title:
Extragalactic magnetism with SOFIA (SALSA Legacy Program) -- III: First
  data release and on-the-fly polarization mapping characterization

abstract:
  We describe the data processing of the Survey on extragALactic magnetiSm with
SOFIA (SALSA Legacy Program). This first data release presents 33% (51.34h out
of 155.7h, including overheads) of the total awarded time taken from January
2020 to December 2021. Our observations were performed using the newly
implemented on-the-fly mapping (OTFMAP) technique in the polarimetric mode. We
present the pipeline steps to obtain homogeneously reduced high-level data
products of polarimetric maps of galaxies for use in scientific analysis. Our
approach has a general design and can be applied to sources smaller than the
field-of-view of the HAWC+ array in any given band. We estimate that the OTFMAP
polarimetric mode offers a reduction of observing overheads by a factor 2.34,
and an improvement in sensitivity by a factor 1.80 when compared to previously
obtained polarimetric observations using the chopping and nodding mode. The
OTFMAP is a significant optimization of the polarimetric mode of HAWC+ as it
ultimately reduces the cost of operations of SOFIA/HAWC+ by increasing the
science collected per hour of observation up to an overall factor of 2.49. The
OTFMAP polarimetric mode is the standard observing strategy of SALSA. The
results and quantitative analysis of this first data release are presented in
Papers IV and V of the series.


title: Surface critical properties of the three-dimensional clock model

abstract:
  Using Monte Carlo simulations and finite-size scaling analysis, we show that
the $q$-state clock model with $q=6$ on the simple cubic lattice with open
surfaces has a rich phase diagram; in particular, it has an extraordinary-log
phase, besides the ordinary and extraordinary transitions at the bulk critical
point. We prove numerically that the presence of the intermediate
extraordinary-log phase is due to the emergence of an O(2) symmetry in the
surface state before the surface enters the $Z_{q}$ symmetry-breaking region as
the surface coupling is increased at the bulk critical point, while O(2)
symmetry emerges for the bulk. The critical behaviors of the extraordinary-log
transition, as well as the ordinary and the special transition separating the
ordinary and the extraordinary-log transition are obtained.


title: Category-agnostic Segmentation for Robotic Grasping

abstract:
  In this work we introduce DoPose, a dataset of highly cluttered and closely
stacked objects for segmentation and 6D pose estimation. We show how using
careful choice of synthetic data and fine-tuning on our real dataset along with
a rational training can boost the performance of already existing CNN
architectures to generalize on real data and produce comparable results to SOTA
methods even without post-processing or refinements. Our DoPose dataset,
network models, pipeline code and ROS driver are available online.


title: Bernstein - von Mises theorem and misspecified models: a review

abstract:
  This is a review of asymptotic and non-asymptotic behaviour of Bayesian
methods under model specification. In particular we focus on consistency, i.e.
convergence of the posterior distribution to the point mass at the best
parametric approximation to the true model, and conditions for it to be locally
Gaussian around this point. For well specified regular models, variance of the
Gaussian approximation coincides with the Fisher information, making Bayesian
inference asymptotically efficient. In this review, we discuss how this is
affected by model misspecification. We also discuss approaches to adjust
Bayesian inference to make it asymptotically efficient under model
misspecification.


title:
Omnimodal topological polarization of bilayer networks: theoretical
  characterization in the Maxwell limit and experiments on a 3D-printed
  prototype

abstract:
  Two-dimensional, critically-coordinated mechanical networks can exhibit
topologically polarized modes that are exponentially localized to a particular
edge; however, the coupling of such in-plane deformation to out-of-plane
curvature remains largely unexplored. Here, we investigate a class of
mechanical bilayers as a model system for designing topologically protected
edge modes beyond the in-plane paradigm. In particular, we apply the
spring-mass modeling framework, familiar to topological mechanics, to bilayers
with mirror-symmetric layers adjoined via a particular choice of interlayer
connections, and we show that a unified topological index can be defined,
thereby characterizing the omnimodal topological polarization of both in-plane
and out-of-plane modes. Furthermore, we quantify the manner in which these
flexural modes are lifted to finite frequencies while remaining localized when
the harmonic springs are replaced by elastic beams. This analysis is supported
by experiments conducted on a prototypical lattice fabricated via additive
manufacturing where we confirm both the edge selectivity and the frequency
selectivity of the mechanical structure.


title:
Vacancy formation energies and migration barriers in multi-principal
  element alloys

abstract:
  Multi-principal element alloys (MPEAs) continue to garner interest as
structural and plasma-facing materials due to their structure stability and
increased resistance to radiation damage. Despite sensitivity of mechanical
behavior to irradiation and point-defect formation, there has been scant
attention on understanding vacancy stability and diffusion in refractory-based
MPEAs. Using density-functional theory, we examine vacancy stability and
diffusion barriers in body-centered cubic (Mo0.95W0.05)0.85Ta0.10(TiZr)0.05.
The results in this MPEA show strong dependence on environment, originating
from local lattice distortion associated with charge-transfer between
neighboring atoms that vary with different chemical environments. We find a
correlation between degree of lattice distortion and migration barrier: (Ti,
Zr) with less distortion have lower barriers, while (Mo, W) with larger
distortion have higher barriers, depending up local environments. Under
irradiation, our findings suggest that (Ti, Zr) are significantly more likely
to diffuse than (Mo, W) while Ta shows intermediate effect. As such, material
degradation caused by vacancy diffusion can be controlled by tuning composition
of alloying elements to enhance creep strength at extreme operating
temperatures and harsh conditions.


title:
Linearity Characterization and Uncertainty Quantification of
  Spectroradiometers via Maximum Likelihood and the Non-parametric Bootstrap

abstract:
  A rigorous uncertainty quantification for "flux-addition," (also known as the
"combinatoric technique") a well-known technique in the radiometry field, is
investigated. A probabilistic model for digital readings at given fluxes and
values of polynomial coefficients describing the nonlinear instrument response
is proposed. Maximum likelihood estimates (MLEs) of the unknown fluxes and
polynomial coefficients are recommended. A non-parametric bootstrap algorithm
is introduced to quantify uncertainty, e.g., to calculate standard errors. The
model and bootstrap algorithm are studied by simulation. These simulation are
performed using fluxes and polynomial coefficients consistent with those
observed during the characterization of the Orbiting Carbon Observatory-2, for
each of its focal planes. The MLEs are found to be approximately unbiased, and
confidence intervals derived from the bootstrap replicates are found to be
consistent with their target coverage of 95 %. For the polynomial coefficients
representing the linearization function, the relative bias was less than 1 %
and the observed coverages range from 90 % to 97 %. An experimental data set
from the NIST Beam Conjoiner, which implements flux addition with a broadband
light-source and a set of neutral density filters, is also considered. In this
example, a complete calibration is generated assuming that one flux level is
well characterized by the spectroradiometer. The uncertainty contribution
attributable to estimation of the instrument's nonlinear response is less than
0.02 % over most of its range.


title: Exocomets size distribution in the $\beta$ Pictoris planetary system

abstract:
  The star $\beta$ Pictoris harbors a young planetary system, which is
characterized by the presence of a gaseous and dusty debris disk, at least two
massive planets and many minor bodies. For more than thirty years, exocomets
transiting the star have been detected using spectroscopy, probing the gaseous
part of the cometary comas and tails.
  The detection of the dusty component of the tails can be performed through
photometric observations of the transits. Since 2018, the Transiting Exoplanet
Survey Satellite has observed $\beta$ Pic for a total of 156 days. Here we
report an analysis of the TESS photometric data set with the identification of
a total of 30 transits of exocomets. Our statistical analysis shows that the
number of transiting exocomet events ($N$) as a function of the absorption
depth ($AD$) in the light curve follows a power law in the form $dN(AD) \propto
AD^{-\alpha}$, where $\alpha=2.3\pm 0.4$. This distribution of absorption depth
leads to a differential comet size distribution proportional to $R^{-\gamma}$,
where $\gamma =3.6 \pm 0.8$, showing a striking similarity to the size
distribution of comets in the Solar system and the distribution of a
collisionally relaxed population ($\gamma_{\rm D}= 3.5$).


title: Personalized Federated Learning with Multiple Known Clusters

abstract:
  We consider the problem of personalized federated learning when there are
known cluster structures within users. An intuitive approach would be to
regularize the parameters so that users in the same cluster share similar model
weights. The distances between the clusters can then be regularized to reflect
the similarity between different clusters of users. We develop an algorithm
that allows each cluster to communicate independently and derive the
convergence results. We study a hierarchical linear model to theoretically
demonstrate that our approach outperforms agents learning independently and
agents learning a single shared weight. Finally, we demonstrate the advantages
of our approach using both simulated and real-world data.


title: Generative Adversarial Networks for Image Super-Resolution: A Survey

abstract:
  Single image super-resolution (SISR) has played an important role in the
field of image processing. Recent generative adversarial networks (GANs) can
achieve excellent results on low-resolution images with small samples. However,
there are little literatures summarizing different GANs in SISR. In this paper,
we conduct a comparative study of GANs from different perspectives. We first
take a look at developments of GANs. Second, we present popular architectures
for GANs in big and small samples for image applications. Then, we analyze
motivations, implementations and differences of GANs based optimization methods
and discriminative learning for image super-resolution in terms of supervised,
semi-supervised and unsupervised manners. Next, we compare performance of these
popular GANs on public datasets via quantitative and qualitative analysis in
SISR. Finally, we highlight challenges of GANs and potential research points
for SISR.


title:
FlameNEST: Explicit Profile Likelihoods with the Noble Element
  Simulation Technique

abstract:
  We present FlameNEST, a framework providing explicit likelihood evaluations
in noble element particle detectors using data-driven models from the Noble
Element Simulation Technique. FlameNEST provides a way to perform statistical
analyses on real data with no dependence on large, computationally expensive
Monte Carlo simulations by evaluating the likelihood on an event-by-event basis
using analytic probability elements convolved together in a single TensorFlow
multiplication. Furthermore, this robust framework creates opportunities for
simple inter-collaborative analyses which will be fundamental for the future of
experimental dark matter physics.


