Abstract:
  Inspired by our previous work on the boundedness of Toeplitz operators, we
introduce weak BMO and VMO type conditions, denoted by BWMO and VWMO,
respectively, for functions on the open unit disc of the complex plane. We show
that the average function of a function $f$ in BWMO is boundedly oscillating,
and the analogous result holds for $f$ in VWMO. The result is applied for
generalizations of known results on the essential spectra and norms of Toeplitz
operators. Finally, we provide examples of functions satisfying the VWMO
condition which are not in the classical VMO or even in BMO.


Abstract:
  Understanding the low-temperature pure state structure of spin glasses
remains an open problem in the field of statistical mechanics of disordered
systems. Here we study Monte Carlo dynamics, performing simulations of the
growth of correlations following a quench from infinite temperature to a
temperature well below the spin-glass transition temperature $T_c$ for a
one-dimensional Ising spin glass model with diluted long-range interactions. In
this model, the probability $P_{ij}$ that an edge $\{i,j\}$ has nonvanishing
interaction falls as a power-law with chord distance,
$P_{ij}\propto1/R_{ij}^{2\sigma}$, and we study a range of values of $\sigma$
with $1/2<\sigma<1$. We consider a correlation function $C_{4}(r,t)$. A dynamic
correlation length that shows power-law growth with time $\xi(t)\propto
t^{1/z}$ can be identified in the data and, for large time $t$, $C_{4}(r,t)$
decays as a power law $r^{-\alpha_d}$ with distance $r$ when $r\ll \xi(t)$. The
calculation can be interpreted in terms of the maturation metastate averaged
Gibbs state, or MMAS, and the decay exponent $\alpha_d$ differentiates between
a trivial MMAS ($\alpha_d=0$), as expected in the droplet picture of spin
glasses, and a nontrivial MMAS ($\alpha_d\ne 0$), as in the
replica-symmetry-breaking (RSB) or chaotic pairs pictures. We find nonzero
$\alpha_d$ even in the regime $\sigma >2/3$ which corresponds to short-range
systems below six dimensions. For $\sigma < 2/3$, the decay exponent $\alpha_d$
follows the RSB prediction for the decay exponent $\alpha_s = 3 - 4 \sigma$ of
the static metastate, consistent with a conjectured statics-dynamics relation,
while it approaches $\alpha_d=1-\sigma$ in the regime $2/3<\sigma<1$; however,
it deviates from both lines in the vicinity of $\sigma=2/3$.


Abstract:
  Different theories of gravity can admit the same black hole solution, but the
parameters usually have different physical interpretations. In this work we
study in depth the linear term $\beta r$ in the redshift function of black
holes, which arises in conformal gravity, de Rham-Gabadadze-Tolley (dRGT)
massive gravity, $f(R)$ gravity (as approximate solution) and general
relativity. Geometrically we quantify the parameter $\beta$ in terms of the
curvature invariants. Astrophysically we found that $\beta$ can be expressed in
terms of the cosmological constant, the photon orbit radius and the innermost
stable circular orbit (ISCO) radius. The metric degeneracy can be broken once
black hole thermodynamics is taken into account. Notably, we show that under
Hawking evaporation, different physical theories with the same black hole
solution (at the level of the metric) can lead to black hole remnants with
different values of their physical masses with direct consequences on their
viability as dark matter candidates. In particular, the mass of the graviton in
massive gravity can be expressed in terms of the cosmological constant and of
the formation epoch of the remnant. Furthermore the upper bound of remnant mass
can be estimated to be around $0.5 \times 10^{27}$ kg.


Abstract:
  This study reports the magnetization switching induced by spin-orbit torque
(SOT) from the spin current generated in Co2MnGa magnetic Weyl semimetal (WSM)
thin films. We deposited epitaxial Co2MnGa thin films with highly B2-ordered
structure on MgO(001) substrates. The SOT was characterized by harmonic Hall
measurements in a Co2MnGa/Ti/CoFeB heterostructure and a relatively large spin
Hall efficiency of -7.8% was obtained.The SOT-induced magnetization switching
of the perpendicularly magnetized CoFeB layer was further demonstrated using
the structure. The symmetry of second harmonic signals, thickness dependence of
spin Hall efficiency, and shift of anomalous Hall loops under applied currents
were also investigated. This study not only contributes to the understanding of
the mechanisms of spin-current generation from magnetic-WSM-based
heterostructures, but also paves a way for the applications of magnetic WSMs in
spintronic devices.


Abstract:
  In this article, we derive analytically the complex optical spectrum of a
pulsed laser source obtained when a frequency comb generated by phase
modulation is input into a synchronized intensity modulator. We then show how
this knowledge of the spectrum may help to achieve unprecedented accuracy
during the experimental spectrum correction step usually carried out with an
optical spectrum processor. In numerical examples, for a given average power we
present up to a 75 % increase in peak power and an enhancement of the
extinction ratio by at least three orders of magnitude. This method also
enables large-factor rate-multiplications of these versatile coherent sources
using the Talbot effect with negligible degradation of the signal.


Abstract:
  The analysis of 20 years of spectrophotometric data of the double shell
planetary nebula PM\,1-188 is presented, aiming to determine the time evolution
of the emission lines and the physical conditions of the nebula, as a
consequence of the systematic fading of its [WC\,10] central star whose
brightness has declined by about 10 mag in the past 40 years. Our main results
include that the [\ion{O}{iii}], [\ion{O}{ii}], [\ion{N}{ii}] line intensities
are increasing with time in the inner nebula as a consequence of an increase in
electron temperature from 11,000 K in 2005 to more than 14,000 K in 2018, due
to shocks. The intensity of the same lines are decreasing in the outer nebula,
due to a decrease in temperature, from 13,000 K to 7,000 K, in the same period.
The chemical composition of the inner and outer shells was derived and they are
similar. Both nebulae present subsolar O, S and Ar abundances, while they are
He, N and Ne rich. For the outer nebula the values are 12+log He/H=
11.13$\pm$0.05, 12+log O/H = 8.04$\pm$0.04, 12+log N/H= 7.87$\pm$0.06, 12+log
S/H = 7.18$\pm$0.10 and 12+log Ar = 5.33$\pm$0.16. The O, S and Ar abundances
are several times lower than the average values found in disc non-Type I PNe,
and are reminiscent of some halo PNe. From high resolution spectra, an outflow
in the N-S direction was found in the inner zone. Position-velocity diagrams
show that the outflow expands at velocities in the $-$150 to 100 km s$^{-1}$
range, and both shells have expansion velocities of about 40 km s$^{-1}$.


Abstract:
  We investigate long-lived particles (LLPs) produced in pair from neutral
currents and decaying into a displaced electron plus two jets at the LHC,
utilizing the proposed minimum ionizing particle timing detector at CMS. We
study two benchmark models: the R-parity-violating supersymmetry with the
lightest neutralinos being the lightest supersymmetric particle and two
different $U(1)$ extensions of the standard model with heavy neutral leptons
(HNLs). The light neutralinos are produced from the standard model $Z$-boson
decays via small Higgsino components, and the HNLs arise from decays of a heavy
gauge boson, $Z'$. By simulating the signal processes at the HL-LHC with the
center-of-mass energy $\sqrt{s}=$ 14 TeV and integrated luminosity of 3
ab$^{-1}$, our analyses indicate that the search strategy based on a timing
trigger and the final state kinematics has the potential to probe the parameter
space that is complementary to other traditional LLP search strategies such as
those based on the displaced vertex.


Abstract:
  Algorithms produce a growing portion of decisions and recommendations both in
policy and business. Such algorithmic decisions are natural experiments
(conditionally quasi-randomly assigned instruments) since the algorithms make
decisions based only on observable input variables. We use this observation to
develop a treatment-effect estimator for a class of stochastic and
deterministic decision-making algorithms. Our estimator is shown to be
consistent and asymptotically normal for well-defined causal effects. A key
special case of our estimator is a multidimensional regression discontinuity
design. We apply our estimator to evaluate the effect of the Coronavirus Aid,
Relief, and Economic Security (CARES) Act, where hundreds of billions of
dollars worth of relief funding is allocated to hospitals via an algorithmic
rule. Our estimates suggest that the relief funding has little effect on
COVID-19-related hospital activity levels. Naive OLS and IV estimates exhibit
substantial selection bias.


Abstract:
  Concurrent accesses to databases are typically encapsulated in transactions
in order to enable isolation from other concurrent computations and resilience
to failures. Modern databases provide transactions with various semantics
corresponding to different trade-offs between consistency and availability.
Since a weaker consistency model provides better performance, an important
issue is investigating the weakest level of consistency needed by a given
program (to satisfy its specification). As a way of dealing with this issue, we
investigate the problem of checking whether a given program has the same set of
behaviors when replacing a consistency model with a weaker one. This property
known as robustness generally implies that any specification of the program is
preserved when weakening the consistency. We focus on the robustness problem
for consistency models which are weaker than standard serializability, namely,
causal consistency, prefix consistency, and snapshot isolation. We show that
checking robustness between these models is polynomial time reducible to a
state reachability problem under serializability. We use this reduction to also
derive a pragmatic proof technique based on Lipton's reduction theory that
allows to prove programs robust. We have applied our techniques to several
challenging applications drawn from the literature of distributed systems and
databases.


Abstract:
  Given the prevalence of pre-trained contextualized representations in today's
NLP, there have been several efforts to understand what information such
representations contain. A common strategy to use such representations is to
fine-tune them for an end task. However, how fine-tuning for a task changes the
underlying space is less studied. In this work, we study the English BERT
family and use two probing techniques to analyze how fine-tuning changes the
space. Our experiments reveal that fine-tuning improves performance because it
pushes points associated with a label away from other labels. By comparing the
representations before and after fine-tuning, we also discover that fine-tuning
does not change the representations arbitrarily; instead, it adjusts the
representations to downstream tasks while preserving the original structure.
Finally, using carefully constructed experiments, we show that fine-tuning can
encode training sets in a representation, suggesting an overfitting problem of
a new kind.


Abstract:
  In this article, we discuss how to solve information-gathering problems
expressed as rho-POMDPs, an extension of Partially Observable Markov Decision
Processes (POMDPs) whose reward rho depends on the belief state. Point-based
approaches used for solving POMDPs have been extended to solving rho-POMDPs as
belief MDPs when its reward rho is convex in B or when it is
Lipschitz-continuous. In the present paper, we build on the POMCP algorithm to
propose a Monte Carlo Tree Search for rho-POMDPs, aiming for an efficient
on-line planner which can be used for any rho function. Adaptations are
required due to the belief-dependent rewards to (i) propagate more than one
state at a time, and (ii) prevent biases in value estimates. An asymptotic
convergence proof to epsilon-optimal values is given when rho is continuous.
Experiments are conducted to analyze the algorithms at hand and show that they
outperform myopic approaches.


