title:
An amplitude equation modeling the single-to-double crest wave
  transition in orbital shaken cylindrical containers

abstract:
  The container motion along a planar circular trajectory at a constant angular
velocity, i.e. orbital shaking, is of interest in several industrial
applications, e.g. for fermentation processes or in cultivation of stem cells,
where good mixing and efficient gas exchange are the main targets. Under these
external forcing conditions, the free surface typically exhibits a primary
steady state motion through a single-crest dynamics, whose wave amplitude, as a
function of the external forcing parameters, shows a Duffing-like behaviour.
However, previous experiments in lab-scale cylindrical containers have unveiled
that, owing to the excitation of super-harmonics, diverse dynamics are
observable in certain driving-frequency ranges. Among these super-harmonics,
the double-crest dynamics is particularly relevant, as it displays a notably
large amplitude response, that is strongly favored by the spatial structure of
the external forcing. In the inviscid limit and with regards to circular
cylindrical containers, we formalize here a weakly nonlinear analysis via
multiple timescale method of the full hydrodynamic sloshing system, leading to
an amplitude equation suitable to describe such a super-harmonic dynamics and
the resulting single-to-double crest wave transition. The weakly nonlinear
prediction is shown to be in fairly good agreement with previous experiments
described in the literature. Lastly, we discuss how an analogous amplitude
equation can be derived by solving asymptotically for the first super-harmonic
of the forced Helmholtz-Duffing equation with small nonlinearities.


title:
Domain Knowledge Aids in Signal Disaggregation; the Example of the
  Cumulative Water Heater

abstract:
  In this article we present an unsupervised low-frequency method aimed at
detecting and disaggregating the power used by Cumulative Water Heaters (CWH)
in residential homes. Our model circumvents the inherent difficulty of
unsupervised signal disaggregation by using both the shape of a power spike and
its time of occurrence to identify the contribution of CWH reliably. Indeed,
many CHWs in France are configured to turn on automatically during off-peak
hours only, and we are able to use this domain knowledge to aid peak
identification despite the low sampling frequency. In order to test our model,
we equipped a home with sensors to record the ground-truth consumption of a
water heater. We then apply the model to a larger dataset of energy consumption
of Hello Watt users consisting of one month of consumption data for 5k homes at
30-minute resolution. In this dataset we successfully identified CWHs in the
majority of cases where consumers declared using them. The remaining part is
likely due to possible misconfiguration of CWHs, since triggering them during
off-peak hours requires specific wiring in the electrical panel of the house.
Our model, despite its simplicity, offers promising applications: detection of
mis-configured CWHs on off-peak contracts and slow performance degradation.


title:
Contribution of Different Handwriting Modalities to Differential
  Diagnosis of Parkinson's Disease

abstract:
  In this paper, we evaluate the contribution of different handwriting
modalities to the diagnosis of Parkinson's disease. We analyse on-surface
movement, in-air movement and pressure exerted on the tablet surface.
Especially in-air movement and pressure-based features have been rarely taken
into account in previous studies. We show that pressure and in-air movement
also possess information that is relevant for the diagnosis of Parkinson's
Disease (PD) from handwriting. In addition to the conventional kinematic and
spatio-temporal features, we present a group of the novel features based on
entropy and empirical mode decomposition of the handwriting signal. The
presented results indicate that handwriting can be used as biomarker for PD
providing classification performance around 89% area under the ROC curve (AUC)
for PD classification.


title: Recent neutron focusing experiments using polycapillary lens in CSNS

abstract:
  Higher neutron current densities can provide convenience for neutron
experiments. Using neutron optical focusing elements, large flux beams
transported to sample can be achieved. As one kind of focusing elements,
polycapillary lens is very suitable for neutron absorption experiments such as
PGAA and NDP technology. At present, a Neutron Physics and Application
Spectrometer was in construction in CSNS, which is the first pulsed neutron
source in China. To provide some suggestions and ideas for the following design
of enhanced PGAA or NDP instrument with polycapillary lens in CSNS, a first
neutron focusing experiment using polycapillary lens in CSNS was conducted. For
0.5-12.6 polychromatic beam, a focal spot with FWHM of 800 was obtained. As the
value of wavelength increased, the beam size, transmission efficiency and gain
increased. For cold neutron, the gain maintained in a level of 7.


title:
A portable sub Hertz ultra-stable laser over 1700km highway
  transportation

abstract:
  We present a subHz linewidth portable ultrastable laser with the mass and
volume of are 40kg and 400mm*280mm*450mm, respectively, that meets the
requirements of automatic frequency locking and road transportation. A dynamic
analytical model of the physical parts of ultrastable laser is established, and
the first order resonance frequency is determined by FEA and well agrees with
the experimentally measured result. To verify the transport performance of the
portable ultrastable laser, it is tested for 100 km actual road transportation
and 60 min continuous vibration, corresponding to 1700 km road transportation.
The success of the test demonstrated that the portable ultrastable laser was
very robust. Meanwhile, the portable ultrastable lasers shows that the median
of the linewidth distribution is approximately 0.78 Hz, and the fractional
frequency instability is less than 3E-15 at 1 to 10 s averaging time. This
value approaches the total noise of 2.0E-15 including thermal noise and
residual amplitude modulation. The robust suggested that the portable
ultrastable laser might be a good candidate such as optical frequency transfer
and metrological systems.


title:
113 km Free-Space Time-Frequency Dissemination at the 19th Decimal
  Instability

abstract:
  Optical clock networks play important roles in various fields, such as
precise navigation, redefinition of "second" unit, and gravitational tests. To
establish a global-scale optical clock network, it is essential to disseminate
time and frequency with a stability of $10^{-19}$ over a long-distance
free-space link. However, such attempts were limited to dozens of kilometers in
mirror-folded configuration. Here, we take a crucial step toward future
satellite-based time-frequency disseminations. By developing the key
technologies, including high-power frequency combs, high-stability and
high-efficiency optical transceiver systems, and efficient linear optical
sampling, we demonstrate free-space time-frequency dissemination over two
independent links with femtosecond time deviation, $3\times10^{-19}$ at 10,000
s residual instability and $1.6\times10^{-20}\pm 4.3\times10^{-19}$ offset.
This level of the stability retains for an increased channel loss up to 89 dB.
Our work can not only be directly used in ground-based application, but also
firmly laid the groundwork for future satellite time-frequency dissemination.


title: Congruences of Hurwitz class numbers on square classes

abstract:
  We extend a holomorphic projection argument of our earlier work to prove a
novel divisibility result for non-holomorphic congruences of Hurwitz class
numbers. This result allows us to establish Ramanujan-type congruences for
Hurwitz class numbers on square classes, where the holomorphic case parallels
previous work by Radu on partition congruences. We offer two applications. The
first application demonstrates common divisibility features of Ramanujan-type
congruences for Hurwitz class numbers. The second application provides a
dichotomy between congruences for class numbers of imaginary quadratic fields
and Ramanujan-type congruences for Hurwitz class numbers.


title:
DefGraspSim: Physics-based simulation of grasp outcomes for 3D
  deformable objects

abstract:
  Robotic grasping of 3D deformable objects (e.g., fruits/vegetables, internal
organs, bottles/boxes) is critical for real-world applications such as food
processing, robotic surgery, and household automation. However, developing
grasp strategies for such objects is uniquely challenging. Unlike rigid
objects, deformable objects have infinite degrees of freedom and require field
quantities (e.g., deformation, stress) to fully define their state. As these
quantities are not easily accessible in the real world, we propose studying
interaction with deformable objects through physics-based simulation. As such,
we simulate grasps on a wide range of 3D deformable objects using a GPU-based
implementation of the corotational finite element method (FEM). To facilitate
future research, we open-source our simulated dataset (34 objects, 1e5 Pa
elasticity range, 6800 grasp evaluations, 1.1M grasp measurements), as well as
a code repository that allows researchers to run our full FEM-based grasp
evaluation pipeline on arbitrary 3D object models of their choice. Finally, we
demonstrate good correspondence between grasp outcomes on simulated objects and
their real counterparts.


title:
Liars are more influential: Effect of Deception in Influence
  Maximization on Social Networks

abstract:
  Detecting influential users, called the influence maximization problem on
social networks, is an important graph mining problem with many diverse
applications such as information propagation, market advertising, and rumor
controlling. There are many studies in the literature for influential users
detection problem in social networks. Although the current methods are
successfully used in many different applications, they assume that users are
honest with each other and ignore the role of deception on social networks. On
the other hand, deception appears to be surprisingly common among humans within
social networks. In this paper, we study the effect of deception in influence
maximization on social networks. We first model deception in social networks.
Then, we model the opinion dynamics on these networks taking the deception into
consideration thanks to a recent opinion dynamics model via sheaf Laplacian. We
then extend two influential node detection methods, namely Laplacian centrality
and DFF centrality, for the sheaf Laplacian to measure the effect of deception
in influence maximization. Our experimental results on synthetic and real-world
networks suggest that liars are more influential than honest users in social
networks.


title: Model Comparison in Approximate Bayesian Computation

abstract:
  A common problem in natural sciences is the comparison of competing models in
the light of observed data. Bayesian model comparison provides a statistically
sound framework for this comparison based on the evidence each model provides
for the data. However, this framework relies on the calculation of likelihood
functions which are intractable for most models used in practice. Previous
approaches in the field of Approximate Bayesian Computation (ABC) circumvent
the evaluation of the likelihood and estimate the model evidence based on
rejection sampling, but they are typically computationally intense. Here, I
propose a new efficient method to perform Bayesian model comparison in ABC.
Based on recent advances in posterior density estimation, the method
approximates the posterior over models in parametric form. In particular, I
train a mixture-density network to map features of the observed data to the
posterior probability of the models. The performance is assessed with two
examples. On a tractable model comparison problem, the underlying exact
posterior probabilities are predicted accurately. In a use-case scenario from
computational neuroscience -- the comparison between two ion channel models --
the underlying ground-truth model is reliably assigned a high posterior
probability. Overall, the method provides a new efficient way to perform
Bayesian model comparison on complex biophysical models independent of the
model architecture.


