title:
Linking the properties of accreting white dwarfs with the ionization
  state of their ambient medium

abstract:
  Steadily accreting white dwarfs (WDs) are efficient sources of ionization and
thus, are able to create extended ionized nebulae in their vicinity. These
nebulae represent ideal tools for the detection of accreting WDs, given that in
most cases the source itself is faint. In this work, we combine radiation
transfer simulations with known H and He accreting WD models, providing for the
first time the ionization state and the emission line spectra of the formed
nebulae as a function of the WD mass, the accretion rate and the chemical
composition of the accreted material. We find that the nebular optical line
fluxes and radial extent vary strongly with the WD's accretion properties,
peaking in systems with WD masses of 0.8 - 1.2 $\rm~M_{\odot}$. Projecting our
results on the 'BPT' diagnostic diagrams, we show that accreting WDs nebulae
possess characteristics distinct from those of H II-like regions, while they
share similar line ratios with the galactic low-ionization emission-line
regions. Finally, we compare our results to the relevant constraints imposed by
the lack of ionized nebulae in the vicinity of supersoft X-ray sources (SSSs)
and Type Ia supernova remnants - sources which are related to steadily
accreting WDs. The large discrepancies uncovered by our comparison rule out any
steadily accreting WD as a potential progenitor of the studied remnants and
additionally require the ambient medium around the SSSs to be less dense than
0.2 $\rm~cm^{-3}$. We discuss possible alternatives that could bridge the
incompatibility between the theoretical expectations and the relevant
observations.


title:
Predicting Winners of the Reality TV Dating Show $\textit{The Bachelor}$
  Using Machine Learning Algorithms

abstract:
  $\textit{The Bachelor}$ is a reality TV dating show in which a single
bachelor selects his wife from a pool of approximately 30 female contestants
over eight weeks of filming (American Broadcasting Company 2002). We collected
the following data on all 422 contestants that participated in seasons 11
through 25: their Age, Hometown, Career, Race, Week they got their first 1-on-1
date, whether they got the first impression rose, and what "place" they ended
up getting. We then trained three machine learning models to predict the ideal
characteristics of a successful contestant on $\textit{The Bachelor}$. The
three algorithms that we tested were: random forest classification, neural
networks, and linear regression. We found consistency across all three models,
although the neural network performed the best overall. Our models found that a
woman has the highest probability of progressing far on $\textit{The Bachelor}$
if she is: 26 years old, white, from the Northwest, works as an dancer,
received a 1-on-1 in week 6, and did not receive the First Impression Rose. Our
methodology is broadly applicable to all romantic reality television, and our
results will inform future $\textit{The Bachelor}$ production and contestant
strategies. While our models were relatively successful, we still encountered
high misclassification rates. This may be because: (1) Our training dataset had
fewer than 400 points or (2) Our models were too simple to parameterize the
complex romantic connections contestants forge over the course of a season.


title: Comparing constellations across cultures

abstract:
  Cultural astronomy reveals ways in which perception and culture have shaped
the interpretation of the night sky.


title:
Robust Beamforming for Localization-Aided Millimeter Wave Communication
  Systems

abstract:
  In this letter, we investigate a robust beamforming problem for
localization-aided millimeter wave (mmWave) communication systems. To handle
this problem, we propose a novel restriction and relaxation (R&R) method. The
proposed R&R method aims at minimizing the total transmit power while the
positioning error follows a Gaussian distribution. Specifically, in the
restriction phase of R&R, the probabilistic constraint is transformed into the
deterministic form by using the Bernsteintype inequality. In the relaxation
phase of R&R, the non-convex optimization problem is reformulated into a convex
semidefinite program (SDP) by using semidefinite relaxation (SDR) and
firstorder Taylor expansion methods. To the best of our knowledge, we first
consider the impact of the distribution of the positioning error on the channel
state information (CSI), which further influences the data rate. Numerical
results present the trade-off of the beamforming between the communication and
positioning.


title:
Universal location of Yang Lee edge singularity for a one-component
  field theory in $1\le d \le 4$

abstract:
  We determine the universal location of the Yang-Lee edge singularity in the
entire relevant domain of spatial dimensions $1\le d \le 4$ for the Ising
universality class. To that end, we present analytical results for $d=1,2,4$
and near four dimensions. For $d=3$ and a set of fractional dimensions, we
perform numerical calculations using a systematic Functional Renormalization
Group approach.


title:
The diversity of rotation curves of simulated galaxies with cusps and
  cores

abstract:
  We use $\Lambda$CDM cosmological hydrodynamical simulations to explore the
kinematics of gaseous discs in late-type dwarf galaxies. We create
high-resolution 21-cm 'observations' of simulated dwarfs produced in two
variations of the EAGLE galaxy formation model: one where supernova-driven gas
flows redistribute dark matter through gravitational interaction and form
constant-density central 'cores', and another where the central 'cusps' survive
intact. We 'observe' each galaxy along multiple sight lines and derive a
rotation curve for each observation using a conventional tilted-ring approach
to model the gas kinematics. We find that the modelling process introduces
systematic discrepancies between the recovered rotation curve and the actual
circular velocity curve driven primarily by (i) non-circular gas orbits within
the discs; (ii) the finite thickness of gaseous discs, which leads to overlap
of different radii in projection; and (iii) departures from dynamical
equilibrium. Dwarfs with dark matter cusps often appear to have a core, whilst
the inverse error is less common. These effects naturally reproduce an observed
trend which other models struggle to explain: late-type dwarfs with more
steeply-rising rotation curves appear to be dark matter-dominated in the inner
regions, whereas the opposite seems to hold in galaxies with core-like rotation
curves. We conclude that if similar effects affect the rotation curves of
observed dwarfs, a late-type dwarf population in which all galaxies have
sizeable dark matter cores is most likely incompatible with current
measurements.


title: Error Identification Strategies for Python Jupyter Notebooks

abstract:
  Computational notebooks -- such as Jupyter or Colab -- combine text and data
analysis code. They have become ubiquitous in the world of data science and
exploratory data analysis. Since these notebooks present a different
programming paradigm than conventional IDE-driven programming, it is plausible
that debugging in computational notebooks might also be different. More
specifically, since creating notebooks blends domain knowledge, statistical
analysis, and programming, the ways in which notebook users find and fix errors
in these different forms might be different. In this paper, we present an
exploratory, observational study on how Python Jupyter notebook users find and
understand potential errors in notebooks. Through a conceptual replication of
study design investigating the error identification strategies of R notebook
users, we presented users with Python Jupyter notebooks pre-populated with
common notebook errors -- errors rooted in either the statistical data
analysis, the knowledge of domain concepts, or in the programming. We then
analyzed the strategies our study participants used to find these errors and
determined how successful each strategy was at identifying errors. Our findings
indicate that while the notebook programming environment is different from the
environments used for traditional programming, debugging strategies remain
quite similar. It is our hope that the insights presented in this paper will
help both notebook tool designers and educators make changes to improve how
data scientists discover errors more easily in the notebooks they write.


title:
Geographic Spines in the 2020 Census Disclosure Avoidance System TopDown
  Algorithm

abstract:
  The TopDown Algorithm (TDA) first produces differentially private counts at
the nation and then produces counts at lower geolevels (e.g.: state, county,
etc.) subject to the constraint that all query answers in lower geolevels are
consistent with those at previously estimated geolevels. This paper describes
the three sets of definitions of these geolevels, or the geographic spines,
that are implemented within TDA. These include the standard Census geographic
spine and two other spines that improve accuracy in geographic areas that are
far from the standard Census spine, such as cities, towns, and/or AIAN areas.
The third such spine, which is called the optimized spine, also modifies the
privacy-loss budget allocated to the entities within the geolevels, or the
geounits, to ensure the privacy-loss budget is used efficiently within TDA.


title: No Redshift Evolution of Dust Temperatures from $0 < z < 2$

abstract:
  Some recent literature has claimed there to be an evolution in galaxies' dust
temperatures towards warmer (or colder) spectral energy distributions (SEDs)
between low and high redshift. These conclusions are driven by both theoretical
models and empirical measurement. Such claims sometimes contradict one another
and are prone to biases in samples or SED fitting techniques. What has made
direct comparisons difficult is that there is no uniform approach to fitting
galaxies' infrared/millimeter SEDs. Here we aim to standardize the measurement
of galaxies' dust temperatures with a python-based SED fitting procedure,
MCIRSED. We draw on reference datasets observed by IRAS, Herschel, and Scuba-2
to test for redshift evolution out to $z\sim2$. We anchor our work to the
L$_{IR}$-$\lambda_{peak}$ plane, where there is an empirically observed
anti-correlation between IR luminosity and rest-frame peak wavelength (an
observational proxy for luminosity-weighted dust temperature) such that
$\lambda_{peak}=\lambda_{t}({L_{IR}}/{L_{t}})^{\eta}$ where
$\eta=-0.09\pm0.01$, L$_{t}=10^{12}$ L$_{\odot}$, and $\lambda_{t}=92\pm2\mu$m.
We find no evidence for redshift evolution of galaxies' temperatures, or
$\lambda_{peak}$, at fixed L$_{IR}$ from $0<z<2$ with >99.99% confidence. Our
finding does not preclude evolution in dust temperatures at fixed stellar mass,
which is expected from a non-evolving L$_{IR}$-$\lambda_{peak}$ relation and a
strongly evolving SFR-M$_\star$ relation. The breadth of dust temperatures at a
given L$_{IR}$ is likely driven by variation in galaxies' dust geometries and
sizes and does not evolve. Testing for L$_{IR}$-$\lambda_{peak}$ evolution
toward higher redshift ($z\sim5-6$) requires better sampling of galaxies' dust
SEDs near their peaks (observed $\sim$200-600$\mu$m) with $<$1 mJy sensitivity.
This poses a significant challenge to current instrumentation.


title:
A local rigidity theorem for minimal two-spheres in an electrovacuum
  spacetime

abstract:
  The purpose of this article is to prove that, under suitable constrains on
the electrovacuum spacetime $M$, if $\Sigma\subset M$ is an embedded strictly
stable minimal two-sphere which locally maximizes the charged Hawking mass,
then there exist a neighborhood of it in $M$ isometric to the
Reissner-Nordstr\"om-de Sitter space. At the same time, motived by
\cite{Brendle}, we will deduce an estimate for area of a two-sphere which is
locally area minimizing in an electrovacuum spacetime. Moreover, if the
equality holds, then there exist a neighborhood of it in $M$ isometric to the
charged Nariai space.