title:
Fast Cross-Correlation for TDoA Estimation on Small Aperture Microphone
  Arrays

abstract:
  This paper introduces the Fast Cross-Correlation (FCC) method for Time
Difference of Arrival (TDoA) Estimation for pairs of microphones on a small
aperture microphone array. FCC relies on low-rank decomposition and exploits
symmetry in even and odd bases to speed up computation while preserving TDoA
accuracy. FCC reduces the number of flops by a factor of 4.5 and the execution
speed by factors of 8.2, 2.6 and 2.7 on a Raspberry Pi Zero, a Raspberry Pi 4
and a Nvidia Jetson TX2 devices, respectively, compared to the state-of-the-art
Generalized Cross-Correlation (GCC) method that relies on the Fast Fourier
Transform (FFT). This improvement can provide portable microphone arrays with
extended battery life and allow real-time processing on low-cost hardware.


title:
Fractional Josephson effect versus fractional charge in
  superconducting-normal metal hybrid circuits

abstract:
  Fractionally charged excitations play a central role in condensed matter
physics, and can be probed in different ways. If transport occurs via
dissipation-less supercurrents, they manifest as a fractional Josephson effect,
whereas in dissipative transport they can be revealed by the transport
statistics. However, in a regime where supercurrents and lossy currents
coincide, a full understanding of the relationship between these two transport
phenomena is still missing. Here, we unify them by means of topological phase
transitions in the system-detector dynamics. We show that already conventional
superconductor-normal metal hybrid circuits exhibit a variety of topological
phases, including an open quantum system version of a fractional Josephson
effect. Surprisingly, quasiparticles, usually considered a detrimental side
effect, are here a necessary ingredient to observe nontrivial transport
behaviour.


title:
FFT-based Homogenization at Finite Strains using Composite Boxels
  (ComBo)

abstract:
  Computational homogenization is the gold standard for concurrent multi-scale
simulations (e.g., FE2) in scale-bridging applications. Experimental and
synthetic material microstructures are often represented by 3D image data. The
computational complexity of simulations operating on such three-dimensional
high-resolution voxel data comprising billions of unknowns induces the need for
algorithmically and numerically efficient solvers. The inability of voxelized
3D geometries to capture smooth material interfaces accurately, along with the
necessity for complexity reduction, motivates a special local coarse-graining
technique called composite voxels [Kabel,M. et al. (2015)]. Composite voxels
condense multiple fine-scale voxels into a single voxel obeying a
theory-inspired constitutive model by employing laminate theory. Composite
voxels enhance local field quality at a modest computational cost. Our
contribution comprises the generalization towards composite boxels (ComBo) that
are nonequiaxed, a feature that can pay off for materials with a preferred
direction. A novel image-based normal detection algorithm is devised which
improves the accuracy by around 30\% against the orientation cf. [Kabel,M. et
al. (2015) ]. Further, the use of ComBo for finite strain simulations is
studied in detail. An efficient implementation is proposed, and an essential
back-projection algorithm preventing physically inadmissible states is
developed, which improves robustness. Various examples show the efficiency of
ComBo and the proposed algorithmic enhancements for nonlinear mechanical
problems. The general usability is emphasized by examining and comparing the
performance of myriad Fast Fourier Transform (FFT) based solvers including a
detailed description of the new Doubly-Fine Material Grid (DFMG). All of the
employed schemes benefit from the ComBo discretization.


title:
Standardized Evaluation of Machine Learning Methods for Evolving Data
  Streams

abstract:
  Due to the unspecified and dynamic nature of data streams, online machine
learning requires powerful and flexible solutions. However, evaluating online
machine learning methods under realistic conditions is difficult. Existing work
therefore often draws on different heuristics and simulations that do not
necessarily produce meaningful and reliable results. Indeed, in the absence of
common evaluation standards, it often remains unclear how online learning
methods will perform in practice or in comparison to similar work. In this
paper, we propose a comprehensive set of properties for high-quality machine
learning in evolving data streams. In particular, we discuss sensible
performance measures and evaluation strategies for online predictive modelling,
online feature selection and concept drift detection. As one of the first
works, we also look at the interpretability of online learning methods. The
proposed evaluation standards are provided in a new Python framework called
float. Float is completely modular and allows the simultaneous integration of
common libraries, such as scikit-multiflow or river, with custom code. Float is
open-sourced and can be accessed at https://github.com/haugjo/float. In this
sense, we hope that our work will contribute to more standardized, reliable and
realistic testing and comparison of online machine learning methods.


title: A unified model for the evolution of cataclysmic variables

abstract:
  We give an updated version of the analytical equation of state used in the
Cambridge stellar evolution code (STARS) as a free to use open-source package
that we have used to model cool white dwarfs down to temperatures
$\log_{10}(T_\mathrm{eff}/\mathrm{K})\:=\;3$. With this update in the STARS
code we model the secular evolution of cataclysmic variable (CV) stars using a
double dynamo model wherein there is an interplay between two $\alpha-\Omega$
dynamos, one in the convective envelope and the other at the boundary of a
slowly rotating shrinking radiative core and the growing convective envelope.
We confirm that this model provides a physical formalism for the interrupted
magnetic braking paradigm. In addition, our model also provides a mechanism for
extra angular momentum loss below the period gap. We construct the relative
probability distribution of orbital periods $P_\mathrm{orb}$ using the {mass}
distribution of white dwarfs in cataclysmic variables and find that our model
excellently reproduces the period gap and the observed period minimum spike in
CV distribution. We also compare the evolutionary trajectories from our model
with those of other empirical models and find agreement between the two. We
also report good agreement between our modelled systems and observational data.


title: New physics searches with the ILD detector at the ILC

abstract:
  Although the LHC experiments have searched for and excluded many proposed new
particles up to masses close to 1 TeV, there are many scenarios that are
difficult to address at a hadron collider. This talk will review a number of
these scenarios and present the expectations for searches at an
electron-positron collider such as the International Linear Collider. The cases
discussed include the light Higgsino, the stau lepton in the coannihilation
region relevant to dark matter, and heavy vector bosons coupling to the
s-channel in e$^+$e$^-$ annihilation. The studies are based on the ILD concept
at the ILC.


title: The Halo Bispectrum Multipoles in Redshift Space

abstract:
  We present the analysis of the halo bispectrum in redshift-space in terms of
its multipoles, monopole, quadrupole and hexadecapole, measured from a large
set of simulations. We fit such measurements with a tree-level model in
perturbation theory that depends on linear and nonlinear bias parameters as
well as on the growth rate $f$ of density fluctuations. The likelihood analysis
takes advantage of a very large set of mock catalogs, enabling a robust
estimation of the covariance properties for all multipoles. We compare the
numerical estimate of the covariance matrix to its Gaussian prediction finding
discrepancies of 10% or less for all configurations with the sole exception of
the squeezed triangles in the monopole case. We find the range of validity of
the tree-level model, for the total simulation volume of about 1000 $h^{-3}\,
{\rm Gpc}^3$, reaches a maximum wavenumber of $0.08 \, h \, {\rm Mpc}^{-1}$ for
the monopole, while it is limited to $0.06$ and $0.045\, h \, \rm{Mpc}^{-1}$
respectively for quadrupole and hexadecapole. Despite this, the addition of the
quadrupole to the analysis allows for significant improvements on the
determination of the model parameters and specifically on $f$, similarly to the
power spectrum case. Finally, we compare our numerical estimate for the full
covariance with its theoretical prediction in the Gaussian approximation and
find the latter to work remarkably well in the context of simulation boxes with
periodic boundary condition.


title:
Comprehensive analysis of southern eclipsing systems with pulsating
  components: The cases of HM Pup, V632 Sco and TT Vel

abstract:
  This work presents an extensive analysis of the properties of three southern
semi-detached eclipsing binaries hosting a pulsating component, namely HM Pup,
V632 Sco, and TT Vel. Systematic multi-filtered photometric observations were
obtained using telescopes located in Australia and Chile mostly between
2018-2021. These observations were combined with data from the Transiting
Exoplanet Survey Satellite (TESS) mission for a detailed analysis of
pulsations. Spectral types and radial velocities were determined from spectra
obtained with the Australian National University's 2.3 m telescope and Wide
Field Spectrograph. The data are modelled and the absolute parameters of all
components are derived. The light curve residuals are further analysed using
Fourier transformation techniques for the determination of the pulsation
frequencies. Using theoretical models, the most probable modes of the principal
oscillations are also identified. Eclipse-timing variation analysis is also
made for all systems and the most likely mechanisms modulating the orbital
period are proposed. The physical properties of these systems are compared with
other similar cases and the locations of their components are plotted in the
M-R and HR diagrams. Finally, the pulsational properties of the oscillating
components are compared with currently known systems of this type within the
orbital-pulsation period and log g-pulsation period diagrams. These systems are
identified as oEA stars by definition, with the primaries to be pulsating stars
of $\delta$ Scuti type, while evidence of mass flow from the evolved secondary
components is present in their Na I D spectra.


title: Rotationally Equivariant 3D Object Detection

abstract:
  Rotation equivariance has recently become a strongly desired property in the
3D deep learning community. Yet most existing methods focus on equivariance
regarding a global input rotation while ignoring the fact that rotation
symmetry has its own spatial support. Specifically, we consider the object
detection problem in 3D scenes, where an object bounding box should be
equivariant regarding the object pose, independent of the scene motion. This
suggests a new desired property we call object-level rotation equivariance. To
incorporate object-level rotation equivariance into 3D object detectors, we
need a mechanism to extract equivariant features with local object-level
spatial support while being able to model cross-object context information. To
this end, we propose Equivariant Object detection Network (EON) with a rotation
equivariance suspension design to achieve object-level equivariance. EON can be
applied to modern point cloud object detectors, such as VoteNet and PointRCNN,
enabling them to exploit object rotation symmetry in scene-scale inputs. Our
experiments on both indoor scene and autonomous driving datasets show that
significant improvements are obtained by plugging our EON design into existing
state-of-the-art 3D object detectors.


