Abstract:
  This paper presents a cross channel context model for latents in deep image
compression. Generally, deep image compression is based on an autoencoder
framework, which transforms the original image to latents at the encoder and
recovers the reconstructed image from the quantized latents at the decoder. The
transform is usually combined with an entropy model, which estimates the
probability distribution of the quantized latents for arithmetic coding.
Currently, joint autoregressive and hierarchical prior entropy models are
widely adopted to capture both the global contexts from the hyper latents and
the local contexts from the quantized latent elements. For the local contexts,
the widely adopted 2D mask convolution can only capture the spatial context.
However, we observe that there are strong correlations between different
channels in the latents. To utilize the cross channel correlations, we propose
to divide the latents into several groups according to channel index and code
the groups one by one, where previously coded groups are utilized to provide
cross channel context for the current group. The proposed cross channel context
model is combined with the joint autoregressive and hierarchical prior entropy
model. Experimental results show that, using PSNR as the distortion metric, the
combined model achieves BD-rate reductions of 6.30% and 6.31% over the baseline
entropy model, and 2.50% and 2.20% over the latest video coding standard
Versatile Video Coding (VVC) for the Kodak and CVPR CLIC2020 professional
dataset, respectively. In addition, when optimized for the MS-SSIM metric, our
approach generates visually more pleasant reconstructed images.


Abstract:
  We study the generalized multidimensional bin packing problem (GVBP) that
generalizes both geometric packing and vector packing. Here, we are given $n$
rectangular items where the $i^{\textrm{th}}$ item has width $w(i)$, height
$h(i)$, and $d$ nonnegative weights $v_1(i), v_2(i), \ldots, v_{d}(i)$. Our
goal is to get an axis-parallel non-overlapping packing of the items into
square bins so that for all $j \in [d]$, the sum of the $j^{\textrm{th}}$
weight of items in each bin is at most 1. This is a natural problem arising in
logistics, resource allocation, and scheduling. Despite being well studied in
practice, surprisingly, approximation algorithms for this problem have rarely
been explored.
  We first obtain two simple algorithms for GVBP having asymptotic
approximation ratios $6(d+1)$ and $3(1 + \ln(d+1) + \varepsilon)$. We then
extend the Round-and-Approx (R&A) framework [Bansal-Khan, SODA'14] to wider
classes of algorithms, and show how it can be adapted to GVBP. Using more
sophisticated techniques, we obtain better approximation algorithms for GVBP,
and we get further improvement by combining them with the R&A framework. This
gives us an asymptotic approximation ratio of $2(1+\ln((d+4)/2))+\varepsilon$
for GVBP, which improves to $2.919+\varepsilon$ for the special case of $d=1$.
We obtain further improvement when the items are allowed to be rotated. We also
present algorithms for a generalization of GVBP where the items are high
dimensional cuboids.


Abstract:
  We introduce the COMBO-based approach for EUD parsing and its implementation,
which took part in the IWPT 2021 EUD shared task. The goal of this task is to
parse raw texts in 17 languages into Enhanced Universal Dependencies (EUD). The
proposed approach uses COMBO to predict UD trees and EUD graphs. These
structures are then merged into the final EUD graphs. Some EUD edge labels are
extended with case information using a single language-independent expansion
rule. In the official evaluation, the solution ranked fourth, achieving an
average ELAS of 83.79%. The source code is available at
https://gitlab.clarin-pl.eu/syntactic-tools/combo.


Abstract:
  We study the mobility edges in a variety of one-dimensional tight binding
models with slowly varying quasi-periodic disorders. It is found that the
quasi-periodic disordered models can be approximated by an ensemble of periodic
models. The mobility edges can be determined by the overlaps of the energy
bands of these periodic models. We demonstrate that this method provides an
efficient way to find out the precise location of mobility edge in
qusi-periodic disordered models. Based on this approximate method, we also
propose an index to indicate the degree of localization of each eigenstate.


Abstract:
  The concept of linear set in projective spaces over finite fields was
introduced by Lunardon in 1999 and it plays central roles in the study of
blocking sets, semifields, rank-distance codes and etc. A linear set with the
largest possible cardinality and rank is called maximum scattered. Despite two
decades of study, there are only a limited number of maximum scattered linear
sets of a line $\mathrm{PG}(1,q^n)$. In this paper, we provide a large family
of new maximum scattered linear sets over $\mathrm{PG}(1,q^n)$ for any even
$n\geq 6$ and odd $q$. In particular, the relevant family contains at least
  \[ \begin{cases}
  \left\lfloor\frac{q^t+1}{8rt}\right\rfloor,& \text{ if }t\not\equiv
2\pmod{4};\\[8pt]
  \left\lfloor\frac{q^t+1}{4rt(q^2+1)}\right\rfloor,& \text{ if }t\equiv
2\pmod{4},
  \end{cases} \]
  inequivalent members for given $q=p^r$ and $n=2t>8$, where
$p=\mathrm{char}(\mathbb{F}_q)$. This is a great improvement of previous
results: for given $q$ and $n>8$, the number of inequivalent maximum scattered
linear sets of $\mathrm{PG}(1,q^n)$ in all classes known so far, is smaller
than $q^2$. Moreover, we show that there are a large number of new maximum
rank-distance codes arising from the constructed linear sets.


Abstract:
  The advent of comprehensive synaptic wiring diagrams of large neural circuits
has created the field of connectomics and given rise to a number of open
research questions. One such question is whether it is possible to reconstruct
the information stored in a recurrent network of neurons, given its synaptic
connectivity matrix. Here, we address this question by determining when solving
such an inference problem is theoretically possible in specific attractor
network models and by providing a practical algorithm to do so. The algorithm
builds on ideas from statistical physics to perform approximate Bayesian
inference and is amenable to exact analysis. We study its performance on three
different models and explore the limitations of reconstructing stored patterns
from synaptic connectivity.


Abstract:
  Intestinal parasitic infection leads to several morbidities to humans
worldwide, especially in tropical countries. The traditional diagnosis usually
relies on manual analysis from microscopic images which is prone to human error
due to morphological similarity of different parasitic eggs and abundance of
impurities in a sample. Many studies have developed automatic systems for
parasite egg detection to reduce human workload. However, they work with high
quality microscopes, which unfortunately remain unaffordable in some rural
areas. Our work thus exploits a benefit of a low-cost USB microscope. This
instrument however provides poor quality of images due to limitation of
magnification (10x), causing difficulty in parasite detection and species
classification. In this paper, we propose a CNN-based technique using transfer
learning strategy to enhance the efficiency of automatic parasite
classification in poor-quality microscopic images. The patch-based technique
with sliding window is employed to search for location of the eggs. Two
networks, AlexNet and ResNet50, are examined with a trade-off between
architecture size and classification performance. The results show that our
proposed framework outperforms the state-of-the-art object recognition methods.
Our system combined with final decision from an expert may improve the real
faecal examination with low-cost microscopes.


Abstract:
  Minimizing cross-entropy over the softmax scores of a linear map composed
with a high-capacity encoder is arguably the most popular choice for training
neural networks on supervised learning tasks. However, recent works show that
one can directly optimize the encoder instead, to obtain equally (or even more)
discriminative representations via a supervised variant of a contrastive
objective. In this work, we address the question whether there are fundamental
differences in the sought-for representation geometry in the output space of
the encoder at minimal loss. Specifically, we prove, under mild assumptions,
that both losses attain their minimum once the representations of each class
collapse to the vertices of a regular simplex, inscribed in a hypersphere. We
provide empirical evidence that this configuration is attained in practice and
that reaching a close-to-optimal state typically indicates good generalization
performance. Yet, the two losses show remarkably different optimization
behavior. The number of iterations required to perfectly fit to data scales
superlinearly with the amount of randomly flipped labels for the supervised
contrastive loss. This is in contrast to the approximately linear scaling
previously reported for networks trained with cross-entropy.


Abstract:
  Simultaneous trajectory prediction for multiple heterogeneous traffic
participants is essential for the safe and efficient operation of connected
automated vehicles under complex driving situations in the real world. The
multi-agent prediction task is challenging, as the motions of traffic
participants are affected by many factors, including their individual dynamics,
their interactions with surrounding agents, the traffic infrastructures, and
the number and modalities of the target agents. To further advance the
trajectory prediction techniques, in this work we propose a three-channel
framework together with a novel Heterogeneous Edge-enhanced graph ATtention
network (HEAT), which is able to deal with the heterogeneity of the target
agents and traffic participants involved. Specifically, the agent's dynamics
are extracted from their historical states using type-specific encoders. The
inter-agent interactions are represented with a directed edge-featured
heterogeneous graph, and then interaction features are extracted using the
proposed HEAT network. Besides, the map features are shared across all agents
by introducing a selective gate mechanism. And finally, the trajectories of
multi-agent are executed simultaneously. Validations using both urban and
highway driving datasets show that the proposed model can realize simultaneous
trajectory predictions for multiple agents under complex traffic situations,
and achieve state-of-the-art performance with respect to prediction accuracy,
demonstrating its feasibility and effectiveness.


Abstract:
  We show the existence of a natural Dirichlet-to-Neumann map on Riemannian
manifolds with boundary and bounded geometry, such that the bottom of the
Dirichlet spectrum is positive. This map regarded as a densely defined operator
in the $L^2$-space of the boundary admits Friedrichs extension. We focus on the
spectrum of this operator on covering spaces and total spaces of Riemannian
principal bundles over compact manifolds.


Abstract:
  This paper proceeds an approximate calculation of ultimate time survival
probability for bi-seasonal discrete time risk model when premium rate equals
two. The same model with income rate equal to one was investigated in 2014 by
Damarackas and \v{S}iaulys. In general, discrete time and related risk models
deal with possibility for a certain version of random walk to hit a certain
threshold at least once in time. In this research, the mentioned threshold is
the line $u+2t$ and random walk consists from two interchangeably occurring
independent but not necessarily identically distributed random variables. Most
of proved theoretical statements are illustrated via numerical calculations.
Also, there are raised a couple of conjectures on a certain recurrent
determinants non-vanishing.