title:
Left fractional Sobolev space via Riemann$-$Liouville derivatives on
  time scales and its application to a fractional boundary value problem on
  time scales

abstract:
  We first prove the equivalence of two definitions of Riemann-Liouville
fractional integral on time scales, then by the concept of fractional
derivative of Riemann-Liouville on time scales, we introduce fractional Sobolev
spaces, characterize them, define weak fractional derivatives, and show that
they coincide with the Riemann-Liouville ones on time scales. Next, we prove
equivalence of some norms in the introduced spaces and derive their
completeness, reflexivity, separability and some imbeddings. Finally, as an
application, by constructing an appropriate variational setting, using the
mountain pass theorem and the genus properties, the existence of weak solutions
for a class of Kirchhoff-type fractional p-Laplacian systems on time scales
with boundary condition is studied, and three results of the existence of weak
solutions for this problem is obtained.


title: One-Bit Compressive Sensing: Can We Go Deep and Blind?

abstract:
  One-bit compressive sensing is concerned with the accurate recovery of an
underlying sparse signal of interest from its one-bit noisy measurements. The
conventional signal recovery approaches for this problem are mainly developed
based on the assumption that an exact knowledge of the sensing matrix is
available. In this work, however, we present a novel data-driven and
model-based methodology that achieves blind recovery; i.e., signal recovery
without requiring the knowledge of the sensing matrix. To this end, we make use
of the deep unfolding technique and develop a model-driven deep neural
architecture which is designed for this specific task. The proposed deep
architecture is able to learn an alternative sensing matrix by taking advantage
of the underlying unfolded algorithm such that the resulting learned recovery
algorithm can accurately and quickly (in terms of the number of iterations)
recover the underlying compressed signal of interest from its one-bit noisy
measurements. In addition, due to the incorporation of the domain knowledge and
the mathematical model of the system into the proposed deep architecture, the
resulting network benefits from enhanced interpretability, has a very small
number of trainable parameters, and requires very small number of training
samples, as compared to the commonly used black-box deep neural network
alternatives for the problem at hand.


title: EEG based Emotion Recognition: A Tutorial and Review

abstract:
  Emotion recognition technology through analyzing the EEG signal is currently
an essential concept in Artificial Intelligence and holds great potential in
emotional health care, human-computer interaction, multimedia content
recommendation, etc. Though there have been several works devoted to reviewing
EEG-based emotion recognition, the content of these reviews needs to be
updated. In addition, those works are either fragmented in content or only
focus on specific techniques adopted in this area but neglect the holistic
perspective of the entire technical routes. Hence, in this paper, we review
from the perspective of researchers who try to take the first step on this
topic. We review the recent representative works in the EEG-based emotion
recognition research and provide a tutorial to guide the researchers to start
from the beginning. The scientific basis of EEG-based emotion recognition in
the psychological and physiological levels is introduced. Further, we
categorize these reviewed works into different technical routes and illustrate
the theoretical basis and the research motivation, which will help the readers
better understand why those techniques are studied and employed. At last,
existing challenges and future investigations are also discussed in this paper,
which guides the researchers to decide potential future research directions.


title:
The EL-X8 computer and the BOL detector Networking, programming,
  time-sharing and data-handling in the Amsterdam nuclear research project
  `BOL' A personal historical review

abstract:
  From 1967 to 1974, an Electrologica X8 computer was installed at the
Institute for Nuclear Research (IKO) in Amsterdam, primarily for online and
offline evaluation of experimental data, an application quite different from
its `brother's', X8's. During that time, the nuclear detection system `BOL' was
in operation to study nuclear reactions. The BOL detector embodied a new and
bold concept. It consisted of a large number of state-of-the-art detection
units, mounted in a spherical arrangement around a target in a beam of nuclear
particles. Two minicomputers performed data acquisition and control of the
experiment and supported online visual display of acquired data. The X8
computer, networked with the minicomputers, allowed fast high-level data
processing and analysis. Pioneering work in both experimental nuclear physics
as well as in programming, turned out to be a surprisingly good combination.
For the network with the X8 and the minicomputers, advanced software layers
were developed to efficiently and flexibly program extensive data handling.


title:
Forward Link Analysis for Full-Duplex Cellular Networks with Low
  Resolution ADC/DAC

abstract:
  In this work, we consider a full-duplex (FD) massive multiple-input
multiple-output (MIMO) cellular network with low resolution analog-to-digital
converters (ADCs) and digital-to-analog converters (DACs). Our first
contribution is to propose a unified framework for forward link analysis where
matched filter precoders are applied at the FD base stations (BSs) under
channel hardening. Second, we derive expressions for the
signal-to-quantization-plus-interference-plus-noise ratio (SQINR) for general
and special cases. Finally, we quantify effects of quantization error, pilot
contamination, and full duplexing for a hexagonal cell lattice on spectral
efficiency and cumulative distribution function (CDF) to show that FD
outperforms half duplex (HD) in a wide variety of scenarios.


title:
Characterization of the $n$-dimensional Sierpi\'nski carpet as an
  inverse limit of closed balls ($n \neq 3$)

abstract:
  In this paper we generalize for any dimension different than $3$ a theorem of
Tshishiku and Walsh that characterizes the Sierpi\'nski carpet as a limit set
of maps from the disc to the sphere.


title: NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reconstruction

abstract:
  While NeRF has shown great success for neural reconstruction and rendering,
its limited MLP capacity and long per-scene optimization times make it
challenging to model large-scale indoor scenes. In contrast, classical 3D
reconstruction methods can handle large-scale scenes but do not produce
realistic renderings. We propose NeRFusion, a method that combines the
advantages of NeRF and TSDF-based fusion techniques to achieve efficient
large-scale reconstruction and photo-realistic rendering. We process the input
image sequence to predict per-frame local radiance fields via direct network
inference. These are then fused using a novel recurrent neural network that
incrementally reconstructs a global, sparse scene representation in real-time
at 22 fps. This global volume can be further fine-tuned to boost rendering
quality. We demonstrate that NeRFusion achieves state-of-the-art quality on
both large-scale indoor and small-scale object scenes, with substantially
faster reconstruction than NeRF and other recent methods.


title: A Contrastive Objective for Learning Disentangled Representations

abstract:
  Learning representations of images that are invariant to sensitive or
unwanted attributes is important for many tasks including bias removal and
cross domain retrieval. Here, our objective is to learn representations that
are invariant to the domain (sensitive attribute) for which labels are
provided, while being informative over all other image attributes, which are
unlabeled. We present a new approach, proposing a new domain-wise contrastive
objective for ensuring invariant representations. This objective crucially
restricts negative image pairs to be drawn from the same domain, which enforces
domain invariance whereas the standard contrastive objective does not. This
domain-wise objective is insufficient on its own as it suffers from shortcut
solutions resulting in feature suppression. We overcome this issue by a
combination of a reconstruction constraint, image augmentations and
initialization with pre-trained weights. Our analysis shows that the choice of
augmentations is important, and that a misguided choice of augmentations can
harm the invariance and informativeness objectives. In an extensive evaluation,
our method convincingly outperforms the state-of-the-art in terms of
representation invariance, representation informativeness, and training speed.
Furthermore, we find that in some cases our method can achieve excellent
results even without the reconstruction constraint, leading to a much faster
and resource efficient training.


title:
A new approach to constrained total variation solvation models and the
  study of solute-solvent interface profiles

abstract:
  In the past decade, variational implicit solvation models (VISM) have
achieved great success in solvation energy predictions. However, all existing
VISMs in literature lack the uniqueness of an energy minimizing solute-solvent
interface and thus prevent us from studying many important properties of the
interface profile. To overcome this difficulty, we introduce a new constrained
VISM and conduct a rigorous analysis of the model. Existence, uniqueness and
regularity of the energy minimizing interface has been studied. A necessary
condition for the formation of a sharp solute-solvent interface has been
derived. Moreover, we develop a novel approach to the variational analysis of
the constrained model, which provides a complete answer to a question in our
previous work [55]. Model validation and numerical implementation have been
demonstrated by using several common biomolecular modeling tasks. Numerical
simulations show that the solvation energies calculated from our new model
match the experimental data very well.


title: Braid group action and quasi-split affine $\imath$quantum groups I

abstract:
  This is the first of two papers on quasi-split affine quantum symmetric pairs
$\big(\widetilde{\mathbf U}(\widehat{\mathfrak g}), \widetilde{{\mathbf
U}}^\imath \big)$, focusing on the real rank one case, i.e., $\mathfrak{g}=
\mathfrak{sl}_3$ equipped with a diagram involution. We construct explicitly a
relative braid group action of type $A_2^{(2)}$ on the affine $\imath$quantum
group $\widetilde{{\mathbf U}}^\imath$. Real and imaginary root vectors for
$\widetilde{{\mathbf U}}^\imath$ are constructed, and a Drinfeld type
presentation of $\widetilde{{\mathbf U}}^\imath$ is then established. This
provides a new basic ingredient for the Drinfeld type presentation of higher
rank quasi-split affine $\imath$quantum groups in the sequel.


title:
PCA-RF: An Efficient Parkinson's Disease Prediction Model based on
  Random Forest Classification

abstract:
  In this modern era of overpopulation disease prediction is a crucial step in
diagnosing various diseases at an early stage. With the advancement of various
machine learning algorithms, the prediction has become quite easy. However, the
complex and the selection of an optimal machine learning technique for the
given dataset greatly affects the accuracy of the model. A large amount of
datasets exists globally but there is no effective use of it due to its
unstructured format. Hence, a lot of different techniques are available to
extract something useful for the real world to implement. Therefore, accuracy
becomes a major metric in evaluating the model. In this paper, a disease
prediction approach is proposed that implements a random forest classifier on
Parkinson's disease. We compared the accuracy of this model with the Principal
Component Analysis (PCA) applied Artificial Neural Network (ANN) model and
captured a visible difference. The model secured a significant accuracy of up
to 90%.


