Abstract:
  Predicting the altered acoustic frames is an effective way of self-supervised
learning for speech representation. However, it is challenging to prevent the
pretrained model from overfitting. In this paper, we proposed to introduce two
dropout regularization methods into the pretraining of transformer encoder: (1)
attention dropout, (2) layer dropout. Both of the two dropout methods encourage
the model to utilize global speech information, and avoid just copying local
spectrum features when reconstructing the masked frames. We evaluated the
proposed methods on phoneme classification and speaker recognition tasks. The
experiments demonstrate that our dropout approaches achieve competitive
results, and improve the performance of classification accuracy on downstream
tasks.


Abstract:
  The Euclidean path integral is well approximated by instantons. If instantons
are dynamical, then instantons are necessarily complexified. These fuzzy
instantons can have various physical applications. In slow-roll inflation
models, fuzzy instantons can explain the probability distribution of the
initial conditions of the universe. Even though the potential shape does not
satisfy the slow-roll conditions following the swampland criteria, still the
fuzzy instantons can explain the origin of the universe. If we extend the
Euclidean path integral beyond the no-boundary proposal, we may study fuzzy
Euclidean wormholes that have various physical applications in cosmology and
black hole physics. We summarize them and discuss possible future research
topics.


Abstract:
  On a Riemannian manifold of dimension $n$ we extend the known analytic
results on Yang-Mills connections to the class of connections called
$\Omega$-Yang-Mills connections, where $\Omega$ is a smooth, not necessarily
closed, $(n-4)$-form. Special cases include $\Omega$-anti-self-dual connections
and Hermitian-Yang-Mills connections over general complex manifolds. By a key
observation, a weak compactness result is obtained for moduli space of smooth
$\Omega$-Yang-Mills connections with uniformly $L^2$ bounded curvature, and it
can be improved in the case of Hermitian-Yang-Mills connections over general
complex manifolds. A removable singularity theorem for singular
$\Omega$-Yang-Mills connections on a trivial bundle with small energy
concentration is also proven. As an application, it is shown how to compactify
the moduli space of smooth Hermitian-Yang-Mills connections on unitary bundles
over a class of balanced manifolds of Hodge-Riemann type. This class includes
the metrics coming from multipolarizations, and in particular, the Kaehler
metrics. In the case of multipolarizations on a projective algebraic manifold,
the compactification of smooth irreducible Hermitian-Yang-Mills connections
with fixed determinant modulo gauge transformations inherits a complex
structure from algebro-geometric considerations.


Abstract:
  We propose a method based on cluster expansion to study the low activity/high
temperature phase of a continuous particle system confined in a finite volume,
interacting through a stable and finite range pair potential with negative
minimum in presence of non free boundary conditions.


Abstract:
  In spin-orbit-coupled magnetic materials, the usually applied Curie-Weiss law
can break down. This is due to potentially sharp temperature-dependence of the
local magnetic moments. We therefore propose a modified Curie-Weiss formula
suitable for analysis of experimental susceptibility. We show for octahedrally
coordinated materials of $d^5$ filling that the Weiss constant obtained from
the improved formula is in excellent agreement with the calculated Weiss
constant from microscopic exchange interactions. Reanalyzing the measured
susceptibility of several Kitaev candidate materials with the modified formula
resolves apparent discrepancies between various experiments regarding the
magnitude and anisotropies of the underlying magnetic couplings.


Abstract:
  We investigate the formation and control of stationary optical patterns in a
cold Rydberg atomic gas via double electromagnetically induced transparency. We
show that, through the modulational instability of plane-wave state of a laser
field with two polarization components, the system undergoes a spontaneous
symmetry breaking and hence the emergence of plentiful self-organized spatial
optical structures, which can be manipulated by the ratio between the cross-
and self-Kerr nonlinearities, the nonlocality degree of the Kerr
nonlinearities, and the populations initially prepared in the two atomic ground
states. Interestingly, a crossover from mixture to separation in space (optical
phase separation) of the two polarization components occurs when the ratio
between the cross- and self-Kerr nonlinearities exceeds a critical value. We
also show that the system supports nonlocal two-component spatial optical
solitons and vortices when the parameters of the system are selected suitably.
The rich diversity and active controllability of the self-organized optical
structures reported here provide a way for realizing novel optical patterns and
solitons and their structural phase transitions based on Rydberg atomic gases.


Abstract:
  The accelerations of tracer and light particles in compressible homogeneous
isotropic turbulence (CHIT) is investigated by using data from direct numerical
simulations (DNS) up to turbulent Mach number $M_t =1$. For tracer particles,
the flatness factor of acceleration components, $F_a$, increases gradually for
$M_t \in [0.3, 1]$. On the contrary, $F_a$ for light particles develops a
maximum around $M_t \sim 0.6$. The PDF of longitudinal acceleration of tracers
is increasingly skewed towards the negative value as $M_t$ increases. By
contrast, for light particles, the skewness factor of longitudinal
acceleration, $S_a$, firstly becomes more negative with the increase of $M_t$,
and then goes back to $0$ when $M_t$ is larger than $0.6$. Similarly,
differences among tracers and light particles appear also in the zero-crossing
time of acceleration correlation. It is argued that all these phenomenons are
intimately linked to the flow structures in compression regions, e.g. close to
shocklets.


Abstract:
  We propose an algorithm to generate inner and outer polyhedral approximations
to the upper image of a bounded convex vector optimization problem. It is an
outer approximation algorithm and is based on solving norm minimizing
scalarizations. Unlike Pascolleti-Serafini scalarization used in the literature
for similar purposes, it does not involve a direction parameter. Therefore, the
algorithm is free of direction-biasedness. We also propose a modification of
the algorithm by introducing a suitable compact subset of the upper image,
which helps in proving for the first time the finiteness of an algorithm for
convex vector optimization. The computational performance of the algorithms is
illustrated using some of the benchmark test problems, which shows promising
results in comparison to a similar algorithm that is based on
Pascoletti-Serafini scalarization.


Abstract:
  In this work we present all the possible solutions for a static cylindrical
symmetric spacetime in the Einstein-Aether (EA) theory. As far as we know, this
is the first work in the literature that considers cylindrically symmetric
solutions in the theory of EA. One of these solutions is the generalization in
EA theory of the Levi-Civita (LC) spacetime in General Relativity (GR) theory.
We have shown that this generalized LC solution has unusual geodesic
properties, depending on the parameter $c_{14}$ of the aether field. The
circular geodesics are the same of the GR theory, no matter the values of
$c_{14}$. However, the radial and $z$ direction geodesics are allowed only for
certain values of $\sigma$ and $c_{14}$. The $z$ direction geodesics are
restricted to an interval of $\sigma$ different from those predicted by the GR
and the radial geodesics show that the motion is confined between the origin
and a maximum radius. The latter is not affected by the aether field but the
velocity and acceleration of the test particles are Besides, for
$0\leq\sigma<1/2$, when the cylindrical symmetry is preserved, this spacetime
is singular at the axis $r=0$, although for $\sigma>1/2$ exists interval of
$c_{14}$ where the spacetime is not singular, which is completely different
from that one obtained with the GR theory, where the axis $r=0$ is always
singular.


Abstract:
  We theoretically investigate the one-dimensional dynamics of a dark soliton
in a two-component immiscible mixture of Bose-Einstein condensates with
repulsive interactions. We analyze the reflection and transmission of a soliton
when it propagates through the domain wall, and we show that a dark-bright
soliton can be dynamically generated by the interaction of the dark soliton
with the domain wall, outside the regime of parameters where stationary
solutions are known to exist. The dynamics of this dark-bright soliton is
harmonic like, with a numerical frequency that is in good agreement with the
predictions of a semi-analytical model.


Abstract:
  Thanks to the widespread availability of large-scale datasets on scholarly
outputs, science itself has come under the microscope with the aim of capturing
a quantitative understanding of its workings. In this study, we leverage
well-established cognitive models coming from anthropology in order to
characterise the personal network of collaborations between scientists, i.e.,
the network considered from the standpoint of each individual researcher
(referred to as ego network), in terms of the cognitive investment they devote
to the different collaborations. Building upon these models, we study the
interplay between the structure of academic collaborations, academic
performance, and academic mobility at different career stages. We take into
account both purely academic mobility (i.e., the number of affiliation changes)
and geographical mobility (i.e., physical relocations to different countries).
For our investigation, we rely on a dataset comprising the geo-referenced
publications of a group of 81,500 authors extracted from Scopus, one of the
biggest repositories of academic knowledge. Our main finding is that there is a
clear correlation between the structure of co-authorship ego networks and
academic performance indices: the more one publishes and the higher their
impact, the larger their collaboration network. However, we observe a capacity
bound effect, whereby, beyond a certain point, higher performances become
increasingly less correlated with large collaboration networks. We also find
that international academic migrants are better at growing their networks than
researchers that only migrate within the same country, but the latter seem to
be better in exploiting their collaboration to achieve higher impact. High
academic mobility does not appear to translate into better academic performance
or larger collaboration networks.


Abstract:
  The negative solution to the famous problem of $36$ officers of Euler implies
that there are no two orthogonal Latin squares of order six. We show that the
problem has a solution, provided the officers are entangled, and construct
orthogonal quantum Latin squares of this size. As a consequence, we find an
example of the long-elusive Absolutely Maximally Entangled state AME$(4,6)$ of
four subsystems with six levels each, equivalently a $2$-unitary matrix of size
$36$, which maximizes the entangling power among all bipartite unitary gates of
this dimension, or a perfect tensor with four indices, each running from one to
six. This special state deserves the appellation golden AME state as the golden
ratio appears prominently in its elements. This result allows us to construct a
pure nonadditive quhex quantum error detection code $(\!(3,6,2)\!)_6$, which
saturates the Singleton bound and allows one to encode a $6$-level state into a
triplet of such states.


Abstract:
  With the multiplication of social media platforms, which offer anonymity,
easy access and online community formation, and online debate, the issue of
hate speech detection and tracking becomes a growing challenge to society,
individual, policy-makers and researchers. Despite efforts for leveraging
automatic techniques for automatic detection and monitoring, their performances
are still far from satisfactory, which constantly calls for future research on
the issue. This paper provides a systematic review of literature in this field,
with a focus on natural language processing and deep learning technologies,
highlighting the terminology, processing pipeline, core methods employed, with
a focal point on deep learning architecture. From a methodological perspective,
we adopt PRISMA guideline of systematic review of the last 10 years literature
from ACM Digital Library and Google Scholar. In the sequel, existing surveys,
limitations, and future research directions are extensively discussed.


