title:
Flexible Bayesian additive joint models with an application to type 1
  diabetes research

abstract:
  The joint modeling of longitudinal and time-to-event data is an important
tool of growing popularity to gain insights into the association between a
biomarker and an event process. We develop a general framework of flexible
additive joint models that allows the specification of a variety of effects,
such as smooth nonlinear, time-varying and random effects, in the longitudinal
and survival parts of the models. Our extensions are motivated by the
investigation of the relationship between fluctuating disease-specific markers,
in this case autoantibodies, and the progression to the autoimmune disease type
1 diabetes. By making use of Bayesian P-splines we are in particular able to
capture highly nonlinear subject-specific marker trajectories as well as a
time-varying association between the marker and the event process allowing new
insights into disease progression. The model is estimated within a Bayesian
framework and implemented in the R-package bamlss.


title:
Cosmological constraints from the convergence 1-point probability
  distribution

abstract:
  We examine the cosmological information available from the 1-point
probability distribution (PDF) of the weak-lensing convergence field, utilizing
fast L-PICOLA simulations and a Fisher analysis. We find competitive
constraints in the $\Omega_m$-$\sigma_8$ plane from the convergence PDF with
$188\ arcmin^2$ pixels compared to the cosmic shear power spectrum with an
equivalent number of modes ($\ell < 886$). The convergence PDF also partially
breaks the degeneracy cosmic shear exhibits in that parameter space. A joint
analysis of the convergence PDF and shear 2-point function also reduces the
impact of shape measurement systematics, to which the PDF is less susceptible,
and improves the total figure of merit by a factor of $2-3$, depending on the
level of systematics. Finally, we present a correction factor necessary for
calculating the unbiased Fisher information from finite differences using a
limited number of cosmological simulations.


title: Morphological Inflection Generation with Hard Monotonic Attention

abstract:
  We present a neural model for morphological inflection generation which
employs a hard attention mechanism, inspired by the nearly-monotonic alignment
commonly found between the characters in a word and the characters in its
inflection. We evaluate the model on three previously studied morphological
inflection generation datasets and show that it provides state of the art
results in various setups compared to previous neural and non-neural
approaches. Finally we present an analysis of the continuous representations
learned by both the hard and soft attention \cite{bahdanauCB14} models for the
task, shedding some light on the features such models extract.


title: On bases that are closed under multiplication

abstract:
  It is well known that there is no basis of the field for real numbers
regarded as a vector space over any proper subfield that is closed under
multiplication. Mabry has extended this result to bases of arbitrary proper
field extensions. The aim of this short communication is to notice that the
proof of the result concerning the reals may be adjusted to a larger class of
algebras (including full matrix algebras); thereby we subsume Mabry's result.


title: Form factors of bound states in the XXZ chain

abstract:
  This work focuses on the calculation of the large-volume behaviour of form
factors of local operators in the XXZ spin-$1/2$ chain taken between the ground
state and an excited state containing bound states. The analysis is rigorous
and builds on various fine properties of the string solutions to the Bethe
equations and certain technical hypotheses. These technical hypotheses are
satisfied for a generic excited state. The results obtained in this work pave
the way for extracting, starting from the first principles, the large-distance
and long-time asymptotic behaviour of the XXZ chain's two-point functions just
as the so-called edge singularities of their Fourier transforms.

categories: math-ph cond-mat.stat-mech math.MP nlin.SI


title: Minimal Constrained Supergravity

abstract:
  We describe minimal supergravity models where supersymmetry is non-linearly
realized via constrained superfields. We show that the resulting actions differ
from the so called "de Sitter" supergravities because we consider constraints
eliminating directly the auxiliary fields of the gravity multiplet.


title: Understanding Deep Neural Networks with Rectified Linear Units

abstract:
  In this paper we investigate the family of functions representable by deep
neural networks (DNN) with rectified linear units (ReLU). We give an algorithm
to train a ReLU DNN with one hidden layer to *global optimality* with runtime
polynomial in the data size albeit exponential in the input dimension. Further,
we improve on the known lower bounds on size (from exponential to super
exponential) for approximating a ReLU deep net function by a shallower ReLU
net. Our gap theorems hold for smoothly parametrized families of "hard"
functions, contrary to countable, discrete families known in the literature. An
example consequence of our gap theorems is the following: for every natural
number $k$ there exists a function representable by a ReLU DNN with $k^2$
hidden layers and total size $k^3$, such that any ReLU DNN with at most $k$
hidden layers will require at least $\frac{1}{2}k^{k+1}-1$ total nodes.
Finally, for the family of $\mathbb{R}^n\to \mathbb{R}$ DNNs with ReLU
activations, we show a new lowerbound on the number of affine pieces, which is
larger than previous constructions in certain regimes of the network
architecture and most distinctively our lowerbound is demonstrated by an
explicit construction of a *smoothly parameterized* family of functions
attaining this scaling. Our construction utilizes the theory of zonotopes from
polyhedral theory.

categories: cs.LG cond-mat.dis-nn cs.AI cs.CC stat.ML


title:
Optimal Oil Production under Mean Reverting L\'evy Models with Regime
  Switching

abstract:
  This paper is concerned with the problem of finding the optimal of extraction
policies of an oil field in light of various financial and economical
restrictions and constraints. Taking into account the fact that the oil price
in worldwide commodity markets fluctuates randomly following global and
seasonal macro-economic parameters, we model the evolution of the oil price as
a mean reverting regime switching jump diffusion process. We formulate this
problem as finite-time horizon optimal control problem. We solve the control
problem using the method of viscosity solutions. Moreover, we construct and
prove the convergence of a numerical scheme for approximating the optimal
reward function and the optimal extraction policy. A numerical example that
illustrates these results is presented.


title: Deformation quantization of principal bundles

abstract:
  We outline how Drinfeld twist deformation techniques can be applied to the
deformation quantization of principal bundles into noncommutative principal
bundles, and more in general to the deformation of Hopf-Galois extensions.
First we twist deform the structure group in a quantum group, and this leads to
a deformation of the fibers of the principal bundle. Next we twist deform a
subgroup of the group of authomorphisms of the principal bundle, and this leads
to a noncommutative base space. Considering both deformations we obtain
noncommutative principal bundles with noncommutative fiber and base space as
well.


title:
Topological algebras of locally solid vector subspaces of order bounded
  operators

abstract:
  Let E be a locally solid vector lattice. In this paper, we consider two
particular vector subspaces of the space of all order bounded operators on E.
With the aid of two appropriate topologies, we show that under some conditions,
they establish both, locally solid vector lattices and topologically complete
topological algebras.


title: Development of a Modes of Collaboration framework

abstract:
  Group work is becoming increasingly common in introductory physics
classrooms. Understanding how students engage in these group learning
environments is important for designing and facilitating productive learning
opportunities for students. We conducted a study in which we collected video of
groups of students working on conceptual electricity and magnetism problems in
an introductory physics course. In this setting, students needed to negotiate a
common understanding and coordinate group decisions in order to complete the
activity successfully. We observed students interacting in several distinct
ways while solving these problems. Analysis of these observations focused on
identifying the different ways students interacted and articulating what
defines and distinguishes them, resulting in the development of the Modes of
Collaboration framework. The Modes of Collaboration framework defines student
interactions along three dimensions: social, discursive, and disciplinary
content. This multi-dimensional approach offers a unique lens through which to
consider group work and provides a flexibility that could allow the framework
to be adapted for a variety of contexts. We present the framework and several
examples of its application here.


title:
Geometry of multi-marginal martingale optimal transportations and
  duality

abstract:
  The theory of Optimal Transport (OT) and Martingale Optimal Transport (MOT)
were inspired by problems in economics and finance and have flourished over the
past decades, making significant advances in theory and practice. MOT considers
the problem of pricing and hedging of a financial instrument, referred to as an
option, assuming its payoff depends on a single asset price. In this paper we
introduce Multi-marginal Martingale Optimal Transport (MMOT) problem, which
considers the more general and realistic situation in which the option payoff
depends on multiple asset prices. We address this problem of pricing and
hedging given market information -- described by multi-marginals -- which is an
intimately relevant setup in the robust financial framework.
  We establish that the MMOT problem, as an infinite-dimensional linear
programming, admits an optimizer for its dual program. Such existence result of
dual optimizers is significant for several reasons: the dual optimizers
describe how a person who is liable for an option payoff can formulate optimal
hedging portfolios, and more importantly, they can provide crucial information
on the geometry of primal optimizers, i.e. the MMOTs. As an illustration, we
show that multiple martingales given marginals must exhibit an extremal
conditional correlation structure whenever they jointly optimize the
expectation of distance-type cost functions.


title: A remark on non-integral p-adic slopes for modular forms

abstract:
  We give a sufficient condition, namely "Buzzard irregularity", for there to
exist a cuspidal eigenform which does not have integral p-adic slope.


title: Super-Sensitive Ancilla-Based Adaptive Quantum Phase Estimation

abstract:
  The super-sensitivity attained in quantum phase estimation is known to be
compromised in the presence of decoherence. This is particularly patent at
blind spots -- phase values at which sensitivity is totally lost. One remedy is
to use a precisely known reference phase to shift the operation point of the
sensor to a less vulnerable phase value. We present here an alternative
approach based on combining the probe with an ancillary degree of freedom
containing adjustable parameters to create an entangled quantum state of higher
dimension. We validate this concept by simulating a configuration of a
Mach-Zehnder interferometer with a two-photon probe and a polarization ancilla
of adjustable parameters, entangled at a polarizing beam splitter. At the
interferometer output, the photons are measured after an adjustable unitary
transformation in the polarization subspace. Through calculation of the Fisher
information and simulation of an adaptive estimation procedure, we show that
optimizing the adjustable polarization parameters using an adaptive measurement
process provides globally super-sensitive unbiased phase estimates for a range
of decoherence levels, without prior information or a reference phase.


