title:
MemoRec: A Recommender System for Assisting Modelers in Specifying
  Metamodels

abstract:
  Model Driven Engineering (MDE) has been widely applied in software
development, aiming to facilitate the coordination among various stakeholders.
Such a methodology allows for a more efficient and effective development
process. Nevertheless, modeling is a strenuous activity that requires proper
knowledge of components, attributes, and logic to reach the level of
abstraction required by the application domain. In particular, metamodels play
an important role in several paradigms, and specifying wrong entities or
attributes in metamodels can negatively impact on the quality of the produced
artifacts as well as other elements of the whole process. During the
metamodeling phase, modelers can benefit from assistance to avoid mistakes,
e.g., getting recommendations like meta-classes and structural features
relevant to the metamodel being defined. However, suitable machinery is needed
to mine data from repositories of existing modeling artifacts and compute
recommendations. In this work, we propose MemoRec, a novel approach that makes
use of a collaborative filtering strategy to recommend valuable entities
related to the metamodel under construction. Our approach can provide
suggestions related to both metaclasses and structured features that should be
added in the metamodel under definition. We assess the quality of the work with
respect to different metrics, i.e., success rate, precision, and recall. The
results demonstrate that MemoRec is capable of suggesting relevant items given
a partial metamodel and supporting modelers in their task.


title:
A New Computational Approach for Solving Linear Bilevel Programs Based
  on Parameter-Free Disjunctive Decomposition

abstract:
  Linear bilevel programs (linear BLPs) have been widely used in computational
mathematics and optimization in several applications. Single-level
reformulation for linear BLPs replaces the lower-level linear program with its
Karush-Kuhn-Tucker optimality conditions and linearizes the complementary
slackness conditions using the big-M technique. Although the approach is
straightforward, it requires finding the big-M whose computation is recently
shown to be NP-hard. This paper presents a disjunctive-based decomposition
algorithm which does not need finding the big-Ms whereas guaranteeing that
obtained solution is optimal. Our experience shows promising performance of our
algorithm.


title: Rotational viscosity in spin resonance of hydrodynamic electrons

abstract:
  In novel ultra-pure materials electrons can form a viscous fluid, which is
fundamentally different by its dynamics from the electron gas in ordinary
conductors with significant density of defects. The shape of the non-stationary
flow of such electron fluid is similar to the alternating flow of blood in
large-radius arteries [J. R. Womersley, J. Physiol. 127, 552 (1955)]. The
rotational viscosity effect is responsible for interconnection between the
dynamics of electron spins and flow inhomogeneities. In particular, it induces
the spin polarization of electrons in a curled flow via an internal spin-orbit
torque acting on electron spins. Here we show that this effect in an electron
fluid placed in a magnetic field leads to a correction to the ac sample
impedance, which has a resonance at the Larmor frequency of electrons. In this
way, via the electrically detected spin resonance the Womersley flow of an
electron fluid can be visualized and the rotational viscosity can be measured.


title: Critical Medical Resource Allocation during COVID-19 Pandemic

abstract:
  In this paper, an optimal resource allocation framework is proposed for the
allocation of critical medical resources among different units during a
pandemic. The framework is developed by considering the dynamics of Pandemic,
hierarchical government structure, and non-uniformity of unit resource
requirement among different units. The cost function is designed to minimize
the difference between the demand, actual allocation, and ideal allocation,
where ideal allocation for a region is considered based on the predicted active
cases in a fraction of predicted total active cases of all regions. Different
cost functions are used at a different level of organization based on the
available information. The model can also accommodate severity of disaster in a
region in this framework. A sample allocation case study is presented for the
allocation of oxygen for different states of India.


title: Characters of normalisers of d-split Levi subgroups in Sp2n(q)

abstract:
  In this paper characters of the normaliser of d-split Levi subgroups in
Sp2n(q) are parametrised with a particular focus on the Clifford theory between
the Levi subgroup and its normaliser. This forms a key step in verifying the
inductive Alperin-McKay condition from Sp\"ath via a criterion given by Sp\"ath
and the author.


title: Machine Learning for Hilbert Series

abstract:
  Hilbert series are a standard tool in algebraic geometry, and more recently
are finding many uses in theoretical physics. This summary reviews work
applying machine learning to databases of them; and was prepared for the
proceedings of the Nankai Symposium on Mathematical Dialogues, 2021.


title: TAPE: Task-Agnostic Prior Embedding for Image Restoration

abstract:
  Learning an generalized prior for natural image restoration is an important
yet challenging task. Early methods mostly involved handcrafted priors
including normalized sparsity, L0 gradients, dark channel priors, etc.
Recently, deep neural networks have been used to learn various image priors but
do not guarantee to generalize. In this paper, we propose a novel approach that
embeds a task-agnostic prior into a transformer. Our approach, named
Task-Agnostic Prior Embedding (TAPE), consists of three stages, namely,
task-agnostic pre-training, task-agnostic fine-tuning, and task-specific
fine-tuning, where the first one embeds prior knowledge about natural images
into the transformer and the latter two extracts the knowledge to assist
downstream image restoration. Experiments on various types of degradation
validate the effectiveness of TAPE. The image restoration performance in terms
of PSNR is improved by as much as 1.45 dB and even outperforms task-specific
algorithms. More importantly, TAPE shows the ability of disentangling
generalized image priors from degraded images, which enjoys favorable transfer
ability to unknown downstream tasks.


title: The Regular Languages of First-Order Logic with One Alternation

abstract:
  The regular languages with a neutral letter expressible in first-order logic
with one alternation are characterized. Specifically, it is shown that if an
arbitrary $\Sigma_2$ formula defines a regular language with a neutral letter,
then there is an equivalent $\Sigma_2$ formula that only uses the order
predicate. This shows that the so-called Central Conjecture of Straubing holds
for $\Sigma_2$ over languages with a neutral letter, the first progress on the
Conjecture in more than 20 years. To show the characterization, lower bounds
against polynomial-size depth-3 Boolean circuits with constant top fan-in are
developed. The heart of the combinatorial argument resides in studying how
positions within a language are determined from one another, a technique of
independent interest.


title: Bayesian Nonparametric Inference for "Species-sampling" Problems

abstract:
  "Species-sampling" problems (SSPs) refer to a broad class of statistical
problems that, given an observable sample from an unknown population of
individuals belonging to some species, call for estimating features of the
unknown species composition of additional unobservable samples from the same
population. Among SSPs, the problems of estimating coverage probabilities, the
number of unseen species and coverages of prevalences have emerged over the
past three decades for being the objects of numerous studies, both in methods
and applications, mostly within the field of biological sciences but also in
machine learning, electrical engineering and information theory. In this paper,
we present an overview of Bayesian nonparametric (BNP) inference for such three
SSPs under the popular Pitman--Yor process (PYP) prior: i) we introduce each
SSP in the classical (frequentist) nonparametric framework, and review its
posterior analyses in the BNP framework; ii) we improve on computation and
interpretability of existing posterior distributions, typically expressed
through complicated combinatorial numbers, by establishing novel posterior
representations in terms of simple compound Binomial and Hypergeometric
distributions. The critical question of estimating the discount and scale
parameters of the PYP prior is also considered and investigated, establishing a
general property of Bayesian consistency with respect to the hierarchical Bayes
and empirical Bayes approaches, that is: the discount parameter can be always
estimated consistently, whereas the scale parameter cannot be estimated
consistently, thus advising caution in posterior inference. We conclude our
work by discussing other SSPs, and presenting some emerging generalizations of
SSPs, mostly in biological sciences, which deal with "feature-sampling"
problems, multiple populations of individuals sharing species and classes of
Markov chains.


title:
Econometric Modeling of Intraday Electricity Market Price with
  Inadequate Historical Data

abstract:
  The intraday (ID) electricity market has received an increasing attention in
the recent EU electricity-market discussions. This is partly because the
uncertainty in the underlying power system is growing and the ID market
provides an adjustment platform to deal with such uncertainties. Hence, market
participants need a proper ID market price model to optimally adjust their
positions by trading in the market. Inadequate historical data for ID market
price makes it more challenging to model. This paper proposes long short-term
memory, deep convolutional generative adversarial networks, and No-U-Turn
sampler algorithms to model ID market prices. Our proposed econometric ID
market price models are applied to the Nordic ID price data and their promising
performance are illustrated.


title:
High-resolution synthetic UV-submm images for Milky Way-mass simulated
  galaxies from the ARTEMIS project

abstract:
  We present redshift-zero synthetic dust-aware observations for the 45 Milky
Way-mass simulated galaxies of the ARTEMIS project, calculated with the SKIRT
radiative transfer code. The post-processing procedure includes components for
star-forming regions, stellar sources, and diffuse dust. We produce and
publicly release realistic high-resolution images for 50 commonly-used
broadband filters from ultraviolet to sub-millimetre wavelengths and for 18
different viewing angles. We compare the simulated ARTEMIS galaxies to observed
galaxies in the DustPedia database with similar stellar mass and star formation
rate, and to synthetic observations of the simulated galaxies of the Auriga
project produced in previous work using a similar post-processing technique. In
all cases, global galaxy properties are derived using SED fitting. We find
that, similar to Auriga, the post-processed ARTEMIS galaxies generally
reproduce the observed scaling relations for global fluxes and physical
properties, although dust extinction at FUV/UV wavelengths is underestimated
and representative dust temperatures are lower than observed. At a resolved
scale, we compare multi-wavelength non-parametric morphological properties of
selected disc galaxies across the data sets. We find that the ARTEMIS galaxies
largely reproduce the observed morphological trends as a function of
wavelength, although they appear to be more clumpy and less symmetrical than
observed. We note that the ARTEMIS and Auriga galaxies occupy adjacent regions
in the specific star formation versus stellar mass plane, so that the synthetic
observation data sets supplement each other.


title:
Pandemic Vulnerability Index of US Cities: A Hybrid Knowledge-based and
  Data-driven Approach

abstract:
  Cities become mission-critical zones during pandemics and it is vital to
develop a better understanding of the factors that are associated with
infection levels. The COVID-19 pandemic has impacted many cities severely;
however, there is significant variance in its impact across cities. Pandemic
infection levels are associated with inherent features of cities (e.g.,
population size, density, mobility patterns, socioeconomic condition, and
health environment), which need to be better understood. Intuitively, the
infection levels are expected to be higher in big urban agglomerations, but the
measurable influence of a specific urban feature is unclear. The present study
examines 41 variables and their potential influence on COVID-19 cases and
fatalities. The study uses a multi-method approach to study the influence of
variables, classified as demographic, socioeconomic, mobility and connectivity,
urban form and density, and health and environment dimensions. This study
develops an index dubbed the PVI-CI for classifying the pandemic vulnerability
levels of cities, grouping them into five vulnerability classes, from very high
to very low. Furthermore, clustering and outlier analysis provides insights on
the spatial clustering of cities with high and low vulnerability scores. This
study provides strategic insights into levels of influence of key variables
upon the spread of infections as well as fatalities, along with an objective
ranking for the vulnerability of cities. Thus it provides critical wisdom
needed for urban healthcare policy and resource management. The pandemic
vulnerability index calculation method and the process present a blueprint for
the development of similar indices for cities in other countries, leading to a
better understanding and improved pandemic management for urban areas and
post-pandemic urban planning across the world.


