Abstract:
  A generalized Kummer surface $X=Km_{3}(A,G_{A})$ is the minimal resolution of
the quotient of a $2$-dimensional complex torus by an order 3 symplectic
automorphism group $G_{A}$. A Kummer structure on $X$ is an isomorphism class
of pairs $(B,G_{B})$ such that $X\simeq Km_{3}(B,G_{B})$. When the surface is
algebraic, we obtain that the number of Kummer structures is linked with the
number of order $3$ elliptic points on some Shimura curve naturally related to
$A$. For each $n\in\mathbb{N}$, we obtain generalized Kummer surfaces $X_{n}$
for which the number of Kummer structures is $2^{n}$. We then give a
classification of the moduli spaces of generalized Kummer surfaces. When the
surface is non algebraic, there is only one Kummer structure, but the number of
irreducible components of the moduli spaces of such surfaces is large compared
to the algebraic case. The endomorphism rings of the complex $2$-tori we study
are mainly quaternion orders, these order contain the ring of Eisenstein
integers. One can also see this paper as a study of quaternion orders
$\mathcal{O}$ over $\mathbb{Q}$ that contain the ring of Eisenstein integers.
We obtain that such order is determined up to isomorphism by its discriminant,
and when the quaternion algebra is indefinite, the order $\mathcal{O}$ is
principal.


Abstract:
  The neural network and quantum computing are both significant and appealing
fields, with their interactive disciplines promising for large-scale computing
tasks that are untackled by conventional computers. However, both developments
are restricted by the scope of the hardware development. Nevertheless, many
neural network algorithms had been proposed before GPUs become powerful enough
for running very deep models. Similarly, quantum algorithms can also be
proposed as knowledge reserves before real quantum computers are easily
accessible. Specifically, taking advantage of both the neural networks and
quantum computation and designing quantum deep neural networks (QDNNs) for
acceleration on Noisy Intermediate-Scale Quantum (NISQ) processors is also an
important research problem. As one of the most widely used neural network
architectures, convolutional neural network (CNN) remains to be accelerated by
quantum mechanisms, with only a few attempts have been demonstrated. In this
paper, we propose a new hybrid quantum-classical circuit, namely Quantum
Fourier Convolutional Network (QFCN). Our model achieves exponential speed-up
compared with classical CNN theoretically and improves over the existing best
result of quantum CNN. We demonstrate the potential of this architecture by
applying it to different deep learning tasks, including traffic prediction and
image classification.


Abstract:
  Our goal, in the context of open-domain textual question-answering (QA), is
to explain answers by showing the line of reasoning from what is known to the
answer, rather than simply showing a fragment of textual evidence (a
"rationale'"). If this could be done, new opportunities for understanding and
debugging the system's reasoning become possible. Our approach is to generate
explanations in the form of entailment trees, namely a tree of multipremise
entailment steps from facts that are known, through intermediate conclusions,
to the hypothesis of interest (namely the question + answer). To train a model
with this skill, we created ENTAILMENTBANK, the first dataset to contain
multistep entailment trees. Given a hypothesis (question + answer), we define
three increasingly difficult explanation tasks: generate a valid entailment
tree given (a) all relevant sentences (b) all relevant and some irrelevant
sentences, or (c) a corpus. We show that a strong language model can partially
solve these tasks, in particular when the relevant sentences are included in
the input (e.g., 35% of trees for (a) are perfect), and with indications of
generalization to other domains. This work is significant as it provides a new
type of dataset (multistep entailments) and baselines, offering a new avenue
for the community to generate richer, more systematic explanations.


Abstract:
  We study the problem of list-decodable linear regression, where an adversary
can corrupt a majority of the examples. Specifically, we are given a set $T$ of
labeled examples $(x, y) \in \mathbb{R}^d \times \mathbb{R}$ and a parameter
$0< \alpha <1/2$ such that an $\alpha$-fraction of the points in $T$ are i.i.d.
samples from a linear regression model with Gaussian covariates, and the
remaining $(1-\alpha)$-fraction of the points are drawn from an arbitrary noise
distribution. The goal is to output a small list of hypothesis vectors such
that at least one of them is close to the target regression vector. Our main
result is a Statistical Query (SQ) lower bound of $d^{\mathrm{poly}(1/\alpha)}$
for this problem. Our SQ lower bound qualitatively matches the performance of
previously developed algorithms, providing evidence that current upper bounds
for this task are nearly best possible.


Abstract:
  In this work, we aim to address the 3D scene stylization problem - generating
stylized images of the scene at arbitrary novel view angles. A straightforward
solution is to combine existing novel view synthesis and image/video style
transfer approaches, which often leads to blurry results or inconsistent
appearance. Inspired by the high quality results of the neural radiance fields
(NeRF) method, we propose a joint framework to directly render novel views with
the desired style. Our framework consists of two components: an implicit
representation of the 3D scene with the neural radiance field model, and a
hypernetwork to transfer the style information into the scene representation.
In particular, our implicit representation model disentangles the scene into
the geometry and appearance branches, and the hypernetwork learns to predict
the parameters of the appearance branch from the reference style image. To
alleviate the training difficulties and memory burden, we propose a two-stage
training procedure and a patch sub-sampling approach to optimize the style and
content losses with the neural radiance field model. After optimization, our
model is able to render consistent novel views at arbitrary view angles with
arbitrary style. Both quantitative evaluation and human subject study have
demonstrated that the proposed method generates faithful stylization results
with consistent appearance across different views.


Abstract:
  In this paper, we propose a simple yet effective method to deal with the
violation of the Closed-World Assumption for a classifier. Previous works tend
to apply a threshold either on the classification scores or the loss function
to reject the inputs that violate the assumption. However, these methods cannot
achieve the low False Positive Ratio (FPR) required in safety applications. The
proposed method is a rejection option based on hypothesis testing with
probabilistic networks. With probabilistic networks, it is possible to estimate
the distribution of outcomes instead of a single output. By utilizing Z-test
over the mean and standard deviation for each class, the proposed method can
estimate the statistical significance of the network certainty and reject
uncertain outputs. The proposed method was experimented on with different
configurations of the COCO and CIFAR datasets. The performance of the proposed
method is compared with the Softmax Response, which is a known top-performing
method. It is shown that the proposed method can achieve a broader range of
operation and cover a lower FPR than the alternative.


Abstract:
  I describe the rationale for, and design of, an agent-based simulation model
of a contemporary online sports-betting exchange: such exchanges, closely
related to the exchange mechanisms at the heart of major financial markets,
have revolutionized the gambling industry in the past 20 years, but gathering
sufficiently large quantities of rich and temporally high-resolution data from
real exchanges - i.e., the sort of data that is needed in large quantities for
Deep Learning - is often very expensive, and sometimes simply impossible; this
creates a need for a plausibly realistic synthetic data generator, which is
what this simulation now provides. The simulator, named the "Bristol Betting
Exchange" (BBE), is intended as a common platform, a data-source and
experimental test-bed, for researchers studying the application of AI and
machine learning (ML) techniques to issues arising in betting exchanges; and,
as far as I have been able to determine, BBE is the first of its kind: a free
open-source agent-based simulation model consisting not only of a
sports-betting exchange, but also a minimal simulation model of racetrack
sporting events (e.g., horse-races or car-races) about which bets may be made,
and a population of simulated bettors who each form their own private
evaluation of odds and place bets on the exchange before and - crucially -
during the race itself (i.e., so-called "in-play" betting) and whose betting
opinions change second-by-second as each race event unfolds. BBE is offered as
a proof-of-concept system that enables the generation of large high-resolution
data-sets for automated discovery or improvement of profitable strategies for
betting on sporting events via the application of AI/ML and advanced data
analytics techniques. This paper offers an extensive survey of relevant
literature and explains the motivation and design of BBE, and presents brief
illustrative results.


Abstract:
  Pinch-off and satellite droplets formation during breakup of near-inviscid
liquid bridge sandwiched between two given equal and coaxial circular plates
have been investigated. The breakup always results in the formation of a
spindle shape which is the precursor of the satellite droplet at the moment of
pinch-off. Interestingly, the slenderness of this spindle is always bigger than
2{\pi} and always results in the formation of only one satellite droplet
regardless of the surface tension and the slenderness of the liquid bridge. We
predict the cone angle of this spindle formed during the pinch-off of inviscid
fluids should be 18.086122158...{\deg}. After pinch-off, the satellite droplets
will drift out of the pinch-off regions in the case of symmetrical short
bridge, and merge again with the sessile drop in the case of unsymmetrical long
bridge. We demonstrate that the velocity of the satellite droplet is consistent
with a scaling model based on a balance between capillary forces and the
inertia at the pinch-off region.


Abstract:
  In this paper, we generalize fractional $q$-integrals by the method of
$q$-difference equation. In addition, we deduce fractional Askey--Wilson
integral, reversal type fractional Askey--Wilson integral and Ramanujan type
fractional Askey--Wilson integral.


Abstract:
  Person Re-Identification (Re-ID) is of great importance to the many video
surveillance systems. Learning discriminative features for Re-ID remains a
challenge due to the large variations in the image space, e.g., continuously
changing human poses, illuminations and point of views. In this paper, we
propose HAVANA, a novel extensible, light-weight HierArchical and
VAriation-Normalized Autoencoder that learns features robust to intra-class
variations. In contrast to existing generative approaches that prune the
variations with heavy extra supervised signals, HAVANA suppresses the
intra-class variations with a Variation-Normalized Autoencoder trained with no
additional supervision. We also introduce a novel Jensen-Shannon triplet loss
for contrastive distribution learning in Re-ID. In addition, we present
Hierarchical Variation Distiller, a hierarchical VAE to factorize the latent
representation and explicitly model the variations. To the best of our
knowledge, HAVANA is the first VAE-based framework for person ReID.


Abstract:
  We classify Frobenius forms, a special class of homogeneous polynomials in
characteristic $p>0$, in up to five variables over an algebraically closed
field. We also point out some of the similarities with quadratic forms.


