Abstract:
  While past information technology (IT) advances have transformed society,
future advances hold even greater promise. For example, we have only just begun
to reap the changes from artificial intelligence (AI), especially machine
learning (ML). Underlying IT's impact are the dramatic improvements in computer
hardware, which deliver performance that unlock new capabilities. For example,
recent successes in AI/ML required the synergy of improved algorithms and
hardware architectures (e.g., general-purpose graphics processing units).
However, unlike in the 20th Century and early 2000s, tomorrow's performance
aspirations must be achieved without continued semiconductor scaling formerly
provided by Moore's Law and Dennard Scaling. How will one deliver the next 100x
improvement in capability at similar or less cost to enable great value? Can we
make the next AI leap without 100x better hardware?
  This whitepaper argues for a multipronged effort to develop new computing
approaches beyond Moore's Law to advance the foundation that computing provides
to US industry, education, medicine, science, and government. This impact
extends far beyond the IT industry itself, as IT is now central for providing
value across society, for example in semi-autonomous vehicles, tele-education,
health wearables, viral analysis, and efficient administration. Herein we draw
upon considerable visioning work by CRA's Computing Community Consortium (CCC)
and the IEEE Rebooting Computing Initiative (IEEE RCI), enabled by thought
leader input from industry, academia, and the US government.


Abstract:
  Symmetry-protected topological edge modes are one of the most remarkable
phenomena in topological physics. Here, we formulate and quantitatively examine
the effect of a quantum bath on these topological edge modes. Using the density
matrix renormalization group method, we study the ground state of a composite
system of spin-1 quantum chain, where the system and the bath degrees of
freedom are treated on the same footing. We focus on the dependence of these
edge modes on the global/partial symmetries of system-bath coupling and on the
features of the quantum bath. It is shown that the time-reversal symmetry(TRS)
plays a special role for an open quantum system, where an emergent partial TRS
breaking will result in a TRS-protected topological mode diffusing from the
system edge into the bath, thus make it useless for quantum computation.


Abstract:
  We revise the sunspot observations made by Galileo Galilei and Christoph
Scheiner in the context of their controversy on the nature of sunspots. Their
sunspot records not included in the current sunspot group database, used as a
basis to calculate the sunspot group number, are analyzed. Within the
documentary sources consulted in this work, we can highlight the sunspot
observations by Scheiner included in the letters sent under the pseudonym
Apelles to Marcus Welser and the first sunspot observations made by Galileo,
which can be consulted in Le opere di Galileo Galilei. These sunspot
observations would extend the temporal coverage for these two observers and
filling some gaps in the current group database in the earliest period where
the data available is sparse. Moreover, we have detected changes in the quality
of the sunspot drawings made by Galileo and Scheiner in their observation
series affecting to the number of groups recorded by the two observers. We also
compare these records with sunspot observations made by other astronomers of
that time. According to this comparison and regarding the same observation
days, Scheiner was generally the astronomer who reported more sunspot groups
while Harriot, Cigoli, and Galileo recorded a similar number of groups. We
conclude these differences are mainly because of the observational method used
by the observers.


Abstract:
  When compact manifolds $X$ and $Y$ are both even dimensional, their Euler
characteristics obey the K\"unneth formula $\chi(X\times Y)=\chi(X) \chi(Y)$.
In terms of the Betti numbers $b_p(X)$, $\chi(X)=\sum_{p}(-1)^p b_p(X)$,
implying that $\chi(X)=0$ when $X$ is odd dimensional. We seek a linear
combination of Betti numbers, called $\rho$, that obeys an analogous formula
$\rho(X\times Y)=\chi(X) \rho(Y)$ when $Y$ is odd dimensional. The unique
solution is $\rho(Y)=-\sum_{p}(-1)^p p b_p(Y)$. Physical applications include:
(1) $\rho \rightarrow (-1)^m \rho $ under a generalized mirror map in $d=2m+1$
dimensions, in analogy with $\chi \rightarrow (-1)^m \chi $ in $d=2m$; (2)
$\rho$ appears naturally in compactifications of M-theory. For example, the
4-dimensional Weyl anomaly for M-theory on $X^4 \times Y^7$ is given by
$\chi(X^4)\rho(Y^7)=\rho(X^4 \times Y^7) $ and hence vanishes when $Y^7$ is
self-mirror. Since, in particular, $\rho(Y\times S^1)=\chi(Y)$, this is
consistent with the corresponding anomaly for Type IIA on $X^4 \times Y^6$,
given by $\chi(X^4)\chi(Y^6)=\chi(X^4 \times Y^6)$, which vanishes when $Y^6$
is self-mirror; (3) In the partition function of $p$-form gauge fields, $\rho$
appears in odd dimensions as $\chi$ does in even.


Abstract:
  In this article we study measurement circuit effects in three-terminal
electrical transport measurements arising from finite line impedances. We
provide exact expressions relating the measured voltages and differential
conductances to their values at the device under test, which allow for spurious
voltage divider effects to be corrected. Finally, we test the implementation of
these corrections with experimental measurements.


Abstract:
  We prove that the Khovanov spectra associated to links and tangles are
functorial up to homotopy and sign.


Abstract:
  Understanding influencing factors is essential for the surveillance and
prevention of infectious diseases, and the factors are likely to vary spatially
and temporally as the disease progresses. Taking daily cases and deaths data
during the coronavirus disease 2019 (COVID-19) outbreak in the U.S. as a case
study, we develop a mobility-augmented geographically and temporally weighted
regression (M-GTWR) model to quantify the spatiotemporal impacts of
social-demographic factors and human activities on the COVID-19 dynamics.
Different from the base GTWR model, we incorporate a mobility-adjusted distance
weight matrix where travel mobility is used in addition to the spatial
adjacency to capture the correlations among local observations. The model
residuals suggest that the proposed model achieves a substantial improvement
over other benchmark methods in addressing the spatiotemporal nonstationarity.
Our results reveal that the impacts of social-demographic and human activity
variables present significant spatiotemporal heterogeneity. In particular, a 1%
increase in population density may lead to 0.63% and 0.71% more daily cases and
deaths, and a 1% increase in the mean commuting time may result in 0.22% and
0.95% increases in daily cases and deaths. Although increased human activities
will, in general, intensify the disease outbreak, we report that the effects of
grocery and pharmacy-related activities are insignificant in areas with high
population density. And activities at the workplace and public transit are
found to either increase or decrease the number of cases and deaths, depending
on particular locations. The results of our study establish a quantitative
framework for identifying influencing factors during a disease outbreak, and
the obtained insights may have significant implications in guiding the
policy-making against infectious diseases.


Abstract:
  We examine the Kerr/CFT correspondence in Einstein gravity extended with
quadratic curvature invariants. We consider two explicit examples in four and
five dimensions and compute the central charges of the asymptotic symmetry
algebras of the near horizon geometries, using the improved version of the BBC
formalism that encompasses the information of the Lagrangian. We find that the
resulting Cardy entropy differs from the Wald entropy, caused by the
Riemann-squared $R^{\mu\nu\rho\sigma}R_{\mu\nu\rho\sigma}$ term.


Abstract:
  We examine the use of linear and non-linear dimensionality reduction
algorithms for extracting low-rank feature representations for speech emotion
recognition. Two feature sets are used, one based on low-level descriptors and
their aggregations (IS10) and one modeling recurrence dynamics of speech (RQA),
as well as their fusion. We report speech emotion recognition (SER) results for
learned representations on two databases using different classification
methods. Classification with low-dimensional representations yields performance
improvement in a variety of settings. This indicates that dimensionality
reduction is an effective way to combat the curse of dimensionality for SER.
Visualization of features in two dimensions provides insight into
discriminatory abilities of reduced feature sets.


Abstract:
  We investigate special structures due to automorphisms in isogeny graphs of
principally polarized abelian varieties, and abelian surfaces in particular. We
give theoretical and experimental results on the spectral and statistical
properties of (2, 2)-isogeny graphs of superspecial abelian surfaces, including
stationary distributions for random walks, bounds on eigenvalues and diameters,
and a proof of the connectivity of the Jacobian subgraph of the (2, 2)-isogeny
graph. Our results improve our understanding of the performance and security of
some recently-proposed cryptosystems, and are also a concrete step towards a
better understanding of general superspecial isogeny graphs in arbitrary
dimension.


Abstract:
  Controlling magnetism using voltage is highly desired for applications, but
remains challenging due to fundamental contradiction between polarity and
magnetism. Here we propose a mechanism to manipulate magnetic domain walls in
ferrimagnetic or ferromagnetic multiferroics using electric field. Different
from those studies based on static domain-level couplings, here the
magnetoelectric coupling relies on the collaborative spin dynamics around
domain walls. Accompanying the reversal of spin chirality driven by
polarization switching, a "rolling-downhill"-like motion of domain wall is
achieved at the nanoscale, which tunes the magnetization locally. Our mechanism
opens an alternative route to pursuit practical and fast converse
magnetoelectric functions via spin dynamics.


Abstract:
  Fourth-order differential equations play an important role in many
applications in science and engineering. In this paper, we present a
three-field mixed finite-element formulation for fourth-order problems, with a
focus on the effective treatment of the different boundary conditions that
arise naturally in a variational formulation. Our formulation is based on
introducing the gradient of the solution as an explicit variable, constrained
using a Lagrange multiplier. The essential boundary conditions are enforced
weakly, using Nitsche's method where required. As a result, the problem is
rewritten as a saddle-point system, requiring analysis of the resulting
finite-element discretization and the construction of optimal linear solvers.
Here, we discuss the analysis of the well-posedness and accuracy of the
finite-element formulation. Moreover, we develop monolithic multigrid solvers
for the resulting linear systems. Two and three-dimensional numerical results
are presented to demonstrate the accuracy of the discretization and efficiency
of the multigrid solvers proposed.


Abstract:
  Context: Open Source Software (OSS) projects rely on a continuous stream of
new contributors for sustainable livelihood. Recent studies reported that new
contributors experience many barriers in their first contribution. One of the
critical barriers is the social barrier. Although a number of studies
investigated the social barriers to new contributors, to the best of our
knowledge, the relationship between the first response to the first
contributions and their future contributions has not been studied
comprehensively.
  Objective: In this registered report, we introduce the study protocols that
investigate the correlation between the first response given to the first
contributions and the future contribution. First, we performed a preliminary
survey to manually explore the sentiments of the first response. Preliminary
analysis confirms that the first responses are mainly neutral.
  Method: Our execution plan includes both qualitative and quantitative
approaches with three research questions. We inspect the first response of the
first contributions, investigate the effects of characteristics of the first
response to the interaction between first-time-contributor and project
contributors, and find the impact of the interactions between other
contributors.