title:
Reliable Visual Question Answering: Abstain Rather Than Answer
  Incorrectly

abstract:
  Machine learning has advanced dramatically, narrowing the accuracy gap to
humans in multimodal tasks like visual question answering (VQA). However, while
humans can say "I don't know" when they are uncertain (i.e., abstain from
answering a question), such ability has been largely neglected in multimodal
research, despite the importance of this problem to the usage of VQA in real
settings. In this work, we promote a problem formulation for reliable VQA,
where we prefer abstention over providing an incorrect answer. We first enable
abstention capabilities for several VQA models, and analyze both their
coverage, the portion of questions answered, and risk, the error on that
portion. For that we explore several abstention approaches. We find that
although the best performing models achieve over 71% accuracy on the VQA v2
dataset, introducing the option to abstain by directly using a model's softmax
scores limits them to answering less than 8% of the questions to achieve a low
risk of error (i.e., 1%). This motivates us to utilize a multimodal selection
function to directly estimate the correctness of the predicted answers, which
we show can triple the coverage from, for example, 5.0% to 16.7% at 1% risk.
While it is important to analyze both coverage and risk, these metrics have a
trade-off which makes comparing VQA models challenging. To address this, we
also propose an Effective Reliability metric for VQA that places a larger cost
on incorrect answers compared to abstentions. This new problem formulation,
metric, and analysis for VQA provide the groundwork for building effective and
reliable VQA models that have the self-awareness to abstain if and only if they
don't know the answer.


title:
Self-dual $CP(2)$ vortex-like solitons in the presence of magnetic
  impurities

abstract:
  We investigate the existence of vortex configurations in two gauged-$CP(2)$
models extended via the inclusion of magnetic impurities. In particular, we
consider both the Maxwell-$CP(2)$ and the Chern-Simons-$CP(2)$ enlarged
scenarios, separately. We choose a $CP(2)$-field configuration with a null
topological charge not only in the simplest (free) case, but also when coupled
to an Abelian gauge field. The implementation of the
Bogomol'nyi-Prasad-Sommerfield (BPS) formalism shows that the effective models
for such a configuration possess a self-dual structure which looks like those
inherent to the gauged sigma models. Therefore, when the $CP(2)$ field is
coupled to the Maxwell term, the corresponding total energy possesses both a
well-defined Bogomol'nyi bound and a quantized magnetic flux. Further, when the
$CP(2)$ scenario is gauged with the Chern-Simons action, the total electric
charge is verified to be proportional to the quantized magnetic flux. In
addition, the analysis verifies that the magnetic impurity contributes to the
BPS potentials and appears in both the models' BPS equations. Next, we
introduce a Gaussian type impurity and solve the self-dual equations via a
finite-difference scheme. The resulting solutions present a nonmonotonic
behavior that flips both the magnetic and electric fields. Finally, we discuss
the topologically trivial solutions in the limit for which the impurity becomes
a Dirac $\delta $-function.


title:
Majorana fermions revealing the true nature of quantum phase slip
  junctions

abstract:
  Quantum circuit theory is a powerful and ever-evolving tool to predict the
dynamics of superconducting circuits. In its language, quantum phase slips are
famously considered to be the exact dual to the Josephson effect. However, if
taken at face value, this duality stipulates that charge must be continuous. We
propose a charge-quantization conserving description of quantum phase slips and
show that this seemingly minute change gives rise to some very different
predictions. When shunting a quantum phase slip junction with a topological
Josephson junction, the new description leads to a complete dissappearance of
the Hofstadter butterfly spectrum. For topologically trivial circuits we argue
that adhering to charge quantization renders realizations of the
Gottesman-Kitaev-Preskill code impossible.


title:
Experimental implementation of quantum algorithm for association rules
  mining

abstract:
  Recently, a quantum algorithm for a fundamentally important task in data
mining, association rules mining (ARM), called qARM for short, has been
proposed. Notably, qARM achieves significant speedup over its classical
counterpart for implementing the main task of ARM, i.e., finding frequent
itemsets from a transaction database. In this paper, we experimentally
implement qARM on both a real IBM quantum computer and an IBM quantum
simulator. In the first place, we design quantum circuits of qARM for a
2$\times$2 transaction database (i.e., a transaction database involving two
transactions and two items), and run it on the real five-qubit IBM quantum
computer "ibmq\_quito" and the IBM quantum simulator "aer\_simulator". For a
larger 4$\times$4 transaction database which would lead to circuits with more
qubits and a higher depth than the currently accessible IBM real quantum
devices can handle, we also construct the quantum circuits of qARM and execute
them on "aer\_simulator" alone. Both experimental results show that all the
frequent itemsets from the two transaction databases are successfully derived
as desired, demonstrating the correctness and feasibility of qARM. Our work may
serve as a benchmarking, and provide prototypes for implementing qARM for
larger transaction databases on both noisy intermediate-scale quantum devices
and universal fault-tolerant quantum computers.


title:
SemAttNet: Towards Attention-based Semantic Aware Guided Depth
  Completion

abstract:
  Depth completion involves recovering a dense depth map from a sparse map and
an RGB image. Recent approaches focus on utilizing color images as guidance
images to recover depth at invalid pixels. However, color images alone are not
enough to provide the necessary semantic understanding of the scene.
Consequently, the depth completion task suffers from sudden illumination
changes in RGB images (e.g., shadows). In this paper, we propose a novel
three-branch backbone comprising color-guided, semantic-guided, and
depth-guided branches. Specifically, the color-guided branch takes a sparse
depth map and RGB image as an input and generates color depth which includes
color cues (e.g., object boundaries) of the scene. The predicted dense depth
map of color-guided branch along-with semantic image and sparse depth map is
passed as input to semantic-guided branch for estimating semantic depth. The
depth-guided branch takes sparse, color, and semantic depths to generate the
dense depth map. The color depth, semantic depth, and guided depth are
adaptively fused to produce the output of our proposed three-branch backbone.
In addition, we also propose to apply semantic-aware multi-modal
attention-based fusion block (SAMMAFB) to fuse features between all three
branches. We further use CSPN++ with Atrous convolutions to refine the dense
depth map produced by our three-branch backbone. Extensive experiments show
that our model achieves state-of-the-art performance in the KITTI depth
completion benchmark at the time of submission.


title: Note on $T\bar{T}$ deformed matrix models and JT supergravity duals

abstract:
  In this work we calculate the partition functions of $\mathcal{N}=1$ type 0A
and 0B JT supergravity (SJT) on 2D surfaces of arbitrary genus with multiple
finite cut-off boundaries, based on the $T\bar{T}$ deformed super-Schwarzian
theories. In terms of SJT/matrix model duality, we compute the corresponding
correlation functions in the $T\bar{T}$ deformed matrix model side by using
topological recursion relations as well as the transformation properties of
topological recursion relations under $T\bar{T}$ deformation. We check that the
partition functions finite cut-off 0A and 0B SJT on generic 2D surfaces match
the associated correlation functions in $T\bar{T}$ deformed matrix models
respectively.


title: Learning to Extract Building Footprints from Off-Nadir Aerial Images

abstract:
  Extracting building footprints from aerial images is essential for precise
urban mapping with photogrammetric computer vision technologies. Existing
approaches mainly assume that the roof and footprint of a building are well
overlapped, which may not hold in off-nadir aerial images as there is often a
big offset between them. In this paper, we propose an offset vector learning
scheme, which turns the building footprint extraction problem in off-nadir
images into an instance-level joint prediction problem of the building roof and
its corresponding "roof to footprint" offset vector. Thus the footprint can be
estimated by translating the predicted roof mask according to the predicted
offset vector. We further propose a simple but effective feature-level offset
augmentation module, which can significantly refine the offset vector
prediction by introducing little extra cost. Moreover, a new dataset, Buildings
in Off-Nadir Aerial Images (BONAI), is created and released in this paper. It
contains 268,958 building instances across 3,300 aerial images with fully
annotated instance-level roof, footprint, and corresponding offset vector for
each building. Experiments on the BONAI dataset demonstrate that our method
achieves the state-of-the-art, outperforming other competitors by 3.37 to 7.39
points in F1-score. The codes, datasets, and trained models are available at
https://github.com/jwwangchn/BONAI.git.


title: Russian Texts Detoxification with Levenshtein Editing

abstract:
  Text detoxification is a style transfer task of creating neutral versions of
toxic texts. In this paper, we use the concept of text editing to build a
two-step tagging-based detoxification model using a parallel corpus of Russian
texts. With this model, we achieved the best style transfer accuracy among all
models in the RUSSE Detox shared task, surpassing larger sequence-to-sequence
models.


title:
Improved cutoff functions for short-range potentials and the Wolf
  summation

abstract:
  A class of radial, polynomial cutoff functions $f_{\textrm{c}n}(r)$ for
short-ranged pair potentials or related expressions is proposed. Their
derivatives up to order $n$ and $n+1$ vanish at the outer cutoff $r_\textrm{c}$
and an inner radius $r_\textrm{i}$, respectively. Moreover, $f_{\textrm{c}n}(r
\le r_\textrm{i}) = 1$ and $f_{\textrm{c}n}(r\ge r_\textrm{c})=0$. It is shown
that the used order $n$ can qualitatively affect results: stress and bulk
moduli of ideal crystals are unavoidably discontinuous with density for $n=0$
and $n=1$, respectively. Systematic errors on energies and computing times
decrease by approximately 25\% for Lennard-Jones with $n=2$ or $n=3$ compared
to standard cutting procedures. Another cutoff function turns out beneficial to
compute Coulomb interactions using the Wolf summation, which is shown to not
properly converge when local charge neutrality is obeyed only in a stochastic
sense. However, for all investigated homogeneous systems with thermal noise
(ionic crystals and liquids), the modified Wolf summation, despite being
infinitely differentiable at $r_\textrm{c}$, converges similarly quickly as the
original summation. Finally, it is discussed how to reduce the computational
cost of numerically exact Monte Carlo simulations using the Wolf summation even
when it does not properly converge.


title:
An Improved Authentication Scheme for BLE Devices with no I/O
  Capabilities

abstract:
  Bluetooth Low Energy (BLE) devices have become very popular because of their
Low energy consumption and hence a prolonged battery life. They are being used
in smart wearable devices, smart home automation system, beacons and many more
areas. BLE uses pairing mechanisms to achieve a level of peer entity
authentication as well as encryption. Although, there are a set of pairing
mechanisms available but BLE devices having no keyboard or display mechanism
(and hence using the Just Works pairing) are still vulnerable. In this paper,
we propose and implement, a light-weight digital certificate based
authentication mechanism for the BLE devices making use of Just Works model.
The proposed model is an add-on to the already existing pairing mechanism and
therefore can be easily incorporated in the existing BLE stack. To counter the
existing Man-in-The-Middle attack scenario in Just Works pairing (device
spoofing), our proposed model allows the client and peripheral to make use of
the popular Public Key Infrastructure (PKI) to establish peer entity
authentication and a secure cryptographic tunnel for communication. We have
also developed a lightweight BLE profiled digital certificate containing the
bare minimum fields required for resource constrained devices, which
significantly reduces the memory (about 90\% reduction) and energy consumption.
We have experimentally evaluated the energy consumption of the device using the
proposed pairing mechanism to demonstrate that the model can be easily deployed
with less changes to the power requirements of the chips. The model has been
formally verified using automatic verification tool for protocol testing.


