title: Reactor Antineutrino Signals at Morton and Boulby

abstract:
  Increasing the distance from which an antineutrino detector is capable of
monitoring the operation of a registered reactor, or discovering a clandestine
reactor, strengthens the Non-Proliferation of Nuclear Weapons Treaty. This
report presents calculations of reactor antineutrino interactions, from
quasi-elastic neutrino-proton scattering and elastic neutrino-electron
scattering, in a water-based detector operated >10 km from a commercial power
reactor. It separately calculates signal from the proximal reactor and
background from all other registered reactors. The main results are interaction
rates and kinetic energy distributions of charged leptons scattered from
quasi-elastic and elastic processes. Comparing signal and background
distributions evaluates reactor monitoring capability. Scaling the results to
detectors of different sizes, target media, and standoff distances is
straightforward. Calculations are for two examples of a commercial reactor
(P_th~3 GW) operating nearby (L~20 km) an underground facility capable of
hosting a detector (~1 kT H2O) project. These reactor-site combinations are
Perry-Morton on the southern shore of Lake Erie in the U.S. and
Hartlepool-Boulby on the western shore of the North Sea in U.K.. The signal
from the proximal reactor is about five times greater at the Morton site than
at the Boulby site due to shorter reactor-site separation distance, larger
reactor thermal power, and greater neutrino oscillation survival probability.
In terms of absolute interaction rate, background from all other reactors is
larger at Morton than at Boulby. However, the fraction of the total rate is
smaller at Morton than at Boulby. Moreover, the Hartlepool power plant has two
cores whereas the Perry plant has a single core. The Boulby site, therefore,
offers an opportunity for demonstrating remote reactor monitoring under more
stringent conditions than does the Morton site.


title: Quasi-Recurrent Neural Networks

abstract:
  Recurrent neural networks are a powerful tool for modeling sequential data,
but the dependence of each timestep's computation on the previous timestep's
output limits parallelism and makes RNNs unwieldy for very long sequences. We
introduce quasi-recurrent neural networks (QRNNs), an approach to neural
sequence modeling that alternates convolutional layers, which apply in parallel
across timesteps, and a minimalist recurrent pooling function that applies in
parallel across channels. Despite lacking trainable recurrent layers, stacked
QRNNs have better predictive accuracy than stacked LSTMs of the same hidden
size. Due to their increased parallelism, they are up to 16 times faster at
train and test time. Experiments on language modeling, sentiment
classification, and character-level neural machine translation demonstrate
these advantages and underline the viability of QRNNs as a basic building block
for a variety of sequence tasks.


title:
Interfacial Control of Dzyaloshinskii Moriya Interaction in Heavy
  Metal_Ferromagnetic Metal Thin Film Heterostructures

abstract:
  The interfacial Dzyaloshinskii Moriya Interaction (DMI) in ultrathin magnetic
thin film heterostructures provides a new approach for controlling spin
textures on mesoscopic length scales. Here we investigate the dependence of the
interfacial DMI constant D on a Pt wedge insertion layer in
Ta_CoFeB_Pt(wedge)_MgO thin films by observing the asymmetric spin wave
dispersion using Brillouin light scattering. Continuous tuning of D by more
than a factor of three is realized by inserting less than one monolayer of Pt.
The observations provide new insights for designing magnetic thin film
heterostructures with tailored D for controlling skyrmions and magnetic domain
wall chirality and dynamics.


title: Neural Architecture Search with Reinforcement Learning

abstract:
  Neural networks are powerful and flexible models that work well for many
difficult learning tasks in image, speech and natural language understanding.
Despite their success, neural networks are still hard to design. In this paper,
we use a recurrent network to generate the model descriptions of neural
networks and train this RNN with reinforcement learning to maximize the
expected accuracy of the generated architectures on a validation set. On the
CIFAR-10 dataset, our method, starting from scratch, can design a novel network
architecture that rivals the best human-invented architecture in terms of test
set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is
0.09 percent better and 1.05x faster than the previous state-of-the-art model
that used a similar architectural scheme. On the Penn Treebank dataset, our
model can compose a novel recurrent cell that outperforms the widely-used LSTM
cell, and other state-of-the-art baselines. Our cell achieves a test set
perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than
the previous state-of-the-art model. The cell can also be transferred to the
character language modeling task on PTB and achieves a state-of-the-art
perplexity of 1.214.


title: Decentralized Caching and Coded Delivery with Distinct Cache Capacities

abstract:
  Decentralized proactive caching and coded delivery is studied in a content
delivery network, where each user is equipped with a cache memory, not
necessarily of equal capacity. Cache memories are filled in advance during the
off-peak traffic period in a decentralized manner, i.e., without the knowledge
of the number of active users, their identities, or their particular demands.
User demands are revealed during the peak traffic period, and are served
simultaneously through an error-free shared link. The goal is to find the
minimum delivery rate during the peak traffic period that is sufficient to
satisfy all possible demand combinations. A group-based decentralized caching
and coded delivery scheme is proposed, and it is shown to improve upon the
state-of-the-art in terms of the minimum required delivery rate when there are
more users in the system than files. Numerical results indicate that the
improvement is more significant as the cache capacities of the users become
more skewed. A new lower bound on the delivery rate is also presented, which
provides a tighter bound than the classical cut-set bound.


title:
Calculations of the dominant long-range, spin-independent contributions
  to the interaction energy between two nonrelativistic Dirac fermions from
  double-boson exchange of spin-0 and spin-1 bosons with spin-dependent
  couplings

abstract:
  Various theories beyond the Standard Model predict new particles with masses
in the sub-eV range with very weak couplings to ordinary matter which can
possess spin-dependent couplings to electrons and nucleons. Present laboratory
constraints on exotic spin-dependent interactions with pseudoscalar and axial
couplings for exchange boson masses between meV and eV are very poor compared
to constraints on spin-independent interactions in the same mass range arising
from spin-0 and spin-1 boson exchange. It is therefore interesting to analyze
in a general way how one can use the strong experimental bounds on
spin-independent interactions to also constrain spin-dependent interactions by
considering higher-order exchange processes. The exchange of a pair of bosons
between two fermions with spin-dependent couplings will possess contributions
which flip spins twice and thereby generate a polarization-independent
interaction energy which can add coherently between two unpolarized objects. In
this paper we derive the dominant long-range contributions to the interaction
energy between two nonrelativistic spin-1/2 Dirac fermions from double exchange
of spin-0 and spin-1 bosons proportional to couplings of the form $g_P^{4}$,
$g_S^{2}g_P^{2}$, and $g_V^{2}g_A^{2}$. Our results for $g_P^{4}$ are in
agreement with previous calculations that have appeared in the literature. We
demonstrate the usefulness of this analysis to constrain spin-dependent
couplings by presenting the results of a reanalysis of data from a short-range
gravity experiment to derive an improved constraint on $(g^N_{P})^2$, the
pseudoscalar coupling for nucleons, in the range between $40$ and $200~\mu$m of
about a factor of 5 compared to previous limits. The spin-independent
contribution from 2-boson exchange with axial-vector couplings requires special
treatment and will be explored in another paper.


title: A strongly geometric general residual intersection

abstract:
  In this paper, we prove a formula of Grauert-Riemenschneider canonical sheaf
and log canonical thresholds for a general residual intersection as well as an
equality of minimal log discrepancies under a general link. We also prove an
evidence that MJ-singularities can be preserved under a general residual
intersection.


title:
Leveraging Social Communities for Optimizing Cellular Device-to-Device
  Communications

abstract:
  Device-to-device (D2D) communications over licensed wireless spectrum has
been recently proposed as a promising technology to meet the capacity crunch of
next generation cellular networks. However, due to the high mobility of
cellular devices, establishing and ensuring the success of D2D transmission
becomes a major challenge. To this end, in this paper, a novel framework is
proposed to enable devices to form multi-hop D2D connections in an effort to
maintain sustainable communication in the presence of device mobility. To solve
the problem posed by device mobility, in contrast to existing works, which
mostly focus on physical domain information, a durable community based approach
is introduced taking social encounters into context. It is shown that the
proposed scheme can derive an optimal solution for time sensitive content
transmission while also minimizing the cost that the base station pays in order
to incentivize users to participate in D2D. Simulation results show that the
proposed social community aware approach yields significant performance gain,
in terms of the amount of traffic offloaded from the cellular network to the
D2D tier, compared to the classical social-unaware methods.


title:
Reduced-space Gaussian Process Regression for Data-Driven Probabilistic
  Forecast of Chaotic Dynamical Systems

abstract:
  We formulate a reduced-order strategy for efficiently forecasting complex
high-dimensional dynamical systems entirely based on data streams. The first
step of our method involves reconstructing the dynamics in a reduced-order
subspace of choice using Gaussian Process Regression (GPR). GPR simultaneously
allows for reconstruction of the vector field and more importantly, estimation
of local uncertainty. The latter is due to i) local interpolation error and ii)
truncation of the high-dimensional phase space. This uncertainty component can
be analytically quantified in terms of the GPR hyperparameters. In the second
step we formulate stochastic models that explicitly take into account the
reconstructed dynamics and their uncertainty. For regions of the attractor
which are not sufficiently sampled for our GPR framework to be effective, an
adaptive blended scheme is formulated to enforce correct statistical steady
state properties, matching those of the real data. We examine the effectiveness
of the proposed method to complex systems including the Lorenz 96, the
Kuramoto-Sivashinsky, as well as a prototype climate model. We also study the
performance of the proposed approach as the intrinsic dimensionality of the
system attractor increases in highly turbulent regimes.


title:
Efficient Branching Cascaded Regression for Face Alignment under
  Significant Head Rotation

abstract:
  Despite much interest in face alignment in recent years, the large majority
of work has focused on near-frontal faces. Algorithms typically break down on
profile faces, or are too slow for real-time applications. In this work we
propose an efficient approach to face alignment that can handle 180 degrees of
head rotation in a unified way (e.g., without resorting to view-based models)
using 2D training data. The foundation of our approach is cascaded shape
regression (CSR), which has emerged recently as the leading strategy. We
propose a generalization of conventional CSRs that we call branching cascaded
regression (BCR). Conventional CSRs are single-track; that is, they progress
from one cascade level to the next in a straight line, with each regressor
attempting to fit the entire dataset. We instead split the regression problem
into two or more simpler ones after each cascade level. Intuitively, each
regressor can then operate on a simpler objective function (i.e., with fewer
conflicting gradient directions). Within the BCR framework, we model and infer
pose-related landmark visibility and face shape simultaneously using Structured
Point Distribution Models (SPDMs). We propose to learn task-specific feature
mapping functions that are adaptive to landmark visibility, and that use SPDM
parameters as regression targets instead of 2D landmark coordinates.
Additionally, we introduce a new in-the-wild dataset of profile faces to
validate our approach.


