Abstract:
  We study the estimation of the linear discriminant with projection pursuit, a
method that is blind in the sense that it does not use the class labels in the
estimation. Our viewpoint is asymptotic and, as our main contribution, we
derive central limit theorems for estimators based on three different
projection indices, skewness, kurtosis and their convex combination. The
results show that in each case the limiting covariance matrix is proportional
to that of linear discriminant analysis (LDA), an unblind estimator of the
discriminant. An extensive comparative study between the asymptotic variances
reveals that projection pursuit is able to achieve efficiency equal to LDA when
the groups are arbitrarily well-separated and their sizes are reasonably
balanced. We conclude with a real data example and a simulation study
investigating the validity of the obtained asymptotic formulas for finite
samples.


Abstract:
  Different from traditional knowledge graphs (KGs) where facts are represented
as entity-relation-entity triplets, hyper-relational KGs (HKGs) allow triplets
to be associated with additional relation-entity pairs (a.k.a qualifiers) to
convey more complex information. How to effectively and efficiently model the
triplet-qualifier relationship for prediction tasks such as HKG completion is
an open challenge for research. This paper proposes to improve the
best-performing method in HKG completion, namely STARE, by introducing two
novel revisions: (1) Replacing the computation-heavy graph neural network
module with light-weight entity/relation embedding processing techniques for
efficiency improvement without sacrificing effectiveness; (2) Adding a
qualifier-oriented auxiliary training task for boosting the prediction power of
our approach on HKG completion. The proposed approach consistently outperforms
STARE in our experiments on three benchmark datasets, with significantly
improved computational efficiency.


Abstract:
  Although there are a small number of work to conduct patent research by
building knowledge graph, but without constructing patent knowledge graph using
patent documents and combining latest natural language processing methods to
mine hidden rich semantic relationships in existing patents and predict new
possible patents. In this paper, we propose a new patent vacancy prediction
approach named PatentMiner to mine rich semantic knowledge and predict new
potential patents based on knowledge graph (KG) and graph attention mechanism.
Firstly, patent knowledge graph over time (e.g. year) is constructed by
carrying out named entity recognition and relation extrac-tion from patent
documents. Secondly, Common Neighbor Method (CNM), Graph Attention Networks
(GAT) and Context-enhanced Graph Attention Networks (CGAT) are proposed to
perform link prediction in the constructed knowledge graph to dig out the
potential triples. Finally, patents are defined on the knowledge graph by means
of co-occurrence relationship, that is, each patent is represented as a fully
connected subgraph containing all its entities and co-occurrence relationships
of the patent in the knowledge graph; Furthermore, we propose a new patent
prediction task which predicts a fully connected subgraph with newly added
prediction links as a new pa-tent. The experimental results demonstrate that
our proposed patent predic-tion approach can correctly predict new patents and
Context-enhanced Graph Attention Networks is much better than the baseline.
Meanwhile, our proposed patent vacancy prediction task still has significant
room to im-prove.


Abstract:
  The fast-growing Emerging Market (EM) economies and their improved
transparency and liquidity have attracted international investors. However, the
external price shocks can result in a higher level of volatility as well as
domestic policy instability. Therefore, an efficient risk measure and hedging
strategies are needed to help investors protect their investments against this
risk. In this paper, a daily systemic risk measure, called FRM (Financial Risk
Meter) is proposed. The FRM-EM is applied to capture systemic risk behavior
embedded in the returns of the 25 largest EMs FIs, covering the BRIMST (Brazil,
Russia, India, Mexico, South Africa, and Turkey), and thereby reflects the
financial linkages between these economies. Concerning the Macro factors, in
addition to the Adrian and Brunnermeier (2016) Macro, we include the EM
sovereign yield spread over respective US Treasuries and the above-mentioned
countries currencies. The results indicated that the FRM of EMs FIs reached its
maximum during the US financial crisis following by COVID 19 crisis and the
Macro factors explain the BRIMST FIs with various degrees of sensibility. We
then study the relationship between those factors and the tail event network
behavior to build our policy recommendations to help the investors to choose
the suitable market for in-vestment and tail-event optimized portfolios. For
that purpose, an overlapping region between portfolio optimization strategies
and FRM network centrality is developed. We propose a robust and
well-diversified tail-event and cluster risk-sensitive portfolio allocation
model and compare it to more classical approaches


Abstract:
  Deep neural networks offer numerous potential applications across geoscience,
for example, one could argue that they are the state-of-the-art method for
predicting faults in seismic datasets. In quantitative reservoir
characterization workflows, it is common to incorporate the uncertainty of
predictions thus such subsurface models should provide calibrated probabilities
and the associated uncertainties in their predictions. It has been shown that
popular Deep Learning-based models are often miscalibrated, and due to their
deterministic nature, provide no means to interpret the uncertainty of their
predictions. We compare three different approaches to obtaining probabilistic
models based on convolutional neural networks in a Bayesian formalism, namely
Deep Ensembles, Concrete Dropout, and Stochastic Weight Averaging-Gaussian
(SWAG). These methods are consistently applied to fault detection case studies
where Deep Ensembles use independently trained models to provide fault
probabilities, Concrete Dropout represents an extension to the popular Dropout
technique to approximate Bayesian neural networks, and finally, we apply SWAG,
a recent method that is based on the Bayesian inference equivalence of
mini-batch Stochastic Gradient Descent. We provide quantitative results in
terms of model calibration and uncertainty representation, as well as
qualitative results on synthetic and real seismic datasets. Our results show
that the approximate Bayesian methods, Concrete Dropout and SWAG, both provide
well-calibrated predictions and uncertainty attributes at a lower computational
cost when compared to the baseline Deep Ensemble approach. The resulting
uncertainties also offer a possibility to further improve the model performance
as well as enhancing the interpretability of the models.


Abstract:
  Forecasting competitions are the equivalent of laboratory experimentation
widely used in physical and life sciences. They provide useful, objective
information to improve the theory and practice of forecasting, advancing the
field, expanding its usage and enhancing its value to decision and
policymakers. We describe ten design attributes to be considered when
organizing forecasting competitions, taking into account trade-offs between
optimal choices and practical concerns like costs, as well as the time and
effort required to participate in them. Consequently, we map all major past
competitions in respect to their design attributes, identifying similarities
and differences between them, as well as design gaps, and making suggestions
about the principles to be included in future competitions, putting a
particular emphasis on learning as much as possible from their implementation
in order to help improve forecasting accuracy and uncertainty. We discuss that
the task of forecasting often presents a multitude of challenges that can be
difficult to be captured in a single forecasting contest. To assess the caliber
of a forecaster, we, therefore, propose that organizers of future competitions
consider a multi-contest approach. We suggest the idea of a forecasting
"athlon", where different challenges of varying characteristics take place.


Abstract:
  We consider the problem of online reinforcement learning for the Stochastic
Shortest Path (SSP) problem modeled as an unknown MDP with an absorbing state.
We propose PSRL-SSP, a simple posterior sampling-based reinforcement learning
algorithm for the SSP problem. The algorithm operates in epochs. At the
beginning of each epoch, a sample is drawn from the posterior distribution on
the unknown model dynamics, and the optimal policy with respect to the drawn
sample is followed during that epoch. An epoch completes if either the number
of visits to the goal state in the current epoch exceeds that of the previous
epoch, or the number of visits to any of the state-action pairs is doubled. We
establish a Bayesian regret bound of $O(B_\star S\sqrt{AK})$, where $B_\star$
is an upper bound on the expected cost of the optimal policy, $S$ is the size
of the state space, $A$ is the size of the action space, and $K$ is the number
of episodes. The algorithm only requires the knowledge of the prior
distribution, and has no hyper-parameters to tune. It is the first such
posterior sampling algorithm and outperforms numerically previously proposed
optimism-based algorithms.


Abstract:
  In our research, we focus on the response to the non-consensual distribution
of intimate or sexually explicit digital images of adults, also referred as
revenge porn, from the point of view of the victims. In this paper, we present
a preliminary expert analysis of the process for reporting revenge porn abuses
in selected content sharing platforms. Among these, we included social
networks, image hosting websites, video hosting platforms, forums, and
pornographic sites. We looked at the way to report abuse, concerning both the
non-consensual online distribution of private sexual image or video (revenge
pornography), as well as the use of deepfake techniques, where the face of a
person can be replaced on original visual content with the aim of portraying
the victim in the context of sexual behaviours. This preliminary analysis is
directed to understand the current practices and potential issues in the
procedures designed by the providers for reporting these abuses.


Abstract:
  In January 2019, the UK Government published its Maritime 2050 on Navigating
the Future strategy. In the strategy, the government highlighted the importance
of digitalization (with well-designed regulatory support) to achieve its goal
of ensuring that the UK plays a global leadership role in the maritime sector.
Ports, the gateways for 95% of UK trade movements, were identified as key sites
for investment in technological innovation. The government identified the
potential of the Internet of Things (IoT), in conjunction with other
information-sharing technologies, such as shared data platforms, and Artificial
Intelligence applications (AI), to synchronize processes within the port
ecosystem leading to improved efficiency, safety, and environmental benefits,
including improved air quality and lower greenhouse gas emissions.


Abstract:
  Consider a nonuniformly hyperbolic map $ T $ modelled by a Young tower with
tails of the form $ O(n^{-\beta}) $, $ \beta>2 $. We prove optimal moment
bounds for Birkhoff sums $ \sum_{i=0}^{n-1}v\circ T^i $ and iterated sums $
\sum_{0\le i<j<n}v\circ T^i\, w\circ T^j $, where $ v,w:M\to \Bbb{R}$ are
(dynamically) H\"older observables. Previously iterated moment bounds were only
known for $ \beta>5$. Our method of proof is as follows; (i) prove that $ T $
satisfies an abstract functional correlation bound, (ii) use a weak dependence
argument to show that the functional correlation bound implies moment
estimates.
  Such iterated moment bounds arise when using rough path theory to prove
deterministic homogenisation results. Indeed, by a recent result of Chevyrev,
Friz, Korepanov, Melbourne & Zhang we have convergence an It\^o diffusion for
fast-slow systems of the form \[
x^{(n)}_{k+1}=x_k^{(n)}+n^{-1}a(x_k^{(n)},y_k)+n^{-1/2}b(x_k^{(n)},y_k) , \quad
y_{k+1}=T y_k \] in the optimal range $ \beta>2. $


Abstract:
  Recently, vision transformers and MLP-based models have been developed in
order to address some of the prevalent weaknesses in convolutional neural
networks. Due to the novelty of transformers being used in this domain along
with the self-attention mechanism, it remains unclear to what degree these
architectures are robust to corruptions. Despite some works proposing that data
augmentation remains essential for a model to be robust against corruptions, we
propose to explore the impact that the architecture has on corruption
robustness. We find that vision transformer architectures are inherently more
robust to corruptions than the ResNet-50 and MLP-Mixers. We also find that
vision transformers with 5 times fewer parameters than a ResNet-50 have more
shape bias. Our code is available to reproduce.


