Abstract:
  The pion and kaon properties in a nuclear medium at nonvanishing temperature
as well as the QCD chiral condensate in the presence of a magnetic field for
various baryon densities are studied in the Nambu-Jona-Lasinio (NJL) model with
the help of the proper-time regularization (PTR) scheme, simulating a QCD
confinement. The density dependent of the quark mass in symmetric nuclear
matter is obtained from the quark-meson coupling (QMC) model, which shares the
same covariant feature with the NJL model, at quark level. We then analyze the
QCD chiral condensates, and dynamical masses for various baryon densities at
finite temperature and magnetic field as well as the pion and kaon masses, pion
and kaon weak-decay constants, pion-and kaon-quark coupling constants, and wave
function renormalization factors for various baryon densities at finite
temperature. We find that the QCD chiral condensates suppress with increasing
temperature and baryon density and enhance under the presence of a magnetic
field, which are consistent with other model predictions. Interestingly, the
wave function renormalization factors for the pion and kaon increase with
respect to temperature and reduce as the baryon density increases are found.


Abstract:
  The recent measurement of the muon anomalous magnetic moment by the Fermilab
E989 experiment, when combined with the previous result at BNL, has confirmed
the tension with the SM prediction at $4.2\,\sigma$ CL, strengthening the
motivation for new physics in the leptonic sector. Among the different particle
physics models that could account for such an excess, a gauged
$U(1)_{L_\mu-L_{\tau}}$ stands out for its simplicity. In this article, we
explore how the combination of data from different future probes can help
identify the nature of the new physics behind the muon anomalous magnetic
moment. In particular, we contrast $U(1)_{L_\mu-L_{\tau}}$ with an effective
$U(1)_{L_\mu}$-type model. We first show that muon fixed target experiments
(such as NA64$\mu$) will be able to measure the coupling of the hidden photon
to the muon sector in the region compatible with $(g-2)_\mu$, and will have
some sensitivity to the hidden photon's mass. We then study how experiments
looking for coherent elastic neutrino-nucleus scattering (CE$\nu$NS) at
spallation sources will provide crucial additional information on the kinetic
mixing of the hidden photon. When combined with NA64$\mu$ results, the
exclusion limits (or reconstructed regions) of future CE$\nu$NS detectors will
also allow for a better measurement of the mediator mass. Finally, the
observation of nuclear recoils from solar neutrinos in dark matter direct
detection experiments will provide unique information about the coupling of the
hidden photon to the tau sector. The signal expected for
$U(1)_{L_\mu-L_{\tau}}$ is larger than for $U(1)_{L_\mu}$ with the same kinetic
mixing, and future multi-ton liquid xenon proposals (such as DARWIN) have the
potential to confirm the former over the latter.


Abstract:
  We model the distribution of the observed profiles of 21 cm line emission
from neutral hydrogen (HI) in central galaxies selected from a statistically
representative mock catalog of the local Universe in the Lambda-cold dark
matter framework. The distribution of these HI velocity profiles (specifically,
their widths $W_{50}$) has been observationally constrained, but has not been
systematically studied theoretically. Our model profiles derive from rotation
curves of realistically baryonified haloes in an N-body simulation, including
the quasi-adiabatic relaxation of the dark matter profile of each halo in
response to its baryons. We study the predicted $W_{50}$ distribution using a
realistic pipeline applied to noisy profiles extracted from our
luminosity-complete mock catalog with an ALFALFA-like survey geometry and
redshift selection. Our default mock is in good agreement with observed ALFALFA
results for $W_{50}\gtrsim700$ km/s, being incomplete at lower widths due to
the intrinsic threshold of $M_r\leq-19$. Variations around the default model
show that the velocity width function at $W_{50}\gtrsim300$ km/s is most
sensitive to a possible correlation between galaxy inclination and host
concentration, followed by the physics of quasi-adiabatic relaxation. We also
study the excess kurtosis of noiseless velocity profiles, obtaining a
distribution which tightly correlates with $W_{50}$, with a shape and scatter
that depend on the properties of the turbulent HI disk. Our results open the
door towards using the shapes of HI velocity profiles as a novel statistical
probe of the baryon-dark matter connection.


Abstract:
  The autonomic nervous system (ANS) regulatory capacity begins before birth as
the sympathetic and parasympathetic activity contributes significantly to the
fetus' development. Several studies have shown how vagus nerve is involved in
many vital processes during fetal, perinatal and postnatal life: from the
regulation of inflammation through the anti-inflammatory cholinergic pathway,
which may affect the functioning of each organ, to the production of hormones
involved in bioenergetic metabolism. In addition, the vagus nerve has been
recognized as the primary afferent pathway capable of transmitting information
to the brain from every organ of the body. Therefore, this hypothesis paper
aims to review the development of ANS during fetal and perinatal life, focusing
particularly on the vagus nerve, to identify possible "critical windows" that
could impact its maturation. These "critical windows" could help clinicians
know when to monitor fetuses to effectively assess the developmental status of
both ANS and specifically the vagus nerve. In addition, this paper will focus
on which factors (i.e. fetal characteristics and behaviors, maternal lifestyle
and pathologies, placental health and dysfunction, labor, incubator conditions,
and drug exposure) may have an impact on the development of the vagus during
the above-mentioned "critical window" and how. This analysis could help
clinicians and stakeholders define precise guidelines for improving the
management of fetuses and newborns, particularly to reduce the potential
adverse environmental impacts on ANS development that may lead to persistent
long-term consequences. Since the development of ANS and the vagus influence
have been shown to be reflected in cardiac variability, this paper will rely in
particular on studies using fetal heart rate variability (fHRV) to monitor the
continued growth and health of both animal and human fetuses.


Abstract:
  Disfluency detection models now approach high accuracy on English text.
However, little exploration has been done in improving the size and inference
time of the model. At the same time, automatic speech recognition (ASR) models
are moving from server-side inference to local, on-device inference. Supporting
models in the transcription pipeline (like disfluency detection) must follow
suit. In this work we concentrate on the disfluency detection task, focusing on
small, fast, on-device models based on the BERT architecture. We demonstrate it
is possible to train disfluency detection models as small as 1.3 MiB, while
retaining high performance. We build on previous work that showed the benefit
of data augmentation approaches such as self-training. Then, we evaluate the
effect of domain mismatch between conversational and written text on model
performance. We find that domain adaptation and data augmentation strategies
have a more pronounced effect on these smaller models, as compared to
conventional BERT models.


Abstract:
  Understanding and modeling the dynamics of human gaze behavior in 360$^\circ$
environments is a key challenge in computer vision and virtual reality.
Generative adversarial approaches could alleviate this challenge by generating
a large number of possible scanpaths for unseen images. Existing methods for
scanpath generation, however, do not adequately predict realistic scanpaths for
360$^\circ$ images. We present ScanGAN360, a new generative adversarial
approach to address this challenging problem. Our network generator is tailored
to the specifics of 360$^\circ$ images representing immersive environments.
Specifically, we accomplish this by leveraging the use of a spherical
adaptation of dynamic-time warping as a loss function and proposing a novel
parameterization of 360$^\circ$ scanpaths. The quality of our scanpaths
outperforms competing approaches by a large margin and is almost on par with
the human baseline. ScanGAN360 thus allows fast simulation of large numbers of
virtual observers, whose behavior mimics real users, enabling a better
understanding of gaze behavior and novel applications in virtual scene design.


Abstract:
  We study the problem of maximizing the geometric mean of $d$ low-degree
non-negative forms on the real or complex sphere in $n$ variables. We show that
this highly non-convex problem is NP-hard even when the forms are quadratic and
is equivalent to optimizing a homogeneous polynomial of degree $O(d)$ on the
sphere. The standard Sum-of-Squares based convex relaxation for this polynomial
optimization problem requires solving a semidefinite program (SDP) of size
$n^{O(d)}$, with multiplicative approximation guarantees of
$\Omega(\frac{1}{n})$. We exploit the compact representation of this polynomial
to introduce a SDP relaxation of size polynomial in $n$ and $d$, and prove that
it achieves a constant factor multiplicative approximation when maximizing the
geometric mean of non-negative quadratic forms. We also show that this analysis
is asymptotically tight, with a sequence of instances where the gap between the
relaxation and true optimum approaches this constant factor as $d \rightarrow
\infty$. Next we propose a series of intermediate relaxations of increasing
complexity that interpolate to the full Sum-of-Squares relaxation, as well as a
rounding algorithm that finds an approximate solution from the solution of any
intermediate relaxation. Finally we show that this approach can be generalized
for relaxations of products of non-negative forms of any degree.


Abstract:
  In 1975, Don Zagier obtained a new version of the Kronecker limit formula for
a real quadratic field which involved an interesting function $F(x)$ which is
now known as the \emph{Herglotz function}. As demonstrated by Zagier, and very
recently by Radchenko and Zagier, $F(x)$ satisfies beautiful properties which
are of interest in both algebraic number theory as well as in analytic number
theory. In this paper, we study $\mathscr{F}_{k,N}(x)$, an extension of the
Herglotz function which also subsumes \emph{higher Herglotz function} of
Vlasenko and Zagier. We call it the \emph{extended higher Herglotz function}.
It is intimately connected with a certain generalized Lambert series. We derive
two different kinds of functional equations satisfied by
$\mathscr{F}_{k,N}(x)$. Radchenko and Zagier gave a beautiful relation between
the integral $\displaystyle\int_{0}^{1}\frac{\log(1+t^x)}{1+t}\, dt$ and $F(x)$
and used it to evaluate this integral at various rational as well as irrational
arguments. We obtain a relation between $\mathscr{F}_{k,N}(x)$ and a
generalization of the above integral involving polylogarithm. The asymptotic
expansions of $\mathscr{F}_{k, N}(x)$ and some generalized Lambert series are
also obtained along with other supplementary results.