Abstract:
  We study the orientifold of the ${\mathcal{N}} = 1$ superconformal field
theories describing D3-branes probing the Suspended Pinch Point singularity, as
well as the orientifolds of non-chiral theories obtained by a specific orbifold
$\mathbb{Z}_n$ of SPP. We find that these models realize a mechanism analogous
to the one recently found for the orientifold of the complex Calabi-Yau cone
over the Pseudo del Pezzo surface PdP$_{3c}$: they all flow to a new IR fixed
point such that the value of the $a$-charge is less than half the one of the
oriented theory. We also find that the value of $a$ coincides with the charge
of specific orientifolds of the toric singularities
$L^{(\bar{n},\bar{n},\bar{n})}$ with $\bar{n}=3n/2$ for $n$ even or
$L^{(\bar{n},\bar{n}+1,\bar{n})}$ with $\bar{n}=(3n{-}1)/2$ for $n$ odd,
suggesting the existence of an IR duality.


Abstract:
  With the increase of variable renewable energy sources (VRES) share in
electricity systems, manystudies were developed in order to determine their
optimal technological and spatial mix. Modern PortfolioTheory (MPT) has been
frequently applied in this context. However, some crucial aspects, important
inenergy planning, are not addressed by these analyses. We, therefore, propose
several improvements andevaluate how each change in formulation impacts
results. More specifically, we address generation costs, system demand, and
firm energy output, present a formal model and apply it to the case of Brazil.
Wefound that, after including our proposed modifications, the resulting
efficient frontier differs strongly fromthe one obtained in the original
formulation. Portfolios with high output standard deviation are not ableto
provide a firm output level at competitive costs. Furthermore, we show that
diversification plays animportant role in smoothing output from VRES portfolios


Abstract:
  Standard methods for the meta-analysis of medical tests without a gold
standard are limited to dichotomous data. Multivariate probit models are used
to analyze correlated binary data, and can be extended to multivariate ordered
probit models to model ordinal data. Within the context of an imperfect gold
standard, they have previously been used for the analysis of dichotomous and
ordinal tests in a single study, and for the meta-analysis of dichotomous
tests. In this paper, we developed a hierarchical, latent class multivariate
probit model for the simultaneous meta-analysis of ordinal and dichotomous
tests without assuming a gold standard. The model can accommodate a
hierarchical partial pooling model on the conditional within-study
correlations, enabling one to obtain summary estimates of joint test accuracy.
Dichotomous tests use probit regression likelihoods and ordinal tests use
ordered probit regression likelihoods. We fitted the models using Stan, which
uses a state-of-the-art Hamiltonian Monte Carlo algorithm. We applied the
models to a dataset in which studies evaluated the accuracy of tests, and test
combinations, for deep vein thrombosis. We first demonstrated the issues with
dichotomising test accuracy data a priori without a gold standard by fitting
models which dichotomised the ordinal test data, and then we applied models
which do not dichotomise the data. Furthermore, we fitted and compared a
variety of other models, including those which assumed conditional independence
and dependence between tests, and those assuming perfect and an imperfect gold
standard.


Abstract:
  We present the first set of cosmological baryonic zoom-in simulations of
galaxies including dissipative self-interacting dark matter (dSIDM). These
simulations utilize the Feedback In Realistic Environments (FIRE-2) galaxy
formation physics, but allow the dark matter to have dissipative
self-interactions analogous to Standard Model forces, parameterized by the
self-interaction cross-section per unit mass, $(\sigma/m)$, and the
dimensionless degree of dissipation, $0<f_{\rm diss}<1$. We survey this
parameter space, including constant and velocity-dependent cross-sections, and
focus on structural and kinematic properties of dwarf galaxies with $M_{\rm
halo} \simeq 10^{10-11} {\rm M}_{\odot}$. Central density profiles of simulated
dwarfs become cuspy when $(\sigma/m)_{\rm eff} \gtrsim 0.1\,{\rm
cm^{2}\,g^{-1}}$ (and $f_{\rm diss}=0.5$ as fiducial). The power-law slopes
asymptote to $\alpha \approx -1.5$ in low-mass dwarfs independent of
cross-section, which arises from a dark matter "cooling flow". Through
comparisons with dark matter only simulations, we find the profile in this
regime is insensitive to the inclusion of baryons. However, when
$(\sigma/m)_{\rm eff} \ll 0.1\,{\rm cm^{2}\,g^{-1}}$, baryonic effects can
produce cored density profiles comparable to non-dissipative cold dark matter
(CDM) runs but at smaller radii. Simulated galaxies with $(\sigma/m) \gtrsim
10\,{\rm cm^{2}\,g^{-1}}$ develop significant coherent rotation of dark matter,
accompanied by halo deformation, but this is unlike the well-defined thin "dark
disks" often attributed to baryon-like dSIDM. The density profiles in this high
cross-section model exhibit lower normalizations given the onset of halo
deformation. For our surveyed dSIDM parameters, halo masses and galaxy stellar
masses do not show appreciable difference from CDM, but dark matter kinematics
and halo concentrations/shapes can differ.


Abstract:
  The celebrated CLEAN algorithm has been the cornerstone of deconvolution
algorithms in radio interferometry almost since its conception in the 1970s.
For all its faults, CLEAN is remarkably fast, robust to calibration artefacts
and in its ability to model point sources. We demonstrate how the same
assumptions that afford CLEAN its speed can be used to accelerate more
sophisticated deconvolution algorithms.


Abstract:
  Generative adversarial networks (GANs) have shown impressive results in both
unconditional and conditional image generation. In recent literature, it is
shown that pre-trained GANs, on a different dataset, can be transferred to
improve the image generation from a small target data. The same, however, has
not been well-studied in the case of conditional GANs (cGANs), which provides
new opportunities for knowledge transfer compared to unconditional setup. In
particular, the new classes may borrow knowledge from the related old classes,
or share knowledge among themselves to improve the training. This motivates us
to study the problem of efficient conditional GAN transfer with knowledge
propagation across classes. To address this problem, we introduce a new GAN
transfer method to explicitly propagate the knowledge from the old classes to
the new classes. The key idea is to enforce the popularly used conditional
batch normalization (BN) to learn the class-specific information of the new
classes from that of the old classes, with implicit knowledge sharing among the
new ones. This allows for an efficient knowledge propagation from the old
classes to the new ones, with the BN parameters increasing linearly with the
number of new classes. The extensive evaluation demonstrates the clear
superiority of the proposed method over state-of-the-art competitors for
efficient conditional GAN transfer tasks. The code is available at:
https://github.com/mshahbazi72/cGANTransfer


Abstract:
  We analyze two different fermionic systems that defy Mott localization
showing a metallic ground state at integer filling and very large Coulomb
repulsion. The first is a multiorbital Hubbard model with a Hund's coupling,
where this physics has been widely studied and the new metallic state is called
a Hund's metal, and the second is a SU(3) Hubbard model with a patterned
single-particle potential designed to display a similar interaction-resistant
metal in a set-up which can be implemented with SU($N$) ultracold atoms. With
simple analytical arguments and exact numerical diagonalization of the
Hamiltonians for a minimal three-site system, we demonstrate that the
interaction-resistant metal emerges in both cases as a compromise between two
different insulating solutions which are stabilized by different terms of the
models. This provides a strong evidence that the Hund's metal is a specific
realization of a more general phenomenon which can be realized in various
strongly correlated systems.


Abstract:
  Motivated by recent experiments, we calculate particle emission from a
Bose-Einstein condensate trapped in a single deep well of a one-dimensional
lattice when the interaction strength is modulated. In addition to pair
emission, which has been widely studied, we observe single-particle emission.
Within linear response, we are able to write closed-form expressions for the
single-particle emission rates and reduce the pair emission rates to
one-dimensional integrals. The full nonlinear theory of single-particle
emission is reduced to a single variable integrodifferential equation, which we
numerically solve.


Abstract:
  We show that, under finitely many ergodicity assumptions, any
multicorrelation sequence defined by invertible measure preserving
$\mathbb{Z}^d$-actions with multivariable integer polynomial iterates is the
sum of a nilsequence and a null sequence, extending a recent result of the
second author. To this end, we develop a new seminorm bound estimate for
multiple averages by improving the results in a previous work of the first,
third and fourth authors. We also use this approach to obtain new criteria for
joint ergodicity of multiple averages with multivariable polynomial iterates on
$\mathbb{Z}^{d}$-systems.


Abstract:
  We report promising results for high-throughput on-field soybean pod count
with small mobile robots and machine-vision algorithms. Our results show that
the machine-vision based soybean pod counts are strongly correlated with
soybean yield. While pod counts has a strong correlation with soybean yield,
pod counting is extremely labor intensive, and has been difficult to automate.
Our results establish that an autonomous robot equipped with vision sensors can
autonomously collect soybean data at maturity. Machine-vision algorithms can be
used to estimate pod-counts across a large diversity panel planted across
experimental units (EUs, or plots) in a high-throughput, automated manner. We
report a correlation of 0.67 between our automated pod counts and soybean
yield. The data was collected in an experiment consisting of 1463 single-row
plots maintained by the University of Illinois soybean breeding program during
the 2020 growing season. We also report a correlation of 0.88 between automated
pod counts and manual pod counts over a smaller data set of 16 plots.


Abstract:
  The paper presents a novel asymptotic distribution for a mle when the
log--likelihood is strictly concave in the parameter for all data points; for
example, the exponential family. The new asymptotic distribution can be seen as
a refinement of the usual normal asymptotic distribution and is comparable to
an Edgeworth expansion. However, it is obtained with weaker conditions than
even those for asymptotic normality. The same technique is then used to find
the exact distribution of the weighted likelihood bootstrap sampler.


Abstract:
  Green communications have always been a target for the information industry
to alleviate energy overhead and reduce fossil fuel usage. In current 5G and
future 6G era, there is no doubt that the volume of network infrastructure and
the number of connected terminals will keep exponentially increasing, which
results in the surging energy cost. It becomes growing important and urgent to
drive the development of green communications. However, 6G will inevitably have
increasingly stringent and diversified requirements for Quality of Service
(QoS), security, flexibility, and even intelligence, all of which challenge the
improvement of energy efficiency. Moreover, the dynamic energy harvesting
process, which will be adopted widely in 6G, further complicates the power
control and network management. To address these challenges and reduce human
intervene, Artificial Intelligence (AI) has been widely recognized and
acknowledged as the only solution. Academia and industry have conducted
extensive research to alleviate energy demand, improve energy efficiency, and
manage energy harvesting in various communication scenarios. In this paper, we
present the main considerations for green communications and survey the related
research on AI-based green communications. We focus on how AI techniques are
adopted to manage the network and improve energy harvesting toward the green
era. We analyze how state-of-the-art Machine Learning (ML) and Deep Learning
(DL) techniques can cooperate with conventional AI methods and mathematical
models to reduce the algorithm complexity and optimize the accuracy rate to
accelerate the applications in 6G. Finally, we discuss the existing problems
and envision the challenges for these emerging techniques in 6G.


