title:
Time-dependent C-operators as Lewis-Riesenfeld invariants in
  non-Hermitian theories

abstract:
  ${\cal C}$-operators were introduced as involution operators in non-Hermitian
theories that commute with the time-independent Hamiltonians and the
parity/time-reversal operator. Here we propose a definition for time-dependent
${\cal C}(t)$-operators and demonstrate that for a particular signature they
may be expanded in terms of time-dependent biorthonormal left and right
eigenvectors of Lewis-Riesenfeld invariants. The vanishing commutation relation
between the ${\cal C}$-operator and the Hamiltonian in the time-independent
case is replaced by the Lewis-Riesenfeld equation in the time-dependent
scenario. Thus, ${\cal C}(t)$-operators are always Lewis-Riesenfeld invariants,
whereas the inverse is only true in certain circumstances. We demonstrate the
working of the generalities for a non-Hermitian two-level matrix Hamiltonian.
We show that solutions for ${\cal C}(t)$ and the time-dependent metric operator
may be found that hold in all three ${\cal PT}$-regimes, i.e., the ${\cal
PT}$-regime, the spontaneously broken ${\cal PT}$-regime and at the exceptional
point.


title: Designing Menus of Contracts Efficiently: The Power of Randomization

abstract:
  We study hidden-action principal-agent problems in which a principal commits
to an outcome-dependent payment scheme (called contract) so as to incentivize
the agent to take a costly, unobservable action leading to favorable outcomes.
In particular, we focus on Bayesian settings where the agent has private
information. This is collectively encoded by the agent's type, which is unknown
to the principal, but randomly drawn according to a finitely-supported,
commonly-known probability distribution. The agent's type determines both the
probability distribution over outcomes and the cost associated with each
agent's action. In Bayesian principal-agent problems, the principal may be
better off by committing to a menu of contracts specifying a contract for each
agent's type, rater than committing to a single contract. This induces a
two-stage process that resembles interactions studied in mechanism design:
after the principal has committed to a menu, the agent first reports a type to
the principal, and, then, the latter puts in place the contract in the menu
that corresponds to the reported type. Thus, the principal's computational
problem boils down to designing a menu of contracts that incentivizes the agent
to report their true type and maximizes expected utility. Previous works showed
that computing an optimal menu of contracts or an optimal (single) contract is
APX-hard, which is in sharp contrast from what happens in non-Bayesian
settings, where an optimal contract can be computed efficiently. Crucially,
previous works focus on menus of deterministic contracts. Surprisingly, in this
paper we show that, if one considers menus of randomized contracts, then an
optimal menu can be computed in polynomial time. Besides this main result, we
also close several gaps in the computational complexity analysis of the problem
of computing menus of deterministic contracts.


title: Learning Cluster Patterns for Abstractive Summarization

abstract:
  Nowadays, pre-trained sequence-to-sequence models such as BERTSUM and BART
have shown state-of-the-art results in abstractive summarization. In these
models, during fine-tuning, the encoder transforms sentences to context vectors
in the latent space and the decoder learns the summary generation task based on
the context vectors. In our approach, we consider two clusters of salient and
non-salient context vectors, using which the decoder can attend more to salient
context vectors for summary generation. For this, we propose a novel clustering
transformer layer between the encoder and the decoder, which first generates
two clusters of salient and non-salient vectors, and then normalizes and
shrinks the clusters to make them apart in the latent space. Our experimental
result shows that the proposed model outperforms the existing BART model by
learning these distinct cluster patterns, improving up to 4% in ROUGE and 0.3%
in BERTScore on average in CNN/DailyMail and XSUM data sets.


title:
Cellular Network Capacity and Coverage Enhancement with MDT Data and
  Deep Reinforcement Learning

abstract:
  Recent years witnessed a remarkable increase in the availability of data and
computing resources in communication networks. This contributed to the rise of
data-driven over model-driven algorithms for network automation. This paper
investigates a Minimization of Drive Tests (MDT)-driven Deep Reinforcement
Learning (DRL) algorithm to optimize coverage and capacity by tuning antennas
tilts on a cluster of cells from TIM's cellular network. We jointly utilize MDT
data, electromagnetic simulations, and network Key Performance indicators
(KPIs) to define a simulated network environment for the training of a Deep
Q-Network (DQN) agent. Some tweaks have been introduced to the classical DQN
formulation to improve the agent's sample efficiency, stability, and
performance. In particular, a custom exploration policy is designed to
introduce soft constraints at training time. Results show that the proposed
algorithm outperforms baseline approaches like DQN and best-fist search in
terms of long-term reward and sample efficiency. Our results indicate that
MDT-driven approaches constitute a valuable tool for autonomous coverage and
capacity optimization of mobile radio networks.


title: A Framework for Distributed Quantum Queries in the CONGEST Model

abstract:
  The Quantum CONGEST model is a variant of the CONGEST model, where messages
consist of $O(\log(n))$ qubits. We give a general framework for implementing
quantum query algorithms in Quantum CONGEST, using the concept of
parallel-queries. We apply our framework for distributed quantum queries in two
settings: when data is distributed over the network, and graph theoretical
problems where the network defines the input. The first is slightly unusual in
CONGEST but our results follow almost directly. The second is more traditional
for the CONGEST model but here we require some classical CONGEST steps to get
our results. In the setting with distributed data, we show how a network can
schedule a meeting in one of $k$ dates using $\tilde{O}(\sqrt{kD}+D)$ rounds,
with $D$ the network diameter. We also give an efficient algorithm for element
distinctness: if all nodes are given numbers, then the nodes can find any
duplicates in $\tilde{O}(n^{2/3}D^{1/3})$ rounds. We also generalize the
protocol for the distributed Deutsch-Jozsa problem from the two-party setting
considered in [arXiv:quant-ph/9802040] to general networks, giving a novel
separation between exact classical and exact quantum protocols in CONGEST. When
the input is the network structure itself, we almost directly recover the
$O(\sqrt{nD})$ round diameter computation algorithm of Le Gall and Magniez
[arXiv:1804.02917]. We also compute the radius in the same number of rounds,
and give an $\epsilon$-additive approximation of the average eccentricity in
$\tilde{O}(D+D^{3/2}/\epsilon)$ rounds. Finally, we give quantum speedups for
the problems of cycle detection and girth computation. We detect whether a
graph has a cycle of length at most $k$ in $O(D+(Dn)^{1/2-1/\Theta(k)})$
rounds. We also give a $\tilde{O}(D+(Dn)^{1/2-1/\Theta(g)})$ round algorithm
for finding the girth $g$, beating the known classical lower bound.


title: How Hard Are Verifiable Delay Functions?

abstract:
  Verifiable delay functions (VDF) are functions that enable a verifier to
verify if a prover has spent a specified number of sequential steps to execute
the function. VDFs are useful in several applications ranging from
non-interactive time-stamping to randomness beacons. A close resemble of VDFs
are interactive proofs, however have the following difference. VDFs stand
sequential against a prover using even poly(T ) parallelism to evaluate the
function for T sequential steps. We know that the class of all interactive
proofs IP = PSPACE. Then it seems natural to ask this question that how hard
are VDFs? Equivalently does this sequentiality against parallel provers add
more power to a Turing machine deciding larger class of languages than IP? In
this paper, we show that the class of all the VDFs, VDF does not belong to IP.
In particular, we construct a VDF from an EXP-complete language and reduce the
EXP-complete language to the derived VDF. Thus if VDF belongs to PSPACE = IP
then EXP belongs to PSPACE = IP which has no proof yet. So VDF does not belong
to IP.


title:
Improving Classification Model Performance on Chest X-Rays through Lung
  Segmentation

abstract:
  Chest radiography is an effective screening tool for diagnosing pulmonary
diseases. In computer-aided diagnosis, extracting the relevant region of
interest, i.e., isolating the lung region of each radiography image, can be an
essential step towards improved performance in diagnosing pulmonary disorders.
Methods: In this work, we propose a deep learning approach to enhance abnormal
chest x-ray (CXR) identification performance through segmentations. Our
approach is designed in a cascaded manner and incorporates two modules: a deep
neural network with criss-cross attention modules (XLSor) for localizing lung
region in CXR images and a CXR classification model with a backbone of a
self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR
data sets. The proposed pipeline is evaluated on Shenzhen Hospital (SH) data
set for the segmentation module, and COVIDx data set for both segmentation and
classification modules. Novel statistical analysis is conducted in addition to
regular evaluation metrics for the segmentation module. Furthermore, the
results of the optimized approach are analyzed with gradient-weighted class
activation mapping (Grad-CAM) to investigate the rationale behind the
classification decisions and to interpret its choices. Results and Conclusion:
Different data sets, methods, and scenarios for each module of the proposed
pipeline are examined for designing an optimized approach, which has achieved
an accuracy of 0.946 in distinguishing abnormal CXR images (i.e., Pneumonia and
COVID-19) from normal ones. Numerical and visual validations suggest that
applying automated segmentation as a pre-processing step for classification
improves the generalization capability and the performance of the
classification models.


title: Estimation of Looming from LiDAR

abstract:
  Looming, traditionally defined as the relative expansion of objects in the
observer's retina, is a fundamental visual cue for perception of threat and can
be used to accomplish collision free navigation. The measurement of the looming
cue is not only limited to vision, and can also be obtained from range sensors
like LiDAR (Light Detection and Ranging). In this article we present two
methods that process raw LiDAR data to estimate the looming cue. Using looming
values we show how to obtain threat zones for collision avoidance tasks. The
methods are general enough to be suitable for any six-degree-of-freedom motion
and can be implemented in real-time without the need for fine matching,
point-cloud registration, object classification or object segmentation.
Quantitative results using the KITTI dataset shows advantages and limitations
of the methods.


title:
Wavebender GAN: An architecture for phonetically meaningful speech
  manipulation

abstract:
  Deep learning has revolutionised synthetic speech quality. However, it has
thus far delivered little value to the speech science community. The new
methods do not meet the controllability demands that practitioners in this area
require e.g.: in listening tests with manipulated speech stimuli. Instead,
control of different speech properties in such stimuli is achieved by using
legacy signal-processing methods. This limits the range, accuracy, and speech
quality of the manipulations. Also, audible artefacts have a negative impact on
the methodological validity of results in speech perception studies.
  This work introduces a system capable of manipulating speech properties
through learning rather than design. The architecture learns to control
arbitrary speech properties and leverages progress in neural vocoders to obtain
realistic output. Experiments with copy synthesis and manipulation of a small
set of core speech features (pitch, formants, and voice quality measures)
illustrate the promise of the approach for producing speech stimuli that have
accurate control and high perceptual quality.