title:
Amplifiers and Suppressors of Selection for the Moran Process on
  Undirected Graphs

abstract:
  We consider the classic Moran process modeling the spread of genetic
mutations, as extended to structured populations by Lieberman et al.\ (Nature,
2005). In this process, individuals are the vertices of a connected graph $G$.
Initially, there is a single mutant vertex, chosen uniformly at random. In each
step, a random vertex is selected for reproduction with a probability
proportional to its fitness: mutants have fitness $r>1$, while non-mutants have
fitness 1. The vertex chosen to reproduce places a copy of itself to a
uniformly random neighbor in $G$, replacing the individual that was there. The
process ends when the mutation either reaches fixation (i.e., all vertices are
mutants), or gets extinct. The principal quantity of interest is the
probability with which each of the two outcomes occurs.
  A problem that has received significant attention recently concerns the
existence of families of graphs, called strong amplifiers of selection, for
which the fixation probability tends to 1 as the order $n$ of the graph
increases, and the existence of strong suppressors of selection, for which this
probability tends to 0. For the case of directed graphs, it is known that both
strong amplifiers and suppressors exist. For the case of undirected graphs,
however, the problem has remained open, and the general belief has been that
neither strong amplifiers nor suppressors exist. In this paper we disprove this
belief, by providing the first examples of such graphs. The strong amplifier we
present has fixation probability $1-\tilde O(n^{-1/3})$, and the strong
suppressor has fixation probability $\tilde O(n^{-1/4})$. Both graph
constructions are surprisingly simple. We also prove a general upper bound of
$1-\tilde \Omega(n^{-1/3})$ on the fixation probability of any undirected
graph. Hence, our strong amplifier is existentially optimal.


title: Class-prior Estimation for Learning from Positive and Unlabeled Data

abstract:
  We consider the problem of estimating the class prior in an unlabeled
dataset. Under the assumption that an additional labeled dataset is available,
the class prior can be estimated by fitting a mixture of class-wise data
distributions to the unlabeled data distribution. However, in practice, such an
additional labeled dataset is often not available. In this paper, we show that,
with additional samples coming only from the positive class, the class prior of
the unlabeled dataset can be estimated correctly. Our key idea is to use
properly penalized divergences for model fitting to cancel the error caused by
the absence of negative samples. We further show that the use of the penalized
$L_1$-distance gives a computationally efficient algorithm with an analytic
solution. The consistency, stability, and estimation error are theoretically
analyzed. Finally, we experimentally demonstrate the usefulness of the proposed
method.


title: A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

abstract:
  Transfer and multi-task learning have traditionally focused on either a
single source-target pair or very few, similar tasks. Ideally, the linguistic
levels of morphology, syntax and semantics would benefit each other by being
trained in a single model. We introduce a joint many-task model together with a
strategy for successively growing its depth to solve increasingly complex
tasks. Higher layers include shortcut connections to lower-level task
predictions to reflect linguistic hierarchies. We use a simple regularization
term to allow for optimizing all model weights to improve one task's loss
without exhibiting catastrophic interference of the other tasks. Our single
end-to-end model obtains state-of-the-art or competitive results on five
different tasks from tagging, parsing, relatedness, and entailment tasks.


title: J-GEM Follow-Up Observations of The Gravitational Wave Source GW151226

abstract:
  We report the results of optical--infrared follow-up observations of the
gravitational wave (GW) event GW151226 detected by the Advanced LIGO in the
framework of J-GEM (Japanese collaboration for Gravitational wave
ElectroMagnetic follow-up). We performed wide-field optical imaging surveys
with Kiso Wide Field Camera (KWFC), Hyper Suprime-Cam (HSC), and MOA-cam3. The
KWFC survey started at 2.26 days after the GW event and covered 778 deg$^2$
centered at the high Galactic region of the skymap of GW151226. We started the
HSC follow-up observations from 12 days after the event and covered an area of
63.5 deg$^2$ of the highest probability region of the northern sky with the
limiting magnitudes of 24.6 and 23.8 for i band and z band, respectively.
MOA-cam3 covered 145 deg$^2$ of the skymap with MOA-red filter 2.5 months after
the GW alert. Total area covered by the wide-field surveys was 986.5 deg$^2$.
The integrated detection probability of all the observed area was $\sim$29%. We
also performed galaxy-targeted observations with six optical and near-infrared
telescopes from 1.61 days after the event. Total of 238 nearby (<100 Mpc)
galaxies were observed with the typical I band limiting magnitude of
$\sim$19.5. We detected 13 supernova candidates with the KWFC survey, and 60
extragalactic transients with the HSC survey. Two third of the HSC transients
were likely supernovae and the remaining one third were possible active
galactic nuclei. With our observational campaign, we found no transients that
are likely to be associated with GW151226.


title:
Photodetection in p-n junctions formed by electrolyte-gated transistors
  of two-dimensional crystals

abstract:
  Transition metal dichalcogenide (TMDC) monolayers have attracted much
attention due to their strong light absorption and excellent electronic
properties. These advantages make this type of two-dimensional crystal a
promising one for optoelectronic device applications. In the case of
photoelectric conversion devices such as photodetectors and photovoltaic cells,
p-n junctions are one of the most important devices. Here, we demonstrate
photodetection with WSe2 monolayer films. We prepare the electrolyte-gated
ambipolar transistors and electrostatic p-n junctions are formed by the
electrolyte-gating technique at 270 K. These p-n junctions are cooled down to
fix the ion motion (and p-n junctions) and we observed the reasonable
photocurrent spectra without the external bias, indicating the formation of p-n
junctions. Very interestingly, two-terminal devices exhibit higher
photoresponsivity than that of three-terminal ones, suggesting the formation of
highly balanced anion and cation layers. The maximum photoresponsivity reaches
5 mA/W in resonance with the first excitonic peak. Our technique provides
important evidence for optoelectronics in atomically thin crystals.


title:
Alternating Direction Method of Multipliers for Sparse Convolutional
  Neural Networks

abstract:
  The storage and computation requirements of Convolutional Neural Networks
(CNNs) can be prohibitive for exploiting these models over low-power or
embedded devices. This paper reduces the computational complexity of the CNNs
by minimizing an objective function, including the recognition loss that is
augmented with a sparsity-promoting penalty term. The sparsity structure of the
network is identified using the Alternating Direction Method of Multipliers
(ADMM), which is widely used in large optimization problems. This method
alternates between promoting the sparsity of the network and optimizing the
recognition performance, which allows us to exploit the two-part structure of
the corresponding objective functions. In particular, we take advantage of the
separability of the sparsity-inducing penalty functions to decompose the
minimization problem into sub-problems that can be solved sequentially.
Applying our method to a variety of state-of-the-art CNN models, our proposed
method is able to simplify the original model, generating models with less
computation and fewer parameters, while maintaining and often improving
generalization performance. Accomplishments on a variety of models strongly
verify that our proposed ADMM-based method can be a very useful tool for
simplifying and improving deep CNNs.


title:
Principal boundary of moduli spaces of abelian and quadratic
  differentials

abstract:
  The seminal work of Eskin-Masur-Zorich described the principal boundary of
moduli spaces of abelian differentials that parameterizes flat surfaces with a
prescribed generic configuration of short parallel saddle connections. In this
paper we describe the principal boundary for each configuration in terms of
twisted differentials over Deligne-Mumford pointed stable curves. We also
describe similarly the principal boundary of moduli spaces of quadratic
differentials originally studied by Masur-Zorich. Our main technique is the
flat geometric degeneration and smoothing developed by
Bainbridge-Chen-Gendron-Grushevsky-M\"{o}ller.


title:
Quantum simulation of gravitational-like waves in minisuperspace with an
  artificial qubit

abstract:
  On the background of the Born-Oppenheimer (adiabatic) approximation, we
investigate the geometrical and topological structure in the theory of quantum
gravity by using the path integral method and halfclassical approximation. As
we know, Berry curvature can be extracted from the linear response of a driven
two-level system to nonadiabatic manipulations of its Hamiltonian. In parameter
space of the Hamiltonian, magnetic monopoles can be artificially simulated.
Ripples occur in Hilbert space when the monopole travels from inside to outside
the surface of energy manifold spanned by system parameters. From this point of
view, we set up the connection between the ripples characterized by the
fidelity of quantum states in Hilbert space and gravitational-like waves in
minisuperspace. This might open a window for the study of geometrical and
topological properties of quantum gravity with the help of quantum physical
systems.


title: Operator Lipschitz functions (English translation)

abstract:
  The purpose of this survey is a comprehensive study of operator Lip\-schitz
functions. A continuous function $f$ on the real line ${\Bbb R}$ os called
operator Lipschitz if $\|f(A)-f(B)\|\le\operatorname{const}\|A-B\|$ for
arbitrary self-adjoint operators $A$ and $B$. We give sufficient conditions and
necessary conditions for operator Lipschitzness. We also study the class of
operator differentiable functions on ${\Bbb R}$ . Next, we consider operator
Lipschitz functions on closed subsets of the plane and introduce the class of
commutator Lipschitz functions on such subsets. An important role for the study
of such classes of functions is played by double operator integrals and Schur
multipliers.


title:
MetaFlow: a Scalable Metadata Lookup Service for Distributed File
  Systems in Data Centers

abstract:
  In large-scale distributed file systems, efficient meta- data operations are
critical since most file operations have to interact with metadata servers
first. In existing distributed hash table (DHT) based metadata management
systems, the lookup service could be a performance bottleneck due to its
significant CPU overhead. Our investigations showed that the lookup service
could reduce system throughput by up to 70%, and increase system latency by a
factor of up to 8 compared to ideal scenarios. In this paper, we present
MetaFlow, a scalable metadata lookup service utilizing software-defined
networking (SDN) techniques to distribute lookup workload over network
components. MetaFlow tackles the lookup bottleneck problem by leveraging
B-tree, which is constructed over the physical topology, to manage flow tables
for SDN-enabled switches. Therefore, metadata requests can be forwarded to
appropriate servers using only switches. Extensive performance evaluations in
both simulations and testbed showed that MetaFlow increases system throughput
by a factor of up to 3.2, and reduce system latency by a factor of up to 5
compared to DHT-based systems. We also deployed MetaFlow in a distributed file
system, and demonstrated significant performance improvement.


title: Power Computations for Intervention Analysis

abstract:
  In many intervention analysis applications time series data may be expensive
or otherwise difficult to collect. In this case the power function is helpful
since it can be used to determine the probability that a proposed intervention
analysis application will detect a meaningful change. Assuming that an
underlying ARIMA or fractional ARIMA model is known or can be estimated from
the pre-intervention time series, the methodology for computing the required
power function is developed for pulse, step and ramp interventions with ARIMA
and fractional ARIMA errors. Convenient formulae for computing the power
function for important special cases are given. Illustrative applications in
traffic safety and environmental impact assessment are discussed.