Abstract:
  The paper studies a probabilistic notion of causes in Markov chains that
relies on the counterfactuality principle and the probability-raising property.
This notion is motivated by the use of causes for monitoring purposes where the
aim is to detect faulty or undesired behaviours before they actually occur. A
cause is a set of finite executions of the system after which the probability
of the effect exceeds a given threshold. We introduce multiple types of costs
that capture the consumption of resources from different perspectives, and
study the complexity of computing cost-minimal causes.


Abstract:
  The field of neuromorphic computing is in a period of active exploration.
While many tools have been developed to simulate neuronal dynamics or convert
deep networks to spiking models, general software libraries for learning rules
remain underexplored. This is partly due to the diverse, challenging nature of
efforts to design new learning rules, which range from encoding methods to
gradient approximations, from population approaches that mimic the Bayesian
brain to constrained learning algorithms deployed on memristor crossbars. To
address this gap, we present Neko, a modular, extensible library with a focus
on aiding the design of new learning algorithms. We demonstrate the utility of
Neko in three exemplar cases: online local learning, probabilistic learning,
and analog on-device learning. Our results show that Neko can replicate the
state-of-the-art algorithms and, in one case, lead to significant
outperformance in accuracy and speed. Further, it offers tools including
gradient comparison that can help develop new algorithmic variants. Neko is an
open source Python library that supports PyTorch and TensorFlow backends.


Abstract:
  How much credit (or blame) should an action taken in a state get for a future
reward? This is the fundamental temporal credit assignment problem in
Reinforcement Learning (RL). One of the earliest and still most widely used
heuristics is to assign this credit based on a scalar coefficient $\lambda$
(treated as a hyperparameter) raised to the power of the time interval between
the state-action and the reward. In this empirical paper, we explore heuristics
based on more general pairwise weightings that are functions of the state in
which the action was taken, the state at the time of the reward, as well as the
time interval between the two. Of course it isn't clear what these pairwise
weight functions should be, and because they are too complex to be treated as
hyperparameters we develop a metagradient procedure for learning these weight
functions during the usual RL training of a policy. Our empirical work shows
that it is often possible to learn these pairwise weight functions during
learning of the policy to achieve better performance than competing approaches.


Abstract:
  1.) Spatio-temporal datasets that are difficult to analyze are common in
ecological surveys. There are software packages available to analyze these
datasets, but many of them require advanced coding skills. There is a growing
need for easy to use packages that researchers can use to analyze common
ecological datasets 2.) We develop a particular generalized linear mixed model
for spatio-temporal point-referenced data that is flexible enough to
accommodate data from most ecological surveys while being structured enough to
facilitate analyses without advanced coding. Our implementation in the staRVe
package uses a computationally efficient version of a nearest neighbour
Gaussian process enabling analysis of relatively large datasets. 3.) A brief
simulation study shows our model produces accurate predictions and forecasts,
while a tutorial analysis of a Carolina wren survey suggests a recommended
workflow for analyses. We also analyze a more complicated scientific survey of
haddock to showcase the capabilities of our model. 4.) Our model and package
are tools that can easily be added to researchers' workflow to help make sense
of data from ecological surveys. We emphasize the ability of our model to
create useful visualisations of data which can then lead to identification of
important trends in species distributions.


Abstract:
  A collisional-radiative non-equilibrium impurity treatment for JOREK 3D
nonlinear magneto-hydrodynamic (MHD) simulations has been developed. The
impurities are represented by super-particles flowing along the fluid velocity
field lines, while ionizing and recombining independently according to ADAS
data and local fluid density and temperature. The non-equilibrium impurity
contributions are then projected back to the fluid field for self-consistent
time evolution. A 2D test case is used to compare the new non-equilibrium
impurity model against previous Coronal Equilibrium (CE) impurity treatment, as
well as to compare the non-equilibrium impurity behavior between the single and
the two temperature model. Further, we conduct benchmark with previously
published coronal non-equilibrium results by other 3D nonlinear MHD codes such
as M3D-C1 and NIMROD. The new non-equilibrium treatment is shown to
successfully capture the early phase cooling by weakly ionized impurities which
the CE model missed. The benchmarks with M3D-C1 and NIMROD show general
agreement in both the integrated quantities and the 2D profile evolution,
despite the difference in the atomic model used. The above comparison and
benchmark cases demonstrate the capability of the non-equilibrium impurity
model for JOREK, paving the way for more sophisticated 3D non-linear Massive
Material Injection (MMI) simulations which have important applications in
disruption mitigation studies.


Abstract:
  Sharing data from clinical studies can facilitate innovative data-driven
research and ultimately lead to better public health. However, sharing
biomedical data can put sensitive personal information at risk. This is usually
solved by anonymization, which is a slow and expensive process. An alternative
to anonymization is sharing a synthetic dataset that bears a behaviour similar
to the real data but preserves privacy. As part of the collaboration between
Novartis and the Oxford Big Data Institute, we generate a synthetic dataset
based on COSENTYX (secukinumab) Ankylosing Spondylitis clinical study. We apply
an Auxiliary Classifier GAN to generate synthetic MRIs of vertebral units. The
images are conditioned on the VU location (cervical, thoracic and lumbar). In
this paper, we present a method for generating a synthetic dataset and conduct
an in-depth analysis on its properties along three key metrics: image fidelity,
sample diversity and dataset privacy.


Abstract:
  In this paper we propose two models describing the dynamics of heavy and
light vehicles on a road network, taking into account the interactions between
the two classes. The models are tailored for two-lane highways where heavy
vehicles cannot overtake. This means that heavy vehicles cannot saturate the
whole road space, while light vehicles can. In these conditions the creeping
phenomenon can appear, i.e. one class of vehicles can proceed even if the other
class has reached the maximal density. The first model we propose couples two
first-order macroscopic LWR models, while the second model couples a
second-order microscopic Follow-the-Leader model with a first-order macroscopic
LWR model. Numerical results show that both models are able to catch some
second-order (inertial) phenomena like stop & go waves. Models are calibrated
by means of real data measured by fixed sensors placed along the A4 Italian
highway Trieste-Venice and its branches, provided by Autovie Venete S.p.A.


Abstract:
  In this paper we present DMC, a model and associated tool for polarimetric
imaging of very long baseline interferometry datasets that simultaneously
reconstructs the full-Stokes emission structure along with the station-based
gain and leakage calibration terms. DMC formulates the imaging problem in terms
of posterior exploration, which is achieved using Hamiltonian Monte Carlo
sampling. The resulting posterior distribution provides a natural
quantification of uncertainty in both the image structure and in the data
calibration. We run DMC on both synthetic and real datasets, the results of
which demonstrate its ability to accurately recover both the image structure
and calibration quantities as well as to assess their corresponding
uncertainties. The framework underpinning DMC is flexible, and its specific
implementation is under continued development.


Abstract:
  We study the general embedding of a $ P(X, \varphi) $ inflationary theory
into a two-field theory with curved field space metric, which was proposed as a
possible way to examine the relation between de Sitter Swampland conjecture and
\textit{k}-inflation. We show that this embedding method fits into the special
type of two-field model in which the heavy field can be integrated out at the
full action level. However, this embedding is not exact due to the upper bound
of the effective mass of the heavy field. We quantify the deviation between the
speed of sound calculated via the $ P(X, \varphi) $ theory and the embedding
two-field picture to next leading order terms. We especially focus on the first
potential slow roll parameter defined in the two-field picture and obtain an
upper bound on it.


Abstract:
  We construct solutions of the fast diffusion equation, which exist for all
$t\in\mathbb{R}$ and are singular on the set $\Gamma(t):= \{ \xi(s) ; -\infty
<s \leq ct \}$, $c>0$, where $\xi\in C^3(\mathbb{R};\mathbb{R}^n)$, $n\geq 2$.
We also give a precise description of the behavior of the solutions near
$\Gamma(t)$.


Abstract:
  We present a compact formula in Mellin space for the four-point tree-level
holographic correlators of chiral primary operators of arbitrary conformal
weights in $(2,0)$ supergravity on $AdS_3 \times S^3$, with two operators in
tensor multiplet and the other two in gravity multiplet. This is achieved by
solving the recursion relation arising from a hidden six-dimensional conformal
symmetry. We note the compact expression is obtained after carefully analysing
the analytic structures of the correlators. Various limits of the correlators
are studied, including the maximally R-symmetry violating limit and flat-space
limit.


Abstract:
  Destructive quantum interference (DQI) and its effects on electron transport
is studied in chemical molecules and finite physical lattices that can be
described by a discrete Hamiltonian. Starting from a bipartite system whose
conductance zeros are known to exist between any two points of a specially
designated set, the interference set, we use the Dyson equation to develop a
general algorithm of determining the zero conductance points in complex
systems, which are not necessarily bipartite. We illustrate this procedure as
it applies to the fulvene molecule. The stability of the conductance zeros is
analyzed in respect with external perturbations.


Abstract:
  We conduct an audit of pricing algorithms employed by companies in the
Italian car insurance industry, primarily by gathering quotes through a popular
comparison website. While acknowledging the complexity of the industry, we find
evidence of several problematic practices. We show that birthplace and gender
have a direct and sizeable impact on the prices quoted to drivers, despite
national and international regulations against their use. Birthplace, in
particular, is used quite frequently to the disadvantage of foreign-born
drivers and drivers born in certain Italian cities. In extreme cases, a driver
born in Laos may be charged 1,000 euros more than a driver born in Milan, all
else being equal. For a subset of our sample, we collect quotes directly on a
company website, where the direct influence of gender and birthplace is
confirmed. Finally, we find that drivers with riskier profiles tend to see
fewer quotes in the aggregator result pages, substantiating concerns of
differential treatment raised in the past by Italian insurance regulators.