title:
The Winning Solution to the iFLYTEK Challenge 2021 Cultivated Land
  Extraction from High-Resolution Remote Sensing Image

abstract:
  Extracting cultivated land accurately from high-resolution remote images is a
basic task for precision agriculture. This report introduces our solution to
the iFLYTEK challenge 2021 cultivated land extraction from high-resolution
remote sensing image. The challenge requires segmenting cultivated land objects
in very high-resolution multispectral remote sensing images. We established a
highly effective and efficient pipeline to solve this problem. We first divided
the original images into small tiles and separately performed instance
segmentation on each tile. We explored several instance segmentation algorithms
that work well on natural images and developed a set of effective methods that
are applicable to remote sensing images. Then we merged the prediction results
of all small tiles into seamless, continuous segmentation results through our
proposed overlap-tile fusion strategy. We achieved the first place among 486
teams in the challenge.


title: Hyperbolic Twisted Torus Links

abstract:
  The twisted torus link $T(p, q; r, s)$ is obtained by twisting $r$ parallel
strands of the $(p, q)$-torus link a total of $s$ full times. In this paper we
find all twisted torus links which are hyperbolic for $\vert s\vert >3$.


title:
DRVC: A Framework of Any-to-Any Voice Conversion with Self-Supervised
  Learning

abstract:
  Any-to-any voice conversion problem aims to convert voices for source and
target speakers, which are out of the training data. Previous works wildly
utilize the disentangle-based models. The disentangle-based model assumes the
speech consists of content and speaker style information and aims to untangle
them to change the style information for conversion. Previous works focus on
reducing the dimension of speech to get the content information. But the size
is hard to determine to lead to the untangle overlapping problem. We propose
the Disentangled Representation Voice Conversion (DRVC) model to address the
issue. DRVC model is an end-to-end self-supervised model consisting of the
content encoder, timbre encoder, and generator. Instead of the previous work
for reducing speech size to get content, we propose a cycle for restricting the
disentanglement by the Cycle Reconstruct Loss and Same Loss. The experiments
show there is an improvement for converted speech on quality and voice
similarity.


title:
Organ Shape Sensing using Pneumatically Attachable Flexible Rails in
  Robotic-Assisted Laparoscopic Surgery

abstract:
  In robotic-assisted partial nephrectomy, surgeons remove a part of a kidney
often due to the presence of a mass. A drop-in ultrasound probe paired to a
surgical robot is deployed to execute multiple swipes over the kidney surface
to localise the mass and define the margins of resection. This sub-task is
challenging and must be performed by a highly skilled surgeon. Automating this
sub-task may reduce cognitive load for the surgeon and improve patient
outcomes. The overall goal of this work is to autonomously move the ultrasound
probe on the surface of the kidney taking advantage of the use of the
Pneumatically Attachable Flexible (PAF) rail system, a soft robotic device used
for organ scanning and repositioning. First, we integrate a shape-sensing
optical fibre into the PAF rail system to evaluate the curvature of target
organs in robotic-assisted laparoscopic surgery. Then, we investigate the
impact of the stiffness of the material of the PAF rail on the curvature
sensing accuracy, considering that soft targets are present in the surgical
field. Finally, we use shape sensing to plan the trajectory of the da Vinci
surgical robot paired with a drop-in ultrasound probe and autonomously generate
an Ultrasound scan of a kidney phantom.


title:
Swarm Fabrication: Reconfigurable 3D Printers and Drawing Plotters Made
  of Swarm Robots

abstract:
  We introduce Swarm Fabrication, a novel concept of creating on-demand,
scalable, and reconfigurable fabrication machines made of swarm robots. We
present ways to construct an element of fabrication machines, such as motors,
elevator, table, feeder, and extruder, by leveraging toio robots and 3D printed
attachments. By combining these elements, we demonstrate constructing a X-Y-Z
plotter with multiple toio robots, which can be used for drawing plotters and
3D printers. We also show the possibility to extend our idea to more
general-purpose fabrication machines, which include 3D printers, CNC machining,
foam cutters, line drawing devices, pick and place machines, 3D scanning, etc.
Through this, we draw a future vision, where the swarm robots can construct a
scalable and reconfigurable fabrication machines on-demand, which can be
deployed anywhere the user wishes. We believe this fabrication technique will
become a means of interactive and highly flexible fabrication in the future.


title:
Enabling Reproducibility and Meta-learning Through a Lifelong Database
  of Experiments (LDE)

abstract:
  Artificial Intelligence (AI) development is inherently iterative and
experimental. Over the course of normal development, especially with the advent
of automated AI, hundreds or thousands of experiments are generated and are
often lost or never examined again. There is a lost opportunity to document
these experiments and learn from them at scale, but the complexity of tracking
and reproducing these experiments is often prohibitive to data scientists. We
present the Lifelong Database of Experiments (LDE) that automatically extracts
and stores linked metadata from experiment artifacts and provides features to
reproduce these artifacts and perform meta-learning across them. We store
context from multiple stages of the AI development lifecycle including
datasets, pipelines, how each is configured, and training runs with information
about their runtime environment. The standardized nature of the stored metadata
allows for querying and aggregation, especially in terms of ranking artifacts
by performance metrics. We exhibit the capabilities of the LDE by reproducing
an existing meta-learning study and storing the reproduced metadata in our
system. Then, we perform two experiments on this metadata: 1) examining the
reproducibility and variability of the performance metrics and 2) implementing
a number of meta-learning algorithms on top of the data and examining how
variability in experimental results impacts recommendation performance. The
experimental results suggest significant variation in performance, especially
depending on dataset configurations; this variation carries over when
meta-learning is built on top of the results, with performance improving when
using aggregated results. This suggests that a system that automatically
collects and aggregates results such as the LDE not only assists in
implementing meta-learning but may also improve its performance.


title:
Unveiling the Zero-Phonon Line of the Boron Vacancy Center by Cavity
  Enhanced Emission

abstract:
  Negatively charged boron vacancies ($V_B^-$) in hexagonal boron nitride (hBN)
exhibit a broad emission spectrum due to strong electron-phonon coupling and
Jahn-Teller mixing of electronic states. As such, the direct measurement of the
zero-phonon line (ZPL) transition of $V_B^-$ in pristine hBN crystals has
remained elusive. Here, we measure the room-temperature ZPL wavelength of
$V_B^-$ to be $773\pm2$ nm by coupling the hBN layer to the discrete high-Q
mode in a proximal Si$_3$N$_4$ nanobeam cavity. As the wavelength of the cavity
mode is tuned, we observe a pronounced intensity resonance, indicative of
coupling of the cavity field to $V_B^-$ in the proximal hBN layer. The observed
resonance has an asymmetric lineshape with a width of $\leq10$ nm at room
temperature. Our observations are consistent with the spatial redistribution of
the $V_B^-$ emission and enhancement of ZPL quantum efficiency (Purcell effect)
as resonance is established. Our findings are supported by spatially resolved
measurements in which a clear intensity maximum is observed at the midpoint of
the nanobeam, in accord with the calculated optical field distribution of the
fundamental cavity mode. Analogous spatial and positional intensity
enhancements were not observed as the cavity mode is tuned through the phonon
sideband (PSB), an observation consistent with rapid dephasing that inhibits
weak coupling cavity QED phenomena. Our results are in excellent agreement with
first-principle calculations opening the way to using $V_B^-$ as cavity
spin-photon interfaces with an active volume defined by the nanocavity mode.


title: On the use and misuse of the Oberbeck-Boussinesq approximation

abstract:
  The Oberbeck-Boussinesq approximation is the most widely employed theoretical
scheme for the study of natural or mixed convection flows. However, the
misunderstanding of this approximated framework is a possibility that may cause
the emergence of paradoxes or, at least, incorrect conclusions. In this note,
the basic features of the Oberbeck-Boussinesq approximation are briefly
recalled and three simple examples where this theoretical scheme may be misused
are provided. Such misuses of the approximation lead to erroneous conclusions
that, in the examples presented in this note, entail violations of the
principle of mass conservation. A discussion about the Oberbeck-Boussinesq
approximation as an asymptotic theory obtained by letting the product of the
thermal expansion coefficient and the reference temperature difference tend to
zero is also presented.


title: Applying Grover's Algorithm to Hash Functions: A Software Perspective

abstract:
  Quantum software frameworks provide software engineers with the tools to
study quantum algorithms as applied to practical problems. We implement
classical hash functions MD5, SHA-1, SHA-2, and SHA-3 as quantum oracles to
study the computational resource requirements of conducting a preimage attack
with Grover's Algorithm. We introduce an improvement to the SHA-3 oracle that
reduces the number of logical qubits required in the Keccak block permutation
by 40%.


title:
Tracking perovskite crystallization via deep learning-based feature
  detection on 2D X-ray scattering data

abstract:
  Understanding the processes of perovskite crystallization is essential for
improving the properties of organic solar cells. In situ real-time
grazing-incidence X-ray diffraction (GIXD) is a key technique for this task,
but it produces large amounts of data, frequently exceeding the capabilities of
traditional data processing methods. We propose an automated pipeline for the
analysis of GIXD images, based on the Faster R-CNN deep learning architecture
for object detection, modified to conform to the specifics of the scattering
data. The model exhibits high accuracy in detecting diffraction features on
noisy patterns with various experimental artifacts. We demonstrate our method
on real-time tracking of organic-inorganic perovskite structure crystallization
and test it on two applications: 1. the automated phase identification and
unit-cell determination of two coexisting phases of Ruddlesden-Popper 2D
perovskites, and 2. the fast tracking of MAPbI$_3$ perovskite formation. By
design, our approach is equally suitable for other crystalline thin-film
materials.


title: From chaos to clock in reverberating neural net. Case study

abstract:
  What is the reason for complex dynamical patterns registered from real
biological neuronal networks? Noise and dynamical reconfiguring of a network
(functional/dynamic connectome) were proposed as possible answers. In this case
study, we report a complex dynamical pattern observed in a simple deterministic
network of 25 neurons with fixed connectome. After a short initial stimulation,
the network is engaged into a complex dynamics, which lasts for a long time.
Eventually, with no external intervention, the dynamics comes to a periodic one
with a short period. The long transient is positively checked for being
chaotic. We conclude that the complex dynamics observed is the output of neural
computation performed in the process of neuronal firings and spikes
propagation.