title: A high precision polarimeter for small telescopes

abstract:
  We describe Mini-HIPPI (Miniature HIgh Precision Polarimetric Instrument), a
stellar polarimeter weighing just 650 grams but capable of measuring linear
polarization to $\sim10^{-5}$. Mini-HIPPI is based on the use of a
Ferroelectric Liquid Crystal (FLC) modulator. It can easily be mounted on a
small telescope and allows study of the polarization of bright stars at levels
of precision which are hitherto largely unexplored. We present results obtained
with Mini-HIPPI on a 35 cm telescope. Measurements of polarized standard stars
are in good agreement with predicted values. Meaurements of a number of bright
stars agree well with those from other high-sensitivity polarimeters.
Observations of the binary system Spica show polarization variability around
the orbital cycle.


title:
An efficient differential quadrature method for fractional
  advection-diffusion equation

abstract:
  This article studies a direct numerical approach for fractional
advection-diffusion equations (ADEs). Using a set of cubic trigonometric
B-splines as test functions, a differential quadrature (DQ) method is firstly
proposed for the 1D and 2D time-fractional ADEs of order $(0,1]$. The weighted
coefficients are determined, and with them, the original equation is
transformed into a group of general ordinary differential equations (ODEs),
which are discretized by an effective difference scheme or Runge-Kutta method.
The stability is investigated under a mild theoretical condition. Secondly,
based on a set of cubic B-splines, we develop a new Crank-Nicolson type DQ
method for the 2D space-fractional ADEs without advection. The DQ
approximations to fractional derivatives are introduced and the values of the
fractional derivatives of B-splines are computed by deriving explicit formulas.
The presented DQ methods are evaluated on five benchmark problems and the
concrete simulations of the unsteady propagation of solitons and Gaussian
pulse. In comparison with the existing algorithms in the open literature,
numerical results finally illustrate the validity and accuracy.


title:
Practical scalability assesment for parallel scientific numerical
  applications

abstract:
  The concept of scalability analysis of numerical parallel applications has
been revisited, with the specific goals defined for the performance estimation
of research applications. A series of Community Climate Model System (CCSM)
numerical simulations were used to test the several MPI implementations,
determine optimal use of the system resources, and their scalability. The
scaling capacity and model throughput performance metrics for $N$ cores showed
a log-linear behavior approximated by a power fit in the form of $C(N)=bN^a$,
where $a$ and $b$ are two empirical constants. Different metrics yielded
identical power coefficients ($a$), but different dimensionality coefficients
($b$). This model was consistent except for the large numbers of N. The power
fit approach appears to be very useful for scalability estimates, especially
when no serial testing is possible. Scalability analysis of additional
scientific application has been conducted in the similar way to validate the
robustness of the power fit approach.


title: LipNet: End-to-End Sentence-level Lipreading

abstract:
  Lipreading is the task of decoding text from the movement of a speaker's
mouth. Traditional approaches separated the problem into two stages: designing
or learning visual features, and prediction. More recent deep lipreading
approaches are end-to-end trainable (Wand et al., 2016; Chung & Zisserman,
2016a). However, existing work on models trained end-to-end perform only word
classification, rather than sentence-level sequence prediction. Studies have
shown that human lipreading performance increases for longer words (Easton &
Basala, 1982), indicating the importance of features capturing temporal context
in an ambiguous communication channel. Motivated by this observation, we
present LipNet, a model that maps a variable-length sequence of video frames to
text, making use of spatiotemporal convolutions, a recurrent network, and the
connectionist temporal classification loss, trained entirely end-to-end. To the
best of our knowledge, LipNet is the first end-to-end sentence-level lipreading
model that simultaneously learns spatiotemporal visual features and a sequence
model. On the GRID corpus, LipNet achieves 95.2% accuracy in sentence-level,
overlapped speaker split task, outperforming experienced human lipreaders and
the previous 86.4% word-level state-of-the-art accuracy (Gergen et al., 2016).


title: Loss-aware Binarization of Deep Networks

abstract:
  Deep neural network models, though very powerful and highly successful, are
computationally expensive in terms of space and time. Recently, there have been
a number of attempts on binarizing the network weights and activations. This
greatly reduces the network size, and replaces the underlying multiplications
to additions or even XNOR bit operations. However, existing binarization
schemes are based on simple matrix approximation and ignore the effect of
binarization on the loss. In this paper, we propose a proximal Newton algorithm
with diagonal Hessian approximation that directly minimizes the loss w.r.t. the
binarized weights. The underlying proximal step has an efficient closed-form
solution, and the second-order information can be efficiently obtained from the
second moments already computed by the Adam optimizer. Experiments on both
feedforward and recurrent networks show that the proposed loss-aware
binarization algorithm outperforms existing binarization schemes, and is also
more robust for wide and deep networks.


title: More on time-reversal anomaly of 2+1d topological phases

abstract:
  We prove an explicit formula conjectured recently by Wang and Levin for the
anomaly of time-reversal symmetry in 2+1 dimensional fermionic topological
quantum field theories. The crucial step is to determine the crosscap state in
terms of the modular S matrix and $\mathsf{T}^2$ eigenvalues, generalizing the
recent analysis by Barkeshli et al. in the bosonic case.


title:
Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium
  Images via Mixture Modeling

abstract:
  Calcium is a ubiquitous messenger in neural signaling events. An increasing
number of techniques are enabling visualization of neurological activity in
animal models via luminescent proteins that bind to calcium ions. These
techniques generate large volumes of spatially correlated time series. A
model-based functional data analysis methodology via Gaussian mixtures is
suggested for the clustering of data from such visualizations is proposed. The
methodology is theoretically justified and a computationally efficient approach
to estimation is suggested. An example analysis of a zebrafish imaging
experiment is presented.


title: Bidirectional Attention Flow for Machine Comprehension

abstract:
  Machine comprehension (MC), answering a query about a given context
paragraph, requires modeling complex interactions between the context and the
query. Recently, attention mechanisms have been successfully extended to MC.
Typically these methods use attention to focus on a small portion of the
context and summarize it with a fixed-size vector, couple attentions
temporally, and/or often form a uni-directional attention. In this paper we
introduce the Bi-Directional Attention Flow (BIDAF) network, a multi-stage
hierarchical process that represents the context at different levels of
granularity and uses bi-directional attention flow mechanism to obtain a
query-aware context representation without early summarization. Our
experimental evaluations show that our model achieves the state-of-the-art
results in Stanford Question Answering Dataset (SQuAD) and CNN/DailyMail cloze
test.


title: Dynamic Coattention Networks For Question Answering

abstract:
  Several deep learning models have been proposed for question answering.
However, due to their single-pass nature, they have no way to recover from
local maxima corresponding to incorrect answers. To address this problem, we
introduce the Dynamic Coattention Network (DCN) for question answering. The DCN
first fuses co-dependent representations of the question and the document in
order to focus on relevant parts of both. Then a dynamic pointing decoder
iterates over potential answer spans. This iterative procedure enables the
model to recover from initial local maxima corresponding to incorrect answers.
On the Stanford question answering dataset, a single DCN model improves the
previous state of the art from 71.0% F1 to 75.9%, while a DCN ensemble obtains
80.4% F1.


title: Convergences of Alternating Projections in CAT($\kappa$) Spaces

abstract:
  We establish the asymptotic regularity and the $\Delta$-convergence of the
sequence constructed by the alternating projections to closed convex sets in a
CAT($\kappa$) space with $\kappa > 0$. Furthermore, the strong convergence of
the alternating von Neumann sequence is presented under certain regularity or
compactness.


title:
Learning to Play in a Day: Faster Deep Reinforcement Learning by
  Optimality Tightening

abstract:
  We propose a novel training algorithm for reinforcement learning which
combines the strength of deep Q-learning with a constrained optimization
approach to tighten optimality and encourage faster reward propagation. Our
novel technique makes deep reinforcement learning more practical by drastically
reducing the training time. We evaluate the performance of our approach on the
49 games of the challenging Arcade Learning Environment, and report significant
improvements in both training time and accuracy.


title:
Non-Orthogonal Multiple Access in Multi-Cell Networks: Theory,
  Performance, and Practical Challenges

abstract:
  Non-orthogonal multiple access (NOMA) is a potential enabler for the
development of 5G and beyond wireless networks. By allowing multiple users to
share the same time and frequency, NOMA can scale up the number of served
users, increase the spectral efficiency, and improve user-fairness compared to
existing orthogonal multiple access (OMA) techniques. While single-cell NOMA
has drawn significant attention recently, much less attention has been given to
multi-cell NOMA. This article discusses the opportunities and challenges of
NOMA in a multi-cell environment. As the density of base stations and devices
increases, inter-cell interference becomes a major obstacle in multi-cell
networks. As such, identifying techniques that combine interference management
approaches with NOMA is of great significance. After discussing the theory
behind NOMA, this paper provides an overview of the current literature and
discusses key implementation and research challenges, with an emphasis on
multi-cell NOMA.


title: Wall and Bed Shear Force in Rectangular Open Channels

abstract:
  This paper studies the effects of velocity gradients and secondary currents
on the distribution of the shear force between the walls and bed of rectangular
open channels. We show that neglecting the effect of secondary currents and
assuming zero-shear division lines does not yield acceptable results. We,
accordingly, introduce a method to determine the percentage of the total shear
force acting on the walls and bed of rectangular open channels, which takes
both the velocity gradients and secondary currents into account. Using the
channel bisectors, along which there is no secondary flows effect, and
orthogonal trajectories to isovels, along which there is no shear stress, we
divide the channel cross section into three major subsections, namely bed area,
walls area, and shared area. The geometry of each subsection is derived given
the location of the maximum velocity. The share of the bed and wall shear
forces from the shared area, are calculated afterward. The results for bed and
walls shear forces agree well with the experimental data with an average
relative error less than 5% for both regular flows and flows carrying suspended
sediment. This method also provides a physics-driven range for the wall and bed
shear forces which nicely covers the experimental data.


title: Reconstruction of graphs via asymmetry

abstract:
  Any graph which is not vertex transitive has a proper induced subgraph which
is unique due to its structure or the way of its connection to the rest of the
graph. We have called such subgraph as an anchor. Using an anchor which, in
fact, is representative of a graph asymmetry, the reconstruction of that graph
reduces to a smaller form of the reconstruction. Therefore, to show that a
graph is reconstructible, it is sufficient to find a suitable anchor that
brings us to a solved form of the problem. An orbit O of a graph G which makes
G\ O to be an anchor or two vertices which makes G \{v,w} to be an anchor with
the conditions that will be mentioned, is sufficient to show that G is
reconstructible. For instance, this fact is enough to show that trees are
reconstructible.


