title: Balanced independent sets in graphs omitting large cliques

abstract:
  Our goal is to investigate a close relative of the independent transversal
problem in the class of infinite $K_n$-free graphs: we show that for any
infinite $K_n$-free graph $G=(V,E)$ and $m\in \mathbb N$ there is a minimal
$r=r(G,m)$ such that for any balanced $r$-colouring of the vertices of $G$ one
can find an independent set which meets at least $m$ colour classes in a set of
size $|V|$. Answering a conjecture of S. Thomass\'e, we express the exact value
of $r(H_n,m)$ (using Ramsey-numbers for finite digraphs), where $H_n$ is
Henson's countable universal homogeneous $K_n$-free graph. In turn, we deduce a
new partition property of $H_n$ regarding balanced embeddings of bipartite
graphs: for any finite bipartite $G$ with bipartition $A,B$, if the vertices of
$H_n$ are partitioned into two infinite classes then there is an induced copy
of $G$ in $H_n$ such that the images of $A$ and $B$ are contained in different
classes.


title:
Measurements of charm mixing and $C\!P$ violation using $D^0 \to K^\pm
  \pi^\mp$ decays

abstract:
  Measurements of charm mixing and $C\!P$ violation parameters from the
decay-time-dependent ratio of $ D^0 \to K^+ \pi^- $ to $ D^0 \to K^- \pi^+ $
decay rates and the charge-conjugate ratio are reported. The analysis uses
$\overline{B}\to D^{*+} \mu^- X$, and charge-conjugate decays, where $D^{*+}\to
D^0 \pi^+$, and $D^0\to K^{\mp} \pi^{\pm}$. The $pp$ collision data are
recorded by the LHCb experiment at center-of-mass energies $\sqrt{s}$ = 7 and 8
TeV, corresponding to an integrated luminosity of 3 fb$^{-1}$. The data are
analyzed under three hypotheses: (i) mixing assuming $C\!P$ symmetry, (ii)
mixing assuming no direct $C\!P$ violation in the Cabibbo-favored or doubly
Cabibbo-suppressed decay amplitudes, and (iii) mixing allowing either direct
$C\!P$ violation and/or $C\!P$ violation in the superpositions of flavor
eigenstates defining the mass eigenstates. The data are also combined with
those from a previous LHCb study of $D^0\to K \pi$ decays from a disjoint set
of $ D^{*+} $ candidates produced directly in $pp$ collisions. In all cases,
the data are consistent with the hypothesis of $C\!P$ symmetry.


title: Integration of geometric rough paths

abstract:
  We build a connection between rough path theory and noncommutative algebra,
and interpret the integration of geometric rough paths as an example of a
non-abelian Young integration. We identify a class of slowly-varying one-forms,
and prove that the class is stable under basic operations. In particular rough
path theory is extended to allow a natural class of time varying integrands.


title: CoSTAR: Instructing Collaborative Robots with Behavior Trees and Vision

abstract:
  For collaborative robots to become useful, end users who are not robotics
experts must be able to instruct them to perform a variety of tasks. With this
goal in mind, we developed a system for end-user creation of robust task plans
with a broad range of capabilities. CoSTAR: the Collaborative System for Task
Automation and Recognition is our winning entry in the 2016 KUKA Innovation
Award competition at the Hannover Messe trade show, which this year focused on
Flexible Manufacturing. CoSTAR is unique in how it creates natural abstractions
that use perception to represent the world in a way users can both understand
and utilize to author capable and robust task plans. Our Behavior Tree-based
task editor integrates high-level information from known object segmentation
and pose estimation with spatial reasoning and robot actions to create robust
task plans. We describe the cross-platform design and implementation of this
system on multiple industrial robots and evaluate its suitability for a wide
variety of use cases.


title: Structure of a model TiO2 photocatalytic interface

abstract:
  The interaction of water with TiO2 is crucial to many of its practical
applications, including photocatalytic water splitting. Following the first
demonstration of this phenomenon 40 years ago there have been numerous studies
of the rutile single-crystal TiO2(110) interface with water. This has provided
an atomic-level understanding of the water-TiO2 interaction. However, nearly
all of the previous studies of water/TiO2 interfaces involve water in the
vapour phase. Here, we explore the interfacial structure between liquid water
and a rutile TiO2(110) surface pre-characterized at the atomic level. Scanning
tunnelling microscopy and surface X-ray diffraction are used to determine the
structure, which is comprised of an ordered array of hydroxyl molecules with
molecular water in the second layer. Static and dynamic density functional
theory calculations suggest that a possible mechanism for formation of the
hydroxyl overlayer involves the mixed adsorption of O2 and H2O on a partially
defected surface. The quantitative structural properties derived here provide a
basis with which to explore the atomistic properties and hence mechanisms
involved in TiO2 photocatalysis.


title:
Well-posedness and decay to equilibrium for the Muskat problem with
  discontinuous permeability

abstract:
  We first prove local-in-time well-posedness for the Muskat problem, modeling
fluid flow in a two-dimensional inhomogeneous porous media. The permeability of
the porous medium is described by a step function, with a jump discontinuity
across the fixed-in-time curve $(x_1,-1+f(x_1))$, while the interface
separating the fluid from the vacuum region is given by the time-dependent
curve $(x_1,h(x_1,t))$. Our estimates are based on a new methodology that
relies upon a careful study of the PDE system, coupling Darcy's law and
incompressibility of the fluid, rather than the analysis of the singular
integral contour equation for the interface function $h$. We are able to
develop an existence theory for any initial interface given by $h_0 \in H^2$
and any permeability curve-of-discontinuity that is given by $f \in H^{2.5}$.
In particular, our method allows for both curves to have (pointwise) unbounded
curvature. In the case that the permeability discontinuity is the set $f=0$, we
prove global existence and decay to equilibrium for small initial data. This
decay is obtained using a new energy-energy dissipation inequality that couples
tangential derivatives of the velocity in the bulk of the fluid with the
curvature of the interface. To the best of our knowledge, this is the first
global existence result for the Muskat problem with discontinuous permeability.


title: Compacting Neural Network Classifiers via Dropout Training

abstract:
  We introduce dropout compaction, a novel method for training feed-forward
neural networks which realizes the performance gains of training a large model
with dropout regularization, yet extracts a compact neural network for run-time
efficiency. In the proposed method, we introduce a sparsity-inducing prior on
the per unit dropout retention probability so that the optimizer can
effectively prune hidden units during training. By changing the prior
hyperparameters, we can control the size of the resulting network. We performed
a systematic comparison of dropout compaction and competing methods on several
real-world speech recognition tasks and found that dropout compaction achieved
comparable accuracy with fewer than 50% of the hidden units, translating to a
2.5x speedup in run-time.


title:
Exploring Coronal Dynamics: A Next Generation Solar Physics Mission
  white paper

abstract:
  Determining the mechanisms responsible for the heating of the coronal plasma
and maintaining and accelerating the solar wind are long standing goals in
solar physics. There is a clear need to constrain the energy, mass and momentum
flux through the solar corona and advance our knowledge of the physical process
contributing to these fluxes. Furthermore, the accurate forecasting of Space
Weather conditions at the near-Earth environment and, more generally, the
plasma conditions of the solar wind throughout the heliosphere, require
detailed knowledge of these fluxes in the near-Sun corona. Here we present a
short case for a space-based imaging-spectrometer coronagraph, which will have
the ability to provide synoptic information on the coronal environment and
provide strict constraints on the mass, energy, and momentum flux through the
corona. The instrument would ideally achieve cadences of $\sim10$~s, spatial
resolution of 1" and observe the corona out to 2~$R_{\sun}$. Such an instrument
will enable significant progress in our understanding of MHD waves throughout
complex plasmas, as well as potentially providing routine data products to aid
Space Weather forecasting.


title: Optimal Key Consensus in Presence of Noise

abstract:
  In this work, we abstract some key ingredients in previous LWE- and
RLWE-based key exchange protocols, by introducing and formalizing the building
tool, referred to as key consensus (KC) and its asymmetric variant AKC. KC and
AKC allow two communicating parties to reach consensus from close values
obtained by some secure information exchange. We then discover upper bounds on
parameters for any KC and AKC. KC and AKC are fundamental to lattice based
cryptography, in the sense that a list of cryptographic primitives based on
LWR, LWE and RLWE (including key exchange, public-key encryption, and more) can
be modularly constructed from them. As a conceptual contribution, this much
simplifies the design and analysis of these cryptosystems in the future.
  We then design and analyze both general and highly practical KC and AKC
schemes, which are referred to as OKCN and AKCN respectively for presentation
simplicity. Based on KC and AKC, we present generic constructions of key
exchange (KE) from LWR, LWE and RLWE. The generic construction allows versatile
instantiations with our OKCN and AKCN schemes, for which we elaborate on
evaluating and choosing the concrete parameters in order to achieve an
optimally-balanced performance among security, computational cost, bandwidth
efficiency, error rate, and operation simplicity.


title:
Electroweak Decay Studies of Highly Charged Radioactive Ions with TITAN
  at TRIUMF

abstract:
  Several modes of electroweak radioactive decay require an interaction between
the nucleus and bound electrons within the constituent atom. Thus, the
probabilities of the respective decays are not only influenced by the structure
of the initial and final states in the nucleus, but can also depend strongly on
the atomic charge. Conditions suitable for the partial or complete ionization
of these rare isotopes occur naturally in hot, dense astrophysical
environments, but can also be artificially generated in the laboratory to
selectively block certain radioactive decay modes. Direct experimental studies
on such scenarios are extremely difficult due to the laboratory conditions
required to generate and store radioactive ions at high charge states. A new
electron-beam ion trap (EBIT) decay setup with the TITAN experiment at TRIUMF
has successfully demonstrated such techniques for performing spectroscopy on
the radioactive decay of highly charged ions.


title: Evaluating genetic drift in time-series evolutionary analysis

abstract:
  The Wright-Fisher model is the most popular population model for describing
the behaviour of evolutionary systems with a finite population size.
Approximations to the model have commonly been used for the analysis of
time-resolved genome sequence data, but the model itself has rarely been tested
against genomic data. Here, we evaluate the extent to which it can be inferred
as the correct model given experimental data. Given genome-wide data from an
evolutionary experiment, we validate the Wright-Fisher model as the better
model for variance in a finite population in contrast to a Gaussian model of
allele frequency propagation. However, we note a range of circumstances under
which the Wright-Fisher model cannot be correctly identified. We discuss the
potential for more rapid approximations to the Wright-Fisher model.