title:
Registered Report: A Laboratory Experiment on Using Different
  Financial-Incentivization Schemes in Software-Engineering Experimentation

abstract:
  Empirical studies in software engineering are often conducted with
open-source developers or in industrial collaborations. Seemingly, this
resulted in few experiments using financial incentives (e.g., money, vouchers)
as a strategy to motivate the participants' behavior; which is typically done
in other research communities, such as economics or psychology. Even the
current version of the SIGSOFT Empirical Standards does mention payouts for
completing surveys only, but not for mimicking the real-world or motivating
realistic behavior during experiments. So, there is a lack of understanding
regarding whether financial incentives can or cannot be useful for
software-engineering experimentation. To tackle this problem, we plan a survey
based on which we will conduct a controlled laboratory experiment. Precisely,
we will use the survey to elicit incentivization schemes we will employ as (up
to) four payoff functions (i.e., mappings of choices or performance in an
experiment to a monetary payment) during a code-review task in the experiment:
(1) a scheme that employees prefer, (2) a scheme that is actually employed, (3)
a scheme that is performance-independent, and (4) a scheme that mimics an
open-source scenario. Using a between-subject design, we aim to explore how the
different schemes impact the participants' performance. Our contributions help
understand the impact of financial incentives on developers in experiments as
well as real-world scenarios, guiding researchers in designing experiments and
organizations in compensating developers.


title: Forgiving Debt in Financial Network Games

abstract:
  A financial system is represented by a network, where nodes correspond to
banks, and directed labeled edges correspond to debt contracts between banks.
Once a payment schedule has been defined, where we assume that a bank cannot
refuse a payment towards one of its lenders if it has sufficient funds, the
liquidity of the system is defined as the sum of total payments made in the
network. Maximizing systemic liquidity is a natural objective of any financial
authority, so, we study the setting where the financial authority offers
bailout money to some bank(s) or forgives the debts of others in order to
maximize liquidity, and examine efficient ways to achieve this. We investigate
the approximation ratio provided by the greedy bailout policy compared to the
optimal one, and we study the computational hardness of finding the optimal
debt-removal and budget-constrained optimal bailout policy, respectively.
  We also study financial systems from a game-theoretic standpoint. We observe
that the removal of some incoming debt might be in the best interest of a bank,
if that helps one of its borrowers remain solvent and avoid costs related to
default. Assuming that a bank's well-being (i.e., utility) is aligned with the
incoming payments they receive from the network, we define and analyze a game
among banks who want to maximize their utility by strategically giving up some
incoming payments. In addition, we extend the previous game by considering
bailout payments. After formally defining the above games, we prove results
about the existence and quality of pure Nash equilibria, as well as the
computational complexity of finding such equilibria.


title:
Missing large-angle correlations versus even-odd point-parity imbalance
  in the cosmic microwave background

abstract:
  Context. The existence of a maximum correlation angle ($\theta_{max} \gtrsim
60^{\circ}$) in the two-point angular temperature correlations of cosmic
microwave background (CMB) radiation, measured by WMAP and Planck, stands in
sharp contrast to the prediction of standard inflationary cosmology, in which
the correlations should extend across the full sky (i.e., $180^{\circ}$). The
introduction of a hard lower cutoff ($k_{min}$) in the primordial power
spectrum, however, leads naturally to the existence of $\theta_{max}$. Among
other cosmological anomalies detected in these data, an apparent dominance of
odd-over-even parity multipoles has been seen in the angular power spectrum of
the CMB. This feature, however, may simply be due to observational
contamination in certain regions of the sky.
  Aims. In attempting to provide a more detailed assessment of whether this
odd-over-even asymmetry is intrinsic to the CMB, we therefore proceed in this
paper, first, to examine whether this odd-even parity imbalance also manifests
itself in the angular correlation function and, second, to examine in detail
the interplay between the presence of $\theta_{max}$ and this observed anomaly.
  Methods. We employed several parity statistics and recalculated the angular
correlation function for different values of the cutoff $k_{min}$ in order to
optimize the fit to the different Planck 2018 data.
  Results. We find a phenomenological connection between these features in the
data, concluding that both must be considered together in order to optimize the
theoretical fit to the Planck 2018 data.
  Conclusions. This outcome is independent of whether the parity imbalance is
intrinsic to the CMB, but if it is, the odd-over-even asymmetry would clearly
point to the emergence of new physics.


title:
Discovering mechanisms for materials microstructure optimization via
  reinforcement learning of a generative model

abstract:
  The design of materials structure for optimizing functional properties and
potentially, the discovery of novel behaviors is a keystone problem in
materials science. In many cases microstructural models underpinning materials
functionality are available and well understood. However, optimization of
average properties via microstructural engineering often leads to
combinatorically intractable problems. Here, we explore the use of the
reinforcement learning (RL) for microstructure optimization targeting the
discovery of the physical mechanisms behind enhanced functionalities. We
illustrate that RL can provide insights into the mechanisms driving properties
of interest in a 2D discrete Landau ferroelectrics simulator. Intriguingly, we
find that non-trivial phenomena emerge if the rewards are assigned to favor
physically impossible tasks, which we illustrate through rewarding RL agents to
rotate polarization vectors to energetically unfavorable positions. We further
find that strategies to induce polarization curl can be non-intuitive, based on
analysis of learned agent policies. This study suggests that RL is a promising
machine learning method for material design optimization tasks, and for better
understanding the dynamics of microstructural simulations.


title:
Antilinear superoperator and quantum geometric invariance for
  higher-dimensional quantum systems

abstract:
  We present an investigation of the antilinear superoperators and their
applications in studying higher-dimensional quantum systems. The antilinear
superoperators are introduced and various properties are discussed. We study
several crucial classes of antilinear superoperators, including antilinear
quantum channels, antilinearly unital superoperators, antiunitary
superoperators and the generalized $\Theta$-conjugation. Then using the Bloch
representation, we present a systematic investigation of the quantum geometric
transformations of higher-dimensional quantum systems. By choosing different
generalized $\Theta$-conjugation, different metrics for the space of Bloch
space-time vectors are obtained, including the Euclidean metric and Minkowskian
metric. Then using these geometric structures, we investigate the entanglement
distribution over a multipartite system restricted by quantum geometric
invariance.


title:
Surface recombination and space-charge-limited photocurrent-voltage
  (PC-V) measurements in (Cd,Mn)Te samples. Kinetics of photocurrent (PC)

abstract:
  Photocurrent-voltage characteristic (PC-V) is a method of determining the
critical parameter in X-ray and gamma-ray detector plates, i.e., the carrier
mobility - lifetime product, mt. We show on the (Cd,Mn)Te samples that the
measurement results depend strongly on the surface treatment and the charge
space distribution. The PC-V characteristics obtained for hv > Eg and hv ~ Eg
indicated that etching with 20% HCl caused an appearance of a significant
concentration of very shallow surface traps at the (Cd,Mn)Te sample surface.
These traps seriously changed the measurements of PC-V characteristics and PC
kinetics. We also noticed a small contribution of holes to photoconductivity in
the PC kinetics. The PC-V characteristics measurements for hv > Eg may test the
detector plate surface quality.


title:
Temporal Subtyping of Alzheimer's Disease Using Medical Conditions
  Preceding Alzheimer's Disease Onset in Electronic Health Records

abstract:
  Subtyping of Alzheimer's disease (AD) can facilitate diagnosis, treatment,
prognosis and disease management. It can also support the testing of new
prevention and treatment strategies through clinical trials. In this study, we
employed spectral clustering to cluster 29,922 AD patients in the OneFlorida
Data Trust using their longitudinal EHR data of diagnosis and conditions into
four subtypes. These subtypes exhibit different patterns of progression of
other conditions prior to the first AD diagnosis. In addition, according to the
results of various statistical tests, these subtypes are also significantly
different with respect to demographics, mortality, and prescription medications
after the AD diagnosis. This study could potentially facilitate early detection
and personalized treatment of AD as well as data-driven generalizability
assessment of clinical trials for AD.


title: Resampling-free bootstrap inference for quantiles

abstract:
  Bootstrap inference is a powerful tool for obtaining robust inference for
quantiles and difference-in-quantiles estimators. The computationally intensive
nature of bootstrap inference has made it infeasible in large-scale
experiments. In this paper, the theoretical properties of the Poisson bootstrap
algorithm and quantile estimators are used to derive alternative
resampling-free algorithms for Poisson bootstrap inference that reduce the
computational complexity substantially without additional assumptions. These
findings are connected to existing literature on analytical confidence
intervals for quantiles based on order statistics. The results unlock bootstrap
inference for difference-in-quantiles for almost arbitrarily large samples. At
Spotify, we can now easily calculate bootstrap confidence intervals for
quantiles and difference-in-quantiles in A/B tests with hundreds of millions of
observations.


title:
What controls the Poisson's ratio of highly incompressible metallic
  glasses?

abstract:
  The manner in which metallic glasses fail under external loading is known to
correlate well with those glasses' Poisson's ratio $\nu$: low-$\nu$
(compressible) glasses typically feature brittle failure patterns with scarce
plastic deformation, while high-$\nu$ (incompressible) glasses typically fail
in a ductile manner, accompanied by a high degree of plastic deformation and
extensive liquid-like flow. Since the technological utility of metallic glasses
depends on their ductility, materials scientists have been concerned with
fabricating high-$\nu$ glassy alloys. To shed light on the underlying mechanism
of high-$\nu$ metallic glasses, we employ computer simulations of a simple
glass-forming model with a single tunable parameter that controls the
interparticle-potential's stiffness. We show that the presented model gives
rise to ultra high-$\nu$ glasses, reaching $\nu\!=\!0.445$ and thus exceeding
the most incompressible laboratory metallic glass. We further show that our
higher-$\nu$ computer glasses host relatively softer quasilocalized glassy
excitations, and establish relations between their associated localization
length, macroscopic elasticity, and mechanical disorder. Finally, we discuss
the possible role of the so-called unjamming transition in controlling the
elasticity of ultra high-$\nu$ glasses.