title:
Thermodynamics of viscoelastic solids, its Eulerian formulation, and
  existence of weak solutions

abstract:
  The thermodynamical model of visco-elastic deformable solids at finite
strains is formulated in a fully Eulerian way in rates. Also effects of thermal
expansion or buoyancy due to evolving mass density in a gravity field are
covered. The Kelvin-Voigt rheology with a higher-order viscosity (exploiting
the concept of multipolar materials) is used, allowing for physically relevant
frame-indifferent stored energies and for local invertibility of deformation.
The model complies with energy conservation and Clausius-Duhem entropy
inequality. Existence and a certain regularity of weak solutions is proved by a
Faedo-Galerkin semi-discretization and a suitable regularization. Subtle
physical limitations of the model are illustrated on thermally expanding
neo-Hookean materials or materials with phase transitions.


title:
Efficient Bayesian estimation and use of cut posterior in semiparametric
  hidden Markov models

abstract:
  We consider the problem of estimation in Hidden Markov models with finite
state space and nonparametric emission distributions. Efficient estimators for
the transition matrix are exhibited, and a semiparametric Bernstein-von Mises
result is deduced, extending the work of Gassiat et al. (2018) on mixture
models to the HMM setting. Following from this, we propose a modular approach
using the cut posterior to jointly estimate the transition matrix and the
emission densities. We derive a general theorem on contraction rates for this
approach, in the spirit of the seminal work of Ghosal and van der Vaart (2007).
We then show how this result may be applied to obtain a contraction rate result
for the emission densities in our setting; a key intermediate step is an
inversion inequality relating $L^1$ distance between the marginal densities to
$L^1$ distance between the emissions. Finally, a contraction result for the
smoothing probabilities is shown, which avoids the common approach of sample
splitting. Simulations are provided which demonstrate both the theory and the
ease of its implementation.


title:
LFW-Beautified: A Dataset of Face Images with Beautification and
  Augmented Reality Filters

abstract:
  Selfie images enjoy huge popularity in social media. The same platforms
centered around sharing this type of images offer filters to beautify them or
incorporate augmented reality effects. Studies suggests that filtered images
attract more views and engagement. Selfie images are also in increasing use in
security applications due to mobiles becoming data hubs for many transactions.
Also, video conference applications, boomed during the pandemic, include such
filters.
  Such filters may destroy biometric features that would allow person
recognition or even detection of the face itself, even if such commodity
applications are not necessarily used to compromise facial systems. This could
also affect subsequent investigations like crimes in social media, where
automatic analysis is usually necessary given the amount of information posted
in social sites or stored in devices or cloud repositories.
  To help in counteracting such issues, we contribute with a database of facial
images that includes several manipulations. It includes image enhancement
filters (which mostly modify contrast and lightning) and augmented reality
filters that incorporate items like animal noses or glasses. Additionally,
images with sunglasses are processed with a reconstruction network trained to
learn to reverse such modifications. This is because obfuscating the eye region
has been observed in the literature to have the highest impact on the accuracy
of face detection or recognition.
  We start from the popular Labeled Faces in the Wild (LFW) database, to which
we apply different modifications, generating 8 datasets. Each dataset contains
4,324 images of size 64 x 64, with a total of 34,592 images. The use of a
public and widely employed face dataset allows for replication and comparison.
  The created database is available at
https://github.com/HalmstadUniversityBiometrics/LFW-Beautified


title: Ages of Main-Sequence Turn-Off Stars from the GALAH Survey

abstract:
  Main sequence turn-off (MSTO) stars are good tracers of Galactic populations
since their ages can be reliably estimated from atmospheric parameters. Based
on the GALAH survey, we use the Yale Rotation Evolution Code to determine ages
of 2926 MSTO stars with mean age uncertainty $\sim$10\% considering the
variation of C and O abundances. Ages of CO-poor stars are systematically
affected by $\sim$10\% due to the C and O abundances, globally shifting to
$\sim$0.5 Gyr older compared to the results using solar metal-mixture. Of the
stars with \mbox{[Fe/H] $\sim$0.3-0.5} or \mbox{[O/Fe]~$\leq$~-0.25}, many have
fractional age differences~$\geq$~20\%, and even reach up to 36\%. The
age-metallicity relation appears to possibly exist two distinct sequences: a
young sequence of stars with age mostly $<$ 7 Gyr, and a relatively older
sequence of stars with age mostly $>$ 7 Gyr, overlapping at 5 Gyr
$\leq$~age~$\leq$ 7 Gyr. Moreover, the trends of abundances to age ratios show
two corresponding sequences, especially in [O/Fe]-age plane. We also find that
[Y/Mg] is a good chemical clock in disk populations. The young sequence and the
old sequence can not be separated based on chemistry or kinematics, therefore
stellar age is an important parameter to distinguish these two sequences in our
sample.


title:
Replacing neural networks by optimal analytical predictors for the
  detection of phase transitions

abstract:
  Identifying phase transitions and classifying phases of matter is central to
understanding the properties and behavior of a broad range of material systems.
In recent years, machine-learning (ML) techniques have been successfully
applied to perform such tasks in a data-driven manner. However, the success of
this approach notwithstanding, we still lack a clear understanding of ML
methods for detecting phase transitions, particularly of those that utilize
neural networks (NNs). In this work, we derive analytical expressions for the
optimal output of three widely used NN-based methods for detecting phase
transitions. These optimal predictions correspond to the output obtained in the
limit of perfectly trained and sufficiently large NNs. The inner workings of
the considered methods are revealed through the explicit dependence of the
optimal output on the input data. By evaluating the analytical expressions, we
can identify phase transitions from data without training NNs. Our theoretical
results are supported by extensive numerical simulations covering, e.g.,
topological, quantum, and many-body localization phase transitions. We expect
similar analyses to provide a deeper understanding of other classification
tasks in condensed matter physics.


title:
An active transverse energy filter to differentiate low energy particles
  with large pitch angles in a strong magnetic field

abstract:
  We present the idea and proof of principle measurements for an
angular-selective active filter for charged particles. The motivation for the
setup arises from the need to distinguish background electrons from signal
electrons in a spectrometer of MAC-E filter type. While a large fraction of the
background electrons exhibit predominantly small angles relative to the
magnetic guiding field (corresponding to a low amount of kinetic energy in the
motion component transverse to the field lines, in the following referred to as
transverse energy) and pass the filter mostly unhindered, signal electrons from
an isotropically emitting source interact with the active filter and are
detected. The concept is demonstrated using a microchannel plate (MCP) as an
active filter element. When correctly aligned with the magnetic field,
electrons with a small transverse energy pass the channels of the MCP without
interaction while electrons with large transverse energies hit the channel
walls and trigger an avalanche of secondary electrons that is subsequently
detected. Due to several drawbacks of MCPs for an actual transverse energy
filter, an alternative detection technique using microstructured Si-PIN diodes
is proposed.


title:
On the Insensitivity of Bit Density to Read Noise in One-bit Quanta
  Image Sensors

abstract:
  The one-bit quanta image sensor is a photon-counting device that produces
binary measurements where each bit represents the presence or absence of a
photon. In the presence of read noise, the sensor quantizes the analog voltage
into the binary bits using a threshold value $q$. The average number of ones in
the bitstream is known as the bit-density and is often the sufficient
statistics for signal estimation. An intriguing phenomenon is observed when the
quanta exposure is at the unity and the threshold is $q = 0.5$. The bit-density
demonstrates a complete insensitivity as long as the read noise level does not
exceeds a certain limit. In other words, the bit density stays at a constant
independent of the amount of read noise. This paper provides a mathematical
explanation of the phenomenon by deriving conditions under which the phenomenon
happens. It was found that the insensitivity holds when some forms of the
symmetry of the underlying Poisson-Gaussian distribution holds.


title:
Study of $\phi$-meson production in $p$$+$Al, $p$$+$Au, $d$$+$Au, and
  $^3$He$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV

abstract:
  Small nuclear collisions are mainly sensitive to cold-nuclear-matter effects;
however, the collective behavior observed in these collisions shows a hint of
hot-nuclear-matter effects. The identified-particle spectra, especially the
$\phi$ mesons which contain strange and antistrange quarks and have a
relatively small hadronic-interaction cross section, are a good tool to study
these effects. The PHENIX experiment has measured $\phi$ mesons in a specific
set of small collision systems $p$$+$Al, $p$$+$Au, and $^3$He$+$Au, as well as
$d$$+$Au [Phys. Rev. C {\bf 83}, 024909 (2011)], at $\sqrt{s_{_{NN}}}=200$ GeV.
The transverse-momentum spectra and nuclear-modification factors are presented
and compared to theoretical-model predictions. The comparisons with different
calculations suggest that quark-gluon plasma may be formed in these small
collision systems at $\sqrt{s_{_{NN}}}=200$ GeV. However, the volume and the
lifetime of the produced medium may be insufficient for observing
strangeness-enhancement and jet-quenching effects. Comparison with calculations
suggests that the main production mechanisms of $\phi$ mesons at midrapidity
may be different in $p$$+$Al versus $p/d/$$^3$He$+$Au collisions at
$\sqrt{s_{_{NN}}}=200$ GeV. While thermal quark recombination seems to dominate
in $p/d/$$^3$He$+$Au collisions, fragmentation seems to be the main production
mechanism in $p$$+$Al collisions.


title:
Game Dynamics Structure Control by Design: an Example from Experimental
  Economics

abstract:
  Game dynamics structure (e.g., endogenous cycle motion) in human subjects
game experiments can be predicted by game dynamics theory. However, whether the
structure can be controlled by mechanism design to a desired goal is not known.
Here, using the pole assignment approach in modern control theory, we
demonstrate how to control the structure in two steps: (1) Illustrate an
theoretical workflow on how to design a state-depended feedback controller for
desired structure; (2) Evaluate the controller by laboratory human subject game
experiments and by agent-based evolutionary dynamics simulation. To our
knowledge, this is the first realisation of the control of the human social
game dynamics structure in theory and experiment.


