title:
Bridging the gap between pricing and reserving with an occurrence and
  development model for non-life insurance claims

abstract:
  Due to the presence of reporting and settlement delay, claim data sets
collected by non-life insurance companies are typically incomplete, facing
right censored claim count and claim severity observations. Current practice in
non-life insurance pricing tackles these right censored data via a two-step
procedure. First, best estimates are computed for the number of claims that
occurred in past exposure periods and the ultimate claim severities, using the
incomplete, historical claim data. Second, pricing actuaries build predictive
models to estimate technical, pure premiums for new contracts by treating these
best estimates as actual observed outcomes, hereby neglecting their inherent
uncertainty. We propose an alternative one step approach suitable for both
non-life pricing and reserving. As such we effectively bridge these two key
actuarial tasks that have traditionally been discussed in silos. Hereto we
develop a granular occurrence and development model for non-life claims that
allows to resolve the inconsistency in traditional pricing techniques between
actual, complete observations on the one hand and best estimates on the other
hand. We illustrate our proposed model on a reinsurance portfolio, where large
uncertainties in the best estimates originate from long reporting and
settlement delays, low claim frequencies and heavy (even extreme) claim sizes.


title:
Enhancement of incoherent bremsstrahlung in proton-nucleus scattering in
  the $\Delta$-resonance energy region

abstract:
  We investigate emission of the bremsstrahlung photons in the scattering of
protons off nuclei at the $\Delta$-resonance energy region. Including
properties of $\Delta$-resonance in the nucleus-target to the bremsstrahlung
model, we find the following. (1) Ratio between incoherent emission and
coherent emission is about $10^{6}$--$10^{7}$ for $p + \isotope[197]{Au}$
(without $\Delta$-resonance) at energy of proton beam $E_{\rm p}$ of 190~MeV,
where the calculated full bremsstrahlung spectrum is in good agreement with
experimental data. This confirms importance of incoherent processes in study of
$\Delta$-resonances in this reaction, which have never been studied yet. We
estimate coherent and incoherent contributions, electric and magnetic
contributions, full bremsstrahlung spectra for the scattering of protons on the
\isotope[12]{C}, \isotope[40]{Ca}, \isotope[208]{Pb} nuclei at $E_{\rm
p}=800$~MeV, we find conditions for the most intensive bremsstrahlung emission.
(2) Transition $p\,N \to \Delta^{+} N$ in the nucleus-target reinforces
emission of bremsstrahlung photons in that reaction at $E_{\rm p}=800$~MeV.
Difference between the spectra for normal nuclei and nuclei with included
$\Delta$-resonance is larger for more light nuclei, but the spectra are larger
for heavier nuclei. (3) Taking into account shortly lived state of
$\Delta$-resonance, we find that the spectrum with $\Delta$-resonance in the
nucleus-target is essentially larger in the high energy photon region than the
spectrum without this $\Delta$-resonance (corresponding calculations for
\isotope[12][\Delta]{C}, \isotope[40][\Delta]{Ca}, \isotope[208][\Delta]{Pb} in
comparison with \isotope[12]{C}, \isotope[40]{Ca}, \isotope[208]{Pb} are
provided). Such an aspect is recommended for registration of
$\Delta$-resonances in nuclei in possible future experiments.


title: The Onsager-Machlup action functional for Mckean-Vlasov SDEs

abstract:
  This paper is devoted to deriving the Onsager-Machlup action functional for
Mckean-Vlasov stochastic differential equations in a class of norms that
dominate $L^2([0,1], \mathbb{R}^d)$, such as supremum norm
$\|\cdot\|_{\infty}$, H$\mathrm{\ddot{o}}$lder norms $\|\cdot\|_{\alpha}$ with
$\alpha<\frac{1}{4}$ and $L^p$-norms with $p>4$ are included. Moreover, the
corresponding Euler-Lagrange equation for Onsager-Machlup action functional is
derived and a example is given.


title: Oriented discrepancy of Hamilton cycles

abstract:
  We propose the following conjecture extending Dirac's theorem: if $G$ is a
graph with $n\ge 3$ vertices and minimum degree $\delta(G)\ge n/2$, then in
every orientation of $G$ there is a Hamilton cycle with at least $\delta(G)$
edges oriented in the same direction. We prove an approximate version of this
conjecture, showing that minimum degree $n/2 + O(k)$ guarantees a Hamilton
cycle with at least $(n+k)/2$ edges oriented in the same direction. We also
study the analogous problem for random graphs, showing that if the edge
probability $p = p(n)$ is above the Hamiltonicity threshold, then, with high
probability, in every orientation of $G \sim G(n,p)$ there is a Hamilton cycle
with $(1-o(1))n$ edges oriented in the same direction.


title:
Lower bounds for Seshadri constants via successive minima of line
  bundles

abstract:
  Given a nef and big line bundle $L$ on a projective variety $X$ of dimension
$d \geq 2$, we prove that the Seshadri constant of $L$ at a very general point
is larger than $(d+1)^{\frac{1}{d}-1}$. This slightly improves the lower bound
$1/d$ established by Ein, K\"uchle and Lazarsfeld. The proof relies on the
concept of successive minima for line bundles recently introduced by Ambro and
Ito.


title:
Comparison of simulated and measured grain volume changes during grain
  growth

abstract:
  The three-dimensional microstructure of Ni, observed after five annealing
intervals, was compared to simulations of grain growth using the threshold
dynamics method with the assumption of capillarity as the only driving force. A
grain-by-grain comparison made it possible to identify the sources of
differences between the simulation and experiment. The most significant
difference was for grains of the smallest sizes, which the simulation predicted
would lose volume and disappear at a greater rate than observed in the
experiment. The loss of grains created errors in the numbers of neighbors of
the remaining grains, and it was found that errors in the simulated grain
volume were correlated to errors in the number of near neighbors. While
anisotropic grain boundary properties likely play a role in the differences,
the size dependence of the errors suggest that it might be necessary to include
a size dependence in the model for grain boundary migration kinetics.


title:
Optimal parameter space for detecting stellar differential rotation and
  centre-to-limb convective variations

abstract:
  The goal of this paper is to investigate the optimal parameters space for
using the reloaded Rossiter-McLaughlin technique to detect differential
rotation (DR) and centre-to-limb convective variations. We simulated a
star-planet system with and without convective effects to map the optimal
regions of the parameter space for retrieving the injected differential
rotation. Our simulations explored all possible ranges of projected obliquity
(spin-orbit angle), stellar inclination, and impact parameter, as well as
differences in instrumental configuration, stellar magnitude, and exposure
time. We find that DR is more easily retrieved at low-impact parameters,
corresponding to system configurations in which the transiting planet crosses
the largest number of stellar latitudes. The main hot-spots for detection (i.e.
areas in which DR detectability is high) are
$120^{\rm{o}}<|\lambda|<180^{\rm{o}}$ for $i_*<90^{\rm{o}}$ and
$|\lambda|<60^{\rm{o}}$ for $i_*>90^{\rm{o}}$ on average, and they tend to
shrink as the impact parameter increases. Additionally, in contrast to the
crucial impact of brightness, we identify that exposure time has a negligible
impact on the difficulty of detecting DR as the increase in signal-to-noise
ratio (S/N) at longer exposure times is counteracted by the degraded sampling
rate. We determine that an ESPRESSO-like setup of instrumental configuration
and sensitivity might retrieve DR up to $V = 12$, compared to $V = 10$ for
HARPS. We reach no clear conclusion about limb-dependent convective effects and
the possible confusion with DR; preliminary results suggest, however, that
under certain circumstances, while it seems that one effect could be mistaken
for the other, the accuracy of the fit (in particular of $\alpha$) does not
hold up under additional scrutiny.


title:
Classical molecular dynamic simulations and modelling of
  inverse-bremsstrahlung heating in low Z weakly-coupled plasmas

abstract:
  Classical molecular-dynamics simulations (CMDS) have been conducted to
investigate one of the main mechanism responsible for absorption of radiation
by matter namely stimulated inverse bremsstrahlung. CMDS of two components
plasmas (electrons and ions) for a large range of electron densities, electron
temperatures, for ionization $Z=1$, were carried out with 2 million particles
using the code LAMMPS. A parameterized model (with 6 adjustable constants),
which encompasses most theoretical models proposed in the past to quantify
heating rate by stimulated inverse bremsstrahlung, serves as a reference for
comparison to our simulations. CMDS results are precise enough to rule out
elements of these past models such as coulomb logarithms depending solely upon
laser pulsation $\omega$ and not upon intensity. The 6 constants of the
parameterized model have been adjusted and the resulting model matches all our
CMDS results and those of previous CMDS in the literature.


title: Noisy Pursuit and Pattern Formation of Self-Steering Active Particles

abstract:
  We consider a moving target and an active pursing agent, modeled as an
intelligent active Brownian particle capable of sensing the instantaneous
target location and adjust its direction of motion accordingly. An analytical
and simulation study in two spatial dimensions reveals that pursuit performance
depends on the interplay between self-propulsion, active reorientation, and
random noise. Noise is found to have two opposing effects: (i) it is necessary
to disturb regular, quasi-elliptical trajectories around the target, and (ii)
slows down pursuit by increasing the traveled distance of the pursuer. We also
propose a strategy to sort active pursuers according to their motility by
circular target trajectories.


title: Materials for Future Calorimeters

abstract:
  Future HEP experiments present stringent challenges to calorimeter materials
in radiation tolerance, time response and project cost. The 2019 report of the
DOE Basic Research Needs Study on High Energy Physics Detector Research and
Development points out three priority research directions for future
calorimetry. Following these research directions letters of interest were
submitted to the Snowmass organized by the Division of Particles and Fields of
the American Physics Society. This report summarizes materials to be developed
in the form of inorganic, liquid (oil- and water-based), and plastic
scintillators and wavelength shifters to advance HEP calorimetry to face the
challenges in radiation hardness, fast timing, and cost-effectiveness. Some of
these materials may also find applications for future HEP time-of-flight
system, and beyond HEP in nuclear physics, hard X-ray imaging and medical
instruments.