Abstract:
  Automating a robotic task, e.g., robotic suturing can be very complex and
time-consuming. Learning a task model to autonomously perform the task is
invaluable making the technology, robotic surgery, accessible for a wider
community. The data of robotic surgery can be easily logged where the collected
data can be used to learn task models. This will result in reduced time and
cost of robotic surgery in which a surgeon can supervise the robot operation or
give high-level commands instead of low-level control of the tools. We present
a data-set of needle insertion in soft tissue with two arms where Arm 1 inserts
the needle into the tissue and Arm 2 actively manipulate the soft tissue to
ensure the desired and actual exit points are the same. This is important in
real-surgery because suturing without active manipulation of tissue may yield
failure of the suturing as the stitch may not grip enough tissue to resist the
force applied for the suturing. We present a needle insertion dataset including
60 successful trials recorded by 3 pair of stereo cameras. Moreover, we present
Deep-robot Learning from Demonstrations that predicts the desired state of the
robot at the time step after t (which the optimal action taken at t yields) by
looking at the video of the past time steps, i.e. n step time history where N
is the memory time window, of the task execution. The experimental results
illustrate our proposed deep model architecture is outperforming the existing
methods. Although the solution is not yet ready to be deployed on a real robot,
the results indicate the possibility of future development for real robot
deployment.


Abstract:
  This paper deals with the issue of concept drift in supervised machine
learn-ing. We make use of graphical models to elicit the visible structure of
the dataand we infer from there changes in the hidden context. Differently from
previous concept-drift detection methods, this application does not depend on
the supervised machine learning model in use for a specific target variable,
but it tries to assess the concept drift as independent characteristic of the
evolution of a dataset. Specifically, we investigate how a graphical model
evolves by looking at the creation of new links and the disappearing of
existing ones in different time periods. The paper suggests a method that
highlights the changes and eventually produce a metric to evaluate the
stability over time. The paper evaluate the method with real world data on the
Australian Electric market.


Abstract:
  Strong Parallel Repetition for Unique Games on Small Set Expanders
  The strong parallel repetition problem for unique games is to efficiently
reduce the 1-delta vs. 1-C*delta gap problem of Boolean unique games (where C>1
is a sufficiently large constant) to the 1-epsilon vs. epsilon gap problem of
unique games over large alphabet. Due to its importance to the Unique Games
Conjecture, this problem garnered a great deal of interest from the research
community. There are positive results for certain easy unique games (e.g.,
unique games on expanders), and an impossibility result for hard unique games.
  In this paper we show how to bypass the impossibility result by enlarging the
alphabet sufficiently before repetition. We consider the case of unique games
on small set expanders for two setups: (i) Strong small set expanders that
yield easy unique games. (ii) Weaker small set expanders underlying possibly
hard unique games as long as the game is mildly fortified. We show how to
fortify unique games in both cases, i.e., how to transform the game so
sufficiently large induced sub-games have bounded value. We then prove strong
parallel repetition for the fortified games. Prior to this work fortification
was known for projection games but seemed hopeless for unique games.


Abstract:
  Absorption induced transparency is an optical phenomenon that occurs in
metallic arrays of nanoholes when materials featuring narrow lines in their
absorption spectra are deposited on top of it. First reported in the visible
range, using dye lasers as cover materials, it has been described as
transmission peaks unexpectedly close to the absorption energies of the dye
laser. In this work, amplification of light is demonstrated in the active
regime of absorption induced transparency. Amplification of stimulated emission
can be achieved when the dye laser behaves as a gain material. Intense
illumination can modify the dielectric constant of the gain material, which in
turn, changes the propagation properties of the plasmonic modes excited in the
hole arrays, providing both less damping to light and further feedback,
enhancing the stimulated emission process.


Abstract:
  We construct the semi-infinite tensor structure on the semiderived category
of quasi-coherent torsion sheaves on an ind-scheme endowed with a flat affine
morphism into an ind-Noetherian ind-scheme with a dualizing complex. The
semitensor product is "a mixture of" the cotensor product along the base and
the derived tensor product along the fibers. The inverse image of the dualizing
complex is the unit object. This construction is a partial realization of the
Semi-infinite Algebraic Geometry program, as outlined in the introduction to
arXiv:1504.00700.


Abstract:
  We study reinforcement learning (RL) with no-reward demonstrations, a setting
in which an RL agent has access to additional data from the interaction of
other agents with the same environment. However, it has no access to the
rewards or goals of these agents, and their objectives and levels of expertise
may vary widely. These assumptions are common in multi-agent settings, such as
autonomous driving. To effectively use this data, we turn to the framework of
successor features. This allows us to disentangle shared features and dynamics
of the environment from agent-specific rewards and policies. We propose a
multi-task inverse reinforcement learning (IRL) algorithm, called \emph{inverse
temporal difference learning} (ITD), that learns shared state features,
alongside per-agent successor features and preference vectors, purely from
demonstrations without reward labels. We further show how to seamlessly
integrate ITD with learning from online environment interactions, arriving at a
novel algorithm for reinforcement learning with demonstrations, called $\Psi
\Phi$-learning (pronounced `Sci-Fi'). We provide empirical evidence for the
effectiveness of $\Psi \Phi$-learning as a method for improving RL, IRL,
imitation, and few-shot transfer, and derive worst-case bounds for its
performance in zero-shot transfer to new tasks.


Abstract:
  We show that general parity-violating 3d conformal field theories show a
double copy structure for momentum space 3-point functions of conserved
currents, stress tensor and marginal scalar operators. Splitting up the CFT
correlator into two parts - called homogeneous and non-homogeneous - we show
that double copy relations exist for each part separately. We arrive at similar
conclusions regarding double copy structures using tree-level correlators of
massless fields in $dS_4$. We also discuss the flat space limit of these
correlators. We further extend the double copy analysis to correlators
involving higher-spin conserved currents, which suggests that the spin-$s$
current correlator can be thought of as $s$ copies of the spin one current
correlator.


Abstract:
  We consider matter density effects in theories with a false ground state.
Large and dense systems, such as stars, can destabilize a metastable minimum
and allow for the formation of bubbles of the true minimum. We derive the
conditions under which these bubbles form, as well as the conditions under
which they either remain confined to the dense region or escape to infinity.
The latter case leads to a phase transition in the universe at star formation.
We explore the phenomenological consequences of such seeded phase transitions.


Abstract:
  The modification of the rotating vector model in the case of magnetars are
calculated. Magnetars may have twisted magnetic field compared with normal
pulsars. The polarization position angle of magnetars will change in the case
of a twisted magnetic field. For a twisted dipole field, we found that the
position angle will change both vertically and horizontally. During the
untwisting process of the magnetar magnetosphere, the modifications of the
position angle will evolve with time monotonously. This may explain the
evolution of the position angle in magnetar PSR J1622-4950 and XTE J1810-197.
The relation between the emission point and the line of sight will also change.
We suggest every magnetospheric models of magnetars also calculate the
corresponding changes of position angle in their models. Order of magnitude
estimation formula for doing this is given. This opens the possibility to
extract the magnetic field geometry of magnetars from their radio polarization
observations.


Abstract:
  Although vital to computer vision systems, few-shot action recognition is
still not mature despite the wide research of few-shot image classification.
Popular few-shot learning algorithms extract a transferable embedding from seen
classes and reuse it on unseen classes by constructing a metric-based
classifier. One main obstacle to applying these algorithms in action
recognition is the complex structure of videos. Some existing solutions sample
frames from a video and aggregate their embeddings to form a video-level
representation, neglecting important temporal relations. Others perform an
explicit sequence matching between two videos and define their distance as
matching cost, imposing too strong restrictions on sequence ordering. In this
paper, we propose Compromised Metric via Optimal Transport (CMOT) to combine
the advantages of these two solutions. CMOT simultaneously considers semantic
and temporal information in videos under Optimal Transport framework, and is
discriminative for both content-sensitive and ordering-sensitive tasks. In
detail, given two videos, we sample segments from them and cast the calculation
of their distance as an optimal transport problem between two segment
sequences. To preserve the inherent temporal ordering information, we
additionally amend the ground cost matrix by penalizing it with the positional
distance between a pair of segments. Empirical results on benchmark datasets
demonstrate the superiority of CMOT.


Abstract:
  For open and singular varieties in positive characteristic p we study the
existence of an integral p-adic cohomology theory which is finitely generated,
compatible with log crystalline cohomology and rationally compatible with rigid
cohomology. We develop such a theory under certain assumptions of resolution of
singularities in positive characteristic, by using cdp- and rh-topologies.
  Without resolution of singularities in positive characteristic, we prove the
existence of a good p-adic cohomology theory for open and singular varieties in
cohomological degree 1, by using split proper generically \'etale
hypercoverings. This is a slight generalisation of a result due to
Andreatta--Barbieri-Viale. We also prove that this approach does not work for
higher cohomological degrees.


Abstract:
  Context. Centaurs go around the Sun between the orbits of Jupiter and
Neptune. Only a fraction of the known centaurs have been found to display
comet-like features. Comet 29P/Schwassmann-Wachmann 1 is the most remarkable
active centaur. It orbits the Sun just beyond Jupiter in a nearly circular
path. Only a handful of known objects follow similar trajectories.
  Aims. We present photometric observations of 2020 MK4, a recently found
centaur with an orbit not too different from that of 29P, and we perform a
preliminary exploration of its dynamical evolution.
  Methods. We analyzed broadband Cousins R and Sloan g', r', and i' images of
2020 MK4 acquired with the Jacobus Kapteyn Telescope and the IAC80 telescope to
search for cometary-like activity, and to derive its surface colors and size.
Its orbital evolution was studied using direct N-body simulations.
  Results. Centaur 2020 MK4 is neutral-gray in color and has a faint, compact
cometary-like coma. The values of its color indexes, (g'-r')=0.42+/-0.04 and
(r'-i')=0.17+/-0.04, are similar to the solar ones. A lower limit for the
absolute magnitude of the nucleus is Hg=11.30+/-0.03 mag which, for an albedo
in the range of 0.1-0.04, gives an upper limit for its size in the interval
(23, 37) km. Its orbital evolution is very chaotic and 2020 MK4 may be ejected
from the Solar System during the next 200 kyr. Comet 29P experienced relatively
close flybys with 2020 MK4 in the past, sometimes when they were temporary
Jovian satellites.
  Conclusions. We confirm the presence of a coma of material around a central
nucleus. Its surface colors place this centaur among the most extreme members
of the gray group. Although its past, present, and future dynamical evolution
resembles that of 29P, more data are required to confirm or reject a possible
connection between the two objects and perhaps others.