Abstract:
  Recent trend towards increasing large machine learning models require both
training and inference tasks to be distributed. Considering the huge cost of
training these models, it is imperative to unlock optimizations in computation
and communication to obtain best performance. However, current logical
separation between computation and communication kernels in deep learning
frameworks misses the optimization opportunities across such barrier. Breaking
this abstraction with a holistic consideration can provide many optimizations
to provide performance improvements in distributed workloads. Manually applying
these optimizations needs modifications in underlying computation and
communication libraries for each scenario, which is time consuming and
error-prone.
  Therefore, we present CoCoNeT, with a DSL to express a program with both
computation and communication. CoCoNeT contains several machine learning aware
transformations to optimize a program and a compiler to generate high
performance kernels. Providing both computation and communication as first
class constructs allows users to work on a high-level abstraction and apply
powerful optimizations, such as fusion or overlapping of communication and
computation. CoCoNeT enables us to optimize data-, model-and pipeline-parallel
workloads in large language models with only a few lines of code. Experiments
show CoCoNeT significantly outperforms state-of-the-art distributed machine
learning implementations.


Abstract:
  Rapid progress in additive manufacturing methods has created a new class of
ultralight and strong architected metamaterials that resemble periodic truss
structures. The mechanical performance of these metamaterials with a very large
number of unit cells is ultimately limited by their tolerance to damage and
defects, but an understanding of this sensitivity has remained elusive. Using a
stretching-dominated micro-architecture and metamaterial specimens comprising
millions of unit-cells we show that not only is the stress intensity factor, as
used in conventional elastic fracture mechanics, insufficient to characterize
fracture but also that conventional fracture testing protocols are inadequate.
Via a combination of numerical calculations and asymptotic analyses, we extend
the ideas of fracture mechanics and develop a general test and design protocol
for the failure of metamaterials.


Abstract:
  A fundamental advantage of Petri net models is the possibility to
automatically compute useful system invariants from the syntax of the net.
Classical techniques used for this are place invariants, P-components, siphons
or traps. Recently, Bozga et al. have presented a novel technique for the
\emph{parameterized} verification of safety properties of systems with a ring
or array architecture. They show that the statement \enquote{for every instance
of the parameterized Petri net, all markings satisfying the linear invariants
associated to all the P-components, siphons and traps of the instance are safe}
can be encoded in \acs{WS1S} and checked using tools like MONA. However, while
the technique certifies that this infinite set of linear invariants extracted
from P-components, siphons or traps are strong enough to prove safety, it does
not return an explanation of this fact understandable by humans. We present a
CEGAR loop that constructs a \emph{finite} set of \emph{parameterized}
P-components, siphons or traps, whose infinitely many instances are strong
enough to prove safety. For this we design parameterization procedures for
different architectures.


Abstract:
  I discuss possible consequences of A. D. Sakharov's hypothesis of
cosmological transitions with changes in the signature of the metric, based on
the path integral approach. This hypothesis raises a number of mathematical and
philosophical questions. Mathematical questions concern the definition of the
path integral to include integration over spacetime regions with different
signatures of the metric. One possible way to describe the changes in the
signature is to admit time and space coordinates to be purely imaginary. It may
look like a generalization of what we have in the case of pseudo-Riemannian
manifolds with a non-trivial topology. The signature in these regions can be
fixed by special gauge conditions on components of the metric tensor. The
problem is what boundary conditions should be imposed on the boundaries of
these regions and how they should be taken into account in the definition of
the path integral. The philosophical question is what distinguishes the time
coordinate among other coordinates but the sign of the corresponding principal
value of the metric tensor. In particular, I try to speculate how the existence
of the regions with different signature can affect the evolution of the
Universe.


Abstract:
  Decomposing taxes by source (labor, capital, sales), we analyze the impact of
automation (1) on tax revenues, (2) the structure of taxation, and (3) identify
channels of impact in 19 EU countries during 1995-2016. Robots and Information
and Communication Technologies (ICT) are different technologies designed to
automate manual (robots) or cognitive tasks (ICT).
  Until 2007, robot diffusion led to decreasing factor and tax income, and a
shift from taxes on capital to goods. ICTs changed the structure of taxation
from capital to labor. We find decreasing employment, but increasing wages and
labor income. After 2008, robots have no effect but we find an ICT-induced
increase in capital income, a rise of services, but no effect on taxation.
Automation goes through different phases with different economic impacts which
affect the amount and structure of taxes. Whether automation erodes taxation
depends (a) on the technology type, (b) the stage of diffusion and (c) local
conditions.


Abstract:
  In this paper, we propose the novel problem of Subteam Replacement: given a
team of people embedded in a social network to complete a certain task, and a
subset of members - subteam - in this team which have become unavailable, find
another set of people who can perform the subteam's role in the larger team.
The ability to simultaneously replace multiple team members is highly
appreciated in settings such as corporate management where team structure is
highly volatile and large-scale changes are commonplace. We conjecture that a
good candidate subteam should have high skill and structural similarity with
the replaced subteam while sharing a similar connection with the larger team as
a whole. Based on this conjecture, we propose a novel graph kernel which
evaluates the goodness of candidate subteams in this holistic way freely
adjustable to the need of the situation. To tackle the significant
computational difficulties, we combine our kernel with a fast approximate
algorithm which (a) employs effective pruning strategies, (b) exploits the
similarity between candidate team structures to reduce kernel computations, and
(c) features a solid theoretical bound obtained from mathematical properties of
the problem. We extensively test our solution on both synthetic and real
datasets to demonstrate its consistency and efficiency. Our proposed graph
kernel results in more suitable replacements being proposed compared to graph
kernels used in previous work, and our algorithm consistently outperforms
alternative choices by finding near-optimal solutions while scaling linearly
with the size of the replaced subteam.


Abstract:
  We describe our straight-forward approach for Tasks 5 and 6 of 2021 Social
Media Mining for Health Applications (SMM4H) shared tasks. Our system is based
on fine-tuning Distill- BERT on each task, as well as first fine-tuning the
model on the other task. We explore how much fine-tuning is necessary for
accurately classifying tweets as containing self-reported COVID-19 symptoms
(Task 5) or whether a tweet related to COVID-19 is self-reporting, non-personal
reporting, or a literature/news mention of the virus (Task 6).


Abstract:
  The multi-point Taylor polynomial, which is the general, unique and of
minimum degree ($mk+m-1$) polynomial $P_{k,m}(x)$ which interpolates a
function's derivatives in multiple points is presented in its explicit form. A
proof that this expression satisfies the multi-point Taylor polynomial's
defining property is given. Namely, it is proven that for a k-differentiable
function $f$ and a set of different m-points $\{a_1,...,a_m\}$, this polynomial
satisfies $P^{(n)}_{k,m}(a_i) = f^{(n)}(a_i)\quad \forall \, i = 1,...,m\quad
\&\quad \forall \, n = 0,...,k$. A discussion regarding previous expressions
presented in the literature, which mostly consisted in recursion formulas and
not explicit formulas, is made.


Abstract:
  We propose a new sensing method based on the measurement of the second-order
autocorrelation of the output of micro- and nanolasers with intensity feedback.
The sensing function is implemented through the feedback-induced threshold
shift, whose photon statistics is controlled by the feedback level in a
characteristic way for different laser sizes. The specific response offers
performances which can be adapted to different kinds of sensors. We propose the
implementation of two schemes capable of providing a quantitative sensing
signal and covering a broad range of feedback levels: one is utilizing the
evolution of g$^{(2)}$(0), the other one is the ratio between central and side
peaks in g$^{(2)}(\tau)$. Laser-threshold-based sensing could, thanks to its
potential sensitivity, gain relevance in biomolecular diagnostics and security
monitoring.


Abstract:
  Technical debt has become a well-known metaphor among software professionals,
visualizing how shortcuts taken during development can accumulate and become a
burden for software projects. In the traditional notion of technical debt,
software developers borrow from the maintainability and extensibility of a
software system, thus they are the ones paying the interest. User experience
(UX) debt, on the other hand, focuses on shortcuts taken to speed up
development at the expense of subpar usability, thus mainly borrowing from
users' efficiency. With this article, we want to build awareness for this
often-overlooked form of technical debt by outlining classes of UX debts that
we observed in practice and by pointing to the lack of research and tool
support targeting UX debt in general.


Abstract:
  High spectral resolution observations toward the low mass-loss rate C-rich,
J-type AGB star Y CVn have been carried out at 7.5, 13.1 and 14.0 um with
SOFIA/EXES and IRTF/TEXES. Around 130 HCN and H13CN lines of bands v2, 2v2,
2v2-v2, 3v2-2v2, 3v2-v2, and 4v2-2v2 have been identified involving lower
levels with energies up to ~3900 K. These lines have been complemented with the
pure rotational lines J=1-0 and 3-2 of the vibrational states up to 2v2
acquired with the IRAM 30 m telescope, and with the continuum taken with ISO.
We have analyzed the data with a ro-vibrational diagram and a code which models
the absorption and emission of the circumstellar envelope of an AGB star. The
continuum is produced by the star with a small contribution from dust grains
comprising warm to hot SiC and cold amorphous carbon. The HCN abundance
distribution seems to be anisotropic. The ejected gas is accelerated up to the
terminal velocity (~8 km/s) from the photosphere to ~3R* but there is evidence
of higher velocities (>9-10 km/s) beyond this region. In the vicinity of Y CVn,
the line widths are as high as ~10 km/s, which implies a maximum turbulent
velocity of 6 km/s or the existence of other physical mechanisms probably
related to matter ejection that involve higher gas expansion velocities than
expected. HCN is rotationally and vibrationally out of LTE throughout the whole
envelope. A difference of about 1500 K in the rotational temperature at the
photosphere is needed to explain the observations at 7.5 and 13-14 um. Our
analysis finds a total HCN column density that ranges from ~2.1E+18 to 3.5E+18
cm^{-2}, an abundance with respect to H2 of 3.5E-05 to 1.3E-04, and a 12C/13C
isotopic ratio of ~2.5 throughout the whole envelope.