Abstract:
  We present optical spectroscopy for 18 halo white dwarfs identified using
photometry from the Canada-France Imaging Survey and Pan-STARRS1 DR1 3$\pi$
survey combined with astrometry from Gaia DR2. The sample contains 13 DA, 1 DZ,
2 DC, and two potentially exotic types of white dwarf. We fit both the spectrum
and the spectral energy distribution in order to obtain the temperature and
surface gravity, which we then convert into a mass, and then an age, using
stellar isochrones and the initial-to-final mass relation. We find a large
spread in ages that is not consistent with expected formation scenarios for the
Galactic halo. We find a mean age of 9.03$^{+2.13}_{-2.03}$ Gyr and a
dispersion of 4.21$^{+2.33}_{-1.58}$ Gyr for the inner halo using a maximum
likelihood method. This result suggests an extended star formation history
within the local halo population.


Abstract:
  According to their strength, the tracing properties of a code can be
categorized as frameproof, separating, IPP and TA. It is known that if the
minimum distance of the code is larger than a certain threshold then the TA
property implies the rest. Silverberg et al. ask if there is some kind of
tracing capability left when the minimum distance falls below the threshold.
Under different assumptions, several papers have given a negative answer to the
question. In this paper further progress is made. We establish values of the
minimum distance for which Reed-Solomon codes do not posses the separating
property.


Abstract:
  By using ab-initio-accurate force fields and molecular dynamics simulations
we demonstrate that the layer stiffness has profound effects on the
superlubricant state of two-dimensional van der Waals heterostructures. These
are engineered to have identical inter-layer sliding energy surfaces, but
layers of different rigidity, so that the effects of the stiffness on the
microscopic friction in the superlubricant state can be isolated. A twofold
increase in the intra-layer stiffness reduces the friction by approximately a
factor six. Most importantly, we find two sliding regimes as a function of the
sliding velocity. At low velocity the heat generated by the motion is
efficiently exchanged between the layers and the friction is independent on
whether the sliding layer is softer or harder than the substrate. In contrast,
at high velocity the friction heat flux cannot be exchanged fast enough, and
the build up of significant temperature gradients between the layers is
observed. In this situation the temperature profile depends on whether the
slider is softer than the substrate.


Abstract:
  Previous studies have predicted the failure of Fourier's law of thermal
conduction due to the existence of wave like propagation of heat with finite
propagation speed. This non-Fourier thermal transport phenomenon can appear in
both the hydrodynamic and (quasi) ballistic regimes. Hence, it is not easy to
clearly distinguish these two non-Fourier regimes only by this phenomenon. In
this work, the transient heat propagation in homogeneous thermal system is
studied based on the phonon Boltzmann transport equation (BTE) under the
Callaway model. Given a quasi-one or quasi-two (three) dimensional simulation
with homogeneous environment temperature, at initial moment, a heat source is
added suddenly at the center with high temperature, then the heat propagates
from the center to the outer. Numerical results show that in quasi-two (three)
dimensional simulations, the transient temperature will be lower than the
lowest value of initial temperature in the hydrodynamic regime within a certain
range of time and space. This phenomenon appears only when the normal
scattering dominates heat conduction. Besides, it disappears in quasi-one
dimensional simulations. Similar phenomenon is also observed in thermal systems
with time varying heat source. This novel transient heat propagation phenomenon
of hydrodynamic phonon transport distinguishes it well from (quasi) ballistic
phonon transport.


Abstract:
  Despite the advances in the autonomous driving domain, autonomous vehicles
(AVs) are still inefficient and limited in terms of cooperating with each other
or coordinating with vehicles operated by humans. A group of autonomous and
human-driven vehicles (HVs) which work together to optimize an altruistic
social utility -- as opposed to the egoistic individual utility -- can co-exist
seamlessly and assure safety and efficiency on the road. Achieving this mission
without explicit coordination among agents is challenging, mainly due to the
difficulty of predicting the behavior of humans with heterogeneous preferences
in mixed-autonomy environments. Formally, we model an AV's maneuver planning in
mixed-autonomy traffic as a partially-observable stochastic game and attempt to
derive optimal policies that lead to socially-desirable outcomes using a
multi-agent reinforcement learning framework. We introduce a quantitative
representation of the AVs' social preferences and design a distributed reward
structure that induces altruism into their decision making process. Our
altruistic AVs are able to form alliances, guide the traffic, and affect the
behavior of the HVs to handle competitive driving scenarios. As a case study,
we compare egoistic AVs to our altruistic autonomous agents in a highway
merging setting and demonstrate the emerging behaviors that lead to a
noticeable improvement in the number of successful merges as well as the
overall traffic flow and safety.


Abstract:
  Improving existing widely-adopted prediction models is often a more efficient
and robust way towards progress than training new models from scratch. Existing
models may (a) incorporate complex mechanistic knowledge, (b) leverage
proprietary information and, (c) have surmounted barriers to adoption. Compared
to model training, model improvement and modification receive little attention.
In this paper we propose a general approach to model improvement: we combine
gradient boosting with any previously developed model to improve model
performance while retaining important existing characteristics. To exemplify,
we consider the context of Mendelian models, which estimate the probability of
carrying genetic mutations that confer susceptibility to disease by using
family pedigrees and health histories of family members. Via simulations we
show that integration of gradient boosting with an existing Mendelian model can
produce an improved model that outperforms both that model and the model built
using gradient boosting alone. We illustrate the approach on genetic testing
data from the USC-Stanford Cancer Genetics Hereditary Cancer Panel (HCP) study.


Abstract:
  Traditional on-die, three-level cache hierarchy design is very commonly used
but is also prone to latency, especially at the Level 2 (L2) cache. We discuss
three distinct ways of improving this design in order to have better
performance. Performance is especially important for systems with high
workloads. The first method proposes to eliminate L2 altogether while proposing
a new prefetching technique, the second method suggests increasing the size of
L2, while the last method advocates the implementation of optical caches. After
carefully contemplating the results in performance gains and the advantages and
disadvantages of each method, we found the last method to be the best of the
three.


Abstract:
  It is known that general relativity (GR) theory is not consistent with the
latest observations. The modified gravity of GR known as $\mathrm{f(R)}$ where
$\mathrm{R}$ is the Ricci scalar, is considered to be a good candidate for
dealing with the anomalies present in classical GR. In this context, we study
static rotating uncharged anti-de Sitter and de Sitter (AdS and dS) black holes
(BHs) using $\mathrm{f(R)}$ theory without assuming any constraints on the
Ricci scalar or on $\mathrm{f(R)}$. We derive BH solutions depend on the
convolution function and deviate from the AdS/dS Schwarzschild BH solution of
GR. Although the field equations have no dependence on the cosmological
constant, the BHs are characterized by an effective cosmological constant that
depends on the convolution function. The asymptotic form of this BH solution
depends on the gravitational mass of the system and on extra terms that lead to
BHs being different from GR BHs but to correspond to GR BHs under certain
conditions. We also investigate how these extra terms are responsible for
making the singularities of the invariants milder than those of the GR BHs. We
study some physical properties of the BHs from the point of view of
thermodynamics and show that there is an outer event horizon in addition to the
inner Cauchy horizons. Among other things, we show that our BH solutions
satisfy the first law of thermodynamics. To check the stability of these BHs we
use the geodesic deviations and derive the stability conditions. Finally, using
the odd-type mode it is shown that all the derived BHs are stable and have a
radial speed equal to one.


Abstract:
  In physical experiments, reference frames are standardly modelled through a
specific choice of coordinates used to describe the physical systems, but they
themselves are not considered as such. However, any reference frame is a
physical system that ultimately behaves according to quantum mechanics. We
develop a framework for rotational (i.e. spin) quantum reference frames, with
respect to which quantum systems with spin degrees of freedom are described. We
give an explicit model for such frames as systems composed of three spin
coherent states of angular momentum $j$ and introduce the transformations
between them by upgrading the Euler angles occurring in classical
$\textrm{SO}(3)$ spin transformations to quantum mechanical operators acting on
the states of the reference frames. To ensure that an arbitrary rotation can be
applied on the spin we take the limit of infinitely large $j$, in which case
the angle operator possesses a continuous spectrum. We prove that rotationally
invariant Hamiltonians (such as that of the Heisenberg model) are invariant
under a larger group of quantum reference frame transformations. Our result is
the first development of the quantum reference frame formalism for a
non-Abelian group.


Abstract:
  We consider a simple scalar dark matter model within the frame of gauged
$L_{\mu}-L_{\tau}$ symmetry. A gauge boson $Z'$ as well as two scalar fields
$S$ and $\Phi$ are introduced to the Standard Model (SM). $S$ and $\Phi$ are SM
singlet but both with $U(1)_{L_{\mu}-L_{\tau}}$ charge. The real component and
imaginary component of $S$ can acquire different masses after spontaneously
symmetry breaking, and the lighter one can play the role of dark matter which
is stabilized by the residual $Z_2$ symmetry. A viable parameter space is
considered to discuss the possibility of light dark matter as well as
co-annihilation case, and we present current $(g-2)_{\mu}$ anomaly, Higgs
invisible decay, dark matter relic density as well as direct detection
constriants on the parameter space.


Abstract:
  Decentralized financial (DeFi) applications on the Ethereum blockchain are
highly interoperable because they share a single state in a deterministic
computational environment. Stakeholders can deposit claims on assets, referred
to as 'liquidity shares', across applications producing effects equivalent to
rehypothecation in traditional financial systems. We seek to understand the
degree to which this practice may contribute to financial integration on
Ethereum by examining transactions in 'composed' derivatives for the assets
DAI, USDC, USDT, ETH and tokenized BTC for the full set of 344.8 million
Ethereum transactions computed in 2020. We identify a salient trend for
'composing' assets in multiple sequential generations of derivatives and
comment on potential systemic implications for the Ethereum network.


Abstract:
  The paper presents the submission of the team indicnlp@kgp to the EACL 2021
shared task "Offensive Language Identification in Dravidian Languages." The
task aimed to classify different offensive content types in 3 code-mixed
Dravidian language datasets. The work leverages existing state of the art
approaches in text classification by incorporating additional data and transfer
learning on pre-trained models. Our final submission is an ensemble of an
AWD-LSTM based model along with 2 different transformer model architectures
based on BERT and RoBERTa. We achieved weighted-average F1 scores of 0.97,
0.77, and 0.72 in the Malayalam-English, Tamil-English, and Kannada-English
datasets ranking 1st, 2nd, and 3rd on the respective tasks.