title:
Stellar collisions in globular clusters: constraints on the IMF of the
  first generation of stars

abstract:
  Globular clusters display an anti-correlation between the fraction of the
first generation of stars ($N({\rm G1})/N({\rm tot})$) and the slope of the
present-day mass function of the clusters ($\alpha_{pd}$), which is
particularly significant for massive clusters. In the framework of the
binary-mediated collision scenario for the formation of the second generation
stars in globular clusters, we test the effect of a varying initial stellar
mass function (IMF) of the G1 stars on the $(N({\rm G1})/N({\rm
tot}))-\alpha_{pd}$ anti-correlation. We use a simple collision model which has
only two inputs, the IMF of G1 stars and the fraction of G1 stars that coalesce
to form G2 stars. We show that a variable efficiency of the collision process
is necessary in order to explain the $(N({\rm G1})/N({\rm tot}))-\alpha_{pd}$
anti-correlation, however, the scatter in the anti-correlation can only be
explained by variations in the IMF, and in particular by variations of the
slope in the mass interval $\approx$ (0.1-0.5) M$_{\odot}$. Our results
indicate that in order to explain the scatter in the $(N({\rm G1})/N({\rm
tot}))-\alpha_{pd}$ relation, it is necessary to invoke variations of the slope
in this mass range between $\approx -0.9$ and $\approx -1.9$. Interpreted in
terms of a Kroupa-like broken power law, this translates into variations of the
mean mass between $\approx 0.2$ and $0.55$ M$_{\odot}$. This level of
variations is consistent with what is observed for young stellar clusters in
the Milky Way and may reflect variations in the physical conditions of the
globular clusters progenitor clouds at the time the G1 population has formed or
to the occurrence of collisions between protostellar embryos before stars
settle on the Main Sequence.


title:
Non-perturbative Green's function method to determine the electronic
  spectral function due to electron-phonon interactions: Application to a
  graphene model from weak to strong coupling

abstract:
  In solid state physics, the electron-phonon interaction (EPI) is central to
many phenomena. The theory of the renormalization of electronic properties due
to EPIs became well established with the theory of Allen-Heine-Cardona, usually
applied to second order in perturbation theory (P2). However, this is only
valid in the weak coupling regime, while strong EPIs have been reported in many
materials. Although non-perturbative (NP) methods have started to arise in the
last years, they are usually not well justified, and it is not clear to what
degree they reproduce the exact theory. To address this issue, we present a
stochastic approach for the evaluation of the non-perturbative interacting
Green's function in the adiabatic limit, and show it is equivalent to the
Feynman expansion to all orders in the perturbation. Also, by defining a
self-energy, we can reduce the effect of broadening needed in numerical
calculations, improving convergence in the supercell size. In addition, we
clarify whether it is better to average the Green's function or self-energy.
Then we apply the method to a graphene tight-binding model, and obtain several
interesting results: (i) The Debye-Waller term, which is normally neglected,
does affect the change of the Fermi velocity. (ii) The P2 and NP self-energies
differ even at room temperature for some k-points, raising the question of how
well P2 works in other materials. (iii) Close to the Dirac point, positive and
negative energy peaks merge. (iv) In the strong coupling regime, a peak appears
at energy E=0, which is consistent with previous works on disorder and
localization in graphene. (v) The spectral function becomes more asymmetric at
stronger coupling and higher temperatures. Finally, in the Appendix we show
that the method has better convergent properties when the coupling is strong
relative to when it is weak, and discuss other technical aspects.


abstract:
  This is the Snowmass Whitepaper on Beam Test Facilities for R&D in
Accelerator Science and Technologies and it is submitted to two topical groups
in the Accelerator Frontier: AF1 and AF6.


title: Bismut connection on Vaisman manifolds

abstract:
  The holonomy of the Bismut connection on Vaisman manifolds is studied. We
prove that if $M^{2n}$ is endowed with a Vaisman structure, then the holonomy
group of the Bismut connection is contained in U$(n-1)$. We compute explicitly
this group for particular types of manifolds, namely, solvmanifolds and some
classical Hopf manifolds.


title: Covariant techniques in Quantum Field Theory

abstract:
  In this paper some techniques useful to perform quantum field theory
computations in a covariant manner are reviewed. In particular the background
field gauge, the zeta function regularization and the heat kernel approach are
highlighted. Some detailed calculations of the Schwinger-de Witt coefficients
of the small proper time expansion of the heat kernel are also repeated in
detail. This work reports lectures given by Enrique \'Alvarez at the
IFT-UAM-CSIC in Madrid.


title: A prediction for the 25th solar cycle maximum amplitude

abstract:
  The minimum - maximum method, belonging to the precursor class of the solar
activity forecasting methods, is based on a linear relationship between
relative sunspot number in the minimum and maximum epochs of solar cycles. In
the present analysis we apply a modified version of this method using data not
only from the minimum year, but also from a couple of years before and after
the minimum. The revised 13-month smoothed monthly total sunspot number data
set from SILSO/SIDC is used. Using data for solar cycle nos. 1-24 the largest
correlation coefficient (CC) is obtained when correlating activity level 3
years before solar cycle minimum with the subsequent maximum (CC = 0.82),
independent of inclusion or exclusion of the solar cycle no. 19. For the next
solar maximum of the cycle no. 25 we predict: Rmax = 121 +- 33. Our results
indicate that the next solar maximum (of the cycle no. 25) will be of the
similar amplitude as the previous one, or even something lower. This is in
accordance with the general middle-term lowering of the solar activity after
the secular maximum in the 20th century and consistent with the Gleissberg
period of the solar activity. The reliability of the 3 years before the minimum
predictor is experimentally justified by the largest correlation coefficient
and verified with the Student t-test. It is satisfactorily explained with the
two empirical well-known findings: the extended solar cycle and the Waldmeier
effect. Finally, we successfully reproduced the maxima of the last four solar
cycles, nos. 21-25, using the 3 years before the minimum method.


title:
Automated detection of foreground speech with wearable sensing in
  everyday home environments: A transfer learning approach

abstract:
  Acoustic sensing has proved effective as a foundation for numerous
applications in health and human behavior analysis. In this work, we focus on
the problem of detecting in-person social interactions in naturalistic settings
from audio captured by a smartwatch. As a first step towards detecting social
interactions, it is critical to distinguish the speech of the individual
wearing the watch from all other sounds nearby, such as speech from other
individuals and ambient sounds. This is very challenging in realistic settings,
where interactions take place spontaneously and supervised models cannot be
trained apriori to recognize the full complexity of dynamic social
environments. In this paper, we introduce a transfer learning-based approach to
detect foreground speech of users wearing a smartwatch. A highlight of the
method is that it does not depend on the collection of voice samples to build
user-specific models. Instead, the approach is based on knowledge transfer from
general-purpose speaker representations derived from public datasets. Our
experiments demonstrate that our approach performs comparably to a fully
supervised model, with 80% F1 score. To evaluate the method, we collected a
dataset of 31 hours of smartwatch-recorded audio in 18 homes with a total of 39
participants performing various semi-controlled tasks.


title:
Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on
  Edge Devices

abstract:
  The prediction accuracy of the deep neural networks (DNNs) after deployment
at the edge can suffer with time due to shifts in the distribution of the new
data. To improve robustness of DNNs, they must be able to update themselves to
enhance their prediction accuracy. This adaptation at the resource-constrained
edge is challenging as: (i) new labeled data may not be present; (ii)
adaptation needs to be on device as connections to cloud may not be available;
and (iii) the process must not only be fast but also memory- and
energy-efficient. Recently, lightweight prediction-time unsupervised DNN
adaptation techniques have been introduced that improve prediction accuracy of
the models for noisy data by re-tuning the batch normalization (BN) parameters.
This paper, for the first time, performs a comprehensive measurement study of
such techniques to quantify their performance and energy on various edge
devices as well as find bottlenecks and propose optimization opportunities. In
particular, this study considers CIFAR-10-C image classification dataset with
corruptions, three robust DNNs (ResNeXt, Wide-ResNet, ResNet-18), two BN
adaptation algorithms (one that updates normalization statistics and the other
that also optimizes transformation parameters), and three edge devices (FPGA,
Raspberry-Pi, and Nvidia Xavier NX). We find that the approach that only
updates the normalization parameters with Wide-ResNet, running on Xavier GPU,
to be overall effective in terms of balancing multiple cost metrics. However,
the adaptation overhead can still be significant (around 213 ms). The results
strongly motivate the need for algorithm-hardware co-design for efficient
on-device DNN adaptation.


title: Creating boundaries along a synthetic frequency dimension

abstract:
  Synthetic dimensions have garnered widespread interest for implementing high
dimensional classical and quantum dynamics on lower dimensional geometries.
Synthetic frequency dimensions, in particular, have been used to experimentally
realize a plethora of bulk physics effects, such as effective gauge potentials,
nontrivial Hermitian as well as non-Hermitian topology, spin-momentum locking,
complex long-range coupling, unidirectional frequency conversion, and
four-dimensional lattices. However, in synthetic frequency dimensions there has
not been any demonstration of boundary effects which are of paramount
importance in topological physics due to the bulk edge correspondence, since
systems exhibiting synthetic frequency dimensions do not support well-defined
sharp boundaries. Here we theoretically elucidate a method to construct
boundaries in the synthetic frequency dimension of dynamically modulated ring
resonators by strongly coupling it to an auxiliary ring, and provide an
experimental demonstration of this method. We experimentally explore various
physics effects associated with the creation of such boundaries in the
synthetic frequency dimension, including confinement of the spectrum of light,
the discretization of the band structure, and the interaction of such
boundaries with the topologically protected one-way chiral modes in a quantum
Hall ladder. The incorporation of boundaries allows us to observe topologically
robust transport of light along the frequency axis, which shows that the
frequency of light can be controlled through topological concepts. Our
demonstration of such sharp boundaries fundamentally expands the capability of
exploring topological physics, and is also of importance for other applications
such as classical and quantum information processing in synthetic frequency
dimensions.