Abstract:
  The muon $(g-2)_\mu$ and $b\to s \bar \mu \mu$ induced $B$ anomalies as hints
of new physics beyond the standard model (SM) have attracted much attention.
These two anomalies indicate that there may exist new interaction specifically
related to muon. A lot of theoretical ideas have been proposed to explain these
anomalies. Gauged flavor specific $U(1)_{B_q-L_\mu}$ is among the promising
ones. The new gauge boson $Z'$ from $U(1)_{B_q-L_\mu}$ interacts with muon and
provides necessary ingredient to solve the $(g-2)_\mu$ anomaly. The $Z'$-quark
coupling can generate flavor changing interactions after diagonalization of
quark mass matrix between weak eigen-state and mass eigen-state basis. We
revisit challenges for such models attempting to explain the $(g-2)_\mu$ and
$B$ anomalies separately or simultaneously. We find although for
$U(1)_{B_q-L_\mu}$ models there is still parameter space to provide solutions
for separately explaining the $(g-2)_\mu$ and $B$ anomalies, there exits no
parameter space for such models to solve both the anomalies simultaneously,
after taking into account existing constraints from $\tau \to \mu \gamma$,
$\tau \to 3 \mu$, neutrino trident and $B_s - \bar B_s$ data. Among them
leptonic processes restrict $Z^\prime$ mass to be less than a few hundred MeV
if required to solve the $(g-2)_\mu$ anomaly, which causes conflict between
data from $B_s - \bar B_s$, $D^0 - \bar D^0$ mixing and also hadron decays with
$Z^\prime$ in the final states. The effects of $U(1)_Y$ and $U(1)_{B_q-L_\mu}$
kinetic mixing on these anomalies are also studied. We find that neither can
these effects do much to bring the two anomalies together to be solved
simultaneously.


Abstract:
  This paper proposes a rate-profile construction method for
polarization-adjusted convolutional (PAC) codes of any code length and rate,
which is capable of maintaining trade-off between the error-correction
performance and decoding complexity of PAC code. The proposed method can
improve the error-correction performance of PAC codes while guaranteeing a low
mean sequential decoding complexity for signal-to-noise ratio (SNR) values
beyond a target SNR value.


Abstract:
  Despite their compelling theoretical properties, Bayesian neural networks
(BNNs) tend to perform worse than frequentist methods in classification-based
uncertainty quantification (UQ) tasks such as out-of-distribution (OOD)
detection. In this paper, based on empirical findings in prior works, we
hypothesize that this issue is because even recent Bayesian methods have never
considered OOD data in their training processes, even though this ``OOD
training'' technique is an integral part of state-of-the-art frequentist UQ
methods. To validate this, we treat OOD data as a first-class citizen in BNN
training by exploring four different ways of incorporating OOD data into
Bayesian inference. We show in extensive experiments that OOD-trained BNNs are
competitive to recent frequentist baselines. This work thus provides strong
baselines for future work in Bayesian UQ.


Abstract:
  We develop the theory of pinchings for non-archimedean analytic spaces. In
particular, we show that although pinchings of affinoid spaces do not have to
be affinoid, pinchings of Hausdorff analytic spaces always exist in the
category of analytic spaces.


Abstract:
  Despite broad application during labor and delivery, there remains
considerable debate about the value of electronic fetal monitoring (EFM). EFM
includes the surveillance of the fetal heart rate (FHR) patterns in conjunction
with the maternal uterine contractions providing a wealth of data about fetal
behavior and the threat of diminished oxygenation and perfusion. Adverse
outcomes universally associate a fetal injury with the failure to timely
respond to FHR pattern information. Historically, the EFM data, stored
digitally, are available only as rasterized pdf images for contemporary or
historical discussion and examination. In reality, however, they are rarely
reviewed systematically. Using a unique archive of EFM collected over 50 years
of practice in conjunction with adverse outcomes, we present a deep learning
framework for training and detection of incipient or past fetal injury. We
report 94% accuracy in identifying early, preventable fetal injury intrapartum.
This framework is suited for automating an early warning and decision support
system for maintaining fetal well-being during the stresses of labor.
Ultimately, such a system could enable a physician to timely respond during
labor and prevent adverse outcomes. When adverse outcomes cannot be avoided,
they can provide guidance to the early neuroprotective treatment of the
newborn.


Abstract:
  The relational approach to quantum states asserts that the physical
description of quantum systems is always relative to something or someone. In
relational quantum mechanics (RQM) it is relative to other quantum systems, in
the (neo-)Copenhagen interpretation of quantum theory to measurement contexts,
and in QBism to the beliefs of the agents. In contrast to the other two
interpretations, in RQM any interaction between two quantum systems counts as a
"measurement", and the terms "observer" and "observed system" apply to
arbitrary systems. We show, in the form of a no-go theorem, that in RQM the
physical description of a system relative to an observer cannot represent
knowledge about the observer in the conventional sense of this term. The
problem lies in the ambiguity in the choice of the basis with respect to which
the relative states are to be defined in RQM. In interpretations of quantum
theory where observations play a fundamental role, the problem does not arise
because the experimental context defines a preferred basis.


Abstract:
  High-dimensional observations are a major challenge in the application of
model-based reinforcement learning (MBRL) to real-world environments. To handle
high-dimensional sensory inputs, existing approaches use representation
learning to map high-dimensional observations into a lower-dimensional latent
space that is more amenable to dynamics estimation and planning. In this work,
we present an information-theoretic approach that employs temporal predictive
coding to encode elements in the environment that can be predicted across time.
Since this approach focuses on encoding temporally-predictable information, we
implicitly prioritize the encoding of task-relevant components over nuisance
information within the environment that are provably task-irrelevant. By
learning this representation in conjunction with a recurrent state space model,
we can then perform planning in latent space. We evaluate our model on a
challenging modification of standard DMControl tasks where the background is
replaced with natural videos that contain complex but irrelevant information to
the planning task. Our experiments show that our model is superior to existing
methods in the challenging complex-background setting while remaining
competitive with current state-of-the-art models in the standard setting.


Abstract:
  We connect two developments aiming at extending Voevodsky's theory of motives
over a field in such a way to encompass non-$\mathbf{A}^1$-invariant phenomina.
One is theory of reciprocity sheaves introduced by Kahn-Saito-Yamazaki. Another
is theory of the triangulated category
$\operatorname{\mathbf{logDM}}^{\operatorname{eff}}$ of logarithmic motives
launched by Binda, Park and {\O}stv{\ae}r. We prove that the Nisnevich
cohomology of reciprocity sheaves is representable in
$\operatorname{\mathbf{logDM}}^{\operatorname{eff}}$.


Abstract:
  We study logarithmic Sobolev inequalities with respect to a heat kernel
measure on finite-dimensional and infinite-dimensional Heisenberg groups. Such
a group is the simplest non-trivial example of a sub-Riemannian manifold. First
we consider logarithmic Sobolev inequalities on non-isotropic Heisenberg
groups. These inequalities are considered with respect to the hypoelliptic heat
kernel measure, and we show that the logarithmic Sobolev constants can be
chosen to be independent of the dimension of the underlying space. In this
setting, a natural Laplacian is not an elliptic but a hypoelliptic operator.
The argument relies on comparing logarithmic Sobolev constants for the
three-dimensional non-isotropic and isotropic Heisenberg groups, and
tensorization of logarithmic Sobolev inequalities in the sub-Riemannian
setting. Furthermore, we apply these results in an infinite-dimensional setting
and prove a logarithmic Sobolev inequality on an infinite-dimensional
Heisenberg group modelled on an abstract Wiener space.


Abstract:
  In this work, a two-dimensional time-fractional subdiffusion model is
developed to investigate the underlying transport phenomena evolving in a
binary medium comprised of two sub-domains occupied by homogeneous material. We
utilise an unstructured mesh control volume method to validate the model
against a derived semi-analytical solution for a class of two-layered problems.
This generalised transport model is then used to perform computational
homogenisation on various two-dimensional heterogenous porous media. A key
contribution of our work is to extend the classical homogenisation theory to
accommodate the new framework and show that the effective diffusivity tensor
can be computed once the cell problems reach steady state at the microscopic
scale. We verify the theory for binary media via a series of well-known test
problems and then investigate media having inclusions that exhibit a molecular
relaxation (memory) effect. Finally, we apply the generalised transport model
to estimate the bound water diffusivity tensor on cellular structures obtained
from environmental scanning electron microscope (ESEM) images for Spruce wood
and Australian hardwood. A highlight of our work is that the computed
diffusivity for the heterogeneous media with molecular relaxation is quite
different from the classical diffusion cases, being dominated at steady-state
by the material with memory effects.


Abstract:
  We consider the $\mathcal{N}=2$ SYM theory with gauge group SU($N$) and a
matter content consisting of one multiplet in the symmetric and one in the
anti-symmetric representation. This conformal theory admits a large-$N$ 't
Hooft expansion and is dual to a particular orientifold of $AdS_{5}\times
S^{5}$. We analyze this gauge theory relying on the matrix model provided by
localization a la Pestun. Even though this matrix model has very non-trivial
interactions, by exploiting the full Lie algebra approach to the matrix
integration, we show that a large class of observables can be expressed in a
closed form in terms of an infinite matrix depending on the 't Hooft coupling
$\lambda$. These exact expressions can be used to generate the perturbative
expansions at high orders in a very efficient way, and also to study
analytically the leading behavior at strong coupling. We successfully compare
these predictions to a direct Monte Carlo numerical evaluation of the matrix
integral and to the Pad\'e resummations derived from very long perturbative
series, that turn out to be extremely stable beyond the convergence disk
$|\lambda|<\pi^2$ of the latter.