Abstract:
  The action of four-dimensional Horndeski gravity coupled to Born-Infeld
electromagnetic fields is given via the Kaluza-Klein process. Dyonic black hole
solution of the theory is constructed. The metric is devoid of singularity at
the origin independent of the parameter selections, this property is different
from the one of Einstein-Born-Infeld black holes. Thermodynamics of the black
hole is studied, thermodynamic quantities are calculated and the first law is
checked to be satisfied. Thermodynamic phase transitions of the black holes are
studied in extended phase space.


Abstract:
  A flow view is the graph of a measurable conjugacy between a substitution or
S-adic subshift and an exchange of infinitely many intervals (IIET) in [0,1].
The natural refining sequence of partitions of the sequence space is
transferred to [0,1] using a canonical addressing scheme, a fixed dual
substitution, and a shift-invariant probability measure. On the flow view, a
sequence is shown horizontally at a height given by the conjugacy.
  The IIET is well approximated by exchanges of finitely many intervals, making
numeric and graphic methods possible. The graphs of the IIETs show forms of
self-similarity, a special case of which is proved. The spectral type of the
conjugacy itself is of particular interest. As an example of utility, some
spectral results for constant-length substitutions are included.


Abstract:
  Existing deep neural network (DNN) based wireless localization approaches
typically do not capture uncertainty inherent in their estimates. In this work,
we propose and evaluate variational and scalable DNN approaches to measure the
uncertainty as a result of changing propagation conditions and the finite
number of training samples. Furthermore, we show that data uncertainty is
sufficient to capture the uncertainty due to non-line-of-sight (NLOS) and,
model uncertainty improves the overall reliability. To assess the robustness
due to channel conditions and out-of-set regions, we evaluate the methods on
challenging massive multiple-input multiple-output (MIMO) scenarios.


Abstract:
  We have formulated elastic seismic full waveform inversion (FWI) within a
deep learning environment. In our formulation, a recurrent neural network is
set up with rules enforcing elastic wave propagation, with the wavefield
projected onto a measurement surface acting as the synthetic data to be
compared with observed seismic data. Gradients for iterative updating of an
elastic model, with a variety of parameterizations and misfit functionals, can
be efficiently constructed within the network through the automatic
differential method. With this method, the inversion based on complex misfits
can be calculated. We survey the impact of different complex misfits (based on
the l2, l1 ) with high order total variation (TV) regulations on multiparameter
elastic FWI recovery of models within velocity/density, modulus/density, and
stiffness parameter/density parameterizations. We analyze parameter cross-talk.
Inversion results on simple and complex models show that the RNN elastic FWI
with high order TV regulation using l1 norm can help mitigate cross-talk issues
with gradient-based optimization methods.


Abstract:
  All pandemics are local; so learning about the impacts of pandemics on public
health and related societal issues at granular levels is of great interest.
COVID-19 is affecting everyone in the globe and mask wearing is one of the few
precautions against it. To quantify people's perception of mask effectiveness
and to prevent the spread of COVID-19 for small areas, we use Understanding
America Study's (UAS) survey data on COVID-19 as our primary data source. Our
data analysis shows that direct survey-weighted estimates for small areas could
be highly unreliable. In this paper we develop a synthetic estimation method to
estimate proportions of mask effectiveness for small areas using a logistic
model that combines information from multiple data sources. We select our
working model using an extensive data analysis facilitated by a new variable
selection criterion for survey data and benchmarking ratios. We propose a
Jackknife method to estimate variance of our proposed estimator. From our data
analysis. it is evident that our proposed synthetic method outperforms direct
survey-weighted estimator with respect to commonly used evaluation measures.


Abstract:
  Semantics has become a key topic of research in Genetic Programming (GP).
Semantics refers to the outputs (behaviour) of a GP individual when this is run
on a data set. The majority of works that focus on semantic diversity in
single-objective GP indicates that it is highly beneficial in evolutionary
search. Surprisingly, there is minuscule research conducted in semantics in
Multi-objective GP (MOGP). In this work we make a leap beyond our understanding
of semantics in MOGP and propose SDO: Semantic-based Distance as an additional
criteriOn. This naturally encourages semantic diversity in MOGP. To do so, we
find a pivot in the less dense region of the first Pareto front (most promising
front). This is then used to compute a distance between the pivot and every
individual in the population. The resulting distance is then used as an
additional criterion to be optimised to favour semantic diversity. We also use
two other semantic-based methods as baselines, called Semantic Similarity-based
Crossover and Semantic-based Crowding Distance. Furthermore, we also use the
NSGA-II and the SPEA2 for comparison too. We use highly unbalanced binary
classification problems and consistently show how our proposed SDO approach
produces more non-dominated solutions and better diversity, leading to better
statistically significant results, using the hypervolume results as evaluation
measure, compared to the rest of the other four methods.


Abstract:
  In this paper, we prove the Khavinson conjecture for hyperbolic harmonic
functions on the unit ball. This conjecture was partially solved in
\cite{JKM2020}.


Abstract:
  We calculate the nucleon matrix element of Weinberg's dimension-6 CP-odd
gluon operator $f^{abc} (\tilde F_{\mu\nu})^a (F^{\mu\rho})^b
(F^{\nu}_{\;\;\rho})^c$ in the instanton vacuum. In leading order of the
instanton packing fraction, the dimension-6 operator is effectively
proportional to the topological charge density $(\tilde F_{\mu\nu})^a
(F^{\mu\nu})^a$, whose nucleon matrix element is given by the flavor-singlet
axial charge and constrained by the $U(1)_A$ anomaly. The nucleon matrix
element of the dimension-6 operator is obtained substantially larger than in
other estimates, because of the strong localization of the nonperturbative
gluon fields in the instanton vacuum. We argue that the neutron electric dipole
moment induced by the dimension-6 operator is nevertheless of conventional
size.


Abstract:
  Recently, the power of unconditional image synthesis has significantly
advanced through the use of Generative Adversarial Networks (GANs). The task of
inverting an image into its corresponding latent code of the trained GAN is of
utmost importance as it allows for the manipulation of real images, leveraging
the rich semantics learned by the network. Recognizing the limitations of
current inversion approaches, in this work we present a novel inversion scheme
that extends current encoder-based inversion methods by introducing an
iterative refinement mechanism. Instead of directly predicting the latent code
of a given real image using a single pass, the encoder is tasked with
predicting a residual with respect to the current estimate of the inverted
latent code in a self-correcting manner. Our residual-based encoder, named
ReStyle, attains improved accuracy compared to current state-of-the-art
encoder-based methods with a negligible increase in inference time. We analyze
the behavior of ReStyle to gain valuable insights into its iterative nature. We
then evaluate the performance of our residual encoder and analyze its
robustness compared to optimization-based inversion and state-of-the-art
encoders.


Abstract:
  Information theory can be used to analyze the cost-benefit of visualization
processes. However, the current measure of benefit contains an unbounded term
that is neither easy to estimate nor intuitive to interpret. In this work, we
propose to revise the existing cost-benefit measure by replacing the unbounded
term with a bounded one. We examine a number of bounded measures that include
the Jenson-Shannon divergence and a new divergence measure formulated as part
of this work. We describe the rationale for proposing a new divergence measure.
As the first part of comparative evaluation, we use visual analysis to support
the multi-criteria comparison, narrowing the search down to several options
with better mathematical properties. The theoretical discourse and conceptual
evaluation in this paper provide the basis for further comparative evaluation
through synthetic and experimental case studies, which are to be reported in a
separate paper.


Abstract:
  We present a class of models of interacting lattice bosons which show
complete Bose-Einstein condensation for the ground state.


Abstract:
  The recent progress of deep convolutional neural networks has enabled great
success in single image super-resolution (SISR) and many other vision tasks.
Their performances are also being increased by deepening the networks and
developing more sophisticated network structures. However, finding an optimal
structure for the given problem is a difficult task, even for human experts.
For this reason, neural architecture search (NAS) methods have been introduced,
which automate the procedure of constructing the structures. In this paper, we
expand the NAS to the super-resolution domain and find a lightweight densely
connected network named DeCoNASNet. We use a hierarchical search strategy to
find the best connection with local and global features. In this process, we
define a complexity-based penalty for solving image super-resolution, which can
be considered a multi-objective problem. Experiments show that our DeCoNASNet
outperforms the state-of-the-art lightweight super-resolution networks designed
by handcraft methods and existing NAS-based design.


Abstract:
  A 2-distance list k-coloring of a graph is a proper coloring of the vertices
where each vertex has a list of at least k available colors and vertices at
distance at most 2 cannot share the same color. We prove the existence of a
2-distance list $(\Delta + 3)$-coloring for graphs with maximum average degree
less than $\frac83$ and maximum degree $\Delta\geq 4$ as well as graphs with
maximum average degree less than $\frac{14}5$ and maximum degree $\Delta\geq
6$.


Abstract:
  The recent surge in the adoption of machine learning techniques for materials
design, discovery, and characterization has resulted in an increased interest
and application of Image Driven Machine Learning (IDML) approaches. In this
work, we review the application of IDML to the field of materials
characterization. A hierarchy of six action steps is defined which
compartmentalizes a problem statement into well-defined modules. The studies
reviewed in this work are analyzed through the decisions adopted by them at
each of these steps. Such a review permits a granular assessment of the field,
for example the impact of IDML on materials characterization at the nanoscale,
the number of images in a typical dataset required to train a semantic
segmentation model on electron microscopy images, the prevalence of transfer
learning in the domain, etc. Finally, we discuss the importance of
interpretability and explainability, and provide an overview of two emerging
techniques in the field: semantic segmentation and generative adversarial
networks.


