title: Quantum Networks for High Energy Physics

abstract:
  Quantum networks of quantum objects promise to be exponentially more powerful
than the objects considered independently. To live up to this promise will
require the development of error mitigation and correction strategies to
preserve quantum information as it is initialized, stored, transported,
utilized, and measured. The quantum information could be encoded in discrete
variables such as qubits, in continuous variables, or anything in-between.
Quantum computational networks promise to enable simulation of physical
phenomena of interest to the HEP community. Quantum sensor networks promise new
measurement capability to test for new physics and improve upon existing
measurements of fundamental constants. Such networks could exist at multiple
scales from the nano-scale to a global-scale quantum network.


title: Exchange energies with forces in density-functional theory

abstract:
  We propose exchanging the energy functionals in DFT with physically
equivalent exact force expressions as a promising route towards efficient yet
accurate approximations to the exchange-correlation potential. In analogy to
the usual energy-based procedure, we split the force difference between the
interacting and auxiliary system into a Hartree, an exchange and a correlation
force. The corresponding potentials are obtained by solving a Poisson equation.
The explicit expression of the exchange potential overcomes conceptual issues
of previous force-based approaches and compares well with the numerically more
involved optimized effective-potential method.


title: Design of a robot for the automatic charging of an electric car

abstract:
  In this paper, a robot with parallel architecture is proposed for charging an
electric vehicle having the charging socket on its front side. Kinematic models
are developed to design the robot for a given workspace that corresponds to the
car's plug placements. A demonstrator composed by commercial components is
shown.


title: Social distancing between particles and objects in the Universe

abstract:
  The novel coronavirus, dubbed COVID-19, upended our lives may be in
irreversible ways during its initial spread throughout the world in March 2020.
It forced us all, willingly or unwillingly, to keep social distance from each
other to slow down the spread of COVID-19. As scientists, we started
speculating what kind of separation is between the constitutes of different
objects in the Universe. In this work, we study the "social" distance between
elements inside various objects, no matter their size, mass, and nature. We
consider things ranging from diamond, baseball to Saturn, asteroid belt or M87
Black Hole, to name a few. We show our results in the form of a fascinating
mass/"social" distance plot, where a cool cartoon figure represents each
object.


title:
Self-distillation Augmented Masked Autoencoders for Histopathological
  Image Classification

abstract:
  Self-supervised learning (SSL) has drawn increasing attention in pathological
image analysis in recent years. However, the prevalent contrastive SSL is
suboptimal in feature representation under this scenario due to the homogeneous
visual appearance. Alternatively, masked autoencoders (MAE) build SSL from a
generative paradigm. They are more friendly to pathological image modeling. In
this paper, we firstly introduce MAE to pathological image analysis. A novel
SD-MAE model is proposed to enable a self-distillation augmented SSL on top of
the raw MAE. Besides the reconstruction loss on masked image patches, SD-MAE
further imposes the self-distillation loss on visible patches. It guides the
encoder to perceive high-level semantics that benefit downstream tasks. We
apply SD-MAE to the image classification task on two pathological and one
natural image datasets. Experiments demonstrate that SD-MAE performs highly
competitive when compared with leading contrastive SSL methods. The results,
which are pre-trained using a moderate size of pathological images, are also
comparable to the method pre-trained with two orders of magnitude more images.
Our code will be released soon.


title: Ramsey degrees and entropy of combinatorial structures

abstract:
  Close connections between various notions of entropy and the apparatus of
category theory have been observed already in the 1980s and more vigorously
developed in the past ten years. The starting point of the paper is the recent
categorical understanding of structural Ramsey degrees, which then leads to a
way to compute entropy of an object in a small category not as a measure of
statistical, but as a measure of its combinatorial complexity. The new entropy
function we propose, the Ramsey entropy, is a real-valued invariant of an
object in an arbitrary small category. We require no additional categorical
machinery to introduce and prove the properties of this entropy. Motivated by
combinatorial phenomena (structural Ramsey degrees) we build the necessary
infrastructure and prove the fundamental properties using only special
partitions imposed on homsets. We conclude the paper with the discussion of the
maximal Ramsey entropy on a category that we refer to as the Ramsey-Boltzmann
entropy.


title:
On design of hybrid diffractive optics for achromatic extended
  depth-of-field (EDoF) RGB imaging

abstract:
  A hybrid imaging system is a simultaneous physical arrangement of a
refractive lens and a multilevel phase mask (MPM) as a diffractive optical
element (DOE). The favorable properties of the hybrid setup are improved
extended-depth-of-field (EDoF) imaging and low chromatic aberrations. We built
a fully differentiable image formation model in order to use neural network
techniques to optimize imaging. At the first stage, the design framework relies
on the model-based approach with numerical simulation and end-to-end joint
optimization of both MPM and imaging algorithms. In the second stage, MPM is
fixed as found at the first stage, and the image processing is optimized
experimentally using the CNN learning-based approach with MPM implemented by a
spatial light modulator. The paper is concentrated on a comparative analysis of
imaging accuracy and quality for design with various basic optical parameters:
aperture size, lens focal length, and distance between MPM and sensor. We point
out that the varying aperture size, lens focal length, and distance between MPM
and sensor are for the first time considered for end-to-end optimization of
EDoF. We numerically and experimentally compare the designs for visible
wavelength interval [400-700]nm and the following EDoF ranges: [0.5-100]m for
simulations and [0.5-1.9]m for experimental tests. This study concerns an
application of hybrid optics for compact cameras with aperture [5-9] mm and
distance between MPM and sensor [3-10]mm.


title: Optical Properties of Superconducting Nd0.8Sr0.2NiO2 Nickelate

abstract:
  The intensive search for alternative non-cuprate high-transition-temperature
($T_c$) superconductors has taken a positive turn recently with the discovery
of superconductivity in infinite layer nickelates. This discovery is expected
to be the basis for disentangling the puzzle behind the physics of high $T_c$
in oxides. In the unsolved quest for the physical conditions necessary for
inducing superconductivity, we report an optical study of a
Nd$_{0.8}$Sr$_{0.2}$NiO$_2$ film measured using optical spectroscopy, at
temperatures above and below the critical temperature $T_c\sim 13$ K. The
normal-state electrodynamics of Nd$_{0.8}$Sr$_{0.2}$NiO$_2$, is described by
the Drude model characterized by a scattering time just above $T_c$ ($\tau \sim
1.7\times 10^{-14}$ s) and a plasma frequency $\omega_p = 8500$ cm$^{-1}$ in
combination with an absorption band in the Mid-Infrared (MIR) around $\omega_0
\sim 4000$ cm$^{-1}$. The MIR absorption indicates the presence of strong
electronic correlation effect in the NiO$_2$ plane similarly to cuprates. Below
$T_c$, a superconducting energy gap ($2\Delta$) of $\sim 3.2$ meV is extracted
from the Terahertz reflectivity using the the Mattis-Bardeen model. From the
Ferrel-Glover-Thinkam Rule applied to the real part of the optical
conductivity, we also estimate a London penetration depth of about 490 nm, in
agreement with a type-II superconductivity in Nd$_{0.8}$Sr$_{0.2}$NiO$_2$
Nickelate.


title:
Leveraging Predictions from Multiple Repositories to Improve Bot
  Detection

abstract:
  Contemporary social coding platforms such as GitHub facilitate collaborative
distributed software development. Developers engaged in these platforms often
use machine accounts (bots) for automating effort-intensive or repetitive
activities. Determining whether a contributor corresponds to a bot or a human
account is important in socio-technical studies, for example, to assess the
positive and negative impact of using bots, analyse the evolution of bots and
their usage, identify top human contributors, and so on. BoDeGHa is one of the
bot detection tools that have been proposed in the literature. It relies on
comment activity within a single repository to predict whether an account is
driven by a bot or by a human. This paper presents preliminary results on how
the effectiveness of BoDeGHa can be improved by combining the predictions
obtained from many repositories at once. We found that doing this not only
increases the number of cases for which a prediction can be made but that many
diverging predictions can be fixed this way. These promising, albeit
preliminary, results suggest that the "wisdom of the crowd" principle can
improve the effectiveness of bot detection tools.


title:
Acoustic-Net: A Novel Neural Network for Sound Localization and
  Quantification

abstract:
  Acoustic source localization has been applied in different fields, such as
aeronautics and ocean science, generally using multiple microphones array data
to reconstruct the source location. However, the model-based beamforming
methods fail to achieve the high-resolution of conventional beamforming maps.
Deep neural networks are also appropriate to locate the sound source, but in
general, these methods with complex network structures are hard to be
recognized by hardware. In this paper, a novel neural network, termed the
Acoustic-Net, is proposed to locate and quantify the sound source simply using
the original signals. The experiments demonstrate that the proposed method
significantly improves the accuracy of sound source prediction and the
computing speed, which may generalize well to real data. The code and trained
models are available at https://github.com/JoaquinChou/Acoustic-Net.


title:
Functional Stability of Discounted Markov Decision Processes Using
  Economic MPC Dissipativity Theory

abstract:
  This paper discusses the functional stability of closed-loop Markov Chains
under optimal policies resulting from a discounted optimality criterion,
forming Markov Decision Processes (MDPs). We investigate the stability of MDPs
in the sense of probability measures (densities) underlying the state
distributions and extend the dissipativity theory of Economic Model Predictive
Control in order to characterize the MDP stability. This theory requires a
so-called storage function satisfying a dissipativity inequality. In the
probability measures space and for the discounted setting, we introduce new
dissipativity conditions ensuring the MDP stability. We then use finite-horizon
optimal control problems in order to generate valid storage functionals. In
practice, we propose to use Q-learning to compute the storage functionals.


title: Linear analysis of the gravitational beam-plasma instability

abstract:
  We investigate the well-known phenomenon of the beam-plasma instability in
the gravitational sector, when a fast population of particles interacts with
the massive scalar mode of an Horndeski theory of gravity, resulting into the
linear growth of the latter amplitude. Following the approach used in the
standard electromagnetic case, we start from the dielectric representation of
the gravita- tional plasma, as introduced in a previous analysis of the Landau
damping for the scalar Horndeski mode. Then, we set up the modified
Vlasov-Einstein equation, using at first a Dirac delta func- tion to describe
the fast beam distribution. This way, we provide an analytical expression for
the dispersion relation and we demonstrate the existence of non-zero growth
rate for the linear evolu- tion of the Horndeski scalar mode. A numerical
investigation is then performed with a trapezoidal beam distribution function,
which confirms the analytical results and allows to demonstrate how the growth
rate decreases as the beam spread increases.