Abstract:
  Modern vehicles equipped with on-board units (OBU) are playing an essential
role in the smart city revolution. The vehicular processing resources, however,
are not used to their fullest potential. The concept of vehicular clouds is
proposed to exploit the underutilized vehicular resources to supplement cloud
computing services to relieve the burden on cloud data centers and improve
quality of service. In this paper we introduce a vehicular cloud architecture
supported by fixed edge computing nodes and the central cloud. A mixed integer
linear programming (MLP) model is developed to optimize the allocation of the
computing demands in the distributed architecture while minimizing power
consumption. The results show power savings as high as 84% over processing in
the conventional cloud. A heuristic with performance approaching that of the
MILP model is developed to allocate computing demands in real time.


Abstract:
  Nambu dynamics is a generalized Hamiltonian dynamics of more than two
variables, whose time evolutions are given by the Nambu bracket, a
generalization of the canonical Poisson bracket. Nambu dynamics can always be
represented in the form of noncanonical Hamiltonian dynamics by defining the
noncanonical Poisson bracket by means of the Nambu bracket. For the time
evolution to be consistent, the Nambu bracket must satisfy the fundamental
identity, while the noncanonical Poisson bracket must satisfy the Jacobi
identity. However, in many degrees of freedom systems, it is well known that
the fundamental identity does not hold. In the present paper we show that, even
if the fundamental identity is violated, the Jacobi identity for the
corresponding noncanonical Hamiltonian dynamics could hold. As an example, we
evaluate these identities for a semiclassical system of two coupled
oscillators.


Abstract:
  How to measure the incremental Return On Ad Spend (iROAS) is a fundamental
problem for the online advertising industry. A standard modern tool is to run
randomized geo experiments, where experimental units are non-overlapping
ad-targetable geographical areas (Vaver & Koehler 2011). However, how to design
a reliable and cost-effective geo experiment can be complicated, for example:
1) the number of geos is often small, 2) the response metric (e.g. revenue)
across geos can be very heavy-tailed due to geo heterogeneity, and furthermore
3) the response metric can vary dramatically over time. To address these
issues, we propose a robust nonparametric method for the design, called Trimmed
Match Design (TMD), which extends the idea of Trimmed Match (Chen & Au 2019)
and furthermore integrates the techniques of optimal subset pairing and sample
splitting in a novel and systematic manner. Some simulation and real case
studies are presented. We also point out a few open problems for future
research.


Abstract:
  Visually realistic GAN-generated images have recently emerged as an important
misinformation threat. Research has shown that these synthetic images contain
forensic traces that are readily identifiable by forensic detectors.
Unfortunately, these detectors are built upon neural networks, which are
vulnerable to recently developed adversarial attacks. In this paper, we propose
a new anti-forensic attack capable of fooling GAN-generated image detectors.
Our attack uses an adversarially trained generator to synthesize traces that
these detectors associate with real images. Furthermore, we propose a technique
to train our attack so that it can achieve transferability, i.e. it can fool
unknown CNNs that it was not explicitly trained against. We demonstrate the
performance of our attack through an extensive set of experiments, where we
show that our attack can fool eight state-of-the-art detection CNNs with
synthetic images created using seven different GANs.


Abstract:
  We study a generalized Blume-Capel model on the simple cubic lattice. In
addition to the nearest neighbor coupling there is a next to next to nearest
neighbor coupling. In order to quantify spatial anisotropy, we determine the
correlation length in the high temperature phase of the model for three
different directions. It turns out that the spatial anisotropy depends very
little on the dilution parameter $D$ of the model and is essentially determined
by the ratio of the nearest neighbor and the next to next to nearest neighbor
coupling. This ratio is tuned such that the leading contribution to the spatial
anisotropy is eliminated. Next we perform a finite size scaling (FSS) study to
tune $D$ such that also the leading correction to scaling is eliminated. Based
on this FSS study, we determine the critical exponents $\nu=0.62998(5)$ and
$\eta=0.036284(40)$, which are in nice agreement with the more accurate results
obtained by using the conformal bootstrap method. Furthermore we provide
accurate results for fixed point values of dimensionless quantities such as the
Binder cumulant and for the critical couplings. These results provide the
groundwork for broader studies of universal properties of the three-dimensional
Ising universality class.


Abstract:
  Sign language translation (SLT) is often decomposed into video-to-gloss
recognition and gloss-to-text translation, where a gloss is a sequence of
transcribed spoken-language words in the order in which they are signed. We
focus here on gloss-to-text translation, which we treat as a low-resource
neural machine translation (NMT) problem. However, unlike traditional
low-resource NMT, gloss-to-text translation differs because gloss-text pairs
often have a higher lexical overlap and lower syntactic overlap than pairs of
spoken languages. We exploit this lexical overlap and handle syntactic
divergence by proposing two rule-based heuristics that generate pseudo-parallel
gloss-text pairs from monolingual spoken language text. By pre-training on the
thus obtained synthetic data, we improve translation from American Sign
Language (ASL) to English and German Sign Language (DGS) to German by up to
3.14 and 2.20 BLEU, respectively.


Abstract:
  A type of polar self-propelled particle generates a torque that makes it
naturally drawn to higher-density areas. The collective behaviour this induces
in assemblies of particles constitutes a new form of phase separation in active
fluids.


Abstract:
  When fonts are used on documents, they are intentionally selected by
designers. For example, when designing a book cover, the typography of the text
is an important factor in the overall feel of the book. In addition, it needs
to be an appropriate font for the rest of the book cover. Thus, we propose a
method of generating a book title image based on its context within a book
cover. We propose an end-to-end neural network that inputs the book cover, a
target location mask, and a desired book title and outputs stylized text
suitable for the cover. The proposed network uses a combination of a
multi-input encoder-decoder, a text skeleton prediction network, a perception
network, and an adversarial discriminator. We demonstrate that the proposed
method can effectively produce desirable and appropriate book cover text
through quantitative and qualitative results.


Abstract:
  The music of Northern Myanmar Kachin ethnic group is compared to the music of
western China, Xijiang based Uyghur music, using timbre and pitch feature
extraction and machine learning. Although separated by Tibet, the muqam
tradition of Xinjiang might be found in Kachin music due to myths of Kachin
origin, as well as linguistic similarities, e.g., the Kachin term 'makan' for a
musical piece. Extractions were performed using the apollon and COMSAR
(Computational Music and Sound Archiving) frameworks, on which the Ethnographic
Sound Recordings Archive (ESRA) is based, using ethnographic recordings from
ESRA next to additional pieces. In terms of pitch, tonal systems were compared
using Kohonen self-organizing map (SOM), which clearly clusters Kachin and
Uyghur musical pieces. This is mainly caused by the Xinjiang muqam music
showing just fifth and fourth, while Kachin pieces tend to have a higher fifth
and fourth, next to other dissimilarities. Also, the timbre features of
spectral centroid and spectral sharpness standard deviation clearly tells
Uyghur from Kachin pieces, where Uyghur music shows much larger deviations.
Although more features will be compared in the future, like rhythm or melody,
these already strong findings might introduce an alternative comparison
methodology of ethnic groups beyond traditional linguistic definitions.


Abstract:
  Enabling out-of-distribution (OOD) detection for DNNs is critical for their
safe and reliable operation in the open world. Despite recent progress, current
works often consider a coarse level of granularity in the OOD problem, which
fail to approximate many real-world fine-grained tasks where high granularity
may be expected between the in-distribution (ID) data and the OOD data (e.g.,
identifying novel bird species for a bird classification system in the wild).
In this work, we start by carefully constructing four large-scale fine-grained
test environments in which existing methods are shown to have difficulties. We
find that current methods, including ones that include a large/diverse set of
outliers during DNN training, have poor coverage over the broad region where
fine-grained OOD samples locate. We then propose Mixture Outlier Exposure
(MixOE), which effectively expands the covered OOD region by mixing ID data and
training outliers, and regularizes the model behaviour by linearly decaying the
prediction confidence as the input transitions from ID to OOD. Extensive
experiments and analyses demonstrate the effectiveness of MixOE for improving
OOD detection in fine-grained settings.


Abstract:
  Embedding static graphs in low-dimensional vector spaces plays a key role in
network analytics and inference, supporting applications like node
classification, link prediction, and graph visualization. However, many
real-world networks present dynamic behavior, including topological evolution,
feature evolution, and diffusion. Therefore, several methods for embedding
dynamic graphs have been proposed to learn network representations over time,
facing novel challenges, such as time-domain modeling, temporal features to be
captured, and the temporal granularity to be embedded. In this survey, we
overview dynamic graph embedding, discussing its fundamentals and the recent
advances developed so far. We introduce the formal definition of dynamic graph
embedding, focusing on the problem setting and introducing a novel taxonomy for
dynamic graph embedding input and output. We further explore different dynamic
behaviors that may be encompassed by embeddings, classifying by topological
evolution, feature evolution, and processes on networks. Afterward, we describe
existing techniques and propose a taxonomy for dynamic graph embedding
techniques based on algorithmic approaches, from matrix and tensor
factorization to deep learning, random walks, and temporal point processes. We
also elucidate main applications, including dynamic link prediction, anomaly
detection, and diffusion prediction, and we further state some promising
research directions in the area.


Abstract:
  Two types of non-holonomic constraints (imposing a prescription on velocity)
are analyzed, connected to an end of a (visco)elastic rod, straight in its
undeformed configuration. The equations governing the nonlinear dynamics are
obtained and then linearized near the trivial equilibrium configuration. The
two constraints are shown to lead to the same equations governing the
linearized dynamics of the Beck (or Pfluger) column in one case and of the Reut
column in the other. Therefore, although the structural systems are fully
conservative (when viscosity is set to zero), they exhibit flutter and
divergence instability. In addition, the Ziegler's destabilization paradox is
found when dissipation sources are introduced. It follows that these features
are proven to be not only a consequence of 'unrealistic non-conservative loads'
(as often stated in the literature), rather, the models proposed by Beck, Reut,
and Ziegler can exactly describe the linearized dynamics of structures subject
to non-holonomic constraints, which are made now fully accessible to
experiments.