title:
Norms of basic operators in vector valued model spaces and de Branges
  spaces

abstract:
  Let $\Omega_+$ be either the open unit disc or the open upper half plane or
the open right half plane. In this paper, we compute the norm of the basic
operator $A_\alpha=\Pi_\Theta T_{b_\alpha}|_{\mathcal{H}(\Theta)}$ in the
vector valued model space $\mathcal{H}(\Theta)=H^m_2 \ominus \Theta H^m_2$
associated with an $m\times m$ matrix valued inner function $\Theta$ in
$\Omega_+$ and show that the norm is attained. Here $\Pi_\Theta$ denotes the
orthogonal projection from the Lebesgue space $L^m_2$ onto
$\mathcal{H}(\Theta)$ and $T_{b_\alpha}$ is the operator of multiplication by
the elementary Blaschke factor $b_{\alpha}$ of degree one with a zero at a
point $\alpha\in \Omega_+$. We show that if $A_\alpha$ is strictly contractive,
then its norm may be expressed in terms of the singular values of
$\Theta(\alpha)$. We then extend this evaluation to the more general setting of
vector valued de Branges spaces.


title:
2-Period Balanced Travelling Salesman Problem: a polynomially solvable
  case and heuristics

abstract:
  We consider the NP-hard 2-period balanced travelling salesman problem. In
this problem the salesman needs to visit a set of customers in two time
periods. A given subset of the customers has to be visited in both periods
while the rest of the customers need to be visited only once, in any of the two
periods. Moreover, it is required that the number of customers visited in the
first period does not differ from the respective number in the second period by
more than p, where p is a given small constant. The objective is to find tours
for visiting the customers in the two periods with minimal total length. We
show that in the case when the underlying distance matrix is a Kalmanson
matrix, the problem can be solved in polynomial time. For the general case, we
propose two new heuristics. A combination of these two heuristics has shown
very favourable results in computational experiments on benchmark instances
from the literature.


title:
Convection Reconciles the Difference in Efficiencies Between Low-Mass
  and High-Mass Common Envelopes

abstract:
  The formation pathways for gravitational-wave merger sources are predicted to
include common envelope (CE) evolution. Observations of high-mass post-common
envelope binaries suggest that energy transfer to the envelope during the CE
phase must be highly efficient. In contrast, observations of low-mass post-CE
binaries indicate energy transfer during the CE phase must be highly
inefficient. Convection, a process present in low-mass and high-mass stars,
naturally explains this dichotomy. Using observations of Wolf-Rayet binaries,
we study the effects of convection and radiative losses on the predicted final
separations of high-mass common envelopes. Despite robust convection in massive
stars, the effect is minimal as the orbit decays well before convection can
transport the liberated orbital energy to the surface. In low-mass systems,
convective transport occurs faster then the orbit decays, allowing the system
to radiatively cool thereby lowering the efficiency. The inclusion of
convection reproduces observations of low-mass and high-mass binaries and
remains a necessary ingredient for determining outcomes of common envelopes.


title:
Local H\"older and maximal regularity of solutions of elliptic equations
  with superquadratic gradient terms

abstract:
  We study the local H\"older regularity of strong solutions $u$ of
second-order uniformly elliptic equations having a gradient term with
superquadratic growth $\gamma > 2$, and right-hand side in a Lebesgue space
$L^q$. When $q > N\frac{\gamma-1}{\gamma}$ ($N$ is the dimension of the
Euclidean space), we obtain the optimal H\"older continuity exponent $\alpha_q
> \frac{\gamma-2}{\gamma-1}$. This allows us to prove some new results of
maximal regularity type, which consist in estimating the Hessian matrix of $u$
in $L^q$. Our methods are based on blow-up techniques and a Liouville theorem.


title: Integration and Packaging

abstract:
  Vertically integrated (3D) combinations of sensors and electronics provide
the ability to fabricate small, fine pitch pixels with very small total
capacitance monolithically integrated with complex circuitry. The small
capacitance, enabled by the fine pixel pitch and low interconnect capacitance
available in 3D hybrid bonding, provides excellent signal/noise with moderate
power. This combination enables fabrication of integrated sensors and
electronics with both excellent position and time resolution. In this white
paper a discussion will be presented on 3D integration advantages, ongoing
projects and prospects in high energy physics and beyond.


title:
Collectivity in Ultra-Peripheral Pb+Pb Collisions at the Large Hadron
  Collider

abstract:
  We present the first full (3+1)D dynamical simulations of ultra-peripheral
Pb+Pb collisions at the Large Hadron Collider. Extrapolating from p+Pb
collisions, we explore whether a quasi-real photon $\gamma^*$ interacting with
the lead nucleus in an ultra-peripheral collision can create a many-body system
exhibiting fluid behavior. Assuming strong final-state interactions, we provide
model results for charged hadron multiplicity, identified particle mean
transverse momenta, and charged hadron anisotropic flow coefficients, and
compare them with experimental data from the ALICE and ATLAS collaborations.
The elliptic flow hierarchy between p+Pb and $\gamma^*$+Pb collisions is
dominated by the difference in longitudinal flow decorrelations and reproduces
the experimental data well. We have demonstrated that our theoretical framework
provides a quantitative tool to study particle production and collectivity for
all system sizes, ranging from central heavy-ion collisions to small asymmetric
collision systems at the Relativistic Heavy-Ion Collider and the Large Hadron
Collider and even at the future Electron-Ion Collider.


title: Constrained mixers for QAOA

abstract:
  The quantum approximate optimization algorithm/quantum alternating operator
ansatz (QAOA) is a heuristic to find approximate solutions of combinatorial
optimization problems. Most literature is limited to quadratic problems without
constraints. However, many practically relevant optimization problems do have
(hard) constraints that need to be fulfilled. In this article we present a
framework for constructing mixing operators that restrict the evolution to a
subspace of the full Hilbert space given by these constraints; We generalize
the "XY"-mixer designed to preserve the subspace of "one-hot" states to the
general case of subspaces given by a number of computational basis states. We
expose the underlying mathematical structure which reveals more of how mixers
work and how one can minimize their cost in terms of number of CNOT gates,
particularly when Trotterization is taken into account. Our analysis also leads
to valid Trotterizations for "XY"-mixer with fewer CNOT gates as is known to
date. In view of practical implementations we also describe algorithms for
efficient decomposition into basis gates. Several examples of more general
cases are presented and analyzed.


title:
WLASL-LEX: a Dataset for Recognising Phonological Properties in American
  Sign Language

abstract:
  Signed Language Processing (SLP) concerns the automated processing of signed
languages, the main means of communication of Deaf and hearing impaired
individuals. SLP features many different tasks, ranging from sign recognition
to translation and production of signed speech, but has been overlooked by the
NLP community thus far. In this paper, we bring to attention the task of
modelling the phonology of sign languages. We leverage existing resources to
construct a large-scale dataset of American Sign Language signs annotated with
six different phonological properties. We then conduct an extensive empirical
study to investigate whether data-driven end-to-end and feature-based
approaches can be optimised to automatically recognise these properties. We
find that, despite the inherent challenges of the task, graph-based neural
networks that operate over skeleton features extracted from raw videos are able
to succeed at the task to a varying degree. Most importantly, we show that this
performance pertains even on signs unobserved during training.


title: Wideband current modulation of diode lasers for frequency stabilization

abstract:
  We present a current-modulation technique for diode-laser systems that is
specifically designed for high-bandwidth laser-frequency stabilization and
wideband frequency modulation with a flat transfer function. It consists of a
dedicated current source and an impedance-matching circuit both placed close to
the laser diode. The transfer behaviour of the system is analysed under
realistic conditions employing an external-cavity diode laser (ECDL) system. We
achieve a phase lag less than 90$\deg$ up to 25 MHz and a gain flatness of
$\pm$ 3 dB in the frequency range of DC to 100 MHz while the passive stability
of the laser system is not impaired. The potential of the presented current
modulation scheme is demonstrated in an optical phase-locked-loop between two
ECDL systems resulting in an improved phase noise of 42 mrad$_{rms}$. Details
on the electronic layout and the printed circuit board design are available as
an open-source project at
https://github.com/TU-Darmstadt-APQ/Laser\_Backplane\_DVI.


title: Complex scaling flows in quench dynamics of interacting particles

abstract:
  Many-body systems driven out of equilibrium can exhibit scaling flows of the
quantum state. For a sudden quench to resonant interactions between particles
we construct a new class of analytical scaling solutions for the time evolved
wave function with a complex scale parameter. These solutions determine the
exact dynamical scaling of observables such as the pair correlation function,
the contact and the fidelity. We give explicit examples of the nonequilibrium
dynamics for two trapped fermions or bosons quenched to unitarity, for ideal
Bose polarons, and for resonantly interacting, Borromean three-body systems.
These solutions reveal universal scaling properties of interacting many-body
systems that arise from the buildup of correlations at short times after the
quench.


title:
Minimization of Eddy Current Artifacts in Sequences with Periodic
  Dynamics

abstract:
  Purpose: To minimize eddy-current artifacts in periodic pulse sequences with
balanced gradient moments as, e.g., used for quantitative MRI.
  Theory and Methods: Eddy current artifacts in balanced sequences result from
large jumps in k-space. In quantitative MRI, one often samples some spin
dynamics repeatedly while acquiring different parts of k-space. We swap
individual k-space lines between different repetitions in order to minimize
jumps within each repetition. This reordering can be formulated as a travelling
salesman problem and we tackle the discrete optimization with a simulated
annealing algorithm.
  Results: The proposed approach is highly effective at minimizing the change
of the readout direction of a 3D koosh-ball trajectory. This decreases the
eddy-current induced perturbations of the magnetization and reduces artifacts
in the derived parameter maps, as demonstrated in the example of a hybrid-state
free precession sequence.
  Conclusion: Beyond this example application, we believe that the approach can
be used for most k-space trajectories, other quantitative MRI methods, as well
as for cine cardiac imaging.