title: Tate Homology and Powered Flybys

abstract:
  In this paper we show that in the planar circular restricted three body
problem there are either infinitely many symmetric consecutive collision orbits
or at least one periodic symmetric consecutive collision orbit for all energies
below the first critical energy value. Using Levi-Civita regularization allows
us to distinguish two different kinds of symmetric consecutive collision orbits
and prove the above claim for both of them separately, one corresponding to a
solar eclipse and the other to a lunar eclipse. By interpreting the orbits as
Hamiltonian chords between two different Lagrangian submanifolds we can use a
perturbed version of $G$-equivariant Lagrangian Rabinowitz Floer homology to
prove the existence of this kind of consecutive collision orbit. To calculate
this homology we show that under certain conditions the $G$-equivariant
Lagrangian Rabinowitz Floer homology is equal to the Tate homology of $G$.


title:
Subquadratic Dynamic Path Reporting in Directed Graphs Against an
  Adaptive Adversary

abstract:
  We study reachability and shortest paths problems in dynamic directed graphs.
Whereas algebraic dynamic data structures supporting edge updates and
reachability/distance queries have been known for quite a long time, they do
not, in general, allow reporting the underlying paths within the same time
bounds, especially against an adaptive adversary.
  In this paper we develop the first known fully dynamic reachability data
structures working against an adaptive adversary and supporting edge updates
and path queries for two natural variants: (1) point-to-point path reporting,
and (2) single-source reachability tree reporting. For point-to-point queries
in DAGs, we achieve $O(n^{1.529})$ worst-case update and query bounds, whereas
for tree reporting in DAGs, the worst-case bounds are $O(n^{1.765})$. More
importantly, we show how to lift these algorithms to work on general graphs at
the cost of increasing the bounds to $n^{1+5/6+o(1)}$ and making the update
times amortized. On the way to accomplishing that, we obtain two interesting
subresults. We give subquadratic fully dynamic algorithms for topological order
(in a DAG), and strongly connected components. To the best of our knowledge,
such algorithms have not been described before.
  Additionally, we provide deterministic incremental data structures for
reachability and shortest paths that handle edge insertions and report the
respective paths within subquadratic worst-case time bounds. For reachability
and $(1+\epsilon)$-approximate shortest paths in weighted digraphs, these
bounds match the best known dynamic matrix inverse-based randomized bounds for
fully dynamic reachability [v.d.Brand, Nanongkai and Saranurak, FOCS'19]. For
exact shortest paths in unweighted graphs, the obtained bounds in the
incremental setting polynomially improve upon the respective best known
randomized update/distance query bounds in the fully dynamic setting.


title:
Wireless Real-Time Capacitance Readout Based on Perturbed Nonlinear
  Parity-Time Symmetry

abstract:
  In this article, we report a vector-network-analyzer-free and real-time LC
wireless capacitance readout system based on perturbed nonlinear parity-time
(PT) symmetry. The system is composed of two inductively coupled reader-sensor
parallel RLC resonators with gain and loss respectively. By searching for the
real mode that requires the minimum saturation gain, the steady-state frequency
evolution as a function of the sensor capacitance perturbation is analytically
deduced. The proposed system can work in different modes by setting different
perturbation point. In particular, at the exceptional point of PT symmetry, the
system exhibits high sensitivity. Experimental demonstrations revealed the
viability of the proposed readout mechanism by measuring the steady-state
frequency of the reader resonator in response to the change of trimmer
capacitor on the sensor side. Our findings could impact many emerging
applications such as implantable medical device for health monitoring,
parameter detection in harsh environment and sealed food packages, etc.


title:
Snowmass White Paper: prospects for measurements of the bottom quark
  mass

abstract:
  In this white paper for the Snowmass '21 community planning exercise we
provide quantitative prospects for bottom quark mass measurements in
high-energy collisions at future colliders that can provide a precise test of
the scale evolution, or "running" of quark masses predicted by QCD.


title: Message Passing Neural Networks for Hypergraphs

abstract:
  Hypergraph representations are both more efficient and better suited to
describe data characterized by relations between two or more objects. In this
work, we present a new graph neural network based on message passing capable of
processing hypergraph-structured data. We show that the proposed model defines
a design space for neural network models for hypergraphs, thus generalizing
existing models for hypergraphs. We report experiments on a benchmark dataset
for node classification, highlighting the effectiveness of the proposed model
with respect to other state-of-the-art methods for graphs and hypergraphs. We
also discuss the benefits of using hypergraph representations and, at the same
time, highlight the limitation of using equivalent graph representations when
the underlying problem has relations among more than two objects.


title:
Measuring hand use in the home after cervical spinal cord injury using
  egocentric video

abstract:
  Background: Egocentric video has recently emerged as a potential solution for
monitoring hand function in individuals living with tetraplegia in the
community, especially for its ability to detect functional use in the home
environment. Objective: To develop and validate a wearable vision-based system
for measuring hand use in the home among individuals living with tetraplegia.
Methods: Several deep learning algorithms for detecting functional hand-object
interactions were developed and compared. The most accurate algorithm was used
to extract measures of hand function from 65 hours of unscripted video recorded
at home by 20 participants with tetraplegia. These measures were: the
percentage of interaction time over total recording time (Perc); the average
duration of individual interactions (Dur); the number of interactions per hour
(Num). To demonstrate the clinical validity of the technology, egocentric
measures were correlated with validated clinical assessments of hand function
and independence (Graded Redefined Assessment of Strength, Sensibility and
Prehension - GRASSP, Upper Extremity Motor Score - UEMS, and Spinal Cord
Independent Measure - SCIM). Results: Hand-object interactions were
automatically detected with a median F1-score of 0.80 (0.67-0.87). Our results
demonstrated that higher UEMS and better prehension were related to greater
time spent interacting, whereas higher SCIM and better hand sensation resulted
in a higher number of interactions performed during the egocentric video
recordings. Conclusions: For the first time, measures of hand function
automatically estimated in an unconstrained environment in individuals with
tetraplegia have been validated against internationally accepted measures of
hand function. Future work will necessitate a formal evaluation of the
reliability and responsiveness of the egocentric-based performance measures for
hand use.


title: Bot Detection in GitHub Repositories

abstract:
  Contemporary social coding platforms like GitHub promote collaborative
development. Many open-source software repositories hosted in these platforms
use machine accounts (bots) to automate and facilitate a wide range of
effort-intensive and repetitive activities. Determining if an account
corresponds to a bot or a human contributor is important for socio-technical
development analytics, for example, to understand how humans collaborate and
interact in the presence of bots, to assess the positive and negative impact of
using bots, to identify the top project contributors, to identify potential bus
factors, and so on. Our project aims to include the trained machine learning
(ML) classifier from the BoDeGHa bot detection tool as a plugin to the
GrimoireLab software development analytics platform. In this work, we present
the procedure to form a pipeline for retrieving contribution and contributor
data using Perceval, distinguishing bots from humans using BoDeGHa, and
visualising the results using Kibana.


title: C*-irreducibility for reduced twisted group C*-algebras

abstract:
  We study C*-irreducibility of inclusions of reduced twisted group C*-algebras
and of reduced group C*-algebras. We characterize C*-irreducibility in the case
of an inclusion arising from a normal subgroup, and exhibit many new examples
of C*-irreducible inclusions.


title: Traveling vortex pairs for 2D Boussinesq equations

abstract:
  In this paper, we study the existence and asymptotic properties of the
traveling vortex pairs for the two-dimensional inviscid incompressible
Boussinesq equations. We construct a family of traveling vorticity pairs, which
constitutes the de-singularization of a pair of point vortices with equal
intensity but opposite sign. Using the improved vorticity method, we also give
limiting position of the supports of vorticities.


title:
Rational penta-inner functions and the distinguished boundary of the
  pentablock

abstract:
  In this note, we give a description of rational maps from the open unit disc
$\mathbb{D}$ to the pentablock that map the boundary of $\mathbb{D}$ to the
distinguished boundary of the pentablock. We also obtain a new characterization
of the distinguished boundary of the pentablock.


title:
SingAug: Data Augmentation for Singing Voice Synthesis with
  Cycle-consistent Training Strategy

abstract:
  Deep learning based singing voice synthesis (SVS) systems have been
demonstrated to flexibly generate singing with better qualities, compared to
conventional statistical parametric based methods. However, neural systems are
generally data-hungry and have difficulty to reach reasonable singing quality
with limited public available training data. In this work, we explore different
data augmentation methods to boost the training of SVS systems, including
several strategies customized to SVS based on pitch augmentation and mix-up
augmentation. To further stabilize the training, we introduce the
cycle-consistent training strategy. Extensive experiments on two public singing
databases demonstrate that our proposed augmentation methods and the
stabilizing training strategy can significantly improve the performance on both
objective and subjective evaluations.


title: Conditional Autoregressors are Interpretable Classifiers

abstract:
  We explore the use of class-conditional autoregressive (CA) models to perform
image classification on MNIST-10. Autoregressive models assign probability to
an entire input by combining probabilities from each individual feature; hence
classification decisions made by a CA can be readily decomposed into
contributions from each each input feature. That is to say, CA are inherently
locally interpretable. Our experiments show that naively training a CA achieves
much worse accuracy compared to a standard classifier, however this is due to
over-fitting and not a lack of expressive power. Using knowledge distillation
from a standard classifier, a student CA can be trained to match the
performance of the teacher while still being interpretable.


title: Equivariant Diffusion for Molecule Generation in 3D

abstract:
  This work introduces a diffusion model for molecule generation in 3D that is
equivariant to Euclidean transformations. Our E(3) Equivariant Diffusion Model
(EDM) learns to denoise a diffusion process with an equivariant network that
jointly operates on both continuous (atom coordinates) and categorical features
(atom types). In addition, we provide a probabilistic analysis which admits
likelihood computation of molecules using our model. Experimentally, the
proposed method significantly outperforms previous 3D molecular generative
methods regarding the quality of generated samples and efficiency at training
time.


title:
Speech Enhancement with Score-Based Generative Models in the Complex
  STFT Domain

abstract:
  Score-based generative models (SGMs) have recently shown impressive results
for difficult generative tasks such as the unconditional and conditional
generation of natural images and audio signals. In this work, we extend these
models to the complex short-time Fourier transform (STFT) domain, proposing a
novel training task for speech enhancement using a complex-valued deep neural
network. We derive this training task within the formalism of stochastic
differential equations, thereby enabling the use of predictor-corrector
samplers. We provide alternative formulations inspired by previous publications
on using SGMs for speech enhancement, avoiding the need for any prior
assumptions on the noise distribution and making the training task purely
generative which, as we show, results in improved enhancement performance.


