title: Acylindrical hyperbolicity and Artin-Tits groups of spherical type

abstract:
  We prove that, for any irreducible Artin-Tits group of spherical type $G$,
the quotient of $G$ by its center is acylindrically hyperbolic. This is
achieved by studying the additional length graph associated to the classical
Garside structure on $G$, and constructing a specific element $x_G$ of $G/Z(G)$
whose action on the graph is loxodromic and WPD in the sense of
Bestvina-Fujiwara; following Osin, this implies acylindrical hyperbolicity.
Finally, we prove that "generic" elements of $G$ act loxodromically, where the
word "generic" can be understood in either of the two common usages: as a
result of a long random walk or as a random element in a large ball in the
Cayley graph.


title: Natural calcium carbonate for biomedical applications

abstract:
  Sea shells are found to be a very rich natural resource for calcium
carbonate. Sea shells are made up of CaCO3 mainly in the aragonite form, which
are columnar or fibrous or microsphere structured crystals. The bioactivity of
nanoparticles of sea shell has been studied in this work. The sea shells
collected were thoroughly washed, dried and pulverized. The powder was sieved
and particles in the range of 45 to 63 microns were collected. The powdered sea
shells were characterized using X-Ray Diffraction and Field Emission Scanning
Electron Microscopy. The XRD data showed that the particles were mainly
microspheres. Traces of calcite and vaterite were also present. Experiments
were conducted to study the aspirin and strontium ranelate drug loading into
the sea shell powder using soak and dry method. Different concentrations of
drug solution was made in ethanol and water. The shell powder was soaked in
drug solutions and was kept soaking for 48 hrs with intermittent
ultrasonication. The mixture was gently dried in a vacuum oven. The in vitro
drug release studies were done using Phosphate Buffered Saline. The FESEM
images displayed a distribution of differently sized and shaped particles. The
sea shells due to its natural porosity and crystallinity are expected to be
useful for drug delivery. About 50% drug entrapment efficiency for aspirin and
39% for strontium ranelate was seen. A burst release of the drug (80 percent)
was observed within two hours for both the drugs studied. Rest of the drug was
released slowly in 19 hrs. Further modification of the sea shell with non toxic
polymers is also planned as a part of this work. Sea shell powder has become a
potential candidate for drug delivery due to all the aforementioned advantages.


title:
On a theorem of Bishop and commutants of Toeplitz operators in
  $\mathbb{C}^n$

abstract:
  We prove an approximation theorem on a class of domains in $\mathbb{C}^n$ on
which the $\overline{\partial}$-problem is solvable in $L^{\infty}$.
Furthermore, as a corollary, we obtain a version of the
Axler-\v{C}u\v{c}kovi\'c-Rao Theorem in higher dimensions.


title:
Harnessing the Power of the Crowd to Increase Capacity for Data Science
  in the Social Sector

abstract:
  We present three case studies of organizations using a data science
competition to answer a pressing question. The first is in education where a
nonprofit that creates smart school budgets wanted to automatically tag budget
line items. The second is in public health, where a low-cost, nonprofit women's
health care provider wanted to understand the effect of demographic and
behavioral questions on predicting which services a woman would need. The third
and final example is in government innovation: using online restaurant reviews
from Yelp, competitors built models to forecast which restaurants were most
likely to have hygiene violations when visited by health inspectors. Finally,
we reflect on the unique benefits of the open, public competition model.


title: Sign changes of the Eisenstein series on the critical line

abstract:
  We prove a quantitative lower bound on the number of nodal domains of the
real-analytic Eisenstein series. The main tool in the proof is a quantitative
restricted QUE theorem where the support of the test function is allowed to
shrink with the Laplace eigenvalue.


title:
Sequential Convolutional Neural Networks for Slot Filling in Spoken
  Language Understanding

abstract:
  We investigate the usage of convolutional neural networks (CNNs) for the slot
filling task in spoken language understanding. We propose a novel CNN
architecture for sequence labeling which takes into account the previous
context words with preserved order information and pays special attention to
the current word with its surrounding context. Moreover, it combines the
information from the past and the future words for classification. Our proposed
CNN architecture outperforms even the previously best ensembling recurrent
neural network model and achieves state-of-the-art results with an F1-score of
95.61% on the ATIS benchmark dataset without using any additional linguistic
knowledge and resources.


title:
Satellite Images Analysis with Symbolic Time Series: A Case Study of the
  Algerian Zone

abstract:
  Satellite Image Time Series (SITS) are an important source of information for
studying land occupation and its evolution. Indeed, the very large volumes of
digital data stored, usually are not ready to a direct analysis. In order to
both reduce the dimensionality and information extraction, time series data
mining generally gives rise to change of time series representation. In an
objective of information intelligibility extracted from the representation
change, we may use symbolic representations of time series. Many high level
representations of time series have been proposed for data mining, including
Fourier transforms, wavelets, piecewise polynomial models, etc. Many
researchers have also considered symbolic representations of time series,
noting that such representations would potentiality allow researchers to avail
of the wealth of data structures and algorithms from the text processing and
bioinformatics communities. We present in this work, one of the main symbolic
representation methods "SAX"(Symbolic Aggregate Approximation) and we
experience this method to symbolize and reduce the dimensionality of a
Satellite Image Times Series acquired over a period of 5 years by
characterizing the evolution of a vegetation index (NDVI).


title:
Incentive Schemes for Mobile Peer-to-Peer Systems and Free Riding
  Problem: A Survey

abstract:
  Mobile peer-to-peer networks are quite prevalent and popular now days due to
advent of business scenarios where all the services are going mobile like
whether it's to find good restaurants, healthy diet books making friends,
job-hunting, real state info or cab-sharing etc. As the mobile users are
increasing day by day, peer-to-peer networks getting bigger and complex. In
contrast to client server system in peer-to-peer network resource sharing is
done on the basis of mutual consent and agreed policies with no central
authority and controlling entity. Incentive schemes for P2P networks are
devised to encourage the participation and to adhere the policies agreed. P2P
services based only on altruistic behaviour of users are facing serious
challenges like Free riding or The tragedy of commons. Free riders are the
users who consume the bandwidth of the system (perform downloading) but don't
show altruistic behaviour (deny uploading) and act as a parasite for the P2P
network. To counter the free riding issue many Incentive schemes are suggested
by the researchers. In this paper we will survey the different incentive
schemes, their architectures keeping eye on how they handle the challenges of
modern P2P network.


title: Precise neural network computation with imprecise analog devices

abstract:
  The operations used for neural network computation map favorably onto simple
analog circuits, which outshine their digital counterparts in terms of
compactness and efficiency. Nevertheless, such implementations have been
largely supplanted by digital designs, partly because of device mismatch
effects due to material and fabrication imperfections. We propose a framework
that exploits the power of deep learning to compensate for this mismatch by
incorporating the measured device variations as constraints in the neural
network training process. This eliminates the need for mismatch minimization
strategies and allows circuit complexity and power-consumption to be reduced to
a minimum. Our results, based on large-scale simulations as well as a prototype
VLSI chip implementation indicate a processing efficiency comparable to current
state-of-art digital implementations. This method is suitable for future
technology based on nanodevices with large variability, such as memristive
arrays.


title: Invisibility Cloak Printed on a Photonic Chip

abstract:
  Invisibility cloak capable of hiding an object can be achieved by properly
manipulating electromagnetic field. Such a remarkable ability has been shown in
transformation and ray optics. Alternatively, it may be realistic to create a
spatial cloak by means of confining electromagnetic field in three-dimensional
arrayed waveguides and introducing appropriate collective curvature surrounding
an object. We realize the artificial structure in borosilicate by femtosecond
laser direct writing, where we prototype up to 5000 waveguides to conceal
millimeter-scale volume. We characterize the performance of the cloak by
normalized cross correlation, tomography analysis and continuous
three-dimensional viewing angle scan. Our results show invisibility cloak can
be achieved in waveguide optics. Furthermore, directly printed invisibility
cloak on a photonic chip may enable controllable study and novel applications
in classical and quantum integrated photonics, such as invisualising a coupling
or swapping operation with on-chip circuits of their own.


title: The geometry of cluster varieties from surfaces

abstract:
  Cluster varieties are geometric objects that have recently found applications
in several areas of mathematics and mathematical physics. This thesis studies
the geometry of a large class of cluster varieties associated to compact
oriented surfaces with boundary.
  The main original contribution of this thesis is to develop the properties of
a particular kind of cluster variety called the symplectic double. We show that
the symplectic double is birational to a certain moduli space of local systems
associated to a doubled surface. We define a version of the notion of measured
lamination on such a surface and prove that the space of all such laminations
is a tropicalization of the symplectic double. We describe a canonical map from
this space of laminations into the algebra of rational functions on the
symplectic double.
  The second main contribution of this thesis is a proof of Fock and
Goncharov's duality conjectures for quantum cluster varieties associated to a
disk with finitely many marked points on its boundary. These duality
conjectures identify a canonical set of elements in the quantized algebra of
functions on a cluster variety satisfying a number of special properties.


title: Strain-controlled fundamental gap and structure of bulk black phosphorus

abstract:
  We study theoretically the structural and electronic response of layered bulk
black phosphorus to in-layer strain. Ab initio density functional theory (DFT)
calculations reveal that the strain energy and interlayer spacing display a
strong anisotropy with respect to the uniaxial strain direction. To correctly
describe the dependence of the fundamental band gap on strain, we used the
computationally more involved GW quasiparticle approach that is free of
parameters and superior to DFT studies, which are known to underestimate gap
energies. We find that the band gap depends sensitively on the in-layer strain
and even vanishes at compressive strain values exceeding about 2%, thus
suggesting a possible application of black P in strain-controlled infrared
devices.


title: Small scale problems of the $\Lambda$CDM model: a short review

abstract:
  The $\Lambda$CDM model, or concordance cosmology, as it is often called, is a
paradigm at its maturity. It is clearly able to describe the universe at large
scale, even if some issues remain open, such as the cosmological constant
problem , the small-scale problems in galaxy formation, or the unexplained
anomalies in the CMB. $\Lambda$CDM clearly shows difficulty at small scales,
which could be related to our scant understanding, from the nature of dark
matter to that of gravity; or to the role of baryon physics, which is not well
understood and implemented in simulation codes or in semi-analytic models. At
this stage, it is of fundamental importance to understand whether the problems
encountered by the $\Lambda$DCM model are a sign of its limits or a sign of our
failures in getting the finer details right. In the present paper, we will
review the small-scale problems of the $\Lambda$CDM model, and we will discuss
the proposed solutions and to what extent they are able to give us a theory
accurately describing the phenomena in the complete range of scale of the
observed universe.