title:
Towards More Efficient EfficientDets and Low-Light Real-Time Marine
  Debris Detection

abstract:
  Marine debris is a problem both for the health of marine environments and for
the human health since tiny pieces of plastic called "microplastics" resulting
from the debris decomposition over the time are entering the food chain at any
levels. For marine debris detection and removal, autonomous underwater vehicles
(AUVs) are a potential solution. In this letter, we focus on the efficiency of
AUV vision for real-time and low-light object detection. First, we improved the
efficiency of a class of state-of-the-art object detectors, namely
EfficientDets, by 1.5% AP on D0, 2.6% AP on D1, 1.2% AP on D2 and 1.3% AP on D3
without increasing the GPU latency. Subsequently, we created and made publicly
available a dataset for the detection of in-water plastic bags and bottles and
trained our improved EfficientDets on this and another dataset for marine
debris detection. Finally, we investigated how the detector performance is
affected by low-light conditions and compared two low-light underwater image
enhancement strategies both in terms of accuracy and latency. Source code and
dataset are publicly available.


title:
Pulses with Minimum Residual Intersymbol Interference for Faster than
  Nyquist Signaling

abstract:
  Faster than Nyquist signaling increases the spectral efficiency of pulse
amplitude modulation by accepting intersymbol interference, where an equalizer
is needed at the receiver. Since the complexity of an optimal equalizer
increases exponentially with the number of the interfering symbols, practical
truncated equalizers assume shorter memory. The power of the resulting residual
interference depends on the transmit filter and limits the performance of
truncated equalizers. In this paper, we use numerical optimizations and the
prolate spheroidal wave functions to find optimal time-limited pulses that
achieve minimum residual interference. Compared to root raised cosine pulses,
the new pulses decrease the residual interference by an order of magnitude, for
example, a decrease by 32 dB is achieved for an equalizer that considers four
interfering symbols at 57% faster transmissions. As a proof of concept, for the
57% faster transmissions of binary symbols, we showed that using the new pulse
with a 4-state equalizer has better bit error rate performance compared to
using a root raised cosine pulse with a 128-state equalizer.


title: Inverse Hamiltonian design by automatic differentiation

abstract:
  An ultimate goal of materials science is to deliver materials with desired
properties at will. In the theoretical study, a standard approach consists of
constructing a Hamiltonian based on phenomenology or first principles,
calculating physical observables, and improving the Hamiltonian through
feedback. However, there is also an approach that bypasses such a cumbersome
procedure, namely, to obtain an appropriate Hamiltonian directly from the
desired properties. Solving the inverse problem has the potential to reach
qualitatively different principles, but most research to date has been limited
to quantitative determination of parameters within known models. Here, we
present a general framework that enables the inverse design of Hamiltonians by
optimizing numerous parameters using automatic differentiation. By applying it
to the quantum anomalous Hall effect (AHE), we show that our framework can not
only rediscover the Haldane model but also automatically generate a new
Hamiltonian that exhibits a six-times larger AHE. In addition, the application
to the photovoltaic effect (PVE) gives an optimal Hamiltonian for electrons
moving on a noncoplanar spin texture, which can generate $\sim 900$~A/m$^2$
under solar radiation. This framework would accelerate materials exploration by
establishing unprecedented models and principles beyond human intuition and
empirical rules.


title: Lowerbounds for Bisimulation by Partition Refinement

abstract:
  We provide time lower bounds for sequential and parallel algorithms deciding
bisimulation on labeled transition systems that use partition refinement. For
sequential algorithms this is $\Omega((m \mkern1mu {+} \mkern1mu n ) \mkern-1mu
\log \mkern-1mu n)$ and for parallel algorithms this is $\Omega(n)$, where $n$
is the number of states and $m$ is the number of transitions. The lowerbounds
are obtained by analysing families of deterministic transition systems,
ultimately with two actions in the sequential case, and one action for parallel
algorithms.
  For deterministic transition systems with one action, bisimilarity can be
decided sequentially with fundamentally different techniques than partition
refinement. In particular, Paige, Tarjan, and Bonic give a linear algorithm for
this specific situation. We show, exploiting the concept of an oracle, that
this approach is not of help to develop a faster generic algorithm for deciding
bisimilarity. For parallel algorithms there is a similar situation where these
techniques may be applied, too.


title: On the benefits of knowledge distillation for adversarial robustness

abstract:
  Knowledge distillation is normally used to compress a big network, or
teacher, onto a smaller one, the student, by training it to match its outputs.
Recently, some works have shown that robustness against adversarial attacks can
also be distilled effectively to achieve good rates of robustness on
mobile-friendly models. In this work, however, we take a different point of
view, and show that knowledge distillation can be used directly to boost the
performance of state-of-the-art models in adversarial robustness. In this
sense, we present a thorough analysis and provide general guidelines to distill
knowledge from a robust teacher and boost the clean and adversarial performance
of a student model even further. To that end, we present Adversarial Knowledge
Distillation (AKD), a new framework to improve a model's robust performance,
consisting on adversarially training a student on a mixture of the original
labels and the teacher outputs. Through carefully controlled ablation studies,
we show that using early-stopping, model ensembles and weak adversarial
training are key techniques to maximize performance of the student, and show
that these insights generalize across different robust distillation techniques.
Finally, we provide insights on the effect of robust knowledge distillation on
the dynamics of the student network, and show that AKD mostly improves the
calibration of the network and modify its training dynamics on samples that the
model finds difficult to learn, or even memorize.


title: CAR: Class-aware Regularizations for Semantic Segmentation

abstract:
  Recent segmentation methods, such as OCR and CPNet, utilizing "class level"
information in addition to pixel features, have achieved notable success for
boosting the accuracy of existing network modules. However, the extracted
class-level information was simply concatenated to pixel features, without
explicitly being exploited for better pixel representation learning. Moreover,
these approaches learn soft class centers based on coarse mask prediction,
which is prone to error accumulation. In this paper, aiming to use class level
information more effectively, we propose a universal Class-Aware Regularization
(CAR) approach to optimize the intra-class variance and inter-class distance
during feature learning, motivated by the fact that humans can recognize an
object by itself no matter which other objects it appears with. Three novel
loss functions are proposed. The first loss function encourages more compact
class representations within each class, the second directly maximizes the
distance between different class centers, and the third further pushes the
distance between inter-class centers and pixels. Furthermore, the class center
in our approach is directly generated from ground truth instead of from the
error-prone coarse prediction. Our method can be easily applied to most
existing segmentation models during training, including OCR and CPNet, and can
largely improve their accuracy at no additional inference overhead. Extensive
experiments and ablation studies conducted on multiple benchmark datasets
demonstrate that the proposed CAR can boost the accuracy of all baseline models
by up to 2.23% mIOU with superior generalization ability. The complete code is
available at https://github.com/edwardyehuang/CAR.


title: Light-Induced Ferromagnetism in Moir\'e Superlattices

abstract:
  Many-body interactions between carriers lie at the heart of correlated
physics. The ability to tune such interactions would open the possibility to
access and control complex electronic phase diagrams on demand. Recently,
moir\'e superlattices formed by two-dimensional materials have emerged as a
promising platform for quantum engineering such phenomena. The power of the
moir\'e system lies in the high tunability of its physical parameters by
tweaking layer twist angle, electrical field, moir\'e carrier filling, and
interlayer coupling. Here, we report that optical excitation can drastically
tune the spin-spin interactions between moir\'e trapped carriers, resulting in
ferromagnetic order in WS2/WSe2 moir\'e superlattices over a small range of
doping at elevated temperatures. Near the filling factor v = -1/3 (i.e., one
hole per three moir\'e unit cells), as the excitation power at the exciton
resonance increases, a well-developed hysteresis loop emerges in the reflective
magnetic circular dichroism (RMCD) signal as a function of magnetic field, a
hallmark of ferromagnetism. The hysteresis loop persists down to charge
neutrality, and its shape evolves as the moir\'e superlattice is gradually
filled, indicating changes of magnetic ground state properties. The observed
phenomenon points to a mechanism in which itinerant photo-excited excitons
mediate exchange coupling between moir\'e trapped holes. This exciton-mediated
interaction can be of longer range than direct coupling between moir\'e trapped
holes, and thus magnetic order can arise even in the dilute hole regime under
optical excitation. This discovery adds a new and dynamic tuning knob to the
rich many-body Hamiltonian of moir\'e quantum matter.


title: RAUM-VO: Rotational Adjusted Unsupervised Monocular Visual Odometry

abstract:
  Unsupervised learning for monocular camera motion and 3D scene understanding
has gained popularity over traditional methods, relying on epipolar geometry or
non-linear optimization. Notably, deep learning can overcome many issues of
monocular vision, such as perceptual aliasing, low-textured areas, scale-drift,
and degenerate motions. Also, concerning supervised learning, we can fully
leverage video streams data without the need for depth or motion labels.
However, in this work, we note that rotational motion can limit the accuracy of
the unsupervised pose networks more than the translational component.
Therefore, we present RAUM-VO, an approach based on a model-free epipolar
constraint for frame-to-frame motion estimation (F2F) to adjust the rotation
during training and online inference. To this end, we match 2D keypoints
between consecutive frames using pre-trained deep networks, Superpoint and
Superglue, while training a network for depth and pose estimation using an
unsupervised training protocol. Then, we adjust the predicted rotation with the
motion estimated by F2F using the 2D matches and initializing the solver with
the pose network prediction. Ultimately, RAUM-VO shows a considerable accuracy
improvement compared to other unsupervised pose networks on the KITTI dataset
while reducing the complexity of other hybrid or traditional approaches and
achieving comparable state-of-the-art results.


title:
Experimental evidence of strong electromagnetic shower enhancement
  induced by high-energy photons in a thick oriented tungsten crystal

abstract:
  We have observed a significant enhancement in the energy deposition and
secondary particle generation by $25$--$100~\mathrm{GeV}$ photons in a
$1~\mathrm{cm}$ thick tungsten crystal oriented along its $\langle 111 \rangle$
lattice axes. At $100~\mathrm{GeV}$, this enhancement, with respect to that
observed without axial alignment, is more than twofold. This is ascribed to the
acceleration of the electromagnetic shower development by the strong axial
electric field. Previous studies of the enhancement of electromagnetic
interactions by coherent effects in this energy range have generally made use
of samples of thickness $1~\mathrm{mm}$ or less. Our results, obtained with a
$1~\mathrm{cm}$ thick crystal, could be crucial in the development of
high-performance photon absorbers, ultra-compact electromagnetic calorimeters
and beam dumps for use at the energy and intensity frontiers.


title: Inverse moving point source problem for the wave equation

abstract:
  In this paper, we consider the problem of identifying a single moving point
source for a three-dimensional wave equation from boundary measurements.
Precisely, we show that the knowledge of the field generated by the source at
six different points of the boundary over a finite time interval is sufficient
to determine uniquely its trajectory. We also derive a Lipschitz stability
estimate for the inversion.