title: Privacy-Preserving Aggregation in Federated Learning: A Survey

abstract:
  Over the recent years, with the increasing adoption of Federated Learning
(FL) algorithms and growing concerns over personal data privacy,
Privacy-Preserving Federated Learning (PPFL) has attracted tremendous attention
from both academia and industry. Practical PPFL typically allows multiple
participants to individually train their machine learning models, which are
then aggregated to construct a global model in a privacy-preserving manner. As
such, Privacy-Preserving Aggregation (PPAgg) as the key protocol in PPFL has
received substantial research interest. This survey aims to fill the gap
between a large number of studies on PPFL, where PPAgg is adopted to provide a
privacy guarantee, and the lack of a comprehensive survey on the PPAgg
protocols applied in FL systems. In this survey, we review the PPAgg protocols
proposed to address privacy and security issues in FL systems. The focus is
placed on the construction of PPAgg protocols with an extensive analysis of the
advantages and disadvantages of these selected PPAgg protocols and solutions.
Additionally, we discuss the open-source FL frameworks that support PPAgg.
Finally, we highlight important challenges and future research directions for
applying PPAgg to FL systems and the combination of PPAgg with other
technologies for further security improvement.


title: Quantum simulation of real-space dynamics

abstract:
  Quantum simulation is a prominent application of quantum computers. While
there is extensive previous work on simulating finite-dimensional systems, less
is known about quantum algorithms for real-space dynamics. We conduct a
systematic study of such algorithms. In particular, we show that the dynamics
of a $d$-dimensional Schr\"{o}dinger equation with $\eta$ particles can be
simulated with gate complexity $\tilde{O}\bigl(\eta d F
\text{poly}(\log(g'/\epsilon))\bigr)$, where $\epsilon$ is the discretization
error, $g'$ controls the higher-order derivatives of the wave function, and $F$
measures the time-integrated strength of the potential. Compared to the best
previous results, this exponentially improves the dependence on $\epsilon$ and
$g'$ from $\text{poly}(g'/\epsilon)$ to $\text{poly}(\log(g'/\epsilon))$ and
polynomially improves the dependence on $T$ and $d$, while maintaining best
known performance with respect to $\eta$. For the case of Coulomb interactions,
we give an algorithm using $\eta^{3}(d+\eta)T\text{poly}(\log(\eta
dTg'/(\Delta\epsilon)))/\Delta$ one- and two-qubit gates, and another using
$\eta^{3}(4d)^{d/2}T\text{poly}(\log(\eta dTg'/(\Delta\epsilon)))/\Delta$ one-
and two-qubit gates and QRAM operations, where $T$ is the evolution time and
the parameter $\Delta$ regulates the unbounded Coulomb interaction. We give
applications to several computational problems, including faster real-space
simulation of quantum chemistry, rigorous analysis of discretization error for
simulation of a uniform electron gas, and a quadratic improvement to a quantum
algorithm for escaping saddle points in nonconvex optimization.


title:
Vehicular Positioning and Tracking in Multipath Non-Line-of-Sight
  Channels

abstract:
  We consider the downlink transmission in a single cell multiple-input
multiple-output system, in which the user equipment correspond to a vehicle
moving along a given trajectory. This system utilizes millimeter wave channels
characterized by multiple non-line-of-sight (NLoS) components. As it has been
pointed out in several related works, in such systems radio access network
(RAN)-based positioning can effectively improve the positioning accuracy
achieved by Global Navigation Satellite Systems. However, the RAN-based
positioning accuracy is highly dependent on the quality of the channel
estimates, especially if multipath propagation is exploited. Recognizing that
the communication channels between the serving base station and the vehicle as
well as the geographical position of the vehicle can be advantageously modeled
as inter-related autoregressive processes, we propose a two-stage Kalman filter
algorithm employing two intertwined filters for channel tracking, position
tracking and abrupt channel change detection. The first Kalman filter tracks
angles-of-departure and angles-of-arrival associated with the communication
channels, which are used to make a coarse position estimation. The second
Kalman filter tracks the position of the vehicle utilizing the kinematic
parameters of the vehicle. Simulation results clearly show the advantages of
using the proposed scheme, which exploits the memoryful property of both the
communication channels and the geographical positions, as compared to employing
previously proposed single-stage or not properly combined filters in NLoS
environments.


title:
It's All In the Teacher: Zero-Shot Quantization Brought Closer to the
  Teacher

abstract:
  Model quantization is considered as a promising method to greatly reduce the
resource requirements of deep neural networks. To deal with the performance
drop induced by quantization errors, a popular method is to use training data
to fine-tune quantized networks. In real-world environments, however, such a
method is frequently infeasible because training data is unavailable due to
security, privacy, or confidentiality concerns. Zero-shot quantization
addresses such problems, usually by taking information from the weights of a
full-precision teacher network to compensate the performance drop of the
quantized networks. In this paper, we first analyze the loss surface of
state-of-the-art zero-shot quantization techniques and provide several
findings. In contrast to usual knowledge distillation problems, zero-shot
quantization often suffers from 1) the difficulty of optimizing multiple loss
terms together, and 2) the poor generalization capability due to the use of
synthetic samples. Furthermore, we observe that many weights fail to cross the
rounding threshold during training the quantized networks even when it is
necessary to do so for better performance. Based on the observations, we
propose AIT, a simple yet powerful technique for zero-shot quantization, which
addresses the aforementioned two problems in the following way: AIT i) uses a
KL distance loss only without a cross-entropy loss, and ii) manipulates
gradients to guarantee that a certain portion of weights are properly updated
after crossing the rounding thresholds. Experiments show that AIT outperforms
the performance of many existing methods by a great margin, taking over the
overall state-of-the-art position in the field.


title: Assessing performance of quantum annealing with non-linear driving

abstract:
  Current generation quantum annealers have already proven to be successful
problem-solvers. Yet, quantum annealing is still very much in its infancy, with
suboptimal applicability. For instance, to date it is still an open question
which annealing protocol will universally cause the fewest diabatic
excitations, and even whether there is a universally optimal strategy.
Therefore, in the present work, we report analytical and numerical studies of
the diabatic excitations arising from non-linear protocols applied to the
transverse field Ising chain, the exactly solvable model that serves as quantum
annealing playground. Our analysis focuses on several driving schemes that
inhibit or facilitate the dynamic phases discussed in a previous work. Rather
remarkably, we find that the paradigmatic Kibble-Zurek behavior can be
suppressed with "pauses" in the evolution, both for crossing and stopping at
the quantum critical point of the system.


title: Consistency of randomized integration methods

abstract:
  For integrable functions we provide a weak law of large numbers for
structured Monte Carlo methods, such as estimators based on randomized digital
nets, Latin hypercube sampling, randomized Frolov point sets as well as
Cranley-Patterson rotations. Moreover, we suggest median modified methods and
show that for integrands in $L^p$ with $p>1$ a strong law of large numbers
holds.


title: Assessing the ultra-central flow puzzle in the Bayesian era

abstract:
  An outstanding problem in the field of relativistic heavy-ion collisions is
the inability for any simulation model to accurately describe experimental flow
data in extremely central collisions -- in particular, models always predict
either an elliptic flow that is too large or triangular flow that is too small
(or both). We reassess the status of this puzzle in light of recent progress in
Bayesian parameter estimation, in which a large model parameter space can be
efficiently explored to determine what parameters are necessary for a good fit
to experimental results, and how well state-of-the-art models are able to
describe data. We explore predictions for flow in ultra-central collisions from
multiple recent Bayesian models that were tuned to various observables in
different collision systems at typical centralities. We find that, while
ultra-central data can now be described with better accuracy than in previous
calculations, tension with experimental observation remains, and progressively
gets worse as one goes to more central collisions. Thus, the physics of
ultra-central collisions is still not fully understood. A resolution to this
puzzle will be an important step in solidifying our understanding of the
physics of the strong interactions in these extreme conditions, as well as
increasing our confidence in the results of precision analyses.


title: A Temporal-oriented Broadcast ResNet for COVID-19 Detection

abstract:
  Detecting COVID-19 from audio signals, such as breathing and coughing, can be
used as a fast and efficient pre-testing method to reduce the virus
transmission. Due to the promising results of deep learning networks in
modelling time sequences, and since applications to rapidly identify COVID
in-the-wild should require low computational effort, we present a
temporal-oriented broadcasting residual learning method that achieves efficient
computation and high accuracy with a small model size. Based on the
EfficientNet architecture, our novel network, named Temporal-oriented
ResNet~(TorNet), constitutes of a broadcasting learning block, i.e. the
Alternating Broadcast (AB) Block, which contains several Broadcast Residual
Blocks (BC ResBlocks) and a convolution layer. With the AB Block, the network
obtains useful audio-temporal features and higher level embeddings effectively
with much less computation than Recurrent Neural Networks~(RNNs), typically
used to model temporal information. TorNet achieves 72.2% Unweighted Average
Recall (UAR) on the INTERPSEECH 2021 Computational Paralinguistics Challenge
COVID-19 cough Sub-Challenge, by this showing competitive results with a higher
computational efficiency than other state-of-the-art alternatives.


title:
A Temporal Learning Approach to Inpainting Endoscopic Specularities and
  Its effect on Image Correspondence

abstract:
  Video streams are utilised to guide minimally-invasive surgery and diagnostic
procedures in a wide range of procedures, and many computer assisted techniques
have been developed to automatically analyse them. These approaches can provide
additional information to the surgeon such as lesion detection, instrument
navigation, or anatomy 3D shape modeling. However, the necessary image features
to recognise these patterns are not always reliably detected due to the
presence of irregular light patterns such as specular highlight reflections. In
this paper, we aim at removing specular highlights from endoscopic videos using
machine learning. We propose using a temporal generative adversarial network
(GAN) to inpaint the hidden anatomy under specularities, inferring its
appearance spatially and from neighbouring frames where they are not present in
the same location. This is achieved using in-vivo data of gastric endoscopy
(Hyper-Kvasir) in a fully unsupervised manner that relies on automatic
detection of specular highlights. System evaluations show significant
improvements to traditional methods through direct comparison as well as other
machine learning techniques through an ablation study that depicts the
importance of the network's temporal and transfer learning components. The
generalizability of our system to different surgical setups and procedures was
also evaluated qualitatively on in-vivo data of gastric endoscopy and ex-vivo
porcine data (SERV-CT, SCARED). We also assess the effect of our method in
computer vision tasks that underpin 3D reconstruction and camera motion
estimation, namely stereo disparity, optical flow, and sparse point feature
matching. These are evaluated quantitatively and qualitatively and results show
a positive effect of specular highlight inpainting on these tasks in a novel
comprehensive analysis.