Abstract:
  In this work, we propose an overlapped speech detection system trained as a
three-class classifier. Unlike conventional systems that perform binary
classification as to whether or not a frame contains overlapped speech, the
proposed approach classifies into three classes: non-speech, single speaker
speech, and overlapped speech. By training a network with the more detailed
label definition, the model can learn a better notion on deciding the number of
speakers included in a given frame. A convolutional recurrent neural network
architecture is explored to benefit from both convolutional layer's capability
to model local patterns and recurrent layer's ability to model sequential
information. The proposed overlapped speech detection model establishes a
state-of-the-art performance with a precision of 0.6648 and a recall of 0.3222
on the DIHARD II evaluation set, showing a 20% increase in recall along with
higher precision. In addition, we also introduce a simple approach to utilize
the proposed overlapped speech detection model for speaker diarization which
ranked third place in the Track 1 of the DIHARD III challenge.


Abstract:
  Incentives have surprisingly inconsistent effects when it comes to
encouraging people to behave prosocially. Classical economic theory, according
to which a specific behavior becomes more prevalent when it is rewarded,
struggles to explain why incentives sometimes backfire. More recent theories
therefore posit a reputational cost offsetting the benefits of receiving an
incentive -- yet unexplained effects of incentives remain, for instance across
incentive types and countries. We propose that social norms can offer an
explanation for these inconsistencies. Ultimately, social norms determine the
reputational costs or benefits resulting from a given behavior, and thus
variation in the effect of incentives may reflect variation in norms. We
implemented a formal model of prosocial behavior integrating social norms,
which we empirically tested on the real-world prosocial behavior of blood
donation. Blood donation is essential for many life-saving medical procedures,
but also presents an ideal testing ground for our theory: Various incentive
policies for blood donors exist across countries, enabling a comparative
approach. Our preregistered analyses reveal that social norms can indeed
account for the varying effects of financial and time incentives on
individual-level blood donation behavior across 28 European countries.
Incentives are associated with higher levels of prosociality when norms
regarding the incentive are more positive. The results indicate that social
norms play an important role in explaining the relationship between incentives
and prosocial behavior. More generally, our approach highlights the potential
of integrating theory from across the economic and behavioral sciences to
generate novel insights, with tangible consequences for policy-making.


Abstract:
  When producing a model to object detection in a specific context, the first
obstacle is to have a dataset labeling the desired classes. In RoboCup, some
leagues already have more than one dataset to train and evaluate a model.
However, in the Small Size League (SSL), there is not such dataset available
yet. This paper presents an open-source dataset to be used as a benchmark for
real-time object detection in SSL. This work also presented a pipeline to
train, deploy, and evaluate Convolutional Neural Networks (CNNs) models in a
low-power embedded system. This pipeline was used to evaluate the proposed
dataset with state-of-art optimized models. In this dataset, the MobileNet SSD
v1 achieves 44.88% AP (68.81% AP50) at 94 Frames Per Second (FPS) while running
on an SSL robot.


Abstract:
  We characterize all random point measures which are in a certain sense stable
under the action of branching. Denoting by $\circledast$ the branching
convolution operation introduced by Bertoin and Mallein (2019), and by
$\mathcal{Z}$ the law of a random point measure on the real line, we are
interested in solutions to the fixed point equation \[ \mathcal E = \mathcal{Z}
\circledast \mathcal E, \] with $\mathcal E$ a random point measure
distribution. Under suitable assumptions, we characterize all solutions of this
equation as shifted decorated Poisson point processes with a uniquely defined
shift.


Abstract:
  In recent years wildfires have caused havoc across the world, especially
aggravated in certain regions, due to climate change. Remote sensing has become
a powerful tool for monitoring fires, as well as for measuring their effects on
vegetation over the following years. We aim to explain the dynamics of
wildfires' effects on a vegetation index (previously estimated by causal
inference through synthetic controls) from pre-wildfire available information
(mainly proceeding from satellites). For this purpose, we use regression models
from Functional Data Analysis, where wildfire effects are considered functional
responses, depending on elapsed time after each wildfire, while pre-wildfire
information acts as scalar covariates. Our main findings show that vegetation
recovery after wildfires is a slow process, affected by many pre-wildfire
conditions, among which the richness and diversity of vegetation is one of the
best predictors for the recovery.


Abstract:
  Dynamical models allow us to connect the motion of a set of tracers to the
underlying gravitational potential, and thus to the total (luminous and dark)
matter distribution. They are particularly useful for understanding the mass
and spatial distribution of dark matter (DM) in a galaxy. Globular clusters
(GCs) are an ideal tracer population in dynamical models, since they are bright
and can be found far out into the halo of galaxies. We aim to test how well
Jeans-Anisotropic-MGE (JAM) models using GCs (positions and line-of-sight
velocities) as tracers can constrain the mass and radial distribution of DM
halos. For this, we use the E-MOSAICS suite of 25 zoom-in simulations of L*
galaxies. We find that the DM halo properties are reasonably well recovered by
the JAM models. There is, however, a strong correlation between how well we
recover the mass and the radial distribution of the DM and the number of GCs in
the galaxy: the constraints get exponentially worse with fewer GCs, and at
least 150 GCs are needed in order to guarantee that the JAM model will perform
well. We find that while the data quality (uncertainty on the radial
velocities) can be important, the number of GCs is the dominant factor in terms
of the accuracy and precision of the measurements. This work shows promising
results for these models to be used in extragalactic systems with a sample of
more than 150 GCs.


Abstract:
  We introduce a method for associating a chain complex to a module over a
combinatorial category, such that if the complex is exact then the module has a
rational Hilbert series. We prove homology--vanishing theorems for these
complexes for several combinatorial categories including: the category of
finite sets and injections, the opposite of the category of finite sets and
surjections, and the category of finite dimensional vector spaces over a finite
field and injections.
  Our main applications are to modules over the opposite of the category of
finite sets and surjections, known as $FS^{op}$ modules. We obtain many
constraints on the sequence of symmetric group representations underlying a
finitely generated $FS^{op}$ module. In particular, we describe its character
in terms of functions that we call character exponentials. Our results have new
consequences for the character of the homology of the moduli space of stable
marked curves, and for the equivariant Kazhdan-Luzstig polynomial of the braid
matroid.


Abstract:
  We revisit the problem of high-dimensional multiple packing in Euclidean
space. Multiple packing is a natural generalization of sphere packing and is
defined as follows. Let $ N>0 $ and $ L\in\mathbb{Z}_{\ge2} $. A multiple
packing is a set $\mathcal{C}$ of points in $ \mathbb{R}^n $ such that any
point in $ \mathbb{R}^n $ lies in the intersection of at most $ L-1 $ balls of
radius $ \sqrt{nN} $ around points in $ \mathcal{C} $. We study the multiple
packing problem for both bounded point sets whose points have norm at most
$\sqrt{nP}$ for some constant $P>0$ and unbounded point sets whose points are
allowed to be anywhere in $ \mathbb{R}^n $. Given a well-known connection with
coding theory, multiple packings can be viewed as the Euclidean analog of
list-decodable codes, which are well-studied for finite fields. In this paper,
we derive various bounds on the largest possible density of a multiple packing
in both bounded and unbounded settings. A related notion called average-radius
multiple packing is also studied. Some of our lower bounds exactly pin down the
asymptotics of certain ensembles of average-radius list-decodable codes, e.g.,
(expurgated) Gaussian codes and (expurgated) Poisson Point Processes. To this
end, we apply tools from high-dimensional geometry and large deviation theory.
Some of our lower bounds on the optimal multiple packing density are the best
known lower bounds. These bounds are obtained via a proxy known as error
exponent. The latter quantity is the best exponent of the probability of
list-decoding error when the code is corrupted by a Gaussian noise. We
establish a curious inequality which relates the error exponent, a quantity of
average-case nature, to the list-decoding radius, a quantity of worst-case
nature. We derive various bounds on the error exponent in both bounded and
unbounded settings which are of independent interest beyond multiple packing.


Abstract:
  We search for scalar and tensor non-standard interactions using
(anti)-electron neutrino disappearance in oscillation data. We found a slight
preference for non-zero CP violation, coming from both tensor and scalar
interactions. The preference for CP violation is lead by Daya Bay low-energy
data with a significance that reaches $\sim1.7\sigma$ in the global analysis
(and $\sim2.1\sigma$ when considering only medium baseline reactors data)
compared to the standard neutrino oscillation scenario.


Abstract:
  In the vicinity of the insulator-to-superfluid quantum phase transition in
its core, a dislocation in a He-4 crystal supports particle-hole excitations
with arbitrary small gaps. These exotic analogs of Frenkel interstitial-vacancy
pairs should manifest themselves in various threshold and thermoactivation
effects. In Worm Algorithm simulations, we reveal the presence of corresponding
small gaps via anomalous thermoactivation behavior of particle number
fluctuations, which we unambiguously associate with dislocations by
"visualization" techniques. Experimentally, the related threshold and
thermoactivation dependencies could be observed in the ultrasound absorption.


Abstract:
  The measurement of the silicon lattice parameter by a separate-crystal
triple-Laue x-ray interferometer is a key step for the kilogram realisation by
counting atoms. Since the measurement accuracy is approaching nine significant
digits, a reliable model of the interferometer operation is demanded to
quantify or exclude systematic errors. This paper investigates both
analytically and experimentally the effect of defocus (a difference between the
splitter-to-mirror distance on the one hand and the analyser-to-mirror one on
the other) on the phase of the interference fringes and the measurement of the
lattice parameter.


Abstract:
  The ability to combine known skills to create new ones may be crucial in the
solution of complex reinforcement learning problems that unfold over extended
periods. We argue that a robust way of combining skills is to define and
manipulate them in the space of pseudo-rewards (or "cumulants"). Based on this
premise, we propose a framework for combining skills using the formalism of
options. We show that every deterministic option can be unambiguously
represented as a cumulant defined in an extended domain. Building on this
insight and on previous results on transfer learning, we show how to
approximate options whose cumulants are linear combinations of the cumulants of
known options. This means that, once we have learned options associated with a
set of cumulants, we can instantaneously synthesise options induced by any
linear combination of them, without any learning involved. We describe how this
framework provides a hierarchical interface to the environment whose abstract
actions correspond to combinations of basic skills. We demonstrate the
practical benefits of our approach in a resource management problem and a
navigation task involving a quadrupedal simulated robot.