Abstract:
  The goal of this study was to improve the post-processing of precipitation
forecasts using convolutional neural networks (CNNs). Instead of
post-processing forecasts on a per-pixel basis, as is usually done when
employing machine learning in meteorological post-processing, input forecast
images were combined and transformed into probabilistic output forecast images
using fully convolutional neural networks. CNNs did not outperform regularized
logistic regression. Additionally, an ablation analysis was performed.
Combining input forecasts from a global low-resolution weather model and a
regional high-resolution weather model improved performance over either one.


Abstract:
  Deep learning can promote the mammography-based computer-aided diagnosis
(CAD) for breast cancers, but it generally suffers from the small sample size
problem. Self-supervised learning (SSL) has shown its effectiveness in medical
image analysis with limited training samples. However, the network model
sometimes cannot be well pre-trained in the conventional SSL framework due to
the limitation of the pretext task and fine-tuning mechanism. In this work, a
Task-driven Self-supervised Bi-channel Networks (TSBN) framework is proposed to
improve the performance of classification model the mammography-based CAD. In
particular, a new gray-scale image mapping (GSIM) is designed as the pretext
task, which embeds the class label information of mammograms into the image
restoration task to improve discriminative feature representation. The proposed
TSBN then innovatively integrates different network architecture, including the
image restoration network and the classification network, into a unified SSL
framework. It jointly trains the bi-channel network models and collaboratively
transfers the knowledge from the pretext task network to the downstream task
network with improved diagnostic accuracy. The proposed TSBN is evaluated on a
public INbreast mammogram dataset. The experimental results indicate that it
outperforms the conventional SSL and multi-task learning algorithms for
diagnosis of breast cancers with limited samples.


Abstract:
  The true topological nature of the Kondo insulator SmB$_6$ remains to be
unveiled. Our previous tunneling study not only found evidence for the
existence of surface Dirac fermions, but it also uncovered that they inherently
interact with the spin excitons, collective excitations in the bulk. We have
extended such a spectroscopic investigation into crystals containing a Sm
deficiency. The bulk hybridization gap is found to be insensitive to the
deficiency up to 1% studied here, but the surface states in Sm-deficient
crystals exhibit quite different temperature evolutions from those in
stoichiometric ones. We attribute this to the topological surface states
remaining incoherent down to the lowest measurement temperature due to their
continued interaction with the spin excitons that remain uncondensed. This
result shows that the detailed topological nature of SmB$_6$ could vary
drastically in the presence of disorder in the lattice. This sensitiveness to
disorder is seemingly contradictory to the celebrated topological protection,
but it can be understood as being due to the intimate interplay between strong
correlations and topological effects.


Abstract:
  Effective environmental planning and management to address climate change
could be achieved through extensive environmental modeling with machine
learning and conventional physical models. In order to develop and improve
these models, practitioners and researchers need comprehensive benchmark
datasets that are prepared and processed with environmental expertise that they
can rely on. This study presents an extensive dataset of rainfall events for
the state of Iowa (2016-2019) acquired from the National Weather Service Next
Generation Weather Radar (NEXRAD) system and processed by a quantitative
precipitation estimation system. The dataset presented in this study could be
used for better disaster monitoring, response and recovery by paving the way
for both predictive and prescriptive modeling.


Abstract:
  This paper is concerned with polynomially generated multiplier invariant
subspaces of the weighted Bergman space $A_{\boldsymbol{\beta}}^2$ in
infinitely many variables. We completely classify these invariant subspaces
under the unitary equivalence. Our results not only cover cases of both the
Hardy space $H^{2}(\mathbb{D}_{2}^{\infty})$ and the Bergman space
$A^{2}(\mathbb{D}_{2}^{\infty})$ in infinitely many variables, but also apply
in finite-variable setting.


Abstract:
  The capability of generalization to unseen domains is crucial for deep
learning models when considering real-world scenarios. However, current
available medical image datasets, such as those for COVID-19 CT images, have
large variations of infections and domain shift problems. To address this
issue, we propose a prior knowledge driven domain adaptation and a dual-domain
enhanced self-correction learning scheme. Based on the novel learning schemes,
a domain adaptation based self-correction model (DASC-Net) is proposed for
COVID-19 infection segmentation on CT images. DASC-Net consists of a novel
attention and feature domain enhanced domain adaptation model (AFD-DA) to solve
the domain shifts and a self-correction learning process to refine segmentation
results. The innovations in AFD-DA include an image-level activation feature
extractor with attention to lung abnormalities and a multi-level discrimination
module for hierarchical feature domain alignment. The proposed self-correction
learning process adaptively aggregates the learned model and corresponding
pseudo labels for the propagation of aligned source and target domain
information to alleviate the overfitting to noises caused by pseudo labels.
Extensive experiments over three publicly available COVID-19 CT datasets
demonstrate that DASC-Net consistently outperforms state-of-the-art
segmentation, domain shift, and coronavirus infection segmentation methods.
Ablation analysis further shows the effectiveness of the major components in
our model. The DASC-Net enriches the theory of domain adaptation and
self-correction learning in medical imaging and can be generalized to
multi-site COVID-19 infection segmentation on CT images for clinical
deployment.


Abstract:
  We revisit the foundational Moment Formula proved by Roger Lee fifteen years
ago. We show that when the underlying stock price martingale admits finite
log-moments E[|log(S)|^q] for some positive q, the arbitrage-free growth in the
left wing of the implied volatility smile is less constrained than Lee's bound.
The result is rationalised by a market trading discretely monitored variance
swaps wherein the payoff is a function of squared log-returns, and requires no
assumption for the underlying martingale to admit any negative moment. In this
respect, the result can derived from a model-independent setup. As a byproduct,
we relax the moment assumptions on the stock price to provide a new proof of
the notorious Gatheral-Fukasawa formula expressing variance swaps in terms of
the implied volatility.


Abstract:
  We present a combined angle-resolved photoemission spectroscopy and
low-energy electron diffraction (LEED) study of the prominent transition metal
dichalcogenide IrTe$_2$ upon potassium (K) deposition on its surface. Pristine
IrTe$_2$ undergoes a series of charge-ordered phase transitions below room
temperature that are characterized by the formation of stripes of Ir dimers of
different periodicities. Supported by density functional theory calculations,
we first show that the K atoms dope the topmost IrTe$_2$ layer with electrons,
therefore strongly decreasing the work function and shifting only the
electronic surface states towards higher binding energy. We then follow the
evolution of its electronic structure as a function of temperature across the
charge-ordered phase transitions and observe that their critical temperatures
are unchanged for K coverages of $0.13$ and $0.21$~monolayer (ML). Using LEED,
we also confirm that the periodicity of the related stripe phases is unaffected
by the K doping. We surmise that the charge-ordered phase transitions of
IrTe$_2$ are robust against electron surface doping, because of its metallic
nature at all temperatures, and due to the importance of structural effects in
stabilizing charge order in IrTe$_2$.


Abstract:
  We theoretically show that two distinctive spin textures manifest themselves
around saddle points of energy bands in a monolayer NbSe$_2$ under external
gate potentials. While the density of states at all saddle points diverge
logarithmically, ones at the zone boundaries display a windmill-shaped spin
texture while the others unidirectional spin orientations. The disparate
spin-resolved states are demonstrated to contribute an intrinsic spin Hall
conductivity significantly while their characteristics differ from each
other.Based on a minimal but essential tight-binding approximation reproducing
first-principles computation results, we established distinct effective Rashba
Hamiltonians for each saddle point, realizing the unique spin textures
depending on their momentum. Energetic positions of the saddle points in a
single layer NbSe$_2$ are shown to be well controlled by a gate potential so
that it could be a prototypical system to test a competition between various
collective phenomena triggered by diverging density of states and their spin
textures in low-dimension.


Abstract:
  The time of the first occurrence of a threshold crossing event in a
stochastic process, known as the first passage time, is of interest in many
areas of sciences and engineering. Conventionally, there is an implicit
assumption that the notional 'sensor' monitoring the threshold crossing event
is always active. In many realistic scenarios, the sensor monitoring the
stochastic process works intermittently. Then, the relevant quantity of
interest is the $\textit{first detection time}$, which denotes the time when
the sensor detects the threshold crossing event for the first time. In this
work, a birth-death process monitored by a random intermittent sensor is
studied, for which the first detection time distribution is obtained. In
general, it is shown that the first detection time is related to, and is
obtainable from, the first passage time distribution. Our analytical results
display an excellent agreement with simulations. Further, this framework is
demonstrated in several applications -- the SIS compartmental and logistic
models, and birth-death processes with resetting. Finally, we solve the
practically relevant problem of inferring the first passage time distribution
from the first detection time.


Abstract:
  Millimeter-wave (mmWave) and sub-Terahertz (THz) frequencies are expected to
play a vital role in 6G wireless systems and beyond due to the vast available
bandwidth of many tens of GHz. This paper presents an indoor 3-D spatial
statistical channel model for mmWave and sub-THz frequencies based on extensive
radio propagation measurements at 28 and 140 GHz conducted in an indoor office
environment from 2014 to 2020. Omnidirectional and directional path loss models
and channel statistics such as the number of time clusters, cluster delays, and
cluster powers were derived from over 15,000 measured power delay profiles. The
resulting channel statistics show that the number of time clusters follows a
Poisson distribution and the number of subpaths within each cluster follows a
composite exponential distribution for both LOS and NLOS environments at 28 and
140 GHz. This paper proposes a unified indoor statistical channel model for
mmWave and sub-Terahertz frequencies following the mathematical framework of
the previous outdoor NYUSIM channel models. A corresponding indoor channel
simulator is developed, which can recreate 3-D omnidirectional, directional,
and multiple input multiple output (MIMO) channels for arbitrary mmWave and
sub-THz carrier frequency up to 150 GHz, signal bandwidth, and antenna
beamwidth. The presented statistical channel model and simulator will guide
future air-interface, beamforming, and transceiver designs for 6G and beyond.


Abstract:
  We aim at measuring the influence of the nondeterministic choices of a part
of a system on its ability to satisfy a specification. For this purpose, we
apply the concept of Shapley values to verification as a means to evaluate how
important a part of a system is. The importance of a component is measured by
giving its control to an adversary, alone or along with other components, and
testing whether the system can still fulfill the specification. We study this
idea in the framework of model-checking with various classical types of
linear-time specification, and propose several ways to transpose it to
branching ones. We also provide tight complexity bounds in almost every case.