title: Fundamental inequalities in the plain-old Stoner-Wohlfarth model

abstract:
  We report two fundamental inequalities in the Stoner-Wohlfarth model.
Specifically, we investigate what is the theoretical limit for the initial
magnetic susceptibility in a system described by the Stoner-Wohlfarth approach.
We also find analytical solutions for the magnetization at the low-fields
regime, and obtain the borderline values for saturation magnetization and
uniaxial-anisotropy constant for such ideal Stoner-Wohlfarth system. We go
beyond and introduce a general mean field theory for interacting
Stoner-Wohlfarth-like systems, thus estimating how the initial magnetic
susceptibility is affected due to the dipolar and exchange interactions inside
the system. By means of a simple insight, from a fundamental inequality for the
magnetic susceptibility of an ideal Stoner-Wohlfarth system, we show its
violation is a signature of the existence of exchange interactions between
nanoparticles in an interacting Stoner-Wohlfarth-like system.


title:
Refinements for Bragg coherent X-ray diffraction imaging: Electron
  backscatter diffraction alignment and strain field computation

abstract:
  Bragg coherent X-ray diffraction imaging (BCDI) allows the three-dimensional
(3D) measurement of lattice strain along the scattering vector for specific
microcrystals. If at least three linearly independent reflections are measured,
the 3D variation of the full lattice strain tensor within the micro-crystal can
be recovered. However, this requires knowledge of the crystal orientation,
which is typically attained via estimates based on crystal geometry or
synchrotron micro-beam Laue measurements. Here, we present an alternative
method to determine the crystal orientation for BCDI measurements, by using
electron backscatter diffraction (EBSD) to align Fe-Ni and Co-Fe alloy
microcrystals on three different substrates. The orientation matrix is
calculated from EBSD Euler angles and compared to the orientation determined
using micro-beam Laue diffraction. The average angular mismatch between the
orientation matrices is less than ~6 degrees, which is reasonable for the
search for Bragg reflections. We demonstrate the use of an orientation matrix
derived from EBSD to align and measure five reflections for a single Fe-Ni
microcrystal using multi-reflection BCDI. Using this dataset, a refined strain
field computation based on the gradient of the complex exponential of the phase
is developed. This approach is shown to increase accuracy, especially in the
presence of dislocations. Our results demonstrate the feasibility of using EBSD
to pre-align BCDI samples and the application of more efficient approaches to
determine the full lattice strain tensor with greater accuracy.


title: Constraining extended scalar sectors at current and future colliders

abstract:
  In this proceeding, I summarize results on various new physics extensions of
the Standard Model, for models with and without dark matter candidates. I
discuss current constraints as well as rates and discovery prospects at future
colliders.


title: Transmogrifiers: Bright of the Exomoon

abstract:
  Though it may be a behavior that has been observed and documented for
millennia, and despite the connection between it and the full moon, the
astronomical community has afforded very little attention to lycanthropy. We
hope to address this deficiency by using the population of known exoplanets as
a natural experiment to better characterize what properties of the moon are
necessary to trigger a transformation into a werewolf. We additionally
investigate which exoplanets are most likely to have exomoons which may cause
werewolves, with a particular focus on LHS 1140 b. We also propose a new
mission called the Werewolves From Infrared Radiation and Spectral-typing
Telescope, or WFIRST, in order to better characterize exoplanetary systems.
This will allow us to explore the impact of stellar type on lycanthropy more
than it has traditionally been considered. We believe this represents a major
step forward in our understanding and recognition of the burgeoning field of
exocryptozoology.


title:
An Illustrative Industry Architecture to Mitigate Potential
  Fragmentation across Central Bank Digital Currency and Commercial Bank Money

abstract:
  Central banks are actively exploring central bank digital currencies (CBDCs)
by conducting research, proofs of concept and pilots. However, adoption of a
CBDC can risk fragmenting both payments markets and retail deposits. In this
paper, we aim to provide a mitigation to this fragmentation risk by presenting
an illustrative industry architecture which places CBDCs and commercial bank
money on a similar footing. We introduce the concept of ecosystems providing a
common programmability layer that interfaces with the account systems at both
commercial banks and the central bank. We focus on a potential United Kingdom
(UK) CBDC, including industry ecosystems interfacing with commercial banks
using Open Banking application programming interfaces (APIs).


title: DeepFry: Identifying Vocal Fry Using Deep Neural Networks

abstract:
  Vocal fry or creaky voice refers to a voice quality characterized by
irregular glottal opening and low pitch. It occurs in diverse languages and is
prevalent in American English, where it is used not only to mark phrase
finality, but also sociolinguistic factors and affect. Due to its irregular
periodicity, creaky voice challenges automatic speech processing and
recognition systems, particularly for languages where creak is frequently used.
  This paper proposes a deep learning model to detect creaky voice in fluent
speech. The model is composed of an encoder and a classifier trained together.
The encoder takes the raw waveform and learns a representation using a
convolutional neural network. The classifier is implemented as a multi-headed
fully-connected network trained to detect creaky voice, voicing, and pitch,
where the last two are used to refine creak prediction. The model is trained
and tested on speech of American English speakers, annotated for creak by
trained phoneticians.
  We evaluated the performance of our system using two encoders: one is
tailored for the task, and the other is based on a state-of-the-art
unsupervised representation. Results suggest our best-performing system has
improved recall and F1 scores compared to previous methods on unseen data.


title: Logit Normalization for Long-tail Object Detection

abstract:
  Real-world data exhibiting skewed distributions pose a serious challenge to
existing object detectors. Moreover, the samplers in detectors lead to shifted
training label distributions, while the tremendous proportion of background to
foreground samples severely harms foreground classification. To mitigate these
issues, in this paper, we propose Logit Normalization (LogN), a simple
technique to self-calibrate the classified logits of detectors in a similar way
to batch normalization. In general, our LogN is training- and tuning-free (i.e.
require no extra training and tuning process), model- and label
distribution-agnostic (i.e. generalization to different kinds of detectors and
datasets), and also plug-and-play (i.e. direct application without any bells
and whistles). Extensive experiments on the LVIS dataset demonstrate superior
performance of LogN to state-of-the-art methods with various detectors and
backbones. We also provide in-depth studies on different aspects of our LogN.
Further experiments on ImageNet-LT reveal its competitiveness and
generalizability. Our LogN can serve as a strong baseline for long-tail object
detection and is expected to inspire future research in this field. Code and
trained models will be publicly available at https://github.com/MCG-NJU/LogN.


title: New calibrations for estimating the N/O ratio in HII regions

abstract:
  We use a sample of 536 \hii\ regions located in nearby spirals, with an
homogeneous determination of their $T_e$-based abundances, to obtain new
empirical calibrations of the N2O2, N2S2, O3N2, and N2 strong-line indices to
estimate the nitrogen-to-oxygen abundance ratio when auroral lines are not
detected. All indices are strongly correlated with the $T_e$-based $\log$(N/O)
for our \hii\ region sample, even more strongly than with $12+\log$(O/H). N2O2
is the most strongly correlated index, and the best fit to the $\log$(N/O)-N2O2
relation is obtained with a second-order polynomial. The derived relation has a
low dispersion ({\em rms}$<$0.09~dex), being valid in the range $-1.74 < $ N2O2
$< 0.62$ (or $-1.81 < $ $\log$(N/O) $< -0.13$). We have compared our
calibration with previous ones and have discussed the differences between them
in terms of the nature of the objects used as calibrators.


title:
Tuning long-range fermion-mediated interactions in cold-atom quantum
  simulators

abstract:
  Engineering long-range interactions in cold-atom quantum simulators can lead
to exotic quantum many-body behavior. Fermionic atoms in ultracold atomic
mixtures can act as mediators, giving rise to long-range RKKY-type interactions
characterized by the dimensionality and density of the fermionic gas. Here, we
propose several tuning knobs, accessible in current experimental platforms,
that allow to further control the range and shape of the mediated interactions,
extending the existing quantum simulation toolbox. In particular, we include an
additional optical lattice for the fermionic mediator, as well as anisotropic
traps to change its dimensionality in a continuous manner. This allows us to
interpolate between power-law and exponential decays, introducing an effective
cutoff for the interaction range, as well as to tune the relative interaction
strengths at different distances. Finally, we show how our approach allows to
investigate frustrated regimes that were not previously accessible, where
symmetry-protected topological phases as well as chiral spin liquids emerge.


title:
CTA-RNN: Channel and Temporal-wise Attention RNN Leveraging Pre-trained
  ASR Embeddings for Speech Emotion Recognition

abstract:
  Previous research has looked into ways to improve speech emotion recognition
(SER) by utilizing both acoustic and linguistic cues of speech. However, the
potential association between state-of-the-art ASR models and the SER task has
yet to be investigated. In this paper, we propose a novel channel and
temporal-wise attention RNN (CTA-RNN) architecture based on the intermediate
representations of pre-trained ASR models. Specifically, the embeddings of a
large-scale pre-trained end-to-end ASR encoder contain both acoustic and
linguistic information, as well as the ability to generalize to different
speakers, making them well suited for downstream SER task. To further exploit
the embeddings from different layers of the ASR encoder, we propose a novel
CTA-RNN architecture to capture the emotional salient parts of embeddings in
both the channel and temporal directions. We evaluate our approach on two
popular benchmark datasets, IEMOCAP and MSP-IMPROV, using both within-corpus
and cross-corpus settings. Experimental results show that our proposed method
can achieve excellent performance in terms of accuracy and robustness.