Abstract:
  In this paper, we first determine Bohr's inequality for the class of harmonic
mappings $f=h+\overline{g}$ in the unit disk $\ID$, where either both
$h(z)=\sum_{n=0}^{\infty}a_{pn+m}z^{pn+m}$ and
$g(z)=\sum_{n=0}^{\infty}b_{pn+m}z^{pn+m}$ are analytic and bounded in $\ID$,
or satisfies the condition $|g'(z)|\leq d|h'(z)|$ in $\ID\backslash \{0\}$ for
some $d\in [0,1]$ and $h$ is bounded. In particular, we obtain Bohr's
inequality for the class of harmonic $p$-symmetric mappings. Also, we
investigate the Bohr-type inequalities of harmonic mappings with a multiple
zero at the origin and that most of results are proved to be sharp.


Abstract:
  High-entropy alloys (HEAs) are materials that consist of equimolar or
near-equimolar multiple principal components but tend to form single phases,
which is a new research topic in the field of metallurgy, have attracted
extensive attention in the past decade. The HEAs families contain the
face-centered-cubic (fcc), body-centered-cubic (bcc), and
hexagonal-close-packed (hcp)-structured HEAs. On one hand, mechanical
properties, e.g. hardness, strength, ductility, fatigue, and elastic moduli,
are essential for practical applications of HEAs. Scientists have explored in
this direction since the advent of HEAs. On the other hand, the pursuit of high
strength and good plasticity is the critical research issue of materials.
Hence, strengthening of HEAs is a crucial issue. Recently, many articles are
focusing on the strengthening strategies of HEAs[1-14]. In this chapter, we
reviewed the recent work on the room-temperature elastic properties and
mechanical behavior of HEAs, including the mechanisms behind the plastic
deformation of HEAs at both low and high temperatures. Furthermore, the present
work examined the strengthening strategies of HEAs, e.g. strain hardening,
grain-boundary strengthening, solid-solution strengthening, and particle
strengthening. The fatigue, creep, and fracture properties were briefly
introduced. Lastly, the future scientific issues and challenges of HEAs were
discussed.


Abstract:
  We study low-rank parameterizations of weight matrices with embedded spectral
properties in the Deep Learning context. The low-rank property leads to
parameter efficiency and permits taking computational shortcuts when computing
mappings. Spectral properties are often subject to constraints in optimization
problems, leading to better models and stability of optimization. We start by
looking at the compact SVD parameterization of weight matrices and identifying
redundancy sources in the parameterization. We further apply the Tensor Train
(TT) decomposition to the compact SVD components, and propose a non-redundant
differentiable parameterization of fixed TT-rank tensor manifolds, termed the
Spectral Tensor Train Parameterization (STTP). We demonstrate the effects of
neural network compression in the image classification setting and both
compression and improved training stability in the generative adversarial
training setting.


Abstract:
  This position paper discusses the relevance of register as a theoretical
framework for chatbot language design. We present the concept of register and
discuss how using register-specific language influence the user's perceptions
of the interaction with chatbots. Additionally, we point several research
opportunities that are important to pursue to establish register as a
foundation for advancing chatbot's communication skills.


Abstract:
  Several works have demonstrated the use of variational autoencoders (VAEs)
for generating levels in the style of existing games and blending levels across
different games. Further, quality-diversity (QD) algorithms have also become
popular for generating varied game content by using evolution to explore a
search space while focusing on both variety and quality. To reap the benefits
of both these approaches, we present a level generation and game blending
approach that combines the use of VAEs and QD algorithms. Specifically, we
train VAEs on game levels and run the MAP-Elites QD algorithm using the learned
latent space of the VAE as the search space. The latent space captures the
properties of the games whose levels we want to generate and blend, while
MAP-Elites searches this latent space to find a diverse set of levels
optimizing a given objective such as playability. We test our method using
models for 5 different platformer games as well as a blended domain spanning 3
of these games. We refer to using MAP-Elites for blending as Blend-Elites. Our
results show that MAP-Elites in conjunction with VAEs enables the generation of
a diverse set of playable levels not just for each individual game but also for
the blended domain while illuminating game-specific regions of the blended
latent space.


Abstract:
  We study the problem of learning Ising models satisfying Dobrushin's
condition in the outlier-robust setting where a constant fraction of the
samples are adversarially corrupted. Our main result is to provide the first
computationally efficient robust learning algorithm for this problem with
near-optimal error guarantees. Our algorithm can be seen as a special case of
an algorithm for robustly learning a distribution from a general exponential
family. To prove its correctness for Ising models, we establish new
anti-concentration results for degree-$2$ polynomials of Ising models that may
be of independent interest.


Abstract:
  We construct non-K\"ahler Calabi-Yau manifolds of dimension $\ge$ 4 with
arbitrarily large 2nd Betti numbers by smoothing normal crossing varieties. The
examples have K3 fibrations over smooth projective varieties and their
algebraic dimensions are of codimension 2.


Abstract:
  Biomedical question-answering (QA) has gained increased attention for its
capability to provide users with high-quality information from a vast
scientific literature. Although an increasing number of biomedical QA datasets
has been recently made available, those resources are still rather limited and
expensive to produce. Transfer learning via pre-trained language models (LMs)
has been shown as a promising approach to leverage existing general-purpose
knowledge. However, finetuning these large models can be costly and time
consuming, often yielding limited benefits when adapting to specific themes of
specialised domains, such as the COVID-19 literature. To bootstrap further
their domain adaptation, we propose a simple yet unexplored approach, which we
call biomedical entity-aware masking (BEM). We encourage masked language models
to learn entity-centric knowledge based on the pivotal entities characterizing
the domain at hand, and employ those entities to drive the LM fine-tuning. The
resulting strategy is a downstream process applicable to a wide variety of
masked LMs, not requiring additional memory or components in the neural
architectures. Experimental results show performance on par with
state-of-the-art models on several biomedical QA datasets.


Abstract:
  We propose a novel scene flow estimation approach to capture and infer 3D
motions from point clouds. Estimating 3D motions for point clouds is
challenging, since a point cloud is unordered and its density is significantly
non-uniform. Such unstructured data poses difficulties in matching
corresponding points between point clouds, leading to inaccurate flow
estimation. We propose a novel architecture named Sparse
Convolution-Transformer Network (SCTN) that equips the sparse convolution with
the transformer. Specifically, by leveraging the sparse convolution, SCTN
transfers irregular point cloud into locally consistent flow features for
estimating continuous and consistent motions within an object/local object
part. We further propose to explicitly learn point relations using a point
transformer module, different from exiting methods. We show that the learned
relation-based contextual information is rich and helpful for matching
corresponding points, benefiting scene flow estimation. In addition, a novel
loss function is proposed to adaptively encourage flow consistency according to
feature similarity. Extensive experiments demonstrate that our proposed
approach achieves a new state of the art in scene flow estimation. Our approach
achieves an error of 0.038 and 0.037 (EPE3D) on FlyingThings3D and KITTI Scene
Flow respectively, which significantly outperforms previous methods by large
margins.


Abstract:
  Thanks to the rapid growth in wearable technologies, monitoring complex human
context becomes feasible, paving the way to develop human-in-the-loop IoT
systems that naturally evolve to adapt to the human and environment state
autonomously. Nevertheless, a central challenge in designing such personalized
IoT applications arises from human variability. Such variability stems from the
fact that different humans exhibit different behaviors when interacting with
IoT applications (intra-human variability), the same human may change the
behavior over time when interacting with the same IoT application (inter-human
variability), and human behavior may be affected by the behaviors of other
people in the same environment (multi-human variability). To that end, we
propose FaiR-IoT, a general reinforcement learning-based framework for adaptive
and fairness-aware human-in-the-loop IoT applications. In FaiR-IoT, three
levels of reinforcement learning agents interact to continuously learn human
preferences and maximize the system's performance and fairness while taking
into account the intra-, inter-, and multi-human variability. We validate the
proposed framework on two applications, namely (i) Human-in-the-Loop Automotive
Advanced Driver Assistance Systems and (ii) Human-in-the-Loop Smart House.
Results obtained on these two applications validate the generality of FaiR-IoT
and its ability to provide a personalized experience while enhancing the
system's performance by 40%-60% compared to non-personalized systems and
enhancing the fairness of the multi-human systems by 1.5 orders of magnitude.


Abstract:
  We consider the properties of a static axially symmetric wormhole described
by an exact solution of Einstein's field equations and investigate how we can
distinguish such a hypothetical object from a black hole. To this aim, we
explore the motion of test particles and photons in the wormhole's space-time
and compare it with the particle dynamics in the well known space-times of
Schwarzschild and Kerr black holes. We show that precise simultaneous
measurement of test particle motion and photon motion may provide the means to
distinguish the wormhole geometry from that of a black hole.


Abstract:
  The performance of image recognition like human pose detection, trained with
simulated images would usually get worse due to the divergence between real and
simulated data. To make the distribution of a simulated image close to that of
real one, there are several works applying GAN-based image-to-image
transformation methods, e.g., SimGAN and CycleGAN. However, these methods would
not be sensitive enough to the various change in pose and shape of subjects,
especially when the training data are imbalanced, e.g., some particular poses
and shapes are minor in the training data. To overcome this problem, we propose
to introduce the label information of subjects, e.g., pose and type of objects
in the training of CycleGAN, and lead it to obtain label-wise transforamtion
models. We evaluate our proposed method called Label-CycleGAN, through
experiments on the digit image transformation from SVHN to MNIST and the
surveillance camera image transformation from simulated to real images.


Abstract:
  Extending computational harmonic analysis tools from the classical setting of
regular lattices to the more general setting of graphs and networks is very
important and much research has been done recently. The Generalized Haar-Walsh
Transform (GHWT) developed by Irion and Saito (2014) is a multiscale transform
for signals on graphs, which is a generalization of the classical Haar and
Walsh-Hadamard Transforms. We propose the extended Generalized Haar-Walsh
Transform (eGHWT), which is a generalization of the adapted time-frequency
tilings of Thiele and Villemoes (1996). The eGHWT examines not only the
efficiency of graph-domain partitions but also that of "sequency-domain"
partitions simultaneously. Consequently, the eGHWT and its associated
best-basis selection algorithm for graph signals significantly improve the
performance of the previous GHWT with the similar computational cost, $O(N \log
N)$, where $N$ is the number of nodes of an input graph. While the GHWT
best-basis algorithm seeks the most suitable orthonormal basis for a given task
among more than $(1.5)^N$ possible orthonormal bases in $\mathbb{R}^N$, the
eGHWT best-basis algorithm can find a better one by searching through more than
$0.618\cdot(1.84)^N$ possible orthonormal bases in $\mathbb{R}^N$. This article
describes the details of the eGHWT best-basis algorithm and demonstrates its
superiority using several examples including genuine graph signals as well as
conventional digital images viewed as graph signals. Furthermore, we also show
how the eGHWT can be extended to 2D signals and matrix-form data by viewing
them as a tensor product of graphs generated from their columns and rows and
demonstrate its effectiveness on applications such as image approximation.


