Abstract:
  Pencil x-ray beam imaging provides superior spatial resolution than other
imaging geometries like sheet beam and cone beam geometries due to the
illumination of a line instead of an area or volume. However, the pencil beam
geometry suffers from long scan times and concerns over dose discourage
laboratory use of pencil beam x-ray sources. Molecular imaging techniques like
XLCT imaging benefit most from pencil beam imaging to accurately localize the
distribution of contrast agents embedded in a small animal object. To
investigate the dose deposited by pencil beam x-ray imaging in XLCT, dose
estimations from one angular projection scan by three different x-ray source
energies were performed on a small animal object composed of water, bone, and
blood with a Monte Carlo simulation platform, GATE (Geant4 Application for
Tomographic Emission). Our results indicate that, with an adequate x-ray
benchtop source with high brilliance and quasi-monochromatic properties like
the Sigray source, the dose concerns can be reduced. With the Sigray source,
the bone marrow was estimated to have a radiation dose of 30 mGy for a typical
XLCT imaging, in which we have 6 angular projections, 100 micrometer scan step
size, and 10^6 x-ray photons per linear scan.


Abstract:
  As of today, the fifth generation (5G) mobile communication system has been
rolled out in many countries and the number of 5G subscribers already reaches a
very large scale. It is time for academia and industry to shift their attention
towards the next generation. At this crossroad, an overview of the current
state of the art and a vision of future communications are definitely of
interest. This article thus aims to provide a comprehensive survey to draw a
picture of the sixth generation (6G) system in terms of drivers, use cases,
usage scenarios, requirements, key performance indicators (KPIs), architecture,
and enabling technologies. First, we attempt to answer the question of "Is
there any need for 6G?" by shedding light on its key driving factors, in which
we predict the explosive growth of mobile traffic until 2030, and envision
potential use cases and usage scenarios. Second, the technical requirements of
6G are discussed and compared with those of 5G with respect to a set of KPIs in
a quantitative manner. Third, the state-of-the-art 6G research efforts and
activities from representative institutions and countries are summarized, and a
tentative roadmap of definition, specification, standardization, and regulation
is projected. Then, we identify a dozen of potential technologies and introduce
their principles, advantages, challenges, and open research issues. Finally,
the conclusions are drawn to paint a picture of "What 6G may look like?". This
survey is intended to serve as an enlightening guideline to spur interests and
further investigations for subsequent research and development of 6G
communications systems.


Abstract:
  Let $A$ and $B$ be $C^*$-algebras, $A$ separable and $I$ an ideal in $B$. We
show that for any completely positive contractive linear map $\psi\colon A\to
B/I$ there is a continuous family $\Theta_t\colon A\to B$, for $t\in
[1,\infty)$, of lifts of $\psi$ that are asymptotically linear, asymptotically
completely positive and asymptotically contractive. If $\psi$ is orthogonality
preserving, then $\Theta_t$ can be chosen to have this property asymptotically.
If $A$ and $B$ carry continuous actions of a second countable locally compact
group $G$ such that $I$ is $G$-invariant and $\psi$ is equivariant, we show
that the family $\Theta_t$ can be chosen to be asymptotically equivariant. If a
linear completely positive lift for $\psi$ exists, we can arrange that
$\Theta_t$ is linear and completely positive for all $t\in [1,\infty)$. In the
equivariant setting, if $A$, $B$ and $\psi$ are unital, we show that
asymptotically linear unital lifts are only guaranteed to exist if $G$ is
amenable. This leads to a new characterization of amenability in terms of the
existence of asymptotically equivariant unital sections for quotient maps.


Abstract:
  Batch normalization is a key component of most image classification models,
but it has many undesirable properties stemming from its dependence on the
batch size and interactions between examples. Although recent work has
succeeded in training deep ResNets without normalization layers, these models
do not match the test accuracies of the best batch-normalized networks, and are
often unstable for large learning rates or strong data augmentations. In this
work, we develop an adaptive gradient clipping technique which overcomes these
instabilities, and design a significantly improved class of Normalizer-Free
ResNets. Our smaller models match the test accuracy of an EfficientNet-B7 on
ImageNet while being up to 8.7x faster to train, and our largest models attain
a new state-of-the-art top-1 accuracy of 86.5%. In addition, Normalizer-Free
models attain significantly better performance than their batch-normalized
counterparts when finetuning on ImageNet after large-scale pre-training on a
dataset of 300 million labeled images, with our best models obtaining an
accuracy of 89.2%. Our code is available at https://github.com/deepmind/
deepmind-research/tree/master/nfnets


Abstract:
  In this paper, we present a general numerical platform for designing
accurate, efficient, and stable numerical algorithms for incompressible
hydrodynamic models that obeys the thermodynamical laws. The obtained numerical
schemes are automatically linear in time. It decouples the hydrodynamic
variable and other state variables such that only small-size linear problems
need to be solved at each time marching step. Furthermore, if the classical
velocity projection method is utilized, the velocity field and pressure field
can be decoupled. In the end, only a few elliptic-type equations shall be
solved in each time step. This strategy is made possible through a sequence of
model reformulations by fully exploring the models' thermodynamic structures.
The generalized Onsager principle directly guides these reformulation
procedures. In the reformulated but equivalent models, the reversible and
irreversible components can be identified, guiding the numerical platform to
decouple the reversible and irreversible dynamics. This eventually leads to
decoupled numerical algorithms, given that the coupling terms only involve
irreversible dynamics. To further demonstrate the numerical platform's power,
we apply it to several specific incompressible hydrodynamic models. The energy
stability of the proposed numerical schemes is shown in detail. The
second-order accuracy in time is verified numerically through time step
refinement tests. Several benchmark numerical examples are presented to further
illustrate the proposed numerical framework's accuracy, stability, and
efficiency.


Abstract:
  We place observational constraints on two models within a class of scenarios
featuring an elastic interaction between dark energy and dark matter that only
produces momentum exchange up to first order in cosmological perturbations. The
first one corresponds to a perfect-fluid model of the dark components with an
explicit interacting Lagrangian, where dark energy acts as a dark radiation at
early times and behaves as a cosmological constant at late times. The second
one is a dynamical dark energy model with a dark radiation component, where the
momentum exchange covariantly modifies the conservation equations in the dark
sector. Using Cosmic Microwave Background (CMB), Baryon Acoustic Oscillations
(BAO), and Supernovae type Ia (SnIa) data, we show that the Hubble tension can
be alleviated due to the additional radiation, while the $\sigma_8$ tension
present in the $\Lambda$-Cold-Dark-Matter model can be eased by the weaker
galaxy clustering that occurs in these interacting models. Furthermore, we show
that, while CMB+BAO+SnIa data put only upper bounds on the coupling strength,
adding low-redshift data in the form of a constraint on the parameter $S_8$
strongly favours nonvanishing values of the interaction parameters. Our
findings are in line with other results in the literature that could signal a
universal trend of the momentum exchange among the dark sector.


Abstract:
  We study analytically how noninteracting weakly active particles, for which
passive Brownian diffusion cannot be neglected and activity can be treated
perturbatively, distribute and behave near boundaries in various geometries. In
particular, we develop a perturbative approach for the model of active
particles driven by an exponentially correlated random force (active
Ornstein-Uhlenbeck particles). This approach involves a relatively simple
expansion of the distribution in powers of the P\'{e}clet number and in terms
of Hermite polynomials. We use this approach to cleanly formulate boundary
conditions, which allows us to study weakly active particles in several
geometries: confinement by a single wall or between two walls in 1D,
confinement in a circular or wedge-shaped region in 2D, motion near a
corrugated boundary, and finally absorption onto a sphere. We consider how
quantities such as the density, pressure, and flow of the active particles
change as we gradually increase the activity away from a purely passive system.
These results for the limit of weak activity help us gain insight into how
active particles behave in the presence of various types of boundaries.


Abstract:
  Quantum Darwinism proposes that the proliferation of redundant information
plays a major role in the emergence of objectivity out of the quantum world. Is
this kind of objectivity necessarily classical? We show that if one takes
Spekkens' notion of noncontextuality as the notion of classicality and the
approach of Brand\~{a}o, Piani and Horodecki to quantum Darwinism, the answer
to the above question is `yes', if the environment encodes sufficiently well
the proliferated information. Moreover, we propose a threshold on this
encoding, above which one can unambiguously say that classical objectivity has
emerged under quantum Darwinism.


Abstract:
  Some audio declipping methods produce waveforms that do not fully respect the
physical process of clipping, which is why we refer to them as inconsistent.
This letter reports what effect on perception it has if the solution by
inconsistent methods is forced consistent by postprocessing. We first propose a
simple sample replacement method, then we identify its main weaknesses and
propose an improved variant. The experiments show that the vast majority of
inconsistent declipping methods significantly benefit from the proposed
approach in terms of objective perceptual metrics. In particular, we show that
the SS PEW method based on social sparsity combined with the proposed method
performs comparable to top methods from the consistent class, but at a
computational cost of one order of magnitude lower.


Abstract:
  The strong constraints from the Fermi-LAT data on the isotropic gamma-ray
background suggest that the neutrinos observed by IceCube might possibly come
from sources that are hidden to gamma-ray observations. A possibility recently
discussed in the literature is that neutrinos may come from jets of collapsing
massive stars which fail to break out of the stellar envelope, and for this
reason they are known as choked jets, or choked Gamma-Ray Bursts (GRBs). In
this paper, we estimate the neutrino flux and spectrum expected from these
sources, focusing on Type II SNe. We perform detailed calculations of pg
interactions, accounting for all the neutrino production channels and
scattering angles. We provide predictions of expected event rates for operating
neutrino telescopes, such as ANTARES and IceCube, as well as for the future
generation telescope KM3NeT. We find that for GRB energies channeled into
protons spanning between 10^51 - 10^53 erg, choked GRBs may substantially
contribute to the observed astrophysical neutrino flux, if their local rate is
80 - 1 Gpc^-3 yr^-1 respectively.