title:
Community Integration Algorithms (CIAs) for Dynamical Systems on
  Networks

abstract:
  Dynamics of large-scale network processes underlies crucial phenomena ranging
across all sciences. Forward simulation of large network models is often
computationally prohibitive. Yet, most networks have intrinsic community
structure. We exploit these communities and propose a fast simulation algorithm
for network dynamics. In particular, aggregating the inputs a node receives
constitutes the limiting factor in numerically simulating large-scale network
dynamics. We develop community integration algorithms (CIAs) significantly
reducing function-evaluations. We obtain a substantial reduction from
polynomial to linear computational complexity. We illustrate our results in
multiple applications including classical and higher-order Kuramoto-type
systems for synchronisation and Cucker--Smale systems exhibiting flocking
behaviour on synthetic as well as real-world networks. Numerical comparison and
theoretical analysis confirm the robustness and efficiency of CIAs.


title: On Sequences in Cyclic Groups with Distinct Partial Sums

abstract:
  A subset of an abelian group is {\em sequenceable} if there is an ordering
$(x_1, \ldots, x_k)$ of its elements such that the partial sums $(y_0, y_1,
\ldots, y_k)$, given by $y_0 = 0$ and $y_i = \sum_{j=1}^i x_i$ for $1 \leq i
\leq k$, are distinct, with the possible exception that we may have $y_k = y_0
= 0$. We demonstrate the sequenceability of subsets of size $k$ of
$\mathbb{Z}_n \setminus \{ 0 \}$ when $n = mt$ in many cases, including when
$m$ is either prime or has all prime factors larger than $k! /2$ for $k \leq
11$ and $t \leq 5$ and for $k=12$ and $t \leq 4$. We obtain similar, but
partial, results for $13 \leq k \leq 15$. This represents progress on a variety
of questions and conjectures in the literature concerning the sequenceability
of subsets of abelian groups, which we combine and summarize into the
conjecture that if a subset of an abelian group does not contain 0 then it is
sequenceable.


title: Completing the Theory of Electron with Gravitational Torsion

abstract:
  We demonstrate that classical and quantum electrodynamics can be completed by
gravitational torsion appearing in Einstein-Cartan-Sciama-Kibble theory of
gravity, providing the missing part of the electron theory. One of the
equations of this theory, a version of Dirac type equation for fermions called
Hehl-Datta equation, contains a gravitational spin-torsion term in the
Lagrangian density for quantum electrodynamics. This term relates the charged
fermion spin to gravitational torsion and provides a mechanical energy
counter-balance to the infinite electromagnetic self-energy. As a result, there
is no "bare" mass for an electron, nor is renormalization required for many
scenarios.


title: On The Role of Social Identity in the Market for (Mis)information

abstract:
  Motivated by recent works in the communication and psychology literature, we
model and study the role social identity -- a person's sense of belonging to a
group -- plays in human information consumption. A hallmark of Social Identity
Theory (SIT) is the notion of 'status', i.e., an individual's desire to enhance
their and their 'in-group's' utility relative to that of an 'out-group'. In the
context of belief formation, this comes off as a desire to believe positive
news about the in-group and negative news about the out-group, which has been
empirically shown to support belief in misinformation and false news.
  We model this phenomenon as a Stackelberg game being played over an
information channel between a news-source (sender) and news-consumer
(receiver), with the receiver incorporating the 'status' associated with social
identity in their utility, in addition to accuracy. We characterize the
strategy that must be employed by the sender to ensure that its message is
trusted by receivers of all identities while maximizing their overall quality
of information. We show that, as a rule, this optimal quality of information at
equilibrium decreases when a receiver's sense of identity increases. We further
demonstrate how extensions of our model can be used to quantitatively estimate
the level of importance given to identity in a population.


title: A Liouville's theorem for some Monge-Amp\`ere type equations

abstract:
  We study a Monge-Amp\`ere type equation that interpolates the classical
{\sigma_2} -Yamabe equation in conformal geometry and the 2-Hessian equation in
dimension 4.


title: Challenges in leveraging GANs for few-shot data augmentation

abstract:
  In this paper, we explore the use of GAN-based few-shot data augmentation as
a method to improve few-shot classification performance. We perform an
exploration into how a GAN can be fine-tuned for such a task (one of which is
in a class-incremental manner), as well as a rigorous empirical investigation
into how well these models can perform to improve few-shot classification. We
identify issues related to the difficulty of training such generative models
under a purely supervised regime with very few examples, as well as issues
regarding the evaluation protocols of existing works. We also find that in this
regime, classification accuracy is highly sensitive to how the classes of the
dataset are randomly split. Therefore, we propose a semi-supervised fine-tuning
approach as a more pragmatic way forward to address these problems.


title:
Robust Reputation Independence in Ranking Systems for Multiple Sensitive
  Attributes

abstract:
  Ranking systems have an unprecedented influence on how and what information
people access, and their impact on our society is being analyzed from different
perspectives, such as users' discrimination. A notable example is represented
by reputation-based ranking systems, a class of systems that rely on users'
reputation to generate a non-personalized item-ranking, proved to be biased
against certain demographic classes. To safeguard that a given sensitive user's
attribute does not systematically affect the reputation of that user, prior
work has operationalized a reputation independence constraint on this class of
systems. In this paper, we uncover that guaranteeing reputation independence
for a single sensitive attribute is not enough. When mitigating biases based on
one sensitive attribute (e.g., gender), the final ranking might still be biased
against certain demographic groups formed based on another attribute (e.g.,
age). Hence, we propose a novel approach to introduce reputation independence
for multiple sensitive attributes simultaneously. We then analyze the extent to
which our approach impacts on discrimination and other important properties of
the ranking system, such as its quality and robustness against attacks.
Experiments on two real-world datasets show that our approach leads to less
biased rankings with respect to multiple users' sensitive attributes, without
affecting the system's quality and robustness.


title:
Semi-explicit integration of second order for weakly coupled
  poroelasticity

abstract:
  We introduce a semi-explicit time-stepping scheme of second order for linear
poroelasticity satisfying a weak coupling condition. Here, semi-explicit means
that the system, which needs to be solved in each step, decouples and hence
improves the computational efficiency. The construction and the convergence
proof are based on the connection to a differential equation with two time
delays, namely one and two times the step size. Numerical experiments confirm
the theoretical results and indicate the applicability to higher-order schemes.


title: Grey Tracks as Probes of Hadronization Dynamics

abstract:
  Energetic quarks liberated from hadrons in nuclear deep-inelastic scattering
propagate through the nuclear medium, interacting with it via several
processes. These include quark energy loss and nuclear interactions of forming
hadrons. One manifestation of these interactions is the enhanced emission of
low-energy charged particles, referred to as grey tracks. We use the
theoretical components of the BeAGLE event generator to interpret grey track
signatures of parton transport and hadron formation by comparing its
predictions to E665 data. We extend the base version of BeAGLE by adding four
different options for describing parton energy loss. The E665 data we used
consists of multiplicity ratios for fixed-target scattering of 490 GeV muons on
Xe normalized to deuterium as a function of the number of grey tracks. We
compare multiplicity ratios for E665 grey tracks to the predictions of BeAGLE,
varying the options and parameters to determine which physics phenomena can be
identified by these data. We find that grey tracks are unaffected by
modifications of the forward production. Thus their production must be
dominated by interactions with hadrons in the backward region. This offers the
advantage that selecting certain particles in the forward region is unlikely to
bias a centrality selection. We see a strong correlation between the number of
grey tracks and the in-medium path length. Our energy loss model does not
reproduce the suppression observed in the projectile region. We see an
underprediction of the proton production rate in backward kinematics,
suggesting that a stronger source of interaction with the nuclear medium is
needed for accurate modeling. These results lay an important foundation for
future spectator tagging studies at both Jefferson Lab and at the Electron-Ion
Collider, where neutron and proton grey track studies will be feasible down to
very small momenta.


title: Hawkes Process Modeling of Block Arrivals in Bitcoin Blockchain

abstract:
  The paper constructs a multi-variate Hawkes process model of Bitcoin block
arrivals and price jumps. Hawkes processes are selfexciting point processes
that can capture the self- and cross-excitation effects of block mining and
Bitcoin price volatility. We use publicly available blockchain datasets to
estimate the model parameters via maximum likelihood estimation. The results
show that Bitcoin price volatility boost block mining rate and Bitcoin
investment return demonstrates mean reversion. Quantile-Quantile plots show
that the proposed Hawkes process model is a better fit to the blockchain
datasets than a Poisson process model.


title: Quantum transport in chaotic cavities with tunnel barriers

abstract:
  We bring together the semiclassical approximation, matrix integrals and the
theory of symmetric polynomials in order to solve a long standing problem in
the field of quantum chaos: to compute transport moments when tunnel barriers
are present and the number of open channels, $M$, is small. In contrast to
previous approaches, ours is non-perturbative in $M$; instead, we arrive at an
explicit expression in the form of a power series in the barrier's
reflectivity, whose coefficients are rational functions of $M$. For general
moments we must require that the barriers are equal and time reversal symmetry
is broken, but for conductance we treat the general situation. Our method
accounts for exponentially small non-perturbative terms that were not
accessible to previous semiclassical approaches. We also show how to include
more than two leads in the system.


title:
Flexible and Efficient Contextual Bandits with Heterogeneous Treatment
  Effect Oracle

abstract:
  Many popular contextual bandit algorithms estimate reward models to inform
decision making. However, true rewards can contain action-independent
redundancies that are not relevant for decision making and only increase the
statistical complexity of accurate estimation. It is sufficient and more
data-efficient to estimate the simplest function that explains the reward
differences between actions, that is, the heterogeneous treatment effect,
commonly understood to be more structured and simpler than the reward.
Motivated by this observation, building on recent work on oracle-based
algorithms, we design a statistically optimal and computationally efficient
algorithm using heterogeneous treatment effect estimation oracles. Our results
provide the first universal reduction of contextual bandits to a
general-purpose heterogeneous treatment effect estimation method. We show that
our approach is more robust to model misspecification than reward estimation
methods based on squared error regression oracles. Experimentally, we show the
benefits of heterogeneous treatment effect estimation in contextual bandits
over reward estimation.