Abstract:
  We consider a randomised version of Kleene's realisability interpretation of
intuitionistic arithmetic in which computability is replaced with randomised
computability with positive probability. In particular, we show that (i) the
set of randomly realisable statements is closed under intuitionistic
first-order logic, but (ii) different from the set of realisable statements,
that (iii) "realisability with probability 1" is the same as realisability and
(iv) that the axioms of bounded Heyting's arithmetic are randomly realisable,
but some instances of the full induction scheme fail to be randomly realisable.


Abstract:
  Photoluminescence (PL) is a light-matter quantum interaction associated with
the chemical potential of light formulated by the Generalized Planck's law.
Without knowing the inherent temperature dependence of chemical potential, the
Generalized Planck's law is insufficient to characterize PL(T). Recent
experiments showed that PL at low temperatures conserves the emitted photon
rate, accompanied by a blue-shift and transition to thermal emission at a
higher temperature. Here, we theoretically study temperature-dependent PL by
including phononic interactions in a detailed balance analysis. Our solution
validates recent experiments and predicts important relations, including i) An
inherent relation between emissivity and the quantum efficiency of a system,
ii) A universal point defined by the pump and the temperature where the
emission rate is fixed to any material, iii) A new phonon-induced quenching
mechanism, and iv) Thermalization of the photon spectrum. These findings are
relevant to and important for all photonic fields where the temperature is
dominant.


Abstract:
  An important step in the task of neural network design, such as
hyper-parameter optimization (HPO) or neural architecture search (NAS), is the
evaluation of a candidate model's performance. Given fixed computational
resources, one can either invest more time training each model to obtain more
accurate estimates of final performance, or spend more time exploring a greater
variety of models in the configuration space. In this work, we aim to optimize
this exploration-exploitation trade-off in the context of HPO and NAS for image
classification by accurately approximating a model's maximal performance early
in the training process. In contrast to recent accelerated NAS methods
customized for certain search spaces, e.g., requiring the search space to be
differentiable, our method is flexible and imposes almost no constraints on the
search space. Our method uses the evolution history of features of a network
during the early stages of training to build a proxy classifier that matches
the peak performance of the network under consideration. We show that our
method can be combined with multiple search algorithms to find better solutions
to a wide range of tasks in HPO and NAS. Using a sampling-based search
algorithm and parallel computing, our method can find an architecture which is
better than DARTS and with an 80% reduction in wall-clock search time.


Abstract:
  We present an analysis of the kinematics of 14 satellites of the Milky Way
(MW). We use proper motions (PMs) from the $Gaia$ Early Data Release 3 (EDR3)
and line-of-sight velocities ($v_{\mathrm{los}}$) available in the literature
to derive the systemic 3D motion of these systems. For six of them, namely the
Carina, Draco, Fornax, Sculptor, Sextans, and Ursa Minor dwarf spheroidal
galaxies (dSph), we study the internal kinematics projecting the stellar PMs
into radial, $V_R$ (expansion/contraction), and tangential, $V_T$ (rotation),
velocity components with respect to the centre of mass. We find significant
rotation in the Carina ($|V_T| = 9.6 \pm 4.5 \ {\rm{km \ s^{-1}}}\>$), Fornax
($|V_T| = 2.8 \pm 1.3 \ {\rm{km \ s^{-1}}}\>$), and Sculptor ($|V_T| = 3.0 \pm
1.0 \ {\rm{km \ s^{-1}}}\>$) dSphs. Besides the Sagittarius dSph, these are the
first measurements of internal rotation in the plane of the sky in the MW's
classical dSphs. All galaxies except Carina show $|V_T| / \sigma_v < 1$. We
find that slower rotators tend to show, on average, larger sky-projected
ellipticity (as expected for a sample with random viewing angles) and are
located at smaller Galactocentric distances (as expected for tidal stirring
scenarios in which rotation is transformed into random motions as satellites
sink into the parent halo). However, these trends are small and not
statistically significant, indicating that rotation has not played a dominant
role in shaping the 3D structure of these galaxies. Either tidal stirring had a
weak impact on the evolution of these systems or it perturbed them with similar
efficiency regardless of their current Galactocentric distance.


Abstract:
  In this paper, a numerical study is conducted to investigate boiling of a
cryogen on a solid surface as well as on a liquid surface. Both single mode and
multi-mode boiling is reported for boiling on to a solid surface. In case of
boiling on a liquid surface, liquid nitrogen is selected as the cryogen
(boiling fluid) and water is chosen as the base fluid (heating fluid).
Different flow instabilities and their underlying consequences during boiling
of a cryogen are also discussed. For the boiling on a solid surface, in the
single mode, bubble growth, its departure, and area weighted average heat flux
are reported, where they increase linearly with increase in the wall superheat.
Asymmetry in the bubble growth and departure of 2nd batch of the vapor bubbles
have been observed due to local fluctuations and turbulence created just after
the pinch off of the 1st batch of vapor bubbles in case of multi-mode boiling
on the solid surface. Boiling of LN2 on a liquid surface is reported for a base
fluid (Water) temperature of 300 K. Vapor film thickness decreases with time
and the minimum film thickness just before rupture is 7.62 micrometer,
dominance of thermocapillary over vapor thrust causes breaking of the vapor
film at 0.0325s. The difference in evaporation rate and vapor generation,
before and after vapor film collapse is significant.


Abstract:
  Generating videos with content and motion variations is a challenging task in
computer vision. While the recent development of GAN allows video generation
from latent representations, it is not easy to produce videos with particular
content of motion patterns of interest. In this paper, we propose Dual Motion
Transfer GAN (Dual-MTGAN), which takes image and video data as inputs while
learning disentangled content and motion representations. Our Dual-MTGAN is
able to perform deterministic motion transfer and stochastic motion generation.
Based on a given image, the former preserves the input content and transfers
motion patterns observed from another video sequence, and the latter directly
produces videos with plausible yet diverse motion patterns based on the input
image. The proposed model is trained in an end-to-end manner, without the need
to utilize pre-defined motion features like pose or facial landmarks. Our
quantitative and qualitative results would confirm the effectiveness and
robustness of our model in addressing such conditioned image-to-video tasks.


Abstract:
  The infinite many symmetries of DS(Davey-Stewartson) system are closely
connected to the integrable deformations of surfaces in $\mathbb{R}^{4}$. In
this paper, we give a direct algorithm to construct the expression of the
DS(Davey-Stewartson) hierarchy by two scalar pseudo-differential operators
involved with $\partial$ and $\hat{\partial}$.


Abstract:
  Young giant planets and brown dwarf companions emit near-infrared radiation
that can be linearly polarized up to several percent. This polarization can
reveal the presence of a circumsubstellar accretion disk, rotation-induced
oblateness of the atmosphere, or an inhomogeneous distribution of atmospheric
dust clouds. We measured the near-infrared linear polarization of 20 known
directly imaged exoplanets and brown dwarf companions with the high-contrast
imager SPHERE-IRDIS at the VLT. We reduced the data using the IRDAP pipeline to
correct for the instrumental polarization and crosstalk with an absolute
polarimetric accuracy <0.1% in the degree of polarization. We report the first
detection of polarization originating from substellar companions, with a
polarization of several tenths of a percent for DH Tau B and GSC 6214-210 B in
H-band. By comparing the measured polarization with that of nearby stars, we
find that the polarization is unlikely to be caused by interstellar dust.
Because the companions have previously measured hydrogen emission lines and red
colors, the polarization most likely originates from circumsubstellar disks.
Through radiative transfer modeling, we constrain the position angles of the
disks and find that the disks must have high inclinations. The presence of
these disks as well as the misalignment of the disk of DH Tau B with the disk
around its primary star suggest in situ formation of the companions. For the 18
other companions, we do not detect significant polarization and place
subpercent upper limits on their degree of polarization. These non-detections
may indicate the absence of circumsubstellar disks, a slow rotation rate of
young companions, the upper atmospheres containing primarily submicron-sized
dust grains, and/or limited cloud inhomogeneity. Finally, we present images of
the circumstellar disks of DH Tau, GQ Lup, PDS 70, Beta Pic, and HD 106906.


Abstract:
  In superconducting quantum circuits (SQCs), chiral routing quantum
information is often realized with the ferrite circulators, which are usually
buck, lossy and require strong magnetic fields. To overcome these problems, we
propose a novel method to realize chiral quantum networks by exploiting the
giant atom effects in SQC platforms. By assuming each coupling point modulated
with time, the interaction becomes momentum-dependent, and the giant atoms will
chirally emit photons due to interference effects. The chiral factor can
approach 1, and both the emission direction and rate can be freely tuned by the
modulating signals. We demonstrate that the high-fidelity state transfer
between remote giant atoms can be realized. Our proposal can be integrated on
the superconducting chip easily, and has the potential to work as a tunable
toolbox for quantum information processing in future chiral quantum networks.


Abstract:
  We extend HPQCD's earlier $n_f=4$ lattice-QCD analysis of the ratio of
$\overline{\mathrm{MSB}}$ masses of the $b$ and $c$ quark to include results
from finer lattices (down to 0.03fm) and a new calculation of QED contributions
to the mass ratio. We find that
$\overline{m}_b(\mu)/\overline{m}_c(\mu)=4.586(12)$ at renormalization scale
$\mu=3$\,GeV. This result is nonperturbative. Combining it with HPQCD's recent
lattice QCD$+$QED determination of $\overline{m}_c(3\mathrm{GeV})$ gives a new
value for the $b$-quark mass: $\overline{m}_b(3\mathrm{GeV}) = 4.513(26)$GeV.
The $b$-mass corresponds to $\overline{m}_b(\overline{m}_b, n_f=5) =
4.202(21)$GeV. These results are the first based on simulations that include
QED.