Abstract:
  We consider an improper reinforcement learning setting where a learner is
given $M$ base controllers for an unknown Markov decision process, and wishes
to combine them optimally to produce a potentially new controller that can
outperform each of the base ones. This can be useful in tuning across
controllers, learnt possibly in mismatched or simulated environments, to obtain
a good controller for a given target environment with relatively few trials.
  \par We propose a gradient-based approach that operates over a class of
improper mixtures of the controllers. We derive convergence rate guarantees for
the approach assuming access to a gradient oracle. The value function of the
mixture and its gradient may not be available in closed-form; however, we show
that we can employ rollouts and simultaneous perturbation stochastic
approximation (SPSA) for explicit gradient descent optimization. Numerical
results on (i) the standard control theoretic benchmark of stabilizing an
inverted pendulum and (ii) a constrained queueing task show that our improper
policy optimization algorithm can stabilize the system even when the base
policies at its disposal are unstable\footnote{Under review. Please do not
distribute.}.


Abstract:
  In this article, we introduce a novel variant of the Tsetlin machine (TM)
that randomly drops clauses, the key learning elements of a TM. In effect, TM
with drop clause ignores a random selection of the clauses in each epoch,
selected according to a predefined probability. In this way, additional
stochasticity is introduced in the learning phase of TM. Along with producing
more distinct and well-structured patterns that improve the performance, we
also show that dropping clauses increases learning robustness. To explore the
effects clause dropping has on accuracy, training time, and interpretability,
we conduct extensive experiments on various benchmark datasets in natural
language processing (NLP) (IMDb and SST2) as well as computer vision (MNIST and
CIFAR10). In brief, we observe from +2% to +4% increase in accuracy and 2x to
4x faster learning. We further employ the Convolutional TM to document
interpretable results on the CIFAR10 dataset. To the best of our knowledge,
this is the first time an interpretable machine learning algorithm has been
used to produce pixel-level human-interpretable results on CIFAR10. Also,
unlike previous interpretable methods that focus on attention visualisation or
gradient interpretability, we show that the TM is a more general interpretable
method. That is, by producing rule-based propositional logic expressions that
are \emph{human}-interpretable, the TM can explain how it classifies a
particular instance at the pixel level for computer vision and at the word
level for NLP.


Abstract:
  An important problem in quantum information is to construct multiqubit
unextendible product bases (UPBs). By using the unextendible orthogonal
matrices, we construct a 7-qubit UPB of size 11. It solves an open problem in
[Quantum Information Processing 19:185 (2020)]. Next, we graph-theoretically
show that the UPB is locally indistinguishable in the bipartite systems of two
qubits and five qubits, respectively. It turns out that the UPB corresponds to
a complete graph with 11 vertices constructed by three sorts of nonisomorphic
graphs. Taking the graphs as product vectors, we show that they are in three
different orbits up to local unitary equivalence. Moreover, we also present the
number of sorts of nonisomorphic graphs of complete graphs of some known UPBs
and their orbits.


Abstract:
  We study the effect of a small fermion mass in the formulation of the
on-shell effective field theory (OSEFT). This is our starting point to derive
small mass corrections to the chiral kinetic theory. In the massless case, only
four Wigner functions are needed to describe positive and negative energy
fermions of left and right chirality, corresponding to the vectorial components
of a fermionic two-point Green's function. As soon as mass correction are
introduced, tensorial components are also needed, while the scalar components
strictly vanish in the OSEFT. The tensorial components are conveniently
parametrized in the so-called spin coherence function, which describe quantum
coherent mixtures of left-right and right-left chiral fermions, of either
positive or negative energy. We show that, up to second order in the energy
expansion, vectorial and tensorial components are decoupled, and obey the same
dispersion law and transport equation, depending on their respective chirality.
We study the mass modifications of the reparametrization invariance of the
OSEFT, and check that vector and tensorial components are related by the
associated symmetry transformations. We study how the macroscopic properties of
the system are described in terms of the whole set of Wigner functions, and
check that our framework allows to account for the mass modifications to the
chiral anomaly equation.


Abstract:
  The Molecular Ridge in the LMC extends several kiloparsecs south from 30
Doradus, and it contains ~30% of the molecular gas in the entire galaxy.
However, the southern end of the Molecular Ridge is quiescent - it contains
almost no massive star formation, which is a dramatic decrease from the very
active massive star-forming regions 30 Doradus, N159, and N160. We present new
ALMA and APEX observations of the Molecular Ridge at a resolution as high as
~16'' (~3.9 pc) with molecular lines 12CO(1-0), 13CO(1-0), 12CO(2-1),
13CO(2-1), and CS(2-1). We analyze these emission lines with our new multi-line
non-LTE fitting tool to produce maps of T_kin, n_H2, and N_CO across the region
based on models from RADEX. Using simulated data for a range of parameter space
for each of these variables, we evaluate how well our fitting method can
recover these physical parameters for the given set of molecular lines. We then
compare the results of this fitting with LTE and X_CO methods of obtaining mass
estimates and how line ratios correspond with physical conditions. We find that
this fitting tool allows us to more directly probe the physical conditions of
the gas and estimate values of T_kin, n_H2, and N_CO that are less subject to
the effects of optical depth and line-of-sight projection than previous
methods. The fitted n_H2 values show a strong correlation with the presence of
YSOs, and with the total and average mass of the associated YSOs. Typical star
formation diagnostics, such as mean density, dense gas fraction, and virial
parameter do not show a strong correlation with YSO properties.


Abstract:
  In this paper, we propose an offline-online strategy based on the Localized
Orthogonal Decomposition (LOD) method for elliptic multiscale problems with
randomly perturbed diffusion coefficient. We consider a periodic deterministic
coefficient with local defects that occur with probability $p$. The offline
phase pre-computes entries to global LOD stiffness matrices on a single
reference element (exploiting the periodicity) for a selection of defect
configurations. Given a sample of the perturbed diffusion the corresponding LOD
stiffness matrix is then computed by taking linear combinations of the
pre-computed entries, in the online phase. Our computable error estimates show
that this yields a good coarse-scale approximation of the solution for small
$p$, which is illustrated by extensive numerical experiments. This makes the
proposed technique attractive already for moderate sample sizes in a Monte
Carlo simulation.


Abstract:
  A novel approach for solving the general absolute value equation $Ax+B|x| =
c$ where $A,B\in \mathbb{R}^{m\times n}$ and $c\in \mathbb{R}^m$ is presented.
We reformulate the equation as a feasibility problem which we solve via the
method of alternating projections (MAP). The fixed points set of the
alternating projections map is characterized under nondegeneracy conditions on
$A$ and $B$. Furthermore, we prove linear convergence of the algorithm. Unlike
most of the existing approaches in the literature, the algorithm presented here
is capable of handling problems with $m\neq n$, both theoretically and
numerically.


Abstract:
  We prove a quantitative version of the classical Tits' alternative for
discrete groups acting on packed Gromov-hyperbolic spaces supporting a convex
geodesic bicombing. Some geometric consequences, as uniform estimates on
systole, diastole, algebraic entropy and critical exponent of the groups, will
be presented. Finally we will study the behaviour of these group actions under
limits, providing new examples of compact classes of metric spaces.


Abstract:
  Recent work has explored how complementary strengths of humans and artificial
intelligence (AI) systems might be productively combined. However, successful
forms of human-AI partnership have rarely been demonstrated in real-world
settings. We present the iterative design and evaluation of Lumilo, smart
glasses that help teachers help their students in AI-supported classrooms by
presenting real-time analytics about students' learning, metacognition, and
behavior. Results from a field study conducted in K-12 classrooms indicate that
students learn more when teachers and AI tutors work together during class. We
discuss implications of this research for the design of human-AI partnerships.
We argue for more participatory approaches to research and design in this area,
in which practitioners and other stakeholders are deeply, meaningfully involved
throughout the process. Furthermore, we advocate for theory-building and for
principled approaches to the study of human-AI decision-making in real-world
contexts.


Abstract:
  We investigate the problem of nonparametric estimation of the trend for
stochastic differential equations with delay and driven by a fractional
Brownian motion through the method of kernel-type estimation for the estimation
of a probability density function.


Abstract:
  High fidelity segmentation of both macro and microvascular structure of the
retina plays a pivotal role in determining degenerative retinal diseases, yet
it is a difficult problem. Due to successive resolution loss in the encoding
phase combined with the inability to recover this lost information in the
decoding phase, autoencoding based segmentation approaches are limited in their
ability to extract retinal microvascular structure. We propose RV-GAN, a new
multi-scale generative architecture for accurate retinal vessel segmentation to
alleviate this. The proposed architecture uses two generators and two
multi-scale autoencoding discriminators for better microvessel localization and
segmentation. In order to avoid the loss of fidelity suffered by traditional
GAN-based segmentation systems, we introduce a novel weighted feature matching
loss. This new loss incorporates and prioritizes features from the
discriminator's decoder over the encoder. Doing so combined with the fact that
the discriminator's decoder attempts to determine real or fake images at the
pixel level better preserves macro and microvascular structure. By combining
reconstruction and weighted feature matching loss, the proposed architecture
achieves an area under the curve (AUC) of 0.9887, 0.9914, and 0.9887 in
pixel-wise segmentation of retinal vasculature from three publicly available
datasets, namely DRIVE, CHASE-DB1, and STARE, respectively. Additionally,
RV-GAN outperforms other architectures in two additional relevant metrics, mean
intersection-over-union (Mean-IOU) and structural similarity measure (SSIM).


Abstract:
  We present a phase-space study of two stellar groups located at the core of
the Orion complex: Brice\~no-1 and Orion Belt Population-near (OBP-near). We
identify the groups with the unsupervised clustering algorithm, Shared Nearest
Neighbor (SNN), which previously identified twelve new stellar substructures in
the Orion complex. For each of the two groups, we derive the 3D space motions
of individual stars using Gaia EDR3 proper motions supplemented by radial
velocities from Gaia DR2, APOGEE-2, and GALAH DR3. We present evidence for
radial expansion of the two groups from a common center. Unlike previous work,
our study suggests that evidence of stellar group expansion is confined only to
OBP-near and Brice\~no-1 whereas the rest of the groups in the complex show
more complicated motions. Interestingly, the stars in the two groups lie at the
center of a dust shell, as revealed via an extant 3D dust map. The exact
mechanism that produces such coherent motions remains unclear, while the
observed radial expansion and dust shell suggest that massive stellar feedback
could have influenced the star formation history of these groups.