title:
VQF: Highly Accurate IMU Orientation Estimation with Bias Estimation and
  Magnetic Disturbance Rejection

abstract:
  The miniaturization of inertial measurement units (IMUs) facilitates their
widespread use in a growing number of application domains. Orientation
estimation is a prerequisite for most further data processing steps in inertial
motion tracking, such as position/velocity estimation, joint angle estimation,
and 3D visualization. Errors in the estimated orientations severely affect all
further processing steps. Few existing publications systematically compare
multiple algorithms on a broad collection of experimental data, and those
publications show that out-of-the-box accuracy of existing algorithms is often
low and that application-specific tuning is required. In the present work, we
propose and extensively evaluate an orientation estimation algorithm that is
based on a novel approach of filtering the acceleration measurements in an
almost-inertial frame and that includes extensions for gyroscope bias
estimation and magnetic disturbance rejection, as well as a variant for offline
data processing. In contrast to all existing work, we perform a comprehensive
evaluation, using a large collection of publicly available datasets and eight
literature methods for comparison. The proposed method consistently outperforms
all literature methods and achieves an average RMSE of 2.9{\deg}, while the
errors obtained with literature methods range from 5.3{\deg} to 16.7{\deg}.
Since the evaluation was performed with one single fixed parametrization across
a very diverse dataset collection, we conclude that the proposed method
provides unprecedented out-of-the-box performance for a broad range of motions,
sensor hardware, and environmental conditions. This gain in orientation
estimation accuracy is expected to advance the field of IMU-based motion
analysis and provide performance benefits in numerous applications. The
provided open-source implementation makes it easy to employ the proposed
method.


title:
Theory of magnetic spin and orbital Hall and Nernst effects in bulk
  ferromagnets

abstract:
  The magnetic spin Hall effect (MSHE) is an anomalous charge-to-spin
conversion phenomenon which occurs in ferromagnetic materials. In contrast to
the conventional spin Hall effect (SHE), being a time-reversal even effect, the
magnetic counterpart is time-reversal odd. In this work, we use ab initio
calculations to investigate the MSHE for the bulk ferromagnets Fe, Co, and Ni.
The magnitudes of the MSHE of Fe and Co are comparable to those of the SHE, but
the MSHE is strongly dependent on the electron lifetime and the MSHE and SHE
can moreover have opposite signs. For Ni the MSHE is smaller than the SHE, but
in general, the MSHE cannot be ignored for spin-orbit torques. Considering a
charge current we analyze how both the MSHE and SHE contribute to a total Hall
angle. We extend our analysis of the MSHE to its orbital counterpart, that is,
the magnetic orbital Hall effect (MOHE), for which we show that the MOHE is in
general smaller than the orbital Hall effect (OHE). We compute furthermore the
thermal analogs, i.e., the spin and orbital Nernst effects, and their magnetic
counterparts. Here our calculations show that the magnetic spin and orbital
Nernst effects of Ni are substantially larger than those of Fe and Co.


title: SDSS-IV MaNGA: Exploring the local scaling relations for N/O

abstract:
  We present, for the first time, the relationship between local stellar mass
surface density, $\mathrm{\Sigma_{*}}$, and N/O derived from SDSS-IV MaNGA
data, using a sample of $792765$ high signal-to-noise ratio star-forming
spaxels. Using a combination of phenomenological modelling and partial
correlation analysis, we find that $\mathrm{\Sigma_{*}}$ alone is insufficient
to predict the N/O in MaNGA spaxels, and that there is an additional dependence
on the local star formation rate surface density, $\mathrm{\Sigma_{SFR}}$. This
effect is a factor of $3$ stronger than the dependence of 12+log(O/H) on
$\mathrm{\Sigma_{SFR}}$. Surprisingly, we find that the local N/O scaling
relations also depend on the total galaxy stellar mass at fixed $\Sigma_{*}$ as
well as the galaxy size at fixed stellar mass. We find that more compact
galaxies are more nitrogen rich, even when $\mathrm{\Sigma_{*}}$ and
$\mathrm{\Sigma_{SFR}}$ are controlled for. We show that $\sim50\%$ of the
variance of N/O is explained by the total stellar mass and size. Thus, the
evolution of nitrogen in galaxies is set by more than just local effects and
does not simply track the build up of oxygen in galaxies. The precise form of
the N/O-O/H relation is therefore sensitive to the sample of galaxies from
which it is derived. This result casts doubt on the universal applicability of
nitrogen-based strong-line metallicity indicators derived in the local
universe.


title: Flat-topped Probability Density Functions for Mixture Models

abstract:
  This paper investigates probability density functions (PDFs) that are
continuous everywhere, nearly uniform around the mode of distribution, and
adaptable to a variety of distribution shapes ranging from bell-shaped to
rectangular. From the viewpoint of computational tractability, the PDF based on
the Fermi-Dirac or logistic function is advantageous in estimating its shape
parameters. The most appropriate PDF for $n$-variate distribution is of the
form:
$p\left(\mathbf{x}\right)\propto\left[\cosh\left(\left[\left(\mathbf{x}-\mathbf{m}\right)^{\mathsf{T}}\boldsymbol{\Sigma}^{-1}\left(\mathbf{x}-\mathbf{m}\right)\right]^{n/2}\right)+\cosh\left(r^{n}\right)\right]^{-1}$
where $\mathbf{x},\mathbf{m}\in\mathbb{R}^{n}$, $\boldsymbol{\Sigma}$ is an
$n\times n$ positive definite matrix, and $r>0$ is a shape parameter. The
flat-topped PDFs can be used as a component of mixture models in machine
learning to improve goodness of fit and make a model as simple as possible.


title:
Differentially Private Federated Learning via Reconfigurable Intelligent
  Surface

abstract:
  Federated learning (FL), as a disruptive machine learning paradigm, enables
the collaborative training of a global model over decentralized local datasets
without sharing them. It spans a wide scope of applications from
Internet-of-Things (IoT) to biomedical engineering and drug discovery. To
support low-latency and high-privacy FL over wireless networks, in this paper,
we propose a reconfigurable intelligent surface (RIS) empowered over-the-air FL
system to alleviate the dilemma between learning accuracy and privacy. This is
achieved by simultaneously exploiting the channel propagation reconfigurability
with RIS for boosting the receive signal power, as well as waveform
superposition property with over-the-air computation (AirComp) for fast model
aggregation. By considering a practical scenario where high-dimensional local
model updates are transmitted across multiple communication blocks, we
characterize the convergence behaviors of the differentially private federated
optimization algorithm. We further formulate a system optimization problem to
optimize the learning accuracy while satisfying privacy and power constraints
via the joint design of transmit power, artificial noise, and phase shifts at
RIS, for which a two-step alternating minimization framework is developed.
Simulation results validate our systematic, theoretical, and algorithmic
achievements and demonstrate that RIS can achieve a better trade-off between
privacy and accuracy for over-the-air FL systems.


title: Modular $q$-holonomic modules

abstract:
  We introduce the notion of modular $q$-holonomic modules whose fundamental
matrices define a cocycle with improved analyticity properties and show that
the generalised $q$-hypergeometric equation, as well as three key $q$-holonomic
modules of complex Chern--Simons theory are modular. This notion explains
conceptually recent structural properties of quantum invariants of knots and
3-manifolds, and of exact and perturbative Chern--Simons theory, and in
addition provides an effective method to solve the corresponding linear
$q$-difference equations. An alternative title of our paper, emphasising the
equations rather than the modules, is: Modular linear $q$-difference equations.


title: Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks

abstract:
  New classes arise frequently in our ever-changing world, e.g., emerging
topics in social media and new types of products in e-commerce. A model should
recognize new classes and meanwhile maintain discriminability over old classes.
Under severe circumstances, only limited novel instances are available to
incrementally update the model. The task of recognizing few-shot new classes
without forgetting old classes is called few-shot class-incremental learning
(FSCIL). In this work, we propose a new paradigm for FSCIL based on
meta-learning by LearnIng Multi-phase Incremental Tasks (LIMIT), which
synthesizes fake FSCIL tasks from the base dataset. The data format of fake
tasks is consistent with the `real' incremental tasks, and we can build a
generalizable feature space for the unseen tasks through meta-learning.
Besides, LIMIT also constructs a calibration module based on transformer, which
calibrates the old class classifiers and new class prototypes into the same
scale and fills in the semantic gap. The calibration module also adaptively
contextualizes the instance-specific embedding with a set-to-set function.
LIMIT efficiently adapts to new classes and meanwhile resists forgetting over
old classes. Experiments on three benchmark datasets (CIFAR100, miniImageNet,
and CUB200) and large-scale dataset, i.e., ImageNet ILSVRC2012 validate that
LIMIT achieves state-of-the-art performance.


title:
Training strategy for a lightweight countermeasure model for automatic
  speaker verification

abstract:
  The countermeasure (CM) model is developed to protect Automatic Speaker
Verification (ASV) systems from spoof attacks and prevent resulting personal
information leakage. Based on practicality and security considerations, the CM
model is usually deployed on edge devices, which have more limited computing
resources and storage space than cloud-based systems. This work proposes
training strategies for a lightweight CM model for ASV, using generalized
end-to-end (GE2E) pre-training and adversarial fine-tuning to improve
performance, and applying knowledge distillation (KD) to reduce the size of the
CM model. In the evaluation phase of the ASVspoof 2021 Logical Access task, the
lightweight ResNetSE model reaches min t-DCF 0.2695 and EER 3.54%. Compared to
the teacher model, the lightweight student model only uses 22.5% of parameters
and 21.1% of multiply and accumulate operands of the teacher model.


title:
Probing the Galactic Halo with RR Lyrae Stars. II. The Substructures of
  the Milky Way

abstract:
  We identify substructures of the Galactic halo using 3,003 type $ab$ RR
Lyraes (RRab) with 6D position-velocity information from the SDSS, LAMOST, and
Gaia EDR3. Based on the information, we define the separation of any two of the
stars in the integrals of motion space and identify substructures by utilizing
the friends-of-friends algorithm. We identify members belonging to several
known substructures: the Sagittarius stream, the Gaia-Enceladus-Sausage (GES),
the Sequoia, and the Helmi streams. In addition to these known substructures,
there are three other substructures possibly associated with globular clusters
NGC 5272, NGC 6656, and NGC 5024, respectively. Finally, we also find three
remaining unknown substructures and one of them has large angular momentum and
a mean metallicity $\rm -2.13\,dex$ which may be a new substructure. As for
GES, we find that it accounts for a large part of substructures in the inner
halo and the range of apocenter distance is from 10 to $34\,\rm kpc$, which
suggests that the GES is mainly distributed in the inner halo. The near
one-third proportion of the GES and the peak value $20\,\rm kpc$ of the
apocenter distances suggest that GES could account for the break in the density
profile of the Galactic halo at Galactocentric distance ${\sim}20-25\,\rm kpc$.
The similarity of comparing the kinematic properties of Gaia-Enceladus-Sausage
with the Hercules-Aquila Cloud and Virgo Overdensity suggests that the three
substructures may have similar origins.


