. We provide self-consistent
atmospheric models and synthetic JWST observations for prime exoplanet targets
in order to identify spectral regions of interest and estimate the number of
transits needed to distinguish between model setups. We select targets which
span a wide range in planetary temperature and surface gravity, ranging from
super-Earths to giant planets, and have a high expected SNR. For all targets we
vary the enrichment, C/O ratio, presence of optical absorbers (TiO/VO) and
cloud treatment. We calculate atmospheric structures and emission and
transmission spectra for all targets and use a radiometric model to obtain
simulated observations. We analyze JWST's ability to distinguish between
various scenarios. We find that in very cloudy planets such as GJ 1214b less
than 10 transits with NIRSpec may be enough to reveal molecular features.
Further, the presence of small silicate grains in atmospheres of hot Jupiters
may be detectable with a single JWST MIRI transit. For a more detailed
characterization of such particles less than 10 transits are necessary.
Finally, we find that some of the hottest hot Jupiters are well fitted by
models which neglect the redistribution of the insolation and harbor
inversions, and that 1-4 eclipse measurements with NIRSpec are needed to
distinguish between the inversion models. Wet thus demonstrate the capabilities
of JWST for solving some of the most intriguing puzzles in current exoplanet
atmospheric research. Further, by publishing all models calculated for this
study we enable the community to carry out similar or retrieval analyses for
all planets included in our target list.


title:
Pinning down the large-x gluon with NNLO top-quark pair differential
  distributions

abstract:
  Top-quark pair production at the LHC is directly sensitive to the gluon PDF
at large x. While total cross-section data is already included in several PDF
determinations, differential distributions are not, because the corresponding
NNLO calculations have become available only recently. In this work we study
the impact on the large-x gluon of top-quark pair differential distributions
measured by ATLAS and CMS at $\sqrt{s}=8$ TeV. Our analysis, performed in the
NNPDF3.0 framework at NNLO accuracy, allows us to identify the optimal
combination of LHC top-quark pair measurements that maximize the constraints on
the gluon, as well as to assess the compatibility between ATLAS and CMS data.
We find that differential distributions from top-quark pair production provide
significant constraints on the large-x gluon, comparable to those obtained from
inclusive jet production data, and thus should become an important ingredient
for the next generation of global PDF fits.


title: Resummation of Transverse Momentum Distributions in Distribution Space

abstract:
  Differential spectra in observables that resolve additional soft or collinear
QCD emissions exhibit Sudakov double logarithms in the form of logarithmic plus
distributions. Important examples are the total transverse momentum $q_T$ in
color-singlet production, N-jettiness (with thrust or beam thrust as special
cases), but also jet mass and more complicated jet substructure observables.
The all-order logarithmic structure of such distributions is often fully
encoded in differential equations, so-called (renormalization group) evolution
equations. We introduce a well-defined technique of distributional scale
setting, which allows one to treat logarithmic plus distributions like ordinary
logarithms when solving these differential equations. In particular, this
allows one (through canonical scale choices) to minimize logarithmic
contributions in the boundary terms of the solution, and to obtain the full
distributional logarithmic structure from the solution's evolution kernel
directly in distribution space. We apply this technique to the $q_T$
distribution, where the two-dimensional nature of convolutions leads to
additional difficulties (compared to one-dimensional cases like thrust), and
for which the resummation in distribution (or momentum) space has been a
long-standing open question. For the first time, we show how to perform the RG
evolution fully in momentum space, thereby directly resumming the logarithms
$[\ln^n(q_T^2/Q^2)/q_T^2]_+$ appearing in the physical $q_T$ distribution. The
resummation accuracy is then solely determined by the perturbative expansion of
the associated anomalous dimensions.


title:
The X-ray Binary Population of the Nearby Dwarf Starburst Galaxy IC 10:
  Variable and Transient X-ray Sources

abstract:
  We have monitored the Cassiopeia dwarf galaxy (IC 10) in a series of 10
Chandra ACIS-S observations to capture its variable and transient X-ray source
population, which is expected to be dominated by High Mass X-ray Binaries
(HMXBs). We present a sample of 21 X-ray sources that are variable between
observations at the 3 sigma level, from a catalog of 110 unique point sources.
We find 4 transients (flux variability ratio greater than 10) and a further 8
objects with ratio > 5. The observations span years 2003 - 2010 and reach a
limiting luminosity of >10$^{35}$ erg/s, providing sensitivity to X-ray
binaries in IC 10 as well as flare stars in the foreground Milky Way. The
nature of the variable sources is investigated from light-curves, X-ray
spectra, energy quantiles, and optical counterparts. The purpose of this study
is to discover the composition of the X-ray binary population in a young
starburst environment. IC 10 provides a sharp contrast in stellar population
age (<10 My) when compared to the Magellanic Clouds (40-200 My) where most of
the known HMXBs reside. We find 10 strong HMXB candidates, 2 probable
background Active Galactic Nuclei, 4 foreground flare-stars or active binaries,
and 5 not yet classifiable sources. Complete classification of the sample
requires optical spectroscopy for radial velocity analysis and deeper X-ray
observations to obtain higher S/N spectra and search for pulsations. A catalog
and supporting dataset are provided.


title:
Abundances of disk and bulge giants from hi-res optical spectra: II. O,
  Mg, Ca, and Ti in the bulge sample

abstract:
  Determining elemental abundances of bulge stars can, via chemical evolution
modeling, help to understand the formation and evolution of the bulge. Recently
there have been claims both for and against the bulge having a different
[$\alpha$/Fe] vs. [Fe/H]-trend as compared to the local thick disk possibly
meaning a faster, or at least different, formation time scale of the bulge as
compared to the local thick disk. We aim to determine the abundances of oxygen,
magnesium, calcium, and titanium in a sample of 46 bulge K-giants, 35 of which
have been analyzed for oxygen and magnesium in previous works, and compare them
to homogeneously determined elemental abundances of a local disk sample of 291
K-giants. We use spectral synthesis to determine both the stellar parameters as
well as the elemental abundances of the bulge stars analyzed here. The method
is exactly the same as was used for analyzing the comparison sample of 291
local K-giants in Paper I of this series. Compared to the previous analysis of
the 35 stars in our sample, we find lower [Mg/Fe] for [Fe/H]>-0.5, and
therefore contradict the conclusion about a declining [O/Mg] for increasing
[Fe/H]. We instead see a constant [O/Mg] over all the observed [Fe/H] in the
bulge. Furthermore, we find no evidence for a different behavior of the
alpha-iron trends in the bulge as compared to the local thick disk from our two
samples.


title: Tensor Network Models of Unitary Black Hole Evaporation

abstract:
  We introduce a general class of toy models to study the quantum
information-theoretic properties of black hole radiation. The models are
governed by a set of isometries that specify how microstates of the black hole
at a given energy evolve to entangled states of a tensor product
black-hole/radiation Hilbert space. The final state of the black hole radiation
is conveniently summarized by a tensor network built from these isometries. We
introduce a set of quantities generalizing the Renyi entropies that provide a
complete set of bipartite/multipartite entanglement measures, and give a
general formula for the average of these over initial black hole states in
terms of the isometries defining the model. For models where the dimension of
the final tensor product radiation Hilbert space is the same as that of the
space of initial black hole microstates, the entanglement structure is
universal, independent of the choice of isometries. In the more general case,
we find that models which best capture the "information-free" property of black
hole horizons are those whose isometries are tensors corresponding to states of
tripartite systems with maximally mixed subsystems.