title: An accelerated proximal gradient method for multiobjective optimization

abstract:
  Many descent methods for multiobjective optimization problems have been
developed in recent years. In 2000, the steepest descent method was proposed
for differentiable multiobjective optimization problems. Afterward, the
proximal gradient method, which can solve composite problems, was also
considered. However, the accelerated versions are not sufficiently studied. In
this paper, we propose a multiobjective accelerated proximal gradient
algorithm, in which we solve subproblems with terms that only appear in the
multiobjective case. We also show the proposed method's global convergence rate
($O(1/k^2)$) under reasonable assumptions, using a merit function to measure
the complexity. Moreover, we present an efficient way to solve the subproblem
via its dual, and we confirm the validity of the proposed method through
preliminary numerical experiments.


title: Error Exponent and Strong Converse for Quantum Soft Covering

abstract:
  How well can we approximate a quantum channel output state using a random
codebook with a certain size? In this work, we study the quantum soft covering
problem. Namely, we use a random codebook with codewords independently sampled
from a prior distribution and send it through a classical-quantum channel to
approximate the target state. When using a random codebook sampled from an
independent and identically distributed prior with a rate above the quantum
mutual information, we show that the expected trace distance between the
codebook-induced state and the target state decays with exponent given by the
sandwiched R\'enyi information. On the other hand, when the rate of the
codebook size is below the quantum mutual information, the trace distance
converges to one exponentially fast. We obtain similar results when using a
random constant composition codebook, whereas the sandwiched Augustin
information expresses the error exponent. In addition to the above large
deviation analysis, our results also hold in the moderate deviation regime.
That is, we show that even when the rate of the codebook size approaches the
quantum mutual information moderately quickly, the trace distance still
vanishes asymptotically.


title:
Learning Dynamics and Structure of Complex Systems Using Graph Neural
  Networks

abstract:
  Many complex systems are composed of interacting parts, and the underlying
laws are usually simple and universal. While graph neural networks provide a
useful relational inductive bias for modeling such systems, generalization to
new system instances of the same type is less studied. In this work we trained
graph neural networks to fit time series from an example nonlinear dynamical
system, the belief propagation algorithm. We found simple interpretations of
the learned representation and model components, and they are consistent with
core properties of the probabilistic inference algorithm. We successfully
identified a `graph translator' between the statistical interactions in belief
propagation and parameters of the corresponding trained network, and showed
that it enables two types of novel generalization: to recover the underlying
structure of a new system instance based solely on time series observations, or
to construct a new network from this structure directly. Our results
demonstrated a path towards understanding both dynamics and structure of a
complex system and how such understanding can be used for generalization.


title:
Minimax Regret for Partial Monitoring: Infinite Outcomes and
  Rustichini's Regret

abstract:
  We show that a version of the generalised information ratio of Lattimore and
Gyorgy (2020) determines the asymptotic minimax regret for all finite-action
partial monitoring games provided that (a) the standard definition of regret is
used but the latent space where the adversary plays is potentially infinite; or
(b) the regret introduced by Rustichini (1999) is used and the latent space is
finite. Our results are complemented by a number of examples. For any $p \in
[1/2,1]$ there exists an infinite partial monitoring game for which the minimax
regret over $n$ rounds is $n^p$ up to subpolynomial factors and there exist
finite games for which the minimax Rustichini regret is $n^{4/7}$ up to
subpolynomial factors.


title:
Does prior knowledge in the form of multiple low-dose PET images (at
  different dose levels) improve standard-dose PET prediction?

abstract:
  Reducing the injected dose would result in quality degradation and loss of
information in PET imaging. To address this issue, deep learning methods have
been introduced to predict standard PET images (S-PET) from the corresponding
low-dose versions (L-PET). The existing deep learning-based denoising methods
solely rely on a single dose level of PET images to predict the S-PET images.
In this work, we proposed to exploit the prior knowledge in the form of
multiple low-dose levels of PET images (in addition to the target low-dose
level) to estimate the S-PET images.


title: Peierls bounds from Toom contours

abstract:
  We review and extend Toom's classical result about stability of trajectories
of cellular automata, with the aim of deriving explicit bounds for monotone
Markov processes, both in discrete and continuous time. This leads, among other
things, to rigorous bounds for a two-dimensional interacting particle system
with cooperative branching and deaths. Our results can be applied to derive
bounds for other monotone systems as well.


title:
Understanding the fragile-to-strong transition in silica from
  microscopic dynamics

abstract:
  In this work, we revisit the fragile-to-strong transition (FTS) in the
simulated BKS silica from the perspective of microscopic dynamics in an effort
to elucidate the dynamical behaviors of fragile and strong glass-forming
liquids. Softness, which is a machine-learned feature from local atomic
structures, is used to predict the microscopic activation energetics and
long-term dynamics. The FTS is found to originate from a change in the
temperature dependence of the microscopic activation energetics. Furthermore,
results suggest there are two diffusion channels with different energy barriers
in BKS silica. The fast dynamics at high temperatures is dominated by the
channel with small energy barriers ($<\sim$1 eV), which is controlled by the
short-range order. The rapid closing of this diffusion channel when lowering
temperature leads to the fragile behavior. On the other hand, the slow dynamics
at low temperatures is dominated by the channel with large energy barriers
controlled by the medium-range order. This slow diffusion channel changes only
subtly with temperature, leading to the strong behavior. The distributions of
barriers in the two channels show different temperature dependences, causing a
crossover at $\sim$3100 K. This transition temperature in microscopic dynamics
is consistent with the inflection point in the configurational entropy,
suggesting there is a fundamental correlation between microscopic dynamics and
thermodynamics.


title:
Multi-Objective Dual Simplex-Mesh Based Deformable Image Registration
  for 3D Medical Images -- Proof of Concept

abstract:
  Reliably and physically accurately transferring information between images
through deformable image registration with large anatomical differences is an
open challenge in medical image analysis. Most existing methods have two key
shortcomings: first, they require extensive up-front parameter tuning to each
specific registration problem, and second, they have difficulty capturing large
deformations and content mismatches between images. There have however been
developments that have laid the foundation for potential solutions to both
shortcomings. Towards the first shortcoming, a multi-objective optimization
approach using the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm
(RV-GOMEA) has been shown to be capable of producing a diverse set of
registrations for 2D images in one run of the algorithm, representing different
trade-offs between conflicting objectives in the registration problem. This
allows the user to select a registration afterwards and removes the need for
up-front tuning. Towards the second shortcoming, a dual-dynamic grid
transformation model has proven effective at capturing large differences in 2D
images. These two developments have recently been accelerated through GPU
parallelization, delivering large speed-ups. Based on this accelerated version,
it is now possible to extend the approach to 3D images. Concordantly, this work
introduces the first method for multi-objective 3D deformable image
registration, using a 3D dual-dynamic grid transformation model based on
simplex meshes while still supporting the incorporation of annotated guidance
information and multi-resolution schemes. Our proof-of-concept prototype shows
promising results on synthetic and clinical 3D registration problems, forming
the foundation for a new, insightful method that can include bio-mechanical
properties in the registration.


title:
Probing light mediators and $(g-2)_{\mu}$ through detection of coherent
  elastic neutrino nucleus scattering at COHERENT

abstract:
  We present the constraints on the parameters of several light boson mediator
models obtained from the analysis of the current data of the COHERENT CE$\nu$NS
experiment. We consider a variety of vector boson mediator models: the
so-called universal, the $B-L$ and other anomaly-free $U(1)'$ gauge models with
direct couplings of the new vector boson with neutrinos and quarks, and the
anomaly-free $L_e-L_\mu$, $L_e-L_\tau$, and $L_\mu-L_\tau$ gauge models where
the coupling of the new vector boson with the quarks is generated by kinetic
mixing with the photon at the one-loop level. We consider also a model with a
new light scalar boson mediator that is assumed, for simplicity, to have
universal coupling with quarks and leptons. Since the COHERENT CE$\nu$NS data
are well-fitted with the cross section predicted by the Standard Model, the
analysis of the data yields constraints for the mass and coupling of the new
boson mediator that depend on the charges of quarks and neutrinos in each model
under consideration. We compare these constraints with the limits obtained in
other experiments and with the values that can explain the muon $g-2$ anomaly
in the models where the muon couples to the new boson mediator.


title:
Impurity-induced excitations in twisted topological van der Waals
  superconductors

abstract:
  Twisted van der Waals materials have provided a new platform realizing
artificial topological superconductivity, as demonstrated in twisted
CrBr$_3$/NbSe$_2$ heterostructures. The emergence of a topological
superconducting state in a twisted van der Waals material provides a new
platform for exploring new strategies to control topological superconductors,
potentially exploiting unique features associated with a moire pattern. In
particular, impurities in moire superconductors can potentially lead to the
unique interplay between atomic and moire length scales, a feature absent in
generic topological superconductors. Here we address the impact of non-magnetic
impurities in a topological moire superconductor, both in the weak and strong
regime, considering both periodic arrays and single impurities in otherwise
pristine infinite moire systems. We demonstrate a fine interplay between
impurity induced modes and the moire length, leading to radically different
spectral and topological properties depending on the relative impurity location
and moire lengths. Our results highlight the key role of impurities in
topological moire superconductors, revealing the key interplay between length
and energies scales in artificial moire systems.


title:
A novel active veto prototype detector with an inner target for improved
  rare event searches

abstract:
  We report the fabrication and performance of an annular, cryogenic,
phonon-mediated veto detector that can host an inner target detector, allowing
substantial reduction in radiogenic backgrounds for rare event search
experiments. A germanium veto detector of mass $\sim$500 g with an outer
diameter of 76 mm and an inner diameter of 28 mm was produced. A 25 mm diameter
germanium inner target detector of mass $\sim$10 g was mounted inside the veto
detector. The detector was designed using inputs from a GEANT4 based
simulation, where it was modeled to be sandwiched between two germanium
detectors. The simulation showed that the background rates (dominantly gamma
interactions) could be reduced by $>$ 90$\%$, and that such an arrangement is
sufficient for aggressive background reduction needed for neutrino and dark
matter search experiments. During testing at the experimental site the veto
detector prototype achieved a baseline resolution of 1.24 $\pm$ 0.02 keV while
hosting a functional inner target detector. The baseline resolution of the
inner target detector was 147 $\pm$ 2 eV. The detectors were operated at mK
temperatures. The experimental results of an identical detector arrangement are
in excellent agreement with the simulation.