title: Final Results of the PICASSO Dark Matter Search Experiment

abstract:
  The PICASSO dark matter search experiment operated an array of 32 superheated
droplet detectors containing 3.0 kg of C$_{4}$F$_{10}$ and collected an
exposure of 231.4 kgd at SNOLAB between March 2012 and January 2014. We report
on the final results of this experiment which includes for the first time the
complete data set and improved analysis techniques including \mbox{acoustic}
localization to allow fiducialization and removal of higher activity regions
within the detectors. No signal consistent with dark matter was observed. We
set limits for spin-dependent interactions on protons of
$\sigma_p^{SD}$~=~1.32~$\times$~10$^{-2}$~pb (90\%~C.L.) at a WIMP mass of 20
GeV/c$^{2}$. In the spin-independent sector we exclude cross sections larger
than $\sigma_p^{SI}$~=~4.86~$\times$~10$^{-5 }$~pb~(90\% C.L.) in the region
around 7 GeV/c$^{2}$. The pioneering efforts of the PICASSO experiment have
paved the way forward for a next generation detector incorporating much of this
technology and experience into larger mass bubble chambers.


title: Large Spin Perturbation Theory

abstract:
  We consider conformal field theories around points of large twist degeneracy.
Examples of this are theories with weakly broken higher spin symmetry and
perturbations around generalised free fields. At the degenerate point we
introduce twist conformal blocks. These are eigenfunctions of certain quartic
operators and encode the contribution, to a given four-point correlator, of the
whole tower of intermediate operators with a given twist. As we perturb around
the degenerate point, the twist degeneracy is lifted. In many situations this
breaking is controlled by inverse powers of the spin. In such cases the twist
conformal blocks can be decomposed into a sequence of functions which we
systematically construct. Decomposing the four-point correlator in this basis
turns crossing symmetry into an algebraic problem. Our method can be applied to
a wide spectrum of conformal field theories in any number of dimensions and at
any order in the breaking parameter. As an example, we compute the spectrum of
various theories around generalised free fields. For theories with higher spin
symmetry we discuss the relation between twist conformal blocks and higher spin
conformal blocks.


title: Data Poisoning: Lightweight Soft Fault Injection for Python

abstract:
  This paper introduces and explores the idea of data poisoning, a light-weight
peer-architecture technique to inject faults into Python programs. This method
requires very small modification to the original program, which facilitates
evaluation of sensitivity of systems that are prototyped or modeled in Python.
We propose different fault scenarios that can be injected to programs using
data poisoning. We use Dijkstra's Self Stabilizing Ring Algorithm to illustrate
the approach.


title: The algebraic structure of quantity calculus

abstract:
  An algebraic structure underlying the quantity calculus is proposed
consisting in an algebraic fiber bundle, that is, a base structure which is a
free Abelian group together with fibers which are one dimensional vector
spaces, all of them bound by algebraic restrictions. Subspaces, tensor product
and quotient spaces are considered as well as homomorphisms to end with a
classification theorem of these structures. The new structure provides an
axiomatic foundation for quantity calculus and gives complete justification
within its framework of the way that quantity calculus is actually performed.
It is hoped that this exposition helps to clarify the role of the interviening
concepts of quantity, quantity value, quantity dimension and their relation
with a system of units, particularly, the SI.


title:
Protein Secondary Structure Prediction Using Deep Multi-scale
  Convolutional Neural Networks and Next-Step Conditioning

abstract:
  Recently developed deep learning techniques have significantly improved the
accuracy of various speech and image recognition systems. In this paper we
adapt some of these techniques for protein secondary structure prediction. We
first train a series of deep neural networks to predict eight-class secondary
structure labels given a protein's amino acid sequence information and find
that using recent methods for regularization, such as dropout and weight-norm
constraining, leads to measurable gains in accuracy. We then adapt recent
convolutional neural network architectures--Inception, ReSNet, and DenseNet
with Batch Normalization--to the problem of protein structure prediction. These
convolutional architectures make heavy use of multi-scale filter layers that
simultaneously compute features on several scales, and use residual connections
to prevent underfitting. Using a carefully modified version of these
architectures, we achieve state-of-the-art performance of 70.0% per amino acid
accuracy on the public CB513 benchmark dataset. Finally, we explore additions
from sequence-to-sequence learning, altering the model to make its predictions
conditioned on both the protein's amino acid sequence and its past secondary
structure labels. We introduce a new method of ensembling such a conditional
model with our convolutional model, an approach which reaches 70.6% Q8 accuracy
on CB513. We argue that these results can be further refined for larger boosts
in prediction accuracy through more sophisticated attempts to control
overfitting of conditional models. We aim to release the code for these
experiments as part of the TensorFlow repository.


title: Estimating Causal Direction and Confounding of Two Discrete Variables

abstract:
  We propose a method to classify the causal relationship between two discrete
variables given only the joint distribution of the variables, acknowledging
that the method is subject to an inherent baseline error. We assume that the
causal system is acyclicity, but we do allow for hidden common causes. Our
algorithm presupposes that the probability distributions $P(C)$ of a cause $C$
is independent from the probability distribution $P(E\mid C)$ of the
cause-effect mechanism. While our classifier is trained with a Bayesian
assumption of flat hyperpriors, we do not make this assumption about our test
data. This work connects to recent developments on the identifiability of
causal models over continuous variables under the assumption of "independent
mechanisms". Carefully-commented Python notebooks that reproduce all our
experiments are available online at
http://vision.caltech.edu/~kchalupk/code.html.


title:
Eve: A Gradient Based Optimization Method with Locally and Globally
  Adaptive Learning Rates

abstract:
  Adaptive gradient methods for stochastic optimization adjust the learning
rate for each parameter locally. However, there is also a global learning rate
which must be tuned in order to get the best performance. In this paper, we
present a new algorithm that adapts the learning rate locally for each
parameter separately, and also globally for all parameters together.
Specifically, we modify Adam, a popular method for training deep learning
models, with a coefficient that captures properties of the objective function.
Empirically, we show that our method, which we call Eve, outperforms Adam and
other popular methods in training deep neural networks, like convolutional
neural networks for image classification, and recurrent neural networks for
language tasks.


title:
Isotonized smooth estimators of a monotone baseline hazard in the Cox
  model

abstract:
  We consider two isotonic smooth estimators for a monotone baseline hazard in
the Cox model, a maximum smooth likelihood estimator and a Grenander-type
estimator based on the smoothed Breslow estimator for the cumulative baseline
hazard. We show that they are both asymptotically normal at rate
$n^{m/(2m+1)}$, where $m\geq 2$ denotes the level of smoothness considered, and
we relate their limit behavior to kernel smoothed isotonic estimators studied
in Lopuha\"a and Musta (2016). It turns out that the Grenander-type estimator
is asymptotically equivalent to the kernel smoothed isotonic estimators, while
the maximum smoothed likelihood estimator exhibits the same asymptotic variance
but a different bias. Finally, we present numerical results on pointwise
confidence intervals that illustrate the comparable behavior of the two
methods.


title:
Counterexamples and Proof Loophole for the C/C++ to POWER and ARMv7
  Trailing-Sync Compiler Mappings

abstract:
  The C and C++ high-level languages provide programmers with atomic operations
for writing high-performance concurrent code. At the assembly language level, C
and C++ atomics get mapped down to individual instructions or combinations of
instructions by compilers, depending on the ordering guarantees and
synchronization instructions provided by the underlying architecture. These
compiler mappings must uphold the ordering guarantees provided by C/C++ atomics
or the compiled program will not behave according to the C/C++ memory model. In
this paper we discuss two counterexamples to the well-known trailing-sync
compiler mappings for the Power and ARMv7 architectures that were previously
thought to be proven correct. In addition to the counterexamples, we discuss
the loophole in the proof of the mappings that allowed the incorrect mappings
to be proven correct. We also discuss the current state of compilers and
architectures in relation to the bug.


title: Spinfoam cosmology with the proper vertex amplitude

abstract:
  The proper vertex amplitude is derived from the EPRL vertex by restricting to
a single gravitational sector in order to achieve the correct semi-classical
behaviour. We apply the proper vertex to calculate a cosmological transition
amplitude that can be viewed as the Hartle-Hawking wavefunction. To perform
this calculation we deduce the integral form of the proper vertex and use
extended stationary phase methods to estimate the large-volume limit. We show
that the resulting amplitude satisfies an operator constraint whose classical
analogue is the Hamiltonian constraint of the Friedmann-Robertson-Walker
cosmology. We find that the constraint dynamically selects the relevant family
of coherent states and demonstrate a similar dynamic selection in standard
quantum mechanics. We investigate the effects of dynamical selection on
long-range correlations.


title:
Many-body subradiant excitations in metamaterial arrays: Experiment and
  theory

abstract:
  Subradiant excitations, originally predicted by Dicke, have posed a
long-standing challenge in physics owing to their weak radiative coupling to
environment. Here we engineer massive subradiance in planar metamaterial arrays
as a spatially extended eigenmode comprising over 1000 metamolecules. By
comparing the near- and far-field response in large-scale numerical simulations
with those in experimental observations we identify correlated
multimetamolecule subradiant states that dominate the total excitation energy.
We show that spatially extended many-body subradiance can also exist in
plasmonic metamaterial arrays at optical frequencies.


title:
Suppression of Upsilon(1S), Upsilon(2S), and Upsilon(3S) production in
  PbPb collisions at sqrt(s[NN]) = 2.76 TeV

abstract:
  The production yields of Upsilon(1S), Upsilon(2S), and Upsilon(3S) quarkonium
states are measured through their decays into muon pairs in the CMS detector,
in PbPb and pp collisions at the centre-of-mass energy per nucleon pair of 2.76
TeV. The data correspond to integrated luminosities of 166 inverse microbarns
and 5.4 inverse picobarns for PbPb and pp collisions, respectively.
Differential production cross sections are reported as functions of Upsilon
rapidity y up to 2.4, and transverse momentum pT up to 20 GeV/c. A strong
centrality-dependent suppression is observed in PbPb relative to pp collisions,
by factors of up to approximately 2 and 8, for the Upsilon(1S) and Upsilon(2S)
states, respectively. No significant dependence of this suppression is observed
as a function of y or pT. The Upsilon(3S) state is not observed in PbPb
collisions, which corresponds to a suppression for the centrality-integrated
data by at least a factor of approximately 7 at a 95% confidence level. The
observed suppression is in agreement with theoretical scenarios modeling the
sequential melting of quarkonium states in a quark gluon plasma.