title: An investigation of the RCB star candidate GDS J0702414-023501

abstract:
  2MASS J07024146-0235017 = GDS J0702414-023501 was included in the "Catalogue
enriched with R CrB stars" on grounds of its near- and mid-infrared colours.
The object, which corresponds to the carbon star IRAS 07001-0230 = CGCS 6197,
has been found to exhibit large amplitude variability in its Bochum Galactic
Disk Survey light curve. Taking into account all available data, GDS
J0702414-023501 is here proposed as a new candidate Mira star.


title: SUSY Meets Her Twin

abstract:
  We investigate the general structure of mirror symmetry breaking in the Twin
Higgs scenario. We show, using the IR effective theory, that a significant gain
in fine tuning can be achieved if the symmetry is broken hardly. We emphasize
that weakly coupled UV completions can naturally accommodate this scenario. We
analyze SUSY UV completions and present a simple Twin SUSY model with a tuning
of around 10% and colored superpartners as heavy as 2 TeV. The collider
signatures of general Twin SUSY models are discussed with a focus on the
extended Higgs sectors.


title:
Metal enrichment leads to low atmospheric C/O ratios in transiting giant
  exoplanets

abstract:
  We predict the carbon-to-oxygen (C/O) ratios in the hydrogen-helium envelope
and atmospheres of a sample of nearly 50 relatively cool ($T_{\mathrm eq}<$
1000 K) transiting gas giant planets. The method involves planetary envelope
metallicity estimates that use the structure models of Thorngren et al. (2016)
and the disk and planetary accretion model of \"Oberg et al. (2011). We find
that nearly all of these planets are strongly metal-enriched which, coupled
with the fact that solid material is the main deliverer of metals in the
protoplanetary disk, implies that the substellar C/O ratios of their accreted
solid material dominate compared to the enhanced C/O ratio of their accreted
gaseous component. We predict that these planets will have atmospheres that are
typically reduced in their C/O compared to parent star values independent of
the assessed formation locations, with C/O $<1$ a nearly universal outcome
within the framework of the model. We expect water vapor absorption features to
be ubiquitous in the atmospheres of these planets, and by extension, other gas
giants.


title:
Effect of Scatterering on Coherent Anti-Stokes Raman Scattering (CARS)
  signals

abstract:
  We develop a computational framework to examine the factors responsible for
scattering-induced distortions of coherent anti-Stokes Raman scattering (CARS)
signals in turbid samples. We apply the Huygens-Fresnel Wave-based Electric
Field Superposition (HF-WEFS) method combined with the radiating dipole
approximation to compute the effects of scattering-induced distortions of focal
excitation fields on the far-field CARS signal. We analyze the effect of
spherical scatterers, placed in the vicinity of the focal volume, on the CARS
signal emitted by different objects (2{\mu}m diameter solid sphere, 2{\mu}m
diameter myelin cylinder and 2{\mu}m diameter myelin tube). We find that
distortions in the CARS signals arise not only from attenuation of the focal
field but also from scattering-induced changes in the spatial phase that
modifies the angular distribution of the CARS emission. Our simulations further
show that CARS signal attenuation can be minimized by using a high numerical
aperture condenser. Moreover, unlike the CARS intensity image, CARS images
formed by taking the ratio of CARS signals obtained using x- and y-polarized
input fields is relatively insensitive to the effects of spherical scatterers.
Our computational framework provide a mechanistic approach to characterizing
scattering-induced distortions in coherent imaging of turbid media and may
inspire bottom-up approaches for adaptive optical methods for image correction.


title: A Benchmark and Comparison of Active Learning for Logistic Regression

abstract:
  Logistic regression is by far the most widely used classifier in real-world
applications. In this paper, we benchmark the state-of-the-art active learning
methods for logistic regression and discuss and illustrate their underlying
characteristics. Experiments are carried out on three synthetic datasets and 44
real-world datasets, providing insight into the behaviors of these active
learning methods with respect to the area of the learning curve (which plots
classification accuracy as a function of the number of queried examples) and
their computational costs. Surprisingly, one of the earliest and simplest
suggested active learning methods, i.e., uncertainty sampling, performs
exceptionally well overall. Another remarkable finding is that random sampling,
which is the rudimentary baseline to improve upon, is not overwhelmed by
individual active learning techniques in many cases.


title:
Heating of galactic gas by dark matter annihilation in ultracompact
  minihalos

abstract:
  The existence of substructure in halos of annihilating dark matter would be
expected to substantially boost the rate at which annihilation occurs.
Ultracompact minihalos of dark matter (UCMHs) are one of the more extreme
examples of this. The boosted annihilation can inject significant amounts of
energy into the gas of a galaxy over its lifetime. Here we determine the impact
of the boost factor from UCMH substructure on the heating of galactic gas in a
Milky Way-type galaxy, by means of N-body simulation. If $1\%$ of the dark
matter exists as UCMHs, the corresponding boost factor can be of order $10^5$.
For reasonable values of the relevant parameters (annihilation cross section
$3\times10^{-26} ~\textrm{cm}^3~ \textrm{s}^{-1}$, dark matter mass 100 GeV,
10% heating efficiency), we show that the presence of UCMHs at the 0.1% level
would inject enough energy to eject significant amounts of gas from the halo,
potentially preventing star formation within $\sim$1 kpc of the halo centre.


title:
Photoluminescence quantum efficiency of Er optical centers in GaN
  epilayers

abstract:
  We report the quantum efficiency of photoluminescence processes of Er optical
centers as well as the thermal quenching mechanism in GaN epilayers prepared by
metal-organic chemical vapor deposition. High resolution infrared spectroscopy
and temperature dependence measurements of photoluminescence intensity from Er
ions in GaN under resonant excitation excitations were performed. Data provide
a picture of the thermal quenching processes and activation energy levels. By
comparing the photoluminescence from Er ions in the epilayer with a reference
sample of Er-doped SiO2, we find that the fraction of Er ions that emits photon
at 1.54 micron upon a resonant optical excitation is approximately 68%. This
result presents a significant step in the realization of GaN:Er epilayers as an
optical gain medium at 1.54 micron.


title: Microstructure-enabled control of wrinkling in nematic elastomer sheets

abstract:
  Nematic elastomers are rubbery solids which have liquid crystals incorporated
into their polymer chains. These materials display many unusual mechanical
properties, one such being the ability to form fine-scale microstructure. In
this work, we explore the response of taut and appreciably stressed sheets made
of nematic elastomer. Such sheets feature two potential instabilities -- the
formation of fine-scale material microstructure and the formation of fine-scale
wrinkles. We develop a theoretical framework to study these sheets that
accounts for both instabilities, and we implement this framework numerically.
Specifically, we show that these instabilities occur for distinct mesoscale
stretches, and observe that microstructure is finer than wrinkles for
physically relevant parameters. Therefore, we relax (i.e., implicitly but
rigorously account for) the microstructure while we regularize (i.e., compute
the details explicitly) the wrinkles. Using both analytical and numerical
studies, we show that nematic elastomer sheets can suppress wrinkling by
modifying the expected state of stress through the formation of microstructure.