title: A simple predictor based on delay-induced negative group delay

abstract:
  A very simple linear signal predictor that uses past predicted values rather
than past signal values for prediction is presented. Man-made or natural
systems utilizing this predictor would not require a memory of input signal
values but only of already predicted, internalized states. This delay-induced
negative group delay (DINGD) predictor affords real-time prediction of signals
without the need for a specific signal model. Its properties are derived
analytically and are numerically tested on various types of broadband input
data.


title: Wide & Deep Learning for Recommender Systems

abstract:
  Generalized linear models with nonlinear feature transformations are widely
used for large-scale regression and classification problems with sparse inputs.
Memorization of feature interactions through a wide set of cross-product
feature transformations are effective and interpretable, while generalization
requires more feature engineering effort. With less feature engineering, deep
neural networks can generalize better to unseen feature combinations through
low-dimensional dense embeddings learned for the sparse features. However, deep
neural networks with embeddings can over-generalize and recommend less relevant
items when the user-item interactions are sparse and high-rank. In this paper,
we present Wide & Deep learning---jointly trained wide linear models and deep
neural networks---to combine the benefits of memorization and generalization
for recommender systems. We productionized and evaluated the system on Google
Play, a commercial mobile app store with over one billion active users and over
one million apps. Online experiment results show that Wide & Deep significantly
increased app acquisitions compared with wide-only and deep-only models. We
have also open-sourced our implementation in TensorFlow.


title: The Higgs singlet extension at LHC Run 2

abstract:
  We discuss the current status of theoretical and experimental constraints on
the real Higgs singlet extension of the Standard Model. For the second neutral
(non-standard) Higgs boson the mass range up to 1 TeV accessible at past and
current collider experiments is considered. We furthermore discuss electroweak
corrections to the H to hh partial decay width within this model.


title:
Multidimensional mode-separable frequency conversion for high-speed
  quantum communication

abstract:
  Quantum frequency conversion (QFC) of photonic signals preserves quantum
information while simultaneously changing the signal wavelength. A common
application of QFC is to translate the wavelength of a signal compatible with
the current fiber-optic infrastructure to a shorter wavelength more compatible
with high quality single-photon detectors and optical memories. Recent work has
investigated the use of QFC to manipulate and measure specific temporal modes
(TMs) through tailoring of the pump pulses. Such a scheme holds promise for
multidimensional quantum state manipulation that is both low loss and
re-programmable on a fast time scale. We demonstrate the first QFC temporal
mode sorting system in a four-dimensional Hilbert space, achieving a conversion
efficiency and mode separability as high as 92% and 0.84, respectively. A
20-GHz pulse train is projected onto 6 different TMs, including superposition
states, and mode separability with weak coherent signals is verified via photon
counting. Such ultrafast high-dimensional photonic signals could enable
long-distance quantum communication with high rates.


title:
Quantum phase transition from bounded to extensive entanglement entropy
  in a frustration-free spin chain

abstract:
  We introduce a continuous family of frustration-free Hamiltonians with
exactly solvable ground states. We prove that the {ground state of our model is
non-degenerate and exhibits} a novel quantum phase transition from bounded
entanglement entropy to a massively entangled state with volume entropy
scaling. The ground state may be interpreted as a deformation away from the
uniform superposition of colored Motzkin paths, showed by Movassagh and Shor to
have a large (square-root) but sub-extensive scaling of entanglement into a
state with an extensive entropy.

categories: quant-ph cond-mat.stat-mech math-ph math.MP


title:
Born-Jordan Pseudodifferential Operators and the Dirac Correspondence:
  Beyond the Groenewold-van Hove Theorem

abstract:
  Quantization procedures play an essential role in microlocal analysis,
time-frequency analysis and, of course, in quantum mechanics. Roughly speaking
the basic idea, due to Dirac, is to associate to any symbol, or observable,
$a(x,\xi)$ an operator $\mathrm{Op}(a)$, according to some axioms dictated by
physical considerations. This led to the introduction of a variety of
quantizations. They all agree when the symbol $a(x,\xi)=f(x)$ depends only on
$x$ or $a(x,\xi)=g(\xi)$ depends only on $\xi$: \[
\mathrm{Op}(f\otimes1)u=fu,\quad\mathrm{Op}(1\otimes g)u=\mathcal{F}%
^{-1}(g\mathcal{F}u) \] where $\mathcal{F}$ stands for the Fourier transform.
Now, Dirac aimed at finding a quantization satisfying, in addition, the key
correspondence \[
\lbrack\mathrm{Op}(a),\operatorname*{Op}(b)]=i\mathrm{Op}(\{a,b\}) \] where
$[\,,\,]$ stands for the commutator and $\{\,,\}$ for the Poisson brackets,
which would represent a tight link between classical and quantum mechanics.
Unfortunately, the famous Groenewold-van Hove theorem states that such a
quantization does not exist, and indeed most quantization rules satisfy this
property only approximately.
  Now, in this note we show that the above commutator rule in fact holds for
the Born-Jordan quantization, at least for symbols of the type $f(x)+g(\xi)$.
Moreover we will prove that, remarkably, this property completely characterizes
this quantization rule, making it the quantization which best fits the Dirac
dream.


title: Boosting quantum annealer performance via sample persistence

abstract:
  We propose a novel method for reducing the number of variables in quadratic
unconstrained binary optimization problems, using a quantum annealer (or any
sampler) to fix the value of a large portion of the variables to values that
have a high probability of being optimal. The resulting problems are usually
much easier for the quantum annealer to solve, due to their being smaller and
consisting of disconnected components. This approach significantly increases
the success rate and number of observations of the best known energy value in
samples obtained from the quantum annealer, when compared with calling the
quantum annealer without using it, even when using fewer annealing cycles. Use
of the method results in a considerable improvement in success metrics even for
problems with high-precision couplers and biases, which are more challenging
for the quantum annealer to solve. The results are further enhanced by applying
the method iteratively and combining it with classical pre-processing. We
present results for both Chimera graph-structured problems and embedded
problems from a real-world application.


title: Which causal structures might support a quantum-classical gap?

abstract:
  A causal scenario is a graph that describes the cause and effect
relationships between all relevant variables in an experiment. A scenario is
deemed `not interesting' if there is no device-independent way to distinguish
the predictions of classical physics from any generalised probabilistic theory
(including quantum mechanics). Conversely, an interesting scenario is one in
which there exists a gap between the predictions of different operational
probabilistic theories, as occurs for example in Bell-type experiments. Henson,
Lal and Pusey (HLP) recently proposed a sufficient condition for a causal
scenario to not be interesting. In this paper we supplement their analysis with
some new techniques and results. We first show that existing graphical
techniques due to Evans can be used to confirm by inspection that many graphs
are interesting without having to explicitly search for inequality violations.
For three exceptional cases -- the graphs numbered 15,16,20 in HLP -- we show
that there exist non-Shannon type entropic inequalities that imply these graphs
are interesting. In doing so, we find that existing methods of entropic
inequalities can be greatly enhanced by conditioning on the specific values of
certain variables.


title:
The Picard group of the graded module category of a generalized Weyl
  algebra

abstract:
  The first Weyl algebra, $A_1 = k \langle x, y\rangle/(xy-yx - 1)$ is
naturally $\mathbb{Z}$-graded by letting $\operatorname{deg} x = 1$ and
$\operatorname{deg} y = -1$. Sue Sierra studied $\operatorname{gr}- A_1$,
category of graded right $A_1$-modules, computing its Picard group and
classifying all rings graded equivalent to $A_1$. In this paper, we generalize
these results by studying the graded module category of certain generalized
Weyl algebras. We show that for a generalized Weyl algebra $A(f)$ with base
ring $k[z]$ defined by a quadratic polynomial $f$, the Picard group of
$\operatorname{gr}- A(f)$ is isomorphic to the Picard group of
$\operatorname{gr}- A_1$. In a companion paper, we use these results to
construct commutative rings which are graded equivalent to generalized Weyl
algebras.


title: The noncommutative schemes of generalized Weyl algebras

abstract:
  The first Weyl algebra over $k$, $A_1 = k \langle x, y\rangle/(xy-yx - 1)$
admits a natural $\mathbb{Z}$-grading by letting $\operatorname{deg} x = 1$ and
$\operatorname{deg} y = -1$. Paul Smith showed that $\operatorname{gr}- A_1$ is
equivalent to the category of quasicoherent sheaves on a certain quotient
stack. Using autoequivalences of $\operatorname{gr}- A_1$, Smith constructed a
commutative ring $C$, graded by finite subsets of the integers. He then showed
$\operatorname{gr}- A_1 \equiv \operatorname{gr}- (C,
\mathbb{Z}_{\mathrm{fin}})$. In this paper, we generalize results of Smith by
using autoequivalences of a graded module category to construct rings with
equivalent graded module categories. For certain generalized Weyl algebras, we
use autoequivalences defined in a companion paper so that these constructions
yield commutative rings.


title: A Fluctuating Lattice Boltzmann Method for the Diffusion Equation

abstract:
  We derive a fluctuating lattice Boltzmann method for the diffusion equation.
The derivation removes several shortcomings of previous derivations for
fluctuating lattice Boltzmann methods for hydrodynamic systems. The comparative
simplicity of this diffusive system highlights the basic features of this first
exact derivation of a fluctuating lattice Boltzmann method.


title:
On the importance of graph search algorithms for DRGEP-based mechanism
  reduction methods

abstract:
  The importance of graph search algorithm choice to the directed relation
graph with error propagation (DRGEP) method is studied by comparing basic and
modified depth-first search, basic and R-value-based breadth-first search
(RBFS), and Dijkstra's algorithm. By using each algorithm with DRGEP to produce
skeletal mechanisms from a detailed mechanism for n-heptane with
randomly-shuffled species order, it is demonstrated that only Dijkstra's
algorithm and RBFS produce results independent of species order. In addition,
each algorithm is used with DRGEP to generate skeletal mechanisms for n-heptane
covering a comprehensive range of autoignition conditions for pressure,
temperature, and equivalence ratio. Dijkstra's algorithm combined with a
coefficient scaling approach is demonstrated to produce the most compact
skeletal mechanism with a similar performance compared to larger skeletal
mechanisms resulting from the other algorithms. The computational efficiency of
each algorithm is also compared by applying the DRGEP method with each search
algorithm on the large detailed mechanism for n-alkanes covering n-octane to
n-hexadecane with 2115 species and 8157 reactions. Dijkstra's algorithm
implemented with a binary heap priority queue is demonstrated as the most
efficient method, with a CPU cost two orders of magnitude less than the other
search algorithms.