Abstract:
  Renormalization-Group (RG) improvement has been frequently applied to capture
the effect of quantum corrections on cosmological and black-hole spacetimes.
This work utilizes an algebraically complete set of curvature invariants to
establish that: On the one hand, RG improvement at the level of the metric is
coordinate-dependent. On the other hand, a newly proposed RG improvement at the
level of curvature invariants is coordinate-independent. Spherically-symmetric
and axially-symmetric black-hole spacetimes serve as physically relevant
examples.


Abstract:
  In this paper, we propose a unified pre-training approach called UniSpeech to
learn speech representations with both unlabeled and labeled data, in which
supervised phonetic CTC learning and phonetically-aware contrastive
self-supervised learning are conducted in a multi-task learning manner. The
resultant representations can capture information more correlated with phonetic
structures and improve the generalization across languages and domains. We
evaluate the effectiveness of UniSpeech for cross-lingual representation
learning on public CommonVoice corpus. The results show that UniSpeech
outperforms self-supervised pretraining and supervised transfer learning for
speech recognition by a maximum of 13.4% and 17.8% relative phone error rate
reductions respectively (averaged over all testing languages). The
transferability of UniSpeech is also demonstrated on a domain-shift speech
recognition task, i.e., a relative word error rate reduction of 6% against the
previous approach.


Abstract:
  CdTe is a key thin-film photovoltaic technology. Non-radiative electron-hole
recombination reduces the solar conversion efficiency from an ideal value of
32% to a current champion performance of 22%. The cadmium vacancy (V_Cd) is a
prominent acceptor species in p-type CdTe; however, debate continues regarding
its structural and electronic behavior. Using ab initio defect techniques, we
calculate a negative-U double-acceptor level for V_Cd, while reproducing the
V_Cd^-1 hole-polaron, reconciling theoretical predictions with experimental
observations. We find the cadmium vacancy facilitates rapid charge-carrier
recombination, reducing maximum power-conversion efficiency by over 5% for
untreated CdTe -- a consequence of tellurium dimerization, metastable
structural arrangements, and anharmonic potential energy surfaces for carrier
capture.


Abstract:
  Quasi-geostrophic (QG) theory describes the dynamics of synoptic scale flows
in the trophosphere that are balanced with respect to both acoustic and
internal gravity waves. Within this framework, effects of (turbulent) friction
near the ground are usually represented by Ekman Layer theory. The troposphere
covers roughly the lowest ten kilometers of the atmosphere while Ekman layer
heights are typically just a few hundred meters. However, this two-layer
asymptotic theory does not explicitly account for substantial changes of the
potential temperature stratification due to diabatic heating associated with
cloud formation or with radiative and turbulent heat fluxes, which, in the
middle latitudes, can be particularly important in about the lowest three
kilometers. To address this deficiency, this paper extends the classical
QG-Ekman layer model by introducing an intermediate, dynamically and
thermodynamically active layer, called the "diabatic layer" (DL) from here on.
The flow in this layer is also in acoustic, hydrostatic, and geostrophic
balance but, in contrast to QG flow, variations of potential temperature are
not restricted to small deviations from a stable and time independent
background stratification. Instead, within the diabatic layer, diabatic
processes are allowed to affect the leading-order stratification. As a
consequence, the diabatic layer modifies the pressure field at the top of the
Ekman layer, and with it the intensity of Ekman pumping seen by the
quasi-geostrophic bulk flow. The result is the proposed extended
quasi-geostrophic three-layer QG-DL-Ekman model for mid-latitude (dry and
moist) dynamics.


Abstract:
  Quantization has become a popular technique to compress neural networks and
reduce compute cost, but most prior work focuses on studying quantization
without changing the network size. Many real-world applications of neural
networks have compute cost and memory budgets, which can be traded off with
model quality by changing the number of parameters. In this work, we use ResNet
as a case study to systematically investigate the effects of quantization on
inference compute cost-quality tradeoff curves. Our results suggest that for
each bfloat16 ResNet model, there are quantized models with lower cost and
higher accuracy; in other words, the bfloat16 compute cost-quality tradeoff
curve is Pareto-dominated by the 4-bit and 8-bit curves, with models primarily
quantized to 4-bit yielding the best Pareto curve. Furthermore, we achieve
state-of-the-art results on ImageNet for 4-bit ResNet-50 with
quantization-aware training, obtaining a top-1 eval accuracy of 77.09%. We
demonstrate the regularizing effect of quantization by measuring the
generalization gap. The quantization method we used is optimized for
practicality: It requires little tuning and is designed with hardware
capabilities in mind. Our work motivates further research into optimal numeric
formats for quantization, as well as the development of machine learning
accelerators supporting these formats. As part of this work, we contribute a
quantization library written in JAX, which is open-sourced at
https://github.com/google-research/google-research/tree/master/aqt.


Abstract:
  Learning quickly and continually is still an ambitious task for neural
networks. Indeed, many real-world applications do not reflect the learning
setting where neural networks shine, as data are usually few, mostly unlabelled
and come as a stream. To narrow this gap, we introduce FUSION - Few-shot
UnSupervIsed cONtinual learning - a novel strategy which aims to deal with
neural networks that "learn in the wild", simulating a real distribution and
flow of unbalanced tasks. We equip FUSION with MEML - Meta-Example
Meta-Learning - a new module that simultaneously alleviates catastrophic
forgetting and favours the generalisation and future learning of new tasks. To
encourage features reuse during the meta-optimisation, our model exploits a
single inner loop per task, taking advantage of an aggregated representation
achieved through the use of a self-attention mechanism. To further enhance the
generalisation capability of MEML, we extend it by adopting a technique that
creates various augmented tasks and optimises over the hardest. Experimental
results on few-shot learning benchmarks show that our model exceeds the other
baselines in both FUSION and fully supervised case. We also explore how it
behaves in standard continual learning consistently outperforming
state-of-the-art approaches.


Abstract:
  The origin of boson peak -- an excess of density of states over Debye's model
in glassy solids -- is still under intense debate, among which some theories
and experiments suggest that boson peak is related to van-Hove singularity.
Here we show that boson peak and van-Hove singularity are well separated
identities, by measuring the vibrational density of states of a two-dimensional
granular system, where packings are tuned gradually from a crystalline, to
polycrystals, and to an amorphous material. We observe a coexistence of well
separated boson peak and van-Hove singularities in polycrystals, in which the
van-Hove singularities gradually shift to higher frequency values while
broadening their shapes and eventually disappear completely when the structural
disorder $\eta$ becomes sufficiently high. By analyzing firstly the strongly
disordered system ($\eta=1$) and the disordered granular crystals ($\eta=0$),
and then systems of intermediate disorder with $\eta$ in between, we find that
boson peak is associated with spatially uncorrelated random flucutations of
shear modulus $\delta G/\langle G \rangle$ whereas the smearing of van-Hove
singularities is associated with spatially correlated fluctuations of shear
modulus $\delta G/\langle G \rangle$.


Abstract:
  We present the discovery of NGTS-19b, a high mass transiting brown dwarf
discovered by the Next Generation Transit Survey (NGTS). We investigate the
system using follow up photometry from the South African Astronomical
Observatory, as well as sector 11 TESS data, in combination with radial
velocity measurements from the CORALIE spectrograph to precisely characterise
the system. We find that NGTS-19b is a brown dwarf companion to a K-star, with
a mass of $69.5 ^{+5.7}_{-5.4}$ M$_{Jup}$ and radius of $1.034
^{+0.055}_{-0.053}$ R$_{Jup}$. The system has a reasonably long period of 17.84
days, and a high degree of eccentricity of $0.3767 ^{+0.0061}_{-0.0061}$. The
mass and radius of the brown dwarf imply an age of $0.46 ^{+0.26}_{-0.15}$ Gyr,
however this is inconsistent with the age determined from the host star SED,
suggesting that the brown dwarf may be inflated. This is unusual given that its
large mass and relatively low levels of irradiation would make it much harder
to inflate. NGTS-19b adds to the small, but growing number of brown dwarfs
transiting main sequence stars, and is a valuable addition as we begin to
populate the so called brown dwarf desert.


Abstract:
  A $3$-Prismatoid $P$ is the convex hull of two convex polygons $A$ and $B$
which lie in parallel planes $H_A, H_B\subset\mathbb{R}^3$. Let $A'$ be the
orthogonal projection of $A$ onto $H_B$. A prismatoid is called nested if $A'$
properly contained in $B$, or vice versa. We show that nested prismatoids can
be edge-unfolded.


Abstract:
  We introduce a simple entropy-based formalism to characterize the role of
mixing in pressure-balanced multiphase clouds, and demonstrate example
applications using Enzo-E (magneto)hydrodynamic simulations. Under this
formalism, the high-dimensional description of the system's state at a given
time is simplified to the joint distribution of mass over pressure ($P$) and
entropy ($K=P/\rho^\gamma$). As a result, this approach provides a way for
(empirically and analytically) quantifying the impact of different initial
conditions and sets of physics on the system evolution. We find that mixing
predominantly alters the distribution along the $K$ direction and illustrate
how the formalism can be used to model mixing and cooling for fluid elements
originating in the cloud. We further confirm and generalize a previously
suggested criterion for cloud growth in the presence of radiative cooling, and
demonstrate that the shape of the cooling curve, particularly at the low
temperature end, can play an important role in controlling condensation.
Moreover, we discuss the capacity of our approach to generalize such a
criterion to apply to additional sets of physics, and to build intuition for
the impact of subtle higher order effects not directly addressed by the
criterion.


Abstract:
  Recently, a first-order differentiator based on time-varying gains was
introduced in the literature, in its non recursive form, for a class of
differentiable signals $y(t)$, satisfying $|\ddot{y}(t)|\leq L(t-t_0)$, for a
known function $L(t-t_0)$, such that $\frac{1}{L(t-t_0)}\left|\frac{d
{L}(t-t_0)}{dt}\right|\leq M$ with a known constant $M$. It has been shown that
such differentiator is globally finite-time convergent. In this paper, we
redesign such an algorithm, using time base generators (a class of time-varying
gains), to obtain a differentiator algorithm for the same class of signals,
with guaranteed convergence before a desired time, i.e., with fixed-time
convergence with an a priori user-defined upper bound for the settling time.
Thus, our approach can be applied for scenarios under time-constraints.
  We present numerical examples exposing the contribution with respect to
state-of-the-art algorithms.