title:
Self-consistent study of Abelian and non-Abelian order in a
  two-dimensional topological superconductor

abstract:
  We perform self-consistent studies of two-dimensional (2D) $s$-wave
topological superconductivity (TSC) with Rashba spin-orbit coupling and Zeeman
field by solving the Bogoliubov-de Gennes equations. In particular, we examine
the effects of a nonmagnetic impurity in detail and show that the nature of the
spin-polarised midgap bound state varies significantly depending on the
material parameters. Most notably, a nonmagnetic impurity in a 2D $s$-wave
topological superconductor can act like a magnetic impurity in a conventional
$s$-wave superconductor, leading to phase transitions of the ground state as
the impurity potential is varied. Furthermore, by solving for the
spin-dependent Hartree potential self-consistently along with the
superconducting order parameter, we demonstrate that topological charge density
waves can coexist with TSC at half filling just as in a conventional $s$-wave
superconductor.


title:
Bifurcations of a large scale circulation in a quasi-bidimensional
  turbulent flow

abstract:
  We report the experimental study of the bifurcations of a large-scale
circulation that is formed over a turbulent flow generated by a spatially
periodic forcing. After shortly describing how the flow becomes turbulent
through a sequence of symmetry breaking bifurcations, we focus our study on the
transitions that occur within the turbulent regime. They are related to changes
in the shape of the probability density function (PDF) of the amplitude of the
large scale flow. We discuss the nature of these bifurcations and how to model
the shape of the PDF.


title: Topological phase in non-centrosymmetric material NaSnBi

abstract:
  We predict that a non-centrosymmetric material NaSnBi locates in a
three-dimensional non-trivial topological phase under ambient pressure based on
first-principle calculations. By deriving the effective model around $\Gamma$
point, we find that the topological phase transition is driven by a Rashba
spin-orbital coupling through an odd number of pairs of band touch because of a
small anisotropic gap caused by quintic dispersion terms. In contrast to
conventional topological insulators, the spin texture of surface Dirac cone is
right-handed and the surface states are strikingly different for different
surface terminations.


title: Multiplicative Nambu structures on Lie groupoids

abstract:
  We study some properties of coisotropic submanifolds of a manifold with
respect to a given multivector field. Using this notion, we generalize the
results of Weinstein \cite{wein} from Poisson bivector field to Nambu-Poisson
tensor or more generally to any multivector field. We also introduce the notion
of Nambu-Lie groupoid generalizing the concepts of both Poisson-Lie groupoid
and Nambu-Lie group. We show that the infinitesimal version of Nambu-Lie
groupoid is the notion of weak Lie-Filippov bialgebroid as introduced in
\cite{bas-bas-das-muk}. Next we introduce coisotropic subgroupoids of a
Nambu-Lie groupoid and these subgroupoids corresponds to, so called coisotropic
subalgebroids of the corresponding weak Lie-Filippov bialgebroid.


title: Wormholes in a viscous universe

abstract:
  We study the static and spherically symmetric wormhole spacetime
configurations at present time in a viscous universe. Considering three classes
of viscous models, i.e., bulk viscosity as a function of Hubble constant $H_0$,
present cosmic temperature $T_0$ and dark energy density $\rho_0$,
respectively, we obtain several wormhole solutions. Through the analysis for
the anisotropic solutions, we conclude that, to some extent, these three
classes of viscous models have very high degeneracy with each other, and
deviate slightly from the ideal fluid consequence. Subsequently, without the
loss of generality, to investigate the traversabilities, energy conditions and
stability for the wormhole solution, we study the wormhole solution of the
constant redshift function of the viscous $\omega$CDM model with a constant
bulk viscosity coefficient. We obtain the following conclusions: the value of
traversal velocity decreases for decreasing bulk viscosity, and the traversal
velocity for a traveler depends on not only the wormhole geometry but also the
effects of cosmological background evolution; the null energy condition will be
violated more clearly when the bulk viscosity or the cosmic expansion velocity
decreases; for the case of a positive surface energy density, the range of the
junction radius decreases and the values of the parameter $\lambda$ are further
restricted when the throat radius of the wormhole $r_0/M$ increases, bulk
viscosity coefficient $\zeta_0$ decreases and present-day Hubble parameter
$H_0$ decreases, respectively; for the case of a negative surface energy
density, by increasing $r_0/M$, decreasing $\zeta_0$ and increasing $H_0$, the
values of the parameter $\lambda$ are less restricted, one may conclude that
the total stability region increases, and the range of the junction radius
increases, increases and decreases, respectively. \end{abstract}


title: An example of a differentiability space which is PI-unrectifiable

abstract:
  We construct a (Lipschitz) differentiability space which has at generic
points a disconnected tangent and thus does not contain positive measure
subsets isometric to positive measure subsets of spaces admitting a Poincar\'e
inequality. We also prove that $l^2$-valued Lipschitz maps are differentiable
a.e., but there are also Lipschitz maps taking values in some other Banach
spaces having the Radon-Nikodym property which fail to be differentiable on
sets of positive measure.


title: Discovery of a variable lead-rich hot subdwarf: UVO 0825+15

abstract:
  UVO0825+15 is a hot bright helium-rich subdwarf which lies in {\it K2} Field
5 and in a sample of intermediate helium-rich subdwarfs observed with {\it
Subaru/HDS}. The {\it K2} light curve shows low-amplitude variations, whilst
the {\it Subaru} spectrum shows Pb{\sc iv} absorption lines, indicative of a
very high lead overabundance. UVO0825+15 also has a high proper motion with
kinematics typical for a thick disk star. Analyses of ultraviolet and
intermediate dispersion optical spectra rule out a short-period binary
companion, and provide fundamental atmospheric parameters of $T_{\rm
off}=38\,900\pm270$\,K, $\log g/{\rm cm\,s^{-2}}=5.97\pm0.11$,
  $\log n_{\rm He}/n_{\rm H}=-0.57\pm0.01$, $E_{B-V}\approx0.03$, and angular
radius $\theta = 1.062\pm0.006\times10^{-11}$ radians (formal errors). The
high-resolution spectrum shows that carbon is $>2$ dex subsolar, iron is
approximately solar and all other elements heavier than argon are at least 2 --
4 dex overabundant, including germanium, yttrium and lead. Approximately 150
lines in the blue-optical spectrum remain unidentified. The chemical structure
of the photosphere is presumed to be determined by radiatively-dominated
diffusion. The {\it K2} light curve shows a dominant period around 10.8\,h,
with a variable amplitude, its first harmonic, and another period at 13.3\,h.
The preferred explanation is multi-periodic non-radial oscillation due to
g-modes with very high radial order, although this presents difficulties for
pulsation theory. Alternative explanations fail for lack of radial-velocity
evidence. UVO0825+15 represents the fourth member of a group of hot subdwarfs
having helium-enriched photospheres and 3--4 dex overabundances of trans-iron
elements, and is the first lead-rich subdwarf to show evidence of pulsations.


title:
Gravitational waves at aLIGO and vacuum stability with a scalar singlet
  extension of the Standard Model

abstract:
  A new gauge singlet scalar field can undergo a strongly first-order phase
transition (PT) leading to gravitational waves (GW) potentially observable at
aLIGO and simultaneously stabilize the electroweak vacuum. aLIGO (O5) is
potentially sensitive to cosmological PTs at $10^7$-$10^8$ GeV, which coincides
with the requirement that the singlet scale is less than the Standard Model
(SM) vacuum instability scale, which is between $10^8$ GeV and $10^{14}$ GeV.
After sampling its parameter space, we identify three benchmark points with a
PT at about $T\approx 10^7$ GeV in a gauge singlet extension of the SM. We
calculate the nucleation temperature, order parameter, characteristic
timescale, and peak amplitude and frequency of GW from bubble collisions during
the PT for the benchmarks and find that, in an optimistic scenario, GW from
such a PT may be in reach of aLIGO (O5). We confirm that the singlet stabilizes
the electroweak vacuum whilst remaining consistent with zero-temperature
phenomenology as well. Thus, this scenario presents an intriguing possibility
that aLIGO may detect traces of fundamental physics motivated by vacuum
stability at an energy scale that is well above the reach of any other
experiment.


title: On bipartite mixed graphs

abstract:
  Mixed graphs can be seen as digraphs that have both arcs and edges (or
digons, that is, two opposite arcs). In this paper, we consider the case where
such graphs are bipartite. As main results, we show that in this context the
Moore-like bound is attained in the case of diameter $k=3$, and that bipartite
mixed graphs of diameter $k\ge 4$ do not exist.


title:
Lindeberg's central limit theorems for martingale like sequences under
  sub-linear expectations

abstract:
  The central limit theorem of martingales is the fundamental tool for studying
the convergence of stochastic processes, especially stochastic integrals and
differential equations. In this paper, general central limit theorems and
functional central limit theorems are obtained for martingale like random
variables under the sub-linear expectation. As applications, the Lindeberg
central limit theorem and functional central limit theorem are obtained for
independent but not necessarily identically distributed random variables, and a
new proof of the L\'evy characterization of a G-Brownian motion without using
stochastic calculus is given. For proving the results, we have also established
Rosenthal's inequality and the exceptional inequality for the martingale like
random variables.


title:
Absence of $^{17}$O Knight-Shift Changes across the First-Order Phase
  Transition Line in Sr$_2$RuO$_4$

abstract:
  We performed $^{17}$O nuclear magnetic resonance measurements on
superconducting (SC) Sr$_2$RuO$_4$ under in-plane magnetic fields. We found
that no new signal appears in the SC state and that the $^{17}$O Knight shifts
obtained from the double-site measurements remain constant across the
first-order phase-transition line, as well as across the second-order
phase-transition line as already reported. The present results indicate that
the SC spin susceptibility does not decrease in the high-field region, although
a magnetization jump in the SC state was reported at low temperatures. Because
the spin susceptibility is unchanged in the SC state in Sr$_2$RuO$_4$, we
suggest that the first-order phase transition across the upper critical field
should be interpreted as a depairing mechanism other than the conventional
Pauli-paramagnetic effect.