title:
Thermodynamically consistent modeling and simulation of multi-component
  two-phase flow model with partial miscibility

abstract:
  A general diffuse interface model with a realistic equation of state (e.g.
Peng-Robinson equation of state) is proposed to describe the multi-component
two-phase fluid flow based on the principles of the NVT-based framework which
is a latest alternative over the NPT-based framework to model the realistic
fluids. The proposed model uses the Helmholtz free energy rather than Gibbs
free energy in the NPT-based framework. Different from the classical routines,
we combine the first law of thermodynamics and related thermodynamical
relations to derive the entropy balance equation, and then we derive a
transport equation of the Helmholtz free energy density. Furthermore, by using
the second law of thermodynamics, we derive a set of unified equations for both
interfaces and bulk phases that can describe the partial miscibility of two
fluids. A relation between the pressure gradient and chemical potential
gradients is established, and this relation leads to a new formulation of the
momentum balance equation, which demonstrates that chemical potential gradients
become the primary driving force of fluid motion. Moreover, we prove that the
proposed model satisfies the total (free) energy dissipation with time. For
numerical simulation of the proposed model, the key difficulties result from
the strong nonlinearity of Helmholtz free energy density and tight coupling
relations between molar densities and velocity. To resolve these problems, we
propose a novel convex-concave splitting of Helmholtz free energy density and
deal well with the coupling relations between molar densities and velocity
through very careful physical observations with a mathematical rigor. We prove
that the proposed numerical scheme can preserve the discrete (free) energy
dissipation. Numerical tests are carried out to verify the effectiveness of the
proposed method.


title:
Toward CL-20 crystalline covalent solids: On the dependence of energy
  and electronic properties on the effective size of CL-20 chains

abstract:
  One-dimensional CL-20 chains have been constructed using CH$_2$ molecular
bridges for the covalent bonding between isolated CL-20 fragments. The energy
and electronic properties of the nanostructures obtained have been analyzed by
means of density functional theory and nonorthogonal tight-binding model
considering Landauer-B\"uttiker formalism. It has been found that such systems
become more thermodynamically stable as the efficient length of the chain
increases. Thus, the formation of bulk covalent CL-20 solids may be
energetically favorable, and such structures may possess high kinetic stability
comparing to the CL-20 molecular crystals. As for electronic properties of pure
CL-20 chains, they are wide-bandgap semiconductors with energy gaps equal to
several electron volts that makes their use in nanoelectronic applications
problematic without any additional modification.


title: Fast deterministic tourist walk for texture analysis

abstract:
  Deterministic tourist walk (DTW) has attracted increasing interest in
computer vision. In the last years, different methods for analysis of dynamic
and static textures were proposed. So far, all works based on the DTW for
texture analysis use all image pixels as initial point of a walk. However, this
requires much runtime. In this paper, we conducted a study to verify the
performance of the DTW method according to the number of initial points to
start a walk. The proposed method assigns a unique code to each image pixel,
then, the pixels whose code is not divisible by a given $k$ value are ignored
as initial points of walks. Feature vectors were extracted and a classification
process was performed for different percentages of initial points. Experimental
results on the Brodatz and Vistex datasets indicate that to use fewer pixels as
initial points significantly improves the runtime compared to use all image
pixels. In addition, the correct classification rate decreases very little.


title: Directional Mean Curvature for Textured Image Demixing

abstract:
  Approximation theory plays an important role in image processing, especially
image deconvolution and decomposition. For piecewise smooth images, there are
many methods that have been developed over the past thirty years. The goal of
this study is to devise similar and practical methodology for handling textured
images. This problem is motivated by forensic imaging, since fingerprints,
shoeprints and bullet ballistic evidence are textured images. In particular, it
is known that texture information is almost destroyed by a blur operator, such
as a blurred ballistic image captured from a low-cost microscope. The
contribution of this work is twofold: first, we propose a mathematical model
for textured image deconvolution and decomposition into four meaningful
components, using a high-order partial differential equation approach based on
the directional mean curvature. Second, we uncover a link between functional
analysis and multiscale sampling theory, e.g., harmonic analysis and filter
banks. Both theoretical results and examples with natural images are provided
to illustrate the performance of the proposed model.


title:
Moving energies as first integrals of nonholonomic systems with affine
  constraints

abstract:
  In nonholonomic mechanical systems with constraints that are affine (linear
nonhomogeneous) functions of the velocities, the energy is typically not a
first integral. It was shown in [Fass\`o and Sansonetto, JNLS, 26, (2016)]
that, nevertheless, there exist modifications of the energy, called there
moving energies, which under suitable conditions are first integrals. The first
goal of this paper is to study the properties of these functions and the
conditions that lead to their conservation. In particular, we enlarge the class
of moving energies considered in [Fass\`o and Sansonetto, JNLS, 26, (2016)].
The second goal of the paper is to demonstrate the relevance of moving energies
in nonholonomic mechanics. We show that certain first integrals of some well
known systems (the affine Veselova and LR systems), which had been detected on
a case-by-case way, are instances of moving energies. Moreover, we determine
conserved moving energies for a class of affine systems on Lie groups that
include the LR systems, for a heavy convex rigid body that rolls without
slipping on a uniformly rotating plane, and for an $n$-dimensional
generalization of the Chaplygin sphere problem to a uniformly rotating
hyperplane.


title:
Electronic and optical properties of strained graphene and other
  strained 2D materials: a review

abstract:
  This review presents the state of the art in strain and ripple-induced
effects on the electronic and optical properties of graphene. It starts by
providing the crystallographic description of mechanical deformations, as well
as the diffraction pattern for different kinds of representative deformation
fields. Then, the focus turns to the unique elastic properties of graphene, and
to how strain is produced. Thereafter, various theoretical approaches used to
study the electronic properties of strained graphene are examined, discussing
the advantages of each. These approaches provide a platform to describe exotic
properties, such as a fractal spectrum related with quasicrystals, a mixed
Dirac-Schr\"odinger behavior, emergent gravity, topological insulator states,
in molecular graphene and other 2D discrete lattices. The physical consequences
of strain on the optical properties are reviewed next, with a focus on the
Raman spectrum. At the same time, recent advances to tune the optical
conductivity of graphene by strain engineering are given, which open new paths
in device applications. Finally, a brief review of strain effects in
multilayered graphene and other promising 2D materials like silicene and
materials based on other group-IV elements, phosphorene, dichalcogenide- and
monochalcogenide-monolayers is presented, with a brief discussion of interplays
among strain, thermal effects, and illumination in the latter material family.


title:
Applying recursive numerical integration techniques for solving high
  dimensional integrals

abstract:
  The error scaling for Markov-Chain Monte Carlo techniques (MCMC) with $N$
samples behaves like $1/\sqrt{N}$. This scaling makes it often very time
intensive to reduce the error of computed observables, in particular for
applications in lattice QCD. It is therefore highly desirable to have
alternative methods at hand which show an improved error scaling. One candidate
for such an alternative integration technique is the method of recursive
numerical integration (RNI). The basic idea of this method is to use an
efficient low-dimensional quadrature rule (usually of Gaussian type) and apply
it iteratively to integrate over high-dimensional observables and Boltzmann
weights. We present the application of such an algorithm to the topological
rotor and the anharmonic oscillator and compare the error scaling to MCMC
results. In particular, we demonstrate that the RNI technique shows an error
scaling in the number of integration points $m$ that is at least exponential.