title:
Statistical and Spatio-temporal Hand Gesture Features for Sign Language
  Recognition using the Leap Motion Sensor

abstract:
  In modern society, people should not be identified based on their disability,
rather, it is environments that can disable people with impairments.
Improvements to automatic Sign Language Recognition (SLR) will lead to more
enabling environments via digital technology. Many state-of-the-art approaches
to SLR focus on the classification of static hand gestures, but communication
is a temporal activity, which is reflected by many of the dynamic gestures
present. Given this, temporal information during the delivery of a gesture is
not often considered within SLR. The experiments in this work consider the
problem of SL gesture recognition regarding how dynamic gestures change during
their delivery, and this study aims to explore how single types of features as
well as mixed features affect the classification ability of a machine learning
model. 18 common gestures recorded via a Leap Motion Controller sensor provide
a complex classification problem. Two sets of features are extracted from a 0.6
second time window, statistical descriptors and spatio-temporal attributes.
Features from each set are compared by their ANOVA F-Scores and p-values,
arranged into bins grown by 10 features per step to a limit of the 250
highest-ranked features. Results show that the best statistical model selected
240 features and scored 85.96% accuracy, the best spatio-temporal model
selected 230 features and scored 80.98%, and the best mixed-feature model
selected 240 features from each set leading to a classification accuracy of
86.75%. When all three sets of results are compared (146 individual machine
learning models), the overall distribution shows that the minimum results are
increased when inputs are any number of mixed features compared to any number
of either of the two single sets of features.


title: Robust Real-time LiDAR-inertial Initialization

abstract:
  For most LiDAR-inertial odometry, accurate initial states, including temporal
offset and extrinsic transformation between LiDAR and 6-axis IMUs, play a
significant role and are often considered as prerequisites. However, such
information may not be always available in customized LiDAR-inertial systems.
In this paper, we propose LI-Init: a full and real-time LiDAR-inertial system
initialization process that calibrates the temporal offset and extrinsic
parameter between LiDARs and IMUs, and also the gravity vector and IMU bias by
aligning the state estimated from LiDAR measurements with that measured by IMU.
We implement the proposed method as an initialization module, which, if
enabled, automatically detects the degree of excitation of the collected data
and calibrate, on-the-fly, the temporal offset, extrinsic, gravity vector, and
IMU bias, which are then used as high-quality initial state values for
real-time LiDAR-inertial odometry systems. Experiments conducted with different
types of LiDARs and LiDAR-inertial combinations show the robustness,
adaptability and efficiency of our initialization method. The implementation of
our LiDAR-inertial initialization procedure LI-Init and test data are
open-sourced on Github and also integrated into a state-of-the-art
LiDAR-inertial odometry system FAST-LIO2.


title:
On a Cahn-Hilliard-Keller-Segel model with generalized logistic source
  describing tumor growth

abstract:
  We propose a new type of diffuse interface model describing the evolution of
a tumor mass under the effects of a chemical substance (e.g., a nutrient or a
drug). The process is described by utilizing the variables $\varphi$, an order
parameter representing the local proportion of tumor cells, and $\sigma$,
representing the concentration of the chemical. The order parameter $\varphi$
is assumed to satisfy a suitable form of the Cahn-Hilliard equation with mass
source and logarithmic potential of Flory-Huggins type (or generalizations of
it). The chemical concentration $\sigma$ satisfies a reaction-diffusion
equation where the cross-diffusion term has the same expression as in the
celebrated Keller-Segel model. In this respect, the model we propose represents
a new coupling between the Cahn-Hilliard equation and a subsystem of the
Keller-Segel model. We believe that, compared to other models, this choice is
more effective in capturing the chemotactic effects that may occur in tumor
growth dynamics (chemically induced tumor evolution and consumption of
nutrient/drug by tumor cells). Note that, in order to prevent finite time
blowup of $\sigma$, we assume a chemical source term of logistic type. Our main
mathematical result is devoted to proving existence of weak solutions in a
rather general setting that covers both the two- and three- dimensional cases.
Under more restrictive assumptions on coefficients and data, and in some cases
on the spatial dimension, we prove various regularity results. Finally, in a
proper class of smooth solutions we show uniqueness and continuous dependence
on the initial data in a number of significant cases.


title:
The kinks, the solitons and the shocks in series connected discrete
  Josephson transmission lines

abstract:
  We analytically study the localized running waves in the discrete Josephson
transmission lines (JTL), constructed from Josephson junctions (JJ) and
capacitors. The quasi-continuum approximation reduces calculation of the
running wave properties to the problem of equilibrium of an elastic rod in the
potential field. Making additional approximation, we reduce the problem to the
motion of the fictitious Newtonian particle in the potential well. We show that
there exist running waves in the form of supersonic kinks and solitons and
calculate their velocities and profiles. We show that the nonstationary smooth
waves which are small perturbations on the homogeneous non-zero background are
described by Korteweg-de Vries equation, and those on zero background -- by
modified Korteweg-de Vries equation. We also study the effect of dissipation on
the running waves in JTL and find that in the presence of the resistors,
shunting the JJ and/or in series with the ground capacitors, the only possible
stationary running waves are the shock waves, whose profiles are also found.

categories: cond-mat.supr-con math-ph math.MP nlin.PS physics.app-ph


title: Computing Multiple Image Reconstructions with a Single Hypernetwork

abstract:
  Deep learning based techniques achieve state-of-the-art results in a wide
range of image reconstruction tasks like compressed sensing. These methods
almost always have hyperparameters, such as the weight coefficients that
balance the different terms in the optimized loss function. The typical
approach is to train the model for a hyperparameter setting determined with
some empirical or theoretical justification. Thus, at inference time, the model
can only compute reconstructions corresponding to the pre-determined
hyperparameter values. In this work, we present a hypernetwork-based approach,
called HyperRecon, to train reconstruction models that are agnostic to
hyperparameter settings. At inference time, HyperRecon can efficiently produce
diverse reconstructions, which would each correspond to different
hyperparameter values. In this framework, the user is empowered to select the
most useful output(s) based on their own judgement. We demonstrate our method
in compressed sensing, super-resolution and denoising tasks, using two
large-scale and publicly-available MRI datasets. Our code is available at
https://github.com/alanqrwang/hyperrecon.


title: Entropy-driven order in an array of nanomagnets

abstract:
  Long-range ordering is typically associated with a decrease in entropy. Yet,
it can also be driven by increasing entropy in certain special cases. We
demonstrate that artificial spin ice arrays of single-domain nanomagnets can be
designed to produce entropy-driven order. We focus on the tetris artificial
spin ice structure, a highly frustrated array geometry with a zero-point Pauli
entropy, which is formed by selectively creating regular vacancies on the
canonical square ice lattice. We probe thermally active tetris artificial spin
ice both experimentally and through simulations, measuring the magnetic moments
of the individual nanomagnets. We find two-dimensional magnetic ordering in one
subset of these moments, which we demonstrate to be induced by disorder (i.e.,
increased entropy) in another subset of the moments. In contrast with other
entropy-driven systems, the discrete degrees of freedom in tetris artificial
spin ice are binary and are both designable and directly observable at the
microscale, and the entropy of the system is precisely calculable in
simulations. This example, in which the system's interactions and ground state
entropy are well-defined, expands the experimental landscape for the study of
entropy-driven ordering.


title:
Probing Relatively Heavier Right-Handed Selectron at the CEPC, $\rm\bf
  {FCC_{ee}}$ and ILC

abstract:
  We employ the low energy Minimal Supersymmetric Standard Model (MSSM) to
explore the parameter space associated with $Z$-pole and Higgs-pole solutions.
Such parameter spaces can not only saturate the cold dark matter relic density
bound within 5$\sigma$ set by the Planck 2018, but also satisfy the other
standard collider mass bounds and B-physics bounds. In particular, we show that
the right-handed selectron can be light. Thus, we propose a search for the
relatively heavier right-handed selectron at the future lepton colliders with
the center-of-mass energy $\sqrt{s}=240$ GeV and integrated luminosity 3000
$\rm{fb^{-1}}$ via mono-photon channel: $e^{+}_{R} e^{-}_{R}\rightarrow {\tilde
\chi_{1}^{0}(bino)}+{\tilde \chi_{1}^{0}(bino)}+{\gamma}$. We show that for the
$Z$-pole case the right-handed selectron will be excluded up to 180 GeV and 210
GeV respectively at 3$\sigma$ and 2$\sigma$, while the right-handed selectron
will be excluded up to 140 GeV and 180 GeV respectively at 3$\sigma$ and
2$\sigma$ in case of Higgs-pole.


title: Snowmass White Paper: The Analytic Conformal Bootstrap

abstract:
  The analytic conformal bootstrap is an array of techniques to characterize,
constrain, and solve strongly interacting quantum field theories using
symmetries, causality, unitarity, and other general principles. In the last
decade, bolstered by the development of new Lorentzian methods, it has been
used to solve conformal field theories at large spin; to place bounds on energy
distributions, event shapes, operator product coefficients, and other
observables; and to understand aspects of quantum gravity in anti-de Sitter
space. We review these advances and highlight several promising areas for
future exploration. Targets include developing new methods to close the gap
between numerical and analytic bounds, extending the bootstrap beyond conformal
fixed points, applications to quantum gravity and cosmology, and building on
ties to condensed matter theory and mathematics.


title:
Weakly interacting Bose gases with generalized uncertainty principle:
  Effects of quantum gravity

abstract:
  We investigate quantum gravity corrections due to the generalized uncertainty
principle on three-dimensional weakly interacting Bose gases at both zero and
finite temperatures using the time-dependent Hatree-Fock-Bogoliubov theory. We
derive useful formulas for the depletion, the anomalous density and some
thermodynamic quantities such as the chemical potential, the ground-state
energy, the free energy, and the superfluid density. It is found that the
presence of a minimal length leads to modify the fluctuations of the condensate
and its thermodynamic properties in the weak and strong quantum gravitational
regimes. Unexpectedly, the interplay of quantum gravity effects and quantum
fluctuations stemming from interactions may lift both the condensate and the
superfluid fractions. We show that quantum gravity minimizes the interaction
force between bosons leading to the formation of ultradilute Bose condensates.
Our results which can be readily probed in current experiments may offer a new
attractive possibility to understand gravity in the framework of quantum
mechanics.


title:
Global Solutions to Nonconvex Problems by Evolution of Hamilton-Jacobi
  PDEs

abstract:
  Computing tasks may often be posed as optimization problems. The objective
functions for real-world scenarios are often nonconvex and/or
nondifferentiatiable. State-of-the-art methods for solving these problems
typically only guarantee convergence to local minima. This work presents
Hamilton-Jacobi-based MoreauAdaptive Descent (HJ-MAD), a zero-order algorithm
with guaranteed convergence to global minima, assuming continuity of the
objective function. The core idea is to compute gradients of the Moreau
envelope of the objective (which is "piece-wise convex") with adaptive
smoothing parameters. Gradients of the Moreau envelope are approximated by
employing the Hopf-Lax formula for the viscous Hamilton-Jacobi equation.
Provided numerical examples illustrate global convergence.