title: Real Quantum Amplitude Estimation

abstract:
  We introduce the Real Quantum Amplitude Estimation (RQAE) algorithm, an
extension of Quantum Amplitude Estimation (QAE) which is sensitive to the sign
of the amplitude. RQAE is an iterative algorithm which offers explicit control
over the amplification policy through an adjustable parameter. We provide a
rigorous analysis of the RQAE performance and prove that it achieves a
quadratic speedup, modulo logarithmic corrections, with respect to unamplified
sampling. Besides, we corroborate the theoretical analysis with a set of
numerical experiments.


title:
Trivalent Categories for Adjoint Representations of Exceptional Lie
  Algebras

abstract:
  We consider the universal pivotal, symmetric, monoidal, $\Bbbk$-linear
category, generated by a Schurian object with a skew-symmetric multiplication,
and study some of its quotients. We show that these quotients give rise to
either vector product algebras or representation categories of exceptional Lie
algebras.


title:
Interaction of Autonomous and Manually-Controlled
  Vehicles:Implementation of a Road User Communication Service

abstract:
  Communication between vehicles with varying degrees of automation is
increasingly challenging as highly automated vehicles are unable to interpret
the non-verbal signs of other road users. The lack of understanding on roads
leads to lower trust in automated vehicles and impairs traffic safety. To
address these problems, we propose the Road User Communication Service, a
software as a service platform, which provides information exchange and cloud
computing services for vehicles with varying degrees of automation. To inspect
the operability of the proposed solution, field tests were carried out on a
test track, where the autonomous JKU-ITS research vehicle requested the state
of a driver in a manually-controlled vehicle through the implemented service.
The test results validated the approach showing its feasibility to be used as a
communication platform. A link to the source code is available.


title:
Propagation of errors and quantitative quantum simulation with quantum
  advantage

abstract:
  The rapid development in hardware for quantum computing and simulation has
led to much interest in problems where these devices can exceed the
capabilities of existing classical computers and known methods. Approaching
this for problems that go beyond testing the performance of a quantum device is
an important step, and quantum simulation of many-body quench dynamics is one
of the most promising candidates for early practical quantum advantage. We
analyse the requirements for quantitatively reliable quantum simulation beyond
the capabilities of existing classical methods for analogue quantum simulators
with neutral atoms in optical lattices and trapped ions. Considering the
primary sources of error in analogue devices and how they propagate after a
quench in studies of the Hubbard or long-range transverse field Ising model, we
identify the level of error expected in quantities we extract from experiments.
We conclude for models that are directly implementable that regimes of
practical quantum advantage are attained in current experiments with analogue
simulators. We also identify the hardware requirements to reach the same level
of accuracy with future fault-tolerant digital quantum simulation. Verification
techniques are already available to test the assumptions we make here, and
demonstrating these in experiments will be an important next step.


title: Matrix Factorizations, Reality and Kn\"orrer periodicity

abstract:
  Motivated by periodicity theorems for Real K-theory and Grothendieck-Witt
theory and, separately, work of Hori-Walcher on Landau-Ginzburg orientifolds,
we introduce and study categories of Real matrix factorizations. Our main
results are generalizations of Kn\"orrer periodicity to categories of Real
matrix factorizations. These generalizations are structurally similar to (1,
1)-periodicity for KR-theory and 4-periodicity for Grothendieck-Witt theory. We
use techniques from Real categorical representation theory which allow us to
incorporate into our main results equivariance for a finite group and discrete
torsion twists.


title: Picard schemes of noncommutative bielliptic surfaces

abstract:
  Let $X$ be a bielliptic surface over the field of complex numbers. We study
the nontrivial elements in the Brauer group of $X$ and realize them as Azumaya
algebras on $X$. Given such an algebra $\mathcal{A}$, we study the moduli
scheme of torsion free $\mathcal{A}$-modules of rank one. Finally we check in
which cases this moduli scheme is a finite \'{e}tale cover of a Lagrangian
subscheme in the corresponding moduli scheme on the canonically covering
abelian surface.


title: Morita equivalence of deformations of Kleinian singularities

abstract:
  In this paper we classify all Morita equivalent pairs of (classical)
generalized Weyl algebras for generic values of the parameters, thus positively
settling a 30 year old question posed by T.Hodges. We also prove a similar
result for noncommutative deformations of arbitrary Kleinian singularities
provided the corresponding parameters are very generic.


title: Survivable Network Design Revisited: Group-Connectivity

abstract:
  In the classical survivable network design problem (SNDP), we are given an
undirected graph $G=(V,E)$ with costs on edges and a connectivity requirement
$k(s,t)$ for each pair of vertices. The goal is to find a minimum-cost subgraph
$H\subseteq V$ such that every pair $(s,t)$ are connected by $k(s,t)$ edge or
(openly) vertex disjoint paths, abbreviated as EC-SNDP and VC-SNDP,
respectively. The seminal result of Jain [FOCS'98, Combinatorica'01] gives a
$2$-approximation algorithm for EC-SNDP, and a decade later, an $O(k^3\log
n)$-approximation algorithm for VC-SNDP, where $k$ is the largest connectivity
requirement, was discovered by Chuzhoy and Khanna [FOCS'09, Theory Comput.'12].
While there is rich literature on point-to-point settings of SNDP, the viable
case of connectivity between subsets is still relatively poorly understood.
  This paper concerns the generalization of SNDP into the subset-to-subset
setting, namely Group EC-SNDP. We develop the framework, which yields the first
non-trivial (true) approximation algorithm for Group EC-SNDP. Previously, only
a bicriteria approximation algorithm is known for Group EC-SNDP [Chalermsook,
Grandoni, and Laekhanukit, SODA'15], and a true approximation algorithm is
known only for the single-source variant with connectivity requirement
$k(S,T)\in\{0,1,2\}$ [Gupta, Krishnaswamy, and Ravi, SODA'10; Khandekar,
Kortsarz, and Nutov, FSTTCS'09 and Theor. Comput. Sci.'12].


title: Monogamy inequality of entanglement of pure tripartite qudit states

abstract:
  We analytically establish an inequality analogous to the
Coffman-Kundu-Wootters inequality, which succinctly describes monogamy of
entanglement in $\mathbb{C}^d\otimes \mathbb{C}^d\otimes \mathbb{C}^d$
dimensional pure states. The derivation of this inequality is based on the
G-concurrence \cite{gour2} measure of entanglement. It is shown that the shared
entanglement of the subsystems of a pure tripartite qudit state always satisfy
a monogamy constraint.


title:
Unlocking High-Accuracy Differentially Private Image Classification
  through Scale

abstract:
  Differential Privacy (DP) provides a formal privacy guarantee preventing
adversaries with access to a machine learning model from extracting information
about individual training points. Differentially Private Stochastic Gradient
Descent (DP-SGD), the most popular DP training method, realizes this protection
by injecting noise during training. However previous works have found that
DP-SGD often leads to a significant degradation in performance on standard
image classification benchmarks. Furthermore, some authors have postulated that
DP-SGD inherently performs poorly on large models, since the norm of the noise
required to preserve privacy is proportional to the model dimension. In
contrast, we demonstrate that DP-SGD on over-parameterized models can perform
significantly better than previously thought. Combining careful hyper-parameter
tuning with simple techniques to ensure signal propagation and improve the
convergence rate, we obtain a new SOTA on CIFAR-10 of 81.4% under (8,
10^{-5})-DP using a 40-layer Wide-ResNet, improving over the previous SOTA of
71.7%. When fine-tuning a pre-trained 200-layer Normalizer-Free ResNet, we
achieve a remarkable 77.1% top-1 accuracy on ImageNet under (1, 8*10^{-7})-DP,
and achieve 81.1% under (8, 8*10^{-7})-DP. This markedly exceeds the previous
SOTA of 47.9% under a larger privacy budget of (10, 10^{-6})-DP. We believe our
results are a significant step towards closing the accuracy gap between private
and non-private image classification.


title:
Exponential mixing for random dynamical systems and an example of
  Pierrehumbert

abstract:
  We consider the question of exponential mixing for random dynamical systems
on arbitrary compact manifolds without boundary. We put forward a robust,
dynamics-based framework that allows us to construct space-time smooth,
uniformly bounded in time, universal exponential mixers. The framework is then
applied to the problem of proving exponential mixing in a classical example
proposed by Pierrehumbert in 1994, consisting of alternating periodic shear
flows with randomized phases. This settles a longstanding open problem on
proving the existence of a space-time smooth (universal) exponentially mixing
incompressible velocity field on a two-dimensional periodic domain while also
providing a toolbox for constructing such smooth universal mixers in all
dimensions.


title:
Making expert processes visible: how and why theorists use assumptions
  and analogies in their research

abstract:
  Understanding how physicists solve problems can guide the development of
methods that help students learn and improve at solving complex problems.
Leveraging the framework of cognitive task analysis, we conducted
semi-structured interviews with theoretical physicists (N=11) to gain insight
into the cognitive processes and skills that they use in their professional
research. Among numerous activities that theorists described, here we elucidate
two activities that theorists commonly characterized as being integral to their
work: making assumptions and using analogies. Theorists described making
assumptions throughout their research process, especially while setting their
project's direction and goals, establishing their model's interaction with
mathematics, and revising their model while troubleshooting. They described how
assumptions about their model informed their mathematical decision making, as
well as instances where mathematical steps fed back into their model's
applicability. We found that theorists used analogies to generate new project
ideas as well as overcome conceptual challenges. Theorists deliberately sought
out or constructed analogous, indicating this is a skill students can practice.
When mapping knowledge from one system to another, theorists sought to use
systems that shared a high degree of mathematical similarity; however, these
systems did not always share similar surface features. We conclude by
discussing connections between the ways theorists use assumption and analogy
and offering potential applications to instruction.