title: Symplectic models for Unitary groups

abstract:
  In analogy with the study of representations of $GL_{2n}(F)$ distinguished by
$Sp_{2n}(F)$, where $F$ is a local field, in this paper we study
representations of $U_{2n}(F)$ distinguished by $Sp_{2n}(F)$. (Only quasi-split
unitary groups are considered in this paper since they are the only ones which
contain $Sp_{2n}(F)$.) We prove that there are no cuspidal representations of
$U_{2n}(F)$ distinguished by $Sp_{2n}(F)$ for $F$ a non-archimedean local
field. We also prove the corresponding global theorem that there are no
cuspidal representations of $U_{2n}({\mathbb A}_k)$ with nonzero period
integral on $Sp_{2n}(k) \backslash Sp_{2n}({\mathbb A}_k)$ for $k$ any number
field or a function field. We completely classify representations of
quasi-split unitary group in four variables over local and global fields with
nontrivial symplectic periods using methods of theta correspondence. We propose
a conjectural answer for the classification of all representations of a
quasi-split unitary group distinguished by $Sp_{2n}(F)$.


title: Dissipative Topological Defects in Coupled Laser Networks

abstract:
  Topologically protected defects have been observed and studied in a wide
range of fields, such as cosmology, spin systems, cold atoms and optics as they
are quenched across a phase transition into an ordered state. Revealing their
origin and control is becoming increasingly important field of research, as
they limit the coherence of the system and its ability to approach a fully
ordered state. Here, we present dissipative topological defects in a 1-D ring
network of phase-locked lasers, and show how their formation is related to the
Kibble-Zurek mechanism and is governed in a universal manner by two competing
time scales of the lasers, namely the phase locking time and synchronization
time of their amplitude fluctuations. The ratio between these two time scales
depends on the system parameters such as gain and coupling strength, and thus
offers the possibility to control the probability of topological defects in the
system. Enabling the system to dissipate to the fully ordered, defect-free
state can be exploited for solving hard combinatorial optimization problems in
various fields. As opposed to unitary systems where quenching is obtained via
external cooling mostly through the edges, our dissipative system is kept
strictly uniform even for fast quenches.


title: The exclusive Drell-Yan process and deeply virtual pion production

abstract:
  In this talk it is reported on analyses of l p -> l pi+ n and pi- p -> l+ l-
n within the handbag approach. It is argued that recent measurements of hard
pion production performed by HERMES and CLAS clearly indicate the occurrence of
strong contributions from transversely polarized photons. The gamma*T -> pi
transitions are described by the transversity GPDs accompanied by twist-3 pion
wave functions. The experiments also require strong contributions from the pion
pole which can be modeled as a classical one-pion exchange. With these
extensions the handbag approach leads to results on cross sections and spin
asymmetries in fair agreement with experiment. This approach is also used for
an estimate of the partial cross sections for the exclusive Drell-Yan process.


title:
On boundary-value problems for a partial differential equation with
  Caputo and Bessel operators

abstract:
  In this work, we investigate a unique solvability of a direct and inverse
source problem for a time-fractional partial differential equation with the
Caputo and Bessel operators. Using spectral expansion method, we give explicit
forms of solutions to formulated problems in terms of multinomial
Mittag-Leffler and first kind Bessel functions.


title:
Moduli of regular singular parabolic connections of spectral type on
  smooth projective curves

abstract:
  We define a moduli space of stable regular singular parabolic connections of
spectral type on smooth projective curves and show the smoothness of the moduli
space and give a relative symplectic structure on the moduli space. Moreover,
we define the isomonodromic deformation on this moduli space and prove the
geometric Painlev\'e property of the isomonodromic deformation.


title: Combining policy gradient and Q-learning

abstract:
  Policy gradient is an efficient technique for improving a policy in a
reinforcement learning setting. However, vanilla online variants are on-policy
only and not able to take advantage of off-policy data. In this paper we
describe a new technique that combines policy gradient with off-policy
Q-learning, drawing experience from a replay buffer. This is motivated by
making a connection between the fixed points of the regularized policy gradient
algorithm and the Q-values. This connection allows us to estimate the Q-values
from the action preferences of the policy, to which we apply Q-learning
updates. We refer to the new technique as 'PGQL', for policy gradient and
Q-learning. We also establish an equivalency between action-value fitting
techniques and actor-critic algorithms, showing that regularized policy
gradient techniques can be interpreted as advantage function learning
algorithms. We conclude with some numerical examples that demonstrate improved
data efficiency and stability of PGQL. In particular, we tested PGQL on the
full suite of Atari games and achieved performance exceeding that of both
asynchronous advantage actor-critic (A3C) and Q-learning.


title: The Bolzano mean-value theorem and partial differential equations

abstract:
  We study the existence of solutions to abstract equations of the form $0 = Au
+ F(u)$, $u\in K\subset E$, where A is an abstract differential operator acting
in a Banach space $E$, $K$ is a closed convex set of constraints being
invariant with respect to resolvents of A and perturbations are subject to
different tangency condition. Such problems are closely related to the
so-called Poicar\'e- Miranda theorem, being the multi-dimensional counterpart
of the celebrated Bolzano intermediate value theorem. In fact our main results
can and should be regarded as infinite-dimensional variants of Bolzano and
Miranda-Poincar\'e theorems. Along with single-valued problems we deal with
set-valued ones, yielding the existence of the so-called constrained equilibria
of set-valued maps. The abstract results are applied to show existence of
(strong) steady state solutions to some weakly coupled systems of drift
reaction-diffusion equations or differential inclusions of this type. In
particular we get the existence of strong solutions to the Dirichlet, Neumann
and periodic boundary problems for elliptic partial differential inclusions
under the presence of state constraints of different type. Certain aspects of
the Bernstein theory for bvp for second order ODE are studied, too. No
assumptions concerning structural coupling (monotonicity, cooperativity) are
undertaken.


title: Reference-Aware Language Models

abstract:
  We propose a general class of language models that treat reference as an
explicit stochastic latent variable. This architecture allows models to create
mentions of entities and their attributes by accessing external databases
(required by, e.g., dialogue generation and recipe generation) and internal
state (required by, e.g. language models which are aware of coreference). This
facilitates the incorporation of information that can be accessed in
predictable locations in databases or discourse context, even when the targets
of the reference may be rare words. Experiments on three tasks shows our model
variants based on deterministic attention.


title:
On the optimal feedforward torque control problem of anisotropic
  synchronous machines: Quadrics, quartics and analytical solutions

abstract:
  The optimal feedforward torque control problem is tackled and solved
analytically for synchronous machines while stator resistance and
cross-coupling inductance are explicitly considered. Analytical solutions for
the direct and quadrature optimal reference currents are found for all major
operation strategies such as Maximum Torque per Current (MTPC) (or often called
as Maximum Torque per Ampere (MTPA)), Maximum Current (MC), Field Weakening
(FW), Maximum Torque per Voltage (MTPV) and Maximum Torque per Flux (MTPF).
Numerical methods (approximating the optimal solutions only) or simplifying
assumptions (neglecting stator resistance and/or cross-coupling inductance) are
no longer necessary. The presented results are based on one simple idea: all
optimization problems (e.g. MTPC, MTPV or MTPF) with their respective
constraints (e.g. current or voltage limit) and the computation of the
intersection point(s) of voltage ellipse, current circle, or torque, MTPC, MTPV
and MTPF hyperbolas are reformulated implicitly as quadrics (quadratic
surfaces) which allow to solve the feedforward torque control problem by
invoking the Lagrangian formalism and by finding the roots of a fourth-order
polynomial analytically. The proposed solutions are applicable to any
anisotropic (or isotropic) synchronous machine independent of the underlying
current control strategy.


title:
Krein's trace formula for unitary operators and operator Lipschitz
  functions (English translation)

abstract:
  The main result of this paper is a description of the space of functions on
the unit circle, for which Krein's trace formula holds for arbitrary pairs of
unitary operators with trace class difference. This space coincides with the
space of operator Lipschitz functions on the unit circle.


title:
A few remarks on boundedness in topological modules and topological
  groups

abstract:
  Let X, Y, and Z be topological modules over a topological ring $R$. In the
first part of the paper, we introduce three different classes of bounded
bigroup homomorphisms from $X\times Y$ into $Z$ with respect to the three
different uniform convergence topologies. We show that these spaces form again
topological modules over $R$. In the second part, we characterize bounded sets
in the arbitrary product of topological groups with respect to the both product
and box topologies.


title:
Characterizations of 2-local derivations and local Lie derivations on
  some algebras

abstract:
  We prove that every 2-local derivation from the algebra
$M_n(\mathcal{A})(n>2)$ into its bimodule $M_n(\mathcal{M})$ is a derivation,
where $\mathcal{A}$ is a unital Banach algebra and $\mathcal{M}$ is a unital
$\mathcal{A}$-bimodule such that each Jordan derivation from $\mathcal{A}$ into
$\mathcal{M}$ is an inner derivation, and that every 2-local derivation on a
C*-algebra with a faithful traceable representation is a derivation. We also
characterize local and 2-local Lie derivations on some algebras such as von
Neumann algebras, nest algebras, Jiang-Su algebra and UHF algebras.


title: Characterizations of centralizers and derivations on some algebras

abstract:
  A linear mapping $\phi$ on an algebra $\mathcal{A}$ is called a centralizable
mapping at $G\in\mathcal{A}$ if $\phi(AB)=\phi(A)B=A\phi(B)$ for each $A$ and
$B$ in $\mathcal{A}$ with $AB=G$, and $\phi$ is called a derivable mapping at
$G\in\mathcal{A}$ if $\phi(AB)=\phi(A)B+A\phi(B)$ for each $A$ and $B$ in
$\mathcal{A}$ with $AB=G$. A point $G$ in $\mathcal{A}$ is called a
full-centralizable point (resp. full-derivable point) if every centralizable
(resp. derivable) mapping at $G$ is a centralizer (resp. derivation). We prove
that every point in a von Neumann algebra or a triangular algebra is a
full-centralizable point. We also prove that a point in a von Neumann algebra
is a full-derivable point if and only if its central carrier is the unit.


title:
Finite nilpotent groups coincide with their $2$-closures in all of their
  faithful permutation representations

abstract:
  Here we show that a finite nilpotent group is 2-closed if and only if it is
either cyclic or a direct product of a generalized quaternion group with a
cyclic group of odd order.