title:
Escaping Data Scarcity for High-Resolution Heterogeneous Face
  Hallucination

abstract:
  In Heterogeneous Face Recognition (HFR), the objective is to match faces
across two different domains such as visible and thermal. Large domain
discrepancy makes HFR a difficult problem. Recent methods attempting to fill
the gap via synthesis have achieved promising results, but their performance is
still limited by the scarcity of paired training data. In practice, large-scale
heterogeneous face data are often inaccessible due to the high cost of
acquisition and annotation process as well as privacy regulations. In this
paper, we propose a new face hallucination paradigm for HFR, which not only
enables data-efficient synthesis but also allows to scale up model training
without breaking any privacy policy. Unlike existing methods that learn face
synthesis entirely from scratch, our approach is particularly designed to take
advantage of rich and diverse facial priors from visible domain for more
faithful hallucination. On the other hand, large-scale training is enabled by
introducing a new federated learning scheme to allow institution-wise
collaborations while avoiding explicit data sharing. Extensive experiments
demonstrate the advantages of our approach in tackling HFR under current data
limitations. In a unified framework, our method yields the state-of-the-art
hallucination results on multiple HFR datasets.


title:
PIE-Net: Photometric Invariant Edge Guided Network for Intrinsic Image
  Decomposition

abstract:
  Intrinsic image decomposition is the process of recovering the image
formation components (reflectance and shading) from an image. Previous methods
employ either explicit priors to constrain the problem or implicit constraints
as formulated by their losses (deep learning). These methods can be negatively
influenced by strong illumination conditions causing shading-reflectance
leakages.
  Therefore, in this paper, an end-to-end edge-driven hybrid CNN approach is
proposed for intrinsic image decomposition. Edges correspond to illumination
invariant gradients. To handle hard negative illumination transitions, a
hierarchical approach is taken including global and local refinement layers. We
make use of attention layers to further strengthen the learning process.
  An extensive ablation study and large scale experiments are conducted showing
that it is beneficial for edge-driven hybrid IID networks to make use of
illumination invariant descriptors and that separating global and local cues
helps in improving the performance of the network. Finally, it is shown that
the proposed method obtains state of the art performance and is able to
generalise well to real world images. The project page with pretrained models,
finetuned models and network code can be found at
https://ivi.fnwi.uva.nl/cv/pienet/.


title:
Can X-ray Observations Improve Optical-UV-based Accretion-Rate Estimates
  for Quasars?

abstract:
  Current estimates of the normalized accretion rates of quasars (L/L_Edd),
rely on measuring the velocity widths of broad optical-UV emission lines (e.g.,
H$\beta$ and Mg II $\lambda2800$). However, such lines tend to be weak or
inaccessible in the most distant quasars, leading to increasing uncertainty in
L/L_Edd estimates at $z > 6$. Utilizing a carefully selected sample of 53
radio-quiet quasars that have H$\beta$ and C IV $\lambda1549$ spectroscopy as
well as {\sl Chandra} coverage, we searched for a robust accretion-rate
indicator for quasars, particularly at the highest-accessible redshifts ($z
\sim 6-7$). Our analysis explored relationships between the H$\beta$-based
L/L_Edd, the equivalent width (EW) of C IV, and the optical-to-X-ray spectral
slope (a_ox). Our results show that EW(C IV) is the strongest indicator of the
H$\beta$-based L/L_Edd parameter, consistent with previous studies, although
significant scatter persists particularly for sources with weak C IV lines. We
do not find evidence for the a_ox parameter improving this relation, and we do
not find a significant correlation between a_ox and H$\beta$-based L/L_Edd.
This absence of an improved relationship may reveal a limitation in our sample.
X-ray observations of additional luminous sources, found at $z \gtrsim 1$, may
allow us to mitigate the biases inherent in our archival sample and test
whether X-ray data could improve L/L_Edd estimates. Furthermore, deeper X-ray
observations of our sources may provide accurate measurements of the hard-X-ray
power-law photon index ($\Gamma$), which is considered an unbiased L/L_Edd
indicator. Correlations between EW(C IV) and a_ox with $\Gamma$-based L/L_Edd
may yield a more robust prediction of a quasar normalized accretion rate.


title:
Long-term variability of Class I methanol masers in the high mass star
  forming region DR21(OH)

abstract:
  High mass stars play an important role in the Interstellar Medium, but much
remains to be known about their formation. Class I methanol masers may be
unique tracers of an early stage of high mass star formation, and a better
understanding of such masers will allow them to be used as more effective
probes of the high mass star forming process. We present an investigation of
the long-term variability of Class I methanol masers at 44 GHz toward the high
mass star forming region DR21(OH). We compare observations taken in 2017 to
2012 and also to 2001 data from the literature. A total of 57 maser spots were
found in the 2017 data, with center velocities ranging between -8.65 km/s to
+2.56 km/s. The masers are arranged in a western and an eastern lobe with two
arcs in each lobe that look like bowshocks, consistent with previous
observations. The general trend is an increase in intensity from 2001 to 2012,
and a decrease from 2012 to 2017. Variability appears to be more prevalent in
the inner arc of the western lobe than in the outer arc. We speculate that this
may be a consequence of episodic accretion, in which a later accretion event
has resulted in ejection of material whose shock reached the inner arc at some
point in time after 2001. We conclude that class I methanol masers are variable
on long timescales (of the order of 5-10 years).


title: System Identification via Nuclear Norm Regularization

abstract:
  This paper studies the problem of identifying low-order linear systems via
Hankel nuclear norm regularization. Hankel regularization encourages the
low-rankness of the Hankel matrix, which maps to the low-orderness of the
system. We provide novel statistical analysis for this regularization and
carefully contrast it with the unregularized ordinary least-squares (OLS)
estimator. Our analysis leads to new bounds on estimating the impulse response
and the Hankel matrix associated with the linear system. We first design an
input excitation and show that Hankel regularization enables one to recover the
system using optimal number of observations in the true system order and
achieve strong statistical estimation rates. Surprisingly, we demonstrate that
the input design indeed matters, by showing that intuitive choices such as
i.i.d. Gaussian input leads to provably sub-optimal sample complexity. To
better understand the benefits of regularization, we also revisit the OLS
estimator. Besides refining existing bounds, we experimentally identify when
regularized approach improves over OLS: (1) For low-order systems with slow
impulse-response decay, OLS method performs poorly in terms of sample
complexity, (2) Hankel matrix returned by regularization has a more clear
singular value gap that ease identification of the system order, (3) Hankel
regularization is less sensitive to hyperparameter choice. Finally, we
establish model selection guarantees through a joint train-validation procedure
where we tune the regularization parameter for near-optimal estimation.

categories: stat.ML cs.LG cs.SY eess.SY math.DS math.OC


title: On the First Beurling--Malliavin theorem

abstract:
  In this article we prove a generalization of the First Beurling--Malliavin
Theorem. In more detail, we establish a new sufficient condition for a function
to be a Beurling--Malliavin majorant. Our result is strictly stronger than that
of the First Beurling--Malliavin Multiplier Theorem. We also show that our
result is strict in a certain sense.


title:
Kinematics and Metallicity of Red Giant Branch Stars in the Northeast
  Shelf of M31

abstract:
  We obtained Keck/DEIMOS spectra of 556 individual red giant branch stars in 4
spectroscopic fields spanning $13-31$ projected kpc along the Northeast (NE)
shelf of M31. We present the first detection of a complete wedge pattern in the
space of projected M31-centric radial distance versus line-of-sight velocity
for this feature, which includes the returning stream component of the shelf.
This wedge pattern agrees with expectations of a tidal shell formed in a radial
merger and provides strong evidence in favor of predictions of Giant Stellar
Stream (GSS) formation models in which the NE shelf originates from the second
orbital wrap of the tidal debris. The observed concentric wedge patterns of the
NE, West (W), and Southeast (SE) shelves corroborate this interpretation
independently of the models. We do not detect a kinematical signature in the NE
shelf region corresponding to an intact progenitor core, favoring GSS formation
models in which the progenitor is completely disrupted. The shelf's photometric
metallicity distribution implies that it is dominated by tidal material, as
opposed to the phase-mixed stellar halo or the disk. The metallicity
distribution ([Fe/H]$_{\rm phot}$ = $-0.42$ $\pm$ $0.01$) also matches the GSS,
and consequently the W and SE shelves, further supporting a direct physical
association between the tidal features.


title:
Exploring the hysteresis properties of nanocrystalline permanent magnets
  using deep learning

abstract:
  We demonstrate the use of model order reduction and neural networks for
estimating the hysteresis properties of nanocrystalline permanent magnets from
microstructure. With a data-driven approach, we learn the demagnetization curve
from data-sets created by grain growth and micromagnetic simulations. We show
that the granular structure of a magnet can be encoded within a low-dimensional
latent space. Latent codes are constructed using a variational autoencoder. The
mapping of structure code to hysteresis properties is a multi-target regression
problem. We apply deep neural network and use parameter sharing, in order to
predict anchor points along the demagnetization curves from the magnet's
structure code. The method is applied to study the magnetic properties of
nanocrystalline permanent magnets. We show how new grain structures can be
generated by interpolation between two points in the latent space and how the
magnetic properties of the resulting magnets can be predicted.


title:
First-principles Landau-like potential for BiFeO$_3$ and related
  materials

abstract:
  In this work we introduce the simplest, lowest-order Landau-like potential
for BiFeO$_3$ and La-doped BiFeO$_3$ as an expansion around the paraelectric
cubic phase in powers of polarization, FeO$_6$ octahedral rotations and
strains. We present an analytical approach for computing the model parameters
from density functional theory. We illustrate our approach by computing the
potentials for BiFeO$_3$ and La$_{0.25}$Bi$_{0.75}$FeO$_3$ and show that,
overall, we are able to capture the first-principles results accurately. The
computed models allow us to identify and explain the main interactions
controlling the relative stability of the competing low-energy phases of these
compounds.