title:
Texture analysis using deterministic partially self-avoiding walk with
  thresholds

abstract:
  In this paper, we propose a new texture analysis method using the
deterministic partially self-avoiding walk performed on maps modified with
thresholds. In this method, two pixels of the map are neighbors if the
Euclidean distance between them is less than $\sqrt{2}$ and the weight
(difference between its intensities) is less than a given threshold. The maps
obtained by using different thresholds highlight several properties of the
image that are extracted by the deterministic walk. To compose the feature
vector, deterministic walks are performed with different thresholds and its
statistics are concatenated. Thus, this approach can be considered as a
multi-scale analysis. We validate our method on the Brodatz database, which is
very well known public image database and widely used by texture analysis
methods. Experimental results indicate that the proposed method presents a good
texture discrimination, overcoming traditional texture methods.


title: Probing the Radio Loud/Quiet AGN dichotomy with quasar clustering

abstract:
  We investigate the clustering properties of 45441 radio-quiet quasars (RQQs)
and 3493 radio-loud quasars (RLQs) drawn from a joint use of the Sloan Digital
Sky Survey (SDSS) and Faint Images of the Radio Sky at 20 cm (FIRST) surveys in
the range $0.3<z<2.3$. This large spectroscopic quasar sample allow us to
investigate the clustering signal dependence on radio-loudness and black hole
(BH) virial mass. We find that RLQs are clustered more strongly than RQQs in
all the redshift bins considered. We find a real-space correlation length of
$r_{0}=6.59_{-0.24}^{+0.33}\,h^{-1}\,\textrm{Mpc}$ and
$r_{0}=10.95_{-1.58}^{+1.22}\,h^{-1}\,\textrm{Mpc}$ {\normalsize{}for} RQQs and
RLQs, respectively, for the full redshift range. This implies that RLQs are
found in more massive host haloes than RQQs in our samples, with mean host halo
masses of $\sim4.9\times10^{13}\,h^{-1}\,M_{\odot}$ and
$\sim1.9\times10^{12}\,h^{-1}\,M_{\odot}$, respectively. Comparison with
clustering studies of different radio source samples indicates that this mass
scale of $\gtrsim1\times10^{13}\,h^{-1}\,M_{\odot}$ is characteristic for the
bright radio-population, which corresponds to the typical mass of galaxy groups
and galaxy clusters. The similarity we find in correlation lengths and host
halo masses for RLQs, radio galaxies and flat-spectrum radio quasars agrees
with orientation-driven unification models. Additionally, the clustering signal
shows a dependence on black hole (BH) mass, with the quasars powered by the
most massive BHs clustering more strongly than quasars having less massive BHs.
We suggest that the current virial BH mass estimates may be a valid BH proxies
for studying quasar clustering. We compare our results to a previous
theoretical model that assumes that quasar activity


title: Change-point detection in panel data via double CUSUM statistic

abstract:
  In this paper, we consider the problem of (multiple) change-point detection
in panel data. We propose the double CUSUM statistic which utilises the
cross-sectional change-point structure by examining the cumulative sums of
ordered CUSUMs at each point. The efficiency of the proposed change-point test
is studied, which is reflected on the rate at which the cross-sectional size of
a change is permitted to converge to zero while it is still detectable. Also,
the consistency of the proposed change-point detection procedure based on the
binary segmentation algorithm, is established in terms of both the total number
and locations (in time) of the estimated change-points. Motivated by the
representation properties of the Generalised Dynamic Factor Model, we propose a
bootstrap procedure for test criterion selection, which accounts for both
cross-sectional and within-series correlations in high-dimensional data. The
empirical performance of the double CUSUM statistics, equipped with the
proposed bootstrap scheme, is investigated in a comparative simulation study
with the state-of-the-art. As an application, we analyse the log returns of S&P
100 component stock prices over a period of one year.


title:
Modelling and forecasting daily electricity load curves: a hybrid
  approach

abstract:
  We propose a hybrid approach for the modelling and the short-term forecasting
of electricity loads. Two building blocks of our approach are (i) modelling the
overall trend and seasonality by fitting a generalised additive model to the
weekly averages of the load, and (ii) modelling the dependence structure across
consecutive daily loads via curve linear regression. For the latter, a new
methodology is proposed for linear regression with both curve response and
curve regressors. The key idea behind the proposed methodology is the dimension
reduction based on a singular value decomposition in a Hilbert space, which
reduces the curve regression problem to several ordinary (i.e. scalar) linear
regression problems. We illustrate the hybrid method using the French
electricity loads between 1996 and 2009, on which we also compare our method
with other available models including the EDF operational model.


title:
Measurement of the acoustic radiation force on a sphere embedded in a
  soft solid

abstract:
  The acoustic radiation force exerted on a small sphere located at the focus
of an ultrasonic beam is measured in a soft gel. It is proved to evolve
quadratically with the local amplitude of the acoustic field. Strong
oscillations of the local pressure are observed and attributed to an acoustic
Fabry-P{\'e}rot effect between the ultrasonic emitter and the sphere. Taking
this effect into account with a simple model, a quantitative link between the
radiation force and the acoustic pressure is proposed and compared to
theoretical predictions in the absence of dissipation. The discrepancy between
experiment and theory suggests that dissipative effects should be taken into
account for fully modeling the observations.


title:
Multiscale interpretation of taut string estimation and its connection
  to Unbalanced Haar wavelets

abstract:
  We compare two state-of-the-art non-linear techniques for nonparametric
function estimation via piecewise constant approximation: the taut string and
the Unbalanced Haar methods. While it is well-known that the latter is
multiscale, it is not obvious that the former can also be interpreted as
multiscale. We provide a unified multiscale representation for both methods,
which offers an insight into the relationship between them as well as
suggesting lessons both methods can learn from each other.


title:
Dynamics of wide binary stars: A case study for testing Newtonian
  dynamics in the low acceleration regime

abstract:
  Extremely wide binary stars represent ideal systems to probe Newtonian
dynamics in the low acceleration regimes (<10e-10 m/s/s) typical of the
external regions of galaxies. Here we present a study of 60 alleged wide binary
stars with projected separation ranging from 0.004 to 1 pc, probing
gravitational accelerations well below the limit were dark matter or modified
dynamics theories set in. Radial velocities with accuracy ~100 m/s were
obtained for each star, in order to constrain their orbital velocity, that,
together with proper motion data, can distinguish bound from unbound systems.
It was found that about half of the observed pairs do have velocity in the
expected range for bound systems, out to the largest separations probed here.
In particular, we identified five pairs with projected separation >0.15 pc that
are useful for the proposed test. While it would be premature to draw any
conclusion about the validity of Newtonian dynamics at these low accelerations,
our main result is that very wide binary stars seem to exist in the harsh
environment of the solar neighborhood. This could provide a tool to test
Newtonian dynamics versus modified dynamics theories in the low acceleration
conditions typical of galaxies. In the near future the GAIA satellite will
provide data to increase significantly the number of wide pairs that, with the
appropriate follow up spectroscopic observations, will allow the implementation
of this experiment with unprecedented accuracy.