title:
An optimization model with stochastic variables for flexible production
  logistics planning

abstract:
  Production logistics has an important role as a chain that connects the
components of the production system. The most important goal of production
logistics plans is to keep the flow of the production system well. However,
compared to the production system, the level of planning, management, and
digitalization of the production logistics system is not high enough, so it is
difficult to respond flexibly when unexpected situations occur in the
production logistics system. Optimization and heuristic algorithms have been
proposed to solve this problem, but due to their inflexible nature, they can
only achieve the desired solution in a limited environment. In this paper, the
relationship between the production and production logistics system is analyzed
and stochastic variables are introduced by modifying the pickup and delivery
problem with time windows (PDPTW) optimization model to establish a flexible
production logistics plan. This model, taking into account stochastic
variables, gives the scheduler a new perspective, allowing them to have new
insights based on the mathematical model. However, since the optimization model
is still insufficient to respond to the dynamic environment, future research
will cover how to derive meaningful results even in a dynamic environment such
as a machine learning model.


title: Decayless oscillations in solar coronal bright points

abstract:
  Decayless kink oscillations of solar coronal loops (or decayless oscillations
for short) have attracted great attention since their discovery. Coronal bright
points (CBPs) are mini-active regions and consist of loops with a small size.
However, decayless oscillations in CBPs have not been widely reported. In this
study, we identified this kind of oscillations in some CBPs using 171 \AA\,
images taken by the Atmospheric Imaging Assembly (AIA) onboard the Solar
Dynamics Observatory (SDO). After using the motion magnification algorithm to
increase oscillation amplitudes, we made time-distance maps to identify the
oscillatory signals. We also estimated the loop lengths and velocity
amplitudes. We analysed 23 CBPs, and found 31 oscillation events in 16 of them.
The oscillation periods range from 1 to 8 minutes (on average about 5 minutes),
and the displacement amplitudes have an average value of 0.07 Mm. The average
loop length and velocity amplitude are 23 Mm and 1.57 \kms, respectively.
Relationships between different oscillation paraments are also examined.
Additionally, we performed a simple forward model to illustrate how these
sub-pixel oscillation amplitudes (less than 0.4 Mm) could be detected. Results
of the model confirm the reliability of our data processing methods. Our study
shows for the first time that decayless oscillations are common in small-scale
loops of CBPs. These oscillations allow for seismological diagnostics of the
Alfv\'{e}n speed and magnetic field strength in the corona.


title:
Photometric redshifts and Galaxy Clusters for DES DR2, DESI DR9, and
  HSC-SSP PDR3 Data

abstract:
  Photometric redshift (photo-z) is a fundamental parameter for
multi-wavelength photometric surveys, while galaxy clusters are important
cosmological probers and ideal objects for exploring the dense environmental
impact on galaxy evolution. We extend our previous work on estimating photo-z
and detecting galaxy clusters to the latest data releases of the Dark Energy
Spectroscopic Instrument (DESI) imaging surveys, Dark Energy Survey (DES), and
Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) imaging surveys and make
corresponding catalogs publicly available for more extensive scientific
applications. The photo-z catalogs include accurate measurements of photo-z and
stellar mass for about 320, 293, and 134 million galaxies with $r<23$, $i<24$,
and $i<25$ in DESI DR9, DES DR2, and HSC-SSP PDR3 data, respectively. The
photo-z accuracy is about 0.017, 0.024, and 0.029 and the general redshift
coverage is $z<1$, $z<1.2$, and $z<1.6$, respectively for those three surveys.
The uncertainties of the logarithmic stellar mass that is inferred from stellar
population synthesis fitting is about 0.2 dex. With the above photo-z catalogs,
galaxy clusters are detected using a fast cluster-finding algorithm. A total of
532,810, 86,963, and 36,566 galaxy clusters with the number of members larger
than 10 are discovered for DESI, DES, and HSC-SSP, respectively. Their photo-z
accuracy is at the level of 0.01. The total mass of our clusters are also
estimated by using the calibration relations between the optical richness and
the mass measurement from X-ray and radio observations. The photo-z and cluster
catalogs are available at ScienceDB
(https://www.doi.org/10.11922/sciencedb.o00069.00003) and PaperData Repository
(https://doi.org/10.12149/101089).


title: Partial Coupling of Optimal Transport for Spoken Language Identification

abstract:
  In order to reduce domain discrepancy to improve the performance of
cross-domain spoken language identification (SLID) system, as an unsupervised
domain adaptation (UDA) method, we have proposed a joint distribution alignment
(JDA) model based on optimal transport (OT). A discrepancy measurement based on
OT was adopted for JDA between training and test data sets. In our previous
study, it was supposed that the training and test sets share the same label
space. However, in real applications, the label space of the test set is only a
subset of that of the training set. Fully matching training and test domains
for distribution alignment may introduce negative domain transfer. In this
paper, we propose an JDA model based on partial optimal transport (POT), i.e.,
only partial couplings of OT are allowed during JDA. Moreover, since the label
of test data is unknown, in the POT, a soft weighting on the coupling based on
transport cost is adaptively set during domain alignment. Experiments were
carried out on a cross-domain SLID task to evaluate the proposed UDA. Results
showed that our proposed UDA significantly improved the performance due to the
consideration of the partial couplings in OT.


title:
First-principles theory of intrinsic spin and orbital Hall and Nernst
  effects in metallic monoatomic crystals

abstract:
  The generation of spin and orbital currents is of crucial importance in the
field of spin-orbitronics. In this work, using relativistic density functional
theory and the Kubo linear-response formalism, we systematically investigate
the spin Hall and orbital Hall effects for 40 monoatomic metals. The spin Hall
conductivity (SHC) and orbital Hall conductivity (OHC) are computed as a
function of the electrochemical potential and the influence of the spin-orbit
interaction strength is also investigated. Our calculations predict a rather
small OHC in $sp$ metals, but a much larger OHC in $d$-band metals, with
maximum values [$\sim 8000\,(\hbar/e)\Omega^{-1}{\rm cm}^{-1}$] near the middle
of the $d$ series. Using the Mott formula, we evaluate the thermal counterparts
of the spin and orbital Hall effects, the spin Nernst effect (SNE) and the
orbital Nernst effect (ONE). We find that the as-yet unobserved ONE is
significantly larger ($\sim 10 \times$) than the SNE and has maximum values for
group 10 elements (Ni, Pd, and Pt). Our work provides a broad overview of
electrically- and thermally-induced spin and orbital transport in monoatomic
metals.


title: Antiparticle of exciton in semimetals

abstract:
  An emergent quantized field enriches quantum many-body systems. We propose an
antiparticle analog of the exciton in semimetals as an emergent collective mode
in interacting electron systems. To this end, we solve the Bethe-Salpeter
equation of a two-band electron-hole model for semimetals, and demonstrate that
inter-band excitations in semimetals are generally comprised of both excitons
and antiparticles of excitons. We clarify that the effective theory of the
inter-band excitations is given by a generalized Klein-Gordon theory. The
particle and antiparticle are characterized by opposite conserved charges,
fostering fertile scattering processes as well as possible experimental
detections of the antiparticles. Our theory provides a comprehensive
understanding of excitonic spectra in generic semimetals, bringing a new
insight into electronic collective phenomena in solids.

categories: cond-mat.mes-hall cond-mat.mtrl-sci cond-mat.str-el hep-th


title: Current status and future of cosmology with 21cm Intensity Mapping

abstract:
  21cm Intensity Mapping (IM) has been proposed about 15 years ago as a cost
effective method to carry out cosmological surveys and to map the 3D
distribution of matter in the universe, over a large range of post EoR
redshifts, from z=0 to z=6. Since then a number of pathfinder instruments have
been built, such as CHIME or Tianlai. Several other ones will be commissioned
in the next few years (HIRAX, CHORD, BINGO), while even larger arrays, with
several thousand antennae are being considered for the next generation
experiments. We will briefly review the 21cm cosmology of the Epoch of
Reionisation (EoR), and we will then focus on IM for late time cosmology. After
presenting some of the promises of this technique to constrain the cosmological
model, dark energy and inflation, we will review some of the instrumental and
scientific challenges of IM surveys. The second part of the paper presents an
overview of the ongoing and future experiments, as well as recent results by
GBT, CHIME and Tianlai.


title: Potential impact of CV-QKD integration on classical WDM network capacity

abstract:
  Continuous-variable quantum key distribution (CV-QKD) could allow QKD and
classical optical signals physically sharing the same optical fibers in
existing networks. However, Raman scattering imposes a limit on the optical
power, which in turn impacts the network capacity for classical traffic in
presence of CV-QKD. Network-planning simulations indicate that maxing out the
CV-QKD capacity in an optical link can adversely impact its classical capacity.
Although preliminary, these results show that designing a mixed classical and
CV-QKD network will require dedicated planning heuristics and tools that
specifically seek a compromise between classical and CV-QKD traffics.


title: On the Population Monotonicity of Independent Set Games

abstract:
  An independent set game is a cooperative game defined on graphs and dealing
with profit sharing in maximum independent set problems. A population monotonic
allocation scheme is a rule specifying how to share the profit of each
coalition among its participants such that every participant is better off when
the coalition expands. In this paper, we provide a necessary and sufficient
characterization for population monotonic allocation schemes in independent set
games. Moreover, our characterization can be verified efficiently.


title:
IITD-DBAI: Multi-Stage Retrieval with Pseudo-Relevance Feedback and
  Query Reformulation

abstract:
  Resolving the contextual dependency is one of the most challenging tasks in
the Conversational system. Our submission to CAsT-2021 aimed to preserve the
key terms and the context in all subsequent turns and use classical Information
retrieval methods. It was aimed to pull as relevant documents as possible from
the corpus. We have participated in automatic track and submitted two runs in
the CAsT-2021. Our submission has produced a mean NDCG@3 performance better
than the median model.