Abstract:
  We study a generating function flowing from the one enumerating a set of
partitions to the one enumerating the corresponding set of noncrossing
partitions; numerical simulations indicate that its limit in the Adjacency
random matrix model on bipartite Erd\"os-Renyi graphs gives a good
approximation of the spectral distribution for large average degrees. This
model and a Wishart-type random matrix model are described using congruence
classes on $k$-divisible partitions.
  We compute, in the $d\to \infty$ limit with $\frac{Z_a}{d}$ fixed, the
spectral distribution of an Adjacency and of a Laplacian random block matrix
model, on bipartite Erd\"os-Renyi graphs and on bipartite biregular graphs with
degrees $Z_1, Z_2$; the former is the approximation previously mentioned; the
latter is a mean field approximation of the Hessian of a random bipartite
biregular elastic network; it is characterized by an isostatic line and a
transition line between the one- and the two-band regions.


Abstract:
  Twenty years ago Breiman (2001) called to our attention a significant
cultural division in modeling and data analysis between the stochastic data
models and the algorithmic models. Out of his deep concern that the statistical
community was so deeply and "almost exclusively" committed to the former,
Breiman warned that we were losing our abilities to solve many real-world
problems. Breiman was not the first, and certainly not the only statistician,
to sound the alarm; we may refer to none other than John Tukey who wrote almost
60 years ago "data analysis is intrinsically an empirical science." However,
the bluntness and timeliness of Breiman's article made it uniquely influential.
It prepared us for the data science era and encouraged a new generation of
statisticians to embrace a more broadly defined discipline. Some might argue
that "The cultural division between these two statistical learning frameworks
has been growing at a steady pace in recent years", to quote Mukhopadhyay and
Wang (2020). In this commentary, we focus on some of the positive changes over
the past 20 years and offer an optimistic outlook for our profession.


Abstract:
  Purpose: The purpose of this paper is to explore possible factors impacting
team performance in healthcare, by focusing on information exchange within and
across hospital's boundaries. Design/methodology/approach: Through a web-survey
and group interviews, the authors collected data on the communication networks
of 31 members of four interdisciplinary healthcare teams involved in a system
redesign initiative within a large US children's hospital. The authors mapped
their internal and external social networks based on management advice,
technical support and knowledge dissemination within and across departments,
studying interaction patterns that involved more than 700 actors. The authors
then compared team performance and social network metrics such as degree,
closeness and betweenness centrality, and computed cross ties and constraint
levels for each team. Findings: The results indicate that highly effective
teams were more inwardly focused and less connected to outside members.
Moreover, highly recognized teams communicated frequently but, overall, less
intensely than the others. Originality/value: Mapping knowledge flows and
balancing internal focus and outward connectivity of interdisciplinary teams
may help healthcare decision makers in their attempt to achieve high value for
patients, families and employees.


Abstract:
  We may attempt to encapsulate what we know about a physical system by a model
structure, $S$. This collection of related models is defined by parametric
relationships between system features; say observables (outputs), unobservable
variables (states), and applied inputs. Each parameter vector in some parameter
space is associated with a completely specified model in $S$. Before choosing a
model in $S$ to predict system behaviour, we must estimate its parameters from
system observations. Inconveniently, multiple models (associated with distinct
parameter estimates) may approximate data equally well. Yet, if these equally
valid alternatives produce dissimilar predictions of unobserved quantities,
then we cannot confidently make predictions. Thus, our study may not yield any
useful result. We may anticipate the non-uniqueness of parameter estimates
ahead of data collection by testing $S$ for structural global identifiability
(SGI). Here we will provide an overview of the importance of SGI, some
essential theory and distinctions, and demonstrate these in testing some
examples.


Abstract:
  The transfer matrix is a powerful technique that can be applied to
statistical mechanics systems as for example in the calculus of the entropy of
the ice model. One interesting way to study such systems is to map it onto a
3-color problem. In this paper, we explicitly build the transfer matrix for the
3-color problem in order to calculate the number of possible configurations for
finite systems with free, periodic in one direction and toroidal boundary
conditions (periodic in both directions)


Abstract:
  K. Costello's Pushforward Formula is not correct as stated. We verify it
holds under an assumption of properness or of a weaker notion we call "purity."
We then check many papers that used this formula to make sure it holds in their
context.


Abstract:
  Obtaining coarse-grained models that accurately incorporate finite-size
effects is an important open challenge in the study of complex, multi-scale
systems. We apply Langevin regression, a recently developed method for finding
stochastic differential equation (SDE) descriptions of realistically-sampled
time series data, to understand finite-size effects in the Kuramoto model of
coupled oscillators. We find that across the entire bifurcation diagram, the
dynamics of the Kuramoto order parameter are statistically consistent with an
SDE whose drift term has the form predicted by the Ott-Antonsen ansatz in the
$N\to \infty$ limit. We find that the diffusion term is nearly independent of
the bifurcation parameter, and has a magnitude decaying as $N^{-1/2}$,
consistent with the central limit theorem. This shows that the diverging
fluctuations of the order parameter near the critical point are driven by a
bifurcation in the underlying drift term, rather than increased stochastic
forcing.


Abstract:
  Artificial Light pollution (AL) in Turkey and in Turkish observatories
between 2012--2020 have been studied using the archival data of Visible
Infrared Imaging Radiometer Suite (VIIRS) instrument. The astroGIS database has
been used in processing the data (https://www.astrogis.org)
\cite{2020MNRAS.493.1204A}. The total energy released to space from Turkey
increased by 80\% in 2019 with respect to 2012. In the span of the dataset, a
steady and continuous increase has been observed throughout all cities of the
country. On the other hand, Dark Sky Park locations, East and Southeast
Anatolian regions and mostly rural areas around the cities kept their AL level
constant. Four demographic parameters have been studied and they were found to
be correlated very well with AL: Population ($R \simeq 0.90$); GDP ($R \simeq
0.87$); Total Power Consumption ($R \simeq 0.66$) and Outdoor Lightening ($R
\simeq 0.67$). Contrary to countries acting to prevent AL increases, Turkey
seems to be at the beginning of an era where AL will arithmetically increase
throughout the country and enormous amount of energy will continuously escape
to space and therefore will be wasted. Therefore, a preventive legislation,
especially for invaluable astronomical site locations such as TURAG, TUG, DAG
and \c{C}AAM where each is counted as a truly dark site due to their SQM
values, has to be enacted in Turkey, in very near future.


Abstract:
  This paper addresses the question of how to align AI systems with human
values and situates it within a wider body of thought regarding technology and
value. Far from existing in a vacuum, there has long been an interest in the
ability of technology to 'lock-in' different value systems. There has also been
considerable thought about how to align technologies with specific social
values, including through participatory design-processes. In this paper we look
more closely at the question of AI value alignment and suggest that the power
and autonomy of AI systems gives rise to opportunities and challenges in the
domain of value that have not been encountered before. Drawing important
continuities between the work of the fairness, accountability, transparency and
ethics community, and work being done by technical AI safety researchers, we
suggest that more attention needs to be paid to the question of 'social value
alignment' - that is, how to align AI systems with the plurality of values
endorsed by groups of people, especially on the global level.


Abstract:
  We investigate optical response of a linear waveguide quantum electrodynamics
(QED) system, namely, an optical cavity coupled to a waveguide. Our analysis is
based on exact diagonalization of the overall Hamiltonian and is therefore
rigorous even in the ultrastrong coupling regime of waveguide QED. Owing to the
counter-rotating terms in the cavity-waveguide coupling, the motion of cavity
amplitude in the phase space is elliptical in general. Such elliptical motion
becomes remarkable in the ultrastrong coupling regime due to the large Lamb
shift comparable to the bare cavity frequency. We also reveal that such
elliptical motion does not propagate into the output field and present an
analytic form of the reflection coefficient that is asymmetric with respect to
the resonance frequency.


Abstract:
  This work analyzes the variation over time of the effect of geographic
distance on knowledge flows. The flows are measured through the citations
exchanged between scientific publications, including and excluding
self-citations. To calculate geographic distances between citing and cited
publication, each publication is associated with a "prevailing" territory,
according to the authors' affiliations. We then apply a gravity model to
account for the research size of the territories, in terms of cognitive
proximity of citing-cited publications. The field of observation is the
2010-2017 world publications citing the 2010-2012 Italian publications, as
indexed in the Web of Science. The results show that in domestic knowledge
flows, geographic proximity remains an influential factor through time,
although with differences among disciplines and trends of attenuating effects.
Finally, we replicate the analyses of knowledge flows but with the exclusion of
self-citations: in this manner the effect of geographic proximity seems
reduced, particularly at the national scale, but the differences (with vs
without self-citations) lessen through time. As shown in previous works, the
effect of distance on continental flows is modest (imperceptible for
intercontinental flows), yet here too time has some influence, including
concerning exclusion of self-citations.


Abstract:
  In this paper, the scale-invariant version of the mean and variance
multi-level Monte Carlo estimate is proposed. The optimization of the
computation cost over the grid levels is done with the help of a novel
normalized error based on t-statistic. In this manner, the algorithm
convergence is made invariant to the physical scale at which the estimate is
computed. The novel algorithm is tested on the linear elastic example, the
constitutive law of which is described by material uncertainty including both
heterogeneity and anisotropy.


Abstract:
  We revisit the Boltzmann equation governing the spectrum of energetic
particles originating from the decay of massive progenitors during the process
of thermalization. We assume that these decays occur when the background
temperature $T$ is much less than the mass $M$ of the progenitor. We pay
special attention to the IR cutoff provided by the thermal bath, and include
the suppression resulting from the interference of multiple scattering
reactions (LPM effect). We solve the resulting integral equation numerically,
and construct an accurate analytical fit of the solutions.