title:
Penerapan E-Service Berbasis Android pada Divisi Pelayanan Perbaikan
  Komputer CV Ria Kencana Ungu (RKU)

abstract:
  Archival information systems in government agency is one of the most used
applications for daily acitivities. One feature in application management
information document is searching. This feature serves to search for documents
from a collection of available information based on keywords entered by the
user. But some researches on a search engine (searching) concluded that the
average user error in the search is quite high due to several factors.
Therefore, we need a development on this feature as search suggestion. This
study discusses the application of the method of approximate string matching
algorithm using levenshtein distance. Levensthein distance algorithm is capable
of calculating the minimum distance conversion of a string into another string
to the optimum. Archiving information system using Levensthein Algorithm String
is an application that will be built to address these problems, this
application will help, especially in the administration to enter or save a
document, locate and make a report that will be seen by government agencies.


title:
Theoretical and observational constraints on the mass-radius relations
  of neutron stars

abstract:
  We investigate theoretical and observational constraints on the mass-radius
relations for neutron stars. For that purpose we consider the model of neutron
stars taking into considerations strong, weak, electromagnetic and
gravitational interactions in the equation of state and integrate the structure
equations within the Hartle-Thorne formalism for rotating configurations. On
the basis of the theoretical restrictions imposed by general relativity,
mass-shedding and axisymmetric secular instabilities we calculate the upper and
lower bounds for the parameters of neutron stars. Our theoretical calculations
have been compared and contrasted with the observational constraints and as a
result we show that the observational constraints favor stiff equations of
state.


title: A model based DC analysis of SiPM breakdown voltages

abstract:
  A new method to determine the breakdown voltage of SiPMs is presented. It is
backed up by a DC model which describes the breakdown phenomenon by distinct
avalanche turn-on ($V_{01}$) and turn off ($V_{10}$) voltages. It is shown that
$V_{01}$ is related to the 'breakdown voltage' that previous DC methods derive
from simple reverse current-voltage measurements, while $V_{10}$ is the 'real'
breakdown voltage commonly obtained from complex gain-voltage measurements. The
proposed method reveals how the microcell population distributes around
$V_{01}$ and $V_{10}$. It is found that if this distribution is assumed to be
normal, then both voltages and even their standard deviation can readily be
extracted from current-voltage curves. Measurements are in good agreement with
the theoretical model.


title:
First test of an enriched $^{116}$CdWO$_4$ scintillating bolometer for
  neutrinoless double-beta-decay searches

abstract:
  For the first time, a cadmium tungstate crystal scintillator enriched in
$^{116}$Cd has been succesfully tested as a scintillating bolometer. The
measurement was performed above ground at a temperature of 18 mK. The crystal
mass was 34.5 g and the enrichment level ~82 %. Despite a substantial pile-up
effect due to above-ground operation, the detector demonstrated a high energy
resolution (2-7 keV FWHM in 0.2-2.6 MeV $\gamma$ energy range), a powerful
particle identification capability and a high level of internal radiopurity.
These results prove that cadmium tungstate is an extremely promising detector
material for a next-generation neutrinoless double-beta decay bolometric
experiment, like that proposed in the CUPID project (CUORE Upgrade with
Particle IDentification).


title:
H(O)TA: estimation of DNA methylation and hydroxylation levels and
  efficiencies from time course data

abstract:
  Methylation and hydroxylation of cytosines to form 5-methylcytosine (5mC) and
5-droxymethylcytosine (5hmC) belong to the most important epigenetic
modifications and their vital role in the regulation of gene expression has
been widely recognized. Recent experimental techniques allow to infer
methylation and hydroxylation levels at CpG dinucleotides but require a
sophisticated statistical analysis to achieve accurate estimates.


title: The impact of non-minimally coupled gravity on vacuum stability

abstract:
  We consider vacuum decay in the presence of a non-minimal coupling to
gravity. We extend the usual thin-wall solution to include the non-minimal
coupling. We also perform a full numerical study and discuss the validity of
the new thin-wall approximation. Implications of a large cosmological constant,
whose influence on the geometry boosts the tunnelling rate, are discussed. Our
results show that the influence of the non-minimal coupling differs
significantly between the cases of Minkowski and deSitter backgrounds. In the
latter the decay probability quickly decreases when the coupling grows and in
fact the vacuum can be made absolutely stable simply due to introduction of the
non-minimal coupling. In the case of Minkowski background the effect is much
weaker and the decay rate even increases for small values of the non-minimal
coupling.


title:
After the interaction: an efficiently star-forming molecular disk in
  NGC5195

abstract:
  We present new molecular gas maps of NGC5195 (alternatively known as M51b)
from the Combined Array for Research in Millimeter Astronomy (CARMA), including
12CO(1-0), 13CO(1-0), CN(1-0), and CS(2-1). NGC5195 has also been detected in
3mm continuum. NGC5195 has a 12CO/13CO ratio consistent with normal
star-forming galaxies. The CN(1-0) intensity is higher than is seen in an
average star-forming galaxy, possibly enhanced in the diffuse
photo-dissociation regions. Stellar template fitting of the nuclear spectrum of
NGC5195 shows two stellar populations: an 80% mass fraction of old (>10Gyr) and
a 20% mass fraction of intermediate-aged (~1Gyr) stellar populations, providing
a constraint on the timescale over which NGC5195 experienced enhanced star
formation during its interaction with M51a. The average molecular gas depletion
timescale in NGC5195 is: tdep=3.08Gyr, a factor of ~2 larger than the depletion
timescales in nearby star-forming galaxies, but consistent with the depletion
seen in CO-detected early-type galaxies. While the radio continuum emission at
centimeter and millimeter wavelengths is present in the vicinity of the nucleus
of NGC5195, we find it is most likely associated with nuclear star formation
rather than a radio-loud AGN. Thus, despite having a substantial interaction
with M51a ~1/2Gyr ago, the molecular gas in NGC5195 has resettled and is
forming stars at an efficiency consistent with settled early-type galaxies at
the present time.


title:
Under Pressure: Quenching Star Formation in Low-Mass Satellite Galaxies
  via Stripping

abstract:
  Recent studies of galaxies in the local Universe, including those in the
Local Group, find that the efficiency of environmental (or satellite) quenching
increases dramatically at satellite stellar masses below ~ $10^8\ {\rm
M}_{\odot}$. This suggests a physical scale where quenching transitions from a
slow "starvation" mode to a rapid "stripping" mode at low masses. We
investigate the plausibility of this scenario using observed HI surface density
profiles for a sample of 66 nearby galaxies as inputs to analytic calculations
of ram-pressure and viscous stripping. Across a broad range of host properties,
we find that stripping becomes increasingly effective at $M_{*} < 10^{8-9}\
{\rm M}_{\odot}$, reproducing the critical mass scale observed. However, for
canonical values of the circumgalactic medium density ($n_{\rm halo} <
10^{-3.5}$ ${\rm cm}^{-3}$), we find that stripping is not fully effective;
infalling satellites are, on average, stripped of < 40 - 70% of their cold gas
reservoir, which is insufficient to match observations. By including a host
halo gas distribution that is clumpy and therefore contains regions of higher
density, we are able to reproduce the observed HI gas fractions (and thus the
high quenched fraction and short quenching timescale) of Local Group
satellites, suggesting that a host halo with clumpy gas may be crucial for
quenching low-mass systems in Local Group-like (and more massive) host halos.


title: Geometrical beaming of stellar mass ULXs

abstract:
  The presence or lack of eclipses in the X-ray lightcurves of ultraluminous
X-ray sources (ULXs) can be directly linked to the accreting system geometry.
In the case where the compact object is stellar mass and radiates
isotropically, we should expect eclipses by a main-sequence to sub-giant
secondary star on the recurrence timescale of hours to days. X-ray lightcurves
are now available for large numbers of ULXs as a result of the latest
XMM-Newton catalogue. We determine the amount of fractional variability that
should be injected into an otherwise featureless lightcurve for a given set of
system parameters as a result of eclipses and compare this to the available
data. We find that the vast majority of sources for which the variability has
been measured to be non-zero and for which available observations meet the
criteria for eclipse searches, have fractional variabilities which are too low
to derive from eclipses and so must be viewed such that theta =< acos(R*/a).
This would require that the disc subtends a larger angle than that of the
secondary star and is therefore consistent with a conical outflow formed from
super-critical accretion rates and implies some level of geometrical beaming in
ULXs.


title: Gravitational Waves from Axion Monodromy

abstract:
  Large field inflation is arguably the simplest and most natural variant of
slow-roll inflation. Axion monodromy may be the most promising framework for
realising this scenario. As one of its defining features, the long-range
polynomial potential possesses short-range, instantonic modulations. These can
give rise to a series of local minima in the post-inflationary region of the
potential. We show that for certain parameter choices the inflaton populates
more than one of these vacua inside a single Hubble patch. This corresponds to
a dynamical phase decomposition, analogously to what happens in the course of
thermal first-order phase transitions. In the subsequent process of bubble wall
collisions, the lowest-lying axionic minimum eventually takes over all space.
Our main result is that this violent process sources gravitational waves, very
much like in the case of a first-order phase transition. We compute the energy
density and peak frequency of the signal, which can lie anywhere in the mHz-GHz
range, possibly within reach of next-generation interferometers. We also note
that this "dynamical phase decomposition" phenomenon and its gravitational wave
signal are more general and may apply to other inflationary or reheating
scenarios with axions and modulated potentials.


title:
Spin density wave order, topological order, and Fermi surface
  reconstruction

abstract:
  In the conventional theory of density wave ordering in metals, the onset of
spin density wave (SDW) order co-incides with the reconstruction of the Fermi
surfaces into small 'pockets'. We present models which display this transition,
while also displaying an alternative route between these phases via an
intermediate phase with topological order, no broken symmetry, and pocket Fermi
surfaces. The models involve coupling emergent gauge fields to a fractionalized
SDW order, but retain the canonical electron operator in the underlying
Hamiltonian. We establish an intimate connection between the suppression of
certain defects in the SDW order, and the presence of Fermi surface sizes
distinct from the Luttinger value in Fermi liquids. We discuss the relevance of
such models to the physics of the hole-doped cuprates near optimal doping.