Abstract:
  In the fractional nonrelativistic potential model, the decomposition of heavy
quarkonium in a hot magnetized medium is investigated. The analytical solution
of the fractional radial Schrodinger equation for the hot-magnetized
interaction potential is displayed by using the conformable fractional
Nikiforov-Uvarov method. Analytical expressions for the energy eigenvalues and
the radial wave function are obtained for arbitrary quantum numbers. Next, we
study the charmonium and bottmonium binding energies for different magnetic
field values in the thermal medium. The effect of the fractional parameter on
the decomposition temperature is also analyzed for charmonium and bottomonium
in the presence of hot magnetized media. We conclude that the dissociation of
heavy quarkonium in the fractional nonrelativistic potential model is more
practical than the classical nonrelativistic potential model.


Abstract:
  The study of advanced quantum devices for energy storage has attracted the
attention of the scientific community in the past few years. Although several
theoretical progresses have been achieved recently, experimental proposals of
platforms operating as quantum batteries under ambient conditions are still
lacking. In this context, this work presents a feasible realization of a
quantum battery in a carboxylate-based metal complex, which can store a finite
amount of extractable work under the form of quantum discord at room
temperature, and recharge by thermalization with a reservoir. Moreover, the
stored work can be evaluated through non-destructive measurements of the
compound's magnetic susceptibility. These results pave the way for the
development of enhanced energy storage platforms through material engineering.


Abstract:
  We define the doubling zeta integral for smooth families of representations
of classical groups. Following this we prove a rationality result for these
zeta integrals and show that they satisfy a functional equation. Moreover, we
show that there exists an apropriate normalizing factor which allows us to
construct $\gamma$-factors for smooth families out of the functional equation.
We prove that under certain hypothesis, specializing this $\gamma$-factor at a
point of the family yields the $\gamma$-factor defined by Piateski-Shapiro and
Rallis.


Abstract:
  Future generation wireless networks are designed with extremely low delay
requirements which makes even small contributed delays important. On the other
hand, software defined networking (SDN) has been introduced as a key enabler of
future wireless and cellular networks in order to make them more flexible. In
SDN, a central controller manages all network equipments by setting the
match-action pairs in flow tables of the devices. However, these flow tables
have limited capacity and thus are not capable of storing the rules of all the
users. In this paper, we consider an SDN-enabled base station (SD-BS) in a cell
equipped with a limited capacity flow table. We analyze the expected delay
incurred in processing of the incoming packets to the SD-BS and present a
mathematical expression for it in terms of density of the users and cell area.


Abstract:
  We train deep generative models on datasets of reflexive polytopes. This
enables us to compare how well the models have picked up on various global
properties of generated samples. Our datasets are complete in the sense that
every single example, up to changes of coordinate, is included in the dataset.
Using this property we also perform tests checking to what extent the models
are merely memorizing the data. We also train models on the same dataset
represented in two different ways, enabling us to measure which form is easiest
to learn from. We use these experiments to show that deep generative models can
learn to generate geometric objects with non-trivial global properties, and
that the models learn some underlying properties of the objects rather than
simply memorizing the data.


Abstract:
  Nano-membrane tri-gate beta-gallium oxide (\b{eta}-Ga2O3) field-effect
transistors (FETs) on SiO2/Si substrate fabricated via exfoliation have been
demonstrated for the first time. By employing electron beam lithography, the
minimum-sized features can be defined with a 50 nm fin structure. For
high-quality interface between \b{eta}-Ga2O3 and gate dielectric, atomic
layer-deposited 15-nm-thick aluminum oxide (Al2O3) was utilized with
Tri-methyl-aluminum (TMA) self-cleaning surface treatment. The fabricated
devices demonstrate extremely low subthreshold slope (SS) of 61 mV/dec, high
drain current (IDS) ON/OFF ratio of 1.5 X 109, and negligible transfer
characteristic hysteresis. We also experimentally demonstrated robustness of
these devices with current-voltage (I-V) characteristics measured at
temperatures up to 400 {\deg}C.


Abstract:
  This technical report outlines the fundamental workings of the game logic
behind Ludii, a general game system, that can be used to play a wide variety of
games. Ludii is a program developed for the ERC-funded Digital Ludeme Project,
in which mathematical and computational approaches are used to study how games
were played, and spread, throughout history. This report explains how general
game states and equipment are represented in Ludii, and how the rule ludemes
dictating play are implemented behind the scenes, giving some insight into the
core game logic behind the Ludii general game player. This guide is intended to
help game designers using the Ludii game description language to understand it
more completely and make fuller use of its features when describing their
games.


Abstract:
  In the classical partial vertex cover problem, we are given a graph $G$ and
two positive integers $R$ and $L$. The goal is to check whether there is a
subset $V'$ of $V$ of size at most $R$, such that $V'$ covers at least $L$
edges of $G$. The problem is NP-hard as it includes the Vertex Cover problem.
Previous research has addressed the extension of this problem where one has
weight-functions defined on sets of vertices and edges of $G$. In this paper,
we consider the following version of the problem where on the input we are
given an edge-weighted bipartite graph $G$, and three positive integers $R$,
$S$ and $T$. The goal is to check whether $G$ has a subset $V'$ of vertices of
$G$ of size at most $R$, such that the edges of $G$ covered by $V'$ have weight
at least $S$ and they include a matching of weight at least $T$. In the paper,
we address this problem from the perspective of fixed-parameter tractability.
One of our hardness results is obtained via a reduction from the bi-objective
knapsack problem, which we show to be W[1]-hard with respect to one of
parameters. We believe that this problem might be useful in obtaining similar
results in other situations.


Abstract:
  Cytoskeletal networks are the main actuators of cellular mechanics, and a
foundational example for active matter physics. In cytoskeletal networks,
motion is generated on small scales by filaments that push and pull on each
other via molecular-scale motors. These local actuations give rise to large
scale stresses and motion. To understand how microscopic processes can give
rise to self-organized behavior on larger scales it is important to consider
what mechanisms mediate long-ranged mechanical interactions in the systems. Two
scenarios have been considered in the recent literature. The first are systems
which are relatively sparse, in which most of the large scale momentum transfer
is mediated by the solvent in which cytoskeletal filaments are suspended. The
second, are systems in which filaments are coupled via crosslink molecules
throughout. Here, we review the differences and commonalities between the
physics of these two regimes. We also survey the literature for the numbers
that allow us to place a material within either of these two classes.


Abstract:
  We are interested in the effect of Dirichlet boundary conditions on the nodal
length of Laplace eigenfunctions. We study random Gaussian Laplace
eigenfunctions on the two dimensional square and find a two terms asymptotic
expansion for the expectation of the nodal length in any square of side larger
than the Planck scale, along a denisty one sequence of energy levels. The proof
relies on a new study of lattice points in small arcs, and shows that the said
expectation is independent of the position of the square, giving the same
asymptotic expansion both near and far from the boundaries.


Abstract:
  In agreement with the gravitational-wave events which are constantly
increasing, new aspects of the internal structure of compact stars have come to
light. A scenario in which a first order transition takes place inside these
stars is of particular interest as it can lead, under conditions, to a third
gravitationally stable branch (besides white dwarfs and neutron stars). This is
known as the twin star scenario. The new branch yields stars with the same mass
as normal compact stars but quite different radii. In the current work, we
focus on hybrid stars undergone a hadron to quark phase transition near their
core and how this new stable configuration arises. Emphasis is to be given
especially in the aspects of the phase transition and its parameterization in
two different ways, namely with Maxwell construction and with Gibbs
construction. Qualitative findings of mass-radius relations of these stars will
also be presented.


Abstract:
  Current densities are induced in the electronic structure of molecules when
they are exposed to external magnetic fields. Aromatic molecular rings sustain
net diatropic ring currents, whereas the net ring current in antiaromatic
molecular rings is paratropic and flows in the opposite, non-classical
direction. We present computational methods and protocols to calculate, analyse
and visualise magnetically induced current densities in molecules. Calculated
ring-current strengths are used for quantifying the degree of aromaticity. The
methods have been demonstrated by investigating ring-current strengths and the
degree of aromaticity of aromatic, antiaromatic and non-aromatic six-membered
hydrocarbon rings. Current-density pathways and ring-current strengths of
aromatic and antiaromatic porphyrinoids and other polycyclic molecules have
been studied. The aromaticity and current density of M\"obius-twisted molecules
has been investigated to find the dependence on the twist and the spatial
deformation of the molecular ring. Current densities of fullerene, gaudiene and
toroidal carbon nanotubes have also been studied.


Abstract:
  Photometric observations of accreting, low-mass, pre-main-sequence stars
(i.e., Classical T Tauri stars; CTTS) have revealed different categories of
variability. Several of these classifications have been linked to changes in
$\dot{M}$. To test how accretion variability conditions lead to different
light-curve morphologies, we used 1D hydrodynamic simulations of accretion
along a magnetic field line coupled with radiative transfer models and a simple
treatment of rotation to generate synthetic light curves. We adopted previously
developed metrics in order to classify observations to facilitate comparisons
between observations and our models. We found that stellar mass, magnetic field
geometry, corotation radius, inclination, and turbulence all play roles in
producing the observed light curves and that no single parameter is entirely
dominant in controlling the observed variability. While the periodic behavior
of the light curve is most strongly affected by the inclination, it is also a
function of the magnetic field geometry and inner disk turbulence. Objects with
either pure dipole fields, strong aligned octupole components, or high
turbulence in the inner disk all tend to display accretion bursts. Objects with
anti-aligned octupole components or aligned, weaker octupole components tend to
show light curves with slightly fewer bursts. We did not find clear monotonic
trends between the stellar mass and empirical classification. This work
establishes the groundwork for more detailed characterization of well-studied
targets as more light curves of CTTS become available through missions such as
the Transiting Exoplanet Survey Satellite (TESS).