title: Snowmass Neutrino Frontier: DUNE Physics Summary

abstract:
  The Deep Underground Neutrino Experiment (DUNE) is a next-generation
long-baseline neutrino oscillation experiment with a primary physics goal of
observing neutrino and antineutrino oscillation patterns to precisely measure
the parameters governing long-baseline neutrino oscillation in a single
experiment, and to test the three-flavor paradigm. DUNE's design has been
developed by a large, international collaboration of scientists and engineers
to have unique capability to measure neutrino oscillation as a function of
energy in a broadband beam, to resolve degeneracy among oscillation parameters,
and to control systematic uncertainty using the exquisite imaging capability of
massive LArTPC far detector modules and an argon-based near detector. DUNE's
neutrino oscillation measurements will unambiguously resolve the neutrino mass
ordering and provide the sensitivity to discover CP violation in neutrinos for
a wide range of possible values of $\delta_{CP}$. DUNE is also uniquely
sensitive to electron neutrinos from a galactic supernova burst, and to a broad
range of physics beyond the Standard Model (BSM), including nucleon decays.
DUNE is anticipated to begin collecting physics data with Phase I, an initial
experiment configuration consisting of two far detector modules and a minimal
suite of near detector components, with a 1.2 MW proton beam. To realize its
extensive, world-leading physics potential requires the full scope of DUNE be
completed in Phase II. The three Phase II upgrades are all necessary to achieve
DUNE's physics goals: (1) addition of far detector modules three and four for a
total FD fiducial mass of at least 40 kt, (2) upgrade of the proton beam power
from 1.2 MW to 2.4 MW, and (3) replacement of the near detector's temporary
muon spectrometer with a magnetized, high-pressure gaseous argon TPC and
calorimeter.


title:
Zeckendorf representation of multiplicative inverses modulo a Fibonacci
  number

abstract:
  Prempreesuk, Noppakaew, and Pongsriiam determined the Zeckendorf
representation of the multiplicative inverse of $2$ modulo $F_n$, for every
positive integer $n$ not divisible by $3$, where $F_n$ denotes the $n$th
Fibonacci number. We determine the Zeckendorf representation of the
multiplicative inverse of $a$ modulo $F_n$, for every fixed integer $a \geq 3$
and for all positive integers $n$ with $\gcd(a, F_n) = 1$. Our proof makes use
of the so-called base-$\varphi$ expansion of real numbers.


title:
On the Difficulty of Epistemic Uncertainty Quantification in Machine
  Learning: The Case of Direct Uncertainty Estimation through Loss Minimisation

abstract:
  Uncertainty quantification has received increasing attention in machine
learning in the recent past. In particular, a distinction between aleatoric and
epistemic uncertainty has been found useful in this regard. The latter refers
to the learner's (lack of) knowledge and appears to be especially difficult to
measure and quantify. In this paper, we analyse a recent proposal based on the
idea of a second-order learner, which yields predictions in the form of
distributions over probability distributions. While standard (first-order)
learners can be trained to predict accurate probabilities, namely by minimising
suitable loss functions on sample data, we show that loss minimisation does not
work for second-order predictors: The loss functions proposed for inducing such
predictors do not incentivise the learner to represent its epistemic
uncertainty in a faithful way.


title: The high-energy frontier of the muon g-2

abstract:
  The long-standing muon g-2 anomaly can be explained by heavy new physics
particles through chirally-enhanced contributions. It has been recently
proposed that a muon collider running at center-of-mass energies of several TeV
could test these new physics scenarios in a model-independent way, through the
study of high-energy processes such as mu+ mu- --> h gamma. In this work, we
validate these findings, based on effective field theories, by considering
renormalizable simplified models and by computing this one-loop process in full
generality. Furthermore, we explore the interplay of direct and indirect
high-energy searches to pin down the details of the underlying new physics
model accommodating the muon g-2 anomaly.


title: DUNE Software and High Performance Computing

abstract:
  DUNE, like other HEP experiments, faces a challenge related to matching
execution patterns of our production simulation and data processing software to
the limitations imposed by modern high-performance computing facilities. In
order to efficiently exploit these new architectures, particularly those with
high CPU core counts and GPU accelerators, our existing software execution
models require adaptation. In addition, the large size of individual units of
raw data from the far detector modules pose an additional challenge somewhat
unique to DUNE. Here we describe some of these problems and how we begin to
solve them today with existing software frameworks and toolkits. We also
describe ways we may leverage these existing software architectures to attack
remaining problems going forward. This whitepaper is a contribution to the
Computational Frontier of Snowmass21.


title: A summary on the UD Kalman Filter

abstract:
  This document contains a concise and unified reference for one of the
existing mechanizations of the UD Kalman filter. Associated matrix algorithms
are also included along with the corresponding references.


title: Fundamental resolution limit of quantum imaging with undetected photons

abstract:
  Quantum imaging with undetected photons relies on the principle of induced
coherence without induced emission and uses two sources of photon-pairs with a
signal- and an idler photon of wavelengths $\lambda_S$ and $\lambda_I$,
respectively. Each pair shares strong quantum correlations in both position and
momentum, which allows to image an object illuminated with idler photons by
just measuring signal photons that never interact with the object. In this
work, we theoretically investigate the transverse resolution of this non-local
imaging scheme through a general formalism that treats propagating photons
beyond the commonly used paraxial approximation. We hereby prove that the
resolution of quantum imaging with undetected photons is diffraction limited to
the longer wavelength of the signal and idler pairs, with a minimum resolvable
resolution of $ \mathrm{max}(\lambda_S,\lambda_I)/2$. Moreover, we conclude
that this result is also valid for other non-local two-photon imaging schemes.


title: REX: Reasoning-aware and Grounded Explanation

abstract:
  Effectiveness and interpretability are two essential properties for
trustworthy AI systems. Most recent studies in visual reasoning are dedicated
to improving the accuracy of predicted answers, and less attention is paid to
explaining the rationales behind the decisions. As a result, they commonly take
advantage of spurious biases instead of actually reasoning on the
visual-textual data, and have yet developed the capability to explain their
decision making by considering key information from both modalities. This paper
aims to close the gap from three distinct perspectives: first, we define a new
type of multi-modal explanations that explain the decisions by progressively
traversing the reasoning process and grounding keywords in the images. We
develop a functional program to sequentially execute different reasoning steps
and construct a new dataset with 1,040,830 multi-modal explanations. Second, we
identify the critical need to tightly couple important components across the
visual and textual modalities for explaining the decisions, and propose a novel
explanation generation method that explicitly models the pairwise
correspondence between words and regions of interest. It improves the visual
grounding capability by a considerable margin, resulting in enhanced
interpretability and reasoning performance. Finally, with our new data and
method, we perform extensive analyses to study the effectiveness of our
explanation under different settings, including multi-task learning and
transfer learning. Our code and data are available at
https://github.com/szzexpoi/rex.


title: ActiveMLP: An MLP-like Architecture with Active Token Mixer

abstract:
  This paper presents ActiveMLP, a general MLP-like backbone for computer
vision. The three existing dominant network families, i.e., CNNs, Transformers
and MLPs, differ from each other mainly in the ways to fuse contextual
information into a given token, leaving the design of more effective
token-mixing mechanisms at the core of backbone architecture development. In
ActiveMLP, we propose an innovative token-mixer, dubbed Active Token Mixer
(ATM), to actively incorporate contextual information from other tokens in the
global scope into the given one. This fundamental operator actively predicts
where to capture useful contexts and learns how to fuse the captured contexts
with the original information of the given token at channel levels. In this
way, the spatial range of token-mixing is expanded and the way of token-mixing
is reformed. With this design, ActiveMLP is endowed with the merits of global
receptive fields and more flexible content-adaptive information fusion.
Extensive experiments demonstrate that ActiveMLP is generally applicable and
comprehensively surpasses different families of SOTA vision backbones by a
clear margin on a broad range of vision tasks, including visual recognition and
dense prediction tasks. The code and models will be available at
https://github.com/microsoft/ActiveMLP.


title: The polar orbit of the warm Neptune GJ436b seen with VLT/ESPRESSO

abstract:
  GJ436b might be the prototype of warm Neptunes that have undergone late
migration induced by an outer companion. Precise determination of the orbital
architecture of such systems is critical to constraining their dynamical
history and evaluating the role of delayed migration in the exoplanet
population. To this purpose we analyzed the Rossiter-McLaughlin (RM) signal of
GJ436 b in two transits - recently observed with ESPRESSO - using three
different techniques. The high level of precision achieved in radial velocity
(RV) measurements allows us to detect the deviation from the Keplerian orbit,
despite the slow rotation of the M dwarf host (vsini = 272.0+40.0-34.0 m/s),
and to measure the sky-projected obliquity ($\lambda$ =
102.5+17.2-18.5$^{\circ}$). The Reloaded RM technique, which allows the stellar
RV field along the transit chord to be analyzed, yields $\lambda$ =
107.5+23.6-19.3$^{\circ}$ and vsini = 292.9+41.9-49.9 m/s. The RM Revolutions
technique, which allows us to fit the spectral profiles from all
planet-occulted regions together, yields $\lambda$ = 114.1+22.8-17.8$^{\circ}$
and vsini = 300.5+45.9-57.0 m/s. The consistent results between these three
techniques, and with published results from HARPS/HARPS-N data, confirm the
polar orbit of GJ436b and support the hypothesis that its origin lies in Kozai
migration. Results from a joint RM Revolutions analysis of the ESPRESSO, HARPS,
and HARPS-N datasets ($\lambda$ = 113.5+23.3-17.3$^{\circ}$; vsini =
293.5+43.7-52.2 m/s) combined with a revised stellar inclination ($i_\star$ =
35.7+5.9-7.6$^{\circ}$ or 144.2+7.6-5.9$^{\circ}$) lead us to constrain the 3D
obliquity $\Psi$ to 103.2+12.8-11.5$^{\circ}$.


title:
Stochastic Learning in Kolkata Paise Restaurant Problem: Classical \&
  Quantum Strategies

abstract:
  We will review the results for stochastic learning strategies, both classical
(one-shot and iterative) and quantum (one-shot only), for optimizing the
available many-choice resources among a large number of competing agents,
developed over the last decade in the context of the Kolkata Paise Restaurant
Problem. Apart from a few rigorous and approximate analytical results, both for
classical and quantum strategies, most of the interesting results on the phase
transition behavior (obtained so far for the classical model) using classical
Monte Carlo simulations. All these, including the applications to computer
science (job or resource allotments in Internet-of-Things), transport
engineering (on-line vehicle hire problems), operation research (optimizing
efforts for delegated search problem, efficient solution of Travelling Salesman
problem), etc will be discussed.