Abstract:
  It is becoming increasingly common to study complex associations between
multiple phenotypes and high-dimensional genomic features in biomedicine.
However, it requires flexible and efficient joint statistical models if there
are correlations between multiple response variables and between
high-dimensional predictors. We propose a structured multivariate Bayesian
variable selection model to identify sparse predictors associated with multiple
correlated response variables. The approach makes use of known structure
information between the multiple response variables and high-dimensional
predictors via a Markov random field (MRF) prior for the latent indicator
variables of the coefficient matrix of a sparse seemingly unrelated regressions
(SSUR). The structure information included in the MRF prior can improve the
model performance (i.e., variable selection and response prediction) compared
to other common priors. In addition, we employ random effects to capture
heterogeneity of grouped samples. The proposed approach is validated by
simulation studies and applied to a pharmacogenomic study which includes
pharmacological profiling and multi-omics data (i.e., gene expression, copy
number variation and mutation) from in vitro anti-cancer drug sensitivity
screening.


Abstract:
  This paper proposes a framework to investigate the value of sharing
privacy-protected smart meter data between domestic consumers and load serving
entities. The framework consists of a discounted differential privacy model to
ensure individuals cannot be identified from aggregated data, a ANN-based
short-term load forecasting to quantify the impact of data availability and
privacy protection on the forecasting error and an optimal procurement problem
in day-ahead and balancing markets to assess the market value of the
privacy-utility trade-off. The framework demonstrates that when the load
profile of a consumer group differs from the system average, which is
quantified using the Kullback-Leibler divergence, there is significant value in
sharing smart meter data while retaining individual consumer privacy.


Abstract:
  Rare-earth (R) nickelates (such as perovskite RNiO3, trilayer R4Ni3O10, and
infinite layer RNiO2) have attracted tremendous interest very recently.
However, unlike widely studied RNiO3 and RNiO2 films, the synthesis of trilayer
nickelate R4Ni3O10 films is rarely reported. Here, single-crystalline
(Nd0.8Sr0.2)4Ni3O10 epitaxial films were coherently grown on SrTiO3 substrates
by high-pressure magnetron sputtering. The crystal and electronic structures of
(Nd0.8Sr0.2)4Ni3O10 films were characterized by high-resolution X-ray
diffraction and X-ray photoemission spectroscopy, respectively. The electrical
transport measurements reveal a metal-insulator transition near 82 K and
negative magnetoresistance in (Nd0.8Sr0.2)4Ni3O10 films. Our work provides a
novel route to synthesize high-quality trilayer nickelate R4Ni3O10 films.


Abstract:
  This paper presents a new mathematical signal transform that is especially
suitable for decoding information related to non-rigid signal displacements. We
provide a measure theoretic framework to extend the existing Cumulative
Distribution Transform [ACHA 45 (2018), no. 3, 616-641] to arbitrary (signed)
signals on $\overline{\mathbb{R}}$. We present both forward (analysis) and
inverse (synthesis) formulas for the transform, and describe several of its
properties including translation, scaling, convexity, linear separability and
others. Finally, we describe a metric in transform space, and demonstrate the
application of the transform in classifying (detecting) signals under random
displacements.


Abstract:
  This study is devoted to the implications of scale-dependent gravity in
Cosmology. Redshift-space distortion data indicate that there is a tension
between $\Lambda$CDM and available observations as far as the value of the rms
density fluctuation, $\sigma_8$, is concerned. It has been pointed out that
this tension may be alleviated in alternative theories in which gravity is
weaker at red-shift $z \sim 1$. We study the evolution of density perturbations
for non-relativistic matter on top of a spatially flat FLRW Universe, and we
compute the combination $A=f \sigma_8$ in the framework of scale-dependent
gravity, where both Newton's constant and the cosmological constant are allowed
to vary with time. Upon comparison between available observational data
(supernovae data as well as redshift-space distortion data) and theoretical
predictions of the model, we determine the numerical value of $\sigma_8$ that
best fits the data.


Abstract:
  Active learning aims to achieve greater accuracy with less training data by
selecting the most useful data samples from which it learns. Single-criterion
based methods (i.e., informativeness and representativeness based methods) are
simple and efficient; however, they lack adaptability to different real-world
scenarios. In this paper, we introduce a multiple-criteria based active
learning algorithm, which incorporates three complementary criteria, i.e.,
informativeness, representativeness and diversity, to make appropriate
selections in the active learning rounds under different data types. We
consider the selection process as a Determinantal Point Process, which good
balance among these criteria. We refine the query selection strategy by both
selecting the hardest unlabeled data sample and biasing towards the classifiers
that are more suitable for the current data distribution. In addition, we also
consider the dependencies and relationships between these data points in data
selection by means of centroidbased clustering approaches. Through evaluations
on synthetic and real-world datasets, we show that our method performs
significantly better and is more stable than other multiple-criteria based AL
algorithms.


Abstract:
  Studies of the production of heavy-flavour baryons are of prominent
importance to investigate hadronization mechanisms at the LHC, in particular
through the study of the evolution of the baryon-over-meson production ratio.
Measurements performed in pp and p--Pb collisions at the LHC have revealed
unexpected features, qualitatively similar to what was observed in heavy-ion
collisions and, in the charm sector, not in line with the expectations based on
previous measurements from $\rm e^+e^-$ colliders and in ep collisions. These
results suggest that charmed baryon formation might not be universal and that
the baryon-over-meson ratio depends on the collision system or multiplicity.
  A review of ALICE measurements of charmed baryons, including $\rm
\Lambda_c^+/D^0$ as a function of charged-particle multiplicity in pp, p--Pb
and Pb--Pb collisions, $\rm \Sigma_c^{0, +, ++}/D^0$ and $\rm \Xi_c^{0, +}/D^0$
as a function of $p_{\rm T}$ in pp collisions and $\rm
\Gamma(\Xi_c^0\rightarrow\Xi^-e^+\nu_e)/\Gamma(\Xi_c^0\rightarrow\Xi^-\pi^+)$,
will be presented. Comparison to phenomenological models will be also
discussed. Emphasis will be given to the discussion of the impact of these
studies on the understanding of hadronization processes.


Abstract:
  In this paper, the interaction between two immiscible fluids with a finite
mobility ratio is investigated numerically within a Hele-Shaw cell. Fingering
instabilities initiated at the interface between a low viscosity fluid and a
high viscosity fluid are analysed at varying capillary numbers and mobility
ratios using a finite mobility ratio model. The present work is motivated by
the possible development of interfacial instabilities that can occur in porous
media during the process of CO$_2$ sequestration, but does not pretend to
analyse this complex problem. Instead, we present a detailed study of the
analogous problem occurring in a Hele-Shaw cell, giving indications of possible
plume patterns that can develop during the CO$_2$ injection. The numerical
scheme utilises a boundary element method in which the normal velocity at the
interface of the two fluids is directly computed through the evaluation of a
hypersingular integral. The boundary integral equation is solved using a
Neumann convergent series with cubic B-Spline boundary discretisation,
exhibiting 6th order spatial convergence. The convergent series allows the long
term non-linear dynamics of growing viscous fingers to be explored accurately
and efficiently. Simulations in low mobility ratio regimes reveal large
differences in fingering patterns compared to those predicted by previous high
mobility ratio models. Most significantly, classical finger shielding between
competing fingers is inhibited. Secondary fingers can possess significant
velocity, allowing greater interaction with primary fingers compared to high
mobility ratio flows. Eventually, this interaction can lead to base thinning
and the breaking of fingers into separate bubbles.


Abstract:
  HTCondor is a major workload management system used in distributed high
throughput computing (dHTC) environments, e.g., the Open Science Grid. One of
the distinguishing features of HTCondor is the native support for data
movement, allowing it to operate without a shared filesystem. Coupling data
handling and compute scheduling is both convenient for users and allows for
significant infrastructure flexibility but does introduce some limitations. The
default HTCondor data transfer mechanism routes both the input and output data
through the submission node, making it a potential bottleneck. In this document
we show that by using a node equipped with a 100 Gbps network interface (NIC)
HTCondor can serve data at up to 90 Gbps, which is sufficient for most current
use cases, as it would saturate the border network links of most research
universities at the time of writing.


Abstract:
  A stochastic process with movement, return, and rest phases is considered in
this paper. For the movement phase, the particles move following the dynamics
of Gaussian process or ballistic type of L\'evy walk, and the time of each
movement is random. For the return phase, the particles will move back to the
origin with a constant velocity or acceleration or under the action of a
harmonic force after each movement, so that this phase can also be treated as a
non-instantaneous resetting. After each return, a rest with a random time at
the origin follows. The asymptotic behaviors of the mean squared displacements
with different kinds of movement dynamics, random resting time, and returning
are discussed. The stationary distributions are also considered when the
process is localized. Besides, the mean first passage time is considered when
the dynamic of movement phase is Brownian motion.


Abstract:
  We reconsider the widely held view that the Mannheim--Kazanas (MK) vacuum
solution for a static, spherically-symmetric system in conformal gravity (CG)
predicts flat rotation curves, such as those observed in galaxies, without the
need for dark matter. This prediction assumes that test particles have fixed
rest mass and follow timelike geodesics in the MK metric in the vacuum region
exterior to a spherically-symmetric representation of the galactic mass
distribution. Such geodesics are not conformally invariant, however, which
leads to an apparent discrepancy with the analogous calculation performed in
the conformally-equivalent Schwarzschild-de-Sitter (SdS) metric, where the
latter does not predict flat rotation curves. This difference arises since the
mass of particles in CG must instead be generated dynamically through
interaction with a scalar field. The energy-momentum of this required scalar
field means that, in a general conformal frame from the equivalence class of CG
solutions outside a static, spherically-symmetric matter distribution, the
spacetime is not given by the MK vacuum solution. A unique frame does exist,
however, for which the metric retains the MK form, since the scalar field
energy-momentum vanishes despite the field being non-zero and radially
dependent. Nonetheless, we show that in both this MK frame and the Einstein
frame, in which the scalar field is constant, massive particles follow timelike
geodesics of the SdS metric, thereby resolving the apparent frame dependence of
physical predictions and unambiguously yielding rotation curves with no flat
region. We also comment on how our analysis resolves the long-standing
uncertainty regarding gravitational lensing in the MK metric. (Abridged)


Abstract:
  Traumatic Brain Injury (TBI) is a common cause of death and disability.
However, existing tools for TBI diagnosis are either subjective or require
extensive clinical setup and expertise. The increasing affordability and
reduction in size of relatively high-performance computing systems combined
with promising results from TBI related machine learning research make it
possible to create compact and portable systems for early detection of TBI.
This work describes a Raspberry Pi based portable, real-time data acquisition,
and automated processing system that uses machine learning to efficiently
identify TBI and automatically score sleep stages from a single-channel
Electroen-cephalogram (EEG) signal. We discuss the design, implementation, and
verification of the system that can digitize EEG signal using an Analog to
Digital Converter (ADC) and perform real-time signal classification to detect
the presence of mild TBI (mTBI). We utilize Convolutional Neural Networks (CNN)
and XGBoost based predictive models to evaluate the performance and demonstrate
the versatility of the system to operate with multiple types of predictive
models. We achieve a peak classification accuracy of more than 90% with a
classification time of less than 1 s across 16 s - 64 s epochs for TBI vs
control conditions. This work can enable development of systems suitable for
field use without requiring specialized medical equipment for early TBI
detection applications and TBI research. Further, this work opens avenues to
implement connected, real-time TBI related health and wellness monitoring
systems.