title:
Knowledge-Spreader: Learning Facial Action Unit Dynamics with Extremely
  Limited Labels

abstract:
  Recent studies on the automatic detection of facial action unit (AU) have
extensively relied on large-sized annotations. However, manually AU labeling is
difficult, time-consuming, and costly. Most existing semi-supervised works
ignore the informative cues from the temporal domain, and are highly dependent
on densely annotated videos, making the learning process less efficient. To
alleviate these problems, we propose a deep semi-supervised framework
Knowledge-Spreader (KS), which differs from conventional methods in two
aspects. First, rather than only encoding human knowledge as constraints, KS
also learns the Spatial-Temporal AU correlation knowledge in order to
strengthen its out-of-distribution generalization ability. Second, we approach
KS by applying consistency regularization and pseudo-labeling in multiple
student networks alternately and dynamically. It spreads the spatial knowledge
from labeled frames to unlabeled data, and completes the temporal information
of partially labeled video clips. Thus, the design allows KS to learn AU
dynamics from video clips with only one label allocated, which significantly
reduce the requirements of using annotations. Extensive experiments demonstrate
that the proposed KS achieves competitive performance as compared to the state
of the arts under the circumstances of using only 2% labels on BP4D and 5%
labels on DISFA. In addition, we test it on our newly developed large-scale
comprehensive emotion database, which contains considerable samples across
well-synchronized and aligned sensor modalities for easing the scarcity issue
of annotations and identities in human affective computing. The new database
will be released to the research community.


title: Which cluster morphism categories are CAT(0)

abstract:
  The cluster morphism category of an hereditary algebra was introduced in [5]
to show that the picture space of an hereditary algebra of finite
representation type is a $K(\pi,1)$ for the associated picture group, thereby
allowing for the computation of the homology of picture groups of finite type
as carried out in [7] for the case of $A_n$.
  In this paper we show that the cluster morphism category is a
$CAT(0)$-category for hereditary algebras of finite or tame type with only
small tubes. As a consequence, we get that the classifying space of the cluster
morphism category is a locally $CAT(0)$ space and, as a consequence of that, we
get that this classifying space is a $K(\pi,1)$.


title: Learning the Effect of Registration Hyperparameters with HyperMorph

abstract:
  We introduce HyperMorph, a framework that facilitates efficient
hyperparameter tuning in learning-based deformable image registration.
Classical registration algorithms perform an iterative pair-wise optimization
to compute a deformation field that aligns two images. Recent learning-based
approaches leverage large image datasets to learn a function that rapidly
estimates a deformation for a given image pair. In both strategies, the
accuracy of the resulting spatial correspondences is strongly influenced by the
choice of certain hyperparameter values. However, an effective hyperparameter
search consumes substantial time and human effort as it often involves training
multiple models for different fixed hyperparameter values and may lead to
suboptimal registration. We propose an amortized hyperparameter learning
strategy to alleviate this burden by learning the impact of hyperparameters on
deformation fields. We design a meta network, or hypernetwork, that predicts
the parameters of a registration network for input hyperparameters, thereby
comprising a single model that generates the optimal deformation field
corresponding to given hyperparameter values. This strategy enables fast,
high-resolution hyperparameter search at test-time, reducing the inefficiency
of traditional approaches while increasing flexibility. We also demonstrate
additional benefits of HyperMorph, including enhanced robustness to model
initialization and the ability to rapidly identify optimal hyperparameter
values specific to a dataset, image contrast, task, or even anatomical region,
all without the need to retrain models. We make our code publicly available at
http://hypermorph.voxelmorph.net.


title: Face Relighting with Geometrically Consistent Shadows

abstract:
  Most face relighting methods are able to handle diffuse shadows, but struggle
to handle hard shadows, such as those cast by the nose. Methods that propose
techniques for handling hard shadows often do not produce geometrically
consistent shadows since they do not directly leverage the estimated face
geometry while synthesizing them. We propose a novel differentiable algorithm
for synthesizing hard shadows based on ray tracing, which we incorporate into
training our face relighting model. Our proposed algorithm directly utilizes
the estimated face geometry to synthesize geometrically consistent hard
shadows. We demonstrate through quantitative and qualitative experiments on
Multi-PIE and FFHQ that our method produces more geometrically consistent
shadows than previous face relighting methods while also achieving
state-of-the-art face relighting performance under directional lighting. In
addition, we demonstrate that our differentiable hard shadow modeling improves
the quality of the estimated face geometry over diffuse shading models.


title: To Find Waldo You Need Contextual Cues: Debiasing Who's Waldo

abstract:
  We present a debiased dataset for the Person-centric Visual Grounding (PCVG)
task first proposed by Cui et al. (2021) in the Who's Waldo dataset. Given an
image and a caption, PCVG requires pairing up a person's name mentioned in a
caption with a bounding box that points to the person in the image. We find
that the original Who's Waldo dataset compiled for this task contains a large
number of biased samples that are solvable simply by heuristic methods; for
instance, in many cases the first name in the sentence corresponds to the
largest bounding box, or the sequence of names in the sentence corresponds to
an exact left-to-right order in the image. Naturally, models trained on these
biased data lead to over-estimation of performance on the benchmark. To enforce
models being correct for the correct reasons, we design automated tools to
filter and debias the original dataset by ruling out all examples of
insufficient context, such as those with no verb or with a long chain of
conjunct names in their captions. Our experiments show that our new sub-sampled
dataset contains less bias with much lowered heuristic performances and widened
gaps between heuristic and supervised methods. We also demonstrate the same
benchmark model trained on our debiased training set outperforms that trained
on the original biased (and larger) training set on our debiased test set. We
argue our debiased dataset offers the PCVG task a more practical baseline for
reliable benchmarking and future improvements.


title:
Active Learning for Computationally Efficient Distribution of Binary
  Evolution Simulations

abstract:
  Binary stars undergo a variety of interactions and evolutionary phases,
critical for predicting and explaining observed properties. Binary population
synthesis with full stellar-structure and evolution simulations are
computationally expensive requiring a large number of mass-transfer sequences.
The recently developed binary population synthesis code POSYDON incorporates
grids of MESA binary star simulations which are then interpolated to model
large-scale populations of massive binaries. The traditional method of
computing a high-density rectilinear grid of simulations is not scalable for
higher-dimension grids, accounting for a range of metallicities, rotation, and
eccentricity. We present a new active learning algorithm, psy-cris, which uses
machine learning in the data-gathering process to adaptively and iteratively
select targeted simulations to run, resulting in a custom, high-performance
training set. We test psy-cris on a toy problem and find the resulting training
sets require fewer simulations for accurate classification and regression than
either regular or randomly sampled grids. We further apply psy-cris to the
target problem of building a dynamic grid of MESA simulations, and we
demonstrate that, even without fine tuning, a simulation set of only $\sim 1/4$
the size of a rectilinear grid is sufficient to achieve the same classification
accuracy. We anticipate further gains when algorithmic parameters are optimized
for the targeted application. We find that optimizing for classification only
may lead to performance losses in regression, and vice versa. Lowering the
computational cost of producing grids will enable future versions of POSYDON to
cover more input parameters while preserving interpolation accuracies.


title: DBSP: Automatic Incremental View Maintenance for Rich Query Languages

abstract:
  Incremental view maintenance has been for a long time a central problem in
database theory. Many solutions have been proposed for restricted classes of
database languages, such as the relational algebra, or Datalog. These
techniques do not naturally generalize to richer languages. In this paper we
give a general solution to this problem in 3 steps: (1) we describe a simple
but expressive language called DBSP for describing computations over data
streams; (2) we give a general algorithm for solving the incremental view
maintenance problem for arbitrary DBSP programs, and (3) we show how to model
many rich database query languages (including the full relational queries,
grouping and aggregation, monotonic and non-monotonic recursion, and streaming
aggregation) using DBSP. As a consequence, we obtain efficient incremental view
maintenance techniques for all these rich languages.


title: Streaming Speaker-Attributed ASR with Token-Level Speaker Embeddings

abstract:
  This paper presents a streaming speaker-attributed automatic speech
recognition (SA-ASR) model that can recognize "who spoke what" with low latency
even when multiple people are speaking simultaneously. Our model is based on
token-level serialized output training (t-SOT) which was recently proposed to
transcribe multi-talker speech in a streaming fashion. To further recognize
speaker identities, we propose an encoder-decoder based speaker embedding
extractor that can estimate a speaker representation for each recognized token
not only from non-overlapping speech but also from overlapping speech. The
proposed speaker embedding, named t-vector, is extracted synchronously with the
t-SOT ASR model, enabling joint execution of speaker identification (SID) or
speaker diarization (SD) with the multi-talker transcription with low latency.
We evaluate the proposed model for a joint task of ASR and SID/SD by using
LibriSpeechMix and LibriCSS corpora. The proposed model achieves substantially
better accuracy than a prior streaming model and shows comparable or sometimes
even superior results to the state-of-the-art offline SA-ASR model.


title:
Decentralized convex optimization under affine constraints for power
  systems control

abstract:
  Modern power systems are now in continuous process of massive changes.
Increased penetration of distributed generation, usage of energy storage and
controllable demand require introduction of a new control paradigm that does
not rely on massive information exchange required by centralized approaches.
Distributed algorithms can rely only on limited information from neighbours to
obtain an optimal solution for various optimization problems, such as optimal
power flow, unit commitment etc.
  As a generalization of these problems we consider the problem of
decentralized minimization of the smooth and convex partially separable
function $f = \sum_{k=1}^l f^k(x^k,\tilde x)$ under the coupled $\sum_{k=1}^l
(A^k x^k - b^k) \leq 0$ and the shared $\tilde{A} \tilde{x} - \tilde{b} \leq 0$
affine constraints, where the information about $A^k$ and $b^k$ is only
available for the $k$-th node of the computational network.
  One way to handle the coupled constraints in a distributed manner is to
rewrite them in a distributed-friendly form using the Laplace matrix of the
communication graph and auxiliary variables (Khamisov, CDC, 2017). Instead of
using this method we reformulate the constrained optimization problem as a
saddle point problem (SPP) and utilize the consensus constraint technique to
make it distributed-friendly. Then we provide a complexity analysis for
state-of-the-art SPP solving algorithms applied to this SPP.