Abstract:
  State of the art radial velocity (RV) exoplanet searches are limited by the
effects of stellar magnetic activity. Magnetically active spots, plage, and
network regions each have different impacts on the observed spectral lines, and
therefore on the apparent stellar RV. Differentiating the relative coverage, or
filling factors, of these active regions is thus necessary to differentiate
between activity-driven RV signatures and Doppler shifts due to planetary
orbits. In this work, we develop a technique to estimate feature-specific
magnetic filling factors on stellar targets using only spectroscopic and
photometric observations. We demonstrate linear and neural network
implementations of our technique using observations from the solar telescope at
HARPS-N, the HK Project at the Mt. Wilson Observatory, and the Total Irradiance
Monitor onboard SORCE. We then compare the results of each technique to direct
observations by the Solar Dynamics Observatory (SDO). Both implementations
yield filling factor estimates that are highly correlated with the observed
values. Modeling the solar RVs using these filling factors reproduces the
expected contributions of the suppression of convective blueshift and
rotational imbalance due to brightness inhomogeneities. Both implementations of
this technique reduce the overall activity-driven RMS RVs from 1.64 m/s to 1.02
m/s, corresponding to a 1.28 m/s reduction in the RMS variation. The technique
provides an additional 0.41 m/s reduction in the RMS variation compared to
traditional activity indicators.


Abstract:
  In meta-analysis of diagnostic test accuracy, summary receiver operating
characteristic (SROC) is a recommended method to summarize the discriminant
capacity of a diagnostic test in the presence of study-specific cutoff values
and the area under the SROC (SAUC) gives the aggregate measure of test
accuracy. SROC or SAUC can be estimated by bivariate modelling of pairs of
sensitivity and specificity over the primary diagnostic studies. However,
publication bias is a major threat to the validity of estimates in
meta-analysis. To address this issue, we propose to adopt sensitivity analysis
to make an objective inference for the impact of publication bias on SROC or
SAUC. We extend Copas likelihood based sensitivity analysis to the bivariate
normal model used for meta-analysis of diagnostic test accuracy to evaluate how
much SROC or SAUC would change with different selection probabilities under
several selective publication mechanisms dependent on sensitivity and/or
specificity. The selection probability is modelled by a selection function on
$t$-type statistic for the linear combination of logit-transformed sensitivity
and specificity, allowing the selective publication of each study to be
influenced by the cutoff-dependent $p$-value for sensitivity, specificity, or
diagnostic odds ratio. By embedding the selection function into the bivariate
normal model, the conditional likelihood is proposed and the bias-corrected
SROC or SAUC can be estimated by maximizing the likelihood. We illustrate the
proposed sensitivity analysis by reanalyzing a meta-analysis of test accuracy
for intravascular device related infection. Simulation studies are conducted to
investigate the performance of proposed methods.


Abstract:
  Video compression is a critical component of Internet video delivery. Recent
work has shown that deep learning techniques can rival or outperform
human-designed algorithms, but these methods are significantly less compute and
power-efficient than existing codecs. This paper presents a new approach that
augments existing codecs with a small, content-adaptive super-resolution model
that significantly boosts video quality. Our method, SRVC, encodes video into
two bitstreams: (i) a content stream, produced by compressing downsampled
low-resolution video with the existing codec, (ii) a model stream, which
encodes periodic updates to a lightweight super-resolution neural network
customized for short segments of the video. SRVC decodes the video by passing
the decompressed low-resolution video frames through the (time-varying)
super-resolution model to reconstruct high-resolution video frames. Our results
show that to achieve the same PSNR, SRVC requires 16% of the bits-per-pixel of
H.265 in slow mode, and 2% of the bits-per-pixel of DVC, a recent deep
learning-based video compression scheme. SRVC runs at 90 frames per second on a
NVIDIA V100 GPU.


Abstract:
  The success and superior performance of deep networks is spreading their
popularity and use to an increasing number of applications. Very recent works,
however, demonstrate that modern day deep networks suffer from
irreproducibility (also referred to as nondeterminism or underspecification).
Two or more models that are identical in architecture, structure, training
hyper-parameters, and parameters, and that are trained on exactly the same
training data, yield different predictions on individual previously unseen
examples. Thus, a model that performs well on controlled test data, may perform
in unexpected ways when deployed in the real world, whose data is expected to
be similar to the test data. We study simple synthetic models and data to
understand the origins of these problems. We show that even with a single
nonlinearity and for very simple data and models, irreproducibility occurs. Our
study demonstrates the effects of randomness in initialization, training data
shuffling window size, and activation functions on prediction
irreproducibility, even under very controlled synthetic data. While, as one
would expect, randomness in initialization and in shuffling the training
examples exacerbates the phenomenon, we show that model complexity and the
choice of nonlinearity also play significant roles in making deep models
irreproducible.


Abstract:
  We present our analysis of a significant data artifact in the official
2019/2021 ASVspoof Challenge Dataset. We identify an uneven distribution of
silence duration in the training and test splits, which tends to correlate with
the target prediction label. Bonafide instances tend to have significantly
longer leading and trailing silences than spoofed instances. In this paper, we
explore this phenomenon and its impact in depth. We compare several types of
models trained on a) only the duration of the leading silence and b) only on
the duration of leading and trailing silence. Results show that models trained
on only the duration of the leading silence perform particularly well, and
achieve up to 85% percent accuracy and an equal error rate (EER) of 15.1%. At
the same time, we observe that trimming silence during pre-processing and then
training established antispoofing models using signal-based features leads to
comparatively worse performance. In that case, EER increases from 3.6% (with
silence) to 15.5% (trimmed silence). Our findings suggest that previous work
may, in part, have inadvertently learned thespoof/bonafide distinction by
relying on the duration of silence as it appears in the official challenge
dataset. We discuss the potential consequences that this has for interpreting
system scores in the challenge and discuss how the ASV community may further
consider this issue.


Abstract:
  We present initial limit Datalog, a new extensible class of constrained Horn
clauses for which the satisfiability problem is decidable. The class may be
viewed as a generalisation to higher-order logic (with a simple restriction on
types) of the first-order language limit Datalog$_Z$ (a fragment of Datalog
modulo linear integer arithmetic), but can be instantiated with any suitable
background theory. For example, the fragment is decidable over any countable
well-quasi-order with a decidable first-order theory, such as natural number
vectors under componentwise linear arithmetic, and words of a bounded,
context-free language ordered by the subword relation. Formulas of initial
limit Datalog have the property that, under some assumptions on the background
theory, their satisfiability can be witnessed by a new kind of term model which
we call entwined structures. Whilst the set of all models is typically
uncountable, the set of all entwined structures is recursively enumerable, and
model checking is decidable.


Abstract:
  Accurate estimation of anthropometric body measurements from RGB images has
many potential applications in industrial design, online clothing, medical
diagnosis and ergonomics. Research on this topic is limited by the fact that
there exist only generated datasets which are based on fitting a 3D body mesh
to 3D body scans in the commercial CAESAR dataset. For 2D only silhouettes are
generated. To circumvent the data bottleneck, we introduce a new 3D scan
dataset of 2,675 female and 1,474 male scans. We also introduce a small dataset
of 200 RGB images and tape measured ground truth. With the help of the two new
datasets we propose a part-based shape model and a deep neural network for
estimating anthropometric measurements from 2D images. All data will be made
publicly available.


Abstract:
  In this letter, we present an update of a method for analysing arrival
directions of ultra-high-energy cosmic rays (UHECRs) above the
Greisen--Zatsepin--Kuz'min cut-off with a deep convolutional neural network
developed originally in Kalashev, Pshirkov, Zotov (2020). Namely, we introduce
energy as another variable employed in the analysis. This allows us to take
into account the intrinsic uncertainties in energy of primary cosmic rays
present in any experiment, which were not taken into account in the previous
study, without any loss of quality of the classifier. We present the
architecture of the new neural network, results of its application to mock maps
of UHECR arrival directions and outline possible directions of a further
improvement of the method.


Abstract:
  An electron-positron pair ($e^- e^+$) is created in vacuum above a critical
electric field strength $E_{crt}$ which is quite large in the laboratory scale.
The photon, thus the field, annihilates in the pair creation process. Here, we
question if the pair creation (at $E=E_{crt}$) introduces a boundary condition
in the electromagnetic state, e.g., similar to the one in angular or linear
momentum. We show that introduction of such a reasonable condition yields
approximately the well-known magnetic flux quanta which normally one obtains
using different arguments.


Abstract:
  Despite the immense popularity of the Automated Program Repair (APR) field,
the question of patch validation is still open. Most of the present-day
approaches follow the so-called Generate-and-Validate approach, where first a
candidate solution is being generated and after validated against an oracle.
The latter, however, might not give a reliable result, because of the
imperfections in such oracles; one of which is usually the test suite. Although
(re-) running the test suite is right under one's nose, in real life
applications the problem of over- and underfitting often occurs, resulting in
inadequate patches. Efforts that have been made to tackle with this problem
include patch filtering, test suite expansion, careful patch producing and many
more. Most approaches to date use post-filtering relying either on test
execution traces or make use of some similarity concept measured on the
generated patches. Our goal is to investigate the nature of these
similarity-based approaches. To do so, we trained a Doc2Vec model on an
open-source JavaScript project and generated 465 patches for 10 bugs in it.
These plausible patches alongside with the developer fix are then ranked based
on their similarity to the original program. We analyzed these similarity lists
and found that plain document embeddings may lead to misclassification - it
fails to capture nuanced code semantics. Nevertheless, in some cases it also
provided useful information, thus helping to better understand the area of
Automated Program Repair.


Abstract:
  In this work, we study the problem of clustering survival data $-$ a
challenging and so far under-explored task. We introduce a novel
semi-supervised probabilistic approach to cluster survival data by leveraging
recent advances in stochastic gradient variational inference. In contrast to
previous work, our proposed method employs a deep generative model to uncover
the underlying distribution of both the explanatory variables and censored
survival times. We compare our model to the related work on clustering and
mixture models for survival data in comprehensive experiments on a wide range
of synthetic, semi-synthetic, and real-world datasets, including medical
imaging data. Our method performs better at identifying clusters and is
competitive at predicting survival times. Relying on novel generative
assumptions, the proposed model offers a holistic perspective on clustering
survival data and holds a promise of discovering subpopulations whose survival
is regulated by different generative mechanisms.