title:
A test for second-order stationarity of time series based on
  unsystematic sub-samples

abstract:
  In this paper, we introduce a new method for testing the stationarity of time
series, where the test statistic is obtained from measuring and maximising the
difference in the second-order structure over pairs of randomly drawn
intervals. The asymptotic normality of the test statistic is established for
both Gaussian and a range of non-Gaussian time series, and a bootstrap
procedure is proposed for estimating the variance of the main statistics.
Further, we show the consistency of our test under local alternatives. Due to
the flexibility inherent in the random, unsystematic sub-samples used for test
statistic construction, the proposed method is able to identify the intervals
of significant departure from the stationarity without any dyadic constraints,
which is an advantage over other tests employing systematic designs. We
demonstrate its good finite sample performance on both simulated and real data,
particularly in detecting localised departure from the stationarity.


title: Degeneracy of Holomorphic Poisson Spectral Sequence

abstract:
  Through the theory of Lie bi-algebroids and generalized complex structures,
one could define a cohomology theory naturally associated to a holomorphic
Poisson structure. It is known that it is the hypercohomology of a bi-complex
such that one of the two operators is the classical
$\overline{\partial}$-operator. Another operator is the adjoint action of the
Poisson bivector with respect to the Schouten-Nijenhuis bracket. The
hypercohomology is naturally computed by one of the two associated spectral
sequences. In a prior publication, the author of this article and his
collaborators investigated the degeneracy of this spectral sequence on the
second page. In this note, the author investigates the conditions for which
this spectral sequence degenerates on the first page. Particular effort is
devoted to nilmanifolds with abelian complex structures.


title:
Continuously tunable pulsed Ti:Sa laser self-seeded by an extended
  grating cavity

abstract:
  A continuously tunable titanium:sapphire (Ti:Sa) laser self-seeded by an
extended grating cavity was demonstrated and characterized. By inserting a
partially reflecting mirror inside the cavity of a classic single-cavity
grating laser, two oscillators are created: a broadband power oscillator, and a
narrowband oscillator with a prism beam expander and a diffraction grating in
Littrow configuration. By coupling the grating cavity oscillation into the
power oscillator, a power-enhanced narrow-linewidth laser oscillation is
achieved. Compared to the classic grating laser, this simple modification
significantly increases the laser output power without considerably broadening
the linewidth. With most of the oscillating laser power confined inside the
broadband power cavity and lower power incident onto the grating, the new
configuration also allows higher pump power, which is typically limited by the
thermal deformation of the grating coating at high oscillation power.


title:
Multiple-change-point detection for high dimensional time series via
  sparsified binary segmentation

abstract:
  Time series segmentation, a.k.a. multiple change-point detection, is a
well-established problem. However, few solutions are designed specifically for
high-dimensional situations. In this paper, our interest is in segmenting the
second-order structure of a high-dimensional time series. In a generic step of
a binary segmentation algorithm for multivariate time series, one natural
solution is to combine CUSUM statistics obtained from local periodograms and
cross-periodograms of the components of the input time series. However, the
standard "maximum" and "average" methods for doing so often fail in high
dimensions when, for example, the change-points are sparse across the panel or
the CUSUM statistics are spuriously large. In this paper, we propose the
Sparsified Binary Segmentation (SBS) algorithm which aggregates the CUSUM
statistics by adding only those that pass a certain threshold. This
"sparsifying" step reduces the impact of irrelevant, noisy contributions, which
is particularly beneficial in high dimensions. In order to show the consistency
of SBS, we introduce the multivariate Locally Stationary Wavelet model for time
series, which is a separate contribution of this work.


title: High-dimensional variable selection via tilting

abstract:
  The paper considers variable selection in linear regression models where the
number of covariates is possibly much larger than the number of observations.
High dimensionality of the data brings in many complications, such as (possibly
spurious) high correlations between the variables, which result in marginal
correlation being unreliable as a measure of association between the variables
and the response. We propose a new way of measuring the contribution of each
variable to the response which takes into account high correlations between the
variables in a data-driven way. The proposed tilting procedure provides an
adaptive choice between the use of marginal correlation and tilted correlation
for each variable, where the choice is made depending on the values of the hard
thresholded sample correlation of the design matrix. We study the conditions
under which this measure can successfully discriminate between the relevant and
the irrelevant variables and thus be used as a tool for variable selection.
Finally, an iterative variable screening algorithm is constructed to exploit
the theoretical properties of tilted correlation, and its good practical
performance is demonstrated in a comparative simulation study.


title: Optimal Control for Generalized Network-Flow Problems

abstract:
  We consider the problem of throughput-optimal packet dissemination, in the
presence of an arbitrary mix of unicast, broadcast, multicast and anycast
traffic, in a general wireless network. We propose an online dynamic policy,
called Universal Max-Weight (UMW), which solves the above problem efficiently.
To the best of our knowledge, UMW is the first throughput-optimal algorithm of
such versatility in the context of generalized network flow problems.
Conceptually, the UMW policy is derived by relaxing the precedence constraints
associated with multi-hop routing, and then solving a min-cost routing and
max-weight scheduling problem on a virtual network of queues. When specialized
to the unicast setting, the UMW policy yields a throughput-optimal cycle-free
routing and link scheduling policy. This is in contrast to the well-known
throughput-optimal Back- Pressure (BP) policy which allows for packet cycling,
resulting in excessive latency. Extensive simulation results show that the
proposed UMW policy incurs a substantially smaller delay as compared to the BP
policy. The proof of throughput-optimality of the UMW policy combines ideas
from stochastic Lyapunov theory with a sample path argument from adversarial
queueing theory and may be of independent theoretical interest.


title: Gaussian Approximations for Probability Measures on $\mathbf{R}^d$

abstract:
  This paper concerns the approximation of probability measures on
$\mathbf{R}^d$ with respect to the Kullback-Leibler divergence. Given an
admissible target measure, we show the existence of the best approximation,
with respect to this divergence, from certain sets of Gaussian measures and
Gaussian mixtures. The asymptotic behavior of such best approximations is then
studied in the small parameter limit where the measure concentrates; this
asymptotic behaviour is characterized using $\Gamma$-convergence. The theory
developed is then applied to understanding the frequentist consistency of
Bayesian inverse problems. For a fixed realization of noise, we show the
asymptotic normality of the posterior measure in the small noise limit. Taking
into account the randomness of the noise, we prove a Bernstein-Von Mises type
result for the posterior measure.


title: Geodesic Convexity Types in Riemannian Manifolds

abstract:
  The usual notion of set-convexity, valid in the classical Euclidean context,
metamorphoses into several distinct convexity types in the more general
Riemannian setting. By studying this phenomenon in reverse, we characterize
complete manifolds for which certain convexity types are assumed a priori to
coincide.


title: Reduction for $SL(3)$ pre-buildings

abstract:
  Given an $SL(3)$ spectral curve over a simply connected Riemann surface, we
describe in detail the reduction steps necessary to construct the core of a
pre-building with versal harmonic map whose differential is given by the
spectral curve.


title:
Representation of compounds for machine-learning prediction of physical
  properties

abstract:
  The representations of a compound, called "descriptors" or "features", play
an essential role in constructing a machine-learning model of its physical
properties. In this study, we adopt a procedure for generating a systematic set
of descriptors from simple elemental and structural representations. First it
is applied to a large dataset composed of the cohesive energy for about 18000
compounds computed by density functional theory (DFT) calculation. As a result,
we obtain a kernel ridge prediction model with a prediction error of 0.041
eV/atom, which is close to the "chemical accuracy" of 1 kcal/mol (0.043
eV/atom). The procedure is also applied to two smaller datasets, i.e., a
dataset of the lattice thermal conductivity (LTC) for 110 compounds computed by
DFT calculation and a dataset of the experimental melting temperature for 248
compounds. We examine the performance of the descriptor sets on the efficiency
of Bayesian optimization in addition to the accuracy of the kernel ridge
regression models. They exhibit good predictive performances.