title:
Event Horizon Telescope Observations as Probes for Quantum Structure of
  Astrophysical Black Holes

abstract:
  The need for a consistent quantum evolution for black holes has led to
proposals that their semiclassical description is modified not just near the
singularity, but at horizon or larger scales. If such modifications extend
beyond the horizon, they influence regions accessible to distant observeration.
Natural candidates for these modifications behave like metric fluctuations,
with characteristic length and time scales set by the horizon radius. We
investigate the possibility of using the Event Horizon Telescope to observe
these effects, if they have a strength sufficient to make quantum evolution
consistent with unitarity. We find that such quantum fluctuations can introduce
a strong time dependence for the shape and size of the shadow that a black hole
casts on its surrounding emission. For the black hole in the center of the
Milky Way, detecting the rapid time variability of its shadow will require
non-imaging timing techniques. However, for the much larger black hole in the
center of the M87 galaxy, a variable black-hole shadow, if present with these
parameters, would be readily observable in the individual snapshots that will
be obtained by the Event Horizon Telescope.


title:
An improved probabilistic approach for linking progenitor and descendant
  galaxy populations using comoving number density

abstract:
  Galaxy populations at different cosmic epochs are often linked together by
comoving cumulative number density in observational studies. Many theoretical
works, however, have shown that the number densities of tracked galaxy
populations evolve in bulk and spread out over time. We present a number
density method for linking progenitor and descendant galaxy populations which
takes both of these effects into account. We define probability distribution
functions that capture the evolution and dispersion of galaxy populations in
comoving number density space, and use these functions to assign galaxies at
one redshift $z_f$ probabilities of being progenitors or descendants of a
galaxy population at another redshift $z_0$. These probabilities are then used
as weights for calculating distributions of physical properties such as stellar
mass, star formation rate, or velocity dispersion within the
progenitor/descendant population. We demonstrate that this probabilistic method
provides more accurate predictions for the evolution of physical properties
then either the assumption of a constant number density or the assumption of an
evolving number density in a bin of fixed width by comparing the predictions
against galaxy populations directly tracked through a cosmological simulation.
We find that the constant number density method performs most poorly at
recovering galaxy properties, the evolving number method density slightly
better, and the probabilistic number density method best of all. The
improvement is present for predictions of both stellar mass as well as inferred
quantities such as star formation rate and velocity dispersion which were not
included in the number density fits. We demonstrate that this method can also
be applied robustly and easily to observational data, and provide a code
package for doing so.


title: Symmetry enriched string-nets: Exactly solvable models for SET phases

abstract:
  We construct exactly solvable models for a wide class of symmetry enriched
topological (SET) phases. Our construction applies to 2D bosonic SET phases
with finite unitary onsite symmetry group $G$ and we conjecture that our models
realize every phase in this class that can be described by a commuting
projector Hamiltonian. Our models are designed so that they have a special
property: if we couple them to a dynamical lattice gauge field with gauge group
$G$, the resulting gauge theories are equivalent to modified string-net models.
This property is what allows us to analyze our models in generality. As an
example, we present a model for a phase with the same anyon excitations as the
toric code and with a $\mathbb{Z}_2$ symmetry which exchanges the $e$ and $m$
type anyons. We further illustrate our construction with a number of additional
examples.


title: Constraints on Assembly Bias from Galaxy Clustering

abstract:
  We constrain the newly-introduced decorated Halo Occupation Distribution
(HOD) model using SDSS DR7 measurements of projected galaxy clustering or
r-band luminosity threshold samples. The decorated HOD is a model for the
galaxy-halo connection that augments the HOD by allowing for the possibility of
galaxy assembly bias: galaxy luminosity may be correlated with dark matter halo
properties besides mass, Mvir. We demonstrate that it is not possible to rule
out galaxy assembly bias using DR7 measurements of galaxy clustering alone.
Moreover, galaxy samples with Mr < -20 and Mr < -20.5 favor strong central
galaxy assembly bias. These samples prefer scenarios in which
high-concentration are more likely to host a central galaxy relative to
low-concentration halos of the same mass. We exclude zero assembly bias with
high significance for these samples. Satellite galaxy assembly bias is
significant for the faintest sample, Mr < -19. We find no evidence for assembly
bias in the Mr < -21 sample. Assembly bias should be accounted for in galaxy
clustering analyses or attempts to exploit galaxy clustering to constrain
cosmology. In addition to presenting the first constraints on HOD models that
accommodate assembly bias, our analysis includes several improvements over
previous analyses of these data. Therefore, our inferences supersede
previously-published results even in the case of a standard HOD analysis.


title:
Chandra X-ray observations of the hyper-luminous infrared galaxy IRAS
  F15307+3252

abstract:
  Hyper-luminous infrared galaxies (HyLIRGs) lie at the extreme luminosity end
of the IR galaxy population with $L_{\rm IR}>10^{13}$L$_\odot$. They are
thought to be closer counterparts of the more distant sub-mm galaxies, and
should therefore be optimal targets to study the most massive systems in
formation. We present deep $Chandra$ observations of IRAS~F15307+3252 (100ks),
a classical HyLIRG located at $z=$0.93 and hosting a radio-loud AGN ($L_{\rm
1.4 GHz}\sim3.5\times10^{25}$ W/Hz). The $Chandra$ images reveal the presence
of extended ($r=160$ kpc), asymmetric X-ray emission in the soft 0.3-2.0 keV
band that has no radio counterpart. We therefore argue that the emission is of
thermal origin originating from a hot intragroup or intracluster medium
virializing in the potential. We find that the temperature ($\sim2$ keV) and
bolometric X-ray luminosity ($\sim3\times10^{43}$ erg s$^{-1}$) of the gas
follow the expected $L_{\rm X-ray}-T$ correlation for groups and clusters, and
that the gas has a remarkably short cooling time of $1.2$ Gyrs. In addition,
VLA radio observations reveal that the galaxy hosts an unresolved compact
steep-spectrum (CSS) source, most likely indicating the presence of a young
radio source similar to 3C186. We also confirm that the nucleus is dominated by
a redshifted 6.4 keV Fe K$\alpha$ line, strongly suggesting that the AGN is
Compton-thick. Finally, Hubble images reveal an over-density of galaxies and
sub-structure in the galaxy that correlates with soft X-ray emission. This
could be a snapshot view of on-going groupings expected in a growing cluster
environment. IRAS~F15307+3252 might therefore be a rare example of a group in
the process of transforming into a cluster.


title:
Long-term stability of the HR 8799 planetary system without resonant
  lock

abstract:
  HR 8799 is a star accompanied by four massive planets on wide orbits. The
observed planetary configuration has been shown to be unstable on a timescale
much shorter than the estimated age of the system (~ 30 Myr) unless the planets
are locked into mean motion resonances. This condition is characterised by
small-amplitude libration of one or more resonant angles that stabilise the
system by preventing close encounters. We simulate planetary systems similar to
the HR 8799 planetary system, exploring the parameter space in separation
between the orbits, planetary masses and distance from the Sun to the star. We
find systems that look like HR 8799 and remain stable for longer than the
estimated age of HR 8799. None of our systems are forced into resonances. We
find, with nominal masses and in a narrow range of orbit separations, that 5 of
100 systems match the observations and lifetime. Considering a broad range of
orbit separations, we find 12 of 900 similar systems. The systems survive
significantly longer because of their slightly increased initial orbit
separations compared to assuming circular orbits from the observed positions. A
small increase in separation leads to a significant increase in survival time.
The low eccentricity the orbits develop from gravitational interaction is
enough for the planets to match the observations. With lower masses, but still
comfortably within the estimated planet mass uncertainty, we find 18 of 100
matching and long-lived systems in a narrow orbital separation range. In the
broad separation range, we find 82 of 900 matching systems. Our results imply
that the planets in the HR 8799 system do not have to be in strong mean motion
resonances.


title:
Feeding cosmic star formation: Exploring high-redshift molecular gas
  with CO intensity mapping

abstract:
  The study of molecular gas is crucial for understanding star formation,
feedback, and the broader ecosystem of a galaxy as a whole. However, we have
limited understanding of its physics and distribution in all but the nearest
galaxies. We present a new technique for studying the composition and
distribution of molecular gas in high-redshift galaxies inaccessible to
existing methods. Our proposed approach is an extension of carbon monoxide
intensity mapping methods, which have garnered significant experimental
interest in recent years. These intensity mapping surveys target the 115 GHz
$^{12}$CO (1-0) line, but also contain emission from the substantially fainter
110 GHz $^{13}$CO (1-0) transition. The method leverages the information
contained in the $^{13}$CO line by cross-correlating pairs of frequency
channels in an intensity mapping survey. Since $^{13}$CO is emitted from the
same medium as the $^{12}$CO, but saturates at a much higher column density,
this cross-correlation provides valuable information about both the gas density
distribution and isotopologue ratio, inaccessible from the $^{12}$CO alone.
Using a simple model of these molecular emission lines, we show that a future
intensity mapping survey can constrain the abundance ratio of these two species
and the fraction of emission from optically thick regions to order $\sim30\%$.
These measurements cannot be made by traditional CO observations, and
consequently the proposed method will provide unique insight into the physics
of star formation, feedback, and galactic ecology at high redshifts.


title: Tomographic image of the proton

abstract:
  We determine, based on the latest experimental Deep Virtual Compton
Scattering experimental data, the dependence of the spatial size of the proton
on the quark's longitudinal momentum. This results in a three-dimensional
momentum-space image and tomography of the proton.


title: Efficient Parallel Learning of Word2Vec

abstract:
  Since its introduction, Word2Vec and its variants are widely used to learn
semantics-preserving representations of words or entities in an embedding
space, which can be used to produce state-of-art results for various Natural
Language Processing tasks. Existing implementations aim to learn efficiently by
running multiple threads in parallel while operating on a single model in
shared memory, ignoring incidental memory update collisions. We show that these
collisions can degrade the efficiency of parallel learning, and propose a
straightforward caching strategy that improves the efficiency by a factor of 4.


title: A Note on the Alon-Kleitman Argument for Sum-free Subset Theorem

abstract:
  In 1990, Alon and Kleitman proposed an argument for the sum-free subset
problem: every set of n nonzero elements of a finite Abelian group contains a
sum-free subset A of size |A|>\frac{2}{7}n. In this note, we show that the
argument confused two different randomness. It applies only to the finite
Abelian group G = (Z/pZ)^s where p is a prime. For the general case, the
problem remains open.