title:
Quasi-orthogonality and intrinsic dimensions as measures of learning and
  generalisation

abstract:
  Finding best architectures of learning machines, such as deep neural
networks, is a well-known technical and theoretical challenge. Recent work by
Mellor et al (2021) showed that there may exist correlations between the
accuracies of trained networks and the values of some easily computable
measures defined on randomly initialised networks which may enable to search
tens of thousands of neural architectures without training. Mellor et al used
the Hamming distance evaluated over all ReLU neurons as such a measure.
Motivated by these findings, in our work, we ask the question of the existence
of other and perhaps more principled measures which could be used as
determinants of success of a given neural architecture. In particular, we
examine, if the dimensionality and quasi-orthogonality of neural networks'
feature space could be correlated with the network's performance after
training. We showed, using the setup as in Mellor et al, that dimensionality
and quasi-orthogonality may jointly serve as network's performance
discriminants. In addition to offering new opportunities to accelerate neural
architecture search, our findings suggest important relationships between the
networks' final performance and properties of their randomly initialised
feature spaces: data dimension and quasi-orthogonality.


title:
Eigenvector-Assisted Statistical Inference for Signal-Plus-Noise Matrix
  Models

abstract:
  In this paper, we develop a generalized Bayesian inference framework for a
collection of signal-plus-noise matrix models arising in high-dimensional
statistics and many applications. The framework is built upon an asymptotically
unbiased estimating equation with the assistance of the leading eigenvectors of
the data matrix. The solution to the estimating equation coincides with the
maximizer of an appropriate statistical criterion function. The generalized
posterior distribution is constructed by replacing the usual log-likelihood
function in the Bayes formula with the criterion function. The proposed
framework does not require the complete specification of the sampling
distribution and is convenient for uncertainty quantification via a Markov
Chain Monte Carlo sampler, circumventing the inconvenience of resampling the
data matrix. Under mild regularity conditions, we establish the large sample
properties of the estimating equation estimator and the generalized posterior
distributions. In particular, the generalized posterior credible sets have the
correct frequentist nominal coverage probability provided that the so-called
generalized information equality holds. The validity and usefulness of the
proposed framework are demonstrated through the analysis of synthetic datasets
and the real-world ENZYMES network datasets.


title: Undecidable problems in quantum field theory

abstract:
  In the hope of providing some intellectual entertainment on April Fools' day
of this dark year, we point out that some questions in quantum field theory are
undecidable in a precise mathematical sense. More concretely, it will be
demonstrated that there is no algorithm answering whether a given 2d
supersymmetric Lagrangian theory breaks supersymmetry or not. It will also be
shown that there is a specific 2d supersymmetric Lagrangian theory which breaks
supersymmetry if and only if the standard Zermelo-Fraenkel set theory with the
axiom of choice is consistent, which can never be proved or disproved as the
consequence of G\"odel's second incompleteness theorem. The article includes a
brief and informal introduction to the phenomenon of undecidability and its
previous appearances in theoretical physics.


title:
GTP-SLAM: Game-Theoretic Priors for Simultaneous Localization and
  Mapping in Multi-Agent Scenarios

abstract:
  Robots operating in complex, multi-player settings must simultaneously model
the environment and the behavior of human or robotic agents who share that
environment. Environmental modeling is often approached using Simultaneous
Localization and Mapping (SLAM) techniques; however, SLAM algorithms usually
neglect multi-player interactions. In contrast, a recent branch of the motion
planning literature uses dynamic game theory to explicitly model noncooperative
interactions of multiple agents in a known environment with perfect
localization. In this work, we fuse ideas from these disparate communities to
solve SLAM problems with game theoretic priors. We present GTP-SLAM, a novel,
iterative best response-based SLAM algorithm that accurately performs state
localization and map reconstruction in an uncharted scene, while capturing the
inherent game-theoretic interactions among multiple agents in that scene. By
formulating the underlying SLAM problem as a potential game, we inherit a
strong convergence guarantee. Empirical results indicate that, when deployed in
a realistic traffic simulation, our approach performs localization and mapping
more accurately than a standard bundle adjustment algorithm across a wide range
of noise levels.


title: MAE-AST: Masked Autoencoding Audio Spectrogram Transformer

abstract:
  In this paper, we propose a simple yet powerful improvement over the recent
Self-Supervised Audio Spectrogram Transformer (SSAST) model for speech and
audio classification. Specifically, we leverage the insight that the SSAST uses
a very high masking ratio (75%) during pretraining, meaning that the vast
majority of self-attention compute is performed on mask tokens. We address this
by integrating the encoder-decoder architecture from Masked Autoencoders are
Scalable Vision Learners (MAE) into the SSAST, where a deep encoder operates on
only unmasked input, and a shallow decoder operates on encoder outputs and mask
tokens. We find that MAE-like pretraining can provide a 3x speedup and 2x
memory usage reduction over the vanilla SSAST using current audio pretraining
strategies with ordinary model and input sizes. When fine-tuning on downstream
tasks, which only uses the encoder, we find that our approach outperforms the
SSAST on a variety of downstream tasks. We further conduct comprehensive
evaluations into different strategies of pretraining and explore differences in
MAE-style pretraining between the visual and audio domains.


title:
Machine learning based data-driven discovery of nonlinear phase-field
  dynamics

abstract:
  One of the main questions regarding complex systems at large scales concerns
the effective interactions and driving forces that emerge from the detailed
microscopic properties. Coarse-grained models aim to describe complex systems
in terms of coarse-scale equations with a reduced number of degrees of freedom.
Recent developments in machine learning (ML) algorithms have significantly
empowered the discovery process of the governing equations directly from data.
However, it remains difficult to discover partial differential equations (PDEs)
with high-order derivatives. In this paper, we present new data-driven
architectures based on multi-layer perceptron (MLP), convolutional neural
network (CNN), and a combination of CNN and long short-term memory (CNN-LSTM)
structures for discovering the non-linear equations of motion for phase-field
models with non-conserved and conserved order parameters. The well-known
Allen--Cahn, Cahn--Hilliard, and the phase-field crystal (PFC) models were used
as the test cases. Two conceptually different types of implementations were
used: (a) guided by physical intuition (such as local dependence of the
derivatives) and (b) in the absence of any physical assumptions (black-box
model). We show that not only can we effectively learn the time derivatives of
the field in both scenarios, but we can also use the data-driven PDEs to
propagate the field in time and achieve results in good agreement with the
original PDEs.


title:
A characterization of finite simple set-theoretic solutions of the
  Yang-Baxter equation

abstract:
  In this paper we present a characterization of finite simple involutive
non-degenerate set-theoretic solutions of the Yang-Baxter equation by means of
left braces and we provide some significant examples.


title: Stacking-Mediated Diffusion of Ruthenium Nanoclusters in Graphite

abstract:
  The diffusion, penetration and intercalation of metallic atomic dopants is an
important question for various graphite applications in engineering and
nanotechnology. We have performed systematic first-principles calculations of
the behaviour of ruthenium nanoclusters on a graphene monolayer and
intercalated into a bilayer. Our computational results show that at a
sufficiently high density of single Ru atom interstitials, intercalated atoms
can shear the surrounding lattice to an AA stacking configuration, an effect
which weakens with increasing cluster size. Moreover, the interlayer stacking
configuration, in turn, has a significant effect on cluster diffusion. We
therefore find different trends in diffusivity as a function of cluster size
and interlayer stacking. For monolayer graphene and an AA graphene bilayer, the
formation of small clusters generally lowers diffusion barriers, while the
opposite behaviour is found for the preferred AB stacking configuration. These
results demonstrate that conditions of local impurity concentration and
interlayer disregistry are able to regulate the diffusivity of metallic
impurities in graphite.


title:
Atomistically-informed continuum modeling and isogeometric analysis of
  2D materials over holey substrates

abstract:
  This work develops, discretizes, and validates a continuum model of a
molybdenum disulfide (MoS$_2$) monolayer interacting with a periodic holey
silicon nitride substrate via van der Waals (vdW) forces. The MoS$_2$ layer is
modeled as a geometrically nonlinear Kirchhoff-Love shell, and vdW forces are
modeled by a Lennard-Jones potential, simplified using approximations for a
smooth substrate topography. The material parameters of the shell model are
calibrated by comparing small-strain tensile and bending tests with atomistic
simulations. This model is efficiently discretized using isogeometric analysis
(IGA) for the shell structure and a pseudo-time continuation method for energy
minimization. The IGA shell model is validated against fully-atomistic
calculations for several benchmark problems with different substrate
geometries. The continuum simulations reproduce deflections, strains and
curvatures predicted by atomistic simulations, which are known to strongly
affect the electronic properties of MoS$_2$, with deviations well below the
modeling errors suggested by differences between the widely-used reactive
empirical bond order and Stillinger-Weber interatomic potentials. Agreement
with atomistic results depends on geometric nonlinearity in some cases, but a
simple isotropic St. Venant-Kirchhoff model is found to be sufficient to
represent material behavior. We find that the IGA discretization of the
continuum model has a much lower computational cost than atomistic simulations,
and expect that it will enable efficient design space exploration in strain
engineering applications. This is demonstrated by studying the dependence of
strain and curvature in MoS$_2$ over a holey substrate as a function of the
hole spacing on scales inaccessible to atomistic calculations. The results show
an unexpected qualitative change in the deformation pattern below a critical
hole separation.


title: Kernel theorems for Beurling-Bj\"orck type spaces

abstract:
  We prove new kernel theorems for a general class of Beurling-Bj\"orck type
spaces. In particular, our results cover the classical Beurling-Bj\"orck spaces
$\mathcal{S}^{(\omega)}_{(\eta)}$ and $\mathcal{S}^{\{\omega\}}_{\{\eta\}}$
defined via weight functions $\omega$ and $\eta$.