Abstract:
  Path finding is a well-studied problem in AI, which is often framed as graph
search. Any-angle path finding is a technique that augments the initial graph
with additional edges to build shorter paths to the goal. Indeed, optimal
algorithms for any-angle path finding in static environments exist. However,
when dynamic obstacles are present and time is the objective to be minimized,
these algorithms can no longer guarantee optimality. In this work, we elaborate
on why this is the case and what techniques can be used to solve the problem
optimally. We present two algorithms, grounded in the same idea, that can
obtain provably optimal solutions to the considered problem. One of them is a
naive algorithm and the other one is much more involved. We conduct a thorough
empirical evaluation showing that, in certain setups, the latter algorithm
might be as fast as the previously-known greedy non-optimal solver while
providing solutions of better quality. In some (rare) cases, the difference in
cost is up to 76%, while on average it is lower than one percent (the same cost
difference is typically observed between optimal and greedy any-angle solvers
in static environments).


Abstract:
  We study the backward stability of running a backward stable eigenstructure
solver on a pencil $S(\lambda)$ that is a strong linearization of a rational
matrix $R(\lambda)$ expressed in the form $R(\lambda)=D(\lambda)+ C(\lambda
I_\ell-A)^{-1}B$, where $D(\lambda)$ is a polynomial matrix and $C(\lambda
I_\ell-A)^{-1}B$ is a minimal state-space realization. We consider the family
of block Kronecker linearizations of $R(\lambda)$, which are highly structured
pencils. Backward stable eigenstructure solvers applied to $S(\lambda)$ will
compute the exact eigenstructure of a perturbed pencil $\widehat
S(\lambda):=S(\lambda)+\Delta_S(\lambda)$ and the special structure of
$S(\lambda)$ will be lost. In order to link this perturbed pencil with a nearby
rational matrix, we construct a strictly equivalent pencil $\widetilde
S(\lambda)$ to $\widehat S(\lambda)$ that restores the original structure, and
hence is a block Kronecker linearization of a perturbed rational matrix
$\widetilde R(\lambda) = \widetilde D(\lambda)+ \widetilde C(\lambda I_\ell-
\widetilde A)^{-1} \widetilde B$, where $\widetilde D(\lambda)$ is a polynomial
matrix with the same degree as $D(\lambda)$. Moreover, we bound appropriate
norms of $\widetilde D(\lambda)- D(\lambda)$, $\widetilde C - C$, $\widetilde A
- A$ and $\widetilde B - B$ in terms of an appropriate norm of
$\Delta_S(\lambda)$. These bounds may be inadmissibly large, but we also
introduce a scaling that allows us to make them satisfactorily tiny. Thus, for
this scaled representation, we prove that the staircase and the $QZ$ algorithms
compute the exact eigenstructure of a rational matrix $\widetilde R(\lambda)$
that can be expressed in exactly the same form as $R(\lambda)$ with the
parameters defining the representation very near to those of $R(\lambda)$. This
shows that this approach is backward stable in a structured sense.


Abstract:
  We present MultiBodySync, a novel, end-to-end trainable multi-body motion
segmentation and rigid registration framework for multiple input 3D point
clouds. The two non-trivial challenges posed by this multi-scan multibody
setting that we investigate are: (i) guaranteeing correspondence and
segmentation consistency across multiple input point clouds capturing different
spatial arrangements of bodies or body parts; and (ii) obtaining robust
motion-based rigid body segmentation applicable to novel object categories. We
propose an approach to address these issues that incorporates spectral
synchronization into an iterative deep declarative network, so as to
simultaneously recover consistent correspondences as well as motion
segmentation. At the same time, by explicitly disentangling the correspondence
and motion segmentation estimation modules, we achieve strong generalizability
across different object categories. Our extensive evaluations demonstrate that
our method is effective on various datasets ranging from rigid parts in
articulated objects to individually moving objects in a 3D scene, be it
single-view or full point clouds.


Abstract:
  Sliced Stein discrepancy (SSD) and its kernelized variants have demonstrated
promising successes in goodness-of-fit tests and model learning in high
dimensions. Despite their theoretical elegance, their empirical performance
depends crucially on the search of optimal slicing directions to discriminate
between two distributions. Unfortunately, previous gradient-based optimisation
approaches for this task return sub-optimal results: they are computationally
expensive, sensitive to initialization, and they lack theoretical guarantees
for convergence. We address these issues in two steps. First, we provide
theoretical results stating that the requirement of using optimal slicing
directions in the kernelized version of SSD can be relaxed, validating the
resulting discrepancy with finite random slicing directions. Second, given that
good slicing directions are crucial for practical performance, we propose a
fast algorithm for finding such slicing directions based on ideas of active
sub-space construction and spectral decomposition. Experiments on
goodness-of-fit tests and model learning show that our approach achieves both
improved performance and faster convergence. Especially, we demonstrate a
14-80x speed-up in goodness-of-fit tests when comparing with gradient-based
alternatives.


Abstract:
  Approximate Bayesian Computation (ABC) now serves as one of the major
strategies to perform model choice and parameter inference on models with
intractable likelihoods. An essential component of ABC involves comparing a
large amount of simulated data with the observed data through summary
statistics. To avoid the curse of dimensionality, summary statistic selection
is of prime importance, and becomes even more critical when applying ABC to
mechanistic network models. Indeed, while many summary statistics can be used
to encode network structures, their computational complexity can be highly
variable. For large networks, computation of summary statistics can quickly
create a bottleneck, making the use of ABC difficult. To reduce this
computational burden and make the analysis of mechanistic network models more
practical, we investigated two questions in a model choice framework. First, we
studied the utility of cost-based filter selection methods to account for
different summary costs during the selection process. Second, we performed
selection using networks generated with a smaller number of nodes to reduce the
time required for the selection step. Our findings show that computationally
inexpensive summary statistics can be efficiently selected with minimal impact
on classification accuracy. Furthermore, we found that networks with a smaller
number of nodes can only be employed to eliminate a moderate number of
summaries. While this latter finding is network specific, the former is general
and can be adapted to any ABC application.


Abstract:
  In [OW2,OW3], the authors studied the nonlinear elliptic system $$
\overline{\partial}^\pi w = 0, \, d(w^*\lambda \circ j) = 0 $$ without
involving symplectization for each given contact triad $(Q,\lambda, J)$, and
established the a priori $W^{k,2}$ elliptic estimates and proved the asymptotic
(subsequence) convergence of the map $w: \dot \Sigma \to Q$ for any solution,
called a contact instanton, on $\dot \Sigma$ under the hypothesis
$\|w^*\lambda\|_{C^0} < \infty$ and $d^\pi w \in L^2 \cap L^4$. The asymptotic
limit of a contact instanton is a `spiraling' instanton along a `rotating' Reeb
orbit near each puncture on a punctured Riemann surface $\dot \Sigma$. Each
limiting Reeb orbit carries a `charge' arising from the integral of $w^*\lambda
\circ j$. In this article, we further develop analysis of contact instantons,
especially the $W^{1,p}$ estimate for $p > 2$ (or the $C^1$-estimate), which is
essential for the study of compactfication of the moduli space and the relevant
Fredholm theory for contact instantons. In particular, we define a Hofer-type
off-shell energy $E^\lambda(j,w)$ for any pair $(j,w)$ with a smooth map $w$
satisfying $d(w^*\lambda \circ j) = 0$, and develop the bubbling-off analysis
and prove an $\epsilon$-regularity result. We also develop the relevant
Fredholm theory and carry out index calculations (for the case of vanishing
charge).


Abstract:
  2019 marked the 75th anniversary of the publication of Erwin
Schr\"{o}dinger's "What is Life?", a short book described by Roger Penrose in
his preface to a reprint of this classic as "among the most influential
scientific writings of the 20th century." In this article, I review the long
argument made by Schr\"{o}dinger as he mused on how the laws of physics could
help us understand "the events in space and time which take place within the
spatial boundary of a living organism." Though Schr\"{o}dinger's book is often
hailed for its influence on some of the titans who founded molecular biology,
this article takes a different tack. Instead of exploring the way the book
touched biologists such as James Watson and Francis Crick, as well as its
critical reception by others such as Linus Pauling and Max Perutz, I argue that
Schr\"{o}dinger's classic is a timeless manifesto, rather than a dated
historical curiosity. "What is Life?" is full of timely outlooks and approaches
to understanding the mysterious living world that includes and surrounds us and
can instead be viewed as a call to arms to tackle the great unanswered
challenges in the study of living matter that remain for 21$^{st}$ century
science.


Abstract:
  We prove decay estimates for generalized eigenfunctions of discrete
Schr\"odinger operators on weighted infinite graphs in the spirit of Agmon.


Abstract:
  This is an extended introduction to discrete valuation rings and Dedekind
domains. Some natural generalizations of Dedekind domains are also (briefly)
discussed including "almost Dedekind domains", Pr\"ufer domains, Krull domains,
and singular integral domains of dimension 1.


Abstract:
  The magnetospheres of the outer planets exhibit turbulent phenomena in an
environment which is qualitatively different compared to the solar wind or the
interstellar medium. The key differences are the finite sizes of the
magnetospheres limited by their physical boundaries, the presence of a strong
planetary background magnetic field and spatially very inhomogeneous plasma
properties within the magnetospheres. Typical turbulent fluctuations possess
amplitudes much smaller than the background field and are characterized by
Alfv\'en times, which can be smaller than the non-linear interaction time
scales. The magnetospheres of the outer planets are thus interesting
laboratories of plasma turbulence. In Jupiter's and Saturn's magnetospheres,
turbulence is well established thanks to the in-situ measurements by several
spacecraft, in particular by the Galileo and Cassini orbiter. In contrast, the
fluctuations in Uranus' and Neptune's magnetospheres are poorly understood due
to the lack of sufficient data. Turbulence in the outer planets' magnetospheres
have important effects on the systems as a whole. The dissipation of the
turbulent fluctuations through wave-particle interaction is a significant heat
source, which can explain the large magnetospheric plasma temperatures.