title: GRIT: General Robust Image Task Benchmark

abstract:
  Computer vision models excel at making predictions when the test distribution
closely resembles the training distribution. Such models have yet to match the
ability of biological vision to learn from multiple sources and generalize to
new data sources and tasks. To facilitate the development and evaluation of
more general vision systems, we introduce the General Robust Image Task (GRIT)
benchmark. GRIT evaluates the performance, robustness, and calibration of a
vision system across a variety of image prediction tasks, concepts, and data
sources. The seven tasks in GRIT are selected to cover a range of visual
skills: object categorization, object localization, referring expression
grounding, visual question answering, segmentation, human keypoint detection,
and surface normal estimation. GRIT is carefully designed to enable the
evaluation of robustness under image perturbations, image source distribution
shift, and concept distribution shift. By providing a unified platform for
thorough assessment of skills and concepts learned by a vision model, we hope
GRIT catalyzes the development of performant and robust general purpose vision
systems.


title: On Quantitative Algebraic Higher-Order Theories

abstract:
  We explore the possibility of extending Mardare et al. quantitative algebras
to the structures which naturally emerge from Combinatory Logic and the
lambda-calculus. First of all, we show that the framework is indeed applicable
to those structures, and give soundness and completeness results. Then, we
prove some negative results which clearly delineate to which extent categories
of metric spaces can be models of such theories. We conclude by giving several
examples of non-trivial higher-order quantitative algebras.


title: Correlation functions in linear chaotic maps

abstract:
  The simplest examples of chaotic maps are linear, area-preserving maps on the
circle, torus, or product of tori; respectively known as the Bernoulli map, the
cat map, and the recently introduced "spatiotemporal" cat map. We study
correlation functions in these maps. For the Bernoulli map, we compute the
correlation functions in a variety of ways: by direct computation of the
integral, through Fourier series, through symbolic dynamics, and through
periodic orbits. In relation to the more standard treatment in terms of
eigenfunctions of the Perron-Frobenius operator, some of these methods are
simpler and also extend to multipoint correlation functions. For the cat map,
we compute correlation functions through a Fourier expansion, review and expand
on a prior treatment of two-point functions by Crawford and Cary, and discuss
the limitations of shadowing. Finally, for the spatiotemporal cat map --
intended to be a model of many-body chaos -- we show that connected correlation
functions of local operators vanish.

categories: nlin.CD cond-mat.stat-mech hep-th math.DS


title:
Unsupervised Multi-Modal Medical Image Registration via
  Discriminator-Free Image-to-Image Translation

abstract:
  In clinical practice, well-aligned multi-modal images, such as Magnetic
Resonance (MR) and Computed Tomography (CT), together can provide complementary
information for image-guided therapies. Multi-modal image registration is
essential for the accurate alignment of these multi-modal images. However, it
remains a very challenging task due to complicated and unknown spatial
correspondence between different modalities. In this paper, we propose a novel
translation-based unsupervised deformable image registration approach to
convert the multi-modal registration problem to a mono-modal one. Specifically,
our approach incorporates a discriminator-free translation network to
facilitate the training of the registration network and a patchwise contrastive
loss to encourage the translation network to preserve object shapes.
Furthermore, we propose to replace an adversarial loss, that is widely used in
previous multi-modal image registration methods, with a pixel loss in order to
integrate the output of translation into the target modality. This leads to an
unsupervised method requiring no ground-truth deformation or pairs of aligned
images for training. We evaluate four variants of our approach on the public
Learn2Reg 2021 datasets \cite{hering2021learn2reg}. The experimental results
demonstrate that the proposed architecture achieves state-of-the-art
performance. Our code is available at https://github.com/heyblackC/DFMIR.


title:
Dynamical purification and the emergence of quantum state designs from
  the projected ensemble

abstract:
  Quantum thermalization in a many-body system is defined by the approach of
local subsystems towards a universal form, describable as an ensemble of
quantum states wherein observables acquire thermal expectation values.
Recently, it was demonstrated that the distribution of these quantum states can
also exhibit universal statistics, upon associating each state with the outcome
of local projective measurements of the complementary subsystem. Specifically,
this collection of pure quantum states -- called the projected ensemble -- can
under certain conditions mimic the behavior of a maximally entropic, uniformly
random ensemble, i.e. form a quantum state-design, representing a "deeper" form
of quantum thermalization. In this work, we investigate the dynamical process
underlying this novel emergent universality. Leveraging a space-time duality
mapping for one-dimensional quantum circuits, we argue that the physics of
dynamical purification, which arises in the context of monitored quantum
systems, constrains the the projected ensemble's approach towards the uniform
distribution. We prove that absence of dynamical purification in the space-time
dual dynamics (a condition realized in dual-unitary quantum circuits with
appropriate initial states and final measurement bases) generically yields
exact state-designs for all moments $k$ at the same time, extending previous
rigorous results [Ho and Choi, Phys. Rev. Lett. 128, 060601 (2022)].
Conversely, we show that, departing from these conditions, dynamical
purification can lead to a separation of timescales between the formation of a
quantum state-design for moment $k=1$ (regular thermalization) and for high
moments $k\gg 1$ ("deep" thermalization). Our results suggest that the
projected ensemble can probe nuanced features of quantum dynamics inaccessible
to regular thermalization, such as quantum information scrambling.

categories: quant-ph cond-mat.dis-nn cond-mat.stat-mech cond-mat.str-el


title:
The Active Galactic Nuclei in the Hobby-Eberly Telescope Dark Energy
  Experiment Survey (HETDEX) I. Sample selection

abstract:
  We present the first Active Galactic Nuclei (AGN) catalog in the Hobby-Eberly
Telescope Dark Energy Experiment Survey (HETDEX) observed between January 2017
and June 2020. HETDEX is an ongoing spectroscopic survey with no pre-selection
based on magnitudes, colors or morphologies, enabling us to select AGN based on
their spectral features. Both luminous quasars and low-luminosity Seyferts are
found in our catalog. AGN candidates are selected with at least two significant
AGN emission lines, such as the LyA and CIV line pair, or with single broad
emission lines (FWHM > 1000 km/s). Each source is further confirmed by visual
inspections. This catalog contains 5,322 AGN, covering an effective sky
coverage of 30.61 deg^2. A total of 3,733 of these AGN have secure redshifts,
and we provide redshift estimates for the remaining 1,589 single broad-line AGN
with no cross matched spectral redshifts from SDSS DR14Q. The redshift range of
the AGN catalog is 0.25 < z < 4.32, with a median of z = 2.1. The bolometric
luminosity range is 10^9-10^14 Lsun with a median of 10^12 Lsun. The median
r-band magnitude of the AGN is 21.6 mag, with 34% of the AGN have r > 22.5, and
2.6% reaching the detection limit at r ~ 26 mag of the deepest imaging surveys
we searched. We also provide a composite spectrum of the AGN sample covering
700 AA - 4400 AA.


title: One-loop diagrams with quadratic propagators from the worldsheet

abstract:
  It is well known that forward limits of tree-level amplitudes (and those
trivalent diagrams they consist of) produce one-loop amplitudes and trivalent
diagrams with propagators linear in the loop momentum. They naturally arise
from one-loop worldsheet formulae, and an important open problem is how to
recombine them into usual one-loop diagrams with quadratic propagators. In this
paper, we study a new collection of worldsheet functions: generalized one-loop
Parke-Taylor factors with tensor numerators, which are conjectured to serve as
a basis for one-loop worldsheet functions with this nice property. We present
all-multiplicity, closed-form expressions for combinations of one-loop
trivalent diagrams with quadratic propagators and tensor numerators to
arbitrary rank (including possible tadpole contributions), produced by any pair
of Parke-Taylor factors. We also briefly comment on reducing worldsheet
functions onto such a basis, and applications to one-loop amplitudes in
physical theories.


title:
A novel connection between integral binary quadratic forms and knot
  polynomials

abstract:
  We establish a novel connection between algebraic number theory and knot
theory. We show that the number of equivalence classes of integral binary
quadratic forms of discriminant $t^2 - 4$ (for $t\neq \pm 2$) is equal to the
number of isotopy classes of links in $\mathbb{S}^3$ with prescribed values
(depending on $t$) of three classical link invariants. The equality arises from
a natural algebraic correspondence between integral binary quadratic forms (of
discriminant $t^2 - 4$ for $t\neq \pm 2$) and isotopy classes of links of braid
index at most three. In particular, the class numbers of certain quadratic
number fields precisely measure the failure of the Alexander/Jones polynomial
to distinguish non-isotopic links of braid index at most three.


title: Toward Compositional Generalization in Object-Oriented World Modeling

abstract:
  Compositional generalization is a critical ability in learning and
decision-making. We focus on the setting of reinforcement learning in
object-oriented environments to study compositional generalization in world
modeling. We (1) formalize the compositional generalization problem with an
algebraic approach and (2) study how a world model can achieve that. We
introduce a conceptual environment, Object Library, and two instances, and
deploy a principled pipeline to measure the generalization ability. Motivated
by the formulation, we analyze several methods with exact} or no compositional
generalization ability using our framework, and design a differentiable
approach, Homomorphic Object-oriented World Model (HOWM), that achieves
approximate but more efficient compositional generalization.


title: Articulated Objects in Free-form Hand Interaction

abstract:
  We use our hands to interact with and to manipulate objects. Articulated
objects are especially interesting since they often require the full dexterity
of human hands to manipulate them. To understand, model, and synthesize such
interactions, automatic and robust methods that reconstruct hands and
articulated objects in 3D from a color image are needed. Existing methods for
estimating 3D hand and object pose from images focus on rigid objects. In part,
because such methods rely on training data and no dataset of articulated object
manipulation exists. Consequently, we introduce ARCTIC - the first dataset of
free-form interactions of hands and articulated objects. ARCTIC has 1.2M images
paired with accurate 3D meshes for both hands and for objects that move and
deform over time. The dataset also provides hand-object contact information. To
show the value of our dataset, we perform two novel tasks on ARCTIC: (1) 3D
reconstruction of two hands and an articulated object in interaction; (2) an
estimation of dense hand-object relative distances, which we call interaction
field estimation. For the first task, we present ArcticNet, a baseline method
for the task of jointly reconstructing two hands and an articulated object from
an RGB image. For interaction field estimation, we predict the relative
distances from each hand vertex to the object surface, and vice versa. We
introduce InterField, the first method that estimates such distances from a
single RGB image. We provide qualitative and quantitative experiments for both
tasks, and provide detailed analysis on the data. Code and data will be
available at https://arctic.is.tue.mpg.de.