title: Algorithms for Fitting the Constrained Lasso

abstract:
  We compare alternative computing strategies for solving the constrained lasso
problem. As its name suggests, the constrained lasso extends the widely-used
lasso to handle linear constraints, which allow the user to incorporate prior
information into the model. In addition to quadratic programming, we employ the
alternating direction method of multipliers (ADMM) and also derive an efficient
solution path algorithm. Through both simulations and real data examples, we
compare the different algorithms and provide practical recommendations in terms
of efficiency and accuracy for various sizes of data. We also show that, for an
arbitrary penalty matrix, the generalized lasso can be transformed to a
constrained lasso, while the converse is not true. Thus, our methods can also
be used for estimating a generalized lasso, which has wide-ranging
applications. Code for implementing the algorithms is freely available in the
Matlab toolbox SparseReg.


title:
Magnonic analog of relativistic Zitterbewegung in an antiferromagnetic
  spin chain

abstract:
  We theoretically investigate the spin wave (magnon) excitations in a
classical antiferromagnetic spin chain with easy-axis anisotropy. We obtain a
Dirac-like equation by linearizing the Landau- Lifshitz-Gilbert equation in
this antiferromagnetic system, in contrast to the ferromagnetic system in which
a Schr\"{o}dinger equation is derived. The Hamiltonian operator in the
Dirac-like equation is a pseudo-Hermitian. We compute and demonstrate the
relativistic Zitterbewegung (trembling motion) in the antiferromagnetic spin
chain by measuring the expectation values of the wave packet position.


title: Lens Modeling Abell 370: Crowning the Final Frontier Field with MUSE

abstract:
  We present a strong lensing analysis on the massive cluster Abell 370 (A370;
z = 0.375), using a combination of deep multi-band Hubble Space Telescope (HST)
imaging and Multi-Unit Spectroscopic Explorer (MUSE) spectroscopy. From only
two hours of MUSE data, we are able to measure 120 redshifts in the Southern
BCG area, including several multiply-imaged lens systems. In total, we increase
the number of multiply-imaged systems with a secure redshift from 4 to 15, nine
of which are newly discovered. Of these, eight are located at z > 3, greatly
extending the redshift range of spectroscopically-confirmed systems over
previous work. Using these systems as constraints, we update a parametric lens
model of A370, probing the mass distribution from cluster to galaxy scales.
Overall, we find that a model with only two cluster- scale dark matter halos
(one for each BCG) does a poor job of fitting these new image constraints.
Instead, two additional mass clumps -- a central "bar" of mass located between
the BCGs, and another clump located within a "crown" of galaxies in the
Northern part of the cluster field -- provide significant improvements to the
fit. Additional physical evidence suggests these clumps are indeed real
features of the system, but with relatively few image constraints in the crown
region, this claim is difficult to evaluate from a modeling perspective.
Additional MUSE observations of A370 covering the entire strong-lensing region
will greatly help these efforts, further improving our understanding of this
intriguing cluster.


title:
Updated fit to three neutrino mixing: exploring the accelerator-reactor
  complementarity

abstract:
  We perform a combined fit to global neutrino oscillation data available as of
fall 2016 in the scenario of three-neutrino oscillations and present updated
allowed ranges of the six oscillation parameters. We discuss the differences
arising between the consistent combination of the data samples from accelerator
and reactor experiments compared to partial combinations. We quantify the
confidence in the determination of the less precisely known parameters
$\theta_{23}$, $\delta_\text{CP}$, and the neutrino mass ordering by performing
a Monte Carlo study of the long baseline accelerator and reactor data. We find
that the sensitivity to the mass ordering and the $\theta_{23}$ octant is below
$1\sigma$. Maximal $\theta_{23}$ mixing is allowed at slightly more than 90%
CL. The best fit for the CP violating phase is around $270^\circ$, CP
conservation is allowed at slightly above $1\sigma$, and values of
$\delta_\text{CP} \simeq 90^\circ$ are disfavored at around 99% CL for normal
ordering and higher CL for inverted ordering.


title:
Hybrid continuous dynamical decoupling: a photon-phonon doubly dressed
  spin

abstract:
  We study the parametric interaction between a single Nitrogen-Vacancy
electronic spin and a diamond mechanical resonator in which the spin is
embedded. Coupling between spin and oscillator is achieved by crystal strain,
which is generated upon actuation of the oscillator and which parametrically
modulates the spins' energy splitting. Under coherent microwave driving of the
spin, this parametric drive leads to a locking of the spin Rabi frequency to
the oscillator mode in the megahertz range. Both the Rabi oscillation decay
time and the inhomogeneous spin dephasing time increase by two orders of
magnitude under this spin-locking condition. We present routes to prolong the
dephasing times even further, potentially to the relaxation time limit. The
remarkable coherence protection that our hybrid spin-oscillator system offers
is reminiscent of recently proposed concatenated continuous dynamical
decoupling schemes and results from our robust, drift-free strain-coupling
mechanism and the narrow linewidth of the high-quality diamond mechanical
oscillator employed. Our findings suggest feasible applications in quantum
information processing and sensing.


title: Entanglement from Topology in Chern-Simons Theory

abstract:
  The way in which geometry encodes entanglement is a topic of much recent
interest in quantum many-body physics and the AdS/CFT duality. This relation is
particularly pronounced in the case of topological quantum field theories,
where topology alone determines the quantum states of the theory. In this work,
we study the set of quantum states that can be prepared by the Euclidean path
integral in three-dimensional Chern-Simons theory. Specifically, we consider
arbitrary 3-manifolds with a fixed number of torus boundaries in both abelian
U(1) and non-abelian SO(3) Chern-Simons theory. For the abelian theory, we find
that the states that can be prepared coincide precisely with the set of
stabilizer states from quantum information theory. This constrains the
multipartite entanglement present in this theory, but it also reveals that
stabilizer states can be described by topology. In particular, we find an
explicit expression for the entanglement entropy of a many-torus subsystem
using only a single replica, as well as a concrete formula for the number of
GHZ states that can be distilled from a tripartite state prepared through path
integration. For the nonabelian theory, we find a notion of "state
universality", namely that any state can be prepared to an arbitrarily good
approximation. The manifolds we consider can also be viewed as toy models of
multi-boundary wormholes in AdS/CFT.


title: Sterile Neutrino Dark Matter with Supersymmetry

abstract:
  Sterile neutrino dark matter, a popular alternative to the WIMP paradigm, has
generally been studied in non-supersymmetric setups. If the underlying theory
is supersymmetric, we find that several interesting and novel dark matter
features can arise. In particular, in scenarios of freeze-in production of
sterile neutrino dark matter, its superpartner, the sterile sneutrino, can play
a crucial role in early Universe cosmology as the dominant source of cold,
warm, or hot dark matter, or of a subdominant relativistic population of
sterile neutrinos that can contribute to the effective number of relativistic
degrees of freedom Neff during Big Bang nucleosynthesis.


title: Quantum Loop Topography for Machine Learning

abstract:
  Despite rapidly growing interest in harnessing machine learning in the study
of quantum many-body systems, training neural networks to identify quantum
phases is a nontrivial challenge. The key challenge is in efficiently
extracting essential information from the many-body Hamiltonian or wave
function and turning the information into an image that can be fed into a
neural network. When targeting topological phases, this task becomes
particularly challenging as topological phases are defined in terms of
non-local properties. Here we introduce quantum loop topography (QLT): a
procedure of constructing a multi-dimensional image from the "sample"
Hamiltonian or wave function by evaluating two-point operators that form loops
at independent Monte Carlo steps. The loop configuration is guided by
characteristic response for defining the phase, which is Hall conductivity for
the cases at hand. Feeding QLT to a fully-connected neural network with a
single hidden layer, we demonstrate that the architecture can be effectively
trained to distinguish Chern insulator and fractional Chern insulator from
trivial insulators with high fidelity. In addition to establishing the first
case of obtaining a phase diagram with topological quantum phase transition
with machine learning, the perspective of bridging traditional condensed matter
theory with machine learning will be broadly valuable.


title:
General relativistic simulations of compact binary mergers as engines of
  short gamma-ray bursts

abstract:
  Black hole - neutron star (BHNS) and neutron star - neutron star (NSNS)
binaries are among the favored candidates for the progenitors of the black hole
- disk systems that may be the engines powering short-hard gamma ray bursts.
After almost two decades of simulations of binary NSNSs and BHNSs in full
general relativity we are now beginning to understand the ingredients that may
be necessary for these systems to launch incipient jets. Here, we review our
current understanding, and summarize the surprises and lessons learned from
state-of-the-art (magnetohydrodynamic) simulations in full general relativity
of BHNS and NSNS mergers as jet engines for short-hard gamma-ray bursts.


title:
Modeling Tunneling for the Unconventional Superconducting Proximity
  Effect

abstract:
  Recently there has been reinvigorated interest in the superconducting
proximity effect, driven by predictions of the emergence of Majorana fermions.
To help guide this search, we have developed a phenomenological model for the
tunneling spectra in anisotropic superconductor-normal metal proximity devices.
We combine successful approaches used in s-wave proximity and standard d-wave
tunneling to reproduce tunneling spectra in d-wave proximity devices, and
clarify the origin of various features. Different variations of the pair
potential are considered, resulting from the proximity-induced
superconductivity. Furthermore, the effective pair potential felt by the
quasiparticles is momentum-dependent in contrast to s-wave superconductors. The
probabilities of reflection and transmission are calculated by solving the
Bogoliubov equations. Our results are consistent with experimental observations
of the unconventional proximity effect and provide important experimental
parameters such as the size and length scale of the proximity induced gap, as
well as the conditions needed to observe the reduced and full superconducting
gaps.