title: Type-Directed Program Synthesis for RESTful APIs

abstract:
  With the rise of software-as-a-service and microservice architectures,
RESTful APIs are now ubiquitous in mobile and web applications. A service can
have tens or hundreds of API methods, making it a challenge for programmers to
find the right combination of methods to solve their task.
  We present APIphany, a component-based synthesizer for programs that compose
calls to RESTful APIs. The main innovation behind APIphany is the use of
precise semantic types, both to specify user intent and to direct the search.
APIphany contributes three novel mechanisms to overcome challenges in adapting
component-based synthesis to the REST domain: (1) a type inference algorithm
for augmenting REST specifications with semantic types; (2) an efficient
synthesis technique for "wrangling" semi-structured data, which is commonly
required in working with RESTful APIs; and (3) a new form of simulated
execution to avoid executing APIs calls during synthesis. We evaluate APIphany
on three real-world APIs and 32 tasks extracted from GitHub repositories and
StackOverflow. In our experiments, APIphany found correct solutions to 29
tasks, with 23 of them reported among top ten synthesis results.


title: Majorana fermions as quanta of a superfluid vacuum

abstract:
  This short article on a thermodynamic signature of Majorana fermions confined
on the surface of a 3D topological superfluid in the presence of a moving
condensate was circulated to a limited audience in Fall 2013. Since recent
experiments are looking at possible signatures of a gas of Majorana excitations
confined in two dimensions on the surface of superfluid $^3$He-B it seems
timely to post this earlier perspective to arXiv.


title:
Excitation and Measurement Patterns for the Identifiability of Directed
  Acyclic Graphs

abstract:
  This paper deals with the design of Excitation and Measurement Patterns (EMP)
for the identification of a class of dynamical networks whose topology has the
structure of a Directed Acyclic Graph (DAG). In addition to the by now well
known condition that the identifiabiltiy of any dynamical network requires that
the sources be excited, the sinks be measured, and all other nodes be either
excited or measured, we show that for DAGs two other types of nodes have
special excitation and measurement requirements. Armed with this result, we
propose a systematic procedure for the design of EMPs that guarantee
identifiability of a network with DAG topology.


title:
Can second-order perturbation theory accurately predict electron density
  of open-shell molecules? The importance of self-consistency

abstract:
  Electron density distribution plays an essential role in predicting molecular
properties. It is also a simple observable from which machine-learning models
for molecular electronic structure can be derived. In the present work, we
present the performance of the one-body M{\o}ller-Plesset second-order
perturbation (OBMP2) theory that we have recently developed. In OBMP2, an
effective one-body Hamiltonian including dynamic correlation at the MP2 level
is derived using the canonical transformation followed by the cumulant
approximation. We evaluate electron density and related properties of three
groups of open-shell systems: atoms and their ions, main-group radicals, and
halogen dimmers. We find that OBMP2 outperforms standard MP2 and density
functional theory in all cases considered here, and its accuracy is comparable
to coupled-cluster singles and doubles (CCSD), a higher-level method. OBMP2 is
thus believed to be an effective method for predicting the accurate electron
density of open-shell molecules.


title:
Towards Differential Relational Privacy and its use in Question
  Answering

abstract:
  Memorization of the relation between entities in a dataset can lead to
privacy issues when using a trained model for question answering. We introduce
Relational Memorization (RM) to understand, quantify and control this
phenomenon. While bounding general memorization can have detrimental effects on
the performance of a trained model, bounding RM does not prevent effective
learning. The difference is most pronounced when the data distribution is
long-tailed, with many queries having only few training examples: Impeding
general memorization prevents effective learning, while impeding only
relational memorization still allows learning general properties of the
underlying concepts. We formalize the notion of Relational Privacy (RP) and,
inspired by Differential Privacy (DP), we provide a possible definition of
Differential Relational Privacy (DrP). These notions can be used to describe
and compute bounds on the amount of RM in a trained model. We illustrate
Relational Privacy concepts in experiments with large-scale models for Question
Answering.


title: Weighted homomorphisms between C*-algebras

abstract:
  We show that a bounded, linear map between C*-algebras is a weighted
$\ast$-homomorphism (the central compression of a $\ast$-homomorphism) if and
only if it preserves zero-products, range-orthogonality, and
domain-orthogonality. It follows that a self-adjoint, bounded, linear map is a
weighted $\ast$-homomorphism if and only if it preserves zero-products.
  As an application we show that a linear map between C*-algebras is completely
positive, order zero in the sense of Winter-Zacharias if and only if it is
positive and preserves zero-products.


title: Probing highly collimated photon-jets with deep learning

abstract:
  Many extensions of the standard model (SM) predict the existence of
axion-like particles and/or dark Higgs in the sub-GeV scale. Two new sub-GeV
particles, a scalar and a pseudoscalar, produced through the Higgs boson exotic
decays, are investigated. The decay signatures of these two new particles with
highly collimated photons in the final states are discriminated from the ones
of SM backgrounds using the Convolutional Neural Networks and Boosted Decision
Trees techniques. The sensitivities of searching for such new physics
signatures at the Large Hadron Collider are obtained.


title:
Complexity factor of spherically anisotropic polytropes from
  gravitational decoupling

abstract:
  In this work we will analyse the complexity factor, proposed by L. Herrera,
of spherically symmetric static matter distributions satisfying a polytropic
equation through the gravitational decoupling method. Specifically, we will use
the 2-steps GD, which is a particular case of the Extended Geometric
Deformation (EGD), to obtain analytic polytropic solutions of Einstein's
equations. In order to give an example, we construct a model satisfying a
polytropic equation of state using Tolman IV as seed solution.


title:
Robust Disentangled Variational Speech Representation Learning for
  Zero-shot Voice Conversion

abstract:
  Traditional studies on voice conversion (VC) have made progress with parallel
training data and known speakers. Good voice conversion quality is obtained by
exploring better alignment modules or expressive mapping functions. In this
study, we investigate zero-shot VC from a novel perspective of self-supervised
disentangled speech representation learning. Specifically, we achieve the
disentanglement by balancing the information flow between global speaker
representation and time-varying content representation in a sequential
variational autoencoder (VAE). A zero-shot voice conversion is performed by
feeding an arbitrary speaker embedding and content embeddings to the VAE
decoder. Besides that, an on-the-fly data augmentation training strategy is
applied to make the learned representation noise invariant. On TIMIT and VCTK
datasets, we achieve state-of-the-art performance on both objective evaluation,
i.e., speaker verification (SV) on speaker embedding and content embedding, and
subjective evaluation, i.e., voice naturalness and similarity, and remains to
be robust even with noisy source/target utterances.


title:
Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the
  NextG Standard

abstract:
  Incorporating artificial intelligence and machine learning (AI/ML) methods
within the 5G wireless standard promises autonomous network behavior and
ultra-low-latency reconfiguration. However, the effort so far has purely
focused on learning from radio frequency (RF) signals. Future standards and
next-generation (nextG) networks beyond 5G will have two significant evolutions
over the state-of-the-art 5G implementations: (i) massive number of antenna
elements, scaling up to hundreds-to-thousands in number, and (ii) inclusion of
AI/ML in the critical path of the network reconfiguration process that can
access sensor feeds from a variety of RF and non-RF sources. While the former
allows unprecedented flexibility in 'beamforming', where signals combine
constructively at a target receiver, the latter enables the network with
enhanced situation awareness not captured by a single and isolated data
modality. This survey presents a thorough analysis of the different approaches
used for beamforming today, focusing on mmWave bands, and then proceeds to make
a compelling case for considering non-RF sensor data from multiple modalities,
such as LiDAR, Radar, GPS for increasing beamforming directional accuracy and
reducing processing time. This so called idea of multimodal beamforming will
require deep learning based fusion techniques, which will serve to augment the
current RF-only and classical signal processing methods that do not scale well
for massive antenna arrays. The survey describes relevant deep learning
architectures for multimodal beamforming, identifies computational challenges
and the role of edge computing in this process, dataset generation tools, and
finally, lists open challenges that the community should tackle to realize this
transformative vision of the future of beamforming.


title:
Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum
  Circuits

abstract:
  Variational quantum algorithms stand at the forefront of simulations on
near-term and future fault-tolerant quantum devices. While most variational
quantum algorithms involve only continuous optimization variables, the
representational power of the variational ansatz can sometimes be significantly
enhanced by adding certain discrete optimization variables, as is exemplified
by the generalized quantum approximate optimization algorithm (QAOA). However,
the hybrid discrete-continuous optimization problem in the generalized QAOA
poses a challenge to the optimization. We propose a new algorithm called
MCTS-QAOA, which combines a Monte Carlo tree search method with an improved
natural policy gradient solver to optimize the discrete and continuous
variables in the quantum circuit, respectively. We find that MCTS-QAOA has
excellent noise-resilience properties and outperforms prior algorithms in
challenging instances of the generalized QAOA.

categories: quant-ph cond-mat.quant-gas cs.LG physics.comp-ph


title: Task Adaptive Parameter Sharing for Multi-Task Learning

abstract:
  Adapting pre-trained models with broad capabilities has become standard
practice for learning a wide range of downstream tasks. The typical approach of
fine-tuning different models for each task is performant, but incurs a
substantial memory cost. To efficiently learn multiple downstream tasks we
introduce Task Adaptive Parameter Sharing (TAPS), a general method for tuning a
base model to a new task by adaptively modifying a small, task-specific subset
of layers. This enables multi-task learning while minimizing resources used and
competition between tasks. TAPS solves a joint optimization problem which
determines which layers to share with the base model and the value of the
task-specific weights. Further, a sparsity penalty on the number of active
layers encourages weight sharing with the base model. Compared to other
methods, TAPS retains high accuracy on downstream tasks while introducing few
task-specific parameters. Moreover, TAPS is agnostic to the model architecture
and requires only minor changes to the training scheme. We evaluate our method
on a suite of fine-tuning tasks and architectures (ResNet, DenseNet, ViT) and
show that it achieves state-of-the-art performance while being simple to
implement.


title: Class Numbers and Pell's Equation $x^2 + 105y^2 = z^2$

abstract:
  Two well-studied Diophantine equations are those of Pythagorean triples and
elliptic curves, for the first we have a parametrization through rational
points on the unit circle, and for the second we have a structure theorem for
the group of rational solutions. Recently, Yekutieli discussed a connection
between these two problems, and described the group structure of Pythagorean
triples and the number of triples for a given hypotenuse. In arXiv:2112.03663
we generalized these methods and results to Pell's equation. We find a similar
group structure and count on the number of solutions for a given $z$ to $x^2 +
Dy^2 = z^2$ when $D$ is 1 or 2 modulo 4 and the class group of
$\mathbb{Q}[\sqrt{-D}]$ is a free $\mathbb{Z}_2$ module, which always happens
if the class number is at most 2. In this paper, we discuss the main results of
arXiv:2112.03663 using some concrete examples in the case of $D=105$.