title:
Thermophysical properties of $n$-hexadecane: Combined Molecular Dynamics
  and experimental investigations

abstract:
  Investigating properties of phase change materials (PCMs) is an important
issue due to their extensive use in heat storage systems and thermal regulation
devices. Improvement of the efficiency of such systems should be based on a
better knowledge of the microscopic mechanisms governing the thermal and
rheological characteristics of PCMs. This may be accomplished by the use of
molecular simulations of the aforementioned quantities and their linkage with
macroscale investigations. In this work, we studied thermophysical properties
of $n$-hexadecane for different temperatures regimes using molecular dynamics
(MD) and carry out several experimental measurements. Particularly, we focused
on the evaluation of various rheological and thermal properties such as thermal
conductivity, $\kappa$, viscosity, $\eta$, diffusion coefficient, $D$, and heat
capacities $C_p$ and $C_v$. Special attention was paid to the comparison of the
results of simulations with experimental ones.

categories: physics.app-ph cond-mat.soft physics.chem-ph physics.comp-ph


title:
Index-zero closed geodesics and stable figure-eights in convex
  hypersurfaces

abstract:
  For each odd $n \geq 3$, we construct a closed convex hypersurface of
$\mathbb{R}^{n+1}$ that contains a non-degenerate closed geodesic with Morse
index zero. A classical theorem of J. L. Synge would forbid such constructions
for even $n$, so in a sense we prove that Synge's theorem is "sharp." We also
construct stable figure-eights: that is, for each $n \geq 3$ we embed the
figure-eight graph in a closed convex hypersurface of $\mathbb{R}^{n+1}$, such
that sufficiently small variations of the embedding either preserve its image
or must increase its length. These index-zero geodesics and stable
figure-eights are mainly derived by constructing explicit billiard trajectories
with "controlled parallel transport" in convex polytopes.


title:
Dataset and Case Studies for Visual Near-Duplicates Detection in the
  Context of Social Media

abstract:
  The massive spread of visual content through the web and social media poses
both challenges and opportunities. Tracking visually-similar content is an
important task for studying and analyzing social phenomena related to the
spread of such content. In this paper, we address this need by building a
dataset of social media images and evaluating visual near-duplicates retrieval
methods based on image retrieval and several advanced visual feature extraction
methods. We evaluate the methods using a large-scale dataset of images we crawl
from social media and their manipulated versions we generated, presenting
promising results in terms of recall. We demonstrate the potential of this
method in two case studies: one that shows the value of creating systems
supporting manual content review, and another that demonstrates the usefulness
of automatic large-scale data analysis.


title:
Edge Waves and Transmissions for Temporal Laminates and Imperfect Chiral
  Interfaces

abstract:
  The analysis of wave patterns in a structure which possesses periodicity in
the spatial and temporal dimensions is presented. The topic of imperfect chiral
interfaces is also considered. Although causality is fundamental for physical
processes, natural wave phenomena can be observed when a wave is split at a
temporal interface. A wave split at a spatial interface is a more common
occurrence; however when the coefficients of the governing equations are
time-dependent, the temporal interface becomes important. Here, the associated
edge waves are studied, and regimes are analysed where the growth of the
solution in time is found. Imperfect interfaces, across which the displacements
are discontinuous, are also considered in the vector case of chiral elastic
systems. Analytical study and asymptotic approximations are supplied with
illustrative numerical examples.


title: Smoothness in pencils of hypersurfaces over finite fields

abstract:
  We study pencils of hypersurfaces over finite fields $\mathbb{F}_q$ such that
each of the $q+1$ members defined over $\mathbb{F}_q$ is smooth.


title:
In situ study of zinc peroxide decomposition to zinc oxide by X-ray
  absorption spectroscopy and reverse Monte-Carlo simulations

abstract:
  The Zn K-edge X-ray absorption spectroscopy has been used to investigate in
situ the decomposition of zinc peroxide (ZnO$_2$) to zinc oxide (ZnO).
Principal component and linear combination analyses of the EXAFS spectra have
been employed to identify the phase composition of the oxide upon heating to
900$^\circ$C. Only the ZnO$_2$ phase has been found up to 180$^\circ$C, whereas
only the nanocrystalline ZnO phase has occurred above 250$^\circ$C. Detailed
structural information on the temperature dependence of the local environment
of zinc atoms has been obtained using the reverse Monte Carlo simulations. A
strong increase of disorder has been found upon approaching the decomposition
temperature, evidenced by the broadening of Zn-O and Zn-Zn pair distribution
functions and related mean-square relative displacements.


title: Orchestrated Value Mapping for Reinforcement Learning

abstract:
  We present a general convergent class of reinforcement learning algorithms
that is founded on two distinct principles: (1) mapping value estimates to a
different space using arbitrary functions from a broad class, and (2) linearly
decomposing the reward signal into multiple channels. The first principle
enables incorporating specific properties into the value estimator that can
enhance learning. The second principle, on the other hand, allows for the value
function to be represented as a composition of multiple utility functions. This
can be leveraged for various purposes, e.g. dealing with highly varying reward
scales, incorporating a priori knowledge about the sources of reward, and
ensemble learning. Combining the two principles yields a general blueprint for
instantiating convergent algorithms by orchestrating diverse mapping functions
over multiple reward channels. This blueprint generalizes and subsumes
algorithms such as Q-Learning, Log Q-Learning, and Q-Decomposition. In
addition, our convergence proof for this general class relaxes certain required
assumptions in some of these algorithms. Based on our theory, we discuss
several interesting configurations as special cases. Finally, to illustrate the
potential of the design space that our theory opens up, we instantiate a
particular algorithm and evaluate its performance on the Atari suite.


title: RED-ACE: Robust Error Detection for ASR using Confidence Embeddings

abstract:
  ASR Error Detection (AED) models aim to post-process the output of Automatic
Speech Recognition (ASR) systems, in order to detect transcription errors.
Modern approaches usually use text-based input, comprised solely of the ASR
transcription hypothesis, disregarding additional signals from the ASR model.
Instead, we propose to utilize the ASR system's word-level confidence scores
for improving AED performance. Specifically, we add an ASR Confidence Embedding
(ACE) layer to the AED model's encoder, allowing us to jointly encode the
confidence scores and the transcribed text into a contextualized
representation. Our experiments show the benefits of ASR confidence scores for
AED, their complementary effect over the textual signal, as well as the
effectiveness and robustness of ACE for combining these signals. To foster
further research, we publish a novel AED dataset consisting of ASR outputs on
the LibriSpeech corpus with annotated transcription errors.


title: The two-loop bispectrum in the effective theory of large-scale structure

abstract:
  We study the bispectrum of large-scale structure in the EFTofLSS including
corrections up to two-loop. We derive an analytic result for the double-hard
limit of the two-loop correction, and show that the UV-sensitivity can be
absorbed by the same four EFT operators that renormalize the one-loop
bispectrum. For the single-hard region, we employ a simplified treatment,
introducing one extra EFT parameter. We compare our results to N-body
simulations, and show that going from one- to two-loop extends the wavenumber
range with percent-level agreement from $k \simeq 0.08$ to
$0.15~h/\mathrm{Mpc}$.


title:
Cohomological supports of tensor products of modules over commutative
  rings

abstract:
  This works concerns cohomological support varieties of modules over
commutative local rings. The main result is that the support of a derived
tensor product of a pair of differential graded modules over a Koszul complex
is the join of the supports of the modules. This generalizes, and gives another
proof of, a result of Dao and the third author dealing with Tor-independent
modules over complete intersection rings. The result for Koszul complexes has a
broader applicability, including to exterior algebras over local rings.


title: A Linear View on Shape Optimization

abstract:
  Shapes do not define a linear space. This paper explores the linear structure
of deformations as a representation of shapes. This transforms shape
optimization to a variant of optimal control. The numerical challenges of this
point of view are highlighted and a novel linear version of the second shape
derivative is proposed leading to particular algorithms of shape Newton type.


title:
Water Oxidation and Hydrogen Evolution with Organic Photooxidants: A
  Theoretical Perspective

abstract:
  In this perspective, we discuss a novel water-splitting scenario, namely the
direct oxidation of water molecules by organic photooxidants in hydrogen-bonded
chromophore-water complexes. In comparison with the established scenario of
semiconductor-based water splitting, the distance of electron transfer
processes is thereby reduced from mesoscopic scales to the {\AA}ngstr{\o}m
scale and the time scale is reduced from milliseconds to femtoseconds, which
suppresses competing loss processes. The concept is illustrated by
computational studies for the heptazine-H2O complex. The excited-state
landscape of this complex has been characterized with ab initio
electronic-structure methods and the proton-coupled electron-transfer dynamics
has been explored with nonadiabatic dynamics simulations. A unique feature of
the heptazine chromophore is the existence of a low-lying and exceptionally
long-lived 1{\pi}{\pi}* state in which a substantial part of the photon energy
can be stored for hundreds of nanoseconds and is available for the oxidation of
water molecules. The calculations reveal that the absorption spectra and the
photochemical functionalities of heptazine chromophores can be systematically
tailored by chemical substitution. The options of harvesting hydrogen and the
problems posed by the high reactivity of OH radicals are discussed.


title:
Strong to weak interacting-topological phase transition of bosons on a
  lattice

abstract:
  We study hard-core bosons on the honeycomb lattice subjected to anisotropic
nearest-neighbor hopping along with anisotropic nearest-neighbor repulsion,
using a quantum Monte Carlo technique. At half-filling, we find a transition
from strong interacting-topological order to weak interacting-topological order
as function of the hopping anisotropy. The strong topological phase is
characterized by a finite topological entanglement entropy, while the weak
topological order is identified with a non-trivial value of the bipartite
entanglement entropy. The dependencies of the various order parameters on the
anisotropies demonstrate abrupt changes in some of the order parameters and
their derivatives, thus revealing the nature of this interacting-topological
phase transition.