Abstract:
  Active metric learning is the problem of incrementally selecting high-utility
batches of training data (typically, ordered triplets) to annotate, in order to
progressively improve a learned model of a metric over some input domain as
rapidly as possible. Standard approaches, which independently assess the
informativeness of each triplet in a batch, are susceptible to highly
correlated batches with many redundant triplets and hence low overall utility.
While a recent work \cite{kumari2020batch} proposes batch-decorrelation
strategies for metric learning, they rely on ad hoc heuristics to estimate the
correlation between two triplets at a time. We present a novel batch active
metric learning method that leverages the Maximum Entropy Principle to learn
the least biased estimate of triplet distribution for a given set of prior
constraints. To avoid redundancy between triplets, our method collectively
selects batches with maximum joint entropy, which simultaneously captures both
informativeness and diversity. We take advantage of the submodularity of the
joint entropy function to construct a tractable solution using an efficient
greedy algorithm based on Gram-Schmidt orthogonalization that is provably
$\left( 1 - \frac{1}{e} \right)$-optimal. Our approach is the first batch
active metric learning method to define a unified score that balances
informativeness and diversity for an entire batch of triplets. Experiments with
several real-world datasets demonstrate that our algorithm is robust,
generalizes well to different applications and input modalities, and
consistently outperforms the state-of-the-art.


Abstract:
  The quality of the semiconductor-barrier interface plays a pivotal role in
the demonstration of high quality reproducible quantum dots for quantum
information processing. In this work, we have measured SiMOSFET Hall bars on
undoped Si substrates in order to investigate the quality of the devices
fabricated in a full CMOS process. We report a record mobility of 17'500 cm2/Vs
with a sub-10 nm oxide thickness indicating a high quality interface, suitable
for future qubit applications. We also study the influence of gate materials on
the mobilities and discuss the underlying mechanisms, giving insight into
further material optimization for large scale quantum processors.


Abstract:
  Recently, the authors have proposed and analyzed isogeometric tearing and
interconnecting (IETI-DP) solvers for multi-patch discretizations in
Isogeometric Analysis. Conforming and discontinuous Galerkin settings have been
considered. In both cases, we have assumed that the interfaces between the
patches consist of whole edges. In this paper, we present a generalization that
allows us to drop this requirement. This means that the patches can meet in
T-junctions, which increases the flexibility of the geometric model
significantly. We use vertex-based primal degrees of freedom. For the
T-junctions, we propose to follow the idea of "fat vertices".


Abstract:
  In a series of publications, Kocherginsky and Gruebele presented a systematic
framework for chemical transport and thermodiffusion to predict the Soret
coefficients from thermodynamics. A macroscopic derivation of the Onsager
reciprocal relations without recourse to microscopic fluctuations or equations
of motion was also discussed. Their important contributions and some confusions
are discussed.


Abstract:
  A planetary system consists of a host star and one or more planets, arranged
into a particular configuration. Here, we consider what information belongs to
the configuration, or ordering, of 4286 Kepler planets in their 3277 planetary
systems. First, we train a neural network model to predict the radius and
period of a planet based on the properties of its host star and the radii and
period of its neighbors. The mean absolute error of the predictions of the
trained model is a factor of 2.1 better than the MAE of the predictions of a
naive model which draws randomly from dynamically allowable periods and radii.
Second, we adapt a model used for unsupervised part-of-speech tagging in
computational linguistics to investigate whether planets or planetary systems
fall into natural categories with physically interpretable "grammatical rules."
The model identifies two robust groups of planetary systems: (1) compact
multi-planet systems and (2) systems around giant stars ($\log{g} \lesssim
4.0$), although the latter group is strongly sculpted by the selection bias of
the transit method. These results reinforce the idea that planetary systems are
not random sequences -- instead, as a population, they contain predictable
patterns that can provide insight into the formation and evolution of planetary
systems.


Abstract:
  The unprecedented wide bandgap tunability (~1 eV) of
Al$_x$In$_{1-x}$As$_y$Sb$_{1-y}$ latticed-matched to GaSb enables the
fabrication of photodetectors over a wide range from near-infrared to
mid-infrared. In this paper, the valence band-offsets in AlxIn1-xAsySb1-y with
different Al compositions are analyzed by tight-binding calculations and X-ray
photoelectron spectroscopy (XPS) measurements. The observed weak variation in
valence band offsets is consistent with the lack of any minigaps in the valence
band, compared to the conduction band.


Abstract:
  In this paper, we give upper and lower bounds for the spectral norms of
r-circulant matrices with the generalized bi-periodic Fibonacci numbers.
Moreover, we investigate the eigenvalues and determinants of these matrices.


Abstract:
  The looping pendulum is a simple physical system consisting of two masses
connected by a string that passes over a rod. We derive equations of motion for
the looping pendulum using Newtonian mechanics, and show that these equations
can be solved numerically to give a good description of the system's dynamics.
The numerical solution captures complex aspects of the looping pendulum's
behavior, and is in good agreement with the experimental results.


Abstract:
  We consider Markov Decision Processes (MDPs) with deterministic transitions
and study the problem of regret minimization, which is central to the analysis
and design of optimal learning algorithms. We present logarithmic
problem-specific regret lower bounds that explicitly depend on the system
parameter (in contrast to previous minimax approaches) and thus, truly quantify
the fundamental limit of performance achievable by any learning algorithm.
Deterministic MDPs can be interpreted as graphs and analyzed in terms of their
cycles, a fact which we leverage in order to identify a class of deterministic
MDPs whose regret lower bound can be determined numerically. We further
exemplify this result on a deterministic line search problem, and a
deterministic MDP with state-dependent rewards, whose regret lower bounds we
can state explicitly. These bounds share similarities with the known
problem-specific bound of the multi-armed bandit problem and suggest that
navigation on a deterministic MDP need not have an effect on the performance of
a learning algorithm.


Abstract:
  All the laws of particle physics are time-reversible. A time arrow emerges
only when ensembles of classical particles are treated probabilistically,
outside of physics laws, and the second law of thermodynamics is introduced. In
quantum physics, despite its intrinsically probabilistic nature, no mechanism
for a time arrow has been proposed. We propose an entropy for quantum physics,
which may conduce to the emergence of a time arrow. The proposed entropy is a
measure of randomness over the degrees of freedom of a quantum state. It is
dimensionless, it is a relativistic scalar, it is invariant under coordinate
transformation of position and momentum that maintain conjugate properties, and
under CPT transformations; and its minimum is positive due to the uncertainty
principle.


Abstract:
  In many real-world games, such as traders repeatedly bargaining with
customers, it is very hard for a single AI trader to make good deals with
various customers in a few turns, since customers may adopt different
strategies even the strategies they choose are quite simple. In this paper, we
model this problem as fast adaptive learning in the finitely repeated games. We
believe that past game history plays a vital role in such a learning procedure,
and therefore we propose a novel framework (named, F3) to fuse the past and
current game history with an Opponent Action Estimator (OAE) module that uses
past game history to estimate the opponent's future behaviors. The experiments
show that the agent trained by F3 can quickly defeat opponents who adopt
unknown new strategies. The F3 trained agent obtains more rewards in a fixed
number of turns than the agents that are trained by deep reinforcement
learning. Further studies show that the OAE module in F3 contains
meta-knowledge that can even be transferred across different games.


Abstract:
  Recent result by Adachi-Iyama-Reiten has shown a bijective correspondence
between support $\tau$-tilting modules and functorially finite torsion classes.
On the other hand, the techniques of gluing torsion classes along a recollement
were investigated by Liu-Vit\'oria-Yang and Ma-Huang. In this article, we show
that gluing torsion classes can be restricted to functorially finiteness
condition in the symmetric ladders of height 2. Then using the above
correspondence with support $\tau$-tilting modules, we present explicit
constructions of gluing of support $\tau$-tilting modules via symmetric ladders
of height 2. Finally, we apply the results to triangular matrix algebras to
give a more detailed version of the known Jasso's reduction and study maximal
green sequences.


Abstract:
  Let $(\Sigma, g)$ be a closed, oriented, negatively curved surface, and fix
pairwise disjoint simple closed geodesics $\gamma_{\star,1}, \dots
\gamma_{\star, r}$. We give an asymptotic growth as $L \to +\infty$ of the
number of primitive closed geodesic of length less than $L$ intersecting
$\gamma_{\star,j}$ exactly $n_j$ times, where $n_1, \dots, n_r$ are fixed
nonnegative integers. This is done by introducing a dynamical scattering
operator associated to the surface with boundary obtained by cutting $\Sigma$
along $\gamma_{\star,1}, \dots, \gamma_{\star, r}$ and by using the theory of
Pollicott-Ruelle resonances for open systems.


Abstract:
  We present exact black hole solutions endowed with magnetic charge coming
from exponential and logarithmic nonlinear electrodynamics (NLED). Classically,
we analyze the null and timelike geodesics, all of which contain both the bound
and the scattering orbits. Using the effective geometry formalism, we found
that photon can have nontrivial stable (both circular and non-circular) bound
orbits. The noncircular bound orbits for the one-horizon case mostly take the
form of precessed ellipse. For the extremal and three-horizon cases we find
many-world orbits where photon crosses the outer horizon but bounces back
without hitting the true (or second, respectively) horizon, producing the
epicycloid and epitrochoid paths. Semiclassically, we investigate their Hawking
temperature, stability, and phase transition. The nonlinearity enables black
hole stability with smaller radius than its RN counterpart. However, for
very-strong nonlinear regime, the thermodynamic behavior tends to be
Schwarzschild-like.