Abstract:
  We prove that in the non-orientable setting, the minimal stretch factor of a
pseudo-Anosov homeomorphism of a surface of genus $g$ with a fixed number of
punctures is asymptotically on the order of $\frac{1}{g}$. Our result adapts
the work of Yazdi to non-orientable surfaces. We include the details of
Thurston's theory of fibered faces for non-orientable 3-manifolds.


Abstract:
  The promise constraint satisfaction problem (PCSP) is a recently introduced
vast generalisation of the constraint satisfaction problem (CSP) that captures
approximability of satisfiable instances. A PCSP instance comes with two forms
of each constraint: a strict one and a weak one. Given the promise that a
solution exists using the strict constraints, the task is to find a solution
using the weak constraints. While there are by now several dichotomy results
for fragments of PCSPs, they all consider (in some way) symmetric PCSPs.
  1-in-3-SAT and Not-All-Equal-3-SAT are classic examples of Boolean symmetric
(non-promise) CSPs. While both problems are NP-hard, Brakensiek and Guruswami
showed [SODA'18] that given a satisfiable instance of 1-in-3-SAT one can find a
solution to the corresponding instance of (weaker) Not-All-Equal-3-SAT. In
other words, the PCSP template (1-in-3,NAE) is tractable.
  We focus on non-symmetric PCSPs. In particular, we study PCSP templates
obtained from the Boolean template (t-in-k,NAE) by either adding tuples to
t-in-k or removing tuples from NAE. For the former, we classify all templates
as either tractable or not solvable by the currently strongest known algorithm
for PCSPs, the combined basic LP and affine IP relaxation of Brakensiek and
Guruswami [SODA'20]. For the latter, we classify all templates as either
tractable or NP-hard.


Abstract:
  Automatic dialogue coherence evaluation has attracted increasing attention
and is crucial for developing promising dialogue systems. However, existing
metrics have two major limitations: (a) they are mostly trained in a simplified
two-level setting (coherent vs. incoherent), while humans give Likert-type
multi-level coherence scores, dubbed as "quantifiable"; (b) their predicted
coherence scores cannot align with the actual human rating standards due to the
absence of human guidance during training. To address these limitations, we
propose Quantifiable Dialogue Coherence Evaluation (QuantiDCE), a novel
framework aiming to train a quantifiable dialogue coherence metric that can
reflect the actual human rating standards. Specifically, QuantiDCE includes two
training stages, Multi-Level Ranking (MLR) pre-training and Knowledge
Distillation (KD) fine-tuning. During MLR pre-training, a new MLR loss is
proposed for enabling the model to learn the coarse judgement of coherence
degrees. Then, during KD fine-tuning, the pretrained model is further finetuned
to learn the actual human rating standards with only very few human-annotated
data. To advocate the generalizability even with limited fine-tuning data, a
novel KD regularization is introduced to retain the knowledge learned at the
pre-training stage. Experimental results show that the model trained by
QuantiDCE presents stronger correlations with human judgements than the other
state-of-the-art metrics.


Abstract:
  We investigate the classical active pure exploration problem in Markov
Decision Processes, where the agent sequentially selects actions and, from the
resulting system trajectory, aims at identifying the best policy as fast as
possible. We propose a problem-dependent lower bound on the average number of
steps required before a correct answer can be given with probability at least
$1-\delta$. We further provide the first algorithm with an instance-specific
sample complexity in this setting. This algorithm addresses the general case of
communicating MDPs; we also propose a variant with a reduced exploration rate
(and hence faster convergence) under an additional ergodicity assumption. This
work extends previous results relative to the \emph{generative
setting}~\cite{pmlr-v139-marjani21a}, where the agent could at each step query
the random outcome of any (state, action) pair. In contrast, we show here how
to deal with the \emph{navigation constraints}, induced by the \emph{online
setting}. Our analysis relies on an ergodic theorem for non-homogeneous Markov
chains which we consider of wide interest in the analysis of Markov Decision
Processes.


Abstract:
  Many COVID-19 patients developed prolonged symptoms after the infection,
including fatigue, delirium, and headache. The long-term health impact of these
conditions is still not clear. It is necessary to develop a way to follow up
with these patients for monitoring their health status to support timely
intervention and treatment. In the lack of sufficient human resources to follow
up with patients, we propose a novel smart chatbot solution backed with machine
learning to collect information (i.e., generating digital diary) in a
personalized manner. In this article, we describe the design framework and
components of our prototype.


Abstract:
  Interpretability in machine learning models is important in high-stakes
decisions, such as whether to order a biopsy based on a mammographic exam.
Mammography poses important challenges that are not present in other computer
vision tasks: datasets are small, confounding information is present, and it
can be difficult even for a radiologist to decide between watchful waiting and
biopsy based on a mammogram alone. In this work, we present a framework for
interpretable machine learning-based mammography. In addition to predicting
whether a lesion is malignant or benign, our work aims to follow the reasoning
processes of radiologists in detecting clinically relevant semantic features of
each image, such as the characteristics of the mass margins. The framework
includes a novel interpretable neural network algorithm that uses case-based
reasoning for mammography. Our algorithm can incorporate a combination of data
with whole image labelling and data with pixel-wise annotations, leading to
better accuracy and interpretability even with a small number of images. Our
interpretable models are able to highlight the classification-relevant parts of
the image, whereas other methods highlight healthy tissue and confounding
information. Our models are decision aids, rather than decision makers, aimed
at better overall human-machine collaboration. We do not observe a loss in mass
margin classification accuracy over a black box neural network trained on the
same data.


Abstract:
  We quantify evolution in the cluster scale stellar mass - halo mass (SMHM)
relation's parameters using 2323 clusters and brightest central galaxies (BCGs)
over the redshift range $0.03 \le z \le 0.60$. The precision on inferred SMHM
parameters is improved by including the magnitude gap ($\rm m_{gap}$) between
the BCG and fourth brightest cluster member (M14) as a third parameter in the
SMHM relation. At fixed halo mass, accounting for $\rm m_{gap}$, through a
stretch parameter, reduces the SMHM relation's intrinsic scatter. To explore
this redshift range, we use clusters, BCGs, and cluster members identified
using the Sloan Digital Sky Survey C4 and redMaPPer cluster catalogs and the
Dark Energy Survey redMaPPer catalog. Through this joint analysis, we detect no
systematic differences in BCG stellar mass, $\rm m_{gap}$, and cluster mass
(inferred from richness) between the datsets. We utilize the Pareto function to
quantify each parameter's evolution. We confirm prior findings of negative
evolution in the SMHM relation's slope (3.5$\sigma$) and detect negative
evolution in the stretch parameter (4.0$\sigma$) and positive evolution in the
offset parameter (5.8$\sigma$). This observed evolution, combined with the
absence of BCG growth, when stellar mass is measured within 50kpc, suggests
that this evolution results from changes in the cluster's $\rm m_{gap}$. For
this to occur, late-term growth must be in the intra-cluster light surrounding
the BCG. We also compare the observed results to Illustris TNG 300-1
cosmological hydrodynamic simulations and find modest qualitative agreement.
However, the simulations lack the evolutionary features detected in the real
data.


Abstract:
  Following the approach of [E1, M1, M2, S1, S2, SZJV] for reaction diffusion
systems, we justify rigorously the Eckhaus stability criterion for stability of
convective Turing patterns, as derived formally by complex Ginzburg-Landau
approximation [SS, NW, WZ]. Notably, our analysis includes also higher-order,
nonlocal, and even certain semilinear hyperbolic systems.


Abstract:
  We introduce a methodology for robust Bayesian estimation with robust
divergence (e.g., density power divergence or {\gamma}-divergence), indexed by
a single tuning parameter. It is well known that the posterior density induced
by robust divergence gives highly robust estimators against outliers if the
tuning parameter is appropriately and carefully chosen. In a Bayesian
framework, one way to find the optimal tuning parameter would be using evidence
(marginal likelihood). However, we numerically illustrate that evidence induced
by the density power divergence does not work to select the optimal tuning
parameter since robust divergence is not regarded as a statistical model. To
overcome the problems, we treat the exponential of robust divergence as an
unnormalized statistical model, and we estimate the tuning parameter via
minimizing the Hyvarinen score. We also provide adaptive computational methods
based on sequential Monte Carlo (SMC) samplers, which enables us to obtain the
optimal tuning parameter and samples from posterior distributions
simultaneously. The empirical performance of the proposed method through
simulations and an application to real data are also provided.


Abstract:
  Magnetic reconnection is invoked as one of the primary mechanisms to produce
energetic particles. We employ large-scale three-dimensional (3D)
particle-in-cell simulations of reconnection in magnetically-dominated
($\sigma=10$) pair plasmas to study the energization physics of high-energy
particles. We identify a novel acceleration mechanism that only operates in 3D.
For weak guide fields, 3D plasmoids / flux ropes extend along the $z$ direction
of the electric current for a length comparable to their cross-sectional
radius. Unlike in 2D simulations, where particles are buried in plasmoids, in
3D we find that a fraction of particles with $\gamma\gtrsim 3\sigma$ can escape
from plasmoids by moving along $z$, and so they can experience the large-scale
fields in the upstream region. These "free" particles preferentially move in
$z$ along Speiser-like orbits sampling both sides of the layer, and are
accelerated linearly in time -- their Lorentz factor scales as $\gamma\propto
t$, in contrast to $\gamma\propto \sqrt{t}$ in 2D. The energy gain rate
approaches $\sim eE_{\rm rec}c$, where $E_{\rm rec}\simeq 0.1 B_0$ is the
reconnection electric field and $B_0$ the upstream magnetic field. The spectrum
of free particles is hard, $dN_{\rm free}/d\gamma\propto \gamma^{-1.5}$,
contains $\sim 20\%$ of the dissipated magnetic energy independently of domain
size, and extends up to a cutoff energy scaling linearly with box size. Our
results demonstrate that relativistic reconnection in GRB and AGN jets may be a
promising mechanism for generating ultra-high-energy cosmic rays.