title: Two-parameter families of uniquely extendable Diophantine triples

abstract:
  Let A,K be positive integers and u=-2,-1,1 or 2. The main contribution of the
paper is a proof that each of the D(u^2)-triples {K,A^2K+2uA,(A+1)^2K+2u(A+1)}
has unique extension to a D(u^2)-quadruple.


title: Dual MCDRR Scheduler for Hybrid TDM/WDM Optical Networks

abstract:
  In this paper we propose and investigate the performance of a dual
multi-channel deficit round-robin (D-MCDRR) scheduler based on the existing
single MCDRR scheduler. The existing scheduler is used for multiple channels
with tunable transmitters and fixed receivers in hybrid time division
multiplexing (TDM)/wavelength division multiplexing (WDM) optical networks. The
proposed dual scheduler will also be used in the same optical networks. We
extend the existing MCDRR scheduling algorithm for n channels to the case of
considering two schedulers for the same n channels. Simulation results show
that the proposed dual MCDRR (D-MCDRR) scheduler can provide better throughput
when compared to the existing single MCDRR scheduler.


title: Patient-Driven Privacy Control through Generalized Distillation

abstract:
  The introduction of data analytics into medicine has changed the nature of
patient treatment. In this, patients are asked to disclose personal information
such as genetic markers, lifestyle habits, and clinical history. This data is
then used by statistical models to predict personalized treatments. However,
due to privacy concerns, patients often desire to withhold sensitive
information. This self-censorship can impede proper diagnosis and treatment,
which may lead to serious health complications and even death over time. In
this paper, we present privacy distillation, a mechanism which allows patients
to control the type and amount of information they wish to disclose to the
healthcare providers for use in statistical models. Meanwhile, it retains the
accuracy of models that have access to all patient data under a sufficient but
not full set of privacy-relevant information. We validate privacy distillation
using a corpus of patients prescribed to warfarin for a personalized dosage. We
use a deep neural network to implement privacy distillation for training and
making dose predictions. We find that privacy distillation with sufficient
privacy-relevant information i) retains accuracy almost as good as having all
patient data (only 3\% worse), and ii) is effective at preventing errors that
introduce health-related risks (only 3.9\% worse under- or over-prescriptions).


title:
Robust Variable and Interaction Selection for Logistic Regression and
  Multiple Index Models

abstract:
  We propose Stepwise cOnditional likelihood variable selection for
Discriminant Analysis (SODA) to detect both main and quadratic interaction
effects in logistic regression and quadratic discriminant analysis (QDA)
models. In the forward stage, SODA adds in important predictors evaluated based
on their overall contributions, whereas in the backward stage SODA removes
unimportant terms so as to optimize the extended Bayesian Information Criterion
(EBIC). Compared with existing methods on QDA variable selections, SODA can
deal with high-dimensional data with the number of predictors much larger than
the sample size and does not require the joint normality assumption on
predictors, leading to much enhanced robustness. We further extend SODA to
conduct variable selection and model fitting for multiple index models.
Compared with existing variable selection methods based on the Sliced Inverse
Regression (SIR) (Li 1991), SODA requires neither the linearity nor the
constant variance condition and is much more robust. Our theoretical analyses
establish the variable-selection consistency of SODA under high-dimensional
settings, and our simulation studies as well as real-data applications
demonstrate superior performances of SODA in dealing with non-Gaussian design
matrices in both classification problems and multiple index models.


title:
Topological Crystalline Magnets: Symmetry-Protected Topological Phases
  of Fermions

abstract:
  We introduce a novel class of interaction-enabled topological crystalline
insulators in two- and three-dimensional electronic systems, which we call
"topological crystalline magnet." It is protected by the product of the
time-reversal symmetry $\mathcal{T}$ and a mirror symmetry or a rotation
symmetry $\mathcal{R}$. A topological crystalline magnet exhibits two
intriguing features: (i) it cannot be adiabatically connected to any Slater
insulator and (ii) the edge state is robust against coupling electrons to the
edge. These features are protected by the anomalous symmetry transformation
property $(\mathcal{R} \mathcal{T})^2=-1$ of the edge state. An anisotropic
response to the external magnetic field can be an experimental signature.

categories: cond-mat.str-el cond-mat.mes-hall cond-mat.mtrl-sci cond-mat.other


title:
Proceedings of the 4th and 5th International Workshop on Trends in
  Functional Programming in Education

abstract:
  This volume contains the proceedings of the Fourth and Fifth International
Workshops on Trends in Functional Programming in Education, TFPIE 2015 and
TFPIE 2016, which were held on June 2, 2015 in Sophia-Antipolis, France, and on
June 7, 2016 at the University of Maryland College Park in the USA,
respectively.


title: Growth of the inner core in the mean-field dynamo model

abstract:
  Application of Parker's dynamo model to the geodynamo with the growing inner
core is considered. It is shown that decrease of the inner core size, where
intensive magnetic field generation takes place, leads to the multi-polar
magnetic field in the past. This effect reflects the decrease of the region of
the effective magnetic field generation. The process is accompanied by increase
of the reversals number and decrease of intensity of the geomagnetic field. The
constraints on the mechanisms of convection in the liquid core are discussed.


title:
Nb/InAs nanowire proximity junctions from Josephson to quantum dot
  regimes

abstract:
  The superconducting proximity effect is probed experimentally in Josephson
junctions fabricated with InAs nanowires contacted by Nb leads. Contact
transparencies $t \sim 0.7$ are observed. The electronic phase coherence length
at low temperatures exceeds the channel length. However, the elastic scattering
length is a few times shorter than the channel length. Electrical measurements
reveal two regimes of quantum transport: (i) the Josephson regime,
characterized by a dissipationless current up to $\sim 100$ nA, and (ii) the
quantum dot regime, characterized by the formation of Andreev Bound States
(ABS) associated with spontaneous quantum dots inside the nanowire channel. In
regime (i), the behaviour of the critical current $I_c$ versus an axial
magnetic field $B_{||}$ shows an unexpected modulation and persistence to
fields $>2$ T. In the quantum dot regime, the ABS are modelled as the
current-biased solutions of an Anderson-type model. The applicability of
devices in both transport regimes to Majorana fermion experiments is discussed.


title:
Two observations on the capacity of the range of simple random walks on
  $\mathbb{Z}^3$ and $\mathbb{Z}^4$

abstract:
  We prove a weak law of large numbers for the capacity of the range of simple
random walks on $\mathbb{Z}^{4}$. On $\mathbb{Z}^{3}$, we show that the
capacity, properly scaled, converges in distribution towards the corresponding
quantity for three dimensional Brownian motion.