title:
Symmetric cohomology and symmetric Hochschild cohomology of
  cocommutative Hopf algebras

abstract:
  Staic defined symmetric cohomology of groups and studied that the secondary
symmetric cohomology group is corresponding to group extensions and the
injectivity of the canonical map from symmetric cohomology to classical
cohomology. In this paper, we define symmetric cohomology and symmetric
Hochschild cohomology for cocommutative Hopf algebras. The first one is a
generalization of symmetric cohomology of groups. We give an isomorphism
between symmetric cohomology and symmetric Hochschild cohomology, which is a
symmetric version of the classical result about cohomology of groups by
Eilenberg-MacLane and cohomology of Hopf algebras by Ginzburg-Kumar. Moreover,
to consider the condition that symmetric cohomology coincides with classical
cohomology, we investigate the projectivity of a resolution which gives
symmetric cohomology.


title:
Efficient Dropout-resilient Aggregation for Privacy-preserving Machine
  Learning

abstract:
  With the increasing adoption of data-hungry machine learning algorithms,
personal data privacy has emerged as one of the key concerns that could hinder
the success of digital transformation. As such, Privacy-Preserving Machine
Learning (PPML) has received much attention from both academia and industry.
However, organizations are faced with the dilemma that, on the one hand, they
are encouraged to share data to enhance ML performance, but on the other hand,
they could potentially be breaching the relevant data privacy regulations.
Practical PPML typically allows multiple participants to individually train
their ML models, which are then aggregated to construct a global model in a
privacy-preserving manner, e.g., based on multi-party computation or
homomorphic encryption. Nevertheless, in most important applications of
large-scale PPML, e.g., by aggregating clients' gradients to update a global
model for federated learning, such as consumer behavior modeling of mobile
application services, some participants are inevitably resource-constrained
mobile devices, which may drop out of the PPML system due to their mobility
nature. Therefore, the resilience of privacy-preserving aggregation has become
an important problem to be tackled. In this paper, we propose a scalable
privacy-preserving aggregation scheme that can tolerate dropout by participants
at any time, and is secure against both semi-honest and active malicious
adversaries by setting proper system parameters. By replacing
communication-intensive building blocks with a seed homomorphic pseudo-random
generator, and relying on the additive homomorphic property of Shamir secret
sharing scheme, our scheme outperforms state-of-the-art schemes by up to
6.37$\times$ in runtime and provides a stronger dropout-resilience. The
simplicity of our scheme makes it attractive both for implementation and for
further improvements.


title:
Wasserstein Distributionally Robust Control of Partially Observable
  Linear Systems: Tractable Approximation and Performance Guarantee

abstract:
  Wasserstein distributionally robust control (WDRC) is an effective method for
addressing inaccurate distribution information about disturbances in stochastic
systems. It provides various salient features, such as an out-of-sample
performance guarantee, while most of existing methods use full-state
observations. In this paper, we develop a computationally tractable WDRC method
for discrete-time partially observable linear-quadratic (LQ) control problems.
The key idea is to reformulate the WDRC problem as a novel minimax control
problem with an approximate Wasserstein penalty. We derive a closed-form
expression of the optimal solution to the approximate problem using a
nontrivial Riccati equation. We further show the guaranteed cost property of
the resulting controller and identify a provable bound for the optimality gap.
Finally, we evaluate the performance of our method through numerical
experiments using both Gaussian and non-Gaussian disturbances.


title:
Enhancing entanglement with the generalized elephant quantum walk from
  localized and delocalized states

abstract:
  Quantum walks are paradigmatic models for studies spanning from fundamental
properties of quantum theory to realizations of quantum algorithms. For many
years, a plethora of important results based on such models appeared in
literature. Recently, a quantum walk with a nonstandard step operator was
proposed and named the elephant quantum walk (EQW). With proper statistical
distribution for the steps, the generalized EQW (gEQW) can be programmable to
exhibit a myriad of dynamical behavior ranging from diffusion and
superdiffusion to ballistic and hyperballistic spreading. Such a rich
phenomenology makes the gEQW a promising model. In this work we study the
influence of the statistics of the step size and the delocalization of the
initial states on the entanglement entropy of the coin. Our results show that
the gEQW generates maximally entangled states for almost all initial coin
states and coin operators considering initially localized walkers and for the
delocalized ones, taking the proper limit, the same condition is guaranteed.


title: Ab initio machine learning of phase space averages

abstract:
  Equilibrium structures determine material properties and biochemical
functions. We propose to machine learn phase-space averages, conventionally
obtained by {\em ab initio} or force-field based molecular dynamics (MD) or
Monte Carlo simulations. In analogy to \textit(ab initio} molecular dynamics
(AIMD), our {\em ab initio} machine learning (AIML) model does not require bond
topologies and therefore enables a general machine learning pathway to ensemble
properties throughout chemical compound space. We demonstrate AIML for
predicting Boltzmann averaged structures after training on hundreds of MD
trajectories. AIML output is subsequently used to train machine learning models
of free energies of solvation using experimental data, and reaching competitive
prediction errors (MAE $\sim$ 0.8 kcal/mol) for out-of-sample molecules --
within milli-seconds. As such, AIML effectively bypasses the need for MD or
MC-based phase space sampling, enabling exploration campaigns throughout CCS at
a much accelerated pace. We contextualize our findings by comparison to
state-of-the-art methods resulting in a Pareto plot for the free energy of
solvation predictions in terms of accuracy and time.


title: Why FIFA should re-draw the 2022 World Cup

abstract:
  The draw for the 2022 FIFA World Cup takes place in April 2022 when three
winners of the play-offs remain unknown. Seeding is based on the FIFA World
Ranking released on 31 March 2022 and these three teams are drawn from the
weakest Pot 4. This paper shows that the official seeding policy does not
balance the difficulty levels of the groups to the extent possible: a better
alternative would be to assign the placeholders according to the highest-ranked
potential winner, similar to the rule used in the UEFA Champions League
qualification. The questionable decision of FIFA has harmed certain nations. In
particular, Ukraine should play against stronger opponents if it manages to
qualify for the World Cup after the Russian invasion has forced a rescheduling
of its match(es). In the spirit of fairness and solidarity, FIFA is strongly
encouraged to repeat the group stage draw following our proposal.


title:
Towards automated design of corrosion resistant alloy coatings with an
  autonomous scanning droplet cell

abstract:
  We present an autonomous scanning droplet cell platform designed for
on-demand alloy electrodeposition and real-time electrochemical
characterization for investigating the corrosion-resistance properties of
multicomponent alloys. Automation and machine learning are currently driving
rapid innovation in high throughput and autonomous materials design and
discovery. We present two alloy design case studies: one focusing on a
multi-objective corrosion resistant alloy optimization, and a case study
highlighting the complexity of the multimodal characterization needed to
provide insight into the underlying structural and chemical factors that drive
observed material behavior. This motivates a close coupling between autonomous
research platforms and scientific machine learning methodology that blends
mechanistic physical models and black box machine learning models. This
emerging research area presents new opportunities to accelerate materials
synthesis, evaluation, and hence discovery and design.


title:
Rebuttal to "Well-posedness and optimal decay rates for the viscoelastic
  Kirchhoff equation"

abstract:
  A paper, entitled "Well-posedness and optimal decay rates for the
viscoelastic Kirchhoff equation" was published in 2016, in the journal Boletim
da Sociedade Paranaense de Matematica. The main results of that paper are
stated in Theorem 1.3. Section 2 is devoted to stability estimates for the
solutions of the Galerkin approximation (2.1)-(2.2) of (1.2). Section 3 is
devoted to the proof of the well-posedness for the Kirchhoff equation (1.2).
This rebuttal is written in view to show the major flaws, and gaps in the
proofs of this publication.


title:
You Don't Know What I Know: On Notion of High-Order Opacity in
  Discrete-Event Systems

abstract:
  In this paper, we investigate a class of information-flow security properties
called opacity in partial-observed discrete-event systems. Roughly speaking, a
system is said to be opaque if the intruder, which is modeled by a passive
observer, can never determine the "secret" of the system for sure. Most of the
existing notions of opacity consider secrets related to the actual behaviors of
the system. In this paper, we consider a new type of secret related to the
knowledge of the system user. Specifically, we assume that the system user also
only has partial observation of the system and has to reason the actual
behavior of the system. We say a system is high-order opaque if the intruder
can never determine that the system user knows some information of importance
based on its own incomparable information. We provide the formal definition of
high-order opacity. Two algorithms are provided for the verification of this
new notion: one with doubly-exponential complexity for the worst case and the
other with single-exponential complexity. Illustrative examples are provided
for the new notion of high-order opacity.


title: Model order reduction of layered waveguides via rational Krylov fitting

abstract:
  Rational approximation recently emerged as an efficient numerical tool for
the solution of exterior wave propagation problems. Currently, this technique
is limited to wave media which are invariant along the main propagation
direction. We propose a new model order reduction-based approach for
compressing unbounded waveguides with layered inclusions. It is based on the
solution of a nonlinear rational least squares problem using the RKFIT method.
We show that approximants can be converted into an accurate finite difference
representation within a rational Krylov framework. Numerical experiments
indicate that RKFIT computes more accurate grids than previous analytic
approaches and even works in the presence of pronounced scattering resonances.
Spectral adaptation effects allow for finite difference grids with dimensions
near or even below the Nyquist limit.


title:
Separation of track- and shower-like energy deposits in ProtoDUNE-SP
  using a convolutional neural network

abstract:
  Liquid argon time projection chamber detector technology provides high
spatial and calorimetric resolutions on the charged particles traversing liquid
argon. As a result, the technology has been used in a number of recent neutrino
experiments, and is the technology of choice for the Deep Underground Neutrino
Experiment (DUNE). In order to perform high precision measurements of neutrinos
in the detector, final state particles need to be effectively identified, and
their energy accurately reconstructed. This article proposes an algorithm based
on a convolutional neural network to perform the classification of energy
deposits and reconstructed particles as track-like or arising from
electromagnetic cascades. Results from testing the algorithm on data from
ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network
identifies track- and shower-like particles, as well as Michel electrons, with
high efficiency. The performance of the algorithm is consistent between data
and simulation.


title: BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection

abstract:
  Single frame data contains finite information which limits the performance of
the existing vision-based multi-camera 3D object detection paradigms. For
fundamentally pushing the performance boundary in this area, a novel paradigm
dubbed BEVDet4D is proposed to lift the scalable BEVDet paradigm from the
spatial-only 3D space to the spatial-temporal 4D space. We upgrade the naive
BEVDet framework with a few modifications just for fusing the feature from the
previous frame with the corresponding one in the current frame. In this way,
with negligible additional computing budget, we enable BEVDet4D to access the
temporal cues by querying and comparing the two candidate features. Beyond
this, we simplify the task of velocity prediction by removing the factors of
ego-motion and time in the learning target. As a result, BEVDet4D with robust
generalization performance reduces the velocity error by up to -62.9%. This
makes the vision-based methods, for the first time, become comparable with
those relied on LiDAR or radar in this aspect. On challenge benchmark nuScenes,
we report a new record of 54.5% NDS with the high-performance configuration
dubbed BEVDet4D-Base, which surpasses the previous leading method BEVDet-Base
by +7.3% NDS.