Abstract:
  It has long been known that the coarse-grained approximation to the black
hole density of states can be computed using classical Euclidean gravity. In
this work we argue for another entry in the dictionary between Euclidean
gravity and black hole physics, namely that Euclidean wormholes describe a
coarse-grained approximation to the energy level statistics of black hole
microstates. To do so we use the method of constrained instantons to obtain an
integral representation of wormhole amplitudes in Einstein gravity and in
full-fledged AdS/CFT. These amplitudes are non-perturbative corrections to the
two-boundary problem in AdS quantum gravity. The full amplitude is likely UV
sensitive, dominated by small wormholes, but we show it admits an integral
transformation with a macroscopic, weakly curved saddle-point approximation.
The saddle is the "double cone" geometry of Saad, Shenker, and Stanford, with
fixed moduli. In the boundary description this saddle appears to dominate a
smeared version of the connected two-point function of the black hole density
of states, and suggests level repulsion in the microstate spectrum. Using these
methods we further study Euclidean wormholes in pure Einstein gravity and in
IIB supergravity on Euclidean AdS$_5\times\mathbb{S}^5$. We address the
perturbative stability of these backgrounds and study brane nucleation
instabilities in 10d supergravity. In particular, brane nucleation
instabilities of the Euclidean wormholes are lifted by the analytic
continuation required to obtain the Lorentzian spectral form factor from
gravity. Our results indicate a factorization paradox in AdS/CFT.


Abstract:
  The availability of accurate computational tools for modeling and simulation
is vital to accelerate the discovery of materials capable of storing hydrogen
(H2) under given parameters of pressure swing and temperature. Previously, we
compiled the H2Bind275 dataset consisting of equilibrium geometries and
assessed the performance of 55 density functionals over this dataset (Veccham,
S. P.; Head-Gordon, M. J. Chem. Theory Comput., 2020, 16, 4963--4982). As it is
crucial for computational tools to accurately model the entire potential energy
curve (PEC), in addition to the equilibrium geometry, we have extended this
dataset with 389 new data points to include two compressed and three elongated
geometries along 78 PECs for H2 binding, forming the H2Bind78x7 dataset.
Assessing the performance of 55 density functionals on this significantly
larger and more comprehensive H2Bind78x7 dataset, we have identified the best
performing density functionals for H2 binding applications: PBE0-DH,
$\omega$B97X-V, $\omega$B97M-V, and DSD-PBEPBE-D3(BJ). Addition of Hartree Fock
exchange improves the performance of density functionals, albeit not uniformly
throughout the PEC. We recommend the usage of wB97X-V and wB97M-V density
functionals as they give good performance for both geometries and energies. In
addition, we have also identified B97M-V and B97M-rV as the best semi-local
density functionals for predicting H2 binding energy at its equilibrium
geometry.


Abstract:
  We present an exact analytical solution to the problem of shear dispersion
given a general initial condition. The solution is expressed as an infinite
series expansion involving Mathieu functions and their eigenvalues. The
eigenvalue system depends on the imaginary parameter $q=2ik$Pe, with $k$ the
wavenumber that determines the tracer scale in the initial condition and Pe the
P\'{e}clet number. Solutions are valid for all Pe, $t>0$, and $k>0$ except at
specific values of $q=q_{\ell}^{EP}$ called Exceptional Points (EPs), the first
occurring at $q_{0}^{EP}\approx1.468i$. For values of $q \lessapprox 1.468i$,
all the eigenvalues are real, different and eigenfunctions decay with time,
thus shear dispersion can be represented as a diffusive process. For values of
$q \gtrapprox 1.468i$, pairs of eigenvalues coalesce at EPs becoming complex
conjugates, the eigenfunctions propagate and decay with time, and so shear
dispersion is no longer a purely diffusive process.
  The limit $q\rightarrow0$ is approached by the small P\'{e}clet number limit
for all finite $k>0$, or equally by the large P\'{e}clet number limit as long
as $2k \ll 1/$Pe. The latter implies $k\rightarrow0$, strong separation of
scales between the tracer and flow. The limit $q\rightarrow\infty$ results from
large P\'{e}clet number for any $k>0$, or from large $k$ and non-vanishing Pe.
  We derive an exact closure that is continuous in wavenumber space. At small
$q$, the closure approaches a diffusion operator with an effective diffusivity
proportional to $U_0^2/\kappa$, for flow speed $U_0$ and diffusivity $\kappa$.
At large $q$, the closure approaches the sum of an advection operator plus a
half-derivative operator (differential operator of fractional order), the
latter with coefficient proportional to $\sqrt{\kappa U_0}$.


Abstract:
  We give quantitative estimates of the Bergman distance through positivity of
capacity densities.


Abstract:
  Here we present the derivation, description and results of a Monte
Carlo-based algorithm for simulating inelastic scattering of photo-electrons
when passing through some scattering medium, such as a gas atmosphere or a
solid material. The code used to run these simulations was written in python
and is freely available online
(https://github.com/surfaceanalytics/montecarlo_electron_scattering).


Abstract:
  In this paper we present a benchmark solution with higher number of
continuous and discrete states and control levers using validated powertrain
component models, where DP fails due to exponential rise in the computation
time. The problem involves 13 states and 4 control levers, with complex
interactions between multiple subsystems. Some of these variables are discrete
while some are continuous. Some have slow dynamics while some have fast
dynamics. A novel three step PS3 algorithm [1] which is presented in our
prequel paper is used to obtain a near-optimal solution. PS3 algorithm makes
use of pseudo spectral method for accurate state estimations. We present three
scenarios where only fuel is minimized, only emissions are minimized and,
lastly a combination of both fuel and emissions are minimized. All three cases
are analyzed for their performance and computation time. The optimal compromise
between fuel consumption and emissions are analyzed using a Pareto-front study.
This large-scale powertrain optimization problem is solved for a P2 parallel
hybrid architecture on a class 6 pick-up & delivery truck.


Abstract:
  We study arithmetic of the algebraic varieties defined over number fields by
applying Lagrange interpolation to fibrations.
  Assuming the finiteness of the Tate-Shafarevich group of a certain elliptic
curve, we show, for Ch\^atelet surface bundles over curves, that the violation
of Hasse principle being accounted for by the Brauer-Manin obstruction is not
invariant under an arbitrary finite extension of the ground field.


Abstract:
  Hand modeling is critical for immersive VR/AR, action understanding, or human
healthcare. Existing parametric models account only for hand shape, pose, or
texture, without modeling the anatomical attributes like bone, which is
essential for realistic hand biomechanics analysis. In this paper, we present
PIANO, the first parametric bone model of human hands from MRI data. Our PIANO
model is biologically correct, simple to animate, and differentiable, achieving
more anatomically precise modeling of the inner hand kinematic structure in a
data-driven manner than the traditional hand models based on the outer surface
only. Furthermore, our PIANO model can be applied in neural network layers to
enable training with a fine-grained semantic loss, which opens up the new task
of data-driven fine-grained hand bone anatomic and semantic understanding from
MRI or even RGB images. We make our model publicly available.


Abstract:
  We discuss new ideas for consideration of loop diagrams and angular integrals
in $D$-dimensions in QCD. In case of loop diagrams, we propose the covariant
formalism of expansion of tensorial loop integrals into the orthogonal basis of
linear combinations of external momenta. It gives a very simple presentation
for the final results and is more convenient for calculations on computer
algebra systems. In case of angular integrals we demonstrate how to simplify
the integration of differential cross sections over polar angles. Also we
derive the recursion relations, which allow to reduce all occurring angular
integrals to a short set of basic scalar integrals. All order
$\epsilon$-expansion is given for all angular integrals with up to two
denominators based on the expansion of the basic integrals and using recursion
relations. A geometric picture for partial fractioning is developed which
provides a new rotational invariant algorithm to reduce the number of
denominators.


Abstract:
  Diverse promising datasets have been designed to hold back the development of
fake audio detection, such as ASVspoof databases. However, previous datasets
ignore an attacking situation, in which the hacker hides some small fake clips
in real speech audio. This poses a serious threat since that it is difficult to
distinguish the small fake clip from the whole speech utterance. Therefore,
this paper develops such a dataset for half-truth audio detection (HAD).
Partially fake audio in the HAD dataset involves only changing a few words in
an utterance.The audio of the words is generated with the very latest
state-of-the-art speech synthesis technology. We can not only detect fake
uttrances but also localize manipulated regions in a speech using this dataset.
Some benchmark results are presented on this dataset. The results show that
partially fake audio presents much more challenging than fully fake audio for
fake audio detection.


Abstract:
  Citizens' assemblies need to represent subpopulations according to their
proportions in the general population. These large committees are often
constructed in an online fashion by contacting people, asking for the
demographic features of the volunteers, and deciding to include them or not.
This raises a trade-off between the number of people contacted (and the
incurring cost) and the representativeness of the committee. We study three
methods, theoretically and experimentally: a greedy algorithm that includes
volunteers as long as proportionality is not violated; a non-adaptive method
that includes a volunteer with a probability depending only on their features,
assuming that the joint feature distribution in the volunteer pool is known;
and a reinforcement learning based approach when this distribution is not known
a priori but learnt online.


Abstract:
  We report our analyses of the multi-epoch (2015-2017) ALMA archival data of
the Class II binary system XZ Tau at Bands 3, 4 and 6. The millimeter dust
continuum images show compact, unresolved (r <~ 15 au) circumstellar disks
(CSDs) around the individual binary stars; XZ Tau A and B, with a projected
separation of ~ 39 au. The 12CO (2-1) emission associated with those CSDs
traces the Keplerian rotations, whose rotational axes are misaligned with each
other (P.A. ~ -5 deg for XZ Tau A and ~ 130 deg for XZ Tau B). The similar
systemic velocities of the two CSDs (VLSR ~ 6.0 km s-1) suggest that the
orbital plane of the binary stars is close to the plane of the sky. From the
multi-epoch ALMA data, we have also identified the relative orbital motion of
the binary. Along with the previous NIR data, we found that the elliptical
orbit (e = 0.742+0.025-0.034, a = 0''.172+0''.002-0''.003, and {\omega} =
-54.2+2.0-4.7 deg) is preferable to the circular orbit. Our results suggest
that the two CSDs and the orbital plane of the XZ Tau system are all misaligned
with each other, and possible mechanisms to produce such a configuration are
discussed. Our analyses of the multi-epoch ALMA archival data demonstrate the
feasibility of time-domain science with ALMA.