Abstract:
  The associations between emergent physical phenomena (e.g.,
superconductivity) and orbital, charge, and spin degrees of freedom of $3d$
electrons are intriguing in transition metal compounds. Here, we successfully
manipulate the superconductivity of spinel oxide Li$_{1\pm
x}$Ti$_2$O$_{4-\delta}$ (LTO) by ionic liquid gating. A dome-shaped
superconducting phase diagram is established, where two insulating phases are
disclosed both in heavily electron-doping and hole-doping regions. The
superconductor-insulator transition (SIT) in the hole-doping region can be
attributed to the loss of Ti valence electrons. In the electron-doping region,
LTO exhibits an unexpected SIT instead of a metallic behavior despite an
increase in carrier density. Furthermore, a thermal hysteresis is observed in
the normal state resistance curve, suggesting a first-order phase transition.
We speculate that the SIT and the thermal hysteresis stem from the enhanced
$3d$ electron correlations and the formation of orbital ordering by comparing
the transport and structural results of LTO with the other spinel oxide
superconductor MgTi$_2$O$_4$, as well as analysing the electronic structure by
first-principles calculations. Further comprehension of the detailed interplay
between superconductivity and orbital ordering would contribute to the
revealing of unconventional superconducting pairing mechanism.


Abstract:
  This paper explores the options available to the anti-realist to defend a
Quinean empirical under-determination thesis using examples of dualities. I
first explicate a version of the empirical under-determination thesis that can
be brought to bear on theories of contemporary physics. Then I identify a class
of examples of dualities that lead to empirical under-determination. But I
argue that the resulting under-determination is benign, and is not a threat to
a cautious scientific realism. Thus dualities are not new ammunition for the
anti-realist. The paper also shows how the number of possible interpretative
options about dualities that have been considered in the literature can be
reduced, and suggests a general approach to scientific realism that one may
take dualities to favour.


Abstract:
  Two-dimensional (2D) magnets have broad application prospects in the
spintronics, but how to effectively control them with a small electric field is
still an issue. Here we propose that 2D magnets can be efficiently controlled
in a multiferroic heterostructure composed of 2D magnetic material and
perovskite oxide ferroelectric (POF) whose dielectric polarization is easily
flipped under a small electric field. We illustrate the feasibility of such
strategy in the bilayer CrI3/BiFeO3(001) heterostructure by using the
first-principles calculations. Different from the traditional POF multiferroic
heterostructures which have strong interface interactions, we find that the
interface interaction between CrI3 and BiFeO3(001) is van der Waals type.
Whereas, the heterostructure has particular strong magnetoelectric coupling
where the bilayer CrI3 can be efficiently switched between ferromagnetic and
antiferromagnetic types by the polarized states of BiFeO3(001). We also
discover the competing effect between electron doping and the additional
electric field on the interlayer exchange coupling interaction of CrI3, which
is responsible to the magnetic phase transition. Our results provide a new
avenue for the tuning of 2D magnets with a small electric field.


Abstract:
  Previous studies have shown that the ground state of systems of nucleons
composed by an equal number of protons and neutrons interacting via
proton-neutron pairing forces can be described accurately by a condensate of
$\alpha$-like quartets. Here we extend these studies to the low-lowing excited
states of these systems and show that these states can be accurately described
by breaking a quartet from the ground state condensate and replacing it with an
"excited" quartet. This approach, which is analogous to the one-broken-pair
approximation employed for like-particle pairing, is analysed for various
isovector and isovector-isoscalar pairing


Abstract:
  This paper focuses on regularisation methods using models up to the third
order to search for up to second-order critical points of a finite-sum
minimisation problem. The variant presented belongs to the framework of [3]: it
employs random models with accuracy guaranteed with a sufficiently large
prefixed probability and deterministic inexact function evaluations within a
prescribed level of accuracy. Without assuming unbiased estimators, the
expected number of iterations is $\mathcal{O}\bigl(\epsilon_1^{-2}\bigr)$ or
$\mathcal{O}\bigl(\epsilon_1^{-{3/2}}\bigr)$ when searching for a first-order
critical point using a second or third order model, respectively, and of
$\mathcal{O}\bigl(\max[\epsilon_1^{-{3/2}},\epsilon_2^{-3}]\bigr)$ when seeking
for second-order critical points with a third order model, in which
$\epsilon_j$, $j\in\{1,2\}$, is the $j$th-order tolerance. These results match
the worst-case optimal complexity for the deterministic counterpart of the
method. Preliminary numerical tests for first-order optimality in the context
of nonconvex binary classification in imaging, with and without Artifical
Neural Networks (ANNs), are presented and discussed.


Abstract:
  Organic-inorganic metal halide perovskites have recently attracted increasing
attention as highly efficient light harvesting materials for photovoltaic
applications. However, the precise control of crystallization and morphology of
organometallic perovskites deposited from solution, considered crucial for
enhancing the final photovoltaic performance, remains challenging. In this
context, here, we report on growing microcrystalline deposits of CH3NH3PbI3
(MAPbI3), by one-step solution casting on cylinde-shaped quartz substrates
(rods). We show that the substrate curvature has a strong influence on
morphology of the obtained polycrystalline deposits of MAPbI3. Although the
crystalline width and length markedly decreased for substrates with higher
curvatures, the photoluminescence (PL) spectral peak positions did not
significantly evolve for MAPbI3 deposits on substrates with different
diameters. The crystalline size reduction and denser coverage of
microcrystalline MAPbI3 deposits on cylinder-shaped substrates with higher
curvatures were attributed to two major contributions, both related to the
annealing step of the MAPbI3 deposits. In particular, the diameter-dependent
variability of the heat capacities and the substrate curvature-enhanced solvent
evaporation rate seemed to contribute the most to the crystallization process
and the resulting morphology changes of MAPbI3 deposits on cylinder-shaped
quartz substrates with various diameters. The longitudinal geometry of
cylinder-shaped substrates provided also a facile solution for checking the PL
response of the deposits of MAPbI3 exposed to the flow of various gaseous
media, such as oxygen, nitrogen and argon. Overall, the approach reported
herein inspires novel, cylinder-shaped geometries of MAPbI3 deposits, which can
find applications in low-cost photo-optical devices, including gas sensors.


Abstract:
  This paper proposes a deep learning framework for classification of BBC
television programmes using audio. The audio is firstly transformed into
spectrograms, which are fed into a pre-trained convolutional Neural Network
(CNN), obtaining predicted probabilities of sound events occurring in the audio
recording. Statistics for the predicted probabilities and detected sound events
are then calculated to extract discriminative features representing the
television programmes. Finally, the embedded features extracted are fed into a
classifier for classifying the programmes into different genres. Our
experiments are conducted over a dataset of 6,160 programmes belonging to nine
genres labelled by the BBC. We achieve an average classification accuracy of
93.7% over 14-fold cross validation. This demonstrates the efficacy of the
proposed framework for the task of audio-based classification of television
programmes.


Abstract:
  We prove that two Enriques surfaces defined over an algebraically closed
field of characteristic different from $2$ are isomorphic if their Kuznetsov
components are equivalent. This improves and complete our previous result joint
with Nuer where the same statement is proved for generic Enriques surfaces.


Abstract:
  Let $\{(A_i,B_i)\}_{i=1}^m$ be a set pair system. F\"{u}redi, Gy\'{a}rf\'{a}s
and Kir\'{a}ly called it {\em $1$-cross intersecting} if $|A_i\cap B_j|$ is $1$
when $i\neq j$ and $0$ if $i=j$. They studied such systems and their
generalizations, and in particular considered $m(a,b,1)$ -- the maximum size of
a $1$-cross intersecting set pair system in which $|A_i|\leq a$ and $|B_i|\leq
b$ for all $i$. F\"{u}redi, Gy\'{a}rf\'{a}s and Kir\'{a}ly proved that
$m(n,n,1)\geq 5^{(n-1)/2}$ and asked whether there are upper bounds on
$m(n,n,1)$ significantly better than the classical bound ${2n\choose n}$ of
Bollob\' as for cross intersecting set pair systems.
  Answering one of their questions, Holzman recently proved that if $a,b\geq
2$, then $m(a,b,1)\leq \frac{29}{30}\binom{a+b}{a}$. He also conjectured that
the factor $\frac{29}{30}$ in his bound can be replaced by $\frac{5}{6}$. The
goal of this paper is to prove this bound.


Abstract:
  The structure and stability of ternary systems prepared with polysorbate 60
and various combinations of cetyl (C16) and stearyl (C18) alcohols (fatty
alcohol 16g, polysorbate 4g, water 180g) were examined as they aged over 3
months at 25oC. Rheological results showed that the consistency of these
systems increased initially during roughly the first week of aging, which was
succeeded by little changes in consistency (systems containing from 30% to 70%
C18, with the 50% C18 system showing the highest consistencies in viscosity and
elasticity) or significant breakdown of structure (remaining systems). The
formation and/or disintegration of all ternary systems were also detected by
microscopy and differential scanning calorimetry experiments. This study
emphasizes the fact that the structure and consistency of ternary systems are
dominantly controlled by the swelling capacity of the lamellar
$\alpha-$crystalline gel phase. When the conversion of this gel phase into
non-swollen $\beta$- or $\gamma$-crystals occurs, systems change from
semisolids to fluids. Molecular dynamics simulations were performed to provide
important details on the molecular mechanism of our ternary systems.
Computational results supported the hypothesis experimentally proposed for the
stability of the mixed system being due to an increase in the flexibility,
hence an increase in the configurational entropy of the chain tip of the
alcohol with a longer hydrocarbon chain (with the highest flexibility observed
in the 50:50 C18:C16 system). This finding is in excellent agreement with
experimental conclusions. Additionally, simulation data show that in the mixed
system, the alcohol with shorter hydrocarbon chain becomes more rigid. These
molecular details could not be available in experimental measurements


Abstract:
  The slow revolution of the Earth and Moon around their barycentrum does not
induce Coriolis accelerations. On the other hand, the motion of Sun and Earth
is a rotation with Coriolis forces which appear not to have been calculated
yet, nor have the inertial accelerations within the system of motion of all
three celestial bodies. It is the purpose of this contribution to evaluate the
related Coriolis and centrifugal terms and to compare them to the available
atmospheric standard terms. It is a main result that the revolution is of
central importance in the combined dynamics of Earth, Moon and Sun. Covariant
flow equations are well known tools for dealing with such complicated flow
settings. They are used here to quantify the effects of the Earth's revolution
around the Earth-Moon barycenter and its rotation around the Sun on the
atmospheric circulation. It is found that the motion around the Sun adds time
dependent terms to the standard Coriolis forces. The related centrifugal
accelerations are presented. A major part of these accelerations is balanced by
the gravitational attraction by Moon and Sun, but important unbalanced
contributions remain. New light on the consequences of the Earth's revolution
is shed by repeating the calculations for a rotating Earth-Moon pair. It is
found that the revolution complicates the atmospheric dynamics.