title:
ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing
  Vaccine Uptake in Nigeria

abstract:
  More than 5 million children under five years die from largely preventable or
treatable medical conditions every year, with an overwhelmingly large
proportion of deaths occurring in under-developed countries with low
vaccination uptake. One of the United Nations' sustainable development goals
(SDG 3) aims to end preventable deaths of newborns and children under five
years of age. We focus on Nigeria, where the rate of infant mortality is
appalling. We collaborate with HelpMum, a large non-profit organization in
Nigeria to design and optimize the allocation of heterogeneous health
interventions under uncertainty to increase vaccination uptake, the first such
collaboration in Nigeria. Our framework, ADVISER: AI-Driven Vaccination
Intervention Optimiser, is based on an integer linear program that seeks to
maximize the cumulative probability of successful vaccination. Our optimization
formulation is intractable in practice. We present a heuristic approach that
enables us to solve the problem for real-world use-cases. We also present
theoretical bounds for the heuristic method. Finally, we show that the proposed
approach outperforms baseline methods in terms of vaccination uptake through
experimental evaluation. HelpMum is currently planning a pilot program based on
our approach to be deployed in the largest city of Nigeria, which would be the
first deployment of an AI-driven vaccination uptake program in the country and
hopefully, pave the way for other data-driven programs to improve health
outcomes in Nigeria.


title:
Individual characteristics associated with risk and time preferences: A
  multi country representative survey

abstract:
  This paper empirically analyzes how individual characteristics are associated
with risk aversion, loss aversion, time discounting, and present bias. To this
end, we conduct a large-scale demographically representative survey across
eight European countries. We elicit preferences using incentivized multiple
price lists and jointly estimate preference parameters to account for their
structural dependencies. Our findings suggest that preferences are linked to a
variety of individual characteristics such as age, gender, and income as well
as some personal values. We also report evidence on the relationship between
cognitive ability and preferences. Incentivization, stake size, and the order
of presentation of binary choices matter, underlining the importance of
controlling for these factors when eliciting economic preferences.


title: Theory and Algorithms for Diffusion Processes on Riemannian Manifolds

abstract:
  We study geometric stochastic differential equations (SDEs) and their
approximations on Riemannian manifolds. In particular, we introduce a simple
new construction of geometric SDEs, using which with bounded curvature. In
particular, we provide the first (to our knowledge) non-asymptotic bound on the
error of the geometric Euler-Murayama discretization. We then bound the
distance between the exact SDE and a discrete geometric random walk, where the
noise can be non-Gaussian; this analysis is useful for using geometric SDEs to
model naturally occurring discrete non-Gaussian stochastic processes. Our
results provide convenient tools for studying MCMC algorithms that adopt
non-standard noise distributions.


title:
Schr\"odinger's FP: Dynamic Adaptation of Floating-Point Containers for
  Deep Learning Training

abstract:
  We introduce a software-hardware co-design approach to reduce memory traffic
and footprint during training with BFloat16 or FP32 boosting energy efficiency
and execution time performance. We introduce methods to dynamically adjust the
size and format of the floating-point containers used to store activations and
weights during training. The different value distributions lead us to different
approaches for exponents and mantissas. Gecko exploits the favourable exponent
distribution with a loss-less delta encoding approach to reduce the total
exponent footprint by up to $58\%$ in comparison to a 32 bit floating point
baseline. To content with the noisy mantissa distributions, we present two
lossy methods to eliminate as many as possible least significant bits while not
affecting accuracy. Quantum Mantissa, is a machine learning-first mantissa
compression method that taps on training's gradient descent algorithm to also
learn minimal mantissa bitlengths on a per-layer granularity, and obtain up to
$92\%$ reduction in total mantissa footprint. Alternatively, BitChop observes
changes in the loss function during training to adjust mantissa bit-length
network-wide yielding a reduction of $81\%$ in footprint. Schr\"{o}dinger's FP
implements hardware encoders/decoders that guided by Gecko/Quantum Mantissa or
Gecko/BitChop transparently encode/decode values when transferring to/from
off-chip memory boosting energy efficiency and reducing execution time.


title: On weak convergence of quasi-infinitely divisible laws

abstract:
  We study a new class of so-called quasi-infinitely divisible laws, which is a
wide natural extension of the well known class of infinitely divisible laws
through the L\'evy--Khinchine type representations. We are interested in
criteria of weak convergence within this class. Under rather natural
assumptions, we state assertions, which connect a weak convergence of
quasi-infinitely divisible distribution functions with one special type of
convergence of their L\'evy--Khinchine spectral functions. The latter
convergence is not equivalent to the weak convergence. So we complement known
results by Lindner, Pan, and Sato (2018) in this field.


title: Unaligned Supervision For Automatic Music Transcription in The Wild

abstract:
  Multi-instrument Automatic Music Transcription (AMT), or the decoding of a
musical recording into semantic musical content, is one of the holy grails of
Music Information Retrieval. Current AMT approaches are restricted to piano and
(some) guitar recordings, due to difficult data collection. In order to
overcome data collection barriers, previous AMT approaches attempt to employ
musical scores in the form of a digitized version of the same song or piece.
The scores are typically aligned using audio features and strenuous human
intervention to generate training labels. We introduce NoteEM, a method for
simultaneously training a transcriber and aligning the scores to their
corresponding performances, in a fully-automated process. Using this unaligned
supervision scheme, complemented by pseudo-labels and pitch-shift augmentation,
our method enables training on in-the-wild recordings with unprecedented
accuracy and instrumental variety. Using only synthetic data and unaligned
supervision, we report SOTA note-level accuracy of the MAPS dataset, and large
favorable margins on cross-dataset evaluations. We also demonstrate robustness
and ease of use; we report comparable results when training on a small, easily
obtainable, self-collected dataset, and we propose alternative labeling to the
MusicNet dataset, which we show to be more accurate. Our project page is
available at https://benadar293.github.io


title:
Membrane-less phonon trapping and resolution enhancement in optical
  microwave kinetic inductance detectors

abstract:
  Microwave Kinetic Inductance Detectors (MKIDs) sensitive to light in the
ultraviolet to near-infrared wavelengths are superconducting micro-resonators
that are capable of measuring photon arrival times to microsecond precision and
estimating each photon's energy. The resolving power of non-membrane MKIDs has
remained stubbornly around 10 at 1 $\mu$m despite significant improvements in
the system noise. Here we show that the resolving power can be roughly doubled
with a simple bilayer design without needing to place the device on a membrane,
avoiding a significant increase in fabrication complexity. Based on modeling of
the phonon propagation, we find that the majority of the improvement comes from
the inability of high energy phonons to enter the additional layer due to the
lack of available phonon states.


title:
The Duality of Networks and Foci: Generative Models of Two-Mode Networks
  from One-Mode Networks

abstract:
  Focus theory describes how shared social statuses, beliefs, and places (i.e.,
foci) can facilitate the formation of social ties, while two-mode projections
provide a method for transforming two-mode data on individuals' memberships in
foci into a one-mode network of their co-memberships. In this paper, I explore
the opposite process: how social ties can facilitate the formation of foci, and
how two-mode data can be generated from a one-mode network. Drawing on theories
of team, social group, and organization recruitment, I propose three models
that describe how such foci might form from the relationships in a social
network. I show that these models can be used to generate empirically plausible
two-mode networks characterized by positively-skewed degree distributions and
an over-representation of four- and six-cycles. I conclude by discussing these
models' limitations, and highlighting how they might be used to study two-mode
networks representing social foci, and to investigate two-mode projection
methods.


title:
On the detailed structure of quantum control landscape for fast single
  qubit phase-shift gate generation

abstract:
  In this work, we study the detailed structure of quantum control landscape
for the problem of single-qubit phase shift gate generation on the fast time
scale. In previous works, the absence of traps for this problem was proven on
various time scales. A special critical point which was known to exist in
quantum control landscapes was shown to be either a saddle or a global
extremum, depending on the parameters of the control system. However, in the
case of saddle the numbers of negative and positive eigenvalues of Hessian at
this point and their magnitudes have not been studied. At the same time, these
numbers and magnitudes determine the relative ease or difficulty for practical
optimization in a vicinity of the critical point. In this work, we compute the
numbers of negative and positive eigenvalues of Hessian at this saddle point
and moreover, give estimates on magnitude of these eigenvalues. We also
significantly simplify our previous proof of the theorem about this saddle
point of the Hessian [Theorem~3 in B.O.~Volkov, O.V.~Morzhin, A.N.~Pechen,
J.~Phys.~A: Math. Theor. {\bf 54}, 215303 (2021)].


title: Measuring the Streaming motion in the Milky Way disc with Gaia EDR3 +

abstract:
  We map the 3D kinematics of the Galactic disc out to 3.5 kpc from the Sun,
and within 0.75 kpc from the midplane of the Milky Way. To this end, we combine
high quality astrometry from \gedrthree{}, with the radial velocities from
\gdrtwo{}, and from major spectroscopic surveys including \apogee{}, \galah{},
and \lamost{}. We construct an axisymmetric model for the mean velocity field,
and then subtract this on a star-by-star basis to obtain the peculiar velocity
field in the Galactocentric components, \vphi{}, \vR, \vz, as well as the
heliocentric line-of-sight, \vlos{}. The velocity residuals are quantified
using the power spectrum, and we find that the peak power ($A$) in the midplane
($|z|<0.25$ kpc) is ($A_{\phi},A_{\rm R},A_{\rm Z},A_{\rm
los}$)=($4.2,8.5,2.6,4.6$), at $0.25 < |z|/[{\rm kpc}] < 0.5$, is
($A_{\phi},A_{\rm R},A_{\rm Z},A_{\rm los}$)=($4.0,7.9,3.6,5.3$), and at $0.5 <
|z|/[{\rm kpc}] < 0.75$, is ($A_{\phi},A_{\rm R},A_{\rm Z},A_{\rm
los}$)=($1.9,6.9,5.2,6.4$). Our results provide for the first time, a measure
of the streaming motion in the disc in the individual components. We find that
streaming is most significant in the Galactocentric radial component, and at
all heights ($|Z|$) probed, but is also not negligible in the other components.
Additionally, we find that the patterns in the velocity field overlap spatially
with models for the Spiral arms in the Milky Way. Finally, we demonstrate using
a simulation that phase mixing of disrupting spiral arms can generate such
residuals in the velocity field, where the radial component is dominant, just
as in the real data. The simulation also suggests that with evolution of time
both the amplitude and the physical scale of the peculiar motion decreases.