Abstract:
  The application of digital technologies in agriculture can improve
traditional practices to adapt to climate change, reduce Greenhouse Gases (GHG)
emissions, and promote a sustainable intensification for food security. Some
authors argued that we are experiencing a Digital Agricultural Revolution (DAR)
that will boost sustainable farming. This study aims to find evidence of the
ongoing DAR process and clarify its roots, what it means, and where it is
heading. We investigated the scientific literature with bibliometric analysis
tools to produce an objective and reproducible literature review. We retrieved
4995 articles by querying the Web of Science database in the timespan
2012-2019, and we analyzed the obtained dataset to answer three specific
research questions: i) what is the spectrum of the DAR-related terminology?;
ii) what are the key articles and the most influential journals, institutions,
and countries?; iii) what are the main research streams and the emerging
topics? By grouping the authors' keywords reported on publications, we
identified five main research streams: Climate-Smart Agriculture (CSA),
Site-Specific Management (SSM), Remote Sensing (RS), Internet of Things (IoT),
and Artificial Intelligence (AI). To provide a broad overview of each of these
topics, we analyzed relevant review articles, and we present here the main
achievements and the ongoing challenges. Finally, we showed the trending topics
of the last three years (2017, 2018, 2019).


Abstract:
  In this thesis, we provide new insights into the theory of cascade feedback
linearization of control systems. In particular, we present a new explicit
class of cascade feedback linearizable control systems, as well as a new
obstruction to the existence of a cascade feedback linearization for a given
invariant control system. These theorems are presented in Chapter 4, where
truncated versions of operators from the calculus of variations are introduced
and explored to prove these new results. This connection reveals new geometry
behind cascade feedback linearization and establishes a foundation for future
exciting work on the subject with important consequences for dynamic feedback
linearization.


Abstract:
  In this work, we prove a novel one-shot multi-sender decoupling theorem
generalising Dupuis result. We start off with a multipartite quantum state, say
on A1 A2 R, where A1, A2 are treated as the two sender systems and R is the
reference system. We apply independent Haar random unitaries in tensor product
on A1 and A2 and then send the resulting systems through a quantum channel. We
want the channel output B to be almost in tensor with the untouched reference
R. Our main result shows that this is indeed the case if suitable entropic
conditions are met. An immediate application of our main result is to obtain a
one-shot simultaneous decoder for sending quantum information over a k-sender
entanglement unassisted quantum multiple access channel (QMAC). The rate region
achieved by this decoder is the natural one-shot quantum analogue of the
pentagonal classical rate region. Assuming a simultaneous smoothing conjecture,
this one-shot rate region approaches the optimal rate region of Yard, Dein the
asymptotic iid limit. Our work is the first one to obtain a non-trivial
simultaneous decoder for the QMAC with limited entanglement assistance in both
one-shot and asymptotic iid settings; previous works used unlimited
entanglement assistance.


Abstract:
  The capability of reinforcement learning (RL) agent directly depends on the
diversity of learning scenarios the environment generates and how closely it
captures real-world situations. However, existing environments/simulators lack
the support to systematically model distributions over initial states and
transition dynamics. Furthermore, in complex domains such as soccer, the space
of possible scenarios is infinite, which makes it impossible for one research
group to provide a comprehensive set of scenarios to train, test, and benchmark
RL algorithms. To address this issue, for the first time, we adopt an existing
formal scenario specification language, SCENIC, to intuitively model and
generate interactive scenarios. We interfaced SCENIC to Google Research Soccer
environment to create a platform called SCENIC4RL. Using this platform, we
provide a dataset consisting of 36 scenario programs encoded in SCENIC and
demonstration data generated from a subset of them. We share our experimental
results to show the effectiveness of our dataset and the platform to train,
test, and benchmark RL algorithms. More importantly, we open-source our
platform to enable RL community to collectively contribute to constructing a
comprehensive set of scenarios.


Abstract:
  In this paper, we describe novel components for extracting clinically
relevant information from medical conversations which will be available as
Google APIs. We describe a transformer-based Recurrent Neural Network
Transducer (RNN-T) model tailored for long-form audio, which can produce rich
transcriptions including speaker segmentation, speaker role labeling,
punctuation and capitalization. On a representative test set, we compare
performance of RNN-T models with different encoders, units and streaming
constraints. Our transformer-based streaming model performs at about 20% WER on
the ASR task, 6% WDER on the diarization task, 43% SER on periods, 52% SER on
commas, 43% SER on question marks and 30% SER on capitalization. Our recognizer
is paired with a confidence model that utilizes both acoustic and lexical
features from the recognizer. The model performs at about 0.37 NCE. Finally, we
describe a RNN-T based tagging model. The performance of the model depends on
the ontologies, with F-scores of 0.90 for medications, 0.76 for symptoms, 0.75
for conditions, 0.76 for diagnosis, and 0.61 for treatments. While there is
still room for improvement, our results suggest that these models are
sufficiently accurate for practical applications.


Abstract:
  Visual sensors serve as a critical component of the Internet of Things (IoT).
There is an ever-increasing demand for broad applications and higher
resolutions of videos and cameras in smart homes and smart cities, such as in
security cameras. To utilize this large volume of video data generated from
networks of visual sensors for various machine vision applications, it needs to
be compressed and securely transmitted over the Internet. H.266/VVC, as the new
compression standard, brings the highest compression for visual data. To
provide security along with high compression, a selective encryption method for
hiding information of videos is presented for this new compression standard.
Selective encryption methods can lower the computation overhead of the
encryption while keeping the video bitstream format which is useful when the
video goes into untrusted blocks such as transcoding or watermarking. Syntax
elements that represent considerable information are selected for the
encryption, i.e., luma Intra Prediction Modes (IPMs), Motion Vector Difference
(MVD), and residual signs., then the results of the proposed method are
investigated in terms of visual security and bit rate change. Our experiments
show that the encrypted videos provide higher visual security compared to other
similar works in previous standards, and integration of the presented
encryption scheme into the VVC encoder has little impact on the bit rate
efficiency (results in 2% to 3% bit rate increase).


Abstract:
  Deep learning based molecular graph generation and optimization has recently
been attracting attention due to its great potential for de novo drug design.
On the one hand, recent models are able to efficiently learn a given graph
distribution, and many approaches have proven very effective to produce a
molecule that maximizes a given score. On the other hand, it was shown by
previous studies that generated optimized molecules are often unrealistic, even
with the inclusion of mechanics to enforce similarity to a dataset of real drug
molecules. In this work we use a hybrid approach, where the dataset
distribution is learned using an autoregressive model while the score
optimization is done using the Metropolis algorithm, biased toward the learned
distribution. We show that the resulting method, that we call learned realism
sampling (LRS), produces empirically more realistic molecules and outperforms
all recent baselines in the task of molecule optimization with similarity
constraints.


Abstract:
  In single-reference coupled-cluster (CC) methods, one has to solve a set of
non-linear polynomial equations in order to determine the so-called amplitudes
which are then used to compute the energy and other properties. Although it is
of common practice to converge to the (lowest-energy) ground-state solution, it
is also possible, thanks to tailored algorithms, to access higher-energy roots
of these equations which may or may not correspond to genuine excited states.
Here, we explore the structure of the energy landscape of variational CC (VCC)
and we compare it with its (projected) traditional version (TCC) in the case
where the excitation operator is restricted to paired double excitations
(pCCD). By investigating two model systems (the symmetric stretching of the
linear \ce{H4} molecule and the continuous deformation of the square \ce{H4}
molecule into a rectangular arrangement) in the presence of weak and strong
correlations, the performance of VpCCD and TpCCD are gauged against their
configuration interaction (CI) equivalent, known as doubly-occupied CI (DOCI),
for reference Slater determinants made of ground- or excited-state Hartree-Fock
orbitals or state-specific orbitals optimized directly at the VpCCD level. The
influence of spatial symmetry breaking is also investigated.


Abstract:
  Multi-agent value-based approaches recently make great progress, especially
value decomposition methods. However, there are still a lot of limitations in
value function factorization. In VDN, the joint action-value function is the
sum of per-agent action-value function while the joint action-value function of
QMIX is the monotonic mixing of per-agent action-value function. To some
extent, QTRAN reduces the limitation of joint action-value functions that can
be represented, but it has unsatisfied performance in complex tasks. In this
paper, in order to extend the class of joint value functions that can be
represented, we propose a novel actor-critic method called NQMIX. NQMIX
introduces an off-policy policy gradient on QMIX and modify its network
architecture, which can remove the monotonicity constraint of QMIX and
implement a non-monotonic value function factorization for the joint
action-value function. In addition, NQMIX takes the state-value as the learning
target, which overcomes the problem in QMIX that the learning target is
overestimated. Furthermore, NQMIX can be extended to continuous action space
settings by introducing deterministic policy gradient on itself. Finally, we
evaluate our actor-critic methods on SMAC domain, and show that it has a
stronger performance than COMA and QMIX on complex maps with heterogeneous
agent types. In addition, our ablation results show that our modification of
mixer is effective.


Abstract:
  We present a chronology of the formation and early evolution of the Oort
cloud by simulations. These simulations start with the Solar System being born
with planets and asteroids in a stellar cluster orbiting the Galactic center.
Upon ejection from its birth environment, we continue to follow the evolution
of the Solar System while it navigates the Galaxy as an isolated planetary
system. We conclude that the range in semi-major axis between 100au and several
10$^3$\,au still bears the signatures of the Sun being born in a
1000MSun/pc$^3$ star cluster, and that most of the outer Oort cloud formed
after the Solar System was ejected. The ejection of the Solar System, we argue,
happened between 20Myr and 50Myr after its birth. Trailing and leading trails
of asteroids and comets along the Sun's orbit in the Galactic potential are the
by-product of the formation of the Oort cloud. These arms are composed of
material that became unbound from the Solar System when the Oort cloud formed.
Today, the bulk of the material in the Oort cloud ($\sim 70$\%) originates from
the region in the circumstellar disk that was located between $\sim 15$\,au and
$\sim 35$\,au, near the current location of the ice giants and the Centaur
family of asteroids. According to our simulations, this population is
eradicated if the ice-giant planets are born in orbital resonance. Planet
migration or chaotic orbital reorganization occurring while the Solar System is
still a cluster member is, according to our model, inconsistent with the
presence of the Oort cloud. About half the inner Oort cloud, between 100 and
$10^4$\,au, and a quarter of the material in the outer Oort cloud, $\apgt
10^4$\,au, could be non-native to the Solar System but was captured from
free-floating debris in the cluster or from the circumstellar disk of other
stars in the birth cluster.