title:
Modelling the Partially Coherent Behaviour of Few-Mode Far-Infrared
  Grating Spectrometers

abstract:
  Modelling ultra-low-noise far-infrared grating spectrometers has become
crucial for the next generation of far-infrared space observatories.
Conventional techniques are awkward to apply because of the partially coherent
form of the incident spectral field, and the few-mode response of the optics
and detectors. We present a modal technique for modelling the behaviour of
spectrometers, which allows for the propagation and detection of partially
coherent fields, and the inclusion of straylight radiated by warm internal
surfaces. We illustrate the technique by modelling the behaviour of the Long
Wavelength Band of the proposed SAFARI instrument on the well-studied SPICA
mission.


title:
Neutrino Scattering Measurements on Hydrogen and Deuterium: A Snowmass
  White Paper

abstract:
  Neutrino interaction uncertainties are a limiting factor in current and
next-generation experiments probing the fundamental physics of neutrinos, a
unique window on physics beyond the Standard Model. Neutrino-nucleon scattering
amplitudes are an important part of the neutrino interaction program. However,
since all modern neutrino detectors are composed primarily of heavy nuclei,
knowledge of elementary neutrino-nucleon amplitudes relies heavily on
experiments performed in the 1970s and 1980s, whose statistical and systematic
precision are insufficient for current needs. In this white paper, we outline
the motivation for attempting measurements on hydrogen and deuterium that would
improve this knowledge, and we discuss options for making these measurements
either with the DUNE near detector or with a dedicated facility.


title: Inequalities governing the peripheral spike activity

abstract:
  A fundamental inequality governing the spike activity of peripheral neurons
is derived and tested against auditory data. This inequality states that the
steady-state firing rate below saturation lies between the arithmetic and
geometric means of the spontaneous and peak activities during adaptation.
Implications towards the development of computational auditory models are
explored.


title: Delicatessen: M-Estimation in Python

abstract:
  M-estimation is a general statistical approach that simplifies and unifies
estimation in a variety of settings. Here, we introduce delicatessen, a Python
library that automates the tedious calculations of M-estimation. To highlight
the utility of delicatessen for data analyses in life science research, we
provide several illustrations: linear regression robust to outliers, estimation
of a dose-response curve, and standardization of results.


title: 2-simplexes and superconformal central charges

abstract:
  The superconformal central charge is an important quantity for theories
emerging from string theory geometrical implementation of Quantum Field Theory
since it is linked, for example, to the scaling dimension of fields. Butti and
Zaffaroni construction of the central charge for toric Calabi-Yau threefold
geometries is a powerful tool but its implementation could be quite tricky.
Here we present an equivalent new construction based on a 2-simplexes
decomposition of the toric diagram.


title: Irreducibility of the zero polynomials of Eisenstein series

abstract:
  Let $E_k$ be the normalized Eisenstein series of weight $k$ on
$\mathrm{SL}_{2}(\mathbb{Z})$. Let $\varphi_k$ be the polynomial that encodes
the $j$-invariants of non-elliptic zeros of $E_k$. In 2001, Gekeler observed
that the polynomials $\varphi_k$ seem to be irreducible (and verified this
claim for $k\leq 446$). We show that $\varphi_k$ is irreducible for infinitely
many $k$.


title:
Measurement-induced phase transition in a classical, chaotic many-body
  system

abstract:
  Local measurements in quantum systems are projective operations which act to
counteract the spread of quantum entanglement. Recent work has shown that
local, random measurements applied to a generic volume-law entanglement
generating many-body system are able to force a transition into an area-law
phase. This work shows that projective operations can also force a similar
classical phase transition; we show that local projections in a chaotic system
can freeze information dynamics. In rough analogy with measurement-induced
phase transitions, this is characterized by an absence of information spreading
instead of entanglement entropy. We leverage a damage-spreading model of the
classical transition to predict the butterfly velocity of the system both near
to and away from the transition point. We map out the full phase diagram and
show that the critical point is shifted by local projections, but remains in
the directed percolation universality class. We discuss the implication for
other classical chaotic many-body systems.


title:
On-chip detection of electro-nuclear transitions in $^{155,157}$Gd
  multi-level spin system

abstract:
  The properties of rare-earth elements diluted in non-magnetic crystals make
them a promising candidate for a quantum memory due to their limited Hilbert
space. The control and readout of the qubit states require a highly sensitive
measurement and large coupling of the spin ensemble with the electro-magnetic
mode of a superconducting resonator. We report sensitive detection of forbidden
transitions of electro-nuclear states from the minority species of $^{155}$Gd
and $^{157}$Gd isotopes which opens the possibility of connecting quantum
states with very different spin projections. Cavity perturbation measurements
seen in the reflected signal allows the detection of about 7.6$\times10^{7}$
spins and the measurement of spin decoherence rate and spin-photon coupling
strength.


title:
Generative Adversarial Network for Future Hand Segmentation from
  Egocentric Video

abstract:
  We introduce the novel problem of anticipating a time series of future hand
masks from egocentric video. A key challenge is to model the stochasticity of
future head motions, which globally impact the head-worn camera video analysis.
To this end, we propose a novel deep generative model -- EgoGAN, which uses a
3D Fully Convolutional Network to learn a spatio-temporal video representation
for pixel-wise visual anticipation, generates future head motion using
Generative Adversarial Network (GAN), and then predicts the future hand masks
based on the video representation and the generated future head motion. We
evaluate our method on both the EPIC-Kitchens and the EGTEA Gaze+ datasets. We
conduct detailed ablation studies to validate the design choices of our
approach. Furthermore, we compare our method with previous state-of-the-art
methods on future image segmentation and show that our method can more
accurately predict future hand masks.


title:
Dark Energy Survey Year 3 results: imprints of cosmic voids and
  superclusters in the Planck CMB lensing map

abstract:
  The CMB lensing signal from cosmic voids and superclusters probes the growth
of structure in the low-redshift cosmic web. In this analysis, we
cross-correlated the Planck CMB lensing map with voids detected in the Dark
Energy Survey Year 3 (Y3) data set ($\sim$5,000 deg$^{2}$), extending previous
measurements using Y1 catalogues ($\sim$1,300 deg$^{2}$). Given the increased
statistical power compared to Y1 data, we report a $6.6\sigma$ detection of
negative CMB convergence ($\kappa$) imprints using approximately 3,600 voids
detected from a redMaGiC luminous red galaxy sample. However, the measured
signal is lower than expected from the MICE N-body simulation that is based on
the $\Lambda$CDM model (parameters $\Omega_{\rm m} = 0.25$, $\sigma_8 = 0.8$).
The discrepancy is associated mostly with the void centre region. Considering
the full void lensing profile, we fit an amplitude $A_{\kappa}=\kappa_{\rm
DES}/\kappa_{\rm MICE}$ to a simulation-based template with fixed shape and
found a moderate $2\sigma$ deviation in the signal with
$A_{\kappa}\approx0.79\pm0.12$. We also examined the WebSky simulation that is
based on a Planck 2018 $\Lambda$CDM cosmology, but the results were even less
consistent given the slightly higher matter density fluctuations than in MICE.
We then identified superclusters in the DES and the MICE catalogues, and
detected their imprints at the $8.4\sigma$ level; again with a
lower-than-expected $A_{\kappa}=0.84\pm0.10$ amplitude. The combination of
voids and superclusters yields a $10.3\sigma$ detection with an
$A_{\kappa}=0.82\pm0.08$ constraint on the CMB lensing amplitude, thus the
overall signal is $2.3\sigma$ weaker than expected from MICE.


title:
Faster Asynchronous Nonconvex Block Coordinate Descent with Locally
  Chosen Stepsizes

abstract:
  Distributed nonconvex optimization problems underlie many applications in
learning and autonomy, and such problems commonly face asynchrony in agents'
computations and communications. When delays in these operations are bounded,
they are called partially asynchronous. In this paper, we present an
uncoordinated stepsize selection rule for partially asynchronous block
coordinate descent that only requires local information to implement, and it
leads to faster convergence for a class of nonconvex problems than existing
stepsize rules, which require global information in some form. The problems we
consider satisfy the error bound condition, and the stepsize rule we present
only requires each agent to know (i) a certain type of Lipschitz constant of
its block of the gradient of the objective and (ii) the communication delays
experienced between it and its neighbors. This formulation requires less
information to be available to each agent than existing approaches, typically
allows for agents to use much larger stepsizes, and alleviates the impact of
stragglers while still guaranteeing convergence to a stationary point.
Simulation results provide comparisons and validate the faster convergence
attained by the stepsize rule we develop.


title:
BinPo: An open-source code to compute the band structure of
  two-dimensional electron systems

abstract:
  We introduce BinPo, an open-source Python code to compute electronic
properties of two-dimensional electron systems. Its usage is focused on the
ABO$_3$ perovskite structure based systems, such as SrTiO$_3$ and KTaO$_3$,
because of their increasing impact in materials community and possible
applications in spintronic devices. BinPo has a Schr\"odinger-Poisson solver to
obtain the self-consistent potential energy in a slab system. The tight binding
slab Hamiltonian of the system is created from the transfer integrals in the
maximally localized Wannier functions basis, thus reaching a higher accuracy
than conventional tight binding methods. The band structure, energy slices, and
other properties, along with different projections and orientations can be
computed. High resolution and publishable figures of the simulations can be
generated. In BinPo, priority has been given to ease-of-use, efficiency,
readability and modularity, therefore becoming suitable to produce reliable
electronic structures simulations at low computational cost. Along with the
code itself, we provide files from first-principles calculations, instructions
of use and detailed examples of its wide range of capabilities. We detail the
approaches used in the code, so that it can be further exploited and adapted to
other problems, such as adding new materials and functionalities which can
strength the initial code scopes.

categories: cond-mat.mtrl-sci cond-mat.mes-hall cond-mat.quant-gas cond-mat.str-el


title: Fog Based Computation Offloading for Swarm of Drones

abstract:
  Due to the limited computing resources of swarm of drones, it is difficult to
handle computation-intensive tasks locally, hence the cloud based computation
offloading is widely adopted. However, for the business which requires low
latency and high reliability, the cloud-based solution is not suitable, because
of the slow response time caused by long distance data transmission. Therefore,
to solve the problem mentioned above, in this paper, we introduce fog computing
into swarm of drones (FCSD). Focusing on the latency and reliability sensitive
business scenarios, the latency and reliability is constructed as the
constraints of the optimization problem. And in order to enhance the
practicality of the FCSD system, we formulate the energy consumption of FCSD as
the optimization target function, to decrease the energy consumption as far as
possible, under the premise of satisfying the latency and reliability
requirements of the task. Furthermore, a heuristic algorithm based on genetic
algorithm is designed to perform optimal task allocation in FCSD system. The
simulation results validate that the proposed fog based computation offloading
with the heuristic algorithm can complete the computing task effectively with
the minimal energy consumption under the requirements of latency and
reliability.