Abstract:
  It is no secret amongst deep learning researchers that finding the optimal
data augmentation strategy during training can mean the difference between
state-of-the-art performance and a run-of-the-mill result. To that end, the
community has seen many efforts to automate the process of finding the perfect
augmentation procedure for any task at hand. Unfortunately, even recent
cutting-edge methods bring massive computational overhead, requiring as many as
100 full model trainings to settle on an ideal configuration. We show how to
achieve equivalent performance in just 6: with Random Unidimensional
Augmentation. Source code is available at https://github.com/fastestimator/RUA


Abstract:
  By processing in the frequency domain (FD), massive MIMO systems can approach
the theoretical per-user capacity using a single carrier modulation (SCM)
waveform with a cyclic prefix. Minimum mean squared error (MMSE) detection and
zero forcing (ZF) precoding have been shown to effectively cancel multi-user
interference while compensating for inter-symbol interference. In this paper,
we present a modified downlink precoding approach in the FD based on
regularized zero forcing (RZF), which reuses the matrix inverses calculated as
part of the FD MMSE uplink detection. By reusing these calculations, the
computational complexity of the RZF precoder is drastically lowered, compared
to the ZF precoder. Introduction of the regularization in RZF leads to a bias
in the detected data symbols at the user terminals. We show this bias can be
removed by incorporating a scaling factor at the receiver. Furthermore, it is
noted that user powers have to be optimized to strike a balance between noise
and interference seen at each user terminal. The resulting performance of the
RZF precoder exceeds that of the ZF precoder for low and moderate input
signal-to-noise ratio (SNR) conditions, and performance is equal for high input
SNR. These results are established and confirmed by analysis and simulation.


Abstract:
  We analyze the orthogonal greedy algorithm when applied to dictionaries
$\mathbb{D}$ whose convex hull has small entropy. We show that if the metric
entropy of the convex hull of $\mathbb{D}$ decays at a rate of
$O(n^{-\frac{1}{2}-\alpha})$ for $\alpha > 0$, then the orthogonal greedy
algorithm converges at the same rate on the variation space of $\mathbb{D}$.
This improves upon the well-known $O(n^{-\frac{1}{2}})$ convergence rate of the
orthogonal greedy algorithm in many cases, most notably for dictionaries
corresponding to shallow neural networks. These results hold under no
additional assumptions on the dictionary beyond the decay rate of the entropy
of its convex hull. In addition, they are robust to noise in the target
function and can be extended to convergence rates on the interpolation spaces
of the variation norm. Finally, we show that these improved rates are sharp and
prove a negative result showing that the iterates generated by the orthogonal
greedy algorithm cannot in general be bounded in the variation norm of
$\mathbb{D}$.


Abstract:
  Network flows are one of the most studied combinatorial optimization problems
with innumerable applications. Any flow on a directed acyclic graph (DAG) $G$
having $n$ vertices and $m$ edges can be decomposed into a set of $O(m)$ paths,
with applications from network routing to assembly of biological sequences. In
some applications, the flow decomposition corresponds to some particular data
that need to be reconstructed from the flow, which require finding paths (or
subpaths) appearing in all possible flow decompositions, referred to as safe
paths.
  Recently, Ma et al. [WABI 2020] addressed a related problem in a
probabilistic framework. Later, they gave a quadratic-time algorithm based on a
global criterion, for a generalized version (AND-Quant) of the corresponding
problem, i.e., reporting if a given flow path is safe. Our contributions are as
follows:
  1- A simple characterization for the safety of a given path based on a local
criterion, which can be directly adapted to give an optimal linear time
verification algorithm.
  2- A simple enumeration algorithm that reports all maximal safe paths on a
flow network in $O(mn)$ time. The algorithm reports all safe paths using a
compact representation of the solution (called ${\cal P}_c$), which is
$\Omega(mn)$ in the worst case, but merely $O(m+n)$ in the best case.
  3- An improved enumeration algorithm where all safe paths ending at every
vertex are represented as funnels using $O(n^2+|{\cal P}_c|)$ space. These can
be computed and used to report all maximal safe paths, using time linear in the
total space required by funnels, with an extra logarithmic factor.
  Overall we present a simple characterization for the problem leading to an
optimal verification algorithm and a simple enumeration algorithm. The
enumeration algorithm is improved using the funnel structures for safe paths,
which may be of independent interest.


Abstract:
  The most advanced D-Wave Advantage quantum annealer has 5000+ qubits,
however, every qubit is connected to a small number of neighbors. As such,
implementation of a fully-connected graph results in an order of magnitude
reduction in qubit count. To compensate for the reduced number of qubits, one
has to rely on special heuristic software such as qbsolv, the purpose of which
is to decompose a large problem into smaller pieces that fit onto a quantum
annealer. In this work, we compare the performance of two implementations of
such software: the original open-source qbsolv which is a part of the D-Wave
Ocean tools and a new Mukai QUBO solver from Quantum Computing Inc. (QCI). The
comparison is done for solving the electronic structure problem and is
implemented in a classical mode (Tabu search techniques). The Quantum Annealer
Eigensolver is used to map the electronic structure eigenvalue-eigenvector
equation to a type of problem solvable on modern quantum annealers. We find
that the Mukai QUBO solver outperforms the Ocean qbsolv for all calculations
done in the present work, both the ground and excited state calculations. This
work stimulates the development of software to assist in the utilization of
modern quantum annealers.


Abstract:
  We derive the Thouless-Anderson-Palmer (TAP) equations for the Ghatak and
Sherrington model. Our derivation, based on the cavity method, holds at high
temperature and at all values of the crystal field. It confirms the prediction
of Yokota.


Abstract:
  One of the most complex and devastating disaster scenarios that the
U.S.~Pacific Northwest region and the state of Oregon faces is a large
magnitude Cascadia Subduction Zone earthquake event. The region's electrical
grid lacks in resilience against the destruction of a megathrust earthquake, a
powerful tsunami, hundreds of aftershocks and increased volcanic activity, all
of which are highly probable components of this hazard. This research seeks to
catalyze further understanding and improvement of resilience. By systematizing
power system related experiences of historical earthquakes, and collecting
practical and innovative ideas from other regions on how to enhance network
design, construction, and operation, important steps are being taken toward a
more resilient, earthquake-resistant grid. This paper presents relevant
findings in an effort to be an overview and a useful guideline for those who
are also working towards greater electrical grid resilience.


Abstract:
  The representation of data and its relationships using networks is prevalent
in many research fields such as computational biology, medical informatics and
social networks. Recently, complex networks models have been introduced to
better capture the insights of the modelled scenarios. Among others, dual
networks -based models have been introduced, which consist in mapping
information as pair of networks containing the same nodes but different edges.
  We focus on the use of a novel approach to visualise and analyse dual
networks. The method uses two algorithms for community discovery, and it is
provided as a Python-based tool with a graphical user interface. The tool is
able to load dual networks and to extract both the densest connected subgraph
as well as the common modular communities. The latter is obtained by using an
adapted implementation of the Louvain algorithm.
  The proposed algorithm and graphical tool have been tested by using social,
biological, and co-authorship networks. Results demonstrate that the proposed
approach is efficient and is able to extract meaningful information from dual
networks. Finally, as contribution, the proposed graphical user interface can
be considered a valuable innovation to the context.


Abstract:
  In this article, we develop an algebraic framework of axioms which abstracts
various high-level properties of multi-qudit representations of generalized
Clifford algebras. We further construct an explicit model and prove that it
satisfies these axioms. Strengths of our algebraic framework include the
minimality of its assumptions, and the readiness by which one may give an
explicit construction satisfying these assumptions. In terms of applications,
this algebraic framework provides a solid foundation which opens the way for
developing a graphical calculus for multi-qudit representations of generalized
Clifford algebras using purely algebraic methods, which is addressed in a
follow-up paper.


Abstract:
  Assuming the Riemann hypothesis we establish explicit bounds for the modulus
of the log-derivative of Riemann's zeta-function in the critical strip.


Abstract:
  Abstract symbolic reasoning, as required in domains such as mathematics and
logic, is a key component of human intelligence. Solvers for these domains have
important applications, especially to computer-assisted education. But learning
to solve symbolic problems is challenging for machine learning algorithms.
Existing models either learn from human solutions or use hand-engineered
features, making them expensive to apply in new domains. In this paper, we
instead consider symbolic domains as simple environments where states and
actions are given as unstructured text, and binary rewards indicate whether a
problem is solved. This flexible setup makes it easy to specify new domains,
but search and planning become challenging. We introduce four environments
inspired by the Mathematics Common Core Curriculum, and observe that existing
Reinforcement Learning baselines perform poorly. We then present a novel
learning algorithm, Contrastive Policy Learning (ConPoLe) that explicitly
optimizes the InfoNCE loss, which lower bounds the mutual information between
the current state and next states that continue on a path to the solution.
ConPoLe successfully solves all four domains. Moreover, problem representations
learned by ConPoLe enable accurate prediction of the categories of problems in
a real mathematics curriculum. Our results suggest new directions for
reinforcement learning in symbolic domains, as well as applications to
mathematics education.


Abstract:
  With the ongoing penetration of conversational user interfaces, a better
understanding of social and emotional characteristic inherent to dialogue is
required. Chatbots in particular face the challenge of conveying human-like
behaviour while being restricted to one channel of interaction, i.e., text. The
goal of the presented work is thus to investigate whether characteristics of
social intelligence embedded in human-chatbot interactions are perceivable by
human interlocutors and if yes, whether such influences the experienced
interaction quality. Focusing on the social intelligence dimensions
Authenticity, Clarity and Empathy, we first used a questionnaire survey
evaluating the level of perception in text utterances, and then conducted a
Wizard of Oz study to investigate the effects of these utterances in a more
interactive setting. Results show that people have great difficulties
perceiving elements of social intelligence in text. While on the one hand they
find anthropomorphic behaviour pleasant and positive for the naturalness of a
dialogue, they may also perceive it as frightening and unsuitable when
expressed by an artificial agent in the wrong way or at the wrong time.