title:
Solstices and Equinoxes in 1703 at the meridian line of St. Maria degli
  Angeli in Rome, and the stellar aberration of Sirius

abstract:
  The 1703 was the first year of full operation of the meridian line in the
Basilica of St. Maria degli Angeli in Rome. The instants of solstices and
equinoxes, the \textit{Anni Cardines}, obtained by comparing transit timings of
Sun and Sirius, also in daytime, are affected either by the East deviation of
the meridian line of about 5' Eastward (geometrical effect), either by the
stellar aberration of Sirius (relativistic effect). Similarly the seasonal
shifts of Sirius' declination observed by Bianchini are here firstly recognized
as depending on the stellar aberration in ecliptic latitude. The eccentricity
of the Earth's orbit and the orientation of its axis in the space can be
measured, since Ptolemy, by knowing the exact timing of the solstices and the
equinoxes. The dates of 1703 equinoxes and solstices have been published by
Francesco Bianchini in local roman mean time, referred to the local (roman)
mean noon, i.e. after the solar meridian mean transit time. By using the
observations of the three lunar eclipses of 1703 we found the equation of time
for that year, and the UTC corresponding timings of the beginning of the
seasons. This operation lead to find the contribution of the Sirius aberration
to the instant calculated by Bianchini with respect to the current celestial
mechanical models of IMCCE.


title: DIOT: Detecting Implicit Obstacles from Trajectories

abstract:
  In this paper, we study a new data mining problem of obstacle detection from
trajectory data. Intuitively, given two kinds of trajectories, i.e., reference
and query trajectories, the obstacle is a region such that most query
trajectories need to bypass this region, whereas the reference trajectories can
go through as usual. We introduce a density-based definition for the obstacle
based on a new normalized Dynamic Time Warping (nDTW) distance and the density
functions tailored for the sub-trajectories to estimate the density variations.
With this definition, we introduce a novel framework \textsf{DIOT} that
utilizes the depth-first search method to detect implicit obstacles. We conduct
extensive experiments over two real-life data sets. The experimental results
show that \textsf{DIOT} can capture the nature of obstacles yet detect the
implicit obstacles efficiently and effectively. Code is available at
\url{https://github.com/1flei/obstacle}.


title: Orthogonal polynomials, Toda lattices and Painlev\'e equations

abstract:
  We give a survey of the connection between orthogonal polynomials, Toda
lattices and related lattices, and Painlev\'e equations (discrete and
continuous).


title: Dissociation of Pseudotensor Mesons through Critical Temperature

abstract:
  We have computed the masses and decay constants of isotriplet
  $\pi_2(1670)$, the isoscalar $\eta_2(1645)$ and $\eta_2(1870)$ states as the
ground-state nonet of $1^1D_2$ case within the Thermal QCD sum rules framework.
This method is applied to the spectral changes of pseudotensor mesons at high
temperatures including quark, gluon, and mixed condensates up to the five
dimensions. We found that their masses and decay constants are insensitive to
temperature in low-temperature regions. At the near-critical temperature, we
observed an exponential decrease of the masses of $\pi_2$, $\eta_2(1645)$, and
$\eta_2(1870)$, but while the decay constant of $\pi_2$ is fallen, that of
$\eta_2(1645)$ and $\eta_2(1870)$ is increased as a function of temperature.
The modification of condensates at $T\neq 0$ is crucial to the change of
considered mesons properties in a hot medium. We discuss and interpret the
implications of the physical meaning of our results.


title: Skyrmions in magnetic materials

abstract:
  Skyrmions are vortex-like textures of magnetic moments found in some
magnetically ordered materials. Here we describe the origin of the skyrmion and
discuss the experimental realization of skyrmions in magnetic materials, the
dynamic properties of the skyrmion and the potentially useful interaction of
skyrmions with electrons. Our discussion is based on the physics of fields and
allows us to touch on notions from field theory and topology that illustrate
how skyrmions can be understood as part of a family of defects in an ordered
field, and hint at other topological objects that await our discovery.


title: Almost positive kernels on compact Riemannian manifolds

abstract:
  We show how to build a kernel \[
K_X(x,y)=\sum_{m=0}^Xh(\lambda_m/{\lambda_X})\varphi_m(x)\overline{\varphi_m(y)}
\] on a compact Riemannian manifold $M$, which is positive up to a negligible
error and such that $K_X(x,x)\approx X$. Here
$0=\lambda_0^2\le\lambda_1^2\le\ldots$ are the eigenvalues of the
Laplace-Beltrami operator on $M$, listed with repetitions, and
$\varphi_0,\,\varphi_1,\ldots$ an associated system of eigenfunctions, forming
an orthonormal basis of $L^2(M)$. The function $h$ is smooth up to a certain
minimal degree, even, compactly supported in $[-1,1]$ with $h(0)=1$, and
$K_X(x,y)$ turns out to be an approximation to the identity.


title: Ab initio simulation of amorphous graphite

abstract:
  A disorder-order transition from amorphous carbon (aC) to layered amorphous
graphene (LAG) has been predicted using ab-initio methods. Amorphous carbon at
densities close to the graphitic density show a strong proclivity to layer in
NVT simulations near 3000K . Our calculations have shown that graphitization is
strongly dependent on the simulation temperature and is not observed under
2500K. The origin of such a disorder-order transition has been studied using
various structural analyses. Each layer of LAG is a layer of amorphous graphene
(aG) including pentagons and heptagons in addition to hexagons and the planes
are separated by about 3.2A. LAG obtained from the NVT simulations were highly
stable. The electronic charge density, especially for the pi and pi$^*$ states,
was computed with the Hyed-Scuseria-Ernzerhof (HSE) hybrid functional and
compared with crystalline graphite (cG). The impact of structural disorder has
been studied in detail, especially the consequences of disorder to electronic
transport.


title: Tangent spaces and T-invariant curves of Schubert varieties

abstract:
  The set of T-invariant curves in a Schubert variety through a T-fixed point
is relatively easy to characterize in terms of its weights, but the tangent
space is more difficult. We prove that the weights of the tangent space are
contained in the rational cone generated by the weights of the T-invariant
curves. In simply laced types, this remains true if "rational" is replaced by
"integral". We also obtain conditions under which every weight of the tangent
space is the weight of a T-invariant curve, as well as a smoothness criterion.
The results rely on equivariant K-theory, as well as the study of different
notions of decomposability of roots.


title: Diffeological Principal Bundles and Principal Infinity Bundles

abstract:
  Given a diffeological group $G$ we prove that the nerve of the groupoid of
diffeological principal $G$-bundles over a diffeological space $X$ is weakly
equivalent to the nerve of the category of $G$-principal $\infty$-bundles over
$X$. Using this we show that the $\v{C}$ech cohomology groups of $X$ as defined
in [Igl20] coincides with the corresponding cohomology groups defined using
higher topos theory.


title:
Broad search for gravitational waves from subsolar-mass binaries through
  LIGO and Virgo's third observing run

abstract:
  We present a search for gravitational waves from the coalescence of binaries
which contain at least one subsolar mass component using data from the LIGO and
Virgo observatories through the completion of their third observing run. The
observation of a merger with a component below $1\,M_{\odot}$ would be a clear
sign of either new physics or the existence of a primordial black hole
population; these black holes could also contribute to the dark matter
distribution. Our search targets binaries where the primary has mass $M_1$
between 0.1-100$\,M_{\odot}$ and the secondary has mass $M_2$ from
0.1-1$\,M_{\odot}$ for $M_1 < 20\,M_\odot$ and 0.01-1$\,M_{\odot}$ for $M_1 \ge
20\,M_\odot$. Sources with $M_1 < 7\,M_\odot, M_2 > 0.5\,M_\odot$ are also
allowed to have orbital eccentricity up to $e_{10} \sim 0.3$. This search
region covers from comparable to extreme mass ratio sources up to $10^4:1$. We
find no statistically convincing candidates and so place new upper limits on
the rate of mergers; our analysis sets the first limits for most subsolar
sources with $7\,M_{\odot}< M_1 < 20\,M_{\odot}$ and tightens limits by $\sim
8\times$ $(1.6\times)$ where $M_1 > 20\,M_{\odot}$ ($M_1 < 7\,M_{\odot}$).
Using these limits, we constrain the dark matter fraction to below 0.3
(0.7)$\%$ for 1 (0.5)$\,M_{\odot}$ black holes assuming a monochromatic mass
function. Due to the high merger rate of primordial black holes beyond the
individual source horizon distance, we also use the lack of an observed
stochastic background as a complementary probe to limit the dark matter
fraction. We find that although the limits are in general weaker than those
from the direct search they become comparable at $0.1 \,M_{\odot}$.


title: 3D ToF LiDAR in Mobile Robotics: A Review

abstract:
  In the past ten years, the use of 3D Time-of-Flight (ToF) LiDARs in mobile
robotics has grown rapidly. Based on our accumulation of relevant research,
this article systematically reviews and analyzes the use 3D ToF LiDARs in
research and industrial applications. The former includes object detection,
robot localization, long-term autonomy, LiDAR data processing under adverse
weather conditions, and sensor fusion. The latter encompasses service robots,
assisted and autonomous driving, and recent applications performed in response
to public health crises. We hope that our efforts can effectively provide
readers with relevant references and promote the deployment of existing mature
technologies in real-world systems.


title: Anharmonic quantum thermal transport across a van der Waals interface

abstract:
  We investigate the anharmonic phonon scattering across a weakly interacting
interface by developing a quantum mechanics-based theory. We find that the
contribution from anharmonic three-phonon scatterings to interfacial thermal
conductance can be cast into Landauer formula with transmission function being
temperature-dependent. Surprisingly, in the weak coupling limit, the
transmission due to anharmonic phonon scattering is unbounded with increasing
temperature, which is physically impossible for two-phonon processes. We
further reveal that the anharmonic contribution in a real heterogeneous
interface (e.g., between graphene and monolayer molybdenum disulfide) can
dominate over the harmonic process even at room temperature, highlighting the
important role of anharmonicity in weakly interacting heterogeneous systems.