Abstract:
  The growth of crystals confined in porous or cellular materials is ubiquitous
in Nature and industry. Confinement affects the formation of biominerals in
living organisms, of minerals in the Earth's crust and of salt crystals
damaging porous limestone monuments, and is also used to control the growth of
artificial crystals. However, the mechanisms by which confinement alters
crystal shapes and growth rates are still not elucidated. Based on novel
\textit{in situ} optical observations of (001) surfaces of NaClO$_3$ and
CaCO$_3$ crystals at nanometric distances from a glass substrate, we
demonstrate that new molecular layers can nucleate homogeneously and propagate
without interruption even when in contact with other solids, raising the
macroscopic crystal above them. Confined growth is governed by the peculiar
dynamics of these molecular layers controlled by the two-dimensional transport
of mass through the liquid film from the edges to the center of the contact,
with distinctive features such as skewed dislocation spirals, kinetic
localization of nucleation in the vicinity of the contact edge, and directed
instabilities. Confined growth morphologies can be predicted from the values of
three main dimensionless parameters.


Abstract:
  With the increasing amount of reliance on digital data and computer networks
by corporations and the public in general, the occurrence of cyber attacks has
become a great threat to the normal functioning of our society. Intrusion
detection systems seek to address this threat by preemptively detecting attacks
in real time while attempting to block them or minimizing their damage. These
systems can function in many ways being some of them based on artificial
intelligence methods. Datasets containing both normal network traffic and cyber
attacks are used for training these algorithms so that they can learn the
underlying patterns of network-based data. The CIDDS-001 is one of the most
used datasets for network-based intrusion detection research. Regarding this
dataset, in the majority of works published so far, the Class label was used
for training machine learning algorithms. However, there is another label in
the CIDDS-001, AttackType, that seems very promising for this purpose and
remains considerably unexplored. This work seeks to make a comparison between
two machine learning models, K-Nearest Neighbours and Random Forest, which were
trained with both these labels in order to ascertain whether AttackType can
produce reliable results in comparison with the Class label.


Abstract:
  For the wave equation $\partial_t^2-\Delta+V$ on $\mathbb{R}^d$ with
compactly supported, real valued potential $V$, we establish a sharp relation
between Sobolev regularity of $V$ and the existence of finite order expansions
as $t\rightarrow 0$ for the relative trace of the wave group.


Abstract:
  The experimental verification of quantum fluctuation relations for driven
open quantum system is currently a challenge, due to the conceptual and
operative difficulty of distinguishing work and heat. The Nitrogen-Vacancy
center in diamond has been recently proposed as a controlled test bed to study
fluctuation relations in the presence of an engineered dissipative channel, in
absence of work [Hern\'andez-G\'omez et al., Phys. Rev. Research 2, 023327
(2020)]. Here, we extend those studies to exploring the validity of quantum
fluctuation relations in a driven-dissipative scenario, where the spin
exchanges energy both with its surroundings because of a thermal gradient, and
with an external work source. We experimentally prove the validity of the
quantum fluctuation relations in the presence of cyclic driving in two cases,
when the spin exchanges energy with an effective infinite-temperature
reservoir, and when the total work vanishes at stroboscopic times -- although
the power delivered to the NV center is non-null. Our results represent the
first experimental study of quantum fluctuation relation in driven open quantum
systems.


Abstract:
  This work concerns with the description of the topological phases of band
insulators of class DIII by using the equivariant cohomology. The main result
is the definition of a cohomology class for general systems of class DIII which
generalizes the well known $\mathbb{Z}_2$-invariant given by the Teo-Kane
formula in the one-dimension case. In the two-dimensional case this cohomology
invariant allows a complete description of the strong and weak phases. The
relation with the KR-theory, the Noether-Fredholm index and the classification
of "Real" gerbes are also discussed.


Abstract:
  Safety in human-robot interaction can be divided into physical safety and
perceived safety, where the latter is still under-addressed in the literature.
Investigating perceived safety in human-robot interaction requires a
multidisciplinary perspective. Indeed, perceived safety is often considered as
being associated with several common factors studied in other disciplines,
i.e., comfort, predictability, sense of control, and trust. In this paper, we
investigated the relationship between these factors and perceived safety in
human-robot interaction using subjective and objective measures. We conducted a
two-by-five mixed-subjects design experiment. The five within-subjects
conditions correspond to (1) baseline, and the manipulations of robot behaviors
to stimulate: (2) discomfort, (3) decreased perceived safety, (4) decreased
sense of control and (5) distrust. Twenty-seven young adult participants took
part in the experiments. Participants were asked to answer questionnaires that
measure the manipulated factors after within-subjects conditions. Besides
questionnaire data, we collected objective measures such as videos and
physiological data. The questionnaire results show a correlation between
comfort, sense of control, trust, and perceived safety. We also discuss the
effect of individual human characteristics (such as personality and gender)
that they could be predictors of perceived safety. We used the physiological
signal data and facial affect from videos for estimating perceived safety where
participants' subjective ratings were utilized as labels. The data from
objective measures revealed that the prediction rate was higher from
physiological signal data. This paper can play an important role in the goal of
better understanding perceived safety in human-robot interaction.


Abstract:
  Single-phase multiferroic materials that allow the coexistence of
ferroelectric and magnetic ordering above room temperature are highly
desirable, motivating an ongoing search for mechanisms for unconventional
ferroelectricity in magnetic oxides. Here, we report an antisite defect
mechanism for room temperature ferroelectricity in epitaxial thin films of
yttrium orthoferrite, YFeO3, a perovskite-structured canted antiferromagnet. A
combination of piezoresponse force microscopy, atomically resolved elemental
mapping with aberration corrected scanning transmission electron microscopy and
density functional theory calculations reveals that the presence of YFe
antisite defects facilitates a non-centrosymmetric distortion promoting
ferroelectricity. This mechanism is predicted to work analogously for other
rare earth orthoferrites, with a dependence of the polarization on the radius
of the rare earth cation. Furthermore, a vertically aligned nanocomposite
consisting of pillars of a magnetoelastic oxide CoFe2O4 embedded epitaxially in
the YFeO3 matrix exhibits both robust ferroelectricity and ferrimagnetism at
room temperature, as well as a noticeable strain-mediated magnetoelectric
coupling effect. Our work uncovers the distinctive role of antisite defects in
providing a novel mechanism for ferroelectricity in a range of magnetic
orthoferrites and further augments the functionality of this family of complex
oxides for multiferroic applications.


Abstract:
  Patients with Frontotemporal Dementia (FTD) have impaired cognitive
abilities, executive and behavioral traits, loss of language ability, and
decreased memory capabilities. Based on the distinct patterns of cortical
atrophy and symptoms, the FTD spectrum primarily includes three variants:
behavioral variant FTD (bvFTD), non-fluent variant primary progressive aphasia
(nfvPPA), and semantic variant primary progressive aphasia (svPPA). The purpose
of this study is to classify MRI images of every single subject into one of the
spectrums of the FTD in a hierarchical order by applying data-driven techniques
of Artificial Intelligence (AI) on cortical thickness data. This data is
computed by FreeSurfer software. We used the Smallest Univalue Segment
Assimilating Nucleus (SUSAN) technique to minimize the noise in cortical
thickness data. Specifically, we took 204 subjects from the frontotemporal
lobar degeneration neuroimaging initiative (NIFTD) database to validate this
approach, and each subject was diagnosed in one of the diagnostic categories
(bvFTD, svPPA, nfvPPA and cognitively normal). Our proposed automated
classification model yielded classification accuracy of 86.5, 76, and 72.7 with
support vector machine (SVM), linear discriminant analysis (LDA), and Naive
Bayes methods, respectively, in 10-fold cross-validation analysis, which is a
significant improvement on a traditional single multi-class model with an
accuracy of 82.7, 73.4, and 69.2.


Abstract:
  Long-term complex activity recognition and localisation can be crucial for
the decision-making process of several autonomous systems, such as smart cars
and surgical robots. Nonetheless, most current methods are designed to merely
localise short-term action/activities or combinations of atomic actions that
only last for a few frames or seconds. In this paper, we address the problem of
long-term complex activity detection via a novel deformable, spatiotemporal
parts-based model. Our framework consists of three main building blocks: (i)
action tube detection, (ii) the modelling of the deformable geometry of parts,
and (iii) a sparsity mechanism. Firstly, action tubes are detected in a series
of snippets using an action tube detector. Next, a new 3D deformable RoI
pooling layer is designed for learning the flexible, deformable geometry of the
constellation of parts. Finally, a sparsity strategy differentiates between
activated and deactivate features. We also provide temporal complex activity
annotation for the recently released ROAD autonomous driving dataset and the
SARAS-ESAD surgical action dataset, to validate our method and show the
adaptability of our framework to different domains. As they both contain long
videos portraying long-term activities they can be used as benchmarks for
future work in this area.


Abstract:
  Deep-learning-based image processing has emerged as a valuable tool in recent
years owing to its high performance. However, the quality of
deep-learning-based methods relies heavily on the amount of training data, and
the cost of acquiring a large amount of data is often prohibitive in medical
fields. Therefore, we performed CT modality conversion based on deep learning
requiring only a small number of unsupervised images. The proposed method is
based on generative adversarial networks (GANs) with several extensions
tailored for CT images. This method emphasizes the preservation of the
structure in the processed images and reduction in the amount of training data.
This method was applied to realize the conversion of mega-voltage computed
tomography (MVCT) to kilo-voltage computed tomography (kVCT) images. Training
was performed using several datasets acquired from patients with head and neck
cancer. The size of the datasets ranged from 16 slices (for two patients) to
2745 slices (for 137 patients) of MVCT and 2824 slices of kVCT for 98 patients.
The quality of the processed MVCT images was considerably enhanced, and the
structural changes in the images were minimized. With an increase in the size
of training data, the image quality exhibited a satisfactory convergence from a
few hundred slices. In addition to statistical and visual evaluations, these
results were clinically evaluated by medical doctors in terms of the accuracy
of contouring. We developed an MVCT to kVCT conversion model based on deep
learning, which can be trained using a few hundred unpaired images. The
stability of the model against the change in the data size was demonstrated.
This research promotes the reliable use of deep learning in clinical medicine
by partially answering the commonly asked questions: "Is our data enough? How
much data must we prepare?"