Abstract:
  Learning representations for graphs plays a critical role in a wide spectrum
of downstream applications. In this paper, we summarize the limitations of the
prior works in three folds: representation space, modeling dynamics and
modeling uncertainty. To bridge this gap, we propose to learn dynamic graph
representation in hyperbolic space, for the first time, which aims to infer
stochastic node representations. Working with hyperbolic space, we present a
novel Hyperbolic Variational Graph Neural Network, referred to as HVGNN. In
particular, to model the dynamics, we introduce a Temporal GNN (TGNN) based on
a theoretically grounded time encoding approach. To model the uncertainty, we
devise a hyperbolic graph variational autoencoder built upon the proposed TGNN
to generate stochastic node representations of hyperbolic normal distributions.
Furthermore, we introduce a reparameterisable sampling algorithm for the
hyperbolic normal distribution to enable the gradient-based learning of HVGNN.
Extensive experiments show that HVGNN outperforms state-of-the-art baselines on
real-world datasets.


Abstract:
  We propose SinIR, an efficient reconstruction-based framework trained on a
single natural image for general image manipulation, including
super-resolution, editing, harmonization, paint-to-image, photo-realistic style
transfer, and artistic style transfer. We train our model on a single image
with cascaded multi-scale learning, where each network at each scale is
responsible for image reconstruction. This reconstruction objective greatly
reduces the complexity and running time of training, compared to the GAN
objective. However, the reconstruction objective also exacerbates the output
quality. Therefore, to solve this problem, we further utilize simple random
pixel shuffling, which also gives control over manipulation, inspired by the
Denoising Autoencoder. With quantitative evaluation, we show that SinIR has
competitive performance on various image manipulation tasks. Moreover, with a
much simpler training objective (i.e., reconstruction), SinIR is trained 33.5
times faster than SinGAN (for 500 X 500 images) that solves similar tasks. Our
code is publicly available at github.com/YooJiHyeong/SinIR.


Abstract:
  This paper studies the model compression problem of vision transformers.
Benefit from the self-attention module, transformer architectures have shown
extraordinary performance on many computer vision tasks. Although the network
performance is boosted, transformers are often required more computational
resources including memory usage and the inference complexity. Compared with
the existing knowledge distillation approaches, we propose to excavate useful
information from the teacher transformer through the relationship between
images and the divided patches. We then explore an efficient fine-grained
manifold distillation approach that simultaneously calculates cross-images,
cross-patch, and random-selected manifolds in teacher and student models.
Experimental results conducted on several benchmarks demonstrate the
superiority of the proposed algorithm for distilling portable transformer
models with higher performance. For example, our approach achieves 75.06% Top-1
accuracy on the ImageNet-1k dataset for training a DeiT-Tiny model, which
outperforms other ViT distillation methods.


Abstract:
  Adversarial examples mainly exploit changes to input pixels to which humans
are not sensitive to, and arise from the fact that models make decisions based
on uninterpretable features. Interestingly, cognitive science reports that the
process of interpretability for human classification decision relies
predominantly on low spatial frequency components. In this paper, we
investigate the robustness to adversarial perturbations of models enforced
during training to leverage information corresponding to different spatial
frequency ranges. We show that it is tightly linked to the spatial frequency
characteristics of the data at stake. Indeed, depending on the data set, the
same constraint may results in very different level of robustness (up to 0.41
adversarial accuracy difference). To explain this phenomenon, we conduct
several experiments to enlighten influential factors such as the level of
sensitivity to high frequencies, and the transferability of adversarial
perturbations between original and low-pass filtered inputs.


Abstract:
  In this paper, we propose a simple yet effective crowd counting and
localization network named SCALNet. Unlike most existing works that separate
the counting and localization tasks, we consider those tasks as a pixel-wise
dense prediction problem and integrate them into an end-to-end framework.
Specifically, for crowd counting, we adopt a counting head supervised by the
Mean Square Error (MSE) loss. For crowd localization, the key insight is to
recognize the keypoint of people, i.e., the center point of heads. We propose a
localization head to distinguish dense crowds trained by two loss functions,
i.e., Negative-Suppressed Focal (NSF) loss and False-Positive (FP) loss, which
balances the positive/negative examples and handles the false-positive
predictions. Experiments on the recent and large-scale benchmark, NWPU-Crowd,
show that our approach outperforms the state-of-the-art methods by more than 5%
and 10% improvement in crowd localization and counting tasks, respectively. The
code is publicly available at https://github.com/WangyiNTU/SCALNet.


Abstract:
  Harmonic generation in atoms and molecules has reshaped our understanding of
ultrafast phenomena beyond the traditional nonlinear optics and has launched
attosecond physics. Harmonics from solids represent a new frontier, where both
majority and minority spin channels contribute to harmonics.} This is true even
in a ferromagnet whose electronic states are equally available to optical
excitation. Here, we demonstrate that harmonics can be generated {mostly} from
a single spin channel in half metallic chromium dioxide. {An energy gap in the
minority channel greatly reduces the harmonic generation}, so harmonics
predominantly emit from the majority channel, with a small contribution from
the minority channel. However, this is only possible when the incident photon
energy is well below the energy gap in the minority channel, so all the
transitions in the minority channel are virtual. The onset of the photon energy
is determined by the transition energy between the dipole-allowed transition
between the O-$2p$ and Cr-$3d$ states. Harmonics {mainly} from a single spin
channel can be detected, regardless of laser field strength, as far as the
photon energy is below the minority band energy gap. This prediction should be
tested experimentally.


Abstract:
  Dynamic Time Warping (DTW) is widely used for temporal data processing.
However, existing methods can neither learn the discriminative prototypes of
different classes nor exploit such prototypes for further analysis. We propose
Discriminative Prototype DTW (DP-DTW), a novel method to learn class-specific
discriminative prototypes for temporal recognition tasks. DP-DTW shows superior
performance compared to conventional DTWs on time series classification
benchmarks. Combined with end-to-end deep learning, DP-DTW can handle
challenging weakly supervised action segmentation problems and achieves state
of the art results on standard benchmarks. Moreover, detailed reasoning on the
input video is enabled by the learned action prototypes. Specifically, an
action-based video summarization can be obtained by aligning the input sequence
with action prototypes.


Abstract:
  Hadronic matrix elements of local four-quark operators play a central role in
non-leptonic kaon decays, while vacuum matrix elements involving the same kind
of operators appear in inclusive dispersion relations, such as those relevant
in $\tau$-decay analyses. Using an $SU(3)_L\otimes SU(3)_R$ decomposition of
the operators, we derive generic relations between these matrix elements,
extending well-known results that link observables in the two different
sectors. Two relevant phenomenological applications are presented. First, we
determine the electroweak-penguin contribution to the kaon CP-violating ratio
$\varepsilon'/\varepsilon$, using the measured hadronic spectral functions in
$\tau$ decay. Second, we fit our $SU(3)$ dynamical parameters to the most
recent lattice data on $K\to\pi\pi$ matrix elements. The comparison of this
numerical fit with results from previous analytical approaches provides an
interesting anatomy of the $\Delta I = \frac{1}{2}$ enhancement, confirming old
suggestions about its underlying dynamical origin.


Abstract:
  We present a machine learning method to predict extreme hydrologic events
from spatially and temporally varying hydrological and meteorological data. We
used a timestep reduction technique to reduce the computational and memory
requirements and trained a bidirection LSTM network to predict soil water and
stream flow from time series data observed and simulated over eighty years in
the Wabash River Watershed. We show that our simple model can be trained much
faster than complex attention networks such as GeoMAN without sacrificing
accuracy. Based on the predicted values of soil water and stream flow, we
predict the occurrence and severity of extreme hydrologic events such as
droughts. We also demonstrate that extreme events can be predicted in
geographical locations separate from locations observed during the training
process. This spatially-inductive setting enables us to predict extreme events
in other areas in the US and other parts of the world using our model trained
with the Wabash Basin data.


Abstract:
  Electronic and optical properties of doped organic semiconductors are
dominated by local interactions between donor and acceptor molecules. However,
when such systems are in crystalline form, long-range order competes against
short-range couplings. In a first-principles study on three experimentally
resolved bulk structures of quaterthiophene doped by (fluorinated)
tetracyanoquinodimethane, we demonstrate the crucial role of long-range
interactions in donor/acceptor co-crystals. The band structures of the
investigated materials exhibit direct band-gaps decreasing in size with
increasing amount of F atoms in the acceptors. The valence-band maximum and
conduction-band minimum are found at the Brillouin zone boundary and the
corresponding wave-functions are segregated on donor and acceptor molecules,
respectively. With the aid of a tight-binding model, we rationalize that the
mechanisms responsible for these behaviors, which are ubiquitous in
donor/acceptor co-crystals, are driven by long-range interactions. The optical
response of the analyzed co-crystals is highly anisotropic. The absorption
onset is dominated by an intense resonance corresponding to a charge-transfer
excitation. Long-range interactions are again responsible for this behavior,
which enhances the efficiency of the co-crystals for photo-induced charge
separation and transport. In addition to this result, which has important
implications in the rational design of organic materials for opto-electronics,
our study clarifies that cluster models, accounting only for local
interactions, cannot capture the relevant impact of long-range order in
donor/acceptor co-crystals.


Abstract:
  Recently, deep-learning based approaches have achieved impressive performance
for autonomous driving. However, end-to-end vision-based methods typically have
limited interpretability, making the behaviors of the deep networks difficult
to explain. Hence, their potential applications could be limited in practice.
To address this problem, we propose an interpretable end-to-end vision-based
motion planning approach for autonomous driving, referred to as IVMP. Given a
set of past surrounding-view images, our IVMP first predicts future egocentric
semantic maps in bird's-eye-view space, which are then employed to plan
trajectories for self-driving vehicles. The predicted future semantic maps not
only provide useful interpretable information, but also allow our motion
planning module to handle objects with low probability, thus improving the
safety of autonomous driving. Moreover, we also develop an optical flow
distillation paradigm, which can effectively enhance the network while still
maintaining its real-time performance. Extensive experiments on the nuScenes
dataset and closed-loop simulation show that our IVMP significantly outperforms
the state-of-the-art approaches in imitating human drivers with a much higher
success rate. Our project page is available at
https://sites.google.com/view/ivmp.