title:
Propagating spin-wave normal modes: A dynamic matrix approach using
  plane-wave demagnetizating tensors

abstract:
  We present a finite-difference micromagnetic approach for determining the
normal modes of spin-waves propagating in extended magnetic films and strips,
which is based on the linearized Landau-Lifshitz equation and uses the dynamic
matrix method. The model takes into account both short range exchange
interactions and long range dipole-dipole interactions. The latter are
accounted for through plane-wave dynamic demagnetization factors, which depend
not only on the geometry and relative positions of the magnetic cells, as usual
demagnetization factors do, but also on the wave vector of the propagating
waves. Such a numerical model is most relevant when the spin-wave medium is
spatially inhomogeneous perpendicular to the direction of propagation, either
in its magnetic properties or in its equilibrium magnetic configuration. We
illustrate this point by studying surface spin-waves in magnetic bilayer films
and spin-waves channelized along magnetic domain walls in perpendicularly
magnetized strips. In both cases, dynamic dipolar interactions produce
non-reciprocity effects, where counter-propagative spin-waves have different
frequencies.


title: The proton and helium anomalies in the light of the Myriad model

abstract:
  A hardening of the proton and helium fluxes is observed above a few hundreds
of GeV/nuc. The distribution of local sources of primary cosmic rays has been
suggested as a potential solution to this puzzling behavior. Some authors even
claim that a single source is responsible for the observed anomalies. But how
probable these explanations are? To answer that question, our current
description of cosmic ray Galactic propagation needs to be replaced by the
Myriad model. In the former approach, sources of protons and helium nuclei are
treated as a jelly continuously spread over space and time. A more accurate
description is provided by the Myriad model where sources are considered as
point-like events. This leads to a probabilistic derivation of the fluxes of
primary species, and opens the possibility that larger-than-average values may
be observed at the Earth. For a long time though, a major obstacle has been the
infinite variance associated to the probability distribution function which the
fluxes follow. Several suggestions have been made to cure this problem but none
is entirely satisfactory. We go a step further here and solve the infinite
variance problem of the Myriad model by making use of the generalized central
limit theorem. We find that primary fluxes are distributed according to a
stable law with heavy tail, well-known to financial analysts. The probability
that the proton and helium anomalies are sourced by local SNR can then be
calculated. The p-values associated to the CREAM measurements turn out to be
small, unless somewhat unrealistic propagation parameters are assumed.


title:
Assessing the quality of restored images in optical long-baseline
  interferometry

abstract:
  Assessing the quality of aperture synthesis maps is relevant for benchmarking
image reconstruction algorithms, for the scientific exploitation of data from
optical long-baseline interferometers, and for the design/upgrade of
new/existing interferometric imaging facilities. Although metrics have been
proposed in these contexts, no systematic study has been conducted on the
selection of a robust metric for quality assessment. This article addresses the
question: what is the best metric to assess the quality of a reconstructed
image? It starts by considering several metrics, and selecting a few based on
general properties. Then, a variety of image reconstruction cases is
considered. The observational scenarios are phase closure and phase referencing
at the Very Large Telescope Interferometer (VLTI), for a combination of two,
three, four and six telescopes. End-to-end image reconstruction is accomplished
with the MiRA software, and several merit functions are put to test. It is
found that convolution by an effective point spread function is required for
proper image quality assessment. The effective angular resolution of the images
is superior to naive expectation based on the maximum frequency sampled by the
array. This is due to the prior information used in the aperture synthesis
algorithm and to the nature of the objects considered. The l1 norm is the most
robust of all considered metrics, because being linear it is less sensitive to
image smoothing by high regularisation levels. For the cases considered, this
metric allows the implementation of automatic quality assessment of
reconstructed images, with a performance similar to human selection.


title:
Combining simulations and solution experiments as a paradigm for RNA
  force field refinement

abstract:
  Recent computational efforts have shown that the current potential energy
models used in molecular dynamics are not accurate enough to describe the
conformational ensemble of RNA oligomers and suggest that molecular dynamics
should be complemented with experimental data. We here propose a scheme based
on the maximum entropy principle to combine simulations with bulk experiments.
In the proposed scheme the noise arising from both the measurements and the
forward models used to back calculate the experimental observables is
explicitly taken into account. The method is tested on RNA nucleosides and is
then used to construct chemically consistent corrections to the Amber RNA force
field that allow a large set of experimental data on nucleosides and
dinucleosides to be correctly reproduced. The transferability of these
corrections is assessed against independent data on tetranucleotides and
displays a previously unreported agreement with experiments. This procedure can
be applied to enforce multiple experimental data on multiple systems in a
self-consistent framework thus suggesting a new paradigm for force field
refinement.

categories: physics.comp-ph cond-mat.stat-mech physics.bio-ph physics.chem-ph q-bio.BM


title:
A generalised comparison principle for the Monge-Amp\`ere equation and
  the pressure in 2D fluid flows

abstract:
  We extend the generalised comparison principle for the Monge-Amp\`ere
equation due to Rauch & Taylor (Rocky Mountain J. Math. 7, 1977) to nonconvex
domains. From the generalised comparison principle we deduce bounds (from above
and below) on solutions of the Monge-Amp\`ere equation with sign-changing
right-hand side. As a consequence, if the right-hand side is nonpositive (and
does not vanish almost everywhere) then the equation equipped with constant
boundary condition has no solutions. In particular, due to a connection between
the two-dimensional Navier-Stokes equations and the Monge-Amp\`ere equation,
the pressure $p$ in 2D Navier-Stokes equations on a bounded domain cannot
satisfy $\Delta p \leq 0$ in $\Omega $ unless $\Delta p \equiv 0$ (at any fixed
time). As a result at any time $t>0$ there exists $z\in \Omega $ such that
$\Delta p (z,t) =0$.


title: AFFACT - Alignment-Free Facial Attribute Classification Technique

abstract:
  Facial attributes are soft-biometrics that allow limiting the search space,
e.g., by rejecting identities with non-matching facial characteristics such as
nose sizes or eyebrow shapes. In this paper, we investigate how the latest
versions of deep convolutional neural networks, ResNets, perform on the facial
attribute classification task. We test two loss functions: the sigmoid
cross-entropy loss and the Euclidean loss, and find that for classification
performance there is little difference between these two. Using an ensemble of
three ResNets, we obtain the new state-of-the-art facial attribute
classification error of 8.00% on the aligned images of the CelebA dataset. More
significantly, we introduce the Alignment-Free Facial Attribute Classification
Technique (AFFACT), a data augmentation technique that allows a network to
classify facial attributes without requiring alignment beyond detected face
bounding boxes. To our best knowledge, we are the first to report similar
accuracy when using only the detected bounding boxes -- rather than requiring
alignment based on automatically detected facial landmarks -- and who can
improve classification accuracy with rotating and scaling test images. We show
that this approach outperforms the CelebA baseline on unaligned images with a
relative improvement of 36.8%.


title:
End-to-End Subtitle Detection and Recognition for Videos in East Asian
  Languages via CNN Ensemble with Near-Human-Level Performance

abstract:
  In this paper, we propose an innovative end-to-end subtitle detection and
recognition system for videos in East Asian languages. Our end-to-end system
consists of multiple stages. Subtitles are firstly detected by a novel image
operator based on the sequence information of consecutive video frames. Then,
an ensemble of Convolutional Neural Networks (CNNs) trained on synthetic data
is adopted for detecting and recognizing East Asian characters. Finally, a
dynamic programming approach leveraging language models is applied to
constitute results of the entire body of text lines. The proposed system
achieves average end-to-end accuracies of 98.2% and 98.3% on 40 videos in
Simplified Chinese and 40 videos in Traditional Chinese respectively, which is
a significant outperformance of other existing methods. The near-perfect
accuracy of our system dramatically narrows the gap between human cognitive
ability and state-of-the-art algorithms used for such a task.


title:
Linear convergence in optimization over directed graphs with
  row-stochastic matrices

abstract:
  This paper considers a distributed optimization problem over a multi-agent
network, in which the objective function is a sum of individual cost functions
at the agents. We focus on the case when communication between the agents is
described by a \emph{directed} graph. Existing distributed optimization
algorithms for directed graphs require at least the knowledge of the neighbors'
out-degree at each agent (due to the requirement of column-stochastic
matrices). In contrast, our algorithm requires no such knowledge. Moreover, the
proposed algorithm achieves the best known rate of convergence for this class
of problems, $O(\mu^k)$ for $0<\mu<1$, where $k$ is the number of iterations,
given that the objective functions are strongly-convex and have
Lipschitz-continuous gradients. Numerical experiments are also provided to
illustrate the theoretical findings.


title: Mapping theorems for Sobolev-spaces of vector-valued functions

abstract:
  We consider Sobolev spaces with values in Banach spaces as they are
frequently useful in applied problems. Given two Banach spaces $X\neq\{0\}$ and
$Y$, each Lipschitz continuous mapping $F:X\rightarrow Y$ gives rise to a
mapping $u\mapsto F\circ u$ from $W^{1,p}(\Omega,X)$ to $W^{1,p}(\Omega,Y)$ if
and only if $Y$ has the Radon-Nikodym Property. But if $F$ is one-sided Gateaux
differentiable no condition on the space is needed. We also study when weak
properties in the sense of duality imply strong properties. Our results are
applied to prove embedding theorems, a multi-dimensional version of the
Aubin-Lions Lemma and characterizations of the space $W^{1,p}_0(\Omega,X)$.


title: Strong approximation of sets of finite perimeter in metric spaces

abstract:
  In the setting of a metric space equipped with a doubling measure that
supports a Poincar\'e inequality, we show that any set of finite perimeter can
be approximated in the BV norm by a set whose topological and measure theoretic
boundaries almost coincide. This result appears to be new even in the Euclidean
setting. The work relies on a quasicontinuity-type result for BV functions
proved by Lahti and Shanmugalingam (2016).