Abstract:
  Autoencoders are widely used in machine learning applications, in particular
for anomaly detection. Hence, they have been introduced in high energy physics
as a promising tool for model-independent new physics searches. We scrutinize
the usage of autoencoders for unsupervised anomaly detection based on
reconstruction loss to show their capabilities, but also their limitations. As
a particle physics benchmark scenario, we study the tagging of top jet images
in a background of QCD jet images. Although we reproduce the positive results
from the literature, we show that the standard autoencoder setup cannot be
considered as a model-independent anomaly tagger by inverting the task: due to
the sparsity and the specific structure of the jet images, the autoencoder
fails to tag QCD jets if it is trained on top jets even in a semi-supervised
setup. Since the same autoencoder architecture can be a good tagger for a
specific example of an anomaly and a bad tagger for a different example, we
suggest improved performance measures for the task of model-independent anomaly
detection. We also improve the capability of the autoencoder to learn
non-trivial features of the jet images, such that it is able to achieve both
top jet tagging and the inverse task of QCD jet tagging with the same setup.
However, we want to stress that a truly model-independent and powerful
autoencoder-based unsupervised jet tagger still needs to be developed.


Abstract:
  We introduce a family of Markov Chain Monte Carlo (MCMC) methods designed to
sample from target distributions with irregular geometry using an adaptive
scheme. In cases where targets exhibit non-Gaussian behaviour, we propose that
adaption should be regional in nature as opposed to global. Our algorithms
minimize the information projection side of the Kullback-Leibler (KL)
divergence between the proposal distribution class and the target to encourage
proposals distributed similarly to the regional geometry of the target. Unlike
traditional adaptive MCMC, this procedure rapidly adapts to the geometry of the
current position as it explores the space without the need for a large batch of
samples. We extend this approach to multimodal targets by introducing a heavily
tempered chain to enable faster mixing between regions of interest. The
divergence minimization algorithms are tested on target distributions with
multiple irregularly shaped modes and we provide results demonstrating the
effectiveness of our methods.


Abstract:
  Within a structural health monitoring (SHM) framework, we propose a
simulation-based classification strategy to move towards online damage
localization. The procedure combines parametric Model Order Reduction (MOR)
techniques and Fully Convolutional Networks (FCNs) to analyze raw vibration
measurements recorded on the monitored structure. First, a dataset of possible
structural responses under varying operational conditions is built through a
physics-based model, allowing for a finite set of predefined damage scenarios.
Then, the dataset is used for the offline training of the FCN. Because of the
extremely large number of model evaluations required by the dataset
construction, MOR techniques are employed to reduce the computational burden.
The trained classifier is shown to be able to map unseen vibrational
recordings, e.g. collected on-the-fly from sensors placed on the structure, to
the actual damage state, thus providing information concerning the presence and
also the location of damage. The proposed strategy has been validated by means
of two case studies, concerning a 2D portal frame and a 3D portal frame railway
bridge; MOR techniques have allowed us to respectively speed up the analyses
about 30 and 420 times. For both the case studies, after training the
classifier has attained an accuracy greater than 85%.


Abstract:
  Polymer blends consisting of two or more polymers are important for a wide
variety of industries and processes, but, the precise mechanism of their
thermomechanical behaviour is incompletely understood. In order to understand
clearly, it is essential to determine the miscibility and interactions between
the components in polymer blend and its macroscopic thermomechanical
properties. In this study, we performed experiments on SEBS and isotactic PP
blends (SP) as well as molecular dynamics simulations, aiming to know the role
played by molecular interactions on the thermomechanical properties. To
investigate the glass transition temperature (Tg) of SEBS, PP and their blends
at different ratio, the unit cell of the polymer molecular structure of each
was established. The LAMMPS molecular dynamics method was used to predict the
density, specific volume, free volume, enthalpy, kinetic energy, potential
energy and bond energy. The (Tg) s of the SEBS, PP and SP blends were predicted
by analysing these properties. Interestingly, the simulated values of the Tg of
SEBS, PP and their blends showed good agreement with our experimental results
obtained from dynamic mechanical analysis (DMA). This technique used in this
work can be used in studying glass transition of other complex polymer blends.


Abstract:
  This article aims to present a unified framework for grading-based voting
processes. The idea is to represent the grades of each voter on d candidates as
a point in R^d and to define the winner of the vote using the deepest point of
the scatter plot. The deepest point is obtained by the maximization of a depth
function. Universality, unanimity, and neutrality properties are proved to be
satisfied. Monotonicity and Independence to Irrelevant Alternatives are also
studied. It is shown that usual voting processes correspond to specific choices
of depth functions. Finally, some basic paradoxes are explored for these voting
processes.


Abstract:
  Zero-shot learning (ZSL) aims to discriminate images from unseen classes by
exploiting relations to seen classes via their attribute-based descriptions.
Since attributes are often related to specific parts of objects, many recent
works focus on discovering discriminative regions. However, these methods
usually require additional complex part detection modules or attention
mechanisms. In this paper, 1) we show that common ZSL backbones (without
explicit attention nor part detection) can implicitly localize attributes, yet
this property is not exploited. 2) Exploiting it, we then propose SELAR, a
simple method that further encourages attribute localization, surprisingly
achieving very competitive generalized ZSL (GZSL) performance when compared
with more complex state-of-the-art methods. Our findings provide useful insight
for designing future GZSL methods, and SELAR provides an easy to implement yet
strong baseline.


Abstract:
  We have studied the charged BTZ black holes in noncommutative spaces arising
from two independent approaches. First, by using the Seiberg-Witten map
followed by a dynamic choice of gauge in the Chern-Simons gauge theory. Second,
by inducing the fuzziness in the mass and charge by a Lorentzian distribution
function with the width being the same as the minimal length of the associated
noncommutativity. In the first approach, we have found the existence of
non-static and non-stationary BTZ black holes in noncommutative spaces for the
first time in the literature, while the second approach facilitates us to
introduce a proper bound on the noncommutative parameter so that the
corresponding black hole becomes stable and physical. We have used a
contemporary tunneling formalism to study the thermodynamics of the black holes
arising from both of the approaches and analyze their behavior within the
context.


Abstract:
  The genericity of Arnold diffusion in the analytic category is an open
problem. In this paper, we study this problem in the following a priori
unstable Hamiltonian system with a time-periodic perturbation
\[\mathcal{H}_\varepsilon(p,q,I,\varphi,t)=h(I)+\sum_{i=1}^n\pm
\left(\frac{1}{2}p_i^2+V_i(q_i)\right)+\varepsilon H_1(p,q,I,\varphi, t), \]
where $(p,q)\in \mathbb{R}^n\times\mathbb{T}^n$,
$(I,\varphi)\in\mathbb{R}^d\times\mathbb{T}^d$ with $n, d\geq 1$, $V_i$ are
Morse potentials, and $\varepsilon$ is a small non-zero parameter. The
unperturbed Hamiltonian is not necessarily convex, and the induced inner
dynamics does not need to satisfy a twist condition. Using geometric methods we
prove that Arnold diffusion occurs for generic analytic perturbations $H_1$.
Indeed, the set of admissible $H_1$ is $C^\omega$ dense and $C^3$ open (a
fortiori, $C^\omega$ open). Our perturbative technique for the genericity is
valid in the $C^k$ topology for all $k\in [3,\infty)\cup\{\infty, \omega\}$.


Abstract:
  We address the fluid-structure interaction of flexible fin models oscillated
in a water flow. Here, we investigate in particular the dependence of
hydrodynamic force distributions on fin geometry and flapping frequency. For
this purpose, we employ state-of-the-art techniques in pressure evaluation to
describe fluid force maps with high temporal and spatial resolution on the
deforming surfaces of the hydrofoils. Particle tracking velocimetry (PTV) is
used to measure the 3D fluid velocity field, and the hydrodynamic stress tensor
is subsequently calculated based on the Navier-Stokes equation. The shape and
kinematics of the fin-like foils are linked to their ability to generate
propulsive thrust efficiently, as well as the accumulation of external contact
forces and the resulting internal tension throughout a flapping cycle.


Abstract:
  In this paper, we shall consider spherically symmetric spacetime solutions
describing the interior of stellar compact objects, in the context of
higher-order curvature theory of the f(R) type. We shall derive the non--vacuum
field equations of the higher-order curvature theory, without assuming any
specific form of the $\mathrm{f(R)}$ theory, specifying the analysis for a
spherically symmetric spacetime with two unknown functions. We obtain a system
of highly non-linear differential equations, which consists of four
differential equations with six unknown functions. To solve such a system, we
assume a specific form of metric potentials, using the Krori-Barua ansatz. We
successfully solve the system of differential equations, and we derive all the
components of the energy-momentum tensor. Moreover, we derive the non-trivial
general form of $\mathrm{f(R)}$ that may generate such solutions and calculate
the dynamic Ricci scalar of the anisotropic star. Accordingly, we calculate the
asymptotic form of the function $\mathrm{f(R)}$, which is a polynomial
function. We match the derived interior solution with the exterior one, which
was derived in \cite{Nashed:2019tuk}, with the latter also resulting in a
non-trivial form of the Ricci scalar. Notably but rather expected, the exterior
solution differs from the Schwarzschild one in the context of general
relativity. The matching procedure will eventually relate two constants with
the mass and radius of the compact stellar object. We list the necessary
conditions that any compact anisotropic star must satisfy and explain in detail
that our model bypasses all of these conditions for a special compact star
$\textit {Her X--1 }$, which has an estimated mass and radius \textit {(mass =
0.85 $\pm 0.15M_{\circledcirc}$\,\, and\, \,radius $= 8.1 \pm 0.41$km)}.


Abstract:
  The deformation theory of curves is studied by using the canonical ideal. The
problem of lifting curves with automorphisms is reduced to a lifting problem of
linear representations. As an application we prove that the dihedral group
$D_{p^h}$ of order $2p^h$ is a local Oort group.


Abstract:
  RISC-V is a relatively new, open instruction set architecture with a mature
ecosystem and an official formal machine-readable specification. It is
therefore a promising playground for formal-methods research.
  However, we observe that different formal-methods research projects are
interested in different aspects of RISC-V and want to simplify, abstract,
approximate, or ignore the other aspects. Often, they also require different
encoding styles, resulting in each project starting a new formalization
from-scratch. We set out to identify the commonalities between projects and to
represent the RISC-V specification as a program with holes that can be
instantiated differently by different projects.
  Our formalization of the RISC-V specification is written in Haskell and
leverages existing tools rather than requiring new domain-specific tools,
contrary to other approaches. To our knowledge, it is the first RISC-V
specification able to serve as the interface between a processor-correctness
proof and a compiler-correctness proof, while supporting several other projects
with diverging requirements as well.