Abstract:
  We study outer Lipschitz geometry of real semialgebraic or, more general,
definable in a polynomially bounded o-minimal structure over the reals, surface
germs. In particular, any definable H\"older triangle is either Lipschitz
normally embedded or contains some "abnormal" arcs. We show that abnormal arcs
constitute finitely many "abnormal zones" in the space of all arcs, and
investigate geometric and combinatorial properties of abnormal surface germs.
We establish a strong relation between geometry and combinatorics of abnormal
H\"older triangles.


Abstract:
  We propose a lattice spin model on a cubic lattice that shares many of the
properties of the 3D toric code and the X-cube fracton model. The model, made
of Z_3 degrees of freedom at the links, has the vertex, the cube, and the
plaquette terms. Being a stabilizer code the ground states are exactly solved.
With only the vertex and the cube terms present, we show that the ground state
degeneracy (GSD) is 3^(L3+3L-1) where L is the linear dimension of the cubic
lattice. In addition to fractons, there are free vertex excitations we call the
freeons. With the addition of the plaquette terms, GSD is vastly reduced to
3^3, with fracton, fluxon, and freeon excitations, among which only the freeons
are deconfined. The model is called the AB model if only the vertex (A_v) and
the cube (B_c) terms are present, and the ABC model if in addition the
plaquette terms (C_p) are included. The AC model consisting of vertex and
plaquette terms is the Z_3 3D toric code. The extensive GSD of the AB model
derives from the existence of both local and non-local logical operators that
connect different ground states. The latter operators are identical to the
logical operators of the Z_3 X-cube model. Fracton excitations are immobile and
accompanied by the creation of fluxons - plaquettes having nonzero flux. In the
ABC model, such fluxon creation costs energy and ends up confining the
fractons. Unlike past models of fractons, vertex excitations are free to move
in any direction and pick up a non-trivial statistical phase when passing
through a fluxon or a fracton cluster.


Abstract:
  Results of analysis of 60010 data photometric observations from the AAVSO
international database are presented, which span 120 years of monitoring. The
periodogram analysis shows the best fit period of 70.74d, a half of typically
published periods for smaller intervals. Contrary to expectation for
deep/shallow minima, the changes between them are not so regular. There may be
series of deep (or shallow) minima without alternations. There may be two
acting periods of 138.5 days and 70.74, so the beat modulation may be expected.
The dependence of the phases of deep minima argue for two alternating periods
with a characteristic life-time of a mode of 30years. These phenomenological
results better explain the variability than the model of chaos.


Abstract:
  In this worldwide spread of SARS-CoV-2 (COVID-19) infection, it is of utmost
importance to detect the disease at an early stage especially in the hot spots
of this epidemic. There are more than 110 Million infected cases on the globe,
sofar. Due to its promptness and effective results computed tomography
(CT)-scan image is preferred to the reverse-transcription polymerase chain
reaction (RT-PCR). Early detection and isolation of the patient is the only
possible way of controlling the spread of the disease. Automated analysis of
CT-Scans can provide enormous support in this process. In this article, We
propose a novel approach to detect SARS-CoV-2 using CT-scan images. Our method
is based on a very intuitive and natural idea of analyzing shapes, an attempt
to mimic a professional medic. We mainly trace SARS-CoV-2 features by
quantifying their topological properties. We primarily use a tool called
persistent homology, from Topological Data Analysis (TDA), to compute these
topological properties. We train and test our model on the "SARS-CoV-2 CT-scan
dataset" \citep{soares2020sars}, an open-source dataset, containing 2,481
CT-scans of normal and COVID-19 patients. Our model yielded an overall
benchmark F1 score of $99.42\% $, accuracy $99.416\%$, precision $99.41\%$, and
recall $99.42\%$. The TDA techniques have great potential that can be utilized
for efficient and prompt detection of COVID-19. The immense potential of TDA
may be exploited in clinics for rapid and safe detection of COVID-19 globally,
in particular in the low and middle-income countries where RT-PCR labs and/or
kits are in a serious crisis.


Abstract:
  Observations of positional offsets between the location of X-ray and radio
features in many resolved, extragalactic jets indicates that the emitting
regions are not co-spatial, an important piece of evidence in the debate over
the origin of the X-ray emission on kpc scales. The existing literature is
nearly exclusively focused on jets with sufficiently deep Chandra observations
to yield accurate positions for X-ray features, but most of the known X-ray
jets are detected with tens of counts or fewer, making detailed morphological
comparisons difficult. Here we report the detection of X-ray-to-radio
positional offsets in 15 extragalactic jets from an analysis of 22 sources with
low-count Chandra observations, where we utilized the Low-count Image
Reconstruction Algorithm (LIRA). This algorithm has allowed us to account for
effects such as Poisson background fluctuations and nearby point sources which
have previously made the detection of offsets difficult in shallow
observations. Using this method, we find that in 55 % of knots with detectable
offsets, the X-rays peak upstream of the radio, questioning the applicability
of one-zone models, including the IC/CMB model for explaining the X-ray
emission. We also report the non-detection of two previously claimed X-ray
jets. Many, but not all, of our sources, follow a loose trend of increasing
offset between the X-ray and radio emission, as well as a decreasing X-ray to
radio flux ratio along the jet.


Abstract:
  In this paper, we deal with an elliptic problem with the Dirichlet boundary
condition. We operate in Sobolev spaces and the main analytic tool we use is
the Lax-Milgram lemma. First, we present the variational approach of the
problem which allows us to apply different functional analysis techniques. Then
we study thoroughly the well-posedness of the problem. We conclude our work
with a solution of the problem using numerical analysis techniques and the free
software freefem++.


Abstract:
  The aim of this article is to study semigroups of composition operators on
the BMOA-type spaces $BMOA_p$, and on their "little oh" analogues $VMOA_p$. The
spaces $BMOA_p$ were introduced by R. Zhao as part of the large family of
F(p,q,s) spaces, and are the M\"{o}bius invariant subspaces of the Dirichlet
spaces $D^p_{p-1}$. We study the maximal subspace of strong continuity,
providing a sufficient condition on the infinitesimal generator of ${\phi}$,
under which $[{\phi}_t,BMOA_p]=VMOA_p$, and a related necessary condition in
the case where the Denjoy - Wolff point of the semigroup is in $\mathbb{D}$.
Further, we characterize those semigroups, for which $[{\phi}_t,
BMOA_p]=VMOA_p$, in terms of the resolvent operator of the infinitesimal
generator of $T_t$. In addition we provide a connection between the maximal
subspace of strong continuity and the Volterra-type operators $T_g$. We
characterize the symbols g for which $T_g$ acting from $BMOA$ to $BMOA_1$ is
bounded or compact, thus extending a related result to the case $p=1$. We also
prove that for $1<p<2$ compactness of $T_g$ on $BMOA_p$ is equivalent to weak
compactness.


Abstract:
  Titanium nitride (TiN) is a paradigm of refractory transition metal nitrides
with great potential in vast applications. Generally, the plasmonic performance
of TiN can be tuned by oxidation, which was thought to be only temperature-,
oxygen partial pressure-, and time-dependent. Regarding the role of
crystallographic orientation in the oxidation and resultant optical properties
of TiN films, little is known thus far. Here we reveal that both the oxidation
resistance behavior and the plasmonic performance of epitaxial TiN films follow
the order of (001) < (110) < (111). The effects of crystallographic orientation
on the lattice constants, optical properties, and oxidation levels of epitaxial
TiN films have been systematically studied by combined high-resolution X-ray
diffraction, spectroscopic ellipsometry, X-ray absorption spectroscopy, and
X-ray photoemission spectroscopy. To further understand the role of
crystallographic orientation in the initial oxidation process of TiN films,
density-functional-theory calculations are carried out, indicating the energy
cost of oxidation is (001) < (110) < (111), consistent with the experiments.
The superior endurance of the (111) orientation against mild oxidation can
largely alleviate the previously stringent technical requirements for the
growth of TiN films with high plasmonic performance. The crystallographic
orientation can also offer an effective controlling parameter to design
TiN-based plasmonic devices with desired peculiarity, e.g., superior chemical
stability against mild oxidation or large optical tunability upon oxidation.


Abstract:
  Recently, the MiniBooNE experiment at Fermilab has updated the results with
increased data and reported an excess of $560.6 \pm 119.6$ electronlike events
($4.7\sigma$) in the neutrino operation mode. In this paper, we propose a
scenario to account for the excess where a Dirac-type sterile neutrino,
produced by a charged kaon decay through the neutrino mixing, decays into a
leptophilic axionlike particle ($\ell$ALP) and a muon neutrino. The
electron-positron pairs produced from the $\ell$ALP decays can be interpreted
as electronlike events provided that their opening angle is sufficiently small.
In our framework, we consider the $\ell$ALP with a mass $m^{}_a =
20\,\text{MeV}$ and an inverse decay constant $c^{}_e/f^{}_a =
10^{-2}\,\text{GeV}^{-1}$, allowed by the astrophysical and experimental
constraints. Then, after integrating the predicted angular or visible energy
spectra of the $\ell$ALP to obtain the total excess event number, we find that
our scenario with sterile neutrino masses within $150\,\text{MeV}\lesssim
m^{}_N \lesssim 380 \,\text{MeV}$ ($150\,\text{MeV}\lesssim m^{}_N \lesssim 180
\,\text{MeV}$) and neutrino mixing parameters between $10^{-10} \lesssim
|U_{\mu 4}|^2 \lesssim 10^{-8}$ ($3\times 10^{-7} \lesssim |U_{\mu 4}|^2
\lesssim 8 \times10^{-7}$) can explain the MiniBooNE data.


Abstract:
  Artificial stock market simulation based on agent is an important means to
study financial market. Based on the assumption that the investors are composed
of a main fund, small trend and contrarian investors characterized by four
parameters, we simulate and research a kind of financial phenomenon with the
characteristics of pyramid schemes. Our simulation results and theoretical
analysis reveal the relationships between the rate of return of the main fund
and the proportion of the trend investors in all small investors, the small
investors' parameters of taking profit and stopping loss, the order size of the
main fund and the strategies adopted by the main fund. Our work are helpful to
explain the financial phenomenon with the characteristics of pyramid schemes in
financial markets, design trading rules for regulators and develop trading
strategies for investors.