title: The Spatially Resolved [CII] Cooling Line Deficit in Galaxies

abstract:
  We present [CII] 158um measurements from over 15,000 resolved regions within
54 nearby galaxies of the KINGFISH program to investigate the so-called [CII]
"line cooling deficit" long known to occur in galaxies with different
luminosities. The [CII]/TIR ratio ranges from above 1% to below 0.1% in the
sample, with a mean value of 0.48+-0.21%. We find that the surface density of
24um emission dominates this trend, with [CII]/TIR dropping as nuInu{24um}
increases. Deviations from this overall decline are correlated with changes in
the gas phase metal abundance, with higher metallicity associated with deeper
deficits at a fixed surface brightness. We supplement the local sample with
resolved [CII] measurements from nearby luminous infrared galaxies and high
redshift sources from z=1.8-6.4, and find that star formation rate density
drives a continuous trend of deepening [CII] deficit across six orders of
magnitude in SFRD. The tightness of this correlation suggests that an
approximate star formation rate density can be estimated directly from global
measurements of [CII]/TIR, and a relation is provided to do so. Several
low-luminosity AGN hosts in the sample show additional and significant central
suppression of [CII]/TIR, but these deficit enhancements occur not in those AGN
with the highest X-ray luminosities, but instead those with the highest central
starlight intensities. Taken together, these results demonstrate that the [CII]
cooling line deficit in galaxies likely arises from local physical phenomena in
interstellar gas.


title:
Deformation of an Amorphous Polymer during the
  Fused-Filament-Fabrication Method for Additive Manufacturing

abstract:
  3D printing is rapidly becoming an effective means of prototyping and
creating custom consumer goods. The most common method for printing a polymer
melt is fused filament fabrication (FFF), and involves extrusion of a
thermoplastic material through a heated nozzle; the material is then built up
layer-by-layer to fabricate a three-dimensional object. Under typical printing
conditions the melt experiences high strain rates within the FFF nozzle, which
are able to significantly stretch and orient the polymer molecules. In this
paper, we model the deformation of an amorphous polymer melt during the
extrusion process, where the fluid must make a 90$^\text{o}$ turn. The melt is
described by a modified version of the Rolie-Poly model, which allows for
flow-induced changes in the entanglement density. The complex polymer
configurations in the cross-section of a printed layer are quantified and
visualised. The deposition process involving the corner flow geometry dominates
the deformation and significantly disentangles the melt.


title:
Finite generating sets for reversible gate sets under general
  conservation laws

abstract:
  It is well-known that the Toffoli gate and the negation gate together yield a
universal gate set, in the sense that every permutation of $\{0,1\}^n$ can be
implemented as a composition of these gates. Since every bit operation that
does not use all of the bits performs an even permutation, we need to use at
least one auxiliary bit to perform every permutation, and it is known that one
bit is indeed enough. Without auxiliary bits, all even permutations can be
implemented. We generalize these results to non-binary logic: For any finite
set $A$, a finite gate set can generate all even permutations of $A^n$ for all
$n$, without any auxiliary symbols. This directly implies the previously
published result that a finite gate set can generate all permutations of $A^n$
when the cardinality of $A$ is odd, and that one auxiliary symbol is necessary
and sufficient to obtain all permutations when the cardinality of $A$ is even.
We also consider the conservative case, that is, those permutations of $A^n$
that preserve the weight of the input word. The weight is the vector that
records how many times each symbol occurs in the word or, more generally, the
image of the word under a fixed monoid homomorphism from $A^*$ to a commutative
monoid. It turns out that no finite conservative gate set can, for all $n$,
implement all conservative even permutations of $A^n$ without auxiliary bits.
But we provide a finite gate set that can implement all those conservative
permutations that are even within each weight class of $A^n$.


title:
`To Have What They are Having': Portfolio Choice for Mimicking
  Mean-Variance Savers

abstract:
  We consider a group of mean-variance investors with mimicking desire such
that each investor is willing to penalize deviations of his portfolio
composition from compositions of other group members. Penalizing norm
constraints are already applied for statistical improvement of Markowitz
portfolio procedure in order to cope with estimation risk. We relate these
penalties to individuals' wish of social learning and introduce a mutual fund
(investment club) aggregating group member preferences unknown for individual
savers. We derive the explicit analytical solution for the fund's optimal
portfolio weights and show advantages to invest in such a fund for individuals
willing to mimic.


title: Diversity Pulse Shaped Transmission in Ultra-Dense Small Cell Networks

abstract:
  In ultra-dense small cell networks, spatial multiplexing gain is a challenge
because of the different propagation conditions. The channels associated with
different transmitreceive pairs can be highly correlated due to the i) high
probability of line-of-sight (LOS) communication between user equipment (UE)
and base station (BS), and ii) insufficient spacing between antenna elements at
both UE and BS. In this paper, we propose a novel transmission technique titled
Diversity Pulse Shaped Transmission (DPST) to enhance the throughput over the
correlated MIMO channels in an ultra-dense small cell network. The fundamental
of DPST is to shape transmit signals at adjacent antennas with distinct
interpolating filters, introducing pulse shaping diversity. In DPST, each
antenna transmits its own data stream with a relative deterministic time
offset-which must be a fraction of the symbol period-with respect to the
adjacent antenna. The delay is interpolated with the pulse shaped signal
generating a virtual MIMO channel that benefits from increased diversity from
the receiver perspective. To extract the diversity, the receiver must operate
in an over-sampled domain and hence a fractionally spaced equaliser (FSE) is
proposed. The joint impact of DPST and FSE helps the receiver to sense a less
correlated channel, eventually enhancing the UE's throughput. Moreover, in
order to minimise the spatial correlation, we aim to optimise the deterministic
fractional delay. Simulation results show that applying DPST to a correlated
channel can approximately enhance the UE throughput by 1.93x and 3.76x in 2x2
and 4x4 MIMO systems, respectively.


title: Uncertainty Quantification for Optical Model Parameters

abstract:
  Although uncertainty quantification has been making its way into nuclear
theory, these methods have yet to be explored in the context of reaction
theory. For example, it is well known that different parameterizations of the
optical potential can result in different cross sections, but these differences
have not been systematically studied and quantified. The purpose of this work
is to investigate the uncertainties in nuclear reactions that result from
fitting a given model to elastic-scattering data, as well as to study how these
uncertainties propagate to the inelastic and transfer channels. We use
statistical methods to determine a best fit and create corresponding 95\%
confidence bands. A simple model of the process is fit to elastic-scattering
data and used to predict either inelastic or transfer cross sections. In this
initial work, we assume that our model is correct, and the only uncertainties
come from the variation of the fit parameters. We study a number of reactions
involving neutron and deuteron projectiles with energies in the range of 5-25
MeV/u, on targets with mass $A$=12-208. We investigate the correlations between
the parameters in the fit. The case of deuterons on $^{12}$C is discussed in
detail: the elastic-scattering fit and the prediction of $^{12}$C(d,p)$^{13}$C
transfer angular distributions, using both uncorrelated and correlated $\chi^2$
minimization functions. The general features for all cases are compiled in a
systematic manner to identify trends. Our work shows that, in many cases, the
correlated $\chi ^2$ functions (in comparison to the uncorrelated $\chi^2$
functions) provide a more natural parameterization of the process. These
correlated functions do, however, produce broader confidence bands. Further
optimization may require improvement in the models themselves and/or more
information included in the fit.


title: Nonlocal wave turbulence in non-Abelian plasmas

abstract:
  We investigate driven wave turbulence in non-Abelian plasmas, in the
framework of kinetic theory where both elastic and inelastic processes are
considered in the small angle approximation. The gluon spectrum, that forms in
the presence of a steady source, is shown to be controlled by nonlocal
interactions in momentum space, in contrast to the universal
Kolmogorov-Zakharov spectra. Assuming strongly nonlocal interactions, we show
that inelastic processes are dominant in the IR and cause a thermal bath to
form below the forcing scale, as a result of a detailed balance between
radiation and absorption of soft gluons by the hard ones. Above the forcing
scale, the inelastic collision term reduces to an inhomogeneous diffusion-like
equation yielding a spectrum that spreads to the UV as $t^{1/2}$, similarly to
elastic processes. Due to nonlocal interactions the non-universal turbulent
spectrum is not steady and flattens when time goes on toward the thermal
distribution. This analysis is complemented by numerical simulations, where we
observe that in the explored time interval the spectral exponent of the
nonlocal turbulent cascade is close to $-2$ in agreement with simulations of
classical Yang-Mills equations.


title: Twisted compactifications of 3d N = 4 theories and conformal blocks

abstract:
  Three-dimensional N = 4 supersymmetric quantum field theories admit two
topological twists, the Rozansky-Witten twist and its mirror. Either twist can
be used to define a supersymmetric compactification on a Riemann surface and a
corre- sponding space of supersymmetric ground states. These spaces of ground
states can play an interesting role in the Geometric Langlands program. We
propose a description of these spaces as conformal blocks for certain
non-unitary Vertex Operator Algebras and test our conjecture in some important
examples. The two VOAs can be constructed respectively from a UV Lagrangian
description of the N = 4 theory or of its mirror. We further conjecture that
the VOAs associated to an N = 4 SQFT inherit properties of the theory which
only emerge in the IR, such as enhanced global symmetries. Thus knowledge of
the VOAs should allow one to compute the spaces of supersymmetric ground states
for a theory coupled to supersymmetric background connections for the full
symmetry group of the IR SCFT. In particular, we propose a conformal field
theory description of the spaces of ground states for the T[SU(N)] theories.
These theories play a role of S-duality kernel in maximally supersymmetric
SU(N) gauge theory and thus the corresponding spaces of supersymmetric ground
states should provide a kernel for the Geometric Langlands duality for special
unitary groups.


title: Dapper: Data Plane Performance Diagnosis of TCP

abstract:
  With more applications moving to the cloud, cloud providers need to diagnose
performance problems in a timely manner. Offline processing of logs is slow and
inefficient, and instrumenting the end-host network stack would violate the
tenants' rights to manage their own virtual machines (VMs). Instead, our Dapper
system analyzes TCP performance in real time near the end-hosts (e.g., at the
hypervisor, NIC, or top-of-rack switch). Dapper determines whether a connection
is limited by the sender (e.g., a slow server competing for shared resources),
the network (e.g., congestion), or the receiver (e.g., small receive buffer).
Emerging edge devices now offer flexible packet processing at high speed on
commodity hardware, making it possible to monitor TCP performance in the data
plane, at line rate. We use P4 to prototype Dapper and evaluate our design on
real and synthetic traffic. To reduce the data-plane state requirements, we
perform lightweight detection for all connections, followed by heavier-weight
diagnosis just for the troubled connections.