title: TOI-3757 b: A low density gas giant orbiting a solar-metallicity M dwarf

abstract:
  We present the discovery of a new Jovian-sized planet, TOI-3757 b, the lowest
density planet orbiting an M dwarf (M0V). It orbits a solar-metallicity M dwarf
discovered using TESS photometry and confirmed with precise radial velocities
(RV) from HPF and NEID. With a planetary radius of $12.0^{+0.4}_{-0.5}$
$R_{\oplus}$ and mass of $85.3^{+8.8}_{-8.7}$ $M_{\oplus}$, not only does this
object add to the small sample of gas giants ($\sim 10$) around M dwarfs, but
also, its low density ($\rho =$ $0.27^{+0.05}_{-0.04}$ $\textrm{g~cm}^{-3}$)
provides an opportunity to test theories of planet formation. We present two
hypotheses to explain its low density; first, we posit that the low metallicity
of its stellar host ($\sim$ 0.3 dex lower than the median metallicity of M
dwarfs hosting gas giants) could have played a role in the delayed formation of
a solid core massive enough to initiate runaway accretion. Second, we use our
eccentricity estimate of $0.14 \pm 0.06$ to estimate the significance of tidal
heating for inflating the radius of TOI-3757 b. The low density and large scale
height of TOI-3757 b makes it an excellent target for transmission spectroscopy
studies of atmospheric escape and composition (TSM $\sim$ 190). We use HPF to
perform transmission spectroscopy of TOI-3757 b using the helium line. Doing
this, we place an upper limit of 6.9 \% (with 90\% confidence) on the maximum
depth of the absorption from the metastable transition of He at $\sim$ 10830
\AA.


title: MDNet: Learning Monaural Speech Enhancement from Deep Prior Gradient

abstract:
  While traditional statistical signal processing model-based methods can
derive the optimal estimators relying on specific statistical assumptions,
current learning-based methods further promote the performance upper bound via
deep neural networks but at the expense of high encapsulation and lack adequate
interpretability. Standing upon the intersection between traditional
model-based methods and learning-based methods, we propose a model-driven
approach based on the maximum a posteriori (MAP) framework, termed as MDNet,
for single-channel speech enhancement. Specifically, the original problem is
formulated into the joint posterior estimation w.r.t. speech and noise
components. Different from the manual assumption toward the prior terms, we
propose to model the prior distribution via networks and thus can learn from
training data. The framework takes the unfolding structure and in each step,
the target parameters can be progressively estimated through explicit gradient
descent operations. Besides, another network serves as the fusion module to
further refine the previous speech estimation. The experiments are conducted on
the WSJ0-SI84 and Interspeech2020 DNS-Challenge datasets, and quantitative
results show that the proposed approach outshines previous state-of-the-art
baselines.


title:
A pressure-robust HHO method for the solution of the incompressible
  Navier-Stokes equations on general meshes

abstract:
  In a recent work [10], we have introduced a pressure-robust Hybrid High-Order
method for the numerical solution of the incompressible Navier-Stokes equations
on matching simplicial meshes. Pressure-robust methods are characterized by
error estimates for the velocity that are fully independent of the pressure. A
crucial question was left open in that work, namely whether the proposed
construction could be extended to general polytopal meshes. In this paper we
provide a positive answer to this question. Specifically, we introduce a novel
divergence-preserving velocity reconstruction that hinges on the solution
inside each element of a mixed problem on a subtriangulation, then use it to
design discretizations of the body force and convective terms that lead to
pressure robustness. An in-depth theoretical study of the properties of this
velocity reconstruction, and their reverberation on the scheme, is carried out
for polynomial degrees $k \in \{0, 1\}$ and meshes composed of general convex
polytopes. The theoretical convergence estimates and the pressure robustness of
the method are confirmed by an extensive panel of numerical examples.


title:
Optimal Correlated Equilibria in General-Sum Extensive-Form Games:
  Fixed-Parameter Algorithms, Hardness, and Two-Sided Column-Generation

abstract:
  We study the problem of finding optimal correlated equilibria of various
sorts: normal-form coarse correlated equilibrium (NFCCE), extensive-form coarse
correlated equilibrium (EFCCE), and extensive-form correlated equilibrium
(EFCE). This is NP-hard in the general case and has been studied in special
cases, most notably triangle-free games, which include all two-player games
with public chance moves. However, the general case is not well understood, and
algorithms usually scale poorly. First, we introduce the correlation DAG, a
representation of the space of correlated strategies whose size is dependent on
the specific solution concept. It extends the team belief DAG of Zhang et al.
to general-sum games. For each of the three solution concepts, its size depends
exponentially only on a parameter related to the game's information structure.
We also prove a fundamental complexity gap: while our size bounds for NFCCE are
similar to those achieved in the case of team games by Zhang et al., this is
impossible to achieve for the other two concepts under standard complexity
assumptions. Second, we propose a two-sided column generation approach to
compute optimal correlated strategies. Our algorithm improves upon the
one-sided approach of Farina et al. by means of a new decomposition of
correlated strategies which allows players to re-optimize their sequence-form
strategies with respect to correlation plans which were previously added to the
support. Our techniques outperform the prior state of the art for computing
optimal general-sum correlated equilibria. For team games, the two-sided column
generation approach vastly outperforms standard column generation approaches,
making it the state of the art algorithm when the parameter is large. Along the
way we also introduce two new benchmark games: a trick-taking game that
emulates the endgame phase of the card game bridge, and a ride-sharing game.


title:
NeILF: Neural Incident Light Field for Physically-based Material
  Estimation

abstract:
  We present a differentiable rendering framework for material and lighting
estimation from multi-view images and a reconstructed geometry. In the
framework, we represent scene lightings as the Neural Incident Light Field
(NeILF) and material properties as the surface BRDF modelled by multi-layer
perceptrons. Compared with recent approaches that approximate scene lightings
as the 2D environment map, NeILF is a fully 5D light field that is capable of
modelling illuminations of any static scenes. In addition, occlusions and
indirect lights can be handled naturally by the NeILF representation without
requiring multiple bounces of ray tracing, making it possible to estimate
material properties even for scenes with complex lightings and geometries. We
also propose a smoothness regularization and a Lambertian assumption to reduce
the material-lighting ambiguity during the optimization. Our method strictly
follows the physically-based rendering equation, and jointly optimizes material
and lighting through the differentiable rendering process. We have intensively
evaluated the proposed method on our in-house synthetic dataset, the DTU MVS
dataset, and real-world BlendedMVS scenes. Our method is able to outperform
previous methods by a significant margin in terms of novel view rendering
quality, setting a new state-of-the-art for image-based material and lighting
estimation.


title:
QuFI: a Quantum Fault Injector to Measure the Reliability of Qubits and
  Quantum Circuits

abstract:
  Quantum computing is a new technology that is expected to revolutionize the
computation paradigm in the next few years. Qubits exploit the quantum physics
proprieties to increase the parallelism and speed of computation.
Unfortunately, besides being intrinsically noisy, qubits have also been shown
to be highly susceptible to external sources of faults, such as ionizing
radiation. The latest discoveries highlight a much higher radiation sensitivity
of qubits than traditional transistors and identify a much more complex fault
model than bit-flip. We propose a framework to identify the quantum circuits
sensitivity to radiation-induced faults and the probability for a fault in a
qubit to propagate to the output. Based on the latest studies and radiation
experiments performed on real quantum machines, we model the transient faults
in a qubit as a phase shift with a parametrized magnitude. Additionally, our
framework can inject multiple qubit faults, tuning the phase shift magnitude
based on the proximity of the qubit to the particle strike location. As we show
in the paper, the proposed fault injector is highly flexible, and it can be
used on both quantum circuit simulators and real quantum machines. We report
the finding of more than 285M injections on the Qiskit simulator and 53K
injections on real IBM machines. We consider three quantum algorithms and
identify the faults and qubits that are more likely to impact the output. We
also consider the fault propagation dependence on the circuit scale, showing
that the reliability profile for some quantum algorithms is scale-dependent,
with increased impact from radiation-induced faults as we increase the number
of qubits. Finally, we also consider multi qubits faults, showing that they are
much more critical than single faults. The fault injector and the data
presented in this paper are available in a public repository to allow further
analysis.


title:
Removing systematics-induced 21-cm foreground residuals by
  cross-correlating filtered data

abstract:
  Observations of the redshifted 21-cm signal emitted by neutral hydrogen
represent a promising probe of large-scale structure in the universe. However,
cosmological 21-cm signal is challenging to observe due to astrophysical
foregrounds which are several orders of magnitude brighter. Traditional linear
foreground removal methods can optimally remove foregrounds for a known
telescope response but are sensitive to telescope systematic errors such as
antenna gain and delay errors, leaving foreground contamination in the
recovered signal. Non-linear methods such as principal component analysis, on
the other hand, have been used successfully for foreground removal, but they
lead to signal loss that is difficult to characterize and requires careful
analysis. In this paper, we present a systematics-robust foreground removal
technique which combines both linear and non-linear methods. We first obtain
signal and foreground estimates using a linear filter. Under the assumption
that the signal estimate is contaminated by foreground residuals induced by
parameterizable systematic effects, we infer the systematics-induced
contamination by cross-correlating the initial signal and foreground estimates.
Correcting for the inferred error, we are able to subtract foreground
contamination from the linearly filtered signal up to the first order in the
amplitude of the telescope systematics. In simulations of an interferometric
21-cm survey, our algorithm removes foreground leakage induced by complex gain
errors by one to two orders of magnitude in the power spectrum. Our technique
thus eases the requirements on telescope characterization for modern and
next-generation 21-cm cosmology experiments.


title: On the dynamics of vortices in viscous 2D flows

abstract:
  We study the 2D Navier--Stokes solution starting from an initial vorticity
mildly concentrated near $N$ distinct points in the plane. We prove
quantitative estimates on the propagation of concentration near a system of
interacting point vortices introduced by Helmholtz and Kirchhoff. Our work
extends the previous results in the literature in three ways: The initial
vorticity is concentrated in a weak (Wasserstein) sense, it is merely $L^p$
integrable for some $p>2$, and the estimates we derive are uniform with respect
to the viscosity.