title:
Detecting Treatment Interference under the K-Nearest-Neighbors
  Interference Model

abstract:
  We propose a model of treatment interference where the response of a unit
depends only on its treatment status and the statuses of units within its
K-neighborhood. Current methods for detecting interference include carefully
designed randomized experiments and conditional randomization tests on a set of
focal units. We give guidance on how to choose focal units under this model of
interference. We then conduct a simulation study to evaluate the efficacy of
existing methods for detecting network interference. We show that this choice
of focal units leads to powerful tests of treatment interference which
outperform current experimental methods.


title: An analytic theory for the dynamics of wide quantum neural networks

abstract:
  Parametrized quantum circuits can be used as quantum neural networks and have
the potential to outperform their classical counterparts when trained for
addressing learning problems. To date, much of the results on their performance
on practical problems are heuristic in nature. In particular, the convergence
rate for the training of quantum neural networks is not fully understood. Here,
we analyze the dynamics of gradient descent for the training error of a class
of variational quantum machine learning models. We define wide quantum neural
networks as parameterized quantum circuits in the limit of a large number of
qubits and variational parameters. We then find a simple analytic formula that
captures the average behavior of their loss function and discuss the
consequences of our findings. For example, for random quantum circuits, we
predict and characterize an exponential decay of the residual training error as
a function of the parameters of the system. We finally validate our analytic
results with numerical experiments.


title: An algebraic approach to Borel CSPs

abstract:
  We adapt tools from the algebraic approach to constraint satisfaction
problems to answer descriptive set theoretic questions about Borel CSPs. We
show that if a structure $\mathcal D$ does not have a Taylor polymorphism, then
the corresponding Borel CSP is $\mathbf{\Sigma}^1_2$-complete. In particular,
by the CSP Dichotomy Theorem, if $\operatorname{CSP}(\mathcal D)$ is
$\mathrm{NP}$-complete, then the Borel version, $\operatorname{csp}_B(\mathcal
D)$, is $\mathbf{\Sigma}^1_2$-complete (assuming $\mathrm{P}\not=\mathrm{NP}$).
We also have partial converses, such as a descriptive analogue of the
Hell--Ne\v set\v ril theorem characterizing $\mathbf{\Sigma}^1_2$-complete
graph homomorphism problems. We show that the structures where every solvable
Borel instance of their CSP has a Borel solution are exactly the width 1
structures. And, we prove a handful of results bounding the projective
complexity of certain bounded width structures.


title: Wordle is NP-hard

abstract:
  Wordle is a single player word-guessing game where the goal is to discover a
secret word $w$ that has been chosen from a dictionary $D$. In order to
discover $w$, the player can make at most $\ell$ guesses, which must also be
words from $D$, all words in $D$ having the same length $k$. After each guess,
the player is notified of the positions in which their guess matches the secret
word, as well as letters in the guess that appear in the secret word in a
different position. We study the game of Wordle from a complexity perspective,
proving NP-hardness of its natural formalization: to decide given a dictionary
$D$ and an integer $\ell$ if the player can guarantee to discover the secret
word within $\ell$ guesses. Moreover, we prove that hardness holds even over
instances where words have length $k = 5$, and that even in this case it is
NP-hard to approximate the minimum number of guesses required to guarantee
discovering the secret word (beyond a certain constant). We also present
results regarding its parameterized complexity and offer some related open
problems.


title: End-to-End Table Question Answering via Retrieval-Augmented Generation

abstract:
  Most existing end-to-end Table Question Answering (Table QA) models consist
of a two-stage framework with a retriever to select relevant table candidates
from a corpus and a reader to locate the correct answers from table candidates.
Even though the accuracy of the reader models is significantly improved with
the recent transformer-based approaches, the overall performance of such
frameworks still suffers from the poor accuracy of using traditional
information retrieval techniques as retrievers. To alleviate this problem, we
introduce T-RAG, an end-to-end Table QA model, where a non-parametric dense
vector index is fine-tuned jointly with BART, a parametric sequence-to-sequence
model to generate answer tokens. Given any natural language question, T-RAG
utilizes a unified pipeline to automatically search through a table corpus to
directly locate the correct answer from the table cells. We apply T-RAG to
recent open-domain Table QA benchmarks and demonstrate that the fine-tuned
T-RAG model is able to achieve state-of-the-art performance in both the
end-to-end Table QA and the table retrieval tasks.


title:
Cyberattack Detection for Nonlinear Leader-Following Multi-Agent Systems
  Using Set-Membership Fuzzy Filtering

abstract:
  This paper is concerned with cyberattack detection in discrete-time,
leader-following, nonlinear, multi-agent systems subject to unknown but bounded
(UBB) system noises. The Takagi-Sugeno (T-S) fuzzy model is employed to
approximate the nonlinear systems over the true value of the state. A
distributed cyberattack detection method, based on a new fuzzy set-membership
filtering method, which consists of two steps, namely a prediction step and a
measurement update step, is developed for each agent to identify two types of
cyberattacks at the time of their occurrence. The attacks are replay attacks
and false data injection attacks affecting the leader-following consensus. We
calculate an estimation ellipsoid set by updating the prediction ellipsoid set
with the current sensor measurement data. Two criteria are provided to detect
cyberattacks based on the intersection between the ellipsoid sets. If there is
no intersection between the prediction set and the estimation set of an agent
at the current time instant, a cyberattack on its sensors is declared. Control
signal or communication signal data of an agent are under a cyberattack if its
prediction set has no intersection with the estimation set updated at the
previous time instant. Recursive algorithms for solving the consensus protocol
and calculating the two ellipsoid sets for detecting attacks are proposed.
Simulation results are provided to demonstrate the effectiveness of the
proposed method.


title: Radio Scattering Horizons for Galactic and Extragalactic Transients

abstract:
  Radio wave scattering can cause severe reductions in detection sensitivity
for surveys of Galactic and extragalactic fast ($\sim$ms duration) transients.
While Galactic sources like pulsars are subject to scattering in the Milky Way
interstellar medium (ISM), extragalactic fast radio bursts (FRBs) can also
experience scattering in their host galaxies and other galaxies intervening
their lines-of-sight. We assess Galactic and extragalactic scattering horizons
for fast radio transients using a combination of NE2001 to model the dispersion
measure (DM) and scattering time ($\tau$) contributed by the Milky Way, and
independently constructed electron density models for other galaxies' ISMs and
halos that account for different galaxy morphologies, masses, densities, and
strengths of turbulence. For FRB source redshifts $z_{\rm s} \lesssim 1$, an
all-sky, isotropic FRB population has values of $\tau$ ranging between $\sim 1\
\mu$s and $\sim 2$ ms at 1 GHz (observer frame) that are dominated by host
galaxies. For a hypothetical, high-redshift ($z_{\rm s}\sim5$) FRB population,
$\tau$ ranges from $\sim 0.01 - 100$s of ms at 1 GHz, and is largely dominated
by intervening galaxies. About $20\%$ of these high-redshift FRBs are predicted
to have $\tau > 5$ ms at 1 GHz (observer frame), and $\gtrsim 40\%$ of FRBs
between $z_{\rm s} \sim 0.5 - 5$ are predicted to have $\tau \gtrsim 1$ ms for
$\nu\leq 800$ MHz. The percentage of FRBs selected against from scattering may
be substantially larger because our scattering predictions are conservative
compared to localized FRBs, and if circumgalactic turbulence causes density
fluctuations larger than those observed from nearby halos.


title:
A comparison of strategies for selecting auxiliary variables for
  multiple imputation

abstract:
  Multiple imputation (MI) is a popular method for handling missing data.
Auxiliary variables can be added to the imputation model(s) to improve MI
estimates. However, the choice of which auxiliary variables to include in the
imputation model is not always straightforward. Including too few may lead to
important information being discarded, but including too many can cause
problems with convergence of the estimation procedures for imputation models.
Several data-driven auxiliary variable selection strategies have been proposed.
This paper uses a simulation study and a case study to provide a comprehensive
comparison of the performance of eight auxiliary variable selection strategies,
with the aim of providing practical advice to users of MI. A complete case
analysis and an MI analysis with all auxiliary variables included in the
imputation model (the full model) were also performed for comparison. Our
simulation study results suggest that the full model outperforms all auxiliary
variable selection strategies, providing further support for adopting an
inclusive auxiliary variable strategy where possible. Auxiliary variable
selection using the Least Absolute Selection and Shrinkage Operator (LASSO) was
the best performing auxiliary variable selection strategy overall and is a
promising alternative when the full model fails. All MI analysis strategies
that we were able to apply to the case study led to similar estimates.


title: A Large-Scale Comparison of Python Code in Jupyter Notebooks and Scripts

abstract:
  In recent years, Jupyter notebooks have grown in popularity in several
domains of software engineering, such as data science, machine learning, and
computer science education. Their popularity has to do with their rich features
for presenting and visualizing data, however, recent studies show that
notebooks also share a lot of drawbacks: high number of code clones, low
reproducibility, etc.
  In this work, we carry out a comparison between Python code written in
Jupyter Notebooks and in traditional Python scripts. We compare the code from
two perspectives: structural and stylistic. In the first part of the analysis,
we report the difference in the number of lines, the usage of functions, as
well as various complexity metrics. In the second part, we show the difference
in the number of stylistic issues and provide an extensive overview of the 15
most frequent stylistic issues in the studied mediums. Overall, we demonstrate
that notebooks are characterized by the lower code complexity, however, their
code could be perceived as more entangled than in the scripts. As for the
style, notebooks tend to have 1.4 times more stylistic issues, but at the same
time, some of them are caused by specific coding practices in notebooks and
should be considered as false positives. With this research, we want to pave
the way to studying specific problems of notebooks that should be addressed by
the development of notebook-specific tools, and provide various insights that
can be useful in this regard.