title:
From the divergence between two measures to the shortest path between
  two observables

abstract:
  We consider two independent and stationary measures over $\chi^\mathbb{N}$,
where $\chi$ finite or countable alphabet. For each pair of $n$-strings in the
product space we define $T_n^{(2)}$ as the length of the shortest path
connecting one string to the other where the paths are generating by the
underlying dynamics of the measure. For ergodic measures with positive entropy
we prove that, for almost every pair of realizations $(x,y)$, $T^{(2)}_n/n$
concentrates in one, as $n$ diverges. Under mild extra conditions we prove a
large deviation principle. This principle is linked to a quantity that compute
the similarity between the two measures that we also introduce. We further
prove its existence and other properties. We also show that the fluctuations of
$T_n^{(2)}$ converge (only) in distribution to a non-degenerated distribution.
Several examples are provided for all results.


title:
Effects of income redistribution on the evolution of cooperation in
  spatial public goods games

abstract:
  Income redistribution is the transfer of income from some individuals to
others directly or indirectly by means of social mechanisms, such as taxation,
public services and so on. Employing a spatial public goods game, we study the
influence of income redistribution on the evolution of cooperation. Two kinds
of evolutionary models are constructed, which describe local and global
redistribution of income respectively. In the local model, players have to pay
part of their income after each PGG and the accumulated income is redistributed
to the members. While in the global model, all the players pay part of their
income after engaging in all the local PGGs, which are centered on himself and
his nearest neighbours, and the accumulated income is redistributed to the
whole population. We show that the cooperation prospers significantly with
increasing income expenditure proportion in the local redistribution of income,
while in the global model the situation is opposite. Furthermore, the
cooperation drops dramatically from the maximum curvature point of income
expenditure proportion. In particular, the intermediate critical points are
closely related to the renormalized enhancement factors.


title:
Enhanced Pinning For Vortices in Hyperuniform Substrates and Emergent
  Hyperuniform Vortex States

abstract:
  Disordered hyperuniformity is a state of matter which has isotropic liquid
like properties while simultaneously having crystalline like properties such as
little variation in the density fluctuations over long distances. Such states
arise for the packing of photoreceptor cells in chicken eyes, jammed particle
assemblies, and in nonequilibrium systems. An open question is what possible
applications could utilize properties of hyperuniformity. One of the major
issues for applications of type-II superconductors is how to achieve high
critical currents by preventing the motion or depinning of vortices, so there
is great interest in understanding which pinning site geometries will lead to
the optimal pinning of vortices. Here, using large scale computational
simulations, we show that vortices in a type-II superconductor with a
hyperuniform pinning arrangement exhibit enhanced pinning compared to an equal
number of pinning sites with a purely random arrangement, and that the
enhancement is robust over a wide range of parameters. The stronger pinning
arises in the hyperuniform arrays due to the suppression of pinning density
fluctuations, permitting higher pin occupancy and the reduction of weak links
that lead to easy flow channeling. We also show that in general, in amorphous
vortex states in the presence of either random or hyperuniform pinning arrays,
the vortices themselves exhibit disordered hyperuniformity due to the repulsive
nature of the vortex-vortex interactions.


title: Phenomenological Review on Quark-Gluon Plasma: Concepts vs. Observations

abstract:
  In this review, we present an up-to-date phenomenological summary of research
developments in the physics of the Quark--Gluon Plasma (QGP). A short
historical perspective and theoretical motivation for this rapidly developing
field of contemporary particle physics is provided. In addition, we introduce
and discuss the role of the quantum chromodynamics (QCD) ground state,
non-perturbative and lattice QCD results on the QGP properties, as well as the
transport models used to make a connection between theory and experiment. The
experimental part presents the selected results on bulk observables, hard and
penetrating probes obtained in the ultra-relativistic heavy-ion experiments
carried out at the Brookhaven National Laboratory Relativistic Heavy Ion
Collider (BNL RHIC) and CERN Super Proton Synchrotron (SPS) and Large Hadron
Collider (LHC) accelerators. We also give a brief overview of new developments
related to the ongoing searches of the QCD critical point and to the
collectivity in small ($p+p$ and $p+A$) systems.


title:
GFA: Exploratory Analysis of Multiple Data Sources with Group Factor
  Analysis

abstract:
  The R package GFA provides a full pipeline for factor analysis of multiple
data sources that are represented as matrices with co-occurring samples. It
allows learning dependencies between subsets of the data sources, decomposed
into latent factors. The package also implements sparse priors for the
factorization, providing interpretable biclusters of the multi-source data


title:
Parsimony, model adequacy and periodic correlation in forecasting time
  series

abstract:
  The merits of the modelling philosophy of Box \& Jenkins (1970) are
illustrated with a summary of our recent work on seasonal river flow
forecasting. Specifically, this work demonstrates that the principle of
parsimony, which has been questioned by several authors recently, is helpful in
selecting the best model for forecasting seasonal river flow. Our work also
demonstrates the importance of model adequacy. An adequate model for seasonal
river flow must incorporate seasonal periodic correlation. The usual
autoregressive-moving average (ARMA) and seasonal ARMA models are not adequate
in this respect for seasonal river flow time series. A new diagnostic check,
for detecting periodic correlation in fitted ARMA models is developed in this
paper. This diagnostic check is recommended for routine use when fitting
seasonal ARMA models. It is shown that this diagnostic check indicates that
many seasonal economic time series also exhibit periodic correlation. Since the
standard forecasting methods are inadequate on this account, it can be
concluded that in many cases, the forecasts produced are sub-optimal. Finally,
a limitation of the arbitrary combination of forecasts is also illustrated.
Combining forecasts from an adequate parsimonious model with an inadequate
model did not improve the forecasts whereas combining the two forecasts of two
inadequate models did yield an improvement in forecasting performance. These
findings also support the model building philosophy of Box \& Jenkins. The
non-intuitive findings of Newbold \& Granger (1974) and Winkler \& Makridakis
(1983) that the apparent arbitrary combination of forecasts from similar models
will lead to forecasting performance is not supported by our case study with
river flow forecasting.


title: New Noise Subtraction Methods in Lattice QCD

abstract:
  Noise subtraction techniques can help reduce the statistical uncertainty in
the extraction of hard to detect signals. We describe new noise subtraction
methods in Lattice QCD which apply to disconnected diagram evaluations. Some of
the noise suppression techniques include polynomial quark matrix methods,
eigenspectrum deflation methods, and combination methods. Our most promising
technique combines polynomial and Hermitian deflation subtraction methods. The
overall goal is to improve the efficiency of Lattice QCD noise method
algorithms.


title: Modeling of novel lateral AlGaAs/GaAs quantum well solar cell

abstract:
  In this paper, a novel lateral quantum well solar cell has been introduced,
and the structural parameters effects of these nano-structures on the
performance of the device have been investigated. For modeling, the continuity
equation has been solved in the quasi neutral regions. However, to analyze the
quantum wells' effects, first the Schrodinger and Poisson equations have been
solved self-consistently. To find the absorption coefficient derived from the
Fermi's golden rule, the obtained Eigen states and energies and also the
effects of multilayers using Transfer Matrix Method have been employed. Then,
to find the solar cell performance parameters, all radiative and non-radiative
recombinations have been accounted. It is found that modifying different
geometrical parameters, including the thickness of the system, the widths of
the wells and barriers, and also some structural parameters such as the
barriers' mole fraction could noticeably influence the characteristics of the
device. So, optimizing these parameters is necessary to obtain a good
efficiency.


title:
FU Orionis outbursts, preferential recondensation of water ice, and the
  formation of giant planets

abstract:
  Ices, including water ice, prefer to recondense onto pre-existing nuclei
rather than spontaneously forming grains from a cloud of vapor. Interestingly,
different potential recondensation nuclei have very different propensities to
actually nucleate water ice at the temperatures associated with freeze-out in
protoplanetary discs. Therefore, if a region in a disc is warmed and then
recooled, water vapor should not be expected to refreeze evenly onto all
available grains. Instead it will preferentially recondense onto the most
favorable grains. When the recooling is slow enough, only the most favorable
grains will nucleate ice, allowing them to recondense thick ice mantles. We
quantify the conditions for preferential recondensation to rapidly create
pebble-sized grains in protoplanetary discs and show that FU Orionis type
outbursts have the appropriate cooling rates to drive pebble creation in a band
about 5 astronomical units wide outside of the quiescent frost line from
approximately Jupiter's orbit to Saturn's (about 4 to 10 au). Those pebbles
could be of the appropriate size to proceed to planetesimal formation via the
Streaming Instability, or to contribute to the growth of planetesimals through
pebble accretion. We suggest that this phenomenon contributed to the formation
of the gas giants in our own Solar System.


title:
Quantum critical scaling in the disordered itinerant ferromagnet
  UCo$_{1-x}$Fe$_x$Ge

abstract:
  Belitz-Kirkpatrick-Vojta (BKV) theory shows in excellent agreement with
experiment that ferromagnetic quantum phase transitions (QPTs) in clean metals
are generally first-order due to the coupling of the magnetization to
electronic soft modes, in contrast to the classical analogue that is an
archetypical second-order phase transition. For disordered metals BKV theory
predicts that the second order nature of the QPT is restored because the
electronic soft modes change their nature from ballistic to diffusive. Our
low-temperature magnetization study identifies the ferromagnetic QPT in the
disordered metal UCo$_{1-x}$Fe$_x$Ge as the first clear example that exhibits
the associated critical exponents predicted by BKV theory.


title: Topology and Geometry of Half-Rectified Network Optimization

abstract:
  The loss surface of deep neural networks has recently attracted interest in
the optimization and machine learning communities as a prime example of
high-dimensional non-convex problem. Some insights were recently gained using
spin glass models and mean-field approximations, but at the expense of strongly
simplifying the nonlinear nature of the model.
  In this work, we do not make any such assumption and study conditions on the
data distribution and model architecture that prevent the existence of bad
local minima. Our theoretical work quantifies and formalizes two important
\emph{folklore} facts: (i) the landscape of deep linear networks has a
radically different topology from that of deep half-rectified ones, and (ii)
that the energy landscape in the non-linear case is fundamentally controlled by
the interplay between the smoothness of the data distribution and model
over-parametrization. Our main theoretical contribution is to prove that
half-rectified single layer networks are asymptotically connected, and we
provide explicit bounds that reveal the aforementioned interplay.
  The conditioning of gradient descent is the next challenge we address. We
study this question through the geometry of the level sets, and we introduce an
algorithm to efficiently estimate the regularity of such sets on large-scale
networks. Our empirical results show that these level sets remain connected
throughout all the learning phase, suggesting a near convex behavior, but they
become exponentially more curvy as the energy level decays, in accordance to
what is observed in practice with very low curvature attractors.