title:
Characteristic function and operator approach to M-indeterminate
  probability densities

abstract:
  We investigate M-indeterminate probability densities by way of the
characteristic function and self adjoint operators. The approach leads to new
methods to construct M-indeterminate probability densities of the Stieltjes
class as well as new non-Stieltjes classes of densities.


title:
Semileptonic decays of $\rho$, $\omega$, $\phi$, $\eta$ and $\eta'$
  mesons in ground and first radially excited states in the $U(3)\times U(3)$
  NJL model

abstract:
  In the extended $U(3)\times U(3)$ NJL model, semileptonic decays of $\rho$,
$\omega$, $\phi$, $\eta$ and $\eta'$ mesons in both the ground and first
radially excited states are described. Quark loops of the anomalous type are
considered, which are based on the vertex $\eta \to VV$. The obtained results
are in satisfactory agreement with the existing experimental data. In addition,
a number of predictions for experimentally unmeasured decays of radially
excited mesons are presented.


title:
Toward RIS-Enhanced Integrated Terrestrial/Non-Terrestrial Connectivity
  in 6G-Enabled IoE Era

abstract:
  The next generation of wireless systems will take the concept of
communications and networking to another level and turn the vision of Internet
of Everything (IoE) into reality. Everywhere connectivity through the
integration of terrestrial, aerial, satellite, maritime, and underwater
communications is expected in 6G-enabled IoE systems, where various services
with satisfying performance are proffered in an uninterrupted universal manner.
The successful realization of this ambitious goal relies on the development of
pioneering solutions which can revolutionize the future of wireless
technologies, while being secure, scalable, and reliable. Reconfigurable
intelligent surface (RIS) is a novel paradigm which has recently been on the
research spotlight and shown promising signs to expedite the evolution of
6G-enabled integrated terrestrial/non-terrestrial (INTENT) IoE networks.
Motivated by the unparalleled properties of this innovatory technology, we
herein set forth the architecture of RIS-enhanced 6G-enabled INTENT IoE
networks and clarify the decisive role of RIS in such an integrated
environment. We also present the key challenges which need to be addressed for
the successful realization of RIS-enhanced 6G systems in all dimensions.


title:
Energy Harvesting Aware Multi-hop Routing Policy in Distributed IoT
  System Based on Multi-agent Reinforcement Learning

abstract:
  Energy harvesting technologies offer a promising solution to sustainably
power an ever-growing number of Internet of Things (IoT) devices. However, due
to the weak and transient natures of energy harvesting, IoT devices have to
work intermittently rendering conventional routing policies and energy
allocation strategies impractical. To this end, this paper, for the very first
time, developed a distributed multi-agent reinforcement algorithm known as
global actor-critic policy (GAP) to address the problem of routing policy and
energy allocation together for the energy harvesting powered IoT system. At the
training stage, each IoT device is treated as an agent and one universal model
is trained for all agents to save computing resources. At the inference stage,
packet delivery rate can be maximized. The experimental results show that the
proposed GAP algorithm achieves around 1.28 times and 1.24 times data
transmission rate than that of the Q-table and ESDSRAA algorithm, respectively.


title:
Benchmarking the performance of a low-cost Magnetic Resonance Control
  System at multiple sites in the open MaRCoS community

abstract:
  Purpose: To describe the current properties and capabilities of an
open-source hardware and software package that is being developed by many sites
internationally with the aim of providing an inexpensive yet flexible platform
for low-cost MRI. Methods: This paper describes three different setups from 50
to 360 mT in different settings, all of which used the MaRCoS console for
acquiring data, and different types of software interfaces (custom-built GUI or
PulSeq overlay) to acquire the data. Results: Images are presented from both
phantoms and in vivo from healthy volunteers to demonstrate the image quality
that can be obtained from the MaRCoS hardware/software interfaced to different
low-field magnets. Conclusions: The results presented here show that a number
of different sequences commonly used in the clinic can be programmed into an
open-source system relatively quickly and easily, and can produce good quality
images even at this early stage of development. Both the hardware and software
will continue to develop, and it is an aim of this paper to encourage other
groups to join this international consortium.


title:
Landscape Analysis for Surrogate Models in the Evolutionary Black-Box
  Context

abstract:
  Surrogate modeling has become a valuable technique for black-box optimization
tasks with expensive evaluation of the objective function. In this paper, we
investigate the relationship between the predictive accuracy of surrogate
models and features of the black-box function landscape. We also study
properties of features for landscape analysis in the context of different
transformations and ways of selecting the input data. We perform the landscape
analysis of a large set of data generated using runs of a surrogate-assisted
version of the Covariance Matrix Adaptation Evolution Strategy on the noiseless
part of the Comparing Continuous Optimisers benchmark function testbed.


title:
Random vector functional link network: recent developments,
  applications, and future directions

abstract:
  Neural networks have been successfully employed in various domain such as
classification, regression and clustering, etc. Generally, the back propagation
(BP) based iterative approaches are used to train the neural networks, however,
it results in the issues of local minima, sensitivity to learning rate and slow
convergence. To overcome these issues, randomization based neural networks such
as random vector functional link (RVFL) network have been proposed. RVFL model
has several characteristics such as fast training speed, simple architecture,
and universal approximation capability, that make it a viable randomized neural
network. This article presents the comprehensive review of the development of
RVFL model, which can serve as the extensive summary for the beginners as well
as practitioners. We discuss the shallow RVFL, ensemble RVFL, deep RVFL and
ensemble deep RVFL models. The variations, improvements and applications of
RVFL models are discussed in detail. Moreover, we discuss the different
hyperparameter optimization techniques followed in the literature to improve
the generalization performance of the RVFL model. Finally, we give potential
future research directions/opportunities that can inspire the researchers to
improve the RVFL architecture further.


title:
The Change that Matters in Discourse Parsing: Estimating the Impact of
  Domain Shift on Parser Error

abstract:
  Discourse analysis allows us to attain inferences of a text document that
extend beyond the sentence-level. The current performance of discourse models
is very low on texts outside of the training distribution's coverage,
diminishing the practical utility of existing models. There is need for a
measure that can inform us to what extent our model generalizes from the
training to the test sample when these samples may be drawn from distinct
distributions. While this can be estimated via distribution shift, we argue
that this does not directly correlate with change in the observed error of a
classifier (i.e. error-gap). Thus, we propose to use a statistic from the
theoretical domain adaptation literature which can be directly tied to
error-gap. We study the bias of this statistic as an estimator of error-gap
both theoretically and through a large-scale empirical study of over 2400
experiments on 6 discourse datasets from domains including, but not limited to:
news, biomedical texts, TED talks, Reddit posts, and fiction. Our results not
only motivate our proposal and help us to understand its limitations, but also
provide insight on the properties of discourse models and datasets which
improve performance in domain adaptation. For instance, we find that non-news
datasets are slightly easier to transfer to than news datasets when the
training and test sets are very different. Our code and an associated Python
package are available to allow practitioners to make more informed model and
dataset choices.


title:
Deep Reinforcement Learning and Convex Mean-Variance Optimisation for
  Portfolio Management

abstract:
  Traditional portfolio management methods can incorporate specific investor
preferences but rely on accurate forecasts of asset returns and covariances.
Reinforcement learning (RL) methods do not rely on these explicit forecasts and
are better suited for multi-stage decision processes. To address limitations of
the evaluated research, experiments were conducted on three markets in
different economies with different overall trends. By incorporating specific
investor preferences into our RL models' reward functions, a more comprehensive
comparison could be made to traditional methods in risk-return space.
Transaction costs were also modelled more realistically by including nonlinear
changes introduced by market volatility and trading volume. The results of this
study suggest that there can be an advantage to using RL methods compared to
traditional convex mean-variance optimisation methods under certain market
conditions. Our RL models could significantly outperform traditional
single-period optimisation (SPO) and multi-period optimisation (MPO) models in
upward trending markets, but only up to specific risk limits. In sideways
trending markets, the performance of SPO and MPO models can be closely matched
by our RL models for the majority of the excess risk range tested. The specific
market conditions under which these models could outperform each other
highlight the importance of a more comprehensive comparison of Pareto optimal
frontiers in risk-return space. These frontiers give investors a more granular
view of which models might provide better performance for their specific risk
tolerance or return targets.


title:
Bubble Chamber Detectors with Light Nuclear Targets: A Snowmass 2021
  White Paper

abstract:
  Neutrino cross sections are a critical ingredient in experiments that depend
on neutrino scattering to reconstruct event kinematics and infer neutrino
characteristics, like NOvA and T2K. An opportunity exists to reduce the 5-10%
broad uncertainty on neutrino cross sections by producing more measurements of
neutrino scattering from light nuclear targets at the relevant energies. Bubble
chambers with light nuclear targets would be ideal for these measurements but
the most recent device designed for use with an accelerator neutrino source is
at least fifty years old. A new bubble chamber with light nuclear targets could
be designed by observing how the technology has progressed for use in dark
matter experiments and producing smaller modular devices that use more
efficient cooling systems. A smaller modular device could also be designed for
deployment to all functioning neutrino beams, but an investigation of the
proper operating characteristics is necessary to adapt newer detectors to the
structure of contemporary neutrino beams.