Abstract:
  Facial Expression Recognition (FER) in the wild is an extremely challenging
task in computer vision due to variant backgrounds, low-quality facial images,
and the subjectiveness of annotators. These uncertainties make it difficult for
neural networks to learn robust features on limited-scale datasets. Moreover,
the networks can be easily distributed by the above factors and perform
incorrect decisions. Recently, vision transformer (ViT) and data-efficient
image transformers (DeiT) present their significant performance in traditional
classification tasks. The self-attention mechanism makes transformers obtain a
global receptive field in the first layer which dramatically enhances the
feature extraction capability. In this work, we first propose a novel pure
transformer-based mask vision transformer (MVT) for FER in the wild, which
consists of two modules: a transformer-based mask generation network (MGN) to
generate a mask that can filter out complex backgrounds and occlusion of face
images, and a dynamic relabeling module to rectify incorrect labels in FER
datasets in the wild. Extensive experimental results demonstrate that our MVT
outperforms state-of-the-art methods on RAF-DB with 88.62%, FERPlus with
89.22%, and AffectNet-7 with 64.57%, respectively, and achieves a comparable
result on AffectNet-8 with 61.40%.


Abstract:
  We study the emergence of chaotic behavior of Follow-the-Regularized Leader
(FoReL) dynamics in games. We focus on the effects of increasing the population
size or the scale of costs in congestion games, and generalize recent results
on unstable, chaotic behaviors in the Multiplicative Weights Update dynamics to
a much larger class of FoReL dynamics. We establish that, even in simple linear
non-atomic congestion games with two parallel links and any fixed learning
rate, unless the game is fully symmetric, increasing the population size or the
scale of costs causes learning dynamics to become unstable and eventually
chaotic, in the sense of Li-Yorke and positive topological entropy.
Furthermore, we show the existence of novel non-standard phenomena such as the
coexistence of stable Nash equilibria and chaos in the same game. We also
observe the simultaneous creation of a chaotic attractor as another chaotic
attractor gets destroyed. Lastly, although FoReL dynamics can be strange and
non-equilibrating, we prove that the time average still converges to an exact
equilibrium for any choice of learning rate and any scale of costs.


Abstract:
  We derive Legendre polynomials using Cauchy determinants with a
generalization to power functions with real exponents greater than -1/2. We
also provide a geometric formulation of Gram-Schmidt orthogonalization using
the Hodge star operator.


Abstract:
  We establish the validity of the Euler$+$Prandtl approximation for solutions
of the Navier-Stokes equations in the half plane with Dirichlet boundary
conditions, in the vanishing viscosity limit, for initial data which are
analytic only near the boundary, and Sobolev smooth away from the boundary. Our
proof does not require higher order correctors, and works directly by
estimating an $L^{1}$-type norm for the vorticity of the error term in the
expansion Navier-Stokes$-($Euler$+$Prandtl$)$. An important ingredient in the
proof is the propagation of local analyticity for the Euler equation, a result
of independent interest.


Abstract:
  Thermal machines exploit interactions with multiple heat baths to perform
useful tasks, such as work production and refrigeration. In the quantum regime,
tasks with no classical counterpart become possible. Here, we consider the
minimal setting for quantum thermal machines, namely two-qubit autonomous
thermal machines that use only incoherent interactions with their environment,
and investigate the fundamental resources needed to generate entanglement. Our
investigation is systematic, covering different types of interactions, bosonic
and fermionic environments, and different resources that can be supplied to the
machine. We adopt an operational perspective in which we assess the
nonclassicality of the generated entanglement through its ability to perform
useful tasks such as Einstein-Podolsky-Rosen steering, quantum teleportation
and Bell nonlocality. We provide both constructive examples of nonclassical
effects and general no-go results that demarcate the fundamental limits in
autonomous entanglement generation. Our results open up a path toward
understanding nonclassical phenomena in thermal processes.


Abstract:
  Electricity supply must be matched with demand at all times. This helps
reduce the chances of issues such as load frequency control and the chances of
electricity blackouts. To gain a better understanding of the load that is
likely to be required over the next 24h, estimations under uncertainty are
needed. This is especially difficult in a decentralized electricity market with
many micro-producers which are not under central control.
  In this paper, we investigate the impact of eleven offline learning and five
online learning algorithms to predict the electricity demand profile over the
next 24h. We achieve this through integration within the long-term agent-based
model, ElecSim. Through the prediction of electricity demand profile over the
next 24h, we can simulate the predictions made for a day-ahead market. Once we
have made these predictions, we sample from the residual distributions and
perturb the electricity market demand using the simulation, ElecSim. This
enables us to understand the impact of errors on the long-term dynamics of a
decentralized electricity market.
  We show we can reduce the mean absolute error by 30% using an online
algorithm when compared to the best offline algorithm, whilst reducing the
required tendered national grid reserve required. This reduction in national
grid reserves leads to savings in costs and emissions. We also show that large
errors in prediction accuracy have a disproportionate error on investments made
over a 17-year time frame, as well as electricity mix.


Abstract:
  Two-dimensional (2D) Dirac states with linear band dispersion have attracted
enormous interest since the discovery of graphene. However, to date, 2D Dirac
semimetals are still very rare due to the fact that 2D Dirac states are
generally fragile against perturbations such as spin-orbit couplings.
Nonsymmorphic crystal symmetries can enforce the formation of Dirac nodes,
providing a new route to establishing symmetry-protected Dirac states in 2D
materials. Here we report the symmetry-protected Dirac states in nonsymmorphic
alpha-antimonene. The antimonene was synthesized by the method of molecular
beam epitaxy. Two Dirac cones with large anisotropy were observed by
angle-resolved photoemission spectroscopy. The Dirac state in alpha-antimonene
is of spin-orbit type in contrast to the spinless Dirac states in graphene. The
result extends the 'graphene' physics into a new family of 2D materials where
spin-orbit coupling is present.


Abstract:
  Neutron diffraction and X-ray pair distribution function (XPDF) experiments
were performed in order to investigate the magnetic and local crystal
structures of Ba2FeSbSe5 and to compare them to the average (i.e. long-range)
structural model, previously obtained by single crystal X-ray diffraction.
Changes in the local crystal structure (i.e. in the second coordination sphere)
are observed upon cooling from 295 K to 95 K resulting in deviations from the
average (i.e. long-range) crystal structure. This work demonstrates, that these
observations cannot be explained by local or long-range magnetoelastic effects
involving Fe-Fe correlations. Instead, we found, that the observed differences
between local and average crystal structure can be explained by Sb-5s lone pair
dynamics. We also find, that below the N\'eel temperature (TN = 58 K), the two
distinct magnetic Fe3+ sites order collinearly, such that a combination of
antiparallel and parallel spin arrangements along the b-axis results. The
nearest-neighbor arrangement (J1 = 6 {\AA}) is fully antiferromagnetic, while
next-nearest-neighbor interactions are ferromagnetic in nature.


Abstract:
  A growing number of applications require the reconstructionof 3D objects from
a very small number of views. In this research, we consider the problem of
reconstructing a 3D object from only 4 Flash X-ray CT views taken during the
impact of a Kolsky bar. For such ultra-sparse view datasets, even model-based
iterative reconstruction (MBIR) methods produce poor quality results.
  In this paper, we present a framework based on a generalization of
Plug-and-Play, known as Multi-Agent Consensus Equilibrium (MACE), for
incorporating complex and nonlinear prior information into ultra-sparse CT
reconstruction. The MACE method allows any number of agents to simultaneously
enforce their own prior constraints on the solution. We apply our method on
simulated and real data and demonstrate that MACE reduces artifacts, improves
reconstructed image quality, and uncovers image features which were otherwise
indiscernible.


Abstract:
  Ultrasound tongue imaging is widely used for speech production research, and
it has attracted increasing attention as its potential applications seem to be
evident in many different fields, such as the visual biofeedback tool for
second language acquisition and silent speech interface. Unlike previous
studies, here we explore the feasibility of age estimation using the ultrasound
tongue image of the speakers. Motivated by the success of deep learning, this
paper leverages deep learning on this task. We train a deep convolutional
neural network model on the UltraSuite dataset. The deep model achieves mean
absolute error (MAE) of 2.03 for the data from typically developing children,
while MAE is 4.87 for the data from the children with speech sound disorders,
which suggest that age estimation using ultrasound is more challenging for the
children with speech sound disorder. The developed method can be used a tool to
evaluate the performance of speech therapy sessions. It is also worthwhile to
notice that, although we leverage the ultrasound tongue imaging for our study,
the proposed methods may also be extended to other imaging modalities (e.g.
MRI) to assist the studies on speech production.


Abstract:
  This paper presents U-LanD, a framework for joint detection of key frames and
landmarks in videos. We tackle a specifically challenging problem, where
training labels are noisy and highly sparse. U-LanD builds upon a pivotal
observation: a deep Bayesian landmark detector solely trained on key video
frames, has significantly lower predictive uncertainty on those frames vs.
other frames in videos. We use this observation as an unsupervised signal to
automatically recognize key frames on which we detect landmarks. As a test-bed
for our framework, we use ultrasound imaging videos of the heart, where sparse
and noisy clinical labels are only available for a single frame in each video.
Using data from 4,493 patients, we demonstrate that U-LanD can exceedingly
outperform the state-of-the-art non-Bayesian counterpart by a noticeable
absolute margin of 42% in R2 score, with almost no overhead imposed on the
model size. Our approach is generic and can be potentially applied to other
challenging data with noisy and sparse training labels.


Abstract:
  In this paper we prove a Faber-Krahn type inequality for the first eigenvalue
of the Hermite operator with Robin boundary condition. We prove that the
optimal set is an half-space and we also address the equality case in such
inequality.