title: An Overview on Machine Translation Evaluation

abstract:
  Since the 1950s, machine translation (MT) has become one of the important
tasks of AI and development, and has experienced several different periods and
stages of development, including rule-based methods, statistical methods, and
recently proposed neural network-based learning methods. Accompanying these
staged leaps is the evaluation research and development of MT, especially the
important role of evaluation methods in statistical translation and neural
translation research. The evaluation task of MT is not only to evaluate the
quality of machine translation, but also to give timely feedback to machine
translation researchers on the problems existing in machine translation itself,
how to improve and how to optimise. In some practical application fields, such
as in the absence of reference translations, the quality estimation of machine
translation plays an important role as an indicator to reveal the credibility
of automatically translated target languages. This report mainly includes the
following contents: a brief history of machine translation evaluation (MTE),
the classification of research methods on MTE, and the the cutting-edge
progress, including human evaluation, automatic evaluation, and evaluation of
evaluation methods (meta-evaluation). Manual evaluation and automatic
evaluation include reference-translation based and reference-translation
independent participation; automatic evaluation methods include traditional
n-gram string matching, models applying syntax and semantics, and deep learning
models; evaluation of evaluation methods includes estimating the credibility of
human evaluations, the reliability of the automatic evaluation, the reliability
of the test set, etc. Advances in cutting-edge evaluation methods include
task-based evaluation, using pre-trained language models based on big data, and
lightweight optimisation models using distillation techniques.


title:
Generating Synthetic Mobility Networks with Generative Adversarial
  Networks

abstract:
  The increasingly crucial role of human displacements in complex societal
phenomena, such as traffic congestion, segregation, and the diffusion of
epidemics, is attracting the interest of scientists from several disciplines.
In this article, we address mobility network generation, i.e., generating a
city's entire mobility network, a weighted directed graph in which nodes are
geographic locations and weighted edges represent people's movements between
those locations, thus describing the entire mobility set flows within a city.
Our solution is MoGAN, a model based on Generative Adversarial Networks (GANs)
to generate realistic mobility networks. We conduct extensive experiments on
public datasets of bike and taxi rides to show that MoGAN outperforms the
classical Gravity and Radiation models regarding the realism of the generated
networks. Our model can be used for data augmentation and performing
simulations and what-if analysis.


title:
In-situ Measurement of the Energy Fraction in Supra-thermal and
  Energetic Particles at ACE, Wind, and PSP Interplanetary Shocks

abstract:
  The acceleration of charged particles by interplanetary shocks (IPs) can
drain a non-negligible fraction of the plasma pressure. In this study, we have
selected 17 IPs observed in-situ at $1\,\text{au}$ by the Advanced Composition
Explorer (ACE) and the Wind spacecraft, and 1 shock at $0.8\,\text{au}$
observed by Parker Solar Probe (PSP). We have calculated the time-dependent
partial pressure of supra-thermal and energetic particles (smaller and greater
than $50\,\text{keV}$ for protons and $30\,\text{keV}$ for electrons,
respectively) in both the upstream and downstream regions. The particle fluxes
were averaged for 1 hour before and 1 hour after the shock time to remove short
time scale effects. Using the MHD Rankine-Hugoniot jump conditions, we find
that the fraction of the total upstream energy flux transferred to
supra-thermal and energetic downstream particles is typically $\lesssim\!16\%$,
in agreement with previous observations and simulations. Notably, by accounting
for errors on all measured shock parameters, we have found that for any given
fast magnetosonic Mach number, $M_{f}\!<7$, the angle between the shock normal
and average upstream magnetic field, $\theta_{Bn}$, is not correlated with the
energetic particle pressure; in particular, the partial pressure of energized
particles does not decrease for $\theta_{Bn} \gtrsim 45^\circ$. The downstream
electron-to-proton energy ratio in the range $\gtrsim\!140\,\text{eV}$ for
electrons and $\gtrsim\!70\,\text{keV}$ for protons exceeds the expected
$\sim\!1\%$ and nears equipartition ($>\!0.1$) for the Wind events.

categories: astro-ph.SR astro-ph.HE physics.plasm-ph physics.space-ph


title: Birational classification of pointless del Pezzo surfaces of degree 8

abstract:
  Let k be an algebraically nonclosed field of characteristic $0$. Recently
J.-L. Colliot-Th\'el\`ene showed that two pointless quadric surfaces over k are
birationally equivalent if and only if they are isomorphic. We show that this
result holds for arbitrary del Pezzo surfaces of degree $8$ with the Picard
number $1$, and describe minimal surfaces birationally equivalent to a given
pointless del Pezzo surface of degree $8$.


title:
A Unified Nonparametric Test of Transformations on Distribution
  Functions with Nuisance Parameters

abstract:
  This paper proposes a simple unified approach to testing transformations on
cumulative distribution functions (CDFs) with nuisance parameters. We consider
testing general parametric transformations on two CDFs, and then generalize the
test for multiple CDFs. We construct the test using a numerical bootstrap
method which can easily be implemented. The proposed test is shown to be
asymptotically size controlled and consistent. Monte Carlo simulations and an
empirical application show that the test performs well on finite samples.


title: Classification of Planar Monomials Over Finite Fields of Small Order

abstract:
  For all finite fields of order up to $2^{30}$, we computationally prove that
there are no planar monomials besides the ones already known.


title:
Characterizing generalized axisymmetric quantum states in $d\times d$
  systems

abstract:
  We introduce a family of highly symmetric bipartite quantum states in
arbitrary dimensions. It consists of all states that are invariant under local
phase rotations and local cyclic permutations of the basis. We solve the
separability problem for a subspace of these states and show that a sizeable
part of the family is bound entangled. We also calculate some of the Schmidt
numbers for the family in $d = 3$, thereby characterizing the dimensionality of
entanglement. Our results allow to estimate entanglement properties of
arbitrary states, as general states can be symmetrized to the considered family
by local operations.


title: Some minimal bimolecular mass-action systems with limit cycles

abstract:
  We discuss three examples of bimolecular mass-action systems with three
species, due to Feinberg, Berner, Heinrich, and Wilhelm. Each system has a
unique positive equilibrium which is unstable for certain rate constants and
then exhibits stable limit cycles, but no chaotic behaviour. For some rate
constants in the Feinberg--Berner system, a stable equilibrium, an unstabe
limit cycle, and a stable limit cycle coexist. All three networks are minimal
in some sense.
  By way of homogenising the three known examples, we construct bimolecular
mass-conserving mass-action systems with four species that admit a stable limit
cycle. The homogenised Feinberg--Berner system and the homogenised
Wilhelm--Heinrich system admit the coexistence of a stable equilibrium, an
unstable limit cycle, and a stable limit cycle.


title: Accounting for Spatial Anonymization in DHS Household Surveys

abstract:
  The household surveys conducted by the Demographic and Health Surveys (DHS)
Program are a key source of data for estimating demographic and health
indicators at a fine spatial scale in low- and middle-income countries. The
surveys provide jittered coordinates for visited clusters, where coordinates
are randomly displaced according to a known jittering distribution. We develop
a geostatistical model that accounts for the positional uncertainty when
analysing DHS surveys, and provide a fast implementation using Template Model
Builder. The approach works for both Gaussian and non-Gaussian likelihoods. A
simulation study with binomial data shows that the new approach performs
equally or better than the common practice of not accounting for jittering,
both in terms of more accurate parameter estimates and improved predictive
measures. The improvement increases with increased amount of jittering. An
analysis of contraceptive use in Kenya shows that the approach is fast and easy
to use in practice.


title: Gradient-type estimates for the dynamic $\varphi^4_2$-model

abstract:
  We prove gradient bounds for the Markov semigroup of the dynamic
$\varphi^4_2$-model on a torus of fixed size $L>0$. For sufficiently large mass
$m>0$ these estimates imply exponential contraction of the Markov semigroup.
Our method is based on pathwise estimates of the linearized equation. To
compensate the lack of exponential integrability of the stochastic drivers we
use a stopping time argument and the strong Markov property in the spirit of
Cass--Litterer--Lyons. Following the classical approach of Bakry-\'Emery, as a
corollary we prove a Poincar\'e/spectral gap inequality for the
$\varphi^4_2$-measure of sufficiently large mass $m>0$ with almost optimal
carr\'e du champ.


title:
Effects of high order interatomic potential on elastic phonon
  scatterings

abstract:
  Interatomic potentials beyond quadratic order provide scattering sources for
phonon transport in lattice. By using a weakly-interacting interface model, we
investigated the relation between the order of interatomic potential and the
multiple-phonon scattering process. We find that high order interatomic
potential not only causes multiple-phonon scattering processes, but also has
significant impacts on elastic phonon scattering processes. Using fourth order
potential as an example, we show that it can significantly affects elastic
phonon scatterings, through the formation of localized phonons. Such impact is
closely related to the correlations of interfacial atoms and it becomes more
significant with increasing temperature. Our work suggests that it is
insufficient to consider only quadratic potential to investigate elastic phonon
transport.


title: Banding vs. Quality: Perceptual Impact and Objective Assessment

abstract:
  Staircase-like contours introduced to a video by quantization in flat areas,
commonly known as banding, have been a long-standing problem in both video
processing and quality assessment communities. The fact that even a relatively
small change of the original pixel values can result in a strong impact on
perceived quality makes banding especially difficult to be detected by
objective quality metrics. In this paper, we study how banding annoyance
compares to more commonly studied scaling and compression artifacts with
respect to the overall perceptual quality. We further propose a simple
combination of VMAF and the recently developed banding index, CAMBI, into a
banding-aware video quality metric showing improved correlation with overall
perceived quality.


title: Galaxy Rotation Curve Measurements with Low Cost 21 cm Radio Telescope

abstract:
  Probing the Universe with atomic hydrogen 21 cm emission is a fascinating and
challenging work in astronomy. Radio telescopes play a vital role in detecting
and imaging these faint signals. Powerful radio telescopes are complex to
construct and operate. We have built a simple, low-cost 21 cm radio telescope
primarily for educational training purposes. The design uses a custom horn
antenna, ready-to-use radio-frequency components, and a software-defined radio
module. The telescope operates efficiently from a rooftop in a city
environment. Using this telescope, we have conducted observations and
successfully detected the 21 cm line emissions from the different directions of
our galactic plane. Based on the Doppler-shift observed in these measurements,
we have successfully derived the Galactic rotation velocity (rotation curve) in
those directions. The paper presents the details of the telescope construction,
21 cm observation, and the Galactic rotation curve derivation.