title:
GPU-accelerated Red Blood Cells Simulations with Transport Dissipative
  Particle Dynamics

abstract:
  Mesoscopic numerical simulations provide a unique approach for the
quantification of the chemical influences on red blood cell functionalities.
The transport Dissipative Particles Dynamics (tDPD) method can lead to such
effective multiscale simulations due to its ability to simultaneously capture
mesoscopic advection, diffusion, and reaction. In this paper, we present a
GPU-accelerated red blood cell simulation package based on a tDPD adaptation of
our red blood cell model, which can correctly recover the cell membrane
viscosity, elasticity, bending stiffness, and cross-membrane chemical
transport. The package essentially processes all computational workloads in
parallel by GPU, and it incorporates multi-stream scheduling and non-blocking
MPI communications to improve inter-node scalability. Our code is validated for
accuracy and compared against the CPU counterpart for speed. Strong scaling and
weak scaling are also presented to characterizes scalability. We observe a
speedup of 10.1 on one GPU over all 16 cores within a single node, and a weak
scaling efficiency of 91% across 256 nodes. The program enables
quick-turnaround and high-throughput numerical simulations for investigating
chemical-driven red blood cell phenomena and disorders.


title:
Improving the Coverage and Spectral Efficiency of Millimeter-Wave
  Cellular Networks using Device-to-Device Relays

abstract:
  The susceptibility of millimeter waveform propagation to blockages limits the
coverage of millimeter-wave (mmWave) signals. To overcome blockages, we propose
to leverage two-hop device-to-device (D2D) relaying. Using stochastic geometry,
we derive expressions for the downlink coverage probability of relay-assisted
mmWave cellular networks when the D2D links are implemented in either uplink
mmWave or uplink microwave bands. We further investigate the spectral
efficiency (SE) improvement in the cellular downlink, and the effect of D2D
transmissions on the cellular uplink. For mmWave links, we derive the coverage
probability using dominant interferer analysis while accounting for both
blockages and beamforming gains. For microwave D2D links, we derive the
coverage probability considering both line-of-sight (LOS) and non-line-of-sight
(NLOS) propagation. Numerical results show that downlink coverage and SE can be
improved using two-hop D2D relaying. Specifically, microwave D2D relays achieve
better coverage because D2D connections can be established under NLOS
conditions. However, mmWave D2D relays achieve better coverage when the density
of interferers is large because blockages eliminate interference from NLOS
interferers. The SE on the downlink depends on the relay mode selection
strategy, and mmWave D2D relays use a significantly smaller fraction of uplink
resources than microwave D2D relays.


title:
The growth with energy of exclusive $J/\Psi$ and $\Upsilon$
  photo-production cross-sections and BFKL evolution

abstract:
  We investigate whether NLO BFKL evolution is capable to describe the energy
dependence of the exclusive photo-production cross-section of vector mesons
$J/\Psi$ and $\Upsilon$ on protons. Our description is based on available NLO
BFKL fits of the proton impact factor in inclusive DIS, which allow us to
construct the necessary scattering amplitude at zero momentum transfer $t=0$.
Assuming an exponential drop-off with $t$, this result allows us to calculate
the exclusive photoproduction cross-section. Comparing our results with both
HERA data (measured by H1 and ZEUS collaborations in $ep$ collision) and LHC
data (measured by ALICE, CMS and LHCb collaborations in ultra-peripheral $pp$
and $pPb$ collision) we find that our framework provides a very good
description of the energy dependence of the $J/\Psi$ and $\Upsilon$
photoproduction cross-section, providing therefore further evidence for BFKL
evolution at the LHC. The available fits of the proton impact factor require on
the other hand an adjustment in the overall normalization.


title: On Almost Entire Solutions Of The Burgers Equation

abstract:
  Solutions that satisfy classically the Burgers equation except, perhaps, on a
closed set S of the plane of potential singularities whose Hausdorff 1-measure
is zero, $H^1(S) = 0$, are necessarily identically constant. We show this under
the additional hypothesis that $S$ is a subset of a countable union of ordered
graphs.


title:
A rain induced landslide 3D model based on molecular dynamics with
  fractal and fractional water diffusion

abstract:
  We present a three-dimensional model, based on cohesive spherical particles,
of rain-induced landslides. The rainwater infiltration into the soil follow the
either the fractional or the fractal diffusion equations. We solve analytically
the fractal diffusion partial differential equation (PDE) with particular
boundary conditions to simulate a rainfall event. Then, for the PDE, we
developed a numerical integration scheme that we integrate with MD (Molecular
Dynamics) algorithm for the triggering and propagation of the simulated
landslide. Therefore we test the numerical integration scheme of fractal
diffusion equation with the analytical solution. We adopt the fractal diffusion
equation in term of gravimetric water content that we use as input of
triggering scheme based on Mohr-Coulomb limit-equilibrium criterion, adapted to
particle level. Moreover, taking into account an interacting force
Lennard-Jones inspired, we use a standard MD algorithm to update particle
positions and velocities. Then we present results for homogeneous and
heterogeneous systems (i.e. composed by particles with same or different radius
respectively). Interestingly, in the heterogeneous case, we observe segregation
effects due to the different volume of the particles. Finally we show the
parameter sensibility analysis both for triggering and propagation phase. Our
simulations confirm the results of our previous two-dimensional model and
therefore the feasible applicability to real cases.


title: On $p$-values

abstract:
  Models are consistently treated as approximations and all procedures are
consistent with this. They do not treat the model as being true. In this
context $p$-values are one measure of approximation, a small $p$-value
indicating a poor approximation. Approximation regions are defined and
distinguished from confidence regions.


title:
Critical exponents for long-range O(n) models below the upper critical
  dimension

abstract:
  We consider the critical behaviour of long-range $O(n)$ models ($n \ge 0$) on
${\mathbb Z}^d$, with interaction that decays with distance $r$ as
$r^{-(d+\alpha)}$, for $\alpha \in (0,2)$. For $n \ge 1$, we study the
$n$-component $|\varphi|^4$ lattice spin model. For $n =0$, we study the weakly
self-avoiding walk via an exact representation as a supersymmetric spin model.
These models have upper critical dimension $d_c=2\alpha$. For dimensions
$d=1,2,3$ and small $\epsilon>0$, we choose $\alpha = \frac 12 (d+\epsilon)$,
so that $d=d_c-\epsilon$ is below the upper critical dimension. For small
$\epsilon$ and weak coupling, to order $\epsilon$ we prove existence of and
compute the values of the critical exponent $\gamma$ for the susceptibility
(for $n \ge 0$) and the critical exponent $\alpha_H$ for the specific heat (for
$n \ge 1$). For the susceptibility, $\gamma = 1 + \frac{n+2}{n+8} \frac
\epsilon\alpha + O(\epsilon^2)$, and a similar result is proved for the
specific heat. Expansion in $\epsilon$ for such long-range models was first
carried out in the physics literature in 1972. Our proof adapts and applies a
rigorous renormalisation group method developed in previous papers with
Bauerschmidt and Brydges for the nearest-neighbour models in the critical
dimension $d=4$, and is based on the construction of a non-Gaussian
renormalisation group fixed point. Some aspects of the method simplify below
the upper critical dimension, while some require different treatment, and new
ideas and techniques with potential future application are introduced.


title: Current systems of coronal loops in 3D MHD simulations

abstract:
  We study the magnetic field and current structure associated with a coronal
loop. Through this we investigate to what extent the assumptions of a
force-free magnetic field break down and where they might be justified. We
analyse a 3D MHD model of the solar corona in an emerging active region with
the focus on the structure of the forming coronal loops. The lower boundary of
this simulation is taken from a model of an emerging active region. As a
consequence of the emerging magnetic flux and the horizontal motions at the
surface a coronal loop forms self-consistently. We investigate the current
density along magnetic field inside (and outside) this loop and study the
magnetic and plasma properties in and around it. We find that the total current
along the loop changes its sign from being antiparallel to parallel to the
magnetic field. This is caused by the inclination of the loop together with the
footpoint motion. Around the loop the currents form a complex non-force-free
helical structure. This is directly related to a bipolar current structure at
the loop footpoints at the base of the corona and a local reduction of the
background magnetic field (i.e. outside the loop) caused by the plasma flow
into and along the loop. The locally reduced magnetic pressure in the loop
allows the loop to sustain a higher density, which is crucial for the emission
in extreme UV. The acting of the flow on the magnetic field hosting the loop
turns out to be also responsible for the observed squashing of the loop. The
complex magnetic field and current system surrounding it can be modeled only in
3D MHD models where the magnetic field has to balance the plasma pressure. A 1D
coronal loop model or a force-free extrapolation can not capture the current
system and the complex interaction of the plasma and the magnetic field in the
coronal loop, despite the fact that the loop is under low-$\beta$ conditions.


title: Composite Spin Crystal Phase in Antiferromagnetic Chiral Magnets

abstract:
  We study the classical antiferromagnetic Heisenberg model on the triangular
lattice with Dzyaloshinskii-Moriya interactions in a magnetic field. We focus
in particular in the emergence of a composite spin crystal phase, dubbed
antiferromagnetic skyrmion lattice, that was recently observed in [Phys. Rev. B
92, 214439 (2015)] for intermediate fields. This complex phase can be made up
from three inter-penetrated skyrmion lattices, one for each sub-lattice of the
original triangular one. Following these recent numerical results, in this
paper we explicitly construct the low-energy effective action that reproduces
the correct phenomenology and could serve as a starting point to study the
coupling to charge carriers, lattice vibrations, structural disorder and
transport phenomena.


title: Parallelizing Word2Vec in Multi-Core and Many-Core Architectures

abstract:
  Word2vec is a widely used algorithm for extracting low-dimensional vector
representations of words. State-of-the-art algorithms including those by
Mikolov et al. have been parallelized for multi-core CPU architectures, but are
based on vector-vector operations with "Hogwild" updates that are
memory-bandwidth intensive and do not efficiently use computational resources.
In this paper, we propose "HogBatch" by improving reuse of various data
structures in the algorithm through the use of minibatching and negative sample
sharing, hence allowing us to express the problem using matrix multiply
operations. We also explore different techniques to distribute word2vec
computation across nodes in a compute cluster, and demonstrate good strong
scalability up to 32 nodes. The new algorithm is particularly suitable for
modern multi-core/many-core architectures, especially Intel's latest Knights
Landing processors, and allows us to scale up the computation near linearly
across cores and nodes, and process hundreds of millions of words per second,
which is the fastest word2vec implementation to the best of our knowledge.