Abstract:
  We introduce WimPyDD, a modular, object-oriented and customizable Python code
that calculates accurate predictions for the expected rates in Weakly
Interacting Massive Particle (WIMP) direct-detection experiments within the
framework of Galilean-invariant non-relativistic effective theory in virtually
any scenario, including inelastic scattering, an arbitrary WIMP spin and a
generic WIMP velocity distribution in the Galactic halo. WimPyDD exploits the
factorization of the three main components that enter in the calculation of
direct detection signals: i) the Wilson coefficients that encode the dependence
of the signals on the ultraviolet completion of the effective theory; ii) a
response function that depends on the nuclear physics and on the main features
of the experimental detector (acceptance, energy resolution, response to
nuclear recoils); iii) a halo function that depends on the WIMP velocity
distribution and that encodes the astrophysical inputs. In WimPyDD these three
components are calculated and stored separately for later interpolation and
combined together only as the last step of the signal evaluation procedure.
This makes the phenomenological study of the direct detection scattering rate
with WimPyDD transparent and fast also when the parameter space of the WIMP
model has a large dimensionality.


Abstract:
  For genus $g=2i\geq4$ and the length $g-1$ partition $\mu =
(4,2,\ldots,2,-2,\ldots,-2)$ of 0, we compute the first coefficients of the
class of $\overline{D}(\mu)$ in
$\mathrm{Pic}_\mathbb{Q}(\overline{\mathcal{R}}_g)$, where $D(\mu)$ is the
divisor consisting of pairs $[C,\eta]\in \mathcal{R}_g$ with $\eta \cong
\mathcal{O}_C(2x_1+x_2+\cdots + x_{i-1}-x_i-\cdots-x_{2i-1})$ for some points
$x_1,\ldots, x_{2i-1}$ on $C$. We further provide several enumerative results
that will be used for this computation.


Abstract:
  Model-based reinforcement learning is a compelling framework for
data-efficient learning of agents that interact with the world. This family of
algorithms has many subcomponents that need to be carefully selected and tuned.
As a result the entry-bar for researchers to approach the field and to deploy
it in real-world tasks can be daunting. In this paper, we present MBRL-Lib -- a
machine learning library for model-based reinforcement learning in continuous
state-action spaces based on PyTorch. MBRL-Lib is designed as a platform for
both researchers, to easily develop, debug and compare new algorithms, and
non-expert user, to lower the entry-bar of deploying state-of-the-art
algorithms. MBRL-Lib is open-source at
https://github.com/facebookresearch/mbrl-lib.


Abstract:
  In this paper we consider the following problem: given $N$ independent
samples from an unknown distribution $\mathcal{P}$ over passwords $pwd_1,pwd_2,
\ldots$ can we generate high confidence upper/lower bounds on the guessing
curve $\lambda_G \doteq \sum_{i=1}^G p_i$ where $p_i=\Pr[pwd_i]$ and the
passwords are ordered such that $p_i \geq p_{i+1}$. Intuitively, $\lambda_G$
represents the probability that an attacker who knows the distribution
$\mathcal{P}$ can guess a random password $pwd \leftarrow \mathcal{P}$ within
$G$ guesses. Understanding how $\lambda_G$ increases with the number of guesses
$G$ can help quantify the damage of a password cracking attack and inform
password policies. Despite an abundance of large (breached) password datasets
upper/lower bounding $\lambda_G$ remains a challenging problem. We introduce
several statistical techniques to derive tighter upper/lower bounds on the
guessing curve $\lambda_G$ which hold with high confidence. We apply our
techniques to analyze $9$ large password datasets finding that our new lower
bounds dramatically improve upon prior work. Our empirical analysis shows that
even state-of-the-art password cracking models are significantly less guess
efficient than an attacker who knows the distribution. When $G$ is not too
large we find that our upper/lower bounds on $\lambda_G$ are both very close to
the empirical distribution which justifies the use of the empirical
distribution in settings where $G$ is not too large i.e., $G \ll N$ closely
approximates $\lambda_G$. The analysis also highlights regions of the curve
where we can, with high confidence, conclude that the empirical distribution
significantly overestimates $\lambda_G$. Our new statistical techniques yield
substantially tighter upper/lower bounds on $\lambda_G$ though there are still
regions of the curve where the best upper/lower bounds diverge significantly.


Abstract:
  We present a novel algorithm attaining excessively fast, the sought solution
of linear systems of equations. The algorithm is short in its basic formulation
and by definition vectorised, while the memory allocation demands trivial,
because for each iteration only one dimension of the given input matrix
$\mathbf x$ is utilized. The execution time is very short compared with
state-of-the-art methods, exhibiting up to $\mathcal{O}(10^3)$ speed-up and low
memory allocation demands, especially for non-square Systems of Linear
Equations, with ratio of equations versus features high (tall systems), or low
(wide systems) accordingly. The accuracy is high and straightforwardly
controlled, and the numerical results highlight the efficiency of the proposed
algorithm, in terms of computation time, solution accuracy and memory
allocations demands. The parallelisation of the algorithm is also presented in
multi-threaded and GPU accelerators' setting. The paper also comprises a
theoretical proof for the algorithmic convergence. Finally, we extend the
implementation of the proposed algorithmic rationale to feature selection
tasks.


Abstract:
  Probing complex language models has recently revealed several insights into
linguistic and semantic patterns found in the learned representations. In this
article, we probe BERT specifically to understand and measure the relational
knowledge it captures in its parametric memory. While probing for linguistic
understanding is commonly applied to all layers of BERT as well as fine-tuned
models, this has not been done for factual knowledge. We utilize existing
knowledge base completion tasks (LAMA) to probe every layer of pre-trained as
well as fine-tuned BERT models(ranking, question answering, NER). Our findings
show that knowledge is not just contained in BERT's final layers. Intermediate
layers contribute a significant amount (17-60%) to the total knowledge found.
Probing intermediate layers also reveals how different types of knowledge
emerge at varying rates. When BERT is fine-tuned, relational knowledge is
forgotten. The extent of forgetting is impacted by the fine-tuning objective
and the training data. We found that ranking models forget the least and retain
more knowledge in their final layer compared to masked language modeling and
question-answering. However, masked language modeling performed the best at
acquiring new knowledge from the training data. When it comes to learning
facts, we found that capacity and fact density are key factors. We hope this
initial work will spur further research into understanding the parametric
memory of language models and the effect of training objectives on factual
knowledge. The code to repeat the experiments is publicly available on GitHub.


Abstract:
  Context: The formation of high-mass star-forming regions from their parental
gas cloud and the subsequent fragmentation processes lie at the heart of star
formation research. Aims: We aim to study the dynamical and fragmentation
properties at very early evolutionary stages of high-mass star formation.
Methods: Employing the NOrthern Extended Millimeter Array (NOEMA) and the IRAM
30m telescope, we observed two young high-mass star-forming regions, ISOSS22478
and ISOSS23053, in the 1.3mm continuum and spectral line emission at a high
angular resolution (~0.8''). Results: We resolved 29 cores that are mostly
located along filament-like structures. Depending on the temperature
assumption, these cores follow a mass-size relation of approximately M~r^2.0,
corresponding to constant mean column densities. However, with different
temperature assumptions, a steeper mass-size relation up to M~r^3.0, which
would be more likely to correspond to constant mean volume densities, cannot be
ruled out. The correlation of the core masses with their nearest neighbor
separations is consistent with thermal Jeans fragmentation. We found hardly any
core separations at the spatial resolution limit, indicating that the data
resolve the large-scale fragmentation well. Although the kinematics of the two
regions appear very different at first sight - multiple velocity components
along filaments in ISOSS22478 versus a steep velocity gradient of more than
50km/s/pc in ISOSS23053 - the findings can be explained within the framework of
a dynamical cloud collapse scenario. Conclusions: While our data are consistent
with a dynamical cloud collapse scenario and subsequent thermal Jeans
fragmentation, the importance of additional environmental properties, such as
the magnetization of the gas or external shocks triggering converging gas
flows, is nonetheless not as well constrained and would require future
investigation.


Abstract:
  In spoken conversations, spontaneous behaviors like filled pause and
prolongations always happen. Conversational partner tends to align features of
their speech with their interlocutor which is known as entrainment. To produce
human-like conversations, we propose a unified controllable spontaneous
conversational speech synthesis framework to model the above two phenomena.
Specifically, we use explicit labels to represent two typical spontaneous
behaviors filled-pause and prolongation in the acoustic model and develop a
neural network based predictor to predict the occurrences of the two behaviors
from text. We subsequently develop an algorithm based on the predictor to
control the occurrence frequency of the behaviors, making the synthesized
speech vary from less disfluent to more disfluent. To model the speech
entrainment at acoustic level, we utilize a context acoustic encoder to extract
a global style embedding from the previous speech conditioning on the
synthesizing of current speech. Furthermore, since the current and previous
utterances belong to the different speakers in a conversation, we add a domain
adversarial training module to eliminate the speaker-related information in the
acoustic encoder while maintaining the style-related information. Experiments
show that our proposed approach can synthesize realistic conversations and
control the occurrences of the spontaneous behaviors naturally.


Abstract:
  As the electromagnetic spectrum becomes more congested and the environments
in which we need to operate become more complicated, control over the
environment itself becomes necessary to ensure the integrity of wireless
communication channels. Wavefront shaping with programmable metasurfaces allows
wave fields to be manipulated in both time and space, providing a method to
interact with the environment. When coupled with deep learning, intelligent
wavefront shaping serves as a catalyst, enabling smart radio environments and
unlocking applications beyond traditional wireless communication networks. In
this paper, we discuss the outlook of intelligent wavefront shaping for wave
propagation in complex environments and highlight its transformative potential.


Abstract:
  This work explores constituency parsing on automatically recognized
transcripts of conversational speech. The neural parser is based on a sentence
encoder that leverages word vectors contextualized with prosodic features,
jointly learning prosodic feature extraction with parsing. We assess the
utility of the prosody in parsing on imperfect transcripts, i.e. transcripts
with automatic speech recognition (ASR) errors, by applying the parser in an
N-best reranking framework. In experiments on Switchboard, we obtain 13-15% of
the oracle N-best gain relative to parsing the 1-best ASR output, with
insignificant impact on word recognition error rate. Prosody provides a
significant part of the gain, and analyses suggest that it leads to more
grammatical utterances via recovering function words.