title:
Hubble Space Telescope Imaging of Luminous Extragalactic Infrared
  Transients and Variables from the SPIRITS Survey

abstract:
  SPIRITS--the SPitzer InfraRed Intensive Transients Survey--searched for
luminous infrared (IR) transients and variables in nearly 200 nearby galaxies
from 2014 to 2019, using the warm Spitzer telescope at 3.6 and 4.5 microns.
Among the SPIRITS variables are IR-bright objects that are undetected in
ground-based optical surveys. We classify them as (1) transients, (2) periodic
variables, and (3) irregular variables. The transients include "SPRITE"s
(eSPecially Red Intermediate-luminosity Transient Events), having maximum
luminosities fainter than supernovae, red IR colors, and a wide range of
outburst durations (days to years). Here we report deep optical and near-IR
imaging with the Hubble Space Telescope (HST) of 21 SPIRITS variables. They
were initially considered SPRITE transients, but many eventually proved instead
to be periodic or irregular variables as more data were collected. HST images
show most of these cool and dusty variables are associated with star-forming
regions in late-type galaxies, implying an origin in massive stars. Two SPRITEs
lacked optical progenitors in deep pre-outburst HST images; however, one was
detected during eruption at J and H, indicating a dusty object with an
effective temperature of ~1050 K. One faint SPRITE turned out to be a dusty
classical nova. About half the HST targets proved to be periodic variables,
with pulsation periods of 670-2160 days; they are likely dusty
asymptotic-giant-branch (AGB) stars with masses of ~5-10 Msun. A few of them
were warm enough to be detected in deep HST frames, but most are too cool. Out
of six irregular variables, two were red supergiants with optical counterparts
in HST images; four were too enshrouded for HST detection.


title:
Validating First-Principles Phonon Lifetimes via Inelastic Neutron
  Scattering

abstract:
  Phonon lifetimes are a key component of quasiparticle theories of transport,
yet first-principles lifetimes are rarely directly compared to inelastic
neutron scattering (INS) results. Existing comparisons show discrepancies even
at temperatures where perturbation theory is expected to be reliable. In this
work, we demonstrate that the reciprocal space voxel ($q$-voxel), which is the
finite region in reciprocal space required in INS data analysis, must be
explicitly accounted for within theory in order to draw a meaningful
comparison. We demonstrate accurate predictions of peak widths of the
scattering function when accounting for the $q$-voxel in CaF$_2$ and ThO$_2$.
Passing this test implies high fidelity of the phonon interactions and the
approximations used to compute the Green's function, serving as critical
benchmark of theory, and indicating that other material properties should be
accurately predicted; which we demonstrate for thermal conductivity.


title:
FASURA: A Scheme for Quasi-Static Massive MIMO Unsourced Random Access
  Channels

abstract:
  This article considers the massive MIMO unsourced random access problem on a
quasi-static Rayleigh fading channel. Given a fixed message length and a
prescribed number of channel uses, the objective is to construct a coding
scheme that minimizes the energy-per-bit subject to a fixed probability of
error. The proposed scheme differs from other state-of-the-art schemes in that
it blends activity detection, single-user coding, pilot-aided and temporary
decisions-aided iterative channel estimation and decoding, minimum-mean squared
error (MMSE) estimation, and successive interference cancellation (SIC). We
show that an appropriate combination of these ideas can substantially
outperform state-of-the-art coding schemes when the number of active users is
more than 100, making this the best performing scheme known for this regime.


title: Differentially Private Estimation of Heterogeneous Causal Effects

abstract:
  Estimating heterogeneous treatment effects in domains such as healthcare or
social science often involves sensitive data where protecting privacy is
important. We introduce a general meta-algorithm for estimating conditional
average treatment effects (CATE) with differential privacy (DP) guarantees. Our
meta-algorithm can work with simple, single-stage CATE estimators such as
S-learner and more complex multi-stage estimators such as DR and R-learner. We
perform a tight privacy analysis by taking advantage of sample splitting in our
meta-algorithm and the parallel composition property of differential privacy.
In this paper, we implement our approach using DP-EBMs as the base learner.
DP-EBMs are interpretable, high-accuracy models with privacy guarantees, which
allow us to directly observe the impact of DP noise on the learned causal
model. Our experiments show that multi-stage CATE estimators incur larger
accuracy loss than single-stage CATE or ATE estimators and that most of the
accuracy loss from differential privacy is due to an increase in variance, not
biased estimates of treatment effects.


title:
Nested spheroidal figures of equilibrium I. Approximate solutions for
  rigid rotations

abstract:
  We discuss the equilibrium conditions for a body made of two homogeneous
components separated by oblate spheroidal surfaces and in relative motion.
While exact solutions are not permitted for rigid rotation (unless a specific
ambient pressure), approximations can be obtained for configurations involving
a small confocal parameter. The problem then admits two families of solutions,
depending on the pressure along the common interface (constant or quadratic
with the cylindrical radius). We give in both cases the pressure and the
rotation rates as a function of the fractional radius, ellipticities and
mass-density jump. Various degrees of flattening are allowed but there are
severe limitations for global rotation, as already known from classical theory
(e.g. impossibility of confocal and coelliptical solutions, gradient of
ellipticity outward). States of relative rotation are much less constrained,
but these require a mass-density jump. This analytical approach compares
successfully with the numerical solutions obtained from the
Self-Consistent-Field method. Practical formula are derived in the limit of
small ellipticities appropriate for slowly-rotating star/planet interiors.


title: SupermarQ: A Scalable Quantum Benchmark Suite

abstract:
  The emergence of quantum computers as a new computational paradigm has been
accompanied by speculation concerning the scope and timeline of their
anticipated revolutionary changes. While quantum computing is still in its
infancy, the variety of different architectures used to implement quantum
computations make it difficult to reliably measure and compare performance.
This problem motivates our introduction of SupermarQ, a scalable,
hardware-agnostic quantum benchmark suite which uses application-level metrics
to measure performance. SupermarQ is the first attempt to systematically apply
techniques from classical benchmarking methodology to the quantum domain. We
define a set of feature vectors to quantify coverage, select applications from
a variety of domains to ensure the suite is representative of real workloads,
and collect benchmark results from the IBM, IonQ, and AQT@LBNL platforms.
Looking forward, we envision that quantum benchmarking will encompass a large
cross-community effort built on open source, constantly evolving benchmark
suites. We introduce SupermarQ as an important step in this direction.


title: Approximate gradient ascent methods for distortion risk measures

abstract:
  We propose approximate gradient ascent algorithms for risk-sensitive
reinforcement learning control problem in on-policy as well as off-policy
settings. We consider episodic Markov decision processes, and model the risk
using distortion risk measure (DRM) of the cumulative discounted reward. Our
algorithms estimate the DRM using order statistics of the cumulative rewards,
and calculate approximate gradients from the DRM estimates using a smoothed
functional-based gradient estimation scheme. We derive non-asymptotic bounds
that establish the convergence of our proposed algorithms to an approximate
stationary point of the DRM objective.


title:
Szeg\"{o}-Weinberger type inequalities for symmetric domains in space
  forms

abstract:
  We consider the Neumann eigenvalue problem for Laplacian on a bounded
multi-connected domain contained in simply connected space forms. Under certain
symmetry assumptions on the domain, we prove Szeg\"{o}-Weinberger type
inequalities for the first $n$ positive Neumann eigenvalues.


title:
Listening to the Universe with Next Generation Ground-Based
  Gravitational-Wave Detectors

abstract:
  In this study, we use simple performance metrics to assess the science
capabilities of future ground-based gravitational-wave detector networks --
composed of A+ or Voyager upgrades to the LIGO, Virgo, and KAGRA observatories
and proposed next generation observatories such as Cosmic Explorer and Einstein
Telescope. These metrics refer to coalescences of binary neutron stars (BNSs)
and binary black holes (BBHs) and include: (i) network detection efficiency and
detection rate of cosmological sources as a function of redshift, (ii)
signal-to-noise ratios and the accuracy with which intrinsic and extrinsic
parameters would be measured, and (iii) enabling multimessenger astronomy with
gravitational waves by accurate 3D localization and early warning alerts. We
further discuss the science enabled by the small population of rare and
extremely loud events. While imminent upgrades will provide impressive advances
in all these metrics, next generation observatories will deliver an improvement
of an order-of-magnitude or more in most metrics. In fact, a network containing
two or three such facilities will detect half of all the BNS and BBH mergers up
to a redshift of $z=1$ and $z=20$, respectively, give access to hundreds of
BNSs and ten thousand BBHs with signal-to-noise ratios exceeding 100, readily
localize hundreds to thousands of mergers to within $1\,{\rm deg^2}$ on the sky
and better than 10% in luminosity distance, respectively, and consequently,
enable mutlimessenger astronomy through follow-up surveys in the
electromagnetic spectrum several times a week. Such networks will further shed
light on potential cosmological merger populations and detect an abundance of
high-fidelity BNS and BBH signals which will allow investigations of the
high-density regime of matter at an unprecedented level and enable precision
tests of general relativity in the strong-filed regime, respectively.


title: Wastewater Pipe Condition Rating Model Using K- Nearest Neighbors

abstract:
  Risk-based assessment in pipe condition mainly focuses on prioritizing the
most critical assets by evaluating the risk of pipe failure. This paper's goal
is to classify a comprehensive pipe rating model which is obtained based on a
series of pipe physical, external, and hydraulic characteristics that are
identified for the proposed methodology. The traditional manual method of
assessing sewage structural conditions takes a long time. By building an
automated process using K-Nearest Neighbors (K-NN), this study presents an
effective technique to automate the identification of the pipe defect rating
using the pipe repair data. First, we performed the Shapiro Wilks Test for 1240
data from the Dept. of Engineering & Environmental Services, Shreveport,
Louisiana Phase 3 with 12 variables to determine if factors could be
incorporated in the final rating. We then developed a K-Nearest Neighbors model
to classify the final rating from the statistically significant factors
identified in Shapiro Wilks Test. This classification process allows
recognizing the worst condition of wastewater pipes that need to be replaced
immediately. This comprehensive model is built according to the
industry-accepted and used guidelines to estimate the overall condition.
Finally, for validation purposes, the proposed model is applied to a small
portion of a US wastewater collection system in Shreveport, Louisiana.
Keywords: Pipe rating, Shapiro Wilks Test, K-Nearest Neighbors (KNN), Failure,
Risk analysis