title: Empirical risk minimization and complexity of dynamical models

abstract:
  A dynamical model consists of a continuous self-map $T: \mathcal{X} \to
\mathcal{X}$ of a compact state space $\mathcal{X}$ and a continuous
observation function $f: \mathcal{X} \to \mathbb{R}$. This paper considers the
fitting of a parametrized family of dynamical models to an observed real-valued
stochastic process using empirical risk minimization. The limiting behavior of
the minimum risk parameters is studied in a general setting. We establish a
general convergence theorem for minimum risk estimators and ergodic
observations. We then study conditions under which empirical risk minimization
can effectively separate the signal from the noise in an additive observational
noise model. The key, necessary condition in the latter results is that the
family of dynamical models has limited complexity, which is quantified through
a notion of entropy for families of infinite sequences. Close connections
between entropy and limiting average mean widths for stationary processes are
established.


title:
Stratified Knowledge Bases as Interpretable Probabilistic Models
  (Extended Abstract)

abstract:
  In this paper, we advocate the use of stratified logical theories for
representing probabilistic models. We argue that such encodings can be more
interpretable than those obtained in existing frameworks such as Markov logic
networks. Among others, this allows for the use of domain experts to improve
learned models by directly removing, adding, or modifying logical formulas.


title:
Learning Interpretability for Visualizations using Adapted Cox Models
  through a User Experiment

abstract:
  In order to be useful, visualizations need to be interpretable. This paper
uses a user-based approach to combine and assess quality measures in order to
better model user preferences. Results show that cluster separability measures
are outperformed by a neighborhood conservation measure, even though the former
are usually considered as intuitively representative of user motives. Moreover,
combining measures, as opposed to using a single measure, further improves
prediction performances.


title: The Hoffmeister asteroid family

abstract:
  The Hoffmeister family is a C-type group located in the central main belt.
Dynamically, it is important because of its interaction with the ${\nu}_{1C}$
nodal secular resonance with Ceres, that significantly increases the dispersion
in inclination of family members at lower semi-major axis. As an effect, the
distribution of inclination values of the Hoffmeister family at semi-major axis
lower than its center is significantly leptokurtic, and this can be used to set
constraints on the terminal ejection velocity field of the family at the time
it was produced. By performing an analysis of the time behaviour of the
kurtosis of the $v_W$ component of the ejection velocity field
(${\gamma}_2(v_W)$), as obtained from Gauss' equations, for different
fictitious Hoffmeister families with different values of the ejection velocity
field, we were able to exclude that the Hoffmeister family should be older than
335 Myr. Constraints from the currently observed inclination distribution of
the Hoffmeister family suggest that its terminal ejection velocity parameter
$V_{EJ}$ should be lower than 25~m/s. Results of a Yarko-YORP Monte Carlo
method to family dating, combined with other constraints from inclinations and
${\gamma}_2(v_W)$, indicate that the Hoffmeister family should be
$220^{+60}_{-40}$~Myr old, with an ejection parameter $V_{EJ}= 20\pm5$~m/s.


title: Exponential Networks and Representations of Quivers

abstract:
  We study the geometric description of BPS states in supersymmetric theories
with eight supercharges in terms of geodesic networks on suitable spectral
curves. We lift and extend several constructions of Gaiotto-Moore-Neitzke from
gauge theory to local Calabi-Yau threefolds and related models. The
differential is multi-valued on the covering curve and features a new type of
logarithmic singularity in order to account for D0-branes and non-compact
D4-branes, respectively. We describe local rules for the three-way junctions of
BPS trajectories relative to a particular framing of the curve. We reproduce
BPS quivers of local geometries and illustrate the wall-crossing of finite-mass
bound states in several new examples. We describe first steps toward
understanding the spectrum of framed BPS states in terms of such "exponential
networks."


title:
Continuous-state branching processes, extremal processes and
  super-individuals

abstract:
  The long-term behaviors of flows of continuous-state branching processes are
characterized through subordinators and extremal processes. The extremal
processes arise in the case of supercritical processes with infinite mean and
of subcritical processes with infinite variation. The jumps of these extremal
processes are interpreted as specific initial individuals whose progenies
overwhelm the population. These individuals, which correspond to the records of
a certain Poisson point process embedded in the flow, are called
super-individuals. They radically increase the growth rate to $+\infty$ in the
supercritical case, and slow down the rate of extinction in the subcritical
one.


title: LOTS about Attacking Deep Features

abstract:
  Deep neural networks provide state-of-the-art performance on various tasks
and are, therefore, widely used in real world applications. DNNs are becoming
frequently utilized in biometrics for extracting deep features, which can be
used in recognition systems for enrolling and recognizing new individuals. It
was revealed that deep neural networks suffer from a fundamental problem,
namely, they can unexpectedly misclassify examples formed by slightly
perturbing correctly recognized inputs. Various approaches have been developed
for generating these so-called adversarial examples, but they aim at attacking
end-to-end networks. For biometrics, it is natural to ask whether systems using
deep features are immune to or, at least, more resilient to attacks than
end-to-end networks. In this paper, we introduce a general technique called the
layerwise origin-target synthesis (LOTS) that can be efficiently used to form
adversarial examples that mimic the deep features of the target. We analyze and
compare the adversarial robustness of the end-to-end VGG Face network with
systems that use Euclidean or cosine distance between gallery templates and
extracted deep features. We demonstrate that iterative LOTS is very effective
and show that systems utilizing deep features are easier to attack than the
end-to-end network.


title: Spin entanglement in elastic electron scattering from lithium atoms

abstract:
  In two recent papers (Phys. Rev. Lett. {\bf 116} (2016) 033201; Phys. Rev. A
{\bf 94} (2016) 032331), the possibility of continuously varying the degree of
entanglement between an elastically scattered electron and the valence electron
of an alkali target was discussed. In order to estimate how well such a scheme
may work in practice, we present results for elastic electron scattering from
lithium in the energy regime of 1$-$5~eV and the full range of scattering
angles $0^\circ - 180^\circ$. The most promising regime for Bell-correlations
in this particular collision system are energies between about 1.5 eV and 3.0
eV, in an angular range around $110^\circ \pm 10^\circ$. In addition to the
relative exchange asymmetry parameter, we present the differential cross
section that is important when estimating the count rate and hence the
feasibility of experiments using this system.


title:
Calibration to American Options: Numerical Investigation of the
  de-Americanization

abstract:
  American options are the reference instruments for the model calibration of a
large and important class of single stocks. For this task, a fast and accurate
pricing algorithm is indispensable. The literature mainly discusses pricing
methods for American options that are based on Monte Carlo, tree and partial
differential equation methods. We present an alternative approach that has
become popular under the name de-Americanization in the financial industry. The
method is easy to implement and enjoys fast run-times. Since it is based on ad
hoc simplifications, however, theoretical results guaranteeing reliability are
not available. To quantify the resulting methodological risk, we empirically
test the performance of the de-Americanization method for calibration. We
classify the scenarios in which de-Americanization performs very well. However,
we also identify the cases where de-Americanization oversimplifies and can
result in large errors.


title:
The generalized Lemaitre-Tolman-Bondi solutions with nonzero pressure in
  modeling the cosmological black holes

abstract:
  In this work we have obtained the set of new exact solutions of the Einstein
equations that generalize the known Lemaitre-Tolman-Bondi solution for the
certain case of nonzero pressure under zero spatial curvature. These solutions
are pretending to describe the black hole immersed in the nonstatic
cosmological background and give a possibility to investigate the problems
concerning the effects of the cosmological expansion in gravitationally bounded
systems. They may also be used as a seed models in the problem of structure
formation in the universe at the epoch of matter and radiation decoupling. It
was shown that each of the solutions obtained contains either the
Reissner-Nordstrom or the Schwarzschild black hole in the central region of the
space. It is demonstrated that the approach of the mass function use in solving
the Einstein equations allows clear physical interpretation of the resulting
solutions that is of much benefit to any their further application.


title: Rescuing the concept of swimming in curved spacetime

abstract:
  It has been argued that an extended, quasi-rigid body evolving freely in
curved spacetime can deviate from its natural trajectory by simply performing
cyclic deformations. More interestingly, in the limit of rapid cycles, the
amount of deviation, per cycle, would depend on the sequence of deformations
but not on how fast they are performed -- like the motion of a swimmer at low
Reynolds number. Here, however, we show that the original analysis which
supported this idea is inappropriate to investigate the motion of extended
bodies in the context of general relativity, rendering its quantitative results
invalid and casting doubts on the reality of this swimming effect. We
illustrate this by showing that the original analysis leads to a non-zero
deviation even in a scenario where no swimming can possibly occur.
Notwithstanding, by applying a fully covariant, local formalism, we show that
swimming in curved spacetime is indeed possible and that, in general, its
magnitude can be of the same order as (fortuitously) anticipated -- although it
is highly suppressed in the particular scenario where it was originally
investigated.


title:
Discrete nature of inhomogeneity: The initial stages and local
  configurations of TiOPc during bilayer growth on Ag(111)

abstract:
  The operation of organic optoelectronic devices relies notably on the bulk
properties of compound molecular species, but even more so on the influence of
interfaces thereof. The identification and characterization of elemental
processes in these critical sections of a device thereby requires well-defined
interfaces with low defect density. In this context titanyl phthalocyanine
(TiOPc) arises as an excellent candidate that reveals the formation of a stable
bilayer structure with a characteristic "up-down" molecular arrangement that
optimizes the dipole-dipole interaction within the bilayer. In our experimental
study, long-range ordered TiOPc bilayers have been grown on Ag(111) surfaces
and analyzed using infrared absorption spectroscopy and scanning tunneling
microscopy. By monitoring the prominent Ti=O stretching mode in IRAS and
identifying local configurations in STM, a microscopic model for the growth of
TiOPc bilayers on Ag(111) is suggested. We demonstrate that defect structures
within these bilayers lead to characteristic vibrational signatures which react
sensitively to the local environment of the molecules. Thermal desorption
spectroscopy reveals a high thermal stability of the TiOPc bilayer up to 500 K,
which is attributed to hydrogen bonds between oxygen of the titanyl unit and
the hydrogen rim of phthalocyanines in the second layer, in addition to
contributions arising from the oppositely oriented axial dipole moments and the
ubiquitous van der Waals interactions.