title: Classification with Ultrahigh-Dimensional Features

abstract:
  Although much progress has been made in classification with high-dimensional
features \citep{Fan_Fan:2008, JGuo:2010, CaiSun:2014, PRXu:2014},
classification with ultrahigh-dimensional features, wherein the features much
outnumber the sample size, defies most existing work. This paper introduces a
novel and computationally feasible multivariate screening and classification
method for ultrahigh-dimensional data. Leveraging inter-feature correlations,
the proposed method enables detection of marginally weak and sparse signals and
recovery of the true informative feature set, and achieves asymptotic optimal
misclassification rates. We also show that the proposed procedure provides more
powerful discovery boundaries compared to those in \citet{CaiSun:2014} and
\citet{JJin:2009}. The performance of the proposed procedure is evaluated using
simulation studies and demonstrated via classification of patients with
different post-transplantation renal functional types.


title: The set splittability problem

abstract:
  The set splittability problem is the following: given a finite collection of
finite sets, does there exits a single set that contains exactly half the
elements from each set in the collection? (If a set has odd size, we allow the
floor or ceiling.) It is natural to study the set splittability problem in the
context of combinatorial discrepancy theory and its applications, since a
collection is splittable if and only if it has discrepancy $\leq1$.
  We introduce a natural generalization of splittability problem called the
$p$-splittability problem, where we replace the fraction $\frac12$ in the
definition with an arbitrary fraction $p\in(0,1)$. We first show that the
$p$-splittability problem is NP-complete. We then give several criteria for
$p$-splittability, including a complete characterization of $p$-splittability
for three or fewer sets ($p$ arbitrary), and for four or fewer sets
($p=\frac12$). Finally we prove the asymptotic prevalence of splittability over
unsplittability in an appropriate sense.


title:
Approximation by partial isometries and symmetric approximation of
  finite frames

abstract:
  We solve the problem of best approximation by partial isometries of given
rank to an arbitrary rectangular matrix, when the distance is measured in any
unitarily invariant norm. In the case where the norm is strictly convex, we
parametrize all the solutions. In particular, this allow us to give a simple
necessary and sufficient condition for uniqueness. We then apply these results
to solve the global problem of approximation by partial isometries, and to
extend the notion of symmetric approximation of frames introduced in M. Frank,
V. Paulsen, T. Tiballi, Symmetric Approximation of frames and bases in Hilbert
Spaces, Trans. Amer. Math. Soc. 354 (2002), 777-793. In addition, we
characterize symmetric approximations of frames belonging to a prescribed
subspace.


title:
Coupling of $t\bar t$ and $\gamma\gamma$ with a strongly interacting
  Electroweak Symmetry Breaking Sector

abstract:
  We report the coupling of an external $\gamma\gamma$ or $t\bar t$ state to a
strongly interacting EWSBS satisfying unitarity. We exploit perturbation theory
for those coupling of the external state, whereas the EWSBS is taken as
strongly interacting. We use a modified version of the IAM unitarization
procedure to model such a strongly interacting regime. The matrix elements
$V_LV_L\to V_LV_L$, $V_LV_L\leftrightarrow hh$, $hh\to hh$,
$V_LV_L\leftrightarrow\{\gamma\gamma,t\bar t\}$, and
$hh\leftrightarrow\{\gamma\gamma,t\bar t\}$ are all computed to NLO in
perturbation theory with the Nonlinear Effective Field Theory of the EWSBS,
within the Equivalence Theorem. This allows us to describe resonances of the
electroweak sector that may be found at the LHC and their effect on other
channels such as $\gamma\gamma$ or $t\bar t$ where they may be discovered.


title: Cosmological neutrino simulations at extreme scale

abstract:
  Constraining neutrino mass remains an elusive challenge in modern physics.
Precision measurements are expected from several upcoming cosmological probes
of large-scale structure. Achieving this goal relies on an equal level of
precision from theoretical predictions of neutrino clustering. Numerical
simulations of the non-linear evolution of cold dark matter and neutrinos play
a pivotal role in this process. We incorporate neutrinos into the cosmological
N-body code CUBEP3M and discuss the challenges associated with pushing to the
extreme scales demanded by the neutrino problem. We highlight code
optimizations made to exploit modern high performance computing architectures
and present a novel method of data compression that reduces the phase-space
particle footprint from 24 bytes in single precision to roughly 9 bytes. We
scale the neutrino problem to the Tianhe-2 supercomputer and provide details of
our production run, named TianNu, which uses 86% of the machine (13,824 compute
nodes). With a total of 2.97 trillion particles, TianNu is currently the
world's largest cosmological N-body simulation and improves upon previous
neutrino simulations by two orders of magnitude in scale. We finish with a
discussion of the unanticipated computational challenges that were encountered
during the TianNu runtime.


title:
Scalable Holistic Analysis of Multi-Source, Data-Intensive Problems
  Using Multilayered Networks

abstract:
  Holistic analysis of many real-world problems are based on data collected
from multiple sources contributing to some aspect of that problem. The word
fusion has also been used in the literature for such problems involving
disparate data types. Holistically understanding traffic patterns, causes of
accidents, bombings, terrorist planning and many natural phenomenon such as
storms, earthquakes fall into this category. Some may have real-time
requirements and some may need to be analyzed after the fact (post-mortem or
forensic analysis.) What is common for all these problems is that the amount
and types of data associated with the event. Data may also be incomplete and
trustworthiness of sources may also vary. Currently, manual and ad-hoc
approaches are used in aggregating data in different ways for analyzing and
understanding these problems.
  In this paper, we approach this problem in a novel way using multilayered
networks. We identify features of a central event and propose a network layer
for each feature. This approach allows us to study the effect of each feature
independently and its impact on the event. We also establish that the proposed
approach allows us to compose these features in arbitrary ways (without loss of
information) to analyze their combined effect. Additionally, formulation of
relationships (e.g., distance measure for a single feature instead of several
at the same time) is simpler. Further, computations can be done once on each
layer in this approach and reused for mixing and matching the features for
aggregate impacts and "what if" scenarios to understand the problem
holistically. This has been demonstrated by recreating the communities for the
AND-Composed network by using the communities of the individual layers.
  We believe that techniques proposed here make an important contribution to
the nascent yet fast growing area of data fusion.


title:
Automated Generation of Multilingual Clusters for the Evaluation of
  Distributed Representations

abstract:
  We propose a language-agnostic way of automatically generating sets of
semantically similar clusters of entities along with sets of "outlier"
elements, which may then be used to perform an intrinsic evaluation of word
embeddings in the outlier detection task. We used our methodology to create a
gold-standard dataset, which we call WikiSem500, and evaluated multiple
state-of-the-art embeddings. The results show a correlation between performance
on this dataset and performance on sentiment analysis.


title:
Web of Science: showing a bug today that can mislead scientific research
  output's prediction

abstract:
  As it happened in all domains of human activities, economic issues and the
increase of people working in scientific research have altered the way
scientific production is evaluated so as the objectives of performing the
evaluation. Introduced in 2005 by J. E. Hirsch as an indicator able to measure
individual scientific output not only in terms of quantity, but also in terms
of quality, h index has spread throughout the world. In 2007, Hirsch proposed
its adoption also as the best to predict future scientific achievement and,
consequently, a useful guide for investments in research and for institutions
when hiring members for their scientific staff. Since then, several authors
have also been using the Thomson ISI Web of Science database to develop their
proposals for evaluating research output. Here, using a software we have
developed, we analyse more than 100 thousand articles and show that a subtle
flaw in Web of Science can inflate the results of info collected, therefore
compromising the exactness and, consequently, the effectiveness of Hirsch's
proposal and its variations.


title:
More Than Peer Production: Fanfiction Communities as Sites of
  Distributed Mentoring

abstract:
  From Harry Potter to American Horror Story, fanfiction is extremely popular
among young people. Sites such as Fanfiction.net host millions of stories, with
thousands more posted each day. Enthusiasts are sharing their writing and
reading stories written by others. Exactly how does a generation known more for
videogame expertise than long-form writing become so engaged in reading and
writing in these communities? Via a nine-month ethnographic investigation of
fanfiction communities that included participant observation, interviews, a
thematic analysis of 4,500 reader reviews and an in-depth case study of a
discussion group, we found that members of fanfiction communities spontaneously
mentor each other in open forums, and that this mentoring builds upon previous
interactions in a way that is distinct from traditional forms of mentoring and
made possible by the affordances of networked publics. This work extends and
develops the theory of distributed mentoring. Our findings illustrate how
distributed mentoring supports fanfiction authors as they work to develop their
writing skills. We believe distributed mentoring holds potential for supporting
learning in a variety of formal and informal learning environments.


title:
Symmetric high order Gautschi-type exponential wave integrators
  pseudospectral method for the nonlinear Klein-Gordon equation in the
  nonrelativistic limit regime

abstract:
  A group of high order Gautschi-type exponential wave integrators (EWIs)
Fourier pseudospectral method are proposed and analyzed for solving the
nonlinear Klein-Gordon equation (KGE) in the nonrelativistic limit regime,
where a parameter $0<\varepsilon\ll1$ which is inversely proportional to the
speed of light, makes the solution propagate waves with wavelength
$O(\varepsilon^2)$ in time and $O(1)$ in space. With the Fourier pseudospectral
method to discretize the KGE in space, we propose a group of EWIs with designed
Gautschi's type quadratures for the temporal integrations, which can offer any
intended even order of accuracy provided that the solution is smooth enough,
while all the current existing EWIs offer at most second order accuracy. The
scheme is explicit, time symmetric and rigorous error estimates show the
meshing strategy of the proposed method is time step $\tau=O(\varepsilon^2)$
and mesh size $h=O(1)$ as $0<\varepsilon\ll1$, which is `optimal' among all
classical numerical methods towards solving the KGE directly in the limit
regime, and which also distinguish our methods from other high order approaches
such as Runge-Kutta methods which require $\tau=O(\varepsilon^3)$. Numerical
experiments with comparisons are done to confirm the error bound and show the
superiority of the proposed methods over existing classical numerical methods.