Abstract:
  Motivated by kidney exchange, we study the following mechanism-design
problem: On a directed graph (of transplant compatibilities among patient-donor
pairs), the mechanism must select a simple path (a chain of transplantations)
starting at a distinguished vertex (an altruistic donor) such that the total
length of this path is as large as possible (a maximum number of patients
receive a kidney). However, the mechanism does not have direct access to the
graph. Instead, the vertices are partitioned over multiple players (hospitals),
and each player reports a subset of her vertices to the mechanism. In
particular, a player may strategically omit vertices to increase how many of
her vertices lie on the path returned by the mechanism.
  Our objective is to find mechanisms that limit incentives for such
manipulation while producing long paths. Unfortunately, in worst-case
instances, competing with the overall longest path is impossible while
incentivizing (approximate) truthfulness, i.e., requiring that hiding nodes
cannot increase a player's utility by more than a factor of $1 + o(1)$. We
therefore adopt a semi-random model where a small ($o(n)$) number of random
edges are added to worst-case instances. While it remains impossible for
truthful mechanisms to compete with the overall longest path, we give a
truthful mechanism that competes with a weaker but non-trivial benchmark: the
length of any path whose subpaths within each player have a minimum average
length. In fact, our mechanism satisfies even a stronger notion of
truthfulness, which we call matching-time incentive compatibility. This notion
of truthfulness requires that each player not only reports her nodes truthfully
but also does not stop the returned path at any of her nodes in order to divert
it to a continuation inside her own subgraph.


Abstract:
  Iterative Green's function, based on cyclic reduction of block tridiagonal
matrices, has been the ideal algorithm, through tight-binding models, to
compute the surface density-of-states of semi-infinite topological electronic
materials. In this paper, we apply this method to photonic and acoustic
crystals, using finite-element discretizations and a generalized eigenvalue
formulation, to calculate the local density-of-states on a single surface of
semi-infinite lattices. The three-dimensional (3D) examples of gapless
helicoidal surface states in Weyl and Dirac crystals are shown and the
computational cost, convergence and accuracy are analyzed.


Abstract:
  Electroweak probes are potential tool to study the properties of the hot and
dense strongly interacting matter produced in relativistic nuclear collisions
due to their unique nature. A selection of the new experimental analysis and
results from theory calculations on electromagnetic and weak probes presented
at the Hard Probes 2020 are discussed in this contribution.


Abstract:
  The goal of the paper is to provide a detailed explanation on how the
(continuous) cosine transform and the discrete(-time) cosine transform arise
naturally as certain manifestations of the celebrated Gelfand transform. We
begin with the introduction of the cosine convolution $\star_c$, which can be
viewed as an "arithmetic mean" of the classical convolution and its "twin
brother", the anticonvolution. The d'Alambert property of $\star_c$ plays a
pivotal role in establishing the bijection between $\Delta(L^1(G),\star_c)$ and
the cosine class $\mathcal{COS}(G),$ which turns out to be an open map if
$\mathcal{COS}(G)$ is equipped with the topology of uniform convergence on
compacta $\tau_{ucc}$. Subsequently, if $G = \mathbb{R},\mathbb{Z}, S^1$ or
$\mathbb{Z}_n$ we find a relatively simple topological space which is
homeomorphic to $\Delta(L^1(G),\star_c).$ Finally, we witness the "reduction"
of the Gelfand transform to the aforementioned cosine transforms.


Abstract:
  Magnetic braking (MB) likely plays a vital role in the evolution of low-mass
X-ray binaries (LMXBs). However, it is still uncertain about the physics of MB,
and there are various proposed scenarios for MB in the literature. To examine
and discriminate the efficiency of MB, we investigate the LMXB evolution with
five proposed MB laws. Combining detailed binary evolution calculation with
binary population synthesis, we obtain the expected properties of LMXBs and
their descendants binary millisecond pulsars. We then discuss the strength and
weakness of each MB law by comparing the calculated results with observations.
We conclude that the $\tau$-boosted MB law seems to best match the
observational characteristics.


Abstract:
  Atomtronics experiments with ultracold atomic gases allow us to explore
quantum transport phenomena of a weakly-interacting Bose-Einstein condensate
(BEC). Here, we focus on two-terminal transport of such a BEC in the vicinity
of zero temperature. By using the tunnel Hamiltonian and Bogoliubov theory, we
obtain a DC Josephson current expression in the BEC and apply it to
experimentally relevant situations such as quantum point contact and planar
junction. Due to the absence of Andreev bound states but the presence of
couplings associated with condensation elements, a current-phase relation in
the BEC is found to be different from one in an s-wave superconductor. In
addition, it turns out that the DC Josephson current in the BEC depends on the
sign of tunneling elements, which allows to realize the so-called $\pi$
junction by using techniques of artificial gauge fields.


Abstract:
  The Galactic B[e] supergiant MWC 137 is surrounded by a large-scale optical
nebula. To shed light on the physical conditions and kinematics of the nebula,
we analyze the optical forbidden emission lines [NII] 6548,6583 and [SII]
6716,6731 in long-slit spectra taken with ALFOSC at the Nordic Optical
Telescope. The radial velocities display a complex behavior but, in general,
the northern nebular features are predominantly approaching while the southern
ones are mostly receding. The electron density shows strong variations across
the nebula with values spreading from about zero to ~800 cm$^{-3}$. Higher
densities are found closer to MWC~137 and in regions of intense emission,
whereas in regions with high radial velocities the density decreases
significantly. We also observe the entire nebula in the two [SII] lines with
the scanning Fabry-Perot interferometer attached to the 6-m telescope of the
Special Astrophysical Observatory. These data reveal a new bow-shaped feature
at PA = 225-245 and a distance 80" from MWC 137. A new H$\alpha$ image has been
taken with the Danish 1.54-m telescope on La Silla. No expansion or changes in
the nebular morphology appear within 18.1 years. We derive a mass of 37 (+9/-5)
solar masses and an age of $4.7\pm0.8$ Myr for MWC 137. Furthermore, we detect
a period of 1.93 d in the time series photometry collected with the TESS
satellite, which could suggest stellar pulsations. Other, low-frequency
variability is seen as well. Whether these signals are caused by internal
gravity waves in the early-type star or by variability in the wind and
circumstellar matter currently cannot be distinguished.


Abstract:
  This article proposes an artificial data generating algorithm that is simple
and easy to customize. The fundamental concept is to perform random permutation
of Monte Carlo generated random numbers which conform to the unconditional
probability distribution of the original real time series. Similar to
constraint surrogate methods, random permutations are only accepted if a given
objective function is minimized. The objective function is selected in order to
describe the most important features of the stochastic process. The algorithm
is demonstrated by producing simulated log-returns of the S\&P 500 stock index.


Abstract:
  Creating programs with block-based programming languages like Scratch is easy
and fun. Block-based programs can nevertheless contain bugs, in particular when
learners have misconceptions about programming. Even when they do not, Scratch
code is often of low quality and contains code smells, further inhibiting
understanding, reuse, and fun. To address this problem, in this paper we
introduce LitterBox, a linter for Scratch programs. Given a program or its
public project ID, LitterBox checks the program against patterns of known bugs
and code smells. For each issue identified, LitterBox provides not only the
location in the code, but also a helpful explanation of the underlying reason
and possible misconceptions. Learners can access LitterBox through an easy to
use web interface with visual information about the errors in the block-code,
while for researchers LitterBox provides a general, open source, and extensible
framework for static analysis of Scratch programs.


Abstract:
  In the first paper of this series (Rhea et al. 2020), we demonstrated that
neural networks can robustly and efficiently estimate kinematic parameters for
optical emission-line spectra taken by SITELLE at the Canada-France-Hawaii
Telescope. This paper expands upon this notion by developing an artificial
neural network to estimate the line ratios of strong emission-lines present in
the SN1, SN2, and SN3 filters of SITELLE. We construct a set of 50,000
synthetic spectra using line ratios taken from the Mexican Million Model
database replicating Hii regions. Residual analysis of the network on the test
set reveals the network's ability to apply tight constraints to the line
ratios. We verified the network's efficacy by constructing an activation map,
checking the [N ii] doublet fixed ratio, and applying a standard k-fold
cross-correlation. Additionally, we apply the network to SITELLE observation of
M33; the residuals between the algorithm's estimates and values calculated
using standard fitting methods show general agreement. Moreover, the neural
network reduces the computational costs by two orders of magnitude. Although
standard fitting routines do consistently well depending on the signal-to-noise
ratio of the spectral features, the neural network can also excel at
predictions in the low signal-to-noise regime within the controlled environment
of the training set as well as on observed data when the source spectral
properties are well constrained by models. These results reinforce the power of
machine learning in spectral analysis.


Abstract:
  We present a polarization-resolved, high-resolution Raman scattering study of
the three consecutive charge density wave (CDW) regimes in $1T$-TaS$_2$ single
crystals, supported by \textit{ab initio} calculations. Our analysis of the
spectra within the low-temperature commensurate (C-CDW) regime shows
$\mathrm{P3}$ symmetry of the system, thus excluding the previously proposed
triclinic stacking of the "star-of-David" structure, and promoting trigonal or
hexagonal stacking instead. The spectra of the high-temperature incommensurate
(IC-CDW) phase directly project the phonon density of states due to the
breaking of the translational invariance, supplemented by sizeable
electron-phonon coupling. Between 200 and 352\,K, our Raman spectra show
contributions from both the IC-CDW and the C-CDW phase, indicating their
coexistence in the so-called nearly-commensurate (NC-CDW) phase. The
temperature-dependence of the symmetry-resolved Raman conductivity indicates
the stepwise reduction of the density of states in the CDW phases, followed by
a Mott transition within the C-CDW phase. We determine the size of the Mott gap
to be $\Omega_{\rm gap}\approx 170-190$ meV, and track its temperature
dependence.


Abstract:
  Often neglected in traditional education, spatial thinking has played a
critical role in science, technology, engineering, and mathematics (STEM)
education. Spatial thinking skills can be enhanced by training, life
experience, and practice. One approach to train these skills is through 3D
modeling (also known as Computer-Aided Design or CAD). Although 3D modeling
tools have shown promising results in training and enhancing spatial thinking
skills in undergraduate engineering students when it comes to novices,
especially middle and high-school students, they are not sufficient to provide
rich 3D experience since the 3D models created in CAD are isolated the actual
3D physical world. Resulting in novice students finding it difficult to create
error-free 3D models that would 3D print successfully. This leads to student
frustration where students are not motivated to create 3D models themselves;
instead, they prefer to download them from online repositories. To address this
problem, researchers are focusing on integrating 3D models and displays into
the physical world with the help of technologies like Augmented Reality (AR).
In this demo, we present an AR application, 3DARVisualizer, that helps us
explore the role of AR as a 3D model debugger, including enhancing 3D modeling
abilities and spatial thinking skills of middle- and high-school students.