title: Valley engineering electron-hole liquids in TMDC monolayers

abstract:
  Electron-hole liquids(EHLs), a correlated state of matter and a thermodynamic
liquid, have recently been found to exist at room temperature in suspended
monolayers of MoS2. Appreciably higher rates of radiative recombination inside
the liquid as compared to free excitons hold promise for optoelectronic
applications such as broadband lasing. In this paper, we show that leveraging
the valley physics in MoS2 may be a route towards achieving tunability of
specific characteristics of an EHL, such as emission wavelength, linewidth, and
most importantly, the liquid density. The conditions under which EHLs form, in
bulk semiconductors as well as TMDC monolayers are quite stringent, requiring
high crystal purity and cryogenic temperatures in bulk semiconductors, and
suspension in monolayers. Using a simple yet powerful model for describing free
excitons and show that a phase transition into the EHL state may be feasible in
substrate-supported monolayer samples. More repeatable experimental
realizations of EHLs may be essential to answer questions regarding the nature
of electron-hole correlations and how they may be used to generate non-trivial
states of light.


title:
Alarm-Based Root Cause Analysis in Industrial Processes Using Deep
  Learning

abstract:
  Alarm management systems have become indispensable in modern industry. Alarms
inform the operator of abnormal situations, particularly in the case of
equipment failures. Due to the interconnections between various parts of the
system, each fault can affect other sections of the system operating normally.
As a result, the fault propagates through faultless devices, increasing the
number of alarms. Hence, the timely detection of the major fault that triggered
the alarm by the operator can prevent the following consequences. However, due
to the complexity of the system, it is often impossible to find precise
relations between the underlying fault and the alarms. As a result, the
operator needs support to make an appropriate decision immediately. Modeling
alarms based on the historical alarm data can assist the operator in
determining the root cause of the alarm. This research aims to model the
relations between industrial alarms using historical alarm data in the
database. Firstly, alarm data is collected, and alarm tags are sequenced. Then,
these sequences are converted to numerical vectors using word embedding. Next,
a self-attention-based BiLSTM-CNN classifier is used to learn the structure and
relevance between historical alarm data. After training the model, this model
is used for online fault detection. Finally, as a case study, the proposed
model is implemented in the well-known Tennessee Eastman process, and the
results are presented.


title:
Probabilistically robust stabilizing allocations in uncertain
  cooperative games

abstract:
  In this paper we consider multi-agent cooperative games with uncertain value
functions for which we establish distribution-free guarantees on the
probability of allocation stability, i.e., agents do not have incentives to
defect the grand coalition to subcoalitions for unseen realizations of the
uncertain parameter. In case the set of stable allocations, the so called core
of the game, is empty, we propose a randomized relaxation of the core. We then
show that those allocations that belong to this relaxed set can be accompanied
by stability guarantees in a probably approximately correct fashion. Finally,
numerical experiments corroborate our theoretical findings.


title:
Training Quantised Neural Networks with STE Variants: the Additive Noise
  Annealing Algorithm

abstract:
  Training quantised neural networks (QNNs) is a non-differentiable
optimisation problem since weights and features are output by piecewise
constant functions. The standard solution is to apply the straight-through
estimator (STE), using different functions during the inference and gradient
computation steps. Several STE variants have been proposed in the literature
aiming to maximise the task accuracy of the trained network. In this paper, we
analyse STE variants and study their impact on QNN training. We first observe
that most such variants can be modelled as stochastic regularisations of stair
functions; although this intuitive interpretation is not new, our rigorous
discussion generalises to further variants. Then, we analyse QNNs mixing
different regularisations, finding that some suitably synchronised smoothing of
each layer map is required to guarantee pointwise compositional convergence to
the target discontinuous function. Based on these theoretical insights, we
propose additive noise annealing (ANA), a new algorithm to train QNNs
encompassing standard STE and its variants as special cases. When testing ANA
on the CIFAR-10 image classification benchmark, we find that the major impact
on task accuracy is not due to the qualitative shape of the regularisations but
to the proper synchronisation of the different STE variants used in a network,
in accordance with the theoretical results.


title: Learning robot motor skills with mixed reality

abstract:
  Mixed Reality (MR) has recently shown great success as an intuitive interface
for enabling end-users to teach robots. Related works have used MR interfaces
to communicate robot intents and beliefs to a co-located human, as well as
developed algorithms for taking multi-modal human input and learning complex
motor behaviors. Even with these successes, enabling end-users to teach robots
complex motor tasks still poses a challenge because end-user communication is
highly task dependent and world knowledge is highly varied. We propose a
learning framework where end-users teach robots a) motion demonstrations, b)
task constraints, c) planning representations, and d) object information, all
of which are integrated into a single motor skill learning framework based on
Dynamic Movement Primitives (DMPs). We hypothesize that conveying this world
knowledge will be intuitive with an MR interface, and that a sample-efficient
motor skill learning framework which incorporates varied modalities of world
knowledge will enable robots to effectively solve complex tasks.


title: Enhancing Speech Recognition Decoding via Layer Aggregation

abstract:
  Recently proposed speech recognition systems are designed to predict using
representations generated by their top layers, employing greedy decoding which
isolates each timestep from the rest of the sequence. Aiming for improved
performance, a beam search algorithm is frequently utilized and a language
model is incorporated to assist with ranking the top candidates. In this work,
we experiment with several speech recognition models and find that logits
predicted using the top layers may hamper beam search from achieving optimal
results. Specifically, we show that fined-tuned Wav2Vec 2.0 and HuBERT yield
highly confident predictions, and hypothesize that the predictions are based on
local information and may not take full advantage of the information encoded in
intermediate layers. To this end, we perform a layer analysis to reveal and
visualize how predictions evolve throughout the inference flow. We then propose
a prediction method that aggregates the top M layers, potentially leveraging
useful information encoded in intermediate layers and relaxing model
confidence. We showcase the effectiveness of our approach via beam search
decoding, conducting our experiments on Librispeech test and dev sets and
achieving WER, and CER reduction of up to 10% and 22%, respectively.


title:
Effect of Snyder-de Sitter model on the Black hole thermodynamics in the
  context of rainbow gravity

abstract:
  One of the foremost goals of theoretical modern physics is to obtain a
reliable theory of quantum gravity. In so doing, new fundamental scales are
being proposed. For example, in the Snyder - de Sitter model, two scales
manifest which relate measurement limits of the position and momentum. In this
work, we study the thermodynamic functions of the Scharwzschild black hole in
the Snyder - de Sitter model within the presence of position-independent and
dependent gravity's rainbow. Heuristically, we prove the presence of a non-zero
lower bound value of the horizon, mass, and temperature of the black hole.
Then, we present the stability and remnant conditions of the black hole
according to the entropy and heat capacity functions.


title:
Online Joint Optimal Control-Estimation Architecture in Distribution
  Networks

abstract:
  In this paper, we propose an optimal control-estimation architecture for
distribution networks, which jointly solves the optimal power flow (OPF)
problem and static state estimation (SE) problem through an online
gradient-based feedback algorithm. The main objective is to enable a fast and
timely interaction between the optimal controllers and state estimators with
limited sensor measurements. First, convergence and optimality of the proposed
algorithm are analytically established. Then, the proposed gradient-based
algorithm is modified by introducing statistical information of the inherent
estimation and linearization errors for an improved and robust performance of
the online control decisions. Overall, the proposed method eliminates the
traditional separation of control and operation, where control and estimation
usually operate at distinct layers and different time-scales. Hence, it enables
a computationally affordable, efficient and robust online operational framework
for distribution networks under time-varying settings.


title: Benchmarking Large-Scale ACOPF Solutions and Optimality Bounds

abstract:
  We present the results of a comprehensive benchmarking effort aimed at
evaluating and comparing state-of-the-art open-source tools for solving the
Alternating-Current Optimal Power Flow (ACOPF) problem. Our numerical
experiments include all instances found in the public library PGLIB with
network sizes up to 30,000 nodes. The benchmarked tools span a number of
programming languages (Python, Julia, Matlab/Octave, and C$++$), nonlinear
optimization solvers (Ipopt, MIPS, and INLP) as well as different mathematical
modeling tools (JuMP and Gravity). We also present state-of-the-art optimality
bounds obtained using sparsity-exploiting semidefinite programming approaches
and corresponding computational times.


title:
A Simulation Approach for Competitive Facility Location with Random
  Utility Maximizing Customers

abstract:
  The competitive facility location problem formalizes the managerial problem
faced by a firm that enters a market already occupied by competitors in which
customers are assumed to choose the facility that maximizes their utility.
Given a discrete choice model that expresses the probability for a customer to
select each location as a function of its attributes, and given the
distribution of these attributes in the population, the objective is to
identify facility locations that maximize the expected market share captured by
the firm.
  We present a simulation approach for solving this problem with any discrete
choice model. Our method exploits the fact that it is possible to aggregate
customers to reduce the dimension of the optimization problem without affecting
its optimal solution. Our experiments indicate that solving the resulting 0-1
linear program produces high quality solutions for problems based on the mixed
multinomial logit model more efficiently than existing methods.
  In many cases, our approach also leads to near-optimal solutions for
large-scale instances based on the multinomial logit model in a fraction of the
computing time required by the state-of-the-art exact method from the
literature. To interpret these results, we introduce an entropy measure to
characterize the properties that influence the performance of our method on
different types of instances. We finally propose potential uses of this novel
information-theoretic perspective in the broader context of optimization
problems based on random utility maximization models.


title:
Stochastic line integrals and stream functions as metrics of
  irreversibility and heat transfer

abstract:
  Stochastic line integrals provide a useful tool for quantitatively
characterizing irreversibility and detailed balance violation in noise-driven
dynamical systems. A particular realization is the stochastic area, recently
studied in coupled electrical circuits. Here, we provide a general framework
for understanding properties of stochastic line integrals and clarify their
implementation for experiments and simulations. For two-dimensional systems,
stochastic line integrals can be expressed in terms of a stream function, the
sign of which determines the orientation of steady-state probability currents.
Additionally, the stream function permits analytical understanding of the
scaling dependence of stochastic area on key parameters such as the noise
strength for both nonlinear and linear springs. Theoretical results are
supported by numerical studies of an overdamped, two-dimensional mass-spring
system driven out of equilibrium.