Abstract:
  Much recent literature has formulated structure-from-motion (SfM) as a
self-supervised learning problem where the goal is to jointly learn neural
network models of depth and egomotion through view synthesis. Herein, we
address the open problem of how to optimally couple the depth and egomotion
network components. Toward this end, we introduce several notions of coupling,
categorize existing approaches, and present a novel tightly-coupled approach
that leverages the interdependence of depth and egomotion at training and at
inference time. Our approach uses iterative view synthesis to recursively
update the egomotion network input, permitting contextual information to be
passed between the components without explicit weight sharing. Through
substantial experiments, we demonstrate that our approach promotes consistency
between the depth and egomotion predictions at test time, improves
generalization on new data, and leads to state-of-the-art accuracy on indoor
and outdoor depth and egomotion evaluation benchmarks.


Abstract:
  Instances-reweighted adversarial training (IRAT) can significantly boost the
robustness of trained models, where data being less/more vulnerable to the
given attack are assigned smaller/larger weights during training. However, when
tested on attacks different from the given attack simulated in training, the
robustness may drop significantly (e.g., even worse than no reweighting). In
this paper, we study this problem and propose our solution--locally reweighted
adversarial training (LRAT). The rationale behind IRAT is that we do not need
to pay much attention to an instance that is already safe under the attack. We
argue that the safeness should be attack-dependent, so that for the same
instance, its weight can change given different attacks based on the same
model. Thus, if the attack simulated in training is mis-specified, the weights
of IRAT are misleading. To this end, LRAT pairs each instance with its
adversarial variants and performs local reweighting inside each pair, while
performing no global reweighting--the rationale is to fit the instance itself
if it is immune to the attack, but not to skip the pair, in order to passively
defend different attacks in future. Experiments show that LRAT works better
than both IRAT (i.e., global reweighting) and the standard AT (i.e., no
reweighting) when trained with an attack and tested on different attacks.


Abstract:
  A Cayley (di)graph $Cay(G,S)$ of a group $G$ with respect to a subset $S$ of
$G$ is called normal if the right regular representation of $G$ is a normal
subgroup in the full automorphism group of $Cay(G,S)$, and is called a
CI-(di)graph if for every $T\subseteq G$, $Cay(G,S)\cong Cay(G,T)$ implies that
there is $\sigma\in Aut(G)$ such that $S^\sigma=T$. We call a group $G$ a
NDCI-group if all normal Cayley digraphs of $G$ are CI-digraphs, and a
NCI-group if all normal Cayley graphs of $G$ are CI-graphs, respectively. In
this paper, we prove that a cyclic group of order $n$ is a NDCI-group if and
only if $8\nmid n$, and is a NCI-group if and only if either $n=8$ or $8\nmid
n$.


Abstract:
  Recently the first example of a family of pro-$p$ groups, for $p$ a prime,
with full normal Hausdorff spectrum was constructed. In this paper we further
investigate this family by computing their finitely generated Hausdorff
spectrum with respect to each of the five standard filtration series: the
$p$-power series, the iterated $p$-power series, the lower $p$-series, the
Frattini series and the dimension subgroup series. Here the finitely generated
Hausdorff spectra of these groups consist of infinitely many rational numbers,
and their computation requires a rather technical approach. This result also
gives further evidence to the non-existence of a finitely generated pro-$p$
group with uncountable finitely generated Hausdorff spectrum.


Abstract:
  While Semi-supervised learning has gained much attention in computer vision
on image data, yet limited research exists on its applicability in the time
series domain. In this work, we investigate the transferability of
state-of-the-art deep semi-supervised models from image to time series
classification. We discuss the necessary model adaptations, in particular an
appropriate model backbone architecture and the use of tailored data
augmentation strategies. Based on these adaptations, we explore the potential
of deep semi-supervised learning in the context of time series classification
by evaluating our methods on large public time series classification problems
with varying amounts of labelled samples. We perform extensive comparisons
under a decidedly realistic and appropriate evaluation scheme with a unified
reimplementation of all algorithms considered, which is yet lacking in the
field. We find that these transferred semi-supervised models show significant
performance gains over strong supervised, semi-supervised and self-supervised
alternatives, especially for scenarios with very few labelled samples.


Abstract:
  In this article, we give a proof of multiplicativity for $\gamma$-factors, an
equality of parabolically induced and inducing factors, in the context of the
Braverman-Kazhdan/Ngo program, under the assumption of commutativity of the
corresponding Fourier transforms and a certain generalized Harish-Chandra
transform. We also discuss the resolution of singularities and their
rationality for reductive monoids, which are among the basic objects in the
program.


Abstract:
  Stationary waves in the condensate of electron-hole pairs in the $n-p$
bilayer system are studied. The system demonstrates the transition from a
uniform (superfluid) to a nonuniform (supersolid) state. The precursor of this
transition is the appearance of the roton-type minimum in the collective mode
spectrum. Stationary waves occur in the flow of the condensate past an
obstacle. It is shown that the roton-type minimum manifests itself in a rather
complicated stationary wave pattern with several families of crests which cross
one another. It is found that the stationary wave pattern is essentially
modified under variation in the density of the condensate and under variation
in the flow velocity. It is shown that the pattern is formed in the main part
by shortwave modes in the case of a point obstacle. The contribution of
longwave modes is clearly visible in the case of a weak extended obstacle,
where the stationary wave pattern resembles the ship wave pattern.


Abstract:
  Let $q$ be an odd prime power. Denote by $r(q)$ the value $q$ modulo 4. In
this paper we establish a correspondence between two types of maximal cliques
of order $\frac{q+r(q)}{2}$ in the Paley graph of order $q^2$.


Abstract:
  Pool boiling is one of the efficient ways to remove heat from high-power
electronics, heat exchangers, and nuclear reactors. Nowadays, the pool boiling
method is tried to exercise frequently due to its ability to remove high heat
flux compared with natural/forced convection, while maintaining at low wall
superheat. But this pool boiling heat transfer capacity is also limited by an
important parameter, called critical heat flux (CHF). At the point of CHF, the
heat transfer coefficient (HTC) drastically decreases due to the change of the
heat transfer regime from nucleate boiling to film boiling. This is why, the
enhancement of CHF, while maintaining low wall superheat is of great interest
to engineers and researchers.


Abstract:
  In a previous paper, second- and fourth-order explicit symplectic integrators
were designed for a Hamiltonian of the Schwarzschild black hole. Following this
work, we continue to trace the possibility of the construction of explicit
symplectic integrators for a Hamiltonian of charged particles moving around a
Reissner-Nordstrom black hole with an external magnetic field. Such explicit
symplectic methods are still available when the Hamiltonian is separated into
five independently integrable parts with analytical solutions as explicit
functions of proper time. Numerical tests show that the proposed algorithms
share the desirable properties in their long-term stability, precision and
efficiency for appropriate choices of step sizes. For the applicability of one
of the new algorithms, the effects of the black hole's charge, the Coulomb part
of the electromagnetic potential and the magnetic parameter on the dynamical
behavior are surveyed. Under some circumstances, the extent of chaos gets
strong with an increase of the magnetic parameter from a global phase-space
structure. No the variation of the black hole's charge but the variation of the
Coulomb part is considerably sensitive to affect the regular and chaotic
dynamics of particles' orbits. A positive Coulomb part is easier to induce
chaos than a negative one.


Abstract:
  Heterogeneous teams of robots, leveraging a balance between autonomy and
human interaction, bring powerful capabilities to the problem of exploring
dangerous, unstructured subterranean environments. Here we describe the
solution developed by Team CSIRO Data61, consisting of CSIRO, Emesent and
Georgia Tech, during the DARPA Subterranean Challenge. These presented systems
were fielded in the Tunnel Circuit in August 2019, the Urban Circuit in
February 2020, and in our own Cave event, conducted in September 2020. A unique
capability of the fielded team is the homogeneous sensing of the platforms
utilised, which is leveraged to obtain a decentralised multi-agent SLAM
solution on each platform (both ground agents and UAVs) using peer-to-peer
communications. This enabled a shift in focus from constructing a pervasive
communications network to relying on multi-agent autonomy, motivated by
experiences in early circuit events. These experiences also showed the
surprising capability of rugged tracked platforms for challenging terrain,
which in turn led to the heterogeneous team structure based on a BIA5 OzBot
Titan ground robot and an Emesent Hovermap UAV, supplemented by smaller tracked
or legged ground robots. The ground agents use a common CatPack perception
module, which allowed reuse of the perception and autonomy stack across all
ground agents with minimal adaptation.


Abstract:
  Black phosphorus (BP) analogous tin(II) sulfide (SnS) has recently emerged as
an attractive building block for electronic devices due to its highly
anisotropic response. Two-dimensional (2D) SnS has shown to exhibit in-plane
anisotropy in optical and electrical properties. However, the limitations in
growing ultrasmall structures of SnS hinder the experimental exploration of
anisotropic behavior in low dimension. Here, we present an elegant approach of
synthesizing highly crystalline nanometer-sized SnS sheets. Ultrasmall SnS
exhibits two distinct valleys along armchair and zig-zag directions due to
in-plane structural anisotropy like bulk SnS. We show that in such SnS
nanosheet dots, the band gaps corresponding to two valleys are increased due to
quantum confinement effect. We particularly observe that SnS quantum dots (QDs)
show excitation energy dependent photoluminescence (PL), which originates from
the two nondegenerate valleys. Our work may open up an avenue to show the
potential of SnS QDs for new functionalities in electronics and
optoelectronics.


Abstract:
  We consider the typical behaviour of random dynamical systems of
order-preserving interval homeomorphisms with a positive Lyapunov exponent
condition at the endpoints. Our study removes any requirement for continuous
differentiability save the existence of finite derivatives of the
homeomorphisms at the endpoints of the interval. We construct a suitable Baire
space structure for this class of systems. Generically within our Baire space,
we show that the stationary measure is singular with respect to the Lebesgue
measure, but has full support on $[0,1]$. This provides an answer to a question
raised by Alsed\`a and Misiurewicz.


Abstract:
  Consumer applications are becoming increasingly smarter and most of them have
to run on device ecosystems. Potential benefits are for example enabling
cross-device interaction and seamless user experiences. Essential for today's
smart solutions with high performance are machine learning models. However,
these models are often developed separately by AI engineers for one specific
device and do not consider the challenges and potentials associated with a
device ecosystem in which their models have to run. We believe that there is a
need for tool-support for AI engineers to address the challenges of
implementing, testing, and deploying machine learning models for a next
generation of smart interactive consumer applications. This paper presents
preliminary results of a series of inquiries, including interviews with AI
engineers and experiments for an interactive machine learning use case with a
Smartwatch and Smartphone. We identified the themes through interviews and
hands-on experience working on our use case and proposed features, such as data
collection from sensors and easy testing of the resources consumption of
running pre-processing code on the target device, which will serve as
tool-support for AI engineers.