title: LiDAR-based 4D Panoptic Segmentation via Dynamic Shifting Network

abstract:
  With the rapid advances of autonomous driving, it becomes critical to equip
its sensing system with more holistic 3D perception. However, existing works
focus on parsing either the objects (e.g. cars and pedestrians) or scenes (e.g.
trees and buildings) from the LiDAR sensor. In this work, we address the task
of LiDAR-based panoptic segmentation, which aims to parse both objects and
scenes in a unified manner. As one of the first endeavors towards this new
challenging task, we propose the Dynamic Shifting Network (DS-Net), which
serves as an effective panoptic segmentation framework in the point cloud
realm. In particular, DS-Net has three appealing properties: 1) Strong backbone
design. DS-Net adopts the cylinder convolution that is specifically designed
for LiDAR point clouds. 2) Dynamic Shifting for complex point distributions. We
observe that commonly-used clustering algorithms are incapable of handling
complex autonomous driving scenes with non-uniform point cloud distributions
and varying instance sizes. Thus, we present an efficient learnable clustering
module, dynamic shifting, which adapts kernel functions on the fly for
different instances. 3) Extension to 4D prediction. Furthermore, we extend
DS-Net to 4D panoptic LiDAR segmentation by the temporally unified instance
clustering on aligned LiDAR frames. To comprehensively evaluate the performance
of LiDAR-based panoptic segmentation, we construct and curate benchmarks from
two large-scale autonomous driving LiDAR datasets, SemanticKITTI and nuScenes.
Extensive experiments demonstrate that our proposed DS-Net achieves superior
accuracies over current state-of-the-art methods in both tasks. Notably, in the
single frame version of the task, we outperform the SOTA method by 1.8% in
terms of the PQ metric. In the 4D version of the task, we surpass 2nd place by
5.4% in terms of the LSTQ metric.


title: Tubular-body theory for viscous flows

abstract:
  Cable-like bodies play a key role in many interdisciplinary systems but are
hard to simulate. Asymptotic theories, called slender-body theories, are
effective but apply in specific regimes and can be hard to extend beyond
leading order. In this letter we develop an exact slender-body-like theory for
the surface traction of cable-like bodies in viscous flow. This theory
expresses the traction as a series of solutions to a well-behaved
one-dimensional Fredholm integral equation of the second kind. This process can
be simply generalised to other systems.


title:
The Impact of a Reduced High-wind Charnock Parameter on Wave Growth With
  Application to the North Sea, the Norwegian Sea and the Arctic Ocean

abstract:
  As atmospheric models move to higher resolution and resolve smaller scales,
the maximum modeled wind speed also tends to increase. Wave models tuned to
coarser wind fields tend to overestimate the wave growth under strong winds. A
recently developed semi-empirical parameterization of the Charnock parameter,
which controls the roughness length over surface waves, substantially reduces
the aerodynamic drag of waves in high winds (above a threshold of 30 m/s). Here
we apply the formulation in a recent version of the wave model WAM (Cycle 4.7),
which uses a modified version of the physics parameterizations by Ardhuin et al
(2010) as well as subgrid obstructions for better performance around complex
topography. The new Charnock formulation is tested with wind forcing from
NORA3, a recently completed non-hydrostatic atmospheric downscaling of the
global reanalysis ERA5 for the North Sea, the Norwegian Sea and the Barents
Sea. Such high-resolution atmospheric model integrations tend to have stronger
(and more realistic) upper-percentile winds than what is typically found in
coarser atmospheric models. A two-year comparison (2011-2012) of a control run
against the run with the modified Charnock parameter shows a dramatic reduction
of the wave height bias in high-wind cases. The added computational cost of the
new physics and the reduction of the Charnock parameter compared to the earlier
WAM physics is modest (14%). A longer (1998-2020) hindcast integration with the
new Charnock parameter is found to compare well against in situ and altimeter
wave measurements both for intermediate and high sea states.


title:
A new tool to search for physics beyond the Standard Model in ${\bar
  B}\to D^{*+}\ell^- {\bar\nu}$

abstract:
  Recent experimental results in $B$ physics from Belle, BaBar and LHCb suggest
new physics (NP) in the weak $b\to c$ charged-current and the $b\to s$
neutral-current processes. Here we focus on the charged-current case and
specifically on the decay modes $B\to D^{*+}\ell^- \bar{\nu}$ with $\ell = e,
\mu,$ and $\tau$. The world averages of the ratios $R_D$ and $R_D^{*}$
currently differ from the Standard Model (SM) by $3.4\sigma$ while $\Delta
A_{FB} = A_{FB}(B\to D^{*} \mu\nu) - A_{FB} (B\to D^{*} e \nu)$ is found to be
$4.1\sigma$ away from the SM prediction in an analysis of 2019 Belle data.
These intriguing results suggest an urgent need for improved simulation and
analysis techniques in $B\to D^{*+}\ell^- \bar{\nu}$ decays. Here we describe a
Monte Carlo Event-generator tool based on EVTGEN developed to allow simulation
of the NP signatures in $B\to D^*\ell^- \nu$, which arise due to the
interference between the SM and NP amplitudes. As a demonstration of the
proposed approach, we exhibit some examples of NP couplings that are consistent
with current data and could explain the $\Delta A_{FB}$ anomaly in $B\to
D^*\ell^- \nu$ while remaining consistent with other constraints. We show that
the $\Delta$-type observables such as $\Delta A_{FB}$ and $\Delta S_5$
eliminate most QCD uncertainties from form factors and allow for clean
measurements of NP. We introduce correlated observables that improve the
sensitivity to NP. We discuss prospects for improved observables sensitive to
NP couplings with the expected 50 ab$^{-1}$ of Belle II data, which seems to be
ideally suited for this class of measurements.


title:
CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual
  Entailment

abstract:
  CLIP has shown a remarkable zero-shot capability on a wide range of vision
tasks. Previously, CLIP is only regarded as a powerful visual encoder. However,
after being pre-trained by language supervision from a large amount of
image-caption pairs, CLIP itself should also have acquired some few-shot
abilities for vision-language tasks. In this work, we empirically show that
CLIP can be a strong vision-language few-shot learner by leveraging the power
of language. We first evaluate CLIP's zero-shot performance on a typical visual
question answering task and demonstrate a zero-shot cross-modality transfer
capability of CLIP on the visual entailment task. Then we propose a
parameter-efficient fine-tuning strategy to boost the few-shot performance on
the vqa task. We achieve competitive zero/few-shot results on the visual
question answering and visual entailment tasks without introducing any
additional pre-training procedure.


title:
Impedance Adaptation by Reinforcement Learning with Contact Dynamic
  Movement Primitives

abstract:
  Dynamic movement primitives (DMPs) allow complex position trajectories to be
efficiently demonstrated to a robot. In contact-rich tasks, where position
trajectories alone may not be safe or robust over variation in contact
geometry, DMPs have been extended to include force trajectories. However,
different task phases or degrees of freedom may require the tracking of either
position or force -- e.g., once contact is made, it may be more important to
track the force demonstration trajectory in the contact direction. The robot
impedance balances between following a position or force reference trajectory,
where a high stiffness tracks position and a low stiffness tracks force. This
paper proposes using DMPs to learn position and force trajectories from
demonstrations, then adapting the impedance parameters online with a
higher-level control policy trained by reinforcement learning. This allows
one-shot demonstration of the task with DMPs, and improved robustness and
performance from the impedance adaptation. The approach is validated on
peg-in-hole and adhesive strip application tasks.


title:
Closing loopholes of measurement-device-independent nonlinear
  entanglement witnesses

abstract:
  The concept of entanglement witnesses form a useful technique to detect
entanglement in realistic quantum devices. Measurement-device-independent
nonlinear entanglement witnesses (MDI-NEWs) are a kind of entanglement
witnesses which eliminate dependence on the correct alignments of measurement
devices for guaranteeing the existence of entanglement and also detect more
entangled states than their linear counterparts. While this method guarantees
entanglement independent of measurement alignments, they are still prone to
serving wrong results due to other loopholes. Here we study the response of
MDI-NEWs to two categories of faults occurring in experiments. In the first
category, the detection loophole, characterized by lost and additional events
of outcomes of measurements, is investigated, and bounds which guarantee
entanglement are obtained in terms of the efficiency of measurement being
performed. In the second category, we study noise associated with the sets of
additional quantum inputs required in MDI-NEW scenarios. In this case, a type
of noise is identified which still allows the MDI-NEWs to guarantee
entanglement. We also show that MDI-NEWs are less or equally robust in
comparison to their linear counterparts under the same noise in additional
quantum inputs, although the former group detects a larger volume of entangled
states in the noiseless scenario than their linear cousins.


title:
Emergent dynamics in an astrocyte-neuronal network coupled via nitric
  oxide

abstract:
  In the brain, both neurons and glial cells work in conjunction with each
other during information processing. Stimulation of neurons can cause calcium
oscillations in astrocytes which in turn can affect neuronal calcium dynamics.
The "glissandi" effect is one such phenomenon, associated with a decrease in
infraslow fluctuations, in which synchronized calcium oscillations propagate as
a wave in hundreds of astrocytes. Nitric oxide molecules released from the
astrocytes contribute to synaptic functions on the basis of the underlying
astrocyte-neuron interaction network. In this study, by defining an
astrocyte-neuronal (A-N) unit as an integrated circuit of one neuron and one
astrocyte, we developed a minimal model of neuronal stimulus-dependent and
nitric oxide-mediated emergence of calcium waves in astrocytes. Incorporating
inter-unit communication via nitric oxide molecules, a coupled network of 1,000
such A-N units is developed in which multiple stable regimes were found to
emerge in astrocytes. We examined the ranges of neuronal stimulus strength and
the coupling strength between A-N units that give rise to such dynamical
behaviors. We also report that there exists a range of coupling strength,
wherein units not receiving stimulus also start showing oscillations and become
synchronized. Our results support the hypothesis that glissandi-like phenomena
exhibiting synchronized calcium oscillations in astrocytes help in efficient
synaptic transmission by reducing the energy demand of the process.


title: Strict stability of extension types

abstract:
  We show that the extension types occurring in Riehl--Shulman's work on
synthetic $(\infty,1)$-categories can be interpreted in the intended semantics
in a way so that they are strictly stable under substitution. The splitting
method used here is due to Voevodsky in 2009. It was later generalized by
Lumsdaine--Warren to the method of local universes.


title:
TaylorBeamformer: Learning All-Neural Beamformer for Multi-Channel
  Speech Enhancement from Taylor's Approximation Theory

abstract:
  While existing end-to-end beamformers achieve impressive performance in
various front-end speech processing tasks, they usually encapsulate the whole
process into a black box and thus lack adequate interpretability. As an attempt
to fill the blank, we propose a novel neural beamformer inspired by Taylor's
approximation theory called TaylorBeamformer for multi-channel speech
enhancement. The core idea is that the recovery process can be formulated as
the spatial filtering in the neighborhood of the input mixture. Based on that,
we decompose it into the superimposition of the 0th-order non-derivative and
high-order derivative terms, where the former serves as the spatial filter and
the latter is viewed as the residual noise canceller to further improve the
speech quality. To enable end-to-end training, we replace the derivative
operations with trainable networks and thus can learn from training data.
Extensive experiments are conducted on the synthesized dataset based on
LibriSpeech and results show that the proposed approach performs favorably
against the previous advanced baselines.