title: Malgrange division by quasianalytic functions

abstract:
  Quasianalytic classes are classes of infinitely differentiable functions that
satisfy the analytic continuation property enjoyed by analytic functions. Two
general examples are quasianalytic Denjoy-Carleman classes (of origin in the
analysis of linear partial differential equations) and the class of infinitely
differentiable functions that are definable in a polynomially bounded o-minimal
structure (of origin in model theory). We prove a generalization to
quasianalytic functions of Malgrange's celebrated theorem on the division of
infinitely differentiable by real-analytic functions.


title:
Fast computation method for comprehensive agent-level epidemic
  dissemination in networks

abstract:
  Two simple agent based models are often employed in epidemic studies: the
susceptible-infected (SI) and the susceptible-infected-susceptible (SIS). Both
models describe the time evolution of infectious diseases in networks in which
vertices are either susceptible (S) or infected (I) agents. Predicting the
effects of disease spreading is one of the major goals in epidemic studies, but
often restricted to numerical simulations. Analytical methods using operatorial
content are subjected to the asymmetric eigenvalue problem, restraining the
usability of standard perturbative techniques, whereas numerical methods are
limited to small populations since the vector space increases exponentially
with population size $N$. Here, we propose the use of the squared norm of
probability vector, $\vert P(t)\vert ^2$, to obtain an algebraic equation which
allows the evaluation of stationary states, in time independent Markov
processes. The equation requires eigenvalues of symmetrized time generators,
which take full advantage of system symmetries, reducing the problem to an
$O(N)$ sparse matrix diagonalization.
  Standard perturbative methods are introduced, creating precise tools to
evaluate the effects of health policies.


title: Chasing New Physics in Stacks of Soft Tracks

abstract:
  In this letter we introduce a new variable $\xi$, namely the number of tracks
associated with the primary vertex, which are not parts of reconstructed
objects such as jets/isolated leptons etc. We demonstrate its usefulness in the
context of new physics searches in the channel monojet$+$missing transverse
momentum (MET). In models such as in compressed supersymmetry, events are often
characterized by a rather large number of soft partons from the cascade decays,
none of which result in reconstructed objects. We find that $\xi$, binned in
$p_T$, can discriminate these new physics events from events due to
$Z+\text{jets}$, that is, the main background in the channel monojet+ MET. The
information contained in soft tracks is largely uncorrelated with traditional
variables such as the effective mass, MET, $p_T$ of the jet, etc. and,
therefore, can be combined with these to increase the discovery potential by
more than $200\%$ (depending on the spectra, of course). In fact, we find that
simple cuts on $\xi(p_T)$ along with cuts on MET, and the effective mass
outperforms sophisticated optimized MultiVariate Analyses using all
conventional variables. One can model the background distribution of $\xi(p_T)$
in an entirely data-driven way, and make these robust against pile-up by
identifying the primary vertex.


title:
Modeling and Inferring Human Intents and Latent Functional Objects for
  Trajectory Prediction

abstract:
  This paper is about detecting functional objects and inferring human
intentions in surveillance videos of public spaces. People in the videos are
expected to intentionally take shortest paths toward functional objects subject
to obstacles, where people can satisfy certain needs (e.g., a vending machine
can quench thirst), by following one of three possible intent behaviors: reach
a single functional object and stop, or sequentially visit several functional
objects, or initially start moving toward one goal but then change the intent
to move toward another. Since detecting functional objects in low-resolution
surveillance videos is typically unreliable, we call them "dark matter"
characterized by the functionality to attract people. We formulate the
Agent-based Lagrangian Mechanics wherein human trajectories are
probabilistically modeled as motions of agents in many layers of "dark-energy"
fields, where each agent can select a particular force field to affect its
motions, and thus define the minimum-energy Dijkstra path toward the
corresponding source "dark matter". For evaluation, we compiled and annotated a
new dataset. The results demonstrate our effectiveness in predicting human
intent behaviors and trajectories, and localizing functional objects, as well
as discovering distinct functional classes of objects by clustering human
motion behavior in the vicinity of functional objects.


title:
Modelling User Preferences using Word Embeddings for Context-Aware Venue
  Recommendation

abstract:
  Venue recommendation aims to assist users by making personalised suggestions
of venues to visit, building upon data available from location-based social
networks (LBSNs) such as Foursquare. A particular challenge for this task is
context-aware venue recommendation (CAVR), which additionally takes the
surrounding context of the user (e.g. the user's location and the time of day)
into account in order to provide more relevant venue suggestions. To address
the challenges of CAVR, we describe two approaches that exploit word embedding
techniques to infer the vector-space representations of venues, users' existing
preferences, and users' contextual preferences. Our evaluation upon the test
collection of the TREC 2015 Contextual Suggestion track demonstrates that we
can significantly enhance the effectiveness of a state-of-the-art venue
recommendation approach, as well as produce context-aware recommendations that
are at least as effective as the top TREC 2015 systems.


title:
Unsupervised Topic Modeling Approaches to Decision Summarization in
  Spoken Meetings

abstract:
  We present a token-level decision summarization framework that utilizes the
latent topic structures of utterances to identify "summary-worthy" words.
Concretely, a series of unsupervised topic models is explored and experimental
results show that fine-grained topic models, which discover topics at the
utterance-level rather than the document-level, can better identify the gist of
the decision-making process. Moreover, our proposed token-level summarization
approach, which is able to remove redundancies within utterances, outperforms
existing utterance ranking based summarization methods. Finally, context
information is also investigated to add additional relevant information to the
summary.


title:
Spatial Aggregation of Holistically-Nested Networks for Automated
  Pancreas Segmentation

abstract:
  Accurate automatic organ segmentation is an important yet challenging problem
for medical image analysis. The pancreas is an abdominal organ with very high
anatomical variability. This inhibits traditional segmentation methods from
achieving high accuracies, especially compared to other organs such as the
liver, heart or kidneys. In this paper, we present a holistic learning approach
that integrates semantic mid-level cues of deeply-learned organ interior and
boundary maps via robust spatial aggregation using random forest. Our method
generates boundary preserving pixel-wise class labels for pancreas
segmentation. Quantitative evaluation is performed on CT scans of 82 patients
in 4-fold cross-validation. We achieve a (mean $\pm$ std. dev.) Dice Similarity
Coefficient of 78.01% $\pm$ 8.2% in testing which significantly outperforms the
previous state-of-the-art approach of 71.8% $\pm$ 10.7% under the same
evaluation criterion.


title:
A Neural Network Approach to Efficient Valuation of Large Portfolios of
  Variable Annuities

abstract:
  Managing and hedging the risks associated with Variable Annuity (VA) products
require intraday valuation of key risk metrics for these products. The complex
structure of VA products and computational complexity of their accurate
evaluation have compelled insurance companies to adopt Monte Carlo (MC)
simulations to value their large portfolios of VA products. Because the MC
simulations are computationally demanding, especially for intraday valuations,
insurance companies need more efficient valuation techniques. Recently, a
framework based on traditional spatial interpolation techniques has been
proposed that can significantly decrease the computational complexity of MC
simulation (Gan and Lin, 2015). However, traditional interpolation techniques
require the definition of a distance function that can significantly impact
their accuracy. Moreover, none of the traditional spatial interpolation
techniques provide all of the key properties of accuracy, efficiency, and
granularity (Hejazi et al., 2015). In this paper, we present a neural network
approach for the spatial interpolation framework that affords an efficient way
to find an effective distance function. The proposed approach is accurate,
efficient, and provides an accurate granular view of the input portfolio. Our
numerical experiments illustrate the superiority of the performance of the
proposed neural network approach compared to the traditional spatial
interpolation schemes.


title:
Stellar mass function of cluster galaxies at z $\sim$ 1.5: evidence for
  reduced quenching efficiency at high redshift

abstract:
  We present the stellar mass functions (SMFs) of passive and star-forming
galaxies with a limiting mass of 10$^{10.1}$ M$_{\odot}$ in four
spectroscopically confirmed Spitzer Adaptation of the Red-sequence Cluster
Survey (SpARCS) galaxy clusters at 1.37 $<$ z $<$ 1.63. The clusters have 113
spectroscopically confirmed members combined, with 8-45 confirmed members each.
We construct $Ks$-band-selected photometric catalogs for each cluster with an
average of 11 photometric bands ranging from $u$ to 8 $\mu$m. We compare our
cluster galaxies to a field sample derived from a similar $Ks$-band-selected
catalog in the UltraVISTA/COSMOS field. The SMFs resemble those of the field,
but with signs of environmental quenching. We find that 30 $\pm$ 20\% of
galaxies that would normally be forming stars in the field are quenched in the
clusters. The environmental quenching efficiency shows little dependence on
projected cluster-centric distance out to $\sim$ 4 Mpc, providing tentative
evidence of pre-processing and/or galactic conformity in this redshift range.
We also compile the available data on environmental quenching efficiencies from
the literature, and find that the quenching efficiency in clusters and in
groups appears to decline with increasing redshift in a manner consistent with
previous results and expectations based on halo mass growth.


title: Square of Hamilton cycle in a random graph

abstract:
  We show that $p=\sqrt{\frac{e}{n}}$ is a sharp threshold for the random graph
$G_{n,p}$ to contain the square of a Hamilton cycle. This improves the previous
results of K\"uhn and Osthus and also Nenadov and \v{S}kori\'c.


title: Zeta Functions of the Dirac Operator on Quantum Graphs

abstract:
  We construct spectral zeta functions for the Dirac operator on metric graphs.
We start with the case of a rose graph, a graph with a single vertex where
every edge is a loop. The technique is then developed to cover any finite graph
with general energy independent matching conditions at the vertices. The
regularized spectral determinant of the Dirac operator is also obtained as the
derivative of the zeta function at a special value. In each case the zeta
function is formulated using a contour integral method, which extends results
obtained for Laplace and Schrodinger operators on graphs.