title: A Deep Neural Network to identify foreshocks in real time

abstract:
  Foreshock events provide valuable insight to predict imminent major
earthquakes. However, it is difficult to identify them in real time. In this
paper, I propose an algorithm based on deep learning to instantaneously
classify a seismic waveform as a foreshock, mainshock or an aftershock event
achieving a high accuracy of 99% in classification. As a result, this is by far
the most reliable method to predict major earthquakes that are preceded by
foreshocks. In addition, I discuss methods to create an earthquake dataset that
is compatible with deep networks.


title: Attention-based Memory Selection Recurrent Network for Language Modeling

abstract:
  Recurrent neural networks (RNNs) have achieved great success in language
modeling. However, since the RNNs have fixed size of memory, their memory
cannot store all the information about the words it have seen before in the
sentence, and thus the useful long-term information may be ignored when
predicting the next words. In this paper, we propose Attention-based Memory
Selection Recurrent Network (AMSRN), in which the model can review the
information stored in the memory at each previous time step and select the
relevant information to help generate the outputs. In AMSRN, the attention
mechanism finds the time steps storing the relevant information in the memory,
and memory selection determines which dimensions of the memory are involved in
computing the attention weights and from which the information is extracted.In
the experiments, AMSRN outperformed long short-term memory (LSTM) based
language models on both English and Chinese corpora. Moreover, we investigate
using entropy as a regularizer for attention weights and visualize how the
attention mechanism helps language modeling.


title:
Convolutional Experts Constrained Local Model for Facial Landmark
  Detection

abstract:
  Constrained Local Models (CLMs) are a well-established family of methods for
facial landmark detection. However, they have recently fallen out of favor to
cascaded regression-based approaches. This is in part due to the inability of
existing CLM local detectors to model the very complex individual landmark
appearance that is affected by expression, illumination, facial hair, makeup,
and accessories. In our work, we present a novel local detector --
Convolutional Experts Network (CEN) -- that brings together the advantages of
neural architectures and mixtures of experts in an end-to-end framework. We
further propose a Convolutional Experts Constrained Local Model (CE-CLM)
algorithm that uses CEN as local detectors. We demonstrate that our proposed
CE-CLM algorithm outperforms competitive state-of-the-art baselines for facial
landmark detection by a large margin on four publicly-available datasets. Our
approach is especially accurate and robust on challenging profile images.


title:
Comparison of dust charging between Orbital-Motion-Limited theory and
  Particle-In-Cell simulations

abstract:
  The Orbital-Motion-Limited (OML) theory has been modified to predict the dust
charge and the results were contrasted with the Whipple approximation [Tang and
Delzanno, Phys. Plasmas 21, 123708 (2014)]. To further establish its regime of
applicability, in this paper the OML predictions (for a non-electron-emitting,
spherical dust grain at rest in a collisionless, unmagnetized plasma) are
compared with Particle-In-Cell simulations that retain the absorption radius
effect. It is found that for large dust grain radius $r_d$ relative to the
plasma Debye length $\lambda_D$, the revised OML theory remains a very good
approximation as, for the parameters considered ($r_d/\lambda_D\le10$, equal
electron and ion temperatures), it yields the dust charge to within $20\%$
accuracy. This is a substantial improvement over the Whipple approximation. The
dust collected currents and energy fluxes, which remain the same in the revised
and standard OML theories, are accurate to within $15-30\%$.


title: Nagaoka's theorem in the Holstein-Hubbard model

abstract:
  Nagaoka's theorem on ferromagnetism in the Hubbard model is extended to the
Holstein-Hubbard model. This shows that Nagaoka's ferromagnetism is stable even
if the electron-phonon interaction is taken into account. We also prove that
Nagaoka's ferromagnetism is stable under the influence of the quantized
radiation field.


title:
On the DoF of Two-way $2\times2\times2$ Relay Networks with or without
  Relay Caching

abstract:
  Two-way relay is potentially an effective approach to spectrum sharing and
aggregation by allowing simultaneous bidirectional transmissions between
source-destinations pairs. This paper studies the two-way $2\times2\times2$
relay network, a class of four-unicast networks, where there are four
source/destination nodes and two relay nodes, with each source sending a
message to its destination. We show that without relay caching the total
degrees of freedom (DoF) is bounded from above by $8/3$, indicating that
bidirectional links do not double the DoF (It is known that the total DoF of
one-way $2\times2\times2$ relay network is $2$.). Further, we show that the DoF
of $8/3$ is achievable for the two-way $2\times2\times2$ relay network with
relay caching. Finally, even though the DoF of this network is no more than
$8/3$ for generic channel gains, DoF of $4$ can be achieved for a symmetric
configuration of channel gains.


title:
Knowledge Graph Representation with Jointly Structural and Textual
  Encoding

abstract:
  The objective of knowledge graph embedding is to encode both entities and
relations of knowledge graphs into continuous low-dimensional vector spaces.
Previously, most works focused on symbolic representation of knowledge graph
with structure information, which can not handle new entities or entities with
few facts well. In this paper, we propose a novel deep architecture to utilize
both structural and textual information of entities. Specifically, we introduce
three neural models to encode the valuable information from text description of
entity, among which an attentive model can select related information as
needed. Then, a gating mechanism is applied to integrate representations of
structure and text into a unified architecture. Experiments show that our
models outperform baseline by margin on link prediction and triplet
classification tasks. Source codes of this paper will be available on Github.


title:
Compact pseudo-Riemannian homogeneous Einstein manifolds of low
  dimension

abstract:
  Let $M$ be pseudo-Riemannian homogeneous Einstein manifold of finite volume,
and suppose a connected Lie group $G$ acts transitively and isometrically on
$M$. In this situation, the metric on $M$ induces a bilinear form
$\langle\cdot,\cdot\rangle$ on the Lie algebra $\mathfrak{g}$ of $G$ which is
nil-invariant, a property closely related to invariance. We study such spaces
$M$ in three important cases. First, we assume $\langle\cdot,\cdot\rangle$ is
invariant, in which case the Einstein property requires that $G$ is either
solvable or semisimple. Next, we investigate the case where $G$ is solvable.
Here, $M$ is compact and $M=G/\Gamma$ for a lattice $\Gamma$ in $G$. We show
that in dimensions less or equal to $7$, compact quotients $M=G/\Gamma$ exist
only for nilpotent groups $G$. We conjecture that this is true for any
dimension. In fact, this holds if Schanuel's Conjecture on transcendental
numbers is true. Finally, we consider semisimple Lie groups $G$, and find that
$M$ is covered by a pseudo-Riemannian product of Einstein manifolds
corresponding to the compact and the non-compact factors of $G$.


title:
Multi-Task Zero-Shot Action Recognition with Prioritised Data
  Augmentation

abstract:
  Zero-Shot Learning (ZSL) promises to scale visual recognition by bypassing
the conventional model training requirement of annotated examples for every
category. This is achieved by establishing a mapping connecting low-level
features and a semantic description of the label space, referred as
visual-semantic mapping, on auxiliary data. Reusing the learned mapping to
project target videos into an embedding space thus allows novel-classes to be
recognised by nearest neighbour inference. However, existing ZSL methods suffer
from auxiliary-target domain shift intrinsically induced by assuming the same
mapping for the disjoint auxiliary and target classes. This compromises the
generalisation accuracy of ZSL recognition on the target data. In this work, we
improve the ability of ZSL to generalise across this domain shift in both
model- and data-centric ways by formulating a visual-semantic mapping with
better generalisation properties and a dynamic data re-weighting method to
prioritise auxiliary data that are relevant to the target classes.
Specifically: (1) We introduce a multi-task visual-semantic mapping to improve
generalisation by constraining the semantic mapping parameters to lie on a
low-dimensional manifold, (2) We explore prioritised data augmentation by
expanding the pool of auxiliary data with additional instances weighted by
relevance to the target domain. The proposed new model is applied to the
challenging zero-shot action recognition problem to demonstrate its advantages
over existing ZSL models.