Abstract:
  Results for high multiplicity pp and p-Pb collisions at the LHC have revealed
that these small collision systems exhibit features of collectivity. To
understand the origin of these unexpected phenomena, the relative transverse
activity classifier ($R_{\rm{T}}$) can be exploited as a tool to disentangle
soft and hard particle production, by studying the yield of charged particles
in different topological regions associated with transverse momentum trigger
particles. This allows to study system size dependence of charged particle
production of different origins and in particular search for jet-quenching
effects. Here, results on the system size and $R_{\rm{T}}$ dependence of
charged particle production in pp, p-Pb and Pb-Pb collisions at $\sqrt{s_{\rm
NN}}$ = 5.02 TeV are presented.


Abstract:
  This paper addresses unsupervised person re-identification (Re-ID) using
multi-label prediction and classification based on graph-structural insight.
Our method extracts features from person images and produces a graph that
consists of the features and a pairwise similarity of them as nodes and edges,
respectively. Based on the graph, the proposed graph structure based
multi-label prediction (GSMLP) method predicts multi-labels by considering the
pairwise similarity and the adjacency node distribution of each node. The
multi-labels created by GSMLP are applied to the proposed selective multi-label
classification (SMLC) loss. SMLC integrates a hard-sample mining scheme and a
multi-label classification. The proposed GSMLP and SMLC boost the performance
of unsupervised person Re-ID without any pre-labelled dataset. Experimental
results justify the superiority of the proposed method in unsupervised person
Re-ID by producing state-of-the-art performance. The source code for this paper
is publicly available on 'https://github.com/uknownpioneer/GSMLP-SMLC.git'.


Abstract:
  In this work, we present near-field image transmission and error vector
magnitude measurement in a rich scattering environment in a metal enclosure. We
check the effect of loading metal enclosure on the performance of SDR based
near-field communication link. We focus on the key communication receiver
parameters to observe the effect of near-field link in presence of
rich-scattering and in presence of loading with RF absorber cones. The
near-field performance is measured by transmitting wideband OFDM-modulated
packets containing image information. Our finding suggests that the performance
of OFDM based wideband near-field communication improves when the metal
enclosure is loaded with RF absorbers. Near-field EVM improves when the
enclosure is loaded with RF absorber cones. Loading of the metal enclosure has
the effect of increased coherence bandwidth. Frequency selectivity was observed
in an empty enclosure which suggests coherence bandwidth less than the signal
bandwidth.


Abstract:
  Learning sensorimotor control policies from high-dimensional images crucially
relies on the quality of the underlying visual representations. Prior works
show that structured latent space such as visual keypoints often outperforms
unstructured representations for robotic control. However, most of these
representations, whether structured or unstructured are learned in a 2D space
even though the control tasks are usually performed in a 3D environment. In
this work, we propose a framework to learn such a 3D geometric structure
directly from images in an end-to-end unsupervised manner. The input images are
embedded into latent 3D keypoints via a differentiable encoder which is trained
to optimize both a multi-view consistency loss and downstream task objective.
These discovered 3D keypoints tend to meaningfully capture robot joints as well
as object movements in a consistent manner across both time and 3D space. The
proposed approach outperforms prior state-of-art methods across a variety of
reinforcement learning benchmarks. Code and videos at
https://buoyancy99.github.io/unsup-3d-keypoints/


Abstract:
  Transistors operating at high frequencies are the basic building blocks of
millimeter-wave communication and sensor systems. The high velocity and
mobility of carriers in graphene can open way for ultra-fast group IV
transistors with similar or even better performance than can be achieved with
III-V based semiconductors. However, the progress of high-speed graphene
transistors has been hampered due to fabrication issues, influence of adjacent
materials, and self-heating effects. Here, we report a graphene field-effect
transistor (FET) on a diamond substrate, with a $f_{max}$ up to 54 GHz for a
gate length of 500 nm. The high thermal conductivity of diamond provides an
efficient heat-sink, and its relatively high optical-phonon energy improves
saturation velocity of carriers in the graphene channel. Moreover, we show that
graphene FETs on diamond, with different gate lengths, exhibit excellent
scaling behavior. These results indicate that graphene FETs on diamond
technology can reach sub-terahertz frequency performance.


Abstract:
  We introduce in this work an efficient numerical method for the simulation of
the quantum Liouville-BGK equation, which models the diffusive transport of
quantum particles. The corner stone to the model is the BGK collision operator,
obtained by minimizing the quantum free energy under the constraint that the
local density of particles is conserved during collisions. This leads to a
large system of coupled nonlinear nonlocal PDEs whose resolution is
challenging. We then define a splitting scheme that separates the transport and
the collision parts, which, exploiting the local conservation of particles,
leads to a fully linear collision step. The latter involves the resolution of a
constrained optimization problem that is is handled with the nonlinear
conjugate gradient algorithm. We prove that the time semi-discrete scheme is
convergent, and as an application of our numerical scheme, we validate the
quantum drift-diffusion model that is obtained as the diffusive limit of the
quantum Liouville-BGK equation.


Abstract:
  In this paper, we propose a new Fully Composite Formulation of convex
optimization problems. It includes, as a particular case, the problems with
functional constraints, max-type minimization problems, and problems of
Composite Minimization, where the objective can have simple nondifferentiable
components. We treat all these formulations in a unified way, highlighting the
existence of very natural optimization schemes of different order. We prove the
global convergence rates for our methods under the most general conditions.
Assuming that the upper-level component of our objective function is
subhomogeneous, we develop efficient modification of the basic Fully Composite
first-order and second-order Methods, and propose their accelerated variants.


Abstract:
  Analytic solutions to the nonlinear radiation diffusion equation with an
instantaneous point source for a non-homogeneous medium with a power law
spatial density profile, are presented. The solutions are a generalization of
the well known solutions for a homogeneous medium. It is shown that the
solutions take various qualitatively different forms according to the value of
the spatial exponent. These different forms are studied in detail for linear
and non linear heat conduction. In addition, by inspecting the generalized
solutions, we show that there exist values of the spatial exponent such the
conduction front has constant speed or even accelerates. Finally, the various
solution forms are compared in detail to numerical simulations, and a good
agreement is achieved.


Abstract:
  We performed integrated modelling of the chemical pathways of formation for
boron nitride nanotube (BNNT) precursors during high-temperature synthesis in a
B/N2 mixture. Modelling includes quantum chemistry, quantum-classical molecular
dynamics, thermodynamic, and kinetic approaches. It is shown that BN compounds
are formed in the interaction of N2 molecules with small boron clusters (N2
molecule fixation) rather than with less reactive liquid boron. We demonstrate
that the transformation and consumption of liquid boron proceeds through the
evaporation of clusters, Bm with m less than or equal to 5 and their subsequent
conversion into BmNn chains. The production of such chains is crucial to the
growth of BNNTs because these chains form the building blocks of bigger and
longer BN chains and rings, which are themselves the building blocks of
fullborenes and BNNTs. Moreover, kinetic modelling revealed that B4N4 and B5N4
species play a major role in the N2 molecule fixation process. The formation of
these species via reactions with B4 and B5 clusters is not adequately described
under the assumption of thermodynamic equilibrium because the accumulation of
both B4N4 and B5N4 depends on the background gas pressure and the gas cooling
rate. Long BN chains and rings, which are precursors of the fullborene and BNNT
growth, form via self-assembly of component B4N4 and B5N4. Our modelling
results (particularly the increased densities of B4N4 and B5N4 species at
higher gas pressures) explain the experimentally observed effect of gas
pressure on the yield of high-quality BNNTs. The catalytic role of hydrogen was
also studied; it is shown that HBNH molecules can be the main precursor of BNNT
synthesis in the presence of hydrogen.


Abstract:
  The next-generation Versatile Video Coding (VVC) standard introduces a new
Multi-Type Tree (MTT) block partitioning structure that supports Binary-Tree
(BT) and Ternary-Tree (TT) splits in both vertical and horizontal directions.
This new approach leads to five possible splits at each block depth and thereby
improves the coding efficiency of VVC over that of the preceding High
Efficiency Video Coding (HEVC) standard, which only supports Quad-Tree (QT)
partitioning with a single split per block depth. However, MTT also has brought
a considerable impact on encoder computational complexity. In this paper, a
two-stage learning-based technique is proposed to tackle the complexity
overhead of MTT in VVC intra encoders. In our scheme, the input block is first
processed by a Convolutional Neural Network (CNN) to predict its spatial
features through a vector of probabilities describing the partition at each 4x4
edge. Subsequently, a Decision Tree (DT) model leverages this vector of spatial
features to predict the most likely splits at each block. Finally, based on
this prediction, only the N most likely splits are processed by the
Rate-Distortion (RD) process of the encoder. In order to train our CNN and DT
models on a wide range of image contents, we also propose a public VVC frame
partitioning dataset based on existing image dataset encoded with the VVC
reference software encoder. Our proposal relying on the top-3 configuration
reaches 46.6% complexity reduction for a negligible bitrate increase of 0.86%.
A top-2 configuration enables a higher complexity reduction of 69.8% for 2.57%
bitrate loss. These results emphasis a better trade-off between VTM intra
coding efficiency and complexity reduction compared to the state-of-the-art
solutions.


Abstract:
  Deep neural networks (DNNs) play an important role in machine learning due to
its outstanding performance compared to other alternatives. However, DNNs are
not suitable for safety-critical applications since DNNs can be easily fooled
by well-crafted adversarial examples. One promising strategy to counter
adversarial attacks is to utilize spectral normalization, which ensures that
the trained model has low sensitivity towards the disturbance of input samples.
Unfortunately, this strategy requires exact computation of spectral norm, which
is computation intensive and impractical for large-scale networks. In this
paper, we introduce an approximate algorithm for spectral normalization based
on Fourier transform and layer separation. The primary contribution of our work
is to effectively combine the sparsity of weight matrix and decomposability of
convolution layers. Extensive experimental evaluation demonstrates that our
framework is able to significantly improve both time efficiency (up to 60\%)
and model robustness (61\% on average) compared with the state-of-the-art
spectral normalization.