title:
$\Lambda_b\to\Lambda_c\tau\bar\nu_\tau$ decay in scalar and vector
  leptoquark scenarios

abstract:
  It has been shown that the anomalies observed in $\bar B\to
D^{(\ast)}\tau\bar\nu_\tau$ and $\bar B\to \bar K\ell^+\ell^-$ decays can be
resolved by adding a single scalar or vector leptoquark to the Standard Model,
while constraints from other precision measurements in the flavour sector can
be satisfied without fine-tuning. To further explore these two interesting
scenarios, in this paper, we study their effects in the semi-leptonic
$\Lambda_b\to\Lambda_c\tau\bar\nu_\tau$ decay. Using the best-fit solutions for
the operator coefficients allowed by the current data of mesonic decays, we
find that (i) the two scenarios give similar amounts of enhancements to the
branching fraction $\mathcal B(\Lambda_b\to\Lambda_c\tau\bar\nu_\tau)$ and the
ratio $R_{\Lambda_c}=\mathcal B(\Lambda_b\to\Lambda_c
\tau\bar\nu_\tau)/\mathcal B(\Lambda_b\to\Lambda_c\ell\bar\nu_\ell)$, (ii) the
two best-fit solutions in each of these two scenarios are also
indistinguishable from each other, (iii) both scenarios give nearly the same
predictions as those of the Standard Model for the longitudinal polarizations
of $\Lambda_c$ and $\tau$ as well as the lepton-side forward-backward
asymmetry. With future measurements of these observables in
$\Lambda_b\to\Lambda_c\tau\bar\nu_\tau$ decay at the LHCb, the two leptoquark
scenarios could be further tested, and even differentiated from the other NP
explanations for the $R_{D^{(\ast)}}$ anomalies. We also discuss the
feasibility for the measurements of these observables at the LHC and the future
$e^+e^-$ colliders.


title: Toric Fano varieties associated to building sets

abstract:
  We characterize building sets whose associated nonsingular projective toric
varieties are Fano. Furthermore, we show that all such toric Fano varieties are
obtained from smooth Fano polytopes associated to finite directed graphs.


title: Chemical analysis of a carbon-enhanced very metal-poor star: CD-27 14351

abstract:
  We present the first time abundance analysis of a very metal-poor
carbon-enhanced star CD-27 14351 based on a high resolution (R ~ 48000) FEROS
spectrum. Our abundance analysis performed using Local Thermodynamic
Equilibrium (LTE) model atmospheres shows that the object is a cool star with
stellar atmospheric parameters, effective temperature Teff = 4335 K, surface
gravity log g = 0.5, microturbulence = 2.42 km/s, and, metallicity [Fe/H] =
-2.6. The star exhibits high carbon and nitrogen abundances with [C/Fe] = 2.89
and [N/Fe] = 1.89. Overabundances of neutron-capture elements are evident in
Ba, La, Ce, and Nd with estimated [X/Fe] > 1, the largest enhancement being
seen in Ce with [Ce/Fe] = 2.63. While the first peak s-process elements Sr and
Y are found to be enhanced with respect to Fe, ([Sr/Fe] = 1.73 and [Y/Fe] =
1.91) the third peak s-process element Pb could not be detected in our spectrum
at the given resolution. Europium, primarily a r-process element also shows an
enhancement with [Eu/Fe] = 1.65. With [Ba/Eu] = 0.12 the object CD-27 14351
satisfies the classification criterion for CEMP-r/s star. The elemental
abundance distributions observed in this star is discussed in light of chemical
abundances observed in other CEMP stars from literature.


title:
Sharp isoperimetric inequalities for small volumes in complete
  noncompact Riemannian manifolds of bounded geometry involving the scalar
  curvature

abstract:
  We provide an isoperimetric comparison theorem for small volumes in an
$n$-dimensional Riemannian manifold $(M^n,g)$ with strong bounded geometry, as
in Definition $2.3$, involving the scalar curvature function. Namely in strong
bounded geometry, if the supremum of scalar curvature function $S_g<n(n-1)k_0$
for some $k_0\in\mathbb{R}$, then for small volumes the isoperimetric profile
of $(M^n,g)$ is less then or equal to the isoperimetric profile of
$\mathbb{M}^n_{k_0}$ the complete simply connected space form of constant
sectional curvature $k_0$. This work generalizes Theorem $2$ of [Dru02b] in
which the same result was proved in the case where $(M^n, g)$ is assumed to be
just compact. As a consequence of our result we give an asymptotic expansion in
Puiseux's series up to the second nontrivial term of the isoperimetric profile
function for small volumes. Finally, as a corollary of our isoperimetric
comparison result, it is shown, in the special case of manifolds with strong
bounded geometry, and $S_g<n(n-1)k_0$ that for small volumes the
Aubin-Cartan-Hadamard's Conjecture in any dimension $n$ is true.


title:
Robustly representing uncertainty in deep neural networks through
  sampling

abstract:
  As deep neural networks (DNNs) are applied to increasingly challenging
problems, they will need to be able to represent their own uncertainty.
Modeling uncertainty is one of the key features of Bayesian methods. Using
Bernoulli dropout with sampling at prediction time has recently been proposed
as an efficient and well performing variational inference method for DNNs.
However, sampling from other multiplicative noise based variational
distributions has not been investigated in depth. We evaluated Bayesian DNNs
trained with Bernoulli or Gaussian multiplicative masking of either the units
(dropout) or the weights (dropconnect). We tested the calibration of the
probabilistic predictions of Bayesian convolutional neural networks (CNNs) on
MNIST and CIFAR-10. Sampling at prediction time increased the calibration of
the DNNs' probabalistic predictions. Sampling weights, whether Gaussian or
Bernoulli, led to more robust representation of uncertainty compared to
sampling of units. However, using either Gaussian or Bernoulli dropout led to
increased test set classification accuracy. Based on these findings we used
both Bernoulli dropout and Gaussian dropconnect concurrently, which we show
approximates the use of a spike-and-slab variational distribution without
increasing the number of learned parameters. We found that spike-and-slab
sampling had higher test set performance than Gaussian dropconnect and more
robustly represented its uncertainty compared to Bernoulli dropout.


title: What Is the Best Practice for CNNs Applied to Visual Instance Retrieval?

abstract:
  Previous work has shown that feature maps of deep convolutional neural
networks (CNNs) can be interpreted as feature representation of a particular
image region. Features aggregated from these feature maps have been exploited
for image retrieval tasks and achieved state-of-the-art performances in recent
years. The key to the success of such methods is the feature representation.
However, the different factors that impact the effectiveness of features are
still not explored thoroughly. There are much less discussion about the best
combination of them.
  The main contribution of our paper is the thorough evaluations of the various
factors that affect the discriminative ability of the features extracted from
CNNs. Based on the evaluation results, we also identify the best choices for
different factors and propose a new multi-scale image feature representation
method to encode the image effectively. Finally, we show that the proposed
method generalises well and outperforms the state-of-the-art methods on four
typical datasets used for visual instance retrieval.


title: Finite dimensional invariant KAM tori for tame vector fields

abstract:
  We discuss a Nash-Moser/ KAM algorithm for the construction of invariant tori
for {\em tame} vector fields. Similar algorithms have been studied widely both
in finite and infinite dimensional contexts: we are particularly interested in
the second case where tameness properties of the vector fields become very
important. We focus on the formal aspects of the algorithm and particularly on
the minimal hypotheses needed for convergence. We discuss various applications
where we show how our algorithm allows to reduce to solving only linear forced
equations. We remark that our algorithm works at the same time in analytic and
Sobolev class.


title: GPU-based Pedestrian Detection for Autonomous Driving

abstract:
  We propose a real-time pedestrian detection system for the embedded Nvidia
Tegra X1 GPU-CPU hybrid platform. The pipeline is composed by the following
state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and
Histograms of Oriented Gradients (HOG) features extracted from the input image;
Pyramidal Sliding Window technique for candidate generation; and Support Vector
Machine (SVM) for classification. Results show a 8x speedup in the target Tegra
X1 platform and a better performance/watt ratio than desktop CUDA platforms in
study.


title:
Amygdala and insula contributions to dorsal-ventral pathway integration
  in the prosodic neural network

abstract:
  Speech prosody enables communication of emotional intentions via modulation
of vocal intonations. Reciprocal interactions between superior temporal (STG)
and inferior frontal gyri (IFG) have been shown to anchor a neural network for
prosodic comprehension, which we refer to as the Prosody Neural Network (PNN).
Although the amygdala is critical for socio-emotional processing, its integral
functional and structural role in processing social information from speech
prosody as well as its role in the PNN is largely unexplored including
inconsistent recent empirical findings. Here, we used magnetoencephalography
and diffusion magnetic resonance imaging of white-matter pathways to establish
that the PNN is characterized by (1) a robust amygdala-cortical functional
connectivity that dynamically evolves as prosodic interpretation progresses,
(2) direct structural fiber connections between amygdala and STG/IFG traversing
a ventral white-matter pathway, and (3) robust amygdala-insula functional
connectivity and structural insula fiber projections to arcuate STG-IFG
connections. These findings support a role for functional and structural
amygdala-centric ventral pathways in combining speech features to form prosodic
percepts. They also highlight insula contributions to prosodic comprehension,
potentially via vertical integration of amygdala-centric ventral processing
into dorsal pathways responsible for prosodic motor articulation and speech
planning.


title:
Surviving in Directed Graphs: A Polylogarithmic Approximation for
  Two-Connected Directed Steiner Tree

abstract:
  In this paper, we study a survivable network design problem on directed
graphs, 2-Connected Directed Steiner Tree (2-DST): given an $n$-vertex weighted
directed graph, a root $r$, and a set of $h$ terminals $S$, find a min-cost
subgraph $H$ that has two edge/vertex disjoint paths from $r$ to any $t\in S$.
2-DST is a natural generalization of the classical Directed Steiner Tree
problem (DST), where we have an additional requirement that the network must
tolerate one failure. No non-trivial approximation is known for 2-DST. This was
left as an open problem by Feldman et al., [SODA'09; JCSS] and has then been
studied by Cheriyan et al. [SODA'12; TALG] and Laekhanukit SODA'14]. However,
no positive result was known except for the special case of a $D$-shallow
instance [Laekhanukit, ICALP'16].
  We present an $O(D^3\log D\cdot h^{2/D}\cdot \log n)$ approximation algorithm
for 2-DST that runs in time $O(n^{O(D)})$, for any $D\in[\log_2h]$. This
implies a polynomial-time $O(h^\epsilon \log n)$ approximation for any constant
$\epsilon>0$, and a poly-logarithmic approximation running in quasi-polynomial
time. We remark that this is essentially the best-known even for the classical
DST, and the latter problem is $O(\log^{2-\epsilon}n)$-hard to approximate
[Halperin and Krauthgamer, STOC'03]. As a by product, we obtain an algorithm
with the same approximation guarantee for the $2$-Connected Directed Steiner
Subgraph problem, where the goal is to find a min-cost subgraph such that every
pair of terminals are $2$-edge/vertex connected.