Abstract:
  Self-supervised pretraining has been shown to yield powerful representations
for transfer learning. These performance gains come at a large computational
cost however, with state-of-the-art methods requiring an order of magnitude
more computation than supervised pretraining. We tackle this computational
bottleneck by introducing a new self-supervised objective, contrastive
detection, which tasks representations with identifying object-level features
across augmentations. This objective extracts a rich learning signal per image,
leading to state-of-the-art transfer accuracy on a variety of downstream tasks,
while requiring up to 10x less pretraining. In particular, our strongest
ImageNet-pretrained model performs on par with SEER, one of the largest
self-supervised systems to date, which uses 1000x more pretraining data.
Finally, our objective seamlessly handles pretraining on more complex images
such as those in COCO, closing the gap with supervised transfer learning from
COCO to PASCAL.


Abstract:
  Chimera states, states of coexistence of synchronous and asynchronous motion,
have been a subject of extensive research since they were first given a name in
2004. Increased interest has lead to their discovery in ever new settings, both
theoretical and experimental. Less well-discussed is the fact that successive
results have also broadened the notion of what actually constitutes a chimera
state. In this article, we critically examine how the results for different
model types and coupling schemes, as well as varying implicit interpretations
of terms such as coexistence, synchrony and incoherence, have influenced the
common understanding of what constitutes a chimera. We cover both theoretical
and experimental systems, address various chimera-derived terms that have
emerged over the years and finally reflect on the question of chimera states in
real-world contexts.


Abstract:
  We study the changes in the short-distance quark structure of the
Nambu-Goldstone boson when the long-distance symmetry-breaking scales are
depleted controllably. We achieve this by studying the valence Parton
Distribution Function (PDF) of pion in 2+1 dimensional two-color QCD, with the
number $N$ of massless quarks as the tunable parameter that slides the theory
from being strongly broken for $N=0$ to the conformal window for $N>4$, where
the theory is gapped by the fixed finite volume. We perform our study
non-perturbatively using lattice simulations with $N=0,2,4,8$ flavors of nearly
massless two-component Wilson-Dirac sea quarks and employ the leading-twist
formalism (LaMET/SDF) to compute the PDF of pion at a fixed valence mass. We
find that the relative variations in the first few PDF moments are only mild
compared to the changes in decay constant, but the shape of the reconstructed
$x$-dependent PDF itself dramatically changes from being broad in the
scale-broken sector to being sharply peaked in the near-conformal region, best
reflected in PDF shape observables such as the cumulants.


Abstract:
  Inferring the connectivity structure of networked systems from data is an
extremely important task in many areas of science. Most of real-world networks
exhibit sparsely connected topologies, with links between nodes that in some
cases may be even associated to a binary state (0 or 1, denoting respectively
the absence or the existence of a connection). Such un-weighted topologies are
elusive to classical reconstruction methods such as Lasso or Compressed Sensing
techniques. We here introduce a novel approach called signal Lasso, where the
estimation of the signal parameter is subjected to 0 or 1 values. The
theoretical properties and algorithm of proposed method are studied in detail.
Applications of the method are illustrated to an evolutionary game and
synchronization dynamics in several synthetic and empirical networks, where we
show that the novel strategy is reliable and robust, and outperform the
classical approaches in terms of accuracy and mean square errors.


Abstract:
  We introduce a two-dimensional electronic insulator that possesses a toric
code topological order enriched by translation symmetry. This state can be
realized from disordering a weak topological superconductor by double-vortex
condensation. It is termed the toric code insulator, whose anyonic excitations
consist of a charge-$e$ chargon, a neutral fermion and two types of visons.
There are two types of visons because they have constrained motion as a
consequence of the fractional Josephson effect of one-dimensional topological
superconductor. Importantly, these two types of visons are related by a
discrete translation symmetry and have a mutual semionic braiding statistics,
leading to a symmetry-enrichment akin to the type in Wen's plaquette model and
Kitaev's honeycomb model. We construct this state using a three-fluid
coupled-wire model, and analyze the anyon spectrum and braiding statistics in
detail to unveil the nature of symmetry-enrichment due to translation. We also
discuss potential material realizations and present a band-theoretic
understanding of the state, fitting it into a general framework for studying
fractionalizaton in strongly-interacting weak topological phases.


Abstract:
  The plasma contributing to emission from the Sun between the cool
chromosphere ($\le 10^4$K) and hot corona ($\ge 10^6$K) has been subjected to
many different interpretations. Here we look at the magnetic structure of this
transition region (TR) plasma, based upon the implications of CLASP2 data of an
active region recently published by Ishikawa et al., and earlier IRIS and SDO
data of quiet regions. Ishikawa et al. found that large areas of sunspot plages
are magnetically unipolar as measured in the cores of \ion{Mg}{2} resonance
lines, formed in the lower transition region under low plasma-$\beta$
conditions. Here we show that IRIS images in the line cores have fibrils which
well aligned with the overlying coronal loop segments seen in the 171 \AA{}
channel of SDO. When the TR emission in active regions arise from plasma
magnetically and thermally connected to the corona, then the line cores can
provide the first credible magnetic boundary conditions for force-free
calculations extended to the corona. We also re-examine IRIS images of dynamic
TR cool loops previously reported as a major contributor to transition region
emission from the quiet Sun. Dynamic cool loops contribute only a small
fraction of the total TR emission from the quiet Sun.


Abstract:
  The mutual information method has demonstrated to be very useful for deriving
the potential order parameter of a system. Although the method suggests some
constraints which help to define this quantity, there is still some freedom in
the definition. The method then results inefficient for cases where we have
order parameters with a large number of constants in the expansion, which
happens when we have many degenerate vacuums. Here we introduce some additional
constraints based on the existence of broken symmetries, which help us to
reduce the arbitrariness in the definitions of the order parameter in the
proposed mutual information method.


Abstract:
  Autoregressive models have been widely used in unsupervised text style
transfer. Despite their success, these models still suffer from the content
preservation problem that they usually ignore part of the source sentence and
generate some irrelevant words with strong styles. In this paper, we propose a
Non-Autoregressive generator for unsupervised text Style Transfer (NAST), which
alleviates the problem from two aspects. First, we observe that most words in
the transferred sentence can be aligned with related words in the source
sentence, so we explicitly model word alignments to suppress irrelevant words.
Second, existing models trained with the cycle loss align sentences in two
stylistic text spaces, which lacks fine-grained control at the word level. The
proposed non-autoregressive generator focuses on the connections between
aligned words, which learns the word-level transfer between styles. For
experiments, we integrate the proposed generator into two base models and
evaluate them on two style transfer tasks. The results show that NAST can
significantly improve the overall performance and provide explainable word
alignments. Moreover, the non-autoregressive generator achieves over 10x
speedups at inference. Our codes are available at
https://github.com/thu-coai/NAST.


Abstract:
  Skrzypulec (2021) raises the question whether motor activation by masked
color primes is based on the same type of color representation as conscious
vision. He postulates that the literature on color processing without awareness
makes an implicit assumption that "conscious" and "unconscious" color
representations have the same properties, in which case priming by masked color
stimuli would indeed indicate that the same complex representation of surface
color can be conscious as well as unconscious. I review some evidence from
response priming by lightness stimuli in the context of a visual illusion that
alters the perceived lightness of the primes (Schmidt et al., 2010). Those
results clearly show that response priming is not driven by color-constant
information but instead by local image contrast, making it unlikely that rapid
response activation by color primes is based on a color-constant representation
of surface color.


Abstract:
  During planet formation, numerous small impacting bodies result in cratering
impacts on large target bodies. A fraction of the target surface is eroded,
while a fraction of the impactor material accretes onto the surface. These
fractions depend upon the impact velocities, the impact angles, and the escape
velocities of the target. This study uses smoothed particle hydrodynamics
simulations to model cratering impacts onto a planar icy target for which
gravity is the dominant force and material strength is neglected. By evaluating
numerical results, scaling laws are derived for the escape mass of the target
material and the accretion mass of the impactor material onto the target
surface. Together with recently derived results for rocky bodies in a companion
study, a conclusion is formulated that typical cratering impacts on terrestrial
planets, except for those on Mercury, led to a net accretion, while those on
the moons of giant planets, e.g., Rhea and Europa, led to a net erosion. Our
newly derived scaling laws would be useful for predicting the erosion of the
target body and the accretion of the impactor for a variety of cratering
impacts that would occur on large rocky and icy planetary bodies during planet
formation and collisional evolution from ancient times to today.


Abstract:
  Safety in reinforcement learning (RL) is a key property in both training and
execution in many domains such as autonomous driving or finance. In this paper,
we formalize it with a constrained RL formulation in the distributional RL
setting. Our general model accepts various definitions of safety(e.g., bounds
on expected performance, CVaR, variance, or probability of reaching bad
states). To ensure safety during learning, we extend a safe policy optimization
method to solve our problem. The distributional RL perspective leads to a more
efficient algorithm while additionally catering for natural safe constraints.
We empirically validate our propositions on artificial and real domains against
appropriate state-of-the-art safe RL algorithms.


Abstract:
  The memory-based diffusion systems have wide applications in practice. Hopf
bifurcations are observed from such systems. To meet the demand for computing
the normal forms of the Hopf bifurcations of such systems, we develop an
effective new algorithm where the memory delay is treated as the perturbation
parameter. To illustrate the effectiveness of the algorithm, we consider a
diffusive predator-prey system with memory-based diffusion and Holling type-II
functional response. By employing this newly developed procedure, we
investigate the direction and stability of the delay-induced mode-1 and mode-2
Hopf bifurcations. Numerical simulations confirm our theoretical findings, that
is the existence of stable spatially inhomogeneous periodic solutions with
mode-1 and mode-2 spatial patterns, and the transition from the unstable mode-2
spatially inhomogeneous periodic solution to the stable mode-1 spatially
inhomogeneous periodic solution.


Abstract:
  We prove that the best so far known constant $c_p=\frac{p^{-p}}{1-p},\,
p\in(0,1)$ of a domination inequality, which originates to Lenglart, is sharp.
In particular, we solve an open question posed by Revuz and Yor. Motivated by
the application to maximal inequalities, like e.g. the Burkholder-Davis-Gundy
inequality, we also study the domination inequality under an additional
monotonicity assumption. In this special case, a constant which stays bounded
for $p$ near $1$ was proven by Pratelli and Lenglart. We provide the sharp
constant for this case.