Abstract:
  Let $S_p(n)$ denote the sum of $p$th powers of the first $n$ positive
integers $1^p + 2^p + \cdots + n^p$. In this paper, first we express $S_p(n)$
in the so-called Faulhaber form, namely, as an even or odd polynomial in $(n +
1/2)$, according as $p$ is odd or even. Then, using the relation $S_p(n) -
S_p(n-1) = n^p$, we derive a recursive formula for the associated Faulhaber
coefficients. Applying Cramer's rule to the corresponding system of equations,
we obtain an explicit determinant formula for the said coefficients.
Furthermore, we show how to convert the (even or odd) Faulhaber polynomials in
$(n+ 1/2)$ into polynomials in $S_1(n)$ for any arbitrary $p$, and vice versa.


Abstract:
  We propose an uncertainty-aware service approach to provide drone-based
delivery services called Drone-as-a-Service (DaaS) effectively. Specifically,
we propose a service model of DaaS based on the dynamic spatiotemporal features
of drones and their in-flight contexts. The proposed DaaS service approach
consists of three components: scheduling, route-planning, and composition.
First, we develop a DaaS scheduling model to generate DaaS itineraries through
a Skyway network. Second, we propose an uncertainty-aware DaaS route-planning
algorithm that selects the optimal Skyways under weather uncertainties. Third,
we develop two DaaS composition techniques to select an optimal DaaS
composition at each station of the planned route. A spatiotemporal DaaS
composer first selects the optimal DaaSs based on their spatiotemporal
availability and drone capabilities. A predictive DaaS composer then utilises
the outcome of the first composer to enable fast and accurate DaaS composition
using several Machine Learning classification methods. We train the classifiers
using a new set of spatiotemporal features which are in addition to other DaaS
QoS properties. Our experiments results show the effectiveness and efficiency
of the proposed approach.


Abstract:
  Feature selection identifies subsets of informative features and reduces
dimensions in the original feature space, helping provide insights into data
generation or a variety of domain problems. Existing methods mainly depend on
feature scoring functions or sparse regularizations; nonetheless, they have
limited ability to reconcile the representativeness and inter-correlations of
features. In this paper, we introduce a novel, simple yet effective
regularization approach, named top-$k$ regularization, to supervised feature
selection in regression and classification tasks. Structurally, the top-$k$
regularization induces a sub-architecture on the architecture of a learning
model to boost its ability to select the most informative features and model
complex nonlinear relationships simultaneously. Theoretically, we derive and
mathematically prove a uniform approximation error bound for using this
approach to approximate high-dimensional sparse functions. Extensive
experiments on a wide variety of benchmarking datasets show that the top-$k$
regularization is effective and stable for supervised feature selection.


Abstract:
  Low-light imaging on mobile devices is typically challenging due to
insufficient incident light coming through the relatively small aperture,
resulting in a low signal-to-noise ratio. Most of the previous works on
low-light image processing focus either only on a single task such as
illumination adjustment, color enhancement, or noise removal; or on a joint
illumination adjustment and denoising task that heavily relies on short-long
exposure image pairs collected from specific camera models, and thus these
approaches are less practical and generalizable in real-world settings where
camera-specific joint enhancement and restoration is required. To tackle this
problem, in this paper, we propose a low-light image processing framework that
performs joint illumination adjustment, color enhancement, and denoising.
Considering the difficulty in model-specific data collection and the ultra-high
definition of the captured images, we design two branches: a coefficient
estimation branch as well as a joint enhancement and denoising branch. The
coefficient estimation branch works in a low-resolution space and predicts the
coefficients for enhancement via bilateral learning, whereas the joint
enhancement and denoising branch works in a full-resolution space and
progressively performs joint enhancement and denoising. In contrast to existing
methods, our framework does not need to recollect massive data when being
adapted to another camera model, which significantly reduces the efforts
required to fine-tune our approach for practical usage. Through extensive
experiments, we demonstrate its great potential in real-world low-light imaging
applications when compared with current state-of-the-art methods.


Abstract:
  Recent measurements of Galactic polarized dust emission have found a nonzero
$TB$ signal, a correlation between the total intensity and the $B$-mode
polarization component. We present evidence that this parity-odd signal is
driven by the relative geometry of the magnetic field and the filamentary
interstellar medium in projection. Using neutral hydrogen morphology and Planck
polarization data, we find that the angle between intensity structures and the
plane-of-sky magnetic field orientation is predictive of the signs of Galactic
$TB$ and $EB$. Our results suggest that magnetically misaligned filamentary
dust structures introduce nonzero $TB$ and $EB$ correlations in the dust
polarization, and that the intrinsic dust $EB$ can be predicted from
measurements of dust $TB$ and $TE$ over the same sky mask. We predict
correlations between $TE$, $TB$, $EB$, and $EE/BB$, and confirm our predictions
using synthetic dust polarization maps from magnetohydrodynamic simulations. We
introduce and measure a scale-dependent effective magnetic misalignment angle,
$\psi_\ell^{dust} \sim 5^\circ$ for $100 \lesssim \ell \lesssim 500$, and
predict a positive intrinsic dust $EB$ with amplitude $\left<D_\ell^{EB}\right>
\lesssim 2.5~\mu\mathrm{K^2_{CMB}}$ for the same multipole range at 353 GHz
over our sky mask. Both the sign and amplitude of the Galactic $EB$ signal can
change with the sky area considered. Our results imply that searches for parity
violation in the cosmic microwave background must account for the nonzero
Galactic $EB$ and $TB$ signals, necessitating revision of existing analyses of
the evidence for cosmic birefringence.


Abstract:
  This paper presents new designs of graph convolutional neural networks (GCNs)
on 3D meshes for 3D object segmentation and classification. We use the faces of
the mesh as basic processing units and represent a 3D mesh as a graph where
each node corresponds to a face. To enhance the descriptive power of the graph,
we introduce a 1-ring face neighbourhood structure to derive novel
multi-dimensional spatial and structure features to represent the graph nodes.
Based on this new graph representation, we then design a densely connected
graph convolutional block which aggregates local and regional features as the
key construction component to build effective and efficient practical GCN
models for 3D object classification and segmentation. We will present
experimental results to show that our new technique outperforms state of the
art where our models are shown to have the smallest number of parameters and
consietently achieve the highest accuracies across a number of benchmark
datasets. We will also present ablation studies to demonstrate the soundness of
our design principles and the effectiveness of our practical models.


Abstract:
  We consider the problem of goodness-of-fit testing for a model that has at
least one unknown parameter that cannot be eliminated by transformation.
Examples of such problems can be as simple as testing whether a sample consists
of independent Gamma observations, or whether a sample consists of independent
Generalised Pareto observations given a threshold. Over time the approach to
determining the distribution of a test statistic for such a problem has moved
towards on-the-fly calculation post observing a sample. Modern approaches
include the parametric bootstrap and posterior predictive checks. We argue that
these approaches are merely approximations to integrating over the posterior
predictive distribution that flows naturally from a given model. Further, we
attempt to demonstrate that shortcomings which may be present in the parametric
bootstrap, especially in small samples, can be reduced through the use of
objective Bayes techniques, in order to more reliably produce a test with the
correct size.


Abstract:
  We study experimentally and theoretically the temperature dependence of
transverse magnetic routing of light emission from hybrid
plasmonic-semiconductor quantum well structures where the exciton emission from
the quantum well is routed into surface plasmon polaritons propagating along a
nearby semiconductor-metal interface. In II-VI and III-V direct band
semiconductors the magnitude of routing is governed by the circular
polarization of exciton optical transitions, that is induced by a magnetic
field. For structures comprising a (Cd,Mn)Te/(Cd,Mg)Te diluted magnetic
semiconductor quantum well we observe a strong directionality of the emission
up to 15% at low temperature of 20 K and magnetic field of 485 mT due to giant
Zeeman splitting of holes mediated via the strong exchange interaction with
Mn$^{2+}$ ions. For increasing temperatures towards room-temperature the
magnetic susceptibility decreases and the directionality strongly decreases to
4% at T = 45 K. We also propose an alternative design based on a non-magnetic
(In,Ga)As/(In,Al)As quantum well structure, suitable for higher temperatures.
According to our calculations, such structure can demonstrate emission
directionality up to 5% for temperatures below 200 K and moderate magnetic
fields of 1 T.


Abstract:
  Adapting optical microscopy methods for nanoscale characterization of defects
in two-dimensional (2D) materials is a vital step for photonic on-chip devices.
To increase the analysis throughput, waveguide-based on-chip imaging platforms
have been recently developed. Their inherent disadvantage, however, is the
necessity to transfer the 2D material from the growth substrate to the imaging
chip which introduces contamination, potentially altering the characterization
results. Here we present a unique approach to circumvent these shortfalls by
directly growing a widely-used 2D material (hexagonal boron nitride, hBN) on
silicon nitride chips, and optically characterizing the defects in the intact
as-grown material. We compare the direct growth approach to the standard wet
transfer method, and confirm the clear advantages of the direct growth. While
demonstrated with hBN in the current work, the method is easily extendable to
other 2D materials.


Abstract:
  This research concerns the estimation of latent linear or polychoric
correlations from fuzzy frequency tables. Fuzzy counts are of particular
interest to many disciplines including social and behavioral sciences, and are
especially relevant when observed data are classified using fuzzy categories -
as for socio-economic studies, clinical evaluations, content analysis,
inter-rater reliability analysis - or when imprecise observations are
classified into either precise or imprecise categories - as for the analysis of
ratings data or fuzzy coded variables. In these cases, the space of count
matrices is no longer defined over naturals and, consequently, the polychoric
estimator cannot be used to accurately estimate latent linear correlations. The
aim of this contribution is twofold. First, we illustrate a computational
procedure based on generalized natural numbers for computing fuzzy frequencies.
Second, we reformulate the problem of estimating latent linear correlations
from fuzzy counts in the context of Expectation-Maximization based maximum
likelihood estimation. A simulation study and two applications are used to
investigate the characteristics of the proposed method. Overall, the results
show that the fuzzy EM-based polychoric estimator is more efficient to deal
with imprecise count data as opposed to standard polychoric estimators that may
be used in this context.