title: Parallel spinors, pp-waves, and gravitational perturbations

abstract:
  We prove that any real, vacuum gravitational perturbation of a 4-dimensional
vacuum pp-wave space-time can be locally expressed, modulo gauge
transformations, as the real part of a Hertz/Debye potential, where the scalar
potential satisfies the wave equation. We discuss relations with complex
perturbations, complex space-times, non-linear structures, and real spaces with
split (ultra-hyperbolic/Kleinian) signature. Motivated by generalized notions
of parallel spinors, we also discuss generalizations of the result to other
space-times.


title: Galois sections and $p$-adic period mappings

abstract:
  Let $K$ be a number field not containing a CM subfield. For any smooth
projective curve $Y/K$ of genus $\geq2$, we prove that the image of the
"Selmer" part of Grothendieck's section set inside the $K_v$-rational points
$Y(K_v)$ is finite for every finite place $v$. This gives an unconditional
verification of a prediction of Grothendieck's section conjecture. In the
process of proving our main result, we also refine and extend the method of
Lawrence and Venkatesh, with potential consequences for explicit computations.


title:
Efficient Semiclassical Evaluation of Electronic Coherences in
  Polyatomic Molecules

abstract:
  Exposing a molecule to intense light pulses may bring this molecule to a
nonstationary quantum state, thus launching correlated dynamics of electronic
and nuclear subsystems. Although much had been achieved in the understanding of
fundamental physics behind the electron-nuclear interactions and dynamics,
accurate numerical simulations of light-induced processes taking place in
polyatomic molecules remain a formidable challenge. Here, we review a recently
developed theoretical approach for evaluating electronic coherences in
molecules, in which the ultrafast electronic dynamics is coupled to nuclear
motion. The presented technique, which combines accurate ab initio on-the-fly
simulations of electronic structure with efficient semiclassical procedure to
compute the dynamics of nuclear wave packets, is not only computationally
efficient, but also can help shed light on the underlying physical mechanisms
of decoherence and revival of the electronic coherences driven by nuclear
rearrangement.


title: Solar Polar Flux Redistribution based on Observed Coronal Holes

abstract:
  We explore the use of observed polar coronal holes (CHs) to constrain the
flux distribution within the polar regions of global solar magnetic field maps
in the absence of reliable quality polar field observations. Global magnetic
maps, generated by the Air Force Data Assimilative Photospheric flux Transport
(ADAPT) model, are modified to enforce field unipolarity thresholds both within
and outside observed CH boundaries. The polar modified and unmodified maps are
used to drive Wang-Sheeley-Arge (WSA) models of the corona and solar wind (SW).
The WSA predicted CHs are compared with the observations, and SW predictions at
the WIND and Ulysses spacecraft are also used to provide context for the new
polar modified maps. We find that modifications of the polar flux never worsen
and typically improve both the CH and SW predictions. We also confirm the
importance of the choice of the domain over which WSA generates the coronal
magnetic field solution but find that solutions optimized for one location in
the heliosphere can worsen predictions at other locations. Finally, we
investigate the importance of low-latitude (i.e., active region) magnetic
fields in setting the boundary of polar CHs, determining that they have at
least as much impact as the polar fields themselves.


title: Flat symplectic Lie algebras

abstract:
  Let $(G,\Omega)$ be a symplectic Lie group, i.e, a Lie group endowed with a
left invariant symplectic form. If $\G$ is the Lie algebra of $G$ then we call
$(\G,\omega=\Om(e))$ a symplectic Lie algebra. The product $\bullet$ on $\G$
defined by $3\omega\left(x\bullet
y,z\right)=\omega\left([x,y],z\right)+\omega\left([x,z],y\right)$ extends to a
left invariant connection $\na$ on $G$ which is torsion free and symplectic
($\na\Om=0)$. When $\na$ has vanishing curvature, we call $(G,\Omega)$ a flat
symplectic Lie group and $(\G,\om)$ a flat symplectic Lie algebra. In this
paper, we study flat symplectic Lie groups. We start by showing that the
derived ideal of a flat symplectic Lie algebra is degenerate with respect to
$\om$. We show that a flat symplectic Lie group must be nilpotent with
degenerate center. This implies that the connection $\na$ of a flat symplectic
Lie group is always complete. We prove that the double extension process can be
applied to characterize all flat symplectic Lie algebras. More precisely, we
show that every flat symplectic Lie algebra is obtained by a sequence of double
extension of flat symplectic Lie algebras starting from $\{0\}$. As examples in
low dimensions, we classify all flat symplectic Lie algebras of dimension
$\leq6$.


title: Unified Simulation, Perception, and Generation of Human Behavior

abstract:
  Understanding and modeling human behavior is fundamental to almost any
computer vision and robotics applications that involve humans. In this thesis,
we take a holistic approach to human behavior modeling and tackle its three
essential aspects -- simulation, perception, and generation. Throughout the
thesis, we show how the three aspects are deeply connected and how utilizing
and improving one aspect can greatly benefit the other aspects. We also discuss
the lessons learned and our vision for what is next for human behavior
modeling.


title: Curriculum Learning for Dense Retrieval Distillation

abstract:
  Recent work has shown that more effective dense retrieval models can be
obtained by distilling ranking knowledge from an existing base re-ranking
model. In this paper, we propose a generic curriculum learning based
optimization framework called CL-DRD that controls the difficulty level of
training data produced by the re-ranking (teacher) model. CL-DRD iteratively
optimizes the dense retrieval (student) model by increasing the difficulty of
the knowledge distillation data made available to it. In more detail, we
initially provide the student model coarse-grained preference pairs between
documents in the teacher's ranking and progressively move towards finer-grained
pairwise document ordering requirements. In our experiments, we apply a simple
implementation of the CL-DRD framework to enhance two state-of-the-art dense
retrieval models. Experiments on three public passage retrieval datasets
demonstrate the effectiveness of our proposed framework.


title: Online convex optimization for data-driven control of dynamical systems

abstract:
  We propose an algorithm based on online convex optimization for controlling
discrete-time linear dynamical systems. The algorithm is data-driven, i.e.,
does not require a model of the system, and is able to handle a priori unknown
and time-varying cost functions. To this end, we make use of a single
persistently exciting input-output sequence of the system and results from
behavioral systems theory which enable it to handle unknown linear
time-invariant systems. Moreover, we consider noisy output feedback instead of
full state measurements and allow general economic cost functions. Our analysis
of the closed loop reveals that the algorithm is able to achieve sublinear
regret, where the measurement noise only adds an additional constant term to
the regret upper bound. In order to do so, we derive a data-driven
characterization of the steady-state manifold of an unknown system. Moreover,
our algorithm is able to asymptotically exactly estimate the measurement noise.
The effectiveness and applicational aspects of the proposed method are
illustrated by means of a detailed simulation example in thermal control.


title: Phase gadget compilation for diagonal qutrit gates

abstract:
  Phase gadgets have proved to be an indispensable tool for reasoning about
ZX-diagrams, being used in optimisation and simulation of quantum circuits and
the theory of measurement-based quantum computation. In this paper we study
phase gadgets for qutrits. We present the flexsymmetric variant of the original
qutrit ZX-calculus, which allows for rewriting that is closer in spirit to the
original (qubit) ZX-calculus. In this calculus phase gadgets look as you would
expect, but there are non-trivial differences in their properties. We devise
new qutrit-specific tricks to extend the graphical Fourier theory of qubits,
resulting in a translation between the 'additive' phase gadgets and a
'multiplicative' counterpart we dub phase multipliers. This enables us to build
different types of qutrit multiple-controlled phase gates. As an application of
these results we find a construction for emulating arbitrary qubit diagonal
unitaries, and specifically find an emulation for the qubit CCZ gate that only
requires three single-qutrit non-Clifford gates to implement - provably lower
than the four T gates needed using just qubit gates.


title:
Generalization of the hartogs-bochner theorem for forms to unbounded
  domains

abstract:
  We give some generalizations of the $C^\infty$ Hartogs-Bochner extension
theorem for differential forms defined in unbounded domains of a complex
analytie manifold in relation with the vanising of some $\bar{\partial}$
cohomologies groups.


title:
KING: Generating Safety-Critical Driving Scenarios for Robust Imitation
  via Kinematics Gradients

abstract:
  Simulators offer the possibility of safe, low-cost development of
self-driving systems. However, current driving simulators exhibit na\"ive
behavior models for background traffic. Hand-tuned scenarios are typically
added during simulation to induce safety-critical situations. An alternative
approach is to adversarially perturb the background traffic trajectories. In
this paper, we study this approach to safety-critical driving scenario
generation using the CARLA simulator. We use a kinematic bicycle model as a
proxy to the simulator's true dynamics and observe that gradients through this
proxy model are sufficient for optimizing the background traffic trajectories.
Based on this finding, we propose KING, which generates safety-critical driving
scenarios with a 20% higher success rate than black-box optimization. By
solving the scenarios generated by KING using a privileged rule-based expert
algorithm, we obtain training data for an imitation learning policy. After
fine-tuning on this new data, we show that the policy becomes better at
avoiding collisions. Importantly, our generated data leads to reduced
collisions on both held-out scenarios generated via KING as well as traditional
hand-crafted scenarios, demonstrating improved robustness.


title:
Force-gradient sensing and entanglement via feedback cooling of
  interacting nanoparticles

abstract:
  We show that feedback-cooling of two levitated, interacting nanoparticles
enables differential sensing of forces and the observation of stationary
entanglement. The feedback drives the two particles into a stationary,
non-thermal state which is susceptible to inhomogeneous force fields and which
exhibits entanglement for sufficiently strong inter-particle couplings. We
demonstrate that force-gradient sensing at the zepto-Newton per micron range is
feasible and that entanglement due to the Coulomb interaction between charged
particles can be realistically observed in state-of-the-art setups.