Abstract:
  The globalization of the electronics supply chain requires effective methods
to thwart reverse engineering and IP theft. Logic locking is a promising
solution, but there are still several open concerns. First, even when applied
at a higher level of abstraction, locking has significant overhead without
improving the security metric. Second, optimizing a security metric is
application-dependent and designers must evaluate and compare alternative
solutions. We propose a framework to optimize the use of behavioral locking
during the high-level synthesis (HLS) of IP cores. Our method operates on
chip's specification (before HLS) and it is compatible with all HLS tools,
complementing industrial EDA flows. The framework supports different
meta-heuristics to explore the design space and to select points to lock
automatically. Our method optimizes a given security metric better than
topological locking: 1) we always identify a valid solution that optimizes the
security metric; 2) we minimize the number of bits used for locking; and 3) we
make a better use of hardware resources.


Abstract:
  Automated Audio Captioning is a cross-modal task, generating natural language
descriptions to summarize the audio clips' sound events. However, grounding the
actual sound events in the given audio based on its corresponding caption has
not been investigated. This paper contributes an AudioGrounding dataset, which
provides the correspondence between sound events and the captions provided in
Audiocaps, along with the location (timestamps) of each present sound event.
Based on such, we propose the text-to-audio grounding (TAG) task, which
interactively considers the relationship between audio processing and language
understanding. A baseline approach is provided, resulting in an event-F1 score
of 28.3% and a Polyphonic Sound Detection Score (PSDS) score of 14.7%.


Abstract:
  The digital transformation is creating basically a digital version of our
physical world and the currency in that digital space is data. Massive amount
of data has been generated ranging from wearable devices monitoring our
physical health every single millisecond to autonomous vehicles generating
roughly 5Tb hourly to even astronomical activities producing an order of
Exabytes on daily basis and then ultra-broadband Internet comes into play,
moving such data to the cloud. Internet traffic therefore has been experiencing
explosive growth and in this context, optical transport networks forming the
backbone of the Internet are pushed for transformation in system capacity.
While the intuitive solution of deploying multiple fibers can address the
pressing demand for increased capacity, doing so does not bring improvement in
economic of scales in terms of cost, power consumption and spectral efficiency.
This necessitates for a different approach so that the fiber capacity could be
utilized in a more efficient manner. In this paper, we focus on innovative
techniques, that is, photonic network coding and partial protection, to reduce
the effective traffic load in order to achieve greater capacity efficiency for
optical transport networks. Specifically, the application of network coding is
examined by upgrading the functionalities of intermediate nodes with
all-optical processing (i.e., encoding and decoding) capabilities. Besides,
partial protection relying on the premise of providing just enough bandwidth in
case of failure events is investigated for saving the redundant protection
capacity. That it takes two to tango, combining photonic network coding and
partial protection therefore bring to light new opportunities and challenges.
In mining such new avenue, we present insights on how to derive compounding
gains to maximize spectral efficiency via a case study.


Abstract:
  At modern construction sites, utilizing GNSS (Global Navigation Satellite
System) to measure the real-time location and orientation (i.e. pose) of
construction machines and navigate them is very common. However, GNSS is not
always available. Replacing GNSS with on-board cameras and visual simultaneous
localization and mapping (visual SLAM) to navigate the machines is a
cost-effective solution. Nevertheless, at construction sites, multiple
construction machines will usually work together and side-by-side, causing
large dynamic occlusions in the cameras' view. Standard visual SLAM cannot
handle large dynamic occlusions well. In this work, we propose a motion
segmentation method to efficiently extract static parts from crowded dynamic
scenes to enable robust tracking of camera ego-motion. Our method utilizes
semantic information combined with object-level geometric constraints to
quickly detect the static parts of the scene. Then, we perform a two-step
coarse-to-fine ego-motion tracking with reference to the static parts. This
leads to a novel dynamic visual SLAM formation. We test our proposals through a
real implementation based on ORB-SLAM2, and datasets we collected from real
construction sites. The results show that when standard visual SLAM fails, our
method can still retain accurate camera ego-motion tracking in real-time.
Comparing to state-of-the-art dynamic visual SLAM methods, ours shows
outstanding efficiency and competitive result trajectory accuracy.


Abstract:
  Quantum simulation on emerging quantum hardware is a topic of intense
interest. While many studies focus on computing ground state properties or
simulating unitary dynamics of closed systems, open quantum systems are an
interesting target of study owing to their ubiquity and rich physical behavior.
However, their non-unitary dynamics are also not natural to simulate on digital
quantum devices. Here, we report algorithms for the digital quantum simulation
of the dynamics of open quantum systems governed by a Lindblad equation using
adaptations of the quantum imaginary time evolution (QITE) algorithm. We
demonstrate the algorithms on IBM Quantum's hardware with simulations of the
spontaneous emission of a two level system and the dissipative transverse field
Ising model. Our work advances efforts to simulate the dynamics of open quantum
systems on quantum hardware.


Abstract:
  This study compares the lexical structure of articles titles and abstracts of
two extremes in MB (management-business research): the AMJ (Academy of
Management Journal), one of its most revered periodicals, and Espacios, the one
that unveiled a structural problem in Latin-American MB. Results showed
significant differences in the median of titles length and abstracts
readability and diversity as AMJ titles length was longer and abstracts both
more diverse and readability-demanding.


Abstract:
  In this paper, a family of novel diffusion adaptive estimation algorithm is
proposed from the asymmetric cost function perspective by combining diffusion
strategy and the linear-linear cost (LLC), quadratic-quadratic cost (QQC), and
linear-exponential cost (LEC), at all distributed network nodes, and named
diffusion LLCLMS (DLLCLMS), diffusion QQCLMS (DQQCLMS), and diffusion LECLMS
(DLECLMS), respectively. Then the stability of mean estimation error and
computational complexity of those three diffusion algorithms are analyzed
theoretically. Finally, several experiment simulation results are designed to
verify the superiority of those three proposed diffusion algorithms.
Experimental simulation results show that DLLCLMS, DQQCLMS, and DLECLMS
algorithms are more robust to the input signal and impulsive noise than the
DSELMS, DRVSSLMS, and DLLAD algorithms. In brief, theoretical analysis and
experiment results show that those proposed DLLCLMS, DQQCLMS, and DLECLMS
algorithms have superior performance when estimating the unknown linear system
under the changeable impulsive noise environments and different types of input
signals.


Abstract:
  Abel's quadratures for integrable Hamiltonian systems are defined up to a
group law of the corresponding Abelian variety $A$. If $A$ is isogenous to a
direct product of Abelian varieties $A\cong A_1\times\cdots\times A_k$, the
group law can be used to construct various Lax matrices on the factors
$A_1,\ldots,A_k$. As an example, we discuss 2-dimensional reducible Abelian
variety $A=E_+\times E_-$, which is a product of 1-dimensional varieties
$E_\pm$ obtained by Euler in his study of the two fixed centres problem, and
the Lax matrices on the factors $E_\pm$.


Abstract:
  Communications system design has been traditionally guided by task-agnostic
principles, which aim at efficiently transmitting as many correct bits as
possible through a given channel. However, in the era of cyber-physical
systems, the effectiveness of communications is not dictated simply by the bit
rate, but most importantly by the efficient completion of the task in hand,
e.g., controlling remotely a robot, automating a production line or
collaboratively sensing through a drone swarm. In parallel, it is projected
that by 2023, half of the worldwide network connections will be among machines
rather than humans. In this context, it is crucial to establish a new paradigm
for designing communications strategies for multi-agent cyber-physical systems.
This is a daunting task, since it requires a combination of principles from
information, communication, control theories and computer science in order to
formalize a general framework for task-oriented communication design. In this
direction, this paper reviews and structures the relevant theoretical work
across a wide range of scientific communities. Subsequently, it proposes a
general conceptual framework for task-oriented communication design, along with
its specializations according to the targeted use case. Furthermore, it
provides a survey of relevant contributions in dominant applications, such as
industrial internet of things, multi-UAV systems, tactile internet, autonomous
vehicles, distributed learning systems, smart manufacturing plants and 5G and
beyond self-organizing networks. Finally, it highlights the most important open
research topics from both the theoretical framework and application points of
view.


Abstract:
  In this paper we prove that the $r$-th ADO polynomial of a knot, for $r$ a
power of prime number, can be expanded as Vassiliev invariants with values in
$\mathbb{Z}$. Nevertheless this expansion is not unique and not easily
computable. We can obtain a unique computable expansion, but we only get $r$
adic topological Vassiliev invariants as coefficients. To do so, we exploit the
fact that the colored Jones polynomials can be decomposed as Vassiliev
invariants and we tranpose it to ADO using the unified knot invariant
recovering both ADO and colored Jones defined in arXiv:2003.09854. Finally we
prove some asymptotic behavior of the ADO polynomials modulo $r$ as $r$ goes to
infinity.


Abstract:
  The excess of $\gamma$ rays in the data measured by Fermi-LAT from the
Galactic center region is one of the most intriguing mysteries in Astroparticle
Physics. This Galactic center excess (GCE), has been measured with respect to
different interstellar emission models (IEMs), source catalogs, data selections
and techniques. Although several proposed interpretations have appeared in the
literature, there are not firm conclusions as to its origin. The main
difficulty in solving this puzzle lies in modeling a region of such complexity
and thus precisely measuring the characteristics of the GCE. In this paper, we
use 11 years of Fermi-LAT data, state of the art IEMs, and the newest 4FGL
source catalog to provide precise measurements of the energy spectrum, spatial
morphology, position, and sphericity of the GCE. We find that the GCE has a
spectrum which is peaked at a few GeV and is well fit with a log-parabola. The
normalization of the spectrum changes by roughly $60\%$ when using different
IEMs, data selections and analysis techniques. The spatial distribution of the
GCE is compatible with a dark matter (DM) template produced with a generalized
NFW density profile with slope $\gamma = 1.2-1.3$. No energy evolution is
measured for the GCE morphology between $0.6-30$ GeV at a level larger than
$10\%$ of the $\gamma$ average value, which is 1.25. The analysis of the GCE
modeled with a DM template divided into quadrants shows that the spectrum and
spatial morphology of the GCE is similar in different regions around the
Galactic center. Finally, the GCE centroid is compatible with the Galactic
center, with best-fit position between
$l=[-0.3^{\circ},0.0^{\circ}],b=[-0.1^{\circ},0.0^{\circ}]$, and it is
compatible with a spherical symmetric morphology. In particular, fitting the DM
spatial profile with an ellipsoid gives a major-to-minor axis ratio between
0.8-1.2.