title:
Charge-order-induced ferroelectricity in LaVO$_{3}$/SrVO$_{3}$
  superlattices

abstract:
  The structure and properties of the 1:1 superlattice of LaVO$_{3}$ and
SrVO$_{3}$ are investigated with a first-principles
density-functional-theory-plus-$U$ (DFT+$U$) method. The lowest energy states
are antiferromagnetic charge-ordered Mott-insulating phases. In one of these
insulating phases, layered charge ordering combines with the layered cation
ordering to produce a polar structure with nonzero spontaneous polarization
normal to the interfaces. This polarization is produced by electron transfer
between the V$^{3+}$ and V$^{4+}$ layers, and is comparable to that of
conventional ferroelectrics. The energy of this polar state relative to the
nonpolar ground state is only 3 meV per vanadium. Under tensile strain, this
energy difference can be further reduced, suggesting that the polar phase can
be induced by applied electric field, yielding an antiferroelectric
double-hysteresis loop. If the system does not switch back to the nonpolar
state on removal of the field, a ferroelectric-type hysteresis loop could be
observed.


title:
Exceptional outburst of the blazar CTA 102 in 2012: The GASP-WEBT
  campaign and its extension

abstract:
  After several years of quiescence, the blazar CTA 102 underwent an
exceptional outburst in 2012 September-October. The flare was tracked from
gamma-ray to near-infrared frequencies, including Fermi and Swift data as well
as photometric and polarimetric data from several observatories. An intensive
GASP-WEBT collaboration campaign in optical and NIR bands, with an addition of
previously unpublished archival data and extension through fall 2015, allows
comparison of this outburst with the previous activity period of this blazar in
2004-2005. We find remarkable similarity between the optical and gamma-ray
behaviour of CTA 102 during the outburst, with a time lag between the two light
curves of ~1 hour, indicative of co-spatiality of the optical and gamma-ray
emission regions. The relation between the gamma-ray and optical fluxes is
consistent with the SSC mechanism, with a quadratic dependence of the SSC
gamma-ray flux on the synchrotron optical flux evident in the post-outburst
stage. However, the gamma-ray/optical relationship is linear during the
outburst; we attribute this to changes in the Doppler factor. A strong
harder-when-brighter spectral dependence is seen both the in gamma-ray and
optical non-thermal emission. This hardening can be explained by convexity of
the UV-NIR spectrum that moves to higher frequencies owing to an increased
Doppler shift as the viewing angle decreases during the outburst stage. The
overall pattern of Stokes parameter variations agrees with a model of a
radiating blob or shock wave that moves along a helical path down the jet.


title:
The systematic error test for PSF correction in weak gravitational
  lensing shear measurement by The ERA Method by Idealizing PSF

abstract:
  We improve the ERA(Ellipticity of Re-smeared Artificial image) method of
PSF(Point Spread Function) correction in weak lensing shear analysis in order
to treat realistic shape of galaxies and PSF. This is done by re-smearing PSF
and the observed galaxy image smeared by a RSF(Re-Smearing Function), and
allows us to use a new PSF with a simple shape and to correct PSF effect
without any approximations and assumptions. We perform numerical test to show
that the method applied for galaxies and PSF with some complicated shapes can
correct PSF effect with systematic error less than 0.1%. We also apply ERA
method for real data of Abell 1689 cluster to confirm that it is able to detect
the systematic weak lensing shear pattern. The ERA method requires less than
0.1 or 1 second to correct PSF for each object in numerical test and real data
analysis, respectively.


title: Differentiability of a two-parameter family of self-affine functions

abstract:
  This paper highlights an unexpected connection between expansions of real
numbers to noninteger bases (so-called {\em $\beta$-expansions}) and the
infinite derivatives of a class of self-affine functions. Precisely, we extend
Okamoto's function (itself a generalization of the well-known functions of
Perkins and Katsuura) to a two-parameter family $\{F_{N,a}: N\in\mathbb{N},
a\in(0,1)\}$. We first show that for each $x$, $F_{N,a}'(x)$ is either $0$,
$\pm\infty$, or undefined. We then extend Okamoto's theorem by proving that for
each $N$, depending on the value of $a$ relative to a pair of thresholds, the
set $\{x: F_{N,a}'(x)=0\}$ is either empty, uncountable but Lebesgue null, or
of full Lebesgue measure. We compute its Hausdorff dimension in the second
case.
  The second result is a characterization of the set
$\mathcal{D}_\infty(a):=\{x:F_{N,a}'(x)=\pm\infty\}$, which enables us to
closely relate this set to the set of points which have a unique expansion in
the (typically noninteger) base $\beta=1/a$. Recent advances in the theory of
$\beta$-expansions are then used to determine the cardinality and Hausdorff
dimension of $\mathcal{D}_\infty(a)$, which depends qualitatively on the value
of $a$ relative to a second pair of thresholds.


title: Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles

abstract:
  Many practical perception systems exist within larger processes that include
interactions with users or additional components capable of evaluating the
quality of predicted solutions. In these contexts, it is beneficial to provide
these oracle mechanisms with multiple highly likely hypotheses rather than a
single prediction. In this work, we pose the task of producing multiple outputs
as a learning problem over an ensemble of deep networks -- introducing a novel
stochastic gradient descent based approach to minimize the loss with respect to
an oracle. Our method is simple to implement, agnostic to both architecture and
loss function, and parameter-free. Our approach achieves lower oracle error
compared to existing methods on a wide range of tasks and deep architectures.
We also show qualitatively that the diverse solutions produced often provide
interpretable representations of task ambiguity.


title: Modeling Group Dynamics Using Probabilistic Tensor Decompositions

abstract:
  We propose a probabilistic modeling framework for learning the dynamic
patterns in the collective behaviors of social agents and developing profiles
for different behavioral groups, using data collected from multiple information
sources. The proposed model is based on a hierarchical Bayesian process, in
which each observation is a finite mixture of an set of latent groups and the
mixture proportions (i.e., group probabilities) are drawn randomly. Each group
is associated with some distributions over a finite set of outcomes. Moreover,
as time evolves, the structure of these groups also changes; we model the
change in the group structure by a hidden Markov model (HMM) with a fixed
transition probability. We present an efficient inference method based on
tensor decompositions and the expectation-maximization (EM) algorithm for
parameter estimation.


title: Proactive Decision Support using Automated Planning

abstract:
  Proactive decision support (PDS) helps in improving the decision making
experience of human decision makers in human-in-the-loop planning environments.
Here both the quality of the decisions and the ease of making them are
enhanced. In this regard, we propose a PDS framework, named RADAR, based on the
research in Automated Planning in AI, that aids the human decision maker with
her plan to achieve her goals by providing alerts on: whether such a plan can
succeed at all, whether there exist any resource constraints that may foil her
plan, etc. This is achieved by generating and analyzing the landmarks that must
be accomplished by any successful plan on the way to achieving the goals. Note
that, this approach also supports naturalistic decision making which is being
acknowledged as a necessary element in proactive decision support, since it
only aids the human decision maker through suggestions and alerts rather than
enforcing fixed plans or decisions. We demonstrate the utility of the proposed
framework through search-and-rescue examples in a fire-fighting domain.


title:
Numerical Modeling of 3.5 micron Dual-Wavelength Pumped Erbium Doped
  Mid-Infrared Fiber Lasers

abstract:
  The performance of mid-infrared erbium doped fiber lasers has dramatically
improved in the last few years. In this paper we present a numerical model that
provides valuable insight into the dynamics of a dual-wavelength pumped fiber
laser that can operate on the 3.5 micron and 2.8 micron bands. This model is a
much needed tool for optimizing and understanding the performance of these
laser systems. Comparisons between simulation and experimental results for
three different systems are presented.


title: Persuasion with Limited Communication

abstract:
  We examine information structure design, also called "persuasion" or
"signaling", in the presence of a constraint on the amount of communication. We
focus on the fundamental setting of bilateral trade, which in its simplest form
involves a seller with a single item to price, a buyer whose value for the item
is drawn from a common prior distribution over $n$ different possible values,
and a take-it-or-leave-it-offer protocol. A mediator with access to the buyer's
type may partially reveal such information to the seller in order to further
some objective such as the social welfare or the seller's revenue.
  In the setting of maximizing welfare under bilateral trade, we show that
$O(\log(n) \log \frac{1}{\epsilon})$ signals suffice for a $1-\epsilon$
approximation to the optimal welfare, and this bound is tight. As our main
result, we exhibit an efficient algorithm for computing a $\frac{M-1}{M} \cdot
(1-1/e)$-approximation to the welfare-maximizing scheme with at most M signals.
  For the revenue objective, we show that $\Omega(n)$ signals are needed for a
constant factor approximation to the revenue of a fully informed seller. From a
computational perspective, however, the problem gets easier: we show that a
simple dynamic program computes the signaling scheme with M signals maximizing
the seller's revenue.
  Observing that the signaling problem in bilateral trade is a special case of
the fundamental Bayesian Persuasion model of Kamenica and Gentzkow, we also
examine the question of communication-constrained signaling more generally. In
this model there is a sender (the mediator), a receiver (the seller) looking to
take an action (setting the price), and a state of nature (the buyer's type)
drawn from a common prior. We show that it is NP-hard to approximate the
optimal sender's utility to within any constant factor in the presence of
communication constraints.


title:
Generating weights for the Weil representation attached to an even order
  cyclic quadratic module

abstract:
  We develop geometric methods to study the generating weights of free modules
of vector valued modular forms of half-integral weight, taking values in a
complex representation of the metaplectic group. We then compute the generating
weights for modular forms taking values in the Weil representation attached to
cyclic quadratic modules of order 2p^r, where p is a prime greater than three.
We also show that the generating weights approach a simple limiting
distribution as p grows, or as r grows and p remains fixed.


title:
Robust and scalable Bayesian analysis of spatial neural tuning function
  data

abstract:
  A common analytical problem in neuroscience is the interpretation of neural
activity with respect to sensory input or behavioral output. This is typically
achieved by regressing measured neural activity against known stimuli or
behavioral variables to produce a "tuning function" for each neuron.
Unfortunately, because this approach handles neurons individually, it cannot
take advantage of simultaneous measurements from spatially adjacent neurons
that often have similar tuning properties. On the other hand, sharing
information between adjacent neurons can errantly degrade estimates of tuning
functions across space if there are sharp discontinuities in tuning between
nearby neurons. In this paper, we develop a computationally efficient block
Gibbs sampler that effectively pools information between neurons to de-noise
tuning function estimates while simultaneously preserving sharp discontinuities
that might exist in the organization of tuning across space. This method is
fully Bayesian and its computational cost per iteration scales
sub-quadratically with total parameter dimensionality. We demonstrate the
robustness and scalability of this approach by applying it to both real and
synthetic datasets. In particular, an application to data from the spinal cord
illustrates that the proposed methods can dramatically decrease the
experimental time required to accurately estimate tuning functions.