Abstract:
  Optimal transport (OT) has recently found widespread interest in machine
learning. It allows to define novel distances between probability measures,
which have shown promise in several applications. In this work, we discuss how
to computationally approach general non-linear OT problems within the framework
of Riemannian manifold optimization. The basis of this is the manifold of
doubly stochastic matrices (and their generalization). Even though the manifold
geometry is not new, surprisingly, its usefulness for solving general
non-linear OT problems has not been popular. To this end, we specifically
discuss optimization-related ingredients that allow modeling the OT problem on
smooth Riemannian manifolds by exploiting the geometry of the search space. We
also discuss extensions where we reuse the developed optimization ingredients.
We make available the Manifold optimization-based Optimal Transport, or MOT,
repository with codes useful in solving OT problems in Python and Matlab. The
codes are available at \url{https://github.com/SatyadevNtv/MOT}.


Abstract:
  Light diffraction at an aperture is a basic problem that has generated a
tremendous amount of interest in optics. Some of the most significant
diffraction results are the Fresnel-Kirchhoff and Rayleigh-Sommerfeld formulas.
These theories are based on solving the wave equation using Green's theorem and
result in slightly different expressions depending on the particular boundary
conditions employed. In this paper, we propose another approach for solving the
diffraction by a thin screen, which includes apertures, gratings,
transparencies etc. We show that, applying the first order Born approximation
to 2D objects, we obtain a general diffraction formula, without angular
approximations. We discuss several common approximations and place our results
in the context of existing theories.


Abstract:
  Decoder diversity is a powerful error correction framework in which a
collection of decoders collaboratively correct a set of error patterns
otherwise uncorrectable by any individual decoder. In this paper, we propose a
new approach to design the decoder diversity of finite alphabet iterative
decoders (FAIDs) for Low-Density Parity Check (LDPC) codes over the binary
symmetric channel (BSC), for the purpose of lowering the error floor while
guaranteeing the waterfall performance. The proposed decoder diversity is
achieved by training a recurrent quantized neural network (RQNN) to
learn/design FAIDs. We demonstrated for the first time that a machine-learned
decoder can surpass in performance a man-made decoder of the same complexity.
As RQNNs can model a broad class of FAIDs, they are capable of learning an
arbitrary FAID. To provide sufficient knowledge of the error floor to the RQNN,
the training sets are constructed by sampling from the set of most problematic
error patterns - trapping sets. In contrast to the existing methods that use
the cross-entropy function as the loss function, we introduce a
frame-error-rate (FER) based loss function to train the RQNN with the objective
of correcting specific error patterns rather than reducing the bit error rate
(BER). The examples and simulation results show that the RQNN-aided decoder
diversity increases the error correction capability of LDPC codes and lowers
the error floor.


Abstract:
  The freeze-in production of Feebly Interacting Massive Particle (FIMP) dark
matter in the early universe is an appealing alternative to the well-known -
and constrained - Weakly Interacting Massive Particle (WIMP) paradigm. Although
challenging, the phenomenology of FIMP dark matter has been receiving growing
attention and is possible in a few scenarios. In this work, we contribute to
this endeavor by considering a $Z^\prime$ portal to fermionic dark matter, with
the $Z^\prime$ having both vector and axial couplings and a mass ranging from
MeV up to PeV. We evaluate the bounds on both freeze-in and freeze-out from
direct detection, atomic parity violation, leptonic anomalous magnetic moments,
neutrino-electron scattering, collider, and beam dump experiments. We show that
FIMPs can already be tested by most of these experiments in a complementary
way, whereas WIMPs are especially viable in the $Z^\prime$ low mass regime, in
addition to the $Z^\prime$ resonance region. We also discuss the role of the
axial couplings of $Z^\prime$ in our results. We therefore hope to motivate
specific realizations of this model in the context of FIMPs, as well as
searches for these elusive dark matter candidates.


Abstract:
  Smooth periodic travelling waves in the Camassa--Holm (CH) equation are
revisited. We show that these periodic waves can be characterized in two
different ways by using two different Hamiltonian structures. The standard
formulation, common to the Korteweg--de Vries (KdV) equation, has several
disadvantages, e.g., the period function is not monotone and the quadratic
energy form may have two rather than one negative eigenvalues. We explore the
nonstandard formulation and prove that the period function is monotone and the
quadratic energy form has only one simple negative eigenvalue. We deduce a
precise condition for the spectral and orbital stability of the smooth periodic
travelling waves and show numerically that this condition is satisfied in the
open region where the smooth periodic waves exist.


Abstract:
  Given the great success of Deep Neural Networks(DNNs) and the black-box
nature of it,the interpretability of these models becomes an important
issue.The majority of previous research works on the post-hoc interpretation of
a trained model.But recently, adversarial training shows that it is possible
for a model to have an interpretable input-gradient through
training.However,adversarial training lacks efficiency for interpretability.To
resolve this problem, we construct an approximation of the adversarial
perturbations and discover a connection between adversarial training and
amplitude modulation. Based on a digital analogy,we propose noise modulation as
an efficient and model-agnostic alternative to train a model that interprets
itself with input-gradients.Experiment results show that noise modulation can
effectively increase the interpretability of input-gradients model-agnosticly.


Abstract:
  The Gamma Factory is a proposal to back-scatter laser photons off a beam of
partially-stripped ions at the LHC, producing a beam of $\sim 10$ MeV to $1$
GeV photons with intensities of $10^{16}$ to $10^{18}~\text{s}^{-1}$. This
implies $\sim 10^{23}$ to $10^{25}$ photons on target per year, many orders of
magnitude greater than existing accelerator light sources and also far greater
than all current and planned electron and proton fixed target experiments. We
determine the Gamma Factory's discovery potential through "dark Compton
scattering," $\gamma e \to e X$, where $X$ is a new, weakly-interacting
particle. For dark photons and other new gauge bosons with masses in the
1~to~100 MeV range, the Gamma Factory has the potential to discover extremely
weakly-interacting particles with just a few hours of data and will probe
couplings as low as $\sim 10^{-9}$ with a year of running. The Gamma Factory
therefore may probe couplings lower than all other terrestrial experiments and
is highly complementary to astrophysical probes. We outline the requirements of
an experiment to realize this potential and determine the sensitivity reach for
various experimental configurations.


Abstract:
  Real-world classification domains, such as medicine, health and safety, and
finance, often exhibit imbalanced class priors and have asynchronous
misclassification costs. In such cases, the classification model must achieve a
high recall without significantly impacting precision. Resampling the training
data is the standard approach to improving classification performance on
imbalanced binary data. However, the state-of-the-art methods ignore the local
joint distribution of the data or correct it as a post-processing step. This
can causes sub-optimal shifts in the training distribution, particularly when
the target data distribution is complex. In this paper, we propose Radial-Based
Combined Cleaning and Resampling (RB-CCR). RB-CCR utilizes the concept of class
potential to refine the energy-based resampling approach of CCR. In particular,
RB-CCR exploits the class potential to accurately locate sub-regions of the
data-space for synthetic oversampling. The category sub-region for oversampling
can be specified as an input parameter to meet domain-specific needs or be
automatically selected via cross-validation. Our $5\times2$ cross-validated
results on 57 benchmark binary datasets with 9 classifiers show that RB-CCR
achieves a better precision-recall trade-off than CCR and generally
out-performs the state-of-the-art resampling methods in terms of AUC and
G-mean.


Abstract:
  We describe in this short note a technique to convert an implicit surface
into a Signed Distance Function (SDF) while exactly preserving the zero
level-set of the implicit. The proposed approach relies on embedding the input
implicit in the final layer of a neural network, which is trained to minimize a
loss function characterizing the SDF.


Abstract:
  We develop a second order correction to commonly used density functional
approximations (DFA) to eliminate the systematic delocalization error. The
method, based on the previously developed global scaling correction (GSC), is
an exact quadratic correction to the DFA for the fractional charge behavior and
uses the analytical second derivatives of the total energy with respect to
fractional occupation numbers of the canonical molecular orbitals. For small
and medium-size molecules, this correction leads to ground-state orbital
energies that are highly accurate approximation to the corresponding
quasiparticle energies. It provides excellent predictions of ionization
potentials, electron affinities, photoemission spectrum and photoexcitation
energies beyond previous approximate second order approaches, thus showing
potential for broad applications in computational spectroscopy.


Abstract:
  This paper does not describe a working system. Instead, it presents a single
idea about representation which allows advances made by several different
groups to be combined into an imaginary system called GLOM. The advances
include transformers, neural fields, contrastive representation learning,
distillation and capsules. GLOM answers the question: How can a neural network
with a fixed architecture parse an image into a part-whole hierarchy which has
a different structure for each image? The idea is simply to use islands of
identical vectors to represent the nodes in the parse tree. If GLOM can be made
to work, it should significantly improve the interpretability of the
representations produced by transformer-like systems when applied to vision or
language


Abstract:
  The structural flexibility at three substitution sites in LaFeAsO enabled
investigation of the relation between superconductivity and structural
parameters over a wide range of crystal compositions. Substitutions of Nd for
La, Sb or P for As, and F or H for O were performed. All these substitutions
modify the local structural parameters, while the F/H-substitution also changes
band filling. It was found that the superconducting transition temperature
$T_{c}$ is strongly affected by the pnictogen height $h_{Pn}$ from the Fe-plane
that controls the electron correlation strength and the size of the $d_{xy}$
hole Fermi surface (FS). With increasing $h_{Pn}$, weak coupling
superconductivity switches to the strong coupling one where the $d_{xy}$ hole
FS is crucially important.


Abstract:
  The aim of the paper is to recover some results of
Cardaliaguet-Nolen-Souganidis in \cite{CNS} and Xin-Yu in \cite{XY} about the
homogenization of the G--equation, using different and simpler techniques. The
main mathematical issue is the lack of coercivity of the Hamiltonians. In our
approach we consider a multivalued dynamics without periodic invariants sets, a
family of intrinsic distances and perform an approximation by a sequence of
coercive Hamiltonians.