title: Ubiquity of the quantum boomerang effect in localized systems

abstract:
  A particle with finite initial velocity in a disordered potential comes back
and in average stops at the original location. This phenomenon dubbed quantum
boomerang effect (QBE) has been recently observed in an experiment simulating
the quantum kicked-rotor model [Phys. Rev. X 12, 011035 (2022)]. We provide
analytical arguments that support QBE in a wide class of disordered systems.
Sufficient conditions to observe the real-space QBE effect are (a) Anderson
localization, (b) the reality of the spectrum for the case of non-Hermitian
systems, (c) the ensemble of disorder realizations $\{H\}$ be invariant under
the application of $\mathcal{R\, T}$, and (d) the initial state is an
eigenvector of $\mathcal{R\, T}$, where $\mathcal{R}$ is a reflection $x
\rightarrow -x$ and $\mathcal{T}$ is the time-reversal operator. The QBE can be
observed in momentum-space in systems with dynamical localization if conditions
(c) and (d) are satisfied with respect to the operator $\mathcal{T}$ instead of
$\mathcal{RT}$. These conditions allow the observation of the QBE in
time-reversal symmetry broken models, contrarily to what was expected from
previous analyses of the effect, and to a large class of non-Hermitian models.
We provide examples of QBE in ladder models with magnetic flux breaking
time-reversal symmetry and in a non-Hermitian random-hopping model. Whereas the
QBE straightforwardly applies to noninteracting many-body systems, we argue
that breaking of RT (T) symmetry is responsible for the absence of real-
(momentum-)space QBE in weakly interacting bosonic systems.


title: Quantum Control of Spin Qubits Using Nanomagnets

abstract:
  Single-qubit gates are essential components of a universal quantum computer.
Without selective addressing of individual qubits, scalable implementation of
quantum algorithms is not possible. When the qubits are discrete points or
regions on a lattice, the selective addressing of magnetic spin qubits at the
nanoscale remains a challenge due to the difficulty of localizing and confining
a classical divergence-free field to a small volume of space. Herein we propose
a new technique for addressing spin qubits using voltage-control of nanoscale
magnetism, exemplified by the use of voltage control of magnetic anisotropy
(VCMA). We show that by tuning the frequency of the nanomagnet's electric field
drive to the Larmor frequency of the spins confined to a nanoscale volume, and
by modulating the phase of the drive, single-qubit quantum gates with
fidelities approaching those for fault-tolerant quantum computing can be
implemented. Such single-qubit gate operations have the advantage of remarkable
energy efficiency, requiring only tens of femto-Joules per gate operation, and
lossless, purely magnetic field control (no E-field over the target volume).
Their physical realization is also straightforward using existing foundry
manufacturing techniques.


title:
General lossless polarization and phase transformation using bilayer
  metasurfaces

abstract:
  Metasurface optical elements enable wavefront control and polarization
manipulation with subwavelength resolution. Metasurfaces made of linearly
birefringent meta-atoms such as rectangular nano-posts are commonly used to
control phase and polarization, but a single layer of these meta-atoms cannot
implement transformations with elliptically polarized or circularly polarized
eigenstates (i.e., chiral transformations). Here, it is shown that two cascaded
metasurface layers comprising linearly birefringent meta-atoms provide
sufficiently many degrees of freedom to implement arbitrary unitary phase and
polarization transformations. A systematic design method for such
metastructures is described and used to design a bifocal metalens that cannot
be implemented by a single layer of birefringent meta-atoms. The presented
design and implementation techniques introduce a systematic approach for
realizing the most general form of lossless polarization and phase
transformations using metasurfaces with high efficiency.


title: Optimizing Observables with Machine Learning for Better Unfolding

abstract:
  Most measurements in particle and nuclear physics use matrix-based unfolding
algorithms to correct for detector effects. In nearly all cases, the observable
is defined analogously at the particle and detector level. We point out that
while the particle-level observable needs to be physically motivated to link
with theory, the detector-level need not be and can be optimized. We show that
using deep learning to define detector-level observables has the capability to
improve the measurement when combined with standard unfolding methods.


title: Exploiting Explainable Metrics for Augmented SGD

abstract:
  Explaining the generalization characteristics of deep learning is an emerging
topic in advanced machine learning. There are several unanswered questions
about how learning under stochastic optimization really works and why certain
strategies are better than others. In this paper, we address the following
question: \textit{can we probe intermediate layers of a deep neural network to
identify and quantify the learning quality of each layer?} With this question
in mind, we propose new explainability metrics that measure the redundant
information in a network's layers using a low-rank factorization framework and
quantify a complexity measure that is highly correlated with the generalization
performance of a given optimizer, network, and dataset. We subsequently exploit
these metrics to augment the Stochastic Gradient Descent (SGD) optimizer by
adaptively adjusting the learning rate in each layer to improve in
generalization performance. Our augmented SGD -- dubbed RMSGD -- introduces
minimal computational overhead compared to SOTA methods and outperforms them by
exhibiting strong generalization characteristics across application,
architecture, and dataset.


title: Effect of quantum coherence on Landauer's principle

abstract:
  Quantum Landauer's principle provides a fundamental lower bound for energy
dissipation occurred with information erasure in the quantum regime. While most
studies have related the entropy reduction incorporated with the erasure to the
lower bound~(entropic bound), recent efforts have also provided another lower
bound associated with the thermal fluctuation of the dissipated
energy~(thermodynamic bound). The coexistence of the two bounds has stimulated
comparative studies of their properties; however, these studies were performed
for systems where the time-evolution of diagonal (population) and off-diagonal
(coherence) elements of the density matrix are decoupled. In this paper, we
aimed to broaden the comparative study to include the influence of quantum
coherence induced by the tilted system--reservoir interaction direction. By
examining their dependence on the initial state of the information-bearing
system, we find that the following properties of the bounds are generically
held regardless of whether the influence of the coherence is present or not:
the entropic bound serves as the tighter bound for a sufficiently mixed initial
state, while the thermodynamic bound is tighter when the purity of the initial
state is sufficiently high. The exception is the case where the system dynamics
involve only phase relaxation; in this case, the two bounds coincide when the
initial coherence is zero; otherwise, the thermodynamic bound serves the
tighter bound. We also find the quantum information erasure inevitably
accompanies constant energy dissipation caused by the creation of
system--reservoir correlation, which may cause an additional source of
energetic cost for the erasure.


title: A class of singular bilinear maximal functions

abstract:
  Lebesgue space bounds $L^{p_1}({\mathbb R}^1) \times L^{p_2}(^1) \to
L^q({\mathbb R}^1)$ are established for certain maximal bilinear operators. The
proof combines a trilinear smoothing inequality with Calder\'on-Zygmund theory.
  A reference to overlapping work of other authors on one observation has been
added.


title:
Hybrid normal mode and energy flux model for an ideal oceanic wedge
  environment with radial sound speed front

abstract:
  Energy flux is an acoustic propagation model that calculates the
locally-averaged intensity without computing explicit eigenvalues or tracing
rays. The energy flux method has so far only been used for two-dimensional
problems that have collapsed the third dimension by rotational or translational
symmetry. This report outlines the derivation and implementation of a
three-dimensional ocean acoustic propagation model using a combination of
normal modes and the energy flux method. This model is specifically derived for
a wedge environment with a radial sound speed front at some distance from the
shoreline. The hybrid energy flux model's output is compared to that of another
propagation model for this environment that is built on normal modes alone.
General agreement in the shape, location, and amplitude of caustic features is
observed with some discrepancies that may be attributable to inherent
differences in the model derivations. This work serves as a stepping-stone
toward developing a more generalized three-dimensional energy flux model.


title: Mid-Infrared studies of dusty sources in the Galactic Center

abstract:
  Mid-Infrared (MIR) images of the Galactic center show extended gas and dust
features along with bright IRS sources. Some of these dust features are a part
of ionized clumpy streamers orbiting Sgr~A*, known as the mini-spiral. We
present their proper motions over 12 year time period and report their flux
densities in $N$-band filters {and derive their spectral indices}. The
observations were carried out by VISIR at ESO VLT. High-pass filtering led to
the detection of several resolved filaments and clumps along the mini-spiral.
Each source was fit by a 2-D Gaussian profile to determine the offsets and
aperture sizes. We perform aperture photometry to extract fluxes in two
different bands. We present the proper motions of the largest consistent set of
resolved and reliably determined sources. In addition to stellar orbital
motions, we identify a stream-like motion of extended clumps along the
mini-spiral. We also detect MIR counterparts of the radio tail components of
the IRS7 source. They show a clear kinematical deviation with respect to the
star. They likely represent Kelvin-Helmholtz instabilities formed downstream in
the shocked stellar wind. We also analyze the shape and the orientation of the
extended late-type IRS3 star that is consistent with the ALMA sub-mm detection
of the source. Its puffed-up envelope with the radius of $\sim 2\times
10^6\,R_{\odot}$ could be the result of the red-giant collision with a nuclear
jet, which was followed by the tidal prolongation along the orbit.


title: On the convergence of Denjoy-Wolff points

abstract:
  We show that the Denjoy-Wolff points of a sequence of analytic self-maps of
the disk, that converges pointwise, converge to the Denjoy-Wolff point of the
limit map. Applications that are potentially useful in free probability are
presented, but knowledge of this subject is not necessary for their statements
or proofs.


title: A Trace Formula on Stationary Kaluza-Klein Spacetimes

abstract:
  We prove relativistic versions of the ladder asymptotics from V. Guillemin
and A. Uribe [Journal of Differential Geometry, 32(2):315-347, 1990] on
principal bundles over globally hyperbolic, stationary, spatially compact
spacetimes equipped with a Kaluza-Klein metric. This involves understanding the
distribution of the frequency spectrum for the wave equation on a Kaluza-Klein
spacetime when restricted to the isotypic subspace of an irreducible
representation of the structure group, in the limit that the weight of the
representation approaches infinity in the Weyl chamber. This is a direct
generalization of the results from A. Strohmaier and S. Zelditch [Indagationes
Mathematicae 32 (2021), 323-363] and is closely related to Strohmaier-Zelditch
[Advances in Mathematics, Volume 376, 2021, 107434] and O. Islam
arXiv:2109.09219. Furthermore we show how to apply these results to frequency
asymptotics for the massive Klein-Gordon equation on vector bundles as one
takes the representation defining the vector bundle to infinity.