title:
Certified machine learning: A posteriori error estimation for
  physics-informed neural networks

abstract:
  Physics-informed neural networks (PINNs) are one popular approach to
introduce a priori knowledge about physical systems into the learning
framework. PINNs are known to be robust for smaller training sets, derive
better generalization problems, and are faster to train. In this paper, we show
that using PINNs in comparison with purely data-driven neural networks is not
only favorable for training performance but allows us to extract significant
information on the quality of the approximated solution. Assuming that the
underlying differential equation for the PINN training is an ordinary
differential equation, we derive a rigorous upper limit on the PINN prediction
error. This bound is applicable even for input data not included in the
training phase and without any prior knowledge about the true solution.
Therefore, our a posteriori error estimation is an essential step to certify
the PINN. We apply our error estimator exemplarily to two academic toy
problems, whereof one falls in the category of model-predictive control and
thereby shows the practical use of the derived results.


title: Weakly toll convexity and proper interval graphs

abstract:
  A walk $u_0u_1 \ldots u_{k-1}u_k$ is a \textit{weakly toll walk} if $u_0u_i
\in E(G)$ implies $u_i = u_1$ and $u_ju_k\in E(G)$ implies $u_j=u_{k-1}$. A set
$S$ of vertices of $G$ is {\it weakly toll convex} if for any two non-adjacent
vertices $x,y \in S$ any vertex in a weakly toll walk between $x$ and $y$ is
also in $S$. The {\em weakly toll convexity} is the graph convexity space
defined over weakly toll convex sets. Many studies are devoted to determine if
a graph equipped with a convexity space is a {\em convex geometry}. An
\emph{extreme vertex} is an element $x$ of a convex set $S$ such that the set
$S\backslash\{x\}$ is also convex. A graph convexity space is said to be a
convex geometry if it satisfies the Minkowski-Krein-Milman property, which
states that every convex set is the convex hull of its extreme vertices. It is
known that chordal, Ptolemaic, weakly polarizable, and interval graphs can be
characterized as convex geometries with respect to the monophonic, geodesic,
$m^3$, and toll convexities, respectively. Other important classes of graphs
can also be characterized in this way. In this paper, we prove that a graph is
a convex geometry with respect to the weakly toll convexity if and only if it
is a proper interval graph. Furthermore, some well-known graph invariants are
studied with respect to the weakly toll convexity.


title:
Scaling exponents of Mellin amplitudes for deriving bounds on flat space
  S-matrices from bounds on chaos

abstract:
  We study an inequality between a scaling exponent $A$ in the Regge limit of
tree-level flat space S-matrices with external massless scalars and another
scaling exponent $A'$ in the Regge limit of the corresponding four-point scalar
conformal correlators by using scaling exponents of Mellin amplitudes. We
derive $A'\ge A$, which leads to the Regge growth bound of tree-level flat
space S-matrices from the chaos bound in the flat space limit of the AdS/CFT
correspondence, from polynomial boundedness of the Mellin amplitudes for local
bulk descriptions. We also show $A'=A$ from the conformal block expansion in
the $t$-channel with finite intermediate spins when coefficients are not small
in the flat space limit.


title:
Charm- and Bottom-Quark Production in Au$+$Au Collisions at
  $\sqrt{s_{_{NN}}}$ = 200 GeV

abstract:
  The invariant yield of electrons from open-heavy-flavor decays for $1<p_T<8$
GeV/$c$ at midrapidity $|y|<0.35$ in Au$+$Au collisions at $\sqrt{s_{_{NN}}}$ =
200 GeV has been measured by the PHENIX experiment at the Relativistic Heavy
Ion Collider. A displaced-vertex analysis with the PHENIX silicon-vertex
detector enables extraction of the fraction of charm and bottom hadron decays
and unfolding of the invariant yield of parent charm and bottom hadrons. The
nuclear-modification factors $R_{AA}$ for electrons from charm and bottom
hadron decays and heavy-flavor hadrons show both a centrality and a quark-mass
dependence, indicating suppression in the quark-gluon plasma produced in these
collisions that is medium sized and quark-mass dependent.


title:
Room Temperature Gate Tunable Non Reciprocal Charge Transport in Lattice
  Matched InSb/CdTe Heterostructures

abstract:
  The manipulation of symmetry provides an effective way to tailor the physical
orders in solid-state systems. With the breaking of both the inversion and
time-reversal symmetries, non-reciprocal magneto-transport may emerge in
assorted non-magnetic systems to enrich spintronic physics. Here, we report the
observation of the uni-directional magneto-resistance (UMR) in the
lattice-matched InSb/CdTe film up to room temperature. Benefiting from the
strong built-in electric field of $0.13 \mathrm{~V} \cdot \mathrm{nm}^{-1}$ in
the hetero-junction region, the resulting Rashba-type spin-orbit coupling and
quantum confinement warrant stable angular-dependent second-order charge
current with the non-reciprocal coefficient 1-2 orders of magnitude larger than
most non-centrosymmetric materials at 298 K. More importantly, this
heterostructure configuration enables highly-efficient gate tuning of the
rectification response in which the enhancement of the UMR amplitude by 40% is
realized. Our results advocate the narrow-gap semiconductor-based hybrid system
with the robust two-dimensional interfacial spin texture as a suitable platform
for the pursuit of controllable chiral spin-orbit devices and applications.


title: We cannot believe we overlooked these Majorana discoveries

abstract:
  In 2011-2012 we performed experiments on hybrid superconductor-semiconductor
nanowire devices which yielded signatures of Majorana fermions based on
zero-bias peaks in tunneling measurements. The research field that grew out of
those findings and other contemporary works has advanced significantly, and a
lot of new knowledge and insights were gained. However, key smoking gun
evidence of Majorana is still lacking. In this paper, we report that while
reviewing our old data recently, armed with a decade of knowledge, we realized
that back in 2012 our results contained two breakthrough Majorana discoveries.
Specifically, we have observed quantized zero-bias peaks, the hallmark of ideal
Majorana states. Furthermore, we have observed the closing and re-opening of
the induced gap perfectly correlated with the emergence of the zero-bias peak -
clear evidence of the topological quantum phase superconducting transition.
These insights should pave the way to topological Majorana qubits, and you
should also check supplementary information for important disclosures.


title:
Plug-and-Play Methods for Integrating Physical and Learned Models in
  Computational Imaging

abstract:
  Plug-and-Play Priors (PnP) is one of the most widely-used frameworks for
solving computational imaging problems through the integration of physical
models and learned models. PnP leverages high-fidelity physical sensor models
and powerful machine learning methods for prior modeling of data to provide
state-of-the-art reconstruction algorithms. PnP algorithms alternate between
minimizing a data-fidelity term to promote data consistency and imposing a
learned regularizer in the form of an image denoiser. Recent highly-successful
applications of PnP algorithms include bio-microscopy, computerized tomography,
magnetic resonance imaging, and joint ptycho-tomography. This article presents
a unified and principled review of PnP by tracing its roots, describing its
major variations, summarizing main results, and discussing applications in
computational imaging. We also point the way towards further developments by
discussing recent results on equilibrium equations that formulate the problem
associated with PnP algorithms.


title:
Quantum coherence versus interferometric visibility in a generalized
  Mach-Zehnder interferometer

abstract:
  The double-slit interferometer and the Mach-Zehnder interferometer (MZI) with
balanced beam splitters are prototypical setups for investigating the quantum
wave-particle duality. These setups induced researchers to make a quantitative
association of interferometric visibility (IVI) with the wave aspect of a
quantum system (WAQ). Recently, it was realized that quantum coherence (QC) can
be better suited than IVI for quantifying the WAQ. In this article,
investigating a MZI with general beam splitters, we show that IVI is in general
not adequate to quantify the WAQ, even for a bi-dimensional closed quantum
system. Using IBM quantum computers, we experimentally verify our theoretical
findings by doing a full quantum simulation of the optical MZI with general
beam splitters.


title: Efficient and Eventually Consistent Collective Operations

abstract:
  Collective operations are common features of parallel programming models that
are frequently used in High-Performance (HPC) and machine/ deep learning (ML/
DL) applications. In strong scaling scenarios, collective operations can
negatively impact the overall application performance: with the increase in
core count, the load per rank decreases, while the time spent in collective
operations increases logarithmically.
  In this article, we propose a design for eventually consistent collectives
suitable for ML/ DL computations by reducing communication in Broadcast and
Reduce, as well as by exploring the Stale Synchronous Parallel (SSP)
synchronization model for the Allreduce collective. Moreover, we also enrich
the GASPI ecosystem with frequently used classic/ consistent collective
operations -- such as Allreduce for large messages and AlltoAll used in an HPC
code. Our implementations show promising preliminary results with significant
improvements, especially for Allreduce and AlltoAll, compared to the
vendor-provided MPI alternatives.


title: Atmosphere Loss in Oblique Super-Earth Impacts

abstract:
  Using smoothed particle hydrodynamics we model giant impacts of Super-Earth
mass rocky planets between an atmosphere-less projectile and an atmosphere-rich
target. In this work we present results from head-on to grazing collisions. The
results of the simulations fall into two broad categories: 1) one main
post-collision remnant containing material from target and projectile; 2) two
main post-collision remnants resulting from `erosive hit-and-run' collisions.
All collisions removed at least some of the target atmosphere, in contrast to
the idealised hit-and-run definition in which the target mass is unchanged. We
find that the boundary between `hit-and-run' collisions and collisions that
result in the projectile and target accreting/merging to be strongly correlated
with the mutual escape velocity at the predicted point of closest approach. Our
work shows that it is very unlikely for a single giant impact to remove all of
the atmosphere. For all the atmosphere to be removed, head-on impacts require
roughly the energy of catastrophic disruption (i.e. permanent ejection of half
the total system mass) and result in significant erosion of the mantle. We show
that higher impact angle collisions, which are more common, are less efficient
at atmosphere removal than head-on collisions. Therefore, single collisions
that remove all the atmosphere without substantially disrupting the planet are
not expected during planet formation.