title: Proteins analysed as virtual knots

abstract:
  Long, flexible physical filaments are naturally tangled and knotted, from
macroscopic string down to long-chain molecules. The existence of knotting in a
filament naturally affects its configuration and properties, and may be very
stable or disappear rapidly under manipulation and interaction. Knotting has
been previously identified in protein backbone chains, for which these
mechanical constraints are of fundamental importance to their molecular
functionality, despite their being open curves in which the knots are not
mathematically well defined; knotting can only be identified by closing the
termini of the chain somehow. We introduce a new method for resolving knotting
in open curves using virtual knots, a wider class of topological objects that
do not require a classical closure and so naturally capture the topological
ambiguity inherent in open curves. We describe the results of analysing
proteins in the Protein Data Bank by this new scheme, recovering and extending
previous knotting results, and identifying topological interest in some new
cases. The statistics of virtual knots in protein chains are compared with
those of open random walks and Hamiltonian subchains on cubic lattices,
identifying a regime of open curves in which the virtual knotting description
is likely to be important.


title:
Dark Matter Phenomenology of SM and Enlarged Higgs Sectors Extended with
  Vector Like Leptons

abstract:
  We will investigate the scenario in which the Standard Model (SM) Higgs
sector and its 2-doublet extension (called the Two Higgs Doublet Model or 2HDM)
are the "portal" for the interactions between the Standard Model and a
fermionic Dark Matter (DM) candidate. The latter is the lightest stable neutral
particle of a family of vector-like leptons (VLLs). We will provide an
extensive overview of this scenario combining the constraints purely coming
from DM phenomenology with more general constraints like Electro-weak Precision
Tests (EWPT) as well as with collider searches. In the case that the new
fermionic sector interacts with the SM Higgs sector, constraints from DM
phenomenology force the new states to lie above the TeV scale. This requirement
is relaxed in the case of 2HDM. Nevertheless, strong constraints coming from
Electroweak Precision Tests (EWPT) and the Renormalization Group Equations
(RGEs) limit the impact of VLFs on collider phenomenology.


title:
A dual consistent finite difference method with narrow stencil second
  derivative operators

abstract:
  We study the numerical solutions of time-dependent systems of partial
differential equations, focusing on the implementation of boundary conditions.
The numerical method considered is a finite difference scheme constructed by
high order summation by parts operators, combined with a boundary procedure
using penalties (SBP-SAT).
  Recently it was shown that SBP-SAT finite difference methods can yield
superconvergent functional output if the boundary conditions are imposed such
that the discretization is dual consistent. We generalize these results so that
they include a broader range of boundary conditions and penalty parameters. The
results are also generalized to hold for narrow-stencil second derivative
operators. The derivations are supported by numerical experiments.


title: Variable Computation in Recurrent Neural Networks

abstract:
  Recurrent neural networks (RNNs) have been used extensively and with
increasing success to model various types of sequential data. Much of this
progress has been achieved through devising recurrent units and architectures
with the flexibility to capture complex statistics in the data, such as long
range dependency or localized attention phenomena. However, while many
sequential data (such as video, speech or language) can have highly variable
information flow, most recurrent models still consume input features at a
constant rate and perform a constant number of computations per time step,
which can be detrimental to both speed and model capacity. In this paper, we
explore a modification to existing recurrent units which allows them to learn
to vary the amount of computation they perform at each step, without prior
knowledge of the sequence's time structure. We show experimentally that not
only do our models require fewer operations, they also lead to better
performance overall on evaluation tasks.


title: Query Complexity of Tournament Solutions

abstract:
  A directed graph where there is exactly one edge between every pair of
vertices is called a {\em tournament}. Finding the "best" set of vertices of a
tournament is a well studied problem in social choice theory. A {\em tournament
solution} takes a tournament as input and outputs a subset of vertices of the
input tournament. However, in many applications, for example, choosing the best
set of drugs from a given set of drugs, the edges of the tournament are given
only implicitly and knowing the orientation of an edge is costly. In such
scenarios, we would like to know the best set of vertices (according to some
tournament solution) by "querying" as few edges as possible. We, in this paper,
precisely study this problem for commonly used tournament solutions: given an
oracle access to the edges of a tournament T, find $f(T)$ by querying as few
edges as possible, for a tournament solution f. We first show that the set of
Condorcet non-losers in a tournament can be found by querying $2n-\lfloor \log
n \rfloor -2$ edges only and this is tight in the sense that every algorithm
for finding the set of Condorcet non-losers needs to query at least $2n-\lfloor
\log n \rfloor -2$ edges in the worst case, where $n$ is the number of vertices
in the input tournament. We then move on to study other popular tournament
solutions and show that any algorithm for finding the Copeland set, the Slater
set, the Markov set, the bipartisan set, the uncovered set, the Banks set, and
the top cycle must query $\Omega(n^2)$ edges in the worst case. On the positive
side, we are able to circumvent our strong query complexity lower bound results
by proving that, if the size of the top cycle of the input tournament is at
most $k$, then we can find all the tournament solutions mentioned above by
querying $O(nk + \frac{n\log n}{\log(1-\frac{1}{k})})$ edges only.


title: Fractal Dimension of Self-Affine Signals: Four Methods of Estimation

abstract:
  This paper serves as a complementary material to a poster presented at the
XXXVI Dynamics Days Europe in Corfu, Greece, on June 6th-10th in 2016.
  In this study, fractal dimension ($D$) of two types of self-affine signals
were estimated with help of four methods of fractal complexity analysis.
  The methods include the Higuchi method for the fractal dimension computation,
the estimation of the spectral decay ($\beta$), the generalized Hurst exponent
($H$), and the detrended fluctuation analysis. For self-affine processes, the
next relation between the fractal dimension, Hurst exponent, and spectral decay
is valid: $D=2-H=\frac{5-\beta}{2}$. Therefore, the fractal dimension can be
get from any of the listed characteristics.
  The goal of the study is to find out which of the four methods is the most
reliable. For this purpose, two types of test data with exactly given fractal
dimensions ($D = 1.2, 1.4, 1.5, 1.6, 1.8$) were generated: the graph of the
self-affine Weierstrass function and the statistically self-affine fractional
Brownian motion. The four methods were tested on the both types of time series.
Effect of noise added to data and effect of the length of the data were also
investigated.
  The most biased results were obtained by the spectral method. The Higuchi
method and the generalized Hurst exponent were the most successful.


title:
The evolution of white dwarfs resulting from helium-enhanced,
  low-metallicity progenitor stars

abstract:
  Some globular clusters host multiple stellar populations with different
chemical abundance patterns. This is particularly true for $\omega$ Centauri,
which shows clear evidence of a helium- enriched sub-population characterized
by a helium abundance as high as $Y= 0.4$. We present a whole and consistent
set of evolutionary tracks from the ZAMS to the white dwarf stage appropriate
for the study of the formation and evolution of white dwarfs resulting from the
evolution of helium-rich progenitors. Different issues of the white dwarf
evolution and their helium-rich progenitors have been explored. In particular,
the final mass of the remnants, the role of overshooting during the
thermally-pulsing phase, and the cooling of the resulting white dwarfs differ
markedly from the evolutionary predictions of progenitor stars with standard
initial helium abundance. Finally, the pulsational properties of the resulting
white dwarfs are also explored. We find that, for the range of initial masses
explored in this paper, the final mass of the helium-rich progenitors is
markedly larger than the final mass expected from progenitors with the usual
helium abundance. We also find that progenitors with initial mass smaller than
$M_\star \simeq 0.65\,M_\odot$ evolve directly into helium-core white dwarfs in
less than 14~Gyr, and that for larger progenitor masses the evolution of the
resulting low-mass carbon-oxygen white dwarfs is dominated by residual nuclear
burning. For helium-core white dwarfs, we find that they evolve markedly faster
than their counterparts coming from standard progenitors. Also, in contrast
with what occurs for white dwarfs resulting from progenitors with the standard
helium abundance, the impact of residual burning on the cooling time of white
dwarfs is not affected by the occurrence of overshooting during the
thermally-pulsing phase of progenitor stars.


title:
Two-photon exchange correction to $2S$-$2P$ splitting in muonic helium-3
  ions

abstract:
  We calculate the two-photon exchange correction to the Lamb shift in muonic
helium-3 ions within the dispersion relations framework. Part of the effort
entailed making analytic fits to the electron-$^3$He quasielastic scattering
data set, for purposes of doing the dispersion integrals. Our result is that
the energy of the 2$S$ state is shifted downwards by two-photon exchange
effects by 15.14(49) meV, in good accord with the result obtained from a
potential model and effective field theory calculation.


title: Operator Tail Dependence of Copulas

abstract:
  A notion of tail dependence based on operator regular variation is introduced
for copulas, and the standard tail dependence used in the copula literature is
included as a special case. The non-standard tail dependence with marginal
power scaling functions having possibly distinct tail indexes is investigated
in detail. We show that the copulas with operator tail dependence, incorporated
with regularly varying univariate margins, give rise to a rich class of the
non-standard multivariate regularly varying distributions. We also show that
under some mild conditions, the copula of a non-standard multivariate regularly
varying distribution has the standard tail dependence of order 1. Some
illustrative examples are given.


title: Expert Gate: Lifelong Learning with a Network of Experts

abstract:
  In this paper we introduce a model of lifelong learning, based on a Network
of Experts. New tasks / experts are learned and added to the model
sequentially, building on what was learned before. To ensure scalability of
this process,data from previous tasks cannot be stored and hence is not
available when learning a new task. A critical issue in such context, not
addressed in the literature so far, relates to the decision which expert to
deploy at test time. We introduce a set of gating autoencoders that learn a
representation for the task at hand, and, at test time, automatically forward
the test sample to the relevant expert. This also brings memory efficiency as
only one expert network has to be loaded into memory at any given time.
Further, the autoencoders inherently capture the relatedness of one task to
another, based on which the most relevant prior model to be used for training a
new expert, with finetuning or learning without-forgetting, can be selected. We
evaluate our method on image classification and video prediction problems.


title:
Dark solitons generation and their instability dynamics in two
  dimensional condensates

abstract:
  We analyze numerically the formation and the subsequent dynamics of
two-dimensional matter wave dark solitons in a Thomas-Fermi rubidium condensate
using various techniques. An initially imprinted sharp phase gradient leads to
the dynamical formation of a stationary soliton as well as very shallow grey
solitons, whereas a smooth gradient only creates grey solitons. The depth and
hence, the velocity of the soliton is provided by the spatial width of the
phase gradient, and it also strongly influences the snake-instability dynamics
of the two dimensional solitons. The vortex dipoles stemming from the unstable
soliton exhibit rich dynamics. Notably, the annihilation of a vortex dipole via
a transient dark lump or a vortexonium state, the exchange of vortices between
either a pair of vortex dipoles or a vortex dipole and a single vortex, and so
on. For sufficiently large width of the initial phase gradient, the solitons
may decay directly into vortexoniums instead of vortex pairs, and also the
decay rate is augmented. Later, we discuss alternative techniques to generate
dark solitons, which involve a Gaussian potential barrier and time-dependent
interactions, both linear and periodic. The properties of the solitons can be
controlled by tuning the amplitude or the width of the potential barrier. In
the linear case, the number of solitons and their depths are determined by the
quench time of the interactions. For the periodic modulation, a transient
soliton lattice emerges with its periodicity depending on the modulation
frequency, through a wave number selection governed by the local Bogoliubov
spectrum. Interestingly, for sufficiently low barrier potential, both Faraday
pattern and soliton lattice coexist. The snake instability dynamics of the
soliton lattice is characteristically modified if the Faraday pattern is
present.