Abstract:
  The aim of this article is to show the global existence of both martingale
and pathwise solutions of stochastic equations with a monotone operator, of the
Ladyzenskaya-Smagorinsky type, driven by a general Levy noise. The classical
approach based on using directly the Galerkin approximation is not valid.
Instead, our approach is based on using appropriate approximations for the
monotone operator, Galerkin approximations and on the theory of martingale
solutions.


Abstract:
  Machine learning has been proven to be effective in various application
areas, such as object and speech recognition on mobile systems. Since a
critical key to machine learning success is the availability of large training
data, many datasets are being disclosed and published online. From a data
consumer or manager point of view, measuring data quality is an important first
step in the learning process. We need to determine which datasets to use,
update, and maintain. However, not many practical ways to measure data quality
are available today, especially when it comes to large-scale high-dimensional
data, such as images and videos. This paper proposes two data quality measures
that can compute class separability and in-class variability, the two important
aspects of data quality, for a given dataset. Classical data quality measures
tend to focus only on class separability; however, we suggest that in-class
variability is another important data quality factor. We provide efficient
algorithms to compute our quality measures based on random projections and
bootstrapping with statistical benefits on large-scale high-dimensional data.
In experiments, we show that our measures are compatible with classical
measures on small-scale data and can be computed much more efficiently on
large-scale high-dimensional datasets.


Abstract:
  The population of the elderly people has kept increasing rapidly over the
world in the past decades. Solutions that are able to effectively support the
elderly people to live independently at their home are thus urgently needed.
Ambient assisted living (AAL) aims to provide products and services with
ambient intelligence to build a safe environment around people in need. With
the high prevalence of multiple chronic diseases, the elderly people often need
different levels of care management to prolong independent living at home. An
effective AAL system should provide the required clinical support as an
extension to the services provided in hospitals. Following the rapid growth of
available data, together with the wide application of machine learning
technologies, we are now able to build intelligent ambient assisted systems to
fulfil such a request. This paper discusses different levels of intelligence in
AAL. We also introduce our solution for building an intelligent AAL system with
the discussed technologies. Taking semantic web technology as its backbone,
such an AAL system is able to aggregate information from different sources,
solve the semantic gap between different data sources, and perform adaptive and
personalized carepath management based on the ambient environment.


Abstract:
  Following the theory of information measures based on the cumulative
distribution function, we propose the fractional generalized cumulative
entropy, and its dynamic version. These entropies are particularly suitable to
deal with distributions satisfying the proportional reversed hazard model. We
study the connection with fractional integrals, and some bounds and comparisons
based on stochastic orderings, that allow to show that the proposed measure is
actually a variability measure. The investigation also involves various notions
of reliability theory, since the considered dynamic measure is a suitable
extension of the mean inactivity time. We also introduce the empirical
generalized fractional cumulative entropy as a non-parametric estimator of the
new measure. It is shown that the empirical measure converges to the proposed
notion almost surely. Then, we address the stability of the empirical measure
and provide an example of application to real data. Finally, a central limit
theorem is established under the exponential distribution.


Abstract:
  The most commonly used interface between a video game and the human user is a
handheld "game controller", "game pad", or in some occasions an "arcade stick."
Directional pads, analog sticks and buttons - both digital and analog - are
linked to in-game actions. One or multiple simultaneous inputs may be necessary
to communicate the intentions of the user. Activating controls may be more or
less convenient depending on their position and size. In order to enable the
user to perform all inputs which are necessary during gameplay, it is thus
imperative to find a mapping between in-game actions and buttons, analog
sticks, and so on. We present simple formats for such mappings as well as for
the constraints on possible inputs which are either determined by a physical
game controller or required to be met for a game software, along with methods
to transform said constraints via a button-action mapping and to check one
constraint set against another, i.e., to check whether a button-action mapping
allows a controller to be used in conjunction with a game software, while
preserving all desired properties.


Abstract:
  Safety constraints and optimality are important, but sometimes conflicting
criteria for controllers. Although these criteria are often solved separately
with different tools to maintain formal guarantees, it is also common practice
in reinforcement learning to simply modify reward functions by penalizing
failures, with the penalty treated as a mere heuristic. We rigorously examine
the relationship of both safety and optimality to penalties, and formalize
sufficient conditions for safe value functions: value functions that are both
optimal for a given task, and enforce safety constraints. We reveal the
structure of this relationship through a proof of strong duality, showing that
there always exists a finite penalty that induces a safe value function. This
penalty is not unique, but upper-unbounded: larger penalties do not harm
optimality. Although it is often not possible to compute the minimum required
penalty, we reveal clear structure of how the penalty, rewards, discount
factor, and dynamics interact. This insight suggests practical, theory-guided
heuristics to design reward functions for control problems where safety is
important.


Abstract:
  This paper critically examines arguments against independence, a measure of
group fairness also known as statistical parity and as demographic parity. In
recent discussions of fairness in computer science, some have maintained that
independence is not a suitable measure of group fairness. This position is at
least partially based on two influential papers (Dwork et al., 2012, Hardt et
al., 2016) that provide arguments against independence. We revisit these
arguments, and we find that the case against independence is rather weak. We
also give arguments in favor of independence, showing that it plays a
distinctive role in considerations of fairness. Finally, we discuss how to
balance different fairness considerations.


Abstract:
  The International Olympiad in Cryptography NSUCRYPTO is the unique Olympiad
containing scientific mathematical problems for professionals, school and
university students from any country. Its aim is to involve young researchers
in solving curious and tough scientific problems of modern cryptography. In
2020, it was held for the seventh time. Prizes and diplomas were awarded to 84
participants in the first round and 49 teams in the second round from 32
countries. In this paper, problems and their solutions of NSUCRYPTO'2020 are
presented. We consider problems related to attacks on ciphers and hash
functions, protocols, permutations, primality tests, etc. We discuss several
open problems on JPEG encoding, Miller -- Rabin primality test, special bases
in the vector space, AES-GCM. The problem of a modified Miller -- Rabin
primality test was solved during the Olympiad. The problem for finding special
bases was partially solved.


Abstract:
  We give two examples which show that rational nef and anti-nef polytopes are
not uniform even for klt surface pairs, answering a question of Chen-Han. We
also show that rational nef polytopes are uniform when the Cartier indices are
uniformly bounded.


Abstract:
  Let $G=(V,E)$ be a simple graph. A dominating set of $G$ is a subset
$D\subseteq V$ such that every vertex not in $D$ is adjacent to at least one
vertex in $D$. The cardinality of a smallest dominating set of $G$, denoted by
$\gamma(G)$, is the domination number of $G$. A dominating set $D$ is an
accurate dominating set of $G$, if no $|D|$-element subset of $V\setminus D$ is
a dominating set of $G$. The accurate domination number, $\gamma_a(G)$, is the
cardinality of a smallest accurate dominating set $D$. In this paper, after
presenting preliminaries, we count the number of accurate dominating sets of
some specific graphs.


Abstract:
  We construct conforming finite elements for the spaces
$H(\text{sym}\,\text{Curl})$ and $H(\text{dev}\,\text{sym}\,\text{Curl})$.
Those are spaces of matrix-valued functions with symmetric or
deviatoric-symmetric $\text{Curl}$ in a Lebesgue space, and they appear in
various models of nonstandard solid mechanics. The finite elements are not
$H(\text{Curl})$-conforming. We show the construction, prove conformity and
unisolvence, and point out optimal approximation error bounds.


Abstract:
  Assessment of replicability is critical to ensure the quality and rigor of
scientific research. In this paper, we discuss inference and modeling
principles for replicability assessment. Targeting distinct application
scenarios, we propose two types of Bayesian model criticism approaches to
identify potentially irreproducible results in scientific experiments. They are
motivated by established Bayesian prior and posterior predictive model-checking
procedures and generalize many existing replicability assessment methods.
Finally, we discuss the statistical properties of the proposed replicability
assessment approaches and illustrate their usages by simulations and examples
of real data analysis, including the data from the Reproducibility Project:
Psychology and a systematic review of impacts of pre-existing cardiovascular
disease on COVID-19 outcomes.


Abstract:
  This paper proposes a macroscopic model to describe the equilibrium
distribution of passenger arrivals for the morning commute problem in a
congested urban rail transit system. We use a macroscopic train operation
sub-model developed by Seo et al (2017a,b) to express the interaction between
the dynamics of passengers and trains in a simplified manner while maintaining
their essential physical relations. The equilibrium conditions of the proposed
model are derived and a solution method is provided. The characteristics of the
equilibrium are then examined through analytical discussion and numerical
examples. As an application of the proposed model, we analyze a simple
time-dependent timetable optimization problem with equilibrium constraints and
reveal that a "capacity increasing paradox" exists such that a higher dispatch
frequency can increase the equilibrium cost. Furthermore, insights into the
design of the timetable are obtained and the timetable influence on passengers'
equilibrium travel costs are evaluated.


Abstract:
  5G D2D Communication promises improvements in energy and spectral efficiency,
overall system capacity, and higher data rates. However, to achieve optimum
results it is important to select wisely the Transmission mode of the D2D
Device to form clusters in the most fruitful positions in terms of Sum Rate and
Power Consumption. Towards this end, this paper investigates the use of
Distributed Artificial Intelligence (DAI) and innovative to D2D, Machine
Learning (ML) approaches to achieve satisfactory results in terms of Spectral
Efficiency (SE), Power Consumption (PC) and execution time, with the creation
of clusters and backhauling D2D network under existing Base Station/Small Cell.
Additionally, one of the major factors that affect the creation of high-quality
clusters under a D2D network is the number of the Devices. Therefore, this
paper focuses on a small (<=200) number of Devices, with the purpose to
identify the limits of each approach in terms of number of devices.
Specifically, to identify where it is beneficial to form a cluster, investigate
the critical point that gains increases rapidly and at the end examine the
applicability of 5G requirements. Additionally, prior work presented a
Distributed Artificial Intelligence (DAI) Solution/Framework in D2D and a DAIS
Transmission Mode Selection (TMS) plan was proposed. In this paper DAIS is
further examined, improved in terms of thresholds evaluation, evaluated, and
compared with other approaches (AI/ML). The results obtained demonstrate the
exceptional performance of DAIS, compared to all other related approaches in
terms of SE, PC, execution time and cluster formation efficiency. Also, results
show that the investigated AI/ML approaches are also beneficial for
Transmission Mode Selection (TMS) in 5G D2D communication, even with a smaller
(i.e., >=5 D2D Relay,>=50 D2D Multi Hop Relay) numbers of devices as a lower
limits.