title: On The Robustness of Offensive Language Classifiers

abstract:
  Social media platforms are deploying machine learning based offensive
language classification systems to combat hateful, racist, and other forms of
offensive speech at scale. However, despite their real-world deployment, we do
not yet comprehensively understand the extent to which offensive language
classifiers are robust against adversarial attacks. Prior work in this space is
limited to studying robustness of offensive language classifiers against
primitive attacks such as misspellings and extraneous spaces. To address this
gap, we systematically analyze the robustness of state-of-the-art offensive
language classifiers against more crafty adversarial attacks that leverage
greedy- and attention-based word selection and context-aware embeddings for
word replacement. Our results on multiple datasets show that these crafty
adversarial attacks can degrade the accuracy of offensive language classifiers
by more than 50% while also being able to preserve the readability and meaning
of the modified text.


title:
Information loss and run time from practical application of quantum data
  compression

abstract:
  We examine information loss, resource costs, and run time from practical
application of quantum data compression. Compressing quantum data to fewer
qubits enables efficient use of resources, as well as applications for quantum
communication and denoising. In this context, we provide a description of the
quantum and classical components of the hybrid quantum autoencoder algorithm,
implemented using IBM's Qiskit language. Utilizing our own data sets, we encode
bitmap images as quantum superposition states, which correspond to linearly
independent vectors with density matrices of discrete values. We successfully
compress this data with near-lossless compression using simulation, and then
run our algorithm on an IBMQ quantum chip. We describe conditions and run times
for compressing our data on quantum devices.


title: Locality-aware Qubit Routing for the Grid Architecture

abstract:
  Due to the short decohorence time of qubits available in the NISQ-era, it is
essential to pack (minimize the size and or the depth of) a logical quantum
circuit as efficiently as possible given a sparsely coupled physical
architecture. In this work we introduce a locality-aware qubit routing
algorithm based on a graph theoretic framework. Our algorithm is designed for
the grid and certain "grid-like" architectures. We experimentally show the
competitiveness of algorithm by comparing it against the approximate token
swapping algorithm, which is used as a primitive in many state-of-the-art
quantum transpilers. Our algorithm produces circuits of comparable depth
(better on random permutations) while being an order of magnitude faster than a
typical implementation of the approximate token swapping algorithm.


title:
Update on the Systematics in the ALMA Proposal Review Process after
  Cycle 8

abstract:
  We present an updated analysis of systematics in the Atacama Large
Millimeter/submillimeter Array (ALMA) proposal ranks from Carpenter (2020) to
include the last two ALMA cycles, when significant changes were introduced in
the proposal review process. In Cycle 7, the investigator list on the proposal
cover sheet was randomized such that the reviewers were aware of the overall
proposal team but did not know the identity of the principal investigator (PI).
In Cycle 8, ALMA adopted distributed peer review for most proposals and
implemented dual-anonymous review for all proposals, in which the identity of
the proposal team was not revealed to the reviewers. The most significant
change in the systematics in Cycles 7 and 8 compared to previous cycles is
related to the experience of PIs in submitting ALMA proposals. PIs that submit
a proposal every cycle tend to have ranks that are consistent with average in
Cycles 7 and 8 whereas previously they had the best overall ranks. Also, PIs
who submitted a proposal for the second time show improved ranks over previous
cycles. These results suggest some biases related to the relative prominence of
the PI have been present in the ALMA review process. Systematics related to
regional affiliation remain largely unchanged in that PIs from Chile, East
Asia, and non-ALMA regions tend to have poorer overall ranks than PIs from
Europe and North America. The systematics of how one region ranks proposals
from another region are also investigated. No significant differences in the
overall ranks based on gender of the PI are observed.

categories: astro-ph.IM astro-ph.EP astro-ph.GA astro-ph.SR


title: Global Matching with Overlapping Attention for Optical Flow Estimation

abstract:
  Optical flow estimation is a fundamental task in computer vision. Recent
direct-regression methods using deep neural networks achieve remarkable
performance improvement. However, they do not explicitly capture long-term
motion correspondences and thus cannot handle large motions effectively. In
this paper, inspired by the traditional matching-optimization methods where
matching is introduced to handle large displacements before energy-based
optimizations, we introduce a simple but effective global matching step before
the direct regression and develop a learning-based matching-optimization
framework, namely GMFlowNet. In GMFlowNet, global matching is efficiently
calculated by applying argmax on 4D cost volumes. Additionally, to improve the
matching quality, we propose patch-based overlapping attention to extract large
context features. Extensive experiments demonstrate that GMFlowNet outperforms
RAFT, the most popular optimization-only method, by a large margin and achieves
state-of-the-art performance on standard benchmarks. Thanks to the matching and
overlapping attention, GMFlowNet obtains major improvements on the predictions
for textureless regions and large motions. Our code is made publicly available
at https://github.com/xiaofeng94/GMFlowNet


title:
Coherent structure detection and the inverse cascade mechanism in
  two-dimensional Navier-Stokes turbulence

abstract:
  Coherent structures are ubiquitously observed in nature, experiments and
numerical simulations, however they lack a rigorous unique mathematical
definition until this date. While the amount of literature in this field is
steadily growing without a tendency of developing a definition which is
rigorously agreed upon, the general influence onto the essential structure
formation process, namely the inverse energy cascade mechanism, is usually not
discussed. Therefore, the present study conducts a comparison between several
structure detection schemes comprised of the vorticity magnitude, Okubo-Weiss
criterion, and Lagrangian coherent structures, with regard to the inverse
cascade in two-dimensional hydrodynamic turbulence. For that, the recently
introduced vortex scaling theory determines the threshold choices of these
methods. We observe that all methods exhibit qualitatively similar properties
with respect to the cascade behavior. Furthermore, coherent structures have a
smaller contribution to the inverse energy cascade, as compared to their
residual counterparts. This has two reasons: $(i)$ There is a higher
misalignment tendency of strain-rate and subgrid stress tensors within coherent
regions, leading to a decreased energy cascade efficiency. $(ii)$ Coherent
structures exhibit shape preserving properties and are less affected by the
vortex thinning mechanism.


title:
Observation of Nonclassical Photon Statistics in Single-Bubble
  Sonoluminescence

abstract:
  A cavitation bubble inside a liquid, under a specific set of conditions, can
get trapped in an antinode of the ultrasonically driven standing wave and
periodically emits visible photons (1,2). This conversion of sound to light
phenomenon, known as sonoluminescence, can be seen with unaided eyes and occurs
in multi- or single-bubble regimes. The sonoluminescence radiation spectrum
analysis attributes a temperature of about 6,000 Kelvin to the bubble (3-5) --
close to the sun's surface temperature. The bubble dynamics, thermodynamics,
and other physicochemical properties are well-explored and studied over the
past decades (6-8). Notwithstanding that several theories such as the thermal
(black-body radiation) model (9-11), shock wave theory (12), phase transition
radiation (13,14), and vacuum quantum electrodynamics (15,16) -- were proposed,
the underlying physics of sonoluminescence has hitherto remained a puzzle.
Here, we experimentally investigated the photon number statistics of the
emitted photons from single-bubble sonoluminescence (SBSL), employing two
distinctly different techniques, i.e., measuring multiphoton correlations and
photon number (using a photon-number resolving detector). Our findings show
that the emitted photons from SBSL possess sub-Poissonian statistics,
indicating the nonclassical nature of the SBSL. Our observation of
sub-Poissonian statistics of emitted photons may help to explain the physics of
SBSL. In addition, considering the importance of nonclassical light sources in
quantum technologies, this discovery would be an exciting milestone in paving
the route towards a bright quantum photonics source.


title:
Fast Toeplitz eigenvalue computations, joining
  interpolation-extrapolation matrix-less algorithms and simple-loop
  conjectures: the preconditioned setting

abstract:
  Under appropriate technical assumptions, the simple-loop theory allows to
deduce various types of asymptotic expansions for the eigenvalues of Toeplitz
matrices $T_{n}(f)$ generated by a function $f$, unfortunately, such a theory
is not available in the preconditioning setting, that is for matrices of the
form $T_{n}^{-1}(g)T_{n}(l)$ with $l,g$ real-valued, $g$ nonnnegative and not
identically zero almost everywhere. Independently and under the milder
hypothesis that $f=\frac{l}{g}$ is even and monotonic over $[0,\pi]$,
matrix-less algorithms have been developed for the fast eigenvalue computation
of large preconditioned matrices of the type above, within a linear complexity
in the matrix order: behind the high efficiency of such algorithms there are
the expansions as in the case $g\equiv 1$, combined with the extrapolation
idea, and hence we conjecture that the simple-loop theory has to be extended in
such a new setting, as the numerics strongly suggest.Here we focus our
attention on a change of variable, followed by the asymptotic expansion of the
new variable, and we consider new matrix-less algorithms ad hoc for the current
case. Numerical experiments show a much higher precision till machine precision
and the same linear computation cost, when compared with the matrix-less
procedures already proposed in the literature.


title:
A Unified Description for Polarization-Transfer Mechanisms in Magnetic
  Resonance in Static Solids: Cross Polarization and DNP

abstract:
  Polarization transfers are crucial building blocks in magnetic resonance
experiments, i.e., they can be used to polarize insensitive nuclei and
correlate nuclear spins in multidimensional NMR spectroscopy. The polarization
can be transferred either across different nuclear spin species or from
electron spins to the relatively low-polarized nuclear spins. The former route
occurring in solid-state NMR (ssNMR) can be performed via cross polarization
(CP), while the latter route is known as dynamic nuclear polarization (DNP).
Despite having different operating conditions, we opinionate that both
mechanisms are theoretically similar processes in ideal conditions, i.e., the
electron is merely another spin-1/2 particle with a much higher gyromagnetic
ratio. Here, we show that the CP and DNP processes can be described using a
unified theory based on average Hamiltonian theory (AHT) combined with
fictitious operators. The intuitive and unified approach has allowed new
insights on the cross effect (CE) DNP mechanism, leading to better design of
DNP polarizing agents and extending the applications beyond just
hyperpolarization. We explore the possibility of exploiting theoretically
predicted DNP transients for electron-nucleus distance measurements--like
routine dipolar-recoupling experiments in solid-state NMR.