title:
Semi-supervised Learning using Denoising Autoencoders for Brain Lesion
  Detection and Segmentation

abstract:
  The work presented explores the use of denoising autoencoders (DAE) for brain
lesion detection, segmentation and false positive reduction. Stacked denoising
autoencoders (SDAE) were pre-trained using a large number of unlabeled patient
volumes and fine tuned with patches drawn from a limited number of patients
(n=20, 40, 65). The results show negligible loss in performance even when SDAE
was fine tuned using 20 patients. Low grade glioma (LGG) segmentation was
achieved using a transfer learning approach wherein a network pre-trained with
High Grade Glioma (HGG) data was fine tuned using LGG image patches. The weakly
supervised SDAE (for HGG) and transfer learning based LGG network were also
shown to generalize well and provide good segmentation on unseen BraTS 2013 &
BraTS 2015 test data. An unique contribution includes a single layer DAE,
referred to as novelty detector(ND). ND was trained to accurately reconstruct
non-lesion patches using a mean squared error loss function. The reconstruction
error maps of test data were used to identify regions containing lesions. The
error maps were shown to assign unique error distributions to various
constituents of the glioma, enabling localization. The ND learns the non-lesion
brain accurately as it was also shown to provide good segmentation performance
on ischemic brain lesions in images from a different database.


title: Tuned and Non-Higgsable U(1)s in F-theory

abstract:
  We study the tuning of U(1) gauge fields in F-theory models on a base of
general dimension. We construct a formula that computes the change in
Weierstrass moduli when such a U(1) is tuned, based on the Morrison-Park form
of a Weierstrass model with an additional rational section. Using this formula,
we propose the form of "minimal tuning" on any base, which corresponds to the
case where the decrease in the number of Weierstrass moduli is minimal.
Applying this result, we discover some universal features of bases with
non-Higgsable U(1)s. Mathematically, a generic elliptic fibration over such a
base has additional rational sections. Physically, this condition implies the
existence of U(1) gauge group in the low-energy supergravity theory after
compactification that cannot be Higgsed away. In particular, we show that the
elliptic Calabi-Yau manifold over such a base has a small number of complex
structure moduli. We also suggest that non-Higgsable U(1)s can never appear on
any toric bases. Finally, we construct the first example of a threefold base
with non-Higgsable U(1)s.


title:
Training an Interactive Humanoid Robot Using Multimodal Deep
  Reinforcement Learning

abstract:
  Training robots to perceive, act and communicate using multiple modalities
still represents a challenging problem, particularly if robots are expected to
learn efficiently from small sets of example interactions. We describe a
learning approach as a step in this direction, where we teach a humanoid robot
how to play the game of noughts and crosses. Given that multiple multimodal
skills can be trained to play this game, we focus our attention to training the
robot to perceive the game, and to interact in this game. Our multimodal deep
reinforcement learning agent perceives multimodal features and exhibits verbal
and non-verbal actions while playing. Experimental results using simulations
show that the robot can learn to win or draw up to 98% of the games. A pilot
test of the proposed multimodal system for the targeted game---integrating
speech, vision and gestures---reports that reasonable and fluent interactions
can be achieved using the proposed approach.


title:
Continuously Phase-Modulated Standing Surface Acoustic Waves for
  Separation of Particles and Cells in Microfluidic Channels Containing
  Multiple Pressure Nodes

abstract:
  This paper describes continuously phase-modulated standing surface acoustic
waves (CPM-SSAW) and its application for particle separation in multiple
pressure nodes. A linear change of phase in CPM-SSAW applies a force to
particles whose magnitude depends on their size and contrast factors. During
continuous phase modulation, we demonstrate that particles with the target
dimension are translated in the direction of moving pressure nodes, whereas
smaller particles show oscillatory movements. The rate of phase modulation is
optimized for separation of target particles from the relationship between mean
particle velocity and period of oscillation. The developed technique is applied
to separate particles of the target dimension from the particle mixture.
Furthermore, we also demonstrate human keratinocyte cells can be separated in
the cell and bead mixture. The separation technique is incorporated with a
microfluidic channel spanning multiple pressure nodes, which is advantageous
over separation in a single pressure node in terms of high throughput.


title:
Absolute frequency measurement with uncertainty below $1\times 10^{-15}$
  using International Atomic Time

abstract:
  The absolute frequency of the $^{87}{\rm Sr}$ clock transition measured in
2015 was reevaluated using an improved frequency link to the SI second. The
scale interval of International Atomic Time (TAI) that we used as the reference
was calibrated for an evaluation interval of five days instead of the
conventional interval of one month which is regularly employed in Circular T.
The calibration on a five-day basis removed the uncertainty in assimilating the
TAI scale of the five-day mean to that of the one-month mean. The reevaluation
resulted in the total uncertainty of $10^{-16}$ level for the first time
without local cesium fountains. Since there are presumably no correlations
among systematic shifts of cesium fountains worldwide, the measurement is not
limited by the systematic uncertainty of a specific primary frequency standard.


title: Visual Dialog

abstract:
  We introduce the task of Visual Dialog, which requires an AI agent to hold a
meaningful dialog with humans in natural, conversational language about visual
content. Specifically, given an image, a dialog history, and a question about
the image, the agent has to ground the question in image, infer context from
history, and answer the question accurately. Visual Dialog is disentangled
enough from a specific downstream task so as to serve as a general test of
machine intelligence, while being grounded in vision enough to allow objective
evaluation of individual responses and benchmark progress. We develop a novel
two-person chat data-collection protocol to curate a large-scale Visual Dialog
dataset (VisDial). VisDial v0.9 has been released and contains 1 dialog with 10
question-answer pairs on ~120k images from COCO, with a total of ~1.2M dialog
question-answer pairs.
  We introduce a family of neural encoder-decoder models for Visual Dialog with
3 encoders -- Late Fusion, Hierarchical Recurrent Encoder and Memory Network --
and 2 decoders (generative and discriminative), which outperform a number of
sophisticated baselines. We propose a retrieval-based evaluation protocol for
Visual Dialog where the AI agent is asked to sort a set of candidate answers
and evaluated on metrics such as mean-reciprocal-rank of human response. We
quantify gap between machine and human performance on the Visual Dialog task
via human studies. Putting it all together, we demonstrate the first 'visual
chatbot'! Our dataset, code, trained models and visual chatbot are available on
https://visualdialog.org


abstract:
  We develop a quantum-mechanical theory for Landau damping of surface plasmons
in metal nanostructures larger that the characteristic length for nonlocal
effects. We show that the electron surface scattering, which facilitates
plasmon decay in small nanostructures, can be incorporated into the metal
dielectric function on par with phonon and impurity scattering. The derived
surface scattering rate is determined by the plasmon local field polarization
relative to the metal-dielectric interface and is highly sensitive to the
system geometry. We illustrate our model by providing analytical results for
surface scattering rate in some common shape nanostructures.