Abstract:
  For any nonconstant f,g in C(x) such that the numerator H(x,y) of f(x)-g(y)
is irreducible, we compute the genus of the normalization of the curve
H(x,y)=0. We also prove an analogous formula in arbitrary characteristic when f
and g have no common wildly ramified branch points, and generalize to (possibly
reducible) fiber products of nonconstant morphisms of curves f:A-->D and
g:B-->D.


Abstract:
  This paper introduces a feedback-based temperature controller design for
intelligent regulation of food internal temperature inside of standard
convection ovens. Typical convection ovens employ an open-loop control system
that requires a person to estimate the amount of time needed to cook foods in
order to achieve desired internal temperatures. This approach, however, can
result in undesired results with the final food temperatures being too high or
too low due to the inherent difficulty in accurately predicting required
cooking times without continuously measuring internal states and accounting for
noise in the system. By implementing and introducing a feedback controller with
a full-order Luenberger observer to create a closed-loop system, an oven can be
instrumented to measure and regulate the internal temperatures of food in order
to automatically control oven heat output and confidently achieve desired
results.


Abstract:
  Stabilized cat codes can provide a biased noise channel with a set of
bias-preserving (BP) gates, which can significantly reduce the resource
overhead for fault-tolerant quantum computing. All existing schemes of BP
gates, however, require adiabatic quantum evolution, with performance limited
by excitation loss and non-adiabatic errors during the adiabatic gates. In this
work, we apply a derivative-based leakage suppression technique to overcome
non-adiabatic errors, so that we can implement fast BP gates on Kerr-cat qubits
with improved gate fidelity while maintaining high noise bias. When applied to
concatenated quantum error correction, the fast BP gates can not only improve
the logical error rate but also reduce resource overhead, which enables more
efficient implementation of fault-tolerant quantum computing.


Abstract:
  We study some asymptotic properties of cylinder processes in the plane
defined as union sets of dilated straight lines (appearing as mutually
overlapping infinitely long strips) derived from a stationary independently
marked point process on the real line, where the marks describe thickness and
orientation of individual cylinders. Such cylinder processes form an important
class of (in general non-stationary) planar random sets. We observe the
cylinder process in an unboundedly growing domain $\rho K$ when $\rho \to
\infty\,$, where the set $K$ is compact and star-shaped w.r.t. the origin ${\bf
o}$ being an inner point of $K$. Provided the unmarked point process satisfies
a Brillinger-type mixing condition and the thickness of the typical cylinder
has a finite second moment we prove a (weak) law of large numbers as well as a
formula of the asymptotic variance for the area of the cylinder process in
$\rho K$. Due to the long-range dependencies of the cylinder process, this
variance increases proportionally to $\rho^3$.


Abstract:
  Depth information matters in RGB-D semantic segmentation task for providing
additional geometric information to color images. Most existing methods exploit
a multi-stage fusion strategy to propagate depth feature to the RGB branch.
However, at the very deep stage, the propagation in a simple element-wise
addition manner can not fully utilize the depth information. We propose
Global-Local propagation network (GLPNet) to solve this problem. Specifically,
a local context fusion module(L-CFM) is introduced to dynamically align both
modalities before element-wise fusion, and a global context fusion
module(G-CFM) is introduced to propagate the depth information to the RGB
branch by jointly modeling the multi-modal global context features. Extensive
experiments demonstrate the effectiveness and complementarity of the proposed
fusion modules. Embedding two fusion modules into a two-stream encoder-decoder
structure, our GLPNet achieves new state-of-the-art performance on two
challenging indoor scene segmentation datasets, i.e., NYU-Depth v2 and SUN-RGBD
dataset.


Abstract:
  We prove the Second Vanishing Theorem for local cohomology modules of an
unramified regular local ring in its full generality and provide a new proof of
the Second Vanishing Theorem in prime characteristic $p$. As an application of
our vanishing theorem for unramified regular local rings, we extend our
topological characterization of the highest Lyubeznik number of an
equi-characteristic local ring to the setting of mixed characteristic. An upper
bound of local cohomological dimension in mixed characteristic is also obtained
by partially extending Lyubeznik's vanishing theorem in prime characteristic
$p$ to mixed characteristic.


Abstract:
  Recent X-ray observations by Jiang et al. have identified an active galactic
nucleus (AGN) in the bulgeless spiral galaxy NGC 3319, located just
$14.3\pm1.1\,$Mpc away, and suggest the presence of an intermediate-mass black
hole (IMBH; $10^2\leq M_\bullet/\mathrm{M_{\odot}}\leq10^5$) if the Eddington
ratios are as high as 3 to $3\times10^{-3}$. In an effort to refine the black
hole mass for this (currently) rare class of object, we have explored multiple
black hole mass scaling relations, such as those involving the (not previously
used) velocity dispersion, logarithmic spiral-arm pitch angle, total galaxy
stellar mass, nuclear star cluster mass, rotational velocity, and colour of NGC
3319, to obtain ten mass estimates, of differing accuracy. We have calculated a
mass of $3.14_{-2.20}^{+7.02}\times10^4\,\mathrm{M_\odot}$, with a confidence
of 84% that it is $\leq$$10^5\,\mathrm{M_\odot}$, based on the combined
probability density function from seven of these individual estimates. Our
conservative approach excluded two black hole mass estimates (via the nuclear
star cluster mass, and the fundamental plane of black hole activity
$\unicode{x2014}$ which only applies to black holes with low accretion rates)
that were upper limits of $\sim$$10^5\,{\rm M}_{\odot}$, and it did not use the
$M_\bullet\unicode{x2013}L_{\rm 2-10\,keV}$ relation's prediction of
$\sim$$10^5\,{\rm M}_{\odot}$. This target provides an exceptional opportunity
to study an IMBH in AGN mode and advance our demographic knowledge of black
holes. Furthermore, we introduce our novel method of meta-analysis as a
beneficial technique for identifying new IMBH candidates by quantifying the
probability that a galaxy possesses an IMBH.


Abstract:
  We propose a novel method to extract global and local features of functional
time series. The global features concerning the dominant modes of variation
over the entire function domain, and local features of function variations over
particular short intervals within function domain, are both important in
functional data analysis. Functional principal component analysis (FPCA),
though a key feature extraction tool, only focus on capturing the dominant
global features, neglecting highly localized features. We introduce a FPCA-BTW
method that initially extracts global features of functional data via FPCA, and
then extracts local features by block thresholding of wavelet (BTW)
coefficients. Using Monte Carlo simulations, along with an empirical
application on near-infrared spectroscopy data of wood panels, we illustrate
that the proposed method outperforms competing methods including FPCA and
sparse FPCA in the estimation functional processes. Moreover, extracted local
features inheriting serial dependence of the original functional time series
contribute to more accurate forecasts. Finally, we develop asymptotic
properties of FPCA-BTW estimators, discovering the interaction between
convergence rates of global and local features.


Abstract:
  A volatility surface is an important tool for pricing and hedging
derivatives. The surface shows the volatility that is implied by the market
price of an option on an asset as a function of the option's strike price and
maturity. Often, market data is incomplete and it is necessary to estimate
missing points on partially observed surfaces. In this paper, we show how
variational autoencoders can be used for this task. The first step is to derive
latent variables that can be used to construct synthetic volatility surfaces
that are indistinguishable from those observed historically. The second step is
to determine the synthetic surface generated by our latent variables that fits
available data as closely as possible. As a dividend of our first step, the
synthetic surfaces produced can also be used in stress testing, in market
simulators for developing quantitative investment strategies, and for the
valuation of exotic options. We illustrate our procedure and demonstrate its
power using foreign exchange market data.


Abstract:
  Most methods for publishing data with privacy guarantees introduce randomness
into datasets which reduces the utility of the published data. In this paper,
we study the privacy-utility tradeoff by taking maximal leakage as the privacy
measure and the expected Hamming distortion as the utility measure. We study
three different but related problems. First, we assume that the data-generating
distribution (i.e., the prior) is known, and we find the optimal privacy
mechanism that achieves the smallest distortion subject to a constraint on
maximal leakage. Then, we assume that the prior belongs to some set of
distributions, and we formulate a min-max problem for finding the smallest
distortion achievable for the worst-case prior in the set, subject to a maximal
leakage constraint. Lastly, we define a partial order on privacy mechanisms
based on the largest distortion they generate. Our results show that when the
prior distribution is known, the optimal privacy mechanism fully discloses
symbols with the largest prior probabilities, and suppresses symbols with the
smallest prior probabilities. Furthermore, we show that sets of priors that
contain more uniform distributions lead to larger distortion, while privacy
mechanisms that distribute the privacy budget more uniformly over the symbols
create smaller worst-case distortion.


Abstract:
  We propose a novel high-fidelity expressive speech synthesis model, UniTTS,
that learns and controls overlapping style attributes avoiding interference.
UniTTS represents multiple style attributes in a single unified embedding space
by the residuals between the phoneme embeddings before and after applying the
attributes. The proposed method is especially effective in controlling multiple
attributes that are difficult to separate cleanly, such as speaker ID and
emotion, because it minimizes redundancy when adding variance in speaker ID and
emotion, and additionally, predicts duration, pitch, and energy based on the
speaker ID and emotion. In experiments, the visualization results exhibit that
the proposed methods learned multiple attributes harmoniously in a manner that
can be easily separated again. As well, UniTTS synthesized high-fidelity speech
signals controlling multiple style attributes. The synthesized speech samples
are presented at https://jackson-kang.github.io/paper_works/UniTTS/demos.