title:
Generation of second maximal subgroups and the existence of special
  primes

abstract:
  Let $G$ be a finite almost simple group. It is well known that $G$ can be
generated by 3 elements, and in previous work we showed that 6 generators
suffice for all maximal subgroups of $G$. In this paper we consider subgroups
at the next level of the subgroup lattice - the so-called second maximal
subgroups. We prove that with the possible exception of some families of rank 1
groups of Lie type, the number of generators of every second maximal subgroup
of $G$ is bounded by an absolute constant. We also show that such a bound holds
without any exceptions if and only if there are only finitely many primes $r$
for which there is a prime power $q$ such that $(q^r-1)/(q-1)$ is prime. The
latter statement is a formidable open problem in Number Theory. Applications to
random generation and polynomial growth are also given.


title:
An Empirical Study of Continuous Connectivity Degree Sequence
  Equivalents

abstract:
  In the present work we demonstrate the use of a parcellation free
connectivity model based on Poisson point processes. This model produces for
each subject a continuous bivariate intensity function that represents for
every possible pair of points the relative rate at which we observe tracts
terminating at those points. We fit this model to explore degree sequence
equivalents for spatial continuum graphs, and to investigate the local
differences between estimated intensity functions for two different
tractography methods. This is a companion paper to Moyer et al. (2016), where
the model was originally defined.


title: The kinematical behavior of ORLs and CELs in PNe with [WC] central star

abstract:
  In Planetary Nebulae (PNe) and HII regions ionic abundances can be derived by
using collisionally excited lines (CELs) or recombination lines (ORLs). Such
abundances do not coincide for the same ion and usually abundances from ORLs
are larger than those from CELs by factors of 2 or larger. The origin of the
discrepancy, known as the Abundance Discrepancy Factor is an open problem in
astrophysics of gaseous nebulae. It has been attributed to temperature
fluctuations in the plasma, tiny metal-rich inclusions embedded in the H-rich
plasma, gas inhomogeneities or other processes. In this work we analyze the
kinematical behavior of CELs and ORLs in two PNe ionized by [WC] stars, finding
that kinematics of ORLS is incompatible with the kinematics of CELs. In
particular the expansion velocities from CELs and ORLs for the same ion are
different, indicating that ORLs seem to be produced in zones nearer the central
star than CELs. This is in agreement with results found by other authors for
individual PNe.


title:
Upscaling High-Quality CVD Graphene Devices to 100 Micron-Scale and
  Beyond

abstract:
  We describe a method for transferring ultra large-scale CVD-grown graphene
sheets. These samples can be fabricated as large as several cm$^2$ and are
characterized by magneto-transport measurements on SiO$_2$ substrates. The
process we have developed is highly effective and limits damage to the graphene
all the way through metal liftoff, as shown in carrier mobility measurements
and the observation of the quantum Hall effect. The charge-neutral point is
shown to move drastically to near-zero gate voltage after a 2-step
post-fabrication annealing process, which also allows for greatly diminished
hysteresis.


title: Conformal higher spin scattering amplitudes from twistor space

abstract:
  We use the formulation of conformal higher spin (CHS) theories in twistor
space to study their tree-level scattering amplitudes, finding expressions for
all three-point anti-MHV amplitudes and all MHV amplitudes involving positive
helicity conformal gravity particles and two negative helicity higher spins.
This provides the on-shell analogue for the covariant coupling of CHS fields to
a conformal gravity background. We discuss the restriction of the theory to a
ghost-free unitary subsector, analogous to restricting conformal gravity to
general relativity with a cosmological constant. We study the flat-space limit
and show that the restricted amplitudes vanish, supporting the conjecture that
in the unitary sector the S-matrix of CHS theories is trivial. However, by
appropriately rescaling the amplitudes we find non-vanishing results which we
compare with chiral flat-space higher spin theories.


title:
An operational characterization of the notion of probability by
  algorithmic randomness and its applications

abstract:
  The notion of probability plays an important role in almost all areas of
science and technology. In modern mathematics, however, probability theory
means nothing other than measure theory, and the operational characterization
of the notion of probability is not established yet. In this paper, based on
the toolkit of algorithmic randomness we present an operational
characterization of the notion of probability, called an ensemble. Algorithmic
randomness, also known as algorithmic information theory, is a field of
mathematics which enables us to consider the randomness of an individual
infinite sequence. We use the notion of Martin-Loef randomness with respect to
Bernoulli measure to present the operational characterization. As the first
step of the research of this line, in this paper we consider the case of finite
probability space, i.e., the case where the sample space of the underlying
probability space is finite, for simplicity. We give a natural operational
characterization of the notion of conditional probability in terms of ensemble,
and give equivalent characterizations of the notion of independence between two
events based on it. Furthermore, we give equivalent characterizations of the
notion of independence of an arbitrary number of events/random variables in
terms of ensembles. Moreover, we show that the independence of events/random
variables is equivalent to the independence in the sense of van Lambalgen's
Theorem, in the case where the underlying finite probability space is
computable. In the paper we make applications of our framework to information
theory, cryptography, and the simulation of a biased coin using fair coins, in
order to demonstrate the wide applicability of our framework to the general
areas of science and technology.


title:
Brownian yet non-Gaussian diffusion: from superstatistics to
  subordination of diffusing diffusivities

abstract:
  A growing number of biological, soft, and active matter systems are observed
to exhibit normal diffusive dynamics with a linear growth of the mean squared
displacement, yet with a non-Gaussian distribution of increments. Based on the
Chubinsky-Slater idea of a diffusing diffusivity we here establish and analyze
a minimal model framework of diffusion processes with fluctuating diffusivity.
In particular, we demonstrate the equivalence of the diffusing diffusivity
process with a superstatistical approach with a distribution of diffusivities,
at times shorter than the diffusivity correlation time. At longer times a
crossover to a Gaussian distribution with an effective diffusivity emerges.
Specifically, we establish a subordination picture of Brownian but non-Gaussian
diffusion processes, that can be used for a wide class of diffusivity
fluctuation statistics. Our results are shown to be in excellent agreement with
simulations and numerical evaluations.


title: Ear Recognition: More Than a Survey

abstract:
  Automatic identity recognition from ear images represents an active field of
research within the biometric community. The ability to capture ear images from
a distance and in a covert manner makes the technology an appealing choice for
surveillance and security applications as well as other application domains.
Significant contributions have been made in the field over recent years, but
open research problems still remain and hinder a wider (commercial) deployment
of the technology. This paper presents an overview of the field of automatic
ear recognition (from 2D images) and focuses specifically on the most recent,
descriptor-based methods proposed in this area. Open challenges are discussed
and potential research directions are outlined with the goal of providing the
reader with a point of reference for issues worth examining in the future. In
addition to a comprehensive review on ear recognition technology, the paper
also introduces a new, fully unconstrained dataset of ear images gathered from
the web and a toolbox implementing several state-of-the-art techniques for ear
recognition. The dataset and toolbox are meant to address some of the open
issues in the field and are made publicly available to the research community.


title:
Visualizing and Understanding Curriculum Learning for Long Short-Term
  Memory Networks

abstract:
  Curriculum Learning emphasizes the order of training instances in a
computational learning setup. The core hypothesis is that simpler instances
should be learned early as building blocks to learn more complex ones. Despite
its usefulness, it is still unknown how exactly the internal representation of
models are affected by curriculum learning. In this paper, we study the effect
of curriculum learning on Long Short-Term Memory (LSTM) networks, which have
shown strong competency in many Natural Language Processing (NLP) problems. Our
experiments on sentiment analysis task and a synthetic task similar to sequence
prediction tasks in NLP show that curriculum learning has a positive effect on
the LSTM's internal states by biasing the model towards building constructive
representations i.e. the internal representation at the previous timesteps are
used as building blocks for the final prediction. We also find that smaller
models significantly improves when they are trained with curriculum learning.
Lastly, we show that curriculum learning helps more when the amount of training
data is limited.


title:
The Escaramujo Project: instrumentation courses during a road trip
  across the Americas

abstract:
  The Escaramujo Project was a series of eight hands-on laboratory courses on
High Energy Physics and Astroparticle Instrumentation, in Latinamerican
Institutions. The Physicist Federico Izraelevitch traveled on a van with his
wife and dogs from Chicago to Buenos Aires teaching the courses. The sessions
took place at Institutions in Mexico, Guatemala, Costa Rica, Colombia, Ecuador,
Peru and Bolivia at an advanced undergraduate and graduate level. During these
workshops, each group built a modern cosmic ray detector based on plastic
scintillator and silicon photomultipliers, designed specifically for this
project. After the courses, a functional detector remained at each institution
to be used by the faculty to facilitate the training of future students and to
support and enable local research activities. The five-days workshops covered
topics such as elementary particle and cosmic ray Physics, radiation detection
and instrumentation, low-level light sensing with solid state devices,
front-end analog electronics and object-oriented data analysis (C++ and ROOT).
Throughout this initiative, about a hundred of talented and highly motivated
young students were reached. With the detector as a common thread, they were
able to understand the designing principles and the underlying Physics involved
in it, build the device, start it up, characterize it, take data and analyze
it, mimicking the stages of a real elementary particle Physics experiment.
Besides the aims to awaken vocations in science, technology and engineering,
The Escaramujo Project was an effort to strengthen the integration of
Latinamerican academic institutions into the international scientific
community.