title: Multi-sensor large-scale dataset for multi-view 3D reconstruction

abstract:
  We present a new multi-sensor dataset for 3D surface reconstruction. It
includes registered RGB and depth data from sensors of different resolutions
and modalities: smartphones, Intel RealSense, Microsoft Kinect, industrial
cameras, and structured-light scanner. The data for each scene is obtained
under a large number of lighting conditions, and the scenes are selected to
emphasize a diverse set of material properties challenging for existing
algorithms. In the acquisition process, we aimed to maximize high-resolution
depth data quality for challenging cases, to provide reliable ground truth for
learning algorithms. Overall, we provide over 1.4 million images of 110
different scenes acquired at 14 lighting conditions from 100 viewing
directions. We expect our dataset will be useful for evaluation and training of
3D reconstruction algorithms of different types and for other related tasks.
Our dataset and accompanying software will be available online.


title: Solving Simulation Systematics in and with AI/ML

abstract:
  Training an AI/ML system on simulated data while using that system to infer
on data from real detectors introduces a systematic error which is difficult to
estimate and in many analyses is simply not confronted. It is crucial to
minimize and to quantitatively estimate the uncertainties in such analysis and
do so with a precision and accuracy that matches those that AI/ML techniques
bring. Here we highlight the need to confront this class of systematic error,
discuss conventional ways to estimate it and describe ways to quantify and to
minimize the uncertainty using methods which are themselves based on the power
of AI/ML. We also describe methods to introduce a simulation into an AI/ML
network to allow for training of its semantically meaningful parameters. This
whitepaper is a contribution to the Computational Frontier of Snowmass21.


title:
Detection of multiple retinal diseases in ultra-widefield fundus images
  using deep learning: data-driven identification of relevant regions

abstract:
  Ultra-widefield (UWF) imaging is a promising modality that captures a larger
retinal field of view compared to traditional fundus photography. Previous
studies showed that deep learning (DL) models are effective for detecting
retinal disease in UWF images, but primarily considered individual diseases
under less-than-realistic conditions (excluding images with other diseases,
artefacts, comorbidities, or borderline cases; and balancing healthy and
diseased images) and did not systematically investigate which regions of the
UWF images are relevant for disease detection. We first improve on the state of
the field by proposing a DL model that can recognise multiple retinal diseases
under more realistic conditions. We then use global explainability methods to
identify which regions of the UWF images the model generally attends to. Our
model performs very well, separating between healthy and diseased retinas with
an area under the curve (AUC) of 0.9206 on an internal test set, and an AUC of
0.9841 on a challenging, external test set. When diagnosing specific diseases,
the model attends to regions where we would expect those diseases to occur. We
further identify the posterior pole as the most important region in a purely
data-driven fashion. Surprisingly, 10% of the image around the posterior pole
is sufficient for achieving comparable performance to having the full images
available.


title:
Displaced fat-jets and tracks to probe boosted right-handed neutrinos in
  the $U(1)_{B-L}$ model

abstract:
  We investigate the pair-production of Right-Handed Neutrinos (RHNs) via a
$B-L$ $Z'$ boson and the detection prospects at the High-Luminosity run of the
LHC (HL-LHC) and a future $pp$ collider (FCC-hh). We focus on RHN states with a
mass of $10-70$ GeV which naturally results in displaced vertices for small
active-sterile mixing strengths. Being produced through a mass resonance with
$m_{Z'} \ge 1$ TeV, the RHNs are heavily boosted, leading to collimated decay
products that give rise to fat-jets. We investigate the detection prospect of
dedicated signatures in the inner detector and the muon spectrometer, namely a
pair of displaced fat-jets and the associated tracks, respectively. We find
that both the HL-LHC and FCC-hh can be sensitive to small active-sterile mixing
$V_{\mu N} > 10^{-6}$ and $V_{\mu N} > 10^{-7}$ with the number of events
reaching $\mathcal{O}(10)$ and $\mathcal{O}(10^3)$, respectively. This allows
probing the generation of light neutrino masses through the Seesaw mechanism in
this scenario.


title: Sparse recovery properties of discrete random matrices

abstract:
  Motivated by problems from compressed sensing, we determine the threshold
behavior of a random $n\times d$ $\pm 1$ matrix $M_{n,d}$ with respect to the
property "every $s$ columns are linearly independent". In particular, we show
that for every $0<\delta <1$ and $s=(1-\delta)n$, if $d\leq
n^{1+1/2(1-\delta)-o(1)}$ then whp every $s$ columns of $M_{n,d}$ are linearly
independent, and if $d\geq n^{1+1/2(1-\delta)+o(1)}$ then whp there are some
$s$ linearly dependent columns.


title: Locality of staggered overlap operators

abstract:
  We give an explicit proof for the locality of staggered overlap operators.
The proof covers the original two flavor construction by Adams as well as a
single flavor version. As in the case of Neuberger's operator, an admissibility
condition for the gauge fields is required.


title: GATSPI: GPU Accelerated Gate-Level Simulation for Power Improvement

abstract:
  In this paper, we present GATSPI, a novel GPU accelerated logic gate
simulator that enables ultra-fast power estimation for industry sized ASIC
designs with millions of gates. GATSPI is written in PyTorch with custom CUDA
kernels for ease of coding and maintainability. It achieves simulation kernel
speedup of up to 1668X on a single-GPU system and up to 7412X on a multiple-GPU
system when compared to a commercial gate-level simulator running on a single
CPU core. GATSPI supports a range of simple to complex cell types from an
industry standard cell library and SDF conditional delay statements without
requiring prior calibration runs and produces industry-standard SAIF files from
delay-aware gate-level simulation. Finally, we deploy GATSPI in a
glitch-optimization flow, achieving a 1.4% power saving with a 449X speedup in
turnaround time compared to a similar flow using a commercial simulator.


title:
Estimating the age-dependent physical parameters of kiwifruit with
  non-contact acoustic measurements

abstract:
  We present non-contact acoustic measurements of the mechanical properties of
a golden kiwifruit. With a laser source in emission, we measure the transmitted
acoustic waves using a rotating laser ultrasound detector with a fibre head.
Two main propagating waves are observed; a low-frequency Rayleigh wave, and a
high frequency wave which propagates through the outer flesh of the fruit.
Theoretical modeling of this second wave enables estimates of the wave velocity
in the two outer layers of the fruit flesh, and of several viscoelastic
parameters. In particular, we find that both acoustic wave velocities, Young's
modulus $E$, and bulk modulus $K$ evolve with fruit age, while the
high-frequency wave velocity and $K$ also differ between outer and inner fruit
layers.

categories: physics.app-ph eess.SP physics.bio-ph physics.flu-dyn physics.ins-det


title:
The role of inter-regional mobility in forecasting SARS-CoV-2
  transmission

abstract:
  In this paper, we present a method to forecast the spread of SARS-CoV-2
across regions with a focus on the role of mobility. Mobility has previously
been shown to play a significant role in the spread of the virus, particularly
between regions. Here, we investigate under which epidemiological circumstances
incorporating mobility into transmission models yields improvements in the
accuracy of forecasting, where we take the situation in the Netherlands during
and after the first wave of transmission in 2020 as a case study. We assess the
quality of forecasting on the detailed level of municipalities, instead of on a
nation-wide level. To model transmissions, we use a simple mobility-enhanced
SEIR compartmental model with subpopulations corresponding to the Dutch
municipalities. We use commuter information to quantify mobility, and develop a
method based on maximum likelihood estimation to determine the other relevant
parameters. We show that taking inter-regional mobility into account generally
leads to an improvement in forecast quality. However, at times when policies
are in place that aim to reduce contacts or travel, this improvement is very
small.


title: Abstract Excision and $\ell^1$-Homology

abstract:
  We use the abstract setting of excisive functors in the language of
$\infty$-categories to show that the best approximation to the
$\ell^1$-homology functor by an excisive functor is trivial.
  Then we make an effort to explain the used language on a conceptual level for
those who do not feel at home with $\infty$-categories, prove that the singular
chain complex functor is indeed excisive in the abstract sense, and show how
the latter leads to classical excision statements in the form of Mayer-Vietoris
sequences.


title:
Cold atmospheric plasma activated deionized water using helium, argon,
  and nitrogen as feeding gas for cancer therapy

abstract:
  Plasma activated medium, containing reactive oxygen species (ROS) and
reactive nitrogen species (RNS), has been used for cancer treatment as an
indirect treatment method. Here we report cold atmospheric plasma (CAP)
activated deionized (DI) water using helium (He), argon (Ar), and nitrogen (N2)
as feeding gas for the cancer therapy. Basic characteristics of CAP activated
DI water with different feeding gases were diagnosed and the effects of three
solutions on the breast cancer cells and gastric cancer cells were also
recorded. Our findings show that DI water treated by CAP with He feeding gas
has a much stronger effect on apoptosis in precultured breast cancer cells and
gastric cancer cells. These results are attributed to the higher concentration
of ROS generated in CAP-activated DI water using He as feeding gas.


title: Deep Learning the Shape of the Brain Connectome

abstract:
  To statistically study the variability and differences between normal and
abnormal brain connectomes, a mathematical model of the neural connections is
required. In this paper, we represent the brain connectome as a Riemannian
manifold, which allows us to model neural connections as geodesics. We show for
the first time how one can leverage deep neural networks to estimate a
Riemannian metric of the brain that can accommodate fiber crossings and is a
natural modeling tool to infer the shape of the brain from DWMRI. Our method
achieves excellent performance in geodesic-white-matter-pathway alignment and
tackles the long-standing issue in previous methods: the inability to recover
the crossing fibers with high fidelity.


title:
An Empirical Study of Graphormer on Large-Scale Molecular Modeling
  Datasets

abstract:
  This technical note describes the recent updates of Graphormer, including
architecture design modifications, and the adaption to 3D molecular dynamics
simulation. The "Graphormer-V2" could attain better results on large-scale
molecular modeling datasets than the vanilla one, and the performance gain
could be consistently obtained on downstream tasks. In addition, we show that
with a global receptive field and an adaptive aggregation strategy, Graphormer
is more powerful than classic message-passing-based GNNs. Graphormer-V2
achieves much less MAE than the vanilla Graphormer on the PCQM4M quantum
chemistry dataset used in KDD Cup 2021, where the latter one won the first
place in this competition. In the meanwhile, Graphormer-V2 greatly outperforms
the competitors in the recent Open Catalyst Challenge, which is a competition
track on NeurIPS 2021 workshop, and aims to model the catalyst-adsorbate
reaction system with advanced AI models. All models could be found at
\url{https://github.com/Microsoft/Graphormer}.