title: On tangent cones of Schubert varieties

abstract:
  We consider tangent cones of Schubert varieties in the complete flag variety,
and investigate the problem when the tangent cones of two different Schubert
varieties coincide. We give a sufficient condition for such coincidence, and
formulate a conjecture that provides a necessary condition. In particular, we
show that all Schubert varieties corresponding to the Coxeter elements of the
Weyl group have the same tangent cone. Our main tool is the notion of pillar
entries in the rank matrix counting the dimensions of the intersections of a
given flag with the standard one. This notion is a version of Fulton's
essential set. We calculate the dimension of a Schubert variety in terms of the
pillar entries of the rank matrix.


title:
Contributions of microtubule dynamic instability and rotational
  diffusion to kinetochore capture

abstract:
  Microtubule dynamic instability allows search and capture of kinetochores
during spindle formation, an important process for accurate chromosome
segregation during cell division. Recent work has found that microtubule
rotational diffusion about minus-end attachment points contributes to
kinetochore capture in fission yeast, but the relative contributions of dynamic
instability and rotational diffusion are not well understood. We have developed
a biophysical model of kinetochore capture in small fission-yeast nuclei using
hybrid Brownian dynamics/kinetic Monte Carlo simulation techniques. With this
model, we have studied the importance of dynamic instability and microtubule
rotational diffusion for kinetochore capture, both to the lateral surface of a
microtubule and at or near its end. Over a range of biologically relevant
parameters, microtubule rotational diffusion decreased capture time, but made a
relatively small contribution compared to dynamic instability. At most,
rotational diffusion reduced capture time by 25%. Our results suggest that
while microtubule rotational diffusion can speed up kinetochore capture, it is
unlikely to be the dominant physical mechanism. In addition, we found that when
microtubules undergo dynamic instability, lateral captures predominate even in
the absence of rotational diffusion. Counterintuitively, adding rotational
diffusion to a dynamic microtubule increases the probability of end-on capture.


title:
Unconventional and conventional quantum criticalities in
  CeRh$_{0.58}$Ir$_{0.42}$In$_5$

abstract:
  An appropriate description of the state of matter that appears as a second
order phase transition is tuned toward zero temperature, {\it viz.}
quantum-critical point (QCP), poses fundamental and still not fully answered
questions. Experiments are needed both to test basic conclusions and to guide
further refinement of theoretical models. Here, charge and entropy transport
properties as well as AC specific heat of the heavy-fermion compound
CeRh$_{0.58}$Ir$_{0.42}$In$_5$, measured as a function of pressure, reveal two
qualitatively different QCPs in a {\it single} material driven by a {\it
single} non-symmetry-breaking tuning parameter. A discontinuous sign-change
jump in thermopower suggests an unconventional QCP at $p_{c1}$ accompanied by
an abrupt Fermi-surface reconstruction that is followed by a conventional
spin-density-wave critical point at $p_{c2}$ across which the Fermi surface
evolves smoothly to a heavy Fermi-liquid state. These experiments are
consistent with some theoretical predictions, including the sequence of
critical points and the temperature dependence of the thermopower in their
vicinity.


title: Focused Meeting Summarization via Unsupervised Relation Extraction

abstract:
  We present a novel unsupervised framework for focused meeting summarization
that views the problem as an instance of relation extraction. We adapt an
existing in-domain relation learner (Chen et al., 2011) by exploiting a set of
task-specific constraints and features. We evaluate the approach on a decision
summarization task and show that it outperforms unsupervised utterance-level
extractive summarization baselines as well as an existing generic
relation-extraction-based summarization method. Moreover, our approach produces
summaries competitive with those generated by supervised methods in terms of
the standard ROUGE score.


title:
Generalized wave polynomials and transmutations related to perturbed
  Bessel equations

abstract:
  The transmutation (transformation) operator associated with the perturbed
Bessel equation is considered. It is shown that its integral kernel can be
uniformly approximated by linear combinations of constructed here generalized
wave polynomials, solutions of a singular hyperbolic partial differential
equation arising in relation with the transmutation kernel. As a corollary of
this results an approximation of the regular solution of the perturbed Bessel
equation is proposed with corresponding estimates independent of the spectral
parameter.


title: Thermal insulator transition induced by interface scattering

abstract:
  We develop an effective medium model of thermal conductivity that accounts
for both percolation and interface scattering. This model accurately explains
the measured increase and decrease of thermal conductivity with loading in
composites dominated by percolation and interface scattering, respectively. Our
model further predicts that strong interface scattering leads to a sharp
decrease in thermal conductivity, or an insulator transition, at high loadings
when conduction through the matrix is restricted and heat is forced to diffuse
through particles with large interface resistance. The accuracy of our model
and its ability to predict transitions between insulating and conducting states
suggest it can be a useful tool for designing materials with low or high
thermal conductivity for a variety of applications.


title:
FPMax: a 106GFLOPS/W at 217GFLOPS/mm2 Single-Precision FPU, and a
  43.7GFLOPS/W at 74.6GFLOPS/mm2 Double-Precision FPU, in 28nm UTBB FDSOI

abstract:
  FPMax implements four FPUs optimized for latency or throughput workloads in
two precisions, fabricated in 28nm UTBB FDSOI. Each unit's parameters, e.g
pipeline stages, booth encoding etc., were optimized to yield 1.42ns latency at
110GLOPS/W (SP) and 1.39ns latency at 36GFLOPS/W (DP). At 100% activity,
body-bias control improves the energy efficiency by about 20%; at 10% activity
this saving is almost 2x.
  Keywords: FPU, energy efficiency, hardware generator, SOI


title:
Teaching Physics in the first years of Elementary School to children
  with ADHD

abstract:
  In this paper we report on a Science Fair activity developed at Colegio Pedro
II, a traditional Brazilian school, with a group of eight 8-12 years old
Attention Deficit Hyperactivity Disorder (ADHD) students. ADHD is usually a
condition associated with underachievement at school. As part of working toward
scientific literacy for students, we explored the idea of conservation of
energy based on STS paradigm. At the same time, the learning experience was
designed to stimulate children's poor executive function, or, more
specifically, their ability to manage time and planning future tasks.


title:
Spin glass state and enhanced spiral phase in doped delafossite oxide
  CuCrO2

abstract:
  In this work, we study the doping effects on the magnetic states of CuCrO2
based on the classical frustrated spin model [Lin et al., Phys. Rev. B 89,
220405(R) (2014)]. Several experimental observations can be well reproduced by
the Monte Carlo simulations of the modified spin models. Our work suggests that
the disorder induced by V/Al doping cooperated with the frustration in the
system may contribute to the emergence of the spin glass state. Furthermore,
the hole-doping by Mg2+ substituting Cr3+ enhances the quantum fluctuations and
bond disorder which modulate the biquadratic exchanges, and in turn results in
the promotion of the spiral phase, consistent with the experimental report.


title:
Probabilistic Forecasting and Simulation of Electricity Markets via
  Online Dictionary Learning

abstract:
  The problem of probabilistic forecasting and online simulation of real-time
electricity market with stochastic generation and demand is considered. By
exploiting the parametric structure of the direct current optimal power flow, a
new technique based on online dictionary learning (ODL) is proposed. The ODL
approach incorporates real-time measurements and historical traces to produce
forecasts of joint and marginal probability distributions of future locational
marginal prices, power flows, and dispatch levels, conditional on the system
state at the time of forecasting. Compared with standard Monte Carlo simulation
techniques, the ODL approach offers several orders of magnitude improvement in
computation time, making it feasible for online forecasting of market
operations. Numerical simulations on large and moderate size power systems
illustrate its performance and complexity features and its potential as a tool
for system operators.


title:
Spin structure factors of chiral quantum spin liquids on the kagome
  lattice

abstract:
  We calculate dynamical spin structure factors for gapped chiral spin liquid
states in the spin-1/2 Heisenberg antiferromagnet on the kagome lattice using
Schwinger-boson mean-field theory. In contrast to static (equal-time) structure
factors, the dynamical structure factor shows clear signatures of time-reversal
symmetry breaking for chiral spin liquid states. In particular, momentum
inversion $\bm{k} \to -\bm{k}$ symmetry as well as the six-fold rotation
symmetry around the $\Gamma$-point are lost. We highlight other interesting
features, such as a relatively flat onset of the two-spinon continuum for the
\emph{cuboc1} state. Our work is based on the projective symmetry group
classification of time-reversal symmetry breaking Schwinger-boson mean-field
states by Messio, Lhuillier, and Misguich.


title: Stability of twisted states in the continuum Kuramoto model

abstract:
  We study a nonlocal diffusion equation approximating the dynamics of coupled
phase oscillators on large graphs. Under appropriate assumptions, the model has
a family of steady state solutions called twisted states. We prove a sufficient
condition for stability of twisted states with respect to perturbations in the
Sobolev and BV spaces. As an application, we study stability of twisted states
in the Kuramoto model on small-world graphs.


title:
Static Output Feedback Control for Nonlinear Systems subject to
  Parametric and Nonlinear Uncertainties

abstract:
  This work addresses the design of static output feedback control of
discrete-time nonlinear systems satisfying a local Lipschitz continuity
condition with time-varying uncertainties. The controller has also a guaranteed
disturbance attenuation level (Hinfty performance). Thanks to the linearity of
the proposed LMIs in both the admissible Lipschitz constant of the system and
the disturbance attenuation level, they can be simultaneously optimized through
convex multiobjective optimization. The optimization over Lipschitz constant
adds an extra important and new feature to the controller, robustness against
nonlinear uncertainty. The resulting controller is robust against both
nonlinear additive uncertainty and time-varying parametric uncertainties.
Explicit norm-wise and element-wise bounds on the tolerable nonlinear
uncertainty are derived.


title: First-Principles Calculations of Glycine Formation on Cu(110) Surface

abstract:
  The geometrical structures of singlet or triplet NCH3 molecules adsorbing on
Cu(110) surface with presence of Cu adatoms have been determined by
first-principle calculation method. The two distinguishable structures match
the patterns of experimental scanning tunneling microscopy (STM) image,
so-called zigzag and rectangle structures. The singlet NCH3 molecules arrange
as zigzag, when the triplet NCH3 molecules array as rectangle. Since NCH3 can
be found in interstellar environment and its radicals are chemically active, we
arise a question whether this system can be utilized to study surface chemical
reaction of interstellar medium (ISM). Therefore, the potential surface of NCH3
isomerization to HNCH2 subsequently binding with CO on Cu(110) surface has been
calculated using density functional theory (DFT). The calculation results that
HNCH2-CO can be produced on triplet state. However, the reaction is endothermic
which is assumed to be occurred at warmer regions of solar systems or in
specific planets having terrestrial heat (like Earth).