title:
Optimising low-Reynolds-number predation via optimal control and
  reinforcement learning

abstract:
  We seek the best stroke sequences of a finite-size swimming predator chasing
a non-motile point or finite--size prey at low Reynolds number. We use optimal
control to seek the globally-optimal solutions for the former and RL for
general situations. The predator is represented by a squirmer model that can
translate forward and laterally, rotate and generate a stresslet flow. We
identify the predator's best squirming sequences to achieve the time-optimal
(TO) and efficiency-optimal (EO) predation. For a point prey, the TO squirmer
executing translational motions favours a two-fold L-shaped trajectory that
enables it to exploit the disturbance flow for accelerated predation; using a
stresslet mode significantly expedites the EO predation, allowing the predator
to catch the prey faster yet with lower energy consumption and higher predatory
efficiency; the predator can harness its stresslet disturbance flow to suck the
prey towards itself; compared to a translating predator, its compeer combining
translation and rotation is less time--efficient, and the latter occasionally
achieves the TO predation via retreating in order to advance. We also adopt RL
to reproduce the globally-optimal predatory strategy of chasing a point prey,
qualitatively capturing the crucial two--fold attribute of TO path. Using a
numerically emulated RL environment, we explore the dependence of the optimal
predatory path on the size of prey. Our results might provide useful
information that help design synthetic microswimmers such as \textit{in vivo}
medical micro-robots capable of capturing and approaching objects in viscous
flows.


title:
Variational Density Functional Calculations of Excited States: Conical
  Intersection and Avoided Crossing in Ethylene Bond Twisting

abstract:
  Theoretical studies of photochemical processes require a description of the
energy surfaces of excited electronic states, especially near degeneracies,
where transitions between states are most likely. Systems relevant to
photochemical applications are typically too large for high-level
multireference methods, and while time-dependent density functional theory
(TDDFT) is efficient, it can fail to provide the required accuracy. A
variational, time-independent density functional approach is applied to the
twisting of the double bond and pyramidal distortion in ethylene, the
quintessential model for photochemical studies. By allowing for symmetry
breaking, the calculated energy surfaces exhibit the correct topology around
the twisted-pyramidalized conical intersection even when using a semilocal
functional approximation, and by including explicit self-interaction
correction, the torsional energy curves are in close agreement with published
multireference results. The findings of the present work point to the
possibility of using a single determinant time-independent density functional
approach to simulate nonadiabatic dynamics, even for large systems where
multireference methods are impractical and TDDFT is often not accurate enough.


title:
An Evolution System for a Class of Age-Structured Diffusive Population
  Equations

abstract:
  Kato's theory on the construction of strongly continuous evolution systems
associated with hyperbolic equations is applied to the linear equation
describing an age-structured population that is subject to time-dependent
diffusion. The evolution system is used to provide conditions for the
well-posedness of the corresponding quasilinear equation.


title:
Nonlinear master relation in microscopic mechanical response of
  semiflexible biopolymer networks

abstract:
  A network of semiflexible biopolymers, known as the cytoskeleton, and
molecular motors play fundamental mechanical roles in cellular activities. The
cytoskeletal response to forces generated by molecular motors is profoundly
linked to physiological processes. However, owing to the highly nonlinear
mechanical properties, the cytoskeletal response on the microscopic level is
largely elusive. The aim of this study is to investigate the microscopic
mechanical response of semiflexible biopolymer networks by conducting
microrheology (MR) experiments. Micrometer-sized colloidal particles, embedded
in semiflexible biopolymer networks, were forced beyond the linear regime at a
variety of conditions by using feedback-controlled optical trapping. This
high-bandwidth MR technology revealed an affine elastic response, which showed
stiffening upon local forcing. After scaling the stiffening behaviors, with
parameters describing semi-flexible networks, a collapse onto a single master
curve was observed. The physics underlying the general microscopic response is
presented to justify the collapse, and its potentials/implications to elucidate
cell mechanics is discussed.


title: A nonlocal model describing tumor angiogenesis

abstract:
  In this paper we study the onset of angiogenesis and derive a new model to
describe it. This new model takes the form of a nonlocal Burgers equation with
both diffusive and dispersive terms. For a particular value of the parameters,
the equation reduces to $$ \partial_t p-\frac{1}{2}(-\Delta)^{(\alpha-1)/2}H
\partial_t p=-\frac{1}{2}(-\Delta)^{\alpha/2} p+ p\partial_x p-\partial_x p, $$
where $H$ denotes the Hilber transform. In addition to the derivation of the
new model, we also prove a number of well-posedness results. Finally, some
preliminary numerics are shown. These numerics suggest that the dynamics of the
equation is rich enough to have solutions that blow up in finite time.


title:
Evaluation of websites of state public health agencies during the
  COVID-19 pandemic demonstrating the degree of effort to design for
  accessibility

abstract:
  Since the beginning of the pandemic, every state public health agency in the
United States has created and maintained a website dedicated to COVID 19. Our
goal was to evaluate these websites for conformity to accessibility guidelines.
Specifically, we assessed, on a scale of increasing levels of accessibility
compliance requirements, the results of the efforts made by website developers
to incorporate and meet accessibility compliance criteria. We focused on
homepages and vaccine pages in 49 states. For this study, we used the automated
AChecker tool to assess conformance to the WCAG 2.0 guidelines at A, AA and AAA
levels of conformance, and conformance with the Section 508c standard. We also
manually rated, on a scale 0 (none) to 3 (highest), the specific accessibility
features, if any, that web developers had included on the pages. We found that
accessibility violations were prevalent across states but to varying degrees
for a specific accessibility criterion. Although violations were detected in
all 4 POUR accessibility principles, the most number of known violations
occurred in meeting the perceivability and operability principles. Most
violations in 508c guidelines occurred in not providing functional text in
scripting languages and in not providing text equivalents for nontext. The
degree of effort and conformance significantly varied between states; a
majority of states exhibited a lower degree of effort, while a few attempted
innovative ways to enhance accessibility on their websites. The efforts seemed
to focus on meeting the minimum threshold. It is not clear if websites were
designed proactively for accessibility.


title: Study of the $h \gamma Z$ coupling at the ILC

abstract:
  We studied the $e^+e^- \to h \gamma$ process at the full simulation level,
using a realistic detector model to study the feasibility to constrain the SM
effective field theory (SMEFT) $h \gamma Z$ coefficient, $\zeta_{AZ}$, at the
ILC. Assuming International Large Detector (ILD) operating at 250 GeV ILC, it
is shown that the $e^+e^- \to h\gamma$ process is much more difficult to
observe than naively expected if there is no BSM contribution. We thus put
upper limits on the cross section of this process. The expected combined 95%
C.L. upper limits for full polarisations $(P_{e^-}, P_{e^+})=(-100\%, +100\%)$
and $(+100\%, -100\%)$ are $\frac{\sigma_{h \gamma}^{L}}{\sigma_{S M}^{L}}<5.0$
and $\frac{\sigma_{h \gamma}^{R}}{\sigma_{S M}^{R}}<61.9$, respectively. The
resultant 95% C.L. limit on $\zeta_{AZ}$ is $-0.020<\zeta_{A Z}<0.003$.


title:
Microscopic theory of photo-assisted electronic transport in
  normal-metal/BCS-superconductor junctions

abstract:
  We investigate photo assisted electronic transport in a
normal-metal/BCS-superconductor junction with a microscopic Hamiltonian
approach, for several types of periodic voltage drives applied on the
normal-metal side. The time-dependent current and the photo-assisted noise are
computed to all orders of the tunneling Hamiltonian using a
Keldysh-Nambu-Floquet approach. An excess noise analysis allows one to
determine to what extent pure electronic excitations with a small number of
electrons per period can be generated by the different drives. When the
superconducting gap is small compared to the drive frequency, the junction
behaves like a normal-metal junction and minimal excess noise is reached for
Lorentzian voltage drives carrying an integer charge (levitons). In the
opposite regime of a large-gap, the excess noise vanishes for half-quantized
levitons, giving rise to the perfect transmission of a Cooper pair on the
superconducting side. This microscopic approach also allows us to address the
intermediate regime, when the drive frequency is comparable to the gap,
allowing us to study the non-trivial interplay between Andreev reflection and
quasiparticle-transfer processes. Our analysis also shows the appearance of
Tien-Gordon-type relations connecting the current and noise in the AC-driven
junction to their DC counterpart, which we investigate in details. Finally, the
possibility to build a reliable on-demand source of Cooper pairs with this
setup is examined using realistic experimental parameters.


title:
Centrality and transverse momentum dependence of higher-order flow
  harmonics of identified hadrons in Au+Au collisions at $\sqrt{s_{\rm NN}}$ =
  200 GeV

abstract:
  We present high-precision measurements of elliptic, triangular, and
quadrangular flow $v_{2}$, $v_{3}$, and $v_{4}$, respectively, at midrapidity
($|\eta|<1.0$) for identified hadrons $\pi$, $p$, $K$, $\varphi$, $K_s$,
$\Lambda$ as a function of centrality and transverse momentum in Au+Au
collisions at the center-of-mass energy $\sqrt{s_{\rm NN}}=$ 200 GeV. We
observe similar $v_{n}$ trends between light and strange mesons which indicates
that the heavier strange quarks flow as strongly as the lighter up and down
quarks. The number-of-constituent-quark scaling for $v_{2}$, $v_{3}$, and
$v_{4}$ is found to hold within statistical uncertainty for 0-10$\%$, 10-40$\%$
and 40-80$\%$ collision centrality intervals. The results are compared to
several viscous hydrodynamic calculations with varying initial conditions, and
could serve as an additional constraint to the development of hydrodynamic
models.


title:
Matching and maximum likelihood decoding of a multi-round subsystem
  quantum error correction experiment

abstract:
  Quantum error correction offers a promising path for performing quantum
computations with low errors. Although a fully fault-tolerant execution of a
quantum algorithm remains unrealized, recent experimental developments, along
with improvements in control electronics, are enabling increasingly advanced
demonstrations of the necessary operations for applying quantum error
correction. Here, we perform quantum error correction on superconducting qubits
connected in a heavy-hexagon lattice. The full processor can encode a logical
qubit with distance three and perform several rounds of fault-tolerant syndrome
measurements that allow the correction of any single fault in the circuitry.
Furthermore, by using dynamic circuits and classical computation as part of our
syndrome extraction protocols, we can exploit real-time feedback to reduce the
impact of energy relaxation error in the syndrome and flag qubits. We show that
the logical error varies depending on the use of a perfect matching decoder
compared to a maximum likelihood decoder. We observe a logical error per
syndrome measurement round as low as $\sim0.04$ for the matching decoder and as
low as $\sim0.03$ for the maximum likelihood decoder. Our results suggest that
more significant improvements to decoders are likely on the horizon as quantum
hardware has reached a new stage of development towards fully fault-tolerant
operations.