Abstract:
  This letter presents a combined measurement of the energy spectra of
atmospheric $\nu_e$ and $\nu_\mu$ in the energy range between $\sim$100 GeV and
$\sim$50 TeV with the ANTARES neutrino telescope. The analysis uses 3012 days
of detector livetime in the period 2007--2017, and selects 1016 neutrinos
interacting in (or close to) the instrumented volume of the detector, yielding
shower-like events (mainly from $\nu_e+\overline \nu_e$ charged current plus
all neutrino neutral current interactions) and starting track events (mainly
from $\nu_\mu + \overline \nu_\mu$ charged current interactions). The
contamination by atmospheric muons in the final sample is suppressed at the
level of a few per mill by different steps in the selection analysis, including
a Boosted Decision Tree classifier. The distribution of reconstructed events is
unfolded in terms of electron and muon neutrino fluxes. The derived energy
spectra are compared with previous measurements that, above 100 GeV, are
limited to experiments in polar ice and, for $\nu_\mu$, to Super-Kamiokande.


Abstract:
  The Android operating system is the most spread mobile platform in the world.
Therefor attackers are producing an incredible number of malware applications
for Android. Our aim is to detect Android's malware in order to protect the
user. To do so really good results are obtained by dynamic analysis of
software, but it requires complex environments. In order to achieve the same
level of precision we analyze the machine code and investigate the frequencies
of ngrams of opcodes in order to detect singular code blocks. This allow us to
construct a database of infected code blocks. Then, because attacker may modify
and organized differently the infected injected code in their new malware, we
perform not only a semantic comparison of the tested software with the database
of infected code blocks but also a structured comparison. To do such comparison
we compute subgraph isomorphism. It allows us to characterize precisely if the
tested software is a malware and if so in witch family it belongs. Our method
is tested both on a laboratory database and a set of real data. It achieves an
almost perfect detection rate.


Abstract:
  The emission of multi-MeV ($\gamma$-ray) photons from the interaction of a
high-powered laser pulse with a dense plasma target is studied using
particle-in-cell simulations. A new set of diagnostic techniques is presented
and applied to analyze the intense field and ultra-relativistic electrons. Such
methods elucidate the dominant processes responsible for efficient
laser-to-$\gamma$ energy conversion, which include nonlinear Compton scattering
and magneto-bremsstrahlung radiation, and provide a clear picture of the
interaction on a microscopic level. We identify regions in the plasma target of
high photon energy-density and obtain an energy conversion efficiency as high
as 30%. The essential characteristics of the interaction are validated with
full-3D simulations.


Abstract:
  We study the problem of a planner who resolves risk-return trade-offs - like
financial investment decisions - on behalf of a collective of agents with
heterogeneous risk preferences. The planner's objective is a two-stage utility
functional where an outer utility function is applied to the distribution of
the agents' certainty equivalents from a given decision. Assuming lognormal
risks and heterogeneous power utility preferences for the agents, we
characterize optimal behavior in a setting where the planner can let each agent
choose between different options from a fixed menu of possible decisions,
leading to a grouping of the agents by risk preferences. These optimal decision
menus are derived first for the case where the planner knows the distribution
of preferences exactly and then for a case where he faces uncertainty about
this distribution, only having access to upper and lower bounds on agents'
relative risk aversion. Finally, we provide tight bounds on the welfare loss
from offering a finite menu of choices rather than fully personalized
decisions.


Abstract:
  CPT invariance is one of the most fundamental symmetries in nature and it
plays a major role in the formulation of Quantum Field Theory. Although no
definitive signal of CPT violation has been observed so far, there are many
reasons to carefully investigate various low-energy phenomena that can provide
better probes to test CPT symmetry. In this context, neutrino experiments are
expected to provide more stringent bounds on CPT invariance violation when
compared to the existing bounds from the Kaon system. In this work, we
investigate the sensitivity of the upcoming long-baseline experiments: Hyper
Kamiokande (T2HK, T2HKK), ESSnuSB and DUNE to constrain the CPT violating
parameters $\Delta(\delta_{CP})$, $\Delta(m^2_{31})$ and $\Delta(\sin^2
\theta_{23})$, which characterize the difference between neutrino and
antineutrino oscillation parameters. Further, we analyse neutrino and
antineutrino data independently and constrain the oscillation parameters
governing them by considering the combination of these experiments (DUNE+T2HKK
and DUNE+ESSnuSB). In addition, assuming CPT symmetry is violated in nature, we
study the individual ability of the aforementioned experiments to establish CPT
violation. We found that the experiments Hyper-K (T2HK, T2HKK) and ESSnuSB,
along with DUNE, will be able to establish CPT violation in their proposed
run-times.


Abstract:
  Random walks are frequently used as a model for very diverse physical
phenomena. The Monte Carlo method is a versatile tool for the study of the
properties of systems modelled as random walks. Often, each walker is
associated with a statistical weight, used in the estimation of observable
quantities. Weights are typically assumed to be positive; nonetheless, some
applications require the use of positive and negative weights or complex
weights, and often pose particular challenges with convergence. In this paper,
we examine such a case from the field of nuclear reactor physics, where the
negative particle weights prevent the power iteration algorithm from converging
on the sought fundamental eigenstate of the Boltzmann transport equation. We
demonstrate how the use of weight cancellation allows convergence on the
physical eigenstate. To this end, we develop a novel method to perform weight
cancellation in an exact manner, in three spatial dimensions. The viability of
this algorithm is then demonstrated on a reactor physics problem.


Abstract:
  In this paper, we present a maximum likelihood method for estimating the
parameters of a univariate Hawkes process with self-excitation or inhibition.
Our work generalizes techniques and results that were restricted to the
self-exciting scenario. The proposed estimator is implemented for the classical
exponential kernel and we show that, in the inhibition context, our procedure
provides more accurate estimations than current alternative approaches.


Abstract:
  I present an overview of some recent advancements on the empirical analysis
and theoretical modeling of the process of price formation in financial markets
as the result of the arrival of orders in a limit order book exchange. After
discussing critically the possible modeling approaches and the observed
stylized facts of order flow, I consider in detail market impact and
transaction cost of trades executed incrementally over an extended period of
time, by comparing model predictions and recent extensive empirical results. I
also discuss how the simultaneous presence of many algorithmic trading
executions affects the quality and cost of trading.


Abstract:
  Fluid flows in coupled systems consisting of a free-flow region and the
adjacent porous medium appear in a variety of environmental settings and
industrial applications. In many applications, fluid flow is non-parallel to
the fluid-porous interface that requires a generalisation of the Beavers-Joseph
coupling condition typically used for the Stokes-Darcy problem. Generalised
coupling conditions valid for arbitrary flow directions to the interface are
recently derived using the theory of homogenisation and boundary layers. The
aim of this work is the mathematical analysis of the Stokes-Darcy problem with
these generalised interface conditions. We prove the existence and uniqueness
of the weak solution of the coupled problem. The well-posedness is guaranteed
under a suitable relationship between the permeability and the boundary layer
constants containing geometrical information about the porous medium and the
interface. We numerically study the validity of the obtained results for
realistic problems and provide a benchmark for numerical solution of the
Stokes-Darcy problem with generalised interface conditions.


Abstract:
  We formulate a data-driven, physics-constrained closure method for
coarse-scale numerical simulations of turbulent fluid flows. Our approach
involves a closure scheme that is non-local both in space and time, i.e. the
closure terms are parametrized in terms of the spatial neighborhood of the
resolved quantities but also their history. The data-driven scheme is
complemented with a physical constrain expressing the energy conservation
property of the nonlinear advection terms. We show that the adoption of this
physical constrain not only increases the accuracy of the closure scheme but
also improves the stability properties of the formulated coarse-scale model. We
demonstrate the presented scheme in fluid flows consisting of an incompressible
two-dimensional turbulent jet. Specifically, we first develop one-dimensional
coarse-scale models describing the spatial profile of the jet. We then proceed
to the computation of turbulent closures appropriate for two-dimensional
coarse-scale models. Training data are obtained through high-fidelity direct
numerical simulations (DNS). We also showcase how the developed scheme captures
the coarse-scale features of the concentration fields associated with inertial
tracers, such as bubbles and particles, carried by the flow but not following
the flow. We thoroughly examine the generalizability properties of the trained
closure models for different Reynolds numbers, as well as, radically different
jet profiles from the ones used in the training phase. We also examine the
robustness of the derived closures with respect to the grid size. Overall the
adoption of the constraint results in an average improvement of 26% for
one-dimensional closures and 29% for two-dimensional closures, being notably
larger for flows that were not used for training.


Abstract:
  End-to-end models have achieved impressive results on the task of automatic
speech recognition (ASR). For low-resource ASR tasks, however, labeled data can
hardly satisfy the demand of end-to-end models. Self-supervised acoustic
pre-training has already shown its amazing ASR performance, while the
transcription is still inadequate for language modeling in end-to-end models.
In this work, we fuse a pre-trained acoustic encoder (wav2vec2.0) and a
pre-trained linguistic encoder (BERT) into an end-to-end ASR model. The fused
model only needs to learn the transfer from speech to language during
fine-tuning on limited labeled data. The length of the two modalities is
matched by a monotonic attention mechanism without additional parameters.
Besides, a fully connected layer is introduced for the hidden mapping between
modalities. We further propose a scheduled fine-tuning strategy to preserve and
utilize the text context modeling ability of the pre-trained linguistic
encoder. Experiments show our effective utilizing of pre-trained modules. Our
model achieves better recognition performance on CALLHOME corpus (15 hours)
than other end-to-end models.


Abstract:
  We investigate the stability of the wave equation with spatial dependent
coefficients on a bounded multidimensional domain. The system is stabilized via
a scattering passive feedback law. We formulate the wave equation in a
port-Hamiltonian fashion and show that the system is semi-uniform stable, which
is a stability concept between exponential stability and strong stability.
Hence, this also implies strong stability of the system. In particular,
classical solutions are uniformly stable. This will be achieved by showing that
the spectrum of the port-Hamiltonian operator is contained in the left half
plane $\mathbb{C}_{-}$ and the port-Hamiltonian operator generates a
contraction semigroup. Moreover, we show that the spectrum consists of
eigenvalues only and the port-Hamiltonian operator has a compact resolvent.