title:
Improving empirical interatomic potentials for predicting thermophysical
  properties by using an irreducible derivatives approach: The case of thorium
  dioxide

abstract:
  The accuracy of physical property predictions using classical molecular
dynamics simulations is determined by the quality of the empirical interatomic
potentials (EIPs). We introduce a training approach for EIPs, based on direct
comparisons of the second- and third-order interatomic force constants (IFCs)
between EIP and density functional theory (DFT) calculations. This work's
unique aspect is the utilization of irreducible derivatives (IDs) of the total
energy, which leverage on the symmetry of the crystalline structure and provide
a minimal representation of the IFCs. Our approach is tailored toward accurate
predictions of thermal conductivity, thus requiring knowledge of both harmonic
and anharmonic IFCs by matching second- and third-order displacement
derivatives, whereas second-order strain derivatives are needed for determining
the elastic constants. We demonstrate this approach as an efficient and robust
manner in which to train EIPs for predicting phonons and related properties, by
optimizing parameters of an embedded-atom method potential for ThO$_2$, which
is used as a model system for fluorite oxides. Our ID-trained EIP provides
thermophysical properties in great agreement with DFT, and outperforms
previously widely utilized EIP for ThO$_2$ in phonon dispersion and thermal
conductivity calculations. It also provides reasonable estimates of thermal
expansion and the formation energies of simple defects.


title: HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling

abstract:
  4D human sensing and modeling are fundamental tasks in vision and graphics
with numerous applications. With the advances of new sensors and algorithms,
there is an increasing demand for more versatile datasets. In this work, we
contribute HuMMan, a large-scale multi-modal 4D human dataset with 1000 human
subjects, 400k sequences and 60M frames. HuMMan has several appealing
properties: 1) multi-modal data and annotations including color images, point
clouds, keypoints, SMPL parameters, and textured meshes; 2) popular mobile
device is included in the sensor suite; 3) a set of 500 actions, designed to
cover fundamental movements; 4) multiple tasks such as action recognition, pose
estimation, parametric human recovery, and textured mesh reconstruction are
supported and evaluated. Extensive experiments on HuMMan voice the need for
further study on challenges such as fine-grained action recognition, dynamic
human mesh reconstruction, point cloud-based parametric human recovery, and
cross-device domain gaps.


title:
Capturing the ground state of uranium dioxide from first principles:
  crystal distortion, magnetic structure, and phonons

abstract:
  Uranium dioxide (UO$_2$) remains a formidable challenge for first-principles
approaches, due to the complex interplay among spin-orbit coupling, Mott
physics, magnetic ordering, and crystal distortions. Here we use DFT+$U$ to
explore UO$_2$ at zero temperature, incorporating all the aforementioned
phenomena. The technical challenge is to navigate the many metastable
electronic states produced by DFT+$U$, which is acomplished using $f$-orbital
occupation matrix control to search for the ground state. We restrict our
search to the high-symmetry ferromagnetic phase, including spin-orbit coupling,
which produces a previously unreported occupation matrix. This newfound
occupation matrix is then used as an initialization to explore the broken
symmetry phases. We find the oxygen cage distortion of the 3k antiferromagnetic
state to be in excellent agreement with experiments, and both the spin-orbit
coupling and the Hubbard $U$ are critical ingredients. We demonstrate that only
select phonon modes have a strong dependence on the Hubbard $U$, whereas
magnetic ordering has only a small influence overall. We perform measurements
of the phonon dispersion curves using inelastic neutron scattering, and our
calculations show good agreement when using reasonable values of $U$. The
quantitative success of DFT+$U$ warrants exploration of thermal transport and
other observables within this level of theory.


title:
The minimal Archimedean order unitization of seminormed ordered vector
  spaces

abstract:
  Archimedean order unit (AOU) spaces have many favourable properties, so it is
useful to have ways of turning a general ordered vector space into an AOU
space. One such construction is given by the Archimedeanization of Paulsen and
Tomforde, provided that the original space already has order units. In this
paper, we give a different construction, which assumes the presence of a
seminorm instead of an order unit. We show that our order unitization is
minimal, by showing that it satisfies a universal property similar to that of
the Archimedeanization of Paulsen and Tomforde. Additionally, we study the
properties of our unitization, and show that it agrees on the self-adjoint part
of a non-unital $C^*$-algebra with the $C^*$-algebraic unitization.


title: Arithmetic Upper and Lower Bounds for the Denumerant Function

abstract:
  We use an old elementary arithmetic argument to find new upper and lower
bounds for Sylvester's denumerant function. These bounds are tight enough to
get the asymptotic behavior of the denumerant.


title: Running away from the T-parameter solution to the W mass anomaly

abstract:
  We show that it is essential to include renormalisation group (RG) effects
for determining the SMEFT parameter space consistent with the CDF W-mass
anomaly at the matching scale. This is because operators that are only weakly
constrained--for instance those probed by diboson and Higgs data--can have a
contribution to the W-boson mass via one-loop RG effects, that is comparable to
the tree-level contribution of the much more strongly constrained operators
related to electroweak precision observables. We RG evolve the low energy SMEFT
parameter space consistent with the anomaly to the matching scale and find a
much larger allowed region. In particular we find that it is possible to have a
vanishing or even negative T -parameter at the matching scale. This will
hopefully lead to a larger set of UV completions that can explain the anomaly.
For the one-loop contributions to be important a relatively low new physics
scale around 800 GeV is required. This enhances the possibility of probing this
new physics in direct and indirect searches in the recent future.


title: Foundations for learning from noisy quantum experiments

abstract:
  Understanding what can be learned from experiments is central to scientific
progress. In this work, we use a learning-theoretic perspective to study the
task of learning physical operations in a quantum machine when all operations
(state preparation, dynamics, and measurement) are a priori unknown. We prove
that, without any prior knowledge, if one can explore the full quantum state
space by composing the operations, then every operation can be learned. When
one cannot explore the full state space but all operations are approximately
known and noise in Clifford gates is gate-independent, we find an efficient
algorithm for learning all operations up to a single unlearnable parameter
characterizing the fidelity of the initial state. For learning a noise channel
on Clifford gates to a fixed accuracy, our algorithm uses quadratically fewer
experiments than previously known protocols. Under more general conditions, the
true description of the noise can be unlearnable; for example, we prove that no
benchmarking protocol can learn gate-dependent Pauli noise on Clifford+T gates
even under perfect state preparation and measurement. Despite not being able to
learn the noise, we show that a noisy quantum computer that performs entangled
measurements on multiple copies of an unknown state can yield a large advantage
in learning properties of the state compared to a noiseless device that
measures individual copies and then processes the measurement data using a
classical computer. Concretely, we prove that noisy quantum computers with
two-qubit gate error rate $\epsilon$ can achieve a learning task using $N$
copies of the state, while $N^{\Omega(1/\epsilon)}$ copies are required
classically.


title:
NMTScore: A Multilingual Analysis of Translation-based Text Similarity
  Measures

abstract:
  Being able to rank the similarity of short text segments is an interesting
bonus feature of neural machine translation. Translation-based similarity
measures include direct and pivot translation probability, as well as
translation cross-likelihood, which has not been studied so far. We analyze
these measures in the common framework of multilingual NMT, releasing the
NMTScore library (available at https://github.com/ZurichNLP/nmtscore). Compared
to baselines such as sentence embeddings, translation-based measures prove
competitive in paraphrase identification and are more robust against
adversarial or multilingual input, especially if proper normalization is
applied. When used for reference-based evaluation of data-to-text generation in
2 tasks and 17 languages, translation-based measures show a relatively high
correlation to human judgments.


title:
Linear Temporal Logic Modulo Theories over Finite Traces (Extended
  Version)

abstract:
  This paper studies Linear Temporal Logic over Finite Traces (LTLf) where
proposition letters are replaced with first-order formulas interpreted over
arbitrary theories, in the spirit of Satisfiability Modulo Theories. The
resulting logic, called LTLf Modulo Theories (LTLfMT), is semi-decidable.
Nevertheless, its high expressiveness comes useful in a number of use cases,
such as model-checking of data-aware processes and data-aware planning. Despite
the general undecidability of these problems, being able to solve satisfiable
instances is a compromise worth studying. After motivating and describing such
use cases, we provide a sound and complete semi-decision procedure for LTLfMT
based on the SMT encoding of a one-pass tree-shaped tableau system. The
algorithm is implemented in the BLACK satisfiability checking tool, and an
experimental evaluation shows the feasibility of the approach on novel
benchmarks.


title:
Revisiting Witten-O'Raifeartaigh Inflation for a Non-minimally Coupled
  Scalar Field

abstract:
  In this work, we revisit the Witten-O'Raifeartaigh model of inflation, in
which the potential takes a $\operatorname{log}^2(\phi/M)$ form, when the
scalar field is non-minimally coupled to gravity. We investigate the impact of
the coupling in the prediction of the inflationary parameters, thereby
affecting the viability of the model. We find that a small coupling of order
$\xi\sim10^{-3}$ is preferred by data at the $n_s-r$ plane level, and that the
presence of a non-zero $\xi$ allows for a large interval of the mass scale $M$,
in which it is possible to achieve a low tensor-to-scalar ratio. We also
establish constraints imposed by a subsequent reheating era, in which its
duration and temperature can be related to CMB observables, which in return,
restricts the possible values for the $n_s$ and $r$ parameters.


title: Bilinear value networks

abstract:
  The dominant framework for off-policy multi-goal reinforcement learning
involves estimating goal conditioned Q-value function. When learning to achieve
multiple goals, data efficiency is intimately connected with the generalization
of the Q-function to new goals. The de-facto paradigm is to approximate Q(s, a,
g) using monolithic neural networks. To improve the generalization of the
Q-function, we propose a bilinear decomposition that represents the Q-value via
a low-rank approximation in the form of a dot product between two vector
fields. The first vector field, f(s, a), captures the environment's local
dynamics at the state s; whereas the second component, {\phi}(s, g), captures
the global relationship between the current state and the goal. We show that
our bilinear decomposition scheme substantially improves data efficiency, and
has superior transfer to out-of-distribution goals compared to prior methods.
Empirical evidence is provided on the simulated Fetch robot task-suite and
dexterous manipulation with a Shadow hand.


title: NeurMiPs: Neural Mixture of Planar Experts for View Synthesis

abstract:
  We present Neural Mixtures of Planar Experts (NeurMiPs), a novel planar-based
scene representation for modeling geometry and appearance. NeurMiPs leverages a
collection of local planar experts in 3D space as the scene representation.
Each planar expert consists of the parameters of the local rectangular shape
representing geometry and a neural radiance field modeling the color and
opacity. We render novel views by calculating ray-plane intersections and
composite output colors and densities at intersected points to the image.
NeurMiPs blends the efficiency of explicit mesh rendering and flexibility of
the neural radiance field. Experiments demonstrate superior performance and
speed of our proposed method, compared to other 3D representations in novel
view synthesis.