title: Some characteristics of the simple Boolean quadric polytope extension

abstract:
  Following the seminal work of Padberg on the Boolean quadric polytope $BQP$
and its LP relaxation $BQP_{LP}$, we consider a natural extension: $SATP$ and
$SATP_{LP}$ polytopes, with $BQP_{LP}$ being projection of the $SATP_{LP}$ face
(and $BQP$ -- projection of the $SATP$ face). We consider a problem of integer
recognition: determine whether a maximum of a linear objective function is
achieved at an integral vertex of a polytope. Various special instances of
3-SAT problem like NAE-3-SAT, 1-in-3-SAT, weighted MAX-3-SAT, and others can be
solved by integer recognition over $SATP_{LP}$. We describe all integral
vertices of $SATP_{LP}$. Like $BQP_{LP}$, polytope $SATP_{LP}$ has the
Trubin-property being quasi-integral (1-skeleton of $SATP$ is a subset of
1-skeleton of $SATP_{LP}$). However, unlike $BQP$, not all vertices of $SATP$
are pairwise adjacent, the diameter of $SATP$ equals 2, and the clique number
of 1-skeleton is superpolynomial in dimension. It is known that the fractional
vertices of $BQP_{LP}$ are half-integer (0, 1 or 1/2 valued). We show that the
denominators of $SATP_{LP}$ fractional vertices can take any integral value.
Finally, we describe polynomially solvable subproblems of integer recognition
over $SATP_{LP}$ with constrained objective functions. Based on that, we solve
some cases of edge constrained bipartite graph coloring.


title: Boosting Image Captioning with Attributes

abstract:
  Automatically describing an image with a natural language has been an
emerging challenge in both fields of computer vision and natural language
processing. In this paper, we present Long Short-Term Memory with Attributes
(LSTM-A) - a novel architecture that integrates attributes into the successful
Convolutional Neural Networks (CNNs) plus Recurrent Neural Networks (RNNs)
image captioning framework, by training them in an end-to-end manner. To
incorporate attributes, we construct variants of architectures by feeding image
representations and attributes into RNNs in different ways to explore the
mutual but also fuzzy relationship between them. Extensive experiments are
conducted on COCO image captioning dataset and our framework achieves superior
results when compared to state-of-the-art deep models. Most remarkably, we
obtain METEOR/CIDEr-D of 25.2%/98.6% on testing data of widely used and
publicly available splits in (Karpathy & Fei-Fei, 2015) when extracting image
representations by GoogleNet and achieve to date top-1 performance on COCO
captioning Leaderboard.


title: Uniform Sampling through the Lov\'asz Local Lemma

abstract:
  We propose a new algorithmic framework, called "partial rejection sampling",
to draw samples exactly from a product distribution, conditioned on none of a
number of bad events occurring. Our framework builds (perhaps surprising) new
connections between the variable framework of the Lov\'asz Local Lemma and some
classical sampling algorithms such as the "cycle-popping" algorithm for rooted
spanning trees. Among other applications, we discover new algorithms to sample
satisfying assignments of k-CNF formulas with bounded variable occurrences.


title:
Remarks on Propositional Logics and the categorial relationship between
  Institutions and {\Pi}-Institutions

abstract:
  In this work we explore some applications of the notions of Institution and
{\Pi}-Institution in the setting of propositional logics and establish a
precise categorial relation between these notions, i.e., we provide a pair of
functors that establishes an adjunction between the categories Inst and
{\Pi}-Inst.


title:
Three-Dimensional, Time-Dependent Simulation of Free-Electron Lasers
  with Planar, Helical, and Elliptical Undulators

abstract:
  Free-electron lasers (FELs) have been built ranging in wavelength from
long-wavelength oscillators using partial wave guiding through ultraviolet
through hard x-ray that are either seeded or start from noise (SASE). In
addition, FELs that produce different polarizations of the output radiation
ranging from linear through elliptic to circular polarization are currently
under study. In this paper, we develop a three-dimensional, time-dependent
formulation that is capable of modeling this large variety of FEL
configurations including different polarizations. We employ a modal expansion
for the optical field, i.e., a Gaussian expansion with variable polarization
for free-space propagation. This formulation uses the full Newton-Lorentz force
equations to track the particles through the optical and magnetostatic fields.
As a result, arbitrary three-dimensional representations for different
undulator configurations are implemented, including planar, helical, and
elliptical undulators. In particular, we present an analytic model of an
APPLE-II undulator to treat arbitrary elliptical polarizations. To model
oscillator configurations, and allow propagation of the optical field outside
the undulator and interact with optical elements, we link the FEL simulation
with the optical propagation code OPC. We present simulations using the
APPLE-II undulator model to produce elliptically polarized output radiation,
and present a detailed comparison with recent experiments using a tapered
undulator configuration at the Linac Coherent Light Source. Validation of the
nonlinear formation is also shown by comparison with experimental results
obtained in the SPARC SASE FEL experiment at ENEA Frascati, a seeded tapered
amplifier experiment at Brookhaven National Laboratory, and the 10-kW Upgrade
Oscillator experiment at the Thomas Jefferson National Accelerator Facility.


title:
The truncated lognormal distribution as a luminosity function for
  SWIFT-BAT gamma-ray bursts

abstract:
  The determination of the luminosity function (LF) in gamma ray bursts (GRBs)
depends on the adopted cosmology, each one characterized by its corresponding
luminosity distance. Here we analyse three cosmologies: the standard cosmology,
the plasma cosmology, and the pseudo-Euclidean universe. The LF of the GRBs is
firstly modeled by the lognormal distribution and the four broken power law,
and secondly by a truncated lognormal distribution. The truncated lognormal
distribution fits acceptably the range in luminosity of GRBs as a function of
the redshift.


title:
Noncommutative maximal ergodic theorems for spherical means on the
  Heisenberg group

abstract:
  We prove maximal ergodic theorems for spherical averages on the Heisenberg
groups acting on $L_p$ spaces over measure spaces not necessarily commutative,
that is, on noncommutative $L_p$ spaces. The scale of $p$ is optimal in the
reduced Heisenberg group case. We also obtain the corresponding individual
ergodic theorems and differential theorems in the noncommutative setting. The
results can be regarded as noncommutative analogues of Nevo-Thangavelu's
ergodic theorems. The approach of proof involves recent developments in
noncommutative $L_p$ spaces and in noncommutative harmonic analysis, in
addition to the spectral theory used in the commutative setting.


title: A Differentiable Physics Engine for Deep Learning in Robotics

abstract:
  An important field in robotics is the optimization of controllers. Currently,
robots are often treated as a black box in this optimization process, which is
the reason why derivative-free optimization methods such as evolutionary
algorithms or reinforcement learning are omnipresent. When gradient-based
methods are used, models are kept small or rely on finite difference
approximations for the Jacobian. This method quickly grows expensive with
increasing numbers of parameters, such as found in deep learning. We propose
the implementation of a modern physics engine, which can differentiate control
parameters. This engine is implemented for both CPU and GPU. Firstly, this
paper shows how such an engine speeds up the optimization process, even for
small problems. Furthermore, it explains why this is an alternative approach to
deep Q-learning, for using deep learning in robotics. Finally, we argue that
this is a big step for deep learning in robotics, as it opens up new
possibilities to optimize robots, both in hardware and software.


title:
Perturbative QCD and beyond: azimuthal angle correlations in
  deuteron-deuteron scattering from Bose-Einstein correlations

abstract:
  In this paper, we found within the framework of perturbative QCD, that in
deuteron-deuteron scattering the Bose-Einstein correlations due to two parton
showers production, induce azimuthal angle correlations, with three correlation
lengths: the size of the deuteron ($R_D$), the proton radius ($R_N$), and the
size of the BFKL Pomeron which, is closely related to the saturation momentum
($R_c \sim 1/Q_s$). These correlations are independent of the values of
rapidities of the produced gluons (long range rapidity correlations), for large
rapidities ($\bas |y_1 - y_2| \geq 1$), and have no symmetry with respect to
$\phi \to \pi - \phi$ ($ \vec{p}_{T1} \to - \vec{p}_{T1}$). Therefore, they
give rise to $v_n$ for all values of $n$, not only even values. The
contributions with the correlation length $R_D$ and $R_N$ crucially depend on
the non-perturbative contributions, and to obtain estimates of their values,
requiries a lot of modeling, while the correlations with $R_c \sim 1/Q_s$ have
a perturbative QCD origin, and can be estimated in the ColorGlass Condensate
(CGC) approach.


title: A generalization of the Nakayama functor

abstract:
  In this paper we introduce a generalization of the Nakayama functor for
finite-dimensional algebras. This is obtained by abstracting its interaction
with the forgetful functor to vector spaces. In particular, we characterize the
Nakayama functor in terms of an ambidextrous adjunction of monads and comonads.
In the second part we develop a theory of Gorenstein homological algebra for
such Nakayama functor. We obtain analogues of several classical results for
Iwanaga-Gorenstein algebras. One of our main examples is the module category
$\Lambda\text{-}\operatorname{Mod}$ of a $k$-algebra $\Lambda$, where $k$ is a
commutative ring and $\Lambda$ is finitely generated projective as a
$k$-module.


title: Twenty (simple) questions

abstract:
  A basic combinatorial interpretation of Shannon's entropy function is via the
"20 questions" game. This cooperative game is played by two players, Alice and
Bob: Alice picks a distribution $\pi$ over the numbers $\{1,\ldots,n\}$, and
announces it to Bob. She then chooses a number $x$ according to $\pi$, and Bob
attempts to identify $x$ using as few Yes/No queries as possible, on average.
  An optimal strategy for the "20 questions" game is given by a Huffman code
for $\pi$: Bob's questions reveal the codeword for $x$ bit by bit. This
strategy finds $x$ using fewer than $H(\pi)+1$ questions on average. However,
the questions asked by Bob could be arbitrary. In this paper, we investigate
the following question: Are there restricted sets of questions that match the
performance of Huffman codes, either exactly or approximately?
  Our first main result shows that for every distribution $\pi$, Bob has a
strategy that uses only questions of the form "$x < c$?" and "$x = c$?", and
uncovers $x$ using at most $H(\pi)+1$ questions on average, matching the
performance of Huffman codes in this sense. We also give a natural set of
$O(rn^{1/r})$ questions that achieve a performance of at most $H(\pi)+r$, and
show that $\Omega(rn^{1/r})$ questions are required to achieve such a
guarantee.
  Our second main result gives a set $\mathcal{Q}$ of $1.25^{n+o(n)}$ questions
such that for every distribution $\pi$, Bob can implement an optimal strategy
for $\pi$ using only questions from $\mathcal{Q}$. We also show that
$1.25^{n-o(n)}$ questions are needed, for infinitely many $n$. If we allow a
small slack of $r$ over the optimal strategy, then roughly $(rn)^{\Theta(1/r)}$
questions are necessary and sufficient.