title:
Improving State-of-the-Art in One-Class Classification by Leveraging
  Unlabeled Data

abstract:
  When dealing with binary classification of data with only one labeled class
data scientists employ two main approaches, namely One-Class (OC)
classification and Positive Unlabeled (PU) learning. The former only learns
from labeled positive data, whereas the latter also utilizes unlabeled data to
improve the overall performance. Since PU learning utilizes more data, we might
be prone to think that when unlabeled data is available, the go-to algorithms
should always come from the PU group. However, we find that this is not always
the case if unlabeled data is unreliable, i.e. contains limited or biased
latent negative data. We perform an extensive experimental study of a wide list
of state-of-the-art OC and PU algorithms in various scenarios as far as
unlabeled data reliability is concerned. Furthermore, we propose PU
modifications of state-of-the-art OC algorithms that are robust to unreliable
unlabeled data, as well as a guideline to similarly modify other OC algorithms.
Our main practical recommendation is to use state-of-the-art PU algorithms when
unlabeled data is reliable and to use the proposed modifications of
state-of-the-art OC algorithms otherwise. Additionally, we outline procedures
to distinguish the cases of reliable and unreliable unlabeled data using
statistical tests.


title:
A Self-Supervised, Differentiable Kalman Filter for Uncertainty-Aware
  Visual-Inertial Odometry

abstract:
  Traditionally, visual-inertial-odometry (VIO) systems rely on filtering or
optimization-based frameworks for robot egomotion estimation. While these
methods are accurate under nominal conditions, they are prone to failure in
degraded environments, where illumination changes, fast camera motion, or
textureless scenes are present. Learning-based systems have the potential to
outperform classical implementations in degraded environments, but are,
currently, less accurate than classical methods in nominal settings. A third
class, of hybrid systems, attempts to leverage the advantages of both systems.
  Herein, we introduce a framework for training a hybrid VIO system. Our
approach uses a differentiable Kalman filter with an IMU-based process model
and a robust, neural network-based relative pose measurement model. By
utilizing the data efficiency of self-supervised learning, we show that our
system significantly outperforms a similar, supervised system, while enabling
online retraining. To demonstrate the utility of our approach, we evaluate our
system on a visually degraded version of the EuRoC dataset. Notably, we find
that, in cases where classical estimators consistently diverge, our estimator
does not diverge or suffer from a significant reduction in accuracy. Finally,
our system, by properly utilizing the metric information contained in the IMU
measurements, is able to recover metric scale, while other self-supervised
monocular VIO approaches cannot.


title: Geometry of Data

abstract:
  Topological data analysis asks when balls in a metric space $(X,d)$
intersect. Geometric data analysis asks how much balls have to be enlarged to
intersect. We connect this principle to the traditional core geometric concept
of curvature. This enables us, on one hand, to reconceptualize curvature and
link it to the geometric notion of hyperconvexity. On the other hand, we can
then also understand methods of topological data analysis from a geometric
perspective.


title:
Mixture Components Inference for Sparse Regression: Introduction and
  Application for Estimation of Neuronal Signal from fMRI BOLD

abstract:
  Sparse linear regression methods including the well-known LASSO and the
Dantzig selector have become ubiquitous in the engineering practice, including
in medical imaging. Among other tasks, they have been successfully applied for
the estimation of neuronal activity from functional magnetic resonance data
without prior knowledge of the stimulus or activation timing, utilizing an
approximate knowledge of the hemodynamic response to local neuronal activity.
These methods work by generating a parametric family of solutions with
different sparsity, among which an ultimate choice is made using an information
criteria. We propose a novel approach, that instead of selecting a single
option from the family of regularized solutions, utilizes the whole family of
such sparse regression solutions. Namely, their ensemble provides a first
approximation of probability of activation at each time-point, and together
with the conditional neuronal activity distributions estimated with the theory
of mixtures with varying concentrations, they serve as the inputs to a Bayes
classifier eventually deciding on the verity of activation at each time-point.
We show in extensive numerical simulations that this new method performs
favourably in comparison with standard approaches in a range of realistic
scenarios. This is mainly due to the avoidance of overfitting and underfitting
that commonly plague the solutions based on sparse regression combined with
model selection methods, including the corrected Akaike Information Criterion.
This advantage is finally documented in selected fMRI task datasets.


title:
Extracting perfect GHZ states from imperfect weighted graph states via
  entanglement concentration

abstract:
  Photonic GHZ states serve as the central resource for a number of important
applications in quantum information science, including secret sharing, sensing,
and fusion-based quantum computing. The use of photon-emitter entangling gates
is a promising approach to creating these states that sidesteps many of the
difficulties associated with intrinsically probabilistic methods based on
linear optics. However, the efficient creation of high-fidelity GHZ states of
many photons remains an outstanding challenge due to both coherent and
incoherent errors during the generation process. Here, we propose an
entanglement concentration protocol that is capable of generating perfect GHZ
states using only local gates and measurements on imperfect weighted graph
states. We show that our protocol is both efficient and robust to incoherent
noise errors.


title:
Model predictive control and moving horizon estimation for adaptive
  optimal bolus feeding in high-throughput cultivation of E. coli

abstract:
  We discuss the application of a nonlinear model predictive control (MPC) and
a moving horizon estimation (MHE) to achieve an optimal operation of E. coli
fed-batch cultivations with intermittent bolus feeding. 24 parallel experiments
were considered in a high-throughput microbioreactor platform at 10 mL scale.
The robotic island in question can run up to 48 fed-batch processes in parallel
with an automated liquid handling and online and atline analytics. The
implementation of the model-based monitoring and control framework reveals that
there are mainly three challenges which need to be addressed; First, the inputs
are given in an instantaneous pulsed form by bolus injections, second, online
and atline measurement frequencies are severely imbalanced, and third,
optimization for the distinctive multiple reactors can be either parallelized
or integrated. We address these challenges by incorporating the concept of
impulsive control systems, formulating multi-rate MHE with identifiability
analysis, and suggesting criteria for deciding the reactor configuration. In
this study, we present the key elements and background theory of the
implementation with \textit{in silico} simulations for the bacterial fed-batch
cultivation.


title:
Prompt electron and tau neutrinos and antineutrinos in the forward
  region at the LHC

abstract:
  Neutrino fluxes at high rapidity and at high energy are sensitive to QCD
dynamics of heavy-flavor production in kinematic regions where measurements
have not yet been made. The FASER$\nu$ and SND@LHC experiments scheduled for
Run 3 at the LHC and the proposed Forward Physics Facility with a suite of
experiments during the High-Luminosity LHC phase will probe neutrinos at high
pseudorapidity. This short paper reports on recent evaluations of the prompt
$\nu_\tau+\bar\nu_\tau$ and $\nu_e+\bar\nu_e$ double-differential cross
sections in $pp$ collisions at the Large Hadron Collider from the production
and decays of $D_s^\pm$ and $D^\pm$, respectively. For $\sqrt{s}=14$ TeV, the
double-differential neutrino energy and pseudorapidity distributions are
evaluated at NLO QCD. Data tables with these predictions are presented. Future
work needed to refine predictions of neutrino and antineutrino fluxes in the
forward region at the LHC is discussed.


title:
Wigner's friends, tunnelling times and Feynman's "only mystery of
  quantum mechanics"

abstract:
  Recent developments in elementary quantum mechanics have seen a number of
extraordinary claims regarding quantum behaviour, and even questioning internal
consistency of the theory. These are, we argue, different disguises of what
Feynman described as quantum theory's "only mystery".


title: High Energy Physics Opportunities Using Reactor Antineutrinos

abstract:
  Nuclear reactors are uniquely powerful, abundant, and flavor-pure sources of
antineutrinos that continue to play a vital role in the US neutrino physics
program. The US reactor antineutrino physics community is a diverse interest
group encompassing many detection technologies and many particle physics
topics, including Standard Model and short-baseline oscillations, BSM physics
searches, and reactor flux and spectrum modeling. The community's aims offer
strong complimentary with numerous aspects of the wider US neutrino program and
have direct relevance to most of the topical sub-groups composing the Snowmass
2021 Neutrino Frontier. Reactor neutrino experiments also have a direct
societal impact and have become a strong workforce and technology development
pipeline for DOE National Laboratories and universities. This white paper,
prepared as a submission to the Snowmass 2021 community organizing exercise,
will survey the state of the reactor antineutrino physics field and summarize
the ways in which current and future reactor antineutrino experiments can play
a critical role in advancing the field of particle physics in the next decade.


title:
Aperiodic and linearly repetitive Lorentz gases of finite horizon are
  not exponentially mixing

abstract:
  We prove that aperiodic and linearly repetitive Lorentz gases with finite
horizon are not mixing with exponential or stretched exponential speed in any
dimension for any class of H\"older observables. We also bound the polynomial
speed of mixing for observables in the H\"older space $H_{\alpha}$ depending on
$\alpha$.


title:
A Novel Perspective to Look At Attention: Bi-level Attention-based
  Explainable Topic Modeling for News Classification

abstract:
  Many recent deep learning-based solutions have widely adopted the
attention-based mechanism in various tasks of the NLP discipline. However, the
inherent characteristics of deep learning models and the flexibility of the
attention mechanism increase the models' complexity, thus leading to challenges
in model explainability. In this paper, to address this challenge, we propose a
novel practical framework by utilizing a two-tier attention architecture to
decouple the complexity of explanation and the decision-making process. We
apply it in the context of a news article classification task. The experiments
on two large-scaled news corpora demonstrate that the proposed model can
achieve competitive performance with many state-of-the-art alternatives and
illustrate its appropriateness from an explainability perspective.