title:
Accelerating cosmological inference with Gaussian processes and neural
  networks -- an application to LSST Y1 weak lensing and galaxy clustering

abstract:
  Studying the impact of systematic effects, optimizing survey strategies,
assessing tensions between different probes and exploring synergies of
different data sets require a large number of simulated likelihood analyses,
each of which cost thousands of CPU hours. In this paper, we present a method
to accelerate cosmological inference using emulators based on Gaussian process
regression and neural networks. We iteratively acquire training samples in
regions of high posterior probability which enables accurate emulation of data
vectors even in high dimensional parameter spaces. We showcase the performance
of our emulator with a simulated 3x2 point analysis of LSST-Y1 with realistic
theoretical and systematics modelling. We show that our emulator leads to
high-fidelity posterior contours, with an order of magnitude speed-up. Most
importantly, the trained emulator can be re-used for extremely fast impact and
optimization studies. We demonstrate this feature by studying baryonic physics
effects in LSST-Y1 3x2 point analyses where each one of our MCMC runs takes
approximately 5 minutes. This technique enables future cosmological analyses to
map out the science return as a function of analysis choices and survey
strategy.


title: Protein Representation Learning by Geometric Structure Pretraining

abstract:
  Learning effective protein representations is critical in a variety of tasks
in biology such as predicting protein function or structure. Existing
approaches usually pretrain protein language models on a large number of
unlabeled amino acid sequences and then finetune the models with some labeled
data in downstream tasks. Despite the effectiveness of sequence-based
approaches, the power of pretraining on smaller numbers of known protein
structures has not been explored for protein property prediction, though
protein structures are known to be determinants of protein function. We first
present a simple yet effective encoder to learn protein geometry features. We
pretrain the protein graph encoder by leveraging multiview contrastive learning
and different self-prediction tasks. Experimental results on both function
prediction and fold classification tasks show that our proposed pretraining
methods outperform or are on par with the state-of-the-art sequence-based
methods using much less data. All codes and models will be published upon
acceptance.


title: Distribution-free Prediction Sets Adaptive to Unknown Covariate Shift

abstract:
  Predicting sets of outcomes -- instead of unique outcomes -- is a promising
solution to uncertainty quantification in statistical learning. Despite a rich
literature on constructing prediction sets with statistical guarantees,
adapting to unknown covariate shift -- a prevalent issue in practice -- poses a
serious challenge and has yet to be solved. In the framework of semiparametric
statistics, we can view the covariate shift as a nuisance parameter. In this
paper, we propose a novel flexible distribution-free method, PredSet-1Step, to
construct prediction sets that can efficiently adapt to unknown covariate
shift. PredSet-1Step relies on a one-step correction of the plug-in estimator
of coverage error.
  We theoretically show that our methods are asymptotically probably
approximately correct (PAC), having low coverage error with high confidence for
large samples. PredSet-1Step may also be used to construct asymptotically
risk-controlling prediction sets. We illustrate that our method has good
coverage in a number of experiments and by analyzing a data set concerning HIV
risk prediction in a South African cohort study. In experiments without
covariate shift, PredSet-1Step performs similarly to inductive conformal
prediction, which has finite-sample PAC properties. Thus, PredSet-1Step may be
used in the common scenario if the user suspects -- but may not be certain --
that covariate shift is present, and does not know the form of the shift. Our
theory hinges on a new bound for the convergence rate of Wald confidence
interval coverage for general asymptotically linear estimators. This is a
technical tool of independent interest.


title:
Spatial Consistency Loss for Training Multi-Label Classifiers from
  Single-Label Annotations

abstract:
  As natural images usually contain multiple objects, multi-label image
classification is more applicable "in the wild" than single-label
classification. However, exhaustively annotating images with every object of
interest is costly and time-consuming. We aim to train multi-label classifiers
from single-label annotations only. We show that adding a consistency loss,
ensuring that the predictions of the network are consistent over consecutive
training epochs, is a simple yet effective method to train multi-label
classifiers in a weakly supervised setting. We further extend this approach
spatially, by ensuring consistency of the spatial feature maps produced over
consecutive training epochs, maintaining per-class running-average heatmaps for
each training image. We show that this spatial consistency loss further
improves the multi-label mAP of the classifiers. In addition, we show that this
method overcomes shortcomings of the "crop" data-augmentation by recovering
correct supervision signal even when most of the single ground truth object is
cropped out of the input image by the data augmentation. We demonstrate gains
of the consistency and spatial consistency losses over the binary cross-entropy
baseline, and over competing methods, on MS-COCO and Pascal VOC. We also
demonstrate improved multi-label classification mAP on ImageNet-1K using the
ReaL multi-label validation set.


title:
Functional universality in slow-growing microbial communities arises
  from thermodynamic constraints

abstract:
  The dynamics of microbial communities is incredibly complex, determined by
competition for metabolic substrates and cross-feeding of byproducts. Species
in the community grow by harvesting energy from chemical reactions that
transform substrates to products. In many anoxic environments, these reactions
are close to thermodynamic equilibrium and growth is slow. To understand the
community structure in these energy-limited environments, we developed a
microbial community consumer-resource model incorporating energetic and
thermodynamic constraints on an interconnected metabolic network. The central
ingredient of the model is product inhibition, meaning that microbial growth
may be limited not only by depletion of metabolic substrates but also by
accumulation of products. We demonstrate that these additional constraints on
microbial growth cause a convergence in the structure and function of the
community metabolic network -- independent of species composition and
biochemical details -- providing a possible explanation for convergence of
community function despite taxonomic variation observed in many natural and
industrial environments. Furthermore, we discovered that the structure of
community metabolic network is governed by the thermodynamic principle of
maximum heat dissipation. Overall, the work demonstrates how universal
thermodynamic principles may constrain community metabolism and explain
observed functional convergence in microbial communities.

categories: q-bio.PE cond-mat.stat-mech nlin.AO physics.bio-ph q-bio.MN


title:
Observation of spin-correlated exciton-polaritons in a van der Waals
  magnet

abstract:
  Strong coupling between light and material excitations is emerging as a
powerful tool to engineer the properties of solid-state systems.
Spin-correlated excitations in magnetic crystals that couple strongly to
optical cavities promise control over collective quantum phenomena, but their
suitable electronic resonances have yet to be found. Here we report strong
light-matter coupling in nickel phosphorus trisulfide ($\textrm{NiPS}_3$), a
van der Waals magnet with strongly correlated magnetic and electronic degrees
of freedom. A previously unobserved class of polaritonic quasiparticles emerges
from the strong coupling between its spin-correlated excitons and the photons
inside a microcavity. The hybridization with light offers unique opportunities
to study the origin and interactions of magnetically coupled excitations in
antiferromagnetic insulators. Our work introduces van der Waals magnets to the
field of strong light-matter physics and provides a path towards the design and
control of correlated quantum materials via cavity quantum electrodynamics.


title: Chiral 480nm absorption in the hemoglycin space polymer

abstract:
  A 1494 Dalton hemoglycin space polymer of Glycine18 Hydroxy-glycine4 Fe2O4
termed the core unit is part of a polymer of Glycine, Si, Fe and O that forms
tubes, vesicles and a lattice structure isolated from CV3 meteorites and
characterized by mass spectrometry, FIB/SIMS and X-ray analysis. In
Hartree-Fock calculations the polymer has an absorption of blue light at 480nm
that is dependent on rectus R (= dextro D) chirality in a hydroxy-glycine
residue whose C-terminus is bonded to an iron atom. The absorption originates
in the Fe II state as a consequence of chiral symmetry breaking. The infrared
spectrum is presented. We discuss how the core unit could have been selected
4.5 billion years ago in our protoplanetary disc by blue light from the early
sun.


title:
BSM $\nu$ physics: complementarity across energies -- a white paper for
  Snowmass 2021

abstract:
  We reiterate that there is significant complementarity between low-energy
experiments and high-energy colliders in exploring new physics associated with
neutrino properties and their mass generation mechanisms. Signals of the new
physics in the two energy regimes may be correlated with each other from the
same underlying dynamics. We demonstrate the complementary nature by presenting
the physics reaches for the Seesaw models of Type I, II and III, and for
general neutrino interactions in an effective field theory framework, and in a
$Z'$ model.


title:
Comment on "Direct and Converse Flexoelectricity in Two-Dimensional
  Materials"

abstract:
  In a recent letter [Phys. Rev. Lett. 127, 216801 (2021)], the authors
introduced the flexovoltage coefficient $\varphi$ for quantifying the
flexoelectric effect in 2D materials. In particular, they introduced the metric
term $\varphi^\text{M}$ -- quadrupolar moment of the unperturbed charge density
-- which they claim to have been neglected in the formulation of Ref. [Phys.
Rev. Mat. 5, L030801 (2021)] for the transversal flexoelectric coefficient
$\mu_\text{T}$, and used it to explain the significant disagreement in results.
Here, we clarify that $\mu_\text{T}$ has been properly defined in Ref. [Phys.
Rev. Mat. 5, L030801 (2021)], and that the suggested relation : $\varphi =
\mu_{\rm T}/\epsilon_0 + \varphi^\text{M}, \, \epsilon_0: \text{vacuum
permittivity} $, inferred from numerical tests [Phys. Rev. Lett. 127, 216801
(2021)], is unjustified.


title:
Scaling limit of the heavy-tailed ballistic deposition model with
  $p$-sticking

abstract:
  Ballistic deposition is a classical model for interface growth in which unit
blocks fall down vertically at random on the different sites of $\mathbb{Z}$
and stick to the interface at the first point of contact, causing it to grow.
We consider an alternative version of this model in which the blocks have
random heights which are i.i.d. with a heavy (right) tail, and where each block
sticks to the interface at the first point of contact with probability $p$
(otherwise, it falls straight down until it lands on a block belonging to the
interface). We study scaling limits of the resulting interface for the
different values of $p$ and show that there is a phase transition as $p$ goes
from $1$ to $0$.


title:
Gegenbauer expansions and addition theorems for a binomial and
  logarithmic fundamental solution of the even-dimensional Euclidean
  polyharmonic equation

abstract:
  On even-dimensional Euclidean space for integer powers of the Laplace
operator greater than or equal to half the dimension, a fundamental solution of
the polyharmonic equation has binomial and logarithmic behavior. Gegenbauer
polynomial expansions of these fundamental solutions are obtained through a
limit applied to Gegenbauer expansions of a power-law fundamental solution of
the polyharmonic equation. This limit is accomplished through parameter
differentiation. By combining these results with previously derived azimuthal
Fourier series expansions for these binomial and logarithmic fundamental
solutions, we are able to obtain addition theorems for the azimuthal Fourier
coefficients. These logarithmic and binomial addition theorems are expressed in
Vilenkin polyspherical geodesic polar coordinate systems and as well in
generalized Hopf coordinates on spheres in arbitrary even dimensions.