title:
Non-Monotonic Spatial Reasoning with Answer Set Programming Modulo
  Theories

abstract:
  The systematic modelling of dynamic spatial systems is a key requirement in a
wide range of application areas such as commonsense cognitive robotics,
computer-aided architecture design, and dynamic geographic information systems.
We present ASPMT(QS), a novel approach and fully-implemented prototype for
non-monotonic spatial reasoning -a crucial requirement within dynamic spatial
systems- based on Answer Set Programming Modulo Theories (ASPMT).
  ASPMT(QS) consists of a (qualitative) spatial representation module (QS) and
a method for turning tight ASPMT instances into Satisfiability Modulo Theories
(SMT) instances in order to compute stable models by means of SMT solvers. We
formalise and implement concepts of default spatial reasoning and spatial frame
axioms. Spatial reasoning is performed by encoding spatial relations as systems
of polynomial constraints, and solving via SMT with the theory of real
nonlinear arithmetic. We empirically evaluate ASPMT(QS) in comparison with
other contemporary spatial reasoning systems both within and outside the
context of logic programming. ASPMT(QS) is currently the only existing system
that is capable of reasoning about indirect spatial effects (i.e., addressing
the ramification problem), and integrating geometric and qualitative spatial
information within a non-monotonic spatial reasoning context.
  This paper is under consideration for publication in TPLP.


title:
Tight Algorithms for Vertex Cover with Hard Capacities on Multigraphs
  and Hypergraphs

abstract:
  In this paper we give a f-approximation algorithm for the minimum unweighted
Vertex Cover problem with Hard Capacity constraints (VCHC) on f-hypergraphs.
This problem generalizes standard vertex cover for which the best known
approximation ratio is also f and cannot be improved assuming the unique game
conjecture. Our result is therefore essentially the best possible. This
improves over the previous 2.155 (for f=2) and 2f-approximation algorithms by
Cheung, Goemans and Wong (CGW).
  At the heart of our approach is to apply iterative rounding to the problem
with ideas coming from several previous works. We also give a faster
implementation of the method based on certain iteratively rounding the solution
to certain CGW-style covering LPs.
  We note that independent of this work, Kao [#kao2017iterative] also recently
obtained the same result.


title: Color Dipole Picture of Deep Inelastic Scattering, Revisited

abstract:
  Based upon the color-dipole picture, we provide closed analytic expressions
for the longitudinal and the transverse photoabsorption cross sections at low
values of the Bjorken variable of x<0.1. We compare with the experimental data
for the longitudinal-to-transverse ratio of the (virtual) photoabsorption cross
section and with our previous fit to the experimental data for the total
photoabsorption cross section. Scaling in terms of the low-x scaling variable
eta(W^2,Q^2) is analyzed in terms of the reduced cross section of deep
inelastic scattering.


title: Matroid Online Bipartite Matching and Vertex Cover

abstract:
  The Adwords and Online Bipartite Matching problems have enjoyed a renewed
attention over the past decade due to their connection to Internet advertising.
Our community has contributed, among other things, new models (notably
stochastic) and extensions to the classical formulations to address the issues
that arise from practical needs. In this paper, we propose a new generalization
based on matroids and show that many of the previous results extend to this
more general setting. Because of the rich structures and expressive power of
matroids, our new setting is potentially of interest both in theory and in
practice.
  In the classical version of the problem, the offline side of a bipartite
graph is known initially while vertices from the online side arrive one at a
time along with their incident edges. The objective is to maintain a decent
approximate matching from which no edge can be removed. Our generalization,
called Matroid Online Bipartite Matching, additionally requires that the set of
matched offline vertices be independent in a given matroid. In particular, the
case of partition matroids corresponds to the natural scenario where each
advertiser manages multiple ads with a fixed total budget.
  Our algorithms attain the same performance as the classical version of the
problems considered, which are often provably the best possible. We present
$1-1/e$-competitive algorithms for Matroid Online Bipartite Matching under the
small bid assumption, as well as a $1-1/e$-competitive algorithm for Matroid
Online Bipartite Matching in the random arrival model. A key technical
ingredient of our results is a carefully designed primal-dual waterfilling
procedure that accommodates for matroid constraints. This is inspired by the
extension of our recent charging scheme for Online Bipartite Vertex Cover.


title: Fully Dynamic Spanners with Worst-Case Update Time

abstract:
  An $\alpha$-spanner of a graph $ G $ is a subgraph $ H $ such that $ H $
preserves all distances of $ G $ within a factor of $ \alpha $. In this paper,
we give fully dynamic algorithms for maintaining a spanner $ H $ of a graph $ G
$ undergoing edge insertions and deletions with worst-case guarantees on the
running time after each update. In particular, our algorithms maintain: (1) a
$3$-spanner with $ \tilde O (n^{1+1/2}) $ edges with worst-case update time $
\tilde O (n^{3/4}) $, or (2) a $5$-spanner with $ \tilde O (n^{1+1/3}) $ edges
with worst-case update time $ \tilde O (n^{5/9}) $. These size/stretch
tradeoffs are best possible (up to logarithmic factors). They can be extended
to the weighted setting at very minor cost. Our algorithms are randomized and
correct with high probability against an oblivious adversary. We also further
extend our techniques to construct a $5$-spanner with suboptimal size/stretch
tradeoff, but improved worst-case update time.
  To the best of our knowledge, these are the first dynamic spanner algorithms
with sublinear worst-case update time guarantees. Since it is known how to
maintain a spanner using small amortized but large worst-case update time
[Baswana et al. SODA'08], obtaining algorithms with strong worst-case bounds,
as presented in this paper, seems to be the next natural step for this problem.


title: Azimuthal instabilities of the Gribov-Levin-Ryskin equation

abstract:
  We introduce the phenomenology of elliptic flow in nuclear collisions, and
argue that its scaling across energies, rapidities and system sizes could be
suggestive of a QCD-based rather than a hydrodynamical explanation. As a
hypothesis for such an explanation, we show that the GLR equation develops
unstable modes when the parton distribution function is generalized to depend
on azimuthal angle. This generally means the structure function aquires an
azimuthal dependence. We argue that this process is a plausible alternative
explanation for the origin of elliptic flow, one that naturally respects the
scaling experimentally observed.


title: Holographic Metal-Insulator Transition in Higher Derivative Gravity

abstract:
  We introduce a Weyl term into the Einstein-Maxwell-Axion theory in four
dimensional spacetime. Up to the first order of the Weyl coupling parameter
$\gamma$, we construct charged black brane solutions without translational
invariance in a perturbative manner. Among all the holographic frameworks
involving higher derivative gravity, we are the first to obtain metal-insulator
transitions (MIT) when varying the system parameters at zero temperature.
Furthermore, we study the holographic entanglement entropy (HEE) of strip
geometry in this model and find that the second order derivative of HEE with
respect to the axion parameter exhibits maximization behavior near quantum
critical points (QCPs) of MIT. It testifies the conjecture in 1502.03661 and
1604.04857 that HEE itself or its derivatives can be used to diagnose quantum
phase transition (QPT).


title: Cohen-Lenstra Moments for Some Nonabelian Groups

abstract:
  Cohen and Lenstra detailed a heuristic for the distribution of odd p-class
groups for imaginary quadratic fields. One such formulation of this
distribution is that the expected number of surjections from the class group of
an imaginary quadratic field k to a fixed odd abelian group is 1. Class field
theory tells us that the class group is also the Galois group of the Hilbert
class field, the maximal unramified abelian extension of k, so we could
equivalently say the expected number of unramified G-extensions of k is
1/#Aut(G) for a fixed abelian group G. We generalize this to asking for the
expected number of unramified G-extensions Galois over $\mathbb{Q}$ for a fixed
finite group G, with no restrictions placed on G. We review cases where the
answer is known or conjectured by Boston-Wood, Boston-Bush-Hajir, and Bhargava,
then answer this question in several new cases. In particular, we show when the
expected number is zero and give a nontrivial family of groups realizing this.
Additionally, we prove the expected number for the quaternion group $Q_8$ and
dihedral group $D_4$ of order 8 is infinite. Lastly, we discuss the special
case of groups generated by elements of order 2 and give an argument for an
infinite expected number based on Malle's conjecture.


title:
coxphMIC: An R Package for Sparse Estimation of Cox Proportional Hazards
  Models

abstract:
  In this paper, we describe an R package named coxphMIC, which implements the
sparse estimation method for Cox proportional hazards models via approximated
information criterion (Su et al., 2016 Biometrics). The developed methodology
is named MIC which stands for "Minimizing approximated Information Criteria". A
reparameterization step is introduced to enforce sparsity while at the same
time keeping the objective function smooth. As a result, MIC is computationally
fast with a superior performance in sparse estimation. Furthermore, the
reparameterization tactic yields an additional advantage in terms of
circumventing post-selection inference. The MIC method and its R implementation
are introduced and illustrated with the PBC data.


title:
Representing Documents and Queries as Sets of Word Embedded Vectors for
  Information Retrieval

abstract:
  A major difficulty in applying word vector embeddings in IR is in devising an
effective and efficient strategy for obtaining representations of compound
units of text, such as whole documents, (in comparison to the atomic words),
for the purpose of indexing and scoring documents. Instead of striving for a
suitable method for obtaining a single vector representation of a large
document of text, we rather aim for developing a similarity metric that makes
use of the similarities between the individual embedded word vectors in a
document and a query. More specifically, we represent a document and a query as
sets of word vectors, and use a standard notion of similarity measure between
these sets, computed as a function of the similarities between each constituent
word pair from these sets. We then make use of this similarity measure in
combination with standard IR based similarities for document ranking. The
results of our initial experimental investigations shows that our proposed
method improves MAP by up to $5.77\%$, in comparison to standard text-based
language model similarity, on the TREC ad-hoc dataset.


title: Hairy AdS Solitons

abstract:
  We construct exact hairy AdS soliton solutions in Einstein-dilaton gravity
theory. We examine their thermodynamic properties and discuss the role of these
solutions for the existence of first order phase transitions for hairy black
holes. The negative energy density associated to hairy AdS solitons can be
interpreted as the Casimir energy that is generated in the dual filed theory
when the fermions are antiperiodic on the compact coordinate.