Abstract:
  We develop methods for forming prediction sets in an online setting where the
data generating distribution is allowed to vary over time in an unknown
fashion. Our framework builds on ideas from conformal inference to provide a
general wrapper that can be combined with any black box method that produces
point predictions of the unseen label or estimated quantiles of its
distribution. While previous conformal inference methods rely on the assumption
that the data points are exchangeable, our adaptive approach provably achieves
the desired coverage frequency over long-time intervals irrespective of the
true data generating process. We accomplish this by modelling the distribution
shift as a learning problem in a single parameter whose optimal value is
varying over time and must be continuously re-estimated. We test our method,
adaptive conformal inference, on two real world datasets and find that its
predictions are robust to visible and significant distribution shifts.


Abstract:
  We analyze a fully discrete finite element numerical scheme for the
Cahn-Hilliard-Stokes-Darcy system that models two-phase flows in coupled free
flow and porous media. To avoid a well-known difficulty associated with the
coupling between the Cahn-Hilliard equation and the fluid motion, we make use
of the operator-splitting in the numerical scheme, so that these two solvers
are decoupled, which in turn would greatly improve the computational
efficiency. The unique solvability and the energy stability have been proved
in~\cite{CHW2017}. In this work, we carry out a detailed convergence analysis
and error estimate for the fully discrete finite element scheme, so that the
optimal rate convergence order is established in the energy norm, i.e.,, in the
$\ell^\infty (0, T; H^1) \cap \ell^2 (0, T; H^2)$ norm for the phase variables,
as well as in the $\ell^\infty (0, T; H^1) \cap \ell^2 (0, T; H^2)$ norm for
the velocity variable. Such an energy norm error estimate leads to a
cancellation of a nonlinear error term associated with the convection part,
which turns out to be a key step to pass through the analysis. In addition, a
discrete $\ell^2 (0;T; H^3)$ bound of the numerical solution for the phase
variables plays an important role in the error estimate, which is accomplished
via a discrete version of Gagliardo-Nirenberg inequality in the finite element
setting.


Abstract:
  In this paper, the Hankel transform of the generalized q-exponential
polynomial of the first form (q, r)-Whitney numbers of the second kind is
established using the method of Cigler. Consequently, the Hankel transform of
the first form (q, r)-Dowling numbers is obtained as special case.


Abstract:
  In the article, we investigate the diquark-diquark-antiquark type fully-heavy
pentaquark states with the spin-parity $J^P={\frac{1}{2}}^-$ via the QCD sum
rules, and obtain the masses $M_{cccc\bar{c}}=7.93\pm 0.15\,\rm{GeV}$ and
$M_{bbbb\bar{b}}=23.91\pm0.15\,\rm{GeV}$. We can search for the fully-heavy
pentaquark states in the $J/\psi \Omega_{ccc}$ and $\Upsilon \Omega_{bbb}$
invariant mass spectrum in the future.


Abstract:
  Optical phenomena associated with extremely localized field should be
understood with considerations of nonlocal and quantum effects, which pose a
hurdle to conceptualize the physics with a picture of eigenmodes. Here we first
propose a generalized Lorentz model to describe general nonlocal media under
linear mean-field approximation and formulate source-free Maxwell's equations
as a linear eigenvalue problem to define the quasinormal modes. Then we
introduce an orthonormalization scheme for the modes and establish a canonical
quasinormal mode framework for general nonlocal media. Explicit formalisms for
metals described by quantum hydrodynamic model and polar dielectrics with
nonlocal response are exemplified. The framework enables for the first time
direct modal analysis of mode transition in the quantum tunneling regime and
provides physical insights beyond usual far-field spectroscopic analysis.
Applied to nonlocal polar dielectrics, the framework also unveils the important
roles of longitudinal phonon polaritons in optical response.


Abstract:
  Smart contracts are distributed, self-enforcing programs executing on top of
blockchain networks. They have the potential to revolutionize many industries
such as financial institutes and supply chains. However, smart contracts are
subject to code-based vulnerabilities, which casts a shadow on its
applications. As smart contracts are unpatchable (due to the immutability of
blockchain), it is essential that smart contracts are guaranteed to be free of
vulnerabilities. Unfortunately, smart contract languages such as Solidity are
Turing-complete, which implies that verifying them statically is infeasible.
Thus, alternative approaches must be developed to provide the guarantee. In
this work, we develop an approach which automatically transforms smart
contracts so that they are provably free of 4 common kinds of vulnerabilities.
The key idea is to apply runtime verification in an efficient and provably
correct manner. Experiment results with 5000 smart contracts show that our
approach incurs minor run-time overhead in terms of time (i.e., 14.79%) and gas
(i.e., 0.79%).


Abstract:
  We present a structure preserving discretization of the fundamental spacetime
geometric structures of fluid mechanics in the Lagrangian description in 2D and
3D. Based on this, multisymplectic variational integrators are developed for
barotropic and incompressible fluid models, which satisfy a discrete version of
Noether theorem. We show how the geometric integrator can handle regular fluid
motion in vacuum with free boundaries and constraints such as the impact
against an obstacle of a fluid flowing on a surface. Our approach is applicable
to a wide range of models including the Boussinesq and shallow water models, by
appropriate choice of the Lagrangian.


Abstract:
  Modularity of neural networks -- both biological and artificial -- can be
thought of either structurally or functionally, and the relationship between
these is an open question. We show that enforcing structural modularity via
sparse connectivity between two dense sub-networks which need to communicate to
solve the task leads to functional specialization of the sub-networks, but only
at extreme levels of sparsity. With even a moderate number of interconnections,
the sub-networks become functionally entangled. Defining functional
specialization is in itself a challenging problem without a universally agreed
solution. To address this, we designed three different measures of
specialization (based on weight masks, retraining and correlation) and found
them to qualitatively agree. Our results have implications in both neuroscience
and machine learning. For neuroscience, it shows that we cannot conclude that
there is functional modularity simply by observing moderate levels of
structural modularity: knowing the brain's connectome is not sufficient for
understanding how it breaks down into functional modules. For machine learning,
using structure to promote functional modularity -- which may be important for
robustness and generalization -- may require extremely narrow bottlenecks
between modules.


Abstract:
  Being able to spot defective parts is a critical component in large-scale
industrial manufacturing. A particular challenge that we address in this work
is the cold-start problem: fit a model using nominal (non-defective) example
images only. While handcrafted solutions per class are possible, the goal is to
build systems that work well simultaneously on many different tasks
automatically. The best peforming approaches combine embeddings from ImageNet
models with an outlier detection model. In this paper, we extend on this line
of work and propose PatchCore, which uses a maximally representative memory
bank of nominal patch-features. PatchCore offers competitive inference times
while achieving state-of-the-art performance for both detection and
localization. On the standard dataset MVTec AD, PatchCore achieves an
image-level anomaly detection AUROC score of $99.1\%$, more than halving the
error compared to the next best competitor. We further report competitive
results on two additional datasets and also find competitive results in the few
samples regime.


Abstract:
  Novel many-body and topological electronic phases can be created in
assemblies of interacting spins coupled to a superconductor, such as
one-dimensional topological superconductors with Majorana zero modes (MZMs) at
their ends. Understanding and controlling interactions between spins and the
emergent band structure of the in-gap Yu-Shiba-Rusinov (YSR) states they induce
in a superconductor are fundamental for engineering such phases. Here, by
precisely positioning magnetic adatoms with a scanning tunneling microscope
(STM), we demonstrate both the tunability of exchange interaction between spins
and precise control of the hybridization of YSR states they induce on the
surface of a bismuth (Bi) thin film that is made superconducting with the
proximity effect. In this platform, depending on the separation of spins, the
interplay between Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction, spin-orbit
coupling, and surface magnetic anisotropy stabilizes different types of spin
alignments. Using high-resolution STM spectroscopy at millikelvin temperatures,
we probe these spin alignments through monitoring the spin-induced YSR states
and their energy splitting. Such measurements also reveal a quantum phase
transition between the ground states with different electron number parity for
a pair of spins in a superconductor tuned by their separation. Experiments on
larger assemblies show that spin-spin interactions can be mediated in a
superconductor over long distances. Our results show that controlling
hybridization of the YSR states in this platform provides the possibility of
engineering the band structure of such states for creating topological phases.


Abstract:
  We describe a numerical method that simulates the interaction of the helium
atom with sequences of femtosecond and attosecond light pulses. The method,
which is based on the close-coupling expansion of the electronic configuration
space in a B-spline bipolar spherical harmonic basis, can accurately reproduce
the excitation and single ionization of the atom, within the electrostatic
approximation. The time dependent Schr\"odinger equation is integrated with a
sequence of second-order split-exponential unitary propagators. The asymptotic
channel-, energy- and angularly-resolved photoelectron distributions are
computed by projecting the wavepacket at the end of the simulation on the
multichannel scattering states of the atom, which are separately computed
within the same close-coupling basis. This method is applied to simulate the
pump-probe ionization of helium in the vicinity of the $2s/2p$ excitation
threshold of the He$^+$ ion. This work confirms the qualitative conclusions of
one of our earliest publications [L Argenti and E Lindroth, Phys. Rev. Lett.
{\bf 105}, 53002 (2010)], in which we demonstrated the control of the $2s/2p$
ionization branching-ratio. Here, we take those calculations to convergence and
show how correlation brings the periodic modulation of the branching ratios in
almost phase opposition. The residual total ionization probability to the
$2s+2p$ channels is dominated by the beating between the $sp_{2,3}^+$ and the
$sp_{2,4}^+$ doubly excited states, which is consistent with the modulation of
the complementary signal in the $1s$ channel, measured in 2010 by Chang and
co-workers~[S Gilbertson~\emph{et al.}, Phys. Rev. Lett. {\bf 105}, 263003
(2010)].


Abstract:
  Over the past decade, unprecedented progress in the development of neural
networks influenced dozens of different industries, including weed recognition
in the agro-industrial sector. The use of neural networks in agro-industrial
activity in the task of recognizing cultivated crops is a new direction. The
absence of any standards significantly complicates the understanding of the
real situation of the use of the neural network in the agricultural sector. The
manuscript presents the complete analysis of researches over the past 10 years
on the use of neural networks for the classification and tracking of weeds due
to neural networks. In particular, the analysis of the results of using various
neural network algorithms for the task of classification and tracking was
presented. As a result, we presented the recommendation for the use of neural
networks in the tasks of recognizing a cultivated object and weeds. Using this
standard can significantly improve the quality of research on this topic and
simplify the analysis and understanding of any paper.