title: Kaon Flavour Physics Strikes Back

abstract:
  In this short presentation I emphasize the increased importance of kaon
flavour physics in the search for new physics (NP) that we should witness in
the rest of this decade and in the next decade. The main actors will be the
branching ratios for the rare decays $K^+\rightarrow\pi^+\nu\bar\nu$ and
$K_{L}\rightarrow\pi^0\nu\bar\nu$, to be measured by NA62 and KOTO, and their
correlations with the ratio $\varepsilon'/\varepsilon$ on which recently
progress by lattice QCD and large $N$ dual QCD approach has been made implying
a new flavour anomaly. Further correlations of $K^+\rightarrow\pi^+\nu\bar\nu$,
$K_{L}\rightarrow\pi^0\nu\bar\nu$ and $\varepsilon'/\varepsilon$ with
$\varepsilon_K$, $\Delta M_K$, $K_L\to\mu^+\mu^-$ and $K_L\to\pi^0\ell^+\ell^-$
will help us to identify indirectly possible NP at short distance scales. This
talk summarizes the present highlights of this fascinating field including some
results from concrete NP scenarios.


title: Time-of-arrival correlations

abstract:
  We propose that measurements of time-of-arrival correlations in multi-partite
systems can sharply distinguish between different approaches to the
time-of-arrival problem. To show this, we construct a Positive-Operator-Valued
measure for two distinct time-of-arrival measurements in a bipartite system,
and we prove that the resulting probabilities differ strongly from ones defined
in terms of probability currents. We also prove that time-of-arrival
correlations are entanglement witnesses, a result suggesting the use of
temporal observables for quantum information processing tasks. Finally, we
construct the probabilities for sequential time-of-arrival measurements on a
single particle. We derive the state-reduction rule for time-of-arrival
measurements; it differs significantly from the standard one, because
time-of-arrival measurements are not defined at a single predetermined moment
of time.


title:
Distributed Simultaneous Inference in Generalized Linear Models via
  Confidence Distribution

abstract:
  We propose a distributed method for simultaneous inference for datasets with
sample size much larger than the number of covariates, i.e., N >> p, in the
generalized linear models framework. When such datasets are too big to be
analyzed entirely by a single centralized computer, or when datasets are
already stored in distributed database systems, the strategy of
divide-and-combine has been the method of choice for scalability. Due to
partition, the sub-dataset sample sizes may be uneven and some possibly close
to p, which calls for regularization techniques to improve numerical stability.
However, there is a lack of clear theoretical justification and practical
guidelines to combine results obtained from separate regularized estimators,
especially when the final objective is simultaneous inference for a group of
regression parameters. In this paper, we develop a strategy to combine
bias-corrected lasso-type estimates by using confidence distributions. We show
that the resulting combined estimator achieves the same estimation efficiency
as that of the maximum likelihood estimator using the centralized data. As
demonstrated by simulated and real data examples, our divide-and-combine method
yields nearly identical inference as the centralized benchmark.


title:
Monte Carlo simulations of Photospheric emission in relativistic
  outflows

abstract:
  We study the spectra of photospheric emission from highly relativistic
gamma-ray burst outflows using a Monte Carlo (MC) code. We consider the
Comptonization of photons with a fast cooled synchrotron spectrum in a
relativistic jet with photon to electron number ratio $N_{\gamma}/N_e = 10^5$.
For all our simulations, we use mono-energetic protons which interact with
thermalised electrons through the Coulomb interaction. The photons, electrons
and protons are cooled adiabatically as the jet expands outwards. We find that
the initial energy distribution of the protons and electrons do not have any
appreciable effect on the photon peak energy and the power-law spectrum above
the peak energy. We also find that the Coulomb interaction between the
electrons and the protons does not affect the output photon spectrum
significantly as the energy of the electrons is elevated only marginally. The
peak energy and the spectral indices for the low and high energy power-law
tails of the photon spectrum remain practically unchanged even in the presence
of electron-proton coupling. Increasing the initial optical depth $\tau_{in}$
results in shallower photon spectrum below the peak energy ($f_{\nu} \propto
\nu^{1.1}$ for $\tau_{in} = 2$ to $f_{\nu} \propto \nu^{0.3}$ for $\tau_{in} =
16$) and fewer photons at the high-energy tail, although $f_{\nu} \propto
\nu^{-0.5}$ above the peak energy up to $\sim 1$ MeV, independent of
$\tau_{in}$. The peak energy of the seed photon spectrum $E_{\gamma,peak}$
determines the peak energy and the shape of the output photon spectrum. Lastly,
we find that our simulation results are quite sensitive to $N_{\gamma}/N_e$,
for $N_{e} = 10^3$. For almost all our simulations, we obtain an output photon
spectrum with power-law tail above $E_{\gamma,peak}$ extending up to $\sim 1$
MeV.


title:
Exact Model Reduction by a Slow-Fast Decomposition of Nonlinear
  Mechanical Systems

abstract:
  We derive conditions under which a general nonlinear mechanical system can be
exactly reduced to a lower-dimensional model that involves only the most
flexible degrees of freedom. This Slow-Fast Decomposition (SFD) enslaves
exponentially fast the stiff degrees of freedom to the flexible ones as all
oscillations converge to the reduced model defined on a slow manifold. We
obtain an expression for the domain boundary beyond which the reduced model
ceases to be relevant due to a generic loss of stability of the slow manifold.
We also find that near equilibria, the SFD gives a mathematical justification
for two modal-reduction methods used in structural dynamics: static
condensation and modal derivatives. These formal reduction procedures, however,
are also found to return incorrect results when the SFD conditions do not hold.
We illustrate all these results on mechanical examples.


title: NoiseOut: A Simple Way to Prune Neural Networks

abstract:
  Neural networks are usually over-parameterized with significant redundancy in
the number of required neurons which results in unnecessary computation and
memory usage at inference time. One common approach to address this issue is to
prune these big networks by removing extra neurons and parameters while
maintaining the accuracy. In this paper, we propose NoiseOut, a fully automated
pruning algorithm based on the correlation between activations of neurons in
the hidden layers. We prove that adding additional output neurons with entirely
random targets results into a higher correlation between neurons which makes
pruning by NoiseOut even more efficient. Finally, we test our method on various
networks and datasets. These experiments exhibit high pruning rates while
maintaining the accuracy of the original network.


title:
Wino dark matter annihilation through the radiative formation of bound
  states

abstract:
  The most dramatic "Sommerfeld enhancements" of neutral-wino-pair annihilation
occur when the wino mass is tuned to near critical values where there is a
zero-energy S-wave resonance at the neutral-wino-pair threshold. If the wino
mass is larger than the critical value, the resonance is a wino-pair bound
state. If the wino mass is near a critical value, low-energy winos can be
described by a zero-range effective field theory in which the winos interact
nonperturbatively through a contact interaction. The parameters of the
zero-range effective field theory can be determined by matching wino scattering
amplitudes calculated by solving the Schr\"odinger equation for a
nonrelativistic effective field theory in which the winos interact through a
potential due to the exchange of electroweak gauge bosons. The utility of the
zero-range effective field theory is illustrated by calculating the rate for
formation of an S-wave bound state in the collision of two neutral winos
through the emission of two soft photons.


title: GaDei: On Scale-up Training As A Service For Deep Learning

abstract:
  Deep learning (DL) training-as-a-service (TaaS) is an important emerging
industrial workload. The unique challenge of TaaS is that it must satisfy a
wide range of customers who have no experience and resources to tune DL
hyper-parameters, and meticulous tuning for each user's dataset is
prohibitively expensive. Therefore, TaaS hyper-parameters must be fixed with
values that are applicable to all users. IBM Watson Natural Language Classifier
(NLC) service, the most popular IBM cognitive service used by thousands of
enterprise-level clients around the globe, is a typical TaaS service. By
evaluating the NLC workloads, we show that only the conservative
hyper-parameter setup (e.g., small mini-batch size and small learning rate) can
guarantee acceptable model accuracy for a wide range of customers. We further
justify theoretically why such a setup guarantees better model convergence in
general. Unfortunately, the small mini-batch size causes a high volume of
communication traffic in a parameter-server based system. We characterize the
high communication bandwidth requirement of TaaS using representative
industrial deep learning workloads and demonstrate that none of the
state-of-the-art scale-up or scale-out solutions can satisfy such a
requirement. We then present GaDei, an optimized shared-memory based scale-up
parameter server design. We prove that the designed protocol is deadlock-free
and it processes each gradient exactly once. Our implementation is evaluated on
both commercial benchmarks and public benchmarks to demonstrate that it
significantly outperforms the state-of-the-art parameter-server based
implementation while maintaining the required accuracy and our implementation
reaches near the best possible runtime performance, constrained only by the
hardware limitation. Furthermore, to the best of our knowledge, GaDei is the
only scale-up DL system that provides fault-tolerance.


title: Gauge Invariant Perturbations of the Schwarzschild Spacetime

abstract:
  Beginning with the pioneering work of Regge and Wheeler (Phys. Rev. 108,
1957), there have been many studies of perturbations away from the
Schwarzschild spacetime background. In particular several authors (e.g.
Moncrief, Ann. Phys 88, 1974) have investigated gauge invariant quantities of
the Regge-Wheeler (RW) gauge. Steven Detweiler also investigated perturbations
of Schwarzschild in his own gauge, which he denoted the "easy (EZ) gauge", and
which he was in the process of adapting for use in the second-order self-force
problem. We present here a compilation of some of his working results, arising
from notes for which there seems to have been no manuscript in preparation. In
particular, we list the gauge invariant quantities used by Detweiler, as well
as explain the process by which he found them.


title: Maps of simplicial spectra whose realizations are cofibrations

abstract:
  Given a map of simplicial topological spaces, mild conditions on degeneracies
and the levelwise maps imply that the geometric realization of the simplicial
map is a cofibration. These conditions are not formal consequences of model
category theory, but depend on properties of spaces, and similar results have
not been available for any model for the stable homotopy category. In this
paper we prove such results for symmetric spectra. Consequently, we get a set
of conditions which ensure that the geometric realization of a map of
simplicial symmetric spectra is a cofibration. These conditions are very
user-friendly in that they are simple, often easily checked, and do not require
computation of a latching object or any other knowledge of Reedy theory.


title: Generative Deep Neural Networks for Dialogue: A Short Review

abstract:
  Researchers have recently started investigating deep neural networks for
dialogue applications. In particular, generative sequence-to-sequence (Seq2Seq)
models have shown promising results for unstructured tasks, such as word-level
dialogue response generation. The hope is that such models will be able to
leverage massive amounts of data to learn meaningful natural language
representations and response generation strategies, while requiring a minimum
amount of domain knowledge and hand-crafting. An important challenge is to
develop models that can effectively incorporate dialogue context and generate
meaningful and diverse responses. In support of this goal, we review recently
proposed models based on generative encoder-decoder neural network
architectures, and show that these models have better ability to incorporate
long-term dialogue history, to model uncertainty and ambiguity in dialogue, and
to generate responses with high-level compositional structure.