Abstract:
  Recent observations of extrasolar gas giants suggest super-stellar C/O ratios
in planetary atmospheres, while interior models of observed extrasolar giant
planets additionally suggest high heavy element contents. Furthermore, recent
observations of protoplanetary disks revealed super-solar C/H ratios, which are
explained by inward drifting and evaporating pebbles, enhancing the volatile
content of the disk. We investigate how the inward drift and evaporation of
volatile rich pebbles influences the atmospheric C/O ratio and heavy element
content of giant planets growing by pebble and gas accretion. To achieve this
goal, we perform semi analytical 1D models of protoplanetary disks including
the treatment of viscous evolution and heating, pebble drift and simple
chemistry to simulate the growth of planets from planetary embryos to Jupiter
mass objects by accretion of pebbles and gas while they migrate through the
disk. Our simulations show that the composition of the planetary gas atmosphere
is dominated by the accretion of vapour, originating from inward drifting
evaporating pebbles. This process allows the giant planets to harbour large
heavy element contents. In addition, our model reveals that giant planets
originating further away from the central star have a higher C/O ratio on
average due to the evaporation of methane rich pebbles in the outer disk. These
planets can then also harbour super-solar C/O ratios, in line with exoplanet
observations. However, planets formed in the outer disk harbour a smaller heavy
element content, due to a smaller vapour enrichment of the outer disk. Our
model predicts that giant planets with low/large atmospheric C/O should harbour
a large/low total heavy element content. We further conclude that the inclusion
of pebble evaporation at evaporation lines is a key ingredient to determine the
heavy element content and composition of giant planets.


Abstract:
  We assume the anisotropic model of the Universe in the framework of varying
speed of light $c$ and varying gravitational constant $G$ theories and study
different types of singularities. For the singularity models, we write the
scale factors in terms of cosmic time and found some conditions for possible
singularities. For future singularities, we assume the forms of varying speed
of light and varying gravitational constant. For regularizing big bang
singularity, we assume two forms of scale factors: sine model and tangent
model. For both the models, we examine the validity of null energy condition
and strong energy condition. Start from the first law of thermodynamics, we
study the thermodynamic behaviours of $n$ number of Universes (i.e.,
Multiverse) for (i) varying $c$, (ii) varying $G$ and (iii) both varying $c$
and $G$ models. We found the total entropies for all the cases in the
anisotropic Multiverse model. We also found the nature of the Multiverse if
total entropy is constant.


Abstract:
  A new federated learning (FL) framework enabled by large-scale wireless
connectivity is proposed for designing the autonomous controller of connected
and autonomous vehicles (CAVs). In this framework, the learning models used by
the controllers are collaboratively trained among a group of CAVs. To capture
the varying CAV participation in the FL training process and the diverse local
data quality among CAVs, a novel dynamic federated proximal (DFP) algorithm is
proposed that accounts for the mobility of CAVs, the wireless fading channels,
as well as the unbalanced and nonindependent and identically distributed data
across CAVs. A rigorous convergence analysis is performed for the proposed
algorithm to identify how fast the CAVs converge to using the optimal
autonomous controller. In particular, the impacts of varying CAV participation
in the FL process and diverse CAV data quality on the convergence of the
proposed DFP algorithm are explicitly analyzed. Leveraging this analysis, an
incentive mechanism based on contract theory is designed to improve the FL
convergence speed. Simulation results using real vehicular data traces show
that the proposed DFP-based controller can accurately track the target CAV
speed over time and under different traffic scenarios. Moreover, the results
show that the proposed DFP algorithm has a much faster convergence compared to
popular FL algorithms such as federated averaging (FedAvg) and federated
proximal (FedProx). The results also validate the feasibility of the
contract-theoretic incentive mechanism and show that the proposed mechanism can
improve the convergence speed of the DFP algorithm by 40% compared to the
baselines.


Abstract:
  According to "Social Disorganization" theory, criminal activity increases if
the societal institutions that might be responsible for maintaining order are
weakened. Do large apartment buildings, which often have fairly transient
populations and low levels of community involvement, have disproportionately
high rates of crime? Do these rates differ during the daytime or nighttime,
depending when residents are present, or away from their property? This study
examines four types of "acquisitive" crime in Milwaukee during 2014. Overall,
nighttime crimes are shown to be more dispersed than daytime crimes. A spatial
regression estimation finds that the density of multiunit housing is positively
related to all types of crime except burglaries, but not for all times of day.
Daytime robberies, in particular, increase as the density of multiunit housing
increases.


Abstract:
  In this paper we present the theory of oscillation numbers and dual
oscillation numbers for continuous Lagrangian paths in $\mathbb{R}^{2n}$. Our
main results include a connection of the oscillation numbers of the given
Lagrangian path with the Lidskii angles of a special symplectic orthogonal
matrix. We also present Sturmian type comparison and separation theorems for
the difference of the oscillation numbers of two continuous Lagrangian paths.
These results, as well as the definition of the oscillation number itself, are
based on the comparative index theory (Elyseeva, 2009). The applications of
these results are directed to the theory of Maslov index of two continuous
Lagrangian paths. We derive a formula for the Maslov index via the Lidskii
angles of a special symplectic orthogonal matrix, and hence we express the
Maslov index as the oscillation number of a certain transformed Lagrangian
path. The results and methods are based on a generalization of the recently
introduced oscillation numbers and dual oscillation numbers for conjoined bases
of linear Hamiltonian systems (Elyseeva, 2019 and 2020) and on the connection
between the comparative index and Lidskii angles of symplectic matrices
(\v{S}epitka and \v{S}imon Hilscher, 2020).


Abstract:
  We employ the periodic Anderson model with superconducting correlations in
the conduction band at half filling to study the behavior of the in-gap bands
in a heterostructure consisting of a molecular layer deposited on the surface
of a conventional superconductor. We use the dynamical mean-field theory to map
the lattice model on the superconducting single impurity model with
self-consistently determined bath and use the continuous-time hybridization
expansion (CT-HYB) quantum Monte Carlo and the iterative perturbation theory
(IPT) as solvers for the impurity problem. We present phase diagrams for square
and triangular lattice that both show two superconducting phases that differ by
the sign of the induced pairing, in analogy to the $0$ and $\pi$ phases of the
superconducting single impurity Anderson model and discuss the evolution of the
spectral function in the vicinity of the transition. We also discuss the
failure of the IPT for superconducting models with spinful ground state and the
behavior of the average expansion order of the CT-HYB simulation.


Abstract:
  We consider the problem of service placement at the network edge, in which a
decision maker has to choose between $N$ services to host at the edge to
satisfy the demands of customers. Our goal is to design adaptive algorithms to
minimize the average service delivery latency for customers. We pose the
problem as a Markov decision process (MDP) in which the system state is given
by describing, for each service, the number of customers that are currently
waiting at the edge to obtain the service. However, solving this $N$-services
MDP is computationally expensive due to the curse of dimensionality. To
overcome this challenge, we show that the optimal policy for a single-service
MDP has an appealing threshold structure, and derive explicitly the Whittle
indices for each service as a function of the number of requests from customers
based on the theory of Whittle index policy.
  Since request arrival and service delivery rates are usually unknown and
possibly time-varying, we then develop efficient learning augmented algorithms
that fully utilize the structure of optimal policies with a low learning
regret. The first of these is UCB-Whittle, and relies upon the principle of
optimism in the face of uncertainty. The second algorithm, Q-learning-Whittle,
utilizes Q-learning iterations for each service by using a two time scale
stochastic approximation. We characterize the non-asymptotic performance of
UCB-Whittle by analyzing its learning regret, and also analyze the convergence
properties of Q-learning-Whittle. Simulation results show that the proposed
policies yield excellent empirical performance.


Abstract:
  This work presents a naive algorithm for parameter transfer between different
architectures with a computationally cheap injection technique (which does not
require data). The primary objective is to speed up the training of neural
networks from scratch. It was found in this study that transferring knowledge
from any architecture was superior to Kaiming and Xavier for initialization. In
conclusion, the method presented is found to converge faster, which makes it a
drop-in replacement for classical methods. The method involves: 1) matching:
the layers of the pre-trained model with the targeted model; 2) injection: the
tensor is transformed into a desired shape. This work provides a comparison of
similarity between the current SOTA architectures (ImageNet), by utilising TLI
(Transfer Learning by Injection) score.


Abstract:
  The majority of current approaches in autonomous driving rely on
High-Definition (HD) maps which detail the road geometry and surrounding area.
Yet, this reliance is one of the obstacles to mass deployment of autonomous
vehicles due to poor scalability of such prior maps. In this paper, we tackle
the problem of online road map extraction via leveraging the sensory system
aboard the vehicle itself. To this end, we design a structured model where a
graph representation of the road network is generated in a hierarchical fashion
within a fully convolutional network. The method is able to handle complex road
topology and does not require a user in the loop.


Abstract:
  Understanding the critical condition and mechanism of the droplet wetting
transition between Cassie-Baxter state and Wenzel state triggered by an
external electric field is of considerable importance because of its numerous
applications in industry and engineering. However, such a wetting transition on
a patterned surface is still not fully understood, e.g., the effects of
electro-wetting number, geometry of the patterned surfaces, and droplet volume
on the transition have not been systematically investigated. In this paper, we
propose a theoretical model for the Cassie-Baxter- Wenzel wetting transition
triggered by applying an external voltage on a droplet placed on a
mircopillared surface or a porous substrate. It is found that the transition is
realized by lowering the energy barrier created by the intermediate composite
state considerably, which enables the droplet to cross the energy barrier and
complete the transition process. Our calculations also indicate that for fixed
droplet volume, the critical electrowetting number (voltage) will increase
(decrease) along with the surface roughness for a micro-pillar patterned
(porous) surface, and if the surface roughness is fixed, a small droplet tends
to ease the critical electrowetting condition for the transition. Besides,
three dimensional phase diagrams in terms of electrowetting number, surface
roughness, and droplet volume are constructed to illustrate the
Cassie-Baxter-Wenzel wetting transition. Our theoretical model can be used to
explain the previous experimental results about the Cassie-Baxter-Wenzel
wetting transition reported in the literature.