Abstract:
  Primordial black hole (PBH) is a fascinating candidate for the origin of
binary merger events observed by LIGO-Virgo collaboration. The spatial
distribution of PBHs at formation is an important feature to estimate the
merger rate. We investigate the clustering of PBHs formed by Affleck-Dine (AD)
baryogenesis, where dense baryon bubbles collapse to form PBHs. We found that
formed PBHs show a strong clustering due to the stochastic dynamics of the AD
field. Including the clustering, we evaluate the merger rate and isocurvature
perturbations of PBHs, which show significant deviations from those without
clustering.


Abstract:
  The ratios $R_{\tau/P}\equiv \Gamma(\tau \to P \nu_\tau [\gamma]) / \Gamma(P
\to\mu \nu_\mu[\gamma])$ ($P=\pi, K$) provide sensitive tests of lepton
universality $\left|g_\tau/g_\mu\right|=1$ and are a useful tool for new
physics searches. The radiative corrections to $R_{\tau/P}$ are computed
following a large-$N_C$ expansion to deal with hadronic effects: Chiral
Perturbation Theory is enlarged by including the lightest multiplets of
spin-one heavy states such that the relevant Green functions are well-behaved
at high energies. We find $\delta R_{\tau/\pi}=(0.18\pm 0.57 )\%$ and $\delta
R_{\tau/K}=(0.97\pm 0.58 )\%$, which imply
$\left|g_\tau/g_\mu\right|_\pi=0.9964\pm 0.0038$ and
$\left|g_\tau/g_\mu\right|_K=0.9857\pm 0.0078$, compatible with and at
$1.8\sigma$ of lepton universality, respectively. We test unitarity and bind
non-standard effective interactions with the $\tau \to P \nu_\tau [\gamma]$
decays.


Abstract:
  The development of four-dimensional (4D) scanning transmission electron
microscopy (STEM) using fast detectors has opened-up new avenues for addressing
some of long-standing challenges in electron imaging. One of these challenges
is how to image severely distorted crystal lattices, such as at a dislocation
core. Here we introduce a new 4D-STEM technique, called Cepstral STEM, for
imaging disordered crystals using electron diffuse scattering. Local
fluctuations of diffuse scattering are captured by scanning electron
nanodiffraction (SEND) using a coherent probe. The harmonic signals in electron
diffuse scattering are detected through Cepstral analysis and used for imaging.
By integrating Cepstral analysis with 4D-STEM, we demonstrate that information
about the distortive part of electron scattering potential can be separated and
imaged at nm spatial resolution. We apply our technique to the analysis of a
dislocation core in SiGe and lattice distortions in high entropy alloy.


Abstract:
  Recent work in human-computer interaction has explored the use of
conversational agents as facilitators for group goal-oriented discussions.
Inspired by this work and by the apparent lack of tooling available to support
it, we created Diplomat, a Python-based framework for building conversational
agent facilitators. Diplomat is designed to support simple specification of
agent functionality as well as customizable integration with online chat
services. We document a preliminary user study we conducted to help inform the
design of Diplomat. We also describe the architecture, capabilities, and
limitations of our tool, which we have shared on GitHub.


Abstract:
  Max-plus equations are derived from tropically discretized Sel'kov model via
ultradiscretization. These max-plus equations possess common dynamical
structures with the discretized model: Neimark-Sacker bifurcation and limit
cycles. The limit cycles of the ultradiscrete max-plus equations have seven
discrete states. Furthermore, these max-plus equations exhibit excitability.
Relationship between the tropically discretized model and the derived max-plus
equations is also discussed based on numerical results. It is found that the
derived max-plus equations correspond to a limiting case of the tropically
discretized model.


Abstract:
  We present a number of new piecewise-polynomial kernels for image
interpolation. The kernels are constructed by optimizing a measure of
interpolation quality based on the magnitude of anisotropic artifacts. The
kernel design process is performed symbolically using Mathematica computer
algebra system. Experimental evaluation involving 14 image quality assessment
methods demonstrates that our results compare favorably with the existing
linear interpolators.


Abstract:
  AQ Men is a nova-like variable which is presumed to have a tilted, precessing
accretion disc. Grazing eclipses in this system have been speculated to be
useful in exploring the geometry of its accretion disc. In this work we
analysed TESS observations of AQ Men, which provide the best light curve of
this object thus far. We show that the depths of the eclipses are changing with
the orientation of the accretion disc, which means that they can serve as a
direct test of the tilted accretion disc models. The precession period of the
accretion disc is increasing during the TESS observations. However, it is still
shorter than the period determined in the previous studies. The amplitude of
the variability related to the precession of the accretion disc varies, and so
does the shape of this variability. Moreover, we have detected a positive
superhump that was previously unseen in AQ Men. Interestingly, the positive
superhump has a strongly non-sinusoidal shape, which is not expected for a
nova-like variable.


Abstract:
  Fooling people with highly realistic fake images generated with Deepfake or
GANs brings a great social disturbance to our society. Many methods have been
proposed to detect fake images, but they are vulnerable to adversarial
perturbations -- intentionally designed noises that can lead to the wrong
prediction. Existing methods of attacking fake image detectors usually generate
adversarial perturbations to perturb almost the entire image. This is redundant
and increases the perceptibility of perturbations. In this paper, we propose a
novel method to disrupt the fake image detection by determining key pixels to a
fake image detector and attacking only the key pixels, which results in the
$L_0$ and the $L_2$ norms of adversarial perturbations much less than those of
existing works. Experiments on two public datasets with three fake image
detectors indicate that our proposed method achieves state-of-the-art
performance in both white-box and black-box attacks.


Abstract:
  In an online survey in December 2020, 385 journalists in Germany were
surveyed about platforms in journalism and about their frequency of use and
willingness to adopt emerging technologies. Journalists have a commitment to
publish on a journalism platform on a full-time basis. Freelancers have a
higher commitment than employed journalists. A platform subscription model is
rated more attractive than advertising for a platform. Employed journalists on
the other hand consider advertising more attractive than freelance journalists.
For German journalists it is important that the platform is developed in Europe
or Germany and that it sets high standards on data protection. Multimedia forms
and interactive elements are used occasionally, often or always. Stories or
Reels are predominantly not used. AI software as well as editorial analytics
are rarely or never used. Apart from stories or reels, journalists intend to
use multimedia forms and interactive elements more often in the future. They
are receptive to software for research process documentation as well as to the
analysis of indicators of their own publications. Software as a support for
text production, image selection or headline suggestions is mostly rejected.


Abstract:
  Privacy scoring aims at measuring the privacy violation risk of a user over
an online social network (OSN). Existing work in the field rely on possibly
biased or emotional survey data and focus only on personel purpose OSNs like
Facebook. In contrast to existing work, in this thesis, we work with real-world
OSN data collected from LinkedIn, the most popular professional-purpose OSN
(ProOSN). Towards this end, we developed an extensive crawler to collect all
relevant profile data of 5,389 LinkedIn users, modelled these data using both
relational and graph databases and quantitatively analyzed all privacy risk
scoring methods in the literature. Additionally, we propose a novel scoring
method that consider the granularity of data an OSN user shares on her profile
page. Extensive experimental evaluation of existing and proposed scoring
methods indicates the effectiveness of the proposed solution.


Abstract:
  Given a polynomial with integral coefficients, one can inquire about the
possible residues it can take in its image modulo a prime $p$. The sum over
these residues can sometimes be computed independently of the choice of prime
$p$; for example, Gauss showed that the sum over quadratic residues vanishes
modulo a prime. In this paper we provide a closed form for the sum over
distinct residues in the image of Lucas polynomials of arbitrary degree over
all primes, and prove a complete characterization of the size of the image set.
Our result provides the first non-trivial classification of such a sum for a
family of polynomials of unbounded degree.


Abstract:
  Blockchains have a two-sided reputation: they are praised for disrupting some
of our institutions through innovative technology for good, yet notorious for
being slow and expensive to use. In this work, we tackle this issue with
concurrency, yet we aim to take a radically different approach by valuing
simplicity. We embrace the simplicity through two steps: first, we formulate a
simple runtime mechanism to deal with conflicts called concurrency delegation.
This method is much simpler and has less overhead, particularly in scenarios
where conflicting transactions are relatively rare. Moreover, to further reduce
the number of conflicting transactions, we propose using static annotations
attached to each transaction, provided by the programmer. These annotations are
pseudo-static: they are static with respect to the lifetime of the transaction,
and therefore are free to use information such as the origin and parameters of
the transaction. We propose a distributor component that can use the output of
this pseudo-static annotations and use them to effectively distribute
transactions between threads in the least-conflicting way. We name the outcome
of a system combining concurrency delegation and pseudo-static annotations as
SonicChain. We evaluate SonicChain for both validation and authoring tasks
against a common workload in blockchains, namely, balance transfers, and
observe that it performs expectedly well while introducing very little overhead
and additional complexity to the system.


Abstract:
  We revisit the critical two-dimensional Ashkin-Teller model, i.e. the
$\mathbb{Z}_2$ orbifold of the compactified free boson CFT at $c=1$. We solve
the model on the plane by computing its three-point structure constants and
proving crossing symmetry of four-point correlation functions. We do this not
only for affine primary fields, but also for Virasoro primary fields, i.e.
higher twist fields and degenerate fields.
  This leads us to clarify the analytic properties of Virasoro conformal blocks
and fusion kernels at $c=1$. We show that blocks with a degenerate channel
field should be computed by taking limits in the central charge, rather than in
the conformal dimension. In particular, Al. Zamolodchikov's simple explicit
expression for the blocks that appear in four-twist correlation functions is
only valid in the non-degenerate case: degenerate blocks, starting with the
identity block, are more complicated generalized theta functions.


Abstract:
  Radar has long been a common sensor on autonomous vehicles for obstacle
ranging and speed estimation. However, as a robust sensor to all-weather
conditions, radar's capability has not been well-exploited, compared with
camera or LiDAR. Instead of just serving as a supplementary sensor, radar's
rich information hidden in the radio frequencies can potentially provide useful
clues to achieve more complicated tasks, like object classification and
detection. In this paper, we propose a new dataset, named CRUW, with a
systematic annotator and performance evaluation system to address the radar
object detection (ROD) task, which aims to classify and localize the objects in
3D purely from radar's radio frequency (RF) images. To the best of our
knowledge, CRUW is the first public large-scale dataset with a systematic
annotation and evaluation system, which involves camera RGB images and radar RF
images, collected in various driving scenarios.