title: Arithmetic Okounkov bodies and positivity of adelic Cartier divisors

abstract:
  In algebraic geometry, theorems of K\"uronya and Lozovanu characterize the
ampleness and the nefness of a Cartier divisor on a projective variety in terms
of the shapes of its associated Okounkov bodies. We prove the analogous result
in the context of Arakelov geometry, showing that the arithmetic ampleness and
nefness of an adelic $\mathbb{R}$-Cartier divisor $\overline{D}$ are determined
by arithmetic Okounkov bodies in the sense of Boucksom and Chen. Our main
results generalize to arbitrary projective varieties criteria for the
positivity of toric metrized $\mathbb{R}$-divisors on toric varieties
established by Burgos Gil, Moriwaki, Philippon and Sombra. As an application,
we show that the absolute minimum of $\overline{D}$ coincides with the infimum
of the Boucksom--Chen concave transform, and we prove a converse to the
arithmetic Hilbert-Samuel theorem under mild positivity assumptions. We also
establish new criteria for the existence of generic nets of small points and
subvarieties.


title:
A trace inequality of Ando, Hiai and Okubo and a monotonicity property
  of the Golden-Thompson inequality

abstract:
  The Golden-Thompson trace inequality which states that $Tr\, e^{H+K} \leq
Tr\, e^H e^K$ has proved to be very useful in quantum statistical mechanics.
Golden used it to show that the classical free energy is less than the quantum
one. Here we make this G-T inequality more explicit by proving that for some
operators, notably the operators of interest in quantum mechanics, $H=\Delta$
or $H= -\sqrt{-\Delta +m}$ and $K=$ potential, $Tr\, e^{H+(1-u)K}e^{uK}$ is a
monotone increasing function of the parameter $u$ for $0\leq u \leq 1$. Our
proof utilizes an inequality of Ando, Hiai and Okubo (AHO): $Tr\,
X^sY^tX^{1-s}Y^{1-t} \leq Tr\, XY$ for positive operators X,Y and for
$\tfrac{1}{2} \leq s,\,t \leq 1 $ and $s+t \leq \tfrac{3}{2}$. The obvious
conjecture that this inequality should hold up to $s+t\leq 1$, was proved false
by Plevnik. We give a different proof of AHO and also give more counterexamples
in the $\tfrac{3}{2}, 1$ range. More importantly we show that the inequality
conjectured in AHO does indeed hold in this range if $X,Y$ have a certain
positivity property -- one which does hold for quantum mechanical operators,
thus enabling us to prove our G-T monotonicity theorem.


title: QCD Axion Search with ILC Beam Facility

abstract:
  One of the most promising methods to search for axions is a
light-shining-through-walls (LSW) experiment. In this work, we discuss the
possibility of performing an LSW experiment at the ILC experiment, where photon
beams are generated for positron production. The photon beam is energetic and
intense; the energy is of order MeV and the number of photons is about
$10^{24}$ per year. Due to the high energy and intensity, this LSW experiment
can reveal the parameter region of the axion unexplored by previous
ground-based experiments.


title: Restricted Forensic Levenshtein Distance

abstract:
  String edit distances have been used for decades in applications ranging from
spelling correction and web search suggestions to DNA analysis. In short tandem
repeat (STR) regions of the genome, motifs of nucleotides 2-6 base pairs long
repeat consecutively, and these motifs most often expand and contract as a
whole unit. This phenomenon of slippage of the polymerase on the template is
referred to as stutter in forensic DNA analysis. String edit distances that
only consider single-character edits fail to capture true DNA sequence
similarity in STR regions. In forensic applications, stutter appears in vitro
as an artifact of the PCR amplification. Forensic DNA analysis now also
utilizes data from massively parallel sequencing (MPS) beyond just length-based
analysis by capillary electrophoresis data. The algorithm presented here
measures distance between sequences, using dynamic programming, that allows the
addition or deletion of motifs as a separate edit type from single-nucleotide
insertion, deletion, and substitution. Forensic examples are shown, but the
applications extend to sequence alignment and string similarity in other
biological applications.


title: GPU Accelerated Automatic Differentiation With Clad

abstract:
  Automatic Differentiation (AD) is instrumental for science and industry. It
is a tool to evaluate the derivative of a function specified through a computer
program. The range of AD application domain spans from Machine Learning to
Robotics to High Energy Physics. Computing gradients with the help of AD is
guaranteed to be more precise than the numerical alternative and have a low,
constant factor more arithmetical operations compared to the original function.
Moreover, AD applications to domain problems typically are computationally
bound. They are often limited by the computational requirements of
high-dimensional parameters and thus can benefit from parallel implementations
on graphics processing units (GPUs). Clad aims to enable differential analysis
for C/C++ and CUDA and is a compiler-assisted AD tool available both as a
compiler extension and in ROOT. Moreover, Clad works as a plugin extending the
Clang compiler; as a plugin extending the interactive interpreter Cling; and as
a Jupyter kernel extension based on xeus-cling. We demonstrate the advantages
of parallel gradient computations on GPUs with Clad. We explain how to bring
forth a new layer of optimization and a proportional speed up by extending Clad
to support CUDA. The gradients of well-behaved C++ functions can be
automatically executed on a GPU. The library can be easily integrated into
existing frameworks or used interactively. Furthermore, we demonstrate the
achieved application performance improvements, including (~10x) in ROOT
histogram fitting and corresponding performance gains from offloading to GPUs.


title: Orbital-selective Mott phase as a dehybridization fixed point

abstract:
  Studies on the iron-based superconductors and related strongly correlated
systems have focused attention on bad-metal normal state in proximity to
antiferromagnetic order. An orbital-selective Mott phase (OSMP) has been
extensively discussed as anchoring the orbital-selective correlation phenomena
in this regime. Motivated by recent experiments, we advance the notion that an
OSMP is synonymous to correlation-driven dehybridization. This idea is
developed in terms of a competition between inter-orbital hopping and dynamical
spatial spin correlations. Within effective models that arise from extended
dynamical mean-field theory (EDMFT), and using a combination of continuous-time
quantum Monte Carlo and analytical methods, we show how the OSMP emerges as a
stable dehybridization fixed point. Concomitantly, the stability of the OSMP is
demonstrated. Connections of this mechanism with partial
localization-delocalization transition in other strongly correlated metals are
discussed.


title: The least singular value of a random symmetric matrix

abstract:
  Let $A$ be a $n \times n$ symmetric matrix with $(A_{i,j})_{i\leq j} $,
independent and identically distributed according to a subgaussian
distribution. We show that $$\mathbb{P}(\sigma_{\min}(A) \leq
\varepsilon/\sqrt{n}) \leq C \varepsilon + e^{-cn},$$ where $\sigma_{\min}(A)$
denotes the least singular value of $A$ and the constants $C,c>0 $ depend only
on the distribution of the entries of $A$. This result confirms a folklore
conjecture on the lower-tail asymptotics of the least singular value of random
symmetric matrices and is best possible up to the dependence of the constants
on the distribution of $A_{i,j}$. Along the way, we prove that the probability
$A$ has a repeated eigenvalue is $e^{-\Omega(n)}$, thus confirming a conjecture
of Nguyen, Tao and Vu.


title:
Cosmology Intertwined: A Review of the Particle Physics, Astrophysics,
  and Cosmology Associated with the Cosmological Tensions and Anomalies

abstract:
  In this paper we will list a few important goals that need to be addressed in
the next decade, also taking into account the current discordances between the
different cosmological probes, such as the disagreement in the value of the
Hubble constant $H_0$, the $\sigma_8$--$S_8$ tension, and other less
statistically significant anomalies. While these discordances can still be in
part the result of systematic errors, their persistence after several years of
accurate analysis strongly hints at cracks in the standard cosmological
scenario and the necessity for new physics or generalisations beyond the
standard model. In this paper, we focus on the $5.0\,\sigma$ tension between
the {\it Planck} CMB estimate of the Hubble constant $H_0$ and the SH0ES
collaboration measurements. After showing the $H_0$ evaluations made from
different teams using different methods and geometric calibrations, we list a
few interesting new physics models that could alleviate this tension and
discuss how the next decade's experiments will be crucial. Moreover, we focus
on the tension of the {\it Planck} CMB data with weak lensing measurements and
redshift surveys, about the value of the matter energy density $\Omega_m$, and
the amplitude or rate of the growth of structure ($\sigma_8,f\sigma_8$). We
list a few interesting models proposed for alleviating this tension, and we
discuss the importance of trying to fit a full array of data with a single
model and not just one parameter at a time. Additionally, we present a wide
range of other less discussed anomalies at a statistical significance level
lower than the $H_0$--$S_8$ tensions which may also constitute hints towards
new physics, and we discuss possible generic theoretical approaches that can
collectively explain the non-standard nature of these signals.[Abridged]


title:
Twisted Ways to Find Plane Structures in Simple Drawings of Complete
  Graphs

abstract:
  Simple drawings are drawings of graphs in which the edges are Jordan arcs and
each pair of edges share at most one point (a proper crossing or a common
endpoint). We introduce a special kind of simple drawings that we call
generalized twisted drawings. A simple drawing is generalized twisted if there
is a point $O$ such that every ray emanating from $O$ crosses every edge of the
drawing at most once and there is a ray emanating from $O$ which crosses every
edge exactly once.
  Via this new class of simple drawings, we show that every simple drawing of
the complete graph with $n$ vertices contains $\Omega(n^{\frac{1}{2}})$
pairwise disjoint edges and a plane path of length $\Omega(\frac{\log n }{\log
\log n})$. Both results improve over previously known best lower bounds. On the
way we show several structural results about and properties of generalized
twisted drawings. We further present different characterizations of generalized
twisted drawings, which might be of independent interest.


title:
Performance Analysis and Optimal Node-Aware Communication for Enlarged
  Conjugate Gradient Methods

abstract:
  Krylov methods are a key way of solving large sparse linear systems of
equations, but suffer from poor strong scalabilty on distributed memory
machines. This is due to high synchronization costs from large numbers of
collective communication calls alongside a low computational workload. Enlarged
Krylov methods address this issue by decreasing the total iterations to
convergence, an artifact of splitting the initial residual and resulting in
operations on block vectors. In this paper, we present a performance study of
an Enlarged Krylov Method, Enlarged Conjugate Gradients (ECG), noting the
impact of block vectors on parallel performance at scale. Most notably, we
observe the increased overhead of point-to-point communication as a result of
denser messages in the sparse matrix-block vector multiplication kernel.
Additionally, we present models to analyze expected performance of ECG, as well
as, motivate design decisions. Most importantly, we introduce a new
point-to-point communication approach based on node-aware communication
techniques that increases efficiency of the method at scale.