title: Holonomy groups of Lorentzian manifolds

abstract:
  In this paper, a survey of the recent results about the classification of the
connected holonomy groups of the Lorentzian manifolds is given. A
simplification of the construction of the Lorentzian metrics with all possible
connected holonomy groups is obtained. As the applications, the Einstein
equation, Lorentzian manifolds with parallel and recurrent spinor fields,
conformally flat Walker metrics and the classification of 2-symmetric
Lorentzian manifolds are considered.


title:
Origin of multistate resistive switching in Ti/manganite/Si$O_x$/Si
  heterostructures

abstract:
  We report on the growth and characterization of
Ti/$La_{1/3}$$Ca_{2/3}$Mn$O_3$/Si$O_x$/n-Si memristive devices. We demonstrate
that using current as electrical stimulus unveils an intermediate resistance
state, in addition to the usual high and low resistance states that are
observed in standard voltage controlled experiments. Based on thorough
electrical characterization (impedance spectroscopy, current-voltage curves
analysis), we disclose the contribution of three different microscopic regions
of the device to the transport properties: an ohmic incomplete metallic
filament, a thin manganite layer below the filament tip exhibiting
Poole-Frenkel like conduction, and the SiOx layer with an electrical response
well characterized by a Child-Langmuir law. Our results suggest that the
existence of the SiOx layer plays a key role in the stabilization of the
intermediate resistance level, indicating that the combination of two or more
active RS oxides adds functionalities in relation to single-oxide devices. We
understand that these multilevel devices are interesting and promising as their
fabrication procedure is rather simple and they are fully compatible with
standard Si-based electronics.


title: QBF Solving by Counterexample-guided Expansion

abstract:
  We introduce a novel generalization of Counterexample-Guided Inductive
Synthesis (CEGIS) and instantiate it to yield a novel, competitive algorithm
for solving Quantified Boolean Formulas (QBF). Current QBF solvers based on
counterexample-guided expansion use a recursive approach which scales poorly
with the number of quantifier alternations. Our generalization of CEGIS removes
the need for this recursive approach, and we instantiate it to yield a simple
and efficient algorithm for QBF solving. Lastly, this research is supported by
a competitive, though straightforward, implementation of the algorithm, making
it possible to study the practical impact of our algorithm design decisions,
along with various optimizations.


title: On the de Rham-Wu decomposition for Riemannian and Lorentzian manifolds

abstract:
  It is explained how to find the de~Rham decomposition of a Riemannian
manifold and the Wu decomposition of a Lorentzian manifold. For that it is
enough to find parallel symmetric bilinear forms on the manifold, and do some
linear algebra. This result will allow to compute the connected holonomy group
of an arbitrary Riemannian or Lorentzian manifold.


title:
A Review on Mechanics and Mechanical Properties of 2D Materials -
  Graphene and Beyond

abstract:
  Since the first successful synthesis of graphene just over a decade ago, a
variety of two-dimensional (2D) materials (e.g., transition
metal-dichalcogenides, hexagonal boron-nitride, etc.) have been discovered.
Among the many unique and attractive properties of 2D materials, mechanical
properties play important roles in manufacturing, integration and performance
for their potential applications. Mechanics is indispensable in the study of
mechanical properties, both experimentally and theoretically. The coupling
between the mechanical and other physical properties (thermal, electronic,
optical) is also of great interest in exploring novel applications, where
mechanics has to be combined with condensed matter physics to establish a
scalable theoretical framework. Moreover, mechanical interactions between 2D
materials and various substrate materials are essential for integrated device
applications of 2D materials, for which the mechanics of interfaces (adhesion
and friction) has to be developed for the 2D materials. Here we review recent
theoretical and experimental works related to mechanics and mechanical
properties of 2D materials. While graphene is the most studied 2D material to
date, we expect continual growth of interest in the mechanics of other 2D
materials beyond graphene.


title:
Dirac type operators on the quantum solid torus with global boundary
  conditions

abstract:
  We define a noncommutative space we call the quantum solid torus. It is an
example of a noncommutative manifold with a noncommutative boundary. We study
quantum Dirac type operators subject to Atiyah-Patodi-Singer like boundary
conditions on the quantum solid torus. We show that such operators have compact
inverse, which means that the corresponding boundary value problem is elliptic.


title:
Spatiotemporal dynamics and reliable computations in recurrent spiking
  neural networks

abstract:
  Randomly connected networks of excitatory and inhibitory spiking neurons
provide a parsimonious model of neural variability, but are notoriously
unreliable for performing computations. We show that this difficulty is
overcome by incorporating the well-documented dependence of connection
probability on distance. Spatially extended spiking networks exhibit
symmetry-breaking bifurcations and generate spatiotemporal patterns that can be
trained to perform dynamical computations.


title:
Social influence makes self-interested crowds smarter: an optimal
  control perspective

abstract:
  It is very common to observe crowds of individuals solving similar problems
with similar information in a largely independent manner. We argue here that
crowds can become "smarter," i.e., more efficient and robust, by partially
following the average opinion. This observation runs counter to the widely
accepted claim that the wisdom of crowds deteriorates with social influence.
The key difference is that individuals are self-interested and hence will
reject feedbacks that do not improve their performance. We propose a
control-theoretic methodology to compute the degree of social influence, i.e.,
the level to which one accepts the population feedback, that optimizes
performance. We conducted an experiment with human subjects ($N = 194$), where
the participants were first asked to solve an optimization problem
independently, i.e., under no social influence. Our theoretical methodology
estimates a $30\%$ degree of social influence to be optimal, resulting in a
$29\%$ improvement in the crowd's performance. We then let the same cohort
solve a new problem and have access to the average opinion. Surprisingly, we
find the average degree of social influence in the cohort to be $32\%$ with a
$29\%$ improvement in performance: In other words, the crowd self-organized
into a near-optimal setting. We believe this new paradigm for making crowds
"smarter" has the potential for making a significant impact on a diverse set of
fields including population health to government planning. We include a case
study to show how a crowd of states can collectively learn the level of
taxation and expenditure that optimizes economic growth.


title: How hard is the tensor rank?

abstract:
  We investigate the computational complexity of tensor rank, a concept that
plays fundamental role in different topics of modern applied mathematics. For
tensors over any integral domain, we prove that the rank problem is polynomial
time equivalent to solving a system of polynomial equations over this integral
domain. Our result gives a complete description of the algorithmic complexity
of tensor rank and allows one to solve several known open problems. In
particular, the tensor rank over $\mathbb{Z}$ turns out to be undecidable,
which answers the question posed by Gonzalez and Ja'Ja' in 1980. We generalize
our result and prove that the symmetric rank admits a similar description of
computational complexity as the one we give for usual rank. In particular,
computing the symmetric rank of a rational tensor is shown to be NP-hard, which
proves a recent conjecture of Hillar and Lim. As a byproduct of our approach,
we get a similar characterization of the algorithmic complexity of the minimal
rank matrix completion problem, which gives a complete answer to the question
discussed in 1999 by Buss, Frandsen, and Shallit.


title:
Photo-z-SQL: integrated, flexible photometric redshift computation in a
  database

abstract:
  We present a flexible template-based photometric redshift estimation
framework, implemented in C#, that can be seamlessly integrated into a SQL
database (or DB) server and executed on-demand in SQL. The DB integration
eliminates the need to move large photometric datasets outside a database for
redshift estimation, and utilizes the computational capabilities of DB
hardware. The code is able to perform both maximum likelihood and Bayesian
estimation, and can handle inputs of variable photometric filter sets and
corresponding broad-band magnitudes. It is possible to take into account the
full covariance matrix between filters, and filter zero points can be
empirically calibrated using measurements with given redshifts. The list of
spectral templates and the prior can be specified flexibly, and the expensive
synthetic magnitude computations are done via lazy evaluation, coupled with a
caching of results. Parallel execution is fully supported. For large upcoming
photometric surveys such as the LSST, the ability to perform in-place photo-z
calculation would be a significant advantage. Also, the efficient handling of
variable filter sets is a necessity for heterogeneous databases, for example
the Hubble Source Catalog, and for cross-match services such as SkyQuery. We
illustrate the performance of our code on two reference photo-z estimation
testing datasets, and provide an analysis of execution time and scalability
with respect to different configurations. The code is available for download at
https://github.com/beckrob/Photo-z-SQL.


title: Change-point detection for L\'evy processes

abstract:
  Since the work of Page in the 1950s, the problem of detecting an abrupt
change in the distribution of stochastic processes has received a great deal of
attention. In particular, a deep connection has been established between
Lorden's minimax approach to change-point detection and the widely used CUSUM
procedure, first for discrete-time processes, and subsequently for some of
their continuous-time counterparts. However, results for processes with jumps
are still scarce, while the practical importance of such processes has
escalated since the turn of the century. In this work we consider the problem
of detecting a change in the distribution of continuous-time processes with
independent and stationary increments, i.e. L\'evy processes, and our main
result shows that CUSUM is indeed optimal in Lorden's sense. This is the most
natural continuous-time analogue of the seminal work of Moustakides for
sequentially observed random variables that are assumed to be i.i.d. before and
after the change-point. From a practical perspective, the approach we adopt is
appealing as it consists in approximating the continuous-time problem by a
suitable sequence of change-point problems with equispaced sampling points, and
for which a CUSUM procedure is shown to be optimal.


title: $\sigma$-PBW Extensions of Skew Armendariz Rings

abstract:
  The aim of this paper is to investigate a general notion of $\sigma$-PBW
extensions over Armendariz rings. As an application, the properties of being
Baer, quasi-Baer, p.p. and p.q.-Baer are established for these extensions. We
generalize several results in the literature for Ore extensions of injective
type and skew PBW extensions.


title: A Liouville Type Theorem for Steady-State Navier-Stokes Equations

abstract:
  A Liouville type theorem is proven for the steady-state Navier-Stokes
equations. It follows from the corresponding theorem on the Stokes equations
with the drift. The drift is supposed to belong to a certain Morrey space.