title:
Detection and characterization of microseismic events from fiber-optic
  DAS data using deep learning

abstract:
  Microseismic analysis is a valuable tool for fracture characterization in the
earth's subsurface. As distributed acoustic sensing (DAS) fibers are deployed
at depth inside wells, they hold vast potential for high-resolution
microseismic analysis. However, the accurate detection of microseismic signals
in continuous DAS data is challenging and time-consuming. We design, train, and
deploy a deep learning model to detect microseismic events in DAS data
automatically. We create a curated dataset of nearly 7,000 manually-selected
events and an equal number of background noise examples. We optimize the deep
learning model's network architecture together with its training
hyperparameters by Bayesian optimization. The trained model achieves an
accuracy of 98.6% on our benchmark dataset and even detects low-amplitude
events missed during manual labeling. Our methodology detects more than 100,000
events allowing the reconstruction of spatio-temporal fracture development far
more accurately and efficiently than would have been feasible by traditional
methods.


title: Snowmass White Paper: Strong CP Beyond Axion Direct Detection

abstract:
  We sketch recent progress and promising future directions for research
connected with the strong CP problem. Topics surveyed include axion dark matter
substructure and its gravitational detection; axion model building and the
quality problem; experimental tests of ultraviolet solutions; and connections
to lattice QCD.


title:
Extending the reach of quantum computing for materials science with
  machine learning potentials

abstract:
  Solving electronic structure problems represents a promising field of
application for quantum computers. Currently, much effort has been spent in
devising and optimizing quantum algorithms for quantum chemistry problems
featuring up to hundreds of electrons. While quantum algorithms can in
principle outperform their classical equivalents, the polynomially scaling
runtime, with the number of constituents, can still prevent quantum simulations
of large scale systems. We propose a strategy to extend the scope of quantum
computational methods to large scale simulations using a machine learning
potential, trained on quantum simulation data. The challenge of applying
machine learning potentials in today's quantum setting arises from the several
sources of noise affecting the quantum computations of electronic energies and
forces. We investigate the trainability of a machine learning potential
selecting various sources of noise: statistical, optimization and hardware
noise.Finally, we construct the first machine learning potential from data
computed on actual IBM Quantum processors for a hydrogen molecule. This already
would allow us to perform arbitrarily long and stable molecular dynamics
simulations, outperforming all current quantum approaches to molecular dynamics
and structure optimization.


title: Snowmass2021 Cosmic Frontier White Paper: Rubin Observatory after LSST

abstract:
  The Vera C. Rubin Observatory will begin the Legacy Survey of Space and Time
(LSST) in 2024, spanning an area of 18,000 square degrees in six bands, with
more than 800 observations of each field over ten years. The unprecedented data
set will enable great advances in the study of the formation and evolution of
structure and exploration of physics of the dark universe. The observations
will hold clues about the cause for the accelerated expansion of the universe
and possibly the nature of dark matter. During the next decade, LSST will be
able to confirm or dispute if tensions seen today in cosmological data are due
to new physics. New and unexpected phenomena could confirm or disrupt our
current understanding of the universe. Findings from LSST will guide the path
forward post-LSST. The Rubin Observatory will still be a uniquely powerful
facility even then, capable of revealing further insights into the physics of
the dark universe. These could be obtained via innovative observing strategies,
e.g., targeting new probes at shorter timescales than with LSST, or via modest
instrumental changes, e.g., new filters, or through an entirely new instrument
for the focal plane. This White Paper highlights some of the opportunities in
each scenario from Rubin observations after LSST.


title:
Spectral analysis and representations]{Spectral analysis near regular
  point of reducibility and representations of Coxeter groups

abstract:
  For a tuple of square matrices $A_1,...,A_n$ the determinantal hypersurface
is defined as \begin{eqnarray*} &\sigma(A_1,...,A_n)= \\ &\Big\{[x_1:\cdots
:x_n]\in \C{\mathbb P}^{n-1}: det(x_1A_1+\cdots +x_nA_n)=0\Big \}.
\end{eqnarray*} In this paper we develop a local spectral analysis near a
regular point of reducibility of a determinantal hypersurface. We prove a
rigidity type theorem for representations of Coxeter groups as an application


title: Coloring graphs with forbidden almost bipartite subgraphs

abstract:
  A conjecture of Alon, Krivelevich, and Sudakov states that, for any graph
$F$, there is a constant $c(F) > 0$ such that $\chi(G) \leq (c(F) + o(1))
\Delta / \log\Delta$ for all $F$-free graphs $G$ of maximum degree $\Delta$.
The only graphs $F$ for which this conjecture has been verified so far -- by
Alon, Krivelevich, and Sudakov themselves -- are the so-called almost bipartite
graphs, i.e., graphs that can be made bipartite by removing at most one vertex.
Equivalently, a graph is almost bipartite if it is a subgraph of the complete
tripartite graph $K_{1,t,t}$ for some $t \in \mathbb{N}$. The best heretofore
known upper bound on $c(F)$ for almost bipartite $F$ is due to Davies, Kang,
Pirot, and Sereni, who showed that $c(K_{1,t,t}) \leq t$. We prove that in fact
$c(F) \leq 4$ for any almost bipartite graph $F$, thus making the bound
independent of $F$ in all the known cases of the conjecture. We also establish
a more general version of this result in the setting of DP-coloring (also known
as correspondence coloring) and consider some algorithmic consequences.


title:
The Hubble constant from galaxy cluster scaling-relation and SNe Ia
  observations: a consistency test

abstract:
  In this paper, we propose a self-consistent test for a Hubble constant
estimate using galaxy cluster and type Ia supernovae (SNe Ia) observations. The
approach consists, in a first step, of obtaining the observational value of the
galaxy cluster scaling-relation $Y_{SZE}D_{A}^{2}/C_{XZS}Y_X = C $ by combining
the X-Ray and SZ observations of galaxy clusters at low redshifts ($z < 0.1$)
from the first {\it Planck mission} all-sky data set ($0.044 \leq z \leq
0.444$), along with SNe Ia observations and making use of the cosmic distance
duality relation validity. Then, by considering a flat $\Lambda$CDM model for
$D_A$, the constant $C$ from the first step and the Planck prior on $\Omega_M$
parameter, we obtain $H_0$ by using the galaxy cluster data with $z>0.1$. As a
result, we obtain a low $H_0$ estimate, $H_0=63.583^{+6.496}_{-5.685}$
km/s/Mpc, in full agreement with the latest results from {\it Planck mission}.


title:
Promising Technologies and R&D Directions for the Future Muon Collider
  Detectors

abstract:
  Among the post-LHC generation of particle accelerators, the muon collider
represents a unique machine with capability to provide very high energy
leptonic collisions and to open the path to a vast and mostly unexplored
physics programme. However, on the experimental side, such great physics
potential is accompanied by unprecedented technological challenges, due to the
fact that muons are unstable particles. Their decay products interact with the
machine elements and produce an intense flux of background particles that
eventually reach the detector and may degrade its performance. In this paper,
we present technologies that have a potential to match the challenging
specifications of a muon collider detector and outline a path forward for the
future R&D efforts.


title:
Arbitrary Beam Pattern Approximation via RISs with Measured Element
  Responses

abstract:
  Smart radio environments (SREs) are seen as a key rising concept of next
generation wireless networks, where propagation channels between transmitters
and receivers are purposely controlled. One promising approach to achieve such
channel flexibility relies on semipassive reflective Reconfigurable intelligent
surfaces (RISs), which can shape the bouncing multipath signals for enhancing
communication quality of service, making localization feasible in adverse
operating conditions, or reducing unwanted electromagnetic emissions. This
paper introduces a generic framework that aims at optimizing the end-to-end
precoder controlled by RISs, so that arbitrary beam patterns can be generated,
given a predefined lookup table of RIS element-wise complex reflection
coefficients. This method is validated and illustrated for different targeted
beam patterns in both the far-field and the near-field regimes, while
considering the prior characterization of real-life RIS hardware prototypes.
These results show how, and to which extent, RIS configuration optimization can
approximate the desired beams under realistic hardware limitations and
low-complexity implementation practicability, or conversely, which RIS
elements' lookup tables would be more suitable. The latter can provide useful
guidelines for future RIS hardware designs.


title: Enriching a predicate and tame expansions of the integers

abstract:
  Given a structure $\mathcal{M}$ and a stably embedded $\emptyset$-definable
set $Q$, we prove tameness preservation results when enriching the induced
structure on $Q$ by some further structure $\mathcal{Q}$. In particular, we
show that if $T=\text{Th}(\mathcal{M})$ and $\text{Th}(\mathcal{Q})$ are stable
(resp., superstable, $\omega$-stable), then so is the theory $T[\mathcal{Q}]$
of the enrichment of $\mathcal{M}$ by $\mathcal{Q}$. Assuming stability of $T$
and a further condition on $Q$ related to the behavior of algebraic closure, we
also show that simplicity and NSOP$_1$ pass from $\text{Th}(\mathcal{Q})$ to
$T[\mathcal{Q}]$. We then prove several applications for tame expansions of
weakly minimal structures and, in particular, the group of integers. For
example, we construct the first known examples of strictly stable expansions of
$(\mathbb{Z},+)$. More generally, we show that any stable (resp., superstable,
simple, NIP, NTP$_2$, NSOP$_1$) countable graph can be defined in a stable
(resp., superstable, simple, NIP, NTP$_2$, NSOP$_1$) expansion of
$(\mathbb{Z},+)$ by some unary predicate $A\subseteq \mathbb{N}$.


title: On the general Smarandache's sigma product of digits

abstract:
  This paper investigates the behaviour of one of the most famous Smarandache's
sequence given by A061076 on oeis. In particular we first study the behaviour
of two sequences (A061077, A061078) strictly connected with the main
Smarandache's sigma product of digits. We'll solve some open problems such as
the determination of an upper bound for these sequences (which hold for all
$n\in\mathbb{N}$) and the determination of a closed formula for each $a(n)$ and
$b(n)$. Then combining these results it will be possible to understand the
behaviour of the general sequence $c(n)$. Every result will be accompanied by
Wolfram Mathematica scripts examples in order to support our thesis.


title:
FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text
  Processing

abstract:
  We present a benchmark suite of four datasets for evaluating the fairness of
pre-trained language models and the techniques used to fine-tune them for
downstream tasks. Our benchmarks cover four jurisdictions (European Council,
USA, Switzerland, and China), five languages (English, German, French, Italian
and Chinese) and fairness across five attributes (gender, age, region,
language, and legal area). In our experiments, we evaluate pre-trained language
models using several group-robust fine-tuning techniques and show that
performance group disparities are vibrant in many cases, while none of these
techniques guarantee fairness, nor consistently mitigate group disparities.
Furthermore, we provide a quantitative and qualitative analysis of our results,
highlighting open challenges in the development of robustness methods in legal
NLP.