title: Neuromorphic Data Augmentation for Training Spiking Neural Networks

abstract:
  Developing neuromorphic intelligence on event-based datasets with spiking
neural networks (SNNs) has recently attracted much research attention. However,
the limited size of event-based datasets makes SNNs prone to overfitting and
unstable convergence. This issue remains unexplored by previous academic works.
In an effort to minimize this generalization gap, we propose neuromorphic data
augmentation (NDA), a family of geometric augmentations specifically designed
for event-based datasets with the goal of significantly stabilizing the SNN
training and reducing the generalization gap between training and test
performance. The proposed method is simple and compatible with existing SNN
training pipelines. Using the proposed augmentation, for the first time, we
demonstrate the feasibility of unsupervised contrastive learning for SNNs. We
conduct comprehensive experiments on prevailing neuromorphic vision benchmarks
and show that NDA yields substantial improvements over previous
state-of-the-art results. For example, NDA-based SNN achieves accuracy gain on
CIFAR10-DVS and N-Caltech 101 by 10.1% and 13.7%, respectively.


title:
Cryogenic User Facilities for R&D on Noble Liquid Detectors and Low
  Temperature Devices

abstract:
  Cryogenic test facilities are critical infrastructure for physics experiments
in a variety of fields, perhaps most notably for particle detection with noble
liquid detectors, low-temperature device development, and quantum information
research. However, considerable investment and technical knowledge are required
to construct and operate such facilities. This white paper discusses proposals
for user facilities aimed at broadening the availability of testing
capabilities for the scientific community.


title: Memristors, Spintronics and 2D Materials for Future Computing Systems

abstract:
  In a data-driven economy, virtually all industries benefit from advances in
information technology -- powerful computing systems are critically important
for rapid technological progress. However, this progress might be at risk of
slowing down if we do not address the discrepancy between our current computing
power demands and what the existing technologies can offer. Key limitations to
improving the energy efficiency are the excessive growth of data transfer costs
associated with the von Neumann architecture and the fundamental limits of
complementary metal-oxide-semiconductor (CMOS) technologies, such as
transistors. In this perspective article, we discuss three technologies that
will likely play an essential role in future computing systems: memristive
electronics, spintronics, and electronics based on 2D materials. We present
some representative applications for each and speculate how these could fit
within future digital, quantum and neuromorphic systems.


title:
Classification of irreducible Harish-Chandra modules over full toroidal
  Lie algebras and higher-dimensional Virasoro algebras

abstract:
  In this paper, we completely classify the irreducible Harish-Chandra modules
over the full toroidal Lie algebra, which is a natural higher-dimensional
analogue of the affine-Virasoro algebra. As a by-product, we also obtain the
classification of all possible irreducible Harish-Chandra modules over the
higher-dimensional Virasoro algebra. These directly generalize the well-known
result of O. Mathieu [Invent. Math. 1992] for the classical Virasoro algebra
and also the recent work of Billig-Futorny [J. Reine Angew. Math. 2016] for the
higher rank Witt algebra. In particular, we show that any irreducible
Harish-Chandra module over the higher-dimensional Virasoro algebra turns out to
be either a quotient of a module of tensor fields on a torus or a highest
weight type module up to a twist of an automorphism, as conjectured by Eswara
Rao in 2004.


title: MIGHTEE-HI: the HI Size-Mass relation over the last billion years

abstract:
  We present the observed HI size-mass relation of $204$ galaxies from the
MIGHTEE Survey Early Science data. The high sensitivity of MeerKAT allows us to
detect galaxies spanning more than 4 orders of magnitude in HI mass, ranging
from dwarf galaxies to massive spirals, and including all morphological types.
This is the first time the relation has been explored on a blind homogeneous
data set which extends over a previously unexplored redshift range of $0 < z <
0.084$, i.e. a period of around one billion years in cosmic time. The sample
follows the same tight logarithmic relation derived from previous work, between
the diameter ($D_{\rm HI}$) and the mass ($M_{\rm HI}$) of HI discs. We measure
a slope of $0.501\pm 0.008$, an intercept of $-3.252^{+0.073}_{-0.074}$, and an
observed scatter of $0.057$ dex. For the first time, we quantify the intrinsic
scatter of $0.054 \pm 0.003$ dex (${\sim} 10 \%$), which provides a constraint
for cosmological simulations of galaxy formation and evolution. We derive the
relation as a function of galaxy type and find that their intrinsic scatters
and slopes are consistent within the errors. We also calculate the $D_{\rm HI}
- M_{\rm HI}$ relation for two redshift bins and do not find any evidence for
evolution with redshift. These results suggest that over a period of one
billion years in lookback time, galaxy discs have not undergone significant
evolution in their gas distribution and mean surface mass density, indicating a
lack of dependence on both morphological type and redshift.


title: Contact Process on Weighted Planar Stochastic Lattice

abstract:
  We study the absorbing state phase transition in the contact process on the
Weighted Planar Stochastic (WPS) Lattice. The WPS lattice is multifractal. Its
dual network has a power-law degree distribution function and is also embedded
in a bidimensional space. Moreover, it represents a novel way to introduce
coordination disorder in lattice models. We investigated the critical behavior
of the disordered system using extensive simulations. Our results show the
critical behavior is distinct from that on a regular lattice, suggesting it
belongs to a different universality class. We evaluate the exponent governing
the bond fluctuations and our results agree with the Harris-Barghathi-Vojta
criterium for relevant fluctuations.


title:
Optimization and readout-noise analysis of a hot vapor EIT memory on the
  Cs D1 line

abstract:
  Quantum memories promise to enable global quantum repeater networks. For
field applications, alkali metal vapors constitute an exceptional storage
platform, as neither cryogenics, nor strong magnetic fields are required. We
demonstrate a technologically simple, in principle satellite-suited quantum
memory based on electromagnetically induced transparency on the cesium D1 line.
The focus lies on the analysis of the trade-off between end-to-end efficiency
and signal-to-noise ratio, both being key parameters in applications. For
coherent pulses containing one photon on average, we achieve storage and
retrieval with end-to-end efficiencies of $\eta_{\text{e2e}}=(13\pm 2)\%$,
which correspond to internal memory efficiencies of of
$\eta_{\text{mem}}=(33\pm 1)\%$. Simultaneously, we achieve a noise level
corresponding to $\mu_1=0.07(2)$ signal photons, for which we present
strategies for further minimization.


title: Moral Dilemmas for Moral Machines

abstract:
  Autonomous systems are being developed and deployed in situations that may
require some degree of ethical decision-making ability. As a result, research
in machine ethics has proliferated in recent years. This work has included
using moral dilemmas as validation mechanisms for implementing decision-making
algorithms in ethically-loaded situations. Using trolley-style problems in the
context of autonomous vehicles as a case study, I argue (1) that this is a
misapplication of philosophical thought experiments because (2) it fails to
appreciate the purpose of moral dilemmas, and (3) this has potentially
catastrophic consequences; however, (4) there are uses of moral dilemmas in
machine ethics that are appropriate and the novel situations that arise in a
machine-learning context can shed some light on philosophical work in ethics.


title: Symmetry Group Equivariant Architectures for Physics

abstract:
  Physical theories grounded in mathematical symmetries are an essential
component of our understanding of a wide range of properties of the universe.
Similarly, in the domain of machine learning, an awareness of symmetries such
as rotation or permutation invariance has driven impressive performance
breakthroughs in computer vision, natural language processing, and other
important applications. In this report, we argue that both the physics
community and the broader machine learning community have much to understand
and potentially to gain from a deeper investment in research concerning
symmetry group equivariant machine learning architectures. For some
applications, the introduction of symmetries into the fundamental structural
design can yield models that are more economical (i.e. contain fewer, but more
expressive, learned parameters), interpretable (i.e. more explainable or
directly mappable to physical quantities), and/or trainable (i.e. more
efficient in both data and computational requirements). We discuss various
figures of merit for evaluating these models as well as some potential benefits
and limitations of these methods for a variety of physics applications.
Research and investment into these approaches will lay the foundation for
future architectures that are potentially more robust under new computational
paradigms and will provide a richer description of the physical systems to
which they are applied.


title:
Hierarchy Configuration Interaction: Combining Seniority Number and
  Excitation Degree

abstract:
  We propose a novel partitioning of the Hilbert space, hierarchy configuration
interaction (hCI), where the excitation degree (with respect to a given
reference determinant) and the seniority number (i.e., the number of unpaired
electrons) are combined in a single hierarchy parameter. The key appealing
feature of hCI is that each hierarchy level accounts for all classes of
determinants whose number share the same scaling with system size. By surveying
the dissociation of multiple molecular systems, we found that the overall
performance of hCI usually exceeds or, at least, parallels that of
excitation-based CI. For higher orders of hCI and excitation-based CI, the
additional computational burden related to orbital optimization usually do not
compensate the marginal improvements compared with results obtained with
Hartree-Fock orbitals. The exception is orbital-optimized CI with single
excitations, a minimally correlated model displaying the qualitatively correct
description of single bond breaking, at a very modest computational cost.

categories: physics.chem-ph cond-mat.mtrl-sci cond-mat.str-el nucl-th physics.comp-ph


title:
On the Euclidean Action of de Sitter Black Holes and Constrained
  Instantons

abstract:
  We compute the on-shell Euclidean action of Schwarzschild-de Sitter black
holes, and take their contributions in the gravitational path integral into
account using the formalism of constrained instantons. Although Euclidean de
Sitter black hole geometries have conical singularities for generic masses,
their on-shell action is finite and is shown to be equal to minus the sum of
the black hole and cosmological horizon entropy. We apply this result to
compute the probability for a nonrotating, neutral arbitrary mass black hole to
nucleate spontaneously in empty de Sitter space, which separates into a
constant and a "non-perturbative" contribution, the latter corresponding to the
proper saddle-point instanton in the Nariai limit. We also speculate on some
further applications of our results, most notably the possible non-perturbative
corrections to anti-podal correlators in the de Sitter vacuum.


title: Hindman's Theorem in the hierarchy of Choice Principles

abstract:
  In the context of $\mathsf{ZF}$, we analyze a version of Hindman's finite
unions theorem on infinite sets, which normally requires the Axiom of Choice to
be proved. We establish the implication relations between this statement and
various classical weak choice principles, thus precisely locating the strength
of the statement as a weak form of the $\mathsf{AC}$.