Abstract:
  The stochastic block model (SBM) and degree-corrected block model (DCBM) are
network models often selected as the fundamental setting in which to analyze
the theoretical properties of community detection methods. We consider the
problem of spectral clustering of SBM and DCBM networks under a local form of
edge differential privacy. Using a randomized response privacy mechanism called
the edge-flip mechanism, we develop theoretical guarantees for differentially
private community detection, demonstrating conditions under which this strong
privacy guarantee can be upheld while achieving spectral clustering convergence
rates that match the known rates without privacy. We prove the strongest
theoretical results are achievable for dense networks (those with node degree
linear in the number of nodes), while weak consistency is achievable under mild
sparsity (node degree greater than $\sqrt{n}$). We empirically demonstrate our
results on a number of network examples.


Abstract:
  A beautifully simple free generating set for the commutator subgroup of a
free group was constructed by Tomaszewski. We give a new geometric proof of his
theorem, and show how to give a similar free generating set for the commutator
subgroup of a surface group. We also give a simple representation-theoretic
description of the structure of the abelianizations of these commutator
subgroups and calculate their homology.


Abstract:
  Accurate and comprehensive diatomic molecular spectroscopic data have long
been vital in a wide variety of applications for measuring and monitoring
astrophysical, industrial and other gaseous environments. These data are also
used extensively for benchmarking quantum chemistry and applications from
quantum computers, ultracold chemistry and the search for physics beyond the
standard model. Useful data can be highly detailed like line lists or summative
like molecular constants, and obtained from theory, experiment or a
combination. There are plentiful (though not yet sufficient) data available,
but these data are often scattered. For example, molecular constants have not
been compiled since 1979 despite the existing compilation still being cited
more than 200 times annually. Further, the data are interconnected but updates
in one type of data are not yet routinely applied to update interconnected
data: in particular, new experimental and ab-initio data are not routinely
unified with other data on the molecule. This paper provide information and
strategies to strengthen the connection between data producers (e.g. ab-initio
electronic structure theorists and experimental spectroscopists), data
modellers (e.g. line list creators and others who connect data on one aspect of
the molecule to the full energetic and spectroscopic description) and data
users (astronomers, chemical physicists etc). All major data types are
described including their source, use, compilation and interconnectivity.
Explicit advice is provided for theoretical and experimental data producers,
data modellers and data users to facilitate optimal use of new data with
appropriate attribution.


Abstract:
  Data markets have the potential to foster new data-driven applications and
help growing data-driven businesses. When building and deploying such markets
in practice, regulations such as the European Union's General Data Protection
Regulation (GDPR) impose constraints and restrictions on these markets
especially when dealing with personal or privacy-sensitive data. In this paper,
we present a candidate architecture for a privacy-preserving personal data
market, relying on cryptographic primitives such as multi-party computation
(MPC) capable of performing privacy-preserving computations on the data.
Besides specifying the architecture of such a data market, we also present a
privacy-risk analysis of the market following the LINDDUN methodology.


Abstract:
  The Einstein-Podolsky-Rosen (EPR) steering, which is regarded as a category
of quantum nonlocal correlations, owns the asymmetric property in contrast with
the entanglement and the Bell nonlocality. For the multipartite EPR steering,
monogamy, which limits the two observers to steer the third one simultaneously,
emerges as an essential property. However, more configurations of shareability
relations in the reduced subsystem which are beyond the monogamy could be
observed by increasing the numbers of measurement setting, in which the
experimental verification is still absent. Here, in an optical experiment, we
provide a proof-of-principle demonstration of shareability of the EPR steering
without constraint of monogamy in the three-qubit system, in which Alice could
be steered by Bob and Charlie simultaneously. Moreover, based on the reduced
bipartite EPR steering detection, we verify the genuine three-qubit
entanglement. This work provides a basis for an improved understanding of the
multipartite EPR steering and has potential applications in many quantum
information protocols, such as multipartite entanglement detection and quantum
cryptography.


Abstract:
  HD 163296 is a Herbig Ae star that underwent a dramatic $\sim$0.8 magnitude
drop in brightness in the V photometric band in 2001 and a brightening in the
near-IR in 2002. Because the star possesses Herbig-Haro objects travelling in
outflowing bipolar jets, it was suggested that the drop in brightness was due
to a clump of dust entrained in a disk wind, blocking the line-on-sight toward
the star. In order to quantify this hypothesis, we investigated the brightness
drop at visible wavelengths and the brightening at near-IR wavelengths of HD
163296 using the Monte Carlo Radiative Transfer Code, HOCHUNK3D. We created
three models to understand the events. Model 1 describes the quiescent state of
the system. Model 2 describes the change in structure that led to the drop in
brightness in 2001. Model 3 describes the structure needed to produce the
observed 2002 brightening of the near-IR wavelengths. Models 2 and 3 utilize a
combination of a disk wind and central bipolar flow. By introducing a filled
bipolar cavity in Models 2 and 3, we were able to successfully simulate a
jet-like structure for the star with a disk wind and created the drop and
subsequent increase in brightness of the system. On the other hand, when the
bipolar cavity is not filled, Model 1 replicates the quiescent state of the
system.


Abstract:
  Finding nearest neighbors in high-dimensional spaces is a fundamental
operation in many diverse application domains. Locality Sensitive Hashing (LSH)
is one of the most popular techniques for finding approximate nearest neighbor
searches in high-dimensional spaces. The main benefits of LSH are its
sub-linear query performance and theoretical guarantees on the query accuracy.
In this survey paper, we provide a review of state-of-the-art LSH and
Distributed LSH techniques. Most importantly, unlike any other prior survey, we
present how Locality Sensitive Hashing is utilized in different application
domains.


Abstract:
  We introduce a design of electrically isolated floating bilayer GaAs quantum
wells (QW) in which application of a large gating voltage controllably and
highly reproducibly induces charges that remain trapped in the bilayer after
removal of the gating voltage. At smaller gate voltages, the bilayer is fully
electrically isolated from external electrodes by thick insulating barriers.
This design permits full control of the total and differential densities of two
coupled 2D electron systems. The floating bilayer design provides a unique
approach for studying systems inaccessible by simple transport measurements. It
also provides the ability to measure the charge transfer between the layers,
even when the in-plane resistivities of the 2D systems diverge. We measure the
capacitance and inter-layer tunneling spectra of the QW bilayer with
independent control of the top and bottom layer electron densities. Our
measurements display strongly enhanced inter-layer tunneling current at the
total filling factor of 1, a signature of exciton condensation of a strongly
interlayer-correlated bilayer system. With fully tunable densities of
individual layers, the floating bilayer QW system provides a versatile platform
to access previously unavailable information on the quantum phases in electron
bilayer systems.


Abstract:
  There is an extensive literature on the asymptotic order of Sudler's
trigonometric product $P_N (\alpha) = \prod_{n=1}^N |2 \sin (\pi n \alpha)|$
for fixed or for "typical" values of $\alpha$. In the present paper we
establish a structural result, which for a given $\alpha$ characterizes those
$N$ for which $P_N(\alpha)$ attains particularly large values. This
characterization relies on the coefficients of $N$ in its Ostrowski expansion
with respect to $\alpha$, and allows us to obtain very precise estimates for
$\max_{1 \le N \leq M} P_N(\alpha)$ and for $\sum_{N=1}^M P_N(\alpha)^c$ in
terms of $M$, for any $c>0$. Furthermore, our arguments give a natural
explanation of the fact that the value of the hyperbolic volume of the
complement of the figure-eight knot appears generically in results on the
asymptotic order of the Sudler product and of the Kashaev invariant.


Abstract:
  We present a detailed analysis of a cool-core galaxy cluster Abell 3017, at a
redshift of z=0.219, which has been identified to be merging with its companion
cluster Abell 3016. This study has made use of X-ray Chandra, UV (GALEX),
optical (ESO/VLT), mid-infrared (WISE), and radio uGMRT observations of this
cluster. Using various image processing techniques, such as unsharp masking,
2-d fits using Beta models, contour binning and the use of surface brightness
profiles, we show the existence of a pair of X-ray cavities, at a projected
distance of $\sim$20'' (70 kpc) and $\sim$16'' (57 kpc), respectively from the
core of Abell~3017. We also detect an excess of X-ray emission located at 25''
$\sim$(88 kpc) south of the centre of Abell 3017, is likely due to the bulk
motions in the ICM either by gas sloshing or ram-pressure striping due to a
merger. We find that the radio lobes are responsible for the observed X-ray
cavities detected in this system. The lower values of the mid-IR WISE colour
[W1-W2] and [W2-W3] imply that the central BCG of Abell~3017 is a star-forming
galaxy. The current star formation rate of the central BCG, estimated from the
${\rm H\alpha}$ and GALEX FUV luminosities, are equal to be $\sim 5.06\pm 0.78$
Msun yr$^{-1}$ and $\sim 9.20\pm 0.81$ Msun yr$^{-1}$, respectively. We detect,
for the first time, a radio phoenix $\sim$150 kpc away from the radio core,
with a spectral index of ($\alpha \!\leq\! -1.8$). We also report the detection
of $\rm~Pa_\alpha$ emission in this cluster using ESO VLT SINFONI imaging data.


Abstract:
  An asymptotic formula is given for the number of y-smooth numbers up to x in
a Beatty sequence corresponding to an irrational number of finite type.


Abstract:
  The extreme fragility of deep neural networks when presented with tiny
perturbations in their inputs was independently discovered by several research
groups in 2013, but in spite of enormous effort these adversarial examples
remained a baffling phenomenon with no clear explanation. In this paper we
introduce a new conceptual framework (which we call the Dimpled Manifold Model)
which provides a simple explanation for why adversarial examples exist, why
their perturbations have such tiny norms, why these perturbations look like
random noise, and why a network which was adversarially trained with
incorrectly labeled images can still correctly classify test images. In the
last part of the paper we describe the results of numerous experiments which
strongly support this new model, and in particular our assertion that
adversarial perturbations are roughly perpendicular to the low dimensional
manifold which contains all the training examples.