Abstract:
  We consider estimating the effect of a treatment on the progress of subjects
tested both before and after treatment assignment. A vast literature compares
the competing approaches of modeling the post-test score conditionally on the
pre-test score versus modeling the difference, namely the gain score. Our
contribution resides in analyzing the merits and drawbacks of the two
approaches in a multilevel setting. This is relevant in many fields, for
example education with students nested into schools. The multilevel structure
raises peculiar issues related to the contextual effects and the distinction
between individual-level and cluster-level treatment. We derive approximate
analytical results and compare the two approaches by a simulation study. For an
individual-level treatment our findings are in line with the literature,
whereas for a cluster-level treatment we point out the key role of the cluster
mean of the pre-test score, which favors the conditioning approach in settings
with large clusters.


Abstract:
  We report a systematic measurement of cumulants, $C_{n}$, for net-proton,
proton and antiproton multiplicity distributions, and correlation functions,
$\kappa_n$, for proton and antiproton multiplicity distributions up to the
fourth order in Au+Au collisions at $\sqrt{s_{\mathrm {NN}}}$ = 7.7, 11.5,
14.5, 19.6, 27, 39, 54.4, 62.4 and 200 GeV. The $C_{n}$ and $\kappa_n$ are
presented as a function of collision energy, centrality and kinematic
acceptance in rapidity, $y$, and transverse momentum, $p_{T}$. The data were
taken during the first phase of the Beam Energy Scan (BES) program (2010 --
2017) at the BNL Relativistic Heavy Ion Collider (RHIC) facility. The
measurements are carried out at midrapidity ($|y| <$ 0.5) and transverse
momentum 0.4 $<$ $p_{\rm T}$ $<$ 2.0 GeV/$c$, using the STAR detector at RHIC.
We observe a non-monotonic energy dependence ($\sqrt{s_{\mathrm {NN}}}$ = 7.7
-- 62.4 GeV) of the net-proton $C_{4}$/$C_{2}$ with the significance of
3.1$\sigma$ for the 0-5\% central Au+Au collisions. This is consistent with the
expectations of critical fluctuations in a QCD-inspired model. Thermal and
transport model calculations show a monotonic variation with $\sqrt{s_{\mathrm
{NN}}}$. For the multiparticle correlation functions, we observe significant
negative values for a two-particle correlation function, $\kappa_2$, of protons
and antiprotons, which are mainly due to the effects of baryon number
conservation. Furthermore, it is found that the four-particle correlation
function, $\kappa_4$, of protons plays a role in determining the energy
dependence of proton $C_4/C_1$ below 19.6 GeV, which cannot be understood by
the effect of baryon number conservation.


Abstract:
  We address the challenge of policy evaluation in real-world applications of
reinforcement learning systems where the available historical data is limited
due to ethical, practical, or security considerations. This constrained
distribution of data samples often leads to biased policy evaluation estimates.
To remedy this, we propose that instead of policy evaluation, one should
perform policy comparison, i.e. to rank the policies of interest in terms of
their value based on available historical data. In addition we present the
Limited Data Estimator (LDE) as a simple method for evaluating and comparing
policies from a small number of interactions with the environment. According to
our theoretical analysis, the LDE is shown to be statistically reliable on
policy comparison tasks under mild assumptions on the distribution of the
historical data. Additionally, our numerical experiments compare the LDE to
other policy evaluation methods on the task of policy ranking and demonstrate
its advantage in various settings.


Abstract:
  Significant clustering around the rarest luminous quasars is a feature
predicted by dark matter theory combined with number density matching
arguments. However, this expectation is not reflected by observations of
quasars residing in a diverse range of environments. Here, we assess the
tension in the diverse clustering of visible $i$-band dropout galaxies around
luminous $z\sim6$ quasars. Our approach uses a simple empirical method to
derive the median luminosity to halo mass relation, $L_{c}(M_{h})$ for both
quasars and galaxies under the assumption of log-normal luminosity scatter,
$\Sigma_{Q}$ and $\Sigma_{G}$. We show that higher $\Sigma_{Q}$ reduces the
average halo mass hosting a quasar of a given luminosity, thus introducing at
least a partial reversion to the mean in the number count distribution of
nearby Lyman-Break galaxies. We generate a large sample of mock Hubble Space
Telescope fields-of-view centred across rare $z\sim6$ quasars by resampling
pencil beams traced through the dark matter component of the BlueTides
cosmological simulation. We find that diverse quasar environments are expected
for $\Sigma_{Q}>0.4$, consistent with numerous observations and theoretical
studies. However, we note that the average number of galaxies around the
central quasar is primarily driven by galaxy evolutionary processes in
neighbouring halos, as embodied by our parameter $\Sigma_{G}$, instead of a
difference in the large scale structure around the central quasar host,
embodied by $\Sigma_{Q}$. We conclude that models with $\Sigma_{G}>0.3$ are
consistent with current observational constraints on high-z quasars, and that
such a value is comparable to the scatter estimated from hydrodynamical
simulations of galaxy formation.


Abstract:
  Here we develop a method for investigating global strong solutions of
partially dissipative hyperbolic systems in the critical regularity setting.
Compared to the recent works by Kawashima and Xu, we use hybrid Besov spaces
with different regularity exponent in low and high frequency. This allows to
consider more general data and to track the exact dependency on the dissipation
parameter for the solution. Our approach enables us to go beyond the L^2
framework in the treatment of the low frequencies of the solution, which is
totally new, to the best of our knowledge. Focus is on the one-dimensional
setting (the multi-dimensional case will be considered in a forthcoming paper)
and, for expository purpose, the first part of the paper is devoted to a toy
model that may be seen as a simplification of the compressible Euler system
with damping. More elaborated systems (including the compressible Euler system
with general increasing pressure law) are considered at the end of the paper.


Abstract:
  The input space of a neural network with ReLU-like activations is partitioned
into multiple linear regions, each corresponding to a specific activation
pattern of the included ReLU-like activations. We demonstrate that this
partition exhibits the following encoding properties across a variety of deep
learning models: (1) {\it determinism}: almost every linear region contains at
most one training example. We can therefore represent almost every training
example by a unique activation pattern, which is parameterized by a {\it neural
code}; and (2) {\it categorization}: according to the neural code, simple
algorithms, such as $K$-Means, $K$-NN, and logistic regression, can achieve
fairly good performance on both training and test data. These encoding
properties surprisingly suggest that {\it normal neural networks well-trained
for classification behave as hash encoders without any extra efforts.} In
addition, the encoding properties exhibit variability in different scenarios.
{Further experiments demonstrate that {\it model size}, {\it training time},
{\it training sample size}, {\it regularization}, and {\it label noise}
contribute in shaping the encoding properties, while the impacts of the first
three are dominant.} We then define an {\it activation hash phase chart} to
represent the space expanded by {model size}, training time, training sample
size, and the encoding properties, which is divided into three canonical
regions: {\it under-expressive regime}, {\it critically-expressive regime}, and
{\it sufficiently-expressive regime}. The source code package is available at
\url{https://github.com/LeavesLei/activation-code}.


Abstract:
  Light binary convolutional neural networks (LB-CNN) are particularly useful
when implemented in low-energy computing platforms as required in many
industrial applications. Herein, a framework for optimizing compact LB-CNN is
introduced and its effectiveness is evaluated. The framework is freely
available and may run on free-access cloud platforms, thus requiring no major
investments. The optimized model is saved in the standardized .h5 format and
can be used as input to specialized tools for further deployment into specific
technologies, thus enabling the rapid development of various intelligent image
sensors. The main ingredient in accelerating the optimization of our model,
particularly the selection of binary convolution kernels, is the Chainer/Cupy
machine learning library offering significant speed-ups for training the output
layer as an extreme-learning machine. Additional training of the output layer
using Keras/Tensorflow is included, as it allows an increase in accuracy.
Results for widely used datasets including MNIST, GTSRB, ORL, VGG show very
good compromise between accuracy and complexity. Particularly, for face
recognition problems a carefully optimized LB-CNN model provides up to 100%
accuracies. Such TinyML solutions are well suited for industrial applications
requiring image recognition with low energy consumption.


Abstract:
  Given a Lie group $G$, we elaborate the dynamics on $T^*T^*G$ and $T^*TG$,
which is given by a Hamiltonian, as well as the dynamics on the Tulczyjew
symplectic space $TT^*G$, which may be defined by a Lagrangian or a Hamiltonian
function. As the trivializations we adapted respect the group structures of the
iterated bundles, we exploit all possible subgroup reductions (Poisson,
symplectic or both) of higher order dynamics.


Abstract:
  X-ray polarimetry promises us an unprecedented look at the structure of
magnetic fields and on the processes at the base of acceleration of particles
up to ultrarelativistic energies in relativistic jets. Crucial pieces of
information are expected from observations of blazars (that are characterized
by the presence of a jet pointing close to the Earth), in particular of the
subclass defined by a synchrotron emission extending to the X-ray band
(so-called high synchrotron peak blazars, HSP). In this review, I give an
account of some of the models and numerical simulations developed to predict
the polarimetric properties of HSP at high energy, contrasting the predictions
of scenarios assuming particle acceleration at shock fronts with those that are
based on magnetic reconnection, and I discuss the prospects for the
observations of the upcoming Imaging X-ray Polarimetry Explorer (IXPE)
satellite.


Abstract:
  In robotic bin-picking applications, the perception of texture-less, highly
reflective parts is a valuable but challenging task. The high glossiness can
introduce fake edges in RGB images and inaccurate depth measurements especially
in heavily cluttered bin scenario. In this paper, we present the ROBI
(Reflective Objects in BIns) dataset, a public dataset for 6D object pose
estimation and multi-view depth fusion in robotic bin-picking scenarios. The
ROBI dataset includes a total of 63 bin-picking scenes captured with two active
stereo camera: a high-cost Ensenso sensor and a low-cost RealSense sensor. For
each scene, the monochrome/RGB images and depth maps are captured from sampled
view spheres around the scene, and are annotated with accurate 6D poses of
visible objects and an associated visibility score. For evaluating the
performance of depth fusion, we captured the ground truth depth maps by
high-cost Ensenso camera with objects coated in anti-reflective scanning spray.
To show the utility of the dataset, we evaluated the representative algorithms
of 6D object pose estimation and multi-view depth fusion on the full dataset.
Evaluation results demonstrate the difficulty of highly reflective objects,
especially in difficult cases due to the degradation of depth data quality,
severe occlusions and cluttered scene. The ROBI dataset is available online at
https://www.trailab.utias.utoronto.ca/robi.