Abstract:
  Quantized transports of fermions are topological phenomena determined by the
sum of the Chern numbers of all the energy bands below the Fermi energy. For
bosonic excitations, e.g. phonons and magnons in a crystal, topological
transport is dominated by the Chern number of the lowest energy band because
the energy distribution of the bosons is limited below the thermal energy.
Here, we demonstrate the existence of topological transport by bosonic magnons
in a lattice of magnetic skyrmions - topological defects formed by a
vortex-like texture of spins. We find a distinct thermal Hall signal when the
ferromagnetic spins in an insulating polar magnet GaV4Se8 form magnetic
skyrmions. Its origin is identified as the topological thermal Hall effect of
magnons in which the trajectories of these magnons are bent by an emergent
magnetic field produced by the magnetic skyrmions. Our theoretical simulations
confirm that the thermal Hall effect is indeed governed by the Chern number of
the lowest energy band of the magnons in a triangular lattice of magnetic
skyrmions. Our findings lay a foundation for studying topological phenomena of
other bosonic excitations.


Abstract:
  In this paper, we study the $L(2, 1)$-Labeling of the Mycielski and the
iterated Mycielski of graphs in general. For a graph $G$ and all $t\geq 1$, we
give sharp bounds for $\lambda(M^t(G))$ the $L(2, 1)$-labeling number of the
$t$-th iterated Mycielski in terms of the number of iterations $t$, the order
$n$, the maximum degree $\bigtriangleup$, and $\lambda(G)$ the $L(2,
1)$-labeling number of $G$. For $t=1$, we present necessary and sufficient
conditions between the $4$-star matching number of the complement graph and
$\lambda(M(G))$ the $L(2, 1)$-labeling number of the Mycielski of a graph, with
some applications to special graphs. For all $t\geq 2$, we prove that for any
graph $G$ of order $n$, we have $2^{t-1}(n+2)-2\leq \lambda(M^t(G))\leq
2^{t}(n+1)-2$. Thereafter, we characterize the graphs achieving the upper bound
$2^t(n+1)-2$, then by using the Marriage Theorem and Tutte's characterization
of graphs with a perfect $2$-matching, we characterize all graphs without
isolated vertices achieving the lower bound $2^{t-1}(n+2)-2$. We determine the
$L(2, 1)$-labeling number for the Mycielski and the iterated Mycielski of some
graph classes.


Abstract:
  We prove the Farrell-Jones Conjecture for mapping tori of automorphisms of
virtually torsion-free hyperbolic groups. The proof uses recently developed
geometric methods for establishing the Farrell-Jones Conjecture by
Bartels-L\"{u}ck-Reich, as well as the structure theory of mapping tori by
Dahmani-Krishna.


Abstract:
  We discuss the role of combinators in the development of the modern
conception of computation over the course of the past century. We describe how
ideas about formalism and mathematical logic led to the introduction of
combinators in 1920 as an extension of the discovery of Nand as a basis for
basic logic. We then discuss how combinators informed lambda calculus and
symbolic computation, and their relationship to the development of practical
computation. We finally describe recent views of combinators in terms of the
computational universe of possible programs, and a recent approach to the
fundamental theory of physics.


Abstract:
  We suggest an underlying mechanism that governs the growth of a network of
concepts, a complex network that reflects the connections between different
scientific concepts based on their co-occurrences in publications. To this end,
we perform empirical analysis of a network of concepts based on the preprints
in physics submitted to the arXiv.org. We calculate the network characteristics
and show that they cannot follow as a result of several simple commonly used
network growth models. In turn, we suggest that a simultaneous account of two
factors, i.e., growth by blocks and preferential selection, gives an
explanation of empirically observed properties of the concepts network.
Moreover, the observed structure emerges as a synergistic effect of these both
factors: each of them alone does not lead to a satisfactory picture.


Abstract:
  One useful standard method to compute eigenvalues of matrix polynomials ${\bf
P}(z) \in \mathbb{C}^{n\times n}[z]$ of degree at most $\ell$ in $z$ (denoted
of grade $\ell$, for short) is to first transform ${\bf P}(z)$ to an equivalent
linear matrix polynomial ${\bf L}(z)=z{\bf B}-{\bf A}$, called a companion
pencil, where ${\bf A}$ and ${\bf B}$ are usually of larger dimension than
${\bf P}(z)$ but ${\bf L}(z)$ is now only of grade $1$ in $z$. The eigenvalues
and eigenvectors of ${\bf L}(z)$ can be computed numerically by, for instance,
the QZ algorithm. The eigenvectors of ${\bf P}(z)$, including those for
infinite eigenvalues, can also be recovered from eigenvectors of ${\bf L}(z)$
if ${\bf L}(z)$ is what is called a "strong linearization" of ${\bf P}(z)$.
  In this paper we show how to use algorithms for computing the Hermite Normal
Form of a companion matrix for a scalar polynomial to direct the discovery of
unimodular matrix polynomial cofactors ${\bf E}(z)$ and ${\bf F}(z)$ which, via
the equation ${\bf E}(z){\bf L}(z){\bf F}(z) = \mathrm{diag}( {\bf P}(z), {\bf
I}_n, \ldots, {\bf I}_n)$, explicitly show the equivalence of ${\bf P}(z)$ and
${\bf L}(z)$. By this method we give new explicit constructions for several
linearizations using different polynomial bases. We contrast these new
unimodular pairs with those constructed by strict equivalence, some of which
are also new to this paper. We discuss the limitations of this experimental,
computational discovery method of finding unimodular cofactors.


Abstract:
  We argue that the exact single-particle Green's function ($G$) in quantum
many-body theory does not conserve particle number because the single-particle
basis is incomplete. We conclude that the exact $G$ is not a probability
amplitude and is not $\Phi$-derivable in the Kadanoff-Baym sense. This sets up
a number of inconsistencies involving normalization, the definition of $G$,
interpretation of the spectral function, and $\Phi$-derivability. Our result
suggests that, in the most general case and in the most literal sense, $G$ is
not suitable for computing particle addition/removal spectra.


Abstract:
  In this work we develop an implementation of the Wang--Landau algorithm
[Phys. Rev. Lett. \textbf{86}, 2050-2053 (2001)]. This algorithm allows us to
find the density of states (DOS), a function that, for a given system,
describes the proportion of states that have a certain energy. The
implementation uses the Python language for the algorithm itself, and it can
take advantage of any library, such as the powerful LAMMPS library, for the
computation of energy. Therefore, the resulting implementation is simple and
flexible without sacrificing efficiency. This implementation also considers
recent developments in the parallelization of the code for faster computation.
We establish the soundness and effectiveness of our implementation by studying
well-known systems such as the Ising model, the Lennard--Jones and EAM solids.
We have found that our implementation can find the DOS with very good precision
in a reasonable amount of time. Therefore, we are equipped with a very powerful
and flexible implementation that can be easily used in order to study more
realistic models of matter.


Abstract:
  Machine-learning systems such as self-driving cars or virtual assistants are
composed of a large number of machine-learning models that recognize image
content, transcribe speech, analyze natural language, infer preferences, rank
options, etc. Models in these systems are often developed and trained
independently, which raises an obvious concern: Can improving a
machine-learning model make the overall system worse? We answer this question
affirmatively by showing that improving a model can deteriorate the performance
of downstream models, even after those downstream models are retrained. Such
self-defeating improvements are the result of entanglement between the models
in the system. We perform an error decomposition of systems with multiple
machine-learning models, which sheds light on the types of errors that can lead
to self-defeating improvements. We also present the results of experiments
which show that self-defeating improvements emerge in a realistic stereo-based
detection system for cars and pedestrians.


Abstract:
  In this paper, we propose a modification to the density approach to Stein's
method for intervals for the unit circle $\mathbb{S}^1$ which is motivated by
the differing geometry of $\mathbb{S}^1$ to Euclidean space. We provide an
upper bound to the Wasserstein metric for circular distributions and exhibit a
variety of different bounds between distributions; particularly, between the
von-Mises and wrapped normal distributions, and the wrapped normal and wrapped
Cauchy distributions.


Abstract:
  This contribution acts as an introduction to the requirements of the machine
protection system. As the first step, the basics interactions of fast charged
particles, neutrons and ${\gamma}$-rays with matter are summarized. The
architecture of a machine protection system based on beam loss detection is
described. Personal safety issues and personal dosimetry are discussed,
including the concept of radiation shielding.


Abstract:
  We investigate atmospheric responses of modeled hypothetical Earth-like
planets in the habitable zone of the M-dwarf AD Leonis to reduced oxygen (O2),
removed biomass (dead Earth), varying carbon dioxide (CO2) and surface relative
humidity (sRH). Results suggest large O2 differences between the reduced O2 and
dead scenarios in the lower but not the upper atmosphere. Ozone (O3) and
nitrous oxide (N2O) also show this behavior. Methane depends on hydroxyl (OH),
its main sink. Abiotic production of N2O occurs in the upper layers.
Chloromethane (CH3Cl) decreases everywhere on decreasing biomass. Changing CO2
(from x1 to x100 present atmospheric level (PAL)) and surface relative humidity
(sRH) (from 0.1 percent to 100 percent) does not influence CH3Cl as much as
lowering biomass. Therefore, CH3Cl can be considered a good biosignature.
Changing sRH and CO2 has a greater influence on temperature than O2 and biomass
alone. Changing the biomass produces ~6 kilometer (km) in effective height (H)
in transmission compared with changing CO2 and sRH ( about 25km). In
transmission O2 is discernible at 0.76 microns for greater than 0.1 PAL. The O3
9.6 micron band was weak for the low O2 runs and difficult to discern from dead
Earth, however O3 at 0.3 microns could serve as an indicator to distinguish
between reduced O2 and dead Earth. Spectral features of N2O and CH3Cl
corresponded to some km H. CH4 could be detectable tens of parsecs away with
ELT except for the 10-4 and 10-6 PAL O2 scenarios. O2 is barely detectable for
the 1 PAL O2 case and unfeasible at lower abundances.