Abstract:
  In the physics of the Fractional Quantum Hall (FQH) effect, a zoo of Abelian
topological phases can be obtained by varying the magnetic field. Aiming to
reach the same phenomenology in spin-like systems, we propose a family of
SU($N$)-symmetric models in the fundamental representation, on the square
lattice with short-range interactions restricted to triangular units, a natural
generalization for arbitrary $N$ of an SU($3$) model studied previously where
time-reversal symmetry is broken explicitly. Guided by the recent discovery of
SU($2$)$_1$ and SU($3$)$_1$ chiral spin liquids (CSL) on similar models we
search for topological SU($N$)$_1$ CSL in some range of the Hamiltonian
parameters via a combination of complementary numerical methods such as exact
diagonalizations (ED), infinite density matrix renormalization group (iDMRG)
and infinite Projected Entangled Pair State (iPEPS). Extensive ED on small
(periodic and open) clusters up to $N=10$ and an innovative SU($N$)-symmetric
version of iDMRG to compute entanglement spectra on (infinitely-long) cylinders
in all topological sectors provide unambiguous signatures of the SU($N$)$_1$
character of the chiral liquids. An SU($4$)-symmetric chiral PEPS, constructed
in a manner similar to its SU($2$) and SU($3$) analogs, is shown to give a good
variational ansatz of the $N=4$ ground state, with chiral edge modes
originating from the PEPS holographic bulk-edge correspondence. Finally, we
discuss the possible observation of such Abelian CSL in ultracold atom setups
where the possibility of varying $N$ provides a tuning parameter similar to the
magnetic field in the physics of the FQH effect.


Abstract:
  In 2006, Fowler and Foemmel defined ten core Continuous Integration (CI)
practices that could increase the speed of software development feedback cycles
and improve software quality. Since then, these practices have been widely
adopted by industry and subsequent research has shown they improve software
quality. However, there is poor understanding of how organizations implement
these practices, of the benefits developers perceive they bring, and of the
challenges developers and organizations experience in implementing them. In
this paper, we discuss a multiple-case study of three small- to medium-sized
companies using the recommended suite of ten CI practices. Using interviews and
activity log mining, we learned that these practices are broadly implemented
but how they are implemented varies depending on their perceived benefits, the
context of the project, and the CI tools used by the organization. We also
discovered that CI practices can create new constraints on the software process
that hurt feedback cycle time. For researchers, we show that how CI is
implemented varies, and thus studying CI (for example, using data mining)
requires understanding these differences as important context for research
studies. For practitioners, our findings reveal in-depth insights on the
possible benefits and challenges from using the ten practices, and how project
context matters.


Abstract:
  Graph neural networks (GNNs) manifest pathologies including over-smoothing
and limited discriminating power as a result of suboptimally expressive
aggregating mechanisms. We herein present a unifying framework for stochastic
aggregation (STAG) in GNNs, where noise is (adaptively) injected into the
aggregation process from the neighborhood to form node embeddings. We provide
theoretical arguments that STAG models, with little overhead, remedy both of
the aforementioned problems. In addition to fixed-noise models, we also propose
probabilistic versions of STAG models and a variational inference framework to
learn the noise posterior. We conduct illustrative experiments clearly
targeting oversmoothing and multiset aggregation limitations. Furthermore, STAG
enhances general performance of GNNs demonstrated by competitive performance in
common citation and molecule graph benchmark datasets.


Abstract:
  Online social media (OSM) has emerged as a prominent platform for debate on a
wide range of issues. Even celebrities and public figures often share their
opinions on a variety of topics through OSM platforms. One such subject that
has gained a lot of coverage on Twitter is the Novel Coronavirus, officially
known as COVID-19, which has become a pandemic and has sparked a crisis in
human history. In this study, we examine 29 million tweets over three months to
study highly influential users, whom we refer to as leaders. We recognize these
leaders through social network techniques and analyze their tweets using text
analysis. Using a community detection algorithm, we categorize these leaders
into four clusters: research, news, health, and politics, with each cluster
containing Twitter handles (accounts) of individual users or organizations.
E.g., the health cluster includes the World Health Organization (@WHO), the
Director-General of WHO (@DrTedros), and so on. The emotion analysis reveals
that (i) all clusters show an equal amount of fear in their tweets, (ii)
research and news clusters display more sadness than others, and (iii) health
and politics clusters are attempting to win public trust. According to the text
analysis, the (i) research cluster is more concerned with recognizing symptoms
and the development of vaccination; (ii) news and politics clusters are mostly
concerned with travel. We then show that we can use our findings to classify
tweets into clusters with a score of 96% AUC ROC.


Abstract:
  This research focuses on the algorithms and approaches for learning Hidden
Markov Models (HMMs) and compares HMM learning methods and algorithms. HMM is a
statistical Markov model in which the system being modeled is assumed to be a
Markov process. One of the essential characteristics of HMMs is their learning
capabilities. Learning algorithms are introduced to overcome this
inconvenience. One of the main problems of the newly proposed algorithms is
their validation. This research aims by using the theoretical and experimental
analysis to 1) compare HMMs learning algorithms proposed in the literature, 2)
provide a validation tool for new HMM learning algorithms, and 3) present a new
algorithm called Asexual Reproduction Optimization (ARO) with one of its
extensions - Modified ARO (MARO) - as a novel HMM learning algorithm to use the
validation tool proposed. According to the literature findings, it seems that
populationbased algorithms perform better among HMMs learning approaches than
other algorithms. Also, the testing was done in nine benchmark datasets. The
results show that MARO outperforms different algorithms in objective functions
in terms of accuracy and robustness.


Abstract:
  How to learn long-range dependencies from 3D point clouds is a challenging
problem in 3D point cloud analysis. Addressing this problem, we propose a
global attention network for point cloud semantic segmentation, named as
GA-Net, consisting of a point-independent global attention module and a
point-dependent global attention module for obtaining contextual information of
3D point clouds in this paper. The point-independent global attention module
simply shares a global attention map for all 3D points. In the point-dependent
global attention module, for each point, a novel random cross attention block
using only two randomly sampled subsets is exploited to learn the contextual
information of all the points. Additionally, we design a novel point-adaptive
aggregation block to replace linear skip connection for aggregating more
discriminate features. Extensive experimental results on three 3D public
datasets demonstrate that our method outperforms state-of-the-art methods in
most cases.


Abstract:
  We prove that four different ways of defining Cartesian fibrations and the
Cartesian model structure are all Quillen equivalent: On marked simplicial
sets, on bisimplicial spaces, on bisimplicial sets, on marked simplicial
spaces. The main way to prove these equivalences is by using the Quillen
equivalences between quasi-categories and complete Segal spaces as defined by
Joyal-Tierney and the straightening construction due to Lurie.


Abstract:
  The fault diagnostic model trained for a laboratory case machine fails to
perform well on the industrial machines running under variable operating
conditions. For every new operating condition of such machines, a new
diagnostic model has to be trained which is a time-consuming and uneconomical
process. Therefore, we propose a quick learning mechanism that can transform
the existing diagnostic model into a new model suitable for industrial machines
operating in different conditions. The proposed method uses the Net2Net
transformation followed by a fine-tuning to cancel/minimize the maximum mean
discrepancy between the new data and the previous one. The fine-tuning of the
model requires a very less amount of labelled target samples and very few
iterations of training. Therefore, the proposed method is capable of learning
the new target data pattern quickly. The effectiveness of the proposed fault
diagnosis method has been demonstrated on the Case Western Reserve University
dataset, Intelligent Maintenance Systems bearing dataset, and Paderborn
university dataset under the wide variations of the operating conditions. It
has been validated that the diagnostic model trained on artificially damaged
fault datasets can be used to quickly train another model for a real damage
dataset.


Abstract:
  Experiments by Chretien and co-workers suggest that mitochondria are 10oC
hotter than their surroundings. Steady-state theoretical estimates place this
difference at a maximum of 10^-5 oC. This million-fold disagreement may be
called the hot mitochondrion paradox. It is suggested that every proton
translocated via ATP synthase sparks a picosecond temperature-difference spike
of the order of magnitude measured by Chretien et al. Time-averaging of these
spikes recovers the theoretical value. Further, a temporal and spatial
superposition of the fluorescence intensity of a very large number of molecular
thermometer molecules in the sample can give the appearance of a steady signal.
The inner mitochondrial membrane appears to be flanked by temperature
differences fluctuating in time and along the membrane s surface, with hot and
cold spots as ultrashort temperature spikes.


Abstract:
  Object classification is a significant task in computer vision. It has become
an effective research area as an important aspect of image processing and the
building block of image localization, detection, and scene parsing. Object
classification from low-quality images is difficult for the variance of object
colors, aspect ratios, and cluttered backgrounds. The field of object
classification has seen remarkable advancements, with the development of deep
convolutional neural networks (DCNNs). Deep neural networks have been
demonstrated as very powerful systems for facing the challenge of object
classification from high-resolution images, but deploying such object
classification networks on the embedded device remains challenging due to the
high computational and memory requirements. Using high-quality images often
causes high computational and memory complexity, whereas low-quality images can
solve this issue. Hence, in this paper, we investigate an optimal architecture
that accurately classifies low-quality images using DCNNs architectures. To
validate different baselines on lowquality images, we perform experiments using
webcam captured image datasets of 10 different objects. In this research work,
we evaluate the proposed architecture by implementing popular CNN
architectures. The experimental results validate that the MobileNet
architecture delivers better than most of the available CNN architectures for
low-resolution webcam image datasets.


Abstract:
  The correction to the Coulomb energy due to virtual production of $e^+e^-$
pairs, which is on the order of one percent of the Coulomb energy at nuclear
scales is discussed. The effects of including a pair-production term in the
semi-empirical mass formula and the correction to the Coulomb barrier for a
handful of nuclear collisions using the Bass and Coulomb potentials are
studied. With an eye toward future work using Constrained Molecular Dynamics
(CoMD) model, we also calculate the correction to the Coulomb energy and force
between protons after folding with a Gaussian spatial distribution.