title:
Autonomous choices among deterministic evolution-laws as source of
  uncertainty

abstract:
  We provide evidence of an extreme form of sensitivity to initial conditions
in a family of one-dimensional self-ruling dynamical systems. We prove that
some hyperchaotic sequences are closed-form expressions of the orbits of these
pseudo-random dynamical systems. Each chaotic system in this family exhibits a
sensitivity to initial conditions that encompasses the sequence of choices of
the evolution rule in some collection of maps. This opens a possibility to
extend current theories of complex behaviors on the basis of intrinsic
uncertainty in deterministic chaos.


title: Cancelation free formula for the antipode of linearized Hopf monoid

abstract:
  Many combinatorial Hopf algebras $H$ in the literature are the functorial
image of a linearized Hopf monoid $\bf H$. That is, $H={\mathcal K} ({\bf H})$
or $H=\overline{\mathcal K} ({\bf H})$. Unlike the functor $\overline{\mathcal
K}$, the functor ${\mathcal K}$ applied to ${\bf H}$ may not preserve the
antipode of ${\bf H}$. In this case, one needs to consider the larger Hopf
monoid ${\bf L}\times{\bf H}$ to get $H={\mathcal K} ({\bf
H})=\overline{\mathcal K}({\bf L}\times{\bf H})$ and study the antipode in
${\bf L}\times{\bf H}$. One of the main results in this paper provides a
cancelation free and multiplicity free formula for the antipode of ${\bf
L}\times{\bf H}$. From this formula we obtain a new antipode formula for $H$.
We also explore the case when ${\bf H}$ is commutative and cocommutative. In
this situation we get new antipode formulas that despite of not being
cancelation free, can be used to obtain one for $\overline{\mathcal K}({\bf
H})$ in some cases. We recover as well many of the well-known cancelation free
formulas in the literature. One of our formulas for computing the antipode in
${\bf H}$ involves acyclic orientations of hypergraphs as the central tool. In
this vein, we obtain polynomials analogous to the chromatic polynomial of a
graph, and also identities parallel to Stanley's (-1)-color theorem. One of our
examples introduces a {\it chromatic} polynomial for permutations which counts
increasing sequences of the permutation satisfying a pattern. We also study the
statistic obtained after evaluating such polynomial at $-1$. Finally, we sketch
$q$ deformations and geometric interpretations of our results. This last part
will appear in a sequel paper in joint work with J. Machacek.


title:
Citation algorithms for identifying research milestones driving
  biomedical innovation

abstract:
  Scientific activity plays a major role in innovation for biomedicine and
healthcare. For instance, fundamental research on disease pathologies and
mechanisms can generate potential targets for drug therapy. This co-evolution
is punctuated by papers which provide new perspectives and open new domains.
Despite the relationship between scientific discovery and biomedical
advancement, identifying these research milestones that truly impact biomedical
innovation can be difficult and is largely based solely on the opinions of
subject matter experts. Here, we consider whether a new class of citation
algorithms that identify seminal scientific works in a field, Reference
Publication Year Spectroscopy (RPYS) and multi-RPYS, can identify the
connections between innovation (e.g. therapeutic treatments) and the
foundational research underlying them. Specifically, we assess whether the
results of these analytic techniques converge with expert opinions on research
milestones driving biomedical innovation in the treatment of Basal Cell
Carcinoma. Our results show that these algorithms successfully identify the
majority of milestone papers detailed by experts (Wong and Dlugosz 2014)
thereby validating the power of these algorithms to converge on independent
opinions of seminal scientific works derived by subject matter experts. These
advances offer an opportunity to identify scientific activities enabling
innovation in biomedicine.


title: A universal rank-size law

abstract:
  A mere hyperbolic law, like the Zipf's law power function, is often
inadequate to describe rank-size relationships. An alternative theoretical
distribution is proposed based on theoretical physics arguments starting from
the Yule-Simon distribution. A modeling is proposed leading to a universal
form. A theoretical suggestion for the "best (or optimal) distribution", is
provided through an entropy argument. The ranking of areas through the number
of cities in various countries and some sport competition ranking serves for
the present illustrations.


title: Dynamic multiscaling in magnetohydrodynamic turbulence

abstract:
  We present the first study of the multiscaling of time-dependent velocity and
magnetic-field structure functions in homogeneous, isotropic
magnetohydrodynamic (MHD) turbulence in three dimensions. We generalize the
formalism that has been developed for analogous studies of time-dependent
structure functions in fluid turbulence to MHD. By carrying out detailed
numerical studies of such time-dependent structure functions in a shell model
for three-dimensional MHD turbulence, we obtain both equal-time and dynamic
scaling exponents.


title: Spanning Trees in Multipartite Geometric Graphs

abstract:
  Let $R$ and $B$ be two disjoint sets of points in the plane where the points
of $R$ are colored red and the points of $B$ are colored blue, and let
$n=|R\cup B|$. A bichromatic spanning tree is a spanning tree in the complete
bipartite geometric graph with bipartition $(R,B)$. The minimum (respectively
maximum) bichromatic spanning tree problem is the problem of computing a
bichromatic spanning tree of minimum (respectively maximum) total edge length.
  1. We present a simple algorithm that solves the minimum bichromatic spanning
tree problem in $O(n\log^3 n)$ time. This algorithm can easily be extended to
solve the maximum bichromatic spanning tree problem within the same time bound.
It also can easily be generalized to multicolored point sets.
  2. We present $\Theta(n\log n)$-time algorithms that solve the minimum and
the maximum bichromatic spanning tree problems.
  3. We extend the bichromatic spanning tree algorithms and solve the
multicolored version of these problems in $O(n\log n\log k)$ time, where $k$ is
the number of different colors (or the size of the multipartition in a complete
multipartite geometric graph).


title:
Dynamical masses and non-homology of massive elliptical galaxies grown
  by dry mergers

abstract:
  We study whether dry merger-driven size growth of massive elliptical galaxies
depends on their initial structural concentration, and analyse the validity of
the homology hypothesis for virial mass determination in massive ellipticals
grown by dry mergers. High-resolution simulations of a few realistic merger
trees, starting with compact progenitors of different structural concentrations
(S\'ersic indices n), show that galaxy growth has little dependence on the
initial S\'ersic index (larger n leads to slightly larger size growth), and
depends more on other particulars of the merger history. We show that the
deposition of accreted matter in the outer parts leads to a systematic and
predictable breaking of the homology between remnants and progenitors, which we
characterize through the evolution, during the course of the merger history, of
virial coefficients K = GM/Re \sigma^2 associated to the most commonly-used
dynamical and stellar mass parameters. The virial coefficient for the luminous
mass, K , is about 50 per cent larger at the z = 2 start of the merger
evolution than in z = 0 remnants. Ignoring virial evolution leads to biased
virial mass estimates. We provide K corresponding to a variety of dynamical and
stellar mass parameters, and provide recipes for the dynamical determination of
galaxy masses. For massive, non-compact ellipticals, the popular expression M =
5 Re \sigma^2 / G underestimates the dynamical mass within the luminous body by
factors of up to 4; it instead provides an approximation to the total stellar
mass with smaller uncertainty than current stellar population models.


title:
Stability properties of the Euler-Korteweg system with nonmonotone
  pressures

abstract:
  We establish a relative energy framework for the Euler-Korteweg system with
non-convex energy. This allows us to prove weak-strong uniqueness and to show
convergence to a Cahn-Hilliard system in the large friction limit. We also use
relative energy to show that solutions of Euler-Korteweg with convex energy
converge to solutions of the Euler system in the vanishing capillarity limit,
as long as the latter admits sufficiently regular strong solutions.


title:
Charged-particle nuclear modification factors in PbPb and pPb collisions
  at sqrt(s[NN]) = 5.02 TeV

abstract:
  The spectra of charged particles produced within the pseudorapidity window
abs(eta) < 1 at sqrt(s[NN]) = 5.02 TeV are measured using 404 inverse
microbarns of PbPb and 27.4 inverse picobarns of pp data collected by the CMS
detector at the LHC in 2015. The spectra are presented over the transverse
momentum ranges spanning 0.5 < pt < 400 GeV in pp and 0.7 < pt < 400 GeV in
PbPb collisions. The corresponding nuclear modification factor, R[AA], is
measured in bins of collision centrality. The R[AA] in the 5% most central
collisions shows a maximal suppression by a factor of 7-8 in the pt region of
6-9 GeV. This dip is followed by an increase, which continues up to the highest
pt measured, and approaches unity in the vicinity of pt = 200 GeV. The R[AA] is
compared to theoretical predictions and earlier experimental results at lower
collision energies. The newly measured pp spectrum is combined with the pPb
spectrum previously published by the CMS Collaboration to construct the pPb
nuclear modification factor, R[pA], up to 120 GeV. For pt > 20 GeV, R[pA]
exhibits weak momentum dependence and shows a moderate enhancement above unity.


title: Local Geometry of Even Clifford Structures on Conformal Manifolds

abstract:
  We introduce the concept of a Clifford-Weyl structure on a conformal
manifold, which consists of an even Clifford structure parallel with respect to
the tensor product of a metric connection on the Clifford bundle and a Weyl
structure on the manifold. We show that the Weyl structure is necessarily
closed except for some "generic" low-dimensional instances, where explicit
examples of non-closed Clifford-Weyl structures can be constructed.


title:
Computational helioseismology in the frequency domain: acoustic waves in
  axisymmetric solar models with flows

abstract:
  Local helioseismology has so far relied on semi-analytical methods to compute
the spatial sensitivity of wave travel times to perturbations in the solar
interior. These methods are cumbersome and lack flexibility. Here we propose a
convenient framework for numerically solving the forward problem of
time-distance helioseismology in the frequency domain. The fundamental quantity
to be computed is the cross-covariance of the seismic wavefield. We choose
sources of wave excitation that enable us to relate the cross-covariance of the
oscillations to the Green's function in a straightforward manner. We illustrate
the method by considering the 3D acoustic wave equation in an axisymmetric
reference solar model, ignoring the effects of gravity on the waves. The
symmetry of the background model around the rotation axis implies that the
Green's function can be written as a sum of longitudinal Fourier modes, leading
to a set of independent 2D problems. We use a high-order finite-element method
to solve the 2D wave equation in frequency space. The computation is
`embarrassingly parallel', with each frequency and each azimuthal order solved
independently on a computer cluster. We compute travel-time sensitivity kernels
in spherical geometry for flows, sound speed, and density perturbations under
the first Born approximation. Convergence tests show that travel times can be
computed with a numerical precision better than one millisecond, as required by
the most precise travel-time measurements. The method presented here is
computationally efficient and will be used to interpret travel-time
measurements in order to infer, e.g., the large-scale meridional flow in the
solar convection zone. It allows the implementation of (full-waveform)
iterative inversions, whereby the axisymmetric background model is updated at
each iteration.