Abstract:
  We give an explicit formula for the zeroth $\mathbb{A}^1$-homology sheaf of a
smooth proper variety. We also provide a simple proof of a theorem of
Kahn-Sujatha which describes hom sets in the birational localization of the
category of smooth varieties.


Abstract:
  The diffusion of innovations theory has been studied for years. Previous
research efforts mainly focus on key elements, adopter categories, and the
process of innovation diffusion. However, most of them only consider single
innovations. With the development of modern technology, recurrent innovations
gradually come into vogue. In order to reveal the characteristics of recurrent
innovations, we present the first large-scale analysis of the adoption of
recurrent innovations in the context of mobile app updates. Our analysis
reveals the adoption behavior and new adopter categories of recurrent
innovations as well as the features that have impact on the process of
adoption.


Abstract:
  Important advances have recently been achieved in developing procedures
yielding uniformly valid inference for a low dimensional causal parameter when
high-dimensional nuisance models must be estimated. In this paper, we review
the literature on uniformly valid causal inference and discuss the costs and
benefits of using uniformly valid inference procedures. Naive estimation
strategies based on regularisation, machine learning, or a preliminary model
selection stage for the nuisance models have finite sample distributions which
are badly approximated by their asymptotic distributions. To solve this serious
problem, estimators which converge uniformly in distribution over a class of
data generating mechanisms have been proposed in the literature. In order to
obtain uniformly valid results in high-dimensional situations, sparsity
conditions for the nuisance models need typically to be made, although a double
robustness property holds, whereby if one of the nuisance model is more sparse,
the other nuisance model is allowed to be less sparse. While uniformly valid
inference is a highly desirable property, uniformly valid procedures pay a high
price in terms of inflated variability. Our discussion of this dilemma is
illustrated by the study of a double-selection outcome regression estimator,
which we show is uniformly asymptotically unbiased, but is less variable than
uniformly valid estimators in the numerical experiments conducted.


Abstract:
  The planar Hall effect (PHE), wherein a rotating magnetic field in the plane
of a sample induces oscillating transverse voltage, has recently garnered
attention in a wide range of topological metals and insulators. The observed
twofold oscillations in $\rho_{yx}$ as the magnetic field completes one
rotation are the result of chiral, orbital and/or spin effects. The
antiperovskites $A_3B$O ($A$ = Ca, Sr, Ba; $B$ = Sn, Pb) are topological
crystalline insulators whose low-energy excitations are described by a
generalized Dirac equation for fermions with total angular momentum $J = 3/2$.
We report unusual sixfold oscillations in the PHE of Sr$_3$SnO, which persisted
nearly up to room temperature. Multiple harmonics (twofold, fourfold and
sixfold), which exhibited distinct field and temperature dependencies, were
detected in $\rho_{xx}$ and $\rho_{yx}$. These observations are more diverse
than those in other Dirac and Weyl semimetals and point to a richer interplay
of microscopic processes underlying the PHE in the antiperovskites.


Abstract:
  In this paper, we derive a stability result for $L_1$ and $L_{\infty}$
perturbations of diffusions under weak regularity conditions on the
coefficients. In particular, the drift terms we consider can be unbounded with
at most linear growth, and we do not require uniform convergence of perturbed
diffusions. Instead, we require a weaker convergence condition in a special
metric introduced in this paper, related to the Holder norm of the diffusion
matrix differences. Our approach is based on a special version of the
McKean-Singer parametrix expansion.


Abstract:
  Given a generically finite local extension of valuation rings $V \subset W$,
the question of whether $W$ is the localization of a finitely generated
$V$-algebra is significant for approaches to the problem of local
uniformization of valuations using ramification theory. Hagen Knaf proposed a
characterization of when $W$ is essentially of finite type over $V$ in terms of
classical invariants of the extension of associated valuations. Knaf's
conjecture has been verified in important special cases by Cutkosky and
Novacoski using local uniformization of Abhyankar valuations and resolution of
singularities of excellent surfaces in arbitrary characteristic, and by
Cutkosky for valuation rings of function fields of characteristic $0$ using
embedded resolution of singularities. In this paper we prove Knaf's conjecture
in full generality.


Abstract:
  The relationship between words in a sentence often tells us more about the
underlying semantic content of a document than its actual words, individually.
In this work, we propose two novel algorithms, called Flexible Lexical Chain II
and Fixed Lexical Chain II. These algorithms combine the semantic relations
derived from lexical chains, prior knowledge from lexical databases, and the
robustness of the distributional hypothesis in word embeddings as building
blocks forming a single system. In short, our approach has three main
contributions: (i) a set of techniques that fully integrate word embeddings and
lexical chains; (ii) a more robust semantic representation that considers the
latent relation between words in a document; and (iii) lightweight word
embeddings models that can be extended to any natural language task. We intend
to assess the knowledge of pre-trained models to evaluate their robustness in
the document classification task. The proposed techniques are tested against
seven word embeddings algorithms using five different machine learning
classifiers over six scenarios in the document classification task. Our results
show the integration between lexical chains and word embeddings representations
sustain state-of-the-art results, even against more complex systems.


Abstract:
  In this paper, we consider a reconfigurable intelligent surface
(RIS)-assisted two-way relay network, in which two users exchange information
through the base station (BS) with the help of an RIS. By jointly designing the
phase shifts at the RIS and beamforming matrix at the BS, our objective is to
maximize the minimum signal-to-noise ratio (SNR) of the two users, under the
transmit power constraint at the BS. We first consider the single-antenna BS
case, and propose two algorithms to design the RIS phase shifts and the BS
power amplification parameter, namely the SNR-upper-bound-maximization (SUM)
method, and genetic-SNR-maximization (GSM) method. When there are multiple
antennas at the BS, the optimization problem can be approximately addressed by
successively solving two decoupled subproblems, one to optimize the RIS phase
shifts, the other to optimize the BS beamforming matrix. The first subproblem
can be solved by using SUM or GSM method, while the second subproblem can be
solved by using optimized beamforming or maximum-ratio-beamforming method. The
proposed algorithms have been verified through numerical results with
computational complexity analysis.


Abstract:
  We give a double copy construction for the symmetries of the self-dual
sectors of Yang-Mills (YM) and gravity, in the light-cone formulation. We find
an infinite set of double copy constructible symmetries. We focus on two
families which correspond to the residual diffeomorphisms on the gravitational
side. For the first one, we find novel non-perturbative double copy rules in
the bulk. The second family has a more striking structure, as a
non-perturbative gravitational symmetry is obtained from a perturbatively
defined symmetry on the YM side.
  At null infinity, we find the YM origin of the subset of extended
Bondi-Metzner-Sachs (BMS) symmetries that preserve the self-duality condition.
In particular, holomorphic large gauge YM symmetries are double copied to
holomorphic supertranslations. We also identify the single copy of
superrotations with certain non-gauge YM transformations that to our knowledge
have not been previously presented in the literature.


Abstract:
  We use a Hamiltonian (transition matrix) description of height-restricted
Dyck paths on the plane in which generating functions for the paths arise as
matrix elements of the propagator to evaluate the length and area generating
function for paths with arbitrary starting and ending points, expressing it as
a rational combination of determinants. Exploiting a connection between random
walks and quantum exclusion statistics that we previously established, we
express this generating function in terms of grand partition functions for
exclusion particles in a finite harmonic spectrum and present an alternative,
simpler form for its logarithm that makes its polynomial structure explicit.


Abstract:
  We prove that for a domain $\Omega \subset \mathbb{R}^n$, being
$(\epsilon,\delta)$ in the sense of Jones is equivalent to being an extension
domain for bmo$(\Omega)$, the nonhonomogeneous version of the space of function
of bounded mean oscillation on $\Omega$. In the process we demonstrate that
these conditions are equivalent to local versions of two other conditions
characterizing uniform domains, one involving the presence of length cigars
between nearby points and the other a local version of the quasi-hyperbolic
uniform condition. Our results show that the definition of bmo$(\Omega)$ is
closely connected to the geometry of the domain.


Abstract:
  There is accelerating interest in developing memory devices using
antiferromagnetic (AFM) materials, motivated by the possibility for
electrically controlling AFM order via spin-orbit torques, and its read-out via
magnetoresistive effects. Recent studies have shown, however, that high current
densities create non-magnetic contributions to resistive switching signals in
AFM/heavy metal (AFM/HM) bilayers, complicating their interpretation. Here we
introduce an experimental protocol to unambiguously distinguish current-induced
magnetic and nonmagnetic switching signals in AFM/HM structures, and
demonstrate it in IrMn$_3$/Pt devices. A six-terminal double-cross device is
constructed, with an IrMn$_3$ pillar placed on one cross. The differential
voltage is measured between the two crosses with and without IrMn$_3$ after
each switching attempt. For a wide range of current densities, reversible
switching is observed only when write currents pass through the cross with the
IrMn$_3$ pillar, eliminating any possibility of non-magnetic switching
artifacts. Micromagnetic simulations support our findings, indicating a complex
domain-mediated switching process.


Abstract:
  Graph Convolutional Networks (GCNs) are increasingly adopted in large-scale
graph-based recommender systems. Training GCN requires the minibatch generator
traversing graphs and sampling the sparsely located neighboring nodes to obtain
their features. Since real-world graphs often exceed the capacity of GPU
memory, current GCN training systems keep the feature table in host memory and
rely on the CPU to collect sparse features before sending them to the GPUs.
This approach, however, puts tremendous pressure on host memory bandwidth and
the CPU. This is because the CPU needs to (1) read sparse features from memory,
(2) write features into memory as a dense format, and (3) transfer the features
from memory to the GPUs. In this work, we propose a novel GPU-oriented data
communication approach for GCN training, where GPU threads directly access
sparse features in host memory through zero-copy accesses without much CPU
help. By removing the CPU gathering stage, our method significantly reduces the
consumption of the host resources and data access latency. We further present
two important techniques to achieve high host memory access efficiency by the
GPU: (1) automatic data access address alignment to maximize PCIe packet
efficiency, and (2) asynchronous zero-copy access and kernel execution to fully
overlap data transfer with training. We incorporate our method into PyTorch and
evaluate its effectiveness using several graphs with sizes up to 111 million
nodes and 1.6 billion edges. In a multi-GPU training setup, our method is
65-92% faster than the conventional data transfer method, and can even match
the performance of all-in-GPU-memory training for some graphs that fit in GPU
memory.