title: Improved spread-location visualization

abstract:
  The spread-location plot has often been used as a diagnostic plot suitable
for many types of fitted statistical models. The spread-location plot which
plots the absolute residual or square-root absolute residual versus fitted
value along with a robust loess smooth is a useful replacement for the
customary practice of plotting residuals versus fitted values. In this note, we
show that neither absolute residual or square-root absolute residual is always
appropriate for error distributions likely to be encountered in actual
applications. Hence we recommend a multipanel display showing a suitable
transformation of the absolute residual versus fitted value along with a
boxplot to judge the symmetry achieved by the transformation. We conclude with
an illustrative example.


title: Struwe-like solutions for the Stochastic Harmonic Map Flow

abstract:
  We give a new result on the well-posedness of the two-dimensional Stochastic
Harmonic Map flow, whose study is motivated by the Landau-Lifshitz-Gilbert
model for thermal fluctuations in micromagnetics. We construct strong solutions
that belong locally to the spaces $C([s,t);H^1)\cap L^2([s,t);H^2)$, $0\leq
s<t\leq T$. It that sense, these maps are a counterpart of the so-called
"Struwe solutions" of the deterministic model. We also give a natural criterion
of uniqueness that extends A.\ Freire's Theorem to the stochastic case. Both
results are obtained under the condition that the noise term has a trace-class
covariance in space.


title: An on-chip architecture for self-homodyned nonclassical light

abstract:
  In the last decade, there has been remarkable progress on the practical
integration of on-chip quantum photonic devices yet quantum state generators
remain an outstanding challenge. Simultaneously, the quantum-dot
photonic-crystal-resonator platform has demonstrated a versatility for creating
nonclassical light with tunable quantum statistics, thanks to a newly
discovered self-homodyning interferometric effect that preferentially selects
the quantum light over the classical light when using an optimally tuned Fano
resonance. In this work, we propose a general structure for the cavity quantum
electrodynamical generation of quantum states from a waveguide-integrated
version of the quantum-dot photonic-crystal-resonator platform, which is
specifically tailored for preferential quantum state transmission. We support
our results with rigorous Finite-Difference Time-Domain and quantum optical
simulations, and show how our proposed device can serve as a robust generator
of highly pure single- and even multi-photon states.


title: Thermal Brillouin noise observed in silicon optomechanical waveguide

abstract:
  Stimulated Brillouin scattering was recently observed in nanoscale silicon
waveguides. Surprisingly, thermally-driven photon-phonon conversion in these
structures had not yet been reported. Here, we inject an optical probe in a
suspended silicon waveguide and measure its phase fluctuations at the output.
We observe mechanical resonances around 8 GHz with a scattering efficiency of
$10^{-5} \, \text{m}^{-1}$ and a signal-to-noise ratio of 2. The observations
are in agreement with a theory of noise in these waveguides as well as with
stimulated measurements. Our scheme may simplify measurements of mechanical
signatures in nanoscale waveguides and is a step towards a better grasp of
thermal noise in these new continuum optomechanical systems.


title:
Universality and the collapse of multifractality in Barkhausen
  avalanches

abstract:
  Barkhausen effect in ferromagnetic materials provides an excellent area for
investigating scaling phenomena found in disordered systems exhibiting
crackling noise. The critical dynamics is characterized by random pulses or
avalanches with scale-invariant properties, power-law distributions, and
universal features. However, the traditional Barkhausen avalanches statistics
may not be sufficient to fully characterize the complex temporal correlation of
the magnetic domain walls dynamics. Here we go beyond power laws and focus on
the multifractal scenario to quantify the temporal scaling characteristics of
Barkhausen avalanches in polycrystalline and amorphous ferromagnetic films with
thicknesses from $50$ nm to $1000$ nm. We show that the multifractal properties
are dependent on the film thickness, and insensitive to the structural
character of the materials. Further, we observe for the first time the collapse
of the multifractality in the domain walls dynamics as the thickness is
reduced, and the multifractal behavior gives place to a monofractal one over
the entire range of time scales. The reorganization in the temporal scaling
characteristics of Barkhausen avalanches is understood as an universal
restructuring associated to the dimensional transition, from three to
two-dimensional magnetization dynamics.


title: A Two Pronged Progress in Structured Dense Matrix Multiplication

abstract:
  Matrix-vector multiplication is one of the most fundamental computing
primitives. Given a matrix $A\in\mathbb{F}^{N\times N}$ and a vector $b$, it is
known that in the worst case $\Theta(N^2)$ operations over $\mathbb{F}$ are
needed to compute $Ab$. A broad question is to identify classes of structured
dense matrices that can be represented with $O(N)$ parameters, and for which
matrix-vector multiplication can be performed sub-quadratically. One such class
of structured matrices is the orthogonal polynomial transforms, whose rows
correspond to a family of orthogonal polynomials. Other well known classes
include the Toeplitz, Hankel, Vandermonde, Cauchy matrices and their extensions
that are all special cases of a ldisplacement rank property. In this paper, we
make progress on two fronts:
  1. We introduce the notion of recurrence width of matrices. For matrices with
constant recurrence width, we design algorithms to compute $Ab$ and $A^Tb$ with
a near-linear number of operations. This notion of width is finer than all the
above classes of structured matrices and thus we can compute multiplication for
all of them using the same core algorithm.
  2. We additionally adapt this algorithm to an algorithm for a much more
general class of matrices with displacement structure: those with low
displacement rank with respect to quasiseparable matrices. This class includes
Toeplitz-plus-Hankel-like matrices, Discrete Cosine/Sine Transforms, and more,
and captures all previously known matrices with displacement structure that we
are aware of under a unified parametrization and algorithm.
  Our work unifies, generalizes, and simplifies existing state-of-the-art
results in structured matrix-vector multiplication. Finally, we show how
applications in areas such as multipoint evaluations of multivariate
polynomials can be reduced to problems involving low recurrence width matrices.


title: The additive structure of the squares inside rings

abstract:
  When defining the amount of additive structure on a set it is often
convenient to consider certain sumsets; Calculating the cardinality of these
sumsets can elucidate the set's underlying structure. We begin by investigating
finite sets of perfect squares and associated sumsets. We reveal how arithmetic
progressions efficiently reduce the cardinality of sumsets and provide
estimates for the minimum size, taking advantage of the additive structure that
arithmetic progressions provide. We then generalise the problem to arbitrary
rings and achieve satisfactory estimates for the case of squares in finite
fields of prime order. Finally, for sufficiently small finite fields we
computationally calculate the minimum for all prime orders.


title:
Flat ORAM: A Simplified Write-Only Oblivious RAM Construction for Secure
  Processors

abstract:
  Oblivious RAM (ORAM) is a cryptographic primitive which obfuscates the access
patterns to a storage thereby preventing privacy leakage. So far in the current
literature, only `fully functional' ORAMs are widely studied which can protect,
at a cost of considerable performance penalty, against the strong adversaries
who can monitor all read and write operations. However, recent research has
shown that information can still be leaked even if only the write access
pattern (not reads) is visible to the adversary. For such weaker adversaries, a
fully functional ORAM turns out to be an overkill causing unnecessary
overheads. Instead, a simple `write-only' ORAM is sufficient, and, more
interestingly, is preferred as it can offer far more performance and energy
efficiency than a fully functional ORAM.
  In this work, we present Flat ORAM: an efficient write-only ORAM scheme which
outperforms the closest existing write-only ORAM called HIVE. HIVE suffers from
performance bottlenecks while managing the memory occupancy information vital
for correctness of the protocol. Flat ORAM resolves these bottlenecks by
introducing a simple idea of Occupancy Map (OccMap) which efficiently manages
the memory occupancy information resulting in far better performance. Our
simulation results show that, on average, Flat ORAM only incurs a moderate
slowdown of $3\times$ over the insecure DRAM for memory intensive benchmarks
among Splash2 and $1.6\times$ for SPEC06. Compared to HIVE, Flat ORAM offers
$50\%$ performance gain on average and up to $80\%$ energy savings.


title:
Crowd Guilds: Worker-led Reputation and Feedback on Crowdsourcing
  Platforms

abstract:
  Crowd workers are distributed and decentralized. While decentralization is
designed to utilize independent judgment to promote high-quality results, it
paradoxically undercuts behaviors and institutions that are critical to
high-quality work. Reputation is one central example: crowdsourcing systems
depend on reputation scores from decentralized workers and requesters, but
these scores are notoriously inflated and uninformative. In this paper, we draw
inspiration from historical worker guilds (e.g., in the silk trade) to design
and implement crowd guilds: centralized groups of crowd workers who
collectively certify each other's quality through double-blind peer assessment.
A two-week field experiment compared crowd guilds to a traditional
decentralized crowd work model. Crowd guilds produced reputation signals more
strongly correlated with ground-truth worker quality than signals available on
current crowd working platforms, and more accurate than in the traditional
model.


title:
Null Angular Momentum and Weak KAM Solutions of the Newtonian $N$-Body
  Problem

abstract:
  In [Arch. Ration. Mech. Anal. 213 (2014), 981-991] it has been proved that in
the Newtonian $N$-body problem, given a minimal central configuration $a$ and
an arbitrary configuration $x$, there exists a completely parabolic orbit
starting on $x$ and asymptotic to the homothetic parabolic motion of $a$,
furthermore such an orbit is a free time minimizer of the action functional. In
this article we extend this result in abundance of completely parabolic motions
by proving that under the same hypothesis it is possible to get that the
completely parabolic motion starting at $x$ has zero angular momentum. We
achieve this by characterizing the rotation invariant weak KAM solutions as
those defining a lamination on the configuration space by free time minimizers
with zero angular momentum.


title:
Averaging of nonlinear Schr\"odinger equations with strong magnetic
  confinement

abstract:
  We consider the dynamics of nonlinear Schr\"odinger equations with strong
constant magnetic fields. In an asymptotic scaling limit the system exhibits a
purely magnetic confinement, based on the spectral properties of the Landau
Hamiltonian. Using an averaging technique we derive an associated effective
description via an averaged model of nonlinear Schr\"odinger type. In a special
case this also yields a derivation of the LLL equation.