Abstract:
  The numerical solution of dynamical systems with memory requires the
efficient evaluation of Volterra integral operators in an evolutionary manner.
After appropriate discretisation, the basic problem can be represented as a
matrix-vector product with a lower diagonal but densely populated matrix. For
typical applications, like fractional diffusion or large scale dynamical
systems with delay, the memory cost for storing the matrix approximations and
complete history of the data then would become prohibitive for an accurate
numerical approximation. For Volterra-integral operators of convolution type,
the \emph{fast and oblivious convolution quadrature} method of Sch\"adle,
Lopez-Fernandez, and Lubich allows to compute the discretized valuation with
$N$ time steps in $O(N \log N)$ complexity and only requiring $O(\log N)$
active memory to store a compressed version of the complete history of the
data. We will show that this algorithm can be interpreted as an
$\mathcal{H}$-matrix approximation of the underlying integral operator and,
consequently, a further improvement can be achieved, in principle, by resorting
to $\mathcal{H}^2$-matrix compression techniques. We formulate a variant of the
$\mathcal{H}^2$-matrix vector product for discretized Volterra integral
operators that can be performed in an evolutionary and oblivious manner and
requires only $O(N)$ operations and $O(\log N)$ active memory. In addition to
the acceleration, more general asymptotically smooth kernels can be treated and
the algorithm does not require a-priori knowledge of the number of time steps.
The efficiency of the proposed method is demonstrated by application to some
typical test problems.


Abstract:
  Abridged for arXiv: In this work, we apply a powerful new technique in order
to observationally derive accurate assembly histories through a self-consistent
combined stellar dynamical and population galaxy model. We present this
approach for three edge-on lenticular galaxies from the Fornax3D project -- FCC
153, FCC 170, and FCC 177 -- in order to infer their mass assembly histories
individually and in the context of the Fornax cluster. The method was tested on
mock data from simulations to quantify its reliability. We find that the
galaxies studied here have all been able to form dynamically-cold (intrinsic
vertical velocity dispersion $\sigma_z \lesssim 50\ {\rm km}\ {\rm s}^{-1}$)
stellar disks after cluster infall. Moreover, the pre-existing (old) high
angular momentum components have retained their angular momentum (orbital
circularity $\lambda_z > 0.8$) through to the present day. Comparing the
derived assembly histories with a comparable galaxy in a low-density
environment -- NGC 3115 -- we find evidence for cluster-driven suppression of
stellar accretion and merging. We measured the intrinsic stellar
age--velocity-dispersion relation and find that the shape of the relation is
consistent with galaxies in the literature across redshift. There is tentative
evidence for enhancement in the luminosity-weighted intrinsic vertical velocity
dispersion due to the cluster environment. But importantly, there is an
indication that metallicity may be a key driver of this relation. We finally
speculate that the cluster environment is responsible for the S0 morphology of
these galaxies via the gradual external perturbations, or `harassment',
generated within the cluster.


Abstract:
  Knowledge distillation (KD) has been actively studied for image
classification tasks in deep learning, aiming to improve the performance of a
student model based on the knowledge from a teacher model. However, there have
been very few efforts for applying KD in image regression with a scalar
response, and there is no KD method applicable to both tasks. Moreover,
existing KD methods often require a practitioner to carefully choose or adjust
the teacher and student architectures, making these methods less scalable in
practice. Furthermore, although KD is usually conducted in scenarios with
limited labeled data, very few techniques are developed to alleviate such data
insufficiency. To solve the above problems in an all-in-one manner, we propose
in this paper a unified KD framework based on conditional generative
adversarial networks (cGANs), termed cGAN-KD. Fundamentally different from
existing KD methods, cGAN-KD distills and transfers knowledge from a teacher
model to a student model via cGAN-generated samples. This unique mechanism
makes cGAN-KD suitable for both classification and regression tasks, compatible
with other KD methods, and insensitive to the teacher and student
architectures. Also, benefiting from the recent advances in cGAN methodology
and our specially designed subsampling and filtering procedures, cGAN-KD also
performs well when labeled data are scarce. An error bound of a student model
trained in the cGAN-KD framework is derived in this work, which theoretically
explains why cGAN-KD takes effect and guides the implementation of cGAN-KD in
practice. Extensive experiments on CIFAR-10 and Tiny-ImageNet show that we can
incorporate state-of-the-art KD methods into the cGAN-KD framework to reach a
new state of the art. Also, experiments on RC-49 and UTKFace demonstrate the
effectiveness of cGAN-KD in image regression tasks, where existing KD methods
are inapplicable.


Abstract:
  For a complex Lie group G and a prime number p, Totaro had conjectured that
the dimension of the singular cohomology with Z/p-coefficients of classifying
space of G is bounded above by that of the de Rham cohomology of the
classifying stack of (the split form of) G in characteristic p. This conjecture
was recently proven by Kubrak--Prikhodko. In this note, we give a shorter
proof.


Abstract:
  In this paper, we propose some variational formulations with the use of high
order impedance boundary condition (HOIBC) to solve the scattering problem. We
study the existence and uniqueness of the solution. Then, a discretization of
these formulations is done. We give validations of the HOIBC obtained with a
MoM code that show the improvement in accuracy over the standard impedance
boundary condition (SIBC) computations.


Abstract:
  Bayesian experimental design (BED) aims at designing an experiment to
maximize the information gathering from the collected data. The optimal design
is usually achieved by maximizing the mutual information (MI) between the data
and the model parameters. When the analytical expression of the MI is
unavailable, e.g., having implicit models with intractable data distributions,
a neural network-based lower bound of the MI was recently proposed and a
gradient ascent method was used to maximize the lower bound. However, the
approach in Kleinegesse et al., 2020 requires a pathwise sampling path to
compute the gradient of the MI lower bound with respect to the design
variables, and such a pathwise sampling path is usually inaccessible for
implicit models. In this work, we propose a hybrid gradient approach that
leverages recent advances in variational MI estimator and evolution strategies
(ES) combined with black-box stochastic gradient ascent (SGA) to maximize the
MI lower bound. This allows the design process to be achieved through a unified
scalable procedure for implicit models without sampling path gradients. Several
experiments demonstrate that our approach significantly improves the
scalability of BED for implicit models in high-dimensional design space.


Abstract:
  Medical Visual Question Answering (VQA) is a multi-modal challenging task
widely considered by research communities of the computer vision and natural
language processing. Since most current medical VQA models focus on visual
content, ignoring the importance of text, this paper proposes a multi-view
attention-based model(MuVAM) for medical visual question answering which
integrates the high-level semantics of medical images on the basis of text
description. Firstly, different methods are utilized to extract the features of
the image and the question for the two modalities of vision and text. Secondly,
this paper proposes a multi-view attention mechanism that include
Image-to-Question (I2Q) attention and Word-to-Text (W2T) attention. Multi-view
attention can correlate the question with image and word in order to better
analyze the question and get an accurate answer. Thirdly, a composite loss is
presented to predict the answer accurately after multi-modal feature fusion and
improve the similarity between visual and textual cross-modal features. It
consists of classification loss and image-question complementary (IQC) loss.
Finally, for data errors and missing labels in the VQA-RAD dataset, we
collaborate with medical experts to correct and complete this dataset and then
construct an enhanced dataset, VQA-RADPh. The experiments on these two datasets
show that the effectiveness of MuVAM surpasses the state-of-the-art method.


Abstract:
  We explore hierarchical black hole (BH) mergers in nuclear star clusters
(NSCs), globular clusters (GCs) and young star clusters (YSCs), accounting for
both original and dynamically assembled binary BHs (BBHs). We find that the
median mass of both first- and nth-generation dynamical mergers is larger in
GCs and YSCs with respect to NSCs, because the lighter BHs are ejected by
supernova kicks from the lower-mass clusters. Also, first- and nth-generation
BH masses are strongly affected by the metallicity of the progenitor stars: the
median mass of the primary BH of a nth-generation merger is $\sim{}24-38$
M$_\odot$ ($\sim{}9-15$ M$_\odot$) in metal-poor (metal-rich) NSCs. The maximum
BH mass mainly depends on the escape velocity: BHs with mass up to several
thousand M$_\odot$ form in NSCs, while YSCs and GCs host BHs with mass up to
several hundred M$_\odot$. Furthermore, we calculate the fraction of mergers
with at least one component in the pair-instability mass gap ($f_{\rm PI}$) and
in the intermediate-mass BH regime ($f_{\rm IMBH}$). In the fiducial model for
dynamical BBHs with metallicity $Z=0.002$, we find $f_{\rm PI}\approx{}0.05$,
$0.02$ and $0.007$ ($f_{\rm IMBH}\approx{}0.01$, $0.002$ and $0.001$) in NSCs,
GCs and YSCs, respectively. Both $f_{\rm PI}$ and $f_{\rm IMBH}$ drop by at
least one order of magnitude at solar metallicity. Finally, we investigate the
formation of GW190521 by assuming that it is either a nearly equal-mass BBH or
an intermediate-mass ratio inspiral.


Abstract:
  We consider an interacting particle system with two species under strong
competition dynamics between the two species. Then, through the hydrodynamic
limit procedure for the microscopic model, we derive a one-phase Stefan type
free boundary problem with non-linear diffusion, letting the competition rate
divergent. Non-linearity of diffusion comes from a zero-range dynamics for one
species while we impose the other species to weakly diffuse according to the
Kawasaki dynamics for technical reasons, which macroscopically corresponds to
the vanishing viscosity method.


Abstract:
  Visual place recognition is a challenging task in computer vision and a key
component of camera-based localization and navigation systems. Recently,
Convolutional Neural Networks (CNNs) achieved high results and good
generalization capabilities. They are usually trained using pairs or triplets
of images labeled as either similar or dissimilar, in a binary fashion. In
practice, the similarity between two images is not binary, but rather
continuous. Furthermore, training these CNNs is computationally complex and
involves costly pair and triplet mining strategies.
  We propose a Generalized Contrastive loss (GCL) function that relies on image
similarity as a continuous measure, and use it to train a siamese CNN.
Furthermore, we propose three techniques for automatic annotation of image
pairs with labels indicating their degree of similarity, and deploy them to
re-annotate the MSLS, TB-Places, and 7Scenes datasets.
  We demonstrate that siamese CNNs trained using the GCL function and the
improved annotations consistently outperform their binary counterparts. Our
models trained on MSLS outperform the state-of-the-art methods, including
NetVLAD, and generalize well on the Pittsburgh, TokyoTM and Tokyo 24/7
datasets. Furthermore, training a siamese network using the GCL function does
not require complex pair mining. We release the source code at
https://github.com/marialeyvallina/generalized_contrastive_loss.