title: Many Models for Water Waves

abstract:
  This document is an announcement and preview of a memoir whose full version
is available on the Open Math Notes repository of the American Mathematical
Society (OMN:202109.111309). In this memoir, I try to provide a fairly
comprehensive picture of (mostly shallow water) asymptotic models for water
waves. The work and presentation is heavily inspired by the book of D. Lannes,
yet extends the discussion into several directions, notably high order and
fully dispersive models, and internal/interfacial waves.


title: Binary codes that do not preserve primitivity

abstract:
  A code $X$ is not primitivity preserving if there is a primitive list
${\mathbf w} \in {\tt lists} X$ whose concatenation is imprimitive. We
formalize a full characterization of such codes in the binary case in the proof
assistant Isabelle/HOL. Part of the formalization, interesting on its own, is a
description of $\{x,y\}$-interpretations of the square $xx$ if $|y| \leq |x|$.
We also provide a formalized parametric solution of the related equation
$x^jy^k = z^\ell$.


title:
Finite-part integral representation of the Riemann zeta function at odd
  positive integers and consequent representations

abstract:
  The values of the Riemann zeta function at odd positive integers,
$\zeta(2n+1)$, are shown to admit a representation proportional to the
finite-part of the divergent integral $\int_0^{\infty} t^{-2n-1}
\operatorname{csch}t\,\mathrm{d}t$. Integral representations for $\zeta(2n+1)$
are then deduced from the finite-part integral representation. Certain
relations between $\zeta(2n+1)$ and $\zeta'(2n+1)$ are likewise deduced, from
which integral representations for $\zeta'(2n+1)$ are obtained.


title:
Using Evolutionary Coupling to Establish Relevance Links Between Tests
  and Code Units. A case study on fault localization

abstract:
  Many software engineering techniques, such as fault localization, operate
based on relevance relationships between tests and code. These relationships
are often inferred through the use of dynamic test execution information (test
execution traces) that approximate the link between relevant code units and
asserted, by the tests, program behaviour. Unfortunately, in practice dynamic
information is not always available due to the overheads introduced by the
instrumentation or the nature of the production environments. To deal with this
issue, we propose CEMENT, a static technique that automatically infers such
test and code relationships given the projects' evolution. The key idea is that
developers make relevant changes on test and code units at the same period of
time, i.e., co-evolution of tests and code units reflects a probable link
between them. We evaluate CEMENT on 15 open source projects and show that it
indeed captures relevant links. Additionally, we perform a fault localization
case study where we compare CEMENT with an existing Information Retrieval-based
Fault Localization (IRFL) technique and show that it achieves comparable
performance. A further analysis of our results reveals a small overlap between
the faults successfully localized by the two approaches suggesting
complementarity. In particular, out of the 39 successfully localized faults,
two are common while CEMENT and IRFL localize 16 and 21. These results
demonstrate that test and code evolutionary coupling can effectively support
test and debugging activities.


title:
Magnetic circular dichroism within the Algebraic Diagrammatic
  Construction scheme of the polarisation propagator up to third order

abstract:
  We present an implementation of the B term of Magnetic Circular Dichroism
within the Algebraic Diagrammatic Construction (ADC) scheme of the polarization
propagator and its Intermediate State Representation. As illustrative results,
the MCD spectra of the ADC variants ADC(2), ADC(2)-x and ADC(3) of the
molecular systems uracil, 2-thiouracil, 4-thiouracil, purine, hypoxanthine,
1,4-naphthoquinone, 9,10-anthraquinone and 1-naphthylamine, are computed and
compared with results obtained using the Coupled-Cluster Singles and
Approximate Doubles (CC2) method, literature TD-DFT results as well as with
available experimental data.


title: Localized radial roll patterns in higher space dimensions

abstract:
  Localized roll patterns are structures that exhibit a spatially periodic
profile in their center. When following such patterns in a system parameter in
one space dimension, the length of the spatial interval over which these
patterns resemble a periodic profile stays either bounded, in which case
branches form closed bounded curves ("isolas"), or the length increases to
infinity so that branches are unbounded in function space ("snaking"). In two
space dimensions, numerical computations show that branches of localized rolls
exhibit a more complicated structure in which both isolas and snaking occur. In
this paper, we analyse the structure of branches of localized radial roll
solutions in dimension 1+$\varepsilon$, with $0<\varepsilon\ll1$, through a
perturbation analysis. Our analysis sheds light on some of the features visible
in the planar case.


title: Spatially Localized Structures in Lattice Dynamical Systems

abstract:
  We investigate stationary, spatially localized patterns in lattice dynamical
systems that exhibit bistability. The profiles associated with these patterns
have a long plateau where the pattern resembles one of the bistable states,
while the profile is close to the second bistable state outside this plateau.
We show that the existence branches of such patterns generically form either an
infinite stack of closed loops (isolas) or intertwined s-shaped curves
(snaking). We then use bifurcation theory near the anti-continuum limit, where
the coupling between edges in the lattice vanishes, to prove existence of
isolas and snaking in a bistable discrete real Ginzburg--Landau equation. We
also provide numerical evidence for the existence of snaking diagrams for
planar localized patches on square and hexagonal lattices and outline a
strategy to analyse them rigorously.


title: Localized patterns in planar bistable weakly coupled lattice systems

abstract:
  Localized planar patterns in spatially extended bistable systems are known to
exist along intricate bifurcation diagrams, which are commonly referred to as
snaking curves. Their analysis is challenging as techniques such as spatial
dynamics that have been used to explain snaking in one space dimension no
longer work in the planar case. Here, we consider bistable systems posed on
square lattices and provide an analytical explanation of snaking near the
anti-continuum limit using Lyapunov--Schmidt reduction. We also establish
stability results for localized patterns, discuss bifurcations to asymmetric
states, and provide further numerical evidence that the shape of snaking curves
changes drastically as the coefficient that reflects the strength of the
spatial coupling crosses a finite threshold.


title: Openness of Regular Regimes of Complex Random Matrix Models

abstract:
  Consider the general complex polynomial external field $$
V(z)=\frac{z^{k}}{k}+\sum_{j=1}^{k-1} \frac{t_j z^j}{j}, \qquad t_j \in
\mathbb{C}, \quad k \in \mathbb{N}. $$ Fix an equivalence class $\mathcal{T}$
of admissible contours whose members approach $\infty$ in two different
directions and consider the associated max-min energy problem. When $k=2p$, $p
\in \mathbb{N}$, and $\mathcal{T}$ contains the real axis, we show that the set
of parameters $t_1, \cdots, t_{2p-1}$ which gives rise to a regular $q$-cut
max-min (equilibrium) measure, $1 \leq q \leq 2p-1 $, is an open set in
$\mathbb{C}^{2p-1}$. We use the implicit function theorem to prove that the
endpoint equations are solvable in a small enough neighborhood of a regular
$q$-cut point. We also establish the real-analyticity of the real and imaginary
parts of the end-points for all $q$-cut regimes, $1 \leq q \leq 2p-1$, with
respect to the real and imaginary parts of the complex parameters in the
external field. Our choice of even $k$ and the equivalence class $\mathcal{T}
\ni \mathbb{R}$ of admissible contours is only for the simplicity of exposition
and our proof extends to all possible choices in an analogous way.


title: Snowmass Whitepaper: The Belle II Detector Upgrade Program

abstract:
  We describe the planned near-term and potential longer-term upgrades of the
Belle II detector at the SuperKEKB electron-positron collider in Tsukuba,
Japan. These upgrades will allow increasingly sensitive searches for possible
new physics beyond the Standard Model in flavor, tau, electroweak and dark
sector physics that are both complementary to and competitive with the LHC and
other experiments. We encourage the instrumentation-frontier community to
contribute and study upgrade ideas as part of the Snowmass process.


title: Tame sets in homogeneous spaces

abstract:
  We prove the existence of strongly tame sets in affine algebraic homogenenous
spaces of linear algebraic Lie groups. We also show that $(\mathbb{C}^n,A)$ for
a discrete tame set enjoy the relative density property, and we provide
examples of Stein manifolds admitting non-equivalent tame sets.


title:
Metamorphosis of the Landau transition in the flow of a
  resonantly-driven bistable polariton fluid

abstract:
  The onset of dissipation in a flowing superfluid has been the subject of
numerous investigations. Motivated by recent experiments, we theoretically
analyze the analogous phenomenon for a one-dimensional "quantum fluid of light"
which is resonantly driven, and exhibits bistability. The flow is found to
abruptly change multiple times when the fluid velocity or the potential
strength are increased. In contrast to the classical Landau transition to a
time-dependent flow, the transitions take place between stationary states and
involve the fluid bistability in an essential way.


title: A General Framework for Impermanent Loss in Automated Market Makers

abstract:
  We provide a framework for analyzing impermanent loss for general Automated
Market Makers (AMMs) and show that Geometric Mean Market Makers (G3Ms) are in a
rigorous sense the simplest class of AMMs from an impermanent loss viewpoint.
In this context, it becomes clear why automated market makers like Curve
([Ego19]) require more parameters in order to specify impermanent loss. We
suggest the proper parameter space on which impermanent loss should be
considered and prove results that help in understanding the impermanent loss
characteristics of different AMMs.


title: Time-dependent Hamiltonian Simulation Using Discrete Clock Constructions

abstract:
  In this work we provide a new approach for approximating an ordered operator
exponential using an ordinary operator exponential that acts on the Hilbert
space of the simulation as well as a finite-dimensional clock register. This
approach allows us to translate results for simulating time-independent systems
to the time-dependent case. Our result solves two open problems in simulation.
It first provides a rigorous way of using discrete time-displacement operators
to generate time-dependent product and multiproduct formulas. Second, it
provides a way to allow qubitization to be employed for certain time-dependent
formulas. We show that as the number of qubits used for the clock grows, the
error in the ordered operator exponential vanishes, as well as the entanglement
between the clock register and the register of the state being acted upon. As
an application, we provide a new family of multiproduct formulas (MPFs) for
time-dependent Hamiltonians that yield both commutator scaling and
poly-logarithmic error scaling. This in turn, means that this method
outperforms existing methods for simulating physically-local, time-dependent
Hamiltonians. Finally, we apply the formalism to show how qubitization can be
generalized to the time-dependent case and show that such a translation can be
practical if the second derivative of the Hamiltonian is sufficiently small.