Abstract:
  A prerequisite for the comprehensive understanding of many-body quantum
systems is a characterization in terms of their entanglement structure. The
experimental detection of entanglement in spatially extended many-body systems
describable by quantum fields still presents a major challenge. We develop a
general scheme for certifying entanglement and demonstrate it by revealing
entanglement between distinct subsystems of a spinor Bose-Einstein condensate.
Our scheme builds on the spatially resolved simultaneous detection of the
quantum field in two conjugate observables which allows the experimental
confirmation of quantum correlations between local as well as non-local
partitions of the system. The detection of squeezing in Bogoliubov modes in a
multi-mode setting illustrates its potential to boost the capabilities of
quantum simulations to study entanglement in spatially extended many-body
systems.


Abstract:
  A search for long-lived particles, which have come to rest within the ATLAS
detector, is presented. The subsequent decays of these long-lived particles can
produce high-momentum jets, resulting in large out-of-time energy deposits in
the ATLAS calorimeters. These decays are detected using data collected during
periods in the LHC bunch structure when collisions are absent. The analysed
dataset is composed of events from proton-proton collisions produced by the
Large Hadron Collider at a centre-of-mass energy of $\sqrt{s}=13$ TeV and
recorded by the ATLAS experiment during 2017 and 2018. The dataset used for
this search corresponds to a total live time of 579 hours. The results of this
search are used to derive lower limits on the mass of gluino $R$-hadrons,
assuming a branching fraction $B(\tilde{g} \rightarrow q \bar{q}
\tilde{\chi}_1^0)=100$%, with masses of up to 1.4 TeV excluded for gluino
lifetimes of $10^{-5}$ to $10^3$ s.


Abstract:
  PySDM is an open-source Python package for simulating the dynamics of
particles undergoing condensational and collisional growth, interacting with a
fluid flow and subject to chemical composition changes. It is intended to serve
as a building block for process-level as well as computational-fluid-dynamics
simulation systems involving representation of a continuous phase (air) and a
dispersed phase (aerosol), with PySDM being responsible for representation of
the dispersed phase. The PySDM package core is a Pythonic high-performance
implementation of the Super-Droplet Method (SDM) Monte-Carlo algorithm for
representing collisional growth, hence the name. PySDM has two alternative
parallel number-crunching backends available: multi-threaded CPU backend based
on Numba and GPU-resident backend built on top of ThrustRTC. The usage examples
are built on top of four simple atmospheric cloud modelling frameworks: box,
adiabatic parcel, single-column and 2D prescribed flow kinematic models. In
addition, the package ships with tutorial code depicting how PySDM can be used
from Julia and Matlab.


Abstract:
  Holographic complexity in the "complexity equals action" approach is
reconsidered relaxing the requirement of reparameterization invariance of the
action with the prescription that the action vanish in any vacuum causal
diamond. This implies that vacuum anti-de Sitter space plays the role of the
reference state. Moreover, the complexity of an anti-de Sitter-Schwarzschild
black hole becomes intrinsically finite and saturates Lloyd's bound after a
critical time. It is also argued that several artifacts, such as the unphysical
negative-time interval, can be removed by truly considering the bulk dual of
the thermofield double state.


Abstract:
  $n$-Dimensional fuzzy sets are a fuzzy set extension where the membership
values are n-tuples of real numbers in the unit interval [0,1] increasingly
ordered, called n-dimensional intervals. The set of n-dimensional intervals is
denoted by $L_n([0,1])$. This paper aims to investigate semi-vector spaces over
a weak semifield and aggregation functions concerning an admissible order on
the set of $n$-dimensional intervals and the construction of aggregation
functions on $L_n([0,1])$ based on the operations of the semi-vector spaces. In
particular, extensions of the family of OWA and weighted average aggregation
functions are investigated. Finally, we develop a multi-criteria group
decision-making method based on n-dimensional aggregation functions with
respect to an admissible order and give an illustrative example.


Abstract:
  The analysis of quantitative hemodynamics and luminal pressure may add
valuable information to aid treatment strategies and prognosis for aortic
dissections. This work directly compared in vitro 4D-flow magnetic resonance
imaging (MRI), catheter-based pressure measurements, and computational fluid
dynamics that integrated fluid-structure interaction (CFD FSI). Experimental
data was acquired with a compliant 3D-printed model of a type-B aortic
dissection (TBAD) that was embedded into a physiologically tuned flow circuit.
In vitro flow and pressure information were used to tune the CFD FSI Windkessel
boundary conditions. Results showed very good overall agreement of complex flow
patterns, true to false lumen flow splits, and pressure distribution. This work
demonstrates feasibility of a tunable experimental setup that integrates a
patient-specific compliant model and provides a test bed for exploring critical
imaging and modeling parameters that ultimately may improve the prognosis for
patients with aortic dissections.


Abstract:
  The Laplacian matching polynomial of a graph $G$, denoted by
$\mathscr{L\hspace{-0.7mm}M}(G,x)$, is a new graph polynomial whose all roots
are nonnegative real numbers. In this paper, we investigate the location of
zeros of the Laplacian matching polynomials. Let $G$ be a connected graph. We
show that $0$ is a root of $\mathscr{L\hspace{-0.7mm}M}(G, x)$ if and only if
$G$ is a tree. We prove that the number of distinct positive zeros of
$\mathscr{L\hspace{-0.7mm}M}(G,x)$ is at least equal to the length of the
longest path in $G$. It is also established that the zeros of
$\mathscr{L\hspace{-0.7mm}M}(G,x)$ and $\mathscr{L\hspace{-0.7mm}M}(G-e,x)$
interlace for each edge $e$ of $G$. Using the path-tree of $G$, we present a
linear algebraic approach to investigate the largest zero of
$\mathscr{L\hspace{-0.7mm}M}(G,x)$ and particularly to give tight upper and
lower bounds on it.


Abstract:
  Two-sample tests have been one of the most classical topics in statistics
with wide application even in cutting edge applications. There are at least two
modes of inference used to justify the two-sample tests. One is usual
superpopulation inference assuming the units are independent and identically
distributed (i.i.d.) samples from some superpopulation; the other is finite
population inference that relies on the random assignments of units into
different groups. When randomization is actually implemented, the latter has
the advantage of avoiding distributional assumptions on the outcomes. In this
paper, we will focus on finite population inference for censored outcomes,
which has been less explored in the literature. Moreover, we allow the
censoring time to depend on treatment assignment, under which exact permutation
inference is unachievable. We find that, surprisingly, the usual logrank test
can also be justified by randomization. Specifically, under a Bernoulli
randomized experiment with non-informative i.i.d. censoring within each
treatment arm, the logrank test is asymptotically valid for testing Fisher's
null hypothesis of no treatment effect on any unit. Moreover, the asymptotic
validity of the logrank test does not require any distributional assumption on
the potential event times. We further extend the theory to the stratified
logrank test, which is useful for randomized blocked designs and when censoring
mechanisms vary across strata. In sum, the developed theory for the logrank
test from finite population inference supplements its classical theory from
usual superpopulation inference, and helps provide a broader justification for
the logrank test.


Abstract:
  Deep models trained on large-scale RGB image datasets have shown tremendous
success. It is important to apply such deep models to real-world problems.
However, these models suffer from a performance bottleneck under illumination
changes. Thermal IR cameras are more robust against such changes, and thus can
be very useful for the real-world problems. In order to investigate efficacy of
combining feature-rich visible spectrum and thermal image modalities, we
propose an unsupervised domain adaptation method which does not require
RGB-to-thermal image pairs. We employ large-scale RGB dataset MS-COCO as source
domain and thermal dataset FLIR ADAS as target domain to demonstrate results of
our method. Although adversarial domain adaptation methods aim to align the
distributions of source and target domains, simply aligning the distributions
cannot guarantee perfect generalization to the target domain. To this end, we
propose a self-training guided adversarial domain adaptation method to promote
generalization capabilities of adversarial domain adaptation methods. To
perform self-training, pseudo labels are assigned to the samples on the target
thermal domain to learn more generalized representations for the target domain.
Extensive experimental analyses show that our proposed method achieves better
results than the state-of-the-art adversarial domain adaptation methods. The
code and models are publicly available.


Abstract:
  High-dimensional sparse generalized linear models (GLMs) have emerged in the
setting that the number of samples and the dimension of variables are large,
and even the dimension of variables grows faster than the number of samples.
False discovery rate (FDR) control aims to identify some small number of
statistically significantly nonzero results after getting the sparse penalized
estimation of GLMs. Using the CLIME method for precision matrix estimations, we
construct the debiased-Lasso estimator and prove the asymptotical normality by
minimax-rate oracle inequalities for sparse GLMs. In practice, it is often
needed to accurately judge each regression coefficient's positivity and
negativity, which determines whether the predictor variable is positively or
negatively related to the response variable conditionally on the rest
variables. Using the debiased estimator, we establish multiple testing
procedures. Under mild conditions, we show that the proposed debiased
statistics can asymptotically control the directional (sign) FDR and
directional false discovery variables at a pre-specified significance level.
Moreover, it can be shown that our multiple testing procedure can approximately
achieve a statistical power of 1. We also extend our methods to the two-sample
problems and propose the two-sample test statistics. Under suitable conditions,
we can asymptotically achieve directional FDR control and directional FDV
control at the specified significance level for two-sample problems. Some
numerical simulations have successfully verified the FDR control effects of our
proposed testing procedures, which sometimes outperforms the classical knockoff
method.


Abstract:
  We propose a simple, intuitive yet powerful method for human-object
interaction (HOI) detection. HOIs are so diverse in spatial distribution in an
image that existing CNN-based methods face the following three major drawbacks;
they cannot leverage image-wide features due to CNN's locality, they rely on a
manually defined location-of-interest for the feature aggregation, which
sometimes does not cover contextually important regions, and they cannot help
but mix up the features for multiple HOI instances if they are located closely.
To overcome these drawbacks, we propose a transformer-based feature extractor,
in which an attention mechanism and query-based detection play key roles. The
attention mechanism is effective in aggregating contextually important
information image-wide, while the queries, which we design in such a way that
each query captures at most one human-object pair, can avoid mixing up the
features from multiple instances. This transformer-based feature extractor
produces so effective embeddings that the subsequent detection heads may be
fairly simple and intuitive. The extensive analysis reveals that the proposed
method successfully extracts contextually important features, and thus
outperforms existing methods by large margins (5.37 mAP on HICO-DET, and 5.7
mAP on V-COCO). The source codes are available at
$\href{https://github.com/hitachi-rd-cv/qpic}{\text{this https URL}}$.