Abstract:
  Small-sized unmanned surface vehicles (USV) are coastal water devices with a
broad range of applications such as environmental control and surveillance. A
crucial capability for autonomous operation is obstacle detection for timely
reaction and collision avoidance, which has been recently explored in the
context of camera-based visual scene interpretation. Owing to curated datasets,
substantial advances in scene interpretation have been made in a related field
of unmanned ground vehicles. However, the current maritime datasets do not
adequately capture the complexity of real-world USV scenes and the evaluation
protocols are not standardised, which makes cross-paper comparison of different
methods difficult and hiders the progress. To address these issues, we
introduce a new obstacle detection benchmark MODS, which considers two major
perception tasks: maritime object detection and the more general maritime
obstacle segmentation. We present a new diverse maritime evaluation dataset
containing approximately 81k stereo images synchronized with an on-board IMU,
with over 60k objects annotated. We propose a new obstacle segmentation
performance evaluation protocol that reflects the detection accuracy in a way
meaningful for practical USV navigation. Seventeen recent state-of-the-art
object detection and obstacle segmentation methods are evaluated using the
proposed protocol, creating a benchmark to facilitate development of the field.


Abstract:
  With vast mmWave spectrum and narrow beam antenna technology, precise
position location is now possible in 5G and future mobile communication
systems. In this article, we describe how centimeterlevel localization accuracy
can be achieved, particularly through the use of map-based techniques. We show
how data fusion of parallel information streams, machine learning, and
cooperative localization techniques further improve positioning accuracy.


Abstract:
  Batch Normalization (BN) is a commonly used technique to accelerate and
stabilize training of deep neural networks. Despite its empirical success, a
full theoretical understanding of BN is yet to be developed. In this work, we
analyze BN through the lens of convex optimization. We introduce an analytic
framework based on convex duality to obtain exact convex representations of
weight-decay regularized ReLU networks with BN, which can be trained in
polynomial-time. Our analyses also show that optimal layer weights can be
obtained as simple closed-form formulas in the high-dimensional and/or
overparameterized regimes. Furthermore, we find that Gradient Descent provides
an algorithmic bias effect on the standard non-convex BN network, and we design
an approach to explicitly encode this implicit regularization into the convex
objective. Experiments with CIFAR image classification highlight the
effectiveness of this explicit regularization for mimicking and substantially
improving the performance of standard BN networks.


Abstract:
  The variational discrete element method developed in [28] for dynamic
elasto-plastic computations is adapted to compute the deformation of elastic
Cosserat materials. In addition to cellwise displacement degrees of freedom
(dofs), cellwise rotational dofs are added. A reconstruction is devised to
obtain P 1 non-conforming polynomials in each cell and thus constant strains
and stresses in each cell. The method requires only the usual macroscopic
parameters of a Cosserat material and no microscopic parameter. Numerical
examples show the robustness of the method for both static and dynamic
computations in two and three dimensions.


Abstract:
  Surgical state estimators in robot-assisted surgery (RAS) - especially those
trained via learning techniques - rely heavily on datasets that capture surgeon
actions in laboratory or real-world surgical tasks. Real-world RAS datasets are
costly to acquire, are obtained from multiple surgeons who may use different
surgical strategies, and are recorded under uncontrolled conditions in highly
complex environments. The combination of high diversity and limited data calls
for new learning methods that are robust and invariant to operating conditions
and surgical techniques. We propose StiseNet, a Surgical Task Invariance State
Estimation Network with an invariance induction framework that minimizes the
effects of variations in surgical technique and operating environments inherent
to RAS datasets. StiseNet's adversarial architecture learns to separate
nuisance factors from information needed for surgical state estimation.
StiseNet is shown to outperform state-of-the-art state estimation methods on
three datasets (including a new real-world RAS dataset: HERNIA-20).


Abstract:
  We examine the dielectron invariant mass spectrum and the variation of the
polarization asymmetry as a function of that mass in the event generator of the
five-dimensional Bethe-Heitler differential cross section of the conversion of
linearly polarized photons that we have implemented recently as a Geant4
Physics Model. We compare the results obtained with simulated samples to
analytical expressions.
  Studies of $b \to s \gamma$ decays at LHC and Belle II experiments could
provide hints of physics beyond the standard model in flavor-changing neutral
current processes; issues have been reported in the simulation of the
properties of the background induced by genuine $\gamma$-ray conversions in
their detectors. Our results demonstrate that using the five-dimensional model
would help solve these issues.


Abstract:
  The underground muon detector of the Pierre Auger Observatory is aimed at
attaining direct measurements of the muonic component of extensive air showers
produced by cosmic rays with energy from $10^{16.5}$ eV up to the region of the
ankle (around $10^{18.7}$ eV). It consists of two nested triangular grids of
underground scintillators with 433 m, and 750 m spacings and a total of 71
positions, each with 192 scintillator strips (30 m$^2$) deployed 2.3 m
underground. The light produced by impinging muons in the scintillators is
propagated with optical fibers towards an array of silicon photomultipliers. In
this work, we present the development, validation, and performance of an
end-to-end tool for simulating the response of the underground muon detector to
single-muon signals, which constitutes the basis for further simulations of the
whole array. Laboratory data and simulation outcomes are found consistent,
showing that with the underground muon detector we can measure single muons,
with an efficiency of 99 %, up to about 1050 particles arriving at exactly the
same time in 30 m$^2$ of scintillator.


Abstract:
  We study $N$ vicious Brownian bridges propagating from an initial
configuration $\{a_1 < a_2 < \ldots< a_N \}$ at time $t=0$ to a final
configuration $\{b_1 < b_2 < \ldots< b_N \}$ at time $t=t_f$, while staying
non-intersecting for all $0\leq t \leq t_f$. We first show that this problem
can be mapped to a non-intersecting Dyson's Brownian bridges with Dyson index
$\beta=2$. For the latter we derive an exact effective Langevin equation that
allows to generate very efficiently the vicious bridge configurations. In
particular, for the flat-to-flat configuration in the large $N$ limit, where
$a_i = b_i = (i-1)/N$, for $i = 1, \cdots, N$, we use this effective Langevin
equation to derive an exact Burgers' equation (in the inviscid limit) for the
Green's function and solve this Burgers' equation for arbitrary time $0 \leq
t\leq t_f$. At certain specific values of intermediate times $t$, such as
$t=t_f/2$, $t=t_f/3$ and $t=t_f/4$ we obtain the average density of the
flat-to-flat bridge explicitly. We also derive explicitly how the two edges of
the average density evolve from time $t=0$ to time $t=t_f$. Finally, we discuss
connections to some well known problems, such as the Chern-Simons model, the
related Stieltjes-Wigert orthogonal polynomials and the Borodin-Muttalib
ensemble of determinantal point processes.


Abstract:
  We propose and analyze a specific asymptotic stochastic order for random
processes based on the measure of departure discussed in the literature. As
applications, we stochastically compare mixtures of order statistics and record
values coming from two different homogeneous samples, as the sample size
becomes large.


Abstract:
  We report on observations of the Be/X-ray binary system SwiftJ1626.6-5156
performed with NuSTAR during a short outburst in March 2021, following its
detection of by the MAXI monitor and Spektrum-Roentgen-Gamma (SRG) observatory.
Our analysis of the broadband X-ray spectrum of the source confirms the
presence of two absorption-like features at energies ~9 and ~17 keV previously
reported in literature and interpreted as the fundamental cyclotron resonance
scattering feature (CRSF) and its first harmonic (based on RXTE data). The
better sensitivity and energy resolution of NuSTAR, combined with the low
energy coverage of NICER, allowed us to detect two additional absorption-like
features at ~4.9 keV and ~13 keV. We conclude, therefore, that in total four
cyclotron lines are observed in the spectrum of SwiftJ1626.6-5156: the
fundamental CRSF at ~4.9 keV and three higher spaced harmonics. This discovery
makes SwiftJ1626.6-5156 the second accreting pulsar, after 4U0115+63, whose
spectrum is characterized by more than three lines of a cyclotronic origin, and
implies the source has the weakest confirmed magnetic field among all X-ray
pulsars B~4E11 G. This discovery makes SwiftJ1626.6-5156 one of prime targets
for the upcoming X-ray polarimetry missions covering soft X-ray band such as
IXPE and eXTP.


Abstract:
  Sentiment analysis is one of the most fundamental tasks in Natural Language
Processing. Popular languages like English, Arabic, Russian, Mandarin, and also
Indian languages such as Hindi, Bengali, Tamil have seen a significant amount
of work in this area. However, the Marathi language which is the third most
popular language in India still lags behind due to the absence of proper
datasets. In this paper, we present the first major publicly available Marathi
Sentiment Analysis Dataset - L3CubeMahaSent. It is curated using tweets
extracted from various Maharashtrian personalities' Twitter accounts. Our
dataset consists of ~16,000 distinct tweets classified in three broad classes
viz. positive, negative, and neutral. We also present the guidelines using
which we annotated the tweets. Finally, we present the statistics of our
dataset and baseline classification results using CNN, LSTM, ULMFiT, and
BERT-based deep learning models.


Abstract:
  Most of the turbulence models in practice are based on the assumption of a
linear relation between Reynolds stresses and mean flow strain rates which
generally provides a good approximation in case of attached and fully turbulent
flows. A two dimensional numerical study has been carried out over NACA 0021
with k-\omega SST model with non-linear correction at Re = 120,000 for various
angles of attack which experiences the formation of a laminar separation bubble
(LSB). A correct prediction of LSB requires an accurate resolution of
anisotropy in Reynolds stresses. For comparison with other linear models, the
simulations are also performed with k-kl-\omega, k-\omega SST and Spalart
Allmaras. The performance of these models is assessed through aerodynamic lift,
drag, pressure and friction coefficients. It is found that the non-linear
k-\omega SST and k-kl-\omega transition model provide comparable quality of
prediction in lift and drag coefficients (in spite of the fact that non-linear
k-\omega SST involves solving less number of transport equation than the
transition model) as observed in the experiments whereas k-\omega SST and SA
models under predict the drag coefficient value at low angle of attack due to
inability to capture the separation induced transition. It is also observed
that the location of laminar separation bubble is captured accurately when
nonlinear or transition model is used as opposed to the SA or linear SST
models, which lack in the ability to predict the same.