Abstract:
  The formation of $\alpha$ particle on nuclear surface has been a fundamental
problem since the early age of nuclear physics. It strongly affects the
$\alpha$ decay lifetime of heavy and superheavy elements, level scheme of light
nuclei, and the synthesis of the elements in stars. However, the
$\alpha$-particle formation in medium-mass nuclei has been poorly known despite
its importance. Here, based on the $^{48}{\rm Ti}(p,p\alpha)^{44}{\rm Ca}$
reaction analysis, we report that the $\alpha$-particle formation in a
medium-mass nucleus $^{48}{\rm Ti}$ is much stronger than that expected from a
mean-field approximation, and the estimated average distance between $\alpha$
particle and the residue is as large as 4.5 fm. This new result poses a
challenge of describing four nucleon correlations by microscopic nuclear
models.


Abstract:
  Giant spin-splitting was recently predicted in collinear antiferromagnetic
materials with a specific class of magnetic space group. In this work, we have
predicted a two-dimensional (2D) antiferromagnetic Weyl semimetal (WS), CrO
with large spin-split band structure, spin-momentum locked transport properties
and high N\'eel temperature. It has two pairs of spin-polarized Weyl points at
the Fermi level. By manipulating the position of the Weyl points with strain,
four different antiferromagnetic spintronic states can be achieved: WSs with
two spin-polarized transport channels (STCs), WSs with single STC,
semiconductors with two STCs, and semiconductors with single STC. Based on
these properties, a new avenue in spintronics with 2D collinear
antiferromagnets is proposed.


Abstract:
  Being the seventh most spoken language in the world, the use of the Bangla
language online has increased in recent times. Hence, it has become very
important to analyze Bangla text data to maintain a safe and harassment-free
online place. The data that has been made accessible in this article has been
gathered and marked from the comments of people in public posts by celebrities,
government officials, athletes on Facebook. The total amount of collected
comments is 44001. The dataset is compiled with the aim of developing the
ability of machines to differentiate whether a comment is a bully expression or
not with the help of Natural Language Processing and to what extent it is
improper if it is an inappropriate comment. The comments are labeled with
different categories of harassment. Exploratory analysis from different
perspectives is also included in this paper to have a detailed overview. Due to
the scarcity of data collection of categorized Bengali language comments, this
dataset can have a significant role for research in detecting bully words,
identifying inappropriate comments, detecting different categories of Bengali
bullies, etc. The dataset is publicly available at
https://data.mendeley.com/datasets/9xjx8twk8p.


Abstract:
  Let $\Omega \Subset \mathbb R^n$, $f \in C^1(\mathbb R^{N\times n})$ and
$g\in C^1(\mathbb R^N)$, where $N,n \in \mathbb N$. We study the minimisation
problem of finding $u \in W^{1,\infty}_0(\Omega;\mathbb R^N)$ that satisfies \[
\big\| f(\mathrm D u) \big\|_{L^\infty(\Omega)} \! = \inf \Big\{\big\|
f(\mathrm D v) \big\|_{L^\infty(\Omega)} \! : \ v \! \in
W^{1,\infty}_0(\Omega;\mathbb R^N), \, \| g(v) \|_{L^\infty(\Omega)}\!
=1\Big\}, \] under natural assumptions on $f,g$. This includes the
$\infty$-eigenvalue problem as a special case. Herein we prove existence of a
minimiser $u_\infty$ with extra properties, derived as the limit of minimisers
of approximating constrained $L^p$ problems as $p\to \infty$. A central
contribution and novelty of this work is that $u_\infty$ is shown to solve a
divergence PDE with measure coefficients, whose leading term is a divergence
counterpart equation of the non-divergence $\infty$-Laplacian. Our results are
new even in the scalar case of the $\infty$-eigenvalue problem.


Abstract:
  This paper presents the detailed simulation of a double-pixel structure for
charged particle detection based on the 3D-trench silicon sensor developed for
the TIMESPOT project and a comparison of the simulation results with
measurements performed at $\pi-$M1 beam at PSI laboratory. The simulation is
based on the combined use of several software tools (TCAD, GEANT4, TCoDe and
TFBoost) which allow to fully design and simulate the device physics response
in very short computational time, O(1-100 s) per simulated signal, by
exploiting parallel computation using single or multi-thread processors. This
allowed to produce large samples of simulated signals, perform detailed studies
of the sensor characteristics and make precise comparisons with experimental
results.


Abstract:
  We investigate the possibility that radio-bright active galactic nuclei (AGN)
are responsible for the TeV--PeV neutrinos detected by IceCube. We use an
unbinned maximum-likelihood-ratio method, 10 years of IceCube muon-track data,
and 3388 radio-bright AGN selected from the Radio Fundamental Catalog. None of
the AGN in the catalog have a large global significance. The two most
significant sources have global significance of $\simeq$ 1.5$\sigma$ and
0.8$\sigma$, though 4.1$\sigma$ and 3.8$\sigma$ local significance. Our
stacking analyses show no significant correlation between the whole catalog and
IceCube neutrinos. We infer from the null search that this catalog can account
for at most 30\% (95\% CL) of the diffuse astrophysical neutrino flux measured
by IceCube. Moreover, our results disagree with recent work that claimed a
4.1$\sigma$ detection of neutrinos from the sources in this catalog, and we
discuss the reasons of the difference.


Abstract:
  We propose a generalization of the coherent anomaly method to extract the
critical exponents of a phase transition occurring in the steady-state of an
open quantum many-body system. The method, originally developed by Suzuki [J.
Phys. Soc. Jpn. {\bf 55}, 4205 (1986)] for equilibrium systems, is based on the
scaling properties of the singularity in the response functions determined
through cluster mean-field calculations. We apply this method to the
dissipative transverse-field Ising model and the dissipative XYZ model in two
dimensions obtaining convergent results already with small clusters.


Abstract:
  As a step towards quantization of Higher Spin Gravities we construct the
presymplectic AKSZ sigma-model for $4d$ Higher Spin Gravity which is AdS/CFT
dual of Chern-Simons vector models. It is shown that the presymplectic
structure leads to the correct quantum commutator of higher spin fields and to
the correct algebra of the global higher spin symmetry currents. The
presymplectic AKSZ model is proved to be unique, it depends on two coupling
constants in accordance with the AdS/CFT duality, and it passes some simple
checks of interactions.


Abstract:
  Electric, intelligent, and network are the most important future development
directions of automobiles. Intelligent electric vehicles have shown great
potentials to improve traffic mobility and reduce emissions, especially at
unsignalized intersections. Previous research has shown that vehicle passing
order is the key factor in traffic mobility improvement. In this paper, we
propose a graph-based cooperation method to formalize the conflict-free
scheduling problem at unsignalized intersections. Firstly, conflict directed
graphs and coexisting undirected graphs are built to describe the conflict
relationship of the vehicles. Then, two graph-based methods are introduced to
solve the vehicle passing order. One method is an optimized depth-first
spanning tree method which aims to find the local optimal passing order for
each vehicle. The other method is a maximum matching algorithm that solves the
global optimal problem. The computational complexity of both methods is also
derived. Numerical simulation results demonstrate the effectiveness of the
proposed algorithms.


Abstract:
  As an indispensable part of modern human-computer interaction system, speech
synthesis technology helps users get the output of intelligent machine more
easily and intuitively, thus has attracted more and more attention. Due to the
limitations of high complexity and low efficiency of traditional speech
synthesis technology, the current research focus is the deep learning-based
end-to-end speech synthesis technology, which has more powerful modeling
ability and a simpler pipeline. It mainly consists of three modules: text
front-end, acoustic model, and vocoder. This paper reviews the research status
of these three parts, and classifies and compares various methods according to
their emphasis. Moreover, this paper also summarizes the open-source speech
corpus of English, Chinese and other languages that can be used for speech
synthesis tasks, and introduces some commonly used subjective and objective
speech quality evaluation method. Finally, some attractive future research
directions are pointed out.


Abstract:
  The internet advertising market is a multi-billion dollar industry, in which
advertisers buy thousands of ad placements every day by repeatedly
participating in auctions. In recent years, the industry has shifted to
first-price auctions as the preferred paradigm for selling advertising slots.
Another important and ubiquitous feature of these auctions is the presence of
campaign budgets, which specify the maximum amount the advertisers are willing
to pay over a specified time period. In this paper, we present a new model to
study the equilibrium bidding strategies in first-price auctions for
advertisers who satisfy budget constraints on average. Our model dispenses with
the common, yet unrealistic assumption that advertisers' values are independent
and instead assumes a contextual model in which advertisers determine their
values using a common feature vector. We show the existence of a natural
value-pacing-based Bayes-Nash equilibrium under very mild assumptions, and
study its structural properties. Furthermore, we generalize the existence
result to standard auctions and prove a revenue equivalence showing that all
standard auctions yield the same revenue even in the presence of budget
constraints.


Abstract:
  We study the dynamics of a ferrofluid thin film confined in a Hele-Shaw cell,
and subjected to a tilted nonuniform magnetic field. It is shown that the
interface between the ferrofluid and an inviscid outer fluid (air) supports
traveling waves, governed by a novel modified Kuramoto--Sivashinsky-type
equation derived under the long-wave approximation. The balance between energy
production and dissipation in this long-wave equations allows for the existence
of dissipative solitons. These permanent traveling waves' propagation velocity
and profile shape are shown to be tunable via the external magnetic field. A
multiple-scale analysis is performed to obtain the correction to the linear
prediction of the propagation velocity, and to reveal how the nonlinearity
arrests the linear instability. The traveling periodic interfacial waves
discovered are identified as fixed points in an energy phase plane. It is shown
that transitions between states (wave profiles) occur. These transitions are
explained via the spectral stability of the traveling waves. Interestingly,
multiperiodic waves, which are a non-integrable analog of the double cnoidal
wave, are also found to propagate under the model long-wave equation. These
multiperiodic solutions are investigated numerically, and they are found to be
long-lived transients, but ultimately abruptly transition to one of the stable
periodic states identified.