Abstract:
  In this paper, we present in-depth transfer matrix analyses of the Anderson
transition in three non-Hermitian (NH) systems, NH Anderson, U(1) and Peierls
models, that belong to NH class AI$^{\dagger}$ or NH class A. We first argue a
general validity of the transfer matrix analysis, and clarify the symmetry
properties of the Lyapunov exponents, scattering ($S$) matrix and two-terminal
conductance in these NH models. The unitarity of the $S$ matrix is violated in
NH systems, where the two-terminal conductance can take arbitrarily large
values. Nonetheless, we show that the transposition symmetry of a Hamiltonian
leads to the symmetric nature of the $S$ matrix as well as the reciprocal
symmetries of the Lyapunov exponents and conductance in certain ways in these
NH models. Finite size scaling data are fitted by polynomial functions, from
which we determine the critical exponent $\nu$ at different single-particle
energies and system parameters of the NH models. We conclude that the critical
exponents of the NH class AI$^{\dagger}$ and the NH class A are $\nu=1.19 \pm
0.01$ and $\nu=1.00 \pm 0.04$, respectively. In the NH models, a distribution
of the two-terminal conductance is not Gaussian. Instead, it contains small
fractions of huge conductance values, which come from rare-event states with
huge transmissions amplified by on-site NH disorders. Nonetheless, a geometric
mean of the conductance enables the finite-size scaling analysis. We show that
the critical exponents obtained from the conductance analysis are consistent
with those from the localization length in these three NH models.


Abstract:
  Nitrogen fertilizers have a detrimental effect on the environment, which can
be reduced by optimizing fertilizer management strategies. We implement an
OpenAI Gym environment where a reinforcement learning agent can learn
fertilization management policies using process-based crop growth models and
identify policies with reduced environmental impact. In our environment, an
agent trained with the Proximal Policy Optimization algorithm is more
successful at reducing environmental impacts than the other baseline agents we
present.


Abstract:
  We study molecular emission in a massive condensation at the border of the
HII region RCW 120, paying particular attention to the Core 1 and Core 2
objects, the most massive fragments of the condensation found previously by
ALMA. The latter fragment was previously suggested to host a high-mass analogue
of Class 0 young stellar object. We present spectra of molecular emission in
the 1 mm range made with the APEX telescope. We detect CH$_3$OH and C$^{34}$S
lines in Core 1 and Core 2. The CH$_3$CN series and the SO$_2$ lines are only
found in Core 2. We estimate gas physical parameters using methanol lines and
obtain gas temperature less than 100 K in both regions. Molecular hydrogen
number density in Core 2 is in the range of $10^5-10^7$ cm$^{-3}$ and is more
uncertain in Core 1. However, the detection of the CH$_3$CN lines corresponding
to highly excited transitions ($E_{\rm u}> 400$~K) in Core~2 indicates that the
region contains hot gas, while the abundances of CH$_3$OH, CS, SO$_2$ and
CH$_3$CN are quite low for a hot core stage. We propose that Core 2 is in the
warm-up phase prior to the establishing of the hot gas chemistry. We suggest
that Core 2 is in the beginning of the hot core stage. There are no detected
CH$_3$CN lines in Core 1, therefore, it might be on an even less evolved
evolutionary stage.


Abstract:
  We extend the results about Hensel minimality to include also the mixed
characteristic case. This completes our axiomatic framework for tame
non-archimedean geometry over Henselian valued fields of characteristic zero.
We prove a Jacobian property, a strong form of Taylor approximations of
definable functions, resplendency results and cell decomposition, all under
1-h-minimality. We obtain a diophantine application of counting rational points
of bounded height on Hensel minimal curves.


Abstract:
  The world is still fighting against COVID-19, which has been lasting for more
than a year. Till date, it has been a greatest challenge to human beings in
fighting against COVID-19 since, the pathogen SARS-COV-2 that causes COVID-19
has significant biological and transmission characteristics when compared to
SARS-COV and MERS-COV pathogens. In spite of many control strategies that are
implemented to reduce the disease spread, there is a rise in the number of
infected cases around the world. Hence, a mathematical model which can describe
the real nature and impact of COVID-19 is necessary for the better
understanding of disease transmission dynamics of COVID-19. This article
proposes a new compartmental SEIRUC mathematical model, which includes the new
state called convalesce (C). The basic reproduction number $\mathcal{R}_0$ is
identified for the proposed model. The stability analysis are performed for the
disease free equilibrium ($\mathcal{E}_0$) as well for the endemic equilibrium
($\mathcal{E}_*$) by using the Routh-Hurwitz criterion. The graphical
illustrations of the proposed mathematical results are provided to validate the
theoretical results.


Abstract:
  The $SU(N)$ Yang-Mills matrix model admits self-dual and anti-self-dual
instantons. When coupled to $N_f$ flavors of massless quarks, the Euclidean
Dirac equation in an instanton background has $n_+$ positive and $n_-$ negative
chirality zero modes. We show that the index $(n_+ - n_-)$ is equal to a
suitably defined instanton charge. Further, we show that the path integral
measure is not invariant under a chiral rotation, and relate the non-invariance
of the measure to the index of the Dirac operator. Axial symmetry is broken
anomalously, with the residual symmetry being a finite group. For $N_f$
fundamental fermions, this residual symmetry is $\mathbb{Z}_{2N_f}$, whereas
for adjoint quarks it is $\mathbb{Z}_{4N_f}$.


Abstract:
  In this paper, following the ideas in Marsden et al.[18], we set up the
regular reduction theory of a regular controlled Lagrangian (RCL) system with
symmetry and momentum map, by using Legendre transformation and Euler-Lagrange
vector field, and this reduction is an extension of symmetric reduction theory
of a regular Lagrangian system under regular controlled Lagrangian equivalence
conditions. Considering the completeness of reduction, in order to describe
uniformly the RCL systems defined on a tangent bundle and on its regular
reduced spaces, we first define a kind of RCL systems on a symplectic fiber
bundle. Then we give a good expression of the dynamical vector field of the RCL
system, such that we can describe the RCL-equivalence for the RCL systems.
Moreover, we introduce regular point and regular orbit reducible RCL systems
with symmetries and momentum maps, by using the reduced Lagrange symplectic
forms and the reduced Euler-Lagrange vector fields, and prove the regular point
and regular orbit reduction theorems for the RCL systems and regular Lagrangian
systems, which explain the relationships between RpCL-equivalence,
RoCL-equivalence for the reducible RCL systems with symmetries and
RCL-equivalence for the associated reduced RCL systems, as well as the
relationship of equivalences of the regular reducible Lagrangian systems,
$R_p$-reduced Lagrangian systems and $R_o$-reduced Lagrangian systems.


Abstract:
  We introduce the notion of real phase structure on rational polyhedral fans
in Euclidean space. Such a structure consists of an assignment of affine spaces
over $\mathbb{Z}/2\mathbb{Z}$ to each top dimensional face of the fan subject
to two conditions. Given an oriented matroid we can construct a real phase
structure on the fan of the underlying matroid. Conversely, we show that from a
real phase structure on a matroid fan we can produce an orientation of the
underlying matroid. Thus real phase structures are cryptomorphic to matroid
orientations. The topes of the orientated matroid are recovered immediately
from the real phase structure. We also provide a direct way to recover the
signed circuits of the oriented matroid from the real phase structure.


Abstract:
  We prove that a certain pair of isospectral planar sets are distinguished by
torsional rigidity.


Abstract:
  Hippocampal reverse replay is thought to contribute to learning, and
particularly reinforcement learning, in animals. We present a computational
model of learning in the hippocampus that builds on a previous model of the
hippocampal-striatal network viewed as implementing a three-factor
reinforcement learning rule. To augment this model with hippocampal reverse
replay, a novel policy gradient learning rule is derived that associates place
cell activity with responses in cells representing actions. This new model is
evaluated using a simulated robot spatial navigation task inspired by the
Morris water maze. Results show that reverse replay can accelerate learning
from reinforcement, whilst improving stability and robustness over multiple
trials. As implied by the neurobiological data, our study implies that reverse
replay can make a significant positive contribution to reinforcement learning,
although learning that is less efficient and less stable is possible in its
absence. We conclude that reverse replay may enhance reinforcement learning in
the mammalian hippocampal-striatal system rather than provide its core
mechanism.


Abstract:
  Modern neural networks excel at image classification, yet they remain
vulnerable to common image corruptions such as blur, speckle noise or fog.
Recent methods that focus on this problem, such as AugMix and DeepAugment,
introduce defenses that operate in expectation over a distribution of image
corruptions. In contrast, the literature on $\ell_p$-norm bounded perturbations
focuses on defenses against worst-case corruptions. In this work, we reconcile
both approaches by proposing AdversarialAugment, a technique which optimizes
the parameters of image-to-image models to generate adversarially corrupted
augmented images. We theoretically motivate our method and give sufficient
conditions for the consistency of its idealized version as well as that of
DeepAugment. Our classifiers improve upon the state-of-the-art on common image
corruption benchmarks conducted in expectation on CIFAR-10-C and improve
worst-case performance against $\ell_p$-norm bounded perturbations on both
CIFAR-10 and ImageNet.


Abstract:
  We study the Higgs mode in a Bardeen-Cooper-Schrieffer (BCS) superconductor.
Motivated by the observation that U(1) symmetry of the BCS Hamiltonian is not
essential for the Higgs mode, we study the Ising-like Hamiltonian in the
pseudospin representation. We show that the Higgs mode emerges as the lowest
excited state of the Ising-like Hamiltonian due to spontaneous breakdown of
$\mathbb{Z}_2$ symmetry under the time-reversal operation $\mathcal T$ in the
pseudospin space. We further predict the existence of multiple Higgs modes that
have quantized energy $2(n+1)\Delta_0$ ($0\le n\le N_{k_F}$), where $\Delta_0$
is the superconducting gap, $n$ is an integer, and $N_{k_F}$ is the number of
states on the Fermi surface.


Abstract:
  The island rule for the entanglement entropy is applied to an eternal
Reissner-Nordstr\"om black hole. The key ingredient is that the black hole is
assumed to be in thermal equilibrium with a heat bath of an arbitrary
temperature and so the generalized entropy is treated as being off-shell.
Taking the on-shell condition to the off-shell generalized entropy, we find the
generalized entropy and then obtain the entanglement entropy following the
island rule. For the non-extremal black hole, the entanglement entropy grows
linearly in time and can be saturated after the Page time as expected. The
entanglement entropy also has a well-defined Schwarzschild limit. In the
extremal black hole, the island prescription provides a logarithmically growing
entanglement entropy in time and a constant entanglement entropy after the Page
time. In the extremal black hole, the boundary of the island hits the curvature
singularity where the semi-classical approximations appear invalid. To avoid
encountering the curvature singularity, we apply this procedure to the Hayward
black hole regular at the origin. Consequently, the presence of the island in
extremal black holes can provide a finite entanglement entropy, which might
imply non-trivial vacuum configurations of extremal black holes.