title: Specification Tests for the Propensity Score

abstract:
  This paper proposes new nonparametric diagnostic tools to assess the
asymptotic validity of different treatment effects estimators that rely on the
correct specification of the propensity score. We derive a particular
restriction relating the propensity score distribution of treated and control
groups, and develop specification tests based upon it. The resulting tests do
not suffer from the "curse of dimensionality" when the vector of covariates is
high-dimensional, are fully data-driven, do not require tuning parameters such
as bandwidths, and are able to detect a broad class of local alternatives
converging to the null at the parametric rate $n^{-1/2}$, with $n$ the sample
size. We show that the use of an orthogonal projection on the tangent space of
nuisance parameters facilitates the simulation of critical values by means of a
multiplier bootstrap procedure, and can lead to power gains. The finite sample
performance of the tests is examined by means of a Monte Carlo experiment and
an empirical application. Open-source software is available for implementing
the proposed tests.


title: Convex functions on dual Orlicz spaces

abstract:
  In the dual $L_{\Phi^*}$ of a $\Delta_2$-Orlicz space $L_\Phi$, that we call
a dual Orlicz space, we show that a proper (resp. finite) convex function is
lower semicontinuous (resp. continuous) for the Mackey topology
$\tau(L_{\Phi^*},L_\Phi)$ if and only if on each order interval
$[-\zeta,\zeta]=\{\xi: -\zeta\leq \xi\leq\zeta\}$ ($\zeta\in L_{\Phi^*}$), it
is lower semicontinuous (resp. continuous) for the topology of convergence in
probability. For this purpose, we provide the following Koml\'os type result:
every norm bounded sequence $(\xi_n)_n$ in $L_{\Phi^*}$ admits a sequence of
forward convex combinations $\bar\xi_n\in\mathrm{conv}(\xi_n,\xi_{n+1},...)$
such that $\sup_n|\bar\xi_n|\in L_{\Phi^*}$ and $\bar\xi_n$ converges a.s.


title: Astrophysics Source Code Library: Here we grow again!

abstract:
  The Astrophysics Source Code Library (ASCL) is a free online registry of
research codes; it is indexed by ADS and Web of Science and has over 1300 code
entries. Its entries are increasingly used to cite software; citations have
been doubling each year since 2012 and every major astronomy journal accepts
citations to the ASCL. Codes in the resource cover all aspects of astrophysics
research and many programming languages are represented. In the past year, the
ASCL added dashboards for users and administrators, started minting Digital
Objective Identifiers (DOIs) for software it houses, and added metadata fields
requested by users. This presentation covers the ASCL's growth in the past year
and the opportunities afforded it as one of the few domain libraries for
science research codes.


title: Tensor networks for dynamic spacetimes

abstract:
  Existing tensor network models of holography are limited to representing the
geometry of constant time slices of static spacetimes. We study the possibility
of describing the geometry of a dynamic spacetime using tensor networks. We
find it is necessary to give a new definition of length in the network and
propose a definition based on the mutual information. We show that by
associating a set of networks with a single quantum state and making use of the
mutual information based definition of length, a network analogue of the
maximin formula can be used to calculate the entropy of boundary regions.


title: Foundations of Structural Causal Models with Cycles and Latent Variables

abstract:
  Structural causal models (SCMs), also known as (nonparametric) structural
equation models (SEMs), are widely used for causal modeling purposes. In
particular, acyclic SCMs, also known as recursive SEMs, form a well-studied
subclass of SCMs that generalize causal Bayesian networks to allow for latent
confounders. In this paper, we investigate SCMs in a more general setting,
allowing for the presence of both latent confounders and cycles. We show that
in the presence of cycles, many of the convenient properties of acyclic SCMs do
not hold in general: they do not always have a solution; they do not always
induce unique observational, interventional and counterfactual distributions; a
marginalization does not always exist, and if it exists the marginal model does
not always respect the latent projection; they do not always satisfy a Markov
property; and their graphs are not always consistent with their causal
semantics. We prove that for SCMs in general each of these properties does hold
under certain solvability conditions. Our work generalizes results for SCMs
with cycles that were only known for certain special cases so far. We introduce
the class of simple SCMs that extends the class of acyclic SCMs to the cyclic
setting, while preserving many of the convenient properties of acyclic SCMs.
With this paper we aim to provide the foundations for a general theory of
statistical causal modeling with SCMs.


title: Approximate Near Neighbors for General Symmetric Norms

abstract:
  We show that every symmetric normed space admits an efficient nearest
neighbor search data structure with doubly-logarithmic approximation.
Specifically, for every $n$, $d = n^{o(1)}$, and every $d$-dimensional
symmetric norm $\|\cdot\|$, there exists a data structure for
$\mathrm{poly}(\log \log n)$-approximate nearest neighbor search over
$\|\cdot\|$ for $n$-point datasets achieving $n^{o(1)}$ query time and
$n^{1+o(1)}$ space. The main technical ingredient of the algorithm is a
low-distortion embedding of a symmetric norm into a low-dimensional iterated
product of top-$k$ norms.
  We also show that our techniques cannot be extended to general norms.


title: The Origin of the Iron-Rich Knot in Tycho's Supernova Remnant

abstract:
  X-ray observations of supernova remnants (SNRs) allow us to investigate the
chemical inhomogeneity of ejecta, offering unique insight into the
nucleosynthesis in supernova explosions. Here we present detailed imaging and
spectroscopic studies of the "Fe knot" located along the eastern rim of the
Type Ia SNR Tycho (SN 1572) using Suzaku and Chandra long-exposure data.
Surprisingly, the Suzaku spectrum of this knot shows no emission from Cr, Mn,
or Ni, which is unusual for the Fe-rich regions in this SNR. Within the
framework of the canonical delayed-detonation models for SN Ia, the observed
mass ratios M_Cr/M_Fe < 0.023, M_Mn/M_Fe < 0.012, and M_Ni/M_Fe < 0.029 (at 90%
confidence) can only be achieved for a peak temperature of (5.3-5.7) x 10^9 K
and a neutron excess of < 2.0 x 10^-3. These constraints rule out the deep,
dense core of a Chandrasekhar-mass white dwarf as the origin of the Fe knot,
and favors either incomplete Si burning or the alpha-rich freeze-out regime,
probably close to their boundary. An explosive He burning regime is a possible
alternative, although this hypothesis is in conflict with the main properties
of this SNR.


title:
ModelHub: Towards Unified Data and Lifecycle Management for Deep
  Learning

abstract:
  Deep learning has improved state-of-the-art results in many important fields,
and has been the subject of much research in recent years, leading to the
development of several systems for facilitating deep learning. Current systems,
however, mainly focus on model building and training phases, while the issues
of data management, model sharing, and lifecycle management are largely
ignored. Deep learning modeling lifecycle generates a rich set of data
artifacts, such as learned parameters and training logs, and comprises of
several frequently conducted tasks, e.g., to understand the model behaviors and
to try out new models. Dealing with such artifacts and tasks is cumbersome and
largely left to the users. This paper describes our vision and implementation
of a data and lifecycle management system for deep learning. First, we
generalize model exploration and model enumeration queries from commonly
conducted tasks by deep learning modelers, and propose a high-level domain
specific language (DSL), inspired by SQL, to raise the abstraction level and
accelerate the modeling process. To manage the data artifacts, especially the
large amount of checkpointed float parameters, we design a novel model
versioning system (dlv), and a read-optimized parameter archival storage system
(PAS) that minimizes storage footprint and accelerates query workloads without
losing accuracy. PAS archives versioned models using deltas in a
multi-resolution fashion by separately storing the less significant bits, and
features a novel progressive query (inference) evaluation algorithm. Third, we
show that archiving versioned models using deltas poses a new dataset
versioning problem and we develop efficient algorithms for solving it. We
conduct extensive experiments over several real datasets from computer vision
domain to show the efficiency of the proposed techniques.


title: Hopf images in locally compact quantum groups

abstract:
  This manuscript is devoted to the study of the concept of a generating subset
(a.k.a. Hopf image of a morphism) in the setting of locally compact quantum
groups. The aim of this paper is to provide an accurate description of the Hopf
image of a given morphism. We extend and unify the previously existing
approaches for compact and discrete quantum groups and present some results
that can shed light on some local perspective in the theory of quantum groups.
In particular, we provide a characterization of fullness of Hopf image in the
language of partial actions as well as in representation-theoretic terms in the
spirit of representation $C^*$-categories, extending some known results not
only to the broader setting of non-compact quantum groups, but also
encompassing a broader setting of generating subsets.


title:
Relaxation Dynamics in Photoexcited Chiral Molecules Studied by
  Time-Resolved Photoelectron Circular Dichroism: Toward Chiral Femtochemistry

abstract:
  Unravelling the main initial dynamics responsible for chiral recognition is a
key stepin the understanding of many biological processes. However this
challenging task requires a sensitive enantiospecic probe to investigate
molecular dynamics on their natural femtosecond timescale. Here we show that,
in the gas phase, the ultrafast relaxationdynamics of photoexcited chiral
molecules can be tracked by recording Time-ResolvedPhotoElectron Circular
Dichroism (TR-PECD) resulting from the photoionisation bya circularly polarized
probe pulse. A large forward/backward asymmetry along theprobe propagation axis
is observed in the photoelectron angular distribution. Its evolution with
pump-probe delay reveals ultrafast dynamics that are inaccessible in
theangle-integrated photoelectron spectrum nor via the usual electron emission
anisotropyparameter ($\beta$). PECD, which originates from the electron
scattering in the chiral molecular potential, appears as a new sensitive
observable for ultrafast molecular dynamicsin chiral systems.


title: Reply to Comment on "Dislocation Structure and Mobility in hcp $^4$He"

abstract:
  In their Comment [arXiv:1609.06174 (2016)], Kuklov and Svistunov argue that
(a) our discussion of the role of basal-plane dislocations in mass-flow
junction experiments in our recent Letter (Phys. Rev. Lett. 117, 045301 (2016))
is misleading, (b) our results do not provide new insight into dislocation
dissociation nor superfluidity of dislocation cores and (c) our calculations
lack control of the numerical data. Here we offer our Reply.