Abstract:
  To construct the rotation curve of the Galaxy, classical Cepheids with proper
motions, parallaxes and line-of-sight velocities from the Gaia DR2 Catalog are
used in large part. The working sample formed from literature data contains
about 800 Cepheids with estimates of their age. We determined that the linear
rotation velocity of the Galaxy at a solar distance is $V_0=240\pm3$~km
s$^{-1}$. In this case, the distance from the Sun to the axis of rotation of
the Galaxy is found to be $R_0=8.27\pm0.10$~kpc. A spectral analysis of radial
and residual tangential velocities of Cepheids younger than 120 Myr showed
close estimates of the parameters of the spiral density wave obtained from data
both at present time and in the past. So, the value of the wavelength
$\lambda_{R,\theta}$ is in the range of [2.4--3.0] kpc, the pitch angle
$i_{R,\theta}$ is in the range of [$-13^\circ$,$-10^\circ$] for a four-arm
pattern model, the amplitudes of the radial and tangential perturbations are
$f_R\sim12$~km s$^{-1}$ and $f_\theta\sim9$~km s$^{-1}$, respectively.
Velocities of Cepheids older than 120 Myr are currently giving a wavelength
$\lambda_{R,\theta}\sim5$~kpc. This value differs significantly from one that
we obtained from the samples of young Cepheids. An analysis of positions and
velocities of old Cepheids, calculated by integrating their orbits backward in
time, made it possible to determine significantly more reliable values of the
parameters of the spiral density wave: wavelength $\lambda_{R,\theta}=2.7$~kpc,
amplitudes of radial and tangential perturbations are $f_R=7.9$~km s$^{-1}$ and
$f_\theta=5$~km s$^{-1}$, respectively.


Abstract:
  Intraoperative Gamma Probe (IPG) remains the current gold standard modality
for sentinel lymph node identification and tumor removal in cancer patients.
However, even alongside the optical dyes they do not meet with <5% false
negative rates (FNR) requirement, a key metric suggested by the American
Society of Clinical Oncology (ASCO). We are aiming to reduce FNR by using time
of flight (TOF) PET detector technology in the limited angle geometry system by
using only two detector buckets in coincidence, where one small-area detector
is placed above the patient and the other with larger detection-area, placed
just under the patient bed. For proof of concept, we used two Hamamatsu TOF PET
detector modules (C13500-4075YC-12) featuring 12x12 array of 4.2x4.2x20 mm3 LFS
crystal pixels, one-one coupled to silicon photomultiplier (SiPM) pixels.
Detector coincidence timing resolution (CTR) measured 271 ps FWHM for the whole
detector. We 3D printed lesion phantom containing spheres with 2-10 mm in
diameter, representing lymph nodes, and placed it inside a 10-liter warm
background water phantom. Experimental results show that with sub-minute data
acquisition, 6 mm diameter spheres can be identified in the image when a lesion
phantom with 10:1 activity ratio to background is used. Simulation results are
in good agreement with the experimental data, by resolving 6 mm diameters
lesions with 60 seconds acquisition time, in 25 cm deep background water
phantom with 10:1 activity ratio. The image quality improves as the CTR
improves in the simulation, and with decreasing background water phantom depth
or lesion to background activity ratio, in the experiment. With the results
presented here we conclude that limited angle TOF PET detector is a major step
forward for intraoperative applications in that, improved lesion detectability
is beyond what the conventional Gamma- and NIR-based probes could achieve.


Abstract:
  To facilitate effective human-robot interaction (HRI), trust-aware HRI has
been proposed, wherein the robotic agent explicitly considers the human's trust
during its planning and decision making. The success of trust-aware HRI depends
on the specification of a trust dynamics model and a trust-behavior model. In
this study, we proposed one novel trust-behavior model, namely the reverse
psychology model, and compared it against the commonly used disuse model. We
examined how the two models affect the robot's optimal policy and the
human-robot team performance. Results indicate that the robot will deliberately
"manipulate" the human's trust under the reverse psychology model. To correct
this "manipulative" behavior, we proposed a trust-seeking reward function that
facilitates trust establishment without significantly sacrificing the team
performance.


Abstract:
  We explore variations of the dust extinction law of the Milky Way by
selecting stars from the Swift/UVOT Serendipitous Source Catalogue,
cross-matched with Gaia DR2 and 2MASS to produce a sample of 10,452 stars out
to ~4kpc with photometry covering a wide spectral window. The near ultraviolet
passbands optimally encompass the 2175A bump, so that we can simultaneously fit
the net extinction, quoted in the V band (A$_V$), the steepness of the
wavelength dependence ($\delta$) and the bump strength (E$_b$). The methodology
compares the observed magnitudes with theoretical stellar atmospheres from the
models of Coelho. Significant correlations are found between these parameters,
related to variations in dust composition, that are complementary to similar
scaling relations found in the more complex dust attenuation law of galaxies -
that also depend on the distribution of dust among the stellar populations
within the galaxy. We recover the strong anticorrelation between A$_V$ and
Galactic latitude, as well as a weaker bump strength at higher extinction.
$\delta$ is also found to correlate with latitude, with steeper laws towards
the Galactic plane. Our results suggest that variations in the attenuation law
of galaxies cannot be fully explained by dust geometry.


Abstract:
  We present evidence that many common convolutional neural networks (CNNs)
trained for face verification learn functions that are nearly equivalent under
rotation. More specifically, we demonstrate that one face verification model's
embeddings (i.e. last-layer activations) can be compared directly to another
model's embeddings after only a rotation or linear transformation, with little
performance penalty. This finding is demonstrated using IJB-C 1:1 verification
across the combinations of ten modern off-the-shelf CNN-based face verification
models which vary in training dataset, CNN architecture, method of angular loss
calculation, or some combination of the 3. These networks achieve a mean true
accept rate of 0.96 at a false accept rate of 0.01. When instead evaluating
embeddings generated from two CNNs, where one CNN's embeddings are mapped with
a linear transformation, the mean true accept rate drops to 0.95 using the same
verification paradigm. Restricting these linear maps to only perform rotation
produces a mean true accept rate of 0.91. These mappings' existence suggests
that a common representation is learned by models despite variation in training
or structure. We discuss the broad implications a result like this has,
including an example regarding face template security.


Abstract:
  This paper presents an advance on image interpolation based on ant colony
algorithm (AACA) for high-resolution image scaling. The difference between the
proposed algorithm and the previously proposed optimization of bilinear
interpolation based on ant colony algorithm (OBACA) is that AACA uses global
weighting, whereas OBACA uses a local weighting scheme. The strength of the
proposed global weighting of the AACA algorithm depends on employing solely the
pheromone matrix information present on any group of four adjacent pixels to
decide which case deserves a maximum global weight value or not. Experimental
results are further provided to show the higher performance of the proposed
AACA algorithm with reference to the algorithms mentioned in this paper.


Abstract:
  Portrait matting is an important research problem with a wide range of
applications, such as video conference app, image/video editing, and
post-production. The goal is to predict an alpha matte that identifies the
effect of each pixel on the foreground subject. Traditional approaches and most
of the existing works utilized an additional input, e.g., trimap, background
image, to predict alpha matte. However, providing additional input is not
always practical. Besides, models are too sensitive to these additional inputs.
In this paper, we introduce an additional input-free approach to perform
portrait matting using Generative Adversarial Nets (GANs). We divide the main
task into two subtasks. For this, we propose a segmentation network for the
person segmentation and the alpha generation network for alpha matte
prediction. While the segmentation network takes an input image and produces a
coarse segmentation map, the alpha generation network utilizes the same input
image as well as a coarse segmentation map that is produced by the segmentation
network to predict the alpha matte. Besides, we present a segmentation encoding
block to downsample the coarse segmentation map and provide feature
representation to the residual block. Furthermore, we propose border loss to
penalize only the borders of the subject separately which is more likely to be
challenging and we also adapt perceptual loss for portrait matting. To train
the proposed system, we combine two different popular training datasets to
improve the amount of data as well as diversity to address domain shift
problems in the inference time. We tested our model on three different
benchmark datasets, namely Adobe Image Matting dataset, Portrait Matting
dataset, and Distinctions dataset. The proposed method outperformed the MODNet
method that also takes a single input.


Abstract:
  The academic, socioemotional, and health impacts of school policies
throughout the COVID-19 pandemic have been a source of many important questions
that require accurate information about the extent of onsite schooling that has
been occurring throughout the pandemic. This paper investigates school
operational status data sources during the COVID-19 pandemic, comparing
self-report data collected nationally on the household level through a
Facebook-based survey with data collected at district and county levels
throughout the country. The percentage of households reporting in-person
instruction within each county is compared to the district and county data at
the state and county levels. The results show high levels of consistency
between the sources at the state level and for large counties. The consistency
levels across sources support the usage of the Facebook-based COVID-19 Symptom
Survey as a source to answer questions about the educational experiences,
factors, and impacts related to K-12 education across the nation during the
pandemic.


Abstract:
  We review the theoretical aspects relevant in the description of high energy
heavy ion collisions, with an emphasis on the learnings about the underlying
QCD phenomena that have emerged from these collisions.


Abstract:
  Bounded rationality is an important consideration stemming from the fact that
agents often have limits on their processing abilities, making the assumption
of perfect rationality inapplicable to many real tasks. We propose an
information-theoretic approach to the inference of agent decisions under
Smithian competition. The model explicitly captures the boundedness of agents
(limited in their information-processing capacity) as the cost of information
acquisition for expanding their prior beliefs. The expansion is measured as the
Kullblack-Leibler divergence between posterior decisions and prior beliefs.
When information acquisition is free, the homo economicus agent is recovered,
while in cases when information acquisition becomes costly, agents instead
revert to their prior beliefs. The maximum entropy principle is used to infer
least-biased decisions based upon the notion of Smithian competition formalised
within the Quantal Response Statistical Equilibrium framework. The
incorporation of prior beliefs into such a framework allowed us to
systematically explore the effects of prior beliefs on decision-making in the
presence of market feedback, as well as importantly adding a temporal
interpretation to the framework. We verified the proposed model using
Australian housing market data, showing how the incorporation of prior
knowledge alters the resulting agent decisions. Specifically, it allowed for
the separation of past beliefs and utility maximisation behaviour of the agent
as well as the analysis into the evolution of agent beliefs.