Abstract:
  Adaptive control is subject to stability and performance issues when a
learned model is used to enhance its performance. This paper thus presents a
deep learning-based adaptive control framework for nonlinear systems with
multiplicatively-separable parametrization, called adaptive Neural Contraction
Metric (aNCM). The aNCM approximates real-time optimization for computing a
differential Lyapunov function and a corresponding stabilizing adaptive control
law by using a Deep Neural Network (DNN). The use of DNNs permits real-time
implementation of the control law and broad applicability to a variety of
nonlinear systems with parametric and nonparametric uncertainties. We show
using contraction theory that the aNCM ensures exponential boundedness of the
distance between the target and controlled trajectories in the presence of
parametric uncertainties of the model, learning errors caused by aNCM
approximation, and external disturbances. Its superiority to the existing
robust and adaptive control methods is demonstrated using a cart-pole balancing
model.


Abstract:
  We present a general approach to constructing permutation tests that are both
exact for the null hypothesis of equality of distributions and asymptotically
correct for testing equality of parameters of distributions while allowing the
distributions themselves to differ. These robust permutation tests transform a
given test statistic by a consistent estimator of its limiting distribution
function before enumerating its permutation distribution. This transformation,
known as prepivoting, aligns the unconditional limiting distribution for the
test statistic with the probability limit of its permutation distribution.
Through prepivoting, the tests permute one minus an asymptotically valid
$p$-value for testing the null of equality of parameters. We describe two
approaches for prepivoting within permutation tests, one directly using
asymptotic normality and the other using the bootstrap. We further illustrate
that permutation tests using bootstrap prepivoting can provide improvements to
the order of the error in rejection probability relative to competing
transformations when testing equality of parameters, while maintaining
exactness under equality of distributions. Simulation studies highlight the
versatility of the proposal, illustrating the restoration of asymptotic
validity to a wide range of permutation tests conducted when only the
parameters of distributions are equal.


Abstract:
  PredDiff is a model-agnostic, local attribution method that is firmly rooted
in probability theory. Its simple intuition is to measure prediction changes
while marginalizing features. In this work, we clarify properties of PredDiff
and its connection to Shapley values. We stress important differences between
classification and regression, which require a specific treatment within both
formalisms. We extend PredDiff by introducing a new, well-founded measure for
interaction effects between arbitrary feature subsets. The study of interaction
effects represents an inevitable step towards a comprehensive understanding of
black-box models and is particularly important for science applications. As
opposed to Shapley values, our novel measure maintains the original linear
scaling and is thus generally applicable to real-world problems.


Abstract:
  Automatically tuning software configuration for optimizing a single
performance attribute (e.g., minimizing latency) is not trivial, due to the
nature of the configuration systems (e.g., complex landscape and expensive
measurement). To deal with the problem, existing work has been focusing on
developing various effective optimizers. However, a prominent issue that all
these optimizers need to take care of is how to avoid the search being trapped
in local optima -- a hard nut to crack for software configuration tuning due to
its rugged and sparse landscape, and neighboring configurations tending to
behave very differently. Overcoming such in an expensive measurement setting is
even more challenging. In this paper, we take a different perspective to tackle
this issue. Instead of focusing on improving the optimizer, we work on the
level of optimization model. We do this by proposing a meta
multi-objectivization model (MMO) that considers an auxiliary performance
objective (e.g., throughput in addition to latency). What makes this model
unique is that we do not optimize the auxiliary performance objective, but
rather use it to make similarly-performing while different configurations less
comparable (i.e. Pareto nondominated to each other), thus preventing the search
from being trapped in local optima.
  Experiments on eight real-world software systems/environments with diverse
performance attributes reveal that our MMO model is statistically more
effective than state-of-the-art single-objective counterparts in overcoming
local optima (up to 42% gain), while using as low as 24% of their measurements
to achieve the same (or better) performance result.


Abstract:
  We present a method for generating comparative summaries that highlights
similarities and contradictions in input documents. The key challenge in
creating such summaries is the lack of large parallel training data required
for training typical summarization systems. To this end, we introduce a hybrid
generation approach inspired by traditional concept-to-text systems. To enable
accurate comparison between different sources, the model first learns to
extract pertinent relations from input documents. The content planning
component uses deterministic operators to aggregate these relations after
identifying a subset for inclusion into a summary. The surface realization
component lexicalizes this information using a text-infilling language model.
By separately modeling content selection and realization, we can effectively
train them with limited annotations. We implemented and tested the model in the
domain of nutrition and health -- rife with inconsistencies. Compared to
conventional methods, our framework leads to more faithful, relevant and
aggregation-sensitive summarization -- while being equally fluent.


Abstract:
  In this article we investigate the unitary dynamics of squashed entanglement
and concurrence measures in Werner state and maximally entangled mixed states
(MEMS) under two different Hamiltonians. The aim of the present study is two
fold. The first part of the study deals with the dynamics under Heisenberg
Hamiltonian and the second part deals under bi-linear bi-quadratic Hamiltonian
which is the extension of first Hamiltonian. In both the parts we investigate
the dynamical trade off and balancing points for squashed entanglement and
concurrence. During the study, we also found the results of entanglement sudden
death (ESD) with Heisenberg Hamiltonian in Werner state under concurrence
measure. In second part, we investigate the special result for bi-linear
bi-quadratic Hamiltonian; it does not disturb squashed entanglement and
concurrence in both the states and exhibit the robust character for both of the
states.


Abstract:
  Using nonlinear mathematical models and experimental data from laboratory and
clinical studies, we have designed new combination therapies against COVID-19.


Abstract:
  In this work, we introduce the new scene understanding task of Part-aware
Panoptic Segmentation (PPS), which aims to understand a scene at multiple
levels of abstraction, and unifies the tasks of scene parsing and part parsing.
For this novel task, we provide consistent annotations on two commonly used
datasets: Cityscapes and Pascal VOC. Moreover, we present a single metric to
evaluate PPS, called Part-aware Panoptic Quality (PartPQ). For this new task,
using the metric and annotations, we set multiple baselines by merging results
of existing state-of-the-art methods for panoptic segmentation and part
segmentation. Finally, we conduct several experiments that evaluate the
importance of the different levels of abstraction in this single task.


Abstract:
  This report aims to survey multi-agent Q-Learning algorithms, analyze
different game theory frameworks used, address each framework's applications,
and report challenges and future directions. The target application for this
study is resource management in the wireless sensor network.
  In the first section, the author provided an introduction regarding the
applications of wireless sensor networks. After that, the author presented a
summary of the Q-Learning algorithm, a well-known classic solution for
model-free reinforcement learning problems.
  In the third section, the author extended the Q-Learning algorithm for
multi-agent scenarios and discussed its challenges.
  In the fourth section, the author surveyed sets of game-theoretic frameworks
that researchers used to address this problem for resource allocation and task
scheduling in the wireless sensor networks. Lastly, the author mentioned some
interesting open challenges in this domain.


Abstract:
  The competition between kinetic energy and Coulomb interactions in electronic
systems can lead to complex many-body ground states with competing
superconducting, charge density wave, and magnetic orders. Here we study the
low temperature phases of a strongly interacting zinc-oxide-based high mobility
two dimensional electron system that displays a tunable metal-insulator
transition. Through a comprehensive analysis of the dependence of electronic
transport on temperature, carrier density, in-plane and perpendicular magnetic
fields, and voltage bias, we provide evidence for the existence of competing
correlated metallic and insulating states with varying degrees of spin
polarization. Our system features an unprecedented level of agreement with the
state-of-the-art Quantum Monte Carlo phase diagram of the ideal jellium model,
including a Wigner crystallization transition at a value of the interaction
parameter $r_s\sim 30$ and the absence of a pure Stoner transition. In-plane
field dependence of transport reveals a new low temperature state with partial
spin polarization separating the spin unpolarized metal and the Wigner crystal,
which we examine against possible theoretical scenarios such as an
anti-ferromagnetic crystal, Coulomb induced micro-emulsions, and disorder
driven puddle formation.


Abstract:
  Multiple imputation is increasingly used in dealing with missing data. While
some conventional multiple imputation approaches are well studied and have
shown empirical validity, they entail limitations in processing large datasets
with complex data structures. Their imputation performances usually rely on
expert knowledge of the inherent relations among variables. In addition, these
standard approaches tend to be computationally inefficient for medium and large
datasets. In this paper, we propose a scalable multiple imputation framework
mixgb, which is based on XGBoost, bootstrapping and predictive mean matching.
XGBoost, one of the fastest implementations of gradient boosted trees, is able
to automatically retain interactions and non-linear relations in a dataset
while achieving high computational efficiency. With the aid of bootstrapping
and predictive mean matching, we show that our approach obtains less biased
estimates and reflects appropriate imputation variability. The proposed
framework is implemented in an R package misle. Supplementary materials for
this article are available online.


Abstract:
  We derive the chiral effective Lagrangian for excited heavy-light mesons from
QCD under proper approximations. We focus on the chiral partners with
$j_l^P=\frac{3}{2}^+$ and $j_l^P=\frac{3}{2}^-$ which amounts to ($1^+,2^+$)
and ($1^-,2^-$) states respectively. The low energy constants including the
masses of the chiral partners are calculated. The calculated spectrum for the
excited mesons are found roughly consistent with experimental data. In
addition, our results indicate that quantum numbers of $B_J(5970)$ can be
identified with $1^-$ or $2^-$.


Abstract:
  One dimensional analysis is presented of solitary positive potential plasma
structures whose velocity lies within the range of ion distribution velocities
that are strongly populated: so called "slow" electron holes. It is shown that
to avoid the self-acceleration of the hole velocity away from ion velocities it
must lie within a local minimum in the ion velocity distribution. Quantitative
criteria for the existence of stable equilibria are obtained. The background
ion distributions required are generally stable to ion-ion modes unless the
electron temperature is much higher than the ion temperature. Since slow
positive potential solitons are shown not to be possible without a significant
contribution from trapped electrons, it seems highly likely that such observed
slow potential structures are indeed electron holes.